#!/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.embeddings import SMILESEmbedder
from spec_rag.faiss_index import build_hnsw_index, save_index
from spec_rag.io import ensure_dir, load_smiles, save_embeddings, save_smiles


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Build FAISS HNSW index from SMILES.")
    parser.add_argument("--smiles-path", required=True)
    parser.add_argument("--model-name", default="seyonec/ChemBERTa-zinc-base-v1")
    parser.add_argument("--out-dir", required=True)
    parser.add_argument("--batch-size", type=int, default=64)
    parser.add_argument("--max-length", type=int, default=256)
    parser.add_argument("--pooling", choices=["cls", "mean"], default="cls")
    parser.add_argument("--no-normalize", action="store_true")
    parser.add_argument("--metric", choices=["cosine", "l2"], default="cosine")
    parser.add_argument("--m", type=int, default=32)
    parser.add_argument("--ef-construction", type=int, default=200)
    parser.add_argument("--ef-search", type=int, default=128)
    parser.add_argument("--dedupe", action="store_true")
    parser.add_argument("--device", default="cuda")
    return parser.parse_args()


def main() -> None:
    args = parse_args()
    if args.device == "cuda":
        try:
            import torch
        except Exception:
            torch = None
        if torch is None or not torch.cuda.is_available():
            print("CUDA not available; falling back to CPU.")
            args.device = "cpu"
    out_dir = ensure_dir(args.out_dir)
    smiles = load_smiles(args.smiles_path)
    if args.dedupe:
        seen = set()
        smiles = [s for s in smiles if not (s in seen or seen.add(s))]

    embedder = SMILESEmbedder(
        model_name=args.model_name,
        device=args.device,
        pooling=args.pooling,
        batch_size=args.batch_size,
        max_length=args.max_length,
        normalize=not args.no_normalize,
    )
    embeddings = embedder.encode(smiles)

    save_embeddings(out_dir / "smiles_embeddings.npy", embeddings)
    save_smiles(out_dir / "smiles.txt", smiles)

    index = build_hnsw_index(
        embeddings,
        m=args.m,
        ef_construction=args.ef_construction,
        ef_search=args.ef_search,
        metric=args.metric,
    )
    save_index(index, out_dir / "smiles.index")
    print(f"Saved index and embeddings to {out_dir}")


if __name__ == "__main__":
    main()