{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "fb106e20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json, pickle\n",
    "import numpy as np\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from seqeval.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.metrics import CategoricalAccuracy\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "00347a5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribusi label SRL:\n",
      "ARG1: 2597\n",
      "O: 1487\n",
      "V: 943\n",
      "ARGM-LOC: 781\n",
      "ARGM-TMP: 736\n",
      "ARG0: 685\n",
      "ARG2: 445\n",
      "ARGM-MNR: 221\n",
      "ARGM-CAU: 113\n",
      "ARGM-MOD: 90\n",
      "ARGM-ADV: 29\n",
      "ARGM-NEG: 18\n",
      "ARGM-PRP: 18\n",
      "ARGM-SRC: 16\n",
      "ARGM-COM: 16\n",
      "ARGM-PRD: 13\n",
      "ARGM-DIR: 13\n",
      "ARG3: 12\n",
      "ARGM-DIS: 8\n",
      "ARGM-BNF: 6\n",
      "I-TIME: 4\n",
      "ARGM-FRQ: 2\n",
      "ARGM-ORD: 2\n",
      "ARGM-EXT: 2\n",
      "ARGM-REC: 2\n",
      "B-TIME: 2\n",
      "Osoe: 1\n",
      "ARGM-ADJ: 1\n",
      "ARGM-EX: 1\n",
      "\n",
      "Distribusi label NER:\n",
      "O: 5782\n",
      "B-LOC: 551\n",
      "I-QUANT: 224\n",
      "B-QUANT: 203\n",
      "I-LOC: 202\n",
      "I-EVENT: 151\n",
      "I-DATE: 146\n",
      "B-EVENT: 143\n",
      "B-DATE: 139\n",
      "I-PER: 121\n",
      "B-TIME: 113\n",
      "B-PER: 110\n",
      "I-TIME: 98\n",
      "B-ETH: 83\n",
      "I-ETH: 65\n",
      "B-ORG: 60\n",
      "I-ORG: 24\n",
      "B-MISC: 22\n",
      "B-RES: 8\n",
      "B-MIN: 6\n",
      "I-MISC: 4\n",
      "I-TERM: 3\n",
      "B-REL: 2\n",
      "B-TERM: 2\n",
      "I-RES: 2\n",
      "\n",
      "Total kalimat: 799\n",
      "Total token: 8264\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "data = []\n",
    "\n",
    "with open(\"../dataset/dataset_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
    "    tokens, ner_labels, srl_labels = [], [], []\n",
    "    \n",
    "    for line in f:\n",
    "        line = line.strip()\n",
    "        if not line:\n",
    "            if tokens:\n",
    "                data.append({\n",
    "                    \"tokens\": tokens,\n",
    "                    \"labels_ner\": ner_labels,\n",
    "                    \"labels_srl\": srl_labels\n",
    "                })\n",
    "                tokens, ner_labels, srl_labels = [], [], []\n",
    "        else:\n",
    "            token, ner, srl = line.split(\"\\t\")\n",
    "            tokens.append(token)\n",
    "            ner_labels.append(ner)\n",
    "            srl_labels.append(srl)\n",
    "\n",
    "# Preprocessing\n",
    "sentences = [[tok.lower() for tok in item[\"tokens\"]] for item in data]\n",
    "labels_ner = [item[\"labels_ner\"] for item in data]\n",
    "labels_srl = [item[\"labels_srl\"] for item in data]\n",
    "\n",
    "# Hitung total label SRL\n",
    "srl_counter = Counter()\n",
    "for srl_sequence in labels_srl:\n",
    "    srl_counter.update(srl_sequence)\n",
    "\n",
    "print(\"Distribusi label SRL:\")\n",
    "for label, count in srl_counter.most_common():\n",
    "    print(f\"{label}: {count}\")\n",
    "\n",
    "# Hitung total label NER\n",
    "ner_counter = Counter()\n",
    "for ner_sequence in labels_ner:\n",
    "    ner_counter.update(ner_sequence)\n",
    "\n",
    "print(\"\\nDistribusi label NER:\")\n",
    "for label, count in ner_counter.most_common():\n",
    "    print(f\"{label}: {count}\")\n",
    "\n",
    "# Total kalimat dan token\n",
    "total_kalimat = len(data)\n",
    "total_token = sum(len(item[\"tokens\"]) for item in data)\n",
    "\n",
    "print(\"\\nTotal kalimat:\", total_kalimat)\n",
    "print(\"Total token:\", total_token)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ac8eb374",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tagging \n",
    "words = sorted({w for s in sentences for w in s})\n",
    "ner_tags = sorted({t for seq in labels_ner for t in seq})\n",
    "srl_tags = sorted({t for seq in labels_srl for t in seq})\n",
    "\n",
    "word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
    "word2idx[\"PAD\"], word2idx[\"UNK\"] = 0, 1\n",
    "\n",
    "tag2idx_ner = {t: i for i, t in enumerate(ner_tags)}\n",
    "tag2idx_srl = {t: i for i, t in enumerate(srl_tags)}\n",
    "idx2tag_ner = {i: t for t, i in tag2idx_ner.items()}\n",
    "idx2tag_srl = {i: t for t, i in tag2idx_srl.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "80356f1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39\n"
     ]
    }
   ],
   "source": [
    "# encoding\n",
    "\n",
    "X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n",
    "y_ner = [[tag2idx_ner[t] for t in seq] for seq in labels_ner]\n",
    "y_srl = [[tag2idx_srl[t] for t in seq] for seq in labels_srl]\n",
    "\n",
    "maxlen = max(len(s) for s in sentences)\n",
    "print(maxlen)\n",
    "\n",
    "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n",
    "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n",
    "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n",
    "\n",
    "y_ner = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]\n",
    "y_srl = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]\n",
    "\n",
    "X = np.array(X)\n",
    "y_ner = np.array(y_ner)\n",
    "y_srl = np.array(y_srl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fe219c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_ner_train, y_ner_test, y_srl_train, y_srl_test = train_test_split(\n",
    "    X, y_ner, y_srl,            \n",
    "    test_size=0.20,             \n",
    "    random_state=42,\n",
    "    shuffle=True            \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9636b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_6\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_6\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_6       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ embedding_6         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │    <span style=\"color: #00af00; text-decoration-color: #00af00\">130,816</span> │ input_layer_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_6     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ embedding_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ner_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,225</span> │ bidirectional_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ srl_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">39</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">29</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,741</span> │ bidirectional_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_6       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)        │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ embedding_6         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │    \u001b[38;5;34m130,816\u001b[0m │ input_layer_6[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mEmbedding\u001b[0m)         │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_6     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m66,048\u001b[0m │ embedding_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ner_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m, \u001b[38;5;34m25\u001b[0m)    │      \u001b[38;5;34m3,225\u001b[0m │ bidirectional_6[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ srl_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m39\u001b[0m, \u001b[38;5;34m29\u001b[0m)    │      \u001b[38;5;34m3,741\u001b[0m │ bidirectional_6[\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │                   │\n",
       "└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">203,830</span> (796.21 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m203,830\u001b[0m (796.21 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">203,830</span> (796.21 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m203,830\u001b[0m (796.21 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 10ms/step - loss: 1.8462 - ner_output_accuracy: 0.9063 - ner_output_loss: 0.7988 - srl_output_accuracy: 0.8035 - srl_output_loss: 1.0474 - val_loss: 0.8543 - val_ner_output_accuracy: 0.9196 - val_ner_output_loss: 0.3470 - val_srl_output_accuracy: 0.8340 - val_srl_output_loss: 0.5073\n",
      "Epoch 2/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.7499 - ner_output_accuracy: 0.9186 - ner_output_loss: 0.3147 - srl_output_accuracy: 0.8535 - srl_output_loss: 0.4352 - val_loss: 0.6488 - val_ner_output_accuracy: 0.9325 - val_ner_output_loss: 0.2552 - val_srl_output_accuracy: 0.8750 - val_srl_output_loss: 0.3936\n",
      "Epoch 3/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.5438 - ner_output_accuracy: 0.9362 - ner_output_loss: 0.2241 - srl_output_accuracy: 0.9011 - srl_output_loss: 0.3197 - val_loss: 0.5199 - val_ner_output_accuracy: 0.9534 - val_ner_output_loss: 0.1888 - val_srl_output_accuracy: 0.9000 - val_srl_output_loss: 0.3311\n",
      "Epoch 4/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.3795 - ner_output_accuracy: 0.9596 - ner_output_loss: 0.1522 - srl_output_accuracy: 0.9356 - srl_output_loss: 0.2273 - val_loss: 0.4739 - val_ner_output_accuracy: 0.9631 - val_ner_output_loss: 0.1609 - val_srl_output_accuracy: 0.9090 - val_srl_output_loss: 0.3131\n",
      "Epoch 5/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.2946 - ner_output_accuracy: 0.9706 - ner_output_loss: 0.1146 - srl_output_accuracy: 0.9507 - srl_output_loss: 0.1799 - val_loss: 0.4509 - val_ner_output_accuracy: 0.9678 - val_ner_output_loss: 0.1383 - val_srl_output_accuracy: 0.9114 - val_srl_output_loss: 0.3125\n",
      "Epoch 6/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.2389 - ner_output_accuracy: 0.9789 - ner_output_loss: 0.0907 - srl_output_accuracy: 0.9578 - srl_output_loss: 0.1482 - val_loss: 0.4452 - val_ner_output_accuracy: 0.9692 - val_ner_output_loss: 0.1355 - val_srl_output_accuracy: 0.9165 - val_srl_output_loss: 0.3097\n",
      "Epoch 7/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.1932 - ner_output_accuracy: 0.9821 - ner_output_loss: 0.0719 - srl_output_accuracy: 0.9663 - srl_output_loss: 0.1214 - val_loss: 0.4347 - val_ner_output_accuracy: 0.9704 - val_ner_output_loss: 0.1281 - val_srl_output_accuracy: 0.9228 - val_srl_output_loss: 0.3066\n",
      "Epoch 8/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.1375 - ner_output_accuracy: 0.9875 - ner_output_loss: 0.0495 - srl_output_accuracy: 0.9763 - srl_output_loss: 0.0880 - val_loss: 0.4387 - val_ner_output_accuracy: 0.9710 - val_ner_output_loss: 0.1257 - val_srl_output_accuracy: 0.9204 - val_srl_output_loss: 0.3130\n",
      "Epoch 9/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.1198 - ner_output_accuracy: 0.9895 - ner_output_loss: 0.0440 - srl_output_accuracy: 0.9801 - srl_output_loss: 0.0758 - val_loss: 0.4530 - val_ner_output_accuracy: 0.9715 - val_ner_output_loss: 0.1357 - val_srl_output_accuracy: 0.9204 - val_srl_output_loss: 0.3173\n",
      "Epoch 10/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0948 - ner_output_accuracy: 0.9914 - ner_output_loss: 0.0358 - srl_output_accuracy: 0.9846 - srl_output_loss: 0.0590 - val_loss: 0.4690 - val_ner_output_accuracy: 0.9716 - val_ner_output_loss: 0.1320 - val_srl_output_accuracy: 0.9202 - val_srl_output_loss: 0.3370\n",
      "Epoch 11/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0820 - ner_output_accuracy: 0.9926 - ner_output_loss: 0.0303 - srl_output_accuracy: 0.9860 - srl_output_loss: 0.0518 - val_loss: 0.4649 - val_ner_output_accuracy: 0.9710 - val_ner_output_loss: 0.1344 - val_srl_output_accuracy: 0.9208 - val_srl_output_loss: 0.3305\n",
      "Epoch 12/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0625 - ner_output_accuracy: 0.9952 - ner_output_loss: 0.0218 - srl_output_accuracy: 0.9902 - srl_output_loss: 0.0407 - val_loss: 0.5084 - val_ner_output_accuracy: 0.9712 - val_ner_output_loss: 0.1403 - val_srl_output_accuracy: 0.9213 - val_srl_output_loss: 0.3681\n",
      "Epoch 13/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0495 - ner_output_accuracy: 0.9957 - ner_output_loss: 0.0173 - srl_output_accuracy: 0.9923 - srl_output_loss: 0.0321 - val_loss: 0.4905 - val_ner_output_accuracy: 0.9718 - val_ner_output_loss: 0.1420 - val_srl_output_accuracy: 0.9226 - val_srl_output_loss: 0.3485\n",
      "Epoch 14/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0447 - ner_output_accuracy: 0.9957 - ner_output_loss: 0.0180 - srl_output_accuracy: 0.9934 - srl_output_loss: 0.0267 - val_loss: 0.5138 - val_ner_output_accuracy: 0.9710 - val_ner_output_loss: 0.1449 - val_srl_output_accuracy: 0.9205 - val_srl_output_loss: 0.3689\n",
      "Epoch 15/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0353 - ner_output_accuracy: 0.9980 - ner_output_loss: 0.0116 - srl_output_accuracy: 0.9942 - srl_output_loss: 0.0236 - val_loss: 0.5168 - val_ner_output_accuracy: 0.9679 - val_ner_output_loss: 0.1456 - val_srl_output_accuracy: 0.9197 - val_srl_output_loss: 0.3712\n",
      "Epoch 16/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0315 - ner_output_accuracy: 0.9969 - ner_output_loss: 0.0122 - srl_output_accuracy: 0.9957 - srl_output_loss: 0.0193 - val_loss: 0.5367 - val_ner_output_accuracy: 0.9694 - val_ner_output_loss: 0.1467 - val_srl_output_accuracy: 0.9231 - val_srl_output_loss: 0.3900\n",
      "Epoch 17/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0229 - ner_output_accuracy: 0.9988 - ner_output_loss: 0.0073 - srl_output_accuracy: 0.9971 - srl_output_loss: 0.0157 - val_loss: 0.5494 - val_ner_output_accuracy: 0.9691 - val_ner_output_loss: 0.1525 - val_srl_output_accuracy: 0.9213 - val_srl_output_loss: 0.3969\n",
      "Epoch 18/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0235 - ner_output_accuracy: 0.9985 - ner_output_loss: 0.0081 - srl_output_accuracy: 0.9971 - srl_output_loss: 0.0154 - val_loss: 0.5552 - val_ner_output_accuracy: 0.9710 - val_ner_output_loss: 0.1546 - val_srl_output_accuracy: 0.9216 - val_srl_output_loss: 0.4006\n",
      "Epoch 19/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0184 - ner_output_accuracy: 0.9989 - ner_output_loss: 0.0065 - srl_output_accuracy: 0.9976 - srl_output_loss: 0.0119 - val_loss: 0.5899 - val_ner_output_accuracy: 0.9686 - val_ner_output_loss: 0.1588 - val_srl_output_accuracy: 0.9196 - val_srl_output_loss: 0.4311\n",
      "Epoch 20/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0157 - ner_output_accuracy: 0.9989 - ner_output_loss: 0.0057 - srl_output_accuracy: 0.9982 - srl_output_loss: 0.0100 - val_loss: 0.5719 - val_ner_output_accuracy: 0.9692 - val_ner_output_loss: 0.1592 - val_srl_output_accuracy: 0.9215 - val_srl_output_loss: 0.4126\n",
      "Epoch 21/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0142 - ner_output_accuracy: 0.9987 - ner_output_loss: 0.0061 - srl_output_accuracy: 0.9990 - srl_output_loss: 0.0081 - val_loss: 0.5963 - val_ner_output_accuracy: 0.9705 - val_ner_output_loss: 0.1665 - val_srl_output_accuracy: 0.9220 - val_srl_output_loss: 0.4298\n",
      "Epoch 22/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0114 - ner_output_accuracy: 0.9989 - ner_output_loss: 0.0047 - srl_output_accuracy: 0.9990 - srl_output_loss: 0.0067 - val_loss: 0.6074 - val_ner_output_accuracy: 0.9707 - val_ner_output_loss: 0.1674 - val_srl_output_accuracy: 0.9215 - val_srl_output_loss: 0.4400\n",
      "Epoch 23/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0080 - ner_output_accuracy: 0.9994 - ner_output_loss: 0.0030 - srl_output_accuracy: 0.9993 - srl_output_loss: 0.0050 - val_loss: 0.6179 - val_ner_output_accuracy: 0.9692 - val_ner_output_loss: 0.1691 - val_srl_output_accuracy: 0.9215 - val_srl_output_loss: 0.4488\n",
      "Epoch 24/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0094 - ner_output_accuracy: 0.9988 - ner_output_loss: 0.0038 - srl_output_accuracy: 0.9990 - srl_output_loss: 0.0057 - val_loss: 0.6226 - val_ner_output_accuracy: 0.9686 - val_ner_output_loss: 0.1738 - val_srl_output_accuracy: 0.9229 - val_srl_output_loss: 0.4488\n",
      "Epoch 25/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0086 - ner_output_accuracy: 0.9993 - ner_output_loss: 0.0032 - srl_output_accuracy: 0.9991 - srl_output_loss: 0.0054 - val_loss: 0.6720 - val_ner_output_accuracy: 0.9696 - val_ner_output_loss: 0.1785 - val_srl_output_accuracy: 0.9232 - val_srl_output_loss: 0.4935\n",
      "Epoch 26/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0094 - ner_output_accuracy: 0.9993 - ner_output_loss: 0.0032 - srl_output_accuracy: 0.9983 - srl_output_loss: 0.0062 - val_loss: 0.6702 - val_ner_output_accuracy: 0.9699 - val_ner_output_loss: 0.1800 - val_srl_output_accuracy: 0.9244 - val_srl_output_loss: 0.4902\n",
      "Epoch 27/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0071 - ner_output_accuracy: 0.9996 - ner_output_loss: 0.0023 - srl_output_accuracy: 0.9989 - srl_output_loss: 0.0048 - val_loss: 0.6484 - val_ner_output_accuracy: 0.9708 - val_ner_output_loss: 0.1834 - val_srl_output_accuracy: 0.9221 - val_srl_output_loss: 0.4650\n",
      "Epoch 28/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0092 - ner_output_accuracy: 0.9992 - ner_output_loss: 0.0036 - srl_output_accuracy: 0.9988 - srl_output_loss: 0.0055 - val_loss: 0.6592 - val_ner_output_accuracy: 0.9668 - val_ner_output_loss: 0.1848 - val_srl_output_accuracy: 0.9216 - val_srl_output_loss: 0.4744\n",
      "Epoch 29/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0071 - ner_output_accuracy: 0.9990 - ner_output_loss: 0.0034 - srl_output_accuracy: 0.9995 - srl_output_loss: 0.0037 - val_loss: 0.6624 - val_ner_output_accuracy: 0.9696 - val_ner_output_loss: 0.1908 - val_srl_output_accuracy: 0.9220 - val_srl_output_loss: 0.4716\n",
      "Epoch 30/30\n",
      "\u001b[1m320/320\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 9ms/step - loss: 0.0053 - ner_output_accuracy: 0.9994 - ner_output_loss: 0.0025 - srl_output_accuracy: 0.9995 - srl_output_loss: 0.0028 - val_loss: 0.6863 - val_ner_output_accuracy: 0.9668 - val_ner_output_loss: 0.1852 - val_srl_output_accuracy: 0.9216 - val_srl_output_loss: 0.5011\n"
