"""Merge an Objectverse Diary LoRA adapter into its base Hugging Face model."""

from __future__ import annotations

import argparse
import json
from pathlib import Path
from typing import Any


ADAPTER_WEIGHT_FILES = ("adapter_model.safetensors", "adapter_model.bin")


def validate_adapter_source(adapter: str | Path, *, base_model: str) -> dict[str, object]:
    adapter_text = str(adapter)
    adapter_path = Path(adapter_text)
    if adapter_path.exists():
        if not adapter_path.is_dir():
            raise ValueError(f"Adapter path is not a directory: {adapter_path}")
        config_path = adapter_path / "adapter_config.json"
        if not config_path.exists():
            raise ValueError(f"Adapter directory is missing adapter_config.json: {adapter_path}")
        if not any((adapter_path / name).exists() for name in ADAPTER_WEIGHT_FILES):
            raise ValueError(
                "Adapter directory is missing adapter_model.safetensors or adapter_model.bin."
            )
        config = _read_adapter_config(config_path)
        configured_base = config.get("base_model_name_or_path")
        if configured_base and str(configured_base) != base_model:
            raise ValueError(
                f"Adapter base model is {configured_base!r}, expected {base_model!r}."
            )
        return {
            "adapter": str(adapter_path),
            "adapter_type": "local",
            "adapter_base_model": configured_base or "",
        }

    if "/" not in adapter_text:
        raise FileNotFoundError(f"Adapter source does not exist: {adapter_text}")
    return {
        "adapter": adapter_text,
        "adapter_type": "hub",
        "adapter_base_model": "",
    }


def plan_merge(
    *,
    base_model: str,
    adapter: str | Path,
    output: Path,
    dry_run: bool,
) -> dict[str, object]:
    summary = validate_adapter_source(adapter, base_model=base_model)
    summary.update(
        {
            "base_model": base_model,
            "output": str(output),
            "dry_run": dry_run,
        }
    )
    if dry_run:
        summary["merged"] = False
        return summary

    merge_lora_adapter(
        base_model=base_model,
        adapter=str(adapter),
        output=output,
    )
    summary["merged"] = True
    summary["files"] = sorted(path.name for path in output.iterdir() if path.is_file())
    return summary


def merge_lora_adapter(
    *,
    base_model: str,
    adapter: str,
    output: Path,
) -> None:
    from peft import PeftModel
    from transformers import AutoModelForCausalLM, AutoTokenizer

    output.mkdir(parents=True, exist_ok=True)
    model = AutoModelForCausalLM.from_pretrained(
        base_model,
        torch_dtype="auto",
        device_map={"": "cpu"},
        low_cpu_mem_usage=True,
    )
    peft_model = PeftModel.from_pretrained(model, adapter)
    merged = peft_model.merge_and_unload(safe_merge=True)
    merged.save_pretrained(
        output,
        safe_serialization=True,
        max_shard_size="2GB",
    )

    tokenizer = AutoTokenizer.from_pretrained(adapter if Path(adapter).exists() else base_model)
    tokenizer.save_pretrained(output)

    metadata = {
        "base_model": base_model,
        "adapter": adapter,
        "output": str(output),
        "format": "merged-hf",
    }
    (output / "objectverse_merge_metadata.json").write_text(
        json.dumps(metadata, indent=2, sort_keys=True),
        encoding="utf-8",
    )


def _read_adapter_config(config_path: Path) -> dict[str, object]:
    try:
        payload = json.loads(config_path.read_text(encoding="utf-8"))
    except json.JSONDecodeError as exc:
        raise ValueError(f"Invalid adapter_config.json: {exc.msg}") from exc
    if not isinstance(payload, dict):
        raise ValueError("adapter_config.json must contain a JSON object.")
    return payload


def _print_json(payload: dict[str, Any]) -> None:
    print(json.dumps(payload, indent=2, sort_keys=True), flush=True)


def _parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--base-model", required=True)
    parser.add_argument("--adapter", required=True)
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--dry-run", action="store_true")
    return parser.parse_args()


def main() -> None:
    args = _parse_args()
    _print_json(
        plan_merge(
            base_model=args.base_model,
            adapter=args.adapter,
            output=args.output,
            dry_run=args.dry_run,
        )
    )


if __name__ == "__main__":
    try:
        main()
    except Exception as exc:
        raise SystemExit(str(exc)) from exc