"""
Batch Processing Enhancements for Video Processing

Provides:
1. Resume checkpoint system
2. Error summary with detailed failure tracking
3. Batch INSERT transactions for database efficiency
4. Memory monitoring
5. Structured per-run logging
6. Performance metrics and timing

Usage:
    from batch_processor import BatchProcessor

    processor = BatchProcessor()
    processor.process_videos(video_keys, label="720p")
"""

import os
import json
import time
import gc
from datetime import datetime
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, asdict
from contextlib import contextmanager

from runtime_paths import checkpoint_file, log_file
from utils import atomic_write_json

# Try to import psutil for memory monitoring
try:
    import psutil
    HAS_PSUTIL = True
except ImportError:
    HAS_PSUTIL = False
    print("Warning: psutil not installed. Memory monitoring disabled.")


@dataclass
class VideoProcessingResult:
    """Result of processing a single video."""
    natural_key: str
    status: str  # 'success', 'error', 'skipped'
    stage_completed: str  # 'thumbnails', 'embeddings', 'faces', 'complete'
    error_message: Optional[str]
    duration_seconds: float
    thumbnail_count: int
    embedding_count: int
    face_count: int
    timestamp: str


@dataclass
class BatchStats:
    """Statistics for a batch processing run."""
    run_id: str
    started_at: str
    completed_at: Optional[str]
    total_videos: int
    processed: int
    skipped: int
    failed: int
    total_thumbnails: int
    total_embeddings: int
    total_faces: int
    avg_time_per_video: float
    errors: List[Dict[str, str]]


class StructuredLogger:
    """Structured logging with per-run log files."""

    def __init__(self, run_id: str, log_dir: str | None = None):
        self.run_id = run_id
        self.log_dir = log_dir or os.path.dirname(log_file())
        os.makedirs(self.log_dir, exist_ok=True)

        # Create run-specific log file
        self.log_file = os.path.join(self.log_dir, f"processing_{run_id}.log")
        self.error_file = os.path.join(self.log_dir, f"errors_{run_id}.csv")
        self.metrics_file = os.path.join(self.log_dir, f"metrics_{run_id}.json")

        # Initialize error CSV
        with open(self.error_file, 'w') as f:
            f.write("timestamp,natural_key,stage,error_type,error_message\n")

        self.log("INFO", f"Batch processing run started: {run_id}")

    def log(self, level: str, message: str, natural_key: str = None):
        """Log a message with timestamp and optional video key."""
        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]

        if natural_key:
            entry = f"[{timestamp}] [{level}] [{natural_key}] {message}"
        else:
            entry = f"[{timestamp}] [{level}] {message}"

        # Only print important messages to console
        if level in ('ERROR', 'WARNING', 'INFO'):
            print(entry)

        # Always write to log file
        with open(self.log_file, 'a') as f:
            f.write(entry + "\n")

    def log_error(self, natural_key: str, stage: str, error_type: str, error_message: str):
        """Log an error to both log file and error CSV."""
        timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')

        self.log("ERROR", f"Stage: {stage}, Type: {error_type}, Message: {error_message}", natural_key)

        # Append to error CSV (escape commas in message)
        safe_message = error_message.replace('"', '""').replace('\n', ' ')
        with open(self.error_file, 'a') as f:
            f.write(f'{timestamp},{natural_key},{stage},{error_type},"{safe_message}"\n')

    def log_progress(self, current: int, total: int, natural_key: str, status: str, duration: float):
        """Log progress with ETA calculation."""
        percent = (current / total) * 100
        self.log("INFO", f"[{current}/{total}] ({percent:.1f}%) - {status} in {duration:.1f}s", natural_key)

    def save_metrics(self, stats: BatchStats):
        """Save batch statistics to JSON file."""
        atomic_write_json(self.metrics_file, asdict(stats), indent=2)
        self.log("INFO", f"Metrics saved to {self.metrics_file}")


class CheckpointManager:
    """Manages processing checkpoints for resume capability."""

    def __init__(self, run_id: str, checkpoint_dir: str | None = None):
        self.run_id = run_id
        self.checkpoint_dir = checkpoint_dir or os.path.dirname(checkpoint_file(run_id))
        os.makedirs(self.checkpoint_dir, exist_ok=True)

        self.checkpoint_file = (
            os.path.join(self.checkpoint_dir, f"checkpoint_{run_id}.json")
            if checkpoint_dir is not None
            else checkpoint_file(run_id)
        )
        self.checkpoint_data = self._load_checkpoint()

