From 42816580aa7eed9f7ab580f0886ecb6048da0e74 Mon Sep 17 00:00:00 2001 From: akhdanre Date: Sun, 20 Apr 2025 10:26:19 +0700 Subject: [PATCH] feat: adding dataset and working on ner and srl model --- NER/lstm_ner_srl.ipynb | 203 -- NER/test_ner.py | 2 + NER_SRL/lstm_ner_srl.ipynb | 2696 ++++++++++++++++++++ NER_SRL/multi_task_bilstm_model.keras | Bin 0 -> 1034314 bytes NER_SRL/tag2idx_ner.pkl | Bin 0 -> 116 bytes NER_SRL/tag2idx_srl.pkl | Bin 0 -> 287 bytes NER_SRL/word2idx.pkl | Bin 0 -> 2085 bytes dataset/dataset_ner_srl.json | 3238 +++++++++---------------- 8 files changed, 3785 insertions(+), 2354 deletions(-) delete mode 100644 NER/lstm_ner_srl.ipynb create mode 100644 NER_SRL/lstm_ner_srl.ipynb create mode 100644 NER_SRL/multi_task_bilstm_model.keras create mode 100644 NER_SRL/tag2idx_ner.pkl create mode 100644 NER_SRL/tag2idx_srl.pkl create mode 100644 NER_SRL/word2idx.pkl diff --git a/NER/lstm_ner_srl.ipynb b/NER/lstm_ner_srl.ipynb deleted file mode 100644 index 05503bd..0000000 --- a/NER/lstm_ner_srl.ipynb +++ /dev/null @@ -1,203 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 8, - "id": "fcdce269", - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import numpy as np\n", - "from keras.models import Model\n", - "from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n", - "from keras.utils import to_categorical\n", - "from keras.preprocessing.sequence import pad_sequences\n", - "from sklearn.model_selection import train_test_split\n", - "from seqeval.metrics import classification_report\n", - "import pickle" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d568e8f2", - "metadata": {}, - "outputs": [], - "source": [ - "# === LOAD DATA ===\n", - "with open(\"../dataset/dataset_ner_srl.json\", \"r\", encoding=\"utf-8\") as f:\n", - " data = json.load(f)\n", - "\n", - "sentences = [[token.lower() for token in item[\"tokens\"]] for item in data]\n", - "ner_labels = [item[\"labels_ner\"] for item in data]\n", - "srl_labels = [item[\"labels_srl\"] for item in data]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "e9653d99", - "metadata": {}, - "outputs": [], - "source": [ - "# === VOCABULARY ===\n", - "words = list(set(word for sentence in sentences for word in sentence))\n", - "word2idx = {word: idx + 2 for idx, word in enumerate(words)}\n", - "word2idx[\"PAD\"] = 0\n", - "word2idx[\"UNK\"] = 1\n", - "\n", - "all_ner_tags = sorted(set(tag for seq in ner_labels for tag in seq))\n", - "all_srl_tags = sorted(set(tag for seq in srl_labels for tag in seq))\n", - "tag2idx_ner = {tag: idx for idx, tag in enumerate(all_ner_tags)}\n", - "tag2idx_srl = {tag: idx for idx, tag in enumerate(all_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": null, - "id": "9d3a37b3", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'O'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[11], line 9\u001b[0m\n\u001b[1;32m 7\u001b[0m X \u001b[38;5;241m=\u001b[39m pad_sequences(X, maxlen\u001b[38;5;241m=\u001b[39mmaxlen, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, value\u001b[38;5;241m=\u001b[39mword2idx[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPAD\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 8\u001b[0m y_ner \u001b[38;5;241m=\u001b[39m pad_sequences(y_ner, maxlen\u001b[38;5;241m=\u001b[39mmaxlen, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, value\u001b[38;5;241m=\u001b[39mtag2idx_ner[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mO\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m----> 9\u001b[0m y_srl \u001b[38;5;241m=\u001b[39m pad_sequences(y_srl, maxlen\u001b[38;5;241m=\u001b[39mmaxlen, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[43mtag2idx_srl\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mO\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 10\u001b[0m y_ner_cat \u001b[38;5;241m=\u001b[39m [to_categorical(seq, num_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(tag2idx_ner)) \u001b[38;5;28;01mfor\u001b[39;00m seq \u001b[38;5;129;01min\u001b[39;00m y_ner]\n\u001b[1;32m 11\u001b[0m y_srl_cat \u001b[38;5;241m=\u001b[39m [to_categorical(seq, num_classes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m(tag2idx_srl)) \u001b[38;5;28;01mfor\u001b[39;00m seq \u001b[38;5;129;01min\u001b[39;00m y_srl]\n", - "\u001b[0;31mKeyError\u001b[0m: 'O'" - ] - } - ], - "source": [ - "\n", - "# === ENCODING ===\n", - "X = [[word2idx.get(w, word2idx[\"UNK\"]) for w in s] for s in sentences]\n", - "y_ner = [[tag2idx_ner[t] for t in ts] for ts in ner_labels]\n", - "y_srl = [[tag2idx_srl[t] for t in ts] for ts in srl_labels]\n", - "\n", - "maxlen = max(len(x) for x in X)\n", - "X = pad_sequences(X, maxlen=maxlen, padding=\"post\", value=word2idx[\"PAD\"])\n", - "y_ner = pad_sequences(y_ner, maxlen=maxlen, padding=\"post\", value=tag2idx_ner[\"O\"])\n", - "y_srl = pad_sequences(y_srl, maxlen=maxlen, padding=\"post\", value=tag2idx_srl[\"O\"])\n", - "y_ner_cat = [to_categorical(seq, num_classes=len(tag2idx_ner)) for seq in y_ner]\n", - "y_srl_cat = [to_categorical(seq, num_classes=len(tag2idx_srl)) for seq in y_srl]\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5c264df", - "metadata": {}, - "outputs": [], - "source": [ - "# split dataset \n", - "X_temp, X_test, y_ner_temp, y_ner_test, y_srl_temp, y_srl_test = train_test_split(\n", - " X, y_ner_cat, y_srl_cat, test_size=0.1, random_state=42\n", - ")\n", - "X_train, X_val, y_ner_train, y_ner_val, y_srl_train, y_srl_val = train_test_split(\n", - " X_temp, y_ner_temp, y_srl_temp, test_size=0.1111, random_state=42 # ~10% of total\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "712c1789", - "metadata": {}, - "outputs": [], - "source": [ - "#training model\n", - "input_layer = Input(shape=(maxlen,))\n", - "embedding = Embedding(input_dim=len(word2idx), output_dim=64)(input_layer)\n", - "bilstm = Bidirectional(LSTM(units=64, return_sequences=True))(embedding)\n", - "out_ner = TimeDistributed(Dense(len(tag2idx_ner), activation=\"softmax\"), name=\"ner_output\")(bilstm)\n", - "out_srl = TimeDistributed(Dense(len(tag2idx_srl), activation=\"softmax\"), name=\"srl_output\")(bilstm)\n", - "\n", - "model = Model(inputs=input_layer, outputs=[out_ner, out_srl])\n", - "model.compile(\n", - " optimizer=\"adam\",\n", - " loss={\"ner_output\": \"categorical_crossentropy\", \"srl_output\": \"categorical_crossentropy\"},\n", - " metrics={\"ner_output\": \"accuracy\", \"srl_output\": \"accuracy\"}\n", - ")\n", - "\n", - "model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98feee87", - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "# === TRAINING ===\n", - "history = model.fit(\n", - " X_train,\n", - " {\"ner_output\": np.array(y_ner_train), \"srl_output\": np.array(y_srl_train)},\n", - " validation_data=(X_val, {\"ner_output\": np.array(y_ner_val), \"srl_output\": np.array(y_srl_val)}),\n", - " batch_size=2,\n", - " epochs=10\n", - ")\n", - "\n", - "# === SAVE ===\n", - "model.save(\"NER_SRL/multi_task_bilstm_model.keras\")\n", - "with open(\"NER_SRL/word2idx.pkl\", \"wb\") as f:\n", - " pickle.dump(word2idx, f)\n", - "with open(\"NER_SRL/tag2idx_ner.pkl\", \"wb\") as f:\n", - " pickle.dump(tag2idx_ner, f)\n", - "with open(\"NER_SRL/tag2idx_srl.pkl\", \"wb\") as f:\n", - " pickle.dump(tag2idx_srl, f)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aeef32c1", - "metadata": {}, - "outputs": [], - "source": [ - "# evaluation\n", - "y_pred_ner, y_pred_srl = model.predict(X_test)\n", - "\n", - "y_true_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_ner_test]\n", - "y_pred_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_pred_ner]\n", - "\n", - "y_true_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_srl_test]\n", - "y_pred_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_pred_srl]\n", - "\n", - "print(\"\\nšŸ“Š [NER] Test Set Classification Report:\")\n", - "print(classification_report(y_true_ner, y_pred_ner))\n", - "\n", - "print(\"\\nšŸ“Š [SRL] Test Set Classification Report:\")\n", - "print(classification_report(y_true_srl, y_pred_srl))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/NER/test_ner.py b/NER/test_ner.py index 8fbecbc..a7f3dd8 100644 --- a/NER/test_ner.py +++ b/NER/test_ner.py @@ -15,6 +15,8 @@ with open("NER/tag2idx.pkl", "rb") as f: idx2tag = {i: t for t, i in tag2idx.items()} +print(idx2tag) + maxlen = 100 diff --git a/NER_SRL/lstm_ner_srl.ipynb b/NER_SRL/lstm_ner_srl.ipynb new file mode 100644 index 0000000..2f958dd --- /dev/null +++ b/NER_SRL/lstm_ner_srl.ipynb @@ -0,0 +1,2696 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 76, + "id": "fcdce269", + "metadata": {}, + "outputs": [], + "source": [ + "from keras.models import Model\n", + "from keras.layers import Input, Embedding, Bidirectional, LSTM, TimeDistributed, Dense\n", + "from keras.utils import to_categorical\n", + "from keras.preprocessing.sequence import pad_sequences\n", + "from sklearn.model_selection import train_test_split\n", + "from seqeval.metrics import classification_report\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import nltk\n", + "from nltk.corpus import stopwords\n", + "from nltk.tokenize import word_tokenize\n", + "\n", + "from Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n", + "\n", + "from collections import Counter\n", + "import re\n", + "import string\n", + "import pickle\n", + "import json\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "92b6b57f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package stopwords to /home/akeon/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package punkt to /home/akeon/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package punkt_tab to /home/akeon/nltk_data...\n", + "[nltk_data] Package punkt_tab is already up-to-date!\n", + "[nltk_data] Downloading package wordnet to /home/akeon/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nltk.download(\"stopwords\")\n", + "nltk.download(\"punkt\")\n", + "nltk.download(\"punkt_tab\")\n", + "nltk.download(\"wordnet\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "d568e8f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "28 sentences\n", + "=== NER LABEL COUNTS ===\n", + "O -> 338 labels\n", + "B-LOC -> 17 labels\n", + "V -> 2 labels\n", + "B-MISC -> 1 labels\n", + "B-TIME -> 1 labels\n", + "I-TIME -> 2 labels\n", + "I-LOC -> 1 labels\n", + "B-QUANT -> 2 labels\n", + "I-QUANT -> 3 labels\n", + "B-DATE -> 2 labels\n", + "\n", + "=== SRL LABEL COUNTS ===\n", + "ARG1 -> 116 labels\n", + "ARGM-LOC -> 13 labels\n", + "AM-NEG -> 2 labels\n", + "V -> 34 labels\n", + "ARGM-SRC -> 9 labels\n", + "O -> 69 labels\n", + "AM-QUE -> 5 labels\n", + "ARGM-BNF -> 4 labels\n", + "ARG2 -> 24 labels\n", + "ARGM-MNR -> 1 labels\n", + "ARG0 -> 16 labels\n", + "AM-TMP -> 25 labels\n", + "AM-PRP -> 1 labels\n", + "AM-MOD -> 5 labels\n", + "AM-ADV -> 1 labels\n", + "AM-CAU -> 1 labels\n", + "AM-EXT -> 6 labels\n", + "AM-MNR -> 9 labels\n", + "AM-DIS -> 2 labels\n", + "AM-FRQ -> 2 labels\n", + "ARGM-PNC -> 1 labels\n", + "R-ARG1 -> 3 labels\n", + "AM-LOC -> 14 labels\n", + "AM-DIR -> 4 labels\n", + "ARGM-CAU -> 3 labels\n" + ] + } + ], + "source": [ + "# === LOAD DATA ===\n", + "with open(\"../dataset/dataset_ner_srl.json\", \"r\", encoding=\"utf-8\") as f:\n", + " data = json.load(f)\n", + "\n", + "sentences = [[token.lower() for token in item[\"tokens\"]] for item in data]\n", + "ner_labels = [item[\"labels_ner\"] for item in data]\n", + "srl_labels = [item[\"labels_srl\"] for item in data]\n", + "\n", + "print(len(sentences), \"sentences\")\n", + "\n", + "# === COUNTERS ===\n", + "ner_counter = Counter()\n", + "srl_counter = Counter()\n", + "\n", + "for ner_seq in ner_labels:\n", + " ner_counter.update(ner_seq)\n", + "\n", + "for srl_seq in srl_labels:\n", + " srl_counter.update(srl_seq)\n", + "\n", + "# === PRINT RESULT ===\n", + "print(\"=== NER LABEL COUNTS ===\")\n", + "for label, count in ner_counter.items():\n", + " print(f\"{label} -> {count} labels\")\n", + "\n", + "print(\"\\n=== SRL LABEL COUNTS ===\")\n", + "for label, count in srl_counter.items():\n", + " print(f\"{label} -> {count} labels\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "95f16969", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "old [['keberagaman', 'potensi', 'sumber', 'daya', 'alam', 'indonesia', 'tidak', 'lepas', 'dari', 'proses', 'geografis', 'yang', 'terjadi', '.'], ['bagaimana', 'proses', 'geografis', 'di', 'indonesia', '?'], ['bagaimana', 'pengaruh', 'proses', 'geografis', 'bagi', 'keragaman', 'alam', 'dan', 'keragaman', 'sosial', 'masyarakat', 'indonesia', '?'], ['bagaimana', 'mengoptimalkan', 'peranan', 'sumber', 'daya', 'manusia', 'dalam', 'mengelola', 'sumber', 'daya', 'alam', 'indonesia', '?'], ['apakah', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'sudah', 'memenuhi', 'syarat', 'untuk', 'mengolah', 'pariwisata', 'yang', 'dimilikinya', '?'], ['bagaimana', 'lembaga', 'sosial', 'yang', 'akan', 'mewadahi', 'untuk', 'mengolah', 'sumber', 'daya', 'alam', 'dan', 'sumber', 'daya', 'manusianya', '?'], ['kalian', 'juga', 'perlu', 'memahami', ',', 'bahwa', 'keragaman', 'sosial', 'dan', 'budaya', 'telah', 'menarik', 'kedatangan', 'bangsa-bangsa', 'asing', 'sejak', 'ribuan', 'tahun', 'yang', 'lalu', '.'], ['perkembangan', 'hindu-buddha', 'di', 'indonesia', 'tidak', 'lepas', 'dari', 'perkembangan', 'perdagangan', 'dan', 'pelayaran', 'pada', 'awal', 'abad', 'masehi', '.'], ['bangsa', 'indonesia', 'patut', 'bersyukur', 'karena', 'proses', 'geografis', 'dan', 'keragaman', 'alam', 'yang', 'dimiliki', '.'], ['indonesia', 'merupakan', 'negara', 'terluas', 'di', 'asia', 'tenggara', '.'], ['luas', 'daratan', 'indonesia', 'sebesar', '1.910.932,37', 'km2', '.'], ['dan', 'lautan', 'indonesia', 'mencapai', '5,8', 'juta', 'km2', '.'], ['letak', 'indonesia', 'sangat', 'menguntungkan', 'bagi', 'kehidupan', 'masyarakat', '.'], ['selain', 'memiliki', 'letak', 'geografis', 'yang', 'sangat', 'menguntungkan', ',', 'indonesia', 'juga', 'memiliki', 'letak', 'geologis', ',', 'iklim', ',', 'dan', 'cuaca', 'yang', 'sangat', 'menguntungkan', '.'], ['kalian', 'tentu', 'sering', 'membincangkan', 'tentang', 'musim', 'dan', 'hubungannya', 'dengan', 'aktivitas', 'sehari-hari', '.'], ['masyarakat', 'memiliki', 'kebiasaan', 'di', 'musim', 'hujan', 'dan', 'musim', 'kemarau', 'baik', 'berhubungan', 'dengan', 'mata', 'pencaharian', 'dan', 'kesenangan', '(', 'hobi', ')', '.'], ['kalian', 'juga', 'sering', 'memperhatikan', 'prakiraan', 'cuaca', 'untuk', 'merancang', 'kegiatan', 'harian', '.'], ['cuaca', 'dan', 'iklim', 'inilah', 'bagian', 'penting', 'yang', 'memengaruhi', 'aktivitas', 'masyarakat', 'indonesia', '.'], ['cuaca', 'adalah', 'kondisi', 'rata-rata', 'udara', 'pada', 'saat', 'tertentu', 'di', 'suatu', 'wilayah', 'yang', 'relatif', 'sempit', 'dan', 'dalam', 'waktu', 'yang', 'singkat', '.'], ['iklim', 'merupakan', 'kondisi', 'cuaca', 'rata-rata', 'tahunan', 'pada', 'suatu', 'wilayah', 'yang', 'luas', '.'], ['indonesia', 'memiliki', 'iklim', 'tropis', 'yang', 'memiliki', 'dua', 'musim', 'yaitu', 'musim', 'hujan', 'dan', 'musim', 'kemarau', '.'], ['musim', 'hujan', 'terjadi', 'pada', 'bulan', 'oktober-maret', ',', 'sedangkan', 'musim', 'kemarau', 'terjadi', 'pada', 'bulan', 'april-september', '.'], ['semakin', 'ke', 'timur', 'curah', 'hujan', 'semakin', 'sedikit', '.'], ['hal', 'ini', 'karena', 'hujan', 'telah', 'banyak', 'jatuh', 'dan', 'menguap', 'di', 'bagian', 'barat', '.'], ['keadaan', 'iklim', 'dapat', 'diamati', 'dengan', 'memperhatikan', 'unsur-unsur', 'cuaca', 'dan', 'iklim', '.'], ['unsur-unsur', 'tersebut', 'antara', 'lain', ',', 'penyinaran', 'matahari', ',', 'suhu', 'udara', ',', 'kelembaban', 'udara', ',', 'angin', ',', 'dan', 'hujan', '.'], ['tanaman', 'tropis', 'memiliki', 'banyak', 'varietas', 'yang', 'kaya', 'akan', 'hidrat', 'arang', 'terutama', 'tanaman', 'bahan', 'makanan', 'pokok', '.'], ['berikut', 'pengaruh', 'unsur-unsur', 'iklim', 'terhadap', 'tanaman', ':']]\n", + "new [['keberagaman', 'potensi', 'sumber', 'daya', 'alam', 'indonesia', 'tidak', 'lepas', 'dari', 'proses', 'geografis', 'yang', 'terjadi', '.'], ['bagaimana', 'proses', 'geografis', 'di', 'indonesia', '?'], ['bagaimana', 'pengaruh', 'proses', 'geografis', 'bagi', 'keragaman', 'alam', 'dan', 'keragaman', 'sosial', 'masyarakat', 'indonesia', '?'], ['bagaimana', 'mengoptimalkan', 'peranan', 'sumber', 'daya', 'manusia', 'dalam', 'mengelola', 'sumber', 'daya', 'alam', 'indonesia', '?'], ['apakah', 'sumber', 'daya', 'manusia', 'di', 'indonesia', 'sudah', 'memenuhi', 'syarat', 'untuk', 'mengolah', 'pariwisata', 'yang', 'dimilikinya', '?'], ['bagaimana', 'lembaga', 'sosial', 'yang', 'akan', 'mewadahi', 'untuk', 'mengolah', 'sumber', 'daya', 'alam', 'dan', 'sumber', 'daya', 'manusianya', '?'], ['kalian', 'juga', 'perlu', 'memahami', ',', 'bahwa', 'keragaman', 'sosial', 'dan', 'budaya', 'telah', 'menarik', 'kedatangan', 'bangsa-bangsa', 'asing', 'sejak', 'ribuan', 'tahun', 'yang', 'lalu', '.'], ['perkembangan', 'hindu-buddha', 'di', 'indonesia', 'tidak', 'lepas', 'dari', 'perkembangan', 'perdagangan', 'dan', 'pelayaran', 'pada', 'awal', 'abad', 'masehi', '.'], ['bangsa', 'indonesia', 'patut', 'bersyukur', 'karena', 'proses', 'geografis', 'dan', 'keragaman', 'alam', 'yang', 'dimiliki', '.'], ['indonesia', 'merupakan', 'negara', 'terluas', 'di', 'asia', 'tenggara', '.'], ['luas', 'daratan', 'indonesia', 'sebesar', '1.910.932,37', 'km2', '.'], ['dan', 'lautan', 'indonesia', 'mencapai', '5,8', 'juta', 'km2', '.'], ['letak', 'indonesia', 'sangat', 'menguntungkan', 'bagi', 'kehidupan', 'masyarakat', '.'], ['selain', 'memiliki', 'letak', 'geografis', 'yang', 'sangat', 'menguntungkan', ',', 'indonesia', 'juga', 'memiliki', 'letak', 'geologis', ',', 'iklim', ',', 'dan', 'cuaca', 'yang', 'sangat', 'menguntungkan', '.'], ['kalian', 'tentu', 'sering', 'membincangkan', 'tentang', 'musim', 'dan', 'hubungannya', 'dengan', 'aktivitas', 'sehari-hari', '.'], ['masyarakat', 'memiliki', 'kebiasaan', 'di', 'musim', 'hujan', 'dan', 'musim', 'kemarau', 'baik', 'berhubungan', 'dengan', 'mata', 'pencaharian', 'dan', 'kesenangan', '(', 'hobi', ')', '.'], ['kalian', 'juga', 'sering', 'memperhatikan', 'prakiraan', 'cuaca', 'untuk', 'merancang', 'kegiatan', 'harian', '.'], ['cuaca', 'dan', 'iklim', 'inilah', 'bagian', 'penting', 'yang', 'memengaruhi', 'aktivitas', 'masyarakat', 'indonesia', '.'], ['cuaca', 'adalah', 'kondisi', 'rata-rata', 'udara', 'pada', 'saat', 'tertentu', 'di', 'suatu', 'wilayah', 'yang', 'relatif', 'sempit', 'dan', 'dalam', 'waktu', 'yang', 'singkat', '.'], ['iklim', 'merupakan', 'kondisi', 'cuaca', 'rata-rata', 'tahunan', 'pada', 'suatu', 'wilayah', 'yang', 'luas', '.'], ['indonesia', 'memiliki', 'iklim', 'tropis', 'yang', 'memiliki', 'dua', 'musim', 'yaitu', 'musim', 'hujan', 'dan', 'musim', 'kemarau', '.'], ['musim', 'hujan', 'terjadi', 'pada', 'bulan', 'oktober-maret', ',', 'sedangkan', 'musim', 'kemarau', 'terjadi', 'pada', 'bulan', 'april-september', '.'], ['semakin', 'ke', 'timur', 'curah', 'hujan', 'semakin', 'sedikit', '.'], ['hal', 'ini', 'karena', 'hujan', 'telah', 'banyak', 'jatuh', 'dan', 'menguap', 'di', 'bagian', 'barat', '.'], ['keadaan', 'iklim', 'dapat', 'diamati', 'dengan', 'memperhatikan', 'unsur-unsur', 'cuaca', 'dan', 'iklim', '.'], ['unsur-unsur', 'tersebut', 'antara', 'lain', ',', 'penyinaran', 'matahari', ',', 'suhu', 'udara', ',', 'kelembaban', 'udara', ',', 'angin', ',', 'dan', 'hujan', '.'], ['tanaman', 'tropis', 'memiliki', 'banyak', 'varietas', 'yang', 'kaya', 'akan', 'hidrat', 'arang', 'terutama', 'tanaman', 'bahan', 'makanan', 'pokok', '.'], ['berikut', 'pengaruh', 'unsur-unsur', 'iklim', 'terhadap', 'tanaman', ':']] \n", + " 28\n" + ] + } + ], + "source": [ + "# text preprocessing\n", + "stop_words = set(stopwords.words(\"indonesian\")) \n", + "factory = StemmerFactory()\n", + "stemmer = factory.create_stemmer()\n", + "\n", + "with open(\"../normalize_text/normalize.json\", \"r\", encoding=\"utf-8\") as file:\n", + " normalization_dict = json.load(file)\n", + " \n", + "def text_preprocessing(text):\n", + " \n", + " # if(text == \"?\" or text == \".\" or text == \"!