{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"import os\n",
"\n",
"os.environ['TORCH_LOGS'] = '+dynamic'\n",
"import pylab as pl"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
" WeightNorm.apply(module, name, dim)\n",
"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:123: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
" warnings.warn(\n"
]
},
{
"ename": "TypeError",
"evalue": "CustomAlbert.forward() got an unexpected keyword argument 'attention_mask'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[3], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkokoro\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m generate\n\u001b[1;32m 9\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHow could I know? It\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms an unanswerable question. Like asking an unborn child if they\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mll lead a good life. They haven\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt even been born.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 10\u001b[0m audio, out_ps \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvoicepack\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;66;03m# 4️⃣ Display the 24khz audio and print the output phonemes\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display, Audio\n",
"File \u001b[0;32m~/Projects/DeepLearning/TTS/Kokoro-82M/kokoro.py:147\u001b[0m, in \u001b[0;36mgenerate\u001b[0;34m(model, text, voicepack, lang, speed)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTruncated to 510 tokens\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 146\u001b[0m ref_s \u001b[38;5;241m=\u001b[39m voicepack[\u001b[38;5;28mlen\u001b[39m(tokens)]\n\u001b[0;32m--> 147\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mref_s\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspeed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m ps \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mnext\u001b[39m(k \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m VOCAB\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m==\u001b[39m v) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m tokens)\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out, ps\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/utils/_contextlib.py:116\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 115\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 116\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Projects/DeepLearning/TTS/Kokoro-82M/kokoro.py:119\u001b[0m, in \u001b[0;36mforward\u001b[0;34m(model, tokens, ref_s, speed)\u001b[0m\n\u001b[1;32m 117\u001b[0m input_lengths \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mLongTensor([tokens\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 118\u001b[0m text_mask \u001b[38;5;241m=\u001b[39m length_to_mask(input_lengths)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m--> 119\u001b[0m bert_dur \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m~\u001b[39;49m\u001b[43mtext_mask\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mint\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 120\u001b[0m d_en \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mbert_encoder(bert_dur)\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 121\u001b[0m s \u001b[38;5;241m=\u001b[39m ref_s[:, \u001b[38;5;241m128\u001b[39m:]\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"\u001b[0;31mTypeError\u001b[0m: CustomAlbert.forward() got an unexpected keyword argument 'attention_mask'"
]
}
],
"source": [
"from models import build_model\n",
"import torch\n",
"device = \"cpu\" #'cuda' if torch.cuda.is_available() else 'cpu'\n",
"model = build_model('kokoro-v0_19.pth', device)\n",
"voicepack = torch.load('voices/af.pt', weights_only=True).to(device)\n",
"\n",
"# 3️⃣ Call generate, which returns a 24khz audio waveform and a string of output phonemes\n",
"from kokoro import generate\n",
"text = \"How could I know? It's an unanswerable question. Like asking an unborn child if they'll lead a good life. They haven't even been born.\"\n",
"audio, out_ps = generate(model, text, voicepack)\n",
"\n",
"# 4️⃣ Display the 24khz audio and print the output phonemes\n",
"from IPython.display import display, Audio\n",
"display(Audio(data=audio, rate=24000, autoplay=True))\n",
"print(out_ps)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 640, 348])\n"
]
},
{
"data": {
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ABsAEAAVABcAGQAOAAQACwACAAQABwD6//j/+f/9//b/7v/w//P/8//0//f/9f/y//T/9f/z//7/+f/0//X/9//2/wMA8v/t//n/4//4/+3/2v/h/+L/5P/j/+n/2//j/+7/4//t/+j/7f/w//j/7f/z//n/5//1/+r/8v/1//n/7//1/wgA9/8AAPr/+P/+/xAA/f8DABMA/P8gAAkAEAAnAAwAHAAoABsAKgAmAA8AJgATABYAHQATABQAGAAYAAgAKQARABQAEgAMABIABwAMAO3/CgDo//X/8//F//H/2f/d/9z/2//f//T/5v/r//j/2f/5//H/4v/v/+H/zP/X/8X/sf+1/5r/mf+w/5X/k/+p/4j/pP+r/43/rf+U/43/q/+W/5j/pP+F/4H/pv+I/4L/nP94/5//p/+L/7X/m/+p/8v/rv+4/8r/w//B/9j/yf/E/9v/1v/8/+D/zv8DAA0ACgAvACQAFAB0AGEAYgCVAGgAkQC2AJMA1ADKALYA+QDyAO0A6gAFARUBJgEgARIBKgFHAU4BMAEtATMBWgFOARgBLQE1ASoBBQHYAKwAlgCkAGYAQgAVAPj/DwDs/93/s/+9/7b/rv/O/4P/lf+V/6T/pP91/0D/eP9O/xL/U//Z/iL/9P7I/uH+wf6R/nT+af7R/RD+wP1G/Xj9D/3U/OT8XvxW/Gv8JPwq/AL8Dvz0+zv8A/zL+2X81Ptt/H/8GPzD/Hv83Pwn/S79d/0T/kP+zP4t/zj/egD4AMYBdgIAA0oEsgWxBkoHQgjzCFgKAgs3C8kLkgtwDI0MPQwmDDsLogtAC8UKiAptCSEJwAhmCIkHnAbABT8FgwRGA2gCXQF0ANH/8v73/af93Pzo/MD8CPwU/C38DfyB/JT8O/zQ/PD8+/wd/Uz9yPyi/bP9Mv3X/Sf9hP0v/vn9dv2V/Uj9u/zG/HH72flO+cb3aPYv9ZHyT/F78KzuT+1M7LzqDOre6RTp1ehs6MrnM+gc6DXoh+hM6FLoCulo6nvqsOzW7G7uJ/KW8zv4JfmC+yAA/QMrCcQLDQ+eE3QaniKHJion2SjEKqIthzCPLRco6iZlJUIkmyKGHK4X6RZpF1UW8hLwDS8NRw6dDtwLgQXeAJz+cABG/Q336/La7cjwT/J172XuluyL8Uz3OPkQ+7j7hP/tBfgILwgOB8MHqQmWC8wJvwaFBa0FEgekBjAG8AMPBYoGUgbeBr4E/wPnArwCTAA+/oz5gPa79Z7xkvB87Wfr8epJ65LqSOw/7EnsGO+r7rTw5fCL8ODwpfHc8LHw3u9I7VXuC+6P7Vrtd+zj6pLty+4K7gjx/u538LfxYPCF8Snxqu808I/x3+928U3xX/OW9ob3h/mO+638uwIdDo4S4xTYF/MZ6h9nJfojXCTyIloedx0JHq4ZMRhkGB8VkhQgErQSCRT2E6ESARS6EP0L6gkxBV8FEAP7/c/2x/W69PD0bvh09fj2F/sq/8QBJwO4A9cHIQuCCl4JugVyBUAIIgjcBkUEuQRuCHUJIwv9CBMKFgufCxYJIQaBA6EA3AH6+xT6HfZ+9F71EvUi9Mj0sPYC9zL72flk+7r76/ux+i/7GPhL9NrzAe6679Ltm+3S6xTsd+oq66DumukW7V7p0eh+6T3nf+Nn4+Hho9765qDfOOBu4Gbd3uRs5L3kY+XG5qPop+7O8iP2zvtQ+0gCRAjHBwIKTRGKHsgnGzm5QUY9RD1QSYZVJlNdR7I1fC/cKt8lUxyYDoAFwggTC6wErv/1+9cFqQtaCEP+1vMT8nLzMfaP7Tbm5uQM5Knthe9E9fr+pAftD+YRthLfFQEcIBooG+MRHA57CmkEPgaIA7cHwAoaCZwFAgaMCCgJ2wZpACj47fPl6yfoR+fw5NXqtOnL6qPrLvFS+xEATAKL/nD9aPq3+GLzBe+X6sXpceit48jh8uVN64nvK/Qz8RP1sPL18hXv6Ola5ZjkyuAQ2fHVAtR22TvdLeO44PriMOBc6Hfr9+tb8MbsGvEH8Ib84/tGALQEQQVREksP5g5dGRc1H03lVKROx0TfTl1bB1rlTHMyJh3WHRYZKAz//t34jAKuBmsENACN/joExg4aD6sA5fVM65vs2PCR7QfuuupH8gL7VwFuC9MTNRwjHWwd1BQ1ELUQ8wxuDfEGiQGlAa4EwwluDu8Ptw9zDhELTAME/Kv0te6V7XXlwOA83CbeZ+hn8CT4e/pu+9782wFYAn7/4PlR8T/wROqL6GroXOWF64fwrfN28w/za/DC8QHy/uxG6A7fktmC2N7T5dPs2GDYIN8Q3VzdruT86APwZ++Z58PgM+AG5aTmkuUa45npJ/YC+BwIugfdEoUdNCRGJ8YaPBg1JYlLi10MYFpM6k3RU85kg2cbT20s6hoFGhEI9Aq69Gr27vr9/wz+t/ZA9In3eAtpACP5VeaW08jnh/eC+eP46fGk+WANhRuqHSAgSxoSGxsc0g0JCMEBAAbdDCQIWQq6CggMzBD/GKYScglA+7TzQfFs5o/lZt7W5N3kEe5o6w3wxPSC+00Fyfvf+lXxMfUq9KHyufA3+Az6FvvO+273T/gC/E75q/RW7SjjyuN13VviteCS5J/jVeEY3djVH9dM2qTfCN4M11LMP8zC1hztzO8i7GzhReDf6TLtD/hO8+jtyuxS9LT5eQNLEjEXBia1Hu0R3hmQMmFa5mnXa9Ral06BUIRjGmRuR5Uriw/QCnwE+ANDAmcGRgEUBeQAJ/Z5+Db7+AVE+lLr79Q51kPjjPf4BcUCmwPsBzsSkhuKICQYRBT2EEQFcvvt/lkGERcdIF0bxhM6EkYTwhK5Dtj9ePLF6H3jqd9p3n3oDfDa9QPypfG18v3x1foa9l3vb+bK6Yvw7PO692X7SAA0BFUGIwRyAbX/WvwN9WrwTepn687od+f24QjiHuAM5PHfBOAv15/JQdG50UfdCNQc1gHY+t605j3lXd4j1+PhmOF53urfctw25dzzEvmSByAGhQzbF04kuB2jE5YPNR7gTXtpg26kWm5cvGR3ap5mWUg9KREZ1xVgDQUPxw8XE3obgRmhBmPzoOV16Drvg+UP3DzM8tCW5+UAKQmqC1YNCgj2C5MFIQKTAzwGtgseDDAK5xD3H4UqSy9sJ28apBnjEX0HCP8C/kYHhgav+ortO+n97DTutuaI3f3dCt/R3UvddOBW7Kv5xPoi+tn23vH/82T1kftw+Hz88fVN9CfzGfVo9z/1ZvP/6TDi4Nne2rXYAuJc34/iNtyw2G/ZIN0g4+XfEeVw2hfXgtdx2FnfQt5N3anchtyO52fukfSL9NADEhJsEXsOJAXuEZYckRkYC5YSkT4Aa8R1x2MQWUVcN2u7VlQ24h2uG8cpviy7JO4aByUiKhMqDQ238IzaONxs4EPjLegB5dn1LPc+BSr9fvjQ+HX21fJz6ADuke8pDMcbiR31ItAbjRp1EtMRrQ1xFCwQYA37EHoNExxCHrEeYBDj/07q3OPr3d7oq+0m78bymuuD7RXhgupc5J7rWegP3vrk1ebp9F72MP4P/DH1afF+4lno7ezj8iD4qu5M673nouiR6XPlMORr34HgLt222G/bzty44bHdpNrV1tfXBNjr2j3bAttu3YjjQex96//wkfPg+qP+cAYkCpILDBFAD14M5xPuMa9IPWnwcU9es0wrS/NPLEJjQLU0FDSNPNM7jC1VKgAs6yIcE18Eue9m4tzuPfax++H8Z/Z16zLwPe/q6Crnj+/Q6MLtLfA991oAdxUkFRoK6QhU/yAFlAj/GhkV2iqDKEIkQh1eGJIQhgk1C+v9ygF7/hcCtwHQCFkBTPuo8y/kY+FQ3n/fkuBf4HPm9ehe89DpYeUe3OfZWeGp4HDokOfr7f3tUfA97tnt3OhA6ZPi6+N85pjmA+l66ijwV+oJ5YncTdOn2EbXtNfn3YnaBeMy2LjkNuI25N/sYuWI7sXhEvTU9En9MAqhBSoM+QPhFOUfLkbyZtxX20d6RPlHrUl4TI5IYEMET8FPCjttOw40rCuWH6kbPhR7BW4I+gNMB/gK3gKQ86Pk4uE/46viReoZ56jpce3N7sHuSPF48/PxOfYa9jf2hv+BD5YYgSiWJ7Eh3Rz7F9QPWhETGlkaeCG/JAIe2xZQDikCtf/O/IP4VvKb9MDyVvMu8WroA+FR2qzVSNRe0SLRgNrg3ljkt96M2pLU7NH51HPXbN434aDj0+Zr6GbnGuNo5ELrke1S6Dfrd+Nv5+jpF+mK7Q/oB+3c3IDpBec46sT3VPNA9uLlQ/KZ75b5VgMWAR4C7/r1Bv0NETK4TxZJsTNmLhMshS9uP6FCkkIYVDdZskaCQos0BCsQJRAtYDFGLjQwfSeIIi0XZwjb+9/wvfIM+YH0yP4Y/ZLwWerb4ijd/tmR3+DfMewp9k71S/cJ+nH0gfYV/En9fAnDDyMXChnSFyUS0xFKF4gYjRhdFAgTBBzkGhMWfhEzBQYFqPp590P0OPb99Q3x4+sm3inZI9ey0wjS7dFCy8DRf86XzHXPONCfz8HI6MMyyO7TZ9YY3B7gWuH53Dnh6tx83OXmOul/8G70GfPj6WzwfvIS8ZT3qvfx+57yNwBU/vEDAwl+/6MEWvtZDaMRgieCOUAqbiaSLIcoLS0JLWA0v0IhSb1N2jYZP3w5GjHlNCoxfT+YPdk5DzHtJIQjpBuAFhQSLRA2FLgOzgvIAav3SffL8Tjtseie6Z3sPPKm6/7m1ur35yDoReYP6fXx/viH/ZT4xvak//z5k/++/NECbwzfCf4PnAXZDd4LvgVfAgwCUQfoAyQEM/8d+Rr6PvHz62HrQ99p40ThAeS53A3a+dlZzrfT4Mqc0IPRjc4UzcPPeNeAyobRTtLI3DPgVtZv33To6e//6F7kv+1o82H25vU0/BMIKwW4AAv8WQrvDvkOMRI/E54NfwtqEXEYVSMZI4AnrShPKa0YXRlpIxAvoC6VLEk1dCsmK0UfnyRVMcsuDDDDL7AxVi1kIaocDCUgJnwiqR+iH+gfdRl6D04MKA2EDCQJQQHHBS0Cd/0J/Y73VPkp8TftIPBr73/1Q/EA8lDvB+m16Gjknu0170LzcvKZ8pbucOjV7t/vwfZJ+K79ePk194/z4vA19GL4JPyH9gP2PfVJ8Mft1++y7EPxbPIw73/sHukT6VXoqecH6eXo/+pm6nfqXuem4rnnc+Qz6QvsEvHd8Z7uD/Ee7UTyW/Nj9jD6ff0C/tX9gQLUAOIEYQGWBA0IhwgFC7QKrg8kDR8N9AxvC7sMQA3yD/USUhTaFGMN8gq6CrwPZxDWEa4WrQ+0Ea0MgAucDiYPHxHvD5YTqhLEDtsLCQyDDXUOKw6nD94P3g3RCxkJ3gd/CzALggmXCzUHMAk/B1MGUAWWA0sHlwAwBRgCZAC+AVz/GgL3/oL9c/yn/ET+zf7o+AT90Pu1+aP4lfkC+mb6ufvO95b5TPYj9/D0nvaU9xj4J/iR9SD4TPN686r3MPXs9jb2xvRz9xP0SvSP8jP1nfeW9uH12PY/+Ev0xfUA9Tj6sfky+Ur7KPhb+kv5t/qv+279hvwP/ff+9/17/uT/Wf+IAHwBUABOA/0BYgPuAysDfwR6A68DRwV+BEIFeAUyBLEF6gNsAzMEvAMNBfUEnQIyA3oC4wGuAcICugEQAjYCogDQARUAdQDP/2IAXACtAO0An//a//X+Vf6JAHIAYgCLAVkAjv9qAHUAfgDgAbkBVAI4AfsAdAHEAQ4DgAO4ArADVwMnAqkCtAKpA+4DewOIAwoD3AJPAhEC7gFlAgEDOwFpAscBZADLAIoA9wBiAbQAIwDXAMcAywC+/4EAbAD+/woAqv+aAXABIgFPAaEAUwEPAeoA0QHLAQUCHAKPAbUB7QCRAJUBHgGdAWoBsgDQAJb/lf/C/8f/3/9z/0f/i/70/gD+8v25/eT96v1A/Uj9y/w+/bH80fxr/M/8Vvw//ID8B/xB/Hf8/vsZ/Ir8kvs9/D78jPuL/HL8pvwz/On7SvyU/ND8QvzO/Rf9N/0r/dL8yP2N/cP9C/6B/of+hv5Z/pX++f4x/xf/Vv9b/6L/qf/Q/7f/z/8fAPD/ZgA/AMQAIwBsALgAGACfAHcAjgAcAeoAnAClAJ4A9QDhAAUBBgFVAUwBKAFjAXUBsgHGAb0BjwEMAvsBQQJ0AvQBSAJAAi0CrAJTAp8CTwJPAmcCFgJ4AhcCUgJHAioCDwLXAaQBuAHRAcgBhgF+AXsBPAE7AdcAwQDeAOgACgH3AIoAkQAuADsASgAZAEIAaQCXABAANQCt/77/ff81/ycAuv+b/0X/Vv9u//H+cP5z/mD/9v7K/lz+Fv5z/if+dv6F/hf+aP58/jX+TP5b/rH+7/7W/rX+FP8v/3z/uf90/4f/CgA0AFgAWABUAN0A6ADRAPMA8AAuAV8BQQF9AWIBFAFcAXcBZAFVAVsBewE4ASkB1wAAARsB6ADwAKMAhABnAJsAkABgADEARABnAGIANwAaAAwAJQA6ACYAUQAuAD4AKABDAFAAVwBWAGEAdwBjAIEAVABaAGUAWQB8AHIASQA9AEgAJAAQAPT/9v8DANP/1/+s/5L/Xv9A/yv/Lf8n/xj/Ev/j/rv+rf6m/pf+pv6Z/qP+lv6b/o/+kv6Q/rH+0v7Y/u/+Cv8u/0P/V/9d/6D/x//9/xUAFAA8AF4AegCVANMA+AAGAR4BHAEmATsBRwFlAYABdgFgAV0BVQFNATMBLgEfARAB+gDLAL8ArQCKAGkAWQAuAA8A9//T/7X/p/+X/3v/ZP9H/z7/N/8o/x//Iv8Z/xr/F/8Z/yr/Kv8t/0L/UP9O/2L/eP+B/4n/mf+q/6r/vP/T/9b/2v/i/+X/7v/y//P//v/w/+f/5P/m/+D/yv+6/6j/oP+k/5v/hP92/2f/W/9a/1P/V/9O/0r/Tf9J/07/Uf9m/3r/ev+K/5X/m/+x/8z/2P/3/w0AIQA0AEUAXQBqAIAAlwCjALIAwwDNAM8A0wDbANoA4QDgAOcA3gDXAMUAxQDAAK8AqACbAJkAhQB2AGAATQA/AD0AKwAiAB0ACAD7//T/9P/o/+f/5f/v/+f/6//g/+T/6v/o//f/7P/z//n/9P8DABwACAD4/wMA9v8BAOT/4v/2/+L/4P/J/8j/yf+6/6X/rP+q/6H/lv+S/4j/fv+B/5P/m/+K/4v/k/+V/4n/lP+g/6//t//H/8z/0f/Q/9n/8//z/wAACAAaAB8AKAArACwAPQBLAFYAWQBdAF4AYQBgAGEAdQBoAG4AcQB3AHEAawBvAHcAdwBxAHoAcAByAG0AawBuAHAAcgB0AHMAbgBkAF8AXgBbAGMAXwBdAFkATQBDAEQAOgA0AC0AIAAgABUABAD///n/8P/o/93/1P/M/8X/vf+w/63/pv+i/5//m/+V/5H/iv+J/4//jf+T/5X/kv+U/5r/nv+r/7j/w//J/83/zP/S/+L/8P/8/wQADgAUABkAIwAuACwALQA4AD0ARABNAFMATABJAEcARQBNAE8ATgBMAEkAPQA9AD8AQwBEADsANgA3ADYALgAsACwALwAjACYAJAAiABIACgAgABcADAABABYAFAAAAO7/8f/5//7/9//x//v/6//j/9//7f/m/+T/4f/n/+j/y//S/9b/zv/N/67/xP/L/8b/r/+h/7L/t/+2/5b/sf+b/6b/lP+k/73/if+9/4f/p/+G/5b/yf+r/9D/l//e/9j/pv/T/5T/z//+/+3/8v+5/xAAy//k/xQABAAwAP//5v8HAAAABQANABIATwAQAEIA+/8SADgA/P9KADcAKAD7/9v/TgBUAI4AYwA0ADQA2/82AA4ATQA9ADgAKQD6/xQAOAA0APH///8GADcATwA+ANP/7P9jAPb/RgDk/9n/HAATAEcA5v/g/4T/4P9XAPb/AQDW/87/sf8CAEgAAQB7AAcAfv8AALD/lv++/9v/1/89AHwAif9W/5z/zf/3/3oA8v97/x0Ae/9G/xoA+f/c/0UAXwCD/47/YADC//gAMwBA/1f/bP8mACgAPAHY/6D/qwD7/iv/JQEsAjMBiP8JAGP8Fvx3+00LahZ6CoT5dPQ/82L3ZwhIBtwGuf46+hH2S/M7/5QHng2OCL74Z/go/Sn7IQXeCtgEcfl79i8DWgKw/6r39vc7B4UGRw2cBvv5GehJ8FoYZRkUCVULu/oi2FHx8/XYD3gk6wndA/DmLPAM8UX0kxzsE6UIp/4X3ZfnlfGc/SoW2g+O/NDwAfY/+jnzagF1C7YLrf4+8tQFLvy4+u31l/5FCsQH0wsv/b//SviK+QYAsQdJDID8zPos/Z/+wQTuBbgGXwSxAJ4ErAJjAJEAPvu9AawF7gUKBD77LP3m+CL+sAQNA3gHwAIvA5z6ivsNAnAArAYUARsC5f5N/CgDuQL1AEgC6vya/7T/5/2ZACcAugBE/hH9If3oAAUAZQB3AAcBp/1w/K79dAENBNH/gQARAt/9O/zn/VUD1gQpAecA/gPuAYv+cv+PAUQEiQI8AxADAQAc/jcBpgCmAQIAkv4sAnD9t/uW/SD9v/+zAlUAKwDr/Kr66ftF+TgBXge3BKQBVviQ93b6nPoZA+AD8gXaA+/4x/gA+4f7OwJbBG4EZgUwADgBNACy+2sDsQEoA3sHI/8J/xf4gPqhASYC1gF5Arj6Xfpx+W/43wAg/30CLP9q/V36vPk2+qoBRwP5/7sCe/zn/az7wv6tBDwHlwVzAiUAWAEnBosG3ganB8oGtAOBBgMEFgeVBV8BsgTlAnQBsADKACcCIANi/ev7OPrF/FT+3/7XAF78Wvnk9R73Zfne+9z8Mf7w+xH7M/gq+Cn7wv3HAAUDRQKMAzYBrP21A1gCMAoKCSMIPwgYCM8HaQhiCcQHsguACJYIMgboBAoEYwa8Bc4CGv9L/5j8ufuh+4P1U/eG8tfxa/Ky8ebx4uuL597r3ekH7LPtYeiu59veGt3F4pjwKQA2ALz2xfNL7Kzr0PR4AW0STxtQGjQJvwYcB/ELjxryHlUtHizfJMogFRKOFF8afR5BKQIn4h4uF9MKswiiBfoDoAs9CeoGQP+J8Mnu7ehu6aTv2+1x8gXsgeMG4uDeaeNl50/pse3J7kvwW/BX77HxCfVm+wUE8wfKCe8KDgvFD6ASZxXyGPoZFB7DHNgbqRqYGEkZuReeGZcXYBMkDlQK8AgxB1MFwgE3/gz6M/V88V7w2O9o79ftpO1U6pHn7uW35HLowuns6rjs++yr7iTwa+9U8or15/jH/Mv+TwE0AvIEaQZsBzIJ1gkaDHANEA69DSANDQwpC90KywgKCFoHywYIB8MEPQH7/uj8Fvv6+Rf5dfnY+Qz4T/Ul8xrzgfMq9AT27va6+PL4mfhg+dT5//oC/SoAEAPyBIkFLQXZBZwGHAeQCLAJggp5C04LegpsCVYH+QU8BgkHNwerBbkDpwH0/ur8Ivwo/cr9JPwJ+4f4M/dm99/2Sfle+qf62/lp+ab54fm1+7n8s/5+APIA1QDLATYCoAPfBPMECQZ9Bv0GAgdMBywG8gTyBBcFswV+BfkDCQPoAZ0AlP+Y/jX+Hf4b/iD9S/xx+8r5GPmE+RD6C/v4+uD69/q9+oj69fmk+i38z/33/r//3f/g/5r/IgA0Aa8BrwLpAxwFpQU3Be8DiANvAx0DKwSHBXoGJAZCBJECAwJzASABoAHyAUkC9wE6AcX/gf6a/Rf94v01/xsADgCj/9v+Xv1a/bv93f1f/5AACwF+AP7/rP71/or/x/93AOgAtgHYAdUBCgG9ADsABQCrAKQBFALhAmkCPAHOAD4Af/9Q/4IAAQEcAsMBAQBI/2j+3/0h/o7+K//U/7X/p/4g/lL9bvz3/D/93P2y/vT+9/65/oL+4P1Y/pf+QP96AAsBwgGNARcBogDwAA0BpAFlAoACvAJPAqkBPAEhAQwBMQEjAdgAsgAXABn/Bf/W/tD+if4k/hD+Gv5H/vz9Lf4c/kP+s/7s/pr/EgB7AK4AegCKAOoAlgEkAqQCEwPrApgCLQIGAg0CBAJKAkcCGgKNAQwBVgC7/3T/Ff8p//P+7v7E/sb+ZP7//e79yv0s/on+//58/83/zv/h//f/GQBcALQALQG/Ae0BkQF0AUcB6ACfAFgAXwCWAHQA8P9Q/67+Pf4X/vP96f3n/Q7+E/4e/hr+HP5e/qH+Ff9j/8X/LQCkAAABLgFWAYkBuAH2ASsCPwIeAuIBsgGNAVoB9ACsAGgAJACl/1X/O/8k/+P+W/4h/i3+hv7c/hP/ZP+I/5b/p//r/10ADwGuAQYCKgIbAi0CIwIZAvEBFgI0Av4BwgFDAd8AQgCH/+n+uv6p/n7+Vv7d/Vj95fyv/ML8Dv1A/Y/9/f11/uf+Jf9I/4r/DgCGAB0BlwEDAmcCiAJQAgcC0gG0Aa0BoAF7AScBrAAdAIf/4v5L/uH9uv24/b79sP1s/SL98vwA/Tr9kf0M/qf+KP+O//T/MABZAKIAEAGWARUCdAK8AuYC1gKWAlACDwL2AfMB4gGpAVQB2gBRAN//cf8q//r+5P7I/rf+l/59/lT+N/4w/lP+rv4f/5L/5v8nAEoAfQCuAPMAPgGJAcUB9wEWAgkC1AGXAWUBOAEbAfsAygCBADgA6P+h/0H/5/6o/pj+jv6A/nv+Z/5h/nz+gv6P/sD++v4y/3//zv8LAFAAcQCPALQA2QDmAAQBIgE4AT4BIwH+ANgAtwB8AFMAKQASAPn/0P+f/2b/Ov8V/wD/7v7o/vT+Af8i/zT/Pf9H/3b/pP/K//b/GgBOAIgAsAC+ANIA0wDRAO4A8gDsAOgA4QDHAL0AmQBzAF0ANQAIAOj/3f++/7f/oP+Q/3n/bf9y/2f/dv9y/5r/sP+6/9X/9P8kADAAOQBJAF8AhQClAJUAmQCcAKsAlwB5AGYAZQB9AGoAJAD///X/4f/b/8L/zv+6/8b/sv+4/6X/k/+1/+H/5P8EAPr/FQAwACgAWAAsAFcABQD9/xQASwBdACQA4v+a/0//8P6e/sz/awBb//v9+f2h/tP+LP7t/EL/xwBSAU8BQwCT/in/JQFzAQoB2QFOA30DzAInARsA+wDFAIcAUwCyAPsAHABGAEf/G/88/hL9tP2m/oL+tf78/gf/gf8P/3b+5f7c/2AA1wA2AZMBwAGlAbgAyABGAYcBiwFOATgBTgFVAaAA9v9n/3P/eP9s/zj/zP7x/iP/If/5/sD+rv7U/hv/Rf+R/+v/NgB/AIYAiQBtAD8APgB1ALEA2wDqALoAeQBUAAAAs/+W/5n/2f/h/7b/av9o/1T/L/8b/yD/W/+0/+f/y//E/8//9f8FAAQAAAA4AGsAagBtAG4AUABTACwA/P/r//f/9f/w//r/zf/K/7v/p/+p/53/hP+V/7//3v/d/+v/6P/0/+3/1//m/wYAIAAeAB0AHQAyABwADAD6/wsADQAZACYAHAAXABgADgD8/wkAFgAfACkAMgAuADwAOwAkABcAHgAkAC0ALwArACEAIwAhABQA/f/n//X/9//x//X/+P/q//T/9v/x//X/9P/y/wUAHQAgACkAKQAcAB8AIgATABoAIgAlACgAJQAfABEABgD2//z/AQD+//3//v/8//3//f/+/wMAAAAHAAkADgAcACkAOQA4AD0ANQA4ADIAKgAsADIAOgA9AD8AMAAmACEAHgAcACAAGwAUABYAFQAYABEACQAHAAgAFAAcAB4AEAAKAA8ADQAVABMAEQATAA8ACAAHAAcABAAHAA0ABgAKAAYAAAD9//f/9f/v//z/8P/v/+j/7f/y/+v/2P/N/9H/yv/T/87/0P/A/8H/sP+n/6b/mf+Z/5v/rv+t/7z/uP+z/73/v//L/87/4P/h/+3/9f/7/wQA/v/3//L/8v/q/+f/5v/w/+X/4//c/+L/8//t//b/6v/0/+r/6//6//3/BwABAAoABQAAAPT//v8OACAADQAAAAEA/f8IAP7/EwASABUACAACABkAAgDy/+3/AAAeACEALgAXAA0A//8EACIADQAHAPb/EQA1ACQAMgALAAoADQAGACQABwAgAPn/+v8EAAIAIgAMAAAA7f8KAP7/+f8GAA0ADAD///f/6v8EAOn/FwA8ACoA1v+z/wUA+P8ZANH/zP/8/+r/9P/j/xMAIgASAAAA9v8fAEcATQA5AFQADQDt/woA7f8kACkARgBVAE8ABwDW/9n/r//p/wsACAC///L/8v+0/+T/5//+/wkAEAAWAIYA9ADHABABuwDWAHcAPAAiAPr/xwCGAIgASgDf/5f/bf+V//f/VABxANv/oP94/13/rP8xAMEArgBoADkA/wBEAcwAdwDOAAIBCQFZAAYALwH4AKQA3//+/08AUwAJ/yv+jf49/p/+Vv4P/8H+HgDs/93/9v4O/HX65vmw+hH6IftI+pf9RQAE/XT67PSS9xv4yvn1+lH64foS9S74zPK+6nTeVNRu2L3sjxW2P5xfYlHNIP//kQVrFDMRkfmj7jENuCdHIZ0CH/NA7o/lEtbsxYrWl/EX/kT/4AxUIV0qCB4UBWsB7QhlCU8Bhv3cCvQXyRgADvQB6/9U+P/sGOB23L/hnunz9lgFyRVaGZYPqwEV+3r/FwSUBIUErgo4EqYQGggP/9D/LwicCjAFnPzB9h3zevFF8p/3kALYCWYJIwZ9AYb7KvVX8un1Uv9VB9QG4QXsBy8L3wmgAjH6ofb8+S/9TgGlBAUFiwJUAA/+5frL96DyjPHp9bn6lf5xAScCBgO0BFMDhABb/nz7mfylAZAEXAYQBp8DVALnAMb9uPtL/O/6Q/nS+cH6FP1u/kL8m/uA+975n/pD/XX/rQK+BMwDXAMrAjsBvgGPAnQBYgFuAE79t/yC+237B/0J/ZP7pfpt+yX8Yvxr+9H5EP0yAW4DtQUaB6oHAwcUBSQAmfya+l35FPso/ID83PyL/dL9Hf+GAM8A5gAp/kb8v/v1/LD9F/4lAPIASQG+/bH7kvxZ/Er7rvp++/79mQCkALb/VgGUASD/If71/G//cQEsA7cDEgQiCG8E3QZLCooJzA/1DX8H6wLABMIFLwgxBncGvQkrA2D93wQYKNFRlV50SuQt6RZJAfXdecBIvBfWMOwi8+35vQJ3AtPzieR/5T/2AwMXDDQWYigmLiAmnRdiDwwKWACi8mLoCuS+43jl4uuE8Tnxze++7+3w2/BE77nuEPisBBwR3B77Ky4pZCEhDVP4eu0D6fnoXPDG9KfwjvKZ8rXwNu9u6Zvk2OkT7oDyxfeX/c0EOA0UDvsKxgYyA9n65/CN7PDsUffy/wr9GvuH9xXwe+nj4wLumgN8EmsTQgwyCI4G6AAC/LL3VAFsCPcIWQelAFoDpQZaBJQB2gFUADcCKP29+mkEnwqSDTYEYffQ+qwDsAdtC/YPFRjbHbAVVwglAK37Hvq287Tw7vRi+Wr9A/8J/6v+Zf9O/rT8DgBQBRsHIwp5CWUJ7AunC5QKMwihBPwDngHt/CH9PPwr+3z5vfea+PT95P+O/vT/+AHFBUkLUQ7GDnUO/A3iCmEGjQSbAtkCTwfwCjsLhQnNAcD7BPgo98f74f6lA0cJNgy5DYAN1wo1Ch4JEQanA8MAh/7U/E783voW96n03/KX8KHs2uyr7LHuWPHr70DwtPPb+M33RvXM8u7wNe8e85jurepM6ILc+toZ33LipejO6hHp2+0M8C3yBPKu78bvqfVY+PzwPOy+8tELojXOWtFw/3/ZeAlbBjKgCdbp7tfuzcXJC9or8aj7r/w088/rO/KL/BcI1Q4IEcwWoB5gJYIsvigZI/UeAhKVAYD1i+wh5RLoQOrP7GD2BP+1AWAFpwfSBD4DEAnvDf8WiCeLL68s7iHVDyT8zvMJ7VXp2+nG8Mf1ivnX/GD+kP1W/m387vZU9w36dfxcAGkEgwRoCYsGJgIN/fb2W/F47MzoGuO64wrifODx5m/rJ+0w78Lwk/CU8kjuk+1+7mrqv+0a6cfpwPEx8V/quOoI6d7k4+jg577mdepw7xzt9emh7BDxuPBI9Z/3HfzhFc05a1ElWzJaBE1pO1In1hS0/fz29PbK8dnxRfwjAVn9Cvn89W374QUnEFMLIxCOHEcnAyWvJLMmISRaHdcRVgEP9aX3M+4a5mzoA/HS81P98gLaAHME0gUjBbcIfRWWHOEiZCLWGjMSMAd8/k71ve367fjx3++X8LDwm+6o8hf2rPiUATQIMgsXC/0Iwgg3BkYDSP0C+NnzB+8r6j/niuPp4vPgn97N31bgZeF25YPsQ/RG+5T+KwLNAf0AcPnH7nzqd+eB4y3hlOGO4dTiY+CK4G3lm+7d+aL/FAkJFrId4h1TGjoLjvsX+f/9Qw/KKr1F/lavXudWvT6OIkwK9vhn7Efq8ukc6DHu6vfrAHYLjRPYFPsSugz6A3T/oQOJEFcenCZtLJkoYhp3B0DyiuAC2DnaB+AV67/7rAesCosN+QslCSUJjQioCIgPghaPGr0dux9kHPYOLQAo7nvgU9rf2MTdMecv7tzxN/lwAPQJ/Q0gDfsIiwQxAtj+v/3E+eP1nu2g6Nrhqd2p3WveStwK3A/g3eCX6KHsjepz7ivzZfTx9ij2be4G6AjfZdvM2ofe9emh75v1rf1wAQAAeQW8BK4EKwSdAYkCxRKeL1JEw1O6WalUZUKWLbwT7vzk7sDprurd8O77S/6s/sgG9w7AEY0XuBeiEJoP0A/RD18UoBu0HPcaZBNRAu3ztOvn6afojexU8m77TANbCLoL1gzWEAQSQhStFTcVUxTlFBsQagpvBUX6pPRW8cjqY+kv6w7rN++F9474Tfmh+i/97f4iAykEfQGNAT3+Zvcr8V7vNOkN6F7q/+kr6Xzr3+hD5sPnuOMy3x3jgeNF4lbnk+y57tnzAvMc70zu9u4P7LLjO+TR5erovfJd/H38DwfwDXkKUAgnA8j7ivx/BNsPPiR7PY1Q4FKlSw5B/DGvH+wMo/tL8+Ly+PDZ7tL1cwB4CPsN/RN0FH0VoRNYDj0O/Q/8D1USSxfoFJYRVAjIAKb17Os55cHmDfEQ/JECYwXdDAEOABCiErUX4RlGHjAYsQ4NCwoEeP2N+i33vvCV7pLnN+Pf4wznJe2G+H8CSgcTCykKewhIBd3+9vec9O/w3uyo6xrqQejW5fvh598C40HjAOFQ4izhAeE94zHkQuYF6vPr8u1d7oPu+u+W7rfw5fK+7Vrwl/QJ9Nr3Mf/w/mH+Vv3O9yr4zP/7EUUk5UCYUdFPpkk/RtI1ciUrFKb9HPcY9U/vUu4K+Az94QdMEagW7homG+YTMA9MEAQQYRLbFFAXVxiEFPUKrwJZ+jPxteqO6SHuCPeJ/pMEuQzrFPwWzRpOHkQepRyTGEwSSA71BiD8qfOW8K/wCe/l7ELrNulG6WHu5vKL++QCzQclCRwLrwWO/8r4wPLk66Lp0ud14x7iDeCg3h7hP+eL5j3ooun25DXf0N6n2nTb6+Dt4k3mzey87UDv/+5j7xDvquwc78vxxPJG96n6+Pi5+ErywvFI+1UKDx/6Me5A3UrfR8E+DDmGLbof8hGqAev7qfqE+JH6LQMlCWsRbRWiFiwYlBaCE8QQoRSWFhcZ0Bj3GLgSQwucAf75nvOc74fw0/O4/dADdwc+CwMTKhZkGDIbkR3ZHsEevRlWEXQKQ/899Z7xeu8H7wPvOe7e7Rrx1vSa+wUCJgbbBpYB3f3G+QT3V/V+9YLwg++E67nlJ+JV36PcBt2n4JDjW+ci537odeUR4/nfXN+U3lrfPt/l37niG+fw6jfs2/HG8YruwvAD83vytPjU+YP1tvNT7pDuBgK6GVQwZ0TqTm1NHkYMOowsVSECFekHi/1l+bPzZ/KR+R0GlBAIGmgd+Bo7GaoUHBH3EmYXgxrdG3gYtxKsC48CM/yq9VHyH/NO9rP6JAD/BKYJaREDGUIfyR1BHFoZjBeJFcMSlQzrA877KvO675XuJfDC7oDzrvR49iL4Cf1JAIsG/whdBVUB5/1Y9s7wVe4358DktuSZ4WreGOAV3kLhj+Qt5pnk5Oa942jhu+Co4QDjUue86nHrh+yr60Lpl+bd5efhHuK94uzkK+qq7wj1rPoU+9762vyOBaEXPSmiOHlDn0elRvk/lzMuJxcaoQ0v/9n1APGx7uLyXvtUByIV2R11H1MfNxyQG9YWchiWF9wTrhGVCxUFqwLc/cj4X/lD9zb5ZvtIATYBXgn5Ec8WRR4TIT8daR3lGEURzRB3Dj8MWQRb/Erw/eo27LTuP/Et+pb5Ff3dAMT/KAGBCCUHgQHv/Yv2LPOo8abtseSm5/febt3Y203aRNZ83dvf7OVt5XXpv+lU6Ubv6OeP6Lbs9eRz4zbkA9474Bne4t9t5h/ppOxb8NjzdfT27n7t7u04+64IvxlQL9w56ETJTSFJDUK1NQUhGBStANvyVutP6cDs2PQd/kQPNh8sJxAo2yJ0IKEb0xYZErMM5gklC/0DkwEPAzQAmgBR/X77Y/rv+4AB2gddDwIaYR2vHswfQRhrFPoULRUEEQYPaQrFAAj7gvQU7nHwJPNz8/771gBcAagGdwbCB14KGQZZAw4AcvbK7fXnquIh4VzeWd+y3MzceN5U3nDfB+LB4Irh+OZx6hXsl+387gLsee6Q7Wbo0OQV4ezYTNXN1ffYy+B36HPuCfGO9NzzsfWSABYRDCRaNHQ9kEN2RCE+fDdpKYUapw81Aib4jvBI67fvdvqFBFkQZxkkIJckcSJOHfMZEhUjEWQPkgxHCqcHDQRbARf/6f0MAaj/iv8YAAwBtwu6FiMXMBtMHdEasB3RGFQWUhdFEqYNTAdqAH39cfQA8AvxIfLX9S36Sf/lBdAHSQhkB7UH2gab/xb5APH97bfoGOXd4nvf89yZ3lPcH91V33veDeOE5Nvlrera7cntJ+9g6SDqsOz75lzhmt0A2YvZx9qS2y7gReQP6rXoJ+pA7J7vr/uFDGAavSm5OCtDL0nqROE6di45I4MUHwQe9/XuZ+0J8CX3iQFDDEoWrR8oIwkicyJ7Hm8bMBVzCZIGuATiAR8DAACD/R8Agvv1+9YA7wHsBvsLnRKwFTcYbBpWHBEe+R+/G90XfRYnD68HaACC+jDzaPWj9o33TvzHAToB3AbPCugK8w48DJACnPoV9SrwTvBa7Y/p5eXV5ULkFuSR4//fLuA04TDfcuH75WLoj+wS7aHsKOym65rpB+bK4snenthd1UbVltcT3WfimOaK6d/qkOt87jj3XAkLHBcq0DbbPQs+nz+/OZMrnyByEhAD2/i67/Tn1+sY9e0A6g2CGschFiG0HxUdzByNGqERdwijBRMCRwGwARYBhQSyA6j+3/0+AOH/dwOYDEoSIxhdG4YZHBu1HWseGR9YHgUY1BDmBkIAQPcC8uP0Evfy+Er7z/2fANIHEwiLCqwM3AlOBd3+KfkX9aPwmOwg7InojOUe4vPetdw/28/aItxs4Hbjrebk6W7qpuoq64Dp5+cv5S7gKt3t2+jZttwU4frkeOnP6zPsYumT5WjjHOmW+akQ7iNDM90+SER/RM88Ri/aIUgXyAoH/LXuJuZY5NDu9v6BDxYdOCNBJH0j7R/aGvcWjRGXDSEI7wNrAmwD1QUwCmoKNwerAVb8Ofse/MICjQigD0AVvBj7HeEhRSR/JdkhSBs6EgwFj/0O+HHzkvTM9rX5zP5xAOwBNwVFB8UF/ARzBIoCXgBi/YT4w/To89PxWfAf673kU94n3dPcfd3z4IrlEuiA6LToFube5ovmxeHs4R3f5Not2YPYmdts4Q3kLOnU7ADn2OS83EzbzOa191gJdx9RLAM1GkAbQ7A+UjXoKPAXXwk++qDuQui+5rjtq/j7Co4Zgh0rHyIhPxy7GRAaCRj0F6QRKAc3A38DVAJ9BD4DSwAe/mT6lPpDAWsETwutFO4alR4XH4IeVSECIewdNRoYE20MtAJ7+ov3jvgN97P4uPpc/Hb+rwHIBJAJbQoFCPkFIwIA/2H65PVC9M7yU+zW66DqZuhU5dLi6+Ar4W7h0d4m3xvgGuDp32TlNugJ5jvk4uQ54uzhaOQ25GDo1uon45XhRd+j2P/aKO05AWkTnSAwLHQ3YjyrPjE4BzMrKFoUigAY+QvsdOHv5YjtRvslCqgTJBtXIBoe7SAGJFMjhB4REOwHzANT/XT8fQESABD9Uf2p+ff8ZABPAzMIzg2dDgMTTRixHY8n6ygBK1clfBjQCd4ADfiS9of29/Jm82nzc/ca/PMDFgg1DnYNTQnuBWkCn/+r/Bj8sPmN+uP0zO9c63fm/OAK3+neh92v30XegeAH40bkFeYk6+jr9OcP5/vjs+Jh3xLd392Y4T7jIuQN5nXiMuNF4qrrGf52D/kcSS2aNlg6kkHvPBc2lSjpFsUFrvhc6g/kluRq6xr5AgLvDngbhyB1ImclZCPBHewXXQ/DCGgEbwEq/7v/OAGkAHEARP8B/zD+ZwMOCsYKBQybEI0UwBrkIaklhSc/JIkZ1Q/kB9sBl/pW9nj1QfN/8ujz4/az+lcA5wGyBfQFPgP5//3+l/21+oj7Ffpd9lPvtelm5H/h9N5w3PHdet7t3n3dK+Jd5gDny+Xm5WzkK+G74QDgGeFR4Z/iw+W96BTnb+Sx5Gzm+e+m+g8NOiFKKpYwWTnxPcA5HTMuJaca/Avf+ezrUOXt5uPskvgGCB8UJhYfHZkhQiP+I04hbBsRF+wNpAXiAiIBMf3X/B39YfwI+7L67/7MAnwIfwslEn8V8hqEHHUhPCYFI48fBhh3EK8GlgEe+534yvPI7rfup+1q8974Of+EBWoHQwTTBeIFbgK6Asv9vPlu9HDtwOga5a3f0drX2s/Zh91k35PjsuRZ5ArjjuJs5VXft96D3Obfat9L4szkbu3i7y7rnezx6Kfs+PBy/lIL8xueI3kuNjjOOTs8DjPfKkochwqI+aT1te476pzxBPevAlEKbxHMGHQh8SKWI5MhtxuHGIQMJwh7BZkBff/GAUH+l/zB/lX+wwP9BegJxwurDkcPNhKPFmgbrSHIIU8hKRqzEGEHogEo+m7zEO6f6iLrk+z57/j0DPuZ/h4BVQBUAQP/Nf7v/dr8Bvtz9Q7vqunw5Rjgx90i2r/Zq9gh1oTVB9fK2I/cU9+S30HiUuLQ5b7rA/JR9tv4EPdi86Lw6+zM79j77QuVGWwi5SZlKZMwBDT0MNgojx5lEGED9PmR81DyQfZA/20I1BCpE9EWLBlrH74gFx3pGSoVXQ2fBz8G7QLiBtcGUAUWA4H+Zfww/igBRAQpCLEIkQ0UDzgPVBbvGyUf+x0AF2QOTwclAML5wvhb8qLv7On05ynq/Osa8oX4Sf8E/ucBbP8ABHECl/5n/Nb0me7e5Q/h+Nq13gPa79yC3cDZbdqV2wHez9194tvf7uRh5KLoUe5F8zf5v/op/MX2C/iG9VsBmQ5+F+obmyGYJYkoKS5IKSAmVR0JFIYIuv8z+AL3Zvjl/1EJ4gybEXoUMBetGbUd4RwlGowW7RAhCnIGcAVTBOQEggMJ/1f81fpQ/B0A6gX7Cv8MqA0sD6ERJhPkF44YYxfzEw8NJQbZ/vj5H/fB9cnx5OwN5//nvepO7sj0xvjE/Iv97vzF/Zj+8vs9+tP2Q+9h6jvjud+e3z3ev9ws3T3cJdvh2abbMeF73wXmo+Z86kXsJvRH98L72vyK+cP+ivphBe8I/xnMIWklzSFfKWgp+iPuIFgTng7ABDX9EPUM92z22f+PCDcPkReZF8YZEB1YHUIclhoPFAcNWQpzBUAEMAMuAEoADv1M/CL7xv0vAEsGDwh1C30NNA8AFQgZMhxmGY0WKg/oCaEBKfsY9zHys+3g6aDmeuQq6HDrNfQF+3z8sf/8AHX/Z/7X+ov2MvXg7Rvq++UT4TLh0uLv4kTm3+HG3tzfvduX4JHgluKO4vrkSOZh8Kz1V/7FA7ABogLD/WoFtRN/HtwfJCF9HWwhYSAzHg8bDBhPEAQHrfyi+oH50fuPBUsOvxTcFUwYsxdvHX0djB8QGfIUgwxbBucDxAP+BWsFWgbBAOb9Jv0vAVACcgZvB8kGhgi2C24PkhLTFXUWiBUIEPwICgH8+jT30vTL8JTuTuun6Fzqketa7dnuMfIC89L14fQM9Tv1xPQK9xfzE/Az6wLphuS35VDgdd+d3vLapt/p3IfgW+Ki5nHpF/EZ8/n5Uv0X/Lj/1/qH/acGRBA3FYAZuhg5HQkdqhk0GX0YUxWFDeIDnf+u/eb8ywOyC+EQ6hVrGR0ZjBmRGlse9h2TGi0RaAibBdoGvwh9CSoKrgbzAbkAkAKEBHEK5AwYDNIJWQlxCoEOwRICFDERAw7hCn4CG/1u+J/4OPUe9ILtDerW6bjssu/38OPzo/E19KnwtfNX8XD0e/Kn7//rcOsO7EzoCus85VXnaeWS49PhwuAy3wThSOLK41rrZe/79hb7dfqO/r3/IAD/BRIJdQzED/MSaRYUFxATdxYMGmMZ3hMTDLIMjAweB38HTgloCw4OBRCVEUgUoBLKFgYaQBeDFoQQHRJrENQM2goMDgMKuQdUA+ECogZ/BgIH5gIxAysElwRwBQcLpA1IENgPvwxtC6kGGgRGA/H9b/lO8U3reOpi6MrmZOlE6u/rsepr57LqWO3i8oH3U/cu96r3qfZh+dL0Le9i6Ubhpt0v2mvWHdfB2VjcCuRr51vvAvbM/MIGKQkYByEDtABLBfcJagn6CuQKTQwnE7QUGhSGEO4QUxa5EzIMQwkrCH0O3BD1DQYTtRK4FwsZfhhLE38QChBXFVIPsAlwDO4MVBOtDysKrQgxDVsPVBF8B4kGCAT9AOUAdgH0AvUEuwWBBf0EkAEYBsoFugmrBqIAB/uH+dP1gPV27wbqG+my5aHmneGE4gznRuxP8EPyw/B09GH22/ab9+Tw6u5f6/zmzOdS4m/dH9/k4eTjVOUr5IjryvOf+Or+X/5rAXoGtwj6BsEF+geOEUMVshNQFc0T0hIJEp4QnRMpERkMqQqnB1oGjQZdEH8ZiRp1FQgVkxMxGCcZShhwFpcRaxBYCwMJ3geGC2oMdw0pB6gEDQbEBXMJ8QhICmgJkAamBc4ElQQ2BogJSwk5Bmz/GPzd+SH65Pi59qbzVe6Q7DbrcusW7B7xtPHE8cft6uv97S7vyfCm8fDrIutV6r/orerF7ELwxPAl7rXo9eUE5ELrhe9y9ELzd++H76P0FflR/ncFpQXBBuv/if2u/54IZBG9EnASOhG8FLYWqRjOFVkR/g80D7UKZAbiBzYKcBCXE3sS0RGKEkcXkhg4GHkThw/sEKQRCw4BCq8I8woEDdAKXAliA+sDaARsBA0Fif/8/08EkwNQAwIBzgAqBPcI8AY//zf6Lfde+Pb1AvJU7wbvOfC478nqy+rf64/z+/TE76LtZe3X8Vj0DfQv7WLs+O/t7xDqjunI6DTpwu6g60rt6ezb8Tb77/n791P1BPeu+d35Wfg//CH/BwVWCFUHBwfcBxMR1RTbE1oNYww0DQwPWxFWE4gUsRQlF40ZyhOaEiYSWBQ/FDQNqwq4CIQPgg9IE14NDQwWDQERDhARCnML9AmtEVQPWQSsAfoEbAP7BPUBWvpR+R/4lPmQ+GP1h/f4/Nj9nvfk8o3y0vXR9+b21++P7n/u1vI6+LvyMfWp+Vz3evl/7oLohey/7dPqVeSu5svmDusP8BXzPfJ29srz7++I83rxRfLI99D2s/YX91D4bf2DA88IrgjQBYADQQKoAfsFXALxBigIKwhFDR4OsxRhFgAYrRywGkwTmRY2FhsXWRlPEs4Sww8HDZIMGQzzCnsJ/g16DSgKdgplCW0MfQtXB0QJlAbvBsAEpglSB94A/gHDBDoDfwEhACr8xfsk99j3GvU09nvxnvRV9tvz+fMA8S31x/NM9zf1BvJW8eLvs/Tc8BDs+ewh7qnwXfKn7Ijs6vDw7tPzR/KW7vvtyvEP+vb4Rvb9+Rj7ZP1y/AL7bPso/Db91P5z+5L5Gv/jAIQH3wb9ByUIZwguC4gLxgzZDbwNJw0qEZcPYBBYE5QT/BKJEnsONg5+EHoMowx8DoULPAxQDpsKQxCaD8YP0w8JDdIOAwpTB1MIbQL3Ac0Fn/8pBUoDL/8WAAf/8P5Q/TX+4/mA/Ez97PYa9zz8DPnQ+Bz6QvKh9cH2yPJL9Mj2GvFu8xLyD/P09kDuP/e3973zPvSt6nXu//Ot8GzzwPer9bTymvgn+Sz5iPb8+pf8w/qg+Gf2bf6o+hj+MgQpAwT/2gBEBrECxwAeAvAFogRKB6MKvAIBCOAH2gYfDaUMtwJ7CIQMNQncDrsGPgiiDZgKFQIzDPEFVAmpDUEJzQVVAGcQagUUCGsKkv/FCK4Ljf49DmsHkPxEBm0ClAkJAX37jwP3ADL/y/9M+mMAHfnG8wb9ZAEd+5LqcgUlAeX3E/2x8J0DwPld9Fj3cv9M9ZvnnQu1AfroJvnW9RIFswkS6DP4tALw/kEDcu918SwDVfs693cFb/TM7yP++P2KASP3P/bU/0sC6gAfAqT8EQAo/pcL5gR37NYIef62BdgCMPkrCr7+QwcxC1L9bgX5AIUA5hSl+Hr8FBPsAQAFvg1YBZT6VgMoDNMAWgO5+gYM7wTK9/gJ8/2p/hwKkgKA++QJDAAa/A0S9vcd+ZYbIusuAwAW3/S+/CEA8QruC5jvbu7CGIP6JvezA+z2rg5J8Qn4agiA/0nzxvfOFDXsk/tc/Un9QgyQ5gMCCQ8a5zf8mxRY5RQEVgt/6pYLz/Vx7pEX9PgW6e4XKvdi8eMPhgFE/zP7MvbTDUcHXeFYDRD8jPz8AqP37xLZ6jgAIgfh94QApvZpB8T6evRdEorvQgRYAWn6DQuB+bb46ALKEOboBQctC4D5DQC4+ckPq/sp9VwNSQDLA+r3PPgqDrsA0fjkAIH9VgvQAlbssxcY9hAKzvirBYsTg9+rHvLvCgBxFX3ubAul/uf+xguk+9T1LhHA+4kAJgwO9/777RiS7uEAzg0U60cPLvx4A7j06QeYA+X3bw8g6aUT3/huA+0CmPYuD+PyrAsT/l/8hwdrC0flyA95DEnijBcR96fyRxNf/OHpaQzRBPv4m/ruC4v2KvwABxP1Ugaf+qMB7Pm/ABv/uPmjAHcGZ/YB+D0bWtfqEzgCPeaKIJniIwZaEvbsLvvoFwXxrftSEhHvc/93DPT4IPRMHPTrCwK9Cgj4KwxJ45QaVP7g+XEJMvtwBUX9UAWWAdMDd+ipILTsnQc/CD7pMyFl7275vBku7DUAOxm/3lUjtffA3FwzoepJ8uchBeHMDFsTpeBzF/3+IfFiEpj1nAkA75IPkQKR7AwYvfH9Bu4A3/e0Cf/3fwJX/SH94BJd5GIExhXG6TkEEgJSA4/x9Rbm7Oj7vSJ8xIQxnvR05Dkpzd8tCbUJT+x0EDv3PvS2JXLZgv79KJbQOxx0/ePq/iZm3p4CsBnq78btNSN+5mUE4Q8h4gUm5OIFDIgG1e0iFvnyF/mxHYjlZgV+Eird5yHt8Jr3MxYo57gc5PBI+X4cyOAbEW0ICuV7IMDqRwSmEBflzw9FCt/nzAoXEBnggRH/BgH0hAPSAuPsPR0H7rb/3QeA8woNq+6UGvjhhA3iBvXsLA7V/pnw7xjx41EOogxN26sos+IlCREDnfqsAH/6rQoj8z0SDuL4HUXwjv40DY3vZQ5J+/b1vw6C+RfyPiC30ycm1+5c+P0TavQwBXr7LQIy+DogUdE9H9UBBOp/Hqng0R+O6aMHpAXm+8UDgvT9DpH4aQMrA+/2uQxuBEnsvRTP+PT/6QOb9hsRQval+kkLGgDo+s8DPP+KA7H6aQZK+lsH+fjeAngNKOIHIYLrVwaeDKvgeyeY23cRBwE89EYGHAav8M0L6Pu38H0e3eS2BV4HnvVTB07+v/oxC8zr6BZ89dvzuh2V1tQfuvDJ98sXPuBmFtv0yAWt9rMGr/4w9O8XHeAeF8n8rO5/F+/xU/04DpfuGgdwBZz3UvuME67ylfj+GILh4Rs48Az7Oxd24cAZKPKEBF4GX+4+ETf6/Auj5NoU/wI28BsRD+10Fhn2N/IFG1LuBAVNBCH6GAb2Ad70QxRx9Rr/ewnS+NACdAk98VwLYQC/88QXeuibDFsItOtbEu7+ofPkCEH/Pf3EAtT6FQUl+rcH0fP1CYUBtfOaDLn19Agm/Bj65Aml+w78b/3HCaH7Fvi/Cjz5lP4mCOjxRBJz8sH4RhWj80L6vgtA+Sr36BXa6NILrwCM96EMlvJVAxkGLPzR+T4H8ABU9XMOH/YiBCL8VQPb+/AF8QP96RcjT+FnDuwBXveeEBPnMB524+sWD/K6CRj5Tf+0EoPgaSOt5B4MEAQq7vMhINuaF5UAx+swGgLxAADpCdbuqBWQ9qzy7R2l6XYBww5d6QIX6u59BlH/PvcPFzvjyBhj8ZX/NBDo5dgaze2CAQgKzfK7BdUGa+/xBnULsOqoFebxRAB9C371MQQ0AQj2eAvf+UX8iQij9AUNyvIdBvcAN/dDCjr0pwxB85wGl/glDWXwjAdaBRTxdBCW8b0HOP7bAdj3sAg6/fb4PxFW6gURDv/S6QAeLe3DAlAHVPfCBhP/GPgPEdn2MfQoHj3e5RpT8Xr+URCz7agJ8QKl+zcEV/iqAosM8uxYCJwCJPp4DNjtBwzd+x8GAvjXBOv9tgIr/gYBtv1JAsUC7fDiFwrqiwv2/vD2mQ119mj+Ygdi+XsDiQWf7HEXKvOK+4IQBe6hEpvsgAqmANj5OQqU9hAGwvamEOLrMgowCp/rCRLQ98AArv/1A2H33w0s8GUHTAJp/KQDj/q1BL//7fzU/0UE9frjANUBOgZz71oT5/IzBH4GZ/JxDkHx/REU7rAMWPsX+xkOAPBqCvECyvEtD378ofqXDbvufQypAKHuUxVi8YQBbQxZ79gLDPqU/zIKv+8DC3X7xgF0AkT5bwUo/iP/tv6uBJj32gYGAEv3YA+n7OEN+/sLAfn5hAdfAPD2Sw1g7x4LpAMf6hwduOtZACMWJt40GXj5iPFgGSfnaRXX9Gj2hRdx7o0CzwX1+XMFSvyT+iYQqO5lBOYMFe7ZDW/3GwIoBU/5ggA8DNDqthM19a8Cz/+D/JELJfHWCSP+j/34BNL4ugPkBBv2Qwgu+FEHMv6t+s4Gy/5Z/mf+FAJcA/L3ZARtAhH6mwdn88kOMPUJBvP+6folBC4EN/V+C/L6KgGI/9b/RgL8AAL2tgkeAjvzWQwY+k8AZwfQ7xEPTPuN/AIKIe4iD+79CvQ9EY/xEAXQBs3sDhmH7esGvv8U9rAPUvGfBCwDYQCO9gAOpfTzBXX7Bv6fCML0Agtj+Vj9lQVWA1PzXA43+HAAGwHr/MgKAvEFB5z/YgHD+xYDEP/i/2MDDP1r+8UNWPQE/iUMefMWCvv4bQBGCtLsoxFB+RH8egcm/ZT/1v+FACADHffWByf/L/sBB9b7MAFI/4UBDwPD9vwHh/9A+c4HKfy5A1X2PAl//ej7Kge6+/r86wdf9OkNTPa/APwIHfASDCMApvfvBiz9/PzmBqr3HgeF/Nb7MwYL+nQGZPXHB7z+PPtIB+X6RQDkBXTzTg+/9tX5PhN157AXd+9/BIAFY/tt/JwEhv4hAzn81f1MCDn50ANH/pMCQvx/Avr97gENAIT8EQUo/P8EgPpNBYj/fvjkCcv2SQbAAHX1tRB89Br/Zgra8g4NWfd5+ugTme1+AQoIWf04BG/x0g7u+tj88gWl++4DVvoJBe0FEOxxFKEAWOokFOf3dQAIA5X50Aj4+av8cgp3+fL+UwJAAMD6sQwD9jD7bQxJ+WwFKPY6Bs4GGPT4AwcBcv+D/PoHxPgOANACxwTQ9zMDxgAZAVn7FAGdBhf0pAZrAu78Jv9P/c4J8vah/8cMDvCBCdT+Wva+FJDmbBCKAK30jA3M78gT2/QI++QM5PhY+5UIAvzX/7z/5ABtAQr5xwvR9cwHsvamBhb85gEvARD9DP1IB1X65/rmDsHzcgI8ABgCtfxh/GoHpv6D9G4JjQlq7VoQ6+7IEP36ovMPEu72+P5OAN3/TgV9AOTxbxK39qL+AgPV/dL9KAmr9mUBDwcm9qsPYukXEBD/QvZ0BRv/cQeb8csBFg179zb5SAcbAEQEMvVeDKj7afcjCZj9+AR49SMGSgJIAFf4LglC+zsAvgBHAjz6HQJ4Bgn4CAKK+yEPZvY7+3AFhgT4+z74Qgos/UT+sv+l/lYILfZQBnQCHfZoDX31LQNpAx31IRA/9HYCawPa+k4HEvn6Al0HavN7BHoEbfojAmj+wALz+aYFlwVC9yMCxf/RBEb9w/2BAVn/iAHa/I/8TQ0591X6VwqVAJv7k/mOB2UFGPLwACcMJ/0E9pACogP5Bdj3N/eFDeoB2/lY++kDZQiY+0TwYhG1/139Sfvi+oIXwu8W+HoOev0JAQb1nAD7EIrwpQSgAkb5DBCl7hIGsP/6/7sABvcCC3f91/dcDDH2FQb7A3/0jQe99YkOGfqc8/UObP+h+s0Ezfm6BXP/nvuoArj7kAsT+Fv6ZAdvBO37NPllBwsE+veK/mQHv/24/rj+GgRN/9L8YwO9+20DAwL7+iwCjQRz/bj9Zf/eBdn2GAFeDm7yMPubDpUEBO+Y+RsWgv5C8UABZwzy/6LzVv0gDkv/lfMX/18MDwNp7E4C6wzVAT/2FfxfDJQAG/lT/AX/eAljARXxVwjaBKn/h/3c+s8N3vby/bACgf8xBZj5tAEhBrwA0/mj/6YHEPrF/h/8KwkO/AX+DwHXBBn+J/3ZAsn+8AA4+FkN5v1l8FwIVgnT+3X3zgUOBBH6d/7kBqEAkPtP/WQFNwG6APD6mQfQBh30BgIoBqL6KvkzC7b/tAE79+kGdgzY7ab8zAaaCOz4XO0oDmAQe+mU808btAUS7AP06RydCKLYwQbMFkH0DvXdAYsDQwr68Y0EsQ4e9gkB4vlhCs36BvVNBrsIPvXHAh0KKvp2A5f7Gg2N+BX19waPBL3/0vCN/q0YDPjQ6zgNvhJk+eflrwoEG1DkHPBzE0AKp/ZY8hYOhwpo9vP4HAMeBdH+mfv19A8MogYU8dYB+AI5Cjv8U/G6CyoLHfgY+b74RQ/VCAnnfP28FuMFSerU/wUU2v7S9Dz1fBEECivnSgBVDN0H+fTE8YMWuASC6+b/EQq0B6Px2fJQE/QFj/OX+cMHlwxz9JX7uQnKAMj3U/gPCm4FPfFs9tERZApu6+H1YhZgCWTrLPc8DW4FQfEr+DwJZAeQ99/6qwfZBjL4gvtEBij/ovpz/3UB6PxxBfD/H/dfBMwHs/rs9rUFMwUQ/G75vwAfBKf+wvzc/U4AAgFMAVP7BANeBNn5cwHJALkCJfwW+nAA2AUd/Mj6EAWeA4IDTfg8BMAFyPqMAFD/3ANp/rz6OgOtBrj/9PtuBCgEOQEl/wX9nwRRAtv83/8SAWwFrf2D/0kGcAEd/swD5QLM/4z+uv1LB1r9e/4aAjwBhAZu/L0ALQVAAUr9Of+SBJD9Jf5TA9MBKgAxAv4BxwBYAoEA6f76/1kB5v/D/08AKwSd/1MBrAOG/P8DgwF+/X3/AwLHAEn9/gBLAfgBev6EADcDnv+b/rcAXgCz/t7+EP/iAs3+rv4NArH+WQBi/vr+zP8H/m4AMgBc/rH/PgCc/xf/Jf6cAML+sP1q/y0AfABG/an+VgON/wP7pACQAJv/pf7s+woC+P9f/YYAPv5RAFQAEP3m/4z/Ov/7/kT+Y//O/0P/EP6vADEAl/69/R8AQwH2/fT9+gBJAAT++f93/10Ayf93/isAfQAj/z//AgD0/yMA9v2g/ycCl/7m/ssAsACHAKv+M/+yAEcAhv6D/68AAwAHACD/3gCqAET/SwDV/5H/oQBl/+D+TQE6AI7/vQC6/8cAmAAh/0cA8wC5/wQA/AC+/70AdQALAHkBy/57AOMBrP9n/yMBDwGv/+8Am/9RAbIAEP/CAVAAkv+dAcr/oACZAIb/UQEJABAARQD7AIcALgDx/6YAwABE/2QAogD9/54ApP9BAIQBF//t/5wAUwA6ADX/PAF5ADr/AgA2ALwAOP/u/9gANABl/2YAFADs/xAAqv9NAKn/gABw/0YAdQCL/y4Aaf8XAE8Aff/n//n/MgDQ/9j/zf8EACkALP/K/zgAsv/P/+//0/8fAKL/3v9q/+D/xP+r/3H/kf9+AHb/e//M/8//5P+a/37/bP8mAJD/g//8/0r/9f+C/+H/uv9B/wsAx/9Y/9z/nf/4/8j/if90/73/BQAr/xQAp//H/7X/u//m/5v/sv/3/5b/0//i/6r/wf+d/+//xP/R/9z/5f+m/+3/7v+l/7H/CwC5/7n/6P/8/+X/tP/c/wQA7/+x//b/7//1/+//5f8CAN7/4v8wAPT/0f8VAC4A3v8AACgA/v8KAAMAQQDz//f/KAA1AB0A4P9IAB0AEAA8AAMAOAAvABkANwApACAANwALACkAPAA9APv/HgCFABEA3f9FAIMAEgDs/0YAaQAzAOX/UQCBAAoADgBiAE4ALADz/0EAigAfAPj/QgBlADAALABtAHAANAAhAD0AdQACACAAiABOACQAVABwAGkAKAAkAHcATwAsAB4ANAB8ADIANwBBAFIAUgAHAE0APgBNAB0APgBkAAsARAAgAGUALAAUAEYASAA0AC0AQgATADkAPgA0ABsAJQBOACIAFABEAD8ABgAaADsALAAUACIALgAWAB4AAQAyADoAAAAQACoAHwAEAAUAIQAhAP3/DAAaACwAAgAOABUADgATAAYAGwAQABsACQAJABQACAABAA4AEAAEABYABgAAAA4ADgAAAAoACAD9/wYACQAGAAAABQAIAAsACgAEAAsABAAEAAcAAwACAAYABQAKAAAABgAEAAoADAAAAA8AAAAKAAMA/P8LAAgABAAAAAkAAAADAAAABgADAAQA/v8GAA4A//8CAAgACgABAAQABgACAAYAAwAJAA0AAAAEAAUAAwADAP//AgAHAAIAAAACAAEA/f///wAA/f/+//z/AAD7//v/+P/z//f/+P/5//n/9//3//T/+P/5//j/9//2//D/8//1//X/+v/v//H/+v/1//X/9//7//3/9P/z//b/+v////f/+v/3/+v/9f/7/wMAAgD1//r/+//5//X/9v/7//7/+v/7/wMA9v/6//f/+P/+//z/AQAAAAIA+v/3//j/9v/0//f/AAAFAP3/8P/x/wAA9//5//r/AwAHAPn/9//2//v/+P/9/wIAAgD///z/8v8BAAIA+P8AAAMA/v/5/+P/7v8FAAsABgD9/wgA//8BAO7/9v8CAAgA/P/x////9//5/+3/+P8KAA4A/P////v/9P/1//z/FQABAOv/7v8HABMA+f/u/wMADwD1//H/BQAMAAAA7f8JABYA8P/3/wAAEwAjAAAA///d//r/FQA5AD4A+f/5//n/BwDg//r/CAABADYAGwAxAAEA2f/m//r/CwDa//X/JQAaABwA6P/h/+//AAArAPj/0v/n//7/FADC/5L/+/8YAAQA5P/v//T/y//R/wAA/P8YAO//9f/X/8X/IgAiACsA5P/k/8P/4P/0/+z/HABDAB0A2P+q/3P/ff/v/xkAUAAXAL7/tf9d/zH/YP9+/3wA6gD9/+3+Nf25/qsCYgJVAtUA0v5q/M78DP7z/wgDwgPZAmL+1/vk+gP+TwO8A2YCBwFz/HH77fzB/wcDDgDe/Jv+iwG7AFD+jwB2A0gC7/Z89HIP3hi7EmH88O+P8pDwpgXQCW4PHAgZ+VH6HfSe9Sj7yQh8EY8ICQCs90b0fPqmAPgFKANFAS0CcvsM+rf5PP+JBnQEuf9H/pj+If62/rIADAMcA3cCkQD3/i79VgEJAgkEugNVAYYBMwAg/9X/ygAKAL8BXwEyAigBXP+y/Un/wv+SAowB9/7H//H+t/6T/UT++P+sAYkA5/7X/ej9Xf9ZAC4ANP9vABH/QQCZ/nr+lgB3//7+tf1VAc0CdwFrAI/+5fwP/qz9xQB5AzABFgB9/vb9pP+QAvACKgNB/ukATQDT+iYAYgVrCDUAJfkl/asCsv1c+0EBYgr+BBn5B/leAFr+nQERBXP9/AGZ/5UCuQS1AMwEwgHF/loE5AKNAiX8bP5vBM4EE/3k+1oCZQIG/Cj3eQBzAmz/i/jJ/7H/mQCi/EX+pwDv/or/v/sYADX9GAAn/eb+kwIZAR79rv7aBMAIaP80+5gB6wTZBdb/pQRjA/r9k/36/iAEIQRaALP/hQBOAHL6UPzqBHMFWAE0+QH9NAJEAan/mPsVApsAzfpO/CQC7ANx/Zr58f0uAXf/K/7JAYEBV/12+7n8AQaz/0z7rfxTBIwEDP4AAQsDBADB+UH9CAbBCBf93v1s/tj/JwGEAFUECP9o/Hn+9wGnAYL/u/5EAv377fcf//0E4wPiAY/7X/af+OP9QAbqBcr+9vzA/Jn6qf6M/ZIBnwETAb8B6/2n/VL86P6FAZEBAwDSAYcBhP49/ikAXQHBAO8Aqf8m/awChwKW/gT7Tv4TBp0BgwBJAhUByP3C+6wAegbcBDL/3vvi/1IE5f/o/8UAJwJoAkv+Z/5LAXICMQW2Awv9yfvr/SwEIQSNA8oDFv5j+mn/twSFAykAkALN/yv+SP7e/5cDc/91/3f+3wBFAHH8j/5kAf8Bi/7d/eIC9fyc+0oB/AEQAzn9TP0OAMn+kwQtBS38rvrK/+gDFAOaAFwEKf+3+xP+MQFvA/UBegKh/0f/GPw6/UgA1gMOBCMBS/9N/KL8uQDxAroBzABR/tz/A//O/vwA0P9LAXIB4/0o/Zj79v/jBCwFEP8Y/Az+pv5kAQ4BJARUAsgAHf3l+64ARgMWA8EAqgGS/yr9+Puc/xIEXASBALf73/tn/QQCr/8I/wsBLP1B/pL8XPz+/qwCiQHt+W/3If9mAv//yvzj/ln+dPyi/pf+gAG3/UT+SgFqARn/bfnD/CoEqgE5AHX/DwCb/9T6mgA7BAcD1//y+0UA7AIhARkAhQAjA77/ef/tAAACHgKY/tgC+QKhAF/+Gv1sAg4FRQHX/8j/+v9bAAH/zgKgA/wB1P2m/YgA2QNDAhkAOAG3Ad8APv5mAa4CuAHeAEIAugByAZgBOQNmAdL/6v7t/rcBYwJLAQcAJP0CADcAkv4nATr+Rv9y/Qf9DP7C/O3+m/0B/Hr6Hvmd+936t/yQ/J34x/ap9pr47fk6++P4x/g89oj2GPjN92P8Ffst+Lj2kfdW/J37OfrQ+uL4WvsG/cP/MAGM/kYAbADcAioFcgTUBZ8JdQi7B+4JYwkTDdEMYw8qEv4PIhA5Ek4QwxOAFfgRKhRvEpQTBxP3EVgU0RBFD5sNrg0OD/YKiQ1iBikGywUPAgsEuf7d/8r7N/r/+QT3IPUE9t30APIy8cLtWO+67w3uLO+o6prqKe4x6djt9+rm6nbuzeco7qHr/+zX7EHpHeyE7Lbsd+yG71Dtku1p673tePG08RbyRvEM8a3wnPiI/uECsAWJ/Ir9Gv6bBssRzA3GEbcNlg8TE/EUwhU4GOsaLB2qIEAdSx8UHrceqCCUHLAZshriGc0fjB2HF7YTRg8aDD4NygzGC2gOxAW/Bpv9bPsiADj4af+R+vP42vpN9eP3L/We81rzkPXi9OD4svf+9MH4iPKM9eH26/ag/Df6Y/nn+Lf1GPZu9rX0V/jR9/v0O/XW7UzuZOu86vzrjekL6qflz+Uh4rvk4eBm38/j8uBT6JvhO+ET6KvrNvm19l70vfDt8RH9zAWWDHEILQopCi8UZRm9Fpob4hgBIFIm/CbvKRkjYCOCJ7IjHCpkIv8gZCXZITQkRRixEzUULRKGFagQEAiiB9EGUwUABIr7UPp5+XT5ov20+Gf3bPaK84H0b/RS9Ln4U/iS+jz54PbC+G/3RPqw/MUBsP/kAI/7f/tH/un7jgHu+nn7tfu29876Q/X48TDxEu0q7M7oZuh450Tl6eGM3t3a2Nl+3XLcW96n3fTZ1taC2i3jFO269kDxI+xu5hnz7QXcBqcOGQYSCgUW9xc3HjMZqB4AKZAqAS+FLdMmRC1lLWUtki30JHErwydRKMQnWxrkGuoRfxXzFbwQlg8YBgoFPwNKAYb8XPhP9836X/xI+pb2xvBj8Hz1DPUm+b/3F/he+/n2u/qi9qz51v5xAJUEkAHd/mAAgf6SAtwBdv/MAGz8fQBT+0L5ZfjJ8Sr2sO8X72TrHuNV6nXfxeT+36TY0d3w0Z/bxda914PcCNUA2f3RwtdS5cXsIPa97EbnMung8rUEOwajDEoMeQ5zFeAV4RiEFuogEitbL8Iw/yi2JY0kzyqQLhIreCxwKsUnNSWdHbEZShXGGVgbHRUsEakHRwi7BA8H4AFD/TMAx/yJAXn6UfjN9Z/0RvwY+7D7QPzC9y/8Cvry+qD8svoVA0QCKwQkA7H8UABJ/oUDNwZ1AO8FPf23/yz8hvYS+tbzCvog93/zj+7t52HlgeRW4yPilODT3ZbdmtkG1hTSc9PM1Wfb3dkt2eXUI9T24tvnn/NI7sLpnu