#!/usr/bin/env python3
"""Main execution script for SAM3 metrics evaluation."""

import argparse
import json
import logging
import sys
from pathlib import Path

from metrics_evaluation.config.config_loader import load_config
from metrics_evaluation.extraction.cvat_extractor import CVATExtractor
from metrics_evaluation.inference.sam3_inference import SAM3Inferencer
from metrics_evaluation.metrics.metrics_calculator import MetricsCalculator
from metrics_evaluation.utils.logging_config import setup_logging
from metrics_evaluation.visualization.visual_comparison import VisualComparator

logger = logging.getLogger(__name__)


def write_metrics_summary(metrics: dict, output_path: Path) -> None:
    """Write human-readable metrics summary.

    Args:
        metrics: Metrics dictionary
        output_path: Path to output file
    """
    with open(output_path, "w") as f:
        f.write("=" * 80 + "\n")
        f.write("SAM3 EVALUATION METRICS SUMMARY\n")
        f.write("=" * 80 + "\n\n")

        aggregate = metrics["aggregate"]

        f.write(f"Total Images Evaluated: {aggregate['total_images']}\n\n")

        for threshold_str, threshold_data in aggregate["by_threshold"].items():
            iou = threshold_data["iou_threshold"]
            f.write(f"\n{'='*80}\n")
            f.write(f"IoU Threshold: {iou:.0%}\n")
            f.write(f"{'='*80}\n\n")

            overall = threshold_data["overall"]

            f.write("Overall Metrics:\n")
            f.write(f"  True Positives:  {overall['true_positives']}\n")
            f.write(f"  False Positives: {overall['false_positives']}\n")
            f.write(f"  False Negatives: {overall['false_negatives']}\n")
            f.write(f"  Precision:       {overall['precision']:.2%}\n")
            f.write(f"  Recall:          {overall['recall']:.2%}\n")
            f.write(f"  F1-Score:        {overall['f1']:.2%}\n")
            f.write(f"  mAP:             {overall['map']:.2%}\n")
            f.write(f"  mAR:             {overall['mar']:.2%}\n\n")

            f.write("Per-Class Metrics:\n")
            f.write("-" * 80 + "\n")
            f.write(f"{'Class':<20} {'GT':>6} {'Pred':>6} {'TP':>6} {'FP':>6} {'FN':>6} {'Prec':>8} {'Rec':>8} {'F1':>8}\n")
            f.write("-" * 80 + "\n")

            for label, stats in sorted(threshold_data["by_label"].items()):
                f.write(
                    f"{label:<20} "
                    f"{stats['gt_total']:>6} "
                    f"{stats['pred_total']:>6} "
                    f"{stats['tp']:>6} "
                    f"{stats['fp']:>6} "
                    f"{stats['fn']:>6} "
                    f"{stats['precision']:>8.2%} "
                    f"{stats['recall']:>8.2%} "
                    f"{stats['f1']:>8.2%}\n"
                )

            f.write("\n")

            # Confusion Matrix
            cm = threshold_data["confusion_matrix"]
            labels = cm["labels"]
            matrix = cm["matrix"]

            if labels:
                f.write("Confusion Matrix:\n")
                f.write("-" * 80 + "\n")

                # Header
                header = "Actual \\ Pred |"
                for label in labels:
                    header += f" {label[:10]:>10} |"
                f.write(header + "\n")
                f.write("-" * len(header) + "\n")

                # Rows
                for i, actual_label in enumerate(labels):
                    row = f"{actual_label[:13]:>13} |"
                    for j in range(len(labels)):
                        row += f" {matrix[i][j]:>10} |"
                    f.write(row + "\n")

                f.write("\n")

        f.write("=" * 80 + "\n")
        f.write("END OF REPORT\n")
        f.write("=" * 80 + "\n")

    logger.info(f"Wrote metrics summary to {output_path}")


def main() -> int:
    """Main execution function.