\"): return text\n", + " # lowercase\n", + " text = text.lower()\n", + " \n", + " # remove punctuation\n", + " # text = text.translate(str.maketrans(\"\", \"\", string.punctuation))\n", + " \n", + " # remove extra spaces\n", + " text = re.sub(r\"\\s+\", \" \", text).strip()\n", + " \n", + " # tokenize\n", + " # tokens = word_tokenize(text)\n", + " \n", + " # normalization\n", + " # tokens = normalization_dict.get(text, text) \n", + " \n", + " \n", + " # stemming\n", + " # tokens = stemmer.stem(tokens)\n", + " \n", + " \n", + " # remove stopwords\n", + " # tokens = [word for word in tokens if word not in stop_words]\n", + " \n", + " # print(f\"Original: {text}\")\n", + " # print(f\"Normalized: {tokens}\")\n", + " \n", + " return text\n", + "\n", + "# sentences = [text_preprocessing(\" \".join(sentence)) for sentence in sentences]\n", + "print(\"old\", sentences)\n", + "preprocessing_sentences = []\n", + "\n", + "for text in sentences:\n", + " result = []\n", + " for i in range(len(text)):\n", + " text[i] = text_preprocessing(text[i])\n", + " result.append(text[i])\n", + " preprocessing_sentences.append(result)\n", + "\n", + "print(\"new\", preprocessing_sentences, \"\\n\", len(preprocessing_sentences))\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "e9653d99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['mengelola', 'memengaruhi', 'mata', 'yaitu', 'april-september', 'kehidupan', 'matahari', ')', '.', 'pencaharian', 'cuaca', 'bagaimana', 'bangsa-bangsa', 'selain', 'kalian', 'singkat', 'tropis', 'dapat', '(', 'kebiasaan', 'barat', 'lembaga', 'lain', 'masyarakat', 'letak', 'waktu', 'sehari-hari', 'dimiliki', 'dengan', 'hobi', 'ke', 'sumber', 'hal', 'karena', 'mengoptimalkan', 'relatif', 'prakiraan', 'bahan', 'sedikit', 'kedatangan', 'dan', 'musim', 'kondisi', 'kesenangan', 'bagi', 'peranan', 'semakin', 'makanan', 'manusianya', 'akan', 'tentang', 'juga', 'sosial', 'asia', 'pada', 'terluas', 'wilayah', 'negara', 'sedangkan', 'lautan', 'oktober-maret', 'pengaruh', 'awal', '1.910.932,37', 'bersyukur', 'iklim', 'tersebut', 'merupakan', 'rata-rata', 'timur', 'kelembaban', 'angin', 'memperhatikan', 'curah', 'berikut', 'jatuh', 'perkembangan', 'suhu', 'tidak', 'patut', '5,8', ',', 'abad', 'daya', 'merancang', 'juta', 'udara', 'adalah', 'penting', 'dalam', 'bagian', 'syarat', 'mencapai', 'ini', 'indonesia', 'unsur-unsur', 'hidrat', 'hujan', 'keragaman', 'diamati', 'tanaman', 'sempit', 'bahwa', 'berhubungan', 'memenuhi', 'keadaan', 'menguap', 'kaya', 'pariwisata', 'membincangkan', 'di', '?', 'suatu', 'asing', 'tahunan', 'memahami', 'tenggara', 'dua', 'telah', 'lalu', 'sering', 'menguntungkan', 'proses', 'inilah', 'antara', 'bangsa', ':', 'hindu-buddha', 'budaya', 'harian', 'keberagaman', 'arang', 'daratan', 'terhadap', 'tentu', 'kemarau', 'sudah', 'potensi', 'apakah', 'menarik', 'lepas', 'yang', 'geologis', 'hubungannya', 'dimilikinya', 'sangat', 'sebesar', 'baik', 'penyinaran', 'pokok', 'sejak', 'varietas', 'saat', 'mengolah', 'bulan', 'perlu', 'manusia', 'masehi', 'luas', 'pelayaran', 'banyak', 'dari', 'alam', 'ribuan', 'terjadi', 'memiliki', 'tertentu', 'mewadahi', 'terutama', 'km2', 'tahun', 'kegiatan', 'geografis', 'untuk', 'perdagangan', 'aktivitas']\n", + "{'mengelola': 2, 'memengaruhi': 3, 'mata': 4, 'yaitu': 5, 'april-september': 6, 'kehidupan': 7, 'matahari': 8, ')': 9, '.': 10, 'pencaharian': 11, 'cuaca': 12, 'bagaimana': 13, 'bangsa-bangsa': 14, 'selain': 15, 'kalian': 16, 'singkat': 17, 'tropis': 18, 'dapat': 19, '(': 20, 'kebiasaan': 21, 'barat': 22, 'lembaga': 23, 'lain': 24, 'masyarakat': 25, 'letak': 26, 'waktu': 27, 'sehari-hari': 28, 'dimiliki': 29, 'dengan': 30, 'hobi': 31, 'ke': 32, 'sumber': 33, 'hal': 34, 'karena': 35, 'mengoptimalkan': 36, 'relatif': 37, 'prakiraan': 38, 'bahan': 39, 'sedikit': 40, 'kedatangan': 41, 'dan': 42, 'musim': 43, 'kondisi': 44, 'kesenangan': 45, 'bagi': 46, 'peranan': 47, 'semakin': 48, 'makanan': 49, 'manusianya': 50, 'akan': 51, 'tentang': 52, 'juga': 53, 'sosial': 54, 'asia': 55, 'pada': 56, 'terluas': 57, 'wilayah': 58, 'negara': 59, 'sedangkan': 60, 'lautan': 61, 'oktober-maret': 62, 'pengaruh': 63, 'awal': 64, '1.910.932,37': 65, 'bersyukur': 66, 'iklim': 67, 'tersebut': 68, 'merupakan': 69, 'rata-rata': 70, 'timur': 71, 'kelembaban': 72, 'angin': 73, 'memperhatikan': 74, 'curah': 75, 'berikut': 76, 'jatuh': 77, 'perkembangan': 78, 'suhu': 79, 'tidak': 80, 'patut': 81, '5,8': 82, ',': 83, 'abad': 84, 'daya': 85, 'merancang': 86, 'juta': 87, 'udara': 88, 'adalah': 89, 'penting': 90, 'dalam': 91, 'bagian': 92, 'syarat': 93, 'mencapai': 94, 'ini': 95, 'indonesia': 96, 'unsur-unsur': 97, 'hidrat': 98, 'hujan': 99, 'keragaman': 100, 'diamati': 101, 'tanaman': 102, 'sempit': 103, 'bahwa': 104, 'berhubungan': 105, 'memenuhi': 106, 'keadaan': 107, 'menguap': 108, 'kaya': 109, 'pariwisata': 110, 'membincangkan': 111, 'di': 112, '?': 113, 'suatu': 114, 'asing': 115, 'tahunan': 116, 'memahami': 117, 'tenggara': 118, 'dua': 119, 'telah': 120, 'lalu': 121, 'sering': 122, 'menguntungkan': 123, 'proses': 124, 'inilah': 125, 'antara': 126, 'bangsa': 127, ':': 128, 'hindu-buddha': 129, 'budaya': 130, 'harian': 131, 'keberagaman': 132, 'arang': 133, 'daratan': 134, 'terhadap': 135, 'tentu': 136, 'kemarau': 137, 'sudah': 138, 'potensi': 139, 'apakah': 140, 'menarik': 141, 'lepas': 142, 'yang': 143, 'geologis': 144, 'hubungannya': 145, 'dimilikinya': 146, 'sangat': 147, 'sebesar': 148, 'baik': 149, 'penyinaran': 150, 'pokok': 151, 'sejak': 152, 'varietas': 153, 'saat': 154, 'mengolah': 155, 'bulan': 156, 'perlu': 157, 'manusia': 158, 'masehi': 159, 'luas': 160, 'pelayaran': 161, 'banyak': 162, 'dari': 163, 'alam': 164, 'ribuan': 165, 'terjadi': 166, 'memiliki': 167, 'tertentu': 168, 'mewadahi': 169, 'terutama': 170, 'km2': 171, 'tahun': 172, 'kegiatan': 173, 'geografis': 174, 'untuk': 175, 'perdagangan': 176, 'aktivitas': 177, 'PAD': 0, 'UNK': 1}\n", + "['B-DATE', 'B-LOC', 'B-MISC', 'B-QUANT', 'B-TIME', 'I-LOC', 'I-QUANT', 'I-TIME', 'O', 'V']\n", + "['AM-ADV', 'AM-CAU', 'AM-DIR', 'AM-DIS', 'AM-EXT', 'AM-FRQ', 'AM-LOC', 'AM-MNR', 'AM-MOD', 'AM-NEG', 'AM-PRP', 'AM-QUE', 'AM-TMP', 'ARG0', 'ARG1', 'ARG2', 'ARGM-BNF', 'ARGM-CAU', 'ARGM-LOC', 'ARGM-MNR', 'ARGM-PNC', 'ARGM-SRC', 'O', 'R-ARG1', 'V']\n", + "{'B-DATE': 0, 'B-LOC': 1, 'B-MISC': 2, 'B-QUANT': 3, 'B-TIME': 4, 'I-LOC': 5, 'I-QUANT': 6, 'I-TIME': 7, 'O': 8, 'V': 9}\n", + "{'AM-ADV': 0, 'AM-CAU': 1, 'AM-DIR': 2, 'AM-DIS': 3, 'AM-EXT': 4, 'AM-FRQ': 5, 'AM-LOC': 6, 'AM-MNR': 7, 'AM-MOD': 8, 'AM-NEG': 9, 'AM-PRP': 10, 'AM-QUE': 11, 'AM-TMP': 12, 'ARG0': 13, 'ARG1': 14, 'ARG2': 15, 'ARGM-BNF': 16, 'ARGM-CAU': 17, 'ARGM-LOC': 18, 'ARGM-MNR': 19, 'ARGM-PNC': 20, 'ARGM-SRC': 21, 'O': 22, 'R-ARG1': 23, 'V': 24}\n", + "{0: 'B-DATE', 1: 'B-LOC', 2: 'B-MISC', 3: 'B-QUANT', 4: 'B-TIME', 5: 'I-LOC', 6: 'I-QUANT', 7: 'I-TIME', 8: 'O', 9: 'V'}\n", + "{0: 'AM-ADV', 1: 'AM-CAU', 2: 'AM-DIR', 3: 'AM-DIS', 4: 'AM-EXT', 5: 'AM-FRQ', 6: 'AM-LOC', 7: 'AM-MNR', 8: 'AM-MOD', 9: 'AM-NEG', 10: 'AM-PRP', 11: 'AM-QUE', 12: 'AM-TMP', 13: 'ARG0', 14: 'ARG1', 15: 'ARG2', 16: 'ARGM-BNF', 17: 'ARGM-CAU', 18: 'ARGM-LOC', 19: 'ARGM-MNR', 20: 'ARGM-PNC', 21: 'ARGM-SRC', 22: 'O', 23: 'R-ARG1', 24: 'V'}\n" + ] + } + ], + "source": [ + "words = list(set(word for sentence in preprocessing_sentences for word in sentence))\n", + "word2idx = {word: idx + 2 for idx, word in enumerate(words)}\n", + "word2idx[\"PAD\"] = 0\n", + "word2idx[\"UNK\"] = 1\n", + "\n", + "all_ner_tags = sorted(set(tag for seq in ner_labels for tag in seq))\n", + "all_srl_tags = sorted(set(tag for seq in srl_labels for tag in seq))\n", + "tag2idx_ner = {tag: idx for idx, tag in enumerate(all_ner_tags)}\n", + "tag2idx_srl = {tag: idx for idx, tag in enumerate(all_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()}\n", + "\n", + "print(words)\n", + "print(word2idx)\n", + "print(all_ner_tags)\n", + "print(all_srl_tags)\n", + "print(tag2idx_ner)\n", + "print(tag2idx_srl)\n", + "print(idx2tag_ner)\n", + "print(idx2tag_srl)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "9d3a37b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[132 139 33 85 164 96 80 142 163 124 174 143 166 10 0 0 0 0\n", + " 0 0 0 0]\n", + " [ 13 124 174 112 96 113 0 0 0 0 0 0 0 0 0 0 0 0\n", + " 0 0 0 0]\n", + " [ 13 63 124 174 46 100 164 42 100 54 25 96 113 0 0 0 0 0\n", + " 0 0 0 0]\n", + " [ 13 36 47 33 85 158 91 2 33 85 164 96 113 0 0 0 0 0\n", + " 0 0 0 0]\n", + " [140 33 85 158 112 96 138 106 93 175 155 110 143 146 113 0 0 0\n", + " 0 0 0 0]\n", + " [ 13 23 54 143 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12 12 12 22 22 22 22 22 22 22 22]\n", + " [ 2 2 2 14 14 4 24 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22]\n", + " [17 17 17 13 12 4 24 22 24 6 6 6 22 22 22 22 22 22 22 22 22 22]\n", + " [14 14 8 24 7 24 14 14 22 14 22 22 22 22 22 22 22 22 22 22 22 22]\n", + " [14 14 22 22 22 14 14 22 14 14 22 14 14 22 14 22 22 14 22 22 22 22]\n", + " [13 13 24 14 14 23 7 7 14 14 7 14 14 14 14 22 22 22 22 22 22 22]\n", + " [12 14 14 14 2 15 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22 22]]\n", + "y_ner cat \n", + " \n", + "[array([[0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 0., 0., 0., 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"print(\"y_srl \\n \")\n", + "print(y_srl)\n", + "print(\"y_ner cat \\n \")\n", + "print(y_ner_cat)\n", + "print(\"y_srl cat \\n \")\n", + "print(y_srl_cat)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "a5c264df", + "metadata": {}, + "outputs": [], + "source": [ + "# split dataset \n", + "X_temp, X_test, y_ner_temp, y_ner_test, y_srl_temp, y_srl_test = train_test_split(\n", + " X, y_ner_cat, y_srl_cat, test_size=0.1, random_state=42\n", + ")\n", + "X_train, X_val, y_ner_train, y_ner_val, y_srl_train, y_srl_val = train_test_split(\n", + " X_temp, y_ner_temp, y_srl_temp, test_size=0.1111, random_state=42 # ~10% of total\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "712c1789", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "ā”ƒ Layer (type)        ā”ƒ Output Shape      ā”ƒ    Param # ā”ƒ Connected to      ā”ƒ\n",
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{ + "cell_type": "code", + "execution_count": 84, + "id": "98feee87", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 5.4074 - ner_output_accuracy: 0.7423 - ner_output_loss: 2.2128 - srl_output_accuracy: 0.2337 - srl_output_loss: 3.1946 - val_loss: 4.5710 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 1.6993 - val_srl_output_accuracy: 0.6061 - val_srl_output_loss: 2.9475\n", + "Epoch 2/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 4.1426 - ner_output_accuracy: 0.9434 - ner_output_loss: 1.3429 - srl_output_accuracy: 0.5027 - srl_output_loss: 2.7998 - val_loss: 1.8463 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3212 - val_srl_output_accuracy: 0.6061 - val_srl_output_loss: 1.6901\n", + "Epoch 3/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.1865 - ner_output_accuracy: 0.9540 - ner_output_loss: 0.3207 - srl_output_accuracy: 0.5062 - srl_output_loss: 1.8658 - val_loss: 1.5277 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3891 - val_srl_output_accuracy: 0.6061 - val_srl_output_loss: 1.3498\n", + "Epoch 4/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.8984 - ner_output_accuracy: 0.9315 - ner_output_loss: 0.4461 - srl_output_accuracy: 0.5915 - srl_output_loss: 1.4523 - val_loss: 1.4642 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3424 - val_srl_output_accuracy: 0.6818 - val_srl_output_loss: 1.2855\n", + "Epoch 5/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.8848 - ner_output_accuracy: 0.9281 - ner_output_loss: 0.3844 - srl_output_accuracy: 0.6055 - srl_output_loss: 1.5004 - val_loss: 1.3921 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3394 - val_srl_output_accuracy: 0.7121 - val_srl_output_loss: 1.2169\n", + "Epoch 6/10\n", + "\u001b[1m11/11\u001b[0m \u001b[32mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.7063 - ner_output_accuracy: 0.9498 - ner_output_loss: 0.2498 - srl_output_accuracy: 0.5942 - srl_output_loss: 1.4566 - val_loss: 1.3408 - val_ner_output_accuracy: 0.9545 - val_ner_output_loss: 0.3570 - val_srl_output_accuracy: 0.7121 - val_srl_output_loss: 1.1573\n", + "Epoch 7/10\n", + "\u001b[1m11/11\u001b[0m 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IiIiIiIiISJ5TUUpERERERERERPKcndkB7oXFYuHEiRO4u7tjGIbZcURERERERERE7ntWq5XLly9TqlQpbGyy7g9VoItSJ06cwM/Pz+wYIiIiIiIiIiLyL0ePHqVMmTJZfl+gi1Lu7u7wz0l6eHiYHeeepaSksHLlSlq2bIm9vb3ZcUTua7ofRfIX3ZMi+YvuSZH8Q/ej5Efx8fH4+fll1G2yUqCLUteH7Hl4eBSaopSLiwseHh76w0TEZLofRfIX3ZMi+YvuSZH8Q/ej5Ge3m2pJE52LiIiIiIiIiEieU1FKRERERERERETynIpSIiIiIiIiIiKS5wr0nFIiIiIiIiIi95u0tDRSUlLgnzml7OzsSExMJC0tzexocp+wt7fH1tb2no+jopSIiIiIiIhIAWC1Wjl16hSXLl3KtM7X15ejR4/edlJpkZzk5eWFr6/vPf3uVJQSERERERERKQCuF6R8fHxwcXHBMAwsFgtXrlzBzc0NGxvN0CO5z2q1kpCQwJkzZwAoWbJkto+lopSIiIiIiIhIPpeWlpZRkCpWrFjGeovFQnJyMk5OTipKSZ5xdnYG4MyZM/j4+GR7KJ9+sSIiIiIiIiL53PU5pFxcXMyOIgL/81u8/tvMDhWlRERERERERAoIzRsl+UVO/BZVlBIRERERERERkTynopSIiIiIiIiIFBqGYbBkyRKzY9yxGTNm0LJly1w7/kMPPcSQIUPuePtz587h4+PDsWPHci3TdSpKiYiIiIiIiEiOMwzjlq+xY8dmuW90dDSGYRAVFZXjuXr16kX79u1z/LjZkZiYyKhRoxgzZkzGupzO9/333/Pmm2/e8fbFixfn6aefzpQpt+jpeyIiIiIiIiKS406ePJmxPH/+fEaPHs2BAwcy1rm5uZmULP9YtGgRHh4ehIaG3vW+KSkp2Nvb33a7okWL3vWxn3nmGerUqcP777+frf3vlHpKiYiIiIiIiEiO8/X1zXh5enpiGEbGZx8fHz766CPKlCmDo6MjtWrVYvny5Rn7BgQEABAcHIxhGDz00EMAREZG0qJFC4oXL46npydNmzZl27ZtOZr7jz/+ICQkBEdHR0qWLMnw4cNJTU3N+H7RokUEBQXh7OxMsWLFaN68OVevXgVgzZo1hISE4OrqipeXF6GhocTExGTZ1rx582jbtm3G57FjxzJ79mx+/PHHjB5la9asyeg5Nn/+fJo2bYqTkxNz5szh/PnzdO/endKlS+Pi4kJQUBDfffddpjb+PXzP39+fd955h969e+Pu7k7ZsmWZPn16pn2qVatGqVKl+OGHH3LkmmZFRSkRERERERGRAshqtZKQnMq15DQSklPz7GW1Wu85+6RJk/jwww/54IMP2LlzJ2FhYbRr145Dhw4BsHnzZgBWr17NyZMn+f777wG4fPkyPXv2ZN26dWzcuJHAwEBat27N5cuX7zkTwPHjx2ndujX16tVjx44dTJs2jRkzZvDWW2/BP72/unfvTu/evdm3bx9r1qyhY8eOWK1WUlNTad++PU2bNmXnzp1s2LCBfv363fIpdevWraNu3boZn1955RW6dOnCo48+ysmTJzl58iSNGjXK+H748OEMHjyYffv2ERYWRmJiInXq1OHXX39l9+7d9OvXj6eeeirj+mXlww8/pG7dumzfvp0BAwbQv3//TL3YAEJCQli7du09XM3b0/A9ERERERERkQLoWkoa1ceuyvN2974RhovDvZUTPvjgA4YNG0a3bt0AmDBhAr///jsTJ07kk08+wdvbG4BixYrh6+ubsd/DDz+c6TjTp0/Hy8uLP/74g8cee+yeMgF8+umn+Pn5MXXqVAzDoHLlypw4cYJhw4YxevRoTp48SWpqKh07dqRcuXIABAUFAXDhwgXi4uJ47LHHKF++PABVqlTJsq1Lly4RFxdHqVKlMta5ubnh7OxMUlJSpvO+bsiQIXTs2DHTuldeeSVjeeDAgaxYsYIFCxYQEhKSZdutW7dmwIABAAwbNoyPP/6Y33//nUqVKmVsU6pUKbZv335H1y271FNKRERERERERPJMfHw8J06cuGEepdDQUPbt23fLfU+fPk3fvn0JDAzE09MTDw8Prly5QmxsbI5k27dvHw0bNszUuyk0NJQrV65w7NgxatasySOPPEJQUBBPPPEEX3zxBRcvXoR/5m7q1asXYWFhtG3blkmTJmWaV+vfrl27BoCTk9Md5/vfXlUAaWlpvPnmmwQFBVG0aFHc3NxYsWLFba9HjRo1MpavD6s8c+ZMpm2cnZ1JSEi442zZoZ5SIiIiIiIiIgWQs70tu8e24HL8Zdw93LGxyZt+J872tnnSzs307NmT8+fPM2nSJMqVK4ejoyMNGzYkOTk5T9q3tbVl1apVrF+/npUrVzJlyhRef/11Nm3aREBAADNnzmTQoEEsX76c+fPnM3LkSFatWkWDBg1uOFaxYsUwDCOjqHUnXF1dM31+//33mTRpEhMnTiQoKAhXV1eGDBly2+vx7wnSDcPAYrFkWnfhwoWMHmu5RT2l8pGE5NQ72EpEREREREQkvZDg4mCHs4MtLg52efa61RxJd8LDw4NSpUoRERGRaX1ERARVq1YFwMHBAf7pCfTvbQYNGkTr1q2pVq0ajo6OnDt37p7y/K8qVaqwYcOGTPNmRURE4O7uTpkyZeCf6x4aGsq4cePYvn07Dg4OmSYEDw4OZsSIEaxfv57q1aszd+7cm7bl4OBA1apV2bt37w3r/33eWYmIiODxxx/nySefpGbNmjzwwAMcPHgwm2ef2e7duwkODs6RY2VFRal8IjL6Ai0nRbD9/L3d3CIiIiIiIiL53dChQ5kwYQLz58/nwIEDDB8+nKioKAYPHgyAj48Pzs7OLF++nNOnTxMXFwdAYGAg33zzDfv27WPTpk2Eh4fj7Ox81+3HxcURFRWV6XX06FEGDBjA0aNHGThwIPv37+fHH39kzJgx/Oc//8HGxoZNmzbxzjvvsGXLFmJjY/n+++85e/YsVapU4ciRI4wYMYINGzYQExPDypUrOXTo0C3nlQoLC2PdunWZ1vn7+7Nz504OHDjAuXPnSElJyXL/wMDAjJ5b+/bt47nnnuP06dN3fT3+LSEhga1bt9KyZct7PtataPhePvHf/Wc4HZ/Ed1dt6HHuKhVLepkdSURERERERCRXDBo0iLi4OF5++WXOnDlD1apV+emnnwgMDATAzs6OyZMn88YbbzB69GgaN27MmjVrmDFjBv369aN27dr4+fnxzjvvZJro+06tWbPmhl5Affr04csvv2Tp0qUMHTqUmjVrUrRoUfr06cPIkSPhn15ef/75JxMnTiQ+Pp5y5crx4Ycf0qpVK06fPs3+/fuZPXs258+fp2TJkrzwwgs899xzWebo06cPdevWJS4uDk9PTwD69u3LmjVrqFu3LleuXOH333/H39//pvuPHDmSv//+m7CwMFxcXOjXrx/t27fPKOJl148//kjZsmVp3LjxPR3ndgxrTjzL0STx8fF4enoSFxeHh4eH2XHuSWqahe5fbCQy+iKVS7ix5MUHcTJxnK7I/S4lJYWlS5fSunXrG8Zbi0je0z0pkr/onhTJe4mJiRw5coSAgIBME2NbLBbi4+Px8PDIszmlJGc98cQT1K5dmxEjRpgdJUODBg0YNGgQPXr0yHKbrH6T3EW9Rr/YfMLO1oaJXWrgZm9l/+krjP5xt9mRRERERERERCSXvf/++7i5uZkdI8O5c+fo2LEj3bt3z/W2VJTKR3zcHekZaMHGgAVbjrFwy1GzI4mIiIiIiIhILvL392fgwIFmx8hQvHhxXn311Xue0P5OqCiVz1T0tDLo4QoAjPpxN/tPxZsdSUREREREREQkx6kolQ/1bxJA04reJKZYGPDtNi4nZj3TvoiIiIiIiIhIQaSiVD5kY2PwcddalPR04u9zVxn+/S4K8Hz0IiIiIiIiIiI3UFEqnyrq6sDUHrWxszH4dedJvt4QY3YkEREREREREZEco6JUPlanXBFGtK4CwFu/7iXq6CWzI4mIiIiIiIiI5AgVpfK53qH+tKruS0qalRfmbONSQrLZkURERERERERE7pmKUvmcYRhM6FwD/2IuHL90jf8s2IHFovmlRERERERERKRgU1GqAPBwsueT8No42Nnw3/1n+OzPw2ZHEhEREREREcmXDMNgyZIlZse4YzNmzKBly5Y5esyHHnqIIUOGZHxu0KABixcvztE2coKKUgVEtVKevNGuGgAfrDjAxr/Pmx1JREREREREJEuGYdzyNXbs2Cz3jY6OxjAMoqKicjxXr169aN++fY4fNzsSExMZNWoUY8aMydV2Ro4cyfDhw7FYLLnazt1SUaoA6VrPj461S2OxwsDvtnPmcqLZkURERERERERu6uTJkxmviRMn4uHhkWndK6+8YnZE0y1atAgPDw9CQ0NztZ1WrVpx+fJlli1blqvt3C0VpQoQwzB4q311KpZw4+zlJAZ/F0Wa5pcSERERERGRfMjX1zfj5enpiWEYGZ99fHz46KOPKFOmDI6OjtSqVYvly5dn7BsQEABAcHAwhmHw0EMPARAZGUmLFi0oXrw4np6eNG3alG3btuVo7j/++IOQkBAcHR0pWbIkw4cPJzU1NeP7RYsWERQUhLOzM8WKFaN58+ZcvXoVgDVr1hASEoKrqyteXl6EhoYSExOTZVvz5s2jbdu2GZ9XrlyJk5MTly5dyrTd4MGDefjhhwE4f/483bt3p3Tp0ri4uBAUFMR33313y3OytbWldevWzJs3L9vXJTeoKFXAuDjY8Wl4HVwdbNnw93k+XnXQ7EgiIiIiIiJiBqsVkq9CSkL6e169rPfeOWLSpEl8+OGHfPDBB+zcuZOwsDDatWvHoUOHANi8eTMAq1ev5uTJk3z//fcAXL58mZ49e7Ju3To2btxIYGAgrVu35vLly/ecCeD48eO0bt2aevXqsWPHDqZNm8aMGTN466234J/eX927d6d3797s27ePNWvW0LFjR6xWK6mpqbRv356mTZuyc+dONmzYQL9+/TAMI8v21q1bR926dTM+P/LII3h5eWWa/yktLY358+cTHh4O/wz5q1OnDr/++iu7d++mX79+PPXUUxnXLCshISGsXbs2B65SzrEzO4DcvQo+brzbqQaDvtvO1N//oo5/EZpV8jE7loiIiIiIiOSllARsxpfBK6/bfe0EOLje0yE++OADhg0bRrdu3QCYMGECv//+OxMnTuSTTz7B29sbgGLFiuHr65ux3/XeQtdNnz4dLy8v/vjjDx577LF7ygTw6aef4ufnx9SpUzEMg8qVK3PixAmGDRvG6NGjOXnyJKmpqXTs2JFy5coBEBQUBMCFCxeIi4vjscceo3z58gBUqVIly7YuXbpEXFwcpUqVylhna2tLt27dmDt3Ln369AHgt99+49KlS3Tq1AmA0qVLZxr6OHDgQFasWMGCBQsICQnJsr1SpUpx9OhRLBYLNjb5o49S/kghd61dzVI81SD9BnhpfhTHL10zO5KIiIiIiIjIbcXHx3PixIkb5lEKDQ1l3759t9z39OnT9O3bl8DAQDw9PfHw8ODKlSvExsbmSLZ9+/bRsGHDTL2bQkNDuXLlCseOHaNmzZo88sgjBAUF8cQTT/DFF19w8eJFAIoWLUqvXr0ICwujbdu2TJo0iZMnT2bZ1rVr6f+Od3JyyrQ+PDycNWvWcOLECQDmzJlDmzZt8PJKLz+mpaXx5ptvEhQURNGiRXFzc2PFihW3vQbOzs5YLBaSkpLu4QrlLPWUKsBGPlaFHccusfNYHC/M2caC5xriYKc6o4iIiIiIyH3B3gXL8GPEX76Mh7t73vV+sXfJm3ZuomfPnpw/f55JkyZRrlw5HB0dadiwIcnJyXnSvq2tLatWrWL9+vWsXLmSKVOm8Prrr7Np0yYCAgKYOXMmgwYNYvny5cyfP5+RI0eyatUqGjRocMOxihUrhmEYGUWt6+rVq0f58uWZN28e/fv354cffmDWrFkZ37///vtMmjSJiRMnEhQUhKurK0OGDLntNbhw4QKurq44Ozvn4BW5N6pgFGCOdrZ80qM2Hk52RB29xLvLbl1RFhERERERkULEMNKH0dm7pL/n1esWcyTdCQ8PD0qVKkVERESm9REREVStWhUABwcH+KdX0L+3GTRoEK1bt6ZatWo4Ojpy7ty5e8rzv6pUqcKGDRuw/s+8WREREbi7u1OmTBn45yFkoaGhjBs3ju3bt+Pg4MAPP/yQsX1wcDAjRoxg/fr1VK9enblz5960LQcHB6pWrcrevXtv+C48PJw5c+bw888/Y2NjQ5s2bTLlefzxx3nyySepWbMmDzzwAAcP3n6+6d27dxMcHHzX1yQ3qShVwPkVdeGjLrUAmBkRzdJdWXcNFBEREREREckPhg4dyoQJE5g/fz4HDhxg+PDhREVFMXjwYAB8fHxwdnZm+fLlnD59mri4OAACAwP55ptv2LdvH5s2bSI8PDxbPX/i4uKIiorK9Dp69CgDBgzg6NGjDBw4kP379/Pjjz8yZswY/vOf/2BjY8OmTZt455132LJlC7GxsXz//fecPXuWKlWqcOTIEUaMGMGGDRuIiYlh5cqVHDp06JbzSoWFhbFu3bob1oeHh7Nt2zbefvttOnfujKOjY8Z3gYGBGb219u3bx3PPPcfp06dve85r166lZcuWd32tcpOpRamxY8diGEamV+XKlc2MVCA1r1qC55o+AMCri3Zy5NxVsyOJiIiIiIiIZGnQoEH85z//4eWXXyYoKIjly5fz008/ERgYCICdnR2TJ0/m888/p1SpUjz++OMAzJgxg4sXL1K7dm2eeuopBg0ahI/P3T/4a82aNQQHB2d6jRs3jtKlS7N06VI2b95MzZo1ef755+nTpw8jR46Ef3p5/fnnn7Ru3ZqKFSsycuRIPvzwQ1q1aoWLiwv79++nU6dOVKxYkX79+vHCCy/w3HPPZZmjT58+LF26NKPodl2FChUICQlh586dGU/du27kyJHUrl2bsLAwHnroIXx9fWnfvv0tz/f48eOsX7+eZ5555q6vVW4yrNYceJZjNo0dO5ZFixaxevXqjHV2dnYUL178jvaPj4/H09OTuLg4PDw8cjFp3khJSWHp0qW0bt0ae3v7u9o3Nc1Cjy82sTn6ApV93VnyQihO9ra5llWksLuX+1FEcp7uSZH8RfekSN5LTEzkyJEjBAQEZJoY22KxEB8fj4eHR755oprcnSeeeILatWszYsSIXGtj2LBhXLx4kenTp+fYMbP6TXIX9RrTf7F2dnb4+vpmvO60ICWZ2dnaMKVHMMVcHdh/6jJjftxjdiQRERERERERuY33338fNze3XG3Dx8eHN998M1fbyA7Ti1KHDh2iVKlSPPDAA4SHh+fYYxzvRyU8nJjULRjDgPlbjrJo6zGzI4mIiIiIiIjILfj7+zNw4MBcbePll1+mRIkSudpGdtiZ2Xj9+vWZNWsWlSpV4uTJk4wbN47GjRuze/du3N3db9g+KSmJpKSkjM/x8fHwT/fhlJSUPM2eG66fw72cS31/TwY2K8/k/x5m5JJdVPZxoZLvjddSRG4tJ+5HEck5uidF8hfdkyJ5LyUlBavVisViwWKxZKy/PiPP9e9E8orFYsFqtZKSkoKtbebpg+707wdT55T6t0uXLlGuXDk++ugj+vTpc8P3Y8eOZdy4cTesnzt3Li4uLnmUMv+zWOHzfTbsj7PBx8nKyzXScNL0UiIiIiIiIgXW9alv/Pz8cHBwMDuOCMnJyRw9epRTp06Rmpqa6buEhAR69Ohx2zml8lVRCqBevXo0b96cd99994bvbtZTys/Pj3PnzhWaic5XrVpFixYt7nnCyPNXk3n80w2cjk+iTXVfPu4ShGEYOZZVpLDLyftRRO6d7kmR/EX3pEjeS0xM5OjRo/j7+2eaVNpqtXL58mXc3d31bz7JU4mJiURHR+Pn53fTic6LFy9+26KUqcP3/u3KlSscPnyYp5566qbfOzo64ujoeMN6e3v7QvWXYU6cj6+XPZ+G16br5xv5dfcpGpQvxlMN/XMso8j9orD9+SJS0OmeFMlfdE+K5J20tDQMw8DGxibTU/auD9m7/p1IXrGxscEwjJv+XXCnfzeY+ot95ZVX+OOPP4iOjmb9+vV06NABW1tbunfvbmasQqNOuaIMb1UZgDd/2cfOY5fMjiQiIiIiIiIiAmYXpY4dO0b37t2pVKkSXbp0oVixYmzcuBFvb28zYxUqfR4MIKxaCZLTLAyYs424BE1GKSIiIiIiIiLmM3X43rx588xs/r5gGAbvda7JvpPriL2QwMsLo5j+VF1sbDTWWERERERERETMowGn9wFP5/T5pRzsbFi97wzT1/5tdiQRERERERGR+9Jvv/1GlSpVSEtLy5Xj9+rVi/bt29/x9snJyfj7+7Nly5ZcyXMrKkrdJ6qX9mRs22oAvL/iAJv+Pm92JBERERERESnkevXqhWEYjB8/PtP6JUuWZHpa4Jo1azAM46avU6dOATB27NiMdba2tvj5+dGvXz8uXLhwywxjx46lVq1auXSGd+/VV19l5MiR2NraQi7kmzRpErNmzbrj7R0cHHjllVcYNmxYjmW4UypK3Ue6h/jRIbg0aRYrA7/bztnLSWZHEhERERERkULOycmJCRMmcPHixdtue+DAAU6ePJnp5ePjk/F9tWrVOHnyJLGxscycOZPly5fTv3//XD6DnLNu3ToOHz5Mp06d7nrflJQ7myPa09MTLy+vuzp2eHg469atY8+ePXed616oKHUfMQyDtztUJ9DHjTOXkxg8bztpFqvZsURERERERKQQa968Ob6+vrz77ru33dbHxwdfX99MLxub/y9d2NnZ4evrS+nSpWnevDlPPPEEq1atuqd8u3bt4uGHH8bZ2ZlixYrRr18/rly5kvH9mjVrCAkJwdXVFS8vL0JDQ4mJiQFgx44dNGvWDHd3dzw8PKhTp84th8HNmzePFi1a4OTkBMCsWbMYN24cO3bsyOgFdr2Xk2EYTJs2jXbt2uHq6srbb79NWloaffr0ISAgAGdnZypVqsSkSZMytfHv4XsPPfQQgwYN4tVXX6Vo0aL4+voyduzYTPsUKVKE0NDQPJ/7W0Wp+4yLgx3TnqyNi4Mt6w+fZ9Lqg2ZHEhERERERkWywWq0kpCRwLfUaCSkJefayWu+uc4OtrS3vvPMOU6ZM4dixYzl2/tHR0axYsQIHB4dsH+Pq1auEhYVRpEgRIiMjWbhwIatXr+bFF18EIDU1lfbt29O0aVN27tzJhg0b6NevX8bQw/DwcMqUKUNkZCRbt25l+PDh2NvbZ9ne2rVrqVu3bsbnrl278vLLL2f0ADt58iRdu3bN+H7s2LF06NCBXbt20bt3bywWC2XKlGHhwoXs3buX0aNH89prr7FgwYJbnufs2bNxdXVl06ZNvPfee7zxxhs3FPNCQkJYu3Zttq9ldpj69D0xRwUfd97tGMTgeVFM+f0v6vgXpWlFb7NjiYiIiIiIyF24lnqNhvMa5nm7m3pswsXe5a726dChA7Vq1WLMmDHMmDEjy+3KlCmT6XO5cuUyDSnbtWsXbm5upKWlkZiYCMBHH3101+dw3dy5c0lMTOTrr7/G1dUVgKlTp9K2bVsmTJiAvb09cXFxPPbYY5QvXx6AKlWqZOwfGxvL0KFDqVy5MgCBgYG3bC8mJoZSpUplfHZ2dsbNzS2jB9i/9ejRg2eeeSbTunHjxmUsBwQEsGHDBhYsWECXLl2ybLdGjRqMGTMmI+PUqVP57bffaNGiRcY2pUqVyugBllfUU+o+9Xit0oTXL4vVCkPmbefEpWtmRxIREREREZFCbMKECcyePZt9+/Zluc3atWuJiorKeC1dujTT95UqVSIqKorIyEiGDRtGWFgYAwcOzHamffv2UbNmzYyCFEBoaCgWi4UDBw5QtGhRevXqRVhYGG3btmXSpEmcPHkyY9v//Oc/PPvsszRv3pzx48dz+PDhW7Z37dq1jKF7d+J/e1Vd98knn1CnTh28vb1xc3Nj+vTpxMbG3vI4NWrUyPS5ZMmSnDlzJtM6Z2dnEhIS7jhbTlBPqfvYqMeqsuPYJXYfj+fFuduY/1xD7G1VpxQRERERESkInO2c2dBtA5cvX8bd3T3T3Eu53W52NGnShLCwMEaMGEGvXr1uuk1AQMAtJ+l2cHCgQoUKAIwfP542bdowbtw43nzzzWxluhMzZ85k0KBBLF++nPnz5zNy5EhWrVpFgwYNGDt2LD169ODXX39l2bJljBkzhnnz5tGhQ4ebHqt48eJ3NOH7df9bLOOfOaleeeUVPvzwQxo2bIi7uzvvv/8+mzZtuuVx/j2k0DAMLBZLpnUXLlzA2ztvR1GpAnEfc7K35dMedXB3smNb7CXGL9tvdiQRERERERG5Q4Zh4GLvgrOdMy72Lnn2uj6fUnaMHz+en3/+mQ0bNuTINRg5ciQffPABJ06cyNb+VapUYceOHVy9ejVjXUREBDY2NlSqVCljXXBwMCNGjGD9+vVUr16duXPnZnxXsWJFXnrpJVauXEnHjh2ZOXNmlu0FBwezd+/eTOscHBxIS0u7o7wRERE0atSIAQMGEBwcTIUKFW7bO+tO7d69m+Dg4Bw51p1SUeo+V7aYCx8+UROAGeuOsHz3ydvuIyIiIiIiIpIdQUFBhIeHM3ny5Jt+f+bMGU6dOpXplZKSkuXxGjZsSI0aNXjnnXdu2e61a9cyDQuMiori8OHDhIeH4+TkRM+ePdm9eze///47AwcO5KmnnqJEiRIcOXKEESNGsGHDBmJiYli5ciWHDh2iSpUqXLt2jRdffJE1a9YQExNDREQEkZGRmeac+rewsDDWrVuXaZ2/vz9HjhwhKiqKc+fOkZSUlOX+gYGBbNmyhRUrVnDw4EFGjRpFZGTkLc/9Tq1du5aWLVvmyLHulIpSQstqvvRr8gAAQxfuJPrc1dvuIyIiIiIiIpIdb7zxxg1Dx66rVKkSJUuWzPTaunXrLY/30ksv8eWXX3L06NEstzl48CDBwcGZXs899xwuLi6sWLGCCxcuUK9ePTp37swjjzzC1KlTAXBxcWH//v106tSJihUr0q9fP1544QWee+45bG1tOX/+PE8//TQVK1akS5cutGrVKtNE5P8WHh7Onj17OHDgQMa6Tp068eijj9KsWTO8vb357rvvstz/ueeeo2PHjnTt2pX69etz/vx5BgwYcMvrcyc2bNhAXFwcnTt3vudj3Q3DerfPcsxH4uPj8fT0JC4uDg8PD7Pj3LOUlBSWLl1K69atb/kIyVxpO81Cjy82Ehl9kaolPfh+QCOc7G3zNINIfmLm/SgiN9I9KZK/6J4UyXuJiYkcOXKEgICATBNlWywW4uPj8fDwyLM5peTeDB06lPj4eD7//HOzo2To2rUrNWvW5LXXXrvjfbL6TXIX9Rr9YgUAe1sbpnSvTTFXB/aejGfcz3vuYC8RERERERERuRuvv/465cqVy7K3WF5LTk4mKCiIl156Kc/bVlFKMvh6OjGpWzCGAd9tPsr3246ZHUlERERERESkUPHy8uK1117LNz3bHBwcGDlyJM7O2Xuq4r3IH1dA8o0HA4sz+JFAAF7/YTcHT182O5KIiIiIiIiIFEIqSskNBj4cSOPA4lxLSaP/t1u5mpRqdiQRERERERERKWRUlJIb2NoYTOxaC18PJw6fvcqI73dRgOfDFxEREREREZF8SEUpualibo5M7RGMrY3BTztO8O2mWLMjiYiIiIiIiEghoqKUZKmuf1GGP1oZgDd/3svOY5fMjiQiIiIiIiIihYSKUnJLzzYOoGXVEiSnWRgwZxtxCSlmRxIRERERERGRQkBFKbklwzB4/4ma+BV15tjFa7y8cIfmlxIRERERERGRe6ailNyWp7M9n/aog4OtDav3nWb6n3+bHUlERERERESkQPrtt9+oUqUKaWlpOXbMXr160b59+4zP3bp148MPP8yx4+cWFaXkjgSV8WR026oAvLfiAJuPXDA7koiIiIiIiORzvXr1wjAMxo8fn2n9kiVLMAwj4/OaNWswDOOmr1OnTgEwduzYjHW2trb4+fnRr18/Lly49b9Px44dS61atXLpDO/eq6++ysiRI7G1tc21NkaOHMnbb79NXFxcrrWRE1SUkjsWXr8sj9cqRZrFysDvtnHuSpLZkURERERERCSfc3JyYsKECVy8ePG22x44cICTJ09mevn4+GR8X61aNU6ePElsbCwzZ85k+fLl9O/fP5fPIOesW7eOw4cP06lTp1xtp3r16pQvX55vv/02V9u5VypKyR0zDIN3OgRRwceN0/FJDJ63nTSL5pcSERERERGRrDVv3hxfX1/efffd227r4+ODr69vppeNzf+XLuzs7PD19aV06dI0b96cJ554glWrVt1Tvl27dvHwww/j7OxMsWLF6NevH1euXMn4fs2aNYSEhODq6oqXlxehoaHExMQAsGPHDpo1a4a7uzseHh7UqVOHLVu2ZNnWvHnzaNGiBU5OTgAcPHgQwzDYv39/pu0+/vhjypcvD0BaWhp9+vQhICAAZ2dnKlWqxKRJk257Xm3btmXevHnZvi55QUUpuSuujnZMC6+Ns70tEX+dZ9Jvh8yOJCIiIiIicl+yWq1YEhKwXLuW/p5Hr7t9+JWtrS3vvPMOU6ZM4dixYzl2/tHR0axYsQIHB4dsH+Pq1auEhYVRpEgRIiMjWbhwIatXr+bFF18EIDU1lfbt29O0aVN27tzJhg0b6NevX8bQw/DwcMqUKUNkZCRbt25l+PDh2NvbZ9ne2rVrqVu3bsbnihUrUrduXebMmZNpuzlz5tCjRw8ALBYLZcqUYeHChezdu5fRo0fz2muvsWDBglueW0hICJs3byYpKf+OcrIzO4AUPIEl3HmnY3Vemr+DKf89RN1yRWhS0dvsWCIiIiIiIvcV67VrHKpbD4DTedhupW1bMVxc7mqfDh06UKtWLcaMGcOMGTOy3K5MmTKZPpcrV449e/ZkfN61axdubm6kpaWRmJgIwEcffXTX53Dd3LlzSUxM5Ouvv8bV1RWAqVOn0rZtWyZMmIC9vT1xcXE89thjGT2XqlSpkrF/bGwsQ4cOpXLlygAEBgbesr2YmBhKlSqVaV14eDhTp07lzTffhH96T23dujVj6J29vT3jxo3L2D4gIIANGzawYMECunTpkmVbpUqVIjk5mVOnTlGuXLlsXJ3cp55Ski0dgsvQPaQsVisMmR/FybhrZkcSERERERGRfGzChAnMnj2bffv2ZbnN2rVriYqKyngtXbo00/eVKlUiKiqKyMhIhg0bRlhYGAMHDsx2pn379lGzZs2MghRAaGgoFouFAwcOULRoUXr16kVYWBht27Zl0qRJnDx5MmPb//znPzz77LM0b96c8ePHc/jw4Vu2d+3atYyhe9d169aN6OhoNm7cCP/0kqpdu3ZGoQvgk08+oU6dOnh7e+Pm5sb06dOJjY29ZVvOzs4AJCQk3OVVyTvqKSXZNqZtVXYeu8SeE/G8OHc78/o1wN5WdU4REREREZG8YDg7E7glkvjLl/Fwd88091Jut5sdTZo0ISwsjBEjRtCrV6+bbhMQEICXl1eWx3BwcKBChQoAjB8/njZt2jBu3LiMXka5YebMmQwaNIjly5czf/58Ro4cyapVq2jQoAFjx46lR48e/PrrryxbtowxY8Ywb948OnTocNNjFS9e/IYJ3319fXn44YeZO3cuDRo0YO7cuZkmb583bx6vvPIKH374IQ0bNsTd3Z3333+fTZs23TL39acSenvn35FNqiBItjnZ2/JpeG3cnezYGnORCcv238FeIiIiIiIikhMMw8DGxQUbZ+f09zx6XZ9PKTvGjx/Pzz//zIYNG3LkGowcOZIPPviAEydOZGv/KlWqsGPHDq5evZqxLiIiAhsbGypVqpSxLjg4mBEjRrB+/XqqV6/O3LlzM76rWLEiL730EitXrqRjx47MnDkzy/aCg4PZu3fvDevDw8OZP38+GzZs4O+//6Zbt26Z8jRq1IgBAwYQHBxMhQoVbtsjC2D37t2UKVOG4sWL3/H1yGsqSsk9KVfMlfc71wTgy3VHWL77lNmRREREREREJJ8KCgoiPDycyZMn3/T7M2fOcOrUqUyvlJSULI/XsGFDatSowTvvvHPLdq9du5ZpWGBUVBSHDx8mPDwcJycnevbsye7du/n9998ZOHAgTz31FCVKlODIkSOMGDGCDRs2EBMTw8qVKzl06BBVqlTh2rVrvPjii6xZs4aYmBgiIiKIjIzMNOfUv4WFhbFu3bob1nfs2JHLly/Tv39/mjVrlmneqcDAQLZs2cKKFSs4ePAgo0aNIjIy8pbnyz9DIVu2bHnb7cykolR+YbVixKzDOfmc2Unu2qPVfXn2wQAAhi7cQcz5q7fdR0RERERERO5Pb7zxBhaL5abfVapUiZIlS2Z6bd269ZbHe+mll/jyyy85evRoltscPHiQ4ODgTK/nnnsOFxcXVqxYwYULF6hXrx6dO3fmkUceYerUqQC4uLiwf/9+OnXqRMWKFenXrx8vvPACzz33HLa2tpw/f56nn36aihUr0qVLF1q1apVpUvJ/Cw8PZ8+ePRw4cCDTend3d9q2bcuOHTsIDw/P9N1zzz1Hx44d6dq1K/Xr1+f8+fMMGDDgltckMTGRJUuW0Ldv31tuZzbDerfPcsxH4uPj8fT0JC4uDg8PD7Pj3Jvlr8HGTzhS/BHKPDf/lo+QzI9S0ix0m76RrTEXqVbKg8X9G+Fkb2t2LJFsS0lJYenSpbRu3brA3Y8ihZHuSZH8RfekSN5LTEzkyJEjBAQEZJoo22KxEB8fj4eHR57NKSX3ZujQocTHx/P555/nWhvTpk3jhx9+YOXKlbnWRla/Se6iXqNfbH5RqRUAZc//CVcLXm8pe1sbpvYIpqirA3tOxPPGLzeOkRURERERERG5373++uuUK1cuy95iOcHe3p4pU6bk2vFziopS+YX/g1hKBmNrTcFmyxdmp8mWkp7OTOxaC8OAuZtiWbL9uNmRRERERERERPIVLy8vXnvttVzt2fbss89mmqg9v1JRKr8wDCwNBwJgs2UGJF0xO1G2NKnozcCHAwEY8f0uDp2+bHYkEREREREREcmHVJTKR6yV2nDFsQRG4iXY/q3ZcbJt8COBPFihONdS0ug/ZxtXk1LNjiQiIiIiIiIi+YyKUvmJjS1/+aTPLcWGqZCW9WMv8zNbG4OJ3WpRwsORv85c4fUfdlGA59MXERERERERkVygolQ+c7Tog1hdvSHuKOxZYnacbCvu5sjUHrWxtTFYEnWCuZtjzY4kIiIiIiIiIvmIilL5jMXGAUvdvukfIiZBAe5hVM+/KK+GpU+sNu6nvew+Hmd2JBERERERERHJJ1SUyocsdZ4Be1c4vQsO/2Z2nHvSr8kDNK9SguQ0C/3nbCXuWsEckigiIiIiIiIiOUtFqfzIuQjU6Zm+HDHJ7DT3xDAMPnyiJmWKOHP0wjWGLtyh+aVEREREREREREWpfKvBALCxgyN/wvFtZqe5J54u9kwLr4ODrQ0r957my7VHzI4kIiIiIiIikmeSk5OpUKEC69evz5Xjr1mzBsMwuHTp0h3vM3z4cAYOHJgree6UilL5lZcfVO+cvlzAe0sBBJXxZFTbqgCMX76fLdEXzI4kIiIiIiIiuezs2bP079+fsmXL4ujoiK+vL2FhYURERGRs4+/vj2EYGIaBi4sLQUFBfPnll5mOc7dFl+joaAzDICoqKsfPKTs+++wzAgICaNSoEeRCvkaNGnHy5Ek8PT3veJ9XXnmF2bNn8/fff+dIhuxQUSo/Cx2U/r7vJzh/2Ow09+zJ+mVpV7MUaRYrL87dzvkrSWZHEhERERERkVzUqVMntm/fzuzZszl48CA//fQTDz30EOfPn8+03RtvvMHJkyfZvXs3Tz75JH379mXZsmWm5c5JVquVqVOn0qdPn7veNzk5+Y62c3BwwNfXF8Mw7vjYxYsXJywsjGnTpt11rpyiolR+VqIaVGgBVgts+MTsNPfMMAze7RhEeW9XTsUnMmR+FGkWzS8lIiIiIiJSGF26dIm1a9cyYcIEmjVrRrly5QgJCWHEiBG0a9cu07bu7u74+vrywAMPMGzYMIoWLcqqVatyLVtSUhKDBg3Cx8cHJycnHnzwQSIjIzO+v3jxIuHh4Xh7e+Ps7ExgYCAzZ86EfwpFL774IiVLlsTJyYly5crx7rvvZtnW1q1bOXz4MG3atMlYFxAQAEBwcDCGYfDQQw8B0KtXL9q3b8/bb79NqVKlqFQp/Yn233zzDXXr1s24Tj169ODMmTMZx/t3T7JZs2bh5eXFihUrqFKlCm5ubjz66KOcPHkyU7a2bdsyb968HLqqd09FqfzuwSHp71Fz4MpZs9PcM1dHO6Y9WQdne1vWHjrHlP8eMjuSiIiIiIhIgWS1WklJSiM1OY2UpLx73enDq9zc3HBzc2PJkiUkJd3ZSBmLxcLixYu5ePEiDg4O93iFsvbqq6+yePFiZs+ezbZt26hQoQJhYWFcuJA+1cyoUaPYu3cvy5YtY9++fUybNo3ixYsDMHnyZH766ScWLFjAgQMHmDNnDv7+/lm2tXbtWipWrIi7u3vGus2bNwOwevVqTp48yffff5/x3W+//caBAwdYtWoVv/zyCwApKSm8+eab7NixgyVLlhAdHU2vXr1ueY4JCQl88MEHfPPNN/z555/ExsbyyiuvZNomJCSEY8eOER0dna3reK/sTGlV7ly5UChdB45vhc3T4eHXzU50zyqWcOftDtX5z4IdTPrtEHXKFaFxoLfZsURERERERAqU1GQLX760Ns/b7TepKfaOtrfdzs7OjlmzZtG3b18+++wzateuTdOmTenWrRs1atTItO2wYcMYOXIkSUlJpKamUrRoUZ599tlcyX/16lWmTZvGrFmzaNWqFQBffPEFq1atYsaMGQwdOpTY2FiCg4OpW7cu/DPv1XWxsbEEBgby4IMPYhgG5cqVu2V7MTExlCpVKtM6b+/0fwMXK1YMX1/fTN+5urry5ZdfZirK9e7dO2P5gQceYPLkydSrV48rV67g5uZ203ZTUlL47LPPKF++PAAvvvgib7zxRqZtrueKiYm5ZWEtt6inVH5nGBA6OH1583RIumJ2ohzRsXYZuof4YbXCkHlRnIpLNDuSiIiIiIiI5LBOnTpx4sQJfvrpJx599FHWrFlD7dq1mTVrVqbthg4dSlRUFP/973+pX78+H3/8MRUqVMiVTIcPHyYlJYXQ0NCMdfb29oSEhLBv3z4A+vfvz7x586hVqxavvvpqpqfm9erVi6ioKCpVqsSgQYNYuXLlLdu7du0aTk5Od5wvKCjohl5iW7dupW3btpQtWxZ3d3eaNm0K/xTIsuLi4pJRkAIoWbJkpiF/AM7OzvBPryozqKdUQVD5MSj6AFz4G7Z/Aw36m50oR4xpW40dR+PYezKeF+du47t+DbC3VZ1URERERETkTtg52PDsx425fDked3cPbGzy5t9Tdg53146TkxMtWrSgRYsWjBo1imeffZYxY8ZkGn5WvHhxKlSoQIUKFVi4cCFBQUHUrVuXqlWr5sIZ3F6rVq2IiYlh6dKlrFq1ikceeYQXXniBDz74gNq1a3PkyBGWLVvG6tWr6dKlC82bN2fRokU3PVbx4sXZtWvXHbft6uqa6fPVq1cJCwsjLCyMOXPm4O3tTWxsLGFhYbecCN3e3j7TZ8Mwbhh6eX244vWeW3lNFYCCwMYWGg1MX97wCaSlmJ0oRzjZ2/JpeG3cHe3YEnOR91ccMDuSiIiIiIhIgWEYBvaOttg52GLvmHevu3nC281UrVqVq1evZvm9n58fXbt2ZcSIEffUTlbKly+Pg4MDERERGetSUlKIjIzMVATz9vamZ8+efPvtt0ycOJHp06dnfOfh4UHXrl354osvmD9/PosXL84o8PxbcHAw+/fvz1QQut4TKi0t7bZ59+/fz/nz5xk/fjyNGzemcuXKN/R4yq7du3djb29PtWrVcuR4d0s9pQqKmj3g93cg7ijs+QFqdDE7UY7wL+7K+0/U4PlvtzH9z7+pW64ILav53sGeIiIiIiIikp+dP3+eJ554gt69e1OjRg3c3d3ZsmUL7733Ho8//vgt9x08eDDVq1dny5YtGfM6AezatSvThOGGYVCzZs0sj3PgwI2dH6pVq0b//v0ZOnQoRYsWpWzZsrz33nskJCTQp08fAEaPHk2dOnWoVq0aSUlJ/PLLL1SpUgWAjz76iJIlSxIcHIyNjQ0LFy7E19cXLy+vm2Zo1qwZV65cYc+ePVSvXh0AHx8fnJ2dWb58OWXKlMHJyQlPT8+b7l+2bFkcHByYMmUKzz//PLt37+bNN9+85fW7U2vXrqVx48YZw/jymnpKFRT2TlD/+fTliElwh087KAgerV6S3qHpj8N8eeEOYs+bM5ZVREREREREco6bm1vG/FBNmjShevXqjBo1ir59+zJ16tRb7lu1alVatmzJ6NGjM61v0qQJwcHBGa86derc8jjdunXLtH1wcDCnT59m/PjxdOrUiaeeeoratWvz119/sWLFCooUKQL/9GQaMWIENWrUoEmTJtja2jJv3jwA3N3dee+996hbty716tUjOjqapUuXZjl8slixYnTo0IE5c+ZkrLOzs2Py5Ml8/vnnlCpV6pZFOm9vb2bNmsXChQupWrUq48eP54MPPrjled+pefPm0bdv3xw5VnYY1jt9lmM+FB8fj6enJ3FxcXh4eJgd556lpKSwdOlSWrdufcPYTwCuXYSPqkHKVQhfDIHNzYiZK5JTLXSdvoHtsZeoXtqDRc83wsn+9k9zEMktt70fRSRP6Z4UyV90T4rkvcTERI4cOUJAQECmSbMtFgvx8fF4eOTdnFJy93bu3EmLFi04fPhwlk/Ly2vLli3j5ZdfZufOndjZ3f1Auqx+k9xFvUa/2ILEuQjU+WciuIiJZqfJUQ52NnzSozZFXOzZfTyeN3/Za3YkERERERERkRxRo0YNJkyYwJEjR8yOkuHq1avMnDkzWwWpnKKiVEHTcADY2EH0Wji+1ew0OaqUlzMfd62FYcCcTbH8GHXc7EgiIiIiIiIiOaJXr14EBQWZHSND586dqV+/vqkZVJQqaDzLQNAT6csRk81Ok+MequTDi80qADDi+138deay2ZFEREREREREJBeoKFUQNRqY/r7vJzh/2Ow0OW5I84o0Kl+MhOQ0+n+7jYTkVLMjiYiIiIiIiEgOU1GqICpRDQJbgtUCG279xIKCyNbGYFK3YHzcHTl05gqv/7CbAjwfv4iIiIiIiIjchIpSBVXo4PT37XPgyhmz0+Q4b3dHpnQPxtbG4Iftx/lu81GzI4mIiIiIiIhIDlJRqqAqFwql60JaEmyebnaaXFH/gWK80rISAGN/3sPu43FmRxIRERERERGRHKKiVEFlGP/fW2rzF5B0xexEueK5Jg/wSGUfklMtDJizjbhrKWZHEhEREREREZEcoKJUQVa5DRQtD4mXYNvXZqfJFTY2Bh92qUlpL2diLyQwdOEOzS8lIiIiIiIiUgioKFWQ2dj+/5P4NnwCaYWzF5GXiwOfhtfGwdaGlXtPM2PdEbMjiYiIiIiIiNyx5ORkKlSowPr163PsmGvWrMEwDC5dugTA8uXLqVWrFhaLJcfayG0qShV0NbuDqw/EH4Pd35udJtfU9PNi5GNVABi/bD9bYy6YHUlERERERERu4+zZs/Tv35+yZcvi6OiIr68vYWFhREREZGzj7++PYRgYhoGLiwtBQUF8+eWXmY7z7wLM7URHR2MYBlFRUTl+Ttnx2WefERAQQKNGjXKtjUcffRR7e3vmzJmTa23kNBWlCjp7J2jwfPpyxCQoxEPbnmpQjrY1S5FqsfLCnO2cv5JkdiQRERERERG5hU6dOrF9+3Zmz57NwYMH+emnn3jooYc4f/58pu3eeOMNTp48ye7du3nyySfp27cvy5YtMy13TrJarUydOpU+ffrkelu9evVi8uTJud5OTlFRqjCo2xsc3ODMHvhrtdlpco1hGLzbMYgHvF05FZ/IkPlRpFkKbxFORERERESkILt06RJr165lwoQJNGvWjHLlyhESEsKIESNo165dpm3d3d3x9fXlgQceYNiwYRQtWpRVq1blWrakpCQGDRqEj48PTk5OPPjgg0RGRmZ8f/HiRcLDw/H29sbZ2ZnAwEBmzpwJ/wzFe/HFFylZsiROTk6UK1eOd999N8u2tm7dyuHDh2nTpk3GukaNGjFs2LBM2509exZ7e3v+/PNPAL755hvq1q2bcW169OjBmTNnbnlebdu2ZcuWLRw+fDjb1yYvqShVGDgXgTq90pcjJpmdJle5OdoxLbwOTvY2rD10jqn//cvsSCIiIiIiIqawWq2kJCaSkpSY/p5Hrzt9+JSbmxtubm4sWbKEpKQ7G+lisVhYvHgxFy9exMHB4R6vUNZeffVVFi9ezOzZs9m2bRsVKlQgLCyMCxfSp4oZNWoUe/fuZdmyZezbt49p06ZRvHhxACZPnsxPP/3EggULOHDgAHPmzMHf3z/LttauXUvFihVxd3fPWBceHs68efMyXcv58+dTqlQpGjduDEBKSgpvvvkmO3bsYMmSJURHR9OrV69bnlfZsmUpUaIEa9euvedrlBfszA4gOaTBANj0GUSvhWNboUwdsxPlmkq+7rzVPohXFu5g4m8HqetfhNAKxc2OJSIiIiIikqdSk5KY+kyXPG930OxF2Ds53XY7Ozs7Zs2aRd++ffnss8+oXbs2TZs2pVu3btSoUSPTtsOGDWPkyJEkJSWRmppK0aJFefbZZ3Ml/9WrV5k2bRqzZs2iVatWAHzxxResWrWKGTNmMHToUGJjYwkODqZu3brwz7xX18XGxhIYGMiDDz6IYRiUK1fulu3FxMRQqlSpTOu6dOnCkCFDWLduXUYRau7cuXTv3h3DMADo3bt3xvYPPPAAkydPpl69ely5cgU3N7cs2ytVqhQxMTHZujZ5TT2lCgvP0hD0zx9G6wt3bymAznXK0LWuH1YrDJ63ndPxiWZHEhERERERkX/p1KkTJ06c4KeffuLRRx9lzZo11K5dm1mzZmXabujQoURFRfHf//6X+vXr8/HHH1OhQoVcyXT48GFSUlIIDQ3NWGdvb09ISAj79u0DoH///sybN49atWrx6quvZnpqXq9evYiKiqJSpUoMGjSIlStX3rK9a9eu4fSvIp63tzctW7bMmJT8yJEjbNiwgfDw8Ixttm7dStu2bSlbtizu7u40bdoU/imK3YqzszMJCQl3dU3Mop5ShUmjgbBjLuz9Cc4fhmLlzU6Uq8Y9Xo2dx+PYdzKegXO3M7dvfexsVWcVEREREZH7g52jIy/OXED85Xg83D2wscmbfw/ZOTre1fZOTk60aNGCFi1aMGrUKJ599lnGjBmTaSha8eLFqVChAhUqVGDhwoUEBQVRt25dqlatmgtncHutWrUiJiaGpUuXsmrVKh555BFeeOEFPvjgA2rXrs2RI0dYtmwZq1evpkuXLjRv3pxFixbd9FjFixdn165dN6wPDw9n0KBBTJkyhblz5xIUFERQUBD805srLCyMsLAw5syZg7e3N7GxsYSFhZGcnHzL7BcuXMDb2zuHrkTu0r/gC5MSVSEwDLDC+ilmp8l1Tva2fBpeGzdHOzZHX+D9lQfMjiQiIiIiIpJnDMPA3skJe0en9Pc8el0fXpZdVatW5erVq1l+7+fnR9euXRkxYsQ9tZOV8uXL4+DgQERERMa6lJQUIiMjMxXBvL296dmzJ99++y0TJ05k+vTpGd95eHjQtWtXvvjiC+bPn8/ixYsz5qP6t+DgYPbv33/DXFyPP/44iYmJLF++nLlz52bqJbV//37Onz/P+PHjady4MZUrV77tJOcAiYmJHD58mODg4Lu+LmZQT6nCJnQwHFoBUXOh2Wvg5mN2olwVUNyV9zvXoP+cbXz+x9/ULVeUFlVLmB1LRERERETkvnf+/HmeeOIJevfuTY0aNXB3d2fLli289957PP7447fcd/DgwVSvXp0tW7ZkzOsEsGvXrkwThhuGQc2aNbM8zoEDN3ZeqFatGv3792fo0KEULVqUsmXL8t5775GQkECfPn0AGD16NHXq1KFatWokJSXxyy+/UKVKFQA++ugjSpYsSXBwMDY2NixcuBBfX1+8vLxumqFZs2ZcuXKFPXv2UL169Yz1rq6utG/fnlGjRrFv3z66d++e8V3ZsmVxcHBgypQpPP/88+zevZs333zzltcMYOPGjTg6OtKwYcPbbpsfqChV2JRrBGXqwbFI2PQ5PDLK7ES5rlVQSZ4J9WdmRDQvL4ji10GN8SvqYnYsERERERGR+5qbm1vG/FDX53Hy8/Ojb9++vPbaa7fct2rVqrRs2ZLRo0ezdOnSjPVNmjTJtJ2trS2pqalZHqdbt243rDt69Cjjx4/HYrHw1FNPcfnyZerWrcuKFSsoUqQIAA4ODowYMYLo6GicnZ1p3Lgx8+bNA8Dd3Z333nuPQ4cOYWtrS7169Vi6dGmWwyeLFStGhw4dmDNnDu+++26m78LDw2ndujVNmjShbNmyGeu9vb2ZNWsWr732GpMnT6Z27dp88MEHtGvX7pbX7bvvviM8PBwXl4Lxb2LDeqfPcsyH4uPj8fT0JC4uDg8PD7Pj3LOUlBSWLl1K69atsbe3z/6B9v0M858EJ094aS84Zj0rf2GRnGqh6/QNbI+9RI0ynix8viGOdrZmx5ICLMfuRxHJEbonRfIX3ZMieS8xMZEjR44QEBCQadJsi8VCfHw8Hh55N6eU3L2dO3fSokULDh8+fMsn592Lc+fOUalSJbZs2UJAQECutPG/svpNchf1Gv1iC6NKraFYBUiMg22zzU6TJxzsbJjaozZeLvbsPBbHW7/sMzuSiIiIiIiICAA1atRgwoQJHDlyJNfaiI6O5tNPP82TglROUVGqMLKxTX8SH8CGTyAtxexEeaK0lzMfd60FwDcbY/hpxwmzI4mIiIiIiIgA0KtXr4yn6+WGunXr0rVr11w7fm5QUaqwqtEN3EpA/HHYvdjsNHmmWSUfXmxWAYDhi3fy15krZkcSERERERERkZtQUaqwsneC+s+nL0dMgoI7ddhde6lFRRo+UIyE5DQGzNlKQnLWk96JiIiIiIiIiDlUlCrM6vYGBzc4sxcOrTI7TZ6xtTGY1L0W3u6OHDx9hZFLdlOA5/MXERERERHJoH/bSH6RE7/FfFOUGj9+PIZhMGTIELOjFB7OXlCnV/pyxCSz0+QpH3cnJncLxsaA77cdZ37kUbMjiYiIiIiIZNv1J10mJCSYHUUE/ue3eC9PYbXLwTzZFhkZyeeff06NGjXMjlL4NBgAmz6HmHVwbAuUqWt2ojzTsHwxXm5ZifdXHGD0T3sIKuNJtVKeZscSERERERG5a7a2tnh5eXHmzBkAXFxcMAwDi8VCcnIyiYmJ2Njkm34nUohZrVYSEhI4c+YMXl5e2NraZvtYphelrly5Qnh4OF988QVvvfWW2XEKH8/SUKMLRM1J7y3V9RuzE+Wp/k3LsyX6Ar8fOMuAOdv4eeCDeDhlv4orIiIiIiJiFl9fX4CMwhT/FAiuXbuGs7MzhmGYmE7uN15eXhm/yewyvSj1wgsv0KZNG5o3b66iVG5pNDC9KLXvZzh/GIqVNztRnrGxMfioSy0em7KOmPMJvLpwJ9OerK0/rEVEREREpMAxDIOSJUvi4+NDSkoKACkpKfz55580adLknoZRidwNe3v7e+ohdZ2pRal58+axbds2IiMj72j7pKQkkpKSMj7Hx8fDPzfh9RuyIPvfP1RyVJEK2FZoic1fK0lbNxFL649y9vj5nJuDwaSuNej+5WaW7znFF38e5plG5cyOJflcrt2PIpItuidF8hfdkyLmu14QsFgspKamYmtrmyNFApE7YbFYsFgsWX5/p38/GFaTpu4/evQodevWZdWqVRlzST300EPUqlWLiRMn3nSfsWPHMm7cuBvWz507FxcXl1zPXJAVvXKAxofeJs2wZ1W1D0my9zI7Up7786TB4mhbbAwrg6qlEeBudiIRERERERGRwichIYEePXoQFxeHh4dHltuZVpRasmQJHTp0yFTJTUtLwzAMbGxsSEpKuqHKe7OeUn5+fpw7d+6WJ1lQpKSksGrVKlq0aJHz3S6tVmxnt8bmeCRpjV7C0uz1nD1+AWC1Whk8fyfL9pzG18ORHwc0pKirg9mxJJ/K1ftRRO6a7kmR/EX3pEj+oftR8qP4+HiKFy9+26KUacP3HnnkEXbt2pVp3TPPPEPlypUZNmzYTbsdOjo64ujoeMN6e3v7QnXz5dr5PDgE5odju+0rbJu+DI73X1eh956oyf7TERw5d5Wh3+9hVq962NhofinJWmH780WkoNM9KZK/6J4UyT90P0p+cqe/RdOeF+nu7k716tUzvVxdXSlWrBjVq1c3K1bhVqk1FKsAiXGwdbbZaUzh7mTPp+G1cbSz4c+DZ/nk97/MjiQiIiIiIiJyXzKtKCUmsLGBRoPSlzd+CqnJZicyRZWSHrzZPr3w+fHqg6z/65zZkURERERERETuO/mqKLVmzZosJzmXHFKjK7iVgPjjsHux2WlM06WuH0/UKYPFCoPmbed0fKLZkURERERERETuK/mqKCV5wN4JGvRPX46YBObMc58vvPF4dSr7unPuSjIDv9tOalrWj7MUERERERERkZylotT9qM4z4OAOZ/fBoVVmpzGNs4Mtn4bXxs3Rjs1HLvDByoNmRxIRERERERG5b6godT9y9oK6vdKXI+7v4ZIPeLsxoVMNAD774zC/7TttdiQRERERERGR+4KKUverBgPAxh5iIuBopNlpTNWmRkl6NfIH4D8LdnD0QoLZkUREREREREQKPRWl7lcepdInPQdYP8nsNKZ7rXUVavp5EXcthRfmbiMpNc3sSCIiIiIiIiKFmopS97NGA9Pf9/0C5/4yO42pHOxs+KRHMJ7O9uw8Fsfbv+4zO5KIiIiIiIhIoaai1P3MpzJUbAVYYf1ks9OYrkwRFz7uWhOArzfE8POOE2ZHEhERERERESm0VJS634UOTn/f8R1c1iTfD1cuwYCHygMwfPFODp+9YnYkERERERERkUJJRan7XbmG4Fcf0pJh02dmp8kX/tOiIvUDinI1OY0B327jWrLmlxIRERERERHJaSpKyf/3loqcAUmXzU5jOjtbG6Z0D6a4myMHTl9m5JLdWK1Ws2OJiIiIiIiIFCoqSkn6vFLFAiEpDrbONjtNvuDj4cSU7sHYGLB42zEWbDlqdiQRERERERGRQkVFKQEbGwgdlL684RNITTY7Ub7QsHwxXm5ZCYDRP+5h74l4syOJiIiIiIiIFBoqSkm6Gl3BzRcun4Ddi8xOk2/0b1qeZpW8SUq1MGDOVi4nppgdSURERERERKRQUFFK0tk5QoP+6csRk8FiMTtRvmBjY/BRl1qU9nIm+nwCwxbv1PxSIiIiIiIiIjlARSn5f3WfAQd3OLsP/lpldpp8o4irA1N7BGNva7B01ylmrY82O5KIiIiIiIhIgaeilPw/J8/0whRAxCSz0+QrwWWL8FrrKgC8s3Qf22Mvmh1JREREREREpEBTUUoya9AfbOwhJgKORpqdJl/p1cifNkElSUmz8sKcbVy8qgnhRURERERERLJLRSnJzKMU1Oyavhwx0ew0+YphGIzvFIR/MRdOxCXy0oIoLBbNLyUiIiIiIiKSHSpKyY0aDUp/3/8rnDtkdpp8xd3Jnk/D6+BoZ8OaA2eZ9sdhsyOJiIiIiIiIFEgqSsmNvCtBpdaAFdZPMTtNvlO1lAdvPF4NgA9XHmD94XNmRxIREREREREpcFSUkpsLHZz+vuM7uHzK7DT5Tpe6fnSqXQaLFQZ9F8WZ+ESzI4mIiIiIiIgUKCpKyc2VbQB+9SEtGTZ9ZnaafMcwDN5qX51KJdw5dyWJgd9tJzXNYnYsERERERERkQJDRSnJWuiQ9PfIryAx3uw0+Y6zgy2fPlkbVwdbNh25wEerDpodSURERERERKTAUFFKslbxUSheEZLiYNtss9PkS+W93RjfqQYAn645zH/3nzY7koiIiIiIiEiBoKKUZM3G5v+fxLfhU0hNNjtRvtS2ZimeblgOgJfm7+DYxQSzI4mIiIiIiIjkeypKya3V6ALuJeHyCdi10Ow0+dbrbapQs4wncddSeGHudpJTNb+UiIiIiIiIyK2oKCW3ZucIDfqnL6+fDBYVW27G0c6WqT1q4+lsz46jl3jr171mRxIRERERERHJ11SUktur0wscPeDsfji00uw0+ZZfURc+6lITgK83xDBj3RGzI4mIiIiIiIjkWypKye05eULdZ9KXIyaZnSZfe6RKCV59tBIAb/26l192njA7koiIiIiIiEi+pKKU3Jn6/cHGHmLXw9HNZqfJ1/o3Lc/TDcthtcJ/5u9gw+HzZkcSERERERERyXdUlJI741ESanZNX1ZvqVsyDIMxbavxaDVfktMs9PtmC/tPxZsdS0RERERERCRfUVFK7lyjwenv+3+FswfNTpOv2doYTOxWi3r+RbicmErPrzZz/NI1s2OJiIiIiIiI5BsqSsmd864IldoAVtgwxew0+Z6TvS1fPl2PQB83Tscn0eurzVxKSDY7loiIiIiIiEi+oKKU3J3Qf3pL7ZgHl0+ZnSbf83SxZ1bvEHw9nDh05gp9v95CYkqa2bFERERERERETKeilNydsvXBrwGkJcPGaWanKRBKezkzq3c93B3tiIy+yEvzo0izWM2OJSIiIiIiImIqFaXk7j04JP19y1eQqAm870RlXw+mP10XB1sblu0+xRs/78FqVWFKRERERERE7l8qSsndCwyD4pUgKR62zjI7TYHRsHwxPupaE4DZG2L47I+/zY4kIiIiIiIiYhoVpeTu2dhA6KD05Y2fQqom775Tj9UoxajHqgIwYfl+vt92zOxIIiIiIiIiIqZQUUqyJ+gJcC8Jl0/CrgVmpylQ+jwYQL8mDwDw6qKd/HnwrNmRRERERERERPKcilKSPXaO0GBA+nLEZLBYzE5UoAx/tDKP1ypFqsVK/2+3svt4nNmRRERERERERPKUilKSfXV6gaMHnDsAh1aYnaZAsbExeL9zTUIrFONqchq9Zm4m9nyC2bFERERERERE8oyKUpJ9Th5Qt3f6csQks9MUOA52Nnz2ZB2qlPTg3JVkes7czPkrSWbHEhEREREREckTKkrJvan/PNg6QOwGiN1kdpoCx93JnlnP1KO0lzNHzl2l9+wtJCSnmh1LREREREREJNepKCX3xqMk1OiavqzeUtlSwsOJ2b1D8HKxZ8fRSwycu53UNM3RJSIiIiIiIoWbilJy70IHAwYc+BXOHjQ7TYFUwceNGT3r4mhnw2/7zzByyW6sVqvZsURERERERERyjYpScu+KB0LlNunL6yebnabAqlOuKJO7B2NjwLzIo0xcfcjsSCIiIiIiIiK5RkUpyRmhg9Pfd86H+JNmpymwwqr58sbj1QGY9Nshvtsca3YkERERERERkVyhopTkDL8QKNsQ0pJh0zSz0xRoTzYox8CHKwDw+g+7WL33tNmRRERERERERHKcilKSc0KHpL9vmQmJcWanKdD+06IiT9Qpg8UKL363jW2xF82OJCIiIiIiIpKjVJSSnBPYErwrQ1I8bJ1ldpoCzTAM3ukYxEOVvElMsdBnViSHz14xO5aIiIiIiIhIjlFRSnKOjQ00GpS+vHEapCaZnahAs7e14ZMetalRxpOLCSn0/GozZy4nmh1LREREREREJEeoKCU5K+gJcC8Jl0/CroVmpynwXB3t+KpXPcoVc+HYxWs8MzOSy4kpZscSERERERERuWcqSknOsnOABgPSlyMmgcVidqICr7ibI1/3DqGYqwN7TsTT/9ttJKfquoqIiIiIiEjBpqKU5Lw6vcDRE84dhIPLzU5TKJQr5srMZ+rh4mDLur/OMWzxTiwWq9mxRERERERERLJNRSnJeU4eUK93+nLEJLPTFBo1ynjxSXhtbG0Mfth+nPdWHDA7koiIiIiIiEi2qSgluaP+82DrAEc3QuxGs9MUGs0q+TC+YxAAn/1xmFkRR8yOJCIiIiIiIpItKkpJ7nD3hZrd0pfVWypHPVHXj1daVgRg3C97WbrrpNmRRERERERERO6ailKSexoNAgw4sBTOaqhZTnqhWQWebFAWqxWGzI9i09/nzY4kIiIiIiIicldUlJLcUzwQKrdJX14/2ew0hYphGIxrV52WVUuQnGqh79dbOHj6stmxRERERERERO6YilKSu0KHpL/vmA/xGmaWk2xtDCZ3D6ZOuSLEJ6bS86vNnIy7ZnYsERERERERkTuiopTkLr96ULYRWFJg0zSz0xQ6Tva2zOhZl/LerpyMS6TXV5HEXUsxO5aIiIiIiIjIbakoJbkvdHD6+5aZkBhndppCx8vFgdm9Q/Bxd+TA6cv0+3oLiSlpZscSERERERERuSUVpST3BbYE7yqQFJ9emJIcV6aIC7OeCcHd0Y5NRy7w8oIdWCxWs2OJiIiIiIiIZElFKcl9NjYQOih9eeM0SE0yO1GhVLWUB58/VQd7W4Nfd53kjV/2YrWqMCUiIiIiIiL5k4pSkjeqdwb3UnDlFOxcYHaaQqtRheJ82KUWALPWRzP9z7/NjiQiIiIiIiJyUypKSd6wc4CGA9KXIyaBxWJ2okKrXc1SjGxTBYB3l+1nyfbjZkcSERERERERuYGKUpJ36vQCR084fwgOLjM7TaH2bOMH6PNgAABDF+1g3aFzZkcSERERERERyURFKck7ju5Qr0/6csQks9MUeq+3rsJjNUqSkmbluW+2sPu4nnwoIiIiIiIi+YeKUpK36j8Ptg5wdBPEbjQ7TaFmY2PwYZeaNHygGFeT03hmViRHLySYHUtEREREREQEVJTKPy6dSeDXT3Zx7bQtFkshfmKaewmo2T19Wb2lcp2jnS2fP12Hyr7unL2cRM+vNnPharLZsURERERERERUlMovdq05xvH9lzi/zYX5b24hanUsSQkpZsfKHY0GAQYcWApn9pudptDzcLJn1jMhlPZy5u9zV+kzO5JryWlmxxIREREREZH7nIpS+UTNR/yo2bwMhr2Vy+cSiVj0F7NGrOeP7w5w8dRVs+PlrOIVoMpj6cvrp5id5r7g6+nE7N718HS2Z3vsJQZ+t53UND0BUURERERERMyjolQ+4VHMmfqPB1Cy2RUad6tA0VKupCalsfuP48wdu4mfJ0cRvesc1sIytC90SPr7zvkQf8LsNPeFCj7ufNmzLo52Nqzed5pRP+7Bai0kvycREREREREpcFSUymdsbKFKaEm6jQrh8ZeCCahZHAyI3XuBXz/ZyZwxG9nx36MkX0s1O+q9KVMXyoWCJQU2TjM7zX2jnn9RJnULxjDgu82xTPnvX2ZHEhERERERkfuUilL5lGEYlKlUhNb9a/DUmw2p1dwPB2c74s5eY92CQ8waHsGf8w9y6XQBfppa6OD09y0z4dols9PcNx6t7ssb7aoB8NGqg8yPjDU7koiIiIiIiNyHVJQqADyKOxPaOZCe7zaiaY9KFPF1ISUpjV2/H2POmI38MnUHsXvOF7yhfYEtwacqJF+GrTPNTnNfeaqhPy80Kw/Aaz/s5r/7T5sdSURERERERO4zKkoVIA5OdlRvUpruY+rTbnAt/IOKgQExu8/z85QdzB23iV1rjpGcWECG9hnGP0/iI30IX2qS2YnuK6+0rESn2mVIs1h5Yc52oo6qt5qIiIiIiIjkHRWlCiDDMPCrUpQ2L9QkfFwDaj7sh4OTLZdOJ/DnvIPMHh7BugWHiDtbAIb2Ve8EHqXhyun0Sc8lzxiGwfhOQTSp6M21lDR6z4rkyLlC9qRHERERERERybdUlCrgvHxceLBLID3Hh9KkW0W8SriQnJjGjv8e5dvRG/n1050c3Xch/z5lzc4BGgxIX46YDBaL2YnuK/a2NkwLr01QaU8uXE3m6a82cfayeqyJiIiIiIhI7lNRqpBwcLIj6KEy9BhTn7YDa1K2WjGwQvTOc/w0KYrv3tjM7j+Pk5KUZnbUG9XpCU6ecP4QHFhqdpr7jqujHV/1qkfZoi4cvXCNZ2Zt5kpSARkCKiIiIiIiIgWWqUWpadOmUaNGDTw8PPDw8KBhw4YsW7bMzEgFnmFjULZaMdoOTB/aF9SsDPaOtlw8eZU/5h5g9ogIIhb/Rfy5a2ZH/X+O7lDv2fTliImQX3t1FWLe7o7M7h1CUVcHdh+PZ8CcbaSkqdeaiIiIiIiI5B5Ti1JlypRh/PjxbN26lS1btvDwww/z+OOPs2fPHjNjFRpeJVxo0rUivcaH8mCXQDy9nUlKSCVqVSzfjtrA0mk7OXbgYv4Y2hfyHNg6wrFIiN1odpr7UkBxV77qVQ9ne1v+PHiWYYt35o/fhoiIiIiIiBRKphal2rZtS+vWrQkMDKRixYq8/fbbuLm5sXGjihI5ycHZjpoP+xE+rgFtXqiBX9WiWK1wZMc5fvx4O/Pf2szedSdISTZxaJ97CajVPX05YpJ5Oe5ztfy8+DS8NrY2Bt9vO84HKw+YHUlEREREREQKqXwzp1RaWhrz5s3j6tWrNGzY0Ow4hZJhY+AfVJx2g2rRfUx9qjctjZ2jLeePX+X3b/cze0QEG374i8sXEs0J2HAgYMDBZXBmnzkZhGaVfXi3QxAAn/x+mG82RJsdSURERERERAohO7MD7Nq1i4YNG5KYmIibmxs//PADVatWvem2SUlJJCX9/5PB4uPjAUhJSSElJSXPMueW6+eQF+fiXtyBRp0foE7rshzYeJo9f57g8vlEtq2IZfvKWPxrFqN609L4lvfAMIxczwOAZzlsKz+Gzf6fsaybRFrbKXnTrtygQy1fjl+8yqT/Hmb0T3so6mJHy6olzI6Vp/LyfhSR29M9KZK/6J4UyT90P0p+dKe/R8Nq8qQxycnJxMbGEhcXx6JFi/jyyy/5448/blqYGjt2LOPGjbth/dy5c3FxccmjxIWT1QqJZ2y5EuNA0vn/r1Xau6fh5p+MS8lUDNvcz1Hk6mGaHByHxbBlVdUPSXQomvuNyk1ZrbDgbxvWn7HBzrAyoGoa5T3MTiUiIiIiIiL5XUJCAj169CAuLg4Pj6z/IWl6UerfmjdvTvny5fn8889v+O5mPaX8/Pw4d+7cLU+yoEhJSWHVqlW0aNECe3t703JcOHGVPX+e4ODmM6SlpD+BzcnNjsqNSlL1wZK4FXHM1fZtv2mHTex60hq8gOWRG4uQkndS0yy8OG8Hv+0/i6ezHd89G0Kgj5vZsfJEfrkfRSSd7kmR/EX3pEj+oftR8qP4+HiKFy9+26KU6cP3/s1isWQqPP0vR0dHHB1vLIjY29sXipvPkpY+0bjZ51OinBclnvKiUcdA9kacYNeaY1y5kETUyqPsWH2M8sHe1GhWBt/ynrkztO/Bl2Duemy3fY1t01fB2Svn25A7Ym8PU3vUIfzLjWyLvcSzX2/j+wGh+Ho6mR0tz5h9P4pIZronRfIX3ZMi+YfuR8lP7vS3aOpE5yNGjODPP/8kOjqaXbt2MWLECNasWUN4eLiZsUxx6fQp5owYTMLpE2ZHyeDkak/tluV46s2GtHouiNIVvbBarPy19Qzff7CNhe9uYf/Gkxm9qXJMYAvwqQrJl2HLVzl7bLlrzg62zOhZjwe8XTkRl0ivmZuJT9R4dREREREREbk3phalzpw5w9NPP02lSpV45JFHiIyMZMWKFbRo0cLMWKbYvGQBF08c5+Sa5Rzdu8vsOJnY2NrwQLA37f9Tm64jQ6gaWhJbexvOxl7mt1n7mP1aBJt++purl27ew+2uGQaEDk5f3vQZpJj0NEDJUMTVgdnPhODt7sj+U5fp9/UWklLTzI4lIiIiIiIiBZipRakZM2YQHR1NUlISZ86cYfXq1fdlQQrg4Weex79mHaxpafz8wVvE7t5pdqSbKl7GjWZPVaHXu6E0aP8AbkUcuXY5hS1Lo/n6tfWsnLGHU3/H3XtD1TuBRxm4chp2zs+J6HKP/Iq6MOuZerg52rHx7wu8vGAHFku+mpJOREREREREChBTi1Ly/+wcHGg9+FVcSvmRmpzMDxPG5dvCFICTmz11HvXnqbcaEta3OiUreGKxWDkUeZrF721l4fgtHNh0irTUbA7ts7WHhgPSl9dPBksODxGUbKlWypPPnqyDva3BLztP8vbSfWZHEhERERERkQJKRal8xM7BAd/GzfGvWYfU5KR8X5jin6F9Fer40PGVOnR5rR6VG5XE1s6GM9HxrJ65l69fW8/mX45wNS4bQ/tqPw1OnnD+Lzjwa27El2x4MLA473euCcCMdUf4cu3fZkcSERERERGRAkhFqXzGxtaO1oNfJSC47v8UpnaYHeuOeJd155Gnq9Dz3UbUb/cArp4OJMQnE/nLEb5+bT2rZu7hdHT8nR/Q0R3q9U1fXjcRrBoqll+0Dy7NiFaVAXjr1338GHXc7EgiIiIiIiJSwKgolQ/ZOTjQ7uXX/6cw9UaBKUwBOLs7ULe1P0+904iWz1bD9wFPLGlWDm46zaLxW1j83hYORZ4mLe0OhuTVfw5sHeH4FojdkBfx5Q71a/IAz4T6A/DKwh2s/+uc2ZFERERERESkAMlWUero0aMcO3Ys4/PmzZsZMmQI06dPz8ls9zU7e/sCXZgCsLW1IbBuCTq9WocnRtSlUgNfbOwMTv0dz8oZe/jmtfVsWXqEhPjkrA/i5gO1eqQvR0zKs+xye4ZhMKpNVdoElSQlzUq/b7ay98Rd9IQTERERERGR+1q2ilI9evTg999/B+DUqVO0aNGCzZs38/rrr/PGG2/kdMb7VmEoTF3nU86D5r2q0vOdUELaBuDi4cDVuGQ2/XSE2a9F8NusvZyNvXzznRsNBAw4uBzOaGLt/MTGxuDDLjWpH1CUK0mp9Jq5mWMXE8yOJSIiIiIiIgVAtopSu3fvJiQkBIAFCxZQvXp11q9fz5w5c5g1a1ZOZ7yvFabCFICLhwP12gTw9DuNaNG7KiUCPLCkWtm/8RQL3onk+w+28tfWM5mH9hUrD1XbpS9HTDYtu9yck70t05+uS6US7py5nETPrzZz8eoter+JiIiIiIiIZLcolZKSgqOjIwCrV6+mXbv0gkHlypU5efJkziaUmxamYnZFmR3rntja2VAxxJfOw+rSaVgdKoaUwMbW4ORfcaz4YjffjtzA1uXRXLvyT3EjdHD6+64FEKdJtfMbT2d7ZvWuR0lPJw6fvcqzX28hMSXN7FgiIiIiIiKSj2WrKFWtWjU+++wz1q5dy6pVq3j00UcBOHHiBMWKFcvpjHKTwtSS994s8IWp63wDPGnRuxpPv9OIum38cXa358rFJDYu+ZvZw9fz36/3cc5aEfwbgyUVNn5qdmS5iZKezszuHYKHkx1bYy4y6LvtpFn0xEQRERERERG5uWwVpSZMmMDnn3/OQw89RPfu3alZsyYAP/30U8awPsl51wtTD9SuV+gKUwCuno7Ub/sAPd8JpXmvKniXdSct1cK+9SeZ/1YkPxx7icOJDbBs+RquXTI7rtxExRLufNmzHg52Nqzce5oxP+3GalVhSkRERERERG6UraLUQw89xLlz5zh37hxfffVVxvp+/frx2Wef5WQ++Rc7e3va/ue1QluYArC1t6FSg5I8MaIuHYfWoUJdHwwbgxPHDJZfGsY3x99n2+yfSbyaYnZUuYmQgKJM6loLw4BvN8byye9/mR1JRERERERE8qFsFaWuXbtGUlISRYoUASAmJoaJEydy4MABfHx8cjqj/Mv9UJgCMAyDkuU9CXu2Ok+/3ZA6rcrh5GThisWHDVGlmT08gt+/3c/541fMjir/0iqoJGPbVgPgg5UHWbjlqNmRREREREREJJ/JVlHq8ccf5+uvvwbg0qVL1K9fnw8//JD27dszbdq0nM4oN3G/FKaucyviRIPHy9NzfGMeLvEtxe3+JjXFwt51J5j35maWfLydv6POYtEcRvlGz0b+PN+0PADDv9/F7wfOmB1JRERERERE8pFsFaW2bdtG48aNAVi0aBElSpQgJiaGr7/+msmTJ+d0RsnCDYWpQvBUvtuxc3KkyqN16VLsZTr4f0r54OIYNgbHD1xk2We7+HbUBraviiUpQUP78oNhj1aiY3Bp0ixWBny7jR1HNReYiIiIiIiIpMtWUSohIQF3d3cAVq5cSceOHbGxsaFBgwbExMTkdEa5hUyFqZTk+6IwRe2nMZy9KJW4ikebxPDUWw2pHVYOR1c7Lp9PZP3iv5g1PII/5h7gwsmrZqe9rxmGwfhONWgcWJxrKWn0nhVJzHn9NxEREREREZFsFqUqVKjAkiVLOHr0KCtWrKBly5YAnDlzBg8Pj5zOKLdx3xWmHN0gpG/68rqJuBdxpGGH8vR6N5RmT1WmWGlXUpMt7P7zON+N28RPk7YTvfMcVg3tM4WDnQ3TnqxDtVIenL+azNNfbebclSSzY4mIiIiIiIjJslWUGj16NK+88gr+/v6EhITQsGFD+KfXVHBwcE5nlDtw3xWmQp4DW0c4vgVi1gNg52BL1dBSdB0ZQvuXgnmgljeGAUf3XeTXT3fy7ZiN7PjtKEnXUs1Of99xc7Rj5jP18CvqTMz5BHrPiuRqkv47iIiIiIiI3M+yVZTq3LkzsbGxbNmyhRUrVmSsf+SRR/j4449zMp/chfuqMOXmDcHh6csRkzJ9ZRgGpSsVodXzQTz5ZkOCW5TF0cWO+LPXWLfwELOHR/DnvINcPKVhZHnJx92J2c+EUMTFnp3H4nhh7jZS0ixmxxIRERERERGTZKsoBeDr60twcDAnTpzg2LFjAISEhFC5cuWczCd36aaFqZ2FtDDV8EUwbODQCji996abeBR3plGnCvR8N5SHwitRtJQrKUlp7FpzjLljN/HzlChidp/X0L488oC3G1/1qoeTvQ1rDpzlte93YbXq2ouIiIiIiNyPslWUslgsvPHGG3h6elKuXDnKlSuHl5cXb775JhaLej6Y7YbC1HuFtDBVrDxUaZe+vP7WT320d7SlWuPSdBsVQrshtfCvURwMiN1zgV+m7mDO2I3s/P0oyYkaUpbbgssW4ZMetbExYOHWY3y06qDZkURERERERMQE2SpKvf7660ydOpXx48ezfft2tm/fzjvvvMOUKVMYNWpUzqeUu5ZRmKoTUrgLU6GD0t93LYS4Y7fd3DAM/CoXpc2AGjz5RkNqNvfDwdmOuDPXWDv/ELOGR7B2wUEunUnI/ez3sUeqlOCdDkEATPnvX3y7UU/tFBERERERud9kqyg1e/ZsvvzyS/r370+NGjWoUaMGAwYM4IsvvmDWrFk5n1Kyxc7enrYvjSjchanSdcC/MVhSYeO0u9rV09uZBzsH0vPdRjTtXpEivi6kJKax87/HmDNmI798soPYvec1vCyXdAspy5DmgQCM/nE3K/acMjuSiIiIiIiI5KFsFaUuXLhw07mjKleuzIULF3Iil+SQ+6IwFTok/X3rLLh28a53d3Cyo3rTMnQfXZ+2g2pSLqgYWCFm13l+nryD78ZtYteaYxralwsGPxJI9xA/LFYY9N12tsbozw8REREREZH7RbaKUjVr1mTq1Kk3rJ86dSo1atTIiVySgwp9YarCI1CiOiRfgS1fZfswho1B2arFeOyFmoSPa0CNh8tg72TLxVMJ/DnvILNHrGfdokPEnb2Wo/HvZ4Zh8Obj1Xmksg9JqRb6zN7CX2eumB1LRERERERE8kC2ilLvvfceX331FVWrVqVPnz706dOHqlWrMmvWLD744IOcTyn37GaFqeid282OlTMMA0IHpy9v/AxSEu/5kF4lXGjcpSK9xofSuGsgnj7OJF9LZcfqo3w7egO/frqTo/svaGhfDrCztWFKj2Bq+XlxKSGFnl9t5nT8vf83FBERERERkfwtW0Wppk2bcvDgQTp06MClS5e4dOkSHTt2ZM+ePXzzzTc5n1JyxL8LUz++92bhKUxV6wCefnD1DOz4LscO6+BkR41mfoSPbcBjL9akbLWiYIXonef4aWIU897czO4/j5OSlJZjbd6PXBzs+KpXPQKKu3L80jV6zYwkPjHF7FgiIiIiIiKSi7JVlAIoVaoUb7/9NosXL2bx4sW89dZbXLx4kRkzZuRsQslRdvb2tPtPISxM2dpDwxfSl9dPAUvOFokMG4Ny1YvRdmAteoytT1DT0tg52nLhxFX+mHuA2SMiWL/4L+LPaWhfdhV1deDr3iEUd3Nk38l4nv9mK8mpFrNjiYiIiIiISC7JdlFKCi5bu0JamAp+Cpy84MJh2P9rrjVTxNeVJt0r0Wt8KA8+EYhHcSeSElLZviqWb0dtYNlnuzh5OC7X2i/M/Iq6MOuZerg62LL+8HleWbgDi0VDJEVERERERAojFaXuU4WyMOXoBiH90pcjJkIuz/fk6GxHzUf8CH+jIW0G1MCvShGsVvg76izfv7+VlV/u5vIFzY10t6qX9uSzp+pgZ2Pw044TjF++3+xIIiIiIiIikgtUlLqP3bQwtWOb2bHuTUg/sHOC41shJiJPmrSxMfCvUZx2g4PpPro+VRqVBAMObTnD3LEb2bL0CKnJmnPqbjQO9Oa9zulP8pz+59/MWHfE7EgiIiIiIiKSw+zuZuOOHTve8vtLly7dax7JY9cLUz999C5/b93Mj++/xeNDR+Jfs7bZ0bLHzRtqhcOWGRAxCfwfzNPmi5Zy5eGnqxD0UBnWLjjIyb/i2PTTEfZGnOTBzoEE1CqOYRh5mqmg6li7DKfjk5iwfD9v/rIXH3dH2tYsZXYsERERERERySF31VPK09Pzlq9y5crx9NNP515ayRXXC1Pl69ZP7zH1/lsFu8dUwxfAsIFDK+H0HlMieJd1p8PLtWnRpyquXo5cPp/Iss938dOkKM6fuGJKpoLo+aYP0KuRPwAvL9jB+sPnzI4kIiIiIiIiOeSuekrNnDkz95KIqWzt7Gn70nB+/ng8h7dsKtg9poqVhyrtYO+S9CfxdfjMlBiGYVCxni8BNbzZujyaqFVHObb/IvPfiiSoaWnqPRaAk6u9KdkKCsMwGPVYVc5cTmTprlM89/VWFvZvSGVfD7OjiYiIiIiIyD3SnFKS4XphqlD0mAodnP6+ayFcOmpqFHtHWxo8Xp7uY+rzQC1vrBYrO38/xpzRG9mz9rieLncbtjYGH3WpRYh/US4npdLzq80cv3TN7FgiIiIiIiJyj1SUkkwKTWGqdG0IaAKWVNg4zew0AHh6O9Pq+SDaDa5FkZKuJF5NYc2cAyx8N5ITf2k+tltxsrfli6frEujjxun4JHp+tZlLCclmxxIREREREZF7oKKU3ODfhakl7xfQp/Jd7y21dRZcu2h2mgx+VYrSdWQ9HnwiEAdnO84dvcIPH2xj5Yw9XLmYaHa8fMvTxZ7ZvUPw9XDirzNX6Pv1FhJT9FRDERERERGRgkpFKbmp/y9MNSAtJaVgFqbKPwIlgiDlKkTOMDtNJra2NtR8xI8n32hA1calwIBDkaeZM2YjW5ZGk6piy02V8nJmVu96uDvZERl9kSHzokjT8EcREREREZECSUUpyVJ6YWpYwS1MGcb/95ba9Bmk5L95iJzdHWgWXpku/8fefcdHUeZ/AP/MzPb0QgqkUqX3bsdTQb1D7zxP1EMF9RCw4Nl7OXun2LCdYrnf2T2wodKLUqRJCyQESEJIr1tm5vfH7G52N5uQkDKb5PO+29fsPvPMzHcjS8gnz/PM3aOR3CsKLoeCDV8ewIcPb8CBLYVQVQYugU5JisTrV42CSRLxzc58PPzVTn6diIiIiIiIOiCGUtSoDh9MDZwKRKUBVYXAbx/qXU2DuqVF4OJ/jsAfrh2AsGgzyo/XYtlr2/HlS1tRfLRK7/JCzvhecXj+sqEQBODf63LwyoosvUsiIiIiIiKiZmIoRSfUoYMpyQiMn609XzsfUEJ3WpwgCOg7JgnTHhqLkZPTIRlEHN5dgo8e24hV/9kLe7VT7xJDyoVDuuP+CwYAAJ7+Zg8+2XRY75KIiIiIiIioGRhKUZMEDaa2btK7rKYZcRVgjQGKDwC7v9a7mhMyWQwY96deuPzBscgcGg9VUbHtx8N4/4H12LnqCBSuoeR17amZuOH0ngCAOz/ZhhV7C/UuiYiIiIiIiJqIoRQ1Wb1g6tnHOkYwZQoDRl+nPV/9ItBB1h+K6mbFlFlD8MebhiEmyYbaSid+XrIH//fELzi6v1Tv8kLGneefgqnDusOlqJj1/iZsP1ymd0lERERERETUBAylqFk8wVTv0R0smBp7A2CwAEc3A9mr9a6mWVIHxOKy+8fg1Ev7wGQ14HhuJT57djO+e3MnKktq9S5Pd6Io4Om/DMXE3nGodsi45p2NOFRUrXdZREREREREdAIMpajZJIMRF97SwYKpsHhg+JXa8zUv6V1Ns0mSiKGTUnHlI+Mw4NTugADs+6UASx5cj1+XZsPlDN21stqDySDi1StHYkByJI5XOjD97Y0oqrTrXRYRERERERE1gqEUnZQOGUyNnw0IIrD/eyB/h97VnBRrhAlnXXkKLr1rFJJ6RsHlULDhywP48OENOLC1EGoHmZrYFiIsRrxzzWj0iLbi4PEqXPvur6h2uPQui4iIiIiIiBrAUIpOWocLpmJ7AgP+pD1fO1/valokIT0Sl9w+An+4dgDCokwoP16LZa9ux5cvbUXx0Sq9y9NNQqQF/54xBtE2I37LLcWcD7bAJSt6l0VERERERERBMJSiFgkWTB0M5WBq4s3adsd/gdJcvatpEUEQ0HdMEqY9PA4jz0+HaBBweHcJPnpsI1b9Zy/s1U69S9RFr27heHP6aJgNIn7cfQz3frajS48gIyIiIiIiClUMpajF6oKp8ZCdTnwRysFU9+FA5hmA4gLWL9K7mlZhshgwbmovTHtwLDKHxkNVVGz78TDef2A9dq46AkXpeoHMyPQYLJg2AqIAfPxrLl78YZ/eJREREREREVEAhlLUKrRg6o6OEUx5RktteheoLta7mlYT1c2GKbOG4KKbhiImyYbaSid+XrIH//fEL8jbX6p3ee3uDwMS8ejUQQCAl5bvwwcbDuldEhEREREREflgKEWtpsMEU73OBpIGA84q4Nc39a6m1aUNiMNl94/BqZf2gcki4XhuJT59djO+e3MnKku61h3prhibjpvO7g0AuO/z7fhhV4HeJREREREREZEbQylqVR0imBIEYOIt2vMNrwHOGr0ranWSJGLopFRc8ch4DJiYDAjAvl8KsOSh9fh1WTZcTlnvEtvNrX/oi7+OSoGiAnM+3IzNh0r0LomIiIiIiIgYSlFb6BDB1ICpQFQaUFUIbP1A72rajC3ShLOu6o9L7xqFpJ5RcNllbPjiAD58eAMObC3sEguAC4KAf108GGf164Zap4IZ7/yCrMJKvcsiIiIiIiLq8hhKUZsI+WBKMgAT5mjP184HlM49cighPRKX3D4C51wzAGFRJpQfr8WyV7fjq5e3ojivSu/y2pxRErHwihEYmhKFkmonpr+1EcfKa/Uui4iIiIiIqEtjKEVtJuTvyjf8SsAaA5QcBH7/Su9q2pwgCOg3NgnTHh6HEeenQzQIyP29BB8/uhGr/7MP9mqn3iW2KZvJgLeuHo2MOBsOl9Tg6rd/QUVt537PREREREREoYyhFLUpyWAI3WDKFAaMuV57vuYloAtMZQMAk8WA8VN7YdqDY5ExJB6KouK3H3Ox5MH12LnqCBSl834d4sLNePfaMYgPN2FXXjlmvb8ZDpeid1lERERERERdEkMpanMhHUyNuR4wWIGjm4HsVXpX066iutlwwY1DcNFNQxGTZENNhRM/L9mD/z75K/L2l+pdXptJjwvDW1ePhs0kYfX+47jjv7916iCOiIiIiIgoVDGUonYRssFUWLw2jQ/u0VJdUNqAOFx2/xhM/EtvmCwSCg9V4NNnN+P7t3aissSud3ltYkhKNBZdMQIGUcDnW4/iqW93610SERERERFRl8NQitpNvWDqmUdxcMuvepcFjJ8NCCKw/wcgf4fe1ehCkkQMOycNVzwyHv0nJgMCsHdjAZY8tB6/LsuGy9n5FoI/s18CnvzzEADAaysO4O01B/UuiYiIiIiIqEthKEXtyhNM9RkzAbLLpY2Y0juYis0EBkzVnq99Wd9adGaLNOHsq/rj0rtGIalnJFx2GRu+OIAPH96Ag78VQu1k6279ZWQKbj+vHwDgka934X/b8vQuiYiIiIiIqMtgKEXtTjIYcMHNd4RWMDXxZm27/b9A6SF9awkBCemRuOT2kTjnmgGwRZlQfrwWS1/Zjq/m/4bivCq9y2tVN57ZC1eNS4eqArd+vBXrDxTpXRIREREREVGXwFCKdBFywVT3YUDPMwFVBtYt0q+OECIIAvqNTcIVD4/DiPPSIRoE5O4qxsePbsTq/+yDvdqpd4mtQhAEPPTHgThvYCIcsoLr/v0r9uRX6F0WERERERFRp8dQinQTcsGUZ7TU5neB6mL96ggxJosB4y/uhcsfGIuMIfFQFBW//ZiLJQ+ux641R6F2gjvXSaKAl/42HKPSY1BR68LVb29EXlmt3mURERERERF1agylSFfBgqkDW37Rp5ieZwFJgwFnNfDLm/rUEMKiE2y44MYhuGjuUEQn2lBT4cRP7+3G/z35K/KyyvQur8UsRgmLp49C74Rw5JXVYsa/N6HapXdVREREREREnRdDKdJdYDD15bP/0ieYEgRg4i3a8w2vAs6a9q+hA0gbGIe/3T8GE//SGyaLhMJDFfj0mU34/q2dqCyx611ei0TbTHj32jFIjDRj37EqvL5bws97C1FpZzpFRERERETU2hhKUUjwBlNjdQ6mBkwFotOA6uPA1iXtf/0OQjKIGHZOGq54ZDz6T0wGBGDvxgIseWg9Nn2TDZdT1rvEk9Yj2op3rhmDcLMBBysEXPfeFgx9+DtcvGgNnv5mN1bvO44aR8d9f0RERERERKGCoRSFDMlgwAU36RxMSQZg/Fzt+dr5gMLwoTG2SBPOvqo/Lr1rFJJ6RsJll7H+8wP48JGNOPhbIVS1Y6431T85Eh/MGI2x3RSkxFghKyq2HCrFop+zcOWbGzDk4W/x11fX4fnv92L9gSLYXfxzQkRERERE1FwMpSikhEQwNfwKwBoLlGQDv3/ZvtfuoBLSI3HJP0finKv7wxZlQnlhDZa+sh1fzf8NxXlVepd3UvonR2BabwU/zTsNq+88C8/8ZQguGd4DyVEWOGUVG7OL8fLyffjb6+sx5KHvMO2N9Zi/fB825RTDKSt6l09ERERERBTyDHoXQBTIE0z97+WnsW/DWnz57L/wx9vuRc8Ro9unAFMYMOZ6YMWTwOoXtSl9gtA+1+7ABFFAv3HJyBzWDZuW5WDr8kPI3VWMjx/diMFnpWD0hZkwWzvmXzkpMTZcOsqGS0elQlVV5BRVY92BIqzNKsK6rCIcr7RjbZb2+rnvAZtJwqiMWIzvGYcJveIwsHskDBJ/B0BEREREROSrY/6ESJ1evWDquXYOpsZcD6x5CcjbCmSvAjJPb5/rdgImiwHjL+6F/hOTsea/+5G97Th+W56LvRvzMW5qL/QfnwxB7LghnyAIyIgPQ0Z8GC4fkwZVVZFVWIl17lBq/YEilFQ7sXJvIVbuLQQARJgNGJMZi/G94jCuZxwGJEdC7MBfAyIiIiIiotbAUIpClieYWvryM9i7YU37BlNhccCIq4CNr2ujpRhKNVt0gg0X3DgEOTuLsPo/+1BaUI2f3tuNHSuO4LTL+iK5V5TeJbYKQRDQOyECvRMicNX4DCiKij0FFd6QasPBIlTUurB89zEs330MABBtM2JspnskVe949EkIh8DReERERERE1MUwlKKQJhkMmHLT7cDLaP9gavxs4JfFQNZyIH87kDS47a/ZCaUPjEPK/THY/vNh/PL1QRQeqsCnz2xC37GJmHBxb4RFm/UusVWJooD+yZHonxyJa0/NhKyo2HW0HOsOHMfarCL8crAYpdVOfLuzAN/uLAAAxIebMLZnnHe6X2Z8GEMqIiIiIiLq9BhKhZDNxzZ32LuVtSXdgqmYDGDgxcCOT4A1LwN/fqNtr9eJSQYRw85JQ98xSVj/eRZ+X5uHvRsKcGDrcYyanI5hk9IgGTvnmkuSKGBwShQGp0Th+tN7wSkr2H6kDOvc61H9mlOM45UO/G9bHv63LQ8AkBhpxviecRjfKw4TesUjNdam99sgIiIiIiJqdQylQsQX+7/AfWvuw0jTSJyrnAsjjHqXFFJ0C6Ym3qyFUjs+ASbdD0Snte31OjlbpAln/70/Bp7eA6s+3ouCg+VY//kB7FqTh1Mv7YOMwXGdfoSQURIxIi0GI9JiMPus3rC7ZPyW6w6pDhzH5pxSFJTb8fnWo/h861EAQI9oK8b3ivMGVd2jrXq/DSIiIiIiohZjKBUi7LIdoiBik2MT5q2Yh+fPeh42I0dH+AoWTF007x70Gjmm7S6aPBToeRZw4Cdg3UJg8lNtd60uJDEjEn++fST2bszH2s+yUF5Yg6WLtiFtQCxO/WsfxCSF6V1iuzEbJIzJjMWYzFjcjD6odcrYnFOCdQe0kVRbc0txpLQG/910GP/ddBgAkBFn8y6aPr5XHBIiLHq/DSIiIiIiomZjKBUi/trvr4g1xeKOlXdgTd4aXP3N1Vh0ziLEW+P1Li2kBAZTXz3/eNsHUxNv1kKpzf8GzrgTsMW23bW6EEEU0G9cMjKHdcOmZdnY+kMuDu0qxkePbMTgs1Mw+oJMmK1d768oi1HChN7xmNBb++xX2V34NafEPZKqCNsPlyK7qBrZRdX4cGMuAKB3Qrh3FNW4nnGIDTPp/C6IiIiIiIhOrOv9xBfCzkg5A9eGX4uPnR/j9+LfceXSK/HKOa8gMypT79JCijeYmi9g7/rVbR9M9TwTSBoC5G/TFj4/4462uU4XZbIYMP7i3ug/oTvW/HcfsrcX4bcfcrF3Qz7GTe2F/uOTIYide0pfY8LMBpzRtxvO6NsNAFBe68QvB4u9IdWuvHLsP1aJ/ccq8d76HADAKUkR3ul+Y3vGIcrK6cBERERERBR6GEqFmFRDKt456x3M/XkucitycdWyqzD/7PkYnjBc79JCimQwYMrcfwJA2wdTgqCNlvpkBrDhVWDCXMDINX1aW3SiDRfMHoqcnUVY/Z99KC2oxk/v7cbOlUdw2mV9kdQzSu8SQ0KkxYhJ/RMxqX8iAKC02oH1B4qx3j3db09BBXbna4+312RDFICB3aO8IdXozFiEm/lXPxERERER6a9z3u6qg0uLSMP7U97H4PjBKLOXYea3M/F9zvd6lxVyPMFU33GnQna58NXzjyNr08a2udiAqUB0OlBdBGxd0jbXIABA+sA4/O3+MZjw594wWiQcy6nAJ09vwg9v70JVqV3v8kJOtM2E8wcl4aE/DsS3t56OX+87BwumDccVY9PQs1sYFBXYfqQMr688gGve+QVDH/4OFy9ag6e/2Y1V+wpR45D1fgtERERERNRFMZQKUbGWWLx53ps4M+VMOBQHbvv5Niz5nWFIoMBg6svn2iiYkgzaCCkAWDsfkF2tfw3ykgwihv8hDVc8PA6nTEgGAOzZkI8lD67H5m9zIDsVvUsMWfHhZlw4pDv+dfFg/HjbmdhwzyS8eNkwXDYqFWmxNsiKii2HSrHo5yxc9eZGDHn4W/z11XV4/vu9WJdVhFonQyoiIiIiImofnMMRwqwGK1446wU8ufFJfLznYzy58UnkVeZh3qh5EAXmiR6BU/m+fO5x/PG2NpjKN+wK4KfHgZJs4PcvgUGXtO75qZ6wKDMm/b0/Bp3eA6s+3ouCg+VY91kWdq0+iomX9kHG4DgIQtddb6opEiMtmDq8B6YO7wEAOFxS7V2Pan1WEY6W1WJjdjE2Zhfj5eX7YDaIGJke4104fUhKNEwG/n1DREREREStT9efNJ544gmMHj0aERERSEhIwNSpU7Fnzx49Swo5BtGAe8fei5tH3AwAeHfXu7hj5R2wy5zG5Mt3xJQit9GIKZMNGHuD9nzNS4Cqtu75qUGJGZH48+0jMenq/rBFmlBWWIOli7bh6wW/oSS/Su/yOpSUGBsuHZWK5/86DGvuOhsrbj8TT1wyGH8c2h3dIsywuxSszSrCc9/vxV9eXYdhj3yHv7+1Ea/8nIWtuaVwyRylRkRERERErUPXkVIrVqzA7NmzMXr0aLhcLtxzzz0499xzsWvXLoSFhelZWkgRBAEzB89Eoi0RD6x9AN9mf4vjNcfx0lkvIcrMxZ89JIMBF9x0O9CWI6bGXK8FUnlbgYMrgZ5ntN65qVGCKOCUccnoOawbfl2ajd+W5+LQzmJ89PtGDDk7BaMuyITZysGfzSEIAtLjwpAeF4bLx6RBVVVkFVZhXdZxbSTVgWIUVzmwcm8hVu4tBABEmA0YkxmL8b3iMK5nHAYkR0LswndHJCIiIiKik6frT3DffPON3+t33nkHCQkJ2LRpE04//XTd6gpVF/W6CN1s3XDrT7diU8Em/H3Z3/HKOa+ge3h3vUsLGaIktW0wZYsFhl8FbHwNWPMiQykdmCwGTLikNwZM7I41/92H7O1F2PpDLvZsyMe4qb3Qf3wyBIYkJ0UQBPROCEfvhHBcNT4DiqJi77EKrN2vTffbcKAI5bUuLN99DMt3HwMARFmNGNcz1j3dLx59E8M5pZKIiIiIiJokpIYVlJWVAQBiY2OD7rfb7bDb66atlZeXAwCcTiecTmc7Vdl2PO+hsfcyMn4kFp+zGDf9fBMOlB3AFUuvwPwz56NfTL92rDT0nTvrFiiKgv0b1+LL5x7HBbfcgczho1vn5KNvgOGXxRCyfoQzdzOQNLh1zkvNEhZrxLnXD8ChncVY9+kBlB2rwU/v7caOFYcx4S+9kJgZ2aLzN+Xz2BX0irOiV1wKrhqbAllRsTu/AusOFGP9wWL8mlOCshonvt1ZgG93FgAA4sJMGJsZg7GZsRiXGYvMeBtDKmoV/EwShRZ+JolCBz+PFIqa+udRUNXQWBhHURT88Y9/RGlpKVavXh20z0MPPYSHH364XvsHH3wAm83WDlWGjjKlDP+u/DcKlAKYYMLlYZejj7GP3mWFFFVRULD2R1QeOgiIIpJPPQdhKemtcu6R2YuQUrIeuTHjsTljVquck06eqgCV2UaU7zdDlbUAxNbDiai+dkiWkPgrrlOSVSC3EthXLmBfmYADFQKcin8AFWVU0TtKRZ9IFX2iVMSZAWZURERERESdW3V1NaZNm4aysjJERjY8YCBkQqlZs2Zh2bJlWL16NVJSUoL2CTZSKjU1FcePH2/0TXYUTqcT33//Pf7whz/AaDSesH+FowK3rboNvxb8CoNgwP1j78dFPS9ql1o7CkWW8e2iF7BvwxqIkgFTbr4DPUe0woip/G0wvnk2VEGC68ZfgOi01iiXWqi63IGNX2Vj73pt1I7RLGH4eakYfGYPSMbm3dehuZ9HAhwuBduOlGG9eyTVltwyOFz+C6P3iLZ4R1GN6xmL5CiLbvVSx8LPJFFo4WeSKHTw80ihqLy8HPHx8ScMpUJi+t6cOXPw9ddfY+XKlQ0GUgBgNpthNpvrtRuNxk714Wvq+4k1xuK1P7yG+9fcj6UHl+LB9Q+isLYQ1w+5ntNlPIxGXHjzHfjf/Gexd90qLH3pafzxtrvRa+TYlp03dSTQ62wIWT/C+OvrwOSnWqtiaoGoOCP+cPVADDkjFav+sxcFB8ux8cts7FlXgFMv7YP0wXHN/mx0tr9f2pLRCIzvnYDxvRMAALVOGZsPlWB9VhHWZhVha24pjpTW4tMtR/HplqMAgIw4m3fR9PG94pAQwZCKGsfPJFFo4WeSKHTw80ihpKl/FnUNpVRVxdy5c/HZZ5/h559/RmZmpp7ldEgmyYQnTnsCyWHJeHPHm1iwdQHyqvJw37j7YBBDInPUnShJuGDuPwEAe9etwpfPPdE6wdTEm4GsH4HN/wbOuFNbBJ1CQmJmJP58+0js2ZCPdZ9loaywBv9btA1pA+Nw6qW9EZPEu3u2B4tRwoRe8ZjQKx7zAFQ7XPg1uwTrDmgh1fbDpcguqkZ2UTU+3JgLAOjVLQwTesV7g6rYMJPeb4OIiIiIiNqIrqnF7Nmz8cEHH+CLL75AREQE8vPzAQBRUVGwWq16ltahiIKIW0begqSwJDyx8Ql8su8THKs+hmfPeBY2Y9daa6shbRJMZZ4BJA8F8n4DNr4BnHln6xVMLSaIAk4Zn4yew7rh12XZ+G15Lg7tLMJHvxdjyNkpGH1BJkxWBrftyWYy4PS+3XB6324AgIpaJ37JLsY690iqXXnlyCqsQlZhFd5bnwMAOCUpAuN7xWF8zziMzYxDlI2//SMiIiIi6ix0/YnslVdeAQCceeaZfu1vv/02rr76ap2q6rj+dsrfkGBLwJ0r78SqI6tw7bfXYsGkBYi3xutdWkgIFkxdNO9u9B51ksGUIGijpf57LbDxNWDCXMDEEDDUmKwGTLikNwZM7I7V/92HnO1F2PpDLvZsLMD4qT1xyrhkCCKnu+ohwmLE2ack4uxTEgEApdUObDiohVTrsoqwp6ACu/O1x9trsiEIwMDukdpIqp5xGJ0Zi3Azg0UiIiIioo5K9+l71LrOTjsbi89bjLnL52Jn0U5cufRKvHrOq8iIytC7tJAQGEx99XwLg6n+fwJiMoCSbGDrEmDMda1bMLWa6EQbLpw9FNnbj2P1/+1D2bEa/Pjv3dix4ghO+1tfJGVG6V1ilxdtM+G8gUk4b2ASAKCo0o71B4qx7sBxrM0qwoHCKuw4Uo4dR8rx+soDkEQBQ1KiMN69HtWo9FhYTZLeb4OIiIiIiJqIv2LuhIZ2G4r3pryHWT/MQm5FLq5adhXmnz0fwxKG6V1aSPAEUwKAPS0NpiQDMH4OsPSfwNr5wMhrtDYKWRmD45HaPxbbfjyMX5YexLGcCnzy1Cb0G5eE8Rf3QlhU/ZspkD7iws24YEgyLhiSDAAoKK/F+gPaKKp1B4qQU1SNLYdKseVQKRb9nAWjJGB4agzGuaf7DU+LhsXIkIqIiIiIKFTxp+dOKj0yHe9Nfg9zls/BjqIdmPndTDx12lOYlD5J79JCgihJmOIeMdXiYGrYFcDPTwClOcDvXwCD/tz6BVOrkgwihp+bhr5jE7H+8yzsXpePPevzcWBLIUZNycDQs1P1LpGCSIy04E/DeuBPw3oAAI6U1nin+q3LOo6jZbXYmF2MjdnFeHn5PpgNIkakxWBczzikxVkRYzMhNqzuYTPxWyARERERkZ74L/JOLM4ahzfPexN3rLwDKw6vwK0/34q7xtyFaf2n6V1aSGi1YMpkA8bcAPz8OLDmJWDgJdp6UxTywqLMmDR9AAae3gOrPt6HY9nlWPdZFnatPorxl/TUuzw6gR7RVvxlZAr+MjIFqqriUHG1dxTVuqwiHKuwa88PFAU93mIUEWszITbc5B9YudtifdpiwrQ+EtcfIyIiIiJqNQylOjmb0YYXz3oRj294HP+39//wxMYnkF+Vj1tG3gJREPUuT3etFkyNuQ5Y86J2J76DK4CeZzbhIAoVSZlR+MsdI7FnQz7WfpaFssIafPPaTpjjrNgRdhQp/WIR1yMcIgOJkCUIAtLjwpAeF4a/jUmDqqrIKqzCugNF2JxTgsIKO4qqHCipcqC4ygGHrKDWqeBoWS2OltU28RpAlNVYF1z5BFZx7tAqMMyymSQIDKmJiIiIiIJiKNUFGEQD7h93P7qHd8dLm1/C2zvfRn5VPh479TGYJJPe5ekuaDB1613oPXpc009iiwVG/B3Y8Ko2WoqhVIcjiAJOGZ+MnsO64del2fjtx1zYiwxY+98sAFkwWSQk9YpGcu8odO8djYSMCBi4XlHIEgQBvRPC0TshHFeNS/fbp6oqqhwySqoc3qAqcFtcrYVXnrayGidUFSitdqK02okDqGpSHWaDqAVXNhPiAkdkeUItn30xNiMMEn9hQERERERdA0OpLkIQBMwcPBOJtkQ8sOYBLMtehsKaQrx09kuINEXqXZ7uvMGUIGDP2pX46oUnmx9MjbsR2PgGkPWjNmIqeWhblkxtxGQ1YMKfe6PvuAQs+2gNIqVEFBysgKNWxqGdRTi0U5sKJhoEJKZHIrl3FJJ7RyOpZxQsYUa9y6cmEAQB4WYDws0GpMbamnSMS1ZQUu1EiTusCvbw3VdU5YDDpcDuUpBXVou8Jo7GArTRWHE+UwbjfEdj+Ww90wzDOBqLiIiIiDoohlJdzEW9LkK8NR63/nwrfi34FdOXTceiSYuQHJ6sd2m6EyUJU+bcBgAnF0zFpAODLgG2/x/w7b3A6f8EUscCRmvbFk5tIirBisjeDkyZMhiSKKHoSBWO7itFXlYpju4vQ025A3lZZcjLKgO+PQQIQFz3MCT3rhtNFR5j0fttUCsxSCK6RZjRLaJpd2dUVRXVDrkutKqumzpY7+HeV+oejVVW40RZjRM43rTRWCaDtjaWf2BlRGyYGbFh2jYmzIg49zbGZoKRo7GIiIiIKAQwlOqCxncfj3fPfxc3/nAj9pfux5VLr8SicxahX2w/vUvTXYuDqQk3Adv/C2Sv0h6SCUgZDWScBmSepj03NO2HWgodoiSiW1oEuqVFYOikVKiqirLCGuTtL0Xe/jIc3V+KsmM1KDpShaIjVdix4ggAICLWguQ+UUjuFY3uvaMRk2SDwHWpugRBEBBmNiCsmaOxymqcQQMrv+mF1Q4UV2rP7S4FDpeC/PJa5Jc3fTRWpMVQfxphwFpZvo9ws4GjsYiIiIio1TGU6qL6xfbDkguWYNYPs7C/dD+mfzMdL5z5AsZ3H693abprUTCVPAS46jNg6wdaKFWRB+Ss0R4rngQMFi2YyjxdC6p6jAQMXNeroxEEAdEJNkQn2NB/QncAQHW5wy+kOp5bgYriWlRsqMXeDQUAAEuYEUm9tFFUyb2j0C0tApKBI1ZIY5BExIWbERfe9OC6xiGjqMqOkiqntq12oKjSM5XQiWK/fdr0Q1UFymtdKK91IbuouknXMUmid5RVsNDKs/i7Z7RWtM0EE/9sExEREdEJMJTqwpLCkvDu5Hdxy0+34Jf8X3DjDzfikYmP4KJeF+ldmu5aFEz1Okt7qCpQfAA4uFILqA6uAqqO1Y2iAgCjTZvil3maFlJ1Hw5IXJeoI7JFmtBrRAJ6jUgAADhqXSg4UI6j+7UpfwUHylFb5UT2tuPI3nYcAGAwikjMjPRO+UvqGQWThX8tU9NZTRJSTDakxDStv6yo9UdjuUdf1YVZ/o8apwyHrKCg3I6CcnuTa4vwHY0VOL3QZ00sz75IC0djEREREXU1/Omni4s0ReLVc17Ffavvw7LsZbhn9T3Ir8rHzMEzu/wPB/WDqSdw0a13N30qnyAAcb20x6hrtJDq+F53SLVae1QfBw78pD0AwBQOpI2rm+6XNBSQ+DHtiEwWA1IHxCJ1QCwAQHYpKMytQN6+MuRlaSOqaqucOLK3FEf2lgLuPzLxqRHeNamSe0fDFsmRdNR6JFHwBkVNVeOQ600j9J1eWFzpf7fCkmoHFBWoqHWhotaFnCaOxjKIQl1wFRBYxYWZEGkWsbdUQOqRMsRFWBFpMSLSaoTEKbFEREREHRZ/2iWYJBOePP1JJIUn4e0db+PlLS8jryoP94y9Bwaxa/8R8QRTgiBg95oVzQ+mfAkC0K2f9hhznRZSHfvdPYpqpTbFr6YE2P+D9gAAcySQNr5uJFXSYECUWv19UtuTDCKSMqOQlBmF4UiDqqgoKahG3v5SbTTV/jJUFNWi8FAFCg9VYNuPhwH3guvJvaPRvbe2NlVUgrXLB8bUvqwmCT1MVvSIbtpNGxTPaKwgo65KfMMsn/ZqhwyXoqKwwo7CisZGY0l45fcNfi0RZgMirUZEBT5s2jbSEnx/pNXIBd+JiIiIdNa1EwfyEgUR80bOQ5ItCU9ufBL/t/f/cKz6GJ4+/WnYjE1bpLezEiUJk2fPA4CWB1O+BAFIHKA9xt4AKApwbKc2zS97FZC9BrCXAfu+1R4AYIkC0k8FMk7VgqqEgYDIH6o6IkEUEJschtjkMAw8rQcAoLKk1rsmVd7+MhQdrUTZsRqUHavB7rV5AABrpAnde0VpQVWfaMT1CIPIH6wphIjuEU8xYSb06ta0Y2qdcqNTCEuqHTheYUfusWKoBgvKa12odsgAgAq7CxV2F46U1jS71jCT5A2oggZbASFW3XMDzAb+goCIiIiopRhKkZ9p/ach0ZaIO1fdiRWHV2DGtzOwYNICxFnj9C5NV20WTPldRNRGQiUNBsbfCCgykL9Nm+Z3cBWQsxaoLQP2/E97AIA1FsiYCGScroVU3U7Rwi7qkMJjLOgz2oI+oxMBALVVTuQfKEPe/jLk7S9FQU45asodyNpSiKwthQAAo1lCUq8oJLsXUE/IjITRxB+WqWOxGCUkR1mRHNXwaCyn04mlS5diypQzYDQa4XApKK91oqxGe5QHbOs/XCh376+wuwAAVQ4ZVQ4ZR8uafufCuprFRoMrbZSW/6gtz8Ni5GeUiIiICAylKJhJ6ZOw2LoYc3+cix1FO3Dl0ivx6h9eRXpkut6l6apdgin/C2oLn3cfDkyYC8guIO83INu9JlXOOqCmGPj9K+0BALb4ulFUGacD8X0YUnVgljAjMgbHI2NwPADA5ZRxLLsCeVmlOLqvDPlZpXDUysjdVYzcXcUAAFES0C0twnuHv+Re0bCEc/F86nxMBhHx4WbEN+NuhR4uWUFFrauB8MqJ8toGwq1qLdBSVaDWqaDW2bzF331r9w+vDI2GW3VTEY2wmSRO4SUiIqJOg6EUBTUsYRjem/we/vHDP3C48jCuXHolFkxagKHdhupdmq6CBVMX3noX+owe3/YXlwxAykjtceqtgOwEjm6pu7vfoQ3awum7PtceABCeqIVUGacBmacDsT0ZUnVgBqOE7n20aXsjz9fW7ik+Wlk35W9fKarKHCg4WI6Cg+XY8r12XExymLYmlTuoioi18Ida6tIMkuidYthciqJ6Ay3fkVpBw60gI7cUFXC4lCasnxWcURK8I7Aam3IYaa2/lla4mXc4JCIiotDCUIoalBGVgfenvI85y+dgZ9FOzPh2Bp4+/WmcnXa23qXpKjCY+vqFJ9svmPIlGYHUMdrj9H8CLjtwZHPdwum5G4HKAmDHJ9oDACK61y2annkaEJPRvjVTqxJFAfEpEYhPicDgM1Ogqioqimq9a1Ll7S9FSX41SvKqUJJXhZ2rjgIAwmPMWkDVKwrd+0QjNjkMAu9gRtQkoihoI5dszR+BqKoqKu2uesGVtg0+css31HIpKpyyiiL3nRCbSxIFv1FZTVlLy9MvwmyAyL8niIiIqJUxlKJGxVvj8dZ5b+H2lbdj5eGVuPXnW3HXmLtw+SmX612arkImmPJlMAPp47XHGXcAzlrg8C/uRdNXa88rjgLbPtYeABCV5jPd7zQgOlW/+qnFBEFAZLwVkfFWnDIuGQBQU+FAXlbd4unHD1WgssSOfb8UYN8vBQAAs82AJPeaVMm9opCQHgnJyMXTiVqbIAiIsBgRYTEiJaZ5x6qqihqn7DeVMGi41cC0RIdLgayoKKl2oqTaeRK1a3c6DFwfq9G1tHz2Swy0iIiIKAiGUnRCNqMNL531Eh5b/xg+2fcJHt/wOPKr8nHziJshCl33B9eQDKZ8GS1a2JR5mvbaUQ0c3lh3d78jm4CyQ8BvH2gPQBs5leEzkiqyu65vgVrOGmFCz2Hd0HOYdhs0p11GwcEyHHWPpMo/WA57tQs524uQs70IACAZRCRkeNalikZSryiYrfx2QaQnQRBgMxlgMxkaXRC+IbW+gVYjC8KX+z3XAq4apwxVBcprXSivdSEXzb/TYYS5bjphhMUAs1GCSRJhNoowG0SYDZJ7qz1MnjZjwOuA52aj6D6P7z6tjVMViYiIQh9/yggRqqKg9P33IYSF6V1KUAbRgAfHP4ju4d0xf8t8vLXjLeRX5ePRiY/CJDV/TY7OIuSDKV8mG9DzTO0BAI4q4NB693S/Vdr6VCXZ2mPLe1qf2F51o6gyTgMiEnV9C9RyRrOElFNikXJKLABAlhUUHa7E0X2lyMvSgqqaCqd7+l8ZgBxAAOJ6hHsXT+/eOxph0c1fXJqI9GMxSrAYJSRGWpp9rN0l+00vDAy0ggVcnrYqhwwAqLC7UGF34Uhp8wOtk1UvwPK8NvoHYGaD5A2zzO79Jkn0hl7++xvoGyQ8MxsYjBEREZ0IQ6kQceypp1D87r/RvXdvqBdcABhD725ZgiDg+iHXI9GWiIfWPoSlB5fieM1xvHDWC4g0Repdnm6CBVOTrp2FvuNPhSUsXO/yGmYKA3pP0h4AUFteF1Jlr9Lu9FecpT02vaP1ie9bN4oq4zQgLF7Xt0AtJ0kiEtIjkZAeiWHnaFOESguqtYBqXymOZpWhvLAGRYcrUXS4Ett/PgwAiIy3ILl3tDeoik608Ycvok7KbJDQLUJCt4jmh9FOWdEWfPeZVlhRq00ndLgU2F0K7C4ZdqcCh+x+7ZRhD9zvfa7td8gK7E7F3a9uvy9PWwVcrfjVaB7fcMt3RFfw0V91o8Pqgi7/QMyvb6OjzeqO41pgREQUygRVVVW9izhZ5eXliIqKQllZGSIjO3YoUrN9B3KmT4daXY2wP/wBqS++AEGS9C6rQWuPrsW8n+ehylmF3tG98co5ryApLEnvsnSlyDKWLXweu9esAAAIgoik3n2QPmQ40gcPQ3KfUyAZOlAOXFMKHFrnnu63EsjfASDgr4uEAXV398s4FbDF6lVtq3M6nVi6dCmmTJkCYwiGxO2pqtTusy5VKYoOVyLwO4c1wojkXlpAldw7GvGp4ZCkrju9l1ofP5N0IqqqwiH7hlnBAizfkEuu6+sJxZwBAZinb8B+R0BY5vs61P5lbZSEeiO8gk6BDBKINRieGSVIUPDbls0YP3YMLCYjjJIAoyTCIAkwSSIMkuht87QbRa1NEgX+IoOoFfF7JIWipuY1DKVCSNnKVTgyaxYEWUb05X9D0gMPhPQ37N3Fu3HjDzeisKYQCbYELJq0CP1i++ldlq4UWcb6Tz/CnrWrUHz0sN8+k9WKlAGDkT54ONKHDENs95SQ/u9bT3UxkLO2brrfsZ0BHQQgcVDdKKr0CYA1WqdiW47f3Btmr3Gh4EDd4ukFB8shB4xQMJglJGVGIrlXFJL7RCMxIxImSwcKZSnk8DNJHYGqqnApaqMjvhyBo8OcQUaDBQRmfq9d9c8b2FfpAP+6N3mCKp/wqi64CrZPhFEMDL7qQi+jJAT0qR+KeY4xiCJMBm0beH1PoGYQBZgM2tZoEBmoUUjj90gKRQylOiCn04mVTzyJ7h9+CKgq4ufMQbc5s/Uuq1F5lXmY9cMsZJVlIdwYjhfOegHjksfpXVZIKD9eiJztW5CzbSsObd+Kmopyv/0Rcd2QPmQY0gcPQ9rgYbBFRulW60mpOg7krKlbOL1wd0AHAUge4p7udzqQNh6wdJzPKb+5N53sVHDsUAXy3COp8rLKYK/2ny4jiAK6pYb7TfmzRnTd9eio+fiZJGo6l6wEHcWlBWGyd+RY4PRI38As8NjA89Q6XTheXIqwiEi4FBUuWYFTVuGUFThlBS5ZG7nmUlTIHSElawZviOUNrkQYDf6BmkESYXIHYFqoVT9Q0/r4Bl/1A7V64ZxPoNbgqDSfQM17DUmAgYFap8XvkRSKGEp1QJ6/TCaWl6PwX48DABIfuB+x06bpXVqjyuxluPmnm7GpYBMMogGPTHgEF/W6SO+yQoqqKDiWfQA527ciZ9sWHNmzC7LT/5bcCRm9kD5Um+rXo98AGEwd7Af2ymN1o6iyVwNF+/z3CxLQfZh7ut/pQNo4wBy6a27xm/vJUxUVxXlVyNtf6r3LX2WJvV6/6EQburun+yX3jkZkvIX/WKYG8TNJFFqa85lUFBVORQuqnD7hVV1wpcDpCuyj9XPJ2ogyb7s3APM/j99rRYHDpcKl+FwjMDRTVDhcSpcO1LyjzAxauGV0r4FmlLSpmiaf52ap7rnvfpMk+LcFbH2PMRsC2wSYJcn73MBp/yeN3yMpFDU1r+FcihAU9be/QS0tw/GFC1Hw6GMwxMQgcvJkvctqUJQ5Cq//4XXcu/pefJP9De5ZfQ8KqgswY9AM/oDpJogiEnv2RmLP3hjzp7/Aaa/Fkd27kL1tCw5t24LCQ9k4lp2FY9lZ+OWL/8JgMqPHKQOQPmQ4MoYMR3xaRuh/LcMTgEF/1h4AUJ6nhVPZK7WgquQgcGST9ljzEiAagO4j3NP9TgVSx2l3CKQOTxAFxPUIR1yPcAw6IwUAUFFc63eHv+KjVSgtqEZpQTV2rckDAIRFmbwBVXLvKMT1COcCvUREnYAoCjCLEswd8CcPT6DmCcgaC9Sc7pCrsUBN69O0QM33PA0Fav7nOHGgpp1H1uVreSKigLoQLCDoqhd6uYMsk0Fyr5sW0M8QJGSTtFFr2jkEmHwCsfrn9rmexBsGELWlDvitoWuInzMbruIilH74EY7ccSekqCiETZigd1kNMkkmPHX6U0gKS8I7O9/BS5tfQl5lHu4eezcMIv+YBTKaLcgYOgIZQ0cAAKpKS7yjqHK2b0VVSbH2fNsWrARgi4pG+uBh3kXTw2Pj9H4LJxaZDAy5VHsAQNlhLaTyLJxeegg4vFF7rHoOEI1Ayqi6u/uljAGMzb91OYWmiFgL+o1NQr+x2g0Raquc3oAqb38pjuVUoKrMgf2bjmH/pmMAAJNFQlKvKPeUvygkZETCYAzdG0AQEVHn05kCNU9AFhioeW4Q4PTduqeAOgP2OVwKHLLq3+buH9jP6XsO73617piA9SgV1feumaHFM7KsboSYWC/QChaoBRtBdqKRZkb3yLSmjDQzSWLo/+Ka6AQ4fS+EBA67VGUZR+bdhopvv4VosyHt3XdhHTxI7zJPaMnvS/DUxqegQsWZKWfiqdOfgs3IETBNpaoqig4fQs62rcjZthm5v++Ay+4/9SkuJU0LqIYMQ2r/wTBaOmB4U5KjTffzBFXl/gvDQzIDKaPrFk5PGQUYmn878pPFYdDty+mQcSy73B1SlSHvQBmctf6/yRUNAhLSItG9TxSSe0UjqVcULGH8b9NV8DNJFFr4maSWUlXVOwLMN8RyyIEhWGCgJcPpUmGXtdFp9QKxgGBMe+0J32TveZw+d+v09HN6r99xfkQ2udcUUxUXbBYzjJIIyR2iSaLgXlvMs7C/ELBP9O6vO04I2BfsuMD+ot9xgf09feof5/PavU6ap00UwMCtg+OaUh1QsG/uisOB3OtvQPX69ZBiYpD+wRKYMzP1LvWEfsj5AXetugt22Y7B8YMx/+z5iLN2gNE9IcjldCJv7+/I2b4V2b9tQcHB/fC937QoGdCjX3/vKKqEnr0gih1sNImqatP7PIumH1wFVOb79zFYgdQx7pDqdKD7cMDQdutu8R/b+lJkBUVHqrx3+MvbX4rqcod/JwGI6x6G5F7RSHYHVRGxHTCgpSbhZ5IotPAzSZ2ZZ5SZXxgWGIDJQdrqjRLzCdICRokFDdLkwPOofiPWHLLS6dY5a4xR8g/OpIDAyyAGC7989wUPyAxi/UDNc/OAYOes2xf8Ov77fI7z7Bd9zuHTt7NjKNUBNfTNXa6swqHp01G7cyeM3bsj/cMPYExM1LXWpthybAvm/jgXZfYypEak4tVzXkVaZJreZXV4NRXlOLRjm/vOfltQXnjMb78lPAJpg4a67+w3HFEJof9npR5VBYqy6tajyl4FVBX69zHatMXSPXf3Sx4GSK03tp7/2A4tqqqirLDGG1DlZZWhtKC6Xr+IWAuSe0d57/IXk2SD0AW+6XcF/EwShRZ+Jon0IbvXEvMNt6rtDiz/8WdMOPU0CKIEpzu8csruBf8Vbdqm7J7O6TmHS1G9i/17+3vbfY+rW7/Mf1/Adbz7fffVncelBLtO57uhQFMIAurCKk+w5Q6xpg7vgTvPP0XvEluMC513IlJ4GFJffw05066AIycHuTOvQ/r770GKitK7tEYNTxiO9ya/h1k/zEJuRS6uXHolFkxagCHdhuhdWodmjYhEv/Gnot/4U6GqKkrzj2pT/bZvwaEd21BbWYG961dj7/rVAIDopGSkDxmB9CHDkDZwCMy2ML3fwokJAhDfW3uMulYLqQr3uKf7uaf8VRcBWT9qDwAwRQDp49139zsNSB4KdLQRY9QgQRAQnWBDdIIN/SckAwCqyx3IyypF3r4y5GWVojC3EhXFtajYWIu9GwsAAOYwA5J7RsEWaYLBLMFokmC0SDCYJBjN7odJ0vZ5n4vefZKBazUQERFR6JBEAZIoweKzzqbTaUCSDTglKaJDhsSqWhdO1Qu/ZM8+z/po/oGZ33PfQE2uC8fq+jaxj3df8FpccgPXD9angdBNVRu+8UBFrbNeW2fGUKqDMMTFIfXNN5Fz+eWw79uH3Fk3Iu3NxRCtVr1La1RmVCben/I+Zi+fjV1FuzDj2xl4+vSncVbaWXqX1ikIgoCY5B6ISe6BYeddAEWWkbd/r3fB9Lx9u1Gan4fS/P/ht+/+B0EUkdS7LzKGDEf64OFI6t0XkqED/DUgCEDCKdpjzHWAogCFv9eNospeDdSWAvu+0x4AYI4C0ifU3d0vcTAg8lbDnYkt0oRewxPQa3gCAMBR60LBgXIczdIWTy84UA57lQvZ24tO+hqCgIDAqi7YqguwDDCaRL9+RrM7+LLUvfa2mbW+Em99TURERARB0Ka7GSX4hW2dhSd0a2rQFW3reMFiS3SAn0bJw5TSA6mLFyPnqqtQs3kzjtxyK1IWzIcQ4ml4vDUeb5/3Nm5bcRtWH1mNW36+BfeMuQeXnXKZ3qV1OqIkoUe//ujRrz8mXDoN9uoq5O7c7p7qtxUleUeQt3c38vbuxrr/fgiT1YrUgUPc61ENR0xy944xKkQUgcSB2mPcP7SQqmBH3XpUOWsBexmwd5n2AABLdN0oqszTgG79GVJ1MiaLAakDYpE6IBYAIMsKCg9V4Fh2BRw1TjjtCpwOGU67DJddhtPh3noe3n0KZPcdgVQVcNbK9RZdbw2iQQgSWPmO4hKDjuzyPvfbp4VjBpN2DKcsEhEREYUG39AN6HyhW0sxlOpgLP36IvWVRTh07QxUrliBvPvuR/ITj0MI8R+ubUYb5p89H4+tfwyf7PsEj214DHlVebhpxE0QhdCuvSMz28LQe/Q49B49DgBQfvwYcrZtRfa2LTi04zfUVpQj69cNyPp1AwAgIr4b0gdrd/VLGzQUtsjQniLqJYpA8hDtMX42oMhA3m/aCKrsVUDOOm0k1e6vtQcA2OKA9InaelQZpwHd+mnDYqjTkCQRSZlRSMps/p9jRVbgdCh1oVVAmOV9HiToCnxe91qB0y5DdQ/hVlwq7C4X7NWuVn/vBqPYSJilTU884XRGT0jG6YxERERE1EYYSnVAtpEj0eOFF3B47lyUffEFpLg4JN5xu95lnZBBNODB8Q8iKSwJC7cuxJs73kR+dT4enfAojFJoj/bqLCLjEzD47HMx+OxzoSoKjmUfQPY2bcH0o3t2oeJ4IXb89B12/PQdIAhIzOyF9MHDkD5kOLr3GwBDiI/K8xIloMcI7THxJkB2AXlbgYMrtZDq0HptTarfv9QeABCWoI2kyjxNC6ki0/V+F6QjURJhtoowW1v326SqqlBcqn9g5fM88LXLHWQF3+c7yksL0DxcTgUupwKgddck8E5n9AZWJ5jOGBiKWXynN7r7cjojERERUZfFu++FkObexaT008+Qd889AICE2/+JuBkz2qHK1vH5/s/x