2.Análisis_de_noticias.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2ecab6ff-dc62-4229-b951-56caaadbc0cc",
   "metadata": {},
   "source": [
    "# **Caso práctico 2: Análisis de noticias**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6c06dc-1af5-4611-b6f8-a2e483e14a7a",
   "metadata": {},
   "source": [
    "## 1. Análisis de sentimiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4671ac36-8412-44ae-9f90-2088297fbc59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset mlsum (/Users/dani/.cache/huggingface/datasets/mlsum/es/1.0.0/77f23eb185781f439927ac2569ab1da1083195d8b2dab2b2f6bbe52feb600688)\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import pipeline\n",
    "\n",
    "import rubrix as rb\n",
    "\n",
    "classifier = pipeline(\n",
    "    \"text-classification\", \n",
    "    model=\"finiteautomata/beto-sentiment-analysis\", \n",
    "    return_all_scores=True,\n",
    "    truncation=True,\n",
    "    padding=True\n",
    ")\n",
    "\n",
    "classifier = rb.monitor(classifier, dataset=\"noticias_en_es\", sample_rate=1.0)\n",
    "\n",
    "dataset = load_dataset(\"mlsum\", \"es\", split=\"test[0:500]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3cdb423-0fbf-493b-9d10-d936da4ac2ea",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "baba4fe7f1344f6b8e219ee352df1245",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/500 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['date', 'summary', 'text', 'title', 'topic', 'url', 'prediction'],\n",
       "    num_rows: 500\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.map(\n",
    "    lambda r: {\"prediction\": classifier(r[\"summary\"])},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2facda74-71d1-480c-90aa-a5f288c64a59",
   "metadata": {},
   "source": [
    "## 2. Categorización de texto (zero-shot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55f0221d-c93a-43e9-b8f8-bade0ae3e8fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = pipeline(\"zero-shot-classification\", \n",
    "                       model=\"Recognai/zeroshot_selectra_medium\")\n",
    "\n",
    "labels = [\"política\", \"cultura\", \"sociedad\", \"economia\", \"deportes\", \"ciencia y tecnología\"]\n",
    "template = \"Esta noticia habla de {}.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d285669-ea83-4aaa-99e1-d36ce47dc7c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = rb.monitor(classifier, dataset=\"noticias_en_es_categorias\", sample_rate=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d65fdcc-cca8-48aa-b2a8-f4d3fe262637",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset.map(\n",
    "    lambda r: {\"prediction\": classifier(r[\"summary\"], candidate_labels=labels, hypothesis_template=template)},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e733898b-2ab1-404c-9df2-0e3de27b7129",
   "metadata": {},
   "source": [
    "## 3. Categorización de texto (etiquetado programático usando weak supervision)\n",
    "\n",
    "![Labeling workflow](https://raw.githubusercontent.com/recognai/rubrix-materials/main/tutorials/weak_supervision/weak_supervision.svg \"Labeling workflow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "21a9a11a-60af-4730-8324-8c36fc054614",
   "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>termino</th>\n",
       "      <th>polaridad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>abiertamente</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>abrazo</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abrazos</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>absorbente</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>absorbentes</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        termino polaridad\n",
       "0  abiertamente  positive\n",
       "1        abrazo  positive\n",
       "2       abrazos  positive\n",
       "3    absorbente  positive\n",
       "4   absorbentes  positive"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "crisol = pd.read_csv(\"datos/crisol.csv\", sep=\";\", names=[\"termino\", \"polaridad\"]) ; crisol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "982264b1-0c42-43ee-90a2-4b08c0261017",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fs6i9f5Du1QBABxju2X+HbkC8lO7vJI3X/xV4M/CbwL8Ae7b6Z4DTBv3v4suXr/n7avPWduCwtrwBeAdwPbCs1Y4Abmjtq4FTWvtdwFhrLwBe0tr7A6NA2v7v3GG8O1v7z4EPtfYBwD2t/VHgHa29CPgesHCic5h3VwbNQ1X1zdb+B+Bo4P6q+l6rrQd+F3gC+ClwUZI/BJ6c6gBV9d/AfUmOTLIf8Grgm22sNwA3J7m9Lf/67E9J0nPc/VV1e2vfSnfC/h3gK20u+TzdyRrgjcBXWvtLPfsI8NEkm4B/o/t7bkOTjLsBOLG1TwLGP0tYDqxpY3eAFwCvmGgn8+63iZodP8h4nO5VwC936n6J7XC6E/aJwLuBo6YxzmV0//G+C1xRVZUkwPqqOnsmBy5pt/VUT/tndCfxx6vqsGns41S6dy3eUFX/l+QBupP4hKpqS5LHkvw28Ed0rzSgGyxvq6op/djnfL0yeEWSN7b2HwO3AEuTHNxq7wT+PcmLgL2r6lq6l0qv3cm+fgy8eIJxrqD7E9qn0A0G6F7WnZjkZQBJ9k0y7V8AlLTb+xFwf5K3A6RrfI66EXhba5/cs83ewKMtCN7C078++mzzGMCXgQ/QnQ83tdp1wJ+1/+CS5HXPdrDzNQzuAc5MshnYB7gQOIPu5dYdwM+Bz9H9x7m6XVJ9A3jfTvZ1MfC58Q+Qe1dU1TZgM/DKqrqp1e6m+xnF19p+N/L0pZ0kTcepwKok3wHu4um/3/Je4H1tjjmY7i1vgEuB4TbPnUb3rgVV9RjwzSR39j740uNyuqGyoad2LrAnsCnJXW15QvPu0dJ+PEIlSYOU5IXA/7Rb0yfT/TB5oH/oa75+ZiBJz2VvAD7VbuE8DvzJYA9nHl4ZSJL6b75+ZiBJ6iPDQJJkGEiSDANJEoaBJAn4f0NJNytT5/vqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crisol.polaridad.