check_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljb/miniconda3/envs/sde/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import segmentation_models_pytorch as smp\n",
    "model = smp.UnetPlusPlus(\n",
    "    encoder_name=\"efficientnet-b0\",\n",
    "    in_channels=3,\n",
    "    encoder_depth=3, \n",
    "    decoder_channels=(64, 32, 16),\n",
    "    classes=1,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "成功添加代理\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth\" to /home/ljb/.cache/torch/hub/checkpoints/efficientnet-b2-8bb594d6.pth\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[27], line 73\u001b[0m\n\u001b[1;32m     70\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m  \n\u001b[1;32m     71\u001b[0m os\u001b[39m.\u001b[39msystem(\u001b[39m'\u001b[39m\u001b[39msource ~/add_proxy.sh\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> 73\u001b[0m model \u001b[39m=\u001b[39m timeUnetPlusPlus(\n\u001b[1;32m     74\u001b[0m     model_channels\u001b[39m=\u001b[39;49m\u001b[39m64\u001b[39;49m,\n\u001b[1;32m     75\u001b[0m     encoder_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mefficientnet-b2\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m     76\u001b[0m     in_channels\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m,\n\u001b[1;32m     77\u001b[0m     encoder_depth\u001b[39m=\u001b[39;49m\u001b[39m4\u001b[39;49m, \n\u001b[1;32m     78\u001b[0m     decoder_channels\u001b[39m=\u001b[39;49m(\u001b[39m64\u001b[39;49m, \u001b[39m32\u001b[39;49m, \u001b[39m16\u001b[39;49m, \u001b[39m8\u001b[39;49m),\n\u001b[1;32m     79\u001b[0m     classes\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m,\n\u001b[1;32m     80\u001b[0m     )\n",
      "Cell \u001b[0;32mIn[27], line 9\u001b[0m, in \u001b[0;36mtimeUnetPlusPlus.__init__\u001b[0;34m(self, model_channels, *args, **kwargs)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m, model_channels, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[0;32m----> 9\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m     11\u001b[0m     \u001b[39m# self.timeEmb\u001b[39;00m\n\u001b[1;32m     12\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel_channels \u001b[39m=\u001b[39m model_channels\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/site-packages/segmentation_models_pytorch/decoders/unetplusplus/model.py:74\u001b[0m, in \u001b[0;36mUnetPlusPlus.__init__\u001b[0;34m(self, encoder_name, encoder_depth, encoder_weights, decoder_use_batchnorm, decoder_channels, decoder_attention_type, in_channels, classes, activation, aux_params)\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[39mif\u001b[39;00m encoder_name\u001b[39m.\u001b[39mstartswith(\u001b[39m\"\u001b[39m\u001b[39mmit_b\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m     72\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mUnetPlusPlus is not support encoder_name=\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(encoder_name))\n\u001b[0;32m---> 74\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoder \u001b[39m=\u001b[39m get_encoder(\n\u001b[1;32m     75\u001b[0m     encoder_name,\n\u001b[1;32m     76\u001b[0m     in_channels\u001b[39m=\u001b[39;49min_channels,\n\u001b[1;32m     77\u001b[0m     depth\u001b[39m=\u001b[39;49mencoder_depth,\n\u001b[1;32m     78\u001b[0m     weights\u001b[39m=\u001b[39;49mencoder_weights,\n\u001b[1;32m     79\u001b[0m )\n\u001b[1;32m     81\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdecoder \u001b[39m=\u001b[39m UnetPlusPlusDecoder(\n\u001b[1;32m     82\u001b[0m     encoder_channels\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencoder\u001b[39m.\u001b[39mout_channels,\n\u001b[1;32m     83\u001b[0m     decoder_channels\u001b[39m=\u001b[39mdecoder_channels,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     87\u001b[0m     attention_type\u001b[39m=\u001b[39mdecoder_attention_type,\n\u001b[1;32m     88\u001b[0m )\n\u001b[1;32m     90\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msegmentation_head \u001b[39m=\u001b[39m SegmentationHead(\n\u001b[1;32m     91\u001b[0m     in_channels\u001b[39m=\u001b[39mdecoder_channels[\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m],\n\u001b[1;32m     92\u001b[0m     out_channels\u001b[39m=\u001b[39mclasses,\n\u001b[1;32m     93\u001b[0m     activation\u001b[39m=\u001b[39mactivation,\n\u001b[1;32m     94\u001b[0m     kernel_size\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m,\n\u001b[1;32m     95\u001b[0m )\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/site-packages/segmentation_models_pytorch/encoders/__init__.py:85\u001b[0m, in \u001b[0;36mget_encoder\u001b[0;34m(name, in_channels, depth, weights, output_stride, **kwargs)\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[1;32m     78\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m     79\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mWrong pretrained weights `\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m` for encoder `\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m`. Available options are: \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\n\u001b[1;32m     80\u001b[0m                 weights,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     83\u001b[0m             )\n\u001b[1;32m     84\u001b[0m         )\n\u001b[0;32m---> 85\u001b[0m     encoder\u001b[39m.\u001b[39mload_state_dict(model_zoo\u001b[39m.\u001b[39;49mload_url(settings[\u001b[39m\"\u001b[39;49m\u001b[39murl\u001b[39;49m\u001b[39m\"\u001b[39;49m]))\n\u001b[1;32m     87\u001b[0m encoder\u001b[39m.\u001b[39mset_in_channels(in_channels, pretrained\u001b[39m=\u001b[39mweights \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m     88\u001b[0m \u001b[39mif\u001b[39;00m output_stride \u001b[39m!=\u001b[39m \u001b[39m32\u001b[39m:\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/site-packages/torch/hub.py:746\u001b[0m, in \u001b[0;36mload_state_dict_from_url\u001b[0;34m(url, model_dir, map_location, progress, check_hash, file_name)\u001b[0m\n\u001b[1;32m    744\u001b[0m         r \u001b[39m=\u001b[39m HASH_REGEX\u001b[39m.\u001b[39msearch(filename)  \u001b[39m# r is Optional[Match[str]]\u001b[39;00m\n\u001b[1;32m    745\u001b[0m         hash_prefix \u001b[39m=\u001b[39m r\u001b[39m.\u001b[39mgroup(\u001b[39m1\u001b[39m) \u001b[39mif\u001b[39;00m r \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m--> 746\u001b[0m     download_url_to_file(url, cached_file, hash_prefix, progress\u001b[39m=\u001b[39;49mprogress)\n\u001b[1;32m    748\u001b[0m \u001b[39mif\u001b[39;00m _is_legacy_zip_format(cached_file):\n\u001b[1;32m    749\u001b[0m     \u001b[39mreturn\u001b[39;00m _legacy_zip_load(cached_file, model_dir, map_location)\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/site-packages/torch/hub.py:611\u001b[0m, in \u001b[0;36mdownload_url_to_file\u001b[0;34m(url, dst, hash_prefix, progress)\u001b[0m\n\u001b[1;32m    609\u001b[0m file_size \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    610\u001b[0m req \u001b[39m=\u001b[39m Request(url, headers\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mUser-Agent\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mtorch.hub\u001b[39m\u001b[39m\"\u001b[39m})\n\u001b[0;32m--> 611\u001b[0m u \u001b[39m=\u001b[39m urlopen(req)\n\u001b[1;32m    612\u001b[0m meta \u001b[39m=\u001b[39m u\u001b[39m.\u001b[39minfo()\n\u001b[1;32m    613\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(meta, \u001b[39m'\u001b[39m\u001b[39mgetheaders\u001b[39m\u001b[39m'\u001b[39m):\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/urllib/request.py:216\u001b[0m, in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    215\u001b[0m     opener \u001b[39m=\u001b[39m _opener\n\u001b[0;32m--> 216\u001b[0m \u001b[39mreturn\u001b[39;00m opener\u001b[39m.\u001b[39;49mopen(url, data, timeout)\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/urllib/request.py:519\u001b[0m, in \u001b[0;36mOpenerDirector.open\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    516\u001b[0m     req \u001b[39m=\u001b[39m meth(req)\n\u001b[1;32m    518\u001b[0m sys\u001b[39m.\u001b[39maudit(\u001b[39m'\u001b[39m\u001b[39murllib.Request\u001b[39m\u001b[39m'\u001b[39m, req\u001b[39m.\u001b[39mfull_url, req\u001b[39m.\u001b[39mdata, req\u001b[39m.\u001b[39mheaders, req\u001b[39m.\u001b[39mget_method())\n\u001b[0;32m--> 519\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_open(req, data)\n\u001b[1;32m    521\u001b[0m \u001b[39m# post-process response\u001b[39;00m\n\u001b[1;32m    522\u001b[0m meth_name \u001b[39m=\u001b[39m protocol\u001b[39m+\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m_response\u001b[39m\u001b[39m\"\u001b[39m\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/urllib/request.py:536\u001b[0m, in \u001b[0;36mOpenerDirector._open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    533\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n\u001b[1;32m    535\u001b[0m protocol \u001b[39m=\u001b[39m req\u001b[39m.\u001b[39mtype\n\u001b[0;32m--> 536\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_chain(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhandle_open, protocol, protocol \u001b[39m+\u001b[39;49m\n\u001b[1;32m    537\u001b[0m                           \u001b[39m'\u001b[39;49m\u001b[39m_open\u001b[39;49m\u001b[39m'\u001b[39;49m, req)\n\u001b[1;32m    538\u001b[0m \u001b[39mif\u001b[39;00m result:\n\u001b[1;32m    539\u001b[0m     \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/urllib/request.py:496\u001b[0m, in \u001b[0;36mOpenerDirector._call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[39mfor\u001b[39;00m handler \u001b[39min\u001b[39;00m handlers:\n\u001b[1;32m    495\u001b[0m     func \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(handler, meth_name)\n\u001b[0;32m--> 496\u001b[0m     result \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49margs)\n\u001b[1;32m    497\u001b[0m     \u001b[39mif\u001b[39;00m result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    498\u001b[0m         \u001b[39mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/urllib/request.py:1391\u001b[0m, in \u001b[0;36mHTTPSHandler.https_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mhttps_open\u001b[39m(\u001b[39mself\u001b[39m, req):\n\u001b[0;32m-> 1391\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdo_open(http\u001b[39m.\u001b[39;49mclient\u001b[39m.\u001b[39;49mHTTPSConnection, req,\n\u001b[1;32m   1392\u001b[0m         context\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_context, check_hostname\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_check_hostname)\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/urllib/request.py:1348\u001b[0m, in \u001b[0;36mAbstractHTTPHandler.do_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1346\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1347\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1348\u001b[0m         h\u001b[39m.\u001b[39;49mrequest(req\u001b[39m.\u001b[39;49mget_method(), req\u001b[39m.\u001b[39;49mselector, req\u001b[39m.\u001b[39;49mdata, headers,\n\u001b[1;32m   1349\u001b[0m                   encode_chunked\u001b[39m=\u001b[39;49mreq\u001b[39m.\u001b[39;49mhas_header(\u001b[39m'\u001b[39;49m\u001b[39mTransfer-encoding\u001b[39;49m\u001b[39m'\u001b[39;49m))\n\u001b[1;32m   1350\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mOSError\u001b[39;00m \u001b[39mas\u001b[39;00m err: \u001b[39m# timeout error\u001b[39;00m\n\u001b[1;32m   1351\u001b[0m         \u001b[39mraise\u001b[39;00m URLError(err)\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/http/client.py:1286\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1283\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrequest\u001b[39m(\u001b[39mself\u001b[39m, method, url, body\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, headers\u001b[39m=\u001b[39m{}, \u001b[39m*\u001b[39m,\n\u001b[1;32m   1284\u001b[0m             encode_chunked\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m   1285\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1286\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_request(method, url, body, headers, encode_chunked)\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/http/client.py:1332\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1328\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(body, \u001b[39mstr\u001b[39m):\n\u001b[1;32m   1329\u001b[0m     \u001b[39m# RFC 2616 Section 3.7.1 says that text default has a\u001b[39;00m\n\u001b[1;32m   1330\u001b[0m     \u001b[39m# default charset of iso-8859-1.\u001b[39;00m\n\u001b[1;32m   1331\u001b[0m     body \u001b[39m=\u001b[39m _encode(body, \u001b[39m'\u001b[39m\u001b[39mbody\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m-> 1332\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mendheaders(body, encode_chunked\u001b[39m=\u001b[39;49mencode_chunked)\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/http/client.py:1281\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1279\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1280\u001b[0m     \u001b[39mraise\u001b[39;00m CannotSendHeader()\n\u001b[0;32m-> 1281\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_output(message_body, encode_chunked\u001b[39m=\u001b[39;49mencode_chunked)\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/http/client.py:1041\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1039\u001b[0m msg \u001b[39m=\u001b[39m \u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\r\u001b[39;00m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mjoin(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_buffer)\n\u001b[1;32m   1040\u001b[0m \u001b[39mdel\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_buffer[:]\n\u001b[0;32m-> 1041\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(msg)\n\u001b[1;32m   1043\u001b[0m \u001b[39mif\u001b[39;00m message_body \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1044\u001b[0m \n\u001b[1;32m   1045\u001b[0m     \u001b[39m# create a consistent interface to message_body\u001b[39;00m\n\u001b[1;32m   1046\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(message_body, \u001b[39m'\u001b[39m\u001b[39mread\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m   1047\u001b[0m         \u001b[39m# Let file-like take precedence over byte-like.  