#!/usr/bin/env python3
"""Train multiple YOLO models on the merged shoe dataset and compare ROI performance."""

from __future__ import annotations

import argparse
import json
import os
import sys
from pathlib import Path
from statistics import mean


REPO_ROOT = Path(__file__).resolve().parent
PYDEPS = REPO_ROOT / ".pydeps"
if PYDEPS.exists():
    sys.path.insert(0, str(PYDEPS))

from ultralytics import YOLO  # noqa: E402


DEFAULT_MODELS = ["yolov8s.pt", "yolo11s.pt", "yolo26s.pt"]


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Train and compare YOLO shoe detectors")
    parser.add_argument("--data", default="datasets/shoe-public-mix/data.yaml", help="Training dataset yaml")
    parser.add_argument("--roi-dir", default="datasets/roi-shoes", help="Real-world ROI evaluation directory")
    parser.add_argument("--project", default="runs/shoe_compare", help="Ultralytics project directory")
    parser.add_argument("--models", nargs="+", default=DEFAULT_MODELS, help="Model checkpoints or aliases")
    parser.add_argument("--epochs", type=int, default=40, help="Training epochs for each model")
    parser.add_argument("--imgsz", type=int, default=640, help="Training image size")
    parser.add_argument("--batch", type=int, default=16, help="Training batch size")
    parser.add_argument("--workers", type=int, default=8, help="Data loader workers")
    parser.add_argument("--device", default="0", help="Training device, e.g. 0 or cpu")
    parser.add_argument("--seed", type=int, default=42, help="Random seed")
    parser.add_argument("--patience", type=int, default=20, help="Early stopping patience")
    return parser.parse_args()


def safe_name(model_name: str) -> str:
    stem = Path(model_name).stem
    return stem.replace(".", "_").replace("-", "_")


def evaluate_roi(model_path: Path, roi_dir: Path, save_dir: Path, device: str) -> dict:
    model = YOLO(str(model_path))
    results = model.predict(
        source=str(roi_dir),
        conf=0.1,
        save=True,
        project=str(save_dir.parent),
        name=save_dir.name,
        exist_ok=True,
        verbose=False,
        device=device,
    )

    per_image = []
    max_confs = []
    hit_count = 0
    for result in results:
        n = 0 if result.boxes is None else len(result.boxes)
        hit = n > 0
        if hit:
            hit_count += 1
            max_confs.append(float(result.boxes.conf.max().item()))
        per_image.append(
            {
                "image": Path(result.path).name,
                "detections": n,
                "max_conf": float(result.boxes.conf.max().item()) if hit else 0.0,
            }
        )

    summary = {
        "roi_total": len(per_image),
        "roi_hits": hit_count,
        "roi_hit_rate": hit_count / len(per_image) if per_image else 0.0,
        "roi_mean_max_conf": mean(max_confs) if max_confs else 0.0,
        "per_image": per_image,
    }
    return summary


def main() -> None:
    args = parse_args()
    os.environ.setdefault("YOLO_CONFIG_DIR", str(REPO_ROOT / ".ultralytics"))

    data = Path(args.data)
    roi_dir = Path(args.roi_dir)
    project = Path(args.project)
    project.mkdir(parents=True, exist_ok=True)

    if not data.exists():
        raise FileNotFoundError(f"Dataset yaml not found: {data}")
    if not roi_dir.exists():
        raise FileNotFoundError(f"ROI directory not found: {roi_dir}")

    compare_summary = []
    for model_name in args.models:
        run_name = f"{safe_name(model_name)}_shoe_{args.imgsz}"
        print("=" * 80)
        print(f"Training {model_name} -> {run_name}")
        print("=" * 80)

        model = YOLO(model_name)
        train_results = model.train(
            data=str(data.resolve()),
            epochs=args.epochs,
            imgsz=args.imgsz,
            batch=args.batch,
            workers=args.workers,
            device=args.device,
            seed=args.seed,
            patience=args.patience,
            project=str(project.resolve()),
            name=run_name,
            exist_ok=True,
            pretrained=True,
            cos_lr=True,
            amp=True,
            verbose=True,
        )

        best_path = Path(train_results.save_dir) / "weights" / "best.pt"
        roi_save_dir = Path(train_results.save_dir) / "roi_eval"
        roi_summary = evaluate_roi(best_path, roi_dir, roi_save_dir, args.device)

        record = {
            "model": model_name,
            "run_name": run_name,
            "save_dir": str(Path(train_results.save_dir).resolve()),
            "best_pt": str(best_path.resolve()),
            "metrics": {
                "map50": float(train_results.results_dict.get("metrics/mAP50(B)", 0.0)),
                "map50_95": float(train_results.results_dict.get("metrics/mAP50-95(B)", 0.0)),
                "precision": float(train_results.results_dict.get("metrics/precision(B)", 0.0)),
                "recall": float(train_results.results_dict.get("metrics/recall(B)", 0.0)),
            },
            "roi_eval": roi_summary,
        }
        compare_summary.append(record)

        summary_path = Path(train_results.save_dir) / "roi_eval_summary.json"
        summary_path.write_text(json.dumps(record, indent=2, ensure_ascii=False), encoding="utf-8")

        compare_json = project / "compare_summary.json"
        compare_json.write_text(json.dumps(compare_summary, indent=2, ensure_ascii=False), encoding="utf-8")

        print(json.dumps(record, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()