"""
=============================================================================
THE FACTORY - Configuración
=============================================================================
"""

import os
from enum import Enum
from dataclasses import dataclass, field
from typing import Dict, List, Optional


class JobState(Enum):
    """Estados posibles de un job."""
    PENDING = "PENDING"
    QUEUED = "QUEUED"
    RUNNING = "RUNNING"
    EVALUATING = "EVALUATING"
    CONVERGED = "CONVERGED"
    EXHAUSTED = "EXHAUSTED"
    FAILED = "FAILED"
    CANCELLED = "CANCELLED"


class FunctionType(Enum):
    """Tipos de función soportados."""
    TEXT_GENERATION = "TEXT_GENERATION"
    IMAGE_GENERATION = "IMAGE_GENERATION"
    CODE_GENERATION = "CODE_GENERATION"
    DOCUMENT_GENERATION = "DOCUMENT_GENERATION"
    AUDIO_GENERATION = "AUDIO_GENERATION"
    VIDEO_GENERATION = "VIDEO_GENERATION"


@dataclass
class ModelConfig:
    """Configuración de un modelo."""
    name: str
    provider: str
    cost_per_1k_input: float
    cost_per_1k_output: float
    max_tokens: int = 4096
    supports_images: bool = False
    supports_streaming: bool = True


@dataclass
class FactoryConfig:
    """Configuración global de THE FACTORY."""

    # API Keys
    anthropic_api_key: str = field(default_factory=lambda: os.environ.get("ANTHROPIC_API_KEY", ""))
    openai_api_key: str = field(default_factory=lambda: os.environ.get("OPENAI_API_KEY", ""))
    replicate_api_key: str = field(default_factory=lambda: os.environ.get("REPLICATE_API_KEY", ""))

    # Límites por defecto
    default_max_cycles: int = 5
    default_budget_usd: float = 1.0
    default_timeout_ms: int = 120000

    # Convergencia
    convergence_threshold: float = 0.85
    diminishing_returns_threshold: float = 0.02
    diminishing_returns_min_confidence: float = 0.70

    # Modelos por función
    models: Dict[str, ModelConfig] = field(default_factory=lambda: {
        # Text generation
        "claude-sonnet": ModelConfig(
            name="claude-sonnet-4-20250514",
            provider="anthropic",
            cost_per_1k_input=0.003,
            cost_per_1k_output=0.015,
            max_tokens=8192,
            supports_images=True
        ),
        "claude-haiku": ModelConfig(
            name="claude-haiku-4-20250514",
            provider="anthropic",
            cost_per_1k_input=0.00025,
            cost_per_1k_output=0.00125,
            max_tokens=8192,
            supports_images=True
        ),
        "gpt-4o": ModelConfig(
            name="gpt-4o",
            provider="openai",
            cost_per_1k_input=0.005,
            cost_per_1k_output=0.015,
            max_tokens=4096,
            supports_images=True
        ),
        "gpt-4o-mini": ModelConfig(
            name="gpt-4o-mini",
            provider="openai",
            cost_per_1k_input=0.00015,
            cost_per_1k_output=0.0006,
            max_tokens=4096,
            supports_images=True
        ),
        # Image generation
        "flux-pro": ModelConfig(
            name="black-forest-labs/flux-1.1-pro",
            provider="replicate",
            cost_per_1k_input=0.05,  # por imagen
            cost_per_1k_output=0.0,
            max_tokens=0
        ),
        "flux-schnell": ModelConfig(
            name="black-forest-labs/flux-schnell",
            provider="replicate",
            cost_per_1k_input=0.003,  # por imagen
            cost_per_1k_output=0.0,
            max_tokens=0
        ),
    })

    # Modelo por defecto por función
    default_models: Dict[FunctionType, str] = field(default_factory=lambda: {
        FunctionType.TEXT_GENERATION: "claude-sonnet",
        FunctionType.CODE_GENERATION: "claude-sonnet",
        FunctionType.DOCUMENT_GENERATION: "claude-sonnet",
        FunctionType.IMAGE_GENERATION: "flux-schnell",
        FunctionType.AUDIO_GENERATION: "claude-sonnet",  # placeholder
        FunctionType.VIDEO_GENERATION: "claude-sonnet",  # placeholder
    })

    # Modelo para evaluación
    evaluator_model: str = "claude-haiku"

    def get_model(self, name: str) -> Optional[ModelConfig]:
        """Obtiene configuración de modelo."""
        return self.models.get(name)

    def get_default_model(self, function: FunctionType) -> ModelConfig:
        """Obtiene modelo por defecto para una función."""
        model_name = self.default_models.get(function, "claude-sonnet")
        return self.models[model_name]