8NqH4VJdGJs0Fi+c9QIiTBF6l9WlOWtrcXj3Tu96VMcPZfvtN5jNSOk/yBtSxaemd9wRE7ITOLK57u5+uRsAV61fFzU8CXlSDyRlnALREgmYwwFTuHsb4d6G+TwPr9tygXXSgaqocDmVIIGV/zRFLehyeUd5uRroF2w6Y1vync4YbISXZBRwJD8Xffr1gsVmhMligMkiwejemqwGGM11W6NZ6rh/RxF1ALz7HlHo4OeRQhHvvtcFRF9yMeSSYhx75lkce+ZZSDGxiL7kYr3LapKpvaeim7Ub5v08DxvyN2D6N9OxaNIiJIUl6V1al2W0WJA5bCQyh40EAFSWFOPQ9q3ekKqqtATZWzche+smAEBYTCzSBw1F+pDhSBs8DOExsTq/g2aQjEDaWO1x+u2Ayw4c2VS3cHruRgiV+eiOfOC3Tc0/v9HmH1KZI9wBVmCoFeS1Kczd373PGMa1r6hJBFHwhjGtTZEV/1FbDa7N1dbTGU3YmpPbpJoFAf4hlSe8CgizjEHavAGXxaCN7mLARURERNQmOFIqhJxswl3w9DMofustQJKQMn8+Is7uOHe2+73od9y4/EYcrzmOBFsCXjnnFfSN6at3WRRAVVUcz83xBlSHd+2Ay2H36xOfmq4tmD5kOFL6D4TRbNGt3hZz1sKVvRa7VnyCgb3TIbmqAXsl4KgCHBXu55U+2wptq7T+2kCAEBBoBQu5wv2DrGCvvSGXjWtnUcho+nRGBfZqB37ftQdpKZmQHQoctTKctS44amW/585aF1r9XzYCYDL7BllND7h8+zLgos6EIzOIQgc/jxSKOFKqC0n4522Qi4tR9vnnOHLrrUh7603YRo7Uu6wm6R/XH0umLMGsH2bhQNkBTF82HS+e9SLGJo/VuzTyIQgCuqVloFtaBkZdeDFcTieO7tnlDakKDmbheG4OjufmYNP/PodkMKB7vwFIHzIcGUOGIyGjZ8gvxu/HaIGacRoO7qpA/4lTIDXlm7uqaiOuvCFVlU9wFRhkBQm2/EIudx9VAaDW7a9shfcmiP4hle9oLVNYE0Mun2mLBgtDLjppgiBAMgqQjCIsYY1/zpxOJ466tmPilF6N/oNbVVW4HAoctS44a2U4fMIq/yCr7rVvv6ABlwrvvqoWv+mGAy6j35YBFxEREXV+DKU6AUEUkfzoI5BLSlC5YgVyZ92I9Pfeg6Vfxxhx1D28O/49+d+46cebsPnYZvzjh3/g0YmP4sKeF+pdGjXAYDQibdBQpA0aitMAVJeXIXfnNmT/tgU527eg4nghcnduQ+7ObVj94buwREQibdBQpA8ehowhwxHZLUHvt9D6BAEwWrRHWHzLz6eqgLPGfyRWg6O1Ghm95aiqa4OqBV32cu1R0RrvW2pgSmJzpy262wzmViiKujJB8JnG2MIbiDYn4HIGbNsr4PJMM2xKwGUM2PoFXCYJgsiAi4iIiNoXQ6lOQjAa0ePFF3Do2hmo2bIFuTNnIv3DD2FK6aF3aU0SZY7C6+e+jntW3YPvcr7D3avuRn5VPmYMmsHfAncAtsgo9Bt/GvqNPw2qqqIk7yhytmt39cvduQ21FeXYu24V9q5bBQCISe6B9CHDkD54OFIHDoHZZtP7LYQeQQBMNu0R3gohnqIAzurGR2/5Blh+QViQ185q7byqDNSWaY/WIBpPEHKF+4zoaiTk8mx5Z09qgVYPuJwKHDXBA6wGAy67rB0TsPUEXM5aGc42DLg863KZzBKM1oZCLwZcREREdHIYSnUiotWK1FdfQc6VV8G+bx9yZ8xA+gdLYIiL07u0JjFLZjxzxjNI+jUJ/971b7y0+SXkV+Xj7jF3Q+LdzDoMQRAQ270HYrv3wPDzLoTsciF//17kbN+C7G1bkL9/L0ryjqAk7wi2fvs/baRfn1O8d/VL7t0XosT/3q1OFLWgxhwOtMaNLhW56VMUHVVBQq6APp67HypOoKZEe7QGyayFWZ6HJQowR7qfR2rPLe7X5kj3/gj/dlMEF5unFhME990FTe0QcAUEWA67DKd7264Bl7n+XRFNVgOMJgmSQYRkFCBKovbcIEI0CO7n2laUREhG92v3c1ESgvSvey2KAn+ZRURE1IEwlOpkpKgopC5+AzmXT4MjJwe519+AtHffhRQepndpTSIKIm4ffTuSwpLwzC/P4OM9H6OgugBPn/40rAar3uXRSZAMBvQ4ZQB6nDIAEy69AvbqKhzauQ0527YiZ9tmlObn4eieXTi6ZxfW/fcDmKw2pA0agvTBw5E+ZBiik7rzB4xQJEpaaGNppZtMyC6fkKqqaaO1Glu7S3a4z2sHqu1A9fEWFCf4BFuBIZbneVQD7T5BF6cmUitpi4DLWVs/yAoWYDnsjU1fdN9N0TfgKnO01ttuGgFaSCUJ7hDLJ+TyBlg+oZdfsCX49fE+lwIDtEaCMU9oFhCgSQbteCIiIvLHUKoTMiYmIvXNxciZdgVqd+7E4blzkPraaxBNJr1La7KrBlyFRFsi7l51N37O/Rkzv52J+ZPmI9YSq3dp1EJmWxj6jB6PPqPHAwDKjhW4p/ptxaEdv6G2sgL7f1mP/b+sBwBEdkv0TvVLGzwU1vDWGOZDIUcyANYY7dEaXI6AIKsCqC2vW0/L+7wi4HmZf7vi1IaQeI7DkRa8R3Pd6CzfkVreEKsJ7aZwLixPrco34LJFtuzfCY0FXH4Bll2G4lIhuxTILgWKS4HsUiHLCmSnAkWu2ye7+yk+rxV3P9ndD753W1Sh7XMCqJVb/PVpTYKAIMGYb4gVEHi5gzVvyBYQrAUN2YxCXZAWEJoFDdDc5xc53ZKIiHTCUKqTMmdmIvX113Fo+nRUr1uPo7ffgR7PPwehA02LOjfjXMRb4zH3x7nYdnwbrlp6FV455xWkRabpXRq1oqiERAyZdD6GTDofiiLj2IEs5GzfipxtW3Bkz+8oLyzA9uXfYvvybwFBQGJmb2QMHY70wcOQ3Lc/DLztLQVjMAGGWMDWgiBbVbVphd7gqixIiNWEoMvhvm2ibAeqCrXHyRJEbTph0BCrCUGXZ9QW19qiNtCaAVdTqaoKRVH9Qi4txPIJvWTVHWK5Qy6nogVbfqGXb38FstMdktU7n+rTxyck89nn29+/Vk9gpjT4fvQiuEeYiYbGRpb5h1qCCBTnW/BzyV4YjBJEUYAoCe6t1l+UBAjudkkSvc+9D9++7n2CJECSBAiiGNDP8xDr+vqe292PiIg6FoZSnZh18CCkLJiPQzf8AxXffov8R6OR9OCDHWoq1IjEEXhvynuY9f0sHKo4hCuXXokFkxZgSLchepdGbUAUJST17ouk3n0x9uK/wllbi8O/70D2Nm3R9KLDh1BwYB8KDuzDhs/+A4PZjNT+g5A+ZATShwxDXEpah/rzTSFOEACjVXu0ZLF5RW5gVFaQoKvWvc8v5HI/V2X33RPLtEdLGKwnPw3R0260cdQW6U4QtABDkrR1rEKJJzDzG/3VhJFgwcI0pYHQS/YEaK66oC0wQKs3Gs3dx79WwOVUgGYHZkbsPVrQql+3FhHQYDhWF2KJASGXf39BdIdiQUKweuGYTzAW7HpiwPU85/QL3QJq0a4fcG739QQB/HcOEXU6DKU6ubAJE9Dj6adwZN5tKP3oYxji4tFt7hy9y2qWnlE9seSCJbjxhxvxe/HvmPHtDDx9+tM4K+0svUujNma0WJA5fBQyh48CAFQUH8eh7b8hZ9sW5GzfiuqyUhzcugkHt24CAITHxCJt8DBkDBmOtMHDEBbdSlPBiFpClFo+NVFVAWdNQHBVFiTEaqy9vO6uia4aoLIGqGzBD5OC1MDorIggI7WCLDDv6SfxnyLUOdUFZqG3lpSqqt5wLHCUWbApkoFhmuJS4LC7sGvnLvTtewoECFBk96g1WYUqu8+heJ579ine56qsQnb3UxXV73jF3e77WlW0/p5zq2qwNwZvf01oTeFsDfVCNG/IFSRECxhp5gnC/EI393kkz7GGuvNIBgGi6J76GbBf8gZwAft9j/Vp84y28x01R0QEhlJdQ+TkyZBLS5H/8CM4vnAhpLhYxE6bpndZzRJvjcfb57+N21bchjVH1uCWn2/BvWPvxV/7/VXv0qgdRcTGY+AZkzDwjElQVRXHD2UjZ5t2V78jv+9EZUkxdq38EbtW/ggA6JaWgbQhw5ExeBh69B8Io9mi91sgOjmCAJhs2iMi6eTPIzuDh1VNWm/LZ2SXqmgjt2pLtUdLGMOaNA1RMNjQvWQfhH0GwOoeqWWyuUez2dwPqxYCElGjBEHwTss7WU6nE7k1v2H4uakw6jCVXlV8QizfwMsdYHnDLUXxCboa66sEhGJ1fT2BWF3g5nOewGOaGLJ5zx3YT6mrK5jOErp51ljzDapEyb1WmtRwoOUN0Bo4NrAt8NhgAZnvsd4gThLcYZsniPMJ9HiXT6JWxVCqi4i5/HK4jhfh+MKFKHj0MRhiYhA5ebLeZTVLmDEM88+ej0fXPYrP9n+GR9c/iryqPNw0/CZ+Y+iCBEFAt/RMdEvPxKiLLoHL4cCRPbu09ah+24Jj2VkoPJSNwkPZ2PT1Z5CMRvToNwDpQ7T1qBIyekIQQ++310RtSjJq62y1dK0tR1Xj0xCbst6Wq1Y7n7NKe1TmN3pZA4DRAJC9oPH6DBZ3UBVWN/3SFOYfXpl8Qqx6bT7tgW2mMEAycdoiUQgQRAGSKHTawZaqokJRGwjCgoVsin9fuZFwzDdk87521b327nMpQds8I9w855R9+imeEXCeKajuNgRkbKrvTQk6IE9Q5RugSQYhYAqn/4ixoKPRGhxt5hue1QVi3vDM9zoGEaoqw1EmouhIFUwmo3tknPY5EX2minrbvK8ZsJH+Oulf4xRM/JzZcBUXofTDj3DkjjshRUUhbMIEvctqFqNoxMMTHkZyWDIW/bYIi7cvRn5VPh6Z8AiMXLi3SzOYTEgfPAzpg4cB065GdXkZDm3f6l40fSsqigpxaMdvOLTjN6wCYI2IRNrgYd47+0XGd9P7LRB1DIIAmMO1R0u4HHXTDZuw3pZSU4biglzEhlsgumq1qYjOam1ao2daIqCFXa5aoKakxW81KEGsH1QFjtZqaQjG0V5EXZ4gCpCgrZfWGWg3JPAPqoIFWr5tdft8Ai+fNu95GmmT/Y71vY5/m1/Q5vIP4oKNWlNcKhSXDJcuX82GhOGTtZubfZQgwHujAE9Q5V3TzPNc8L+5gOd1sP5B+/j2FX3b4H8d0f+63uAs4LXYUJvUQM3B+kv16+G6bfpgKNWFCIKApPvug1xSiopvvkHunLlIf/ddWAcP0ru0ZhEEAbOGzUJSWBIeXvcwvj7wNQprCvHCmS8gwhShd3kUImyRUThl4hk4ZeIZUFUVxUcPI2fbVuRs34LcndtRU1GOPWtXYs/alQCAmO4pyBgyHOlDhiF1wGAIBoacRG3KYAIMcUBYXJO6y04n1ixdiilTpkAMnCqkKFoQ5QmqHAGBlV+bT3tgm6PKva/GPYKrpq6P4v51vqpod1T03FWxLUjmgHDLZ+RX0BDMN+AKNq0xIAQzmDnai4jalSgKEE0dM2HzjDiTZf+QzD/kcu8PCLSUBtoCj61rO5mwTVvnrbqqBiaTGaoKv1FynucNvj8VUF0qlMDhbF1UYMAliAgI0oR6QdoJQ7Jg4ZokQBTq9xElAQnpEcgc2nV+Yc5QqosRJAndn34KuWWlqF63HrnXX4/0D5bAnJmpd2nNdnGfi9HN1g3zfp6HDXkbMP2b6Vg0aRGSwlqw3gp1SoIgIK5HKuJ6pGLE5Isgu1zI27fbPYpqC/L370PJ0cMoOXoYW775CqIkIalXX1QbTNik2GEND4fJYoXRaoPZaoXJaoPRYoXJqj2MZgt/q0KkJ1GsW3OrrcjOIOFVsEDrRMGYT+Dl11YN7/wW2a49WrpeV4OEgPAqMPDybQsIvJoagnXWOVVE1OV4p4oaQ3fZB6fTiaVLl2LKlDMaXOPNuw6bT1DluzabGrhP9Qm2fAOuev0BRfFfH01133Qg6LkCr93I+ZvSx/ecjZ5fhd85GqIqKmRFhZ7D4Aae1p2hFHVuosmElPkLcGj6dNTu3IncGTOR/uEHMCYm6l1as53a41S8c/47uPGHG7GvZB+uXHolXjnnFfSJ6aN3aRTCJIMBKf0HIaX/IEz865WorapE7o5tyNm+BTnbtqK0IA9H9/4OAFiz67cTn1AQYLJYvMGVyRtY2bSt57V7a7RaYbIE7PPpL3GUFlHokYyAFKXdSbAtqKo22ssvzAoYreUXgjWhzREQiskOz8Xq1vJqK5IpYG0vn/DK4F6QXhDdW8lnKwa8NjSjr0+7aAiyr6XXO1G7oX4bf2FBRCHCG67pXUgIUFVP6IWTC718+jd4rKr6BXN+xwa0+b2WVST3jtb7S9SuGEp1UVJ4GFJffw05066AIycHuTNnIv299yBFd7wPwIC4AVhywRL84/t/ILs8G9OXTceLZ72IMclj9C6NOghLWDj6jJ2APmO1NdbKjuUja/Ov2Lx6JZITE+Cy18JRUwNHbU3dtroaztpaqKoCqKrWXlMDlBS3uB7JYPAPt3y2RneAZfaO1qoffHn6mCw2mCwWLuhO1BEIQt0oJTRtSmOzya76gZffKK/AEKyhYKyR6Y/e0V4O7VFb1jbvpaMQggVnAcGWaGh+6OYXoDWlr+/1goVqDZ1DDBLYaX0FBUgu3QJhDwCjqe69CoL7uegTznn2iXX7m73Pvb/BfaL2HoiITkBwT5uDBIZ0IYChVBdmiItD6ptvIufyy2Hftx+5s25E2ltvQrRa9S6t2XqE98D7U97H3B/nYsuxLbjhhxvw2MTHcEHPC/QujTqgqIQkDJ50HnLtMs6dMqXhYdCqCpfd7g6rqusFV84ard1eUwNnbbU3uKrXx/3a5bADAGSXC3JFOWorylvl/RgbCrd8R3QFGd1V16euTTIaOVWRqKOSDIAUCVgi2+b8qgq47I1Pa/QEYaoMKLK2Rpci+7yWtTXCVBlQXAH7Guvr0664mt43sF1xNdI3yDlV5QRfE8V9XAe9xVgjDADGAMBBvSsJ4BtS+YZWotgG+wJCuFbb5xPQ1QvepBPs8xzb0L4gD9FQFzp6nzfltbtNMta95ghBImomhlJdnCmlB1IXL0bOVVehZssWHLnlVqQsmA+hgR/CQ1mUOQpvnPsG7l51N77P+R53rboLBdUFuGbgNfwhmtqEIAgwWiwwWiwIi45p8fkUWXaHVf4BVl1w5Rt8NdSn7nhV0X5YctbWwFlbg9aYqCNKkn9gZfMd0WWrW2erXhBmg8kW2McCkXcZI+o8BAEwWrQHYvWupn1oKwo3MUAL0t5oCHaCcMx7bDNCN2/Y19LrKVBkJ0qKixATHQ0Ral1Ip82HqXuuKie5T/bv1+T/Js3sT62vWcFWA0FXvbaWnLMJ15CaUkMTzsufOYiajaEUwdKvL1JfWYRD185A5YoVyLvvfiQ/8XiHnPJjlsx49oxn8cwvz+D939/HC5teQF5lHu4acxck/vBLIU6UJFjCw2EJD2/xuVRVhcvpgLOmBnZ3gOUXbnkCrIaCL99wrLYGLrs2ikuRZdRWVaK2qnXuPGYwm4OGWp7RW8bAdbcsAfs8UxmtVhiMJgbQRNS+PCNSuuC/MWSnE6sbuiNmW2gssFLkuoAw6D7FvV8O0n6ifUoD5zvRPt/rNrTPvf9k9jUa7vl+PZqwzxM6ereuJrxuZPSfp09X5DfyzDe4Mp5kgNa01yJE9Ms7AHHVLsBgrJtq6zcFtxnTfH3b600vbu6ae0LD5ydiKEUetpEj0eOFF3B47lyUffEFpLg4JN5xu95lnRRREHHnmDuRHJaMZ399Fh/t+QgF1QV46vSnYDV0vKmJRCdDEAQYTWYYTWbYolq+VpyiyHDW1tYFVt5RW/7TFp1+YVd1QN+68EuRZQCAy26Hy25HdVnL7zImiKJ/uOUOrsye6YdWm3cqotlm8742W23aiC+ffVxsnogoxIgiAP4QG1IUpS6gChpiNSXoam4YFtAmN3TtNn6tysG/JqpSt6ZeO5IAnAIA+e162ZZrSkjmnUZ7MmvhSXVTXdv9HC0I9mzxQFQPvf/rtBuGUuQVcfZZSH7sMeTdfTeK33oLhrhYxM2YoXdZJ+3vA/+OxLBE3LPqHvyU+xNmfjcTC85egBhLy6dZEXU1oijBbAuD2RbW4nOpqgrZ6fQZrVVdb5SW03dR+SChll9fe612XkWBvaoK9qqWT1SUjMa6wMrqnnro99rmHbWlBVzWegGX2WqDwWzm6C0iIuqcRBEQTQBMelfS/lS1/QM439feME57LbscOJSdhbTUFEjwHZ0XOG24ievl+bUHHtNKa+4BWj+5gYCvKxt5NXDRS3pX0W4YSpGf6IunQi4uxrFnnsGxZ56FFBOL6Esu1rusk3ZexnmIt8bjph9vwrbCbbhq2VV4ZdIrSI1M1bs0oi5LEAQYTCYYTCbYIqNafD5VUeCorfWOzPJfX0u7U6InyLJXV7sDrWr365q65zXV3mmKstOJGmcZaspbducwQRCDB1pWG8w+o7fMNu1uiv4juKx+I7i4/hYREVGIEARtgXcpNEZWK04nti1dipQpUyCFytrAvtNET+bGE76BV1PX6WuwXTm56zY52DvROZoS7Pm0W1o+y6EjYShF9cTNuBauoiIUv/UW8u6/H1J0NCLOPkvvsk7ayMSReG/ye5j1wyzklOfgymVXYuGkhRgUP0jv0oioFQiiCLNNC3Zaym+x+Wrtzol1AVbjgZajum66or2mGlBVqGrd6K2KFtZmNFsaDLQam5roncLobjeEyj9WiYiIqPPyrCUFKWTCOwpNDKUoqIR/3ga5uBhln3+OI7feirS33oRt5Ei9yzppPaN74v0p72P28tn4vfh3XPvttXjm9GdwRuoZepdGRCGktRabV1UVTnutO9iq9llwXguu6gdcPvs87dVauyJri8U67bVw2mtRVVLcotokgyFgxJbP9EO/gKuBqYnuuy4azRZOTSQiIiKiFmEoRUEJoojkRx+BXFqKyp9/Ru6sG5H+3nuw9Ourd2knrZutG94+/23c9vNtWHN0DW766SbcO/Ze/LXfX/UujYg6GUEQtPDGYkU44lp0LpfTWRdS1fiGWb5TEoOP6PKO9Kqu9q69JbtcqKkoR01FeQvfo+izgLwWVJn9Ai1rkICLUxOJiIiIqA5DKWqQYDSixwvP49CMmajZvBm5M2ci/cMPYUrpuHcCCDOGYf6k+Xhk3SP4fP/neHT9o8ivysfc4XP5G38iCkkGoxEGY1SL199SZLlukfggUxOdtTUNBlreKYo11XBU10BVFW1qYnUV7NUtX1jeYDafMNCSTGaUZR3A7xFWWMPCYbRYYLRY3Xda1J4bLRYYjCb+fU5ERETUQTCUokaJVitSX1mEnCuvgn3fPuTOmIH0D5bAENey3/zrySga8ciER5AcloxXfnsFb2x/AwXVBXho/EMwcr4zEXVSoiTBEhYOS1jLpya67Hb/aYYNTk2sqRdo+b6WnU4AgMtuh8tuR1VpyQmv//2vaxrdLwgijBaLFlRZbe7AygKTxQqj2R1kWSwwWa3eIMvk2Zrrwi2Tz7EGE++iSERERNQWGErRCUlRUUhd/AZyLp8GR04Ocq+/AWnvvgspvOW3hteLIAi4cdiNSApLwiPrHsGXWV/iWPUxvHDmCwg3tewHNiKizkwQBPcoJQsQE9uic3mnJtbUBJ2i6Ds1sbaqEoeysxEXHQ2XvRbO2ho47LVw1tbCUVvjvXOiqire49HC9bd83rR3NFbd1lo3Wisg+PIEXr4juEw+W5PFCoOZQRcRERERQylqEmNiIlLfXIycaVegdudOHJ47B6mvvQbRZNK7tBa5pM8l6GbthttW3Ib1eesx/ZvpWDRpERLDEvUujYio02vO1ESn04mlS5diypQpMAa5g6CiyHDZ7e67J2qhlbO2Fg57jTe4cvrs07Z14ZajpqbuGPfWsw4XVNUdjtWg5ZMV3QRBC7DcgZZ3BJclyAguv6213jHerdkCQRRbq0IiIiKiNsdQiprMnJmJ1Ndfx6Hp01G9bj2O3n4Hejz/HASpYy9Qe1rKaXj7/Lcx+4fZ2FuyF1cuuxKvTHoFvWN6610aERE1kShK3jWoWouqKHA67N7Ayi/s8gu3arxBljfQcu/TXvsfo51cdbfXtFq9ANxTFC31RnL5h12W+iO5rFaYzIGjv6wwWsxciJ6IiIjaDEMpahbr4EFIWTAfh274Byq+/Rb5j0Yj6cEHO/wUhIFxA/H+lPcx64dZyC7Pxt+X/R0vnf0SRieN1rs0IiLSiSCK3ul2rUVVFLgcDm3R+QaCLGetNirLaa/16VMbMJLLf9QXVBUA4LRrI7yqy0pbrWaD2Rx83S33QvPeIMtvn+9C9FbvlE+TxQqDyQxRFDmqi4iIiBhKUfOFTZiAHk8/hSPzbkPpRx/DEBePbnPn6F1Wi6VEpOC9ye9h7o9zsbVwK274/gb869R/YXLmZL1LIyKiTkIQRW9AE4aYVjmnqqpwOeze8MpRU+03NdE3+Ko36sve0L5aqKoC+CxEj7JWKdePIIrugEryeS7Wfy5J2mtBbPox3ucSRMm9bbRfA+cVtOv7tvmdu4nXF4Wm1hykj9TQdXzaO/gvCImIqGtiKEUnJXLyZMilpch/+BEcX7gQUlwsYqdN07usFou2ROONc9/A3avuxg+HfsAdK+9AQVUBpg+czn/sERFRSBLc61MZzRbYTrw8V5OoqgqX0+E/FTHIulsOT7DV6BTHur6qovhfR1EgKwoAV+sU3pUJQqPBVbODvIB2TzAnCNprVRCQn5eHb7P3QHLvgyC4g0MBgvu51ia420T3c/fW5zkEnzZ3OwL6BJ5bEAUADZ8bQY7z1hhYU2DdogAhyLkbq6nBc3vfY8BxAuquFeQ4IqKugKEUnbSYyy+Hq6gYxxcsQMGjj8EQE4PIyR1/VJHFYMGzZzyLZ359Bkt+X4LnNj2HvKo83DH6DkhcV4OIiLoAQRBgNJlhNJmBJixE3xSqqkJ2OuF02KEqClRFgaLIPs+VBp7LUGQFqrtvw/3q9wnW1/+a9a9/onP69ZVlqGrgcXKj12+wVvkEXwtVOdEXGIosA7LcKv+9mmpPTla7Xq8raUrAhyABX/3ArvHQTpQkSAYDJIMBosGoPZcMEA0GSO7Xone/odH9krvN97X3nAb/Y/yPM3LEH1EXxVCKWiR+9o2Qi4tQ8sGHOHLHnZCiohA2YYLeZbWYJEq4c/SdSA5LxrO/PosPdn+AguoCPHnak7AYLHqXR0RE1OEIggCDyQRDB79zr15UVQ0SgNUP8FSfkKs5odiJ+gQGeC6nE7t27kL//qdAFAR3fao7QPPUqrVBVaCq0Np8+sCnT11/BfDrq9Y7TlVVwPc67v4I6BOsFm+/gHOjoeO0kze9FkWFChXw66ueOFQM+t9cgdq+GaO+BAGSJAUJseoHXU0J0Hz7aMEbAzSiUMRQilpEEAQk3nsvXMUlqPjmG+TOmYv0d9+FdfAgvUtrMUEQMH3gdCTaEnHP6nuw/NByzPxuJuafPR8xltZZB4SIiIioKbTRLRIgSgiFcdtOpxNHZRHDJ0+B0WjUu5wOIWhA5g6xAgMvNBry1Q+8/MO+gHDO91qBx3lCR5cLsssJ2eWC7HK5X2ttissFWfa0N9BHluvtr+vjDOhf1y/gC+Td79TrP1JTCUJdWCW1boDm+9p3FFtjAZoCwFlRjorjhTBZLN7jRFGCaJAgSgaGaBSyGEpRiwmShO5PP4XcslJUr1uP3OuvR/oHS2DOzNS7tFZxfub5iLfG46afbsJvhb/h78v+jkXnLEJqRKrepRERERFRB1EXLOpdSWhQ3VNOfcOtoIGYywVFDh50nUyAVi8gk08yQHM6ITtDKz57+6uPG9wniKI7FJPcYZU2gkwL1bStKIrB2yXJ2+YNvALaRIPBvb6c737tepJkgCC5ry95ru3uL0naCDlPbX7tdW0M2jovhlLUKkSTCSnzF+DQ9Omo3bkTuTNmIv3DD2BMTNS7tFYxKmkU3pv8Hmb9MAvZ5dm4cumVWDRpEQbGD9S7NCIiIiKiDkfwGW0U6mPtmhOgeYMs2QXFJbdugObbLyBMs9faIQqA4pKDThdVFQUuxYHQH4bWdJ6gzRtsBQRqgthIu29w5g68tPaWBW2iJHqDNTEgVBODBG2BbV0xaGMoRa1GCg9D6huvI2faFXBkZyN35kykv/cepOhovUtrFb2ie+H9Ke/jxh9uxJ6SPbjm22vw7BnP4vSU0/UujYiIiIiI2kioB2hOpxNLly7FlCnadFpVUSDLMhTZpYVp7kBNkWVvWKYoQdpkl7tdey77HKf4nE/2trmgyIp764LsCrimJ8gLrMUT2HnaPOdTAvq76q5xoqCts2RtgihiyKTzcc7MG/Uupd0wlKJWZYiNRerixciZNg32ffuRO+tGpL31JkSrVe/SWkWCLQHvnP8O5v08D+vy1uGmH2/CfePuw1/6/kXv0oiIiIiIiCCIIgyiCHSi9d78graA8EtuQpssu6DKckDQpgVpqhIYtGmvtXaf87oDM+95AtobDAEDwjhP4AdVDfo+uxqGUtTqTCk9kPrGG8i56irUbNmCI7fcipQF8yF0kr8Uw03hWHjOQjy09iF8mfUlHl73MPKr8jF72OwuOdySiIiIiIioLXXGoE1R5LqRZt6wygWDyax3ae2Ky+xRm7D064vUV1+BYDajcsUK5N13f6dKfY2iEY9NfAw3DLkBAPDattdw35r74FQ6y8BRIiIiIiIiaiuiKMFgNMJkscISHg5bVDQiYuNhDY/Qu7R2pWsotXLlSlx00UXo3r07BEHA559/rmc51MpsI0agx4svAJKEsi++wLFnn9O7pFYlCALmDJ+DB8c/CEmQ8GXWl5j9w2xUOir1Lo2IiIiIiIgo5OkaSlVVVWHo0KFYuHChnmVQG4o46ywkP/YYAKD4rbdQ9OabepfU6v7S9y94+eyXYTVYsS5vHa759hocqz6md1lEREREREREIU3XUGry5Ml47LHHcPHFF+tZBrWx6IunIuH22wEAx555FqWffqZ3Sa3u9JTT8fZ5byPWEovdxbvxly//gvd3vQ+H7NC7NCIiIiIiIqKQ1KEWOrfb7bDb7d7X5eXlgPsWmE5nx1/Lx/MeOsN7CRT596vgOF6I0rffQd799wMR4Qg780y9y2pVfaP64p1z38GtK25FVlkWnvrlKby36z3MGjILkzMmQxS4hFtH0pk/j0QdET+TRKGFn0mi0MHPI4Wipv55FFQ1yH0IdSAIAj777DNMnTq1wT4PPfQQHn744XrtH3zwAWw2WxtXSC2mqkj8v/8iatMmKAYDDs+cidrMDL2ranWyKmOzYzN+rP0RFWoFACBJTMK51nPRx9CHd+gjIiIiIiKiTq26uhrTpk1DWVkZIiMjG+zXoUKpYCOlUlNTcfz48UbfZEfhdDrx/fff4w9/+AOMnehWl75Ulwt5t9yC6hUrIUZEoMc7b8Pct6/eZbWJGlcNPtzzId7Z9Q4qndri5yMTRuKmYTdhcPxgvcujE+gKn0eijoSfSaLQws8kUejg55FCUXl5OeLj408YSnWo6Xtmsxlms7leu9Fo7FQfvs72fvwYjUh98UUcmjETNZs3I+8fs5D+4YcwpfTQu7JWZzQaccOwG3DZKZdh8fbF+HD3h9h0bBOmfzcd56Sdg5tG3ITMqEy9y6QT6NSfR6IOiJ9JotDCzyRR6ODnkUJJU/8scpEbanei1YrUVxbB3KcPXIWFyJ0xA66iIr3LajPRlmj8c/Q/8fXFX+NPvf4EURDxw6EfcPEXF+OhtQ+hoKpA7xKJiIiIiIiI2p2uoVRlZSW2bt2KrVu3AgAOHjyIrVu34tChQ3qWRe1AiopC6uLFMHbvDkdODnKvux5yZaXeZbWp5PBkPHbqY/jkok9wZuqZkFUZn+z7BBd+diFe3PQiyh3lepdIRERERERE1G50DaV+/fVXDB8+HMOHDwcAzJs3D8OHD8cDDzygZ1nUToyJCUh9czGkmBjU7tqFw3PmQnE49C6rzfWO6Y35Z8/Hu+e/i2HdhqFWrsWbO97E5E8m450d78Au25twFiIiIiIiIqKOTddQ6swzz4SqqvUe77zzjp5lUTsyZ2Yi9fXXIdpsqF6/HkdvvwOqLOtdVrsYkTgC/578b7x81svoFdUL5Y5yPLfpOVzw6QX4bN9nkJWu8XUgIiIiIiKirolrSpHurIMHIWXhAghGIyq+/Rb5jz6KELkpZJsTBAFnpZ2FT/74CR6Z8AgSbYkoqC7AA2sfwJ+//DN+PPRjl/laEBERERERUdfCUIpCQtj48ej+zNOAIKD0o49xfMFCvUtqV5Io4eI+F+Pri7/GbSNvQ6QpElllWbj5p5vx92V/x+aCzXqXSERERERERNSqGEpRyIg8/3wkPXA/AOD4woUo/uADvUtqdxaDBVcPuhrL/rwMMwbNgEWyYGvhVkz/ZjrmLp+LfSX79C6RiIiIiIiIqFUwlKKQEnP55YifMwcAUPDoYyhftkzvknQRaYrELSNvwf8u+R/+0vcvkAQJPx/+GX/+8s+4b/V9yKvM07tEIiIiIiIiohZhKEUhJ372jYiZdjmgqjhyx52oWrtW75J0k2BLwIPjH8Rnf/oMf0j/A1So+CLrC1z42YV49pdnUVpbqneJRERERERERCeFoRSFHEEQkHjvvYiYfD7gdCJ3zlzUbN+ud1m6yozKxPNnPo8lU5ZgVOIoOBQH3t31LiZ/OhlvbHsD1c5qvUskIiIiIiIiahaGUhSSBElC96eegm38OKjV1ci9/gbYDxzUuyzdDek2BG+d9xYWTVqEvjF9UemsxMtbXsaFn12I/+z5D5yKU+8SiYiIiIiIiJqEoRSFLNFkQsr8BbAMHAi5pAS5M2fCWVCgd1m6EwQBp6Wchv+76P/w+KmPo0d4DxTWFOLR9Y/iki8uwfc530NVVb3LJCIiIiIiImoUQykKaVJ4GFLfeB2mjAw4jx5F7syZkEu5jhIAiIKIi3pdhC+nfom7xtyFGHMMssuzMe/nebhi6RXYmLdR7xKJiIiIiIiIGsRQikKeITYWqYsXw5CQAPu+/ciddSOUmhq9ywoZJsmEK/pfgaWXLMU/hv4DVoMV249vx4zvZuAfP/wDu4t3610iERERERERUT0MpahDMKX0QOriNyBGRqJmyxYcueVWqE6un+Qr3BSO2cNmY+klS/G3fn+DQTBgzZE1uPSrS3HXqrtwuOKw3iUSEREREREReTGUog7D0rcvUl99BYLZjMoVK5B33/1QFUXvskJOvDUe9467F19M/QKTMyYDAP534H+46POL8MSGJ1BUU6R3iUREREREREQMpahjsY0YgR4vvgBIEsq++ALHnn1O75JCVlpkGp4+42l8dOFHGJ88Hi7FhQ92f4Apn07BK1tfQZWzSu8SiYiIiIiIqAtjKEUdTsRZZyH5X48BAIrfegtFb76pd0khbWDcQLx+7ut4/Q+vY0DcAFS7qrHot0WY8ukUfPD7B3DKnAZJRERERERE7Y+hFHVI0VOnIuH22wEAx555FqWffqZ3SSFvfPfx+PCCD/HMGc8gLSINxbXFeGLjE/jj53/E0gNLoaicCklERERERETth6EUdVhxM65F7IxrAQB599+Pih9/0rukkCcKIs7POB+fT/0c9429D3GWOByuPIw7V92Jv339N6w9shaqqupdJhEREREREXUBDKWoQ0v45z8RdfHFgCzjyK23onrTJr1L6hCMohGXnXIZll6yFHOGzUGYMQy/F/+OG364Add9dx12HN+hd4lERERERETUyTGUog5NEAQkP/oIws88E6rdjtx/zELtnj16l9Vh2Iw23DD0Biy9ZCmu7H8ljKIRG/I34PL/XY7bfr4NOeU5epdIREREREREnRRDKerwBIMBPV54HtaRI6FUVCB35nVwHD6sd1kdSqwlFneOuRNfXfwVLup5EQQI+C7nO/zp8z/h0XWPorC6UO8SiYiIiIiIqJNhKEWdgmi1InXRQpj79IGrsBC5M2bCVVSkd1kdTo/wHnj8tMfxfxf9H07rcRpkVcZ/9v4HF3x2AV7e/DIqHBV6l0hERERERESdBEMp6jSkqCikLl4MY/fucOTkIPe66yFXVupdVofUL7YfFp2zCG+f9zaGdBuCGlcN3tj+BqZ8OgX/3vlvOGSH3iUSERERERFRB8dQijoVY2ICUt9cDCk2FrW7duHwnLlQHAxQTtaopFF4f/L7ePHMF5ERmYFSeyme+fUZXPjZhfgy60vIiqx3iURERERERNRBMZSiTsecmYnU11+HaLOhev16HL39Dqgyw5OTJQgCJqVPwmd/+gwPjX8ICdYE5FXl4d7V9+IvX/0FKw+vhKqqepdJREREREREHQxDKeqUrIMGImXhAghGIyq+/Rb5jz7K4KSFDKIBf+77Z3x9yde4ZcQtiDBFYH/pfsxePhtXf3M1th7bqneJRERERERE1IEwlKJOK2z8eHR/5mlAEFD60cc4vmCh3iV1ClaDFTMGz8CyS5bhmoHXwCSasPnYZly17Crc/OPNOFB6QO8SiYiIiIiIqANgKEWdWuT55yPpgfsBAMcXLkTxkiV6l9RpRJmjMG/UPPzvkv/h4t4XQxRE/Jj7Iy7+8mI8uPZB5Ffl610iERERERERhTCGUtTpxVx+OeLnzgEAFDz2L5QvW6Z3SZ1KUlgSHpn4CD7946c4O/VsKKqCT/d9igs/uxDPb3oeZfYyvUskIiIiIiKiEMRQirqE+BtvRMy0aYCq4sgdd6Jq7Vq9S+p0ekX3wktnv4T3Jr+HEQkjYJfteHvH25j86WS8teMt1Lpq9S6RiIiIiIiIQghDKeoSBEFA4r33IGLy+YDTidw5c1GzfbveZXVKwxKG4Z3z38GCsxegd3RvVDgq8MKmF3DBZxfgk72fwKW49C6RiIiIiIiIQgBDKeoyBElC96eeQtiE8VCrq5F7/Q2wHziod1mdkiAIOCP1DPz3ov/isYmPITksGceqj+GhdQ/hki8vwfKc5bwbIhERERERURfHUIq6FNFkQo+X58MyaBDkkhIcmjkDzoICvcvqtCRRwp96/wlfXfwV/jnqn4gyR+Fg2UHc8vMtuHLZlfg1/1e9SyQiIiIiIiKdMJSiLkcKD0Pq66/BlJEB19E85M6cCbm0VO+yOjWzZMb0gdOx7JJluG7wdbBIFmwr3IZrvr0GN/5wI/YU79G7RCIiIiIiImpnDKWoSzLExiJ18WIYEhJg37cfubNuhFJTo3dZnV6EKQI3jbgJSy9Zir/2/SskQcKqI6tw6VeX4t7V9+Jo5VG9SyQiIiIiIqJ2wlCKuixTSg+kLn4DYmQkarZswZFbboXqdOpdVpfQzdYN94+/H5//6XOcm34uVKj4MutLXPjZhXj6l6dRUluid4lERERERETUxhhKUZdm6dsXqa++AsFiQeWKFci7736oiqJ3WV1GRlQGnjvzOXx4wYcYkzQGTsWJ93a9hymfTsFrv72Game13iUSERERERFRG2EoRV2ebcQI9HjxBUCSUPbFFzj27HN6l9TlDIofhMXnLsar57yKU2JPQaWzEgu2LsAFn12Aj3d/DKfCEWxERERERESdDUMpIgARZ56J5H89BgAofustFL35pt4ldTmCIGBij4n4+MKP8dRpT6FHeA8crzmOxzY8hqmfT8U32d9AUTmKjYiIiIiIqLNgKEXkFj11KhLuuAMAcOyZZ1H6yad6l9QliYKIKT2n4KupX+HuMXcj1hKLQxWHcPuK23H5/y7H+rz1epdIRERERERErYChFJGPuGuvQdzMGQCAvAceQMWPP+ldUpdllIyY1n8all6yFDcOvRE2gw27inbhuu+uw/XfXY9dRbv0LpGIiIiIiIhagKEUUYBut92GqIsvBmQZR269FdWbNuldUpcWZgzDrGGzsPSSpZh2yjQYRAPW5a3DZV9fhjtW3IHc8ly9SyQiIiIiIqKTwFCKKIAgCEh+9BGEn3kmVLsduf+Yhdo9e/Quq8uLs8bh7rF348upX2JK5hQAwLLsZfjj53/Ev9b/C8drjutdIhERERERETUDQymiIASDAT1eeB7WkSOhVFQgd+Z1cBw+rHdZBCA1IhVPnf4U/nPhfzCx+0S4VBc+2vMRpnw6BQu3LkSlo1LvEomIiIiIiKgJGEoRNUC0WpG6aCHMffvCVViIQzNmwFVUpHdZ5NY/rj9e/cOrWHzuYgyKG4QaVw1e/e1VTPl0Cpb8vgQO2aF3iURERERERNQIhlJEjZCiopD6xhsw9ugBZ84h5F53PeRKjsQJJWOTx+KDCz7Ac2c8h4zIDJTYS/Dkxifxx8//iK+yvoKiKnqXSEREREREREEwlCI6AWNiAlIXvwEpNha1u3bh8Jy5UBwchRNKBEHAuRnn4tM/fYr7x92PeGs8jlQewT2r78Ffv/orVh9ZDVVV9S6TiIiIiIiIfDCUImoCc2YmUl9/HaLNhur163H09jugyrLeZVEAo2jEX/v9Ff+7+H+4afhNCDeGY0/JHsz6YRZmfDcD2wu3610iERERERERuTGUImoi66CBSFm0EILRiIpvv0X+o49y9E2IshltuG7IdVh2yTL8fcDfYRSN+CX/F0xbOg3zfp6Hg2UH9S6RiIiIiIioyzPoXQBRRxI2bhy6P/MMjtx6K0o/+hiGuHh0mztH77KoAdGWaNw++nZc0f8KLNy6EF9lfYXvc77Hj4d+xMV9LsasobOQYEvw9lcdDshVVVCqquAoLYXl0CHY9+yFGh4GwWqFaDZDsFohmEwQBEHX90ZERERERG1LVVXA81AUQFWhAt7nUBRo4xTq9kNV647zHBPsPCoA1fc8KqACUkQ4DN266f3W2w1DKaJmijz/PMgl9yP/4UdwfOFCSLExiL3iCr3L6rJURYFSXQOlqhJKpftRVQW5shJKZRWUykqYqipxc1UMriqahD25W1BRegxW+0fY5PwPEtRwhDskqFVVUAPWCksDkLtwUf2LCgIEi8UbUvlvLRDNlga3otUCoaGtxQLR4r9lAEZERETUMqqiaD/0u7cNPVdlRQsJmrI/SN9g+1VZrgsdgu53X0f1aVMUoBn7ZZcL8fv34/jvv0MURHf44Q45FNUnKPGEJu5QBap2LhV1YQlUqD7HeM+jwidMCTgmWFvgeRQFKppynoDwBqr/+zjZ8wQLk3DiY/QQfdllSH74IV2urQeGUkQnIebyy+EqLsbx+QtQ8Ni/YIiJQeSUKXqX1aEoDodfiKRUVtYFSVX1gyVv2FQV0FZd3axvGH39qwBQjsD78wlWK8SwMNTKLlhECWptLZTaWsCzjpiqQq2pgVxTA5SWtsaXo2GeAMwSLLQyQ7RYG9yKFjOEpm6tVghGIwMwIiLq0BocneB5rahBRib4/LCrqH4jHk6uv1oXPAT29/5wHaS/6gkIVP/nPteo398nOAjorwUlJ+oP93llra/vc59ApHn7A0KUBvYHPaZZ+08QMPm0dQWxAEpXrNC7DApGEABR1LaCoP1726dNAOr2iyJEi0XvitsVQymikxR/442Qi4pR8sEHOHLnXRCjohA+caLeZbUpVZahVFd7QyQtSKqqC5aqKv2DJE/YVFW/TXU6W7c4SYIUHg4xLAxieLj7EVbXFubfJths2FGThY8Of4Us51FUm4DImERcO+ZGXNRnKhRZwdKlSzFlyhQYjUbt/TudUOx2qDU1ddtaO9RabavU1ngDLLWmFoq9oW3AOYJsgwZgbU0QGhzx1egIr+ZuLRYGYEQhTvX+Vl8GZDnoVnXJgOKz9dvn0o736aPKrrpzen7rrrp/2Pab7nCCdr/f3NfvX6+93r7mttff16xr+B3TUHsTr9HguZTmXVdV3SMNTuYYeAOT1OJi5C5ZAgGC/4gFnxEN3tESgSMaGuivImBERGMhk2ekg8/IB6IWEUVAkrR/o4giBFHU2nyfSyIEQWxwvyCJQGP7T3TOIMf7tXmfCxBECRBFKACys7OR2asnRMkAQXSHHoInCIF2LniCEPe/+wLaBL9jhIbP421r5DzeNp8QxnOeem2+5w3S5nsen7a6+urOXb8t8NwB5wkSEvmd29vmcx73exME/zBJQAMBFDWKoRTRSRIEAYn33Qu5tATlS5fh8NybkP7uO7AOHqx3aX5UVYXqMyqpbjRSlXfKm19bZaV/uFQVMCqplQk2GyTfICksTAuOvCGST5v3tdYmhdcdJ5jNzf5LfyKAscosfJX1FRZuXYiD1QW4f/1DeHf3+5g9ZHa9hewFoxGS0QiEh7fyV8GfqqqAOwBTamqgnmhba9eCME8g1syt9x/xqgq1uhpydTVQ0qZvUfvG3cCIr6aN8Gpo9Jj/VrRYAAZg1ABt+kNAmOJ9rgCyq/n7ggQ1De5zydpIgMb2udzHB+4LFhad5L6GgieiE7ECsB/Su4pWFDh6wed1vZENvj94en8QFxvv73nt/YE8yPHB+te7XuDxgt+1vT+Yo6HrBbwOFog01CbWf34y+wXJ8159notB9gcLc060vxlhUL22DsrpdOKXpUsx2ueXqUQdhaB24NuHlZeXIyoqCmVlZYiMjNS7nBZzOp31RmZQ6FMcDhz+xz9QtXYdpJgYpC9ZAnPPzBafV5Vl/2ltVVV1U9sC23yDpKrquhCpshJyVRXgcrXKe/UyGIKPSgrzbasfIvm1hYdDtNkgSFLr1naSal21+HD3h1i8fTHKHeUAgEQxEend0mGUjDCIBhhEAyRB8j43ika/15IowSC420V3u6C1G8Xg5zAIhnrt3uOFuvN6j3efz/f4lgQu3gCsWUFW3eiwhrbaqDHfrXasLr/F9vyDE6gbIu1+Xm8b0Ca0cj8IgLeCk+lXr79nE+wcQfp5ftvYinU26/otqFOFisJjhYiPjYEga6NtvKGNogAuV9022D7fEMbdptdaER2eZySB+4GAbbA2v31+v7H2+XPh9/D5cxDQrn2Om3dMc9u1EEAM0nYS56+3r6Fz+bQH2dfc9uDnauI1Ao4J1i4rMjZt2YKRI0fBYDTWjUYIHB3h+zpghEWD/d3XCtpfCB76+AU37r/z641qEBsIiXyPJ+qA+HMkhaKm5jUcKUXUQqLJhB4vz8ehq69G7Y4dODRzBtJeew2C0egzta3payTJVVqwpLbBqCTRZvMJkcIhhtnc4VKQ6W7e9rB6AVRnXHzbYrDgmkHX4JI+l+CtHW9hye9LUCAXoKCgQO/STsgTZjUUdp0oMAsasHkCMosRBlvAuUUDDEJk3XmDXC/ouQUJBhkQHS4YHQokpwzR4YRk17aiwwXR7oTgcEGodQB2B9TaWqj2Wig1DW8bmh7pDRwC1pNoTgzByCK0hAFoh4msmuYELQYJEAP2GSQIYsA+g0H7ITlwn8F9nBhwnGTQRgP4Xq+xfd42n30Gg3ukQ+A+g/v6IgRvXQZ3aCQ1vM8zrYW6PKfTiSqHA+Fnn8UfgomI6KQxlCJqBVJ4GFJffw05066AIzsbBy76Y+ud3GgMCIUamdoWVtfmO7XNOyqpAw9Lbi9R5ijcOvJWXNbnMrz97dsYPGwwIAIuxQWX4oJTcUJWZLhU7bWsyHAqTrhU7bmnn6y6293PPe0uxeU91nO872vPft9z+e5Xg8QksipD7qRTbQw2A4zhDQRmfkFaJCQxxhu8GQQJRkWExSXA4hJgEk0wSyaYRTPMBjPMohFm0QKTZIRFNMMkmWASjbBIFpjc/UyiESbJqD2XjDBLFhjgO7LP/d/Cuw4LvFvveizN6ecdtVO/X1udt3n9VO/uhq6v+h7XyteXXS78tn07ho0YAYPZ7A1M6gUuPmGKN1TxC1q0bWMhk3cUBhERERG1KYZSRK3EEBuLtDcXI3fWjbDv2xcwKilwaluwRbh9p7v59DOZ9H5rXVI3azcMMg3C5IzJIfUbYFmRvSGXU3H6BV6yIsOpOuvCroBAyzcwC3q8b3uwsOwEgZlf+KY2cs2AUM/TPxjP8aFCFESYJTMskgVmg3srmWExWLxtgfstBkvQY8wGM6yS1afNZ797n0Fs2fTMzsTpdKLCaEQEpyYQERERdRoMpYhakbFHD2R+8TmgqhyVRG1CEiVIkGCSOldYqapq4yPKGgi8Ggzf1LpRbE7FCYfsQK1cC7vLDrts9z6vlWtR66r1awu23zNCTVEV1LhqUOOqAext/3Vp7RDMsz+wzbM1ilwUnoiIiIjaD0MpolZWt3goETWVIAjedalCjaqqcCpOv6DqRIFWrau2wZDLLtsb3R+KIZjvqK4ThWQMwYiIiIioqULvX/9EREQhRBAEbc0pyQS0wwC1hkIwu2xHjaumwVFdNXJNpw7BjKIRR6uPYtemXTBK2oL9kiBBFETvc+9D1NoNggGiKHrbRUGEQTTU7fM51nef73m8x/qcJ9g1vOfz2UdEREREjWMoRUREFEI6Qwh2ov2Kqt0N8WRCsPV71rftF6QVBQZffiGXT4DlG3wFDcsCz+NzXNDz+AZsLTmPTz2e8/meJ/AagYFdsGsYRAMECBAEAaIgas8hQPu/4N3n2RIREVHnxlCKiIioCwulEKyxqY9Vjirs3rsbmb0yAcF910lV9i7+r6iK32tPm0txaVvVBUVR6p6ril9fz7EnOo9vv4YW6PdwqS7t7oJK239dOyu/kMonvPIGWu7gKrCfINR/7dtPhFgXhAXp53t+Ae5j3c89o+DqXQ8NHNtA4Hai9xb0uAbO4Xlv9Y49wXENvrcG+vm+N0VRcLDmIPZv3Q9JlPzq8Pv6+L5HrUODAWS9Y4P8t2voeWD/xv4bBTs2sL/v64b+W5zo/TT2Z9jvug29H58+Db4fwec6LX0/jXy+REGs/9q3j/tzFXj9YOFz4LFE1LUxlCIiIqJ2c7IhmNPpxNLcpZgyLLTuvtdQuFUv5AoIvjwhWWPBV4PBW5BreIO2Rq7RLudRZShK/fN4pog2lwoVqqr6NlCIWbVrld4lUAcXNFBtJAjzDfY8QRjg3xYY/gULXBsMmH1eB+sjCmKDYWTQ7cnuO0HQ7rtVVRW51bnYtGETJEmqF0YiWOjbSOiJICFm4Nev3nEBQavv1yiwn+9/+2DXRQNhbdD3EXjOht5HI4F0Q+dr7P17jw34s9CUev3C5CD1drN2Q0ZURjM+RR0bQykiIiKikyQKIkRJhBGhE5SFIk9A5QnxPG2q53/u4ElVvS3eaZ7eNvfW71hPmwq/4/zafM4ZeK7AfgqUulqC9Q9yLqjwHueprynHeY8N9nXw1OP5GgQ5R9Cvg8+16339TvB+6n0Ng30dPM9VFbIi48DBA8jIyIDovuNwsHN7avDuD/I+A9saqtsTStZr9+kfeKzv+/Lr08CfkYauEVizb5+g5wl830H++wV7n4H9G3tvfl+zht5nkK/9ifqrwT5HPn+mWpsK7Q687hfUApuyNuldArWCS/teigfGP6B3Ge2GoRQRERERtSnPelQAAEnvaqg1OJ1OLM1fiikjQ2v0IrU936BOgVIv4AoWpgYLSRsKnoOFoMFCwqB9T/b4JvSBJwgOEnr6hsv12nyCwWCBr18YHhgKniBo9rS5ZBf27NmDvn37QhAF77GB/20QEKwG/neqd1xAkNtgeOzTL7BGBAloA68b7LiGQlcECWpP+D4Cr+tzznr1BgmmT/j+A49r6vsP/G/hPq6btduJP4idCEMpIiIiIiIiahLfaV0SU+aQ4HQ6sTRnKaYMYkhMHQ/vV0xERERERERERO2OoRQREREREREREbU7hlJERERERERERNTuGEoREREREREREVG7YyhFRERERERERETtjqEUERERERERERG1O4ZSRERERERERETU7hhKERERERERERFRu2MoRURERERERERE7Y6hFBERERERERERtTuGUkRERERERERE1O4YShERERERERERUbtjKEVERERERERERO2OoRQREREREREREbU7hlJERERERERERNTuQiKUWrhwITIyMmCxWDB27Fhs3LhR75KIiIiIiIiIiKgN6R5Kffzxx5g3bx4efPBBbN68GUOHDsV5552HY8eO6V0aERERERERERG1Ed1Dqeeffx7XXXcdrrnmGgwYMACvvvoqbDYb3nrrLb1LIyIiIiIiIiKiNmLQ8+IOhwObNm3C3Xff7W0TRRHnnHMO1q1bV6+/3W6H3W73vi4vLwcAOJ1OOJ3Odqq67XjeQ2d4L0QdHT+PRKGFn0mi0MLPJFHo4OeRQlFT/zzqGkodP34csiwjMTHRrz0xMRG7d++u1/+JJ57Aww8/XK/9u+++g81ma9Na29P333+vdwlE5MbPI1Fo4WeSKLTwM0kUOvh5pFBSXV3dpH66hlLNdffdd2PevHne12VlZUhLS8P48eMRERGha22twel04qeffsJZZ50Fo9GodzlEXRo/j0ShhZ9JotDCzyRR6ODnkUJRRUUFAEBV1Ub76RpKxcfHQ5IkFBQU+LUXFBQgKSmpXn+z2Qyz2ex97Zm+l5mZ2Q7VEhERERERERFRU1VUVCAqKqrB/bqGUiaTCcxM2lkAAAqUSURBVCNHjsTy5csxdepUAICiKFi+fDnmzJlzwuO7d++O3NxcREREQBCEdqi4bZWXlyM1NRW5ubmIjIzUuxyiLo2fR6LQws8kUWjhZ5IodPDzSKFIVVVUVFSge/fujfbTffrevHnzMH36dIwaNQpjxozBiy++iKqqKlxzzTUnPFYURaSkpLRLne0pMjKSf5kQhQh+HolCCz+TRKGFn0mi0MHPI4WaxkZIeegeSl122WUoLCzEAw88gPz8fAwbNgzffPNNvcXPiYiIiIiIiIio89A9lAKAOXPmNGm6HhERERERERERdQ6i3gVQHbPZjAcffNBvMXci0gc/j0ShhZ9JotDCzyRR6ODnkToyQT3R/fmIiIiIiIiIiIhaGUdKERERERERERFRu2MoRURERERERERE7Y6hFBERERERERERtTuGUiFi4cKFyMjIgMViwdixY7Fx40a9SyLqkp544gmMHj0aERERSEhIwNSpU7Fnzx69yyIiAE8++SQEQcAtt9yidylEXdaRI0dw5ZVXIi4uDlarFYMHD8avv/6qd1lEXZIsy7j//vuRmZkJq9WKXr164dFHHwWXjaaOhKFUCPj4448xb948PPjgg9i8eTOGDh2K8847D8eOHdO7NKIuZ8WKFZg9ezbWr1+P77//Hk6nE+eeey6qqqr0Lo2oS/vll1/w2muvYciQIXqXQtRllZSUYOLEiTAajVi2bBl27dqF5557DjExMXqXRtQlPfXUU3jllVewYMEC/P7773jqqafw9NNPY/78+XqXRtRkvPteCBg7dixGjx6NBQsWAAAURUFqairmzp2Lu+66S+/yiLq0wsJCJCQkYMWKFTj99NP1LoeoS6qsrMSIESOwaNEiPPbYYxg2bBhefPFFvcsi6nLuuusurFmzBqtWrdK7FCICcOGFFyIxMRFvvvmmt+3Pf/4zrFYr3n//fV1rI2oqjpTSmcPhwKZNm3DOOed420RRxDnnnIN169bpWhsRAWVlZQCA2NhYvUsh6rJmz56NCy64wO97JRG1vy+//BKjRo3CpZdeioSEBAwfPhxvvPGG3mURdVkTJkzA8uXLsXfvXgDAb7/9htWrV2Py5Ml6l0bUZAa9C+jqjh8/DlmWkZiY6NeemJiI3bt361YXEWmjFm+55RZMnDgRgwYN0rscoi7po48+wubNm/HLL7/oXQpRl3fgwAG88sormDdvHu655x788ssvuOmmm2AymTB9+nS9yyPqcu666y6Ul5fjlFNOgSRJkGUZ//rXv3DFFVfoXRpRkzGUIiJqwOzZs7Fjxw6sXr1a71KIuqTc3FzcfPPN+P7772GxWPQuh6jLUxQFo0aNwuOPPw4AGD58OHbs2IFXX32VoRSRDv7zn/9gyZIl+OCDDzBw4EBs3boVt9xyC7p3787PJHUYDKV0Fh8fD0mSUFBQ4NdeUFCApKQk3eoi6urmzJmDr7/+GitXrkRKSore5RB1SZs2bcKxY8cwYsQIb5ssy1i5ciUWLFgAu90OSZJ0rZGoK0lOTsaAAQP82vr3749PPvlEt5qIurLbb78dd911F/72t78BAAYPHoycnBw88cQTDKWow+CaUjozmUwYOXIkli9f7m1TFAXLly/H+PHjda2NqCtSVRVz5szBZ599hh9//BGZmZl6l0TUZU2aNAnbt2/H1q1bvY9Ro0bhiiuuwNatWxlIEbWziRMnYs+ePX5te/fuRXp6um41EXVl1dXVEEX/H+klSYKiKLrVRNRcHCkVAubNm4fp06dj1KhRGDNmDF588UVUVVXhmmuu0bs0oi5n9uzZ+OCDD/DFF18gIiIC+fn5AICoqChYrVa9yyPqUiIiIuqt5xYWFoa4uDiu80akg1tvvRUTJkzA448/jr/+9a/YuHEjXn/9dbz++ut6l0bUJV100UX417/+hbS0NAwcOBBbtmzB888/j2uvvVbv0oiaTFBVVdW7CAIWLFiAZ555Bvn5+Rg2bBhefvlljB07Vu+yiLocQRCCtr/99tu4+uqr270eIvJ35plnYtiwYXjxxRf1LoWoS/r6669x9913Y9++fcjMzMS8efNw3XXX6V0WUZdUUVGB+++/H5999hmOHTuG7t274/LLL8cDDzwAk8mkd3lETcJQioiIiIiIiIiI2h3XlCIiIiIiIiIionbHUIqIiIiIiIiIiNodQykiIiIiIiIiImp3DKWIiIiIiIiIiKjdMZQiIiIiIiIiIqJ2x1CKiIiIiIiIiIjaHUMpIiIiIiIiIiJqdwyl/r+9+wmx6e/jAP4+onFnUMNk3GwkmoaiRJnYYGFGKRpJ3TRjM02YbJSayIg1O3chbIgaRbPwp1hOiY0xi2GtJiEbptiMZ6Opm+d5en7PmDvU61Wnzvl+z7n3892++57PAQAAAKDuhFIAAH+xoijy4MGD+S4DAOAfE0oBAPyfent7UxTFL0dnZ+d8lwYA8MdbON8FAAD8zTo7O3Pz5s2asYaGhnmrBwDgb2GnFADALDQ0NGTVqlU1R3Nzc/Lz1bpqtZqurq6USqWsXbs29+7dq3l+fHw8u3fvTqlUyooVK9LX15evX7/W3HPjxo1s3LgxDQ0NKZfLOXnyZM38p0+fcvDgwTQ2Nmb9+vUZGRmpw8oBAGZHKAUAMIfOnTuX7u7ujI2NpVKp5MiRI5mYmEiSTE1NZe/evWlubs7Lly8zPDycp0+f1oRO1Wo1J06cSF9fX8bHxzMyMpJ169bV/MeFCxdy+PDhvH79Ovv27UulUsnnz5/rvlYAgH+i+PHjx4/5LgIA4G/U29ubW7duZfHixTXjg4ODGRwcTFEU6e/vT7VanZnbvn17tmzZkqtXr+batWs5c+ZM3r17l6ampiTJw4cPs3///kxOTqa1tTWrV6/OsWPHcunSpX9bQ1EUOXv2bC5evJj8DLqWLFmSR48e6W0FAPzR9JQCAJiFXbt21YROSbJ8+fKZ846Ojpq5jo6OvHr1KkkyMTGRzZs3zwRSSbJjx45MT0/n7du3KYoik5OT2bNnz3+tYdOmTTPnTU1NWbZsWT58+DDrtQEAzCWhFADALDQ1Nf3yOt3vUiqV/qf7Fi1aVHNdFEWmp6fnpCYAgN9FTykAgDn0/PnzX67b29uTJO3t7RkbG8vU1NTM/OjoaBYsWJC2trYsXbo0a9asybNnz+peNwDAXLNTCgBgFr5//57379/XjC1cuDAtLS1JkuHh4WzdujU7d+7M7du38+LFi1y/fj1JUqlUcv78+fT09GRoaCgfP37MwMBAjh49mtbW1iTJ0NBQ+vv7s3LlynR1deXLly8ZHR3NwMDAPKwWAOD3EUoBAMzC48ePUy6Xa8ba2try5s2b5OeX8e7evZvjx4+nXC7nzp072bBhQ5KksbExT548yalTp7Jt27Y0Njamu7s7ly9fnvmtnp6efPv2LVeuXMnp06fT0tKSQ4cO1XmVAAC/n6/vAQDMkaIocv/+/Rw4cGC+SwEA+OPoKQUAAABA3QmlAAAAAKg7PaUAAOaILgkAAP+ZnVIAAAAA1J1QCgAAAIC6E0oBAAAAUHdCKQAAAADqTigFAAAAQN0JpQAAAACoO6EUAAAAAHUnlAIAAACg7oRSAAAAANTdvwDOFqtDVpDH1wAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# === TRAINING ===\n", + "history = model.fit(\n", + " X_train,\n", + " {\"ner_output\": np.array(y_ner_train), \"srl_output\": np.array(y_srl_train)},\n", + " validation_data=(X_val, {\"ner_output\": np.array(y_ner_val), \"srl_output\": np.array(y_srl_val)}),\n", + " batch_size=2,\n", + " epochs=10\n", + ")\n", + "\n", + "# === SAVE ===\n", + "model.save(\"multi_task_bilstm_model.keras\")\n", + "with open(\"word2idx.pkl\", \"wb\") as f:\n", + " pickle.dump(word2idx, f)\n", + "with open(\"tag2idx_ner.pkl\", \"wb\") as f:\n", + " pickle.dump(tag2idx_ner, f)\n", + "with open(\"tag2idx_srl.pkl\", \"wb\") as f:\n", + " pickle.dump(tag2idx_srl, f)\n", + " \n", + " \n", + "history_dict = history.history\n", + "\n", + "# === LOSS ===\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.plot(history_dict[\"loss\"], label=\"Total Loss (train)\")\n", + "plt.plot(history_dict[\"val_loss\"], label=\"Total Loss (val)\")\n", + "plt.plot(history_dict[\"ner_output_loss\"], label=\"NER Loss (train)\")\n", + "plt.plot(history_dict[\"val_ner_output_loss\"], label=\"NER Loss (val)\")\n", + "plt.plot(history_dict[\"srl_output_loss\"], label=\"SRL Loss (train)\")\n", + "plt.plot(history_dict[\"val_srl_output_loss\"], label=\"SRL Loss (val)\")\n", + "\n", + "plt.title(\"Model Loss per Epoch\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Loss\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n", + "# === ACCURACY ===\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "plt.plot(history_dict[\"ner_output_accuracy\"], label=\"NER Accuracy (train)\")\n", + "plt.plot(history_dict[\"val_ner_output_accuracy\"], label=\"NER Accuracy (val)\")\n", + "plt.plot(history_dict[\"srl_output_accuracy\"], label=\"SRL Accuracy (train)\")\n", + "plt.plot(history_dict[\"val_srl_output_accuracy\"], label=\"SRL Accuracy (val)\")\n", + "\n", + "plt.title(\"Model Accuracy per Epoch\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Accuracy\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "aeef32c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:5 out of the last 5 calls to .one_step_on_data_distributed at 0x7fc3e3f1b010> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n", + "\u001b[1m1/1\u001b[0m \u001b[32mā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”ā”\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 264ms/step\n", + "\n", + "šŸ“Š [NER] Test Set Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " LOC 0.00 0.00 0.00 3\n", + "\n", + " micro avg 0.00 0.00 0.00 3\n", + " macro avg 0.00 0.00 0.00 3\n", + "weighted avg 0.00 0.00 0.00 3\n", + "\n", + "\n", + "šŸ“Š [SRL] Test Set Classification Report:\n", + " precision recall f1-score support\n", + "\n", + " CAU 0.00 0.00 0.00 1\n", + " LOC 0.00 0.00 0.00 1\n", + " MOD 0.00 0.00 0.00 1\n", + " RG0 0.00 0.00 0.00 1\n", + " RG1 0.00 0.00 0.00 10\n", + " RG2 0.00 0.00 0.00 1\n", + " _ 0.00 0.00 0.00 2\n", + "\n", + " micro avg 0.00 0.00 0.00 17\n", + " macro avg 0.00 0.00 0.00 17\n", + "weighted avg 0.00 0.00 0.00 17\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG1 seems not to be NE tag.\n", + " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n", + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: V seems not to be NE tag.\n", + " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n", + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG2 seems not to be NE tag.\n", + " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n", + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARGM-LOC seems not to be NE tag.\n", + " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n", + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: ARG0 seems not to be NE tag.\n", + " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n", + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-MOD seems not to be NE tag.\n", + " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n", + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/sequence_labeling.py:171: UserWarning: AM-CAU seems not to be NE tag.\n", + " warnings.warn('{} seems not to be NE tag.'.format(chunk))\n", + "/mnt/disc1/code/thesis_quiz_project/lstm-quiz/myenv/lib64/python3.10/site-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + } + ], + "source": [ + "# evaluation\n", + "y_pred_ner, y_pred_srl = model.predict(X_test)\n", + "\n", + "y_true_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_ner_test]\n", + "y_pred_ner = [[idx2tag_ner[np.argmax(tok)] for tok in seq] for seq in y_pred_ner]\n", + "\n", + "y_true_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_srl_test]\n", + "y_pred_srl = [[idx2tag_srl[np.argmax(tok)] for tok in seq] for seq in y_pred_srl]\n", + "\n", + "print(\"\\nšŸ“Š [NER] Test Set Classification Report:\")\n", + "print(classification_report(y_true_ner, y_pred_ner))\n", + "\n", + "print(\"\\nšŸ“Š [SRL] Test Set Classification Report:\")\n", + "print(classification_report(y_true_srl, y_pred_srl))\n", + "\n", + "\n", + "# import numpy as np\n", + "\n", + "# # Prediksi model (output = probabilitas)\n", + "# y_pred_ner = model.predict(X_test)[0]\n", + "# y_pred_ner_idx = np.argmax(y_pred_ner, axis=-1)\n", + "# y_true_ner_idx = np.argmax(y_ner_test, axis=-1)\n", + "\n", + "# # Mapping ke string\n", + "# y_pred_ner_str = []\n", + "# y_true_ner_str = []\n", + "\n", + "# for y_true_seq, y_pred_seq in zip(y_true_ner_idx, y_pred_ner_idx):\n", + "# true_seq = []\n", + "# pred_seq = []\n", + "# for t, p in zip(y_true_seq, y_pred_seq):\n", + "# if idx2tag_ner[t] != \"PAD\":\n", + "# true_seq.append(idx2tag_ner[t])\n", + "# pred_seq.append(idx2tag_ner[p])\n", + "# y_true_ner_str.append(true_seq)\n", + "# y_pred_ner_str.append(pred_seq)\n", + "\n", + "# from seqeval.metrics import classification_report\n", + "# print(\"\\nšŸ“Š [NER] Test Set Classification Report:\")\n", + "# print(classification_report(y_true_ner_str, y_pred_ner_str))\n", + "\n", + "\n", + "# from collections import Counter\n", + "\n", + "# flat_preds = [tag for seq in y_pred_ner_str for tag in seq]\n", + "# print(Counter(flat_preds))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "5a18da05", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def plot_confusion_matrix(y_true_flat, y_pred_flat, labels, title=\"Confusion Matrix\"):\n", + " cm = confusion_matrix(y_true_flat, y_pred_flat, labels=labels)\n", + " plt.figure(figsize=(10, 8))\n", + " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n", + " xticklabels=labels, yticklabels=labels)\n", + " plt.title(title)\n", + " plt.xlabel(\"Predicted\")\n", + " plt.ylabel(\"Actual\")\n", + " plt.xticks(rotation=45)\n", + " plt.yticks(rotation=0)\n", + " plt.tight_layout()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "cee30988", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AAPAZzSUAAAAAwGc0lwAAAAAAn9FcAgAAAAB8RnMJAAAAAPBZgNMF4OwyTzldAQAAAFC4QopZVxLa9B6nS1DGhilOl5ALk0sAAAAAgM9oLgEAAAAAPitmA2gAAAAAcJiLGZ0NqQAAAAAAfEZzCQAAAADwGctiAQAAAMAbLpfTFRRJTC4BAAAAAD5jcgkAAAAA3uCCPlakAgAAAADwGc0lAAAAAMBnLIsFAAAAAG9wQR8rJpcAAAAAAJ8xuQQAAAAAb3BBHytSAQAAAAD4jOYSAAAAAOAzlsUCAAAAgDe4oI8Vk0sAAAAAKMH++OMP9e3bVxEREQoNDVWjRo30/fffux83xmjMmDGqUqWKQkND1bZtW23fvt3r16G5BAAAAABvuPyc3/IpNTVVV1xxhQIDA/Xxxx9ry5Yteu6551S+fHn3MRMnTtSLL76oV199VWvWrFHp0qUVHx+vzMxM72IxxhivnoHzKvOU0xUAAAAAhSukmH1YL7TlSKdLUMbqZ/J13MMPP6xvvvlGX331lfVxY4yqVq2qBx54QA8++KAkKS0tTZUrV9bs2bPVs2fPfNfE5BIAAAAASqj33ntPl19+uW6++WZddNFFatq0qaZNm+Z+fOfOnUpOTlbbtm3d+8LDw9WiRQutWrXKq9eiuQQAAAAAb7hcjm9ZWVlKT0/32LKysnKV+uuvv+qVV15RvXr19Mknn2jw4MG69957NWfOHElScnKyJKly5coez6tcubL7sfyiuQQAAACAYiYxMVHh4eEeW2JiYq7jcnJydOmll+qpp55S06ZNdeedd+qOO+7Qq6++WuA10VwCAAAAQDEzatQopaWleWyjRo3KdVyVKlV08cUXe+xr0KCBdu/eLUmKjIyUJO3bt8/jmH379rkfyy+aSwAAAADwhtNXinX5KTg4WGXLlvXYgoODc5V6xRVXaNu2bR77fv75Z9WsWVOSVLt2bUVGRmrFihXux9PT07VmzRrFxcV5FUsxuy4TAAAAACC/7r//frVq1UpPPfWUunfvrrVr1+r111/X66+/LklyuVwaNmyYnnjiCdWrV0+1a9fWY489pqpVq6pTp05evRbNJQAAAAB4w+VyuoJ8a9asmd59912NGjVKEyZMUO3atTV58mT16dPHfcxDDz2kY8eO6c4779Thw4d15ZVXatmyZQoJCfHqtYrlstj+/fvL5XK5t4iICLVv314bN27M8zm7du3yeE6ZMmUUGxurIUOGaPv27dbnrFq1Sv7+/urQoUOer/33rVatWpKkNm3aWB+/6667CiGRwrVw/jwltLtWzZo2Up+eN2vTWXK+kJCLHbnkjWzsyMWOXOzIxY5c7MjFjlwuPDfccIM2bdqkzMxMbd26VXfccYfH4y6XSxMmTFBycrIyMzP12WefqX79+l6/TrFsLiWpffv22rt3r/bu3asVK1YoICBAN9xwwz8+77PPPtPevXv1ww8/6KmnntLWrVt1ySWXeKwxPm3GjBkaOnSovvzyS/3555+SpP/7v/9zv+7evXslSbNmzXJ//d1337mff8cdd3gcu3fvXk2cOLFAcyhsyz7+SJMmJmrQ3UO0cPG7io6O0eBBA5WSkuJ0aY4iFztyyRvZ2JGLHbnYkYsdudiRix25oDAV2+YyODhYkZGRioyMVJMmTfTwww9rz549OnDgwFmfFxERocjISNWpU0c33XSTPvvsM7Vo0UIDBw5Udna2+7ijR49q0aJFGjx4sDp06KDZs2dLf91Q9PTrnr56Urly5dxfV6pUyX2OUqVKeRwbGRmpsmXLFlomhWHunFnq0q27OnXuqrpRURo9drxCQkK09J0lTpfmKHKxI5e8kY0dudiRix252JGLHbnYkUsBKQIX9CmKimZVXjp69KjefPNNRUVFKSIiwqvn+vn56b777tNvv/2mdevWufe/9dZbiomJUXR0tPr27auZM2fKGFMI1RddJ0+c0NYtm9UyrpV7n5+fn1q2bKWNP2xwtDYnkYsdueSNbOzIxY5c7MjFjlzsyMWOXFDYim1z+cEHHygsLExhYWEqU6aM3nvvPS1atEh+ft6/pZiYGOmvz2WeNmPGDPXt21f6awluWlqaVq5c6dV5p06d6q7x9DZv3jyv63NK6uFUZWdn52rYIyIidPDgQcfqchq52JFL3sjGjlzsyMWOXOzIxY5c7MilALlczm9FULFtLq+55holJSUpKSlJa9euVXx8vBISEvTbb78pISHB3czFxsb+47lOTyRdf/0hbdu2TWvXrlWvXr0kSQEBAerRo4dmzJjhVY19+vRx13h6u/HGG/M8PisrS+np6R5bVlaWV68JAAAAAE4otrciKV26tKKiotxfT58+XeHh4Zo2bZqmT5+ujIwMSVJgYOA/nmvr1q3SXzcQ1V9Ty1OnTqlq1aruY4wxCg4O1pQpUxQeHp6vGsPDwz1q/CeJiYkaP368x75HHxur0WPG5fscBal8ufLy9/fP9QHvlJQUVaxY0ZGaigJysSOXvJGNHbnYkYsdudiRix252JELCluxnVz+ncvlkp+fnzIyMlStWjVFRUUpKipKNWvWPOvzcnJy9OKLL6p27dpq2rSpTp06pTfeeEPPPfecx8Txhx9+UNWqVbVgwYJCew+jRo1SWlqaxzZi5KhCe71/EhgUpAYXx2rN6lXufTk5OVqzZpUaX9LUsbqcRi525JI3srEjFztysSMXO3KxIxc7cilATl/Mp4he0KfYTi6zsrKUnJwsSUpNTdWUKVN09OhRdezY8azPS0lJUXJyso4fP64ff/xRkydP1tq1a/Xhhx/K399fS5cuVWpqqgYOHJhrQtm1a1fNmDEj3/eqPH78uLvG04KDg1W+fHnr8cHBwQoODvbYl3kqXy9VaG7pd5see2SkYmMbqmGjxnpz7hxlZGSoU+cuzhbmMHKxI5e8kY0dudiRix252JGLHbnYkQsKU7FtLpctW6YqVapIksqUKaOYmBgtXrxYbdq0Oevz2rZtK/11m5CaNWvqmmuu0euvv+5evjpjxgy1bdvWuvS1a9eumjhxojZu3KjGjRv/Y43Tpk3TtGnTPPbFx8dr2bJlXr1XJ7VPuF6phw5p6pQXdfDgAUXHNNDU16Yr4gJfOkEuduSSN7KxIxc7crEjFztysSMXO3JBYXKZC+3+GsWM05NLAAAAoLCFFLORV2jrCU6XoIyVY5wuIZeiuVgXAAAAAFCsFLPfEQAAAACAw/yK5n0mncbkEgAAAADgM5pLAAAAAIDPWBYLAAAAAN4ooveZdBqpAAAAAAB8xuQSAAAAALzh4oI+NkwuAQAAAAA+o7kEAAAAAPiMZbEAAAAA4A0u6GNFKgAAAAAAn9FcAgAAAAB8xrJYAAAAAPAGV4u1YnIJAAAAAPAZk0sAAAAA8AYX9LEiFQAAAACAz2guAQAAAAA+Y1ksAAAAAHiDC/pYMbkEAAAAAPiMySUAAAAAeIML+liRCgAAAADAZzSXAAAAAACfsSwWAAAAALzBBX2smFwCAAAAAHzG5BIAAAAAvMEFfaxIBQAAAADgM5pLAAAAAIDPWBYLAAAAAN7ggj5WTC4BAAAAAD5jcgkAAIACl3Ei2+kSiqTQIH+nSwAKDc0lAAAAAHiDq8VakQoAAAAAwGdMLgEAAADAG0wurUgFAAAAAOAzmksAAAAAgM9YFgsAAAAA3uA+l1ZMLgEAAAAAPmNyCQAAAADe4II+VqQCAAAAAPAZzSUAAAAAwGcsiwUAAAAAb3BBHysmlwAAAAAAn9FcAgAAAAB8xrJYAAAAAPAGV4u1IhUAAAAAgM+YXAIAAACAN7igjxWTSwAAAACAz2guAQAAAAA+Y1ksAAAAAHjBxbJYKyaXAAAAAACfMbkEAAAAAC8wubRjcgkAAAAA8BnNJQAAAADAZyyLBQAAAABvsCrWisklAAAAAMBnNJcAAAAAAJ+VyOayf//+crlc7i0iIkLt27fXxo0b83zOrl275HK5lJSUlOcx3377ra6//nqVL19eISEhatSokZ5//nllZ2fnOvbzzz/X9ddfr4iICJUqVUoXX3yxHnjgAf3xxx8F9j7Pl4Xz5ymh3bVq1rSR+vS8WZvOkuOFhFzsyCVvZGNHLnbkYkcuduTiaclbC9Wneydde2UzXXtlM91+ay99+/WXTpdVZPD94rszew2ntqKoRDaXktS+fXvt3btXe/fu1YoVKxQQEKAbbrjhnM/37rvvqnXr1vrXv/6lzz//XD/99JPuu+8+PfHEE+rZs6eMMe5jX3vtNbVt21aRkZFasmSJtmzZoldffVVpaWl67rnnCugdnh/LPv5IkyYmatDdQ7Rw8buKjo7R4EEDlZKS4nRpjiIXO3LJG9nYkYsdudiRix255HZR5coaMvR+zZ63WLPnLdZlzVvoofvv0a+/bHe6NMfx/YLC5DJndkUlRP/+/XX48GEtXbrUve/rr7/WVVddpf3796tSpUq5nrNr1y7Vrl1bGzZsUJMmTTweO3bsmGrWrKnWrVtryZIlHo+9//77uvHGG7Vw4UL16NFDv//+u+rWrau7775bL7zwQq7XOXz4sMqVK5fv95J5Kt+HFoo+PW9WbMNGemT0GElSTk6Orvt3a/XqfYsG3nGns8U5iFzsyCVvZGNHLnbkYkcudkU1l4wTuVd2Oem61i11z7ARurFzV0frCA3yd/T1i+r3S0gxu8xomR5znC5BRxb1c7qEXErs5PJMR48e1ZtvvqmoqChFRER4/fxPP/1UKSkpevDBB3M91rFjR9WvX18LFiyQJC1evFgnTpzQQw89ZD2XN42l006eOKGtWzarZVwr9z4/Pz+1bNlKG3/Y4GhtTiIXO3LJG9nYkYsdudiRix25/LPs7GwtX/aRMjIy1KjxJU6X4yi+X1DYitnvCPLvgw8+UFhYmPTX5LFKlSr64IMP5OfnfT/9888/S5IaNGhgfTwmJsZ9zPbt21W2bFlVqVLFp/qLgtTDqcrOzs7VkEdERGjnzl8dq8tp5GJHLnkjGztysSMXO3KxI5e87dj+s+7o10snTpxQaGgpPfPci6pdN8rpshzF9wsKW4mdXF5zzTVKSkpSUlKS1q5dq/j4eCUkJOi3335TQkKCwsLCFBYWptjY2HyfMz8riI0x5/wB26ysLKWnp3tsWVlZ53QuAACAC1nNWrX0xsJ3NOONhepycw9NGPOIdv6yw+myUEI4fTEfLuhznpUuXVpRUVGKiopSs2bNNH36dB07dkzTpk3T9OnT3Y3nRx999I/nql+/viRp69at1se3bt3qPqZ+/fpKS0vT3r17va45MTFR4eHhHtuzzyR6fZ6CUr5cefn7++f6gHdKSooqVqzoWF1OIxc7cskb2diRix252JGLHbnkLTAwSNVr1FTMxbG6+97hiqofrUUL5jpdlqP4fkFhK7HN5d+5XC75+fkpIyND1apVczeeNWvW/MfnXnfddapQoYL1Sq/vvfeetm/frl69ekmSunXrpqCgIE2cONF6rsOHD+f5OqNGjVJaWprHNmLkKK/eZ0EKDApSg4tjtWb1Kve+nJwcrVmzSo0vaepYXU4jFztyyRvZ2JGLHbnYkYsdueSfMUYnTpx0ugxH8f1ScJyeWhbVyWWJ/cxlVlaWkpOTJUmpqamaMmWKjh49qo4dO571edu2bcu1LzY2Vq+99pp69uypO++8U/fcc4/Kli2rFStWaMSIEerWrZu6d+8uSapevbpeeOEF3XPPPUpPT9ett96qWrVq6ffff9cbb7yhsLCwPG9HEhwcrODgYI99Tl8t9pZ+t+mxR0YqNrahGjZqrDfnzlFGRoY6de7ibGEOIxc7cskb2diRix252JGLHbnkNvXF5xV3xdWqXKWKjh87pk8//kDrv1+ryVOnOV2a4/h+QWEqsc3lsmXL3BfVKVOmjGJiYrR48WK1adPmrM/r2bNnrn179uxRt27d9Pnnn+vJJ5/UVVddpczMTNWrV0+PPvqohg0b5vHbg7vvvlv169fXpEmT1LlzZ2VkZKhWrVq64YYbNHz48EJ4t4WnfcL1Sj10SFOnvKiDBw8oOqaBpr42XREX+NIJcrEjl7yRjR252JGLHbnYkUtuqYcOafxjDyvl4AGFhZVR3Xr1NXnqNLVo2Sofzy7Z+H5BYSqR97ksSZyeXAIAAJyLonafy6LC6ftcFlXF7T6X4b2d//xu2vxbnC4hlwvmM5cAAAAAgMJDcwkAAAAA8FkxG0ADAAAAgLOK6tVancbkEgAAAADgMyaXAAAAAOAFJpd2TC4BAAAAAD6juQQAAAAA+IxlsQAAAADgBZbF2jG5BAAAAAD4jMklAAAAAHiByaUdk0sAAAAAgM9oLgEAAAAAPmNZLAAAAAB4g1WxVkwuAQAAAAA+Y3IJAAAAAF7ggj52TC4BAAAAAD6juQQAAAAA+IxlsQAAAADgBZbF2jG5BAAAAAD4jOYSAAAAAEqocePGyeVyeWwxMTHuxzMzMzVkyBBFREQoLCxMXbt21b59+87ptVgWCwAAAABeKG7LYmNjY/XZZ5+5vw4I+P9t4P33368PP/xQixcvVnh4uO655x516dJF33zzjdevQ3MJAAAAACVYQECAIiMjc+1PS0vTjBkzNH/+fF177bWSpFmzZqlBgwZavXq1WrZs6dXrsCwWAAAAALzhKgKbF7Zv366qVauqTp066tOnj3bv3i1JWrdunU6ePKm2bdu6j42JiVGNGjW0atUqr2NhcgkAAAAAxUxWVpaysrI89gUHBys4ONhjX4sWLTR79mxFR0dr7969Gj9+vK666ir9+OOPSk5OVlBQkMqVK+fxnMqVKys5OdnrmphcAgAAAEAxk5iYqPDwcI8tMTEx13EJCQm6+eab1bhxY8XHx+ujjz7S4cOH9dZbbxV4TUwuAQAAAMALReGCPqNGjdLw4cM99v19amlTrlw51a9fXzt27FC7du104sQJHT582GN6uW/fPutnNP8Jk0sAAAAAKGaCg4NVtmxZjy0/zeXRo0f1yy+/qEqVKrrssssUGBioFStWuB/ftm2bdu/erbi4OK9rYnIJAAAAAF4oCpPL/HrwwQfVsWNH1axZU3/++afGjh0rf39/9erVS+Hh4Ro4cKCGDx+uChUqqGzZsho6dKji4uK8vlKsaC4BAABQGEKD/J0uAYCk33//Xb169VJKSooqVaqkK6+8UqtXr1alSpUkSS+88IL8/PzUtWtXZWVlKT4+XlOnTj2n13IZY0wB148ClHnK6QoAAACAwhVSzEZekXe87XQJSp7WzekScilmf4wAAAAA4KzitCz2fOKCPgAAAAAAn9FcAgAAAAB8xrJYAAAAAPACy2LtmFwCAAAAAHzG5BIAAAAAvMHg0orJJQAAAADAZzSXAAAAAACfsSwWAAAAALzABX3smFwCAAAAAHzG5BIAAAAAvMDk0o7JJQAAAADAZzSXAAAAAACfsSwWAAAAALzAslg7JpcAAAAAAJ/RXAIAAAAAfMayWAAAAADwBqtirZhcAgAAAAB8xuQSAAAAALzABX3smFwCAAAAAHxGcwkAAAAA8BnLYgEAAADACyyLtWNyCQAAAADwGZNLAAAAAPACk0s7JpcAAAAAAJ8V2+ayf//+crlc7i0iIkLt27fXxo0b83zOrl275HK55O/vrz/++MPjsb179yogIEAul0u7du3yOD4pKcl93LvvvquWLVsqPDxcZcqUUWxsrIYNG+ZxrhMnTmjixIm65JJLVKpUKVWsWFFXXHGFZs2apZMnTxZ4FoVt4fx5Smh3rZo1baQ+PW/WprNkfCEhFztyyRvZ2JGLHbnYkYsdudiRix25oLAU2+ZSktq3b6+9e/dq7969WrFihQICAnTDDTf84/OqVaumN954w2PfnDlzVK1atbM+b8WKFerRo4e6du2qtWvXat26dXryySc9GsYTJ04oPj5eTz/9tO688059++23Wrt2rYYMGaKXXnpJmzdv9uEdn3/LPv5IkyYmatDdQ7Rw8buKjo7R4EEDlZKS4nRpjiIXO3LJG9nYkYsdudiRix252JGLHbkUjDOHXE5tRVGxbi6Dg4MVGRmpyMhINWnSRA8//LD27NmjAwcOnPV5/fr106xZszz2zZo1S/369Tvr895//31dccUVGjFihKKjo1W/fn116tRJL7/8svuYyZMn68svv9SKFSs0ZMgQNWnSRHXq1FHv3r21Zs0a1atXz8d3fX7NnTNLXbp1V6fOXVU3Kkqjx45XSEiIlr6zxOnSHEUuduSSN7KxIxc7crEjFztysSMXO3JBYSrWzeWZjh49qjfffFNRUVGKiIg467E33nijUlNT9fXXX0uSvv76a6Wmpqpjx45nfV5kZKQ2b96sH3/8Mc9j5s2bp7Zt26pp06a5HgsMDFTp0qXz/Z6cdvLECW3dslkt41q59/n5+ally1ba+MMGR2tzErnYkUveyMaOXOzIxY5c7MjFjlzsyKUAuYrAVgQV6+bygw8+UFhYmMLCwlSmTBm99957WrRokfz8zv62AgMD1bdvX82cOVOSNHPmTPXt21eBgYFnfd7QoUPVrFkzNWrUSLVq1VLPnj01c+ZMZWVluY/Zvn27YmJiCugdOiv1cKqys7NzNesRERE6ePCgY3U5jVzsyCVvZGNHLnbkYkcuduRiRy525ILCVqyby2uuuUZJSUlKSkrS2rVrFR8fr4SEBP32229KSEhwN56xsbG5njtgwAAtXrxYycnJWrx4sQYMGPCPr1e6dGl9+OGH2rFjh0aPHq2wsDA98MADat68uY4fPy5JMsac8/vJyspSenq6x3Zm4woAAAAARVWxbi5Lly6tqKgoRUVFqVmzZpo+fbqOHTumadOmafr06e7G86OPPsr13EaNGikmJka9evVSgwYN1LBhw3y/bt26dXX77bdr+vTpWr9+vbZs2aJFixZJkurXr6+ffvrpnN5PYmKiwsPDPbZnn0k8p3MVhPLlysvf3z/XB7xTUlJUsWJFx+pyGrnYkUveyMaOXOzIxY5c7MjFjlzsyKXgOH0xHy7ocx64XC75+fkpIyND1apVczeeNWvWtB4/YMAAffHFF/maWualVq1aKlWqlI4dOyZJ6t27tz777DNt2JB73frJkyfdx9mMGjVKaWlpHtuIkaPOuTZfBQYFqcHFsVqzepV7X05OjtasWaXGl+T+TOmFglzsyCVvZGNHLnbkYkcuduRiRy525ILCFuB0Ab7IyspScnKyJCk1NVVTpkzR0aNH//HCPKfdcccduvnmm1WuXLl8HT9u3DgdP35c119/vWrWrKnDhw/rxRdf1MmTJ9WuXTtJ0rBhw/Thhx/q3//+tx5//HFdeeWVKlOmjL7//ns988wzmjFjhpo0aWI9f3BwsIKDgz32ZZ7KV2mF5pZ+t+mxR0YqNrahGjZqrDfnzlFGRoY6de7ibGEOIxc7cskb2diRix252JGLHbnYkYsduaAwFevmctmyZapSpYokqUyZMoqJidHixYvVpk2bfD0/ICDAqyUArVu31ssvv6xbb71V+/btU/ny5dW0aVN9+umnio6Olv5qEJcvX64XXnhBr732mh588EGVKlVKDRo00L333uvV8tuioH3C9Uo9dEhTp7yogwcPKDqmgaa+Nl0RF/jSCXKxI5e8kY0dudiRix252JGLHbnYkUvBKKrLUp3mMr5cgQaFzunJJQAAAFDYQorZyKvuAx87XYJ+eS7B6RJyKWZ/jAAAAADgLAaXdiXqgj4AAAAAAGfQXAIAAAAAfMayWAAAAADwAhf0sWNyCQAAAADwGZNLAAAAAPACg0s7JpcAAAAAAJ/RXAIAAAAAfMayWAAAAADwAhf0sWNyCQAAAADwGc0lAAAAAMBnLIsFAAAAAC+wKtaOySUAAAAAwGdMLgEAAADAC35+jC5tmFwCAAAAAHxGcwkAAAAA8BnLYgEAAADAC1zQx47JJQAAAADAZ0wuAQAAAMALLkaXVkwuAQAAAAA+o7kEAAAAAPiMZbEAAAAA4AVWxdoxuQQAAAAA+IzmEgAAAADgM5bFAgAAAIAXuFqsHZNLAAAAAIDPmFwCAAAAgBeYXNoxuQQAAAAA+IzmEgAAAADgM5bFAgAAAIAXWBVrx+QSAAAAAOAzJpcAAAAA4AUu6GPH5BIAAAAA4DOaSwAAAACAz1gWCwAAAABeYFWsHZNLAAAAAIDPaC4BAAAAAD5jWSwAAAAAeIGrxdoxuQQAAAAA+IzJJQAAAAB4gcGlHZNLAAAAAIDPaC4BAAAAAD5jWSwAAAAAeIEL+tgxuQQAAAAA+IzJJQAAAAB4gcGlHZNLAAAAAIDPaC4BAAAAAD5jWSwAAAAAeIEL+tgxuQQAAAAA+IzJJQAAAAB4gcGlHZNLAAAAAIDPaC4BAAAAAD5jWSwAAAAAeIEL+tgxuQQAAAAA+IzmEgAAAADgM5pL/KOF8+cpod21ata0kfr0vFmbNm50uqQigVzsyCVvZGNHLnbkYkcuduRiRy525OI7l8v5rShytLns37+/XC6Xe4uIiFD79u21MR/f4Hv27NGAAQNUtWpVBQUFqWbNmrrvvvuUkpLicVytWrU0efLkXM8fN26cmjRpkmv/qlWr5O/vrw4dOuR6bNeuXXK5XLrooot05MgRj8eaNGmicePGuY852zZ79ux8JuS8ZR9/pEkTEzXo7iFauPhdRUfHaPCggblyvtCQix255I1s7MjFjlzsyMWOXOzIxY5cUJgcn1y2b99ee/fu1d69e7VixQoFBATohhtuOOtzfv31V11++eXavn27FixYoB07dujVV1/VihUrFBcXp0OHDp1zPTNmzNDQoUP15Zdf6s8//7Qec+TIEU2aNMn6WPXq1d3vZ+/evXrggQcUGxvrsa9Hjx7nXN/5NnfOLHXp1l2dOndV3agojR47XiEhIVr6zhKnS3MUudiRS97Ixo5c7MjFjlzsyMWOXOzIpWD80zDpfGxFkePNZXBwsCIjIxUZGakmTZro4Ycf1p49e3TgwIE8nzNkyBAFBQXp008/VevWrVWjRg0lJCTos88+0x9//KFHH330nGo5evSoFi1apMGDB6tDhw55ThiHDh2q559/Xvv378/1mL+/v/v9REZGKiwsTAEBAR77QkNDz6m+8+3kiRPaumWzWsa1cu/z8/NTy5attPGHDY7W5iRysSOXvJGNHbnYkYsdudiRix252JELCpvjzeWZjh49qjfffFNRUVGKiIiwHnPo0CF98sknuvvuu3M1aZGRkerTp48WLVokY4zXr//WW28pJiZG0dHR6tu3r2bOnGk9T69evRQVFaUJEyZ4/RrFSerhVGVnZ+f6s4iIiNDBgwcdq8tp5GJHLnkjGztysSMXO3KxIxc7crEjFxQ2x5vLDz74QGFhYQoLC1OZMmX03nvvadGiRfLzs5e2fft2GWPUoEED6+MNGjRQamrqWSefeZkxY4b69u0r/bVcNy0tTStXrsx1nMvl0tNPP63XX39dv/zyi9evk5esrCylp6d7bFlZWQV2fgAAAAC+c/piPr6sin366aflcrk0bNgw977MzEwNGTJEERERCgsLU9euXbVv3z6vz+14c3nNNdcoKSlJSUlJWrt2reLj45WQkKDffvtNCQkJ7sYzNjbW43n/NJkMCgryqo5t27Zp7dq16tWrlyQpICBAPXr00IwZM6zHx8fH68orr9Rjjz3m1eucTWJiosLDwz22Z59JLLDze6t8ufLy9/fP9QHvlJQUVaxY0bG6nEYuduSSN7KxIxc7crEjFztysSMXO3LBd999p9dee02NGzf22H///ffr/fff1+LFi7Vy5Ur9+eef6tKli9fnd7y5LF26tKKiohQVFaVmzZpp+vTpOnbsmKZNm6bp06e7G8+PPvpIkhQVFSWXy6WtW7daz7d161ZVqlRJ5cqVkySVLVtWaWlpuY47fPiwwsPD3V/PmDFDp06dUtWqVRUQEKCAgAC98sorWrJkifX5+qvrX7RokTZsKJg16qNGjVJaWprHNmLkqAI597kIDApSg4tjtWb1Kve+nJwcrVmzSo0vaepYXU4jFztyyRvZ2JGLHbnYkYsdudiRix25FBynL+ZzLhf0OXr0qPr06aNp06apfPny7v1paWmaMWOGnn/+eV177bW67LLLNGvWLH377bdavXq1V68R4HVVhczlcsnPz08ZGRmqVq1arscjIiLUrl07TZ06Vffff7/H5y6Tk5M1b948DRkyxL0vOjpa69aty3We9evXKzo6WpJ06tQpvfHGG3ruued03XXXeRzXqVMnLViwQHfddVeuczRv3lxdunTRww8/7PP71l8XNwoODvbYl3mqQE59zm7pd5see2SkYmMbqmGjxnpz7hxlZGSoU2fvf5NRkpCLHbnkjWzsyMWOXOzIxY5c7MjFjlwuXEOGDFGHDh3Utm1bPfHEE+7969at08mTJ9W2bVv3vpiYGNWoUUOrVq1Sy5Yt8/0ajjeXWVlZSk5OliSlpqZqypQpOnr0qDp27Jjnc6ZMmaJWrVopPj5eTzzxhGrXrq3NmzdrxIgRql+/vsaMGeM+9v7779dVV12lJ598Ul26dFF2drYWLFigVatWaerUqdJfn/tMTU3VwIEDPaaZktS1a1fNmDHD2lxK0pNPPqnY2FgFBDgeZaFon3C9Ug8d0tQpL+rgwQOKjmmgqa9NV8QFvnSCXOzIJW9kY0cuduRiRy525GJHLnbkUnJkZWXluj6LbVglSQsXLtT69ev13Xff5XosOTlZQUFB7pWfp1WuXNndp+WXy5zLZVULSP/+/TVnzhz312XKlFFMTIxGjhyprl27nvW5u3bt0rhx47Rs2TLt379fxhh16dJFc+fOValSpTyO/fTTTzVhwgRt2bJFfn5+atSokcaPH6+rr75aktSxY0fl5OToww8/zPU6a9euVYsWLfTDDz+obNmyql27tjZs2KAmTZq4jxk0aJBef/11jR07VuPGjfN4/rhx47R06VIlJSWdU0ZOTy4BAACAwhZSzOY0Vz//jdMl6Nr05Ro/frzHPls/smfPHl1++eVavny5+7OWbdq0UZMmTTR58mTNnz9ft912W65GtXnz5rrmmmv0zDPP5LsmR5vLgjR27Fg9//zzWr58uVej26KO5hIAAAAlHc2l95YPuTxfk8ulS5eqc+fO8vf3d+/Lzs52fxzxk08+Udu2bZWamuoxvaxZs6aGDRum+++/P981FbM/xryNHz9etWrV0urVq9W8efM8b2UCAAAAAMVdXktg/+7f//63Nm3a5LHvtttuc68YrV69ugIDA7VixQr36tFt27Zp9+7diouL86qmEtNc6q+QAAAAAKAw+XKfyfOtTJkyatiwoce+0qVLKyIiwr1/4MCBGj58uCpUqKCyZctq6NChiouL83pFaIlqLgEAAAAA3nnhhRfk5+enrl27KisrS/Hx8e6Ln3qjxHzmsqTiM5cAAAAo6YrbZy7bTP7W6RL0xbBWTpeQCx9MBAAAAAD4jOYSAAAAAOCzYjaABgAAAABnFacL+pxPTC4BAAAAAD5jcgkAAAAAXnAxurRicgkAAAAA8BnNJQAAAADAZyyLBQAAAAAvsCrWjsklAAAAAMBnNJcAAAAAAJ+xLBYAAAAAvODHulgrJpcAAAAAAJ8xuQQAAAAALzC4tGNyCQAAAADwGc0lAAAAAMBnLIsFAAAAAC+4WBdrxeQSAAAAAOAzJpcAAAAA4AU/BpdWTC4BAAAAAD6juQQAAAAA+IxlsQAAAADgBS7oY8fkEgAAAADgMyaXAAAAAOAFBpd2TC4BAAAAAD6juQQAAAAA+IxlsQAAAADgBZdYF2vD5BIAAAAA4DOaSwAAAACAz1gWCwAAAABe8GNVrBWTSwAAAACAz5hcAgAAAIAXXNzo0orJJQAAAADAZzSXAAAAAACfsSwWAAAAALzAqlg7JpcAAAAAAJ8xuQQAAAAAL/gxurRicgkAAAAA8BnNJQAAAADAZyyLBQAAAAAvsCrWjsklAAAAAMBnNJcAAAAAAJ+xLBYAAAAAvOBiXawVk0sAAAAAgM+YXAIAAACAFxhc2jG5BAAAAAD4jOYSAAAAAOAzlsUCAAAAgBf8WBdrxeQSAAAAAOAzJpcAAAAA4AXmlnZMLgEAAAAAPqO5BAAAAAD4rEQ0l/3795fL5XJvERERat++vTZu3Jjr2DZt2ngc+/etTZs2kqRatWpp8uTJ7ufVqlVLLpdLCxcuzHXO2NhYuVwuzZ49O9fxf9+efvrpQsuhsCycP08J7a5Vs6aN1KfnzdpkyfVCRC525JI3srEjFztysSMXO3KxIxc7cvHd2fqJ87UVRSWiuZSk9u3ba+/evdq7d69WrFihgIAA3XDDDbmOe+edd9zHrV27VpL02Wefufe98847eb5G9erVNWvWLI99q1evVnJyskqXLp3r+AkTJrjPe3obOnRogbzf82XZxx9p0sREDbp7iBYuflfR0TEaPGigUlJSnC7NUeRiRy55Ixs7crEjFztysSMXO3KxIxcUphLTXAYHBysyMlKRkZFq0qSJHn74Ye3Zs0cHDhzwOK5ChQru4ypVqiRJioiIcO+rUKFCnq/Rp08frVy5Unv27HHvmzlzpvr06aOAgNzXRipTpoz7vKc3WxNalM2dM0tdunVXp85dVTcqSqPHjldISIiWvrPE6dIcRS525JI3srEjFztysSMXO3KxIxc7ckFhKjHN5ZmOHj2qN998U1FRUYqIiCiw81auXFnx8fGaM2eOJOn48eNatGiRBgwYUGCvUZScPHFCW7dsVsu4Vu59fn5+atmylTb+sMHR2pxELnbkkjeysSMXO3KxIxc7crEjFztyKTh+Lue3oqjENJcffPCBwsLCFBYWpjJlyui9997TokWL5OdXsG9xwIABmj17towxevvtt1W3bl01adLEeuzIkSPdNZ3evvrqqwKtpzClHk5VdnZ2rgY9IiJCBw8edKwup5GLHbnkjWzsyMWOXOzIxY5c7MjFjlxQ2EpMc3nNNdcoKSlJSUlJWrt2reLj45WQkKDffvtNCQkJ7uYuNjbWp9fp0KGDjh49qi+//FIzZ84869RyxIgR7ppOb5dffnmex2dlZSk9Pd1jy8rK8qleAAAAAAXL6Yv5FNUL+uT+oGAxVbp0aUVFRbm/nj59usLDwzVt2jRNnz5dGRkZkqTAwECfXicgIEC33HKLxo4dqzVr1ujdd9/N89iKFSt61PRPEhMTNX78eI99jz42VqPHjPOp5nNVvlx5+fv75/qAd0pKiipWrOhITUUBudiRS97Ixo5c7MjFjlzsyMWOXOzIBYWtxEwu/87lcsnPz08ZGRmqVq2aoqKiFBUVpZo1a/p87gEDBmjlypW66aabVL58+QKpV5JGjRqltLQ0j23EyFEFdn5vBQYFqcHFsVqzepV7X05OjtasWaXGlzR1rC6nkYsdueSNbOzIxY5c7MjFjlzsyMWOXFDYSszkMisrS8nJyZKk1NRUTZkyRUePHlXHjh0L/LUaNGiggwcPqlSpUmc97siRI+6aTitVqpTKli1rPT44OFjBwcEe+zJPFUDBPril32167JGRio1tqIaNGuvNuXOUkZGhTp27OFuYw8jFjlzyRjZ25GJHLnbkYkcuduRiRy4Fo4iuSnVciWkuly1bpipVqkh/3QIkJiZGixcvVps2bQrl9fJzFdoxY8ZozJgxHvsGDRqkV199tVBqKgztE65X6qFDmjrlRR08eEDRMQ009bXpirjAl06Qix255I1s7MjFjlzsyMWOXOzIxY5cUJhcxhjjdBHIm9OTSwAAAKCwhRSzkdet8zc6XYLe6N3Y6RJyKbGfuQQAAAAAnD80lwAAAAAAnxWzATQAAAAAOMuPC/pYMbkEAAAAAPiM5hIAAAAA4DOWxQIAAACAF1zc6NKKySUAAAAAwGdMLgEAAADAC8wt7ZhcAgAAAAB8lq/J5XvvvZfvE954442+1AMAAAAAKIby1Vx26tQpXydzuVzKzs72tSYAAAAAKLL8uKCPVb6ay5ycnMKvBAAAAABQbHFBHwAAAADwAoNLu3NqLo8dO6aVK1dq9+7dOnHihMdj9957b0HVBgAAAAAoJrxuLjds2KDrr79ex48f17Fjx1ShQgUdPHhQpUqV0kUXXURzCQAAAAAXIK9vRXL//ferY8eOSk1NVWhoqFavXq3ffvtNl112mSZNmlQ4VQIAAABAEeFyuRzfiiKvm8ukpCQ98MAD8vPzk7+/v7KyslS9enVNnDhRjzzySOFUCQAAAAAo0rxuLgMDA+Xn97+nXXTRRdq9e7ckKTw8XHv27Cn4CgEAAACgCHG5nN+KIq8/c9m0aVN99913qlevnlq3bq0xY8bo4MGDmjt3rho2bFg4VQIAAAAAijSvJ5dPPfWUqlSpIkl68sknVb58eQ0ePFgHDhzQ66+/Xhg1AgAAAACKOK8nl5dffrn7/1900UVatmxZQdcEAAAAAEWWX1Fdl+owryeXAAAAAAD8ndeTy9q1a5/10re//vqrrzUBAAAAAIoZr5vLYcOGeXx98uRJbdiwQcuWLdOIESMKsjYAAAAAKHJYFWvndXN53333Wfe//PLL+v777wuiJgAAAABAMVNgn7lMSEjQkiVLCup0AAAAAFAkuVwux7eiqMCay7ffflsVKlQoqNMBAAAAAIoRr5fFNm3a1KNTNsYoOTlZBw4c0NSpUwu6PgAAAABAMeB1c3nTTTd5NJd+fn6qVKmS2rRpo5iYmIKuDwAAAMVQdo5xuoQiyd+vaC5nhHe4n6Od183luHHjCqcSAAAAAECx5XXT7e/vr/379+fan5KSIn9//4KqCwAAAACKJKcv5lNiLuhjjH2JQ1ZWloKCggqiJgAAAABAMZPvZbEvvvii9FeXPn36dIWFhbkfy87O1pdffslnLgEAAADgApXv5vKFF16Q/ppcvvrqqx5LYIOCglSrVi29+uqrhVMlAAAAABQRXJfJLt/LYnfu3KmdO3eqdevW+uGHH9xf79y5U9u2bdMnn3yiFi1aFG61AAAAAIB8e+WVV9S4cWOVLVtWZcuWVVxcnD7++GP345mZmRoyZIgiIiIUFhamrl27at++fef0Wl5/5vLzzz9X+fLlz+nFAAAAAADnz7/+9S89/fTTWrdunb7//ntde+21uummm7R582ZJ0v3336/3339fixcv1sqVK/Xnn3+qS5cu5/RaLpPXFXry0LVrVzVv3lwjR4702D9x4kR99913Wrx48TkVArvMU05XAAAA4D3uc2nHfS7tQry+QaKzhr/3k9Ml6Pkbz/16NxUqVNCzzz6rbt26qVKlSpo/f766desmSfrpp5/UoEEDrVq1Si1btvTqvF5PLr/88ktdf/31ufYnJCToyy+/9PZ0AAAAAIDzIDs7WwsXLtSxY8cUFxendevW6eTJk2rbtq37mJiYGNWoUUOrVq3y+vxe/47g6NGj1luOBAYGKj093esCAAAAAKA4KQr3mczKylJWVpbHvuDgYAUHB+c6dtOmTYqLi1NmZqbCwsL07rvv6uKLL1ZSUpKCgoJUrlw5j+MrV66s5ORkr2vyenLZqFEjLVq0KNf+hQsX6uKLL/a6AAAAAACAdxITExUeHu6xJSYmWo+Njo5WUlKS1qxZo8GDB6tfv37asmVLgdfk9eTyscceU5cuXfTLL7/o2muvlSStWLFC8+fP19tvv13gBQIAAAAAPI0aNUrDhw/32GebWuqvW0dGRUVJki677DJ99913+r//+z/16NFDJ06c0OHDhz2ml/v27VNkZKTXNXndXHbs2FFLly7VU089pbfffluhoaG65JJL9N///lcVKlTwugAAAAAAKE6KwnWZ8loCmx85OTnKysrSZZddpsDAQK1YsUJdu3aVJG3btk27d+9WXFyc1+c9p+sydejQQR06dJAkpaena8GCBXrwwQe1bt06ZWdnn8spAQAAAAAFbNSoUUpISFCNGjV05MgRzZ8/X1988YU++eQThYeHa+DAgRo+fLgqVKigsmXLaujQoYqLi/P6SrE61+ZSf101dsaMGVqyZImqVq2qLl266OWXXz7X0wEAAABAsVAErueTb/v379ett96qvXv3Kjw8XI0bN9Ynn3yidu3aSZJeeOEF+fn5qWvXrsrKylJ8fLymTp16Tq/l1X0uk5OTNXv2bM2YMUPp6enq3r27Xn31Vf3www9czKeQcJ9LAABQHHGfSzvuc2lX3O5z+dCH25wuQRM7RDtdQi75vlpsx44dFR0drY0bN2ry5Mn6888/9dJLLxVudQAAAACAYiHfvyP4+OOPde+992rw4MGqV69e4VYFAAAAAEWUX3FaF3se5Xty+fXXX+vIkSO67LLL1KJFC02ZMkUHDx4s3OoAAAAAAMVCvpvLli1batq0adq7d68GDRqkhQsXqmrVqsrJydHy5ct15MiRwq0UAAAAAFBkeXVBn7/btm2bZsyYoblz5+rw4cNq166d3nvvvYKt8ALHBX0AAEBxxAV97Ligj11xu6DPIx/97HQJeur6+k6XkEu+J5c20dHRmjhxon7//XctWLCg4KoCAAAAABQrBfI7An9/f3Xq1EmdOnUqiNMBAAAAQJHF9XzsfJpcAgAAAAAgmksAAAAAQEEoZh+dBQAAAABncZ9Luwtqctm/f/+zfi60TZs2GjZsWJ6PHzp0SMOGDVPNmjUVFBSkqlWrasCAAdq9e3euY5OTkzV06FDVqVNHwcHBql69ujp27KgVK1YU2Ps5XxbOn6eEdteqWdNG6tPzZm3auNHpkooEcrEjl7yRjR252JGLHbnYkUtu677/Tvfdc5euu/YqXdooRp+v+MzpkooMvl9QWC6o5tIXhw4dUsuWLfXZZ5/p1Vdf1Y4dO7Rw4ULt2LFDzZo106+//uo+dteuXbrsssv03//+V88++6w2bdqkZcuW6ZprrtGQIUMcfR/eWvbxR5o0MVGD7h6ihYvfVXR0jAYPGqiUlBSnS3MUudiRS97Ixo5c7MjFjlzsyMUuMyND9evH6OFHxzhdSpHC90vBcLmc34oimst8evTRR/Xnn3/qs88+U0JCgmrUqKGrr75an3zyiQIDAz2axrvvvlsul0tr165V165dVb9+fcXGxmr48OFavXq1o+/DW3PnzFKXbt3VqXNX1Y2K0uix4xUSEqKl7yxxujRHkYsdueSNbOzIxY5c7MjFjlzsrrjqag25d5iu/Xc7p0spUvh+QWGiucyHnJwcLVy4UH369FFkZKTHY6Ghobr77rv1ySef6NChQzp06JCWLVumIUOGqHTp0rnOVa5cufNYuW9OnjihrVs2q2VcK/c+Pz8/tWzZSht/2OBobU4iFztyyRvZ2JGLHbnYkYsducAbfL+gsNFc5sOBAwd0+PBhNWjQwPp4gwYNZIzRjh07tGPHDhljFBMTc97rLGiph1OVnZ2tiIgIj/0RERE6ePCgY3U5jVzsyCVvZGNHLnbkYkcuduQCb/D9UnD8XM5vRdEF2VzOmzdPYWFh7u2rr77K1/OMMQVyTF6ysrKUnp7usWVlZZ3z+QAAAADgfLkgm8sbb7xRSUlJ7u3yyy8/6/GVKlVSuXLltHXrVuvjW7dulcvlUlRUlOrVqyeXy6WffvrJ67oSExMVHh7usT37TKLX5yko5cuVl7+/f64PeKekpKhixYqO1eU0crEjl7yRjR252JGLHbnYkQu8wfdLwfFzuRzfiqILsrksU6aMoqKi3FtoaOhZj/fz81P37t01f/58JScnezyWkZGhqVOnKj4+XhUqVFCFChUUHx+vl19+WceOHct1rsOHD+f5OqNGjVJaWprHNmLkKB/eqW8Cg4LU4OJYrVm9yr0vJydHa9asUuNLmjpWl9PIxY5c8kY2duRiRy525GJHLvAG3y8obAFOF1DUHDhwQElJSR77qlSpoqeeekorVqxQu3btNHHiRDVs2FA7d+7U6NGjdfLkSb388svu419++WVdccUVat68uSZMmKDGjRvr1KlTWr58uV555ZU8J6DBwcEKDg722Jd5qpDeaD7d0u82PfbISMXGNlTDRo315tw5ysjIUKfOXZwtzGHkYkcueSMbO3KxIxc7crEjF7vjx49pzxn3Iv/jj9+17aetKhseripVqjpam5P4fkFhorn8m/nz52v+/Pke+x5//HGNHj1aq1ev1oQJEzRo0CAlJyerQoUKSkhI0JtvvqkaNWq4j69Tp47Wr1+vJ598Ug888ID27t2rSpUq6bLLLtMrr7ziwLs6d+0TrlfqoUOaOuVFHTx4QNExDTT1temKuMCXTpCLHbnkjWzsyMWOXOzIxY5c7LZs/lF3Dujn/vr5Z5+WJHW8sZPGP/m0g5U5i++XglFEV6U6zmV8uQINCp3Tk0sAAIBzkZ3DPzFt/IvqZT4dFlLMRl6Pf7bD6RL0WNsop0vI5YL8zCUAAAAAoGAVs98RAAAAAICzGEDbMbkEAAAAAPiMySUAAAAAeMElRpc2TC4BAAAAAD6juQQAAAAA+IxlsQAAAADgBS7oY8fkEgAAAADgMyaXAAAAAOAFJpd2TC4BAAAAAD6juQQAAAAA+IxlsQAAAADgBZeLdbE2TC4BAAAAAD6juQQAAAAA+IxlsQAAAADgBa4Wa8fkEgAAAADgMyaXAAAAAOAFrudjx+QSAAAAAOAzmksAAAAAgM9YFgsAAAAAXvBjXawVk0sAAAAAgM+YXAIAAACAF7gViR2TSwAAAACAz2guAQAAAAA+Y1ksAAAAAHiB6/nYMbkEAAAAAPiM5hIAAAAA4DOWxQIAAACAF/zEulgbmksAAAAUOH/u1QBccGguAQAAAMALXNDHjs9cAgAAAAB8RnMJAAAAAPAZy2IBAAAAwAt8pNiOySUAAAAAwGdMLgEAAADAC35c0ceKySUAAAAAwGc0lwAAAAAAn7EsFgAAAAC8wKpYOyaXAAAAAACf0VwCAAAAAHzGslgAAAAA8AJXi7VjcgkAAAAA8BmTSwAAAADwAoNLOyaXAAAAAACf0VwCAAAAAHzGslgAAAAA8AITOjtyAQAAAAD4jMklAAAAAHjBxRV9rJhcAgAAAAB8RnMJAAAAAPAZy2IBAAAAwAssirVjcgkAAAAA8BmTSwAAAADwgh8X9LFicgkAAAAA8BnNJQAAAADAZzSX+EcL589TQrtr1axpI/XpebM2bdzodElFArnYkUveyMaOXOzIxY5c7MjFjlzsyMV3riKwFUVFsrns37+/OnXqdNZjsrOz9cILL6hRo0YKCQlR+fLllZCQoG+++cbjuHHjxqlJkya5nr9r1y65XC4lJSXleiwmJkbBwcFKTk7O9VibNm3kcrm0cOFCj/2TJ09WrVq1PI7Ja2vTpk2+s3Daso8/0qSJiRp09xAtXPyuoqNjNHjQQKWkpDhdmqPIxY5c8kY2duRiRy525GJHLnbkYkcuKExFsrn8J8YY9ezZUxMmTNB9992nrVu36osvvlD16tXVpk0bLV269JzP/fXXXysjI0PdunXTnDlzrMeEhIRo9OjROnnypPXxd955R3v37tXevXu1du1aSdJnn33m3vfOO++cc33n29w5s9SlW3d16txVdaOiNHrseIWEhGjpO0ucLs1R5GJHLnkjGztysSMXO3KxIxc7crEjFxSmYtlcvvXWW3r77bf1xhtv6Pbbb1ft2rV1ySWX6PXXX9eNN96o22+/XceOHTunc8+YMUO9e/fWLbfcopkzZ1qP6dWrlw4fPqxp06ZZH69QoYIiIyMVGRmpSpUqSZIiIiLc+ypUqHBOtZ1vJ0+c0NYtm9UyrpV7n5+fn1q2bKWNP2xwtDYnkYsdueSNbOzIxY5c7MjFjlzsyMWOXAqOy+X8VhQVy+Zy/vz5ql+/vjp27JjrsQceeEApKSlavny51+c9cuSIFi9erL59+6pdu3ZKS0vTV199leu4smXL6tFHH9WECRPOuYktDlIPpyo7O1sREREe+yMiInTw4EHH6nIaudiRS97Ixo5c7MjFjlzsyMWOXOzIBYWtWDaXP//8sxo0aGB97PT+n3/+2evzLly4UPXq1VNsbKz8/f3Vs2dPzZgxw3rs3XffrZCQED3//PNev05esrKylJ6e7rFlZWUV2PkBAAAA+O5s11c5X1tRVKSby3nz5iksLMy9nTlFNMac9blBQUFev97MmTPVt29f99d9+/bV4sWLdeTIkVzHBgcHa8KECZo0aVKB/aYnMTFR4eHhHtuzzyQWyLnPRfly5eXv75/rA94pKSmqWLGiY3U5jVzsyCVvZGNHLnbkYkcuduRiRy525ILCVqSbyxtvvFFJSUnu7fLLL5ck1atXT1u3brU+5/T++vXrS38tYU1LS8t13OHDhyVJ4eHhkqQtW7Zo9erVeuihhxQQEKCAgAC1bNlSx48fz3Vl2NP69u2rmjVr6oknniiQ9ztq1CilpaV5bCNGjiqQc5+LwKAgNbg4VmtWr3Lvy8nJ0Zo1q9T4kqaO1eU0crEjl7yRjR252JGLHbnYkYsdudiRCwpbgNMFnE2ZMmVUpkyZXPt79eql3r176/3338/1ucvnnntOVatWVbt27SRJ0dHR+v3337Vv3z5VrlzZfdz69esVEhKiGjVqSH9dyOfqq6/Wyy+/7HG+WbNmacaMGbrjjjty1eHn56fExER16dJFgwcP9vn9BgcHKzg42GNf5imfT+uTW/rdpsceGanY2IZq2Kix3pw7RxkZGerUuYuzhTmMXOzIJW9kY0cuduRiRy525GJHLnbkUjCK9ITOQUW6ucxLz5499dZbb6lfv3569tln9e9//1vp6el6+eWX9cEHH2jZsmUKDAyUJMXHxys6Olq9evXSE088ocjISK1fv16jR4/WfffdJ39/f508eVJz587VhAkT1LBhQ4/Xuv322/X8889r8+bNio2NzVVLhw4d1KJFC7322msezWtJ0T7heqUeOqSpU17UwYMHFB3TQFNfm66IC3zpBLnYkUveyMaOXOzIxY5c7MjFjlzsyAWFyWX+6cOLDujfv78OHz581vtVnjp1SpMnT9bs2bO1fft2nThxQhUqVNBXX32liy++2OPYP//8U4888og+//xzHThwQLVr19att96q4cOHKzAwUEuWLFH37t31559/WhvEiy++WO3bt9fzzz+vNm3aqEmTJpo8ebL78VWrVqlVq1aqWbOmdu3a5fHcXbt2qXbt2tqwYYOaNGnidRZOTy4BAACAwhZSzEZebyX96XQJ6t6kqtMl5FIkm8tzsX79erVt21YDBw7Us88+63Q5BYbmEgAAACUdzaX3imJzWWKWC1966aVasWKFSpcurV9++cXpcgAAAADgglLMfkdwdk2bNlXTplzpCgAAAEDhKZp3mXReiZlcAgAAAACcQ3MJAAAAAPBZiVoWCwAAAACFzeViYawNk0sAAAAAgM+YXAIAAACAF5jQ2ZELAAAAAMBnNJcAAAAAUEIlJiaqWbNmKlOmjC666CJ16tRJ27Zt8zgmMzNTQ4YMUUREhMLCwtS1a1ft27fP69eiuQQAAAAAL7hcLse3/Fq5cqWGDBmi1atXa/ny5Tp58qSuu+46HTt2zH3M/fffr/fff1+LFy/WypUr9eeff6pLly7e52KMMV4/C+dN5imnKwAAAAAKV0gxuxLMuxuTnS5BnRtHntPzDhw4oIsuukgrV67U1VdfrbS0NFWqVEnz589Xt27dJEk//fSTGjRooFWrVqlly5b5PjeTSwAAAADwgqsIbFlZWUpPT/fYsrKy/rH2tLQ0SVKFChUkSevWrdPJkyfVtm1b9zExMTGqUaOGVq1a5VUuNJcAAAAAUMwkJiYqPDzcY0tMTDzrc3JycjRs2DBdccUVatiwoSQpOTlZQUFBKleunMexlStXVnKydxPaYjaABgAAAACMGjVKw4cP99gXHBx81ucMGTJEP/74o77++utCqYnmEgAAAAC84MX1dApNcHDwPzaTZ7rnnnv0wQcf6Msvv9S//vUv9/7IyEidOHFChw8f9phe7tu3T5GR3n2uk2WxAAAAAFBCGWN0zz336N1339V///tf1a5d2+Pxyy67TIGBgVqxYoV737Zt27R7927FxcV59VpMLgEAAACghBoyZIjmz5+v//znPypTpoz7c5Th4eEKDQ1VeHi4Bg4cqOHDh6tChQoqW7ashg4dqri4OK+uFCtuRVL0cSsSAAAAlHTF7VYk72/a53QJ6tiocr6Oy+uemLNmzVL//v0lSZmZmXrggQe0YMECZWVlKT4+XlOnTvV6WSzNZRFHcwkAAICSjubSe/ltLs+nYvbHCAAAAADOKgoX9CmKuKAPAAAAAMBnNJcAAAAAAJ+xLBYAAAAAvOAS62JtmFwCAAAAAHzG5BIAAAAAvMAFfeyYXAIAAAAAfEZzCQAAAADwGctiAQAAAMALflzQx4rJJQAAAADAZ0wuAQAAAMALXNDHjsklAAAAAMBnNJcAAAAAAJ+xLBYAAAAAvMCyWDsmlwAAAAAAn9FcAgAAAAB8xrJYAAAAAPCCi/tcWjG5BAAAAAD4jMklAAAAAHjBj8GlFZNLAAAAAIDPaC4BAAAAAD5jWSwAAAAAeIEL+tgxuQQAAAAA+IzJJQAAAAB4wcXg0orJJQAAAADAZzSXAAAAAACfsSwWAAAAALzABX3smFwCAAAAAHxGcwkAAAAA8BnLYgEAAADAC36sirVicgkAAAAA8BmTSwAAAADwAhf0sWNyCQAAAADwGc0lAAAAAMBnLIsFAAAAAC+4WBVrxeQSAAAAAOCzEttc9u/fX506dfLYN3v2bLlcrrNuu3bt0rhx49SkSRP388aNGyeXy6X27dvnep1nn31WLpdLbdq0yXX837eYmJhCfteFY+H8eUpod62aNW2kPj1v1qaNG50uqUggFztyyRvZ2JGLHbnYkYsdudiRix25+M5VBLaiqMQ2lzY9evTQ3r173VtcXJzuuOMOj33Vq1e3PrdKlSr6/PPP9fvvv3vsnzlzpmrUqJHr+NjYWI/z7t27V19//XWhvbfCsuzjjzRpYqIG3T1ECxe/q+joGA0eNFApKSlOl+YocrEjl7yRjR252JGLHbnYkYsdudiRCwrTBdVchoaGKjIy0r0FBQWpVKlSHvv8/f2tz73ooot03XXXac6cOe593377rQ4ePKgOHTrkOj4gIMDjvJGRkapYsWKhvr/CMHfOLHXp1l2dOndV3agojR47XiEhIVr6zhKnS3MUudiRS97Ixo5c7MjFjlzsyMWOXOzIBYXpgmoufTVgwADNnj3b/fXMmTPVp08fBQUFOVpXYTl54oS2btmslnGt3Pv8/PzUsmUrbfxhg6O1OYlc7Mglb2RjRy525GJHLnbkYkcuduRScPxcLse3oojm0gs33HCD0tPT9eWXX+rYsWN66623NGDAAOuxmzZtUlhYmMd21113nfeafZF6OFXZ2dmKiIjw2B8REaGDBw86VpfTyMWOXPJGNnbkYkcuduRiRy525GJHLihsJf5WJPPmzdOgQYPcX3/88ce66qqrzulcgYGB6tu3r2bNmqVff/1V9evXV+PGja3HRkdH67333vPYV7Zs2bOePysrS1lZWR77jH+wgoODz6leAAAAADhfSnxzeeONN6pFixbur6tVq+bT+QYMGKAWLVroxx9/zHNqKUlBQUGKiory6tyJiYkaP368x75HHxur0WPGnXO9vihfrrz8/f1zfcA7JSWlWH5+tKCQix255I1s7MjFjlzsyMWOXOzIxY5cCk7RXJTqvBK/LLZMmTKKiopyb6GhoT6dLzY2VrGxsfrxxx/Vu3fvAqtTkkaNGqW0tDSPbcTIUQX6Gt4IDApSg4tjtWb1Kve+nJwcrVmzSo0vaepYXU4jFztyyRvZ2JGLHbnYkYsdudiRix25oLCV+MllYfjvf/+rkydPqly5cnkec+rUKSUnJ3vsc7lcqly5cp7PCQ7OvQQ281QBFOyDW/rdpsceGanY2IZq2Kix3pw7RxkZGerUuYuzhTmMXOzIJW9kY0cuduRiRy525GJHLnbkUkAYXVrRXJ6D0qVL/+MxmzdvVpUqVTz2BQcHKzMzsxArK3jtE65X6qFDmjrlRR08eEDRMQ009bXpirjAl06Qix255I1s7MjFjlzsyMWOXOzIxY5cUJhcxhjjdBHIm9OTSwAAAKCwhRSzkdfqXw47XYJa1s17FaVTitkfIwAAAAA4y8W6WKsSf0EfAAAAAEDhY3IJAAAAAF5wMbi0YnIJAAAAAPAZzSUAAAAAwGcsiwUAAAAAL7Aq1o7JJQAAAADAZzSXAAAAAACfsSwWAAAAALzBulgrJpcAAAAAAJ8xuQQAAAAAL7gYXVoxuQQAAAAA+IzmEgAAAADgM5bFAgAAAIAXXKyKtWJyCQAAAADwGZNLAAAAAPACg0s7JpcAAAAAAJ/RXAIAAAAAfMayWAAAAADwButirZhcAgAAAAB8xuQSAAAAALzgYnRpxeQSAAAAAOAzmksAAAAAgM9YFgsAAAAAXnCxKtaKySUAAAAAwGc0lwAAAAAAn7EsFgAAAAC8wKpYOyaXAAAAAACfMbkEAABAgSvf7B6nSyiSUr+b4nQJKAiMLq2YXAIAAAAAfEZzCQAAAADwGctiAQAAAMALLtbFWjG5BAAAAAD4jMklAAAAAHjBxeDSisklAAAAAMBnNJcAAAAAAJ+xLBYAAAAAvMCqWDsmlwAAAAAAn9FcAgAAAAB8xrJYAAAAAPAG62KtmFwCAAAAAHzG5BIAAAAAvOBidGnF5BIAAAAA4DOaSwAAAACAz1gWCwAAAABecLEq1orJJQAAAADAZ0wuAQAAAMALDC7tmFwCAAAAAHxGcwkAAAAA8BnNJQAAAAB4w1UENi98+eWX6tixo6pWrSqXy6WlS5d6PG6M0ZgxY1SlShWFhoaqbdu22r59u9ex0FwCAAAAQAl27NgxXXLJJXr55Zetj0+cOFEvvviiXn31Va1Zs0alS5dWfHy8MjMzvXodLugDAAAAACVYQkKCEhISrI8ZYzR58mSNHj1aN910kyTpjTfeUOXKlbV06VL17Nkz36/D5BIAAAAAvOAqAv/LyspSenq6x5aVleX1e9m5c6eSk5PVtm1b977w8HC1aNFCq1at8upcNJcAAAAAUMwkJiYqPDzcY0tMTPT6PMnJyZKkypUre+yvXLmy+7H8YlksAAAAAHjBVQRudDlq1CgNHz7cY19wcLBj9YjJZeHZs2ePBgwYoKpVqyooKEg1a9bUfffdp5SUFKdL89rC+fOU0O5aNWvaSH163qxNGzc6XVKRQC525JI3srEjFztysSMXO3KRqlYK18wnbtXvnz+jQ6ue13dvPaJLL64hSQoI8NMT996k7956RAe/fU6/fvqkpj9+i6pUCne6bEfw/VIyBAcHq2zZsh7buTSXkZGRkqR9+/Z57N+3b5/7sfyiuSwEv/76qy6//HJt375dCxYs0I4dO/Tqq69qxYoViouL06FDh5wuMd+WffyRJk1M1KC7h2jh4ncVHR2jwYMGFssmuSCRix255I1s7MjFjlzsyMWOXKRyZUL139nDdfJUjjrdM1VNuz6ph59/R6npxyVJpUKC1KRBdT097WPF9XpGPR+Ypvo1K2vx5EFOl37e8f2Cv6tdu7YiIyO1YsUK97709HStWbNGcXFxXp3LZYwxhVDjBS0hIUE//vijfv75Z4WGhrr3Jycnq27durr11lv1yiuv5OtcmacKsdB86NPzZsU2bKRHRo+RJOXk5Oi6f7dWr963aOAddzpbnIPIxY5c8kY2duRiRy525GJXVHMp3+ye8/Zaj997o+IuqaO2Ayfn+zmXXVxDX897SPUTHtOe5NRCre9Mqd9NOW+vZVNUv19CitmH9X5OPu50CaofWSrfxx49elQ7duyQJDVt2lTPP/+8rrnmGlWoUEE1atTQM888o6efflpz5sxR7dq19dhjj2njxo3asmWLQkJC8v06TC4L2KFDh/TJJ5/o7rvv9mgs9dfIuU+fPlq0aJGKQ09/8sQJbd2yWS3jWrn3+fn5qWXLVtr4wwZHa3MSudiRS97Ixo5c7MjFjlzsyOV/OrRupPVbdmvexAH6bUWiVi0Yqds6tzrrc8qWCVVOTo4OH8k4b3U6je+XC9f333+vpk2bqmnTppKk4cOHq2nTphoz5n+/ZHjooYc0dOhQ3XnnnWrWrJmOHj2qZcuWedVYiuay4G3fvl3GGDVo0MD6eIMGDZSamqoDBw6c99q8lXo4VdnZ2YqIiPDYHxERoYMHDzpWl9PIxY5c8kY2duRiRy525GJHLv9Tu1pF3XHzVdqx+4BuvPtlTVv8tZ57qJv6dGxhPT44KEBP3HuT3lq2TkeOeXeT+OKM75cC5CoCmxfatGkjY0yubfbs2f97Oy6XJkyYoOTkZGVmZuqzzz5T/fr1vY6lmA2gi49zmUxmZWXlujeN8Q92/KpPAAAARZmfn0vrt+zW2CnvS5J+2Pa7YqOq6I5uV2re+2s8jg0I8NObEwfK5XLp3qcWOVQxUDIxuSxgUVFRcrlc2rp1q/XxrVu3qnz58qpUqVKux2z3qnn2Ge/vVVNQypcrL39//1wf8E5JSVHFihUdq8tp5GJHLnkjGztysSMXO3KxI5f/ST6Yrq2/et6P76edyaoeWd5jX0CAn+Y9M1A1qpTXDYOnXFBTS/H9gvOA5rKARUREqF27dpo6daoyMjzX8CcnJ2vevHnq0aOHXJab44waNUppaWke24iRo85j9Z4Cg4LU4OJYrVm9yr0vJydHa9asUuNLmjpWl9PIxY5c8kY2duRiRy525GJHLv+zKulX1a95kce+ejUu0u69//8K/acby7o1KqnDXVN0KO2YA5U6i++XguMqAv8rilgWWwimTJmiVq1aKT4+Xk888YRq166tzZs3a8SIEapWrZqefPJJ6/OCg3MvgXX6arG39LtNjz0yUrGxDdWwUWO9OXeOMjIy1KlzF2cLcxi52JFL3sjGjlzsyMWOXOzIRXrpzf/q89kPaMSA67Rk+Xo1i62lAV2v0D2PL5D+aiznP3u7msZUV5f7XpW/n0uVI8pIkg6lHdfJU9kOv4Pzh+8XFCaay0JQr149ff/99xo7dqy6d++uQ4cOKTIyUp06ddLYsWNVoUIFp0vMt/YJ1yv10CFNnfKiDh48oOiYBpr62nRFXOBLJ8jFjlzyRjZ25GJHLnbkYkcu0rotu9XjgWmaMPRGPXJngnb9kaIRzy7Rwo+/lyRVrVROHds0liStXeS5Kuy62/9PX63b7kjdTuD7pWBYFiGC+1wWfU5PLgEAAM7F+bzPZXHi9H0ui6ridp/LHfudv4VN1EWh+Tjq/OIzlwAAAAAAnxWz3xEAAAAAgLNYFWvH5BIAAAAA4DOaSwAAAACAz1gWCwAAAADeYF2sFZNLAAAAAIDPmFwCAAAAgBdcjC6tmFwCAAAAAHxGcwkAAAAA8BnLYgEAAADACy5WxVoxuQQAAAAA+IzJJQAAAAB4gcGlHZNLAAAAAIDPaC4BAAAAAD5jWSwAAAAAeIN1sVZMLgEAAAAAPqO5BAAAAAD4jGWxAAAAAOAFF+tirZhcAgAAAAB8xuQSAAAAALzgYnBpxeQSAAAAAOAzmksAAAAAgM9YFgsAAAAAXmBVrB2TSwAAAACAz5hcAgAAAIAXuKCPHZNLAAAAAIDPaC4BAAAAAD5jWSwAAAAAeIV1sTYuY4xxugjkLfOU0xUAAAAAhSukmI28fk894XQJ+lf5IKdLyIVlsQAAAAAAnxWz3xEAAAAAgLO4Wqwdk0sAAAAAgM+YXAIAAACAFxhc2jG5BAAAAAD4jOYSAAAAAOAzlsUCAAAAgBe4oI8dk0sAAAAAgM+YXAIAAACAF1xc0seKySUAAAAAwGc0lwAAAAAAn7EsFgAAAAC8wapYKyaXAAAAAACf0VwCAAAAAHzGslgAAAAA8AKrYu2YXAIAAAAAfMbkEgAAAAC84GJ0acXkEgAAAADgM5pLAAAAAIDPWBYLAAAAAF5wcUkfKyaXAAAAAACfMbkEAAAAAG8wuLRicgkAAAAA8BnNJQAAAADAZyyLBQAAAAAvsCrWjsklAAAAAMBnNJeFoGPHjmrfvr31sa+++koul0sbN24873Wdq4Xz5ymh3bVq1rSR+vS8WZuKUe2FiVzsyCVvZGNHLnbkYkcuduRiRy525OI7l8v5rSiiuSwEAwcO1PLly/X777/nemzWrFm6/PLL1bhxY0dq89ayjz/SpImJGnT3EC1c/K6io2M0eNBApaSkOF2ao8jFjlzyRjZ25GJHLnbkYkcuduRiRy4oTDSXheCGG25QpUqVNHv2bI/9R48e1eLFizVw4EDHavPW3Dmz1KVbd3Xq3FV1o6I0eux4hYSEaOk7S5wuzVHkYkcueSMbO3KxIxc7crEjFztysSMXFCaay0IQEBCgW2+9VbNnz5Yxxr1/8eLFys7OVq9evRytL79OnjihrVs2q2VcK/c+Pz8/tWzZSht/2OBobU4iFztyyRvZ2JGLHbnYkYsdudiRix25FBxXEfhfUURzWUgGDBigX375RStXrnTvmzVrlrp27arw8HBHa8uv1MOpys7OVkREhMf+iIgIHTx40LG6nEYuduSSN7KxIxc7crEjFztysSMXO3JBYaO5LCQxMTFq1aqVZs6cKUnasWOHvvrqq7Muic3KylJ6errHlpWVdR6rBgAAAIBzQ3NZiAYOHKglS5boyJEjmjVrlurWravWrVvneXxiYqLCw8M9tmefSTyvNZ+pfLny8vf3z/UB75SUFFWsWNGxupxGLnbkkjeysSMXO3KxIxc7crEjFztyKThOXymWq8VegLp37y4/Pz/Nnz9fb7zxhgYMGCDXWb4TRo0apbS0NI9txMhR57XmMwUGBanBxbFas3qVe19OTo7WrFmlxpc0dawup5GLHbnkjWzsyMWOXOzIxY5c7MjFjlxQ2AKcLqAkCwsLU48ePTRq1Cilp6erf//+Zz0+ODhYwcHBHvsyTxVykf/gln636bFHRio2tqEaNmqsN+fOUUZGhjp17uJsYQ4jFztyyRvZ2JGLHbnYkYsdudiRix25oDDRXBaygQMHasaMGbr++utVtWpVp8vxWvuE65V66JCmTnlRBw8eUHRMA019bboiLvClE+RiRy55Ixs7crEjFztysSMXO3KxIxcUJpc5814ZKHKcnlwCAAAAhS2kmI28Uo9nO12Cypfyd7qEXIrZHyMAAAAAOKuoXlDHaVzQBwAAAADgMyaXAAAAAOAFlxhd2jC5BAAAAAD4jOYSAAAAAOAzlsUCAAAAgBe4oI8dk0sAAAAAgM9oLgEAAAAAPmNZLAAAAAB4gVWxdkwuAQAAAAA+Y3IJAAAAAN5gdGnF5BIAAAAA4DOaSwAAAACAz1gWCwAAAABecLEu1orJJQAAAADAZ0wuAQAAAMALLgaXVkwuAQAAAAA+o7kEAAAAAPiMZbEAAAAA4AVWxdoxuQQAAAAA+IzmEgAAAADgM5bFAgAAAIA3WBdrxeQSAAAAAOAzJpcAAAAA4AUXo0srJpcAAAAAUMK9/PLLqlWrlkJCQtSiRQutXbu2wF+D5hIAAAAASrBFixZp+PDhGjt2rNavX69LLrlE8fHx2r9/f4G+jssYYwr0jChQmaecrgAAAAAoXCHF7MN6ReHf6N5k1qJFCzVr1kxTpkyRJOXk5Kh69eoaOnSoHn744QKricklAAAAABQzWVlZSk9P99iysrJyHXfixAmtW7dObdu2de/z8/NT27ZttWrVqoItygD5kJmZacaOHWsyMzOdLqVIIRc7crEjFztysSMXO3KxI5e8kY0duRR/Y8eONZI8trFjx+Y67o8//jCSzLfffuuxf8SIEaZ58+YFWhPLYpEv6enpCg8PV1pamsqWLet0OUUGudiRix252JGLHbnYkYsdueSNbOzIpfjLysrKNakMDg5WcHCwx74///xT1apV07fffqu4uDj3/oceekgrV67UmjVrCqymYra6GQAAAABgayRtKlasKH9/f+3bt89j/759+xQZGVmgNfGZSwAAAAAooYKCgnTZZZdpxYoV7n05OTlasWKFxySzIDC5BAAAAIASbPjw4erXr58uv/xyNW/eXJMnT9axY8d02223Fejr0FwiX4KDgzV27Nh8jd4vJORiRy525GJHLnbkYkcuduSSN7KxI5cLS48ePXTgwAGNGTNGycnJatKkiZYtW6bKlSsX6OtwQR8AAAAAgM/4zCUAAAAAwGc0lwAAAAAAn9FcAgAAAAB8RnMJAAAAAPAZzSUAALgg5eTkOF0CChnXrcwtJSWF730UGppL4DziLzk7cvHe0qVL9csvvzhdBooA/vvx3vTp07Vnzx75+V2Y/wy6EL5nMjIylJWVpT179igzM9PpcoqMw4cPKzo6WvPnz3e6FJRQF+ZPVRS63377TRs2bHC6jCIhIyNDx48fV3p6ulwul9PlFDm7du3Syy+/rPHjx2vv3r1Ol1Ms3HPPPRo4cKBKlSrldCnn3c6dO/XFF184XYbjsrKylJqaKkn8XPHSfffdp0cffVTZ2dlOl3Le/fTTT1qzZk2J/57ZunWr+vbtq8svv1x169ZVXFycHn74YafLKhJKlSqlq666Su+9957S09OdLgclEM0lCtyGDRvUtGlTbdu2zelSHPfzzz/rnnvu0e2336433njjgvhtsTc2bdqkdu3aaf369Tp48KAiIiKcLqnIGz58uN566y19+umnqlKlitPlnFdJSUmKjo7Wnj17nC7FUT///LNuv/12JSQkaOrUqU6XU6wMHz5cb7zxhpYvX65atWo5Xc559cMPP+iSSy7RN99843QphWrTpk2Ki4tTlSpVNGzYML311luqWbOmJk+erI4dO+rkyZNOl+iooKAg/fvf/9Z///tfHTx4UGJ5OAqaAQpQUlKSKV26tHnooYecLsVxGzduNJUrVzbDhw83ixcvNidPnnS6pCJl27ZtpmLFimbUqFFkk0/jx483LpfLfP3118YYY06cOOF0SedNUlKSCQsLMyNHjnS6FEdt3LjRVKlSxTz44IPmww8/NMeOHXO6pGLjiSeeMC6Xy3zzzTdOl3LeJSUlmdDQUPPII484XUqh2r9/v2natKl5+OGHc+2fMmWKKV26tOnRo4dj9TktJyfH/f+bNm1qevbs6Wg9KJloLlFgfvjhB+tfXmvWrDH79+93rC4n/Pbbb6ZOnTrmwQcf9Nh/5g/2C1lWVpa5/fbbzS233GKOHz/u3k8+eRs2bJjx9/c3sbGx5uabbzaHDh0yxhhz6tQpp0srdBs3bjShoaFm9OjRHvu/+OILc+DAAcfqOt92795t6tSpY4YPH+6xn/9u/tmwYcNMcHCwCQkJMf379zeHDx92uqTzJikpyZQqVSpXw/X++++bHTt2OFZXYVi/fr1p2LCh2bRpk/tnY3Z2tjHGmMOHD5snnnjClCpVyrz77rsOV3r+ZGZmenx9+pe5EydONJdddpn7e4CfIygoLItFgfj111/VunVr9erVS08++aR7/4QJE9S7d29lZGQ4Wt/59umnn6pGjRq69957PZbC2j7nciEulQ0MDNTq1atVv359hYaGuvefzuf0Eh0uwvA/gwYN0rx587R27VpNmDBBe/bsUb9+/ZSWliZ/f/8S/dmxnTt3Ki4uTt26ddPjjz/u3v/EE0+oQ4cOOnz4sKP1nU8ff/yxIiMjNWLECI/9/Fw5u/vvv18zZ87Upk2btGbNGi1dulR33XWXjhw54nRphe6PP/5QmzZt1LlzZyUmJrq/L5588kndc889Je5n7A8//KAdO3aoYcOG8vf3lzHGfdGm8PBw9e7dW4GBgdqxY4fTpZ4XO3fuVM+ePTVr1iz3v8MCAgIkSb169dKvv/6quXPnSnx2GwWI5hI+y87O1u7duxUcHKzSpUvrp59+kiQ9/fTTeumll/TSSy+pRo0aTpd5Xq1cuVJZWVmqXr16rh/Yp/9yP3bsmFJTUy+4H+jZ2dn6888/tWfPHkVHR0uSTp065XHM6X8MTJkyxX3RkgvVTz/9pPfee08ffvihLr30Ut1www0aMmSIDh48qFtuuaXEN5hpaWkKCgqSJK1bt06SNHHiRL344ot6++23FRUVles5JbWx+vrrrxUQEKDIyMhcj51+zydOnJD4h6Lb+vXr9cEHH+irr75SvXr11LhxY3344YdatmyZ7rzzzhLfYG7fvl2XXHKJdu7cqaSkJLlcLiUmJuqFF17Qa6+9ptjYWKdLLFCnfx4sWbJEsvx3ULt2bdWpU0d//PGHI/Wdb5mZmTp16pTuvPNOtW/fXo888oiOHDmirKws/etf/9JDDz2kJUuWcI0MFCiaS/hkw4YNatOmjdq0aaMnn3xS33zzjV5//XXdf//9eu655zRv3jwlJCR4PGfnzp2O1Xs+5OTkKDAw0D2R+/vFA07/ZTdp0iT3X4AXgtP/6PX391eVKlV08cUX69VXX9WhQ4cUEBCQqyFYv369lixZckFNpv7uo48+0qlTp/T777+rWbNmOnnypIKCgtSzZ88S32AeP35cWVlZatKkid5//3198803mjJlioYOHapnnnlGCxYsUPv27T2es3HjRqkEN1ZhYWFKSUnR8ePHcz12+j3379+fi/z8Zd68eapXr562b9+uxo0bu3/GtGrVqsQ3mKffU5s2bfToo48qIiJCd999t4YMGaLJkydr3rx5io+P93jOr7/+6lC1BadWrVoqW7as3njjDf3222/u/adXw6Smpio0NFSXXXaZg1WePw0aNND777+vdevWKSYmRm+99ZYaNmyo0aNH68cff1S7du2UlpbmnuRyYR8UCKfX5aL4Ov05jjM///P666+b2NhYExoaaqZNm2bM39bxjx071lx55ZXmyJEjJW59/5nv55VXXjEul8t88cUXxvz1mY8zH09JSTE9evQw//nPfxyp9XzbsWOHGTp0qPnoo4/c+8aMGWPKli1rnnzySZOamprrOWPGjDEJCQkmLS3tPFfrvJycHLNlyxZz0UUXmdtuu81s3LjR/djpzxGdOnXKzJ0718TFxZmbbrrJ/RmykvDf1ZYtW8wNN9xgli5dajIyMowxxnz11Vembt26xuVymSlTpriPPf1+R48ebVq1amVSUlIcq7uwnP7M2FNPPWVCQkLMf/7zn1yfJzPGmPT0dHPrrbdeMD9XzuaXX34xDRo0MM2bN3d/5v/vn0/+5ptvTLly5Uzv3r1Nenq6Q5UWvK1bt5omTZqY6dOnu/d98sknpmPHjsbf39/938+ZeYwcOdKULl3apKenF/ufIUuWLDFBQUHmlltuMT/++KPHY6NHjza1atUyu3btcqw+p2RmZprU1FTz4IMPmiuuuMIEBgaasWPHmooVK5qmTZuaI0eOOF0iSgiaS5yTLVu2mNKlS7sv3nPm1T7nzp1rGjdubAYPHmw2b97s3v/YY4+ZgIAAs27dOkdqLiwZGRkmMzPT7Ny505i//rG3b98+06xZM1OpUiWzatWqXM8ZM2aMadKkidmzZ48DFZ9fGzduNLVq1TJ9+/Y1r7zyisdj1113nSlVqpR54IEHzG+//WbMX1eRve+++0xERITZtGmTQ1UXDQsWLDANGzY0t99+u/nhhx/c+89sMN98803TqlUr06pVK4+LIxVXWVlZpmXLlsblcpnrr7/efPTRR+4LUqxdu9bUrVvX9OrVy6xdu9b9nNM/W77//nsHKy9Yp3+uJCcnu3/BkpOTY5o0aWLq1atnPv/8c3fjfboZGDNmjGncuLHZvXu3o7UXBTk5OebDDz80bdq0MXFxcXk2mN9++60pX768iY+Pd+dZnGVlZZlbbrnFuFwu07x5c4+fuacbzObNm3v8PfzYY4+ZsLAws2bNGoeqLlinTp0yr776qgkICDDR0dFmwIAB5tFHHzW9e/c25cuXN+vXr3e6RMcdOHDAzJo1y7Ru3dqUKlXKlC9f/oK78CIKD80lvPbDDz+YihUrmoiICPPHH3+495/ZYE6bNs00bdrU3Hnnnea3335z/8a9JP3jz/zVZPfq1cs0bNjQVKpUybRq1cpMmjTJnDhxwnzxxRemQYMGpmzZsuall14yX3/9tVmyZIkZOHCgCQ8PNxs2bHC6/EK3bds2U7lyZfPwww+bo0ePWo/p0aOHqVSpkilbtqypV6+eadq0qbn44osviHzycuY/gBcuXGhiYmLO2mC+/vrrZvz48Y7UWhjefPNNU79+fVO9enVz6aWXmmXLlrkbzK+++srUqVPHdO/e3WzZssWMGzfOhISElKhfWm3ZssV069bNXHLJJSY0NNQ0atTIPP7448b8dTXM2NhYU7VqVfPYY4+ZTZs2mQULFpjBgwebsmXLmqSkJKfLLzJON5hXXnmladmyZZ4N5ueff24GDRrkUJUFb9KkSaZcuXJmyJAhplWrVua1115zP3a6wbz88svNtm3bzKRJk0rk383GGLN69WrTpUsXExsba6644gpz9913m61btzpdlqP+PpXet2+fWbNmjfnll18cqwklD80lvLJhwwYTGhpq7rzzThMZGWni4+PNtm3b3I+f+Zf2tGnTTIsWLUx0dLQJDg4ucX95bdy40ZQrV87cddddZvLkyWb+/PnmiiuuMOXLlzddunQxWVlZZu3ataZHjx7G39/fhIaGmvr165v4+PgLYiKXnZ1thg0bZvr37+/xfZGSkmK2bdtm3n//fZOVlWXMX7eUmDRpknnkkUfM0qVLPX5pcSGZN2+eSUpKck/Bz9wfHR1tBg4caG0wz1waWZyXtJ1+H9u2bTP9+/c3n3zyiWnXrp1p0KBBrgYzOjraVK1a1ZQuXbpE/Ww5/XNl8ODBZsaMGWbmzJmmc+fOxuVymVtuucXs37/f/PHHH6Z9+/YmIiLCuFwuU6dOHXP99ddfED9Xzua9994zH3zwgTl+/Lj7l53Z2dnmk08+MS1btjQtW7Y0+/btM+Yst/Apzv/9nH7PJ0+eNFdffbUZMGCAGTRokLn00kvdH1MxfzWYnTt3NqGhoSVu4v93p06dcv+ZnvlzEkDhoblEvm3bts2EhISYESNGGPPXZ1oqVKhg4uPjzc8//+w+7sy/tF955RXTtGlTj8+MlQT79u0zjRo1ynXfsBMnTpiHH37YVKpUyQwcOND9l/2WLVvM6tWrze7du0vUZ3vO5tSpU+baa681gwcPdu/7z3/+Y/r162fCw8ONn5+fufTSSy/IG5rbTJ8+3bhcLlOpUiVTv359c9ddd5lXXnnFvSRy+fLlJjo62tx1110eU93i/I/h0zIzM82JEyc89vXu3dvcdNNNxhhjrr32WtOoUSOPBvPLL780l156qUezXdzt27fPNGnSxIwcOdJj//79+83LL79sQkJCzF133eXev3PnTrN27Vpz8ODBC+bnSl6mTZtmXC6Xcblcpm7duqZ///5m2rRp7uX2K1asMPHx8aZ58+bue6OeudqmODtzVUhOTo45efKkeeqpp8yIESPMTz/9ZAYOHGiaNGni0WC+//77plevXiX+FxJn/nwsCT8rgeKA5hL5Nm/ePPeFAE7/pfzLL7+YiIiIszaYJfGCLKtXrzbNmjUzP//8s/u9nv7H8fHjx80dd9xhLrroIvPtt986XOn5d+Y/2B5++GETFxdnFi5caB599FFTo0YNM2DAAPPWW2+Z33//3dStW9f07t3b4/kX6j8ANm/ebBo2bGgqVqxoXnjhBRMfH29iYmLcKwTeffddc//995umTZuawYMHl5jPDf3444+mffv25tFHH/VYsnbgwAHTsmVL8/XXX5sTJ06YuLg407hxY/Ppp5+6G8y/3xy8uFuzZo2JjY01W7ZsMTk5OR7/LRw5csQkJiYal8tlli5d6midRdHy5ctN48aNTbt27czNN99sHnjgAVOxYkVTr149c+2115rnnnvOjBs3zrRs2dJce+215uDBg06XXCB++uknc9lll5lhw4aZX3/91f337ffff2/KlCljvvjiC/Pnn3+a22+/3Vx66aVmxowZ7uceO3bMwcoBlFQ0l/hHf79IyOl/8Py9wWzfvn2eDWZJM336dBMaGuq+Qudpp5fd7N+/35QvX95MnDjRoQqdsWvXLtOmTRuzY8cOY/6aLnXu3NlUr17d1KhRw8yfP9/jIkYjR440rVq1KhEX0igIP/30k6levbrp3bu3+eOPP0xmZqaZM2eOGT58uKlZs6Zp0aKFezpz5hSiuDrz4j2NGzc24eHhZsKECWbhwoXG/DW9PL064MSJE6Z169amevXqZsWKFcaUwF9EvPbaayY8PNz99d+X8W3dutWUK1fOvPTSSw5UV/QtW7bMXHHFFeaWW24xGzZsMOnp6earr74yffr0MfHx8cbf39+ULVvWuFwu88wzzzhdrs+OHz9u+vTpY1wul/H39zf9+vUz7du3d/9Sc9KkSeb222835q9rJdx1112mdu3a5o033nC4cgAlWYDTt0JB0bZ582bdeeeduu6663TXXXepQoUKCgwMVE5OjgICAnTy5EnVqVNHa9euVfPmzXX//fdr0qRJiomJkb+/v9PlF5ry5cvLGKMtW7YoLi5OOTk58vPzk5/f/24dGx4erv/X3p0HRFXu/wN/HxDGAfmGigsgCiibiKwukLggIaUJKoiZl7Qw1FxQS3PBQtxwyYW6aplLSV7L7dqiCJokimIKiqCALCKKipgapCwzn98fP+dcRiAXgoGZz+uf4pwzM585zpk57/M853k6dOiAhw8fqrrURiWVSpGTk4OgoCDs27cPnp6ecHBwwOPHjyGVSvHKK6+I2xIRbty4AQcHB7RooblfRYrPDhHBxsYGsbGxGDx4MIKDg7Fv3z4EBwcDAGbNmoXS0lJs3boVEokEISEhqi693nR1dREdHY133nkHLi4ueOONN/Dw4UMsXLgQx44dg5GREVatWgU/Pz/07dsXcXFxGD58OMzNzQE1nM/Szs4OZWVl2L9/P0aMGCF+nyjY2trCxMQE+fn5KquxKZLJZNDW1saQIUMgk8mwePFiREZG4qOPPkK/fv3Qr18/PH78GOfPn0dqaiqKioowZ84cVZddb1KpFGPHjoWuri5SUlJgY2MDGxsbBAYGwtfXF/n5+fjrr79w+/Zt9OzZE5MnT4ZEIkG/fv1UXTpjTJ2pOt2ypm3JkiVkbm5Otra2NHLkSAoMDKTMzEylezwULZi5ubkkCAIFBATUuH9K3fz555/UqVMn8Z4wemouy7t371K/fv1o165dRGrYwvK06i2Pt27dop49e1LPnj2VBqapvg8ePXpE8+fPp44dO2rs6H1xcXE1poxQtPZnZGRQp06dyNvbWxyApDbNdYCKvLw82rNnjzivWlJSknif3G+//Ua3bt2i9957j/z9/UkQBEpKSlL7Y4ietFwbGxvTyJEjlQZKk8lkJJPJxK7Cu3fvVmmdTcGhQ4fo/v37Ys+a6t3xDx06RH369KHAwEBxruHaNNfjJzc3lzZs2CD+ffjwYQoKCiJnZ2cqKCigK1eu0ObNm8nS0pIEQaDY2FhxW8Ugaowx1lA4XLK/FR8fT2PHjqXc3FxKTEykwMBAsrKyopCQkFon6s7Ly6MrV66opNbGojghUcyjFRQUVGOajQULFpCFhYVGzDd38eJFsre3F7sqEhEVFRVRz549ycnJiXJzc5W237FjBwUHB5Opqana3Df4ojZv3kyCIFDHjh1p/fr1dOjQoRrbZGRkkKmpKfn6+qrd/GOhoaFkYmJCu3btEo+dpKQk6tatG/n5+Ynd68vKytRm7r26PB2at27dSoIg0Pjx42scH+Hh4WRhYSEOUqOpNm3aRIIg0AcffEBjxoyhjIyMGqHp559/pj59+lBQUBAlJiaqrNZ/WlVVFUVERFC7du1o9erV4vK4uDgaPnw4ubi4iNPyFBcX09mzZ4k04AInY6zp4HDJnsnDw4MmTpwo/n3y5Enq0KEDCYJAQUFBtHbtWnrw4IFa/njl5eXVCEcKxcXFtHTpUjIwMCB7e3sKCwuj8PBwGjduHBkaGmpMcFK0LhkbGz8zYKalpdGMGTNo0qRJSi0zmubIkSMUGhpK0dHRNGXKFOratSu9/fbb9MsvvygNUpORkUEmJibk4uIitvI1ZxcvXqTPPvuMiIhGjRpFDg4OFBMTIwbM06dPU7du3WjEiBFKoVLdvlsKCwuVLig8PXjPunXrSBAEsrOzoxkzZtCCBQsoODiYJ4B/4siRI9S+fXuKjo6mmTNnkqmpKU2ZMqXGvYQ//vgj9e3blwYPHqw0HkBzdf78edqxYwfl5ubS/PnzycbGRum+/vj4ePL39ydnZ2cxUD/92WKMsYbG4ZLVSdG1NSEhgby8vMQWyQkTJpC1tTXt3buXQkNDydTUlNzc3NRu5EaZTEbe3t5kbGwsDlDztPv371NCQgL5+PiQg4MDubm50aRJkygjI6PR61WVgwcPkq+vLw0ZMoSkUikdOXJEXFc9YCpaW+7du6fxoxTm5uaSlZUV7dmzh+hJ6A4JCSEPDw/y9PSkI0eOiIH8woULNGPGDBVXXH8pKSkkkUho0aJF4jI/P786A+bo0aPVqsVJoby8nPz9/cnd3Z1+/PFHcfnTAeDQoUM0ZswYsrS0pD59+tCkSZM0tgt5dXK5nEpLS2nSpEm0bds2IiLas2cPffnll6Srq0ujR4+m1atXi79fR44coU8++UTFVddfamoqCYIgTgWWn59Pc+fOrTNg9u7dWy2PH8ZY08fhkj1TYWEhubm50ZYtW2jy5MlkbGxMycnJRE+mAvi71r3m7vbt29S3b1+yt7en7OxspXVPnww+fvyYHj16pNaj5NYmKyuLrK2taevWrRQeHk5SqZTi4uLE9UVFReTq6kpmZmYa351Pce8cPRlxuFevXmJgyMjIID09PbKzs6OePXuSi4sLzZs3T+nxzbUFIi0tjfT09Gj+/PlET90f5+/vTw4ODrRz504xYCYnJ1Pbtm0pODhYLUcSPnv2LPn4+JCvr6/S7QXV79t++m9N+155lvDwcOrevbv4WcrPzyepVEqenp7k5uZGZmZmNH/+fKULWc31+Ll06RLp6elRZGSk0vK6AubRo0fJy8uLBgwYQI8ePWq275sx1jxxuGRETwLk1q1bKSAggIKCgmj+/PlUUlIintDs3LlT7Pqo7pMuP624uJjc3NxqDZj0ZDj4tWvX0s2bN1VSX2OrrYV6w4YN1Lt3b8rKyqL333+f9PT0lFowb968SZ6enpSTk9PI1TYNW7duFS/IVFVVkVwup/T0dBo0aBBdunSJbt++Te3ataN3332X6MkULgsWLKBx48apuPL6u3TpErVr144GDhyotLz6oF+1Bcxz587V2WOgOVOc6KemppKXl1eNgKlYX15eTp9//jnFx8crLddEW7ZsqfWz0Lt3b9q5cyfl5+dTu3btKCQkhP744w8qLi6md999l2bOnKmSev9J6enp1KZNG3JzcxOPmeoXZ+oKmMePH1ea9okxxhoLh0tGaWlp5OTkRN7e3uTt7U1eXl6kp6dHLi4udOzYMaqsrKSbN29Sv379aMmSJUQaeBW9esCsfu9OeXk5TZs2jQRB0Ih7CC9evEjm5uYUFRWl1KXvypUrNHjwYDFAjR8/nvT09JRaMDXtM6OQl5dH/fv3JycnJ0pJSVFaFxoaSmZmZtS2bVsaP358nfdVNtdgkZqaSlKplHr37k0tW7akTZs2Ka1/ugXT2dmZvv76a7XvNq3490xJSSEvLy8aMmQIHThwQFz/6NEjmjx5Muno6KhlwH4RpaWlZGZmRj169BBHn5bL5VRZWUkLFiwgHx8fat26NQUHB9cYWE2huR8/9vb25O7uThEREVRSUkL01Ei3ioBpb29Pn376qQorZowxDpcaLzU1lQwMDGju3Llil8Xy8nJKTk4mW1tbsrW1pdTUVCIimjVrFnXu3Fktu6lVV/1EpPrJb0lJCbm4uIgtmFVVVTR16lTS09PTiEE2ZDKZOGH3gAEDyN7engICAig2NpZkMhnNmDGDBg8eTPRkv4WGhpIgCPTrr7+qunSVO3LkCPn7+5Obm5vSZ6WwsJBsbGxo5MiRdR5XzfXE+OLFiySRSMSusEuXLiUtLa2/DZheXl706quv0oMHDxq93oZS279r9fd88eLFGi2YM2bMIH19ffr9998btdamqqioiFxcXGqMPn358mUyNDQkLy8vpe2rB6/mfPwIgkCLFy8mIqLZs2eTi4sLRURE0L1794hqCZhTp06lXr16UUlJSbN934yx5o/DpQZLS0ujVq1aUXh4OFG1H2HFf69cuULm5ubk4+NDREQPHjwgY2Nj8cdOndQ2op7iBPDatWu0c+dOomotmA4ODjR27FjS19cXh33XBLdu3SJfX1/q3LkzxcfH01tvvUVDhw4lJycn+uyzz8jS0pJOnjxJ9GQaienTp2v0ICTVQ0R8fDy9+eabSgHzzz//pBEjRpC/v7+4nTqcFMrlcgoNDa3RivI8AVOduvIVFhZSYGAgHTt2TFym6NpYUFBA+/fvJ6rWRfaNN96gwYMHk1Qq1ajvlafVdgwUFRWRo6MjOTk5KXWvj4iIoGHDhtGtW7caucqGU1lZSVFRURQREaG0fPbs2eTq6lpnwCwoKPjbeXEZY6wxcLjUUDKZjMaMGUOCINR5r6BcLqcvvviCJBIJnT9/nv766y+aNm2a2nXTyszMpKlTp9KIESPEecMUP9j5+flkYmJC06dPF094iouLycnJiQRBqNHNURMUFxeTs7MzeXp6UkZGBt25c4fCw8PJzc2NBEGg3377TdUlqlz1Y6p6d2BFwOzVq5fYIyAlJYVatmxJ3377rUpqbSh1Tdb+PAFTXeTk5JC7uzsNHTqUTpw4IS7Pzc2lV155hT788EPxu+bChQvk6upKRkZGGvm9Up3i+Hk6ZCoCprOzsxii9u/fT+3bt6fjx4+rpNaG8vDhQ/H/qx8btQVMTb3lgDHWNHG41GC3b9+mPn361LiPkKr9qF+6dIkEQRCvvFe/SqoOUlNTqV27duTv709jxowhHR0dWrVqFdGT/dOlSxeaOHFijZOc4uJiKigoUFHVqqcImD169BBbEfLz88UpWNSh9e1lbdy4kby9ven06dPisuonf3FxcfT666+Tv78/FRUVUWlpKXl4eNCKFStUVPE/r/r7rS00KgLml19+2ciVNb6srCxxqh7FZ6Jjx44UEhJSa28RTR9R+YcffiALCwtxXz39XXLz5k2ys7Oj/v37i8t69+6tFoP3VO9CrXjfiuOntoC5ZMkS8R5MxhhrKjhcarhnjYS6Z88esre3pzt37hCpWWi4cOECSaVS8Z4wmUxGU6dOpbCwMCovL6ecnBxau3YtkZq97xf1rHtQbW1t1a41uz5OnTpF5ubmNHr0aDpz5oy4vHrg+vbbb8nS0lLsMnz27FmV1PpPqq1rueI9FxQUiF3LFVasWEGCIIhzFaozRcD09fWlr776ig4dOlTjvkBN/o6pLjY2VmzdVxw/T++b5ORk6tChg9itWB0GU/u7LtTXr1+ngwcPKoXPOXPmkKWlJUVFRandRV/GWPOmBabRjIyMcOjQIUilUvj7+yM7O1tpfUJCArp37w6pVAoAEARBRZX+s65fv47Bgwdj2LBhWLp0KQBAS0sLxcXF+PXXX9GjRw/MnTsXrVu3BtTofT+vJxeegGrvvaqqCi1atEBBQQFiYmLQpk0bxMbGolWrVhg1ahQyMzNVXLXqKPYXEcHd3R3ff/89zp8/j5UrV+LMmTMAAG1tbVRVVQEA/P398fDhQ6SlpQEA3NzcxOdpjrKysjB9+nSMGjUKa9asAQDI5XJoa2vj2rVr6Nu3L5KTk5UeM3fuXKxZswZ9+vRRUdWNx8rKChs2bICWlhb27duHVq1aQUvr///8EhEEQdC475i6+Pj4ICwsDKamppg0aRLOnDkDQRAgl8vF46NDhw6QSCTi39bW1kAzPn4AoLy8HIWFhVizZg0SExMBADo6OsjLy0OPHj1w4sQJtGzZEnK5HAAQFRWFsWPHIjAwUPwsMcZYU8DfSBqm+o+v4kTXyMgIsbGxkEgkSgFz0aJF2L17NyIiItCqVSuV1dwQZDIZLCwsUF5ejpMnTwIAVqxYgR9//BEBAQGYM2cO0tPTsXTpUly4cEHV5TaquoJCixYtcO3aNbi7uyM5ORlEBCMjIxw+fBhlZWWYMGECKisrVV2+ShARHj58CEEQ8PjxY/Tq1QvffPMNLly4gFWrVokBs0WLFgCAnJwcdOnSBV26dFF6nuYYMC5cuIB+/fqhsLAQEokE8+bNw+rVq6GlpYU7d+5gwIABGDp0KNatW1fjsTNnzoSdnZ1K6m5sVlZWWLt2LYgIS5YswalTp4Bm+m/+T6uqqkJJSQn++OMPAICXlxdmz54NMzMzTJkyBWfOnIGWlpa4rx4/foyOHTuKF/8UmvO+tLS0xI4dOyCTybBkyRLxO8PDwwOBgYGIiooCnlwElclkAIDIyEhYWFiotG7GGKtB1U2nrOG96EioLi4uNHnyZLUfsVDRVW348OEUEhJC7du3p9jYWHH9tWvXSBAE2rx5s0rrbEwvew9qSUmJ0hQBmuTf//43jRo1ioyNjal79+40btw4sZvw6dOnycrKivz8/Oi///0vlZWVUXp6Orm5udHbb7+t6tLrjbuWv7isrCwaNmwY9e3bl5KSklRdjspFR0dTUFAQtWvXjmxtbWncuHHiYFdJSUnk5+dHNjY2dPz4cXr48CFdvnyZHB0dKSgoSNWlN4hndaFmjLGmjsOlmnvRkVDv3r1LPXv2JEEQNGLuxszMTHrttddIKpWK+0cul1NFRQUVFhaSo6Mj/fDDD6ous1FwUHhxs2fPJmNjY1q0aBF9+eWXNG7cODI3N6c2bdqIo4MmJyfTwIEDydramjp27Eiurq40ZswY8Tma674sKCggIyMjCgwMVFoeFBREjo6OZGVlRQEBAbR9+3aV1dhUXb58mQICAjR+8B7F8RMZGUmrV6+mmTNnUseOHcnc3Jz27NlDRES///47BQcHk5aWFllZWZGTkxONGzdOfI7mevz8naysLHrjjTfo9ddfVxplWB3fK2NM/XC4VGM8EurzuXr1Kvn4+NDrr7+uNI1GeHg4WVhYaMS+4KDw4r766ivq1KlTjYnu4+LiyN3dnVq3bi2Ownzjxg06e/Ysfffdd5SYmChu25xbJPLy8qhXr140fPhw8T0tX76c9PT0KDIykr766iuys7MjKysrsSWK/U9dU7Voiq+//ppMTExqXMTMy8sjW1tbsrS0FNeVlpZSQkIC7d+/X2nKkeZ8/DxLZmamOMqwYu5gxhhrDjhcqiluhXox1acLOH/+PEVFRVHLli01ovWWOCi8lAkTJtD06dOJnhxD1U90jx49StbW1jRmzBilER6rU4fjjruWsxel+NxPmDBBnD5EMapw9dGFTU1N6a233nrm86gz7kLNGGuOeEAfNcQjob44xWiOOjo68PX1xcKFC5GYmAhnZ2dVl9YozM3NERMTg4qKCqxcuRITJ07E2rVrsX//fixcuBAhISE4fPgwrl69Kg40ocnKysrw66+/om3btuIyLS0tccAsLy8veHl54dy5c2jZsmWtz6EOx52VlRXWr1+PR48eISYmBnPmzIGPjw+ICJWVldDW1kbPnj3Rpk0bVZfKmghBEFBZWYmzZ89CV1dXaZ22tjbkcjnMzMwQGhqK06dP48GDB3U+j7qzsrLCqlWr0KlTJ5iYmKi6HMYYey4cLtUQj4T6cqysrLB69Wr07dsXKSkpcHV1VXVJjYqDwvPT19eHhYUFkpKSxFFi8eSEVzFVgJ+fH0pKSnD37l1xmTqytrbGxo0b4enpiaNHj+LEiRMQBAE6OjrYvHkzHj58qBHTjbDnp6OjA0NDQ/H3R1tbW7wwo5hWw9LSEkVFReII1M15mpH6sLW1RUxMDDp37qzqUhhj7LlwuFRD3Ar18mxsbLBnzx7Y29uruhSV4KDw/BwdHXHy5EmcPHlSnNZHMWchAOTm5qJXr164ceNGna0v6qJr1674/PPPQURYunQpUlJSsHLlSqxatQp79+6FmZmZqktkTYTiQou/vz/OnDmDzZs3A08uzFRVVYnTbJSXl2PAgAEoLi5GUVGRWl+geZanW3gZY6wpE0hTLwdqgKysLEydOhWJiYmIjIzE7NmzQUSoqqrCnTt3MHToUCxcuBABAQGqLpU1MdnZ2Zg+fTqICMuXL0dcXBw++eQTnDp1SmO6CtdFESDLy8vh4eGBP//8E9HR0fDw8ICBgQEAoLi4GP3798f169chkUgQEhKCuXPnqn2rb3Z2NmbNmoXk5GT88ccfSEpK0rgeAOz5ZGVlYcyYMSgvL0dYWBgmTpworrt79y769++PK1euwMTEBKNHj8aiRYtgaGio0poZY4w9G4dLNZeTk4MpU6ZAW1sb8+bNg6enJwBg0aJF2LlzJxISErhVgdWKg0Lt5HI5BEGAIAhITU1FcHAwrl+/jiFDhsDHxwe3b9/G7t270alTJyxfvhwymQzm5uYac2KcmZmJOXPmYNmyZRrbA4DVTS6Xi11fz549i5CQENy4cQMDBw7EwIEDUVpail27dsHU1BRbt27FnTt3YGFhIV64YYwx1rRxuNQA3ArFXhYHhf95Mrq2eGKclpYGBwcHlJaWYubMmUhOTkZubi769+8PR0dHLFu2TNUlq0xlZSV0dHRUXQZTsf/85z/o0KEDBg0aBACoqqpCixYtAADp6emwt7fH5cuXsWvXLhw4cAB3796Fk5MTXF1dERkZqeLqGWOMvQwOlxqCW6HYy9LUoHD48GEUFxdDKpXW6Dq+YsUKREdH4/Tp02LLv0wmw927d9GhQwdxu+qtNIxpknv37mHYsGHQ19dHREQEPDw8xHWLFy8We84YGxuLXc3v3r0LQ0NDMYDy8cMYY80Pf2trCE0fCZW9PE0MlvPmzcP777+PNWvWYPTo0ViwYIG4buXKlVixYgW2bdsGMzMzcaARbW1tpWBZvZWTMU3Tpk0bbN++HRKJBIsXLxZHLl+2bBlWrVqF9evXw9jYGKg2EqyRkZEYLPn4YYyx5olbLjWMprZCMfa8wsLCsGPHDhw5cgSdO3fG/v37sXz5cly8eBGCICA4OBizZs1C//79VV0qY01e9dsyQkNDkZ6ejl69emHIkCGqLo0xxlgD4HDJGGNPLF68GIsXL0Zubq44r1x8fDw+/vhjvPnmm9DR0YGNjQ1GjRql6lIZazays7MxY8YMCIKAadOmwdfXV9UlMcYYayAcLhljDMD9+/fh7e0NHR0dfP7553B1dYVMJoOzszPKy8vh5uaG+Ph4tGzZEl988QWGDRum6pIZazaysrIQFhYGuVyO8PBwvPrqq6ouiTHGWAPgGxoYYwyAoaEhvvvuO7Rt2xaffvopEhIS4OnpCRMTE5w6dQoxMTEoKChAWVkZ9u7dq+pyGWtWrK2tsX79emhra2PJkiU4duyYqktijDHWADhcMsbYE9bW1li7di2qqqowYsQIVFRU4PDhw2jbti3Ky8shkUgwaNAgCIIA7vTB2IuxsrLCunXrUFJSgnPnzqm6HMYYYw2AwyVjjFVjZWWF6OhouLi4oHXr1khKSgIASCQS3Lp1C2lpaejevTsEQVB1qYw1O1ZWVjh48CA++ugjVZfCGGOsAfA9l4wxVgvFKJdyuRxLly6Fm5sb7O3t0bVrVxw8eFDV5THW7Cnmt2SMMaY+OFwyxlgdsrOzERYWhoqKCly8eBFOTk6IjY0FeIJ3xhhjjLEaOFwyxtjfyM7OxujRo9G9e3fExMQAHCwZY4wxxmrF4ZIxxp7hzp07aN++PcDBkjHGGGOsThwuGWPsOfE9YowxxhhjdePL74wx9pw4WDLGGGOM1Y3DJWOMMcYYY4yxeuNwyRhjjDHGGGOs3jhcMsYYY4wxxhirNw6XjDHGGGOMMcbqjcMlY4wxxhhjjLF643DJGGNMY40fPx7+/v7i3wMHDkRYWFij13H8+HEIgoD79+83+mszxhhj/xQOl4wxxpqc8ePHQxAECIIAXV1ddOvWDYsXL0ZVVVWDvu6+ffsQGRn5XNtyIGSMMcaUtVB1AYwxxlhtfH19sW3bNpSXl+OXX37BBx98AB0dHcybN09pu4qKCujq6v4jr9mmTZt/5HkYY4wxTcQtl4wxxpokiUSCjh07okuXLpg8eTK8vb1x8OBBsSvr0qVLYWJiAhsbGwDA9evXMXr0aBgaGqJNmzbw8/NDfn6++HwymQyzZs2CoaEh2rZtizlz5oCIlF7z6W6x5eXlmDt3LszMzCCRSNCtWzd8/fXXyM/Px6BBgwAArVu3hiAIGD9+PABALpdj+fLlsLCwgFQqhaOjI/bs2aP0Or/88gusra0hlUoxaNAgpToZY4yx5orDJWOMsWZBKpWioqICAHD06FFkZmYiLi4OP/30EyorKzFkyBAYGBjgxIkTOHnyJFq1agVfX1/xMWvWrMH27duxdetWJCYm4t69e9i/f//fvmZwcDB27dqFDRs24PLly9i8eTNatWoFMzMz7N27FwCQmZmJoqIirF+/HgCwfPlyfPPNN9i0aRPS09Mxc+ZMjBs3DgkJCcCTEDxy5Ei8+eabSE1NRUhICD7++OMG3nuMMcZYw+NusYwxxpo0IsLRo0cRGxuLadOmobi4GPr6+tiyZYvYHXbnzp2Qy+XYsmULBEEAAGzbtg2GhoY4fvw4fHx8sG7dOsybNw8jR44EAGzatAmxsbF1vm5WVha+//57xMXFwdvbGwBgaWkprld0oW3fvj0MDQ2BJy2dy5YtQ3x8PNzd3cXHJCYmYvPmzRgwYAA2btyIrl27Ys2aNQAAGxsbpKWlISoqqoH2IGOMMdY4OFwyxhhrkn766Se0atUKlZWVkMvlGDt2LD799FN88MEHcHBwULrP8sKFC7h69SoMDAyUnuPx48fIycnBgwcPUFRUhD59+ojrWrRoATc3txpdYxVSU1Ohra2NAQMGPHfNV69exV9//YXXXntNaXlFRQWcnZ0BAJcvX1aqA4AYRBljjLHmjMMlY4yxJmnQoEHYuHEjdHV1YWJighYt/veTpa+vr7RtaWkpXF1dERMTU+N52rVr91KvL5VKX/gxpaWlAICff/4ZpqamSuskEslL1cEYY4w1FxwuGWOMNUn6+vro1q3bc23r4uKC3bt3o3379vi///u/WrcxNjbGmTNn0L9/fwBAVVUVzp07BxcXl1q3d3BwgFwuR0JCgtgttjpFy6lMJhOXde/eHRKJBAUFBXW2eNrZ2eHgwYNKy06fPv1c75MxxhhrynhAH8YYY83e22+/DSMjI/j5+eHEiRPIy8vD8ePHMX36dBQWFgIAZsyYgRUrVuDAgQO4cuUKpkyZ8rdzVJqbm+Odd97Bu+++iwMHDojP+f333wMAunTpAkEQ8NNPP6G4uBilpaUwMDDAhx9+iJkzZ2LHjh3IycnB+fPnER0djR07dgAAJk2ahOzsbHz00UfIzMzEd999h+3btzfSnmKMMcYaDodLxhhjzZ6enh5+++03dO7cGSNHjoSdnR3ee+89PH78WGzJnD17Nv71r3/hnXfegbu7OwwMDDBixIi/fd6NGzciICAAU6ZMga2tLSZOnIiysjIAgKmpKSIiIvDxxx+jQ4cOmDp1KgAgMjIS4eHhWL58Oezs7ODr64uff/4ZFhYWAIDOnTtj7969OHDgABwdHbFp0yYsW7aswfcRY4wx1tAEqmskA8YYY4wxxhhj7DlxyyVjjDHGGGOMsXrjcMkYY4wxxhhjrN44XDLGGGOMMcYYqzcOl4wxxhhjjDHG6o3DJWOMMcYYY4yxeuNwyRhjjDHGGGOs3jhcMsYYY4wxxhirNw6XjDHGGGOMMcbqjcMlY4wxxhhjjLF643DJGGOMMcYYY6zeOFwyxhhjjDHGGKs3DpeMMcYYY4wxxurt/wG1+IRrWBRtawAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Flatten label\n", + "y_true_flat_ner = [tag for seq in y_true_ner for tag in seq]\n", + "y_pred_flat_ner = [tag for seq in y_pred_ner for tag in seq]\n", + "\n", + "# Buat plot\n", + "plot_confusion_matrix(\n", + " y_true_flat_ner, \n", + " y_pred_flat_ner, \n", + " labels=list(tag2idx_ner.keys()), \n", + " title=\"NER Confusion Matrix\"\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "4ba2b85c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_true_flat_srl = [tag for seq in y_true_srl for tag in seq]\n", + "y_pred_flat_srl = [tag for seq in y_pred_srl for tag in seq]\n", + "\n", + "plot_confusion_matrix(\n", + " y_true_flat_srl, \n", + " y_pred_flat_srl, \n", + " labels=list(tag2idx_srl.keys()), \n", + " title=\"SRL Confusion Matrix\"\n", + ")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/NER_SRL/multi_task_bilstm_model.keras b/NER_SRL/multi_task_bilstm_model.keras new file mode 100644 index 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"labels_srl": [], - "labels_ner": ["O", "O", "O"] - }, - { - "tokens": [ - "Semakin", - "tinggi", - "tumbuhan", - "itu", - "semakin", - "dalam", - "pula", - "akarnya", - "menghunjam", - "ke", - "bumi" - ], - "labels_srl": [], - "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-LOC"] - }, - { - "tokens": [ - "hingga", - "tidak", - "mudah", - "tumbang", - "dari", - "terpaan", - "angin", - "badai", - "atau", - "bencana", + "Keberagaman", + "potensi", + "sumber", + "daya", "alam", - "lainnya" + "Indonesia", + "tidak", + "lepas", + "dari", + "proses", + "geografis", + "yang", + "terjadi", + "." ], - "labels_srl": [], - "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"] + "labels_ner": [ + "O", + "O", + "O", + "O", + "O", + "B-LOC", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O" + ], + "labels_srl": [ + "ARG1", + "ARG1", + "ARG1", + "ARG1", + "ARG1", + "ARGM-LOC", + "AM-NEG", + "V", + "ARGM-SRC", + "ARGM-SRC", + "ARGM-SRC", + "ARGM-SRC", + 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+ "ARG1", + "ARG1", + "ARG1", + "ARGM-BNF", + "ARG2", + "ARG2", + "O", + "ARG2", + "ARG2", + "ARG2", + "ARGM-LOC", + "O" ] }, { "tokens": [ - "Jika", - "tidak", - "demikian", + "Bagaimana", + "mengoptimalkan", + "peranan", + "sumber", + "daya", "manusia", - "yang", - "melupakan", - "budaya", - "bangsanya" + "dalam", + "mengelola", + "sumber", + "daya", + "alam", + "Indonesia", + "?" ], - "labels_srl": [], - "labels_ner": ["O", "O", "O", "O", "O", "O", "B-MISC", "B-MISC"] - }, - { - "tokens": [ - "akan", - "mudah", - "terombang-ambing", - "oleh", - "terpaan", - "budaya", - "asing", - "yang", - "lebih", - "kuat" - ], - "labels_srl": [], - "labels_ner": ["O", "O", "O", "O", "O", "B-MISC", "B-MISC", "O", "O", "O"] - }, - { - "tokens": [ - "sehingga", - "dengan", - "sendirinya", - "kehilangan", - "identitas", - "diri" - ], - "labels_srl": [], - "labels_ner": ["O", "O", "O", "O", "B-MISC", "I-MISC"] - }, - { - "tokens": [ - "Jadi", - "bangsa", - "yang", - "gampang", - "meninggalkan", - 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"O", + "O", + "O", + "O", + "O", + "B-LOC", + "O" + ], + "labels_srl": [ + "ARGM-CAU", + "ARGM-CAU", + "ARGM-CAU", + "ARG0", + "AM-TMP", + "AM-EXT", + "V", + "O", + "V", + "AM-LOC", + "AM-LOC", + "AM-LOC", + "O" + ] }, { "tokens": [ - "Fenomena", - "alam", - "yang", - "terjadi", - "itu", - "merupakan", - "bagian", - "tak", - "terpisahkan", - "dari", - "aktivitas", - "panjang", - "bumi", - "kita", - "sejak", - "proses", - "terjadinya", - "alam", - "semesta", - "ratusan", - "ribuan", - "bahkan", - "juta", - "tahun", - "yang", - "lalu" + "Keadaan", + "iklim", + "dapat", + "diamati", + "dengan", + "memperhatikan", + "unsur-unsur", + "cuaca", + "dan", + "iklim", + "." + ], + "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"], + "labels_srl": [ + "ARG1", + "ARG1", + "AM-MOD", + "V", + "AM-MNR", + "V", + "ARG1", + "ARG1", + "O", + "ARG1", + "O" + ] + }, + { + "tokens": [ + "Unsur-unsur", + "tersebut", + "antara", + "lain", + ",", + "penyinaran", + "matahari", + ",", + "suhu", + "udara", + ",", + "kelembaban", + "udara", + ",", + "angin", + ",", + "dan", + "hujan", + "." ], - "labels_srl": [], "labels_ner": [ "O", "O", @@ -2167,95 +1095,103 @@ "O", "O", "O", - "B-LOC", "O", "O", "O", "O", "O", "O", - "B-TIME", - "I-TIME", - "I-TIME", - "I-TIME", - "I-TIME", - "I-TIME", + "O" + ], + "labels_srl": [ + "ARG1", + "ARG1", "O", + "O", + "O", + "ARG1", + "ARG1", + "O", + "ARG1", + "ARG1", + "O", + "ARG1", + "ARG1", + "O", + "ARG1", + "O", + "O", + "ARG1", "O" ] }, { "tokens": [ - "Proses", - "tersebut", - "secara", - "geologis", - "mengalami", - "beberapa", - "tahapan", - "atau", - "pembabakan", - "waktu" + "Tanaman", + "tropis", + "memiliki", + "banyak", + "varietas", + "yang", + "kaya", + "akan", + "hidrat", + "arang", + "terutama", + "tanaman", + "bahan", + "makanan", + "pokok", + "." ], - "labels_srl": [], - "labels_ner": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"] + "labels_ner": [ + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + "O", + 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"labels_ner": ["B-TIME", "O", "B-PER", "O", "O", "O", "O", "B-PER"] + "labels_ner": ["O", "O", "O", "O", "O", "O", "O"], + "labels_srl": ["AM-TMP", "ARG1", "ARG1", "ARG1", "AM-DIR", "ARG2", "O"] } ]