2p7y4DyAdTCEYO8AxbFZ8YiRi8HA8bUCdfLtUu9y2oJ24luSUAKwstuyoxKgAnHiRsH1kbCRh6FAQZcRZdE5YMQwZ2Bd4C3AWVAVL/dP7g/aL+/vt++Zv2UPhS++/+hv1r/ZX6Rfz2/Dn/mQAM/0EEmQL0BfMD2QFpAccAFAMtBfQCWgIwAK/7Kfs1+bT1zPbK86/0afRv6sjr+uBH49DjXOBD4z3c09xW2WfV4NKQ04zWhdzX3TbapdUb02DfluvX8zjzAOzT64jwggBTCHUJzw92Dx4WRRb3FpkZWxlUKCctYS5uKwImfCJqI6Io2CkuK0Aq2yrLItwdjRizFDcWehoLG40V8A7QB30EGAP8Bv4DlwRjA1EBswAN+1H6hfjZ+tr//v8A/wn+kPrk+277D/2P/h//iAN7AkECa/+o/DT9AP73/x4C8gBbAar+x/jL9UfzufTf98T3afYO8cXrWunf5GzjWeJw5VXkGuVe4YXZItf70t7X8dlM3Y3hTN5w3LXY0Nd64DbpJfUK9aLxafLg8uT97QLHCGsNKRDrFgcZlxUAFQ4Y6x+0KYkrtyh4ItAfGCOwJKkluyYQJmgl2CLvHYkXrRS9Fr8YXBh2Ff0PQgtTCTcHEAbqBPsFSQf+BSID4P/C+7D8sv64/1kC4gDsAH7+ef2t/IP89/7M/+oBzQBWALj+/ft2/Kb7Q/zG/T/+4vz++uH35PRa9ELzzvRS8/Tx/fDq7BnqTefT5N3k++TN5CnjDd+H3U7bMtsL3MDbm9xx3NTeGN8/3g/guOGi54HsgPDE86j0ZPng/FgASARmCR8OJhNfFmsU5BYAF6Ed2yEcIvEkzSEiIqciqiLeIuYj5CLcIqMgmB7gHD0aQRnNF5oXtRSDFdARLw72DDcJOwrPCXEJFQgRBg4ELwOLAqkAuwJRAW4CRQNmABAAb/7v/ob/rwCa/5z/IP/9/OL+bfv7+1D8rfqX/Gf6dPk8+Aj3QfYK9v/zq/Lt8arvG++97HHq8uhp5jTljuMp4Rnhsd4e3mrciNvU2y3aFNss2ivbD9yV3areCuB841zmxeqt7QPxefRQ+AL9YAHlA3cHzgw9EKUU0BWIFv8YSRzcINsi8CLuIkMj3SNhJXQl5iT1Jf0lKSW+ItgfQB7yHW0e6hz0GtIXQRbFFG0SWRHNDnkOZA6rDJwLfgghBysGIQWHBagDfgOKAq8BbgBf/7X+r/5V/3z+zv7X/Ev89Puw+qr7FPs2+qT59feP9+327/Us9a/zf/Jl8eTv2e1s7Bfrgung583lBOPv4JPflN5M3UzcLttH2qDZ4dgM2avX19jZ2YHan9yQ3dzfreFr5RXpruxm8G70lvjY+2oAeQPPBm0KkQ5pEiEVOBgVGpAbSx6DIMQi2CPxJPUlpyXMJq4mqiZGJtol2CRHI84iXiGqHw4eyxzQGmYZZBf5FVkUoRKlEQ0Pqg2EDCYLEAoUCQEH/gWRBJwDCQNeAaoBJwAYAC3/j/5J/lb9j/1n/CX8OPvn+mb6wvmf+MT3gfY69Uv0CvMK8vLw++887r/sqepA6Srns+VC5FDiKeEL36zdpdvo2c3Zmdis2InYMdjV2DfYItlk2YjagNwi3w7iIeUa6Znr7e+H8374w/x2AE0FkAhiDI4PABOfFcYYCxtZHU0fJiEDI8cjJCWUJVQmLSbfJrEmQib6JeQkAySRIp8hECDUHi4duBvyGQEYehaRFGUTDxLAEAkPdw0uDJgKfAkLCPAG3wXHBCcEvgLCAQcBIgCl//D+W/6//TT9l/wC/Or6RvqH+Y349/cL90D2NvU19ELzEfK18OrvNe4H7W3rFuri6OPmmOVb4xfiNODA3vnchduz2mfZPtmY2I3YdtiE2O7YrNmB2qrbfd2R387iquV36VXtC/Fl9Vb5mv1oAYQFbgkLDWIQPxOnFeUXWBqSHMAekSCUItwj+iTPJfklYCaNJucmyCZRJqYlsSR6I4EiNSHGH38ewRxzG4gZ1RdiFoIUYBPGEVoQBg85DQoMZApECUMI3QbqBdEE8AP7Au8B4wAfADr/if6p/bL8JfyI+xv7nfoK+o350fg2+F33M/Yt9R/0N/OI8UDwwO5A7bvrOuqi6BPnkuUb5IviRuAc3+Pcftss2rTYN9gk1zXXD9f41mTXI9jX2LLZQ9vp3CHfrOFA5TbpGO2N8ab1ofm9/TUCLQbACbIN/xDXE0EWqRiYGoMcAh8AIWki/CNyJfwlhyb2JjonMycrJ+0m7CU3JU4kRCP3IdwgkB+6HSkcZBqXGPoWhRXpE20S0BBJD7AN/gu7CnUJgwiwB4sGagVIBEcDHQIGAf//zf7t/Z78rPu9+hj60vk5+Qn5cvgC+FX3tvb29R31L/QE86bxAfCk7vLsg+vp6Yvo/eas5TbkReID4STfwd103Ajb69nC2PnXjdcj1znXytdU2ETZYtpe2+Hc4N4Z4SHkiefA64rvy/MY+AT8SgCCBN4IfAxBEH4TZRaCGJwayBy5HsEgpiKDJIwl8iaBJ9InMChBKFEosidPJ6cmnSWOJJwjeCL2IMIfKB4kHFkamBjZFhQVrBMkEmMQxQ4NDWkL4AnICJgHkQZ+BVUEPwMAAiMB3/8I//P93Pze+8L6KPpT+Rb5zviW+Ef47fd+9+j2W/aQ9ef0sPOr8kjxle9A7pXsHOup6TnoteYh5YbjN+KY4NPerN0C3Lvap9mB2NLXK9ch1y3XUNfr177Ykdm92kDcCN5V4Abjd+ZK6l3u0fLo9tz6/v4yAx4H4ArIDkwSYRUeGLkarhzPHjUhNCPmJI4mNijIKEQprinHKZ8plyn5KAwoSCcaJgAlhCOXIh0hRB/DHQ8cLhp4GAwXhxXyE1QS0RABD1YN0AuSCkkJlQiHBzQGIQUJBMwChAGlAJL/rP5v/Zb8jvu4+kD6v/mV+Sr5yfhH+H734vb69dj0xvOw8kHx2+9y7qTshuv26aHoL+fO5aDk5+K+4UHgwt5k3cXbpdpZ2RvYodfC1nzWtta+1lTXv9cH2SjaHtts3UffEOI85QTpPu0b8dn1IPrA/QACKQY/CvANRhE6FZcXiBrcHPAeayFNI1AlcCb/J8coWylmKdkp8Sk6KTYpMyhrJ0QmuyTAI2Ei5CDgHgYdFBs8GZgXzBXCFBwTsRE4EEUO/AxECzMKSwk4CJkHNQZdBSQE7gL9AdYASwBl/4X+qv2x/P77ffu2+r76WPok+qf57fh4+CP3Fvan9Hbz4fFT8KzuMe2J68bpg+im5mblJOSS4mLhOeDb3nPdCtyw2mzZQNiG1+HWgdY41mvW+9ZG10TYgNnm2jfcKt5s4Injm+Zz6jbv6/Lw95T7uv8tBD0IDg1/EKEUlxg+G7UdZSCkImcknyZgKO8p4SqSK7MrlyuzK2Mr0Sr6KbkpJCiqJjUlUiPmIS0gbB5cHFgaVBjDFqQUTBOtEbkPhQ6mDEALwwl9CLEHnQbeBdEE8AMQA7MBBgEhAKb/8f5Z/tj90Pyd/H773/oY+2f6uPr/+W/5NfmZ96b2PvU59EvzwfGB8NvuOO2U66zpzOf/5WfkHeOT4Vngzt7j3Kvb0dnq2NPXhdb51nnVltWx1SjVbtYM1kjXf9hw2XDbwNy03y7j2ea766/vifSn+Bn8JgDUA44IVAyzEJQUhxeMGggdNR+6IOIjmSXkKDMrhSswLXQsLi0wLSIs7yyrLDQstyreKL0mzCTWIpYh6h+CHvActBpFGYkW7xQqEpsRfBA2Dx4OmQv4CVcIIAfzBd0FZASABIADdALAAasAHwDO/8P/lf4p/4X+7/0I/hL9yvwv/NL6HvrX+Ab40/YL9VrzGfG576Lt9utW6lXoCeZQ5STjDeGT393dc90E3ILbYNnx19TXoNZ21oXVw9Wf1rPVzdei19vXq9g62NzaF9s33pzgi+Ld51Hrie4o8c73evvO/9YCHAfsCvQNEhMbFUcXdhvjHkYf6CIQI+QlrybRKkIqoStdL34r7iwGK0Mr/ytaKsUqzCiPJrwk8iA0HxQe0h2xGv4ZHxesFGYTZRA9ERUN9A01C7YIBgkuBkIHfwR4BQsDxgEzAg3/TQDC/WP//v3I/er99/pI/Pv57fpZ+K/55ffS9w74v/B69FXtmvCo733qce1Z58nnSOKT4CfiA+CN3q7ce9vo2UfV+NXO0C3ccd7Y11DQQc73z1/Or9hu08ja0d6j2xDZnNqh2d3cJOFN5kDvtfSKAuwAPATu/YP/cQieEOghZSF2KCso7yNpIQojzCZAKNg0CTZ8OVY4ky0ZJ1ckBSdRK+st7i2tLGgmiyCEF9oTJBEqFmQbNhulGVsNGwfYAiAEwgYRCNgGKgqCB70FKwIB/uz+MAHaBrME9QgYBasFdwYzBEMCYAGRBMsCdgj7Bn0DTgd9AAQBIf8g/Hb9lfmP+bj4Pvha9szzXutA50HjGeFd5bXlT+eG5Obdwtaa1LrUu9MC2XPXNds63MnXgtOYznDQuNG61vHa2dxh4ELgIN0L3oHcFuKL5/7v2fQ09fvzOvGQ+9sJoRk3HOEVTBHQEGwawSbHJ7cuYTTKMnYzPiefH4AikCoxOJE7CDQxLKwfLxsEGzMbqh3sH1Ah/h1PGPAOaQZRAkMHEgjQDMILAQapBZYCDP+q/kb9p/5MA7YFZQmhBjsIKQQYAsECTgQLBc8JRg3gC5IPxgjABLcAzgBVAqAHxgveB3oJl/+B+5/3+PGs9bz0Hvhj93rzH+4K5wHgktwz3VDfAOOP5LDgut0z1oXSktDQzrbWmdT23kzdDNuj2e3MdNEAz8zWJN/g4LLnzuVG5PLgvt3h40PpBveM/Aj8zv3C9pv/hA1OGNcjoCB6HOcerCHyLTItFzJPNoY1TTtpMdYrHCdWKZ4w9TMtNFEqciUrGrwZkxsWFJ4aKRAUE7gQJg1nC0wBOQTU/rAB8/5E/ln+awE8BtgCFwPQ/qn7Zv6OAfIFuQxJEfQNtA0RB6gFFAUPBp8LIgwXE3wPDQtCBWsBc//hAnsCmgL5/7P9Uvrd88j14uxl7c/sQ+jQ6Xfl9uAt3tndfeDt3M7dtNl707LalNR92JHZO9H52wvRt9rL3HHRNN+uznzXhtoJ1wTkL95U5cjhXuGJ4GTlUekT9M38HfcZ/hTzEv+WEuAgcC4nJmwi6h0dIsUvwy5eOAY+jkLXQXo7cy/9IrgmmiixMiM0njYnKL0h1xheE0YPAgsgDB8IgxQVDz4OLgEB/RL5YvYh+8n1T/qwAwYGLgmMCGn+Yv5y/P/+wwVzDAkSkBNlE7oPSgjjCDUESQjpDNgN4BAZDJgKdAZQBFwEdP5k/ID4pvYE+gf33fcs8YjuG+kR5bLiN97P4PLdcOMM4gfgT+C41cHZStP41DvXGtbB2prd+do02dXa7M5F3SHSOtqe2t/SBOGj17bjZ+GH34/ebuOy5aPqrfhf7Zf7gvV59ikKygs4KvEpKyz2KDscYSY4K2wxdjnZOqQ+xUUoOno2YCJjIdgmkSXiM8gkBiZlIdQZaB19DcsI5//5/msGEAkLDC8JigfpAS7+YvhO8rf3AP4yBxEQpw5+D2UKkQd1B1AExwk7DXsR9hiPFtwXUBMhDmMLdQMtBH8DtAU6C5cKmAhsB7T9Vfoq8jTu9esO7TLw+vRw9drxA+wG3nze6NWg3BvdheDj46vhi+XX3LDaDdQH0ErVGdqR3Z3f89sf2dzdKdw73VrYj9DT17DWTuNQ4uHiiOJY4pDo1+GS69/jEvDs+Cb7gQPy+/0H4BStJ6UyVS+sJAAk3Su+MCI7rDbONjk/az6PPWg02CQXJhQhgCddKSYgxCAlGjcZEBlgEBkJ7AJA/qwCi/6zAo4BmwSHCHMFPQI1+oj4MfeQ/rwDawtyDxgS+BG3D0kPgws3Cx8LvwpdD24RKRQKFnARXRHBCKAFzAFX/LUA5/3KAyUFVAJ0APz0y+8A7HLolusy6vPpt+2W6v/sg+a94CLd79RP2wLZ7N0f4tXeJeVX4GXf0NpF1NbSKNUz1lDdpuGo3RDlTtqL29PY39Mw2l7Zmd9v4nLlm+Ys7WztwvHz9OntivTz82oAZBhsJgc4/zOCLikoiiQfLAorUTGUNfE9N0VWRbQ8By0cJGwf4iGDIEMgBBopGhUf5h6GHeMRHwek/Mv5i/jm+7z+Owj8DIsNsglJ/Rf3DvOJ9xT84gS2CuwPmhNYFMgTdRFTDuMK2wgDCJYJgAzZD3ITKxSREXIMRAU5/vn5Gfeo93f7nf1MAD78jvek7mjpkuRg41rjEuTP5YnlsOe65KjlKuCJ3hTaDNfH2B7YG96/4eLk8eZH5QbhJ94R29zYn9t+29vfr+OQ5VjpPOZO5Pzfbt153SHhB+X76ILvlPEe9zr5IvmK/Mr/6QtnF/IgKSclKA0qzi0nMQcvnDG5Lk0zLDjXN3E4QjKILq4ruChsJeIhMRpGF8AV3hSaFh4UERCLDb0JKwaQA2//2P3P/o//mwE9ASP/Tf6C/If+c//1/2f/tv8CAQoE7gfPCBUKxAgDCEsFiAS8ATEAvwBF/9gC5AFtAtQBR/0m/Tz6o/fD9zD16fSM9Y30cPVq9J7zzvIJ8bPwmu+s7uftwe1Z7cTtcezv6xjrtOrG6+Dp0uoN6uDqIeuV6uHpmOmC6uTpP+sg6evpl+qB6mvrXeq06M/oxeo47BDvcu617izvgPD+8jnzFPQJ9rr5Mv62AmIFJwckC5oOwxHpE5ATsRR+Fg4a3RzRHpkfeSF0Iw8lDSeTJSMl0SNSI6giUSLOIbMhxCFKIfkgWB4IHaAayhfjFnoUXBLUEBkOhA3CC5MKowgjBiQFagMXAvL/B/7C+3f7g/oQ+m/6v/iw+EL3bPXc9LXzd/MF9Lf0ffVe9mj3ovhS+Y/6DPrF+Qb5IfjD+On32PlD+Qb5Uvm/9tj1nPN98Yfw7e8o72DulOxJ6nnojeZs5dbjAuOW4pzhYOEe4M3e/94m387f0t9w30PfgN/G4B3hQuJZ4+TjjeVf5xDpousU7oTwFvRu9wT7pv46Ai4G4gloDoQR0hSeGP8aPR+LIkIl9ieXKQUspy1dLz8xojBPMSQxVTBnMKMvwi4HLVIskCoDKYAnuCRFIZ4eIBuYF9UUrxHpDvcMjgpyB6oENQGb/j78/fpc+bL4T/hx9gX20vRh8030fvOe8+DzHPQq9dz0HfZB9ZT16/Uj9ln2Z/Zv9gf1QvYp9qj31/fQ9sf1D/SS8wbyvfE+8JPvG+667aDsueua67HpGOnP5/bm2+Sd5DPjeeJ3453ijOIJ4qHhquD74Jjg+N9F4N/fpeCf4XLj0uTI5pboXOr/7PruMPIR9Sv5Mv15AXcG5QnuDTURBhS7Fw4bRh7kIOsjWibbKNMqSSxtLVYuSC/fLusuWy5dLWssrSsYKmYpXCdHJSwjRSFUH+kbVxmCFUATshDiDdcKIgjiBREEvgLhABr/ff0U/LH6WvoF+Tr4Kvi597v3e/cB96X2A/eH94L3jPdv9y33T/fR9/33Ifjj+AD5PPlw+SP56Pjb+NP44/cv9yX2H/Wp9PrzJvMs8jjxcvBF75ju5+yB647qB+lU6BrnL+ZW5SflW+Qs5IHjHON54pThhuFn4J7geeC+4CHiiuOc5BTmD+eg6PbqB+3T7zTzKPf6+hP/fwKxBT8JeAxqD4AS3xV/GGEbKh4lIIsigCRoJrAnHCnxKcQpuCmHKQopmChnKEgngyYCJq8kRCOOIaUfRh0NG1wYABWAEucPng3YCzEKgwjaBmMFUANiAaL/wv0z/Ef7Wvo6+eP4Qvju97D3rPev96X3MPiW9w73Tvf79l/3r/fz93b4KPn8+ef5VvpK+mP6KPrY+f349/d89z/28vX69IX0tPOx8hXy0fDh76fuYe1C7FHrX+oX6UromOcG54rmqeU35W3kKeR644fiL+Ly4WHiieKr41nkAuVi5jfng+j/6ZrsN+9B8/r3Gvt7/nkBcARJB4gKXA3WD4cTjxbnGBYbOB3rHh0hQiPAJGUlDybXJUglsiVKJTElAyUDJdQkYySkI+QhciAHH/scfxp/FycUPxH3DhYNnwtVCiMJpAfkBUQEHAIpAHT+8/zE+576jPlR+K/3zffL9wX4Wvgl+FH4//ev91v3QPfr9w340PiB+QH68fqZ+3b8/vxH/VL9e/y4+6X6FPkN+PH2J/ZJ9Zv0rvPX8nzyg/HH8JHvm+5t7U7sbevH6YHpBekJ6RjpmOjR6F3orehF6GvnJ+fE5vzmUOcK6MLop+nF6t3rtuzV7YPvhvEA9fX4cvw9/7gBOQRpBgYJNwvwDM0P1BITFfoWiBjGGdsb/x2vH30gwyAFIU0gYCDVHz4fGh/QHl0fHx++Hgoehxy2G6UaZxi/FXESiQ9WDcYLcwoVCZII8gfgBt8F1QP7AbYAFP8Z/p/8aPsy+nv5f/mD+S76Wfq/+rf6hPod+j/53/h7+Ob4JvnM+U76p/rl+4P8Uf2c/U/96fwp/E37Qfoz+az4L/gP+On3KffH9uj1wPUy9XT0m/Mg8nnxkPCz7+XuI+607c3txO2e7UDtau3Y7f/t2e4x7gPuBe5b7X7t4+zm7C/tgO7j797w7fFH8jjz+fP09Ir1/PZs+Rz8iP/cAd0DygW1B9cJhwslDekO5BA1E0AVjxbaFxYZ1RpfHGYdvR1yHWsdIR32HCscbBvQGpQaQhrIGcMYOxdDFnkUoRK5D7cMpQk3B/YF7AMUA/oB4ADOAGf/Vf7N/Ej7svpu+Tb5G/io9+H3nPey+Nj4R/my+az5KPoZ+g/69fnP+Qz6TfqN+gz7dPtN/OT8S/3P/YX9hv34/Df8uPvf+mP68vmm+Z75c/kj+db4L/il97r2zfUR9Sr0sfMJ85PyNPII8gfyH/Jr8pXyuPLa8gTzSfOa8xH0e/Rj9XL2b/dd+Lf4Gvle+b75Kfpz+vj6lvv++2r8GPzx+6n7svtC/IT8tP0x/hb/w/8/AGQBYALNA2sFCQdzCM0JigpQC00McA3tDnUQ+xEZE+ATRhRwFNMUtRT7FKcUNBT0E+0S3hIoEj4SzRGREHsPbQ3YC0oKughTBxIGsgSHAwwCsQB1/4T+Iv62/Yb9sPzu+0H7f/pV+jv6d/ri+nj7+Psw/JL88fxe/eD94P3j/dL91v1W/lr+ev6N/rv+/f4F/7j+7f01/Tn8qPtc+wz7Rfvs+uL6fvow+ur5Hfkb+dr4/vja+CD4Mvet9qr2G/en96n3rvfe9yD4yPhk+dT5Xfrc+rT7Avwk/Z/9gv6g/2L/ef9b/sj9Qv32/Gv8qvua+5b7MPxx/DD87fvw+4f7jfuP+qD5y/jU91j3mPYP96T3VvmV+gr7TPto+2P8Mv5E/08AeQCBAJ8BuwI5BTsIWAuCDgwQghDHEPkPGRCeD8MOKQ++DigPtg72DXMN+wy7DM0LfAqxCP8GUAWQA7QBcABd/5P/4/8kADYA6v9yACYBtQEMAXL/3P20/ZH+zQAdAlIDbASOBEwFcAQ2BPYDgQSYBHYEwgMdA+UExQQIBh4ERQJnAbL/VgCg//7+a/4Z/cD7JfsP+p36CPoW+pf55PhK+Wv4xfmg+Vf6k/ow+of6cvr0+u76jPvr+3D8/PxY/Ff8gPyu/PX9Ov5d/pD+3f1A/qj+Gv8NABEAMwBBACIAxgAzAeIBBQIRAX8Arv/U/5cABACCAK4AIQGEAscBewHdAF0AfwAZANn/d//Y/1//Ef92/gn+n/6y/vD+lf6S/pv+af6i/Zz8k/yt/TL/3v8TANz/XAAQATsBNwHbAAwBBQBh/8v9Rf04/pX+1f+c/nj9H/uy+bz4hfes9yv2jvWe8//v2+6R7UXwW/Jl8SbwRu0q7UXvqe+88ZbzPvQB+7n7vwKVBB0HNQcLBxEKJAp4Eu4MDxIMDnkN1BmJHRsyCDfJMTwm+xC9CJ4GIwXYAEL5NfJ09qr6AwL7BGAGiAZyAfn7fPFb7Tvr1O0n84z6GAD7BZAHUAiHDIAOQhB7DSQGBwBY/H78sgEVBhIM1gwNDuYMOQv6CYoFGwFR/Ub55vW082LxLPQF+Iz99P9bAE/9dfpq+Fn2zfX69Kn0bvUg+L768v6BAYsDXQS6A2cBwv1i+oz3rve1+FP6jPy6/Vz/9P9J/yP+Gfxx+sz4ffZj9fz0efai+dL7Qf5W/9T/EgCV/r38zfov+iP7Ufz9/Tv/RAGLA2cF6gUnBdEDjAJDAcv/vP5a/nn/mwAYArIC1QJ5AyIDsgJMAZL/lP5x/dj8A/zp+6z8Ov6n/3IAZwA3AHQA1P/H/xH/7v5a/4X/CADGAPwBDwMmBNcDKQOZAmoB7QBeAO3/OQBiAIwA7gDmAEUBjQHiARsC3gGkAe4AowBhAIYA+QBHAWEB8QBfADUALwBrAFsA6P8QABoAyABeAbgBXgK2ArYCiAIjAvsBFALjAcMBnAHwAY4CzQKjAhkCAQITAigC0gEVAcoAfQDEALgAngDbAPsAKQHzAGwA5P+//5L/0v+J/1n/g/+W/4EAyAAuATIBAAHzAIkAAgCA/4D/kP8VAEQAeADWACwBXQGXAdsA5gCjAJsAlwDu/woA+v9bAMIAPQEiAZkB6QCnABYAAACDADoAEgBw/7D/8/++AGcBLQE9AcgAnQApADQAk/+S/zj/UP5X/1L+7P8UADoA9P/q/hb+bf6//Yb9V/1d/Jr9Jv1e/hv+SP/M/9T/0P+b/nH/2v5w/rr9B/1z/Vf+lv5T/t/+vv7L/wj/DP6P/Sr90/1g/V39/PzJ/Yz+Gv8q/4v/Zv/Z/5H/FP7J/gn+/P6P/9D+2f9U/+D/OwDU/ywA7v++/1v/Xf/Q/lH/Sv9A/4X/Uf+xAPoBfANwA5QCOgEtAe4ABwA7/yD+gf4e/8//MADwAKQBXQJJAscBGQErAJT/RP+6/2IADAFHAR4BVQHOATgCgwIrAkgBSgCD/3D/d/+O/4z/rP8KAHUAzQDIAOcA6gAUAa0A4/9X//T++v4q/1H/r//w/w0AGwDP/7L/qv9q/wH/Yv5K/nL+gv7F/uD+Pv+u/+D/vP9P/9L+h/55/or+pv7U/jD/ev8LAIQA8gAbAeEAzQCAAFoAGgD+/woAIAAhAGoAqADbAOYAnABXAOn/6f/Z/87/pf92/4T/rv/u/z0AlQDWAOIAmgBBADsAbwD2AD0BTAFQAT4BcgHEAQsCMQIcAsoBeAEgAdcAiAB1AFwAZABwAEwAWwBfAKEAmQBgAC4AIgAkAN7/tf+E/5L/0P8ZAHsAogCTAJEAdgChAMUAwgB7ACEADwD2//7/3v/r//H/CADp/7//hP9P/x3/8v7u/sb+vv61/t7+CP9N/4j/zv8GADwAVgAmABcAAwA2AE0ARQAmABwAKABHAGgAfACEAHgAXQA2ACAABgAbAB0AKwAdACoANQBfAKAAuAC+AKEAhQBmAHUAZwBjAEMAOQA3AE8AZQCAAIgAdABPACgAGgALABEA2v++/6H/uf/a//7/GgAmAEgASABOAEAAIAAeAAwA+v/l/+b/6v8BACQAOABMADUAKgASABgAFQABAOf/0v/E/9//5//0/////f/r/9D/z//E/73/qf+n/7P/w//F/9n/1//0//z/+P/0/+n/7P/Z/+D/1f/g/9T/zf/E/6v/qP+q/7T/oP+d/4r/mP+9/8j/xP/F/+b/7//1/9//5v/w//7/CwAGACAAJQAsAC8ALQAzACwAMgAvAEcATABjAG4AWQA/AC8AQgA9AEYAJAAcACMAHwAcAAUABAAIABwAFAARAAoADQA1ADMALQAcAA0AHgA0ACYAJwBNAF0AUAAmAAMAHQBOAFUAJAANACwAOAA4AAwACwANABUA+P/3/83/pv/c/wAA7f/I/5z/xv/m/7r/q/91/6z/d/86/zP/Pf91/+/+hf4P/zD/Tv9V/mX/JABL/5b9Fv2T/Xv94/uS+Ub9iwCTAhEBlv3N/EICbwftBR4Ah/6yAbwBJQGL/tv+mAJFAJz/AAB8AQcCvvwq/jwAUwJ8/hH66vwMAmMD4v4t/bv/FwQ+A/f/cv8rAX4BW/+5/h7/sP9E/kD8av8RAgMDugHL/3EA7wGUAm0B5//R/gb/Kf+d/4X/Pf8HAIUA9gAmAegA6AByADIAff99/7T/Fv9//oX+kP9mAFkAAAAfAJkAAQHnACgAbP9a/wr/CP8K//v+Lv8C/xL/U/8AADwA5/9l/zT/dv+2/67/UP8q/0j/nf+u/4//jv/b//j/EQAeADIADgDd/8P/tP/C/7j/kP9u/6r/tf/J//H/CABEAGQAYwBFAEYASAA2AEEALQBOACIA8P/s//7/MwBTAE0ATgBkAF8AaABfAFYAagBuAE8ALgAjADYAOQAvACkASwBWAEwARwA5ADgAOwAmABIADgAcABgAMABQAFwAZABlAFQAQAAwACsAGgD9//n/6P/S/9P/5v/s/wAACAD9/wYAHgAWABQA///o/+v/6f/X/9j/5f/r/woACwANAAgADwAPABwAHAAKAAQA9P8BAAgACgAFAAoACwAUACAAHQApACsAMgAlACkAKAAtACgAJQA4AEcAVgBXAFAARgBJAEgASABFAEUAQAA+AD0ARgBUAFQARgA9ADMAOQBFAEEAMwArADEALwA/ADkAPwA+AD8ANgAyAC4AKwAtADoANgA9AEIAPwA4ACwAKwAdADQAIgAgAA8AFwAVAA0AAgDz//z/7v/w/+H/7P/j/+f/0f+//7f/qf+k/5X/ov+b/6//rf+m/6f/nv+t/7D/v/+7/8H/y//S/9n/0P/O/9P/0P/N/8T/vv/R/8P/vP+y/6//v//A/9T/1v/m/+b/6//3/+j/4P/O/8b/uf+3/6//s/+z/83/xv/G/9D/yP/f/8n/0//A/77/rv+x/8P/vf+e/53/v//h//b/4P/l/9f/3//R/+n/1v/U/9T/t//o/8P/8//S/9P/x/++/x8A+/85AP7/3v/m/9f/CQAAANr/u/+5/8n/4P8KAB4ACQAoAPv/+P8CAOj/AAD3/+T/pf+5//b/GwBoADgARgA9ADYAPQA5AC4ABgA9AEEAdABTAFoAigBpAGwADAAtAGwAfgB4ACIAMgBOAHgARgBAABoAAQAJADkAZAD//wAAyf8OAHEAnwAQAdUAwAD7AAcBjAH0AJ4AVwA0AM4AsgAXAd0AGgHgAYoBhQIOAuEBPQJRAfABLgHVAfQA+f8O/9H9Nf+z/y7/A/8W/1j+UgBh/xj/ff+A/4j+pPyW+Y/2JPYL96T4Ovei+Vb0Ovf+9qP6+ACB/CT8HfL889jxp/E18ofzvvLZ5M/gXer9C1ModihCE9AMGhsjLjc3yiFMCA72per46v3yKPan8JvoLelp+9kQVBgmEUIMFBAtFrERwANC9xrzxfi+/Hr5QfPw79j0wAIbEUcWpRN/DawGywfRC2EL5gTZ+SrwW/JC+Wf+ggBx/2cDmAd5DYsPVw77CoMFHAES/w/9IPiq85vw+PKS+TUAlwWOCSsLHwz1DO4MSQv8BjL/jPeV8UDuge7o8Jvy4PRN9xL6UQDLBIsI2ghoBr8BbP15+wT5q/gC9tnzivO/9DH4uPy1Aj0G+wcUBx0FuwXHBtkGUgT0/4j6Afem9qb4c/xj/nz+2Pxu/eX/RQKHBBEDnQAU/ZT5kfe896f4hPjS+HH3c/d1+bX8kgAvBNAD4AHCAMD/5QDLAPv/Kv7H/K76UPlG+479jf94AOz+tQAEA4YEJgePCKwJFAp6CZkHAwgoBpwE9AOZAsgADP99/Zj9qgHhA90FzQadBuQGxwe9CZAKvQgFBcUAGQBrArUD3gNCBIsDxAJNBH0DkQJAAgv9n/qq+Hr1EPOA8oLzDvRO9rDyufPC9Ef5OPsa+gv4XvP38rPy7fKm7SHv8uZF6U/q8+3V9Z316/kl+CUAXQJnB5QFBQ31DnUGFgP1BGwcEDb9PUkkphpzGs0hujJPJMwGyvMw4c3e3vZf/sH6CvHr6Ij0zBJgIa8hghmxDcILvQqnC9D/Rfkd8K/vGvEw73Tyqvv5CNkU0xd7EO0Sbw4qEKIVuw4gBIH30ejy75b9CANAAyj9KfsEA/kLYhOOE3cLqQF791T5sPqQ+lP1oPCY8S/1lPwiAmQEqQgvBhMDqQScA2sClQC8+EX0t/Tm8yv45ffe+Bn46vcY+iX7OP3k/C34NfGH7qjsPu/b7ynqmuWw5gnrsu7i9AjxrvGe86Pxr/YO9GX1EPJc7Qfu9ufV6A3mBuO950/lGu2g7NnpNfH37wf4Ovvs9JPvb/VlBJ0akSnTJV4jICQPNd5Alz+2M28cLA24CKoMsA7nDNb1hegy7/38wBjhHOsT7QxeDf8T1R7cHZYNxAHk9Fb1E/vw/Jz4bPUn9lf+kwpQDkITRhCbDowTKhDKDC0HnP30+2kAYv9MAEgApP5RB3cO0hIUEIILYQRSBDUEHgHk+mvvJe6o77j3/v4g/8P9KgKoBaMNlhJJDVsHogGf/Ob9Df1S+Rj2HfNI9DX1+/jo+Zr6k/r1+ID1zPJy8t/wv/BZ7nXoOuj65trr/e+Q8KXthulD6G/s2vDa7NrsVuM15f3jAOQ35Ungo98830PhNuR/63XrmvTu+UP6Lfpw+Ub8cAsaHsgi9STnJZonQze4Rv1Dmzx5J20WQRI5FzsX+Qlk+j3tH/PAAB4OxxJSD9AO2hF4FrUaJBldDiIHaAEG9/ryfPCB72n1nvx//LX+7QOPCHcV9xteGrQUOQziCIILdwz+Ct0Bkf2e+zABDAgSC4INgAvOCp8IbgueB24Is/+a/PX30PKQ9aH3u/3x/08BmvxnBM0GkQpHC4YBePib9KHx5fBT8S3pZeOD5E/n3+l+8QvstOiq6XDm8Ofu5bLfQNtj2JjX6Nmb2l/aOdyd3YviuONt4YzeUt9d4qDhNuT+4cPk/uxX9tD/qgN4A/8EPBEmIx0uMDBaKgUqLC7gOfk6xjP0KWMXWRSsE7YWzRJBCir+jP4RBMoLfBIZD3URfA8jECMSkRKgDnMMLQb8AK/+vf7l+nwBFAWhBfkJ2wcrDi8TORu8GDsX7hJVDkAQLhBXD40KBAkjBMsH3wobCXEMqAbVAz8B8f5V/dT6E/ZT8cPyMvDl8/n19PYI/ur9Sf/JAW79Bv/b+QDz4+yM5f3f5dnj2O7Wt9da2SzZndu04vDkCOhk6hPp3ejb5VnjmeC14ALdZtYv1MLR69D+00nVi9mq47Dny+zQ8Uz5CwVXD88SbBH9DYsM5BVSITItTTHXLTorkTTCPO1FnEUMN2cqjhzUGJgZXhoKEOQDevz2+2MIHA+wEu4UQxLvFMUTZRQOEvwPOAtAAcL9Z/TO9j73Ev0wApgFFgwEDLcUxRilHbAgJR63FRcQtgzGCI0LjAfqAi0GVwIrBiUHgAf8CykGYgfJ/mz7xva68SrwYfBS79nqKO0I6mjyb/Pj9Z7yNPD/6bLjeuTF3CrhTNYA1IPOTc7l0krZ4N2o2qjhs9fe4cTkLuF/5aneDtSR0WXQ8Mlm2bHR+9M92f/XKOnx9PL+ggZDDa8JwhP5EooWFhzZIecmJi0RNB8t6j2RP9hGOUi/NsstgSYVIPUdVhslDosH9gH5AF0PdRltGGoayhPhE6cafRubGVQPlwd/+379tf+w/NAA4v5aAvsIUBAZE4obihotHL0bSRZ6F0YQuA+DCmEKZAYMCOkGigN6C/wFwQbeBYoAIf7x/Cr2PPHj8ULrrOkf6hProuyW763uR+7a73/uxuwp5rPjL9w62LDb8tZ71N/SptBS0PDYttnH2JDZV9QW0/PXYNxI1qjZEM4B0ubTcNid34HbNeBP4eTrkfEAADH7+QajC8IHDxDiDZETliJkMbgtvzVkNLo2xEZvTpVD5jpPK28Xgh0mG8AYewwBAET6TgXpFBwcwiG+FVsZQxsSHRMiHxyVC6AGPQEg+tj/Tf15AQ8FKQuXC0QT0xYHGq0eXhnDFpYQ3Qu+C6QQzwq/DWoJBwaMDFINmg93DNoHDv/N/sj6Q/aE8tzq4ujG5zjrr+rP7VnuafDA8uTxfu9a7BrpUuWp4Y/aXdmd0o7UY9Nk1CrUT9L80qTUhdeX1DvVm8w00V3MSdJx05fNYtWWy/rX1t5F46rrHfH+8JX0iANaAU0QbQutBrkM5xD8JG0tOzmCOp045Dz1STxNL0o7PU4lVx7nG4sZ5RNYDYcCigICENsXhB7EIuAeGB8dITwepRf/EWIKngU4Aa//I/+X/r8IXA2fER4YvBWEFkIZWxbLF08RRg1lCtoHJwx8CzMPSBAyDgoPCw4FDbwN3QhQAcP7dPJe7+nsne2n7Ibrx+sM7ZnyafKR9dDtRe8m6pTlmuMw2iPZCNXw16zV6daQ077Rk9Vb2CbZ/NOO0ADFOMs3yCPMFc+vxPnNecXm1NHaVd5N5a3loOq66pb77PTsAT/+ZPzPBRgAoRKeGZgr/zh3O5g5ZUWRS7ROM1A9PGwt2SN6HFMY+RnSDcoN3AgtEaoZRyLiJ3Aj5SOnHOIavhVeFIUKhgvw/3MD5QIdBCIP6A7AGaIXLRiIE9AW3xXlE/wRnA0sCHMKiQtADQoYURS0FZcR7A4lDxgQPgyAAh/6U+4l7ZbtXO5y7sXsOuwx7u7wufPc8ofwwO5X5rrj9tzY2jHZddcA1qfSLNJ3zrfT/9Pq19rTBM7hyoTJvcySz1DPU8jnygrCc81j0rnSpNoc2cXf6+Fd8yvz1ACCARAACAnEBOYWXRpJK0g1FTb0N0hDEUo9T6JPJEAvNUwpZyXGHCQiLhMBEMgOhAq+HB8eMyP0IfIa/hbAF5UUVRKDDVMIWQN8Av4HOQlzFOAUTxeFGmUVZxcPFeMTaxLUC3oJaQcqBikNShF4Fg4XXBYBEmESfRSrDjoLbQE19f7vD+9G7s7xSe4C7Z3rwu2R8ezwK+9j7IXj/OAE3pjXoNoM2EHWadYh1F/Tr9NB1qTVXtQu0nfJEcvDyiPPKNA0z1HN38t3y0bRb9Xd1Q3brNeO3tHisu4J+DQAjAPzBIsHGw0bHwoqmjSVNkA2EzZqRQNMyEtPRp81Ni4yJCclpSBaHUkVJREyEMMUIxx7HsUfdht1G4ERoxJsDyMJeQ9JB1wExQkGBMQREBVOFvIZERKnFY4NBhE9EV0M+A1GCsQLrw5NGIoY4hwGGlEWRBWlENcOEQYeA+j6J/kl8R/3r/Sn9cH4De5C8t/sQO3X7pbkBuIP3CbYGts21l/cJ9L82P3Si9MG1xTPGtr0xijUKMyOxenVkMT40rfNnMdq0fLEP9BJ0V3Tk9pz2rzjTee38mL4sf3hBOH9jQVkD5od7zHQMPEzPzbHOpxL0UYaP4g1ciTKIk4gLhwbG+4XIBeSGF8acSEYIucfax3tF44SJQ1oC9kGXQZ3CbgHFgpzEtoTuRrDGmgXkRbQDjsQkQu2Cc0Ivwc0CxMROhlOG9Uf7R6SHVYg+BpwExYOpgBT/3z9u/nt/Kz3xvmd+8z5Jfq89bjv6e4w6cviR9+t2dvX39tt21HceN0o2B/Z7dat1r7TAM40yuLDpchHyJbKpsy3yIvOFMw60FDRdMq+0H/SBNVw29nfheQk8KX2X/vQ/ZkFZxXhH+su6i1OL9wwoj32RPQ/2joLKu8lYybzJmYjrR4oGL8YohxZH50hZxxzGiIXWRMyD+EKBgkFBjYLMghGCzQOcRAsF+YXxhdkElcSGg/SD8gMMQ0mDYgNeRSlF3AaTB/9Hq8c7hvbGL8UhRFDC+sGxAPAAOED5f4bAT7/yfoy+1/zZvHF7zXpFejz35DdGdxS3i/iON1H3BjWrNWZ21HX2NVyz37Ehc1YxsDM9MzVwIDL5sDryhLPzsf6zzbL/tDI1L7ckN3+40Dqsuvj8ljyvAAHDWkhDy0zLPcoITOEQL5DB0b5MeEhWCMPJAIpqSrtIE4lviKQJpgsjiIBJf4c8BI8DqYEAwPSA4EF7QrECIILZg6JEawYzxXuErALPwiKCBUKnAtRC+ANLROoGscfKyOtItsg+R77GrgUgBBvDMcK6wk2B/0HTgaQCQMMjQYtBFr7Nvdg9Mbu0erh4sffzN5n4YriSeFZ3vHZPtnI2E3Vn9Kty/PG3sgFyC7LMMq8xxfJb8gfzMbLf8mVx4TJmsxcznnXydXP3r7mlue08DDytgAED7waGiRmIY8igC2YNrs5ATerLAYmNyiILckunzDdLHcrfSqHKMApPCRFICwayxF+CwUJKQdXB14JYQlnCcUI1QmyCokNewsNB4oGCANlCAoKmgjpDI0LQhPuGEUazBxpGecYaBkaF84V9BLbDx0OqwuIC4ULUQyhCh4HXgH4+l34tvX38vDuh+gA5JziD+Js5E7jF+Im3qTYR9dI19vYbNlr1xPSdNIn0r3Xz9nB1jPWMdH50HPRR9I50+TVm9Y82pHeZuNZ65vvuPFw9J/4VP0sBvkJtAvBD1oR2hdFHZkdxx9rHX4cvR6XH/Mh7iPMIvMhWSIiItokvSQiI+IfUh2XGsYa5RoFGA8a6hXQFbEWgRMCFqoT5BAtEMoLXgswC+4IuQn4B8UH7wgDCZkKagpGCdgIbQeaBlQGIwT1A6cCiAOqBPQCcgO1AoUDJQTEAx4BjP+F/+P9xv34+tz5N/p6+f/4lvbx8RTx0PFc8AfwZesF6LHm/OV15iHkFeEw3kfc5dlZ2VLYMNYo1/LVBNWO1WvWINkJ21vbTNpk2rfbaeA54/fkrecb6X7ukPMI+I/7p/1pAHMCgQS4B7gKig60EscV9hivHZAinyfCKoYrDi0mLeYuqy+LLz0wZjBcMIUwlTBLMMEw7i6ILHkqkCh/J2clDyEFHdkZxxenF7YWxhQnEysRAQ/9DGcLOwn7B9sEMwF//5f8x/4J/5P9df0C+hH5A/hE9sr0dfIu8OHuGu0q6zPqA+hy5p7kauJZ4UDf091N2/DXBdd81RfV39Pw0bPQRM9Iz2vONM7Izc/N4c09zpzPwtDg0mPVOti02mvd5t+B49rnsuym8Un0/ff6+5n/NwV2CcwMDBF2FF8Zph2BIZImhigNLLQtEi+UMSszpTVeNeU1hDV4Ne81djUlNHIzXjJfMJ8uMSzHKYYo7iZsJLYhOB4eHGEZ6hdcFr4UihN0EHQOdQutCOsIPAb7BAMDzwBlABr+//0V+1j5Gfev9dzzp/GV8MzsTux86sTqmuk95/jlJOOq4SHg99703P7bW9kA2LPVYNS909rRA9HNziPOyctBzBLMVswDzsfNDc+CzzzRMtPc1RDZstzd4IHkd+hd7PTwhfW/+sL+ogJjBzsLORAnFMsY5RzBIFYlYid0KtIs+C4sMQ8yVDPBM8Q0VDXzNV819zTJNAkzlTG7L04u1yxuKrcn9SQ5Isohhh9EHekaMRnvFxMVVRMcEKwOHg2EC6kI/AW7BBoDwAIqAW3/3P2j++X5A/hD9fbzqPJU8Qrwz+027L/q4un16BrnY+Vt45rhz9//3ZDc39qX2cnXwNXl02vSNdF00K7PYM4qzmvNHc5tzrbOxc/rz73RKdPc1ZnZkt0B4tvlLOmi7ATx6/Qk+Qv9mwDqBDAJrw2bEeUVdxrLHlIityRuJ7YpjCwUL0EwJTETMpAzrzRfNfc1vzXFNbY1HTQfMvAvqi0pLOopdyc1JUAjQyIrIFAe8hvHGYYXlBRgEfgNygvjCSgIDgbFBCgDrQEtAL79p/s0+cP3j/Wc8/Lx/+86777t8uxS69bpb+i45hXlqeL74A3fb92J20TaZdgo1zPWXNQ801rRqNB8z4XOc84bzvjN8c5Nz9HPNtFi0vfUG9iR3MnfUOOu5iTp5u1p8QH1aPlt/dwB1AUFCgAOcBKFFyobCx4iIQAksiZIKbArFC4HMB0yFjORMx411DVENtM16jT6M1oy9TCkLtkswyv1KV8o8iW/I0YiQCDcHfQaAhgrFdwSMBBxDYoL9AlzCIAGagSnAZr/if3F+pT4B/Ys9JDy0fA574PtReze6hTpaOdc5ajjEOIW4DPeItyo2svYc9cL1kbUAdNv0TjQ3c4JzuTMVMx6zBbNls1DznXPFNBu0hnUitZ52kPeyuIP5mXph+zS8IT1K/mI/ZgBogXGCeQNTxGgFS0a3h22IAYjwyUOKCArdC1WLw8xdTLSM2w0LTXQNRI2ijXONE0zdjEgMK8uPy3lKx4qCCgBJtgj9iG0HzQdexrMF94U8RGQD/sMPgt4CUUH4QRSAtD/Kv0F+1v4PPZD9BryjfCF7hftk+sv6qHopebT5KjiBeEq37Ld69tf2vPYCtf71TjUMNPn0ZnQ4M9HzsfNG80Wzc/NfM54z03QsNEb00zVEdhi2wXg3uMo583qyu1u8T/2Qfrv/Y0CjQZrCkgONhL7FeMZEB5UIL4iQiWpJ8Iq2CzILogwcjH2Mp8z6jOQNGg0dTTxMrExFzBILqct7ivZKgYp7yYYJZ0ieSA7HqQbDBlrFsMTKBGrDrMMhgrzCNoG/QOFAX3+MPwO+qv3gPWH86PxBPBp7m7s/eps6ernGuYT5A/iWOAb3zfd59sL2nHYTdeB1U3UytLJ0c7QsM/DzifOV86aznzP8c+i0PPRmdNt1cbXoNsO31DjWufe6TztTfE59Sv5KP3KAJ8E7AixDBUQRhQKGAYcBh/kIGAjliWYKBArBi2jLqovNjHoMXsy8jLpMuMyxTF1MNUu+CwcLP4quCk0KGkmSCRJIlogFR6lG/MYMRZQE90QVg5mDIwKwgjTBgQEDwH7/Xb7GfkS9/f05vIK8WrvvO0o7KbqBemR52jltOOU4dXfct7/3LvbBdre2BLX4dWh1FbTkNJb0afQx89fz4DPidBP0V/S19Ob1IHWf9gG243eouLI5j/qv+3F8Fb0tvh7/HUAkAQFCLELkQ/oEocWdRrjHWogyiL0JKomRiniK+Mtei/MMJUx3zGzMsgymjIYMmExDTAwLuwsHivyKQMpNycxJcIiYSBlHiIc2xkhF2cU3hEoD9sMSQovCEMG4QNdAUH+M/uZ+EP2WfRS8mPwo+767EDrlOn85z3mzuQZ4zDhX9/T3XHcXtsm2tfYTtfU1Z3UQ9Oj0n3RK9GN0BnQc9AS0ITRG9KR0wTV7dUz2IjZAd0S4PXjPOl87Gvw1fPO9q/6jv6nAiUGAQpsDoQRfhU8GVccSiD7IqslMSfIKEUrES2YLzAxTzL9Mo4zNTTiM5MzyjLLMX0w1S67LL4qkClfKEAnCSU7IssfPR33GmcYPRV3EsoPmQ35CjIIXgYSBFkCsP/2++H42PX/8zryEfBJ7kLsxepm6V7nz+Xb40PiH+Hr3nDdc9tZ2tnZjdgL2B7W7tT/08XSdtIx0Q3RwdCH0DLRzdHX0gnUENYr16PYu9qy3Nnf1ONl6PTrHPCH83n2mvpS/msCbAZWCt0NjBHmFA8YyxshHz4iASU0J5AosSr3LOcu9DAgMsoy6jJuM4kzUTPgMiUyLDFML5MtfyujKZEoOCc6JckiLCDJHUkbChluFo8TBhHhDT8LawjaBfcDrwF0/3/8Z/lv9sjz6vEB8D3ufuyf6rbo1eYp5crjWuLu4EnfZ934223acNkt2GHXcNb/1AzUqdI00qLRjNFn0erQVNGB0aHS09Mv1efWhNh82kXc0N6q4cDlpupZ7r3x3PQZ+Kr7CADwAzwHqgt0D40SwhULGZocVyDnIysmrCeqKdMrDy4hMKAxujIhM6IzwDM9Mzwz4TIzMtEw1i7ULNwquilmKO0mxSQ0IrEf/Bx8GhwYlxWnEuYP4wzVCTwHDgW/AmQAt/1j+i/3VvQn8irwee527DXqKehX5sHkTePZ4VfgAt9s3bnb4Nmw2NLXLdef1ivV/tML06PSgtIG0u3Rx9ER0uzSy9Oz1CfWQNgt2gPc+90r4EzjneeH6xbvl/Kv9Vv5M/3YAIQEiwiEDOsPiBNwFnwZlB3rIO0jQyZRKB0qCixbLsUvNjF5MuQy9jITM8AyeDLcMTkxxC+5LR8s4ylrKP0mNSUoI2IgCR5hGx8ZxhYOFJARWw4GDB8JRwb9A2UBhP98/Kj5gPYy86bxMe9t7W3rxehx5xnl6ONM4pDg6N8K3u3cIdsq2V3YVNfN1tbVbtTb0//S19Kx0kfShNLB0mzT+9O81DTW4Ne32bzbjd1k31ri2eVr6V7tKPGt9AP4yPtW/64Cxga+CkIOwhHyFAcYXhvqHlUi4iRcJ4UpESu5LKsuTjCgMYIyzjIBM/wyADM/MsYx3DBEL3ctDStKKW4n+SUBJN8hgR/VHKYa6xeQFdUS0w8KDTMKcgeUBF4COwDJ/Qz7BPjm9FDyEfDi7fLr9unl57DlEeSP4mfhNuAe39bdRtwF21PZXNiS193WEtYj1WTUddPr0u3SzdLE0ubS8tKy09zUNdaG11/ZU9tG3Zbf2OGq5JDo9eyq8Av0OPdr+pv+mQIuBuIJhg1BEdMUAxjvGl0eNCJXJcsn2ylcK2gtwi9zMVoyDTOqMxw0NzSdM10zrDJJMgcx0i6dLD4q9CgzJ1klMiN4ICAezBsoGXUWiBMFEYcORAtDCEIF4QKtAD/+V/sl+Jj1A/OU8FLuSOzn6d7nC+b545PiJeEU4Ozezt1k3Arbvdl52LbX1tYp1ifVhdTM017Tj9Nt01/Tu9Mh1PrU09Wm1nvY1tns27jded/B4V/kiOiv6zTv1vLs9ZP5Hv2+ABEE4AfiC1QPfhJZFYMYMRzEHysjqiXjJx0qISwjLkUv/TAeMukyUzP1MgEzmzK0MhkyxDAtL28tfyujKconpSWsI6MhjB+vHB8aoxceFdES2Q+WDGwJywYiBIIBE/90/N35Y/eI9M7xie997ZLrU+lX5/bkR+P+4aHgtd8z3jLd7NuA2ljZJNhI15PW3tUJ1QLUcdNF08vSIdMk02DT39N11KvVndZC2PDZqNuj3ePfL+Lf5FLo2etH78byTPZf+f784AB6BBcIKAyMD+oStBbJGU8dryAbJLkmJimxKystVi8AMTMyPjPKMxY0+jP7M7czLTN8MoIx3C/4LQIsKypSKHYmQyTfITYfrBxEGlMX3xQPEn8PTAw7CVYGSQP2AAv+xvvR+FD20vMl8QDvY+zR6nzoZebp5LjiUeEc4Arf8t2C3JnbG9rv2A7YydYc1i/VrNTA0/nSstJ60rfSjdIu03DTPdQw1SfW2tdE2WHb8tz13nXhQOQ35/Lq0u7/8av1Cfmv/BMAHwTUB0kLdQ+NErUVEBmzHEIgXyNwJhMpHStYLU4v7TBpMoMzgTR6NK40qzRQNDI0rDPXMk0xoy+tLdMr7ykEKAImsiMsIVkevRvQGFkWhxNvEEoNEQpJB/4DhQGv/gX8YPkz9qjzp/Bl7hXst+mu51nlc+OG4fDfvN533VzcOttB2h/ZHthK1zzWvdUL1TzUdtPf0tXSdNKm0ubS7NLN01fUrNXd1hLYTtqc24zdl9+14V/kZudc67PuIPIM9kf5/vwWAWcEPQiuC1EP0xJ0FRAZGBzaH0Ij/yVZKTsr8y0QMH4xQDPoM9k0/DT3NCQ1mjRZNAY0JzPOMVIwpC7BLA4r5SiGJiYk+SFHH2QcEhoDF3wUohEwDkoL8gcQBTwCMP+a/K/5A/dL9IzxNe+D7JzqY+gA5iDkF+KU4BDfv92I3FzbMtpN2TbYU9eV1vnVeNWF1JLUotON08nTftM+1BbUA9V31UDWw9fM2JXaHdwv3tnft+Eg5H7mdum87BbwOPO69uz5Z/3KAD8E4wcdC4IOdBHHFM8X6RqQHmwhTyRqJ9opUixwLlgwBzLlMgM0MjQqNGg0QTQSNDszpTKwMRwwDS9qLV0rnimXJxYliiITIG8dzRryFyQVDxLzDhYM2QjkBbAC//8e/U364Pfw9NDyG/DW7YnrNelE5/jkdeNv4fPfod7+3PXb39q/2e/YENhI15rW3tVi1arUl9Tv083T/NPD00jUb9QZ1YfVnta+1/bYctog3Ffeud8U4i3kXOYm6fvrUO8w8tX1QflL/Nr/ZQPmBlIK2g0SEUEUcxe0Gs0d2SD1I9YmiynEKxku+y+PMfoyvjNdNG80uTSENA40uDPKMgUyiDCGL+It/SumKkkoRybnI7ohER9gHOsZ2BbrE+kQ2A2lCs0HvATuAQv/X/zQ+SX3uvRK8gXwku176y3p9ObO5BLjK+FT3wTeUNwz2/XZENks2EXX19YX1qvVD9Xj1GXUOtRi1B3UrNSe1DvVydVn1r/Xmdgx2rLbi91h3z3hV+MX5VLnj+kz7Mru8PFK9TL4svvK/gQCJQV9CMsLgg4JEu8UyxfbGuQd1CCDIz8mqijLKvIsGS96MNAxtjKDM9Mz1jP/M3Ez6zJgMkMx+i/XLjotrSviKSUoMyb4I/0hQh/xHBoaGhd4FBoRaw6OC5sI4wUMA3wAyv1U+5/4Q/YM9Kvxgu8/7Rbr6+gO5/nkDuNb4crfjN4D3Qzc09oU2mDZVtgJ2CfXNtfa1n3Wq9Yw1nDWWtaA1uHWONfc14nYbdl12p/b/9x53gLgx+Ff4zPlMOf16PjqTu2Q7xHy+fTA96T6of3JAKsDmwblCeEM9A/mEugVxRifG4oePyHRIysmnSh8KoYsRy6FL6UwZzEnMi4yaTJLMsYxZzFwMJovbi4SLc4rBSqUKKQmzCQBI6IglR7jG2wZkRbYEyMRPw7OC88IYwatAz4Bp/4L/NX5I/c89dzylfCx7lXsc+p46HXms+TV4mTh6N+A3lXdF9xW213ap9kW2XPYJdjP16XXTddI10fXZtd216nXNtiA2BrZs9mW2nPbptzG3RHfzeAo4vPjiuVK5yLpA+sL7QTvhvHX83L2VvkO/Nn+pQGMBJUHhwpWDWkQORMJFtQYgxsKHnYgDiMuJV8niSk3K+AsSC48LxkwfzDWMOkwtTB1MKUvDi/5LdIsmisSKssoJCeoJeQjASL7H+QdqBs5GdQWVRTkEUsPywwyCsIHOAXTAlkACP7z+8L5t/d89W/zW/Fi7yHtPOta6X3n7uU55MLiQeE/4Pve4N0b3Q/cn9ss26naVNoD2tzZutmp2Z7ZwtkM2k7a3NpW2+3b0NyH3XTeed+Z4Kbh9+KD5MflhecA6afqXOwB7uXvtvHN8/H1avjA+kv98/9HAtgEfwcPCpAMMQ+yEQwUbRbEGAEb/BwCH/ogziKrJFcm7ycXKUsqRiuZKy4sUywsLAYsjyv+KhoqQCkpKNYmuiU5JOwifyHnH1oefxyqGp4YnRZwFCsS8g+5DYILMgnwBsQEmwJgAHP+h/yl+uD4FPdy9Zrz9PFE8Gzu1Owv66jpFOjO5ozlVOQ34xbiM+FM4LnfG9+t3ize793C3W7dVN0S3SDdFN1b3aDd4N2R3gHfy9+g4Fzhd+Js45DkxeUX52/otekb623s/u1y7+nwbPIT9Mf1hveX+WD7kv3Q/+kBJQRWBrEIwQr+DBkPHREzEwYV/xbEGIkaTRzhHYsf/yBBIqojpiSYJWsm4iZUJ3knjSc/J+YmXCabJb4kySPDIo4hciAHH8YdURzHGmEZmRf9FRwUPRJgEEoOdAxgCmQIdgZxBJECpgDr/hf9YfvH+Tj4tPYd9afzJvKk8Bfvoe0l7N7qrulb6DvnR+Zo5ZDk6+Mw47DiWOIC4sDhc+FX4TThQOFE4V3hleHH4SHikOL74p7jYuT95ALm4ubn5+Xo6uks6yPseO2Z7uTvHvFl8tLzA/Vc9tH3UfnX+p/8OP4TAPMBvAO8BZoHegldCzoNCg/uELUSVhTwFYoX9RhuGs0bAh1DHkkfTCAcIbQhMyKDIqQijyJfIgYiliEIIUggdB+GHnMdZRwpG+AZnRhIF+YVbBTqElgRvg8CDlwMmArvCFsHmgUEBFgC4AA4/6P9K/yr+mT5EPjn9qH1cPQ88x3y4fCz76Lupu3Q7M7rFOs86qjpAulR6PjnaOcs5/fmsuaX5m/ma+Z05oHmoebS5iTnf+cH6H/oBum66WHqFuvt67nsie2C7nTvcvB78YfyjfOs9Lj11/b39w/5Tfpo+6X8yP0b/1gAhAHlAioEkwXRBh8Icgm4CvwLJw1ZDmwPmRCcEZUSkhN1FFEVHBbWFnUXEBiGGOoYQBlmGXcZdhlgGToZ6xiUGDMYtxcuF4IW4hUhFU8UdBN+EpARexB4D2kONA0XDOMKwwmMCEUHIAbnBLMDdQI6AQQA0f6c/WP8QPsT+gP59Pfv9uz19fQb9CHzUvJ88bfw/u9D77PuAe5+7QLtkOww7N/rruuD62nrW+tr63frouu66wbsQOyJ7PTsV+3Y7VLu5O5l7xLwwPBp8STy4/Kf83f0ZPU99iD39vfs+Mb5ofqJ+2L8L/0Q/gH/2v+iAIEBaQI7AxUE3QTIBaUGagdKCCoJ+wm5CncLLgztDIgNJQ7DDk8P2g9SEMoQMRGREeERLRJdEpYSyxLeEu4S9xLuEsgSrxKBEkMS6hGIERERghD9D0wPnA7lDS4NUAx5C6IKtQnSCNIH4wbXBesE2gPXAt4BuwCw/5f+gv1z/IX7e/qL+aL4s/fR9uv1HvVc9KLzBfN58uLxdfH58JDwI/DY75fvU+8+7yHvF+8R7xfvI+9C72bvkO/W7xjwb/Da8D7xr/Ex8qXyNPO980n05/SD9Sr2yPZv9xf4wfhh+Rn6xvpu+x78zPyC/Sf+3/6I/y4AxgBwARkCtgJdA/IDoAQzBc4FYgb6BpMHMgjOCFgJ7gl/ChcLjAsQDJIM/AxjDcoNJQ5qDqsO2Q4QDykPNA87D0EPMg8SD+4Ouw6IDjUO6A2EDRkNpgwfDJML+QpaCqYJ/whFCIQHzQYQBlcFpQTcAxgDXAKZAdEABwBH/4H+0P0H/Vj8oPvq+k36o/kE+XT49/dj9/T2fvYW9rb1VfUI9br0fvRE9Br07PPY87fzs/Ox87TzxPPJ8+/zA/Q29Gb0m/TU9BL1XfWj9f31Tfaz9hn3fvfy92z47/hr+eT5afr0+nL79PuA/AX9g/0O/ob+/f5+//7/bADgAFsBzgFEAqsCFgN8A94DSQSoBAYFUwWwBf4FTgaTBsgGCgdIB38HtAfnBwEIMwhVCHYIjQiiCLkIwwi+CLUIsAiUCIcIZAhBCB8I7Qe7B4sHUwcTB9UGiwZMBv4FsAVdBQYFqwRLBO8DjQMxA8gCXAL5AZ0BLQG3AFgA6P+K/yb/y/5x/gz+tv1Z/Qn9uvxn/A38zPuK+0D7AfvC+or6WPos+g766/m9+ar5mvmF+XX5dfl2+X/5ivma+bH5yPnd+fr5JfpH+mr6lfrQ+vr6L/tt+5773/se/Fr8lfzQ/BD9WP2O/cv9I/5c/pj+2/4h/1r/lv/Z/xgAVACOAMgA+QAzAWkBnwHRAf4BMwJgAoYCrQLTAvgCGgM6A2QDfgOaA7UDywPjA/YDCAQcBCYEMQREBEgETARXBFgEXQRUBE0EQgQ5BCwEHwQJBPYD6APLA7cDmgN9A1sDPAMdA/0C1gK0ApECZQJCAh4C8AHFAZwBbAFCARgB5wC0AIMAVAAeAOz/vv+Q/1n/JP/y/sP+j/5j/jj+B/7d/bH9hf1h/UH9Gv3//OH8wfyp/Jb8fPxq/F78TfxC/Dv8Nfwy/Dv8PfxD/ET8Uvxe/G78hfyb/LD8x/zn/AL9Hf09/V79gv2d/bL91f0A/iL+Q/5m/o7+uP7U/vz+JP9I/2v/kP+1/9f/+/8eAEIAZACFAKUAyADlAAcBKAE/AVgBbwGIAaUBugHLAd4B8wEHAhUCJQI4AkICRgJTAlsCYgJkAmgCagJlAmUCYQJdAloCTwJKAjkCLQIlAh4CCAL6AfAB2AHEAbMBoQGGAXIBWwFCASkBFAH7AOQAxwCpAIkAagBUADYAEgD7/9f/u/+h/4L/bf9O/zL/I/8R//X+4P7L/r7+rf6h/pn+jv6H/nr+d/5v/nb+fP57/nn+ff57/nz+gf6D/pD+lf6c/qX+rv61/sf+yf7W/uT+5v79/gH/DP8k/y3/P/9E/0//Zf9r/3j/jf+d/63/uv/E/9X/6P/2/woAGAAkADEAQwBQAFUAYwBzAHcAgwCUAJ0AogCqALQAwgDHAM8A1gDgAOsA8AD4APkABAEGAQoBDAETARMBFgEaARkBHgEZARwBGQEbARYBFAERARQBDAEJAQkBAAEBAfkA8gDuAOYA3ADUAMgAvQCyAKUAnQCQAIYAcwBmAFgARgA0ACwAGwAHAPv/6P/h/8//v/+1/6r/of+T/4j/ff9w/2f/Xv9T/1L/Sv9B/z3/Nv8y/yn/I/8i/yL/HP8g/x7/Fv8Z/x3/Hv8g/yb/Lf8x/zf/OP87/0L/TP9U/13/af9y/3T/fP+J/47/lP+f/6n/t//G/9H/1v/Z/+X/8P/7/wAABgASACAAHwAoADQAOABFAEQASwBTAFUAWgBdAGQAawBpAHAAdgB5AHEAagCAAHkAcgByAIMAiwB+AHEAdAB7AHwAcQB1AIIAbQBqAG4AcgBlAGMAZQBwAGMAOABCAFYALwA0ABUAEQAvAB4AEAADABgAAwAAANL/+//s/+L/5v/M/+L/o//P/7P/rf+k/57/yv+6/5D/b/+x/5n/Z/9o/13/iP+n/4z/af9t/5D/Wv+M/53/h/+j/3L/av9//4f/kf+K/5T/ff93/2v/Wf+I/4z/i/+Z/4f/WP8x/zz/if+V/73/s/94/4H/7v4q/4f/p//A/17/gv95/47/AADs/+D/4P8cAAMA5f/7/woAKABlAOf/HAAlAAcAngAhAFgAEgCr/xMASQDTAGcBSgFtAJEAuwCjAHECaQK6AfIAa/+z/5//oACeAV4CUwJ8AFQAfAD8AJ8AnAJ/BH8DZAKEAOz/svyM/Cj/nv4p/rQAAgK3AAUCVAFMANYACv9L/2AA6/9Z/y0C5wKoAq4DCwMuAyQCoQOoAXIBjgJoAMYCPwUvBUANThIpEA8Cz/+d/+f2Jf2O+MT4f/jq9cL4D/mw+hH6bvnh+uf8afvI+yT7Gfl/+TH5N/nN+K34mPiB+XT6kvud+377Q/ya+6T8D/2d/QP+sf5p/0kA1AATACgAVv+T/vP+Mv9m/7n/JwDaAMsAGwGtAV0B5wAOAKv/WP9C/xsA1gDyADwBXwHnAN8AtACPAPUAUwHPAGsAOQAoAA0ABwCg/6n/MQDC//b/jwAkAR8BIwFxATABPAHvALwABAFSAaoB7QFBAnoCewLCAm8DsgMZBKIEogSMBMsEgwXzBXAGuwYjBs4FBQZNBmwGkgZVBhcGtwXqBKcElgRFBBUEjAPfAiECPQG3ABwAev8Z/wz/Xf9k//X+nP5T/jX+B/7H/dr9j/0k/RH92Pyi/F78Cvzy+/j7OfyL/Nn8F/04/cn9Pv46/hT+1f28/Zf9rv2n/UX9vPxG/OT7evse+2j6xPlD+ZL4XfgT+Mv3+vfB99T3pfeT9//34vf89zj4Gvhk+GX4g/jU+Bz5uPno+X/6GPvi+479Ov/fAAID6QSuBugI9QphDegPfBEIE5EU/BWcF6oYBRnrGJQYkRh0GIsXJhbNFE8T7xG8ED4PrQ3eCxsKmghNB8MFaAT6An4B3/9s/vX8bPt1+j/5UPin97H2F/bA9R31G/X49Cb19PWJ9mP3Ifjc+Dr6kfuv/Lf9Xf4C/43/uf/O/7H/G//v/lH+cf17/Fn7QPo0+Rf47/YP9qH0gPMt8gnx1e+I7mft/uvo6r/p1ug86J/n3ubT5tPmD+fW5x/ox+hc6SXq/OoR7PHsre0V77nvevE+83X0nfaA+J37sv+TAzgIfAx/EBYVghkoHrgiPCaPKQEsmS1LL3AvlC+eLnMs9SrJJ6okDSF6HPEY8hQoEasNbQmDBQYCvv5R/Gn6ivgE94j1XvRJ83TyzPGA8ZnxhvG38aTx+vHe8t/zWPW39gz4+/mo+8P9EgDmAUsEKAZJCAsKgwsnDeENgw7FDl8OVA6XDRsMgQriB0UFPAOQAGr+oftM+Cz1+vGF70/tS+sd6d7mxuQl4+fhHeE84PHfjd+B37bfut+g4Fjh0eLW40TlieYK6AXqf+un7SnvC/G68ib09/XR98D52fud/TL/OgHLA3AHUgtvDxsTrBbJGgQfCSTlKNosHjDyMts0DDdIOH44SDgUNpo0qTE6LuQqYCXKIDIbzRW4ENkKXwVl/+75OvVZ8ejtg+tz6KLm2eTv4wbkt+MZ5a3lkect6Wzrfu3m73/ynfVb+Gz7WP6VALYD6QQdCJsJlwwwDh4PbhAAEEMR6RBpEYUQ6Q9PDvsMfwu6CEwHzwObAbX+SfvL+CL1M/Ko7qHrsOj35ZjjyeBz3tnbcNoL2R7YvNe21uTWHdfB10vZU9pl3OTdSOAQ43Dl1uhi69DuevEN9Vz4ZPsT/7IBYwVXCNsLZQ/UEloX2xtAILUk0yiDLO8wDzUBOWs8oT6IQDdBRUHOQNg+njwaOdw0QTCBKsYk8x0YF/oP1AjaAQT7dPSB7Yvnj+F43Sravdck1kzUB9QG1LfVEtgC21fezuES5lXqQe/e8234t/w7AZUFoQlRDfIPdRJNFLUWWBiWGUgaqhk4GRYYfRdFFpgUphLvDx0NiQpjB0EENwFb/VP6RvY687vvvOs+6SflYuKV31DcXtqs18/VR9Rh0i7SRtFB0VbSLNIr1IHVo9dx2qXcM+Db4uDmkOpG7kDy6PVV+Wf8WQABA94GRAohDekP/xGpFeYYDR4HI2wmvylVLPQv5jOXOEc8zT0KP0w/XT9rP78+nzwdOQA1XjDsKkolGh+KF2sQ7ggGAhr7bPS17ZXmMuEn3MDY2dWq08fRR9Cd0InRJ9RV13baAN6w4Qzm8uos8G31O/q+/joDPQdSC/oO8hGKFKwWphg1GlcbnBtUG1cacRkzGN8WJBVoEi8PkQtkCA8FJwJ3/sH6ivZ28hrvXuuy6D3lKOL93gPcA9q313zWx9R705LSNdKs0iPTTdQ51bPWZdjN2kvdFeA34w7mVeme7B3wuPNd99r6X/6oAaMFJwmVDAYQmRJsFrUaZx8oJHoniypJLfswyDWEOcQ8nD7uPoM/9T+sP7U+OTzrOFw0TS97KhYkpB31Fk0PFQgbARP6D/Pk60vlQt8e2kLXjtQD0nzQrM68zl/Q/tJD1vjYUtwY4E3kF+qZ75/04/lf/goDzQcMDAwQEhO7FU8YKRolHCQdCB12HH0bVxqWGdwXkxWcEtMO9AuFCH4FGQIx/nz6efbO8jXv1Ou36N7l5uL133Hd3too2YjXM9YS1TXU5NP8037U7NRr1qDXINo13FreS+F+43Dnvuof7prxuvR4+Ln7MP8+AkMFYwjfC9YOHxGSFNAXIhzoINgkLSjWKpsu2TK1Nsw6SD0KPiI/fD+BP1M/vz1IO+k2aDKnLXEn8yEzG7kTJQxeBFH9AvYh70rot+FM3DXY4NQd0vvPDM7HzYnO5NDq09PWvNoz3hLjOOjV7dDzEPmA/hUDwgdDDEsQFhQRFzsZWhuvHNsdPx6BHRIdaxt2GsoYARbBE6cPswx4CbUF0gJK/vj67Pbt8g/wSuyz6anmxOMl4U3ec9zB2kLZBdjf1uPVoNWx1SbW8tYV2MLZRdtX3dTfYuIm5VHofOuT7unxHvVW+Bf7/P3KAGADUwbZCEQLVA1uD0kSwRXMGZYdgiATI9AlAyqNLpYyMDaxN/k4+zmnO4Y8jztZOgI3STN3LzsrKybqH34ZbhL8CpQEz/169n3vi+iw4nrdYdqJ12zUdNIB0UrR9NLa1YjYCttX3rLi0Odx7f7yjfdJ/OYArQUjCuoNSxGqExwWUBi3Ge0aHxuGGpQZZBh7FyYWHRSMES0OFguLCOAFKAMEAIf8WPky9qrz6PCu7VLri+hn5hrkk+F630vdF9wK29nZwthB2MPXLtjo2HTZO9tU3JPe+uDf4gvmmegF7Crv/PFo9Sn4G/sB/qcAsAJjBecHxAnfC9kNuRCzE5YXJRtsHcIfRyN1J2krui8CMmEzNzXpNpo44jhBOJg2ZzOYMFctpyiTI80dSxfDECEK8gNh/Vj2LPCh6W/kd+DV3AjaBtdB1ebUk9W61xvaWNyH3zvjAOgi7ffxDfeS+0AAHgV3CSgNWRDkEnEVmhcMGTMaBBp3GcAYgReZFu8UzBJJEAMNLQr6Bh8ENgHO/ar6W/d89Jzx1e4l7JDpd+dW5Y/jeuGb3zjektzf22fbgtqM2l/aY9qz2w/cE96139/gLeSL5ZXopuu87XLxtvPN9gz6Qfzs/n8BggNdBr4IbAqcDOcN+RB9FF4XuxqzHOUe8SHeJRwq9SybLz4xPTLpM5w1HzYrNX8z6zC/LYYqSSc2InMcpRYyEBQKOgRN/nD33/AR6/nl6eHg3jLcONmh103X89fi2TDcuN644ZHloeq678L0nfkR/s0CggfvC60PhhIWFR8X6xiWGvoa/BrbGVsY9RYNFWkTzRCHDeIJMQbOAqL/b/wH+ZX1PvKP78fsXOpH6O/lcOTD4nzhT+Cj3tTdH9173KnchdxV3CzdSd2m3uLfn+Bt403kkuZb6avqv+2172XycfVR9yv6mvwx/hMBdwP4BKAH9QjlCpcNLBBEFLYWhhmLHOIeniLCJsYqti3hL4YxajPnNAM2YTbeNPIyTDBYLdIpZCVuIEIa6BPPDZ8HXgFL+wP1te5V6Rbl3eHC3nzcytpX2f3Zvdvx3bbgpuNF53rrPPCQ9Qr6Q/7sAtkGJAvADqsRBRRzFVsXSBjsGAEZ2xelFtIUBhMWEXQOzQt+COgEzwGS/of7qPiD9cfy9u+S7Y7rMemE56PlSuQf47Dh0+B73xPfuN5H3nDeSN693kbf+98b4VHilONG5Qfnyejv6vzsq+/H8fPzfPan+DL7Qv1t/xUBCANeBSQH+QjQCswMkA9hE9sW5xkqHD8f3CJnJhcr6i2cL4cxiTLnM7A0dDQ9MzYwRS3hKYIlZSE/HAYWyw/ACREEhv4h+YTzo+2+6D7lu+Kw4FffId6w3eLe3eD/47bm7ulz7R7x6vUe+qD+bwLMBX8JkwyqDyUSqBPwFEYVmBXKFfoUORQvEtwPiw3YCswI4gXqAtH/Mfyp+fv2qfSh8sbvze2s6+jpyOgT5zDmz+Sd4+TiM+IL4lvhVuEA4eDgYOHj4c3iTuMR5B/l++WG5xbpbup07P3tC/A08svzRPZJ+Df6nvw8/rwA9wLkBJoHRAkqDEgQZBS4GOcbFR/BIq8mkiubL70xgzNeNUA2bjfYNi01iDKgLo0r1yasIbYc9xXXD6MJQwNo/qT4qvOF7mLphubo46ziHuIk4crhAeP45Dfo0+qG7s7xavW/+fb8/gB7BH4HQwpCDNcOpRBDEjITyBJ+EhwSpBHqECoPMw2GCgYI9gU9A7gA4f0i+2X4t/W781bxQu//7AnrAelx52XmI+Xj47bim+Eq4dbgfODh4FLgEOFI4enhX+Od45DlP+YS5+joiumR68LsQ+4k8ALxE/PH9E/2K/jG+cr7uv2Q/wIC5AM0Bj0JSA27EWQWpxpZHlUilSaoK8IvwzITNag2Ajj8OGw4wDagMyQwiCy0J8Ei/BymFl4QGAohBGr+EvlI9KjvjOty6FbmNOXk5NrkoeUr52npeex178nyM/Z++Sv9iADGA/AGfgnGC5kN/Q67EPERXhIYEjURbBCSD4kOPw3ICl8I2wV7A+QAf/6a+xn5kvYU9Hfyoe9q7sfryemG6F/mI+ab5JbjC+Nb4WvhR+Hf4Lrh1OC54WHiW+J55GvkAObM5tfmuujZ6LHq7+uo7B/uKe+i8Fny3vMW9db2/vef+pX8Gf6uAOgCNAawCt4PFxXRGfMd+yJGJ4EslzFHNBI3wzjROQE7BToTOMM0DDC9LD0nJiLbHHEVvQ8YCQ8DFf6/+Oj0vPCH7bHr0enL6WXq4+o+7IbuYfE79H73iPrj/K//cAJNBVoHnwmTCykMgw0eDnIOVA8mD5MOig0tDEcMowpRCScItQSeA1EBbf+w/Yf6+fiR9oj0UPNG8NDu5uxZ6mHpMudH5tHkvuPE4inh4eB54A7g5t/63+XfguCO4eHhpuJM40DkR+Xu5aTnQuij6QXrhusQ7Q/ube9I8THyD/Qp9Tv3zfmm+gf+4QBIBWkLLRFCFwwcRiEKJ8orOjE/NW43kDreO+E8UDwGOpw2STG1LJUnxyFoHBEWJw9UCB8C2/z695f0avHR7sTtue0o7g/vcfAi8mn08veH+/D+CAJcBDgG6wcICtgKpQzeDH0Nlw0qDdENUQxmDF0L5gngCasIeQh2B6wFyQSKAicBFQBP/l38o/oL+H72JfTY8U/wpuw066roQuZi5Qzj2+HI4ATfC98+3jTesd7t3S/feN824PzhA+J34ynka+TV5S3maudr6FPpdepU6yTs8u0e7yTwfvIB84/1uveb+Qf98v/NBWkL0xEVGfQd0SPBKRQvFDSqN5U6ETxUPcM9sTvOOPIzWC7ZKM4iVx13FhQQ5wnOAvj9HPl99QTzAPF/8HjwB/I28wn1yvbd+Jj7pv6xAt4EvAeICRoKBgzjC8EM3Az3C8kMgwu5C0YLaQlrCdQHegfKB8wGmQcZBpQFJwUcAxgDzgCP/+n9SfvQ+bb2k/ST8WvuB+yy6JzmE+Rq4pTg/95E3vfcV92L3Cvdg93j3bXf3N/94cXiXOMi5ezkI+bP5QfmX+fp5pnouOg46e/qs+pO7aPtW+498SfxQvUZ98v5T/+zA4wMlBLIGbwgySVsLQkzHDh0O0A96j6jP0M/JTx2Ny8xwSqXJPEdURgkESoLbwWs//n7w/fr9YP05vMv9TX2aPkV/Cb+UwALAmQEzwb7CUkLSAyTDAUMnAxYC9gKWQlGB+0HVwZLB0oHzAX1BmkFMAeqB/oHFQreCLgJFQmABxgHuwQ3AoT/Ofwa+aL2I/Pz7/Lruudi5ejhoeBN3i7cCtxv2rXbKtwp3IPebt524GXiYOKe5RvluuaL5+PkJefF4x3kGOT84FzjBeHy4pXkkOTa51Poq+pD7l/wU/RQ+DL8IAMbCqESlhr+IEUofC22M3Q5zjurPmU/zz6DPq47YTcDMS4pJyJ4GgEU1w5YCGsD3/4E+wb5sfd/9z73c/g2+x/+2wFyBTYH0Qj6CQcLQQzsDIUNFwy/C4oKCQmwCA4GHQV6AyQDwQQuBYgHEAjkCH8KvgrcDBcNIA1iDVsLGAsCCeoFeAM//kP6QfbO8RTvOOuj56bkI+Fd39/d5twi3XvcT93k3kXgJ+O05FLloucX50zoZukh5xjp0eU95XfkGuCE4czcg92/3CLbAd+S3h7jXOW553zrcO1v8QT1Xvg3/VkCFgmMEQQZdiFKJtgsdzJ5NTw7hzv9O7I80jlsOJ0zFy3aJmYeDhiVEcoL7gfpA/EAmv77/Cv9h/2Z/pkAjAF1BYEIfwvZDd8NDw4BDcsMNgsgCl0IeAYkBfADAwMQAr0BAwGHAtsDvgbDCbMLhg5FD/cQLRHREF4QCA4fDaoJ9QZsA2n+dvrI9DHw1evD6DPmU+T/4nHhpeHP4Zzin+Nn5NHlJufQ6PvpqOqE6t7p1ueH5grk/eFc4d7dI94a21PaSNpB2D7a9NiC2+nd2OAJ5dboPewN7zHzbPTn+DD72/6wBMcJAxMMGdsgvSZ6KyYx7jS7N5E4wzhDN442cjM5L5AoniGRGrkT6A5SCa8FrAI8AUsALQGTAdICkARMBWwIkgmTDJYOFQ7hDsAMFAtrCZ8GVwT9AQIBXwBmARMCLwOPBL4FnAhxCv0NaxCyEvUUExX+FSIUEhKYDisK8QVtAeb9MPk/9g7xMu4664Xovui25v/nLOhl6QPr2Ot97CvssevA6hzqHOmH6OXm9OXt4wDi1d9l3cfbKtqx2RPZN9lS2a/ZRto02q3bWdy/3mbiUeWd6k3uhvIC9oP41PvF/bwB2wUDC5kT8hp7I+Ip4i7pM4U2ODofObQ4bDfKNI8z5i4RKRUicRo2EzgNPwcUBAwB9/8cADEAlQJvA7EFRQZIB2EJogrGDLEN2wwMDLEKzAdXBkkD3wBJAFL/ugC3ARQEJgaFCJcLowwpEDYR6xMIFXQUtxQzETkQaAuQBgUCo/td+B70BfH77Wvsb+o86obqgukw7LjrMe4F71TvgPBi707vFezX6iDnz+X842LglODa21nctNp92Irapdeu2i/adts13RDdtd8331LhluG45IbmCuq47LPtD/Le8s32rfl2/LkBFgmbEacb3SUeLeo1rDrQPuQ/GD5UPEE34zQIL/kpkyMcGzQVegxACFEDmAD/ABsAlQMhBu0JSQ04DgQPHQ5aDkANgQxcCroHawbAAhsCbf4U/TL8ivvM/gwAtQW6CIoNoxFyFNAXHxkSGs8YqBhuFbMTFRDmCsIG7wAT/Ln3QfTV8Obvde6f7rDvXvDa8X3yUfPh8oDzqvHg8HTuiuvr6UDmQOSP4fje993E3PPbhNzu2mzdNdw63tzfYt364endP+B24MfdqOAC3pTfXOHt4oLl++j56bDtIPHw8lr4IvsbARsJMBKBHC0m9y74NcA7gj93P3k9JzmxMvMt7ydDIqkczhWCD9gKQQfyBLUE2QLOBFAHGAtzEC0TsBQgFaATqhFGD4wKnwcLAs7/S/yg+kz6S/h6+Wb5PPyDANAFyApKEQ8VbBrfHTMeGB+YGoQXBRJWDK4HbgFZ/Vj4C/Xy8nXx3/Hd8S/zWfR69tL4b/q++yf7wfmB91T0IfH/7EXp6OR44Xrfstwu3frbBtxE3RXdst6u4KPgIuN74jfieeMY4Jjilt1B3ljc9Nqe3lXd0eKY4wLp4ep38CTzXvZr/Lr8dwWaCjoU0B7hJ74uYzYeO3Q9Rj+QOu00ATCzKMIiMx64Fd0RngyWCHYHRAaGBmoHwwjGC6QPlhRAF9cW+RYzEu4Q5wsWBooArvrV98j1EvdY9nb5ifu3/v0DtwjqDSkTCxdTGlgeFyCrIHsdUxgXEd4KigQX/vv4hfSh8fjxtPLx82r3LPg6+7T83P1O/kT+7/ud+M31gu/87BfnQuMu4Grdht2j3NLePN804mbk8uUI53znGuZu5fDkvd/i4JLaVdkQ2THUC9iN1b/Yu9xV4BDmhev8787zbvlX+bX+5f4pAdAGnQnsE5waziNNK0wxFTZCOUw5yDVkMYApzyQWH3YalhUgEU4NvgtcC+AKZQv+CqcLcg1TEOESDhWHFG4SUg/GCy0HBwPD/PX3nfWb9FD4Jfu7//QDtwiCDj8TAhg7Gl0bVhy5G+kbkRp8FhYSPgs3BfQAc/wE+mX4hfeS+hb9BgKABJQFQwUjAhcAvvrS9hTwDOvH5VriJOFT39rg5N8k4vnj6eXa6TvqGeyy617qHOmR5gTil9/F2VzWXNWCz/rTFc8h0zbVYdZM3iDgeufd6gHxI/JW+LX3avrE/DL7EwHvAo8LoBORHTAlfS5MNAE6MTvgOR00pC1hJwAeTxv0EgERkg1KC8cLygyMDWQPuA6FD1URFhLBFDQR+BAgC18I+wOF/gD75fY39sb2+vqG/1oGOAxRESYV/RjuGekaBBm0FVAUkRFLEBwOjgoMCDAFFwNKAnUAmAFJAZECTwRuBN8FywTMAUv+N/nA8znwiOob58HkiuIq5Gfkeeaq6MzpYuwx7NrsYOxt6hjpZOVl4eTe4toS2Q/Y0dTk1oTVktfN2b7Z/tzS3YDgTeN85ovo7ewf7jTxnvQh9Qb6KPsv/wUG6QvEFm8fpyekMDE1UDoEOwM3mjIVKUAiyRskFWISWA7DDBkOHA9hEhIVkhSNFTUURhP8EzQRWQ/2C6kGuQQAACz9Hvvf9jL4Ovho/FoC3galDG8QWRMeFiwX3RUoFDgQQQ2XC7AJMwlrCDgHTQdZB40H0Ah7CAMI5geuBfkFEwS2AZH/z/kh9y7ybu+17NTpcuiN5/XnpemF6rvrxuvf6r3qVehR5zrkjOEF38vc8NtF2xvcz9v63HndAd0d3xfdJN6D3QPcpt6+3iLiD+YG6EHtRPAa9Mn4B/rR/gAA5AZVDMIUwx0lJGIskTG3M+42BjEpLf8lWRt5GVAQrBB9D+QOXRStFisbbB5BHXcbjxkSFBoS0QzaBpcEEP9d/t/9lvsr/uv81v8mAxQGjgtADhYR7BKdE0ITiRJCDuoKCgj4BOkGUAb2B/EKjQtRD8QQVQ9AEHALVgkMBm8B/f7q+0X4PvhJ9Wv1MPXO8sHzIPDy8AvuC+4x627pT+ez5KXkcOG74bvepN5N3pndbt7N3ejd2d9q3xzhGeGF35fg9d3P3Pvbi9mN2xLdjd8D5TfoVO5482n3xfvd/bwA0ANACKAOkhXIHT0kySpIMCwyLzSYLjUpTCG9GckW7xLlEooTDxVwGR0dxB/zIEQdphnaEzUOYgpPBIIB8/2+/EL+Mf/cAXgDTARJBikI0QnnDNgMrw2QDZgMxgz/CyAKLgr8CIEJlgzHDHgRxhFlE6YSWRFkDiYMSwj0AgABUvof+9T4OPgT+lr37/ju9zn2KPYG8uLvquyP6FPni+Ot4vDh0d9w4jLgH+LJ4gvgaeM53zbhc+CX3nffdN0D3bHcmtu+2T3bjdn63C3fLuGb5lfpwu2+8urzAPiD+Yb6UwAwAhYM8hIEG0AkfSjQL0wyXjGPLqkl4h5ZGaAUthRCE6MUGxg7HMEhEiX+I8Egkxo7FFYOLgj1AbH95foi+8b+5gC0BfYGowjLCekJjAqLCpcInwc2BrEFOwgXCG4KsQplC7YNdw8OEXUTTBKHEjURTA4TDssJ1gewA5AAef5q/mX+/v48/0X9zf1a+1L5oPZB8DPteOjP5Rnme+Ox5VHlqeZZ6S3ozejQ5mnij+IA3QHci9vR1jrcAtkM3G7eAdt/37/cU92a33LdK+E345zlTusU7sDxUvbq9zv8uADOBH0NjBKKGkAgdiVLKqQr4ipKJiwh5BsHGvgXixmBGTIcDyD5IvEmViUEIuwbcBQcDm8IwwIgAEX+i/+gA1sHjwvNDWYNHQxtCRcGwQM8AQf/Rf/J/7ECvgcMCnIOcA/UD/0RxhCqEcYQeQ06DTsKoQnZCRcH3ga2BCMDuwT0A3gEbQRmABAA7/v++C720++z7IXo3ubq5hbnL+fR50nouOdX6AblCuIG4KzarttV2T7YmdzM2dbfOuBg3sji9dut3jfcftgt3Z/aouFg5l3p6PDQ8tb3lvsw/Kb/xQFHB6AOrRXdHGgiaCc6KqArMCl1JPEf+xpZGhsbuRxQIa0iuSYjKEUnMCXsHVcW9g5OCIUFUASkA98GOAi7DKEPIhAQD9EKswWvAUb+O/wc/fL8vwASBHoI5AwQD74P0Q6XDYULkAuyCc4IrQgBB94IywjcCF8JoAcQB1MGFgXDA+0CLP9V/SL60vXv9D3wtu5W7QHrkezz6+TrvOul6ejnIeaq4oHf1NxI2bHZG9n72SfcPdzu3infCt9e3uDbttps2WTYw9lY233fGeUt6B3ur++O8/b2yPcN/NL9dgRuDJUVPh2pI3cmCSlVKi4n3CSfHskaORs0HD4hECaeJ0ssbCrAKcolnh0nGN0OsgocBxAHhwkkDOgPZxFzEkgRmw2VCeUC+f2z+tn4LvyE/jkErAjlC5gP4A8bEOQNxwoKCAAGywXiBpoIgAoPDEMNsA2gDVkMFgpsB7EEgwEtAJ39lvyp+w35EPmG9Yv0e/IU8DPvHuw56jfoKubF5SDkteJe4Vjesd5w3GPcgNxe2b/bu9iJ2WnaytYr2sHWntjr2RPZpN0S3qriz+SX5+DpL+zL7wXyIPdc+0gDJAxzFqod6CPiJCUlxSX/IJ0gsxroGHAbCh6nJVIr+yzoL4gsZygXJGMaVBWwDnMKIwszDJQQBRY4F7MY/BQpEJMK0gM1/mn5tPeO+Hr+5wN7C3UPKxEgEWEOMAtyB04DLgEaAR4DKAjACwYQwRF0EUIQ7wxXCeQFzwFbAE/+3P63/yEAGAGu/u/8V/hO9Tbx9O2x65vojegc5/bnZ+jr5k7m8uIJ4G3eNds82l/Z4dYy2R7YvNnG2/LYrNt92LjZYtr52X7d/t1S4pXjo+cW6Qfs6e5P8Cf1wvi3ANQJ0xMDGxghVSFrIk4izh7yHsUZKxmoHBYgrCn+LjkxwjMELooq3CPWGq0W6g9yDtgPDRKUGNMc2h4vHhIYixG6CZECVf12+fb4gftjAfAHbw4kEe4Q+A0LCaIEJgCH/db8pv4UA9UInA33EHERcw8yDH4HEgPp/zL9q/3p/bn/KgKbAuYDCAGK/Zb4XPOk767s1eoB6uvpIupQ6/Xq/ulM5yTjLd/x25vZ5dj32J7Yz9pf24rdw97r3BDdodlE2afYrNh/28ndn+Ky5Y/pEutq7Y3v0vD687b1YfsAA3YMMxYkHEofTSDxH+wfzx6CHfkbTx3IIY0n4S6MMeUyti/qKZUkIB0bGCMV7RLJFCoX/hnSHsUeMB6NGL4P2ghZAXv+rf3t/U4B5QSbCc4N/w0vDMkGgQGi/Uz7Xvx4/usCqAe+C8QObg7BDBQJIwTOAI/9oP0I/yIBKwTqBGgFBAQrATf9hPjk89vwUe9I743wzfDW8Prvie1R63Png+Mz4N/cntzO3FHeqt9R4HjgBuAT34PdQ9xi2hba49kd2s7bNN0M4MHiu+RU53zo2eq67UXwefNg9mn6UwDzBksO3BOGFysazBu7HZgekR/yHzYhACSzJhAqVitXK5cqeyfAJOAgpB10HGQb1xu+GyEbHBuCGgEZZRYKEkcNLQr8B2QIagg9CMkIAAj/B8sGHwXBAqwAN/9K/yAA6gERBAMFzwWWBHgDhQH8/9T+dv2A/ZL9fP5A/xL/fP7x/E/7svlo+Fz30/aH9lv2F/Yc9dTz9/H674/tuuuy6V7o4+cD5zDnWubI5ZXlQeTS45fiZOHw4HHgn+Dl4KrgyODi4BnhLOL54hDkYuXK5tLo6+oa7fDviPLY9U75U/1+AZUFqAnPDP4PGxLMFMgW/BisG5sdOCAkIg4kgCU8JpQmMCbyJPgjryNFI2sjtCLBIZQgeR9uHtkczxpIGDQWCRTYEsAReRBnD8cNDAwoCnYI8wbRBc4EXAPKAuIB0gGrAZkA6/8h/nH97fxw/GT8tftq+wH7xPqc+v35afnG+Fr4xfem90L3/Pav9q71NvXd85byQPGi76ruf+077A7rXOkB6PbmreW75KTjoOLe4TPhl+AG4Hbf5d6f3pHelN4I35rfh+Cb4RzjeuTy5XHn9+g062ntzPBF9N73Q/wyABsEfwe0CqYNlBB7EyMWBhm5G7AeMyH6ImUkfSWhJpMnKSglKJEnZSd2J80nWCc/Jswk0yKNIfQfXB5UHCQaHBg6FrUU+BL/ENkODg3/CjkJYwf+BbcEZgNnAsQAuP/k/v39ef2+/MX7ZPvY+pL6Q/qI+ZT54fit+Ev48vf79433oPfb9lj2uvXs9FP0RPNP8m/xPfDc7ojtGOza6hHquuhk5xjm0OT347vi+uER4VfgKeB83wPfV97D3Rze+t173tfee9/j4BbiJeR/5VTnMelp6zTuL/G+9Cb4bPyYAPQExggZDNYPJBNnFn0ZYhwqH/whUSQnJmQnaChIKfUpdSqAKm4qACr3KXMpdihVJ7wl+yM2IlkgCx76G94ZwReXFVMTAhHwDlQNWAubCZYHzgXABFADCAKKADz/VP7O/Ub9OvyB+wH7hPpI+sr5+fiu+HT4Ivjk9zX3pPZm9g727PWe9fX0a/SN8/nySPJF8ZHwH+8x7tTswuvI6jfpi+gr53DmUOUY5Pri/uFV4VHg9t853xHfBN/j3hPfW9/J34ngjuFz4hPkOuXs5g/pFevO7ULwYfP99jr7uP9dA7kG+wmuDdcRLBX8F6Ua7BzkH44iASQ+JTcmaCd0KB4pYinoKAgpRikbKXooJyfVJZkkSCPtIcUfRR1rG4EZehcwFcQSfRCJDhgNJAsbCVIHmgV5BBEDqQFoAPv+PP5t/af8pfu5+hr6Wfka+XT4x/c+98z2ofal9mr2MfYz9hf2L/bn9XT19PR/9CL0bfOO8nLxV/Ag79TtluxL6/jpqeiI5xrmGeXl4+Di/OHP4Gfghd9I3wffud7F3nzeD9+J3yPgCeEt4kvj+eQN5+boaevo7R3xBPUi+Tj9owAXBHAHvQu8D7QSJxbMGLkbjx4nIZkisyOOJZom5yepKPUo9CgWKW8painRKM8n1iZ4JXskSiNRIQIfUh3BG58ZiBf7FIAS1hBbD3YNaQteCcMHeAblBFgDvQFuAGf/fP51/Rf8Rvta+rr5Rvlc+Kr33/aB9j32Hvb89az1zPW99cH1lvUf9bL0XvQq9Hrz2vK18aXwoe9A7g3tmOtM6tfo3udZ5tvkoON34nrhauDS35fecd4j3sTd5d1q3dXdVt4X3/rfLOEq4qrj7+W850LqseyP71Lzb/dv+9P+RwKOBckJAA4SEYsUhRdnGnIdHiC1ITUjLSWPJhIo9yiEKbEpCSqBKogqCyoUKVUoBCcOJtQkvyKhIA0fRh0NG9AYBRadE+QRLRAdDh8M8AlNCPUGIgWWA+YBhQCP/4P+a/0b/Bf7OPpv+en4B/hS96/2KvYW9vv13PXp9cP10vXY9XP1QvW89Gf0F/R487/ytfG88HnvUO4M7aXrgOoa6fLnnuZp5TPkN+Nj4h3hquCj3yXf795i3mzeB9563sfeUd854AbhI+Jn41PlE+dM6aPrSu7p8bz1dvm5/C0AhQOgB9sLAg91EpYVuhixG7MediAKImAk8yWfJ64oLCmBKRgqwSr+KnYqpym1KNYn2iaNJcEjUCEUIDMeARzGGboWfRTAEiYR6A6LDE8KYAhNB4oFpwMaAoUAov/S/n79EfwV+zT6qPkU+eD38PZU9tT1tPWK9Qj13vTu9NX03fS69Df0B/TN82zz9fI08kLxUvBp707uX+0t7NLqqumQ6I3neOZe5UPkReOl4tThCOFZ4Nrfwd9j32TfSN9H3w3ggeCc4VXiSOMQ5ZvmBuni6oHtf/AS9CT4Mvu//sQBmAWhCUsNhxB6E+cW4BkxHS8fvyD5IukkyiYHKMsoHCnZKZgq6CrvKjcqaCmfKKgncCbEJJUiACGQH1IdHxtuGNMVDRQyEiEQ3A3CC5YJFgiABnkEHQNGASgAO//d/Xz8YPs5+iL5rPhS92z2xfXN9Fz0UPTW85/z1PNe82/zh/My8x3zBPNz8kDy4PHt8C7wIO817k/tj+wP69fp1+h8597mqeWm5GHj5uIv4pjhE+EE4Frg699s4CTgQ+D44EXh0uJU45DkGeax593pKeya7lrxyfSu+F78of8iA3IGqQpaDpgR2xTRF0IbBx5SIM4h5iO1JVkntSgIKaYpDSreKs0qgSroKSYpfihZJ9Ml5iMQIkMg0h51HAEaXhfxFPsSGhHqDj4MggpgCNwG/QQUA28B2/8e/3z9ofwM+8T5RfkJ+Ir3bPZe9dj0JfS4803z6PKx8pTyv/KG8ofyefIa8lDyL/LA8XHx4PAS8Lrv2u737TftHuwl60LqcelI6K/nrube5TjlauS24/bioOIg4hXi4uG64b7h5OFk4jPj7uPb5Cbmf+eK6WTr8O0w8GfzMvda+g7+xwBPBN8HvAsZD9oRShUJGFMbsR1jH1chQiP6JMEmrycAKLooTCkiKvop7CnZKCko3Cc4Jh0l2iItIbsfzx21G/YYoxZ2FLASxxAwDvQL+QkXCGgGegS0AswA2f9n/gX94Psn+nf5SPjS93/2uPUn9fXzL/RY80bzIvME8+Dyx/IZ85by8/K88qLydvIi8rjxDfGW8Kjv9+4v7j7tLuxF6xrqXOlz6IPnlea+5TvlU+QC5CXj0+K04onig+KI4qfi1OLG42XkZ+XA5vHnlOmq67Ttv++Y8pv1nvgq/Bv/GwJUBaMIAgzuDjAS/hT8F5caxRzcHoUgoyL/I4MliCZHJ/knfygTKfEoCSkSKLQn3SZ6JSckcCIGIR0fix0PG/EYyhaMFLESYxBIDvQLMAoGCEQGbgRpAiwBkf8t/sD8a/sz+h/5QfhD9xv2b/Va9MrzT/OE8mTy1PHi8VbxR/H68IzwuvBR8D3wwu9678TuV+7i7cTsaeyV69XqMOpE6Ybo5udc56jm9+Wv5Q/lruSj5ODjOuQF5CPkV+SV5CfllOXb5k7n+egW6obrYe1r73jxvPP49kr58fz4/60CrgUcCf8L6Q5kEp4UxRdJGl0cYR5MIO0hZSMoJbwl3SZiJ6wnTChQKDsotidKJzgmKyXTIwIizSDdHhEdLhu5GNIWpBSYEloQcg5IDBYKjggxBqgE/QJHAQkAeP4B/Zr7gvpB+VH4ePdT9nf1xPSr8zbzk/L08bbxN/EE8VrwQ/C/73/vU++q7nXuse1N7YnsDex565vqJepT6bDoGuiF59rmaOYt5nPlXOUL5aTk2OSe5PLkGeWI5erlXuZr5//nROlJ6pPrS+2c7n7wZPJo9L/2fvkA/N/+3wFKBCQHGQr4DIMPfhLTFEsX9hnLG98dkB9SIeIiTCRyJSEm6SZ8J+MnOigaKLwnJyd5JowlPyTqInMhzx8iHjsc9xkCGBMW3BO5EZ4PSA0yCx4J4QbBBCEDKQFc/9v91fuE+gH5xPd99l/1NPTr8mjyAPFM8Kzvve5p7tjtU+2H7HTs3et761Trd+pn6rbpg+ks6aDonOgM6PznfucS5wvnjua75nfme+ah5nLm3ubi5hznqecW6JnoVOny6bPqu+vN7N/tIu918MjxZvML9bL2u/jF+h39rP/yAWgE4AZwCQgMtw7rEIwTFBZnGK8afhxrHgMg9SEaI0EkViXwJa8mDScvJyonJyd8JhkmDSUKJNMiViEUIA8elRxYGo4YaxYXFFgS0Q/aDZwLQQkSB/8E4QLWAB3/Df1g+7z5+fep9jL15/O18pHxZfAq72juUu247CbsRev46lLq7Omq6UfpI+ne6MHoX+g76BPo4Of958jn4ufD58Pn0+e55/znEOiH6LXoKumy6f3pzeol6wzsweyU7W/uQe9t8EvxxfKv8wz1Z/ah9xz5gPo7/Ln9q/9NAVEDSQUABygJCQsNDfEOwxCdEoEUTBaxF0oZthroG0IdKR7sHsofZCDvIFEhfiGIIXUhMCHTIFsgjB+2Hs4dixxoG+0ZVRj1FjcVlBPCEfgP9Q04DD4KOwiNBngE2wLmADn/a/3o+1j6nPiG9+L10fR98zTyS/Er8FDvTe6f7ebsb+zX61DrG+uY6nbqPur/6djpx+m26abpxOmn6bLpxenZ6RnqHOo66n7qxeou65brOOyp7Czt5e2S7oHvKPAH8f/x1/L38970Afb+9hX4W/lu+qL7wPzx/Sr/gADIAQkDZgSdBfEGUgi0CfkKUQy+De0OQBBjEYMSbhM0FGwVGhb4FqoXNRi9GCEZlhnFGfEZ6hn4GfAZqRklGf4YWBi5FxQXPhZHFUoUOxPWEdcQWg/mDX0M7gpKCeMHgwbyBH0DywE5AOb+Xv0P/J/6K/kO+Mz2wvVq9JnzwvLH8RjxNvCw7xLvcO4F7u7tqO1X7Tft6Oz57PLs3uwS7TftTO2e7fPtJ+5+7u3uQO+r7z3wuPBU8fXxrfJN89/zs/SM9VL2G/fa95X4S/lg+i/78/vp/NP9YP42/yoA3gDiAZECWwPyA94EgAU1BjMHwQdJCN0IWAneCbwKLAuyCywMfQzkDBwNNQ1yDZsNyg0LDgAOBg71De8Nog2TDZINMA0UDd0MmwxEDMsLbQshC6sKLQqsCQEJhwj4B0EHrgYMBkgFrAThAx4DggLAARgBWACU/93+P/6V/fP8V/zR+0r7m/ot+qz5Kvm/+FP41Pdw9yD3xvaw9nH2Q/YI9vH12/XX9en17vXw9Q32JfZc9qn2rfbx9lX3kvfQ9yn4fvjZ+C/5ofkK+nf63foh+5f7AvyB/N/8UP27/Qr+av7G/hz/XP+u/wIAXQC/AAQBJwF4Aa0B3wE0AnMCsALhAiMDRQN4A6gDygMBBBwEQwRcBHMEkAS2BOAEzwS6BMEE0ATSBNcE1wTgBL4ElQSFBGkEYAQ+BBEE6QPJA3wDawNTAywDBQO0AogCWwJaAg0CrQFjASgB/QD6AMcAWgAvAPb/yf+f/3r/Vf8U/+f+0f6c/m/+Jv79/Qn+5v36/en9hf1v/Wf9Nv0v/Ub9d/1y/Ur9Rf1A/Vr9dP1u/ar9x/3O/b79jf2k/br92/0j/kb+Nv4+/v39Dv5N/nT+w/7w/vf+0f7R/tb+Bv9A/4j/a/8w/0T/ev+Y/4v/t//y/y4ARQBkAHgAqgDWAM0A+ADqAM0A4QAUAWYBaAE8ASgBEAHvAAYBKQFXAVUBPQEdAeYAwQC8AOgAGgESAbgAkgCCAGcAVwBQAG4AWQAzADgAJQAPAPX/2//R/+j/BAD9//P/1/+n/8D/yf/a/wYACgD4/+z/6P8BAAIASgCKABYA3//Y//v/QgCCAFcAZABiAGIAaQCGANgAowCSAHQAmwC5AFYA3P/2/1MAiwCFACoAKAArANn/3P/n/wAAKABrAIYADwC//zv/S/8hAGMA8f/N/yEANwArAAIACACv/7r/XQB8AK0AqwDX/83/UP9t/1YAYwB3AAIAN/9U/73/FABjAOT/qv8q//v+N/8x/5v/PP+H/pb+Pf59/on/9f+K/8b+Sv7x/oz/YP/aAYgBjPnw8w8ByRPoGLQOEvrX5Dvopf9jC/IQggie947w5PKw/g0KqA6RCoD9pPcM+Of4zgJ0CsMGqvw89ij2FvsxAq8D5QKFAL78/vqS/X3/xAAXAaX/Jf8YALgCXP4f+uH9KAHjATsENwJSAl8ADfnN+hQGQAurAOj4gv27/zr+3P6nAzUJeQGR+0EBeAAk9zf8wgWyBK4BBf/g/cb7k/qz/E4CzwPQACT+QP2O/Lj9VgW9BZf8P/c1/4MAXf/xA8AEPACo+2H8MAGX/sH6WwaCCP4AdPp4+nf+UQErBYkJNgV7+vD6Wv/HAYECRQPqCEkJevbU6Q/9kxPxGKb/gu1J+3IBDgIK+lEExhx6ASHmMP4BBdb9mhCyDjH6Oe7Q9OIN8w3M/6v4HPfOBq0Et/wGD6n/nPQi+OT/vw3lAd4BpPmR9XD8EA18Efz7gvAz+33+vgJBCVMBugqp8SrmQRQPDVjwPw26Bq7oWu50DFwNRgVrAvLyHPVU/BEGHwG2D28DteDM+LYX/gaJ8C0EMgVA8S/7DQx3A3gBGQNr9Cn7ZPkEBusRcP9h9z38Of9EBND8R/eDEeUMVPZz9rL1vQPcCecDgg7mAfrezvFMHB4LPPYOBF0E8PINAUUIK/2E+X0DzQg9A8/62fOVBbwEMfo0AI0JDwXf+G/vYQSoGDHw8vMCE3350fvQBwf3BAF8/+760wbxBc4GMwAA8qv0PgOsCuwKdwSB7jr0PRCY+hD28xTEBhLsofF4CisS1O6y8mUa9gdZ4w31sBBGBOj1LwSADXfxRe4dBWYPMvqU+t4XSuvb3Psi/xL85WsAMgHoAbcEuQLt+XLt5w1ECkoBJwnu9YfwN/wADEYSSvsfCAcFzdSO/rgknwdA998CRQGv77D9BgeOAPcVcAon5Fb8MhGP60z3BSnxB1blhP0zCjT6OfgqDIMRW/W86ZQKwRLn9JPuVA0lDn/zp/zUCsAAo/Tl/sIJ+QKJ9jz7Mw/qARnwGv4TENH/vPp/B5f8QOXeBGkUYgDxDB3/2ue756D7KCbDHwvtxei16VAHsBA7+SUMgQ+W+vXj3OVcDU4k4QeQ7H7yKANOANP31gGnFiINI+uI7DT8Ugh8FJYBGfEPAXwADPMfBhwZL/u96e74WQQiDtYHhAUO95/t0vtnE9UGSPa+BMv7Cwlm/5X2vPrkBesKsQgc8GX1QhEI/c/8JAdc+ZX4qRRIBzTv7PPjD5cDrOA8EDUwce3g0goEqyE1+Vzy3hPGAO713/CD+m4QHgw/BFPyz/KMBHEIjvys/JcCwApa+UTyvw6R/HHyJP/8EAoLx+8w7xIOVg3E8nDsuwwUEzP0oAEDB1XrtvkBD0v/pAE3CEQGA/fK4NL5AybxCLf43vsF7Ur7zhImB7wAe/Z19fgMvf4G/fYFxBOc8nHkXAVwCOkLhQmh+Iz84Ouy8DkU7Rd1Dl3r/Os0ADP+nAKWAqodOg500/vxjg5ICSwASffEFIoRtOPC3qcK3BkrDYv8Qu8HAVb8kv92FVf+KvG//kP9SwcMAVEBrwZN/Q/6nfy1+93+VxYiBljxdfLQ+O0HvBQiBHT0i/jy+kH0Dw1nGbrzNfHPARwDL++rE04gK+nk4gUE6gh/DTIGDfe1+5L0pwgrA6ADEQui8/P58PxhCH0I/vHMBZEH4e33BhoNyv1Z98QBrAPz9H4FAgbm/AX5hQhCCXHyBPokCK77jAbpArDyhAiMBUH5Tuz7CyIkxfzw16zubh0cDQb/8ftj8J8CqwYrA2gHavPr/fEFRPRiCLMRTPD69owBZgaMATr8gg1+9uD8OQUP8/f7SwejDB0RtvWL5hD2AA6RFa/7CgL2BVzheexUIXQTy/h2/W3/JOtG73MjgBVk8tvvIfAeC/8QPvp9AlUG/PTx7FMHQhhc97X09AMNCCv3Wvi6AgkGOAcq8sn/1wir/tv7EfT/BcEP+vol8670+Q/vEkUAMvZT5O8AhhMuAQL+pf8WBO//IO8L/AgW+P7N8wIOSAKn64z4eQyhCmIE7fXg8rwKbQEWBd4ICeoe80QTJQlV+R0Jaf0+9b/6ofmhFKsIQPGt+vcKFQjC9RLwJf4kFPcOlvs8+QnpPfouF/wOLvbr904KHv1a76n/5RJ3BZr9X/gm/cn+lPqJB9gUdfvP53sCUwd0Ab0Ig/YZ8/oM/wbV7iUAdRIaBUjuXOrfCmAVE/74/fr83fJ6/yIJv/9pBwUEd/bQA976xeg8Bo8h+wbS5Gz6dw329RH4eRQSEIT1+/Bx82MAQxBeDGH7pgNO+czvsvZREQkXpPc0+Cb1W/bMDH4Tde8t9asUrv7p7WgByxUhAaTp8/gmAT4N5BW9/PDq9/ZgCEH4kgqGGI3wmfJ1BKf1YAE7HAsE1Ork+Dz/VQIBBm8MnAhU7Anwef+6BbEagAy934b6OAoH8GcIMhS6/+T6pez09V8d3AvL79r5wv6HAlkCSviWB0QF6PtPDnv3+OhD/+MNVRMSByTi1e7KDwQMMwE+AcgAh+9C+OYRaAYQ+L8Fsfo+8AEKZhkL7rXoiwu7EZ8BGPzR8xX2XhQhBAbyx/29B/L/u/ahAlER4gF77+HxdAUAF38CNvTnAF3+XO4I/7QR6RfU+7TiovdKD/sBuu8NFZEZXeoq2u4EChhTCgMJ9vTM6a0EHQq+/Sj+KgbG+l/yTxOmENDu+PPgBmkA6PMOBlUYdwPm66D4pwa3+jEB5gbgDMwIk+v75kgHPhImC0n/Zvkd+AHuTv55HiYTfegN5+cESA6YAfkJdAFP5+f5Vg8/AjoGSgO28BP6Xg0WBpL6lvfo+4MENgC5BvUMMO2V+AASGPBLAtoV7/CW+X4EtgQ3/7D5vQij/u76IwN6A8EGKv8N9DH3cgs1C5ftmgTaFfb3e/AjA8f56v/NDe4Ds/tQ/dj4E/ofCwL+XQE9CrT3/fuUAl/8Y/z+Ak8OaANU6lP/zQwi9LQBcROy95n3ff6O/ZEHmAr1/b/xqAWvARXxbgENFFr+PvqFBE/96vLJAFgP2Pkn+isJEgGK+u4Csvkx+zIIkADh+rUHuwnj82DtUwFqFNAB1PlBB5H/W/wY+BzwsAeeF9cI+vAy6toO6QxD8O32ZwRhDZwLI/Q18/EEpAP09ML3iB7UE6Dn9OIfBkIXBfMP9CMfnQ255LrtcAztCs30xPxBDNYI5vWZ8Y0I0wuB+Tv5PgOpDG/6lO5xBUUHOQTs/+v6sAQeAQ/7c/buBLQOie9O+iIZiP827EH8jxHRBSjxKgLYCvL6Qvr3/t//GQDo/YYMGgRq7/cBjAgq8nf2EQkxD9sDtPbA+QD5Nf0DDT0HVwqeA/jkj+0KDYER/vtMCDYQ2ex+6T8ChA9vAkr9ZQxa/Uv3WAQy/Hn4eASrBAoDOgqn+tbqAAQzEjn1YPK4DNAPiPY773AJDwWo7OgFWxkqAffk0ezoEqcTL/+R9u30NAJBDXX5Rf1VCUv4RPVcADITSg3K8y7wh/gv/2gOPxEfCr3yiezH/W/8GgkSC3IDtv8UBor01eg1/zQPgRQKBCntv/VrApkBUQq6B5/+Pupn9PMR1Q3Y+y/+r/e59dQNiwUUAEgFYfow9bD/2/5/BnQJvf9WAR73XPKoBtUOTAYPAdL2Tupv/lQUmgP9AREECAEA7+HzFxUvB2XtrvsqDlAN0vKi8lgPvQq98T7w0Qb/DNAG6fyZADv7be1m+B8Nhw+iBwMAGfhT847xIAGeDDYQcA2r+TnlH++FCP0T1A1W/YXxCvRd/vEHnBCaAnH5R/WE9gwCqwvTByb/iQXQ9grmj/90FE0OnAha9V7xRvaE+zMLLBQOCl31e/DC+fD+TAHDEY4Ct+oBBe8YffbB5M4DfRZXARTzLgRzBF/0Jfl1C1IGKPvf/bD2dwKUFoQElO3X8Gf6nAt+FeML7/jC5xDvqwZmGycM1PXw9nT3tfs4/5cI6hDIAVP1HviB9wkFtgz7AcYAAPSk9LwKTQM8AugKHPhB80/0Av4fFZwNufjr9GLy8P21EBAU9wWB7bPrAvx2CUAOlg3TBBLw3eTjAbcbfAwY+dP0IPkZ/5j50wM2E74EW/RN9SUCcQKx/SMDRwg2/qj+dgBd+I73kP9VDmwIG/5h+KP24f70CuAAzPh6BB38EPvZBa4CkgJCAeP8cQIV/lj9vgCk/wcDVP4b/H0F/AWt+gr5DgcoB3/4EQLE/hjzC/8+CLAHZQU0AQ7+tvcu9A8E4Qjt//gCbwH7+oP67v6IBfMHYwFp+OL6w/9Y/4r80wAaB0kA6PkSBj4N/vgO71n+WQRABuoGF/++/AX+1/ol/8QKhAgM/qz0Rf0vBH/2bv1SCmUIqgOd+8r/rPr48L//Lgp1DoUCzupk9JELZwrzApT+APnp+Mn/WQcoAab+L/15+0ID1QQ5A3b6h/gcBmMI0P7W+CX+4gVzA1EBDv5+ABcEN/y4/LQD+/0c+BYAKQyrCab4vPc7/dL7pADZBa8CdgHc/Vj33/tlAFoIFAsR/377HPpg9TX29P1lDgARLwFR/Xn+Pfry+hz8qAfPCXb9Ffu//rkBGwP+AtoBQf3W/Nr9Zf+bAnIC/f1996j//AivCEYAyvTO8wX/4wfSDPgCBP3b/xn4lPjg/kAD7wfHBHUCbQDl9vz45/0VAeQGGgSRBBEG//ZS9zcC7gCwApMEkv8CANgBdgOpAFD7jf5Q/8UA0QNwAHX9a/p49zsDqQRZAz0Etfkc+1f4Z/grBvYL0wyj+y7sl/pkBC4EAQRkAF0B2ADS/DH/Rf9JAg8ErPuG/dcCbwQ5Avb/LQFIAr8AFAJEAXn9Ev2R+hD/aAKxA2UERvxt99L/PwStAuj8b/m4+/z+3QKY/6D+CQKJBXwDuP4r/8z5QvmUAtcEkQZeBXD9DwB0/Zv+VQQO/vL/Ef4cABYDavxAAQMIAQM/ASP+C/79/gf7b/y1/Z8DGghD/6z8B/8q/skBqf9rAB0A+vsd/JH7Mf1sCE8J1v/B+fX5bAKx/c/87gPSBEkCLf5H/aEAfQEvAxwEk/yY/vb+C/59AcUALP/XAWgETgPJ/Sv6Ift+/P7/LwAKBDAF8f8//FD7/P9SBd8D+v8o/ob+4f4q/Tn/kwPuAXIBQgL/Aqn/XfyD/L/6of75Ak0FNAXJ/xf8LftKAOEDDAHPAaIAAfwD++3/+QInA9//2ADM/vf80P7N/08Bk/9z/5wCIAQqAIv9wvs+ABwDyQFqAkkBFP8m/R38gf/bAjQBTABgAXH/o/6C/0UCzwBs/sb/IABq/53/hAEIAPf9Ff6fALz/hP7MANb/7P4AAAgB5AH7/33/GwIn/y//9gDR/8YBtAAiABsBggDkAVkAZv43AJr/QQBoAaf/ef4sAHUBP/+d/N7+iP5j/fr+eAANAVoA3P8LAHH/Wv/8AHT+bP8+AUAAXgCY/sH9yP9xAeMC+gAH/+cADAETAXcBkv+F/fr7Vf/MA3cCCAJ9Aov/z/0J/yMCuwGl/QL8vfsX/XD/EwIUACv/sP5b/Xj+kf9X/1H/CAEPA7kDLAPUA58AFv1i/gX+k/5tAnAD5AKdAEL/FP59/YT+AAAqALL/VQCxABQBRwFYAAkBUAKxAG7+Iv+8/3v/JgC6/0v/YwD4ABEATwDQ/yP/Bf7e/s8AIAGvAAj/LQBZAqMCsgAO/gz+uQDfAKgBIAKGAVMCYQEY/23+e/4E/lH+bv+iAW4CmgG9/8v+A/5c/wL/cv4U/yoAbgISAvEBtQG+AIAA3v4c/t3+mv/j/wX9qv2IAMgBvAKIAaf/kf5q/rj/tgA7AHX/DP+S/+IAwAG7AJ8AfgCS/w4AEAEWAf4AoQD2/or+nP9dAC7/Mf7//Av9B/8QATQDQwLEAEH/Pv6//hoA2gDIAI4AgAAlAkMCfQIGAo0AL/7i/BH9Cf2C/S3/+QABAZcB5gCX/2T/Jv/X/rD/UgB8AEYAaf8pAHMAvACaAfP/jf/a/Yf9lv4Q/kv/wv/y/xAAtP78/a/+nP7P/53/ngAgAhkDbQN+Aa8B/wBoAOT+Mv/K/uD9jv5E/tcA5gCdAYkBvP/9/+kArgA0AZoAt/+a/8X9AwCb/90AigAZACQCvAErAS//aP1c/c3+2/7n/2cB4QHIAEn/YP/f/18BqgCcAKABpgE7AhH/4P2Z/bf+Yv4M/d393P1lAXgCQwNBA7ICJQLl/47/zP7b/3wAJABL/yL+B/4O/yYAyf/F/y0A9f9lAFT/fP0j/iX/ygAWAekB/QCkADsAy/6//xMApQC4AH/+6v1e/g3+vv8WAE0BVAGn/4/+Uvy2/QYB6AO1BgAFyAER/u38Sv0G/kL/f/5d/k/+l/+UANsAjP4c/b36KPqm/If+tABFAWcCRAKcAj0BPv8QAID/QAIYAhMBWwB6/j8A0f5CAfsB6wKMAnQAT//i/uD+vv3S/Z/79PxA/oYA7AJSA5UBCf+4/hj/vQAbABcAxQCEAS4DSAJ0AywDRgOhAYj/9f5n//H+8/2q/YP+DgCb/1YBnwKpBRADNgCz/c3+SwH8AhEDWwKXAgkAVv5f+678Yv5D/kz+Pv+dAS4DdgJSAAsA5wAaAowC6/5z/6j+cf8aAiYC0AL//jX8TfwZ/7IBHgKM/qz75PtO/JD9tf6PAWMERwO6AEH/qP/sARcBlf74/Z7/nAHbAHH+S/53/yX/ov9d/ob/YAIYAqMBb//f/X/+B/6V/p8AJQEpAo4CzQHmAM4A/f2B/N/6lPrS/f/+MwHF/4T/oP7s/x0Alf79/db8If7S/cX/Ev9BAI0AggAZAjIC0QIKAvT/LP3K/SH+aP9Y/ib7gfuu/Y0BfgLNAEz+Pf5e/5EARQINAtYCu//d/Tn+dwBtAjIAmf1j+03+8/+HA5cClgHMAPoAswRtBokIagM//3j7SfzM/tkBswO3A3UCC/7I/7UD8wc+CYYDmP8LAWUEuQbTBAgCkwHHAi8CNgQBBp8HkgQU/lr6nPu1AWsEhgI+AHsAgQL5BTUGfweeBrECAP+a/Oz9KwGUAXD+Z/wO+yf+FwA7AT0DoAH8AAn/Af5k/iP9zfvp/DP+K//rAHf+4vwT/Mn6Jfv/+mv5jfdR9LHzZfhh/cn+iP2w+ez2jPeu95j5xPn3+SH5MPnP+SD8Cf7T/YH8oPqC+2T8cvwT+vr2JPXu9pD5b/2b/nL/L/6a/NT7HP5qAbMBT/6x+nr5vPpp/rz/7wIWBYkGbAQRArf/GAEnAV4BiwNlBscJJgkBCK8FLQevCS4LiQyMDXUNPwzJCi0KkAx3DSQL6AjbBZgGMgnaCScKhQk1CP8GnwbTBl4JgAppCVkJGQiLCeII0gfXBcYEbgNnA2wDEAOyA2z/KvxZ+UT6UPwv/t38Qv4w/jf9Wfwk+3n8Zv1g+673KPgV+Wr7Mvh+9MvxUPI58pXx7fFi8mL0CfJO8ZLwc/RK9cj08PIX8qLznvN09KbxzPNl88r10PTd9GH1H/bQ9cfy1PMY8rT1wPXX9jj3ivlm+3n8b/7p+5v/oP65/5AAfwEsAxsFVgRZAX8DygI/B/sIHAgFCxoMVA1QDo4QzBHoFRYULhPyFBgUPBdRFoUVLRS6Eo8PbxA2EBoPQQ0HCX4Jcgs2DH8LowtYCxUNwAy9CgEKZAjTBb0DmwLcAzEFwAPSAIj/qv+MAKkAvf6R/dL7D/sc+yj8HP09/ZD8RPz5/LP8t/x3+uj3SPV88bnvT+4j7ULssOve6//t+e5u7mDvKe++8JDxBfJf8+jz5/I38cbvUe+58A7w++7g7VTti+7376TwpfJE89L1VvjE+vL7ZPzD/Fj9yP3x+5b8kPvZ/Mr7kPoo+7/+yANVCLQMug4dEtUQqg8IETYVVRo1HCcaXBeoGJoZEht8GrAYXxdTFJkQ2A2uDLwKOgn8BRIGrAlADX0Phg9/D50QkRKFEB4OCQvvCAAI+gR3AtgA9ACs/33/eP51AKUD6ALQAcP/BgCRAKQAXv65/Xv97/xk/Fz6n/nG+U74E/f/9CDzzfEi8Avv5u1p7d7rNO0L7YzvovBB8l7zc/O68xn0LvU39RD1UvEC8KDuj+5O7k3u2uzw7LHskuwP7wnwZvHT8XXzgfOU9bn20/ip/Db9Bv6p/qz/1P+k/xv++f+GBdYGNggMB9EH6wlXC2gOhxRsG98dAB7uG68dER+LHPwX1RFYDgwM9wf9BWIE4wOhAxQHlQu9Ep0V4RRSFEcRhBIMESIQUQulBikAkf8BAB4BugBT/s7+TQAhAy0DkwVhBIwEoQGLAaEBwgLIADX+p/5H/jAAYf6S/Dv6Wvm69Rb0LvOV8PDvFu127PztbO7W7//x4PR+9tT3Hvc3+H/4DvbK9DTyGfEB7pLpdua55SDnpOeb6W7rhe8X8lTz/fYL+YT90/7l/hn94/vs+a33A/Yl8j7xVe/v8LPy2vSl+Oz9tANcCgcRThaIG8obNRlRGN8Z8xz0Hr0ckhomGkUaVhooGUsY5RZcFQURIA2JCmYH/gQ2Aq8BbwKXBQkG+waMB/oJlQ6tDwkPygsxCCUGTQViAhsBff+6/DL7EPqM+pj+hAAjAY8ApwDfAPz/df8P/9QApAEdAp0AuwAFAAb/Bf0u+r/4bfYE8/Tvee1Z6hvpnegc6mvtfPFP9Dr3nvnU+/P9cf+8/6D8Lvlf89vu++pT54Ll2uSJ5YjlGOhY6tbv9/Rz+Dr7f/w8/ur91P2f+v74JPVn8trvee337wTzkfbJ+RX+EgIYCvMPlBOFF3UYvRisGHgZhxzWITEjASPoITchTyO6IXodERhXEikMfAhHA1P/tPxi+sL66/29A+YHPwuXDM8Olw9LEe8QPg7iCYAFfQHz/Uf8QfnF92/3G/p0/DEAuQI1AyAEWAXqBdgFyAUxA/0BRwCr/VL9H/2V/O/7ffq/+J74b/eT9QL1WvNJ81HyW/Ej8WjxivJ49CP2m/cd+jj6PvtZ+2X6lvmy9wz1W/I58DXuvew46vfpH+qt667u/+8Y8Y3zkvSs9I32RfZy9kX3gfW082r02/R39XD2Nffy+Mb7dv+AAagD1Qf+ChgNXA76DucOPxK3FMcYyB0WI50mdiaMJrMlcCXIIc0ccBRkDn0HYAA9+oH1wfQt9qP59P3PBCQJRA5LETkTDBbwFTkSYg1HBwIB4v39+Qz28PTc89D0xvi3/LP/AAOgBH0FwgfXCKgJKAhDBl0CKACh/lf9lvsX+kv56feg+AD3qfUb9HfzsvEQ8svx4fAk8ony2PNQ9h350/ko/Cv8K/y5/AL71/hK9l7zOvGt8BLui+xm61bqsevP7aHvx/GS87T03/Uz9tr2+fYp90D36Paq9ef1XvVd9bj2jPfN+bn7Zf6k/5QDfQcKC+QNFxE7ERsQhhFUEYIWeCA/Jhgnjyh2Jdsj7yVTJGQekBmSEEsFyv3c91XzCPKU8xH1S/qdAHcGowqTDocRXRTlFSIUPhDKCKYBqvwd+YL2+fVP9bP0N/cq+gP+TQLxBVYHcQisCM0HfAY3BHwCEABL/0j9UvtT+QH5l/gO+V75hPeE9XTzK/Eh76rvze/p8EHypPKq85j2ePmQ/Gb/UwCRADkALf2++fn1RvLO7svsLuuX6dzpsuoz7OLuwPMO9kX4/fmp+Ov3CfgN99/0aPS28gLyU/Ps9Kf2qPfe+vH8Zf97A2wGogdICowMkgsYDLoLgAs0DwYWRR7WJIcp/CljKGcnQijZJhQiRxpCDgsEqfuU9RHy2vEV8gn25ft9AVUIzA2FEVYUjxfAF2oVjw8uCAcAW/qE97j1avTK81D0s/Wr+iQAZwRQB4gJygmUCXIJ2AdxBVcDAwG9/gr9ufq7+Dj4IvmC+iz7rPl89jrzFPEY8O/wvPG18YTxs/Ft8vn0tfh7+1T+EgDdAA4ArP33+b31ofHl7rPs3On259LmvuY56aztTfLG9m754fr5+sb6Nfq++M32qPTn8p7wwu+m7tvvcPLG9Wj6Vv47AjEF3AnXDAwQThL8EqEQYw6BD/0R9RoMJSwp/SgbKs4nYCeaKe0leh32FKcJRf0/9sDw9+1q7vrxIvdv/i0FXgs+EEATGhfxGFYXgRLZCwQC3voH9w706PIo84vzJPQv+E393AIYB2gKjAvdCr8JNQcWBNUBQQFu/0f+Ivws+XT4lPqe/Bj+xP2H+WP1ffLt7+bue+/m7v7uavCa8V708PhP/VQBMQSCBPICEwCr+3L3XvOw79rrR+hE5bHjTOW66ETtevEv9fb2Svlj+5D7S/x1/P/69vhI9yz0NvIY8qrxGfKj89/11/bB+NT7X/8aBE0Kbw8CErkTzhKVEgQXzR+LKB0uli7DKg0obCdRJ7oj3xyfEsUHgv5l9nzxOu+v7grwDvae+2IB6wfwC/4OVxSvF1UW+hPvDLUDJ/6C+hn3r/a79v30xvX592b7TAGKBgkJyArDC9AJBgiwBakDVwJTAsQAwf1e+hr4ufhW+nv7e/rQ+X72PPQj8+fydvEk87bztvKz9O73DPk8/JIAdwDMALQBG/6J+bj2nPIY7xftruqm57fmXOdx6SXtUfKa9Qj4u/oE/JD7ifuR+gj4NPfk9XzzyPCT71HvB/FH87z18PcI+pj8MP/gAbcFlwqmDesP9BAXEMUOpw/tEz8cYiYALZYtHioFKAYogCcUI4saAg4nAnb5wfHU7B7sZu0r8qn6zAGpCOAO6RI/FucZmxn7Fa4PcQWQ+yX2YPNy8nbzzfPN8yf2kfoPAKkGYAsGDVgNygv+B94FKgTqAe0Ahf/u++b60PuI+7f70vsZ+g/54fjC9qr0APOd8Tfxn/K+81L1O/is+xv+bACbAuMCmwGZ/o/5UPS18Fbtcuqw6DvnvubV5+PqG+8i88r2zvny+u/6c/qb+N739/bv9FL0lvNr8jLy6vEH8/n15/bH9zf5Hfmy+sr96wDlBDgJMwykD0oSlRKNE20WfxyaJD8srC8FLusp0ib4I9IhoR3cEisHGf5C9WXwQe/27tPxP/fe/ZQFmQx3EE0TjhVoF20VfxBvCeoAr/mZ9YfzSPNH9TD1zvbA+u3+/AP1CPYKKwzoC14JBwcPBPQAAgBL/03+RP3J+yz8P/25/Bv79/lw93319fIH8YvvUu7P7qfv6/Hb9Zz5TfxlAJIC1QKnAmoALvza9sXxNO1i6qDnIOUf5DTlhehi7Qn0oPhm+3L9uv3w+3v6Kfjo9LLzqfEw7rbtYu5j7yTzwvY2+Sf8v/3n/VD/xAAGAr0DYgfRCPAJagu1C3EL7Q6tFS4f2SlwL8AwdS/ALWArRilMIRcXcg3WAiT4uvJi74HtV/Hz9ln9zQWbDekQvhRwFxsYphaOElILWQPW+2P2f/XB9Zb1LfYM+B/7OgCXBJAHDAkpCagIxgckBlQEgAL1AEYA4P9p/5z+m/0H/G37x/o8+hD5bfdf9Jjxxu9G7kXvb/CB8cfzIPkO+7D9MwHKAuEBswIT/4j3G/PQ7WXp/eeS55fk2uc06pDrPfCU9Rn35vnF+nf37/aq9BrxtO9479Lt3e8X8a/ykvYS+bT6//0u/kT9YP9r//D+vQEWBMgGyQqLCyoMyA1nD8cV3yLDK4UvkDDLLTgqAyoRJ2EfxRccDZAAePcJ8vXsee2Q8nL53gEOCtsOqhEHFS4XmRiyF1QSkQllAIj4DPXx9MP1tvcn+V/6of14Af0DagZCCN8HJwbRA2QBJ//H/osA2wLgAwMENAN3Aa8ASQDM/Tf6ovVN7+/qsehg5wfpHO0I8ST2ufqZ/VUAUAMNBNADWgJW/W/2vfHr7KLot+cF52fm1eeK6anr4+8H81T18faz9of09PMg87zx9PGa8lnyivOo9Cz10/f0+nf8CvzY+3z6p/gn+b36nvwoAUwGXwpeDogQXxGwFJsbDiUnLYUyTTOLLqgqtyh6JHMe0BbtC40CVPti9ATw7O/w8W/2dP72BGcKeg4SEC0SXRUOFXISxQ2FBlgAvfwQ+9P5DvuP+6z6NPzZ/ov/aAJXAyICegIxA/wCwgK+AnYCYgSiBvMGxAS4A+4AAv+b/TT6PvXA8nLv5+up653rE+2n8LvzqvWQ+8j9NgB3AsEBjP7M/sL6XPVD8ZHuButw6ezpTOfz5szo5egT64XvtfDw8ZHyO/PA8wf1sPT+85r1wvXN9IP1cPR99CH1rPW49rH1j/Y1+Rf7YP4MBNsIdg9bEvIS2xM7ExQU5Bn9IfIq6zDEL88skSv/KhsoXCGDF8MLUABZ9+DwkuxO66jvWvckAR0KyQ9/EnkUvRWJF/MWixFQCtwAtfn99tv2C/kq/Df9eP62/4EAbQIWA50DkASgAzgC6wAn/5oAVAPhBdQIJgmZB+YFYgIF/g76KfXl8Nnt2OuA6tPq5ewX8Ij0iflj/VoAmgIiA/gBWAAA/uD5RvYZ86HvGe1b64zp5ui56Kjotemm6zjsnOys7UDuIu/o8PDx+fJX9RD3dfi7+d/5j/gC+Oz3Affx9ar0dPMY8u3yS/Yg+6UAVgd4DXkSZBamFi4XLhsFJIYsrTLBMwQwIC0xK/4mXh/JF0ANrgS2+/DzWfGo8LjxGPa1/BMFDQ6sDzgRNRPeEzEVSBMsDWUHvQH2/OH8Mf7J/XD9J/1h/VL/1gFeAUQAgv/J/iMBGgJ+AuUBZQMHBc4GrAicCEIFvwH//bH4Ofax8hru3Ot/6/fsL/BY8l70cfY++Vz8qf/AAHUAOv6S+674VfYA9L/xC/Ay7V3tI+0V7fvrGusZ6ujq4Oy07Yzu6+3k7THuhvA58tfz2/VM9ub2TPfq9kj2pfdW98X1Ovfb9sT1WfZI+YX9UASsChAO6Q9eDyoQ0hJLHuEqWDKtNBs2rzTcMfgvTScXHWETNgp+/qH0wOzq6Jjp4vAU/NwDbgz7ElITthOuF7gVRBKTD/oIswILAD39evtr/cL/ngG5AkoC3AA1/q78JP52/sz+eP8+AFz/oQI2Bw0KEwx9DPkJdQQ8Acn65PTI8XPvV+zu7Fnuge7s8HDyl/Xa+Qn9+f14/XH7PfsK+8H4N/mM+D726/Qc88bvX+3860LooOe65+bn3ub+5rboJush7sHw0fI08/v1vfYc95L2YfWN8jPyGvIX8N3vi+8t8YjyIvcD/OoBwAd7DZgR5RK6Ey4RZhIFHGoo8zDENuk2PjXmNWIzUSmzH+AVBAuMAjf5CvF57QzwkvSd/JAFfwvwDtAOQQ21DEQMlQtlCloIXAViBFsFSQeTCcEKXAoLCHAEbP/u+d316vJk8tzzy/cE/fkASwXYCeMNWRDkEJoMVQfeAUf7iPWt8Vzuo+yZ7WrugvBc8bTy6fJL8vnynfNc9Wn3jvkz+fv5EPs1+oD62Pjg9kf0F/Gf7AbopeMj4M7g9uFX5Wfps+vS7qjxmvOC9ZP3PveC9m311fI18OPuZO7h7rTwxPLr9Gj3avr+/IUCAgdPDPYQ8hTWFd0TMRTwFnAhGy9qOoM+9T99OwM0kC6HJM4ZKA/TBeL6aPTf71bu5PIT+B0C2Qe2DXsN0gnWCA4K9grZCGoK6AdOCv0K/AfJCEgIXAmyBnQD7fxN+KTxj+6V8Zv00fwFAZEE2gb0CbMJegzQC7UKqwjfAWn8WviD9WTxCfQG9J720/dW9AXxne+x73PvpvIU9Or2tvZZ9vD5Gfwp/nn+Z/tY93z1PfDU6jjo4uXz5VPnuuh36AzpJep+7CvuHPDi71Pu2O1/7Ybt8u648DrwvPJW9Ar2Z/U49bP0RvbQ/N/+ZgENBeoKHg7yELoSvRIkFsYaxSO7K7g3dT1POk82RDQnLjQktRmxC/cDW/+E+GPyYvGA9Pn7OwOtCCwNIQ2/CDsH7gceCc4KoQtaCf8Jmwt/C2EKJAzlCoMGqQLD/RP6a/WS9PDxH/dX/AQBfwRWBsIIuAgMCYcEXQNHAIH9vvxQ+136sflJ+cP2u/hP9vj0DfJp7STt8u1W8KPxy/RV8+H3W/rD+oX8tPvD+ab4GvZm8iryXu9f7RTrKeqR6ULrlum36IjoIepE65vrOewP6q3rBOx571nurvEV8TDyfvVt96r7P/dK++n5JP3/AtkEJwUHCF0Pcw2qEF4NTQ1tF68hmS+3N7o8QDqeNBYuMSpnI/4XwQwZAtgAZ/9R+zn3Jvm4/FUEQgm0BusF/QKT/9sApwd9DIIOYQ8eD3wQrBL/ENgL4gdpA8f/C/ze+dD3ufZo+FP7oAHCA44EpwMZA6gDAwMwATL+EP3x/fT+RwEVAvv/o/wu+cL2WPRq8XbtGusv6wTuLPCI8ozy3vR29eP4NPrh+Y/51/WB9CjyzPAo8O3vIu557XXsE+tq6S/prOYe6LLoUOyx7AHuc+1o61/s3Oum7lTrtO+17QTy1fRG99X9vPyiAh8CKAVOB74IGAeGB/wNowpJDCAJugxwHC8rkDePPCE+FDl+MXgngh1QF2YPnwizAw4FoAblA7wB+gD/A+wGPAicAtH/Wf3S/VEBbAgDEAISVBUZFvYUShKaDTEFpwDE/Uf75Pps/PD7t/yf/9IBRAVmBbkCTgESAPH/C/3r+0L7qvwAAcUCGAWOA7EAUPuK9wb0lPFL77nv8vB28l70t/OC8/HyyvLW8TX0Z/U19XL1nPM99LH1NPV79MLxJfDw6wHqcubc5fzlHOfV6B7pYu5H7jnwcu6w62bonej26Mjne+2W7RLxSPVI9ib8Afwm/8b9W/9QAT4BlgOsA1gMhAt7DqIMChBBHBUmlDUQPMNCXkD+N/8tEii5IvQZfBMeC14MSQyRCFkDUQL+AKwAUf9P+oz6q/gR+M35JQLAClMRlBSzFq0WeRX0D4sIDgS8AIwBBAESBF0DOgITAYAAZgE2AI3+ivuH+0n8v/wa/Zj9mP0d/wIBHwKHAvr+rfl09EzytvHk8pP0pPXi9mb29fSh8b7wJO/P74rwbPKF8tvzwfQ39Fr22vV+9r3z7fOq8IPvxu3e62fqr+s07IPreO2g6xrsG+0k7ODptenC59TnbuwQ7lHvmPO38vn3c/f0+DX4JfvF/xz/WgNtAToJHQvgDtAPXhMcF98VFB0VJMk02EA2QF83fzPYLUcl0xt+ELgOpRJzERYKJgebA0UA+/yk+Bn5/PvL+Zf18/em/ukFbAtADVIQrRX3FPQNNQkeBAMEFwXsBnYIbQoYCZoCxQFE/8MAqP9m/YH7uPtA/l78Nv3P/Un9mP8u/bf6j/jl+KD2cfYv91/35fl9+Xr5vPY/+q73o/fo89byx/Oe9b/4svT/9JvznPNv96n2YfXo8+PwcfCs7pTwa+sN7Vrrs+qb7bzr3+sD6Wzqvugg6kbt1OuB7sbw5vDA89LwOu9B7urvzPTw9fb5zPrX/a4ByAKaB2MItQn4C7wLWgxQDa4MNBMhIS8xyTzgPiQ8TDVwMVIn+RlREhAPyxITFTsSXwyiCDEEQwHp/a777fdX9IjyPvGd+NL+NgYVCgEPag9YD4IMfAhzBf4F5wf0Bk8LkAnpCeIH3AWpAeoCNgH3/iv+BfyG/aL92wA+/lYAVP6T+FH3ePIU8+X0n/do+zj/3AAM/ZD7rveM9of12PQ09ZL3I/r9+m/8jvso+m32C/RE8cfwlPAm8G7wCfLR8irz+fJH8DbuX+vD6W3oIOl06fHpxOuj68zsN+2L7R7vgfBo8pjxcvIW7+bvSvJj8qP4BvjM/Hz+vgB7Acf+1AOGBLoL1A8vEPwTJBUfGZ8ebiojNT08jj3kNbAycysKI9kWHRLcEucWuRh8D44LAwRoArH7RPfB9lzzt/br9C34AP4g/2UCeAPhBhwKMgmxB04H+ggjCkEKlQqwDawNAA9QCbcGhQWRAvcCRAGwAkEBbAHM/fj83vwD+iT2BPHa7k/uTvC28jn0rvn9/Hv92f5G+kz4tPWz9AL1K/Wg9873VfvL/TX8+fpP90P0JPMT8jbyr/Ke89zyCfL/8WPyJfH+7nftbutb7ADsAOzP6m7rU+zX62TuNe127UHus+9u8XPyIfMe8rD09PeA+Zv9Tv08/hIAzQDH/pr+6P15/kcGnQqKDlkQ+xOdE0gcESeJLwM36jkKM1stPivfHzYZaBV/FU0XFh6GGG8QKAxjBSv7WvrU9tf0t/i5+ej35PylAf39aQGDArMDTwSfBZ//9f9VBDgFNwj+DdAOTA+CDgIKMgc2BqcFggLKBT0GvgYdA+3+h/kx93D1zPGe78fuEPJy87b38/hA+nD6DfmU9yr0vvRI9PL01Plf+87++P4QAPb+Hv1S+4D1ePXO85P1AvbZ93/3ZvYB9ZPymPK58ILuB+uy6trp8emo6hbpjeqN65LrDu3o7ZjvQu0Q8GHvQvHQ8nTx0/KL9Uv54/f7+Qv6fvze/Yb/X/0YA88HWQlyDVMNcQ7mDawTexqUKEs3BjoQOVY2ly4EJ3kduhfmFgId9iBIH2QeChfHDCQCr/ro9j72APSM8gf0EPlS+/n75/uz/Y0Au/8V/lf72/wv//EDWgdcDPIQWxHrDxoMVwjsBKoDOgKUBBAJ1QtaCi8H7gHP/Kr5X/NT70fsIe5q7rXxgPR09SH5D/eT9oLyVPGe8AHxHvX696P8Hf8CAA8Aff13+/73pvah9m33qPkQ+fr5U/gy9vTztPA+757uuO4o7sfs7eud6nrqGeti6iTrNuz+67Ts+ewX7ifvBPHR8eLxFPPO8jL0HPQD+QP62/3rAB0DNgaGBIAGkgINB1MJzgtSD6AQyBJpEtQZByP5Lpo3HjbKMtksQykSIHQY8BScFwEetR7DHUcVUBGZCDQBHvtM9+72oPar97j4UPru+4P8Af0f/vv8Nf5e/Dr9HP1FAHYEFgmfDZYO5w/EDRgM1wfDBegF7QZ1CGMJRwlnBqIDX/7e+aT2ovP68MDvLfCc8Pfy6vWI+Nb5ifjx9gD1UPQD9Pjz0PZs+iL/QwG1AnMCav8V/Jf4c/YO9yH4nvhv+iD8Gfzy+dD2OPP88N7wT+8q7vTugu5N76LvpO4w7ezrgOrA6RnrxOzJ7S/wafAI8G7yJvFr8i7zCvNs88L01vrm+af+eQCm/9ACnwDK/yX7uAO5BI8Gewv8CwgPBRJ2G0YesC0TNWExayttKRslUB9yHPUUShgoIIojLhw8F/APHwj8Ai39OPvI+Wv6E/jv91/7A/w8+935T/uO/Mj7Jfo89y75/v2tAiwHCgwSDv4OGw4FC/MIywfnB0MJgwzFDYQL8wmUBfEAcP2/98v0v/Js9Czz3fNN9Xr1O/jS9yH3u/Nk87TyRPP99Z/2IvkY++r+0wBsAXP/3Pqd+JH3rPns+j38IPxV/G79tPwk+vH1g/OX8Sbyb/Hg7/buqu4m7hPukO0j7HPrPOq96efp1usT7dztDPAb72/yPfRt9JL1sPHH9c31rPvr/ev+TAMDAdEETP54AOABCQLlBmoIgQ28C9cNGgzwEYwkDS4KMtswDS4QKjQniyEbF6cYzx0UH6wfEx7wF1gPFQr9Amz/ff9V/dj39vbh+Kn4sPgN+Mf4Cvm9/FL7efgJ+BT5svpt/gcE3geZC9gNjw0EC5YLQwoPCt4KFgt0C/cK3gpdBroCBv/A+nn5ePd79B/yXfFA8dfxxvNe8+nyVvTb89nzlfSs8zPzSfWP+P77/f44AXX/iP/0/un9Tv0L/Eb7VPoU/DH9ZvzO+4b6xPht+LT1u/Me8GnvjO0/7C7ts+sE7OTqPusX7P3r1esC6vXqYO0t8CrzbfOd9Yr1jvf2+JH48Peq98v4gvqx/l7/PgElA5AEcAJ/AlYBT/8rAqsCDwRIBfAGCwm/Eloj2S1iMiox6Cv5KSYmJR99F7wX4hwQIAYibiAXHNwW3Q3FBr8B3/5i+wT16PW19vT7DPmx+d/7of4j/2/6kvZE80r4dPds+9f+WgYCCQ0NDg57C78NygjsCHEGQAnGCMYJoAr0BhAJNATTAoX+6vmv91nzifJg7ubvz/Ck8X7z1fHL85b0bPQC9P/yqvW391H6lPyS/XUB4ABlAkEBpgBEAM/8Rv2w+fb6SfjJ93D4Gfho+db13PXm8SHxv+5a6/bqZelO6tvpqep76g7q2Os47GbunO9E7wPxoPFI9J31VfdE+g/8vv6J/Nv7wfkT+HX6YfpL/QL/9gHqAkgE4QUFBFwE7QSTBHgF3QVSBDcIlREsIGgrHDM+M7sxvTIlLgMmgxxjGJUaUB+XH2QcWxlKGHEV/BAHCwwEO/8P+GPzGPGV8CvzdPRk+T39nv+8/lz6Xvmr9qz3fffy9yj8Sv9nBKQGngokDLINsw95DfcMPgrECAcGRQX5BHcEMwb1AhEAdPtH+Mn02/Hw8Gfvz/Ap73XvTvB68kn0KfTf9f71y/hH+Nn4Qvo5/KD+I//BAcgCxgQ1A0wBrf4Z/BD6sPh2+MD4TvjX9jX2A/Wm89fw7u+37eTteuuz6l/sNO0s7m/tCe+B7yjymvED8Ffxh/OW9J32Jfhk+Bf6AfvM++T5Vvs2+S36Af3n/Y4ABgAvBKwCAgXTBMQDHwQWA3MFlQMLBZMC3QRIDUUZnyROKsYtdSxMLEUpkSOEHQQaHRhzFjMXhhSiEYYPMg/EDRIOHQxhBGv+gfh39FfyKfNK82n1H/s3/u3/Nv9D/rn97P7b/g7+/v0s/tP/9wCbA+oH4A3wEPoRkhCsDS0KNAgpAwP/wvvt+Qv56fh6+t73nvk1+Vj5xfcf9s7ynu8p8Dzw7/FP9Gr2JPiA+2H+JwDAAMAAaf8v/zP/9v1y/MT7Y/xV/sz/i//G/R/9mPxN+ob3b/Qz8sLwv+8R7kHt7e2n7tbtBO+K7+/vau9h74ruYu9c8bzwc/KE80L4n/gP/Of5M/me+Q/5mPvq9yj7Z/jj/Fb+7/+CAo//QQPW/pMAeP81/67/S//1A4wCEQUkBI4GTRLkHsYp0S3jLjsscyscLEAnECPpHvMZ/RYMFh8TTQ6QDNAMEg2SDrALDwMb+0/29/KF8ZfxKfD+7hHyCPfQ+eX8HP7R/hcBygNlAyYAMf/c/Sn/sgIOB0kKjQ1mDyoPhxBaEPsNcAghA7D8mPmr9yz0avNy8q/zsfRB9gL29fSN9XzzEvTp88Hy4vGy8ZvzcfZO+xf+6f8BAtoCtwOZBCcEMwJ3//j9gvxY/ML7Rvq6+eD6Bftf+UD4OvZ29V/0RfJU7h3t3ewi7Ivtde3p7aruofB38AvycPOf8x31//U990n3Ovl++Wj63vu5++D7cPtj/M38XPxe/W38M//J/iYAzf9C/zkAUf5//2H90gILAzYDXQPbA8MFdgvjFyEgzCvdMBIvVi1CMmYxTyzrJpQd8hgfFq8Q5AaoBf4GiwjvCxoJiAZLArj+efoI+Dj2NfHf7sjrQe2G8afzLfdg+zkBkAQfB5gGFQU/Bu4EmgTQA5UF3QUiBqMGgggGDNsNZw64Ck4I/AQiAKX5RvbE8qzvLexu6gbrKO5L8Rzy4/WQ+OD78Pjl+KP4q/oC+jb5j/n0+O78bPz2/xgCsgYcBU0EEwQoATYBOvz5+Qb1UvQt8ofxwvGY7+vwJ/A98wbzU/Kn8XzwfPD+7h/v5e3o7QrtT+0K8M3zcfXj9uP3ffqx/TT+N/8A/jMAVf+ZAOD/BP7R/HX5hPsI+wX9nfrs+Ur7kfzE/qD+MgGeAo0GFgiCB0QI9gg6Dk4YJiUcLNksWC17LiU0bzYKM7UquSPUHmoX5xFgCycHpAOvA10EzwMrAUb8lvn3+d/7Y/js8cbsQusU6z7sge1h8Jb1EPpD/X8A+gRECT0MMw13DQYNJAu4CCgHywYbCAMJrwjHB+8HdwdsBnAFywK//nP5mPOM7tPrqeqq6DLn8Ocm6qbudPJE9gT5UPwR/zoAkQEtAMP+s/zy/bL/LACb/ir83P2SADQEOQN9ATz/5v3i/PP5Nfjg8/zxku497cXr5eoT7Kvsoe+s7xfxRPE386X0uPSY9aH0M/Wa9Gv1wfbU+En6xvol/Pj9QgCPAaQBiwGVAT8A3v4U/MD7svpu+iH6GvpS+5/7Mf5R/l8BeAOTBEYG5gcmCd4HsAovEGYaoiQ7KaEp/ylkL00zLjQwMNspqiKaG48UZwxtBy8EtwFm/1b/VP9e/uX8uPxr/cL+5fxj90bx8u3L7Rzt6e3/7a7vuvFs9Qf6WABzByMLlg2LDs4PnQ91Dq0LpgkPCdkG6AMgACH/wf+1AXUC2gHSACT+R/s3+C/3K/ZW9DTwRuvF6SPrGu4h8AnywvO29qb6HP1U/+wAwgLqAlIDPwKWAEn/A/9a/4X/pf/g/QX9lfx0/Tr9LP3y+kn4+vX/8y3yNfAw7oHrT+vf6rTrA+3s7qHwFfPQ9Er2Z/mV+kb7/vp/+oL68Prt+hH6dvr9+qH7NPy//IL+DAC+ACkA8v79/9r/QwB0/239Iv25+zf84/tf/yIA4QDYAjIEUweSCYQQuRezI6YqximIJzMqyi9yM5Ay6igAIC0Z5xM0D34M1QfzAZr9m/vf/dH/1P4X/Kj7m/wa/JX4t/Px77DvGvBp797upe3A7YLwXfez/jAElwYwB8YJuQ5AExcV4hIvD3ELgwnfCNwGMQSK/439gP3Q/jr+D/yl+YP4w/nW+cj3lPM/8G7uU+/07xPv3uz168Htj/LO95X6pfte/PH+kgOSBxYIogYSBEcDBwQyBAsCg/66+3b6MfuM+yP67fdm9oz27PbR9uf08fF28I3vju847+PtquxP7Sjv1fE39Nj03/WI+J/78v1o/pn9Hv2T/TL/9f51/WX7sPrC++L9tf7I/DT9xvwl/0gBKAHAABT/A//R/ZMApQCw/w//8/9cAqYDdAZzCKMRZh1qI40kSyb5KAMvLjXlM0cuLii+H/QY9xbLE4wNUgXz/UX7Rf7h//n9B/wU/CX92v21/O74Dfei9Sj0lfM/8YHt9+rZ7FTxpfYr+oX6CvwsAY8IXA/jEokTNBEEEDcQsg8YDmYJQgRMADj/0P3Z+3f5Q/dR9/H4Qvn49/j29/VF9i73WPYm83XwVO5K7lHwnvG78QPyY/N79nj7Y/+kATwDOgVIBwYJYQkZB+YESwNDAssAAP6n+Q72RfUl9WX1OfTy8RjxR/LK84r0b/SU8x7zCfQe9dr0HfRk86zzfPVl9zX3lfab9qb3Wvum/SD+Sf00/IX8MP+sAbUAqv9i/Qn9Tv2k/cv9cfwv/J36Wvoa+yX85fvu/Kf/5ABLAnwB0AKfCm4U4xsOILIhXyOvKDkwNDYnOM4yXyluIkghWyGkHGMTdAhTASz/kf/w/mj95/ob+BT4MvkF+rv44Pa39Xb0nvKm7u/q7eme61zuKPDr8Czxr/PR+f0B9Ak4DvUPOBExE7QWyRg4GLQUdw9eCvYGygScARL+dvpf97r1oPTj8+rzaPSF9JL0jfTF88HyDfKa8Q3yjPKu8azwEvAK8VXz9vZe+ob8NP7i/3gCoQU4CHoJhglGCFUGHQSSAqkAr/4D/J/4S/Z59ODyB/Iy8iDzy/Pw88Tz6PNp9fD2//cL+NP2p/Ut9bj1l/Z49hr2V/X+9ff39Pk9/FP94f41AP4BfQN6A7IC5QERAf//3/6Z/PP6Kfqf+b/4e/lf+gn7iv0EAB4CYAQMBj8JbhBmGawedh+VH8ch/CjlL64wJyx7JmYhLx+OHxwd5RZ/DqkF7gB+AdIBW/+R+wv4MvdH+J/4jfha+Br39PQv9L7ygvHI7+PuBvAp8YPyY/Fi8eH1oPtQASAF2AbaCV8NDRG1E8MUYxQEEs0P0wwSCYcGfQLT/rf7OvjH9QHz0vHf8dfxwPIL8+fyV/OI89v0MfbF9gf2dfR+86LzD/XJ9dL1fPad97X56vv0/RAA4gF1BIsFlAVjBVAElANyAlAAxP0y+8b4LvbH8/Lyt/IC83vyYfHu8aXzAvY59yf4P/gZ+AH5mfkE+rv5lPiE9yD4B/lO+cb5LfoD+9f8vP57AFkC7AF3AfQBAAJnAtEALf/F/WL8+PuC+7b7dvwb/Rf+fwAjAgwDmgW3C/ATUBp0HF4c2x/9JogtwC+uLYMptSVkIwEj4iIAH00W5QtjBX8FJgfMBPn+UfjB9R/3yvgP+ab34vS78aHwq/Fq8nnx+e6i7EbtjO8I8ULyc/N09hb71/6FAhAGtQkSDYIQbxPKFDoUQxNZEr8Qmg4CCz8HaQOFAC79W/qe9+Xzt/G28OfwFPFr757tM+4Z8NLxBfJe8SrxZvGU8jr0L/bQ9l32kfbp9637mf5g/wAAvABxAtIEvQVYBaYEOgNzAqkBNgAV/nj73vhF97f2xfVi9IXyqPF/8Y/y6PP+86Lz2PPS9CT2P/e399H36vfz+F75KfrO+r76MPx2/Zv+oP9a/8D/bAH7At0CEwFDAPQAzgFmARsADP/U/tr/6QBVArsDgAO7A1oH1g64FZcYMBgLGWkf/SbRKvUqdSe2I7siZyLgIjIgzRg1ED0KgAl6CTIGcQA++x35FvnL+Dn4Ufdt9gb1cvPd8i3yL/H07+3uGu807xTuJ+4Z8D7z2PZe+X374f4BA4YHswtuDnEQpxF5EvISgBKuENwNTgsGCeUF5gHq/TH7J/kF9+f0ffIR8Zzwm/CX8GXwRfCk8GXx6PEe8j3ybvIz8570uvV79sD2j/dx+fj7yf4vAIYAfAG0AmMEcwXCBVwF5gMuAoAAwf96/h/8wPkO+Hj2AfXD82Tz8vPs80rzqfKZ81v1pPby9s72Dfe695v4m/n7+pz6ivlN+p37Z/2R/a38Cv3G/bv+rv5L/rH+Yv/J/yj/Y/5i/xUAxwCCAmsDEgOTAlYF7gugE68XgxYRFp4bVyQLK28s8igWJZsjgyTTJgsnnyDvFZEN7AtUDioN0gbD/gP64vlD+r35SPgD9pvzkvF28S3yf/Gv71LtF+0O7gft6usO7JHuO/Kd9Gj2CPj1+i8A2wW1CmgNAw7vDlkRdBSZFREU3hAgDdMKMQlTB2IEVf9g+rf35PYd9kb0i/H27+jvkfBy8XLx2PCR8JTwmvH88l3zH/M58t/yz/SH9sn3dfjI+WH7Zf2j/0wBngJ8AwkEqgSoBCYEjAOuArsBRQBK/k780vqa+XT4wPf+9g/2+vTg9M71jPaf9j/2RfZp9gX3AfjC+Kz45ve398n4Mvqq+lz6Avr9+nT8x/wd/TP+IP+S/4v/3v/rAAkBaAHqAmgExwS6AxkF9AoyEnYWbRbvFRgaOCLsKOAqZilFJu8jwCTaJgsm7x9xF1IQhA1gDWcLEQZa/4P7Tfrd+bT5o/jE9qX14/Tz8+Lyq/G38Mrv+u6r7T7sXevO67Pt3O/o8eHzX/bX+Rz+rQI6BxALIA4nEHIR3RJIFOAUiBP9EMwN5QqCCM4FQgN3AD396flE99P1nfV69Wf03fLF8QzypfL78p3yDPLI8V3xP/El8YjxafIl8/PztvT/9Qb4rPkz+xH9Of8AAcMBogFaAXACogOaAxQCmf/p/WL9l/2b/Xf8hPrS+Dj48Pgk+hX6xPiG9w33WvfJ94f3B/eD9oX1VvUE9TT1hfaw9hn3RPdY91j4Sfn5+jb8AP24/d/9p/1Y/igAlgE2AzQD1AF/ApwG3w3bE6wWMBeeFx8chCRWKy8sjSi6JH4kSCYQJ1YkLh2dFfMQcQ+BDsELCwcOAlj/pP4Z/nH88Po/+i35LPdk9OjxafD977nuBey86Vzo5+ct6GPpAuzm7p3x4PMs9lz6lAD2Bg8LWQxlDJkNJxHfFN0VVxPmDr8L9AoJC0IKMQfYAlj/3/0w/jX+4fzI+o/5Yflc+dH4Avd09fL0o/TK83HxGO9e7rvu1+8B8GvvzO/q8Nfy1vTo9gL5p/rv+3r8OP3d/sIAigGIAGP/Qv/j/5EAFgDo/jr+Sv6s/qv+CP6K/bX9GP68/Yb8RPvF+tv6MPoG+Yz3jvbE9gr2lfWv9aL1JvbS9QL23/YQ+J35Mvqa+vH7bP3f/SD/SwHiAwoGCAbhBioLIRNnGZ4ZCxifGk8hPifWKHkl1CHYIRYjeiIcH9QaTRdoE2EQOw5DDLkKSghNBWQDlgLBAXz//vz/+6n6n/gl9STx/O4i7r3tf+ta6NLmQOco6T/ra+3d71HyMvUH+KP7LQBqBCUHQAd7BxAJqQtsDawMvAoECYIIwghACMgG0wV6BYgF7gRqA4UC1AK7A58DuAEi/wH9/PtL+zT61ffB9FfyLPBn74Tvwe9e7wvune2m7s3wgfJz89bzj/Sp9Xb2+vYn9//3IvlS+YP4B/jD+EL6hPvs+wX8qfzb/f/+iP9XAGkBQAIKAtQAUgCgAHUB2QDc/nv9vvzW/G38GftU+lv6jPoO+iz56/jx+YP7NfwR/F779ftX/ggBSAPSA30D3gQYCaUOahIzE/oSFRTqF3gcVB5CHeUbsRstHMUcnhwBG9sYOxcJFiIV1hN0EkARQxAmD+IM+gnoBx0HWgaoA1f/UfsL+YL4bPZ483XxCPAZ7+Ttoe1s7sXv+/Bc8VPxdvIe9dD3+fgf+W35bvpW/Ej99v1L/pv+df9RAK8AAAHpARYD3gM4BDEEMQSyBO0EMAVyBFsDfgJmAVkAr/+N/n397vsL+tz5C/mF+Mz3bfa19nL2LvXJ9ED1yfV69crzJPPF87z0zfRD88DyF/Rp9bj1RfUO9v73MPnP+cn5YPus/Xv+Jv7n/fL+vwDCAQ0BLwB9AF0BMwLGAosC/QHJAcACBQTrAygDggJPAoEDhwR1A3MBsQDkATwDsQKQABz/4v8lApECxQD6/xAB4AIfBIQDMQNhBPoFfwYiBnYGBgebB64HlAfRB1sIqAizCAcJ6QmSCmAK2wrBCxUMMQxPDG4MNgztCzMLXwoICrAJ9ghzB9YFYwVXBdYE1AM8ApYBxAGYARkBKgBF/xn/+f6b/u39QP38/Jb88fuI+2D73/rG+rz6s/rF+u76HPtj+8X73vsH/Cr8fvx6/An8GvwS/On7w/tj+/H60frb+vn6EvvI+rD6v/os+3T7T/v3+vn6VPtr+w37wfqz+tn62vrD+sH6bvp6+o/6tvrm+g378fr3+mz7t/uh+5H7Avxs/I/8e/yw/Bz9if3P/fj9IP5a/qf+HP+c/8H/9f8/AGcAnQDzAE4BOAFCAT4BVAFZAXwBzgF/AR8BEwFQAYEBcQEBAfgA9wAMASgBDgH6ABwBGwEEAR0BHwFFATYBHwEuAXUBjwGMAYsBjwHCAfcBCQIBAvQB+gE0AlgCVgJJAkACQQJlAnwCsQLPAroCvQLrAlMDmQO0A80D7AM2BJgE6AT1BAEFOQWBBbwF1gXRBe4FGQY+BmMGPQYjBiQGPwZHBhgGrgVlBXYFXAX8BIoEIwTaA5oDPAPPAmMCAALBAW0B+QCQADEA6v+z/2D/4v6H/jP+5P2n/Vz9+/yJ/DX8+fvE+3b7Fvu2+mP6MfoS+s75kflq+U/5I/n2+N/4u/i1+L/4tfiQ+IP4hfio+Mn4y/jL+Pf4NPly+ar50fkQ+mD6qfrx+i37Zfuu++r7JPxW/Iz8rvzo/Bv9SP2B/bb98P05/nj+o/7k/ij/cP+r/+X/BwAcAD0AgADLAOgA8QANAUUBgAGmAdAB/QEgAj4CbgKrAtkC8wIPA0EDawOTA70D3gP+AykEVgR+BKcExQToBBMFPQV5BZsFvgXkBQkGKAZOBnAGewZ/Bn0GjgaZBpMGcgZkBkMGFwb7Bd8FrAVvBS8F5gS0BG4EGwTRA4gDOAPTAnYCLALfAYYBKwHiAIgAMADg/5T/Wf8N/8D+ev4+/v/9vf2J/VT9Iv3v/Kz8dPxL/CL89fvK+5T7Yfsz+xf7/frU+rP6kfqA+mf6UPpD+jn6M/pA+kP6Tvpe+nD6lvqs+s369/os+2H7jPu/+/b7N/yB/M78Bv1P/Zn94v0p/mr+sf7y/jf/bv+c/9f/FwBVAIEAmgDEAOgAEwE+AVYBbAGLAZwBtQHMAdoB4wHmAewB/wEFAvQB8QHoAewB7gHoAeAB1wHNAcoB1wHLAdEB4gHvAeAB3AHFAbcBpwGXAacBhwFtAV4BVgFSAUIBDAEJAR0BBAH1AMkAxgDgAMcAwwCzAKYAqACXAIkAlgCiAKMApgCfAJ4AtADIAO8ABQH1AAMBLQE8AUQBVQFiAXQBfwGJAYoBjQGcAaYBpwGfAaIBoQGuAasBoAGeAZEBiAF+AW4BVQFKATQBGwEDAeYA1AC7AKYAjAB2AFYAPwAoABkA///b/8H/pP+N/2//U/85/yL/Av/p/tb+v/6q/pb+hf5t/l3+R/4z/iT+D/4A/vX95f3V/cv9uf23/a/9nv2Y/ZH9kP2F/YD9ff15/Xr9ff2D/Yr9kv2Z/az9vf3I/dL94f36/Q/+IP4+/lX+bf6K/qf+yP7q/g7/Nf9X/3z/nP+9/+f/EQA3AFcAewClAMIA3wAHASMBNQFRAWgBgAGYAaUBqgGvAb8BzAHPAcsBzQHLAdABxQG9Ab0BtwGqAZcBjgGJAYABcQFlAVsBSwE5ATcBMwEjAQAB8wD9AO8A1gDDAMoAwQCpAIsAiACBAH0AYABWAGcARgA7ADcAQAAtAAwABgApAA4Az//V/+f/yv+u/5H/gv+s/6r/cf9Q/2T/Zf9L/x7/Pv8+/y//F/8M/yT/+v4h/wL/4P7u/hH/OP8g//X+Av8z/xT//f4k/xX/N/9k/zj/Iv9F/53/cP+E/8L/tf/d/8H/oP/V/wAAYQA+AOX/JABSAGwAOgCMALgAYwB0AGQAkwCBAGIA1wC0AMUAvQBtAJQAPQAoAG0AzwDxAHcAGwAWACMAkABrACgAAwCd/87/SgB2AO//HAAfANP/YQDZ/8b/DQAiAH//if7C/in/nv8Q/5z+Af/Z/tD+L/8N//v+zv4+/xf/0v2G/qz+Pv/Q/vf9Vv4z/wUBkP/G/bn9y/69/zQAK/44/Rn/mP7B/nv/j/5l/j8AaAB4/qP/OACo/3kApv9u/0j/RADu/+sAZwHF/74BpgD3AVsFvwW3Anr/vAJQB54FQQKX/qwMsRdxBZbv+f+DEBX9rvex9bQETwvi9UDt8AR+HUz+teez/SIMVALk+hT6ZfzvBpH7S/WlA5gBC/dz//4ENAH+/lP4UQJRB5/1D/dJCsAFmPyj8cL87BEU/bT6LfZj+i0NswR+8bfwkwrMEM7w7uzv/78I8gio6LDudhC/Cjzxr/M097MQCQ385vv1UglvB1n8g+xo+vAbYfrY67YE/xMp9Q3xmhPJ/14FxvHE/5ALwv1LBaj7awDgAlsEfwO+BAb8OwLUDtb/A/sGALwHqQbF96wAyAgtBQb/zgE6/LQDDxh19MDvTwgDDB8D/fiz9/YEwgvw+yD+XPhiApkLN/e9+SUFTQIF/HP3GAGgALX6MQIz/hX/Pwbp/IT2SQeeBkP7jPuj/fAH1gP58wv68g5bCZv56fhM/w8NKgLO9x0FbwTjAUz+6vpSAkUCOwHiAOD/f/7e/F7/aQYEAyjzY/qRB6gAzfWf+RAC2wTD/af1ngBhBGH+N/tp/SAGoAOE+rL+BgTz/if+TQW7B77/LP1nASIJrQif/tYC0AoMBSkCewJ0B0YIGwGaB/UI0gARA4MKSQfrA7UDUQdQClYDdwGeBooGNAdcA1sBkweAA58AVgPVBCADNP2iANEG6P/1+sr8+QErA+33TPjNAoYB1vmk9vn8PwJk+pny3vn6/+L6PvSi9mL/S/7g9AD1cvxj/