     ]
    }
   ],
   "source": [
    "input_layer = Input(shape=(maxlen,))\n",
    "embed = Embedding(len(word2idx), 64)(input_layer)\n",
    "bilstm = Bidirectional(LSTM(64, return_sequences=True))(embed)\n",
    "\n",
    "ner_output = TimeDistributed(\n",
    "    Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\"\n",
    ")(bilstm)\n",
    "srl_output = TimeDistributed(\n",
    "    Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\"\n",
    ")(bilstm)\n",
    "\n",
    "model = Model(inputs=input_layer, outputs=[ner_output, srl_output])\n",
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss={\n",
    "        \"ner_output\": \"categorical_crossentropy\",\n",
    "        \"srl_output\": \"categorical_crossentropy\",\n",
    "    },\n",
    "    metrics={\n",
    "        \"ner_output\": [CategoricalAccuracy(name=\"accuracy\")],\n",
    "        \"srl_output\": [CategoricalAccuracy(name=\"accuracy\")],\n",
    "    },\n",
    ")\n",
    "\n",
    "callbacks = [\n",
    "    tf.keras.callbacks.EarlyStopping(monitor=\"val_loss\",\n",
    "                                     patience=3,\n",
    "                                     restore_best_weights=True),\n",
    "    tf.keras.callbacks.ModelCheckpoint(\n",
    "        \"best_model.keras\", monitor=\"val_loss\", save_best_only=True)\n",
    "]\n",
    "\n",
    "model.summary()\n",
    "history = model.fit(\n",
    "    X_train, {\"ner_output\": y_ner_train, \"srl_output\": y_srl_train}, \n",
    "    validation_data=(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}),\n",
    "    batch_size=2,\n",
    "    epochs=30,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "# ---------- 6. Simpan artefak ----------\n",
    "model.save(\"multi_task_lstm_ner_srl_model.keras\")\n",
    "with open(\"word2idx.pkl\", \"wb\") as f:\n",
    "    pickle.dump(word2idx, f)\n",
    "with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n",
    "    pickle.dump(tag2idx_ner, f)\n",
    "with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n",
    "    pickle.dump(tag2idx_srl, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "82dfe902",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ner_accuracy = history.history['ner_output_accuracy']\n",
    "srl_accuracy = history.history['srl_output_accuracy']\n",
    "epochs = list(range(1, len(ner_accuracy) + 1))\n",
    "\n",
    "ner_loss = history.history['ner_output_loss']\n",
    "srl_loss = history.history['srl_output_loss']\n",
    "val_ner_loss = history.history['val_ner_output_loss']\n",
    "val_srl_loss = history.history['val_srl_output_loss']\n",
    "\n",
    "\n",
    "# --------- Plot Accuracy ---------\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs, ner_accuracy, marker='o', label='NER Accuracy')\n",
    "plt.plot(epochs, srl_accuracy, marker='s', label='SRL Accuracy')\n",
    "plt.title('Training Accuracy per Epoch')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0.7, 1.0)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"accuracy_plot.png\")  # Simpan sebagai file gambar\n",
    "plt.show()\n",
    "\n",
    "# --------- Plot Loss ---------\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(epochs, ner_loss, label='NER Loss (Train)')\n",
    "plt.plot(epochs, val_ner_loss, label='NER Loss (Val)')\n",
    "plt.plot(epochs, srl_loss, label='SRL Loss (Train)')\n",
    "plt.plot(epochs, val_srl_loss, label='SRL Loss (Val)')\n",
    "plt.title('Loss per Epoch')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"loss_plot.png\")  \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3a55990b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics names: ['loss', 'compile_metrics', 'ner_output_loss', 'srl_output_loss']\n",
      "loss: 0.6862505674362183\n",
      "compile_metrics: 0.18517984449863434\n",
      "ner_output_loss: 0.5010706782341003\n",
      "srl_output_loss: 0.9668269157409668\n",
      "[NER] Token-level Accuracy: 0.97\n",
      "[SRL] Token-level Accuracy: 0.92\n",
      "[NER] Classification Report (test set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        DATE       0.45      0.56      0.50        18\n",
      "         ETH       0.84      0.80      0.82        20\n",
      "       EVENT       0.47      0.58      0.52        26\n",
      "         LOC       0.63      0.74      0.68       121\n",
      "        MISC       0.00      0.00      0.00         3\n",
      "         ORG       0.38      0.25      0.30        12\n",
      "         PER       0.71      0.58      0.64        26\n",
      "       QUANT       0.77      0.77      0.77        43\n",
      "         RES       0.00      0.00      0.00         5\n",
      "        TERM       0.00      0.00      0.00         2\n",
      "        TIME       0.47      0.42      0.44        19\n",
      "\n",
      "   micro avg       0.62      0.64      0.63       295\n",
      "   macro avg       0.43      0.43      0.42       295\n",
      "weighted avg       0.60      0.64      0.62       295\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# evaluation\n",
    "\n",
    "print(\"Metrics names:\", model.metrics_names)\n",
    "\n",
    "\n",
    "results = model.evaluate(\n",
    "    X_test,\n",
    "    {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test},\n",
    "    verbose=0\n",
    ")\n",
    "for name, value in zip(model.metrics_names, results):\n",
    "    print(f\"{name}: {value}\")\n",
    "    \n",
    "def token_level_accuracy(y_true, y_pred):\n",
    "    total, correct = 0, 0\n",
    "    for true_seq, pred_seq in zip(y_true, y_pred):\n",
    "        for t, p in zip(true_seq, pred_seq):\n",
    "            if t.sum() == 0:  # skip PAD\n",
    "                continue\n",
    "            total += 1\n",
    "            if t.argmax() == p.argmax():\n",
    "                correct += 1\n",
    "    return correct / total\n",
    "\n",
    "\n",
    "def decode(pred, true, idx2tag):\n",
    "    out_true, out_pred = [], []\n",
    "    for p_seq, t_seq in zip(pred, true):\n",
    "        t_labels, p_labels = [], []\n",
    "        for p_tok, t_tok in zip(p_seq, t_seq):\n",
    "            if t_tok.sum() == 0:          # token PAD → lewati\n",
    "                continue\n",
    "            t_labels.append(idx2tag[t_tok.argmax()])\n",
    "            p_labels.append(idx2tag[p_tok.argmax()])\n",
    "        out_true.append(t_labels)\n",
    "        out_pred.append(p_labels)\n",
    "    return out_true, out_pred\n",
    "\n",
    "# prediksi hanya pada test set\n",
    "y_pred_ner, y_pred_srl = model.predict(X_test, verbose=0)\n",
    "\n",
    "true_ner, pred_ner = decode(y_pred_ner, y_ner_test, idx2tag_ner)\n",
    "\n",
    "acc_ner = token_level_accuracy(y_ner_test, y_pred_ner)\n",
    "acc_srl = token_level_accuracy(y_srl_test, y_pred_srl)\n",
    "\n",
    "print(f\"[NER] Token-level Accuracy: {acc_ner:.2f}\")\n",
    "print(f\"[SRL] Token-level Accuracy: {acc_srl:.2f}\")\n",
    "# print(idx2tag_ner)\n",
    "print(\"[NER] Classification Report (test set):\")\n",
    "print(classification_report(true_ner, pred_ner, digits=2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "547d1533",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[SRL] Classification Report (test set):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ADV       0.00      0.00      0.00         6\n",
      "         BNF       0.00      0.00      0.00         1\n",
      "         CAU       0.10      0.17      0.12        12\n",
      "         COM       0.00      0.00      0.00         4\n",
      "         DIR       0.00      0.00      0.00         0\n",
      "         LOC       0.42      0.57      0.49        61\n",
      "         MNR       0.28      0.36      0.32        36\n",
      "         MOD       0.60      0.71      0.65        17\n",
      "         NEG       0.50      0.75      0.60         4\n",
      "         PRD       1.00      0.50      0.67         2\n",
      "         PRP       0.00      0.00      0.00         3\n",
      "         RG0       0.68      0.69      0.68        93\n",
      "         RG1       0.52      0.59      0.55       256\n",
      "         RG2       0.07      0.06      0.07        47\n",
      "         SRC       0.00      0.00      0.00         2\n",
      "        TIME       0.00      0.00      0.00         1\n",
      "         TMP       0.38      0.45      0.41        44\n",
      "           _       0.83      0.69      0.75       166\n",
      "\n",
      "   micro avg       0.52      0.55      0.53       755\n",
      "   macro avg       0.30      0.31      0.29       755\n",
      "weighted avg       0.54      0.55      0.54       755\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG0 seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-LOC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG1 seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG2 seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-PRP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-MOD seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-CAU seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-TMP seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-COM seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-ADV seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-MNR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-NEG seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-PRD seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-SRC seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-BNF seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-DIR seems not to be NE tag.\n",
      "  warnings.warn('{} seems not to be NE tag.'.format(chunk))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "true_srl, pred_srl = decode(y_pred_srl, y_srl_test, idx2tag_srl)\n",
    "# print(idx2tag_srl)\n",
    "print(\"[SRL] Classification Report (test set):\")\n",
    "print(classification_report(true_srl, pred_srl, digits=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "8d44d51e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flat_true_ner = [t for seq in true_ner for t in seq]\n",
    "flat_pred_ner = [p for seq in pred_ner for p in seq]\n",
    "\n",
    "labels_ner = sorted(list(set(flat_true_ner + flat_pred_ner)))\n",
    "cm_ner = confusion_matrix(flat_true_ner, flat_pred_ner, labels=labels_ner)\n",
    "\n",
    "plt.figure(figsize=(12, 10))  \n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_ner, display_labels=labels_ner)\n",
    "disp.plot(xticks_rotation=45, cmap=plt.cm.Blues, ax=plt.gca())  \n",
    "\n",
    "plt.title(\"Confusion Matrix NER\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "918dbefb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IQURERERERERU4/CZfyIiIiIiIiINx+KfiIiIiIiISMOx+CciIiIiIiLScCz+iYiIiIiIiDQci38iIiIiIiIiDaej7gBUfYqLi3H79m2YmppCIpGoOw4REREREVUBuVyOhw8fws7ODlpaNfN+7qNHj5Cfn6/uGKXo6enBwMBA3TGqBYt/DXb79m3Y29urOwYREREREVWDW7duoX79+uqOUWmPHj2CoakVUJij7iil2Nra4saNGxr5BQCLfw1mamoKALj8902YmpqpO44SbS0xeyKwhwQRERERie6hTAYnR3vF5/2aJj8/HyjMgX6zUYC2nrrj/KcoH3cvbUR+fj6Lf6pZSgpZU1MzmJmx+K8IFv9EREREVFPU+M+u2nqQCFT8y9UdoJqx+CciIiIiIiLVk2g9nkQhUpZqoNlHR0REREREREQs/omIiIiIiIg0Hbv9ExERERERkepJAIg0boFAUaoD7/wTERERERERaTgW/0REREREREQajt3+iYiIiIiISPU42r9KafbRERERERERERGLfyIiIiIiIiJNx27/REREREREpHoSiWCj/QuUpRrwzj8RERERERGRhuOd/9fYiYRrWLPlEM4l3ULqfRk2LhmHdzu3UCyXy+VYsuFXbP4xDrKsXLRp7ojQmYPRuEEdxTp/p9xDyKrdiD9/A/kFhWjmVA+zP3wXb3o1qbKc4TuOIXzXH0i5kwEAcHW0xYxxPdG9gxsAIDLmOHbuO41zSf8gK/sRbhxaAnNToyp7/cpYEbEPv/x+DldvpsJAXxdtWjRCyOS+cG5oo5Y8z9oQfRSrthzCvXQZ3J3rYcmM9+Dl1lDdsYTMdfzsNazafBDnLqfg7n0Ztix9Hz5dPNSaqYSI7SVirsXr92DJhr1K85wdbBC/Y67aMpUQ+fqCgOeSuTQnl8jXvojtxVyVI/L1BUHbjF4vvPP/GsvJzYebcz0sCXyvzOWrNh/EhuhYLJs1GL999zGMDPXgN20dHuUVKNYZ9sm3KCwqxq7Vk3EwcgbcnO0w/JP1SE2XVVlOOxsLzJvUB79vnIHDkTPQqVUT+AduwF9/3wEA5D7KR9f2TfHx6O5V9pov68TZaxj3XifsDw/ErtWTUVBYhAFTViM7N0/d0bBr/xnMWRmDWeN64cjmWXB3roeBU9YgLeMhc5UhJzcP7k3qYelMP7XmeJao7SVqLtdGdXF570LFtPe76WrNU0LU6wsCn0vm0oxcol77orYXc1WOqNcXBG4ztSsZ7V+kSYNp9tFVobi4OGhra8PHx0dpfnJyMiQSiWKytLRE586dcezYsVL7kMlkmDt3Ltzc3GBoaAgrKyu0bt0aoaGhePDggWI9uVyO4OBg1K1bF4aGhujWrRuuXr1a5cfUrUMzBI33LfMbUblcjm+jjuLjMd7o1akF3JzrYc28Ebh7X4q9secBAOmZWbh+Kw1TR3aHm3M9NG5QB8ET+yDnUT4uPynMq0LPt5qje0c3NG5QB04OdTBnYm8YG+nj9MVkAMCEoW9j2ihvtHJ3rLLXfFk7Vk3CsN7t0LRxXTRvUh9r5/njn7sPkPjXLXVHw9qthzGyXwcM79Mero3qYsXsITAy0MOWn+KYqwzdO7phzoTe8H1bnDsGELi9RM2lo60Fm9pmisnKwkSteUqIen1B4HPJXJqRS9RrX9T2Yq7KEfX6gsBtRq8XFv8VFBYWhilTpiA2Nha3b98utfzgwYO4c+cOYmNjYWdnB19fX6SmpiqWZ2RkoF27doiIiEBgYCBOnjyJs2fPYsGCBUhISMDWrVsV64aGhuLrr7/GN998g5MnT8LY2Bg9evTAo0ePVHa8N2+n4166DJ1auyjmmZkYoqWbA05deFx0W5obw8mhDqJ/jUd2bh4KC4uwcfdxWNcyhYerfbXkKioqxs79Z5CTm4/WzcXvJiXLenzOapmp5zGEEvkFhUi8fAtd2vx3PrW0tNC5jQtOXbjBXDWEqO0lai4AuH4rDU17BcGz7zy8PycSt+5mqDWP6EQ9l8ylGblEJWp7MZfmYJuRKPjMfwVkZWUhKioKp0+fxt27dxEZGYmgoCCldaysrGBrawtbW1sEBQXhhx9+wMmTJ9GnTx8AQFBQEFJSUnDlyhXY2dkptnNwcIC3tzfkcjnw5I77ypUrMWfOHPTt2xcAsGnTJtjY2GD37t0YMmTIc3Pm5eUhL++/7uUy2ct3vb/3pNu+taWp0nxrS1PFMolEgp2rJmHkzO/g+M5MaGlJULuWCX5YOR4WVVzsXrp2Gz3GLsej/EIYG+pjc+g4uDaqW6WvUdWKi4sxe8UOtPVohGZOdhXYovqkZ2ahqKi4jPNphqvJqc/d7nXNJSpR20vUXF5uDbFmnj+cHGyQel+KJRv24t33v8SJHz6DqbGB2nKJTNRzyVyakUtUorYXc2kOttkLcLR/leKd/wqIjo6Gq6srXFxc4O/vj/DwcEWx/qzc3Fxs2rQJAKCnpwc8KQKjoqLg7++vVPg/TfLkQrtx4wbu3r2Lbt26KZaZm5ujbdu2iIt7cbegRYsWwdzcXDHZ21fP3fcScrkcs5ZuR+1apvj5m4+wL+wTvNupBfwD1+PufWmVvpaTQx0c3fIpDoR/goCBb2Li51tw+XrVPVpQHQJDo/HX33cQtmCMuqMQvZa6d3RDv24t4e5cD13bN8P2ryZA+jAXuw+eVXc0IiIiIpVj8V8BYWFh8Pf3BwD07NkTUqkUR48eVVqnQ4cOMDExgbGxMZYtWwYvLy907doVAJCWlobMzEy4uLgobePl5QUTExOYmJhg6NChAIC7d+8CAGxslEeHt7GxUSx7ntmzZ0MqlSqmW7de/jnzOlZmj7M/MwhJWsZDxbJjp69g//E/seGLUWjr0QgervYInTkYBvp6iPo1/qVfuyx6ujpoZG8Nz6YNEDypD9yd7fBt1NEKbKkeM0Kjse/YRfy8birq2dRSdxxYWZhAW1urjPMpU5xP5hKfqO0laq5nmZsawalBHVy/labuKMIS9Vwyl2bkEpWo7cVcmoNtRqJg8V+OpKQkxMfHK4pzHR0d+Pn5ISwsTGm9qKgoJCQkYOfOnXByckJkZCR0dXVfuO+YmBgkJiaiR48eyM3NfeWs+vr6MDMzU5peloOdFepYmeHYqSuKeQ+zc3H2z5uKZ+1zH+UDACTPjIqppSVBcXHZPSOqSnGxHPn5BRVYU7XkcjlmhEZjz5Fz+GndVDjUq63uSMCTL088Xe1x9FSSYl5xcTFiT11B6+bqGyhR1FyiErW9RM31rKycPNz49z5sa5urO4qwRD2XzKUZuUQlansxl+Zgm72IAKP7K430r9nlMZ/5L0dYWBgKCwuVuuvL5XLo6+tj9erVinn29vZwdnaGs7MzCgsL0b9/f1y8eBH6+vqwtraGhYUFkpKSlPbdoEEDAICpqSkyMzMBALa2tgCA1NRU1K373zPtqamp8PT0rNJjy8rJw41//rsDlnI7HReu/INaZkaob2uJD/06Y0XkPjSyt0YDOyssXr8HtrXN0atTCwBAq+aOsDA1wuT5WxA4ticM9XWx+ccTSLmdju4d3aos5/w1P6Fb+2aob1sLWTl52LHvNP44ew07vp4IAEi9L8O9DJnibt6la7dhYmyA+ja1UMvcuMpyVETgkmjs2HcaW5d9ABMjA6Tefzw+gpmJAQwN9FSa5VkTh72DiZ9vxhtNG6ClW0Os2/Y7snPzMLx3O+YqQ1ZOHm48dYf45u10XEj6BxbmRrC3tVRbLlHbS8Rcc1fuQs+3msO+riXupEmxeP0eaGtpYWAPL7VlKiHq9QVBzyVzaU4uUa99UduLuSpH1OsLArcZvV4k8uc9vE4oLCxE/fr1MXPmTHh7eyst69evHwIDA9GzZ084OjoiISFBUZzL5XI0a9YMH3zwAaZPf/yb0h9++CG+//77UgP+AcDo0aORmZmJ3bt3Qy6Xw87ODoGBgfjkk0+AJwP31alTB5GRkS8c8O9ZMpkM5ubm+PfegzJ7ARw/cxX9Jq0qNd/v3TZYHewPuVyOJRt+xabdJyDLykXbFo0QOnMwGjeoo1g38a8ULPjmF5z7KwUFhUVwbVQXnwT0RLcOzV6YTVur4oNpTPnf94g9fQWp92UwMzGAm5Mdpo7sjrfbugIAFq//FaHf7S213erg4RjmW7k3VMkrDvJRq/XkMuevCfbHMAHe3NdHH8WqzQdxL/0hmjeph8WB76GVu/p/NUHEXH+cuYLe478uNX+oT1usDRmhlkwlRGwvEXMFBIUjLuEaMqQ5qF3LBG09GmHuxN5wrG+ttkwlRL6+IOC5ZC7NySXytS9iezFX5Yh8faGK20wmk8HGyhxSqfSVevuqS0mdou/1ESQ6+uqOoyAvzEPema9qbLuWh8X/C+zevRt+fn64d+8ezM2Vu4nOmjULhw8fxvbt20sV/wCwbt06hISE4MaNGzAyMkJ6ejo6dOiA7OxszJ8/H61atYKxsTHOnz+PTz/9FO7u7ti5cycAYMmSJVi8eDE2btwIR0dHzJ07F+fPn8elS5dgYFDxEarLK/7VqTLFvyq9avFPRERERFTdNKb4bzVNvOL/9Moa267l0eyHGl5RWFgYunXrVqrwB4CBAwfi9OnTz/05vVGjRqGgoEDxaICVlRXi4+MxcuRILF26FG3atEHz5s0REhICPz8/bNiwQbHtzJkzMWXKFHzwwQdo3bo1srKy8Ntvv1Wq8CciIiIiIiIqwTv/Gox3/iuPd/6JiIiISHS88189NP3OPwf8IyIiIiIiItVTjLIvCJGyVAPNPjoiIiIiIiIiYvFPREREREREpOnY7Z+IiIiIiIhUTyJ5PIlCpCzVgHf+iYiIiIiIiDQci38iIiIiIiIiDcdu/0RERERERKR6HO1fpTT76IiIiIiIiIiIxT8RERERERGRpmO3fyIiIiIiIlI9jvavUrzzT0RERERERKTheOf/NaCjrQUdbbG+50l/mKfuCGWyNNFTd4QySTT8W0giIiIiIqpeLP6JiIiIiIhI9Tjav0pp9tEREREREREREYt/IiIiIiIiIk3Hbv9ERERERESkehKJWF3tNXycLYFamoiIiIiIiIiqA4t/IiIiIiIiIg3Hbv9ERERERESkelqSx5MoRMpSDXjnn4iIiIiIiEjDsfgnIiIiIiIi0nDs9k9ERERERESqJ9ESbLR/gbJUA80+OiIiIiIiIiJi8U9ERERERESk6djtn4iIiIiIiFRPInk8iUKkLNWAd/6JiIiIiIiINBzv/JPC8bPXsGrzQZy7nIK792XYsvR9+HTxUFon6cZdhKzajeNnr6GoqBgujrbYGDoO9raW1ZLpm62HsOy7XzF6wFuYM7kfAGDY9LWIP/e30npDe7fH/6YPUvz7/OUULN2wBxev/AOJRIIWrvaY9WFvNG1sV2XZwnccQ/iuP5ByJwMA4OpoixnjeqJ7Bzc8kGZj8fpf8fvJy/gn9QGsLEzg07kFgsb7wMzEsMoyVNaG6KNYteUQ7qXL4O5cD0tmvAcvt4ZqyyNqrsXr92DJhr1K85wdbBC/Y67aMpWoyN+puqj7PJbXNlk5efh89Y/49eh5ZEiz4WBnhQ/8OiNg4Ftqz7Z4/R7s2n8W/6Y+gK6uNjxdG2DOxN5o5a6+v4MvI/dj/pqfMH5IFyz6ZFAFtqg+Il/3EODarym5VkTswy+/n8PVm6kw0NdFmxaNEDK5L5wb2qgtU1lEuvYh4HkUKVd57w1yuRyLvt2DTbtPQJqVi7YtGmH5p35o3KCOSnPWlGufNB/v/FdQXFwctLW14ePjozQ/OTkZEolEMVlaWqJz5844duxYqX3IZDLMnTsXbm5uMDQ0hJWVFVq3bo3Q0FA8ePBAsd6uXbvg7e0NKysrSCQSJCYmquQYc3Lz4N6kHpbO9Ctz+Y1/0tDr/RVwbmiLX779CH9sm43AsT1hoKdbLXnOX07BD7/8H1wb1S21zM+nHeJ2zFNMMz/wVSzLzs1DwKcbYFenFnau+Qg/fDUZxkYGGDNzPQoKi6osn52NBeZN6oPfN87A4cgZ6NSqCfwDN+Cvv+/gzn0p7tyXYv5H/XB822ysCR6OQ3GXMOWLrVX2+pW1a/8ZzFkZg1njeuHI5llwd66HgVPWIC3jodoyiZzLtVFdXN67UDHt/W66WvOUKO/vVF1EOI/ltc2cL3fiUNwlfDt/JE5Gz8H4IV0wc+l2/Hr0vNqzNW5QB6Ez3sPxbUHYu+FjNLCzxIDJq3H/gXr+Ds7+eRORMcfh5lxPLa//LFGvewhy7deUXCfOXsO49zphf3ggdq2ejILCIgyYshrZuXlqy/Qs0a59Ec+jSLnKe2/4atNBfBt1FCtmD8GBiEAYGeph4JQ1eJRXoNKcNeHaV5uS0f5FmjSYZh9dFQoLC8OUKVMQGxuL27dvl1p+8OBB3LlzB7GxsbCzs4Ovry9SU1MVyzMyMtCuXTtEREQgMDAQJ0+exNmzZ7FgwQIkJCRg69b/isLs7Gy8+eabWLJkicqODwC6d3TDnAm94ft22XdT/rf2Z3Tv4Ib5U/uhhYs9HOtb493OLWBtaVrlWbJz8/Dxwu+x4JP3YGZqVGq5ob4urC3NFJOpsYFi2fWUe8iU5eCjMT3QqEEdNHG0xdSR3rj/4CH+TX1Qal8vq+dbzdG9oxsaN6gDJ4c6mDOxN4yN9HH6YjKaNbbDpiXj0POt5nCsb41OrV3w2YTe2HfsIgqr8AuIyli79TBG9uuA4X3aw7VRXayYPQRGBnrY8lOcWvKInktHWws2tc0Uk5WFiVrzlCjv71RdRDiP5bXNyfM3MNSnLd70aoIGdlYYPeBNuDvXw9lLN9We7b2erdGlrSsa1q+Npo3r4otpA/Aw+xH+vFr6vzfVLSsnDx8ER+KroKGwMFVfT6WniXrdQ5Brv6bk2rFqEob1boemjeuieZP6WDvPH//cfYDEv26pLdPTRLz2RTyPIuV60XuDXC7HN9t+R2BAD7zbuQXcneth3ecjcfe+FHuOnlNpTtGvfXp9sPivgKysLERFRWHChAnw8fFBZGRkqXWsrKxga2sLd3d3BAUFQSaT4eTJk4rlQUFBSElJQXx8PMaMGYMWLVrAwcEB3t7e2LZtGyZOnKhYd8SIEQgODka3bt1UdozlKS4uxoHjf8KpQR0MnLIazt6fotvopdhzpHrePEO+2oUubZuho1eTMpf/eOgsWvebi14BS7F0wx7kPspXLHO0t0YtMyNs/zUe+QWFeJRXgO2/nkRjBxvUt61VLXmLioqxc/8Z5OTmo3Xzsru8ybJyYWpsAB0d7WrJ8CL5BYVIvHwLXdq4KOZpaWmhcxsXnLpwQ+V5RM8FANdvpaFpryB49p2H9+dE4tbdDLXmEZnI5/FpbVs4Ym/sBdy+lwm5XI5jp6/g75R7eLttU3VHU5JfUIiNMcdhZmII9yaqv/s4IzQK3h3d0aWtq8pfu6YR9doXNdezZFmPAAC1zEp/ya8Ool37op5HUXM96+a/6UhNl6FLm//Op7mJIbzcGuLU+WS1ZhPt2qfXB5/5r4Do6Gi4urrCxcUF/v7+mDZtGmbPng1JGaNB5ubmYtOmTQAAPT094EnhHBUVBX9/f9jZlf3MeVn7qqy8vDzk5f3XfUgmk73yPkukZWQhKycPKzcewGcTfBEyuR8Oxl3CiJnf4ed1U9HRy7nKXuuXwwn48+o/iFk3rczlfbq+ATubWrCxMsfl67cRun4PbtxKw9r5owEAJkYG+P7LiRg/NwJrthwAADSsVxsRSz6AjnbVFt6Xrt1Gj7HL8Si/EMaG+tgcOq7MxxTSM7OwLPw3jOrXoUpfv6LSM7NQVFRcqpeGtaUZrianPne71zWXl1tDrJnnDycHG6Tel2LJhr149/0vceKHz5R6mdBjop7HZy2Z8R6mLdwGN5850NHWgpaWFr76bCg6tnRSdzQAwG/HLmDcZxHIeVQA29pmiFk9WeU9TnbuP41zl2/h8MaZKn3dmkrUa1/UXE8rLi7G7BU70NajEZo5Vd14PC9LxGtf1PMoaq5npaY//hxsbaWcs46VKe6lV91n5MoS7dpXO472r1Is/isgLCwM/v7+AICePXtCKpXi6NGj6NKli2KdDh06QEtLCzk5OZDL5fDy8kLXrl0BAGlpacjMzISLi4vSfr28vJCUlAQA6N27N7Zt2/ZKORctWoTPP//8lfbxPMXyYgBAr87NMXHYOwCA5i71EX/+OsJ3/VFlxf/tew/wvzW7sTH0Q+g/ZyyBIb7tFf/fpVFd1LE0w4jAb3Dz3/twqFcbj/IKMHtpNLzcHbFyjj+KiuX4LvoIxgWFIWbdNBjoV90YBU4OdXB0y6eQZeXip8OJmPj5Fvz8zVSlLwBkWbnwm/4NXBxtMeuDd6vstan6dO/opvj/7s710Mq9IZr3Dsbug2cxoq96vsChV7c+6ihOX0jG1uUfwr6uJU4kXMOM0GjY1jYX4k7fW62aIPb72UjPzMKm3ScwJigcByMCq+XRqrL8c/cBZi/fiV2rJ1fp+yRRWQJDo/HX33ewd4P6x1PhtU+qJNK1T68fFv/lSEpKQnx8PGJiYgAAOjo68PPzQ1hYmFLxHxUVBVdXV1y8eBEzZ85EZGQkdHVf/B+QmJgY5OfnY9asWcjNzX3lrLNnz8bHH3+s+LdMJoO9vf0r7xcArCxMoKOtBVdH5bvaTRxt8X+J16vkNQDgzyv/IP1BFvp++KViXlFxMU6dv47Nu4/j0r4l0NZWflrFo2kDAMDN24+L/58OncU/qRnYvnoKtLQer/vlZ8Ph1XcuDh6/CN933qiyvHq6Omhkbw0A8GzaAAmXbuLbqKP4cvYQAMDD7Ed476N1MDXSx+bQ96Grhi7/eHL+tLW1Sg3Ek5YhQx0rM7VkEjnXs8xNjeDUoA6u30pTdxQh1YTzmPsoH/9b+zM2L30fPd50B558sXPxyj9YveWQEMW/saE+Gtlbo5G9NVo3d4TXgM+x+ccT+HhMD5W8/rnLKUjLeIguI/4bb6aoqBgnEv7Ghu2xSD2+stT77+tO1Gtf1FwlZoRGY9+xi/h1/TTUs6mex/EqQ9RrX9TzKGquZ9k8yZKW/hC2tc0V8++lP0TzJvXVkkm0a59ePyz+yxEWFobCwkKl7vpyuRz6+vpYvXq1Yp69vT2cnZ3h7OyMwsJC9O/fHxcvXoS+vj6sra1hYWGhuMtfokGDx0WrqakpMjMzXzmrvr4+9PX1X3k/ZdHT1cEbzRxw9aZyd66/U+7Bvm7VvXm1b+mMX8MClebNCo1CI/s6+HDo22X+x/evvx8PiFXH8vGb/KNH+dB68usLJbS0JJAAKJbLqyxrWYqL5cjPfzyCrCwrF4OmroW+ng6+X/6hWu8m6OnqwNPVHkdPJSl+Aqe4uBixp65g3HudmKscWTl5uPHvffjVbqPuKEKqCeexoLAIBYVF0HqmO5+Wlla1vy+8rOJiOfILClX2ep1au+D4tiCleZPnb4FzQxt8NLI7C/8yiHrti5pLLpdj5tLt2HPkHH7+5iM41KuttixPE/XaF/U8iprrWQ71rGBjZYajp5LQ3OVxsS/LysWZP5MRMOhNlWYR9doXgmgj7IuUpRqw+H+BwsJCbNq0CcuXL4e3t7fSsn79+mHbtm3o2bNnqe0GDRqE4OBgrF27FtOnT4eWlhYGDx6MLVu2IDg4+LnP/atbVk4ebjx1Z/Pm7XRcSPoHFuZGsLe1xNQR3RAQFI4ObzjhrVZNcDDuEn47dhE/f/NRlWUwMTJAk2d6Fxga6KGWmRGaONbFzX/v4+fDCejS1hUWZsa4/PdtLFj7E1q3aATXxo/btWOrJlj87S+Y99UujOz/JoqL5fh222Foa2uhnWfVPds7f81P6Na+Gerb1kJWTh527DuNP85ew46vJ0KWlYuBU9ci91E+vp0/Eg+zHuHhk8FdatcyUcsHiYnD3sHEzzfjjaYN0NKtIdZt+x3ZuXkY3rudyrOInmvuyl3o+VZz2Ne1xJ00KRav3wNtLS0M7OGltkwlyvs7VRcRzmN5bdOxpROCv94NQwNd2Nta4vjZa4j6NR5fTBug1myW5sZYHr4PvTo1h01tc2RkZuG77bG4k5aJvl1bVnu2EqbGBqWePzUy1IOlubHan0sV9bqHINd+TckVuCQaO/adxtZlH8DEyACp9x8/d21mYgBDAz215RL52hfxPIqUq7z3hvFD38ay8N/QyN4aDvWssPCbPbCtbQ6fzqr95RBRr316/bD4f4FffvkFDx48wNixY2Fubq60bODAgQgLCyuz+JdIJJg6dSpCQkLw4YcfwsjICAsXLsSRI0fQpk0bzJ8/H61atYKxsTHOnz+PuLg4uLu7K7bPyMhASkqK4icFS3oM2NrawtbWttqON/Gvm+g9/mvFvz/7chcAYKhPW6wNGQHftz2wYvYQfBm5H58u3wGnBnWwack4tPdsXG2ZnqWrq43jZ64gcmcscnLzUbeOBXp2ao6J/t0V6zRuYIP1CwKwatN+vDf5a2hpSdDMqR7Cl3xQpd3R0jIeYsLnm5F6XwYzEwO4Odlhx9cT8XZbV/xx5irOXHw8kqzXgPlK2yXuDkEDO6sqy1FRA7y9cD8zCwu/3fOky1s97Ph6ktq76ImY6997mRg3JwIZ0hzUrmWCth6NcCDiE9SupZpnr1+kvL9TdRHhPJbXNmELAjB/zY/4YO5GPJDlwN7WEnMm+CJgYPXfAXpRthWzh+Bqcip+2HMS6ZnZsDQ3whvNHPDr+ulo2rj0AKKvI1Gvewhy7deUXOE7jwEAfMd/pTR/TbA/hqm5mBWViOdRpFzlvTd8NLIbcnLzMH3hNkizctHOozF2fD1R5b0xee2TKCRyuaD9HQXQu3dvFBcXY8+ePaWWxcfHo23btjh37hw8PDyQkJAAT09PxfKcnBzUr18fn376KWbOfDxyrFQqxZIlSxATE4MbN25AS0sLzs7O6Nu3L6ZNmwZLy8d3LyIjIzFmzJhSrzlv3jyEhIRUOL9MJoO5uTlS06UwMxPnGSwASH+YV4G1VM/SRMxvX6vi1yCIiIiISDPIZDLYWJlDKhXvc35FlNQp+u/8DxIdcX5JSV74CHmH59bYdi0Pi38NxuK/8lj8ExEREZHoWPxXD00v/jV7RAMiIiIiIiIi4jP/REREREREpAYc7V+lNPvoiIiIiIiIiIjFPxEREREREZGmY7d/IiIiIiIiUj2J5PEkCpGyVAPe+SciIiIiIiLScCz+iYiIiIiIiDQcu/0TERERERGRGgg22r+G3xvX7KMjIiIiIiIiIhb/RERERERERJqO3f6JiIiIiIhI9Tjav0qx+Ce1MDfSVXcEIiIiIiKi1wa7/RMRERERERFpON75JyIiIiIiItWTSMQa7V/Du/0L1NJEREREREREVB1Y/BMRERERERFpOHb7JyIiIiIiItWTaAnW7V+gLNVAs4+OiIiIiIiIiFj8ExEREREREWk6dvsnIiIiIiIi1ZNIxBphX6Qs1YB3/omIiIiIiIg0HIt/IiIiIiIiIg3Hbv9ERERERESkehztX6U0++iIiIiIiIiIiMU/ERERERERkaZjt38iIiIiIiJSPY72r1Is/qlcG6KPYtWWQ7iXLoO7cz0smfEevNwaquz1i4qKEfrdXuz47RTuZTyEbW0zDPFpi4/H9IDkyR/oL7+fw8aYP3Du8i08kOXg8KaZaN6kfrXm+jJyP375/Ryu3kyFgb4u2jR3xLwpfeHsYAMAeCDNxuL1v+L3k5fxT+oDWFmYwKdzCwSN94GZiWG1ZnsedZ/LmpTr+NlrWLX5IM5dTsHd+zJsWfo+fLp4qDVTCRHbS5Rc5Z23xev3YNf+s/g39QF0dbXh6doAcyb2Rit31eZcEbFP+f2jRSOETO4L54Y2Ks3xLFGve1FzlRDh2meulyfq32MJ0dpL5FyL1+/Bkg17leY5O9ggfsdctWVCDXgPo9cHu/1XUFxcHLS1teHj46M0Pzk5GRKJRDFZWlqic+fOOHbsWKl9yGQyzJ07F25ubjA0NISVlRVat26N0NBQPHjwAABQUFCAWbNmoXnz5jA2NoadnR1GjhyJ27dvq+xYn7Zr/xnMWRmDWeN64cjmWXB3roeBU9YgLeOhyjJ8vfkgInf9gUWB7+H4tiDMndQHq7YcwoboWMU6OY/y0NajEeZO6qOyXMfPXsPY997CvrBPsGvVJBQUFWHglDXIzs0DANy5L8Wd+1LM/6gfjm+bjTXBw3Eo7hKmfLFVZRmfJsK5rEm5cnLz4N6kHpbO9FNrjmeJ2l6i5CrvvDVuUAehMx6/l+zd8DEa2FliwOTVuP9AtTlPnL2Gce91wv7wQOxaPRkFhUUYMGW14v1DXUS97kXNBYGufeZ6eaL+PULQ9hI5FwC4NqqLy3sXKqa9301XdySh38Po9cLiv4LCwsIwZcoUxMbGllmIHzx4EHfu3EFsbCzs7Ozg6+uL1NRUxfKMjAy0a9cOERERCAwMxMmTJ3H27FksWLAACQkJ2Lr1cUGYk5ODs2fPYu7cuTh79ix27dqFpKQk9OmjuqL2aWu3HsbIfh0wvE97uDaqixWzh8DIQA9bfopTWYZTF26gZ6fm8O7ohgZ2Vujzzhvo0sYVCZduKtYZ3KsNAsf2QufWLirLtePriRjm2w5NG9eFe5P6WBPsj3/uPsC5v24BAJo1tsOmJePQ863mcKxvjU6tXfDZhN7Yd+wiCguLVJazhAjnsibl6t7RDXMm9Ibv22J9My9qe4mSq7zz9l7P1ujS1hUN69dG08Z18cW0AXiY/Qh/XlXtF6w7Vk3CsN6P3z+aN6mPtfMev38kPnn/UBdRr3tRc0Gga5+5Xp6of48QtL1EzgUAOtpasKltppisLEzUHUno9zC1KxntX6RJg2n20VWRrKwsREVFYcKECfDx8UFkZGSpdaysrGBrawt3d3cEBQVBJpPh5MmTiuVBQUFISUlBfHw8xowZgxYtWsDBwQHe3t7Ytm0bJk6cCAAwNzfHgQMHMHjwYLi4uKBdu3ZYvXo1zpw5g5SUFJUed35BIRIv30KXNv8V1FpaWujcxgWnLtxQWY7WzR1x7NQV/J1yDwBw8eq/iD93HV3bN1VZhoqQZT0CAFiYG71gnVyYGhtAR0dbhcnEOZc1JZeoRG0vUXOVJ7+gEBtjjsPMxBDuTeqpNUvJ+0cts+e/f5B4RL32mevViPL3KGp7iZqrxPVbaWjaKwiefefh/TmRuHU3Q92RiITBZ/4rIDo6Gq6urnBxcYG/vz+mTZuG2bNnK543f1pubi42bdoEANDT0wMAFBcXIyoqCv7+/rCzsyvzNcraVwmpVAqJRAILC4sX5szLy0Ne3n9d1GQyWYWPsSzpmVkoKiqGtaWp0nxrSzNcTU597nZV7aOR3fAw+xHa+y2AtpYERcVyBI33waCerVWWoTzFxcUIWrETbT0aoVnjss9xemYWloX/hlH9Oqg8nyjnsqbkEpWo7SVqruf57dgFjPssAjmPCmBb2wwxqyer9c5QcXExZq/Y8fj9w6ns9w8Sk6jXPnO9PJH+HkVtL1FzAYCXW0OsmecPJwcbpN6XYsmGvXj3/S9x4ofPYGpsoNZsRCJg8V8BYWFh8Pf3BwD07NkTUqkUR48eRZcuXRTrdOjQAVpaWsjJyYFcLoeXlxe6du0KAEhLS0NmZiZcXJS7pHt5eSEpKQkA0Lt3b2zbtq3Uaz969AizZs3C0KFDYWZm9sKcixYtwueff14lxyySHw8lYOe+0/h2/ki4ONbFxav/YM6Xu2Bb2xxDfNqqOx4AYEbodvx1/Q5+XT+tzOWyrFz4Tf8GLo62mPXBuyrPR0T/eatVE8R+PxvpmVnYtPsExgSF42BEYKkPsqoSGBqNv/6+g70b1P9cKtHrjn+PNVv3jm6K/+/uXA+t3Buiee9g7D54FiP6qv7mC1UAR/tXKXb7L0dSUhLi4+MxdOhQAICOjg78/PwQFhamtF5UVBQSEhKwc+dOODk5ITIyErq6ui/cd0xMDBITE9GjRw/k5uaWWl5QUIDBgwdDLpdj3bp15WadPXs2pFKpYrp169WeVbOyMIG2tlapwVvSMmSoY/XiLyKqUsiqHzF1ZDf07+6FZk52GNyrDT4c8ja+2nRAZRleZObSaOz74yJ+WjsF9WxqlVr+MPsR3vtoHUyN9LE59H3oqrjLPwQ6lzUll6hEbS9Rcz2PsaE+Gtlbo3VzR6yaOxw62lrY/OMJtWSZERqNfccu4ud1U8t8/yCxiXrtM9fLEe3vUdT2EjVXWcxNjeDUoA6u30pTdxQiIbD4L0dYWBgKCwthZ2cHHR0d6OjoYN26ddi5cyekUqliPXt7ezg7O6N///5YuHAh+vfvr+iCb21tDQsLC8Vd/hINGjSAk5MTTE1L320qKfxv3ryJAwcOlHvXHwD09fVhZmamNL0KPV0deLra4+ip/3IXFxcj9tQVtG7u+Er7rozcR/nQeuZbOG1tCYqL5SrLUBa5XI6ZS6Ox58h5/Lh2Chzq1S61jiwrFwOnrIGerja+X/4hDPRf/IVQdRHlXNaUXKIStb1EzVVRxcVy5BcUqvQ15XI5ZoRGY8+Rc/hp3dQy3z9IfKJe+8xVOaL+PYraXqLmKktWTh5u/HsftrXN1R2FSAjs9v8ChYWF2LRpE5YvXw5vb2+lZf369cO2bdvQs2fPUtsNGjQIwcHBWLt2LaZPnw4tLS0MHjwYW7ZsQXBw8HOf+y9RUvhfvXoVv//+O6ysrKr82Cpq4rB3MPHzzXijaQO0dGuIddt+R3ZuHob3bqeyDN5vuuPLyP2oZ2sJV0dbXLjyD77Z9juG+f6X4YE0G/+kPsDd+4+/kLl28/HggHWszGBTTd9CzwiNxo59Z/D9svdhYmSA1PuPx1gwMzGAoYHe48J/6lrkPsrHt/NH4mHWIzx8MohQ7VqPvzVXJRHOZU3KlZWThxtP3Sm4eTsdF5L+gYW5EextLdWWS9T2EiXXi86bpbkxlofvQ69OzWFT2xwZmVn4bnss7qRlom/XlirNGbgkGjv2ncbWZR+U+f6hLqJe96LmgkDXPnO9PFH/HiFoe4mca+7KXej5VnPY17XEnTQpFq/fA20tLQzs4aXWXCK/h6lbyc+lC0OkLNVAIpfL1Xv7VGC7d++Gn58f7t27B3Nz5W8MZ82ahcOHD2P79u1wdHREQkICPD09FcvXrVuHkJAQ3LhxA0ZGRkhPT0eHDh2QnZ2N+fPno1WrVjA2Nsb58+fx6aefwt3dHTt37kRBQQEGDRqEs2fP4pdffoGNjY1in5aWlopBBCtCJpPB3NwcqenSV+oFsD76KFZtPoh76Q/RvEk9LA58D63cG770/gCgsKi4wutmZT/CovV78OvR87j/IAu2tc3Qv7sXAsf2hJ7u4++vtv1yElO/+L7UtjPG9sTM9yv+jL22VsX/4C3bTClz/urg4Rjm2w5/nLmKPhO+LnOdxN0haGBX8S91qupNsTrOpabm+uPMFfQeX/r8DfVpi7UhI9SSqYSI7SVKrhedtxWzh+D9OZE482cy0jOzYWluhDeaOSAwoCdaujmoNGet1pPLnL8m2B/D1PjhWdTrXtRcJUS49pnr5Yn691hCtPYSOVdAUDjiEq4hQ5qD2rVM0NajEeZO7A3H+tZqzVUd72EymQw2VuaQSl/tc766lNQpBr1XQ6JrqO44CvKCXDz6eXKNbdfysPh/gd69e6O4uBh79uwptSw+Ph5t27bFuXPn4OHhUar4z8nJQf369fHpp59i5syZwJNR+5csWYKYmBjcuHEDWlpacHZ2Rt++fTFt2jRYWloiOTkZjo5ld5n6/ffflQYZLE9VFf/VoTLFvypVpvhXJaG+ESUiIiIitWLxXz1Y/FONxeK/8lj8ExEREZHoNKX4N+yzRrjiP/enSTW2XcvDAf+IiIiIiIiINByLfyIiIiIiIiINx9H+iYiIiIiISPUkTyZRiJSlGvDOPxEREREREZGGY/FPREREREREpOHY7Z+IiIiIiIhUTiKRiPWrViJlqQa8809ERERERESk4Vj8ExEREREREWk4dvsnIiIiIiIilWO3f9XinX8iIiIiIiIiDcfin4iIiIiIiEjDsds/qYWONr93IiIiIiJ6nbHbv2qxAiMiIiIiIiLScCz+iYiIiIiIiDQci38iIiIiIiJSuZJu/yJNr2Lx4sWQSCSYNm2aYt6jR48wadIkWFlZwcTEBAMHDkRqaqrSdikpKfDx8YGRkRHq1KmDGTNmoLCwUGmdI0eOoGXLltDX14eTkxMiIyMrnY/FPxEREREREdErOHXqFL799lu0aNFCaf706dPx888/Y/v27Th69Chu376NAQMGKJYXFRXBx8cH+fn5OHHiBDZu3IjIyEgEBwcr1rlx4wZ8fHzw9ttvIzExEdOmTcO4ceOwb9++SmVk8U9ERERERESqJxFweglZWVkYPnw4NmzYgFq1ainmS6VShIWFYcWKFXjnnXfg5eWFiIgInDhxAv/3f/8HANi/fz8uXbqELVu2wNPTE7169cL//vc/rFmzBvn5+QCAb775Bo6Ojli+fDmaNm2KyZMnY9CgQfjyyy8rlZPFPxEREREREdETMplMacrLy3vh+pMmTYKPjw+6deumNP/MmTMoKChQmu/q6ooGDRogLi4OABAXF4fmzZvDxsZGsU6PHj0gk8nw559/KtZ5dt89evRQ7KOiWPwTERERERERPWFvbw9zc3PFtGjRoueu+8MPP+Ds2bNlrnP37l3o6enBwsJCab6NjQ3u3r2rWOfpwr9kecmyF60jk8mQm5tb4ePSqfCaRERERERERFWkKgbZq1JPsty6dQtmZmaK2fr6+mWufuvWLXz00Uc4cOAADAwMVBbzZfHOPxEREREREdETZmZmStPziv8zZ87g3r17aNmyJXR0dKCjo4OjR4/i66+/ho6ODmxsbJCfn4/MzEyl7VJTU2FrawsAsLW1LTX6f8m/y1vHzMwMhoaGFT4uFv9EREREREREldS1a1dcuHABiYmJiqlVq1YYPny44v/r6uri0KFDim2SkpKQkpKC9u3bAwDat2+PCxcu4N69e4p1Dhw4ADMzMzRr1kyxztP7KFmnZB8VxW7/REREREREpHISCQTr9l+51U1NTeHu7q40z9jYGFZWVor5Y8eOxccffwxLS0uYmZlhypQpaN++Pdq1awcA8Pb2RrNmzTBixAiEhobi7t27mDNnDiZNmqTocTB+/HisXr0aM2fOREBAAA4fPozo6Gjs2bOnUnlZ/BMRERERERFVgy+//BJaWloYOHAg8vLy0KNHD6xdu1axXFtbG7/88gsmTJiA9u3bw9jYGKNGjcL8+fMV6zg6OmLPnj2YPn06vvrqK9SvXx/fffcdevToUaksErlcLq/SoyNhyGQymJubIzVdqjRgBRERERER1VwymQw2VuaQSmvm5/ySOsV88HpIdI3UHUdBXpADafQHNbZdy8M7/0RERERERKRyEgg22n9l+/3XMCz+qVwboo9i1ZZDuJcug7tzPSyZ8R683BqqOxZzVcKKiH345fdzuHozFQb6umjTohFCJveFc0ObCmxd/URss+Nnr2HV5oM4dzkFd+/LsGXp+/Dp4qHWTCVEbC/mqpywHccQvvMYbt3JAAC4NrLFjLG90L2jm1pziXzdQ9BzyVzMxVzi5OLnHaIXE3K0/7i4OGhra8PHx0dpfnJysuK3ICUSCSwtLdG5c2ccO3as1D5kMhnmzp0LNzc3GBoawsrKCq1bt0ZoaCgePHigWK9Lly6QSCRYvHhxqX34+PhAIpEgJCSkQrl79OgBbW1tnDp1qtSy0aNHK3Lr6urCxsYG3bt3R3h4OIqLi4EnPxUhkUjwf//3f2Xuv2vXrhgwYECFslSVXfvPYM7KGMwa1wtHNs+Cu3M9DJyyBmkZD1Wag7lezYmz1zDuvU7YHx6IXasno6CwCAOmrEZ2bp5ac0HgNsvJzYN7k3pYOtNPrTmeJWp7MVfl2NWxwLzJffH7ppk4vHEG3mrVBMMD1+Ovv++oNZeo1z0EPpfMxVzMJU4uft4hejEhi/+wsDBMmTIFsbGxuH37dqnlBw8exJ07dxAbGws7Ozv4+voq/e5hRkYG2rVrh4iICAQGBuLkyZM4e/YsFixYgISEBGzdulVpf/b29oiMjFSa9++//+LQoUOoW7duhTKnpKTgxIkTmDx5MsLDw8tcp2fPnrhz5w6Sk5Oxd+9evP322/joo4/g6+uLwsJCeHl5wcPDo8ztk5OT8fvvv2Ps2LEVylNV1m49jJH9OmB4n/ZwbVQXK2YPgZGBHrb8FKfSHMz1anasmoRhvduhaeO6aN6kPtbO88c/dx8g8a9bas0Fgduse0c3zJnQG75vi3PXEwK3F3NVTq9OzeHd0Q2NG9SBk4MN5k7sA2MjfZy+eEOtuUS97iHwuWQu5mIucXLx807N8/SNXVEmTSZc8Z+VlYWoqChMmDABPj4+pYpyALCysoKtrS3c3d0RFBQEmUyGkydPKpYHBQUhJSUF8fHxGDNmDFq0aAEHBwd4e3tj27ZtmDhxotL+fH19cf/+fRw/flwxb+PGjfD29kadOnUqlDsiIgK+vr6YMGECtm3bhtzc3FLr6Ovrw9bWFvXq1UPLli0RFBSEH3/8EXv37lUc59ixYxEVFYWcnBylbSMjI1G3bl307NmzQnmqQn5BIRIv30KXNi6KeVpaWujcxgWnLqjvAypzvTpZ1iMAQC0z9Q6wUpPaTASithdzvZqiomLs3H8aObn5aN3cUd1xhCTquWQu5mIucXKVhZ93iJQJV/xHR0fD1dUVLi4u8Pf3R3h4OJ73gwS5ubnYtGkTAEBPTw8AUFxcjKioKPj7+8POzq7M7Z79RkdPTw/Dhw9HRESEYl5kZCQCAgIqlFkulyMiIgL+/v5wdXWFk5MTduzYUaFt33nnHXh4eGDXrl0AgOHDhyMvL09pe7lcjo0bN2L06NHQ1tZ+7r7y8vIgk8mUpleRnpmFoqJiWFuaKs23tjTDvfRX2zdzqU9xcTFmr9iBth6N0Myp7L8RVakpbSYKUduLuV7On9f+Rf1OH8Om4zR8vCgKm5e+D9dGFett9roR9VwyF3Mxlzi5nsXPO0SlCVf8h4WFwd/fH3jSTV4qleLo0aNK63To0AEmJiYwNjbGsmXL4OXlha5duwIA0tLSkJmZCRcXF6VtvLy8YGJiAhMTEwwdOrTU6wYEBCA6OhrZ2dmIjY2FVCqFr69vhTIfPHgQOTk5it9Z9Pf3R1hYWIWP2dXVFcnJyQAAS0tL9O/fX6nr/++//47k5GSMGTPmhftZtGjR45/MeDLZ29tXOAO9PgJDo/HX33cQtuDF1xMRVS9nBxvEfj8bByMCETDwTUwM2YzL19X7zD8Rkabg550aQiLgpMGEKv6TkpIQHx+vKM51dHTg5+dXqpCOiopCQkICdu7cCScnJ0RGRkJXV/eF+46JiUFiYiJ69OhRZpd8Dw8PODs7Y8eOHQgPD8eIESOgo6P8YwgLFy5UfIFgYmKClJQUAEB4eDj8/PwU6w8dOhTHjx/H33//XaHjlsvlSr0RAgICEBsbq9g+PDwcnTt3hpOT0wv3M3v2bEilUsV069arPd9kZWECbW2tUgORpGXIUMdKfb97yVwvb0ZoNPYdu4if101FPZta6o5TI9pMJKK2F3O9HD1dHTSyt4Zn0waYN7kv3J3r4Zsfjqg7lpBEPZfMxVzMJU6up/HzDlHZhCr+w8LCUFhYCDs7O+jo6EBHRwfr1q3Dzp07IZVKFevZ29vD2dkZ/fv3x8KFC9G/f3/k5T0exdPa2hoWFhZISkpS2neDBg3g5OQEU1PTUq9bIiAgAGvWrMGOHTvK7PI/fvx4JCYmKiY7OztkZGQgJiYGa9euVWSuV68eCgsLnzvw37P++usvODr+95xn165d0aBBA0RGRkImk2HXrl0VGuhPX18fZmZmStOr0NPVgaerPY6e+q8ti4uLEXvqilqfS2WuypPL5ZgRGo09R87hp3VT4VCvtlrzlBC5zUQkansxV9UolsuRn1+o7hhCEvVcMhdzMZc4ucDPO0Tl0qnAOipRWFiITZs2Yfny5fD29lZa1q9fP2zbtq3Mwe4GDRqE4OBgrF27FtOnT4eWlhYGDx6MLVu2IDg4+LnP/Zdl2LBhCAwMhIeHB5o1a1ZquaWlJSwtLZXmff/996hfvz52796tNH///v1Yvnw55s+f/8Ln9A8fPowLFy5g+vTpinlaWloYM2YMwsLCUK9ePejp6WHQoEEVPo6qNHHYO5j4+Wa80bQBWro1xLptvyM7Nw/De7dTSx7mejmBS6KxY99pbF32AUyMDJB6//HzZWYmBjA00FNrNlHbLCsnDzdupSn+ffN2Oi4k/QMLcyPY21q+cNvqJGp7MVflfL76R3Tr4AZ721p4mPMIO347jT/OXMXOVRMrsHX1EfW6h8DnkrmYi7nEycXPOzWQYCPsywXKUh2EKf5/+eUXPHjwAGPHjoW5ubnSsoEDByIsLKzM4l8ikWDq1KkICQnBhx9+CCMjIyxcuBBHjhxBmzZtMH/+fLRq1QrGxsY4f/484uLi4O7uXmaGWrVq4c6dO+U+QvC0sLAwDBo0qNQ+7e3tMXv2bPz222/w8fEBngzId/fuXRQVFSE1NRW//fYbFi1aBF9fX4wcOVJp+zFjxmD+/PkICgrC0KFDYWhoWOFMVWmAtxfuZ2Zh4bd7cC/9IZo3qYcdX09Sexcl5qqc8J3HAAC+479Smr8m2B/D1PwfHVHbLPGvm+g9/mvFvz/78vGgnEN92mJtyAi15RK1vZircu4/yMKEkE1IvS+DmYkB3JzqYeeqiXi7bVO15hL1uofA55K5mIu5xMnFzztELyaRP28ofRXr3bs3iouLsWfPnlLL4uPj0bZtW5w7dw4eHh5ISEiAp6enYnlOTg7q16+PTz/9FDNnzgQASKVSLFmyBDExMbhx4wa0tLTg7OyMvn37Ytq0aYo7+F26dIGnpydWrlxZZi5PT0/069cPISEhpZadOXMGrVq1Qnx8PFq3bl1q+bvvvgsDAwPs2rULo0ePxsaNG4EnYxnUqlULHh4eGDZsGEaNGgUtrdJPYPTo0QP79+9/7v7LI5PJYG5ujtR06Ss/AkBERERERGKQyWSwsTKHVFozP+eX1Cm1hoZBS0+9P8X4tOL8HDzYNrbGtmt5hCn+qeqx+CciIiIi0jyaUvxbDgsXrvjP2BpQY9u1PEIN+EdEREREREREVY/FPxEREREREZGGE2bAPyIiIiIiInp9SAQb7V+kLNWBd/6JiIiIiIiINByLfyIiIiIiIiINx27/REREREREpHqSJ5MoRMpSDXjnn4iIiIiIiEjDsfgnIiIiIiIi0nDs9k9EREREREQqx9H+VYt3/omIiIiIiIg0HIt/IiIiIiIiIg3Hbv9ERERERESkcuz2r1q8809ERERERESk4Vj8ExEREREREWk4dvsnIiIiIiIilWO3f9XinX8iIiIiIiIiDcfin4iIiIiIiEjDsds/ERERERERqRy7/asW7/wTERERERERaTgW/0REREREREQajt3+iYiIiIiISPUkTyZRiJSlGvDOPxEREREREZGGY/FPREREREREpOHY7Z+IiIiIiIhUjqP9qxbv/BMRERERERFpOBb/RERERERERBqOxT+Va0P0UbToEwzbjtPQbfRSnPkzWd2RAOZ6KaJmY67KYa7KYa7KEzUbc1UOc1UOc1WOiLmOn72GIdO/QdNeQajVejL2HDmn7khKRGwzdSvp9i/SpMmELv7j4uKgra0NHx8fpfnJyclKJ8jS0hKdO3fGsWPHSu1DJpNh7ty5cHNzg6GhIaysrNC6dWuEhobiwYMHivW6dOkCiUSCxYsXl9qHj48PJBIJQkJCnps1MjJSKZOJiQm8vLywa9cupfVKXueHH35Qmr9y5Uo0bNjwufsrmb777rsKtl7V2LX/DOasjMGscb1wZPMsuDvXw8Apa5CW8VClOZhLc7MxF3Mxlzi5RM7GXMzFXMxVnpzcPLg3qYelM/3UmqMsorYZvV6ELv7DwsIwZcoUxMbG4vbt26WWHzx4EHfu3EFsbCzs7Ozg6+uL1NRUxfKMjAy0a9cOERERCAwMxMmTJ3H27FksWLAACQkJ2Lp1q9L+7O3tERkZqTTv33//xaFDh1C3bt1y85qZmeHOnTu4c+cOEhIS0KNHDwwePBhJSUlK6xkYGGDOnDkoKCio8P5KpuHDh5eboyqt3XoYI/t1wPA+7eHaqC5WzB4CIwM9bPkpTqU5mEtzszEXczGXOLlEzsZczMVczFWe7h3dMGdCb/i+7aHWHGURtc3o9SJs8Z+VlYWoqChMmDABPj4+pYpyALCysoKtrS3c3d0RFBQEmUyGkydPKpYHBQUhJSUF8fHxGDNmDFq0aAEHBwd4e3tj27ZtmDhxotL+fH19cf/+fRw/flwxb+PGjfD29kadOnXKzSyRSGBrawtbW1s4Ozvjiy++gJaWFs6fP6+03tChQ5GZmYkNGzZUeH8lk6GhYbk5qkp+QSESL99ClzYuinlaWlro3MYFpy7cUFkO5tLcbMzFXMwlTi6RszEXczEXc9VkbLPnU3cXf3b7F0R0dDRcXV3h4uICf39/hIeHQy6Xl7lubm4uNm3aBADQ09MDABQXFyMqKgr+/v6ws7Mrc7tnT66enh6GDx+OiIgIxbzIyEgEBARUOn9RURE2btwIAGjZsqXSMjMzM3z22WeYP38+srOzK73v58nLy4NMJlOaXkV6ZhaKiophbWmqNN/a0gz30l9t38ylWqJmYy7mYi5xckHgbMzFXMzFXDUZ24xEIWzxHxYWBn9/fwBAz549IZVKcfToUaV1OnToABMTExgbG2PZsmXw8vJC165dAQBpaWnIzMyEi4uL0jZeXl4wMTGBiYkJhg4dWup1AwICEB0djezsbMTGxkIqlcLX17dCmaVSqWLfenp6mDBhAtavX4/GjRuXWnfixIkwMDDAihUrKrQ/ExMT2NravvD1Fy1aBHNzc8Vkb29fodxERERERESk2YQs/pOSkhAfH68oznV0dODn54ewsDCl9aKiopCQkICdO3fCyckJkZGR0NXVfeG+Y2JikJiYiB49eiA3N7fUcg8PDzg7O2PHjh0IDw/HiBEjoKOjo7TOwoULlYrylJQUAICpqSkSExORmJiIhIQELFy4EOPHj8fPP/9c6nX09fUxf/58LFu2DPfv3y8z69P7S0xMxIkTJ154bLNnz4ZUKlVMt27deuH65bGyMIG2tlapgUjSMmSoY2X2SvtmLtUSNRtzMRdziZMLAmdjLuZiLuaqydhmLyARcNJgQhb/YWFhKCwshJ2dHXR0dKCjo4N169Zh586dkEqlivXs7e3h7OyM/v37Y+HChejfvz/y8vIAANbW1rCwsCg12F6DBg3g5OQEU1PTUq9bIiAgAGvWrMGOHTvK7PI/fvx4paK85LECLS0tODk5wcnJCS1atMDHH3+MLl26YMmSJWW+jr+/PxwcHPDFF1+Uufzp/Tk5OaFRo0YvbDd9fX2YmZkpTa9CT1cHnq72OHrqvzYsLi5G7KkraN3c8ZX2zVzMxlzMxVxi5RI5G3MxF3MxV03GNiNRCFf8FxYWYtOmTVi+fLlSgX3u3DnY2dlh27ZtZW43aNAg6OjoYO3atcCTwnnw4MHYsmVLmb8U8CLDhg3DhQsX4O7ujmbNmpVabmlpqVSUP9sz4Gna2tpl9jAoybho0SKsW7cOycli/s7nxGHvYNPuE9j2y/8h6cZdfLw4Ctm5eRjeux1z1aBcImdjLuZiLnFyiZyNuZiLuZirPFk5ebiQ9A8uJP0DALh5Ox0Xkv7BrbsZas0FgduMXi/Pr1rV5JdffsGDBw8wduxYmJubKy0bOHAgwsLC0LNnz1LbSSQSTJ06FSEhIfjwww9hZGSEhQsX4siRI2jTpg3mz5+PVq1awdjYGOfPn0dcXBzc3d3LzFCrVi3cuXOn3EcIniWXy3H37l3gySCEBw4cwL59+xAcHPzcbXx8fNC2bVt8++23sLGxqdTrqcIAby/cz8zCwm/34F76QzRvUg87vp6k9i5KzKU52ZiLuZhLnFwiZ2Mu5mIu5ipP4l830Xv814p/f/blLgDAUJ+2WBsyQo3JxG0zdRNthH2RslQHifx5Q+irSe/evVFcXIw9e/aUWhYfH4+2bdvi3Llz8PDwQEJCAjw9PRXLc3JyUL9+fXz66aeYOXMm8GTQvCVLliAmJgY3btyAlpYWnJ2d0bdvX0ybNg2WlpYAgC5dusDT0xMrV64sM5enpyf69euHkJCQMpdHRkZizJgxin/r6+vDwcEBo0aNwqxZs6Ctrf3c14mLi0OHDh3g4OCg6AEQGRmJadOmITMz8yVbEpDJZDA3N0dquvSVHwEgIiIiIiIxyGQy2FiZQyqtmZ/zS+qUeh9sg5aekbrjKBTn5+Df9UNrbLuWR7jin6oOi38iIiIiIs3D4r96aHrxL1y3fyIiIiIiItJ87PavWsIN+EdEREREREREVYvFPxEREREREZGGY7d/IiIiIiIiUjkJBOv2D3GyVAfe+SciIiIiIiLScCz+iYiIiIiIiDQcu/0TERERERGRynG0f9XinX8iIiIiIiIiDcfin4iIiIiIiEjDsds/ERERERERqZ7kySQKkbJUA975JyIiIiIiItJwLP6JiIiIiIiINBy7/RMREREREZHKcbR/1eKdfyIiIiIiIiINx+KfiIiIiIiISMOx2z8RERERERGpHLv9qxbv/BMRERERERFpOBb/RERERERERBqO3f6JiIiIiIhI5SSSx5MoRMpSHXjnn4iIiIiIiEjDsfgnIiIiIiIi0nDs9k9EREREREQq97jbvzh97QWKUi1455+IiIiIiIhIw7H4JyIiIiIiItJw7PZPREREREREqifYaP8QKUs14J1/IiIiIiIiIg3HO//0XCsi9uGX38/h6s1UGOjrok2LRgiZ3BfODW3UHQ2L1+/Bkg17leY5O9ggfsdctWUqsSH6KFZtOYR76TK4O9fDkhnvwcutobpjAQJnEzHX7XuZCFn1Iw7G/YncRwVwrF8ba4L98UYzB7XmgqDtJWIukd/DRM4GAc8lczEXczFXZYiaTdRc9PrgnX96rhNnr2Hce52wPzwQu1ZPRkFhEQZMWY3s3Dx1RwMAuDaqi8t7Fyqmvd9NV3ck7Np/BnNWxmDWuF44snkW3J3rYeCUNUjLeKjuaMJmEzFXpiwHPcetgK6OFrZ/NRH