File size: 82,805 Bytes
4be4a27 df4616d 4be4a27 58e2c7a 4be4a27 df4616d 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 df4616d 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 df4616d 4be4a27 58e2c7a 4be4a27 df4616d 4be4a27 df4616d 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 e284b6a 4be4a27 e284b6a 58e2c7a 4be4a27 79ecfdc 4be4a27 79ecfdc 4be4a27 58e2c7a 4be4a27 58e2c7a 4be4a27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🔧 Notebook 02 — Preprocesamiento de Texto\n",
"\n",
"Construimos y validamos el pipeline de limpieza de texto **paso a paso**.\n",
"\n",
"El texto crudo de YouTube tiene ruido que engaña al modelo: URLs, menciones, caracteres raros (`\\xa0`), contracciones rotas (`don t`).\n",
"Antes de vectorizar necesitamos texto limpio y normalizado.\n",
"\n",
"### Herramientas\n",
"- **`re`** → expresiones regulares para limpiar ruido estructural\n",
"- **`NLTK`** → lista curada de 179 stopwords en inglés\n",
"- **`spaCy`** → lematización con modelo de lenguaje real `en_core_web_sm`\n",
"- **`MLflow`** → registrar qué configuración de preprocesamiento usamos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Imports y configuración\n",
"\n",
"Cargamos todo desde el YAML. Ningún valor hardcodeado en el notebook."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROJECT_ROOT : /mnt/c/Users/under/Documents/F5/3_Projects/Project_9_Equipo3/Project_YT\n",
"Python : 3.10.20\n"
]
}
],
"source": [
"import re\n",
"import sys\n",
"import yaml\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import mlflow\n",
"import nltk\n",
"import spacy\n",
"from nltk.corpus import stopwords\n",
"from pathlib import Path\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Ruta raiz — \n",
"PROJECT_ROOT = Path.cwd().parent\n",
"sys.path.insert(0, str(PROJECT_ROOT))\n",
"\n",
"plt.rcParams['figure.figsize'] = (12, 5)\n",
"plt.rcParams['axes.spines.top'] = False\n",
"plt.rcParams['axes.spines.right'] = False\n",
"\n",
"print(f'PROJECT_ROOT : {PROJECT_ROOT}')\n",
"print(f'Python : {sys.version.split()[0]}')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Configuracion de preprocesamiento:\n",
" lowercase: True\n",
" remove_urls: True\n",
" remove_mentions: True\n",
" remove_emojis: True\n",
" remove_special_chars: True\n",
" remove_stopwords: True\n",
" lemmatize: True\n",
" min_token_length: 2\n",
" language: en\n",
" custom_stopwords: ['youtube', 'video', 'watch', 'like', 'comment', 'channel', 'stefan', 'peggy', 'masri', 'ferguson', 'cigar', 'hubbard']\n"
]
}
],
"source": [
"# Carga de configuracion desde YAML\n",
"CONFIG_PATH = PROJECT_ROOT / 'configs' / 'features.yaml'\n",
"\n",
"with open(CONFIG_PATH) as f:\n",
" config = yaml.safe_load(f)\n",
"\n",
"prep_cfg = config['preprocessing']\n",
"print('Configuracion de preprocesamiento:')\n",
"for k, v in prep_cfg.items():\n",
" print(f' {k}: {v}')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"spaCy : core_web_sm v3.8.0\n",
"NLTK : 198 stopwords en ingles\n"
]
}
],
"source": [
"# Descargar recursos NLTK\n",
"nltk.download('stopwords', quiet=True)\n",
"nltk.download('punkt', quiet=True)\n",
"\n",
"# Cargar modelo spaCy\n",
"# Si no lo tienes instalado: python -m spacy download en_core_web_sm\n",
"nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])\n",
"\n",
"print(f\"spaCy : {nlp.meta['name']} v{nlp.meta['version']}\")\n",
"print(f'NLTK : {len(stopwords.words(\"english\"))} stopwords en ingles')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Carga de datos\n",
"\n",
"Las rutas vienen del YAML de pipeline, no se escriben a mano."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset : (1000, 15)\n",
"Texto : \"Text\"\n",
"Target : \"IsToxic\"\n",
"Sublabels: ['IsAbusive', 'IsProvocative', 'IsHatespeech', 'IsRacist', 'IsObscene']\n",
"\n",
"Muestra texto crudo:\n",
" -> \"You call yourself an anarchist but defend a cop shooting an unarmed civilian. I'm highly disappointe\"\n",
" -> 'My mother told me the same thing.\\xa0 God Bless this woman.'\n",
" -> 'Love it I same the saem thing Go Peggy! #stupid \\xa0\\nYa Killing ya selves more quicker than\\xa0 STLPD cou'\n"
]
}
],
"source": [
"PIPELINE_PATH = PROJECT_ROOT / 'configs' / 'pipeline.yaml'\n",
"\n",
"with open(PIPELINE_PATH) as f:\n",
" pipeline_cfg = yaml.safe_load(f)\n",
"\n",
"DATA_PATH = PROJECT_ROOT / pipeline_cfg['data']['raw_path']\n",
"TEXT_COL = pipeline_cfg['data']['text_column']\n",
"TARGET_BIN = pipeline_cfg['data']['target_binary']\n",
"SUBLABELS = pipeline_cfg['data']['target_multilabel']\n",
"\n",
"df = pd.read_csv(DATA_PATH)\n",
"print(f'Dataset : {df.shape}')\n",
"print(f'Texto : \"{TEXT_COL}\"')\n",
"print(f'Target : \"{TARGET_BIN}\"')\n",
"print(f'Sublabels: {SUBLABELS}')\n",
"print()\n",
"print('Muestra texto crudo:')\n",
"for t in df[TEXT_COL].sample(3, random_state=42):\n",
" print(f' -> {repr(t[:100])}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Pipeline paso a paso\n",
"\n",
"Construimos cada función por separado para entender su efecto antes de encadenarlas.\n",
"\n",
"```\n",
"texto raw\n",
" → [1] lowercase\n",
" → [2] regex: URLs, @menciones, \\xa0, contracciones rotas, números\n",
" → [3] spaCy: tokenización + lematización\n",
" → [4] NLTK: filtrado stopwords + tokens cortos + puntuación\n",
" → texto limpio\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASO 1 — Lowercase\n",
"-----------------------------------------------------------------\n",
" ANTES : Stephan, Thank you for the video. It takes all the available\n",
" DESPUES: stephan, thank you for the video. it takes all the available\n",
"\n",
" ANTES : Body cams should also air what the police run into day to da\n",
" DESPUES: body cams should also air what the police run into day to da\n",
"\n",
" ANTES : This is a really sad story. Someone is killed for no reason,\n",
" DESPUES: this is a really sad story. someone is killed for no reason,\n",
"\n"
]
}
],
"source": [
"# ── PASO 1: Lowercase ──\n",
"# Por que: 'BLACK' y 'black' son la misma palabra.\n",
"# Sin esto el modelo aprende 'BLACK' y 'black' como features distintas.\n",
"\n",
"def to_lowercase(text: str) -> str:\n",
" \"\"\"Convierte el texto a minusculas.\"\"\"\n",
" return str(text).lower()\n",
"\n",
"# Validacion visual\n",
"print('PASO 1 — Lowercase')\n",
"print('-' * 65)\n",
"for ex in df[TEXT_COL].sample(3, random_state=1).tolist():\n",
" print(f' ANTES : {ex[:60]}')\n",
" print(f' DESPUES: {to_lowercase(ex)[:60]}')\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASO 2 — Limpieza Regex\n",
"-----------------------------------------------------------------\n",
" ANTES : 'Check this out http://youtube.com/watch?v=abc123 ok?'\n",
" DESPUES: 'Check this out ok?'\n",
"\n",
" ANTES : \"Hey @username you're so stupid\\xa0\\xa0 really\"\n",
" DESPUES: 'Hey youre so stupid really'\n",
"\n",
" ANTES : 'dont\\nyou see?\\n\\nThis is wrong!!!'\n",
" DESPUES: 'dont you see? This is wrong!!!'\n",
"\n",
" ANTES : 'He has 100 guns and 50 knives'\n",
" DESPUES: 'He has guns and knives'\n",
"\n"
]
}
],
"source": [
"# ── PASO 2: Limpieza con Regex ────────────────────────────────────────────\n",
"# Por que regex: hay ruido sistematico en comentarios de YouTube.\n",
"# El EDA mostro: \\xa0 embebidos, saltos de linea, URLs, @menciones.\n",
"\n",
"\n",
"def clean_regex(text: str) -> str:\n",
" \"\"\"Limpieza con expresiones regulares.\"\"\"\n",
" # URLs completas\n",
" text = re.sub(r'http\\S+|www\\.\\S+', '', text)\n",
" # Menciones @usuario\n",
" text = re.sub(r'@\\w+', '', text)\n",
" # Saltos de linea y tabulaciones -> espacio\n",
" text = re.sub(r'[\\n\\t\\r]', ' ', text)\n",
" # Caracteres no-ASCII: \\xa0, emojis, caracteres especiales\n",
" text = re.sub(r'[^\\x00-\\x7F]+', ' ', text)\n",
" # Apostrofes: \"don't\" -> \"dont\", \"it's\" -> \"its\"\n",
" text = re.sub(r\"'\", '', text)\n",
" # Numeros solos sin contexto lexico util\n",
" text = re.sub(r'\\b\\d+\\b', '', text)\n",
" # Espacios multiples -> uno solo\n",
" text = re.sub(r'\\s+', ' ', text)\n",
" return text.strip()\n",
"\n",
"# Validacion con casos problematicos reales del dataset (ejemplo)\n",
"print('PASO 2 — Limpieza Regex')\n",
"print('-' * 65)\n",
"test_cases = [\n",
" 'Check this out http://youtube.com/watch?v=abc123 ok?',\n",
" 'Hey @username you\\'re so stupid\\xa0\\xa0 really',\n",
" 'dont\\nyou see?\\n\\nThis is wrong!!!',\n",
" 'He has 100 guns and 50 knives',\n",
"]\n",
"for tc in test_cases:\n",
" print(f' ANTES : {repr(tc)}')\n",
" print(f' DESPUES: {repr(clean_regex(tc))}')\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PASO 3+4 — Lematizacion (spaCy) + Filtrado (NLTK)\n",
"-----------------------------------------------------------------\n",
" ANTES : black people are being killed by cops\n",
" DESPUES: black people kill cop\n",
"\n",
" ANTES : you are so stupid and racist thug\n",
" DESPUES: stupid racist thug\n",
"\n",
" ANTES : running faster than the police officers\n",
" DESPUES: run fast police officer\n",
"\n",
" ANTES : these cops are criminals and killers\n",
" DESPUES: cop criminal killer\n",
"\n",
" ANTES : Hi, stefan\n",
" DESPUES: hi\n",
"\n"
]
}
],
"source": [
"# ── PASO 3 + 4: Lematizacion con spaCy + filtrado con NLTK ───────────────\n",
"#\n",
"# Por que spaCy para LEMATIZAR:\n",
"# Conoce gramatica inglesa real. // 'running' -> 'run' | 'cops' -> 'cop' | 'better' -> 'good'\n",
"\n",
"# Por que NLTK para STOPWORDS:\n",
"# Lista curada de 179 palabras funcionales (the, is, at, which...)\n",
"# Mas explicita y facil de personalizar que la lista interna de spaCy\n",
"\n",
"\n",
"STOP_WORDS = set(stopwords.words('english'))\n",
"\n",