JL4+fYJ+xP7nfaD90j6kvsT+Hf3mvsp/mj81PV99uv8+f0g+jD32fjp+Xz3tvhR+//7UvmQ91P7nvmd9vn2Afhv+0v5g/ON9E74ifjX+TD4W/j8/B35ofnx+yT7Bv0y+1r8pv5g/Cr9AP9nBPMMlw4mDCkJJg37FLQURBGsD9YOwhI6E84PkhKBE7sXJRv8GOsZbhcrFaUWchcpFqERkAnYBs4JHApyCFIGHwXDAwQC9f0e/Gf7tPn393TzfO+q7h7vHO+C8GDwLfGk8RPxQfH48PDw6fEk9EfzPPJZ83v1LfmS/Nj+9QFDAyYC3wL0A8QE0AYZBU4EHgXOBXYGXwX1BLMGQQoMCdAFiQHr/9gB9gF6/7T8tPr1+bn5XPiC+Lz44vnh9sL0f/Jq8vTzL/IS86jxqvHK8ePxe/PO9Fn2pPff91H2LfVn9pH4H/rm+hD7/vvr/GL+8/8rAQsB4QFOA6cBfACP/27/oAAOAGgAkv92/Q79U/xP/h8AHP+I+/X35PhK/moFlAZ1BMQDUwirC78MbQwzDJgNXg7pDpcLUA2gEToY0hqNGsQa0hmBGlUaPBobGmkYWBOrDnwMFQ9DECkQwgziCR0IugXWApf95fzU+on4wfNn7w3vE/Dh8nzzXfLu8YDx5vBr7wTwJvEy8uXynvEX8830q/kD/UcA2APPBKkFagSlBUwH3AhtCeoIngmWCdcKkQuzC1MMqAuDC94InQV6A+cBwgGrANz+iv2C+6X5MPiA9/n2FfX58tTwte8E7yDvsu3T7Bju9O+u8AHwFO++7snwevOc9B3z4PLR9LL2wPii+Bz53/pd/d//w/7N/XX8E/7PALMBjAAZ/nH9LP18/cP92/yJ/IH8Efuo+dP3q/mn+b/7W/0G/Ej4vfP59iH/LgvHDhUKGAaZCgAUmhj2FjYSaRFEFu0Zfxc+FocZTiFiJjMlOCB8Gg0bGRyHG44ZJBV7EGQL7QpXDMIMywuIBysEzgDx/Ub5TfRc8xXzjvON8H/tteuu7TvyVfRp9FLxnO8R777wkvLo89D20flF/Ar9OP25/zEE+wgtCrYH3QWgBecH0wnNChALkgqNCi4JOgiRB+AG4gVmBL0DbwEa/rX6VvkM+8j8CvwX9+fx1u+f8BDyMfFt773tLu437m/uG+6o7uzvevBa8UTwOvG58YLzo/Rk9j74Lvn0+Qz6bfup/NX9gvys+4H7Yv1v/oD9Qfz1+uX8ff1R/br8K/tn/L36PPsw+Vj4zfhW9sn2mfT69234CPuq/MP7qftH+i7/SgbFD98SWw4HCy8PBBgzHpQcDxhoFmYaUh/lHnYe7R+oI34lLiK6HVwZORmQGWcYrRW/EIsMWAmbCIQI0AZDA8b+6vqy9zn0wPEI8HXwBvDB70Ttau3v7TTwcPGM8e/yvfHQ86HzlfbI9/H6zf6xARgEnQNoA50EMgi/C88LnAkqCDgIxgpFC4ALUQksBxoF1wLWAXMA1/62/IX67PpE+q/4S/WX8l/z9vNG9FLw0uz56t/rHu7k7kPv1e5b7y7wi/G88snyZ/Jk8jL0zPUw9xT3qfed+XX8iP7w/dj8HfxT/Qj+u/0I/JX7UPw6/iD/J/6s/Hz7mfxy/O/6i/gh9un2Ivcc+UH4WveM98/2lfkL+Tb8Ifv7/GH+n/4O/2f8NABQBo0SUhjEFyEUthXAHOwiByQQH/sbgBwuIE8gPCCuINwi2iRvI4kgMRunFyMUSxH3D8MNFgsqBoIC5ACpAKcALf46+mX1HfHH7cnryOsY7bvuQu8/7xzvTfDq8ZP0t/bi91T4w/er+DT6mv5dA1sHkQnxCOsHTgfFCBAL6Av2Cq8IAAcYB5gH5wf9BioFOQOWADf+uvuo+Tb46/aw9hL2xfVi9MfypfE48aXx4fDk7zDud+137avu9+9i8UXy6vKV83XzEPSP8zv09fSH9gb4MPhe+E34MPrd+w79Bf3L+xn7aPql+k77h/oO+1n6Gvsv/Ez7KvyB+QX6X/lF+XD5u/dp+Vv5lPo2+oD6Ifwt/Vr+1P0c/tb/AAKRBN0EkQUjBtoHMAwQEggYIhszG38ZZhkxG3UdlR0uHWIcEBwuG+waRho+GxodzxwTG4AWJhKBDQMLFAtKC+0JDAcIA/z/8P76/rr+9fxn+t32YvNA8u3y1fQq9in3p/cU+CP5OvgB+HH40vrl/Vb/uP/g/4YAqQJLBPME+AXfBOAEqgMfAgADKwLdA24DIQKLAXH+Y/1i+hn6sfmF+AX3C/T88lnyiPNo9AT0Y/O78fPvgO++7pnwsfDK8B7x5/Bn8u3ytfNp83rzlfMH9HLzEvT78w30yvU69u33Svg1+Fv4UPdz93X3K/cW9632kfdY+Df6RftJ+276lfn8+WT7pv2v/ev8KvyQ/I7+IQGRAhgCJAGrAGgCPwPHBJEFqwXmBdcGqQngC38NOgzFDDoNKhI9FiYX9xWwExEXCxo3HTccdhifFkMWExedGEMZaBijFnoUqhNXE2kSgBCfDQcLOgl3BwAGEwReA28CIAHw/3j+fv0R/KL6ZvmO+UL5q/nk+GP49vgJ+jD7PPsF++D6YvsQ/Af9i/yE/dz9ZP5A/3n+Af5f/ar8j/yt+zT7gfrT+Xn58Pg2+eL4YPga9571TPPr8rHztfPJ8y3yffEy8WzxH/J68kLyw/LI8m/y5fLg8Xzy1fI29A31lfXO9bP0E/WW9e/2svhV+Rf5iPdb9t33lPjN+r/6a/t1+1n6qvsZ/cv+VP+c/hD+nv/0/3YAVv/m/88AmALJAyYEXwRrA0IEiQN+Bd8GJwiuCNAGLgeYB18IdQkdCmsK7wtOC1wL5QusCg8MSwutC1UM/wupDPALmQsADZsNAw8tD6oOow0uDFYMtgxSDR4MHQz5Ck4M1gykDJMMpwr9CWoJ9ghBCLsHWAZdBpoFHgYaBrsFcwRpApEBFQEtAdcAyP9M/vv9df00/mT9D/1r/En8Kvxi+4D7hPoy+pv6gflt+fH5Y/l6+qP6dPqf+UT4DPjJ99P3H/jT+BD5U/jd97P3NfjU93/3BvYx9Vb2CfZJ9kD22PXK9rn2OPc59xP29vVU9qP2Efdv9sD2kPag9tj4L/gq+Nv4wvji9xj4sPk/+yz7a/qT+rP7Wvyx/Tb+4vyy/Q/+9f5rAGMA3P6X/ycABQEZAgQE4gJgAbgBGQJvBFEDsANKBAcF/QXnB1wFMQUmBDsFbwYwB68HsAXXBqoHwQd6B6oI+wbfCOEFhgYoB9cH0gd9Br0H9gcpCRcI5QWfBbkHFwYPBsAHMAcvB70FaQWoBckEMwXPBRUEKwTTBAQFegUnBPYEAQMDBG4DAgImAk0B/QJbA/MBRgPeApQBnQBBAUsBZAFPAcf+bP5G/SwAGABc/kn+df0H/Rb+Of3l/A/8e/sa/XD8q/3q/7H7Tvqb+9j6yfst+iH86fgQ+vH8Z/y0+5T6G/iZ+Ob86fwh/IX2Ivke+in7Xv7z++v4IvpD/gb6Sfv++0r5gvmz+AIA9P6f+vP7MfjW/SABTf2P/Zz7Qfy9/QT8xADbAK/+3/7Q/nD/gP8kAOf/Of+i/t7/QAKKAQwBBwBLAFkChwDRAS4A/QB5AOT/ZQElA5wD9QDgABMBugJgAoMA3AECASwBrQN5BCgDJgPyAIL/WgL0AkwDhAKRAbkCfQOAA3EF4AOfA1kA9gH7AscBEAHX/3AE3QE8AtcCaARUA5r/fwBAAZoCwQHp/RP/IQKDATYBcwA9AhkCvf/7/pEAzv6l/1kCnwFdAHD74v+2AS3/aAKy/sr/4P7B/nb/Jf8Y+1P9fQCn/v7/9f6WASv9Lf6w/Qz+mQI4/i39JfwZALD/cP2O/+//kP6U/v7/Qv9L/6v6GABc/z8BOAALAND/kADW/kT96AI0/Q4BhPz1AAECdP8gAfP+FAFl/08C2ADQAl37wP7fAz3/GgOH/wME6gHJ/wsALgPb/ej8XQFe/ogCAv8g/qkADgKkACgDuP1j/nb/wPxjAar+mwA9AZj9zvuIAA4D7/2//s39aAJj/0//dv8++/0BqPpqAsEAY/9eAp76gwRyAPD81ACu/LL/XAD0/NQBsP+0AhUBXPsHAfH9K/+z/a8ABgB7/5MA1gF8/9X/fQBf/qMCtvrN/gv/+QGfAyQAxf6g/5H/TwLEAU3+UwJy/u4BVv9//YcD+vxCAa8CcwDeAsD95PzkAtIBTAC0APr9ygEW/50BYwED/1ACJP+R/10B/P6TAkb/af3k/6r/QARUAKz/cwDy/i0CfwE0ADAB3f/o/8f/IQGrACX/+/1aAAIDMABHAVn/rv5OACn+GQBQ/jH9VgEa/m4DegNA/lkA2f+M+94B8AADAav+8PyZBLf9awA2AIcBlf6aAJj+WQJ1AGr+GALr/h4E6/15AjL+qP5c/RUBpQD4AtQDX/3SAdv8zgBPALH+GQAHABsBrQKIAGj+DgBj/0gAXQC+/j79fP5wA9ADgP5b/N7/2QD/AUj+eAAN/h3/FwB1AB4Cdf7L/z7+cf8sAL0Ar/+7ACL/fgCvABEAr/9rAFb9o/+x/0X/ZAK4/h4C2wBsAD7/1QA4/tMAqv5EAOECo/15AIf+5f8P/mgA4v5XArv+HQEc/3L9sgGH/78Ax/3mAaz/RAAPACwAVf9PAPwAl/+3/er99wEiAucAcAGKAEkBZv5T/hoCMAOR/6r+DABT/2YB0v4kAzgAyf81AmD/wf/P/3b9VQCNAK0CzQFe/hIAdwDOAO0B8gCD/Fv/Vf+oAYQAuv4+AkoApv8dA3D+7P+O/+H/VgC3/m8AXgFFAhf+vgBD/RACwgGP/rH/Dv0cAcX/WAEiAD4AKv/V/n0By/+nAQD/+wCb/vAAoP4SAfz/3v+0AHwAlf8s/jwBjv13AlX97ALy/nf/4v+U/6P+CAHKAZv9NABs/d4AJP+I/9IAef5b/6cAlf4W/30AmP76/uT+AwCuAYf/rP7++73+5v/YAs8ApQAo/1v/bwGs/xwAM/8OAhb/tv3d/6cBBgJU/3v/av/rAC39nALU/XAAygB7/eAAJv6HAZz/lv++/0YCTf0SAa38XQH4/4wALwDk/+j+G/8xAEL/+v+T/ucDCf7G/w3/cQASANoApgBhALP/vgC0AMj9fgITADwAqgBfAPT+Zf+OAJoBIv/b/5cAPf86AV0ADgF4/qX/hf9oALABuwGFAKn+ff9eAXsBD/8BAcAAVgBH/gMAeAC5ALD/av+x/48BuQFyARP+c/4TAWsAUAL6/MoARP6SArMARwAhAUz+LgFy/UUDof96AAD+rP9lAd7/RgH5/q3/DP40ANYAjAEa/8b/pQBlAOAAHf5UAHT/D/+zAeT9j/97/jcAhQHp/7QBuv4e//3+xgArAv//9f9w/ZsANf8XAov/QwDe/iT/yAL6/RgA+fuKAA3/ogGtAOT+LALC/ZwBEv4kAQcBYf+sAHf+O/73AIUA9f6KAQz/DAA0/1T+awCn/+j/ZgBI/8P/sgCdACQBjgCh/nD/ogBX/zAAf/3jAJYABAPkAEz9K/9n/4D/8P8KAGUBYv59ATUByP8+/+P9QgAZAH8CjP41Aan9QQEIABoDj/9M/4j+yf/yAJn+5P/j/Y8AxAGKAlT/1v+z/XoBtgCI/y8AMP88AfMATv2lAdr9+QJ6AaD/AgC0/fgAWwCN/97/eQAQASUBav+KAOYASf/O/soAUf+mAX/++P7C/3kAXgP3/2z9JwD2/jkAH/8X/swBNgE5AeIB1v6WAHL/Ev+UAVH/q/+I/gEBF/9oAZQAdAF5ALr+7QDS/1QA4//rAeD+XgBs/g7/UAB//wUClP/W/7X/iP07AIkACACA/kn/hgLH/2kCGf++/YX/Dv9UAaz+7wBQAfH9sgGkAZQAFv+D/w0CQ//JATH/dv4gAMz/QgF0AT4A2v++/+/+awNX+7QAbf70/8YCMv7tAPn96QJfAc7/l/0a/v/+gP8sAF8A2f/EAG8Ccf9B/0r+6v6xAPj/h/83AQ8AVgDS/5r/+gGl/+H8mf6EAXUBawFj/ikAwv5MAMoBEAHc/Ff8+QDpAEYBKQNP/mj/GAB7/UABj/6HAeT+a/6NAgACYP7B/gb/iP/UAUz+/wCE/6IBiQE8/TAApf7+AFYBugCw/zb+YwHvAnz+Pf4+AMQCkgCo/8f97/+IAAD/4wBlAcMAPv8FANQBIwDZ/TwBgf5F/5b/XQEnAoH9RQHt/9UAYgET/Lj+cf5gAYgCowFSAL7+cP/TATkAmv95/wgAIADn/b8A3QBwANf/3f3EANEAmP7X/hH/pQHgAAABNv9e/v0BNv+gAUz95/6SAhv9XwL7/UD+NQCb/iIEmP+Q/GYC4P03AlD/a/+sArz+kQBAAIIAzgCD/8n8XQBbAsH/KACQ/Zj9ygPKAP7/nADm+n8EBP86AHwBLfx6BCH/yQHQ/goBC/0Y/tABO//6AY/+BQFa/iT/RgJ3/uv+BP6e/4L/sQFRAjH9wv9gAfoApP6l/boAuf+d/70BMP9gACYDOP46Ad/+uQB0Agf9OwLu/qz+ygKJAIr+9P4PAUsC4v1O/foAWgEzAKwAW/6k/1wCs/6K/mv9pgBnAur8uQH9/W//PALp+7cCl/4/AaX/Ov2q/wMBWQJEAEYAu/uiAykAPQIZ/7b9egHfAHwAIv8BAcr/LQGm/5IB/Pu4/zwBIf8QAaIA2AM//sf9GwAwACkBpQGy/VH9zQGbAgsBHv63/3X/ywKn/Q4Av/zf/zwDCf7VAk8A6QBG/df/w//vATP+BfzjAej90gTnAbD9iAHk/EsAzQD2/v4DkPwG/xEBlQHZALv81gAeAX8Ahv6B/3MAWwAP/8MBnwJBALH+v/63AFkCw/9//RkBRwAMAen/zv8XABz9vgAeAD0A0/4gACH/CgCXAm3+IgEH/mz+yf1u/0QAQADvAYUBSwBr/ZkCl/5J/2QBaADb/xv/xQBvAR0BewKR/7T8DQEJARv/uvx2AS0DKwKvAB7/FgDA/pAAB/92/5T/VwH7/nr+YABmAOj/hP4R/df/Ef+w/9MAdgD7AAYAdP9IALsAhP+DAHAANAGT/qEANgHEAjH+XQBiAO7+WQJc/qQBCf6uAiICcP4gAHr/qgCM/H0A//+CAND+cQC3AD0ANAB/AIn8bwBnAOsAxP6B/FsD5P4wA0oB3AJ9/u/9dwISARb9/QA/AMr/wf+CATMDNPzi/msAG/8WBC0AzPy1/nEAkAMP/4oAEQRp+8D+gACV/r3/M//F/+D/RAB9AdL91f0fAfQBcP7y/XQAov3a/zUBHgH4/6cBSADO/wT/kgK4ABv8GwOjAJH94v9N/dkEsf/A/WUEd/2MAHj9m/18BQT+RAJIAHT++AM0/eD97f9TADwCo/21/yn+cv/0AML9aQFXAJAAL/gcAZsGd/x/+2AB8AOeAkj5xf+QAyj9DATU+zwDggVX+4n9ogLTA8j82fm1BUQEb/xlATb9fAZU/mD8dQM6/ekERPj4AW8GGf3kAFX/5AAoApD84f5I/xH/rwN8/o//eAMm//kCHv0A/xgGj/zS/oT9UwOIAu37KP/KBBP/jvwdAKwAP/9I+8b/NgNJA2r8qQA2Ag8Bjv/g/VcAXwGc/1r+JwA/ARMDfQAw/7wCvgCt/qwAgP2+AScEx/0BAz0AegHEAYD6pAEHAi//RAAS+3UDdgGmAD781wJ/A4D4rgMX/KcHZfwZ9/sHhv3dAhn9J/lVCJv9EfptBQX8CQPQ/f36RAgt/w37PgQSAf7+ZABU/ukA/wCc/rMB+gH0+scDBf6A/xYAfv3l/7sC/PlgAtsBJPqpA6X7fQPA/j/9IANJ/7T/sP1cAy7+6ACAAFv78wSG/tr9UgZn/hT+MwIQAOwC6PpN/70I/vsx/2IC9v/ZAAn/wQC4ADj/lv6QAM8AYQBe/Qn/0gQA/QMAEwJU/ogBL/yTBTv89AChAKcAxQAz/7UCMfsIB+z7wP3CBSP8JgL6AFj7UQhi/KX+3QP2+3YDfgFR+H0H8P7o+sgH7/aFBnz/MvnqBvf+xvyJAnz9dwChAyf5wQIoAuH7sAC2AMb8ewK9/6/9D/8BAsf7HAId/uX+ywIx+iMGdfoCABoCt/wfAdH/Jv8p/0f+1gDR/6X9KwHp/J4DkfwP/vMAagAaAG79VwBG/wQAb/4wAbr9UwAPANYAcv61/8YCxfsYBXb+l/04A0f+jgHjAC//tQAcALEA3AAP/tIBkwC7ALz+mAE2AGr+2APZ/WUAQQFi/6wAQAFS/m4BYQAf/0YD7fuqA8v+Yv/uAdj+sADD/wMCif4vAQMAlQC/AH//awF4/yIA1wCLAK7/QAEPAFEAPAGB/60AMwBmAOMAev/uAKcAw//cAM//nQAuADkAtwCm/6wA+f9+AHkADf+TASf/9QBKAI7/+ADW/8EAvv9BAPL/UQBHAFsADQBvAIAAt/+XAFwA9f8nAEQAfgDd/yQA4gCg/7kAMQDz/w0BeP+7AAYABgDLAHr/vgAuAAIAfADt/xwAdgAyAAgAYgDT/ycAJQAbACgA5/9DANb/GgBn/3wAgf8nAA4AbP+EAFv/NACY/xoAtf+5/xUAfv8jAIX/0P9hABr/WwDT/wb/TgCC/9H/AQAs//7/+v8p/38AKv/K/yMATP/0/7D/1P+k/8j/1/9w/xsAlP/V//P/V/8SAJv/5P8UAE//LADz/2P/SQCQ//T/8v+z//b/PAB8/ysADACa/yoAnf/k/x4A0f+0/1IA4P9BAI//7f8aAPr/BgAEAAAAtv9dAGj/XwD//8v/XQCb/yUAMAC0/+v/UgCo/0UABADS/zcA5/83ANX/EwAmANb/LwAUALv/bwDG/0IAy/8gACMA6P8/AMr/eQCe/1cAvv9SAAgA4f86AP3/7/9XAPL/2/9vAKX/PQALAOH/GQAkAO3/UADI/yIADwDV/ysA9P8IAB8ACQDH/1cA5f/Q/yQA4v8wAMn/SgDj/9X/LADQ/zUAo/8tANz/HADd/+v/GwDH/zYAr/8jANf/GwDY/+L/RAC0/wgA6P8AAOT/AQDp/9//GADk/wQA3v8OANf/DQD0/+j//P/T/y0Ayf8oAPH/9v/t//X//f/i/wYAz/8gAOL/3P8CAO3/9P/2//3/yP8hANb/9v/y/8//CwDL/xEAzP8LANz/6//k/+f/+f/x/wEA2f/w/9D/BgDL/xYA9f/a/+//2f8JAM//AwD//8D/IgDa/+r/+P/1/+H/+f/x/+v/AwDY/wYA9f/4/8//GwDK/wEA///r//7/5//y//b/+//e/wkA5f8FAOv/5/8IAPP/6f8AAPT/8//y/////f/o/wMA+P8AAP3/AgDz//r/+//w/xEA7P/9/wAA+v8KAP3/8/8QAPf/AAAIAPP/EQD4/wYA///8/wAADQD4/w8A8v8AABcA+/8EAPX/CgAKAAAAAQABAAkA+f8SAPj//v/+////DQACAO//8/8FAAAABAATAAoADAAHAPP/LADn/wsAJAAJAPr/DwAUAA0ADwD//yMA//8XAPn/AwAhAAEAEwD9/xUAEAD9/woABQAaAPn/FAAVAPj/GwD0/yMAAgADAA4AFAAIAAYAEAD+/xcADAAMAP7/BwASAAYAAAAEAA4A//8IAAQABAABAAoACAD1/wIA9P8IAAMA/P/2/wAA/v/8////+P/8//n/AQD8/wYA9v8JAPf/+/8DAP3/BwD4/wgA+f8BAPz/AAABAAAABQD6/wgA/v///wQABAD//wEAAAD8/wUA//8BAP7/AAD//wEABAAAAAAAAAABAP//AgAAAAAAAgD//wAAAgAAAAIABQD9/wcA+f8CAAMA/P8FAAAABAD6/wUA+f8CAAEAAAACAP//AwD+/wQA//8DAAAAAAABAAIABgD+/wEAAQAFAAYA/f8DAP7/AAADAP7//v8AAAMA/f8DAP7//P8AAP3//v/8//3/AgD8//z/+v/0//z/+v/4//z/+f/6//n/+P/8//v/AAD4//L/9//2//7////z//b//v/7//n//f8AAPv//P/7//n/+f8DAAMA/v/8/+z/BgDx/wIACQDz/wYA+v8IAAAA9f8CAP7/8/8BAPv//f8AAPT/9/8HAOr/AAD2/wAADQDk/woA//8BAPj/AAALAAQA5//z/xAA7v/+//7/AAD//wIA8f8KAO//8f8QAAAACQDu/wgAEAD5/+j/LADN//n/GwDn/xgA1P8YANz/JgDi/x8A3//1/0AAuf8eAAgA2f8CAPv/4/8qAOj/xv8CAPv/6P8gAMP/UgDe/9n/OACy/xYADgAUAPP/2f/7/1EAAADc/xUA2f9nALf/z/86AOH/EwD+/wkAEgAGAOb/EwAMAAsAAwANAAIA6v/z/xYADAD7/wAABwASAP7/BwD8/wEACAADAP3/DwAAAAAACwDz/wIAAgADAAcABAACAPn/9v8DAAcA/P8CAPn/CAAEAP3/FgDw//D/9P8BAAAAAgD5/wIACQAGAAQA9/8CAPz/BQAHAPn///8DAAAA/v///wIA+P/8/wgAAAAAAAUA/P/7/xQA+//t//z//v8EAP7///8EAP///f8CAPb/BQD///3/BAD8/wEAAAAAAAYABwAAAP//9f/6/wAABQAEAAAAAgD7//v/AAD5/wYABAACAAQAAQAAAPv/AQD3/+//CgAXAP//BAAVAAYA/f8KAP7/AAAHABoAAgAAAAQA9f8jAPv/8v8HAAUABAARAAAABQAYAPD//v8HAA4AAQAFAAIABwD9////DAABAAIA+v8HAAAA+////xUAAgD///3//f/v/wMA7f/Q//n/AgDp/93/7/8TAAEADQA5AO7/9v8hACYAAADp/8r/CgDs/ywAQwC+//n/vf/Z//X/JgDw/7b/6P8FACUAYv8+APYAe//o/0YAtf9c/wYAtgDw/yb/LQAeAZX/jP+XACkAPv+a/0UArv+G/ywAuP/y/87/W/9SAG3/xf+AAP7+nQAXALT/kQKY//n92P8K/fP94gIBAAz/EQCr/4/+0P9P/7MB4wDs/sYC5wBJAnj/VgJwAQAAAgCH/vQA8P4RAIAAYwCMAI4AnP9AAM/+tv5BACb/AgAwAMD+NP9U/2L/+P+r/2f+2P4t/z/+p/84/6P/QQDv/+z/UwBU/4r/VAAdAMb/sv/9/+oApgAIADIAFgA9AG//ZwABABkA5//9/zYARwAeAFoAvwAxAJ0AEgCAAOAAEgDJ/0kAaQBuALsAgwBBABoAf/8eAFgAQwC9/xUAOwADAAIADACNAIsA0QDdAJUA+f9NAO//JQD9//H/DgBHADcAj/+9/5P/DACl/wAAMf/j/uT/Xv9P/0//UP+u/zAAk/+V/8r/h/8GAPr/HgARADoAPgAOAFMA6P/u/yEAAgDP/04ARwD6/8cAHgHSAEkBggBeAPgAlQDaANj/z/9T/0EAmgAn/3L/Fv+D/sP+AP82/pv+yP/J/hAAXgGBAIkCaQHM/yz/cP/J/8b/HgA8/8sAVwAwAFwAK/9C/1P/tf8f/73+wv6i/z0A2P/9/+7/7P+E/73/SP+m/t7+vP/8AKcA/ACLAHAAEwEgARYB7AA7AUEBzQGMAWsB1ACdACQBxwE2AZoAegCDADYAHwAnAIf/TAA3AFUAuAAfABX/WP/o/sf+dv/z/v3+5f82/8j/KgCc/wv/7P5h/+7+O/+3/m3/4v86AL4AkgBFAM3/xv80AFb/mP8qAJMAbwGRAT4BVgCmAAAA/v9eABAAtQDiALMBrwFtAQEBwQDGAZgBwwFzAZcBlQGpARgCOQJvAlICXwI/AdcAUgAcAN8BRQJfAWkB8wDs/5cAWgBK/w8Agf9x/7T/Bv97/pD+3v8E/4b/y/9K/vn+VP54/t3+IP+2/2H//P8S/tr9lP6A/Vf/TP7k/fH94PxN/f/88P07/eH90/3j/ED9XPzv+oz70fv9+or8e/p/+jf6UPno+YL6Y/qJ+Hj5a/iK+Bz5TvhR+Sz6K/mE+tH6HflF+lr6KPlT/bX7evo9/a35VPvR/FQAhgGnBj0JegaCDFIL0QqyDuwMQA69Ey4SCBPrFHUUhxXeFxYYwxTFFx0SQhAHFMMNZRDXEbYNuQ/RC/EG5AP1AJ//MvyN/yr6NvpR+Qny4/RU8KXw0+5j7vLvFfCA8QjwxPTw9Df3e/qu9sH6bvpe+aH+W/9SAS4D5wVIBLgFDwYbAloFrwQIAzgFAQI+ARMB6f+W/nT7BfqS9Yj0y/KF74Dwu+7I7hjvcOtA7a3l9Ocf59vk3eqs5eXoeOdL66fq1+xA7vXrZ/FP8p7x+PF59BXx//on+sL7F/8S++j/1/2MAiwAswVoBRAJHw22BH0KqAarEwcmRDMkNS8vdTA/JJonbCJFG9om7yxRNeQz3zByJSQe/SEKHkMc2xQGDVMKDgmuCEMG+wAqAcf7PPWE7dnhHdtG3AnlEelK6xbqm+Qp4yrm2+HF5NPpLu9Y9+D/ewLyA1EKuApfDsgP2g2nCZ4LrBCfFBgdOB5THaAdohXSD90GUgPiAPgAYwEBAYb/V/kw9oTuj+3v6ajnreUG5DnkauUn6PHpHeyR6/DrW+qA6kDrOe168132pf30/ur/hgDm+5T8O/pD/nP8owCHALL/VwOx/yD9pPol+DjzW/XI7ijxUO+h7o3yVOsB8Efnsuha5zPkn+uJ5oXv2e0z8Njx3fTb9cP2zf96/A8HbQYWDXsZqiTxMGM1KTqJN38ysC6WLJwwCzb2OhQ+nDzcPPcw0Ch8Ik4d8xcLFbYOyAVvBVYAnfsj+vP2Yep56eHiv9jI28zY/9xh4u3kc+Wk5kLlkOWk5n3orvO99JACzQTRDVYQLxKpFzkRzRk2EYcWqBQaGMMZ6xutHUgXIxjyDEMI3AOe/Cv7dPta9Nb1b/GW7APthenD5gnmv+X03znjc+Rp5brs7O177s/wYPKK8J/zW/Xj+D79zwHgA8oFSgjvAVAG5v6ZAqgC5f7DAnP/bQEp/VL+Wvfc9VnzTe677sbr9+wv6rHt7Okq64jr6eeV7DjjmepV5wLtmu/H8AH4YPbVA7z3hQGi/5n/3wQjCMUOowv0GewWOSb/OUU4pjZ3N+UwES5TLKsmhyR+MFY2si/ZMTUlKBviF18Q2w1yCfAE0wGYADQBQ/t290D0Y/Cf8UTpfOOC5SvhXetG8KbvzvRS76LxZO7c8WnyBPUOAiwEcw1tD0QPlxBiEEQQOQwqD7QLuQufDpgNkxARECIOwwkVBH3+9fdC88jzMvCb8z/ylPFj8Ozrb+yR5XrocuXG5nnt2+tk8aryLPS49yr2s/jd83n5Wvlc+S4CNv9ZBDQEOQIb/9v8ePl/9QT4F/oh97/6RvU98ZHyHuvn6mHmp+gq5n/om+uJ53zsceiz6v7q/eto7jvsVPFE7R71eva3+oYACwCNBdwCWwU6/hoDyAbqCUMQWxTPFfIVEyB+IjQuVjfpNfEw+jNYLjMmPSTUIKcjACwIMEEmNSQ6G70SNA2TCNIF1AGZAhcAVf6v+2r40/Mw8ZHyfvCh6grqfOjA5zLw9vUb8+z6evj+9f/4xvhK+gP//grsChoUiBYTEkETLxPdD7kNrg68CCsLdg3rCt0M8ApeB7oDdP3q9kvw6e3x6Xnqwu3F6w3u5Ou66bbqDenm6enoQO7w8H3y8vXI9uX5Cf0gAEn//gBWATEArwDjBXwF5waqBmQBGQC5/Zj6SfXw9mb10/S/9Qbwz+zo6pnpfOUl5dnjrOFu5W7jv+ib533oSO756ZvyRPAW8cPyx/E//Uj5lALqA1AASweMAqQDaf/NB4gMrQ7vFnwTNhU9FqkggiRyL7o6njEUMBYvZCpCJNAjpSHsIfQstyukHzgdrBfGDVMNYwoOBEwCYwFe+jP4iPpO9I7xR/Tn8PrvQOv56SzlCus88AXwX/Yn9pH3Dfbh+nr5bP58BBoI0wu5EHgRHBFzExEQjw8+DmYLwAaNCMIFegjVCakGZgNg//D36fFl78Donujq6anojOjN6n7po+pa7wDsY+uq8ILu/vHM9Hf3L/mnAC4EagJpB3wFGgMOBLQE+QNkA+cGPAWCApMBC/wW+Bf2UPXp78zuYezV6XLkueYa5svjB+QI4PbhMt+V5S7la+YG7HXuMO3c7YLyJ/Lz8hH81/pb/JAD8wCUANwBgwaVALoJMQ3+CqYOGA7tDTAPdx5sJiMwyzZmMekwCS5hLAIjDSC1JKcmfi14KKAkmiARHqQZVQ96DTwFBgDv+zX2Svgv+NT5n/Y19532AvBT7DjnVOfI6kTto+5Q8dT1L/jU+m77J/yq/xYB0QWmBxwL8g1NEuoWeRfqGM0TPBChDWsH0wSWA5MERwWLBQ8DZfv291rxG+vv6OrlROGC4iLlWuj56vrvCe/27V3wwOr97DHw//T79cP7cAA8ATAGagNUAk8E6QQfBagC1QH4/rL/mv7l+7b8CPdL9p3y4O/X6rvoruWo4DfkMuAO4UPiT+Lv41DlwerM6BHtrO+t7TLzHO668HvxmvWO+9H7LwZdBaALfQn+COUN4w2/D34LvQ14D8AVqxfZH2wqBTcBPBU50zaQMCIxkCSHINIc8SCkKDEnTyYWHnEd6RXGEZ4LWgHj/Xn16vPb8D7zRvRS9/H44/VB9CHuluuL6efq3+ss767wvPZP+UEB0wGiBGEI6QdvCzcHzQvfCAoQRxLXEgAZ2RZvFF4NYgizAgMB3P9I+qr7dfx++fD4J/S47sPtpeuf51Hko+Xx5BTq7+9t8J70P/eo+TL3I/rW9mr4RPrA+6f87/12BGABLgheBU8EJAHx/hn7vvTI9i3wzfHa8tnxXvB58ULuVupj6p7myeKk34jgSN0k5Gzo4enS7Wzva/Dd7VnylO8X7MvxFfAX9iT9fAM/BB0JXg+xB88Npwu3CPUJixDgDNsM2xBvDz4XMi4jNEoztTdJL7YtoSxnKpMY4x/xHUgbBR5gHBAWNxVjF1wODRHkCHIBBPaD9cvvIvLJ83jwsfl2+EP8Ifje9TrykvKx827u6fIt8vr23/vBAegFHwt7DtINaA4eDEENOwoGDFwJHwp/CEwJFglNCDQIRQOiAcz8oPkT9FbyE+4V8Nfvge//7wbvMvDp7izzcfCp88Pyc/I39aD0//hx+DL+Bf+kAjMDWgGHAbf96/5l/OP8Vfru+Jf3pvfY9xr1+/Nt8G/vzu2N6yvoruba5OnjD+Vu4xPlEuV554Lph+zQ7mDtkvDe7TzynPN59G71TvR2/Ib8GAXgBfIGqQuuC3oQNgqmEBsN7QzKD2wO4hB+DzQZ3ButLCA4Dzj7NBU1FTJlLUsqNB+OGjQarRmaEmEUtRG3EEwQog/tDL4HzP+V9VHwFO6F7BPoz+li693xnvN796f3uviJ+sj30vem9033nvZ4+lr/6QTHC2wQZhKfF0sYMhWTErwN/AcuBPgBzP+6/60C2wCiAPj/KP2F+mv3afMf7jTsJ+o051znR+dm6FfsyvEJ9n/47vqO+Wj56/ve+wD7YvyS+2/8Ev8JAnoByQS4A0wCJQN2ARD96PiS9Yfw5fEE7lnriekA6mTnAOkk6CHlO+YN5a7k8uMs6EHlluj06n3rm+4N74vyde/Y9bD19Ph1/Hz9BwRxAZwJygbmCCYLjwv4DJwK7xC8C1kOFQ69DRQVxB0UKc8udjfzNuM0czT2M/swXyzIJHUbUhkmFZ4Q8Qs+DR8MKQ/UDo4J5AXmAPL6I/f49PfubOkL5cPm7egr7xDwbvQw96z61/07/Xn/Qf8S/z/9xwFKA6UG4QlYDf0QRBcCGT8VOhRhDZkJ5AWOAnP+vfoI993y6PN385n0KPYl9rj1mPUz8q7uDuzK6qLryuyn7tbtHfK581X5Tv3bAO0DkwOtA7UAawFo/jH+Wvxl/Cj74Pqj+4v7NP0r/D/6BvVl9Avwnuv36OnkKuJj4VHjGOJr5bjmH+f06v3s5/Dc7/bxv+6d7y3xLvN69zn2xfp8+swBagOaBa8H8wbUCpIIlQqNCAsLYgyGDuMRhhA7EWEQARhrJbkwhjMDM0owSTEUNMUz7S0JKG0ixhnwFFAQYw3zCD4ItweOCDgHEgVG/Rv5ZPgY9xD0DO7x6ZHi5OXG51DqmO2c8ZHzdPee/dT+TAHTAhcDlQQ+CDMHLAZxBvkIpg1HElAUHRNOEm4QYA9yDVwJPgTd+y32nvMl8Bzvhe137WTuDPKw8j/yo/Pl8djxCPQh8+3w6/Hx70rySfWa+EP5Mf1UAIL/fARkBHIEHAWrA6UA1/7z/qr7Y/uI+RT2+vNn8i7wkO0h7w/sVOsO6NzlbOTr5DnnBePe5M3jAuWI6xHsbPDA8PL1A/jJ+pAAdPle/5f9S/8LAXICmAQ+ArYJyweyCQsQCA8sEKAT6RJZD08PmhSGGt4mlS2PK28ttzCoMU8zBDLGLKcjKBzdE6EMags8B48DcAL9A+MDxgLE/4/9V//q/578Q/aa7vjpZOig6JDosOrY7K3tl/K592z+3AKjB5QJdApFDasK7girB+oIQQiQCfsJ6gdzCDYKhAyYDLIMsga8AV3++vqh9aTzlPDc6/7qB+hR6ZDrZ/BW8t720vmN+JP6yfg7+sj5evm0+E34Dvi29ev3Qfqn/g4BvwKUAnQCtQIfAqcBsQBa/Xf3a/Nl7wPtKuty6qfoc+mT6WDpkerI61PtMu1k7gLtjuyo67jrOO0e7yLzePSY97D6QP1HAGECTwR8Aw4FhwS7AicDWAIKBAUHZQv0DE8PnxBcEKwSChiRH2koyCyUK6cqTSwhMRszPDJNKiAlmB+5GhIU8w+ODUwJwgdDAUwAMf94/9f83Pt//Hz5Vfcf8Rntwezv67XqkOoH6+nrG+0m8Gz1u/uRAbYEngchCnkLlw14DtsQTBArDu4Kngh1CU8KawqICCEH+QONAn0AW/5l/KP6TveX8lPvl+vk6e3piOtC63Dsye2h7w7y0fVG+M/5W/zw+yH8kfwN/gf97/yY/CD8if5N/5r+Jv5e/l3/fwB2ABj+J/os+Kj1tvTh8nbvcOyE6Ozo+udC6EHpWunY6grrqOxL7bnw8fGR8oT0kvXq9yP5GfpW+sT+SgD2ALwBoABzAuQDVQUeBP0EeAZVCb8MpA2/DCgNpQ/xFZ4gkiYMKOImwikNLegyNzYnMfoqTyWmHxUbrhiiEyEMsAcpBGUBvAHa/5n9Xv2o/l39H/up9ynzoPCw8PHtv+rx50PmZubn6OjtI/EU9n352f7IAwsJwAz6D/ERSBJiEgEQSg4FDCELdQmpCEEHxgT4AhUBvQGFAogBNACE/F75XfWH81fyGPAL7tXp5Ogd6NjpPOty7Q3xbvP29Zv33fu//kcAKwDwAFABngC4/1X9Gf5k/uf9af2M/ev8A/x1/Lf7L/xg+nb3fvRR8gbwf+1069noP+g35rLmHOaH6AfssO0d8E7xjfRU9o/68fpT+xb8WPuJ/ID8j/3+/P/9F/+eACgCngRFCCwKbw3TDewMSA9kFM4aPiFxJQ0kUiRpKbAuiDPEMzMvRinUJb4jnB/pHKYWPg/HCOoD9wEeALH+z/oh+mD6W/px91L0Y/T882HzZO/a63bpuOjN553o5Ovw7dLwSvI/9gL+fwRbCS4NFBEwEyUVEhW+E5sTrxGnDsoKxwZxA24AXf6p/S792vzU+jP5Gvhh+H345/eW9hvzU/BS7arswOzk7C/sqes/7EHupfL49Zz5EPyD/kkA0AJKBN8D5QMkAgkCzf8j/un6EvnR98T2bff79ND07vI59F/z1/Ku8ljx9PAL76/u5+uT7PHrXevw7DjtVO6Z7gTxjfNk98H4yfjJ+j37c/1+/68AZwAKAdgBzwKPBPkGzQfGCMwKswueDBcQwBekHaAj8STvJDYpdzCoNKszDDGeK/8nQSTLIOsczhcSEJ0IlgSKAgYCwf5d/Nj60foz+kv53Pi29y33z/Qz82DwCe4E7LnqLeuD7HvtVu2U7xjzn/ia/tUDyQcJC/sNshBmFA4WfxUdE3oQIA6AC0EI4QQ2Apb/bP1E+4r6xPkk+SX5ePm2+db4fPdt9Rb1jfR68x3yuO+n7R/tE+5X70bxt/HC8rf0jvd8+hz9Vf+t/w0AQv/2/1v/if4H/Rv6PviQ9nv1bvRt9DrzBfPb8gzzEvRy9ET0CPTf82nz8vKQ8sHx9PCB8H3wFfGn8O7xQvLW9EX3U/gv+hT7EP+dANsCRAS7BG8F7gVwCFAJ4Aq+CN4IWg/PFYgbRh6fH00i5Sg5L2UyjTNPMDwrkycyJ7IkKx5GFsQN3wdUBbwDMf+P+3X5I/iQ+Nf4ofiJ9zb4/fi091z1h/L27wXvU++t7qPsU+sM68jsXPFF9v76Cv5mAdgFTguHED4UDxbfFToVxxNWEkYQrQ36CAkFSgGV/ZD7qfkm+dr39fbV9bj1O/Yy9kP34vZc9vT0s/IR8q/xQfFn8Avvwe777gfwtfHy82T25/cf+nf7Mv3s/pX/pABPAMP/PP5//I371vqt+cf3HfYQ9BXzRvPF8xn0DvPc8ZjxsvJw9NH0jPT/8mHz+fNQ9RX2xPTx9f/04Pcg+Xv5d/tH/CL/1P8bA24DpwTrBc4HhAvvCzsNMAw+ESoazx5PIBMhzSKDJ7kuwzBtLlssQyggJXkk0CKQHdcUig28CI0HwQUwAg39g/kN+aP40fhm9xH3cfVX9Sz1HvN68WPvYe/O7mzuAe1j67nrU+5d8sL1n/jJ+4T+1ALbCFANBBF4EjsSMBIgE6QTFxL/Dq4KzQZdBL0B6f6l/Az6dPgO99v1ZfVC9dD1mvXQ9XP1hfTO8wXzbvMV8/3x0fAp7wHvqO/T8GHy7/KX83D0wvZv+bj78vw7/Y7+I/+o/6f/Av96/mf96Pvw+RD5nfd99sj1lPQy9ODy4vIp87jzGvQt9E/0yvPC9cn1bvYq9lb1u/ZK9ln4fPjD+Gj6AfyC/SP+TwFGAmYFrwfICJwLuw3REjoXFB3fIOQgxSI/KBEuRzC3L3IsVyrKKWAo6yXDIckazxN4DrYL1QmQBQwAkPud+YH4zvdb9j/1dfRE8wnytPBn8KHvo+5i7RXtb+wu65zr4Ox779TxBvSk9vP5ef5AAzQILQwPD9oQXxKkFD4WRxaSFIsRRg6gCw4JdwZxAzn/gPtt+dn3qfaA9ZP0WfS389nz3PM+9BH0SfMC87jyXvKw8PLvDu/Z7lrvL+9t7yDw9/Aq8qr0Jvfd+BD6YvvV/I7+ef/o/47/5v7B/XL8/PtS+pj5ufcx9qT1HfU+9X/0KfXk9Lf1dfbr9vj3bvdj+Mv4Ufk4+Tb45fgY+lD7x/sq/LT8e/4eAWYDfAacCDIKewwaEgIZ2B30H1ggryPoKNMtii7GLUAsWimfKHYnHyV+IJca7RSVEPwNogpGBloB7v0w/Gj5Ffib9lv1SvVr867x/u+f75Hu8+1g7dDr+OoC6nDrH+0b70rwuPFY9Mr3Lfy7/7kDngYYCpIMNQ/kEYASvhPIEv4RyBB9DpAMxgk/B44E9QHh/mr84Ppc+UT4+fYA9p/13/Ta9OL0XfTn8zPzs/Le8XTxTvCL73Tv0O4Z78Du++4B8K/w2vGT8xn1UPbj9934jPoM/Ij8Zv06/f789vyG/Gz8t/vL+rP53Pht+Gz4XPgt9yr3zvaf90X4rPdp+AD4RvnJ+d35jPo5+if8x/yQ/b3+oP7x/8oBJwQ+BrAH8AiVDGUStxfmGq4crx8QI6wnCitqLLsqCilsKL4mjibgIuYdnRjXE2URCw4qCrwFygG1/rD88PqE+Br3efZG9WT09PJe8e3wMPBS75Puzeyt65rrrOsL7UbucO+R8GPyl/Uz+fr8GABUAwYGCAkyDMcOsBBaEcIRMhGiEK4P3Q3cCzwJ5gZMBNMBpv8q/Yr7Bfqs+Nb3p/YI9pr1LvWP9XP1hvSh8/HybvLc8R/xefB2703uy+3a7QDuou4+743vvPDo8cTzqvW79u74L/rd+gD84vzI/fT9Mf7u/UP9iPwe/Ar8oPu1+j/6cfmm+A/6cfnQ+fb5ifkc+1f69PsD/aX83/2B/jD/z/+hAH0BuAPEBH4FfwcFCh4OexGcFcsYmxodHZkhoSUxJwQotSa4JoMmXyVRJOMgOhxAGPYUERI4DwML4AaoA+EAgv6c/Kb6KfkG+FH2QPVH9OfyufJH8nvwrO9F7jHtH+3P7MLt6O0m7lvv0PBF8+f1vfiG+9b9UADEArYFwQhwCvQLUgxwDAkNCw0KDewLTgorCJYGZQXHA1wCIgCJ/jX9yPv7+uD54Phy+GD3yvZe9gn1kfTE89/yQ/IX8SPwje+L7vXtKu7w7QjuH+687rDvgvA48m3zc/Te9Qj3oPi0+QL7GPwk/MH87/xi/ZP9ff2S/Uj9kfwd/ef8KPxP/Rj8C/1M/VP82v1K/fH9Pf///mP/ZgAjANgBTwPFAyYGcwZ+B6QK0w1rEYwTwRQXFwoa0BwTH/AgACFQIHIgaSHVIKgeoxwLGn8XvBTmEkAQ3AwMCnYHjgUCA44AwP5T/QL8LvrW9772p/VX9C30kfLF8O7v3e7/7jjviO7i7hzvuO9R8ZjyV/RE9gb4//kO/Bf+FgBPAjQEbAW3Bj0H6wfdCBMJPgmFCIUHuQb1BUsFSwQCA48BdwBP/z3+R/0a/D/7Lvrs+AT4C/f09fD0DPQM8+DxC/EV8H3v++437mHuDe7l7X3uiO457xjw3/Ag8gDz2PNM9ZH20vd8+Sz6BPs7/M38BP6c/g3/rv+D/xcAqACGAJ4AZACzAEIB4gAVAcIAKgHRAUoCEwOQAkcDNQRQBekGBAiMCOAJvAspDpwRrBJGFNYVEBeSGk0cTBxOHS4dFB21Hd8c/xs+GsAXLhZmFNIRmg8PDZQKPAjoBcIDcQH//+T9ufxm+7v4kPfO9rX1IfW68xfy6/Hd8NDw7fBH8HjwE/CT8I7xhPJc82j0xfUC94D4SPrr+0D9HP9NAKYBxwJwA8oEVwW0BSMG0gWxBdIFOAXnBC0ESQPfAtUBDgFeAGr/lv6m/ZT8lvtk+qj5VPj49jv2xvQH9JnyivFU8f/vVO9U79Du0O4Z7/Xu+O8z8KnwXPLY8t3zNPXt9Yb36vin+R376Pu6/B/+yP6Z/zsAlABSAcUBEwJRAlgC8wIcA08DcAMGAw4EMwSGBEgFqQR3BRYGvwZCCLII+wiBCgwMtw3JD6oQGhJGExwUiRa8F2sXRxhvGDEYlBjJFy0X5RUlFD4T5xG+D/MNdwxtCrwI9AbFBEUDuwEdADn/Zv1o+7j6p/mN+MH3SvaM9b/0wvPa8z7ze/KI8krySfK/8tTykvMV9Gn0p/WL9mr3l/iR+bH6DPzi/P79JP/Y/98AtAEdAuMCTQNkA/YD9QPBA+QDpwNDA9gCPwL7AU0BOQCi/7H+d/2H/G77OvrT+JH3uvZp9QT0WfOI8onxBfGW8G7wFPD775LwtPDx8LLxePIm89vz//T59az27vfz+ND52Pqn+wT9qv0d/pz/+f+HAEcBjgGcArcCOQPXA6EDSATSBCMFnAWVBe4F8AbvBpkH2wgbCckJzQomDNUNmw4tDwcRfxE+Ej4UWhRnFCoVZRWXFTkVgBRuFF4TJxKNEWoQtQ56DXUMxgpVCcQHQQZHBbsDKgJhAcf/Zf6W/VX8ivtw+iH5VPik9+n2OvZ79fn0fvQj9DH0B/T08/vzgfTy9B31yPVp9j73Gfjk+IL5ZPqP+3b8Pv3q/dr+kv8XAN0AgwGcAecBWQKWApkCVwJ6AigCnAF5ASwBVABn/8T+Nv4e/QP8N/vz+eH4B/gp9xr2C/Uv9Aj0g/O18rDyY/J08q7ysvId863zxfOD9Gj10vXE9nj3JvhE+d/5sPoC/Hv8Uf12/gH/7P+CABsBKwJ5AiwD3APEA24EAwVSBfQF+wUpBv8G9QZ1B30IiwguCb8JawrvC7EMSw15DqsOQQ/PEDYRQhHTEQcSKRIWEu8RDhJHEWcQRRC2D5EOtQ0JDSUMxAqHCdsIrgcRBuUEQgTeAi8BLAB6/0/+t/wk/Ez79PlX+db4CfhT96b2kvZX9n71b/Wh9Xn1bfWp9en1OfZg9tn2xvfo90H4a/nS+Sf6BvvZ+4f86PyO/Wb+i/7Z/s7/RwAQAE4AvwChAKAAnQCuAE0AYv9z/3j/Sf7J/Xr9lfwO/BT7kPov+r74PPgc+BP30fZP9pD1xPVS9RL1bvXQ9Db10vV49R72ufYT99D3VvjD+Mj5OPr1+lT8hfzT/Er+nf4d/1UA/f9VAc0BOQHfAuICxQIIBLADHAT1BGQEnwX0BZAFJQeSB3AHqQjGCd8KlAvVCyINQQ6FDm0PqhC/EM0QghEKEjQSoxF5ESES9xACEIAQmw+QDnoNpAxuDMEKPQnxCIwHGAY+BQoE6QKCAWUAoP+u/vr8PfzT+1P6rfkZ+Rr4z/cb90D2gvb59X712PWF9af14fW39U725vap9j33Kfg2+ND4U/nr+b/6D/tn+3b8yvzq/Oz9M/6L/r3+C/9p/6r/Pv9h/9L/MP9b/+n+SP6e/tz9Ev0l/Q/8lfs1+yT6Jfpg+Wz4avgV+JD3FPf19tL2vPZ79mr2Afe+9pL2XPeW9573WPhb+D/5rvm1+QL7X/vb+7H8//zB/cv+0v6l/0kAlgCDAc4B/AHOAkQDVwMbBFQEjAQqBYoFQAbUBuMGrwfBCDMJMgp5C+ILjgxrDSoOCA92D7wPgRC3EHQQBBEWEdoQqxAfENAPnw+TDh4OpA2ADCMMywqUCS4J0AemBrAFYgSHAysC+gBKABz/1v3j/GP8R/te+t/53Pi1+Of3BfdU93b2PPZn9sv1M/Yt9gb2ovaA9sL2Rfdo9/L3Sfie+Bz5tPkG+ob6FvtU+wD8gPyi/Cv9ef2+/SD+Cv5k/nP+Of5l/k7+//3e/c39c/0G/az8TvwR/JT7A/uj+jH65fl9+Qb5yPh/+Dr4CfjH9+L3wfeN98v3F/gj+Cv4ffgL+YP5fvkb+q/6EvuZ+xP8n/wS/Zn9Q/7F/kv/tf8fAO0APQGbAdsBawIYAy4DkgPWA14EEwXNBHYFXgaxBkUHSgfACDkKTgrzCrULowyrDZUNmg46DzsP9w8NEFkQhhBlEEcQHhC5D10PBw+gDsMNfA1qDIULVQvbCfIIVggPBzUGDgXxA2kD2wGYAPb/FP/B/d38OvyY+4b6ivkv+cP4//eF9z33s/bE9ln2hvaV9jj2f/a99sz2oved9133WPjV+AT5VvnJ+W/6+/oa+5z7N/xp/Ir8E/0o/UD9dv1a/Xr9ff0j/Rz99Pxf/ID8Evyo+0379PqQ+jT6kvlU+S75dfhN+Pj3tfe392D3Dfdy9zj3Efdy94H30/dd+Cj4j/ie+Xb50Pmy+uj6qfvv+zT8jv2d/c397f5Z/7r/VQCSAIEB6QH1AdICNwOHAxsEdgSwBGQF9wVaBrUGPAfnB+AIewlNCicLLwtODCQN+QwGDpoOnQ5xDz8PiA8SEIIP6g/fDy4POg+5DoIORg5hDdYMQQxgC5wK3QnqCL4H/gYqBrYEpgObAq0BbgAn/03+kv1k/FT7vPrv+Vj5hPi995j3PveB9mT2cvZI9ib2B/Zi9qz2s/bL9lz31vfd93b43fgy+bn5FPq5+jD7/vqG+0n8RvyA/In8wvwn/fb8wPwK/dX8lPyP/C38GPzR+yn7BPv4+kP67/nI+Vb5EvmX+D74qPgm+H73vfes99z3tfdA9/H3I/jR91P4e/gT+Vb5QPkR+rT66fpg+9r7gPxD/Uv94f3a/kf/kP8dAJYAVQGyAdEBqwJBA3QD3AOIBC4FiwUABp4GQgfuB70I9Am7Ch0LIAzODG4Nmw7YDmoPQRA9EL0QChFNEY4R8hAoEWsRyBB3EDEQ7g+RD0EO3A2DDW0Msgt1CtAJ9QiJB4UGqQVeBCsDDwIXAeL/sP7j/a38+vvB+u/5hPlY+Ln3fvfT9mj2Dvag9eD1nvVO9X31nvW99Qj2GfY29tb2DPdP9+L3GPh/+CP5Rvm8+Tn6Vvr++hf7Hfuv+7j7/fsY/M77Jfwl/O771fuT+477efvu+rP6qPo2+ub5k/lc+SP57viT+Hb4Qvj09zn4//fL9/L32ff690X47vdX+O/4vPgn+Un5rPly+mv6wfqM+/X7aPzo/E/9Ov6n/uX+q/9kAOsAaQEQAn0CQgPUA4IEIAWLBWIGXQdHCAwJIwrxChMMrQwrDXQOTQ/FD08QzhAREbkR0BHhEUUSHhINEuQR0xHTEX8RxhDmEHMQTg85D4YO+A0hDd8LOQtaCvEI7QexBk8FYASeAlgBcgDi/qL9vfx6+3v6mfmF+B/4f/eH9kz26/Vr9UP1EvX79Pf0vfTd9B/1APU19X31n/XV9Tz2ePa69u/2fvf690P4i/jJ+GL5ufkB+g/6YPre+u/6zfrq+hn7BPvJ+qH6zfpy+gv6D/rD+X/5M/km+Tj5zviZ+LT4lvhg+FP4gPiO+JL4m/i5+Nj4F/le+Xn56/kv+pT6B/tQ++D7f/zt/Jn9C/57/kr/nv9MAPQARgH9AbEC3wJGA9kD0QQUBQQF+AWsBkEHqQd3CLwJhAp5CkoLNQyyDE8Ntg2YDv8O7g5LDx8Q5Q+pD1sQdRAZECMQURAeEBQQuA+WD1kPkQ5zDukN6QxODMULpAqtCZ8IVQedBkAF9gMQA78BdQCF/2X+dv2b/Lj7Dvta+pr59/hW+AD4vfc19/b2tPZr9iX2Bfb59fL1sPWw9fv10fXM9dj1bPZ/9m729PY796L3mve+92f4j/h4+Ab5Lflf+XH5aPnF+YX5l/m4+eD5k/l1+dz5uPmB+bv5uPm6+er5n/np+f35BfoY+sX52vlF+lr6Pfp2+vH64voE+4D7V/vA+5P8wPwQ/U39oP2U/tj++f5p/+n/SQCMAMsAowE5AgoCiAL5Ao4DyAPgA44EHQVmBfMFmgY8BwUIJQjbCK0JrgleCiILbQvYCxIMTgy3DOAMEg1MDUgNbQ3nDcUNng2rDeYNug1cDSoN8QzDDAgMggvfCl0KZgmDCMkHuAbgBdIE4wPrAucB1QBhAH7/iP7+/VL94PxS/LL7Jvvs+nP6HfrM+T757fi7+HL4Hfjo97r3kPda92v3VPdc94H3ffed97b3vfcV+Cr4/fd3+Fj4lviq+G/46/j/+OT43/gB+Qj5Y/kd+WD54fnQ+RH65fk1+p/6lfqT+uL68/oW+yX7DPtk+5X7efuB+6f70/sJ/PT7L/yh/M781fwy/Yf91P0T/kf+cP7J/g3/Lf+q/8v/IgAoAHgA2AAbAWgBoQE7AoIC2AIkA8MDNwTgBEgF1AWRBvoGoQf7B3wIwAgnCZkJ0AlbCokKyQooC2kLsAvgCzYMVgyVDJgMvAy5DJgMfAw0DA0MWAsOC7gK9QmHCc8IEAgHB2QGzgUKBeIDlwNAA1oCVAGMAKIAjv8R/1/+6v3F/YX8z/uy+0z7jPod+rj50vl1+bv4p/jj+MX4jfhT+C34cfgY+JT31ffV96/3Zfda97r3lPc995f3zveq9373G/d492/3Svcx91/3W/dk9yP3P/eD90n3Ovdu98X3ufcB+DT4cfiz+LT4J/nN+cn5dfro+j/7cfte++D7YPwb/W/9P/3m/Vr+Zv6P/rT+Wv9s/yL/Of+L/1b//v+QAJ0AxAB1AYEBcgFYAtcCKANkA0IEcwRYBZEFHwYuB6UHNgh6CJIJwQkkCswKVwtPC8gL7Av4CwsNugwgDR8Oag4ED1UPbQ+uDz4PNw/wDjQOSw7BDK0LDgudCk0JqgitBxQHGQazA/kFtAPCAlsCcQP9AbD/dv/N/+X+bPu4/cj7Tf3e84L1HwDY+LXv1vfa+i/tLfR4877x8vPB7PD71vXW7vf7RPlp/HL2wfkR/jf6NfZN9CT6tvTp8aTwXvXd9K/vn/R79yz5p/Y6+AL6uvll+O30cfdy9mH1jfLo8aHzT/Gl8bfxUfNJ9BH0A/Vr9pv2VPdp99z2SvjG9zb2lPax9mX2VfYr9ib3avhS+NL4evpw+gL8e/0r/ysCQwQ4B3kJEQyaDnMRwBI1E1UV0hUjFZQV9xb/F9wY+xm5HN4eBSA7ImEjWyQoJAYj7CFuIG4eEBvFGGAXfhS9EqIRDBDcD4oNfQyIC+MIyQfiBDAB3f74+oz3JvaC8rPyefGX7lDw4u99777vMO8A73Pvh+0c7JntK+xO7Drtl+6z8IHxNfSX9uX3UPqu+7T8bv3L/LP+rv0W/iL/iv8ZACoBHQOEAwYFXwRiBtME2QTlA9kAuAGK/jT9Cvyz+jL6BPkW97r3ZPaH9C71B/Oy8T3xNO7h7DztsOoU6kfqN+u46RDq6+tH7Y3skezu72buU+/V7RXxoPA+70b04vNs9Vb2wfld+sP9k/5b/1ADowKiBd0G1go0DogPFxJIFowVSRfFHGAahBoFIKYelB1fIFAiViNxIpsmmifZJF4l1iWOIwwiMCAZIP4cmhrrGnQWLBYXFkgSyQ7ADaEJYwXBAsD/AP2K+Uj4/PTS8iLxBvBT7gXtHOxm6eXoROeV5rXkN+ZZ533lPujo6DDprOuf7TXvi+8I8d7zBPRg9NL2nfkX+qX8ov7z/44BdwKKBJoE3wW0BRwGhQZpBeoEagXsBAsDOwNgAigBcv9x/m78hvrg+Yf3svYj8yTzO/JJ7mHvGu3h68zrH+oZ6ZbpVefL6VjpUufd7Ffoxuvj7LHqVPAX7hLx3PMW82f1QPnJ+S385/7h/iMD2wN5BgkLpA40E7IUGBYrF0AaBBklGHYeth4zHyEg9iUqJuQhHSnDKk4mjSUvKLgkzSFCIpQioiCAH4IgYhsWGcUYlxOJD7EPVArjBe8EHwFZ/nP9nfuH+Ob2K/OU76jtHOsM6Y3nTOiz50jmqOY25pjmveVB56ToDeZi6CTqBuuo7MHuwfMe9Pz1Pfjo+OD4PvrS/Mv9bADfARsEoARlBUkGbAVcBuUGngScBHsEYwKSAyICJQI/Air/7P0G+wz6J/jE9sj15POg83DwJvDM70zso+1x7KrpIO0Y55Dqa+sp5qLvRuj96pDu7uiH8E7tqO/B9Fvx1PXT+db3AP2z/tD8BwOmAdcEzwoNDRAVcRSoFl4ZvxceGSoY0hxtIMAfZCKpJkkm/CP3J5wpFiW1JS4nySLhIjMj7SGQItYhjiCiGekYBBe4D7QOEw8uDJ8HkgfSBI796v0R/OX2APX18knxnOxn7L7sFerS6sXrZOmo5mjl4+bm5DTmvem16GDrT+zS7Lztve4K8p3zSfRF9mb3rvgz+m/7nP7RAGwAvgJ+ApcBVAOfAn4EDwVpA7MFywPzAYoCSAGuAXD/KP/b/KP6ofqP+Lf4D/YX9jD2rPHc8fjvge6O7t7s5O0e7dfr9e1g7TPrz+0O7Dvs0uzJ7JHvle/w8az0lfQb9YL5J/nb+p78AP2kAWoB7QTLCx0OARRPFGkRYROLE/MVOBaoGzAiDyANIz0m1iAAIfkj/SbyJGMjPSm8IwYkHiZlIugihB9rIDAaCRTyGBwRpxJhFKUOBQ9tBR4FTwAm+hkAq/nN+cz54fI99JLtMe3g7T7qK+566mDoOekz5nrnKuhs6FTqTegm6wTrI+m+7D3tS+9s8tjxhfSO82L0w/eF9kD7afoU+wr+LP0//i/+dP8qAb//tQDx/5n+cf71/i3/p/00/6f8cfsa+ab5dvhA9bT5B/ea85/0svAT83zwcfBp9EjuZfJp8Pnsye/z7OrvI/H17qbzcvJL8YT0O/Rx9or4SvgX/a/89/kwAcABqwa5DQAOmxDSDD0OURHpDbYWhRoBG78hcCAdIaUeSx5zI1chKiXGKRwmDyfbJS0keSTrH90hiSDzG2ce7xmrFr4XNxMSFGsNAwhCCUUBYgJXAsn+Vv6b+Dz4RfQ67d7wvO1u7RHvb+z56xTo/ueD5/7k/ecs6TzpUus06nrrUOrw6rnsbe6G8LDxZPNi9Pz0HvW69Sr5SPkd+U79fvvX/Wz9wv2d/hD8S/8t/mv8YgC2/XD9GP3B+oH79fbP+Tn6PfiS+LT1SfU49Cby2fO48f/yu/Qv8m7yq/Be7mHvrfHX8cnzbPJL9V/0iPKi91X2cfl9/e77FgCQ/Jb+3QSjBJoPdA97D3QPsgk3EJAPLhIQHYYccSCFIPkcwhsyF/8eZyQCJDgpFilNIw0hnR/0HnEcOB54IvseuBxcGxAVbhH5EX8QhA0hCj4J2gbGA7AC2f+u+7T5nPc39J7y6PCX8Vbx3u/u7srqH+cL5xjnkOhe6jHrs+wR67Lo0+dF6PrpS+3x74/zPPOc8TLy7fDG8r30N/co+z77z/tp/Iv4r/oz+4f6cv0D/oP+Qf38+tP8A/pc+Fr7y/kI+iv4OfiH9xvz+fTC9W/y5fNF8xD0yfLX8GLy/u5K8PTyWfHv8ijzpfLP85DyPvW79sH4Evss+1/8Uvs1/HwBMweSC9wKawzPCocGCA65EO0RNBmyHawd1xl+Gaga3xcQH+Im+iaxJkYmICM2HlwfJyEUIegjiiV1IVAcnxgiFrETlhTMFOES5w5iCxEI3QRWA3YAgf+H/J35oPiR9LLzSvJn8Nbw++t/7CXp5ef86wfoIes26mLn5umz5MLpE+rE57fwu+yd7rnvjuud8HLumPJe9gHykPi09qX0dviM9Tz42Ph5+ML8h/kx+Ur7/vfL+DP5F/g7+fL4rPny+ED1PfZg9cjz0/UA9hL2K/Yt9Uj1dvGC8FvzAfP49GD3jffN9a30OvUP9T/3rvsn/uD+ff1H/q/9mP8KCCcN9g4CDuQLhAoyCqIR6xZEGMce9B7EGzoaQhmuHGUf0iXxKg0nLSYEIkIeISDEIAUkzyPWIjMkbhpAFmMWDRJyFdgUfxKPDwQIIAdKBNf/QAI2/o38gPqY9gb1sfCU8THyPO6M7a7qGelT6T7oeuv76BLoYehK5wnonefk6errKuuY7VftcOzV7QjwivJq8jj16fSB9ED1wvWL+Xr4K/mo/CT5d/oV+3/5Fft8+Wj7vPta+Uv7Ofnb96P4Bvag9ub14fQH+L31v/QY9oXw9/H88VHwfvR78u706/U38Cn0E/IV8vb3Ivea+wf75fmn+tX4TgDMBf0Hzg77C9UHVwb5CTsPuRFxGjIduhsjGy0Y7xfhGdEgeSgeKCcqqifyIKMfLSFoJSImhifAKHkioB62G6UYnRmuGWMaBRfFD98OIAvmBhMHswTQAlD/+/uK+uj1R/Sj9ObwOPAC7QTrYur154PrMeqq5hroGeUW5ArmUefG6W7p/+pI7F3peun96tLstO+s8Uv1zfTo8cHzKfSR9On3LPkJ+2X7xvlE+YL5sviB+oj8zfrE+0v78PYz+Kr4w/Ze+Rf1HvYr92zxUfaB9ffx2/Rc763yOfLy7Xr2tPLZ8j70te9q8or0Lfat+RL4+/iR+Kj5fgGuB3wKGwXkBlUFwQNqDwwSbRMZHVgZUxfOFvEUvRoNHRsmBS1XJ0slFiNIHmQhWiTRKJYpoygyKqMeUBr0HOQXyxuUHOgaXhe9DV8N3QkKBbEHcAQzAw4Ahfth+Vjyt/Io8z7wnvCZ7Gvp2+m06Ljp4ue85UjmD+Y45vXmkujg6RPq2+o160fpD+sv7eLwwvXi8zX0xPNd8RX0S/f2+MP79Py6+5v7EPmL92n69/pz/cEAuvuk+jP5nPXz+EX2q/cE+oL0f/lW9e/wIPaW7O/xW/Mj7XD2xe/38Pv0uutB8JLxYO81+Jz1GPb497HzmfpgABkGYwjOBOgDzAIBB/cM3g5vGk4cuxitGU0XPxW1Fl0hpyvjLD4ttyuuIYYf2yG4JB4qDi62MP4p/iGZH2MYABY+HfEesBuGGD0TaQsJB5YFtgNMAygB1P8M/Cr13fIo8MHuMO8F7I/pKOY752Do+eX755/jiOI75MjhaufY55zo3u0165Trj+tr6XrutPOy9lb68/eX9tr2rPYK+kX90/7jACYBtf/8/cL7KPx+/YEBogB9/jP+Afkp+PP3v/VH+F31vffZ9rTvOPLb6xjr1e2A64/vc+wt7CDukuen53frtOq87SLwLvE978XvxPdJ/cIBbwALAvf/1v33CoEOhg4iG3scARrJGcAZ/ht4G70n+DJ7MIIv6i61Jwwo5ygHLXUxFS+1NaUsRSTVJeYa6xzeIHgciR1iFPgQeg53BukGPAA8//P9NPgU+cvxvO8K7pbpUet75PTj8+P24z3nluL6457f5t6c5KThQ+jU6NPofu7o6Pft6Owz6+31hPXU+m/7IPf6+/n3jfxvALX96wI0Ac8BFAHD/iEAJP0rAVkBRv3I/SD5Nvoo9+72Qflz8rH0BfM87wfvQOuQ7OLqien868DpZOgx6f7obece50XsCO2K6s7u1fDp7y31RgDMAw8C5/+yBu4DNwPAFIgUIBeEIt0gmCCuG48cWSRnIlAxyDjKMasxSC4+K7InRilvMAcuHTATMpcoxyL4HC8a/Bk/GOgWOBTXDoMLmQhHAkH+Rvk09kn18vCL8OTuJ+on6xPmI+Fj4GTcreHX4uDhKuWC4MnfUeE84VXk+uVK6kXuXO8X8dbvu/Co8lX2x/ze+yv+GAFC/w0DpwJFAgkDxAJqBTMGnAQbBQIEGAGHAbj/Rfv3+gD7ivnf+vX2s/S284zsTO0u7Jboa+sx6Vzq7uhZ45jkPuOP4ZDlXei15p7rqeqz6Dzr/+tk9uT4JgGNCXwBwwPpBhoHBQ0fEkQdEh8sI+4oJiGbIWEhQSbELX0yATrJNh0zzzJhLb0q1Cp8LVsveC8pMakpdCJtHi4XaBcZEjkQhhKTCmEMYgfa/FL7qPBV8MHvEeqT8FrrXulj6EPfpN2w2RfZUN9A3+3hheMc4Mvhyt804IfjDOQc6yvvXfB89BHy2vSr9aP1RPzE+Wb+iwNDArcGLwOtArkCy//bA8YCGgMiBH4C9AHD/EP7q/ek9G/42PTE9m30Xe7/8MvovOcO6Xzk9ef45mnlaeZM4uzhcuTc4mrnAeq9593sxOw47LPyOfY+/rUC6gVtCRgIwgiADq4RbBXPHXYgnCYDKZYnJSmSJjcqti6SMJk2ozYRNow1wDLhMT8tkyvZK4YqJitWKecmQiIkHRAYShLODC0JRQgPBiUEzwCs+n/1ju/O6nvoF+W75d/kM+Py4a/etN1Q3PXa2tuU25vbDN/y37vj3eU25bLozueT67/tZO1c88n0C/mB/OT8X/+w/VX/LwKIAekE6wQaBn8IRQZaBh0EVwKpAh8Bwf/g/tP7Y/s++7j5gPhD9XzzcfHJ77jseuy17bLtEu5q66bpjegJ6DXqrOuX6hztaO7I7hXxTvEv8hL0hvin/yMEpwUyCPoIugodDYUOjBB5FJgZZx6KInAhRyCCINEi9CRTJj0ocikOKmIrpizBKekmUCUGJPoihyITITkg0B0nG6IYJROPENEMsQipB3cEGwKgALz8jvom9l7ynfFB7k/tD+3a6l/qeekj6FHolubG5pfnJOdd6SHqDeoa7F3tX+4S8ETxhPHu8eTzr/Zh+dP6fftl++n6Wvxk/v/+5wCBAHv/GwHIAHoBIf+e/Yn+wP46ANj/2/1J/KP74/sr/Cr6Z/n091f4b/pz+i/40Pa19j/47Ph1+C/5Nfcl+Ez5Dvoh++L6DPpS+uv7yv3j/V/9f/5Q/hsA+QBUAvQC6wGfAsMCIwTABKsDAAX0BeAFSgcxBswFugT3A+QF3gQ6BpcGCwVtBdoFjwYoBqMD7AKtA5wE9wUFBdYFIgUvBBkEoATNBAAEtwMqBIAFNgYxBgUF7wT4BIEGdwanBh4HVwbgBiAHPAcOB3QGCAbQBuUGFQdCBqoFxAXtBJIEygTgBEkDxQFLAacCXwJ3AX8Awv6N/k3+N/5p/Zr80/sl/DX8tvuG+sf5nPnT+SL6XPnF+XD5A/pG+vP5wfl6+XD5/fnj+fj5QPpL+s36u/oC+4/6uPr5+hb7zPqm+s36C/sK+zr76Pra+oz7Yvtv+yr7jPq7+rP7yfut+5P7q/ti/B/8Ivxw/M37hvxt/XP9of16/Zz94v34/bD+xP6j/hj/jP8oAGQASAAxAPUAuAE9AhkCxwEgApwCEAOTA08DTwO8A8gDOgQjBEUEfgSaBPYEAAXLBC0FQwVTBVgFJgVvBaQF5AUJBvUFqAWRBYoF8AVVBmgGfwaMBrgGhwZhBk4GMgZZBpUGmgaOBhoGwQW7BZ4FiwUQBZAEUAQNBM4DqgNxAzEDlALvAZsBIAHGABgAkf9D/+L+df7p/Vj9+vyg/FX86Pts+x37zPqd+k36Kfok+iX6qPmo+af5n/nD+cL51fn8+RP6N/p2+nf60fqM+gv7gvvu+0L8xPu/+0784vwS/YP8Svxg/cr9tP1f/YT9mf23/dH91v23/aL9i/2J/en9xf16/fn8Wf2S/aP9QP0k/UP99fx2/bz9sv3a/QX+CP5V/vf9AP4T/k7+8/5E/zD/H/9l/9D//v86AM4ASgDz/wUBwQFWAocCKAITAuACIgQcBGwDMQMdBOkEYQUkBLMEkgfPBzMHYAd8B/8FkQUfBk8GjQgFCvkILAVOBMQITApGB6sAeP6NAxQJ4ghFBfwA1/w8AJ4G2QlXBj3/Zf7MAd8E1ARsAiMDWwTlADj8zvzmAykK6QKJ9pD2EQGyCzUIvfs89LX3zgNnCJQB9PeA8+P4kgQKCNH9DvQ69FH9egF4/2X7cfk4+/j7j/wj+2f95fyV+x35qfgk+479Hf6F+oP6LPtv/Bb7Qflr+Vv6zPxx/G/6B/ns+g/+f/wF+0/7BPwT+Qv69/8GBLUARfO38SEIHBSgBVbsbelw9+wCqgoSAIj3RfZm+Bj/wgUgA0z5+/PA+CEBkgMCBPD+7vne+GH6iAABA/P9Gvcw9/r/kAluCGn+c/eL+gkF7wnmA3P7qPli/wcISQnwAuT+ZP8KBPYGJQZOAz0ErwQJAsv/jgOIDEkMWQK695f9YwnNDuEGhvyE/NkDwgsyCdIDUQEyBFwG+AXfAwMIrggzBvABKwFeAcAAgghfBqQG/AF9/r4CkgPqA/UBawBX/yEA5AGyBYwEtgHg/nL9ZwJRAaz7mPkQ+9sCUgfhA4QAuvt8+7//WQCs+w/48fw4BaMGuv81+M/4PP/zAMP+9fun+v373f0R/1ABwgAh/Ij54vgz/CEANgGV/Qf5//ji/msEvASl/k336PaG+pEATALU/un6mvs8/kQB+f/v+rz4wvhN/ssDLgI1/CP4zfmOAC4DJQFV+zn4/fsdAFoCPQBy/Cn7Lv9rA0ICjf6G/Fn8eP6WARwEKQaNBBkB6P6W/6wEsQYxA07/CAGrCIoPRBDMCV8CMQEeBlMNfQ8jCSwGJwuzEDYUAw7vBxoHtQZwC18MogtFDBIMwwr7CT4Ijgg7CFUGZgUZBAsC0AFeBJkGLgd4Agf+r/rs+n/8Nv0F/ZP8Jfxe/bb9kvuS+cL1Q/YN+cL6/fzv+3f5GPiB9n740vzh/qn7YPcJ9r36Af8j/V77Zfvn/hwCz/4r+FLzL/Re+67/of9w+1j5I/lm+Lv1e/Ck7nLvvfOH9u73lPZh83Duvupx6Qjrfu337IPsv+rQ7PrwOPM08j3taOfZ5aXq2e2q8fDxk+/38xT07fhU/C/3y/bL81T1svuX+1gBXwjwD0kaARoVFesPnAtHDyUSXRJ6GrUeGSm4LaAntyQDGmwXnRnjGmQhHCRqJM0leyIWHvoZbhOqDywNaAogCq4JyAl7BjgEk/84/on8mvW38Ffodudy6jDv2vLd85HxQvBX7NjoKOW84sXmieqp8xb36/l6+2z6mvrM+Vv5svt/ACUEUgptDEQNyg+xDL4NgA1DDe4O7wwXDcMNig8+EXMQywshBiP//PxM/BL+iP/B/GT6Qffi9NDySO1y5ZzjKuL96CrtNusd6gbi39/Q3efceOCs4cjlROjo6ELpo+aV5Izkp+O+5wDrIu+X9QH3cvqb+XD2Bvbn9Fj1QvmA+oYCZA7AGNYkSyHlGAEQEgmYEBwXLh6KJXwoQjE8NYoxZSoNHjEaDB9KJNstoC4ALwUuaSm/JWAdpRaKEC4PgAt0DbkLNA4UDvcIQgIH9fDt7uXf51Tm1+oj7FXuffLp7THr8uDA23nZSd2E44rqn/FB9bn5V/iE9h/0R/KR9m77agKxCNkO7RIJFz8XpBNtEhUNShALEosUKRoRGf8bcRmtFLsQzQiZBFv/8vwJ+xP8A/09/L/7gvG47abjzd5d3VfXz9ur29PgVeQO4tnef9ia0jTR+dCE1MjaEuBw5ubmWudp45TiAeNx5NnqbO4A9kD6xf0i/3n/4P8rAj4D1gNcCNkMNBtQJ50vlDBvJ1IgzxxzHVMluyiJLm43BDq4QIM5QS76JFEbZCGkJlQsujHRLF0qzSIHGe8P1AhWBbEGtgdFBb4Ee/3R/KX3ou9g6xriQ+EC4u/hUuWh5BTnLOrC6LLoXOO/39/hJeNn673wx/XT/ZD+jgSNAjQA3QFA/qYG5QrqEk8bUBt7HggZjxYiE90QhxBBERQT1BSRGI8VDRSzC08Em/7898b2FvSt80L05fI28mXvY+nV5HbdAtk+12fVkNrr3LbgkOMH4IreZ9s812nYGdl02XPjfOQB7cTwgOu68nTpze8d8dftkPdx9M39ogAdAusD5AE4BJQFmQViBYUJCA+XHjQoEi1JJggb0BbhEBYafR9gJKkwazICOOoxgyTQHBwSXBlrI08pDDAoLQUl0B9IFIENNAjABaULMwoYDccIVgK6/YD3DPL87DfrAuqL6pPqNOnf53fp7eoJ6xDryeUb5QfkneYC7Ontd/Ut9/77Lv0n/Of8+vwsAbQEFwm0DVYRdRQ9FhAUUxH8DzwPWBIdE2cT8hKqD9cRIA5UDOkIJgLBAFj83vlq+FvzQ/XC8fnwOfDf5p3nUN433uHdLNnX4JfcVeFK45XcPuAv2yTaEt382zTdluS25Hjsb+9R6dHx/eZy74LxNu0N+U70/PyJABoAywTPAawDPwa6AjcGZQsDEg8hsCW0KFEgHhnVGlAUoh+cIdYisy8TMEI0dC9kI4Qf+BjWHron+ibDK0gq9CFOIa0Xaw81Dw8J+w8zDFQK9glc//gAmPvl9fX10u897yLuPOlX6kznU+py7JProeyu5zfnmOad5TLphOpg7qzyofbv9t348fg9+PP8r/tvAdICKweeCzAK4Q08CskLdw0vDJgOCQy2C44MgwuqDW8LSwqMCJID9QKK/IX6Wfny9Xr52vYF9770cO+/677l9uMJ4g/kGeWo5ubnQOUM5ubhQd/z3y3eL+Gf5uLl5+qW61Hpf+4s6uztye5X73X0NPWT+W37Mf5r/lwCSQF8BGYJfgTRDZoLuQ6ZG8cZfiLdHo4bqxwXF9ofXh3cHqsolyXCLMYrryPTIXIbWSBSIj8jgCiMIsohViF1GJMVGBJ+D4oRRxEfEFYMpweQBTkB2/2V/OT4Mfif94X0OfLa77juEO/l7qjtAeyA6YroPujU5wrpH+lR7Artp+4o73HtO+9n7inyR/Ni9pj6Gfr+/nn94f3S/wH+BQI9AWADUAUhBUwIFwZABoAFhgOWBZADxwOaAqIA7AFS/8v/Dv35+oX6rPcK9270+PID8r3xxvG38DDwEO6p7YDsBOsL69PpHesm7Hzt6e467+/vWPCX8F/xYfIs83n1Yvem+Lz6kftZ/W3+0v+sAsECzwUqB0wHugoNDHkOXxEYEgIU/BLOE5kUixJtFUkVzRbtGdAa7hvmGiQa4RgWGOYYyhlzGRsa7RqqGNgYzRYqEyoTFxGFES8QUw60DHoJ/AgWB4YErgIaAWv+Uv7d+7H4EvmQ9fL2X/bN8xL1yfHw8InwOe6X7lzvlO+t8EPxQfG18SbyxfEX8sTyh/Po9Wn2Rfcx+Lv3hPlN+uz5nPuv+3j8Ov6X/Xr9wf0q/U3+0P7A/iP/VP7b/sz9EP44/TX9kf24/GD+LPz4/JP8cfre+/D6M/pt+9D6qPmO+ov5C/nU+Vn5PPnT+Tj6xPlc+tX59Pml+vv6EfzT+178RP09/Rn++f43/tz/aABTAFIBbwGNATYCNgOzAnwDqAOGA1oEXgRMBKMExQQgBVIF4AT/BNEErARgBZMFTwV9BlIGggbDBn0GXAbgBo4Hfwd6CFMI+gjxCDcJAQmECM0I3QhzCSkJ3Qn9CCsJ5QjlBzEINQefB1cHwAayBtMFfQWGBeYEWQQFBJUDOgMOA1oCtAFuAd4AmQAXALL/Mf/l/ov++/14/U39pfw8/Cj8HPs/+7H66vm++TX5qvhw+A/4WPd+9/X2Nvfn9rL2n/ZT9qH2Ufbf9m32PfcP9/r26Pcq9xH4Hfgs+Oj4G/nC+Sf6gPqq+sj6BPtj+7X76PuJ/JP8Af1o/Rj9ov1S/dz98v0Z/sP+Ov7s/t3+1P54/yD/U/9y/1r/zf+q//b/GwBrAMIA4wAXARQBOAEcAV8BNQHQAQwCSgLfAn8C9gK7AtsC/gKnAlkD9QKtA9ED1wNfBDkEvASWBMME3gTzBGMFvwXXBXUGgAbKBvwGFAc1B10HiAeLBwMIAghtCEQIaQhTCGQIrghgCH4IIgj/B/MHlAdiB/gGlwaBBhgG6AU1BcQERASZAzIDXALNASwBvwASALX/6/4m/qf9y/xx/LT7PfvT+lb6+/mH+Qr5t/hJ+PX3p/dd90f37/YK98L2vvbN9rn2yvbi9vj2Cfc991L3dPfA9/33JPiC+Mb45vg7+Vz5hfnb+f35f/pa+vH6Ffsz+wL8rvsQ/Ev8TPzM/CP9Wf2+/S7+Pf6z/s3+r/5F/zD/wv86AEgA8wDdADsBWAFuAcIB2gE9AngCzALzAiYDRwNqA6oDyAMgBEoEfwS8BOIEEwVfBX0FvQUCBhoGfgagBsMG7gYOBzEHbAd9B6gHuQfTB94HxgfUB7QHrQeZB4UHRwckB9cGkQZoBvgFwgVvBQcFvARUBO0DjwMLA6QCJAK0AVIB2wBjAA0Alv8o/8f+X/75/Yv9PP3Y/Jb8OPzo+6r7Yfsg+9v6ofpo+i36APrP+bT5nfl0+W35X/lW+T/5NPkq+Rv5Lvk9+VH5i/mP+bf50fnk+RX6MPpy+qH6+vof+3H7rfvM+xv8Pvx9/MT8Ef1B/Zn95f0