298 lines
10 KiB
Plaintext
298 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ed60d28c",
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"metadata": {},
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"source": [
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"# Libreries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "95cf533e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from transformers import AutoTokenizer\n",
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"from Docker.config import settings\n",
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"from pathlib import Path\n",
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"from transformers import AutoConfig\n",
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"import os"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c9b7265a",
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"metadata": {},
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"source": [
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"# Functions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "6fd7de78",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_text_from_file(file_path: str) -> str:\n",
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" \"\"\"\n",
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" Load text content from a specified file.\n",
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"\n",
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" Args:\n",
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" file_path: Path to the .txt file to load.\n",
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"\n",
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" Returns:\n",
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" The text content of the file.\n",
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"\n",
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" Raises:\n",
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" FileNotFoundError: If the file does not exist.\n",
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" IOError: If the file cannot be read.\n",
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" \"\"\"\n",
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" try:\n",
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" with open(file_path, \"r\", encoding=\"utf-8\") as file:\n",
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" return file.read()\n",
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" except FileNotFoundError:\n",
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" raise FileNotFoundError(f\"El archivo '{file_path}' no existe.\")\n",
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" except IOError as error:\n",
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" raise IOError(f\"Error al leer '{file_path}': {error}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "6389092e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def infer_context_window(model_id: str, tokenizer_obj) -> int:\n",
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" \"\"\"Infer context window from tokenizer/model config.\"\"\"\n",
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" large_sentinel = int(1e9)\n",
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"\n",
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" tokenizer_limit = getattr(tokenizer_obj, \"model_max_length\", None)\n",
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" if isinstance(tokenizer_limit, int) and 0 < tokenizer_limit < large_sentinel:\n",
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" return tokenizer_limit\n",
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"\n",
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" config = AutoConfig.from_pretrained(model_id)\n",
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"\n",
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" for field_name in (\n",
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" \"max_position_embeddings\",\n",
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" \"n_positions\",\n",
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" \"seq_length\",\n",
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" \"model_max_length\",\n",
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" ):\n",
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" value = getattr(config, field_name, None)\n",
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" if isinstance(value, int) and value > 0:\n",
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" return value\n",
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"\n",
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" raise ValueError(\n",
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" \"No se pudo inferir la ventana de contexto del modelo. \"\n",
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" \"Define context_window manualmente.\"\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "7190080b",
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"metadata": {},
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"outputs": [],
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"source": [
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"def run_token_count_test() -> dict[str, int | bool]:\n",
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" \"\"\"Run token count test across all raw .txt files.\"\"\"\n",
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" raw_dir = Path(settings.raw_path)\n",
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" txt_files = sorted(raw_dir.glob(\"*.txt\"))\n",
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"\n",
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" if not txt_files:\n",
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" print(f\"No se encontraron .txt en: {raw_dir}\")\n",
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" return {\n",
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" \"total_tokens_without_special\": 0,\n",
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" \"total_tokens_with_special\": 0,\n",
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" \"fits_without\": True,\n",
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" \"fits_with\": True,\n",
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" }\n",
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"\n",
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" total_tokens_without_special = 0\n",
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" total_tokens_with_special = 0\n",
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"\n",
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" print(f\"Modelo: {model_name}\")\n",
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" print(f\"Ventana de contexto detectada: {context_window}\")\n",
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" print(f\"Archivos analizados: {len(txt_files)}\")\n",
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" print(\"-\" * 80)\n",
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"\n",
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" for file_path in txt_files:\n",
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" content = load_text_from_file(str(file_path))\n",
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" token_ids_without_special = tokenizer.encode(\n",
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" content, add_special_tokens=False\n",
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" )\n",
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" token_ids_with_special = tokenizer.encode(content)\n",
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"\n",
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" count_without_special = len(token_ids_without_special)\n",
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" count_with_special = len(token_ids_with_special)\n",
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"\n",
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" total_tokens_without_special += count_without_special\n",
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" total_tokens_with_special += count_with_special\n",
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"\n",
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" print(\n",
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" f\"{file_path.name:<35} \"\n",
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" f\"sin especiales: {count_without_special:>8} | \"\n",
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" f\"con especiales: {count_with_special:>8}\"\n",
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" )\n",
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"\n",
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" print(\"-\" * 80)\n",
