TIF_E41211115_lstm-quiz-generator/NER_SRL/adjst_model_lstm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 548,
   "id": "263af9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle, tensorflow as tf, numpy as np\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import (\n",
    "    Input,\n",
    "    Embedding,\n",
    "    SpatialDropout1D,\n",
    "    Bidirectional,\n",
    "    LSTM,\n",
    "    TimeDistributed,\n",
    "    Dense,\n",
    ")\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.model_selection import train_test_split\n",
    "from collections import Counter\n",
    "from itertools import zip_longest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 549,
   "id": "4fc87f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total kalimat  628\n",
      "NER Label       Count      || SRL Label       Count     \n",
      "-------------------------------------------------------\n",
      "O               4165       || O               2146      \n",
      "B-TIME          189        || ARGM-TMP        1282      \n",
      "B-PER           313        || ARG0            939       \n",
      "B-LOC           551        || V               719       \n",
      "I-PER           226        || ARG1            1277      \n",
      "B-DATE          335        || ARGM-LOC        495       \n",
      "I-DATE          643        || ARG2            285       \n",
      "B-ETH           213        || ARGM-MOD        39        \n",
      "I-ETH           217        || ARGM-MNR        37        \n",
      "B-EVENT         60         || ARGM-NEG        6         \n",
      "I-EVENT         38         || ARGM-DIR        41        \n",
      "I-LOC           32         || ARGM-CAU        21        \n",
      "B-MISC          14         ||                           \n",
      "I-MISC          3          ||                           \n",
      "I-TIME          46         ||                           \n",
      "B-ORG           20         ||                           \n",
      "I-ORG           17         ||                           \n",
      "B-QUANT         46         ||                           \n",
      "B-MAT           97         ||                           \n",
      "B-UNIT          44         ||                           \n",
      "I-UNIT          1          ||                           \n",
      "I-MAT           16         ||                           \n",
      "I-QUANT         1          ||                           \n"
     ]
    }
   ],
   "source": [
    "data = []\n",
    "with open(\"../dataset/new_ner_srl.tsv\", encoding=\"utf-8\") as f:\n",
    "    tok, ner, srl = [], [], []\n",
    "    for line in f:\n",
    "        line = line.strip()\n",
    "        if not line:\n",
    "            if tok:\n",
    "                data.append({\"tokens\": tok, \"labels_ner\": ner, \"labels_srl\": srl})\n",
    "                tok, ner, srl = [], [], []\n",
    "        else:\n",
    "            t, n, s = line.split(\"\\t\")\n",
    "            tok.append(t.lower())\n",
    "            ner.append(n.strip())\n",
    "            srl.append(s.strip())\n",
    "\n",
    "print(\"total kalimat \", len(data))\n",
    "# ——————————————————\n",
    "sentences = [d[\"tokens\"] for d in data]\n",
    "labels_ner = [d[\"labels_ner\"] for d in data]\n",
    "labels_srl = [d[\"labels_srl\"] for d in data]\n",
    "\n",
    "ner_counter = Counter(label for seq in labels_ner for label in seq)\n",
    "\n",
    "srl_counter = Counter(label for seq in labels_srl for label in seq)\n",
    "\n",
    "# print(\"Total per label NER:\")\n",
    "# for label, count in ner_counter.items():\n",
    "#     print(f\"{label}: {count}\")\n",
    "\n",
    "# print(\"\\nTotal per label SRL:\")\n",
    "# for label, count in srl_counter.items():\n",
    "#     print(f\"{label}: {count}\")\n",
    "\n",
    "print(f\"{'NER Label':<15} {'Count':<10} || {'SRL Label':<15} {'Count':<10}\")\n",
    "print(\"-\" * 55)\n",
    "\n",
    "for (ner_label, ner_count), (srl_label, srl_count) in zip_longest(ner_counter.items(), srl_counter.items(), fillvalue=('', '')):\n",
    "    print(f\"{ner_label:<15} {ner_count:<10} || {srl_label:<15} {srl_count:<10}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "48553e6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "PAD_TOKEN = \"<PAD>\"\n",
    "words = sorted({w for s in sentences for w in s})\n",
    "\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",
    "ner_tags.insert(0, PAD_TOKEN)\n",
    "srl_tags.insert(0, PAD_TOKEN)\n",
    "\n",
    "word2idx = {w: i + 2 for i, w in enumerate(words)}\n",
    "word2idx[\"PAD\"] = 0\n",
    "word2idx[\"UNK\"] = 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": 551,
   "id": "096967e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = [[word2idx.get(w, 1) 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(map(len, X))\n",
    "pad_id = tag2idx_ner[PAD_TOKEN]\n",
    "\n",
    "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=0)\n",
    "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=pad_id)\n",
    "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=pad_id)\n",
    "\n",
    "mask = (y_ner != pad_id).astype(\"float32\")  # shape (N, L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 552,
   "id": "a26893cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "splits = train_test_split(\n",
    "    X, y_ner, y_srl, mask, test_size=0.2, random_state=42, shuffle=True\n",
    ")\n",
    "X_tr, X_te, ner_tr, ner_te, srl_tr, srl_te, m_tr, m_te = splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "1b4a1c61",
   "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_39\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_39\"\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",
       "│ tokens (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ embed (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │     <span style=\"color: #00af00; text-decoration-color: #00af00\">84,864</span> │ tokens[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ spatial_dropout1d_… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ embed[<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\">SpatialDropout1D</span>)  │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ not_equal_38        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ tokens[<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\">NotEqual</span>)          │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_76    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">66,048</span> │ spatial_dropout1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │ not_equal_38[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_77    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)   │     <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │ bidirectional_76… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)     │                   │            │ not_equal_38[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_74 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_77… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │ not_equal_38[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_75 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │ bidirectional_77… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │ not_equal_38[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ner_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>)    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,560</span> │ time_distributed… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │ not_equal_38[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ srl_output          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">34</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">845</span> │ time_distributed… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">TimeDistributed</span>)   │                   │            │ not_equal_38[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</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",
       "│ tokens (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ -                 │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ embed (\u001b[38;5;33mEmbedding\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │     \u001b[38;5;34m84,864\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ spatial_dropout1d_… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │          \u001b[38;5;34m0\u001b[0m │ embed[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
       "│ (\u001b[38;5;33mSpatialDropout1D\u001b[0m)  │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ not_equal_38        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m)        │          \u001b[38;5;34m0\u001b[0m │ tokens[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "│ (\u001b[38;5;33mNotEqual\u001b[0m)          │                   │            │                   │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_76    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m66,048\u001b[0m │ spatial_dropout1… │\n",
       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ bidirectional_77    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m128\u001b[0m)   │     \u001b[38;5;34m98,816\u001b[0m │ bidirectional_76… │\n",
       "│ (\u001b[38;5;33mBidirectional\u001b[0m)     │                   │            │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_74 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │      \u001b[38;5;34m8,256\u001b[0m │ bidirectional_77… │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ time_distributed_75 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m64\u001b[0m)    │      \u001b[38;5;34m8,256\u001b[0m │ bidirectional_77… │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ ner_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m24\u001b[0m)    │      \u001b[38;5;34m1,560\u001b[0m │ time_distributed… │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n",
       "│ srl_output          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m34\u001b[0m, \u001b[38;5;34m13\u001b[0m)    │        \u001b[38;5;34m845\u001b[0m │ time_distributed… │\n",
       "│ (\u001b[38;5;33mTimeDistributed\u001b[0m)   │                   │            │ not_equal_38[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\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\">268,645</span> (1.02 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m268,645\u001b[0m (1.02 MB)\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\">268,645</span> (1.02 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m268,645\u001b[0m (1.02 MB)\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"
    }
   ],
   "source": [
    "embed_dim = 64\n",
    "lstm_units = 64\n",
    "drop_embed = 0.45\n",
    "drop_lstm = 0.35\n",
    "\n",
    "inp = Input(shape=(maxlen,), name=\"tokens\")\n",
    "emb = Embedding(len(word2idx), embed_dim, mask_zero=True, name=\"embed\")(inp)\n",
    "emb = SpatialDropout1D(drop_embed)(emb)\n",
    "\n",
    "x = Bidirectional(\n",
    "    LSTM(\n",
    "        lstm_units,\n",
    "        return_sequences=True,\n",
    "        dropout=drop_lstm,\n",
    "        recurrent_dropout=drop_lstm,\n",
    "    )\n",
    ")(emb)\n",
    "x = Bidirectional(\n",
    "    LSTM(\n",
    "        lstm_units,\n",
    "        return_sequences=True,\n",
    "        dropout=drop_lstm,\n",
    "        recurrent_dropout=drop_lstm,\n",
    "    )\n",
    ")(x)\n",
    "\n",
    "ner_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
    "ner_out = TimeDistributed(\n",
    "    Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\"\n",
    ")(ner_head)\n",
    "\n",
    "srl_head = TimeDistributed(Dense(lstm_units, activation=\"relu\"))(x)\n",
    "srl_out = TimeDistributed(\n",
    "    Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\"\n",
    ")(srl_head)\n",
    "\n",
    "model = Model(inp, [ner_out, srl_out])\n",
    "\n",
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(3e-4),\n",
    "    loss={\n",
    "        \"ner_output\": \"sparse_categorical_crossentropy\",\n",
    "        \"srl_output\": \"sparse_categorical_crossentropy\",\n",
    "    },\n",
    "    metrics={\n",
    "        \"ner_output\": [\"sparse_categorical_accuracy\"],\n",
    "        \"srl_output\": [\"sparse_categorical_accuracy\"],\n",
    "    },\n",
    "    # sample_weight_mode=\"temporal\"\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "f41d6012",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 29ms/step - loss: 4.8275 - ner_output_loss: 2.6099 - ner_output_sparse_categorical_accuracy: 0.1894 - srl_output_loss: 2.2176 - srl_output_sparse_categorical_accuracy: 0.1207 - val_loss: 3.1748 - val_ner_output_loss: 1.5757 - val_ner_output_sparse_categorical_accuracy: 0.1998 - val_srl_output_loss: 1.5992 - val_srl_output_sparse_categorical_accuracy: 0.1438 - learning_rate: 3.0000e-04\n",
      "Epoch 2/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 25ms/step - loss: 3.1583 - ner_output_loss: 1.5923 - ner_output_sparse_categorical_accuracy: 0.1895 - srl_output_loss: 1.5659 - srl_output_sparse_categorical_accuracy: 0.1422 - val_loss: 3.0394 - val_ner_output_loss: 1.4981 - val_ner_output_sparse_categorical_accuracy: 0.2038 - val_srl_output_loss: 1.5413 - val_srl_output_sparse_categorical_accuracy: 0.1422 - learning_rate: 3.0000e-04\n",
      "Epoch 3/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 24ms/step - loss: 3.0438 - ner_output_loss: 1.4899 - ner_output_sparse_categorical_accuracy: 0.1944 - srl_output_loss: 1.5539 - srl_output_sparse_categorical_accuracy: 0.1501 - val_loss: 2.7795 - val_ner_output_loss: 1.3766 - val_ner_output_sparse_categorical_accuracy: 0.2124 - val_srl_output_loss: 1.4029 - val_srl_output_sparse_categorical_accuracy: 0.1790 - learning_rate: 3.0000e-04\n",
      "Epoch 4/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 24ms/step - loss: 2.6481 - ner_output_loss: 1.3084 - ner_output_sparse_categorical_accuracy: 0.2154 - srl_output_loss: 1.3398 - srl_output_sparse_categorical_accuracy: 0.1819 - val_loss: 2.2805 - val_ner_output_loss: 1.1081 - val_ner_output_sparse_categorical_accuracy: 0.2446 - val_srl_output_loss: 1.1724 - val_srl_output_sparse_categorical_accuracy: 0.2141 - learning_rate: 3.0000e-04\n",