    def _load_checkpoint(self) -> Dict[str, Any]:
        """Load existing checkpoint if available."""
        if os.path.exists(self.checkpoint_file):
            try:
                with open(self.checkpoint_file, 'r', encoding="utf-8") as f:
                    return json.load(f)
            except (OSError, json.JSONDecodeError) as exc:
                raise ValueError(
                    f"Checkpoint file {self.checkpoint_file} is invalid or unreadable: {exc}"
                ) from exc
        return {
            "run_id": self.run_id,
            "started_at": datetime.now().isoformat(),
            "processed_keys": [],
            "failed_keys": [],
            "skipped_keys": [],
            "last_key": None
        }

    def mark_processed(self, natural_key: str):
        """Mark a video as successfully processed."""
        if natural_key not in self.checkpoint_data["processed_keys"]:
            self.checkpoint_data["processed_keys"].append(natural_key)
        self.checkpoint_data["last_key"] = natural_key
        self._save_checkpoint()

    def mark_failed(self, natural_key: str):
        """Mark a video as failed."""
        if natural_key not in self.checkpoint_data["failed_keys"]:
            self.checkpoint_data["failed_keys"].append(natural_key)
        self.checkpoint_data["last_key"] = natural_key
        self._save_checkpoint()

    def mark_skipped(self, natural_key: str):
        """Mark a video as skipped (already processed)."""
        if natural_key not in self.checkpoint_data["skipped_keys"]:
            self.checkpoint_data["skipped_keys"].append(natural_key)
        self._save_checkpoint()

    def is_processed(self, natural_key: str) -> bool:
        """Check if a video was already processed in this run."""
        return natural_key in self.checkpoint_data["processed_keys"]

    def get_pending_keys(self, all_keys: List[str]) -> List[str]:
        """Get keys that haven't been processed yet."""
        processed = set(self.checkpoint_data["processed_keys"])
        failed = set(self.checkpoint_data["failed_keys"])
        skipped = set(self.checkpoint_data["skipped_keys"])

        completed = processed | failed | skipped
        return [k for k in all_keys if k not in completed]

    def _save_checkpoint(self):
        """Save checkpoint to disk."""
        self.checkpoint_data["updated_at"] = datetime.now().isoformat()
        atomic_write_json(self.checkpoint_file, self.checkpoint_data, indent=2)

    def get_summary(self) -> Dict[str, int]:
        """Get summary of processing status."""
        return {
            "processed": len(self.checkpoint_data["processed_keys"]),
            "failed": len(self.checkpoint_data["failed_keys"]),
            "skipped": len(self.checkpoint_data["skipped_keys"])
        }


class MemoryMonitor:
    """Monitor system memory and manage resources."""

    def __init__(self, min_free_mb: int = 2000, warning_threshold_mb: int = 4000):
        self.min_free_mb = min_free_mb
        self.warning_threshold_mb = warning_threshold_mb
        self.last_check = 0
        self.check_interval = 10  # seconds

    def get_memory_status(self) -> Dict[str, Any]:
        """Get current memory status."""
        if not HAS_PSUTIL:
            return {"available": True, "free_mb": -1, "percent_used": -1}

        mem = psutil.virtual_memory()
        free_mb = mem.available / (1024 ** 2)

        return {
            "available": free_mb >= self.min_free_mb,
            "free_mb": round(free_mb, 1),
            "percent_used": mem.percent,
            "total_mb": round(mem.total / (1024 ** 2), 1)
        }

    def ensure_memory_available(self, logger: StructuredLogger = None) -> bool:
        """
        Check if enough memory is available, attempt cleanup if not.

        Returns:
            True if memory is available, False if critically low
        """
        if not HAS_PSUTIL:
            return True

        status = self.get_memory_status()

        if not status["available"]:
            if logger:
                logger.log("WARNING", f"Low memory: {status['free_mb']}MB free, attempting cleanup")

            # Force garbage collection
            gc.collect()

            # Try to clear GPU cache if torch is available
            try:
                import torch
                if torch.cuda.is_available():
                    torch.cuda.empty_cache()
            except ImportError:
                pass

            # Re-check after cleanup
            status = self.get_memory_status()

            if not status["available"]:
                if logger:
                    logger.log("ERROR", f"Memory still low after cleanup: {status['free_mb']}MB free")
                return False

        elif status["free_mb"] < self.warning_threshold_mb:
            if logger:
                logger.log("WARNING", f"Memory getting low: {status['free_mb']}MB free")

        return True

    def should_check(self) -> bool:
        """Determine if it's time to check memory again."""
        now = time.time()
        if now - self.last_check > self.check_interval:
            self.last_check = now
            return True
        return False


class PerformanceTracker:
    """Track timing for processing stages."""

    def __init__(self):
        self.stage_times = {}
        self.video_times = []
        self.current_video_start = None
        self.current_stages = {}

    def start_video(self, natural_key: str):
        """Start timing a video."""
        self.current_video_start = time.time()
        self.current_stages = {}

    def start_stage(self, stage_name: str):
        """Start timing a processing stage."""
        self.current_stages[stage_name] = time.time()

    def end_stage(self, stage_name: str):
        """End timing a processing stage."""
        if stage_name in self.current_stages:
            duration = time.time() - self.current_stages[stage_name]

            if stage_name not in self.stage_times:
                self.stage_times[stage_name] = []
            self.stage_times[stage_name].append(duration)

            return duration
        return 0

    def end_video(self) -> float:
        """End timing for current video."""
        if self.current_video_start:
            duration = time.time() - self.current_video_start
            self.video_times.append(duration)
            return duration
        return 0

    def get_stats(self) -> Dict[str, Any]:
        """Get timing statistics."""
        stats = {
            "videos_timed": len(self.video_times),
            "total_time_seconds": sum(self.video_times),
            "avg_time_per_video": sum(self.video_times) / len(self.video_times) if self.video_times else 0,
            "stages": {}
        }

        for stage, times in self.stage_times.items():
            stats["stages"][stage] = {
                "count": len(times),
                "total_seconds": round(sum(times), 2),
                "avg_seconds": round(sum(times) / len(times), 2) if times else 0,
                "min_seconds": round(min(times), 2) if times else 0,
                "max_seconds": round(max(times), 2) if times else 0
            }

        return stats

    def get_eta(self, remaining_videos: int) -> str:
        """Estimate time remaining."""
        if not self.video_times:
            return "Unknown"

        avg_time = sum(self.video_times) / len(self.video_times)
        remaining_seconds = avg_time * remaining_videos

        if remaining_seconds < 60:
            return f"{int(remaining_seconds)} seconds"
        elif remaining_seconds < 3600:
            return f"{int(remaining_seconds / 60)} minutes"
        elif remaining_seconds < 86400:
            hours = remaining_seconds / 3600
            return f"{hours:.1f} hours"
        else:
            days = remaining_seconds / 86400
            return f"{days:.1f} days"


def generate_run_id() -> str:
    """Generate a unique run ID."""
    return datetime.now().strftime('%Y%m%d_%H%M%S')


# ============================================================
# Helper function to get bottleneck analysis
# ============================================================

def analyze_processing_bottlenecks() -> Dict[str, Any]:
    """
    Analyze the current processing pipeline for bottlenecks.

    Returns:
        Analysis of potential bottlenecks and recommendations
    """
    bottlenecks = []
    recommendations = []

    # Check FFmpeg usage pattern
    bottlenecks.append({
        "component": "Thumbnail Resizing",
        "issue": "FFmpeg called separately for each thumbnail (2 calls per thumbnail)",
        "impact": "High - 100+ subprocess calls per video",
        "fix": "Use single FFmpeg command with scale filter chain or parallel processing"
    })

    # Check database patterns
    bottlenecks.append({
        "component": "Database Writes",
        "issue": "Each category/embedding is a separate INSERT with new connection",
        "impact": "High - 3000+ connections per video at scale",
        "fix": "Use batch transactions (one transaction per video, not per insert)"
    })

    # Check logging
    bottlenecks.append({
        "component": "Logging",
        "issue": "Every image categorization logs full results",
        "impact": "Medium - Disk I/O and log file bloat",
        "fix": "Log only summaries and errors (implemented in StructuredLogger)"
    })

    # Model loading
    bottlenecks.append({
        "component": "Model Loading",
        "issue": "Models are lazily loaded but should persist across videos",
        "impact": "Low - Models stay in memory after first load",
        "fix": "Verify models persist; consider explicit model manager"
    })

    recommendations = [
        "Batch category/embedding inserts into one transaction per video",
        "Replace individual FFmpeg calls with batch image processing",
        "Set up per-run logging with StructuredLogger",
        "Enable memory monitoring with MemoryMonitor",
        "Use CheckpointManager for resume capability",
        "Process 50-100 videos first to validate performance"
    ]

    return {
        "bottlenecks": bottlenecks,
        "recommendations": recommendations,
        "estimated_improvement": "40-60% reduction in processing time with batch optimizations"
    }


if __name__ == "__main__":
    # Print bottleneck analysis
    print("=" * 60)
    print("PROCESSING BOTTLENECK ANALYSIS")
    print("=" * 60)

    analysis = analyze_processing_bottlenecks()

    print("\nBottlenecks Identified:")
    for i, b in enumerate(analysis["bottlenecks"], 1):
        print(f"\n{i}. {b['component']}")
        print(f"   Issue: {b['issue']}")
        print(f"   Impact: {b['impact']}")
        print(f"   Fix: {b['fix']}")

    print("\n\nRecommendations:")
    for r in analysis["recommendations"]:
        print(f"  • {r}")

    print(f"\n{analysis['estimated_improvement']}")