"""
Processing Orchestrator - Coordinates document processing across tracks.

This module provides a unified orchestration layer for document processing,
separating the high-level flow control from track-specific implementations.
"""

import logging
import time
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union

from app.models.unified_document import (
    ProcessingTrack,
    UnifiedDocument,
    DocumentMetadata,
    ElementType,
)

logger = logging.getLogger(__name__)


# ============================================================================
# Data Classes
# ============================================================================

@dataclass
class ProcessingConfig:
    """Configuration for document processing."""
    detect_layout: bool = True
    confidence_threshold: float = 0.5
    output_dir: Optional[Path] = None
    lang: str = "ch"
    layout_model: str = "ppyolov2_r50vd_dcn_365e_publaynet"
    preprocessing_mode: str = "auto"
    preprocessing_config: Optional[Dict] = None
    table_detection_config: Optional[Dict] = None
    force_track: Optional[str] = None  # "direct" or "ocr"
    use_dual_track: bool = True


@dataclass
class TrackRecommendation:
    """Recommendation for which processing track to use."""
    track: ProcessingTrack
    confidence: float
    reason: str
    metrics: Dict[str, Any] = field(default_factory=dict)


@dataclass
class ProcessingResult:
    """Result of document processing."""
    document: Optional[UnifiedDocument] = None
    legacy_result: Optional[Dict] = None
    track_used: ProcessingTrack = ProcessingTrack.DIRECT
    processing_time: float = 0.0
    success: bool = True
    error: Optional[str] = None


# ============================================================================
# Pipeline Interface
# ============================================================================

class ProcessingPipeline(ABC):
    """Abstract base class for processing pipelines."""

    @property
    @abstractmethod
    def track_type(self) -> ProcessingTrack:
        """Return the processing track type for this pipeline."""
        pass

    @abstractmethod
    def process(
        self,
        file_path: Path,
        config: ProcessingConfig
    ) -> ProcessingResult:
        """
        Process a document through this pipeline.

        Args:
            file_path: Path to the document
            config: Processing configuration

        Returns:
            ProcessingResult with the extracted document
        """
        pass

    @abstractmethod
    def can_process(self, file_path: Path) -> bool:
        """
        Check if this pipeline can process the given file.

        Args:
            file_path: Path to the document

        Returns:
            True if the pipeline can process this file type
        """
        pass


# ============================================================================
# Direct Track Pipeline
# ============================================================================

class DirectPipeline(ProcessingPipeline):
    """Pipeline for processing editable PDFs via direct text extraction."""

    def __init__(self):
        self._engine = None
        self._office_converter = None

    @property
    def track_type(self) -> ProcessingTrack:
        return ProcessingTrack.DIRECT

    @property
    def engine(self):
        """Lazy-load DirectExtractionEngine."""
        if self._engine is None:
            from app.services.direct_extraction_engine import DirectExtractionEngine
            self._engine = DirectExtractionEngine(
                enable_table_detection=True,
                enable_image_extraction=True,
                min_image_area=200.0,
                enable_whiteout_detection=True,
                enable_content_sanitization=True
            )
        return self._engine

    @property
    def office_converter(self):
        """Lazy-load OfficeConverter."""
        if self._office_converter is None:
            from app.services.office_converter import OfficeConverter
            self._office_converter = OfficeConverter()
        return self._office_converter

    def can_process(self, file_path: Path) -> bool:
        """Check if file is processable (PDF or Office document)."""
        suffix = file_path.suffix.lower()
        return suffix in ['.pdf', '.docx', '.doc', '.xlsx', '.xls', '.pptx', '.ppt']

    def process(
        self,
        file_path: Path,
        config: ProcessingConfig
    ) -> ProcessingResult:
        """Process document using direct text extraction."""
        start_time = time.time()

        try:
            logger.info(f"DirectPipeline: Processing {file_path.name}")

            # Handle Office document conversion
            actual_path = file_path
            if self._is_office_document(file_path):
                actual_path = self._convert_office_to_pdf(file_path, config.output_dir)
                if actual_path is None:
                    return ProcessingResult(
                        success=False,
                        error=f"Failed to convert Office document: {file_path.name}",
                        track_used=ProcessingTrack.DIRECT,
                        processing_time=time.time() - start_time
                    )