      "Epoch 5/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 24ms/step - loss: 2.2634 - ner_output_loss: 1.1069 - ner_output_sparse_categorical_accuracy: 0.2370 - srl_output_loss: 1.1565 - srl_output_sparse_categorical_accuracy: 0.2011 - val_loss: 2.0012 - val_ner_output_loss: 0.9473 - val_ner_output_sparse_categorical_accuracy: 0.2631 - val_srl_output_loss: 1.0539 - val_srl_output_sparse_categorical_accuracy: 0.2206 - learning_rate: 3.0000e-04\n",
      "Epoch 6/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 25ms/step - loss: 1.9864 - ner_output_loss: 0.9526 - ner_output_sparse_categorical_accuracy: 0.2486 - srl_output_loss: 1.0338 - srl_output_sparse_categorical_accuracy: 0.2096 - val_loss: 1.8169 - val_ner_output_loss: 0.8461 - val_ner_output_sparse_categorical_accuracy: 0.2829 - val_srl_output_loss: 0.9708 - val_srl_output_sparse_categorical_accuracy: 0.2337 - learning_rate: 3.0000e-04\n",
      "Epoch 7/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 29ms/step - loss: 1.8446 - ner_output_loss: 0.8630 - ner_output_sparse_categorical_accuracy: 0.2612 - srl_output_loss: 0.9816 - srl_output_sparse_categorical_accuracy: 0.2187 - val_loss: 1.7110 - val_ner_output_loss: 0.7857 - val_ner_output_sparse_categorical_accuracy: 0.2834 - val_srl_output_loss: 0.9253 - val_srl_output_sparse_categorical_accuracy: 0.2430 - learning_rate: 3.0000e-04\n",
      "Epoch 8/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 30ms/step - loss: 1.7250 - ner_output_loss: 0.8011 - ner_output_sparse_categorical_accuracy: 0.2681 - srl_output_loss: 0.9239 - srl_output_sparse_categorical_accuracy: 0.2264 - val_loss: 1.5871 - val_ner_output_loss: 0.7190 - val_ner_output_sparse_categorical_accuracy: 0.2901 - val_srl_output_loss: 0.8681 - val_srl_output_sparse_categorical_accuracy: 0.2512 - learning_rate: 3.0000e-04\n",
      "Epoch 9/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 30ms/step - loss: 1.6804 - ner_output_loss: 0.7667 - ner_output_sparse_categorical_accuracy: 0.2711 - srl_output_loss: 0.9137 - srl_output_sparse_categorical_accuracy: 0.2291 - val_loss: 1.4840 - val_ner_output_loss: 0.6594 - val_ner_output_sparse_categorical_accuracy: 0.2937 - val_srl_output_loss: 0.8246 - val_srl_output_sparse_categorical_accuracy: 0.2558 - learning_rate: 3.0000e-04\n",
      "Epoch 10/10\n",
      "\u001b[1m251/251\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 29ms/step - loss: 1.3918 - ner_output_loss: 0.6476 - ner_output_sparse_categorical_accuracy: 0.2810 - srl_output_loss: 0.7442 - srl_output_sparse_categorical_accuracy: 0.2508 - val_loss: 1.3737 - val_ner_output_loss: 0.6048 - val_ner_output_sparse_categorical_accuracy: 0.2979 - val_srl_output_loss: 0.7690 - val_srl_output_sparse_categorical_accuracy: 0.2591 - learning_rate: 3.0000e-04\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True),\n",
    "    tf.keras.callbacks.ReduceLROnPlateau(patience=2, factor=0.5, min_lr=1e-5),\n",
    "]\n",
    "\n",
    "history = model.fit(\n",
    "    X_tr,\n",
    "    [ner_tr, srl_tr],  # y → LIST (pos 0 = ner_output, 1 = srl_output)\n",
    "    sample_weight=[m_tr, m_tr],  # sama‑persis urutan\n",
    "    validation_data=(X_te, [ner_te, srl_te], [m_te, m_te]),\n",
    "    \n",
    "    batch_size=2,\n",
    "    epochs=10,\n",
    "    callbacks=callbacks,\n",
    "    verbose=1,\n",
    ")\n",
    "\n",
    "\n",
    "# =========================\n",
    "# 7.  Save artefacts\n",
    "# =========================\n",
    "model.save(\"lstm_ner_srl_model.keras\")\n",
    "for fname, obj in [\n",
    "    (\"word2idx.pkl\", word2idx),\n",
    "    (\"tag2idx_ner.pkl\", tag2idx_ner),\n",
    "    (\"tag2idx_srl.pkl\", tag2idx_srl),\n",
    "]:\n",
    "    with open(fname, \"wb\") as f:\n",
    "        pickle.dump(obj, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "430794b9",
   "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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mW7duWFpaYmVlRYsWLQqd7rZhwwZ69uyJiYkJAHZ2drrP6ejoCIC9vb3unL29PcuXL2fgwIGYm5vz/vvvk5uby4QJE/Dy8sLV1ZUGDRrw2Wef5bnPf6fqde3alenTp/Pqq6/q7jlv3rw819ja2tKhQwc2bNhQpu+zKLKPUyXZGRrL/F/P5fnXE1drE94Z0JDevq6VGpsQQgghRHlJz86l4dxdeulLA8QlZ9B43u5itT/3bgBmRsX/9VelUvHBBx8watQopk+fTs2aNcsQ7b8iIiLYtWsXRkZGpe4jNTWVgIAA2rVrx4kTJ4iPj2fixIlMmzaNwMBAcnJyGDRoEJMmTeKHH34gKyuL48eP66YZjh49mmbNmrF8+XJUKhUhISEYGhoWeL+goCBGjRpVohjnzZvHokWL+PTTTzEwMECtVlOzZk02btyIsbEx//zzD8899xyurq4MHz68wH6+/fZbZs6cybFjxzh69Chjx46lQ4cO9OzZU9emdevWBAUFlSi+kpLEqRLcH3L+77953B9yXv50c0mehBBCCCGqgCeffJKmTZvyzjvv8M033xTY7r9JlYeHR56pY2fOnMHCwoLc3FwyMrT/cP7xxx+XOq7169eTkZHB2rVrMTc3B2DZsmUMGDCAxYsXY2hoSFJSEv3798fLywuABg0a6K6Piopi9uzZ1K9fHwAfH59C7xcZGYmbm1uJYhw1ahTjxo3Lc27+/Pmo1WqSk5Np3Lgxx44d48cffyw0cfLz8+Odd97Rxbls2TL27duXJ3Fyc3PTjaaVF0mcKliuWsP8X88VOOSsAOb/eo6eDV1QKfWzWZcQQgghRFVhaqji3LsBxWp7PDyRsWtOFNkucFwrWnvaFevepbF48WK6d+/OrFmzCmwTFBSEpaWl7vV/R2/q1avHtm3byMjIYN26dYSEhPDiiy+WKh6A8+fP06RJE13SBNChQwfUajUXL16kc+fOjB07loCAAHr27Im/vz/Dhw/H1VX7j/MzZ85k4sSJfPfdd7qpg/cTrIdJT0/XTdMrrpYtW+Y798UXX7B69WoiIyPJyMggKyuryAIYfn5+eV67uroSHx+f55ypqSlpaWkliq+kZI1TBTsenphvceODNEBsUgbHwwuvsiKEEEIIUR0pFArMjAyKdXTyccTV2oSC/ilZcW+pQycfx2L192A1vJLo3LkzAQEBzJkzp8A2np6eeHt76w4PD4887xsZGeHt7Y2vry+LFi1CpVIxf/78UsVTXGvWrOHo0aO0b9+ejRs3UrduXf766y+4N43u7Nmz9OvXj/3799OwYUO2bNlSYF8ODg7FKpLxoAeTOu6tk5o1axbjx4/n559/5uTJk4wbN46srKxC+/lvEqpQKFCr1XnOJSYm6tZalRdJnCpY/N2Ck6bStBNCCCGEeFSplAreGdAQ7iVJD7r/+p0BDStkls6iRYv49ddfOXr0qF76e+utt/jwww+JiYkp1fUNGjTg9OnTpKam6s4dPnwYpVJJvXr1dOeaNWvGnDlzOHLkCL6+vqxfv173Xt26dXn55ZfZvXs3gwcPZs2aNQXer1mzZpw7d65UsT4YX/v27Zk6dSp+fn54e3sTFhZWpj7vCw0NpVmzZnrpqyCSOFUwJ8viDXEWt50QQgghxKOst68ry59ujot13t+NXKxNKnRdeOPGjRk9ejRLly596Pvx8fHExcXlOQorwd2uXTv8/Pz44IMPCr1veno6ISEheY6wsDBGjx6NiYkJY8aMITQ0lD/++IMXX3yRZ555BmdnZ8LDw5kzZw5Hjx4lMjKS3bt3c/nyZRo0aEB6ejrTpk3jwIEDREZGcvjwYU6cOJFnDdR/BQQEcOjQoRJ8Y/n5+PgQHBzMrl27uHLlCnPnzuXEiaKnYhZHUFAQvXr10ktfBZE1ThWstacdrtYmxCVlPHSdk+LeXwTFmacrhBBCCPE46O3rSs+GLhwPTyT+bgZOltrflSp6Pfi7777Lxo0bH/reg6M89x09epS2bdsW2N/LL7/M2LFjee2113B3d39om0uXLuUbSenRowd79+5l165dzJgxg1atWmFmZsaQIUN0BSfMzMy4cOEC3377LQkJCbi6uvLCCy8wZcoUcnJySEhI4Nlnn+XGjRs4ODgwePDgQqcOjh49mldffZWLFy8+9LMWx5QpUzh16hQjR44EYOTIkTz//PPs2LGjVP3dd/ToUZKSkhg6dGiZ+imKQlPSgvbVXHJyMtbW1iQlJWFlZVUpMdyvqscDexDcpwCpqlcM2dnZbN++nb59+xZaOlMIfZDnTVQ0eeZERSuvZy4jI4Pw8HA8PT1LXFhAVD2zZ88mOTmZr7/+ukz93K+qZ2VllWevq9J66qmnaNKkCW+88cZD3y/sOSxJbiBT9SpBQUPOAG/2ayBJkxBCCCGEqHLefPNNPDw88hVmqExZWVk0btyYl19+udzvJVP1Ksl/h5x/OB7FX1cT+TvyNhM7VXZ0QgghhBBC5GVjY1PgqE5lMTIy4q233qqQe8mIUyVSKRW087LniaY1ePcJXxQK2BEax/nY5MoOTQghhBBCCPEASZyqiLrOlvRrrJ2it3Tf5coORwghhBBCCPEASZyqkOk9fHSjThfiZNRJCCGEEEKIqkISpyqkrrMlfWXUSQghhBBCiCpHEqcqZnp37ajT9jMy6iSEEEIIIURVIYlTFVPPxZK+98qRf77vSmWHI4QQQgghhJDEqWqa3sMHgN/PxHIx7m5lhyOEEEIIIcRjTxKnKqieiyV9G7sAsHS/rHUSQgghxGPsTjTEhBR83Imu7AgfaVlZWXh7e3PkyJFyu8eBAwdQKBTcuXOn2Ne8/vrrvPjii+UW08NI4lRF3R912i6jTkIIIYR4XN2JhmUtYEWXgo9lLcolebp58yZTp06lVq1aGBsb4+LiQkBAAIcPH9a1qV27NgqFAoVCgZmZGY0bN2bVqlV5+ilpUhAREYFCoSAkJETvn6k0vvrqKzw9PWnfvj2BgYG6z1vQERERUeJ7tG/fntjYWKytrYt9zaxZs/j222+5evVqie9XWpI4VVH1Xazo29gFjUZGnYQQQgjxmEpLgJzMwtvkZGrb6dmQIUM4deoU3377LZcuXWLbtm107dqVhIS893r33XeJjY0lNDSUp59+mkmTJrFjxw69x1MZNBoNy5YtY8KECQA89dRTxMbG6o527doxadKkPOfc3d1112dlZRXrPkZGRri4uKBQKIodm4ODAwEBASxfvrwUn6x0JHGqwh4cdbp0Q0adhBBCCPEI0GggK7V4R0568frMSS9efxpNsbq7c+cOQUFBLF68mG7duuHh4UHr1q2ZM2cOAwcOzNPW0tISFxcX6tSpw2uvvYadnR179uwpzTdTLJmZmUyfPh0nJydMTEzo2LEjJ06c0L1/+/ZtRo8ejaOjI6ampvj4+LBmzRq4l8hMmzYNV1dXTExM8PDwYOHChQXe6++//yYsLIx+/foBYGpqiouLi+4wMjLCzMxM9/r1119nyJAhvP/++7i5uVGvXj0AvvvuO1q2bKn7rkaPHs3Nmzd19/nvqFxgYCA2Njbs2rWLBg0aYGFhQe/evYmNjc0T34ABA9iwYYOev+GCGVTYnUSJ1Xexoo+vCztC41i67zLLRjWv7JCEEEIIIcomOw0+cNNvn6t7F6/dGzFgZF5kMwsLCywsLNi6dStt27bF2Ni4yGvUajVbtmzh9u3bGBkZFS+eUnj11VfZvHkz3377LR4eHixZsoSAgACuXLmCnZ0db7/9NufOnWPHjh04ODhw5coV0tO1CejSpUvZtm0bP/74I7Vq1SI6Opro6IKnOQYFBVG3bl0sLS2LHd++ffuwsrLKkzxmZ2fz3nvvUa9ePeLj45k5cybPP/88u3btKrCftLQ0PvzwQ7777juUSiVPP/00s2bN4vvvv9e1ad26NdeuXSMiIoLatWsXO8bSksSpipvew4cdoXH8fiaWGTfu4uNc/AdXCCGEEEKUnIGBAYGBgUyaNImvvvqK5s2b06VLF0aMGIGfn1+etq+99hpvvfUWmZmZ5OTkYGdnx8SJE8slrtTUVJYvX05gYCB9+vQBYOXKlezZs4dvvvmG2bNnExUVRbNmzWjZsiXcW4d1X1RUFD4+PnTs2BGFQoGHh0eh94uMjMTNrWRJrrm5OatWrcqTPI4fP173c506dfj0009p06YNKSkpWFlZPbSf7OxsvvrqK7y8vACYNm0a7777bp4292OLjIyUxElAA1crejdyYefZOJbuv8LnI5tVdkhCCCGEEKVnaKYd+SmOuH+KN5o0fie4+BXdztCsePe9t8apX79+BAUF8ddff7Fjxw6WLFnCqlWrGDt2rK7d7NmzGTt2LLGxscyePZvnn38eb2/vYt+nJMLCwsjOzqZDhw66c4aGhrRu3Zrz588DMHXqVIYMGcLJkyfp1asXgwYNon379gCMHTuWnj17Uq9ePXr37k3//v3p1atXgfdLT0/HxMSkRDE2btw434jb33//zbx58zh9+jS3b99GrVbDvUTO19f3of2YmZnpkiYAV1dX4uPj87QxNTWFe6NTFUHWOFUD99c6/fZPDJdlrZMQQgghqjOFQjtdrjiHgWnx+jQwLV5/JSg+AGBiYkLPnj15++23OXLkCGPHjuWdd97J08bBwQFvb286derEpk2bmD59OufOnSvRffSpT58+REZG8vLLLxMTE0OPHj2YNWsWAM2bNyc8PJz33nuP9PR0hg8fztChQwvsy8HBgdu3b5fo/ubmeadCpqamEhAQgJWVFd9//z0nTpxg8+bNUETxCENDwzyvFQoFmv+sUUtMTATA0dGxRDGWliRO1UBDNysCGjmj0cDn+69UdjhCCCGEEI+lhg0bkpqaWuD77u7uPPXUU8yZM6dc7u/l5YWRkVGekujZ2dmcOHGChg0b6s45OjoyZswY1q1bx6effsqKFSt071lZWfHUU0+xcuVKNm7cyObNm3UJyH81a9aMCxcu5EtYSuLChQskJCSwaNEiOnXqRP369fONHJVWaGgohoaGNGrUSC/9FUWm6lUT03v4sOvsDX79J4bpPbzxdpK1TkIIIYR4xJnZg4Fx4SXJDYy17fQoISGBYcOGMX78ePz8/LC0tCQ4OJglS5bwxBNPFHrtjBkz8PX1JTg4WLfOCODMmTN5iiwoFAqaNGlSYD8XL17Md65Ro0ZMnTqV2bNnY2dnR61atViyZAlpaWm6kuFz586lRYsWNGrUiMzMTH777TcaNGgAwMcff4yrqyvNmjVDqVSyadMmXFxcsLGxeWgM3bp1IyUlhbNnzxY4pa4otWrVwsjIiM8//5znnnuO0NBQ3n///VL19V9BQUF06tRJN2WvvFVq4rRw4UJ+/vlnLly4gKmpKe3bt2fx4sW60oUF2bRpE2+//TYRERH4+PiwePFi+vbtW2FxV4ZGbtYENHJm19kbLN13haWy1kkIIYQQjzobd5j2d+H7NJnZa9vpkYWFBW3atOGTTz7RrStyd3dn0qRJvPHGG4Ve27BhQ3r16sXcuXPZvn277nznzp3ztFOpVOTk5BTYz4gRI/Kdi46OZtGiRajVap555hnu3r1Ly5Yt2bVrF7a2tnBvT6Q5c+YQERGBqakpnTp10pXstrS0ZMmSJVy+fBmVSkWrVq3Yvn07SuXDJ6HZ29vz5JNP8v333xdatrwwjo6OBAYG8sYbb7B06VKaN2/OkiVLGDRoUKn6e9CGDRuYN29emfspLoWmLGNvZdS7d29GjBhBq1atyMnJ4Y033iA0NJRz587lmx9535EjR+jcuTMLFy6kf//+rF+/nsWLF3Py5MliZcLJyclYW1uTlJRUYBWPqupsTBL9lh5CoYA9L3fB28miskOqNNnZ2Wzfvp2+ffvmmwMrhL7J8yYqmjxzoqKV1zOXkZFBeHg4np6eJS4yIKqGf/75h549exIWFoaFhX5+91Sr1SQnJ2NlZVVg0laUHTt28Morr/DPP/9gYFD4WFBhz2FJcoNKXeO0c+dOxo4dS6NGjWjSpAmBgYFERUXx999/F3jNZ599Ru/evZk9ezYNGjTgvffeo3nz5ixbtqxCY68Mjdys6dXw/lqny5UdjhBCCCGEeMT5+fmxePFiwsPDKzuUPFJTU1mzZk2RSZM+Vak1TklJSQDY2dkV2Obo0aPMnDkzz7mAgAC2bt360PaZmZlkZv47LzY5ORnu/ctKdna2niKvOM938WT3uRtsOx3D1M6eeDkWvYnbo+j+n111/DMU1Y88b6KiyTMnKlp5PXPZ2dloNBrUarWuBLWofp599lm4N1KkD/cnvN1/Nkpj8ODBxY5JrVaj0WjIzs5GpVLlea8kz3yVSZzUajUvvfQSHTp0KHTKXVxcHM7OznnOOTs7ExcX99D2CxcuZP78+fnO7969GzOz4tfyr0oa2yo5c1vJm+uDeNbn8f5L6MFdqYUob/K8iYomz5yoaPp+5gwMDHBxcSElJaXQ0tPi8XT3bsVss5OVlUV6ejoHDx7Mt66sJHtAVZnE6YUXXiA0NJRDhw7ptd85c+bkGaFKTk7G3d2dXr16Vbs1Tvd5NE1m0PK/OJWg5INRnajzGI46ZWdns2fPHnr27Cnz/0W5k+dNVDR55kRFK69nLiMjg+joaCwsLGSNk9DRaDTcvXsXS0tLFCXcW6s0MjIyMDU1pXPnzg9d41RcVSJxmjZtGr/99hsHDx6kZs2ahbZ1cXHhxo0bec7duHEDFxeXh7Y3NjbG2Ng433lDQ8Nq+39GTT3s8W/gzN7zN/gqKIJPnmpa2SFVmur85yiqH3neREWTZ05UNH0/c7m5uSgUCpRKZamLAIhHz/3pdfefjfKmVCpRKBQPfb5L8rxX6hOs0WiYNm0aW7ZsYf/+/Xh6ehZ5Tbt27di3b1+ec3v27KFdu3blGGnV85K/DwC/hFwn7GZKZYcjhBBCCCHEI61SE6cXXniBdevWsX79eiwtLYmLiyMuLo709HRdm2effTbP7sszZsxg586dfPTRR1y4cIF58+YRHBzMtGnTKulTVA7fGtb4N3BCrYFl+69UdjhCCCGEEEI80io1cVq+fDlJSUl07doVV1dX3bFx40Zdm6ioKGJjY3Wv27dvz/r161mxYgVNmjThp59+YuvWraXezbg6m9GjLtwbdboqo05CCCGEEEKUm0pd41ScvXcPHDiQ79ywYcMYNmxYOUVVfTSuqR112ns+nmX7r/DxY7zWSQghhBBCiPIkq/SqufujTltl1EkIIYQQj7BcdS4n4k6w/ep2TsSdIFedW9khPXb27dtHgwYNyM3V33c/b948mjb99x//X3/9dV588UW99a9PkjhVc41rWtOj/r21Tn/IWichhBBCPHr2Ru4lYHMA43eN57Wg1xi/azwBmwPYG7m33O45duxYFAoFixYtynN+69ateUpoHzhwAIVC8dDj/j6j8+bN051TqVS4u7szefJkEhMTC43hv0lFZXv11Vd56623UKlUfPTRR9ja2pKRkZGvXVpaGlZWVixdurTE95g1axbffvstV69e1VPU+iOJ0yNgxr0Ke1tPXSf8VmplhyOEEEIIoTd7I/cy88BMbqTl3Y4mPi2emQdmlmvyZGJiwuLFi7l9+3aRbS9evEhsbGyew8nJSfd+o0aNiI2NJSoqijVr1rBz506mTp1abrHr26FDhwgLC2PIkCEAPPPMM6SmpvLzzz/na/vTTz+RlZXF008/XeL7ODg4EBAQwPLly/UStz5J4vQI8KtpQ/f6UmFPCCGEENVHWnZagUdmbibcm5636PgiNORfF6+5979FxxflmbZXUJ+l4e/vj4uLCwsXLiyyrZOTEy4uLnmOB/coMjAwwMXFhRo1auDv78+wYcPYs2dPqeK678yZM3Tv3h1TU1Ps7e2ZPHkyKSn/Lt04cOAArVu3xtzcHBsbGzp06EBkZCQAp0+fplu3blhaWmJlZUWLFi0IDg4u8F4bNmygZ8+eug1knZycGDBgAKtXr87XdvXq1QwaNAg7Oztee+016tati5mZGXXq1OHtt98mOzu70M81YMAANmzYUIZvpnxUiQ1wRdnN6OHD/gvxbA25zovdvantYF7ZIQkhhBBCFKjN+jYFvtepRie+9P+Sk/En8400/deNtBucjD9JK5dWAPTe3JvbmflHiM6MOVPiGFUqFR988AGjRo1i+vTp1KxZs8R9PExERAS7du3CyMio1H2kpqYSEBBAu3btOHHiBPHx8UycOJFp06YRGBhITk4OgwYNYtKkSfzwww9kZWVx/Phx3TTD0aNH06xZM5YvX45KpSIkJKTQzWCDgoIYNWpUnnMTJkygf//+REZG4uHhAcDVq1c5ePAgu3btAsDS0pLAwEDc3Nw4c+YMkyZNwsLCgilTphR4r9atW3Pt2jUiIiKoXbt2qb8jfZMRp0dEE3cbutVzJFet4XMZdRJCCCHEI+Bm2k29tiuNJ598kqZNm/LOO+8U2q5mzZpYWFjojkaNGuV5/8yZM1hYWGBqaoqnpydnz57ltddeK3Vc69evJyMjg7Vr1+Lr60v37t1ZtmwZ3333HTdu3CA5OZmkpCT69++Pl5cXDRo0YMyYMdSqVQvubfnj7+9P/fr18fHxYdiwYTRp0qTA+0VGRuLm5pbnXEBAAG5ubqxZs0Z3LjAwEHd3d3r06AHAW2+9Rfv27alduzYDBgxg1qxZbNq0qdDPdv8+90fHqgoZcXqEzPCvyx8Xb8qokxBCCCGqvGOjjhX4nkqpAsDRzLFYfT3YbueQnXqILq/FixfTvXt3Zs2aVWCboKAgLC0tda//O3pTr149tm3bRkZGBuvWrSMkJKRM1ePOnz9PkyZNMDf/9/e9Dh06oFaruXjxIp07d2bs2LEEBATQs2dP/P39GT58OK6urgDMnDmTiRMn8t133+mmDnp5eRV4v/T0dN00vftUKhVjxowhMDCQd955B41Gw7fffsu4ceN00xQ3btzI0qVLCQsLIyUlhZycHKysrAr9bKampnCvyERVIiNOj5CmD4w6SYU9IYQQQlRlZoZmBR7GKmMAmjs1x9nMGQWKh/ahQIGLmQvNnZoX2W9ZdO7cmYCAAObMmVNgG09PT7y9vXXH/alr9xkZGeHt7Y2vry+LFi1CpVIxf/78MsVVlDVr1nD06FHat2/Pxo0bqVu3Ln/99Rfcq9h39uxZ+vXrx/79+2nYsCFbtmwpsC8HB4eHFskYP348UVFR7N+/n3379hEdHc24ceMAOHr0KKNHj6Zv37789ttvnDp1ijfffJOsrKxC475fbdDRsXiJc0WRxOkRM8Nfu6/TllPXiUyQCntCCCGEqL5UShWvt34d7iVJD7r/+rXWr+lGqMrTokWL+PXXXzl69Khe+nvrrbf48MMPiYmJKdX1DRo04PTp06Sm/vv73uHDh1EqldSrV093rlmzZsyZM4cjR47g6+vL+vXrde/VrVuXl19+md27dzN48OA8U+7+q1mzZpw7dy7feS8vL7p06cLq1atZs2YN/v7+uqTxyJEjeHh48Oabb9KyZUt8fHyKNf0uNDQUQ0PDfNMdK5skTo+Ypu42dL0/6iRrnYQQQghRzfl7+PNx149xMnPKc97ZzJmPu36Mv4d/hcTRuHFjRo8eXeDeRPHx8cTFxeU5Cqse165dO/z8/Pjggw8KvW96ejohISF5jrCwMEaPHo2JiQljxowhNDSUP/74gxdffJFnnnkGZ2dnwsPDmTNnDkePHiUyMpLdu3dz+fJlGjRoQHp6OtOmTePAgQNERkZy+PBhTpw4QYMGDQqMIyAggEOHDj30vQkTJvDzzz+zZcsWJkyYoDvv4+NDVFQUGzZsICwsjKVLlxY6qnVfUFAQnTp10k3ZqyokcXoEzeih3dfpZxl1EkIIIcQjwN/Dn11DdrE6YDWLOy1mdcBqdg7ZWWFJ033vvvsuarX6oe/Vq1cPV1fXPMfff/9daH8vv/wyq1atIjo6usA2ly5dolmzZnmOKVOmYGZmxq5du0hMTKRVq1YMHTqUHj16sGzZMgDMzMy4cOECQ4YMoW7dukyePJkXXniBKVOmoFKpSEhI4Nlnn6Vu3boMHz6cPn36FDp1cPTo0Zw9e5aLFy/me2/IkCEYGxtjZmbGoEGDdOcHDhzIyy+/zLRp02jatClHjhzh7bffLvQ74V7p80mTJhXZrqIpNBpN/sL4j7Dk5GSsra1JSkoqcmFadTZm9XH+vHST4S1rsmRowRVSqqvs7Gy2b99O3759Cy2dKYQ+yPMmKpo8c6Kildczl5GRQXh4OJ6envkKC4jqZ/bs2SQnJ/P111+XqR+1Wk1ycjJWVlZ59roC2LFjB6+88gr//PMPBgb6qWNX2HNYktxARpweUTP8taNOm09eJyqhalUkEUIIIYQQ1c+bb76Jh4dHgaNu+pCamsqaNWv0ljTpkyROj6jmtWzpXPd+hb3LlR2OEEIIIYSo5mxsbHjjjTfyjRLp09ChQ2nTpuDNkSuTJE6PMN1aJxl1EkIIIYQQokyq3hjY4+BONKQlFPy+mT3YuJf5Ni08tKNOBy/d5Is/rrB4qF+Z+xRCCCGEEOJxJIlTRbsTDctaQE5mwW0MjGHa33pJnmb08OHgpZtsPnmNad29cbcr2wZwQgghhBBCPI5kql5FS0soPGkC7fuFjUiVQAsPWzr5OJCj1vDFH7KvkxBCCCGEEKUhidNj4KV7FfZ++vsa0Ymy1kkIIYQQQoiSksTpMdDCw0436vTlARl1EkIIIYQQoqQkcXpM3K+wtylYRp2EEEIIIYQoKUmcHhMta9vR0VtGnYQQQghRvWTHxJB+9myBR3ZMTGWH+EjLysrC29ubI0eO6K3PiIgIbG1tCQkJAeDcuXPUrFmT1NRUvd2jPEji9BiZ4f/vqNO12zLqJIQQQoiqLTsmhrDefYgYMrTAI6x3n3JJnm7evMnUqVOpVasWxsbGuLi4EBAQwOHDh3VtateujUKhQKFQYGZmRuPGjVm1alWefg4cOIBCoeDOnTvFum9ERAQKhUKXVFS2r776Ck9PT9q3b8+NGzcwNDRkw4YND207YcIEmjdvXuJ7NGzYkLZt2/Lxxx/rIeLyI4nTY6TVA6NOX/wRVtnhCCGEEEIUKuf2bTRZWYW20WRlkXP7tt7vPWTIEE6dOsW3337LpUuX2LZtG127diUhIW/l43fffZfY2FhCQ0N5+umnmTRpEjt27NB7PJVBo9GwbNkyJkyYAICzszP9+vVj9erV+dqmpqby448/6tqW1Lhx41i+fDk5OTlljru8SOJU0czstfs0FUWjKZfbz9BV2IuWUSchhBBCVBp1WlrBR2YRW7eUot+SuHPnDkFBQSxevJhu3brh4eFB69atmTNnDgMHDszT1tLSEhcXF+rUqcNrr72GnZ0de/bsKXX8RcnMzGT69Ok4OTlhYmJCx44dOXHihO7927dvM3r0aBwdHTE1NcXHx4c1a9bAvWl306ZNw9XVFRMTEzw8PFi4cGGB9/r7778JCwujX79+unMTJkxg3759REVF5Wm7adMmcnJyGD16NDt37qRjx47Y2Nhgb29P//79CQsr/B/te/bsSWJiIn/++WcZvp3yJRvgVjQbd+3mtg/bpynjDmx9HpKvw28zYOx2MLbQ6+1b1bajg7c9h68k8OWBMD54srFe+xdCCCGEKI6LzVsU+J55l87U+vrrUvV7pYc/uQ8ZgWpw4Xyx+7CwsMDCwoKtW7fStm1bjI2L/kdvtVrNli1buH37NkZGRiWOu7heffVVNm/ezLfffouHhwdLliwhICCAK1euYGdnx9tvv825c+fYsWMHDg4OXLlyhfT0dACWLl3Ktm3b+PHHH6lVqxbR0dFER0cXeK+goCDq1q2LpaWl7lzfvn1xdnYmMDCQuXPn6s6vWbOGwYMHY2NjQ2pqKjNnzsTPz4+UlBTmzp3Lk08+SUhICErlw8dtjIyMaNq0KUFBQfTo0UOv35m+yIhTZbBxB7em+Y86XWHMr9pRqdjT8NM4yNX/cOWMHnUB2BQczfU76XrvXwghhBCiOjMwMCAwMJBvv/0WGxsbOnTowBtvvME///yTr+1rr72GhYUFxsbGDB06FFtbWyZOnFgucaWmprJ8+XL+97//0adPHxo2bMjKlSsxNTXlm2++ASAqKopmzZrRsmVLateujb+/PwMGDNC95+PjQ8eOHfHw8KBjx46MHDmywPtFRkbi5uaW55xKpWLMmDEEBgaiuTdDKiwsjKCgIMaPHw/3pjkOHjwYb29vmjZtyurVqzlz5gznzp0r9PO5ubkRGRlZ5u+pvEjiVNXYe8HIjWBgApd3w/ZX9D5tr7WnHe297MnO1fDlH1JhTwghhBAVr97Jvws8ai5dWup+vfftfWifJTVkyBBiYmLYtm0bvXv35sCBAzRv3pzAwMA87WbPnk1ISAj79++nTZs2fPLJJ3h7e5c6/sKEhYWRnZ1Nhw4ddOcMDQ1p3bo1589rR9SmTp3Khg0baNq0Ka+++mqeanhjx44lJCSEevXqMX36dHbv3l3o/dLT0zExMcl3fvz48YSHh/PHH3/AvdGm2rVr0717dwAuX77MyJEjqVOnDlZWVtSuXRvuJW6FMTU1Ja2E0yorkiROVZF7KxiyClDA34FwSP8VRu7v6/SjjDoJIYQQohIozcwKPooxNa6k/ZaGiYkJPXv25O233+bIkSOMHTuWd955J08bBwcHvL296dSpE5s2bWL69OlFjqyUpz59+hAZGcnLL79MTEwMPXr0YNasWQA0b96c8PBw3nvvPdLT0xk+fDhDhw4tsC8HBwduP2Tao4+PD506dWLNmjWo1WrWrl3LuHHjUCgUAAwYMIDExERWrlzJsWPHOHbsGNxbY1WYxMREHB0dy/gNlB9JnKqqBgOgz2Ltz/vehX9+1Gv3berY066OjDoJIYQQQhRXw4YNC91ryN3dnaeeeoo5c+aUy/29vLwwMjLKUxI9OzubEydO0LBhQ905R0dHxowZw7p16/j0009ZsWKF7j0rKyueeuopVq5cycaNG9m8eTOJiYkPvV+zZs24cOGCbkregyZMmMDmzZvZvHkz169fZ+zYsQAkJCRw8eJF3nrrLXr06EGDBg0emnw9TGhoKM2aNSvRd1KRpDhEVdZmCtyJgqPLtEUjLF3As7Peup/h78PRFQn8GBzNC928cbMx1VvfQgghhBBlZWBri8LIqNCS5AojIwxsbfV634SEBIYNG8b48ePx8/PD0tKS4OBglixZwhNPPFHotTNmzMDX15fg4GBatmypO3/mzJk8RRYUCgVNmjQpsJ+LFy/mO9eoUSOmTp3K7NmzsbOzo1atWixZsoS0tDRdGfC5c+fSokULGjVqRGZmJr/99hsNGjQA4OOPP8bV1ZVmzZqhVCrZtGkTLi4u2NjYPDSGbt26kZKSwtmzZ/H19c3z3rBhw5g+fTpTpkyhV69euLu7A2Bra4u9vT0rVqzA1dWVqKgoXn/99UK/M+7tX3X9+nX8/f2LbFtZJHGq6nq+B0nRcO4X2PA0TNgFTg300nXbOva0rWPHX1cT+fLAFRYMkgp7QgghhKg6DN3c8Nq5o9B9mgxsbTH8TwGDsrKwsNCtV7q/rsjd3Z1JkybxxhtvFHptw4YN6dWrF3PnzmX79u2685075/3Hb5VKVeieRSNGjMh3Ljo6mkWLFqFWq3nmmWe4e/cuLVu2ZNeuXdjeSx6NjIyYM2cOERERmJqa0qlTJ92GtZaWlixZsoTLly+jUqlo1aoV27dvL7DSnb29PU8++STff/99vrLlZmZmjBgxghUrVuiKQgAolUo2bNjA9OnT8fX1pV69eixdupSuXbsW+r398MMP9OrVCw8Pj0LbVSaF5mFjb4+w5ORkrK2tSUpKwsrKqrLDKZ7sDFj7BET/BVY1YeJesHLVS9dHwxIYufIvjFRKDszuWm1GnbKzs9m+fTt9+/bF0NCwssMRjzh53kRFk2dOVLTyeuYyMjIIDw/H09PzoUUGRNX3zz//0LNnT8LCwrCw0M82OWq1muTkZKysrFAqlWRlZeHj48P69evzFL7Ql8Kew5LkBrLGqTowNIGRP4C9NyRfg/XDIPOuXrpu56UddcrKVbP8QOEbkwkhhBBCiMeLn58fixcvJjw8vNzuERUVxRtvvFEuSZM+SeJUXZjZweifwNwR4s7Aj2MgN1svXd/f12njiWhik6TCnhBCCCGE+NfYsWNp3Lj8lnR4e3szZcqUcutfXyRxqk7sPGHURjAwhbB98NvLetnjqZ2XPW08ZdRJCCGEEEKIgkjiVN3UaAHD1oBCCae+g4Mf6qXbl/y1o04bjsuokxBCCCGEEP8liVN1VK8P9Fmi/fmPBRDyQ5m7bOdlT2sZdRJCCCFEOXjMapGJKkZfz58kTtVV60nQYYb2523T4OqBMnf5kr8P3Bt1ikvKKHN/QgghhHi83a/Ql5aWVtmhiMdY1r19wFQqVZn6kX2cqrMe8yDpGoRuho3PwPid4Nyo1N21q2NP69p2HI9IZPmBK8x/wrcYVwkhhBBCPJxKpcLGxob4+Hi4t/ePQqGo7LBEJVOr1WRlZZGRkVHgHlL6vNfNmzcxMzPDwKBsqY8kTtWZUgmDlsPdOIg8DOuGavd4sq5Rqu4UCgUv+fswatUxfjgRzdSu3rhYy54LQgghhCg9FxcXAF3yJIRGoyE9PR1TU9MKSaSVSiW1atUq870kcaruDIxhxPfwTQDcugjrh8O4HWBSus1923n9O+r01Z9hzBtY+hEsIYQQQgiFQoGrqytOTk5kZ+tnKxVRvWVnZ3Pw4EE6d+5cIZt8GxkZ6WVkSxKnR4GpLYzeBN/0hBuh8OMz2j2fVCV/EBUKBTP8fRi96hjrj0cxtasXzlYy6iSEEEKIslGpVGVeYyIeDSqVipycHExMTCokcdIXKQ7xqLD10O7xZGiuLRSxbXqp93hq72VPq9q2ZOVIhT0hhBBCCCGQxOkR49YMhgWCQgWn18OBRaXqRqFQMKOHdl+n9cejuJEsFfaEEEIIIcTjTRKnR03dXtDvI+3Pfy6Ck9+VqpsO3va09JBRJyGEEEIIIajsxOngwYMMGDAANzc3FAoFW7duLfKa77//niZNmmBmZoarqyvjx48nISGhQuKtNlqOg06vaH/+dQZc2VviLrQV9rSjTj8cjyJeRp2EEEIIIcRjrFITp9TUVJo0acIXX3xRrPaHDx/m2WefZcKECZw9e5ZNmzZx/PhxJk2aVO6xVjvd3wa/p0CTCz+Ogdh/StxFB297WnjYkpmjZvmfMuokhBBCCCEeX5WaOPXp04cFCxbw5JNPFqv90aNHqV27NtOnT8fT05OOHTsyZcoUjh8/Xu6xVjsKBQxcBrU7QVYKfD8M7kSXsAvtvk4A64/JqJMQQgghhHh8Vaty5O3ateONN95g+/bt9OnTh/j4eH766Sf69u1b4DWZmZlkZmbqXicnJ8O9+vGP/l4CChgSiMHafihuXkDz/VBynv0dTKyL3UMbD2ua17LhZNQdvvzjMm/2rV+uERfX/T+7R//PUFQF8ryJiibPnKho8syJilSVnreSxKDQaEpZs1rPFAoFW7ZsYdCgQYW227RpE+PHjycjI4OcnBwGDBjA5s2bC6wBP2/ePObPn5/v/Pr16zEzM9Nb/FWZadYtOl16D9Ps29y0aMBRr9lolMXPmS/cUbD8vApDhYa3m+dibVSu4QohhBBCCFEh0tLSGDVqFElJSVhZWRXatlolTufOncPf35+XX36ZgIAAYmNjmT17Nq1ateKbb7556DUPG3Fyd3fn1q1bRX45j5S4Mxh81x9FVipq32HkDvxSO52vGDQaDU+tPM6p6CTGtffgjT71yj3comRnZ7Nnzx569uxZrTZOE9WTPG+ioskzJyqaPHOiIlWl5y05ORkHB4diJU7VaqrewoUL6dChA7NnzwbAz88Pc3NzOnXqxIIFC3B1dc13jbGxMcbGxvnOGxoaVvofVIVybw7D18L3w1GGbkJp6wE93i725S/1rMeY1cdZfzyaqd28cbI0Kddwi+ux+3MUlUqeN1HR5JkTFU2eOVGRqsLzVpL7V6t9nNLS0lAq84asUqng3qiIKIK3Pwz4TPtz0IcQvKbYl3b2caBZLRsyc9R8/efV8otRCCGEEEKIKqhSE6eUlBRCQkIICQkBIDw8nJCQEKKiogCYM2cOzz77rK79gAED+Pnnn1m+fDlXr17l8OHDTJ8+ndatW+Pm5lZpn6Naaf4MdHlN+/Pvr8Cl3cW67MF9nb4/Fkn8XamwJ4QQQgghHh+VmjgFBwfTrFkzmjVrBsDMmTNp1qwZc+fOBSA2NlaXRAGMHTuWjz/+mGXLluHr68uwYcOoV68eP//8c6V9hmqp6xxoMkq7x9OmsRBzqliXdfZxoKm7DRnZalbIqJMQQgghhHiMVOoap65duxY6xS4wMDDfuRdffJEXX3yxnCN7xCkU2il7d2Pg6gFY/xRM2AO2HkVcpt3XaeyaE6w7FsmULl44WuZfPyaEEEIIIcSjplqtcRJ6ZGAEw78DZ19IuaHdIDf9dpGXdanrSJP7o04HwyokVCGEEEIIISqbJE6PMxMrGPUjWLrBrYuwYTTkZBZ6yf1RJ4Dv/ork5t3C2wshhBBCCPEokMTpcWddA57+CYytIPIwbJ0KanWhl3R9YNRpZZCsdRJCCCGEEI8+SZwEODeCp74DpQGEboZ98wttrlAoeKmHdtRp7dEIbqXIqJMQQgghhHi0SeIktOp0hYHLtD8f/hROrCq0edd6jjSpaa0ddTooo05CCCGEEOLRJomT+FfTkdDtTe3P22fDxR0FNn1wX6e1RyNl1EkIIYQQQjzSJHESeXWeDc2eAY0afhoP1/8usGnXeo741bQmPTtXRp2EEEIIIcQjTRInkZdCAf0/Aa8ekJ2m3eMpMbyApv9W2Ft7NJIEGXUSQgghhBCPKEmcRH4qQxj+Lbg0htSb8P1QSEt8aNNu9Zx0o04rpMKeEEIIIYR4REniJB7O2BJGbQJrd0i4Aj+MhOyMfM0UCgUz7lfYOyKjTkIIIYQQ4tEkiZMomJUrjN4ExtYQ/RdsmfLQPZ6613eicY17a52CHj6tTwghhBBCiOpMEidROKcGMGIdKA3h3FbY83a+JnlGnY5GkJiaVQmBCiGEEEIIUX4kcRJF8+wMg5Zrfz66DI59na9JjwZO+NawIi0rl5Wy1kkIIYQQQjxiJHESxeM3DHrM1f684zU4/1uet7WjTtp9nb49IqNOQgghhBDi0SKJkyi+jjOhxThAA5snQPSJPG/7N3CikZuMOgkhhBBCiEePJE6i+BQK6Psh+ARATgb88BQkhD3wtoKX/LWjTmtl1EkIIYQQQjxCJHESJaMygKGrwbUppCVo93hKvaV7+/6oU2pWLqtk1EkIIYQQQjwiJHESJWdsAaN+BJtakHgVfhgB2enwnwp73x6J4LaMOgkhhBBCiEeAJE6idCydYfRPYGID107A5omgzgWgZ0NnGrreG3U6JKNOQgghhBCi+pPESZSeYz0Y+QOojODCb7DrTbg/6uSvHXUKPCyjTkIIIYQQovqTxEmUjUd7ePIr7c/HlsPRLwDo9cCo0zeHwis3RiGEEEIIIcpIEidRdr5DoOe72p93vQlnt6JQKJh+b61ToKx1EkIIIYQQ1ZwkTkI/2k+HVpO0ezz9PBmijtGroTMNXK1IycyRUSchhBBCCFGtSeIk9EOhgD6LoV5fyM2EH0agTAzTVdgLPBLBnTQZdRJCCCGEENWTJE5Cf5QqGPIN1GgB6Ynw/RB6eSip72Ipo05CCCGEEKJak8RJ6JeRGYzcCLa14XYEyg0jeKVrDQDWHJZRJyGEEEIIUT1J4iT0z8IRRm8GU1u4/jf+596kobMZKZk5rJZRJyGEEEIIUQ1J4iTKh4M3jNwAKmMUl3aw3P5HQMOawxEkpWVXdnRCCCGEEEKUiCROovzUagtDVgIKPK6u502bvdzNzOGbwzLqJIQQQgghqhdJnET5avgEBLwPwKSMNfRXHmXNoXAZdRJCCCGEENWKJE6i/LV7AdpMBeBjo+XUzwqVUSchhBBCCFGtSOIkKkbA+1C/P0bksNLoIw4cDiIpXUadhBBCCCFE9SCJk6gYShUMWYWmRitsFKl8oV7Ihv0nKjsqIYQQQgghikUSJ1FxDE1RjNpAqrkH7sqbdDzxAklJtys7KiGEEEIIIYokiZOoWOYOmI7bwh2FFY24SkLg05CbU9lRCSGEEEIIUShJnESFUzp4Edrla9I1RtS5fYjMX2eCRlPZYQkhhBBCCFEgSZxEpWjfuQ+LzGah1igwDvkWDn1S2SEJIYQQQghRIEmcRKVQKhW07P0M83Oe1Z7YNx/+2VTZYQkhhBBCCPFQkjiJStO3sStH7IewIqef9sTWqRB+sLLDEkIIIYQQIh9JnESlUSkVTO/hw8KckeyiLaizYcPTEH++skMTQgghhBAiD0mcRKXq29gVLycrpmc8R4xVE8hMgnVDITm2skMTQgghhBBCRxInUanujzplYsRTydPJtfOG5Guwfhhk3q3s8IQQQgghhABJnERV0K+xK16O5kRnmLKuzodg7ghxZ+DHMZCbXdnhCSGEEEIIIYmTqHz3R50APv47m9Sh34OhGYTtg99elj2ehBBCCCFEpTOo7AAeR9kxMeTcvl3g+wa2thi6uVVoTJWtv58bS/ddJuxmKmvC7Zg2dDVsGAWnvgMbD+gyu7JDFEIIIYQQj7FKHXE6ePAgAwYMwM3NDYVCwdatW4u8JjMzkzfffBMPDw+MjY2pXbs2q1evrpB49SE7Joaw3n2IGDK0wCOsdx+yY2IqO9QK9eCo08qgcO56+EPf/2nf/GMBhPxQuQEKIYQQQojHWqUmTqmpqTRp0oQvvvii2NcMHz6cffv28c0333Dx4kV++OEH6tWrV65x6lPO7dtosrIKbaPJyip0ROpR1d/PjTqO5iSlZ/PtkQhoNRE6vKR9c9s0uHqgskMUQgghhBCPqUqdqtenTx/69OlT7PY7d+7kzz//5OrVq9jZ2QFQu3btcoxQVCSVUsH07j68tDGEVYfCGdO+NpY93oGkaAjdDBtGw8DPwa4O5ORgnRYBsafB4N5jbGYPNu6V/TGEEEIIIcQjqFqtcdq2bRstW7ZkyZIlfPfdd5ibmzNw4EDee+89TE1NH3pNZmYmmZmZutfJyckAZGdnk51d8RXbcnJyit2uMuKrbL0bOuJpb0Z4QhprDl1lapc60G8pqsQIlDF/w0/jADAEugJc/PdajcqYnKnHwLpmpcUvHk33/1t8HP+bFJVDnjlR0eSZExWpKj1vJYmhWiVOV69e5dChQ5iYmLBlyxZu3brF888/T0JCAmvWrHnoNQsXLmT+/Pn5zu/evRszM7MKiDov4+vX8ShGu6vPTSW9jie3O3chy8W5AiKrOjraKghPUPHVgcs4J1/ARAX2Zr3oyN+FXqfIzeTwnm0kmckopCgfe/bsqewQxGNGnjlR0eSZExWpKjxvaWlpxW6r0GiqRq1nhULBli1bGDRoUIFtevXqRVBQEHFxcVhbWwPw888/M3ToUFJTUx866vSwESd3d3du3bqFlZVVOX2agmWcO8e1p0YUu33NjRswadgQgJR9+0k7egQT38YY+zbCyNMThUpVjtFWjly1hj5LDxOekMYr/t4816UOxJ7GcHWPIq/NHr8PXJtUSJzi8ZGdnc2ePXvo2bMnhoaGlR2OeAzIMycqmjxzoiJVpectOTkZBwcHkpKSiswNqtWIk6urKzVq1NAlTQANGjRAo9Fw7do1fHx88l1jbGyMsbFxvvOGhoaV8geVY1C8r9xpzuuok5KxaNgQxb0404MOkrz5Z5I3/giA0swMk4YNMfHzw7SxLxZdu6IsYMpidWIITPf34eWNp/nmSCTjOnlhUczvzdDAAOQvfFFOKuvvDfH4kmdOVDR55kRFqgrPW0nuX602wO3QoQMxMTGkpKTozl26dAmlUknNmo/Wuhazli1xnP6iLmkCsOrTF7tx4zBr2RKFmRnqtDTSgoNJXL2a6y/PRPPAHM2UQ4e5e+AAOQkJlfQJymaAnxt1HMy5k5bN2qMRxb/w8m5Iji3P0IQQQgghxGOoUkecUlJSuHLliu51eHg4ISEh2NnZUatWLebMmcP169dZu3YtAKNGjeK9995j3LhxzJ8/n1u3bjF79mzGjx9fYHGIqsbA1haNoQGK7IKLRGgMDTCwtc133qJjByw6dtC2yc0lMyyMjDOhpIeeITchEdUDw4sJX39N2okT2nu6uWLq2xhTv8aY+DbGxLcRKguLcvl8+mKgUjKtuzczfzzNyoNXGetpT7FWpP3xvvZwqAuencGzC9TuCGZ25R+0EEIIIYR4ZFVq4hQcHEy3bt10r2fOnAnAmDFjCAwMJDY2lqioKN37FhYW7NmzhxdffJGWLVtib2/P8OHDWbBgQaXEXxpKF2femW5PZuKth76vQIGxnQM/FFEQQqFSYVK3LiZ162IzZHC+9419fMhJTCTr6lVyYmK5GxPL3d27AVDZ2+NzKAiFQgFAVkQEBq6uKB8ypbEyDWzixuf7rxB+K5Xf/olheHEucqgLty7DrUva48QqQAGufvcSqa5Qqy0YV+3EUQghhBBCVC2Vmjh17dqVwmpTBAYG5jtXv379KlGBo7ROxp/kglECuCgKaXWLk/EnaeXSqtT3cZn7NgC5KSlknD1Hxpl/SD8TSsaZMxh5e+mSJoDIsePIuXVLm4g1boxpY19MGvth7FUHRTHXFpUHA5WSad28eWXTabacvF68xGnwSrD1gIjDEP4nhB+Emxe0+z3FnoYjn4PSAGq2updIddb+bFC1kkYhhBBCCFG1VKviEI+Cm2k3i9Xu9YOv09uzN880fAYXc5dS309lYYF5m9aYt2mtO6fOytL9nJucjCYrC3JyyDh3joxz57izcSMAClNTrJ8YiOu8ebr2Go0mT9JV3p5o6sbn+y8TmWBKjpkRBuqsghsbGGs3wTW1hQb9tQfA3TgID4LwA3D1ICRFQdRR7fHnYjAw1Y5C1emiTaRcm4Ly0atWKIQQQgghSk8SpwrmaOZYrHbx6fGsPbeWZxo+ozt3PuE8xipjPK09y5S8KI2MdD+rrKzwOXyInJgY0s+Ekn7mHzLOhJJx9izq1FQUBv8Wp1CnpXGlZy9MGjTApLEvpo0bY+Lri6GTU6ljKYqBSsmL3X14ZVMaA/mMzePqYWqoIjsnh8OHD9OhQwdtJT3QJk027vk7sXQBv2HaAyAxXDsSdX9EKvUmXP1DewAYW2vXRd1PpBzrQwUmi0IIIYQQouqRxKmCNXdqjrOZM/Fp8WjIP01RgQJHM0deafkKV25fyTPa9Nmpzzh8/TCu5q60d2tPhxodaOPaBiujsu1HpVAoMKxRA8MaNbDqHQCARq0mKzwcxQNJVsb58+QmJJB66BCphw7pzhu4uGDa2BfrJwdj2b3bQ+9RFvdHnc4lwLcRNjzXxQuys0kyu67ds6mkZSztPLVHizGg0UD8+X8TqYhDkJkEF3/XHgDmTtoE6n4iZSsb7AohhBBCPG4kcapgKqWK11u/zswDM1GgyJM8KdCOasxpPQd/D3/w/Pc6jUaDkdIII6URsamxbL68mc2XN6NSqPBz9KOre1fG+47XW5wKpRJjL68850wbN6b2pk1khJ65t17qHzKvhJETF8fduDjMWv27JisrMpKby77Qjko19sWkQQOUJialikVbYc+HWZtOs+LgVZ5p64GRvgrpKxTg3FB7tH0OcnMg7jRcvTcaFfUXpMZD6E/aA8DG49+KfZ6dwbLwQh5CCCGEEKL6k8SpEvh7+PNx149ZdHwRN9Ju6M47mznzWuvXtEnTfygUCpZ2X0p6TjrBccEcjjnM4euHiUiO4FT8KYxURnkSp31R+/Bz8Cv21MDiUBgZYdrYF9PGvtiO1J5Tp6aSce4c6WdCMe/YUdc27eQpkn/9leRff9WeMDDA2MdHl0hZdO6CoXPxp/gNaurGum3HuHv9Glt/VDPQzwXj69fJOHdOt6mwga0thm5uZfuQKgOo0UJ7dJoJOZlw7cS9ROpPuP433ImEU99pD9BO5bufRNXuCKY2ZYtBCCGEEEJUOQpNYWXtHkHJyclYW1uTlJSElVXZpriVVa46l5PxJ7mZdhNHM0eaOzVHVcKiBNdTrnP4+mEcTB3oXqs7ALfSb9HtR+2Uubq2deng1oEONTrQzKkZRiqjInrUj4xLl7i7Z8+9faZCyb2Vt/x6zeVfYnmvFH3GxUtkXrqEaWNfDD08Hrp+KzsmhksBvVE+sMnvfymMjPDauaPsyVNhMu9qR6GuHtCOSMWdgQenXCqU2umD9xOpWu3AqFg7UIkqLDs7m+3bt9O3b99K3+FcPB7kmRMVTZ45UZGq0vNWktxARpwqkUqpKlPJcYAaFjUYXi9voe5b6bdoZN+IcwnnuHT7EpduX2LN2TWYGpjSyqUVoxuMpr1b+zJGX7j7e0xxb5phTmysdnrfvWl+po0b69om79xBwvKvAFBaWWHq2wiTxn73yqI3xtDZmZzbtwtNmgA0WVnk3L5dvomTsSX49NQeAGmJEBH079S+hMsQc0p7HP4UlIbg3vrfRKpGCzComORVCCGEEELojyROj6D6dvXZ0H8DiRmJHI05ypGYIxy+fpiEjAQOXjtI79q9dW1jUmK4kHiBNq5tMDc0L5d4FAoFhm5uGLq5YRXQK9/7hs7OmDTxI/P8BdTJyaQeOUrqkaO69+ts314ucemFmR00fEJ7ACRdfyCR+hOSr0PkYe1x4AMwNAePdv8mUi5+oNTXgi0hhBBCCFFeJHF6hNmZ2NGvTj/61emHRqPh0u1LHLp+KM9o086InXzy9ycYKAxo6tSUDjU60N6tPfXt6qNUVMwv9LYjRmA7YgSarCwyLl2+Nyp1howzoWTfuIFRbQ/Szp0vVl/qyp54al0DmozQHhoNJF7VJlBX/9QmVGkJcGWv9gDtnlO1O95LpLqAg4+UPhdCCCGEqIIkcXpMKBQK6tnVo55dvTznTQ1MqWVZi6i7UQTfCCb4RjCfnfwMOxM72ru155WWr+Bg6lAxMRoZYerbCFPfRtiOGAH3NutVKJWcvZ6EZTH6OHs9ida+5R5q8SgUYO+lPVqOB7Ua4s/9u39UxGFIvw3nf9UeAJau9yr23ava97B9qYQQQgghRIWTxOkxN7L+SEbWH0l0crS2Ul/MYY7FHiMxI5G9kXuZ136eru3BawcxMzCjiVMTDJUVs5Dv/ma9iWlZxUqcvvzzCidNnOlS15H6LpZl2ihY75RKcPHVHu1egNxsiAmB8HuFJqKOwd1Y+Gej9gCw9fx3/6jancFCf1UShRBCCCFE8UniJABwt3JnhNUIRtQfQXZuNiE3Q7h29xrGKmNdm4+DPyYsKQxzQ3Nau7Smg1sH2tdoj7tl+Y+K2JkVr6BCdGI6B3ZcYNGOC7hYmdClriNd6znSwccBK5MqViVIZQjurbRH59mQnQ7Rx/8dkbp+Em6Hw9/h8Heg9hqnRv8mUh4dwKRyK0MKIYQQQjwuJHES+RiqDGnl0ipPxb/s3Gzq2dUjMSOR25m3+SP6D/6I/gMADysP+tfpz3NNniu3mBrVsCaqGO0md67Dzkxr1EF/4BdygY1x3dkYbIdKqaBFLVu61NMmUg1drarWaBSAoak2KarTRfs6Ixkij/ybSN0Ihfiz2uOvL0GhArdm/yZS7m20ffzXnWjt2qqCmNnLlEAhhBBCiCJI4iSKxVBlyOLOi1Fr1JxPPM/h69oNeP+5+Q+RyZHEpMTo2uaqc1l3fh1tXdtS17auXhIUIztb1IZGKLOzCmyjNjRicNdGDHd1JeznBWRHXKZ31AmO+rRlZa3OHI/QcDwikf/tuoijpbFuNKqTtyPWZlVsNAq0o0n1emsPgNRb2gQq/KA2mUq8CteDtUfQR6Ay/rf0eZ0u2qTqbhwsa6HdyLcgBsYw7W9JnoQQQgghCiGJkygRpUJJI/tGNLJvxGS/yaRkpXAs7hguZi66NucTz/Nh8IcAOJo60t6tPR1qdKCdaztsTGxKdV9DNzfq7tpBUPBlvj4Yzq2UfxMBBwtjpnT2pFNLH90eTm7z3uHWF1+QeuQoHS4epsOVv7jZ3p9fGvVke4KKm3cz+enva/z09zWUCmhey/ZeIuVEIzcrlMoqNhoFYO4AvoO1B/dGkh5MpO7Gaiv3RQTBHwvAyBKcfQtPmkD7flqCJE5CCCGEEIWQxEmUiYWRBT1q9ch3vlONTgTfCOZm+k1+CfuFX8J+QYGCRvaNmN58Ou3c2pX4XoZubnQf6EaX/p05eiWe3UHH6NWpDe28nVD9J9Exa9GCWqtXk3bylDaBOnwYx6BdTDyyl1enT+dy98EcuBjPgUs3uRKfQnDkbYIjb/PRnks4WBjRua4jXeo60tnHEVvzKrphrY07NButPTQaSLgCV+8VmogI0lbsiz5ajI6EEEIIIURRJHESeufr4MuX/l+SmZvJyRsntRvwxhzm8u3LhCaE5qnIF3orlAuJF+jg1gFXC9di3kGNyuwqBlYhqMwcAQdA9dCWZs2bUeubVaSdOsWtL74k9dAhzOrVpaOPAx19HHgLuHY7jT8v3eTAxZscuXKLWylZ/HzyOj+fvI5SAU3cbeha14ku9Rzxq2FdNUejFArtHlAOPtB6krb0+Y0zEPIDHFte2dEJIYQQQlR7kjiJcmOsMqadWzvaubXjFV7hRuoNjsQcoYlTE12brVe2svGitvS2p7UnHdw60KFGB1o6t8TEwCRfn3sj97Lo+CJupN0AYNO+TTibOfN669fx9/AvMBazZs2otWolGefOYdygge78rRUrUYaFMWzqc4xu05KsHDXBkYn8eVGbSF28cZdTUXc4FXWHT/Zews7ciM4+DnSt50QnHwfsLYwLvGelUirBtYl2JEoSJyGEEEKIMpPESVQYZ3NnnvR5Ms85Hxsfmjg24cytM4QnhROeFM668+swUhrRwrkFn3b7FDNDM7iXNM08MBMNmjx9xKfFM/PATD7u+nGhyROAScOGup/VGRkkfvMNuUlJJP36K9YD+mP/3HO09/KkvZcDc/o2IOZOOgfvjUYdunKLxNQstobEsDUkBoUC/GpY06WeE13rOdKkpk2+KYPVRk56ZUcghBBCCFGlSeIkKtVT9Z/iqfpPkZSZxLHYYxyJOcKh64e4kXaDqLtRmBpoy2vnqnOZe2RuvqQJQIMGBQoWH19MN/duqJQPn7b3X0oTE9xXruDWF1+S8uefJP2yjaRff8Oqfz8cnpuKcR1P3GxMGdG6FiNa1yI7V83fkbc5cPEmf166yfnYZE5fS+L0tSSW7ruMjZkhnXwc6VrXkc51HXG0rKKjUQ+zeRI8tQ7cmlZ2JEIIIYQQVZIkTqJKsDa2plftXvSq3QuNRsPVpKvcTL+pK2V+PO44d7PuFni9Bg1xaXGcjD+ZZ/+popj6+eH+9Veknwnl1pdfkvLHHyRv+5Xk337HZe7b2I4YoWtrqFLSto49bevY83qf+txIztBO6bsUT9DlW9xJy+bX0zH8elpbmr1xDWtdyfOm7jYYqJRl+o7KVVI0rPKHHnOh3TTtVD8hhBBCCKEjiZOochQKBV42XnjZeOnOPbhPVGFupt0s1T1NG/vivvxL0kPPahOoP//ErE0b3fsatRrFf5IJZysThrdyZ3grd3Jy1ZyKvqOt1HfxJmdjkjlzPYkz15NY9scVrEwM6HSvUl/Xuo44WeVfv1UuzOy1+zQVVpJcZQyeHeHKPtjzNoTtg0FfgVVxi3UIIYQQQjz6JHES1UItq1rFaudo5lim+5j6NsL9yy/IjonR7QkFEDvnDTTZ2Tg8PxVjb+981xmolLSqbUer2nbMDqhP/N0MDl66xYGL2tGopPRsfv8nlt//iQWgoasVXetp941qVssGw/IajbJx125um5ZQcBsze7CuCSe/hZ1ztCXNl7eHJ76A+n3LJy4hhBBCiGpGEidRLTR3ao6zmTPxafEPXecE4GLmQnOn5py8cZKwpDD61+mvWyNVUg8mTdnx8ST99hvk5pK8YweWvQNwmDoVk7p1C7zeydKEoS1qMrRFTXJy1Zy+dufetL6b/HMtiXOxyZyLTebLA2FYmhjQ0duBrvUc6VLXCRdrPY9G2bgXb3PbFmPBowP8NB7i/oENI6HleOj1PhiZ6TcmIYQQQohqRhYyiGpBpVTxeuvXAVCQt3Kd4t7/Xmv9GiqlihVnVvDu0Xfp+VNPPjv5GXGpcWW6t6GTE56bf8KyVy/QaLi7YyfhA5/g2ksvk3HxUpHXG6iUtPCwY2avemyb1pHgt/z5eHgTnmjqhq2ZIXczctgRGsdrm8/QduE+en96kIU7znM0LIGsHHWZYi8xBx+YuBfav6h9HbwaVnSFuDMVG4cQQgghRBUjiZOoNvw9/Pm468c4mTnlOe9s5qwrRa7RaOjo1pEaFjVIykxi1ZlV9Nnch1f/fJV/bv5T6nub1K9PzaWf4fnLViwDAgC4u3Mn4U88QfLOXSXqy8HCmMHNa/LZiGYEv9WTLc+3Z0YPH5q626BQwIW4u3z951VGrvyL5u/tYfLaYNYfiyLmTgWVDDcwhl4L4JmtYOECty7Cyu5w9AvtxrpCCCGEEI8hhUajefi8p0dUcnIy1tbWJCUlYWVlVdnhiFLIVedyPOY4e47uoWe7nrR2a52vBHmuOpcD1w6w7tw6gm8E684/4fUECzouKHMMGRcvcWv5ctL++guvvXtQWVjAvb2hlCaln2qXmJpF0GXtvlEHL90kITUrz/t1nS3uVepzomVtW4wNild6vdRSE2DbNLi4XfvaqwcMWg6WzuV73yomOzub7du307dvXwwNDSs7HPEYkGdOVDR55kRFqkrPW0lyA1njJKodlVJFS+eWxBvF09K55UP3bVIpVfSo1YMetXpwIfEC686tY3v4dpo7N9e1Sc9JJzMnExsTmxLHYFKvLjU//YTc5GRd0qTRaIh85lkMnJ1wfP75PJvtFpeduRFPNK3BE01roFZrCI1J4sDFmxy4GE9I9B0u3Ujh0o0UVgaFY2akor2XA13qaSv1udsVvg4pV63heHgi8XczcLI0obWnXdEb9prbw4j12il7u97UVtxb3h4GfQl1A0r8+YQQQgghqitJnMQjr75dfRZ0XMBLLV7C0shSd37rla18HPwxA7wGMLrB6Dzlz4tL9cC/TGSeP09GaCic0ZCydx8W3bvj8MLzmDZqVKq4lUoFfjVt8Ktpw/QePtxJyyLo8i3dBry3UjLZe/4Ge8/fAMDL0Zyu9ZzoWs+RVrXtMDH8N6HcGRrL/F/PEZuUoTvnam3COwMa0tu3iLLjCgW0mqAtHLF5Itw4A+uHQ+vJ0PNdMCxdAQ4hhBBCiOpEEifx2HAwdcjzOjgumIzcDDZd2sSmS5to79aepxs8TYcaHVAqSr78z6RhQ+r8uo1by78ieft2UvbvJ2X/fiy6dsXhhRcwbexbpvhtzIwY0MSNAU3cUKs1nItN5sDFeP68dJOTUXcIu5lK2M1wvjkUjqmhinZe9nSt54gCBXN/Cc1XizAuKYOp606y/OnmRSdPAE71YdI+2Dsf/voCjq+AiEMwZBU4ly45FEIIIYSoLqQ4hHhsfdjlQ9YErKFHrR4oUHAk5gjP73ueJ7Y+wY8XfyxVn8be3tT46EPq/P4bVgMGgFJJyoEDRAwbRlpwcDF6KB6lUoFvDWumdfdh03PtOflWT74Y1ZxhLWriZGlMenYu+y/EM/eXs7z9kKQJ0J2b/+s5ctXFXOpoYAy9P4CnN4O5E8SfgxXd4NjX8HgtlxRCCCHEY0YSJ/HYUigUtHRpyafdPuX3wb/zTMNnsDC0ICI5gkPXD5Wpb+M6dajxvyXU+e03rJ8YiHGDBpg2/3d9Vc7t23r4BP+yNjOkn58r/xvWhGNv9GD79E682rse9V0sC71OA8QmZXA8PLFkN/T2h6lHwCcAcjNhx6va6XspN8v2QYQQQgghqihJnIQA3C3debXVq+wdtpfXW7/OhMYTdO9du3uNWX/OIiQ+hJIWoTSu44nb4sXU3rgBhVL7n5s6PZ2r/QcQNWky6SEhev8sCoWChm5WPN/Vm6ldi7duK/5uRjFa/YeFI4zaCH0/BJUxXN4Ny9vB5b0l70sIIYQQooqTxEmIB5gbmjO6wWiaODbRnfvhwg/sitjFMzueYdTvo/jt6m9k52aXqF+lkZHu57TgYHLv3CE1KIiIESOJmjiJtFOn9Po57nOyLF5p9OK2y0ehgNaTYPIBcGoIqTfh+yGw43XILkUyJoQQQghRRUniJEQRnvB+gsE+gzFSGhGaEMqcoDkEbA5gxT8rSMwo4RQ3wKJTJ7x2bMd68GBQqUg9dIjIkaOImjCRtJP6TaBae9rham1CEUXH+eNiPFk5Zdjc1rkhTPoD2jynfX1sOazqAfHnS9+nEEIIIUQVIomTEEWoa1uX+e3ns2fYHqY1nYajqSM302/y+anPGfzLYLLVJRt9AjCqVQu3D97Ha+cOrIcOAQMDUg8fJnLUKLKiovQWu0qp4J0B2v2k/ps8Pfh6xcGrDF5+mCvxKaW/maEJ9FkMozaBuSPcCIUVXeH4SikcIYQQQohqTxInIYrJzsSOKU2msGvILhZ2Wkgj+0b0q9MPQ6V2x2uNRsNfsX+h1hR/5MbI3R23BQvw2rkDm2FDserbF6NatXTv6yOJ6u3ryvKnm+NinXc6nou1CV893Zyvnm6OjZkhodeT6f95EN8djSjxWq486vbSFo7w9oecDNg+C34YCam3yvxZhBBCCCEqi+zjJEQJGaoM6V+nP/08++UZbQq+Ecyk3ZPwsPJgVP1RDPIehJmhWbH6NKpZE9f33suTsGRdu05Y336YtWiBwwvPY966dalj7u3rSs+GLhwPTyT+bgZOlia09rRDpdSOOzWrZcusTacJunyLt385y/4L8SwZ2gRHS+PS3dDCSTvydPxr2DMXLu2A5e3hya/Aq3upP4cQQgghRGWRESchSkmhUGCk+rfoQ1xqHJaGlkQmR7Lw+EL8N/nzvxP/49rdayXq8770UydBoSDt2DGinh1D5LNjSD12vNTxqpQK2nnZ80TTGrTzstclTQDOViZ8O641c/s3xMhAyR8Xb9L704PsPXej1PdDqYS2U2HSfnCsDyk34LsnYdebkJNZ+n6FEEIIISqBJE5C6MkArwHsHbaXN9u8SW2r2tzNvsvac2vpt6UfL//xMkmZSSXqz3rAALx37cRmxFNgaEja8eNEjRlD5NPPkPrXsbJNp3sIpVLB+I6ebJvWgfouliSkZjFxbTBvbDlDWlZO6Tt2aaytutdqovb10WXawhE3L+ktdiGEEEKI8iaJkxB6ZGZoxoj6I/hl0C982eNL2ru1R61Rc+XOFSyN/t2MtrhJj6GbG67z5uG9exe2o0aiMDQkLTiYay+8gDqlDIUcClHfxYqtL3RgYkdPANYfi6L/54c4c61kiV8ehqbQ7yMY8QOY2UPcGfi6MwSvlsIRQgghhKgWJHESohwoFUo61ezE1z2/ZsvALcxtNxelQvufW2ZuJkN+HcLy08tJSE8oVn+Grq64zJ2L157d2I4ahd34cagstYmYRqMh/fRpvY5AmRiqeKt/Q9ZNaIOzlTFXb6by5JeH+eKPK+Sqy3Cf+n21hSPqdIOcdPjtZdj4NKSVvKy7EEIIIURFksRJiHLmbetNK5dWute7InZx+fZlvgz5kp4/9eStQ29xIfFCsfoydHHBZe7bOL7wgu5c2rFjRDw1gshRo0k5fFivCVRHHwd2zuhMH18XctQa/rfrIiNX/EV0YlrpO7V0gad/hl7vg9IQLvymLRxx9YDe4hZCCCGE0DdJnISoYH08+7Ck8xIaOzQmW53NL2G/MOzXYYzfNZ79UfvJVeeWqL/Mq1dRGBmRfuoU0RMmEjlyFClBh3QJVHZMDOlnzxZ4ZMfEFNq/rbkRX45uzv+G+mFupOJ4RCJ9Pwti66nrpf8SlEpoPw0m7QOHunA3FtYO0lbgy8kqfb9CCCGEEOVEypELUcEMlYb08exDH88+nL55mnXn1rEncg8n4k5wIu4Evwz6hTrWdYrdn92oUVj28Cfhm1Xc2fgj6SEhRE+ahGmTJtiMHEHc3HfQZBWcjCiMjPDauQNDN7eC2ygUDGvpThtPe17aeIqTUXd4aWMI+y/E894gX6xNDUv8PQDg2gQm/wm73oC/18Dhz+DqnzDkG3DwLl2fQgghhBDloFJHnA4ePMiAAQNwc3NDoVCwdevWYl97+PBhDAwMaNq0abnGKER5auLYhP91+R87h+xkgu8E+nj2yZM0/Rr2K9HJ0UX2Y+jshMsbb+C1Zzd2Y8agMDEh/fRpbn62tNCkCUCTlUXO7dvFireWvRk/TmnHy/51USkVbDsdQ59PD3I0rHhrtR7KyAwGfApPrQNTW4gNga87wcm1UjhCCCGEEFVGpSZOqampNGnShC+++KJE1925c4dnn32WHj16lFtsQlQkF3MXXmrxEks6L9Gdu5F6g7mH59JvSz+m75/O8djjRa5fMnRywnnO63jv2Y3duHHYjhyh91gNVEpm+Puw6bl2eNibEZOUwahVf7Fwx3myctSl77jBAG3hCM/OkJ0G216ETWMgvXhJnRBCCCFEearUxKlPnz4sWLCAJ598skTXPffcc4waNYp27dqVW2xCVLa0nDTaurVFg4Y/ov9gwu4JDP11KFsubyEzt/ANZA0cHXF+7VXMO3Qot/ia17Jl+/ROPNXSHY0Gvv7zKoO+OMyV+Lul79TKDZ75Bfzng9IAzv0CyztAxCF9hi6EEEIIUWLVbo3TmjVruHr1KuvWrWPBggVFts/MzCQz899fMpOTkwHIzs4mOzu7XGMV5ef+n92j/GdY06wmS7ssJTwpnA2XNvDr1V+5dPsSc4/M5ZO/P+HDTh/SzKlZoX3k5BRv49qcnJxSfZdGSljwRAM6+9jx1i/nOBebTL+lh3i9d11Gt3ZHoVCUuE8A2rwAtTpgsHUyisSraAL7o27/EurOr4KqlOupyuBxeN5E1SLPnKho8syJilSVnreSxKDQ6LN2cRkoFAq2bNnCoEGDCmxz+fJlOnbsSFBQEHXr1mXevHls3bqVkJCQAq+ZN28e8+fPz3d+/fr1mJmZ6S1+Icpbujqd4Kxg/sr8izRNGq9avYqp0hSATE0mxgrjfNcYX7+Ox9LPi+w7cvqLZNaoUab4krJg/RUlF5K0A9kNbdSM9FJjZVT6PlW5GTS+/j0eCX8CcNusDn/XnkqqsXOZYhVCCCGEAEhLS2PUqFEkJSVhZWVVaNtqkzjl5ubStm1bJkyYwHPPPQf3kqKiEqeHjTi5u7tz69atIr8cUXVlZ2ezZ88eevbsiaFhxY9AVKYcdQ6X71ymgV0D3bkxu8ZgoDRgVP1RdK3RFZVSBUDGuXNce6rodU4113+PSePGZY5Nrdbw3bEoluy+TFaOGjtzQz4Y1Ige9Z3K1K/iwq+ofn8ZRcYdNEbm5AYsRtP4KSjtiFYJPc7Pm6gc8syJiibPnKhIVel5S05OxsHBoViJU6mm6kVHR6NQKKhZsyYAx48fZ/369TRs2JDJkyeXLuoi3L17l+DgYE6dOsW0adMAUKvVaDQaDAwM2L17N927d893nbGxMcbG+f8l3tDQsNL/oETZPY5/joYY4ufsp3sdnRzN+cTz5GhyOHXzFDUsajCy/kie9HkSQ4Pi/SeujonBsHlzvcQ3sbM3neo6M2PDKS7E3eW570MY1aYWb/VrgJlRKWcHNx4MtVrDz1NQRB7C4NdpcHU/9P8ETG30EndxPI7Pm6hc8syJiibPnKhIVeF5K8n9S1UcYtSoUfzxxx8AxMXF0bNnT44fP86bb77Ju+++W5oui2RlZcWZM2cICQnRHc899xz16tUjJCSENm3alMt9hajq3K3c2TlkJ5MaT8LG2IbrKdf5MPhD/Df581XkejAq4i8EAwPM9JQ03VfPxZJfpnVgUidPANYfi6L/0kP8c+1O6Tu1rgljtkH3t7WFI87+DF91hMgj+gtcCCGEEKIApfrn39DQUFq3bg3Ajz/+iK+vL4cPH2b37t0899xzzJ07t1j9pKSkcOXKFd3r8PBwQkJCsLOzo1atWsyZM4fr16+zdu1alEolvr6+ea53cnLCxMQk33khHjfO5s5Mbz6dyX6T+f3q76w7v44rd66w+uY2jJYMZffpn9CQd1auAu00t5e6v02De5vfZly8SE78TSw6dSxzTMYGKt7s15Cu9Zx45cfTXL2VyuAvj/Byz7o818ULlbIU0+yUKug8C+p0g80T4HY4BPaDTrOgy2ugqnb1boQQQghRTZRqxCk7O1s3/W3v3r0MHDgQgPr16xMbG1vsfoKDg2nWrBnNmmkrg82cOZNmzZrpEq/Y2FiioqJKE6IQjyUTAxOG1B3CzwN/ZmWvlQzyGsSWu0FcdYFwF0We4/65DyJWkqvOJef2ba5NfZ7oKVNIXLu2yD2jiquDtwM7X+pEv8au5Kg1/G/XRUasOEp0YlrpO63ZAp4LgqajQaOGg0tgTW9IDNdLzEIIIYQQ/1WqxKlRo0Z89dVXBAUFsWfPHnr37g1ATEwM9vb2xe6na9euaDSafEdgYCAAgYGBHDhwoMDr582bV2hhCCEeVwqFgraubRnoPZAbaTcKbKdBQ1xaHCfjT6I0N8esbVtQq7nxwULi5s5Fk5Wll3hszIxYNqoZHw5rgrmRihMRt+n7WRBbTl0rfYJmbAmDvoShq8HYGq6dgK86wemNeolZCCGEEOJBpUqcFi9ezNdff03Xrl0ZOXIkTZo0AWDbtm26KXxCiMp3M+1msdspjYxwfX8BTq+9Bkoldzb9RNT4CeTcvq2XWBQKBUNb1GTHjM608LDlbmYOL288zYs/nCIprQz7OPgOgamHoFY7yLoLWybD5omQkaSXuIUQQgghKG3i1LVrV27dusWtW7dYvXq17vzkyZP56quv9BmfEKIMHM0cS9ROoVBgP24s7su/RGlhQVpwMBFDh5Fx6ZLeYqplb8bGyW2Z2bMuKqWC3/6JpfdnBzkSdqv0ndrUgrG/Q7e3QKGCM5u0hSOijuktbiGEEEI83kqVOKWnp5OZmYmtrS0AkZGRfPrpp1y8eBEnp7Lt1yKE0J/mTs1xNnPWFYJ4GGOVMY3t8+7hZNGlC7U3/IBhrVpkX7/Orc+L3kS3JAxUSqb38GHz1PbUtjcjNimD0auOsXD7eTJzckvXqVIFXWbD+J1g4wF3omBNHziwGHJz9Bq/EEIIIR4/pUqcnnjiCdauXQvAnTt3aNOmDR999BGDBg1i+fLl+o5RCFFKKqWK11u/Dg9U0fuvzNxMNlzckO+8sbc3tTduwHroEFwXLCiX+Jq62/D79E6MaOWORgNfH7zKk18c4fKNu6Xv1L01PHcI/EaAJhcOfKCtvHc7Up+hCyGEEOIxU6rE6eTJk3Tq1AmAn376CWdnZyIjI1m7di1Lly7Vd4xCiDLw9/Dn464f42SWdzTYxcyFyX6T6eXRi9ENRj/0WgNbW9wWLEBlba07d2frVtSZmXqLz9zYgEVD/Pj6mRbYmhlyLjaZ/p8f4tsjEaUvHGFiBYO/hsErwcgSov/STt0785Pe4hZCCCHE46VUm56kpaVhaWkJwO7duxk8eDBKpZK2bdsSGSn/qitEVePv4U83926cjD/JzbSbOJo50typOSqlKk87tUZNZm4mpgamD+0ncf16brz7Hnd+2EDNZZ9j4Fi8NVTFEdDIhWbuNsz66R8OXrrJO9vOsv9CPP8b5oeTpUnpOvUbrh2B2jwJrh3X7v10ZS/0/Z+2Kp8QQgghRDGVasTJ29ubrVu3Eh0dza5du+jVqxcA8fHxWFlZ6TtGIYQeqJQqWrm0om+dvrRyaZUvadJoNHxw7AMm7Z5EclbyQ/swrl0bpZUV6adPEz5sOOlnz+o1RicrEwLHtmLegIYYGSj589JNen8axJ5zBZdUL5JtbRi3A7q8DgolnP5BO/p0LVifoQshhBDiEVeqxGnu3LnMmjWL2rVr07p1a9q1awf3Rp/ub2YrhKhe4lLj2BG+g9M3TzNx10TuZNzJ18a8fXs8f9yIkacnOXFxRI5+muSdu/Qah1KpYGwHT357sSMNXK1ITM1i0tpg5vx8hrSsUhZ5UBlAtzkwdjtY14LbEfBNLzj4P1CXshiFEEIIIR4rpUqchg4dSlRUFMHBweza9e8vTT169OCTTz7RZ3xCiAriauHK6oDV2JnYcT7xPON3j+dWev4S4Ua1a1N74wbMO3ZEk5HB9Zde4uYXX5R+PVIB6jpbsvWF9kzuXAeAH45H0W/pIU5H50/ois2jHTwXpN37SZML+xfAtwPgTrT+AhdCCCHEI6lUiROAi4sLzZo1IyYmhmvXrgHQunVr6tevr8/4hBAVqJ5dPVYHrMbR1JHLty8zftd4bqTmnyansrLC/avl2I0ZA8CtZV+QERqq93iMDVS80bcB6ye2wcXKhPBbqQxZfoRl+y+Tqy5lomZqA0O+gUFfgZEFRB6GrzrA2S36Dl8IIYQQj5BSJU5qtZp3330Xa2trPDw88PDwwMbGhvfeew+1Wq3/KIUQFcbLxovA3oG4mLsQnhTOuF3jiEmJyddOYWCA85zXcV3wHk6vvYpp48YP7U8f2ns7sPOlTvTzcyVHreHD3Zd46uujRCemla5DhQKajtSOPtVoCRlJsGksbH0BMlP0Hb4QQgghHgGlSpzefPNNli1bxqJFizh16hSnTp3igw8+4PPPP+ftt9/Wf5RCiApVy6oWgb0DqWlRk5iUGC4mXiywrc3QodiPHat7nXXtGumnT+s9JhszI5aNbMZHw5pgYWxAcORt+nwWxM8nr5V+mqBdHe2GuZ1mAQoIWQdfd4brJ/UdvhBCCCGquVKVI//2229ZtWoVAwcO1J3z8/OjRo0aPP/887z//vv6jFEIUQlqWNQgsHcgoQmhdKvVrVjX5KakcG3qVLIio3B9fwHWAwboNSaFQsGQFjVp7WnHyxtDCI68zcwfT7PvQjwfDGqMtZlhyTtVGUKPt8GrO/w8GRLD4Jue0G4aNHxCW4kvJwfrtAiIPQ0G9/7aNLMHG3e9fj4hhBBCVF2lSpwSExMfupapfv36JCYm6iMuIUQV4GzujLO5s+51bEosd7PvUte2bgFXKDB0r0Xm5SvEzH6VzEuXcXz5JRTKUi+nfCh3OzM2TG7L8gNhfLrvMr//E8vJyNt8NKwJ7b0dStdp7Q4w9RD8+hKc2wqHP9UegCHQFeDBgTcDY5j2tyRPQgghxGOiVL/NNGnShGXLluU7v2zZMvz8/PQRlxCiirmZdpMJuycwftd4ziY8fP8mlYU5NZd9jv3kyQAkrFzJtWkvkpuSqvd4DFRKXuzhw+ap7fF0MCc2KYPR3xzjg+3nycwpZYlxU1sYFghdXiu6bU4mpCWU7j5CCCGEqHZKlTgtWbKE1atX07BhQyZMmMCECRNo2LAhgYGBfPjhh/qPUghR6YwNjLE1tiUpM4mJuyYSEh/y0HYKpRKnmS/j9r8lKIyMSNm/n8iRI8m6V31T35q62/Dbix0Z2dodjQZWHLzKoC+OcOnG3dJ1qFBAvb76DlMIIYQQ1VypEqcuXbpw6dIlnnzySe7cucOdO3cYPHgwZ8+e5bvvvtN/lEKISmdlZMWKXito4dyClOwUJu+ZzIm4EwW2tx4wAI/v1qJydCDz8mXi3nuv3GIzNzZg4WA/VjzTAjtzI87HJjPg80MEHg7X+/5SQgghhHg8lXrhgZubG++//z6bN29m8+bNLFiwgNu3b/PNN9/oN0IhRJVhbmjOcv/ltHVtS3pOOlP3TuXI9SMFtjdt0gTPTZuw6NYN13fLL3G6r1cjF3a+1IkudR3JzFEz79dzjF1zgvjkjHK/txBCCCEebfpdsS2EeOSZGpiyrMcyOtfsTGZuJtP2T+NozNEC2xu6uOC+/EsMnZ1051KCgtDk5JRLfE6WJgSOa8X8gY0wNlDy56Wb9P4siN1n4/R/s5D1kJut/36FEEIIUeVI4iSEKDFjlTGfdv2Unh49qWFRAx9bn2Jfm7x9O9GTJhM99Xly75ZyHVIRFAoFY9rX5tcXO9LA1YrE1Cwmf/c3r2/+h9RMPSZsx7/W7vsUWXDiKIQQQohHgyROQohSMVQZsqTzEgJ7B+JgWoIS4CoDFCYmpAYFEfHUCLIiIsotxrrOlmx9oT1TOtdBoYANJ6LptzSIkOg7+rmBiTXEn4M1veGXaZAm2zEIIYQQj6oS7eM0ePDgQt+/c0dPv4wIIaoFA6UB9qb2utdbr2wlV53LkLpDCrzGKqAXhjVrcO2FaWRdvUr4UyOo+cnHmLdvXy4xGhuomNO3AV3qOfLKj6eJSEhjyPIjvNTDh6ldvTBQPeTfj8zstfs05WQW3LGBMYz9HY6vhJPfwqnv4MLv0GsBNB2lrc4nhBBCiEdGiRIna2vrIt9/9tlnyxqTEKIaCr0VyjtH3kGtUZOlzmJk/ZEFtjVt1AjPTT9ybdqLpJ8+TdSkyTi/MQe70aPLLb72Xg7snNGZN7ee4bd/YvlozyX+vHSTT55qirudWd7GNu7azW3v7dOUnZPD4cOH6dChA4YG9/7aNLPXthu4VJso/TYT4s/CL89DyPfQ72Nwyr9RuBBCCCGqpxIlTmvWrCm/SIQQ1Voj+0Y83eBp1p5bywfHPiAzJ5OxvmMLbG/g6Eittd8SN3cuSb9s48Z7CzBt1AjTpk3LLUZrM0M+H9mM7vWdmPvLWYIjb9PnsyDmDWzEkOY1UDw4SmTjrj0AsrNJMrsOrk3A0DB/x7XawpQ/4a/lcGAhRB6GrzpA++nQeTYYmeW/RgghhBDViqxxEkLohUKhYFbLWUxqPAmAj/7+iK9Of1XoPkpKY2NcFy3CadYr2E+eXK5J04NxDm5ekx0zOtGqti0pmTnM2nSaaetPcSctq/Qdqwyhw3R44TjU6wfqHDj0MXzZBi7t0udHEEIIIUQlkMRJCKE3CoWC6c2n82KzFwH4IuQLlp5aWmjypFAosJ84EaeZL+vO5dy6RWZYWLnG6m5nxobJ7ZgdUA8DpYLfz8TS+9Mgjly5VbaObdxh5HoYsR6sasKdKFg/HDY+DUnX9RW+EEIIISqYJE5CCL2b7DeZWS1nAbDqzCqCrgcV+1p1VhbXpr1IxFMjSDl4sByjBJVSwQvdvNk8tT11HMyJS85g1KpjvP/7OTJzcgHIVWs4Fp7I37cUHAtPJFddcBKYR/1+8MIx7XQ9hQrO/wpftIajX0Bu+exhJYQQQojyU6I1TkIIUVxjGo3BWGVM9N1oOtXoVOzrNOnpKAwMUKekEP3cVJxmz8Zu7Ji864/0rIm7Db9N78h7v53nh+NRrAwKJ+jyLYa3dGdl0FVikzIAFWsvB+NqbcI7AxrS29e16I6NLaDXe9BkBPz2MkQfg11vQMgPMOBTqNmy3D6TEEIIIfRLRpyEEOVmRP0RzG41W5f0ZOZmkqvOLfQalbU1tVZ/g82woaBWE794MbFvvIk6qwzrj4rBzMiAhYMbs/LZltiZG3Eh7i7v/nbuXtL0r7ikDKauO8nO0Njid+7cCMbthAFLwcQGbpyBVf7aSnzpso2DEEIIUR1I4iSEqBBZuVnM2D+DOUFzyFZnF9pWYWSEy7vv4vzGG6BUkrRlC1FjxpKTkFDucfZs6Mzv0ztibPDwvx7vT9Sb/+u54k/bA1AqocUYePFvaDJK21PwN7CsJfzzIxSyDkwIIYQQlU8SJyFEhQi9FcqxuGPsiNjB7D9nk5Vb+AiSQqHA7tlncF+xAqWlJemnThHz2usVEmvErTQyc9QFvq8BYpMyOB6eWPLOzR3gyeUw5jdwqAupN+HnSbB2INy6XLbAhRBCCFFuJHESQlSI5s7N+bTrpxgqDdkXtY+X/niJzNzMIq+z6NiB2hs3YtLED5e33qyQWOPvZhSjVfHbPZRnJ3juMHR/GwxMIPwgLG8Pf3wA2WXoVwghhBDlQhInIUSF6eLehWU9lmGiMiHoehAv7HuBtOy0Iq8zruNJ7Q0bMKpdW3cu/cyZQsucl4WTpYle2xXIwAg6z4Ln/wLvnpCbBX8uhuXt4Mq+svUthBBCCL2SxEkIUaHau7Vnuf9yzAzMOBZ7jKl7p5KSlVLkdQ9W1Us5dJiIp0YQM/tV1Bn6H51p7WmHq7UJRdXxC44sQXnywth5wuhNMHwtWLpC4lVYNxh+Gg9348revxBCCCHKTBInIUSFa+nSkhW9VmBpaMml25e4nlKyjWFz4uNBqST5t9+IfOZZsm/E6zU+lVLBOwMaAuRLnh58/dHuS4xc+RfX76SX/aYKBTR8Al44Dm2fB4USQjfDslZwfCUUUY1QCCGEEOVLEichRKVo4tiEVQGrWO6/nHp29Up0rc3gJ6n1zTeorK3JOHOGiGHDSD8Tqtf4evu6svzp5rhY552O52JtwvLRzfnfUD/MjVQcD0+k96cH2XY6Rj83NrGC3gth8gFwaw6ZybB9FqzqATGn9HMPIYQQQpSYJE5CiErT0L4hTZ2a6l6fTTjLrfRbxbrWvE1rav+0CSNvL3Li44l8+mmSt2/Xa3y9fV059Fp31o1vybM+uawb35JDr3WnT2NXhrV05/fpnWjqbsPdjBym/3CKmRtDuJtReKn1YnNtAhP3Qr+PwNhamzSt7A47XoOMZP3cQwghhBDFJomTEKJKuJB4gUm7JzFu5zjiUou3rsfI3Z3aGzZg0aULmsxMrs98hbST+h2VUSkVtPG0o4WDhjaedqiU/07Wq+1gzqbn2jG9uzdKBfx86jp9lwbxd2QpypQ/jFIFrSbCtBPQeBho1HDsK+30vbNbZO8nIYQQogJJ4iSEqBLMDc2xNLQkIjmCsTvHFnvdk8rCgppffoHdhPFYDx2CabOmxbhKfwxVSmb2qsePU9pR09aU6MR0hn11lI/3XCInt+C9oErE0hmGrIJntoKdF6TEwaax8P1QbSEJIYT4f3v3HR5VlT5w/HunpvcekpBCS6EEDB1BqSJrr+ii+7OsYkVdce27rmV1WXRXUde2LiqWXVSUjjTpECCNloQU0nsvk8z8/rhhQqQFSTIp7+d5zgNz77n3nju5DPPmnPMeIUSnk8BJCNEtBDkH8cnMTwh2DianOod5q+aRWZnZrmMVrRbfJ57A/09/smbfa66qwpSX18mtbjWqvwcrH57ItSMCMVvgrQ3HuOG9HWSW1HTcRcKnwH3bYfJToDVA6np4ZyxseR2azr8mlhBCCCF+PQmchBDdhr+TPx/P/JhQ11AKagu4Y/UdpJWntft4RaN+pFmam8l57DGO33Ajtfu7LqGCi52eRTcN582bh+Nsp2N/VjlXvLmVr/dmd9yaU3o7mLwQ7tsBYZOhqR5+egnenQDHt3bMNYQQQghxGgmchBDdio+DDx/P+JiB7gMprivmztV3crzi+AWdo7mykqbCIpqLi8n67TzKl3/bae09k6uGB7Lq4YnEhXpQ09jME98k8MDn+ymvbey4i3hFqEP3rvsQHH2g+Cj8+0r4371QXdRx1xFCCCEESOAkhOiOPO09+WjGR0R5RjHQfSD+jv4XdLzO3Z3+ny3FedpULCYTeU89RcHrr2Np7rq1kPq5O/DF3WN4YsYgdBqFHxPzmLl4K9vT2pc1sF0UBWKuV5NHXHKXuspUwjL45yjY+zGYO2iOlRBCCCEkcBJCdE+uRlf+Nf1fvHXZW9jp7NpxRFsaR0cC33wTr/vvA6D0w484cf98mqurO6G1Z6bVKMyfEsH/7h9HqJcj+ZX1zP1gF6+sOkRjUwcGNfZuatryuzaAXwzUl8MPj8BHMyA/seOuI4QQQvRhEjgJIbotZ4MzDnoHACwWC28feJtdebvafbyi0eD90EME/O0NFKOR6s2byXnk0U5s8ZkN7efGDw9O4Ja4ICwWeG9zOtcu2UZqYQcHcf1Gwt2bYOarYHCCE7vhvUthzdPQ0HUBoxBCCNEb2TRw2rJlC3PmzCEgIABFUfj223PPQ/jf//7HtGnT8Pb2xsXFhbFjx7JmzZoua68QwnZ+SP+Bdw++y/wN89l64sKSILjOnk3I0v+gDwnGZ0HXB04AjkYdr1w7lHdvG4m7g56knEqu/MdWlu7M7LjEEQBaHYy5Tx2+F3kVWJphxz/h7Tg49EPHXUcIIYToY2waONXU1DBs2DDefvvtdtXfsmUL06ZNY+XKlezbt48pU6YwZ84c9ndh1iwhhG3M6D+DyUGTaWhu4KGND7Eha8MFHW8fE0P4jz9iFxlp3daY2b505x1pZrQfqx+ZxMQBXtSbzDzzbRJ3f7qXkuoOTifuEgA3fgq3fg1uIVCZA1/Ohc9vhvKsjr2WEEII0QfYNHCaNWsWL730Etdcc0276i9evJg//OEPXHLJJQwYMICXX36ZAQMGsGLFik5vqxDCtgxaA4smL2J6yHSazE08tukxVh9ffUHnUHQ669/rDh4kfc5vyH/pL1iamjqhxWfn62LHv++M45nZQzBoNaw/VMiMxVvZdKSw4y82cDrcvxMmPgYaPRxdBW+Php8XQ7Op468nhBBC9FK6dtTptsxmM1VVVXh4eJy1TkNDAw0Nrb/JraysBMBkMmEyyZeGnurkz05+hn3PS2NfQq/o+THjR57c+iS1jbXMCZtzweep3n8AS2MjZUuXUp+ait8bb6B1dTlj3c563uaNCSIuxI3HvkngWGENd3y8h9+OCeaJ6QOw02s77kKKHiY9BUOuRbv6cTRZO2D981gOLqN51utYgsZ03LVEh5DPONHV5JkTXak7PW8X0gbF0qGD6389RVFYvnw5V199dbuP+etf/8qrr77K4cOH8fHxOWOdF154gRdffPG07Z9//jkODg4X1WYhhG2YLWa+r/uevY170aDhYeeH8dR6XvB5nJKS8PvyKzSNjTR6eZFzxzxM3t6d0uZzaWyGFVkatuSrgwD87C38dkAzgY6dcDGLhaDSn4nKXYaxqQqATM9LSQ64EZPOuRMuKIQQQnRftbW13HrrrVRUVODicuZfoJ7UYwOnzz//nLvvvpvvvvuOqVOnnrXemXqcgoKCKC4uPu+bI7ovk8nEunXrmDZtGnq93tbNETZgsVh4I/4NwlzDuC7iul99noYjR8h78CGa8vLQODvj98brOIwb16ZOVz1vm48WsXB5MsXVjei1Ck9MH8i8McFoNErHX6y2FO3GP6E5sBQAi70HzZe/iGXozer6UMKm5DNOdDV55kRX6k7PW2VlJV5eXu0KnHrkUL1ly5Zx11138fXXX58zaAIwGo0YjcbTtuv1epv/oMTFk59j3/bHMX9s87rWVGtNX95e+uhoQr/5mhMPPkRdfDy5991PyNKlOMSOOL1uJz9vU6MCWB3iyZPfJLDhcCEvrzrC1tQS3rhhGL4uF76W1Tm5+sLVb0Ps7fDDoyiFKeh+eBASl8HsReAzuGOvJ34V+YwTXU2eOdGVusPzdiHX73HrOH3xxRfceeedfPHFF8yePdvWzRFCdBNl9WXMXTmXf+7/5wWn99Z5ehL8yce4XnMNTpdNwX74MEy5udQlJ1OXnEx9SgrGnBzqU1Ks20y5uZ1yH15ORj6YN4o/Xx2NnV7D1mPFzFy8hTXJ+Z1yPYLHwL1bYNqfQO8Amdvg3fGw/kVorO2cawohhBA9kE17nKqrq0lNTbW+Pn78OAcOHMDDw4Pg4GCeeuopcnJy+PTTT6FleN68efN48803GT16NPn56hcJe3t7XF1dbXYfQgjb23xiM6nlqaSWp9LQ3MCCkQtQLmDImcZgwP/lv2AxmWjKzydt5iwsjY3W/SHAibf+YX2tGAyEr16FPiCgw+9FURRuHxPC2DAPHvriACl5ldz7n33cEhfMs1cOwcHQwR/dWj2MfxiiroFVT8KRlfDzIkj6Bq54AwbO6NjrCSGEED2QTXuc9u7dy4gRIxgxQh0Ss2DBAkaMGMFzzz0HQF5eHllZreuNvP/++zQ1NTF//nz8/f2t5eGHH7bZPQghuoerI67myUueBOCT5E94ZfcrmC3mCzqHoihoDAaaysraBE1nYmlspKms7KLafD4RPs4snz+OeyeFoSjwxe4srnzrZxJOlHfOBd2C4ZYv4ObPwaWfut7T5zfCl7dBRU7nXFMIIYToIWza4zR58uRzDqn55JNP2rzetGlTF7RKCNFT3RZ5G0adkT/v+DNfHP6CxuZGnh3zLFpNB6b27mJGnZanrhjCpQO9WfDVQdKLa7j2ne08Om0gv780HG1nJI4YPBtCL4XNr8GOt+HQCkjbCFP+CHH3grZHTo8VQgghLkqPm+MkhBDncsPAG3hpwktoFA3/PfZfnt72NE3mrl3gtjOMi/Bi9SMTuSLGjyazhdfXHOGWf+0kp7yucy5odILpf1bnPwWNhsZqWPNH+NdkOLG3c64phBBCdGMSOAkhep3fhP+G1ya+hlbRcqDwAOUNnTS0rYu5ORh4+9ZYXr9+KA4GLbuPlzJz8RZWHOycRBUA+EXDnathzltg5wb5ifDBVPhhAdT1jvdVCCGEaA8JnIQQvdLM0Jm8ddlbfDD9A7zsvTr1Wua6Tur1OQNFUbhhVBArH5rI8CA3quqbePCL/Sz48gBV9Z20ArtGAyPnwYP7YNitgAX2fgj/HAUJX0H3WA5QCCGE6FQSOAkheq1J/SbRz7mf9fXuvN3UNXVskGNuaCBt+gxOPPQwNbt3X3Aq9F+rv5cjX/9+LA9dFoFGgf/tz+GKt7ayL7O08y7q6AXXLIF5P4DXQKgpgv/dDZ/+BoqPdd51hRBCiG5AAichRJ+wKXsT9667l/vX30+NqabDzlu7axdNRUVUrV1L1m/ncfyqqyn76qsu6YXSazUsmD6Ir+4dSz93e7JL67jh3R0sWneUpuYLyyh4QUInwu+3wWXPgs4Ojm+BJeNg48tgqofybMg9cPZSnt15bRNCCCE6iaRGEkL0Ca5GV4w6I3sL9nLvuntZMnUJzgbnM9bVubujGAznTEmuGAzo3N2xj4oi9PvvKPvscyq+/56Go0fJf+55Cv+2CLfrrsPjjnnofXw68c5gVH8PVj48kRe+S+Z/+3N4a8Mxth4rYvFNwwnxdOyci+oMMOlxiL4OVj4OqevVLHwHvoCqPDCfY9igzggP7AO3oM5pmxBCCNEJpMdJCNEnjPAZwQfTP8DF4MLBooPctfYuyuvPnNxAHxBA+OpV9P/vN/T/7zf0+3IZmQ89SL8vl1m3nbr4rd3Agfi/+AIDNm3E58kn0QcFYa6ooPSjjzBXVnbJ/bnY6Vl003DevHk4znY69meVc8WbW/l6b3bnDh/0CIW538AN/wZnf6jIOnfQBNDUALUlndcmIYQQohNI4CSE6DOivaL5aMZHuBvdSSlJ4Xdrf0dxXfEZ6+oDArCPisI+Kgq7yEgaAgOxi4y0bjsZNJ1K6+qK5513EL56Ff2WvIPnPfdgjIiw7i9cvJjSzz/HXNNxQwV/6arhgax6eCJxoR7UNDbzxDcJPPD5fsprz72g70VRFIi6GubvhujrO+86QgghhA1J4CSE6FMGeQzi45kf42XvxbGyY/xuze+oaKjo0GsoWi3OU6bgs+BR67amoiJKPvyIgj/9mWOXTib/5ZdpzMjo0Oue1M/dgS/uHsMTMwah0yj8mJjHrDe3sj3tzEFih7FzgXEPdu41hBBCCBuRwEkI0eeEu4XzycxP8HP0I9Yn9qxznTqSxsEB3yeewBASgrm6mrJP/0PazFlk3XMP1Vu2YDF3bDIHrUZh/pQI/nvfOEK9HMmrqGfuB7t4ZdUhGps6MXFEezV3Uup0IYQQopNI4CSE6JNCXEL4YvYXPDvmWTRK538Uahwd8fjt7YStWknQv97H8dJJANRs2Ur2PfdStmxZp1x3WJAbPzw4gVvigrBY4L3N6Vy7ZBuphdWdcr12W3oNrPwD5CXYth1CCCFEO0ngJITos7zsvdBqtACYzCZe2vkS6RXpnXpNRaPBaeJEgt97j/A1q/GY91u03l64zJplrVOXmEhDWlqHXdPRqOOVa4fy7m0jcXfQk5RTyZX/2MrSnZldtu7UaRqqYPd78N5EeHci7HofajtxDSohhBDiIkngJIQQwDsH3uHLI19y5+o7OVp2tEuuaQgJwfeppxiwcSM6d3fr9oK/vEz67CvJ+t3vqPrpJyzNzR1yvZnRfqx+ZBITB3hRbzLzzLdJ3P3pXkqqGzrk/Bdk1usQdQ1oDZCfAKuegL8Ngq/vUFObmzvmnoUQQoiOIoGTEEIAv438LUM8hlBaX8rv1vyO5JLkLru2omtdUs9cX4/W0xM0Gmq27+DE/fNJmzGTkg8/orni4pNY+LrY8e8743hm9hAMWg3rDxUyY/FWNh0pvOhzA+Dgqa7TdC46IwyaBTd8Ao8dgVl/Bb8YaG6E5OWw9DpYHAM/vQSlxzumXUIIIcRFUiw2G6dhG5WVlbi6ulJRUYGLi4utmyN+JZPJxMqVK7niiivQ6/W2bo7oJSoaKrh//f0kFCfgrHfmnanvEOMVw+7c3azbsY5pY6cRFxBnHd7XmRpP5FD2xeeUf/NfzC0Bk2Jnh/cjD+N5xx0dco2U3Eoe+XI/RwvU+U53jOvPwlmDsdNf5P2VZ597nSYHzzMvfpt3EPZ/BglfwqlrbPWfCCNugyG/AYPDxbWth5DPONHV5JkTXak7PW8XEhtI4CR6pO70D070LtWN1czfMJ/4wngMGgOOekfKGsqs+30dfFkYt5CpIVO7pD3mujoqfviBsqWf0XDkCIGL/47LzJnqvvp6FJ2uTY/Vhao3NfPqqsN8sl1NjT7I15k3bxnOYD8bfj6a6uHISti/FNJ+Alr+mzK6QPS1MOJ2CByprh/VS8lnnOhq8syJrtSdnrcLiQ1kqJ4QQpzCyeDEkqlLGOA2gEZzY5ugCaCwtpAFmxawPnN9l7RHY2+P+w03EPrtckI+W4rz5Zdb95X++1NSp02n+L33aSorO+d5zsZOr+WF30Tx8R2X4OVk4EhBFb/5xzY+/Pk4ZrONfq+mt1MDpNv/B48kwpRnwC0EGiph3yfwweXwzhjY/g+o7qAhhkIIIcR5SOAkhBC/YNQaqWg883wiS0vvx2u7X6O5CxMYKIqCw8iRKKf8Zq5qzRqa8vIo+vvfSb10MrlP/ZG65F83N2vKYB9WPzKJywf70Nhs5s8/pDDv490UVNZ34F38Cm5BcOkT8NABmPcDDL0ZdPZQdBjWPgOLhsCyuXBkFTQ32batQgghejUJnIQQ4hfiC+MprD17T4YFC/m1+cQXxndpu34p5IvP8X/lFeyiorA0NlKxfDkZ111Pxi23Urlm7QWfz8vJyAfzRvHnq6Ox02vYeqyYmYu3sCY5v1Paf0E0GgidCNe+B48fgSsXQ+AoMDfB4R/gi5vVIGrts1B0xNatFUII0QtJ4CSEEL9QVFvUofU6i8ZoxO2aq+n/zdeEfPE5LldcAToddfv3U/3TT7/qnIqicPuYEH54cAKR/i6U1Zq49z/7eOp/idQ2dpMeHTtXGHUn3L0B7t8JYx8ABy+oKYTtb8HbcfDBNNj3b6ivtHVrhRBC9BISOAkhxC94O3i3q15jc2Ont6U9FEXBYcQIAhf9jYifNuA1fz4e835r3V9/5Ag5f/gDdQkJ7T5nhI8zy+eP495JYSgKfLE7iyvf+pmEE+XtOLoL+QyBGX+Bxw7DzZ/DoCtA0cKJ3bDiIXVtqOX3QcY26Fu5kIQQQnQwCZyEEOIXYn1i8XXwReHcWdue3/48L+96mYqGi19fqaPofXzwfvAB7CIjrdvKli6l8vsVZNx4E8dvvImK77/H3Hj+oM+o0/LUFUP47P9G4+diR3pxDde+s523N6bSbKvEEWej1cPg2XDLF7DgEEz7E3gOAFMtHPwcPrkC/hELW96Aihxbt1YIIUQPJIGTEEL8glajZWHcQoDTgqeTr4d6DcWMmS8Of8E1311DfZONkyicg9tNN+N61VUoej31CQnk/uFJUi+7nKK33sJUcP6sdOMivFj9yERmRfvRZLbw+poj3PKvneSU13VJ+y+Ysy+Mfxge2AP/tw5ifwsGJyhNh5/+DIujYen16mK7TQ22bq0QQogeQgInIYQ4g6khU1k0eRE+Dj5ttvs6+PL3yX/ns9mf8cH0D4hwi+CqiKuw09nZrK3nYx8dRcBrrxKxaSPeDz+EzseH5uJiit9ZQsbNN2Mxm897DjcHA+/MjeWv1w/FwaBl9/FSZi7ewoqDuV1yD7+KokBQHPzmH/D4Ubh6CYSMB4sZUtfB13eoQ/lWPQn5ibZurRBCiG7u16+aKIQQvdzUkKlMCZrC7tzdrNuxjmljpxEXEIdWowVgtP9ovp7zNc2W1rTkiUWJfJD4AY+PepwglyAbtv50Ok9PvO67D8+77qJq/XpKl36G45gxKBr1d2iW5mYqV67CedpUNHanB4KKonDjqCDi+nvwyJcHOJBdzoNf7Gfj4UJevCoKZ7tuvGimwRGG36qWkjQ48Bkc+Byq8mDXu2rxH6YurhtzPdi727rFQgghuhnpcRJCiHPQarSM8h3FMMMwRvmOsgZNJ+k0Ooxao/X1G3vf4Kfsn7jqu6v4+76/U2OqsUGrz03R63GZNYv+ny3F6/77rNurN28m94knSJ08hcK/LcKUe+bepP5ejnz9+7E8dFkEGgX+tz+HK97ayr7M0i68i4vgGQ6XPwePJsPcbyDyKtDoIe8grHwc3hgE3/wO0n6CLlyrSwghRPcmgZMQQnSg58Y+x1j/sZjMJj5K+ogrl1/Jt6nfYracfzicLSja1kDQ0tiILsCf5vJySv71L1KnTuPEgw9Rs2s3ll9kpNNrNSyYPoiv7h1LP3d7skvruOHdHSxad5SmZvVem80WdqSV8N2BHHaklXS/hBIaLQyYBjd+Co8dgZmvgW80NDdA0n/hP9fAm8Ng48tQlmHr1gohhLAxGaonhBAdKNwtnPemvcfmE5t5fc/rZFVl8ey2Z1l2eBl/HP1HhnoPtXUTz8pl5kycp06lauNGypZ+Ru2uXVStW0fVunUYBwwg+NN/o3NvO4RtVH8PVj48kee/S2b5/hze2nCMrceKuHZEIO9sSiOvojVphr+rHc/PiWRmtL8N7u48HD1hzO9h9L1qz9P+pZD4FVRkw+bX1NJ/ojqUb8gcMDjYusVCCCG6mPQ4CSFEB1MUhclBk1l+1XIeG/kYjnpHkkuSOVZ2zNZNOy9Fp8Nl2jRC/v0Jod9/h9tNN6HY26Po9Wjd3Kz1mquqrH93sdPz95uG8+bNw3G207E/q5xnv0tuEzQB5FfUc9/SeFYn5XXpPV0QRYGA4TD7DXjsKFz3IYRNARTI2ArL71ETSqx4BE7sk7WhhBCiD5EeJyGE6CQGrYE7ou/gyvAr+erIV1wdcbV137GyYwQ5B3XrbHx2Awfi/+IL+Cx4lKbCQhRFTcXeXFVF6pTLcLjkEtxvm4vjuHEoisJVwwMZHuTGLS99i31d9RnPqQD/+KKWaX++Hq3m3Otk2ZzeTk0UEXM9lGfBgS/gwFL17/s+Vov3EBhxGwy9CZzat3CyEEKInkkCJyGE6GRe9l7cP/x+6+u6pjru33A/GjQ8NuoxpoVMswYl3ZHW1RWtq6v1dc2OHZirq6neuJHqjRsxhIXhPvdWXK+6mryjGSxZ8yoGc9NZz9eo0bHnykjGjI3qojvoAG7BMPlJmPSE2vO0fykc+h6KDsHap2H98zBwpjqUL2IqaOW/VyGE6G1kqJ4QQnSx7KpsLBYLuTW5PLb5MX635nccLj1s62a1m8v06YStXIn73LloHBxoTE+n4M8vkTp5Msr7b58zaAIwmJsozy/qsvZ2KI0Gwi6F6/6lJpSYvQgCR4K5CQ7/AF/cBH+PhHXPQ3H3H5ophBCi/SRwEkKILjbQfSArrlnBfcPuw6g1srdgLzeuuJEXd7xIaX3PSOltDAvF79lniNiyGd+nn8bQvz/m6mqcd25u1/Hf7s9hW2rxadn6ehR7N7jk/+Dun+C+HTD2AXDwguoC2LYY/jkKPpwB8f+Bhqp2nFAIIUR3JoGTEELYgL3OnvuH38+Kq1cws/9MLFj45ug3XPm/Kymq7Tm9MVonJzxuv42wlT8S9K9/4X7rLe067lhhNXM/2MWcf/7M9wdzrSnMeyzfSJjxF1hwCG5aqg7bUzSQvRO+f0BdG+rb+yFzuySUEEKIHkoGYQshhA35O/nz+qWvc/Pgm3lt92v0c+6Ht0PPSzKgaDQ4TZyA1sOd8s8+O2/930R6saRIQ1JOJQ99sZ+/utvzfxNCuemSIBwMPfi/Jp1BTVc+ZA5U5kHCMnU+VEkqHPhMLR5hakKJYbeAS0DrseXZUFui/r2pCdfaDDU1uq7l/XDwBLcg29yXEEIICZyEEKI7GOk7ki9mf0FtU611W0FNAa/teY0HRzxIqGuoTdvX0aZ/+Geu/f39/G/AFP69I4MTZXW8uCKFxeuP8duxIcwb1x8vJ6Otm3lxXPxhwqMw/hHI3gX7/wNJy6E0HTb8CX56SU0kMeI28I2BJWOgqQEAPTAZ4Mgp59MZ4YF9EjwJIYSNSOAkhBDdhFajxdngbH391v63WJe5jo1ZG7l1yK3cO+xeXAwuNm1jh2lowCXAl4enDuCeSWGsWLWbjGX/Za3rAP6xoZH3tqRz/ch+3D0xjFAvR1u39uIoCgSPUcvM1yDlW9j/GWRth2Nr1WLnag2azqqpQe2RksBJCCFsQgInIYTopu6OuZuKhgo2n9jMpymf8kP6Dzw44kGuibgGrUZr6+ZdlIC/vYHTpEkA2Bu0XF6UTOH+H5kDVDi6sd1rELuyI5m1fQCXDg3inknhjAxxt3WzL57RSe1hGnEbFJ8cvvc5VOfbumVCCCHOQ5JDCCFEN9XftT//vPyfLJm6hFDXUErrS3lxx4vc/OPN7M3fa+vmnZHO3R3FYDhnHcVgwGHECLTOrb1rxoEDcZ42FcXBAdeacmZl7uKFXR+z7MfnGPvBy9z7t5Vcv2Q761IKMJt7SXIFrwiY+jw8mqz2RAkhhOjWpMdJCCG6uQmBExjtP5qvjnzF2wfe5nDpYbac2MIov1G2btpp9AEBhK9eRVNZ2Vnr6Nzd0QcEtNnmNGkSTpMmYW5spHbXbqo3baJ640bIzWVkaRp1Dk7szSxj76d7md2QxfS4cKZfOwV7o74L7qqTaXXqML722PAniLkBwi8DZ9/ObpkQQohTSOAkhBA9gF6jZ+6QuVwRegUfJn7IPUPvse7Lrc7FzeiGg97Bpm08SR8QcFpg1F4agwGniRNwmjgByzNP03DsGI3p6fw0bjIfb8vgs52ZzNnwJSGrCjjwmjO1sWOIuvYKfKZMROPYw+dCtUfaBrUA+MVA+OVqgomg0WpGPyGEEJ1GAichhOhB3O3cefySx62vLRYLC7cuJKc6hwUjF3BF6BUoimLTNnYURVGwGzgQu4EDcQEWzhrM/eODOJA6gLqECtzqq3Dbvo6y7eso1uowXHIJvtdchetVV9m66Z1nxO1QkAS5+yE/US3bFoPBCUInqT1REVPBo3dlYRRCiO5AAichhOjBCmsLrWXh1oUsO7yMhXELifKKsnXTOoWLiyOTPvsXDXX1bPx6LRkr1jAw7SD+tSU079zBT/UGBl0yhcgAFyxmM/UJCdjFxKBoe3YyDatL7oKA4VBdBOkbIbWlB6qmCI6sVAuoa0VFTFV7pEIngqEP9MYJIUQnk8BJCCF6MF9HX767+js+Tf6UfyX+iwNFB7j5x5u5OuJqHo59GC97L1s3sVMY7e2Y+dvfYLl9DpuPFLLk223o92zniFMwSW9tZeIAL+73N+G24G607u7qHKopU3CcMB6tk5Otm386B091naZzpSTXGdV6AE7eMPRGtZjNUJAIqesh9SfI3qmuFbX7fbVoDeocqoipavGJVFOkCyGEuCCKxWLpJemJ2qeyshJXV1cqKipwcekl66H0QSaTiZUrV3LFFVeg1/eCyeGiW+spz1tBTQFvxr/JivQVADjqHfn3zH8zyGOQrZvWJRJPVPDeljRWJuZhtsCEnIMsOPgN9o11rZX0ehwvGYXT5Mm4zJqFztvblk1uqzxbXacJMDU1sW3bNsaPH49e1/I7TgfP9q3hVF8JGVtbAqn1UJ7Vdr+zf8vcqMsgbAo4eHTG3Ygepqd8zoneoTs9bxcSG0iPkxBC9BK+jr68PPFlbhp8E6/tfo3G5kYi3CJs3awuE9PPlX/eGkt2aS0f/nycL/doucE/msiS40wtP8rE4iPYF+RQs30HNdt3YBw82Bo4NVdVobG3R9HZ8L9Ft6DWwMhkosIhB/yHwYV+qbBzgcGz1WKxQEmaOpwvdT0c3wpVeXBgqVpQIHBkS2/U5erfe/gaYUII0Vlsuo7Tli1bmDNnDgEBASiKwrfffnveYzZt2kRsbCxGo5GIiAg++eSTLmmrEEL0FMO8h7H0iqW8O+1d60K59U31PP3z06SWpdq6eZ0uyMOBF34TxfaFl/HwjCHk9o/k7wOu4Nqxj/Lo7KdJufpO9BMm4RAbaz2m6O+LOTZ+AjlP/IGKH3+kubLSpvfQYRRFXS9q9L0w92t4MgNu/xbGPagO2cMCOXth86vw4TT4axh8NQ/i/wOVubZuvRBCdCs27XGqqalh2LBh/O53v+Paa689b/3jx48ze/Zsfv/73/PZZ5+xYcMG7rrrLvz9/ZkxY0aXtFkIIXoCjaJpM7/p38n/5vu07/kx/UduHHQj84fPx9XoatM2djZ3RwMPXT6AeyaF8d/4E/xrSzqHS+Ax3DH4xXDd9yncNTGMcG8nag/sp7migsoVK6hcsQK0WhxGjsRpyhScJl+KMbSXZKnT20H4FLVMfwkqciDtJ7U3Kn0T1JdDyrdqATW4OpmpL3iserwQQvRRNg2cZs2axaxZs9pd/9133yU0NJS//e1vAAwZMoSff/6Zv//97xI4CSHEOVwRdgWHSw+zPms9Xxz+gpXHVzJ/+HxuGHgDOk3vHrVtp9cyd3QIN18SzLqUfN7bks7+rHK+2J3Nsj3ZTBviy71vvMuQsmyqN26katMmGlPTqN29m9rduylbupTw9eusad4tFkuvSfmOayDE3q4WczPkxLfOjcrZB4UpatnxT9DZqxn6Tq4d5RkuSSaEEH1Kj/rfcseOHUydOrXNthkzZvDII4+c9ZiGhgYaGlqzFFW2DL8wmUyYTKZObK3oTCd/dvIzFF2hNzxvfnZ+/HXCX9mdv5s39r1BakUqL+96mS8Pf8njIx9ntN9oWzexS1w+yIvLBnqyL6ucf23N4KcjRaxNKWBtSgGxwW7cNe0WLn/wIZpzcqjZspmazVswDhhAU1MTABaTicw5v8FuaAyOky7FYcJ4tG5uHd5Omz1zfsPVMuFxqC1FydiMJm0jSvoGlOoCOLZWLYDFNRhz+GVYwi7D0n8iGJ27tq2iQ/WGzznRc3Sn5+1C2tBtsuopisLy5cu5+uqrz1pn4MCB3HnnnTz11FPWbStXrmT27NnU1tZib29/2jEvvPACL7744mnbP//8cxwcHDrwDoQQomdotjSzt3Ev6+vXU2epY7BuMLc53WbrZtlEfi1szNOwp0ih2aL2nvjYWZgSYOYSbwt6DWqChZaeFfv0dILee996vEVRqOsfQs3gIdQMGUyjj0/v7IWxWHCuP4FvZQI+lYl41BxFa2my7jajpdQpgkLnoRS6xFBhHwyKTadRCyFEu9TW1nLrrbe2K6terw+cztTjFBQURHFxsaQj78FMJhPr1q1j2rRpNk9jKXq/3vq8VTRU8H7S+9w08CaCnYOt23QaHY76vrVgamFVA5/uyOLzPdlU1asBgZeTgdtHB3NrXBBuDurP3dLcTH1iErWbN1GzeQuNx461OY/nYwtwv+OOi25Pt3/mGmtQMrehpP+EJv0nlNL0Nrstjt5YQierPVKhU8Cxd64n1pt0+2dO9Crd6XmrrKzEy8ur96Uj9/Pzo6CgoM22goICXFxczhg0ARiNRoxG42nb9Xq9zX9Q4uLJz1F0pd72vHnpvfjjmD+22fbm7jf5OednHol9hDnhc9D0kV6DQA89T82O5MGpA1m2O4uPfj5ObkU9f9+Qyntbj3PTJUH834RQ+rk7YLhkFC6XjILHH8eUk0PVpk1Ub9pM7c6dOI8Za31GqjZtomL5tzhNmYzTpEnoPC58vaRu+8zp3SBytloASo+3pDzfAMe3oNQUoSR9jSbpazXluf8wNd15xFTodwlou+E9CejOz5zolbrD83Yh1+9RgdPYsWNZuXJlm23r1q1j7NixNmuTEEL0FrWmWg4UHqC4rphntj3DssPLeDLuSYb7DLd107qMk1HHXRPDmDeuPz8k5PLe5nQO51fx8bYMPt2RyZVD/blnUhhRAWpGQn1gIB5z5+Ixdy7mmhqUU36JV7V2HVVr1lC1Zg0oCvbDhrVk6ZuMceCA0xJMmHJzaSorA6CpqQljTg71KSk0tawtpXN3Rx8Q0KXvR7t5hILHXXDJXdDUCNm71AQTaRsgPxHyDqhl69/A6AKhk1rXjnILtnXrhRCiXWw6VK+6uprUVHVNkREjRrBo0SKmTJmCh4cHwcHBPPXUU+Tk5PDpp59CSzry6Oho5s+fz+9+9zt++uknHnroIX788cd2Z9W7kNWBRffVnVacFr1fX3reGpsb+ezQZ7yX8B41phoArgy7kkdiH8HX0dfWzetyFouFrceKeW9LGttSS6zbJ0R4cc+kMCYO8Dprhr36lBSq1q+natMmGlIOtdmnDwgg9Ltv0TqrCRVMubmkzZyFpbHxrG1RDAbCV6/qvsHT2VQVtKY8T/sJ6krb7vca2Jqpr/940J95BInoXH3pc07YXnd63i4kNrBpj9PevXuZMmWK9fWCBQsAmDdvHp988gl5eXlkZWVZ94eGhvLjjz/y6KOP8uabb9KvXz8++OADSUUuhBAdxKA1cGf0ncwJn8Nb8W/xbeq3/JD+AxuyNrB4ymLGBYyzdRO7lKIoTBrozaSB3iTlVPD+lnR+TMzj59Rifk4tZoi/C/dOCmP2UH/02rbDGu0iI7GLjMT7oYcw5edTvWkz1Rs3UrNzJxpHB2vQBFD8wYfnDJoALI2NNJWV9bzAydkXht+iFnOz2vOU2hJIndgDxUfVsmsJaI1q8HQykPIe1DuTbQgheqRukxyiq0iPU+/QnX5TIXq/vvy8JRcn8+ruV8mqymLFNStwMcjnZnZpLR/+fJwv92RTZ2oGINDNnjvH9+fmuGCcjOf+naS5rg5TXh7GsDD1dX09R+JGw3kCJ4D+//0G+6ioDrqTbqCuHI5vVudGpW6AyhNt97sEqsP5wi+HsMlgf4bU7+XZUFty+vaTHDzBLajj297L9OXPOdH1utPz1mN6nIQQQnRvUV5RfDrrU05Un7AGTRaLhTf2vsGc8DkM9hhs6yZ2uSAPB174TRQPXz6ApTsz+feODHLK63jpx0O8teEYt40J4Y7x/fFxtjvj8Rp7e2vQRMvaUG7XXE35l1+d/+JmM7V792IID0fn7t6Rt2Ub9m4QeZVaLBa15yl1vRpEZW6DyhyI/1Qtihb6jVJ7osIvh4DhUJkL/xwJTQ1nv4bOCA/sk+BJCHHRJHASQghxToqiEOTc+qVzbeZaPk35lKWHlnLtgGt5cMSDeNhdeMa4ns7d0cCDlw/g7klh/C8+hw+2ppNeXMM7m9L4YOtxro0N5K6JYUT4OJ3zPFpnZ9xuvLFdgVNTfgEnHnxQPc7DA2N4OIaIcIzhERjDwzAOHtxzAypFUYfmeQ+CsfPBVKcGTyd7o4qPqEknsnfBxr+AvYcaPJ0raAJ1f22JBE5CiIsmgZMQQogLMtRrKDP7z2R1xmq+OfoNa46v4ffDfs8tg29Bf0qa6WZzM/GF8RTVFuHt4E2sTyxajdambe8Mdnott44O5uZLglh3qID3NqcRn1XOsj3ZLNuTzdQhvvz+0jBG9b/44LKpqhJ9QACm3FyaS0upLS2lds8e636v++/H+yE1sGoqKaFixQo1qIoIR+fnd9ZEFt2S3r4l895U9XV5dkvK8/WQvllNMpH2k61bKYToQyRwEkIIcUH8nfx5/dLXuXnwzby2+zUOlR7i9b2v8/XRr/nDJX9gYr+JrM9cz6u7X6WgtnXtPV8HXxbGLWRqyFSbtr+zaDQKM6L8mBHlx96MUt7bks66lALWH1JLbLAb90wKZ1qkL1rNrwtg7AYNIuKnDZhramhIP05jehoNqWk0pKXRkJaKceAAa936lBQKX32ttX0ODhjCwzGGh2OMCMdpyhSM4eEdcu9dwi0IRt6hlmYTnNgLBz6D/f85/7Gmuq5ooRCil5PASQghxK8y0nckX8z+gm9Tv+Wt/W+RUZnBon2LqGuq4/HNj2Ohbe6hwtpCFmxawKLJi3pt8HTSqP4ejOrvQWphNR9sTed/8TnEZ5Xz+6X7CPVy5K6JoVwX2w87/a/rgdM4OmIfE419TPQ56jjhPGMGDWmpNGZkYq6tpT4xkfrERAB03t7WwKkuIYHSf3+KMSJcDa4iIjAEBaF01yQBWj2EjFV7pdoTOH0yGwJGqBn7QiZA8Giwc+2KlgohehEJnIQQQvxqWo2W6wZex/T+03k/4X3GBYzj2W3PnhY0AViwoKDw2u7XmBI0pVcO2/ulCB8nXr1uKAumD+Tf2zP4z45MjhfX8PTyJP6+7ijzxvbn9rEhOLq7oxgM513H6ULmLznEjsAhdgS0JKBozMqiIS2NxjS1l8ouMtJaty4hkcoff2x7Ar0eQ0gwxvAIPO++G/voHpzNz9IMOXvVsu1NUDTgF6MGUf3HQ/BYcOh78/SEEBdGAichhBAXzdngzGOjHmNP/p42w/N+yYKF/Np84gvjucTvki5toy35ONvxxIzB3Dc5gi/3ZPPRz8fJKa/jb+uO8s6mNG66JIg7l/2PtNQTvLflOMXVrQkPvJyM3DsplImjBvzqNZwUvV4doneWoXkOo0bivWCBGlSlpdGQno6ltpbG1DQaU9PwmDfPWrf8v/+l5F8ftBn2ZwgPxxgWhsa+my5ee+uXUFsGmT9D5nYoTYe8g2rZ+bZaxyeqpUeqpTh527rVQohuRgInIYQQHaaotqhD6/U2TkYd/zchlN+ODeHHhDze25LOobxKPtmewac7wGwBdN5wynJF6cDu7dUsCVOY2Ulr39oNHozd4NbU8hazmaa8vJa5U+lt504dOUJjRgaNGRlUb9jQehJFQR8QQL933sZu0CAAmsrKUPR6tE7nzizY6Zz8YOBMdRFeUNOYZ26HjJZAqvgIFCarZff7ah2vgWoA1X8ChIwDlx628LAQosNJ4CSEEKLDeDu077f0qzNWc0XYFZ3enu5Kr9Vw9YhArhoewM+pxby3OY2fU8+8iKsFUIAXV6QwLdLvVyeWuBCKRoM+MBB9YCBOkya12ed17704T5miJqVIV3ukGtLSaC4txZSTg87T01q39MMPKfngQ3R+fhjDwlpTp0eovVVatzMsaHshHDzVdZrOt46Tg2fbbS4BEHO9WgCqi9TU55nbIGObGkAVH1XLvo/VOu6hrXOkQsaBe8jFtV0I0eNI4CSEEKLDxPrE4uvgS2Ft4RnnOZ3kadf6RdZisXC07CgD3Qf2rHTZHUBRFCYO8Ean0Zw1cKIleMqrqGf38VLGhnuetV5X0Hl6ohs7FsexY9tsbyotpTE9He0pgVNTkdqz2JSfT1N+PjXbt7c5JmLTRvR+fgDUHTyIua5ODai8vNr3LLgFYbp+FU35WWdvr18w+vOt4eTkDVFXqwWgthSydqhBVOY2yE+AsuNq2b9UreMa1NIj1TK0zyNMXYtKCNFrSeAkhBCiw2g1WhbGLWTBpgUoKG2CJwX1S+XDsQ+3yap3oOgAv131WyLcIrgy7Epmh83Gz9HPJu23lcKq+nbV+/MPyfx2bH+mRvri5WTs9HZdCJ2HBzqPtgkWAl57Dd+nn26TlOJkL1VzZSU6X19r3ZIPP6Jq7VoANK6uGMPCWudPhUfgOHYMiq7t1xZTbi5pN/7uvEk1wlevurD5YQ4eMHi2WgDqKyBrV+scqdz9UJENCcvUAuDsr/ZEnZwj5T1IAikhehkJnIQQQnSoqSFTWTR50RnXcXoy7snTUpGnlqdi0BhILU9lcfxi3ox/kzi/OOaEz2FqyFQc9Y42uIuu5eNs1656KXlVLPxfIprliYwK8WB6lC8zovwI8nDo9Db+WloXFxxGjMBhxIg2280NDW16lXTe3uhDgjFlZWOuqKBu/37q9u8HQDEaGRS/z1q3bNkyzNXVoNefM2gCsDQ20lRW9qsTawBq6vKB09UC0FANJ3a39EhtV7P1VeVB0n/VAuDgpQZS/SeogZRPJGg0v74NQgibk8BJCCFEh5saMpUpQVOIL4ynqLYIbwdvYn1iz5iC/IaBNzCj/wzWZqxlRdoK4gvj2ZW/i135u3hp50t8eeWXhLmF2eQ+ukpcqAf+rnbkV9SfcYCj0pJd7/axwaxLKSQxp4LdGaXszijlpR8PEenvoi6+G+3LIF/nHjHkUWNs22Pm9+wzAJjr62nMyGhZ2DeVxrR0sFhQtK3PTtmyL2k4fLjL22xldILwy9RCywK7J/a2zpPK3gO1xXDoe7UA2Lm19kj1Hw++MaCVr2FC9CTyL1YIIUSn0Gq07U457mJw4fqB13P9wOvJqc7hh7Qf+CH9BxqaG+jv2t9ab0PmBvyd/BniMaRHBAftpdUoPD8nkvuWxqO0zGk66eRd/vnqKGZG+/PQ5QPJKa9jbXI+a5Lz2X28lJS8SlLyKvn7+qOEeDqoQVSULyOC3NF0QTKJjqSxszsty98vuV45m/qwMOqSkzFlZp73nBXffY+5pga7yCi0Tp3Qg6m3h9CJagFoaoTc+Nasfdm7oL4cjqxUC4DBGYLHtCacCBiuLuwrhOi2FIvFcvbZu71QZWUlrq6uVFRU4OLiYuvmiF/JZDKxcuVKrrjiCvTddWV70WvI82YbFouF4rpia6Y+k9nE1K+nUlpf2mvnQ61OyuPFFSnkVbTOefJ3teP5OZHMjPY/4zGlNY2sP1TA2uR8thwrprHJbN3n7WxkWqQ6nG9smCcGXe8aKlaXnEzGdde3/wBFwRAWhn10FHbRMdjHjsA+qgsW9m1uUteMOjlHKnMHNFS0raN3gKC41kV5A0eqGQG7iHzOia7UnZ63C4kNpMdJCCFEt6QoSpv05pUNlYzyHcWm7E2nzYe6MvxKpoVM6/HzoWZG+zMt0o8dqYWs3bqL6RNHMzbC55wpyD0cDdw4KogbRwVR09DE5qNFrEnO56dDhRRVNfD5riw+35WFs52Oywb7MCPKj0sHeuNo7DtfARxGj6YxK4umvDwaWxJVVHz3PU6XXkrQe+9a65Uv/xa7QQMxDhiA0pFf5rQ66DdSLeMfBnMzFCS1Zu3L3A51pZC+SS0AWiP0u6Q1a1+/S8DQfeeyCdEX9J1PTSGEED2ap70nf5v8NyobK1mXsY4V6SvYV7DPOh8qvTydBaMW2LqZF02rURgd6kHJIQujQz0uaN0mR6OOK2L8uSLGn8YmMzvSS1iTnM+6lAKKqhr47kAu3x3IxaDTMGmAF9Oj/Jg6xBcPR0On3pOt+fzhCeyjomgqLqYuKYn6xCTqk5JwHNeaUt1UUEjeU09BSyY+45DB2EfHYBcdjX1MNIbQ0DbzrC6KRgv+w9Qy9n4wm6HocNu1pGoKW3qofm45Rg+Bsa1zpIJGg9G5Y9ojhGgXCZyEEEL0KC4GF64beB3XDbyuzXyo2WGzrXX25O/hp6yfmBM+p9fNh2ovg07DpQO9uXSgNy9dFc3+7DLWJBewJjmfzJJa1h8qZP2hQjSKmpxiRpQf06P8CHSzt3XTO43OywvnyZNxnjz5tH3mmmocx42lLikZc2Ul9QcTqD+YYN3vcccd+C58Uq3b2EhTfj76oKCOebY0GvCNVEvc3WCxQElq6xypzG1QmaPOlcreBT8vAqUl+Do5Ryp4DNhf5ILCQohzksBJCCFEjxXoFMi9w+7lnqH3tPkC+99j/+XH9B9Zemgp4a7hXBl+JVeGXdmr5kNdCI1GYWSIByNDPHhq1mCOFFSxJkkNolLyKtmZXsrO9FJeXJFCTKArM1rSnEf4OHXroFPn7o5iMJx3HSedu/t5z2UMCyP4o4+wWCyYsrKoS0yiPjFR7aFKScEucoi1bn1CApm33Y7G1RX7qCjsYmKwj4nGLjoana/vxb9nigJeA9Qy6k41kCrLaB3Wl/EzlGeqCShy42H7P9Q0In7RrXOkgseB43kWSy7PhtqWhZebmnCtzVDnYp1cL8vBE863eLAQfYgkhxA9UneaVCh6P3neep5tOdtYnrqcjVkbaTSrX6oVFC7xu4Qrw67kN+G/OWNq9O6iK5+57NJa1iTnsza5gD2ZpZz6rSDMy5HpLRn6hvVz65YZ+ky5uTSVlZ11v87d/eLWcAIszc3Q3IxiUIc0Vnz/PXlPP4PFZDqtrtbbC/8X/4TzZVPUYy2Wzgk+K06cMkdqm9pD9UveQ1rnSIWMB+fWBYcpz4Z/joSmhrNfQ2eEB/ZJ8CQ6XHf6f1WSQwghhOjTxgeOZ3zgeCobK1mfuZ4VaSvYW7CX3fm7Ka4r5uqIq611O+2LbQ8R5OHAXRPDuGtiGMXVDaxPUXuitqWWkF5cw7ub03h3cxq+LkamR/oxI8qP0WEe6LXdI0OfPiDgogOj81G0WjhlfpPrb36Dy8yZ1B89Rn1SonXeVENqKs1Fxeg8Wnu4Kv63nOK338YuOhq7mGjsY2Kwi4pC63yR85Nc+8Gwm9QCUJV/So/UNig61Fr2fKDW8YxomSM1QV1X6lxBE6j7a0skcBKihQROQgghei0XgwvXDriWawdcS251Lj+m/4iXvZc1UKprquOGFTcwMXBin54PdZKXk5Gb44K5OS6YqnoTm46oGfo2HSmioLKB/+zM5D87M3G113P5YB+mt2Toszd03967zqIYDNhHR2EfHcXJMMlcV0f9ocMYh7QO66tLSsSUm4spN5eqtWut2w39+2MXE4PPIw+jDwy8+AY5+0H0dWoBqClumR+1XU0wkZ+k9kqVpEL8vy/+ekL0QRI4CSGE6BMCnAK4e+jdbbZtzt5MZmUmmZWZMh/qF5zt9MwZFsCcYQE0NDWzPbU1Q19JTSP/25/D//bnYKfXMGmANzOi/Lh8iA9uDr07Q9+5aOztcYgd0Wabz4IFuMyYqfZMtWTzM+Xk0JiRQWNGBr5PLbTWLf38c+qTk1t6paKxGzTQOjzwgjl6QeRv1AJQVwZZO1uz9uUeAMznO4uaOl0IATLHSeY49VTdaWys6P3keeu9TGYT23O2syJ9xRnnQz0+6nGGeA4573k6vF3d+JlrNlvYl1nGmuR81iTnc6KszrpPq1EYE9aSoS/SDz9XO5u2tbtqKi2lPjmZhrQ0PO+4w7o96//uombbNutrRa/HOHgwdtFR2EfH4PqbOR23vlTmdvh41vnrafTgGwX+Q8FvKPjFqK8lFbq4CN3pM07mOAkhhBDtoNfouTToUi4NupSqxirWZa5rMx/KSe9krVtSV4Kr0RWdpm//16nVKMSFehAX6sEzs4eQklfJmuQC1ibnczi/im2pJWxLLeG575IZFuRmzdAX7u3UjrP3DToPD5wmTsRp4sQ22z3uuAO7mGjrOlPNFRXUJyZSn5hIpfOPuF7TOjev/NtvUbQ67GOi0QcHo2gucM6Zvp2L6ZpNkHdALW0aG6YGUX4x4DdM/dPZT80IKEQv1bc//YUQQogWzgbnNvOhduXtIsildVL8izte5GDRQa4IvYIrw68k0iOyT8+HAlAUhagAV6ICXFkwbSAZxTWsTclnTXIB8VllHMwu52B2OX9dfYQIHydrEBUT6Nrn37szcZo4AaeJE6AlaYnpxAnqk5KoS0wCs7lNcFT89juYsrMB0Dg7t/RKRWMXraZGb0/CDFONlqaGswdcOqMZ/f/9B8xNkJ/YWipzoDRdLSnftR7g4HVKMNXSO+U1QF3wV4heQAInIYQQ4hcCnAK4ZsA11temZhPJJcmU1pey9NBSlh5aSphrGHPC5zA7dDb+Tv42bW930d/LkXsmhXPPpHAKq+pZl1LAmuQCdqQVk1pYTWphNW9vTCPA1Y7pUX5Mj/Ilrr8Hum6Soa87URQFQ1AQhqAgXGa1HVJnaW7GafJk6hMSqD98GHNVFbU7dlK7YycAxsghhP3vf9b6tXv2YAgNReflZd1mKigm7UcfLOazB7CKxkL4rUb0I2ZD5FWtO2qK2wZS+YlQfBRqiyF9o1pO0tmrC/ueGlD5RIJReiBFzyOBkxBCCHEeeq2e1detZkfuDlakrWBj9kbSK9J5M/5N3ox/k5sH3czTY562dTO7FR9nO+aODmHu6BAq6kxsOlJozdCXW1HPJ9sz+GR7Bu4Oei4fovZETRzghZ1eeifOR9Fq8Xv6jwBYTCYaUlOpS0ykPjGJuuQkHIa3JqgwNzaS+bv/A5MJnb8/9tFR2EXHoNU1nTNoArCYFZoaNJw2A8XRC8KnqOUkUx0UHmoJpBJa/kwCUw3k7FNL6x2AZ3hrr9TJP09dZ0qIbkgCJyGEEKId9Bo9k/pNYlK/SVQ1VqnrQ6WvYE/+Hvq79rfWq2qsYn/hfsYFjOvz86FOcrXXc9XwQK4aHki9qZmfjxWzJjmf9YcKKKs18c2+E3yz7wQOBi2XDlQz9E0Z7IOrffdKjNEdKXo9dkOGYDdkCNx4I7QM8zupqbAIQ3AwjenpNOXlUZWXR9W69e2/QHuDGb09BMaq5SSzGcqOnxJIJUJeAlTnt6ZGT27tGcPRp7Vn6mQyCo8wGeonug35RBdCCCEukLPBmWsGXMM1A64hrzoPJ0PrsKO1GWt5YccLeNh5yHyoM7DTa5ka6cvUSF+ams3syVAz9K1Nzie3op5VSfmsSspHp1EYG+7ZkqHPFx+Xc2foazZb2H28lMKqenyc7YgL9UCr6Zvv+anPmqFfIOE//kBzdQ31KcnUJyVTn5RI7b54mgoKznuu8q++wjRmDIawMAwhIWjsLiBTokaj9ix5hkNU69BXqgtPH+pXcgxqCiFtg1pO0juoWfx+OdTP0M7kFkJ0IAmchBBCiIvwy/lNjeZGPOw8ZD5UO+i0GsaGezI23JPn50SSlFNpTXN+rLCarceK2XqsmGe/S2JEkBszovyYEeVHfy/HNudZnZTHiytSyKuot27zd7Xj+TmRzIyW9xtA6+SIY1wcjnFxANQlJ5Nx3fXnPa78y68o//IrALwefADv+fMBaCopoWr9BoxhoRjCwtB6eLT/lwNOPhBxuVpOaqyFwpS2vVMFyWCqhRN71HKSogHPAacEUy0BlZP3hb0pQlwgCZyEEEKIDnTL4Fu4fuD1Z5wP9fb+t9l00yZcja5nPb7Z3Mzegr0cbDyIT4EPcQFxaPvAUCVFUYjp50pMP1cenzGI9KJq1iQXsCY5nwPZ5cRnqeWVVYcZ5OvMjChfpkf5kV1ay/2fxfPLRSnzK+q5b2k8S26LleDpIjhNmUJzWRkNx49jDAuzbq9LTCT/+eetrzWurhhD1SDKGBaK05QpGMPD238hgwP0G6WWk8zNaua+vIOn9E4lQE0RFB9RS9I3pzTW75Rhfi3BlHuo2vMlRAeQwEkIIYToYGebD2XQGtoETe8nvM9gj8GMDRiLXqNnfeZ6Xt39KgW16hCqrzd8ja+DLwvjFjI1ZKoN76jrhXk7cd9kJ+6bHE5+RT3rWtKc70wv4UhBFUcKqnjrp1Q0CqcFTaBuU4AXV6QwLdKvzw7bu1heD8zHPipKnTd1ytwpjb0DjpMm0ph+HFNODuaKCuoOHKDugLrek87X1xo41cbvp/TjjzCEhmEIC8UYFoYhNBSt83kW0dVo1XTmXgMg5pTesaqCXyShSICSNHXuVGo+pK5rrat3BL/oXwz1G6LOyRLiAkngJIQQQnSiU+dDmcwm6/b8mnz+uf+fWLDgYedBlGcUW3O2nnZ8YW0hCzYtYNHkRX0ueDrJz9WO28f25/ax/amoNbHhsNoT9dPhQkzNZwqbVBYgr6Ke3cdLGRvu2aVt7m0URWmzuK3j6DgcR6vD/sz19TRmZtKYnk5DejqN6cfVZBUt6pOSzpiQQuftjSEsDO9HHsZhhJoJ0NLYCDrduRf0dfZVy4BT/j00VJ9lqF8NZO9Si/VmtOA18PShfo7tfEbKs6G25Oz7HTzBLejs+0WPJYGTEEII0UX0mtYscQoKtw65lVXHV1FaX3rGoAnAggUFhdd2v8aUoCl9Ytjeubg66Lk2th/Xxvbjm73ZPP5NwnmP2Xy0iOFBbtgb+vZ7dyqduzuKwaAGKmehGAzo3N3Pey6NnR12gwZhN2jQGfc7jhuL79NP05CeRmP6cTXDX1GRtZw6N6r8v/+l4K+vYwjtj/HUHqrzJacwOkFQnFpOam5SM/f9sneqtgSKDqkl8avW+i6BvwimYsCtf9uhfuXZ8M+R0NRw9jdEZ4QH9knw1AtJ4CSEEELYgK+jOgTvsVGP8e/kf/Nm/JtnrWvBQn5tPj/n/MylQZd2aTu7s0D39mVWe3dzGh/9fJzYEDcmRHgxPsKLmEDXPr3wrj4ggPDVq2gqKwOgqamJbdu2MX78eHQ69euhzt0dfUDARV/LGBGBMSKizbbmqioajx+nIT0dQ8QA6/aG48ex1NXRkHKIhpRDbU+kKPRf9gX2w4apddPTaS4pOXtyCq0OfAarZegN6jaLBaryW4KoU+ZOlaZDZY5ajq5uPYfB+ZShfkPVoOhcQROo+2tLJHDqhSRwEkIIIWxIr9ET4Ni+L6cP/PQA4a7hxHjHMNR7KEO9hhLuFt5n14uKC/XA39WO/Ir6M85zArDXa3Gz15FX2cDO9FJ2ppfyxtqjONvpGBvmyYQBaiAV5uXY51LG6wMCrIGRyWSiISMDu8hI9PrOXz9L6+yM/dCh2A8d2ma77xNP4H7LLTQeP94y9O+4dQigubISfXCwtW75199Q+vHHcIbkFIawMBzHjEHj8IvgWlHAxV8tA6e3bm+oUof25Se2JqMoPASNVZC1Qy2iz+ubn7RCCCFEN+Lt0P40ymkVaaRVpPFt6rcA2Ovs+WbONwS7qF8oTc0m9Nq+sXCsVqPw/JxI7lsaj0LbJBEnQ6C/3zSMGVF+ZJTU8nNqMduOFbM9rZjK+ibWphSwNkVNxOHvasf4CC8mRHgxLsITH+cLWK9IdBhFr8cYGooxNBQuu8y63WKx0Fxa2mbooMbBAX2/fmdMTgEQsXmzNXCqWLGChmOp6tC/8HA1OYVT6/prGJ0heIxaTmo2QfGxtkP9cuMxldbS1HD23kqd0Yz++FZw8ADXoDZzw0TPJoGTEEIIYWOxPrH4OvhSWFuI5Qx9JwoKvg6+fHbFZ6SUppBQlEBCcQJJxUmYLWYCnFp7rF7c8SK78ncx1Guo2ivlPZQhHkOw0/XOQGBmtD9Lbos9bR0nv1+s4xTq5UiolyO3jwmh2WwhKadCDaRSi9mbUUZeRT3f7DvBN/tOADDI11kNpAZ4EhfqiZNRvjLZkqIo6DzbJm/wfvABvB984IzJKUx5eeh8Wn8hUblmDdXrN7Q5XufjY+2h8nniCTT2v8i0p9WDb6Raht0EgGn/OtLmPojFfPZgSNFYCOd59OueATtXdYif39DWVOleA9Vzix5HPgWEEEIIG9NqtCyMW8iCTQtQUNoET0pL38mTcU/i4+iDj6MPk4MmA2C2mMmryWszVC+pOIn8mnzya/JZm7kWAJ2iY6DHQIZ5D2Nh3EI0Su+a2zMz2p9pkX7sPl5KYVU9Ps52xIV6nDUFuVajMCzIjWFBbsyfEkFdYzN7M0utgVRybqU15flH246j0yiMCHaz9kgNC3JD34fnR3U350tOAeAyYwY6b++2ySkKC2kqLKTu4EF8n3nGWjd34VPUHz1yxuQUTRXV5wyaACxmhSb7EPSabKivgIytajlJa1BTop8aUPlFq71eoluTwEkIIYToBqaGTGXR5EVt1nEC8HXw5cm4J8+YilyjaAh0Cmyz7fPZn5NckszBooNqz1RRAiX1JaSUpFDfVN8maFq0dxH2OnuGeg8l2iv6nAvzdndajfKrU47bG7RMHODNxAFqD0VZTSM70kusgVRmSS17MsrYk1HG4vXHcDRoGRPm2dIj5cUAH6c+Nz+qp3GdMwfXOXOsr5srK1uSUxzHXFXZJv15fUoKDUePnjE5hc63nc/YZc/BhCug6EjbFOn5idBQqc6jyjvY9hiPsNYkFH5D1d4pJ18Z6teNSOAkhBBCdBNTQ6YyJWgKu3N3s27HOqaNnUZcQNwFpSB30Dtwid8lXOJ3CbTMDcmrySOhKAGzxWyt12Ru4ovDX1Df3Dq8rb9Lf2vSiRG+IxjoPrCD77BncHc0cEWMP1fEqMP8sktr2ZZazM+pxWxPK6G0ppENhwvZcLgQAG9nozVb3/gIT/xdZXHV7k7r4oL9sGHWDH2n6vfWmy1D/k5PTqG0s7fWXFOrZuDzbwmArDvMUJ7ZNpjKS4CqXDWzX2k6pHzXWt/R+5RgquVPz3B1cWDR5SRwEkIIIboRrUbLKN9RFBoKGeU76qLXbVIUhQCngDbzoGgJnB4d+SgJxQkkFiWSVZVFRmUGGZUZfJ/2PZP7TeYfl//DWn9j1kYiPSPxdfS9qPb0REEeDtwcF8zNccGYzRYO5Ve2BFIl7D5eQlFVA8v357B8fw4A4d6OLUGUF2PCPHG1l/ksPYmhf38M/fufMTlF7ebV5PzxpfOeI+uxl3BYtoaQ/3xq3WaurVWTVXiEqiXyqtYDaorbJqHIS4CSY1BTBGk/qeUkvQP4RrXtnfKNBL0E7J1NAichhBCiD7LT2XHrkFu5lVsBKKsvI7E4kYSiBBKLExntP9paN78mn4c2PgSAj4MPw7yHEeOlpkSP9IzEXtd3vrBpNApRAa5EBbhyz6RwGpqaic8st/ZIJZwoJ62ohrSiGj7dkYlGgaH9WtePig1xw6iT3oKe5mRyCv2g4e0+RntKBkCLxULqlMvQODtjFxXVUiKxj4pC6+YGjl4QPkUtJzXWqinRT643lZegpkw31cKJPWqxNlCjJp34Ze+U468bvirOTAInIYQQQuBu586kfpOY1G/SafvK6ssY7DGYY2XHKKwtZF3mOtZlrgNAq2h5cMSD/F/M/0FLwgpa5l/1BUadlrHhnowN9+TxGYOoqDOxM73EGkilF9VwILucA9nl/HNjKnZ6DXGhnkyIUOdIDfFzQXOWJBai5wr+9ycY+vWzvm7Ky6O5ooLmigpMJ05QtWaNdZ++Xz/crrsWr/vua3sSgwP0G6mWk8zNUJLW0jN1Su9UbTEUHVZL4tet9V0CfxFMxYB7f5k39St1i8Dp7bff5vXXXyc/P59hw4bxj3/8g7i4uLPWX7x4MUuWLCErKwsvLy+uv/56XnnlFezsemeqVSGEEMKWhngO4es5X1NrqiWlJMXaM5VQlEBhXSH+jv7WuvEF8Ty08SFrj9RQr6HEeMXgZudm03voKq72emZE+TEjyg+A3PI6trUkmfg5tYTi6ga2HC1iy9EiADwcDYwL97T2SAV5OJznCqIn0Dg5oQ9sTdyiDwhg4K6d1KekUJ+cTF1yMvXJKZiysjCdOIG5psZat6msjIwbbjxzz5RGC94D1RJzvXqAxQJV+acHU2XHoTJHLUdXtzbO6HJ6MOU9GHSGLn2PeiKbB05ffvklCxYs4N1332X06NEsXryYGTNmcOTIEXx8fE6r//nnn7Nw4UI++ugjxo0bx9GjR7njjjtQFIVFixbZ5B6EEEKIvsBB78Aov1GM8htl3ZZfk4+zoTWNcmJxIlWNVWzP3c723O3W7SEuIcR4xXBH1B0M8jh72ujeJsDNnhtGBXHDqCAsFgtHC6qt2fp2pquJJn5IyOOHhDwAQjwdrGnPx4Z54u4oX2a7E527O4rBgKWx8ax1FIOhzUK9J2ldXXEcOxbHsWOt25orKqhPSUHn2zp3sD4lBdOJE2fsmbKLisLt+utxmjjhlAsq4OKvloEzWrfXV0JBUuvcqbwEdehfQyVkblPLSRo9+Aw+PUW6Xc/NtNkZbB44LVq0iLvvvps777wTgHfffZcff/yRjz76iIULF55Wf/v27YwfP55bb1XHZPfv359bbrmFXbt2dXnbhRBCiL7Oz9GvzevbIm9jtP9oEosSSShWe6UyKjPIrMwkszKTWwffaq279cRWdubttPZM+Tn69eq03oqiMMjPmUF+zvzfhFBMzWYOZJfz87FitqcVsz+rnMySWjJLsvh8VxaKAtEBrtZAalR/d+z0Mj/KlvQBAYSvXkVTWdlZ6+jc3dEHBJx1/6lOBlOnchg+nOBPPlZ7ppKS2vRMmU6cwHH8OGvd+sOHKX5nCXbR0W17pgDsXCBknFpOamqE4qNte6byE6GhojXLH5+11nfv3xJEDWvtnXIJ+HVD/cqzobakpR1NuNZmqCnZdS3hiIMnuAVd+Hm7kE0Dp8bGRvbt28dTTz1l3abRaJg6dSo7duw44zHjxo1j6dKl7N69m7i4ONLT01m5ciW33377Ges3NDTQ0NBgfV1ZWQmAyWTCZDJ1+D2JrnHyZyc/Q9EV5HkTXa2nP3MDXAYwwGUA14ZfC0BFQwVJJUkklSQR5hxmva/1mev5X+r/rMd52XsR4xlDjFcM0Z7RDPMahl7bvox0zeZm9hftp7iuGC97L0Z4j7jojIRdYXigM8MDnXlgcijVDU3syShje1oJ29NKOVpYTWJOBYk5Fby7OQ2DTsOoYDfGhXsyLtyDSH+Xsy7ye6F6+jPXpby90Xl7n7PKRb2PBgOGkSMxjByJS8um5spKGg4doiElBeOoUdbzV+/dS9XatVStXWs9XBcYgDEyErvIKJxmzkB/ylwrUMBzkFqiblA3WSxQkY2Sn4hSkIhSkKT+WZkDZRlqObTCegaLgycW3+iWEoPFN6YlRfo5woqKE+iWjEZpVr+T64HJAEdaq1i0Rpru2wWu/c56ms5wIT8rxWKxWNpRr1Pk5uYSGBjI9u3bGXtKtP2HP/yBzZs3n7UX6a233uLxxx/HYrHQ1NTE73//e5YsWXLGui+88AIvvvjiads///xzHBxkHLEQQghhK0dMRzhqOkp2czb5zfmYMbfZ/5TLUzhqHAHIbcpFp+jw0nidlngiuTGZH+t+pNJSad3morgw2342UYaoLrqbjlfRCMcqFI60lIrGtkGSg9bCAFcLA10tDHK14GUnc/77GkNBAY6HD2M8kYNdTg6GkpI2+7PvuZu68HAA7I5nYJ9xnIbAQOoDAzE7Op7z3PqmKlzrsnCtzcK1LhPXukyc6vPQ/OLfKUCTYqDKvh8V9iFUOIRQYR9MpV0QzVojAK61GUw+8tx572fToD9R4dD/At+Fi1NbW8utt95KRUUFLi4u56xr86F6F2rTpk28/PLLvPPOO4wePZrU1FQefvhh/vznP