"""
OCR Job Worker - Background Task for Queue Processing

Runs as an asyncio background task in FastAPI.
Continuously polls the job queue and processes OCR requests IN PARALLEL.

Architecture:
    FastAPI startup
        ↓
    start_job_worker()
        ↓
    asyncio.create_task(_job_worker_loop())
        ↓
    while True:
        # Process up to OCR_WORKERS jobs concurrently
        jobs = get_pending_jobs(limit=available_slots)
        for job in jobs:
            asyncio.create_task(_process_job(job))
        await asyncio.sleep(0.1)
"""

import asyncio
import logging
import os
import time
from pathlib import Path
from typing import Optional, Set

from .job_queue import job_queue, OCRJobStatus, OCRJob
from .ocr_worker_pool import ocr_worker_pool
from backend.modules.data_entry.schemas.ocr import ExtractionData

logger = logging.getLogger(__name__)

# Global task reference
_job_worker_task: Optional[asyncio.Task] = None
_cleanup_task: Optional[asyncio.Task] = None
_shutdown_event: Optional[asyncio.Event] = None
_active_tasks: Set[asyncio.Task] = set()  # Track active job tasks
_concurrency_semaphore: Optional[asyncio.Semaphore] = None  # Limit concurrent jobs

# Configuration
POLL_INTERVAL_SECONDS = 0.1  # How often to check for new jobs (faster for parallel)
CLEANUP_INTERVAL_SECONDS = 3600  # Clean expired jobs every hour
OCR_TIMEOUT_SECONDS = 120  # Max time for OCR processing


async def _job_worker_loop() -> None:
    """
    Main worker loop - processes jobs from queue IN PARALLEL.

    Runs continuously until shutdown. Uses semaphore to limit
    concurrent jobs to OCR_WORKERS count. Launches jobs as
    background tasks without waiting for completion.
    """
    global _shutdown_event, _active_tasks, _concurrency_semaphore

    # Get max concurrent jobs from env (matches worker pool size)
    max_concurrent = int(os.getenv('OCR_WORKERS', '2'))
    _concurrency_semaphore = asyncio.Semaphore(max_concurrent)
    _active_tasks = set()

    logger.info(f"[JobWorker] Starting PARALLEL worker loop (max_concurrent={max_concurrent})...")
    _shutdown_event = asyncio.Event()

    consecutive_errors = 0
    max_consecutive_errors = 10

    while not _shutdown_event.is_set():
        try:
            # Clean up completed tasks
            done_tasks = {t for t in _active_tasks if t.done()}
            for task in done_tasks:
                _active_tasks.discard(task)
                # Check for exceptions
                try:
                    task.result()
                except Exception as e:
                    logger.error(f"[JobWorker] Task failed: {e}")

            # Check if we have capacity for more jobs
            active_count = len(_active_tasks)
            available_slots = max_concurrent - active_count

            if available_slots > 0:
                # Get next pending job
                job = await job_queue.get_next_pending()

                if job:
                    consecutive_errors = 0
                    # Launch job processing as background task
                    task = asyncio.create_task(_process_job_with_semaphore(job))
                    _active_tasks.add(task)
                    logger.debug(f"[JobWorker] Launched job {job.id} (active={len(_active_tasks)}/{max_concurrent})")
                else:
                    # No pending jobs - wait briefly
                    try:
                        await asyncio.wait_for(
                            _shutdown_event.wait(),
                            timeout=POLL_INTERVAL_SECONDS
                        )
                        if _shutdown_event.is_set():
                            break
                    except asyncio.TimeoutError:
                        pass
            else:
                # At capacity - wait for a slot to free up
                await asyncio.sleep(POLL_INTERVAL_SECONDS)

        except asyncio.CancelledError:
            logger.info("[JobWorker] Worker loop cancelled")
            break

        except Exception as e:
            consecutive_errors += 1
            logger.error(f"[JobWorker] Error in worker loop ({consecutive_errors}/{max_consecutive_errors}): {e}")

            if consecutive_errors >= max_consecutive_errors:
                logger.error("[JobWorker] Too many consecutive errors, stopping worker")
                break

            await asyncio.sleep(min(consecutive_errors * 2, 30))

    # Wait for active tasks to complete on shutdown
    if _active_tasks:
        logger.info(f"[JobWorker] Waiting for {len(_active_tasks)} active tasks to complete...")
        await asyncio.gather(*_active_tasks, return_exceptions=True)

    logger.info("[JobWorker] Worker loop stopped")


async def _process_job_with_semaphore(job: OCRJob) -> None:
    """
    Process job with semaphore to limit concurrency.

