"""Queue processing functionality for video processing"""

import logging
import asyncio
from typing import List, Optional, Dict, Any, Set, ClassVar
from datetime import datetime

from ..queue.types import QueuePriority, QueueMetrics, ProcessingMetrics
from ..queue.models import QueueItem

logger = logging.getLogger("VideoArchiver")

class QueueProcessor:
    """Handles processing of video queue items"""

    _active_items: ClassVar[Set[int]] = set()
    _processing_lock: ClassVar[asyncio.Lock] = asyncio.Lock()

    def __init__(self):
        self._metrics = ProcessingMetrics()

    async def process_item(self, item: QueueItem) -> bool:
        """
        Process a single queue item

        Args:
            item: Queue item to process

        Returns:
            bool: Success status
        """
        if item.id in self._active_items:
            logger.warning(f"Item {item.id} is already being processed")
            return False

        try:
            self._active_items.add(item.id)
            start_time = datetime.now()

            # Process item logic here
            # Placeholder for actual video processing
            await asyncio.sleep(1)

            processing_time = (datetime.now() - start_time).total_seconds()
            self._update_metrics(processing_time, True, item.size)
            return True

        except Exception as e:
            logger.error(f"Error processing item {item.id}: {str(e)}")
            self._update_metrics(0, False, 0)
            return False

        finally:
            self._active_items.remove(item.id)

    def _update_metrics(self, processing_time: float, success: bool, size: int) -> None:
        """Update processing metrics"""
        if success:
            self._metrics.record_success(processing_time)
        else:
            self._metrics.record_failure("Processing error")

    def get_metrics(self) -> QueueMetrics:
        """Get current processing metrics"""
        total = self._metrics.total_processed
        if total == 0:
            return QueueMetrics(
                total_items=0,
                processing_time=0,
                success_rate=0,
                error_rate=0,
                average_size=0,
            )

        return QueueMetrics(
            total_items=total,
            processing_time=self._metrics.avg_processing_time,
            success_rate=self._metrics.successful / total,
            error_rate=self._metrics.failed / total,
            average_size=0,  # This would need to be tracked separately if needed
        )