"""
Bulk Loader Module

This module provides high-volume data loading capabilities for triple stores,
enabling efficient batch processing with progress tracking and error recovery.

Key Features:
    - High-volume data loading strategies
    - Batch processing and chunking
    - Progress monitoring and reporting
    - Error handling and recovery with retries
    - Performance optimization
    - Memory management for large datasets
    - Stream-based loading

Main Classes:
    - BulkLoader: Main bulk loading coordinator
    - LoadProgress: Bulk loading progress representation dataclass

Example Usage:
    >>> from semantica.triple_store import BulkLoader
    >>> loader = BulkLoader(batch_size=1000, max_retries=3)
    >>> progress = loader.load_triples(triples, store_adapter)
    >>> print(f"Loaded {progress.loaded_triples}/{progress.total_triples} triples")
    >>> validation = loader.validate_before_load(triples)

Author: Semantica Contributors
License: MIT
"""

from typing import Any, Dict, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
import time

from ..utils.exceptions import ValidationError, ProcessingError
from ..utils.logging import get_logger
from ..utils.progress_tracker import get_progress_tracker
from ..semantic_extract.triple_extractor import Triple


@dataclass
class LoadProgress:
    """Bulk loading progress information."""
    total_triples: int
    loaded_triples: int
    failed_triples: int = 0
    current_batch: int = 0
    total_batches: int = 0
    progress_percentage: float = 0.0
    elapsed_time: float = 0.0
    estimated_remaining: Optional[float] = None
    metadata: Dict[str, Any] = field(default_factory=dict)


class BulkLoader:
    """
    High-volume data loading system for triple stores.
    
    • High-volume data loading strategies
    • Batch processing and chunking
    • Progress monitoring and reporting
    • Error handling and recovery
    • Performance optimization
    • Memory management for large datasets
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
        """
        Initialize bulk loader.
        
        Args:
            config: Configuration dictionary
            **kwargs: Additional configuration options:
                - batch_size: Batch size for loading (default: 1000)
                - max_retries: Maximum retry attempts (default: 3)
                - retry_delay: Delay between retries in seconds (default: 1.0)
        """
        self.logger = get_logger("bulk_loader")
        self.config = config or {}
        self.config.update(kwargs)
        self.progress_tracker = get_progress_tracker()
        
        self.batch_size = self.config.get("batch_size", 1000)
        self.max_retries = self.config.get("max_retries", 3)
        self.retry_delay = self.config.get("retry_delay", 1.0)
    
    def load_triples(
        self,
        triples: List[Triple],
        store_adapter: Any,
        **options
    ) -> LoadProgress:
        """
        Load triples in bulk.
        
        Args:
            triples: List of triples to load
            store_adapter: Triple store adapter instance
            **options: Additional options:
                - batch_size: Override default batch size
                - progress_callback: Callback function for progress updates
                - stop_on_error: Stop loading on first error (default: False)
        
        Returns:
            Load progress information
        """
        tracking_id = self.progress_tracker.start_tracking(
            module="triple_store",
            submodule="BulkLoader",
            message=f"Loading {len(triples)} triples in bulk"
        )
        
        try:
            start_time = time.time()
            batch_size = options.get("batch_size", self.batch_size)
            stop_on_error = options.get("stop_on_error", False)
            progress_callback = options.get("progress_callback")
            
            total_triples = len(triples)
            total_batches = (total_triples + batch_size - 1) // batch_size
            
            loaded_count = 0
            failed_count = 0
            
            self.logger.info(f"Starting bulk load: {total_triples} triples in {total_batches} batches")
            
            # Process in batches
            for batch_num in range(total_batches):
                self.progress_tracker.update_tracking(tracking_id, message=f"Processing batch {batch_num + 1}/{total_batches}...")
                batch_start = batch_num * batch_size
                batch_end = min(batch_start + batch_size, total_triples)
                batch = triples[batch_start:batch_end]
                
                # Load batch with retries
                batch_loaded = 0
                for attempt in range(self.max_retries):
                    try:
                        if hasattr(store_adapter, "bulk_load"):
                            result = store_adapter.bulk_load(batch, **options)
                        elif hasattr(store_adapter, "add_triples"):
                            result = store_adapter.add_triples(batch, **options)
                        else:
                            raise ProcessingError("Store adapter does not support bulk loading")
                        
                        batch_loaded = len(batch)
                        loaded_count += batch_loaded
                        break
                        
                    except Exception as e:
                        if attempt < self.max_retries - 1:
                            self.logger.warning(f"Batch {batch_num} failed, retrying: {e}")
                            time.sleep(self.retry_delay * (attempt + 1))
                        else:
                            self.logger.error(f"Batch {batch_num} failed after {self.max_retries} attempts: {e}")
                            failed_count += len(batch)
                            
                            if stop_on_error:
                                self.progress_tracker.stop_tracking(tracking_id, status="failed", message=f"Bulk load stopped due to error: {e}")
                                raise ProcessingError(f"Bulk load stopped due to error: {e}")
                
