#!/usr/bin/env python3 """Sync datasets and model artifacts with Hugging Face Hub.""" from __future__ import annotations import argparse import os from pathlib import Path ROOT = Path(__file__).resolve().parents[1] def require_hf() -> None: try: import huggingface_hub # noqa: F401 except ImportError as exc: raise SystemExit("Install huggingface_hub: pip install huggingface_hub") from exc def hf_token() -> str | None: return os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN") def upload_dataset(repo_id: str, folder: Path, private: bool) -> None: from huggingface_hub import HfApi api = HfApi(token=hf_token()) api.create_repo(repo_id=repo_id, repo_type="dataset", exist_ok=True, private=private) api.upload_folder( folder_path=str(folder), repo_id=repo_id, repo_type="dataset", commit_message="Scriptwriter dataset sync", ) visibility = "private" if private else "public" print(f"Dataset uploaded ({visibility}): https://huggingface.co/datasets/{repo_id}") def download_dataset(repo_id: str, folder: Path) -> None: from huggingface_hub import snapshot_download folder.mkdir(parents=True, exist_ok=True) snapshot_download( repo_id=repo_id, repo_type="dataset", local_dir=str(folder), token=hf_token(), ) print(f"Dataset downloaded to {folder}") def upload_model(repo_id: str, folder: Path, private: bool) -> None: from huggingface_hub import HfApi api = HfApi(token=hf_token()) api.create_repo(repo_id=repo_id, repo_type="model", exist_ok=True, private=private) api.upload_folder( folder_path=str(folder), repo_id=repo_id, repo_type="model", commit_message="Scriptwriter LoRA adapter sync", ) visibility = "private" if private else "public" print(f"Model uploaded ({visibility}): https://huggingface.co/models/{repo_id}") def download_model(repo_id: str, folder: Path) -> None: from huggingface_hub import snapshot_download folder.mkdir(parents=True, exist_ok=True) snapshot_download( repo_id=repo_id, repo_type="model", local_dir=str(folder), token=hf_token(), ) print(f"Model downloaded to {folder}") def main() -> None: parser = argparse.ArgumentParser(description="Sync scriptwriter artifacts with HF Hub") sub = parser.add_subparsers(dest="cmd", required=True) up_ds = sub.add_parser("upload-dataset") up_ds.add_argument("--repo", default=os.environ.get("HF_DATASET_REPO", "")) up_ds.add_argument("--folder", type=Path, default=ROOT / "data" / "processed") up_ds.add_argument("--public", action="store_true", help="Upload as public (default: private)") down_ds = sub.add_parser("download-dataset") down_ds.add_argument("--repo", default=os.environ.get("HF_DATASET_REPO", "")) down_ds.add_argument("--folder", type=Path, default=ROOT / "data" / "processed") up_m = sub.add_parser("upload-model") up_m.add_argument("--repo", default=os.environ.get("HF_MODEL_REPO", "")) up_m.add_argument("--folder", type=Path, default=ROOT / "models" / "lora") up_m.add_argument("--public", action="store_true", help="Upload as public (default: private)") down_m = sub.add_parser("download-model") down_m.add_argument("--repo", default=os.environ.get("HF_MODEL_REPO", "")) down_m.add_argument("--folder", type=Path, default=ROOT / "models" / "lora") args = parser.parse_args() require_hf() if args.cmd == "upload-dataset": if not args.repo: raise SystemExit("Set --repo or HF_DATASET_REPO") upload_dataset(args.repo, args.folder, private=not args.public) elif args.cmd == "download-dataset": if not args.repo: raise SystemExit("Set --repo or HF_DATASET_REPO") download_dataset(args.repo, args.folder) elif args.cmd == "upload-model": if not args.repo: raise SystemExit("Set --repo or HF_MODEL_REPO") upload_model(args.repo, args.folder, private=not args.public) elif args.cmd == "download-model": if not args.repo: raise SystemExit("Set --repo or HF_MODEL_REPO") download_model(args.repo, args.folder) if __name__ == "__main__": main()