"""
Casos de uso para Forecasting.

Implementan la lógica de aplicación para pronósticos,
orquestando servicios de dominio y transformando DTOs.
"""

from typing import List
from app.domain.services.forecast_service import ForecastService
from app.domain.models.time_series import TimeSeries
from app.domain.models.forecast_config import ForecastConfig
from app.application.dtos.forecast_dtos import (
    ForecastInputDTO,
    ForecastOutputDTO,
    MultiForecastInputDTO,
    MultiForecastOutputDTO,
    SeriesInputDTO
)
from app.utils.logger import setup_logger

logger = setup_logger(__name__)


class ForecastUnivariateUseCase:
    """
    Caso de uso: Pronóstico Univariado.
    
    Responsabilidad: Ejecutar pronóstico para una serie temporal única.
    """
    
    def __init__(self, forecast_service: ForecastService):
        """
        Inicializa el caso de uso.
        
        Args:
            forecast_service: Servicio de dominio para forecasting
        """
        self.forecast_service = forecast_service
        logger.info("ForecastUnivariateUseCase initialized")
    
    def execute(self, input_dto: ForecastInputDTO) -> ForecastOutputDTO:
        """
        Ejecuta el caso de uso.
        
        Args:
            input_dto: Datos de entrada validados
        
        Returns:
            ForecastOutputDTO: Resultado del pronóstico
        
        Raises:
            ValueError: Si los datos son inválidos
            RuntimeError: Si falla el pronóstico
        """
        logger.info(f"Executing forecast for series: {input_dto.series_id}")
        
        # Validar entrada
        input_dto.validate()
        
        # Convertir DTO a modelos de dominio
        series = TimeSeries(
            values=input_dto.values,
            timestamps=input_dto.timestamps,
            series_id=input_dto.series_id,
            freq=input_dto.freq
        )
        
        config = ForecastConfig(
            prediction_length=input_dto.prediction_length,
            quantile_levels=input_dto.quantile_levels,
            freq=input_dto.freq
        )
        
        # Ejecutar servicio de dominio
        try:
            result = self.forecast_service.forecast_univariate(series, config)
            logger.info(f"Forecast completed: {len(result.timestamps)} periods")
        except Exception as e:
            logger.error(f"Forecast failed: {e}", exc_info=True)
            raise RuntimeError(f"Forecast execution failed: {str(e)}") from e
        
        # Convertir resultado a DTO
        output_dto = ForecastOutputDTO(
            timestamps=result.timestamps,
            median=result.median,
            quantiles=result.quantiles,
            series_id=result.series_id,
            metadata={
                "prediction_length": config.prediction_length,
                "freq": config.freq,
                "context_length": len(series.values)
            }
        )
        
        return output_dto


class ForecastMultiSeriesUseCase:
    """
    Caso de uso: Pronóstico Multi-Series.
    
    Responsabilidad: Ejecutar pronósticos para múltiples series.
    """
    
    def __init__(self, forecast_service: ForecastService):
        """
        Inicializa el caso de uso.
        
        Args:
            forecast_service: Servicio de dominio para forecasting
        """
        self.forecast_service = forecast_service
        logger.info("ForecastMultiSeriesUseCase initialized")
    
    def execute(self, input_dto: MultiForecastInputDTO) -> MultiForecastOutputDTO:
        """
        Ejecuta el caso de uso para múltiples series.
        
        Args:
            input_dto: Datos de entrada con múltiples series
        
        Returns:
            MultiForecastOutputDTO: Resultados de todos los pronósticos
        """
        logger.info(f"Executing forecast for {len(input_dto.series_list)} series")
        
        # Validar entrada
        input_dto.validate()
        
        # Configuración compartida
        config = ForecastConfig(
            prediction_length=input_dto.prediction_length,
            quantile_levels=input_dto.quantile_levels,
            freq=input_dto.freq
        )
        
        # Convertir DTOs a modelos de dominio
        time_series_list: List[TimeSeries] = []
        for series_dto in input_dto.series_list:
            series = TimeSeries(
                values=series_dto.values,
                timestamps=series_dto.timestamps,
                series_id=series_dto.series_id,
                freq=input_dto.freq
            )
            time_series_list.append(series)
        
        # Ejecutar servicio de dominio
        results = []
        successful = 0
        failed = 0
        
        for ts in time_series_list:
            try:
                result = self.forecast_service.forecast_univariate(ts, config)
                
                output_dto = ForecastOutputDTO(
                    timestamps=result.timestamps,
                    median=result.median,
                    quantiles=result.quantiles,
                    series_id=result.series_id,
                    metadata={
                        "prediction_length": config.prediction_length,
                        "freq": config.freq,
                        "context_length": len(ts.values)
                    }
                )
                results.append(output_dto)
                successful += 1
                
            except Exception as e:
                logger.error(f"Forecast failed for series {ts.series_id}: {e}")
                failed += 1
                # Continuar con las siguientes series
        
        logger.info(f"Multi-series forecast completed: {successful} successful, {failed} failed")
        
        # Crear DTO de salida
        multi_output = MultiForecastOutputDTO(
            results=results,
            total_series=len(input_dto.series_list),
            successful=successful,
            failed=failed
        )
        
        return multi_output