"# Stopwords custom: palabras tematicas sin valor discriminante\n",
"CUSTOM_STOPWORDS = set(config['preprocessing']['custom_stopwords'])\n",
"STOP_WORDS = STOP_WORDS | CUSTOM_STOPWORDS\n",
"\n",
"MIN_TOKEN_LEN = prep_cfg.get('min_token_length', 2)\n",
"\n",
"def lemmatize_and_filter(text: str) -> str:\n",
" \"\"\"Lematiza con spaCy y filtra stopwords con NLTK.\"\"\"\n",
" doc = nlp(text) # Separación de palabras\n",
" tokens = [\n",
" token.lemma_\n",
" for token in doc\n",
" if not token.is_punct # sin puntuacion\n",
" and not token.is_space # sin espacios\n",
" and len(token.text) >= MIN_TOKEN_LEN # tokens de al menos 2 chars\n",
" and token.lemma_ not in STOP_WORDS # sin stopwords\n",
" ]\n",
" return ' '.join(tokens)\n",
"\n",
"\n",
"# Validacion: ver exactamente que hace la lematizacion (ejemplo)\n",
"print('PASO 3+4 — Lematizacion (spaCy) + Filtrado (NLTK)')\n",
"print('-' * 65)\n",
"test_texts = [\n",
" 'black people are being killed by cops',\n",
" 'you are so stupid and racist thug',\n",
" 'running faster than the police officers',\n",
" 'these cops are criminals and killers',\n",
" 'Hi, stefan'\n",
"]\n",
"for tt in test_texts:\n",
" result = lemmatize_and_filter(tt)\n",
" print(f' ANTES : {tt}')\n",
" print(f' DESPUES: {result}')\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PIPELINE COMPLETO — Ejemplos reales del dataset\n",
"======================================================================\n",
"[NO TOXICO ]\n",
" CRUDO : You call yourself an anarchist but defend a cop shooting an unarmed civilian. I'm highly disappointe\n",
" LIMPIO : call anarchist defend cop shoot unarmed civilian highly disappointed beginning refer square know blu\n",
"\n",
"[NO TOXICO ]\n",
" CRUDO : My mother told me the same thing. God Bless this woman.\n",
" LIMPIO : mother tell I thing god bless woman\n",
"\n",
"[NO TOXICO ]\n",
" CRUDO : Love it I same the saem thing Go Peggy! #stupid \n",
"Ya Killing ya selves more quicker than STLPD cou\n",
" LIMPIO : love saem thing go stupid ya kill ya self quick stlpd could ever wakeup\n",
"\n",
"[TOXICO ]\n",
" CRUDO : Next time they do that, line up some cars and start making burnout smoke to \"riot gas\" them. Non let\n",
" LIMPIO : next time line car start make burnout smoke riot gas non lethal eficient double check actualy non le\n",
"\n",
"[NO TOXICO ]\n",
" CRUDO : He was Robbing the Store and Being a Big Man .\n",
"If you Play with Fire you Will Get Burnt .?\n",
"The Polic\n",
" LIMPIO : rob store big man play fire get burn police job\n",
"\n",
"[NO TOXICO ]\n",
" CRUDO : The crazy thing is I thought offices never do anything I want of random people over\n",
" LIMPIO : crazy thing think office never anything want random people\n",
"\n"
]
}
],
"source": [
"# ── PIPELINE COMPLETO ──\n",
"\n",
"def preprocess_text(text: str) -> str:\n",
" \"\"\"\n",
" Pipeline completo de preprocesamiento NLP.\n",
"\n",
" Pasos:\n",
" 1. lowercase\n",
" 2. limpieza regex (URLs, menciones, chars especiales)\n",
" 3. lematizacion con spaCy\n",
" 4. filtrado stopwords con NLTK\n",
"\n",
" \"\"\"\n",
" text = to_lowercase(text)\n",
" text = clean_regex(text)\n",
" text = lemmatize_and_filter(text)\n",
" return text\n",
"\n",
"# Verificacion con ejemplos reales del dataset\n",
"print('PIPELINE COMPLETO — Ejemplos reales del dataset')\n",
"print('=' * 70)\n",
"for _, row in df.sample(6, random_state=42).iterrows():\n",
" label = 'TOXICO ' if row[TARGET_BIN] else 'NO TOXICO '\n",
" print(f'[{label}]')\n",
" print(f' CRUDO : {row[TEXT_COL][:100]}')\n",
" print(f' LIMPIO : {preprocess_text(row[TEXT_COL])[:100]}')\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Aplicar el pipeline al dataset completo\n",
"\n",
"Procesamos las 1000 filas y validamos que no haya pérdida de información crítica."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Procesando dataset completo...\n",
"Completado: 1000 comentarios procesados\n"
]
}
],
"source": [
"print('Procesando dataset completo...')\n",
"df['clean_text'] = df[TEXT_COL].apply(preprocess_text)\n",
"print(f'Completado: {len(df)} comentarios procesados')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" CRUDO LIMPIO REDUCCION\n",
"----------------------------------------------------\n",
" mean 33.8 16.5 51.2%\n",
" median 19.0 9.0 52.6%\n",
" min 1.0 1.0 0.0%\n",
" max 815.0 373.0 54.2%\n",
"\n",
"Comentarios vacios tras limpieza : 0\n"
]
}
],
"source": [
"# Estadisticas antes vs despues\n",
"df['tokens_raw'] = df[TEXT_COL].str.split().str.len()\n",
"df['tokens_clean'] = df['clean_text'].str.split().str.len()\n",
"\n",
"print(f\"{'':20} {'CRUDO':>8} {'LIMPIO':>8} {'REDUCCION':>11}\")\n",
"print('-' * 52)\n",
"for stat in ['mean', 'median', 'min', 'max']:\n",
" raw = getattr(df['tokens_raw'], stat)()\n",
" clean = getattr(df['tokens_clean'], stat)()\n",
" pct = (1 - clean / raw) * 100 if raw > 0 else 0\n",
" print(f' {stat:18} {raw:8.1f} {clean:8.1f} {pct:10.1f}%')\n",
"\n",
"print()\n",
"empty_after = (df['tokens_clean'] == 0).sum()\n",
"print(f'Comentarios vacios tras limpieza : {empty_after}')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABQkAAAHqCAYAAACnYcjKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAnktJREFUeJzs3Xd8FHX+x/H3bMmmJwQIIYQOgvQmGEGJgISiwokFjBSPs4IKeuohSlUp51lQwLOBKIjtREVEOijtBEUpisKBiCSgQAhJSLLZnd8f/BizJKGEJAvs6/l4zOOxM9/vfOczuyDjZ77FME3TFAAAAAAAAICAZfN3AAAAAAAAAAD8iyQhAAAAAAAAEOBIEgIAAAAAAAABjiQhAAAAAAAAEOBIEgIAAAAAAAABjiQhAAAAAAAAEOBIEgIAAAAAAAABjiQhAAAAAAAAEOBIEgIAAAAAAAABjiQhgLNiGIYMw1CtWrX8HUpAuBi+70GDBln3sWLFCuv4+XJvu3fvtmJJSkryaywAAJSH8+Xf4HORlJRk3cfu3bslnV//pq9YscKKZdCgQX6N5ULE9wf4B0lC4CJVq1Yt6x/W020FEzcAAAA4P/A8BwAoTw5/BwAACExffvmlJCk4ONivcVStWtWKJSoqyq+xAACAkjuf/k1v2bKlFUuVKlX8GgsAnCmShMBF6oMPPlBOTo61f9NNNyktLU2SNGXKFLVs2dIqa9q0abnHd7HKyspSWFiYv8O4IHTo0MHfIUiSXC7XeRMLAAAF8Tx3ds6nf9OjoqLOm1jKG8/DwIWL4cbARapNmzbq0KGDtblcLqusadOmPmWGYWjkyJG69NJLFRISooiICLVr107//ve/ZZrmaa+1dOlSuVwuGYahihUr6vvvv5ckZWZmasyYMWrSpIlCQkIUGRmppKQkff755z7nnzx/zNdff62rr75aoaGhiouL0+OPPy6v12vV93q9euqpp6x2g4ODVaNGDfXs2VOvv/66T9slmXNn4cKF6tGjhypXrqygoCBVq1ZNN954o3755RdJhedI+c9//qMWLVrI5XLpn//85ynnwyk4bKigP/74QwMGDFBUVJSio6M1YMAA/fHHH8XGmJeXp0mTJqlFixYKCwtTaGiomjdvrokTJyovL++U93fgwAE5HA4ZhqHmzZv7lOXm5ioyMlKGYSg+Pl4ej0eS9O9//1tt2rRReHi4XC6XqlWrpi5dumjy5Mln+rUWUtRvM3PmTOv4mDFjNHXqVNWqVUthYWHq0aOHfv31V+Xk5OiBBx5QpUqVFBERoVtuuUWHDh0qtu2ff/5Z1157rcLDw1WpUiUNGTJEWVlZVt1T/V4ZGRnn9HcDAIBzwfPc2Snu3/QxY8ZYx19//XWNHTtWVatWVWRkpPr166f09HQdOnRI/fv3V1RUlGJiYnT33Xf7JGiLur+OHTsqNDRU8fHxeuKJJ5Sfn2/VP9Wcemlpabr//vtVt25duVwuRUdHKykpSe+//36he/rwww/VoUMHRUVFKSgoSHFxcerQoYMeffTR0/6uBe97xowZeu6551S3bl0FBwerdevWWrx4caFzzjS2k7+PVatWKTExUSEhIRoyZMgp45JO/7xdnFWrVummm25S/fr1FR0draCgIMXHx+vmm2+2/syecOzYMT388MOqX7++XC6XwsLCVLt2bd1www366KOPfOr+/vvvevDBB626FSpUUM+ePbVu3brT3gtwUTEBBISaNWuakkxJ5vLly63jhw4dMhs2bGiVnbz17dvXp50Tx2vWrGmapmlu2LDBjIiIMCWZUVFR5oYNG0zTNM309HSzadOmxbY7depUq81du3ZZx6tWrWqGhIQUqv/qq69a9ceNG1dsu+3btz9lvKczduzYYts+8b0tX77cOla7dm3TMAxrf/To0T7307Fjx2J/hxNyc3PNli1bFrpes2bNiow/JyfHvOqqq4qN86qrrjJzc3NPeZ/dunWz6v/000/W8Y8//tg6Pnz4cNM0TXPWrFnFXqtatWqn/U4HDhxY5J+9ou5txowZ1vG6desW+Z307t270PGUlBSfa544Hh0dbVapUqVQ/W7dull1i/u9zvbvBgAAZS1Qn+eK0rFjR6v+rl27CsVQ8N/00aNHn/L5olu3bmbbtm0LHR85cmSR95eQkGCGhYUVqn/XXXdZ9Qs+Lw4cONA6/r///c+Mi4sr9t4fffRRq+6KFStMm81WbF23233K76jgfTdo0KDQ+U6n01y1alWJYiv4fcTHx5vBwcFF3m9RzvZ5u2B7EyZMKPbc0NBQc9u2bVbdv/71r8XWLfjs+Msvv5gJCQlF1nM6nebHH398yvsBLib0JAQC3GOPPaYff/xR0vE30v/5z3/02muvqUKFCpKkuXPn6t133y3y3J9//lndu3fX0aNHFR4ergULFqh169aSpJEjR2rz5s2SpB49euizzz7TrFmzFBcXJ0kaPny4fv3110JtpqamqlWrVvr44491//33W8f//e9/W58//vhjSVJ0dLTefvttLVmyRLNmzdLdd9+tqlWrlvi72LBhg0aPHm3tDx48WJ9++qneeecd3XTTTbLZCv8nc9euXWrTpo3ef/99zZs3T1deeeVZX3fGjBn69ttvJUkVK1bUG2+8offff1+ZmZlF1n/++ee1atUqSVL16tU1Z84cvfPOO6pRo4ak429Yn3vuuVNe87bbbrM+f/DBB0V+PlHnxPftcDj08ssva+nSpZo9e7Yeeugh1a5d+2xv94zt3LlTjzzyiD7++GNVq1ZNkvT9999r/vz5euaZZzRnzhyFhIRIOv7n9MiRI4XaSE9PV0JCgubNm6cXX3xRoaGhko6/vf70009Pef1z+bsBAEB54nnuzO3evVuTJ0/Wu+++q4iICEnHnwu2bdum1157TdOnTy8y3oL27t2r9u3b69NPP9X48eNlt9ut+if3ZjvZvffeaw0ZT0pK0ieffKJnn33WmqN50qRJWr9+vSTp008/tXpfPv3001q6dKnmzp2rxx9/XI0aNSo0MuVUduzYoXHjxmn+/PlKTk6WJLndbg0bNqxEsRW0b98+JSQk6O2339aCBQvUu3fvYuMoyfN2QW3bttWLL76oTz75RMuXL9fixYs1adIkSVJ2drbPM/CJP2M1a9bUBx98oEWLFun111/XgAEDrL8bJ+577969kqQBAwZo4cKFmj59usLDw+V2u/XXv/7VZxQKcFHzd5YSQPko6s2zx+MxK1SoYB3fvHmzVf/FF1+0jvfq1cs6fuJYxYoVzdq1a5uSzJCQEHPFihVWnYLtBgUFmUuWLDG//PJL88svvzTvvfdeq41nnnnGNE3fN5FBQUFmWlqa1U5oaKgpHe8RdsLll19uSsd7sa1du9bMysoqle/ogQcesOLo169fsfUKvtkMDw83Dx486FN+tj0Ju3fvXuQb+cWLFxd602+apk8Pw08//dQ6/umnn1rHmzdvfsp7zczMtN6At2rVyjTN4z0ao6OjTUnmpZdeatXt27ev9XZ2yZIl5pEjR07Z9slK2pPwiiuusI4PGTLEOt6/f3/reM+ePa3jmzZtKtS2JPPnn3+2jo8cOdI6/te//tU0zaJ/r5L83QAAoKzxPPenkvYkvPXWW63jBZ8jnnjiCet448aNrePp6emF2g4NDbWOm6ZppqSkWGXjxo0zTbPonnAHDx60RqC4XC7zjz/+sNp46KGHrPoPPPCAaZqm+Y9//MM69v777/vUPxMF77tgz7n09HTrN5Fk7tmz56xjK/h92Gw288cffzyjmEryvF2wJ2FWVpY5ZswYs2nTpj73cGJr2bKlVfdEr8jmzZub3377rZmTk1PoOgXvOy4uzvoz/uWXX5p/+ctfrHY/+OCDM7o/4EJHT0IggP3+++86fPiwJCk0NFRNmjSxytq2bWt9/umnnwqde/DgQe3atUvS8Z5tHTt2tMr++OMPq928vDx16dJFV155pa688kpNmzbNqvfDDz8Uardhw4bWCnA2m816y5eenm7VGTx4sCTpt99+U2JiosLDw1WvXj3dddddRcZ6pgqee+21157ROe3bt1dMTEyJrylJ//vf/6zPl112mfW54G9QUME427VrV2T9030PYWFh1lveb775Rrt27dKSJUus7zklJcWqe/vtt8swDGVnZ6tLly6KiopS9erVddttt2nDhg2nv8ESKng/Bb/jNm3aWJ8rVapkfS74Z6TgefXq1SuyzYLf+8nO5e8GAADliee5s1MazxcNGzb0WT35TJ8vfv75Z2sewbp166pixYpFtnHi/lNSUqx5KG+66SZVqlRJVapU0Q033KAlS5YUf5NFKPjMGBUVpQYNGvjEfLaxFVS/fn2f9k6lJM/bBfXr109jxozR5s2blZ2dXai8qD9j3333nVq2bKmwsDA1atRIDz74oFJTUyUd72F54r7T0tKsP+NXXnmlz7yFRf05By5GJAkBSFKh4QqnG75wYliFJE2cOFH79+8/62sW1W2/YNd/6fgQ15P97W9/0+eff67+/furSZMmCgoK0s6dO/XKK6+oY8eORT7MlZUTD8AFFfzuTiz8ccKpFiM5VTtlUf/kIccnhhobhqFbb73VKuvatatWr16tO+64Qy1btlRoaKj27t2r2bNnq2PHjqd8GD4XBR++Cw49iYyMLLL+iQe8Uznb76ioc0rSBgAA5YHnudM7X54vzqSNJk2aaOPGjbr//vvVrl07RUVF6cCBA/roo4+UnJysNWvWlOr1Slq3qOfhsrBnzx598sknkqTw8HBNmzZNK1as0IoVK6w6BRfHGT9+vDWMuUGDBjIMQz/88IOee+45de3a1WehmdNhuDECBUlCIIBVrlxZ0dHRko7/w7d161arrOB8I5dcckmhcxMSEjR8+HBJx+fl69mzp/WPZ6VKlayHw/DwcB09elSmafpsHo9HM2bMKFHcpmmqW7dumjVrljZv3qzMzExrPpW0tLQSPzAVvM/PPvvsjM4p6qGp4MPniXldJOmrr74q8gGjTp061ueCPfOKmvPl5Dj/+9//Flm/qN/sZF26dFFsbKyk43MVnZi35YorrvCZa9A0TSUmJuqVV17RN998o6NHj+pf//qXpONzvyxcuPC01/KXQ4cOaceOHdZ+we+o4Pd+snP5uwEAQHniea78bd++XRkZGdb+mT5f1KtXz3p23Llzpw4ePFhkGyd+K9M01bhxY73wwgtat26d0tPTrZe6Xq9X8+bNO+OYCz4zHjlyRNu3b/eJ+WxjK+hsEo4led4+4bfffrM+Jycn65577lHHjh19Vv0+Wd++ffXee+/pxx9/1NGjR3XjjTdKkrZs2aKffvrJ577r1q2r/Pz8Qn/O8/LyNG7cuLOKFbhQFX6lAyBg2Gw29e3bVy+//LKk40MaRo8ercOHD/tMKNyvX78iz3/mmWe0Y8cOffrpp9q4caNuuukmffLJJ3I4HOrXr5+mTZumzMxMde3aVffff78qVaqkvXv3asuWLfrPf/6jN954Q0lJSWcd94033qiIiAhdeeWVSkhIUH5+vk9yLTc31/p84h/9mjVravfu3adsNyUlRS+88IIkac6cOQoLC1OvXr2UlZWljz/+WHfddZeuuuqq08YXHR2tihUr6uDBg9qxY4fuvvtuNWjQQM8880yR9a+//np9/vnnkqRRo0YpJCRE4eHhGjFiRJH1b731VmtS7CFDhujo0aMyDEP/+Mc/rDrF/WYFORwO9e3bV1OmTNE333xjHS/Yw1CS7r//fqWmpuqaa65R9erV5XA49OWXX1rlBb/v89Gtt96qxx9/XHv37tXzzz9vHe/Vq1ex55zr3w0AAMpLIDzPnW+ysrJ0yy23aOjQofruu+80d+5cq+xUzxcVK1ZUcnKyFi5cqNzcXN18880aPny4du7c6TOE+8RvNXnyZK1YsUI9e/ZUjRo1FBYWpi+++MKqdzbf0TvvvKOGDRuqZcuWeumll6xkcMuWLVW9enVJOqvYSupcnrdr1qxpfV62bJneeecd2e12PfbYY0XWb9++vVq2bKm2bduqWrVqOnr0qLZt22aV5+bmKiYmRt27d9eCBQu0c+dOXX/99Ro8eLAiIiL0yy+/6Ntvv9V//vMfrV27VrVq1TqnewcuCOU6AyIAvylqomvTPD5Zb8OGDQtN+nti69u3r+n1eq36J46fWGwiMzPTbNGiRaHFIA4fPmw2bdq02HYLxnG2C3107ty52DarVKniM5H0yfGezqhRo04bb3ETKRc0YsSIQudXrVrVWhik4P3k5uaazZs3L1S/fv36Rcafk5NjXnnllcXGedVVV5m5ublndL/r16/3OdfpdBaaFHvw4MHFXiskJMTcuXPnKa9R0oVLRo8ebR0vOPH2jBkzzrjtmJgYMyEhoVDc11xzjfXnurg/f2f7dwMAgLIWqM9zRSnpwiVn8hxxurZr1qxpRkZGFor7b3/7m9VGcc+LO3futBbUKGp79NFHrbrjx48vtp7NZjO/+uqrU35HBe+74MJ3JzaHw+Fz32cT26l+79M5l+ftgovNnNjat29f5HNl3bp1i71Oo0aNzPz8fNM0TfOXX34p8nmx4HbizwFwsWO4MRDgYmJitG7dOo0YMUINGjSQy+VSWFiYLrvsMk2fPl1z5sw55RCCsLAwzZ8/X/Hx8ZKkN954Q6NGjVJ0dLTWrl2r8ePHq3nz5goJCVFoaKjq16+vG2+8Ue+8844uv/zyEsV877336pZbblHdunUVHh4uh8OhatWqKSUlRV999ZXPcN+zNXbsWH322Wfq1q2bKlasKKfTqfj4eN1www0+Q3BPZ9SoUbrzzjsVHR1tvSFdvXp1kbEFBQVp8eLFSklJUWRkpCIjI3XzzTf7zK9SkMvl0uLFizVx4kQ1a9ZMISEhCg4OVtOmTTVhwgQtWrRIQUFBZxRn27ZtfYZ9dO/e3Weiaun4G9+BAweqQYMGioqKkt1uV2xsrHr37q0vv/zylMNq/C0iIkJffvmlrrvuOoWFhSkmJkZ33323/vOf/5x2aMy5/t0AAKC88DxXvmrVqqWVK1cqKSlJISEhiouL02OPPabp06ef9tw6derom2++0dChQ1W7dm05nU5FRkbqqquu0rvvvquJEydadXv06KG77rpLTZo0UYUKFWS32xUTE6OuXbvqiy++UPv27c845uHDh+ull15S3bp1FRQUpJYtW2r+/Pk+vUDPJrZzcS7P22+99ZYGDhyoSpUqKTo6Wv3799enn35aZN0RI0aoV69eqlmzpkJDQ+V0OlWrVi3dfffdWrZsmTUnZ40aNfTtt9/q4YcfVsOGDRUcHKyIiAg1bNhQAwYM0CeffGL1tgQudoZpnsFMrAAAXEDOZpg5AADA6ezevdtKYHXs2LHYl7nnkzFjxmjs2LGSpBkzZmjQoEH+DQjAeY+ehAAAAAAAAECAI0kIAAAAAAAABDiShAAAAAAAAECAY05CAAAAAAAAIMDRkxAAAAAAAAAIcCQJAQAAAAAAgABHklCSaZrKyMgQI68BAAAuDjzfAQAAnB2ShJKOHj2qqKgoHT16tFyvm5Wbo+gHblH0A7coKzfHpywnJ1839lyoG3suVE5OfrnGBQAAcKHz1/MdAADAhYokIQAAAAAAABDgSBICAAAAAAAAAY4kIQAAAAAAABDgSBICAAAAAAAAAY4kIQAAAAAAABDgHP4OAACAQGSapvLz8+XxePwdCgKQ0+mU3W73dxgAAOA84vF45Ha7/R0GSqC0nu1IEvqR0+7Qo91utD4XZLfbdNOtda3PAICLR15enlJTU5Wdne3vUBCgDMNQQkKCwsPD/R0KAADwM9M0lZaWpvT0dH+HgnMQHR2tuLg4GYZR4jYM0zTNUozpgpSRkaGoqCgdOXJEkZGR/g4HAHAR83q9+vnnn2W321W5cmUFBQWd0z/kwNkyTVO///67srOzVb9+/Yu2RyHPdwAAnJnU1FSlp6crNjZWoaGhPJteYEzTVHZ2tg4cOKDo6GhVrVq1xG3RkxAAgHKUl5cnr9er6tWrKzQ01N/hIEBVrlxZu3fvltvtvmiThAAA4PQ8Ho+VIKxYsaK/w0EJhYSESJIOHDig2NjYEj/fkST0I6/Xq+37f5MkNahSTTabrUCZqd9+zZQkVaseLpuNTD4AXEwK/jcfKG/0EAAAAJKsOQh5eX3hO/EbnstLYJKEfnTMnafEiX+XJP02+U2FuYKtsrw8j4bfu1qS9PaHXRQczE8FAAAAAABKHy8QL3yl8RvSjQEAAPjNsGHDNGjQIH+HAQAAAAQ8uqcBAHAeOHbMo9wcb5lewxVsU0jI2Q09+Oqrr/TUU09p3bp1Mk1TNWvWVEpKioYNG6agoKAyihQAAAAofytWrNDVV1+tw4cPKzo62t/hlDuShAAAnAdyc7zavCmzzBKFrmCbmrYIP6sk4fz589WvXz+NHz9eb731lipVqqQff/xREydOVGpqqmrWrGnVdbvdcjqdZRE6AAAAypk366jM7Kxyu54RGiZbWMSZ1T3NsNrRo0drzJgxJYrjiiuuUGpqqqKiokp0/oWOJCEAAOeJ3Byvjh0r296EZ8o0Td1///169NFHNWzYMOt4w4YNNXPmTO3evVuGYeiNN97QU089paNHj2r9+vWqXbu2z5vXYcOGKT09XTNnzpQkrVq1SkOGDNGuXbvUtWtXVahQwee6GzZs0AMPPKCtW7cqPj5eTzzxhPr161dOdw0AAABJMrOzlLNmibzZmWV+LVtouIKv6CKdYZIwNTXV+vzuu+9q1KhR2r59u3UsPDy8xLEEBQUpLi6uxOdf6JiTEAAAFPLzzz9r165dp03QffLJJ9qwYYN27dp12jYPHz6s66+/XkOHDlV6erpuv/12vf3221Z5enq6unXrpr59++r333/X9OnTdccdd2j16tXnfD8AAAA4O97sTJlZR8t8O9tEZFxcnLVFRUXJMAxrPzY2Vs8++6wSEhLkcrnUokULLVy4UNLxl+BdunRRcnKyTNOUJB06dEgJCQkaNWqUpOPDjQ3DUHp6unW91atXKykpSaGhoapQoYKSk5N1+PBhSVJubq7uv/9+xcbGKjg4WB06dNDXX39dCt++f5AkBAAAhfz++++SpGrVqp2y3ujRoxUdHa3Q0NDTtjl//nzFx8frrrvuksPh0HXXXadOnTpZ5Z999pkqV66s++67T06nUx07dtStt96qN99889xuBgAAAAHhhRde0L/+9S8988wz+v7775WcnKzrr79eP//8swzD0Jtvvqmvv/5aU6ZMkSTdfffdqlatmpUkPNmmTZvUuXNnNWrUSGvXrtVXX32l6667Th6PR5L0yCOP6MMPP9Sbb76pb775RvXq1VNycrIOHTpUbvdcmhhu7EdOu0P3dbrO+lyQ3W7T9TfUsj4DAFCeKlWqJEn67bffVLdu3WLr1ahR44zb3Ldvn888hpJUs2ZN5eTkSJL27t2rWrVq+ZTXqVNHq1atOuNrAAAAIHA988wzevTRR9W3b19J0qRJk7R8+XI9//zzmjp1qqpVq6Z///vfGjBggNLS0rRgwQJ9++23cjiKTo9NnjxZbdq00bRp06xjjRs3liRlZWVp+vTpmjlzprp37y5JevXVV7V48WK9/vrrevjhh8v4bksfSUI/CnI4NL7XbUWWOZ02DRjcsJwjAgDguEsuuUS1atXS3LlzNXLkyGLr2Wx/vsg6Mf9Ldna2NSdhamqqQkJCJEnx8fH65ZdffM7fs2ePYmNjJUkJCQnavXu3T/nu3buVkJBwrrcDAACAi1xGRob27dun9u3b+xxv3769vvvuO2v/pptu0kcffaSJEydq+vTpql+/frFtbtq0STfddFORZTt37pTb7fa5ntPpVNu2bfXDDz+c4934B13UyllWfp7+yM0stGXl5/k7NAAALIZh6MUXX9TEiRP14osv6uDBg5Kkn376SYMHDy6U7JOO9z6sUaOG3nzzTXm9Xi1fvlwLFiywynv27KnffvtNr776qvLz8/XZZ59p2bJlVnmPHj104MABTZs2Tfn5+fryyy81e/ZsDRgwoOxvGDhH3qyj8vyedk6bN+uov28DAICLXnZ2tjZu3Ci73a6ff/75lHVPvOwOFPQkLGfHPHlae/AXHfO45TVNHco4omC7U9fWaqowR5BVz+s19cfvxyRJlSqHyGY79RLfAIALnyu47N7dlaTta6+9Vp9//rmefPJJPfHEE5KODy/u37+/qlatWuQ5b7zxhu655x49/fTT6tmzp/r27Su32y1JiomJ0ccff6yhQ4dq+PDhuuaaa5SSkmLN6VKhQgV9/vnnGjZsmEaMGKH4+HhNnz5dHTp0KOFdA+XnXFeBPNuVHQEAgK/IyEjFx8dr9erV6tixo3V89erVatu2rbX/0EMPyWaz6fPPP1ePHj3Us2dPn3myC2rWrJmWLl2qsWPHFiqrW7eugoKCtHr1amtKHbfbra+//lrDhg0r3ZsrJyQJ/eCYx60sT55y3W499up0SVKXJ6f51MnL8+jevx6fg+ntD7soOJifCgAuZq5gm5q2CC/za5ytDh06WCvCnezEqnAFde7cWT/99FOx7SUlJWnLli3Flrdt21Zr1qw56ziB88GJVSBLdG4pxwIAQCB6+OGHNXr0aNWtW1ctWrTQjBkztGnTJs2ePVvS8YXy3njjDa1du1atWrXSww8/rIEDB+r7779XhQoVCrU3YsQINW3aVPfee6/uvvtuBQUFafny5brppptUqVIl3XPPPXr44YcVExOjGjVqaPLkycrOztbgwYPL+9ZLBZknAADOAyEhdoWE2P0dBgAAACDpeC/38niJZQstvRfl999/v44cOaKHHnpIBw4cUKNGjfTJJ5+ofv36+v333zV48GCNGTNGrVq1kiSNHTtWixYt0t1336133323UHuXXHKJFi1apMcee0xt27ZVSEiI2rVrp379+kmSJk6cKK/Xq/79++vo0aNq06aNvvjiiyITjhcCkoQAAAAAAACwGKFhx6fBKMfrlcSgQYM0aNAga99ms2n06NEaPXp0obqVK1dWWlqazzGn06kNGzZY+0lJSYVGy3Ts2FGrV68u8vrBwcGaMmWKpkyZUqL4zzckCQEAAAAAAGCxhUUwT24AYnVjAAAAAAAAIMCRJAQAAAAAAAACHElCAAAAAAAAIMAxJ2E583hM5eV5lZvvVb7H1OVNmstp2GR6DB1Jd+vE/Jhut1dXd6kmScrLMxUc7MegAQAAAAAAcFEjSVjOvF4p/XC+DmfnSZLaN+igSJdLOVmGvv8pU7k5fy4w3qBhvFzBNnk9/ooWAAAAAAAAgYDhxn7g8Zg+m9djyuORcnO8OnbMdyuYNAQAwN8aN26s+fPnl0nbs2fP1hVXXFEmbQMAAAA4NXoS+pFpmjqWlyOH6ZF5YpxxgbLcnHxJtkJlAICLT1Z+no558sr0GiH2IIU5gs6oblJSknr37q1hw4b5HN+6dWsZRHZcSkqKUlJSyqx9AAAAAMUjSehH+Z58vfL5DElS9/tfkOT8syzfq3fnfC9JapuY5IfoAADl6ZgnT2sP/qJjHneZtB9idyqxYs0zThICAAAACCwMNwYA4DxxzONWlievTLbSSj7WqlVL8+bNkyTNnDlTLVq00KhRo1SpUiXFxcXp3Xff1erVq9WkSRNFRUVp8ODB8nqPT52xYsUKRUdH68UXX1TVqlUVFxen0aNHWz3mT7R3wv79+3XzzTercuXKqlGjhkaOHKn8/PxSuQ8AAACgPA0aNEi9e/f2dxinRE9CAABQYlu2bNFf//pXpaWl6c0339Sdd96p5ORkrVy5Urm5uWrZsqXmzZunG264QZJ09OhRffPNN9q5c6f27Nmja665RnXq1NHAgQMLtX3rrbcqLi5Ou3bt0sGDB9WjRw+FhYXpscceK+/bBAAACCjlMRVOQWczLY50POH25ptvasKECfrHP/5hHZ83b57+8pe/nNO0bWPGjNG8efO0adOmErdRlBdeeOG8n06OJCEAACixypUr6/7775ck9evXT3/72980ePBgVaxYUZLUsWNHffPNN1aS0Ov1atKkSQoNDVXDhg01dOhQvfXWW4WShL/99puWLVumtLQ0hYeHKzw8XCNHjtSYMWNIEgIAAJSxsp4Kp6CSTosTHBysSZMm6a677lKFChXKKLrSExUV5e8QTovhxgAAoMSqVKlifQ4NDS3yWGZmprUfHBys2NhYa79mzZr67bffCrW7d+9eBQcH+7RVp04d7d27t1TjBwAAQNHKciqc0pgWp0uXLoqLi9OECRNOWe/DDz9U48aN5XK5VKtWLf3rX/8qtu7MmTM1duxYfffddzIMQ4ZhaObMmZKkPXv2qFevXgoPD1dkZKRuvvlm7d+/X5L0448/KjQ0VHPmzLHaeu+99xQSEqJt27ZJKjzc2Ov1avLkyapXr55cLpdq1Kihp556yirfvHmzOnXqpJCQEFWsWFF33nmnz3N1WSBJCAAAyk1OTo4OHDhg7e/Zs0fVqlUrVC8hIUE5OTnWg5ck7d69WwkJCeUSJwAAAM5vdrtdTz/9tF588cViXyRv3LhRN998s/r27avNmzdrzJgxeuKJJ6zE38luueUWPfTQQ2rcuLFSU1OVmpqqW265RV6vV7169dKhQ4e0cuVKLV68WP/73/90yy23SJIaNmyoZ555Rvfee6/27NmjvXv36u6779akSZPUqFGjIq81YsQITZw4UU888YS2bdumOXPmWC/Is7KylJycrAoVKujrr7/W+++/ryVLlmjo0KHn/sWdAsONAQBAkfLz85WTk2PtG4Zxzm3abDaNGDFCL730kvbs2aOpU6dqzJgxhepVq1ZNV199tf7+97/r5Zdf1sGDB/XUU08VOXchAAAAAtNf/vIXtWjRQqNHj9brr79eqPzZZ59V586d9cQTT0iSLrnkEm3btk3//Oc/NWjQoEL1Q0JCFB4eLofDobi4OOv44sWLtXnzZu3atUvVq1eXJM2aNUuNGzfW119/rcsuu0z33nuvFixYoNtuu01BQUG67LLLdN999xUZ99GjR/XCCy/opZdesp5v69atqw4dOkiS5syZo5ycHM2aNUthYWGSpJdeeknXXXedJk2a5DPapjSRJPQjw7Dp0uoNFGS3y274duq02QzVrV9Rdrtks5/7/5QBAM5/IXbnedX2ww8/rIcfftjar1mz5jnHERERoRYtWqhOnTryer268847i038zZkzR0OHDlXNmjUVEhKilJQUPfLII+ccAwAAAC4ekyZNUqdOnfT3v/+9UNkPP/ygXr16+Rxr3769nn/+eXk8Htnt9jO6xg8//KDq1atbCUJJatSokaKjo/XDDz/osssukyS98cYbuuSSS2Sz2bR169ZiX7L/8MMPys3NVefOnYstb968uZUgPBG31+vV9u3bSRJejBx2u5Jbd1aUy6Ugh1OS1yqz223qcFUthYTY5HQyKhwALnYh9iAlVjz3JNzprnGmVqxYcdo6gwYNKvQG9uQV24oaynHfffcV+Vb15Pbi4uL0wQcfnEm4AAAACFBXXXWVkpOTNWLEiCJ7B5an7777TllZWbLZbEpNTVXVqlWLrBcSElLOkZ0ZkoQAAJwHwhxBZ72iGwAAAABp4sSJatGihRo0aOBz/NJLL9Xq1at9jq1evVqXXHJJsb0Ig4KC5PF4CrXz66+/6tdff7V6E27btk3p6enWnIOHDh3SoEGDNHLkSKWmpiolJUXffPNNkQnB+vXrKyQkREuXLtXf/va3QuWXXnqpZs6cqaysLKs34erVq2Wz2QrdY2mii5ofmaYpd75befnuQj0vTNOU2+2R2+0pVAYAAAAAAIDjmjZtqpSUFE2ZMsXn+EMPPaSlS5dq/Pjx+umnn/Tmm2/qpZdeKnJo8gm1atXSrl27tGnTJv3xxx/Kzc1Vly5drGt88803+u9//6sBAwaoY8eOatOmjSTp7rvvVvXq1fX444/r2WeflcfjKfY6wcHBevTRR/XII49o1qxZ2rlzp9atW2fNq5iSkqLg4GANHDhQW7Zs0fLly3Xfffepf//+ZTbUWCJJ6Ff5nnxNnf+qJnz4knLy83zL8r2aM2uTXv/3N8rL9RbTAgAAF46kpCSlp6f7OwwAAACcgRC7U2H2oDLfSmte7nHjxsnr9c2ftGrVSu+9957mzp2rJk2aaNSoURo3btwphyX36dNH3bp109VXX63KlSvrnXfekWEY+vjjj1WhQgVdddVV6tKli+rUqaN3331X0vFFTBYsWKC33npLDodDYWFhevvtt/Xqq6/q888/L/I6TzzxhB566CGNGjVKl156qW655RYdOHBAkhQaGqovvvhChw4d0mWXXaYbb7xRnTt31ksvvVQq31VxDJNuasrIyFBUVJSOHDmiyMjIMr1WauZRvbd1mw5n58id79bU+a9Kkpbe/4IO/+TUsWPH/0C73R7NmbVJkjT1tSRVqRpcpnEBAMpHTk6Odu3apdq1ays4mP+2wz8C4c9heT7fSZLn9zRlL5knM+toic43wiIU2qW37JXjTl8ZAIBSUtwzQVZ+no558k5xZukKsTP1zrkqjec75iQEAAAAAACAhfmyAxPDjQEAAAAAAIAAR5IQAAAAAAAACHAkCQEAAAAAAIAAR5IQAACUqxYtWmjmzJmSpNmzZ+uKK67wb0BlKDo6WitWrPB3GAAAAMBpkST0I8MwVD++rhol1JfN8P0pbIahmrWiVaduBdn4lQAA5SwpKUmGYWjJkiU+x//5z3/KMAwNGzasVK6TkpKiNWvWlEpbF7vly5fr6quvVlRUlKKjowuVHzhwQH379lXlypVVuXJl/f3vf5fH4yn/QAEAwAXH6/X6OwSco9L4DVnd2I8cdod6tk1WlMsll8Mp6c8f1O6wKalzXYWE2OQMsvsvSABAwGrQoIFmzJihLl26WMdmzJihhg0b+jEq/3C73XI6nX6NISwsTH/9619122236aGHHipU3r9/f8XFxemXX35Renq6evbsqUmTJumxxx7zQ7QAAOBCEBQUJJvNpn379qly5coKCgqSYRj+DgtnwTRN5eXl6ffff5fNZlNQUMlXpSZJCAAAitS3b19NmTJFR44cUVRUlNavXy9JateunU+9nTt3atiwYVq3bp1CQ0N1xx136LHHHpPt/7vCv/TSS5o0aZKys7N19913+5w7c+ZMPf/889q0aZMk6dlnn9X06dOVlpam2NhYDR8+XEOHDpUk7d69W7Vr19asWbM0duxY/fHHH+rdu7deffVVOZ1OZWZmKiUlRWvXrlVubq6aN2+uF198Uc2bNy/2Ht955x1NnDhRu3btUoUKFTR27FgNGjRIY8aM0YYNG1S9enW9++67uv322xUREaFNmzZp3rx51vnR0dGaN2+ekpKS5PV6NXr0aL3yyiuy2+0aOXKkz7VM09Szzz6radOm6fDhw2rbtq2mTZumOnXqnNHv0bZtW7Vt27bI4ctZWVlavHixduzYodDQUIWGhmrYsGEaPXo0SUIAAFAsm82m2rVrKzU1Vfv27fN3ODgHoaGhqlGjhvUMXhIkCQEAOA/k5OQXW2azGQoq0Kv8VHUNw5DLVXTd4OCz+2c/Ojpa3bp10zvvvKO7775bb7zxhm6//XZt3brVqpOdna3OnTtr2LBh+vDDD5WWlqYePXqoatWqGjx4sJYtW6aRI0dq4cKFat26tcaOHastW7YUe82aNWtq2bJlSkhI0IoVK9SjRw+1bNlS7du3t+p8/vnn+vbbb3X06FG1a9dOs2fP1qBBg+T1enXrrbdqzpw5stvtevTRR3XzzTfrxx9/LPKN+KeffqqhQ4fq/fffV1JSkv744w/99ttvVvnChQv12muv6cUXX1ReXp4mT558yu9r5syZmjlzplauXKkaNWpoyJAhOnr0qFX+1ltv6dlnn9XChQtVv359jRw5Utddd52+++47ORwOTZw4UV999ZXmz59/Rr9PQaZpWtsJXq9Xv/zyizIyMhQZGXnWbQIAgMAQFBSkGjVqKD8/n6lKLlB2u10Oh+Oce4GSJPQjd75bU+e/Kkm66v4XJP05jMnt9mjOrE2SpKktknzKAAAXn9v6LCm2rFWbynpsbGtrf/Cty5WbW/QDXKOmFTRu4p89/e69faUyMtySpA8+63bWcd1+++16/PHHNXDgQH344YfasmWL/vGPf1jln332mSpUqGDNUVijRg098MADmjNnjgYPHqzZs2crJSVFiYmJkqQxY8bopZdeKvZ6ffr0sT5fffXVSk5O1ooVK3yShKNGjVJERIQiIiLUrVs3bdy4UYMGDVJkZKRuueUWq97YsWM1ZcoU7du3T9WqVSt0rWnTpumBBx5Qp06dJEmxsbGKjY21yps0aaJBgwZJkhyO0z8yzZ49W/fdd581HHvixInWAi3S8STh/fffr6ZNm0qSnn76ab366qv673//qyuuuMLnez1b4eHhuuqqqzR69Gi9/PLLOnTokF544QVJIkkIAABOyzAMOZ1Ov0+vAv/y65IYY8aMkWEYPlvBeY5ycnI0ZMgQVaxYUeHh4erTp4/279/v08aePXvUs2dPhYaGKjY2Vg8//LDy84vvYQEAAM5c586dlZqaqvHjxysxMVFxcXE+5bt379aWLVsUHR1tbQ899JDS0tIkSfv27VPNmjWt+k6nU1WrVi32erNnz1arVq0UExOj6OhoLViwQH/88YdPnYIxhIWFWb31jh07pnvvvVe1atVSZGSkatWqJUmFzj/hl19+Uf369YuNpUaNGsWWFeXke61SpYpcLpe1v3fvXismSXK5XIqPj9fevXvP6jrFmT17to4dO6Z69eqpS5cuuvXWW2UYhipUqFAq7QMAAODi5veehI0bN/ZZObHgm/rhw4frs88+0/vvv6+oqCgNHTpUN9xwg1avXi1J8ng86tmzp+Li4rRmzRqlpqZqwIABcjqdevrpp8v9XgAAKKm3P+xSbJnN5jts4PU5Vxdb9+QhBtNmdDynuGw2mwYOHKinnnpKH3zwQaHy6tWrq3Xr1lq3bl2R58fHx+uXX36x9t1ut1JTU4usu2fPHg0cOFALFy5UUlKSHA6Hevfu7TOE9lT+9a9/aePGjfrqq6+UkJCg9PR0VahQodjza9asqR07dhTb3snzuYSHhys7O9vaz8rKUkZGRrH3euDAAeXm5lr7CQkJ2r17t7Wfl5enffv2KSEh4Yzu73QSEhL04YcfWvvTp09XmzZtFBYWVirtAwAA4OLm156E0vGkYFxcnLVVqlRJknTkyBG9/vrrevbZZ9WpUye1bt1aM2bM0Jo1a6z/EVm0aJG2bdumt99+Wy1atFD37t01fvx4TZ06VXl5ef68LQAAzkpwsKPYLeikVe5PVbfgfIQn1y2p4cOHa9GiRbruuusKlV177bXav3+/pk2bppycHHk8Hm3fvt1aXKNfv36aPXu21q9fr7y8PI0bN05ZWVlFXiczM1OmaSo2NlY2m00LFizQokWLzjjOjIwMBQcHq0KFCsrMzDztgh133XWXXnjhBa1cuVJer1cHDhzQt99+W2z9Vq1aae3atfrxxx+Vk5Ojxx57zCcp269fP02dOlXbt2/XsWPHNGLECJ9E42233aaXXnpJ27ZtU25urh5//HFVq1ZNbdu2PaP783q9ysnJsZ5xcnJylJOTY5X/+OOPSk9Pl8fj0YoVK/Tkk09q3LhxZ9Q2AAAA4Pck4c8//6z4+HjVqVNHKSkp2rNnjyRp48aNcrvd6tLlz54VDRs2VI0aNbR27VpJ0tq1a9W0aVNVqVLFqpOcnKyMjAyfSdUBAEDJxcTEqEuXLkXOURMeHq4lS5Zo6dKlqlWrlipWrKhbb73VGm7cpUsXjR8/Xn369FHVqlXl9XrVpEmTIq/TqFEjjRw5Up06dVLFihX17rvv6vrrrz/jOB988EHZ7XZVqVJFTZo0seZBLE7v3r317LPPasiQIYqKitJll12mzZs3F1u/U6dOuuuuu3TFFVeoXr16atq0qSIiIqzyv/71r7rtttt05ZVXqk6dOmrZsqVP+YABA3Tffffp2muvVVxcnL777jt9+umn1iiKp59+Wt27dy/2+qtWrVJISIiSk5N15MgRhYSEKCQkxCpfvny5GjRooIiICD3wwAOaNm2aunU7+3koAQAAEJgM80zH8JSBzz//XJmZmWrQoIFSU1M1duxY/fbbb9qyZYs+/fRT3X777T7DdCSpbdu2uvrqqzVp0iTdeeed+uWXX/TFF19Y5dnZ2QoLC9OCBQuKfdDOzc31aTcjI0PVq1fXkSNHynxi79TMo3pv6zYdzs7xWbhk6f0v6PBPTh075pV00sIlryWpStXgMo0LAFA+cnJytGvXLtWuXVvBwfy3Hf4RCH8OMzIyFBUVVS7Pd5Lk+T1N2Uvmycw6evrKRTDCIhTapbfsleNOXxkAAKAM+LUnYffu3XXTTTepWbNmSk5O1oIFC5Senq733nuvTK87YcIERUVFWVv16tXL9HoAAACBYsKECbrssssUERGh2NhY9e7dW9u3b/epk5SUVGjxurvvvtunDovTAQAAlC+/DzcuKDo6Wpdccol27NihuLg45eXlKT093afO/v37rVUN4+LiCq12fGL/5NUXCxoxYoSOHDlibb/++mvp3sgZMgxDtarUUP2qtWUzfH8Km2GoWkKkatSMku28+pUAAACKt3LlSg0ZMkTr1q3T4sWL5Xa71bVr10JzUd5xxx1KTU21tsmTJ1tlJxany8vL05o1a/Tmm29q5syZGjVqVHnfDgAAQMDw++rGBWVmZmrnzp3q37+/WrduLafTqaVLl6pPnz6SpO3bt2vPnj3WHEOJiYl66qmndODAAcXGxkqSFi9erMjISDVq1KjY67hcLrlcrrK/odNw2B3qnXitolwuuRxOSV6rzO6wqUtyfYWE2OQ8acJ6AACA89XChQt99mfOnKnY2Fht3LhRV111lXU8NDS02Je6JxanW7JkiapUqaIWLVpo/PjxevTRRzVmzBgFBQWV6T0AAAAEIr/2Ufv73/+ulStXavfu3VqzZo3+8pe/yG63q1+/foqKitLgwYP14IMPavny5dq4caNuv/12JSYm6vLLL5ckde3aVY0aNVL//v313Xff6YsvvtDjjz+uIUOGnBdJQAAAgEB35MgRSccXwClo9uzZqlSpkpo0aaIRI0YoOzvbKmNxOgAAgPLn156Ee/fuVb9+/XTw4EFVrlxZHTp00Lp161S5cmVJ0nPPPSebzaY+ffooNzdXycnJmjZtmnW+3W7X/Pnzdc899ygxMVFhYWEaOHCgxo0b569bAgAAwP/zer0aNmyY2rdv77Oq9a233qqaNWsqPj5e33//vR599FFt375d//nPfyRJaWlpPglCSdb+iZWzT1bUwnQAAAA4c35NEs6dO/eU5cHBwZo6daqmTp1abJ2aNWtqwYIFpR1auXDnu/Xvz2fIkHTVkH9Jcv5Z5vbovTnfS5KatrjKpwwAAOBCMGTIEG3ZskVfffWVz/E777zT+ty0aVNVrVpVnTt31s6dO1W3bt0SXWvChAkaO3bsOcULAAAQyFgSw8/yPflye4peqS8/36v8fG+RZQAAAOezoUOHav78+Vq+fLkSEhJOWbddu3aSpB07dkgq2eJ058vCdAAAABcqkoQAAAAoNaZpaujQofroo4+0bNky1a5d+7TnbNq0SZJUtWpVSccXp9u8ebMOHDhg1Tnd4nQul0uRkZE+GwAAAM4cSUIAAFCuWrRooZkzZ0o6vnjFFVdc4Zc4xowZo969e1v74eHh2rx5c5lc6+mnn1a/fv3KpO3zzZAhQ/T2229rzpw5ioiIUFpamtLS0nTs2DFJ0s6dOzV+/Hht3LhRu3fv1ieffKIBAwboqquuUrNmzSSxOB0AAIA/kCQEAACFJCUlyTAMLVmyxOf4P//5TxmGoWHDhpXKdVJSUrRmzZpSaetcZWZmqmnTpmXS9mOPPaZ33nmn1NpbvHixWrVqpYiICDVq1EgLFy4stbbP1fTp03XkyBElJSWpatWq1vbuu+9KkoKCgrRkyRJ17dpVDRs21EMPPaQ+ffro008/tdo4sTid3W5XYmKibrvtNg0YMIDF6QAAAMqQXxcuAQAA568GDRpoxowZ6tKli3VsxowZatiwoR+jwv/+9z/95S9/0dy5c9WjRw8tWLBAffr00ebNm1WnTh1/hyfTNE9ZXr16da1cufK07VzIi9MBAABciOhJCAAAitS3b199/vnnOnLkiCRp/fr1kv5cZOKEnTt36rrrrlPlypVVs2ZNPfnkk/J6/1x466WXXlL16tVVsWJFjRw50ufcmTNnqkWLFtb+s88+q/r16ysiIkJ169bVSy+9ZJXt3r1bhmHorbfeUr169RQdHa1BgwbJ7XZLOt4TsFevXoqNjVVUVJSuuuoqfffdd2d8v4ZhWHPjjRkzRtdee63uuusuRUVFqXbt2lqxYoXmzZunevXqqUKFCj73cuI+HnvsMVWsWFE1atTQtGnTrPKThzbv2LFDycnJiomJUd26dfX888+fcZwLFy5Uq1atdO2118pms+naa69V27ZtNWvWrDNuAwAAADgZSUI/MgxD1SrGq2blBNlkFCqrEheuqvERMviVAOCil5WbU+yW484747rH8oqve7aio6PVrVs3a5jsG2+8odtvv92nTnZ2tjp37qzOnTvrt99+05dffqm5c+dqxowZkqRly5Zp5MiReu+995SamipJ2rJlS7HXrFmzppYtW6aMjAy99tprevjhh7V69WqfOp9//rm+/fZbbdu2TUuXLtXs2bMlSV6vV7feeqt27dql/fv3q2XLlrr55ptP27OtOIsWLVJycrIOHTqk/v3767bbbtPHH3+s7777TqtXr9a//vUvffPNN1b9LVu2yDAMpaam6t1339U//vEPrVq1qlC7+fn5uvbaa9W8eXPt27dPH330kSZPnqw5c+ZYdaKjo/XVV18VGZfX6y10T16vV99//32J7hMAAACQGG7sVw67Qzdd2VtRLpdcziBJf/a6cDhs6tazgUJCbAoKsvsvSABAuaj2yMBiy7o2aqn37vqHtV//8TuVnZdbZN329Rrps/tGW/vNxg7VwayjkqT0F94967huv/12Pf744xo4cKA+/PBDbdmyRf/4x5+xfPbZZ6pQoYI1R2GNGjX0wAMPaM6cORo8eLBmz56tlJQUJSYmSjreo65g78CT9enTx/p89dVXKzk5WStWrFD79u2t46NGjVJERIQiIiLUrVs3bdy4UYMGDVJkZKRuueUWq97YsWM1ZcoU7du3T9WqVTvre2/durVuuOEGScd7VY4fP17/+Mc/FBYWpkaNGqlZs2b65ptv1KpVK0lSWFiYxowZI6fTqcTERKWkpGjWrFm66qqrfNpdv369UlNT9eSTTyooKEjNmjXT0KFDNXPmTN16662SpPT09GLjuuaaa/T3v/9d8+bN07XXXqv58+dr9erVSkpKOut7BAAAAE6gjxoAAChW586dlZqaqvHjxysxMVFxcXE+5bt379aWLVsUHR1tbQ899JDS0tIkSfv27VPNmjWt+k6nU1WrVi32erNnz1arVq0UExOj6OhoLViwQH/88YdPnYIxhIWF6ejR40nQY8eO6d5771WtWrUUGRmpWrVqSVKh889UlSpVrM+hoaFFHsvMzLT24+Pj5XQ6rf2aNWvqt99+K9Tu3r17FR8fr6CgIOtYnTp1tHfv3jOKq0GDBnr33Xc1duxYxcbG6vXXX1ffvn1VsWLFM785AAAA4CT0JAQA4Dzw2+Q3iy2z23zf6f385CvF1rWdNEfF96OL77V3Jmw2mwYOHKinnnpKH3zwQaHy6tWrq3Xr1lq3bl2R58fHx+uXX36x9t1utzXs+GR79uzRwIEDtXDhQiUlJcnhcKh3795nPFz4X//6lzZu3KivvvpKCQkJSk9PV4UKFUo83Phs7du3T26320oU7tmzp8gejAkJCYXq7t69WwkJCWd8rV69eqlXr17Wfrt27TRwYPG9UQEAAIDToSehH7nz3fr3gjf0z3kv65jbd9iY2+3R3NnfaeZr3yo3x+OnCAEA5SXMFVzsFuwMOuO6IUHF1y2p4cOHa9GiRbruuusKlV177bXav3+/pk2bppycHHk8Hm3fvl0rVqyQJPXr10+zZ8/W+vXrlZeXp3HjxikrK6vI62RmZso0TcXGxspms2nBggVatGjRGceZkZGh4OBgVahQQZmZmXrsscdKdL8llZWVpfHjxysvL0/r16+3hlqfrG3btqpSpYpGjRql3NxcbdmyRS+++OJZJfk2bNig/Px8HT16VOPGjdOhQ4dIEvqbYcgIDZcRFnHKTefwdxEAAKAskST0s2N5OcrOPVZkWW5OvnJy8ss5IgAAfMXExKhLly4+Q2lPCA8P15IlS7R06VLVqlVLFStW1K233moNN+7SpYvGjx+vPn36qGrVqvJ6vWrSpEmR12nUqJFGjhypTp06qWLFinr33Xd1/fXXn3GcDz74oOx2u6pUqaImTZpY8yCWlyZNmig/P19Vq1bVjTfeqKeeekpXX311oXpOp1Pz58/Xxo0bFRcXp+uvv14PPvigNR+hdPx7/fLLL4u91ogRIxQTE6OEhAR9//33Wr58ucLCwsrkvnBmjgUHK7N5a2W17XDKzXtZBxKFAADgvGSY5TUG5zyWkZGhqKgoHTlyRJGRkWV6rdTMo3pv6zYdzs6RO9+tqfNflSQtvf8FHf7JqWPHji9e4nZ7NGfWJknS1NeSVKUqD5MAcDHIycnRrl27VLt2bQUH89/2i8XMmTP1/PPPa9OmTf4O5YwEwp/D8ny+k6Tfjx7SVz+sUXZ2RrF1Ql2hSoytp7D/fiXz/xcUOsEIi1Bol96yV44r5mwAAICyxZyEAAAAQCnIzj2mzJyih9MDAACc7xhuDAAAAAAAAAQ4koQAAADnaNCgQRfMUGMAAACgKCQJAQAAAAAAgADHnIR+ZBiGqkRXlt2wySajUFnFSqGy2SSDVC4AAAAAAADKEElCP3LYHeqXdJOiXC65nEGSvH+WOWy6ttelCgmxKSjI7r8gAQBlwuv1nr4SUEZM0/R3CAAAADjPkCQEAKAcBQUFyWazad++fapcubKCgoJkGMbpTwRKiWma+v3332UYhpxOp7/DAQAAwHmCJCEAAOXIZrOpdu3aSk1N1b59+/wdDgKUYRhKSEiQ3c5oBQAAABxHktCP3PluzVo6VzbDUMe/PamCP0d+vlfzPtwqw5CatrxCEm/6AeBiERQUpBo1aig/P18ej8ff4SAAOZ1OEoQAAADwQZLQz44eOypJMuU7N5BpmsrKzPv/nfKOCgBQ1k4M9WS4JwAAAIDzAevmAgAAAAAAAAGOJCEAAAAAAAAQ4EgSAgAAAAAAAAGOJCEAAAAAAAAQ4EgSAgAAAAAAAAGO1Y39LCaiguyGIUOGz3HDMBQVHSybTTqpCAAAAAAAAChVJAn9yOlwakDnfopyuRTsDJLktcocDpt692mskBCbXC67/4IEAAAAAADARY/hxgAAAAAAAECAI0kIAAAAAAAABDiGG/uRO9+td1Z+ILthqOOgMSr4c+TnezX/4x9ks0lNW14uyemvMAEAAAAAAHCRI0noZ4eOHpYkmTJ9jpumqSPpOf+/U95RAQAAAAAAIJAw3BgAAAAAAAAIcCQJAQAAAAAAgABHkhAAAAAAAAAIcCQJAQAAAAAAgABHkhAAAAAAAAAIcKxu7GcRIRGyGYYMGT7HDcNQWHiQDEM6qQgAAAAAAAAoVSQJ/cjpcGpwcn9FuVwKdgZJ8lplDodNN97SVCEhNrlcdv8FCQAAAAAAgIsew40BAAAAAACAAEeSEAAAAAAAAAhwDDf2o3xPvt7/8iPZDZuS+o9UwZ8jP9+rhZ9tl80mNWvVVpLTb3ECAAAAAADg4kaS0I9M09T+9N8lSV6ZhcoO/pF9/LO30KkAAAAAAABAqWG4MQAAAAAAABDgSBICAAAAAAAAAY4kIQAAAAAAABDgSBICAAAAAAAAAY4kIQAAAAAAABDgWN3Yz0KCgmUYRpFlrmCHii4BAAAAAAAASg9JQj9yOpy6q8dfFeVyKcTp0mF5/yxz2tU3pblCQmxyBdv9GCUAAAAAAAAudgw3BgAAAAAAAAIcSUIAAAAAAAAgwDHc2I/yPfn6aM18OWw2JfV7RAV/jvx8r5Z88bNsNkPNWrWW5PRbnAAAAAAAALi4kST0I9M09dvBfZIkr8xCZfvTMo9/9hY6FQAAAAAAACg1DDcGAAAAAAAAAhxJQgAAAAAAACDAnTdJwokTJ8owDA0bNsw6lpOToyFDhqhixYoKDw9Xnz59tH//fp/z9uzZo549eyo0NFSxsbF6+OGHlZ+fX87RAwAAAAAAABeu8yJJ+PXXX+vf//63mjVr5nN8+PDh+vTTT/X+++9r5cqV2rdvn2644Qar3OPxqGfPnsrLy9OaNWv05ptvaubMmRo1alR53wIAAAAAAABwwfJ7kjAzM1MpKSl69dVXVaFCBev4kSNH9Prrr+vZZ59Vp06d1Lp1a82YMUNr1qzRunXrJEmLFi3Stm3b9Pbbb6tFixbq3r27xo8fr6lTpyovL89ftwQAAAAAAABcUPyeJBwyZIh69uypLl26+BzfuHGj3G63z/GGDRuqRo0aWrt2rSRp7dq1atq0qapUqWLVSU5OVkZGhrZu3Vo+N3COHHaHnPaiF5l2OGxyOPz+EwEAAAAAAOAi59cM1Ny5c/XNN99owoQJhcrS0tIUFBSk6Ohon+NVqlRRWlqaVadggvBE+Ymy4uTm5iojI8Nn8wenw6mh192px268TyFOl2+Z066UgS31t7tbyxVs90t8AAAAZ2vChAm67LLLFBERodjYWPXu3Vvbt2/3qcO80wAAAOcfvyUJf/31Vz3wwAOaPXu2goODy/XaEyZMUFRUlLVVr169XK8PAABwsVq5cqWGDBmidevWafHixXK73eratauysrKsOsw7DQAAcP7xW5Jw48aNOnDggFq1aiWHwyGHw6GVK1dqypQpcjgcqlKlivLy8pSenu5z3v79+xUXFydJiouLK/TW+cT+iTpFGTFihI4cOWJtv/76a+neHAAAQIBauHChBg0apMaNG6t58+aaOXOm9uzZo40bN0pi3mkAAIDzld+ShJ07d9bmzZu1adMma2vTpo1SUlKsz06nU0uXLrXO2b59u/bs2aPExERJUmJiojZv3qwDBw5YdRYvXqzIyEg1atSo2Gu7XC5FRkb6bP6Q78nXvLXzNWfVPOXmu33KPPleLfniZy349Ce58zx+iQ8AAOBcHTlyRJIUExMjKXDmnQYAALjQFL1iRjmIiIhQkyZNfI6FhYWpYsWK1vHBgwfrwQcfVExMjCIjI3XfffcpMTFRl19+uSSpa9euatSokfr376/JkycrLS1Njz/+uIYMGSKXy1Xomucb0zS1e/8eSZLX9Er6c+5Br2nqt73H50r0ev0RHQAAwLnxer0aNmyY2rdvbz3fldW807m5ucrNzbX2/TXnNAAAwIXKb0nCM/Hcc8/JZrOpT58+ys3NVXJysqZNm2aV2+12zZ8/X/fcc48SExMVFhamgQMHaty4cX6MGgAAAJI0ZMgQbdmyRV999VWZX2vChAkaO3ZsmV8HAADgYnVeJQlXrFjhsx8cHKypU6dq6tSpxZ5Ts2ZNLViwoIwjAwAAwNkYOnSo5s+fr1WrVikhIcE6HhcXZ807XbA34cnzTv/3v//1ae90806PGDFCDz74oLWfkZHB4nQAAABnwW9zEgIAAODiY5qmhg4dqo8++kjLli1T7dq1fcpbt25dJvNOny9zTgMAAFyozquehAAAALiwDRkyRHPmzNHHH3+siIgIaw7BqKgohYSEKCoq6qKfdxoAAOBCRJIQAAAApWb69OmSpKSkJJ/jM2bM0KBBgyQx7zQAAMD5iCQhAAAASo1pmqetw7zTAAAA5x+ShH7kdDg1rPe9inK5FOJ06bC8f5Y57Ro4uLVCQmxyBdv9GCUAAAAAAAAudixcAgAAAAAAAAQ4koQAAAAAAABAgGO4sR/le/L1xcalctpsSupTR9Kfw4o9+V59uXKX7HZDzVs1l+T0W5wAAAAAAAC4uJEk9CPTNPXzvp2SJK/pVcEkodc09cvu9OOfvUWcDAAAAAAAAJQShhsDAAAAAAAAAY4kIQAAAAAAABDgSBICAAAAAAAAAY4kIQAAAAAAABDgSBICAAAAAAAAAY4kIQAAAAAAABDgHP4OIJA57A4NufYORbpcCnYESTL/LHPYdOuAFgoJsSnIRS4XAAAAAAAAZYfskx8ZhiGnw6kgh1OGYRQuc9rldNoLlQEAAAAAAACliSQhAAAAAAAAEOAYbuxH+R6Plm5aoSC7XVfH1ZFkt8o8Hq/Wrt4ju11q3rqZ/4IEAAAAAADARY8koR+Zplc//LpdkuQxvSqYJPR6Te38+eDxzx6zqNMBAAAAAACAUsFwYwAAAAAAACDAkSQEAAAAAAAAAhxJQgAAAAAAACDAkSQEAAAAAAAAAhxJQgAAAAAAACDAkSQEAAAAAAAAApzD3wEEMofdoTu7367IoCAFO4IkmX+WOWy65dZmCg6xKchFLhcAAAAAAABlhyShHxmGoVBXiMJcLhmGoYJJQsMwFBziVEiI7f/LAAAAAAAAgLJBFzUAAAAAAAAgwNGT0I/yPR6t2rJaQXa7ro6rI8lulXk8Xn29fq8cDkPNWzf2X5AAAAAAAAC46JEk9CPT9Or7XVskSR7Tq4JJQq/X1PYffj/+2dPIH+EBAAAAAAAgQDDcGAAAAAAAAAhwJAkBAAAAAACAAFfi4cZZWVlauXKl9uzZo7y8PJ+y+++//5wDAwAAAAAAAFA+SpQk/Pbbb9WjRw9lZ2crKytLMTEx+uOPPxQaGqrY2FiShAAAAAAAAMAFpETDjYcPH67rrrtOhw8fVkhIiNatW6dffvlFrVu31jPPPFPaMQIAAAAAAAAoQyVKEm7atEkPPfSQbDab7Ha7cnNzVb16dU2ePFmPPfZYaccIAAAAAAAAoAyVaLix0+mUzXY8vxgbG6s9e/bo0ksvVVRUlH799ddSDfBi5rA7dPs1tynSFSSXw+lb5rCpz81N5Aq2yRnE+jIAAAAAAAAoOyVKErZs2VJff/216tevr44dO2rUqFH6448/9NZbb6lJkyalHeNFyzAMRYVFKsrlks2wSfL6lIVHuBQSYpPNZvgvSAAAAAAAAFz0StRF7emnn1bVqlUlSU899ZQqVKige+65R7///rteeeWVUg0QAAAAAAAAQNkqUU/CNm3aWJ9jY2O1cOHCUgsokHi8Hq3Ztl5Bdrs6Va2rgjlbj8erbzfuk8NhqEWbS/0XJAAAAAAAAC56JUoSonR4vV5t3LFJkpSfPEAFk4Rer6mtm/dLkjz5Df0QHQAAAAAAAALFGScJW7VqpaVLl6pChQpq2bKlDKP4efK++eabUgkOAAAAAAAAQNk74yRhr1695HK5JEm9e/cuq3gAAAAAAAAAlLMzThKOHj26yM8AAAAAAAAALmwlWt3466+/1vr16wsdX79+vTZs2HDOQQEAAAAAAAAoPyVKEg4ZMkS//vproeO//fabhgwZcs5BAQAAAAAAACg/JUoSbtu2Ta1atSp0vGXLltq2bds5BwUAAAAAAACg/JzxnIQFuVwu7d+/X3Xq1PE5npqaKoejRE0GJIfdof6d+io8yCmXw+lb5rDp+hsaKdhlkzOoRLlcAAAAAAAA4IyUKPvUtWtXjRgxQkeOHLGOpaen67HHHtM111xTasFd7AzDUMXIGMVGVZLNsBUqq1AhRDEVQ2SzGX6KEAAAAAAAAIGgRN3+nnnmGV111VWqWbOmWrZsKUnatGmTqlSporfeeqtUAwQAAAAAAABQtkqUJKxWrZq+//57zZ49W999951CQkJ0++23q1+/fnI6nadvAJIkj9ej/27fqGCHQ52q1lXBjp0ej1ebv0uTw2GoRZtw/wUJAAAAAACAi16JJxAMCwvTnXfeWZqxBByv16v12zdIkvI791PBJKHXa+q7b1MlSX/Nv8Qf4QEAAAAAACBAlDhJ+PPPP2v58uU6cOCAvF6vT9moUaPOOTAAAAAAAAAA5aNEScJXX31V99xzjypVqqS4uDgZxp8LaxiGQZIQAAAAAAAAuICUKEn45JNP6qmnntKjjz5a2vEAAAAAAAAAKGe201cp7PDhw7rppptKOxYAAAAAAAAAflCiJOFNN92kRYsWlXYsAAAAAAAAAPygREnCevXq6YknntCgQYP0r3/9S1OmTPHZztT06dPVrFkzRUZGKjIyUomJifr888+t8pycHA0ZMkQVK1ZUeHi4+vTpo/379/u0sWfPHvXs2VOhoaGKjY3Vww8/rPz8/JLcFgAAAAAAABCQSjQn4SuvvKLw8HCtXLlSK1eu9CkzDEP333//GbWTkJCgiRMnqn79+jJNU2+++aZ69eqlb7/9Vo0bN9bw4cP12Wef6f3331dUVJSGDh2qG264QatXr5YkeTwe9ezZU3FxcVqzZo1SU1M1YMAAOZ1OPf300yW5tXJlt9vVt2MfhTuDFGR3nlRmU8/rG8rlMuQMKlEuFwAAAAAAADgjJUoS7tq1q1Quft111/nsP/XUU5o+fbrWrVunhIQEvf7665ozZ446deokSZoxY4YuvfRSrVu3TpdffrkWLVqkbdu2acmSJapSpYpatGih8ePH69FHH9WYMWMUFBRUKnGWFZthU1yFKopyuWS32SR5/yyzGapUOUwhITbZbEbxjQAAAAAAAADn6Jy6qOXl5Wn79u2lMrzX4/Fo7ty5ysrKUmJiojZu3Ci3260uXbpYdRo2bKgaNWpo7dq1kqS1a9eqadOmqlKlilUnOTlZGRkZ2rp1a7HXys3NVUZGhs8GAACA0rFq1Spdd911io+Pl2EYmjdvnk/5oEGDZBiGz9atWzefOocOHVJKSooiIyMVHR2twYMHKzMzsxzvAgAAILCUKEmYnZ2twYMHKzQ0VI0bN9aePXskSffdd58mTpx4Vm1t3rxZ4eHhcrlcuvvuu/XRRx+pUaNGSktLU1BQkKKjo33qV6lSRWlpaZKktLQ0nwThifITZcWZMGGCoqKirK169epnFXNp8Xg92vDzt1r94wa5Pb6JVo/Hqy3fp2nTN6nKz/cW0wIAAMD5JysrS82bN9fUqVOLrdOtWzelpqZa2zvvvONTnpKSoq1bt2rx4sWaP3++Vq1apTvvvLOsQwcAAAhYJUoSjhgxQt99951WrFih4OBg63iXLl307rvvnlVbDRo00KZNm7R+/Xrdc889GjhwoLZt21aSsM7YiBEjdOTIEWv79ddfy/R6xfF6vfpq61ot+e5L5Xs9J5WZ2vj1b1q3Zq88+aZf4gMAACiJ7t2768knn9Rf/vKXYuu4XC7FxcVZW4UKFayyH374QQsXLtRrr72mdu3aqUOHDnrxxRc1d+5c7du3rzxuAQAAIOCUKEk4b948vfTSS+rQoYMM48/58ho3bqydO3eeVVtBQUGqV6+eWrdurQkTJqh58+Z64YUXFBcXp7y8PKWnp/vU379/v+Li4iRJcXFxhVY7PrF/ok5RXC6XtaLyiQ0AAADlZ8WKFYqNjVWDBg10zz336ODBg1bZ2rVrFR0drTZt2ljHunTpIpvNpvXr1xfZHtPJAAAAnJsSJQl///13xcbGFjqelZXlkzQsCa/Xq9zcXLVu3VpOp1NLly61yrZv3649e/YoMTFRkpSYmKjNmzfrwIEDVp3FixcrMjJSjRo1Oqc4AAAAUDa6deumWbNmaenSpZo0aZJWrlyp7t27y+M5PrIiLS2t0LOmw+FQTExMsVPKnC/TyQAAAFyoSrS6cZs2bfTZZ5/pvvvukyQrMfjaa69ZCbwzMWLECHXv3l01atTQ0aNHNWfOHK1YsUJffPGFoqKiNHjwYD344IOKiYlRZGSk7rvvPiUmJuryyy+XJHXt2lWNGjVS//79NXnyZKWlpenxxx/XkCFD5HK5SnJrAAAAKGN9+/a1Pjdt2lTNmjVT3bp1tWLFCnXu3LlEbY4YMUIPPvigtZ+RkUGiEAAA4CyUKEn49NNPq3v37tq2bZvy8/P1wgsvaNu2bVqzZo1Wrlx5xu0cOHBAAwYMUGpqqqKiotSsWTN98cUXuuaaayRJzz33nGw2m/r06aPc3FwlJydr2rRp1vl2u13z58/XPffco8TERIWFhWngwIEaN25cSW4LAAAAflCnTh1VqlRJO3bsUOfOnRUXF+czUkSS8vPzdejQoWKnlHG5XLwkBgAAOAclShJ26NBBmzZt0sSJE9W0aVMtWrRIrVq10tq1a9W0adMzbuf1118/ZXlwcLCmTp16ypXxatasqQULFpzxNQEAAHB+2bt3rw4ePKiqVatKOj6lTHp6ujZu3KjWrVtLkpYtWyav16t27dr5M1QAAICLVomShJJUt25dvfrqq6UZCwAAAC4CmZmZ2rFjh7W/a9cubdq0STExMYqJidHYsWPVp08fxcXFaefOnXrkkUdUr149JScnS5IuvfRSdevWTXfccYdefvllud1uDR06VH379lV8fLy/bgsAAOCiVqIk4Z49e05ZXqNGjRIFE2jsdrv6tO+l8CCnguzOk8psSu5xiYKCDDmDSrS+DAAAgF9s2LBBV199tbV/Yq7AgQMHavr06fr+++/15ptvKj09XfHx8eratavGjx/vM1x49uzZGjp0qDp37mxNPzNlypRyvxcAAIBAUaIkYa1atU65ivGJlelwajbDpuqVqynK5ZLdZpPk/bPMZiiuaoRCQmyy2c5txWgAAIDylJSUJNM0iy3/4osvTttGTEyM5syZU5phAQAA4BRKlCT89ttvffbdbre+/fZbPfvss3rqqadKJTAAAAAAAAAA5aNEScLmzZsXOtamTRvFx8frn//8p2644YZzDiwQeLwebdm9TcEOhzpXrSfpzx6DXq+pn378XU6noRZtwv0XJAAAAAAAAC56pTrZXYMGDfT111+XZpMXNa/Xq+Xff6nPv1kutzffp8zj8Wr92l/11ao98uQXP1wHAAAAAAAAOFcl6kmYkZHhs2+aplJTUzVmzBjVr1+/VAIDAAAAAAAAUD5KlCSMjo4utHCJaZqqXr265s6dWyqBAQAAAAAAACgfJUoSLlu2zCdJaLPZVLlyZdWrV08OR4maBAAAAAAAAOAnJcroJSUllXIYAAAAAAAAAPylRAuXTJgwQW+88Uah42+88YYmTZp0zkEBAAAAAAAAKD8lShL++9//VsOGDQsdb9y4sV5++eVzDgoAAAAAAABA+SnRcOO0tDRVrVq10PHKlSsrNTX1nIMKFHabXb0u76FQp1NOu+9PYbfb1PmaegpyGXI4jWJaAAAAAAAAAM5diZKE1atX1+rVq1W7dm2f46tXr1Z8fHypBBYIbDabasfVUpTLJYfNLslboMxQQo0ohYTYZLeXqMMnAAAAAAAAcEZKlCS84447NGzYMLndbnXq1EmStHTpUj3yyCN66KGHSjVAAAAAAAAAAGWrREnChx9+WAcPHtS9996rvLw8SVJwcLAeffRRjRgxolQDvJh5vB79+OvPCnU61LlqPUl/Div2ek39b8dBOYNsatEm3H9BAgAAAAAA4KJXoiShYRiaNGmSnnjiCf3www8KCQlR/fr15XK5Sju+i5rX69Xib5dJku6/vKckp1Xm8Xi1+stfJEk39avpj/AAAAAAAAAQIM5psru0tDQdOnRIdevWlcvlkmmapRUXAAAAAAAAgHJSoiThwYMH1blzZ11yySXq0aOHtaLx4MGDmZMQAAAAAAAAuMCUKEk4fPhwOZ1O7dmzR6GhodbxW265RQsXLiy14AAAAAAAAACUvRLNSbho0SJ98cUXSkhI8Dlev359/fLLL6USGAAAAAAAAIDyUaKehFlZWT49CE84dOgQi5cAAAAAAAAAF5gSJQmvvPJKzZo1y9o3DENer1eTJ0/W1VdfXWrBAQAAAAAAACh7JRpuPHnyZHXu3FkbNmxQXl6eHnnkEW3dulWHDh3S6tWrSzvGi5bdZlePy7oq1OmU0+77U9jtNnXsVEdBQYYcTsNPEQIAAAAAACAQlChJ2KRJE/3000966aWXFBERoczMTN1www0aMmSIqlatWtoxXrRsNpsuqVZPUS6XHDa7JG+BMkO1aldQSIhNdnuJOnwCAAAAAAAAZ+Ssk4Rut1vdunXTyy+/rJEjR5ZFTAAAAAAAAADK0Vl3UXM6nfr+++/LIpaA4/V69dNvO7T115+U7/WcVGZq967D2rnjkDwebzEtAAAAAAAAAOeuRONYb7vtNr3++uulHUvA8Xg9WvD1In2w5jO5Pfm+ZR6vVi77nxYv3Kl8t+mnCAEAAAAAABAISjQnYX5+vt544w0tWbJErVu3VlhYmE/5s88+WyrBAQAAAAAAACh7Z5Uk/N///qdatWppy5YtatWqlSTpp59+8qljGKzECwAAAAAAAFxIzipJWL9+faWmpmr58uWSpFtuuUVTpkxRlSpVyiQ4AAAAAAAAAGXvrJKEpuk7N97nn3+urKysUg0IAAAAuFgZNruM0PDCx0PDJVuJpgsHAAAoFSWak/CEk5OGAAAAAIoW5AiSPbqiMpu3ljwe30K7XceCHDJyMxViD1KYI8g/QQIAgIB1VklCwzAKzTnIHIQAAADA6TnsDuXKq/8e2Kns7AzfQrtTdne6wsIilVixJklCAABQ7s56uPGgQYPkcrkkSTk5Obr77rsLrW78n//8p/QivIjZbDZd07KTQp0OOW2+P4XdblP7K2vKGWST3UEiFgAA4GKRnXtMmTknTdnjcMqRnyubx+2foAAAQMA7qyThwIEDffZvu+22Ug0m0NhtdjWu2VBRLpccdrskr1Vmsxmqd0klhYTY5HAwPw0AAAAAAADKzlklCWfMmFFWcQAAAAAAAADwE7qo+ZHX69WutN36ad//lO/1nFRmau+eI/pld7o8Hm8xLQAAAAAAAADn7pxWN8a58Xg9+njdAknS31p2kuT8s8zj1dLFOyRJ1/au5o/wAAAAAAAAECDoSQgAAAAAAAAEOJKEAAAAAAAAQIAjSQgAAAAAAAAEOJKEAAAAAAAAQIAjSQgAAAAAAAAEOJKEAAAAAAAAQIBz+DuAQGaz2XR1sysV7HDIafP9Kex2m9olVpfTacjuMPwUIQAAAAAAAAIBSUI/stvsal6nqaJcLjnsdkleq8xmM9SwUaxCQmxyOOjwCQAAAAAAgLJD9gkAAAAAAAAIcCQJ/chrevXr779p94Ff5fF6fcu8ptJSj+q3vRkyTdNPEQIAAAAAACAQMNzYjzwejz5c/bEkaUDTKyU5C5R59cWCnyRJ3a+vqvTD7rNu3xVsU0iIvVRiBQAAAAAAwMWLJOEFIC/P1PZtmcrN8Z6+8v9zBdvUtEU4SUIAAAAAAACcFknCC0RujlfHjp15khAAAAAAAAA4U8xJCAAAAAAAAAQ4koQAAAAAAABAgCNJCAAAAAAAAAQ4koQAAAAAAABAgGPhEj+y2Wzq0DhRwQ6HHDb7SWWGWl9WTSEhNtnthp8iBAAAAAAAQCAgSehHdptdbeq3VJTLJafdIenP1YvtdpuaNItTdAWHHA46fAIAAAAAAKDs+DX7NGHCBF122WWKiIhQbGysevfure3bt/vUycnJ0ZAhQ1SxYkWFh4erT58+2r9/v0+dPXv2qGfPngoNDVVsbKwefvhh5efnl+etAAAAAAAAABcsvyYJV65cqSFDhmjdunVavHix3G63unbtqqysLKvO8OHD9emnn+r999/XypUrtW/fPt1www1WucfjUc+ePZWXl6c1a9bozTff1MyZMzVq1Ch/3NJZ8ZpepR3er98Opsnj9fqWeU398XuW0lIz5fWafooQAAAAAAAAgcCvScKFCxdq0KBBaty4sZo3b66ZM2dqz5492rhxoyTpyJEjev311/Xss8+qU6dOat26tWbMmKE1a9Zo3bp1kqRFixZp27Ztevvtt9WiRQt1795d48eP19SpU5WXl+fP2zstj8ejuSs/1GtL3lGex31SmVefffKj3p29VW63t5gWAAAAzj+rVq3Sddddp/j4eBmGoXnz5vmUm6apUaNGqWrVqgoJCVGXLl30888/+9Q5dOiQUlJSFBkZqejoaA0ePFiZmZnleBcAAACB5bya7O7IkSOSpJiYGEnSxo0b5Xa71aVLF6tOw4YNVaNGDa1du1aStHbtWjVt2lRVqlSx6iQnJysjI0Nbt24t8jq5ubnKyMjw2QAAAFA6srKy1Lx5c02dOrXI8smTJ2vKlCl6+eWXtX79eoWFhSk5OVk5OTlWnZSUFG3dulWLFy/W/PnztWrVKt15553ldQsAAAAB57xZuMTr9WrYsGFq3769mjRpIklKS0tTUFCQoqOjfepWqVJFaWlpVp2CCcIT5SfKijJhwgSNHTu2lO8AAAAAktS9e3d17969yDLTNPX888/r8ccfV69evSRJs2bNUpUqVTRv3jz17dtXP/zwgxYuXKivv/5abdq0kSS9+OKL6tGjh5555hnFx8eX270AAAAEivOmJ+GQIUO0ZcsWzZ07t8yvNWLECB05csTafv311zK/JgAAAKRdu3YpLS3NZ6RIVFSU2rVr5zNSJDo62koQSlKXLl1ks9m0fv36IttlpAgAAMC5OS+ShEOHDtX8+fO1fPlyJSQkWMfj4uKUl5en9PR0n/r79+9XXFycVefk1Y5P7J+oczKXy6XIyEifDQAAAGXvxEiPokaCFBwpEhsb61PucDgUExNzypEiUVFR1la9evUyiB4AAODi5dckoWmaGjp0qD766CMtW7ZMtWvX9ilv3bq1nE6nli5dah3bvn279uzZo8TERElSYmKiNm/erAMHDlh1Fi9erMjISDVq1Kh8bqQU2AxDrmCbQkL+3AAAAHBmGCkCAABwbvw6J+GQIUM0Z84cffzxx4qIiLDeDEdFRSkkJERRUVEaPHiwHnzwQcXExCgyMlL33XefEhMTdfnll0uSunbtqkaNGql///6aPHmy0tLS9Pjjj2vIkCFyuVz+vL2zEhHhUOglksdzPDmYl2taZYbhr6gAAABK14mRHvv371fVqlWt4/v371eLFi2sOgVfAEtSfn6+Dh06dMqRIhfSsx8AAMD5xq9JwunTp0uSkpKSfI7PmDFDgwYNkiQ999xzstls6tOnj3Jzc5WcnKxp06ZZde12u+bPn6977rlHiYmJCgsL08CBAzVu3Ljyuo0Ss9lsategjSJdLpk2r746sEcZx3IlSV6PqbpJoaoVUUEOB1lCAABwcahdu7bi4uK0dOlSKymYkZGh9evX65577pF0fKRIenq6Nm7cqNatW0uSli1bJq/Xq3bt2vkrdAAAgIuaX5OEpmmetk5wcLCmTp2qqVOnFlunZs2aWrBgQWmGVi7sNrsSL22rhIgIOR0OZebl6UhurlUe196lLrUT5HAw9BgAAFw4MjMztWPHDmt/165d2rRpk2JiYlSjRg0NGzZMTz75pOrXr6/atWvriSeeUHx8vHr37i1JuvTSS9WtWzfdcccdevnll+V2uzV06FD17duXlY0BAADKiF+ThAAAALj4bNiwQVdffbW1/+CDD0qSBg4cqJkzZ+qRRx5RVlaW7rzzTqWnp6tDhw5auHChgoODrXNmz56toUOHqnPnztaokilTppT7vQAAAAQKkoR+ZJqmDh09LIcnR15vzUJl2b97tD8oW9E1Qv0UIQAAwNlLSko65YgRwzA0bty4U04PExMTozlz5pRFeAAAACgCSUI/yvfk661lcyVJfZo39ykz3dK3rxzVt9qsKa909Ed4AAAAKE9MQw0AAPyIJCEAAADgbza7JEPenGMy8/LkyTwgeb0lasoIDZMtLKJ04wMAABc9koQAAACAnxk2m+TJl+fwIXmc0Tr23UaZ2Zln3Y4tNFzBV3SRSBICAICzRJIQAAAAOF/k50sej8zsTJlZR8/69JL1PQQAAJBs/g4AAAAAAAAAgH+RJAQAAAAAAAACHElCAAAAAAAAIMAxJ6Ef2Ww2ta7XQuFBQXLY7L6Fdin+cpfqRsXIbjf8EyAAAAAAAAACAklCP7Lb7LqyyRVKiIiQ0+H7U9jshmp3DlaP2jXkcNDhEwAAAAAAAGWH7BMAAAAAAAAQ4EgS+pFpmjqSlaFDmUfk9XoLleWke3T4j1x5vaafIgQAAAAAAEAgYLixH+V78jVj8duSpOsav+BTZrqljVOPaqM2acorHf0RHgAAAAAAAAIEPQkBAAAAAACAAEeSEAAAAAAAAAhwJAkBAAAAAACAAEeSEAAAAAAAAAhwJAkBAAAAAACAAEeSEAAAAAAAAAhwDn8HEMgMw6ZmtZso3OmU3XZSvtYmxbUOUq3IaNlshn8CBAAAAAAAQEAgSehHDrtdnZpfpYSICAU5nD5lNoehut1CdW3t2nI66fAJAAAAAACAskP2CQAAAAAAAAhwJAn9yDRNZeceU2ZOtkzTLFTmzvIq86i7UBkAAAAAAABQmhhu7Ef5nny98vkMSVJygxd8yky39N8pGfqvvtGUVzr6IzwAAAAAAAAECHoSAgAAAAAAAAGOJCEAAAAAAAAQ4EgSAgAAAAAAAAGOJCEAAAAAAAAQ4EgSAgAAAAAAAAGOJCEAAAAAAAAQ4Bz+DiCQGYZNl1ZvoDCnU3bbSflamxTbzKmE8CjZbIZ/AgQAAAAAAEBAIEnoRw67XcmtOyshIkJBDqdPmc1hqP51Ybq2dl05nXT4BAAAAAAAQNkh+wQAAAAAAAAEOJKEfmSaptz5buW682SaZqEyT56pvFxPoTIAAAAAAACgNJEk9KN8T76mzn9VI+c+pxx3nk+Z6ZbW/fOIRg/ZoLw8r58iBAAAAAAAQCAgSQgAAAAAAAAEOJKEAAAAAAAAQIAjSQgAAAAAAAAEOJKEAAAAAAAAQIAjSQgAAAAAAAAEOJKEAAAAAAAAQIBz+DuAQGYYhurH11WIwyGbcVK+1iZVbOhU1bAI2Qz/xAcAAAAAAIDAQJLQjxx2h3q2TVZCRIRcTqdPmc1hqGGfMF1bu76cTrufIgQAAAAAAEAgYLgxAAAAAAAAEOBIEgIAAAAAAAABjiShH7nz3Xp+3jT9/a1JOpaX61PmzTO1+ql0jfjbeuXmevwUIQAAAAAAAAIBSUIAAAAAAAAgwLFwCQAAAHAeMWx2GaHhp61n5rul3JxyiAgAAAQCkoQAAADAeSLI4ZQ9uqIym7eWPKeecibY7Zbt669IFAIAgFJBkhAAAAA4TzjsDuXKq/8e2Kns7Ixi64W6QpUYW09hDqdMkoQAAKAUkCQEAAAAzjPZuceUmZPl7zAAAEAAYeESAAAAAAAAIMDRk9CPDMNQrSo1FOxwyGaclK+1SRXqOhQbGi6b4Z/4AAAAAAAAEBhIEvqRw+5Q78RrlRARIZfT6VNmcxhq1Ddc19ZuIKfT7qcIAQAAAAAAEAgYbgwAAAAAAAAEOJKEAAAAAAAAQIDza5Jw1apVuu666xQfHy/DMDRv3jyfctM0NWrUKFWtWlUhISHq0qWLfv75Z586hw4dUkpKiiIjIxUdHa3BgwcrMzOzHO+i5Nz5br306SsaMedZHcvL9Snz5plaOzldo+79Wrm5Hj9FCAAAAAAAgEDg1yRhVlaWmjdvrqlTpxZZPnnyZE2ZMkUvv/yy1q9fr7CwMCUnJysnJ8eqk5KSoq1bt2rx4sWaP3++Vq1apTvvvLO8buGc5Xvy5fa4iyzzuiV3nrecIwIAAChbY8aMkWEYPlvDhg2t8pycHA0ZMkQVK1ZUeHi4+vTpo/379/sxYgAAgIufXxcu6d69u7p3715kmWmaev755/X444+rV69ekqRZs2apSpUqmjdvnvr27asffvhBCxcu1Ndff602bdpIkl588UX16NFDzzzzjOLj48vtXgAAAHDmGjdurCVLllj7Dsefj6XDhw/XZ599pvfff19RUVEaOnSobrjhBq1evdofoQIAAASE83ZOwl27diktLU1dunSxjkVFRaldu3Zau3atJGnt2rWKjo62EoSS1KVLF9lsNq1fv77cYwYAAMCZcTgciouLs7ZKlSpJko4cOaLXX39dzz77rDp16qTWrVtrxowZWrNmjdatW+fnqAEAAC5e522SMC0tTZJUpUoVn+NVqlSxytLS0hQbG+tT7nA4FBMTY9UpSm5urjIyMnw2AAAAlJ+ff/5Z8fHxqlOnjlJSUrRnzx5J0saNG+V2u31eFDds2FA1atSwXhQDAACg9J23ScKyNGHCBEVFRVlb9erV/R0SAABAwGjXrp1mzpyphQsXavr06dq1a5euvPJKHT16VGlpaQoKClJ0dLTPOQVfFBeFl8AAAADn5rxNEsbFxUlSoUmq9+/fb5XFxcXpwIEDPuX5+fk6dOiQVacoI0aM0JEjR6zt119/LeXoAQAAUJzu3bvrpptuUrNmzZScnKwFCxYoPT1d7733Xonb5CUwAADAuTlvk4S1a9dWXFycli5dah3LyMjQ+vXrlZiYKElKTExUenq6Nm7caNVZtmyZvF6v2rVrV2zbLpdLkZGRPps/GIahahXjVadKdRmGcVKhFFnDrtqXROjkIgAAgItJdHS0LrnkEu3YsUNxcXHKy8tTenq6T52CL4qLwktgAACAc+PX1Y0zMzO1Y8cOa3/Xrl3atGmTYmJiVKNGDQ0bNkxPPvmk6tevr9q1a+uJJ55QfHy8evfuLUm69NJL1a1bN91xxx16+eWX5Xa7NXToUPXt2/eCWNnYYXfopit7KyEiQsHOIJ8ym9NQ0/4RurZ2AwU57X6KEAAAoOxlZmZq586d6t+/v1q3bi2n06mlS5eqT58+kqTt27drz5491oviorhcLrlcrvIKGQAA4KLj1yThhg0bdPXVV1v7Dz74oCRp4MCBmjlzph555BFlZWXpzjvvVHp6ujp06KCFCxcqODjYOmf27NkaOnSoOnfuLJvNpj59+mjKlCnlfi8AAAA4M3//+9913XXXqWbNmtq3b59Gjx4tu92ufv36KSoqSoMHD9aDDz6omJgYRUZG6r777lNiYqIuv/xyf4cOAABw0fJrkjApKUmmaRZbbhiGxo0bp3HjxhVbJyYmRnPmzCmL8M4LNsOQzSa5gn1Hhufnm3K7i//uAAAAzld79+5Vv379dPDgQVWuXFkdOnTQunXrVLlyZUnSc889Z738zc3NVXJysqZNm+bnqAEAAC5ufk0SBjp3vltvLHpLNsNQ+4cn+pR580z9d9oRfWffoNEvtlHsJZLH82ei0J5v166teadMFDKXIQAAOB/NnTv3lOXBwcGaOnWqpk6dWk4RAQAAgCShnx3Lyym2zJ1tyq18/ffQL0pLz5HXczwhGO4KUvvKteVwGMUmCR1OQzKk9MPus4rHFWxTSAhzIAIAAAAAAAQSkoQXgOx8tzJyc+XxnPnwYrvdUF6uqR+3Zik3x3tG57iCbWraIpwkIQAAAAAAQIAhSXiRy83x6tixM0sSAgAAAAAAIDDZTl8FAAAAAAAAwMWMJCEAAAAAAAAQ4EgSAgAAABcTw/B3BAAA4ALEnIR+ZBiGqkRXltNul3Hyw5whRcY7FBXs4jkPAAAAZybIJRmGPL+nnXNTRmiYbGERpRAUAAC4EJAk9COH3aF+STcpISJCwc4gnzKb09AVd8Xomktqa9WB//kpQgAAAFxIDIdT3pxjytvwpbzZmSVuxxYaruArukgkCQEACBgkCQEAAICLjDc7U2bW0ZKfX4qxAACACwNzEgIAAAAAAAABjp6EfuTOd2vW0rly2Ay1H/6kT5nXbWrFc39ovSNdVzwa6acIAQAAAAAAEAhIEvrZ0WMnhoGYvgWmlJPuVY7yChUBAAAAAAAApYnhxgAAAAAAAECAI0kIAAAAAAAABDiShAAAAAAAAECAI0kIAAAAAAAABDiShAAAAAAAAECAY3VjP4uJqCCnzSbJ8C0wpPDKdoUFBRUqAgAAAAAAAEoTSUI/cjqcGtC5nxIiIhQSFORTZnMa6jC0oq65pLZWHfifnyIEAAAAAABAIGC4MQAAAAAAABDg6EkIAAAAXIAMm11GaLjvsZBQye573Mx3S7k55R0eAAC4wJAk9CN3vlvvrPxATptN7e8b7VPmdZv66qWD+jboqNoMC/NThAAAADgfBTmCZI+uqMzmrSWPp0CBS7aQYHkLHA92u2X7+isShQAA4JRIEvrZoaOH//+T6VtgSpm/e5SpY5JZOElosxlyBRceLZ6fb8rtNgsdBwAAwMXDYXcoV17998BOZWdn/FngCpG9YmV59qdKHrdCXaFKjK2nMIdTJklCAABwCiQJL0Auh12REQ4Zl7jl8fgmCu35du3amuenyAAAAFCesnOPKTMny9o3ZMruzlV+bpaU7/ZjZAAA4EJDkvACFGSzK09ufXVgtzKO5VrHw11Bal+5thwOo8RtGyU/FQAAAAAAABcokoQXsMy8PB3JzT19xTPkcBqSIaUfPru3zq5gm0JC7KUWBwAAAAAAAMoXSUJY7HZDebmmftyapdwc7xmd4wq2qWmLcJKEAAAAAAAAFzCShCgkN8erY8fOLEkIAAAAAACACx9JQj+LCImQw2ZIOmkyQEMKjrYpxOEoVAQAAACUOlewDIdTkmSEhks222lOAAAAFxOShH7kdDg1OLm/EiIiFBIU5FNmcxpKGl5J11xSW6sO/M9PEQIAACAguILlvayDcpzHk4Sy23UsyCEjN9OnWog9SGGOoCIaAAAAFzqShAAAAECAMxxO5TidWntgh7JzsyW7U3Z3umzBIVadELtTiRVrkiQEAOAiRZIQAAAAgCQpOzdbmTlZksMpR36ubB4WpwMAIFCQJPSjfE++3v/yIzntdnW49zGfMq/b1Jp/H9LW4Cw1vyekmBYAAAAAAACAc0eS0I9M09T+9N+tz76FUsa+fGUoX81MkoQAAAAAAAAoOyQJAQAAgIuYYbMfX634VHVYzRgAgIBHkhAAAAC4SAU5gmSPrqjM5q0lj6fYejaHU2ZEpGRjDkIAAAIVSUIAAADgIuWwO5Qrr/57YKeyszOKrVcxoqKaRjaXDHoTAgAQqEgSAgAAABe57Nxjx1ctLkaoK7QcowEAAOcjkoQXGZvNkCvYJpfLkM0muYKPvw3OzzfldpunORsAAAAAAACBiCShn4UEBctmGEWWOUMNBf1fe/ceFtV55wH8e87cmOEiCAJiBLzEaxCNRoNG0660YNysNmlrE9pgTaN2ddXGuEraxiZtom36ZHOpm7S7W+2TmzbdaLJqNMaIt6KoEe9BVBRjQYw3GAbm+ts/DBMHZmBErnO+n+fxeeSc95zzvr95ObznN+e8Rxf8vDAmvQ5RkXooA5xQVYHdZEf8AMDtVqFz6VB6zNEmicIA1SciIiIiIiIioi6CScIOZNAbMOuBGbgjMhJmo8lnnWpUMHFxD3xrQB/sqDwT1P6Mqg4OOLGr8ixq3U5EddPh6hUXLHoDxvXoA71eafUkod6gAApw7arzlrYzhakwmzkxNhERERERERFRZ8AkYQiyOhyocTuguvSosjvhcbfdY8Y6nQKHXfD5sRrY6zxBbWMKU5E2PIJJQiIiIiIiIiKiToJJQmoV9joPamuDSxISERERURfAKWWIiIg0hUnCDuRyu7D27+th0utwX+oin3Uep2DvyqsoMddi8AxTgD0QEREREbUBVQdAgafW5l3k0bshDgfc1krAE9yXw4olHGp4ZBtVkoiIiFoTk4QdSERw4fI/vP/3XQlcPevEVTgxSJgkJCIiIqL2o6gq4HbBfakC4rox97TbFA63IRq1hw5AbNZm96FaIhA2NhNgkpCIiKhLYJKQiIiIiIj8EpcT+CpJCL0TcLshNiukprrZbTkRDRERUdeidnQFiIiIiIgoRCmc2JCIiKir4J2EREREREQUFEXVQbFENFtOXE7AaAIUBe5LFbd/XM5tSERE1OaYJCQiIiIiomYZ9UboomNhTR8JuN1Nlg1zOqE/UwJPXS0c+3fCE8QchoFwbkMiIqL2wSQhERERERE1S6/Tww4PCitPw2arCljOYrIgI74/IvU3LjU8Qc5hGAjnNiQiImofTBJ2ML1ODwX+52rRGQCd2jrTRqqqAlNY4325XAKnU/xsQURERETUmM1eC2tdTUdXg4iIiFoZk4QdyKA3YO6DM3FHZCTMRpPPOtWo4Fu/iMe3BvTBjsozt3Uck16HqEg9lAFOuN2+iUKdS4fSY452TxRyDmsiIiIiam+emmqI7fYSnJwfkYiIQhWThBpgVHVwwIldlWdRVWv3Lo8wGTE+oS/CI3Sw13lgMilQVcAUprbpHYZ6gwIowLWrzlvazhSmwmzW3fLxamvdsNfd2oMqLT0WEREREXVeYqtB3d8/afEciZwfkYiIQhmThBpidThw3f51krDhHYYGg8BusiN+AAC70e8dhgaDAr3+69sA6xOLeoMC1AZXD51OgcMu+PxYTdDJO1OYirThES1K3NnrPDhSZG2XYxERERFR53Y7cyRyfkQiIgplTBJ2IJfbhfWFmxCm12N86s981nlcggNvXcO58GL0+2HbfEwN7zA0GFVEddPBaVWREZsKvV7xSRIaDAr6DDXCrXfftOxGYrH3nQaUHPbc0t2H9joPamvbZ6jVnsciIiIioiCZwqDoDU0WUSwRnKuGiIioHTBJ2IFEBGcvlgEAPNIggeUBLpU4cAkO9H00rk3rUX+HoREqVJceTofq90UnpjAVHoMbuytLYbU7AAAGo4rEWDPSjHc0Siq2tvYcG3IcSkRERNRyiqqDEmYGdLobST5/jCa400aiTtfMi/p0OsBsRoC93DqjKcBrA32JywnY61rrqERERJ0ek4TUiEnn/0Unep0Ck0VBzQWn97FlI1REuXSAsW3r1NJ5DBUFcLtvLXHZ3nMmEhEREYUSo94IXXQsqo1GKOYweNJHAm53o3Kq3gB3ZBT2lOyHrTbw47/dusVjbMIduFR9pcnjmhUdzHV1gAQY+6kqxOOGO/0e1AWRJQxzOqHu28VEIRERaUbIJAlXrFiBF198ERUVFUhPT8drr72G0aNHd3S1uiSjzv+LThIiwjE24o4OucuuJfMYAkBUNx369Le0+bE4jyEREVHb4Biv69Hr9LDDg8JLZ2CvMcN9sRxwN/7yNTYyFmlR6bA56mCtC/zGYUukC7UeF/adPoCa6mv+y5jMGJt4J5TPj0FqbX7L6GJioR80HHVGAwounIDN7r/cjf1ZkBHfH+F6A4RJQiIi0oiQSBKuWbMGTz75JN544w2MGTMGL7/8MrKyslBcXIz4+PiOrl6X1fBFJ5HGlt0u2BovO6l3q3MLNnxkui2PRURERK2LY7yuzeasQ51ThcteA7gaJwktplv7Irem1gprzTW/64yKAl1kDKoHDPZ71+KNQibow0wQQzfYnPYmE5NERERaFBJJwpdeeglPPPEEfvzjHwMA3njjDWzYsAF//vOfsWTJkg6unTaoSuM5DIEbicBe/fVwqq3zshN/GiYh67lcbTc/oj+cx5CIiKh1cYxHwfLevVh5GjZblf9CJjPiPX0xpFsioDT/RbKiNp5PUbFE3Jgj8SY1Lgdq3Y4m92XWGRGu9/3C3VNTDbE1SFQqSnCDShFABIolHGp4ZMBiLa1bS3ls1httCvTIdxCaaxMREbWdLp8kdDgcOHDgAPLy8rzLVFVFZmYmCgoKOrBm2mFUdYiI0CO+wRyGwI15DI0WwY7Ss6iqu3FXYv3LTtJNvREeoWv0SK+iNB5X1N992DAR6S8JWU/n0uHKhfa5E/DmeQybGtfd3C5FAXR6Be4mkpkN41C/jasFidWWzJlYW+u+pce7W1o/zudIREQNcYxHLWGz1wa8Q1CBNJswq1c/r6K14XyKBgNqDTrgq/kRVUWFUwEKL52Gzel/3xa9ERlxfRDmcAGer8ZVqgpxOmDfmw+PzfrVQU035kvUNz8mCnO5YTh5HGH3TACaSKjVuh0ouHwOtX4e9wYAs86AjNiUoJKEwSQcxe2Asfoq1M8KAIe9ybL+qJYIhI3NbLJNXU0wcQOCT9a29v6IqOX8ftnTAp3py5EunyT88ssv4Xa7kZCQ4LM8ISEBn3/+ud9t7HY77Dc9Rnv9+nUAQFVVgG8dW1G1tRpGtxNmcUMnboj9xh9sa/XXywHALQKn86vOVheJMI8Lnq/yLqrLAWtVlU/5hstdLid0dR6EedxBlTeLG3qXG7o6D3QuNajyAL7axoXLVZdxoLwCNrvvAKS7xYKhPWLhqKuBo+7GHzOPC3CGATDaENHLCctNOShVURBpMaDK5vBJkBkMKqzuOkTc4YDlpheR6HQKHKqC/RX/gM3x9bHDDAYMj06EqtOhpsYFVW+DwXhjOwH8vtGufrmiqKipcXu3aa48AOj1Kq5e8aD8Qh0ie6hwKo0HYgYx4Oo/3HB99UFazDokJJlgczlg9zRf3rtNTxO+KKuD09UgeSfi/5tnERgMOqT0MaNbtMFPSwK7fs2Jc6W1N47VxP7rl/vUz+lutjwAGPRqi+rWUnV1jROf/pqmKIDb4Ibd44Sq3uhr9cLaaNB1q0nZekyyEnU9Lfl9b8/f9cjISCgdfIv8rY7xOnJ8BwDV1VWAR4XaxNvcPG4F1dVWv+UU0UOxu+BxBS4TzL7q96PCAEBp8phN7a/hfpqrv799KS402kew8biZuIDqqioobiVguWD2pYgeHpsD1Ybmj6nCgC+rq3C8vBT2uq/nLlRMRqjXyuGxVkHcHnSzRKJvfCqsX3yBGrv/C0W3IQxVdTp4zp3xzm+oRnaDrndfOD2ABzd+rxXVAGudHYevX4DDETgJZDQakd6tFyIVHRw1NuhMgft4td0KW7U1YJJQdAZYDVUwmpo/H122W3HkegXsHlfAMgaXG2mKGRbVAEHgcgAgIo3OM6oHzbapqwkmbiZVj2HdEuE2Nf8O72D3l9YtEbFB7I+IWs795UXYDxVCHLc4j9pNFKMZpvTR0MW1z5OQzY3xunySsCWWLVuGZ599ttHy3r17d0Btbhj9x7UB1324pR0rQkRERJp3/fp1REVFdXQ1bklnHN8RERERdSbNjfG6fJIwLi4OOp0OFy9e9Fl+8eJFJCYm+t0mLy8PTz75pPdnj8eDK1euIDY2ts2+Na+qqkLv3r1x/vz5Ljfobi2MAWMAMAYAYwAwBgBjADAGQOeNQWRkxz/ycqtjPI7vOhfGpmmMT2CMTWCMTWCMTWCMTWBajE1zY7wunyQ0Go0YOXIktm7diqlTpwK4MSjcunUr5s6d63cbk8kEk8nksyw6OrqNa3pDVFSUZjpfIIwBYwAwBgBjADAGAGMAMAYAY+DPrY7xOL7rnBibpjE+gTE2gTE2gTE2gTE2gTE2X+vySUIAePLJJ5Gbm4tRo0Zh9OjRePnll1FTU+N9Ex4RERERdT0c4xERERG1n5BIEk6bNg2XLl3CM888g4qKCgwfPhybNm1qNNE1EREREXUdHOMRERERtZ+QSBICwNy5cwM+XtwZmEwmLF26tNFjMFrCGDAGAGMAMAYAYwAwBgBjADAGwejMYzx+foExNk1jfAJjbAJjbAJjbAJjbAJjbBpTRKR93rNMREREREREREREnZLa0RUgIiIiIiIiIiKijsUkIRERERERERERkcYxSUhERERERERERKRxTBK2kxUrViA1NRVhYWEYM2YMCgsLO7pKrWbHjh148MEHkZSUBEVRsG7dOp/1IoJnnnkGPXv2hNlsRmZmJkpKSnzKXLlyBTk5OYiKikJ0dDQef/xxWK3WdmxFyy1btgz33HMPIiMjER8fj6lTp6K4uNinTF1dHebMmYPY2FhERETg4YcfxsWLF33KlJWVYfLkybBYLIiPj8eiRYvgcrnasykt9vrrr2PYsGGIiopCVFQUMjIy8NFHH3nXh3r7/Vm+fDkURcGCBQu8y0I9Dr/61a+gKIrPv0GDBnnXh3r76124cAE//OEPERsbC7PZjLS0NOzfv9+7PtTPiampqY36gaIomDNnDgBt9AO3241f/vKX6NOnD8xmM/r164df//rXuHka6FDvB1oRyuO7YLXGuT9UaH1M3JTmYjN9+vRG/Sg7O9unTKjGhtcSgQUTm2984xuN+s7s2bN9yoRibHj9FVhzsdFqnwmaUJtbvXq1GI1G+fOf/yzHjh2TJ554QqKjo+XixYsdXbVWsXHjRvn5z38u77//vgCQtWvX+qxfvny5dOvWTdatWyeHDh2Sf/mXf5E+ffpIbW2tt0x2drakp6fLnj17ZOfOndK/f3955JFH2rklLZOVlSUrV66Uo0ePSlFRkTzwwAOSnJwsVqvVW2b27NnSu3dv2bp1q+zfv1/uvfdeGTt2rHe9y+WSu+66SzIzM+XgwYOyceNGiYuLk7y8vI5o0i378MMPZcOGDXLy5EkpLi6Wp59+WgwGgxw9elREQr/9DRUWFkpqaqoMGzZM5s+f710e6nFYunSpDB06VMrLy73/Ll265F0f6u0XEbly5YqkpKTI9OnTZe/evXLmzBnZvHmznDp1ylsm1M+JlZWVPn1gy5YtAkC2bdsmItroB88//7zExsbK+vXrpbS0VN577z2JiIiQV155xVsm1PuBFoT6+C5Yt3vuDyVaHxM3pbnY5ObmSnZ2tk8/unLlik+ZUI0NryUCCyY2999/vzzxxBM+fef69eve9aEaG15/BdZcbLTaZ4LFJGE7GD16tMyZM8f7s9vtlqSkJFm2bFkH1qptNPyj7/F4JDExUV588UXvsmvXronJZJJ3331XRESOHz8uAGTfvn3eMh999JEoiiIXLlxot7q3lsrKSgEg27dvF5Eb7TUYDPLee+95y5w4cUIASEFBgYjcGDipqioVFRXeMq+//rpERUWJ3W5v3wa0kpiYGPnv//5vzbW/urpa7rzzTtmyZYvcf//93iShFuKwdOlSSU9P97tOC+0XEVm8eLHcd999Addr8Zw4f/586devn3g8Hs30g8mTJ8uMGTN8lj300EOSk5MjItrsB6FIS+O7ptzuuT9UcUwcWKAk4ZQpUwJuo5XYiPBaoikNYyMiPuNtf7QSGxHtXn8Foz42IuwzzeHjxm3M4XDgwIEDyMzM9C5TVRWZmZkoKCjowJq1j9LSUlRUVPi0v1u3bhgzZoy3/QUFBYiOjsaoUaO8ZTIzM6GqKvbu3dvudb5d169fBwB0794dAHDgwAE4nU6fGAwaNAjJyck+MUhLS0NCQoK3TFZWFqqqqnDs2LF2rP3tc7vdWL16NWpqapCRkaG59s+ZMweTJ0/2aS+gnX5QUlKCpKQk9O3bFzk5OSgrKwOgnfZ/+OGHGDVqFL73ve8hPj4eI0aMwH/9139512vtnOhwOPDWW29hxowZUBRFM/1g7Nix2Lp1K06ePAkAOHToEHbt2oVJkyYB0F4/CEVaH981dDvnfq3g733z8vPzER8fj4EDB+KnP/0pLl++7F2npdho/VqiKQ1jU+/tt99GXFwc7rrrLuTl5cFms3nXaSE2Wr/+akrD2NTTep9pir6jKxDqvvzyS7jdbp8OBgAJCQn4/PPPO6hW7aeiogIA/La/fl1FRQXi4+N91uv1enTv3t1bpqvweDxYsGABxo0bh7vuugvAjfYZjUZER0f7lG0YA38xql/XFRw5cgQZGRmoq6tDREQE1q5diyFDhqCoqEgT7QeA1atX47PPPsO+ffsardNCPxgzZgxWrVqFgQMHory8HM8++yzGjx+Po0ePaqL9AHDmzBm8/vrrePLJJ/H0009j3759mDdvHoxGI3JzczV3Tly3bh2uXbuG6dOnA9DG7wEALFmyBFVVVRg0aBB0Oh3cbjeef/555OTkANDe38ZQpPXx3c1u99yvFfy9b1p2djYeeugh9OnTB6dPn8bTTz+NSZMmoaCgADqdTjOx0fK1RHP8xQYAHn30UaSkpCApKQmHDx/G4sWLUVxcjPfffx9AaMeG11+BBYoNoO0+EwwmCYla0Zw5c3D06FHs2rWro6vS7gYOHIiioiJcv34df/vb35Cbm4vt27d3dLXazfnz5zF//nxs2bIFYWFhHV2dDlF/lxQADBs2DGPGjEFKSgr++te/wmw2d2DN2o/H48GoUaPwwgsvAABGjBiBo0eP4o033kBubm4H1679/c///A8mTZqEpKSkjq5Ku/rrX/+Kt99+G++88w6GDh2KoqIiLFiwAElJSZrsBxTaeO6n1vCDH/zA+/+0tDQMGzYM/fr1Q35+PiZOnNiBNWtfWr6WaE6g2MycOdP7/7S0NPTs2RMTJ07E6dOn0a9fv/auZrvS+vVXUwLFZsiQIZruM8Hg48ZtLC4uDjqdrtGbhC5evIjExMQOqlX7qW9jU+1PTExEZWWlz3qXy4UrV650qRjNnTsX69evx7Zt23DHHXd4lycmJsLhcODatWs+5RvGwF+M6td1BUajEf3798fIkSOxbNkypKen45VXXtFM+w8cOIDKykrcfffd0Ov10Ov12L59O1599VXo9XokJCRoIg43i46OxoABA3Dq1CnN9IOePXt6v6WsN3jwYO+jd1o6J547dw6ffPIJfvKTn3iXaaUfLFq0CEuWLMEPfvADpKWl4Uc/+hF+9rOfYdmyZQC01Q9CldbHd0251XO/VvD3/tb07dsXcXFxOHXqFABtxEbr1xJNCRQbf8aMGQMAPn0nVGOj9euvpgSKjT9a6jPBYJKwjRmNRowcORJbt271LvN4PNi6davPM/Ghqk+fPkhMTPRpf1VVFfbu3ettf0ZGBq5du4YDBw54y3z66afweDzeX9jOTEQwd+5crF27Fp9++in69Onjs37kyJEwGAw+MSguLkZZWZlPDI4cOeIz+NmyZQuioqIaJRy6Co/HA7vdrpn2T5w4EUeOHEFRUZH336hRo5CTk+P9vxbicDOr1YrTp0+jZ8+emukH48aNQ3Fxsc+ykydPIiUlBYA2zon1Vq5cifj4eEyePNm7TCv9wGazQVV9h1g6nQ4ejweAtvpBqNL6+K4pt3ru1wr+3t+aL774ApcvX0bPnj0BhHZseC0RWHOx8aeoqAgAfPpOKMbGH61df92K+tj4o+U+41cHvzhFE1avXi0mk0lWrVolx48fl5kzZ0p0dLTP23K6surqajl48KAcPHhQAMhLL70kBw8elHPnzomIyPLlyyU6Olo++OADOXz4sEyZMkX69OkjtbW13n1kZ2fLiBEjZO/evbJr1y6588475ZFHHumoJt2Sn/70p9KtWzfJz8/3eY26zWbzlpk9e7YkJyfLp59+Kvv375eMjAzJyMjwrq9/zfq3v/1tKSoqkk2bNkmPHj26zGvWlyxZItu3b5fS0lI5fPiwLFmyRBRFkY8//lhEQr/9gTR8c1aox2HhwoWSn58vpaWlsnv3bsnMzJS4uDiprKwUkdBvv4hIYWGh6PV6ef7556WkpETefvttsVgs8tZbb3nLhPo5UeTGW16Tk5Nl8eLFjdZpoR/k5uZKr169ZP369VJaWirvv/++xMXFyb//+797y2ihH4S6UB/fBet2z/2hROtj4qY0FZvq6mp56qmnpKCgQEpLS+WTTz6Ru+++W+68806pq6vz7iNUY8NricCai82pU6fkueeek/3790tpaal88MEH0rdvX5kwYYJ3H6EaG15/BdZUbLTcZ4LFJGE7ee211yQ5OVmMRqOMHj1a9uzZ09FVajXbtm0TAI3+5ebmioiIx+ORX/7yl5KQkCAmk0kmTpwoxcXFPvu4fPmyPPLIIxIRESFRUVHy4x//WKqrqzugNbfOX9sByMqVK71lamtr5V//9V8lJiZGLBaLfOc735Hy8nKf/Zw9e1YmTZokZrNZ4uLiZOHCheJ0Otu5NS0zY8YMSUlJEaPRKD169JCJEyd6/0CJhH77A2mYJAz1OEybNk169uwpRqNRevXqJdOmTZNTp05514d6++v93//9n9x1111iMplk0KBB8qc//clnfaifE0VENm/eLAAatUtEG/2gqqpK5s+fL8nJyRIWFiZ9+/aVn//852K3271ltNAPtCCUx3fBao1zf6jQ+pi4KU3Fxmazybe//W3p0aOHGAwGSUlJkSeeeKJRwj1UY8NricCai01ZWZlMmDBBunfvLiaTSfr37y+LFi2S69ev++wnFGPD66/AmoqNlvtMsBQRkTa9VZGIiIiIiIiIiIg6Nc5JSEREREREREREpHFMEhIREREREREREWkck4REREREREREREQaxyQhERERERERERGRxjFJSEREREREREREpHFMEhIREREREREREWkck4REREREREREREQaxyQhERERERERERGRxjFJSETUyeTn50NRFFy7dq3dj/2rX/0Kw4cPb/fjEhEREXUEjru+dvbsWSiKgqKiIgDtGxtFUbBu3bo2Pw4RNY1JQiLqdKZPnw5FUbB8+XKf5evWrYOiKB1Uq66lsw06iYiIqHPiuOv2heq4a+zYsSgvL0e3bt3a/Fjl5eWYNGlSmx+HiJrGJCERdUphYWH47W9/i6tXr3Z0VYLicDg6ugpERERELcJxF/ljNBqRmJjYLsnixMREmEymNj8OETWNSUIi6pQyMzORmJiIZcuWBSzj71vbl19+Gampqd6fp0+fjqlTp+KFF15AQkICoqOj8dxzz8HlcmHRokXo3r077rjjDqxcudJnP+fPn8f3v/99REdHo3v37pgyZQrOnj3baL/PP/88kpKSMHDgQADAkSNH8E//9E8wm82IjY3FzJkzYbVam2zrxo0bMWDAAJjNZnzzm9/0OU69Xbt2Yfz48TCbzejduzfmzZuHmpoav/tbtWoVnn32WRw6dAiKokBRFKxatQoAUFZWhilTpiAiIgJRUVH4/ve/j4sXLwas2+nTp9G3b1/MnTsXIgK73Y6nnnoKvXr1Qnh4OMaMGYP8/HyfY0dHR2Pz5s0YPHgwIiIikJ2djfLycm+Z/Px8jB49GuHh4YiOjsa4ceNw7ty5JmNEREREbYfjLl8cd3297c2PG9cfb/369Rg4cCAsFgu++93vwmaz4S9/+QtSU1MRExODefPmwe12e/eTmpqKX//613jkkUcQHh6OXr16YcWKFT7Havi4cXOfLceTRG2DSUIi6pR0Oh1eeOEFvPbaa/jiiy9ua1+ffvop/vGPf2DHjh146aWXsHTpUvzzP/8zYmJisHfvXsyePRuzZs3yHsfpdCIrKwuRkZHYuXMndu/e7R103fzN9datW1FcXIwtW7Zg/fr1qKmpQVZWFmJiYrBv3z689957+OSTTzB37tyAdTt//jweeughPPjggygqKsJPfvITLFmyxKfM6dOnkZ2djYcffhiHDx/GmjVrsGvXroD7nTZtGhYuXIihQ4eivLwc5eXlmDZtGjweD6ZMmYIrV65g+/bt2LJlC86cOYNp06b53c/hw4dx33334dFHH8Uf/vAHKIqCuXPnoqCgAKtXr8bhw4fxve99D9nZ2SgpKfFuZ7PZ8Pvf/x5vvvkmduzYgbKyMjz11FMAAJfLhalTp+L+++/H4cOHUVBQgJkzZ/JxJiIiog7EcdfXOO5qms1mw6uvvorVq1dj06ZNyM/Px3e+8x1s3LgRGzduxJtvvok//vGP+Nvf/uaz3Ysvvoj09HQcPHgQS5Yswfz587Flyxa/x2jus+V4kqgNCRFRJ5ObmytTpkwREZF7771XZsyYISIia9eulZtPW0uXLpX09HSfbf/jP/5DUlJSfPaVkpIibrfbu2zgwIEyfvx4788ul0vCw8Pl3XffFRGRN998UwYOHCgej8dbxm63i9lsls2bN3v3m5CQIHa73VvmT3/6k8TExIjVavUu27Bhg6iqKhUVFX7bmpeXJ0OGDPFZtnjxYgEgV69eFRGRxx9/XGbOnOlTZufOnaKqqtTW1vrdr7/YfPzxx6LT6aSsrMy77NixYwJACgsLfbbbvXu3xMTEyO9//3tv2XPnzolOp5MLFy747HfixImSl5cnIiIrV64UAHLq1Cnv+hUrVkhCQoKIiFy+fFkASH5+vt96ExERUfviuIvjrnqlpaUCQA4ePCgiItu2bfOJjb/jzZo1SywWi1RXV3uXZWVlyaxZs7w/p6SkSHZ2ts+xpk2bJpMmTfL+DEDWrl0rIs1/thxPErUd3klIRJ3ab3/7W/zlL3/BiRMnWryPoUOHQlW/Pt0lJCQgLS3N+7NOp0NsbCwqKysBAIcOHcKpU6cQGRmJiIgIREREoHv37qirq8Pp06e926WlpcFoNHp/PnHiBNLT0xEeHu5dNm7cOHg8HhQXF/ut24kTJzBmzBifZRkZGT4/Hzp0CKtWrfLWJSIiAllZWfB4PCgtLQ06DidOnEDv3r3Ru3dv77IhQ4YgOjraJ75lZWX41re+hWeeeQYLFy70Lj9y5AjcbjcGDBjgU5ft27f7xMVisaBfv37en3v27OmNbffu3TF9+nRkZWXhwQcfxCuvvOLzSAwRERF1HI67OO5qTsPjJSQkIDU1FRERET7L6utQr2GcMzIyAvaz5j5bjieJ2o6+