import os
import argparse
import numpy as np
import pandas as pd
from tqdm import tqdm

from rdkit.Chem import AllChem
from ogb.graphproppred import GraphPropPredDataset

def getmorganfingerprint(mol):
    return list(AllChem.GetMorganFingerprintAsBitVect(mol, 2))

def getmaccsfingerprint(mol):
    fp = AllChem.GetMACCSKeysFingerprint(mol)
    return [int(b) for b in fp.ToBitString()]

def main(dataset_name):
    dataset = GraphPropPredDataset(name=dataset_name)

    df_smi = pd.read_csv(f"dataset/{dataset_name}/mapping/mol.csv.gz".replace("-", "_"))
    smiles = df_smi["smiles"]

    mgf_feat_list = []
    maccs_feat_list = []
    for ii in tqdm(range(len(smiles))):
        rdkit_mol = AllChem.MolFromSmiles(smiles.iloc[ii])

        mgf = getmorganfingerprint(rdkit_mol)
        mgf_feat_list.append(mgf)

        maccs = getmaccsfingerprint(rdkit_mol)
        maccs_feat_list.append(maccs)

    mgf_feat = np.array(mgf_feat_list, dtype="int64")
    maccs_feat = np.array(maccs_feat_list, dtype="int64")
    print("morgan feature shape: ", mgf_feat.shape)
    print("maccs feature shape: ", maccs_feat.shape)

    save_path = f"./dataset/{dataset_name}".replace("-", "_")
    print("saving feature in %s" % save_path)
    np.save(os.path.join(save_path, "mgf_feat.npy"), mgf_feat)
    np.save(os.path.join(save_path, "maccs_feat.npy"), maccs_feat)

if __name__=="__main__":
    parser = argparse.ArgumentParser(description='gnn')
    parser.add_argument("--dataset_name", type=str, default="ogbg-molhiv")
    args = parser.parse_args()

    main(args.dataset_name)