b/ln+gf7E/vj+Of9w/6T/3P8WAEoAeQCkANsA/gAoAVYBagGXAbkB1wHyARACKAJNAnECcQKSAqQCtALeAu8CCAMfAzEDTgNwA30DhQOmA7YDxQPmA/EDBgQTBB8ELAQ7BEYETARRBEkEUgRaBFQEWgRJBEYEKwQgBAYE6APPA6IDkQNmA0kDHQPtAqoChAJHAg0C0wGSAWMBGQHWAJAATQD+/8j/ff81//T+of5g/h/+4v2n/W39MP0K/dX8pvx5/EX8Ivz9++T7yvu8+7H7ovul+7H7qfuc+7D7sPvF+9H78vsa/DD8UPxp/Jf8t/zb/O78GP1M/Wz9mv2//eb9D/42/mT+jv6g/sX+7v4K/yv/UP9u/5P/uP/Q/+//CgAeADkAVABiAHsAlwC6AMUA3AD2AAABHQE7AUwBXQFvAYMBoAGoAbcB4AHlAfUBDAIlAjECPgJWAmkCewKMApkCnwKsArgCwQLMAtQC2QLfAuAC2wLVAtICxwK8AroCqgKfAooCcQJeAkYCKwIRAu4BzQGxAZIBaQFIASIBBQHeALgAjQBsAEUAGwD0/9D/sP+F/2//Tv8u/xP/+P7j/s7+uv6s/p7+kv6O/or+hv6H/oT+hf6L/pD+l/6V/p3+pf6r/rT+wv7M/tX+3P7f/uj+4/7q/vH+9P73/v/+Af8E/wv/Af8G/wD/+/77/vr+8f74/vn++/77/vj+Av8A/wf/DP8X/xn/Iv8r/zz/Tv9f/2r/df+S/6D/rf/A/9n/7v/3//7/DwApADwASwBaAGwAdwB+AI0AlQCaAKQAsgC7ALwAwgDGAMoAzADKAMgA0ADPAM4AzQDNAMgAxQC/ALwAvQCwALAAqwClAKAAnwCfAJgAjgCRAIwAhQCFAIIAggCDAIcAhACEAIIAfwCCAHsAewB9AIEAfgCAAIIAfAB3AHYAdgBmAGIAWABRAEgAQQA4ADEAKgAcAAgA+v/0/+b/z//H/7L/qv+m/5L/g/95/2b/X/9V/0H/Ov8r/yH/IP8i/xT/HP8R/w7/FP8D/xj/LP89/y//Ov8y/0b/Sv9D/4D/fP+T/43/pv/E/87/uP/D/wAA6f8DAP3/FAArAA4ARwA3ACEARQBDAEoASQBNAGEAXABnAF4AcQBrAGIAewBlAFwAdgBxAGkAZwBsAHEAXgBoAGsAXgBlAFwAYgBaAFkAVQBVAFQASQBCAEgASwA+AD4ANgA2ADAAKgAwABsAJQAdABMAGgAZAAoADwASABIAFAALABAADQAOABIADwANABEAEgAXABUAFwAUABUAGQAaACQAGgAYAB8AHgAjACEAHwAXABoAFAARAAsACAAJAAgAAwD4//v/8f/n/93/2v/T/8n/0P/I/7//vv+8/7T/r/+s/6z/pv+r/6v/pv+n/67/rf+t/7L/sv+6/8T/wv/A/9L/0//X/+L/5v/u//H/+//8/wQACgARACEAGgAfACMAIgApAC4AKwAtACwAKQAsACcAJQAgACIAIAAYABoAEwAMAA4ACgAAAPr/9v/t/+f/6P/q/+X/3f/c/9n/2P/S/8v/z//N/8j/xv/J/8z/yP/E/83/0f/K/83/0P/S/9b/2P/c/9j/3v/k/+D/4f/n/+T/7P/t//T/AAD6/////v8AAAIA/f/7//n/+f/9/wIA/f////v/+f/6//T/+P/0//f/8v/t/+v/6f/t/+3/6P/s/+v/4P/k/+j/4P/m/+j/6P/q/+r/6//r//D/8P/z//f/9v/8/////P8AAP3//v/+/wQAAAAHAAMACAANAAsAEQAPABgAFgAdABgAGAAWAB4AFgAaACEAHgApACEAKQApADYANwA9AEAARgBHAEIATQBJAE4AUQBYAF4AWwBeAGoAbABeAF4AVQBbAFEASABaAFQAWwBSAFMAVgBJAEYARABDADkAMQAvACoAJQAdABwAGQAYABUADwALAAAAAgAAAPj/+v/6//f/7//k/+L/5f/o/+T/3P/n/+f/8P/q/9//4//h/+//4//q/9v/6v/6//b/7//e/+3/7//y/+T/9f/t/wAA8v/2/wMA8P///+z/AQD7//L/7//1//f/7//y/wMAAQD9//T/7P8LAAAA8v/f/xIADADo/+f/+f/8//P/5P/y//r/3//5/+z/EAADAA0AAwD7/xQA3v8BAPL/AwAAAPv/CAAAABYACAAWABsA//8FAAAAGAAZACQAEgAWAEQAJgAXAO7/FQAZABIANAAgAC8A//80AAwAFwAiAOv/JQAOAFQAFQABAAUADQBKAAQA6//3/xoALAD//xMADQAzABUAzv/k/wQAGwDu/wIA9/8EABUA8f/s/93/7//1/83/4f/r/y4A5f/v/9v/2f8JAPb/2f+Y/wsAIwDa/7//zf/8/wgA1f8LACAAwP/N/9L/0v/z/1n/zf/W/9b/of95//D/rf8PAKL/rv/B/6v/3f/w/9v/0v9LAHYAt/+R/+n/4v/9/2f/Y//d/8j/0f+z/8//bwCpAMcASgBAAGcAnv+f/yD/yP+S/3v/sf55/jQABQA8AUMB2wIYA3oC4/+m/VsASfsx8YjuJf9EBNsDNAJ6BJcLrA3JBvb6EgYWB0ABo/j9+LoD1Q8mA171agDd/84CCvma9JgFKQmeB/gLigNTC0MNz/r3/TjxLfS8/GDuePuJ+PEBKAovBugGoQSTAXn96vs67SL0wPTw97wCWgGJBHoN6QjSDNkGdgHj/+b87fe48y743fbPAZH+IwHcB9gGugqMB4n/cgVJAn78FP+B9hkClgAa/YEBSvlPBiz/9v55/ZD88ADi+xf8jf2PAVcE7gfIAw8HCwNsBcH8gv4K+ff4lP1FAPX89v78AuUBew3+/qsB0wFjAFb/w/c2+TICKv4S/gL3+fp2Arb6Wvl+9R79ygGO/7f9mQBrBR8Hxf+EATb/yQFf/SL6vv9FAC8ElwKL/zMBggTf/tEB3PsI/3IC7//hA73/FwICA1H99PvW+235o/sU+fD6hf+L/vsAWf+g/moDDgCv/4z+kf1D/2P9wQCYANoC4QTBAE4COwCyADUC0P9hAqUABwJ+Ap0AYwDrAMUB6wBzAvUCQwPpAzcCdALuAMYBqQHa/3QCiABAAHcBFQLzA80B/gHdADUBowI1AG4ALP/iAW4AOf9m/5f9vQDv/X7/lf7//cEBXf6hAHL/OPw9/y78/v2O/Kb7wv/7/zkBugAmAQkCQgHkAGkA0/4jAfz/sAGTAD0AUgJrAXYDRgCRAN8DWwPxA2sCfQJDBBECpQH7/5YARgKUAa7/cv9SACwBfP+x/3z/kP5QACL+/P2R/rL+zvxk/Tf+g/6l/y7/EQErAOoAnf84/YD9dP3g/u3+VADWAB8B+AHi/ywAOf+e/iD//f2G/eD/Yv/k/pAAb/+eAFAA+/+b/i//Qf96/cj7bv61/1kA4P+5ANf+SQC6AZz+ygFWAMIAGgD3AIsAkv/R/qkAPf+sATQDzP/aASAAVAHAATQBlgHRAXECngJQAQQB7gCjAaEAnAJRA4UDBAWDARkDkgGqAGoANv3g/mD/J/+H/4P+UwBjAEz/sgAT/+n/S/+b/QL9Ef3J/U/91PyU/fD8sP0r/2394fxs/Yb91/w0/UH8LPw+/dX9d/3Y/EX+WPzo/DT96PqF/Sv9c/2u/zb+1P9L/2D9Lv5T/On8AP3i/Nb85vum/Ff8Fv3y/EX88vu8/IX8lvxv+6z5oPpj+Rv7Mfr8+Uf8j/yY/G77g/o++sL7ZPoY+rf6V/yp/V7/Lf82/rABcQL6A1sGYgYwBzUJlAi3CAcLagt8DUMNow4UEToSvhTyE/gSHxSCFK4TUBRxEtkRohJOEiYTXRHTEVEOjg1WDY4I0glfBYUFygSOAscBtf26/aj6ePnC+Lr2jPZI9VP07PMZ82vyd/Ay8RDxjPJT8zXygfRN8xz2/vWg9sb3O/YQ+2r6WfzL/fz7gP35/W/9dvxi/vP8mfwz/ST80vt8+4b5ufal99v1MvSt8sfwVfAn7xzvHeso6rvrXenc6sjpa+hy6HLq9eov6UTrgenB6tztku978EDxgfd69/P6rgB5/VMCvwRhCJkOZhawGk0b9x+tImYm0SQSJRYiVybOLSksBi+uLI8sIS5fK2grJCklJr0llSDZHHkbcRUkE50PsA3CC5kIKgQ3/Pj4T/MA8Hjso+lk6OHmWOgQ5avlzuTT4q7klONx5tTndemJ6kzr+e+a8c/2wvj0+3UApQJYBrsFzQkWCz4NghFvEU8U8BQaFWMTWhKSEqYOeA9LDq0LEgteB2cDEf+t/GT5qfS08w/vNesj6kPl9OOk4OnfJd653cbd6dnZ2ZbXCNgU2ZHZpdo03RPfgeEk5EPkzOcZ6cjsX+498bXzXvOS+jf5Ff2CAP4A/gP5BTwJwQZjDEsOFwzjD78RtQ04D+QSRxSKHfoohyrqJsIqzyk1JhopkCSIHkkmjyxqKusqYinSJNUkvyYfJL0eRCLjG2cVVhR3DNcLNQdkBpEEmAPmBeD8WPe777vsm+2u6bXpn+Zz6GPq0elb6CboSOs47LHudu2779fx5/Qb9hv1jPtc/pgF8wfPBUwIQgdgDHMLYAwLD40OWRRdE0ISrBBjDuMM0glVCX0HSARvApD80vjN+In3PPV68kHvR+we7CDoHuPV4Jbfq+G44vvhWODl3lbhcN574EPg+d4V5UXjMued5lvor+wY673yAvFd9Fb4gPVg+8b15/pW+y753wKQ/VQELwShBL4GdgL4CoYEtAdTCgoFWwquCIMJxAkrE2EeJCKmKCAkJiGzJXYlxyDpHDYddSA6Jq0q9CQRJJ8mfCR8JgEl2yHyG/QX2hGPC+UNtAhsBhoGOAWTBqMBuP2x8pLwAO5g6e/qouSP513nIuj96YHpCu4o7YfwMvHp7qD0iPI49YL4HPqeAfkCTQlhB0oJIgy4CcwPxg1WEDcQUA9LEIUNjxBQDNALLwrKBm8GAAI4/yz5xfd/9XbyvvJG78rsfeuM6cTm+uSS433g598y4iDgUeFM4ijgt+J74wzlEeN05VrnG+c87tLsHe4Y8MLxJfUf9Hb49/Ud+PX9m/ow/MT6i/uj/SD/sgJu/PL/LwHv/G8D9v/RAaECrgZzCBcGWQxgBaQKcBRfHFsmiCmsKtomEyz8LBsocSYnI1gnbitZMBwtNCjrJ2InvShHJWEjnBtjFLAQ9guKBoQDUQEz/WX+iwB1+6HzSu+p6GjkhuUq40Pg/+Ic5sblWOia7LvsQPHx9NX1afbB+fv6pvwjAlcEIAlODLgPVhH4EZ4ShREuEwUTmBJWEsIOpA/TDqwN9Qv4BrAE6/9f/9j7GPfK80/uF+6Q7DbtGezx5xnnreWR5RDlueMG4rDgu+Mm5YPl2+eb6HHqIO1E72rvf+8H8rHyr/OE9fX11fUC9yH6n/lU+lX5r/c+96P5C/o09ar2J/VW97H51vmm9uX0zfZ18sr0J/f39pH67//uAHoA+AN+AYkEYxXpHpUjmycnJ4Mk+yvCL0cnsihyLcMtwzEjNvguxypTLTcraSiQKjYmPBz1FsIRiQuwB0cEHv8d/CL9Y/rN8xvsj+b247jgpODG3WLcv9xc4Mri4+OD6RLsqO1C8YP1LfZp+En9a/7RAU0JGQ2HDhMTJxUwFO4WIhgWFxoWDxZ/EoEP8Q/WDUoMYgiWBV8CP//W/b/4R/NZ7/Xtluvs6DPpO+bX5JTltOSl4hvjXuNM4sXkz+fw6ArpW+s67Nrt2fHJ8X7xQPHh8mn1KvWV+OL1bPeL+tf7Jf9w+pn8kvm1+Tz8K/ZN9vTwV/Ov8h3ybvZq8FH0E/NX87z0dfX793v2qvs3+wb7xPxH/V4CMhC5HislfSraKcoq2i8WMist/Sb8K5UtoTI8NQowgi+2Mf4zXS/ELKUngxpIFAMN0AUSBHoB0/2V+8P/Cftq9OPuNOcX40XimN0u1iLXSdea2hTiJ+dm6gbvqvS79KX3wPjm9Ub7uwD4BmUL+A5CEGUSBRgDGREZjxgqFp0V6BR1EEINqQkwCmcLnQs8CeUDmADe+lT2U/Pz7TPsO+qw6BHpT+gN6TLmTeYS5ivmuucw5ezkieR255Hrh+468OfvBvMG9Lf2zvZv9cj0FfZB+Yb4Cfui+Zz5Pftf+2z7Vfnj+dH3yvYL+PfysPIx8ULvnPGU8BHzHPH68njzqfH69372tPd1+mX6Xf1a/+cBmwRbDkkcJiQ6KV8sZCrML4Exai1HKvMoZC28LqwyES9hLRUxfi8ZLhwq6iNkGzgVqg6PBl0FEgPY/Yz9a/xc+CT1sPHE6vXlaOUz4ILcct3p3JreduQn6eLq8fBJ81f0QPZp+Gf4WPs8AicDbAlpDFEPtBHbFDQWphOrFqETXBIaEqYPgw3JDJcM+Qq6Cs0IvgN2/1b8K/ey9Qzyoe4t7dPs0+tM6irr0ufB6GbpFOdo5pfnqedV55PriOx07SXyNPN/8cbzyPXc8133ffgS97P4gPua+t35tftW+db4NvuV+RH2DPc08nLzEfNJ84HxF+8x8uDta/Pj8HbwLPIA8fvz1PB19uP0hvbb+1z82P8DAFkFtweaFCQiIiapJ6YpcSz3LJMwBy0/J9kq9C4aLnwtOCxsLLoqJS4zLFckjB+3F+kOaQnEBqMCdv0w/H37//bt92XyXOtH6I/lIeIR343cDtl53Jfhv+WW6kTvufFF9c74U/c5+U78wv1rAVkEFwgoC74RERXtFSEZkBf8FvwUcBKmEIIOxA6DDLYL+wkBByEEyQBr/0781/nB9Mvvx+1y6yrrTeos6Zbp6+mo6gPq/eiS6PDnP+n86szqJOzT7LfusPId9Vz2p/XO9uP1Qfg++Zb38PdY+Bz59vqQ+yr68Pnj+OD6FPeS9jv13e9Z9DPwS/Ez8gfwDfYc8SP1EfQr8e70g/OE9vf1ivtl/Hr8UwGpAkUGFg1wGNAc4iW8K8AoOivULigwrS0PLRArpirTLmMvJiuoKwcthS2gLVgokSG4GDgV0w5zCOoFRv07+wX6Qfcx9tnxiu8M6QPnNeWq3Ifb3dnV2K7c9OAQ5IXnOe0Y8TbyufZS9wn3u/ou+6r8s/8/BEAJ7Q24E3EUzBXuF8kVPRahE6YQOQ9CDroNVwuiCh8IhAfOBgAEUAAl+4P3qPKL8Sjv4Ouf65fqX+vP6m/qLunV51HoX+e25vrlseb25oHoIeo261vuAvAR8rryKvT+82T1bfXA9bf3uvcC+pX5evvp+t76KPx6+8D7A/ty+bf3M/jE91j4Kfi495j4MffQ+Cn3kPQQ9iL26/dR+Mj7cvzv/tkEbQaLCFIMFxP0F+EgZCWKI3ojzyewKpgpbinHJzQnRSuFLfEpjSmsKpsstSz3KiMnwx6wGoMXbRHwDbgHhgLJ/yj9RvxX9+7zh/Ci7VHsjOhN4/jf2d1U3t/gW+CU4i7kreda6qnrpu3B7n3xd/Tz9FD1HfiM+fP/oQOfBkYK6gtqD9QP+hAhESwP+w9jD3gN6wz0CrcKWgkzCt8IKAWXA7n/9vxS+jv3wfO08CDv4+xn61TqAujA59vmlean5b7k8eKQ4eDhQOIL5PziKOVJ5Y/oJuyV62/wLvAo81D1W/SG+LL4Pvru+xz+twEBA0cEugSgBnUIFQo0CXAHggjKB/YIkwhXBwUI7wfaCZcJIAqnCiIK3AsoDBAMsQu9C4UNaw9gEQsSaRLpE8IVXhXHFEYU0xMnE3ISdxNwEt4S4hN6FOcUdRWcFVUUNxNpEcUPsA21DIsKPQdUB8gGPQXBBOgCKgJYAS8AqP12+kD5rvfz9tb1GfVm9I70vPXA9Tn2Nfbc9vv1+fUO9oD01fSG9Xn3xfeT+Mv50Prw/EP+tv5s/nP+E/9z/m3+sv53/Rj+Y/7C/qz+Bv7J/aD9iP0f/bL7w/l9+QX5afi994T3ufbi9m/3k/dd9yv3Ofc699P3Y/eC97j21fdT+NX3IfkM+Sz6UPpA+6H7Jfwx/cn9+v7F/lL/mP/Z/7gA7gDHAD0B4gGqAtwBHgJnAiEDOgP4Al8D/QF0ArsBOwEjAcsAUQCd/xgA7/+R/z7/dv9o/1z/Q/8g/yL/RP/9/qr+pf7X/gD/GP/T/5f/PQCyAPIADgH6AH0BmAAAAaEAZABUAHoA/ADDAMUAlADOAAMBGwF0AKYAXQBAAPn/OwAwADkAtQC1ACoBDgGkAXoB+QE0AtgCQQNlAw8E1gNjBBMFNQV7Bc8F7wWLBpIG3Qb6BlcHxQfqB/MHIwidCDQIPggqCMMIiwg+CD4IvgfeB5cHwwdoBxkHwgZwBj8GEwalBUIF6wSbBHMEpwOBA94CkwIuApgB7gA9AOf/V/+q/v39ZP2P/Bv8Vfuh+gT6Xvno+FX4r/ch96H2NPbr9av1LPXz9OL0ifSR9Hr0U/Rs9I30s/Ty9Cj1W/XC9ev1Ovbl9v72gvcn+KT4Avle+cz5PPqm+hv7oPu1+yX8ifz9/H393f0V/qL+8f5E/47/yf8kAF8ArwDXABMBJQFgAZEBwwH7AUICXAKjAt8C7QItA3QDowPnAykEPwSoBPEENAWMBdkFCAZJBn4GsQYnB2oHlwfZBxAIUgh7CI0IxgjgCNUI/gj4COMIzAiUCJYIZwg4CAAIsAdyBxwHyAZ+BhgGoAUqBcQESQTxA3wD7QKLAisCtQE/AdIAXgD//5T/OP/Q/nv+E/7B/XH9Ef3h/Jj8NvwG/MD7YPs1++b6kPpt+kj6G/ra+a75kvl9+WD5NPlU+Sn5KPkh+Qr5OPkq+Tb5Uvlq+Zj5sfnX+RT6UPp/+pX6wvoT+0r7cfur+8n7Bvw4/HD8vvzw/Db9V/2d/ef9Df5U/o7+w/4l/1L/kf/X/wEAaQCgAOIAKAFkAbEB5wEkAmMCmQLLAvgCEgNFA14DiAOkA6UDvAO+A8EDyAO7A7oDqQOoA5MDYQNUAyoDGQMDA9cCrgKZAnECXQJDAicC/QHcAcIBnAGHAV8BRQEWAfsA8QDXALwArACJAIUAjAB9AH4AggCEAGMAYABfAFMALgAhADoANAAxACsAOQBRAFUAOwBMAHsAbQBvAF8AcgCWAIIAsAC4ALsA6gDyAP0AHAFAAWQBeQF/AYQBpwGuAcoB6AHLAdEB/wH7Af0B/gHyAQgC8wHZAcYBrwGYAXEBTAErAfwAxACkAHIALgD5/83/mP9b/xb/zf6W/lP+Df7N/YT9WP0b/d/8r/yH/Fb8NPwS/Pj75vvJ+7X7p/uW+4v7ift/+4n7jPuV+6T7u/vN++L7/vsU/DX8Svxo/ID8k/y6/Nf86vwE/Sv9R/1l/YD9o/3N/ez9EP4u/mD+hf6o/sr+9v4h/1H/hv+v/+T/HwBVAJAA0AAGAUABdgG1AfABKwJgAqAC5QIZA1UDlAPTAw0EQARtBKYE0gQCBSsFRgVuBYYFmwWrBcAFzgXTBdgFzAXBBaoFlQV7BWIFNgX5BMcEkARSBBME0gOFA0ID9QKaAlEC/wGoAVABAgGpAEoA+P+q/1j/DP/C/nf+O/78/b79h/1L/Q/94vyt/ID8V/wv/BL89PvX+9D7wPup+6z7pvul+6f7r/u5+7770fvs+wL8Efwz/FD8dfyd/MP86vwP/Tr9Wv2J/bX92f32/Rz+Qv5o/on+p/7J/uj+DP8s/0r/cv+P/6v/yf/k////FwA4AFUAZQCEAJkApwDGAOAA7gAEARYBIQExAUABSwFOAVcBYQFnAWkBbQFtAWUBYwFbAUgBQgE4ASsBHgEPAfUA7QDiAMoAtQCnAJoAfgBzAGQATAA2ADUAIgAZABgACwAAAPz////2//H/9/8BAPz/BgD8//7/EQAQABkAIAAxADEAKwBOAHwAagBWAHwAfQCKAHYAiQClAJ8ApwCkAMAAzwDJALkAzADWAMsAzwDVAMgAvwDDANAA1ADAAMEAxgDDALsAtQC0ALkAqwCnAKgAoQCSAI8AmgCFAHUAeAB9AG8AZwBhAFMATwBQAEQAOwAwACAAHQANAP//DAD2/+n/6f/i/9P/yP/E/8P/u/+v/6f/lP+R/4v/ff92/23/av9i/1j/T/9C/zv/Nf8t/zL/J/8h/xf/Cv8G/wT//f79/vX+7f71/u3+5v7r/uz+8f7w/u/+8/72/vv+AP8E/w7/GP8a/yr/Nv8//0n/T/9d/23/dv+H/5b/o/+1/8T/0v/j//P/AAATACYAMQA6AE8AYABoAHQAhQCSAJkApwCxALQAtADAAMcAxwDJAM4AzQDMAM0AyQDJAMcAxADEAMUAuQC8ALgAtgCxAKoApgCjAKIAmwCWAI8AiwCEAIMAhACBAHYAbABuAGkAYQBdAF4AWQBNADsANAA4ADgALwAjACEAGgAOAAwACgABAPz/+P/y/+j/3f/Y/8//yv/F/7z/uv+2/7X/r/+m/6L/nP+Z/5P/kf+L/47/if+E/4j/gv+E/3//e/+B/4T/hf+H/4//kv+R/5T/nf+f/6L/p/+v/7P/r/+y/77/uv/C/83/0P/U/9L/0P/W/9z/1v/Y/9z/3//d/9v/1P/a/9T/zP/R/9T/zf/N/8v/yf/O/83/zf/P/8z/y//U/9b/3P/b/9f/0f/U/9r/6v/s/+b/9v8GABcAIwAvADIAOwA4AC8ARwBQAEsAXwBuAHcAbwBpAIgAjgCRAJMAnACgAKQAogC0ALUAqgCzAK8AuQCzAK4AuwDCAMYAugDCAM4AxQCsALMAsgC3ALcAtwDAALMAqgCzALoAtwCtALgAsQCqAKgAoQCdAKoAlACJAIsAkwCGAGQAZgBVAEQAPAA3ACAAHwAjAAgA5v/k/+T/yP+z/7L/r/+d/4P/a/9m/13/af9e/0z/Uv9J/yP/Gf8n/wn/Dv9K/0f/D/8I/xH/9P4D/yT/Hv8e/1r/VP9I/1b/WP9R/1r/hP9//4D/mf+G/4b/nv+7/8f/tP/S/+r/tP+2/+X/1f/z/wAA5//3/+z/2P/j//3/5f/Y/yEAKAAMADoAPQApABsAIQA3ACUAPABZAFMArACNALAA0QBdABkAawA3ABcA4gAGAR0BkAA/AHMAQABaAA4ANgDdAKcArQCHAF8AVgA6ACcAbABWAEoAlAB/AHwANgAxAB0ABwAqAA8AUwDlAK8AfAE0AaEA6AABABQAvQALAXcBrgAMAtsBYP9sAqkHnwRdAbEBVf8h/SD+y//lAo8DcwEWAiv/B/4C+2/9iABh/7oCQwMuAmL+x/2/++X55/2n/eH9Lv/FANUAYf4a/V79uf1U/an9xP5T/0n/5v9p/1j9jf7c/5v+3vyB/WEAq/37/KH+Sf/U/pP8s/0j/vr+cP/P/NT9PwIg/wD9+v8M/RT/if4w+y8BNAlKBE3+0gJX/k72+vyHAbYEJwatAXYExgenBXP6Iv8WBGsBZAIzAAkIdwam/jX/HgCfAJ39Y/yWAsoFpgOEAPUApgKi/Qb4j/xNA+oBn/5IAFUCZACq+sL53f9+/+L+jwCyAqoCFf+J/YX+pv77/rj/xgD0Ac4A6v+p/0cAWQAe/9H/MAKyAZQAEQHCAaMA1f+tACkB//8nALIBWQFZAVEACACdANYAmQB0AJcA2wBzAIz/2f8mAHEAfgCS//T/HQCg/0//Wv+VAHEACwAXAID/X/88ACgAAgAKAEgA2/8IAH8A1f+9/yYA2v8NAJEARwBiAKAAsgAWALP/pv9XACEAtQCzAWAAyv9MAC8A8v9BAMQAswCcAFcAJQB0ACkA9v9EAFwA/v/e/+3/2P+e/+H/1v/y/x8A9P7Z/gX/Of///nj+nf7F/j7/ZP6b/cL+pf96/nH9nP72/jL+0f1F/mH/Ef9o/kD+FP6M/Wj+wf6U/Wn+Nv6u/rb+df23/UP+J/9x/nP96/3s/pf9jv0m/zL/Mf5R/sf+cP0u/tr/Ff9Z/tn/CQBi/jr+f//W//j+k/+SAQMBVwDG/y3/TgDGAI8BkAGpAXQCxAFZAQcCQgOlA4IDuAOnA9sDNAVZBfMEUwUtBSYGOgZVBikHoQakBh4HjwbkBoMHiwerB0EH9AamBuIFEAZxBmQGEgYyBSsEJAMTAyYDQwNfApcBzwEJAVb/9f7Z/4z/V/6D/Qn95vwI/FL7nfzI+3j64/qi+ln6VPlL+Yn6C/rq+Bz5afm7+JL4e/m/+kf6Sfjh+KD6uPkX+cv6WPsf+Wb51Pod+yL7MPro+oj7yfoU+/n76ftT/Nf6xPvK/n38Efq8/Hb/xf00+yv8aP3e/BP93vyn/Wn+Hf3A+zv8NP5v/1f9O/0T/8b++P5m/wX/DABXAAP/q//BAI8BQQC4AToD3AESApoD1gTIA5EEuQZsBzYHrQjLCDwJmQlxCiMLBArRCtELAAzAC6EKegpoDIMM3gtaC5cL6QueC0ULWAtWC7MKqglbCcMI9gZgBiYF4gPIA38CZwCX/zL/Qf7K/df8bvwS/Db6f/q/+qb5rfnG+Eb4KfhX94P3D/cB9yH3+PZl9+H2Lvcx+Pb3a/j5+Ar5EPp6+qH6Svs8+wL8tvxk/Jr8Gf3n/Q/+E/7y/UX+5P7x/lL/bP9J/4n/dv9M/5j/Iv9I//b+T/5Y/tv9vf1V/dv8Af2V/Ar8SfzF+8n7Ivz0+0L7t/oz+/v6dPq3+sv6k/o0+oj5v/r6+pz66vrl+hP71vs8/Gv8QP1P/fD9MP4m/mH+l//e/+v/bQB3ACkBogFmAj8DGgQaBLYErQX0BugH+wjPCU0KQwvLC3IM7wwbDT0NEQ6xDXANrg2oDSEOCw7NDQ0OzA3PDdENcQ3vDb0NCA0WDOwKYgrpCVAI6wa5BeED+AJyARoAcP+y/en8C/wh++X6w/lh+fn4dPhh+Lv3O/fY9p72NvbY9Xn1dvUJ9a/09fQl9Tz1KPUJ9k72APeF9yX4B/mk+Yj6Ivt3+xX8sfzX/Jz9k/3H/d/9+f0Y/oH+9v6+/gD/b/+T/93/QgAmALQAiABnAKsAPwACANr/2v7K/rn+7v09/dj8l/wy/Cn8vPvQ+5z7ivtT+6T7mfsu+wP7+fog+9L6pfr2+Vf6p/pP+o/6efp0+uT6H/sD/Kf8ZvzE/Hv9OP6g/rT+e//e/3EAAAEKAfABgQIYAwwEpAQ/BeIFoAbKB4UI+QnaCoILvAwMDekNzw5ODw8QLRCNEAoRyhAXEXAReBFEESgRGRHUECkQ+g/BD3sPsw55DXwMOwuZCi4J5QdeBkIEzAKFAbD/kv7N/MH6G/oA+fD3xPbL9V/18/Rx9BH0nvN28zHzOPNx80HzWPMw8zjz0fPx8yH0sPQG9cj1WfbH9mv3e/g8+QX6rfp5+2T80fyd/RL+rP5G/03/lf8BAPP/LAAqACUALgD//wUA7P8RAK3/M/9k/xT//P55/rL90v0k/Xz8Xfya+/D6pvrW+ej5fvnJ+Av5svid+MT4evi0+CP5Ifm0+bT5CPqt+u36WvvB+2L81/ws/bT9AP6u/q3/qv9KAKIA4gD2ARUCqwJGA0QDJASNBBkFwgXBBYUGVAfEBy0IhQhtCZEKigs0DEgMswzjDasOvQ4aDyEPag+ADzEPeg9CDwoPyQ48DhYOzg04DQwNnQwPDFgLvgoeCgIJPwj/BukF9AQeA94BhgD9/gD+bfwP+wn6lfjw95n3b/bG9SP15fQd9bb0cfRi9GH0qfTm9Mz0zPTS9AP1QPWo9aL18fWe9pb2c/fu9zP4lPnP+XX6bfu0+yL9o/3A/c3+AP+B/yUA2P84AEIALgBPAOD/sP97/zT/9v70/o/+3/3P/Wv9S/0S/SL8FfyU+/j6+Pob+p75YPnF+Oz4Tfj59wT4nff29yv4T/hg+Hj4Ifml+Sn6vvrX+pz7NPzK/LH9lf1l/vD++P5TAHkAjwA/AWABTwI+A8YCzAOTBJYEIwYUBsEG1QcRCDkJBwo5CjgLYgyQDXAOKQ+PD9IPfRG0ERsSdBKFEV4SXxLIETASHhGbEMcQmg8VD7QO3A2YDaEMhgvKCugJrQjcB+EG7gTXA0ICxgDJ/7j9Mvwi+2T5oPhm93r1U/WU9JnzkPOO8pLy0PJS8u/yK/P68prz5fMO9Kb0pvQU9Wb15fU+9rf2EPdW92v47vhk+YL6HfuB+/n8Of1h/pT/P/+RANcA2AApApoBfQHFARwBSgHlAAMAxf8+/5n+Uv7b/e/8Wvw2/Hv7mfvK+gH67fkX+TL52/jl94/3U/fs9uH2t/Zx9m72mPas9m33g/d69674yPia+a36i/qy+0X8wfwC/j/+5v7W/wsAgACUAdgBpAI2AzgDWQQMBVwFaQa3BgoHkAjiCMQJ1wofC1EMxQ3eDusPmxBjEBsSFROtEr8TRxPfEr4T3RJ7EsISKRE5EeQQjA+LD6MOmw1ZDawMUgvZCogJGAjRBz0GpQSjA48BBwAI/9n8sftW+jL4MPcO9tH0NvRc82PyWvL98c/xN/Lr8TzyxfLo8oXz+vMM9KL0OfWS9QP2WPbX9lD3/vdq+B75yvld+n37Efzb/L79hP5T/zwAhQAfAeoBxQFVAkoCBQI9AqoB7ADYABcAUv8L/8T9Vv3R/MH7S/sC+/r5o/mI+T/4dfj09wL3Xfeg9sL1YPaZ9Sv19vXx9KD1IPaT9ZP2JvcC9zn4IPkh+ZX69/qE+/r8ev0B/lj/av/o/70BcwGYAlwDMQPWBJQFtgUbB5IHwwfpCTUKIAu1DO4MhA4UEBERahIcExcT2RSIFVwVNhbYFU0V0BVVFYYUlRT6EqMSOhKuECYQRA+/DfwMKwxxCswJ9QcyBmwF3wPIAWsAjP6Z/JT7ivka+Kn2HvWq8znzAfLp8FzxFfAm8JLw7u/o8I3xJPF78uTyLvPc9MX0nPXk9gr3w/fc+ET5APrj+kn7IPzp/L79Vf4V/1v/VQChAW0BDQKhAogCQQN9A6YCLAN4AsQBJAJ7AOD/P/+A/RL9V/yX+gH6BvnI9573nPbn9aD1mPSD9I30xvPR85nzVvOp88bzyvPw81v06fSp9RL2fvZ393L4b/mk+gP7DfxB/Q3+Wv+4/7QAqwFDAisD6gNPBDYF/AVbBpoHrweNCHYJsgkZCyYM1QzUDcsODBBGEpASDxNSFIAUtBU5Fv4VLxaNFZ4V8BWcFMUTPRPBEpER3xABELoOpg2YDCEMegp+CZ0H0QZXBVEDtwI/AJX+AP1S+7j5kfhG9gj1zfS18pbyrvGQ8Bfx2fDM8FLxYPFr8dfyPfOl8+T00vQS9uv2YPeP+AP5yfm7+sf7KPzr/En+7P67/48AOwEjAtAChwMIBIMEtwQuBR0FqwSuBPUDkwOKAqYBqwB5/+396vzA+wT6//gh93L2QfUw9MTzofKY8WfxIPF28DHw2u/J7xXwQPAJ8Nfw7vCz8eTyQPPk8zH1MPZp99/4RPnb+hf8O/36/Uv/zv+RANUB9AEpA1gDLASKBI0FZAUWBr0HoAaqCIIJJgpADNQMGw6UEAkSLxNCFRcVNhfqGGsYrhlTGY8ZrhniGFgYIRj7FrYVLRWGE3QTjxEpEHcPfA3QDDoLjQm1B/YF3QN7Ak0Avv3a+3X5GPg79h70M/JF8UTwMO/v7gjuG+5E7mPuBe+w75jwgPFz8ibz1PTW9f72Kvis+GD6Efs8/Fb9W/3e/vH/eQBFAbwB1QLZA/MDxQSiBbUFVAZ/Bm8GcwYlBrEFpwRsAyYDtgFbALX+y/zs+4z5DPiS9uj0jfOp8tvwgPDB7zHuyu5G7cLtpe397PjtUO557mTvdfAV8czyHPOn9I32bPfI+Er6jfu7/Kz9if7n/2gA/wB1Ai0DUQLNAzoFEgXwBIIFHAb/BR8GAwcPCLgG1AdaCv4LPAyHDSEQfhHpEh8U7BWkFoIWShe1GFcXpRbaFiwWIxWlFJ4T4xEAEdcPZBABD9YN/wwADCcL6AleCXQHmwXKA4oBb/9J/cn6Efme9xz15/Lt8ZnwWfD279LvX/Ct8HPxWPI59M70ofb492b45Pny+sj79fyp/RL+Vv9q/9v/7QCAAUkCSwNwAxEEZwQZBcUFuQVgBQAFwARlA1YCuQDr/wn+Ffz8+Rv4Ivfj9Z70xvKp8Y/wnfDT72bvF+8O77fuZe7N7pHtxe1V7m3uP+6C7orukfBS8Q7yDPTS9Fr2H/jl+oL7MP5//qT/4QHXACsC2gEuAGMAgAHw/Wb+yv7D/MD+zPyG/RD+AP15AeoGhwRrBfYLvw+eFosX8RqDH90eKiNBJ+UlwSVFJHwjOyNuHSAc0BhCFrgSbw8uDSoIYQUQBcUE/QDs/+H7nPyG+yP4P/oL9sDzmfMK8T/x3vAb7jDwIfEE8A/zDfR79SH6VPvR/4cC9AJGBzwIAws3DN8Miwx6C9IJlQmRCYYF+QOTAMH/SP3P+oz6zfhg9mX1YfaD9NnzbfOA9K/07vN383/0dvSH86L1CPah9b31M/a99lL4m/cw+QH6tPh6+bn6Bfx1+2P7w/ku+0v64vh2+Sj3mfW79Bf1SPPy8hnxTPH88tbwTPKZ8m3zmvRB9bf1wve698D30fsC+rb7Zvwc/Cb//P5GALICvwNuA9cFxQbVCD0MSgw0D/QQ3RLOGYAfZCPLJHYkSiegKhArcihxJfAf7xlEFjATzQzJBAYAg/zR+WP2gPVn9aj0z/XS+ET6B/s3/K7+6AHVAawAxv94AGv/o/8F/4X90PyH/UEAZwBaAWYDcQbiCEMKJgqJCiQLFQsfCtwGkgKc/+78cPoW+EXzfvHd7yTvbe8e8AjxO/PN9Yv3p/oG+/38Nv/oABABQQA1//H+jv4Y/k3+lPst+0n6MvvI+/z60/vL+2H7yvo/+0/6bvlG+FD3uPWC8y/x0vBB8FnvuO7u7Iftz+7e8E7yefOg8kPycPWi9oP4HPj99aH3jPlM+Q/85/xN+9b/n/xBAdICgP93A7ACSQJHAm4C3//kBegCnARACBkEoAlCD/YXQiApIVMd9SNxJogqmi1rJiEf8xivFO0R8A36AsL9g/h89YT0wvVu9vT3bfvS/MgBewK2BGMItgrRCNkIIgUOAdoAVf7W/0/9UfoK+bz6Hv1jANsDlQW0BuIHpQhoCccJ4Ac/Bm8B7vz++FL3c/VA9MzxBPBl8Ffx7fQp96L6w/z1/soA7wFJAUwClgLWACIAV/tC+a34APdr+OP4lvZ99lr3lvit/Hj8bvyo+6b5cPkg+lj54/WD8yjwLvBY8FPwh+/X7z3v4PBr9Av0l/bq9ir2B/iX9wv3mvj89oT27/fP9Pr2e/iO9yb+Rfxk/Vv/yvwrASICwQHbA1UCJf9RAcH/MQMMBo0D2gUTBKMGoREdGmMgsyLjIgYo+yx1MHsxJC5pJU8cMBUUE/sNOQRI/Kv0l/Fz8eHzfvXd9/j4gf5JBJUFhAcrCJYLqgxDCSgEGgFQ/tj8av5w/WP6/vlb+tj98gIKBRkJgwmjCDoIbgjWCMoGbAMV/kz62/aa9KTzQPPr8Tvzg/PE85H3tPqJ/8QB1gE+AWkBkQF/AQcAcP22+5f5Rfd99dX1sPZm+Lv4HvjO+Ev6+Psv/c77uvlv+EP3EfZr83ryM/Ey8Njv6u7872fxivKp83v0nPQc9tf2hvWi9QL10vRA9dHzaPPk8zL06/XK9iP4N/oh+278a/7i/l7/MQG0ALsBZwK7AmkDVQKQAqAExgc1CEAHogi5D24ZmR+WI7UkAiZMLHExHDH2K8ggaxkkFo8Pywf6/Rv1iPBP7/3wWPMJ9OD3mf6DBEwJ4wsTDZoPsRAWDr0LkAXk/2r+o/xu+5L5R/cf+HX8tP7xAyQHnwZ9CkUKxgmNCcsFsAKKAJn6+PZZ9a/xmfPv8mDy0fXN9nr5YP54/1YBbgP4ATUDlAGh/oP9m/pv+dr3dvXY9Lr1dPYu+K367/pw/PT9cf53/pD82/kJ+eT2N/Qe8qvvXO6G7Qvvpu+D8VnwvPH08+HzbPZc9vP0jPON8hvxXvTX887yefTb80H3Lfq9+3v/QQFrABwAdv+g/sn9z/2f/A77Mfoj+cn8zAFDBLMHjQgsCNUN+BiKI+woESbQJcwpTi6bMlYtmyRaGzoT2g9fDaoDuvqw9LXwwvEf8lr0ePZv+zr/7ASiCB4IjAvIDZIPGQ5/CBoCJwJRAMT+3P9t+iz65vtb/tMDnwVDBe8GAAcOByMH/wO0AU7+LPyx+Xb38vSk8uL0Wfat92z5ufoL++79qf+hAFcCAf+8/fL8x/t1+0P6Pfip9xf4yvgO+hj59/qK/ZD+x/89/gf98PsX/Az8Pvsl9zXza/IS8QfzzfLP8v7wfvD870D0wfVq9LP12/Ct8HPxlfFE8m/zpu/m8uT07fTp+Wr61Pw8ACkAj/tw/eL6cPvl/h373vi998j5d/+BBkgFXQUkCHwMHRi+H6EkdCZTJZEn6y1IL1cqQCN2GQkUEQ/xCCYC3PjN84jykPJD9eP1DPgZ/hcEHwmIDYIMOQ3uDwsPFQ+gCesD8P9v/pn8Ff1C+z37Ov2W/hACpgRWB94GYAgRBy8H/wISACD9r/tI+ff3Q/eW9F/2LPf0+739KAHV/2oBAwLpALAAvP7t/Mb50vh884LzHPPE80P3HPfZ9qf4iPrg/fQBawCD/yX+5/oU+r34bfTz83HxXO/C7ijsqe6C7z/xvvHz8mjzmfPz8ojzZvUv9AP0p++W73/wCvLH9RX3kvV89Xn36vlH/uX92fuk++T8S//GAosBcAI2BxAK/wpACJYG1QvWF3kdLSEuIMUemiUgLvkwryyQJBMalBabDxwKJwXK/AD2tO967Xjy/vnX+wIAsgP2CKQQmxN9E5gTmRKIEAwMiwRV/hX76/n/+Bv5Lfjk+LD6z/4ZBDIJSQsZCS8INAaCBPoD5wCx+4f5Uve99iT4P/fA+Mj8Kf/BAnoCHAGAAsf/ov91/Sr57PWZ8wLybvNT9CPzb/aV9xn8Nv7S/Zb9eP10/NX6wvnb81HzC+4H7LPtwuz97k/wpO8v8FX1u/Qv+nH5iPO49fHy8vHm8t/uwutf8AjuL/Ic9NfvsfWE+KD6if39+8H5Pf64/ED/xAFEAEMClQXIBtEGiQmrCT0Sshr9HyQjHyWfJeQp7i9bL6Aquh9bFRwQuw6CCC0A6Pap8L/y0vZ4+cP6Kv7LAe0KjxA2D2ERwRF/EScRhgzaBaUD/f5Z/Jf8EfoD/BP9R/2dAEcDpQSkCXAIuAXhBhgDcAIUAoP9WPsN+y/4O/qp/ML7GP2p/SgAqwFuAZz/Lv/W/W78MvqP9kz2gPWq9TH2ZfUu9fX4Sfyb/Nz8Avy/+xz97/r19iT17/Pd8Gfw9+367LzsDe/G8WzyafLD8cHzD/Qh+Mr1r/Sx8VTt+OzE8Grw/+5z8d3sZfHt9AP28vq3/Jr6Sv3B/gz+GQRgA0MFvAe0A88C8wQLByEPXxrZHAkhJSJwJKctlzPqMEMrUCDBExoQ5AqaBc/7IPFk68ruJvPw+MT+sQBaCAUPdxTuF4UY8xNAFGoRdAkGBAf9E/nj+ML5Qvg9+/v7TP6EAyIGrgdxCYoJtgexB7kCGAFbAHL9m/xg/G378/sn/iX/LAJUAoQAXP9M/nj8rft3+TP2ZvVg8rnyAPV+9Z33/vrc+/D9Ov9C/Yv+aP3g+pb5FvbC8UHuFeyL65bsqeuW60LsBO6g8abz9vMd9JPzevOB9L3wBe7S7HLrZ+3W7q7tXfCt8Yz1SvpC++f8w/yE/7YAbgOjAxIDDAM9BzAIlwVSB50HqRCIICMoOyccKe0nzixCNeYxGifLHN0OIAUqAvz6ffPY7EDpm+rO8Wn5BgF0COQOzBRKGKYamRnVF8MTkhCgCs8Bz/pI9jD2GPhw/Er9Bf+O/0ICDAiZCy0MbgvuBqECJQLs/3z/VP6++9H65PwM/iz/KwIfAxgD8QPz/l/62fjt9W70HPTP7wvt6+2Q8e72C/tR/rz/pAGcA+UC8//G/bv3JfOC72Dp3uUP41DikeSa5yfqZe1/8H/zyveM+MP4xPbS8/jvhO6C67vorutj6g/tZ+8J8Yb2uvmD/M0AcgBJAYMFmASRBuAH7ARlBmYG2gQcC3UToRwTJS0rwS4EL90xETVENEotnR/tD8UGx/9a9g7v1OjF5Z/ojPDe+OAAFgo8Ew8ctyFPIgAdYxqkFkwPxAitAIz3nfER8tLy7Paw+/39xQCCBLkFpgoyDlYMIQuZBcQBWwAl/j79hv4t/Zz99f9DAGcBhgEuAH///Pyl9sLxu+557F/tDu4b77Xw5PJt95L+CwRXB/MI5AWfAgf9DvhF9O/u4ecZ4mDdbtuK3wHkC+l17QnwGfRc+W36iftl+7/2L/Of7hbrousv7CLt6e6j8FbzaPW3+o3/2f4tAJwB9QFDAzMGhgeWCNUIdwgYDLMTPh9wJhEvrzGaLrIu7DPvMnctlyOPEe0Ew/2o9Anvh+7X6AvpHPFU9v7+2QkzEsUZqB6rHHoYnRWvEXENGAgJAfD3l/Sr9Mj1f/nl/soAdgFWA6ECigULCeEIdAbKA57/x/7a/6sAYwGOApwDEgOKAxQC4P7y/Vj8cvhJ9cXvNuvq6QDs7+968+X3jvrG+wAAkwMcBkUIJwUHAA75hvQL70rrL+qn5lPkf+ST49zlXOsK70HzTvXI9r7zwfQV9qjzbfXO9WHxKPJG8WTvd/Jd9Wv2GPbF9jT0vPTO+eP8+f9eB1EHfQc2C0QJagvXGU0kgSkZLeUpqynvMoY33DEkLOwbPg6jCLH/mvSz8TPrheur7Prwvvdy/fYJDg9jFD4bZxdFFOUT6w1bC64Fkf9o/N74zPh0+Ar92v9//6kDOAN8BE8EvAJfBJICOgMRBLf+XP4IARQCpgmLCI8EPgR/ART+k/w79wHzOvHZ7EvpfOhU6h3ud/Tp+uL9DACzAxYDLwRWBD0Asft29ILvf+rZ5sPmtOSt5GnnW+an6W/tlexq7h7xyfIQ8hzyAPGy8Mz1JPhd+Ln6YPom+EH6Mfog9pz2uvQl86/0MfbZ+g4DOgmUDFcO1BAhFxcgqyzrMLEvcS05LJYs0C0tKsgf9xRHCQP+aPiH9q3xRPG98cnz+PonBF0IhQ5wFUIX+hkPFwIQAAyxCnsHigbuA3n+PfzO+0D+WAOrBNAAngDA/tf+9gESAN0AbAGk/54CaQQwA5QFpQdmCUILkgVX/sf67PRb8yTymfDu7gHrYuhH697wEvgWAVAB3wEOAEX9VwFtBN78n/dS8fHn+uks5WnkhOnS5ZHlouYv6Lnt+e5E75jyhPIz8vjx+vEk9R32tva49WP3OfVc8pzymO+x8lfylPDd8djyj/fTAGcGcA0QEzETKRZOFToYOCSoLNwvQzAIKQsqsy/cLAImzRwfEDsHuf6b9k7yC/Hz8wzz7ve8/Q4D3wp1EvEVUxjfGPYUABKODeYI+wTSAwEB2P6j/RD9P/+v/70CMQPn/3r+X/4q/s7+lAGzAE4EiAQjALAEpAjrCd0LrwZJ/3f9h/ZX9KrzMu8G7V7rFO1g7GXtS/I7+aX+1QLTAKL/dAKPApYBk/6e+VTxBfFj7z/s3ew/7NvnOOh258/laOrs6lvnnedc6Ero8esv7gnxl/Xl+ZX7sf3p/o39A/5P/jr4mPMA8Q3vxe/F8oj4pf2BBTAMbBCxFf0b8B9PKUYybTJEL3gs6Sr3Kjspnh8vE6AHJgCe+TX0MvJH8Czv3/Vn/HYBdQrOEG0VmBoiG5EUmhKwD6QLzwgkCLAEnf/9/eP8pwElBSACPv7I//X9Nf1l/Wv7K/4TAJP/tAPBCPcGzgdBDJsLMQrRA+L6tvat8afr+Oha6OjmlOkR68TwG/Qu+ccBogU+BvsC3P/4/P79CvkT9aXy1+0866TqxOqK67Xqo+hH6dHnNucn5/rn9umR61vtN/AY9Aj3Gfqc/ZD9W/1T/Ov4Yfe/9bnxR+9k8LPwufXC+kT91AGRCjkTRRgSHPcYJhSQFYYd9yTxKQsscScJJZYn+CZSIxUgpBhKDCkCu/lx8bTx//Qa9z37AABSBHYJmRBoFVUYaBgoE5QKHwXKA20E9QXnB2wFBwRJAnQC+wTqA9YCG/wX9S3wMO9e8+L48/7dA4MG5wjACkILEQ0rDlkJGwJe+LTud+pP6M7pAO1i72vuSu4S8UX29fsSAZP/Gfw9+dX2h/hM+e352vb69hj0QfHE8J3ulOyz6cHnt+Iv48zj2OGd6KHsF/Ao8g31/Pdh+lv7z/um/IH5F/qv9SX1K/fF9SD0FfeH9uP2nPtw/7EEcwrfEPYR4xXhFy0YDRqUHu0lnirtMJov3ypqKtQpyiGpF8EOwgR7/1r5l/KB8bD0wfc8/jUFfAnMDP0NEw/8EDkQ4A1iDHUJPgWeBKgE0wcdCUsI0wYwBCQCkv+D/t76Lvk99Q/16vfZ+sj+igLqBfsIzQnsBlYE5gHm/4T+JPtd9ob0QvAB8dPz0Pb59sD2lPV281L00PLj83324vd79uf4Efpu+pP7q/zh+vn33fQV7u7t2+uC5wXn6eg26i3rYe/p74nyjfGx8tPzn/HN8EPtYe/D8f305/VY+3P9bfso/0b8gP58/4T+hwEYAXP9xf6jCHwPQRe0Fs4WVReeFj8cBiRmLtEy9y+QJAEkRh8vGn8YPw5uAsP63PQN7q31hPkb/yAFmAWKBtIIJQuMDoMSphEjD8UIzgSoBuYKDAzPD2YKxAOYAIv+eP2U/Cr66fO69InyhfRH+kT/HgGJBbsHHQYhB8gBPwCO/+H8a/mW+Mf2ofWu9u726PkC+IT2D/Py8qjyjPJ+8s/w9fOu8+X3wPkB/Ef8Cf1d+4n3LfiF8zryhO3V6NfoD+r06Xzp6evg7T7xJ/L/8YDyjPPd9Hj0z/VG9CPz6/K894f6U/yV//v7g/5o/47/9v9p/+b+3Pz1ApAFPAvyEN0VjRneGMcYABPKF9IjuC4lMW4wBitLJRomRyGzGLkQogZT+nTzPfLU8hX0N/mL/rcE4gjQCcsISwiXCn4L3wmkCD0HOwUiCMYN9A9nDScK4QQqACv+Qvqx9tP0GvZP9Hv1DvjU+dP+qwIdBZcCwAGd/6H7QPvK+cz5Y/nR+DD4vPeL+0v4A/hP+O74vvbn81XzovE28zD00/Ry+U/8NPvH/UD9lvsu9xL0RvA98MzvkewF7eDtOe1Y7hLww/Cw7zPy2u/V7a3v2e5q8iTyG/WH81/4UPpR+Ir6HvmO+8P37PrN+1v/SAIR/1IBTf7UArMAQgPGCCoIaQgjC0ARkxP3GbIWVhpuKuszLznHOCoxXCipICAV8w3oC1gILvz69fb2yPw5AjT/8gCLAGYFXv5R+Lb5jAN3CAoKgRFjEagWXxasFAkSWBGaBbL89fhD9Gf22vMv9Wj0QPcW92r5AfxO+i78Rvpx/fL7QP1HAJ0A1v9nAHEA+vwj/Ob2aPQi9vT1afSI9if4F/iT9pHylPRD9q34V/ZV9a310fUm+an0Jfcl9X72x/Iu7zXyMPNM9U/xUPOK8vb0BfS17RvvGfA28OPu7O0q8GLxDPQ39Wn5/fya/dD+0/3wAKAAL/7v+7r4XPmP+G75O/oi/MACAQUbCnsLNgzNEOEQzROTFWYYsRJgEfYYPyWWN9k80jnhMcEuUyRVF7kRJgqWBSMAv/oc+fH+YgE7/x8Ah/8r/br3zvC+7kP1iP6pB5AK7w0KDzMOoA0/Cs4KtgamA/b8vPun/FP8tP0G/Gb8Mvr2+aX2wfU19rf3JfsH/vcAMwBDABL+Lf9S/cr8ffq89GH0s/Le9RP2c/lt+6z8XfxL9sH3fPW5+OL4sPco+qL4e/l59mH6GP5m//X7+Pez9jT05PTI8zD0ovW19kDzkPKf8hD0qvOw8sPyvvHH9NvxhPP78lv2Evfn9oX5hPiP/CD7a/yr+/79CP5A+8gAff8P/9cCEwOCBH0FfAcFA8kFiAiPB7YJzwp5DrMN3g8JEIARkhyDJlgwwzPAM/Qrlh8YFw8PKBDYDIsHpAAz/8P+Q/6Z/+z7jPum/Ev5N/Uh9fr13fl5AxELaQ7jDH4LKQi6BZoHJgZfB7cDvAE9/7j/1QL0AhoC1v10/Ff4kfXs9VD2N/hr+jb9TPpg+Db2dPXk9mX27PZU9WX4wvqM/D/+pf/oAdYAIf8t/bT6r/mX+Ln6Vv0OAP8Bn//0/MT5+PdI9Mz0BPZD9f/1AfaY9R71S/iA9074SPeK88zxefGT9FP0OPYr9UL1svY69kH3OPhM+Lb3YPhA+Nf4e/l0+0b7xf3k/sr8t/2T+kL8A/xz/hEBAv7+Ah7+uQBE/xEA6AUyA2IGrP2rBLkFSgmmD4kQIxM0DtQSMxagKno9UjiIKSAhyBr2Fw8W6Q68Ce8OuRAUB5IGqQMn/Zb2APL58s70f/VI8pv0L/7QBG8I8AULAiUEYwH7AN4AHAQgCTILAw/wC7UJuAbeBOz/T/4+/CD7OPp++xj83PyPAJL77/ij8eHszep/7X7xFPZC+Tz4I/ky/Rv8wPoe+qz3M/vA/JP+V/9IAtMCCAE5AID8Lfo9+O/18/qx/GT9qvvo+VX6mPiE9mrvB+0X7hHw9vTe+eP6FPnh9X717PWe9sj0VvK18sD1XvcY+Pv5O/sA+2z8sftK/MP8Kfuf+/77sv7p/EABFABzAcoA7P3U/S/52/yS+bn+xAEjAY8HHwSrCaoFsgXPBnIEOQVE/wwLyAxYEmITYRM1HWwjqi1GKtYtGSjEFlAMpQq2EBUUnRRoD4cRlxFXB836Wvd/9u70Ovfc97L8yQAfAF39FwPyAnn61vdB+Gb7ZgH5AygHiAr3C5kImASKB78DuP4e/Yv+AQT3BiQHXwdXBSwDdfk78s7u0Os17sLxN/ij/Cb/w/xp+D31bfBZ77ryyvYY+yb/QgLdAogBTv6m+zn8q/3W/CP/WgAoAigCsgEVAmX/Ffzq9e30J/bD+BT57vph+7D6E/cS8tju++6T7VbsevA39OH4wfjP+Gr30fja9/f0JfVT9/D3rfvT/dUApQLzAD/+P/23/Ef7tP7GAI8E5gQ2Amb+I/qi+vD3D/qz/r77jAOYAHEDCQJRAg4F8ACJA3H5jAL/BgMNKhLhFacaExdrGzAa3yo5OAUx+iFXGlManRmUGpAVjRYyHXkWgwi2A7gAdfsz9djzs/eD/Mf66fBJ8an1GPVT873xvvAz9bX1ffnC/q4CmQXcAUYG3gb8BO0DYgIBBTMJcwyFDvAK0gm7Aj7+5/3u+Q35Jfc39pr1SPgv+df3cfPa7YTpaO1N713wbfNA9zX7RP0M+lT2tPbj9mj6vP1KA2AFRwZsBbUEZQUpA8L+pf3k/kYBIQOSAZP/1/6Q/EL63/Y79L7ze/Bo8vDxjvQi9FPyg/Nm8y71ZfPk8ZHzX/cR+kD9f/1QAM399PwL/F/8J/9k//D/yAIyAwQCfwEX/5kCswEoA8v/tv3W/gb8r/4QAIn74P6M+w78Fvs2/ZoAmwH6BW394f9aATYFBAfQDxwTiRKZEjkMYBfIKVIyaSwSIw4cFBjqGQYWlxGFGIgd8hliGMUP1gCO/cvz9vhv/Yb/mP7/86/25fG98KHwXeqm7x/07/Rw9471gfoJ/4gAKAH7/qP/aQBJ/70EVgqSDwYQ9wh7BngDMgMjAhkA1gEPA1ICg/6U+cD3AfW182ryR/Ce8hbzXPO/8h/xuPCv8RDx7/L58172DPir+Kj8i/yoAEQAov+//6X/3/65AJ8E4waKBrUFegMeAB8A2foK+oL74fzB+0/8Xfny9kL0p/BK7xvu2PHS8M31BPf189D2p/Rs9bj2bfZr+h/9D/97/sP+7gDbAIT/O/8r/9EBZQOUA6IFIAYPBQ8DpADc/pYAEv8HAVwAbP+E/5P8gv4r+4786f3N/M7/hv5M/+ECGAROBr4IBQ31DbAPpRQzGB0h5SfgJosniyYlINYeQxsbHM4c6R7yH0gaHhojEfsJeAUj/lL/J/9L/7L7I/jm9jH0mPEh6kXpOeon7obtMvFD9Zn5lfuA9Ej2GPXM+7z8Qv+nBxcIPg0BC5kI1QokBkkGewaAB7IKmgfVBnEFewE7/2v5ovcS+uH3bvl69cXzlfPJ8DbwIu7D7dnuYe/q74vyuvP99RH1M/RI9jT4bPqU+6L6nP87ApECSwWEAd4EngNOA/oDwP+8Az4AHAF/Ajz+wf5m+/74X/js93b3r/ae9Cz1iPTw9Kf2RvTM9y34Dvff96f3UPmu+UH7DPzQ/mEBfwBh/8X/EwGWAHkBWgEPAjIEbwROAbUBsP6V/isC0wALAp7/Ev4X/af8IP4r/QL/AwF8/Ev9T/2J+9f9UwPMA9cFIwY+A0wIRxBiGcoa3h9vHVYX4RamFTcUjxg2HI8eNCSBI9IYFhDeCsgHoQlVDH0O2w2eDhsHbQCL+/H0vu/08DP1ZPms/Gb6p/Ua8y3wpeyj7BrvwPU5+vn/lQEfAdMAQP0b+8n83wHWBHEHgQjiCX4KOQhSBb0A/wCSADn/fwIpBMMEXQG3/vD67/dc9LXvUfGj8k/26/VH9lH38/K77+/sOu2C7+zw3fOD+Gj7p/x6+4L5zPkK+Gj4xfwhAYoElwOBBe4DZwK9/4z8lP8bARECIQBGASkCWwCo/Vn7cvjU+W35pfk9/DT7rftF+e35O/qD+OH5ofid+RT8jPsM/cn7SPye/B/9Bf3B/Oz8Cv4G/+L+7gAtAJEBKwCvABYBvAEFAMv+df9UANEBkv8XAFP/of/A/wL+9vtR/tT+uv/rAGkAGf8G/q0DbQftDpIU5xBtD10P2w7oDisQbBVeG8MhziPDGx0YuRMID8oQBxPaGhcckhnIE+AL2giJAyr+Cv0ZASsFywWjAb/6UPZS8fLuWu6D76r0rPaT9wv3Kva09MXyofED9F/3DvxJ/3EAtgNkBJAD/ADi/Xz96v+sA1AJAwzvC5EIDgMO/4T8zPwj/w8AvQH5AST/ofz/9kXy2u+d8fvyh/RQ9mL1LvS48ajveu597uvuwe4k80j3M/nY+Oj37Pa49zz4CPeT+z79OAKLAxQE3wRzACv/EP6z/wMF/AWABswG7QQDAwL/cP2//YgA/AGtA+QCxwHz/rD7o/se/Kr9+/0p/wYA5QBi/3r9Ufyo+6v8gf7Q/5QBZwBl/7P+F/6I/Zn85P7hAB8DCAFo//D9e/2//e/8qf7z/4UAHv+f/jT+Pf5f/Gj8t/wu/Cr8wPk8+9//3wJ9A0cDcwLbAFQBdAJfAxgHrQnXDMwO/g+QDIgIYAmrCuUPghQjFxUXuBNiEHoO4wyNDTwOFxCtEnwRXw9hCvwHDgUTAkkDZwMiBEUDFwKBAOX9svpR+T34+fiq+TH6Mfy9+7P7XviY+Ab5Evdu+tv7ff5DAPn/xwAHAMn/G/8IAMQCEAU7BIEFVQUZBEYD2ADeAckBYwNfAr0BFQL1/4f+JfyV+in6Gvss+1f8qPra+G/3J/V/9cz0V/Wz9zL3ivcY9hn0L/Uy9PH1Kfa1+CT5+/hC+Qb5cPmp+SD7hvpW/cL99P2v/pP+Ev7n/nD/mP/AAOUA7gADAVYAkwBhAdkAMgFs/24AjgD4//7/F/5w/p7++v7L/WD9c/z0/Bf+ovz5/YX7p/y7/C77jP6c/RH+5fv0+6L9k/3T/IX6cfrX+jH7Y/mM+mv9lv11/Nn7N/tV+pb5qfkh/cwAYQLX/5L9e/4j/pr/JAEKBP0GxwhXCrcItAjUCE4JyAtcDm4QNBInFEMTnhLlEHQQKxHWEFITThMkFEkS/A04DX8LFgunCf0HGgnuCOQHZwTCAaoAo/9c/vz8//x//JT8jvtQ+5j58vgT99r3Qvrs+vT8Hvu0+0z8ofwj/vj9V/77/0MBbAM9A/gCtwLkAvIDbAS2BKoE6gRRBKgE2gOfA8kBsgDPACYBRAGD/4r9Zvu5+s36Lfnh+Sb46vZJ9+n0mPWX8wzzTvOQ8zf1PPRb8xnzBfP08xf1BPXM9Zj3vfce+W/5i/kT+0X6TfwO/TX+zgBa/7MAdQC5APEB1QB6AikCcgOOBK0CZgJWAt4BnwIAAosBoQEkAfgAhP8LAH//i/3e/Sb9U/66/l/88vth+7n7TfyS+/b7Uvs1+4L7F/vQ+yH7bvs3/N/8A/yK++v7df3l/S/+kv6x/u//BP+R/4n/4QDvAJkBowK4AqICzgHsAd4BagNJA00E0ANwAiADJQLHAr8COgL2A0oEzQOZAm0CXgJEA1gDGwPTBOsElAXvBEoFmQUFBc8EWQVbBm0IrAhICOMHLQccB0sGeQd3B6MIjwh0CK8H5QZIBhcFEAbOBUYGkAaYBU8FmwSbA2cDfwLEAsYCqAKTAssBagH3AC4Ag/+z/5L/+v8e/x7/sv70/Ur+Qv3n/Yj+G/7h/W797PzX/ND8u/x2/N78Y/1W/Jj8MfvB+lH7E/t8/EH78/vk+ij6ffog+l/6tfoR+0v74/va+pr6mfl/+v36F/v3+1v76fsz/Cv7+vtS+377F/yZ/Hn9JP3B/Rf9gf1u/Xf9sv2c/kT+if6h/zsAyACL/77/gP8XAQQBbgFXA+YCDgQjAyADRQPYAusCKgTlBZMFkgULBXMFKwUqBB8E0gSOBWsF1QRhBUkFOQTJAvEClAKnAs0B+wAOAg8B8QDy/5L/KP/j/cH8DP51/Xf+Wv25/KP9V/zM/D/7FPzS+4n84/yf/Zz9Vv2C/Iz83v3l/OX8H/20/VX/8f7k/UX+4v3M/eb8t/3C/bP9zP28/Y/+Pv4J/GH79vva/FX80Pta/Bv8x/yF+2f6BvsZ+ov6Gfyb+2L94Pqu++b8HfoO/QT6Ifz0/SP8Kv/l/e7+/P3N/Tv/1QBVAHcBSgJ4At0DgQFhBPkHbApuCi0J/AmbCTYK9gksCiQOaQ+tEKsQcg8XDWYK5wq7DKMP5Q+EDxYO/AsYCmwHRwefBhoHkgciCIYHOwRhAM7+bP7G/Vz/zf1K/7f9HvxV+/D5t/kl+Jn5jfop/Mv7t/pV+rT5m/nE+sD7ff3S/Yf9p/76/sr97/3d/fP/QgKqAc8CuACOAI7/xf7vAPgAJwEkAf3/7/+E/k38n/t6++b8df3c/MX7M/rC+Cj4QPeH+In4vPgu+YP4O/lv96T2ZPZ192b5GPq5+g37k/qq+lv7Tftb/ET8uv35/3QACQEIADYAOQHlAMYBdAIfA1oENAQYBfsELwS/A+cCigPYA+cDswTcA/wDzgJLATwBDgC3AHkAbwBAAFX/yP7Y/rb9lv0I/Zv8f/0f/T391vwU/bD9v/1i/V79L/0b/on+BP8VAOr/VwBcAC8A7QDcADMBnAEnAhYDGwNUA54CGAIQAvABuwLOAssCPgOjApkCNwIPATQBgAD2AEABYQBBAen/0v84/0n+Mv+F/tD+0f4i/4v/7P6q/Wz9q/0y/tz+Nf/u/5L/qv/d/hj/Y/8e/y8APQCLAT4CdgHDAcEAjQDyADQBiwKxAhADsQJ0AlAC0wHaADABHgK1AqEDvgLrARAC2gBIARAB4ADtAQcBjQGPAVoBIgHe/0r/of8hALAA0ADVAGQABgCk/p3+w/4S/+b/gv/GAAEABf9g/qH99v2M/tv+XP/k/9z+2P46/kj+Mf6o/aT+Yv/r/6L/x/5j/k7+YP5J/vr+dv+A/04A8f4d/439MP1j/qj+UwCA/z7/5P47/iP++P1V/dH94f5w//EAq/9z/u39M/12/rL+c/84ADoAQAAYAKP/tP/Y/p/+5/8mAQgCxgEbAdoAmQA+ANMA5ACqAS4BkgEPAjgCCwJeAdYB1wFdAgkC4QEvAt0BEgJ0A1EDrQNpAh4B1AGFAfYB6AELAncDZAMkAwIDiwF0AagApgBxAq4CgwOzA5wC8AISAfv/NwDj/08CuAIEA6YCiQBL/5f93/z+/Tf/7wCjAWMAW/9Z/Zn72vrI+s37LP7r/nH/af1r+yT6lviK+dr5m/uA/fP9ff0l/P75s/mg+Gf5w/pG/BD+OP2F/TT8Bfuh+un5f/vu/I39zP4M/j7+jf2q/Dn9y/zP/fv93v6r/jP+/v0B/4v+Qv49/gf+fP+N/hX+d/5J/+b/NgBA/x8At/+w/1P/mf+uAbEC2AOoA1ICNAK8AgYD8QPrBMAGqgekBxIGUwYhBq8G7AaYBywKXQptCtwI5gcNCPUHigh4CbwJXAmmCHcHsgcUB5YGjgY7BukGWQauBYIERAMBA7QChAJ/AiYC8QFlAYMABAD//tj+Yv5C/rH+cP74/oD+r/28/WP9uP23/XD9Qv5F/jb+Ev4m/tX+mP7Y/dD9Z/1J/qL++v37/eL96P16/UX84Pv2+0z7x/sW++v7bvzH+kr64fgD+f74Cfiy+MX4pfls+b34j/jT90L3tvbq9sf3LPnN+TL6+/mN+TD5DPnB+FD5cPqb+6j8iv0W/hn+fP20/AT8fPyw/Wz+3f/OACsBKAFSANv/uf8L/y4A7wBcAr0C4QG/AY0BoAF2Af4BHwL5AlIDLgNDA1MDZgNAA2YDLASBBC8E0AOnAwMEkgRSBcAFbgXMBdYFbAUoBdwENAVOBeIFcQbtBv0GlwYtBnQG+wXABbwFXQaQBnQG5wb5BdgFRAWmBDoFxQRABLsEYASeBBoEbwMAAwsDqQIFAr4BRQEUAcAACwHoAOsAsAA4AI7/5f5I/oL+if6M/uf+sv6M/jb+1/0Y/Sb9OP0A/d38hP1j/WT8hPz6+7L8xvxj/AT9XPxZ/Ev8z/tz+xb7tvp0+4r7ufuw+5r7c/uZ+p36J/p/+m76o/p4+4T8Qvwh/Pz7+/q/+kv6zflv+4z8DP25/bj9df7n/QD9Gvwh/L37xPys/ZH+2v7P/o//jv+H/yL/S/4Q/rT+cv6e/0UBewE9ATgALgCCAIj/1P+CAK4BaAKkAucCmwK9Af8BwgDMAGUBfAE4A1cDqAQ7BNsDlAP+ARkCOALqApcCRgJmA/4DIgSJA7ACtgEFAnMC2gElAlQCOAJJA0YDrwLvAV0ByAD6AKYBzgFuAqMCZgJQAugBnAD6AMwAxgDHARkClwLuAdgAdAHhAMv/OwFeAVEChQK4AVQB9f/Z/8n+5f4e/wz/LgFrAqoCuwHs/2f/0v21/IL9bP4G/yr/9QBjAMH/af89/9//AP5g/rD+fv7i/ij+z/3w/5UA3v/y/x7/uv96/n7+Bv5Z/h0AYwA/ATj/cP/P/8D+P/41/5P/8/+7/yD//v+W/8X/z/7X/c39lP5G/6r+mP5c/0//+P9U/2b+yf0m/iH+zf0q/bL95/8d/3MAu/8iAOUA5/19/SL+OP+D/8L9Vv5/ALgADAFrAAAAxv5z/nv+2v5RAOv/6f+IAIMA2f9rAGj/WgBAAIz/OQD0ALMBeAHNAJMAr/4o/nL/J/89AQgCbQLdASkBxAHp/2z+R/58/uT/FAGWAmYD4wIMAp4BPAAHAIT/h/4n/+kAngFeAl8CMwMVA6ABlwGSAa0Arv6//fT/JgK1AcsCVgFkAwcEEgGJ/3P+BwB+AdL/lv8HAZUBJwIBAd0BuwE6/7D+nv7M/4UAoP+/ADgCygHHAV0BBf/f/dP8Of5A/28ATABSAbsCwgIfAwn+NP4d/Ar8x/zS/yQCAwLEAuABWQF0/1UAn/w6+/X8Lf8wAV8CSP8IAbcA6v4iAAr8HfvC/JH+7gH+/wsARwFK/zv/SfzC/KP8Tvxq/6T/sAF+AVgAgP/6/kIA+PyR/mX+Nf6U/s3+KANSAhP+kP+dAPoAAwCb/u0A3gFjAUD+qf0z/p8BswAm/YQBBQEX/3r/Gv4SArD/Kf7S/un8uwCvA4kB1P9A/lX/mf/M/PP/sf36/2MDvgD8Ac8CSP0z/zEAlP9BACj8owCq/xIB+wNSA4sB8gACBFb+5v7M/f784wHu/+UFSwJ7ACEBvvy7AqgCN/8m/jH+lv4+Aa4AdwKMAR8BbAE7AaIAovtz/MMBIQKM/4f/9v9tAm4B0gEqA+/+/P3K/X78oAHzAGX+HQBRArYD+AL2/T/+AP4v/v0AMP+X/iP/+AJ8A4AB6gHf/6n8RP2A/c/+rf51/cICSQQzA4sBxwH7Aqr6hf7L/t/9nfzm+vABIAJwAhQBdANWAZz+1f0c/Vb+LP4U+kb+kgHTAKYBOwBFBAcCS/3r/Lb+wvx5/dYAwwHYAef8/P9xA8X/ygKq/jj+C/9i/94AXP7S+rP/9wJh/7IAfQB1AWD9Z/+C/17/dAKQ/9gAS/toAGUBJv94AXT+0QC0ALgAmf8D/178RABA/zoCmwFJALD/1AGU/3v9wQEF++4A5/yT/3EBEgCNAq3+hwF0ACABLwGsAML63P5BAWz/RgNlAbUFpwLLAR4BOQDa+3f7AABG/zECggECAbgCfQTwAJoCD/4S/Cb+rPsFAe//6QABA8b9w/1IAj8Bof0a/xH+qQLf/nYBTwF++00CZPrRATMBQwCcAq/7TAU/A+P+uv/6+/v+EwFI/0gCZQBoBAgC8fua/5P+Zv8z/pEA0v8FAvgAJQIz/63/zQGN/gIBH/jW/az/6gAOAzwBCgHi/jD+8AGfAVz+fgFA/0gA6PvI/QUEHf3oASIC3gBTAmn84/xhAiID6/9z/vr+CwGi/ukBuP8VAB8DCP2//3T/gf/VA9/9p/28/+r+JgM5APX/gQBr/TADFQPQAGwAp/3S/2MA3AGgAO393v7L/2ADKAJnAAEBJ/6OAAv+iv0d/hb87gB+/wYFqgQ8/gAAZv6F+yYCNABrARD+k/wmBSH96AD0AQYBYP5X/53/+gIV/+b99wIsAL4DnfxSASz94vyf/b8BewIzBMME/v2rASX7p/6K/1/9vf86APgC5gTzACAAhAAU/xP/bf6E/k/6vf1bBagFFv8o/CX/bgETAr/9DAEh/e3+qf+BAKUAlP2K/5v+pv8SACsBmQDcANX/bAHKAcIAcf2v/zr8lP6M/UH+AwbsAVwDCAEiAecAtAAL/cn/E/7Y/0sC5/y+AXX/x/8f/v4Aq/91A0//+QF7/9P6xwHz/rQAy/zfAGL/+gAFAqgB+P90AE0C7v4D+yX7UAA1AvMCbAF3AaQCBf/7/bwBwgM3/1r8Uv4T/qf/Df+bA9EBFQEtBL//q/72/rf6jf7m/usCDALO/pQApQE1AmsDcgL8+dv9ffySAAsA3f2sA+YABgE4BOX+7v5f/4/+Df4h/QAATQK+AhD9/v8C/mcBSQOM/W/+r/xm/yj/CwCI/yoA1f8F/ogDNwBDAowA7f+1/q8A7P0pAdYAf//0AiIBw/92/qUBh/5XAvD9BQQTAGD+9f9r/rH+rQJJAhD8s/+h/twBu/84/mQBS/7O/psAcv6D/igBJf6b/VYAhP/PAlP/iv1R+73+FP8eBE8DYgFLAYH/4gP9/g3/DgDyAxz+lvww/20DYgRn/98ARwALAnT8fgO5/CUByv+f+ycATv52A1L/6v4pADsEf/1cAfb5uwA+AML/OAJC/6f/2PzD/tQAr/9l/74Fef1r/uX+VQAYAc//2wBoABIAeALfAK78tAPi/2T/iQC+ACP+Rv5xAckBTP7a/gwBmv8OAdUArgDF/Yz+5f2Z/84C8wJEAU3+r/45AYEB1P7oAAMBIgAV/Sz/UwBhAFQA7/5oAA4CnwF8Aqb7j/3W/98A/gJs+yAB2f2LBEoATgAVAtT+ZQDb+rgDjQAJAcD8DQBEAh3/XAEr/u3/pfy//y4BzgK//nH/ZAEtAKcCavv9ABz/jf6DAuj7kACA/r0AyQEeAFUC7f46/7r+CgGRAw4AxP8e/EAApf90Asn/SQFV/moAEgRf/BMA8Pn5AFD+9gEsAQv+agNv/WUDrP0rAbUBg/4vAo/9TPxYAXz/6v70AoT/2AEU/wr+IwGC/rAAyv/u/r7/DwGQAW0BrwJC/uX+fABX/osA5vuhAYkA3wQmAVT8NABW/jv/Ef9AADkDJfzZAUAC6v7q/y/8TAH0/0ACEf63AQ7++gAY/7UE6P51/1X+wP9cAfX86/9n/WwADQPzAm/+ygCh/MoBMwF4/XoANP/UAQ4C1PoVAkT9IQQEA8f/gv8s/bwAPADS/n/+EAHvAdABqP9zAPcAGP8M/gsBwP0KAkb+YP+iAIz/sQRYACf9z/+S/cr/av5T/KYCkQIrAegDMv4eAsj+1P0QAj/+BAA5/YsB/P26AcQAHgL4Adr9VQAPAcEAff/AARv9VAHQ/dj93//r//IDRwC5/9z+qP3//8j/cgAm/An/fgTn/joFcf7K/J4AF/6XAfz8YQGcAiX9qwE6AooAbP6LACACPP/lAgL+1f3LAK3+RAJ7AcX/jAHh/o7/KAXw95UCD/3y/sYEePzoAV39QARMA+H+7/zu/UT9SQD6//T/RgEyAasDnv7F/vr+nv08AQYAHP+AAncA2wCQ/5b+hwJg/9/72f7vAQECKgM8/toArf0GAE8CtADX+5n6/gFEArgBMwZb/JoAc//f+nACh/wkA9j+3v3+A2wBHf4v/v79pf9VASn/EgFQAKcC4QHg+5L/BP23ADAB1ABBAX78WgORA7n+0/3G/u0DEf+XAeb6kAFY/h3/BgIGAfcCXP2DAJkCFwD5+2MBz/yh//7+WwESA4j9MQLv/ykApAEr+33+S/13AbMCJQLVAEv9Vf9QAdUAeQC+/+T/sv8M/kcBWQEIAEr/AP0xAOMA5v6U/woAmQIJAMABmv6P/EwDs/1JAov9Nf4OBTz7PwRA/rj71wCH/YMFbv7a+2cE6fzwAjH/4/9KAw7/g/8RAPL/hwGi/rn7FAGEA8r+eADU/4X7iwZL/13/2gH797wHJPxbADcD9focB3b9fgSU/BECgPyB/Q4CT/1KA0H+4gJ7/oX/xQF1/iL/PP2f/yv/7gHBAYD+W/8lAjQBBv9L/nv/SAH7/sMAz/7T/4IEEv0JA0D/qf8GBI/7+ASW/cX9HAJo/xIAuP6KAMkDfP6h/ZkBk/8RAvj+OQCn/84BAP8C/a//Z/5LBML7JAMD/sj9GASd+YIG9/w5AaL+Jv7r/3X/BALY/8ACsfluBjj8XgSL/ZP+rQG+/joCLv1qAx39VwMJ/iQE5vlf/8QB0vyBBOH8nwVf/NoAQgFq/ZoA9wD0/z/+cAHdAPQBy/3WAXH9sANS/YAA8ft0/1IDlf3jA9b9QAMq/KYDNf5QAFH9sfvlBMT7XwQsAKr+cgJX/cD/YADd/mQGjvyGAKD+sP9aAWP8ZgNO/isBWv59ADUCvf9V/90AFgK3/77+aP/y/xwBMwDi/ZwCof+EAXz/af+oAAL7EgEe/i0BQf6AAQQB2/+YAmf+4AKM/OH+vPvn/9H/eAGjA43/3wDF/aED1/0v/5EAdAC9/pH/nwCfAWcAYwEGAPf9AgJI/5n+rftLAbkC2wFYAXz+EwDB/zoAef9r/iUAmQEU/4b+BAD+ACv/XAGi+20B+f3YAKoBsf/9ATr/mf7Y/xQBO/92Ak3+cwJO/sgBlwD5Ae3+8f/f/ub9DASq/FICaP0hA7cBH/0fAnz+gwCl+/4AJ/8WAcP+OgGfAFEAygBZ/33+ngAWAVn/0//P/PcBi/2SA3gANgL0/tb/zwIMAFL/E//8/1/+Vf8aAokBJ/4r/f3/6P/BBGgB0f5x/Zf/qABn/5IAHgIo/qr92AB8/+IA4/94ALwAZQCD/qv9Mf///4sBIP/P/0IBXf/V/5UAk/+1/68C9v5y/5v9jwKZAk/+2wB3/+D+lAA9/uYA7P9E/+0CyP+M/kn+Bf8jA7z/gwAxAosACACR/TT/x/8AAGcBjABb/vf+LwCT/20Ak/93Ahv/3/oOAl4AigB4/X0BxQPbAU38u/7M/sf/8wCg/qQEwv/O/6L8hAETAtv8av7XAC4ENP4yAFv+UQKh/3v+6gLm/gEAnPsuAT4DtQCF/xUANgCgAKz/n/0m///+JgHtAbv++AHE/5sA2AGZ/vwBzf7l/Uj+MQAVAv4AD/9kAPwAE/51AdP+A/2C/qD96QLlAlH//P9XAHwA7gLd/mb/2v/r/AAAP//aAAMCt/85/ssCwgC+AUn+SfwqAPsBlwJyAh/+g/48Akz/7v9x/yj/iP92/ngCKQHU//H8xAErA9b9Tv+E/ucDCv+e/TwA1wEU/5j+JP0XAtIB2v1pACH/oAE3/wT/a/17AyEAtwSw/Xf/qP+H/n4AaP5lAzr+VwKP/McCpv4WAhT+tv/v/Vv/IAIp/tYBZv4RAqb+xgA9/hIBKP99ADEBiP5nAvj+7/9I/uj/Cf+yASQAKgKnAVP+TQHP/6n9u/1a/5wCrQLJ/bkBkv+jAAABMP5r/43+LACgADsAC/+M/63+yQDWAK8AJgEuADL/7P7S/5j/uf6eAHEC0QFT/2n+5QClAGb/IvzwAv7/bQHa/yP/fwIq/ZMB/f4pAlj83f/p/awBkgNQ/VIA5Px6AqH/t/+SAMsBof1t/r8BeP98A/v9OQGB/77+5f/D/0MB5gBi//X+5f9aAGEAagDP/vH/egEOAiX/N/7T//P/igL7/2wC0/z6AcT9qv9k//D/EAD4/sUDmPt0APz+bQQR/8v+bAG8/Sb+j/+l/97/MwAH/loATAA4AXQBkf7ZAH8BxvsbA6D/pf9iA1j+7QFw/vP+aP/G/0b/SgUy//D9OQHb/C4DHwFj/YoBOv1EAeQDMP2IAj36tgNE/0cDJv4IAY78ev21Ab/9PAOB+ykE//pYA2QBlAFRATv8KACz/QwD+/zEAU/91QJ7AR//0AMM/gX/dP6ZALX/3/92/rYA+gIZ/kkD6P2C/tsCVv+VAGH8zQFRADACWvzZBTT9rf6+APf9MwLw/QUBW/7UAcL9kgHp/QMChgF8/pD/Fv8QAhUBkf7t/NEC2wDY/n3/vP+aAcb/VQAwAOb/yv8y/7AA9v4AAfn+vQBwAvP+WP+g/ML/dAJdAwIAlP3I/icAmQLEAM4ADf06AHsAYP/s/gMAAAI3ACP+p//l/7P/1AAd/okBYf9D/3oBPv/z/2n/k/8QA9YAXgHD/wv9qgCeArX+O/56/+sAcgL0/VUAf//g/3gAHf/X/i8A4/+r/ukAEQAYAAcDuf68Acz/if7F/+H9UgA1/y0A7f+YA5T8VgGO/zgBmQFW/PYAIP/cAV7/kQTF/bEAxP45/QUC3/xuAQb+dAK8AbP/Q/5jAHYAG/4tAaf/Vv9kAIL+xgGoAn4AwAAi/D4AAgKW/9n8uf77/gcDGgF3ALMAdf/uACn/GwDo/zUAXv0jAgAAPAK5/0b/0f9r/wkBMv7o/p38/wIT/z4Ct/9f/4P/Lf/KAMYBWgAY//f/PP5WAbr+1wA2AfH+z//jAGgA7QCC/7//9/54AE7+OAJ//+IAAAFL/rwE1v2R/6gAqgBa/wb/8P8yAuv/Bf7cAAf/6P2cAEL+qAKe/5T9uQEOAAoCm/zVAFH+fgAl/tEAVQPQ/foB/v0xACcC2v6f/7f+MQASAVL+i//jAAcBlwBoAZT8CQHkAfEAYACs/nkCo/2z/4T+YgDv/7kBVQFL/3//bv6w/6v/pwHl/lv/CQBBAHwAVwGF/8z9IQA6AEUBAf4hAdMAs/0iAecAxABw/yj/6v+AAoT+XgAw/eD+5gFA/eECzf9FAvUB1//o//z8yf/q/kMCRf7JAP7/eQA2AP3+sQIi/9kBG/5yANv9jwBv/wAAkgE7ADUCcv1v/8P9+AHv/8H/Yf47AQgBKP+zAKL/MwEz/a0B+P8A/87/Q/4bAI4BYgCqAKT/6gCKAPj8gv8WAFcA3v/HAEgB9f9P/3D/KgBgAmL/1/+3////+f/+/hkBDAB8/1r/5gDUAPH/B//N/iEATAF7/hD/pADDAHMAxv7iAGEBAABVAGT/o/6/AA4BdQAaAD79dwAm/s3/rgAqAe8Bdf+Q/uz/8wG4/8n/OP6wAAUAtwAHAYj/6/7LAVsA4v8P/nn+G/+Y/zYBrAEUAfD+DwGW/YoC+P+F/9798f6XAqj/CgDc/q8At/2KAYUBOQEi/iT/o/+JAPgBzwAxAGL9DwKoAPz+5P/YACH+r/5aAaoBVAAb/sIBfv8LApMCPf69/YL+FgHX/3cBVv7s/7f9EQIY//X9TAIe/5b/LP9KAtv+d/4l/zkBNgD8/wgBDf7P/iMBsgALAn3/k/9E/zsBQgHI/hD++f/cAoz+ywD2AHIB5f5b/8YBg/9YAI4Agv9C/or+mgKu/on//v9V/y//kQCQ/8X90wByAbgD4P56/8D/QP36ANL/Wv+vAkYA9ADZ/o8BQP6M/TP/KwABAcn+GAGI/04AsAFGAWYAAgFr/vj+X/+mAAgA8f9qAOr//wCvANj/nf/n/3QAdP/o/UQAeP5XARH/iwCxAnj/HAGR/tH/Gv+T/YcAu//F/+QAOgE5/9kA6wDIAHb/uPzuAMEABwF9/7z/9QCIAVn+DwByAPH/bv/r/qQAagCCAYP/t/6R/2UBeACqAPL+8/8A/4f/HgGn/7T+gAD8ADAAEwJP/b3+wP+OAEcCgP+h/yIAdv2b/hUB9QIsAe4AjP/x/ugAK/5J/1P+4QJ4AQL/RQArACwAQgA/AIQA5P1r/zcB1f5qAEH9MAC+ApMBNgHi/7z/jf7b/fQAxQGL/uT9wgC8ALj+JAHs//UBuADP/ccAav69/mH/LP8pBGcCgP8pAPj+pAD3/p/+s/9v/kIApP8x/yUAywCDABEAHwDMAFb/Bv4o/xcA0QHA/2EBAgHTAH7/7P5yAaD+Sf/x/UMAsP4KAOMBGQAAAtgA3QARAG3+EgBkADH/cAB1/j4BKACN/8j/VwCTAUz/xP4r/0gAMv9i/0AAHAJrAAUA1v/J/WIA2P7L/8sALAGkANv/4//W/kQC+f5bAOn+Lf7mATL9/gBBARAA1wEE/1MBNADt/3cAJv+HAaL/ov6g/6//0wCg/xcAIQCj/5P/Kv8rAOX/ugA9/5z/GQCW/zEAu/8xAMUAu/9D/67/BwAlAHj/TACFAOD/Pf+//+AAAADS/1sA8f+JAer/o/54AOkAHADr/lgA9QD2/l3//P8iAWYBk/8a/679rP8IAMD/SADx/97/HADRAND/ff+x/2L/UgC4AAwB3f+7/x0BfgBzAYr/Ov/u/3wAWwBo//3/Zv/r///+/f8QAe7/RQD6/2YAKwC0/2QAKQAZ/w0AtP9OABEBZf/1//P+XAAxAYsAy/+A/2X/dgDHAB4ATABr/2T/Yv9qAWwAVv8I/y0AugCUAGIAif94/5X/sAATAPP/V//q/oUA1wCwAFYAyf95//X+gP/NACkAHf+yAGkA/P9aACAApQDN/1j/RQBtAOP/Tv8u/woAiABsADUAq/8nAK7/of8+AND/IADy/6f/xf+4//T/hAARAKf/4P86AEEAUQANAPT/vQB8AEkAAQDa/7j/Tv/G/xIA9/+n/5T/8P9pAA8Ayf9s/1n/yv8YADQAUwBjAAkA8/8vAAMAfP+d/2//4v/Y/9T/QAA3AFYAhwBEANf/sP8aAKsAcgD4//H/aQBuAH8AHACFAB8A0P8VAPj/gv/9/j3/eP8XAF4AFADv/9//GwA4AIb/g/95/+b/2P/z/x0AFgBXAOH/hgCtAD8A2P+8/5H/FgCYAGMAeABGABIA7P8LAPT/0v+p/7n/7v/+/ygA6f/r/x0A+P/O/xQA+/8AAOX/8v85APn/6v8oAO7/6/8FAKf/xf+v/2j/xv8ZAC0AcgArAEoAZQBIABEA4f8GAB8AOQAOANz/7/8QABEAzf/u/zMAy/+U/5X/yf/n/+//6f8UAEMA5//T/7T/jf/P/xkAggCSAFwAIAA6AOr/4/8LAPv/TABqAHsAQwANACsAHwDx//r/2P/3/xsAGADN/+T/KQA1AFcAGgDX/77/7f8BACIABgDx//3/BQBFANv/nP+f/9H/0v8AAEcAAwAXADoALQAOAB4AJAD8/wAA/v8tAEQAJwADABgAIwDf/73/3P/f/87/7//0/x0A8v/S/9z/BwAcAPX/AAAAACYAIwD2/wkAHgAnACEA7//v/wYACQDe//3/4/8EABkAAAA1ADMAUQAxAP//CQAcAOD/2v8KAAIA9f/L/wIAXQAbABYADgAVAAMAwv/b/+f/8f/v//3/KQA6ADoAOgAiACAAHQACAPb/4P/P/8z/+/9DACUAFQAPABEA3f+p/7P/uv/k//H/7f/t/wYAAQAhABoAFQAkABUAxv/L//P/CwANAPT/HAD5/+z/2//G/wIA9f/D/9r/2v/3/wkA8P8NADIAIQArAAAA8//S/73/5P/l/wAA8v/b/+P/1P/P/+b/4v/9/93/5//z//L/DAASADgARwBOADEAOgAlAOj/1//P/wEAEgAZACEA+/8OACIAIAAcAP3/+f/0//P/DgDv//3/BAD8/yAAFAAAAPD/yv/c/+//7v/n/+z/FAANAAkAAgAAAA4A/P8BABIADAARAAsAHAAsACAACAD9//P/2P/y//v/AQD2/wIAEgAAAAEA+P8QACAADADz/+b/3P/w/+7/7P/0/+7/5v/k/+v/5f/t//H/+v/z/+//8v/5/w0AEAAPABYAEgANAAwAEAAW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type=\"audio/wav\" />\n",
" Your browser does not support the audio element.\n",
" </audio>\n",
" "
],
"text/plain": [
"<IPython.lib.display.Audio object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"hˌaʊ kʊd aɪ nˈoʊ? ɪts ɐn ʌnˈænsɚɹəbəl kwˈɛstʃən. lˈaɪk ˈæskɪŋ ɐn ʌnbˈɔːɹn tʃˈaɪld ɪf ðeɪl lˈiːd ɐ ɡˈʊd lˈaɪf. ðeɪ hˈævənt ˈiːvən bˌɪn bˈɔːɹn.\n"
]
}
],
"source": [
"from kokoro import phonemize, tokenize, length_to_mask\n",
"import torch.nn.functional as F\n",
"model = model\n",
"speed = 1.\n",
"\n",
"ps = phonemize(text, \"a\")\n",
"tokens = tokenize(ps)\n",
"\n",
"tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)\n",
"\n",
"# tokens = torch.nn.functional.pad(tokens, (0, 510 - tokens.shape[-1]))\n",
"input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)\n",
"\n",
"text_mask = length_to_mask(input_lengths).to(device)\n",
"bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())\n",
"\n",
"\n",
"d_en = model.bert_encoder(bert_dur).transpose(-1, -2)\n",
"\n",
"ref_s =voicepack[tokens.shape[1]]\n",
"s = ref_s[:, 128:]\n",
"\n",
"d = model.predictor.text_encoder.inference(d_en, s)\n",
"x, _ = model.predictor.lstm(d)\n",
"\n",
"duration = model.predictor.duration_proj(x)\n",
"duration = torch.sigmoid(duration).sum(axis=-1) / speed\n",
"pred_dur = torch.round(duration).clamp(min=1).long()\n",
"max_mels = pred_dur.sum().item()\n",
"pred_aln_trg = torch.zeros(input_lengths, max_mels)\n",
"\n",
"c_start = F.pad(pred_dur,(1,0), \"constant\").cumsum(dim=1)[0,0:-1]\n",
"c_end = c_start + pred_dur[0,:]\n",
"\n",
"for row, cs, ce in zip(pred_aln_trg, c_start, c_end):\n",
" row[cs:ce] = 1\n",
" \n",
"en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)\n",
"print(en.shape)\n",
"F0_pred, N_pred = model.predictor.F0Ntrain(en, s)\n",
"t_en = model.text_encoder.inference(tokens)\n",
"asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)\n",
"output = model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().detach().cpu().numpy()\n",
"\n",
"from IPython.display import display, Audio\n",
"display(Audio(data=output, rate=24000, autoplay=True))\n",
"print(out_ps)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "Error",
"evalue": "Unable to infer type of dictionary: Cannot infer concrete type of torch.nn.Module",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m scrpt \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjit\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscript\u001b[49m\u001b[43m(\u001b[49m\u001b[43mMODEL\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/jit/_script.py:1429\u001b[0m, in \u001b[0;36mscript\u001b[0;34m(obj, optimize, _frames_up, _rcb, example_inputs)\u001b[0m\n\u001b[1;32m 1427\u001b[0m prev \u001b[38;5;241m=\u001b[39m _TOPLEVEL\n\u001b[1;32m 1428\u001b[0m _TOPLEVEL \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m-> 1429\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43m_script_impl\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1430\u001b[0m \u001b[43m \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1431\u001b[0m \u001b[43m \u001b[49m\u001b[43moptimize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1432\u001b[0m \u001b[43m \u001b[49m\u001b[43m_frames_up\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_frames_up\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1433\u001b[0m \u001b[43m \u001b[49m\u001b[43m_rcb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_rcb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1434\u001b[0m \u001b[43m \u001b[49m\u001b[43mexample_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexample_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1435\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1437\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prev:\n\u001b[1;32m 1438\u001b[0m log_torchscript_usage(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscript\u001b[39m\u001b[38;5;124m\"\u001b[39m, model_id\u001b[38;5;241m=\u001b[39m_get_model_id(ret))\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/jit/_script.py:1154\u001b[0m, in \u001b[0;36m_script_impl\u001b[0;34m(obj, optimize, _frames_up, _rcb, example_inputs)\u001b[0m\n\u001b[1;32m 1151\u001b[0m obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m__prepare_scriptable__() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(obj, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__prepare_scriptable__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m obj \u001b[38;5;66;03m# type: ignore[operator]\u001b[39;00m\n\u001b[1;32m 1153\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, \u001b[38;5;28mdict\u001b[39m):\n\u001b[0;32m-> 1154\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m create_script_dict(obj)\n\u001b[1;32m 1155\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m 1156\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m create_script_list(obj)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/jit/_script.py:1066\u001b[0m, in \u001b[0;36mcreate_script_dict\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_script_dict\u001b[39m(obj):\n\u001b[1;32m 1054\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1055\u001b[0m \u001b[38;5;124;03m Create a ``torch._C.ScriptDict`` instance with the data from ``obj``.\u001b[39;00m\n\u001b[1;32m 1056\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1064\u001b[0m \u001b[38;5;124;03m zero copy overhead.\u001b[39;00m\n\u001b[1;32m 1065\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1066\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mScriptDict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mError\u001b[0m: Unable to infer type of dictionary: Cannot infer concrete type of torch.nn.Module"
]
}
],
"source": [
"scrpt = torch.jit.script(model)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 143, 640])\n",
"torch.Size([1, 640, 143])\n",
"torch.Size([1, 512, 143])\n",
"torch.Size([1, 143, 348])\n",
"en.shape=torch.Size([1, 640, 348])\n",
"s.shape=torch.Size([1, 128])\n",
"en.dtype=torch.float32\n",
"s.dtype=torch.float32\n",
"torch.Size([1, 512, 143])\n",
"torch.Size([1, 512, 348])\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fbbc0db3220>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(d.shape)\n",
"print(d.transpose(-1,-2).shape)\n",
"print(d_en.shape)\n",
"print(pred_aln_trg.unsqueeze(0).shape)\n",
"print(f\"{en.shape=}\")\n",
"print(f\"{s.shape=}\")\n",
"print(f\"{en.dtype=}\")\n",
"print(f\"{s.dtype=}\")\n",
"\n",
"print(t_en.shape)\n",
"print(asr.shape)\n",
"pl.imshow(pred_aln_trg[:,:])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Export StyleTTS2 model"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0101 18:56:11.998000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:3557] create_symbol s0 = 143 for L['args'][0][0].size()[1] [2, 510] (_export/non_strict_utils.py:109 in fakify), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"s0\"\n",
"I0101 18:56:12.007000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:4857] set_replacement s0 = 143 (range_refined_to_singleton) VR[143, 143]\n",
"I0101 18:56:12.008000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:5106] eval Eq(s0, 143) [guard added] (mp/ipykernel_2488298/2554868606.py:17 in forward), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED=\"Eq(s0, 143)\"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0101 18:56:27.383000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:3317] create_unbacked_symint u0 [-int_oo, int_oo] (_subclasses/fake_impls.py:390 in local_scalar_dense)\n",
"I0101 18:56:27.387000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:5106] runtime_assert u0 >= 0 [guard added] (_refs/__init__.py:4957 in arange), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED=\"u0 >= 0\"\n",
"W0101 18:56:28.575000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:5124] failed during evaluate_expr(u0, hint=None, size_oblivious=False, forcing_spec=False\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] failed while running evaluate_expr(*(u0, None), **{'fx_node': False})\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] Traceback (most recent call last):\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] File \"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/recording.py\", line 262, in wrapper\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] return retlog(fn(*args, **kwargs))\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] File \"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py\", line 5122, in evaluate_expr\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] return self._evaluate_expr(orig_expr, hint, fx_node, size_oblivious, forcing_spec=forcing_spec)\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] File \"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py\", line 5238, in _evaluate_expr\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] raise self._make_data_dependent_error(\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: u0)\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] \n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] Potential framework code culprit (scroll up for full backtrace):\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] File \"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_ops.py\", line 759, in decompose\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] return self._op_dk(dk, *args, **kwargs)\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] \n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] For more information, run with TORCH_LOGS=\"dynamic\"\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"u0\"\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] \n",
"E0101 18:56:28.576000 2488298 site-packages/torch/fx/experimental/recording.py:298] For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n"
]
},
{
"ename": "GuardOnDataDependentSymNode",
"evalue": "Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: u0)\n\nPotential framework code culprit (scroll up for full backtrace):\n File \"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_ops.py\", line 759, in decompose\n return self._op_dk(dk, *args, **kwargs)\n\nFor more information, run with TORCH_LOGS=\"dynamic\"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"u0\"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File \"/rhome/eingerman/Projects/DeepLearning/TTS/Kokoro-82M/models.py\", line 471, in F0Ntrain\n x2, _temp = self.shared(x1)\n\nTo fix the error, insert one of the following checks before this call:\n 1. torch._check(x.shape[2])\n 2. torch._check(~x.shape[2])\n\n(These suggested fixes were derived by replacing `u0` with x.shape[2] or x1.shape[1] in u0 and its negation.)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mGuardOnDataDependentSymNode\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[39], line 61\u001b[0m\n\u001b[1;32m 58\u001b[0m dynamic_shapes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokens\u001b[39m\u001b[38;5;124m\"\u001b[39m:{\u001b[38;5;241m0\u001b[39m:batch, \u001b[38;5;241m1\u001b[39m:token_len}}\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m# with torch.no_grad():\u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m export_mod \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstyle_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# export_mod = torch.export.export(style_model, args=( tokens, ), strict=False)\u001b[39;00m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/__init__.py:270\u001b[0m, in \u001b[0;36mexport\u001b[0;34m(mod, args, kwargs, dynamic_shapes, strict, preserve_module_call_signature)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mScriptModule):\n\u001b[1;32m 265\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExporting a ScriptModule is not supported. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMaybe try converting your ScriptModule to an ExportedProgram \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 268\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124musing `TS2EPConverter(mod, args, kwargs).convert()` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 269\u001b[0m )\n\u001b[0;32m--> 270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_export\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreserve_module_call_signature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreserve_module_call_signature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 277\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 278\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1017\u001b[0m, in \u001b[0;36m_log_export_wrapper.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1011\u001b[0m log_export_usage(\n\u001b[1;32m 1012\u001b[0m event\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexport.error.unclassified\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1013\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39merror_type,\n\u001b[1;32m 1014\u001b[0m message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m 1015\u001b[0m flags\u001b[38;5;241m=\u001b[39m_EXPORT_FLAGS,\n\u001b[1;32m 1016\u001b[0m )\n\u001b[0;32m-> 1017\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 1018\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 1019\u001b[0m _EXPORT_FLAGS \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:990\u001b[0m, in \u001b[0;36m_log_export_wrapper.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 989\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m--> 990\u001b[0m ep \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 992\u001b[0m log_export_usage(\n\u001b[1;32m 993\u001b[0m event\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexport.time\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 994\u001b[0m metrics\u001b[38;5;241m=\u001b[39mend \u001b[38;5;241m-\u001b[39m start,\n\u001b[1;32m 995\u001b[0m flags\u001b[38;5;241m=\u001b[39m_EXPORT_FLAGS,\n\u001b[1;32m 996\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mget_ep_stats(ep),\n\u001b[1;32m 997\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/exported_program.py:114\u001b[0m, in \u001b[0;36m_disable_prexisiting_fake_mode.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(fn)\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m unset_fake_temporarily():\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1880\u001b[0m, in \u001b[0;36m_export\u001b[0;34m(mod, args, kwargs, dynamic_shapes, strict, preserve_module_call_signature, pre_dispatch, allow_complex_guards_as_runtime_asserts, _is_torch_jit_trace)\u001b[0m\n\u001b[1;32m 1877\u001b[0m \u001b[38;5;66;03m# Call the appropriate export function based on the strictness of tracing.\u001b[39;00m\n\u001b[1;32m 1878\u001b[0m export_func \u001b[38;5;241m=\u001b[39m _strict_export \u001b[38;5;28;01mif\u001b[39;00m strict \u001b[38;5;28;01melse\u001b[39;00m _non_strict_export\n\u001b[0;32m-> 1880\u001b[0m export_artifact \u001b[38;5;241m=\u001b[39m \u001b[43mexport_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[operator]\u001b[39;49;00m\n\u001b[1;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreserve_module_call_signature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43moriginal_state_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43moriginal_in_spec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1889\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_complex_guards_as_runtime_asserts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1890\u001b[0m \u001b[43m \u001b[49m\u001b[43m_is_torch_jit_trace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1891\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1892\u001b[0m export_graph_signature: ExportGraphSignature \u001b[38;5;241m=\u001b[39m export_artifact\u001b[38;5;241m.\u001b[39maten\u001b[38;5;241m.\u001b[39msig\n\u001b[1;32m 1894\u001b[0m forward_arg_names \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1895\u001b[0m _get_forward_arg_names(mod, args, kwargs) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_torch_jit_trace \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1896\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1683\u001b[0m, in \u001b[0;36m_non_strict_export\u001b[0;34m(mod, args, kwargs, dynamic_shapes, preserve_module_call_signature, pre_dispatch, original_state_dict, orig_in_spec, allow_complex_guards_as_runtime_asserts, _is_torch_jit_trace, dispatch_tracing_mode)\u001b[0m\n\u001b[1;32m 1667\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _fakify_script_objects(mod, fake_args, fake_kwargs, fake_mode) \u001b[38;5;28;01mas\u001b[39;00m (\n\u001b[1;32m 1668\u001b[0m patched_mod,\n\u001b[1;32m 1669\u001b[0m new_fake_args,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1672\u001b[0m map_fake_to_real,\n\u001b[1;32m 1673\u001b[0m ):\n\u001b[1;32m 1674\u001b[0m _to_aten_func \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1675\u001b[0m _export_to_aten_ir_make_fx\n\u001b[1;32m 1676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch_tracing_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmake_fx\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1681\u001b[0m )\n\u001b[1;32m 1682\u001b[0m )\n\u001b[0;32m-> 1683\u001b[0m aten_export_artifact \u001b[38;5;241m=\u001b[39m \u001b[43m_to_aten_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[operator]\u001b[39;49;00m\n\u001b[1;32m 1684\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatched_mod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1685\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_fake_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1686\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_fake_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1687\u001b[0m \u001b[43m \u001b[49m\u001b[43mfake_params_buffers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1688\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_fake_constant_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1689\u001b[0m \u001b[43m \u001b[49m\u001b[43mproduce_guards_callback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_produce_guards_callback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1690\u001b[0m \u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_tuplify_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1691\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1692\u001b[0m \u001b[38;5;66;03m# aten_export_artifact.constants contains only fake script objects, we need to map them back\u001b[39;00m\n\u001b[1;32m 1693\u001b[0m aten_export_artifact\u001b[38;5;241m.\u001b[39mconstants \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 1694\u001b[0m fqn: map_fake_to_real[obj] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, FakeScriptObject) \u001b[38;5;28;01melse\u001b[39;00m obj\n\u001b[1;32m 1695\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fqn, obj \u001b[38;5;129;01min\u001b[39;00m aten_export_artifact\u001b[38;5;241m.\u001b[39mconstants\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 1696\u001b[0m }\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:637\u001b[0m, in \u001b[0;36m_export_to_aten_ir\u001b[0;34m(mod, fake_args, fake_kwargs, fake_params_buffers, constant_attrs, produce_guards_callback, transform, pre_dispatch, decomp_table, _check_autograd_state, _is_torch_jit_trace)\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;66;03m# This _reparametrize_module makes sure inputs and module.params/buffers have the same fake_mode,\u001b[39;00m\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# otherwise aot_export_module will error out because it sees a mix of fake_modes.\u001b[39;00m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;66;03m# And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about.\u001b[39;00m\n\u001b[1;32m 630\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mutils\u001b[38;5;241m.\u001b[39mstateless\u001b[38;5;241m.\u001b[39m_reparametrize_module(\n\u001b[1;32m 631\u001b[0m mod,\n\u001b[1;32m 632\u001b[0m fake_params_buffers,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 635\u001b[0m stack_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 636\u001b[0m ), grad_safe_guard, _ignore_backend_decomps(), _compiling_state_context(): \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m--> 637\u001b[0m gm, graph_signature \u001b[38;5;241m=\u001b[39m \u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43maot_export_module\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 638\u001b[0m \u001b[43m \u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 639\u001b[0m \u001b[43m \u001b[49m\u001b[43mfake_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 640\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrace_joint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 641\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 642\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecompositions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecomp_table\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 643\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfake_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 644\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 646\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_maybe_fixup_gm_and_output_node_meta\u001b[39m(old_gm, new_gm):\n\u001b[1;32m 647\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(old_gm, torch\u001b[38;5;241m.\u001b[39mfx\u001b[38;5;241m.\u001b[39mGraphModule):\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1611\u001b[0m, in \u001b[0;36m_non_strict_export.<locals>._tuplify_outputs.<locals>._aot_export_non_strict\u001b[0;34m(mod, args, kwargs, **flags)\u001b[0m\n\u001b[1;32m 1605\u001b[0m new_preserved_call_signatures \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 1606\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_export_root.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m i \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m preserve_module_call_signature\n\u001b[1;32m 1607\u001b[0m ]\n\u001b[1;32m 1608\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _wrap_submodules(\n\u001b[1;32m 1609\u001b[0m wrapped_mod, new_preserved_call_signatures, module_call_specs\n\u001b[1;32m 1610\u001b[0m ):\n\u001b[0;32m-> 1611\u001b[0m gm, sig \u001b[38;5;241m=\u001b[39m \u001b[43maot_export\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwrapped_mod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mflags\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1612\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExported program from AOTAutograd:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, gm)\n\u001b[1;32m 1614\u001b[0m sig\u001b[38;5;241m.\u001b[39mparameters \u001b[38;5;241m=\u001b[39m pytree\u001b[38;5;241m.\u001b[39mtree_map(_strip_root, sig\u001b[38;5;241m.\u001b[39mparameters)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py:1246\u001b[0m, in \u001b[0;36maot_export_module\u001b[0;34m(mod, args, decompositions, trace_joint, output_loss_index, pre_dispatch, dynamic_shapes, kwargs)\u001b[0m\n\u001b[1;32m 1243\u001b[0m full_args\u001b[38;5;241m.\u001b[39mextend(args)\n\u001b[1;32m 1245\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx():\n\u001b[0;32m-> 1246\u001b[0m fx_g, metadata, in_spec, out_spec \u001b[38;5;241m=\u001b[39m \u001b[43m_aot_export_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1247\u001b[0m \u001b[43m \u001b[49m\u001b[43mfn_to_trace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1248\u001b[0m \u001b[43m \u001b[49m\u001b[43mfull_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1249\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecompositions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecompositions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1250\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_params_buffers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams_len\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1251\u001b[0m \u001b[43m \u001b[49m\u001b[43mno_tangents\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 1252\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1253\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1254\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1255\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1256\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trace_joint:\n\u001b[1;32m 1258\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mflattened_joint\u001b[39m(\u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 1259\u001b[0m \u001b[38;5;66;03m# The idea here is that the joint graph that AOTAutograd creates has some strict properties:\u001b[39;00m\n\u001b[1;32m 1260\u001b[0m \u001b[38;5;66;03m# (1) It accepts two arguments (primals, tangents), and pytree_flattens them\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# This function \"fixes\" both of the above by removing any tangent inputs,\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;66;03m# and removing pytrees from the original FX graph.\u001b[39;00m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py:1480\u001b[0m, in \u001b[0;36m_aot_export_function\u001b[0;34m(func, args, num_params_buffers, decompositions, no_tangents, pre_dispatch, dynamic_shapes, kwargs)\u001b[0m\n\u001b[1;32m 1477\u001b[0m fake_mode, shape_env \u001b[38;5;241m=\u001b[39m construct_fake_mode(flat_args, aot_config)\n\u001b[1;32m 1478\u001b[0m fake_flat_args \u001b[38;5;241m=\u001b[39m process_inputs(flat_args, aot_config, fake_mode, shape_env)\n\u001b[0;32m-> 1480\u001b[0m fx_g, meta \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_aot_dispatcher_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1481\u001b[0m \u001b[43m \u001b[49m\u001b[43mflat_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1482\u001b[0m \u001b[43m \u001b[49m\u001b[43mfake_flat_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1483\u001b[0m \u001b[43m \u001b[49m\u001b[43maot_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1484\u001b[0m \u001b[43m \u001b[49m\u001b[43mfake_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1485\u001b[0m \u001b[43m \u001b[49m\u001b[43mshape_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1486\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1487\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fx_g, meta, in_spec, out_spec\u001b[38;5;241m.\u001b[39mspec\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py:522\u001b[0m, in \u001b[0;36mcreate_aot_dispatcher_function\u001b[0;34m(flat_fn, fake_flat_args, aot_config, fake_mode, shape_env)\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_aot_dispatcher_function\u001b[39m(\n\u001b[1;32m 515\u001b[0m flat_fn,\n\u001b[1;32m 516\u001b[0m fake_flat_args: FakifiedFlatArgs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 519\u001b[0m shape_env: Optional[ShapeEnv],\n\u001b[1;32m 520\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[Callable, ViewAndMutationMeta]:\n\u001b[1;32m 521\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m dynamo_timed(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreate_aot_dispatcher_function\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 522\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_create_aot_dispatcher_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 523\u001b[0m \u001b[43m \u001b[49m\u001b[43mflat_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfake_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maot_config\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfake_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshape_env\u001b[49m\n\u001b[1;32m 524\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_functorch/aot_autograd.py:623\u001b[0m, in \u001b[0;36m_create_aot_dispatcher_function\u001b[0;34m(flat_fn, fake_flat_args, aot_config, fake_mode, shape_env)\u001b[0m\n\u001b[1;32m 621\u001b[0m ctx \u001b[38;5;241m=\u001b[39m nullcontext()\n\u001b[1;32m 622\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx:\n\u001b[0;32m--> 623\u001b[0m fw_metadata \u001b[38;5;241m=\u001b[39m \u001b[43mrun_functionalized_fw_and_collect_metadata\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 624\u001b[0m \u001b[43m \u001b[49m\u001b[43mflat_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 625\u001b[0m \u001b[43m \u001b[49m\u001b[43mstatic_input_indices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maot_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstatic_input_indices\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 626\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_input_mutations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maot_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeep_inference_input_mutations\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 627\u001b[0m \u001b[43m \u001b[49m\u001b[43mis_train\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mneeds_autograd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 628\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maot_config\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 629\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m_dup_fake_script_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfake_flat_args\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 631\u001b[0m req_subclass_dispatch \u001b[38;5;241m=\u001b[39m requires_subclass_dispatch(\n\u001b[1;32m 632\u001b[0m fake_flat_args, fw_metadata\n\u001b[1;32m 633\u001b[0m )\n\u001b[1;32m 635\u001b[0m output_and_mutation_safe \u001b[38;5;241m=\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28many\u001b[39m(\n\u001b[1;32m 636\u001b[0m x\u001b[38;5;241m.\u001b[39mrequires_grad\n\u001b[1;32m 637\u001b[0m \u001b[38;5;66;03m# view-type operations preserve requires_grad even in no_grad.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 652\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m fw_metadata\u001b[38;5;241m.\u001b[39minput_info\n\u001b[1;32m 653\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/collect_metadata_analysis.py:173\u001b[0m, in \u001b[0;36mrun_functionalized_fw_and_collect_metadata.<locals>.inner\u001b[0;34m(*flat_args)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m disable_above, mode, suppress_pending:\n\u001b[1;32m 171\u001b[0m \u001b[38;5;66;03m# precondition: The passed in function already handles unflattening inputs + flattening outputs\u001b[39;00m\n\u001b[1;32m 172\u001b[0m flat_f_args \u001b[38;5;241m=\u001b[39m pytree\u001b[38;5;241m.\u001b[39mtree_map(_to_fun, flat_args)\n\u001b[0;32m--> 173\u001b[0m flat_f_outs \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mflat_f_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# We didn't do any tracing, so we don't need to process the\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;66;03m# unbacked symbols, they will just disappear into the ether.\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;66;03m# Also, prevent memoization from applying.\u001b[39;00m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fake_mode:\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/utils.py:182\u001b[0m, in \u001b[0;36mcreate_tree_flattened_fn.<locals>.flat_fn\u001b[0;34m(*flat_args)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;28;01mnonlocal\u001b[39;00m out_spec\n\u001b[1;32m 181\u001b[0m args, kwargs \u001b[38;5;241m=\u001b[39m pytree\u001b[38;5;241m.\u001b[39mtree_unflatten(flat_args, tensor_args_spec)\n\u001b[0;32m--> 182\u001b[0m tree_out \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 183\u001b[0m flat_out, spec \u001b[38;5;241m=\u001b[39m pytree\u001b[38;5;241m.\u001b[39mtree_flatten(tree_out)\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m flat_out:\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_functorch/_aot_autograd/traced_function_transforms.py:863\u001b[0m, in \u001b[0;36mcreate_functional_call.<locals>.functional_call\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 859\u001b[0m out \u001b[38;5;241m=\u001b[39m PropagateUnbackedSymInts(mod)\u001b[38;5;241m.\u001b[39mrun(\n\u001b[1;32m 860\u001b[0m \u001b[38;5;241m*\u001b[39margs[params_len:], \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 861\u001b[0m )\n\u001b[1;32m 862\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 863\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mmod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mparams_len\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(out, (\u001b[38;5;28mtuple\u001b[39m, \u001b[38;5;28mlist\u001b[39m)):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 867\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGraph output must be a (). This is so that we can avoid \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 868\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpytree processing of the outputs. Please change the module to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 869\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave tuple outputs or use aot_module instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 870\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1598\u001b[0m, in \u001b[0;36m_non_strict_export.<locals>._tuplify_outputs.<locals>._aot_export_non_strict.<locals>.Wrapper.forward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1594\u001b[0m tree_out \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfx\u001b[38;5;241m.\u001b[39mInterpreter(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_export_root)\u001b[38;5;241m.\u001b[39mrun(\n\u001b[1;32m 1595\u001b[0m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 1596\u001b[0m )\n\u001b[1;32m 1597\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1598\u001b[0m tree_out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_export_root\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1599\u001b[0m flat_outs, out_spec \u001b[38;5;241m=\u001b[39m pytree\u001b[38;5;241m.\u001b[39mtree_flatten(tree_out)\n\u001b[1;32m 1600\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(flat_outs)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"Cell \u001b[0;32mIn[39], line 46\u001b[0m, in \u001b[0;36mStyleTTS2.forward\u001b[0;34m(self, tokens)\u001b[0m\n\u001b[1;32m 42\u001b[0m pred_aln_trg\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mvstack(pred_aln_trg_list)\n\u001b[1;32m 44\u001b[0m en \u001b[38;5;241m=\u001b[39m d\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m@\u001b[39m pred_aln_trg\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m---> 46\u001b[0m F0_pred, N_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpredictor\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mF0Ntrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43men\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 47\u001b[0m t_en \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext_encoder\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39minference(tokens)\n\u001b[1;32m 48\u001b[0m asr \u001b[38;5;241m=\u001b[39m t_en \u001b[38;5;241m@\u001b[39m pred_aln_trg\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mto(device)\n",
