00b_first_steps.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn.objects as so\n",
    "from typing import List\n",
    "import datasets\n",
    "from datasets import concatenate_datasets\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import FeatureUnion, Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.multioutput import ClassifierChain\n",
    "\n",
    "from utils import *\n",
    "sns.set_theme(rc={'figure.figsize': [14, 8]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_datasets = dict()\n",
    "eval_datasets = dict()\n",
    "predictions = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d136dbe577eb4549b4763ee77e3dcbec",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Resolving data files:   0%|          | 0/433 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default-60be5c4bd1ad25f6\n",
      "Reusing dataset text (/home/alex/.cache/huggingface/datasets/text/default-60be5c4bd1ad25f6/0.0.0/acc32f2f2ef863c93c2f30c52f7df6cc9053a1c2230b8d7da0d210404683ca08)\n",
      "Parameter 'function'=<function get_dataset.<locals>.attach_labels at 0x7fbb97a484c0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "054353f2f38d4fe8ab1725c41ee464b4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/433 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_datasets['default'], eval_datasets['default'] = get_split_dataset(\"data/en\", seed=41)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "tmp_train, tmp_eval = zip(*[get_split_dataset(data_dir) for data_dir in [\"data/en\", \"data/it\", \"data/ru\"]])\n",
    "train_datasets['default_all'] = concatenate_datasets(tmp_train)\n",
    "eval_datasets['default_all'] = concatenate_datasets(tmp_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  5,  6,  7,  8,  9, 10, 12, 13])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop_classes = ['Capacity_and_resources', 'Economic', 'Public_opinion', 'Cultural_identity', 'Quality_of_life']\n",
    "drop_classes = ['Capacity_and_resources', 'Economic', 'Public_opinion', 'Cultural_identity']\n",
    "keep_classes = np.nonzero([int(c not in drop_classes) for c in categories])[0]\n",
    "keep_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "662baac4857d4184b3798643e3a3814f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/346 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_datasets['drop_labels'] = train_datasets['default'].map(lambda s: {'text': s['text'], 'labels': list(np.array(s['labels'])[keep_classes])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_drop_predictions(predictions, references):\n",
    "    converted = np.zeros((len(references[0]), len(references)))\n",
    "    converted[keep_classes] = predictions.T\n",
    "    return converted.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_economics_dataset(path:str, categories:List):\n",
    "    data = pd.read_csv(path)\n",
    "    labels = [int(c == 'Economic') for c in categories]\n",
    "    data = pd.DataFrame({'text': data.headline + ' ' + data.text, 'labels': [labels for i in data.index]})\n",
    "    return datasets.Dataset.from_pandas(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'utf-8' codec can't decode byte 0x89 in position 63094: invalid start byte",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m economics_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mload_economics_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdata/US-Economic-News.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcategories\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m economics_dataset \u001b[38;5;241m=\u001b[39m economics_dataset\u001b[38;5;241m.\u001b[39mtrain_test_split(\u001b[38;5;241m0.1\u001b[39m, seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtest\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      3\u001b[0m economics_dataset\n",
      "Cell \u001b[0;32mIn[7], line 2\u001b[0m, in \u001b[0;36mload_economics_dataset\u001b[0;34m(path, categories)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_economics_dataset\u001b[39m(path:\u001b[38;5;28mstr\u001b[39m, categories:List):\n\u001b[0;32m----> 2\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m     labels \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mint\u001b[39m(c \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEconomic\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m categories]\n\u001b[1;32m      4\u001b[0m     data \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m: data\u001b[38;5;241m.\u001b[39mheadline \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m data\u001b[38;5;241m.\u001b[39mtext, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m: [labels \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m data\u001b[38;5;241m.\u001b[39mindex]})\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    947\u001b[0m )\n\u001b[1;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/io/parsers/readers.py:611\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n\u001b[1;32m    610\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m parser:\n\u001b[0;32m--> 611\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mparser\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1778\u001b[0m, in \u001b[0;36mTextFileReader.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m   1771\u001b[0m nrows \u001b[38;5;241m=\u001b[39m validate_integer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnrows\u001b[39m\u001b[38;5;124m\"\u001b[39m, nrows)\n\u001b[1;32m   1772\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1773\u001b[0m     \u001b[38;5;66;03m# error: \"ParserBase\" has no attribute \"read\"\u001b[39;00m\n\u001b[1;32m   1774\u001b[0m     (\n\u001b[1;32m   1775\u001b[0m         index,\n\u001b[1;32m   1776\u001b[0m         columns,\n\u001b[1;32m   1777\u001b[0m         col_dict,\n\u001b[0;32m-> 1778\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[attr-defined]\u001b[39;49;00m\n\u001b[1;32m   1779\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnrows\u001b[49m\n\u001b[1;32m   1780\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1781\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1782\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py:230\u001b[0m, in \u001b[0;36mCParserWrapper.read\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlow_memory:\n\u001b[0;32m--> 230\u001b[0m         chunks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_reader\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_low_memory\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnrows\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m         \u001b[38;5;66;03m# destructive to chunks\u001b[39;00m\n\u001b[1;32m    232\u001b[0m         data \u001b[38;5;241m=\u001b[39m _concatenate_chunks(chunks)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/_libs/parsers.pyx:808\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader.read_low_memory\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/_libs/parsers.pyx:866\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/_libs/parsers.pyx:852\u001b[0m, in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.0/lib/python3.9/site-packages/pandas/_libs/parsers.pyx:1965\u001b[0m, in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0x89 in position 63094: invalid start byte"
     ]
    }
   ],
   "source": [
    "economics_dataset = load_economics_dataset('data/US-Economic-News.csv', categories)\n",
    "economics_dataset = economics_dataset.train_test_split(0.1, seed=42)['test']\n",
    "economics_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'labels'],\n",
       "    num_rows: 1146\n",
       "})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_datasets['economics'] = concatenate_datasets([train_datasets['default'], economics_dataset])\n",
    "train_datasets['economics'].shuffle(seed=42)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df_cats_eval = pd.DataFrame({\n",
    "    \"cats\": categories, \n",
    "    \"totals\": np.sum(eval_dataset[\"labels\"], axis=0), \n",
    "})\n",
    "df_cats_eval = df_cats_eval.sort_values(by=\"totals\")\n",
    "sns.barplot(x=\"totals\", y=\"cats\", data=df_cats_eval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "df_cats_train = pd.DataFrame({\n",
    "    \"cats\": categories, \n",
    "    \"totals\": np.sum(train_datasets['default'][\"labels\"], axis=0), \n",
    "})\n",
    "df_cats_train = df_cats_train.sort_values(by=\"totals\")\n",
    "sns.barplot(x=\"totals\", y=\"cats\", data=df_cats_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_split_length = 5 * 250\n",
    "num_splits = 8000 // min_split_length\n",
    "def get_split(sample, min_split_length=min_split_length):\n",
    "    \"\"\"Adds new features of min length min_split_length to every sample.\"\"\"\n",
    "    new_sample = {'labels': sample['labels']}\n",
    "    text = sample['text']\n",
    "    split_nr = 0\n",
    "    for i in range(num_splits):\n",
    "        idx = text.find('.', min_split_length)+1\n",
    "        if not idx:\n",
    "            new_sample[f'split{split_nr}'] = \"\"\n",
    "        else:\n",
    "            new_sample[f'split{split_nr}'] = text[:idx].strip()\n",
    "        text = text[idx:]\n",
    "        split_nr += 1\n",
    "    return new_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ace3a69a053449d6a0e79902faf556a5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/346 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['labels', 'split0', 'split1', 'split2', 'split3', 'split4', 'split5'],\n",
       "    num_rows: 346\n",
       "})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_datasets['split_features'] = train_datasets['default'].map(get_split, remove_columns=train_datasets['default'].column_names)\n",
    "train_datasets['split_features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ad7d8ec9f924670b882d502c8deba5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/87 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['labels', 'split0', 'split1', 'split2', 'split3', 'split4', 'split5'],\n",
       "    num_rows: 87\n",
       "})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_datasets['split_features'] = eval_datasets['default'].map(get_split, remove_columns=eval_datasets['default'].column_names)\n",
    "eval_datasets['split_features']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_split(samples, min_split_length=min_split_length):\n",
    "    \"\"\"Creates new samples of minimum length min_split_length with identical labels.\"\"\"\n",
    "    splits = list()\n",
    "    labels = list()\n",
    "    for text,label in zip(samples['text'], samples['labels']):\n",
    "        while idx := text.find('.', min_split_length)+1:\n",
    "            splits.append(text[:idx].strip())\n",
    "            labels.append(label)\n",
    "            text = text[idx:]\n",
    "        if len(text) > 500:\n",
    "            splits.append(text.strip())\n",
    "            labels.append(label)\n",
    "    return {'splits': splits, 'labels': labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ede937f97940437a84a1d59b44b5624f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['labels', 'splits'],\n",
       "    num_rows: 1458\n",
       "})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_datasets['split_samples'] = train_datasets['default'].map(get_split, batched=True, remove_columns=train_datasets['default'].column_names)\n",
    "train_datasets['split_samples']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e013892ae24f4d59af75dc77c701ff28",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2472d71630db4b39b070d2bc78cde493",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b8f897d9ab545089ae403da78f7a565",
       "version_major": 2,
       "version_minor": 0
      },
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     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "44e37ee9afcd4eb18ed63d3e2a9bb7b3",
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    },
    {
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       "model_id": "b2fdc7566f304c6a8ae8b607d0eba8f5",
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     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "822796e4297d4fbea405be9eccf4364a",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4f675eed44b54912911c62badef9c0e8",
       "version_major": 2,
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "62d03b36512840719010a349c49c2924",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d0e505adb174de38dd7afeb3a7f5cea",
       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de213150ea074a5aa5ccad22613d22bf",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "419cea7459a94626bbaa1968ca72608a",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7aaae2834da949539d183f1544ed123e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a15d9c52a6e24239b5b5b50a174a27ff",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08b4c2850c0d4ca6bbcc9756614e3f03",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated 151 samples.\n"
     ]
    }
   ],
   "source": [
    "train_datasets['resampled'] = oversample(train_datasets['default'], 4)\n",
    "print(f\"Duplicated {train_datasets['resampled'].shape[0] - train_datasets['default'].shape[0]} samples.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5c23200033341b6b1f0bf36914a0e8d",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97732c8e86764190ae61df1cf21e5a36",
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     },
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    },
    {
     "data": {
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       "model_id": "466c261f104e4d21aa0284a4ef13072b",
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    {
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       "model_id": "561e625438424e9f9c4d2a5f4d8b1315",
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    {
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       "model_id": "ca92366b293144819125cd52d2b9b148",
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       "model_id": "13ba2df71d4a4d9488abc38c4bf2aeec",
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    },
    {
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       "model_id": "9d6e08dfc111478489667c398a774e0e",
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    },
    {
     "data": {
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       "model_id": "04ff245af6074079bc4e3d94c370a2db",
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     },
     "metadata": {},
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    },
    {
     "data": {
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       "model_id": "fcffea4fe0d047269b5a54cf7f68c9d6",
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    {
     "data": {
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       "model_id": "daf9bd65a7984aac8705dbc788431def",
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       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abca3f73a5cc4d3185de23fe913eb769",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e9fbe31afeb439b9b68331b28de1958",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated 151 samples.\n"
     ]
    }
   ],
   "source": [
    "train_datasets['split_features_resampled'] = oversample(train_datasets['split_features'], 4)\n",
    "print(f\"Duplicated {train_datasets['split_features_resampled'].shape[0] - train_datasets['split_features'].shape[0]} samples.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cats = pd.DataFrame({\n",
    "    \"cats\": categories, \n",
    "    \"totals\": np.sum(train_datasets['default'][\"labels\"], axis=0) / np.sum(train_datasets['default'][\"labels\"]), \n",
    "    \"totals_resampled\": np.sum(train_datasets['resampled'][\"labels\"], axis=0) / np.sum(train_datasets['resampled'][\"labels\"])\n",
    "})\n",
    "df_cats = df_cats.sort_values(by=\"totals_resampled\").melt(id_vars=\"cats\")\n",
    "sns.barplot(x=\"value\", y=\"cats\", hue=\"variable\", data=df_cats).set_title('Relative Label Counts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'default': Dataset({\n",
       "     features: ['text', 'labels'],\n",
       "     num_rows: 346\n",
       " }),\n",
       " 'drop_labels': Dataset({\n",
       "     features: ['text', 'labels'],\n",
       "     num_rows: 346\n",
       " }),\n",
       " 'economics': Dataset({\n",
       "     features: ['text', 'labels'],\n",
       "     num_rows: 1146\n",
       " }),\n",
       " 'split_features': Dataset({\n",
       "     features: ['labels', 'split0', 'split1', 'split2', 'split3', 'split4', 'split5'],\n",
       "     num_rows: 346\n",
       " }),\n",
       " 'split_samples': Dataset({\n",
       "     features: ['labels', 'splits'],\n",
       "     num_rows: 1458\n",
       " }),\n",
       " 'resampled': Dataset({\n",
       "     features: ['text', 'labels'],\n",
       "     num_rows: 497\n",
       " }),\n",
       " 'split_features_resampled': Dataset({\n",
       "     features: ['labels', 'split0', 'split1', 'split2', 'split3', 'split4', 'split5'],\n",
       "     num_rows: 497\n",
       " })}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'default': Dataset({\n",
       "     features: ['text', 'labels'],\n",
       "     num_rows: 87\n",
       " }),\n",
       " 'split_features': Dataset({\n",
       "     features: ['labels', 'split0', 'split1', 'split2', 'split3', 'split4', 'split5'],\n",
       "     num_rows: 87\n",
       " })}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# TfIdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ........ (step 1 of 2) Processing vectorizer, total=   0.3s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   5.3s\n"
     ]
    }
   ],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('vectorizer', TfidfVectorizer()),\n",
    "    ('svc', OneVsRestClassifier(SVC()))\n",
    "], verbose = True)\n",
    "pipeline.fit(train_datasets['default']['text'], train_datasets['default']['labels'])\n",
    "references = np.array(eval_datasets['default']['labels'])\n",
    "predictions['tfidf_svc'] = pipeline.predict(eval_datasets['default']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ........ (step 1 of 2) Processing vectorizer, total=   0.3s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   3.9s\n"
     ]
    }
   ],
   "source": [
    "pipeline.fit(train_datasets['drop_labels']['text'], train_datasets['drop_labels']['labels'])\n",
    "predictions['tfidf_svc_drop_labels'] = convert_drop_predictions(pipeline.predict(eval_datasets['default']['text']), references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ........ (step 1 of 2) Processing vectorizer, total=   0.4s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=  21.4s\n"
     ]
    }
   ],
   "source": [
    "pipeline.fit(train_datasets['economics']['text'], train_datasets['economics']['labels'])\n",
    "predictions['tfidf_svc_economics'] = pipeline.predict(eval_datasets['default']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ........ (step 1 of 2) Processing vectorizer, total=   0.3s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=  35.7s\n"
     ]
    }
   ],
   "source": [
    "pipeline.fit(train_datasets['split_samples']['splits'], train_datasets['split_samples']['labels'])\n",
    "predictions['tfidf_svc_split_samples'] = pipeline.predict(eval_datasets['default']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ........ (step 1 of 2) Processing vectorizer, total=   0.5s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   8.3s\n"
     ]
    }
   ],
   "source": [
    "pipeline.fit(train_datasets['resampled']['text'], train_datasets['resampled']['labels'])\n",
    "predictions['tfidf_svc_resampled'] = pipeline.predict(eval_datasets['default']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "X_train = tfidf_vectorizer.fit_transform(train_datasets['resampled']['text'])\n",
    "X_eval = tfidf_vectorizer.transform(eval_datasets['default']['text'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "chains = [ClassifierChain(SVC(), order=\"random\", random_state=i) for i in range(10)]\n",
    "for i,chain in enumerate(chains):\n",
    "    print(f'Fitting chain {i+1}/{len(chains)}\\r', end='')\n",
    "    chain.fit(X_train, train_datasets['resampled']['labels'])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "chain_predictions = {f'chain{i}':chain.predict(X_eval) for (i,chain) in enumerate(chains)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_chain = ClassifierChain(SVC(), order=np.argsort(np.sum(train_datasets['default']['labels'], axis=0))[::-1])\n",
    "custom_chain.fit(X_train, train_datasets['resampled']['labels'])\n",
    "#chain_predictions['custom_chain'] = custom_chain.predict(X_eval)\n",
    "chain_predictions = {'custom_chain': custom_chain.predict(X_eval)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>MicroF1</th>\n",
       "      <th>MacroF1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>custom_chain</td>\n",
       "      <td>0.678201</td>\n",
       "      <td>0.431065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Method   MicroF1   MacroF1\n",
       "0  custom_chain  0.678201  0.431065"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_f1_scores(chain_predictions, references)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "np.array(categories)[chains[1].order_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_embeddings = dict()\n",
    "eval_embeddings = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/.pyenv/versions/3.9.0/lib/python3.9/site-packages/torch/cuda/__init__.py:88: UserWarning: HIP initialization: Unexpected error from hipGetDeviceCount(). Did you run some cuda functions before calling NumHipDevices() that might have already set an error? Error 101: hipErrorInvalidDevice (Triggered internally at ../c10/hip/HIPFunctions.cpp:110.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n"
     ]
    }
   ],
   "source": [
    "model = SentenceTransformer('all-mpnet-base-v2')\n",
    "train_embeddings['default_mpnet_base'] = model.encode(train_datasets['default']['text'])\n",
    "eval_embeddings['default_mpnet_base'] = model.encode(eval_datasets['default']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
    "train_embeddings['default'] = model.encode(train_datasets['default']['text'])\n",
    "eval_embeddings['default'] = model.encode(eval_datasets['default']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_embeddings['resampled'] = model.encode(train_datasets['resampled']['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_embeddings['split_samples'] = model.encode(train_datasets['split_samples']['splits'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('svc', OneVsRestClassifier(SVC()))\n",
    "], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ............ (step 1 of 2) Processing scaler, total=   0.0s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   0.5s\n"
     ]
    }
   ],
   "source": [
    "classifier.fit(train_embeddings['default_all'], train_datasets['default_all']['labels'])\n",
    "predictions['minilm_svc_all'] = classifier.predict(eval_embeddings['default'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ............ (step 1 of 2) Processing scaler, total=   0.0s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   0.2s\n"
     ]
    }
   ],
   "source": [
    "classifier.fit(train_embeddings['default'], train_datasets['default']['labels'])\n",
    "predictions['minilm_svc'] = classifier.predict(eval_embeddings['default'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ............ (step 1 of 2) Processing scaler, total=   0.0s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   0.3s\n"
     ]
    }
   ],
   "source": [
    "classifier.fit(train_embeddings['resampled'], train_datasets['resampled']['labels'])\n",
    "predictions['minilm_svc_resampled'] = classifier.predict(eval_embeddings['default'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ............ (step 1 of 2) Processing scaler, total=   0.0s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   8.3s\n"
     ]
    }
   ],
   "source": [
    "classifier.fit(train_embeddings['split_samples'], train_datasets['split_samples']['labels'])\n",
    "predictions['minilm_svc_split_samples'] = classifier.predict(eval_embeddings['default'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ............ (step 1 of 2) Processing scaler, total=   0.0s\n",
      "[Pipeline] ............... (step 2 of 2) Processing svc, total=   0.4s\n"
     ]
    }
   ],
   "source": [
    "classifier.fit(train_embeddings['default_mpnet_base'], train_datasets['default']['labels'])\n",
    "predictions['mpnet_svc'] = classifier.predict(eval_embeddings['default_mpnet_base'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_chain.fit(train_embeddings['default_mpnet_base'], train_datasets['default']['labels'])\n",
    "predictions['mpnet_svc_chain'] = custom_chain.predict(eval_embeddings['default_mpnet_base'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/.pyenv/versions/3.9.0/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: libc10_cuda.so: cannot open shared object file: No such file or directory\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.utils.data import DataLoader, TensorDataset, Sampler\n",
    "from torchmetrics.classification import MultilabelF1Score\n",
    "from typing import List, Iterator\n",
    "\n",
    "from custom_classes.custom_sampler import ContrastSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Trainer:\n",
    "    def __init__(self, model, loader, eval_loader, criterion, optimizer, n_classes, n_train_classes=None, convert_predictions=None):\n",
    "        self.model = model\n",
    "        self.loader = loader\n",
    "        self.eval_loader = eval_loader\n",
    "        self.criterion = criterion\n",
    "        self.optimizer = optimizer\n",
    "        if not n_train_classes:\n",
    "            n_train_classes = n_classes\n",
    "        self.metrics = {\n",
    "            'macro': MultilabelF1Score(num_labels=n_classes, average='macro'),\n",
    "            'micro': MultilabelF1Score(num_labels=n_classes, average='micro'),\n",
    "            'train_macro': MultilabelF1Score(num_labels=n_train_classes, average='macro'),\n",
    "            'train_micro': MultilabelF1Score(num_labels=n_train_classes, average='micro'),\n",
    "        }\n",
    "        self.convert_predictions = convert_predictions\n",
    "        \n",
    "    def train(self, n_epochs):\n",
    "        epoch_losses = list()\n",
    "        microf1 = list()\n",
    "        macrof1 = list()\n",
    "        train_microf1 = list()\n",
    "        train_macrof1 = list()\n",
    "        for i in range(100):\n",
    "            losses = list()\n",
    "            torch.set_grad_enabled(True)\n",
    "            self.model.train()\n",
    "            for batch in self.loader:\n",
    "                embeddings, labels = batch\n",
    "                prediction_probs = self.model(embeddings)\n",
    "                loss = self.criterion(prediction_probs, labels)\n",
    "                losses.append(loss.detach())\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                self.optimizer.zero_grad()\n",
    "\n",
    "            self.model.eval()\n",
    "            torch.set_grad_enabled(False)\n",
    "            preds = list()\n",
    "            labels = list()\n",
    "            for batch in self.eval_loader:\n",
    "                embeddings, ls = batch\n",
    "                preds.append(torch.round(torch.sigmoid(self.model(embeddings))))\n",
    "                labels.append(ls)\n",
    "            preds = torch.cat(preds)\n",
    "            labels = torch.cat(labels)\n",
    "            if self.convert_predictions:\n",
    "                preds = torch.tensor(self.convert_predictions(preds.numpy(), labels.numpy()))\n",
    "            microf1.append(self.metrics['micro'](preds, labels).numpy())\n",
    "            macrof1.append(self.metrics['macro'](preds, labels).numpy())\n",
    "            preds = list()\n",
    "            labels = list()\n",
    "            for batch in loader:\n",
    "                embeddings, ls = batch\n",
    "                preds.append(torch.round(torch.sigmoid(self.model(embeddings))))\n",
    "                labels.append(ls)\n",
    "            preds = torch.cat(preds)\n",
    "            labels = torch.cat(labels)\n",
    "            train_microf1.append(self.metrics['train_micro'](preds, labels).numpy())\n",
    "            train_macrof1.append(self.metrics['train_macro'](preds, labels).numpy())\n",
    "\n",
    "            epoch_losses.append(np.mean(losses))\n",
    "        return epoch_losses, microf1, macrof1, train_microf1, train_macrof1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_torch_metrics(losses, microf1, macrof1, train_microf1, train_macrof1):\n",
    "    fig, ax = plt.subplots()\n",
    "    loss = ax.plot(losses, label='Loss')\n",
    "    ax2 = ax.twinx()\n",
    "    m1 = ax2.plot(microf1, c='red', label='MicroF1')\n",
    "    m2 = ax2.plot(macrof1, c='orange', label='MacroF1')\n",
    "    m3 = ax2.plot(train_microf1, c='red', label='Train MicroF1', linestyle='dotted')\n",
    "    m4 = ax2.plot(train_macrof1, c='orange', label='Train MacroF1', linestyle='dotted')\n",
    "    ax2.axhline(np.max(microf1), c='red', linestyle='dashed')\n",
    "    ax2.axhline(np.max(macrof1), c='orange', linestyle='dashed')\n",
    "    lns = loss+m1+m2+m3+m4\n",
    "    labs = [l.get_label() for l in lns]\n",
    "    ax2.legend(lns, labs)\n",
    "    plt.show()\n",
    "    argmax_microf1 = np.argmax(microf1)\n",
    "    print(\"MicroF1: \", microf1[argmax_microf1], \" @ \", argmax_microf1)\n",
    "    print(\"MacroF1: \", np.max(macrof1), \" @ \", np.argmax(macrof1))\n",
    "    print(\"MacroF1: \", macrof1[argmax_microf1], \" @ \", argmax_microf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['default', 'default_mpnet_base'])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criterion = nn.BCEWithLogitsLoss()\n",
    "torch_datasets = dict()\n",
    "torch_eval_datasets = dict()\n",
    "torch_datasets['default'] = TensorDataset(torch.tensor(train_embeddings['default']), torch.tensor(train_datasets['default']['labels'], dtype=torch.float32))\n",
    "#torch_datasets['drop_labels'] = TensorDataset(torch.tensor(train_embeddings['default']), torch.tensor(train_datasets['drop_labels']['labels'], dtype=torch.float32))\n",
    "torch_eval_datasets['default'] = TensorDataset(torch.tensor(eval_embeddings['default']), torch.tensor(eval_datasets['default']['labels'], dtype=torch.float32))\n",
    "#torch_datasets['resampled'] = TensorDataset(torch.tensor(train_embeddings['resampled']), torch.tensor(train_datasets['resampled']['labels'], dtype=torch.float32))\n",
    "#torch_datasets['split_samples'] = TensorDataset(torch.tensor(train_embeddings['split_samples']), torch.tensor(train_datasets['split_samples']['labels'], dtype=torch.float32))\n",
    "\n",
    "#torch_datasets['drop_labels_mpnet'] = TensorDataset(torch.tensor(train_embeddings['default_mpnet_base']), torch.tensor(train_datasets['drop_labels']['labels'], dtype=torch.float32))\n",
    "torch_datasets['default_mpnet_base'] = TensorDataset(torch.tensor(train_embeddings['default_mpnet_base']), torch.tensor(train_datasets['default']['labels'], dtype=torch.float32))\n",
    "torch_eval_datasets['default_mpnet_base'] = TensorDataset(torch.tensor(eval_embeddings['default_mpnet_base']), torch.tensor(eval_datasets['default']['labels'], dtype=torch.float32))\n",
    "\n",
    "torch_datasets.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MiniLM Embedding Default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = len(train_datasets['default']['labels'][0])\n",
    "torch_model = nn.Sequential(\n",
    "    nn.Linear(384, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, n_classes),\n",
    "    nn.Dropout(),\n",
    ")\n",
    "optimizer = torch.optim.AdamW(torch_model.parameters())\n",
    "train_sampler = ContrastSampler(torch_datasets['default'], 1, 50)\n",
    "loader = DataLoader(torch_datasets['default'], batch_sampler=train_sampler)\n",
    "eval_loader = DataLoader(torch_eval_datasets['default'], batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MicroF1:  0.7121464  @  13\n",
      "MacroF1:  0.51794744  @  96\n",
      "MacroF1:  0.4412659  @  13\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(torch_model, loader, eval_loader, criterion, optimizer, n_classes)\n",
    "epoch_losses, microf1, macrof1, train_microf1, train_macrof1 = trainer.train(100)\n",
    "plot_torch_metrics(epoch_losses, microf1, macrof1, train_microf1, train_macrof1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MiniLM Embedding Resampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = len(train_datasets['resampled']['labels'][0])\n",
    "torch_model = nn.Sequential(\n",
    "    nn.Linear(384, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, n_classes),\n",
    "    nn.Dropout(),\n",
    ")\n",
    "optimizer = torch.optim.AdamW(torch_model.parameters())\n",
    "train_sampler = ContrastSampler(torch_datasets['resampled'], 1, 50)\n",