/Pss8+eVv+pp55iwYIF1teVlZUEBQUxffr08745ovsymUysW7eOadOmodfL+hiic8nzJrpaX3nmruAK69/rmuo4XHqYpJIkEosTKW0o5YapN1j33/fTfezK34WT3okozyiiPaOJ8YqhvL6cZbuWYaHt74GrLFUsq13GX0f+lcuDLu/S++oMFouF48W1bE9Xe6N2Hi+lqr6Jg6UKB0vVOoFudowNU3ujxoV54OlkbNe5m80WdqYV8dOOfVw2diRjwr07rCdLdK1Te6YaUg4xad48tC3fd4tff4Py1a1zpk72TKm9U5HYxcaiOU+itWZTHeaiw3CyZyo/EaUwGZ2pFvfadNxr06EldrMoGvAIV3umnL3a1f7x48eD/+mLEnemk6PR2sOmgZOXlxdarZaCgoI22wsKCvDz8zvjMc8++yy33347d911FwAxMTHU1NRwzz338PTTT6PRtP0tlNFoxGg8/YNDr9f36v+M+gr5OYquJM+b6Gp96ZnT6/XEBcYRF3jmrLr2OnvsdfZUm6rZlb+LXfnnnttswYKCwt/2/Y1p/af1iGF75zMowMCgADfunBBOU7OZxJwKa9rzfZll5JTX8018Dt/EqwvxDvZzVrP1DfBidKgHDobTv/atTsrjxRUp5FXUA1o+PXYAf1c7np8Tycxo/zO0QnRnek9P7CZMgAkTTtvnOGI45uIi6pKTMWVm0ZSTS1NOLjXr1gMQsfEn9M5qopfa+HjMtXXYRUW2TXSh10NInFpOMjdD6fG2603lJ6DUFEHJMZSSY+1vv06nXqMLXchnrE0DJ4PBwMiRI9mwYQNXX301AGazmQ0bNvDAAw+c8Zja2trTgiOtVv0wtOGoQyGEEEJ0on9c/g+azE2klqdaU6Hvyt9Ffk3+WY+xYCG/Np8deTuYEHj6F8meTKfVMCLYnRHB7jxw2QBqG9X5UdtSi/n5WDEpeZUczq/icH4VH/x8HL1WYUSwuzXt+bB+rqw/VMB9S+P55ben/Ip67lsaz5LbYiV46kVcZs3CZdYsaOmZqk85RH1yEvXJyTTm5KA7pdOi5IMPqf7pJ2hJiKEmn2hNj94mmNJowStCLdHXtW6vKmhNkX58K6Rv7MK77Rw2H6q3YMEC5s2bx6hRo4iLi2Px4sXU1NRYs+z99re/JTAwkFdeeQWAOXPmsGjRIkaMGGEdqvfss88yZ84cawAlhBBCiN5Hp9Ex2GMwgz0Gc+OgG1mZvpIntz553uPmb5hPjFcMsb6xTAqc1Cadem/hYNBx6UBvLh2oJi0oqW5ge5q6EO/WY8XklNex+3gpu4+XsmjdUZwMWkzmXw5wVFnUFAK8uCKFaZF+MmyvF9K6uOA4ZjSOY0afcb8+IAB9SDCmzCxMubmYcnOtCSgUOzsG7duL0vK9u+HYMbReXqenYHf2BedpMGAahF+O6e9baGo4+8LYOqOZ7t6/bvPA6aabbqKoqIjnnnuO/Px8hg8fzurVq/FtyWeflZXVpofpmWeeQVEUnnnmGXJycvD29mbOnDn85S9/seFdCCGEEKKreTucO7PZSWaLmYNFBzlYdJDqxmpr4GRqNrEhewMjfUa2+1w9haeTkTnDApgzLACLxUJWaa11/ajtaSWU1547k5gFyKuoZ/fxUsaGe3ZZu0X34PfM08DTp/RMJVOfnERdcjI6D09r0ASQs2ABDcdS1Z6pk71SLenRTwZTpoJi0n70wWI+exCuaCyE31qMvn2Z3G3C5oETwAMPPHDWoXmbNm1q81qn0/H888/z/PPPd1HrhBBCCNEdxfrE4uvgS2Ft4WnJIQAUFHwdfPlgxgccKDxAfGE8k4MmW/cnlyTzxOYnAAh2Dmak70hifWMZ6TOSfs79es2aUoqiEOLpSIinI3NHh2A2W1iyOY3X1xw577GvrznMrGh/Yvq5EhXggrNdd+8TEB3pTD1TllPSd1uamrA0q1n2rD1T69ZZ9zuOG0fwRx/SVFF1zqAJwGJWaKqo6ta9Tt0icBJCCCGEuFBajZaFcQtZsGkBCkqb4ElB/ZL2ZNyThLiEEOISwlURV7U5vr65nsEegzlSeoSsqiyyqrJYnrocAB97H54e8zSXBV/WxXfV+TQahdhg93bUhPiscuKzygE11XmYlyND+7kRE+jK0H6uRAa4nDHphOi9lFOSKSg6HeErf6S5qor65JSWnim1NGZmovNuyaZn59q+k7e3no3Iky6EEEKIHmtqyFQWTV7Eq7tfpaC2NUuvr4MvT8Y9ydSQqWc9doz/GL6e8zWVjZVqj1RBPPsK9pFUkkRhXSEedh7Wuj9l/cTyY8vVHinfkQzxHIJe051/N35ucaEe+LvakV9Rf8Z5Tgrg7mjgzvH9Sc6pJDGngpzyOtKKakgrqmH5fjVzn0aBAT7OxPRTA6mYQFeG+Ltgp5d5532J1tn5tJ6p5qoqzLW16gtn3/adqL31bEQCJyGEEEL0aFNDpjIlaArxhfEU1Rbh7eBNrE9su1OQuxhcmNRvEpP6TQKgvqmexOJEojxbF8/dnrudTSc2semEOoXAXmfPUO+hjPQZyUjfkQz3GY5Ba+ikO+x4Wo3C83MiuW9pPErLnKaTTg6oevma6DZZ9YqrG0jMqSDxRAUJJypIzCmnoLKBIwVVHCmo4pt9JwDQaRQG+jqrgVQ/V4YGujHIzxmD7uyJAUTvo3V2RtuS3ry3kMBJCCGEED2eVqPlEr9LOuRcdjq7085146AbCXIOYl/BPuIL46loqGBX3i525anrSa2/fj2+jupvy7Mrs3ExuuBq7N7DjmZG+7PktthT1nFS+Z1lHScvJyNTBvkwZZCPdVtBZb0aSOVUkHiinIQTFZTUNJKSV0lKXiXL9mQDYNBqGOLvbA2kYvq5MsDHCZ1WginRc0jgJIQQQghxHgPdBzLQfSDzouZhtphJL08nvjCevQV7KagpsAZNAC/vfpltOdsY4D6AWJ9YRvqN7LaZ+2ZG+zMt0o8dqYWs3bqL6RNHMzbCp90pyH1d7PCNtGNqpHr/FouFvIp6Ek5UkHCinMQctXeqos7EwRMVHDxRAWQBYNRpiApwaTNnKszbSdKfi25LAichhBBCiAugUTREuEcQ4R7BjYNubLPPYrFQXl+OBQtHy45ytOwoy44sAyDIOYjxAeN5eszTNmr5mWk1CqNDPSg5ZGF0qMdFBS6KohDgZk+Amz0zo9UFVS0WC9mldSTklFuH+SXlVFDV0NQm+QSAg0FLdIBrmzlT/T0d0UgwJboBCZyEEEIIITqIoih8ceUXFNcVE18QT3xhPPEF8RwuPUx2VTbHK463qf/a7tcIdgkm1ieWAe4D0Ci9b+iaoigEezoQ7OnAlUPVRXrMZgsZJTXWHqnEExUk5VZQ29jM7oxSdmeUWo93NuqIbumROjnUL8jDvteki+8LdO7uKAYDlsbGs9ZRDIbTF9HtZiRwEkIIIYToYF72XkzvP53p/acDUNVYxYHCA+i1rZn4SutLWXpoqfW1i8GFET4jrJn7Ij0i29TvTTQahTBvJ8K8nbhqeCAAzWYL6UXVLYkn1KF+ybmVVDU0sSO9hB3pJdbjXe311h6pof3cGNrPFX9XOwmmuil9QADhq1fRVFYGQFNTE9u2bWP8+PHodGo4onN3Rx/QjVe/lcBJCCGEEKLzORucmdhv4mnb5w+fT3xBPAeKDlDZWMnmE5vZfGIzANcNuI4Xxr0AQLO5mYbmBhz0Dl3e9q6i1SgM8HVmgK8z143sB4Cp2UxqYTUJLYknEnMqOJRXSUWdia3Hitl6rNh6vJeTgZhAV2L6uTG0pYfKx8XOhnckTqUPCLAGRiaTiYaMDOwiI9Hre84vByRwEkIIIYSwAQ87D34/7PcAmMwmjpQeYV/BPvYV7GN/4X6G+wy31j1cepjbVt5GpGckI31HEusbywifEd0+c9/F0ms1DPF3YYi/Cze1JDpsaGrmaH51mzlTRwqqKK5uZOORIjYeKbIe7+tiJCbQzTrMLybQFS8no+1uSPRoEjgJIYQQQtiYXqMn2iuaaK9oa+a+ZkuzdX9ySTJNliYSihNIKE7g4+SPUVCIcI9gpM9Ibhp0ExHuETa9h65i1GnVIKifK7Sst1pvauZQXmWbOVPHCqsoqGygoLKA9YdaF0cOdLNv6ZlqTUDh5nDha3A1my3sPl5KYVU9Ps52xF1kYg3R/UngJIQQQgjRzWgUTZtEETcMvIHxgeOJL4i39kplVGZwrOwYx8qOWedSASQVJ3Gs7BgjfUcS5Bx03nk/zeZm9hbs5WDjQXwKfIgLiGv34sHdhZ1ey4hgd0YEtyYXqG1sIiW3ss2cqfTiGnLK68gpr2N1cr61brCHQ0viCTWgig50xcXu7EPIViflnbb+lf9Z1r8SvYcETkIIIYQQ3ZyiKAQ6BRLoFMic8DkAFNcVs79wP/EF8cR4xVjr/pD+A58d+gxaklSM9B2priflO/K0zH3rM9fz6u5XKahVe2S+3vA1vg6+LIxbyNSQqV1+nx3JwaBjVH8PRvX3sG6rqjeRnFtpXbQ34UQ5mSW1ZJWq5ceEPGvdMG/HlkBKHeoX6e+Co1HH6qQ87lsaj+UX18uvqOe+pfEsuS1WgqdeSgInIYQQQogeyMvei2kh05gWMq3N9lCXUGJ9YkksTqS4rpg1GWtYk7EGWpJU/HDND3jYebA+cz0LNi3A8osQoLC2kAWbFrBo8qIeHzz9krOdnjFhnowJ87Ruq6g1qT1Sp8yZyimvI72ohvSiGr49kAuARoFwb0dOlNWfFjQBWAAFeHFFCtMi/WTYXi8kgZMQQgghRC9y0+CbuGnwTdQ31ZNUnMS+gn3EF8azv3A/jnpH3I3uNJubeXX3q6cFTQAWLCgovLb7NaYETelxw/YulKuDngkDvJgwwMu6raS6gcScCmvPVOKJCvIr6zlWWHPOc1mAvIp6dh8vZWy45znrip5HAichhBBCiF7ITmfHKL9RjPIbBUCTuYn8mnwURSG+IN46PO9MLFjIr80nvjCeS/wu6cJWdw+eTkYmD/Jh8iAf67bCyno+2JrO+1uPn/NYgL/8mMIVQ/2JDXZnWD837A29O/jsKyRwEkIIIYToA3QaHf2c1fWRimqLzlv/l/XePfgu/Zz7MdRraLuSTvQ2Pi52TBns267AKSm3kqTcSmhZn2qIvzOxwe7WEuRh3+fev95AAichhBBCiD7G28H7gupVNlby9oG3rdtdja5Ee0Uz1GsoMV4xxHjF4Gbn1mnt7S7iQj3wd7Ujv+LM85wUwMPJwD0TwziQXU58VhkFlQ0k5VSSlFPJpzsyAfByMhIb7EZsiBpIDe3nip1eeqW6OwmchBBCCCH6mFifWHwdfCmsLTzjPCcFBV8HX2J9YgFobG5k7pC5JBYlcqj0EBUNFWzL2ca2nG0AXBNxDX8a/ydoGRKYUpLCYI/BGLQXvj5Sd6bVKDw/J5L7lsajtMxpOulk/9Ffro62ZtWzWCzkVtQTn1lGfFYZ8VnlpORWUFzdwNqUAtamqMMldRqFyAAXYoPdGRHsRmywO/3cpVequ5HASQghhBCij9FqtCyMW8iCTQtQUNoET0pLCPBk3JPWxBBe9l4sjFsIgKnZxJGyIyQUJZBYnEhicSIx3q3p0I+VHWPuyrnoNXoGewxWe6S81V6pYOfgHh8MzIz2Z8ltsaet4+R3hnWcFEUh0M2eQDd75gwLgJbFepNyKtRAKrOcfVllFFU1kNCS0e+T7eqxPs5GdWhfiBpIRQdKr5StSeAkhBBCCNEHTQ2ZyqLJi9qs4wTg6+DLk3FPnjUVuV6rJ9ormmivaOs2i6U18CqqK8LN6EZ5Q7k1sOKwus/V6Mof4/7IFWFXWI/riYHUzGh/pkX6sft4KYVV9fg42xEX6tGuFOR2em2b9aUsFgsnyuqIzypjf5Y6vC8lt5LCqgZWJ+dbF+rVaxUiA1zVIX7B7sSGuBPgatcj37+eSgInIYQQQog+amrIVKYETWF37m7W7VjHtLHTiAuIu+AU5Kd+eZ/UbxJbbtrCiaoTJBQnkFScREJxAodLDlPRUIG7nbu17vqs9fx939+J8YphqLc6X6qnDPHTapQOSTmuKApBHg4EeThw1fBAAOoam0m09kqpQ/yKqxs4mF3OwexyPt6WAYCvi9orNTLEnRHB7kQHumDUSa9UZ5HASQghhBCiD9NqtIzyHUWhoZBRvqM6ZN0mRVEIcgkiyCWI2WGz4ZQhfmGuYdZ6CUUJZFdlk12VzcrjK6El+99g98HEeMdwR9QdBDgFXHR7ehp7g5a4UA/iQk/vlToZSKXkVVJQ2cCqpHxWJam9UgathqhAl9YMfiFu+Lva2/hueg8JnIQQQgghRKc7OcTvVHcPvZux/mNJKG6ZL1WUSFlDGUklSSSVJPHbyN9a667NWMvRsqPWnqlTe656uzP1StU2NpFwonWu1P6sMkpqGtmfVc7+rHI+RE2b7u9q15p0IsSdqADplfq1JHASQgghhBA24WJwYVzgOMYFjoOWnpWc6hwSixM5UnqEQKdAa92Vx1eyIWuD9XU/p37WpBMnS0f0lvUUDgYdY8I8GROmDhe0WCxkldZaA6n4rDIO51eRV1HPj4l5/JiYB4BBpyEmsO1cKV8XOxvfTc8ggZMQQgghhOgWFEWhn3M/+jn3Y1borDb7Lg++HEe9IwlFCWRUZnCi+gQnqk+w6vgq9Bo9O2/diRY1cDpYdBBXgyvBLsFoFI2N7qZrKYpCiKcjIZ6OXDNCXei4puHUXik1JXpZrYl9mWXsyyyDll6pQDd7axr02BB3Iv1dMOj6xvt2ISRwEkIIIYQQ3d6c8DnMCZ8DQEVDBcnFya1Z+6BNQok/7fgTR8uO4mxwtvZGDfUeSrRXNB52Hja7h67maNQxNtzTmsTCYrGQUVLbZl2pI/mV5JTXkVNexw8Jaq+UUadhaD/XliF+6lwpH2fplZLASQghhBBC9CiuRtc2Q/xO1WxuxtngjFFrpKqxiu2529meu926f7TfaD6Y8YH1dZO5CZ3mwr8SN5ubiS+Mp6i2CG8Hb2J9Yrv9UEFFUQj1ciTUy5HrRqq9UtUNTSRkl1sDqfisMsprTezJKGNPRpn12H7u9i1JJ9S5UkP8XdBr+1avlAROQgghhBCi19BqtHwy8xNMZhPHyo6RWJRoTT5xvOI4nvatKcSbzc1M+WoKAU4BrXOlvGPo79L/nEP81meuP+P6VwvjFp51/avuysmoY1yEF+MivKClVyq9uMaavW9/VhlHCqo4UVbHibI6vj+YC4CdXsPQQDdGhLgxsmWIn5eT8bzXazZb2HW8lH3FCp7HSxkb4dOu9a+6AwmchBBCCCFEr6PX6In0jCTSM5KbuAmAqsYqakw11joZlRmUN5RT3lBOSkkKXx75EgBnvTPRXtFcGX4lvwn/TZvzrs9cz4JNC7BgabO9sLaQBZsWsGjyoh4XPJ1KURTCvZ0I93bihlFBAFTVmziY3TJXqmWh3oo6E7szStmdUWo9NtjDwdojFRvszmA/Z3Sn9EqtTsrjxRUp5FXUA1o+PbYXf1c7np8Tycxof5vc74WQwEkIIYQQQvQJzgZnnA3O1tdhrmGsvm61NRV6YnEiKSUpVJmq2JG3g6HeQ611S+pKeGXXK2zL3XZa0ARgwYKCwmu7X2NK0JRuP2zvQjjb6ZkwwIsJA9ReKbO5pVcqq4z9LVn8jhZWkVVaS1ZpLd8eUHul7PVada5UiDtYYMnmtNPOnV9Rz31L41lyW2y3D54kcBJCCCGEEH2SoigEOgUS6BTIzP4zAaxD/JKKk9oETonFiazJXHPO81mwkF+bT3xhPJf4XdLp7bcVjUYhwseJCB8nbmzplaqsN3Egq3Wu1P6sMqrqm9h1vJRdx0vPei4LoAAvrkhhWqRftx62J4GTEEIIIYQQLU4d4neqUNdQZoTMOG/wBJBckkxaeRpDPIcw0H0g9jr7Tmxx9+Bip2fSQG8mDfSGll6ptKJq4rPKWJ2Yz8ajRWc91gLkVdSz+3ipNQNgdySBkxBCCCGEEOcR4hLCTYNvalfglFOVw7IjywDQKlpCXUOJ9IxkiMcQhngOIcozCjtd707vrdEoDPB1ZoCvM3Z67TkDp5MKq+q7pG2/lgROQgghhBBCtEOsTyy+Dr4U1haecZ6TgoKvgy+j/EZxovoEKSUplNaXklqeSmp5Kt+nfQ/Af2b9h+E+wwE4VnaMsvoyBnsOxsXg0uX31BXauwZUd18rSgInIYQQQggh2kGr0bIwbiELNi1AQWkTPCmoc3OejHuSqSFTmdF/BhaLhaK6IlJKUjhUcoiU0hSOlB5hoPtA63FfHfnK2jsV5Bxk7ZU62UPlbudugzvtWHGhHvi72pFfUX+GcFOd4+TnakdcaPdenFgCJyGEEEIIIdppashUFk1edMZ1nE4GTScpioKPgw8+Dj5MDpp8xvM5G5wJdAokpzqH7KpssquyWZu51rp/y01brMFTRkUGTgYnvOy9OvUeO5pWo/D8nEjuWxqP0jKn6aSTqSCenxPZrRNDIIGTEEIIIYQQF2ZqyFSmBE0hvjCeotoivB28ifWJ/VUpyB+KfYiHYh+ioqGCQ6WH1J6pkhQOlR6iobmhTY/Tq7tfZVvuNrztvRniOYQhHkOsiSx8HXxRlO4beMyM9mfJbbGnrOOk8pN1nIQQQgghhOi9tBpth6YcdzW6MsZ/DGP8x1i3NTQ3tKlT31yPgkJRXRFFJ4rYcmKLdV+wczA/XPODNXgqqy/DzejWrYKpmdH+TIv0Y0dqIWu37mL6xNGMjfDp9j1NJ0ngJIQQQgghRDdk1BrbvP5k5ifUmmo5WnaUlJIUa89UWnkavo5te5zmrpxLeUM5kR6RbeZMBbsEo1E0NrgblVajMDrUg5JDFkaHevSYoAkJnIQQQgghhOg5HPQODPcZbs3KR0vPVFl9mfV1ramWgpoCGs2N7Mrfxa78Xa3H6xyYFTqLF8a9YN1mtphtGkz1FBI4CSGEEEII0YMZtUb8HP2srx30Duy8dSdpFWkcKjlEckkyh0oPcbT0KLVNtW2ONTWbuPSrS9W1pjzU+VJDPIcQ7haOXqO3wd10XxI4CSGEEEII0cvotXoGewxmsMdgrhlwDQBN5iYyKjLaJLFILU+lqrGKhKIEEooSWo/X6BnoPpDrB17P9QOvt8k9dDcSOAkhhBBCCNEH6DQ6Itwj2mwb6D6Q76/+3rrW1MnMflWmKpJLkpnWMM1aN7sqm0c3PmrtlRriMYRBHoOw19m3uw3N5mb2FuzlYONBfAp8iAuI+1XZCG1BAichhBBCCCH6KK1GS6hrKKGuocwOmw2AxWLhRNUJUkpTGOQ+yFo3pSSFI2VHOFJ2hOWpywHQKBrCXMMY4jGEGwbdwAifEWe91vrM9W3Wv/p6w9f4OviyMG5hm/WvuqtuMQvs7bffpn///tjZ2TF69Gh27959zvrl5eXMnz8ff39/jEYjAwcOZOXKlV3WXiGEEEIIIXorRVEIcgliRv8Z9Hftb90e5xfH4imLuXfovUzqNwlve2/MFjOp5amsSF9BUW2Rte7+wv38YfMf+CTpE3bl7eL71O9ZsGlBm0WDAQprC1mwaQHrM9d36T3+Gjbvcfryyy9ZsGAB7777LqNHj2bx4sXMmDGDI0eO4OPjc1r9xsZGpk2bho+PD9988w2BgYFkZmbi5uZmk/YLIYQQQgjRF7jbuXN58OVcHny5dVtRbRGHStVFe0/N9Lc3fy+rMlaxKmPVOc9pwYKCwmu7X2NK0JRuPWzP5oHTokWLuPvuu7nzzjsBePfdd/nxxx/56KOPWLhw4Wn1P/roI0pLS9m+fTt6vZrpo3///qfVE0IIIYQQQnQubwdvvB28mdRvUpvtEwInoCgKKSUp7C/YT3F98VnPYcFCfm0+8YXxHbqocEezaeDU2NjIvn37eOqpp6zbNBoNU6dOZceOHWc85vvvv2fs2LHMnz+f7777Dm9vb2699VaefPJJtNrTI9SGhgYaGlpXXa6srATAZDJhMpk65b5E5zv5s5OfoegK8ryJribPnOhq8syJjhbhEkGEi5qIYnXGav64/Y/nPSa/Kh+TZ9c+gxfyzNs0cCouLqa5uRlfX9822319fTl8+PAZj0lPT+enn35i7ty5rFy5ktTUVO6//35MJhPPP//8afVfeeUVXnzxxdO2r127FgcHhw68G2EL69ats3UTRB8iz5voavLMia4mz5zoDOmm9HbVSz2YysqUrs1bUFtb245aKpsP1btQZrMZHx8f3n//fbRaLSNHjiQnJ4fXX3/9jIHTU089xYIFC6yvKysrCQoKYvr06bi4uHRx60VHMZlMrFu3jmnTplmHbArRWeR5E11NnjnR1eSZE52p2dzMD9//QFFtERYsp+1XUPBx8OG+Ofd1+Rynk6PR2sOmgZOXlxdarZaCgrbZNQoKCvDz8zvjMf7+/uj1+jbD8oYMGUJ+fj6NjY0YDIY29Y1GI0aj8bTz6PV6+WDoBeTnKLqSPG+iq8kzJ7qaPHOiM+jR81TcUyzYtAAFpU3wpKAAsDBuIXZGu65v2wU87zZNR24wGBg5ciQbNmywbjObzWzYsIGxY8ee8Zjx48eTmpqK2Wy2bjt69Cj+/v6nBU1CCCGEEEII25saMpVFkxfh49A2a7avgy+LJi/qEes42Xyo3oIFC5g3bx6jRo0iLi6OxYsXU1NTY82y99vf/pbAwEBeeeUVAO677z7++c9/8vDDD/Pggw9y7NgxXn75ZR566CEb34kQQgghhBDibKaGTGVK0BR25+5m3Y51TBs7jbiAuG6dgvxUNg+cbrrpJoqKinjuuefIz89n+PDhrF692powIisrC42mtWMsKCiINWvW8OijjzJ06FACAwN5+OGHefLJJ214F0IIIYQQQojz0Wq0jPIdRaGhkFG+o3pM0ER3CJwAHnjgAR544IEz7tu0adNp28aOHcvOnTu7oGVCCCGEEEIIYeM5TkIIIYQQQgjRE0jgJIQQQgghhBDnIYGTEEIIIYQQQpyHBE5CCCGEEEIIcR4SOAkhhBBCCCHEeUjgJIQQQgghhBDnIYGTEEIIIYQQQpyHBE5CCCGEEEIIcR4SOAkhhBBCCCHEeUjgJIQQQgghhBDnIYGTEEIIIYQQQpyHBE5CCCGEEEIIcR46Wzegq1ksFgAqKytt3RRxEUwmE7W1tVRWVqLX623dHNHLyfMmupo8c6KryTMnulJ3et5OxgQnY4Rz6XOBU1VVFQBBQUG2booQQgghhBCiG6iqqsLV1fWcdRRLe8KrXsRsNpObm4uzszOKoti6OeJXqqysJCgoiOzsbFxcXGzdHNHLyfMmupo8c6KryTMnulJ3et4sFgtVVVUEBASg0Zx7FlOf63HSaDT069fP1s0QHcTFxcXm/+BE3yHPm+hq8syJribPnOhK3eV5O19P00mSHEIIIYQQQgghzkMCJyGEEEIIIYQ4DwmcRI9kNBp5/vnnMRqNtm6K6APkeRNdTZ450dXkmRNdqac+b30uOYQQQgghhBBCXCjpcRJCCCGEEEKI85DASQghhBBCCCHOQwInIYQQQgghhDgPCZyEEEIIIYQQ4jwkcBI9yiuvvMIll1yCs7MzPj4+XH311Rw5csTWzRJ9xKuvvoqiKDzyyCO2boroxXJycrjtttvw9PTE3t6emJgY9u7da+tmiV6oubmZZ599ltDQUOzt7QkPD+fPf/4zkjdMdJQtW7YwZ84cAgICUBSFb7/9ts1+i8XCc889h7+/P/b29kydOpVjx47ZrL3nI4GT6FE2b97M/Pnz2blzJ+vWrcNkMjF9+nRqamps3TTRy+3Zs4f33nuPoUOH2ropohcrKytj/Pjx6PV6Vq1aRUpKCn/7299wd3e3ddNEL/Taa6+xZMkS/vnPf3Lo0CFee+01/vrXv/KPf/zD1k0TvURNTQ3Dhg3j7bffPuP+v/71r7z11lu8++677Nq1C0dHR2bMmEF9fX2Xt7U9JB256NGKiorw8fFh8+bNTJo0ydbNEb1UdXU1sbGxvPPOO7z00ksMHz6cxYsX27pZohdauHAh27ZtY+vWrbZuiugDrrzySnx9ffnwww+t26677jrs7e1ZunSpTdsmeh9FUVi+fDlXX301tPQ2BQQE8Nhjj/H4448DUFFRga+vL5988gk333yzjVt8OulxEj1aRUUFAB4eHrZuiujF5s+fz+zZs5k6daqtmyJ6ue+//55Ro0Zxww034OPjw4gRI/jXv/5l62aJXmrcuHFs2LCBo0ePAnDw4EF+/vlnZs2aZeumiT7g+PHj5Ofnt/m/1dXVldGjR7Njxw6btu1sdLZugBC/ltls5pFHHmH8+PFER0fbujmil1q2bBnx8fHs2bPH1k0RfUB6ejpLlixhwYIF/PGPf2TPnj089NBDGAwG5s2bZ+vmiV5m4cKFVFZWMnjwYLRaLc3NzfzlL39h7ty5tm6a6APy8/MB8PX1bbPd19fXuq+7kcBJ9Fjz588nKSmJn3/+2dZNEb1UdnY2Dz/8MOvWrcPOzs7WzRF9gNlsZtSoUbz88ssAjBgxgqSkJN59910JnESH++qrr/jss8/4/PPPiYqK4sCBAzzyyCMEBATI8ybEGchQPdEjPfDAA/zwww9s3LiRfv362bo5opfat28fhYWFxMbGotPp0Ol0bN68mbfeegudTkdzc7Otmyh6GX9/fyIjI9tsGzJkCFlZWTZrk+i9nnjiCRYuXMjNN99MTEwMt99+O48++iivvPKKrZsm+gA/Pz8ACgoK2mwvKCiw7utuJHASPYrFYuGBBx5g+fLl/PTTT4SGhtq6SaIXu/zyy0lMTOTAgQPWMmrUKObOncuBAwfQarW2bqLoZcaPH3/aEgtHjx4lJCTEZm0SvVdtbS0aTduvglqtFrPZbLM2ib4jNDQUPz8/NmzYYN1WWVnJrl27GDt2rE3bdjYyVE/0KPPnz+fzzz/nu+++w9nZ2ToG1tXVFXt7e1s3T/Qyzs7Op82fc3R0xNPTU+bViU7x6KOPMm7cOF5++WVuvPFGdu/ezfvvv8/7779v66aJXmjOnDn85S9/ITg4mKioKPbv38+iRYv43e9+Z+umiV6iurqa1NRU6+vjx49z4MABPDw8CA4O5pFHHuGll15iwIABhIaG8uyzzxIQEGDNvNfdSDpy0aMoinLG7R9//DF33HFHl7dH9D2TJ0+WdOSiU/3www889dRTHDt2jNDQUBYsWMDdd99t62aJXqiqqopnn32W5cuXU1hYSEBAALfccgvPPfccBoPB1s0TvcCmTZuYMmXKadvnzZvHJ598gsVi4fnnn+f999+nvLycCRMm8M477zBw4ECbtPd8JHASQgghhBBCiPOQOU5CCCGEEEIIcR4SOAkhhBBCCCHEeUjgJIQQQgghhBDnIYGTEEIIIYQQQpyHBE5CCCGEEEIIcR4SOAkhhBBCCCHEeUjgJIQQQgghhBDnIYGTEEIIIYQQQpyHBE5CCCHEOSiKwrfffmvrZgghhLAxCZyEEEJ0W3fccQeKopxWZs6caeumCSGE6GN0tm6AEEIIcS4zZ87k448/brPNaDTarD1CCCH6JulxEkII0a0ZjUb8/PzaFHd3d2gZRrdkyRJmzZqFvb09YWFhfPPNN22OT0xM5LLLLsPe3h5PT0/uueceqqur29T56KOPiIqKwmg04u/vzwMPPNBmf3FxMddccw0ODg4MGDCA77//3rqvrKyMuXPn4u3tjb29PQMGDDgt0BNCCNHzSeAkhBCiR3v22We57rrrOHjwIHPnzuXmm2/m0KFDANTU1DBjxgzc3d3Zs2cPX3/9NevXr28TGC1ZsoT58+dzzz33kJiYyPfff09ERESba7z44ovceOONJCQkcMUVVzB37lxKS0ut109JSWHVqlUcOnSIJUuW4OXl1cXvghBCiM6mWCwWi60bIYQQQpzJHXfcwdKlS7Gzs2uz/Y9//CN//OMfURSF3//+9yxZssS6b8yYMcTGxvLOO+/wr3/9iyeffJLs7GwcHR0BWLlyJXPmzCE3NxdfX18CAwO58847eemll87YBkVReOaZZ/jzn/8MLcGYk5MTq1atYubMmfzmN7/By8uLjz76qFPfCyGEELYlc5yEEEJ0a1OmTGkTGAF4eHhY/z527Ng2+8aOHcuBAwcAOHToEMOGDbMGTQDjx4/HbDZz5MgRFEUhNzeXyy+//JxtGDp0qPXvjo6OuLi4UFhYCMB9993HddddR3x8PNOnT+fqq69m3LhxF3nXQgghuhsJnIQQQnRrjo6Opw2d6yj29vbtqqfX69u8VhQFs9kMwKxZs8jMzGTlypWsW7eOyy+/nPnz5/PGG290SpuFEELYhsxxEkII0aPt3LnztNdDhgwBYMiQIRw8eJCamhrr/m3btqHRaBg0aBDOzs7079+fDRs2XFQbvL29mTdvHkuXLmXx4sW8//77F3U+IYQQ3Y/0OAkhhOjWGhoayM/Pb7NNp9NZEzB8/fXXjBo1igkTJvDZZ5+xe/duPvzwQwDmzp3L888/z7x583jhhRcoKiriwQcf5Pbbb8fX1xeAF154gd///vf4+Pgwa9Ysqqqq2LZtGw8++GC72vfcc88xcuRIoqKiaGho4IcffrAGbkIIIXoPCZyEEEJ0a6tXr8bf37/NtkGDBnH48GFoyXi3bNky7r//fvz9/fniiy+IjIwEwMHBgTVr1vDwww9zySWX4ODgwHXXXceiRYus55o3bx719fX8/e9/5/HHH8fLy4vrr7++3e0zGAw89dRTZGRkYG9vz8SJE1m2bFmH3b8QQojuQbLqCSGE6LEURWH58uVcffXVtm6KEEKIXk7mOAkhhBBCCCHEeUjgJIQQQgghhBDnIXOchBBC9Fgy2lwIIURXkR4nIYQQQgghhDgPCZyEEEIIIYQQ4jwkcBJCCCGEEEKI85DASQghhBBCCCHOQwInIYQQQgghhDgPCZyEEEIIIYQQ4jwkcBJCCCGEEEKI85DASQghhBBCCCHO4/8BDPXbzc+8YnsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_training_history(history):\n",
