import os

from tqdm import tqdm
import networkx as nx
import torch
from torch.utils.data import Dataset

atom_number_index_dict = {
    1: 0,  # H
    6: 1,  # C
    7: 2,  # N
    8: 3,  # O
    9: 4  # F
}
atom_index_number_dict = {v: k for k, v in atom_number_index_dict.items()}
max_atom_number = max(atom_number_index_dict.keys())


def atom_number2index(atom_number):
    return atom_number_index_dict[atom_number]


def atom_index2number(atom_index):
    return atom_index_number_dict[atom_index]


class PreprocessedQM9Dataset(Dataset):
    def __init__(self, dataset):
        self.dataset = dataset
        self.processed_data = []
        if dataset is not None:
            self._preprocess()
    def _preprocess(self):
        i = 0
        for g, label in tqdm(self.dataset):
            g.ndata["Z_index"] = torch.tensor([atom_number2index(z.item()) for z in g.ndata["Z"]])
            g.ndata["sample_idx"] = i
            self.processed_data.append((g, label))

    def __len__(self):
        return len(self.processed_data)

    def __getitem__(self, idx):
        return self.processed_data[idx]

    def save_dataset(self, save_dir):
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        torch.save(self.processed_data, os.path.join(save_dir,"QM9_dataset_processed.pt"))
    def load_dataset(self, dataset_path):
        self.processed_data = torch.load(dataset_path)