                # Update progress
                elapsed = time.time() - start_time
                progress = (loaded_count / total_triples * 100) if total_triples > 0 else 0.0
                estimated_remaining = None
                if loaded_count > 0:
                    estimated_remaining = (elapsed / loaded_count) * (total_triples - loaded_count)
                
                progress_info = LoadProgress(
                    total_triples=total_triples,
                    loaded_triples=loaded_count,
                    failed_triples=failed_count,
                    current_batch=batch_num + 1,
                    total_batches=total_batches,
                    progress_percentage=progress,
                    elapsed_time=elapsed,
                    estimated_remaining=estimated_remaining,
                    metadata={
                        "batch_size": batch_size,
                        "store_type": store_adapter.__class__.__name__
                    }
                )
                
                # Call progress callback if provided
                if progress_callback:
                    progress_callback(progress_info)
                
                self.logger.debug(f"Progress: {progress:.1f}% ({loaded_count}/{total_triples} triples)")
            
            elapsed = time.time() - start_time
            
            final_progress = LoadProgress(
                total_triples=total_triples,
                loaded_triples=loaded_count,
                failed_triples=failed_count,
                current_batch=total_batches,
                total_batches=total_batches,
                progress_percentage=100.0,
                elapsed_time=elapsed,
                estimated_remaining=0.0,
                metadata={
                    "success": failed_count == 0,
                    "throughput": loaded_count / elapsed if elapsed > 0 else 0.0
                }
            )
            
            self.logger.info(
                f"Bulk load completed: {loaded_count}/{total_triples} triples loaded "
                f"in {elapsed:.2f}s ({loaded_count / elapsed:.0f} triples/sec)"
            )
            
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                              message=f"Bulk load completed: {loaded_count}/{total_triples} triples loaded")
            return final_progress
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def load_from_file(
        self,
        file_path: str,
        store_adapter: Any,
        **options
    ) -> LoadProgress:
        """
        Load triples from file.
        
        Args:
            file_path: Path to RDF file
            store_adapter: Triple store adapter
            **options: Additional options:
                - format: File format (turtle, ntriples, rdfxml)
                - chunk_size: Chunk size for reading large files
        
        Returns:
            Load progress information
        """
        format = options.get("format", "turtle")
        chunk_size = options.get("chunk_size", 10000)
        
        # This would parse the file and extract triples
        # For now, return placeholder
        self.logger.warning("File loading not fully implemented - would parse and load RDF file")
        
        return LoadProgress(
            total_triples=0,
            loaded_triples=0,
            metadata={"file_path": file_path, "format": format}
        )
    
    def load_from_stream(
        self,
        triples_stream: Any,
        store_adapter: Any,
        **options
    ) -> LoadProgress:
        """
        Load triples from stream.
        
        Args:
            triples_stream: Stream of triples
            store_adapter: Triple store adapter
            **options: Additional options
        
        Returns:
            Load progress information
        """
        # Collect triples from stream in batches
        batch = []
        total_loaded = 0
        
        for triple in triples_stream:
            batch.append(triple)
            
            if len(batch) >= self.batch_size:
                # Load batch
                progress = self.load_triples(batch, store_adapter, **options)
                total_loaded += progress.loaded_triples
                batch = []
        
        # Load remaining triples
        if batch:
            progress = self.load_triples(batch, store_adapter, **options)
            total_loaded += progress.loaded_triples
        
        return LoadProgress(
            total_triples=total_loaded,
            loaded_triples=total_loaded,
            metadata={"source": "stream"}
        )
    
    def validate_before_load(
        self,
        triples: List[Triple],
        **options
    ) -> Dict[str, Any]:
        """
        Validate triples before loading.
        
        Args:
            triples: List of triples to validate
            **options: Validation options
        
        Returns:
            Validation results
        """
        errors = []
        warnings = []
        
        # Check for empty triples
        empty_triples = [t for t in triples if not t.subject or not t.predicate or not t.object]
        if empty_triples:
            errors.append(f"{len(empty_triples)} triples have empty components")
        
        # Check for invalid URIs
        invalid_uris = []
        for triple in triples:
            if not triple.subject.startswith("http") and not triple.subject.startswith("<"):
                invalid_uris.append(f"Subject: {triple.subject}")
            if not triple.predicate.startswith("http") and not triple.predicate.startswith("<"):
                invalid_uris.append(f"Predicate: {triple.predicate}")
        
        if invalid_uris:
            warnings.append(f"{len(invalid_uris)} triples may have invalid URIs")
        
        # Check confidence scores
        low_confidence = [t for t in triples if t.confidence < 0.5]
        if low_confidence:
            warnings.append(f"{len(low_confidence)} triples have low confidence scores")
        
        return {
            "valid": len(errors) == 0,
            "errors": errors,
            "warnings": warnings,
            "total_triples": len(triples),
            "valid_triples": len(triples) - len(empty_triples)
        }