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" print(\n",
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" f\"TOTAL sin especiales: {total_tokens_without_special} tokens\"\n",
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" )\n",
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" print(\n",
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" f\"TOTAL con especiales: {total_tokens_with_special} tokens\"\n",
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" )\n",
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"\n",
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" fits_without = total_tokens_without_special <= context_window\n",
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" fits_with = total_tokens_with_special <= context_window\n",
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"\n",
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" print(\n",
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" f\"¿Cabe en ventana ({context_window}) sin especiales? \"\n",
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" f\"{'Sí' if fits_without else 'No'}\"\n",
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" )\n",
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" print(\n",
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" f\"¿Cabe en ventana ({context_window}) con especiales? \"\n",
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" f\"{'Sí' if fits_with else 'No'}\"\n",
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" )\n",
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"\n",
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" if not fits_with:\n",
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" overflow = total_tokens_with_special - context_window\n",
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" print(\n",
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" f\"Exceso aproximado: {overflow} tokens\"\n",
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" )\n",
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"\n",
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" return {\n",
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" \"total_tokens_without_special\": total_tokens_without_special,\n",
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" \"total_tokens_with_special\": total_tokens_with_special,\n",
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" \"fits_without\": fits_without,\n",
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" \"fits_with\": fits_with,\n",
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" }"
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]
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},
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{
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"cell_type": "markdown",
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"id": "04e0f72f",
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"metadata": {},
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"source": [
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"# Model Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "19c815e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"model_name = os.getenv(\"HF_EMB_MODEL_NAME\")\n",
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"if not model_name:\n",
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" raise ValueError(\n",
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" \"No se encontró HF_EMB_MODEL_NAME en variables de entorno.\"\n",
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" )\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
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"context_window = infer_context_window(model_name, tokenizer)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "22bcc0fe",
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"metadata": {},
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"source": [
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"# Test"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "f6517705",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Modelo: Qwen/Qwen3-Embedding-0.6B\n",
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"Ventana de contexto detectada: 131072\n",
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"Archivos analizados: 24\n",
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"--------------------------------------------------------------------------------\n",
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"10_Execution_model_in_avap.txt sin especiales: 10349 | con especiales: 10350\n",
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"11_Conditional_statements.txt sin especiales: 524 | con especiales: 525\n",
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"12_Loop_statement.txt sin especiales: 594 | con especiales: 595\n",
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"13_Api_inbound_interface.txt sin especiales: 415 | con especiales: 416\n",
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"14_Working_with_libraries.txt sin especiales: 873 | con especiales: 874\n",
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"15_Function_declaration.txt sin especiales: 394 | con especiales: 395\n",
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"16_Appendix.txt sin especiales: 9209 | con especiales: 9210\n",
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"17_Architecture_memory_foundations.txt sin especiales: 1086 | con especiales: 1087\n",
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"18_Input_output_management.txt sin especiales: 1104 | con especiales: 1105\n",
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"19_Control_logic_decision_structures.txt sin especiales: 1166 | con especiales: 1167\n",
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"1_Introduction.txt sin especiales: 1072 | con especiales: 1073\n",
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"20_Concurrency_asynchrony.txt sin especiales: 1049 | con especiales: 1050\n",
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"21_Persistance_connectors_orm.txt sin especiales: 1135 | con especiales: 1136\n",
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"22_System_utilities_transformation.txt sin especiales: 882 | con especiales: 883\n",
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"23_Function_architecture_scopes.txt sin especiales: 604 | con especiales: 605\n",
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"24_Master_example.txt sin especiales: 241 | con especiales: 242\n",
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"2_Dynamic_Programming_Language.txt sin especiales: 707 | con especiales: 708\n",
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"3_Notation.txt sin especiales: 1368 | con especiales: 1369\n",
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"4_Lexics.txt sin especiales: 750 | con especiales: 751\n",
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"5_Data_Model.txt sin especiales: 605 | con especiales: 606\n",
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"6_Data_Types.txt sin especiales: 611 | con especiales: 612\n",
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"7_Working_With_Variables.txt sin especiales: 601 | con especiales: 602\n",
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"8_How_to_work_with_comments.txt sin especiales: 726 | con especiales: 727\n",
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"9_Expressions_in_avap.txt sin especiales: 646 | con especiales: 647\n",
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"--------------------------------------------------------------------------------\n",
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"TOTAL sin especiales: 36711 tokens\n",
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"TOTAL con especiales: 36735 tokens\n",
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"¿Cabe en ventana (131072) sin especiales? Sí\n",
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"¿Cabe en ventana (131072) con especiales? Sí\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'total_tokens_without_special': 36711,\n",
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" 'total_tokens_with_special': 36735,\n",
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" 'fits_without': True,\n",
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" 'fits_with': True}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"test_result = run_token_count_test()\n",
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"test_result"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "assistance-engine",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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