"""
Dependency Injection para FastAPI.

Provee instancias de servicios, repositorios y casos de uso
usando el sistema de DI de FastAPI.
"""

from functools import lru_cache
from fastapi import Depends

# Infrastructure
from app.infrastructure.ml.model_factory import ModelFactory
from app.infrastructure.config.settings import get_settings

# Domain
from app.domain.interfaces.forecast_model import IForecastModel
from app.domain.interfaces.data_transformer import IDataTransformer
from app.domain.services.forecast_service import ForecastService
from app.domain.services.anomaly_service import AnomalyService

# Application
from app.application.use_cases.forecast_use_case import (
    ForecastUnivariateUseCase,
    ForecastMultiSeriesUseCase
)
from app.application.use_cases.anomaly_use_case import DetectAnomaliesUseCase
from app.application.use_cases.backtest_use_case import BacktestUseCase

# Utils
from app.utils.dataframe_builder import DataFrameBuilder
from app.utils.logger import setup_logger

# Get settings instance
settings = get_settings()

logger = setup_logger(__name__)

# ============================================================================
# Infrastructure Layer Dependencies
# ============================================================================

# Singleton para el modelo de forecasting
_model_instance: IForecastModel = None


def get_forecast_model() -> IForecastModel:
    """
    Dependency: Modelo de forecasting (Singleton).
    
    Usa Chronos-2 por defecto. El modelo se carga una sola vez
    y se reutiliza en todas las requests.
    
    Returns:
        IForecastModel: Instancia del modelo
    """
    global _model_instance
    
    if _model_instance is None:
        logger.info("Initializing forecast model (first time)")
        _model_instance = ModelFactory.create(
            model_type="chronos2",
            model_id=settings.model_id,
            device_map=settings.device_map
        )
        logger.info(f"Model loaded: {_model_instance.get_model_info()}")
    
    return _model_instance


def get_data_transformer() -> IDataTransformer:
    """
    Dependency: Transformador de datos.
    
    Returns:
        IDataTransformer: Instancia del transformador
    """
    return DataFrameBuilder()


# ============================================================================
# Domain Layer Dependencies
# ============================================================================

def get_forecast_service(
    model: IForecastModel = Depends(get_forecast_model),
    transformer: IDataTransformer = Depends(get_data_transformer)
) -> ForecastService:
    """
    Dependency: Servicio de dominio para forecasting.
    
    Args:
        model: Modelo de forecasting
        transformer: Transformador de datos
    
    Returns:
        ForecastService: Servicio de forecasting
    """
    return ForecastService(model=model, transformer=transformer)


def get_anomaly_service(
    model: IForecastModel = Depends(get_forecast_model),
    transformer: IDataTransformer = Depends(get_data_transformer)
) -> AnomalyService:
    """
    Dependency: Servicio de dominio para detección de anomalías.
    
    Args:
        model: Modelo de forecasting
        transformer: Transformador de datos
    
    Returns:
        AnomalyService: Servicio de anomalías
    """
    return AnomalyService(model=model, transformer=transformer)


# ============================================================================
# Application Layer Dependencies (Use Cases)
# ============================================================================

def get_forecast_univariate_use_case(
    service: ForecastService = Depends(get_forecast_service)
) -> ForecastUnivariateUseCase:
    """
    Dependency: Caso de uso de pronóstico univariado.
    
    Args:
        service: Servicio de forecasting
    
    Returns:
        ForecastUnivariateUseCase: Caso de uso
    """
    return ForecastUnivariateUseCase(forecast_service=service)


def get_forecast_multi_series_use_case(
    service: ForecastService = Depends(get_forecast_service)
) -> ForecastMultiSeriesUseCase:
    """
    Dependency: Caso de uso de pronóstico multi-series.
    
    Args:
        service: Servicio de forecasting
    
    Returns:
        ForecastMultiSeriesUseCase: Caso de uso
    """
    return ForecastMultiSeriesUseCase(forecast_service=service)


def get_detect_anomalies_use_case(
    service: AnomalyService = Depends(get_anomaly_service)
) -> DetectAnomaliesUseCase:
    """
    Dependency: Caso de uso de detección de anomalías.
    
    Args:
        service: Servicio de anomalías
    
    Returns:
        DetectAnomaliesUseCase: Caso de uso
    """
    return DetectAnomaliesUseCase(anomaly_service=service)


def get_backtest_use_case(
    service: ForecastService = Depends(get_forecast_service)
) -> BacktestUseCase:
    """
    Dependency: Caso de uso de backtesting.
    
    Args:
        service: Servicio de forecasting
    
    Returns:
        BacktestUseCase: Caso de uso
    """
    return BacktestUseCase(forecast_service=service)


# ============================================================================
# Utility Functions
# ============================================================================

def reset_model():
    """
    Resetea el modelo (útil para testing).
    
    ADVERTENCIA: Solo usar en tests, no en producción.
    """
    global _model_instance
    _model_instance = None
    logger.warning("Model instance reset")