import pandas as pd, numpy as np, re
from pathlib import Path
from transformers import AutoTokenizer, AutoModel
import torch

DATA_PROC = Path("data/processed")

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

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

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

controls = [("ETHANOL","CCO"), ("H2O2","OO")]
lib = controls + gen_alcohols(180) + gen_aromatics(220) + gen_heterocycles(210)
L0 = pd.DataFrame(lib, columns=["compound","smiles"]).drop_duplicates("compound").reset_index(drop=True)

L0["chembl_id"]   = pd.NA
L0["pubchem_cid"] = pd.NA
def infer_class(s):
    s=str(s)
    if s=="CCO": return "solvent"
    if s=="OO":  return "oxidant"
    if "c1" in s: return "aromatic/heterocycle"
    if s.endswith("O"): return "alcohol"
    return "other"
L0["class"] = L0["smiles"].map(infer_class)
L0["is_control"] = L0["compound"].isin(["ETHANOL","H2O2"])

DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
tok_c = AutoTokenizer.from_pretrained("DeepChem/ChemBERTa-77M-MLM", padding=True, truncation=True)
mdl_c = AutoModel.from_pretrained("DeepChem/ChemBERTa-77M-MLM", use_safetensors=True).eval().to(DEVICE)

@torch.no_grad()
def chemberta_embed(smi: str):
    smi = "".join(str(smi).split())
    toks = tok_c(smi, return_tensors="pt", truncation=True, max_length=128)
    toks = {k:v.to(DEVICE) for k,v in toks.items()}
    hs = mdl_c(**toks).last_hidden_state
    return hs[:,0,:].squeeze(0).cpu().numpy()

rows=[]
for r in L0.itertuples(index=False):
    v = chemberta_embed(r.smiles)
    rows.append([r.compound] + v.tolist())
L = pd.DataFrame(rows, columns=["compound"]+[f"d{i}" for i in range(len(rows[0])-1)])
L = L.merge(L0, on="compound", how="left")

num = L.select_dtypes(include=[float,int]).columns
L[num] = L[num].fillna(L[num].median(numeric_only=True))
L.to_csv(DATA_PROC/"ligand.csv", index=False)
print("ligand.csv ->", DATA_PROC/"ligand.csv", "| shape:", L.shape)