    Acquires semaphore before processing, releases after.
    This ensures we don't exceed OCR_WORKERS concurrent jobs.
    """
    global _concurrency_semaphore

    async with _concurrency_semaphore:
        await _process_job(job)


async def _process_job(job: OCRJob) -> None:
    """
    Process a single OCR job.

    Reads file, submits to worker pool, updates job status,
    and saves metrics for analytics.

    Args:
        job: OCRJob to process
    """
    logger.info(f"[JobWorker] Processing job {job.id}: engine={job.engine}, file={Path(job.file_path).name}")
    start_time = time.time()
    file_size = 0
    file_type = "image/jpeg"

    try:
        # Note: Job already marked as 'processing' atomically in get_next_pending()

        # Read file bytes
        file_path = Path(job.file_path)
        if not file_path.exists():
            raise FileNotFoundError(f"File not found: {file_path}")

        with open(file_path, 'rb') as f:
            file_bytes = f.read()

        file_size = len(file_bytes)
        # Determine file type from job or extension
        file_type = getattr(job, 'mime_type', 'image/jpeg') or 'image/jpeg'

        # Submit to worker pool
        result = await ocr_worker_pool.submit_task(
            image_bytes=file_bytes,
            engine=job.engine,
            preprocessing="auto",
            timeout=OCR_TIMEOUT_SECONDS
        )

        elapsed_ms = int((time.time() - start_time) * 1000)

        if result.get("success"):
            # Job completed successfully
            extraction = result.get("extraction", {})

            # Include raw_texts for analysis (from all OCR engine passes)
            extraction['raw_texts'] = result.get("raw_texts", [])

            # Extract actual OCR processing time from extraction result
            ocr_time_ms = extraction.get('processing_time_ms', 0)

            # Debug: log suggested_payment_mode
            spm = extraction.get('suggested_payment_mode')
            logger.info(f"[JobWorker] Job {job.id} extraction has suggested_payment_mode={spm}")

            await job_queue.update_status(
                job_id=job.id,
                status=OCRJobStatus.completed,
                result=extraction,
                processing_time_ms=elapsed_ms,
                ocr_time_ms=ocr_time_ms
            )

            logger.info(f"[JobWorker] Job {job.id} completed in {elapsed_ms}ms (ocr: {ocr_time_ms}ms)")

            # Save metrics for successful job
            await _save_job_metrics(
                job_id=job.id,
                username=job.created_by or 'unknown',
                engine_requested=job.engine,
                engine_used=extraction.get('ocr_engine', job.engine),
                processing_time_ms=elapsed_ms,
                file_size_bytes=file_size,
                file_type=file_type,
                original_filename=job.original_filename,
                success=True,
                overall_confidence=extraction.get('overall_confidence', 0.0),
                fields_extracted=_count_extracted_fields(extraction),
                needs_manual_review=extraction.get('needs_manual_review'),
                validation_warnings_count=len(extraction.get('validation_warnings', [])),
                validation_errors_count=len(extraction.get('validation_errors', [])),
            )

