import argparse
from pathlib import Path

import numpy as np
import pandas as pd
import torch
from transformers import AutoModel, AutoTokenizer
from tqdm.auto import tqdm

DATA_PROC = Path("data/processed"); DATA_PROC.mkdir(parents=True, exist_ok=True)

DEVICE     = "cuda" if torch.cuda.is_available() else "cpu"
CHEMBERTA  = "seyonec/ChemBERTa-77M-MTR"     # 768-dim



CONTROLS = [("ETHANOL", "CCO"), ("H2O2", "OO")]

def _gen_alcohols(n=150):
    lib = []
    for c in range(1, 21):
        lib.append((f"ALK_{c:02d}", "C" * c + "O"))
    for c in range(3, 13):
        lib.append((f"IALK_{c}", "C(C)" + "C" * (c - 2) + "O"))
    return lib[:n]

def _gen_aromatics(n=200):
    subs   = ["Cl", "Br", "F", "N(=O)=O", "C(=O)O", "C#N", "OCC", "CCN", "CC(=O)O"]
    cores  = ["c1ccccc1", "c1ccc(cc1)"]
    lib, k = [], 0
    for s in subs:
        for c in cores:
            lib.append((f"ARO_{k:03d}", c + s)); k += 1
            if k >= n: return lib
    return lib

def _gen_heterocycles(n=200):
    rings = ["c1ncccc1", "c1occcn1", "n1ccccc1", "c1ccncc1", "c1ccsc1", "c1ncncn1"]
    lib, k = [], 0
    for r in rings:
        lib.append((f"HET_{k:03d}", r)); k += 1
        lib.append((f"HETOH_{k:03d}", r + "O")); k += 1
        if k >= n: break
    while len(lib) < n:
        lib.append((f"HETPAD_{len(lib):03d}", "c1ncncn1"))
    return lib[:n]

def _classify(smiles: str) -> str:
    if smiles == "CCO":  return "solvent"
    if smiles == "OO":   return "oxidant"
    if "c1" in smiles:   return "aromatic/heterocycle"
    if smiles.endswith("O"): return "alcohol"
    return "other"

def build_library() -> pd.DataFrame:
    lib = CONTROLS + _gen_alcohols(180) + _gen_aromatics(220) + _gen_heterocycles(210)
    df  = pd.DataFrame(lib, columns=["compound", "smiles"]).drop_duplicates("compound")
    df["class"]      = df["smiles"].map(_classify)
    df["is_control"] = df["compound"].isin(["ETHANOL", "H2O2"])
    return df.reset_index(drop=True)



def load_chemberta(model_name: str = CHEMBERTA):
    tok = AutoTokenizer.from_pretrained(model_name)
    mdl = AutoModel.from_pretrained(model_name).eval().to(DEVICE)
    return tok, mdl

@torch.no_grad()
def embed_smiles(smiles: str, tok, mdl) -> np.ndarray:
    """Return CLS-token embedding as float32 array."""
    inputs = tok(smiles, return_tensors="pt", truncation=True, max_length=512,
                 padding=True)
    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
    out    = mdl(**inputs)
    return out.last_hidden_state[:, 0, :].squeeze().cpu().numpy().astype(np.float32)



def canonicalize(smiles: str) -> str:
    try:
        from rdkit import Chem, RDLogger
        RDLogger.DisableLog("rdApp.*")
        mol = Chem.MolFromSmiles(smiles)
        return Chem.MolToSmiles(mol) if mol else smiles
    except Exception:
        return smiles



def main(mock: bool = False):
    print(f"Device: {DEVICE}  |  mock={mock}")

    df_lib = build_library()
    df_lib["smiles"] = df_lib["smiles"].map(canonicalize)
    print(f"Library: {len(df_lib)} compounds")

    if not mock:
        tok, mdl = load_chemberta()
        d_lig = 768
    else:
        d_lig = 768
        rng   = np.random.default_rng(42)

    rows = []
    for _, row in tqdm(df_lib.iterrows(), total=len(df_lib)):
        if mock:
            emb = rng.normal(0, 1, d_lig).astype(np.float32)
        else:
            try:
                emb = embed_smiles(row["smiles"], tok, mdl)
            except Exception as e:
                print(f"  ⚠ {row['compound']}: {e}; using zeros")
                emb = np.zeros(d_lig, dtype=np.float32)
        rows.append(emb)

    emb_df = pd.DataFrame(rows, columns=[f"d{j}" for j in range(d_lig)])
    ligand_df = pd.concat([df_lib, emb_df], axis=1)
    ligand_df.to_csv(DATA_PROC / "ligand.csv", index=False)

    # Manifest (no embeddings)
    df_lib.to_csv(DATA_PROC / "ligand_manifest.csv", index=False)
    print(f"\n Saved ligand.csv  ({len(ligand_df)} compounds, d={d_lig})")
    print(f" Saved ligand_manifest.csv")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--mock", action="store_true",
                        help="Use random embeddings (offline mode)")
    args = parser.parse_args()
    main(mock=args.mock)