            # Extract document
            unified_doc = self.engine.extract(
                actual_path,
                output_dir=config.output_dir
            )

            processing_time = time.time() - start_time

            # Update metadata
            if unified_doc.metadata is None:
                unified_doc.metadata = DocumentMetadata()
            unified_doc.metadata.processing_track = ProcessingTrack.DIRECT
            unified_doc.metadata.processing_time = processing_time

            logger.info(f"DirectPipeline: Completed in {processing_time:.2f}s, "
                       f"{len(unified_doc.pages)} pages extracted")

            return ProcessingResult(
                document=unified_doc,
                track_used=ProcessingTrack.DIRECT,
                processing_time=processing_time,
                success=True
            )

        except Exception as e:
            logger.error(f"DirectPipeline: Error processing {file_path.name}: {e}")
            import traceback
            traceback.print_exc()
            return ProcessingResult(
                success=False,
                error=str(e),
                track_used=ProcessingTrack.DIRECT,
                processing_time=time.time() - start_time
            )

    def check_for_missing_images(
        self,
        file_path: Path,
        unified_doc: UnifiedDocument
    ) -> List[int]:
        """
        Check if document has pages with missing inline images.

        Args:
            file_path: Path to the PDF
            unified_doc: Extracted document

        Returns:
            List of page indices with missing images
        """
        return self.engine.check_document_for_missing_images(file_path)

    def render_missing_images(
        self,
        file_path: Path,
        unified_doc: UnifiedDocument,
        page_list: List[int],
        output_dir: Path
    ) -> UnifiedDocument:
        """
        Render inline image regions that couldn't be extracted.

        Args:
            file_path: Path to the PDF
            unified_doc: Document to update
            page_list: Pages with missing images
            output_dir: Directory for output images

        Returns:
            Updated UnifiedDocument
        """
        return self.engine.render_inline_image_regions(
            file_path, unified_doc, page_list, output_dir
        )

    def _is_office_document(self, file_path: Path) -> bool:
        """Check if file is an Office document."""
        return self.office_converter.is_office_document(file_path)

    def _convert_office_to_pdf(
        self,
        file_path: Path,
        output_dir: Optional[Path]
    ) -> Optional[Path]:
        """Convert Office document to PDF."""
        try:
            return self.office_converter.convert_to_pdf(file_path, output_dir)
        except Exception as e:
            logger.error(f"Office conversion failed: {e}")
            return None


# ============================================================================
# OCR Track Pipeline
# ============================================================================

class OCRPipeline(ProcessingPipeline):
    """Pipeline for processing scanned documents via OCR."""

    def __init__(self):
        self._ocr_service = None
        self._converter = None

    @property
    def track_type(self) -> ProcessingTrack:
        return ProcessingTrack.OCR

    @property
    def ocr_service(self):
        """
        Get reference to OCR service.
        Note: This creates a circular dependency that needs careful handling.
        The OCRPipeline should receive the service via dependency injection.
        """
        if self._ocr_service is None:
            raise RuntimeError(
                "OCRPipeline requires OCR service to be set via set_ocr_service()"
            )
        return self._ocr_service

    def set_ocr_service(self, service):
        """Set the OCR service for this pipeline (dependency injection)."""
        self._ocr_service = service

    @property
    def converter(self):
        """Lazy-load OCR to Unified converter."""
        if self._converter is None:
            from app.services.ocr_to_unified_converter import OCRToUnifiedConverter
            self._converter = OCRToUnifiedConverter()
        return self._converter

    def can_process(self, file_path: Path) -> bool:
        """Check if file is processable (images or PDFs)."""
        suffix = file_path.suffix.lower()
        return suffix in ['.pdf', '.png', '.jpg', '.jpeg', '.tiff', '.tif', '.bmp']

    def process(
        self,
        file_path: Path,
        config: ProcessingConfig
    ) -> ProcessingResult:
        """Process document using OCR."""
        start_time = time.time()

        try:
            logger.info(f"OCRPipeline: Processing {file_path.name}")

            # Use OCR service's traditional processing
            ocr_result = self.ocr_service.process_file_traditional(
                file_path,
                detect_layout=config.detect_layout,
                confidence_threshold=config.confidence_threshold,
                output_dir=config.output_dir,
                lang=config.lang,
                layout_model=config.layout_model,
                preprocessing_mode=config.preprocessing_mode,
                preprocessing_config=config.preprocessing_config,
                table_detection_config=config.table_detection_config
            )

            processing_time = time.time() - start_time

            # Convert to UnifiedDocument
            unified_doc = self.converter.convert(
                ocr_result,
                file_path,
                processing_time,
                config.lang
            )

