import argparse
import os
import time

from dgl.data import QM9Dataset
from dgl.dataloading import GraphDataLoader
from rdkit import Chem
from rdkit import RDLogger;
from torch.utils.data import Dataset
import torch.nn.functional as F
from tqdm import tqdm
import ast

from QM9_dataset_class import PreprocessedQM9Dataset

RDLogger.DisableLog('rdApp.*')
import torch
import torch.nn as nn
import torch.optim as optim


QM9_label_keys = ['mu','alpha','homo','lumo','gap','r2','zpve','U0','U','H','G','Cv']



def prepare_main(label_keys=None, cutoff=5.0,save_path="dataset"):
    assert save_path !="","save_path shouldn't be empty"
    if label_keys is None:
        raise ValueError('label_keys cannot be None')
    for label_key in label_keys:
        if label_key not in QM9_label_keys:
            raise ValueError('label_key must be in QM9_label_keys,refer:https://docs.dgl.ai/en/0.8.x/generated/dgl.data.QM9Dataset.html')
    dataset = QM9Dataset(label_keys=label_keys, cutoff=5.0)
    dataset_processed = PreprocessedQM9Dataset(dataset)
    print("Store processed QM9 dataset:",save_path)
    dataset_processed.save_dataset("dataset")
    return dataset_processed

def main():
    parser = argparse.ArgumentParser(description="Prepare QM9 dataset")
    parser.add_argument('--label_keys', nargs='+', help="label keys in QM9 dataset,like 'mu' 'gap'....")
    parser.add_argument('--cutoff', type=float, default=5.0, help="cutoff for atom number")
    parser.add_argument('--save_path', type=str, default="dataset", help="processed_dataset save path")
    args = parser.parse_args()
    prepare_main(label_keys=args.label_keys, cutoff=args.cutoff)

if __name__ == '__main__':
    main()