"""
Export a slim, release-ready weight file from a training checkpoint.

The training checkpoints saved by `save_checkpoint_pinn` bundle optimizer state,
scaler state, and loss history. For public release you only want the model weights.

Usage:
    python export_weights.py --ckpt path/to/train_ckpt.pt --out pytorch_model.bin

Optionally also write safetensors (recommended for distribution):
    python export_weights.py --ckpt path/to/train_ckpt.pt --out pytorch_model.bin --safetensors
"""
import argparse
import torch


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--ckpt", required=True, help="training checkpoint (.pt) path")
    ap.add_argument("--out", default="pytorch_model.bin", help="output weight file")
    ap.add_argument("--safetensors", action="store_true",
                    help="also write a .safetensors file next to --out")
    args = ap.parse_args()

    ckpt = torch.load(args.ckpt, map_location="cpu")

    # Accept either a full training checkpoint dict or a raw state_dict.
    if isinstance(ckpt, dict) and "model" in ckpt:
        state = ckpt["model"]
    else:
        state = ckpt

    # Keep only tensors; drop any non-tensor entries.
    state = {k: v for k, v in state.items() if torch.is_tensor(v)}

    torch.save(state, args.out)
    print(f"[saved] {args.out}  ({len(state)} tensors)")

    if args.safetensors:
        from safetensors.torch import save_file
        st_path = args.out.rsplit(".", 1)[0] + ".safetensors"
        # safetensors requires contiguous tensors and no shared storage.
        clean = {k: v.contiguous().clone() for k, v in state.items()}
        save_file(clean, st_path)
        print(f"[saved] {st_path}")


if __name__ == "__main__":
    main()