"File \u001b[0;32m~/Projects/DeepLearning/TTS/Kokoro-82M/models.py:471\u001b[0m, in \u001b[0;36mProsodyPredictor.F0Ntrain\u001b[0;34m(self, x, s)\u001b[0m\n\u001b[1;32m 466\u001b[0m x1 \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 467\u001b[0m \u001b[38;5;66;03m# torch._check(x1.dim() == 3, lambda: print(f\"Expected 3D tensor, got {x1.dim()}D tensor\"))\u001b[39;00m\n\u001b[1;32m 468\u001b[0m \u001b[38;5;66;03m# torch._check(x1.shape[1] > 0, lambda: print(f\"Shape 2, got {x1.shape[1]}\"))\u001b[39;00m\n\u001b[1;32m 469\u001b[0m \u001b[38;5;66;03m# torch._check(x1.shape[2] > 0, lambda: print(f\"Shape 2, got {x1.shape[2]}\"))\u001b[39;00m\n\u001b[1;32m 470\u001b[0m \u001b[38;5;66;03m# torch._check(x.shape[2] > 0, lambda: print(f\"Shape 2, got {x.shape[2]}\"))\u001b[39;00m\n\u001b[0;32m--> 471\u001b[0m x2, _temp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshared\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx1\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 472\u001b[0m \u001b[38;5;66;03m# torch._check(x.shape[2] > 0, lambda: print(f\"Shape 2, got {x.size(2)}\"))\u001b[39;00m\n\u001b[1;32m 474\u001b[0m F0 \u001b[38;5;241m=\u001b[39m x2\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m 1120\u001b[0m hx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpermute_hidden(hx, sorted_indices)\n\u001b[1;32m 1122\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1123\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1124\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1125\u001b[0m \u001b[43m \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1126\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1127\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1128\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1129\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1130\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1131\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1132\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1133\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1134\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1135\u001b[0m result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\n\u001b[1;32m 1136\u001b[0m \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m 1137\u001b[0m batch_sizes,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1144\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional,\n\u001b[1;32m 1145\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_export/non_strict_utils.py:520\u001b[0m, in \u001b[0;36m_NonStrictTorchFunctionHandler.__torch_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 512\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m 513\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m called at \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m in \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 514\u001b[0m func\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 517\u001b[0m frame\u001b[38;5;241m.\u001b[39mf_code\u001b[38;5;241m.\u001b[39mco_name,\n\u001b[1;32m 518\u001b[0m )\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 520\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 521\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m GuardOnDataDependentSymNode \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 522\u001b[0m _suggest_fixes_for_data_dependent_error_non_strict(e)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_decomp/decompositions.py:3476\u001b[0m, in \u001b[0;36mlstm_impl\u001b[0;34m(input, hx, params, has_biases, num_layers, dropout, train, bidirectional, batch_first)\u001b[0m\n\u001b[1;32m 3474\u001b[0m hidden \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(hx[\u001b[38;5;241m0\u001b[39m], hx[\u001b[38;5;241m1\u001b[39m]))\n\u001b[1;32m 3475\u001b[0m layer_fn \u001b[38;5;241m=\u001b[39m select_one_layer_lstm_function(\u001b[38;5;28minput\u001b[39m, hx, params)\n\u001b[0;32m-> 3476\u001b[0m out, final_hiddens \u001b[38;5;241m=\u001b[39m \u001b[43m_rnn_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3477\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3478\u001b[0m \u001b[43m \u001b[49m\u001b[43mhidden\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3479\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3480\u001b[0m \u001b[43m \u001b[49m\u001b[43mhas_biases\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3481\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3482\u001b[0m \u001b[43m \u001b[49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3483\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3484\u001b[0m \u001b[43m \u001b[49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3485\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3486\u001b[0m \u001b[43m \u001b[49m\u001b[43mlayer_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3487\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3488\u001b[0m final_hiddens \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mfinal_hiddens))\n\u001b[1;32m 3489\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out, torch\u001b[38;5;241m.\u001b[39mstack(final_hiddens[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m0\u001b[39m), torch\u001b[38;5;241m.\u001b[39mstack(final_hiddens[\u001b[38;5;241m1\u001b[39m], \u001b[38;5;241m0\u001b[39m)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_decomp/decompositions.py:3151\u001b[0m, in \u001b[0;36m_rnn_helper\u001b[0;34m(input, hidden, params, has_biases, num_layers, dropout, train, bidirectional, batch_first, layer_fn)\u001b[0m\n\u001b[1;32m 3147\u001b[0m cur_params, cur_hidden, bidir_params, bidir_hidden \u001b[38;5;241m=\u001b[39m params_hiddens(\n\u001b[1;32m 3148\u001b[0m params, hidden, i, bidirectional\n\u001b[1;32m 3149\u001b[0m )\n\u001b[1;32m 3150\u001b[0m dropout \u001b[38;5;241m=\u001b[39m dropout \u001b[38;5;28;01mif\u001b[39;00m (train \u001b[38;5;129;01mand\u001b[39;00m num_layers \u001b[38;5;241m<\u001b[39m i \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0.0\u001b[39m\n\u001b[0;32m-> 3151\u001b[0m fwd_inp, fwd_hidden \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcur_hidden\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcur_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhas_biases\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3152\u001b[0m final_hiddens\u001b[38;5;241m.\u001b[39mappend(fwd_hidden)\n\u001b[1;32m 3154\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bidirectional:\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_decomp/decompositions.py:3333\u001b[0m, in \u001b[0;36mone_layer_lstm\u001b[0;34m(inp, hidden, params, has_biases, reverse)\u001b[0m\n\u001b[1;32m 3331\u001b[0m precomputed_input \u001b[38;5;241m=\u001b[39m precomputed_input\u001b[38;5;241m.\u001b[39mflip(\u001b[38;5;241m0\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m reverse \u001b[38;5;28;01melse\u001b[39;00m precomputed_input\n\u001b[1;32m 3332\u001b[0m step_output \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m-> 3333\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m inp \u001b[38;5;129;01min\u001b[39;00m precomputed_input:\n\u001b[1;32m 3334\u001b[0m hx, cx \u001b[38;5;241m=\u001b[39m lstm_cell(inp, hx, cx, hh_weight, hh_bias, hr_weight, chunk_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m 3335\u001b[0m step_output\u001b[38;5;241m.\u001b[39mappend(hx)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_tensor.py:1119\u001b[0m, in \u001b[0;36mTensor.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1110\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_get_tracing_state():\n\u001b[1;32m 1111\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 1112\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIterating over a tensor might cause the trace to be incorrect. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1113\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing a tensor of different shape won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt change the number of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1117\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 1118\u001b[0m )\n\u001b[0;32m-> 1119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28miter\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munbind\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/functional_tensor.py:534\u001b[0m, in \u001b[0;36mFunctionalTensorMode.__torch_dispatch__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 525\u001b[0m outs_wrapped \u001b[38;5;241m=\u001b[39m pytree\u001b[38;5;241m.\u001b[39mtree_map_only(\n\u001b[1;32m 526\u001b[0m torch\u001b[38;5;241m.\u001b[39mTensor, wrap, outs_unwrapped\n\u001b[1;32m 527\u001b[0m )\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 529\u001b[0m \u001b[38;5;66;03m# When we dispatch to the C++ functionalization kernel, we might need to jump back to the\u001b[39;00m\n\u001b[1;32m 530\u001b[0m \u001b[38;5;66;03m# PreDispatch mode stack afterwards, to handle any other PreDispatch modes underneath\u001b[39;00m\n\u001b[1;32m 531\u001b[0m \u001b[38;5;66;03m# FunctionalTensorMode. If we call func() directly, we would need to exclude PreDispatch\u001b[39;00m\n\u001b[1;32m 532\u001b[0m \u001b[38;5;66;03m# from the TLS in order to avoid infinite looping, but this would prevent us from coming\u001b[39;00m\n\u001b[1;32m 533\u001b[0m \u001b[38;5;66;03m# back to PreDispatch later\u001b[39;00m\n\u001b[0;32m--> 534\u001b[0m outs_unwrapped \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_op_dk\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 535\u001b[0m \u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDispatchKey\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFunctionalize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 536\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs_unwrapped\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 537\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs_unwrapped\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 538\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 539\u001b[0m \u001b[38;5;66;03m# We don't allow any mutation on result of dropout or _to_copy\u001b[39;00m\n\u001b[1;32m 540\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexport:\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/utils/_stats.py:21\u001b[0m, in \u001b[0;36mcount.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 19\u001b[0m simple_call_counter[fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 20\u001b[0m simple_call_counter[fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m] \u001b[38;5;241m=\u001b[39m simple_call_counter[fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1238\u001b[0m, in \u001b[0;36mFakeTensorMode.__torch_dispatch__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m 1235\u001b[0m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_get_dispatch_mode(torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_TorchDispatchModeKey\u001b[38;5;241m.\u001b[39mFAKE) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1236\u001b[0m ), func\n\u001b[1;32m 1237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1239\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 1240\u001b[0m log\u001b[38;5;241m.\u001b[39mexception(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfake tensor raised TypeError\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1692\u001b[0m, in \u001b[0;36mFakeTensorMode.dispatch\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1689\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1691\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache_enabled:\n\u001b[0;32m-> 1692\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cached_dispatch_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1693\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1694\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dispatch_impl(func, types, args, kwargs)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1348\u001b[0m, in \u001b[0;36mFakeTensorMode._cached_dispatch_impl\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1345\u001b[0m FakeTensorMode\u001b[38;5;241m.\u001b[39mcache_bypasses[e\u001b[38;5;241m.\u001b[39mreason] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 1347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output \u001b[38;5;129;01mis\u001b[39;00m _UNASSIGNED:\n\u001b[0;32m-> 1348\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1350\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1943\u001b[0m, in \u001b[0;36mFakeTensorMode._dispatch_impl\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m decomposition_table \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[1;32m 1934\u001b[0m has_symbolic_sizes\n\u001b[1;32m 1935\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1940\u001b[0m )\n\u001b[1;32m 1941\u001b[0m ):\n\u001b[1;32m 1942\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m-> 1943\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdecomposition_table\u001b[49m\u001b[43m[\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1945\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m 1946\u001b[0m \u001b[38;5;66;03m# Decomposes CompositeImplicitAutograd ops\u001b[39;00m\n\u001b[1;32m 1947\u001b[0m r \u001b[38;5;241m=\u001b[39m func\u001b[38;5;241m.\u001b[39mdecompose(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_refs/__init__.py:3956\u001b[0m, in \u001b[0;36munbind\u001b[0;34m(t, dim)\u001b[0m\n\u001b[1;32m 3953\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ()\n\u001b[1;32m 3954\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3955\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[0;32m-> 3956\u001b[0m torch\u001b[38;5;241m.\u001b[39msqueeze(s, dim) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3957\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/utils/_stats.py:21\u001b[0m, in \u001b[0;36mcount.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 19\u001b[0m simple_call_counter[fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 20\u001b[0m simple_call_counter[fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m] \u001b[38;5;241m=\u001b[39m simple_call_counter[fn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1238\u001b[0m, in \u001b[0;36mFakeTensorMode.__torch_dispatch__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m 1235\u001b[0m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_get_dispatch_mode(torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_TorchDispatchModeKey\u001b[38;5;241m.\u001b[39mFAKE) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1236\u001b[0m ), func\n\u001b[1;32m 1237\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1239\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 1240\u001b[0m log\u001b[38;5;241m.\u001b[39mexception(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfake tensor raised TypeError\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1692\u001b[0m, in \u001b[0;36mFakeTensorMode.dispatch\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1689\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1691\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache_enabled:\n\u001b[0;32m-> 1692\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cached_dispatch_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1693\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1694\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dispatch_impl(func, types, args, kwargs)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1348\u001b[0m, in \u001b[0;36mFakeTensorMode._cached_dispatch_impl\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1345\u001b[0m FakeTensorMode\u001b[38;5;241m.\u001b[39mcache_bypasses[e\u001b[38;5;241m.\u001b[39mreason] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 1347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output \u001b[38;5;129;01mis\u001b[39;00m _UNASSIGNED:\n\u001b[0;32m-> 1348\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1350\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m output\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_subclasses/fake_tensor.py:1947\u001b[0m, in \u001b[0;36mFakeTensorMode._dispatch_impl\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m 1943\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m decomposition_table[func](\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1945\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m 1946\u001b[0m \u001b[38;5;66;03m# Decomposes CompositeImplicitAutograd ops\u001b[39;00m\n\u001b[0;32m-> 1947\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecompose\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1948\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m r \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m 1949\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_ops.py:759\u001b[0m, in \u001b[0;36mOpOverload.decompose\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 757\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpy_kernels[dk](\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 758\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39m_dispatch_has_kernel_for_dispatch_key(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname(), dk):\n\u001b[0;32m--> 759\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_op_dk\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 760\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 761\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/sym_node.py:429\u001b[0m, in \u001b[0;36mSymNode.guard_int\u001b[0;34m(self, file, line)\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mguard_int\u001b[39m(\u001b[38;5;28mself\u001b[39m, file, line):\n\u001b[1;32m 427\u001b[0m \u001b[38;5;66;03m# TODO: use the file/line for some useful diagnostic on why a\u001b[39;00m\n\u001b[1;32m 428\u001b[0m \u001b[38;5;66;03m# guard occurred\u001b[39;00m\n\u001b[0;32m--> 429\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape_env\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_expr\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfx_node\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfx_node\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 431\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mint\u001b[39m(r)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/recording.py:262\u001b[0m, in \u001b[0;36mrecord_shapeenv_event.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m args[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mis_recording: \u001b[38;5;66;03m# type: ignore[has-type]\u001b[39;00m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;66;03m# If ShapeEnv is already recording an event, call the wrapped\u001b[39;00m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;66;03m# function directly.\u001b[39;00m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# NB: here, we skip the check of whether all ShapeEnv instances\u001b[39;00m\n\u001b[1;32m 261\u001b[0m \u001b[38;5;66;03m# are equal, in favor of a faster dispatch.\u001b[39;00m\n\u001b[0;32m--> 262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m retlog(\u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 264\u001b[0m \u001b[38;5;66;03m# Retrieve an instance of ShapeEnv.\u001b[39;00m\n\u001b[1;32m 265\u001b[0m \u001b[38;5;66;03m# Assumption: the collection of args and kwargs may not reference\u001b[39;00m\n\u001b[1;32m 266\u001b[0m \u001b[38;5;66;03m# different ShapeEnv instances.\u001b[39;00m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m _extract_shape_env_and_assert_equal(args, kwargs)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5122\u001b[0m, in \u001b[0;36mShapeEnv.evaluate_expr\u001b[0;34m(self, orig_expr, hint, fx_node, size_oblivious, forcing_spec)\u001b[0m\n\u001b[1;32m 5117\u001b[0m \u001b[38;5;129m@lru_cache\u001b[39m(\u001b[38;5;241m256\u001b[39m)\n\u001b[1;32m 5118\u001b[0m \u001b[38;5;129m@record_shapeenv_event\u001b[39m(save_tracked_fakes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 5119\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mevaluate_expr\u001b[39m(\u001b[38;5;28mself\u001b[39m, orig_expr: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msympy.Expr\u001b[39m\u001b[38;5;124m\"\u001b[39m, hint\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, fx_node\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 5120\u001b[0m size_oblivious: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, forcing_spec: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 5121\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 5122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_evaluate_expr\u001b[49m\u001b[43m(\u001b[49m\u001b[43morig_expr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfx_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize_oblivious\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforcing_spec\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforcing_spec\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 5123\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m 5124\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m 5125\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfailed during evaluate_expr(\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m, hint=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m, size_oblivious=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m, forcing_spec=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5126\u001b[0m orig_expr, hint, size_oblivious, forcing_spec\n\u001b[1;32m 5127\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:5238\u001b[0m, in \u001b[0;36mShapeEnv._evaluate_expr\u001b[0;34m(self, orig_expr, hint, fx_node, size_oblivious, forcing_spec)\u001b[0m\n\u001b[1;32m 5236\u001b[0m concrete_val \u001b[38;5;241m=\u001b[39m unsound_result\n\u001b[1;32m 5237\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 5238\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_data_dependent_error(\n\u001b[1;32m 5239\u001b[0m expr\u001b[38;5;241m.\u001b[39mxreplace(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvar_to_val),\n\u001b[1;32m 5240\u001b[0m expr,\n\u001b[1;32m 5241\u001b[0m size_oblivious_result\u001b[38;5;241m=\u001b[39msize_oblivious_result\n\u001b[1;32m 5242\u001b[0m )\n\u001b[1;32m 5243\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 5244\u001b[0m expr \u001b[38;5;241m=\u001b[39m new_expr\n",
"\u001b[0;31mGuardOnDataDependentSymNode\u001b[0m: Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: u0)\n\nPotential framework code culprit (scroll up for full backtrace):\n File \"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_ops.py\", line 759, in decompose\n return self._op_dk(dk, *args, **kwargs)\n\nFor more information, run with TORCH_LOGS=\"dynamic\"\nFor extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"u0\"\nIf you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\nFor more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing\n\nFor C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1\n\nThe following call raised this error:\n File \"/rhome/eingerman/Projects/DeepLearning/TTS/Kokoro-82M/models.py\", line 471, in F0Ntrain\n x2, _temp = self.shared(x1)\n\nTo fix the error, insert one of the following checks before this call:\n 1. torch._check(x.shape[2])\n 2. torch._check(~x.shape[2])\n\n(These suggested fixes were derived by replacing `u0` with x.shape[2] or x1.shape[1] in u0 and its negation.)"
]
}
],
"source": [
"os.environ['TORCH_LOGS'] = '+dynamic'\n",
"os.environ['TORCH_LOGS'] = '+export'\n",
"os.environ['TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED']=\"u0 >= 0\"\n",
"os.environ['TORCHDYNAMO_EXTENDED_DEBUG_CPP']=\"1\"\n",
"os.environ['TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL']=\"u0\"\n",
"\n",
"class StyleTTS2(torch.nn.Module):\n",
" def __init__(self, model, voicepack):\n",
" super().__init__()\n",
" self.model = model\n",
" self.voicepack = voicepack\n",
" \n",
" def forward(self, tokens):\n",
" speed = 1.\n",
" # tokens = torch.nn.functional.pad(tokens, (0, 510 - tokens.shape[-1]))\n",
" device = tokens.device\n",
" input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)\n",
"\n",
" text_mask = length_to_mask(input_lengths).to(device)\n",
" bert_dur = self.model['bert'](tokens, attention_mask=(~text_mask).int())\n",
"\n",
" d_en = self.model[\"bert_encoder\"](bert_dur).transpose(-1, -2)\n",
"\n",
" ref_s = self.voicepack[tokens.shape[1]]\n",
" s = ref_s[:, 128:]\n",
"\n",
" d = self.model[\"predictor\"].text_encoder.inference(d_en, s)\n",
" x, _ = self.model[\"predictor\"].lstm(d)\n",
"\n",
" duration = self.model[\"predictor\"].duration_proj(x)\n",
" duration = torch.sigmoid(duration).sum(axis=-1) / speed\n",
" pred_dur = torch.round(duration).clamp(min=1).long()\n",
" \n",
" c_start = F.pad(pred_dur,(1,0), \"constant\").cumsum(dim=1)[0,0:-1]\n",
" c_end = c_start + pred_dur[0,:]\n",
" indices = torch.arange(0, pred_dur.sum().item()).long().to(device)\n",
"\n",
" pred_aln_trg_list=[]\n",
" for cs, ce in zip(c_start, c_end):\n",
" row = torch.where((indices>=cs) & (indices<ce), 1., 0.)\n",
" pred_aln_trg_list.append(row)\n",
" pred_aln_trg=torch.vstack(pred_aln_trg_list)\n",
" \n",
" en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)\n",
" \n",
" F0_pred, N_pred = self.model[\"predictor\"].F0Ntrain(en, s)\n",
" t_en = self.model[\"text_encoder\"].inference(tokens)\n",
" asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)\n",
" return (asr, F0_pred, N_pred, ref_s[:, :128])\n",
" # output = self.model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().detach().cpu().numpy()\n",
"\n",
"\n",
"style_model = StyleTTS2(model=model, voicepack=voicepack)\n",
"(asr, F0_pred, N_pred, ref_s) = style_model(tokens)\n",
"\n",
"token_len = torch.export.Dim(\"token_len\", min=2, max=510)\n",
"batch = torch.export.Dim(\"batch\")\n",
"dynamic_shapes = {\"tokens\":{0:batch, 1:token_len}}\n",
"\n",
"# with torch.no_grad():\n",
"export_mod = torch.export.export(style_model, args=( tokens, ), dynamic_shapes=dynamic_shapes, strict=False)\n",
"# export_mod = torch.export.export(style_model, args=( tokens, ), strict=False)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0101 18:19:15.402000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:3557] create_symbol s0 = 143 for L['args'][0][0]._base.size()[1] [2, int_oo] (_export/non_strict_utils.py:109 in fakify), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"s0\"\n",
"I0101 18:19:15.407000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:3557] create_symbol s1 = 143 for L['args'][0][0].size()[0] [2, int_oo] (_export/non_strict_utils.py:109 in fakify), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL=\"s1\"\n",
"I0101 18:19:15.420000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:4857] set_replacement s1 = 143 (range_refined_to_singleton) VR[143, 143]\n",
"I0101 18:19:15.422000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:5106] eval Eq(s1, 143) [guard added] (mp/ipykernel_2488298/2011460168.py:16 in forward), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED=\"Eq(s1, 143)\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 143])\n",
"torch.Size([1, s1])\n",
"torch.Size([1, 143])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"I0101 18:19:33.124000 2488298 site-packages/torch/fx/experimental/symbolic_shapes.py:3646] produce_guards\n"
]
},
{
"ename": "UserError",
"evalue": "Constraints violated (token_len)! For more information, run with TORCH_LOGS=\"+dynamic\".\n - Not all values of token_len = L['args'][0][0].size()[0] in the specified range are valid because token_len was inferred to be a constant (143).\nSuggested fixes:\n token_len = 143",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mConstraintViolationError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:670\u001b[0m, in \u001b[0;36m_export_to_aten_ir\u001b[0;34m(mod, fake_args, fake_kwargs, fake_params_buffers, constant_attrs, produce_guards_callback, transform, pre_dispatch, decomp_table, _check_autograd_state, _is_torch_jit_trace)\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 670\u001b[0m \u001b[43mproduce_guards_callback\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgm\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 671\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ConstraintViolationError, ValueRangeError) \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1655\u001b[0m, in \u001b[0;36m_non_strict_export.<locals>._produce_guards_callback\u001b[0;34m(gm)\u001b[0m\n\u001b[1;32m 1654\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_produce_guards_callback\u001b[39m(gm):\n\u001b[0;32m-> 1655\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mproduce_guards_and_solve_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1656\u001b[0m \u001b[43m \u001b[49m\u001b[43mfake_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfake_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1657\u001b[0m \u001b[43m \u001b[49m\u001b[43mgm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1658\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransformed_dynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1659\u001b[0m \u001b[43m \u001b[49m\u001b[43mequalities_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mequalities_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1660\u001b[0m \u001b[43m \u001b[49m\u001b[43moriginal_signature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moriginal_signature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1661\u001b[0m \u001b[43m \u001b[49m\u001b[43m_is_torch_jit_trace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_is_torch_jit_trace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1662\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_export/non_strict_utils.py:305\u001b[0m, in \u001b[0;36mproduce_guards_and_solve_constraints\u001b[0;34m(fake_mode, gm, dynamic_shapes, equalities_inputs, original_signature, _is_torch_jit_trace)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m constraint_violation_error:\n\u001b[0;32m--> 305\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m constraint_violation_error\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/_export/non_strict_utils.py:270\u001b[0m, in \u001b[0;36mproduce_guards_and_solve_constraints\u001b[0;34m(fake_mode, gm, dynamic_shapes, equalities_inputs, original_signature, _is_torch_jit_trace)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 270\u001b[0m \u001b[43mshape_env\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproduce_guards\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mplaceholders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_contexts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_contexts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43mequalities_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mequalities_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_static\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConstraintViolationError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/fx/experimental/symbolic_shapes.py:4178\u001b[0m, in \u001b[0;36mShapeEnv.produce_guards\u001b[0;34m(self, placeholders, sources, source_ref, guards, input_contexts, equalities_inputs, _simplified, ignore_static)\u001b[0m\n\u001b[1;32m 4177\u001b[0m err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(error_msgs)\n\u001b[0;32m-> 4178\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m ConstraintViolationError(\n\u001b[1;32m 4179\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConstraints violated (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdebug_names\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)! \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4180\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFor more information, run with TORCH_LOGS=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m+dynamic\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 4181\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4182\u001b[0m )\n\u001b[1;32m 4183\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(warn_msgs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
