"""
Export server for processing Google Sheets export jobs.

This module handles the actual processing of export jobs queued through
the FastStream message broker, including data retrieval, formatting,
and Google Sheets API integration.
"""

import logging
from typing import Dict, Any, List, Optional
from datetime import datetime
from adapters.sqlite.database import get_db

logger = logging.getLogger(__name__)


class ExportServer:
    """Server for processing Google Sheets export operations."""

    def __init__(self):
        """Initialize ExportServer with database and Google Sheets client."""
        self.db = get_db
        # TODO: Initialize Google Sheets client when implemented
        # self.sheets_client = GoogleSheetsClient()

    async def process_export_job(self, job_data: Dict[str, Any]) -> None:
        """
        Process a Google Sheets export job.

        Args:
            job_data: Dictionary containing job_id, configuration_id, and metadata
        """
        job_id = job_data["job_id"]
        configuration_id = job_data["configuration_id"]

        try:
            logger.info(f"Starting export job {job_id} for configuration {configuration_id}")

            # Update job status to running
            await self._update_job_status(job_id, "running", started_at=datetime.utcnow())

            # Get export configuration
            config = await self._get_export_configuration(configuration_id)
            if not config:
                raise ValueError(f"Export configuration {configuration_id} not found")

            # Process each enabled entity
            total_records = 0
            processed_entities = {}

            for entity_type, mapping in config["entity_mappings"].items():
                if mapping.get("is_enabled", False):
                    logger.info(f"Exporting {entity_type} data")

                    records_count = await self._export_entity(
                        entity_type,
                        config["sheet_id"],
                        mapping,
                        config.get("date_range_start"),
                        config.get("date_range_end")
                    )

                    total_records += records_count
                    processed_entities[entity_type] = {
                        "processed": records_count,
                        "total": records_count,
                        "status": "completed"
                    }

                    logger.info(f"Exported {records_count} {entity_type} records")

            # Update job as completed
            await self._update_job_status(
                job_id,
                "completed",
                completed_at=datetime.utcnow(),
                records_processed=total_records,
                total_records=total_records,
                entities_processed=processed_entities
            )

            logger.info(f"Export job {job_id} completed successfully with {total_records} records")

        except Exception as e:
            logger.error(f"Export job {job_id} failed: {str(e)}")
            await self._update_job_status(
                job_id,
                "failed",
                completed_at=datetime.utcnow(),
                error_message=str(e)
            )
            raise

    async def _export_entity(
        self,
        entity_type: str,
        sheet_id: str,
        mapping: Dict[str, Any],
        date_range_start: Optional[str] = None,
        date_range_end: Optional[str] = None
    ) -> int:
        """
        Export specific entity type to Google Sheets.

        Args:
            entity_type: Type of entity to export (deals, contacts, companies, etc.)
            sheet_id: Google Sheets document ID
            mapping: Field mapping configuration for this entity
            date_range_start: Optional start date filter
            date_range_end: Optional end date filter

        Returns:
            Number of records exported
        """
        try:
            # Get entity data from database
            data = await self._get_entity_data(entity_type, date_range_start, date_range_end)

            if not data:
                logger.warning(f"No data found for {entity_type}")
                return 0

            # Format data according to mapping
            formatted_data = await self._format_data_for_export(data, mapping)

            # Export to Google Sheets
            await self._write_to_google_sheets(sheet_id, mapping["sheet_name"], formatted_data)

            return len(data)

        except Exception as e:
            logger.error(f"Failed to export {entity_type}: {str(e)}")
            raise

    async def _get_entity_data(
        self,
        entity_type: str,
        date_range_start: Optional[str] = None,
        date_range_end: Optional[str] = None
    ) -> List[Dict[str, Any]]:
        """
        Retrieve entity data from the database.

        Args:
            entity_type: Type of entity to retrieve
            date_range_start: Optional start date filter
            date_range_end: Optional end date filter

        Returns:
            List of entity records
        """
        # TODO: Implement database query based on entity type and date range
        # This would query the appropriate table (amo_deals, amo_contacts, etc.)
        # and join with custom fields if needed

        logger.info(f"Retrieving {entity_type} data (date range: {date_range_start} to {date_range_end})")

        # Placeholder implementation
        return []

    async def _format_data_for_export(
        self,
        data: List[Dict[str, Any]],
        mapping: Dict[str, Any]
    ) -> List[List[Any]]:
        """
        Format data according to the export mapping configuration.

        Args:
            data: Raw entity data from database
            mapping: Field mapping configuration

        Returns:
            Formatted data ready for Google Sheets export
        """
        field_mapping = mapping.get("field_mapping", [])

        # Sort field mapping by order
        sorted_fields = sorted(field_mapping, key=lambda x: x.get("order", 0))

        # Create header row
        headers = [field["field_name"] for field in sorted_fields]
        formatted_data = [headers]

        # Format data rows
        for record in data:
            row = []
            for field in sorted_fields:
                field_name = field["field_name"]
                value = record.get(field_name, "")

                # Handle different data types and formatting
                if isinstance(value, datetime):
                    value = value.isoformat()
                elif value is None:
                    value = ""

                row.append(value)

            formatted_data.append(row)

        logger.info(f"Formatted {len(data)} records with {len(headers)} columns")
        return formatted_data

    async def _write_to_google_sheets(
        self,
        sheet_id: str,
        sheet_name: str,
        data: List[List[Any]]
    ) -> None:
        """
        Write formatted data to Google Sheets.

        Args:
            sheet_id: Google Sheets document ID
            sheet_name: Name of the sheet tab
            data: Formatted data to write
        """
        # TODO: Implement Google Sheets API integration
        # This would use the Google Sheets client to write data

        logger.info(f"Writing {len(data)} rows to sheet '{sheet_name}' in document {sheet_id}")

        # Placeholder - would actually write to Google Sheets
        logger.info(f"Successfully wrote data to Google Sheets (placeholder)")

    async def _get_export_configuration(self, configuration_id: int) -> Optional[Dict[str, Any]]:
        """
        Get export configuration from database.

        Args:
            configuration_id: ID of the export configuration

        Returns:
            Export configuration data or None if not found
        """
        # TODO: Implement database query to get export configuration
        # This would query the export_configuration and export_entity_mappings tables

        logger.info(f"Retrieving export configuration {configuration_id}")

        # Placeholder implementation
        return {
            "id": configuration_id,
            "sheet_id": "placeholder_sheet_id",
            "entity_mappings": {
                "deals": {
                    "sheet_name": "Deals",
                    "is_enabled": True,
                    "field_mapping": [
                        {"field_name": "name", "column": "A", "order": 1},
                        {"field_name": "price", "column": "B", "order": 2},
                        {"field_name": "created_at", "column": "C", "order": 3}
                    ]
                }
            }
        }

    async def _update_job_status(
        self,
        job_id: str,
        status: str,
        **kwargs
    ) -> None:
        """
        Update job status in the database.

        Args:
            job_id: Unique identifier of the job
            status: New status (pending, running, completed, failed)
            **kwargs: Additional fields to update
        """
        # TODO: Implement database update
        # This would update the export_jobs table

        logger.info(f"Updating job {job_id} status to {status}")

        update_data = {"status": status, **kwargs}
        logger.debug(f"Job update data: {update_data}")

        # Placeholder - would actually update database
        pass