This\u001b[39;00m\n\u001b[1;32m   1048\u001b[0m         \u001b[39m# is needed to allow the current position of mmap'ed\u001b[39;00m\n\u001b[1;32m   1049\u001b[0m         \u001b[39m# files to be taken into account.\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/http/client.py:979\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    977\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msock \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    978\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mauto_open:\n\u001b[0;32m--> 979\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n\u001b[1;32m    980\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    981\u001b[0m         \u001b[39mraise\u001b[39;00m NotConnected()\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/http/client.py:1451\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1448\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m   1449\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mConnect to a host on a given (SSL) port.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m-> 1451\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mconnect()\n\u001b[1;32m   1453\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tunnel_host:\n\u001b[1;32m   1454\u001b[0m         server_hostname \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tunnel_host\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/http/client.py:945\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    943\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[39;00m\n\u001b[1;32m    944\u001b[0m sys\u001b[39m.\u001b[39maudit(\u001b[39m\"\u001b[39m\u001b[39mhttp.client.connect\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhost, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mport)\n\u001b[0;32m--> 945\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msock \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_connection(\n\u001b[1;32m    946\u001b[0m     (\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhost,\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mport), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtimeout, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msource_address)\n\u001b[1;32m    947\u001b[0m \u001b[39m# Might fail in OSs that don't implement TCP_NODELAY\u001b[39;00m\n\u001b[1;32m    948\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/socket.py:836\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address, all_errors)\u001b[0m\n\u001b[1;32m    834\u001b[0m \u001b[39mif\u001b[39;00m source_address:\n\u001b[1;32m    835\u001b[0m     sock\u001b[39m.\u001b[39mbind(source_address)\n\u001b[0;32m--> 836\u001b[0m sock\u001b[39m.\u001b[39;49mconnect(sa)\n\u001b[1;32m    837\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n\u001b[1;32m    838\u001b[0m exceptions\u001b[39m.\u001b[39mclear()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from torch import nn\n",
    "import torch\n",
    "import math\n",
    "import segmentation_models_pytorch as smp\n",
    "\n",
    "\n",
    "class timeUnetPlusPlus(smp.UnetPlusPlus):\n",
    "    def __init__(self, model_channels, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        \n",
    "        # self.timeEmb\n",
    "        self.model_channels = model_channels\n",
    "        time_embed_dim = model_channels * 4\n",
    "        self.time_embed = nn.Sequential(\n",
    "            nn.Linear(model_channels, time_embed_dim),\n",
    "            nn.SiLU(),\n",
    "            nn.Linear(time_embed_dim, time_embed_dim),\n",
    "        )\n",
    "        self.lns = nn.ModuleList()\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 2))\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 32))\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 24))\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 40))\n",
    "        \n",
    "        self.name = 'timeUnetPlusPlus'\n",
    "        self.initialize()\n",
    "\n",
    "    def timestep_embedding(self, timesteps, dim, max_period=10000):\n",
    "        \"\"\"\n",
    "        Create sinusoidal timestep embeddings.\n",
    "        :param timesteps: a 1-D Tensor of N indices, one per batch element.\n",
    "                        These may be fractional.\n",
    "        :param dim: the dimension of the output.\n",
    "        :param max_period: controls the minimum frequency of the embeddings.\n",
    "        :return: an [N x dim] Tensor of positional embeddings.\n",
    "        \"\"\"\n",
    "        half = dim // 2\n",
    "        freqs = torch.exp(\n",
    "            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n",
    "        ).to(device=timesteps.device)\n",
    "        args = timesteps[:, None].float() * freqs[None]\n",
    "        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n",
    "        if dim % 2:\n",
    "            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n",
    "        return embedding\n",
    "\n",
    "\n",
    "    def forward(self, x, timesteps):        \n",
    "        emb = self.time_embed(self.timestep_embedding(timesteps, self.model_channels))\n",
    "        self.check_input_shape(x)\n",
    "        features = self.encoder(x)\n",
    "        for f in features:\n",
    "            print(f.shape)\n",
    "        print('===================')\n",
    "        for index, f in enumerate(features):\n",
    "            temp_emb = self.lns[index](emb)\n",
    "            while len(temp_emb.shape) < len(f.shape):\n",
    "                temp_emb = temp_emb[..., None]\n",
    "            print(f.shape, temp_emb.shape)\n",
    "            f = f + temp_emb\n",
    "        decoder_output = self.decoder(*features)\n",
    "\n",
    "        masks = self.segmentation_head(decoder_output)\n",
    "\n",
    "        if self.classification_head is not None:\n",
    "            labels = self.classification_head(features[-1])\n",
    "            return masks, labels\n",
    "\n",
    "        return masks\n",
    "import os  \n",
    "os.system('source ~/add_proxy.sh')\n",
    "\n",
    "model = timeUnetPlusPlus(\n",
    "    model_channels=64,\n",
    "    encoder_name=\"efficientnet-b2\",\n",
    "    in_channels=2,\n",
    "    encoder_depth=4, \n",
    "    decoder_channels=(64, 32, 16, 8),\n",
    "    classes=1,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([100, 2, 32, 32])\n",
      "torch.Size([100, 32, 16, 16])\n",
      "torch.Size([100, 24, 8, 8])\n",
      "torch.Size([100, 40, 4, 4])\n",
      "torch.Size([100, 112, 2, 2])\n",
      "===================\n",
      "torch.Size([100, 2, 32, 32]) torch.Size([100, 2, 1, 1])\n",
      "torch.Size([100, 32, 16, 16]) torch.Size([100, 32, 1, 1])\n",
      "torch.Size([100, 24, 8, 8]) torch.Size([100, 24, 1, 1])\n",
      "torch.Size([100, 40, 4, 4]) torch.Size([100, 40, 1, 1])\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 4 is out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m x \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mrand(\u001b[39m100\u001b[39m, \u001b[39m2\u001b[39m, \u001b[39m32\u001b[39m, \u001b[39m32\u001b[39m)\n\u001b[1;32m      2\u001b[0m t \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mrand(\u001b[39m100\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m model(x, t)\u001b[39m.\u001b[39mshape\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n",
      "Cell \u001b[0;32mIn[21], line 56\u001b[0m, in \u001b[0;36mtimeUnetPlusPlus.forward\u001b[0;34m(self, x, timesteps)\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39m===================\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m     55\u001b[0m \u001b[39mfor\u001b[39;00m index, f \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(features):\n\u001b[0;32m---> 56\u001b[0m     temp_emb \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlns[index](emb)\n\u001b[1;32m     57\u001b[0m     \u001b[39mwhile\u001b[39;00m \u001b[39mlen\u001b[39m(temp_emb\u001b[39m.\u001b[39mshape) \u001b[39m<\u001b[39m \u001b[39mlen\u001b[39m(f\u001b[39m.\u001b[39mshape):\n\u001b[1;32m     58\u001b[0m         temp_emb \u001b[39m=\u001b[39m temp_emb[\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m, \u001b[39mNone\u001b[39;00m]\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/site-packages/torch/nn/modules/container.py:295\u001b[0m, in \u001b[0;36mModuleList.__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m    293\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m(\u001b[39mlist\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_modules\u001b[39m.\u001b[39mvalues())[idx])\n\u001b[1;32m    294\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 295\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_modules[\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_abs_string_index(idx)]\n",
      "File \u001b[0;32m~/miniconda3/envs/sde/lib/python3.11/site-packages/torch/nn/modules/container.py:285\u001b[0m, in \u001b[0;36mModuleList._get_abs_string_index\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m    283\u001b[0m idx \u001b[39m=\u001b[39m operator\u001b[39m.\u001b[39mindex(idx)\n\u001b[1;32m    284\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39m-\u001b[39m\u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m<\u001b[39m\u001b[39m=\u001b[39m idx \u001b[39m<\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m)):\n\u001b[0;32m--> 285\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mIndexError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mindex \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m is out of range\u001b[39m\u001b[39m'\u001b[39m\u001b[39m.\u001b[39mformat(idx))\n\u001b[1;32m    286\u001b[0m \u001b[39mif\u001b[39;00m idx \u001b[39m<\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m    287\u001b[0m     idx \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m)\n",
      "\u001b[0;31mIndexError\u001b[0m: index 4 is out of range"
     ]
    }
   ],
   "source": [
    "x = torch.rand(100, 2, 32, 32)\n",
    "t = torch.rand(100)\n",
    "model(x, t).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5000, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "from easydict import EasyDict as edict\n",
    "import torch\n",
    "import torchvision\n",
    "from utils.Datasets import MNISTdataset\n",
    "import pickle\n",
    "from rich.progress import track\n",
    "from utils.normalize import get_total_mean_std\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from utils.data_utils import *\n",
    "import numpy as np\n",
    "def preprocess_mnist_data(args):\n",
    "    if args.filter_number is not None:\n",
    "        train_ds = torchvision.datasets.MNIST(\n",
    "            root=\"./data/\", \n",
    "            train=True, \n",
    "            download=True\n",
    "            )\n",
    "        imgs = []\n",
    "        for img, label in train_ds:\n",
    "            if label != args.filter_number:\n",
    "                continue\n",
    "            imgs.append(torch.Tensor(np.array(img)))\n",
    "        length = int(len(imgs)/1000)*1000\n",
    "        imgs = torch.stack(imgs[:length])\n",
    "    \n",
    "    # check data pickle file\n",
    "    for i in track(range(60), description=\"Preprocessing dataset\"):\n",
    "        if (args.ds_cached_dir / f'new_ds_{i}.pkl').exists():\n",
    "            continue\n",
    "        ts, bridge, drift, source, target = gen_mnist_data((i*1000, (i+1)*1000), order=True, change_epsilons=args.change_epsilons)\n",
    "        _, metadata = normalize_dataset(ts, bridge, drift, source, target)\n",
    "        new_ds = MNISTdataset(ts, bridge, drift, source, target)\n",
    "        new_ds.metadata = metadata\n",
    "        pickle.dump(new_ds, open(args.ds_cached_dir / f'new_ds_{i}.pkl', 'wb'))\n",
    "        \n",
    "    get_total_mean_std(args)\n",
    "    \n",
    "    ret = {\n",
    "        \"nums_sub_ds\": 60\n",
    "    }\n",
    "    \n",
    "    return ret\n",
    "\n",
    "\n",
    "args = edict(\n",
    "    {\n",
    "    'filter_number': 8\n",
    "    }\n",
    ")\n",
    "preprocess_mnist_data(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<rich.panel.Panel object at 0x7f3ce6aba310>\n"
     ]
    }
   ],
   "source": [
    "from rich.scope import render_scope\n",
    "def test(foo: float, bar: float) -> None:\n",
    "    list_of_things = [1, 2, 3, None, 4, True, False, \"Hello World\"]\n",
    "    dict_of_things = {\n",
    "        \"version\": \"1.1\",\n",
    "        \"method\": \"confirmFruitPurchase\",\n",
    "        \"params\": [[\"apple\", \"orange\", \"mangoes\", \"pomelo\"], 1.123],\n",
    "        \"id\": \"194521489\",\n",
    "    }\n",
    "    print(render_scope(locals(), title=\"[i]locals\", sort_keys=False))\n",
    "\n",
    "test(20.3423, 3.1427)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24M\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from utils.bridge.models import UNetModel\n",
    "\n",
    "kwargs = {'in_channels': 2, \n",
    "            'model_channels': 64, \n",
    "            'out_channels': 1, \n",
    "            'num_res_blocks': 4, \n",
    "            'attention_resolutions': (0,), \n",
    "            'dropout': 0.0, \n",
    "            'channel_mult': (1, 2, 4), \n",
    "            'num_classes': None, \n",
    "            'use_checkpoint': False, \n",
    "            'num_heads': 8, \n",
    "            'num_heads_upsample': -1, \n",
    "            'use_scale_shift_norm': True\n",
    "            }\n",
    "\n",
    "model = UNetModel(**kwargs)\n",
    "before_train = None\n",
    "after_train = None\n",
    "print(f\"{int(sum(p.numel() for p in model.parameters())/1e6)}M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([100, 1, 28, 28])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.rand(100, 2, 28, 28)\n",
    "t = torch.rand(100)\n",
    "model(x, t).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "train_ds = torchvision.datasets.MNIST(\n",
    "    root=\"./data/\", \n",
    "    train=True, \n",