oytARNSUCRMmICsrC3l5eZg+fbrPOlVVISI+y5xOZ6N9GAwGn58VRfG7zOPxAACsVitGjhyJt99+u9G+evTo4f3/zQOXtmS1WjFr1izMmzev0brk5ORWP16PHj2QlJSEd999FzNmzEBUVJS3HjqdDgcOHIBOp/PZ5uaBob/Y3vw5rVy5EvPmzcOmTZuwZs0a/OIXv8CWLVtw7733tnpbiIiIKHgcd3Hc1Zxb/XzbCseTRG2DSUIi6vSWL1+O4cOHeyeprtejRw9UVFRARLxzkBQVFd328e6++26sWbMG8fHx3oFaMAYPHoxVq1ahpqbGO5DdvXs3VFVtVPebt/nwww99lu3Zs6dRfY4fP47+/fsHXRej0egzYXT9sc6fP4/z5897v9U+fvw4rl27hiFDhnjLmc1mrF+/Hg888ACysrLw8ccfIzIyEiNGjIDb7UZlZSXGjx8fdF38GTFiBEaMGIG8vDxkZGTgnXfe4aCOiIioE+C4i+OuttAwznv27MHgwYP9lg32s+0M7SIKNXzcmIg6vbS0NOTk5ODVV1/1Wf6Nb3wDly5dwu9+9zucPn0aK1aswEcffXTbx8vJyUFcXBymTJmCnTt3orS0FPn5+Zg3b16Tk3nn5OQgLCwMubm5OHr0KLZt24Z/+7d/w49+9CMkJCT43Wb27NkoKSnBokWLUFxcjHfeecf7Rrx6ixcvxt///nfMnTsXRUVFKCkpwQcffNDkxNypqakoLS1FUVERvvzyS9jtdmRmZnpj+dlnn6GwsBCPPfYY7r//fowaNcpn+/DwcGzYsAF6vR6TJk2C1WrFgAEDkJOTg8ceewzvv/8+SktLUVhYiGXLlmHDhg1Bxba0tBR5eXkoKCjAuXPn8PHHH6OkpCTgIJGIiIjaF8ddHHe1hd27d+N3v/sdTp48iRUrVuC9997D/Pnz/ZZt7rPtTO0iCjVMEhJRl/Dcc881emxh8ODB+M///E+sWLEC6enpKCws9L7N7XZYLBbs2LEDycnJeOihhzB48GA8/vjjqKura/IbbovFgs2bN+PKlSu455578N3vfhcTJ07EH/7wh4DbJCcn43//93+xbt06pKen44033sALL7zgU2bYsGHYvn07Tp48ifHjx2PEiBF45plnkJSUFHC/Dz/8MLKzs/HNb34TPXr0wLvvvgtFUfDBBx8gJiYGEyZMQGZmJvr27Ys1a9b43UdERAQ++ugjiAgmT56MmpoarFy5Eo899hgWLlyIgQMHYurUqdi3b1/Qj99YLBZ8/vnnePjhhzFgwADMnDkTc+bMwaxZs4LanoiIiNoex10cd7W2hQsXYv/+/RgxYgR+85vf4KWXXkJWVpbfss19tp2pXUShRpGGE0sQEREREREREbWC1NRULFiwAAsWLOjoqhBRM3gnIRERERERERERkcYxSUhERERERERERKRxfNyYiIiIiIiIiIhI43gnIRERERERERERkcYxSUhERERERERERKRxTBISERERERERERFpHJOEREREREREREREGsckIRERERERERERkcYxSUhERERERERERKRxTBISERERERERERFpHJOEREREREREREREGsckIRERERERERERkcb9P/dyopTKjJl0AAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 1300x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"💾 Guardado en reports/07_tokens_comparativa.png\n"
]
}
],
"source": [
"# Visualizacion: distribucion tokens antes vs despues\n",
"fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n",
"\n",
"# Comparativa global\n",
"axes[0].hist(df['tokens_raw'], bins=40, alpha=0.6, label='Crudo',\n",
" color='#7F77DD', edgecolor='white')\n",
"axes[0].hist(df['tokens_clean'], bins=40, alpha=0.6, label='Limpio',\n",
" color='#5DCAA5', edgecolor='white')\n",
"axes[0].axvline(df['tokens_raw'].median(), color='#534AB7',\n",
" linestyle='--', lw=1.5, label=f'Mediana crudo: {df[\"tokens_raw\"].median():.0f}')\n",
"axes[0].axvline(df['tokens_clean'].median(), color='#0F6E56',\n",
" linestyle='--', lw=1.5, label=f'Mediana limpio: {df[\"tokens_clean\"].median():.0f}')\n",
"axes[0].set_title('Tokens: crudo vs limpio', fontweight='bold')\n",
"axes[0].set_xlabel('Numero de tokens')\n",
"axes[0].set_ylabel('Frecuencia')\n",
"axes[0].legend(fontsize=9)\n",
"\n",
"# Por clase\n",
"for clase, color in [('Toxico', '#E8593C'), ('No toxico', '#5DCAA5')]:\n",
" mask = df[TARGET_BIN] == (clase == 'Toxico')\n",
" axes[1].hist(df.loc[mask, 'tokens_clean'], bins=30, alpha=0.6,\n",
" label=clase, color=color, edgecolor='white')\n",
"axes[1].set_title('Tokens limpios por clase', fontweight='bold')\n",
"axes[1].set_xlabel('Numero de tokens limpios')\n",
"axes[1].legend()\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(PROJECT_ROOT / 'reports' / 'v2' / '07_tokens_comparativa.png',\n",
" dpi=150, bbox_inches='tight')\n",
"plt.show()\n",
"print('💾 Guardado en reports/07_tokens_comparativa.png')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Comentarios con < 3 tokens tras limpieza: 106\n",
"\n",
" [NO TOXICO]\n",
" CRUDO : I agree with the protestor.\n",
" LIMPIO : \"agree protestor\"\n",
"\n",
" [TOXICO]\n",
" CRUDO : I think ,he kill him\n",
" LIMPIO : \"think kill\"\n",
"\n",
" [NO TOXICO]\n",
" CRUDO : 1 word: Provocateur........\n",
" LIMPIO : \"word provocateur\"\n",
"\n",
" [NO TOXICO]\n",
" CRUDO : I LIKE TURTLES!\n",
" LIMPIO : \"turtle\"\n",
"\n",
" [TOXICO]\n",
" CRUDO : CNN is such Bullcrap!\n",
" LIMPIO : \"cnn bullcrap\"\n",
"\n",
" [TOXICO]\n",
" CRUDO : You all are some dumb as people\n",
" LIMPIO : \"dumb people\"\n",
"\n"
]
}
],
"source": [
"# Casos problematicos: comentarios con menos de 3 tokens tras limpieza\n",
"# Son comentarios que casi desaparecen -> hay que revisarlos\n",
"short_clean = df[df['tokens_clean'] < 3]\n",
"print(f'Comentarios con < 3 tokens tras limpieza: {len(short_clean)}')\n",
"print()\n",
"for _, row in short_clean.head(6).iterrows():\n",
" label = 'TOXICO' if row[TARGET_BIN] else 'NO TOXICO'\n",
" print(f' [{label}]')\n",
" print(f' CRUDO : {row[TEXT_COL][:80]}')\n",
" print(f' LIMPIO : \"{row[\"clean_text\"]}\"')\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Registro en MLflow\n",
"\n",
"Guardamos exactamente qué configuración usamos.\n",
"Si mañana cambiamos `min_token_length` o las custom stopwords,\n",
"podremos comparar qué configuración produjo mejores métricas al modelar.\n",
"\n",
"> Para ver el dashboard: `mlflow ui` desde la raíz del proyecto.\n",
"\n",
"> mlflow ui --backend-store-uri file:./mlruns --port 5001 "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026/05/14 16:23:55 INFO mlflow.tracking.fluent: Experiment with name 'Youtube_project_data' does not exist. Creating a new experiment.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MLflow run registrado\n",
" Run ID : fe6b6538f89b490eb6f586ef555bc82f\n",
" Experimento: Youtube_project_data\n",
" Ver UI : mlflow ui --backend-store-uri file:///mnt/c/Users/under/Documents/F5/3_Projects/Project_9_Equipo3/Project_YT/mlruns\n"
]
}
],
"source": [
"# ── Configuración MLflow ──\n",
"MLFLOW_DIR = PROJECT_ROOT / 'mlruns'\n",
"EXPERIMENT_NAME = 'Youtube_project_data'\n",
"\n",
"mlflow.set_tracking_uri(f\"file:{MLFLOW_DIR}\")\n",
"mlflow.set_experiment(EXPERIMENT_NAME)\n",
"\n",
"\n",
"with mlflow.start_run(run_name='preprocessing_v2'):\n",
"\n",
" # Params: que configuracion usamos\n",
" mlflow.log_param('spacy_model', 'en_core_web_sm')\n",
" mlflow.log_param('nltk_stopwords', 'english')\n",
" mlflow.log_param('custom_stopwords', list(CUSTOM_STOPWORDS))\n",
" mlflow.log_param('min_token_length', MIN_TOKEN_LEN)\n",
" mlflow.log_param('remove_urls', prep_cfg['remove_urls'])\n",
" mlflow.log_param('remove_mentions', prep_cfg['remove_mentions'])\n",
" mlflow.log_param('lemmatize', prep_cfg['lemmatize'])\n",
"\n",
" # Metrics: que produce este preprocesamiento\n",
" reduction = (1 - df['tokens_clean'].mean() / df['tokens_raw'].mean()) * 100\n",
" mlflow.log_metric('avg_tokens_raw', round(df['tokens_raw'].mean(), 2))\n",
" mlflow.log_metric('avg_tokens_clean', round(df['tokens_clean'].mean(), 2))\n",
" mlflow.log_metric('median_tokens_clean', float(df['tokens_clean'].median()))\n",
" mlflow.log_metric('token_reduction_pct', round(reduction, 2))\n",
" mlflow.log_metric('empty_after_clean', int(empty_after))\n",
" mlflow.log_metric('short_texts_lt3', len(short_clean))\n",
"\n",
" # Artefactos: archivos que produce\n",
" out_csv = PROJECT_ROOT / 'data' / 'processed' / 'v2' /'comments_preprocessed.csv'\n",
" df[['CommentId', TEXT_COL, 'clean_text', TARGET_BIN] + SUBLABELS].to_csv(\n",
" out_csv, index=False\n",
" )\n",
" mlflow.log_artifact(str(out_csv))\n",
" mlflow.log_artifact(str(PROJECT_ROOT / 'reports' / 'v2' / '07_tokens_comparativa.png'))\n",
" mlflow.log_artifact(str(CONFIG_PATH))\n",
"\n",
" run_id = mlflow.active_run().info.run_id\n",
" print(f'MLflow run registrado')\n",
" print(f' Run ID : {run_id}')\n",
" print(f' Experimento: Youtube_project_data')\n",
" print(f' Ver UI : mlflow ui --backend-store-uri file://{MLFLOW_DIR}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Conclusiones y decisiones"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"CONCLUSIONES — PREPROCESAMIENTO\n",
"=======================================================\n",
"Pipeline aplicado:\n",
" 1. lowercase\n",
" 2. regex: URLs, @menciones, \\xa0, apostrofes, numeros\n",
" 3. spaCy: lematizacion (en_core_web_sm)\n",
" 4. NLTK: stopwords english + custom\n",
"\n",
"Resultado:\n",
" Reduccion tokens : 51.2%\n",
" Tokens mediana : 9 (antes: 19)\n",
" Comentarios vacios: 0\n",
"\n",
"Decisiones clave:\n",
" NO quitados: black, white, police, cop\n",
" -> EDA mostro que aparecen en ambas clases con contexto distinto\n",
" -> el modelo necesita verlas para aprender por bigrams\n",
" SI quitados: youtube, video, watch, like, comment, channel\n",
" -> ruido tematico sin valor discriminante\n",
" spaCy para lemma, no NLTK Stemmer\n",
" -> stemmer corta letras, lemma entiende gramatica\n",
"\n",
"\n"
]
}
],
"source": [
"reduction = (1 - df['tokens_clean'].mean() / df['tokens_raw'].mean()) * 100\n",
"\n",
"print(f\"\"\"\n",
"CONCLUSIONES — PREPROCESAMIENTO\n",
"{'='*55}\n",
"Pipeline aplicado:\n",
" 1. lowercase\n",
" 2. regex: URLs, @menciones, \\\\xa0, apostrofes, numeros\n",
" 3. spaCy: lematizacion (en_core_web_sm)\n",
" 4. NLTK: stopwords english + custom\n",
"\n",
"Resultado:\n",
" Reduccion tokens : {reduction:.1f}%\n",
" Tokens mediana : {df['tokens_clean'].median():.0f} (antes: {df['tokens_raw'].median():.0f})\n",
" Comentarios vacios: {empty_after}\n",
"\n",
"Decisiones clave:\n",
" NO quitados: black, white, police, cop\n",
" -> EDA mostro que aparecen en ambas clases con contexto distinto\n",
" -> el modelo necesita verlas para aprender por bigrams\n",
" SI quitados: youtube, video, watch, like, comment, channel\n",
" -> ruido tematico sin valor discriminante\n",
" spaCy para lemma, no NLTK Stemmer\n",
" -> stemmer corta letras, lemma entiende gramatica\n",
"\n",
"\"\"\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "py310",
"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": "3.10.20"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|