"\u001b[0;31mConstraintViolationError\u001b[0m: Constraints violated (token_len)! For more information, run with TORCH_LOGS=\"+dynamic\".\n - Not all values of token_len = L['args'][0][0].size()[0] in the specified range are valid because token_len was inferred to be a constant (143).\nSuggested fixes:\n token_len = 143",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mUserError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[33], line 61\u001b[0m\n\u001b[1;32m 58\u001b[0m dynamic_shapes \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokens0\u001b[39m\u001b[38;5;124m\"\u001b[39m:{\u001b[38;5;241m0\u001b[39m:token_len}}\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m# with torch.no_grad():\u001b[39;00m\n\u001b[0;32m---> 61\u001b[0m export_mod \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexport\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[43mtokens\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# export_mod = torch.export.export(test_model, args=( tokens[0,:], ), strict=False).run_decompositions()\u001b[39;00m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mprint\u001b[39m(export_mod)\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/__init__.py:270\u001b[0m, in \u001b[0;36mexport\u001b[0;34m(mod, args, kwargs, dynamic_shapes, strict, preserve_module_call_signature)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mod, torch\u001b[38;5;241m.\u001b[39mjit\u001b[38;5;241m.\u001b[39mScriptModule):\n\u001b[1;32m 265\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 266\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExporting a ScriptModule is not supported. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 267\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMaybe try converting your ScriptModule to an ExportedProgram \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 268\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124musing `TS2EPConverter(mod, args, kwargs).convert()` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 269\u001b[0m )\n\u001b[0;32m--> 270\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_export\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 271\u001b[0m \u001b[43m \u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 272\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 273\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 274\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 275\u001b[0m \u001b[43m \u001b[49m\u001b[43mstrict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstrict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 276\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreserve_module_call_signature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreserve_module_call_signature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 277\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 278\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1017\u001b[0m, in \u001b[0;36m_log_export_wrapper.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1010\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1011\u001b[0m log_export_usage(\n\u001b[1;32m 1012\u001b[0m event\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexport.error.unclassified\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 1013\u001b[0m \u001b[38;5;28mtype\u001b[39m\u001b[38;5;241m=\u001b[39merror_type,\n\u001b[1;32m 1014\u001b[0m message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m 1015\u001b[0m flags\u001b[38;5;241m=\u001b[39m_EXPORT_FLAGS,\n\u001b[1;32m 1016\u001b[0m )\n\u001b[0;32m-> 1017\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 1018\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 1019\u001b[0m _EXPORT_FLAGS \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:990\u001b[0m, in \u001b[0;36m_log_export_wrapper.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 988\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 989\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m--> 990\u001b[0m ep \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 991\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 992\u001b[0m log_export_usage(\n\u001b[1;32m 993\u001b[0m event\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexport.time\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 994\u001b[0m metrics\u001b[38;5;241m=\u001b[39mend \u001b[38;5;241m-\u001b[39m start,\n\u001b[1;32m 995\u001b[0m flags\u001b[38;5;241m=\u001b[39m_EXPORT_FLAGS,\n\u001b[1;32m 996\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mget_ep_stats(ep),\n\u001b[1;32m 997\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/exported_program.py:114\u001b[0m, in \u001b[0;36m_disable_prexisiting_fake_mode.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(fn)\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m unset_fake_temporarily():\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1880\u001b[0m, in \u001b[0;36m_export\u001b[0;34m(mod, args, kwargs, dynamic_shapes, strict, preserve_module_call_signature, pre_dispatch, allow_complex_guards_as_runtime_asserts, _is_torch_jit_trace)\u001b[0m\n\u001b[1;32m 1877\u001b[0m \u001b[38;5;66;03m# Call the appropriate export function based on the strictness of tracing.\u001b[39;00m\n\u001b[1;32m 1878\u001b[0m export_func \u001b[38;5;241m=\u001b[39m _strict_export \u001b[38;5;28;01mif\u001b[39;00m strict \u001b[38;5;28;01melse\u001b[39;00m _non_strict_export\n\u001b[0;32m-> 1880\u001b[0m export_artifact \u001b[38;5;241m=\u001b[39m \u001b[43mexport_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[operator]\u001b[39;49;00m\n\u001b[1;32m 1881\u001b[0m \u001b[43m \u001b[49m\u001b[43mmod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1882\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1883\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1884\u001b[0m \u001b[43m \u001b[49m\u001b[43mdynamic_shapes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1885\u001b[0m \u001b[43m \u001b[49m\u001b[43mpreserve_module_call_signature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1886\u001b[0m \u001b[43m \u001b[49m\u001b[43mpre_dispatch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1887\u001b[0m \u001b[43m \u001b[49m\u001b[43moriginal_state_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1888\u001b[0m \u001b[43m \u001b[49m\u001b[43moriginal_in_spec\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1889\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_complex_guards_as_runtime_asserts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1890\u001b[0m \u001b[43m \u001b[49m\u001b[43m_is_torch_jit_trace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1891\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1892\u001b[0m export_graph_signature: ExportGraphSignature \u001b[38;5;241m=\u001b[39m export_artifact\u001b[38;5;241m.\u001b[39maten\u001b[38;5;241m.\u001b[39msig\n\u001b[1;32m 1894\u001b[0m forward_arg_names \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1895\u001b[0m _get_forward_arg_names(mod, args, kwargs) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_torch_jit_trace \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1896\u001b[0m )\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:1683\u001b[0m, in \u001b[0;36m_non_strict_export\u001b[0;34m(mod, args, kwargs, dynamic_shapes, preserve_module_call_signature, pre_dispatch, original_state_dict, orig_in_spec, allow_complex_guards_as_runtime_asserts, _is_torch_jit_trace, dispatch_tracing_mode)\u001b[0m\n\u001b[1;32m 1667\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _fakify_script_objects(mod, fake_args, fake_kwargs, fake_mode) \u001b[38;5;28;01mas\u001b[39;00m (\n\u001b[1;32m 1668\u001b[0m patched_mod,\n\u001b[1;32m 1669\u001b[0m new_fake_args,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1672\u001b[0m map_fake_to_real,\n\u001b[1;32m 1673\u001b[0m ):\n\u001b[1;32m 1674\u001b[0m _to_aten_func \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1675\u001b[0m _export_to_aten_ir_make_fx\n\u001b[1;32m 1676\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dispatch_tracing_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmake_fx\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1681\u001b[0m )\n\u001b[1;32m 1682\u001b[0m )\n\u001b[0;32m-> 1683\u001b[0m aten_export_artifact \u001b[38;5;241m=\u001b[39m \u001b[43m_to_aten_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore[operator]\u001b[39;49;00m\n\u001b[1;32m 1684\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatched_mod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1685\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_fake_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1686\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_fake_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1687\u001b[0m \u001b[43m \u001b[49m\u001b[43mfake_params_buffers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1688\u001b[0m \u001b[43m \u001b[49m\u001b[43mnew_fake_constant_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1689\u001b[0m \u001b[43m \u001b[49m\u001b[43mproduce_guards_callback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_produce_guards_callback\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1690\u001b[0m \u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_tuplify_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1691\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1692\u001b[0m \u001b[38;5;66;03m# aten_export_artifact.constants contains only fake script objects, we need to map them back\u001b[39;00m\n\u001b[1;32m 1693\u001b[0m aten_export_artifact\u001b[38;5;241m.\u001b[39mconstants \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 1694\u001b[0m fqn: map_fake_to_real[obj] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, FakeScriptObject) \u001b[38;5;28;01melse\u001b[39;00m obj\n\u001b[1;32m 1695\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m fqn, obj \u001b[38;5;129;01min\u001b[39;00m aten_export_artifact\u001b[38;5;241m.\u001b[39mconstants\u001b[38;5;241m.\u001b[39mitems()\n\u001b[1;32m 1696\u001b[0m }\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/export/_trace.py:672\u001b[0m, in \u001b[0;36m_export_to_aten_ir\u001b[0;34m(mod, fake_args, fake_kwargs, fake_params_buffers, constant_attrs, produce_guards_callback, transform, pre_dispatch, decomp_table, _check_autograd_state, _is_torch_jit_trace)\u001b[0m\n\u001b[1;32m 670\u001b[0m produce_guards_callback(gm)\n\u001b[1;32m 671\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ConstraintViolationError, ValueRangeError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 672\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UserError(UserErrorType\u001b[38;5;241m.\u001b[39mCONSTRAINT_VIOLATION, \u001b[38;5;28mstr\u001b[39m(e)) \u001b[38;5;66;03m# noqa: B904\u001b[39;00m\n\u001b[1;32m 674\u001b[0m \u001b[38;5;66;03m# Run runtime asserts pass before creating input/output specs, since size-related CSE/DCE might affect output signature.\u001b[39;00m\n\u001b[1;32m 675\u001b[0m \u001b[38;5;66;03m# Overwrite output specs afterwards.\u001b[39;00m\n\u001b[1;32m 676\u001b[0m flat_fake_args \u001b[38;5;241m=\u001b[39m pytree\u001b[38;5;241m.\u001b[39mtree_leaves((fake_args, fake_kwargs))\n",
"\u001b[0;31mUserError\u001b[0m: Constraints violated (token_len)! For more information, run with TORCH_LOGS=\"+dynamic\".\n - Not all values of token_len = L['args'][0][0].size()[0] in the specified range are valid because token_len was inferred to be a constant (143).\nSuggested fixes:\n token_len = 143"
]
}
],
"source": [
"os.environ['TORCH_LOGS'] = '+dynamic'\n",
"os.environ['TORCH_LOGS'] = '+export'\n",
"class test(torch.nn.Module):\n",
" def __init__(self, model, voicepack):\n",
" super().__init__()\n",
" self.model = model\n",
" self.voicepack = voicepack\n",
" self.model.text_encoder.lstm.flatten_parameters()\n",
" \n",
" def forward(self, tokens0):\n",
" tokens = tokens0.unsqueeze(0)\n",
" print(tokens.shape)\n",
" # speed = 1.\n",
" # # tokens = torch.nn.functional.pad(tokens, (0, 510 - tokens.shape[-1]))\n",
" # device = tokens.device\n",
" input_lengths = torch.LongTensor([tokens0.shape[-1]]).to(device)\n",
"\n",
" # text_mask = length_to_mask(input_lengths).to(device)\n",
" # bert_dur = self.model['bert'](tokens, attention_mask=(~text_mask).int())\n",
"\n",
" # d_en = self.model[\"bert_encoder\"](bert_dur).transpose(-1, -2)\n",
"\n",
" # ref_s = self.voicepack[tokens.shape[1]]\n",
" # s = ref_s[:, 128:]\n",
"\n",
" # d = self.model[\"predictor\"].text_encoder.inference(d_en, s)\n",
" # x, _ = self.model[\"predictor\"].lstm(d)\n",
"\n",
" # duration = self.model[\"predictor\"].duration_proj(x)\n",
" # duration = torch.sigmoid(duration).sum(axis=-1) / speed\n",
" # pred_dur = torch.round(duration).clamp(min=1).long()\n",
" \n",
" # c_start = F.pad(pred_dur,(1,0), \"constant\").cumsum(dim=1)[0,0:-1]\n",
" # c_end = c_start + pred_dur[0,:]\n",
" # indices = torch.arange(0, pred_dur.sum().item()).long().to(device)\n",
"\n",
" # pred_aln_trg_list=[]\n",
" # for cs, ce in zip(c_start, c_end):\n",
" # row = torch.where((indices>=cs) & (indices<ce), 1., 0.)\n",
" # pred_aln_trg_list.append(row)\n",
" # pred_aln_trg=torch.vstack(pred_aln_trg_list)\n",
" \n",
" # en = d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)\n",
"\n",
" en = torch.rand((1, 640, 2*tokens.shape[-1]))\n",
" s = torch.rand((1,128))\n",
" F0_pred, N_pred = self.model[\"predictor\"].F0Ntrain(en, s)\n",
" t_en = self.model[\"text_encoder\"].inference(tokens)\n",
" asr = t_en @ pred_aln_trg.unsqueeze(0).to(device)\n",
" return (asr, F0_pred, N_pred, ref_s[:, :128])\n",
" # output = self.model.decoder(asr, F0_pred, N_pred, ref_s[:, :128]).squeeze().detach().cpu().numpy()\n",
"\n",
"\n",
"test_model = test(model=model, voicepack=voicepack)\n",
"(asr, F0_pred, N_pred, ref_s) = test_model(tokens[0,:])\n",
"\n",
"token_len = torch.export.Dim(\"token_len\") #, min=2, max=510)\n",
"dynamic_shapes = {\"tokens0\":{0:token_len}}\n",
"\n",
"# with torch.no_grad():\n",
"export_mod = torch.export.export(test_model, args=( tokens[0,:], ), dynamic_shapes=dynamic_shapes, strict=False)\n",
"# export_mod = torch.export.export(test_model, args=( tokens[0,:], ), strict=False).run_decompositions()\n",
"print(export_mod)\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.float32\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import torch.nn.functional as F\n",
"\n",
"# pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item())\n",
"c_start = F.pad(pred_dur,(1,0), \"constant\").cumsum(dim=1)[0,0:-1]\n",
"c_end = c_start + pred_dur[0,:]\n",
"indices = torch.arange(0, pred_dur.sum().item()).to(device)\n",
"\n",
"pred_aln_trg_list=[]\n",
"for cs, ce in zip(c_start, c_end):\n",
" row = torch.where((indices>=cs) & (indices<ce), 1., 0.)\n",
" pred_aln_trg_list.append(row)\n",
"\n",
"pred_aln_trg=torch.vstack(pred_aln_trg_list)\n",
"# print(pred_aln_trg)\n",
"# pl.imshow(pred_aln_trg)\n",
"pl.plot(pred_aln_trg[:,50])\n",
"# print(pred_dur.shape)\n",
"print(row.dtype)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 142])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c_start[:,:-2].shape"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "The size of tensor a (23) must match the size of tensor b (143) at non-singleton dimension 2",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[48], line 41\u001b[0m\n\u001b[1;32m 36\u001b[0m pred_aln_trg[flat_indices\u001b[38;5;241m.\u001b[39mT[\u001b[38;5;241m0\u001b[39m], flat_indices\u001b[38;5;241m.\u001b[39mT[\u001b[38;5;241m1\u001b[39m]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pred_aln_trg\n\u001b[0;32m---> 41\u001b[0m pred_aln_trg \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_alignment_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_lengths\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpred_dur\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 42\u001b[0m pl\u001b[38;5;241m.\u001b[39mimshow(pred_aln_trg)\n",
"Cell \u001b[0;32mIn[48], line 22\u001b[0m, in \u001b[0;36mcreate_alignment_matrix\u001b[0;34m(input_lengths, pred_dur)\u001b[0m\n\u001b[1;32m 19\u001b[0m col_indices \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39marange(pred_dur\u001b[38;5;241m.\u001b[39mmax()\u001b[38;5;241m.\u001b[39mitem())\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mrepeat(input_lengths, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m# Create a mask based on durations\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m mask \u001b[38;5;241m=\u001b[39m \u001b[43mcol_indices\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpred_dur\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;66;03m# Create offset indices for the columns\u001b[39;00m\n\u001b[1;32m 25\u001b[0m offset \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((torch\u001b[38;5;241m.\u001b[39mtensor([\u001b[38;5;241m0\u001b[39m]), cum_dur[\u001b[38;5;241m0\u001b[39m, :\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]))\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mrepeat(\u001b[38;5;241m1\u001b[39m, pred_dur\u001b[38;5;241m.\u001b[39mmax()\u001b[38;5;241m.\u001b[39mitem())\n",
"\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (23) must match the size of tensor b (143) at non-singleton dimension 2"
]
}
],
"source": [
"\n",
"\n",
"def create_alignment_matrix(input_lengths, pred_dur):\n",
" \"\"\"Creates an alignment matrix without explicit loops.\n",
"\n",
" Args:\n",
" input_lengths: Number of input units (int).\n",
" pred_dur: Predicted durations (torch.Tensor of shape (1, input_lengths)).\n",
"\n",
" Returns:\n",
" pred_aln_trg: Alignment matrix (torch.Tensor of shape (input_lengths, pred_dur.sum())).\n",
" \"\"\"\n",
" total_duration = pred_dur.sum().item()\n",
" pred_aln_trg = torch.zeros(input_lengths, total_duration)\n",
"\n",
" # Calculate cumulative durations\n",
" cum_dur = torch.cumsum(pred_dur, dim=1)\n",
"\n",
" # Create indices for filling the matrix\n",
" row_indices = torch.arange(input_lengths).unsqueeze(1).repeat(1, pred_dur.max().item())\n",
" col_indices = torch.arange(pred_dur.max().item()).unsqueeze(0).repeat(input_lengths, 1)\n",
"\n",
" # Create a mask based on durations\n",
" mask = col_indices < pred_dur.unsqueeze(1)\n",
"\n",
" # Create offset indices for the columns\n",
" offset = torch.cat((torch.tensor([0]), cum_dur[0, :-1])).unsqueeze(1).repeat(1, pred_dur.max().item())\n",
"\n",
" # Apply the mask and offset to generate the final column indices\n",
" final_col_indices = (col_indices + offset) * mask\n",
"\n",
" # Flatten indices and create a flattened index tensor\n",
" flat_row_indices = row_indices[mask].long()\n",
" flat_col_indices = final_col_indices[mask].long()\n",
" flat_indices = torch.stack([flat_row_indices, flat_col_indices], dim=1)\n",
"\n",
" # Scatter ones into the alignment matrix\n",
" pred_aln_trg[flat_indices.T[0], flat_indices.T[1]] = 1\n",
"\n",
" return pred_aln_trg\n",
"\n",
"\n",
"pred_aln_trg = create_alignment_matrix(input_lengths.item(), pred_dur)\n",
"pl.imshow(pred_aln_trg)\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([143])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input_lengths"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pred_aln_trg = torch.zeros(input_lengths, pred_dur.sum().item())\n",
"c_frame = 0\n",
"\n",
"for i in range(pred_aln_trg.size(0)):\n",
" pred_aln_trg[i, c_frame:c_frame + pred_dur[0,i].item()] = 1\n",
" c_frame += pred_dur[0,i].item()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Module.eval of StyleTTS2()>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"style_model.eval"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CustomAlbert(\n",
" (embeddings): AlbertEmbeddings(\n",
" (word_embeddings): Embedding(178, 128, padding_idx=0)\n",
" (position_embeddings): Embedding(512, 128)\n",
" (token_type_embeddings): Embedding(2, 128)\n",
" (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n",
" (dropout): Dropout(p=0, inplace=False)\n",
" )\n",
" (encoder): AlbertTransformer(\n",
" (embedding_hidden_mapping_in): Linear(in_features=128, out_features=768, bias=True)\n",
" (albert_layer_groups): ModuleList(\n",
" (0): AlbertLayerGroup(\n",
" (albert_layers): ModuleList(\n",
" (0): AlbertLayer(\n",
" (full_layer_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" (attention): AlbertAttention(\n",
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
" (attention_dropout): Dropout(p=0, inplace=False)\n",
" (output_dropout): Dropout(p=0, inplace=False)\n",
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
" )\n",
" (ffn): Linear(in_features=768, out_features=2048, bias=True)\n",
" (ffn_output): Linear(in_features=2048, out_features=768, bias=True)\n",
" (activation): NewGELUActivation()\n",
" (dropout): Dropout(p=0, inplace=False)\n",
" )\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (pooler): Linear(in_features=768, out_features=768, bias=True)\n",
" (pooler_activation): Tanh()\n",
")"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model['bert']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "only integer tensors of a single element can be converted to an index",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[17], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m pred_aln_trg1 \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(input_lengths, pred_dur\u001b[38;5;241m.\u001b[39msum()\u001b[38;5;241m.\u001b[39mitem(), dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 9\u001b[0m batch_indices \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39marange(input_lengths\u001b[38;5;241m.\u001b[39mitem())\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m \u001b[43mpred_aln_trg1\u001b[49m\u001b[43m[\u001b[49m\u001b[43mbatch_indices\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_indices\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_indices\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 13\u001b[0m pl\u001b[38;5;241m.\u001b[39mimshow(pred_aln_trg1)\n",
"\u001b[0;31mTypeError\u001b[0m: only integer tensors of a single element can be converted to an index"
]
}
],
"source": [
"# Process durations\n",
"\n",
"cumsum_dur = torch.cumsum(pred_dur, dim=1).to(device)\n",
"end_indices = cumsum_dur - 1\n",
"start_indices = torch.cat([torch.zeros(1, 1, dtype=torch.long).to(device), end_indices[:, :-1] + 1], dim=1)\n",
"\n",
"# Create binary alignment target\n",
"pred_aln_trg1 = torch.zeros(input_lengths, pred_dur.sum().item(), dtype=torch.float32)\n",
"batch_indices = torch.arange(input_lengths.item()).unsqueeze(1)\n",
"\n",
"pred_aln_trg1[batch_indices, start_indices: end_indices + 1] = 1\n",
"\n",
"pl.imshow(pred_aln_trg1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"batch_indices"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([143, 329])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pred_aln_trg1 = torch.zeros(input_lengths, pred_dur.sum().item()).to(device)\n",
"a = torch.arange(pred_aln_trg1.size(0))[:, None].repeat(1, pred_dur.size(1)).to(device)\n",
"b = (torch.arange(pred_dur.size(1)).repeat(pred_aln_trg1.size(0), 1).to(device) < pred_dur).to(torch.float32).to(device)\n",
"print(pred_aln_trg.dtype, pred_aln_trg1.dtype, a.dtype, b.dtype)\n",
"print(a.device, b.device, pred_dur.device)\n",
"pred_aln_trg1.scatter_(1, \n",
" a, \n",
" b)\n",
"\n",
"pl.imshow(pred_aln_trg1.detach().cpu().numpy())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "Expected index [1, 25] to be smaller than self [143, 329] apart from dimension 1 and to be smaller size than src [1, 1]",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[8], line 18\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Use scatter_add_ to set the appropriate slices to 1\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(pred_dur\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m1\u001b[39m)):\n\u001b[0;32m---> 18\u001b[0m \t\u001b[43mmask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter_add_\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mstart_indices\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m:\u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpred_dur\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclamp\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpred_aln_trg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m:\u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Apply the mask to pred_aln_trg\u001b[39;00m\n\u001b[1;32m 21\u001b[0m pred_aln_trg \u001b[38;5;241m=\u001b[39m mask\n",
"\u001b[0;31mRuntimeError\u001b[0m: Expected index [1, 25] to be smaller than self [143, 329] apart from dimension 1 and to be smaller size than src [1, 1]"
]
}
],
"source": [
"# Calculate the cumulative sum of durations to get the end indices\n",
"cumulative_durations = torch.cumsum(pred_dur, dim=1).to(device)\n",
"\n",
"# Calculate the start indices by shifting the cumulative durations\n",
"start_indices = cumulative_durations - pred_dur\n",
"\n",
"# Create a tensor of indices for pred_aln_trg\n",
"indices = torch.arange(pred_aln_trg.size(1)).to(device)\n",
"\n",
"# Create a mask tensor initialized to zeros\n",
"mask = torch.zeros_like(pred_aln_trg).to(device)\n",
"\n",
"# Create a tensor to hold the values to scatter\n",
"values = torch.ones_like(pred_dur, dtype=pred_aln_trg.dtype).to(device)\n",
"\n",
"# Use scatter_ to set the appropriate slices to 1\n",
"mask.scatter_(1, start_indices.unsqueeze(2) + torch.arange(pred_dur.max()).unsqueeze(0).unsqueeze(0).to(device), values.unsqueeze(2))\n",
"\n",
"# Apply the mask to pred_aln_trg\n",
"pred_aln_trg = mask"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"device(type='cpu')"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.arange(pred_dur.size(1)).repeat(pred_aln_trg1.size(0), 1).device"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f715c3faf50>"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pl.imshow(pred_aln_trg.detach().cpu().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 143])\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(pred_dur.shape)\n",
"pl.plot(pred_dur[0,:].detach().cpu().numpy().cumsum());"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[50, 157, 43, 135, 16, 53, 135, 46, 16, 43, 102, 16, 56, 156, 57, 135, 6, 16, 102, 62, 61, 16, 70, 56, 16, 138, 56, 156, 72, 56, 61, 85, 123, 83, 44, 83, 54, 16, 53, 65, 156, 86, 61, 62, 131, 83, 56, 4, 16, 54, 156, 43, 102, 53, 16, 156, 72, 61, 53, 102, 112, 16, 70, 56, 16, 138, 56, 44, 156, 76, 158, 123, 56, 16, 62, 131, 156, 43, 102, 54, 46, 16, 102, 48, 16, 81, 47, 102, 54, 16, 54, 156, 51, 158, 46, 16, 70, 16, 92, 156, 135, 46, 16, 54, 156, 43, 102, 48, 4, 16, 81, 47, 102, 16, 50, 156, 72, 64, 83, 56, 62, 16, 156, 51, 158, 64, 83, 56, 16, 44, 157, 102, 56, 16, 44, 156, 76, 158, 123, 56, 4]\n"
]
}
],
"source": [
"ps = phonemize(text)\n",
"tokens = tokenize(ps)\n",
"print(tokens)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from models import build_model\n",
"import torch\n",
"device = \"cpu\" #'cuda' if torch.cuda.is_available() else 'cpu'\n",
"model = build_model('kokoro-v0_19.pth', device)\n",
"voicepack = torch.load('voices/af.pt', weights_only=True).to(device)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"bert = model[\"bert\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"embeddings.word_embeddings.weight torch.Size([178, 128])\n",
"embeddings.position_embeddings.weight torch.Size([512, 128])\n",
"embeddings.token_type_embeddings.weight torch.Size([2, 128])\n",
"embeddings.LayerNorm.weight torch.Size([128])\n",
"embeddings.LayerNorm.bias torch.Size([128])\n",
"encoder.embedding_hidden_mapping_in.weight torch.Size([768, 128])\n",
"encoder.embedding_hidden_mapping_in.bias torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.weight torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.full_layer_layer_norm.bias torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.query.weight torch.Size([768, 768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.query.bias torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.key.weight torch.Size([768, 768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.key.bias torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.value.weight torch.Size([768, 768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.value.bias torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.dense.weight torch.Size([768, 768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.dense.bias torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.weight torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.attention.LayerNorm.bias torch.Size([768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.ffn.weight torch.Size([2048, 768])\n",
"encoder.albert_layer_groups.0.albert_layers.0.ffn.bias torch.Size([2048])\n",
"encoder.albert_layer_groups.0.albert_layers.0.ffn_output.weight torch.Size([768, 2048])\n",
"encoder.albert_layer_groups.0.albert_layers.0.ffn_output.bias torch.Size([768])\n",
"pooler.weight torch.Size([768, 768])\n",
"pooler.bias torch.Size([768])\n"
]
}
],
"source": [
"# show all parameters of model bert\n",
"for name, param in bert.named_parameters():\n",
" print(name, param.requires_grad())\n",
" # print(param)\n",
" # print(param.shape)\n",
" # break"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing LSTM export"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x1.shape=torch.Size([1, 300, 256])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/onnx/symbolic_opset9.py:4279: UserWarning: Exporting a model to ONNX with a batch_size other than 1, with a variable length with LSTM can cause an error when running the ONNX model with a different batch size. Make sure to save the model with a batch size of 1, or define the initial states (h0/c0) as inputs of the model. \n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exported graph: graph(%x : Float(*, 300, 128, strides=[38400, 128, 1], requires_grad=0, device=cpu),\n",
" %onnx::LSTM_194 : Float(2, 1024, strides=[1024, 1], requires_grad=0, device=cpu),\n",
" %onnx::LSTM_195 : Float(2, 512, 128, strides=[65536, 128, 1], requires_grad=0, device=cpu),\n",
" %onnx::LSTM_196 : Float(2, 512, 128, strides=[65536, 128, 1], requires_grad=0, device=cpu)):\n",
" %/lstm/Shape_output_0 : Long(3, strides=[1], device=cpu) = onnx::Shape[onnx_name=\"/lstm/Shape\"](%x), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1081:0\n",
" %/lstm/Constant_output_0 : Long(device=cpu) = onnx::Constant[value={0}, onnx_name=\"/lstm/Constant\"](), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1081:0\n",
" %/lstm/Gather_output_0 : Long(device=cpu) = onnx::Gather[axis=0, onnx_name=\"/lstm/Gather\"](%/lstm/Shape_output_0, %/lstm/Constant_output_0), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1081:0\n",
" %/lstm/Constant_1_output_0 : Long(1, strides=[1], requires_grad=0, device=cpu) = onnx::Constant[value={2}, onnx_name=\"/lstm/Constant_1\"](), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm\n",
" %onnx::Unsqueeze_16 : Long(1, strides=[1], device=cpu) = onnx::Constant[value={0}]()\n",
" %/lstm/Unsqueeze_output_0 : Long(1, strides=[1], device=cpu) = onnx::Unsqueeze[onnx_name=\"/lstm/Unsqueeze\"](%/lstm/Gather_output_0, %onnx::Unsqueeze_16), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm\n",
" %/lstm/Constant_2_output_0 : Long(1, strides=[1], requires_grad=0, device=cpu) = onnx::Constant[value={128}, onnx_name=\"/lstm/Constant_2\"](), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm\n",
" %/lstm/Concat_output_0 : Long(3, strides=[1], device=cpu) = onnx::Concat[axis=0, onnx_name=\"/lstm/Concat\"](%/lstm/Constant_1_output_0, %/lstm/Unsqueeze_output_0, %/lstm/Constant_2_output_0), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1085:0\n",
" %/lstm/ConstantOfShape_output_0 : Float(*, *, *, strides=[128, 128, 1], requires_grad=0, device=cpu) = onnx::ConstantOfShape[value={0}, onnx_name=\"/lstm/ConstantOfShape\"](%/lstm/Concat_output_0), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1085:0\n",
" %/lstm/Transpose_output_0 : Float(300, *, 128, device=cpu) = onnx::Transpose[perm=[1, 0, 2], onnx_name=\"/lstm/Transpose\"](%x), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123:0\n",
" %onnx::LSTM_23 : Tensor? = prim::Constant(), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123:0\n",
" %/lstm/LSTM_output_0 : Float(300, 2, *, 128, device=cpu), %/lstm/LSTM_output_1 : Float(2, *, 128, strides=[128, 128, 1], requires_grad=1, device=cpu), %/lstm/LSTM_output_2 : Float(2, *, 128, strides=[128, 128, 1], requires_grad=1, device=cpu) = onnx::LSTM[direction=\"bidirectional\", hidden_size=128, onnx_name=\"/lstm/LSTM\"](%/lstm/Transpose_output_0, %onnx::LSTM_195, %onnx::LSTM_196, %onnx::LSTM_194, %onnx::LSTM_23, %/lstm/ConstantOfShape_output_0, %/lstm/ConstantOfShape_output_0), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123:0\n",
" %/lstm/Transpose_1_output_0 : Float(300, *, 2, 128, device=cpu) = onnx::Transpose[perm=[0, 2, 1, 3], onnx_name=\"/lstm/Transpose_1\"](%/lstm/LSTM_output_0), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123:0\n",
" %/lstm/Constant_3_output_0 : Long(3, strides=[1], device=cpu) = onnx::Constant[value= 0 0 -1 [ CPULongType{3} ], onnx_name=\"/lstm/Constant_3\"](), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123:0\n",
" %/lstm/Reshape_output_0 : Float(300, *, 256, device=cpu) = onnx::Reshape[allowzero=0, onnx_name=\"/lstm/Reshape\"](%/lstm/Transpose_1_output_0, %/lstm/Constant_3_output_0), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123:0\n",
" %151 : Float(*, 300, 256, strides=[256, 256, 1], requires_grad=1, device=cpu) = onnx::Transpose[perm=[1, 0, 2], onnx_name=\"/lstm/Transpose_2\"](%/lstm/Reshape_output_0), scope: __main__.Model::/torch.nn.modules.rnn.LSTM::lstm # /rhome/eingerman/mambaforge/envs/styletts2/lib/python3.10/site-packages/torch/nn/modules/rnn.py:1123:0\n",
" return (%151)\n",
"\n"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'graph'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 37\u001b[0m\n\u001b[1;32m 34\u001b[0m export_mod \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39monnx\u001b[38;5;241m.\u001b[39mexport(model\u001b[38;5;241m=\u001b[39mmodel, args\u001b[38;5;241m=\u001b[39m( xa, ), dynamic_axes\u001b[38;5;241m=\u001b[39mdynamic_shapes, input_names\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m], f\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel.onnx\u001b[39m\u001b[38;5;124m\"\u001b[39m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, dynamo\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 35\u001b[0m \u001b[38;5;66;03m# export_mod.save(\"model.onnx\")\u001b[39;00m\n\u001b[1;32m 36\u001b[0m \u001b[38;5;66;03m# export_mod.save_diagnostics(\"model_diagnostics.sarif\")\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mexport_mod\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgraph\u001b[49m)\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'graph'"
]
}
],
"source": [
"import torch\n",
"# os.environ['TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED']=\"Eq(s0, 384)\"\n",
"\n",
"# model class containing a single bidirectional LSTM layer\n",
"class Model(torch.nn.Module):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.lstm = torch.nn.LSTM(128, 128, 1, bidirectional=True, batch_first=True)\n",
" #initialize lstm weights\n",
" for name, param in self.lstm.named_parameters():\n",
" if 'weight' in name:\n",
" torch.nn.init.orthogonal_(param)\n",
" elif 'bias' in name:\n",
" torch.nn.init.zeros_(param)\n",
"\n",
" def forward(self, x):\n",
" x1 = x.transpose(-1,-2)\n",
" # print(f\"{x.shape=} {x1.shape=}\")\n",
" x2, _ = self.lstm(x)\n",
" return x2\n",
"\n",
"model = Model()\n",
"model = model.to(\"cpu\")\n",
"model.eval()\n",
"\n",
"#inital input to LSTM in variable x\n",
"xa = torch.zeros((1, 300, 128)).to(\"cpu\")\n",
"x1 = model(xa)\n",
"print(f\"{x1.shape=}\")\n",
"ntokens = torch.export.Dim(\"ntokens\", min=3)\n",
"dynamic_shapes= {\"x\":{0:\"ntokens\"}}\n",
"\n",
"# scripted = torch.jit.script(model)\n",
"torch.onnx.export(model=model, args=( xa, ), dynamic_axes=dynamic_shapes, input_names=[\"x\"], f=\"model.onnx\", verbose=True, dynamo=False)\n",
"# export_mod.save(\"model.onnx\")\n",
"# export_mod.save_diagnostics(\"model_diagnostics.sarif\")\n",
"# print(export_mod.graph)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 143])\n"
]
}
],
"source": [
"from kokoro import phonemize, tokenize\n",
"from models_scripting import load_plbert\n",
"bert = load_plbert()\n",
"\n",
"text = \"How could I know? It's an unanswerable question. Like asking an unborn child if they'll lead a good life. They haven't even been born.\"\n",
"ps = phonemize(text, \"a\")\n",
"tokens = tokenize(ps)\n",
"tokens = torch.LongTensor([[0, *tokens, 0]]).to(device)\n",
"dynamic_shapes = {\"tokens\":{1:'ntokens'}}\n",
"print(tokens.shape)\n",
"torch.onnx.export(model=bert, args=( tokens, ), dynamic_axes=dynamic_shapes, input_names=[\"tokens\"], f=\"bert.onnx\", verbose=False, dynamo=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "Fail",
"evalue": "[ONNXRuntimeError] : 1 : FAIL : Load model from style_model.onnx failed:Node (/Transpose_9) Op (Transpose) [TypeInferenceError] Invalid attribute perm {1, -1, 0}, input shape = {0, 0, 128}",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFail\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[6], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m onnx_model \u001b[38;5;241m=\u001b[39m onnx\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle_model.onnx\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01monnxruntime\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mort\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m ort_session \u001b[38;5;241m=\u001b[39m \u001b[43mort\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mInferenceSession\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstyle_model.onnx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m outputs \u001b[38;5;241m=\u001b[39m ort_session\u001b[38;5;241m.\u001b[39mrun(\u001b[38;5;28;01mNone\u001b[39;00m, {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokens\u001b[39m\u001b[38;5;124m\"\u001b[39m: tokens\u001b[38;5;241m.\u001b[39mnumpy()})\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:465\u001b[0m, in \u001b[0;36mInferenceSession.__init__\u001b[0;34m(self, path_or_bytes, sess_options, providers, provider_options, **kwargs)\u001b[0m\n\u001b[1;32m 462\u001b[0m disabled_optimizers \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdisabled_optimizers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 464\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 465\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_inference_session\u001b[49m\u001b[43m(\u001b[49m\u001b[43mproviders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprovider_options\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdisabled_optimizers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 466\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mRuntimeError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 467\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_enable_fallback:\n",
"File \u001b[0;32m~/mambaforge/envs/styletts2/lib/python3.10/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py:526\u001b[0m, in \u001b[0;36mInferenceSession._create_inference_session\u001b[0;34m(self, providers, provider_options, disabled_optimizers)\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_register_ep_custom_ops(session_options, providers, provider_options, available_providers)\n\u001b[1;32m 525\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_path:\n\u001b[0;32m--> 526\u001b[0m sess \u001b[38;5;241m=\u001b[39m \u001b[43mC\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mInferenceSession\u001b[49m\u001b[43m(\u001b[49m\u001b[43msession_options\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_model_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_config_from_model\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 527\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 528\u001b[0m sess \u001b[38;5;241m=\u001b[39m C\u001b[38;5;241m.\u001b[39mInferenceSession(session_options, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model_bytes, \u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_config_from_model)\n",
"\u001b[0;31mFail\u001b[0m: [ONNXRuntimeError] : 1 : FAIL : Load model from style_model.onnx failed:Node (/Transpose_9) Op (Transpose) [TypeInferenceError] Invalid attribute perm {1, -1, 0}, input shape = {0, 0, 128}"
]
}
],
"source": [
"import onnx\n",
"\n",
"onnx_model = onnx.load(\"style_model.onnx\")\n",
"import onnxruntime as ort\n",
"\n",
"ort_session = ort.InferenceSession(\"style_model.onnx\")\n",
"outputs = ort_session.run(None, {\"tokens\": tokens.numpy()})"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "styletts2",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}