    "    download=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E0RCrrp5JK1mzUE84hpqmdlYc9QXChY3jOI7jOM4LybMpH5uSyE6nhqmeFDS34oJVTyafTU6N9ZMo0FkU5E5md2PpxJwuRVsKnXl7EjN6UsDMfSxa3ZFpn6TqZFiofS9ha4hpLQUT0BTqKcZSxc30eZqoKV1de+6VtFZyL6S9naDJe1XElA5Kb/UzRcP6AsHQPhNjJsbCqkt0MdOHTBd2dV+TgCkLITNtT3onF2VIgeNtTxoDaRsogyKpfq+aBB1qr4+mqRTOaq8PSmift+T6edWsJtjFsouuvqA878Lmr/7Vv8pjjz12Yt9rXvMafv3Xf/35PpXjOI7jOM6dzQ1jjxcJaEtRMzk1oV2kz3NU9GQs8s1ccuuvsS7WfpQ+UNaB3Ct5XUVB6ajCZtWETcfTixhOPS87QYM0R2QqSwu2K01r5V4WDEmCjEJs5W+lFxCrPT6q6Filg6YCQ5zn2FgXsFUg71Vhk/aUcV/IK0j7Ql7Tto2yKlhv0BckFmKX6VeJLmTW/ch+N7IKiXv7Yw7iwF4YWGmaP+IkYvJiuE0xobTotIIwlsBx7vj8dp+jsefKZsVm6BiHSFpH8qjIVok9yAjxuLpLOtTvLAYoofYUBZHas9PVGOhpLo8t1nSRuCmOzZd/+Zfz/ve/f3eS59DX4DiO4ziO86LkmVwXWQQHnBY1t8KxmYZVhhrlbFFbQEC9wC4d5KnZvofcLxybU+Klvt9S7NhJYbMQMLNrE212YYgFFCQUbKxrybkFBSBoL0gxSq6pZpJb6VkQJIY6sDMqFlr5XC/krpWa9VXc5LXVz7BXsL4gfSGuErHLrLrE/mpgHROXui13dVsO4pZ7u2PuCcfshy37Osxf3VLYTGImM7k49fGmdByVnr0wcjWt6MIBV+OK49hxBOSomASyBVSFbDZ/gSVBLoJY6wcqgo4KUWHUWRBfVG6K4ogx8sADD9yMt3Ycx3Ecx7m4nBXtPN/r9eVnUgXGLG64BdHPLe659tVodWxWNUEsrVoJWi+k/ep0VJHQSsbmD8d1gub6MjSDKVUstJsaGgu1Eq4QYqn3oTCOkXEMZOmwELBYe2ymuOMwCGKCDgHdhNqkr9ZK6er6c1t/mh0bIx0YZV2QuxL9KrFajRysBva7kYNu4N7+iLviwH3dIffFQ+4KGx6Il7k3HHG3bNlv7kiexAeTuJGF0Nndb6zjalnz2/1LeCrv859W9/LZ7V1cGdd8tj/gaOjYrDrG2FMGrZHaWoVM/QINRNBRiCgl1c9IirPbJiJnjdS547kpwuY3fuM3ePDBB1mv17zuda/j8ccf55WvfOWZx263W7bb7fz4ypUrN2NJjuM4juNcZJ6vi/kbOB5yVlP+DZr7rzvs9NpOp5ctn188d50jo6GWpE39LTrt19YwXy/ib/oFa+tJyXetyOtAXivDXWHuSUl71alJe1BWu56U6sTYCeemihm7Pv5ZWqJYbM33wVA1RAsxFlSNoIUYMkGNLmQ2ITPEyGHSJhgUHaQN7hTSAJiiayNsO1QVA/JeJO0F0l4TZXs1AS2vIe8ZZb/AKrPaG9lbDez3I/euj9mPA/d0G+7rD7kUNvyBeMhLwjUuhWMeCFe4VwcuqbAv1SXJGMUMsLoN1W2hpi/Xx7CxkbWMbGKPinE1r9mWSEG4FlfkoqQYSLFgWXZletNNa8/NfN/0jrS47IvM8y5sHn74Yd7znvfwmte8hs985jM89thj/PE//sf51V/9VS5dunTd8Y8//vh1PTmO4ziO4zhPy7NNEXu2b7cUJMv3OcsFWQqTM6KWd1HMT/+eJ17/dOedBmIuRUw4KWwsTKVocnIezM0g1tku412R0usuOaxvjfZrWoqY1Sb73mpfCuyEDTSBY4vt+lgWAkhjQcXQUF0ZVSNqdWm6UIhab13I89tuu8wYFeulnj+15v++RkJXZybM7lBeVXGWp9KzhctUeoOuEPpSe2j6kbv6LXd3Gw7iwN2xlpxdChvuDUfcG464pMdc0pFLKtwlHftaxxpkKxQxCoVs7V4msdOEj0Cwwlgyax1Yl4G1jnRSiFIIWhCpKWsnBOKSi61dnpbnXdi86U1vmrdf+9rX8vDDD/OqV72Kf/SP/hHf8z3fc93xjz76KI888sj8+MqVKzz00EPP97Icx3Ecx7nTORVVe6YYuUHc8TOWZ52aB/O0/StTI7/IydfNr1mIjGVDvyy2zxI3izWaSJvbUodb0u7rX+GX29OU+3b8LYh7Lm2WS9rTJhikJYYx96SUuOtJoStoV2bBMusa2Ymd3Vdg7WuqF+8h1MKtyZkJWghSxU3QQqe5ihspKIaKsV115KzVAVlLc4UEKS16WhUkomN1UsZ9Je0Jw6X6OdIa0oGR1wXbz3T7I/0qcc/ehrv62kdzb3/Mng7cHTdcChv2dctaRjpJdJIJGCAUCqPVVLRpe3JuJtdmMJvdmgIcWuRq6bmcD3gqH/BU2uNKWnFtXHE0dmzGyDhEbFBkqK5UvVFvo7VIaKu3bEguSDbIhQs5wKZx07v67733Xr70S7+UT3ziE2c+v1qtWK1WN3sZjuM4juPcySxEzTMOrDzrdTfi9AyYU0JGgp4UMNNxp8q/5ob6pWui9f3n7VMCZtdPMj2enq93c2lZrOeZB12qYJFZ2JQou/jjWyFsWslT2muJZ31zOTpr24Z1BVYF7TOhpYdNQmYSLnXb5o+si30iVsflNIei0+rcBN05FypGlELUjIpREApCFzJDzNWxWRWkKLKCPNbvRjOMucYmA4x7LflsbyfM8rpgq4Kua+rZXj+y3w3c1W25FLcchC37obopKx2bq7IUNYvvqyYZnBA1IzYLmtFqb002YUTZWOCwrDgsK45Kz3Hu2eSOTY4MKZBSqAEJSSHVyGdNILl+NklUMVNowz6bYloM0DG7iB02t0DYXLt2jU9+8pP8hb/wF272qRzHcRzHucicFjWnBc0yDplncGlOC4BFE/4JMXOi3Gu5f3JkFFoZ2FQOVt2TRc/L5KYs5rec3N6taddM37Yn4RKkCYqdmCnTPJfIwrn5Qr7gZ4eFKm7yqp679DU1zDqj9KVFIRuhz3R9IsbMKub2seyEgKkfeydupsc6i5t6C1JLzlQMxWYxE6Xtk1Jjk4Ow6hJjDlhRUmeUYmCgTdhIkd0wT6pAy2taX42R14atCrIqdH0VNQf9QtTEKmr2td7WMtBJYi0jgULA5jDnjKGLUrPRyuzSjE1vDKaM1L6gAeWorDiyFduWjnacO4Yc2ebImAMpKZYUaaJG2q1uWxU3hdmtIRtik1tjUC6mqIGbIGx++Id/mG/+5m/mVa96FZ/+9Kd5+9vfTgiBb/3Wb32+T+U4juM4zouFyZ1Zpn8thg1KCDvHBc4uSXu6vpypKT+EKnAmIaO6eCzX3U+lYSy2d/ubqxJ27ko9J9cLmOv27xyYEpuYiMwCp3SLQZXT89NMl5tt2jR3qPSGxXqb57rEMouZdZdYxzrjZRXSLGCm+/pW075y4rnpPi72q9QytKnkbHrdtG8qUTvuO4oJIsZREYoGcqhfjPYtBjlWUQAt5KBflJ+tCuGuRNcn7trbcu/eMZe6DS9fXePueMxdYculsKlOjQzcHTZV2OjIWhKdnCz1KpRZ1GwWLs0kaEZTRgsMBMbm1lzJa57Ke1xLKw5zz2HqOR47hiGSx4AtStDCIIS5DG0qRTMktVsxSE3UXFCnZuJ5Fza/8zu/w7d+67fyuc99jpe97GW8/vWv50Mf+hAve9nLnu9TOY7jOI7zImLZU7PscbnhkMrTYQKnxM4JR2dq0A+KxcV2CG2OStiJl9bfQhBK0J1bEqoLM5eMyc5FmW+LcrN5XsvTChsoneyETZz21dku875pzku4BReu02DMWJ0ZbaVmIRS6WGe7dCHPwyp7TaxDVRFLQTJ/9dM+zhA+7bhJvCz3TceFVupVWjnXKiS2IZKjMHSR1EyKsqr/beQClFpGB1BWtXwur+vwTVlXp6nvEnvdyEEc5vk0k6jZ1+1CzNQytJ7c3BojtN+xWG3yL03QnCVqNhYZLTJYYCSwsY6N9WxKx3Hp2aSObYqMWckpUJJCK6WTVnomeVGKVqZ7293MoBQo5cKWocFNEDa/8Au/8Hy/pXMbEL7sS851vK26c5/j03/i3nO/5vhrD891/H33nO94gP/5q/7huV9zUfh/H12fZPhM/Hd/+0+f6/gPf+V7z32OT43H5zr+rz35Dec+x4P/88X9f/yOc8dxIpGslZ+1MrFZ1EzuTZvRcePX36AHp833sNBGt7fBkxbreUpsTs0kZqKccGOqmGnbi9jdEheips1GnMvQFuKm7ufE/mk45ezYdDU6eXZLQnNLujrjRWJBb4GwEamxy10TM1FLFTNaWMU0i5n9OLLS3WPYiRC43p05/fzp556OIAUSEGHTbcml/s7jakTEGAVKEXKs5YOmVQgg7PqC9gqyynSrxP56y16XuHd9zB9YHXFPd8xLu6tc0hYUoCO9ZNYynggN6JqDtGTZUzOJmq0FBqpTs7FuFjYb6zgqK67lNdfyiuOptyYHhhQpWWBUZJRafja2HpsRwmhzYIDkdp9aaEAp1bmZRM0FLUe76T02juM4jnMz+at/9a9eNzbgNa95Db/+678OwGaz4S/9pb/EL/zCL7DdbvnGb/xG/s7f+Tvcf//9L8RyneeINEdmGX8sy8GUMd44qWzJDdLILOzK0KwL8zR660JzaYTS6U7ExJ2gmYTM1MQ/CZFlGdlUNnamQ3Na5MzCxpqgaY5MK/0iGNJntKsRyH1fHZI+5rkP5WYyNfGvQiJqmR2ZTjO9JlZaL/L3wtRUn+kkn3BpgBNN9qefO01pdXxTqHNZNBPtgp4hlsI218vboAUDNiGzCYWtGmVUSqi/r5T2Xuua3Nbtj3R9Yn81cu/eMQdx4CWrQ166usY94ZiXxSpsDppbU4MCCmtJBIxOShU2rccmWw1AmObVLJ2ajcUTpWejxerUlCpsruY1h2nFYeo5GnuOh1qGVoZQAwNaCdqUhBYGm0vRwlSKluuNVGp/zeTWFE9FcxzHcZzbli//8i/n/e9///w4xt0/bz/0Qz/E//Q//U/843/8j7nnnnt429vexp/7c3+Of/2v//ULsVTn+WQxuHIWNZNzc8NZMddv25RyFkMtHZtETazOzOTQzE383a6Rv0QoQXYCZGr2X5aMLR2bWcDYjUUN7ErRgmGr6s7Q1ZQxDUbXJ7pYxcxBP7AKib040i96WW4WU4/LShNRq0ux0pGopaaESaLT1PpNJicjnXiP064GXO/WAGSU3JRgQcmm5NaaX0wW20rRAYC9MDJabcafRI6ZUIqSgGx1iKlNCnOVCX2Z088O+oFL3Yb9OHKp23BPOOaeWGfU7Etza6hCraeltTVhswwOqGuuomYawDmVn2WE0QJjK0Orrs1UhhbZlsi2BIYcGYuSi1JyLUGTJEiWufRsl4g2RTszixoxQ8qiv+aCOjUTLmwcx3GcO54YIw888MB1+5966in+7t/9u7z3ve/lv/gv/gsA3v3ud/NlX/ZlfOhDH+Jrv/Zrz3y/7XbLdrudH1+5cuXmLNx59swiRnduzSRqJrdlavRfzothEYF8lsBZxDdPTk3pQxM2Qu51J2aasMndySb+qQxtaujf9b1AibZILduJmereLIYsCtjpoYuhujNhldGQWa0S6y7Rx8Rd3cBeHNmPw4mZKmsdn9H9+EIJ1Eb+ybGYhEsQq70mklHKyfsz1nSWkJnef3JhRostClmrsEHqPltuCwWd15S7ur3SmsS2iR3rmIihMKbAto8MfcSKIMLcT3PP3maOdH75+hoHYctLu2vc3z3FJd3wsnCFAxlZSZ6F2VLIBGG3vfg8mUVfjelCwHRsSs9h6dlYz2iBa3nNUem5llccpRoasB0j4xjISZFRa/nZ2IICmlsTBjsRHFDDAwoy5pqKlksVNVYudICACxvHcRznjuc3fuM3ePDBB1mv17zuda/j8ccf55WvfCUf+chHGMeRN77xjfOxf+gP/SFe+cpX8sEPfvCGwubxxx+/rrzNeeGZXBmZhmVOomYqP5vSy5azZXYvrvc3KkVr/TSlC1hXe2pKKz+roobm1LTZLU245L711kSa4Km9MDa5N11LDmsiZSdk2rbabnClME+UF20xx7FOvF91iYN+4KAb2I8D9/VHHITazF57P465NxzVyOFbIGzqV1l28cbNrQjNzdk10duJ1yx5pmTqKQq5IIymDCjFlI11s9jZWDc7OUpdzxiqrOikCpvDVEvkVIwxB45ixyZmSlFUC+susd+N3Ls65q5uy93dhpf1V7krbHhZvMrL4pUmbI5Zi3GeLuK8DA3gelEzBQVsS3VqjkrPUa6za45SzzZFhhTJKWBN1MgoJ8XNeLIETceC5oK2/hppoQGUhai5oEM6Xdg4juM4dzQPP/ww73nPe3jNa17DZz7zGR577DH++B//4/zqr/4qTzzxBH3fc++99554zf33388TTzxxw/d89NFHeeSRR+bHV65c4aGHHrpZH8F5OmYRs3Nrpl6YE05NDCfjmafXLu7nRDI9/f6yEzWxiZquCZu+9dFEyLPAmSKDa+P53GPT2+zYzM39ncHUFxPLCeECrTm+iRpVm4dT1nmfZS41W8eRu/sN93QbDmJ1Eu4KG+4NR/OF97264UDSMwqG54tw6usM8345tX9pQz17MsamzX3ZWKEzJYugVhgtkEWh1BK1gUAvgaIjGxsZW1LDdK9iVRRJnB/nogQt7HUje3Gck88uxQ13hc0cFHAgAwcysBZjLUK3+Cy5CbZJJuR5Zg3z/tFgY6EloHU1/WzRUzPNq9mU+vg419k1YwkMbW5NSQKpBQa0BLSahrYLDJj7akqNeZ7FzDy/prk23mPjOI7jOLcnb3rTm+bt1772tTz88MO86lWv4h/9o3/E3t7ec3rP1WrFarV6vpboPB3XJZjpqYct6azvq5DpeiQGiBG6WKOZpyb/EKClly1jlZfnqaVgJ6/IrQ3XnMrO8krm/pncL0rOul3J2ZxU1tuJ1DKLJ8WM9jU5TEOd8TIJlyA7EaPNqQmt8V+oF97T/Je7+w0HYeDe7og/0B2xr0Mrjzrmbt3wsnDIJc3co4F92UOfg4i4WYTTkds3IJ/hICQynSVGK9TOo8JyAmkxZaSJhNJxWFZsrAqF2r8Sau/NYrDPFHwQtMwDQKNOwz9rolkxobRenkmAdJY4LIkshU5qEAC0HpoWM10QMtJ6f6btnZg5slVdYwsImMTMUenZlshQItdSdWquDms+f7THZugYNh12HJGtErZC2AhhC2FrxE29D9vq1IRtQcdagiZjRlKBnCEXrJQL69RMuLBxHMdxLhT33nsvX/qlX8onPvEJvuEbvoFhGLh8+fIJ1+bJJ588syfHuYWcmjUzz6g5NWBTQqjCZtUjMULX1ZECIWCrOIuZvJqSzKrDMg++XCSQAdcJnimprIRdZPMkZmwuP5u2d4EAJZ5KLJsETaiiRruMBCPGTIxldl+mi+n5HoiLi2xt81pEjD7UEqqDMHAQt+yFsU27384xwzVquNBRnRFFnrWYuJ04veZJ6BQzxpYoNrkeR03AbKzjSl4zWjwhFK7lVWu+jxymVY1LLpHj1DHmwDYHhhQoRauDBqSiKMZQImOsLk8VHFUkXdE1m3CNTjI9maH5U8WUYXKHCOQmvKbeoJ2wiWxLx9W8ZrTAUamuTLLAtdQzlBrpvMkdx6njcOg52qwYh4gdh1nU6LYmoYUtdSjnaDXuOZ3sq5HcYp5T3Sbn5tYYZoZd0BABFzaO4zjOheLatWt88pOf5C/8hb/AV3/1V9N1HR/4wAd485vfDMDHP/5xfuu3fovXve51L/BKX8QsRM0U4zwLmalfph0nIUAXkdWqOjR9h607LATKOmJ9LR3LK50b/KuwufHgS1jMkqFFK4eFCzM5M4ukM4s7MVN7atp2t+ufka4goc6SCbE6NX3MdDHThUx3AwGjp/apVOem1+rYLEXNSqcI5TQ35QeMIILesiK0m0/BakQyxkgVNUeliZmy5qhU9+NqWc+C4ThXt+Y419tQqlCoYiayzYHURE3KipkgYpgJY2xBA21mzLYE9sLIUew5Kj37OnA17M3f+2DhRErblNBWFolnxerzo4VZaB2Xvq6lRDY5kkrgOHUkU8Yc2KRYAw7GyPa4wwZFNoGwaYKmiRptgQFhhDAUdKhOTe2rKdWpSflkb41d7OGc4MLGcRzHucP54R/+Yb75m7+ZV73qVXz605/m7W9/OyEEvvVbv5V77rmH7/me7+GRRx7hvvvu4+677+YHfuAHeN3rXnfD4ADnFiFaRcty4OYkZJZiJ0YkRmx/DX1HWUfKuqP0StoLlF7InZDWSukg97ThmfU0yxjlE49PPWdahcxuIOZCxEzb0TDduTKooV1B1dA2sFLV5pkyQQt9Kyeb5r2cFjEAsaV3hVnYTKljNUZ5SjurfR/HHOjAwezaZDp55kb8O4VlSVqhMJixMeFq6augsRWfS3dxtaw5yit+P+1znHuupJoilkpgkyPbHMlFGXJgzErKVcyUouSk5Ky1uk1gDIUQC9sx0sfM1bjiarem08x+rGENK83cHY+J2gIKLCzETHVmasR0fbzcHkoglcBQwryusWgNBSjCmEMdHpqVnAIlC2UIyHFAByEe1/IzHSEe7krQ4qaWoIVNIWxb6dlUhpYz5IykDCljeTfL5iJHPruwcRzHce5ofud3fodv/dZv5XOf+xwve9nLeP3rX8+HPvQhXvaylwHw3//3/z2qypvf/OYTAzqdF4gWBiAqJ0VNCIvhm7uZNNJ6aeg7Sh8pq0heB3KvpD0l90LuhbRHFTl9dVnquU45NcJ15WnzTJk2Z8Zk4cYoMCWaTYJGDY0FCbvUshB2pWah9WusYiJKve91J2ymlLClgAkLpwZaHwj1fTrJ7Icta0lVzOjIWod56OXJuOHbp7fmvJzVY5OpJWg1Ea2miR2WFUdlxbW85mpecyXVQZbXxhXHqbo0VcyEWTTkhaApRSlZsLz7j8NinXFjBikrQwrkokStwz43uaPXxHHu6DSj2Anhkqb7EiitxyaV6ggV6hpSUVJRxraW3bqqoLFS12SpzaoZFd209LPt0q2xXQnadBsW5WezqClV1MxiJl/4qGdwYeM4juPc4fzCL/zC0z6/Xq9517vexbve9a5btCLnWSF6UtQErS5NWIicGGva2eTUrCJpP5IOAqUTxn0lraugGQ+qoMnr2syPLkWNPY3ImWKXqzMDQCygNblMYnNkmogRqX0zQapL07ftqIVVSFXgaKYPTdiEKmw6KUTN8+yWybUJbcAjQJAyuzhByixe7gobekknpt5Pbs1aMr1ISx678zhL0EB1awBGpJWhrbha9ria13w+HXAlrbmWV3xue8BR6rk2rDgeO1KuAmIWDSlgJliBkhSKQJZ6PyUfJyEHoyRBghFCYRgjIZRaQthKCfuQ599tFi7WggNMyPM+qoBqg0BnYZWbgJpETK6pbqTqHkmZhm+CjjuXJmymnpoWFjAsnZqCjhkdMqSCjKk6NbmVouWMpV1/DVOAwAUNEXBh8yIkv+H/cO7X/M33nO+C4Eu7/tzncG4uo+VnPugUP/63vvPcr4mH5/tr0Ov+8dvOfY5L/yk980ELVp89Pvc59n/lw+d+jeM4z47JrTkhakKoc2liDQtApIqa1ldT1h15HUgHgXFfySthPJicGhjvMsrKKGvD+nJq+OVuVkw9f7ufRt1MjfxqqNYL2zkxK2SC1iStrqWWdSETpMwuTNQyixjFaulZEyUrTfNf+Tupr5ucmeU8mOnxbrv12UhiX7f0kuep92sZOZAqlqYyNL0De2xuJGomanBA7VE5LCuu5jVP5X1+P+1zdVzz1Ljmc5sDjseOw23PMERKEzRWqnixScRkgVLFAwUkL4RNAILV8AkxSjDSVGoYDA21xHBKtQPqOUwopd6w+rje2J3fqOc/vYYsaAGZ1jJvg+Ya6xy2bT7NxqqwGW0uP9OhEI9yFTXHIzKkGhQw9dXkAinVErTSBE7O3mPjOI7jOI5zUzhr2OY0h0akRjnHgHWB0im5b6VnKyGvIK/bbWXkfaOsCrKXCX0+MR9GTtyf2m5LqRHAuyjg0IRNF6oomZwYbYImNgemu+6+3Zbbrcm/lo7tHBtgfjwN1dT5cXVzOsm19Iw8i5qe0pLQahlaYOfY3ImJaGeRzcgYuc2omQdbtnCAwzbA8njsOB46tttIHgOWdSdmClDkOjEjuYmLSdhkam9VFtA2fygBatXJCQpqpGCzOLb2eptElLXzLe+Ndj5p61icexIybV2yEDZSWAgbm92aMNJina0GBkyRzmOGMe1cmhYYcFrU1Fk2FzcRDVzYOI7jOI5zKxGdy9CmKOcTwza7CKp12GYXsT5S1pG0F8h7ta9mPBDyGsZLkPaNvC5wd6JbjxzsDRyshhtGKN8ogUzFiG17cliW25NomcrHTrswSwEzCZKwEDPTY9iJF9gJmsm9WT4fWuLZWkZUSg0KoNA3p6YDOhE60Qvn1sA03FLnGTXXWk/NU+Mel7d7XB1WXDlaMwyRfBxh0IVIWIqHtm318eSayCRs1DAVNDDHhFuwOVRi2s5h9xtN74ntnBem92znwhbiZRZXu3VIG88zi5lZ2LQI56HGOMft1EtjxE0NCNAho5uxCpvNAK0EzUqLdjabI56tBQlYS0a7yLiwcRzHcRzn1qMt9awN4CTG6tSonnBqrKvzacpKSCshrSHtQV41UXNQsL3M/qUNB+uBl+4fcm9/XPtZmlgBZiECzL0up/tcph6X5X0ntYx3KVqm19Tjygl3ZbevipJO0omSs7AUNUsxIycvOKfn6ntUh6Zu19KzXoQOmUvQatzzndlncxaFUuOem1NzVFYc5Z5ruQYFXBtWXNusGLYdedMSxLayK+taCIu5f6VwQmzMX7/UFL1pptEsaKbeKz0VHw5nvJfMomUWVLNjc1pUTTcDA83shFcTOZqMMFqdT7OtDo1kI2zSbvjm8VBLz4YRG8YqWnLrn5kEjdW+Glu6NRc4RMCFjeM4juM4t5blMM65HK0N1QxaRY0qpnVGTemU3EmdU9MLpa8hAXldsHWm2xu5e3/DH1gf84q9K7x0de2EYwJLYWKn7s8WKjD1uUxzYsp1TsokYOqxu/c72Stj7bGdEDLzV3Fq33Tu6bV17bSSM5qAqeVnXSs7u9Nm2Dxjb83iOxnqrzDPgjluAyy3bd5LHhSGliA2yFzqdb2wafen9gPXixmYnZsTQRO6e43k3fucEDDllLhZCBgp19/qfptfJ7lua64OjWRrAQE18UyHRenZMNYo5+0WUpqHb85zaxaC5qI7NRMubBzHcRzHufXIKbcmhurUdLG6NKrYKlBWgbxS0lpJezAe1KCAvGdwd2Lvri33HBzz6rs/z3+2d5kvWn+W/6z7fTpJdOSdCFmIjjkmuYmN6s5cLyjg5HyYsPiL/Yn9J46RGxxzYzdFT8Q0X3/c9NpJvEznmB7rBeqvWYqaDCeCA66kPa6NK65uVxxuerbHHXIY0WOlOxR0e0owGNeLGRaiZilsOLl9/UDX9nAhbE44LqeEzSRQ5u1laZrZidfX9djidYYkQ1IbuDn10aQ8p54xjNg41oCAYazuzCkBM/fSLEXNBXVqJlzYOI7jOI5z69EbXIRPF/kKJrUEiFYmdOIWaxRzFzPrmLjUbbgnHvOScI37wjXWMp5wS7q5ZKw13LcelaUDsljEbplPMxvmRmLlRu7Jc50zc9b7nS47u5GoOY878lx5uhK40+u6cbzzyXVkrPXYREYLzbEJbHLHmAJpDJSxDrDUEXRbm+3ncrCFoKnbdr3YgbPFDSfLzmZk99oTjk1ZuC5nbefF+ScRc0LsWH1+2leszaQpSF6ImlxgGHdpZ03Q2JiqsIGznZkLLmaWuLBxHMdxHOeFZ3nRP5emNXEDrVRIdoM0gxFDnSOzF0fujhvuCcfcG464Vzet0d4Ii56UKmB0brYPIsTmt1wEt2PJUkAsRUOhnDru2V30nn7dUmyddKl0sS3PSVgVCsWMbJARBotsSx2Uuc2xDtBMrQRtEMJWanLYppZwXSdcliKHM8TNeWgi6WRJmZ0QMSydl7Z9QsDM5Wltfy5tTTaLHkpBUtlFOLehm5NLU+fTVEFT3Zrzj3S4iLiwcRzHcRzn1lPaBe90gVdK/YN4KZCktjaooLlQsta/emer/RMZJE9DEYWx1P6L0QKDBUbTWk4mrddA2gW81D+5176WQiBQsAvVdA9V1BRsFiPZFttYFQ3tKr9wtrg56zJ5KVGWPT8YdFO5XBsWugs0OFkudyMhc3L9dX0jwqb0bEvHce6rsEmRcQyUISALUROPrQkbdg4NcCOXZrd9fnWzSzVrpWhp2UNTe1pmobN0YqyKHZjEzvTf/uToTMe2PpnJpWmihpZudmLY5gWObn4uuLBxHMdxHOeFoVi9Qp6anVujs6hhpSBFoV0A1ovEFuULuxkibep7aYMcC0pGyAhqUntnrOUVmJ0oLctmc47BReG0QzKJmqWgmcRMC+MiL66Nl6/OpwRfXtRnzWV9Vl2xjFUhY0ZB6pweCyAFRZ+1oDl9vum3HE0ZciAVpeRQxW9qZWjtFgarwgYWgqYJiTMcm+fCXFI2lZfNAmbn0kyOzSxkJqFVdsJmFjDQBMpC3JhBLlXUTMM2pyjnFtl8YtDmiyQY4NngwsZxHMdxnFvLXIbTomlN6z6Z/gotCAqpRtxqspPDC1vylRWpF79FGU3JpgwWKKa1nEmkXZzXi/lAm9nYXJyLVXy2Y3JrRsvV+bAyi5mRKmQKMJhSkBMCpjTxstw3bRdblJlJmQeFqhl9u+8EegFt3/FS3NS1Pb2YmZ6vycjCaJFt6RhKZCyBIQXKqEjSub8mtP6auK3/rQA7EbNIP1u6OCdEznl5ph6ZSdBMQua0K7N8DGe7l8V27sxyPs3CrXkxzKU5Ly5sHMdxHMd5YWgXcmaGtIs70bLrqylS+w9y/Uv8JGymQYdWhJyVVJRUQmsyjwyEGs08XbS2i+7JtemeYxP/ncRUzlXMGJs7MxqMVDE40sQgwmi1zyhP4qMJmEnQZJtEybJ/prDWkd5qjPaaOjy0/p/RtVcsxc1ybUuuEzsYg9m8ttEC2xzZpsiQIjaeEjUbI24K8ajsBEf9IMD1QQEnBM45mV/7DOVlNyw1m8XM4vHkKp3el3IVMJOomZybeT6Nl6GdxoWN4ziO4zi3nuVfqad+AZH5L9YAotoask+VohVpggjMpHXM1Ivg3MrSCkqWeoGdW0na5Nrk1ldT5j0Xj7kBn5OiZrQqaIY2GyZP96fES0au39eE0DTXpxRllMSamkBX5ljqKmjCYi2hPVqKmrMEzYnPYHUNowWG0srQikCufVY6laIl6jDLoczhAbB0RNobLsvSOLnvXCxLy4zdf69PJ2bKSRGzFESnBc78v4ecqysziZplCVpxp+YsXNi8CFl9/NPnfs1HNg+d6/gv7Z489zkuCn/pM1977tf8r9deeu7XvOeL/x/nOv6p5/CXnft/6n8592tuR/xvWo5zG2GFadqhmSFWoGSs1MAARKZU3ZYIFaprM5WgzeVo9QK3FGHMgXHp2Figk4S2C/lOSuu5qa5NRy1He67xy7czUxlaFTTVrRkNNqaMKBvbfU8b61rYQr0czFRRmGchI6dEjVKs9s/0kljLyFpHNtJxIAOdpObypFqmhjFSQLSWps1rXLg3y8S2E8fAgLKxjk0rRRtazLOMdRinDhC2RtwY8agQjxI6njGzZSleplk2X0gE8jOJl8Uxcsa+9mGvX9/UQwM7QTPdT8M3cz5Rhmbu2pzAhY3jOI7jOLecufxMpPXWtB6bqccAakla6zeYXZsii+nuVdiYCcnqRfcUIDBaJGBkqe6MLlybi+vT7JhK0EaaU9NKzzbWMVpogqGfk+TOEjDL0rSd6JF5yGnWeuxalKA2PzdKLQPspFz3PZ8lapaCZtqXjTkQIpVdcEDOWkMD0hQYUMVNOM7ocaozXxacEDA32j7fF7t7/Wn35bSAOevYyWlZnN9OHLt4vg3ctClEwBa9NcUWAzhd3Ey4sHEcx3Ec5wXDzFrKWcFMkJxbLHNprswkanb9NSwnx7fI59LS0fIiFS0jKEIwuW4AY259IIWCXjCZsyz3ygbjQvTNbk3pZ5EzWJy/u9MiprRQhrLotwlSWmR27cvJIoTWQzNIprPcIrZbGhtGkad3aU7sa7fp/FOpXM6KZUGnRLQEOhphKOiY0e1Y+1KeRsDIF+pwnHZZnoWAOZFgdlrALN/r9HOTS3OGqNkJIC9JW+LCxnEcx3GcW4oVQxbuDHn3V3YDJCUITWykXNPRUgsQmFybTHNslFxqgMC2dPOFe8DmWxab07+09Z2oyCxuLiJ1Dsyur2bTSs8Oy2ou7zpq22NLkpv6WaD1LJW6XZ+XebuTjLbvdGWRUetxQ7sPWoDEKELXSuK6xbrgxoIGpuADaQKsY1tqcEBKoQYHLJyauDHCcSFc3SJXj+rwSrihi2HPh7uxFEd2vQNzpng5JahOip1T4uSUODrt6NhZ7+8ALmwcx3Ecx3mhKAV00WtjsitJm8ROKUgpbShiEzW2i/Gt13xCskBZOAxZTro2J+banF4GdqE8m8IU71z7ZAaUgTD309T+mp6jsqplaaWrws904dzILGjKFAvdtrdSY547qb9RwFjLSDCbzxEwsiVKK/97Ni4N7NymaYbN7jcVStY5OEDmUrSCDhmGERsGGMbrv5DnWwA8F5Fyo+fPeo/T52jrt2cSVI4LG8dxHMdxXgCm0jPYJaK1efcGoPU5yQXLhqQ6sHMqSau9NnWWTWmlaLsAgUDXekWyKMFsnmtDO8tFEjJLph6WOphzlxY399XMbk3PUeux2ZSOVGo/zeTeTI5NMTkhapIpUQqd1pjnqQ9nEjmdJNY2olIY0RPlaBNPK2jm/btEtG0JjDnMiWi66LHR0QjbjBxvKUfHVdzcbJ6pnO1ZCqln1fh/1nu5mLkhLmwcx3Ecx3nhWLo2hZYKkKHFOEvOSM5oNnS0+YJWRpBBKIMydpFrw4qn4prPpwP2dWBft6x15MC29WJbRtYy0knmQBKdFNaSGaUQEDqZYo7PcHSe5kJSm1gKyDyEckpbUxRtvSlBvrBxoPmMC9xyxlo3lhitcGRwtXQcWsflss/VvMdhWfFU3ueo9FzNa66mNdscOc5dc0Z2tzS7NTLHLk/bKkaUwqV+xTokDuKWp+Ie+2HgnnDM5XDAgW65Gg65pBsOZGAty6b+Z06ju1p6rpY9rqUVm9yxHWMbzNkinkfQVCOeZUjYdsA2Wyyd4dhMuCC48LiwcRzHcRzn1mG2u64thimza2OqLf5ZQdnNuMktGa3YichnTUJJSk7K0dhxbVxxedzjUtg0h2JkU7oqcHTLRjrWMlJkoJPMKJksmU6M0ITD8vJ75yZcvw/qEoO0e5gHf3ZoEzo1UlpRsPKcxc0kasoJp+P6gZeFwmiFjRmHFjm0jqtlj8v5gMOy4lquwu84d1xJezw1rhlyZJMj1pyZVLRuW3VpAHKposZaUENQI2ghmbIKiaPUMfSRXhNHseee2LPS+t1f1S37uuVAt0AtWwPQFiYQKCf2T88dWs9h6dmWjk3uyGUqQ5tmGbVAiVpv12LEi4uXFzkubBzHcRzHubVYwYoi8wTH6trMJWnaxA11ZgelIGMNEKiDGCFEQbegx0rRyLXjVXUStFBM2QsDe2FkX6uIuRQ2rHSkl8Ql3dBJ4kC3HMiASqGntBksy3ku02M9+bglgwGzC9RTWEmmb70nHVXorAl0AjzH7LVsZRY0hXJCxCwb8Wu5FxwW48gCl8uaz+e7uJLXfDbdzVN5j6fSHp8f9jlKPVeHNdfGniFFtinUQae2i8+eh5+25LliUl00awndYlxdjcRQ6GPms93IKib24shdccsqJO7pjqt7Fgb2tX7PNSo6oWIECp0kQttePn+17PH5fBeXxz0Ox55xDJCqYzOJW83UqGUXM07DhY3jOI7jOC8MzZ2ZXZsWjVs1Q+uEybkO6EyllqKNRhgEixC2gnVgQdked1xhSuSCdRhZh5G9MLLSxFNhj7WOrCTNIuegOQmdpNrsPjXQ0wIIFgMrp2GV05yXQCFIqSVvMtJL5pIe00lmPZW70crqgNUXWIo2iZpJ0CzFzNAu7EeDqxY5Kh2fz3fxu+luruU1nxnu4Upa89S4x+9v9jkcew63PUebnpyVMuosWigyx2gvI7WnbTGhaHXdUh+RWNBYuNZlQiisupF1l+i0sN8N7MeBXjMHcaDTTJRMJ5moTQC221prX079/kaOyorfTwdcG1ccjd1uMGdaDmpdzI9xcePgwsZxHMdxnNuBhWtjqohICxiogzylGJpL7ato80s0CTKCDkLeBoZgXNus6LSwDZFN7NiEsV1Yb9kLkZWmOaZ4WzrGUOOLA2UWMKPFRTJYvG6uy2gBxeg0cUlrqdt+K7XqJIFuWmlVpghNgDz3saBniZoRa7HIVdBMc182FloZVy09eyrvzaLmyrDmqe2a46Fjs+0YjzssCYw6i5bdjKCaPjcLmlb5JkUwrSWElgQLSo5G6RUJxhADmy4TQ+Go61jHFV3I7MWRKIU+JHrNdJrpNbHSRCeZ1WJ7qyNHpedaWnGcOoYUsGUZWmY318hwUePMuLBxHMdxHOfWY+1Cf+naTOImZ0wETOvAzlyQMSNjQAcjbA1TIRyDSb0AL10kF+HQhFyULmRWXaIPmS5kDrqBXtPc7D6VqV0Nazqtjs00CHJKEVsOtZwa6scSTiSD3RuP5lKr++I19nXLaJGiR2Qd6SzNs1yeRc/8CZaBAVP/TMYYrc6oybabU5Pb/eW8z9Wyx++lS3x2vIvLaZ8nj+/mqWHNte2KK4dr0hAoRxE9CoSxNuPvBMwpQXPWtgimUHrDApQApQ9YgLErjNEgGNpnNBghZrquujQxFFYxEbWwColVTPPvMrlre2FgWyJXUy2X2wwdNgR0lHpLVOemGJLLrhfLedHjwuZFSPrME+d+zd/67/78uY7/b//04bnPET5217lf8+//4t8692vOy0989rXnOv4Tb9w/9zny5c+c+zXf9rq/eK7j/7f/y7lPwav59+d/keM4zrPEiiEq14sb2PXc5DrAs5ai1VsYFQt1SKNFqSJnA1mULJENMITMkAJ9rO7BmAOrmDjSzFACx2HkONThlCpWwwQskEoVNksRM92noqSFsImaGfvAQdxyV9iiUhisXlp1klArjJbIZ8zOebaU5swAs6iZXJpJzGwstO3QhnD2XMtrLqd9Lg97PDWsubJZcbztGY462Ab0WImHgg5CGLihiJHrHBuqWyNQeqFEqrjpBAtGiQHrquCxLpCCkWJh6AsaDA1V5IRQWMXMKqbZ0anlg4mDGEmmXB1XbMbY+mukDmhNnAyScMfGWeDCxnEcx3GcW8vUgc5C3MCckgYL98YK1gIESK0UbTQ0CjqARggKZQuIkgWyBEpUSlFyVkIo5CIMOdCHTDFhCJEhRoppncmCMVoVLtsSm0OjDDnOomYsgVzqdtQy9/OkFoXcSaYEpW/BBJ1lBhsZrVCknPFFPDvmEjS4TtSMzWEaqM7SUVlxVFZczWuupVrOdTj0HG97tpsONgHdKPFIiIdV1IQtC0Fj5xA2YKGKm9JDCXJK5AgWDYtK6QtZIXeB3Jycsctsu0AXMmMXGLvAUNKcznZtXDGkSMnhRGjArhTtZH/Ns5oL41xoXNg4juM4jvPCYAVEm7hpV85tUKTRLlhzQFKCMSEhoNtM6OsxJdZ7ydW1kQSSlZzqBXXuArkrSCwMXUfsMjFmjruOPibWMXHU9cTWzD6JmDEHhiZixqJVzOQqaCaxpFoIobBNkWv9lsO+p5iyjfXSai11nsrdsiUvEsyeK8WqczO28rONBTaLwZujRTbWcTnv8/l0F58fDvj89oDLmz2uHK4ZjjvsOBKfCsSNEA+hv2KELcTNUszY9aJmEjtTT4tUlyZ3Mgubug2lowqaILtthbJqAqgLlFUgRSP1hW3fEWJhsxo5jD2rLrFdRVJRhhzYbjryJhCG6i5NZWjaBE6NerZnPRTTudi4sHEcx3HuaL7oi76I3/zN37xu/1/8i3+Rd73rXbzhDW/gl37pl04891//1/81P/MzP3OrluicxeTaLMQNsBM4UEVOzlAikjLkjLaSNFOpCWlBQIyynRpY6n2JQilWR5ukOpOlFCHnaTZLizAGolT3ZWwT7sdSxU0u0ibeVzGTU5vnkgUJhmohtvWqGAexRhrvh4GNdaxtZEAX83Ce2yyb3Hp0Mi0kwGR2aiZBM1pkUzo21rG1OnTzOHUcj5E0RmzbnJpNLduLR9Ad1n6leGyzeLlO4BS7TuAg1O+/1yZghBANC0LuhBJtFjmz8ElV+OQV5KxYtBotXYTUBGpp83KCluqqpUDOCklOOjZtfo2Uum4p9gXKRuei4MLGcRzHuaP5t//235Lzbqzir/7qr/IN3/AN/Pk/v+sN/N7v/V7e8Y53zI/398/fC+fcBJbiZtpVdhf+oq0ELWdIubo2Q0K39ZjYVVEjVh2EOTXLmjuQoHSKRepFdFSsU6woY8xNvChBC0GMYSFmcp7ETJ3xUrJgqQ6JJAsEIwfjSA1rAmkdqkuzF0YOy2ru2xmtOi7nDQ+AWoZWrJah1WhnZWQnagYLbEo/OzdHecW1tOLaWEvQNkNHPg7IRgnHQjyqoqa/aqyeKoRtIR6m5no08TKLmeaGFHazYlqJoIkQVwELSumU0iumUgVlX12aKnKYBU3phLyCtBasE1Iyykqw3kgGJVfhqWK1FDArZQhI0t3smnRS3FDaUE4vQ3NwYeM4juPc4bzsZS878fiv/bW/xhd/8RfzJ/7En5j37e/v88ADD9zqpTnPhusavxciJwOikBKMIxIUhoTGGk8cVWoMdKpCRwfQEXRsZVCdkFetLG0MlN4gKikJOQZSn+ceHBEjpZ2gsSxYVmxsYqaAjIqU9v7RsGhspafkGhO9igmowuZaXDdhU+Oiv9BStF0ZmrbAgO7krXQclVWLd66BAdc2KzbHPXIUiIdKvCZ0V4zuEFZXMqvPbQlHI3K4qQ7NsmdlEgvzdv1drBREtfY/9R0WA8SA9RHTJhy7gEWpfU5dDXpIe0ruhbQSwr5Q+hbTva4hBClDGZXSB6b4oVIUBkW2QmghB5Jokd9tveWs/4acFysubBzHcZwLwzAM/IN/8A945JFH6hyUxs///M/zD/7BP+CBBx7gm7/5m/mxH/uxp3Vtttst2+12fnzlypWbum5nwemLVGuuTbGakJZaQpoWLBVsFEyMEKW9trk406BJqhNgYtXJKWCt9K0AI5C0IAIlK6VIHVjZnBlJChkkSZ2bU1oyV64uTu5r8tcQIpsU6bTnOHcclZ79MrQyMWW0TOGZe0HOKlWbytDqHJ2afjYsblMJ2mYqQcsdQw6kVIdvhnGXflZvRjwu6HFCjrbI0WZ2PmwWNGX3W0yPS3teBFQgrZAYIQYYIxIC1kWsKxAE6QM6KBbrbyG5CtKaflfL2WqsN2hQCgVDyV1oP73UoZzj1D/VRE1elKIt1+286HFh4ziO41wYfvEXf5HLly/znd/5nfO+b/u2b+NVr3oVDz74IB/72Mf4kR/5ET7+8Y/zT/7JP7nh+zz++OM89thjt2DFzjNhTdBIShAUGQMMAczQUMWr5HpRq32LBC7VUZEktRwttoCBrvZ95FQjiUsSUhJQamNJKzWTLLWvI1d3ZhoKqeOuz8MCWKQmfhEZgKv9CoB12OdS3KAYl+M+l/SYTo5Zly0qQkBQdgImNBGuKMUyihBE56jnqQytOjW1p+bIVhyWensq77EtHUe55/K4x+Vhj2ubFcOmRjuH1lcTNhCPje6olp/ptWPkaEO5cnV2ZCZ3xpblXVbq7zCJHREkBGQYsRCquOk7JEYkVnGDKtrFWv4XFRk7wiqgY0BMyaO0yGahjLV8MCeljEama+eFeKSELe1mhGEazmrIOMWBe3CAU3Fh4ziO41wY/u7f/bu86U1v4sEHH5z3vfWtb523v/Irv5JXvOIVfP3Xfz2f/OQn+eIv/uIz3+fRRx/lkUcemR9fuXKFhx566OYt3Lkxi0GelksdyJgyIlKHdoqgNNFhdagnQGkzTnIBjW2IZwYZBclGGQUZQ3Vn1Kq4mcRMOlvMLPs8SqzCJqwERCkS2HQ9AH3IfDbW2WxPxHtYa+29CRyhQBDoqIJGgWByQvAE2blNAIMZG6tJaIfWc1hWXMlrrpY9jkrP748HHJWew7Sak9A22w7bBmSrbbDlQhCkRZnZFKUNJ0XN4vs/K0bZJrckhNnNIecqerTU5ZeC1Oo8NFUXR0chbKU5N81Jy4JJFZ850SwdoAjxqMZ6x+MqzMLWCNuCDjVEYtdj4+LGcWHjOI7jXBB+8zd/k/e///1P68QAPPzwwwB84hOfuKGwWa1WrFar532NzhfAwjGQXDAtc0lakamUqaaTmQJWL5aBmo4mTaxEgCZWSi1ZM23la3lXaqa5HZPYiZxFOZSUWtamQ22Ut6ikMTCEyGHXc61fsQqJp9I+l8M+nSTWkuikEMxYS0bNmsgxAsJKFKiDYlSsBgdQ5iS0ya3ZWNfm1dRBnFfSHse542pacW1YtSS0NtRyBBl3Eck6fe5JJE4i5nQp12kxc6MSulLmAAgzaeEDBQFMMsT2HaaCqKBB0dHmyGjT+hoL0lLYpIoaqV/F5NboYISx3qo4q6JG2gBXj3x2wIWN4ziOc0F497vfzctf/nL+zJ/5M0973Ec/+lEAXvGKV9yCVTnPC1ZdhOrWtJI0qJM5AbXak6FZKcmQUmOINdUo4hJrqIDF6rRoL227hgyYUp2GJmK05SrrCWFjVdiUybGxOaBATAAlxchQhGtqfD5kUlH2wkiQwmCRbMpaRnrJrGWkk8xaMp0UOjOyZNZSRU1HmD/+aLBtqWeHZcXVvMfn0108lfe4mtZ8ftjnKPVcHVb8/lF1a8pRRI+VsK29NTrdxjrgVHIVBpZPOh6nS9Ce9mexGrc8/zbMadCgVsWNGagiQdBJPAmUpGA17ax0zPehE/LYHJxSE9zCYMQjozs2wqYQjjNhm5FtTcqzXNyxcQAXNo7jOM4FoJTCu9/9br7jO76DGHf/tH3yk5/kve99L9/0Td/ES17yEj72sY/xQz/0Q3zd130dr33ta1/AFTvnppST5WgitSStPa0iFIN5zmesZWmT2yKlhghUoWKUIGiLhK7CZhEjfIaYkQyabfd+rZcnbOrrNYBsFRNj6CNHQ4eKcXnYY0+HJmIG1lqFzb5sWetIZmRNJlMIUggYnUGRQrZpfo2QkYVTs+JaXnGYVlxLPVeGNcep49p2xWbbMW4jMmodaDlU12Z2OrKhubSo5CkUYFF+dkrUnFWGtvtN2tTOKbJ7SrGb4tdTi4lWg1FBpIY+hAJWS/KkRWljUJI0Ydp+Vas9QWEw4qaKmlqClmsZ4phaKV2+4RKdFxcubBzHcZw7nve///381m/9Ft/93d99Yn/f97z//e/nne98J4eHhzz00EO8+c1v5kd/9EdfoJU6zwVrgxjnC/F24SyqO4dABC0Fs9DK1RSxKj40Se21SVBCFTjTEMk8hQdAjXQuCzFTprKz08LGapR0K5WyIHUQ5RayKnkb2PQdIsaVcc1BHIha2NeBjY2sZYAAYwkUUdAtCIxWUAw9Ne8mtyS0aQjnUelrT03uuTauOBp7jsaO46EjDaEO49xOfTX1VmOSW9DCNP/ldKLYjUTNWc5N632aXJtZVZa8+02WtL4oAA01OABATGt5YOuBKq3cz2p1YBU2oxE3Ze6tkSZsyO1WPBXNqbiwcZ4V9737g+c6/mX/z5ec+xz5c58/92u+/Cu++5kPWvAfvu7vnfsc/+P/7U8880ELXn75fzn3OZ4L8sF/f67jX32+n9Bx7ij+1J/6U2de2Dz00EP80i/90guwIud5pxQsZyTJrjEdagN7yjU5LSg2ZqQLoEoZ2lwVFUKnlEmAtAGSJTaBI9IcG5tL0Wr/zW6f5Op2iIEko/RKiEKJoQ2vlBprXJSskU0wShF+v0uoGMmUQGE/DE3g9KxlYAwbMsIoI0ENJRPmNLQ6nHNEd2VoZc3VvOapcY/f3+5zdVzx1PGa7RjZbjrKYYdslXgsxGPZNdwP062gqVwnDGaeTtQs+3Eml6bU7GbLuTpkUIVGkfk3IbXem1wgR9SspqmNmdIHLCphaL9PFHLf0u6MKmZGI2wzuq2CRo9HGEZkO2IpXf8ZnBctLmwcx3Ecx7l9sdZoPvVz5IJIu9ieZhWF1s9RtM5KKUAoaFGshNqgXqwKH63zVCwIGmtJmmlzDxYuDTY12dtC8JTq6uSClID07YI8CqGDPNSStDIIZVBSiBwPHYexp9fEQdhjtEAOikoBhb5kOkkEjNFGRil0i0i0jDGaVvFjgbFEtqXOq9nkOjtnSIFxiJRtQKbys1aCpml5q4lotfHebthP87Si5syfyObQAAMkUAXPNJZUBUnNZWuvkWiYBcTAUkFyQKNQQpsd1KjCpqDbhA4ZUoEx1TK0lCBPoqo87RqdFwcubBzHcRzHub2ZhkOS57IzrEUKT65AjLXUKbQeD9U6U2Ws2xYVjdW9saC78rEoIFOz+k7E7Lbrxb9YEwTtXsdIGQPdSkGqYAqrOnDSOrA+kIGjrieGgoqxDoltiYwt7GAMLfSgiZy1jXRWKJIplEkWzP01UxnaYV5xlHqOxp7joWO77cibiGwC4VjRgVOzX1qa2FjXLqnMbo21NLHremmeRcKYFUO0ujZGKzUzw0qp379Is7/qQFTJuf5OpdSemzHU30i1umxB6tybsBM2k5iZe2pyRrYjpISNC8fGcXgOwuaXf/mX+et//a/zkY98hM985jP803/6T/mWb/mW+Xkz4+1vfzs/+7M/y+XLl/ljf+yP8dM//dN8yZd8yfO5bsdxHMdxXkRYzojJzsuQXamTJa1/vRet7oBqna8SFAnNsWmPTRWi1vKz2ASOTKVP1prqmzNTCuSdG0FexE2vOqSPxFUVJyZK6akx00EoQSkGQ99xTQ0zodPMfhzmsrTRAgGjb8NeDmRgbYnRMhmjtOGcU3/NUVlxnDuupZ7Dsedw6Dje9uTjCFtFj2oJmm4hHEPY2E7cbE813qdc08RyfoZemhu4IGZAwZozIyaYaP1+ROr7qs6umuSMhQDjOItQibH+RiJVfAYF1TntDqhlhqVUxyc1MTaOrawtwzhi+exZO86Lj3MLm8PDQ77qq76K7/7u7+bP/bk/d93zP/mTP8lP/dRP8ff//t/n1a9+NT/2Yz/GN37jN/If/+N/ZL1ePy+LdhzHcRznRcRUjlYMkercAFW0tJhfCQFUmnsioKE6OSHUC+dY+25Qqft0upiWXUmb2ULQ7GKQ5cQwy7avlXGFbaZ0QmjlXyFAbg37FlpJWhfYxsDRWAd4qhi9VjGz0erEdJIZLMwJaEsySjZltMC2dAwlsk2RMYc6r2ZUdNBaejZwIjRA024op+ayCw7IZSdkziNorjuu7BwZrcloJjq/xxQYYFYHeEoI1X1TadHd7feZ7ifXbSK1GTW5CbHSIp6nfdP8Gp9h4/AchM2b3vQm3vSmN535nJnxzne+kx/90R/lz/7ZPwvAz/3cz3H//ffzi7/4i7zlLW/5wlbrOI7jOM6Lk1Kn2luqF9IANqYqUFhcTEN1bURqg/pC8BBq2ZiE5iSoVndniiFrs1imgY8nLp7naGSr7tHeGlmtCOuuvkZq07sYc0iBGIxdYFQwE65oYSxKLkqUQjElamGtIyrGJe3YWGRtmTJFPZsxthk2m9JxmHuOUs/xGGsJ2hDQjRI2QjwU4lEVN92R7crQJsdmm9EhQetPsbk/5Tm6HXOYQPtt5oqwtiE6O2zX/U7tsUhz4RbuDrr4LctOYJ5Ib5tCDqYyNO+vcXiee2w+9alP8cQTT/DGN75x3nfPPffw8MMP88EPfvBMYbPdbtlut/PjK1euPJ9LchzHcRznorC8kIazL6ZhLkkjF0zT7AhMfR/WelsmobMTNu09JjdjbrA/dXFdrF2I6xw9rGNAk6KpNu1LBh2lpriNdVDomAMhGVstbHIkah3iuSkdKx0paHVnahTCs/5KhMXh1mbHUFPFxOpBcrMv/M96/92Pg9nkQp38nU6/Sk5nXXNqls5pZ8YFjbPgeRU2TzzxBAD333//if3333///NxpHn/8cR577LHncxmO4ziO47wYuGH/R64X0mLX9X6YLsIFsuzcHHhmMTM12VtBcqylVCnXlLRstU9+TlATSpt/gwmWlZyVHIRUlGSBVAKjBQpCsVpu9kyclgIideYLi5uJYGLY/Nyuj+g6blVvyjMIn3mXV5Q5XwDP/L+gm8yjjz7KU089Nd9++7d/+4VekuM4juM4F4mpbOlGguX0oMezyp+eAbEWET27JNO9QAErgpmQS5UmxeSEoJn6akoTN1MiWhChkxoJvdaRvTCyHwf2usRqNRL6jK0KeW2kfSPtQdqHvIa8kvlWeqV0AetCC1ZoPS1nOCSOc6fyvAqbBx54AIAnn3zyxP4nn3xyfu40q9WKu++++8TNcRzHcRznC2YZY3xdnPFOuEzi5bSomVm6NfO+p7EWbHerQkeg5Q9MouYsduKm3gem+0IvmU4yUTO9JlYx0YVM7DJ0hdIXSgdlBaWD3Et9HGW+Way9P4TWb9R6kRznovC8CptXv/rVPPDAA3zgAx+Y9125coUPf/jDvO51r3s+T+U4juM4jnNjznJalo7MdEw5283Z7XsGx6YlqcHOtanbnBA4VdhMCWFSHZvJtWnOzY0IGJ0kVjqy0sRKM6uQ6GOm6xLaZ+iMsirk3ig9lL6Km9zETY2gbklwy0Z9x7lAnLvH5tq1a3ziE5+YH3/qU5/iox/9KPfddx+vfOUr+cEf/EF+4id+gi/5ki+Z454ffPDBE7NuHMdxHMdxbhVzFLFoixpukc5QQwCWAx5Pp2/VNzh71styu3CDUrTpJlgRStGdqDEhL9ybsihJm1CopWhk1jKyrwPbGLmr2zKWgJkwrCKjQLFIzlI/X5a5vycPoEmQrJROCeM0LyacTCBznDuccwubX/mVX+FP/sk/OT9+5JFHAPiO7/gO3vOe9/CX//Jf5vDwkLe+9a1cvnyZ17/+9bzvfe/zGTaO4ziO47yw2ElxAwuBMx1yhqi5/n2eycXZ3c+JZc2xsfle5pKzfIagWRIwgpS5z2alib65NtsQ6bpMKYL1ig1aMxC6WoqmCSzUmTqm7da2HeeicW5h84Y3vOFpG+lEhHe84x284x3v+IIW5tzZ5M9+7pacZ7zS3/RzfPm3/8dzHf97Px3Of5JyfTKM4ziO8zxwYoCknBA3qJx9TXNK1Jzp1szv36KUraahLXtr6vPUAIFWpVYFDXP52dOVoEHts1ExOsn0kllpdW0O4pahhBoX3bfLORPGUSmilNEoo9RMhA5yB9oJFptbM5WjTTePTXYuAM9r3LPjOI7jOM5tg1m7aN+JG6CWpUEdqSKnhMVCvDzt/JQbsBQ0y5I0a9nLp92aXVlam2Fzyr1REToKPZl1EzVjCNwVtqRY/5C2zREVQ8TIWSkSyFtBVrUkLXeCdlCi1V6bqOg0w2deuOy+M8e5Q3Fh4ziO4zjOxWdyamAhcORMwWLXJagt+2rOmscyOTr1bu6tObVtUzLaJGqepvwMqqgBCGJoK0XrJbGWxEoTe2FkNGUvjnOU9LbLjFmwzmoKWqTetJWjBWnzblrUs/fYOBcIFzaO4ziO41xcJtEh14sYKze4qH8WfTVmdkKW7JwaA5PrU9Famdrk1uQpRACZnZqzCAgBYy1TeMCWbMo98Xg+JvWBXjNBCykrR8CwDZRhKkUTcg9hgBIECzUZTUW4Tqa5c+PcwbiwcRzHcRzn4rO8UJ8v3p+hvOyZLu6LIcVmcSBmgMwiZ1mKJnMZ2i5bYEpF24UInO3gdALBjLWMrGWkqHKkK3KoYmg0RdtJN6tINmFcRcpWkbGGCNS454VrE2pvzQ3n2HjfjXMH4sLGcRzHcZwXF8/3BftcY3Y6NACkSHNrDIpgRTET0hz7rIt5Nkqh9ticllyKETB6yYwtRGC0QEHYlkhoJz5MPWMOHHWFHA2LhqlQ4pSORk1Ek1aGplJL9M4SeS5unDsMFzaO4ziO4zjPFTMkV+dGsqHJ0NHQsQoIHSBEQIXcKRnYauQ41ob/ayFxEFdELRyWFYdlxVpGNrYlW2YtSm7aYuqz6SRwoNt5XzZlVRJRMpscKSZc61ccryIlCXkFOkLuWwx0r5Q+oF2Erke6djl4Vhqc7B67yHFud1zYOI7jOI7jnJditc+mFCgFSQVNho2GptrPYgphBNsKJlA6xYCkkU3XoQKHsefquCaIcS2uuapbOklcLYfsSyZT2JgyoowWTvTiBAqd1LQ0gIyyDolVSHQxs4kF6xSLRomCxZqQFqJgXcC6iPQd0vdQFuKlDSmVyX4yaYEKxcWNc1vjwsZxHMdxHOe5UArkeqvCplCSEEajDG18TayiRqw27Werj7exAyCGzCqsUTGeinusdKSTzKFFMsKazNYCAzUOulCFTaAQpBCsipu2m4O4ZZMjfcyEWEjBahlarPNsShRKX10b+g5SRlZ9/RwAOe8EW9umKKKlhi2Yz11zbl9c2DiO4ziO4zwXzCBnJGUYMzIUggimgqnWgZ1S+2zyCCaCZMglkAU2WRCpcc7FhL0wziEALwnX2NctBzKwsUhG2ZSu9d9oLUtjJ2gySieZu+OKoUQO+oHDvicnpawCeQQda1laSkJYB+Jeh5aCjGvIGcywXJCSa1lazvPNcp3/U+fxuGvj3J64sHEcx3EcxzkvdrJ0i1LQMYMKGgUd26yaKG1CZ00nQwRTKCuhaGDoAsddJGrPYeq5Flfs68BhWdX3VyimDBZqsMCpUrSM0Emu4QJa2A8De2GkC5kYMxoLJdicilYic1la6QLShdpjE7Q6T5KxIk3olBY2PX1OpU41dZzbExc2juM4juM4z4VSqsORMowJGSMqQtA6/LKkKm4kCzpS45ULgGCxpqGl0HEcq0i60q9rf4xkLud9cpuUk5FZ0OxK0QyksJZCb5nc9l8tGzax4yAOrLvE2EU2nVE6ayVoQh6NvBLKKqCpw8ZcS9Jygaz1fppxk9sYUTOM3BLUXNw4tycubJw7mi/7kf/fuY7/rq/8+nOf492v+sC5jv8Tf/77z32OS//wQ+d+jeM4jvPCYaVGPEvJkBIyKgwJzAjtGElaI5+z1lkyApSFk2NCDoGhq/02V/o1vSZ6TTyV92exMhFOjdPspQmMdq8U7gkrtqXjrm7Lfjey6SKbvlRRMxq5l5aQJuSVIikgQ0RUsVJgFERyFWGASanuVJt540Vozu2MCxvHcRzHcZznQutDsRiry5FyHXipGQ2tF0WY59nEqLUMrRPCIFgwyiDkQUka2KTIJnccphVHpUel0Et1cAAKBZVCwAhSp94ANUQAQymsZWBfd+VoXchILFg7t7VStBooIDU1rQt1Zk2SeeCoAHWip0EILSRhElLeZ+PcnriwcRzHcRzHeS5YK0UbxioGRCBlyDUhTaNSUiRslNIpkiOSqwtjUerATlVSFxiBo1XPlZCJWvj9cZ9sSsDa7JpMJ4keQApdS0XryPTtuYCxCR0F5e54zKVuw7aPXO0yY6+UlZJ7QVKdaZPWCoVaQpdqshsKMkoNOjBrY2wMkYSFgGjySjTntsWFjeM4juM4znmxAoTqYjS3Y3IyZAoUyAEtBQsBjYqpgFBjn9ctRGCAslWKwDDUIIHD2HMt16Gd6zKyUpnL0oLYzqnB6CWzlrEJn8LdtmFjHXfFLftxZBUTscukGLHWa1ODA6ZhndW1KVRhptmq2IEq0tSg9Qx5boBzu6PPfIjjOI7jvHD88i//Mt/8zd/Mgw8+iIjwi7/4iyeeNzN+/Md/nFe84hXs7e3xxje+kd/4jd84ccznP/95vv3bv527776be++9l+/5nu/h2rVrt/BTOBcRK22YZa7hAZYSNo41SGAYkc2AHA/oZkCOR8JxImwLYWvtBjoIOggyCnkMDCmyTZHDtOI4dxyVnm3pGC20AZ1CMZ1L0VTakE7JHEiqEdG6ncvR9uJY09G6ggVrpWhN1ExlaZ3WkrQgELXeVLGgEBRRraEB6peNzu2N/xfqOI7j3NYcHh7yVV/1VbzrXe868/mf/Mmf5Kd+6qf4mZ/5GT784Q9zcHDAN37jN7LZbOZjvv3bv53/8B/+A//8n/9z/tk/+2f88i//Mm9961tv1UdwLiJmtRQtNUEzDNjxBjs6xg4PsWuH2NVrcOUacvkqevkq8fIR3eUt/dVMf82Ih0Y8gnAshGOlbCKb457Dbc+Vcc3lcY9rrd/mKK8YLc6paMDs1uxL4pJm7lHhXh14iR7yB+Ih93ZH1bnpR7o+wSpTeiOvrM6zWQl5VUME8ipQVoHS15t1AULAYr0naC21E790dG5fvBTNcRzHua1505vexJve9KYznzMz3vnOd/KjP/qj/Nk/+2cB+Lmf+znuv/9+fvEXf5G3vOUt/Nqv/Rrve9/7+Lf/9t/yNV/zNQD8rb/1t/imb/om/sbf+Bs8+OCDt+yzOBcPK4aQMdFdLHIpSGklXLnUEjQNSN+hq46wzegYCIMQBtChNvUzCnlUtmPkcOyJkllprf/Kobo0nSRQ2LctWZp7o4YCAWEthbUk1jKw0sReGOmnmTZdoXSGTm5ND6nUmTtBwbSmuKlQ5/GY1bS3UsvrTBTJdVYPxXazfJ7h+7nBE6ceexiB84XjwsZxHMe5Y/nUpz7FE088wRvf+MZ53z333MPDDz/MBz/4Qd7ylrfwwQ9+kHvvvXcWNQBvfOMbUVU+/OEP81/+l//lde+73W7Zbrfz4ytXrtzcD+LcmZgBBSuKaKlN9bk0QZNr6ZYIqCKSYNUj2xEZOsLWKNHQgZaQJuhGKTEwxI6r2zqgU8UoJmzL2B5XQbDRrs6vkXFeTidKZ4VOCmsdWes4l6Mdxo5tjIy9kZOhayGva2RbSoqpobGmuFkUbKjnkahoi34W1Tq0syWk2WkxUq4XK3Lqufk1C2FkZUqPWww9dZzngAsbx3Ec547liSeeAOD+++8/sf/++++fn3viiSd4+ctffuL5GCP33XfffMxpHn/8cR577LGbsGLnomLFEC0nS7VKqeKmFEyb25EyOmZ0LIRBiBuh9O3wXkgSyEW4rPscrzuOxo5rqxX7ceA4dxx3HUdxRUYpQckIa0kEHdhYnvv7e8mtz2bgoNty3HekHEh7kUIklTpfJ6yqexMGkCTEbZ1zE0YjbAKarDpMxz0yFnSzNw/zlEmAmO3ESFlsTyEKUGfkFEOsVPFn9bHlXN2gnGexYzm7uHGeEy5sHMdxHOcUjz76KI888sj8+MqVKzz00EMv4IqcO4pimLb4Z9hd4DcBIKmgo9VbMnQEjTVEQGMtCUvbeokmYqgY2ZSohdhK0/bDlrWs6CQzamAwpbTxmdpm2nSSWWliHRKrmFh1iaOukHKhrIS8tjp4s1AdowQoaAsYMFU0GSUIISg6lhoskEoVac1gkYWAmYXN9FnnQaZVyNS0uIzlAiUjU1kbgDTXS6oT5uLGOS8ubBzHcZw7lgceeACAJ598kle84hXz/ieffJI/8kf+yHzM7/7u7554XUqJz3/+8/PrT7NarVitVjdn0c7FwxZOzWLbpgt6qCljuSApY6mgY6m9LVshdLUkrHQCVKGRQmQclJyUnJVNH0lFKSYMXWSlaR7UeWg9a0sMNk5agyCFlY7s68BBHNh0G1JRjvc6tmKMQLLqEpVYB4ZKgrCqjo0kIW8MTaCjEbeKjkbYBiRVwSK5iRZj7sOh7MQMhfr5rR1b2hDTlOv+lLGUIOdaiba4d5znggsbx3Ec547l1a9+NQ888AAf+MAHZiFz5coVPvzhD/N93/d9ALzuda/j8uXLfOQjH+Grv/qrAfgX/+JfUErh4YcffqGW7lxAajmatDKrJm6a3hGAUod3Sq7laGFbo5ZjZ4AQI2Agufbl5JVQirABUgpA7blJJXAQBjrNdJLZxI6NRQqLfhsyaxlY68hB3LItgWSBzTqiUp2Q0SD3Nda5jCAZylaQETRV0aOphhvklbSyNEEzSDYkT24NVehY21+sfY6FY5Nqb46kAmMtyRNN1dVSrQNOB+q9JqwoPgnUOS8ubBzHcZzbmmvXrvGJT3xifvypT32Kj370o9x333288pWv5Ad/8Af5iZ/4Cb7kS76EV7/61fzYj/0YDz74IN/yLd8CwJd92Zfxp//0n+Z7v/d7+Zmf+RnGceRtb3sbb3nLWzwRzXn+aE7NCXEjNVGspogtSrFSrhf6qbRSNCNIHdY5D/HcSL3KRykhkoBN7DiKmSiFw9yzl3s2IbIpPYNuGG03xyNIIYjNrs1x6BjiwH5XG3qKSb2FQKEGBkgSTGopmo11gKimWiZnwdAklFAFjubqLE0iRvMkZprImcROEzcSFMkF1RZKIFJFTH1Qv5fQys9E8WmgznPBhY1zR5MvP3Wu4z/3fV927nP81v94fK7j/8pP/Ny5z/Ho//H6VKZnwv6/95zr+If+2w+e+xxe3+zcDvzKr/wKf/JP/sn58dT78h3f8R285z3v4S//5b/M4eEhb33rW7l8+TKvf/3red/73sd6vZ5f8/M///O87W1v4+u//utRVd785jfzUz/1U7f8szgXEKt9Kid2LcUNQNHac5Nr6ZWMCdkkAkAQTCKa64wYSa2RPwu6qn03I1BSdW5EwEzYiyO9JlaaOIw9l0rHoMq6CZpOMge6ZbTAPbH+O9ZJoZiwF3tWIXMl5DoQtO/ISSlJKYMiYxU52tyZaYioZtAtaJImWkBKEzgZpFSBU0XN5NhUgaODodnQoaBR0VSQrdQeHxGklPq9WYvHdl3jPAdc2DiO4zi3NW94wxuuj5VdICK84x3v4B3veMcNj7nvvvt473vfezOW5zg7JiHTnJu6uStNM7PadN/K0RgFGQIhFMRq5LIUyLn23EgWxKi9N6aUGBli4UgLx6uOw7TirrBlYz0jgYLMPTY9mU5SHeCpAzlUL2dbIr1mVAwRY8iBo5DZjpGUlBQjlrQ6NlEpqQYaaFcFjcYmYnItU5tFTJpK0WQncubnhaCGjYZNGlAFLWUXQJBzE346f2/PYkyO45zAhY3jOI7jOM4XwmnXZhkgcKLvpkUdpwRj7S/RIYFWB8SkOSBZAG19K1KFjYCtlDQGhhA5Th2bHDkuPaMFRouMpqylWh0qpfbZ6Mi+7mYyjV1gpT2dZqJkNrmj08Kmi2zHyCZYDSzolBJCFTmdUIbWbxObY5OhJJqY2Qmauf+mVJepujjU9QeqKzWFoOXQAghCjcUOAYLOZWqOc15c2DiO4ziO43yhTK7iHPG8sxtqhLFWQaMKoyLbCLmgLTFMoyIpUraB0AlxY6S1oINioYodUyWFyABcW/fsxTX7ceBaXnMYVgzUuTYBo6dwoFuCFYiwLlXg7Ict29JxVHqudSu2peNKWnGUeja549qwYptDFTlDR0pKToGyDZQsyKBIEzYy7srQtIkYSdSyusmxyfUWthBGoYQaha1hJwRFBB1jS0prImc5D8hxniUubBzHcRzHcZ4vTgucef9yjkuBsSaYybYlmZVAoLodOio5G1IUREjHNVRAB5ChloiNObDNkU3ummMTKLYTAypGRyajrGVEtdBJIkhh0Miq1BK1TenYCwOHccVx7ug1cZR6tjESQ2FMVeSMapQszcURyIJEbWJGsCklLQmWqGNoAjsXZ0oKMJmDB0oUNCgSCgSd+23crXGeKy5sHMdxHMdxnm/O6guzUue2qNakNKuzX8SsujipIFGxENCtUvYiFEjriCmEtZDXSo7GMEQ2qypsNqVjsMhQ4wgA6CgUhCADQQujRdY2tkCByGCBTekYLXJUVlzNa45Kz0HY5zD3HOeOK3FdxVOKHA9dFVPbSMmBkgQbFcsCRShjdZV0rKnWkmuymuT6GKRFX9dENQDJSkkGJSAhIFM52mlR6DjPEhc2juM4juM4twArtWGfcWwRx1b35YyEUJ2cWC/wrYs1ShkYj5USlLwn5A2gyriJXOtW9CHze3t3sdaRl4RrBAqdZIrVsjSA3EKge8n1eTJrUQ50SzZlYx37usdRWdU0tVKFzV4YOc4dR6nnMPaMOXDcdWxTIGdlHAOl1DS1SeSULK1Erc3DmUROELSvLg3s7gFCECS3GGgVZNhHwhYLejI4pH0fU5nfFNCw3Ld77L7PixEXNo7jOI7jOLcCK9RYsJaQZqW6OEXmeS4CWLD6OAgyBjRBGA0dBR2FMoINgWEIHPcdT4173BW2fK67a05CeyaUggJBjIKy1pGMsh92QQPJArooDNtKvWwMWhhSQARyNrIWsgYsSw0bUKmfSQXLNToaM0xryltOAtJS33IVXTrEFpZgyKqvx7ZQBaAKnNLm3JQmYsyq2LFSB3ouv+ezXB8XOxceFzaO4ziO4zi3EDOrjfIi1aURxSiteR7ErA6vTAEdC2FbKF0gbI2wFVAhHytj6Lki8OTqEsUEFeNav2alI11zZ4KUeVul0EtGp/3U7U4SnQTWIowaCBhBqnjoNRK1DgUdQqALmW2OjDGwzYmUAynXnp+clZyllqpNIifXfpzSK2ELpav9QnmopWkWhNDX1LfSKaELRLOaGjes6vfTYrItVyFIu5diWM5VsOS8cHR2ouaEq8NCEDkXEhc2juM4juM4t5Ji1cGYXJuSgVAvzhvVucmQCpoMHQ0dhLCh9tscKykao3Z8/nAfMyFKYbTAShNrHVlJotPEWlITLzX+uZPEmiZ+pIBRXR6F0SIszI9OuiaQjLGEWeCkEtjkyFgCYw4MOZCLziKnFCElxYpSilBiIHVKaPN0QqzDOU2NEgWxUJ0eBckrdIjQRUhVuEjKVfgVg5SwUiBnJLfvrdpHQB1gOn3PoouyNdGdm+Pi5kLiwsZxHMdxHOcWYcWQQC2nEmk3A3Itv1qIG0od5ClN2OzK0ai3rVKCsdl2XA2Zz4UDVIy9MLIXBlaaWGli1KEKHR1r/DO132YiSCFQ6NvcGwqgkJG5FE3F5lK0lSnbXJ2coURSqI9zUcbS3JsijLH24OSsDEARyNT5NXMpWhFMajR0dVoCmjosKBoESQGyQcyQcu3F0daTo4rR0uWgrnRyw6gCkDIN/CwnxY1zIXFh4ziO4ziOcyuxgpnUkrPcWvyby2Aa6sW5KjIqqBK2GYtC3Ai5r8NAS9eCAUpgCD1PZSXlwHHq2Isje3FkPw7shZFL3YZ9HdgPAyUo+7qlk8S+QMDQVnYWMFBqwIBVh2fUwL7V2TdjS1LblshogaFUxyY1oVMQhlz3F5PZzUlFOex7NquOYRNJCrJVLIDFGiqACLnT+vmI6DoQNrUUj2LomJFUsFSQMVW3JuUatJASpKmnZ9dfI2YgpfXn6ElxQ3HX5gLiwsZ5UVH+/a+d+zVveez/eq7jf/7tf+Pc5/jo1/7cuV/D157v8C8/eNu5T/ElP/uZc78m/a//27lf4ziO86KkNNfBDCnMzg0mrccmQ8zImNFB0VEJQx1wGQbBQi3dKptABo7a2266yLaLDCUwxBoBPaWQ7etw3cybnkwLUKsujRY6iwSzKmAsVpFjgZWOjE3YbEsko6SibENHQWr/jSnFlKFUUZMsELUQQ+FQjE0RTAPZAhgErbHQ0yLCqGg0TFpiWrHq0mhG2mBPUcGkikOhlZpNbpjILmxAFSlldm9mceNcSFzYOI7jOI7j3ArMgJrgJVIFyhQiMM2vxKQ5CtSL8lyQsaBjQQcjDK3HZlsHYFb3RmvriVRxM/a1FKyYkIuisisn25SOTvIcAa1SQwWCyULcaC1Ps0I2beImUFAGi4xahdHGIqU9P1qox8YqZkYLJAuzoxMl04Va/pazktQoRWqpmNbBnhVhTHWcDdKey2AiaCtBU4ARZApfgBouYNqcr4wUMNWT4sZHf154XNg4juM4juPcaqxA0dYsPzW91zKzKmia2FFFt7WxP/aKhdqXUoJCESTVi/4yKGkU0qikPjKuI8PewHHXAVBs59gEKVxqQqX21tRwgIzQk8kiNS+tzcLJVsOhT2w3QZNRsgmjxfaaaVtmZ2i0wO93+1we99mLIzFkDrc9R8HIocMGxVCsEywCCGWA0gmhEzTXNDgdBE1aY7GDokNLRBur4BGopWhQe3isVCnTxA3LkrSMcwFxYeM4juM4jnMraXNXRJu4oZVaTRfgOdcyqxxa/0iu0c9DQfuChV1JGlQxkFsTPqqUAlkDW62XecddR9BCr/mEuzKhYihGMCGL0VFL0po3QpbdoM/l4M9JwCzFTkbaMTqfJ6MohooxmrJNdV05K5ssFCJlJWQDTNCBRXKZYImaDGDaQgcMtTbctPUhERRyjcKGAOQqYsRm50aklu7houbC4sLGcRzHcRznVjG5MvNQyYyotNYanRvaRQRLqQ6pHEYQQbtAjHXIZXVuQDPVwUi0lDEofSABCbCiHMaEitFr5jj37IfaZwM1MKCb5rtMKcnTxhkzLqkrrseZVGHTxMz03GkBlE050C2Xwl10kimmrGKa328brFaUSaiVeKmKNWtx0Jp267ERZDGMU1KpIQEpQGiuDYVZ3Fj9Ts4sQvPY5wuHCxvHcRzHcZxbyXwx3WKJJwdBbOcm6O6iW7YDAmgMTcTUxnrJiiZBipK3kFe1TC2vAJq4KcJx36MCXcgc545N6eq8mkbfUtEmudD685fjbAjzczu1kxeiYGmClPn53eN7wxG/Fy6x0hrP/Ll40D6mcS0UDk3IChYVMcW2uwGeOtbWGVNqBHTZzeDUsaWbpTDHPJ8UN211pnMUtEl1fjz2+eLhwsZxHMdxHOeFYOkWNBdnfmoKKssZSxkJGcaEjAERCIPOscYWrF3p25x+Vvo250ZhHCLbmDkeO45zvW0ssrGOgcBgShADDKUKkkncBKqYUSAg6ELY3MjRKdPnmsMIjMxAsUM2oeepbo+McphWbFONhh7GSMlS22D6yTqSlhYHUmq5HRila31GuQYnaFEkhjrMM9bvUtoAT7MCIbTktEWvjZejXUhc2DiO4ziO47zQnFESZcWqmEkJgtb5LVtFberJMSRpLUnrZS7Z0iyYtn4SU1IXOW7v+bnugIKw0sQl3TBYgFgjn1VqkICK0Vmhl9pl02H0UkWFIlXgND9n6eBM+xDQU6onsAE2DFzhalkTMLZt3k3QQsoBEWMIXU106wTTGpagA/PnKdXwoQQlTGV5OkVAaw1iSHXGDSUgIVSBowGR2r9U36CKQctnKBwvT7tjcWHjOI7jOI5zO2KtnMoKlku9WG9DKSUVRIVALd/CanVVdWyM0AvWLvzHLpCsRkF/NuQ6TLPVcn02XuJyd0AvdSDnQRve2ZFZ60hPZi2JlWSCGGvJdCxcnPY+KjvBE0QoVKGzFDgd1t5vZF+33BW2HMYVyZTjVUcxEDGOs2AhkBUQpXSAChZAh+ZIRaHEKkBCp1hUQpfq9zJmSKWmoY2pDvJMuQqgrLWnCXbDUeeve+E0LcvUXOjcMbiwcRzHcRzHuZ0prR/EDMmllqalUC/QRWqfCQoCYTRMhTBA6dr8l00tScsCh3FFKTqnpB12qzp4UxKdJvZ1YC0jax05sCpy1jJyIEOdfyOpOjlmdFLDB4IIakZBUDGKNUdHJnlTS9iC1Lk5ax1Z28i+DuyFgWTKtTgydgEzqXN4xChEcgEdW2Ia0mb/CIjV5LiimO6Eh6SCRkXGXEWOVOdHtEmsaXjnHDTQvl+oKXUNmwIKrHjIwB2ECxvHcRzHcZzbFStYEzRMJWkAQefIYwBNhZymuTj1Yh4TNAEIOSl5ELZFGFYdm6HjcOjZ60aeWF1iHRKrkLgUN6w0sR8G7gob1pK4FI65pBvWOtR7GekpdFJQjF4KnVC3EbomcLCACrRpMiiwlsy+bBk0cE88YrRQBZNJE1o9QY3NGNl2mTF25FHrENJO0RFKEMJK5iGlYaila2El6Gg1FjvFOtR0W10cxlzL91Kogz0BssJAbSQqBZsSCYrNIseKuri5g3Bh4ziO4ziOcztTykmBI1JL0mTqLamRYQGwbXUa4taoic5CaUMvMTANWBa2Bk9RZ9wMObCOVdgcdj3rMHJXHNjEjn2t0dBDiBzYFoBRAmsZ6cj0FApCptDVYTMg0BkUqc8pSqCWzXUU+lbydqRbtqEOEN2Weq9SSEWJITdxJOSoFItkWj9NoZamKbVPJlT3xkTRaAQVbCyL3pv6XWkpbT5QK1MDCG07hBobXUp93+bYiJaduHFue1zYOI7jOI7j3KZYsXbBbbXxXacLbsUAsYCKYKW6N6GJnBKnsq3dzBvJAgJlVEru2BRh6CJjCqy6RBczx33HXhw5igPbLrIXRraxDuIcLRAojBoZJbKWkVESa+pMmowBhdDEjVIHaeqUsCZCL4W1jGSEfd2y0SZsYkSlukAAR6FvSW0wjJEBKBLJUcACFupnnWb6mFgry6vJziFoTYujJmcrYLmmoxEDkrQGCoS8K09rqWkyi5tlap0ucrmd2xUXNo7zDNz39z54ruPf9vHvP/c57v5rv3Pu1/zf/3f/n3Md/x/+T3/73Of4Qw/9n8/9mtc8ps980IL8G//ruc/hOI7zosCqQKCU2uiepPWE1P4QKaVGHOeCxIAFqWVUY53folnJfU1NyyvIvaBZKFHIGyNvOkpnHK8jm76gXeFwPbDqEqsucW21Yj8OHPY921JFTumUfd1yoFv2dUsvmVFGRhlZyzR0s7o3XRM4UDttOgqdGPs6olbIpoRoHJYtnWSOSs/VuOYgbjlMK672Kz4XD9ikyJW4ZhsLaayhAmWoUc9ToEDp6lBPHaFshdIZOtbzW5QasACoav2O2ndoFuv318rNrA1QlZxn56a6Nl6GdifgwsZxHMdxHOc2xmzp2rSSqObcEGye0SIxoKrVxYg6z8aZ+m2kxo6R+9aAb6Cd1HjlLORR2QI5K6nU2TbZFBUjSiGj7Ov6xNpKG7ipUlBazw2FQnVwSitVqylqQme1HK0grHWkoAQpFLQ5NpkghZUmouZaliYr8lwaZgy5lp0VqWu1VrImpZWq2TTXB3KvQIEiaNTqgMVQv7/QZt8E2vG5vcdimKeym3njfTa3PS5sHMdxHMdxblesQDGsXV1L2zc7N0kgxtpvE0MVPynWOZXZ0FGRXJPFSidIgtDVIZjV6QDdCnkllN7IWch9IK0iZjIP0ARIpqx0KjtrwgEli6IU0JqSNlDFRDk1wDMgdCIcaCGYQYFeMxvr6CRxqfRsrOOusOGo9NwV/v/t/Xu0JXdZ54+/Preqvffp091pQuhEEgjogAwQBTVGZxgwWbnoMGrib+TiCMoCZRLUhFEmLC8EZiaIMzqjg7DW9+cAa42Iw2+BzOCAC4EkgzRRolkMIvmaLDDjkHBJ6Ms5Z++q+lx+f3w+VXuf06c7OX3v08+LVatq1659dtU+p6n9zvt53s8EoxIrrsHoiNWRqbWsAN4aojW576bNUdDoUoqmFEmnoQQPlUVR9NnJUj7mcrSU5wClpBbKzoq4USqLnxBAFddGKtHOeLYsbO666y5+4zd+g3vuuYeHHnqID37wg/zIj/zI8PwrX/lK3vOe96x7zTXXXMNHP/rR4z5ZQRAEQRCEc408b8UM4gZV3BftSUrntDSlS+pXyGVpMYKPaKfR3hJmmlhpTJv7b0KVMHUu3/JjhR8rYqXwXhFGitAp1hJ0lRm+8/toqLQnptxvg4UuGYJW2XUpMdJO5T6bkBJBJRylFE1lF2fUl6rpjjoFlmlZTjNm2tIlyyw5Zslx0IwY6Y4DfoxTkZHpWO1qtCqhB9bSKQhOk4wGTR7mqdQw9yaLFACN8qXnposoZ7MTZm0e6qkSKeUQAQi5USeWuGjEpTlb2LKwWV1d5bLLLuOnf/qnuf766zc95tprr+Vd73rX8Liu62M/Q0EQBEEQhHOV0mezKG7UQi8IWudQAK1AlUCBmB0cnRIp9C6FIfqEinm4pfY5Cjq4IpKSIoYcn0zSBCBaQwfMTGTmPFolpsHhSsnYWqjBgE6JUerQRLpk6ZLPZ6oCMc37bAAcBhR0RCBhVCQksnBJgUBLlzSrqcIpzyxVOBWykAK0SrTRoFRCqUSMusy8AaImkV2pkH880eWyu3xtimQU0Za5NloPnyExFmcnr5VKQ0kaKc3L0YQzmi0Lm+uuu47rrrvuqMfUdc3evXuP+aQEQRAEQRCEQizJXT4O4gVKzDNk16bf5yxYC22NahzKGtSsAmtIzmArS7SaVGlCmQ3jJxoz0/gxuRelBu01HTlBbQYc1IkQNWM7yadU1Eq/rpQHDVUKjFKHU7nPBiCkhFUKjUGrhEExUnl/f1QgZRFEIhCYpZa1tMqSatkfJ9S6Y4dp+KabUOnAQTdixdV8UyXaytJaS1SW5DTZulHDTJ+cc63Qnc59Ra0GqyGaUr6nIChUSvNZNilmt2f4YBebbYQzla3FFz1O7rjjDi644AKe8Yxn8NrXvpZHHnnkiMc2TcPBgwfXLYIgCILQc9ddd/HiF7+Yiy66CKUUf/RHfzQ813Udb3jDG3jOc57D0tISF110ET/5kz/JV77ylXU/46lPfSpKqXXLW9/61lN8JYJwHPTOQQx5STkpLYVA8t186TypbaFpSE0DTYtqWtSsRU1b1LTDTDvMaodd87g1j12LuGnErSXsGtgZmKnCrGn0TBOnltm0YmVWs3825tFmiUfbCV9vl/lGt4NvdMt83S/zdb+TR8IOHolLPBomPBocj8bIgdiykhpWUsNaaulSoEuBufTJTk6tLLWyTJRjWRuWVWK3nrJbr7HbrLHLrnGeXWN3NWWnm7GjatgxahjXLdXIo8aBNIrEUSKMEmEEYQR+pObrscZPDGHiiGNHGlWkuiKNKqgrlHMo57I4dDaXqhkzOGLCmc0JDw+49tpruf7667n00kt54IEHeOMb38h1113Hvn37MMYcdvztt9/ObbfddqJPQxAEQdgmHK0Eem1tjb/8y7/kV37lV7jsssv45je/yc///M/zz/7ZP+Ozn/3sumPf/OY38+pXv3p4vLy8fErOXxBOCkdI50ohix60RulcspaDBXLKF0DOSza5VM1rrFZlFgyYJg1uR6zITpHVeJu/w63YCgAfNTEpmmiyW5IUjXHEpAlJMdNtfisUrfJ0BDQ5gCwnpOUwAa0UZbpM2a/RlH0a6hiY6IZlPWWmHdFoVkxNcIqIogm2fByK4DUeiF4NAzZDm9PfVFLoFlQC3WliXUr0QgIfcwKayj1KqOLehAgmzw/KvTZIMtoZzgkXNi95yUuG7ec85zk897nP5elPfzp33HEHV1555WHH33rrrdxyyy3D44MHD3LxxRef6NMSBEEQzlKOVgK9a9cuPvaxj63b95//83/me77ne3jwwQe55JJLhv3Ly8uPu0y6aRqaphkeSzWBcNaQImByz0hMuSk+RpT3gEX5kMUOQAQc6CZgnAaliS6hEpAUqaSMqajxweFrw4GgWK1qqsrzaDVhZD1j27FczRgZz043Y9nOmOiWPXaVZTNloht26hlOeUaqw6lARaRWgUoFnIKRUrikcEpTKzdcTqUiVcpDPZd0Q0Czy1a5pE0lYlLUpsLpLNqazjIDojYkp0loYpXT35JW+BZCZQi1wjQWO7ZoH1FdRLcB1ZVl1uZQgSYLtOQ9yntJRjvDOelxz0972tM4//zzuf/++zcVNnVdS7iAIAiCcMI4cOAASil27969bv9b3/pW3vKWt3DJJZfwspe9jJtvvhlrN78NSjWBsB3I829iiSxWReyUwZSQnZwAymt0F4uQSRhdQsGcyjNxdHZzYtBEHK3XBG/w3jBznql1NMFSG8+ad6y4irHpWIsVy2ac45vNGk6FQaA45VlSLSPlcSoSVGCkshPiVCSk3Iujy+NKBUa6o02GiW4JSeO1Zsk6dBE4XcxN/yFoujLKJgaVwwtUDhEwpr96TbSJpHPPje4iRudZN0prdErQKVSI4D0q6dLLJMrmTOakC5u///u/55FHHuHCCy882W8lCIIgnOPMZjPe8IY38NKXvpSdO3cO+3/u536O5z3veezZs4dPf/rT3HrrrTz00EP85m/+5qY/R6oJhLOZFNMwYLIvnUqhtPkrNR/qqUKe5WIMus2lWKZVufxMQTJ5rk1GET14NMkrQpOII01rLY0LNN7gTGDVVay6mtp6psFxyI4Y65YVN6LWHRPdsqynjHTHTDdF3HRo3QCxDAWdl3oZVVLXVMdIdQStWdPNMEenSwarslsTk8qvL2VoXidizM5TTjiDaMsQT5UILu83LqG7EhHdRrRSqBL1TIgon6Ohhz4bsW3OWLYsbFZWVrj//vuHx1/60pe499572bNnD3v27OG2227jhhtuYO/evTzwwAP80i/9Et/6rd/KNddcc0JPXBAEQRAW6bqOf/7P/zkpJd7xjnese25RpDz3uc+lqip+5md+httvv33TqgGpJhDOeoq4SSHM09MAhc+ZzjHlPpKo0VpnBZGKg+ETKuihL0X7+YBP3eZBntFCHGmSTbQ20Y0cykZWXOBg3eFMYH81Zmw7xrZjZzVlbHKy2XlujYlu2WVyKMCo9ONE3UIM1CbgMHn2DbkcbaQ8E53LQztjivvjMSqyFirGpsWqyJptMTqyYmtmlWWqIbSaUBmS0eX8FaFSmDb3EZlGYdo80NM0vasDeubRIea+mxDnfTbCGcuWhc1nP/tZXvSiFw2P+5vFK17xCt7xjnfwuc99jve85z3s37+fiy66iKuvvpq3vOUtcoMQBEEQThq9qPm7v/s7PvGJT6xzazbj8ssvx3vPl7/8ZZ7xjGecorMUhFNMjGVGS6IvoUpa5d4RAE8WN9qjrEYrhfYJdO5fSToPuKQUrylfIp597lkJKc+GiVXKOspouloRg6a1AR8MbWWYeodPmolthxCCzhq0ipjitsxUyyh5osrnFoloNEapPMxTRaoUiKpjpLvhErtk0KRSjqZxOtBGS0pqKEtrlc1zeWI+x2JdkTS5l0hRoqEB5pHQyekcB200ymgRNWcBWxY2L3zhC7MddwT+5E/+5LhOSBDOdtSf3bvl16z92AVbfs13//jrtnT83W/4T1t+jy++6P+75de8/KlXb+n4A/9oy28hCOvoRc3f/u3f8slPfpInPOEJj/mae++9F601F1yw9X97gnBGkxIQ8xf2lEuqclKaHuZkplKeprSGqHPJVdlnVB7mqbxBRZ1dmk4ROkW0EFoIlSJZ8G1eR5cb8pNLxEbjXcK7SFdbptbhXGDmLSPrWakammhZCQ0h6ZyiZjQj3eKSx6VITAmjVE5JS+DIfTYT3aFTJKBwyuNUGIZ4rsU8yHMc8n9Ir7RnVEIGptbRWEtrHKnTeKdJVqO6MrDTQZzl9LSk81wg7ctA0DaANSXyuSTNxU0/eeEM4KT32AiCIAjC8XC0EugLL7yQH/uxH+Mv//Iv+fCHP0wIgYcffhiAPXv2UFUV+/bt4+677+ZFL3oRy8vL7Nu3j5tvvpmf+Imf4LzzzjtdlyUIJ5UUE6p3XULIZWaAiqVXJMTiXui+tx5iRAMqRLSP6GCIVqMrjWkV0SpCU4SABd1vO4Wp8jpWiVBBspo40kRrs8jpDHXtaYMhJsXMzb+CBhRLumGkOmbJ09GhSUXQaCAyIrtOhgR6xih1dKplpCu6ZJlFx0h1LJsarRJj03GwG2Vh4xzTyrFi83l0zhKsAa9IxpBsdp5ULEEDgGmy06Ubk8eVWJPFn3BGI8JGEARBOKM5Wgn0m970Jv77f//vAHzHd3zHutd98pOf5IUvfCF1XfO+972PN73pTTRNw6WXXsrNN9+8ru9GELYlKULUpaQs2wzZrcnpYaQIxpB8GVybUnYlQiKFXJ2jfEKFlBPRjEL73GsTrULF4tY4RYi5FycElY93Kjs+VSIFhQdSaeq3JZp5ZDw7TMNIO2YpL4HZuksw5Hk1Lg+jwZSSuo5IqwI6RbpkGKlcnuZUoEsGQ8SqQEwKq+OQnNZoi1KJTiViZ4hR5bCBBKbJ5689xEqhfXZ2MDqLGi0DOs90RNgIgiAIZzSPVQJ9tOcAnve85/GZz3zmRJ+WIJy5lHK0LCQCKqkcVRwi6CxiUv8lXXmUC7lczZjcKG+zQ2EaC1qTnCHZ/CU/2tx7Ep3Gj3OpWnAKP8plaaFWhLqUrdWK6BLJgZ8ofK1ZKylrbTBolXA6C5VdZd7Nsp7RpoZaUYZ1JlwxShyJqKBOgS4FItCmjoiiS5plPWU11izphv1mwqE4Ymw6Vn3NQV+z301ovGWlq1hrKlpvmNoab2wuTQtFBKYcKKCiwswMyeXPQ6nyOQpnLCJsBEEQBEEQtht9rw3MBY7Oc2sS5NhiyGVpKeXUL6PnAsda6Hx2KYzOPSZGo20ROkajW0esNKHS2cmxeaCn7sjbvpSnOQBNCIoIzEwiJcWK9UxsS6U9a7FiFh1dMkQgpgSK3GtTEtIikUDCkQgqEYGQYg4GIFDHwJLqMCriVGASGwyJQ3rE2LTUOrDqK2rrqUzu+0lJMYuKQC6tU0Gh6xKOUK4pldk2aClFO9MRYSMIgiAIgrAdGdzMInAWx6+UYZhZFaQ8xNOYucBpO7A2Ozxa5+dM/oKfnM0ORkzEyqBrgwoml28FjQqKaFMpVVO556aPnDaaUBtaHZnVlllwrPq6iBpLl+ZfTXP3T+8sRQwGB4SSTxaJQ09MIDFSkVnqgDUMiZGuAKh1TlLLvTcttfFZTPkKHwzea7qoiJUmdorYQnA54jpZPYg7lIibMx0RNoIgCIIgCNuZzco1F1ROSjF/ae/FTd9P0raHf6nXBmUNWJtnvDiLHllUZ0lO40cG0+bytNApggPjQKXshoCmcwYPrFWBFVcPjs1arJklR5cgFOHVZ7lpzHC+9gjawuGpVQC6nKKWsmOzFFsO6YZad6yFmh2mYcmMWQ0VIWp80KwmhR8bQsjDOkOt0KGU01UG5bKLpazNnxNlEOrwIW6ISnuMElnh5CDCRhAEQRAE4VymlH2tS1IbZuDEnKZm6EfhkGKOkSYElNHgc4paBLSZN9lnlyahkiK0oC2oDpRXJK+KU6Jpo8VHTUAR0uPvYTEb+l0MipgUGtAkKmKJhc7hAiPVgaH05BgiirHtqJ1nZh2dSSSbSLbEWNtcUhedRjuDrhz4ahA2g3iJCVJxpOKCSybi5pQjwkYQBEEQBOFcp3dtAGIRN7D+yzvkErZSuqZCJPmQE9VsnodjuhxQQASjcjmaijlxLFlFbBW+UySnCd7QeksXDE3pr+mSIRSXJpKYT985nJDiYeIGwJDfOwcPBEaqI2rNKHUQodOGickO0MS2VCbgnGfmYukJSiX5rY+w1sTKYipXorNjETOpTP3M65TSkDaX+5pE3JxqRNgIgiAIgiAImXUCZ15elUp5mgoBtMoujs8SJDs6Ch0SKRmIoK3KGsgqos/rpBOhUphWkawieE3rDU2wNNHSREebDCEp4tBHs74k7Wj0KWpGKVxKBGJ2aTTEqJnoBqcCWiVMET0H3YgdVcPMW1arQOw0oYJQZWcpb2v0yJBGFSqlfM0holLMSXMhZFET+22FIoi4OQ2IsBEEQRAEQRDmLM6/WRxKGSNJ6zzkU8UsfKKCGFG+iKAyE4ek0GWtkkJ3eQaO7kD5LBpiyDNkuqjxSROSJqKJKMLGnpUtoAGjwJCoUs5S65QfZt1UylDrjoCi1p5KByoT0CYRTSKZRDKKZJi7Nkbl2Oeu9NqoAEmTVInQDiXSoAicHAsdjnKWwslAhI0gCIIgCIIwlFCpPvkrbui3gSxm+uZ571HkkrWkFCro7NaEBDYLIN3lmTe20iSTm/F1q9AOYqdzn02YuzYh6aEUbZEjlaVtVo5mykDPQMKpSCCwRAsaTIoYEpqIIbLTztjhGlZ9has8odOkKg8Wjb7EPlcKU2tS7fLwUrJblRPkIskrUAqlir+kcoS2Smp9Ep1w0hFhIwhnAOGrX9vya57021t7zeyX/JbfY6KqLb/m/3nqh7d0/D/90V/Y8ntMPnj3ll8jCIIgPD5STChdStIWxQ3kgZ2hSI+g85d8irOj9dyxCBrtTRY4PgubaDWmzaljugO8InhD5w0+6aHHBjb3Oh6r5wby3BuNGsSNK1HXhkRAYUgYnTAqYlRil11jye5gydY4G2hdJNhEdAyiJrpEqHNMNd6iU8pCTmfXSgEoRcKjUvlMhmGeomxOJSJsBEEQBEEQznVSyk5D/3ATcQNz9yalhApF7KjsWGBK/0lK2b1xQIzoqNGtxRRBoz2okOOfY1SEpPBRE5MikNeQ59Ro1rsxj0fcQClHAxylbA4Y4TFDz04EDSPlGZuOyngqG5iZSLCRZAzJUNLRVC5Rs5rkNCloVDJZ3HjAmvlnOPTg6Nx/ozRi25w6RNgIgiAIgiAImb63Rem5uIFsPChNIs1dGcgORShN9GWYp+oHe3aepBXKGKwzJKuwI41pcomX6hTJa7w3zIIbktECir7DZjNxcyT6gZ4OUyLdIpCDAgwBTSKqwCh5ZslSpcCqrXnULrHqasauY1ZZfG0ItUWF7NiEGrRXhJFBhSKMdI65xmhUQy5FYyHuWfmcDiea5pQiwkYQBEEQBEFY79qUdLT+i7rSai56+mCBEEgxoozJz5nsYhBjLk0zJq+tQc8q9Miiu5TDA0IWC94rQtA5RKCUo23ssXkscXOkPpuc7pZTybTKLk0kz7jRKqFTZEk3TEzD2LSMbceqdcxMRbA5QGCYZdOvK42KJged9Z+Vjdm50RpldB54qh+fGBNOLCJsBEEQBEEQhMxGcQPrBE5+GCEw7yXpXxN6QVOa6I0GlXtsVFOjG4dpLaZLBK9QAQiKGHIqWiwLQDhKQvLRytE0CqMUuXYuElGgymwbEgFwKtKVEIGJapjolrHpqG0uRzM24E0iujyss++3iZUiep1dm5iy1EoJYi5wUzGCz4JK9eV5WnEcAW/CFhFhIwiCIAiCIMzZOHel7xEpgmdoGYklIaxPUVO5cR49Fx1KKZLWaOcwzmCWLKYxmIYS/ayJQZUhnTk8YC5u8kwa2GpJmi4zQtXwmkgkqDIbJyWCStQpMDOrPGpWWLEjdtiGaeVYqyqaOpCCItSJWClCpwhV7gsaGo60GsRNLkOLqGBRKZG0EtfmNCDCRhAEQRAE4VxEbe56oA7/Qq60OvIxG4TMfH+fOKDma61IebwNAEmlxxEF8PgwShNSRKOKq6NBRUIq233nTpkqWpVZN1pFtIpYHYYytTyCJ0F/rgvrQeCVfX0qmnD6EWEjCIIgCIKwndn4pXuDcFl0XAbKvkGoLLoPm4mX4anF5xZ+rrMkq/OgS82wkKvV0CphVKJ4PicUoxRhgwuly6wZrfpwgU3eV7TKWYcIG0EQBEEQhO2GUoNQWSdcNhMsZQaNKo7KcJyab6tewBgzFzb9McP7FSdjeG15zmj8rjHdzop2l6FbVvglCOMEdcBVnolrWTItE9PgVMAohjI04HGVoYXSzBJJufQspRIXAIGUS9BIRHKZ2yzBoVhxKI44FEas+Yqpd7TekDqNanUeJtqB7lKewdMkTBPQbUD5iOoiqgvgQx7aGWPuOYopDzMVTikibARBEARBELYpalGosDBIs18Dyuj5MdrMhUwvYvT8NcnouWhZEDBJqTw8ZlHgmLl48kuObsnQjRVhBKGGWCdMFXHOMzIdte6yqCEdVcZsDA7YmIjWixrYXNAEclFah6JLljZZumTw0dBFTYwaQg43yOltJb05JHRIKJ9QMc/xUSHkqOsYS5BAzL1HG/uUhFOCCBtBEARBEITtRHFaelGjjN7cmemFizFZyGidh01qnQWM1jnZTGuSLcKmX5tezKjSe5KFTdJlv6Ick/d1OzTdkqbdqeiWwC9F0ihQ1R2TqmNiO8amY6S6Uh6WebyBAT0ljy2LmyJqOhIhJTpy2loE2qRpkmE1VayEESuhHtwa3xmUV+huwa3pmLs1jc9OTQjZrQnFrfGB5APEuXOzmCYnnHxE2AiCIAiCIGwzDhM1/UwZY+ZOyzBnxmYBYwzJGjCaZAxYncWJycImaTVf6+LIqL5nphczvbhh2J80dBOFX8olaH6SCJOIGQdGVcdS1bJkc+zySHc4IkapTUXNkWKeFwm9Q7NB1HQJAqrMy9HMkmU11syio42WJli6YAheo0octfYslKJFVJvLz3TrszvTl6CFCCHkiOwQinsjouZUI8JGEARBEARhu7BZ834vYLSaz5YxC6VmtggaPW/yT1bPBY3RRJeHckan586MmQuXQcyYImhUeb7s8xOFH4MfJ+IoQhWxzjOuOsa9W6M7nPIYtbkg2EzUbCxDWyRscGrapIlF2LRoWgxdsjSpiJqoCUGTgkL3JWhDOVpChYT2854aYpyLmhhJIZZt6a85XYiwEYQTTPxH37Hl1zzw/xlt+TXP/o4vb+n4iaq2/B7Hwu88+p1bOn7yoc+epDMRBEE4R1F6PiTSmHkPjdEoa4uYsQtlZmYuaCpLLE5NrEweUGkV0eXys+DUIF7WJZyZBYfGrN+HBj/OYQF+EmHZ40Ydy5OGXfWM3dWUXXbKDjNjSTe4DQM4H49L0xP7/6VU+miyU9OXngUUs2TpkmU1VRwKI1Z8zWqomHWWrjPQ6VKGpjAtmDYHB5iZRzUdqmmHoABCBO+zqIm5LC2FkHtsUkSmc55aRNgIgiAIgiBsN/qGfz0XNRhTStHy43UujTM5jnkQM5pQ6bzt5oJmEDYGol1wbBbFTO/U2H5fyoEBo0gaR9yoYzTq2FE37HANS7Zhh5kxUi0j1eVENNSWBA3M+2sCaXBrQik/i6hShpZdmllyw7qJjjYYfDDEYCAqVAkMUD6VcrSE6kJZ/DwwIASS97n0bBA0UoZ2uhBhIwiCIAiCsM1QJZ1sMShgCAjoRY0p/TRWk5whOk2sctlZtIpQ60HMhKoIm6qEAvTCZoM7k/cn0BANuWzNJMI4l5+ZcWA86pjULctVw7Jt2GlnTHTLkm5xKqApc2Yeg6OVoUWYp5+VnpouGVoMbcrLLGZR00RLGy0hamJQpb9mXo6mfcqlaF2AzuelFzF9KlrqS9HCUIYmwQGnHhE2giAIgiAI24hhbk0f2ayLqLF23k9jzRAQECuTnRqn8SNDrFQRNmVdkYWNLTHNvRPjIOm0XuDYRCrCJtkIJoFJ2DKvZly37JlMWbItTxyt8MTqELvslN1mjWU9ZUm1VEphjuDWHE3M9MSUygybXILW99TMkpsv0bEaa9ZixWqZX9N1eX6N7hS67UMDwJQ0NDXrUNOGNJtlRyaVaOchLGA+wyb1QQIS+3xKEWEjCIIgCIKw3ViIdR6S0bSaxzhrPU89K2EB0el1PTWhUkSbRU0sgiZUWdBEk4qwyY5MdAlM3sZG0KBdQJmEMZG67hg5z1LVsquaMrEtO+2UHaYZemtyeEDMjs1CItrjETM9fSJaoI92npegtaXHpk1mncDxSdMFQwwL82timV0zuDUR1flcdtb5ImDiYYJGempOLyJsBEEQBEEQtgslOGDjAM1BzCyWn/XLUIKWXZpQ5Z6aUFP2ZUETHYRRyu6MTcQqZSGjgSqidMK4iDYBYxLOeZwJOBNZqlrGtmOHa9hTrbFkGs53K5xnV1nSzeDWjFTAKY1RakuCZiORxTK03FfTYZjFill068rQZiH32MSgsrDxaqEEDXQX0V2EtoOmITbN+h6atKH0rBc34tacckTYCIIgCIIgnG6O1FNSvtwP5WUL+/o4Z7X4WueyQ+MqVOVyyVm/doZU2dzw70wWM0YRa02oNcEp/FhlAVMpwqiImQpinUvOwiiRXJyLGRPRNmFswJiItWEQMyPrqU0WNzvdjLHpWLIN57k1JrrlPLvKTj1l2SyIGtIRy9AeLxGG0IB+yT011brwgFl0TIuo6YImhTy/RoeFmOeQY57xkeQ9qcvLwGYOjQia08axS2FBEARBOAXcddddvPjFL+aiiy5CKcUf/dEfrXv+la98Jao0SffLtddeu+6YRx99lJe//OXs3LmT3bt386pXvYqVlZVTeBWCsAGlNix63aKMmS/O5uZ/a1FVFixqVKOqCl3XeXs8zstolNeTEWlck0Y1aVwRJxVx5AgjS5g4/Njgx4YwNvixphsr/EjhR+BHWdQMTk2dhnWqcwgAdcDUAVsFrPNUlad2vpSbdYxdx8S1LLk++awt6Wd5EOdEtzkFTXc4QgkNSJjj0DR91HNPSL2w0UT0IHC6ZIjFyYlJE5ImJUWKChKQSilavw4JFct8msUY50VR06ehiag5rYhjIwiCIJzRrK6uctlll/HTP/3TXH/99Zsec+211/Kud71reFzX9brnX/7yl/PQQw/xsY99jK7r+Kmf+ile85rX8N73vveknrsgbEovZFhwYpQ+3IHRuaRMGTMkm6HzturLypSaz6PRKkc3a02sLKk2ebhmn3RmFLFWeW3XJ53Fvp9mlHtpoutdmlJyVuXeGV0HjI1oE3EuYHXEmkhlPU5HnAnUxlNpz8j4YXuHaRibjoluS7Rzx5Jumah5b02lIo7cX6O38N/ewwbXpA8OiKhBwISyHdG5LK0InCYafNSEqCGCWhA3xLLuOVL/jIiZMwYRNoIgCMIZzXXXXcd111131GPqumbv3r2bPvc3f/M3fPSjH+Uv/uIv+K7v+i4Afud3focf/MEf5N//+3/PRRdddMLPWRCOSBEtGwWN6vtg+mP0fMAm1uZjSqoZSs1n0Bid082MIuncL5OsJtZmvZixczETTREubkHYOErqWUk5syk7NDaBy4JGmURV+aHcrLIBo9IgZqyOjExHpfPjsemwKlAXYTPSXYl1bhipjpFuqVSgIuDIoQHmccQ8w+FiZtjfBwcslKGF4srkmGe7MM+mODZRE5Mqw3eyuFHrBI04MWcLImwEQRCEs5477riDCy64gPPOO48f+IEf4N/8m3/DE57wBAD27dvH7t27B1EDcNVVV6G15u677+ZHf/RHD/t5TdPQNM3w+ODBgyf/IoRzh75HZqOoWRQ0Cw6N6mfOODsXNDYP1BzEjMkJZ8OATafygE1TUs2smqeale1YsSByEslCrIqwcaXkzCRMFbHOY22kdh2VDTgdqa3HqEhlApXOwmZsOirtcSpS645ae5wOTEzDSHlq3RVR05egeZzKAunxlqEdUdRsEB8xzcvQhiUtihqFT3lfSgtODXmtEqiNgiZueG8RPGcUImwEQRCEs5prr72W66+/nksvvZQHHniAN77xjVx33XXs27cPYwwPP/wwF1xwwbrXWGvZs2cPDz/88KY/8/bbb+e22247FacvnGsond2aXtT05WRa56b/viRNm/zY2rmgcTYHANhSalaimvsQgNQ7MrYEALgye6bKj/sSs2SzkIlVjmuOVSK5knDmIspGjE2DmKls7p+pTWBkO0bGY3VgZLJosSpS67zPFYfGqVBKzPK+iW5wyjPS3dyxUR1LKpehORIOcKgjujZHEjQbyeEBaig/63tr2mRzGVq0dNHQRJvL0JIiRgVR5Z6a0l8z9Nv0ZWmLiKA5IxFhIwiCIJzVvOQlLxm2n/Oc5/Dc5z6Xpz/96dxxxx1ceeWVx/Qzb731Vm655Zbh8cGDB7n44ouP+1yFc5zFL+y9U7Oxh6aUnw09NNaQXBmsWdniyJRSM6NyudmGvpnBjXEL27YsdY5rzsKmFzOlf8ZGTFmsDVTWU9kwCJrKBHbYJpedbSJmjIrUyuO0x5Coi7CplGekuiJ2WpZUm0WOys5OXnIZmi6fkd6QjPa4RU0RHLG8vi9Fi8WZCagy2ybv89HkUrRY3m/BuRnK0XoRs3EtnHGIsBHOKdR3PXvLr/l/f67a0vH/z/e/Z8vv8YJRu+XXnAqa1G35NZ959NKtvSA+tOX3EISj8bSnPY3zzz+f+++/nyuvvJK9e/fyta99bd0x3nseffTRI/bl1HV9WACBIJwwlJ6Xn5XBmZsGA1izzqmJlSHWlmQVfjQvOfN16ZUpM2eS6WfQkPfXpWemFza29M6UvhnrciCAKSEA1uTysrHrcDowMh0Tm8vKlmwzODJOz9PMRrpDqzg4NFUpMXMqi5yRbnEEKhXmIkeFwa0xzKN6txIcABDpxUwWP6HojkHUoAll6cvQfNR0SeP7RLSkcnhA2uDWyLzNswoRNoIgCMK24u///u955JFHuPDCCwG44oor2L9/P/fccw/Pf/7zAfjEJz5BjJHLL7/8dJ6qcA6idOmfKeJFGb2+l6YXOwuiJo6ysAkjS6h1FjbjPHcmuhzRHK0iVkXEFDHT982EusyeMQk1CsPcmbrucCYwcr70yQRqm1PMKh1Ysi1OB8a6ZWy6ofm/Fyy5NyZ3r1QqoFVZEzEqDkJGq0hFv45zl4aUnRpgpDRaKRxmS59nZL17EsrjxRS0kOaCpi9D65KhLaVoPmhSVKgibrLASRvK0fLPTeLWnNGIsBEEQRDOaFZWVrj//vuHx1/60pe499572bNnD3v27OG2227jhhtuYO/evTzwwAP80i/9Et/6rd/KNddcA8C3f/u3c+211/LqV7+ad77znXRdx0033cRLXvISSUQTTh+qL0XTg1MziBqTwwByhHPejlaX3hlNrHLJWd87E+rSQ1OETY5oLr0ztsyesQntAq6ehwAsVR219YxtN4iZselKqVlgh2lwOlArX5r/Oya62VTEGNJc0KiAoewrz7nhmESlcgKaU1AphQa0UsNgzsdbhrZR1Mz3M5SdLSajZaGTy9B8MrkPp5Soxd6xYWN/zcJ7RBE1ZzoibARBEIQzms9+9rO86EUvGh73vS+veMUreMc73sHnPvc53vOe97B//34uuugirr76at7ylresKyX7/d//fW666SauvPJKtNbccMMN/PZv//YpvxZBABbK0PoZNKX8rHdqiouTFkvQKkOsNWGkCC4P08wDNBVhnPtnQp2IxamJdQSXUC5iq4CrPM4GJnXLyHqWXMtON2NsunXlZRPTlp6ZeXrZkGK20CuzUbiYIjK0SoOAMWphP2BUXlelj8Yx76lxmNxj8zjL0DYTNYtuTT+YM7s1dihDC0nnMrRo8GWGzWLU82J/TS9qlMQ9nzWIsBEEQRDOaF74whcetfzjT/7kTx7zZ+zZs0eGcQqnH6Xngzl7t0bp9T01Zr6N1aQ6i5pQa/xIZ0HjFH7SCxvwkzQfqDmOwyBNWwWsDUzqjrHrGNuOXfWUJduybGc8wa0yMQ3LejaUl410R9VvD70w3VBCNlJhnUgB1hWPmaH5v38ub+mF4ATDXMDMjy/HFbfGqCMLnM1ETd9fs9HbyaVo2ZFZLEnzpb+mDw5IZTjn0FOzIHLEqTl7EGEjCIIgCIJwotkksnjorzFlLo3Rua/G2lx+Zoug0TrHOjuTS9CqXH4WXRY1scqCJlQl8ayUncVRhDqgXcRVnrrKMc3LdcPYduxwDXuqNZZMwy475Ty7ypLuhY1faOzP7s1iP8xIJTQwKiVjG4XKRjY6L5tFOG88ZrEEbbH8bFHIxAXpsji3JhLpUqRNiVmyeYkuL6lilhxNsjSxLMHSBksXNSFoUtBZ1PRxz4uRzyCOzVmCCBtBEARBEITH4gizVQYHRqt1jymPVf+6PizAWrAWVbl5lLOz68RMMoroDNHp7NQsaUKl6CYKP87Cxk9K6VkFYSmSqogeeaqRx9rAUt0OLs3OasaybViyDee7FSa6ZY9dYY9ZYUk3JX65TyiLh/XAOBROGTS6rNVRHZWjsbFfZr1oSYcJl0gkkIYY50Aqc2oSgblDExKsJcMsGR4JSxyKY1ZjxTf8TtZixaEwYn83ZhocK13Nalez2lUcmo5oZ440M9hWYVqFaUH7snQRFSKECCmKe3OGI8JGEARBEARhkY0iZsOX+HUipp9Ho/V80KYqzkw/bLOUnQ3ujDWkImxiZUlOl0GbRdQshgS4LGhiBX6sCKMc4+yXEqFKpCqRRgFd5z6acd1S2cBS1TK2HRPbDqJmh2mY6JbJwoDMPp65b+43xZkxJa1s8cpzD8wRBN7joBc1iwImDHNnyuMNIqYrAqZL0KEISdGR+2Lavm+mrFdjzSw5HvU7OBRHrIQR3+wmrPqaaXDsb8c0wbLWOWato/WG6WpNWrPoNY1ZU5gZmFnCNAnTRrRPKB8hiqg5GxBhIwiCIAiCsBkLgmYzR0apfv6MXthW8yGb/T5VQgLMfNhmHNmcdlYZktN5wGat81BNs1B25sBPynpEFjZVKkEBOSDAjALW5dKzcZUHafalZ2PTMTYtE90y0l0OBSiDM02JYV5MLMvipm/2L/0vW2jqPxIbRU3vxsBczGzmxsxSFjNNMoOQmSWX+2UwzGKVo5uTYS3WrMWaR/0SK6Fm1dcc6Eas+Yqpd6y2Fa03NJ2lay3B67momSlMA6YB3YLpEroroiYkVIiD4BLOXETYCIIgCIIgwNypWSwvO1JpWS9oSn8MxsxDAHoRYxbim3UOAhj6ZupcahZdiW22/Zo8e6ZEOUdXxIxLOcq5TiRXXJoqYFxgPOqonWepalmuGirtWe5FjW7ZaWclrtmzrKcs6XYoQRspjyMOCWZuIblMk/tnNvbQHGsZWi9quhQOEzNt6kXN3Jlpkin9Mo5ZcqzGep0z00THWhE2s+g45Ec0wXKgG7HS1TTBstLUtN7QeUPbOKJXpNagWg1e4aYqOzUNuENgpwk3Tdi1iJlFzMyj2g58yK7NEaKnhTMDETaCIAiCIAgbGBwamLszMJScqd6dMb24WWj8XwwBsHlBZ3cml5jl3plezPSCJlZkx8bOB23GCsIoEQ3EUe6lyS6Nx7mAc54do2aIcN5hmyHCeRiqWUrQRqpjSbeMyrZTAa3mc2UGp6Zc9mJIwGbN/8dC6MVMWR+p1KwrCWYH44hZcqzFehA0K2HELDqaaFkJNU20Q+9MGy0rbc1a5+i8Ydo4vDfETpMaA16hW41uFcqDnZbyswbsLGGbhGkTuo3oNiyUoUUJEDgLEGEjCIIgCILQo/RhTs26HhpYmEEzDwMYggC0LuVmpW+mhAFgFKE2uX+mUviRzgKmUoQKks3xzdFBNFnMJNvPpMkuDVXMLo2J1KOOkfNU1rNcNUxsyw7XsNM21LpjbLqh/Gyim9xXo7tB1GRhk8MCNpsxszg0cx7FfHzlaEMk84Ko6cguzawMz+ySpkVnlyY6DsUxs+TYHyasxYq1UHPQj2iiYxoch3xNG2wuNesq2mCYto62tYSgCTMLnUJ5jW4UulPoDkxThE3pqdEtuLUsasw0YpoibLqA8gFCdmyS9Nmc0YiwEc4Y7KVP2fJrHviprU0Nf9OPv2/L73HDjm9s+TVnIm/86ndt+TV3/qfv3fJrznvPvi2/RhAE4YxiUdSY4l/0PTO96OnnzdRVLjsbAgH0MFQzWUUowQDJkMVM6Z0JI+bRzXUesBlLylm0iThKJJvA5Zk0RiequqOyAbchHGDXwqDNZTMrwzV9Wbcs6XaYS7OkmzKbJi+OxKivrlsoPzvxomYeErAoamZJFXdGr+uh6UvP9ocl1mLFN/0SK75mJdQc6MbMvGPqHStdRests87SNJYYDKEx0GpUUJiZQnUK7cnbAUybHRrlUxY2TcJ0ufxMt1nUmLUW1QRoWmhakvdHnaclnBmIsBEEQRAEQYD16WeL5WeDU7OQdNb31Gide2ls7p+hpJvFqgQCVCUQwCpCPZ9D40e5hybUDGKmDwZILkIdUS5ibJ5JY21g5Dwj66mtZ8m2TGzL2HTsdmuMTceymTEpImaimzJoMxQx44cEtIrs1LjSU9MLGthc1JzwMjQYys9CUgQUs2RpMXNhEytmMZedrcWKFV9z0I9Z9RUH2xFT75h5y2pT4b3JYQCtIXUa1WpUq1A+987MXRpQvgiZJsc52z79rEuYWXZpdBtQTUB1HuUDKZa4Z3FrznhE2AiCIAiCIJQv7/0QzaH8bNGp6d0bXUSNtbmfpnJ5/kzdD9Q0hFqX8rIiZkqpWaj66OYSEjBKuRTNlQGbpdysHnU4G6idZ+I6XEk6m9iWSnt22iannZmWHUXQLJWSs8VBm4Y09NMYErUKVCriFDiyaHHM08/6oACNnqeiDa7N8QucmBIhJboEbdJD2tksOdoFt2Yt1hwKYw6EMSu+5tFuiQNtTjg70IyYtY6mszQzR+w0NAbVaIwH3eR5NFnEgO7yYmZpLmaaVMRMGCKdTVNKz7rs1CgfYNZA25G8z8EBEh5wRrMlYXP77bfzgQ98gC9+8YuMx2O+7/u+j1//9V/nGc94xnDMbDbj9a9/Pe973/tomoZrrrmG3/3d3+VJT3rSCT95QRAEQRCEk0IfErBYftbPozF66KvB2aH0LBZhE+o+HIAsbEo4QD9cM0c4p1x+NiqCxkXM2FNVntp5do1n1Maz5BomtsPpwJJpGZuWWvvBnal1x049zb00KvfR5AjnLGS0SkPqmStRzouCZqOYgfUOzUZRc6yJaLBQhkaOcu5Y6KcpS5sMh0LuqzkURhzwYw51I77ZTDjQjph2jkPTmq61+NaQphbVKsxMo4s7Y9oc2ay7HAiQhU3K2z47M3YWUF1EN34e6dx2EGMWNE2bnZpe1HSd9NecBWxJ2Nx5553ceOONfPd3fzfee974xjdy9dVX84UvfIGlpSUAbr75Zv74j/+Y97///ezatYubbrqJ66+/nj/7sz87KRcgCIIgCIJwwlB6XoIGGwZtLgzcLOukNcmoHBRg9BDhnHtp5uEA0SliDcHlXppQz10aVaKbx+OWcdUxcR17RqtM7DzdrNY+D9cssc0T3bBUAgF6QbOk2iEQoI9v3ph2tthH49T6/pnNBE3ePgFODXOnIyQIKGJSdMkQk6Yts2hm0dElw1qsaFJOO5uG3E8z7VwOBmhcLjtrNHqaE85MA2Zaemka0G0ahI3phc2076EJ6LUOFQJqVsRMiPNI5xCymAlxLmr6/hrpszmj2ZKw+ehHP7ru8bvf/W4uuOAC7rnnHl7wghdw4MABfu/3fo/3vve9/MAP/AAA73rXu/j2b/92PvOZz/C937v1RmRBEARBEIRTwbqI5z7SuRcz2mSnpsyv6YdtptoQK5vLz0Y6J57Vet5DMyrCppSfDcM1J3OXZjxuGTnP7vGUXdWUZddwYX2AHaYpzkzukRnpbkg0WwwBWFIep2C04MLQOyyPw43J20cXL8fj1PT0/TUR6JJeN6NmrUQ55yS0ESthxDe7Cd9sJ6x0NQeaEYemNW3j8Ksul53NFHZVZ1EzyzNoBjHTHkHMdAE1a1DThtR20LUQc/z0EOkcYxYyMUFaSEKL4bg/A+Hkclw9NgcOHABgz549ANxzzz10XcdVV101HPPMZz6TSy65hH379m0qbJqmoWma4fHBgweP55QEQRAEQRAyj9X0vvBlXZm5cFHGDIM2hxk1OgcEoNS6BLQ4coQ699T4cXZrfK1KD01e5x6aMo+mWu/SjEYdS3XL2HXsqqacV03Zaaec71bYYWZDmVmfatb3y2xMNXNKMVIGp8xwTZslmR2L+xJJxHT4l/r1LkzafH9JQ2tSZJYSq1FzKFW51CyO8mya6DgQlpgly1rIyWeroWZ/O+abzYSVtuLQtKaZVYTGoGYaPZsLGtOAXUvYKZgulXCAXHZmpx7VRvTMo2ctqulymdnalNS2Wdz0lP6ZdSVnfU+NODVnBccsbGKM/MIv/ALf//3fz7Of/WwAHn74YaqqYvfu3euOfdKTnsTDDz+86c+5/fbbue222471NARBEARBOJc5mnhZFC56k+M2zqpxbuifecyhm5UlOo0fG2LpqckuTQkJWEg9C3UahE2qcuKZrT2u8kzqlqWqZcnl2Obdbo1ddsoeu8JEN+zUsyEIwA1iJjIqAQCVUtRK48iiZlHYbIWw0BTfRzPPHx8uYPp+GZjPpcn758eF4fU51nktOlbTfDbNwTDiUBzTRMeBMGY2zKYZseorVrqaQ03NWutoGkeY5RjnuahR2BnopoibEghgZxHdRLRP6KnPKWetR83a3DMzm5GaJgsb74/p8xLOTI5Z2Nx44418/vOf51Of+tRxncCtt97KLbfcMjw+ePAgF1988XH9TEEQBEEQtimLQuZIwmWjYIHsuPSvL9sb59Io53IogLNlyGYuM8OoPGjTlqSzat5L40clHMCVcIDFCGeXynaJcB7lxDPrApNRy6jq2FG17KqmTGw7iJodpfxsSTeDS6NVHESNUTkEwMDQL3M89KImkrJg2SBeNgqXXrQ0pVcmJEWTLIHcM5PXljYZIrl/pi81OxTGHOojnEPNqq9pomGlq5kFRxMsa52j9SYnn80qQqtharKgaRVmTQ2zaOxqLjuzsyJohtjmiPLzIZv4soSQS8yipJttR45J2Nx00018+MMf5q677uLJT37ysH/v3r20bcv+/fvXuTZf/epX2bt376Y/q65r6ro+ltMQBEEQBOFcYpgrU4TJ4xEzfYzzENW8EALQ/0xjskPjSnxzXeX4ZquJtSVZNaSdJdMLmXnSWXB9P8180GaoIPWDNl0El7Ajj3WBymWnZrQwj2ZHmUlT646JbvMMGgJOebSKVEQMaUg3y6EA8+s/3lkzi6Jmo6BZFDO5P4Yye8aU4ZpmSDTrMMxilZ+Pji5ZumQ4FEc0MSedrfqaaXCshoo1X9F4y2pX0QZD5w1NZ/Fe41tLmhqU1+ipwjRqHhTQ5OQz04JpE6ZNOTDAJ7TPYQDaxyxgFpaUSt+MlJZtS7YkbFJKvO51r+ODH/wgd9xxB5deeum655///OfjnOPjH/84N9xwAwD33XcfDz74IFdcccWJO2tBEARBEM4tNhM1C2ImH7JB0OgSzQy5rEzNRY1Sau7elP6ZNKpIxpDGjug0yWn8yAwuTR/b3Ds00aosZlwWNGGUS86iS8Q6kWyCOqBdxNjcT+NMYOw8O6omCxrXsNtNhwGby3o2d2p0Nx+oqcrC4W7NiZoz04uaLpVY5k3ETEiKDk2XNKspD9HsMKzGmi5ZZjEHAfTJZl1JOuvFzDQ4VnxN4+2QdOaDzmKmM4Sgia0Br1BdDghQXRY1/UwaM6MImT7OOWGaiGkiKqTcU9MFlI+DW6N6t6Z3bEDim7chWxI2N954I+9973v50Ic+xPLy8tA3s2vXLsbjMbt27eJVr3oVt9xyC3v27GHnzp287nWv44orrpBENEEQBEEQjg+l5y7NokCB9WJmQ4nZMHtmUchoNbwmGQ2VI47yTJowtrncrFrom6ly78zQQ1NBMvP+mWgXxIyLqCpibBzm0jgbWKpaauMHQTM2HTtMw047Y2IadpkpO/WUSRm0mcvQwro5NK6IGlfmz+jjdGp6YvlflyIdeYjmLGVR0yZNhyYmxSxZWkxJLxuvSzJrosslZr6midmRaYKlLaVmTbDMvGXWOrpgaFtD8IbkNanVWcx4hWk0OoDqyC6Nnzs0eQ5NDgkYZtN0KaeetdmpUf2gzRih81nU+EAKMUc4L4gbYXuxJWHzjne8A4AXvvCF6/a/613v4pWvfCUAv/Vbv4XWmhtuuGHdgE5BEARBEIQTwlZEjTF5X59stlHQ6HxcHrJpSZUuKWdZzPiRnocAjIpjU/pmoitrSy43qyLKJrSLuMpjTGRcddTWU1vPsmuoTC4/64dt9pHOte6GuTR9X01FnklTqTjMo+ln0QDDcM08m+Z4Bmemoa8mlO1+iGYvavqSs5bcL9Mmw2qsWIs1a7Ee+mamYe7OrPmKWbA0wTItfTOtt7SNJUade2daDUGhW43qFDqALmJG9YLGz+fSaD8PCdA+odvcV6Pb3E9DcWlUCBDKsM0Qi1MTcsqZlKFtW7ZcivZYjEYj3v72t/P2t7/9mE9KOPOwT71kS8cfeP6FW36PH3/zRx/7oA387O4PbPk1ZyKvf2jrjua+3/2uLR2/591/vuX3OC/u2/JrBEEQTgp92dmiqHk8Ls0wdyaLmKTVEOWco5s1SWviyBLGllhpuiVdHJo+rjmXnOWyszTENidX3BmbMC5incfaSGU946qjMoEl1zIyHSPj2elmVMOgzXbop+lLz5bNdBA1i+lnmw3YdEqvK0M7EfTlZx259Kzvoeldmr6Xpo9ofjTsGOKZ9/tJTjTrala6mi4aVtuKprPZnWnsvMys1aiQ+2V0q1ChODMdZbuImW4uZgYh48G0EeXnTs1hLk1felYcm/nAzeLaxFgEjgQIbDeOa46NIAiCIAjCqUJt7KVZFDV9OMCiS2PtXNDYucBJWoNReW010WpinQds5nk0eRZNqBV+iZJ4lgjj7NKkSUBVAeMizgWsDbl3pupwOjKyHRPbMurdGdtQaz84MyPlH9fQzSxg1guavvxsfW/NsYubeSJaLGEBEFJ2arKwMcMQzT7dbLW4NN/0S6z4LGy+2U6YBcehtma1rei8Ydo4vDfETpNmZnBmdFPETKvQ7YKY6ShihbmI6RIq5mAAFeYujQrz1DPiQi9N786UdfLFpQmxlKIFUkrSX7NNEWEjCIIgCMKZzWIfiV4oP1t0arQawgEGodOLmsWSM53LztCQTJlJY3I/TawW+2kUsZ7PoQkjiOM8h8ZM8gwa5zy1DVTWD+5MpT0T2w1iZodpmJg2uzS9cNHdMJtmpLqcgKY8I+WzU1Nm1DjWC5p8+etFzVCWdrzBAcwT0DoUEUVA0WJoF1LPepGTh2nmyOZD3YiVrmbqHattxVpT4b2mnbnSO5Nnz6iu75dRucys7ftmwMx6d6b0zYT1YkaFlMMAQkJ3AUJChRLlPJScld4Z7yGl4s4s7A/FxRFRs20RYSMIgiAIwhnP+hS0XEY2iBpjQJt5iVrv1BiTnRqjScaAzXHNyRmSVkXYKKLrB2zmsIBuaV561u3IoQBxHDA7sqDZtTRlqWoZF2em0oGxmYuZiW4Hd2ZZTwch0zsyhljWqSSdHT0cYBA1h4mZ9WloRh2bc9MP4OzTz7ri1vTBAGupHsrP+n6alVDzaLvEIV9zsB2xfzam6Sxrs4qusaRWo2amlJgpzFQVRya7M7l/Js+gMV3K202eP2NmIUc2dwEV8syZft0LFNWXlPUuTCxOTUr5mJSPTcO+4thA2Se9NtsRETaCIAiCIJw1rI9pLmKnCJxNnZpFUVOWaHMpWjR5yGY/myYvvUuT45vDOJLqiF7yTJZmTKqOC5ZW2OlmLNmGHSaLmcVSs6WFVLMl3bCkWpwKTFSect+LmMN7Z9SmpWaLM2oWy86O16VZJKZUZtWoYdBmH+U8i1ngzFJOPRuGa4aKla5mpa1Zax1ta7OomZoc1byq86yZRmFmRdS0OdWsDwEwTW7+t9MwBADotXaeZhbjXKz0s2hizKVkpcRsEDFFwAyCpZ9b0zs0aeF1Imq2JSJsBEEQBEE4c+lnz/RDNRdL0BaDAoYY5wVRY81c1DiTBY1WJJdDBKJVxbFZFDWKWBVRUyfSKKJGgdG4ZeeoYWc948LxAXa7KctmxkS3pW8mN/9XKjBRzeDOLKuOUSktGxWBksXLXKAsOjIb3ZhFjiZkHq9bEzZpmA9lEGeAkn6mc1hAGbbZl58Ncc6hZlpEzVpXMe0sTePwrSHNDGpm0K3CThW6DNO0a6kIm3mqmZnF4tIEzLTLAQBNi1qbkbzPDf8L7gswFy/5wVy8wDoB07OulyYtvE7YloiwEQRBEATh1PAYQzaPGt9cys2GpDOloAQC5LIyMwif5My85KwvN7M6CxmjBpcmmjxkM9qcfuaXFH4E3XIJChhF7I6OetSxazLlgskhzqumXDz6JufZVZZ0w7KeDb0yG5v/nYKJUoyUxSmDxRz14zmSONkoRiJpw+OIT6Ecm4Z9w+tJxIU45/w8w/GzBIeSZZYM+8NkKD3LEc41a7HigB/TRMuqrznQjVjzFd9YW2LaOpqZo1tz0Gr0VBdBo7BrYGe55MyuJUwJB7DTiO5KyVnjUa1HTVvUrCW1LXFldUgy27TJf6M4E6EiFETYCIIgCIJw4tlscGQvaIoIUb1A2Shg+hCAjSJGqXm6WS9m+nQzo+Z9M6bEOJvcP5NMHqwZTRE0lrJN3rZ5Ro0fz52aOIpQB1zlqV3HxHVDKMBEt4xUl8vNdIsjlACAMMyeGZr/S0lZdmOOL5o5kgbBslHADIIlzUMAQkk5y9tljSKkeThATIrVEuG8lmr2h0lJPatYCSPWQsVqqFnxFW20rHQ1q13FzFtWZxVt4/BNdmlUq0rZmRp6aXSbcjlan3jWZlEz9NB0IYcC9GVnpScm9YM0NyIiRjgKImwEQRCEM5q77rqL3/iN3+Cee+7hoYce4oMf/CA/8iM/MjyvNvsCDbztbW/jF3/xFwF46lOfyt/93d+te/7222/nX//rf33SzvucZOPvYuGLfB/VPEQxO4eyNosZa3MpmTFzF8bMI5qT6R2ZkmRmirCxRczoLFjydhYxudSMdc5MMgxipt+firCJFfhJnk8TlwJqFLC1Z2nUslS1LLsZO90sp5zpZpg904uaYaCmWhyomftljoeQ4uDQRCIhZXHTuzCLIgagI4uYrvTJBBSzZIh9eVkyhKTpMHTJbohwrjhQ5tFMY8XBbkQbDWu+YrWr6ILJZWedo+sM3TSnnqlWY9Z0FjOz0k/Tgp3l6GbT9oImDcM0c8JZXrNB1AylZhsRUSM8BiJsBEEQhDOa1dVVLrvsMn76p3+a66+//rDnH3rooXWPP/KRj/CqV72KG264Yd3+N7/5zbz61a8eHi8vL5+cEz5X2VBmljc37CsujbIWnEVVVRYzzpKczT0xzixEMJuhyX8uYihCp7gumiJsGLajzdu9gJmLmfn+LJIg2kSyEKri0lQRO/FUdceo6tg9nrJkW86rpuy2a+yyUyYL4QAj3VGR3Zoc0xyHQIA+ptkcp7hZFDRdioOYaVNa58a0SdOhh6Gas+ToyrotYQCzVNElQ1MCAbpkhp6ZaXAc8jWz4Gi8ZaWImaaztJ0lBEXoTB6y6RV6atBddmfsNEc429k8utlOUxY1XcJMe2ET0b1T04R5SIAP6+bMSLmZcCyIsBEEQRDOaK677jquu+66Iz6/d+/edY8/9KEP8aIXvYinPe1p6/YvLy8fdqxwgtjwxX0zQZMP61PLTI5ktkXMVI5UW5IxpDo3+SebI5jTUD62IGp0EShmQdCYTfYVMROLUzMc41IWNroIGwOpzqVnporUo47adSxVHUu2ZYdrcglamUcz0nn2TKXCYaLGlLSz4xUzcHhvzeKsmTYlugVBE1GDoGnJLsziUM0h1SzURdhY1mIpL/MVs+CYBTeUmbXeMuss3ht8Zwhek7yGVqO8QnW57Ey35DjnknSmG4bSs7zkeTS9qFF9CZqPqGGuTO/WyIwZ4fgQYSMIgiBsG7761a/yx3/8x7znPe857Lm3vvWtvOUtb+GSSy7hZS97GTfffDPWbn4bbJqGpmmGxwcPHjxp57ytUHq9qFkUNLrvr9FQOZRzpFEFzhJrRxzZLGbGJvfDOIWv9bxszM4FTCw5yXFBrMwdmyJoNKRetKiFbZNINoECTAIbUSbhXKCqPZX17KhbJi4LmvOqKUu24Ty7xnl2lWU9ZaeeDY5NX36mmUc4O+ZuzcZZM1slkmOYOwJdinQp0ZHnzfQOzWKp2VqsaZPhUByzGmua6DgQxsyio4mWg35EGy3T4FjzFW0wzLwrYsYwax3ea2IwhDJcE69yuZnPIkZ1oL0a5tHkfppUhm/m0rM8aDMOpWe6ybNpGObTFLem8wtuzTyeeQgNEKdG2AIibLYB9sKt/RfIR//L0pbf47WX3rml41+6/NUtv8eZyk3/9x9t6fi/fMd3bPk9zv//fX7Lr9lzaN+WXyMI2533vOc9LC8vH1ay9nM/93M873nPY8+ePXz605/m1ltv5aGHHuI3f/M3N/05t99+O7fddtupOOXtwyaiZrOUM6wdStFSXeUY5rEljCyx0vixJlSK6MCPiqCxG4SNBdSiC5Pm5WXFhWEQNkXE2AgmoXTC2ITWEW0i1ka0jtQ2MHYdY9uxXM2Y2JYdtmW3XWNiWnaZKbvN2jCjJs+m2Xy4plkYrgmbRzdvhUgkprRO1MySKbHMupSc5YGa/SDNA2GJQ2FEkywH/JhVXzMNjpWupo2GqXdMO0cXNE3naFtDjJrQmHViRntQpdxM++LQ+LK0oH0a5tNoXwICmoQKaYhy1j7mKOdYemo6n4duFlFDCBDLunduQESNsGVE2AiCIAjbhv/yX/4LL3/5yxmNRuv233LLLcP2c5/7XKqq4md+5me4/fbbqev6sJ9z6623rnvNwYMHufjii0/eiW8nFpwa4PDoZlXmzNgyX6Y2xMoQak2sFaFW+DJLxo9Lgplb78z0wiWWRpZkemFThIxJxTJJKBuzmDHztbUBoxLWRCrrMSoxtlnUjGzHLjdlbDomuuU8t8pIeZbNNIsaVUIDiqiZBwXMRY1bGLJ5QkrSmM+ZCYkyQHMuauaDNKuh7KyfN7MWKw50Y1ZLudmhts4uTWeLO5NLzWKX3RnVZlGju+LOeDBtfqwCc5fGJ0yb9+XthA6l7KyNebsJ6C67NLr1peSsCJqYQwOS94OgSUcLDhCEx4EIG0EQBGFb8L/+1//ivvvu4w//8A8f89jLL78c7z1f/vKXecYznnHY83Vdbyp4hCOw6NZs7KdhHu+M0iibwwKoHGHsiLXBTwzdUnZq/EThR4pQg5/kfpjkehFTBIxZEDAmgU5ol4WL1r0Lk3AmYE3E6EhlQl7rQG09VgUqExibDqsiY9MyNjkMYKLbYfDmTj1l1A/g1FNGyjNRnpGKlIq4dYImfwQKV2bW5KDnYxc3vVsT6Z2avvzMDoMzV2Pum1mNNYfiiLVQ80i3xEGfBc3+dsxaH9HcVHSdwXeWMDNzZ6ZTQxDAfD13ZkybisjJ82hUyGJGxYTyuYdGxdJH04WceFZ6afAhi5mUIETweTuLmggpkjo/iJsUNwkPEITHgQgbQRAEYVvwe7/3ezz/+c/nsssue8xj7733XrTWXHDBBafgzLY5i45EP6dmoaemnzkzODmmH5qZl1jlkIBQqyxsxnmeTB+/nFyOYB76YmxE2b6kLJeTaZ1wLjsvzmbBYnXEmYDTeXtkOiqdH2cxE6i1Z2JanArUumOkPE75Mp8mBwRMVDPEOU90hyOypLNLUyk1FJmZBZemLz07EW5NTyjiZh4S4NYvJRygnz2zEmoO+ZqVruZgM2La5UCA6bQidprUGFSjcwhAm0vMlFfZhdnYN9PO+2ZMU4RM6MMAirjpQhE5pY8mxnniWZxHOqfFWOcQcj/NOsdGys+EY0eEjSAIgnBGs7Kywv333z88/tKXvsS9997Lnj17uOSSS4BcKvb+97+f//Af/sNhr9+3bx933303L3rRi1heXmbfvn3cfPPN/MRP/ATnnXfeKbuOc4VB1BxWfqbyPqOHWOdYaaLLgiZUxakZUZZEWIokl6CKKBvRJjsyxkaMKcLFZidmZH0WLiYwMh1WRyrtqXUWMLXuqLXH6cBEL4qZDqf8IGSc8mXfhsQz4uDSjIqgWeyj2UzQLLo1xzOccyhDK6KmS4Z2YRkSz2I1FzXdaBimuda6HNncOMLUQqdRjcZMVRYuMzW4M7nUrMQ1D8Im98son7CzgOoW5tCEXD6mfMxixYcsZso2IawXMzGtLzsr69Q7N71bI/01wjEgwkYQBEE4o/nsZz/Li170ouFx3/vyile8gne/+90AvO997yOlxEtf+tLDXl/XNe973/t405veRNM0XHrppdx8883remiE40MtOjKLosYsxD0vRD1nUZP7avxY48eKbimLmm5HyoMyxwm1s8W6QF37oaysMrmUzOnAxLZUJlBpzw7bFiHjB+EyMc0gUkYLImZJN3mf6hipsK5XBsD1s0QpQzbpRYwZBMyiG7MxHGBj6dlxiZrBqWF9X01yw2DNQ3FEEx2Hwoj9fsJKKT870IyYto6VtRrfWFJj0GsG3eZ4ZjPNDo2ZzUvNbJPLykyXyjyanGhmZj67MtM2C5cQShlZmjsxRZQMIiXE9eKlFytD4lksq/WPRdQIx4oIG0EQBOGM5oUvfOH8C9EReM1rXsNrXvOaTZ973vOex2c+85mTcWrbh81Kpo7wZVzpDaVnZejmOjGjdHZm+ue1LqJGk2pHquwgbLJbA6EmryuIdSJVMccvV55J1TGyHqMjtfFMbIvVkSXTUhufhY1pFvpjmsPEzEh1Q0lZLjmLjFSgVrmkzPWxzAsODKwXLWaTlLOt9s9snE0DOdL58H1xiHmepcQsKVaT5VAcMUtuiHNeixUH/IQmWlZCzcFuxJp3rLQ1a01F01l8m0WNajW6uDNmprCz3D9jp6k4Ngk7m8+c6d0Z3Xj0tMtuzLSBrhvEy+DClBk0qS8xg1xitiBa0sYyMxnCKZxgRNgIgiAIwrnARvGyIFw2Eyv5JRvimvvt/rneqTFZuAyBAX3JWXld6reNJi7V+CWHXzJ0k9JTM1GEcSk/m0TSJGDGnqVxw1LVMXHt4M6MTTf0yOwwDbXOZWQ7zCyLFt0VR8YXEdNhSEN/TKXiIGZqZRgpi0bjlDkpH3tI8TAxE0lEYnk+lX3lcRE5MSU6ErOU2B8ta9FxMI7YHyfMouPRsIO1knq2v5swDY5pcDzaTJh5x4HpiOnMETpDWrO59GymsNPcT2NnYFezoHHTImZ8wswipo2oLs5dmlmHalroPGltOk8yKwlm63pjFgWMlJQJpxgRNoIgCIKwnel7XFgoGYP5nJm+yf/xOC69YOnFSv96o0lalzhmk6PCtCZanefNaAVGkbTCTwx+rOkmmna5lJ8tgV9KhFEiLQXs2DMat+wcNSyVQZkb08ucyr0yI91RFzHTC5vepcn9MSEP0Syixql50//clTkxTf6buTGLRBJdCkQiXYoEcplZu5B6FlCEpOjQdEmzmir2hwlrseaRsCOHA8SKb3YTVn3Naqg42I5ogmWtcyX1zNLOLHFqUZ3GrGlMC7pV2LXi0sxSdmzK3BnT5h4a08R5sllZCGE+bybFdZHMj+WmCsKpRISNIAiCIGxXiqgZHJneVSnbwLzBvySWoU0WN8YMogWt17kuSesiVPQgWPLzimQVSZV9VpEUpOEY8CONH+XkMz8pJWijImrqiK4DVd0xcp6x7ZjYdp1L00cy96VmddmeL34QNRUBQ0KrhFFpXSfMZqVmx8rRBM1imVks/+tSpCOVUjOYpfVipkuGlhwKsJZqHvE7WIs1j/olDvoR05h7aFZ8TeMtB9ua1huaztHMHMFr0jSnnulOYZosarK4KQlnLUMvje5K0plPC6EAJRCgbJPSemfmKD0zgnC6EGEjCIIgCNuYxcb+o/XCDGLGZkGTnM3pZVqTnMkCxhYXxiii0Vm4FMHSr6NR2bkZ9i/ug1Bm1IQa/I6Ue2rGpfysDownDTtGDTuqlj31Gku2YYfJjo1RkbokmGXHJpecVWX78FSziFMRTcKRMAocxVBS6qSJmo09M32ZWZcCXYrMUmSWoEMxS4ZZMsMcmn7QZj+fZi1WfKNbZhoc+7sJB7oRa77iYDOi8ZbGG2bTihA0sTHQaJRX2Jku4iUHBeiuxDZP8xwa0ybsLGZxM4tonx0b3YYc19yFeVzzEBZQ0sx6kQOPHQQgCKcQETaCIAiCsB3pS9AWRY0x6wXNYl+M7mOY7RDHnJwZxExeK6LTJKOIThHtegGTdL+vLAuCpz8m1HlGTRgl/Di7NNQBN+6oqsCOUcOuesYO17DbTVmyzVBy1kc0OxWoFgTMYjhA3vYYUo5pVhGzIGpMmTVzMjiSoIHcSxPIfTPZpdE0ybDaD9jEFCFT0yY7lJwdCiMebZdY9RUHuxEHmxFtMKzMatrWELwhTi14hW5KOEAA3cyHbJqmuDRdLkHLEc5xXeqZCimLm9ZDSKhe1JRemhSye0NcKEUTUSOcYYiwEQRBEIRtTp4lo+YzZXpRs5BWNrg0zg5xzFnYKEJtsuti87yZZBTBQbTrRUs0CyLHQDpM5ECoE9FBrCNpHFFVwNae8aijdp6dVcMO17DsGnbaKRPTMtEtte4wxEHg9E6NUx5HLkurCOgFMdOvNdml6aOb9UkUNz0bRQ3kQICQEl2CJhlmyebY5lQzi46DcUwT3SBopsGxEmq+2Y4Hl2a1qXLZ2awitBpajZqZLGJalWfSlAGbuu1n08xTz/LQzYRuE6YJg0vTz6TJAzbnDg2hzKCJYQgD2BgWkFeb9NpI/41wihFhcxJpr/murb/m5ke3/Jo3fuv/3NLxV49Xt/weZypfDdMtHf+C//76Lb/HM3/5i1s6fs/+fVt+D/lvW4IgnAyUnvfOKGPA2XkPjbWDwEm2RDE7Q6od0WnCyGZXxmlCrYhWDdHMyZIFjoW4UcBYSDoNzg0aok0kA5hEchGqiHaR0ajD2cCkbtlZNYxtx3n1Gst2xg7TcEF1kIluh5SzLFayoNHEQdjk/eEwQeNUETSsn0ezOIvmRAUHbOylgcMTz+ZuzdypORTH7A8TZsnxTb/EWqhyhHM3zsKmq/nmbMyss6zNatrGEjsNU5MdmnaedKbb7M5k4ZJFjfYpuzW+9NS085KzQdA0ARXjIGJUKOLG+/lwzRDmc2n6eOfCOlEjbo1wGhFhIwiCIAjbmCH5rAzJPKyXxubtZPUwXyaLGU2scsmZr0vpmQM/yoIm1EXUGIguQXFl8r4iZGwi6QQmgU0onTBVwNqAc4GlumVkPRPXsrOaMTYde9wqO+2MiWnYbdYYqW7on9FEjIo4FTD06zSIGU0aBm2aDaLGoYYZNXNRc/w9Nhs5Woxz79Z0aT5kczVWrJVemkNhNCSdHWhzL81KW+eysz7pbGahU5i1LGpMo7DTImpmqQibImi6hAq57EwFctlZm8MAdJfLzpSP0HmIERXTOpcm+eLSeF9K0cK6FLTHnEsjCKcYETaCIAiCsB0ZYp31QgmaGfppkl3sqym9NM4QK02oNLFWhEoTHPhxFjWhyvHM0eYhmrEqJWiLjowtQsYklI0Yk9AmYEzCmEjtOiobGFnPctVQac+ya1i2RdjYVXaYGUu6YY9ZWdc7kxPO4mFixqg0lJz1vTSw3qVZLD/rB20eL31wQO/WPNZsmkCOc26ZC5u1WHMojFgJNYf8aBiweaAds9Y5Zq1jOq3wnclJZzNTXJo8ZFO3YNdyuZmdJWyTiogpKWcxZTHjc6mZ7kIpN4uotsuCxhcxU4Zrpt6V8T7PpRnK0eZuzVFdGilBE04TImwEQRAEYbuiyqyZ0kejbA4GGATOolNjNbE2+LEhVopuoofSMz9RueG/gjDOPTJhFElVETAuokxEmYS1AWMi1kQqG7Ami5hKB5wJTGzLqMQ39wM2d5iGZTOj1h079ZRJmUmz26xREalVwKj8ZbkXMHC4KzPsX3BnNsY6Lzo1ehA6J9G5WRi4GaGkoJUytDDmQJjwTT9hNdR8o1niYDsaBmw2naVtLWHVodoyj2YGplXY1RIKMEtUq7l3xk4jZhbmIsbHIlxiDgQo82hUL1R8CQbw8/6ZocwsJlIIZV8c+mpSCPkCN3NnRNAIpxkRNoIgCIKw3ekjn0tZWjJ6Lmr61DOjSuqZIvRLBaHO8cxZzOR5M7FKpFFu/Nc2YZ3HmJgdmSJmahMY2Y7KBEamY2Q6ah1YslnMTHTLxDSMlGeimzxgU3dMVNlWnonyw1DNuWiZOy6LQiZf5pHFzPBRnGRRExa+3C+KmlyKBjEpupRn1GTHpmIaKlZ8xZqvmHnHWufmomZm8zyaJs+jsdOcdmanCTMD2yTcasQ0Ebvq0bMOFVJ2ZPq+mVhm0cTSO5MSyfsycLOUn8U+FCDOBcqCoFknZMShEc5QRNgIgiAIwnZFl/6aobdmPmizFzXzMjRdytDUUHLmR4pY50GasSqiZhKgirhxR117nAmMqw6nI7XJQzVHNjsySyanme0okc217ljWs8Nmz4xUN0Q392KmVjBSCqfMugSzjX0xG8vKNuubOVJAwIkQNYtlaIslaBtFTSTPrZmXoeWQgBVfc9DXrHR1Tj1rc/lZM3XExqAag1nVpeRMDWVn1UrCTvMsGnegw8w8erVBTZt5LHPfExMTKWbxMt+3/vmBzUIB1okaETHCmYsIG0EQBEHYhiidv8wPc2oWltRHOhdRE2pdktByApqvFX6UxU2os1MT60QcRfTEY6vAjsmMpaqjtkXMmI5KB3a6GVVxZHaY7ML0PTMj1Q0JZ64IGkNiVBLNHImRAqcUTmlGyh5Tg3/ckDUZWe+kDM9v8h09bLbzSO+zIFrynJr+vbKIgRwUENDEpHgkTtgflvi6X+ahbjff7CZ8o9nBo82Eta5i/3TEbOYInSGuOlSrMFONXcu9NG4V7FrCtODWEnYtl56Z1Q691qBWp6TZrPTLxMNdmA0OTNroxIAIF+GsRoSNIAiCIGxTciKaGubYJNPPsFHZrSmzaaLTeW1zCVp0ufQs2px4Fl0iuQQuYlygqjxjl9PMRsYzsS1j01Ebz5KZl5pt7JsZXBrCEM/siEO5mWMuahwGjUajMEoPjfo9m8Ur9xypeT+mNDyOG47dCmF4X+hSDgQISdMVAdOi6ZIpgkbTJkNE80jYwf4w4RvdMl9vlznYjdjfjjnQjJi1jtnM4RtLag2q0ZhmQ4xzm0WNaROmyQlnObbZozqfm/37BLONYiZ/AOsFjYgYYZshwkYQBEEQtitarwsKwBpSNXdr+gS0MNJEp+jGvUujFpwaiKMIdcSMPaNRx6Tq2FnPhojmsekY6zYLmlJ+NlKeZTPNzozuhnSz3qVZFDWaLGpMSS7bODxzs/SxzcTLRuESUp4bE1JxUlCEpIhoAmoQIQAxHd0ZChvPqYiWWXK0ydCVYZtdMrTJ0kRHQNFExyzm/Qf9iEN+xIF2xDebCdPOsdpUzKYVweucetZqTKswU1XinHPqme4Sdgp2lmfR6CYOs2iGEIAQDxM1A5ulmQnCNkOEjSAIgiBsR/r+kWGOjSaZ9f00eZ17aqKjJJ/lBLRYFdemSuASusqzZ0bOM3YdO1zDsm0Ym+zWjHTukdlhZkOZWd83syhqKiJapfWipmgGzTwAID+eb28UNX0fy2I5GKwXM10RMh26lISpQYzMXRZDSJp4hJK3cATBE1B0ybIaa7pkaGIOAuiDAZpo8dEwDXm7jYaVrmbqHattxeqsousMvrWkmQGfy850l10aM1Von50aO0s5MGAQNQnTZWGjurCQcNYnls1FzWE9NIKwjRFhIwiCIAjblX5+je7L0BRJ5yXahcUxJKEtlqClsigXMTbinGdUemqWbMvSEArgB2EziBndix2/TuT0osao+dyZxaQzmA/Q3MhGUdOleRFam3qX5nBB0yVDi6ErzkrvsGR3JZeJtWlrX4nyzzKshBFdMqyFnG7WREsTDbPgaINlFixNsHTBMO0src9pZ11jSZ2GVqMbjepUKTcrqWcz0F0pP2uyY2PalEVNG1FdTjrL0c4LUc0xrhc1PTI8UzgHEGEjCIIgCNsVnUXNMLPGGWLVLzkoIFQ5LCBW5DK00UICWilBc6OcgLZUt+weTdnpZpxfrbDLToe0M6cCVRExWdi06x0bYi4/KwM2e6emFzVuwxDNjfSipiMMLk230LDf97pkIaPn6WPR0WGYxWooFculYZZZKRnrkjnMmYnp6EM8s0tjWfU10+CYBctqV9NGw8xbms7io6brLMFrQtDEJjszymtUozCdKuIluzO6BdOC8sWl8aUErUlZ2DQR0wRUF9FTj249tB10fh7f3Ced5YvI6yJqNk05E4RthAgbQRAEQdiOLKSipRL7nLQqwQHFqTGLYQFqofyMHBZgs1vTl6BNXMekODU7TMMOM2OiW6qScta7M9XCtiGtEzWOdMTBmjB3azZLQ1vXT0Nu4u9K/0y70Lw/S5YWw1qshx6YtVgPgmYtVkPfyzTktU/miB9l2ETktNHSlfKyWbDD/JnOG1pv6FpLjJpQxAxBZWfGg/a53Ez5Imw6StlZmm+3c2Fjmoj2KYuaNqK7UoLWeZQPhwsaEFEjnJOIsDmJfPlHth5R+f8+5/0n4UyOn7fvf/qWjv9Pd1695fdQ4ej/dWwznvlvvrSl47/tq3dv+T3CYx8iCIJwRjJEPes+AU0TjS4laAvJZ1Uextn31PRlaKqKWBeoS1/NkmtZtg077Yxddo3dZi2XmekOTRwEjVNhSD4zJGoV1vXT9BLCLQzUPLpbk+ZhAaXkrE2JbqHsrEmmODamzIlxrBZhM4uOQ3E0uDOHwqj0vdgsbKLBJ31Ul2bjc23MjszBZkQbDE1nmTUuOzOtIbV6Lma63pHJa+XnYiYLm4QK2a3RPmUR0+a17hK6zSVnuacmorqAaruShBZIPgyzazYbqLlpYIAkognbEBE2giAIgrBd0TnmmX4op+lDA9R8EGefgFblFLRQJ1KdUKOArT31qGNH3bKznrG7WmNPtcoeu8oT7SF2m9Wh3MwQS9pZmKeeHaHsLLs1aggKMMwdmqO5NYFER1onambJrHNpumQGQbMWaw6FMbNkWQkj1koPzCE/oin9L2u+IiSNj5p0BGGzmeBpg6ELmmlT4TuD78wQAqD7qGYPZjZ3ZUwDKhQnxpPFTJcFjApZ4GifUD5iuojyKffRdCH30vgInYcYUU2Xo51jhK7NaWghrBM16QiDN0XUCNsVETaCIAiCsA1RffP9MMOmLJphO1pIlvVrl0g2Ylx2a6oSFjCxLTtsnk2zw8xY1lOW9WwoNzOkdcEAvaAxah7l3AsaYJ1Ds5mo0Zu4N3GIeO77auZzYxbDAXIvjS0Cx7IW6qG5fxocq76iDZapd0y9o4uacARhc6RAsS4YQtC0jSO0OQRAzUwRMAozU6VXBnSTBcwgbAJzMeMTyid0SOgFMaO7CD6iQhY1FOGienfG++zUxDCImpSOEOks5WfCOYIIG0EQBEHYrvSJaCXuOSeg9YM4Kc5NiXmuE7FOpCqi6oirPKOqY6nqWK5m7HIzdts1zrOrPMGs8ASzwm7drHNlFntn4MjuDKwXM4uP83Y5dmEwZyAHBvRuTbsQEjAEApSQgL6fZiWMOBRGTIPjoB+z6quhyb8Jlpm3TFtHiLm5v2czQ2Oj6AlBE70mrdlcatbk2TOmzJ4x0+zAuLXcO2PahJlFVOwdmn4pyWYxlejmhIrZmVH9TJo+9awfvpnmQielBF2X1yHMh29uhjg1wjZHhI0gCIIgbFdKaABGEa0mWT04NTkwIC+hTrm3ps6ixlSBUZVL0HZUDedVU3a7Nc5zqzzRHmS3XmOPnrFbR5xSmN5lWYhoPpKAWdy37lSP0F+zkX7QZkQNyWd9X03v1sxiLkNbixUroWbV1xzoRqz5iiZYDjU1rTc5ernJTf4xKNj4vX8TB2fQBj730JhVnQdpzlQepNmCnSbcWsK04FYDZhoxM49uPCrkSGble7ES5nHNZdAmIbswqRcwKUJMpX9mPnxzsZ/mMEEjIkY4BxFhIwiCIAjbHaVA5e/pSfeLGrbp1wqUiSgd0TpidMTqiFaxpJ7l/pmqhAGYUk6mN5k50zNMmlmIUw4bYlmMUmzmMfgUhrk1TYq0KTFLua9m1ocERDf007TJMEsVh+KItVCz4rOoWfUVa75i6t3g0vig6VqLbw0p6JxctpGjCB0V1NypaYpLM8vujJ1mcWNnEbfiMWse1XSoabtexAwuSywzaEqPTAikENYLmOEDnaebHZZ0JmJGOMcRYSMIgiAI252UIIFKoGK/JFRUeTuUZMoI0WuUzj0kM2+xOnKoG2FUwunARLcEFE55VlOLI3K4AmCTfcfW5xFRBBRrcXTECOdZcqyFepgtc9CPmQbHIV+z0tVMvWO1rWg6m+OYG0f0itQaVKvz9W8ibNRhwmbhOZ/DAeyqwrRZ1LjVhGkSbhqxKwHTROyhBrU6Q81a0qwhxYUG/5gOFzCL5WQiVARhS4iwEQRBEITtSj+JPiR0F9BtRFuFNrnBHZWyc2NVPlblZvzQRVbTiK6zrLmKLhj2t2P2V2MOjMbsslMedTuY6IZKndxQ/IAmJMVarA8bqtmnnPl+Hk00tNGw5vM5r3YV087SBUPTOII3WdA0BoJCtbmMTIUi9h6PPiv7tM/hAKbpl9JH0yV0O083ywEApVdmQdSksOCyLJaVCYJwzIiwEQRBEIRtSEoJVXo0VAjgNTpEtNeYLg+ARKlcnmaBmMvVSIpYKWKCxmu6Kscpr7qKtS4330/sDg7UYya6xemTLGxK8tlarPDRFCGj6aJhFiy+zKBpQp4rE6Jm5i0+6OzQdIYYzOGDMgO5lKwDHbK4mX94m32g6x/qwIKwyf00pk1DZLPuIrqkmmVRE9c5NUDedyTErRGELSPCRhAEQRC2KSlElPc5YUtr9NRjARUNSav85by4FaZShK4f1Jnn2+RhnZaVkWPVRb5ZBb5eL1HZwFLd4kzA6pMbJRyTIiZFFwyJXCLnQ45m9lEToyJGTQiKVCKbo1e5b6ZTKK8hgOkUyqsyCLO4NAvzZNSRKurY3MlRHnRIua+mODV2LaLbiGliDgpo82c/H6IZ5v0yoSipzUIABEE4JkTYCIIgCGcst99+Ox/4wAf44he/yHg85vu+7/v49V//dZ7xjGcMx8xmM17/+tfzvve9j6ZpuOaaa/jd3/1dnvSkJw3HPPjgg7z2ta/lk5/8JDt27OAVr3gFt99+O9Zu49tgnJc4qRBJJX1LdRGtc8N739CfRU6ClL/wR597bqInR0R7RXKa4DTT1tC4yKx1GBNRm9ZvnVhSUjleOSpSzEInlW36dekRyteghr4Z3eU+It0x7M/b2XXRHRCzSNn8zTff3b/eNtmlMW3KpX4+l/31AzVVPzhzMbGsd2qOVIImc2cE4ZjYxv+PLgiCIJzt3Hnnndx4441893d/N9573vjGN3L11VfzhS98gaWlJQBuvvlm/viP/5j3v//97Nq1i5tuuonrr7+eP/uzPwMghMAP/dAPsXfvXj796U/z0EMP8ZM/+ZM45/h3/+7fnc7LO/mUZnR8QCmFaux8nozOQkDFLHCizV/WY5vFjK6zc5MMhFqRbCJaTawT0SRmlc1Da/RJFjaJnEZWRAupBB0koA8/KPtUolxTER7FoelFyLDdzQdlKj8PVOhRj1UGVkIYdCC7M10anBrlI6oJ692aMotmCAqAw9POxK0RhONGpXRmFXEePHiQXbt28UJ+GKvc6T4dQRCEcwqfOu7gQxw4cICdO3ee7tM5jK9//etccMEF3HnnnbzgBS/gwIEDPPGJT+S9730vP/ZjPwbAF7/4Rb7927+dffv28b3f+7185CMf4Z/+03/KV77ylcHFeec738kb3vAGvv71r1NV1WHv0zQNTdMMjw8ePMjFF198Vt2blLV5GdWo0QicI40qqBzJGeLIEp0hOk0Y5fk2oSoDPA2ESpEsRAOxygInOoiWsp3mcdEn8zoSg5AgLYiXBREDC83/aV5apoZSu7Su5Ez7NAgTFdKQEveY57IofmKCWIRN74Q1PgubLkDboXyAWUPqOug8yfvBrVnn1BSHZt0smjPr65kgnDa2cl8Sx0YQBEE4azhw4AAAe/bsAeCee+6h6zquuuqq4ZhnPvOZXHLJJYOw2bdvH895znPWlaZdc801vPa1r+Wv//qv+c7v/M7D3uf222/ntttuO8lXc3LJX5ITKqbca6M8yhuSUqiUUEqhY0JFg0qJaBQq6Fx2ZnKUcTIQjSK2KW/b4uLo+fpxztU8jgvJq74HRqWF7YV9WeykYf/g2gRKitlcwOgidpQvwiaRP6vHqyViKueRnZpc4hfy4mPuaep8cWnivLdm0aXZTNQIgnBciLARBEEQzgpijPzCL/wC3//938+zn/1sAB5++GGqqmL37t3rjn3Sk57Eww8/PByzKGr65/vnNuPWW2/llltuGR73js1ZR5mTorqWlCwKsotgDTpEMJpkNbqyOfbZGZJVJK2ITud9phcx2clJZr4+2W7NInMRM5/Jc/g6bRA+aXBsBlcmptJPVERNiPOfezQ2CI8spuLQS0MsgibEPHyz67Ko6dq5uAkLls9mgkbcGkE4LrYkbB5PE+cLX/hC7rzzznWv+5mf+Rne+c53npgzFgRBEM5JbrzxRj7/+c/zqU996qS/V13X1HV90t/npJIiJJXLn8hf6lOZeK+MzgLHaJTWJGuyyDEGjMox0LYXNrlMDZWdnGHROSr6pDs2MBcuMHdWiiCYC5u0cGwqZWpz14qY5o9DKkNLF7aZ//zHRf+zfIlz7mfV9ALG+9w30/m5Y3Mkd2YxLEBEjSAcM1sSNo+niRPg1a9+NW9+85uHx5PJ5MSdsSAIgnDOcdNNN/HhD3+Yu+66iyc/+cnD/r1799K2Lfv371/n2nz1q19l7969wzF//ud/vu7nffWrXx2e286kmHJqWflirQB0IEWT59sYDUqhrAWtssjp92kNWoFeEDZaz4WPyYomqZOvbNSG8q1FkTNvxl84dohUTvPHQ6TygqhZfLzI4xEXi7NoeuHShwP04mZR6GwMB9gs+UxEjSAcF1sSNh/96EfXPX73u9/NBRdcwD333MMLXvCCYf9kMtn2NwtBEATh5JNS4nWvex0f/OAHueOOO7j00kvXPf/85z8f5xwf//jHueGGGwC47777ePDBB7niiisAuOKKK/i3//bf8rWvfY0LLrgAgI997GPs3LmTZz3rWaf2gk4lKUEKpBRJQaO0yu4BZBGjVG5f0VnIULbVsK1A5VozpbOYUfnB/HmYH39SL2XDF/7N+lE2CoWNvSyLx5R9abOffSSONEyz9M6sO8+FEIB1gQCL5yUIwgnnuHpsNjZx9vz+7/8+//W//lf27t3Li1/8Yn7lV37liK7NZskzgiAIggC5/Oy9730vH/rQh1heXh56Ynbt2sV4PGbXrl286lWv4pZbbmHPnj3s3LmT173udVxxxRV87/d+LwBXX301z3rWs/gX/+Jf8La3vY2HH36YX/7lX+bGG288+8vNHg8pd9anABCyMAn5S73SG0SJ0vOxLQvPHSZe9Ly55rR+TT+S2CgcUbRstVH/ccyVkfIyQTj9HLOw2ayJE+BlL3sZT3nKU7jooov43Oc+xxve8Abuu+8+PvCBD2z6c7ZD8owgCIJwcnjHO94B5P7NRd71rnfxyle+EoDf+q3fQmvNDTfcsG5AZ48xhg9/+MO89rWv5YorrmBpaYlXvOIV60qmtz3rekjCfPNxzoGUr+WCIJwNHPMcm9e+9rV85CMf4VOf+tS6eueNfOITn+DKK6/k/vvv5+lPf/phz2+HWQGCIAjbhTN9js3pQmasCYIgnB5O+hybIzVxbsbll18OcERhsy2SZwRBEARBEARBOK1sSdg8VhPnZtx7770AXHjhhcd0goIgCIIgCIIgCI/FloTNYzVxPvDAA7z3ve/lB3/wB3nCE57A5z73OW6++WZe8IIX8NznPvekXIAgCIIgCIIgCMKWhM1jNXFWVcWf/umf8h//439kdXWViy++mBtuuIFf/uVfPmEnLAiCIAiCIAiCsJEtl6IdjYsvvpg777zzuE5IEARBEARBEARhq+jHPkQQBEEQBEEQBOHMRoSNIAiCIAiCIAhnPSJsBEEQBEEQBEE46xFhIwiCIAiCIAjCWY8IG0EQBEEQBEEQznpE2AiCIAiCIAiCcNYjwkYQBEEQBEEQhLMeETaCIAiCIAiCIJz1iLARBEEQBEEQBOGsR4SNIAiCIAiCIAhnPSJsBEEQBEEQBEE46xFhIwiCIAiCIAjCWY8IG0EQBEEQBEEQznpE2AiCIAiCIAiCcNYjwkYQBEEQBEEQhLMeETaCIAiCIAiCIJz1iLARBEEQBEEQBOGsR4SNIAiCIAiCIAhnPSJsBEEQBEEQBEE46xFhIwiCIAiCIAjCWY8IG0EQBEEQBEEQznpE2AiCIAiCIAiCcNYjwkYQBEEQBEEQhLMee7pPYCMpJQA8HaTTfDKCIAjnGJ4OmP9/sZCRe5MgCMLpYSv3pTNO2Bw6dAiAT/E/T/OZCIIgnLscOnSIXbt2ne7TOGOQe5MgCMLp5fHcl1Q6w/6zXIyRr3zlKywvL6OUWvfcwYMHufjii/k//+f/sHPnztN0hqeHc/Xaz9XrBrl2ufbTc+0pJQ4dOsRFF12E1lKt3BNj5L777uNZz3rWOfl3eTyc7r/psxn57I4d+eyOjTPxc9vKfemMc2y01jz5yU8+6jE7d+48Yz7sU825eu3n6nWDXLtc+6lHnJrD0VrzLd/yLcC5/Xd5PMjnduzIZ3fsyGd3bJxpn9vjvS/Jf44TBEEQBEEQBOGsR4SNIAiCIAiCIAhnPWeVsKnrml/7tV+jruvTfSqnnHP12s/V6wa5drn2c+/az3Tkd3NsyOd27Mhnd+zIZ3dsnO2f2xkXHiAIgiAIgiAIgrBVzirHRhAEQRAEQRAEYTNE2AiCIAiCIAiCcNYjwkYQBEEQBEEQhLMeETaCIAiCIAiCIJz1iLARBEEQBEEQBOGs56wRNm9/+9t56lOfymg04vLLL+fP//zPT/cpnXTe9KY3oZRatzzzmc883ad1Urjrrrt48YtfzEUXXYRSij/6oz9a93xKiV/91V/lwgsvZDwec9VVV/G3f/u3p+dkTzCPde2vfOUrD/s7uPbaa0/PyZ5gbr/9dr77u7+b5eVlLrjgAn7kR36E++67b90xs9mMG2+8kSc84Qns2LGDG264ga9+9aun6YxPDI/nul/4whce9nv/2Z/92dN0xsK5eA/aKo91z9qO/5aPlRNxz3v00Ud5+ctfzs6dO9m9ezevetWrWFlZOYVXceo5EffLc/FzO1H32gcffJAf+qEfYjKZcMEFF/CLv/iLeO9P5aU8JmeFsPnDP/xDbrnlFn7t136Nv/zLv+Syyy7jmmuu4Wtf+9rpPrWTzj/8h/+Qhx56aFg+9alPne5TOimsrq5y2WWX8fa3v33T59/2trfx27/927zzne/k7rvvZmlpiWuuuYbZbHaKz/TE81jXDnDttdeu+zv4gz/4g1N4hiePO++8kxtvvJHPfOYzfOxjH6PrOq6++mpWV1eHY26++Wb+x//4H7z//e/nzjvv5Ctf+QrXX3/9aTzr4+fxXDfAq1/96nW/97e97W2n6YzPbc7le9BWOdo9azv+Wz5WTsQ97+Uvfzl//dd/zcc+9jE+/OEPc9ddd/Ga17zmVF3CaeFE3C/Pxc/tRNxrQwj80A/9EG3b8ulPf5r3vOc9vPvd7+ZXf/VXT8clHZl0FvA93/M96cYbbxwehxDSRRddlG6//fbTeFYnn1/7tV9Ll1122ek+jVMOkD74wQ8Oj2OMae/evek3fuM3hn379+9PdV2nP/iDPzgNZ3jy2HjtKaX0ile8Iv3wD//waTmfU83Xvva1BKQ777wzpZR/z8659P73v3845m/+5m8SkPbt23e6TvOEs/G6U0rpn/yTf5J+/ud//vSdlDBwrt6DtsrR7lnnyr/lY+FY7nlf+MIXEpD+4i/+YjjmIx/5SFJKpf/7f//vKTv308mx3C/lc8scy732f/7P/5m01unhhx8ejnnHO96Rdu7cmZqmObUXcBTOeMembVvuuecerrrqqmGf1pqrrrqKffv2ncYzOzX87d/+LRdddBFPe9rTePnLX86DDz54uk/plPOlL32Jhx9+eN3fwK5du7j88svPib8BgDvuuIMLLriAZzzjGbz2ta/lkUceOd2ndFI4cOAAAHv27AHgnnvuoeu6db/7Zz7zmVxyySXb6ne/8bp7fv/3f5/zzz+fZz/72dx6662sra2djtM7pznX70Fb5Uj3rHPl3/KJ4PHc8/bt28fu3bv5ru/6ruGYq666Cq01d9999yk/5zOJo90v5XPLHMu9dt++fTznOc/hSU960nDMNddcw8GDB/nrv/7rU3j2R8ee7hN4LL7xjW8QQlj3QQI86UlP4otf/OJpOqtTw+WXX8673/1unvGMZ/DQQw9x22238Y//8T/m85//PMvLy6f79E4ZDz/8MMCmfwP9c9uZa6+9luuvv55LL72UBx54gDe+8Y1cd9117Nu3D2PM6T69E0aMkV/4hV/g+7//+3n2s58N5N99VVXs3r173bHb6Xe/2XUDvOxlL+MpT3kKF110EZ/73Od4wxvewH333ccHPvCB03i25x7n8j1oqxztnnUu/Fs+UTyee97DDz/MBRdcsO55ay179uw5pz/Px7pfyud27Pfahx9+eNO/yf65M4UzXticy1x33XXD9nOf+1wuv/xynvKUp/Df/tt/41WvetVpPDPhVPKSl7xk2H7Oc57Dc5/7XJ7+9Kdzxx13cOWVV57GMzux3HjjjXz+85/ftn1kR+JI171Y8/2c5zyHCy+8kCuvvJIHHniApz/96af6NAXhMTnaPWs8Hp/GMxPOFc6V++XxsN3vtWd8Kdr555+PMeawZIavfvWr7N279zSd1elh9+7d/IN/8A+4//77T/epnFL637P8DWSe9rSncf7552+rv4ObbrqJD3/4w3zyk5/kyU9+8rB/7969tG3L/v371x2/XX73R7ruzbj88ssBttXv/WxA7kHHzuI9a7v/Wz6RPJ573t69ew8Lr/De8+ijj8rnucDG++W5/rkdz7127969m/5N9s+dKZzxwqaqKp7//Ofz8Y9/fNgXY+TjH/84V1xxxWk8s1PPysoKDzzwABdeeOHpPpVTyqWXXsrevXvX/Q0cPHiQu++++5z7GwD4+7//ex555JFt8XeQUuKmm27igx/8IJ/4xCe49NJL1z3//Oc/H+fcut/9fffdx4MPPnhW/+4f67o349577wXYFr/3swm5Bx07i/es7fpv+WTweO55V1xxBfv37+eee+4ZjvnEJz5BjHH4jyDC4ffLc/VzOxH32iuuuIL//b//9zph+LGPfYydO3fyrGc969RcyOPhNIcXPC7e9773pbqu07vf/e70hS98Ib3mNa9Ju3fvXpfMsB15/etfn+644470pS99Kf3Zn/1Zuuqqq9L555+fvva1r53uUzvhHDp0KP3VX/1V+qu/+qsEpN/8zd9Mf/VXf5X+7u/+LqWU0lvf+ta0e/fu9KEPfSh97nOfSz/8wz+cLr300jSdTk/zmR8/R7v2Q4cOpX/1r/5V2rdvX/rSl76U/vRP/zQ973nPS9/2bd+WZrPZ6T714+a1r31t2rVrV7rjjjvSQw89NCxra2vDMT/7sz+bLrnkkvSJT3wiffazn01XXHFFuuKKK07jWR8/j3Xd999/f3rzm9+cPvvZz6YvfelL6UMf+lB62tOell7wghec5jM/NzlX70Fb5bHuWdvx3/KxciLueddee236zu/8znT33XenT33qU+nbvu3b0ktf+tLTdUmnhBNxvzwXP7cTca/13qdnP/vZ6eqrr0733ntv+uhHP5qe+MQnpltvvfV0XNIROSuETUop/c7v/E665JJLUlVV6Xu+53vSZz7zmdN9SiedH//xH08XXnhhqqoqfcu3fEv68R//8XT//fef7tM6KXzyk59MwGHLK17xipRSjr/8lV/5lfSkJz0p1XWdrrzyynTfffed3pM+QRzt2tfW1tLVV1+dnvjEJybnXHrKU56SXv3qV2+bL1SbXTeQ3vWudw3HTKfT9C//5b9M5513XppMJulHf/RH00MPPXT6TvoE8FjX/eCDD6YXvOAFac+ePamu6/St3/qt6Rd/8RfTgQMHTu+Jn8Oci/egrfJY96zt+G/5WDkR97xHHnkkvfSlL007duxIO3fuTD/1Uz+VDh06dBqu5tRxIu6X5+LndqLutV/+8pfTddddl8bjcTr//PPT61//+tR13Sm+mqOjUkrpZLlBgiAIgiAIgiAIp4IzvsdGEARBEARBEAThsRBhIwiCIAiCIAjCWY8IG0EQBEEQBEEQznpE2AiCIAiCIAiCcNYjwkYQBEEQBEEQhLMeETaCIAiCIAiCIJz1iLARBEEQBEEQBOGsR4SNIAiCIAiCIAhnPSJsBEEQBEEQBEE46xFhIwiCIAiCIAjCWY8IG0EQBEEQBEEQznr+/4ooL0Eipuh7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 假设我们有一个 (28, 28) 的灰度图像\n",
    "image = torch.Tensor(np.array(train_ds[0][0]))\n",
    "\n",
    "# 调整形状以匹配 (N, C, H, W) 的格式\n",
    "image = image.unsqueeze(0).unsqueeze(0)\n",
    "\n",
    "# 使用双线性插值将图像大小更改为 (224, 224)\n",
    "# 'nearest' | 'linear' | 'bilinear' | 'bicubic' | 'trilinear' | 'area' | 'nearest-exact'\n",
    "resized_image1 = F.interpolate(image, size=(224, 224), mode='bilinear')\n",
    "resized_image2 = F.interpolate(image, size=(224, 224), mode='bilinear')\n",
    "\n",
    "# 最后的结果现在是 (1, 1, 224, 224)\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "axs[0].imshow(image[0,0])\n",
    "axs[1].imshow(resized_image1[0,0])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.unet import UNet\n",
    "import torch\n",
    "model = UNet(\n",
    "    image_channels=2,\n",
    "    ch_mults = (1, 2, 2),\n",
    "    is_attn = (True, True, True),\n",
    "    n_blocks = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([7, 2, 32, 32])\n",
      "9M\n"
     ]
    }
   ],
   "source": [
    "x = torch.rand(7, 2, 32, 32)\n",
    "t = torch.rand(7)\n",
    "print(model(x, t).shape)\n",
    "\n",
    "print(f\"{int(sum(p.numel() for p in model.parameters())/1e6)}M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([7, 2, 32, 32])\n",
      "torch.Size([7, 48, 16, 16])\n",
      "torch.Size([7, 32, 8, 8])\n",
      "torch.Size([7, 56, 4, 4])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([7, 1, 32, 32])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import segmentation_models_pytorch as smp\n",
    "import torch.nn as nn\n",
    "import math\n",
    "class timeUnetPlusPlus(smp.UnetPlusPlus):\n",
    "    def __init__(self, model_channels, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        \n",
    "        # self.timeEmb\n",
    "        self.model_channels = model_channels\n",
    "        time_embed_dim = model_channels * 4\n",
    "        self.time_embed = nn.Sequential(\n",
    "            nn.Linear(model_channels, time_embed_dim),\n",
    "            nn.SiLU(),\n",
    "            nn.Linear(time_embed_dim, time_embed_dim),\n",
    "        )\n",
    "        self.lns = nn.ModuleList()\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 2))\n",
    "        # self.lns.append(nn.Linear(time_embed_dim, 32))\n",
    "        # self.lns.append(nn.Linear(time_embed_dim, 24))\n",
    "        # self.lns.append(nn.Linear(time_embed_dim, 40))\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 48))\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 32))\n",
    "        self.lns.append(nn.Linear(time_embed_dim, 56))\n",
    "        \n",
    "        self.convs = nn.ModuleList()\n",
    "        self.convs.append(nn.Conv2d(2 * 2, 2, 1))\n",
    "        # self.convs.append(nn.Conv2d(32 * 2, 32, 1))\n",
    "        # self.convs.append(nn.Conv2d(24 * 2, 24, 1))\n",
    "        # self.convs.append(nn.Conv2d(40 * 2, 40, 1))\n",
    "        self.convs.append(nn.Conv2d(48 * 2, 48, 1))\n",
    "        self.convs.append(nn.Conv2d(32 * 2, 32, 1))\n",
    "        self.convs.append(nn.Conv2d(56 * 2, 56, 1))\n",
    "        \n",
    "        self.name = 'timeUnetPlusPlus'\n",
    "        self.initialize()\n",
    "\n",
    "    def timestep_embedding(self, timesteps, dim, max_period=10000):\n",
    "        \"\"\"\n",
    "        Create sinusoidal timestep embeddings.\n",
    "        :param timesteps: a 1-D Tensor of N indices, one per batch element.\n",
    "                        These may be fractional.\n",
    "        :param dim: the dimension of the output.\n",
    "        :param max_period: controls the minimum frequency of the embeddings.\n",
    "        :return: an [N x dim] Tensor of positional embeddings.\n",
    "        \"\"\"\n",
    "        half = dim // 2\n",
    "        freqs = torch.exp(\n",
    "            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half\n",
    "        ).to(device=timesteps.device)\n",
    "        args = timesteps[:, None].float() * freqs[None]\n",
    "        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)\n",
    "        if dim % 2:\n",
    "            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)\n",
    "        return embedding\n",
    "\n",
    "    def forward(self, x, timesteps):        \n",
    "        emb = self.timestep_embedding(timesteps, self.model_channels)\n",
    "        emb = self.time_embed(emb)\n",
    "        self.check_input_shape(x)\n",
    "        features = self.encoder(x)\n",
    "        \n",
    "        for f in features:\n",
    "            print(f.shape)\n",
    "        \n",
    "        for index, f in enumerate(features):\n",
    "            temp_emb = self.lns[index](emb)\n",
    "            while len(temp_emb.shape) < len(f.shape):\n",
    "                temp_emb = temp_emb[..., None]\n",
    "            # f = f + temp_emb\n",
    "            f = torch.concat([f, temp_emb.repeat(1, 1, *f.shape[-2:])], dim=1)\n",
    "            f = self.convs[index](f)\n",
    "        decoder_output = self.decoder(*features)\n",
    "\n",
    "        masks = self.segmentation_head(decoder_output)\n",
    "\n",
    "        if self.classification_head is not None:\n",
    "            labels = self.classification_head(features[-1])\n",
    "            return masks, labels\n",
    "\n",
    "        return masks\n",
    "\n",
    "model = timeUnetPlusPlus(\n",
    "    encoder_name=\"efficientnet-b4\",\n",
    "    model_channels=32,\n",
    "    in_channels=2,\n",
    "    encoder_depth=3, \n",
    "    decoder_channels=(64, 32, 16),\n",
    "    classes=1,\n",
    "    )\n",
    "x = torch.rand(7, 2, 32, 32)\n",
    "t = torch.rand(7)\n",
    "model(x, t).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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