#!/usr/bin/env python
from __future__ import annotations

import argparse
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from spec_rag.dataset import build_examples
from spec_rag.faiss_index import load_index
from spec_rag.io import load_embeddings, load_smiles, save_jsonl
from spec_rag.retrieval import search_index


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Build RAG dataset for MolT5/Llama.")
    parser.add_argument("--index-path", required=True)
    parser.add_argument("--smiles-path", required=True)
    parser.add_argument("--spec-embeddings", required=True)
    parser.add_argument("--ground-truth-smiles", default=None)
    parser.add_argument("--ground-truth-mgf", default=None)
    parser.add_argument("--out-jsonl", required=True)
    parser.add_argument("--k", type=int, default=10)
    parser.add_argument("--format", choices=["molt5", "chat"], default="molt5")
    parser.add_argument("--spectrum-token", default="<Spectrum_Token>")
    return parser.parse_args()


def _load_smiles_from_mgf(mgf_path: str) -> list[str]:
    try:
        from pyteomics import mgf  # type: ignore
    except Exception as e:
        raise ImportError(f"pyteomics is required to read MGF: {e}")
    smiles = []
    with mgf.MGF(mgf_path) as reader:
        for spec in reader:
            params = spec.get("params", {})
            smi = params.get("SMILES") or params.get("smiles") or ""
            smiles.append(str(smi).strip())
    return smiles


def main() -> None:
    args = parse_args()
    if bool(args.ground_truth_smiles) == bool(args.ground_truth_mgf):
        raise ValueError("Provide exactly one of --ground-truth-smiles or --ground-truth-mgf.")
    index = load_index(args.index_path)
    smiles = load_smiles(args.smiles_path)
    spec_embeddings = load_embeddings(args.spec_embeddings)
    if args.ground_truth_mgf:
        gt_smiles = _load_smiles_from_mgf(args.ground_truth_mgf)
    else:
        gt_smiles = load_smiles(args.ground_truth_smiles)

    scores, indices = search_index(index, spec_embeddings, args.k)
    contexts = []
    for row in indices:
        contexts.append([smiles[j] if j >= 0 else "" for j in row.tolist()])

    examples = build_examples(
        spectrum_ids=list(range(len(contexts))),
        spectrum_token=args.spectrum_token,
        contexts=contexts,
        targets=gt_smiles,
    )
    if args.format == "chat":
        rows = [ex.as_chat() for ex in examples]
    else:
        rows = [ex.as_molt5() for ex in examples]

    save_jsonl(args.out_jsonl, rows)
    print(f"Wrote {len(rows)} examples to {args.out_jsonl}")


if __name__ == "__main__":
    main()