            # Auto-save receipt for batch jobs
            if job.batch_id:
                auto_save_result = await _auto_save_batch_receipt(
                    job=job,
                    extraction=extraction,
                    file_path=str(file_path)
                )
                if not auto_save_result:
                    # Auto-save failed - mark job as failed
                    # Note: job_queue status already updated to 'completed' above
                    # We need to update it back to failed with the auto-save error
                    logger.warning(
                        f"[JobWorker] Job {job.id} OCR succeeded but auto-save failed"
                    )

        else:
            # Job failed
            error_msg = result.get("error", "Unknown error")

            await job_queue.update_status(
                job_id=job.id,
                status=OCRJobStatus.failed,
                error=error_msg,
                processing_time_ms=elapsed_ms
            )

            logger.warning(f"[JobWorker] Job {job.id} failed after {elapsed_ms}ms: {error_msg}")

            # Save metrics for failed job
            await _save_job_metrics(
                job_id=job.id,
                username=job.created_by or 'unknown',
                engine_requested=job.engine,
                engine_used=job.engine,
                processing_time_ms=elapsed_ms,
                file_size_bytes=file_size,
                file_type=file_type,
                original_filename=job.original_filename,
                success=False,
                error_message=error_msg,
            )

    except Exception as e:
        elapsed_ms = int((time.time() - start_time) * 1000)

        logger.error(f"[JobWorker] Job {job.id} error after {elapsed_ms}ms: {e}")

        await job_queue.update_status(
            job_id=job.id,
            status=OCRJobStatus.failed,
            error=str(e),
            processing_time_ms=elapsed_ms
        )

        # Save metrics for error job
        await _save_job_metrics(
            job_id=job.id,
            username=job.created_by or 'unknown',
            engine_requested=job.engine,
            engine_used=job.engine,
            processing_time_ms=elapsed_ms,
            file_size_bytes=file_size,
            file_type=file_type,
            original_filename=job.original_filename,
            success=False,
            error_message=str(e),
        )

    finally:
        # Cleanup file after processing
        try:
            await job_queue.cleanup_job_file(job.id)
        except Exception as e:
            logger.warning(f"[JobWorker] Failed to cleanup file for job {job.id}: {e}")


async def _cleanup_loop() -> None:
    """
    Periodic cleanup of expired jobs.

    Runs every hour to delete jobs older than 24 hours.
    """
    global _shutdown_event

    logger.info("[JobWorker] Starting cleanup loop...")

    while not _shutdown_event.is_set():
        try:
            # Wait for interval or shutdown
            try:
                await asyncio.wait_for(
                    _shutdown_event.wait(),
                    timeout=CLEANUP_INTERVAL_SECONDS
                )
                if _shutdown_event.is_set():
                    break
            except asyncio.TimeoutError:
                pass  # Normal timeout, do cleanup

            # Run cleanup
            deleted = await job_queue.cleanup_expired()
            if deleted > 0:
                logger.info(f"[JobWorker] Cleanup: deleted {deleted} expired jobs")

        except asyncio.CancelledError:
            logger.info("[JobWorker] Cleanup loop cancelled")
            break

        except Exception as e:
            logger.error(f"[JobWorker] Cleanup error: {e}")
            await asyncio.sleep(60)  # Retry after 1 minute

    logger.info("[JobWorker] Cleanup loop stopped")


async def start_job_worker() -> bool:
    """
    Start the job worker background task.

    Called at FastAPI startup to begin processing queue.

    Returns:
        True if started successfully
    """
    global _job_worker_task, _cleanup_task, _shutdown_event

    if _job_worker_task is not None and not _job_worker_task.done():
        logger.warning("[JobWorker] Already running")
        return True

    try:
        # Initialize job queue
        await job_queue.initialize()

        # Initialize worker pool
        if not ocr_worker_pool.initialize():
            logger.error("[JobWorker] Failed to initialize worker pool")
            return False

        # Pre-warm worker pool in BACKGROUND (don't block startup)
        # First OCR request may be slower if prewarm isn't done yet
        async def _background_prewarm():
            logger.info("[JobWorker] Pre-warming OCR worker pool (background)...")
            warmup_success = await ocr_worker_pool.prewarm(timeout=90.0)
            if warmup_success:
                logger.info("[JobWorker] OCR worker pool pre-warmed successfully")
            else:
                logger.warning("[JobWorker] Worker pool pre-warm failed, first request will be slower")

        asyncio.create_task(_background_prewarm())

        # Start worker loop
        _shutdown_event = asyncio.Event()
        _job_worker_task = asyncio.create_task(_job_worker_loop())

        # Start cleanup loop
        _cleanup_task = asyncio.create_task(_cleanup_loop())

        logger.info("[JobWorker] Started successfully")
        return True

    except Exception as e:
        logger.error(f"[JobWorker] Failed to start: {e}")
        return False


async def stop_job_worker() -> None:
    """
    Stop the job worker background task.