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5058c44-c413-494e-a500-106477fa0ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "crisol = crisol.sample(frac=1.0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "138b63d7-3c55-41c4-8f2f-9a2c34e84a85",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Reusing dataset mlsum (/Users/dani/.cache/huggingface/datasets/mlsum/es/1.0.0/77f23eb185781f439927ac2569ab1da1083195d8b2dab2b2f6bbe52feb600688)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "noticias = load_dataset(\"mlsum\", \"es\", split=\"train[0:10000]\")\n",
    "records = [\n",
    "    rb.TextClassificationRecord(\n",
    "        inputs=noticia[\"summary\"],\n",
    "    )\n",
    "    for noticia in noticias\n",
    "] ; len(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a5ff3fb-b917-4024-a2d4-d5443df62d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BulkResponse(dataset='noticias_en_es_weak_supervision', processed=10000, failed=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rb.delete(name=\"noticias_en_es_weak_supervision\")\n",
    "rb.log(records, name=\"noticias_en_es_weak_supervision\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1959829-ed3b-499e-818f-6454a7cbde74",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rubrix.labeling.text_classification import Rule, WeakLabels\n",
    "\n",
    "terminos_positivos = crisol.query(\"polaridad == 'positive'\")[0:500].values\n",
    "terminos_negativos = crisol.query(\"polaridad == 'negative'\")[0:500].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c875ff62-b0e0-476a-801f-ac9054cdd143",
   "metadata": {},
   "outputs": [],
   "source": [
    "for termino, polaridad in terminos_positivos:\n",
    "    rules.append(\n",
    "        Rule(query=f\"{termino}\", label=\"Positivo\")\n",
    "    )\n",
    "    \n",
    "    \n",
    "for termino, polaridad in terminos_negativos:\n",
    "    rules.append(\n",
    "        Rule(query=f\"{termino}\", label=\"Negativo\")\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2632a917-fcd5-4e77-bd89-414d8a19d856",
   "metadata": {},
   "outputs": [],
   "source": [
    "weak_labels = WeakLabels(rules=rules, dataset=\"noticias_en_es_weak_supervision\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bd1b4cb7-c09a-4733-be71-9ca33026548e",
   "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>polarity</th>\n",
       "      <th>coverage</th>\n",
       "      <th>overlaps</th>\n",
       "      <th>conflicts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fenomenalmente</th>\n",
       "      <td>{}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primas</th>\n",
       "      <td>{Positivo}</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perspicazmente</th>\n",
       "      <td>{}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caritativa</th>\n",
       "      <td>{}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adorado</th>\n",
       "      <td>{}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>empedernido</th>\n",
       "      <td>{}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>espinosa</th>\n",
       "      <td>{Negativo}</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fealdad</th>\n",
       "      <td>{}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>impias</th>\n",
       "      <td>{}</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>{Negativo, Positivo}</td>\n",
       "      <td>0.1411</td>\n",
       "      <td>0.0099</td>\n",
       "      <td>0.0037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1001 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            polarity  coverage  overlaps  conflicts\n",
       "fenomenalmente                    {}    0.0000    0.0000     0.0000\n",
       "primas                    {Positivo}    0.0005    0.0002     0.0000\n",
       "perspicazmente                    {}    0.0000    0.0000     0.0000\n",
       "caritativa                        {}    0.0000    0.0000     0.0000\n",
       "adorado                           {}    0.0000    0.0000     0.0000\n",
       "...                              ...       ...       ...        ...\n",
       "empedernido                       {}    0.0000    0.0000     0.0000\n",
       "espinosa                  {Negativo}    0.0001    0.0000     0.0000\n",
       "fealdad                           {}    0.0000    0.0000     0.0000\n",
       "impias                            {}    0.0000    0.0000     0.0000\n",
