"""Fine-tune YOLO11n card detector on the synthetic poker-table benchmark.

Used for both the smoke (few epochs, fraction of train) and the real run.
After a real run, uploads weights to the Hub model repo.
"""
import argparse
import os

parse = argparse.ArgumentParser()
parse.add_argument("--data", default="/data/benchmark/data.yaml")
parse.add_argument("--weights", default="stock-best.pt")
parse.add_argument("--epochs", type=int, default=80)
parse.add_argument("--patience", type=int, default=15)
parse.add_argument("--batch", type=int, default=16)
parse.add_argument("--imgsz", type=int, default=1280)
parse.add_argument("--fraction", type=float, default=1.0, help="fraction of train set")
parse.add_argument("--device", default=0, help="0 for GPU, cpu for CPU smoke")
parse.add_argument("--project", default="/data/runs")
parse.add_argument("--name", default="poker_cards_ft")
parse.add_argument("--upload-model-repo", default=None,
                   help="e.g. ThreeSixNine/yolo11n-poker-table-cards; skips upload if empty")
parse.add_argument("--trackio-space-id", default=None,
                   help="trackio space id; enables per-epoch metric logging")
parse.add_argument("--trackio-project", default="yolo-poker-cards-ft")
parse.add_argument("--upload-tag", default="",
                   help="suffix for uploaded weight names, e.g. 'mixed' -> "
                        "best_mixed.pt (avoids overwriting prior runs)")
args = parse.parse_args()

if args.trackio_space_id:
    import trackio
    trackio.init(project=args.trackio_project, space_id=args.trackio_space_id)

DOWNLOAD = ("shrimantasatpati/yolov11_playing_cards_detection",
            "detect/yolo11n_playing_cards/weights/best.pt")

if not os.path.exists(args.weights):  # stock weights, idempotent download
    from huggingface_hub import hf_hub_download
    args.weights = hf_hub_download(*DOWNLOAD)
print(f"weights: {args.weights}", flush=True)

from ultralytics import YOLO
model = YOLO(args.weights)

if args.trackio_space_id:
    import trackio

    def _log_epoch(trainer):
        m = getattr(trainer, "metrics", None) or {}
        row = {"epoch": trainer.epoch,
               "loss": float(trainer.loss) if trainer.loss is not None else None}
        for k, v in (m.items() if isinstance(m, dict) else []):
            try:
                row[k] = round(float(v), 4)
            except (TypeError, ValueError):
                pass
        trackio.log(row, step=trainer.epoch)

    model.add_callback("on_fit_epoch_end", _log_epoch)

model.train(data=args.data, epochs=args.epochs, patience=args.patience,
            batch=args.batch, imgsz=args.imgsz, fraction=args.fraction,
            device=args.device, project=args.project, name=args.name,
            exist_ok=True)

metrics = model.val(data=args.data, imgsz=args.imgsz, split="val")
if args.trackio_space_id:
    try:
        d = metrics.results_dict
        trackio.log({f"final/{k}": round(float(v), 4) for k, v in d.items()},
                    step=args.epochs)
        trackio.finish()
    except Exception as e:
        print("trackio final log failed:", e, flush=True)
results_dir = os.path.join(args.project, args.name)
print("== TRAIN DONE ==", flush=True)
print("results_dir:", results_dir, flush=True)
try:
    d = metrics.results_dict
    print("val metrics:", {k: round(float(v), 4) for k, v in d.items()}, flush=True)
except Exception as e:
    print("metrics read failed:", e, flush=True)

if args.upload_model_repo:
    from huggingface_hub import HfApi
    api = HfApi()
    tag = args.upload_tag
    api.upload_file(
        path_or_fileobj=os.path.join(results_dir, "weights", "best.pt"),
        path_in_repo=f"best{'_' + tag if tag else ''}.pt",
        repo_id=args.upload_model_repo,
        repo_type="model",
    )
    api.upload_file(
        path_or_fileobj=os.path.join(results_dir, "weights", "last.pt"),
        path_in_repo=f"last{'_' + tag if tag else ''}.pt",
        repo_id=args.upload_model_repo,
        repo_type="model",
    )
    print(f"uploaded weights to {args.upload_model_repo}", flush=True)