# services/forecasting/app/services/tenant_deletion_service.py
"""
Tenant Data Deletion Service for Forecasting Service
Handles deletion of all forecasting-related data for a tenant
"""

from typing import Dict
from sqlalchemy import select, func, delete
from sqlalchemy.ext.asyncio import AsyncSession
import structlog

from shared.services.tenant_deletion import (
    BaseTenantDataDeletionService,
    TenantDataDeletionResult
)
from app.models import (
    Forecast,
    PredictionBatch,
    ModelPerformanceMetric,
    PredictionCache,
    AuditLog
)

logger = structlog.get_logger(__name__)


class ForecastingTenantDeletionService(BaseTenantDataDeletionService):
    """Service for deleting all forecasting-related data for a tenant"""

    def __init__(self, db: AsyncSession):
        self.db = db
        self.service_name = "forecasting"

    async def get_tenant_data_preview(self, tenant_id: str) -> Dict[str, int]:
        """
        Get counts of what would be deleted for a tenant (dry-run)

        Args:
            tenant_id: The tenant ID to preview deletion for

        Returns:
            Dictionary with entity names and their counts
        """
        logger.info("forecasting.tenant_deletion.preview", tenant_id=tenant_id)
        preview = {}

        try:
            # Count forecasts
            forecast_count = await self.db.scalar(
                select(func.count(Forecast.id)).where(
                    Forecast.tenant_id == tenant_id
                )
            )
            preview["forecasts"] = forecast_count or 0

            # Count prediction batches
            batch_count = await self.db.scalar(
                select(func.count(PredictionBatch.id)).where(
                    PredictionBatch.tenant_id == tenant_id
                )
            )
            preview["prediction_batches"] = batch_count or 0

            # Count model performance metrics
            metric_count = await self.db.scalar(
                select(func.count(ModelPerformanceMetric.id)).where(
                    ModelPerformanceMetric.tenant_id == tenant_id
                )
            )
            preview["model_performance_metrics"] = metric_count or 0

            # Count prediction cache entries
            cache_count = await self.db.scalar(
                select(func.count(PredictionCache.id)).where(
                    PredictionCache.tenant_id == tenant_id
                )
            )
            preview["prediction_cache"] = cache_count or 0

            # Count audit logs
            audit_count = await self.db.scalar(
                select(func.count(AuditLog.id)).where(
                    AuditLog.tenant_id == tenant_id
                )
            )
            preview["audit_logs"] = audit_count or 0

            logger.info(
                "forecasting.tenant_deletion.preview_complete",
                tenant_id=tenant_id,
                preview=preview
            )

        except Exception as e:
            logger.error(
                "forecasting.tenant_deletion.preview_error",
                tenant_id=tenant_id,
                error=str(e),
                exc_info=True
            )
            raise

        return preview

    async def delete_tenant_data(self, tenant_id: str) -> TenantDataDeletionResult:
        """
        Permanently delete all forecasting data for a tenant

        Deletion order:
        1. PredictionCache (independent)
        2. ModelPerformanceMetric (independent)
        3. PredictionBatch (independent)
        4. Forecast (independent)
        5. AuditLog (independent)

        Note: All tables are independent with no foreign key relationships

        Args:
            tenant_id: The tenant ID to delete data for

        Returns:
            TenantDataDeletionResult with deletion counts and any errors
        """
        logger.info("forecasting.tenant_deletion.started", tenant_id=tenant_id)
        result = TenantDataDeletionResult(tenant_id=tenant_id, service_name=self.service_name)

        try:
            # Step 1: Delete prediction cache
            logger.info("forecasting.tenant_deletion.deleting_cache", tenant_id=tenant_id)
            cache_result = await self.db.execute(
                delete(PredictionCache).where(
                    PredictionCache.tenant_id == tenant_id
                )
            )
            result.deleted_counts["prediction_cache"] = cache_result.rowcount
            logger.info(
                "forecasting.tenant_deletion.cache_deleted",
                tenant_id=tenant_id,
                count=cache_result.rowcount
            )

            # Step 2: Delete model performance metrics
            logger.info("forecasting.tenant_deletion.deleting_metrics", tenant_id=tenant_id)
            metrics_result = await self.db.execute(
                delete(ModelPerformanceMetric).where(
                    ModelPerformanceMetric.tenant_id == tenant_id
                )
            )
            result.deleted_counts["model_performance_metrics"] = metrics_result.rowcount
            logger.info(
                "forecasting.tenant_deletion.metrics_deleted",
                tenant_id=tenant_id,
                count=metrics_result.rowcount
            )

            # Step 3: Delete prediction batches
            logger.info("forecasting.tenant_deletion.deleting_batches", tenant_id=tenant_id)
            batches_result = await self.db.execute(
                delete(PredictionBatch).where(
                    PredictionBatch.tenant_id == tenant_id
                )
            )
            result.deleted_counts["prediction_batches"] = batches_result.rowcount
            logger.info(
                "forecasting.tenant_deletion.batches_deleted",
                tenant_id=tenant_id,
                count=batches_result.rowcount
            )

            # Step 4: Delete forecasts
            logger.info("forecasting.tenant_deletion.deleting_forecasts", tenant_id=tenant_id)
            forecasts_result = await self.db.execute(
                delete(Forecast).where(
                    Forecast.tenant_id == tenant_id
                )
            )
            result.deleted_counts["forecasts"] = forecasts_result.rowcount
            logger.info(
                "forecasting.tenant_deletion.forecasts_deleted",
                tenant_id=tenant_id,
                count=forecasts_result.rowcount
            )

            # Step 5: Delete audit logs
            logger.info("forecasting.tenant_deletion.deleting_audit_logs", tenant_id=tenant_id)
            audit_result = await self.db.execute(
                delete(AuditLog).where(
                    AuditLog.tenant_id == tenant_id
                )
            )
            result.deleted_counts["audit_logs"] = audit_result.rowcount
            logger.info(
                "forecasting.tenant_deletion.audit_logs_deleted",
                tenant_id=tenant_id,
                count=audit_result.rowcount
            )

            # Commit the transaction
            await self.db.commit()

            # Calculate total deleted
            total_deleted = sum(result.deleted_counts.values())

            logger.info(
                "forecasting.tenant_deletion.completed",
                tenant_id=tenant_id,
                total_deleted=total_deleted,
                breakdown=result.deleted_counts
            )

            result.success = True

        except Exception as e:
            await self.db.rollback()
            error_msg = f"Failed to delete forecasting data for tenant {tenant_id}: {str(e)}"
            logger.error(
                "forecasting.tenant_deletion.failed",
                tenant_id=tenant_id,
                error=str(e),
                exc_info=True
            )
            result.errors.append(error_msg)
            result.success = False

        return result


def get_forecasting_tenant_deletion_service(
    db: AsyncSession
) -> ForecastingTenantDeletionService:
    """
    Factory function to create ForecastingTenantDeletionService instance

    Args:
        db: AsyncSession database session

    Returns:
        ForecastingTenantDeletionService instance
    """
    return ForecastingTenantDeletionService(db)