/F/UZvpg2ABZmRmrLVELE9hI1l8jvYSJnE/FcMhdzMRdz1fRsouZSN4lEItykyYQs/uPi4qCtrQ0fHx+l+cnJyUonxtLSEp07d8axY8dK7UMmk2Hu3Llwc3ODoaEhrKys0Lp1a4SGhuLBgweK9bp06QKJRILFixeX2oePjw8kEglCQkLKzZyQkID33nsPNjY2MDAwgLOzM95//31cuXKl1Lo9evSAtrY2Tp06VWpZly5dMG3atFLzIyMjYWFhUW6OqrRj1SQM690OTRvXRfMm9bF2nj/+ufsAiX/dUmmO59HR1oJNbTPFZGVhou5IWLv1MEb264DhfdrDtVFdrJg9BEYGetjyU5y6owmbTcRcKzceQD2bWlgzbwS83BrCoV5tvNOuKRzrW6stUwkR20vUXCK/h4mcTcRzyVzMxVzMVdOziZqLXi9CFv9hYWGYMmUKYmNjcfv27VLLDx48iDt37iA2NhZ2dnbw9fVFamqqYnlGRgbatWuHiIgIBAYG4uTJkzh79iwWLFiAhIQEbN26VWl/9vb2iIyMVJr377//4tChQ6hbt265eX/55Re0a9cOeXl5+P777/HXX39hy5YtMDc3x9y5yt3QU1JScOLECUyePBnh4eEv0TrqI8t6BACoJcDdTwC4fisNTXsFwbPvPLw/JxK37maoNU9+QSESL99ClzYuinlaWlro3MYFpy7cYLYalOu3YxfwRtMGGP1pGJy9P0Wn4YuxMea42vKUELW9RM31LNHew54mSjZRzyVzMRdzMVdNziZqLnr9CPfMf1ZWFqKionD69GncvXsXkZGRCAoKUlrHysoKtra2sLW1RVBQEH744QecPHkSffr0AQAEBQUhJSUFV65cgZ2dnWI7BwcHeHt7Qy6XK+3P19cX0dHROH78ODp27AgA2LhxI7y9vZGSkvLCvDk5ORgzZgzeffddxMTEKOY7Ojqibdu2yMzMVFo/IiICvr6+mDBhAtq1a4cVK1bA0NDwFVrsP3l5ecjL+6/LqEwmq5L9AkBxcTFmr9iBth6N0MzJrgJbVC8vt4ZYM88fTg42SL0vxZINe/Hu+1/ixA+fwdTYQC2Z0jOzUFRUDGtLU6X51pZmuJqc+tztVEHUbKLmSv73PsJ3HsPEYe/g4zHeOPvnTXy6fAf0dLUx1Led2nKJ2l6i5nqaaO9hTxMpm6jnkrmYi7mYqyJEzSZqLhFIBBvtX6Qs1UG4O//R0dFwdXWFi4sL/P39ER4eXqpYL5Gbm4tNmzYBAPT09IAnH6KioqLg7++vVPg/7dlnOfT09DB8+HBEREQo5kVGRiIgIKDcvPv27cP9+/cxc+bMMpc/3VVfLpcjIiIC/v7+cHV1hZOTE3bs2FHua1TUokWLYG5urpjs7e2rbN+BodH46+87CFswpsr2+Sq6d3RDv24t4e5cD13bN8P2ryZA+jAXuw+eVXc00gDFxXK0cLFH8KQ+aOFij9ED3sTIfh0QsesPdUejlyTae9jTRM5GREREmkO44j8sLAz+/v4AgJ49e0IqleLo0aNK63To0AEmJiYwNjbGsmXL4OXlha5duwIA0tLSkJmZCRcXF6VtvLy8YGJiAhMTEwwdOrTU6wYEBCA6OhrZ2dmIjY2FVCqFr69vuXmvXr0KAHB1dS133YMHDyInJwc9evQAAPj7+yMsLKzc7Spq9uzZkEqliunWrap5dnRGaDT2HbuIn9dNRT2bWlWyz6pmbmoEpwZ1cP1WmtoyWFmYQFtbq9TALWkZMtSxMlNbLgicTdRcNrXN4NrIVmlek4a2+Ofug+duowqitpeouUqI/B4mWjZRzyVzMRdzMVdFiJpN1Fz0+hGq+E9KSkJ8fLyiONfR0YGfn1+pAjkqKgoJCQnYuXMnnJycEBkZCV1d3RfuOyYmBomJiejRowdyc3NLLffw8ICzszN27NiB8PBwjBgxAjo6yk9FLFy4UPEFgomJCVJSUp7bK6Es4eHh8PPzU+x36NChOH78OP7+++8K7+NF9PX1YWZmpjS9Crlcjhmh0dhz5Bx+WjcVDvVqV0nO6pCVk4cb/96HbW1ztWXQ09WBp6s9jp5KUswrLi5G7KkraN3cUW25RM4maq62Ho1w9eY9pXl/p9xDfVtLtWWCwO0lai6R38NEzSbquWQu5mIu5qrJ2UTNJQItLYlwkyYT6pn/sLAwFBYWKnXXl8vl0NfXx+rVqxXz7O3t4ezsDGdnZxQWFqJ///64ePEi9PX1YW1tDQsLCyQlJSntu0GDBgAAU1PTUs/hlwgICMCaNWtw6dIlxMfHl1o+fvx4DB48WPFvOzs7NGnSBABw+fJltG/f/rnHlpGRgZiYGBQUFGDdunWK+UVFRQgPD8eCBQsAAGZmZpBKpaW2z8zMhLm5agvbwCXR2LHvNLYu+wAmRgZIvf94DAEzEwMYGuipNMuz5q7chZ5vNYd9XUvcSZNi8fo90NbSwsAeXmrNNXHYO5j4+Wa80bQBWro1xLptvyM7Nw/De6vvOXHRs4mYa+LQd9Bj7HIsj9iH/t1a4syfydgYcxxfBpXuNaTybAK2l6i5RH4PEzmbiOeSuZiLuZirpmcTNRe9XoQp/gsLC7Fp0yYsX74c3t7eSsv69euHbdu2oWfPnqW2GzRoEIKDg7F27VpMnz4dWlpaGDx4MLZs2YLg4ODnPvdflmHDhiEwMBAeHh5o1qxZqeWWlpawtFS+8+ft7Y3atWsjNDRUacC/EpmZmbCwsMD333+P+vXrY/fu3UrL9+/fj+XLl2P+/PnQ1taGi4sL9u/fX2o/Z8+eVXzRoCrhOx//hKLv+K+U5q8J9scwNb9R/XsvE+PmRCBDmoPatUzQ1qMRDkR8gtq1TCuwdfUZ4O2F+5lZWPjtHtxLf4jmTephx9eThOjSJWo2EXO1dHPA5qXvY/6an7D0u71wsLPCwo8HYnCv1mrLVELE9hI1l8jvYSJnE/FcMhdzMRdz1fRsouai14tEXpl+69Vo9+7d8PPzw71790rd4Z41axYOHz6M7du3w9HREQkJCfD09FQsX7duHUJCQnDjxg0YGRkhPT0dHTp0QHZ2NubPn49WrVrB2NgY58+fx6effgp3d3fs3LkTANClSxd4enpi5cqVwJNiXVdXF8bGxgAAT09P9OvXDyEhIc/N/uOPP+K9995Dz549MXXqVDg5OeH+/fuIjo5GSkoKfvjhB3h6eqJnz55YvHix0rZSqRR16tTBrl274OPjg+vXr8PNzQ3vv/8+xo0bB319fezZswezZs3Czz//XOYXIM8jk8lgbm6O1HTpKz8CQEREREREYpDJZLCxModUWjM/55fUKS6f7IK2vrG64ygU5WUjafmAGtuu5RHmmf+wsDB069atzK7tAwcOxOnTp5/703WjRo1CQUGB4tEAKysrxMfHY+TIkVi6dCnatGmD5s2bIyQkBH5+ftiwYcNzc1hYWCgK/4rq27cvTpw4AV1dXQwbNgyurq4YOnQopFIpvvjiC5w5cwbnzp3DwIEDS21rbm6Orl27KsY1aNSoEWJjY3H58mV069YNbdu2RXR0NLZv316pwp+IiIiIiIiohDB3/qnq8c4/EREREZHm4Z3/6qHpd/6FeeafiIiIiIiIXh8SiQQSiTgj7IuUpToI0+2fiIiIiIiIiKoHi38iIiIiIiIiDcdu/0RERERERKRyEsnjSRQiZakOvPNPREREREREpOFY/BMRERERERFpOHb7JyIiIiIiIpXjaP+qxTv/RERERERERBqOxT8RERERERGRhmO3fyIiIiIiIlI5dvtXLd75JyIiIiIiItJwLP6JiIiIiIiINBy7/RMREREREZHKSSSPJ1GIlKU68M4/ERERERERkYZj8U9ERERERESk4djtn4iIiIiIiFROAsFG+4c4WaoD7/wTERERERERaTgW/0REREREREQajt3+iYiIiIiISOU42r9q8c4/ERERERERkYZj8U9ERERERESk4djtn4iIiIiIiFROIhFstH+BslQH3vknIiIiIiIi0nAs/omIiIiIiIg0HLv9ExERERERkcpxtH/V4p1/KteG6KNo0ScYth2nodvopTjzZ7K6I+H42WsYMv0bNO0VhFqtJ2PPkXPqjgQInKuEiOcSguYS+VyK2F4QNJeo51HUXCVEPJdgrkpjrsphrsoRNRcEziZqLnp9sPinF9q1/wzmrIzBrHG9cGTzLLg718PAKWuQlvFQrblycvPg3qQels70U2uOZ4maCwKfS1FziXouRW0vUXOJeh5FzQWBzyVzMRdzMVdNziZqLnq9CFP8x8XFQVtbGz4+Pkrzk5OTFaNASiQSWFpaonPnzjh27FipfchkMsydOxdubm4wNDSElZUVWrdujdDQUDx48ECxXpcuXSCRSLB48eJS+/Dx8YFEIkFISEi5mRMSEvDee+/BxsYGBgYGcHZ2xvvvv48rV64orbdx40a0bt0aRkZGMDU1RefOnfHLL78orXPkyBFIJBLUqlULjx49Ulp26tQptY2EuXbrYYzs1wHD+7SHa6O6WDF7CIwM9LDlpziVZ3la945umDOhN3zf9lBrjmeJmgsCn0tRc4l6LkVtL1FziXoeRc0Fgc8lczEXczFXTc4mai51e7rOE2XSZMIU/2FhYZgyZQpiY2Nx+/btUssPHjyIO3fuIDY2FnZ2dvD19UVqaqpieUZGBtq1a4eIiAgEBgbi5MmTOHv2LBYsWICEhARs3bpVaX/29vaIjIxUmvfvv//i0KFDqFu3brl5f/nlF7Rr1w55eXn4/vvv8ddff2HLli0wNzfH3LlzFesFBgbiww8/hJ+fH86fP4/4+Hi8+eab6Nu3L1avXl1qv6ampoiJiSnVNg0aNCg3U1XLLyhE4uVb6NLGRTFPS0sLndu44NSFGyrPQy9P1HMpai5RidpeouaiyhP1XDIXczEXc9XkbKLmotePEAP+ZWVlISoqCqdPn8bdu3cRGRmJoKAgpXWsrKxga2sLW1tbBAUF4YcffsDJkyfRp08fAEBQUBBSUlJw5coV2NnZKbZzcHCAt7c35HK50v58fX0RHR2N48ePo2PHjsCTO/Te3t5ISUl5Yd6cnByMGTMG7777rlKh7ujoiLZt2yIzMxMA8H//939Yvnw5vv76a0yZMkWx3oIFC/Do0SN8/PHH6Nu3L+zt7RXLRo0ahfDwcAwdOhQAkJubix9++AFTp07F//73vxfmysvLQ15enuLfMpnsheuXJz0zC0VFxbC2NFWab21phqvJqc/djsQj6rkUNZeoRG0vUXNR5Yl6LpmLuZiLuSpC1Gyi5qLXjxB3/qOjo+Hq6goXFxf4+/sjPDy8VLFeIjc3F5s2bQIA6OnpAQCKi4sRFRUFf39/pcL/ac924dDT08Pw4cMRERGhmBcZGYmAgIBy8+7btw/379/HzJkzy1xuYWEBANi2bRtMTEzw4Ycfllrnk08+QUFBAXbu3Kk0f8SIETh27JjiC4idO3eiYcOGaNmyZbm5Fi1aBHNzc8X09JcKREREREREIikZ7V+kSZMJUfyHhYXB398fANCzZ09IpVIcPXpUaZ0OHTrAxMQExsbGWLZsGby8vNC1a1cAQFpaGjIzM+Hi4qK0jZeXF0xMTGBiYqK4k/60gIAAREdHIzs7G7GxsZBKpfD19S0379WrVwEArq6uL1zvypUraNy4seJLiqfZ2dnBzMys1PgAderUQa9evRSPJISHh1foCwkAmD17NqRSqWK6detWhbZ7HisLE2hra5UaiCQtQ4Y6VmavtG9SLVHPpai5RCVqe4maiypP1HPJXMzFXMxVEaJmEzUXvX7UXvwnJSUhPj5eUZzr6OjAz88PYWFhSutFRUUhISEBO3fuhJOTEyIjI6Grq/vCfcfExCAxMRE9evRAbm5uqeUeHh5wdnbGjh07EB4ejhEjRkBHR/lJiIULFyq+QDAxMUFKSspzeyWUpTLrlggICEBkZCSuX7+OuLg4DB8+vELb6evrw8zMTGl6FXq6OvB0tcfRU0mKecXFxYg9dQWtmzu+0r5JtUQ9l6LmEpWo7SVqLqo8Uc8lczEXczFXTc4mai56/aj9mf+wsDAUFhYqddeXy+XQ19dXGhDP3t4ezs7OcHZ2RmFhIfr374+LFy9CX18f1tbWsLCwQFJSktK+SwbJMzU1VTyH/6yAgACsWbMGly5dQnx8fKnl48ePx+DBgxX/trOzQ5MmTQAAly9fRvv27Z97bE2aNMEff/yB/Pz8Unf/b9++DZlMptjX03r16oUPPvgAY8eORe/evWFlZfXc16huE4e9g4mfb8YbTRugpVtDrNv2O7Jz8zC8dzu1ZQKArJw83LiVpvj3zdvpuJD0DyzMjWBva8lcZRD1XIqaS9RzKWp7iZpL1PMoai4IfC6Zi7mYi7lqcjZRc6mbaCPsi5SlOqi1+C8sLMSmTZuwfPlyeHt7Ky3r168ftm3bhp49e5babtCgQQgODsbatWsxffp0aGlpYfDgwdiyZQuCg4Of+9x/WYYNG4bAwEB4eHigWbNmpZZbWlrC0lL5g5i3tzdq166N0NDQUiPzA0BmZiYsLCwwZMgQfP311/j222+VBvwDgGXLlkFXVxcDBw4stb2Ojg5GjhyJ0NBQ7N27t8LHUh0GeHvhfmYWFn67B/fSH6J5k3rY8fUktXdRSvzrJnqP/1rx78++3AUAGOrTFmtDRjBXGUQ9l6LmEvVcitpeouYS9TyKmgsCn0vmYi7mYq6anE3UXPR6kchfpl96Fdm9ezf8/Pxw7949mJubKy2bNWsWDh8+jO3bt8PR0REJCQnw9PRULF+3bh1CQkJw48YNGBkZIT09HR06dEB2djbmz5+PVq1awdjYGOfPn8enn34Kd3d3xeB6Xbp0gaenJ1auXAk8KdZ1dXVhbGwMAPD09ES/fv0QEhLy3Ow//vgj3nvvPfTs2RNTp06Fk5MT7t+/j+joaKSkpOCHH34AAEybNg3ffPMNFixYgH79+qGgoABbtmzBokWLsHLlSsWXAkeOHMHbb7+NBw8ewMLCAvn5+ZDJZLCysoJEIsHu3bvRv3//Sj1GIJPJYG5ujtR06Ss/AkBERERERGKQyWSwsTKHVFozP+eX1ClewXugbWCs7jgKRY+ycWa+T41t1/Ko9Zn/sLAwdOvWrVThDwADBw7E6dOnn/tzdaNGjUJBQYHi0QArKyvEx8dj5MiRWLp0Kdq0aYPmzZsjJCQEfn5+2LBhw3NzWFhYKAr/iurbty9OnDgBXV1dDBs2DK6urhg6dCikUim++OILxXorV67E2rVrsW3bNri7u6NVq1aIjY3F7t27S/UGeJqenh5q166t8V1PiIiIiIjoNSXA6P5KI/1reOml1jv/VL1455+IiIiISPNozJ3/eXugI9Cd/8JH2TjzOe/8ExEREREREVENpfbR/omIiIiIiOj1w9H+VYt3/omIiIiIiIg0HIt/IiIiIiIiIg3Hbv9ERERERESkcopR9gUhUpbqwDv/RERERERERBqOd/6JiIiIiIhI5Tjgn2rxzj8RERERERGRhmPxT0RERERERKTh2O2fiIiIiIiIVI4D/qkW7/wTERERERERaTgW/0REREREREQajt3+iYiIiIiISOU42r9q8c4/ERERERERkYZj8U9ERERERESk4djtn4iIiIiIiFSO3f5Vi3f+iYiIiIiIiDQci38iIiIiIiIiDcdu/0RERERERKRyEsnjSRQiZakOvPNPREREREREpOFY/BMRERERERFpOHb7JyIiIiIiIpXjaP+qxTv/RERERERERJW0bt06tGjRAmZmZjAzM0P79u2xd+9exfJHjx5h0qRJsLKygomJCQYOHIjU1FSlfaSkpMDHxwdGRkaoU6cOZsyYgcLCQqV1jhw5gpYtW0JfXx9OTk6IjIx8qbws/omIiIiIiIgqqX79+li8eDHOnDmD06dP45133kHfvn3x559/AgCmT5+On3/+Gdu3b8fRo0dx+/ZtDBgwQLF9UVERfHx8kJ+fjxMnTmDjxo2IjIxEcHCwYp0bN27Ax8cHb7/9NhITEzFt2jSMGzcO+/btq3ReiVwul1fRsZNgZDIZzM3NkZouhZmZmbrjEBERERFRFZDJZLCxModUWjM/55fUKW8u3g8dA2N1x1EofJSNPz71xq1bt5TaVV9fH/r6+hXah6WlJZYuXYpBgwbB2toaW7duxaBBgwAAly9fRtOmTREXF4d27dph79698PX1xe3bt2FjYwMA+OabbzBr1iykpaVBT08Ps2bNwp49e3Dx4kXFawwZMgSZmZn47bffKnV8vPNP5doQfRQt+gTDtuM0dBu9FGf+TFZ3JIC5Xoqo2Zircpircpir8kTNxlyVw1yVw1yVI2ouCJxN1FxUmr29PczNzRXTokWLyt2mqKgIP/zwA7Kzs9G+fXucOXMGBQUF6Natm2IdV1dXNGjQAHFxcQCAuLg4NG/eXFH4A0CPHj0gk8kUvQfi4uKU9lGyTsk+KoPFP73Qrv1nMGdlDGaN64Ujm2fB3bkeBk5Zg7SMh8xVg3KJnI25mIu5xMklcjbmYi7mYq6anE3UXFS2W7duQSqVKqbZs2c/d90LFy7AxMQE+vr6GD9+PGJiYtCsWTPcvXsXenp6sLCwUFrfxsYGd+/eBQDcvXtXqfAvWV6y7EXryGQy5ObmVuq4akzxHxcXB21tbfj4+CjNT05OVowSKZFIYGlpic6dO+PYsWOl9iGTyTB37ly4ubnB0NAQVlZWaN26NUJDQ/HgwQPFel26dIFEIsHixYtL7cPHxwcSiQQhISEvzNuwYUNFJkNDQzRs2BCDBw/G4cOHy8yfmJhY6eNRhbVbD2Nkvw4Y3qc9XBvVxYrZQ2BkoIctP1X+mybmUi9RszEXczGXOLlEzsZczMVczFWTs4maS92erntEmQAoBvArmV7U5d/FxQWJiYk4efIkJkyYgFGjRuHSpUsqbMWKqzHFf1hYGKZMmYLY2Fjcvn271PKDBw/izp07iI2NhZ2dHXx9fZVGUszIyEC7du0QERGBwMBAnDx5EmfPnsWCBQuQkJCArVu3Ku3P3t6+1CiK//77Lw4dOoS6detWKPP8+fNx584dJCUlYdOmTbCwsEC3bt2wYMGCcrct73hUIb+gEImXb6FLGxfFPC0tLXRu44JTF26oNAtzaWY25mIu5hInl8jZmIu5mIu5anI2UXNR1dDT04OTkxO8vLywaNEieHh44KuvvoKtrS3y8/ORmZmptH5qaipsbW0BALa2tqVqvJJ/l7eOmZkZDA0NK5W1RhT/WVlZiIqKwoQJE+Dj41PmTxtYWVnB1tYW7u7uCAoKgkwmw8mTJxXLg4KCkJKSgvj4eIwZMwYtWrSAg4MDvL29sW3bNkycOFFpf76+vrh//z6OHz+umLdx40Z4e3ujTp06FcptamoKW1tbNGjQAJ06dcL69esxd+5cBAcHIykp6YXblnc8ZcnLy4NMJlOaXkV6ZhaKiophbWmqNN/a0gz30l9t38ylWqJmYy7mYi5xckHgbMzFXMzFXBUhajZRc1H1KC4uRl5eHry8vKCrq4tDhw4pliUlJSElJQXt27cHALRv3x4XLlzAvXv3FOscOHAAZmZmaNasmWKdp/dRsk7JPiqjRhT/0dHRcHV1hYuLC/z9/REeHo7n/UhBbm4uNm3aBDz5FgZPTkBUVBT8/f1hZ2dX5nYlXTxK6OnpYfjw4YiIiFDMi4yMREBAwCsdy0cffQS5XI4ff/yxQuuXdTzPs2jRIqWBKezt7V8pKxERERERUXWRAJBIBJoqmX/27NmIjY1FcnIyLly4gNmzZ+PIkSMYPnw4zM3NMXbsWHz88cf4/fffcebMGYwZMwbt27dHu3btAADe3t5o1qwZRowYgXPnzmHfvn2YM2cOJk2apHjUYPz48bh+/TpmzpyJy5cvY+3atYiOjsb06dMr3d41ovgPCwuDv78/AKBnz56QSqU4evSo0jodOnSAiYkJjI2NsWzZMnh5eaFr164AgLS0NGRmZsLFxUVpGy8vL5iYmMDExARDhw4t9boBAQGIjo5GdnY2YmNjIZVK4evr+0rHYmlpiTp16iA5+cWje77oeJ5n9uzZSgNT3Lp165WyWlmYQFtbq9RAJGkZMtSxUt9PijBX5YmajbmYi7nEyQWBszEXczEXc1WEqNlEzUWv7t69exg5ciRcXFzQtWtXnDp1Cvv27UP37t0BAF9++SV8fX0xcOBAdOrUCba2tti1a5die21tbfzyyy/Q1tZG+/bt4e/vj5EjR2L+/PmKdRwdHbFnzx4cOHAAHh4eWL58Ob777jv06NGj0nmFL/6TkpIQHx+vKM51dHTg5+eHsLAwpfWioqKQkJCAnTt3wsnJCZGRkdDV1X3hvmNiYpCYmIgePXqUOVKih4cHnJ2dsWPHDoSHh2PEiBHQ0dFRWmfhwoWKLxBMTEyQkpJS7jHJ5fJSPQ2e9TLHo6+vX2pwilehp6sDT1d7HD313yMKxcXFiD11Ba2bO77SvpmL2ZiLuZhLrFwiZ2Mu5mIu5qrJ2UTNRa8uLCwMycnJyMvLw71793Dw4EFF4Q8ABgYGWLNmDTIyMpCdnY1du3YpnuUv4eDggF9//RU5OTlIS0vDsmXLStWcXbp0QUJCAvLy8vD3339j9OjRL5VXpwLrqFVYWBgKCwuVuuvL5XLo6+tj9erVinn29vZwdnaGs7MzCgsL0b9/f1y8eBH6+vqwtraGhYVFqefsGzRoADx5Nv/ZgRhKBAQEYM2aNbh06RLi4+NLLR8/fjwGDx6s+PfzHisokZ6ejrS0NDg6vvgP/UXHo0oTh72DiZ9vxhtNG6ClW0Os2/Y7snPzMLx3O5XmYC7NzcZczMVc4uQSORtzMRdzMVdNziZqLnXTkkigVc5NUVUSKUt1ELr4LywsxKZNm7B8+XJ4e3srLevXrx+2bduGnj17ltpu0KBBCA4Oxtq1azF9+nRoaWlh8ODB2LJlC4KDg8st0J82bNgwBAYGwsPDQzHowtMsLS1haWlZ4f199dVX0NLSQr9+/Sq8zbPHo0oDvL1wPzMLC7/dg3vpD9G8ST3s+HqS2rsoMZfmZGMu5mIucXKJnI25mIu5mKsmZxM1F71eJPLnjZwngN27d8PPzw/37t2Dubm50rJZs2bh8OHD2L59OxwdHZGQkABPT0/F8nXr1iEkJAQ3btyAkZER0tPT0aFDB2RnZ2P+/Plo1aoVjI2Ncf78eXz66adwd3fHzp07gSfdKjw9PbFy5UoAQGZmJnR1dWFsbAwA8PT0RL9+/RASEvLc7A0bNsTYsWPx/vvvo6CgADdu3MCWLVvw3XffYdGiRZg1axYAIDk5WSn/s/9+3vFUhEwmg7m5OVLTpa/8CAAREREREYlBJpPBxsocUmnN/JxfUqd0CT0IHUNjdcdRKMzNxpGZ3Wpsu5ZH6Gf+w8LC0K1bt1KFPwAMHDgQp0+ffu7P2Y0aNQoFBQWKRwOsrKwQHx+PkSNHYunSpWjTpg2aN2+OkJAQ+Pn5YcOGDc/NYWFhoSj8KyM4OBh169aFk5MTRowYAalUikOHDikK/8p49niIiIiIiIhqMrWP7l/GpMmEvvNPr4Z3/omIiIiINI+m3Pl/e6l4d/5/n8E7/0RERERERERUQwk94B8RERERERFpJolEUu5PoKuSSFmqA+/8ExEREREREWk4Fv9EREREREREGo7d/omIiIiIiEjltCSPJ1GIlKU68M4/ERERERERkYZj8U9ERERERESk4djtn4iIiIiIiFRPItgI+wJFqQ68809ERERERESk4Vj8ExEREREREWk4dvsnIiIiIiIilZNIHk+iEClLdWDxT0REwiooLFZ3hDLp6rDjHBEREdUs/PRCREREREREpOF455+IiIiIiIhUTvLkf6IQKUt14J1/IiIiIiIiIg3H4p+IiIiIiIhIw7HbPxEREREREamcluTxJAqRslQH3vknIiIiIiIi0nAs/omIiIiIiIg0HLv9ExERERERkcpJJBJIJOL0tRcpS3XgnX8iIiIiIiIiDcfin4iIiIiIiEjDsds/ERERERERqZxE8ngShUhZqgPv/BMRERERERFpOBb/RERERERERBqO3f7pucJ2HEP4zmO4dScDAODayBYzxvZC945u6o6GFn2CFbmeNnbQW1g2y08tmQDg+NlrWLX5IM5dTsHd+zJsWfo+fLp4qC3PszZEH8WqLYdwL10Gd+d6WDLjPXi5NVR3LCFziXwu2V6PxSVcw5rvD+Fc0i2k3pchcvE4vNu5BQCgoLAIi779BYdOXMLN2+kwNTFAp1YumDuxD2ytzRX7eCDNxuwVO7D/j4vQ0tKCbxcPfDF9IEyM9Ks1u8jXFwS9xphLM3KJeO2L/HkHgp5HUXMtXr8HSzbsVZrn7GCD+B1z1ZYJgl73otCSSKAlUF97kbJUB975p+eyq2OBeZP74vdNM3F44wy81aoJhgeux19/31F3NBzeOAOX9y5UTDGrJwMA+nV7Q625cnLz4N6kHpbOVN8XEM+za/8ZzFkZg1njeuHI5llwd66HgVPWIC3jIXOVQdRzyfZ66jUf5cPNuR4Wf/JeqWW5j/JxPukffDymBw5GzkDEorH4O+UeRsxcr7TehJBNSLpxF9u/noQtyz5AXOLfCFz8Q/VnF/T6gsDXGHNpRi4Rr32RP++Ieh5FzQUAro3qKn1G3PvddHVHEvK6p9eTsMV/XFwctLW14ePjozQ/OTlZ8XuQEokElpaW6Ny5M44dO1ZqHzKZDHPnzoWbmxsMDQ1hZWWF1q1bIzQ0FA8ePFCs16VLF0gkEixevLjUPnx8fCCRSBASEvLCvA0bNsTKlSufu/zWrVsICAiAnZ0d9PT04ODggI8++gjp6eml1r127RrGjBmD+vXrQ19fH46Ojhg6dChOnz79wgxVrVen5vDu6IbGDerAycEGcyf2gbGRPk5fvKHSHGWpXcsUNrXNFNO+Py7CsX5tdGzprNZc3Tu6Yc6E3vB9W7xvc9duPYyR/TpgeJ/2cG1UFytmD4GRgR62/BTHXGUQ9Vyyvf7TtX0zzP7Qt8y7J2Ymhtjx9ST07dYSTg42aOXuiEWfDMK5y7fwz93Hd/euJN/F4f/7C1/OHgovt4Zo59EYiz4eiJiDZ3E3TVqt2UW9viDwNcZcmpFLxGtf5M87op5HUXMBgI62ltJnRCsLE3VHEvK6p9eTsMV/WFgYpkyZgtjYWNy+fbvU8oMHD+LOnTuIjY2FnZ0dfH19kZqaqliekZGBdu3aISIiAoGBgTh58iTOnj2LBQsWICEhAVu3blXan729PSIjI5Xm/fvvvzh06BDq1q37Ssdy/fp1tGrVClevXsW2bdtw7do1fPPNNzh06BDat2+PjIz/uq+fPn0aXl5euHLlCr799ltcunQJMTExcHV1xSeffPJKOV5FUVExdu4/jZzcfLRu7qi2HGXJLyhE9N5TGN6nPSQa3lXnZeUXFCLx8i10aeOimKelpYXObVxw6oL6PtyImktUbK9XI8t6BIlEAnNTQwDA6Qs3YG5qCM+mDRTrdGrtAi0tCc78mazGpOoj6jXGXJqRqyYQ6fOOqOdR1Fwlrt9KQ9NeQfDsOw/vz4nErbulHxMlcZSM9i/SpMmEfOY/KysLUVFROH36NO7evYvIyEgEBQUprWNlZQVbW1vY2toiKCgIP/zwA06ePIk+ffoAAIKCgpCSkoIrV67Azs5OsZ2DgwO8vb0hl8uV9ufr64vo6GgcP34cHTt2BABs3LgR3t7eSElJeaXjmTRpEvT09LB//34YGj7+0NmgQQO88cYbaNy4MT777DOsW7cOcrkco0ePhrOzM44dOwYtrf++m/H09MRHH330wtfJy8tDXl6e4t8ymeyVcgPAn9f+RY+A5XiUXwhjQ31sXvo+XBu92pchVW3PkfOQZuVimG9bdUcRVnpmFoqKimFtaao039rSDFeTU5+73euaS1Rsr5f3KK8A/1v7I/p3bwlT48fvw/fSH6J2LeW21NHRRi0zI9zLePX3z5pI1GuMuTQjl8hE/Lwj6nkUNRcAeLk1xJp5/nBysEHqfSmWbNiLd9//Eid++AymxgZqzUYkAiHv/EdHR8PV1RUuLi7w9/dHeHh4qWK9RG5uLjZt2gQA0NPTAwAUFxcjKioK/v7+SoX/0569Q6ynp4fhw4cjIiJCMS8yMhIBAQGvdCwZGRnYt28fJk6cqCj8S9ja2mL48OGIioqCXC5HYmIi/vzzT3zyySdKhX8JCwuLF77WokWLYG5urpjs7e1fKTueDJIS+/1sHIwIRMDANzExZDMuX1f/M3BP2/LTCXRr3wx1rV/cPkT0eiooLML7cyIglwNLZw5WdxwiElBN+LxD5eve0Q39urWEu3M9dG3fDNu/mgDpw1zsPnhW3dGIhCBk8R8WFgZ/f38AQM+ePSGVSnH06FGldTp06AATExMYGxtj2bJl8PLyQteuXQEAaWlpyMzMhIuLi9I2Xl5eMDExgYmJCYYOHVrqdQMCAhAdHY3s7GzExsZCKpXC19f3lY7l6tWrkMvlaNq0aZnLmzZtigcPHiAtLQ1Xr14FALi6ur7Ua82ePRtSqVQx3bp165WyA4Cerg4a2VvDs2kDzJvcF+7O9fDND0deeb9VJeVOBo7EJ2Fkvw7qjiI0KwsTaGtrlRqIJy1DhjpWZsxVQ7C9Kq+gsAjjPovArbsZ2P71JMVdfwCoY2WK+w+U27KwsAgPZDmoY/l6tqeo1xhzaUYukYn4eUfU8yhqrrKYmxrBqUEdXL+Vpu4o9BxPj+UmyqTJhCv+k5KSEB8fryjOdXR04Ofnh7CwMKX1oqKikJCQgJ07d8LJyQmRkZHQ1dV94b5jYmKQmJiIHj16IDc3t9RyDw8PODs7Y8eOHQgPD8eIESOgo6P8ZMTChQsVXyCYmJhU+JGA5/VcqOw6L6Kvrw8zMzOlqaoVy+XIzy+s8v2+rK0/x8G6lim8Bfk5HlHp6erA09UeR08lKeYVFxcj9tQVtT7TKGouUbG9Kqek8L/xTxp2fD0JlubGSstbNXeE9GEuzl3+73382JkrKC6Wq/3nqtRF1GuMuTQjV00iwucdUc+jqLnKkpWThxv/3odtbfMKrE2k+YR75j8sLAyFhYVK3fXlcjn09fWxevVqxTx7e3s4OzvD2dkZhYWF6N+/Py5evAh9fX1YW1vDwsICSUlJSvtu0ODxoE6mpqbIzMws8/UDAgKwZs0aXLp0CfHx8aWWjx8/HoMH/9dt9HmPFZRwcnKCRCLBX3/9hf79+5da/tdff6FWrVqwtrZGkyZNAACXL1/GG2+o9yfrAODz1T+iWwc32NvWwsOcR9jx22n8ceYqdq6aqO5owJP/0Hz/8/9hiE9b6OhoqzsOUPIfmae+Xb55Ox0Xkv6BhbkR7G0t1Zpt4rB3MPHzzXijaQO0dGuIddt+R3ZuHob3bsdcZRD1XP4/e/ceV/P9wHH8/a2U6qiUlBJlpShqcrdNxsIKmRGKWTZz3bJfhJHY5G62id26uK/kMmYbw8gMuZTbaGzuyqXUURJdfn/QmaOi1Dnn0/F+enwfj/X9fs+3V58uO5++3/ON4/XE+7zy3/u8dC0DJ/6+gromRrCqZ4rhU6JwPPUKVi34AIVFxbie8fB1/HVNjKBfSw9N7a3xevtm+Hj2D5g/0R8PCgoxeWEC+nZrBWtL1T5RFPXrCwJ/jbFLO7pE/NoX+fmOqJ9HUbumLd6AHq+2gF0Dc6TdzMacb7dCV0cH/bp7arRLxK97ejEJNfkvKCjAihUrsHDhQnh7eytt8/Pzw9q1a9GjR49Sj3v77bcRFhaGpUuXYvz48dDR0cGAAQOwatUqhIWFPXOC/rjBgwcjJCQE7u7uaN68eant5ubmMDev+DephYUF3njjDUXb46/7T09Px+rVqzF06FBIkgQPDw80b94cCxcuhL+/f6nX/WdlZT3zdf/V6dbtHIwKX4Hrt+QwkdWGq6Mt1n81Gl3alf0SBnXbnZSKK+m3Edhbs/+jeVzK6YvoNfJLxduffL4BADDIpx2Whg/RYBnwlrcnbmXlIOKbrbiRcQctmtoi4csxGr9ET9QuUT+XHK//HDtzCX3HfKV4O+zLjQAA/zfbYsJ7PfHr3pMAgNeHzlV63MbIcYo/C7osfCgmL0xAvw+XQEeS4Ovljlkfv62S3seJ+vUFgb/G2KUdXSJ+7Yv8fEfUz6OoXVdvZOG9qTHIzL6LenVlaOfeBL/F/K/UzV3VTcSve1GIdod9kVpUQSqu6rXm1WjTpk3w9/fHjRs3YGqqfNYlNDQUu3btwrp16+Dg4IDk5GR4eHgoti9btgzh4eE4f/48jIyMkJGRgY4dOyI3NxczZ85E69atYWxsjOPHj2PSpElwc3PD+vXrAQBeXl7w8PDA4sWLgUeT7Fq1asHY+OEloh4eHvDz80N4eHi57fb29ujfvz8CAgKU1jdu3Bi3bt1Cx44d0axZM3z22WdwcHDAqVOnMGHCBOTn5+PAgQOKXygkJSWhW7duaNGiBT755BO4uLggJycHW7Zswfbt20vd++Bp5HI5TE1NcT0jWyUvASAiUrUHBUWaTihTLT3hXjVHREQvELlcDisLU2Rn18zn+SXzlD5L96CWoUzTOQoP8nLw4+jONXZcn0WoZy9RUVHo1q1bqYk/APTr1w+HDx8u98/XvfPOO3jw4IHipQEWFhZISkrC0KFDMX/+fLRt2xYtWrRAeHg4/P398d1335XbYWZmppj4V8aCBQvw8ssvKy1bt26Fk5MTDh8+jCZNmmDAgAF46aWXMGLECHTp0gX79+9XupKgbdu2OHz4MBwdHfH++++jWbNm6N27N06dOqX45QQRERERERFRZQh15p+qF8/8E1FNxzP/REREpWnLmf++yxKFO/O/cdRrNXZcn4XPXoiIiIiIiIi0HCf/RERERERERFpOqLv9ExERERER0YtBerSIQqQWVeCZfyIiIiIiIiItx8k/ERERERERkZbjZf9ERERERESkdpIkQZLEudhepBZV4Jl/IiIiIiIiIi3HyT8RERERERGRluNl/0RERERERKR2OtLDRRQitagCz/wTERERERERaTlO/omIiIiIiIi0HC/7JyIiYdXSE/N31MXFxZpOKJO236WYiIi0C+/2r15iPqsiIiIiIiIiomrDyT8RERERERGRluNl/0RERERERKQRWn6lvVB45p+IiIiIiIhIy3HyT0RERERERKTleNk/ERERERERqR3v9q9eFZr8b968ucIH7N27d1V6iIiIiIiIiKiaVWjy7+fnV6GDSZKEwsLCqjYRERERERERUTWq0OS/qKhI9SVERERERET0wtCRHi6iEKlFFap0w7979+5VXwkRERERERERqUSlJ/+FhYX49NNPYWtrC5lMhn///RcAMG3aNERFRamikYiIiIiIiIiqoNKT/1mzZiE2Nhbz5s2Dvr6+Yr2bmxu+//776u4jIiIiIiIiLVRyt3+RFm1W6cn/ihUr8O233yIgIAC6urqK9e7u7jhz5kx19xERERERERFRFVV68n/16lU4OjqWWl9UVIQHDx5UVxcRERERERERVZMK3e3/cc2bN8fevXvRuHFjpfUJCQl4+eWXq7ONNGxRzDb89PsxnL14HbUNaqFtyyYIH9sHTvZWam/Zd/Qcvlq5A8fOXEL6LTlWzX8fPl7uiu1124wt83EzPvTDh0O6qbEU+C5+D75atRM3MuRwc7LF3An94elqr9aGJz1r/DRNtDET6Wu/LKKNl+hdJT6P3Y6ZkZsxcqAXZv/vbbW+X6WvpxYOmD6uD5waK389JR0/j1nLtuDIqYvQ0dVBCydbJHw5Goa19cs9tqqI+rlkF7vYxa6nadk7DJfTMkutH/72q1gQ6q+RJtSA52GaJD1aRCFSiypU+sx/WFgYxo4di7lz56KoqAgbNmzA+++/j1mzZiEsLOy5Q/bv3w9dXV34+Pgorb9w4YLSazDMzc3RuXNn7N27t9Qx5HI5pk2bBldXVxgaGsLCwgJt2rTBvHnzcPv2bcV+Xl5ekCQJc+bMKXUMHx8fSJKE8PDwp/ba29tDkiT88MMPpba5urpCkiTExsaW2v/AgQNK+wYHB8PLy0vxdnh4uOJj1dXVhZ2dHUaMGIHMzNI/yFTtz6Pn8F7/17A9OgQblozFg4JCvDVuCXLz8tXecjcvH25NbTF/Ytk/uM/8EqG0LJkWAEmS0LuLh1o7N2w/gqmLNyL0vZ7YvTIUbk626DcuEjcz76i140nPGj9NEnHMRPraf5KI4yVyV4mjpy4iduM+uDrZqv197zt6DsP7v4ptUf/Dhq/G4EFhIfqNi1T6eko6fh79P1qKLu1d8FtMCHbGhuC9/q9BRwN/c0jUzyW72MUudj3LruUTlJ4Pblzy8OSQXzfNnqAU+XkYvVgqPfnv06cPtmzZgh07dsDY2BhhYWE4ffo0tmzZgjfeeOO5Q6KiojBu3DgkJibi2rVrpbbv2LEDaWlpSExMhI2NDXx9fXH9+nXF9szMTLRv3x4xMTEICQnBwYMHcfToUcyaNQvJyclYs2aN0vHs7OyUJud49JKGnTt3okGDBhVqtrOzQ0xMjNK6AwcOID09HcbGxqX2r127NkJDQ595XFdXV6SlpeHSpUuIiYnBr7/+ilGjRlWoqTolfDUGg3u1R7OXGqBF04ZYOj0QV9JvI+X0ZbW3vNHJFVNH9YJvl7J/S2pVz0Rp+TnxBF71dIJ9w3pq7Vy6ZheG+nVEQO8OcGnSAIsmD4RRbX2s2rxfrR1Petb4aZKIYybS1/6TRBwvkbsAIOduPkaExeKLKYNgVsdQ7e8/4cvRGOz78OvJrWlDRIY9/Ho69tjX0yeLN2CEf2cEv+ONZi81gFNjK/R9oxUM9GupvVfUzyW72MUudj1Lvbp1lJ4PbvvjJBwa1kOnVk4a7RL5eRi9WCo9+QeAV199Fb/99htu3LiBu3fv4o8//oC3t/dzR+Tk5CAuLg6jRo2Cj49PqUk5AFhYWMDa2hpubm6YMmUK5HI5Dh48qNg+ZcoUXLp0CUlJSXj33XfRsmVLNG7cGN7e3li7di1Gjx6tdDxfX1/cunUL+/btU6xbvnw5vL29Ub9+/Qp1BwQEYM+ePbh8+b8ncNHR0QgICICeXulXVIwYMQIHDhzAzz///NTj6unpwdraGra2tujWrRv69++P3377rUJNqiTPuQcAqGtipOmUp7qRIcf2P04isE8Htb7f+w8KkHLmMrzaOivW6ejooHNbZxw6cV6tLTVFTRkzUb72RR0vUbtKTJgXB+9ObvBq56LpFOCxrycz04dfTzcz7+DIyQuwrFsH3YcvgnOPKfD94AscSPlH7W2ifi7ZxS52sauy7j8oQPwvhxDQu4PW38G9JtORJOEWbfZck38AOHz4MFauXImVK1fiyJEjVYqIj4+Hi4sLnJ2dERgYiOjoaBQXF5e5b15eHlasWAEAij81WFRUhLi4OAQGBsLGxqbMxz35Ta+vr4+AgAClM/exsbEICgqqcLeVlRW6d++O5cuXAwDu3r2LuLi4co/h4OCAkSNHYvLkySgqKqrQ+7hw4QK2bdum9GcVy5Ofnw+5XK60VJeioiJMXpSAdu5N0Nyx7DEWxdqtByEzro1ear7kPyMrB4WFRbA0r6O03tLcBDcyqu9zoU1qwpiJ9LUv6niJ2gUA67cfxrEzlxE2prdGO0oUFRVhyqL1D7+eXnr49XTh6i0AwNzvfsZQv45Y98UotHRuCL8xS/DPpRtq7RP1c8kudrGLXZW1dfdxZOfkYbBvO02nEAmj0pP/K1eu4NVXX0Xbtm3x0Ucf4aOPPkKbNm3wyiuv4MqVK88VERUVhcDAQABAjx49kJ2djT179ijt07FjR8hkMhgbG2PBggXw9PRE165dAQA3b95EVlYWnJ2dlR7j6ekJmUwGmUyGQYMGlXq/QUFBiI+PR25uLhITE5GdnQ1fX99KtQcFBSE2NhbFxcVISEjASy+9BA+P8iedU6dOxfnz57F69epy9zlx4gRkMhkMDQ3h4OCAU6dOVejlArNnz4apqalisbOzq9TH8jQh8+Jx+p80RM16t9qOqSqrNx9A/x6tUdtA/ZfLkvapSV/7pOxK+m1MXrge3346TJifBxPmrcPpf9Pw/WfDFOuKHv2ye9hbnRDQqz1aOtsh4uN+cGxcH6u3HHjK0YiIqDyrNv+Jbh2ao4GlmaZTiIRR6cn/e++9hwcPHuD06dPIzMxEZmYmTp8+jaKiIrz33nuVDkhNTUVSUpJicq6npwd/f39ERUUp7RcXF4fk5GSsX78ejo6OiI2NRa1aT38yt3HjRqSkpKB79+7Iy8srtd3d3R1OTk5ISEhAdHQ0hgwZUupy/YiICMUvEGQyGS5duqS03cfHBzk5OUhMTER0dPQzrxywtLRESEgIwsLCcP/+/TL3cXZ2RkpKCg4dOoTQ0FB0794d48aNe+pxAWDy5MnIzs5WLI+/HKEqJsyLx7a9J7Fl2YewtapbLcdUlT+Tz+HsxesY0qej2t+3hZkMuro6pW52czNTjvoWJmrvqQlEHzPRvvZFHS9Ru46duYSbmXfgNWQu6rX/EPXaf4h9R8/hm7g9qNf+QxQWVuwKrOoycX48tv1xEpuXjlP6erJ+NEbODsr3m2lqb4Ur6bdLHUeVRP1csotd7GJXZVxKy8TupFQM9VP/80GqHEkSb9FmlZ7879mzB8uWLVM6y+7s7IyvvvoKiYmJlQ6IiopCQUEBbGxsoKenBz09PSxbtgzr169Hdna2Yj87Ozs4OTmhb9++iIiIQN++fZGf//BOyZaWljAzM0NqaqrSsRs1agRHR0fUqVOn1PstERQUhMjISCQkJJQ5cR85ciRSUlIUy5MvK9DT08OQIUMwffp0HDx4EAEBAc/8mD/++GPk5eVh6dKlZW7X19eHo6Mj3NzcMGfOHOjq6mLGjBnPPK6BgQFMTEyUlqooLi7GhHnx2Lr7GDYv+xCNbdV787znserH/fBoZocWTRuq/X3r19KDh4sd9hz67+uwqKgIiYf+RpsWDmrvqQlEHTNRv/ZFHS9Ru15r44x9a6cgcdUkxfJys0bo36M1EldNgq7uc7/yrVKKi4sxcX48tu4+jh+Xjiv19dTIxgINLE1x9uJ1pfX/XLoJuwbq/aWTqJ9LdrGLXeyqjDVb9sOybh14d3LVdAqRUCr9zMfOzg4PHjwotb6wsLDc19uXp6CgACtWrMDChQuVJtjHjh2DjY0N1q5dW+bj3n77bejp6Skmzzo6OhgwYABWrVpV5l8KeJrBgwfjxIkTcHNzQ/PmzUttNzc3h6Ojo2Ip60Z+QUFB2LNnD/r06YO6dZ/9RE0mk2HatGmYNWsW7tx59p9EmTp1KhYsWFDpj62qQubGI/6XQ/ju02GQGdXG9VtyXL8lR969sq9YUKWcu/k4kXoFJ1IfvrTk4rUMnEi9gsvp//0JRHlOHn7cmayRs/4lRg9+HSs2/Ym1Px1A6vl0fDwnDrl5+Qjo1V5jTajg+GmKiGMm0tf+k0QcL1G76hjXRnNHG6XFyFAf5qbGar1/w4R58Yj/5TC+/fSdMr+eJEnC2MCu+DZuD37cmYx/L9/ErK9/wtmL1xHYW703LoWgn0t2sYtd7KqooqIirN5yAAN92kFPT1fTOYDgz8PoxVJ6JvsM8+fPx7hx4xAZGYnWrVsDj27+99FHH2HBggWVOtZPP/2E27dvY/jw4TA1NVXa1q9fP0RFRaFHjx6lHidJEj788EOEh4fjgw8+gJGRESIiIrB79260bdsWM2fOROvWrWFsbIzjx49j//79cHNzK7Ohbt26SEtLe+ZLCJ6mWbNmuHXrFoyMKn4n8BEjRuDzzz/HmjVr0K7d029E0qFDB7Rs2RIRERFYsmTJc3dWVvT6vQAA35FfKK2PDAvEYDX/cE85fRG9Rn6pePuTzzcAAAb5tMPS8CHAo785W1xcjH7dW6u17XFveXviVlYOIr7ZihsZd9CiqS0Svhyj8cvgKjJ+miLimIn0tf8kEcdL5C4RRK//AwCUvgcBYElYAAb7Pvx6GjWoC/LvP8Ann29AlvwuXJ1sseGrMXBoaKn2XlE/l+xiF7vYVRG7k1JxJf02Antr/hcRJUR+HqZpkiQJ9dcYRGpRBam4vNvqP6Zu3bpKA5Gbm4uCggLFWfCS/zY2NkZmZsV/g9WrVy8UFRVh69atpbYlJSWhXbt2OHbsGNzd3ZGcnKx0I727d++iYcOGmDRpEiZOnAgAyM7Oxty5c7Fx40acP38eOjo6cHJyQp8+fRAcHAxzc3MAgJeXFzw8PLB48eIyuzw8PODn54fw8PBy2+3t7REcHIzg4OAyt5uZmWHx4sUYNmxYufuvXbsWgwcPRufOnbF7924AQHh4ODZt2oSUlBSl4/3www8YNmwYzp49W+Eb+cnlcpiamuJ6RnaVXwJARET/qcD/OjVC25+0EBHRQ3K5HFYWpsjOrpnP80vmKe/EHoC+kUzTOQr37+Zg+bD2NXZcn6VCk/+SP2VXEe+8805Vm6iacPJPRKQanPwTEZEmcfKvGto++a/QZf+c0BMREREREVF1Eu0O+yK1qEKlX/P/uHv37pX6c3Xa+BsSIiIiIiIiopqs0nf7z83NxdixY1G/fn0YGxujbt26SgsRERERERERiaXSk/+JEydi165dWLZsGQwMDPD9999jxowZsLGxwYoVK1RTSURERERERFpFR5KEW7RZpS/737JlC1asWAEvLy+8++67ePXVV+Ho6IjGjRtj9erVCAgIUE0pERERERERET2XSp/5z8zMRJMmTYBHr+8v+dN+r7zyChITE6u/kIiIiIiIiIiqpNKT/yZNmuD8+fMAABcXF8THxwOPrggwMzOr/kIiIiIiIiLSOiV3+xdp0WaVnvy/++67OHbsGABg0qRJiIyMRO3atTF+/HhMmDBBFY1EREREREREVAWVfs3/+PHjFf/drVs3nDlzBkeOHIGjoyNatmxZ3X1EREREREREVEWVnvw/qXHjxmjcuHH11BAREREREdELQZIkSAJday9SiypUaPL/5ZdfVviAH374YVV6iIiIiIiIiKiaVWjy//nnn1foYJIkcfJPRERaT9vPDBAREZH2qdDkv+Tu/kRERERERETVQed57kCvQiK1qIK2f3xERERERERELzxO/omIiIiIiIi0XJXv9k9ERERERERUWbzbv3rxzD8RERERERGRluPkn4iIiIiIiEjLPdfkf+/evQgMDESHDh1w9epVAMDKlSvxxx9/VHcfERERERERaSFJAnQEWrT8qv/KT/7Xr1+P7t27w9DQEMnJycjPzwcAZGdnIyIiQhWNRERERERERFQFlZ78f/bZZ/j666/x3XffoVatWor1nTp1wtGjR6u7j4iIiIiIiIiqqNJ3+09NTcVrr71War2pqSmysrKqq4uIiIiIiIi0WMnl9qIQqUUVKn3m39raGufOnSu1/o8//kCTJk2qq4uIiIiIiIiIqkmlJ//vv/8+PvroIxw8eBCSJOHatWtYvXo1QkJCMGrUKNVUEhEREREREdFzq/Rl/5MmTUJRURG6du2Ku3fv4rXXXoOBgQFCQkIwbtw41VQSERERERGRVpEkCZJAt9gXqUUVKj35lyQJn3zyCSZMmIBz584hJycHzZs3h0wmU00hEREREREREVVJpSf/JfT19dG8efPqrSEhfRe/B1+t2okbGXK4Odli7oT+8HS111jPopht+On3Yzh78TpqG9RC25ZNED62D5zsrTTWVGLf0XP4auUOHDtzCem35Fg1/334eLlrOktBtM8lu9jFLs12Pe1n1oOCQny2bAt+23cKF69mwERWG53bumD62N5oYGmm1s4SIowZu7SvS+TnFRBwvETvgoBtoj8/pBdHpV/z36VLF7z++uvlLs9j//790NXVhY+Pj9L6CxcuKC4FkSQJ5ubm6Ny5M/bu3VvqGHK5HNOmTYOrqysMDQ1hYWGBNm3aYN68ebh9+7ZiPy8vL0iShDlz5pQ6ho+PDyRJQnh4+FN77e3tIUkSfvjhh1LbXF1dIUkSYmNjldb/+eefePPNN1G3bl3Url0bLVq0wKJFi1BYWKi03+Mfr7GxMZycnDBs2DAcOXLkqU2qsmH7EUxdvBGh7/XE7pWhcHOyRb9xkbiZeUcjPQDw59FzeK//a9geHYINS8biQUEh3hq3BLl5+RprKnE3Lx9uTW0xf6K/plNKEfFzyS52sUuzXU/7mXX33n0cP3MZE4Y/bFwx732cu3gdg//3jVobS4gyZuzSvi6Rn1eIOF4id4naJvLzQ00rudu/SIs2q/Tk38PDA+7u7oqlefPmuH//Po4ePYoWLVo8V0RUVBTGjRuHxMREXLt2rdT2HTt2IC0tDYmJibCxsYGvry+uX7+u2J6ZmYn27dsjJiYGISEhOHjwII4ePYpZs2YhOTkZa9asUTqenZ1dqcn51atXsXPnTjRo0KBCzXZ2doiJiVFad+DAAaSnp8PY2Fhp/caNG9G5c2c0bNgQv//+O86cOYOPPvoIn332GQYOHIji4mKl/WNiYpCWloZTp04hMjISOTk5aNeuHVasWFGhtuq0dM0uDPXriIDeHeDSpAEWTR4Io9r6WLV5v9pbSiR8NQaDe7VHs5caoEXThlg6PRBX0m8j5fRljTWVeKOTK6aO6gXfLuL9NlfEzyW72MUuzXY97WeWqcwQGyPHoe8breBkb4U2LRwwb8IApJy+jMvpmWrthEBjxi7t6xL5eYWI4yVyl6htIj8/pBdLpSf/n3/+udKyZMkS/PHHHwgODkatWrUqHZCTk4O4uDiMGjUKPj4+pSblAGBhYQFra2u4ublhypQpkMvlOHjwoGL7lClTcOnSJSQlJeHdd99Fy5Yt0bhxY3h7e2Pt2rUYPXq00vF8fX1x69Yt7Nu3T7Fu+fLl8Pb2Rv369SvUHRAQgD179uDy5f/+xxAdHY2AgADo6f33aorc3Fy8//776N27N7799lt4eHjA3t4e7733HpYvX46EhATEx8crHdvMzAzW1tawt7eHt7c3EhISEBAQgLFjxypdxaBq9x8UIOXMZXi1dVas09HRQee2zjh04rzaOp5FnnMPAFDXxEjTKcIS9XPJLnaxS5yuipDn5EGSJJjKDNX6fkUdM3ZpR9eTRHleIep4idolehuRCCo9+S9PYGAgoqOjK/24+Ph4uLi4wNnZWXGMJ8+El8jLy1Oc/dbX1wcAFBUVIS4uDoGBgbCxsSnzcU/etVFfXx8BAQFKZ+5jY2MRFBRU4W4rKyt0794dy5cvBwDcvXsXcXFxpY6xfft2ZGRkICQkpNQxevXqhaZNm2Lt2rXPfH/jx4/HnTt38Ntvv5W7T35+PuRyudJSFRlZOSgsLIKleR2l9ZbmJriRUbVjV5eioiJMXpSAdu5N0Nyx7M8/ifu5ZBe72CVO17Pcy3+A8CU/op+3J0zUPPkXdczYpR1djxPpeYWo4yVqFwRvo7JJkniLNqu2yf/+/ftRu3btSj8uKioKgYGBAIAePXogOzsbe/bsUdqnY8eOkMlkMDY2xoIFC+Dp6YmuXbsCAG7evImsrCw4OzsrPcbT0xMymQwymQyDBg0q9X6DgoIQHx+P3NxcJCYmIjs7G76+vpVqDwoKQmxsLIqLi5GQkICXXnoJHh4eSvv8/fffAIBmzZqVeQwXFxfFPk/j4uICPLoPQnlmz54NU1NTxWJnZ1epj6cmCpkXj9P/pCFq1ruaTiEi0loPCgrx7uQoFBcXY+EkvmaVtBefVxCRNqv03f7feustpbeLi4uRlpaGw4cPY9q0aZU6VmpqKpKSkrBx48aHMXp68Pf3R1RUFLy8vBT7xcXFwcXFBSdPnsTEiRMRGxv7zJcYbNy4Effv30doaCjy8vJKbXd3d4eTkxMSEhLw+++/Y8iQIUqX6wNAREQEIiIiFG//9ddfaNSokeJtHx8ffPDBB0hMTER0dPRTrxwo72qGiip5/NP+9uTkyZPx8ccfK96Wy+VV+gWAhZkMuro6pW6QcjNTjvoWJs993OoyYV48tu09iZ+/DYatVV1N5whN1M8lu9jFLnG6ylMy8b+cfhubl45T+1l/CDxm7NKOrhKiPa8QdbxE7YLgbUQiqPSZ/8fPLJuamsLc3BxeXl74+eefMX369EodKyoqCgUFBbCxsYGenh709PSwbNkyrF+/HtnZ2Yr97Ozs4OTkhL59+yIiIgJ9+/ZFfv7DO7BaWlrCzMwMqampSsdu1KgRHB0dUadOnVLvt0RQUBAiIyORkJBQ5sR95MiRSElJUSxPvqxAT08PQ4YMwfTp03Hw4EEEBASUOkbTpk0BAKdPny6z4fTp04p9nqbk8Q4ODuXuY2BgABMTE6WlKvRr6cHDxQ57Dv03tkVFRUg89DfatCi/Q9WKi4sxYV48tu4+hs3LPkRj23oaa6kpRP1csotd7BKnqywlE/9/Lt3EpsixMDeTaaRD1DFjl3Z0ifq8QtTxErVL9DYqm44kCbdos0qd+S8sLMS7776LFi1aoG7dqv1GtKCgACtWrMDChQvh7e2ttM3Pzw9r165Fjx49Sj3u7bffRlhYGJYuXYrx48dDR0cHAwYMwKpVqxAWFlbu6/7LMnjwYISEhCj+asGTzM3NYW5u/tRjBAUFYcGCBfD39y9zTLy9vWFubo6FCxeiY8eOSts2b96Ms2fP4tNPP31m6+LFi2FiYoJu3bpV6GOrLqMHv47RM1bi5WaN0MrVHsvW/o7cvHwE9Gqv1o7HhcyNR8K2w1izYARkRrVx/dbD13CZyGrDsLa+xroAIOduPs5fvql4++K1DJxIvQIzUyPYWT/9a0nVRPxcsotd7NJs19N+ZlnXM8U7od/j2JnL+OHzkSgsLFb8vK1ragT9WpW+eLBKRBkzdmlfl8jPK0QcL5G7RG0T+fkhvVgq9X9uXV1deHt74/Tp01We/P/000+4ffs2hg8fDlNTU6Vt/fr1Q1RUVJmTf0mS8OGHHyI8PBwffPABjIyMEBERgd27d6Nt27aYOXMmWrduDWNjYxw/fhz79++Hm5tbmQ1169ZFWlrac/2VghLNmjXDrVu3YGRU9h1hjY2N8c0332DgwIEYMWIExo4dCxMTE+zcuRMTJkzA22+/jQEDBig9JisrC+np6cjPz8fff/+Nb775Bps2bcKKFStgZmb23K3P4y1vT9zKykHEN1txI+MOWjS1RcKXYzR66VT0+r0AAN+RXyitjwwLxGAN/08n5fRF9Br5peLtTz7fAAAY5NMOS8OHaLBMzM8lu9jFLs12Pe1n1qQRb+KXxBMAgNcC5ig9bsvXH+IVz2dftVadRBkzdmlfl8jPK0QcL5G7RG0T+fmhpulU503oqoFILaogFVfyxeitW7fG3LlzFTfce169evVCUVERtm7dWmpbUlIS2rVrh2PHjsHd3R3JyclKN9K7e/cuGjZsiEmTJmHixIkAgOzsbMydOxcbN27E+fPnoaOjAycnJ/Tp0wfBwcGKM/heXl7w8PDA4sWLy+zy8PCAn58fwsPDy223t7dHcHAwgoODy9xuZmaGxYsXY9iwYYp1e/fuxaxZs7B//37cu3cPTk5OePfddxEcHAxdXV3Ffo+/pr927dqwtbXFK6+8gg8//BCtWrV6xqgqk8vlMDU1xfWM7Cq/BICIiIiIiMQgl8thZWGK7Oya+Ty/ZJ7y8bojMDDSzEvKypJ/NweL+nvW2HF9lkpP/n/99VdMnjwZn376KTw9PWFsbKy0XRsHqabi5J+IiIiISPtw8q8a2j75r/Bl/zNnzsT//vc/vPnmmwCA3r17K52lLi4uhiRJKCwsVE0pERERERERaQ1JeriIQqQWVajw5H/GjBkYOXIkfv/9d9UWEREREREREVG1qvDkv+TVAZ07d1ZlDxERERERERFVs0rd7V/S9usgiIiIiIiISC10IEFHoDmmDsRpUYVKTf6bNm36zF8AZGZmVrWJiIiIiIiIiKpRpSb/M2bMgKmpqepqiIiIiIiIiKjaVWryP3DgQNSvX191NURERERERPRC4N3+1Uunojvy9f5ERERERERENVOFJ/8ld/snIiIiIiIiopqlwpf9FxUVqbaEiIiIiIiIXhg60sNFFCK1qEKFz/wTERERERERUc3EyT8RERERERGRlqvU3f6JiIiIiIiIqoMkAToC3VheoBSV4Jl/IiIiIiIiIi3HyT8RERERERGRluNl/0RERERERKR2kiTWpfYitagCz/wTERERERERaTlO/omIiIiIiIi0HC/7JyIiIiIiIrXTkR4uohCpRRV45p+IiIiIiIhIy3HyT0RERERERKTleNk/ERERERERqZ306J8oRGpRBZ75JyIiIiIiItJynPwTERERERERaTle9k9ERERERERqx7v9qxfP/BMRERERERFpOU7+6Zm+i9+Dlr3DYN0pGN2GzceRUxc0nYR9R89h4Piv0aznFNRtMxZbdx/TdJKCiONVQtQ2dlUOuyqHXZUnWltUwl50GhSBRl4haOQVAu+gBfht3ymNNj1OtPEqwa7KYVfliNoFgdtE7aIXh3CT//3790NXVxc+Pj5K6y9cuABJkhSLubk5OnfujL1795Y6hlwux7Rp0+Dq6gpDQ0NYWFigTZs2mDdvHm7fvq3Yz8vLC5IkYc6cOaWO4ePjA0mSEB4e/tRee3t7SJKEAwcOKK0PDg6Gl5eX4u3w8HCl/pLFxcVF6XHnzp1DUFAQGjVqBAMDA9ja2qJr165YvXo1CgoKKjCC1WvD9iOYungjQt/rid0rQ+HmZIt+4yJxM/OO2lsedzcvH25NbTF/or9GO54k6niJ3MYudrFLnC5R22zqm2H62D74fcVE7Fo+Aa+2boqAkG9x+p80jTWVEHG82MWuF7VL5DZRuzSt5LJ/kRZtJtzkPyoqCuPGjUNiYiKuXbtWavuOHTuQlpaGxMRE2NjYwNfXF9evX1dsz8zMRPv27RETE4OQkBAcPHgQR48exaxZs5CcnIw1a9YoHc/Ozg6xsbFK665evYqdO3eiQYMGFWquXbs2QkNDn7mfq6sr0tLSlJY//vhDsT0pKQmtWrXC6dOnERkZiZMnT2L37t147733sGzZMpw6pf6zHEvX7MJQv44I6N0BLk0aYNHkgTCqrY9Vm/erveVxb3RyxdRRveDbxV2jHU8SdbxEbmMXu9glTpeobT1fawHvTq54qVF9ODa2wrTRvWFsZIDDJ89rrKmEiOPFLna9qF0it4naRS8WoSb/OTk5iIuLw6hRo+Dj41NqUg4AFhYWsLa2hpubG6ZMmQK5XI6DBw8qtk+ZMgWXLl1CUlIS3n33XbRs2RKNGzeGt7c31q5di9GjRysdz9fXF7du3cK+ffsU65YvXw5vb2/Ur1+/Qt0jRozAgQMH8PPPPz91Pz09PVhbWyst9erVAwAUFxdj2LBhaNq0Kfbt24devXrByckJTk5OGDRoEP744w+0bNmyQj3V5f6DAqScuQyvts6KdTo6Oujc1hmHTmj+CZdoRB4vUdvYxS52idMleluJwsIirN9+GHfz7qNNCweNtog6Xuxi14vYJXKbqF304hFq8h8fHw8XFxc4OzsjMDAQ0dHRKC4uLnPfvLw8rFixAgCgr68PACgqKkJcXBwCAwNhY2NT5uMkSflaDn19fQQEBCAmJkaxLjY2FkFBQRXudnBwwMiRIzF58mQUFRVV+HGPS0lJwenTpxESEgIdnbI/LU+2Pyk/Px9yuVxpqYqMrBwUFhbB0ryO0npLcxPcyKjasbWRyOMlahu72MUucbogeNupc1fR8LWPYdUpGB/PjsPK+e/DpUnFrtBTFVHHi13sehG7IHCbqF0iKOtl0ZpeKmP27Nlo06YN6tSpg/r168PPzw+pqalK+9y7dw9jxoyBhYUFZDIZ+vXrp3TlOgBcunQJPj4+MDIyQv369TFhwoRSL/nevXs3WrVqBQMDAzg6OpZ5ovxZhJr8R0VFITAwEADQo0cPZGdnY8+ePUr7dOzYETKZDMbGxliwYAE8PT3RtWtXAMDNmzeRlZUFZ2dnpcd4enpCJpNBJpNh0KBBpd5vUFAQ4uPjkZubi8TERGRnZ8PX17dS7VOnTsX58+exevXqcvc5ceKEoqNkGTlyJADg77//BgCl9hs3bijtu3Tp0qc2zJ49G6amporFzs6uUh8DERGRqJwaWyFx9WTsiAlBUL9XMDp8Jc78q/nX/BMR0Ytrz549GDNmDA4cOIDffvsNDx48gLe3N3JzcxX7jB8/Hlu2bMG6deuwZ88eXLt2DW+99ZZie2FhIXx8fHD//n38+eefWL58OWJjYxEWFqbY5/z58/Dx8UGXLl2QkpKC4OBgvPfee9i2bVulevWq6eOustTUVCQlJWHjxo3Ao0vk/f39ERUVpXTjvLi4OLi4uODkyZOYOHEiYmNjUatWracee+PGjbh//z5CQ0ORl5dXaru7uzucnJyQkJCA33//HUOGDIGenvLQREREICIiQvH2X3/9hUaNGinetrS0REhICMLCwuDvX/ZN6JydnbF582aldSYmJuV2W1hYICUlBXh0c8L79+8/9eOcPHkyPv74Y8Xbcrm8Sr8AsDCTQVdXp9SNSG5mylHfovzuF5XI4yVqG7vYxS5xuiB4m34tPTSxswQAeDRrhOS/LuHrH3Zj8ZTSv9RXF1HHi13sehG7IHCbqF1Udb/++qvS27Gxsahfvz6OHDmC1157DdnZ2YiKisKaNWvw+uuvAwBiYmLQrFkzHDhwAO3bt8f27dvx119/YceOHbCysoKHhwc+/fRThIaGIjw8HPr6+vj666/h4OCAhQsXAgCaNWuGP/74A59//jm6d+9e4V5hzvxHRUWhoKAANjY20NPTg56eHpYtW4b169cjOztbsZ+dnR2cnJzQt29fREREoG/fvsjPzwceTcDNzMxKXWrRqFEjODo6ok6dOqXeb4mgoCBERkYiISGhzEv+R44ciZSUFMVS1ssKPv74Y+Tl5ZV7hl5fXx+Ojo5KS8l9BZycnIBHvwQpoaurq9jvyV9GlMXAwAAmJiZKS1Xo19KDh4sd9hz6r6moqAiJh/7W+OssRSTyeInaxi52sUucLtHbnlRUXIz799X/V3AeJ+p4sYtdL2KXyG2idolA03f2L+9u/0++lLpkvvksJfNWc3NzAMCRI0fw4MEDdOvWTbGPi4sLGjVqhP37H97scf/+/WjRogWsrKwU+3Tv3h1yuVxxw/f9+/crHaNkn5JjVHi8K7W3ihQUFGDFihVYuHCh0gT72LFjsLGxwdq1a8t83Ntvvw09PT3FZFtHRwcDBgzAqlWryvxLAU8zePBgnDhxAm5ubmjevHmp7ebm5kqT9rIm4zKZDNOmTcOsWbNw507l/mzHyy+/DBcXFyxYsOC57xugCqMHv44Vm/7E2p8OIPV8Oj6eE4fcvHwE9Gqv0a6cu/k4kXoFJ1KvAAAuXsvAidQruJyeqdEuUcdL5DZ2sYtd4nSJ2jZjyY/Yd/QcLl3LwKlzVzFjyY/448hZ9O/ZWmNNJUQcL3ax60XtErlN1C4qm52dndLLqWfPnv3MxxQVFSE4OBidOnWCm5sbACA9PR36+vowMzNT2tfKygrp6emKfR6f+JdsL9n2tH3kcnmZV7aXR4jL/n/66Sfcvn0bw4cPh6mpqdK2fv36ISoqCj169Cj1OEmS8OGHHyI8PBwffPABjIyMEBERgd27d6Nt27aYOXMmWrduDWNjYxw/fhz79+9XfCKeVLduXaSlpT3zJQTPMmLECHz++edYs2YN2rVrp7StoKBA8Ql8/GOwsrKCJEmIiYnBG2+8gU6dOmHy5Mlo1qwZHjx4gMTERNy8eRO6urpVanseb3l74lZWDiK+2YobGXfQoqktEr4co/FLlFJOX0SvkV8q3v7k8w0AgEE+7bA0fIjGukQdL5Hb2MUudonTJWrbrds5GBW+AtdvyWEiqw1XR1us/2o0urRrprGmEiKOF7vY9aJ2idwmaheV7fLly0pXURsYGDzzMWPGjMHJkyeV/pS7aKTi8m6nr0a9evVCUVERtm7dWmpbUlIS2rVrh2PHjsHd3R3Jycnw8PBQbL979y4aNmyISZMmYeLEicCjyy3mzp2LjRs34vz589DR0YGTkxP69OmD4OBgxWUYXl5e8PDwwOLFi8vs8vDwgJ+fH8LDw8ttt7e3R3BwMIKDgxXr1q5di8GDB6Nz587YvXs3ACA8PBwzZswo9XgDAwPcu3dP8fbff/+NiIgI7Ny5E+np6TA2Noa7uzsCAgIQFBRUocv/S8jlcpiamuJ6RnaVXwJARERERERikMvlsLIwRXZ2zXyeXzJPmfVzCmobl//SbHW7l3sHn7zpUelxHTt2LH788UckJibCweG/l3Ls2rULXbt2xe3bt5XO/jdu3BjBwcEYP348wsLCsHnzZsW93vDoBn9NmjTB0aNH8fLLL+O1115Dq1atlOatMTExCA4OVnqJ/LMIMfkn1eDkn4iIiIhI+3DyrxqVnfwXFxdj3Lhx2LhxI3bv3q24j1uJ7OxsWFpaYu3atejXrx/w6B5vLi4u2L9/P9q3b49ffvkFvr6+SEtLU9wP7ttvv8WECRNw48YNGBgYIDQ0FD///DNOnDihOPbgwYORmZlZ6qaDTyPEZf9ERERERERENcmYMWOwZs0a/Pjjj6hTp47iJd6mpqYwNDSEqakphg8fjo8//hjm5uYwMTHBuHHj0KFDB7Rv//B+D97e3mjevDmGDBmCefPmIT09HVOnTsWYMWMULzcYOXIklixZgokTJyIoKAi7du1CfHx8mVfOPw0n/0RERERERKR2OpIEHUnSdIZCZVuWLVsGPHo5+eNiYmIwbNgwAMDnn38OHR0d9OvXD/n5+ejevbvSX4fT1dXFTz/9hFGjRqFDhw4wNjbGO++8g5kzZyr2cXBwwNatWzF+/Hh88cUXaNiwIb7//vtK/Zk/8LJ/7cbL/omIiIiItI+2XPY/+5djwl32P7mne40d12cR4k/9EREREREREZHq8LJ/IiIiIiIiUjsd6eEiCpFaVIFn/omIiIiIiIi0HCf/RERERERERFqOl/0TERERERGR+kmAQDf7B0RqUQGe+SciIiIiIiLScpz8ExEREREREWk5XvZPREREREREaqcDCToCXWsvUosq8Mw/ERERERERkZbj5J+IiIiIiIhIy/GyfyIiIiIiIlI7SbC7/YvUogo8809ERERERESk5Tj5JyIiIiIiItJyvOyfiIiIiIiI1E5HeriIQqQWVeCZfyIiIiIiIiItx8k/ERERERERkZbjZf9ERERERESkdjqSBB2BbrEvUosq8Mw/ERERERERkZbj5J+IiIiIiIhIy/GyfyIiIiIiIlI7SXq4iEKkFlXgmX8iIiIiIiIiLcfJPxEREREREZGW42X/REREREREpHY6EOxu/xCnRRV45p+e6bv4PWjZOwzWnYLRbdh8HDl1QdNJALuei6ht7KocdlUOuypP1DbRuvYdPYeB479Gs55TULfNWGzdfUyjPU8SbbxKsKty2FVx/J4kejphJ//79++Hrq4ufHx8lNZfuHABkiQpFnNzc3Tu3Bl79+4tdQy5XI5p06bB1dUVhoaGsLCwQJs2bTBv3jzcvn1bsZ+XlxckScKcOXNKHcPHxweSJCE8PPypvfb29oomY2NjtGrVCuvWrVNsDw8PV2zX1dWFnZ0dRowYgczMzHKPY2hoCHt7ewwYMAC7du2q1PhVlw3bj2Dq4o0Ifa8ndq8MhZuTLfqNi8TNzDsa6WGX9rWxi13sEqdL5DYRu+7m5cOtqS3mT/TXWEN5RBwvdrFL1fg9SfR0wk7+o6KiMG7cOCQmJuLatWultu/YsQNpaWlITEyEjY0NfH19cf36dcX2zMxMtG/fHjExMQgJCcHBgwdx9OhRzJo1C8nJyVizZo3S8ezs7BAbG6u07urVq9i5cycaNGhQoeaZM2ciLS0NycnJaNOmDfz9/fHnn38qtru6uiItLQ2XLl1CTEwMfv31V4waNarc46SmpmLFihUwMzNDt27dMGvWrAp1VKela3ZhqF9HBPTuAJcmDbBo8kAY1dbHqs371d7CLu1sYxe72CVOl8htIna90ckVU0f1gm8Xd401lEfE8WIXu1SN35M1T8nd/kVatJmQk/+cnBzExcVh1KhR8PHxKTUpBwALCwtYW1vDzc0NU6ZMgVwux8GDBxXbp0yZgkuXLiEpKQnvvvsuWrZsicaNG8Pb2xtr167F6NGjlY7n6+uLW7duYd++fYp1y5cvh7e3N+rXr1+h7jp16sDa2hpNmzZFZGQkDA0NsWXLFsV2PT09WFtbw9bWFt26dUP//v3x22+/lXucRo0a4bXXXsO3336LadOmISwsDKmpqRUex6q6/6AAKWcuw6uts2Kdjo4OOrd1xqET59XWwS7tbWMXu9glTpfIbaJ2iUrU8WIXu15UHDMShZCT//j4eLi4uMDZ2RmBgYGIjo5GcXFxmfvm5eVhxYoVAAB9fX0AQFFREeLi4hAYGAgbG5syHyc98WsdfX19BAQEICYmRrEuNjYWQUFBz/Ux6OnpoVatWrh//36Z2y9cuIBt27Ypmp/lo48+QnFxMX788cdy98nPz4dcLldaqiIjKweFhUWwNK+jtN7S3AQ3Mqp2bHapl6ht7GIXu8TpgsBtonaJStTxYhe7XlQcMxKFkJP/qKgoBAYGAgB69OiB7Oxs7NmzR2mfjh07QiaTwdjYGAsWLICnpye6du0KALh58yaysrLg7Oys9BhPT0/IZDLIZDIMGjSo1PsNCgpCfHw8cnNzkZiYiOzsbPj6+la6//79+5g9ezays7Px+uuvK9afOHECMpkMhoaGcHBwwKlTpxAaGlqhY5qbm6N+/fq4cKH8G4PMnj0bpqamisXOzq7S7UREREREROqgI+CizYT7+FJTU5GUlKSYnOvp6cHf3x9RUVFK+8XFxSE5ORnr16+Ho6MjYmNjUatWracee+PGjUhJSUH37t2Rl5dXaru7uzucnJyQkJCA6OhoDBkyBHp6yn8NMSIiQvELBJlMhkuXLim2hYaGQiaTwcjICHPnzsWcOXOUbljo7OyMlJQUHDp0CKGhoejevTvGjRtX4bEpLi4udcXC4yZPnozs7GzFcvny5QofuywWZjLo6uqUuhHJzUw56luYVOnY7FIvUdvYxS52idMFgdtE7RKVqOPFLna9qDhmJArhJv9RUVEoKCiAjY0N9PT0oKenh2XLlmH9+vXIzs5W7GdnZwcnJyf07dsXERER6Nu3L/Lz8wEAlpaWMDMzK/X6+EaNGsHR0RF16tQp9X5LBAUFITIyEgkJCWVe8j9y5EikpKQolsdfVjBhwgSkpKTgypUruH37dqmz+vr6+nB0dISbmxvmzJkDXV1dzJgxo0LjkpGRgZs3b8LBwaHcfQwMDGBiYqK0VIV+LT14uNhhz6H/xrGoqAiJh/5Gmxbld6gau7SnjV3sYpc4XSK3idolKlHHi13selFxzEgUQk3+CwoKsGLFCixcuFBpgn3s2DHY2Nhg7dq1ZT7u7bffhp6eHpYuXQo8uoHGgAEDsGrVqjL/UsDTDB48GCdOnICbmxuaN29earu5uTkcHR0Vy+NXBtSrVw+Ojo6wtrZ+6hn6ElOnTsWCBQsq1PjFF19AR0cHfn5+lfp4qmr04NexYtOfWPvTAaSeT8fHc+KQm5ePgF7t1drBLu1tYxe72CVOl8htInbl3M3HidQrOJF6BQBw8VoGTqReweX0zGc+VtVEHC92sUvV+D1Z8zz+J9xFWbSZXgX2UZuffvoJt2/fxvDhw2Fqaqq0rV+/foiKikKPHj1KPU6SJHz44YcIDw/HBx98ACMjI0RERGD37t1o27YtZs6cidatW8PY2BjHjx/H/v374ebmVmZD3bp1kZaW9syXEFSHDh06oGXLloiIiMCSJUsU6+/cuYP09HQ8ePAA58+fx6pVq/D9999j9uzZcHR0VHnX497y9sStrBxEfLMVNzLuoEVTWyR8OUbjlyixS3va2MUudonTJXKbiF0ppy+i18gvFW9/8vkGAMAgn3ZYGj5EY10QdLzYxS5V4/ck0dNJxeXdRl8DevXqhaKiImzdurXUtqSkJLRr1w7Hjh2Du7s7