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LUNA-Training
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"""

Push LUNA training code + config to a Hugging Face model repo.

Uploads everything needed to run LoRA SFT on a GPU instance.



Usage:

    HF_TOKEN=hf_xxx python push_code_to_hf.py

"""

import os
from huggingface_hub import HfApi, create_repo

HF_REPO = "ASTERIZER/LUNA-Training"
TOKEN = os.environ.get("HF_TOKEN")

FILES_TO_PUSH = [
    # Core training scripts
    "sft_train.py",
    "lora_sft_train.py",
    "upload_lora_to_hf.py",
    "upload_full_sft_to_hf.py",
    "chat_full_sft.py",
    "train.py",
    "chat.py",
    "generate.py",

    # Configs
    "rag_mcp_lora_config.yaml",
    "rag_mcp_full_sft_config.yaml",
    "sft_config.yaml",
    "train_config.yaml",

    # Requirements
    "requirements.txt",

    # Validation / benchmarking
    "validate_sft.py",
    "check_sft_alignment.py",
    "validate_and_quantize.py",

    # Dataset builder
    "Base/Datasets/rag_mcp_sft/build_rag_mcp_sft_dataset.py",
    "Base/Datasets/rag_mcp_sft/push_to_hf.py",
    "Base/Datasets/rag_mcp_sft/BUILD_REPORT.md",
    "Base/Datasets/rag_mcp_sft/FINETUNE_COMMANDS.md",
    "Base/Datasets/rag_mcp_sft/README.md",
    "Base/Datasets/rag_mcp_sft/source_manifest.json",
    "Base/Datasets/rag_mcp_sft/sample_preview.json",

    # Tokenizer config (small files only)
    "Base/checkpoints/EleutherAI/pythia-160m/config.json",
    "Base/checkpoints/EleutherAI/pythia-160m/tokenizer_config.json",
    "Base/checkpoints/EleutherAI/pythia-160m/tokenizer.json",

    # Shell scripts
    "setup_and_sft.sh",
    "setup_and_train.sh",

    # GPU run script
    "gpu_train.sh",
    "gpu_full_sft.sh",

    # README
    "README.md",
]


def main():
    if not TOKEN:
        raise RuntimeError("Set HF_TOKEN environment variable")

    api = HfApi(token=TOKEN)

    create_repo(
        repo_id=HF_REPO,
        token=TOKEN,
        repo_type="model",
        exist_ok=True,
        private=False,
    )
    print(f"Repo ready: https://huggingface.co/{HF_REPO}")

    pushed = 0
    for fpath in FILES_TO_PUSH:
        if not os.path.exists(fpath):
            print(f"  SKIP (not found): {fpath}")
            continue
        api.upload_file(
            path_or_fileobj=fpath,
            path_in_repo=fpath,
            repo_id=HF_REPO,
            token=TOKEN,
        )
        print(f"  OK: {fpath}")
        pushed += 1

    print(f"\nPushed {pushed}/{len(FILES_TO_PUSH)} files to https://huggingface.co/{HF_REPO}")


if __name__ == "__main__":
    main()