    Called at FastAPI shutdown to gracefully stop processing.
    """
    global _job_worker_task, _cleanup_task, _shutdown_event

    logger.info("[JobWorker] Stopping...")

    # Signal shutdown
    if _shutdown_event:
        _shutdown_event.set()

    # Cancel worker task
    if _job_worker_task and not _job_worker_task.done():
        _job_worker_task.cancel()
        try:
            await _job_worker_task
        except asyncio.CancelledError:
            pass

    # Cancel cleanup task
    if _cleanup_task and not _cleanup_task.done():
        _cleanup_task.cancel()
        try:
            await _cleanup_task
        except asyncio.CancelledError:
            pass

    # Shutdown worker pool
    ocr_worker_pool.shutdown(wait=True)

    _job_worker_task = None
    _cleanup_task = None
    _shutdown_event = None

    logger.info("[JobWorker] Stopped")


def is_running() -> bool:
    """Check if job worker is running."""
    return _job_worker_task is not None and not _job_worker_task.done()


def estimate_wait_time(queue_position: int) -> int:
    """
    Estimate wait time for a job in queue.

    Args:
        queue_position: Position in queue (1 = next)

    Returns:
        Estimated wait time in seconds
    """
    if queue_position <= 0:
        return 0

    # Get average processing time (synchronous fallback)
    # Default ~7 seconds per job if no data
    avg_time = 7.0

    try:
        # Try to get from queue stats
        import asyncio
        loop = asyncio.get_event_loop()
        if loop.is_running():
            # Can't use sync call in async context, use default
            pass
        else:
            avg_time = loop.run_until_complete(job_queue.get_average_processing_time())
    except Exception:
        pass

    # Estimate: position * average_time
    return int(queue_position * avg_time)


# ============================================================================
# Metrics Helper Functions
# ============================================================================

async def _save_job_metrics(
    job_id: str,
    username: str,
    engine_requested: str,
    engine_used: str,
    processing_time_ms: int = 0,
    file_size_bytes: int = 0,
    file_type: str = "image/jpeg",
    original_filename: Optional[str] = None,
    success: bool = True,
    error_message: Optional[str] = None,
    overall_confidence: float = 0.0,
    fields_extracted: int = 0,
    needs_manual_review: Optional[bool] = None,
    validation_warnings_count: int = 0,
    validation_errors_count: int = 0,
) -> None:
    """
    Save OCR job metrics to database for analytics.

    Called after each job completes (success or failure).
    Errors are logged but don't affect job processing.
    """
    try:
        from backend.modules.data_entry.db.database import get_db_session
        from backend.modules.data_entry.db.crud.ocr_settings import OCRMetricsCRUD

        async with await get_db_session() as session:
            await OCRMetricsCRUD.create(
                session=session,
                job_id=job_id,
                username=username,
                engine_requested=engine_requested,
                engine_used=engine_used,
                processing_time_ms=processing_time_ms,
                file_size_bytes=file_size_bytes,
                file_type=file_type,
                original_filename=original_filename,
                success=success,
                error_message=error_message,
                overall_confidence=overall_confidence,
                fields_extracted=fields_extracted,
                needs_manual_review=needs_manual_review,
                validation_warnings_count=validation_warnings_count,
                validation_errors_count=validation_errors_count,
            )
            logger.debug(f"[JobWorker] Saved metrics for job {job_id}")

    except Exception as e:
        # Log but don't fail - metrics are nice-to-have
        logger.warning(f"[JobWorker] Failed to save metrics for job {job_id}: {e}")


def _count_extracted_fields(extraction: dict) -> int:
    """
    Count number of successfully extracted fields from OCR result.