kpOT4eHhodh+9+5dNGzYEJMmTcLEiRMBANnZ2Zg7dy42btyI8+fPQ0dHB05OTujTpw+Cg4Nhbm4OAPDy8oKHhwcWL15cZpeHhwf8/PwQHh5ebru9vT2Cg4MRHBxc5vbw8HBs2rQJKSkpSut/+OEHDBs2DGfPnoWdnR3s7e1x8eJF4NHLBKytrdG+fXuMHDkSXbp0qdA4lpDL5TA1NcX1jOwqvwSAiIiIiIjEIJfLYWVhiuzsmvk8v2Sesuz3UzCUlf+SbHXLy7mDUV1ca+y4PotQk3+qXpz8ExERERFpH22Z/H8t4OR/pBZP/oV6zT8RERERERERVT9O/omIiIiIiIi0nFA3/CMiIiIiIqIXg44kQUegO+yL1KIKPPNPREREREREpOU4+SciIiIiIiLScrzsn4iIiIiIiDRCuy+0FwvP/BMRERERERFpOU7+iYiIiIiIiLQcL/snIiIiIiIitZOkh4soRGpRBZ75JyIiIiIiItJynPwTERERERERaTle9k9ERERERERqJ0kSJIGutRepRRV45p+IiIiIiIhIy3HyT0RERERERKTleNk/ERERERERqZ2OYGejRWpRBW3/+IiIiIiIiIheeJz8ExEREREREWk5XvZPREREREREase7/asXz/wTERERERERaTlO/omIiIiIiIi0HC/7JyIiIiIiIrWTHi2iEKlFFXjmn4iIiIiIiEjLcfJPREREREREpOV42T8RERERERGpHe/2r148809ERERERESk5Tj5JyIiIiIiItJyvOyfnum7+D34atVO3MiQw83JFnMn9Ienq72ms4TsikrYi+j1e3E5LRMA4NLEGhOG98QbnVw12lVCxDETsWtRzDb89PsxnL14HbUNaqFtyyYIH9sHTvZWGmt6nGjjxS7t6oLAbexiF7vY9TT7jp7DVyt34NiZS0i/Jceq+e/Dx8tdo02PE3HMNE1HsLPRIrWogrZ/fFRFG7YfwdTFGxH6Xk/sXhkKNydb9BsXiZuZd9hVBpv6Zpg+tg9+XzERu5ZPwKutmyIg5Fuc/idNo10QeMxE7Prz6Dm81/81bI8OwYYlY/GgoBBvjVuC3Lx8jTWVEHG82KU9XSK3sYtd7GLXs9zNy4dbU1vMn+iv0Y6yiDpm9GIRfvK/f/9+6OrqwsfHR2n9hQsXFDeIkCQJ5ubm6Ny5M/bu3VvqGHK5HNOmTYOrqysMDQ1hYWGBNm3aYN68ebh9+7ZiPy8vL0iShDlz5pQ6ho+PDyRJQnh4+FN77e3tFU3GxsZo1aoV1q1bp9geHh6u2K6rqws7OzuMGDECmZmZlTqOuixdswtD/ToioHcHuDRpgEWTB8Kotj5Wbd6v9paa0NXztRbw7uSKlxrVh2NjK0wb3RvGRgY4fPK8Rrsg8JiJ2JXw1RgM7tUezV5qgBZNG2Lp9EBcSb+NlNOXNdZUQsTxYpf2dIncxi52sYtdz/JGJ1dMHdULvl3EOdtfQtQxoxeL8JP/qKgojBs3DomJibh27Vqp7Tt27EBaWhoSExNhY2MDX19fXL9+XbE9MzMT7du3R0xMDEJCQnDw4EEcPXoUs2bNQnJyMtasWaN0PDs7O8TGxiqtu3r1Knbu3IkGDRpUqHnmzJlIS0tDcnIy2rRpA39/f/z555+K7a6urkhLS8OlS5cQExODX3/9FaNGjar0cVTt/oMCpJy5DK+2zop1Ojo66NzWGYdOaG4yK2rXkwoLi7B++2HczbuPNi0cNNoi6piJ2vUkec49AEBdEyONdog6XuzSji6R29jFLnaxqybjmJXv8ZO5oizaTOjJf05ODuLi4jBq1Cj4+PiUmpQDgIWFBaytreHm5oYpU6ZALpfj4MGDiu1TpkzBpUuXkJSUhHfffRctW7ZE48aN4e3tjbVr12L06NFKx/P19cWtW7ewb98+xbrly5fD29sb9evXr1B3nTp1YG1tjaZNmyIyMhKGhobYsmWLYruenh6sra1ha2uLbt26oX///vjtt98qfZwn5efnQy6XKy1VkZGVg8LCIlia11Fab2lughsZVTu2NnaVOHXuKhq+9jGsOgXj49lxWDn/fbg0qdgvjlRF1DETtetxRUVFmLwoAe3cm6C5o41GW0QdL3ZpRxcEbmMXu9jFrpqMY0aiEHryHx8fDxcXFzg7OyMwMBDR0dEoLi4uc9+8vDysWLECAKCvrw88etIeFxeHwMBA2NiU/aT9yd/u6OvrIyAgADExMYp1sbGxCAoKeq6PQU9PD7Vq1cL9+/fL3H7hwgVs27ZN0fy8xwGA2bNnw9TUVLHY2dk9VzNVjVNjKySunowdMSEI6vcKRoevxJl/Nf+af3o+IfPicfqfNETNelfTKUREREREz03oyX9UVBQCAwMBAD169EB2djb27NmjtE/Hjh0hk8lgbGyMBQsWwNPTE127dgUA3Lx5E1lZWXB2dlZ6jKenJ2QyGWQyGQYNGlTq/QYFBSE+Ph65ublITExEdnY2fH19K91///59zJ49G9nZ2Xj99dcV60+cOAGZTAZDQ0M4ODjg1KlTCA0NrfRxnjR58mRkZ2crlsuXq/b6ZAszGXR1dUrdiORmphz1LUyqdGxt7CqhX0sPTews4dGsEaaP7QM3J1t8/cNujTaJOmaidpWYMC8e2/aexJZlH8LWqq6mc4QdL3ZpRxcEbmMXu9jFrpqMY1Y+ScBFmwk7+U9NTUVSUpJicq6npwd/f39ERUUp7RcXF4fk5GSsX78ejo6OiI2NRa1atZ567I0bNyIlJQXdu3dHXl5eqe3u7u5wcnJCQkICoqOjMWTIEOjpKf9VxIiICMUvEGQyGS5duqTYFhoaCplMBiMjI8ydOxdz5sxRumGhs7MzUlJScOjQIYSGhqJ79+4YN25cqY5nHedJBgYGMDExUVqqQr+WHjxc7LDnUKpiXVFRERIP/a3R17CL2lWeouJi3L9foNEGUcdM1K7i4mJMmBePrbuPYfOyD9HYtp7GWh4n6nixSzu6RG5jF7vYxa6ajGNGotCrwD4aERUVhYKCAqXL9YuLi2FgYIAlS5Yo1tnZ2cHJyQlOTk4oKChA3759cfLkSRgYGMDS0hJmZmZITU1VOnajRo2AR6+pz8rKKvP9BwUFITIyEn/99ReSkpJKbR85ciQGDBigePvxzgkTJmDYsGGQyWSwsrIq86UFjo6OAKCY0M+YMQOffvqp0n7POo46jB78OkbPWImXmzVCK1d7LFv7O3Lz8hHQq73aW2pC14wlP6JbR1fYWdfFnbv3kPDrYfxx5CzWfzW6Ao9WLVHHTMSukLnxSNh2GGsWjIDMqDau33r4ejwTWW0Y1n76S3RUTcTxYpf2dIncxi52sYtdz5JzNx/nL99UvH3xWgZOpF6BmakR7KzNNdom6pjRi0XIyX9BQQFWrFiBhQsXwtvbW2mbn58f1q5dix49epR63Ntvv42wsDAsXboU48ePh46ODgYMGIBVq1YhLCys3Nf9l2Xw4MEICQmBu7s7mjdvXmq7ubk5zM3L/iFSr149xeS+IqZOnYrXX38do0aNUmqs7HFU4S1vT9zKykHEN1txI+MOWjS1RcKXYzR+iZKoXbdu52BU+ApcvyWHiaw2XB1tsf6r0ejSrplGuyDwmInYFb3+4Z8M9R35hdL6yLBADNbw/6RFHC92aU+XyG3sYhe72PUsKacvotfILxVvf/L5BgDAIJ92WBo+RINl4o6ZpknSw0UUIrWoglRc3h30NGjTpk3w9/fHjRs3YGpqqrQtNDQUu3btwrp16+Dg4IDk5GR4eHgoti9btgzh4eE4f/48jIyMkJGRgY4dOyI3NxczZ85E69atYWxsjOPHj2PSpElwc3PD+vXrAQBeXl7w8PDA4sWLAQBZWVmoVasWjI2NAQAeHh7w8/NDeHh4ue329vYIDg5GcHBwmdvDw8OxadMmpKSkKK1v164d2rRpo7iq4VnHqQi5XA5TU1Ncz8iu8ksAiIiIiIhIDHK5HFYWpsjOrpnP80vmKWv+/BtGsjoVeIR63M25g8Edm9bYcX0WIV/zHxUVhW7dupWa+ANAv379cPjw4XL/jN0777yDBw8eKCbRFhYWSEpKwtChQzF//ny0bdsWLVq0QHh4OPz9/fHdd9+V22FmZqaY+Kva+PHj8f3331f5Jn1ERERERERETxLyzD9VD575JyIiIiLSPtpy5v+HP88Kd+Z/YEenGjuuzyLkmX8iIiIiIiIiqj6c/BMRERERERFpOSHv9k9ERERERETajXf7Vy+e+SciIiIiIiLScpz8ExEREREREWk5XvZPREREREREaic9+icKkVpUgWf+iYiIiIiIiLQcJ/9EREREREREWo6X/RMREREREZHa8W7/6sUz/0RERERERERajpN/IiIiIiIiIi3Hy/6JiIiIiIhI7SRI0BHoDvu82z8RERERERER1Wic/BMRERERERFpOV72T0RERERERGrHu/2rF8/8ExEREREREWk5Tv6JiIiIiIiItBwv+yciIiIiIiK142X/6sUz/0RERERERERajpN/IiIiIiIiIi3Hy/6JiIiIiIhI7aRH/0QhUosq8Mw/ERERERERkZbj5J+IiIiIiIhIy/GyfyIiIiIiIlI7HenhIgqRWlSBZ/6JiIiIiIiItBwn//RU+46ew8DxX6NZzymo22Ystu4+pukkhe/i96Bl7zBYdwpGt2HzceTUBU0nAQJ3QeA2dlUOuyqOP8Oej6ht7KocEbtE/Z4UtQuCfh4hcBcEbhO1S5MkAf9pM07+6anu5uXDrakt5k/013SKkg3bj2Dq4o0Ifa8ndq8MhZuTLfqNi8TNzDvsqmFt7GKXKvFnmPa0sUs7ukT9nhS1S9TPo6hdIreJ2kUvFqEn//v374euri58fHyU1l+4cAGSJCkWc3NzdO7cGXv37i11DLlcjmnTpsHV1RWGhoawsLBAmzZtMG/ePNy+fVuxn5eXFyRJwpw5c0odw8fHB5IkITw8/Km9x44dQ+/evVG/fn3Url0b9vb28Pf3x40bNyrVXVxcjG+//Rbt2rWDTCaDmZkZWrdujcWLF+Pu3buVHseqeKOTK6aO6gXfLu5qfb/PsnTNLgz164iA3h3g0qQBFk0eCKPa+li1eT+7algbu9ilSvwZpj1t7NKOLlG/J0XtEvXzKGqXyG2idtGLRejJf1RUFMaNG4fExERcu3at1PYdO3YgLS0NiYmJsLGxga+vL65fv67YnpmZifbt2yMmJgYhISE4ePAgjh49ilmzZiE5ORlr1qxROp6dnR1iY2OV1l29ehU7d+5EgwYNntp68+ZNdO3aFebm5ti2bRtOnz6NmJgY2NjYIDc3t1LdQ4YMQXBwMPr06YPff/8dKSkpmDZtGn788Uds37690uOobe4/KEDKmcvwauusWKejo4PObZ1x6MR5dtWgNnax60Uk8niJ2sYu7eiiyhH18yhql8htonaJQJLEW7SZsHf7z8nJQVxcHA4fPoz09HTExsZiypQpSvtYWFjA2toa1tbWmDJlCn744QccPHgQvXv3BgBMmTIFly5dwt9//w0bGxvF4xo3bgxvb28UFxcrHc/X1xfx8fHYt28fOnXqBABYvnw5vL29cenSpaf27tu3D9nZ2fj++++hp/dwWB0cHNClS5dS+z6tOz4+HqtXr8amTZvQp08fxWPs7e3Ru3dvyOXychvy8/ORn5+vePtp+9ZkGVk5KCwsgqV5HaX1luYmOHvhermPe1G7IHAbu9j1IhJ5vERtY5d2dFHliPp5FLULAreJ2kUvHmHP/MfHx8PFxQXOzs4IDAxEdHR0qcl6iby8PKxYsQIAoK+vDwAoKipCXFwcAgMDlSb+j5Oe+NWOvr4+AgICEBMTo1gXGxuLoKCgZ/ZaW1ujoKAAGzduLLezIt2rV6+Gs7Oz0sT/8V5TU9Nyjzd79myYmpoqFjs7uwp1EBERERERkXYTdvIfFRWFwMBAAECPHj2QnZ2NPXv2KO3TsWNHyGQyGBsbY8GCBfD09ETXrl2BR5fhZ2VlwdnZWekxnp6ekMlkkMlkGDRoUKn3GxQUhPj4eOTm5iIxMRHZ2dnw9fV9Zm/79u0xZcoUDB48GPXq1UPPnj0xf/58pcv5K9J99uzZUs0VNXnyZGRnZyuWy5cvP9dxRGdhJoOurk6pG6TczJSjvoUJu8ogahu72PUiEnm8RG1jl3Z0UeWI+nkUtQsCt4naJQJJuDv+azchJ/+pqalISkpSTM719PTg7++PqKgopf3i4uKQnJyM9evXw9HREbGxsahVq9ZTj71x40akpKSge/fuyMvLK7Xd3d0dTk5OSEhIQHR0NIYMGaK4jL9ERESE4hcIMplM8ZKAWbNmIT09HV9//TVcXV3x9ddfw8XFBSdOnKhwd0WvGiiLgYEBTExMlBZtpF9LDx4udthzKFWxrqioCImH/kabFg7sqkFt7GLXi0jk8RK1jV3a0UWVI+rnUdQukdtE7aIXj5Cv+Y+KikJBQYHS5frFxcUwMDDAkiVLFOvs7Ozg5OQEJycnFBQUoG/fvjh58iQMDAxgaWkJMzMzpKamKh27UaNGAIA6deogKyurzPcfFBSEyMhI/PXXX0hKSiq1feTIkRgwYIDi7cc7LSws0L9/f/Tv3x8RERF4+eWXsWDBAixfvrxC3U2bNsWZM2eee+yqW87dfJy/fFPx9sVrGTiRegVmpkawszbXWNfowa9j9IyVeLlZI7Rytceytb8jNy8fAb3aa6xJ5C6R29jFLlXizzDtaWOXdnSJ+j0papeon0dRu0RuE7WLXizCTf4LCgqwYsUKLFy4EN7e3krb/Pz8sHbtWvTo0aPU495++22EhYVh6dKlGD9+PHR0dDBgwACsWrUKYWFh5b7uvyyDBw9GSEgI3N3d0bx581Lbzc3NYW7+7P8R6Ovr46WXXip1t/+ndQ8ePBgDBw7Ejz/+WOp1/8XFxZDL5U993X91Szl9Eb1Gfql4+5PPNwAABvm0w9LwIWrreNJb3p64lZWDiG+24kbGHbRoaouEL8do/NIpUbtEbmMXu1SJP8O0p41d2tEl6vekqF2ifh5F7RK5TdQuTdORHi6iEKlFFaTiqlxnrgKbNm2Cv78/bty4UWqSGxoail27dmHdunVwcHBAcnIyPDw8FNuXLVuG8PBwnD9/HkZGRsjIyEDHjh2Rm5uLmTNnonXr1jA2Nsbx48cxadIkuLm5Yf369QAALy8veHh4YPHixQCArKws1KpVC8bGxgAADw8P+Pn5ITw8vMzun376CT/88AMGDhyIpk2bori4GFu2bMGkSZMQExODIUOG4MKFC8/sNjQ0xKBBg7B582ZMnToV3t7esLS0xIkTJ/D5559j3Lhx8PPzq9BYlvyi4HpGtta+BICIiIiI6EUjl8thZWGK7Oya+Ty/ZJ7y85HzMJaJ05+bI8ebng41dlyfRbjX/EdFRaFbt25lnt3u168fDh8+XO6fsHvnnXfw4MEDxUsDLCwskJSUhKFDh2L+/Plo27YtWrRogfDwcPj7++O7774rt8PMzEwx8a+I5s2bw8jICP/73//g4eGB9u3bIz4+Ht9//z2GDHn6b4wf75YkCWvWrMGiRYuwadMmdO7cGS1btkR4eDj69OmD7t27V7iJiIiIiIiICCKe+afqwzP/RERERETaR1vO/P9y5IJwZ/57etrX2HF9FuHO/BMRERERERFR9eLkn4iIiIiIiEjLCXe3fyIiIiIiItJ+kvRwEYVILarAM/9EREREREREWo6TfyIiIiIiIiItx8v+iYiIiIiISO2kR4soRGpRBZ75JyIiIiIiItJynPwTERERERERaTle9k9ERERERERqpwMJOgLdYl9Hyy/855l/IiIiIiIiIi3HyT8RERERERGRluNl/0RERERUoxUUFmk6oUx6ujzPRvQ0vNu/evEnEhEREREREZGW4+SfiIiIiIiISMvxsn8iIiIiIiJSP173r1Y8809ERERERESk5Tj5JyIiIiIiItJyvOyfiIiIiIiI1E569E8UIrWoAs/8ExEREREREWk5Tv6JiIiIiIiItBwv+yciIiIiIiL1kwBJpCvtRWpRAZ75JyIiIiIiItJynPwTERERERERaTle9k9ERERERERqJwl2pb1ILarAM/9EREREREREWo5n/umZvovfg69W7cSNDDncnGwxd0J/eLraa7Tp2o0shH/1I3bsP4W8ew/g0LAeIsMC8XLzxhrtgqDjVULUNnaxq7rsO3oOX63cgWNnLiH9lhyr5r8PHy93xfY5327Fhu1HcfX6bdSqpQsPl0aYOroXWrupf/xEGK8nPWv8NE3EMWNXzezKyb2H2d9uxc97juPW7Ry0aGqLWeP7KZ5HjJ25CnE/Jyk9pkt7F8QvHq3WTggyXjWla863WzH3u1+U1jk1tkJSwjSNNT1OxDGjFwvP/NNTbdh+BFMXb0Toez2xe2Uo3Jxs0W9cJG5m3tFYU5b8Lnq8twi19HSw7ovROBD3CT4LfgtmJkYaayoh4niJ3sYudlWnu3n5cGtqi/kT/cvc/lKj+pg3oT/2rZ2CX777GI1szPHW2CW4dVu9naKM15OeNX6aJOqYsatmdgVHrMWepFRETh+CPasmwautC/qNi0TajSzFPq+3b4aTWz9TLN/OHKbWRgg0XjWlCwBcmjTAmV8iFMsv34/XdBIg+JhplCTgosWEnvzv378furq68PHxUVp/4cIFSJKkWMzNzdG5c2fs3bu31DHkcjmmTZsGV1dXGBoawsLCAm3atMG8efNw+/ZtxX5eXl6QJAlz5swpdQwfHx9IkoTw8PBS28LDw5VayloAYNiwYZAkCSNHjix1jDFjxkCSJAwb9t//VEr2lyQJ+vr6cHR0xMyZM1FQUPAcI/n8lq7ZhaF+HRHQuwNcmjTAoskDYVRbH6s271drx+MWL/8NtlZ1ETl9CDxd7dHYth5eb98MDg0tNdZUQsTxEr2NXeyqTm90csXUUb3g26Xss9X9e7SBVzsX2Desh2YvNcBnwW/hTu49nDp7Ta2doozXk541fpok6pixq+Z15d27j592H0PY2D7o+LIjmthZYuL7b8KhYT3EbPhDsZ+Bvh6sLEwUiyZOMogwXjWpCwD0dHVgVc9EsViYyTSdBAg+ZvTiEHryHxUVhXHjxiExMRHXrpV+YrZjxw6kpaUhMTERNjY28PX1xfXr1xXbMzMz0b59e8TExCAkJAQHDx7E0aNHMWvWLCQnJ2PNmjVKx7Ozs0NsbKzSuqtXr2Lnzp1o0KBBmY0hISFIS0tTLA0bNsTMmTOV1j1+/B9++AF5eXmKdffu3cOaNWvQqFGjUsfu0aMH0tLScPbsWfzvf/9DeHg45s+fX8lRfH73HxQg5cxleLV1VqzT0dFB57bOOHTivNo6nvTr3hN4uVkjDJsUBSfvSXgtYA6Wb9ynsZ4Soo6XyG3sYpcm3X9QgOUb98FEZgi3prZqfb81cbw0SdQxY1fN7CosLEJhYRFq6yu/+rW2gT4OHvtX8fa+o+fQrOcUtB/wGSbMjUNmdq7aGiHQeNWUrhL/Xr6JZj2nwKPPdLw/NRaX0zM1nST8mNGLQ9jJf05ODuLi4jBq1Cj4+PiUmpQDgIWFBaytreHm5oYpU6ZALpfj4MGDiu1TpkzBpUuXkJSUhHfffRctW7ZE48aN4e3tjbVr12L0aOXXbfn6+uLWrVvYt++/ieTy5cvh7e2N+vXrl9kpk8lgbW2tWHR1dVGnTh2ldSVatWoFOzs7bNiwQbFuw4YNaNSoEV5++eVSxzYwMIC1tTUaN26MUaNGoVu3bti8eXO5Y5afnw+5XK60VEVGVg4KC4tgaV5Hab2luQluZFTt2FVx4eotRK/fiyZ2llj/1RgE9XsFkxYmYO1PBzTWBIHHCwK3sYtdmvDr3hNo+NrHsO40HsvW/o6NS8aq9cxQTRsvEYg6ZuyqmV0y49po08IeC6O3If1mNgoLi7Dul0M4fPI8rj/q6NqhGSLDArH+q7EIG9Mbfyafw8Dxy1BYWKS2TlHGq6Z0AYCnqz0ipwdi3ZdjsHCSPy5ey8Cb73+OO7n3NNol8phpmiTgP20m7OQ/Pj4eLi4ucHZ2RmBgIKKjo1FcXFzmvnl5eVixYgUAQF9fHwBQVFSEuLg4BAYGwsbGpszHlVySX0JfXx8BAQGIiYlRrIuNjUVQUFC1fVxBQUFKx4+Ojsa7775boccaGhri/v375W6fPXs2TE1NFYudnV21NIumqKgYLZ3tEDamN1o622HYW69gqF9HpUv1iIjK82rrpkhcPRnboj5G1w7N8e6UaL7mkugFEzl9CIpRjBa9psH2tY/x3bo9eOsNT+g8em7Y9w1P9HitBZo72uDNzi2xeuEHSP7rEvYdPavpdHqKNzq5wq9bK7g52aJrh+ZY98UoZN/Jw6YdRzWdRiQEYSf/UVFRCAwMBB5d/p6dnY09e/Yo7dOxY0fIZDIYGxtjwYIF8PT0RNeuXQEAN2/eRFZWFpydnZUe4+npCZlMBplMhkGDBpV6v0FBQYiPj0dubi4SExORnZ0NX1/favu4AgMD8ccff+DixYu4ePEi9u3bp/g4y1NcXIwdO3Zg27ZteP3118vdb/LkycjOzlYsly9frlKrhZkMuro6pZ4U38yUo76FSZWOXRVW9Uzg0sRaaV1Te2tcSb9d7mPUQdTxgsBt7GKXJhgbGqCJnSXatHDAV9MCoKerg5U//qm291/TxksEoo4Zu2pul0NDS2xe9hEu/D4fKT/OwPboEDwoKERjW4sy97e3rQcLM2Ocv3JLbY0ijVdN6CqLaR0jODaqj38v39RoR00aM9JuQk7+U1NTkZSUpJic6+npwd/fH1FRUUr7xcXFITk5GevXr4ejoyNiY2NRq1atpx5748aNSElJQffu3ZVee1/C3d0dTk5OSEhIQHR0NIYMGQI9PeXXhEVERCh+gSCTyXDp0qUKf2yWlpaKlzHExMTAx8cH9erVK3Pfn376CTKZDLVr10bPnj3h7+9f5k0HSxgYGMDExERpqQr9WnrwcLHDnkOpinVFRUVIPPQ32rRwqNKxq6KdexOcvXhDad0/l26gobW5xpog8HiJ3MYudomgqKgY9x+o72aqNX28NEHUMWNXze8yNjSAdT1TZMnv4veDZ9DztRZl7nftxm1kZt+FlRonaiKOl8hdZcm5m4/zV2/Bup6pRjtq0pipmySJt2gzvQrso3ZRUVEoKChQuly/uLgYBgYGWLJkiWKdnZ0dnJyc4OTkhIKCAvTt2xcnT56EgYEBLC0tYWZmhtTUVKVjl9xYr06dOsjKykJZgoKCEBkZib/++gtJSUmlto8cORIDBgxQvF3eywrKExQUhLFjxwIAIiMjy92vS5cuWLZsGfT19WFjY1PqlxDqMHrw6xg9YyVebtYIrVztsWzt78jNy0dAr/Zqb1E0DXod3YcvxMKYbejbrRWOnLqA5Rv34fMppa/kUHubgOMlehu72FWdcu7m4/xjZ3guXsvAidQrMDM1grmpMRZGb0PP11rAqp4pMrNy8P26RKTdzEKfrq3U2inKeD3paeNnp+FfsIo6ZuyqmV27DpxGcXExHBtb4fzlmwhf8iOcGtfHIN/2yLmbjwVRv8C3izvqm5vgwtVbmLHkRzg0rIcu7V3U2inKeNWUrmmLN6DHqy1g18AcaTezMefbrdDV0UG/7p4a7YLAY0YvFuEm/wUFBVixYgUWLlwIb29vpW1+fn5Yu3YtevToUepxb7/9NsLCwrB06VKMHz8eOjo6GDBgAFatWoWwsLBKTdAHDx6MkJAQuLu7o3nz5qW2m5ubw9z8+Z8E9ejRA/fv34ckSejevXu5+xkbG8PR0fG53091eMvbE7eychDxzVbcyLiDFk1tkfDlGI1eotTKtTFWzn8fMyM3Y/73v6CxjQUiPu6HAT3baKyphIjjJXobu9hVnVJOX0SvkV8q3v7k84c3WB3k0w6LJg/E2QvX8cPWg8jIyoW5qRFebt4YP387Hs1eKvsvuqiKKOP1pKeN39LwIRosE3fM2FUzu+Q5eZi1bAuu3ciCmYkxfLu445ORvqilp4uCgkKcOncNcT8nIftOHqzrmcKrnQsmjXgTBvpPv8K0uokyXjWl6+qNLLw3NQaZ2XdRr64M7dyb4LeY/6Fe3ToVeLRqiTpm9GKRisu7i56GbNq0Cf7+/rhx4wZMTZUv0QkNDcWuXbuwbt06ODg4IDk5GR4eHorty5YtQ3h4OM6fPw8jIyNkZGSgY8eOyM3NxcyZM9G6dWsYGxvj+PHjmDRpEtzc3LB+/XoAgJeXFzw8PLB48WIAQFZWFmrVqgVjY2MAgIeHB/z8/J562T0A2NvbIzg4GMHBwUrrhw0bhqysLGzatAkAFHfiL7k038/PD2ZmZoq/avDk/s9DLpfD1NQU1zOyq/wSACIiIiJRFajxLvyVoacr5CtsSQvI5XJYWZgiO7tmPs8vmafsOX4Zsjri9OfckaNzS7saO67PItxPpKioKHTr1q3UxB8A+vXrh8OHD5f7J+zeeecdPHjwQPHSAAsLCyQlJWHo0KGYP38+2rZtixYtWiA8PBz+/v747rvvyu0wMzNTTPxVoTpek09ERERERERUEcKd+afqwzP/RERE9CLgmX960fDMv2rwzD8RERERERFRdZMEXCopMTERvXr1go2NDSRJKvWy7eLiYoSFhaFBgwYwNDREt27dcPbsWaV9MjMzERAQABMTE5iZmWH48OHIyclR2uf48eN49dVXUbt2bdjZ2WHevHmVbuXkn4iIiIiIiOg55Obmwt3dvdy/4jZv3jx8+eWX+Prrr3Hw4EEYGxuje/fuuHfvnmKfgIAAnDp1Cr/99ht++uknJCYmYsSIEYrtcrkc3t7eaNy4MY4cOYL58+cjPDwc3377baVahbvbPxEREREREZGmPHmPOQMDAxgYGJS5b8+ePdGzZ88ytxUXF2Px4sWYOnUq+vTpAwBYsWIFrKyssGnTJgwcOBCnT5/Gr7/+ikOHDqF169YAgK+++gpvvvkmFixYABsbG6xevRr3799HdHQ09PX14erqipSUFCxatEjplwTPwjP/REREREREpHaSgP8AwM7ODqampopl9uzZz/XxnT9/Hunp6ejWrZtinampKdq1a4f9+/cDAPbv3w8zMzPFxB8AunXrBh0dHRw8eFCxz2uvvQZ9fX3FPt27d0dqaipu375d4R6e+SciIiIiIiJ65PLly0o3/CvvrP+zpKenAwCsrKyU1ltZWSm2paeno379+krb9fT0YG5urrSPg4NDqWOUbKtbt26Fejj5JyIiIiIiInpEW/8sOy/7JyIiIiIiIrWTJPGW6mRtbQ0AuH79utL669evK7ZZW1vjxo0bStsLCgqQmZmptE9Zx3j8fVQEJ/9ERERERERE1czBwQHW1tbYuXOnYp1cLsfBgwfRoUMHAECHDh2QlZWFI0eOKPbZtWsXioqK0K5dO8U+iYmJePDggWKf3377Dc7OzhW+5B+c/BMRERERERE9n5ycHKSkpCAlJQV4dJO/lJQUXLp0CZIkITg4GJ999hk2b96MEydOYOjQobCxsYGfnx8AoFmzZujRowfef/99JCUlYd++fRg7diwGDhwIGxsbAMDgwYOhr6+P4cOH49SpU4iLi8MXX3yBjz/+uFKtfM0/ERERERERqZ30aBHF87QcPnwYXbp0UbxdMiF/5513EBsbi4kTJyI3NxcjRoxAVlYWXnnlFfz666+oXbu24jGrV6/G2LFj0bVrV+jo6KBfv3748ssvFdtNTU2xfft2jBkzBp6enqhXrx7CwsIq9Wf+AEAqLi4ufo6PkWoAuVwOU1NTXM/I1sobVhAREREBQEFhkaYTyqSny4tsSTXkcjmsLEyRnV0zn+eXzFP+OHkFsjri9OfckeMVt4Y1dlyfhWf+iYiIiKhG4ySbiOjZOPknIiIiIiIi9dOG6/5rEP6alIiIiIiIiEjLcfJPREREREREpOV42T8RERERERGpnfTonyhEalEFnvknIiIiIiIi0nKc/BMRERERERFpOV72T0RERERERGonSQ8XUYjUogo8809ERERERESk5Tj5JyIiIiIiItJyvOyfiIiIiIiI1E56tIhCpBZV4Jl/IiIiIiIiIi3HyT8RERERERGRluNl/0RERERERKR+vO5frXjmn4iIiIiIiEjLcfJPz/Rd/B607B0G607B6DZsPo6cuqDpJIBdz0XUNnZVDrsqh12VJ2obuyqHXRW37+g5DBz/NZr1nIK6bcZi6+5jmk5SEHG8IHAXBG4TtYteHJz8Axg2bBgkSVIsFhYW6NGjB44fP15qXy8vL6V9n1y8vLwAAPb29li8eLHicfb29pAkCT/88EOpY7q6ukKSJMTGxpba/8llzpw5KhuHsmzYfgRTF29E6Hs9sXtlKNycbNFvXCRuZt5Rawe7tLeNXexilzhdIrexi12qdDcvH25NbTF/or9GO54k6niJ2iVym6hdmiYJ+E+bcfL/SI8ePZCWloa0tDTs3LkTenp68PX1LbXfhg0bFPslJSUBAHbs2KFYt2HDhnLfh52dHWJiYpTWHThwAOnp6TA2Ni61/8yZMxXHLVnGjRtXLR9vRS1dswtD/ToioHcHuDRpgEWTB8Kotj5Wbd6v1g52aW8bu9jFLnG6RG5jF7tU6Y1Orpg6qhd8u7hrtONJoo6XqF0it4naRS8WTv4fMTAwgLW1NaytreHh4YFJkybh8uXLuHnzptJ+5ubmiv0sLS0BABYWFop15ubm5b6PgIAA7NmzB5cvX1asi46ORkBAAPT0St97sU6dOorjlixl/ZJAVe4/KEDKmcvwauusWKejo4PObZ1x6MR5tXWwS3vb2MUudonTJXIbu9j1IhJ1vETtErlN1C568XDyX4acnBysWrUKjo6OsLCwqLbjWllZoXv37li+fDkA4O7du4iLi0NQUFC1HD8/Px9yuVxpqYqMrBwUFhbB0ryO0npLcxPcyKjasdmlXqK2sYtd7BKnCwK3sYtdLyJRx0vULgjcJmqXCCRJvEWbcfL/yE8//QSZTAaZTIY6depg8+bNiIuLg45O9Q5RUFAQYmNjUVxcjISEBLz00kvw8PAoc9/Q0FBFU8myd+/eco89e/ZsmJqaKhY7O7tqbSciIiIiIqKaiZP/R7p06YKUlBSkpKQgKSkJ3bt3R8+ePXHx4kX07NlTMfl2dXWt0vvx8fFBTk4OEhMTER0d/dSz/hMmTFA0lSytW7cud//JkycjOztbsTz+8oLnYWEmg66uTqkbkdzMlKO+hUmVjs0u9RK1jV3sYpc4XRC4jV3sehGJOl6idkHgNlG76MXDyf8jxsbGcHR0hKOjI9q0aYPvv/8eubm5+O677/D9998rJt8///xzld6Pnp4ehgwZgunTp+PgwYMICAgod9969eopmkoWQ0PDcvc3MDCAiYmJ0lIV+rX04OFihz2HUhXrioqKkHjob7Rp4VClY7OLbexiF7vE6hK5jV3sehGJOl6idoncJmqXCCQBF21W+i5zBACQJAk6OjrIy8uDra1ttR47KCgICxYsgL+/P+rWrVutx65uowe/jtEzVuLlZo3QytUey9b+jty8fAT0as+uGtQlchu72MUucbpEbmMXu1Qp524+zl/+7ybPF69l4ETqFZiZGsHOuvybOauaqOMlapfIbaJ20YuFk/9H8vPzkZ6eDgC4ffs2lixZgpycHPTq1ava31ezZs1w69YtGBkZPXW/O3fuKJpKGBkZVfmMfmW85e2JW1k5iPhmK25k3EGLprZI+HKMxi9RYpf2tLGLXewSp0vkNnaxS5VSTl9Er5FfKt7+5POHf7p5kE87LA0forEuUcdL1C6R20TtoheLVFxcXKzpCE0bNmyY4g78ePQn9lxcXBAaGop+/fqV+7gLFy7AwcEBycnJpW7aZ29vj+DgYAQHB5f59pPMzMywePFiDBs2TLH/xYsXS+33wQcf4Ouvv67QxyWXy2FqaorrGdlq/YUBERERERGpjlwuh5WFKbKza+bz/JJ5SlLqNcjqiNOfc0eOts42NXZcn4WTfy3GyT8RERERkfbh5F81tH3yzxv+EREREREREWk5vuafiIiIiIiI1E569E8UIrWoAs/8ExEREREREWk5Tv6JiIiIiIiItBwv+yciIiIiIiK1k6SHiyhEalEFnvknIiIiIiIi0nKc/BMRERERERFpOV72T0RERERERGonPVpEIVKLKvDMPxEREREREZGW4+SfiIiIiIiISMvxsn8iIiIiIiJSP173r1Y8809ERERERESk5Tj5JyIiIiIiItJyvOyfiIiIiIiI1E569E8UIrWoAs/8ExEREREREWk5Tv6JiIiIiIiItBwv+yciIiIiIiL1kwBJpCvtRWpRAZ75JyIiIiIiItJynPwTERERERERaTle9k9ERERERERqJwl2pb1ILarAM/9EREREREREWo6TfyIiIiIiIiItx8v+iYiIiIiISP143b9a8cw/ERERERERkZbj5J+IiIiIiIhIy/GyfyIiIiIiIlI76dE/UYjUogo8809Pte/oOQwc/zWa9ZyCum3GYuvuY5pOUvgufg9a9g6DdadgdBs2H0dOXdB0EiBwFwRuY1flsKty2FV5oraxq3LYVTGLYrbh9aHzYNf5f3DynoSAkG9x9sJ1jTY9TrTxKiFqFwRuE7WLXhyc/Jdj2LBh8PPzU1oXGxsLSZKeuly4cAHh4eHw8PBQPC48PBySJKFHjx6l3s/8+fMhSRK8vLxK7f/k4uLiouKPurS7eflwa2qL+RP91f6+n2bD9iOYungjQt/rid0rQ+HmZIt+4yJxM/MOu2pYG7vYxS5xukRuYxe7VOXPo+fwXv/XsD06BBuWjMWDgkK8NW4JcvPyNdZUQsTxErlL5DZRu+jFwsl/Jfj7+yMtLU2xdOjQAe+//77SOjs7uzIf26BBA/z++++4cuWK0vro6Gg0atSo1P6urq5Kx01LS8Mff/yhso+tPG90csXUUb3g28Vd7e/7aZau2YWhfh0R0LsDXJo0wKLJA2FUWx+rNu9nVw1rYxe72CVOl8ht7GKXqiR8NQaDe7VHs5caoEXThlg6PRBX0m8j5fRljTWVEHG8RO4SuU3ULk2TJPEWbcbJfyUYGhrC2tpasejr68PIyEhpna6ubpmPrV+/Pry9vbF8+XLFuj///BO3bt2Cj49Pqf319PSUjmttbY169eqp9OOrKe4/KEDKmcvwauusWKejo4PObZ1x6MR5dtWgNnaxi13idIncxi52qZM85x4AoK6JkUY7RB0vUbtEbhO1i148nPyrUVBQEGJjYxVvR0dHIyAgAPr6+tVy/Pz8fMjlcqVFG2Vk5aCwsAiW5nWU1luam+BGhuY+ZlG7IHAbu9jFLnG6IHAbu9ilLkVFRZi8KAHt3JuguaONRltEHS9RuyBwm6hd9OLh5F+NfH19IZfLkZiYiNzcXMTHxyMoKKjMfU+cOAGZTKa0jBw58qnHnz17NkxNTRVLeS9BICIiIqLSQubF4/Q/aYia9a6mU4heCJKAizbjn/p7htWrV+ODDz5QvP3LL7/g1Vdffa5j1apVC4GBgYiJicG///6Lpk2bomXLlmXu6+zsjM2bNyutMzExeerxJ0+ejI8//ljxtlwu18pfAFiYyaCrq1PqBik3M+Wob/H0MXoRuyBwG7vYxS5xuiBwG7vYpQ4T5sVj296T+PnbYNha1dV0jrDjJWoXBG4TtYtePDzz/wy9e/dGSkqKYmndunWVjhcUFIR169YhMjKy3LP+AKCvrw9HR0elpX79+k89toGBAUxMTJQWbaRfSw8eLnbYcyhVsa6oqAiJh/5GmxYO7KpBbexiF7vE6RK5jV3sUqXi4mJMmBePrbuPYfOyD9HYVox7LIk6XqJ2idwmahe9eHjm/xnq1KmDOnXqVGDPinF1dYWrqyuOHz+OwYMHV9txVSXnbj7OX76pePvitQycSL0CM1Mj2Fmba6xr9ODXMXrGSrzcrBFaudpj2drfkZuXj4Be7TXWJHKXyG3sYhe7xOkSuY1d7FKVkLnxSNh2GGsWjIDMqDau33r4GmwTWW0Y1q6e+zI9LxHHS+QukdtE7dI40a61F6lFBTj514Bdu3bhwYMHMDMzK3efgoICpKenK62TJAlWVlZqKPxPyumL6DXyS8Xbn3y+AQAwyKcdloYPUWvL497y9sStrBxEfLMVNzLuoEVTWyR8OUbjl06J2iVyG7vYxS5xukRuYxe7VCV6/V4AgO/IL5TWR4YFYrCGJ2YijpfIXSK3idpFLxapuLi4WNMRIho2bBiysrKwadOmcvfx8vKCh4cHFi9erLQ+PDwcmzZtQkpKSplvPyk4OBgpKSnYvXu3Yv8ZM2aU2s/AwAD37t2r8Mcgl8thamqK6xnZWvsSACIiIiKiF41cLoeVhSmys2vm8/ySecrx89dRp444/XfuyNHSwarGjuuzcPKvxTj5JyIiIiLSPtoy+T9x/oZwk/8WDvVr7Lg+C2/4R0RERERERKTlOPknIiIiIiIi0nK84R8RERERERGpnQRAEugO+wKlqATP/BMRERERERFpOU7+iYiIiIiIiLQcL/snIiIiIiIitZMEu9RepBZV4Jl/IiIiIiIiIi3HyT8RERERERGRluNl/0RERERERKR2kiTY3f4FalEFnvknIiIiIiIi0nKc/BMRERERERFpOV72T0RERERERBrA+/2rEyf/pBHFxcWaTiiTpO0v9CEiIiISWN02YzWdUKbbh5ZoOoGoynjZPxEREREREZGW45l/IiIiIiIiUjve7V+9eOafiIiIiIiISMvxzD8RERERERGpHW/3p148809ERERERESk5Tj5JyIiIiIiItJyvOyfiIiIiIiI1I43/FMvnvknIiIiIiIi0nKc/BMRERERERFpOV72T0RERERERGonPfonCpFaVIFn/omIiIiIiIi0HCf/RERERERERFqOl/0TERERERGR+kmPFlGI1KICnPxTuRbFbMNPvx/D2YvXUdugFtq2bILwsX3gZG+l1o7PY7crd7RwwPRxfeDU+L+OXiO/wL6j55QeN6xvJyyaPFCtrXO+3Yq53/2itM6psRWSEqapteNJ+46ew1crd+DYmUtIvyXHqvnvw8fLXaNNj/sufg++WrUTNzLkcHOyxdwJ/eHpaq+xHlE/jyVEGy+Ru1r2DsPltMxS64e//SoWhPprpAkCf0+y6/mI+LUv8piJNl78mS9+V/A7b2D62D5YtvZ3TFm0HnYNzHF888wy9x02KQo/7kwGANw+tKTU9uFTYrDhtyMAACsLE3wW/BY8mjVCE7t6+CZuD6YsWl/t/aI8pybi5F9Qly9fxvTp0/Hrr7/i1q1baNCgAfz8/BAWFgYLCwu1NPx59Bze6/8aXm7eGAWFhfh06Ra8NW4JDsRPhbGhgVoa8OgJzPD+r+LlZo1RWFiIT5dtQb9xkdgf94lSx1C/jpg8wkfxtmHtWmprfJxLkwbYFDlO8baenuZfXXM3Lx9uTW0R2LsDhkz8TtM5SjZsP4Kpizdi0SR/eLrZ4+u1v6PfuEgcSgiDpXkdjXWJ+HmEwOMlateu5RNQWFisePv0P9fQd+wS+HV7WWNNEPh7kl2VJ+rXvqhjJup48We+uF0vN2+EYX074eTfVxTrrl6/Decek5X2e6dvJ4wL7IYdf55SWj96xkrs3P+X4u3sO3mK/9bX18OtrDtYEP0rRg/uUq3djxPlOTURJ/8C+vfff9GhQwc0bdoUa9euhYODA06dOoUJEybgl19+wYEDB2Bubq7yjoSvxii9vXR6IJy8JyPl9GV0auWo8vev6PhytNLbkWGBaNp9Co6dvoyOj3UY1taHVT0TtXWVR09XR4iOx73RyRVvdHLVdEaZlq7ZhaF+HRHQuwMAYNHkgdi+7xRWbd6P8cO8NdYl4ucRAo+XqF316io/CV28fDscGtZDp1ZOGmuCwN+T7Ko8Ub/2RR0zUceLP/PF7DI21Me3M4fho4i1CAnqoVhfVFSMGxl3lPb19XLHph1HkZt3X2l99p28UvuWuJyWickLH57pD3z0saiCKM+pRcSr/tVLjF9rkpIxY8ZAX18f27dvR+fOndGoUSP07NkTO3bswNWrV/HJJ59opEuecw8AUNfESCPv/8kOM1PljoRfD8PxjUnoODACMyM34+69++UcQbX+vXwTzXpOgUef6Xh/aiwup5e+5Jgeuv+gAClnLsOrrbNinY6ODjq3dcahE+c12ibi51HU8RK160n3HxQg/pdDCOjdAZKk7f97J3WoKV/7ohB5vPgzX8yu+RP9sX3fSexJSn3qfu4udmjpbIdVm/eXcYwBOPfbHOyIDUFAr/bV2ve8RHlOTS8envkXTGZmJrZt24ZZs2bB0NBQaZu1tTUCAgIQFxeHpUuXlnrymp+fj/z8fMXbcrm82rqKiooweVEC2rk3QXNHm2o77vN0TFm0/mHHS/919OveGnbW5mhgaYpT564ifMlmnLt4HSvmva/WPk9Xe0ROD4RjYytcv5WNud/9gjff/xx//vAJ6hjXVmtLTZCRlYPCwqJSlwhampvg7IXrGusS9fMo6niJ2vWkrbuPIzsnD4N922k6hbRETfnaF4Wo48Wf+WJ2vfWGJ9xd7PD6O/Oeue+QPh1w5t80JB1X/uXDrK9/wt5Df+Puvft4vb0LFoT6w9jIAN/G7am2zsoS5Tk1vZg4+RfM2bNnUVxcjGbNmpW5vVmzZrh9+zZu3ryJ+vXrK22bPXs2ZsyYoZKukHnxOP1PGn75brxKjl9RE+atw+l/0/Dzt8FK64f17aT47+aONrCyMIHfmCU4f+UmHBpaqq3v8Uss3Zxs0drNHi16hWHTjqMY0qej2jqoavh51E6rNv+Jbh2ao4GlmaZTiEgg/JkvHlsrM8z+Xz+8NXYJ8u8XPHXf2ga18Hb31pgf9WupbQseW3fi7yswMjTAh0O6aXTyL8pzalFI0sNFFCK1qAIv+xdUcXFxBfZSNnnyZGRnZyuWy5cvV0vLhHnx2Lb3JLYs+xC2VnWr5ZjPY+L8eGz74yQ2Lx33zA5Pt4d3m/338i011ZXNtI4RHBvVx7+Xb2q0Q1QWZjLo6urgZqbya/FuZspR30Kc116K8nkUdWfnrWEAAF7QSURBVLxE7XrcpbRM7E5KxVA/PpGn6lMTvvZFUlPGiz/zNd/l7tII9S1MsHtlKG7u/wI393+BVzyd8IF/Z9zc/wV0dP6bofV53QOGtfXxw9akZx73yMkLsLWqC/1amjn/KcpzanpxcfIvGEdHR0iShNOnT5e5/fTp06hbty4sLUufzTYwMICJiYnSUhXFxcWYMC8eW3cfw+ZlH6Kxbb0qHa8qHRPnx2Pr7uP4cem4CnWc+PsqAMBawzfvybmbj/NXb8G6nqlGO0SlX0sPHi522HPov9fyFRUVIfHQ32jTwkGjbY8T5fMo6niJ2vW4NVv2w7JuHXgLeAM0qrlqwte+SGrKePFnvua7Eg+louPAWXgtcI5iOfrXRaz79TBeC5yDoqL/TpIF9umIXxJPICMr55nHbdG0IW5n5+L+g6dfTVDdRHlOTcTL/gVjYWGBN954A0uXLsX48eOVXvefnp6O1atXY+jQoWq5WVXI3HgkbDuMNQtGQGZUG9dvPbyHgImsNgxr66v8/ZeYMC8eCduOYPWC98vsOH/lJhK2HcEbHZvD3NQYp85dwyefb0DHlx3h6mSrtk4AmLZ4A3q82gJ2DcyRdjMbc77dCl0dHfTr7qnWjifl3M3H+cfOYFy8loETqVdgZmoEO2vV/+WIpxk9+HWMnrESLzdrhFau9li29nfk5uVr9KY8on4eIeh4idyFR09KV285gIE+7aCnp6vpHEDg70l2VZ6oX/uijpmI48Wf+eJ15dzNx+l/0pTW3c27j8zsXKX1Dg3roePLL2FA8LJSx+jxqhsszevg8MkLuJf/AF3auWD8u95Ysmqn0n5uTR8+VzQ2NEC9ujK4NbXFgweFSD2fXi0fCwR6Ti0i6dE/UYjUogpS8fNcX04qdfbsWXTs2BHNmjXDZ599pvSn/vLz8yv8p/7kcjlMTU1xPSP7ua4CqNtmbJnrI8MCMbiKP9wr82Vn3nZcmeuXhAVgsG97XLl+GyPDVuD0P9dw99592FrVhU/nlvhfUHeYyAzLfGx5qvpLlaAp0diffA6Z2XdRr64M7dybYNroXmq970BZ/jjyN3qN/LLU+kE+7bA0fIhGmh73bfwefLVyB25k3EGLpraYE9IfrR+9dEMTRP08lhBtvETv2nXg9KO/Pz0Njo2tNJ0DCPw9ya7nI+LXvshjJtp48We+WF3lPf/c8vVHOPH3FUxZtF6xbtroXhjQsw1a9p5e6rll1w7NEDamNxwaWkKSJJy/chPRCXuxfNOfSvvePrSk1Pu6dC0D7n2mK60ra7+qfkxVeU4tl8thZWGK7Ozne56vaSXzlH+uZKCOQP135HK81NCixo7rs3DyL6iLFy9i+vTp+PXXX5GZmQlra2v4+flh+vTpsLCwqNAxqjr5VyVRv+z457+IiIiINKe8ibKmVWXyrwqc/KuGtk/+edm/oBo3bozY2FhNZxAREREREamG9GgRhUgtKsAb/hERERERERFpOU7+iYiIiIiIiLQcL/snIiIiIiIiteNV/+rFM/9EREREREREWo6TfyIiIiIiIiItx8v+iYiIiIiISO0k6eEiCpFaVIFn/omIiIiIiIi0HCf/RERERERERFqOl/0TERERERGRBkiQhLrHvkgt1Y9n/omIiIiIiIi0HCf/RERERERERFqOl/0TERERERGR2vFu/+rFyT8REREREQnh9qElmk4o0/2CIk0nKBGth2oGXvZPREREREREpOU4+SciIiIiIiLScpz8ExEREREREWk5Tv6JiIiIiIiItBxv+EdERERERERqx7v9qxfP/BMRERERERFpOU7+iYiIiIiIiLQcL/snIiIiIiIitZMe/ROFSC2qwDP/RERERERERFqOk38iIiIiIiIiLcfL/omIiIiIiEjteLd/9eKZfyIiIiIiIiItx8k/ERERERERkZbjZf9UYZ/HbsfMyM0YOdALs//3tlrf70+/H8PZi9dR26AW2rZwwPRxfeDU2Eqxz/jZP2BPUirSb2XD2NAAbVs6YPrY3mhqb622TgCIStiL6PV7cTktEwDg0sQaE4b3xBudXNXaUZ7v4vfgq1U7cSNDDjcnW8yd0B+ervaazhKya9/Rc/hq5Q4cO3MJ6bfkWDX/ffh4uWu0qYSI4yVil8jfjyK3QcDPJbvYpUote4cpvhcfN/ztV7Eg1F8jTY8TbbxE7yqhzuet+5PPIXL1ThxPvYzrt+SImfMe3uzcssx9J8yNw4pN+zDzo774YGAXpW2/7TuFhdG/4vS5azAw0EOHlx2xfO77Km3XNOnRIgqRWlSBZ/4F1KtXL/To0aPMbXv37oUkSTh+/Lham46euojYjfvg6mSr1veLR5Ow4f1fxbao/2HDV2PwoLAQ/cZFIjcvX7GPu4sdlkwLwIG4T5Dw5WgUFxej37ilKCwsUmurTX0zTB/bB7+vmIhdyyfg1dZNERDyLU7/k6bWjrJs2H4EUxdvROh7PbF7ZSjcnGzRb1wkbmbeYVcZ7ublw62pLeZP1PwTv8eJOl4idon8/Shym4ifS3axS5V2LZ+AM79EKJaNS8YCAPy6vayxphIijpfIXSXU/bz17r37cHWyxZz/9X/qfj/vPoYjpy7Aup5pqW0//Z6CsTNWYpBPO+xaGYot3wTjrTc8VVhNLyJO/gU0fPhw/Pbbb7hy5UqpbTExMWjdujVatiz7t4mqkHM3HyPCYvHFlEEwq2OotvdbIuHL0Rjs2x7NXmoAt6YNERkWiCvpt3Hs9GXFPsP6dkLHVo5oZGMBdxc7fDLSF1ev38altAy1tvZ8rQW8O7nipUb14djYCtNG94axkQEOnzyv1o6yLF2zC0P9OiKgdwe4NGmARZMHwqi2PlZt3s+uMrzRyRVTR/WCbxcxzvaXEHW8ROwS+ftR5DYRP5fsYpcq1atbB1b1TBTLtj9OwqFhPXRq5aSxphIijpfIXdDQ89auHZpj8ge+ePMpVwim3cjClEUJWBo+FLX0dJW2FRQUYurn6xE2tg/eeesVvNSoPpwdGqBPt1ZqqKcXCSf/AvL19YWlpSViY2OV1ufk5GDdunUYPny4WnsmzIuDdyc3eLVzUev7LY885x4AwMzUqMztuXn5WL3lABrbWMDWqq6a6/5TWFiE9dsP427efbRp4aCxDgC4/6AAKWcuw6uts2Kdjo4OOrd1xqETmptsiNolKlHHS9Sux4n0/fgkkdpE/Vyyi13qcv9BAeJ/OYSA3h0gafi236KOl6hdJUR73goARUVFGDNzJUYHdIVLkwalth9PvYK0m9nQ0ZHQdehctPCdikHjl+H0P9c00qtWkoCLFuNr/gWkp6eHoUOHIjY2Fp988onifz7r1q1DYWEhBg0aVObj8vPzkZ//36Xwcrm8yi3rtx/GsTOXsWv5xCofqzoUFRVhyqL1aOfeBM1fslHaFpWQiPCvfkRu3n04Na6PDUvGQL+W+r/ET527iu5BC3HvfgGMDQ2wcv77Zf6gV6eMrBwUFhbB0ryO0npLcxOcvXCdXTWEqOMlahcE/X4sIWKbqJ9LdrFLXbbuPo7snDwM9m2n6RRhx0vULgj4vLXEVyt3QE9XB+8P6Fzm9ovXbgEAFkT9ghkf9oVdA3MsW/M73hrzFf6Mm4q6psZqLiZtxTP/ggoKCsI///yDPXv2KNbFxMSgX79+MDUt/TohAJg9ezZMTU0Vi52dXZUarqTfxuSF6/Htp8NQ26BWlY5VXSbMW4fT/6bh+8+GldrWv0cb7F4Zip++/ggvNaqPoCkxuJf/QO2NTo2tkLh6MnbEhCCo3ysYHb4SZ/7V/Ot4iV5EIn8/itxG9KJatflPdOvQHA0szTSdQpUk4vNWADh25hK+i9+DL6cGlns1SVFRMQDgo3e84dvFA+4ujfDF1MGQJGDLrhQ1F5M245l/Qbm4uKBjx46Ijo6Gl5cXzp07h71792LmzJnlPmby5Mn4+OOPFW/L5fIq/QLg2JlLuJl5B15D5irWFRYW4c/kf/DdukRc37cYurrq+/3RxPnx2PbHSWz95qMyL+c3kRnCRGaIlxrVR+sW9mjSNRRbdx9Dv+6t1dYIAPq19NDEzhIA4NGsEZL/uoSvf9iNxVPKvmJDHSzMZNDV1Sl1I56bmXLUtzBhVw0h6niJ2gVBvx9FbhP1c8kudqnDpbRM7E5Kxcp5YtxdXdTxErVLtOetJQ6k/INbt3PQqu90pa7wrzbhu7g9OLwxHFb1Ho6bs8N/f6XKQL8WGtnUw5Xrt9XerE7So3+iEKlFFXjmX2DDhw/H+vXrcefOHcTExOCll15C585lXy4EAAYGBjAxMVFaquK1Ns7Yt3YKEldNUiwvN2uE/j1aI3HVJLX9AC0uLsbE+fHYuvs4flw6Do1t61XoMcXFxch/UKCWxqcpKi7G/fua7dCvpQcPFzvsOZT6X1dR0f/bu++4rOr3f+DXQfYQBBQUcYEiggIOEBdu0UhRc4sjNRXJreBA3HuUZlqWI7UyM800wbJcZVYKiMpQhuDeW4bcr98fX+7zu29GH1HOfb/V69mjR3G44Vyc+5xzv6/rPQ4d/SdFr3OMRY1LVKIeL1HjKo4I12NJRIhN1PeS4+K4dOHrn05QxQpW1FGQR26KerxEjUuUdmthvTr70u9bw+nQlqnyv4721hQ6oB19+9FoooKnVpkYG9LFSzfln8t7nk9Z1+5SVUf9rV/F3jzc8y+w3r1707hx4+jrr7+mr776ikaPHq3TxWesLEypnqv2vHpzM2OytbYosl1JU5Z+R9/HnKLty0eQpbkp3bj9f2sZlLc0JTNTY8q4cpt2/3Ka2vjVJfsKlnTl5n36eMsvZGpiRB2a6fYDfM4nP1L7Zh7k7FiBHj3Npu+j/6Xjpy7QrjWhOo2jOKH921LonK3k416NGnrUoHXf/E5PnuXQgHebclzFePw0h9KzbslfX7p6hxKSL5ONtTk5O9rqLS5Rj5eIcYl8PYocm4jvJcfFcSlNpVLR9p/+or7v+JFhoZXY9UnU4yViXPpstz55mkPpl/9/myHz6h06m3KZbMqbU1VHW7ItNGffyLAcVbK1ItfqDgWxm9Gg4Oa07IufycnBhqo62tLa7YeIiKhrW/0/cpK9OTj5F5ilpSX16dOHpk2bRg8fPqQhQ4rOc38bbNx1nIiI3h21Wmv7J7MGUP+gpmRibEQn4lJp/beH6f7Dp1TR1oqa+bhS9JcTiyxGo7Tb9x7T6Nlf0Y3bD6m8pSl5uDrRrjWh1MbPXadxFKdHx0Z0+/5jWvjZfrp55xHVr+NE368eo/ehlqLGFZd4Seucm7HqByIi6veOH306O0RvcYl6vESMS+TrUeTYRHwvOS6OS2mH/06my9fv0cCu+k2qCxP1eIkal77EJWVSjzFr5K+jVu8mIqI+XXxpdeTAF/odUR8Gk2G5cjRmzjbKzsmlhh41aNcnYWRTvvinW70pJOn//hWFSLEoQQIAfQfBSnbixAlq1qwZdenShfbv31+qn3348CFZW1vTjTsPXnkKQFkT9bTT92N9GGOMMcaYeHKfq/QdgpaHDx+Ss0MFevBAvHb+i1DnKVdv3Rcq/ocPH1KVijav7XH9X7jnX3D+/v7CJsqMMcYYY4wxxl4PnPwzxhhjjDHGGNM5qeBfUYgUixJ4tX/GGGOMMcYYY+wNx8k/Y4wxxhhjjDH2huNh/4wxxhhjjDHGdI/H/esU9/wzxhhjjDHGGGNvOE7+GWOMMcYYY4yxNxwP+2eMMcYYY4wxpnNSwT+iECkWJXDPP2OMMcYYY4wx9obj5J8xxhhjjDHGGHtJa9eupRo1apCpqSn5+fnR33//re+QisXJP2OMMcYYY4wxnZMk8f4trR07dtDEiRMpKiqKTp8+TV5eXtSpUye6efOmEofslXDyzxhjjDHGGGOMvYSVK1fSiBEjaOjQoVSvXj1av349mZub08aNG/UdWhG84N8bDAARET16+FDfoRShjk000suU+xhjjDHG2Bst97lK3yFoefTo/9r3orapX9RDwfIUdTyF4zIxMSETE5Mir8/NzaVTp07RtGnT5G0GBgbUvn17OnHihA4iLh1O/t9gjx49IiIi15rO+g6FMcYYY4wxVsYePXpE1tbW+g6j1IyNjcnR0ZFqC5inWFpakrOzdlxRUVE0e/bsIq+9ffs25efnk4ODg9Z2BwcHSkpKUjzW0uLk/w1WpUoVysrKIisrq1fu0X748CE5OztTVlYWlS9fvsxifFUcV+lwXKUjalwkcGwcV+lwXKXDcZWOqHGRwLFxXKXDcZVOWcYFgB49ekRVqlQps/h0ydTUlNLT0yk3N1ffoRQBoEjuVFyv/+uIk/83mIGBAVWtWrVMf2f58uWFuomqcVylw3GVjqhxkcCxcVylw3GVDsdVOqLGRQLHxnGVDsdVOmUV1+vY46/J1NSUTE1N9R3GK7G3t6dy5crRjRs3tLbfuHGDHB0d9RZXSXjBP8YYY4wxxhhjrJSMjY2pUaNGdOjQIXmbSqWiQ4cOkb+/v15jKw73/DPGGGOMMcYYYy9h4sSJNHjwYGrcuDH5+vrSRx99RE+ePKGhQ4fqO7QiOPlnL8TExISioqKEm+/CcZUOx1U6osZFAsfGcZUOx1U6HFfpiBoXCRwbx1U6HFfpiBoXezV9+vShW7du0axZs+j69evk7e1N0dHRRRYBFIGE1/35EIwxxhhjjDHGGPtPPOefMcYYY4wxxhh7w3HyzxhjjDHGGGOMveE4+WeMMcYYY4wxxt5wnPwzxhhjjDHGGGNvOE7+GWOMMcYYY0xBd+7cIZVKpe8w2FuOk3/G2CtJT0+nnTt36juMIp49e6bvEIqVnZ1NRCR0A4AfAlM6fLwYYy/ibb9X6PLvF+1Y379/n9zc3Ojrr7/WdyjsLcfJP2N6JNqHU2nl5ubSxIkTadOmTUQCJbRxcXEUGhpKV69e1XcoWq5du0ZeXl509OhRMjAwEOZ4qT18+JCIiCRJEubcFCUOTVeuXKG9e/fSmjVriAqOl6jy8/P1HcJ/EvH9FZEox+nu3buUkJBAFy5coCdPnug7HNmjR48oKytLuHv+gwcP6PLly5SVlUVUcK/QxX0/PT2dDh8+rPh+XlRSUhKdPHlSJ/fKZ8+eUU5ODmVlZcnFdhGYm5tTy5Ytae/evfJnLWP6wMk/eyU3b96k5ORk+vvvv7W267uhImqD9969e3TlyhU6e/YskeBJw4swNjamXr160cGDB+mff/4hAwP931Li4+OpcePG5ODgQFWqVNF3OFoeP35MtWvXpr59+9KJEyeEKgCcO3eObGxstBJafV3Hz549owcPHshxiOTs2bPUtWtX2rFjB507d064ESbXrl2j6Oho2r17N929e5fKlSsnzP3w7t27dObMGVq/fj1t3bqV7t69q/fPiuvXr1NMTAxFR0dTTk6OXmPRpFKpKC8vj65cuUIA5OtAn8fr7Nmz1L59e+ratSt5eHjQlClTKCkpSW/xqCUmJlJISAiFhITQrl276OnTp/oOiajgnhocHEzNmjWjjh070vTp04mIyMDAQNH3MS4ujtzc3OSCg77Fx8eTl5cX/fHHH4rvKzExkQYOHEiNGzcmFxcX8vf3p4iICMX3+yKMjY2pXbt29Ntvv9Ht27eJBOowYW8ZMPaS4uPjUatWLbi7u0OSJHTs2BHffPON/H2VSqWXuM6dO4fhw4fj8uXLetl/SRISEtC8eXPUq1cP5ubmGDdunL5DKhNPnjxBu3btMGLECGRnZ+s1ltjYWJibm2PatGl6jeO/JCYmol+/frC3t8eff/4JAMjPz9d3WIiMjIQkSZAkCcuWLZO36/o6Pn/+PN555x34+PigRYsWOHr0KPLy8nQaQ0nOnz8PGxsbTJ8+Hbdu3dJ3OEXEx8fD3d0dbm5usLW1haenJ65fv67vsICCY9ehQwc0atQIZmZmMDc3R+XKlbF27Vrcvn1bLzGdO3cOfn5+6NGjB0aPHq2XGIqTmpqKadOmoXHjxqhRowb8/Pzw9ddfy+ecPj5b4+PjYWFhgXHjxuHgwYOYMWMGTE1NMX36dOTl5ent8z4hIQEVK1bEtGnTcPjwYb3EUJy4uDhYWlpi1KhR2LBhA3r16oUqVapg4cKFOtlveHi4ovt5UXFxcTAzM8P06dMV39eZM2dgbW2NMWPG4IsvvsAPP/yAbt26wcTEBEFBQcjNzVU8hpJoXh8+Pj7o27ev3mJhjJN/9lKuX7+OWrVqYerUqTh79izOnDmDDh06wN/fH3PnzpVvdLpuEKSmpsLZ2RmSJKFTp07CNHwTExNhZ2eHiIgIxMTEYOfOnTAwMMCnn36q79BemuZ7GxUVBWdnZ9y5cwfQUzKbnp4OAwMDzJ07FwDw/PlzAMDixYuxd+9encdTmGYCe/78eeEKADExMfD398ekSZNgbGyMRYsWyd/T1XUcFxcHGxsbDBs2DMuXL4eHhwc8PDyEKOQ9fvwYQUFB+OCDD7S26yvpKSwuLg7m5uYIDw9HWloaNm/eDENDQwwePFivyZk6Nnt7e0yYMAHHjx/Hw4cPERsbi549e8LQ0BCLFy/GgwcPdBpTQkICKlSogMjISK3PiaNHjyI+Pl6nsWg6c+YMXFxc0K9fP0ydOhUff/wxfH19YWFhgQkTJuDatWs6jykpKQnly5fH2LFjtbb3798fLi4uePTokc5jAoCrV6+iXr16CAsL09qu73tpSkoKTE1NMXv2bHnb3bt30bRpU7Rv316x/Z45cwZmZmaYOXOm1vbDhw/rpVipvidFRERobf/pp59w8eLFMt3XzZs34ePjU2RfN2/exCeffAILCwv06dOnTPf5vxTuDFG3AZYuXYpGjRrJx0CUzxD29uDkn72UP/74Ay4uLrh06ZK87ebNmwgLC4Ovry9WrFih85iePn2KqVOnomfPnvjtt99Qo0YNtGnTRu8FgPv376Nbt25FGk4jRoxA//79gdfo5n/37l2t6rn6/589ewZXV1e9jWZQqVTYuXOnnDiqLVy4EObm5jh48KBe4srKysL+/fvlr9UFCWgUAJycnHDq1Cm9xKcZU35+Pho3boyhQ4diw4YNMDAwwNKlS+XXKX2OnjlzBlZWVpgxY4a8bdu2bZAkCevXr9dZHCW5c+cO6tSpozW6SVPhuHQZZ0ZGBkxNTTFx4kR5W35+PmrUqIHAwEC9xQWN9zUyMlKOS1P//v1haWmJQ4cO6Sy+GzduoGHDhkXuV0uWLIEkSRgyZAjOnj2reByFqXvXIyIi8PDhQ63vffDBB7CxsUFUVBSePXum07hWrlwJSZKwdu1a3Lx5U36PFi9eDG9vb719xu7evRt+fn5ITk7Wy/6Lk5eXhwkTJsDe3h5ffPEFoHFOR0REoGXLlnj8+HGZn+dpaWmwsLBASEiI1vZ58+bBwsICFy5cKNP9/S+XL1+GjY0NBgwYAGgcg/nz56N69eplfn2dPn0anp6eSEhI0PpMQ0EbbP78+TA3N8fu3bvLdL8lSUtLQ3BwMDZu3IinT59qfS8rKwsVKlRAVFSUTmJhrDBO/tlLOXXqFJycnHD06FFAo6J5584dDBs2DM2aNUNcXBygw8ZmdnY2tmzZgu+++w4AcPHiRVSvXv0/CwC6iO3WrVto164dtm3bprV92bJlaNKkCVAoKRTVnTt3YGdnh3feeQcrV67U+l52djYiIiLQrFkznQ9NVR+7u3fv4ptvvoGTkxNGjhyJVatWwd7eHgcOHNBJHIXl5OSgR48e8PPzw549e4rEi4Keka5du6Jz58467/m8f/9+kW0xMTHo0qULTp06haVLl+psCkBeXh7at2+PcuXKacU1adIkSJKEFStW4MCBA3jy5AmePHmiSAz/S2xsLCRJwt9//13ia7Kzs4tcG7rw9ddfw9XVFX379pWPz6JFiyBJEry9vTF27FiMGTMGp06d0mkP4J07dyBJEtq1a6e1XaVSydfB8+fP4eXlhdatW+ssrqNHj6JevXo4ffq0nCCsWLECxsbGmD17NmrWrIlhw4YhISFBZzGV1Luek5Mj/3///v1RoUIFraK7rkyfPh3VqlXD4sWLgYL31traGvPmzdN5LGozZ85E7dq1ix3Orb5XZWdnIyUlRSfxqPeZnJyMkSNHomnTpli1ahVQMFrSwsICy5cvV2TfsbGxqFChAkJCQvDvv/8CBcWsihUrlvgZqORn9O+//46AgAA0a9YMsbGxQEEx3s7ODtHR0WW+v02bNsHU1FT+uvDflpaWBmtra63PMyWdP38eQUFBMDQ0RKtWrTBt2jQ8fPhQHg2waNEieHp6IikpSSfxMKaJk3/2Um7cuAEXFxetIbDqxtzt27dRpUoVvcy7LpwYpKSkyAWAGzduyHGqP4x0RbPxoT5O69atQ8uWLbVep+sE8EWoP0RzcnLwzTffoFevXqhYsSLq1auHhQsXyj0KKSkpMDMzw7p163QWW2ZmJtq2bYuMjAyg4Pht374dtWrVgiRJOH78OFBoyL0uxcbGIjAwEB07dtTqcdAsAGzZsgXOzs7IysrSWVyJiYkwNTXF6NGjsW3bNjx+/BgoaCA1bNgQO3bsADR6QnUxkic5ORmurq4ICAgACnoVLS0tMWzYMIwfPx4eHh7w8vJCx44d8emnnyIxMVHxmNRUKhUuXLgAKysrLFq0qMTz6eDBg+jcuXORXlulqHuAnz17hs2bN8PPzw/9+/fH7NmzYW9vj3Xr1uH06dNYsWIFevfuDQcHBzg7O2sNR1bamDFjYGVlhU2bNhW5P6uvgxkzZsDT07PYgpQSlixZggoVKmhtO3r0KH799VegoAhWrVo19OnTR2e92oV71zWpj9ONGzdgbW2NNWvW6CSmwiIiIlCzZk1Mnz4dTk5OWsPt9TEiZ968eXB0dJTvX8UN94+IiNDJ/SslJQXz58+Xp75duHABw4cPR4sWLeRpcUocrydPnsgJ5fHjx1GrVi0MGTIEYWFhsLW1lc9pTUpOa9G89x08eBDvvvsu/P39ERoaikqVKhWb+Kempr7yfo8dOwZTU1N8//33Jb7Gx8cH48ePf+V9lUZ8fDw++OADuLi4oFq1apg8eTISEhLw77//wtnZGfv27QMEmKrC3i6c/LMXcvPmTRw5cgT79u2TG2gxMTEwNDTE/Pnz5depP9A++OAD9OzZU6dxaX7oaH6wJicnywWAzMxMjB49Gu3atVO0oVlSXJpJ35dffin3/ANAeHg4PvzwQ70uSvNf1LHn5OQgMzMTAwYMQOPGjVG+fHnMmjULf/75JxYuXIjWrVvrbF7q4cOH4ePjAz8/P2RmZgIFPdrbt29H9erVMXjwYPm1ui4AqD/MExIS0L59+yIFAPX7fPLkSXh4eMgFDF3ENXv2bEiShDp16mDQoEGoWbMmdu/ejVu3bmHXrl3w9PTErVu38PTpU6xYsQKSJGH16tVlHsuFCxe0Fum6ePEiqlWrBgcHB9jZ2RVpuG7fvh3Dhw9HtWrV5Pdbl7p164bKlSvLPWuFhYeHIyQkRCfDsi9fvoygoCD5+GVnZ2Pjxo1o0qQJJEnSGm2idvz4caxevVonQ9o1e6w//PBDmJiYYNOmTUWGwALAuHHj0LRpU0VHQGn+7k2bNsHa2hpxcXFFGt3qz46ZM2fCz89Pp/PZ1b3ry5YtK1IAQMG6E3Z2dor3XmZmZmLr1q34/PPPi4x+mDZtGoyNjeHv7y/f53WV+KtUKq336+jRo7C1tcWECRO0Pp/U8vPzMXjwYMXX1rl9+zaqV68OGxsbhIeHywtYqgsAlSpVQtOmTeXXl9Vnkbp3ec+ePfI959ixY3BxcYEkSfjkk0/k12qe182aNZOLFGUpMTER3t7e8nQHFLQT3333XZQrV06OR/NaDA8Ph4WFBR4+fPhK51FWVhYqVaqErl27an2Wqs+Xu3fvolmzZti6detL7+NlZWdn4969e5g8eTKaN28OIyMjREVFwd7eHj4+PnpbM4O9vTj5Z//TuXPn0KJFC3Tv3r1Ij9Hq1athYGCAGTNmaCW53bt3x6hRo/QWl/qGr/4wSUlJgYuLC8qXLw8TE5MSG++6igsFDVBPT0+goOfLwMDgP4cU60NmZibWrFmDd955B76+vpgxY4Y81QMFhZVFixbBw8MD1apVg42NDYyNjeVF7HTh119/RZs2bdCoUaMiBQAnJyd5ziF0ML1C8/fn5+cXKQB06tRJ7lVXmzx5Mlq0aKGzXk8UJI6TJ09GuXLl8NNPP2HFihXo0qUL6tati6FDh8LDw0Oeg33//n2sWbMG586dK/M4QkNDIUmSVpJ/8eJFNGrUCG5ubrh79y5QTK+I0j3rly9fxs6dOxEREYG1a9fip59+AgruIx4eHqhZsyZ+++03ucfx+vXriIiIQKVKlXD+/HlFY1M7dOgQWrVqhbZt28ojXLKzs7Fp0yb4+vrivffek+PTTIiUpk5sCr9HY8aMgYmJCTZv3qxVALh//z569+6t6PzX2NhYdO7cWT4ep0+fhpGREaZNm6aViKlUKjnB/PDDDzFixAidz6+PiIhAtWrVsHz5cq0CgEqlwunTp9G0aVNF76/qp/h4e3vDwcEBFStWxB9//KH1mqioKDlGXU0jSUlJwZQpU9CzZ0989tlnuHfvHp4+fYqePXvCycmpyPSD7OxszJo1Cy4uLkhLS1M0tszMTNSqVQvVq1dHt27dMGnSJDm5Tk1NxYgRI9C0aVOtERuv2tObk5ODpk2bQpIkdOnSBT///LM8AuDvv/+WF43UbFNERkbC0NBQkfZPTk4OQkJCIEkSfH19tUYAqgsAvr6+WuvbREZGwtLSEidPniyTGHbt2gVjY2OEhIQUKXDOnDkTNWrU0FmRvSS3bt3Cpk2bEBAQAHNzc1SoUKHYQh9jSuLkn/2nhIQE2NnZYdasWVo3zd9//x1Xr14FAGzcuBEmJibo1KkT+vXrh/fffx8WFhaK9i79V1zqlcELf7j269cPdnZ2eo9L3dj84osv0LFjRyxcuBDGxsZ6XfStOAkJCfDw8EBwcDCCg4MREhICW1tb1K5dG5s2bdJ6bUpKCqKjo9G0aVNUqFChzFfy1fT8+fMiPQTR0dFo3bq1VgFAPQWgRo0a6Nq1q2LxqJ0/fx7t2rXDunXr5PUuNJ05cwZdunSBv78/ZsyYgR9//BFjx45FpUqV9LK6+I0bNzB48GBYWVkhNjYWT548wd69e+Hr6wtzc3M54YWCPXv5+fkYOnQoypcvj19++UXefvHiRdSoUQMBAQFao0h08RSR+Ph4uLq6wtfXV34sp4WFBYYPH46cnBz88ccfaNy4MUxMTNC4cWO0atUKzZo1Q40aNXD69GnF4ipOTEwMgoKC0KpVK60CwMaNG+Hn54fu3bvLCa8uRr4kJycjLCwMzZo1g5ubG95//32tBRLVBQDNEQAzZ86Ei4uLYvcM9aPG1NPQ1OdOZGQkDAwMsHDhQq3pVtnZ2QgPD0fFihUVnVryX73r6gLAsmXL5OlqKCgUBgQEKJYsaD4x4u7du4iOjoa9vT06dOiAx48faxWRwsPD4eLigrlz5yr+qMa4uDg4Ojqic+fO6NChAyRJkhe3vHr1Klq3bg17e3t06tQJP/74I5YuXYqQkBBUqFBBZ1P8tm7dCm9vbwwePBj+/v6YMmVKsVMANBdRfVXbtm1DnTp14OzsjIYNGyI6OlouABw7dgy1atVC7969cf78ecyePRumpqaKtjOWL18OGxsbjBkzBs2aNcNnn30mf09dAGjcuDGSk5OxfPlymJqalmkh4vnz51i/fj0MDQ3l+8+MGTPktTJ0fX/WVPgz68aNGzh58mSZTHlgrLQ4+WclunbtGho0aFDkMTpLly5F+fLl0a9fP3mecnx8PMLCwhAcHKz4Qkn/FZe1tbVWXOre18WLF0OSJEUbAqWJCwXJvyRJsLOzwz///KNYXC8jNjYWlpaWmDJlitac15iYGLRv3x5Vq1Ytdm7ds2fPFO0JSkxMRP/+/TF9+nScP39ea+Gro0ePomXLlmjYsKG8/cGDB9i4cSPq1auHK1euKBaXSqXCmDFjYGBggEWLFsHGxgYLFy5ETEyM1uvOnz+P8PBwuWetc+fOOllU7M6dOzh79iwOHDiAY8eOydsfP36MAQMGwNzcHL///jtQ0DOh7inTxXBelUqFgQMHllgAaNeunaLvnaaUlBT5kZzqpCsxMREzZ86EkZGR/HSO3NxczJ8/HyNGjEDfvn3x6aefIj09XfH4iust3LdvH4KCgtCyZcsiBYDmzZujXbt2OlkkMT4+Hra2thg8eDAmTJiAyMhIODk5wdHREZMmTZJfFxYWBhMTE3z33XeYPn06zMzMFGuUx8bGFvuM8by8PNy7dw9hYWFyz+mSJUswZ84c9O7dG/b29oomSS/Su65ZAHj69CnmzZsHa2trnDlzRpGYLl++jPLly2Po0KFa2728vODj46M1kkktNDQU9evXV2QIuVp8fDwsLS0xc+ZM5OfnQ6VSYcCAATA1NZWLMzdu3EBkZCTq168PW1tbuLm5ISQkRJHRSmrqe6O6qHbmzBn07t0bhw8fxuLFi9GoUSOtAsDFixfRp08fdOjQQR7R9LLU70NycjKGDBmCmJgYdOjQAe7u7kUKAG5ubqhSpQosLCwUG/GoPgZ5eXlo1aoV3n//fYwcORINGzbEhg0b5NfFxMSge/fuMDMzU2wEAgD89ddf6NGjBzw8PNC8eXOEhobqdI0YxkTHyT8r0d69e+Ht7a1101y+fDlsbW0RGhqKgIAAhISEyImCeg6z0j1MLxqXugc4JycHP/74o+I3/9LGdeLECTg7O+t0RekXkZSUBBMTE3kYZeGh8keOHIGPjw/at2+v1Sur9II1z549Q7t27SBJEkxMTFC9enV4enoiLCwM3333HXJycrBv3z707NkTDRs2lBPGhw8f6mQhxbi4ONSoUQPHjx/HwYMH5ZX+g4ODcezYMdy7dw8oaDTm5OTIw1aVlpCQgCZNmsDLywsmJiaQJAkBAQHyUzGePHmCkJAQmJqa4rfffpNjVMLjx4+RlZWFX375Bf/884/WftQFAM3HMqampsLa2hrvvPOO4lM28vPzERoaqjVNRO3OnTtYsWIFDAwM9PZ4prNnz6JLly6YMWOG1sgrFKx9ERgYiFatWsnTcrKzs/Hpp5+iffv2ii8mmZWVBVdXV63HNKKgmNKnTx/Y2dlh7ty58vYJEyZAkiRFeyLPnj0LU1NTLFiwQGv76tWr5XVq7t27h82bN6NevXpwdHREgwYNMGLECEVX4C5t77qrqyuaN28OMzMzRaerHTx4EH5+fmjdurX896ufGOHl5YX+/fujd+/e+Pbbb7Uerac5MqGs3bt3D5aWlkUWxh0wYABMTEyQkJAgj6hTu3DhAh49eqTodI2LFy9iwYIFRUar9OvXDx06dAAAzJ07F76+vpgyZYqc7KelpWldt6WVnZ1dZE2g/v37o1u3bgCAtm3bon79+loFgKNHj6Jhw4aKjC5TjypCwWdGXl4eFi5ciClTpiApKQnDhg2Dt7e3VgHgp59+Qr9+/RRv92iOEOTF9BjTxsk/K1FERARq166ttW3t2rVyz+GGDRvQsmVLBAcH4969e0Xm2YsSl66UJi51b4BoC73k5OTg/fffh52dHfbu3StvV/e4qH3++ecoV66czofRHT58GC1atMCgQYMQFRWFHTt2oFWrVqhevTrq1KmDd999F4MGDYKTkxMaNGigaMNU0/Pnz5GTk4NRo0bJi3E9fPgQ9+/fhyRJ8PDwgLe3N37++WedFnvOnTsHa2trTJw4EbGxsTh37hz27NmDGjVqoGrVqvj888+BgnnXQ4YMQfny5YtdHbosJCcno1evXqhfvz5MTU0hSRKCg4Pl1Y5RQgEgPT1dZ8+obtGiRYnPo7927Rq6dOmCJk2a4OnTpzpd3TwvL08ufDk7O8PMzAxNmzZFjx498P333+PBgwf4+eefMXDgQAQEBOCvv/4CCq5nXawl8eOPP6JVq1a4fv26Vi8gChKltm3bokGDBlpJ4+LFixWb7vLw4UN06NABtra2Wsn0okWLYGlpqbXIJAqSmHv37iE3N1fR4vXL9K5PmDABtra2xU4lKmu//PILOnTogICAAIwbNw6VKlXC1q1bkZGRgR07dmDGjBlwdHSEo6Mj+vTpA+jg837WrFkwMzPDl19+CRQ8qcHIyAienp4ICQmRj+eKFSuQnp6u+CiXGzduoEqVKpAkCQ4ODpg/fz6+/fZb+XuBgYE4cuQIVCoVpk2bJvc8v2pv/9mzZxEYGIgZM2ZodTDcunULTZs2xfHjx5Gbmwt/f380aNAABw8elAsA6v+WpaSkJDRq1Ajjx49HWlqaXGD/999/YWVlhcOHD+Pq1asYPnw4GjZsKL9/KOapTErQPC/18SQKxkTGyT8r0cqVK2Ftbf2fw1mHDx+Ojh076vQ59aWJS5crvIsaV2n9888/6N+/P1q0aKE1X1fz2dw3btyApaUldu7cqfP4YmJi0KJFC/Ts2VNev+Hx48dYs2YNJkyYgMqVK8PAwACSJOl8cZ/169ejUqVKcuNmxIgRqFy5Mnbs2IHQ0FCYmZnp7FFwjx8/RpcuXTBmzJgi37t8+TLc3d3h6uoqJ2A3b97Ee++9h8qVK5f5iIT4+HhUrlwZo0ePxq5duxAbG4stW7bA3t4eXl5eWosgDho0CHZ2dlpFAV1QqVRwd3fH6NGji/0eAHz77bcwNjYu0tuodFwo6NFs3LgxgoODsXz5cuzcuROdOnWCp6cn7OzsEBISgnbt2sHT0xOenp46SRbVZs6cCWdn5xJjP3LkCAwMDLQKikrKzc3FZ599hpYtW8rrfaxevRp2dnZyYalwQqCLz7CX7V1Xak59ZmYmvv76a6xZs0ZOKA8fPizPq1cXBzWlpqZiz549io6OePz4sdb7MXfuXBgaGuLdd9+Fg4MDoqOjkZubi5ycHPzyyy/48MMPYWdnBy8vL0XXH1D/7pCQELRp0watW7fG+PHjUb9+fQQHB2PLli1o3769vNCvSqXCxIkT0b59+1cqRGsu7tegQQNYW1tj7ty5ctGhf//+iIiIAArO/YCAADg7O8uLtpZ18vv06VMMGDAAkiShXLlyGDx4MAIDA+WFKJcvX47hw4cDBff+UaNGoWbNmvjqq6/KNA7G2Mvh5J8Voe552L17N8zNzTF//ny5B139PfV/Q0NDMXr0aJ2sJv0mxaVEJf5VaTYQTp8+jT59+hQpAKj/jp9++gnu7u6KL1aTmZmJb7/9Fl988QWuXbsmx3jo0CH4+/ujW7ducgNH7dq1azh9+rSic7DT09OxcuVKjB8/vsgc3K5du2LZsmXo168fHB0dtXo3Dx06pLNH1F2/fh1ubm5ygUZ97NSN6tTUVJiYmGDq1Knyz9y5c+eVhqUW58yZMzAzM0NkZGSRXs3Tp0+jRo0aaNKkibxKfn5+Pnr06IFq1arppIcIGoWt4OBgeHp6aiU2mtfFhg0b4OnpqbPV8zMyMrB37165Vy0xMRH16tVDt27d5FE3T548webNmzFt2jS4urrCwMAApqamOlmDQG3lypVwdHREenq61nusPnZ3796Fo6Mj1q9fr7W9rGVmZspz53Nzc7Flyxb4+/ujdu3asLa2xokTJ4r8zEcffaSzNSXwkr3rSoiPj4ezszM8PT0hSRJq1KiBjRs3AgULqAYGBqJ58+ZyUSAvL08nPajJycno2bMnPvnkE63rf8mSJZAkCR9++GGxP/fs2TNF762nT5+GJElISEjApUuX8MEHHyAwMBALFy7ErVu3MGrUKHm1e1NTU3mqjUqlKpMFGv/55x/Uq1cPQ4YMQUREBCZPngxXV1d88MEHGDt2LMqVKyef37m5uQgMDFT083n//v0YOnQovL29sXDhQixcuBBOTk4YNmwY2rVrB39/f3m9oPj4eIwbN07xpy4wxl4MJ/8MKKhoJyYmFqnm9+7dG+bm5lizZo3WB9izZ88QHh4OBwcHRXsAOC7laTboNEcklFQAUKlUGDt2LLp3767oXPr4+HhUq1YNfn5+kCQJrVu31urV+fXXX9GsWTP06NGjSAFASWfOnEGdOnUwdOhQLF26tMjUjcWLF8PMzAzu7u5yYUAfww7j4uJQoUIFREdHA4XeW/W80QEDBqBjx46KrT1w6dIlWFhYoHfv3vI29RQSdZJ48uRJSJKEJUuWaP2sLhKywu/LkSNHIEkSRowYoTVPXl0w+fDDD9GjRw+dDPvPzMyEkZER3NzcsGvXLnm0SGJiIjw9PdGhQwetx26ioBDw119/KT7HPyMjAz/99JOcnP37778oV66c1rx+zff44sWL8PLykteUUMLTp08REhICDw8PHDlyBCg4z7/66iv4+fnB19dXngeufj9nz54tJ3RKEbF3XV2Qmz17Ni5fvoyrV6+iSZMmcHd3l9dx2b9/PwIDA+Hv7y/HrfTc6fj4eDg4OGDYsGHFPo994cKFKFeunNYccuhg1EZcXBysrKwwZcoUeVtGRgZGjhyJxo0by0WTBw8eYOXKlXKP/Kser/T0dHz//ffyZ8yJEyfg4uKCIUOG4OjRo7h+/TqGDRuG4OBgSJKEEydOKHpfSktLw+rVq+Wvo6Oj0adPH/j4+CAzMxNJSUn47LPPUKtWLUiSpLXgrS4fN8oY+2+c/DMkJCTAx8cHdevWhSRJiIyMlBve2dnZCAoKgrGxMXr06IEffvgBS5cuxeDBg2FnZ6fonG+OS3lnz55Fnz59sG3btmKTrVOnThUpAMyePRv29vaKrqSsuUr3gwcPcOXKFRgYGGD37t1ar4uJiUGzZs3Qq1evIivrKyElJQX29vaIiIgocUGpR48ewd3dvchTH3Tt9u3bcHBwwAcffCBvK9wYHThwINq2batYDFeuXEGtWrUQFBSklfipG6jqIkSPHj3QtWtXZGdn66RQolnsKPz4wI8++giGhobo1auXPEw9OTkZM2fORPny5XW2ZkNqaiosLS1hZGQEb29vrSQgKSkJnp6eCAwMlJ/SoCuFixLqmMaOHQtJkrBy5coiPxMREQFPT0+tBUKV8PPPP6Nnz55o3ry5fFzUBQB/f38EBQXJo7JmzJih+KPPROxdVxfkevXqpbX9999/h6mpqVw4QcHxDAoKgru7O1JSUhSNKy0tDdWrV8f06dP/M2meNWsWypUrpzWHXEmJiYkoX748xo0bBxSa/paZmYlRo0ahSZMmWLVqVZnve+TIkahSpQq++eYbeXG9EydOwNXVFd26dZPfkydPnuDkyZNlvn9Nz58/x5w5c1CxYkUsX75c3v7LL7+ga9euaNiwoXwt3bp1S36CEc+3Z0w8nPy/5eLi4mBhYYGpU6fit99+Q1RUFMqVK6fV0wsAU6dOhZeXl/z81EGDBsnDdDku/cf1Mp4/f45+/frB3Nwc06dPh4ODA7788kt5sTC1v//+G3369EG7du0QGBgIMzMzRRvMKSkpMDAwkJ+HrE4Q27Vrh8jISISGhuKTTz6Rp078+uuvqFevHkJCQhQdJp6Xl4cRI0agV69eWisuazZu1I3CZcuWoU2bNjpdc0BzKoL6kZbh4eGwsrLCunXrtF6rXgm5b9++mDlzZpG/oyyoG/Dp6enw9vZG+/bttUZoaDbw27Zti+Dg4DLdf0kyMjLQs2dPeUQECv3t2dnZ2Lp1KypUqABjY2NYWFigfv368PDw0FnxTn1sVqxYgYkTJyIwMBDVqlUrtgAQFBSk9YhEpRVXlFCpVEhLS5OHPffs2RNr167Fxo0bMXLkSFhZWSl67DTfv4MHD6Jbt24lFgB69eqFSZMmKb56vqi965oFuUOHDsn3sp9//hk2NjZF7u179uzBe++9p/g0kk8++QSdOnXCw4cP5ffz0qVLOHLkCJYtW4YDBw7Isc6dOxeSJCk+hzw2NhY2NjbyCA31Qr2ao1rUBQA/P78yKwCcOXNGLqL17NkT9evXx/bt2+UCwF9//QVXV1d0795dK+lXKtE+ffo0tmzZgrS0NEyfPh1ubm7y5zMKPoODg4Ph4+MjP25UpVJx4s+YoDj5f4udP38eRkZG8urWKOjhqlChAvr27Vvk9Q8ePEBGRgby8vIUnbPOcelOdHQ0qlativj4eGzatAldunRBw4YNMWbMGMTGxsoNnNjYWAQGBsLR0VHRRvzz58+xYsUKSJIk95BBY1Es9aODqlSpgoEDB8pD2Y8ePap44zQnJwc+Pj7yY8IK02zoXLhwAZIkYcuWLYrGpFbSVITY2Fi0bNkSVatWlXtrVCoVnjx5glmzZsHe3l7RaSjqYkhaWhq8vb3RoUMHrREAz58/x5UrVxAUFCT35Omi57NatWro3LmzViyFk67U1FTExMRg/fr1OH78uOK91po0Fxhs1KgRbt26hSFDhhQpACQnJ8PJyQnvvfeeTh4bWVxRomrVqvjhhx+AgnUm1q1bh+rVq6NixYrw8PBAjx49FBstkZmZiYSEhCJzqmNiYvDuu+8WKQBs27YNdevWhYmJiaKJv6i964ULcu3atUNsbCwyMjLg6OiIyZMnF3ktCj3STSmhoaFo1aqV/PWOHTsQHBwMBwcH2NjYoGbNmlqjApYuXapoQf306dMwNzfHvHnzEBERgRo1auCjjz4qsQAwZswYuLm54dNPP32l/cbGxsLExASzZs2St3Xr1q3EAkDv3r3lhFsJcXFxkCRJnvKQkZGB8PDwEgsAvr6+isbDGHt1nPy/xT7++GNIkoRdu3bJ2+bNmwdJktCuXTssXrwYP/74Y7ENNyUb6ByXbqiTwEGDBsmJ9s2bN5GUlARJkuDt7Y22bdvi1KlTePToEa5evar4XGIUPDM8KioKVlZW+Oabb7Bu3TrY2tpqrf4eFhaGypUr6/SxeXfu3EG1atWwYsUKoIQeOpVKhUmTJiErKwszZsxQdGqEWklTEdTn3J9//ol33nkHkiShfv36aNSoETp37gwnJydFR3CoqQs0aWlp8PLyKjICYNq0afD09MSlS5cUj6Vw8lO4GFH4caW6XNU/IyMD+/btK7JoWZcuXeQhx8HBwahZsya+//57OQm4cOGC4gtvqpVUlFAXANTzeu/fv48rV67g/v37ij1v/dKlS5AkCUZGRnB2dkZERAQ+++wzudAaFxeH4OBgtGzZUj7fcnNz8e233yq+8JiovevQKMilp6fDy8sLzZs3R6VKlTBq1Cj5Nbp6bK+mvXv3wtDQEGFhYRg0aBBsbW0xbtw4uXgzZswY+Pj46KQId/XqVVStWhUTJ06Ut40fPx41atTAxx9/XGwBID09HRMnTnyl9zAhIUEeiYdC67QEBwejfv362LZtm3zt//3337Czs8OgQYMUuc7Onj0rF0A0lVQAOHToENq2bYuAgAA8e/ZMyHYPY4yT/7fetGnTYGRkhOjoaCxZsgQ2NjZYu3Yt1q9fj/DwcLi4uKBevXoICAjAd999x3EJGteriIqKQu3ateWvR44cicqVK2PTpk3o1q0bTE1N0bNnT50u2HPt2jXMnDkTVlZWkCRJHm2gbtjv3bsXzs7OiveUaXr69Cnq1KkjPzoMxTSOT506he7du+ukSIIXnIqAgoLKgQMHMHLkSIwYMQKffvppmSdAhefOQ6Pxql6sTnMEwF9//YVZs2bBwsJCp4+l+1+jEVQqFXJycuTjqosnDqgT2fLly6NevXrYuHGjvFjk/v370aVLF/n9fffdd1G7dm2tXkAlvWhRokaNGvj+++919oSGq1evom7dunByckK/fv3QuXNn1KpVC7Vq1UKHDh2wZ88ezJkzB4MGDUJAQECRxRGVInLvupr6ukxPT4evry8cHR21po7oI2m7f/8+1qxZA39/f7Rq1QoxMTFykg0A27ZtQ+3atcv8aSTFyczMlNeQ0VxM8H8VAF7lEb5nz55FxYoV0bp1a63tmvf14goAp06dwsWLF196vyU5d+4cbG1t0bhxYzkGzb+vpALA4cOHdfb5xxh7OZz8v6U0P9CmTJkCSZJgaGhYZNX0CxcuIDo6Gh07dtRJosVx6Y7mo99atmyJL7/8EiEhIXB0dNR6fN13332n08eGqV25cgXz5s2DlZVVkRWxJ06ciGbNmmk1DpWkPlYbN26EJElYuHAhUMyjHCMjI9G5c2d5UTGl/a+pCOrzVunGfGpqKj7//HPcv39f3p9mglGrVi15mHV6ejoaN24MGxsbWFpaKjr8uiTFjUbQLACEhYXB2NhYXrRKaVevXoWbmxvq1q2LkJAQNG7cGF26dMGYMWOQkJCAypUrazWw27ZtCy8vL7moopSXKUp8/fXXik9z0hxu7eXlhT59+uDnn3/GkydPsHXrVgwfPhzu7u6oVq0azM3NIUkS2rdvr7PeSFF611+kIJeRkSEXwXS9eGRxcnJyiu3FHjduHN55550iT1dRmua9DCUUAF5VXFwczMzM4OvrC1NTU/mRmGqFRwD4+Pjgyy+/VKzQpo7Hw8MD/v7+mDNnjvy3ahas1AUADw8PzJ49W5FYGGNlj5P/t0hiYiKmT5+OjIyMIkOWFyxYAEmS5GeCQ4fVf45Lf9QNm2nTpsHCwgLu7u5yL7vSC0+hhMapOpl4/vw5bt26JY8AUM+lnDVrFiwtLXXaW6yWlZWF4cOHQ5IkzJgxQx5uffbsWUyePBk2NjbCTUUAgMmTJ2tNQSjLc/X69euwt7eXG8TqAgAKksfKlStjyJAhyM/Pl+NLS0tD+/bttYpMSijtaISDBw8iLCwMZmZmOl/c79KlS2jQoAEGDhyIzZs34/fff0fLli3RvXt3WFlZoVGjRlpz23XRuyZqUQIaCXZqaiq8vLzQpk0b/Pnnn/L309LSEBcXh0mTJqFnz544e/as4jFp0nfvemkKcmlpaWjcuDGaNGmi+AiJ/7rnq3uzNb937949hIeHw87OTtF7q3qfmvdQ9fEqvJ7G+PHjUbt2bSxatAh37959pf2eOXMGJiYm8lD/BQsWwMDA4D8LAG3btkXz5s0VedTumTNnIEmS/NjOSZMmoWHDhpgzZ478txYuAISFhaFJkya4c+fOa9kOYuxtw8n/WyI3NxdNmjSBJEmoXbs2Jk+ejB07dmi9ZuLEiTAyMsL27duL/LxSN3SOS3n/lQCpew7S09NRqVIlred0K+2/GqepqalwcXFBYmIirl27hsjISNja2qJ58+aKr9L9X8cLBcdq0qRJMDIyQsWKFVGxYkV4e3vDw8NDXmVfV150KkKPHj2KDNsuK+np6ahQoQJsbGzQtm1brFq1Sn5Pw8LCEBoaWuwTEZR+NvfLjEYwNzeHlZWVTtZC0KSZyDZo0ACBgYHyqIMjR45g8uTJ8uKRrzK0uDRELkqoFR7B0a5duyJPPsjPz1f0mInYu/4yBbmLFy+iZcuWiq698SLXZHx8vPz6VatWoWvXrnBxcVH03voiccXFxWklvSNGjICXl9crJf8qlQojR44s0mv+IgUAJa6zvLw8LFmyBHPmzNHaPmnSJDRq1KjEAkBmZiZu3LhR5vEwxpTByf9bZOnSpVi5ciUOHjyIqKgoVKhQAQMGDMDatWvlhktUVBTMzMx09gxdjktZL9KoUSc606ZNQ1BQkE4+xF+kcTp48GD5OF+7dg1TpkxBtWrVFO2RfdGCRG5uLhISErB69WrMnTsXBw8exJUrVxSLqzilmYrQpUuXV+6h+q8YvvjiC3Tv3h3du3dHgwYN8PHHHyMnJwe3bt1SPMkvjsijEUpSOJFt3bo1jh07ppdY1EQpSpR2BIeuhq+L2rv+sgU5Jd/DF70m1XHduXMHX331FWbPnq3ogpaljUvzfnb9+vVX3n9Ja+m8SAFACZqjdjT3VVwBQB/3dsbYq+Pk/y3y+++/o3z58nLj7erVq5g9ezbMzMzg5+eHzz//HMnJyViwYAHs7e0VGVLGcelOaRs1Bw4cgIGBgbzQkZJK2zhFwRoAt2/fViymFzlegwYNEu75xfqYilC4Ifzrr7+iffv2SEpKQnh4ONzd3bF69Wr5mtD18RJ1NAJKmcgWXotAH/RdlHiZBNvX11fxBFvU3vVXKcgpeZ2+zD1f6VEbrxJXWdB8H4r7O9UFgMJr3pS14p4So46nuALA/PnzdbbeDmOs7HHy/5aZPHkyBgwYIN/s+/Tpg7p162LQoEFo1aoVjIyMsHPnTp3f2DmusvcyjZqhQ4ciMTFR0bhK2zjVVeL4MsdLV0SaipCWloYNGzYUWQSye/fu8nPNR48ejfr16+Ojjz6SE1pdHTtRRyNA4EQWghYlRE2wIWDvusgFOVGvSX3EVVzxWL2PzMxMbNu2Tet7ixcvhiRJ2LRpU5nGoXb58mX06tVL63pWr8GQlZWFvXv3ahUHpk6dilq1amHJkiU6WReIMVb2OPl/y+zcuRP+/v7Iz8/HsGHD4ODgIC+ElJSUhFWrVul8YSSOq+y9bIKtZAOMG6cvR6SpCFeuXIG1tTUkSYKTkxM+/vhjHDx4EAAQHx+PoKAguSgwbNgwNGzYEIsWLdLJAnAin18QPJEVtSghWoKtJlrvuqgFOVGvSX3FlZycjLCwMHTv3h3Lly8HNEYRZGRkoEqVKhg7dmyRn1u5ciXOnz9fJjEUlpqaCn9/f7zzzjtaI3rS0tJgbW2NKVOmaMUJADNnzizzR8UyxnSHk/+3UKtWrWBgYIAqVaroZcX0knBcr07UxhY3Tl+OaFMRrl69ijZt2qBp06bo1q0bgoOD0aJFC/Tr1w8xMTGoW7cu5s2bJ7++f//+aNGihSLrDWgS9fzSJGoiK2pRQrQEG4LeL0QtyIl6Teorrri4OFSsWBHBwcHo27cvjIyMsGzZMgDAjRs3UL16dYwYMUIvo8tSUlIQGBiITp064a+//gIAODo6Yvjw4cXekxhjrzdO/t8i6pv4/v37UadOHezevVtrO8f1esRVElEbW9w4fXkiTkVIT09Hly5d0Lt3b3z00UdISkpC9+7dMXDgQBgaGsLJyQlXr16VX6/5/0oQ9fzSJGIiqyZaUULEBBsC3y9ELMiJek3qK674+HiYmZnJj/PLz89HWFgYxo8fj5ycHKSmpmLVqlWAHtsX6gJAYGAgNmzYgAMHDvCwfsbeUJz8v4WuX78OV1dXzJw5U9+haOG4Xp6ojS1w4/SliTIV4ebNmzhy5Ah++uknOSlMS0vDO++8g9atW2Pfvn1AQeNx3rx52Lp1K6DDXiIRzy81URPZwvGJUpQQNcEW/X4hWkFO1GtSH3FlZmbC3t5ePn/V+vTpAy8vL9SuXRvvvfceNm/e/Ep/W1lISUlBly5d0LlzZ60pAKJ2eDDGXg4n/2+prVu3wsLCAidPntR3KFo4rpcjamNLjRunL06khPHcuXNo0aIFunfvXuRZ1BcvXkRQUBBatmyJb775RrEYXoRo5xcETmQh2DmmJnKCLdr9QvSCHAS9JvURV3p6Opo0aYKuXbvi+PHjAIBFixbB3Nwc8+bNw4YNG+Du7o7atWsLMa0wOTlZngLwxx9/6DscxpgCOPl/S12+fBmtW7dGVlaWvkPRwnG9PJEaW9w4fTkiJYwJCQmws7PDrFmzkJGRIW//7bff5Lne6gJA69atsX379jKPoSSin18iJ7IinWOaREuwCxPlfiFqQU7Ua1KEuNRD6rt27Yrhw4ejUqVKWo/UvXTpEiRJwmeffVZm+3wVKSkpCAoKQtOmTXHixAl9h8MYK2Oc/L/FNB/fIhKO68WI0KgpDjdOX45ICeO1a9fQoEEDhIWFaW1funQprK2t0bdvXzmWixcvolu3bvDx8cF3331X5rEUJur5pUnURFakc6w4oiTYEPR+IWpBTtRrUqS4kpOT0aFDB5iZmckr/atUKuTm5uLy5cvw8vLCzp07FY/jRSUmJuK9995T/EkjjDHd4+SfsdeQSI0aTdw4fXkiJYx79+6Ft7c3EhMT5W3Lly+Hra0tQkNDERAQgJCQEDlRvHDhAvr27av1nitB1POrOCIlspr7EOUcg6AJNgS9X4hakBP1mhQxrosXL6Jjx47o3Lmz1mMyIyMjUbNmTWRmZioeQ2nk5OToOwTGmAI4+WfsNSNiowbcOC0ToiSMERERqF27tta2tWvXyotAbdiwAS1btkRwcDBu374N6OCRdKKeX2qiJrKFiXKOiZhgQ+D7hYgFOVGvSVHjQqHH6p0+fRpLliyBqakpTp8+rfi+GWMMnPwz9noRuVHDjdPSEzVhXLlyJaytrYvMC9c0fPhwdOzYUfGkX03E80tN1EQWgp5joibYIt8vRCzIiXpNihqXmnpOfaVKlWBkZIR///1XJ/tljDFw8s/Y60XkRg03TktHxIRR/Vzn3bt3w9zcHPPnz8e9e/e0vqf+b2hoKEaPHq2zoaEinl8QOJGFoOeYyAm2yPcLEQtyol6TosalKSkpCV27dsXZs2d1ul/GGOPkn7HXiMiNGm6cvjiREsbbt28jMTERSUlJWtt79+4Nc3NzrFmzBjdv3pS3P3v2DOHh4XBwcCjyM0oS8fwSOZEV6RzTJHKCLeL9QuSCnIjXpMhxFZabm6u3fTPG3l4GxBh7bVSqVIlu3rxJGRkZ8rbQ0FBq0aIFERENHz6c3Nzc6OnTp2RtbU1ERIaGhorGpFKpiIioZs2alJeXR9u3b6f79+9rfU/9X2NjY3JxcZG/VpqIx+v69es0YMAA6tevH82ZM4eqV69ORETLli2j7t27U3h4OGVkZJCLiwt99NFHZG1tTcuXL6edO3eWeSxnz56lDh06UPfu3cnd3Z1mzZpFV69eJSKir776itq2bUuTJk2iUaNG0e7du2nZsmU0atQo+uKLL+jAgQPk5uZW5jEVJvL59c8//5CBgQGNGTNG3rZixQpavHgxDRgwgK5du0azZs2S38/ly5eTm5sb+fr6KhqXSOdYYX/++Sc9efKE6tatK28zMzOjH3/8kdauXUsDBw6kjIwMmjBhAt25c4dcXV1p69at8t+gJFHuF3fu3KGkpCRKTk4mA4P/a6YFBwdTUFAQLVy4kLZt20a3bt2Sv5ebm0sRERG0a9cuGjduHBkbG5d5TJpEvSZFjaskRkZGets3Y+wtpu/qA2PsfxOt90f03mLRjpcmUXo+4+LiYGFhgalTp+K3335DVFQUypUrV2QI+NSpU+Hl5QVDQ0O4ublh0KBBOH/+fJnGUpjo55eaiD3FEOgcK46IvbIi3S8SEhLg4+ODunXrQpIkREZG4sqVKwCA7OxsBAUFwdjYGD169MAPP/yApUuXYvDgwbCzs1N00ThRr0lR42KMMVFx8s+YoERt1HDj9NWIkDCeP38eRkZGiIyMlLclJyejQoUK6Nu3b5HXP3jwABkZGcjLy0N2dnaZxlKYqOdXcURMZCHIOVaYSAk2BL1fiFqQE/WaFDUuxhgTGSf/jAlI1EYNN05fnQgJ48cffwxJkrBr1y5527x58yBJEtq1a4fFixfjxx9/REJCQpGfValUisQEgc+vwkRLZAsT4RyDoAk2BL1fiFqQE/WaFDUuxhgTHSf/jAlG1EYNN05fjWgJ47Rp02BkZITo6GgsWbIENjY2WLt2LdavX4/w8HC4uLigXr16CAgI0MkCdaKeX2qiJrKaRDrHREywIfD9QsSCnKjXpKhxMcbY64CTf8YEInKjhhunpSdiwqj5/PYpU6ZAkiQYGhri0KFDWq+7cOECoqOj0bFjR6SkpCgSiyYRzy81URNZCHqOiZpgi36/EK0gJ+o1KWpcjDH2OuDknzGBiN6o4cbpixMpYUxMTMT06dORkZEh9/yqLViwAJIkYefOnfI2fTWQRTu/IHAiC8HOMTWRE2xR7xeiFuQg6DUpclyMMSY6Tv4ZE4yIjRpunJaOSAljbm4umjRpAkmSULt2bUyePBk7duzQes3EiRNhZGRU7LPedVEIEPX8EjmRFekc0yRqgq0myv1C9IKcqNekqHExxtjrgpN/xgQhWqOGG6cvR8SEcenSpVi5ciUOHjyIqKgoVKhQAQMGDMDatWvl9y0qKgpmZmb48ssvFYmhMNHPLwicyIp4jmkSJcHWJNL9QtSCnKjXpKhxMcbY64iTf8b0SNRGDTdOX56ICePvv/+O8uXL459//gEAXL16FbNnz4aZmRn8/Pzw+eefIzk5GQsWLIC9vT0ePHigSBxqop5fxRExkRXxHINgCTYEv1+IVpAT9ZoUNS7GGHtdcfLPmJ6I3qjhxunLEzFhnDx5MgYMGIBnz54BAPr06YO6deti0KBBaNWqFYyMjLBz507cuXNHJ/GIdn4VJloiW5go55ioCbbo9wvRCnIQ+JoUNS7GGHsdcfLPmB6J3KjhxmnpiZww7ty5E/7+/sjPz8ewYcPg4OCAs2fPAgCSkpKwatUq+WtdEPH8EjWR1STSOSZ6gi36/UK0gpyI16TIcTHG2OuIk3/G9Ej0Rg03Tv+31yFhVGvVqhUMDAxQpUoVxMXF6S0ONZHOL5ETWZHPMZETbBHvF5pEK8hBsGvydYiLMcZeN5z8M6ZnIjdquHH630ROGIvbz/79+1GnTh3s3r1bp/sviWjnl4iJrOjnmOgJtkj3i+KIVpAT7ZoUPS7GGHvdcPLPmJ6J3qjhxul/EzFhLMn169fh6uqKmTNn6jUOTSKdX6ImsqKfYyIn2KLdL9RELchBsGtSk6hxMcbY64STf8YEIGKjhhunL0bUhLEkW7duhYWFBU6ePKnXOEQ9v0RMZEU/x0RNsNVEul8UJlJBTtRrUtS4GGPsdWRAjDG9AUBEROHh4eTq6kpr164lLy8vebs+SZJERESNGjUilUpFp06d0tquDyIer9atW9MHH3xAH330EWVnZ1PlypUpMTGRqlevTm5ubrRt2zby9PSkOnXqUHJyMpUvX15vsRIRtWnThpo0aUJVqlTRaxwinl9ERH5+fpSWlkbGxsY0fPhwOnz4MH3//fe0ZcsW+vzzz2np0qXk7u5Otra2OotJ9HPsvffeIyMjIzIyMqIDBw5QTEwMeXh4EBGRm5sbjR8/Xv5al0S8XxTm4OBAUVFRtGrVKvr777/1Gouo16SocTHG2GtJ39UHxphYvT/FEaW3WE204yV6z2dh6l5tUYh2fonYUyzqOfY69MqKdr8o7PLly2jdujWysrL0HYpMtGtSTdS4GGPsdcHJP2OCELlRw43T/03EhPF1Icr5JXoiK/I5JnqCLdr9ojDRCnKiXJOFiRoXY4y9LnjYP2OCEGU4dnGcnJzowIEDVLVqVX2HIhPleL0OQ4tFJ8r5Jerw4tfhHBNp+HpxRLlflMTU1FTfIWgR5ZosTNS4GGPsdcHJP2OCEL1Rw43T4omaML5uRDq/REtkX5dzTOQEW5T7xetEpGtSk6hxMcbY60CCSF0HjDH2Gtu2bRuNGjWKfvvtN/L19dV3OOwVXLlyhQYOHEhbt24VKmEU/RzLzs7m5IwxxhgTlKG+A2CMsTeFyD2frHTUPcWiJbKin2OiHS/GGGOM/X/c888YY2WIez6Z0vgcY4wxxtjL4OSfMcYYY4wxxhh7w/GCf4wxxhhjjDHG2BuOk3/GGGOMMcYYY+wNx8k/Y4wxxhhjjDH2huPknzHGGGOMMcYYe8Nx8s8YY4wxxhhjjL3hOPlnjDHGGGOMMcbecJz8M8YYYzoyZMgQCg4Olr9u3bo1jR8/XudxHD58mCRJovv375f4GkmSaM+ePS/8O2fPnk3e3t6vFFdGRgZJkkRxcXGv9HsYY4wxVhQn/4wxxt5qQ4YMIUmSSJIkMjY2JldXV5o7dy49f/5c8X3/8MMPNG/evBd67Ysk7IwxxhhjJTHUdwCMMcaYvgUGBtKmTZsoJyeHfv75ZxozZgwZGRnRtGnTirw2NzeXjI2Ny2S/tra2ZfJ7GGOMMcb+F+75Z4wx9tYzMTEhR0dHql69Oo0ePZrat29Pe/fuJdIYqr9gwQKqUqUKubm5ERFRVlYW9e7dm2xsbMjW1pa6detGGRkZ8u/Mz8+niRMnko2NDdnZ2dHUqVMJgNZ+Cw/7z8nJofDwcHJ2diYTExNydXWlL7/8kjIyMqhNmzZERFShQgWSJImGDBlCREQqlYoWLVpENWvWJDMzM/Ly8qLvv/9eaz8///wz1alTh8zMzKhNmzZacb6o8PBwqlOnDpmbm1OtWrUoMjKS8vLyirzus88+I2dnZzI3N6fevXvTgwcPtL7/xRdfkLu7O5mamlLdunXp008/LXUsjDHGGCs9Tv4ZY4yxQszMzCg3N1f++tChQ5ScnEy//PIL7du3j/Ly8qhTp05kZWVFx44doz/++IMsLS0pMDBQ/rkVK1bQ5s2baePGjXT8+HG6e/cu7d69+z/3O2jQIPrmm29o9erVlJiYSJ999hlZWlqSs7Mz7dq1i4iIkpOT6dq1a/Txxx8TEdGiRYvoq6++ovXr19O5c+dowoQJNHDgQDpy5AhRQZGiR48e9O6771JcXBwNHz6cIiIiSn1MrKysaPPmzXT+/Hn6+OOPacOGDbRq1Sqt11y8eJG+++47+umnnyg6OppiY2MpNDRU/v727dtp1qxZtGDBAkpMTKSFCxdSZGQkbdmypdTxMMYYY6yUwBhjjL3FBg8ejG7dugEAVCoVfvnlF5iYmGDy5Mny9x0cHJCTkyP/zNatW+Hm5gaVSiVvy8nJgZmZGWJiYgAAlStXxtKlS+Xv5+XloWrVqvK+ACAgIADjxo0DACQnJ4OI8MsvvxQb5++//w4iwr179+Rt2dnZMDc3x59//qn12mHDhqFfv34AgGnTpqFevXpa3w8PDy/yuwojIuzevbvE7y9btgyNGjWSv46KikK5cuVw+fJleduBAwdgYGCAa9euAQBcXFzw9ddfa/2eefPmwd/fHwCQnp4OIkJsbGyJ+2WMMcbYy+E5/4wxxt56+/btI0tLS8rLyyOVSkX9+/en2bNny9+vX7++1jz/+Ph4unjxIllZWWn9nuzsbEpNTaUHDx7QtWvXyM/PT/6eoaEhNW7cuMjQf7W4uDgqV64cBQQEvHDcFy9epKdPn1KHDh20tufm5pKPjw8RESUmJmrFQUTk7+//wvtQ27FjB61evZpSU1Pp8ePH9Pz5cypfvrzWa6pVq0ZOTk5a+1GpVJScnExWVlaUmppKw4YNoxEjRsivef78OVlbW5c6HsYYY4yVDif/jDHG3npt2rShdevWkbGxMVWpUoUMDbU/Hi0sLLS+fvz4MTVq1Ii2b99e5HdVrFjxpWIwMzMr9c88fvyYiIj279+vlXRTwToGZeXEiRM0YMAAmjNnDnXq1Imsra3p22+/pRUrVpQ61g0bNhQpRpQrV67MYmWMMcZY8Tj5Z4wx9tazsLAgV1fXF359w4YNaceOHVSpUqUivd9qlStXppMnT1KrVq2ICnq4T506RQ0bNiz29fXr1yeVSkVHjhyh9u3bF/m+euRBfn6+vK1evXpkYmJCmZmZJY4YcHd3lxcvVPvrr79e+G8lIvrzzz+pevXqNGPGDHnbpUuXirwuMzOTrl69SlWqVJH3Y2BgQG5ubuTg4EBVqlShtLQ0GjBgQKn2zxhjjLFXxwv+McYYY6U0YMAAsre3p27dutGxY8coPT2dDh8+TGPHjqXLly8TEdG4ceNo8eLFtGfPHkpKSqLQ0FC6f/9+ib+zRo0aNHjwYHr//fdpz5498u/87rvviIioevXqJEkS7du3j27dukWPHz8mKysrmjx5Mk2YMIG2bNlCqampdPr0aVqzZo28iN6oUaPowoULNGXKFEpOTqavv/6aNm/eXKq/t3bt2pSZmUnffvstpaam0urVq4tdvNDU1JQGDx5M8fHxdOzYMRo7diz17t2bHB0diYhozpw5tGjRIlq9ejWlpKRQQkICbdq0iVauXFmqeBhjjDFWepz8M8YYY6Vkbm5OR48epWrVqlGPHj3I3d2dhg0bRtnZ2fJIgEmTJlFISAgNHjyY/P39ycrKirp37/6fv3fdunX03nvvUWhoKNWtW5dGjBhBT548ISIiJycnmjNnDkVERJCDgwOFhYUREdG8efMoMjKSFi1aRO7u7hQYGEj79++nmjVrEhXMw9+1axft2bOHvLy8aP369bRw4cJS/b1du3alCRMmUFhYGHl7e9Off/5JkZGRRV7n6upKPXr0oC5dulDHjh2pQYMGWo/yGz58OH3xxRe0adMmql+/PgUEBNDmzZvlWBljjDGmHAklrTzEGGOMMcYYY4yxNwL3/DPGGGOMMcYYY284Tv4ZY4wxxhhjjLE3HCf/jDHGGGOMMcbYG46Tf8YYY4wxxhhj7A3HyT9jjDHGGGOMMfaG4+SfMcYYY4wxxhh7w3HyzxhjjDHGGGOMveE4+WeMMcYYY4wxxt5wnPwzxhhjjDHGGGNvOE7+GWOMMcYYY4yxNxwn/4wxxhhjjDHG2Bvu/wFw7/BV5k6FpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1100x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flat_true_srl = [t for seq in true_srl for t in seq]\n",
    "flat_pred_srl = [p for seq in pred_srl for p in seq]\n",
    "\n",
    "labels_srl = sorted(list(set(flat_true_srl + flat_pred_srl)))\n",
    "cm_srl = confusion_matrix(flat_true_srl, flat_pred_srl, labels=labels_srl)\n",
    "\n",
    "plt.figure(figsize=(11, 9))  \n",
    "\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_srl, display_labels=labels_srl)\n",
    "disp.plot(xticks_rotation=45, cmap=plt.cm.Blues, ax=plt.gca())\n",
    "plt.title(\"Confusion Matrix SRL\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}