            # Update metadata
            if unified_doc.metadata is None:
                unified_doc.metadata = DocumentMetadata()
            unified_doc.metadata.processing_track = ProcessingTrack.OCR
            unified_doc.metadata.processing_time = processing_time

            logger.info(f"OCRPipeline: Completed in {processing_time:.2f}s")

            return ProcessingResult(
                document=unified_doc,
                legacy_result=ocr_result,
                track_used=ProcessingTrack.OCR,
                processing_time=processing_time,
                success=True
            )

        except Exception as e:
            logger.error(f"OCRPipeline: Error processing {file_path.name}: {e}")
            import traceback
            traceback.print_exc()
            return ProcessingResult(
                success=False,
                error=str(e),
                track_used=ProcessingTrack.OCR,
                processing_time=time.time() - start_time
            )


# ============================================================================
# Processing Orchestrator
# ============================================================================

class ProcessingOrchestrator:
    """
    Orchestrates document processing across Direct and OCR tracks.

    This class coordinates the high-level processing flow:
    1. Determines the optimal processing track
    2. Routes to the appropriate pipeline
    3. Handles hybrid mode (Direct + OCR fallback)
    4. Manages result format conversion
    """

    def __init__(self):
        self._document_detector = None
        self._direct_pipeline = DirectPipeline()
        self._ocr_pipeline = OCRPipeline()

    @property
    def document_detector(self):
        """Lazy-load DocumentTypeDetector."""
        if self._document_detector is None:
            from app.services.document_type_detector import DocumentTypeDetector
            self._document_detector = DocumentTypeDetector()
        return self._document_detector

    @property
    def direct_pipeline(self) -> DirectPipeline:
        return self._direct_pipeline

    @property
    def ocr_pipeline(self) -> OCRPipeline:
        return self._ocr_pipeline

    def set_ocr_service(self, service):
        """Set OCR service for the OCR pipeline (dependency injection)."""
        self._ocr_pipeline.set_ocr_service(service)

    def determine_processing_track(
        self,
        file_path: Path,
        force_track: Optional[str] = None
    ) -> TrackRecommendation:
        """
        Determine the optimal processing track for a document.

        Args:
            file_path: Path to the document
            force_track: Optional override ("direct" or "ocr")

        Returns:
            TrackRecommendation with track, confidence, and reason
        """
        # Handle forced track
        if force_track:
            track = ProcessingTrack.DIRECT if force_track == "direct" else ProcessingTrack.OCR
            return TrackRecommendation(
                track=track,
                confidence=1.0,
                reason=f"Forced to use {force_track} track"
            )

        # Use document detector
        try:
            recommendation = self.document_detector.detect(file_path)
            # Convert string track to ProcessingTrack enum
            track_str = recommendation.track
            if isinstance(track_str, str):
                track = ProcessingTrack.DIRECT if track_str == "direct" else ProcessingTrack.OCR
            else:
                track = track_str  # Already an enum
            return TrackRecommendation(
                track=track,
                confidence=recommendation.confidence,
                reason=recommendation.reason,
                metrics=getattr(recommendation, 'metrics', {})
            )
        except Exception as e:
            logger.warning(f"Document detection failed: {e}, defaulting to DIRECT")
            return TrackRecommendation(
                track=ProcessingTrack.DIRECT,
                confidence=0.5,
                reason=f"Detection failed ({e}), using default"
            )

    def process(
        self,
        file_path: Path,
        config: ProcessingConfig
    ) -> ProcessingResult:
        """
        Process a document using the optimal track.