       "total           {Negativo, Positivo}    0.1411    0.0099     0.0037\n",
       "\n",
       "[1001 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weak_labels.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03e5443a-debc-4e88-9837-f11bfb6369ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from snorkel.labeling.model import LabelModel\n",
    "\n",
    "# train our label model\n",
    "label_model = LabelModel()\n",
    "label_model.fit(L_train=weak_labels.matrix(has_annotation=False))\n",
    "\n",
    "# check its performance\n",
    "#label_model.score(L=weak_labels.matrix(has_annotation=True), Y=weak_labels.annotation())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a0653751-6bdf-4421-aeb2-dd920ffa4fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BulkResponse(dataset='noticias_en_es_snorkel', processed=1411, failed=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the part of the weak label matrix that has no corresponding annotation\n",
    "train_matrix = weak_labels.matrix(has_annotation=False)\n",
    "\n",
    "# Get predictions from our label model\n",
    "predictions = label_model.predict_proba(L=train_matrix)\n",
    "predicted_labels = label_model.predict(L=train_matrix)\n",
    "\n",
    "preds = [[('Positivo', pred[0]), ('Negativo', pred[1])] for pred in predictions]\n",
    "\n",
    "# Get the records that do not have an annotation\n",
    "train_records = weak_labels.records(has_annotation=False)\n",
    "\n",
    "# Add the predictions to the records\n",
    "def add_prediction(record, prediction):\n",
    "    record.prediction = prediction\n",
    "    return record\n",
    "\n",
    "train_records_with_lm_prediction = [\n",
    "    add_prediction(rec, pred)\n",
    "    for rec, pred, label in zip(train_records, preds, predicted_labels)\n",
    "    if label != weak_labels.label2int[None] # exclude records where the label model abstains\n",
    "]\n",
    "\n",
    "# Log a new dataset to Rubrix\n",
    "rb.delete(name=\"noticias_en_es_snorkel\")\n",
    "rb.log(train_records_with_lm_prediction, name=\"noticias_en_es_snorkel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dbbab61-2d21-4043-a2e5-bd2e52b9a26a",
   "metadata": {},
   "source": [
    "## 4. Reconocimiento de entidades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0e45c8f6-d369-4665-bbf8-b2056fa71ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:datasets.builder:Reusing dataset mlsum (/Users/dani/.cache/huggingface/datasets/mlsum/es/1.0.0/77f23eb185781f439927ac2569ab1da1083195d8b2dab2b2f6bbe52feb600688)\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "import rubrix as rb\n",
    "\n",
    "from datasets import load_dataset\n",
    "\n",
    "nlp = spacy.load(\"es_core_news_md\")\n",
    "nlp = rb.monitor(nlp, dataset=\"noticias_en_es_ner\", sample_rate=1.0)\n",
    "\n",
    "dataset = load_dataset(\"mlsum\", \"es\", split=\"test[0:10000]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cd6e9838-63ac-44e1-88e1-8d949a0e01b4",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ad004ee5162477ba8bb17dba1cb0fd7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/313 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['date', 'processed', 'summary', 'text', 'title', 'topic', 'url'],\n",
       "    num_rows: 10000\n",
       "})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_entities(record):\n",
    "    docs = nlp.pipe(record[\"summary\"])\n",
    "    return {\"processed\": [doc.text for doc in docs]}\n",
    "\n",
    "dataset.map(\n",
    "    extract_entities,\n",
    "    batched=True, \n",
    "    batch_size=32\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a264cfad-8e9f-45e9-a0a7-e289ce9dcd29",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rubrix.metrics.token_classification import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a35406c6-a514-4d1f-adf1-283c2fc75f8b",
   "metadata": {},
   "outputs": [
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         }
        },
        "title": {
         "text": "Computes the text length measured in number of tokens"
        },
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b31ZXenc+OuPLCTKCQxQNDzr1HmPCh9fmjjazA8INzLROnfGms8zSL8nUWTYaAr04bZic8b+jM5deXfmh3P/QUzKyf0czq63n4IlmZtmapRPTzGrRL/vpg12oLbPjOW1LH2BoiPTTL38EPgagTte36GVmbYx6sFPAUPfbuWtvwDr9sZ3lb8EP67pPuPU5gZYTTGqgoEHVG/PHSPl/l2hl1rb05xIqMHNCkDfmPRp4l5zOZtIvCL7+3KNyYrnSacbPM6PvD5g/Nvl5mTL3ZZkz4UG58NzTRGcYNmz7oAlB9cXkun3y+XfS+p6H07w83rYvQvWvtkOD4hnj+pgwSzedvfLgqKnmvz9+dZIJHHRzQqT3lkyQEsWKhN0XWnbP3v0msHE2Z8mculxe7dywxmYkgZkGODpr6rGHuqYJEcK5Tr75/mdp3GGQ3FDnUjO71NmcstkJzNR42tgHArOcNLi77KYuJnCaPPq+kNdecLi1fO5oKVXi6OwhHcc6nnt2bCLtWtxg/iw7gdnyOY+YYFQ3DdI1tNQZq5NH32/uB3qvuuzGzmaG54tTh5r9nHviuWdWkrkTHwyMaSccGz3wbnOth3udOveRUH2VHkVnntZr+YAJvx4d2Dmw5F5n7Y17ZqGMHtDJhLu6aWj26FPzxRmrIYFFpFXX4eav0r/DrEbD7ubfAg0lnQ+srP1ig