"""
Data Exporter Agent

Exports data to various formats and destinations with support
for scheduling, filtering, and transformation.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class DataExporterAgent(BaseAgent):
    """
    Exports data to various formats and destinations.

    Supports:
    - Multiple formats (CSV, JSON, Parquet, Excel, XML, Avro)
    - Cloud storage (S3, GCS, Azure Blob)
    - Databases and data warehouses
    - APIs and webhooks
    - Compression and encryption
    - Scheduled exports
    """

    def __init__(self):
        super().__init__(
            name='data-exporter',
            description='Export data to various formats and destinations',
            category='data',
            version='1.0.0',
            tags=['export', 'data-transfer', 'etl', 'integration']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Export data to specified destination.

        Args:
            params: {
                'source': {
                    'type': 'database|file|api|dataframe',
                    'connection': {...},
                    'query': str
                },
                'destination': {
                    'type': 'file|s3|gcs|azure|database|api',
                    'path': str,
                    'connection': {...}
                },
                'format': 'csv|json|parquet|excel|xml|avro',
                'options': {
                    'compression': 'none|gzip|snappy|bzip2|lz4',
                    'encryption': bool,
                    'delimiter': str,
                    'encoding': str,
                    'include_header': bool,
                    'chunk_size': int,
                    'overwrite': bool
                },
                'transformations': List[Dict[str, Any]],
                'schedule': str  # Cron expression
            }

        Returns:
            {
                'status': 'success|failed',
                'export_id': str,
                'destination_path': str,
                'format': str,
                'rows_exported': int,
                'file_size_mb': float,
                'compression_ratio': float,
                'execution_time_seconds': float,
                'chunks_created': int,
                'errors': List[str]
            }
        """
        source = params.get('source', {})
        destination = params.get('destination', {})
        format_type = params.get('format', 'csv')
        options = params.get('options', {})

        self.logger.info(
            f"Exporting data to {destination.get('type')} in {format_type} format"
        )

        # Mock export process
        rows = 50000
        file_size = 12.5

        if options.get('compression') and options.get('compression') != 'none':
            compression_ratio = 0.35
            file_size = file_size * compression_ratio
        else:
            compression_ratio = 1.0

        return {
            'status': 'success',
            'export_id': f'export_{format_type}_20251116',
            'destination_type': destination.get('type'),
            'destination_path': destination.get('path', '/exports/data_export.csv'),
            'format': format_type,
            'rows_exported': rows,
            'columns_exported': 18,
            'file_size_mb': round(file_size, 2),
            'original_size_mb': 12.5,
            'compression_ratio': compression_ratio,
            'compression_type': options.get('compression', 'none'),
            'encrypted': options.get('encryption', False),
            'execution_time_seconds': 8.7,
            'chunks_created': rows // options.get('chunk_size', 10000),
            'encoding': options.get('encoding', 'utf-8'),
            'delimiter': options.get('delimiter', ','),
            'include_header': options.get('include_header', True),
            'errors': [],
            'warnings': [
                'Consider using Parquet format for better compression',
                'Enable partitioning for large datasets'
            ] if format_type == 'csv' and rows > 100000 else [],
            'metadata': {
                'export_date': '2025-11-16T10:00:00Z',
                'source_type': source.get('type'),
                'checksum': 'a1b2c3d4e5f6',
                'schema_version': '1.0'
            },
            'next_scheduled_export': '2025-11-17T10:00:00Z' if params.get('schedule') else None
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate export parameters."""
        if 'source' not in params:
            self.logger.error("Missing required field: source")
            return False

        if 'destination' not in params:
            self.logger.error("Missing required field: destination")
            return False

        valid_formats = ['csv', 'json', 'parquet', 'excel', 'xml', 'avro']
        format_type = params.get('format', 'csv')

        if format_type not in valid_formats:
            self.logger.error(f"Invalid format: {format_type}")
            return False

        return True