        Args:
            file_path: Path to the document
            config: Processing configuration

        Returns:
            ProcessingResult with extracted document
        """
        file_path = Path(file_path)
        start_time = time.time()

        logger.info(f"ProcessingOrchestrator: Processing {file_path.name}")

        # Determine track
        recommendation = self.determine_processing_track(
            file_path,
            config.force_track
        )

        logger.info(f"Track recommendation: {recommendation.track.value} "
                   f"(confidence: {recommendation.confidence:.2f}, "
                   f"reason: {recommendation.reason})")

        # Route to appropriate pipeline
        if recommendation.track == ProcessingTrack.DIRECT:
            result = self._execute_direct_with_fallback(file_path, config)
        else:
            result = self._ocr_pipeline.process(file_path, config)

        # Update total processing time
        result.processing_time = time.time() - start_time

        return result

    def _execute_direct_with_fallback(
        self,
        file_path: Path,
        config: ProcessingConfig
    ) -> ProcessingResult:
        """
        Execute direct track with hybrid fallback for missing images.

        Args:
            file_path: Path to the document
            config: Processing configuration

        Returns:
            ProcessingResult (may be HYBRID if OCR was used for images)
        """
        # Run direct extraction
        result = self._direct_pipeline.process(file_path, config)

        if not result.success or result.document is None:
            logger.warning("Direct extraction failed, falling back to OCR")
            return self._ocr_pipeline.process(file_path, config)

        # Check for missing images
        try:
            missing_pages = self._direct_pipeline.check_for_missing_images(
                file_path, result.document
            )

            if missing_pages:
                logger.info(f"Found {len(missing_pages)} pages with missing images, "
                           f"entering hybrid mode")
                return self._execute_hybrid(
                    file_path, config, result.document, missing_pages
                )
        except Exception as e:
            logger.warning(f"Missing image check failed: {e}")

        return result

    def _execute_hybrid(
        self,
        file_path: Path,
        config: ProcessingConfig,
        direct_doc: UnifiedDocument,
        missing_pages: List[int]
    ) -> ProcessingResult:
        """
        Execute hybrid mode: Direct extraction + OCR for missing images.

        Args:
            file_path: Path to the document
            config: Processing configuration
            direct_doc: Document from direct extraction
            missing_pages: Pages with missing images

        Returns:
            ProcessingResult with HYBRID track
        """
        start_time = time.time()

        try:
            # Try OCR for missing images
            ocr_result = self._ocr_pipeline.process(file_path, config)

            if ocr_result.success and ocr_result.document:
                # Merge OCR images into direct result
                images_added = self._merge_ocr_images(
                    direct_doc,
                    ocr_result.document,
                    missing_pages
                )
                logger.info(f"Hybrid mode: Added {images_added} images from OCR")
            else:
                # Fallback: render inline images directly
                logger.warning("OCR failed, rendering inline images as fallback")
                if config.output_dir:
                    direct_doc = self._direct_pipeline.render_missing_images(
                        file_path,
                        direct_doc,
                        missing_pages,
                        config.output_dir
                    )

            # Update metadata
            if direct_doc.metadata is None:
                direct_doc.metadata = DocumentMetadata()
            direct_doc.metadata.processing_track = ProcessingTrack.HYBRID

            return ProcessingResult(
                document=direct_doc,
                track_used=ProcessingTrack.HYBRID,
                processing_time=time.time() - start_time,
                success=True
            )

        except Exception as e:
            logger.error(f"Hybrid processing failed: {e}")
            # Return direct result as-is
            return ProcessingResult(
                document=direct_doc,
                track_used=ProcessingTrack.DIRECT,
                processing_time=time.time() - start_time,
                success=True
            )

    def _merge_ocr_images(
        self,
        direct_doc: UnifiedDocument,
        ocr_doc: UnifiedDocument,
        target_pages: List[int]
    ) -> int:
        """
        Merge image elements from OCR result into direct result.

        Args:
            direct_doc: Target document
            ocr_doc: Source document with images
            target_pages: Page indices to merge images from

        Returns:
            Number of images added
        """
        images_added = 0

        for page_idx in target_pages:
            if page_idx >= len(direct_doc.pages) or page_idx >= len(ocr_doc.pages):
                continue

            direct_page = direct_doc.pages[page_idx]
            ocr_page = ocr_doc.pages[page_idx]

            # Find image elements in OCR result
            for elem in ocr_page.elements:
                if elem.type in [
                    ElementType.IMAGE, ElementType.FIGURE,
                    ElementType.CHART, ElementType.DIAGRAM,
                    ElementType.LOGO, ElementType.STAMP
                ]:
                    # Generate unique element ID
                    elem.element_id = f"ocr_img_{page_idx}_{images_added}"
                    direct_page.elements.append(elem)
                    images_added += 1

        return images_added