    "loader = DataLoader(torch_datasets['resampled'], batch_sampler=train_sampler)\n",
    "eval_loader = DataLoader(torch_eval_datasets['default'], batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MicroF1:  0.65656567  @  27\n",
      "MacroF1:  0.48601806  @  96\n",
      "MacroF1:  0.45249882  @  27\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(torch_model, loader, eval_loader, criterion, optimizer, n_classes)\n",
    "epoch_losses, microf1, macrof1, train_microf1, train_macrof1 = trainer.train(100)\n",
    "plot_torch_metrics(epoch_losses, microf1, macrof1, train_microf1, train_macrof1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MiniLM Embedding Split Samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = len(train_datasets['split_samples']['labels'][0])\n",
    "torch_model = nn.Sequential(\n",
    "    nn.Linear(384, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, n_classes),\n",
    "    nn.Dropout(),\n",
    ")\n",
    "optimizer = torch.optim.AdamW(torch_model.parameters())\n",
    "train_sampler = ContrastSampler(torch_datasets['split_samples'], 1, 50)\n",
    "loader = DataLoader(torch_datasets['split_samples'], batch_sampler=train_sampler)\n",
    "eval_loader = DataLoader(torch_eval_datasets['default'], batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MicroF1:  0.6655629  @  13\n",
      "MacroF1:  0.4835103  @  70\n",
      "MacroF1:  0.4564173  @  13\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(torch_model, loader, eval_loader, criterion, optimizer, n_classes)\n",
    "epoch_losses, microf1, macrof1, train_microf1, train_macrof1 = trainer.train(100)\n",
    "plot_torch_metrics(epoch_losses, microf1, macrof1, train_microf1, train_macrof1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MiniLM Embedding Drop Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/Uni/Thesis/semeval2023task3/custom_classes/custom_sampler.py:19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self._labels = torch.tensor(data_source.tensors[labels_idx].clone().detach()).t()\n"
     ]
    }
   ],
   "source": [
    "n_classes = len(train_datasets['drop_labels']['labels'][0])\n",
    "torch_model = nn.Sequential(\n",
    "    nn.Linear(384, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, n_classes),\n",
    "    nn.Dropout(),\n",
    ")\n",
    "optimizer = torch.optim.AdamW(torch_model.parameters())\n",
    "train_sampler = ContrastSampler(torch_datasets['drop_labels'], 1, 50)\n",
    "loader = DataLoader(torch_datasets['drop_labels'], batch_sampler=train_sampler)\n",
    "eval_loader = DataLoader(torch_eval_datasets['default'], batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MicroF1:  0.6748252  @  18\n",
      "MacroF1:  0.42331082  @  40\n",
      "MacroF1:  0.40793675  @  18\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(torch_model, loader, eval_loader, criterion, optimizer, n_classes=14, n_train_classes=n_classes, convert_predictions=convert_drop_predictions)\n",
    "epoch_losses, microf1, macrof1, train_microf1, train_macrof1 = trainer.train(100)\n",
    "plot_torch_metrics(epoch_losses, microf1, macrof1, train_microf1, train_macrof1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MPNET Embedding Default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/Uni/Thesis/semeval2023task3/custom_classes/custom_sampler.py:19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  self._labels = torch.tensor(data_source.tensors[labels_idx].clone().detach()).t()\n"
     ]
    }
   ],
   "source": [
    "n_classes = len(train_datasets['default']['labels'][0])\n",
    "torch_model = nn.Sequential(\n",
    "    nn.Linear(768, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, n_classes),\n",
    "    nn.Dropout(),\n",
    ")\n",
    "optimizer = torch.optim.AdamW(torch_model.parameters())\n",
    "train_sampler = ContrastSampler(torch_datasets['default_mpnet_base'], 1, 50)\n",
    "loader = DataLoader(torch_datasets['default_mpnet_base'], batch_sampler=train_sampler)\n",
    "eval_loader = DataLoader(torch_eval_datasets['default_mpnet_base'], batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mO1YuO5Rc5R57/JA63ZqIq+mjRT8fIcqI9GCVUGpumfYGSSeuSj14cH/8fh9ffTU/tNjw/Pnf8uyzT3L33fdjMpnwer0MGTLshHNLq2fPzjRp0hQlt5v64ov7MHTodfzww3w+/vhDdu7cwR133MMVVwwps3uWF+nBEqJw0n5EaUnbKURugqOm7CdmwCU47xuP96prUNJScUx5Ffeo0WjJRVcNK2/qgRTUfXsJdOpSugvkPk/Fe4j4RW1xNnkUd8OxeY8JOon/sxXepIHktH4NyNt2VNc2LFn/4q1tDBGLXDsKS/rfpJ1tzO2KWH8nZudGMroYQ/FU9240Wx1QLaWLWVR70oMlisWkKmiajq7rKLljbY8XH5/IsmV/0717T8BIsJo3N7rCBw4cXOL7BYPBULJWkDfffB+HI++QxWbNmvPEE88yc+b0Et9TCCGEqIrC3nsbTGY8I28AIGrUcLT4BHJeMAoKxJ7Xg0Dz5mS//QEA0Zf3JdC2Hc6nngcgps95+LudhfPJZwCIa9sMX9/+5Ex8GS2pNv6OnY71SMXF43xsQsU+Qc1fYDKi1a6DVrt0C6iGb3kC1bOb7Hbvo9tqkdbjX7SwfBZLNYWTdtbyY5vOLbDhKWjxPqhmwlI+xbF9Ir6Ei9HNkbjr34wv4ZLQ8TmtX80bs71BqeIVorqrMQmW7dOPCZs9s+gDS8FzzbV4hwwFQFWNpCqo6ZhNJyZYl17aj/nz59G9e0/27duLx+OhSROji/+9997G7XYzZsxdAHz00Qf89NMCFEXFbrczdeq7/PvvKl599UVatGjF5s2bGD36VuLi4njllRfxeNyEhdm56677aNWqTaExN84dVqDWoJW5hRBCnGJ0HdOG9QRbG3/zbAvmo9tsoQQr2KgxWnRM6HDPkGtCCRIYa/4EGzYKbfvP7EGgxbH5P+6bbiWY+yEoqkr2W++X45MpXMTGezFnrw3NQYpYdxuB6M54kkeV+FrWw99jO/AF2W3fNYYjmsLRzdFG1TtFzT+5yqXbkkLfm7NWwuHFqKftQnM0wVPvejx1rg4VfwjEdCMQ063E8QlR09WYBKuimJTjE6wT959xRme+/voLsrKyWLDgO/r0uZRNmzaecNz3389j0aI/eeut93E4wsnMzAglQzt2bOf++x+ibdv2+P1+hgwZyEMPPU7nzl1ZvnwpDz/8AJ9+OgeLxfiU69ZbR4WGCD766IRQQieEEEJUZ/bXXiZ84jOkLV+DVi+ZzM/n5tl/tIDDUe5bxuTd/8TThW6777iHk2FJ/Z2gvQGao3GJzzU5txC2/2OcTR8BxYQ/8gw0cyzoQdA1TJ69aPaGxsG6Rtjed/HVugzNVhslkI3JuZFgeEt0cySm7LXY976Ps9kEdHMkqvcA5uw1KP5UdGtCnrlNJeGtczW0H42WO8QrvyIYQogT1ZgEyztkaKiXqTyZcnuwCirVrihw/vkX8csvP/Lzzz/w1lvv55tgLV68kIEDr8CROyE1+rhP4JKT69O2rVHqdPfuXVgsFjrnTk7t0qUbFouF3bt3hRKp/IYICiGEEFWCrqNkZaJHx4CuE9urK4E2bUPD+OxvTSHYpCm+i/qAz0fYZ7Pxdz2TYPMWeK+4Ci2pNlqtpMLvUUHsO19FCWTjavoI6DqR/43GH3dOaIFY2/5ZBKK7EAw3FphF1wEdFBXVewDb/ln4ag0gGN4UU84G7Lun4E0aSCCqA9561x67kWIis9M3ueeDOXs1kRvvI8scg7fOVZgzVxCzagAZnRfgjz0L1XcY28E5eJOuwB/XE0+9EXiSr6/gn44Q4igZP1ZCxw8RLEifPn157723aNy4SZ7EqbjsdkmWhBBCVF9Kelro+6gR1xB9Te4cZEXBPfIGfH36hvbbp7yK9WdjWJzizCHikfHYvvwUAC25Pt6rhxnrNlUC68Gvidh4X2jb5NyM2XnsQ9PMzvNxNX4AAMWfSeS627Ad+AoA1b2T+N8bYDvwhbE/kE3E1icxZ/8LgC+xD6lnbyIQ1aHgAHJHzQSiziDtrFV4a/XN3e5A5umfEcgtd+6PP5/Uc3fgj+uZe568vROiMhWrB2vHjh2MHz+ejIwMYmJimDhxIg0bNjzhuPnz5/Pmm2+GCkB88MEHJCQk8Prrr/Pxxx9Tq5YxLrpjx448/vjjJ5xfHRTVgwVQr14yo0ffRuvWBc+T6tGjF3PmfMk555wXGiKYXzLWoMFp+P1+Vq1aQceOnVm5cjmBQIAGDWp2tT0hhBCVQ8nJxvHC8/jOORf/+RehZGUSeeftuG4bS6BLPvNt/H5MmzYSbNsOgPBHHyTs89mkrt8Oqop30JUoTmfocM+Nt+Q5PW3NJvB4ANBj40j/dSHBRsUoEV4OVM8+bAfn4G5wCygmzK5txmK1mhdUGzmtp4SSHhSFYHiz0Lm6JZq0XhvQc0uJa9YkvHWuIpg7zC/oaMzh8/aDOSL3ZlZ0Na7YsQXDjw3/1y1x+BL7nNyTFUKUm2IlWI8//jhDhw5lwIABzJ07l8cee4wPP/wwzzFr165lypQpzJgxg8TERLKzs7FaraH9AwcOZNy4cWUbfSU41oOlFXrcgAGDCt3fp09fDh8+xE03XY/ZbMZut/PGG9NOOM5isfDMM5PyFLl4+umJoflXBfnppwVMnfoa2dlZLFz4BzNnzuDll6fQqFHJx4kLIYSo5opYkUXduQP18CECXbqh2x3Yvp2DFheH//yLUPftw/zfGhSXCwDT2jVEPPwAOc+9SLBNW+xvTyViwqMcWbcNPTERX+9L0Bo0AJ8PwsLwDryi8NhUFY4b5n782k/lTtcxOTeh2eqgW6IxZy4jYvOD+GPOJBDdCVfDu3E1OtaDRT7Vg4+Xp3iEyU5Oy5eOO9d0LLkSQtRoRa6DlZqaSu/evVm6dCkmk4lgMEi3bt348ccfiYs79snLvffeS/fu3Rk8+MRS5K+//joul+ukEqyqsg4WwJ5D2dhtZhKi7RV+75pM1sESonDSfkRxqQdSsCz5K5TcJE6agDZ9BqnrtwFgf2cq5mVLyX53BgAxl1yA4vGEFrHF54PjPiQFQusomZf8TcSTD5P17odo9ZJRt2/DsnY13gt7Q3jVWIOmQFoAS+YyNGsiwfBmmJybifurM9mtXsOTPBKCLlTfoWPFJYS87oiTcqqug1XkIN2UlBSSkpJCazGZTCZq1apFSkpKnuO2bdvGnj17GDZsGJdffjlTp07l+Nztu+++o3///owaNYp//vkn33tlZWWxd+/ePP8OHTp0Ms+vXKiqWugQQSGEEKIkTJs3YZ/2ZukvkJOD9cfvQ0PtbF99QdRN16Pu32fsP/ts3COOK/ft9aG4XcdOf/5FMj+cfWz//ydXcGw+0Jndyfj+V7R6yQBojZvgHTCowpKr6OV9iFwzMrQdse42HNueC22Hb36UsL3Hyq07tj2H5chPxobuJ3plf8L2G8u6BB1NyWo9FV9Cb2O/ySHJlRDipJVZFcFgMMimTZv44IMP8Pl83HjjjdStW5eBAwdy9dVXc8stt2CxWFi8eDG33XYb8+fPJzY2Ns81ZsyYwZQpU/I81rFjR2bPnp1vRnrokIrZXPETOc2qgqZTKfeuyVRVJTExsrLDKHenwnMU5UfaTw2h67ByJbRvbyQzH/0Jjz1ExPXDoU4d2LABWrUq/vXmfwUjR8LPP8MFF8DNo+CKy4hv08JIjPr3J7x/f0J/SSc8CkDi0e0Lzy6751bWtACsfQJa3g22eGh0OViiCTv6u2BVwGEm/Oj26n8gDCKPbi+dA9puaDUIiIQLfsYR3QaHLXd/rVsr+AlVP/K6I07Gqdh+ikyw6tSpw8GDBwkGg6EhgocOHaJOnbxrIdStW5c+ffpgtVqxWq1ccMEFrFmzhoEDB5KYGHoJp0ePHtSpU4ctW7bQtWvXPNcYMWIEl19+eZ7Hjs7jym+IoKZpJzxWEVSTgtcXrJR712SaptX4YQgy1EKcDGk/1YjHQ/SQy3HfcBO+yy4Hl4vwF57De2k/Al26Yf3lR6KvGUzGJ1/hP/9ClMuuhEsuRzdHYP5lITG9zyNn4st4riu41LaSnYVp+zYCHc6ASy7HPK8egebt4XA2mMIhKRyO5ADVu+2Ys1YTs+ElsknGW3cYJNxs7Dj6fJq9kXe7w7y8292W5t1WzoAsgOr586ho1bntiMonQwQLEB8fT6tWrZg3z3jBmjdvHq1atcoz/wqgX79+LFq0CF3X8fv9LFmyhJYtjfKhBw8eDB23YcMG9u3bR6NGjfh/UVFRJCcn5/l3tPJgVWKSIYJCCCH+n8eD9ecfAFB8XlBVlNyh8mp2Fvb338G8YT0Avh5nk/XqVAIdOwGgR8egJyQAEGjTDufDT+AdWHixpMgxtxA17CpjWKCiEOjaDcLCyuvZVbzcn10gqgNpZ60wkishhKgGijVE8IknnmD8+PFMnTqVqKgoJk6cCMDo0aO54447aNeuHX379uW///7j0ksvRVVVevbsGSp48fLLL7Nu3TpUVcVisTBp0qQ8vVrVjVlV0DQ9VI5eCCGEcLz2Mo7JL5D210q0Ro3J/Pq70D4tqTZHdh08Vs0vLAzvNdfmfyGTCfeYO43vAwEi77od9/U3EujUxSg+oetgs+F86DGUzMyalVQdFXQRtWYknuQb8CX2RrM3qOyIhBCi2IqsIlhVVKUqgk6Pn8PpbpJrRWA2yTyssiJVBIUonLSfKs7lwrJkMf7zLyqzS6r79hIz4FJcd9+HZ9CVxFzWB/9ZPXE++UyJrlPd2o7izyR61QDc9UdLz1Ulq25tR1QtMkRQFJuqGj+2/x8mOHhwfwYM6E0wGAw9Nn/+t/Ts2Zkvc1elL0/z539Lnz7nMnLk0NC/3bt3AvDkk48wYEAfevbsjMvlKvxCQgghisW8fClRo4aD1wsOR5kmVwBavWTS/vgbz7DrwG7Hd94F+LueWab3KBFdL3JNrZOh+I6AHkS3RJPR5WdJroQQ1ZIkWKVgCi02fOIfmfj4RJYt+zu0PX/+tzRv3rJM7hsIBIo8pnPnrkyf/nHoX4MGDQHo128A06d/XCZxCCFETWb+dxUR4++F3A/LrL/8iOPF543hef/HtHsXpvX/oR45XH4BHVf+3PXQY/j69i+/e/2fiHW3EbewdWg78r/RxC0+/Vhomx/OUzLdnLkCU/aaPNvmzBXHrrfhbuw7Xwltxyw9h/CN94e24/7qRMT6O4wNtcwKHQshRIWqMa9etv0fh9a1KGueutfirTs0tG0yGQlWfoUuLr20H/Pnz6N7957s27cXj8dDkybGqvQrVixj2rQ38fm8BINBrrtuFBdeaKy9cfjwIV555QX27t0DwIUX9mb48Ot55pknMJlM7N69C5fLxfTpHzNz5nR++GE+AK1ateGuu+7H4XAU+hw6depy8j8IIYSorjQN1AI+U/T5sP75G/7OXdFjYjHt2I7ti89wj76FYJNmWJb8TdjHH+G6dxwAjhefx7xhPVnvfYj3iqvw9hsANlsFPpnyY85YRvjWCWR1mIluicGXeCnBiGMJlrfWZQSiO4W2dXMMutUT2g7f/CgKGhldjGIfERvuRbfGk9nxKwBU3yF0c1ToeF/8BQQdTULbzqaPE4hoU27PTwghKkKNSbAqkin3j3R+PVhnnNGZr7/+gqysLBYs+I4+fS5l06aNADRv3pKpU9/FZDKRlpbKDTcMp2vX7kRFRTFhwqN0796DZ555AYCMjIzQNbds2cyUKe9gt9v5++/F/PDDfN56630cjnCefvpxpk9/l9tuMz7xW7FiGSNHGslgnTp1ee65F8vzRyGEEFWPpmGdNxc9PgF/j16g6yTUT8R113247n8QNI3oQf3wXHc93kFXYt6wjuihV5L9yht4hg7H2/cyvJf2DyVNzocfx3nf+NBCu7rVim63H7tfdU+ugk4UzYduiQXVhurZi8m9i4AlBl+tfnkO9SVdlmfb1fj+PNs5rV9DCR4bhp7d5nV0U0RoO6vDrLznN30sz7YneRRCCFHd1ZgEy1t3aJ5epvJU2BBBRYHzz7+IX375kZ9//oG33no/lGBlZKTz3HMT2Lt3NyaTmaysTHbv3kXjxk347781TJ78Rug6MTExoe/PPfcC7Ll/zFesWMYFF1xMeLjxB+uyywbx6qvHkqjOnbvy9NOTyvw5CyFEVWb54zeUnBxj+JyqEjHhMfwdOxkJVjCI68578Xfrbhzs8RjziDSjcFKg/elkfPo1/rN6Gvtz11/M47gkyn3HPeX9dMqOrhG9oi9EJkHL6QBErbqCoKMJzpaTQNeJXXYhvrjzcLZ4lkBUB9J7rAKldDMIguHN8m5Htj/ZZyCEENVOjUmwKpKigJpbqj0/ffr05eabR9KhwxlER8eEHn/ppefp0eNsnn32BRRF4eqrB+HzeYu8n8NhL/IYIYQ4pfj9mDZuINjOeAPvmPoayuHDoflJGV98g1Yv2TjWbMb1wEPHznU4yJz7/bFtRcF/3gUVFXmFUD0paGF1QFHx1eqHNfbYmpLBiFZoYXWNDUXBF3cu3qSBx04uZXIlhBDCIK+ipaSqCkFNy3dfvXrJjB59GyNH3pjn8ezsbOrUqYOiKCxfvoR9+4z5Vg6Hg7Zt2/PZZ8eKUBw/RPB4nTt35ddff8LlcqLrOvPmzaFLl25l86SEEKIqO+41N3zCY8T2uwhycgDIfuk1Mr776dihDRuBxVLhIVYF1kPfEbeoDebMlQC4T7sdmhwbeuds/jTuBrcd227xHIGYrhUepxBC1FSSYJWSSVXyHSJ41IABg2jWrEWex269dQxvvPEqI0cO5ddff6ZJk2NDKR577CnWrl3N8OFXMWLENcybNyff63bv3oOLL76Em2++nuuuGwLAiBE3FBnvQw/dz+WXXwrA0KFXcM89Y4o8RwghqgrzsqXEndEa0/atAHiuHUHWO9NDQ/e05Ppgr969/fadr2Fybi7dyUEXqtv40M4fdzbu08YQtDcsu+CEEEIUmyw0XApms8r+wzn4Axr1EiOKPkEUiyw0LEThqnv7sb81BSUtDddDRmEDx0sTCTZthnfAIOOAo3+OFAV19y6ihw/BOe4RfJf2Q921k4hHH8Q5/hGCrWtelTnFd4S4v8/EU+dqnM2fLtnJWoDYJT2MtaM6/xAqxnG86t52ROWRtiNOhiw0LEpELaIHSwghRF6m7dswb9oYGupn++pzLH8vDu2P69QW+5RXAdBq1yGYXB89dwkK7bSGZH04u0YmVwC6NYH0br/jbPooYbvfxpL6e/FPVs1kt5mCq9F9+SZXQgghKpYUuSglk6qiaTq6rqPIHzQhhDiBmrKfiIfH4XzwUYLNmpPzzKQ886LSF6+AowuoBwJ4+w1Aq1fP2LZayZr1eSVEXYF0Hce2Z9AtMbhPG4MWlgyaH8fuN/DFX4g//txiXyoQLWsdCiFEVSEJVikdLdWuaXpo4WEhhBDH6GYL5pXLMW1YR7BZ8/yLTpjNoa/OCc9WbICVTsPs3IxmiTaGRyoKqBbSu/6Cbkko1hXCtzyJrphwNXlYeq+EEKKKkCGCpaQWshaWEEKcKkxr12Cb+1Vo2zH5BSLuNCrU6YmJpC1fg++yyysrvEphSVtIzNLzsO2flf8Bug6aFxQTWe3eJ6fVq3mSI92aCIqCEsgGPf9qtUevo/oOoPoOSXIlhBBViCRYpXR8D5YQQpwq1L17cDw3ITSPKuyz2UTeeVuoQIUeHo6ak4OSlWmckN+ivTWcP7Yn/rhzUDSf8UAgh+jll2BJ/RUAx7ZniFnRD4IuUM35rjtlytlI3KK2WA99W/CNFIXsNm+S0+qVcngWQgghSksSrFKSHiwhxKnIsuQvHG9OwbxuLQDu2+8gbeGy0H73TbeR9d6H6FHRlRVipVJ8qaAoOJs9gSf5egBM3gMoupejf3IDkW0JRLYFNazA6wTDm+FNGkTQ0Tjf/aacjaGy7LIwsBBCVC3yqlxKR3uwgtWjyr0Q4hSj7tuLkp4GgJKaiuOF50JrSJWIpmH79GOs384FwDvoStKW/EOgXQdjd+06aPUbyBA1wJS9hviFrbAeXpDn8WB4UzK6/hoqWuFLGkhOq8mFJ0aKiZxWkwlGtst3d8TG+4hZ2Rf0YFmFL4QQooxIglVK/z9EcPToEYwcOZRrr72Sc87pxsiRQxk5cijPPvtksa85Z84XfPppAWP2CzFmzE2cd153so4OyQFWrVpBz56dmTLlFQAWLfqDN954tcTXLszgwf0ZOvSK0HN97bWXAFi2bAk33DCc887rHrq/EKKc+XyhhErds5v4M1pj+8qowmfaugXHi8+j7t0LgHn5UmJ7dMa85l/j+N27sH02GyUj3biW12v8y2X/YBphX3xqbKgqWt16FfOcqhndWgtPveH4Y84ss2sqvlTsO14+IZHKbvMm2a3fAMVUZvcSQghRNqSKYCkpioJy3FpY06bNACAlZT833jic6dM/PuGcQCCA2Vzwj3zgwMGljqdRoyb8/POPDBp0JQDz539L8+YtQ/t79jyHnj3PKdE1i4oX4OmnJ9K4cdM8j9WtW4/x4x/ht99+wefzleieQohSCAaJP6M13ssGkvPci2j1G5D9wiv4zjkPgEC3Mzmy8wCYct+MWywEm7VAizcq1VmWLSFqzM2k/b2SYEwstjlfEjX2FlKXrUZr2IjMmZ+jx8VV1rOrNjRbbXJavlSm17SkLyJ865P4Y7oTiO0eqjao2euj2euX6b2EEEKUjRrVgxU98FJsn+T2APn9xvbnnxjbLpexPedLAJSsTKIHXop13jfGdmqqsf3D98b2wYNED7wUy68/FXg/UzEWGx48uD9vvvk6o0dfxwsvPEtq6hHGjr2ZUaOu5dprr2Lq1GO9Su+993aox2f+/G+5++7beeyxB7n22qu49dZRpKYeKfA+l1zSjwULvst9qi7WrPmXM888K7R//vxveeSRB0Lb8+bNZcSIaxgx4hpuvPE60tJSSUnZT9++FzBlyiuMGjWMefPmsHfvHu6881ZGjLiaUaOGsWTJX4U+X4Dk5Po0a9YCk0k+WRWivCgZ6di+/MzYMJlw3nM/3kv7h/Z7RoxCa9jo2Al2e6jgROD0jmRNn4VWLxkAb9/LSF3yD8EGDY39bdrhHP8IWq0kAPSEBFBr1J+LsqUHCd/0EKpre5lf2lerP+lnrTSSK8C+eyqRa0dB0FPm9xJCCFE2pAfrJJhUBS1Y9Bwsp9PJtGkfAuD1epk4cTIOh4NAIMA994xhyZK/8iRDR23YsJ4ZM2aTlFSbiROf5osvPuXmm2/P9x5169bDarWyc+cO1q1bS69e5xaY4KxatYKPPvqAqVPfJT4+AZfLhclkwuv1kpmZSatWrRkz5i7AGPo4YMDl9Os3kB07tjNmzGhmzvyC2NhYAB55ZBxWqw2AW28dS7du3Yv8eQgh8qdkpIOuo8cW3Vtkf3sqjldexN/1TLT6DfDccHPpb2y3ozVuEtoMtm2Hq23+c3/EiczZ/2Hf+z7+mK74CihKUWqKSjA8d5SA5gfdjxJ0g6ngAhlCCCEqV41KsDLnzD+2YbHk3XY48mzrUdF5t+Pj824nJeU9Px+qohAIFrJGSa4+ffqGvtc0jalTX2Xt2jWATmpqKlu2bM43wWrfvgNJSbUBaNOmLcuXLy30Ppdc0o/vv5/HunVrufvuB/j991/yPe7vvxfTp09f4nOHBzkcjtA+q9XG+edfBIDL5WTr1s1ceullADRq1JimTVuwbt1aevY8G8h/iKAQovjUfXuNniKLhbCPZxL+5COkrtuGnpCAuncPusWKnmT0JPHrr5iUMIJt2+G+/Q68l/Y3CkyIShWI6kBqr7XFXhy4NOy7phC2bzrpZ/6N+7Q7y+0+QgghTp6M+TgJJpNSrHWwHA576PtPP51FdnYW77wznRkzPqFXr3Px+bz5nmc9bv0YVTURDBZeLeq88y7k559/wOPx0KRJ6ZIeuz0MRaqBCVEhzGtXE9elPbZvvgbAd94FOJ9+3hiSh7Fob1yPzsaaU243XHMNjtxiMnpEJMF27Sst9upC9ewl9q9umDMK/4Cq1Nf3HgCOLQ5cXgLhLfDH9kIJuqRioxBCVHGSYJ0EVVUI6jp6CUq1Z2dnEx+fgM1m4/DhQyxa9EeZxeNwOLjttju4/fbCP93s3r0HCxZ8R1paKmDM2fJ6T0zyHI5wmjZtzvffzwNg584dbNu2mTZtZOiQEKVl+f1XrPON36lAm3a47huPP3dobbBVa9yjbw0d6x51E9mTXzfmP9nt8MMPZL/2VqXEXV1p1troahiWrJUlO1HXsR34stAy6Ir3ILGLO2Lf+dpJRlk0f8JF5LSajG45NdcXE0KI6qRGDRGsaCZVAR00XcdUzE8Ur7zyah59dBzDh19FYmISnTp1KdOYLrjg4iKP6dixM8OHj+Suu25DUVSsVgsTJ07O99jHH3+aF154ls8++xiTycQjj0wIzb8qyOrV//LEEw/hdDrRdZ1ffvmR8eMflflZQgCOlyeh+H34Lu0HqorrngcKPDbYpi3BNm2PPXD66XA4u/yDrCFUz160sGQyunwPJkfRJxzHvvNlbIe+xZvYF0wmbAe+IhDegmBkm9AxujkSd8M78SVeWtahCyGEqMYUvSTdL5UoPd1JIJB3vtOBA7uoXfu0Co/FbFYJBDRy3H6OZLiplxiOxSwV805WZf1/VqTExEgOyxvkU45y6BB6dDTYbKh796Al1gKbrcTXkfZTfObMlcQsv4isdu/hS7ocAFP2f9h3TyWn1augWgo93+Tcii3lY1xNHgE9SPyfzfAmXkpOm6mAsT6Vbo0v9+dRVqTtiNKStiNORkW2H7NZJTY2vELuVRQZIngSji42XFSpdiHEKSwnh9hLLyBi/L0AaMn1S5VciZIJhjfDfdod+OPODz1mzl6NNfVXVO++/E/SNayHF+Se3xRX08dAUUG1kHbWSlxNHgJAde8i4Y9GWNIWlvvzEEIIUf3IEMGToEqCJYQoSkQErlvHEujStbIjOaXo5iiczZ7I85i37jB8tS5DN0fme07Yvo+I3DCW9M4/hNadCl3PGs/RV3pdteNs/CBKIKscIhdCCFHdSQ/WSTjag1WcSoJCiFOL7dOPMa37DwDPDTcRaH965QZ0ijA5txK9cgCqe2e++3VzJOg69p2vYD2UdykOT91hZLafeUJydcI1bLVwNXkQX62+hR4nhBDi1CQJ1kmQHiwhTl1Kaiq2r79AOXTIeEDT4OiU1pwcwp97CsdbUyovwFOU6t6Byb0D1EIW4tV92A7OwXr4O9C8hG95HMWfAaoZX9JlFRarEEKImkkSrJOgKgqKokiCJcQpwrR+HaZNGwEwb1hH1M2jMG/aAIBl8UISkhMwL1sKERFkzP2e7Jdfr8xwT0n+hItIO2sVmq12wQepNjI7ziWn9euYs/7FvusNLGm/VVyQQgghajSZg3WSTGrxFhsWQlRzfj/RQwcTaNWarNlf4u/clbSFywjWSwZAq1MX9y1j0Bo0MLZPa1iJwZ56FO8hLBl/46t1GahF/2k7up5UIKYbaT3+QbPXL+8QhRBCnCIkwTpJqmr0YI0ePQK/308g4GfPnt00atQEgObNW/DQQ48X61pz5nyB1+tlyJBhJYphzJibWLduLXPnLiAqynjTsGrVCu644xauvvpaxoy5q0TXK41Vq1Zw//13Ur/+sTLrd9xxDx07dmbKlFf4449fSUnZz4cffkLjxk3LPR4hyop5zb8E2nUAi4WsadMJHm2/YWEEW7QMHRds2gzno09WUpTCsXsq9l1TSOv5L1pYconOleRKCCFEWZIE6ySZchOsadNmAJCSsp8bbxzO9Okfn3BsIBDAbC74Rz5w4OBSx9GoURN+/vlHBg26EoD587+lefOWRZxVfJqmoeQOiSxIw4aNee+9j054vFevc7nyyqu5/fbRZRaPEBXBsvAPYq7oT9a06XgHDCLQpVtlhyQK4GzyCN7ES0qcXAkhhBBlrUYlWNErLsVTdxjeusNA8xO9agCeetfhrXM1BF1E/zMYT/INeGtfgeLPJGr1Nbjr34Iv6TIUXypRa4bjPm0svsRLULwHiVp7Pa6Gd+NPuKjAe6qqgu//FkA+3uDB/bnggotZtWo5jRs35aabbuOJJx7G6XTi8/k466we3HbbnQC8997buN1uxoy5i/nzv+WnnxYQGRnF9u3biIyM4OmnJxEfn5DvfS65pB8LFnzHoEFX4nK5WLPmXy68sDc+nw+Abdu28tJLz+PxuPH5fFx22eVcddVQAHJycnjttZfYuHE9iqLSocPp3HPPON5772127NiO05nDwYMHeOutD1i8+E9mz/4IRVGoWzeZBx54iNjYuEL/Xzp0OL3Q/UJUJfa3pqA7wvFcdz3+s3qS/ewkvBf1qeywRAEUfzq6agVTOIEYSYCFEEJUvhqVYFWGoz1Yuq4X2LvjdDqZNu1DALxeLxMnTsbhcBAIBLjnnjEsWfIXZ5551gnnbdiwnhkzZpOUVJuJE5/miy8+5eabb8/3HnXr1sNqtbJz5w7WrVtLr17nYjKZQvvr1KnDK69MxWq14nK5uOmmEXTt2p2GDRvx2msvYbfbmT59NqqqkpGRETpv/fr/eP/9WcTExLB9+1beemsK7703k4SEBKZNe5PJk19gwoTnANi5czsjRxpJm8ViCfXqCVGZzGtXo+7fj6/3Jcb2imWoBw/i69sfAMfLk1D37yfnxVcAsP70A1psHJ7rrgeTCc+Nt1RW6KIYItfficm1mfRui4o190oIIYQobzXqr1Fm5+PWNFEtebdNjjzbuiU677Y1Pu+2LSnv+QUwqQroOroOBY2e69Pn2FopmqYxdeqrrF27BtBJTU1ly5bN+SZY7dt3ICnJqITVpk1bli9fWmgsl1zSj++/n8e6dWu5++4H+P33X0L7PB4PU6Y8z9atm1EUlSNHDrN162YaNmzEX38t5N13Z6KqRlHJmJiY0Hndu/cIba9atYLu3XuQkGD0og0YMCiUUEHBQwSFqFCahnnVCgKdjYV9wz6cju27uaSu325sz5yB9fdfSctNsBSXCyU7M3R65idfgcVS8XGLUnHXvxGTa4ckV0IIIaoMKdN+kkyhtbAKHibocNhD33/66Syys7N4553pzJjxCb16nYvP5833PKvVGvpeVU0Eg8FCYznvvAv5+ecf8Hg8NGmSt5DE22+/QVxcPO+/P4sZM2bTqlWb0PDBwtjtjiKPEaIqCfv4I2IvvRDTf2sBcN1zPxlzF4T2Ox98jIy53x/bfuQJst/+4NgFJLmqHLqO9dB3mHI2FPt4AH/c2XiSR5RjYEIIIUTJSIJ1kkq62HB2djbx8QnYbDYOHz7EokV/lFksDoeD2267g9tvv/OEfTk52dSqlYTZbGb79q2sXv1vaN9ZZ/Vi9uwP0XPfsBw/RPB4HTt25u+/F5OaegSAb7+dQ5cuXcssfiFKQ8nOIuqG67D+ZCRR3r79yZo6jWDTZoBRPj3YrHnoeD0pSUqoVyGK92DuNwrh254mYvPDRZ+ka0T/M4iwPe+Wb3BCCCFEKciYipNkyh1WV9y1sK688moefXQcw4dfRWJiEp06dSnTeC644OJ8Hx8x4gaeeuoxvvtuLvXrN+D0088I7Rs79h5ee+0lhg8fgslk4owzOnLXXfefcI3GjZtyyy1juPvu23OLXNTj/vsfKjKmV155gT/++I20tFTuuut2oqKimTnzs9I/SXFq03XCH3uQYJNmeEbegO4Ix7RuLcoRI/HXY+PwDh5SyUGK4gjf8hhh+2aQevZmUG1ktXsPzZpk7NS8oFjzHXutBJ3oihXdZD9hnxBCCFHZFP1ot0UVl57uJPB/1foOHNhF7dqnFXBG+TGb1VAs/oDGvsM5xEeHEemwFnGmKExl/X9WpMTESA4fzq7sMKoXXcc+5VXUrEycDxtrykVf3pdA+9NxPvlM6JgCJ0HWINW9/Sj+DMI3P4Sz6ePotiTMGUsxZ6/GU3c4HJ8s6RpRq69FN0eQ3ebt/P9vj/7pOgX+38tCdW87ovJI2xEnoyLbj9msEhsbXiH3Kor0YJ2ko3OwituDJYQoIUXBtGcXam4PFUDmV/PyvrGWN9nVgsm9E9vBOXhrX4HflkQgplsBpdUV/NGdjaTr+P9bzUv41gm4Gt6Nbs1/yQohhBCiskmCdZJU1Vh8t7hzsIQQxeDx4Hh5Et6rriHYtBk5z70Ixy07IAlV9WLK2UQwogWBqNNJ67kW3Rpf+AmKgrvRPaFNc+YqdFMYqj8d+5538MeejS+xdzlHLYQQQpSOJFhlQFUlwRKiLKmZGdinv4seHYO7abO8yZWoVmz7ZxO57lYyuvxAIKZb0cnV/9N1IjbcCehkdFtIWo81aGF1yiVWIYQQoixU+wSrsAV+K4pJVWSI4EmqJlMBRXlyubB98zXeq4ehJdUmbdEK9Fq1KjsqcZK8tfqjeg8QiOpYugsoClmnf4ISdIKiSHIlhBCiyqvWZdrNZitOZ1alvzmXHqyTo+s6TmcWZrMUCakOrN/OIWroYChiXbb8qPv3oWQZi/qqO7YT8cDdmLZsNq775+9E3XEr5rWrASS5qsYs6X8RuWYkaH4wR+BudDeopV9fTAurRzC8edEHCiGEEFVAte7Bio1NJD39MDk5GRV6X1VV0Y5bWDjb5ScQ1FADtgqNoyYxm63ExiZWdhiiGGw/fI/i95d42J555XJiLr2QrGnT8V12OYrLhW3uV3j79CXYrDmBdu3JmPs9gXYdyilyUVFUz17M2atRfQfRwpIrOxwhhBCiQlXrMu2V5f9LTn7yyxZ+/3cfb917buUFJaqFGlPu1uUChwPrD98TbNaMYOOmRZ+j69jffQvvJf3QkuuXf4w1UJVuP7qO6t6B5mhsbAc9YAqr3JhESJVuO6JKk7YjTsapWqa9Wg8RrCoiHRZ8fg2vv+RDpoSoLtSdO1BT9hsbDge4XETcdyfhTz9Z8EluN+EP3Y9y8KBRGW70rZJc1VCO7c8Ru/QcVM9e4wFJroQQQpyiqvUQwari6ALD2S4ftmh7EUcLUT1FPHgf5k0bSVu2GsxmcDjI/GoeWlJSgeeYdmwnbPYsAh074x08pAKjFRXNU3c4uikSzVavskMRQgghKpX0YJWBSIcxeTvb5a/kSIQoPznPvkDOC5ON5CpXsFlz9KhoCASIuO8uTOv+yz04x9jfug1py1ZLclVDqZ692He+ArqOZq+Pu+FYWaNMCCHEKU8SrDIQFerBkgRL1EC50zS1Ro3xXXBxvoeohw5i/fkHLEv+wrRxA/HdTsc6f55xeqIUL6mpwvbNxLHjRVTPnsoORQghhKgyZIhgGTjWg+Wr5EiEKHuOlyai7tlNzkuv5em9Op5Wtx7pfy4xerNcLny9zibYokUFRyoqjOYF1Yar8QN46gxBszeo7IiEEEKIKkN6sMpApPRgiWpGPZASGsaHyxXqpcpXMGiUZS8guTpKj4o2vnE4yH7rfYJNmpVRtKIqCdv7PrFLeqL400FR0RyNKjskIYQQokqRHqwyEGY1YTYp0oMlqgdNI2rENWCxkvHtD0TefTuK00nWR5/mO3/GNe7hwhMwcUoJhLciENkOXZWFwYUQQoj8SIJVBhRFIdJhlR4sUT2oKs4HH0MJGO3Vf2YPFLf7WHKl66AomFetALOZQPvTpXDBKU7xHcaSsQRfrf4EYruTHdu9skMSQgghqiwZIlhGIu0W6cESVZ5y5AgA/nPPx3dhb1AUPNffiPu2sQCYly0lpve5qDt3EP7Mk0TdOAKCsr5bTWVJ/Q3Vk1LkceFbnyLyv1tQ/GkVEJUQQghRvUmCVUYiHRay3dKDJaouy6I/ie/cFsufvxd4jJKTDSYTekICWe9/ROZ7H4HJVHFBigpjS/mMmFUDQkP9bPtnEb1qIEog2zhA10E3kmtn86fJ7Dwf3RJXWeEKIYQQ1YYkWGXEGCIoPVii6gq0boPnymvwd+xc4DH+8y8kY/4v6BGR6NExBNu1r8AIRbkLulBd2wHw1upPdssX0c2Rxr7chEo3RQAQvukBoldeBloA3RxFIKpDZUUthBBCVCsyB6uMRDgsMgdLVE0eD1it6HHxxkLBRZH5VtWK4s8AzYduqwWA6t6JFtYAlBM/P4tZdTkEnWR0+xNMdjz1bwrt89a7Fm+9a0PbwYi26JZYIIj8qRBCCFET7dixg/Hjx5ORkUFMTAwTJ06kYcOGeY5JTU3lwQcfJCUlhUAgQLdu3XjkkUcwF1JduVg9WDt27GDIkCH07t2bIUOGsHPnznyPmz9/Pv3796dfv37079+fI7nzPYLBIE8++SQXXnghF110EZ9//nnxnnU1Eumw4vEF8Qe0yg5FiGN0ncgxNxN1w3VSCbAm0nWi/x1C9D9Xgq6h+DOJX9Qe+85XjP2aD9v+2aAbr0vOxg/gbDEx3+Tr/3mSR+Bq8hCotnJ8AkIIIUTlefzxxxk6dCg//PADQ4cO5bHHHjvhmLfeeosmTZrw7bff8s0337Bu3Tp+/PHHQq9brI8lj958wIABzJ07l8cee4wPP/wwzzFr165lypQpzJgxg8TERLKzs7FajbH93377Lbt37+bHH38kIyODgQMH0r17d5KTk4v7/Ku84xcbjosKq+RohCCUUAW6doOgJj1TNZGi4Gw8HvQAKCq6YiK79Rv4ozsBYM5ZT9S6m8nWA3jqDccff0ElByyEEEKUr5SUFIL/V6ArKiqKqKioPI+lpqayfv16PvjgAwD69evHU089RVpaGnFxx+YcK4qC0+lE0zR8Ph9+v5+kpKRCY1B0vfCPtVNTU+nduzdLly7FZDIRDAbp1q0bP/74Y56b33vvvXTv3p3BgwefcI2bbrqJQYMG0adPHwAmTJhA3bp1ufHGG/Mcl5WVRVZWVp7HrFYrtWrVKvRJCCEAnw+sVggEoGtXGDAAHn+8sqMS5SFzA2RvgeTLCj8u6IPM/yC8IdikQIUQQoia7/zzz2ffvn15HhszZgxjx47N89h///3HuHHj+O6770KPXXrppbzwwgu0adMm9FhGRgZjx45l27ZtuN1uhg0bxn333VdoDEX2YKWkpJCUlIQpt5KYyWSiVq1apKSk5Emwtm3bRnJyMsOGDcPlcnHRRRdx6623oigKKSkp1K1bN3RsnTp1OHDgwAn3mjFjBlOmTMnzWMeOHZk9e3ZRYVa6ddtTGf/GIp68qTsdW0hCKMqQrkP//hAZCUd/Fx59FLxemDTJ2D77bKhXz9hvNhsJVuPGlRezKF+rH4S0f6DOxWAqpMfcZIW4jhUXlxBCCFHJZs2alW8PVmktWLCAFi1aMGPGDJxOJ6NHj2bBggWhjqP8lNnM5WAwyKZNm/jggw/w+XzceOON1K1bl4EDBxb7GiNGjODyyy/P89jRYYbp6U4CVWR+U2JiJIcPZ+d5LOgzClzsTcmkfpy9MsIS1UB+bSc/ypEjhH3+Ce5bbgdFwdZ3IOa1a3DmnhuxJwXF6yE7d9t+4SXo0dF4jl77qReMr8W4l6g+jrYfpelrKP50tDQ/IMV1RNGK+9ojxP+TtiNORkW2H7NZJTY2nDp16hTr+Dp16nDw4EGCwWBolN6hQ4dOOH/mzJk8++yzqKpKZGQk559/PkuXLi00wSpypvPxNwcKvHndunXp06cPVquViIgILrjgAtasWRO6xv79+0PHpqSkULt27RPuFRUVRXJycp5/1WV4YFS4kQhKJUFRFqy//kT4Ew9jXv0PAN7BQ3A++Uxof84Lk8l+7c3QtvvWMXiGDq/wOEXFsR6aD39dZ5RSt8SiOaSHUgghhCit+Ph4WrVqxbx58wCYN28erVq1yjNCDyA5OZk///wTAJ/Px99//02zZs0KvXaRCVZxb96vXz8WLVqEruv4/X6WLFlCy5YtAejTpw+ff/45mqaRlpbGzz//TO/evYv59KsHh81MlMPCj8t3s+9wTmWHI6oZ5fBhIu67C9uXnwHgHXQl6YtXEDhdhnedMjQ/1kPfofgOA2DOXEX0ygGYnJsBMLm3Q/YmlIC8vgghhBBl4YknnmDmzJn07t2bmTNn8uSTTwIwevRo1q5dC8BDDz3EypUr6d+/PwMHDqRhw4ZcddVVhV63yCIXYMyvGj9+PFlZWURFRTFx4kQaN27M6NGjueOOO2jXrh2apjFx4kT+/PNPVFWlZ8+ejBs3DlVVCQaDTJgwgcWLF4eCHjJkSIl+AFV9iCDArgPZvPL5avwBjbFXtKNFg9hKiE5UZQV2lTudRI0eQaDrmbjuKnzipKiZrAfnEL3mOjJP/wxfYh/MWf8SsfFeclq+RCDqdAAS42wcTvNWbqCiWpJhXqK0pO2Ik1EZQwSrgmIlWFVBdUiwAI5kupn82WoOZ7i5sV9rurYqvIyjOLX8f9tRMtLRHeFG9T9dl1LqpzhT9hqCjiZgyv8PhLzREaUlbUeUlrQdcTJO1QSrWAsNi+JLiLbz0PBONK4bzVtz17Fg6W6qSQ4rKlogQPSQy4m6eZSxLcnVKUnxZ2LK2QhAMLJ9gcmVEEIIIaoHSbDKQXiYhXuHdKBLy1p89ttWPv55C5omSZb4P2YznuHX47lmWGVHIiqRY9szxC49F8V3pLJDEUIIIUQZKLMy7SIvi9nEzQPaEBdl44dle0jP9nJT/9ZYLabKDk1UNo8H0749BJs0w3PtiMqORlQyV6P7CER3QbcmVHYoQgghhCgD0oNVjlRFYcj5zbjmwmb8s/kwH/+8pbJDElVAxMMPENPvYpTMjMoORVQmzQu6jm6rhbfOlZUdjRBCCCHKiPRgVYCLOtdny95M1u1IrexQRBXgGns3/q5nokfHVHYoorSCblDDTmreXOT6MShBL1ntZ8j8OyGEEKIGkR6sCtK0XjSpWV7Ss6XEck1gf2sKcR3bgN9YWNr22WyiRg4zKgECYbM+JPLGY8P/HC9NJOaSCwDQGjbCO2RoxQctyox995tEbLyn9BfQdQIR7fFHdZDkSgghhKhhJMGqIE3qRQGwbV9mJUciSsu8bCmmTbnV3ho2xtfrHLBYAFCyMlFT9oXeLCtZWZgOpITO1erUJdCqdcUHLcqF6k/HnLnKGOZXGoqCu+FY3I3uLdvAhBBCCFHpZB2sUihNTf9AUOO2l//k/I71uPqCZuUUmSg3gQBxZ3UimFyfzK/mlfoysp5I9WY9/APBsGRjrSrVCkrJP6MK3/wwvoQ++ON6lfhcaT+itKTtiNKStiNOxqm6DpbMwaogZpNKw9qRbNsvPVjVktlM5kefosXFV3YkorLoQcK3PIxmTSKz83fGY0EXqu8wmv20Yl1C8adhPfQduiW2VAmWEEIIIao+SbAqUJN6Ufyyci/+gIbFLKMzK43Xi3n9fwTO6FTkoWEfTUdx5uC+ZQzBFi0rIDhRZSkmMjovQAm6jG1dJ2bFpcbjXX4u1lwq3RJHevfFoNjKOVghhBBCVBZ5l1+BmtSNJhDU2X1QutorkpKVifWH78FlvDG2vz+N2N7noR48AIB13jc4nn8afL68J+o6lj9/x/Ln76BVjeGponKorm1GSXVrApq9gfGgouBqPI6cZk8XnVxpAcL2zgA9CKZwUOWzLSGEEKKmkgSrAjWpFw1IoYvypmSkY5s9E3XvHgAsS/8mevgQLP+sBMDbtz+ZH8xCi4g09q9agW3uV6GCFbavv8C0dg0oCtlvvEPW9I9BlV+VU5XqSSF26Tk4tj1zwj5f4iUEYrsXeQ3r4XlEbhiLNfWX8ghRCCGEEFWIvGusQLGRNuKjwti6P6uyQ6lZcnKwT3kV8/KlAChpaUTdeRvW338FwN+9Bxlzv8ffqQsAWoPT8PXtD+HGREjnYxNI/2NJqBfC/ubrRI6/FzwesFqNf+KUpdmScDV5GE/dYQUc4Mex7TlsKZ8UeA1f0kDSO/+AL+HicopSCCGEEFWFJFgVrEm9KOnBKgXz8qWY/ltrbOg60QMvxT71dWPbYiF80jNYF/4BgNaoMWkLl+EZOtw4PCISf/ceEBZW8A2OS6Iy5v9C1tvvg03myZzydA0UFXeDW9EcjfI/RjFjTf0FS+aKE3f501A9+wCK1dMlhBBCiOpPEqwK1qReNOnZXtKyPJUdStWmaaj79oY2o266HsebuQmVoqDVqoUeEWFs22ykrt2M654HQvuDLVqWflif2YyWXF8WgD3Fqa4dxP59JuZ8Eqc8FIWMTt+S0/LFE3ZFbLyfmKXnwdHCGEIIIYSo8WSmdQVrenQe1v4s4qIK6VE5xUWNHolp43rSFy0HRSFr2nS0Wkmh/dnvTM9zvB4dU7EBihpPCeagm8LRbLWLPthkB0D1HkBXw9AtMQC4Go/HnNAHTI5yjFQIIYQQVYn0YFWw+rUisJhVGSb4f8yrVhB1w3XgdgPgvnYErvsfDFXvC3TuitageGsNCZGHFijVacHIdmR0/RUtLLlYxyu+VGIXd8Sx4yUIOo1rhDfDW+fKUt1fCCGEENWTJFgVLLTgsCRYeShOpzHPattWAPznXYB34BVgMlVyZKI6M2csI35hS0zOrcU+R/GnY9/5GmjeEg0T1a3xOJs/jafuNcQuPQ/HtmdLE7IQQgghqjlJsCpBk3rR7DqYjT9wCq+tpOvYZs8k7N23APD36EXaqnUE27ar5MBETRIMb4pucqD404p9ju3gHMK3PIbJuaXE9/MkjyLoaIIv/gL8MWeW+HwhhBBCVH+SYFWCowsO7zrFFxy2/bgA248LQNeNghRmmRIoyoYpZxPmzFXoljjSeqwmENO12Od6kq8nvftSgpFtS3dz1YazxXP4488v3flCCCGEqNYkwaoETetFAafggsO6TtjMGagHDxiFK15/i8xPvpJqfaLMRWweR9Sa4aD5jPala9h3v4kpZ33BJ+kaiu8wAMGIFhUUqRBCCCFqGkmwKkF0hI2E6LBTLsFS9+4h4qH7CZvxvvFARETpS6kLUYistu+S1WEmqMb6Zoo/Hcf2FwjbP6vAc2wps4lbfAamnE0VFaYQQgghaiAZk1VJmtSLZvOejMoOo9zZp7yKeeN6sqe8jVa/Aenf/0qwdZvKDkvUUOb0vwnEnIluTSBgTQg9rlvjSe/2O1pY/QLPDUR3w1NvJMHwZhURqhBCCCFqKOk+qCRN6kbVyAWH1f37jMIVug6A4vWguFwQDAIQbNNWhgSKcmHO+oeYFX0I2/N2vvs1ewNQFBRfKibn5hP2B8Ob4mz+NCjysiiEEEKI0qs2PVgRN45EP3AgtO297HI8o0aDy0X00MEnHO+5ehjeq4ehpKYSdcPwE/ePvAHvwCtQ9+0l8vabTtjvvnUsvt6XYNq6hYj77sy702LCMuYe/Oech2ntGiIeHX/C+c6HHifQtRvmZUsJf/bJE/a3u/NRANLnzqfRl++esD/nxVcJNm2G9Yfvsb/5+gn7s994B61eMrY5XxI2/b0T9me99xF6fDy2T2YR9smJw6IyP/4CHA7C3p+G7ZuvT9w/Zz4A9jdew/rTgtDjiteDkpND5udz0WrXIeLeO7DN+Ypg06bodgfqgRTM27fhP6sXwdZtUNxulLRUoq/oH7qGVqcu2W8azzn8kXGY/1ub597BJk3Jeek1ACLuvSNUuv2oQNt2OJ+eCEDkrTeipuzPu79zV5yPPAFA1PXXoqTnrSDn73UOrnvHARB99SDw5E1yfRf1wX37Hcb+gZee8LM5qbZnMWEbNrL0bQ9w3X3/SbW9nKeeJ9iuPZY/fsMx+YUT9xfR9nKemYh68ABqVlaFtj0AwsKMeXuA46WJWBb+kWd3wN4Bz8zrAAh/+gnMK5bl2a/VqYtpxBYUPYBvXk/M69aiBJ2onhQ0R0MCTVtW6bbHsGvgyuGV87pH5be9ynrdA4pse3psHFkfzAQKbnuV+bpHn4vhlruASnjdQ9pedW57dOkEDz8FVMO/uUjbq+y2xxefGvsr4HVPtVnhy89PeI6VQT6qrSRJcXasZpWUI67KDqVEFK8X06aNmNfl/pKYTKAAPj/m9esw7dyBr3sPGQZYU+k6EffdRczVV2D95afKjsagB1E0HwBaWAMwOQo93NniObLavm20W0AJZKH6U8s7SiGEEEKcIhRdzx3LVcWlpzsJVJF1oxITIzl8+ORLrD8/cyUBTeeR6zqXQVTlR92/D/PKFfj6DzC2D6SgJSSGyqqbl/xN1M3Xo6YeAU3Dff2NOJ+ZVPwb+P2EPzuBYLPmeAZeAY7C3yBXZ2XVdiqL/Z2pRDwynkCz5pi2biHrvY/w9busUmOKXHsDloy/STtrOZjCS3Zy0A0mOwSdJT+3ElT39iMqj7QdUVrSdsTJqMj2YzarxMZWjb/l0oNViZrUi2bXgWz8gWBlh1Ko8GcnEHnvWJQc4xdEq13HSK40DfurLxFz+aVgs5Ex/2c8V16NfeYMlMOHi339sI8/wvHGq0TedTvxp7ck/LGHMG3fWvSJpwjT1i1Y530DWuV+wGDatoXwZ57Ee3Ef0n/6k0DHzkTddiPmZUsrNA7FdwTHtuchkAOAp94Iclq+VOIEyb5jMrFLzwE9WC2SKyGEEEJUD5JgVaIm9aIJajq7DuRUdignUI4cCSVJOU88Q/qC39AjIo/tP3yY6GuuIOKZJ/H2H0D6LwsJtD8d99i7wePBPu3N4t3I7cbx0kT8XbqRMWc+vnPOx/7uW8Sd2ZHoIZdjXTA/VCDjlKHrmNauwfH8U8T26krcWZ2IHnUt4Y+ODxUPqXDBIJFjb0W32ch58VVwOMic+RnBuvWIHn4Vpq1byj8G3UgwTa5tOLY/hzV9IQD+uLPxJV5S4sv5Y7sDZhTfkbKMUgghhBCnuGpT5KImalIvGoCt+zJpmhxdydEcx+sl9uJzCJzRiaz3PkRPSEBPOFby2vLXIiJvHoWakU72i6/iGT4yVBkw2LQZ3v4Dsb/3Du7b70CPjin0Vvb33sF0IIXst9/H370H/rN64jx4gLCZMwj78AOir7uaYHJ9vH0uJXBGJwJndCLYuEn5rZ/lcuGY8gphsz4ke8rb+HudU2aXVvfthX/2YHEH0S0WsNnQrTawWtCtNtT9+7HN/xbbd99g2rUTXVXxd+9B9sgbMG/ZjGPaW+ixcbjuO3GSbXmzv/UGlhXLyJo6zejBBPT4eDJnf0ls3wuJvvoK0uf/jF6rVtnfXNeJ2HA3mjUeV9NHCcR0I63nGjT7aaW/psuFbdKXmGZswvb890YbFkIIIYQoA5JgVaLocKux4PD+yl9w2LLwD6w/LsD51HNgs+F85AkCrU4sVBE2cwYR991JsFFj0j/5yii7/n/cd95D2DdfY//gXVx33VfgPZXMDByvvYT3govwd+8RelxLqo3r3nG47rzXqKrz4fvYP/4I5V2j/LYWFU2gw+kETu+I//SOBDp1Rqtb7+R+ALqOdd5cIh5/GNPePWhR0USOvYX0P/4uMkksimnzJhyvT8b25WcQCFDY1XSLBd/Z5+K68168ffoeS2w1DcXpJHzSs2ixsXhuuPmkYioJ06aNhD//FN5L++O94qo8+7RGjcmc9Tkxl/cl+toryfh6PoSX8XA7PWCUV88tZAGcVHJl/ncVkbffhHnLZgJNmxF57x2gaXhGjCqLaIUQQghxipMEq5I1rRfNxt3p6LqOUpHrQ7ndWH/9Gd9FvcFqxbxhHbY5X+K6+z70uHi8g6484RTT5k1EPHgf/rPPJfP9mRARke+lA+064L3gIuxvv4HrptsKLFphn/oaakYGroceyz9Gsxlf3/74+vaHQADT5k1Y/l2F+Z9VmP9dhf3N13EEAgAEa9ch0KkL/k5dCHTugr/96cUulmFav46Ihx/AunghgTbtyHjjHXS7nZhLLiDiwfvJnjqtWNc5Ifx/V+F49WWs87+FsDDco0bjGHY1GanZ4POieH0oPi/4fCg+H3pEBL5zz88/oVNVsidPQcnMJPLB+9GjY/AOHlKquEokECDyjlvQw8PJnjQ53zXMAmd0IuudD4i67hqibhpJ1ozZoQIo+fL5MO3djbpzJ6adOzDtMr6qKfvwDL/+xN4k1UJOq8knPzwyEMDx6ks4XpqIViuJjC++wd+tO1GjriXy/ruMJOv6G0/uHkIIIYQ45UmCVcma1ItmyfqDpGV5iY8Oq7D72j98n/AnHiHrw9nGGhQjbsB9w81G2fX8BAJEjrkJPTycrCnvFJhcHeW68z5iL+uNfdYM3KNvPWG/cugQjrffxDNwEIF2HYoO2Gwm2LqNUf59aO46Fx4P5nVrMf+zEsuK5VhWLsf23TcA6CYTgdZtCbTvgJZcn2C9ZLT6DYyvdeuB1YqSnkb4pGcJ++Bd9OhosidNNt7c5/4MXPc8QPgLz+G9pC++/gOLjhFA17H8vRjHKy9i/f1XtOgYXHffh/vGW9ETEnAkRuIvbTUds5msdz4g+poriBx7C3p0NL6L+pTuWsXkmPIKln9WkfnujEKH//kuvoSciS8Tef9dRN5xK/4evVBSU1FTj6CmHkHJ/aoePoyash/luIIdelgYwdMagqIQee8d6JGReAdeYdx/+yS8SYMIhjc9qQWqTdu3Enn7TVhWrsBzxVXkPP9iKJHNen8mUTcMJ3LcPUaSdcOJ66RUBCU7C/OqlfjPPlcW4xZCCCGqMSnTXgplWXJy54EsJkxfwS0D2tC1VVKZXLO4TFs2E2zUuPDehlyOlycR/vzTZL73YbGTjejL+mDavYu0ZavBas2zL/yh+7F/8C7pi5cTbNy0NOHnSzl8GMuqFZhXLceyYgXmDetQj+StaKgrClqtJGMR5JxsPCNvwDnuYfTYuLwX8/uJ6XcRpl07Sf9jCVpS7cLvnZ5G1E3XY/3jN7TEWrhuGYNn5Cj0yKjQMWXRdpTsLKIH9ce8aQOZn83Bf+ZZBR+bmooeE1Nw4lwI07r/iL34HLx9+5P9zvRineN4dgLhr7wY2tYdDrS4eLT4BPR442uwfgOCDRuhNWxkfK2VZMypc7uJuWog5n9WkvnJVwQ7NyH27+64Gt6Ju9G9JY4fTUPJzsL29ZdEPPEwutVKzqTJoeQtD6+XqNEjsC2YT/azk/DceEvJ73cSlLRUoq8ciGXtarKmTiuwd1LKJYvSkrYjSkvajjgZp2qZdkmwSqEsG0sgqDFm8p+cfXpdhl7YvEyuWRjz2tXoikqwbbtin2Nau4bY3ufivWwg2W+9X+zzLL/+TMzVg8h+5Q08Q4+trq7u2kncWZ3wXH0tOS+9WqL4S8XtxpSyD3XfPtR9ezHt3YO6by+Kz4frtjvynUd2lGnLZmIv6Imv59lkzfq8wJ4FdddOoq+5AtPuXTgfm4B7+PVgt59wXFm1HeXIEWIu64168CAZc+YTbNceJS0V87+rsPz7D+bcoZSmgwcI1kvGM+w6PMOuM1ZVLw6fj5g+52M6kELawmXo8fGhXfbdbxK0Nyywcp9p+1Z0ixUtPqHEa5opGenE9O+Nun8/GXO/R2+WiGaJB9WS/wm6ju3Lz7B9/x1KRgZKZgZqRgZKVgZKVlaop8x3znlkv/Zm4c/f5yNq9Ehs388j5+nncd90W4liLy3l8GFiBl+GaftWgqc1RD14kPSFS0PFRI4nb3REaUnbEaUlbUecDEmwqriammABPD9rFT5/kMdGdimzaxYk+orLMO3aSdqSVcXquTIqCp6LknqE9IVLT+zlKYyuE3PROSg52aQvXhHqRYkcczO2b74mbem/xX/DX4ns094k4uFxZL/0Wr7V5sz/riJ62FXg95H14SeF9iiVZdtR9+4hpt/FKG4XemQ0pt07Q/sCTZsROL0jgZatsS78Hesfv6GrKr6L++C57np85114Qq+WkpON+d9/MK9agfX3X7Eu+pPMGbPxXdL32EG6RtziM/DFnYOz2RPolhK0h6P38WeiqxYw5Z98qfv2EnPL2bDTTMb8n9HqN8j/uIMHiLj3Dmw/LiBYvwFa7Tpo0dHo0THoMTG538cSPK0hvj6XFq/ypN9P1E3XY/vuG3ImPIv7ljElfn7oerGH+CkHDxIzuD+m3bvI/PATtORkYs/PTehnfnbCdeSNjigtaTulFAgYr5Wn8LBdaTviZEiCVcXV5ATr28U7+HrhDl66vQexkbYyu25+lPQ0TBvWE/780wSbNMX5+FPoMbEFHh/+zJM4Xn2JzFmflWq+j/XbuUTfMJysadPxDhiEacN6Ys/tjvvWsTifePpknkrF0TRj+NbK5aT9thitUePQLutPC4gaPRItPoHM2V8SbN6i0EuVddsxbdlMxPh70WNijYqKZ3Qk0L4DelTesv/qzh3YZ84g7OOPUI8cJli/gdGjlZCIedUKLKtWYNq0ESX35SDQuAneIUNx3X3/iTfVg0StuQ6TczPpZy0vdqxKIAvdHEXkmusxOzeQ3m1hvj1TltTfiFk1AG2aHW1/AzK+/SFvYp/baxXx0P0oHg/Ohx835vmVVel+v5+om0dhmzeXQKs2BDqcjr/DGUblyjbt8vZM6jpqyn7Mq1bmFmBZifnff9ASEnDd84BRdbGADzLUlP1ED+qHKSWFzFmf4e/RCwD7O1OJeGQ8Wa9OxXvNtXnOKbL96DrqgRRMWzZj2rIJ8+ZNmLZtI9i4Me7rRxNs1fqkfzxgJPfWP3/HM2RoqYafioonb5JLTt27h+gr+oPZjPPRCfh6X1L+iZauY/53lfFa839D6yuLtB1xMiTBquJqcoKVkurk4WlLuebCZlzUuX6ZXfd4Sk42erhRmCLy5uuxfTMHFAUtIZGcSZPz9lLkMq9YRky/i/Fccy05k6eU7saaRmyvrmC1kf7rIqJGDMWyeCFpy1ejx8UXfX4Voe7bS+w53Qm2bEXG3O/BZCLsww+IeOBuAm3bkznrc/SkoufQVfofKp8P64LvsM/4AOvC3wHQYmPxd+xMoGNn/J06EzijU5E9ldZD81G9+/AkjwKleG+wY5b0JBjRFk/dazA5t+Kpf4Ox4/97fDQ/Yfs+JLi7CdFDBhPocAYZX3wDdjvKoUNE3n8Xtu/n4e/clezX3yTYpFkpfhBF8PuxvzkFy9+LsKz+B/WIsRixbjIRbNGKQPsOKBnpoWGYYJTYD7RuS6DDGUbhlbWrCTRpiuu+8ca8r+MSEXXvHmIG9UM5fJjM2V8SOLP7cc9fI/ryvpj/W0v6n0vQ6iWHduXXfpTsLBzPP41l5XJMW7agZmcdu1RkFMHGTTBvXI/i9eI7qyfuG27C16cvWAoYdlmYYJCwD6YR/swEVGcOOY9OwD32rpJfp5pT9+0lYtw9aEm1yZn4cvFGA1Qg5fBhowDQuv+Mr+vXYU6MJ2vQELz9BxZZpEjk/o4O7IuSkY6WmIh521Z83XvgfOJpAmd0KvoCR99alSQh03Uczz1F+Csv4jv3fDI/mFX2y16UQqX/3RLVmiRYVVxNTrAAHntvKXabmQevLcYLd0npOtFDLkePiMTX6xwix91DzsOP4z/vAiLvvB3zurV4Lr+CnGdeOLbukstF7AU9UbxeYy2o4wo1lJTtk1lE3XErznseIPzlSTjHP4LrngfK6MlVHNvnnxB1+03kPPIEanY2jldfwnvhxWS9M73Yb1iq0h8qdc9u8PuNHrnivAkIuolZNQBXo/vwJVxcspvpGvY9bxG01cGXdHnoYXPGUsK3PE52u/fQwuqB5s/Tq2X95muiRo/E16cv3oGDiHjwPhSnE+f4R3HfcnvF9J7oOur+fZhX/4t59Sosq//FvHYNWnQ0gTM64T+jo7EIdpt2EBYWOsc6fx7hk57FvGEdgRYtcd7/IL5+A44lV+npZH76FYHOXU+4pbpzB3HnnoW/azcyP/069P/z/+1H3bGd6OuuxrR1C/6zehJs1pxAsxYEm7cg2Ky5UZhFUVBSUwn7+CPsM97DtHsXwdp18Fx3Pe7h1xfrgwEA04b1RN4zBsvKFfjOuwDdZML65++k//RniXrG1N27jPllVeTT+ZKyff0FEQ/cg+Jxo3i9eAZdSfYb71RaT56Sk4152RIsS/7GsvofTOv+w3ToYGh/sE5dAq3bYNuzCzZvRneE4xlwOZ5rhhPodmbVGvrm8+F49SXM69cRaNacYLPmBJu3INC0eYUmGuq+vcQMvBQlLY3Mz+cQaNeBsJkzCH/hOdQjh/EMGozzocfRGuRdj085cgTrwt+x/PEb1j9+Q/H5yHrrvWIvWO+Y+AzhL03E1/NsLH8tItCpC5kff37SazECqNu3Ydq311hzsoQfCFTa361AADVlvzFves9u4+vePSjOHAIdOuLv2o1A+9Or7WvJqUISrCqupidY3yzewdyFO3ixPIYJahphH7yLevgQjimv4Ot1jlGwQVXB78fx+mQcL01Ej4oi59kX8A68gvBHx+N4500yvvy22H8cCuT3E9ftdGMB34REUpetrp6foOo6UTdch23eXADcw68nZ+JLJfpjVZUSrJJSXTuI+m80ziYP448/D4JuLBlLjO9LyXpoPo7tz5HZ+Tss6X8RvvkhMs/4Es3RKHTM0TlwAP6Onch+7a0ih2JWGZqGdd5cI9HavIlAqzYoWZkoOTnGG7fTOxZ4atj704gcfy/ZL76K57rrgbztx7LwD6JuvA6ArHc/LN7vaTCI9ecfsb//DtbffjEWtr7gInxnn4u/17nGz/X/33B7PDheeRHH65PRIyPJeep5vIOHoKSmEnd2V4J16pGx4Ndi9YhZfvuF6KGD8XfrTubHX5SoCIr12zmY/1uDd9BVBFu0LPZ5ZUXJzCBi3L2EffU5/k5dyJo6Dds3XxPxzJN4Bg8h+/W3ik6ydB3bpx9j/eUnvFdche/iPiUe2qpkZhjJ1F+LsCxZjHnNapRgEN1sJtCyNcE2bQm0aUugTTsCbdqGRgokJkSQPv8XwmZ/hG3OV6jOHAKNm+C55lq8Q4bmW1SlIpm2bSHylhuxrP6HYMNGxhvp3HUOAaP6aLPmBBs1RotPQIuLNyqTxsYd+z4+4aTfbJ+QXHXsHNqn5GRjn/IKjjenQDCI+4ab8Z1zHtZFf2L54zcsa1cDoEXH4O91DqYtmzBt20rO8y+FfocL4njxecInPYt76HByXn4d63ffEnXLKAItWpH56dfoiYmle0JOJ+EvT8L+5usogQDBWkl4r7waz9XDiv17VJq/W0paKravPkdPSMTbp++xD5+KYF7zL2EfvIv1z99R9+9DCQbz7NcSEtDD7Jj27gGMZT78Z3Qi0PVM/F274e/SrdBpD6LiSYJVxdX0BOvoMMGhFzbjwnIYJqhkZRJ7QS/w+0n/ZVGeqnCQ++n0Xbdh+WcVvh69sC5eiOvGm3E++0KZ3D/svXeIfPA+cp6ZmO+6WNWFkppK9LVX4b2knzE0qoSf/lbnBOv/2XdPJWLTeNLOWmWsU1UQXceS+gv+uLNBzefNj66BomJJW4h91+tkdZh5wnFh776FEgjgvvGWKjccq1iCQWxzv8LxwnOoGelkfjan6PXfcuf+mVetIP2Pv9EanGa0n0NZhL0/jYhHxhFs2swojnHcvMDiMm3bQtj097B9/x2m3buMMGsl4e95Nv5e5+DrdQ6m/fuIuGcs5q1b8AweQs6E5471cgPWed8QPepanPc/iOv+Bwu/34b1xPS9CD02FnXvHmPB8o8+LdYbr6OvH0f5z+iIZ8gwvJdfUbLCO6VkWbyQyDE3ox5IwXXfeFx33htqh47JLxD+3FN4rrya7NfeLDDJUtLTiLj/bsK++Rrd4UBxuQg0a4771rF4Bg8p9Odg2rQR27dzsC6Yj3ntahRdR7da8XfsjP+sHvi798TfuWuhvTx5XnucTmzfziFs9kysfy9Gd4ST/fJr+S4wX+50nbBZHxLxyDh0m43syW/gu7Qf+P2YdmzHtHkT5s0bMW3eZMwt3LUTNSsz/0uFheG+4WZcd95TqjfZhSVXeY5L2Y9j4jOEzZ5p/F9YLPg7d8V/znn4zj2fQIczwGRCyc4i8qbrsf3yE66bbsX5xDP5vn6F2tCQoWS/OjWUdFt+/Zno64cRrFuPzC++yTNcuEi6jvX774h4ZBymvXvwXD0M74UXE/bFZ1h//gElECj271FJ/m6Ztm/F/vZUwj6ZheJ2A0bC6R00GM811xo/m3w+xLF98zX2D97FsnI5usOB96I+RjKdXJ9gcv3QOpZHP5RRDx7AvGwplmV/Y1m2BPPaNSiBALrFQvaUt/FePrj4PytRriTBquJqeoIF8Oh7Swm3mRlfhsMEHc8/TaBTF+MP6ffzyJjzvTEsJD+BAPa3pxI+8WmCdeuR/suishuWEQhgXTDfqOZWHd8gl5Fqm2BpfkDPk/go3oOYc9bhj+1VcBl1wJyxjNjlF5LV5k28dYdVQLBVWDAIPl++Jfzzo+7ZTezZZxI4oyOZX3xDYqwd9+hbsX/4Pt6L+5D95rsnNXw3dJ9dO7Eu/APLoj+wLvwT9fChYyE3OI3sSZPxn39hvudG3jYa25wvyVjwqzFcJ7/rHzxATJ/zIRgkY8GvWP78nag7bsV7cR+y3p9ZaK+D/bWXiXj6Cbx9+pLz7CRs8+YS9ulszOvWolut+C6+BM/VQ43KmKWZV1YYr5fw55/GPvU1gg0bkT11GoFOJ1Z7PbpOoGfIULJfeeOEJMuyeCGRt9+EeuggzvGP4L75dmzz5mJ/4zUs/61BS6yFe/QtuEfeYCQGuo75vzVY583FNu8bzFs2oysKgc5d8Z13Af6zeuLv2LnYvQJQ8GuPadsWIu+8HcuyJbhH3kDOhOeKvq7fj/39d7B9/inegVfgvunWUvUcKelpRN57J7Z5c/H1OofsKW8Xr7Ks34+Sno6aloqaloqSloaalorl78VGr0lUNK4778V9w03F/13bv89IrlJTyfzs63z/n/+fafMm1L278XftXvCojGCQ8CcexvH2VLwXXET22+/nKUJkf20yEU8/XmAvqHnpEqKHXYkeFUXm53OKNedU3bmDiIfux/bzjwRatSF74st55nkqhw8T9tVnhM2ehXn9f8bv0fkXEmjTjmCLlsYw4yZNQ+2gOMV1LEv+wv7m61h/+B4sFjyDh+C+8RbUI4cJ+2QmtvnzUDweAq1a4xkyDM/gISheD/YZ7xM2awZqaiqBps3wXH8jniFDTyjUVCSnE8u/q3BMfAbLimVkzfi4VIW5qgS/H3XvHrTk+mX6mqZkpGPeuAEtMgo9NhYtJtb4/SjtMGFdR0lPQ81IJ9ioSYHXkQSrijsVEqxvFu1g7qIdvDSmBzERZTBM0Okkts95aLWSsC78g5zHnsI95s4iT1MPpKCbLXk+qRZlo7omWNZD84hcdxsZXX4kGFHC4VmaD2vqr/hjuqNbSvhHUxD20XQi770D57iHCf97Ifz5J6477sH54KPlM+9H1zFt2ohl0R8obg/uUaML/aBFyUgn9uwz0WNiSP/pT7D932uX00nMwEsxb9lMxjffh5KwsOnvEfnA3Xj7DSDrnQ9O/OBF1wl/dgKOV18y5jm9/laeNxumtWsI++xjwr78DPXIEbSoaAJt2xn/2rQzhsq1aHViPEU8d3XvHixL/8aydAnW33/BtGunMRz4yWcKHdp8dIiX5+phRpKlquDzET7xGexTXiHYqDHZb72Xd1iormP583ccb7yK9fdf0R3hePtcgmXFCky7d6KrKv6zeuLtNwBf3/5FLnZemEJfe/x+42f9xqv4259O1rsz0Bo2yvdQy++/EvHIOMybNxFs2AjTzh0EmjTF+dRz+C7sXex4Qknn4UM4H3wM921jy6QSqOm/tYQ/8wS2X34iWLceznEP473qmkJ/V/IkVwXMizxZYR9+QMT4ewk2aUrmzM/QTmuIferrRDzxMJ5Bg8l+Y1qBMZrWriFmyEBAIeOzOQWvY+nx4HjjVRyvvoRuMuMa9xDuG24u9E26ae0awj6dhfWnHzDt2hlaO1BXVYKnNSTYvAW2tq1xKhawWtGtNnSrBaw2dKsVxeMhbNaHWFb/gxYXh3vkjbivH33C3E4lMwPbnK8I+2QmlpUr0M1mOLpOYZ++uEeNNoY5n+ScQCU7i+gr+mPeuIHM2V+GqrNWSbqOum8v5g3rMG1YbxSj2bgB09bNKD4fgZatyJ70St4iSCXl82H9+UfCPv8E608LUHy+vCFYrWgxsegxMcYSJw4Hut2Bbg9DtzvAbje2rRbUtDTUgwdRDx3I/XowdL3syVPwDLsu3xAkwariToUEa/8RJ4+8u5RhFzXngk4lGApQCPOyJcQMvBTf+ReS9eEnZVfKWpRKdU2wzFn/ELZ3BjktXwT12BthxZdK2P6ZeJMGodnLpwLmKU/Xib56ENbffgGbjazJU/AOHlLZUeVh/eVHoq8ZjGvs3TgfffLYjmCQqOuvxfrj92R9OBvfxXkXp7a//QYRjz54YqEITSP8kXE43n0b9/CR5EyaXPAbZL8f668/Y/3pB6Ni3oZ1KC4XkFv1sXkLAi1bocfFG2ujRcWgR0ejRRtfdVsY5rWrjaFGS5dg2r/PCCEikkDnLrhvvPmEuAvimPQs4S8+j3vocNy330nkbaOxrP4H97UjjJ6hQhI0039rcUx9DesP3xPo0hVvvwF4+/Qtsw+6ivPaY10wn8ixt4Cuk/3am8ZQvVzqzh1EPP4wtu/nETytITlPPY+v9yVYf/2J8EfGY962Fe9FvXE+9RzBxgUMGXa5sCz5C9v33xH24fsEGzcxks4OZ5TJczyeZdGfhD/1GJZ/VhFo2Qrn/Q+hR0ejHkgx3hweTEE9cADTgRRMWzaBP2D0XJVDchWKaeEfRN0wHEwmPIOuxDHtLTwDBpH95rtFjuwwbd1C9ODLUJxOfL0vMRZSz8pEzcw05nVmZqJkZ6HoOp6Bg3A++WzJ15n0eDBt2xoakmnevAnT5o2Yd+4Ar7fA0wJNm+G+ZQyeK68uVo+hadNGwr74FN1iMZYLKcnQx2JQ0lKJGXAJ6t69ZH75TYFDPUMCAWxffIppz27jdUZV0VXjKyYTmFSCDRudXC+5z4dp00bjNWrtasz/GVU+jx/uGqyXTKBVa4Kt2qDVro39zSmY9u7BPXQ4zkcnnDC1o0C6jnnlcsI+/wTbnC9R09PREhLxXHEl/nPOA7cbNSPD6AXOzEDJSDe2MzJQ3C5jaKfHjeJ2h7YVjwctLg4tqTZarSTja1JttKQkgnXqGq+RBXyYJQlWFXcqJFgAj767lHC7hfHDCp78XhyWvxcTaNKU2EsvBE0j/ZeFFTJXQRSuuiZYBVFdO4hf3KHA4X/mjGVYMpfjTh4JpqrxolcdqSn7CX/yUcLG3cfhRq0qO5x8RdwzlrCPPyLj2x8IdOkGYBTLeXsq2c9OwnPjLfmed3QI4NHJ/eg6kXePIeyTWbhuGYPzyWdK9ql2MIhp53ZM6/7LfROzFvPmTSiZGcab0AL+5AXr1MXf7Uz83brj79qdYOs2Je8h1HWjEtzLk9BVFT06muyXXsfX77KSXaccFPe1R921k6jRI7D8+w+uW8fiuvcB7FNfw/HGa2Ay4bz7ftw33553GKHPh33aWzhemoji9eC+ZQyuu+9DD7Nj/melMfz0z9+xrFiG4vMZb6yvvpacCc+Wb3VAXTeKzDzzJObt2/Ls0iIi0WrXNhYor10H9023Flp0pqyYtm0h6tohRkLabwBZb79f7Dft6p7dRN10Peqhg+hR0bkfGEQd+z4yyphD2fPsMo05MTGSwwczjaGZPi/4jn71QTBo9HZWsQ9v1QMpxPTvjZKZQcbcBQVWOrX89gsRjz2IedPGIq+pJSTgGXQl3iFDCbRtX/Drkq4bw64XL8ScOz/MvGkDit9v7HY4jCU92rYzvrZqQ7BlyxOrRR5XoESPiiLniWfwDhma/33dbizLl2L983es8+Zi3r4NPSwM7yV98V51Db5zzj+56Rn/v5xKCUiCVcWdKgnW3EU7+OZkhgl6vZhXLSdm0GUEk5Mx7dtLxjcLyvVTOVF81THBUl3bQLWjheX/aajq2WeUWM+HY+tT2Pe8Q+rZW8BU/PkiIn9Vuf0o2VnEnnsWusVC+q+LCZv9EZEP3o9r9C04n5lU6LlHy1O7R9yAkp5G2Ddf47xvvFE4oyzLiGsaijPHSLQyM1GzMlFysgm0bG3MdyiLe+k69tcnY/n3H3KemVjyXoRyUqK24/US8fhD2N+fZgwD8/nwDLoS52MT0Orm/7sOoBw8SMQzTxD2ySy0+Hjw+UPrsvnbdcB/9rn4ep2Dv1v3il3fye/H+udv6HYHWu3aBGvVrtRKtkpGOtaffjDWxyvreYPloCq/7hRG3bWTmP69QdPI+GYBWuMmoX2mrVsIf/whbD/9QLBhI3KeeMaYI65pxlzZ3K+KFoRAAMvyZYR9+jHWH783hu+1aoNnyFA8V1yFnpRkDC1e9CfWxQuxLF4YqnKoxccTaH86gbbtCbRrT6Bte4KNGpfowxvT+nVEPnA3lmVL8HXvQc6kyQSbNsO8+h+sf/6OZeEfWJYtQfF60U0m/N174B08BG//AWUyR/dkSYJVxZ0qCda+I04eLeYwQXXnDsK++hzT9m2ou3dh2r0LNWV/nk9oC/vkWFS86viHKnLtKKypv5F6ztZiLyp8PMV3GN1ayhLDIo+q3n4si/4kZlA/fL3OwbJ4Ib6LepM1/eNilS8Pn/AYjjdeBSDniWeMOTmizJSm7djmfoXt809wjbm7RPNAzCuX43j1ZbTEWvjOORd/j7OLP7xJVDlV/XWnMKZNG4kZ0Ac9PIKMb39At9txvDTR+PDA7sB1zwO4b7y52HM1lfQ0bF9/Sdjns425ZCYTWu06mPbtBYyEyn9WL3xn9cTf8+z8l74oDU0jbPZMwic8ipKdje4IDw0vDLRua3xwcfY5+Lv3QI+IPPn7lSFJsKq4UyXBAnjk3aVE2i2MK2CYoOm/tTimTMY25yvQdbQ6dQk2OA2twWkE6yUbpU0bNjK+nsSkaFH2quMfKpNzCybXVnyJBcxD0XxEbLwPf3RXvPWurdjgTjHVof2EP3Q/jnffxt/+dDLmzC9+T4GuY39zClpSEt4rrirfIE9B1aHtiKqpurcd8+p/iL68H3pcPEq20XvtuXYkznEPl359McC0ZTNhn81G3bmDQNdu+HqcTbBlq3IdLqkcOYJj8iQUj8dYTqPH2Sf1HCrCqZpgnbr1squwzi0S+XbxTjJzvEQfHSao61iW/o39tZex/fwjWngE7lvH4r75ttACkeq+vcT0vYicFybjP/OsSnwGoiYJhjcjGF5IaWDVijlnA1pY3h7XiPV3oNnq4moyvpwjFFWJ85En0eomGxPeSzIMS1Gk10oIUeYCHc4g6+PPib76CvydOpMz4TmCbdqe9HWDzZrjfPjxMoiw+PSEhCKHXIuqQRKsKqhLy1p8s3gnKzcf5vwz6mH9cQGO117GsnwpWkICzoceO7ZeyvE0zRjj26xF5QQuahzb/lkEHc0IxBQ+hy+jy495h0HoOormRtEKrjwlaiiHo1jLQQghREXxn3kWRzbtLNmyDUKcBEmwqqB6iRHUiXewYuMh+v46i4hJzxBMTib7+ZfwXHNtgWVQtfoNyPro0wqOVtRYepCILY/hTbiEnCISrFBypQeNeVqKQnbbaeUfoxBCCFEcklyJClS16mqKkC4ta7FpTwaWLz8DIOObH/CMGp1vcqWkpxH+6HiUjPSKDlPUZIqJ1B6rcTV9pFiHR6wbQ/Q/VxinBrLKMzIhhBBCiCpLEqwqqnPLWlh9Xsy7d+IadZNRQhgIf+ZJLEv+ynOsZdFC7NPfQ92zpzJCFTWZOQLNVrxCKYGoDviju6F69hP/e2Ns+2eXc3BCCCGEEFWPJFhVVL2EcHplbsHk9+PrbVRvU9LTsH35GZa/F+c51td/AKkr1xFs174yQhU1UdBD1L9DMaf/XexTPPVH42ryILpiwn3aGALRXcoxQCGEEEKIqknmYFVRiqJwxb/fogEZ9ZsQDuixcaQtWh4qAWpZ+Afmf1bhvuNu9Fq1KjVeUbOY3Lsw5/yHEnSW7ERdQw1k4mz2RLnEJYQQQghR1UkPVhWWeGgPWfZolmUdlwc7HBAWBoD1j98If+l51D27KylCUdOYnFsxZa8lGNGCtB6r8cefX6Lzwzc/QtxfnSHoKqcIhRBCCCGqNkmwqij1QAq2A/v56ewrWbHpcL7HOB95giMbdqDVb1DB0Yka5eha47pO9KoBRGzJXddDUUAp2UuEJ3kUvrjzUILuMg5SCCGEEKJ6kASrirL88L3xzUUXsXF3Opk5Bawn5HBUXFCixrHvfIWYpecaSVZuafXs1m+U+nrB8KZkdpqLbo0vuyCFEEIIIaoRSbCqKPv099AVhRY9O6DrsGzDocoOSdRAmq0egaj2oBlD+vyxZ6GF1ankqIQQQgghqq9iFbnYsWMH48ePJyMjg5iYGCZOnEjDhg3zHPP666/z8ccfUyu32ELHjh15/HFjqNH48eP566+/iI2NBaBPnz7ceuutZfg0ahhNw7R3N8EWrajTuC4NkvayZP0BLupSv7IjEzWE6t6JZm+It86VeOtcWdnhCCGEEELUGMVKsB5//HGGDh3KgAEDmDt3Lo899hgffvjhCccNHDiQcePG5XuNm266iWuvvfbkoj1FmNb9h5qZSc7TEwE4s3VtPvttKwfTXCTFyZBAcXJUz17i/uqKs8kjuBveUdnhCCGEEELUKEUOEUxNTWX9+vX069cPgH79+rF+/XrS0tLKPJisrCz27t2b59+hQ6fe0DjbN3MA8J9rVHDr1joJBfh73YHKC0rUGJolAWeTR/EmDazsUIQQQgghapwie7BSUlJISkrCZDIBYDKZqFWrFikpKcTFxeU59rvvvmPRokUkJiYyduxYzjjjjNC+Dz74gE8//ZT69etz77330qRJkxPuNWPGDKZMmZLnsY4dOzJ79mxiY8NL9QTLS2JiZPld/OvPQFGIb1wPHA4SEyNp1zSB5RsPc+Pl7VEUpfzuLcpdubadYomE2g8RUclRiNKp/PYjqitpO6K0pO2Ik3Eqtp8yW2j46quv5pZbbsFisbB48WJuu+025s+fT2xsLHfffTeJiYmoqsqcOXO48cYb+fnnn0NJ21EjRozg8ssvz/OY1WoFID3dSSCglVW4JyUxMZLDh7PL5+IuFwkpKfjOv4gsZxCcxn06NU/gg/kbWbpmH03qRpfPvUW5K9e2Uwzhmx7El9Abf/y5lRaDKL3Kbj+i+pK2I0pL2o44GRXZfsxmtcp0yBQ5RLBOnTocPHiQYDAIQDAY5NChQ9Spk7fSWGJiIhaLBYAePXpQp04dtmzZAkBSUhKqatxq4MCBuFwuDhw4cbhbVFQUycnJef4dLZpxqrAs+QvF78c9+pY8j3dqXguzSWXJuoOVFJmo7hR/GrbD32HOXl3ZoQghhBBC1FhFJljx8fG0atWKefPmATBv3jxatWp1wvDAgwePvfHfsGED+/bto1GjRifsW7hwIaqqkpSUVCZPoKaxffEZusWCv0u3PI87wsyc3jSe5RsOEtSqRk+eqF50SxxpZy3H3eCWog8WQgghhBClUqwhgk888QTjx49n6tSpREVFMXGiUd1u9OjR3HHHHbRr146XX36ZdevWoaoqFouFSZMmkZiYCMC4ceNITU1FURQiIiJ48803MZvLbHRijWL7eQFoGtjtJ+w7s01tVmw6zPqd6bRrLAu5iuJTXTvQ7A1AtVV2KEIIIYQQNVqxspwmTZrw+eefn/D4tGnTQt8fTbryM3369JJHdgpSDx5AzcjAdcPNkE8C2q5xPA6bmb/XHZAESxSf5iNm1QD8UR3Jbj+9sqMRQgghhKjRpBupCrH8/isAnqHD899vVuncshZL1x/E6wtis5ryPU6IPBQLOc2eQrdIUi6EEEIIUd6KnIMlKk7YJx+jhYcTbNGywGO6t0nC6w/yz5bDFRiZqNYUBV/SAPxxPSs7EiGEEEKIGk8SrKpC07CsXIai65BbjTE/zerHEBdlY8l6qSYoimbf+Tphez+o7DCEEEIIIU4ZkmBVEab161A8HnIeeRIKWUhYVRS6tU7iv+1pZLl8FRihqI4smcuwpC+q7DCEEEIIIU4ZkmBVEdbc+Ve+vv2LPLZ769pous7yDYfKOyxRXek6AFltp5HT8oVKDkYIIYQQ4tQhCVYVETZ7JlpsHFpS7SKPTa4VQXJiOEvWnbhYsxC2lE+IXjUQgm4whaFb4oo8RwghhBBClA1JsKoCtxvT9q3oViuoxfsv6d6mNtv2Z3Eo3VXOwYlqSw9WdgRCCCGEEKccSbCqAMuSv1CCQXImv17sc7q1TkIBKXZRw1nS/iR84/3GkL+gi6jV12HO+iffY1Wv0aPprXM1mR3ngDmiAiMVQgghhKheduzYwZAhQ+jduzdDhgxh586d+R43f/58+vfvT79+/ejfvz9Hjhwp9LqSYFUB1t9/Rbda8XUvfhntuKgwmtePYcm6g+i5821EzWPJ+Btr2h8ogXTMzi2YM/5GCeb2WgbdoAUACNvzHrF/dcbk3GLsK6RQihBCCCGEgMcff5yhQ4fyww8/MHToUB577LETjlm7di1Tpkzh/fffZ968eXz88cdERkYWel1JsKoA25wv0R3h4HCU6LzubWtzIM3Fxt0Z5ROYqBy6hupJAcDV6AHSu/6KbokjENWBtF7/4Y85CwDHzleI+6sTBHLwJfbBU+96gvZGlRm5EEIIIUSlSklJYe/evXn+ZWVlnXBcamoq69evp1+/fgD069eP9evXk5aWlue46dOnM2rUKBITEwGIjIzEZrMVGoO5jJ5LuYuNDa/sEPJITCw8cy22QAAOH4KWLUmsFVWiU6+4sAVXXNiibOIQFabItrP8dtj/HVyyGqzRwPHt4rhz/WdDGCTWqQPUgfqvULIUXVRHZfbaI0450nZEaUnbESejotvPsGHD2LdvX57HxowZw9ixY/M8lpKSQlJSEiaTCQCTyUStWrVISUkhLu5YgbBt27aRnJzMsGHDcLlcXHTRRdx6660ohYwWqjYJVnq6k0BAq+wwAKOhHD6cXSbXMv23lrhAgKzb78JbimsuXX+Qt79Zx8hLWnJ2h7plEpMoP8VpO+bYq7AoybgzFFAKOdbSA+r1gDJqi6LqK8vXHnFqkbYjSkvajjgZFdl+zGaV2NhwZs2aRTCYt9BXVFTJOjGOFwwG2bRpEx988AE+n48bb7yRunXrMnDgwALPkSGClcyy2ihYEDijY6nO79qqFk2To/nyj224PIGyDE1UIJNzC2H7PgQgEN0R92ljZB6VEEIIIUQJ1alTh+Tk5Dz/8kuw6tSpw8GDB0PJWDD4P/buO7yt6nzg+PdqW9bw3k4cxxmOs/cAQiAQRiCEFRpoKVBGKVA6oS2bX0uBlhZKgTLKaCh7BAKBsEIGZO/YznIcx47jPSRZW/f3h8HFOImXbNnJ+3kePyHSvee+cg6S3nvOeU+QyspKUlNTWx2XlpbGWWedhcFgwGKxcPrpp7Nt27ZjxiAJVoTpv/gMVVHQHDzYpfMVRWHh7CE4m/ws+ao4vMGJXhNV/CjRe+6BgDPSoQghhBBCHPfi4+PJzc1lyZIlACxZsoTc3NxW0wOheW3WqlWrUFUVv9/PmjVrGD58+DHblgQrwrSFBYQSEgmlpLZ/8FFkpdiYMSqVTzYcpKJW9sXqj5wjHqV+0jIprS6EEEII0UvuueceFi1axJw5c1i0aBH33nsvANdeey3bt28H4NxzzyU+Pp5zzjmHCy64gJycHC6++OJjtquo/aTG93G5BsvjIWFwOu4bbsJ1573daqrB6eX2p9eQOyCWWy4e3f3YRI9o03dCAVADoDVFLijRb8haCNFV0ndEV0nfEd0RiTVYfYGMYEWQLn8Hit+Pf2zX1l99l91i5LzpWWzZW83O/bXtnyD6BNOhRcR9NRGN51CkQxFCCCGEEGEgCVYE6TZvAsD0yn/C0t4ZEzNJjDHx6md7CIb6xmifOLZg9BB88acTMnZ9iqgQQgghhOg7JMGKIP3WzYSio/HNPjM87ek0XDprCGXVLpZvlhGR/sAfOwPniEelYqAQQgghxHFCEqwI0m3ZhH/aDDxXXxe2NscPTWD4gBjeXVmE0+0PW7sivBRfNVHFf4egO9KhCCGEEEKIMJIEK1KcTrS7CgmMHhvWZhVF4Qezh9LkDfDeqv1hbVt8R6h7yaux8j2i996L1tO18vxCCCGEEKJvkgQrQvQ7tqGoKuZH/4q2sCCsbWcmWZg5Np3PN5VRUiGVf8JN69pD3FcT0dcs73IbnoyrqZ2+gWD00PAFJoQQQgghIk4SrAj5tsCF+8c/IZSREfb2Lzh5ENZoPX97favsjRUmusbNWAp+gaoxEYzKImSI71I7SqA56Q2ZB4czPCGEEEII0QdIghUhuq2bCKZn4HrgYVSLNezt28wGfnPZOEKqysOvbqa6Xtb6dJe+/msMVR+h6qw0TFhM0Dqq+YlgJxLYmvXErRiOvnZlzwQphBBCCCEiShKsCNFt3kRg+AjowXLqaQnR/GrBWDzeIA+9spk6h7fHrnUicA+4kdrp61H1MS2PmQ4+S9zXU1G8lR1rxBCHL/l8ArYxPROkEEIIIYSIKEmwIkCpr0O3vwj92q+w/OqWHr3WgGQrv1wwFqfbz8OvbKbB5evR6x2PNE1FaJ35zX/RWVo9F7CNwx8zHVVvb7cdxV8H1sE48p5E1dl6IlQhhBBCCBFhkmBFgG7rFgA8C3+I98JLevx62Wk2br1kDLUOD399dbOUb+8ky567iNkw94gl1QP2CThGPgUaY/NUwaDniG1Yd1xPzPqzejpUIYQQQggRYZJgRYBuS3OBi6Zf347/5Jm9cs2hmTHcctFoDte6+eurW2jySJLVUY7cv9M4+nnQRh39IDWIfdOF2Hb8BFQVQ+UHxKw9tSXh8iadhzvzGggFeydoIYQQQggREYqqqmqkg+gI//JLUN2HW/7uTZ6PJ/NaCDZh33xxm+M9aZfjTbscxVeDbdsP2z6fcQ3elIvQeEqx7mi70a974M34Es9G69qDpeDnrZ4z6LXUZ/wSf/wstI5tWHbd3uZ8V87dBGKmoKtfS/Tee1s9pyssIPSagYZnlqPVFWAu/kub8525jxKMHoKhailRB/7R5nnHyKcJmTIwHn4LU+lzbZ5vHP0fVEM8xkMvYzr08v/icvspq3Lxoushbrx4CgnVL2CseKfN+Q0TPwQgqvgxDNUftX5SY6Jh/NsAmIseRF/7ZaunVX0cjWMWARC95x50DetaPR8ypuEY9Wzz87tuQ+fY3ur5oDkH54jHALDk34K2aW+r5wPWUbiGPQiAdftP0HgPtX7ePhnXkHsAsG29AsVf2+p5f9xMmrJvA8C+6UIItR518iWchTvrFgi6sW+6CJTWL/9ofU/jOQQaA01ZPydkTMG87yEU1Y+qNbUcY9BraUz+cZf7HkDToN90ue8BOIf9maB1NPqaLzDvf7jt8z3U977VMO5N0JoxHXxG+t7R+h5g33AO32cY/AOqYn8Ykfc9kL7Xn/ueIeNMqlJuBbrW9yL5mQvS9yLZ9wxJE6gacD8Qmfe9E63vKQEXOlcBQVMGIWNKv+97ptNeo6rK0SvvexqdAf2pb7R5jZEgI1gRoDgdqOYo4kcPA2/vFp6IjtKTmmCmuNzJzx9byYdfH6Cyzo2zyU8w2HMFN/oj247r0LkKoIO3IEKmNEKGBAD88afTOPa/rZIrIYQQQoijUQJOdI5tKCEPWncxSigQ6ZBEF/WbEay6OheBQN9IABITrVRVdW0DX6WykoSROTTdeAvBIUPxXP6jMEfXMcWHG9myp5rdB+vZd6gR/ze/29R4M8MyY5gxKpXB6e0XbujvNE370DkL8cXPAq35f0+oKlEHnwRVxT3wZ2G7Xnf6jhDSf0RXSd/pPxRfFaohMdJhtJC+0zt0Deuxb7oQVWfHOfRP2LddQVPWr3ANuTvSoXVLb/YfnU5DbGx0r1yrPbpIB3Ci0W9tXn/lO+sc/FOnRyyOrBQbWSnNlewCwRDF5Q52HaxjT2kDawsqWLW9nBvmjWT80L7zJh8uxvJX8cdMJRSVhbFqKZbdv6d65n5UrRlT2X8wlb1I/fh3cQ+4MdKhCiGEiJSQr/kLrz4WR94TqLrw71n5XYqvGsvuP2AqfwVH7qN4Mq7q0ev1JMVfi2XXbYSMaTQNvKlPJYx9ka5+DfZNF6Ea4qmfsIRQ1AA8KZcSVfIE7sxrCZnSIh2i6CRJsHqZbvMmVI0G1e9Hqa9DjYmNdEjotBpyMuzkZDSPWLk8fv7++laeeGcHV587nOkjUyMcYfgovmoshb/Bm7oA5/C/4En/0Tdl1uMAUDWm5hLq2r5xB0QIIU5kSsCB1pmPzrEDnXMHGl81IWMyQWMqoVY/Kag6OyhK+412kLnozxjqVqCiIaapiIZxrxEyZYSt/RaqivHQy1j2/AEl4CRoysC87094Ui/tl59FWmch9i0L0HhKQQ0SVfIU7oxraMr6OaoxuXuNq0FQQ6DRhyfYPkBftxr75osJGlNomLCEkCkdAFfOHRgr3sVc9ADOEW3XBYZVwInWU0rQMrxnr3MCkSmCXdCd4U7bwovRHihGt2c3zjvuxX3LL8IcXXh4fAH+8dZ2Cg7UccWZQzltfA98qPQixVeDaogHQOvMJ2geCprev78gUy1Ed0j/EV3VX/qO1rUb4+E30Dl2onPuQOsubnkupLMTMiaj8VaiCdS3OTdoSKFx3Bth2chdV7+WmPVzmgsoJM/Htu1KVG00jeNeI2Ab1+32v6V17cZScCuGulX4Y6biyH0MxV9H7IY5OHPuwT3ol2G7Vld1pu8Yqj7Cuv0a0EbRMOZlVH0c5v1/wXj4dVD0uNN/jDvr1rYjMmoIbVMROsdWdI5taNzFaAINKIFGlIADJdCIJtCIEnQR0tlwjHwOX+KcHni1vUtfsxz7lgUEozKbkytjSqvno3fdTlTJU9RNW0vQMix8F1ZD6Bxb0dd8jqHmc/T1a1BUP415T+JNuzx81+HEnSIoCVYXdLmzqCrxeTn4Zp2Od/5FBLJzCGUPDn+AYeIPBHny3Z1s2VvNRTOzOXdaVqRD6hKtYzsxG87Gmft3vCltKxD1pv7yJeeEF/JjOvQffAln9swd6y6S/iO6qj/0HcVXQ+zXU9H4qghG5xCwjCRoHUnAkkfAMrL5/8VvR6iCTWi85Wi9h9F4DqHxHiaq5AlQg9RP/pRQ1ICuBxJwErdmOqgqddNWo+psaJ352DdfgsZXQ+OoZ/Elze3eiw16MBc/gnn/I6haM64h9+NJ/yEozbXHbJsvRl+/jtqTtqLqIzvTpUN9R1WJOvAo0XvuJmAdQ+PY/7Z679Q07cO8/xFM5a8AmubZI/YJ6Bzb0DVuRefYjibYfA1V0ROMykLV21F1NlStjZDOhqqzoupsGKo+ROfcgTP3UTzpkVnHHg766k+xb11I0JxN/fj3UI1JbY5RfDXErR6DP/ZkGse+0r0LBpwYKxdjqPkMQ80XaPw1APito/HHn4aufj36xg3UT1oW1psIkmD1ccdDgqUpPUj8+DwcD/wFzzVtS4X2RYFgiH9/UMCa/ArOnjqAi2cORgnjFIweparNH8YhP5bdt9M08FZCUZkRDak/fMk54QWc2Lf9EEPNZwSNaTSMf4egJTfSUQEndv8xFz2ErnELjaOeO/aedOKIjtR3tK49RB18CsVfjzP3b83ToyPIuv1qjBXvUjdlOUHr6E6fr3XmE7N+DiFjCvWTlnU5MbHk34Kp7EUaJi7FH/u/tdKKtwL7lsvQNW7CNeT/cA+8qfWURFVF59iGofpjDNXL0DoLQGNE1UY1/2ii4Js/te79aN3FeFIuwTn0gTZfrrWObcStOQnXoF/TlHNXl17HMQWc6Bs2oK//unn0IuTBMeJxgtFD2hza7vtO0IO14GZM5a/hSb4QR94TrYtGfYfGXYx5/98wHVrUvI2JxkzAOoqAbTQB61gC1tEELLmgMRz1ckrAgW3bjzDUfIYr+zaasn8f1qmhPU5VMZW9hKXwVwQsw2kYv7hlhs2RmPf/hei991E3aRmBmKlduqTGfQD75kuby78bkvHHn4Yv/jR8cbNa+p7iqyF27UwgRN2UL8O2bk4SrD7ueEiwDEvew371FTQ89RyhrEEExk/sgejCL6SqLFq2m+Wby5g1Lp3LzxyKpo+/mRkPv4V5/8PUTfkSNMZIh9PiRP6C3B8ovirsmy9G59hG06DfYip9HiXkoWHcmwRiJvfotY3lrxJV8iSNY14+6qjZEftPyI+56AF88WcQiJ3WozF+S+vYTtTBp3ENua9X7q5rPOXErRqFovrwJp5D4+hFvT/FV1VB9aOEvBDyoYR8zdsy9JO1IC19R1XR160k6sDjGKo/BkUPhAhY8mgY99YR76L3BkPFYuzbfohr8B9a9kzqCn3tCuyb5uO3T6Zh/DvQya0yDFVLsW9ZQFPWL3ANabunEcEmbDtuwFj5Lu70q3ENuRt93SoMVd8kVb7m/Tr9tnEE7JNADaIE3RByowTdKN/8qSp6mrJ/jT/+9KPGYt12FcaqpdSctLXba5eUgAN9zWfo69egr/+6uRS4GkRFIWAdhdZTBjTvmRSwT2h17rE+tzSecmxbF6Jv3Ihr8J00Dfp1h5IdjfcwSqCRoHkwKNrOv6CQH0vBz4k6tAh32hU4cx/tH/8vBpxYC27FdPh1fHGzaBz9fMsa8KMKuohbNY5Q1EDqJy3rdDKpq1+LfetCCPlxjHoWX/wZR21D17iFmPVn4rdPpGH84rD8Tk/UBEuKXPQi/ZZNqDodxmUfoV/zFbVbCiIdUodoFIUfnjmUKIOWpWtLCARDXHVO37ij3yLowVj5Pr64majGJEL6eIKmAWh8NVJ9J8wUXw26xk34E86IdChhpWkqImbTfDTewzSO+S++xLPxpP2AmI3ziNl4Pg1j/tNjr9lQtRTrzp+iqEGsO3/a/MGmdGybQnPRA0Tv/wvm4r83F27JuLpHYvyWxnsY++ZL0HoPoQQcOEY93+N3j6NKHgc1QFPWLzAX/w1L4S+bv1D14HV1DZuwFNyCtqkIJeRDUX1tjgmaMnEO/SO+pHnhiUVV0XgOEDIN7Fx7oQCmQy+h+OsJRQ0kGDWAoGlg8x3olil1XoyHXsZ84Al0zu2E9Ak0Zf8Wd8ZP0Du2Ytv6I2LXn0H9+HcJmQd1/7V0guKrxlrwC/zWsTRldW/NkT/uFBx5T2Lb8ROsO2/AMerfHf5/SfFVY82/iYBlFK7Bvz/yQVozjaNfIHrvfZiLHyGq7N8AhHQ2/HGn4Uqcgy9+dveLOQBNg/+AsfJdovc/jHP4X7rcjq5hA7ZtP0brKUHVROG3T6Qp65f4Y6YRsE9C1dvRuvZi33whMRvn0jBm0TETv28ZKj/AUvALNEEHDWNewZd0bodjChlT4HvrjTpFo8c54p+ETOlEFz2I1nuYhtEvgc7S9TZ7mNaxE9u2H6Ft2odr8B3fJKMd6JvaaJoG/x5rwS0Yqj7o1PRU4+E3se78KSFjGg0T3yAYPfSYxwdsY3HkPopt5/VE77kT17A/d/haojUZweqCrmbj9ovOR2mop/G5l9AcPkxgSteGeiNFVVXeWL6Pj9aWcNvCcQwbEOEKiGoQJehqnh/v2kPcVxNwDHsIz4AbIhvXMRwPI1i2rT/CWPkuNSftjPiUy3DRNWzCvvliIETD2NdbjVYp3krsmy9E58zHkfcvvKmXhPfadV8Ts2keAUsu3pRLsOz+Pc6hf2qefvQ93+8/+tpV2DeeizflYhR/PcaaT3Bn/ATnsAd75m5u0E3MhrPRuXbhSb6QqEP/oTHvKbxpC8N/rW8ovhriV43Em3gujlHPYt57H9H7/9I8NWjwH8J/QTVE1IHHiN57HyFjCt6kec3TvDT65j8VA6rGAIqGqNIX0Dl34Is9Geewhwha87p1aXPRw0Tvux9vwlk4c//WUk3sWLSuPVh3Xo++YUPbl6KJ+ibZGoDRtQ08FQQsI3APuBFPyqWtRnd09euwb7kEVTHQMP5tgtZR3XotHaaq2LZdiaHqQ+qmriBoGRGWZqOK/45lz100DbwF19D/61gcWy/HUL2sw3EYKt5F37gVX/xp+GOm9sj/c5b8n2M6tIjaGRsJRWV17mRVxXTwX1h2/4GQMRXHiH/gjz3pqHFqvIexb7oIrasAR95TeFMvBdq+72g8ZVgKf4OxagkBSx6NI5/tdt/vDlPpC1gKf0HAMoqGcW+EJbkNK1XFdGgRlsJfNxfoGPUc/rhTOtdGKEDsmubvjHVT17Q/gq+qmIseJLroT/hiptM45uVjTkP8vuhdt2EueZLGkU/jTb2sc7F+z4k6giUJVhd0qbOoKvFDBuC94CKcf/l7j8TVG7z+IL/719fE20z8/ocTen89VsjXPDdbVYlbPQZf/Ok4c/8GgK5hY/PCzA7erYyE/p5g6Rq3Erv2ZAAcwx7GM+D6CEfUffrqT7Fv+yEhfXzzeqsjrEFQ/A3Ytv4AQ92qsCbxWsdOYjacTciQ8M2akXhsWxZgqP2CuilftvmS993+o/hrif16BqrWRN2UlaCNInrPPZgPPIov9mQaR7/UqQ/UdqnqN2tk3qZxzMv4Es/GvuFcdI7t1E1b3fkvfx1k3vdHoosepHba2ua1cKqKJf8mog79B8fwv+HJvCZs19J4DmHdeT2G2i/xJl2AY8Sjx54CGQpgKnuB6H33o/gb8GReg2vwH9qf8nMExsNvY9v+Y/z2Kegc21EVLa4h9zXvhXSk9zRVxVT6HJbdd6BqDDhz/4Y3YQ5aTwla9wE07gNo3SVoPSVo3AfQ2wZQn/wT/HGnHnV0TOvchX3TBShBB41jX8MfO6PTr6OzjIffwrb9qvBXzFNVLLt+TdTBZ3AOexD3gJ8eO46yRdjyb8Q55I+4s24OXxzdpPGUEbd6LN7kC3GM/FeHz1P8DVjzb8ZY+S7ehLNxjHyyQ/3yu+91zqEP4B74s+9MLw0SdfBpzHvvR1GDuAb/DveAn/WJqXmGqo+wbfsxIUMirsG/x5t8Qd9Yqxl0YS34JabyV/DFnUrjyGe7PA3XULkE+9aFOHIfw5Px42Nc04M1/yZMh1/Hk/oDHCMe6/xSiZAf+6YL0Des/6boxdguxQySYPV5/T3B0hbtJW7qeBwP/AU1Lg7/9JMIJXdjeDyCVmw9xAtLC7npwlE9vxHxt4UqAOv2a9B4K2iYuASAqJInCEYNwpd4ds/GEEb9PcGybb4Uff0aVH0MwahBNExYHOmQji7kJerA4xgr3yekj2/eK8eUSsiYRsiYQsiYhs6xDUvBzwlG59Iw/q02JXJbCXqwbb8aY9USXIN+2zx60o0bDBr3AWLWNc+Fr5/0SUvVM8VbSdzXUwkZU6ib8kWrD8bvrqOxbfshhqoPqZ/0KQH7+JZjjOWvYs2/mZAhhYaxr4btzrK56EGi9/2x1RdhjbuE2DUzCEYPp37i0rCvi1ICDuJW5uGPPYnGsf/93xOhALatCzFUL6NxzH/wJZ3X7WsZKpdgzf8ZStCLc/jDeNKu6PC/r+KvJXrfHzEdfA5Vb8c1+M5vEqOOrS3RNWwgZsM5BGxjqZ/wPhpvOdb8n2Oo/QJfzAycIx5rlfhrPOVY83+GoeZTfPGn4RjxRLtToTv63qPxlGLfNB+tu5jGUc93v1reMSjeCuK+nkwwKpv6SZ+Ef12dGsS29QoMVR/SOPo/+JLPP+JhGncxsV/PIGAbS8OE9/vcTbro3X8g6sDj1E1b06GCO1rHNmxbf4TWcwBXzn1ti3G0J+jBtuNajJWLm6flTvsrdUWrsRTcgr5xM77403EMf6TXp5K2R9ewEeuO69A17SGki8GTugBPxlXdGxVVQxgq30frLiFgySVoHUnIkHzM36fir23et82xDVPZi2hdu2nKvp2m7N92bb1ZSywqMevPROM+QO1JW/5XSERVIeRG429A8VdjLfgl+oa1uHLuoinrV13+nFJ8Vd8UvVCom7LiyDfsgk1ovBXNN9iOch1JsPq4/p5gGd9+A9sN19Dwz2ew/+xaGl58Bd/ZHZ+v3JcEQyHuem4dAPddMxmtJowfRt9JqKKK/46p7EXqpm8CRcFU+gJKoAF31s/Dd71e1p8TLF39OmLXz8aZcw8afx1RJU9QM7MIVW+PdGhtGKqXEV34W3TuIvz2SRDyo/GWo/FVotD6Lc8XN5PG0Ys69jpCASwFtxJ16CW88WfQNPh3BOydL1aj+KqaPyh9NdRP+qjNF4CWhfYDf45r6P0tj3/bf0ylL2ItuBnnkPtwZ93apn1dwwZsWxaiCTpoHPl0txOQbwsQeFIvw5H3r1YfpMbyN7DtuAZX9u9pGnx7t67zfVHFj2LZcyd1k79os/CeYBMxG89D59hGw/jFraq9dUqwCcvuPxBV+hx+61gco5474ihmR2gdO7Dsug1D3cpv2nq23TUPGvdBYtfNQtWaqZv8OaohofmJbzef3f17lJAbV/bvcA+8GUPVEqwFt6IEPTiH3o8n49oOfYHqzHuP4qvBvuVSdA0bceY+gif9qvCvd1PV5iS55lPqpqwK7x4/3/VtP2ncSjB6WPP0To0BVWNEVZqnfWpdhWh8ldRN/ap75d17iOKrIW7VaPxxM1vfaPg+VcVU9gKWXb8lpI+ncfQLXa46hxrEUvhrokqfg/ipqDXrUA0JOIf9GW/yRX23ap+qoq9bhanseYwV76GoPvz2ybgzrsKbPP+o1Q2PRF+7iug9d6Bv3NTq8ZA+vnnrAGseQUseIZ0dnXMHOsd2dI7taD0HW44NRmXhyH0Mf/ypYXl5uvo1xK4/k4B5MKCg8dejBBpQVH/LMarGROPIf+FLnt/96zVsImbDHPz2yXjSFqJtaq5+2TxKXozWVwGAY8Q/m7cZOAJJsPq4/p5gRd/5O6JefI7qgv1oDxQTyshAtfW9L6YdtXFXFf98Zzs/Pns4p4zpRhEJNQgooGgwVCzGsuu31E1bg6qPxVD5PoaaL3AO/b9OvSn2Zf05wbJvPB+dcyc1J21D59hO7PozaRz174jvLfZdmqb9WHb/DmPVhwTMOTiHPYQ/Yfb/Dgj50fgqmpMtTzlKyIM3eV7npk+oKlEHHsdc/Bc0/jp88afjyr6tw19klIAD+4a56FwF1I9ffNTKf5b8n2Mqe4GGCR/gjzsJaO4/tcWbiF1zMv6YSccshvHd6l7dmdbYXFVqDgHrSOonfHDEqmzW7T/BWPEW9RM/Dl+1xaCb+FWjmqvbHWWkVPHVfJOoVh0xUW1FVVH8tc3lsZuKWv7U13+N1l3cnMzm3HnM8tAdoqoYK97BUvjL5iRo+EN40n54xC+kSsDRHL/nIPWTPiVoGd7mGI33cPN6l8rFBI2paL3l+G3jcYx8plOJYKffe4Ku5vWWNZ8QiM5tXreVemnYpl0Zy1/FtuO6XpmSp/hqiN57Dxpf1TdVIL+pBqk2V4MEFdfgO3p0tK67zPv+THTRn6ib/FlzdcLvUHzVGKo+wlTxJoaaz/HFn07jyGf+l6x31bfrePb/GXfalbiG3BPxPbk6Q/HVYCp/BVPZC+hcuwnpbPgSz8WbdB6++NOO+r1C69xF9J67MFYvJWhMx5VzB76Es9A5C9A6d6Bz7mweoXIWoISaAFDREIwe0lxy3jrmmz9Hha3U+XeZ996HvnEzIV0Mqj4GVWcnpLN/s2+YHb9tLCFz+PZYNR76L7adzZ8fKhpCpgyCUVkEowYSisoiGJWFN/n8o36OSoLVx/X3BCvmvDkQDFL/4ac9FFXvUlWVP/1nI7UOL3+6bipGfQeGvVUVjacEVReDqrejr1uNffPF1E94j4B9ErqGjUSVPIkr5+7jpnjC9/XXBEtfu4KYjXNb5uSjBon/cgi+uJk4Rj8f6fAg2IR5/yOYDzwKig5X9m24B9zY/S/Lx6AEHJhKn8Nc/BgafzW+2FNoyv4t/tiTj353N+jBvmUB+roVLZUKjyrgJHbNSSiqn7qpX6Hq7STGGfB/OAWt52Dz3fb2KmR+M63RUPUhDePf6lBlsO/SeA8Ts/ZUUDTUTV5+1LUDir+B2DUzQNFSN3UVqs7aqesciengM1gLf0X9hA/wx5189BjdJcSsPwONrwpVa0HVGL8ZoTDAtwUpUNG6S9AE6ludGzSmE4zOoSnrF/jjT+t2zK3i+s56Lk/yfJy5f2/95VQNYttyGYaaT2kY91a71zdUvEf0vv/Dmzy/ufpYJ9e9dOm9JxTAePi171QejMedcTWezGuPPZ22HRrPIWK/nkowehj1kz7q3rSpE4QScBC3agwBax4NE95H07QPY+WHGKo+aN7HihBBUwbuzOtwD7wlrNMcE2O1VNUFw9Zer1NV9PVfYyp7CUPVh2gC9aiaKHzxp+NNmosv8SxUfRyKt4LofQ9gOvQiqjaapqxfNq/dO9pNBTWExr0fjb+BgGX4cXMj+Ei0rr2oioaQKbN33nu6SBKsLujXCZbPR0JOBu4rr8E/42RUgx7/af2/xPWukjoe/O9mLj51MOdMHXj0A0MB0OjQOguJ+3oyjXn/wpv2AzTew0Tt/yuezGvbnUZzvOiXCZaqErNhDhp3CbUztrSMYFh23oix8n1qZu7reiIT8qOvW4mx8n0M1Z/gzry201NA9XWrse64Dq3nIJ6US3ANub93S/MHXUSVPk9U8aNofRX4Y6bii5+N4qtG46tE0/JnFYq/FgWVxrwn8aZd3m7Tuob1xKw/E2/KxThGPkNi6X1Q8JfOlUQOuohdNxuNp4y6KV92fN3EdyoG1k1a1m5VOV3d18RsOBtP2kKceU8co92m9r+IhPzErR73zYaxn7Q7HUnr2oOp7CWUkPt7oxN+CHkBlZApk6A5m2BU9jd/Duz5RfBqkKjiR4ne93+EjKk0jny2ZcQyetftmEueCHuhjqPp1nuPqqKvW01UyRMYqj4ARYc35ULcA25sLizUiXZ09Wuw7LkDnWMHdVNXdXk65oko6sA/sez+HYGobHTuIgACllF4k87Fl3guAevoHpm61y8/t44m5Edftxpj1fsYKj9A6z2Eqmjx26egd2yFkBd3xk9oyv5t90cABSAJVp/XnxMs3YZ1xJ4zm4bn/oP58b+hWu00vNmHiwN0wt/f2Mre0gb+fMM0LFHfu6sR8jfP3Y2diWvI3aAGMZW+gC9hNqGoYyRkx7H++EGlr/6EmM0XtfkiaKj8APvWH1A/fjH++FkdbzDYhKHms+akquqjb+4mmgma0tC6i6mbspygdXSHmmouCDGFkM6OM++fvVL17KiCbkxlL2Eu/jtabxkhrZWQIbF5XzZDIiFDEiFDAgH7JHwJZ3a4WfO+PxFd9GfcmdcRdfBp3BnXtFTO7ChNUxGxa08lZMqgbvInoD32B5ASaMS27UcYaj6nYcx/Ozx1yrz3fqL3P0zD6P/gS54HASd6x1Z0DZvQNW5E37gJrbsYX/xpNOb966jllI2HXsa286c0jH0dX+JZnXqtfZGuYQO27VejcZfQlP1bQoYErIW/pmnAT3ENe7BXYgjXe4+mqYiog//CVPYfNEEnAcsIvMkX4Um5iJA5+8gnhbwYD79FVMlT6B1bCOlicA5/GG/qgm7Hc0IJeojZeB6qxogv6Vy8ief0ymdpf/zc6hA1hK5xE8bKDzBULyMQPQRXzp1hnV4nJMHq8/pzghX1xD+w3PMHqrfvgWgzSmMjobT29zfpD0orndz973XMmTKAS2floKtfg86xA0/mTwCI3vU7AtZRPbpPTn/S7z6oVJWYtaei8ddSO2Nj65GqYBMJywfhSf9hxzbBDLqxFtzSvPA45Cakj8WXcDbepPPxxc9CCbmJ/WoKqiGRuinL2x8VU9XvlDRfecT1KxGhBpu3EwjX6EjIT8z6M9E3bgT7CKomfN6lqSj66k+xb74Ib/L8Y24OrPGUY9tyCTrnThy5/8CbfkWnY9U27SVkSkfrLESh+X07aMokYBtP0JRJVOlzqNpoHHlP4kuc07oNNUjsV5NBY6Ru6uq+u5i+k5RAI5bCX2MqfxUAb8IcGse+2mvT48L93qP4GzAefh3T4TfR138NgN82Hm/KxXiT5xMypaN4K4gqfY6o0n+j8VUSiB6Ge8BP8aQuaDfJF31Hv/vcEn3KiZpghbkmqjgS/bo1BAdmoSY3361VLd1fn9BXZCRZmDYyhU83lDJ7QgYDDr+BsWIxnvQfgcaAa9gDNLh8LP1sD1/tOIxWoxBl1BFl1GE2alv+22LWc9q4DOLtbRfQi8gxVH2A3rGZxrwn2yY8WjO++FkYqpbCsIfb/SIcVfYCpvLXcKc3V3Pyx85oNZdb1UbhHPEY9i0LMBc9RFPOHcdsz1T2Isbqj3AO+3PfSa6g+QtzOKeeafQ0jnoWy67bMU5+GPxdm+fvT5iNK+duLHvvIWAbjzvrljbHaJ0F2DdfjOKvo2Hs6/gTOjmVWaPHMfIZbNuuJGhKxZt0PgHbePz2Ca0We3vSf4Rt+1XYt1xC04AbcQ25t2WBtKFyCbqmPTQeIwnsj1SdDcfIp/HFz8ZQ81nzTYl+vPZI1dvxZF6LJ/NaNJ5SjIffwXj4TSy7f49l9+/xW8eic+5EUf14E+bgHvBT/HGzjqt/UyGEOBoZweqCTmXjqkp8Xg6+Wafj+tVtGD9cgueSy1qSreNBdX0T9z73JeNys7hmdhKqNgq0ZhpcPj5ae4AvNpXhD4aYOCyJKKMOtzfQ8tP0zY+zyU+CvXnzYqu55woTRFq/uhOohohdMx1CfuqmrT3iHjWmspew5t9E7dRVx57WF/QQt3oMQfNgGiZ+eMzLWndcj/Hw69RP/vyo6zs0TfuIW3MSfvskGsa/2+f2rekp3e4/qopt25UYKt+jYfzbrQor6GtXYtu6sLnE77g3urWxZIcEPUTvuRPzwX/ht47GMerfBM1DiFl7CkrQSd30Df06Aelreuu9R+vai7HibQzVy/DbxuHJvJ5gdE6PX1f0nH71uSX6HBnBEj1Cs78ITXUV/slT0W/eiOW+O/GddQ7B4yjBGlD7OI+PfoWbt95L6eQBWKN0fLR2L59vKsUfDDEtL4XzpmeRHHf0O+97Suv5y6tbePTNbfzmsnEYDfLFKtKMFW+jc+Y3jyQcZQNQb8JZWFAwVn5A0zESLNOh/6D1luMY+Uy713UO+zP62uVYd/6Uuilfti39Ggpg23E9qqLHkffkCZNchYWi0Jj3BLFNu7Ftv6q56EVUFsbDb2LdcQNB8yAaxr3ZO2sktSZcwx/GH38a1p03ErvmFDxpP0Dv2IpjxD8lueqngtE5NGX/tnlTVSGEOEHJN5Mepl+3BgD/5Kl4L7qU6t0HCA46ykLgfspvn4whdSYhrZXH397Ob5/6io/XlzBhWBJ/unYqP5k74pjJFcCQjBiuPz+P/eWNPLV4B8FQ3xitPGGFApj3/YmAJa95c8ajUI1JBOyTMVQdY1Qq5MW8/xH8MdOaS5i3Q9XH4sx9FJ0zH3NR2wIA5gN/R9+wDufwvxAyHR9rGXuVzkLDmJdBVbFvuRzz/r9g2341fvtE6id93OsFaHyJZ1M39Sv8MZOJKn2OoCmjeY2OEEII0U9JgtXD9OvXErLHEBzWvEZEjYkF7fFxZ1bjKQfAH38q3pF/59zp2VTWNTFhaCJ/vHYq157XfmL1XeOHJnLFmcPYuq+Glz7aRT+ZvXpcMpW/gq5pL67Bd7Q7QuRNOhe9YysaT+mR2zr0MlpvGa7s2zq8/sKXeBaetMsxF/8NXcPGlsd1jVsw7/sTnuQL8aZc0vEXJFoJmQfTOOo5tM4dRO+9D0/yhTSMfxdVHxeZeEypNIx/F0fuP5pHOXtw/zIhhBCip0mC1cP069bgnzgJNBqiHnsEw4dLIh1SWBiqlhK3ejT6utUtj501eQD/+PnJXHteHimdSKy+a9a4dOZOH8jKbeUsXrU/XOGKTtB4Sone/Qf89kn4Es9p9/hvjzniKFbIh3n/X/HbJzUvcO8E59AHCBmSse78KQQ9zVUId1xLyJCIM/cRWSzfTf6EM3DkPYEz5x4co/7dsr9ZxCgaPBlXRrbUvhBCCBEGkmD1IKWuFt2uQgKTpwIQ9cJzGFZ8EeGowsMfMw13xrX4beNbHlMUBbOpczt8H8n8k7M5aVQq760uZvmWsm63JzohFMC6/SegBmgc+XSHkphg9FAC5hyMlR+0ec5U/ipaz0GaOjF69S1VH4NzxGPoXIVEFz1A9N570Ll24ch7MmIjLccbb9rluAf9UtaxCSGEEGEkRS56kH7DOgD8U6YBULtxB/j9kQype9QQ+trl+ONmoupjcA37U49cRlEUfnTWMBpcPv7z8S7s0QbGDUls/0TRbeb9D2Go/4rGkU93arNFX+K5RJU8geJvQNXbmx8M+THv/wt+2zh88Z0s9/1tuwln4k77EVHFj6IQoinz+laV74QQQggh+hq5bdmD9OvWoup0+Md+M8qjKGDov2sLdI6txGy6AHPRQ0c+IORD69genmtpNdx4wUiyUqw8tXgnuw/Wh6VdcXT62lWYix7Ck/oDvKmXdepcb9K5KKofQ82nLY8ZD7+O1l1MU/bt3ZrO5xr6R0KmdALRQ5v3SxJCCCGE6MMkwepBunVrCIweA2Yz+hXLif7Db1EcjZEOq1P01Z9gKn0RgIBtHPXj3ztq+V1L4W+JWzMD46H/huXaRoOWn18yhjibiUde28Km3VVhaVe0pfhqsO74CUHzIBzD/9rp8wP2SYT0CRiqvpkmGAo0j15ZR+NLOKtbsal6O3VTV1I/+QvQdm1tnxBCCCFEb5Epgj3F50O/eSPuK68BQLu7ENPrr+K6548RDqxzTIcWoXPtwZP+Q1A0+ONPPeJxusatmMqeJ6S1Ys2/mVBUFv7Y6R26htZZgG3blWh8FcC3Ix0KoBCvKDw1VKHYmcLar/LYXDuXcZPPPuq+TP1VVPHfMR/4B6rGhKqNbv7RWVA1ZlRdNCFDIp60HxG05oX/4qqKNf9naHxV1E/+DHSWzrehaPEmnoWx8n0I+TFWvIWuaV9zOfAwFKOQNVdCCCGE6C869C11//793H777dTX1xMTE8ODDz5IVlZWq2P+8Y9/8N///pekpCQAxo8fz9133w2A2+3md7/7HTt37kSr1XLbbbcxa1bnKor1N7ptW1A8HvzfFLjw/OQGPNdc3y8qn1kKfknToF8TMqXhHP4Iqs567EXwqopl129Q9fHUTfkc+6YLsW1dSN3kz9pdx6N17CBm43moiv6bstsqqGrzn9+0DUEGGLeSbXgVjeMVvJ9FoybMxB8/C3/8LILmId3/vYb86Bo3Yaj5An3tl+icO/FkXN1cWlwb1b2222EqfR7Lnrvwxc0kZExHCbpQgk6UYBOKvw7F5UTrPYS55Em8CXNwZ/0Cf8y0sPUl08GnMVZ9iHPoAwRsY7vcji/xXKIOLUJftwJz0cMELHn4Es8NS4xCCCGEEP1FhxKsu+++m4ULFzJv3jwWL17MXXfdxUsvvdTmuAsuuIDbbrutzePPPfccFouFTz75hOLiYi6//HKWLVtGdHR0919BH6VftxZo3mCYUKj5y3A/SK4UbyXG8tcIRA/DM+B6VEN8u+cYD7+Ovn4NjhGPE4rKomHcG8SuOw375kupn/wpqj72iOdpHduI2Xg+qsZEw4QlBKNzjnkd1VvD+hWvoa36nMmhLcRVN5cF91vH4sh7snOjO6qK1lWIofYL9DXL0detRhN0oKIQsI7FHzsdc/EjGCvewZH7d/zxPXNDwFD5IZaCX+CNP4PGsa+C5shVGBV/LVEHnyGq5CmMG87Cb59MU9Yv8CWefeTkN+hB27QPrXs/IWMaAevoI476aR3bsey5A2/CmbgH3Nit1+KLn4WqMWEp+BU6dxENo1+S6nRCCCGEOOG0m2DV1NSQn5/P888/D8DcuXO5//77qa2tJS6uY9N2li5dyp///GcAsrKyGDlyJCtWrODss89udVxjYyONja3XKBkMhpZRsf5Ev24NwYFZqMnJGF9/hagX/03Di6+gJiREOrRjUo1J1J60DVVj7NDxSsBB9O478dvG4Um7AvhmE9Mx/8W+8Xxs235Ew7i32yQOusYt2Deej6qzUD9hCSFzdvvXMsYz+YwbWbb+PH782R4mDXByw5RDxJc+TOzaU2ga/DuaBt7a7vRBXeMWovfciaH2SwACUdl4Uy/FF3cq/tiTWpJKfe0KLPm3ELNpHp7UhTiH/rFDCWdH6erXYtt+FQHbGBpHv3jU5Aqap8g1Zd9G08CbMZX9B/OBx7Fv/QGB6GG4M28AQmhde9A17UHr2ovGU4LC/zZqVjVm/PaJ+GOmEIiZgt8+GVWjx7btx4R0sTjynur+DQCtGV/cLIzVSwlED8eXdH732hNCCCGE6IfaTbDKy8tJTk5Gq9UCoNVqSUpKory8vE2C9cEHH7Bq1SoSExO5+eabGTduHACHDh0iPT295bjU1FQOHz7c5lovvvgijz/+eKvHxo8fzyuvvEJsbN8a7UpMtB79SVWFDWthzpzm4xJjIDmRhGEDQdOH7+gH3KCLAo7x2r5vy5/Adxjtqe+QmGD/3+OJZ4HuWQxrriSx+DaY/J09lWrWw6bzwWiH078g3jKoU2Fefs4IsjJieOTljfzhi2R+c8kqsg/9gei99xFduxSmvQj23LYnug7A1jugeBEYE2DcX2HAReiiB6ID2kwETDwXBp8GO/8PU/5DmGqXwfi/Q9bCbiUjiYlWaCiErQvAnI5+9kckmjp6E8EKKb+GcbdCyevo8v+MtfAXzU/posE6DJKng+0qsA4Fy2Bw7UepWo2hajWG4kdADQIKmJLAUwmnfUpCSuf+DY5q8CVQvRTdmLtITLK3f7zotGO+9whxDNJ3RFdJ3xHdcSL2n7BVCrjsssu44YYb0Ov1rF69mhtvvJEPP/yQ2NgjTw87kiuvvJL58+e3eszwTVnzujoXgUAoXOF2S2Kilaoqx1Gf1xTtI76yEsfoCXiqHDDr7OafGlcvRtl59o3zCBmTcYx8ukPHa117iS34K97UhTjUPPj+78Q6H/Og7UTv+wtOTRburFvQ1a/DvvlCVH0c9eOWEHIngPvov8ujGZpq5dc/GMdjb27jl08WoHAFZ6UP5crQPzF+MJad5ltoSPspmSkxWHVOzPv/SlTJU6BocGf9iqasW5v3a2oCmtq5ftrtaK1zsRbcgv7rK/Dt/jfuzOvx2yagGjs3upqYaKXm4G5i1s9BQUvdmDcJOaLA0fnfAdHnwcS5aJ07UfVxhIypbRM/FTDnwsBzYCAQcKJv3Ii+fg26+nX4B5yGWzup7b9dV1kuQD/Ojt88O3xtihbtvfcIcTTSd0RXSd8R3dGb/Uen0/SZAZl2E6zU1FQqKioIBoNotVqCwSCVlZWkpqa2Oi4x8X8bwc6YMYPU1FT27NnD5MmTSUtLo6ysrGXEq7y8nClTprS5ls1mw2azdfc1RZx+3RoA/GPGYVjyHr5z5vbtkSsANdS8hkYX0+FTonf/DlVjwnmMvYmaBt+Btmkf0XvuRAm6iDrwOKohgfqJHxAyZXQr5Jx0O/dfM5nCknoO1zZRXpPE/YfHMT/6r0zhL+RvWcJXzslckroYvdqIN20hrsF3EDKlt9/49wStI6mf9Ammg88Svfc+7DWfNz9uGoDfPpGAfWLzn9bRxy4l7mvAvvliNP4a6id80KGpkcekKAStIzt+vM6CP24m/riZ3bvu0Wh0+BO6tqmwEEIIIcTxoN0EKz4+ntzcXJYsWcK8efNYsmQJubm5baYHVlRUkJycDEBBQQFlZWUMGtQ87eiss87itddeY9SoURQXF7N9+3b++tfO77XTX+jXryVkj0G/fSvWX91C3ZJPCExum1D2KYoGd9atHT7cUPURxuqPcQ75I6ox+ZjtOvKeQusuIbroAQLmwTRM+ICQKa37MQN2i5EpI757/ZGooTM4VPxfhuy/nRGWl9hYP46d1l8ze9g56HXarl9M0eIZcD2e9B+ia9yKvmEDusaN6Bs2YKp4GwBV0RI0DyYYlU3Q/M1P1CCC5mxCxhRYeTlaVwENY18nYB/fvRcvhBBCCCH6HEVVVbW9g/bt28ftt99OY2MjNpuNBx98kOzsbK699lpuueUWRo0axW233cbOnTvRaDTo9XpuueUWZs5svkve1NTE7bffTkFBARqNht/85jfMnj27U4H2pymCsSdPJpiRSeOi19F/+Tn+0/r2HX1dw3qUQCP+uNM6trYo5CX266mAhrppX4PG0O4pircSc8kTuDOvJ2RKbff4cFC8FeAq5rWt8Xy0toQBSRZ+On8kybHh36xW8Vaib9yIrmE9OtdutE1FaJuKUEJNbY5tzHsKb9rCsMcgjn8yVUd0lfQd0VXSd0R3nKhTBDuUYPUF/SXBUupqSRiWhev2O2j65W97ObKusW39EbqGddSetBU6UD0wav/fsOy9m/pxb+NP6FyiHClb9lbz3JJ8giGVq8/JZeLwXqhMqapofBVomvajdRc1T5VMn0JV1Jyev7Y4LskXHdFV0ndEV0nfEd1xoiZYfXxhUP+j37AOAOPrr6Bf+WWEo+mYxlHP0DD+nQ4lVxpPOdH7H8KbeG6/Sa4AxuYkcPdVk0hLiOaJd3fw8rLd+Hs6YVcUQsYUArHT8KZdTlPOXTDg4p69phBCCCGEiChJsMJMv24tqlaLarEQigvfnkk9QlVBDYHGSNByhLLmRxC9+/egBnAO/VMPBxd+CfYobr98PGdOyuSzTaX8+eWNeP3BSIclhBBCCCGOI5JghZlu3RoCY8ZS/8kKgnmdqO4WAYbqj4hdMx2N+0DHjq/6CFPFWzRl/ZKQOUz7JvUynVbDZacP4YZ5eewvd/DxupJIhySEEEIIIY4jkmCFk8+HfsM6/OMmdmsj2t6iagwETRmEjO1X9FMCDiyFvyQQnUvToF/1QnQ9a3JuMhOGJrJ0TQn1Tm+kwxFCCCGEEMcJSbDCSL9yOYrfj/ZAcaRD6RB//Ok0jnsTNPp2j43eey8aTxmOEY91qGpgf3DxrMEEgiHeXVkU6VCEEEIIIcRxQhKsMNLt2gWA67e/j3Ak7QgFMJa/ASF/hw7X1a/FdPAZ3JnXEYjp4/t5dUJyrJnTxmewcls5pZXOSIcjhBBCCCGOA5JghZF+3RqCA7MIjh0X6VCOyVj5HrYd12Co+az9g0NerPk3ETKlN1fBO86cNyOLKIOO17/YG+lQhBBCCCHEcUASrDBRGuoxLP+MwLDhkQ6lXd7k+dRP+BBfQvv7MZn3P4LOtQvn8EdQddZeiK53WaL0nDcjix37a9lRVBPpcIQQQgghRD8nCVaYaPYXoTQ1EbLaIh3K0akqir8eFAV/3EntFuLQOgsx7/8LnpSL8SWe1TsxRsBp4zNIjDHx2hd7CYX6xb7bQgghhBCij5IEK0zU2DgA/DNOjnAkR2c8/Dpxq8eide1u/2A1hDX/JlSdBeewB3s+uAjS6zRccmoOZVUuVm47FOlwhBBCCCFEPyYJVphoaqoBUJOTIxzJ0QWso/EmzydoHtzusabSZ9E3rMM59AFUQ2IvRBdZE4YlkpNh552V+/H4ApEORwghhBBC9FOSYIWJfnlzwYhQTGyEIzm6oCUXZ+7fQNEe8ziNp5ToPffgi5uFN/UHvRRdZCmKwoLTcmh0+Vi6JvybD6uqyta91byzfC/b9lVT3eBGVWU6ohBCCCHE8UYX6QCOF/ptWwEIJadEOJK29LVfYqhaiivnLtCaj3iM4q9DX/c1+roVGKs+RFFDOEY82i82TA6XwWl2Jucm8fG6EmaOTSPOZmp5TlVVqhs8FJbUUVzuIDvNxpQRyei07d+jOFjp5JVPd1NYUt/qcaNBS1q8mbSEaNISohmSEUNOuj3cL0sIIYQQQvQiSbDCxD9+IsYPlxBK6HvT6fT16zBUL8OVc3fLY0rAgb5uNfq6lehrV6JzbEVBRdWY8MdMwTnsQUJRWZELOkIumjmYTbureGdFEefNyKKwpJ5dJfXsOlhHbaMXaF6z9cXmMt76ch+zJ2Zy6tg0zKa2mzU3uny8s7KIFVsPEW3Sc8WZQ5kzPZudeyo5VO2irNrFoWoXO4pqWb39MACnj8/g0tMGo9cde5RRCCGEEEL0TZJghYmmuhrVHA3mI48QRVJT9m9oGngjaKMAUPy1xH49Ha33EKpiwB8zmabs2/HHnozfPhG0pnZaPH4lxkQxe2ImH60tYfWO5qTHatYzLDOGs6fEMmxADGkJ0eTvr+WjdSW8uXwf739VzMmjUzlzYiYJMVH4AyE+3XiQJV8V4/OHmD0hk/NPyiLapCfGamRoZgxDM2NaXdfp9vP+6mI+2XCQ3aX13DAvj9T46Aj8BoQQQgghRHdIghUm+hXLUU19KzFprhaoEIweAtr/fVmP3nMPGl8FDWNewRd/WkviJZqdNz2LUEglOTaKYQNiSY03o3xvquTI7HhGZsdTUuHg43UH+WJTGZ9tLGX80EQOVjiprHczenA8C07L6VCiZInS84PZQxiRFctzHxRw7wvrWTh7KCePTm1zbSGEEEII0Xcpaj9ZaV9X5yIQCEU6DAASE61UVTlaPRY/OAOMBmryiyIUVVv2TfPRunZRO2MraJqnsOnq1xK7/gyaBt6Ma+gfIxzh8aO20cNnG0tZvuUQsVYjl52Ww8js+DbHHanvfF+dw8uzS/IpOFDH5NwkfjRnOGaT3AsRHes/QhyJ9B3RVdJ3RHf0Zv/R6TTExvaN2T/yrS1MggOzCKWlRTqMVhrznkLXtK8luSLkx1pwK0FjOq7s30U2uONMnM3EJbNyuOjUwWi6OeIUazXyqwVjWbr2AO+s2E/RoUaun5fH4DQpgCGEEEII0ddJmfYw0dRU95kCF4q/AVQV1ZiMP3Z6y+NRJU+hc+7EOfwh0FkiGOHxq7vJVUs7GoVzp2Vx+xXjUVX486JN7NhfE5a2hRBCCCFEz5EEKww0ZaVoDpdDqA9MYQz5sW88H0vBra0e1nhKiS76E96Es/Alzo1MbKLTctLt3Hv1JFLjo3ny3R2UVjkjHZIQQgghhDgGSbDCQFNyAEVVwWiMdCigaPCmzMcXf3qrhy27bgM1hHP4wyfU3lbHA7NJz62XjMag1/LoG9tocPkiHZIQQgghhDgKSbDCQE1KAsA/eWqEIwEULe6sW/Eln9/ykKFqKcbK93Fl30YoamAEgxNdFWcz8fOLR+Nw+3jszW14/cFIhySEEEIIIY5AEqwwUKqb18ZEdA1WyItty0J09WtbPx5swlL4WwLRw3EPvCkysYmwyEqxcf15eRSXN/LsknxCHSgAWl7jwt9Hqm8KIYQQQpwIJMEKA+P77wIQik+IWAwaTyk65w40/rpWj0cXPYzWcwBn7t9AY4hQdCJcxg1N5NLTcti4q4q3lu876nG7Sup4+JXN/OGZtbz6+Z5ejFAIIYQQ4sQmZdrDQFt6EPjfVMFICJkHUzt9PWj+tw5M6ywk6sCjeNIuxx87I2KxifA6c1ImlXVulq4tISk2iplj0wFQVZXCknreW7WfXQfrsUUbGJxuY+XWQ8ydlkWstQ+sERRCCCGEOM7JCFYYBEaNBiAU13Zj2R4XdGM6+ByowVbJleKrxlrwc1StBeeQ+3s/LtFjFEVh4RlDGJkdx38+3s3O4lryi2t58OVNPPzKZg7XNfGD04fw4A3TuP68PFQVlq45EOmwhRBCCCH6lP3797NgwQLmzJnDggULKC4uPuqxRUVFjBkzhgcffLDddmUEKww01VWE7DFg6P0peKbDb2It/AUB60gC9okYqj/FdGgRhqoPUVQ/jXlPohoiN3VR9AytRsNP543kgUUb+dtrWwmpKjEWAwtnD+GUMWkY9FoAjDFRTMtL4cuthzh32kDsFhnFEkIIIYQAuPvuu1m4cCHz5s1j8eLF3HXXXbz00kttjgsGg9x9993Mnj27Q+3KCFYYGJZ9hKrr4VxV/V+hAkPVR0Tvuh0AT9oV1I9ehKH6Y+JW5mHfcgn6ulW4M6+ndtpavGmX92xcImKijDp+fvEYRmXHcfkZQ3nwhmnMnpjZklx969zpAwkEQ3y87mCEIhVCCCGE6FtqamrIz89n7tzm/WHnzp1Lfn4+tbW1bY59+umnOfXUU8nKyupQ2zKCFQaKw4EaHd1j7ZsOPo1lz91Uz9wPWhM65w6MFe/gt00gqux5DHWrUNHgSzgD57CH8CWeJQUtThDxdhM/v2TMMY9JjjUzZUQyX2wu4+ypA7Ca2+8by9YfpLi8kQtPySYhJipc4QohhBBC9Kjy8nKCwdbb2dhsNmw2W5vjkpOT0Wqbb0xrtVqSkpIoLy8nLi6u5bjCwkJWrVrFSy+9xBNPPNGhGPpNghUb23MJTFckJlr/95f0NBg6tPVj4aROAuWnJMbpQVHBagOdEfuOayB6EIz5I8qgKzGa05EJYH1fj/WTY/jD1Z3bo+3yc0b0UCSiuyLRf8TxQfqO6CrpO6I7erv/XH755ZSVlbV67KabbuLmm2/udFt+v58777yTBx54oCUR64h+k2DV1bkI9JH9fBITrVRVOVr+Hl9RgXfCFJzfeSxsVBWUcWji44hadwemspfQBB34YqbjHvNHfInngKIFF+DqgeuLsPp+3+lNT7y7gx1FNTx843SiTfojHlNZ7+a+59eTEGPihnkjeXP5PjbtriI13swVZwwlNyvuiOeJ3hHJ/iP6N+k7oquk74ju6M3+o9NpiI2N5uWXXz7iCNb3paamUlFRQTAYRKvVEgwGqaysJDU1teWYqqoqSkpKuO666wBobGxEVVWcTif333/0InL9JsHqq7T5O1GqqyHgD3/jQTfxK4YRjMpC59gGigZv8nzcA35GwD4+/NcTx7XzpmexobCSTzeUMu+kQW2e9/mDPPH2dgBunD+KpJgobrpwFNv2VfPyJ7t5+NUtTM1LZsGsHCmWIYQQQog+6bsJ0rHEx8eTm5vLkiVLmDdvHkuWLCE3N7fV9MC0tDTWrl3b8vd//OMfNDU1cdtttx2zbSly0U1KdTUKEIoPf4l2JdjUkly5s26l9qTtOEY9J8mV6JLMJAtjcxL4ZP1B3N5Aq+dUVWXRst2UVDq59rwRJH1n3dXowQncf80Uzp/RnKD9/pk1fLml7PvNCyGEEEL0K/fccw+LFi1izpw5LFq0iHvvvReAa6+9lu3bt3e5XRnB6qZvNxcOjjp2oYEutW2IR9XHErCOxjXknrC3L048583I4v4XN/D5plLOnZbV8vjKbeWs2l7OedOzGJPTtqy/Qa/lgpOzmZaXwosfFfLiR7vIyYghPaFvrY0UQgghhOiowYMH88Ybb7R5/Jlnnjni8R1dxyUjWN2kqa4CIJSQGPa2FX8DOscWArbwJ2/ixDQo1cbI7Dg+XncQr695fnLx4UYWLdtN3qC4I04d/K7kODM3XDASnVZh+WYZxRJCCCGE+D5JsLrJ9N/mzch6IsGKXXsKGn8dAaskWCJ8zp8+CKfbzxeby3C6/fzz7R3Yo/Vcd94INBql3fNtZgMThyXx1Y7yliRNCCGEEEI0kwSru7w+oAcSLFXFF3cKAAHr6PC2LU5oORl2cgfG8tG6Ev713k4aXF5unD+qQ/tjfWvW+HTc3iBrCyp6MFIhhBBCiP5HEqxuCg4ZiqooqLGx4W1YUQgZklHRELCODG/b4oR33vQsGl0+du6vZeHsoQxKbVu+9Fhy0u1kJEbz+aZSVFXtoSiFEEIIIfofSbC6SVNdjRofD53YfKxD7XpK0TVsIhg9DLTmsLYtxLABMUzLS+bMSZnMHJvW6fMVRWHWuHRKKpwUlTf2QIRCCCGEEP2TJFjdZHz/XVSl/XUrnWXZdTuGui+kwIXoEYqicO15eVx2+hCULvbfqXkpGA1alm+SYhdCCCGEEN+SBKu7NBpUe0zYm3WnLEBRg7L+SvRZUUYd0/NSWFtQidPdAxttCyGEEEL0Q5JgdVMoJoZA3qiwt6tojQAErGPD3rYQ4TJrXDqBYIhV28rbPbbJ42fRsl2UVDh6ITIhhBBCiMiQBKubNNXVqAltN2btVpvuEgzVHwMQsIY/eRMiXDKSLAzJsLN8SxmhYxS7CIZCPLV4J59vKuPhVzZLkiWEEEKI45YkWN2gW7UCTUM9+MM7PcpU9hKmg88QNA1E1dvD2rYQ4TZrXDqVdW7yi2uPeswbX+xjx/5a5p00CKNBy19e3UJppbMXoxRCCCGE6B2SYHWDEggAEEzPCGu7noyrCBmS8NvHh7VdIXrChGFJWM16vjhKsYuVWw+xbP1BTp+QwbyTBvGbH4xDr9Pw8KubKauSJEsIIYQQxxdJsLohFN88NTA4ZFhY21W1ZrS+SgJWqSAo+j69TsPJo9PYsrea2kZPq+d2H6znpY93MSIrlstOzwEgOdbMb38wDo1G4eFXNnOo2hWJsIUQQggheoQkWN2gqa4CIJSQGLY2FV8VUQf+CSAl2kW/cerYNFDhyy2HWh6rbnDzz3e2k2A38dMLRqLV/O/tJjmuOclCaU6yymskyRJCCCHE8UESrG6IevIfAKiJ4StyYahdSfT+hwBkBEv0GwkxUYwaHM+KbYcIBEN4fAH+8dZ2AkGVWy4eTbRJ3+ac1PhofvODcaiqysOvbKairikCkQshhBBChJckWN2gxsQC4R3B8ibNxRs/m6AhDdUQ3uqEQvSkWePSaXD62LS7imeXFFBa5eSGeXmkxkcf9Zz0hGh+/YNxBIIqD/13sxS+EEIIIUS/JwlWN4QGDETV61FtYaz0pzGg9RwkYB8bvjaF6AWjsuOJt5l4YWkhm3ZXsWBWDqOy49s9LyPRwm9+MA5/IMTd/17HP9/ZzoHDUsZdCCGEEP2TJFjdoFRXNRe6UJTwNKgGMe/7I1rXLgLW0eFpU4heotEonDouDY8vyEmjUjljUmaHz81MsvDHa6dw7vQs8ovruPeF9fz9ja3sLW3oVkw1DR42FFaiHmOPLiGEEEKIcNJFOoD+zPTGq4Ri48LWnrZpP+aih1CAgHVs2NoVorecMTGTOJuJicOSUDp548FqNnDhKdmcNXkAn28qZdn6g/xp0UaGD4hh7vQscgfGdqrN3Qfrefzt7Tjdfm6/fDxDM2M6+WqEEEIIITpPRrC6KhgkFBdHKDk5fE1G5+DMuQ+QCoKifzLotUzLS0Gv6/pbi9mkY+70LB7+6XQuOy2H8tom/vLqFh5+ZTNlHSzpvmLrIR5+ZTPRUXqiTTo+2XCwy/EIIYQQQnSGJFhdpdWCMYrg0OFhbVbXtJuQPoGQMS2s7QrR3xgNWs6cPICHbpjG5WcM5WClk3v+vY43l+/D6w8e8ZxgKMQrn+7hhaWFDB8Yyx0/msApY9PYtLuK6np3L78CIYQQQpyIJMHqqmAQTVVlWCsImvf+H/raFQRso8O3rkuIfk6v03L6hAz+eN1UpuYl8+GaA9zxzFq27KludVyTx8+jb2zjkw0HmT0xg1svaS4Pf/r4DBQUPttUGqFXIIQQQogTiSRYXWRY8h6KuwmCgfA0qIYwlb+K1lMi66+EOAKb2cA1547g9svHYzJoeeytbfzjrW3UNHioqG3i/17aSMGBOq48axgLZw9t2dg4zmZi4vBEVmwtx+0N0/+vQgghhBBHIQlWF6k2GwChrEHhaVDR0DhmEQoqfll/JcRRDc2M4e6rJnHJqYPZWVzLH55dw30vbsDp9vPry8Yyc2x6m3POmJSJ2xtg9fbyCEQshBBCiBOJVBHsIjUmBoDgwDAlWICucSuAlGgXoh06rYazpw5kUm4Sr362lzqHlxvm5ZEYE3XE4wen2RmcZuPTDaWcNiEDjUzBFUIIIUQPkQSrizSHygAIJSSEpT1z0cMYKt8npLMRigpf0ibE8SzBHsVNF47q0LFnTMrkqcU72ba3hrFDwvP/rRBCCCHE93VoiuD+/ftZsGABc+bMYcGCBRQXFx/12KKiIsaMGcODDz7Y8tjtt9/OKaecwrx585g3bx5PPvlktwOPNPM//g4QtiIXGncxWs+B5tErRWZuChFu44cmEms1Ssl2IYQQQvSoDn2Tv/vuu1m4cCEff/wxCxcu5K677jriccFgkLvvvpvZs2e3ee66665j8eLFLF68mJ/+9Kfdi7oPCGTnAOFLsJy5j6IEPQSssv5KiJ6g02o4fUIGBQfqOFjpjHQ4QgghhDhOtZtg1dTUkJ+fz9y5cwGYO3cu+fn51NbWtjn26aef5tRTTyUrK6tLwTQ2NlJaWtrqp7Kysktt9TQ1MRE1Kgqio8PSnrZpD0rILeuvhOhBp4xJw6DXyCiWEEIIIXpMu2uwysvLSU5ORqvVAqDVaklKSqK8vJy4uLiW4woLC1m1ahUvvfQSTzzxRJt2nn/+eV577TUyMzP51a9+xeDBg9sc8+KLL/L444+3emz8+PG88sorxMaGJ5EJF3P1YUhMJDHR2v3G9jwFu/8JgG3gDIgJQ5uizwpLnxFdkgicPmkAn64r4foLxxBjNUY6pE6T/iO6SvqO6CrpO6I7TsT+E5YiF36/nzvvvJMHHnigJRH7rl/84hckJiai0Wh49913+clPfsKnn37a5tgrr7yS+fPnt3rMYDAAUFfnIhAIhSPcbkuMjUJ94w1CKSnUVjm63Z7J4SLK50SriaLamwZhaFP0TYmJVqrk3zeiTspLZulXxbz16S7OP6l/FZSR/iO6SvqO6CrpO6I7erP/6HSaPjMg026ClZqaSkVFBcFgEK1WSzAYpLKyktTU1JZjqqqqKCkp4brrrgOap/qpqorT6eT+++8nOTm55dgLLriABx54gMOHD5Oe3nq/GpvNhu2b/aX6tFCIUFo6wYyMsDTnybwOY8W7qIZE0EhhRyF6Ump8NKOy4/l8cxlnTx2IXidFZYQQQggRPu1+s4iPjyc3N5clS5YAsGTJEnJzc1tND0xLS2Pt2rV8/vnnfP7551x55ZVceuml3H///QBUVFS0HLty5Uo0Gk2rpKvf+WZULfRNoYtuU0PoHNtk/ZUQveSMSRk0unysK6ho81wwFKK8xsWWPdUcqnahqmoEIhRCCCFEf9Wh4ZJ77rmH22+/nSeeeAKbzdZSgv3aa6/llltuYdSoY+9Dc9ttt1FTU4OiKFgsFp588kl0un48UuNwoKmqJBQX3/22Ak7ivp6CJtBIwDq2++0JIdqVlxVHaryZZesPEm3SU1btpKzKRVm1i/IaF4Hg/5IqS5SeIRl2hmTEMDQzhgHJFnTazo16VdQ1sa6gkvUFFfiDKr/9wThi++H6LyGEEEK0T1H7ye3ZPrUG691X4brrcN10K0133detthRfFbYtCzE0rKVuygoCtrHhCVL0STKXve9YvqWMlz7a1fL3eJuRtAQL6YnRpCdEkxxrprzGxZ7SBnaX1lNZ5wbAoNcwOM3OgGQLKXHmlh9btAFFUVraq2nwsL6wkrUFFRw43PxvnpNh52Clk6SYKG5bOB6zqXM3mqT/iK6SviO6SvqO6A5ZgyU67psy9MFB2d1uSjUkEoidgb5xIwFLbrfbE0J0zCmj07CY9MRYjaTFRx8x2cnJsHPymDQA6p3e5mTrYD17Suv5bGMZgeD/bvpEGbUkxzYnW9UNHvaWNQCQlWLl0lk5TM5NIs5mYsf+Gh59Yxv/fGc7t14yRtaACSGEEMcZSbC64pu9r0LfK9LRVVrnToLRw0AjU4aE6C0ajcLE4UkdPj7GYmTS8CQmfXNOSFWpbfBwuK6JwzVNVNS6OVzrYk9pPWaTngtPyWZybhJJseZW7YwcFM9V5wzn2SUFPPdBPtedn4fmOyNfQgghhOjfJMHqip07AVATErvdVPTuP2CoXY4v4axutyWE6D0aRSEhJoqEmChGDurceszpI1NpcPp4Y/k+YixGLjt9SA9FKYQQQojeJglWVzz2GAChMCRYAfMwCPkImsNUkVAI0S+cNWUAdQ4vy9YfJMZi5KwpAyIdkhBCCCHCQCb/d8XkyQCE4hO63ZQ/bgYKKgHz4G63JYToPxRF4bLZQ5g4PInXv9jLmp2HIx2SEEIIIcJAEqyusFgIWW1g7OaaKVVF59wNQDBaRrCEONFoFIVr5+YyLDOG5z4oYGdxbafOD4VUDlW72FBYSZPH30NRCiGEEKIzZIpgZ6kqFBYSio3tdlMa72Fs2y4HICgjWEKckPQ6LTdfNIo/v7yJv722laTYKJJjo0iOM3/nTzN2i4EDhxvZnH+YA4cdFFc4OFjhxOsPAjB6cDw/v3h0q1LxQgghhOh9kmB1kuJohGXLUDK7v15CVbQEonPRuvej6rs/3VAI0T+ZTXp+tWAsn24sba5IWNdEwYE6fEfZ+8+g1zAg2cpJo1PJSrFSVe/mvdXFfLn1EKeODU91UyGEEEJ0jSRYnaQajDBgAMGsMOyBZUxCNSQQ1OhA7joLcUKzW4xcNPN/I9khVaXe4aWitomKOjd1Di85A+OIi9aTGmdGo1FaHbuntIFXP9tD7sBYkr9XGl4IIYQQvUfWYHWWyQReL8GBWd1vK+hG69orFQSFEG1oFIU4m4ncrDhOHZfO/FOyOW1iJukJ0a2Sq2+PvebcXHQaDc8uyScYOvLIlxBCCCF6niRYnaQcLofKSkIxMd1uy5p/MxrvQVl/JYTotjibiSvmDGVfWSMfrimJdDhCCCHECUsSrE4yvfl6c6ELc3S32/LbJ6KAjGAJIcJi6ogUJucm8d6q/Rw47Ih0OEIIIcQJSRKsTvKPGQsQlimCIVNGc1uSYAkhwuSKM4dhNet5+v2d+L6pMCiEEEKI3iMJVicpWi0AoaTk7jUUCqBr3AJIiXYhRPhYovRcfW4u5TVNvPVlUaTDEUIIIU44kmB1km79WgBCCYndakfrLiJ6/0OEtFZUvT0coQkhBAAjB8Vz+vgMPtlwkPxObl78rUAwhNMtmxcLIYQQnSVl2jvJ9PJLQPcTrJA+lkDUIFSdJFdCiPC7eNZgdhbX8twHBdx/zWTMJn2Hz91ZXMuiZbupqG1iSIadaSNTmDQ8iehOtCGEEEKcqGQEq5N8M2cBoMbFdasd1ZCIEnQTsI4MR1hCCNGKUa/l2vNG0OD08cf/bGTF1kP4A8dek1Xb6OGJd3fw11e3oIZUzp02EKfbz0sf7eIX/1jFP9/ezsZdVfiPsgGyEEIIIWQEq9MURYH4eNB171ende5C6zss66+EED1mUKqNn104ksUr9/PC0kLe+nIfs8alc9r4DGzRhpbjAsEQn2w4yHurigmpKhecPIizpwxAr9Ny4SnZHKhw8NWOw6zLr2Dj7iqiTTqmjEjmwlMGYzbJx4gQQgjxXfLJ2Bl+P7rt2yA2tttNWQp/BUgFQSFEzxo3JJGxOQkUltSzbF0J760u5sM1JUzLS+bMSZk0Nvl5+ZPdHKp2MTYngctmDyEpJqrlfEVRyEqxkZViY8FpOeQX1/H1jsN8ueUQ+cV13HzRKFLju79tBUCTJ4A/EMRuMYalPSGEECISJMHqBE1VJfqN62Ho0G635Y+ZhqFuhYxgCSF6nKIo5A6MJXdgLOU1Lj7dUMrq7eWs3FYOQILdxC0XjWbskIRjtqPVaBiVHc+o7Hhmjk3jn+/s4P9e2sj15+cxenB8t2LcfbCex97cRpM3QGq8mRED48jNimX4gJhOrR8TQgghIk0SrE4IJSQSyByAbtiw7jemaf7CEDRnd78tIYTooNT4aH44ZxjzT8lm5dZDoMDp4zMw6LWdamfYgFju+vFEHn9rO4++sZWLTx3MWVMGNE+j7qTNe6p4avFO4m0mzp46gMKSelZuO8Rnm0pRFMhKsZI7MI5pI1NITwjPaJkQQgjRUyTB6gyDAY3TAZmZ3Wsn6EbXsI6gIRW05vDEJoQQnWCJ0nP21IHdaiPBHsXvfjiB5z8s4I3l+yipdPLjs4dj7ESytnLbIV5cuouBKVZuvWQ0VrOBc6eBPxCi6FADBQfqyD9Qx8frSvhySxkP/XQ6UUb56BJCCNF3yadUZ/j9aOrqICmpW83onPkYq5fht+SFKTAhhIgMo17L9efnkZlk4e0viyivcXHzhaOJt5uOeZ6qqny45gBvfVnEyEFx3Dh/JCbD/z6S9DoNwwbEMmxALBecDPvLG7n/xQ18sv4g5580qKdflhBCCNFlUqa9EzS1Nc3/0c0EK2jOJqSNJmAZHYaohBAishRF4dxpWdxy8Wiq6t3c9+J63vpyH3tK6wmG2pZ0D6kqr362l7e+LGLKiGRuuXh0q+TqSAal2hg/NJGP15fIBshCCCH6NBnB6gRVp0c1GlFGjepmQyE0QRdBWzfbEUKIPmRMTgJ3/Ggii5btZumaEj74+gDRJh15g+IYPTiekYPiMZt0/PvDAtbsrGD2xAwuO30Img6u27rgpEFs3l3Fx+tKuGimFAgSQgjRN0mC1QlqfDzVRYdITIuDKkeX2zFUfQQgFQSFEMed1PhofvODcTR5/OwsrmPbvmq2F9WyrqASBbBbDNQ7fVw0M5tzpg7sVFGMjCQLk3KT+HRDKWdMzGy1l5cQQgjRV0iC1Vn67pcLNh94FJA9sIQQxy+zSc+k4UlMGp5ESFU5WOFk275q9pQ2cNHMZGaMSu1Su/NOGsT6wko+XHOAy04fEuaohRBCiO6TBCsCfDHT0Lp2EYzqXgUvIYToDzSKwsAUKwNTrN1uKzU+mukjU/hicxlzJg8g1iqbEgshhOhbpMhFBGgCDYSiskAj01uEEKKzzp8xiFBI5YOviyMdihBCCNGGJFi9TPHXoWvYRNDUzb20hBDiBJUYE8XJo1P5csshqhvckQ5HCCGEaEUSrF6ma9yCzlNMSGePdChCCNFvzZ2ehaIovL+6ONKhCCGEEK1IgtXLglFZAPhjJkc2ECGE6MfibCZOHZvG6u2Hqahr6nI7FbVNhEJqGCMTQghxopMEq5dpvYcACFpHRjgSIYTo386dNhCdVuG9Vfu7dP7O/bX87uk1PPdBPiFVkiwhhBDhIQlWLzNULAZkDywhhOguu8XI6RMyWLOzgrJqV6fODYVUXvt8Dwa9hq93VvDW8n09FKUQQogTjSRYvcxY8Q4qCiEpciGEEN121pQBGAxa3l1Z1KnzVm0vp7TKxdXn5DJrfDpL15bwyYaDPRSlEEKIE4kkWL0sYB1DMGoQKNpIhyKEEP2e1WxgzqRMNu6qYsve6g6d4/EFeGdlEYPTbUwansTls4cyfmgir366h3UFFT0csRBCiOOdJFi9TOs5SNCSG+kwhBDiuHHutCwykyz8+4MC6p3edo//aG0JDU4fC04bgqIoaDQK1503gsEZdp5dkk/hgbpeiFoIIcTxShKsXqRxH0TbtIegISXSoQghxHFDr9Nw/fl5+PxBnlty7IIVdQ4vH60rYdLwJHLS/7ddhkGv5ZaLRpMYE8U/3t5OaaWzN0IXQghxHJIEqxfpaz5FUQOo+thIhyKEEMeVtIRoLjt9CDuL61i27uhrqd5ZUUQopHLxqW0LDVmi9Pzy0rEY9RoeeX0LNQ2engxZCCHEcUoSrF4UMg0AwB97UoQjEUKI48/MsWmMG5LAW1/u48BhR5vnSyocrN5ezukTMkiMiTpiG/F2E7+8dCxef5BHXt+C0+3v6bCFEEIcZyTB6kVad3OVK1mDJYQQ4acoCledk4vVrOdf7+3E6wu2PKeqKq9/sRezScfc6VnHbCcjycLNF46mqt7NH1/awMFenC7oD4R67VpCCCF6hiRYvchQ+R6qxkjIKGuwhBCiJ1ii9Fw7dwQVtU288tmelse3F9WQX1zH+ScNItqkb7ed4QNj+fVl4/D4g/zfSxtYufVQj8Rb5/Dy9Y7DPP9hAbc99RU//euX7D5Y3yPXEkII0Tt0kQ7gRKJv2IiqMYGiRDoUIYQ4buVmxXH21IF8uOYAIwfFMW5oAq99vpek2ChmjUvvcDtDM2O456rJPP3eTp5fWsju0nquOHMYRn3Xt9mobfSw+2A9hSX1FJbUUVnnBsBs1DFsQAw+f4jXPt/LHT+agCKfFUII0S9JgtWLQvpEAtaRkQ5DCCGOexecPIiCA7W8+FEhJZUZlNc08bP5o9BpOzdxwx5t4FcLxrJ41X6WfFXMgcMObpw/ipQ4c7vnBkMhSitd7C1rYE9pPXvLGqhtbC4jH2XUMSwzhtPGpTNsQCyZSRY0GoWV2w7x/IeFrC+sZHJucpdeuxBCiI7Zv38/t99+O/X19cTExPDggw+SlZXV6ph//vOffPjhh2g0GvR6Pb/4xS84+eSTj9muJFi9JeRH6y3Bm3pRpCMRQojjnk6r4brz87jn3+tZ8lUxQzPsjB+a0KW2NBqF+adkk5Nh55n387n3hfVcdfZwJucmE1JVHC4fNY1eahs91DZ6qGn0UlbtZN+hxpZ1YLFWIznpduZMtjM0I6Ylofq+GSNT+WT9Qd76ch/jhiSi18lMfiGE6Cl33303CxcuZN68eSxevJi77rqLl156qdUxo0eP5uqrryYqKorCwkKuuOIKVq1ahclkOmq7kmD1En3NFyhqkJCUaBdCiF6RHGvmR3OG8fInu1lw+pBuT7kblR3PPVdN4snFO3hq8U7e+GIfDS4vgWDrfbeMei3JsVFMH5nCkHQ7ORl24m2mDl1fo1G4dFYOj7y+lS82l3HmpMxuxSyEECea8vJygsFgq8dsNhs2m63VYzU1NeTn5/P8888DMHfuXO6//35qa2uJi4trOe67o1XDhg1DVVXq6+tJSTl6TYV+k2DFxkZHOoRWEhOtnTuhbBMA1oQcrJ09VxxXOt13hPgO6T+dc/4sK+fPGhK29hITrfz9l7PC1t6RzEq0MmtKVtjblb4jukr6juiO3u4/l19+OWVlZa0eu+mmm7j55ptbPVZeXk5ycjJabfO6Wq1WS1JSEuXl5a0SrO969913GTBgwDGTK+hHCVZdnYtAHylfm5hopaqq7R4rxxIVsGABqnWTUDt5rjh+dKXvCPEt6T8njpIKB/c+v56zpgzgklk53W5P+o7oKuk7ojt6s//odBpiY6N5+eWXjziC1V3r1q3j0Ucf5d///nf7sXT7aqJDtE37COliUI1dWwMghBDixDEg2cq0kSl8sqGUWePTSbAfeWNkIYQQraWmpnb4uIqKCoLBIFqtlmAwSGVl5RHP37x5M7/5zW944oknyM7ObrdtWT3bSwzVnxDS2yMdhhBCiH7iwlOyURR4Z0VRpEMRQojjTnx8PLm5uSxZsgSAJUuWkJub22Z64LZt2/jFL37BY489Rl5eXofalgSrFyieMjSeg6j6+EiHIoQQop+Is5k4Y2ImX++s4MBhmaIlhBDhds8997Bo0SLmzJnDokWLuPfeewG49tpr2b59OwD33nsvHo+Hu+66i3nz5jFv3jx27dp1zHYVVVXVYx7RR/TnNVjmogeJ3vdHaqd8RdAm+2CdyGQuu+gO6T8nniZPgNv/9TWZSRZ+fdnYI1YiVFWVw7VNxFlNGA1H3gRZ+o7oKuk7ojsisQarL5A1WD1M49pDVMnT+OJnS3IlhBCiU8wmHefNyOKVT/ewvaiW0YObZ0IEgiF2Haxn8+4qtuytprbRS3JsFLdeMobkDmyC3BE7i2tZvGo/N80fhS3a0OHznG4/j7+1jbOnDmRMjqw7FkKceCTB6kmqin3zJWj8VTQN+FmkoxFCCNEPzRqXzmcbS3lj+V48vgBb9lSzdV8Nbm8Ag05D3qA4zpiYyQdfH+D/XtrAzReNZmhmTLeuGVJVXv10D2XVLt5esY8fn53b4XPfXVnE7tIG6j/dw8jsOLQaWY0ghDixSILV41SCpkz88adFOhAhhBD9kE6r4eKZg3ni3eYNji1ReiYMTWTc0ARGZMVh1DdPCxw3JIG/v7GNh1/ZzNXn5DJt5LH3aTmWTbuqKKt2MSDJwsqt5cwcm86g1PbLHJdWOvlicxkDU6wcOOzgqx2HOXl0WpfjCBdVVbu90bQQQnSU3FbqQfq6Fejc+2nKvh3kjV0IIUQXTRiWyLVzR3D75eP5+80ncfW5uYwbktiSXAEkxZr5w48mMCTDzjNL8nl3ZRFdWWYdUlUWr95ParyZ3ywchzXawH8/3U2onbZUVeW/n+7GbNTxqwVjyUqx8v7qYgLByK6fLq10cttTX7O9qCaicQghThySYPUQ4+G3sRTcSkifgCflkkiHI4QQoh9TFIVpI1MYmhmDRnP0G3bRJj2/XDCWGaNSeG91Mc+8n48/EDzq8UeyaVcVZVUuzpueRbRJz8UzB7OvrJE1Ow8f87wNu6ooLKnnwpmDsUTpueDkQVQ3ePhqx7HP62mvL99LdYOHF5YW4vYGIhqLEOLEIAlWDzFUf4yuaR/ujJ+A1hTpcIQQQpwgdFoNV5+Ty4WnZLMmv4KHX91Cg9PboXNDqsp7q/eTEmdmcm4yANNHpTAo1cYbX+w7aoLi9Qd57fM9ZCZZmDmmeUrgqOx4stNsvL96f1hGsUKqitPt79Q5BcW17CiqZWpeMvVOL28s39ftOIQQoj0dSrD279/PggULmDNnDgsWLKC4uPioxxYVFTFmzBgefPDBlsfcbje33norZ5xxBmeddRZffPFFtwPv61StGVUx4B5wbaRDEUIIcYJRFIW507O4YV4exeUO/vDk6g6N3mzaVUVplYvzZmS1jJRpFIXLzxhKg8vHkq+Kj3je0jUHqG30cvkZQ1vOUxSFC04eRE2jl5Xbyrv9mp55P5/fPvkVlXVNHTo+pKq8vnwfcTYjV509nDMmZrJ8cxm7Suq6HYsQQhxLhxKsu+++m4ULF/Lxxx+zcOFC7rrrriMeFwwGufvuu5k9e3arx5977jksFguffPIJTz31FHfccQcul6v70fdFQRdaRz6mQ//Fk7oA1ZAY6YiEEEKcoCbnJvPzi0dzsNLJ0+/tJBQ6+jqqb0evkuPMTPlm9Opb2Wk2ThqVyrL1Bzlc2zrBqa53s3RtCZNzk9pUL8zLiiMn3c6Sr4o7PVXxu9YXVrI2vwKvL8izHxQc83W0nFNQyYHDDuafnI1ep2X+ydkkxph4YWkhPn/XYxFCiPa0m2DV1NSQn5/P3LlzAZg7dy75+fnU1ta2Ofbpp5/m1FNPJSsrq9XjS5cuZcGCBQBkZWUxcuRIVqxYEYbw+x7zgSeIXXsSSsiNe6CUZhdCCBFZeYPiuO6CUWzdV8Obx5git3l38+jV+d8Zvfqui2Zmo9dpePWzPa0ef+2LvSgKXDorp805345i1Tm8rNjatVGsRpeP/3y8i6wUK1efm8ve0gY+WldyzHMCwRBvr9hHRmI00/KaqykaDVquPGs4FXVu3ltd3KVYhBCiI9pNsMrLy0lOTkarba5UpNVqSUpKory89RtlYWEhq1at4sc//nGbNg4dOkR6enrL31NTUzl8uO2i18bGRkpLS1v9VFZWdvY1RZQn6XxUTRS+uFkELSMiHY4QQgjBuTMGMWt8Oh+tK2HltkNtng+pKotXFR9x9OpbdouR82cMYtu+GrburQYgv7iWjbuqOHdaFnG2I683zh0Yy9DMGJZ8XdzpkSNVVfnPsl14fAGuOTeX6SNTmDg8iXdWFFFS4TjqeV9sLqOq3sPFp+a0ShZHZMVx0uhUPlpbwoHDRz9fCCG6Iyz7YPn9fu68804eeOCBlkSsK1588UUef/zxVo+NHz+eV155hdjY6O6GGVaJidYjP+EshKADw+jfHv0YcUKTfiG6Q/qP6KpbLhtPnaN5NGjYoATysuNbnvtq2yFKq5z8cuF4kpOPvt/VZWflsnrHYV5fvo+Txmfy+vJ9JMeZueKcERj0R//8//F5efz+idVs3FvD+acM7nDMKzeXsXFXFVeeO4KxI1IB+MXCCdz08Oe88NEuHrn1FPS61tdt8vj54OsDjM5J4LQpA9vsf/WzS8ayc//n/GfZbv566ynotFLvqz3yviO640TsP+0mWKmpqVRUVBAMBtFqtQSDQSorK0lNTW05pqqqipKSEq677jqgeSRKVVWcTif3338/aWlplJWVERcXBzSPik2ZMqXNta688krmz5/f6jGDwQBAXZ2LQCCye2l8KzHRSlXV9+58hXxYCn+Frn4dSvQw6nTT4fvHiBPeEfuOEB0k/Ud0VWKilbpaF9ecO5z/e2kj//fvtdxx5USSYqIIqSqLlhaQHGcmN8PWbh+7dNZg/vb6Vn792ApKDju4+cJRNNQfu/BEis1I7sBYXvt0N+Nz4lvt33U0DU4v/3xzS/P6r7ykVnFdedYw/v7GNp55exuXfG9q4tsr9tHo8jFvRhbV1c4jtr1w9hD++c4OFn2wk3OnZbUby4lM3ndEd/Rm/9HpNH1mQKbd2zbx8fHk5uayZMkSAJYsWUJubm5LsgSQlpbG2rVr+fzzz/n888+58sorufTSS7n//vsBOOuss3jttdcAKC4uZvv27Zx88sltrmWz2cjIyGj1k5SUFJYX2tO0TfsxHXoZvasA94CfycbCQggh+pxok55bLx6Nqqo89uY23N4Am3dXc7DSyXnTB6LVtD+aMyo7nrE5CRw47CBvUBxjhyR06NrzThpEo8vHF5vK2j1WVVVe+ngXXn+Ia87NbRPX6MEJzBybxkdrS9h9sL7l8TqHl2XrDjI5N4lBqUcfiZswLIkJwxJZvKqY8prjtOiWECJiOjQufs8997Bo0SLmzJnDokWLuPfeewG49tpr2b59e7vnX3PNNTQ2NnLGGWdw/fXXc99992GxWLoXeR8TtAzDF386IV0cntQFkQ5HCCGEOKLkODM3XjCSitomnlq8s7lyYGwUU0Ycee3Vkfxg9hDGDUngijOGtpmCdzRDM2PIy4pl6doDeHzHLhm/Nr+CzXuqufCUbFLjj3xHesFpOSTGRPHskvyWEvSLV+0nGFK5cGb70xCvOGMoBp2GF5cWElLbr0oohBAdpahq/3hX6fNTBANOEr7IwJ11C64h90UmMNHnyVQL0R3Sf0RXHanvLN9cxksf7wLgJ3NzmT4y9UinhtXesgb+9J+NjBkcz8yx6eQNim2zhqre6eXOZ9eSEm/md5dPOGJFw2/tKa3nzy9v4uTRqZw5aQB3PreW08dnsPCMoR2KZ+XWQzy/tJCTR6ey4LQczCZ9t17f8Ujed0R3nKhTBMNS5EKAteDnKITwx0yPdChCCCFEu04dl06tw8O+ssZOjV51R066nXOnDeTzTaVs3VeD0aBlVHY844cmMGZwAiaDlpc+2oUvEOKac0ccM7kCGJIRw9lTBvLhmgPsKW3AqNcyd0ZWh+M5aXQqZdUuPtlwkK17q7n0tBym5aV0eFROCCGORBKscFBV9HUrAfDbxkU4GCGEEKJjLuxERb9wuWjmYOadNIjCA3Vs2l3Fpj3VbCisRKtRyEqxsu9QI5edlkNKnLlD7V1w8iC2F9VwsNLJ/FOysZkNHY5FURQuO30I0/JSeOnjXTy7pIAVW8v54ZlDSU88vpYyCCF6j0wR7IIjDXdad1yHvvoLak/dc5SzhJCpFqJ7pP+IrurLfScUUtl3qIFNu6vYvLuapNgobr1kTLujV991uLaJL7eUccHJ2R2qUHjEOFSVlVsP8ebyfXh8Qc6YlMn5M7IwGU7se9F9ue+Ivk+mCIpu0TVuIWCX0SshhBCiMzQahSEZMQzJiGHBaUO61EZKnLnL57bEoSjMHJvO+KGJvLl8Hx+tLWFtfgU/OTeX3Ky49hsQQohvyO56YRBV9DBaVyEB29hIhyKEEEKIbrCaDVx1Ti6/v2ICJoOWf76zg9pGT6TDEkL0I5JghYHOVYgCBGT9lRBCCHFcyMmwc8vFowmGVJ77oEBKuQshOkwSrDDw2ycBELCOiXAkQgghhAiX5FgzP5g9hIIDdSxbdzDS4Qgh+glJsMJA37iFkCGJkLHn9xARQgghRO85eXQq44cm8vaKfZRUSLEHIUT7JMHqJkPFuxgr3sFvyQXZN0MIIYQ4riiKwpVnDSM6Ss/T7+fj8wcjHZIQoo+TBKu71CCE3ARskyIdiRBCCCF6gNVs4JpzczlU7eLN5fs6fJ6qqjjdfkoqHGzZU83nm0p5c/k+3vhiL02eQA9GLISIJCnT3k0hU2ZzgQv7+EiHIoQQQogeMnJQPLMnZvDphlJGD45nZHb8EY9rcPlYtr6ELXuqqWn04PO33sNTq1EIqSpFhxr55YIx6HVd27erNzR5/Lz0YT42k46cDDtJMVEoMltHiHZJgtUdqoqucTOAlGgXQgghjnOXnDqYguI6nvuggHuvmYzNbGh5rrrezdJ1JazaVk4gGGLkoHhGDoon3mYkzmYi1mYkzmrCHm1gfWElT7+3k6cW7+TG+SPRavrmhKIPvj7A0rUlLX+3mfXkZMSQk24nJ8POwGQrel3fjF2ISJIEqxs07iIse+4gpLURMqZHOhwhhBBC9CC9Tst15+dx/4vreXFpITddOIrymiY+XHOANTsrUBSYMSqFs6cMJDnOfNR2poxIxtHk47+f7uE/H+/iyrOG97mRIUeTj883lXHK2HRmT0hnb1kDe0ubfzbtrgLAoNOw8IyhnDImLcLRHpuqquw71MjAZEufHjEUxw9JsLpJ1UQRiB4uBS6EEEKIE0BmkoWLZg7mtc/38qdFGykqa0Sv13D6hAzmTM4kzmbqUDuzJ2bS2ORjyVcHsEUbuPCUwcc83u0N8NWOw5gMWkYOisNuMYbj5RzVJxsO4vMHufSMoZi1ChmJFk4d23wzucHpZW9ZA59vKuOFpYUAfTbJ8vqCvPRxIV/vrGDWuHR+OGdYpEMSJwBJsLohZExHCTrwx50U6VCEEEII0UvOmJTJzuJaisoamTs9i9kTM7B+Z7pgR80/OZtGl785yTIbmD0xs80x/kCQLzYf4oOvi3E0+Vsez0yykDcojpGD4hiSYQ/ryEyTx89nG0uZMCyRgSk2qqpal6e3W4xMGJbE6MHx/OOt7by4tBBFgZNH960kq7zGxRPv7OBQtYvMJAtfbjnE7IkZpMZHRzo0cZyTBKsbdA0bUNQgAevYSIcihBBCiF6iURR+fvFoVBV02q6vQVIUhR/OGdoyXdBi1jN1RAoAwVCIr7YfZvHq/dQ2ehmRFcv8U7LRaTTs2F/Dzv21fLL+IB+tLcGg0zBsQCxZKVZirEZiLAZiLEZiLEZs0fpOr/H6dEMpbm+QudOzjnmcXqfl5otG8dhb23nhw0I0isKMUX1jT9C1+RW88FEheq2GXy4YS2aShdv+9TVvf1nEzy4cFenwxHFOEqyuUlXsWy4BpMCFEEIIcaIJV2EKrUbDDfPy+OtrW3luSQEWkx6PL8g7K4sor2liUKqNq8/JZURWXMs5A1OsnDstC48vQGFJPTuLatlRXMuOohrU77WvANZoAyMHxXHVOcPbjdvtDbBs/UHGDUlgQLK13fj1Oi03XziKx97axr8/KEBRYPrI8CdZLo+fNTsrKC5vZFCajeEDYkmNN7dZu+YPhHj98718tqmUnHQ7N8zLa5m2ec6UAbyzcj97yxrISbeHPUYhviUJVlepfoLmYdC0h5Cp7ZC+EEIIIURH6HVabrloFH9+eTOPvL4VgLSEaH42fxTjhyYctQCGyaBjbE4CY3MSgOZRr0aXn3qnl3qnlwanj3qnl4o6N1/tOEy0Sc8PZg85ZiyfbyqlyRvgvBlZHY7foNdy80WjeezNbTz3QQGKojAtL6XD5x9NSFUpKK5j5bZDbNpdTSAYItqkY/WOwwDYow0MHxjL8AExDB8Yi1aj8OS7O9lf3siZkzK5+NTBrUYYz5w0gM83l/H6F3v53eXj+1xhEXH8kASrqzQGIEDAPlEKXAghhBCiW8wmPb9cMIZXPt3D6MHxTMtLQaPp3PcLrUZDrNVIrLVtAQxrlJ5PNhxkQLLlqNP4PL4AH687yKjseLJSbJ26tlGv5ZaLR/PoG1t5dkk+ikLLdMfOqq53s2p7Oau3l1PT6CXapGPm2DROGpXKgGQLVfVuCkvqKTxQR8GBOtbmVwDNUzeNBg0/mz+SCcOS2sZo0HLBSYN48aNdbN5TzfihiV2KT4j2SILVRRr3AXSOnbizfh7pUIQQQghxHIixGPnpBSN7pO1LT8uhtMrJix/tIi0hmkGpbROo5ZsP4XT7OzV69V1GvZafXzyGR9/cyjPv56PTaJg4vG2icyzvrCji/a+KUYC8QXFcMiuHcUMSWhXxSIo1kxRr5pQxaaiqyuHaJgoP1FFe28Tp4zOOWSL/pNGpLFt/kDeX72NMTnyf3YNM9G/Sq7rItv0nKATxy/orIYQQQvRxOq2GGy4YiT3awONvb6fB5Wv1vNcf5KN1JYzIiu3W+iSjoTnJyk6z8ewH+RysdHb43A2Flbz/VTFTRyTz0E+n88sFY5mcm3zMComKopAaH82s8RksnD30mMkVNI/yXXzqYA7XNrFya3mHY/s+VVUpqXB06vWJE4ckWF3kt40BpMCFEEIIIfoHm9nAzReNwuX288Q72wkEQy3PrdhyiEaXj/NnDOr2dYwGLTfNH4XZqOPxt7fhdPvbPae8xsW/PywgO83GVefkEm/v2H5iXTE2J4GhGXbeXbUfjy/Q4fPc3gCbdlfxwtICfv3EV9zz/Hru+fc6lm8p67FY+4L95Y2t+oponyRYXaSEfIR0MYRMAyMdihBCCCFEhwxItnLVObnsKW3gv5/uAZr32lq69gDDMmMYmhkTluvYLUZ+Nn8UtY1enn5vJ6HQ9+sb/o/HF+Cf7+xAp9Vw4wUj0et69uupoihcMiuHRpePZesOHvPY6no3H68r4eFXNnPLoyt5/O3trCuobE4Ezx7OqMHxvPTRLj7ZcOx2wiEYCrF5dxVlVb03arZpdxX3v7iBZ97PJ6Qe/d9QtCZrsLqiqQxd3VoC1jFS4EIIIYQQ/cqUEcmUVDhYuraEAckW1JBKvdPHT+aOCOt1BqfbueLMobz40S7eWVnERTMHtzlGVVVeWFpIeY2LXy0Y21JSvacNTrczcVgiS9eWMHNcOvbo/20UraoqhQfq+HRjKVv2VKMC6QnRnDEpk9HZ8eRk2FuqE04bmcK/Fu/klU/34A+EOGdq+G+8N3kCrNx2iE83HGwp+vHbhePJTLKE/VrfFQiGeGP5PkwGLesLK4m3m7h0Vk6PXvN4IQlWV+z5F7qmAtwJsyIdiRBCCCFEp100czAHK528vGw3ZpOOnHQ7uQNjw36dmWPTKT7s4IOvDzAw2dqm6MWnG0tZV1DJRTOzW+311RsumjmYzXuqeW/Vfn44Zxhef5A1Ow/z6cZSyqpcWKL0nDNtIKeMSSMxJuqIbTSvbcvj2SUFvLl8Hz5/kHknDWq3BLzbG8Co1x6zUmR1g5tPN5SyYushPL4gwzJjuODkbN5eUcRfX9vC7y4f3+6as+5Yua2citombr5wFDuKa/lobQkJdhOnjc/osWseLyTB6or4iShAwD450pEIIYQQQnSaRqNw/bw87n9hA5X1bn4yN6vH9oVaOHsopZVOnvuggNR4M+mJzSMve0rref3zvYzNSeDsHhj5aU9ynJmZY9NYvvkQGkVhTf5hXJ4AmUkWrjpnOFNykzHoj15g41tajYZr545Ar9Xw3upi/IEQF586uM3v0+0NsL6wkq+2l7O7tAGtRiHWaiTeZiLOZiLe3vzflig96woq2bCrEo2iMGl4EmdOzmwpnZ+dZuOBRZv4y6ub+d0VE3pk1M/tDbB4ZRFDM+yMHZLA6Jx4ahs8vPzJbuKsJsYOSQj7NY8niqr2jwmVdXUuAoG+scAuseFVWHcdNTM2EzK3He4W4mgSE61UVTkiHYbop6T/iK6SviOOprLeTeGBOk4enXrEBCtcfafO4eW+F9ZjMmi588qJ+IMq9z6/DoNOy10/nojZpO/2Nbqi0eXj9n99jc8fYvzQBGZPzGRIhr1LyWZIVXl52W6+2FzG6RMymjd1ViH/QC1fbT/Mpt1V+AIhUuLMTBqeREhVqWn0UNvgoabRQ53D17LOKcqo49SxaZw+IeOICdSBww4eemUTtmgjv7t8PLbvTHEMh3dXFvHe6mL+8KMJDE5rrirp9QX58383UV7j4raF449Y6v/7evO9R6fTEBsb3SvXao8kWJ2k+BtI2HUtocqvqJl1UNZgiU6RLzmiO6T/iK6SviO6Kpx9Z09pPQ/9dzN5g+Lw+YPsO9TIH344gQHJ1rC031UVtU3odZqwjASpqsprn+9l2fqD5A6M5XBtE3WO5nVTk3OTmT4qhexU2xETuGAoRIPTR53DS3piNCbDsSea7T5YzyOvbSElzsxvF44LW5Ja7/Tyu3+tYdTgeG783r5sDU4vf/zPRnz+IH/40cSjTp381omaYEkVwU7SNayD8o8IRmVJciWEEEII0UFDMmJYOHsI2/bVUFhSz4/mDIt4cgXNUwXDNc1OURQWnJbD3OlZ7D5Yz4AkCzdeMJJHbjqJH84ZxuC0o4+OaTXNSd7gdHu7yRXA0MwYbrpwFGXVLv7+xja8vmBYXsN7q/YTCIa4aGZ2m+fsFiO3XjKGQFDl729s7VAJ/hORJFid5LdPBkWPP3ZGpEMRQgghhOhXTh2Xzvkzsrjg5EHMGJUa6XB6hKIoXHhKNk/+aiY/v2QME4cn9Vjp+ZHZ8Vx/fh77DjXw+Nvb8HdzttehahcrtpZz6rh0kmOPXEAjLSGamy8aRVW9m8ff3t7tax6PJMHqJK3n0sX5JAAACC1JREFUAKh+AvaJkQ5FCCGEEKJfURSFC07ODsuGxn3dt6Xce9rE/2/vXmOizu4wjj9zkZvoDsgiA7SiprpYI1Roaep2jUjFTQFtkwZjJOmKmtSokMYXaGq0oonExGgCirq+1t4sMZgYEiHbalaDt2ZnpUhBDQ0gOkBEWG8zpy82naypHQb3vzuDfD/vZoYwv5Anc/5PznD+7yXrow8z9fm9QR3+0z/UNzD6xr/rL590KmqKXcVLMoL+3PzvJmj9zzN1p3tIn37e98bv97biFMFxirtfK0l6OT07vIMAAAAAkt5f5JbfGJ2+2KFdH1/VssVpKlkyW/Gxof9f1p3uId3seKRffjBH0+PGPjTjxwtSNDMhTu4Z39xR8RMVBWscbC+GFNN7RrJHycfpgQAAAIgQH2SlKmvuDP3173d18fq/9amnT8VLZit/cdqYu2nGGP2x5V9yxUfpZz/8TsjvGcpJgpMRXxEcBzPFpRfTsqTEXMnGnw4AAACR4534aP36w/f0+49+pIyUaTpzsUO/+/iqbtx5qGAHh19vf6iunsf6xU/nKDqEe38hOHawxsP/Us6Rdin1N+GeBAAAAHit9OR4/bY0W591DegPzR2qPfuZ0t+N17uuL29kHB83RdNioxQfO0XT4qboz590Ki1p6lt78Mi3jYI1DraXQ7L5n0rJ74d7FAAAAOD/stlsWjR3hr4/O0F/u9Wj1n/26+HQF+rqfawnoy/k87+6o1X5q0Wy27kFkRUoWONgopI08JPrSkz/gfToSbjHAQAAAIJy2O1atjhdyxanB54zxujpc5+Gv3ihJ6MvZGQ0N/WdME75dqFgjZNv6ve4wTAAAAAmLJvNpthop2KjnUp2xYZ7nLcOJzUAAAAAgEUoWAAAAABgEQoWAAAAAFiEggUAAAAAFqFgAQAAAIBFKFgAAAAAYBEKFgAAAABYhIIFAAAAABahYAEAAACARShYAAAAAGARChYAAAAAWISCBQAAAAAWoWABAAAAgEUoWAAAAABgEQoWAAAAAFiEggUAAAAAFnGGe4BQORyR1QWdzsiaBxMH2cHXQX7wpsgO3hTZwdfxbeUnkrqCzRhjwj0EAAAAALwNIqfqAQAAAMAER8ECAAAAAItQsAAAAADAIhQsAAAAALAIBQsAAAAALELBAgAAAACLULAAAAAAwCIULAAAAACwCAULAAAAACxCwQIAAAAAi1CwxuHu3bsqLS1VYWGhSktLde/evXCPhAg1ODiojRs3qrCwUMXFxdqyZYsGBgYkSbdu3VJJSYkKCwu1fv16eb3eME+LSFVbW6v58+frzp07ksgOQvPs2TPt3r1bK1asUHFxsXbt2iWJNQxja2lp0erVq7Vq1SqVlJSoqalJEtnB69XU1Cg/P/+VdUoKnpdJkyWDkJWVlZmGhgZjjDENDQ2mrKwszBMhUg0ODporV64EHh84cMDs2LHD+Hw+U1BQYFpbW40xxtTV1ZmqqqpwjYkI5vF4THl5uVm2bJlpb28nOwhZdXW12b9/v/H7/cYYYx4+fGiMYQ1DcH6/3+Tm5pr29nZjjDFtbW0mOzvb+Hw+soPXam1tNT09PYF16r+C5WWyZIkdrBB5vV7dvn1bRUVFkqSioiLdvn07sCsBfJXL5VJeXl7gcXZ2tnp6euTxeBQdHa3c3FxJ0po1a3ThwoVwjYkI9fz5c+3du1d79uwJPEd2EIqRkRE1NDSooqJCNptNkpSUlMQahpDY7XYNDw9LkoaHh5WcnKzBwUGyg9fKzc2V2+1+5blgnzWT6XPIGe4BJore3l7NnDlTDodDkuRwOJScnKze3l4lJiaGeTpEMr/fr9OnTys/P1+9vb1KTU0NvJaYmCi/36+hoSG5XK7wDYmIcuTIEZWUlCg9PT3wHNlBKLq7u+VyuVRbW6urV69q6tSpqqioUExMDGsYgrLZbDp8+LA2b96suLg4jYyM6MSJE1z/YFyC5cUYM2myxA4W8A2rrq5WXFyc1q1bF+5RMAHcvHlTHo9Ha9euDfcomIB8Pp+6u7u1YMECnT17Vtu3b9fWrVs1Ojoa7tEQ4V6+fKnjx4/r6NGjamlp0bFjx1RZWUl2gDfADlaI3G63Hjx4IJ/PJ4fDIZ/Pp/7+/v/ZGgW+qqamRvfv31d9fb3sdrvcbrd6enoCrw8MDMhut7MDgYDW1lZ1dnZq+fLlkqS+vj6Vl5errKyM7GBMbrdbTqcz8BWcrKwsJSQkKCYmhjUMQbW1tam/v185OTmSpJycHMXGxio6OprsIGTBrpeNMZMmS+xghWjGjBnKzMxUY2OjJKmxsVGZmZlv3ZYmrHPo0CF5PB7V1dUpKipKkrRw4UI9ffpU165dkySdOXNGK1euDOeYiDCbNm3SpUuX1NzcrObmZqWkpOjUqVPasGED2cGYEhMTlZeXp8uXL0v68sQur9erjIwM1jAElZKSor6+PnV1dUmSOjs75fV6NWvWLLKDkAW7Xp5M19I2Y4wJ9xATRWdnp6qqqvT48WNNnz5dNTU1mjNnTrjHQgTq6OhQUVGRMjIyFBMTI0lKT09XXV2dbty4od27d+vZs2dKS0vTwYMHlZSUFOaJEany8/NVX1+vefPmkR2EpLu7Wzt37tTQ0JCcTqcqKyu1dOlS1jCM6dy5czp58mTggJRt27apoKCA7OC19u3bp6amJj169EgJCQlyuVw6f/580LxMlixRsAAAAADAInxFEAAAAAAsQsECAAAAAItQsAAAAADAIhQsAAAAALAIBQsAAAAALELBAgAAAACLULAAAAAAwCL/AfduIiWxpCpkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MicroF1:  0.72374797  @  27\n",
      "MacroF1:  0.5564863  @  93\n",
      "MacroF1:  0.5340847  @  27\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(torch_model, loader, eval_loader, criterion, optimizer, n_classes)\n",
    "epoch_losses, microf1, macrof1, train_microf1, train_macrof1 = trainer.train(100)\n",
    "plot_torch_metrics(epoch_losses, microf1, macrof1, train_microf1, train_macrof1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MPNET Embedding Drop Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = len(train_datasets['drop_labels']['labels'][0])\n",
    "torch_model = nn.Sequential(\n",
    "    nn.Linear(768, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, 512),\n",
    "    nn.Dropout(),\n",
    "    nn.ReLU(),\n",
    "    nn.Linear(512, n_classes),\n",
    "    nn.Dropout(),\n",
    ")\n",
    "optimizer = torch.optim.AdamW(torch_model.parameters())\n",
    "train_sampler = ContrastSampler(torch_datasets['drop_labels_mpnet'], 1, 50)\n",
    "loader = DataLoader(torch_datasets['drop_labels_mpnet'], batch_sampler=train_sampler)\n",
    "eval_loader = DataLoader(torch_eval_datasets['default_mpnet_base'], batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MicroF1:  0.66549915  @  12\n",
      "MacroF1:  0.41382328  @  27\n",
      "MacroF1:  0.40278944  @  12\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(torch_model, loader, eval_loader, criterion, optimizer, n_classes=14, n_train_classes=n_classes, convert_predictions=convert_drop_predictions)\n",
    "epoch_losses, microf1, macrof1, train_microf1, train_macrof1 = trainer.train(100)\n",
    "plot_torch_metrics(epoch_losses, microf1, macrof1, train_microf1, train_macrof1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Voting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04fc0cfd8dd0497189c0023fcfc0561c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/346 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8e08b619c04e4ed58593215dcb1ec912",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/87 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_split_embeddings(sample):\n",
    "    new_sample = {'labels': sample['labels']}\n",
    "    for i in range(num_splits):\n",
    "        key = f'split{i}'\n",
    "        new_sample[key] = model.encode(sample[key])\n",
    "    return new_sample\n",
    "train_embeddings['split_features250'] = train_datasets['split_features'].map(get_split_embeddings, remove_columns=train_datasets['split_features'].column_names)\n",
    "eval_embeddings['split_features250'] = eval_datasets['split_features'].map(get_split_embeddings, remove_columns=eval_datasets['split_features'].column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_predict_splits(train_dataset, eval_dataset, classifier, references):\n",
    "    split_keys = [f'split{i}' for i in range(num_splits)]\n",
    "    predictions = list()\n",
    "    train_predictions = list()\n",
    "    for i,key in enumerate(split_keys):\n",
    "        print(f\"Training on split {i+1}/{num_splits}\\r\", end=\"\")\n",
    "        classifier.fit(train_dataset[key], train_dataset[\"labels\"])\n",
    "        train_predictions.append(classifier.predict_proba(train_dataset[key]))\n",
    "        predictions.append(classifier.predict_proba(eval_dataset[key]))\n",
    "    return np.array(train_predictions), np.array(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on split 6/6\r"
     ]
    }
   ],
   "source": [
    "classifier = Pipeline([\n",
    "    #('scaler', StandardScaler()),\n",
    "    #('svc', OneVsRestClassifier(SVC(probability=True))),\n",
    "    ('lr_chain', ClassifierChain(LogisticRegression(), order=np.argsort(np.sum(train_datasets['default'][\"labels\"], axis=0))[::-1])),\n",
    "    #('lr', OneVsRestClassifier(LogisticRegression(class_weight='balanced'))),\n",
    "])\n",
    "train_split_predictions, eval_split_predictions = train_and_predict_splits(train_embeddings['split_features250'], eval_embeddings['split_features250'], classifier, references)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "for i,p in enumerate(eval_split_predictions):\n",
    "    predictions[f'split{i}'] = p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 346, 14)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_split_predictions[:-5].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = Pipeline([\n",
    "    #('scaler', StandardScaler()),\n",
    "    ('svc', OneVsRestClassifier(LogisticRegression(max_iter=1000)))\n",
    "])\n",
    "classifier.fit(np.moveaxis(train_split_predictions[:], 0, 1).reshape(len(train_datasets['split_features']), -1), train_datasets['split_features']['labels'])\n",
    "predictions['minilm_lr_chain_splits_proba'] = classifier.predict(np.moveaxis(eval_split_predictions[:], 0, 1).reshape(len(eval_datasets['split_features']), -1))\n",
    "#predictions['minilm_lr_splits_proba_balanced'] = classifier.predict(np.moveaxis(eval_split_predictions[:], 0, 1).reshape(len(eval_datasets_split), -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>MicroF1</th>\n",
       "      <th>MacroF1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mpnet_svc</td>\n",
       "      <td>0.705281</td>\n",
       "      <td>0.452407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>minilm_lr_chain_splits_proba</td>\n",
       "      <td>0.699659</td>\n",
       "      <td>0.457345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mpnet_svc_chain</td>\n",
       "      <td>0.687609</td>\n",
       "      <td>0.427099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tfidf_svc_resampled</td>\n",
       "      <td>0.686620</td>\n",
       "      <td>0.453399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tfidf_svc</td>\n",
       "      <td>0.679715</td>\n",
       "      <td>0.403498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tfidf_svc_drop_labels</td>\n",
       "      <td>0.679715</td>\n",
       "      <td>0.403498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tfidf_svc_split_samples</td>\n",
       "      <td>0.678024</td>\n",
       "      <td>0.432197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tfidf_svc_economics</td>\n",
       "      <td>0.624521</td>\n",
       "      <td>0.377541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Method   MicroF1   MacroF1\n",
       "5                     mpnet_svc  0.705281  0.452407\n",
       "7  minilm_lr_chain_splits_proba  0.699659  0.457345\n",
       "6               mpnet_svc_chain  0.687609  0.427099\n",
       "4           tfidf_svc_resampled  0.686620  0.453399\n",
       "0                     tfidf_svc  0.679715  0.403498\n",
       "1         tfidf_svc_drop_labels  0.679715  0.403498\n",
       "3       tfidf_svc_split_samples  0.678024  0.432197\n",
       "2           tfidf_svc_economics  0.624521  0.377541"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_f1_scores(predictions, references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>MicroF1</th>\n",
       "      <th>MacroF1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tfidf_svc_resampled</td>\n",
       "      <td>0.663102</td>\n",
       "      <td>0.453229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>minilm_svc_split_samples</td>\n",
       "      <td>0.662139</td>\n",
       "      <td>0.451302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>minilm_svc</td>\n",
       "      <td>0.661871</td>\n",
       "      <td>0.393167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tfidf_svc_split_samples</td>\n",
       "      <td>0.658940</td>\n",
       "      <td>0.458318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>minilm_svc_resampled</td>\n",
       "      <td>0.658407</td>\n",
       "      <td>0.400275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tfidf_svc</td>\n",
       "      <td>0.653430</td>\n",
       "      <td>0.387152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tfidf_svc_drop_labels</td>\n",
       "      <td>0.653430</td>\n",
       "      <td>0.387152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>minilm_lr_chain_splits_proba</td>\n",
       "      <td>0.652482</td>\n",
       "      <td>0.418521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tfidf_svc_economics</td>\n",
       "      <td>0.615686</td>\n",
       "      <td>0.399139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>minilm_svc_all</td>\n",
       "      <td>0.276596</td>\n",
       "      <td>0.096864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Method   MicroF1   MacroF1\n",
       "4           tfidf_svc_resampled  0.663102  0.453229\n",
       "8      minilm_svc_split_samples  0.662139  0.451302\n",
       "5                    minilm_svc  0.661871  0.393167\n",
       "3       tfidf_svc_split_samples  0.658940  0.458318\n",
       "7          minilm_svc_resampled  0.658407  0.400275\n",
       "0                     tfidf_svc  0.653430  0.387152\n",
       "1         tfidf_svc_drop_labels  0.653430  0.387152\n",
       "9  minilm_lr_chain_splits_proba  0.652482  0.418521\n",
       "2           tfidf_svc_economics  0.615686  0.399139\n",
       "6                minilm_svc_all  0.276596  0.096864"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_f1_scores(predictions, references)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predicted_label_counts(predictions, references, ['tfidf_svc', 'tfidf_svc_economics'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1: [0.         0.71264368 0.         0.5        0.63157895 0.18181818\n",
      " 0.55555556 0.61764706 0.72222222 0.         0.85106383 0.\n",
      " 0.17391304 0.64150943]\n",
      "micro-f1: 0.615686274509804\n",
      "macro-f1: 0.3991394250840421\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "per_label_f1(predictions['tfidf_svc_economics'], references)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1: [0.         0.74468085 0.28571429 0.4        0.76190476 0.27272727\n",
      " 0.63157895 0.69135802 0.72       0.13333333 0.89108911 0.\n",
      " 0.16666667 0.64615385]\n",
      "micro-f1: 0.6631016042780749\n",
      "macro-f1: 0.45322907846676197\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "per_label_f1(predictions['tfidf_svc_resampled'], references)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model1, model2 = 'minilm_lr_chain_splits_proba', 'minilm_svc'\n",
    "agreements = calculate_agreements(predictions, references, model1, model2)\n",
    "order = agreements.sort_values(by=[f'{model1} MicroF1', f'{model2} MicroF1'], ascending=True).index\n",
    "ordered_labels = (np.array(train_dataset['labels'])).T[order].T\n",
    "plot_agreements(agreements.iloc[order], ordered_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}