# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "unsloth",
#     "transformers>=4.57",
#     "huggingface_hub",
# ]



# ///
"""Merge the SecureCoder LoRA into the base Qwen3-Coder-30B-A3B-Instruct
checkpoint and upload a 16-bit safetensors repo. GGUF is done in a separate
job (the 30B-A3B MoE does not fit on one 80 GB card when merge and quantise
both run in the same process).

Default base:    unsloth/Qwen3-Coder-30B-A3B-Instruct
Default adapter: Taimwe/securecoder-30b-pro

Run on HF Jobs:
    hf jobs run -d --flavor a100-large --timeout 90m --secrets HF_TOKEN \\
      ghcr.io/astral-sh/uv:python3.12-bookworm \\
      uv run --no-project https://huggingface.co/Taimwe/securecoder-scripts/resolve/main/merge_securecoder.py \\
      -- --adapter Taimwe/securecoder-30b-pro \\
         --output-repo Taimwe/securecoder-30b-pro-merged
"""

from __future__ import annotations

import argparse
import logging
import os
import shutil
import sys
import time

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger("merge")
def parse_args() -> argparse.Namespace:
    p = argparse.ArgumentParser(description="Merge + push the SecureCoder LoRA")
    p.add_argument("--base", default="unsloth/Qwen3-Coder-30B-A3B-Instruct")
    p.add_argument("--adapter", default="Taimwe/securecoder-30b-pro")
    p.add_argument("--output-repo", default="Taimwe/securecoder-30b-pro-merged")
    p.add_argument("--private", action="store_true")
    p.add_argument("--work-dir", default="/data/securecoder-merge")
    p.add_argument("--max-shard-size", default="5GB")
    return p.parse_args()


def main() -> int:
    args = parse_args()
    token = os.environ.get("HF_TOKEN")
    if not token:
        log.error("HF_TOKEN not set")
        return 1

    import torch
    from huggingface_hub import HfApi
    from unsloth import FastLanguageModel

    if not torch.cuda.is_available():
        log.error("no CUDA - merge needs a GPU")
        return 1
    log.info("GPU: %s", torch.cuda.get_device_name(0))

    work = args.work_dir
    if os.path.exists(work):
        shutil.rmtree(work)
    os.makedirs(work, exist_ok=True)

    log.info("loading base %s in 16-bit ...", args.base)
    started = time.time()
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name=args.base,
        max_seq_length=2048,
        dtype=torch.bfloat16,
        load_in_4bit=False,
    )

    log.info("loading adapter %s ...", args.adapter)
    from peft import PeftModel
    model = PeftModel.from_pretrained(model, args.adapter, token=token)
    log.info("merging ...")
    model = model.merge_and_unload()
    log.info("merge done in %.1f min", (time.time() - started) / 60)

    out_dir = os.path.join(work, "merged")
    model.save_pretrained(out_dir, safe_serialization=True, max_shard_size=args.max_shard_size)
    tokenizer.save_pretrained(out_dir)
    log.info("saved merged model to %s", out_dir)

    api = HfApi(token=token)
    api.create_repo(args.output_repo, repo_type="model", exist_ok=True, private=args.private)
    log.info("uploading to %s ...", args.output_repo)
    api.upload_folder(folder_path=out_dir, repo_id=args.output_repo, repo_type="model",
                      commit_message="Merge SecureCoder LoRA into base (16-bit)")
    log.info("merged model live: https://huggingface.co/%s", args.output_repo)

    print("=" * 78)
    print("MERGE COMPLETE")
    print(f"  merged: https://huggingface.co/{args.output_repo}")
    print("  next  : run quantise_securecoder.py separately for GGUF")
    print("=" * 78)
    return 0


if __name__ == "__main__":
    raise SystemExit(main())