    Returns:
        Exit code (0 for success, non-zero for failure)
    """
    parser = argparse.ArgumentParser(
        description="Run SAM3 metrics evaluation against CVAT ground truth"
    )
    parser.add_argument(
        "--config",
        type=str,
        default="config/config.json",
        help="Path to configuration file"
    )
    parser.add_argument(
        "--force-download",
        action="store_true",
        help="Force re-download images from CVAT"
    )
    parser.add_argument(
        "--force-inference",
        action="store_true",
        help="Force re-run SAM3 inference"
    )
    parser.add_argument(
        "--skip-inference",
        action="store_true",
        help="Skip inference, use cached results"
    )
    parser.add_argument(
        "--visualize",
        action="store_true",
        help="Generate visual comparisons"
    )
    parser.add_argument(
        "--log-level",
        type=str,
        default="INFO",
        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
        help="Logging level"
    )

    args = parser.parse_args()

    # Load configuration
    try:
        config = load_config(args.config)
    except Exception as e:
        print(f"ERROR: Failed to load configuration: {e}", file=sys.stderr)
        return 1

    # Setup logging
    cache_dir = config.get_cache_path()
    log_file = cache_dir / "evaluation_log.txt"
    setup_logging(log_file, getattr(logging, args.log_level))

    logger.info("=" * 80)
    logger.info("SAM3 METRICS EVALUATION")
    logger.info("=" * 80)

    try:
        # Phase 1: Extract from CVAT
        logger.info("\n" + "=" * 80)
        logger.info("PHASE 1: CVAT Data Extraction")
        logger.info("=" * 80)

        extractor = CVATExtractor(config)

        if args.force_download:
            logger.info("Force download enabled - will re-download all images")

        image_paths = extractor.run_extraction()

        total_extracted = sum(len(paths) for paths in image_paths.values())
        logger.info(f"Extraction complete: {total_extracted} images extracted")

        if total_extracted == 0:
            logger.error("No images extracted. Aborting.")
            return 1

        # Phase 2: Run SAM3 Inference
        if not args.skip_inference:
            logger.info("\n" + "=" * 80)
            logger.info("PHASE 2: SAM3 Inference")
            logger.info("=" * 80)

            inferencer = SAM3Inferencer(config)
            stats = inferencer.run_inference_batch(image_paths, args.force_inference)

            logger.info(
                f"Inference complete: {stats['successful']} successful, "
                f"{stats['failed']} failed, {stats['skipped']} skipped"
            )

            if stats['successful'] == 0 and stats['skipped'] == 0:
                logger.error("No successful inferences. Aborting.")
                return 1
        else:
            logger.info("Skipping inference (--skip-inference)")

        # Phase 3: Calculate Metrics
        logger.info("\n" + "=" * 80)
        logger.info("PHASE 3: Metrics Calculation")
        logger.info("=" * 80)

        calculator = MetricsCalculator(config)
        metrics = calculator.run_evaluation(cache_dir)

        # Save detailed metrics
        metrics_json_path = cache_dir / "metrics_detailed.json"
        with open(metrics_json_path, "w") as f:
            json.dump(metrics, f, indent=2)
        logger.info(f"Saved detailed metrics to {metrics_json_path}")

        # Write summary
        metrics_summary_path = cache_dir / "metrics_summary.txt"
        write_metrics_summary(metrics, metrics_summary_path)

        # Phase 4: Visualization (optional)
        if args.visualize or config.output.generate_visualizations:
            logger.info("\n" + "=" * 80)
            logger.info("PHASE 4: Visual Comparisons")
            logger.info("=" * 80)

            comparator = VisualComparator()
            comparison_paths = comparator.generate_all_comparisons(cache_dir)
            logger.info(f"Generated {len(comparison_paths)} visual comparisons")

        # Summary
        logger.info("\n" + "=" * 80)
        logger.info("EVALUATION COMPLETE")
        logger.info("=" * 80)

        aggregate = metrics["aggregate"]
        logger.info(f"Images evaluated: {aggregate['total_images']}")

        # Show metrics at 50% IoU
        threshold_50 = aggregate["by_threshold"]["0.5"]
        overall = threshold_50["overall"]

        logger.info(f"\nMetrics at 50% IoU:")
        logger.info(f"  Precision: {overall['precision']:.2%}")
        logger.info(f"  Recall:    {overall['recall']:.2%}")
        logger.info(f"  F1-Score:  {overall['f1']:.2%}")
        logger.info(f"  mAP:       {overall['map']:.2%}")
        logger.info(f"  mAR:       {overall['mar']:.2%}")

        logger.info(f"\nResults saved to:")
        logger.info(f"  Metrics Summary: {metrics_summary_path}")
        logger.info(f"  Detailed JSON:   {metrics_json_path}")
        logger.info(f"  Execution Log:   {log_file}")

        return 0

    except KeyboardInterrupt:
        logger.warning("\nEvaluation interrupted by user")
        return 130

    except Exception as e:
        logger.error(f"\nEvaluation failed with error: {e}", exc_info=True)
        return 1


if __name__ == "__main__":
    sys.exit(main())