    "    # Extract data from history\n",
    "    history_data = history.history\n",
    "    epochs = range(1, len(history_data[\"ner_output_sparse_categorical_accuracy\"]) + 1)\n",
    "\n",
    "    # --- Plot Accuracy ---\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(\n",
    "        epochs,\n",
    "        history_data[\"ner_output_sparse_categorical_accuracy\"],\n",
    "        marker=\"o\",\n",
    "        label=\"NER Accuracy (Train)\",\n",
    "    )\n",
    "    plt.plot(\n",
    "        epochs,\n",
    "        history_data[\"srl_output_sparse_categorical_accuracy\"],\n",
    "        marker=\"s\",\n",
    "        label=\"SRL Accuracy (Train)\",\n",
    "    )\n",
    "\n",
    "    if \"val_ner_output_sparse_categorical_accuracy\" in history_data:\n",
    "        plt.plot(\n",
    "            epochs,\n",
    "            history_data[\"val_ner_output_sparse_categorical_accuracy\"],\n",
    "            marker=\"o\",\n",
    "            linestyle=\"--\",\n",
    "            label=\"NER Accuracy (Val)\",\n",
    "        )\n",
    "        plt.plot(\n",
    "            epochs,\n",
    "            history_data[\"val_srl_output_sparse_categorical_accuracy\"],\n",
    "            marker=\"s\",\n",
    "            linestyle=\"--\",\n",
    "            label=\"SRL Accuracy (Val)\",\n",
    "        )\n",
    "\n",
    "    plt.title(\"Accuracy per Epoch\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Accuracy\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.savefig(\"accuracy_plot.png\")  # Save the accuracy plot\n",
    "    plt.show()\n",
    "\n",
    "    # --- Plot Loss ---\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(\n",
    "        epochs, history_data[\"ner_output_loss\"], marker=\"o\", label=\"NER Loss (Train)\"\n",
    "    )\n",
    "    plt.plot(\n",
    "        epochs, history_data[\"srl_output_loss\"], marker=\"s\", label=\"SRL Loss (Train)\"\n",
    "    )\n",
    "\n",
    "    if \"val_ner_output_loss\" in history_data:\n",
    "        plt.plot(\n",
    "            epochs,\n",
    "            history_data[\"val_ner_output_loss\"],\n",
    "            marker=\"o\",\n",
    "            linestyle=\"--\",\n",
    "            label=\"NER Loss (Val)\",\n",
    "        )\n",
    "        plt.plot(\n",
    "            epochs,\n",
    "            history_data[\"val_srl_output_loss\"],\n",
    "            marker=\"s\",\n",
    "            linestyle=\"--\",\n",
    "            label=\"SRL Loss (Val)\",\n",
    "        )\n",
    "\n",
    "    plt.title(\"Loss per Epoch\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.savefig(\"loss_plot.png\")  # Save the loss plot\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_training_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "e690a0e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 1.  Prediksi\n",
    "# ------------------------------------------------------------------\n",
    "pred_ner_prob, pred_srl_prob = model.predict(X_te, verbose=0)\n",
    "\n",
    "pred_ner = pred_ner_prob.argmax(-1)\n",
    "pred_srl = pred_srl_prob.argmax(-1)\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 2.  Siapkan masker PAD\n",
    "# ------------------------------------------------------------------\n",
    "pad_id = tag2idx_ner[\"<PAD>\"]\n",
    "\n",
    "mask_ner = ner_te != pad_id\n",
    "mask_srl = srl_te != pad_id\n",
    "\n",
    "true_ner_flat = ner_te[mask_ner]\n",
    "pred_ner_flat = pred_ner[mask_ner]\n",
    "\n",
    "true_srl_flat = srl_te[mask_srl]\n",
    "pred_srl_flat = pred_srl[mask_srl]\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 3.  Hitung confusion matrix TANPA PAD\n",
    "# ------------------------------------------------------------------\n",
    "# Buang ID PAD dari label list\n",
    "labels_ner_no_pad = [i for i in range(len(tag2idx_ner)) if i != pad_id]\n",
    "labels_srl_no_pad = [i for i in range(len(tag2idx_srl)) if i != pad_id]\n",
    "\n",
    "cm_ner = confusion_matrix(true_ner_flat, pred_ner_flat, labels=labels_ner_no_pad)\n",
    "\n",
    "cm_srl = confusion_matrix(true_srl_flat, pred_srl_flat, labels=labels_srl_no_pad)\n",
    "\n",
    "# Siapkan label display TANPA PAD\n",
    "display_labels_ner = [idx2tag_ner[i] for i in labels_ner_no_pad]\n",
    "display_labels_srl = [idx2tag_srl[i] for i in labels_srl_no_pad]\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 4.  Plot NER CM (tanpa PAD)\n",
    "# ------------------------------------------------------------------\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "disp_ner = ConfusionMatrixDisplay(\n",
    "    confusion_matrix=cm_ner, display_labels=display_labels_ner\n",
    ")\n",
    "disp_ner.plot(\n",
    "    include_values=True,  # Tampilkan angka\n",
    "    values_format=\"d\",  # Format integer\n",
    "    cmap=plt.cm.Blues,  # Biru-putih\n",
    "    ax=ax,\n",
    "    colorbar=False,\n",
    ")\n",
    "ax.set_title(\"NER Confusion Matrix\", fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 5.  Plot SRL CM (tanpa PAD)\n",
    "# ------------------------------------------------------------------\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "disp_srl = ConfusionMatrixDisplay(\n",
    "    confusion_matrix=cm_srl, display_labels=display_labels_srl\n",
    ")\n",
    "disp_srl.plot(\n",
    "    include_values=True, values_format=\"d\", cmap=plt.cm.Blues, ax=ax, colorbar=False\n",
    ")\n",
    "ax.set_title(\"SRL Confusion Matrix\", fontsize=18)\n",
    "plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "a49f1dfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NER TAG accuracy : 85.12%\n",
      "SRL TAG accuracy : 74.05%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# 3b.  Akurasi token‑level (tanpa PAD)\n",
    "# ------------------------------------------------------------------\n",
    "acc_ner = accuracy_score(true_ner_flat, pred_ner_flat)\n",
    "acc_srl = accuracy_score(true_srl_flat, pred_srl_flat)\n",
    "\n",
    "print(f\"NER TAG accuracy : {acc_ner:.2%}\")\n",
    "print(f\"SRL TAG accuracy : {acc_srl:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "9adad755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[NER] Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      B-DATE       0.94      0.92      0.93        71\n",
      "       B-ETH       0.95      0.83      0.89        47\n",
      "     B-EVENT       0.00      0.00      0.00        11\n",
      "       B-LOC       0.89      0.59      0.71       113\n",
      "       B-MAT       0.00      0.00      0.00        20\n",
      "      B-MISC       0.00      0.00      0.00         3\n",
      "       B-ORG       0.00      0.00      0.00         5\n",
      "       B-PER       0.77      0.73      0.75        60\n",
      "     B-QUANT       0.00      0.00      0.00         7\n",
      "      B-TIME       0.82      0.40      0.54        45\n",
      "      B-UNIT       0.00      0.00      0.00         7\n",
      "      I-DATE       0.92      0.97      0.94       135\n",
      "       I-ETH       0.92      0.86      0.89        51\n",
      "     I-EVENT       0.00      0.00      0.00         3\n",
      "       I-LOC       0.00      0.00      0.00         9\n",
      "       I-MAT       0.00      0.00      0.00         1\n",
      "      I-MISC       0.00      0.00      0.00         0\n",
      "       I-ORG       0.00      0.00      0.00         3\n",
      "       I-PER       0.94      0.79      0.86        43\n",
      "     I-QUANT       0.00      0.00      0.00         0\n",
      "      I-TIME       0.00      0.00      0.00         9\n",
      "      I-UNIT       0.00      0.00      0.00         1\n",
      "           O       0.83      0.98      0.90       855\n",
      "\n",
      "    accuracy                           0.85      1499\n",
      "   macro avg       0.35      0.31      0.32      1499\n",
      "weighted avg       0.81      0.85      0.82      1499\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Recall is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "# (Opsional) tampilkan ringkasan metrik per‑label\n",
    "print(\"\\n[NER] Classification report:\")\n",
    "print(\n",
    "    classification_report(\n",
    "        true_ner_flat,\n",
    "        pred_ner_flat,\n",
    "        labels=labels_ner_no_pad,\n",
    "        target_names=display_labels_ner,\n",
    "        digits=2,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "7cd28380",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[SRL] Classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        ARG0       0.83      0.85      0.84       186\n",
      "        ARG1       0.72      0.59      0.65       245\n",
      "        ARG2       1.00      0.44      0.61        71\n",
      "    ARGM-CAU       0.00      0.00      0.00         1\n",
      "    ARGM-DIR       0.00      0.00      0.00         6\n",
      "    ARGM-LOC       0.81      0.61      0.70       108\n",
      "    ARGM-MNR       0.00      0.00      0.00         7\n",
      "    ARGM-MOD       0.00      0.00      0.00         8\n",
      "    ARGM-NEG       0.00      0.00      0.00         2\n",
      "    ARGM-TMP       0.89      0.84      0.87       274\n",
      "           O       0.64      0.89      0.75       450\n",
      "           V       0.71      0.55      0.62       141\n",
      "\n",
      "    accuracy                           0.74      1499\n",
      "   macro avg       0.47      0.40      0.42      1499\n",
      "weighted avg       0.75      0.74      0.73      1499\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n",
      "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is 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, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n[SRL] Classification report:\")\n",
    "print(\n",
    "    classification_report(\n",
    "        true_srl_flat,\n",
    "        pred_srl_flat,\n",
    "        labels=labels_srl_no_pad,\n",
    "        target_names=display_labels_srl,\n",
    "        digits=2,\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "333745fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def plot_training_history(history):\n",
    "#     epochs = range(1, len(history['loss']) + 1)\n",
    "\n",
    "#     plt.figure(figsize=(14, 6))\n",
    "\n",
    "#     # Plot Loss\n",
    "#     plt.subplot(1, 2, 1)\n",
    "#     plt.plot(epochs, history['loss'], label='Training Loss')\n",
    "#     plt.plot(epochs, history['val_loss'], label='Validation Loss')\n",
    "#     plt.title('Loss During Training')\n",
    "#     plt.xlabel('Epochs')\n",
    "#     plt.ylabel('Loss')\n",
    "#     plt.legend()\n",
    "\n",
    "#     # Plot Accuracy\n",
    "#     plt.subplot(1, 2, 2)\n",
    "#     plt.plot(epochs, history['ner_output_accuracy'], label='NER Train Acc')\n",
    "#     plt.plot(epochs, history['val_ner_output_accuracy'], label='NER Val Acc')\n",
    "#     plt.plot(epochs, history['srl_output_accuracy'], label='SRL Train Acc')\n",
    "#     plt.plot(epochs, history['val_srl_output_accuracy'], label='SRL Val Acc')\n",
    "#     plt.title('Accuracy During Training')\n",
    "#     plt.xlabel('Epochs')\n",
    "#     plt.ylabel('Accuracy')\n",
    "#     plt.legend()\n",
    "\n",
    "#     plt.tight_layout()\n",
    "#     plt.show()\n",
    "\n",
    "# plot_training_history(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "id": "df36e200",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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:\n",
    "#                 continue\n",
    "#             total += 1\n",
    "#             if t.argmax() == p.argmax():\n",
    "#                 correct += 1\n",
    "#     return correct / total\n",
    "\n",
    "# def decode_predictions(pred, true, idx2tag):\n",
    "#     true_out, pred_out = [], []\n",
    "#     for pred_seq, true_seq in zip(pred, true):\n",
    "#         t_labels, p_labels = [], []\n",
    "#         for p_tok, t_tok in zip(pred_seq, true_seq):\n",
    "#             if t_tok.sum() == 0:\n",
    "#                 continue\n",
    "#             t_labels.append(idx2tag[t_tok.argmax()])\n",
    "#             p_labels.append(idx2tag[p_tok.argmax()])\n",
    "#         true_out.append(t_labels)\n",
    "#         pred_out.append(p_labels)\n",
    "#     return true_out, pred_out\n",
    "\n",
    "# results = model.evaluate(X_test, {\"ner_output\": y_ner_test, \"srl_output\": y_srl_test}, verbose=0)\n",
    "# for name, value in zip(model.metrics_names, results):\n",
    "#     print(f\"{name}: {value}\")\n",
    "\n",
    "# y_pred_ner, y_pred_srl = model.predict(X_test, verbose=0)\n",
    "\n",
    "# true_ner, pred_ner = decode_predictions(y_pred_ner, y_ner_test, idx2tag_ner)\n",
    "# true_srl, pred_srl = decode_predictions(y_pred_srl, y_srl_test, idx2tag_srl)\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 Accuracy {acc_ner:.2%}\")\n",
    "# print(f\"SRL Token Accuracy {acc_srl:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "id": "9127cce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"[NER] Classification Report:\")\n",
    "# print(classification_report(true_ner, pred_ner, digits=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "id": "300897b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(\"SRL Classification Resport:\")\n",
    "# print(classification_report(true_srl, pred_srl, digits=2))"
   ]
  }
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