import logging

import numpy as np
import torch.utils.data as data
from PIL import Image
from torchvision.datasets import CIFAR100


IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')



class CIFAR100_truncated(data.Dataset):

    def __init__(self, root, cache_data_set=None,dataidxs=None, train=True, transform=None, target_transform=None, download=False):

        self.root = root
        self.dataidxs = dataidxs
        self.train = train
        self.transform = transform
        self.target_transform = target_transform
        self.download = download

        self.data, self.target = self.__build_truncated_dataset__(cache_data_set)

    def __build_truncated_dataset__(self,cache_data_set):
        # print("download = " + str(self.download))
        if cache_data_set == None:
            cifar_dataobj = CIFAR100(self.root, self.train, self.transform, self.target_transform, self.download)
        else:
            cifar_dataobj =  cache_data_set

            # print("train member of the class: {}".format(self.train))
            # data = cifar_dataobj.train_data
        data = cifar_dataobj.data
        target = np.array(cifar_dataobj.targets)


        if self.dataidxs is not None:
            data = data[self.dataidxs]
            target = target[self.dataidxs]

        return data, target

    def truncate_channel(self, index):
        for i in range(index.shape[0]):
            gs_index = index[i]
            self.data[gs_index, :, :, 1] = 0.0
            self.data[gs_index, :, :, 2] = 0.0

    def __getitem__(self, index):
        """
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        """
        img, target = self.data[index], self.target[index]

        if self.transform is not None:
            img = self.transform(img)

        if self.target_transform is not None:
            target = self.target_transform(target)

        return img, target

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