"""
Data Importer Agent

Imports data from various sources with support for validation,
transformation, and error handling.
"""

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


class DataImporterAgent(BaseAgent):
    """
    Imports data from various sources.

    Supports:
    - Multiple formats (CSV, JSON, Parquet, Excel, XML, Avro)
    - Cloud storage (S3, GCS, Azure Blob)
    - Databases and APIs
    - File uploads and streaming
    - Data validation and cleansing
    - Incremental imports
    """

    def __init__(self):
        super().__init__(
            name='data-importer',
            description='Import data from various sources',
            category='data',
            version='1.0.0',
            tags=['import', 'data-ingestion', 'etl', 'integration']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Import data from specified source.

        Args:
            params: {
                'source': {
                    'type': 'file|s3|gcs|azure|database|api|stream',
                    'path': str,
                    'connection': {...},
                    'format': 'csv|json|parquet|excel|xml|avro'
                },
                'destination': {
                    'type': 'database|dataframe|file',
                    'table': str,
                    'connection': {...}
                },
                'options': {
                    'delimiter': str,
                    'encoding': str,
                    'skip_rows': int,
                    'infer_schema': bool,
                    'validate': bool,
                    'deduplicate': bool,
                    'batch_size': int,
                    'mode': 'append|overwrite|upsert'
                },
                'schema': Dict[str, Any],
                'transformations': List[Dict[str, Any]],
                'error_handling': 'fail|skip|log'
            }

        Returns:
            {
                'status': 'success|failed|partial',
                'import_id': str,
                'rows_imported': int,
                'rows_failed': int,
                'rows_skipped': int,
                'file_size_mb': float,
                'execution_time_seconds': float,
                'validation_errors': int,
                'duplicates_found': int,
                'schema_inferred': bool,
                'errors': List[Dict[str, Any]],
                'warnings': List[str]
            }
        """
        source = params.get('source', {})
        destination = params.get('destination', {})
        options = params.get('options', {})

        source_type = source.get('type')
        source_format = source.get('format', 'csv')

        self.logger.info(
            f"Importing {source_format} data from {source_type}"
        )

        # Mock import process
        total_rows = 75000
        failed_rows = 45
        skipped_rows = 120
        imported_rows = total_rows - failed_rows - skipped_rows
        duplicates = 180

        return {
            'status': 'success' if failed_rows == 0 else 'partial',
            'import_id': f'import_{source_format}_20251116',
            'source_type': source_type,
            'source_format': source_format,
            'source_path': source.get('path'),
            'destination_type': destination.get('type'),
            'destination_table': destination.get('table'),
            'rows_imported': imported_rows,
            'rows_failed': failed_rows,
            'rows_skipped': skipped_rows,
            'rows_total': total_rows,
            'success_rate': round((imported_rows / total_rows) * 100, 2),
            'file_size_mb': 18.3,
            'execution_time_seconds': 12.5,
            'throughput_rows_per_sec': int(total_rows / 12.5),
            'validation_errors': failed_rows,
            'duplicates_found': duplicates,
            'duplicates_removed': duplicates if options.get('deduplicate') else 0,
            'schema_inferred': options.get('infer_schema', True),
            'columns_detected': 22,
            'data_types_inferred': {
                'string': 8,
                'integer': 6,
                'float': 4,
                'datetime': 3,
                'boolean': 1
            },
            'errors': [
                {
                    'row': 1543,
                    'column': 'email',
                    'error': 'Invalid email format',
                    'value': 'not-an-email'
                },
                {
                    'row': 2891,
                    'column': 'age',
                    'error': 'Value out of range',
                    'value': -5
                }
            ] if failed_rows > 0 else [],
            'warnings': [
                'Column "phone" has 12% null values',
                'Date format inconsistent in "created_at" column',
                'Large text values detected in "description" column'
            ],
            'transformations_applied': len(params.get('transformations', [])),
            'mode': options.get('mode', 'append'),
            'batch_size': options.get('batch_size', 1000),
            'batches_processed': total_rows // options.get('batch_size', 1000),
            'metadata': {
                'import_date': '2025-11-16T10:00:00Z',
                'encoding': options.get('encoding', 'utf-8'),
                'delimiter': options.get('delimiter', ','),
                'checksum': 'x1y2z3a4b5c6'
            }
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate import 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_source_types = ['file', 's3', 'gcs', 'azure', 'database', 'api', 'stream']
        source_type = params.get('source', {}).get('type')

        if source_type not in valid_source_types:
            self.logger.error(f"Invalid source type: {source_type}")
            return False

        return True