    Counts non-None values in key fields.
    """
    key_fields = [
        'receipt_number',
        'receipt_date',
        'amount',
        'partner_name',
        'cui',
        'tva_total',
        'address',
        'items_count',
    ]

    count = 0
    for field in key_fields:
        value = extraction.get(field)
        if value is not None and value != '' and value != []:
            count += 1

    # Also count TVA entries if present
    tva_entries = extraction.get('tva_entries', [])
    if tva_entries and len(tva_entries) > 0:
        count += 1

    # Count payment methods if present
    payment_methods = extraction.get('payment_methods', [])
    if payment_methods and len(payment_methods) > 0:
        count += 1

    return count


# ============================================================================
# Auto-Save Batch Receipt Helper
# ============================================================================

async def _auto_save_batch_receipt(
    job: OCRJob,
    extraction: dict,
    file_path: str
) -> bool:
    """
    Automatically create a receipt from OCR result for batch jobs.

    Called when a batch job completes successfully. Creates the receipt,
    attachment, and accounting entries using ReceiptAutoCreateService.

    Args:
        job: Completed OCRJob with batch_id set
        extraction: OCR extraction result dict
        file_path: Path to the original uploaded file

    Returns:
        True if receipt created successfully, False otherwise
    """
    if not job.batch_id:
        return True  # Not a batch job, nothing to do

    logger.info(f"[JobWorker] Auto-saving receipt for batch job {job.id} (batch_id={job.batch_id})")

    try:
        # Import here to avoid circular imports
        from backend.modules.data_entry.db.database import get_db_session
        from backend.modules.data_entry.db.models import BatchUpload
        from backend.modules.data_entry.services.receipt_auto_create import ReceiptAutoCreateService
        from sqlalchemy import select

        # Convert extraction dict to ExtractionData schema
        ocr_result = ExtractionData(**extraction)

        async with await get_db_session() as session:
            # Get batch info to retrieve company_id and user_id
            batch_result = await session.execute(
                select(BatchUpload).where(BatchUpload.id == job.batch_id)
            )
            batch = batch_result.scalar_one_or_none()

            if not batch:
                error_msg = f"Batch {job.batch_id} not found"
                logger.error(f"[JobWorker] Auto-save failed for job {job.id}: {error_msg}")
                await job_queue.update_status(
                    job_id=job.id,
                    status=OCRJobStatus.failed,
                    error=f"Auto-save error: {error_msg}"
                )
                return False

            # Call ReceiptAutoCreateService
            result = await ReceiptAutoCreateService.create_from_ocr_result(
                session=session,
                job_id=job.id,
                ocr_result=ocr_result,
                username=job.created_by or batch.user_id,
                batch_id=job.batch_id,
                company_id=batch.company_id,
                file_path=file_path,
                original_filename=job.original_filename,
                file_hash=job.file_hash  # Pass file_hash for duplicate detection (US-007)
            )

            if result.success:
                logger.info(
                    f"[JobWorker] Auto-save successful for job {job.id}: "
                    f"receipt_id={result.receipt_id}"
                )
                return True
            else:
                error_msg = result.error_message or "Unknown error"
                logger.warning(
                    f"[JobWorker] Auto-save validation failed for job {job.id}: {error_msg}"
                )
                # Update job status to failed with the auto-save error
                await job_queue.update_status(
                    job_id=job.id,
                    status=OCRJobStatus.failed,
                    error=f"Auto-save error: {error_msg}"
                )
                return False

    except Exception as e:
        error_msg = str(e)
        logger.error(f"[JobWorker] Auto-save exception for job {job.id}: {error_msg}")

        # Update job status to failed
        try:
            await job_queue.update_status(
                job_id=job.id,
                status=OCRJobStatus.failed,
                error=f"Auto-save error: {error_msg}"
            )
        except Exception as update_err:
            logger.error(f"[JobWorker] Failed to update job status after auto-save error: {update_err}")

        return False