egutierrez
e3c8979e8d
- cmd/fn/doctor.go - cmd/fn/main.go - cpp/apps/primitives_gallery/playground/tables/CMakeLists.txt - cpp/apps/primitives_gallery/playground/tables/data_table.cpp - cpp/apps/primitives_gallery/playground/tables/data_table_logic.cpp - cpp/apps/primitives_gallery/playground/tables/data_table_logic.h - cpp/apps/primitives_gallery/playground/tables/self_test.cpp - cpp/apps/primitives_gallery/playground/tables/tql.cpp - cpp/apps/primitives_gallery/playground/tables/viz.cpp - cpp/apps/primitives_gallery/playground/tables/viz.h - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
"""genconfig_load_json — carga un GenerationConfig desde un archivo JSON."""
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from __future__ import annotations
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import json
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import os
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import sys
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sys.path.insert(0, os.path.dirname(__file__))
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from generation_config import GenerationConfig
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def genconfig_load_json(path: str) -> GenerationConfig:
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"""Carga y valida un GenerationConfig desde un archivo JSON en disco.
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Usa GenerationConfig.model_validate(data) si pydantic esta disponible
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(version con validacion completa de tipos y literales). En caso de
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fallback a dataclass, construye la instancia manualmente mapeando
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los campos conocidos.
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Args:
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path: Ruta al archivo JSON. Puede ser relativa o absoluta.
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Returns:
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Instancia de GenerationConfig cargada y validada.
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Raises:
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FileNotFoundError: Si el archivo no existe.
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json.JSONDecodeError: Si el contenido no es JSON valido.
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pydantic.ValidationError: Si los datos no cumplen el schema (version pydantic).
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KeyError / TypeError: Si faltan campos obligatorios (version dataclass).
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"""
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abs_path = os.path.abspath(path)
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with open(abs_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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# Intentar deserializacion pydantic (version canonica con validacion)
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try:
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return GenerationConfig.model_validate(data)
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except AttributeError:
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pass
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# Fallback: dataclass — construir manualmente desde el dict
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from lora_ref import LoraRef
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from model_ref import ModelRef
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model_data = data["model"]
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model = ModelRef(
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name=model_data["name"],
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model_type=model_data["model_type"],
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quantization=model_data.get("quantization", "fp16"),
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path=model_data.get("path"),
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)
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loras = [
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LoraRef(
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path=lr["path"],
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weight=lr.get("weight", 1.0),
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scale=lr.get("scale"),
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)
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for lr in data.get("loras", [])
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]
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return GenerationConfig(
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prompt=data["prompt"],
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negative_prompt=data.get("negative_prompt"),
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seed=data["seed"],
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steps=data["steps"],
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cfg_scale=data["cfg_scale"],
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sampler=data["sampler"],
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width=data["width"],
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height=data["height"],
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model=model,
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loras=tuple(loras),
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clip_skip=data.get("clip_skip"),
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)
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