9zRfYQJXvUXM7oFB2afffWDmYFYo/r55pcBefIcXVYdzpgLt69/2PS73Nymv/S/p1Wa2czHuodn1nb+HAEEEEAAAQQQQACBeBOI28DMCRPC+XqbBji6aWAVvDkPWW8vGm9mMjkPh7rMSR/SnK33sElmNtGCpwfJOZUrmT9OSUmVS2+82zws6RI852FKH9xfmzdaTvp3WaH++bzFb8qwcbPFebh0IzC74fY+Zqbb3CcHBM5Tw70q17YXJ8TT5U+6zOzBe243D33OA92xLpKsQpr07whyjvHWC+PMbAxdhnnvoIniBCPOsSbNWiJPTFuU6QNtZoFZJPXpQ7Q+TGtopv2gMz4a33h0RqFu2Q3MdNbVNbf2MO/K69auUaA+ndXS5p6RgZlzmY0fnaGos6KccfXW+59Jl37jAuNBK9SlvDVu6WYeivXh+Fjn64QOWfVFqH7WwExnPL08e2Tgr3/8ebPc1LpfmmWM+pc643DI2JmyePowM2Mrkr5If2wnYB7Rt4PcfP0VZhllVmMz0sBMQ570L94P5zpxwjzn+nTOff13P8ltdw3J1gyz9MZap4Yvf/2zPcN9yDmeE6IEjwH9O/2wwEV1O5rZhw/2uN3sHmlglr5OXeY4aPR0M3sveLbpfUOeNAGd8zVcZ0yP6n+X3HjtZYFu1Rl+F9e9S5o2qCkDe7YOe2w4YzdUX6UfMzoLWEP/9P3izMRt2aiOWTKsm01gpssjazbuEWhL8Hmo86+b/w5cL05gNmdCfxOy6qy7GeP6mpmazhbOmAu3rx1nDSxH9r8rsET9WPdw/g4BBBBAAAEEEEAAAQSOCsRtYPbSa+9Jv4efyRDMpB8Yujzykvqd0iyJdPZxggtnWaXzcPjcUwPlvLP+F6hKv96oSzC/eHNqmplaDdr0k0oVTpDxQ7uZfZ2HwfT7rd+wSW7rONi860uXDNkGZs4DtB4z/fuldKaHhniTRvU073Xq1v9xsxxSZ5a0b17fLEkNnvmT3iurwCx923S52IBHpgZmcjkfYtAliHnz/vcie10Oq30x76kBZglU+i2zwCyS+nTZ1a3tB5plZTpLcOqY3mlm0mU3MHOW/zrL2pxz1yWAuszu/rubmvdsOeNHlwrqA66z6ZLTG2/vI07o4Dygayirs2T03XQa8On40fBBQwjdbPsi1E1SA7MzT60gT468N/DXOhtK/9wZn85f6BLkewY8EZiZGUlfaNi0ePl7sunXP+SXzX+b5dDa/8P7tDdLiMMZm5EGZunHZrjXySNPPmfeF7h87iNSofzxARfnPYnZmWGW3lgr1VldH376baazLJ0QRWesaugdvOkMqFpXVDEhsG6RBmb63kOd+eps+t6/Xg89ZQJ3XcbtbLr8UZdBrn9rhvkjZ0zPfqKfVD3vjDTnpGNGZ7fqvSbcsZHZPTLUWB0/5QWzTDv9LyB034btBkjhggUCM8VsArP3P/5KOvR6NDAzN/hc9J1+M+Yvl0+WP23e6+Ysuy93fCnZuGlzYNaoUybcMRdJXzv/1ukx9NUAbZvXT9Nnoez4MwQQQAABBBBAAAEEEIjjwMyZ/fFA1xZyR+PrMh0Lzuyg4OVvzs7Og9Ljw7qbl8Vn9kU4DcyWrVgTmHXhlM80MFs5TZISEwPntPmvrXJt0/sCS6oyC8z27T8g1ep1CrzDTCsIFZps3b5LrmrY3byDSMOx9JsuD3Rmg+gyq1XvfirPzF0mumxRg7Mpj/YKLE1NX/ZYIc3yVR9lMEgfmD38xBx59oU3zKycUF9+vKXuFeZ9Xum3zAKzSOrTGXaNOww0D7LOu8uCl55mNzBb8c5auWfgE9LgusulYoUTMpx79SpnmaWWmY2f9IGZVjDi8WfNy9m1P0oWL2LOWWdHOS9rz6zv9c8z+2Jp+r4IdVFoyHHxBWekWXroBGaDeraW2/4N67Rs+sAs3L5wyun7oPQ9abrEb+++/WaWpROYaf1Zjc3MAjNd3qtb8DvMQo3NcK8TZ2ltZoFbdgKz9MZ6vuEGZqHecxZuYOaETOnfYZa+TicwC34vmp5jZoHZ85OHmI8PBG86c/fk8sfLM4/2knDHRiRf29X3IOrSz1BfCNZ3QO7Ztz/wDjebwMyZOfn0I/eZ9w4Gb/o+OH0v3OrFT5glsk7gp9ethuV6D545vk/gXhfumAv17jvnuOn7Wv/8z7+3yewXXjfhnW46Y7BPtxZp/p3hByIEEEAAAQQQQAABBBBIKxC3M8yccElnR+jsh1DhjFLpu17OrXmnec+OLu0J3nTWmIZhzsOgW4HZqoXj5PjSJQKHct5P5bz0/otvNkrzu4dmmNGgoYmGcM5L/7WCzEIefaiqefmFgQ8JhHNh6Muy9Qt+x3qPkm1g5rwPK9SskGOdY2aBWST16ZJPXfqp76LSJbTpQ4LsBmb6lbr6rR4IOQMluE2RBGbmBeU9R5mZjyWKF5XTK51ovlCYN2+eQJW2fRHK2yYwC6cv9HqrfsPR62zl848FZjO+veYz6dx3XJrALPj8Qo1NDcyCP5bh7K8zRiudfEKWgZnuH8514rwTa+XCx6Rs6ZKB03KC7lgNzPRdeM77F52T1lljGoa5HZjpLLLgcN5ZFum80zCcsaHnGElg5iyV1Q8b6NcvgzcdA9WrnC1PDO9u/tgmMPt6w8/moyGhPiCjS1Q1jP1q1XQzW9UJzN5cMFZWvfepDBs/23wgQd+96GzhjLlIAzOnbn0P5JCxM8xHa9LPDgzn3wD2QQABBBBAAAEEEEAgngTiNjBzHr50KZu+CFtfiJ3Z1rHXGPls/feydNbDgQdifeBr0WWYeWG9vn9LX3TuVmA24N47zJcUnU2XLOrsn2WzR5p30OhX6q5udE+GMMB56AsOzJw/S/+uNl1qqV/xc2Y+hDvodSnXT7/+kel7lDI7XrizmpwXqKd/31dW56ezjc6v1TbDi9fDrS/4pf+6bO2u3mPlvY+/TLOcK7O2pT+39C/9d85NZ5NoOJvZBxMiCcx0llS5MqUCy3lD+dj2Rag6bQKzcPrCCT71K7ETR/QInIIT5ATPMEt/funHpr4Lav+BQ2lCISds1iWvWc0w0/rDuU40+Ojaf7x511/zhrUDp6Uf1dAXuMdiYObMUAwOpvXDAjfc0dfMfHI7MNP7md7XnM0JOPXLvxpqhjM2nHt2qNmAocaqMxM3/f3dObZ+SVjvlbqFG5jpGNMl8m+/MD7wEQH95Yu+o1C/QBr8yxedeanv9tNfyugsOuf8V73/max+8XHzy5gufceZr3Y6H/QId8xlNzDT+h2X9O92y+oey98jgAACCCCAAAIIIBBvAnEdmOkSvLb3jjLv6Lro/DPMDIgzTzvZfI1Rl2zWrVXdPOw4yzf1Ibt1k+ulcOGCsuz1981MDOf9Uzpw3ArMtK4+XVuYZY9r1n5t3sOjs570HVZO2OJ8rbB7u1ul6nmny1ff/WS+8qZbcGC2Zu16aX/faFP+9luvMw95+uEBfUDVryrqi/b1gb582dKydccu0WWm+m6sS6qcZZZXlSldUs47q5L5cuCX3/xklgIGf/Ut/QWT2fHCDcz0YwgaEOpXSfU9TFdder4kSILoi+W/3fhL4B1MoS5UXWalX4Ub2rudnFC2lJx8YlkpVqRwlvU57y7btWefvDJ7pBQtUkj++Gur1Gl6n/kS5NSxvc3Spczalv5cQn0l01nuqF/3a3pzTSlSuJBs/usf0eWaQ3u3lWJFC0e0JNMJcnTM6lf5dIbkOZUrmhfvO+/Rsu2LUMY2gVm4fauzdXTWjo53Xca2/K2PzDvadHMCs3DGpjObR9/pdu3VF8sff20zHwrQICPcwCyc60TDc/1irr77TpelnvG/CrLhx1/l0UnzzXvXYjEwc+5V+pVLDfmSk1PMx0V05qJubgdmWqfOpLrsonPMuNcgUe89+r5Hvd7CHRuRzDDTY+rHMfRdk53b3CLVLqhsli4/OeslOXhQg9T/ZjCGG5g5s1A1bLv84nPNy/r1xf363jYdk3qf1S8M796zT2Y+/5pZxh48kyv4K5l6fvrLD519qveDl6YPMxbhjLlwA7PvNv4qU+e9LNddVU1OPKG0WZqpy1T1q8vpP/oRbz/80F4EEEAAAQQQQAABBLISiOvATHF0NsXcxW/KB2u/NsGZs+l7sh4b0iXwcmT9ouHgR2eYh2Ld9EHp3g5NpOnNtQIzDQKB2b8zwZy69B1H+nfOl+OcP9flk/87ubyMe6ir+SPnYUpfrq8vEneOoyHIQ/e3lQIF8gXOTwOkHoMmmAdAZz+dwaHLw2pdUVV6dGhs/vzIkWQZ9Oh0E0Dppg/IzmyHz776QcZNWSj6FUZn0/dGjR5wt3nfkLOkL3gQaZjWt1tL87AbasvseJk96DpLsYIf3jSAGPP082Ypk4Ybzhb8VbtQx9b2dOz9qAkpdHNmbWRVn/MQnH7Z2MJlbxs750X6x7IMPp/f//xHrmt2f4avbGro8+yiN0S/+OhsGsjpTCodT5mNH2dJp77gv37tS0142PfhZ8zDuL63LF/ePLJn7/7A2HRmDbrRF+mdNTC75MIzzfJSZ9MvN9ZqfG+GJWlvvrtOuj/4uAR/0TCrvtA69SG/+4DHRT9AoZt+mGJEnw7Sa+gkuaf9reYrmeGMTTXpPXSSmcHjbO2a1xedxfbZ+h/CmmGm5bK6TnQfDTD0IyJrv9gQuB51iZ6+v6pezeppltyFGivOTCv9u1DG+udZvcPMGXfBdTnH0mV++p5FPSfddHbTY88slKlzXw6cjt5nal5exYx5vVfpmMyszlf1lwUPPWXeARa83HHUxHky6/nX0rz0X+9lYwd3TnP/1I9qDLi3dZqvNoYzNiINzPQrkWOfft6ERM6m98BBPduk+eCJvttLX9C/ZulEE15ntuk7LTv3fcwEuro5s9TUU5foj316QeD+o/fSgfe2Nh8Qcbb0gZn+uX5VVGeu6ccsNBAOZ8yF29f6hU4NDZ1/J7Ru/bdNZ1QHf7U00wbzFwgggAACCCCAAAIIxLFA3AdmwX2vD1c7du4xD4qZPTTpzIHDR5LNzBe3t+CHQX14/Gf7Lildslim71fT42sooF9bPK5UsWO+wFlDJD3340oWS/OeK61DZ9pt3bZTihQpZGYrBW+HDx+RLdt2Sp6kpCyPEVzuWMeLxE0DTV3SWLJ4UcmT57+vZmZWh85U2bJthwmRtEz6LdL6Qh3Htm27tc/27ZfiRYukCUHDdWnTY6R5Cb5+xTN4nGrQ1KjdgAxfq7Q933DPK9L9suqLbTt2a7IjJUsUDbmMNdyxqeNbj6UvWtdxkd3UCfs9AAAgAElEQVTtWNeJU6cuz9uz94CULlU8EKRn93g5Uc65XooUKmjue9HedNmnXse6hP1YW1ZjI9Lz1HuIzubSe4LNGHCOq19p1fFQplSJDP2sL+7XGbnH+ppwuOcfzpgLpy79Yqv+W6Htz4l+Duec2AcBBBBAAAEEEEAAgVgXIDCLoR6KdPZEDJ06p5JDAgcOHJKL6nZMMxvFObTOPNN3JunyM10GyIYAAggggAACCCCAAAIIIIAAAtkTIDDLnltUShGYRYXVd5U67/jSZVVnV65oZl/99PMf5t1MuukSSJ3hxIYAAggggAACCCCAAAIIIIAAAtkTIDDLnltUShGYRYXVd5Xqi7v1JeX60QlduqabLjesdUUVadO0Xpr3Qvmu8TQIAQQQQAABBBBAAAEEEEAAgRwQIDDLAeRwD6Ffa9Sv1fGOmXDF4ns/fdG4vp+sQP58Yb3fLb61aD0CCCCAAAIIIIAAAggggAAC4QsQmIVvxZ4IIIAAAggggAACCCCAAAIIIIAAAnEgQGAWB51MExFAAAEEEEAAAQQQQAABBBBAAAEEwhcgMAvfij0RQAABBBBAAAEEEEAAAQQQQAABBOJAgMAsDjqZJiKAAAIIIIAAAggggAACCCCAAAIIhC9AYBa+FXsigAACCCCAAAIIIIAAAggggAACCMSBAIFZHHQyTUQAAQQQQAABBBBAAAEEEEAAAQQQCF+AwCx8K/ZEAAEEEEAAAQQQQAABBBBAAAEEEIgDAQKzOOhkmogAAggggAACCCCAAAIIIIAAAgggEL4AgVn4VuyJAAIIIIAAAggggAACCCCAAAIIIBAHAgRmcdDJNBEBBBBAAAEEEEAAAQQQQAABBBBAIHwBArPwrdgTAQQQQAABBBBAAAEEEEAAAQQQQCAOBAjM4qCTaSICCCCAAAIIIIAAAggggAACCCCAQPgCBGbhW7EnAggggAACCCCAAAIIIIAAAggggEAcCBCYxUEn00QEEEAAAQQQQAABBBBAAAEEEEAAgfAFCMzCt2JPBBBAAAEEEEAAAQQQQAABBBBAAIE4ECAwi4NOpokIIIAAAggggAACCCCAAAIIIIAAAuELEJiFb8WeCCCAAAIIIIAAAggggAACCCCAAAJxIEBgFgedTBMRQAABBBBAAAEEEEAAAQQQQAABBMIXIDAL34o9EUAAAQQQQAABBBBAAAEEEEAAAQTiQIDALA46mSYigAACCCCAAAIIIIAAAggggAACCIQvQGAWvhV7IoAAAggggAACCCCAAAIIIIAAAgjEgQCBWRx0Mk1EAAEEEEAAAQQQQAABBBBAAAEEEAhfgMAsfCv2RAABBBBAAAEEEEAAAQQQQAABBBCIAwECszjoZJqIAAIIIIAAAggggAACCCCAAAIIIBC+AIFZ+FbsiQACCCCAAAIIIIAAAggggAACCCAQBwIEZnHQyTQRAQQQQAABBBBAAAEEEEAAAQQQQCB8AQKz8K3YEwEEEEAAAQQQQAABBBBAAAEEEEAgDgQIzOKgk2kiAggggAACCCCAAAIIIIAAAggggED4AgRm4VuxJwIIIIAAAggggAACCCCAAAIIIIBAHAgQmMVBJ9NEBBBAAAEEEEAAAQQQQAABBBBAAIHwBQjMwrdiTwQQQAABBBBAAAEEEEAAAQQQQACBOBAgMIuDTqaJCCCAAAIIIIAAAggggAACCCCAAALhCxCYhW/FnggggAACCCCAAAIIIIAAAggggAACcSBAYBYHnUwTEUAAAQQQQAABBBBAAAEEEEAAAQTCFyAwC9+KPRFAAAEEEEAAAQQQQAABBBBAAAEE4kCAwCwOOpkmIoAAAggggAACCCCAAAIIIIAAAgiEL0BgFr4VeyKAAAIIIIAAAggggAACCCCAAAIIxIEAgVkcdDJNRAABBBBAAAEEEEAAAQQQQAABBBAIX4DALHwr9kQAAQQQQAABBBBAAAEEEEAAAQQQiAMBArM46GSaiAACCCCAAAIIIIAAAggggAACCCAQvgCBWfhW7IkAAggggAACCCCAAAIIIIAAAgggEAcCBGZx0Mk0EQEEEEAAAQQQQAABBBBAAAEEEEAgfAECs/Ct2BMBBBBAAAEEEEAAAQQQQAABBBBAIA4ECMzioJNpIgIIIIAAAggggAACCCCAAAIIIIBA+AIEZuFbsScCCCCAAAIIIIAAAggggAACCCCAQBwIEJhZdvLmrfsta6A4AggggAACCCCAAAIIIIAAAggg4K5A+eMKulthnNVGYGbZ4QRmloAURwABBBBAAAEEEEAAAQQQQAAB1wUIzOxICczs/ITAzBKQ4ggggAACCCCAAAIIIIAAAggg4LoAgZkdKYFZhH579u6XVBEpWvjo1EYCswgB2R0BBBBAAAEEEEAAAQQQQAABBKIuQGBmR0xgFqbf4uXvypinF8jBg4ekzHEl5OXZIwnMwrRjNwQQQAABBBBAAAEEEEAAAQQQyFkBAjM7bwKzMPxWrF4r/R5+RqaP6yNnnXaKJCYmBEoxwywMQHZBAAEEEEAAAQQQQAABBBBAAIEcFSAws+MmMAvDr1XX4dL05ppy07WXZ9ibwCwMQHZBAAEEEEAAAQQQQAABBBBAAIEcFSAws+MmMMvC7/DhI3Lhte3l4X4dZc0nX0mePElyY53LpXrVs0xJAjO7AUhpBBBAAAEEEEAAAQQQQAABBBBwX4DAzM6UwCwLv63bd8lVDbtLlXNPlw4tb5Affvpdnn52qcwc31fOOv0U2b3/iF0PUBoBBBBAAAGfCaSmpkpysn4iJ2e2PHkSc+ZAHAUBBBBAAAEEEPCQQNGCeTx0trF3qgRmWfTJth27pcYt3WTlwsekbOmSZu+egyfKOZUrSbvm9QnMYm9Mc0YIIIAAArksoIHZp1+kyLrP/3vnZ7ROqUE9kePLEJhFy5d6EUAAAQQQQMC7AgRmdn1HYJaFn/7Qf27NO2XVwnFyfOkSZu8Bj0yV8mVLy92tb2ZJpt34ozQCCCCAgE8FvtuQIHOeS4pq68qVS5VWTVOkWPGcm80W1QZROQIIIIAAAggg4KIASzLtMAnMwvAbOHqa5M+XT/p1bym/bv5bGncYJOOGdJXLq51LYBaGH7sggAACCMSfAIFZ/PU5LUYAAQQQQACB2BIgMLPrDwKzMPx0WWaf4ZPls/Xfm71vb3yddGvbyPw3L/0PA5BdEEAAAQTiToDALO66nAYjgAACCCCAQIwJEJjZdQiBWQR+e/cdkAL580pS0n9LTAjMIgBkVwQQQACBuBEgMIubrqahCCCAAAIIIBCjAgRmdh1DYGbnxwwzSz+KI4AAAgj4U4DAzJ/9SqsQQAABBBBAwDsCBGZ2fUVgZudHYGbpR3EEEEAAAX8KEJj5s19pFQIIIIAAAgh4R4DAzK6vCMzs/AjMLP0ojgACCCDgTwECM3/2K61CAAEEEEAAAe8IEJjZ9RWBmZ0fgZmlH8URQAABBPwpQGDmz36lVQgggAACCCDgHQECM7u+IjCz8yMws/SjOAIIIICAPwUIzPzZr7QKAQQQQAABBLwjQGBm11cEZnZ+BGaWfhRHAAEEEPCnAIGZP/uVViGAAAIIIICAdwQIzOz6isDMzo/AzNKP4ggggAAC/hQgMPNnv9IqBBBAAAEEEPCOAIGZXV8RmNn5EZhZ+lEcAQQQQMCfAgRm/uxXWoUAAggggAAC3hEgMLPrKwIzOz8CM0s/iiOAAAII+FOAwMyf/UqrEEAAAQQQQMA7AgRmdn1FYGbnR2Bm6UdxBBBAAAF/ChCY+bNfaRUCCCCAAAIIeEeAwMyurwjM7PwIzCz9KI4AAggg4E8BAjN/9iutQgABBBBAAAHvCBCY2fUVgZmdH4GZpR/FEUAAAQT8KUBg5s9+pVUIIIAAAggg4B0BAjO7viIws/MjMLP0ozgCCCCAgD8FCMz82a+0CgEEEEAAAQS8I0BgZtdXBGZ2fgRmln4URwABBBDwpwCBmT/7lVYhgAACCCCAgHcECMzs+orAzM6PwMzSj+IIIIAAAv4UIDDzZ7/SKgQQQAABBBDwjgCBmV1fEZjZ+RGYWfpRHAEEEEDAnwIEZv7sV1qFAAIIIIAAAt4RIDCz6ysCMzs/AjNLP4ojgAACCPhTgMDMn/1KqxBAAAEEEEDAOwIEZnZ9RWBm50dgZulHcQQQQAABfwoQmPmzX2kVAggggAACCHhHgMDMrq8IzOz8CMws/SiOAAIIIOBPAQIzf/YrrUIAAQQQQAAB7wgQmNn1FYGZnR+BmaUfxRFAAAEE/ClAYObPfqVVCCCAAAIIIOAdAQIzu74iMLPzIzCz9KM4AggggIA/BQjM/NmvtAoBBBBAAAEEvCNAYGbXVwRmdn4EZpZ+FEcAAQQQ8KcAgZk/+5VWIYAAAggggIB3BAjM7PqKwMzOj8DM0o/iCCCAAAL+FCAw82e/0ioEEEAAAQQQ8I4AgZldXxGY2fkRmFn6URwBBBBAwJ8CBGb+7FdahQACCCCAAALeESAws+srAjM7PwIzSz+KI4AAAgj4U4DAzJ/9SqsQQAABBBBAwDsCBGZ2fUVgZudHYGbpR3EEEEAAAX8KEJj5s19pFQIIIIAAAgh4R4DAzK6vCMzs/AjMLP0ojgACCCDgTwECM3/2K61CAAEEEEAAAe8IEJjZ9RWBmZ0fgZmlH8URQAABBPwpQGDmz36lVQgggAACCCDgHQECM7u+IjCz8yMws/SjOAIIIICAPwUIzPzZr7QKAQQQQAABBLwjQGBm11cEZnZ+BGaWfhRHAAEEEPCnAIGZP/uVViGAAAIIIICAdwQIzOz6isDMzo/AzNKP4ggggAAC/hQgMPNnv9IqBBBAAAEEEPCOAIGZXV8RmNn5EZhZ+lEcAQQQQMCfAgRm/uxXWoUAAggggAAC3hEgMLPrKwIzOz8CM0s/iiOAAAII+FOAwMyf/UqrEEAAAQQQQMA7AgRmdn1FYGbnR2Bm6UdxBBBAAAF/ChCY+bNfaRUCCCCAAAIIeEeAwMyurwjM7PwIzCz9KI4AAggg4E8BAjN/9iutQgABBBBAAAHvCBCY2fUVgZmdH4GZpR/FEUAAAQT8KUBg5s9+pVUIIIAAAggg4B0BAjO7viIws/MjMLP0ozgCCCCAgD8FCMz82a+0CgEEEEAAAQS8I0BgZtdXBGZ2fgRmln4URwABBBDwpwCBmT/7lVYhgAACCCCAgHcECMzs+orAzM6PwMzSj+IIIIAAAv4UIDDzZ7/SKgQQQAABBBDwjgCBmV1fEZjZ+RGYWfpRHAEEEEDAnwIEZv7sV1qFAAIIIIAAAt4RIDCz6ysCMzs/AjNLP4ojgAACCPhTgMDMn/1KqxBAAAEEEEDAOwIEZnZ9RWBm50dgZulHcQQQQAABfwoQmPmzX2kVAggggAACCHhHgMDMrq8IzOz8CMws/SiOAAIIIOBPAQIzf/YrrUIAAQQQQAAB7wgQmNn1FYGZnR+BmaUfxRFAAAEE/ClAYObPfqVVCCCAAAIIIOAdAQIzu74iMLPzIzCz9KM4AggggIA/BQjM/NmvtAoBBBBAAAEEvCNAYGbXVwRmdn4EZpZ+FEcAAQQQ8KcAgZk/+5VWIYAAAggggIB3BAjM7PqKwCxCv+TkZElNFcmTJ8mU3Lx1f4Q1sDsCCCCAAAL+FyAw838f00IEEEAAAQQQiG0BAjO7/iEwi8DvyJFkad75ITmt4onycL+OBGYR2LErAggggEB8CRCYxVd/01oEEEAAAQQQiD0BAjO7PiEwi8DvqZkvydzFb8qV1c4lMIvAjV0RQAABBOJPgMAs/vqcFiOAAAIIIIBAbAkQmNn1B4FZmH7rN2ySviMmS/Oba8sX32wkMAvTjd0QQAABBOJTgMAsPvudViOAAAIIIIBA7AgQmNn1BYFZGH4HDh6S2+4aLMMeaCc//LRZPv7sGwKzMNzYBQEEEEAgfgUIzOK372k5AggggAACCMSGAIGZXT8QmIXhN/bpBZKQkCD3dmwii15ZnSYw273/SBg1sAsCCCCAQDwJHDmSkmPNzZMnMceOFe6BUlNT5dMvUmT2vOieW7lyqXJnS5Hjy0T3OOG2m/0QQAABBBBAAIFYEihaME8snY7nzoXALIsuW/flBrln4ASZM6G/FMifX15+c4189tUPMuDeO6R0qeJCYOa5Mc8JI4AAAlEX+OW3ZHl9ZULUj1OnZqpUrHD0q82xtBGYxVJvcC4IIIAAAgggEK8CBGZ2PU9gloXfkzMWy8QZizPsVapkMVn94uOyeet+ux6gNAIIIICA7wS2/JMgU6Ylyf4D0WtaQoJI107JUqZMavQOYlEzSzIt8CiKAAIIIIAAAgi4IMCSTDtEArMI/dIvySQwixCQ3RFAAIE4ECAwEyEwi4OBThMRQAABBBBAIKYFCMzsuofALEK/F19dLR99ykv/I2RjdwQQQCCuBAjMCMziasDTWAQQQAABBBCISQECM7tuITCz82NJpqUfxRFAAAE/ChCYEZj5cVzTJgQQQAABBBDwlgCBmV1/EZjZ+RGYWfpRHAEEEPCjAIEZgZkfxzVtQgABBBBAAAFvCRCY2fUXgZmdH4GZpR/FEUAAAT8KEJgRmPlxXNMmBBBAAAEEEPCWAIGZXX8RmNn5EZhZ+lEcAQQQ8KMAgRmBmR/HNW1CAAEEEEAAAW8JEJjZ9ReBmZ0fgZmlH8URQAABPwoQmBGY+XFc0yYEEEAAAQQQ8JYAgZldfxGY2fkRmFn6URwBBBDwowCBGYGZH8c1bUIAAQQQQAABbwkQmNn1F4GZnR+BmaUfxRFAAAE/ChCYEZj5cVzTJgQQQAABBBDwlgCBmV1/EZjZ+RGYWfpRHAEEEPCjAIEZgZkfxzVtQgABBBBAAAFvCRCY2fUXgZmdH4GZpR/FEUAAAT8KEJgRmPlxXNMmBBBAAAEEEPCWAIGZXX8RmNn5EZhZ+lEcAQQQ8KMAgRmBmR/HNW1CAAEEEEAAAW8JEJjZ9ReBmZ0fgZmlH8URQAABPwoQmBGY+XFc0yYEEEAAAQQQ8JYAgZldfxGY2fkRmFn6URwBBBDwowCBGYGZH8c1bUIAAQQQQAABbwkQmNn1F4GZnR+BmaUfxRFAAAE/ChCYEZj5cVzTJgQQQAABBBDwlgCBmV1/EZjZ+RGYWfpRHAEEEPCjAIEZgZkfxzVtQgABBBBAAAFvCRCY2fUXgZmdH4GZpR/FEUAAAT8KEJgRmPlxXNMmBBBAAAEEEPCWAIGZXX8RmNn5EZhZ+lEcAQQQ8KMAgRmBmR/HNW1CAAEEEEAAAW8JEJjZ9ReBmZ0fgZmlH8URQAABPwoQmBGY+XFc0yYEEEAAAQQQ8JYAgZldfxGY2fkRmFn6URwBBBDwowCBGYGZH8c1bUIAAQQQQAABbwkQmNn1F4GZnR+BmaUfxRFAAAE/ChCYEZj5cVzTJgQQQAABBBDwlgCBmV1/EZjZ+RGYWfpRHAEEEPCjAIEZgZkfxzVtQgABBBBAAAFvCRCY2fUXgZmdH4GZpR/FEUAAAT8KEJgRmPlxXNMmBBBAAAEEEPCWAIGZXX8RmNn5EZhZ+lEcAQQQ8KMAgRmBmR/HNW1CAAEEEEAAAW8JEJjZ9ReBmZ0fgZmlH8URQAABPwoQmBGY+XFc0yYEEEAAAQQQ8JYAgZldfxGY2fkRmFn6URwBBBDwowCBGYGZH8c1bUIAAQQQQAABbwkQmNn1F4GZnR+BWZBfcrLItm0JlqLhFS9TJjW8HeNwry1b4rsPdu1MkIOHcqbjY3Uc5tQYkASRMqUzXov79ifI3j1x3gf/JMiUaUmy/0D0HBISRLp2SpZYHYffbUiQOc8lRQ9ARMqVS5VWTVOkWHH+TYgqdDYr536cTTiKIYAAAggg4JIAgZkdJIGZnR+BWbrAbP7CJPn2u+gGNrVrpsjVNVIse86/xZcsS5JP1kW3Dy6umioNbkyOSUR9QHt2fqL8+Wd0DVo2S5bKZ8TmQ7oGZhMmJUlqFE+vYAGR9m2TMw3MnluQKJt+jm4f3HJTilStEpv3AmaYMcMsJm+QOXxS3I9zGJzDIYAAAgggkE6AwMxuSBCY2fkRmBGYWY4g94sTmBGYEZi5f11FWiOBGYFZpGPGj/sTmPmxV2kTAggggICXBAjM7HqLwMzOj8CMwMxyBLlfnMCMwIzAzP3rKtIaCcwIzCIdM37cn8DMj71KmxBAAAEEvCRAYGbXWwRmdn4EZgRmliPI/eIEZgRmBGbuX1eR1khgRmAW6Zjx4/4EZn7sVdqEAAIIIOAlAQIzu94iMLPzIzAjMLMcQe4XJzAjMCMwc/+6irRGAjMCs0jHjB/3JzDzY6/SJgQQQAABLwkQmNn1FoGZnR+BGYGZ5QhyvziBGYEZgZn711WkNRKYEZhFOmb8uD+BmR97lTYhgAACCHhJgMDMrrcIzOz8CMwIzCxHkPvFCcwIzAjM3L+uIq2RwIzALNIx48f9Ccz82Ku0CQEEEEDASwIEZna9RWBm50dgRmBmOYLcL05gRmBGYOb+dRVpjQRmBGaRjhk/7k9g5sdepU0IIIAAAl4SIDCz6y0CMzs/AjMCM8sR5H5xAjMCMwIz96+rSGskMCMwi3TM+HF/AjM/9iptQgABBBDwkgCBmV1vEZjZ+RGYEZhZjiD3ixOYEZgRmLl/XUVaI4EZgVmkY8aP+xOY+bFXaRMCCCCAgJcECMzseovAzM6PwIzAzHIEuV+cwIzAjMDM/esq0hoJzAjMIh0zftyfwMyPvUqbEEAAAQS8JEBgZtdbBGZ2fgRmBGaWI8j94gRmBGYEZu5fV5HWSGBGYBbpmPHj/gRmfuxV2oQAAggg4CUBAjO73iIws/MjMCMwsxxB7hcnMCMwIzBz/7qKtEYCMwKzSMeMH/cnMPNjr9ImBBBAAAEvCRCY2fUWgZmdH4EZgZnlCHK/OIEZgRmBmfvXVaQ1EpgRmEU6Zvy4P4GZH3uVNiGAAAIIeEmAwMyutwjM7PwIzAjMLEeQ+8UJzAjMCMzcv64irZHAjMAs0jHjx/0JzPzYq7QJAQQQQMBLAgRmdr1FYGbnR2BGYGY5gtwvTmBGYEZg5v51FWmNBGYEZpGOGT/uT2Dmx16lTQgggAACXhIgMLPrLQIzOz8CMwIzyxHkfnECMwIzAjP3r6tIayQwIzCLdMz4cX8CMz/2Km1CAAEEEPCSAIGZXW8RmNn5EZgRmFmOIPeLE5gRmBGYuX9dRVojgRmBWaRjxo/7E5j5sVdpEwIIIICAlwQIzOx6i8DMzo/AjMDMcgS5X5zAjMCMwMz96yrSGgnMCMwiHTN+3J/AzI+9SpsQQAABBLwkQGBm11sEZnZ+BGYEZpYjyP3iBGYEZgRm7l9XkdZIYEZgFumY8eP+BGZ+7FXahAACCCDgJQECM7veIjCz8yMwIzCzHEHuFycwIzAjMHP/uoq0RgIzArNIx4wf9ycw82Ov0iYEEEAAAS8JEJjZ9RaBmZ0fgRmBmeUIcr84gRmBGYGZ+9dVpDUSmBGYRTpm/Lg/gZkfe5U2IYAAAgh4SYDAzK63CMzC9EtJSZU9e/dJsaKF05TYvHV/mDX4f7fkZJH5C5Pk2+8SotrY2jVT5OoaKVE9hpcrJzAjMCMwy/0rmMCMwCz3R2HunwGBWe73AWeAAAIIIBDfAgRmdv1PYJaFX3Jysjw9e6nMWLA8sOfg+++U+rWqm/9NYPYfIIGZ3cXoVmkCMwIzAjO3rqbs10NgRmCW/dHjn5IEZv7pS1qCAAIIIOBNAQIzu34jMMvCT2eWzXr+NalXu7qULV1SXnvrY+k5eKKse/0ZyZ8vL4FZkB+Bmd3F6FZpAjMCMwIzt66m7NdDYEZglv3R45+SBGb+6UtaggACCCDgTQECM7t+IzCL0G/f/gNSrV4nWbVwnBxfugSBGYFZhCMo+rsTmBGYEZhF/zrL6ggEZgRmWY2RePh7ArN46GXaiAACCCAQywIEZna9Q2AWod/baz6TIWNnycrnx5qSLMn8D5AZZhEOpijtTmBGYEZgFqWLK4JqCcwIzCIYLr7dlcDMt11LwxBAAAEEPCJAYGbXUQRmEfht27FbGncYJP26t5Q6NS4yJXfvP2L+vy7d1P/LqS1PnsScOlTYxzlwMEVmzpWov/S/Tq0UqVsnUZIS035cIDU1VZKT47sPjiSnyPxFIp+sje6HFy6+KFWaNhLJkxR74/DvLSkyfY7In39G1+D25ilS5fxESUhIe5zklFRJzeV7waZfk+XxJxMlNYqXQ8ECIp3ap8jJJyVluEfs2Hn0XrDp5+j2QcMGKXLlpYmSmO5eEAv3419+S5ZJUxJl/4Gwb6ER76hDr3vnFKlYIWMfRFyZywX0fvzpFykye1507xHlyqXKnS1Fji8T3eNkh0fvxxLFazDNOSVwPw51P85Ov7ld5siRnPtIUSz+bOi2J/UhgAACCEQmULRgnsgKsHfaH7FS9adatiwF9h84KG16jJQrq50n3do1CuwfHJi9+0GKbPghug+IeuDGN4uUKB57DwexEJjpA9q6z6PfBw3qxe4DGoFZ7gdmy1ekyB9RDuz0XtCqqUiB/BnvBQRmqZLb92MCMwIz5xcY+6P8Me2CBYVfYGTyC4wsf7jLgR30frxiVfR/LrmuVmrIX2DkQBM5BAIIIIBADAsQmNl1DjPMwvA7dPiIdO03XsqXO04G9WydZkZJ8JLMdZ8myuKl0Q2yKp6SKs1uS5FCBWMv54yFJZnfbUiQOc9Fd7aFzmho1TRFihWPvT7Q4cySzNxfkvn26kR5c1V07wVnVk6Vpo2TJSnEcGdJpkhu349ZksmSTO7HIizJFMnt+3EYP+ayCwIIIICAjwVYkmnXuQRmWfglJydLr6GTzF4j+nSQpH+XoCUm6ZLAxDTvMMvtBzS7oWBfmsDM3tCNGgjMCMxy+wFt3/4EeW5BYtSXZN5yU4pUrRJ6uVNu348JzAjMCMwIzHgPtG0AACAASURBVHQM5Pb92I2fK6gDAQQQQMC7AgRmdn1HYJaF3/oNm+S2joMz7KXLMjvd3oDALEiGwMzuYnSrNIEZgVluP6ARmIkQmBGYEZgRmBGYufWTDfUggAACCGRXgMAsu3JHyxGY2fkRmBGYWY4g94sTmBGYEZixJNP9O0vkNbJEniXyLMlkhlnkdw5KIIAAAgi4KUBgZqdJYGbnR2BGYGY5gtwvTmBGYEZgRmDm/p0l8hoJzAjMCMwIzCK/c1ACAQQQQMBNAQIzO00CMzs/AjMCM8sR5H5xAjMCMwIzAjP37yyR10hgRmBGYEZgFvmdgxIIIIAAAm4KEJjZaRKY2fkRmBGYWY4g94sTmBGYEZgRmLl/Z4m8RgIzAjMCMwKzyO8clEAAAQQQcFOAwMxOk8DMzo/AjMDMcgS5X5zAjMCMwIzAzP07S+Q1EpgRmBGYEZhFfuegBAIIIICAmwIEZnaaBGZ2fgRmBGaWI8j94gRmBGYEZgRm7t9ZIq+RwIzAjMCMwCzyOwclEEAAAQTcFCAws9MkMLPzIzAjMLMcQe4XJzAjMCMwIzBz/84SeY0EZgRmBGYEZpHfOSiBAAIIIOCmAIGZnSaBmZ0fgRmBmeUIcr84gRmBGYEZgZn7d5bIayQwIzAjMCMwi/zOQQkEEEAAATcFCMzsNAnM7PwIzAjMLEeQ+8UJzAjMCMwIzNy/s0ReI4EZgRmBGYFZ5HcOSiCAAAIIuClAYGanSWBm50dgRmBmOYLcL05gRmBGYEZg5v6dJfIaCcwIzAjMCMwiv3NQAgEEEEDATQECMztNAjM7PwIzAjPLEeR+cQIzAjMCMwIz9+8skddIYEZgRmBGYBb5nYMSCCCAAAJuChCY2WkSmNn5EZgRmFmOIPeLE5gRmBGYEZi5f2eJvEYCMwIzAjMCs8jvHJRAAAEEEHBTgMDMTpPAzM6PwIzAzHIEuV+cwIzAjMCMwMz9O0vkNRKYEZgRmBGYRX7noAQCCCCAgJsCBGZ2mgRmdn4EZgRmliPI/eIEZgRmBGYEZu7fWSKvkcCMwIzAjMAs8jsHJRBAAAEE3BQgMLPTJDCz8yMwIzCzHEHuFycwIzAjMCMwc//OEnmNBGYEZgRmBGaR3zkogQACCCDgpgCBmZ0mgZmdH4EZgZnlCHK/OIEZgRmBGYGZ+3eWyGskMCMwIzAjMIv8zkEJBBBAAAE3BQjM7DQJzOz8CMwIzCxHkPvFCcwIzAjMCMzcv7NEXiOBGYEZgRmBWeR3DkoggAACCLgpQGBmp0lgZudHYEZgZjmC3C9OYEZgRmBGYOb+nSXyGgnMCMwIzAjMIr9zUAIBBBBAwE0BAjM7TQIzOz8CMwIzyxHkfnECMwIzAjMCM/fvLJHXSGBGYEZgRmAW+Z2DEggggAACbgoQmNlpEpjZ+RGYEZhZjiD3ixOYEZgRmBGYuX9nibxGAjMCMwIzArPI7xyUQAABBBBwU4DAzE6TwMzOj8CMwMxyBLlfnMCMwIzAjMDM/TtL5DUSmBGYEZgRmEV+56AEAggggICbAgRmdpoEZnZ+BGYEZpYjyP3iBGYEZgRmBGbu31kir5HAjMCMwIzALPI7ByUQQAABBNwUIDCz0yQws/MjMCMwsxxB7hcnMCMwIzAjMHP/zhJ5jQRmBGYEZgRmkd85KIEAAggg4KYAgZmdJoGZnR+BGYGZ5QhyvziBGYEZgRmBmft3lshrJDAjMCMwIzCL/M5BCQQQQAABNwUIzOw0Cczs/GIqMNvyT4JIqmWDwixepkzGAyUni8xfmCTffpcQZi3Z2612zRS5ukZKyMK5/YC2bZtIcnJ02+80PFQf6N8RmBGYEZgRmGXv7upuqdy+H7vbmuzVxv049+/H2es590rFwv147x732nOsmjL7uUQNcmIrXESkUMGMP5/m9s/HOdF2joEAArEvsG9/guTG/ZjAzG5sEJjZ+cVcYDZlWpLsP2DZqGMUT0gQ6dopWQjMUqRY8Yw/lGlgNvPZJNm+I7o/HHZomywVTgqdjvKAlvsPaG+vTpQ3VyVG70IUkTMrp0rTxsmSlJTxMLHwgPbcgkTZ9HN0r4NbbkqRqlVCh+frPk2UxUuj2wcVT0mVZrelZPqAlpv346gOvjArJzDjFxjMMMv9GWb6gMb9OEHi/X4c5m2b3RBAIIoCuXU/JjCz61QCMzs/ArMgP2aYiRCYWV5QLhSPhQc0AjMe0HRGQ7w/oBGYEZjFwv3YhX9WrKrgFxjM+LUaQBRGAAHfCBCYebMrCcws+23z1v2BGuJ9RgOBGYGZ5eXkSvFYeEAjMCMwIzATITAjMIuF+7Er/7BYVEJgRmBmMXwoigACPhIgMPNmZxKYWfYbgdl/gARmBGaWl5MrxWPhAY3AjMCMwIzATG9oLJHP/SXyrvzDYlEJgRmBmcXwoSgCCPhIgMDMm51JYGbZbwRmBGbBQ4glmZYXlAvFCcx4Z44Oo3if8evCpWRdBTPMCMxi4X5sPZAtKyAw435sOYQojgACPhEgMPNmRxKYWfYbgRmBGYGZ5UXkcvFYeEBjhhkzzJhhxgwzZpiJxML92OV/YiKujsCMwCziQUMBBBDwpQCBmTe7lcDMst8IzAjMCMwsLyKXi8fCAxqBGYEZgRmBGYEZgZmOAQIzAjOXf8yhOgQQ8KgAgZk3O47AzLLfCMwIzAjMLC8il4sTmPGApkOKJZkuX1jZqI4lmSzJjIX7cTaGrqtFCMy4H7s6oKgMAQQ8K0Bg5s2uIzCz7DcCMwIzAjPLi8jl4rHwgMYMM2aYMcOMGWbMMGOGGTPMjv4Dzy8wXP5Bh+oQQMCTAgRmnuw2ITCz7DcCMwIzAjPLi8jl4gRmzDDjAc3liyqb1THDjBlmsXA/zubwda0YM8wIzFwbTFSEAAKeFiAw82b3EZhZ9huBGYEZgZnlReRy8Vh4QGOGGTPMmGHGDDNmmDHDjBlmzDBz+UccqkMAAQ8LEJh5s/MIzCz7jcCMwIzAzPIicrk4gRkzzJhh5vJFlc3qmGHGDLNYuB9nc/i6VowZZswwc20wURECCHhagMDMm91HYGbZbwRmBGYEZpYXkcvFY+EBjRlmzDBjhhkzzJhhxgwzZpgxw8zlH3GoDgEEPCxAYObNziMws+w3AjMCMwIzy4vI5eIEZswwY4aZyxdVNqtjhhkzzGLhfpzN4etaMWaYMcPMtcFERQgg4GkBAjNvdh+BmWW/EZgRmBGYWV5ELhePhQc0Zpgxw4wZZswwY4YZM8yYYcYMM5d/xKE6BBDwsACBmTc7j8DMst8IzAjMCMwsLyKXixOYMcOMGWYuX1TZrI4ZZswwi4X7cTaHr2vFmGHGDDPXBhMVIYCApwUIzLzZfQRmlv1GYEZgRmBmeRG5XDwWHtCYYcYMM2aYMcOMGWbMMGOGGTPMXP4Rh+oQQMDDAgRm3uw8AjPLfiMwIzAjMLO8iFwuTmDGDDNmmLl8UWWzOmaYMcMsFu7H2Ry+rhVjhhkzzFwbTFSEAAKeFiAw82b3EZhZ9huBGYEZgZnlReRy8Vh4QGOGGTPMmGHGDDNmmDHDjBlmzDBz+UccqkMAAQ8LEJh5s/MIzCz7jcCMwIzAzPIicrk4gRkzzJhh5vJFlc3qmGHGDLNYuB9nc/i6VowZZswwc20wURECCHhagMDMm91HYGbZbwRmBGYEZpYXkcvFY+EBjRlmzDBjhhkzzJhhxgwzZpgxw8zlH3GoDgEEPCxAYObNziMws+w3AjMCMwIzy4vI5eIEZswwY4aZyxdVNqtjhhkzzGLhfpzN4etaMWaYMcPMtcFERQgg4GkBAjNvdh+BWQT9tnffASlcqECaEgRmBGYEZhFcRDmwayw8oDHDjBlmzDBjhhkzzJhhxgwzZpjlwI89HAIBBDwiQGDmkY5Kd5oEZmH02x9/bZW+Dz8jX2/YJCVLFJU+XVpIzSuqmJIEZgRmBGZhXEQ5uAuBGTPMdLit+zRRFi9NjOrIq3hKqjS7LUUKFUzNcBwCMwIzAjMCMwIzArOo/iNE5Qgg4CkBAjNPdVfgZAnMwui3noMnygllj5OeHZvIF9/8KHf1HiOvzRstJYsXJTAL8ktOFpm/MEm+/S4hDNXs71K7ZopcXSMlZAW5vQRo2zaRmc8myfYd0TXo0DZZKpyU8SGdB7TYeEBjhhkzzAjMCMy4H8fG/Tj7P224U5IlmfwCw52RRC0IIOB1AQIzb/YggVkW/ZacnCzn124nb70wTsocV8Ls3bbnKGl167VS64qqBGYEZmlGEIFZ7t8ImWHGDDMdhcwwy/1rMbd/gZH7ArzDLBbux7k9DgjMuB/n9hjk+AggEBsCBGax0Q+RngWBWRZif/+zQ2o27iFfrZouCQlHZw0NHD1NKp96srRsVIfAjMCMwCzSu06U94+FBzRmmDHDjBlmzDDTW92SZUnyybrozji+uGqqNLgxOcp31uxVHwv34+yduXulCMwIzNwbTdSEAAJeFiAw82bvEZhl0W/f//SbtOwyTD56ZVJgzzGT5ku+fHmlW9tGgT87kpwib7xzRL7dEP2B0LJxohx/XJ4MB/p24yF5dUX0j39drVQ55/T8GQ60e2+yTHk2+j+0lz9BpGG9PJIvb9r3EyWnpMr7nxyWTz6NvsGtNyXIyeXzZjjQDz8fkqXLo3/8q69IlSrn5Jd/M9zAAQ8cSpaZzyXLvv3RPYdCBUVaN0uSAvmSMhxoz/4j0T34v7XnzZMo+dONAf2rXzYflheWhl6u6uaJXVxF5PKL80pSYtqH4UOHU+TFV4/I5j/cPFroutq3SpKihTP2wfrvD8rrK6P7kK5nVK+OyJmn5stwcn9vPSJzFoZeNu2myplniFx7VR7Jk5T2XsD9WESvgL05dC0WKZjx36NYuB8fPJwih49EfxzqmA5lwP049+/H2jc59W9S4YJ5JNRdl/sxPx/n9v04J68D/dk8X56M7w/N7ftxThqE+vcgJ4+f2c/Hh46kiP6MmhNbbhtkdj/ee+CIpEb/EcE8GxTMn/Hn89z++Tgn+t6PxyAwy6JXt+3YLTVu6SZfrpwuif8+GA94ZKpUOrm8tG1Wz49jgjYhgAACCCCAAAIIIIAAAggggAACcS1AYJZF96empsq5Ne+UN+aPkfJljzN7t+o6XFo0qiP1a1WP68FD4xFAAAEEEEAAAQQQQAABBBBAAAE/ChCYhdGrD42dKXnyJEnvzs1l7RcbzEv/318yUYoXKxxGaXZBAAEEEEAAAQQQQAABBBBAAAEEEPCSAIFZGL21fedueWDYZHnv4y+lcKECMuT+O6Ues8vCkGMXBBAIJZCSkip79u6TYkVDh+579x0w9xo2BKIpcPjwETlw6LAULVwwmoehbgSOKXDg4CFJSkyUvHlDvQsvRQ4fOiIFCmR8VyKsCLgpcKxx6OZxqAuBrAT0Z8QjycmSL909kZ8Ns5Lj7xGIjgCBWQSueqMqVFBftB79l2lHcFrsGoHAzl17pd/IZ0T7csa4PoGSE6a/KE/NfCnwv8uWKSUrnx8bQc3sikDWAsnJyfL07KUyY8F/X4YYfP+dgeXda9aul2HjZsuWrTvktIonyvC+HaRShXJZV8weCEQgoO/mfGTiPFn6xvsmmC1zXAl5bHAXOePUCqYWfe3Ap199H6ixyU3XyOD72kRwBHZFIGuBdz/6Uh55cp5s37nH7HzJhWfKgHvvkBLFipj/PXPBcpky7xU5ePCQXHnJefJwv46SP1/Gj+1kfST2QCBzgWONQ/03uf19o9MUDn5FC64IRENg1IS5suzND2T1i4+b6v/4a6v0ffgZ+XrDJilZoqj06dJCal5RJRqHpk4EEAghQGDGsIgbgQ0//iadHhhrgogDBw/KrMf7Bdquy24rn3ayNKpXw/yZhqK6DJcNATcF9LeGs55/TerVri5lS5eU1976WHoOnijrXn9GUlJS5LrmvWTQva2l1pVVZfr8V2X1h1+kCXbdPBfqil8B/cXBi6+ulmY315K8eZPk0UkL5IeffpNnHu1lUK5v0UueGHZPIKzVD94kJXE/jN8RE52Wf/71RlPxBWefKjq7p/uDT0j1KmdKuxY3yHcbf5Xbuw2XRVOHSulSxaXXQ5PknMoVpdMdDaJzMtQatwLHGoevrPxQVr27Tkb07RDwCTUTMm7xaLjrAh+u+8aEY4ePHAkEZvpz4gllj5OeHZvIF9/8KHf1HiOvzRstJYsXdf34VIgAAhkFCMwYFXEj8Pc/O8xDoW6TZi9JE5j1GDhBGlx/udS6omrceNDQ3BfYt/+AVKvXSVYtHCeb//xH+o+aIi/PHmlOTB8gL7q+o7z70hP8UJT7XeXrM3j/469kyGMz5bW5R2dSnHNNG8adr3s8Nhs3dvLzsm/fAXmwx+0yZe7L8teW7dL/nlbmZPX9sUPGzpAlM0bE5slzVr4RCB6H8158U37Z/Lc80KW5b9pHQ2JXYPfe/dK4w0Azg2zgo9NNYKYrE86v3U7eemGcmQ2um75Lu9Wt1/LMErtdyZn5TIDAzGcdSnOyFtCHw/SBmS5BOr50CTPr55QK5eS6qy6WUiWLZV0ZeyBgIfD2ms9kyNhZZvmvzjbTWT+TRvUM1FirSU+ZOOIeOev0UyyOQlEEji0wZtJ82bl7rzzUq60cPHRYql7XQdo0rSuJCQlS+dSTpW7NS5hxyyCKioAuD/7tj7/l8/Ub5Ylpi2TGuL5y9hmnmKXpJ55QWu5sWs8c9/c//5Hrmt0v69+aEZXzoNL4FshsHD45Y7Hoks3zzvqfHF+6pFx16flyeqWT4huL1kdNYODoaXLqKeXlqssulDu6jzCBmf6yv2bjHvLVqumBVwLpfvpvc8tGdaJ2LlSMAAL/CRCYMRriTiBUYLZ4+buye88+OXT4iLy5eq1s3bFLFkwazJdQ42505FyD9Qf0xh0GSb/uLaVOjYtk/pJV8sln38rogXcHTqJhuwHSu3Mzueyic3LuxDhSXAl89tUP0rH3o7J4+nApX/Y4OXIkWaY994oUL1bEhBTL3lgj551ZScYP7RZXLjQ2ZwR0fE2es1Q2btosTRvUlC53NpTjShaT+4Y8KZdWPVv0/Xm67dq9Vy67qYusXT6ZDwDkTNfE1VEyG4frvtxgZjcWKVRQ3v9kvax8b53MfXKAWUbMhoCbAm9/8Lk8PWuJzH6in/yyeUsgMPv+p9+kZZdh8tErkwKH019y5cuXV7q1beTmKVAXAghkIkBgxtCIO4FQgVkwgj4wXt6giwzv016uveriuPOhwdEX2H/goLTpMVKurHaedGt39AeeN99dJ/NfWiWTR98XOIEaDbvLhOH38MN59LskLo/w069/StO7BsuYQZ2lRvXzQxroux8btn1Q3l403rxLig2BaAjoF1uHP/6sHDhwSEb27yijJs4z461d8/rmcL9t3mLerRc8yyIa50Gd8S2Qfhym19B3Sem47Nf96FJhNgTcENixa4+ZQfv4sO7yv5PLy6+b/5a7+4yVZbNGSmKCyNW39pAvV04XfZ+obgMemSqVTi4vbZsdnYHLhgAC0RUgMIuuL7XHoEBWgZmecof7R8sNdS6TW+peGYMt4JS8LKCzGLv2Gy/lyx0ng3q2DkyxX79hk/kAwPI5j5g/c95v9uaCsVLu+FJebjLnHoMC+tWtll2Hy70dG8tN116e6RnqLxAuqNNOXp0zSk4+sWwMtoRT8ouAfuSk38gpZhnSrIWvy8ZNv8uQ++80zdMXYQ98dFrgPXt+aTPtiD2B4HGY/uz03Xo//vKHjOjTPvZOnDPyrECor7E6jXlx2lBp2HaABH+dVV8j06JRncAX1j3bcE4cAY8IEJh5pKM4TfcE0gdm+kJN/UpSlXNPN0HFp199L/qP0SvPjpJTTuIB0T15atKx1mvo0Wn1I/p0kKSkRPPfiUmJkpKcIre0fdAsSbr+mmryzJxlZnnw85OHAIeAqwLbtu+S5l2GSqtG15ovZTqbfhl4y9adojMg9d6Xmpoqz77whkyes0zeWTQ+EO66ejJUFrcC+u9u5f9VMEsst27fJUMfmyXJySnyxPDuZobFre0HmvufvltUf5lw2v9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