import numpy as np
import logging
import torch.utils.data as data
from PIL import Image
from torchvision.datasets import CIFAR10, FashionMNIST, MNIST

logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)

# TODO: truncated seems to be useless
class MNIST_truncated(data.Dataset):

    def __init__(self, root, 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__()

    def __build_truncated_dataset__(self):

        mnist_dataobj = MNIST(self.root, self.train, self.transform, self.target_transform, self.download)

        data = mnist_dataobj.data
        target = mnist_dataobj.targets

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

        return data, target

    def __getitem__(self, index):
        img, target = self.data[index], self.target[index]

        # doing this so that it is consistent with all other datasets
        # to return a PIL Image
        img = Image.fromarray(img.numpy(), mode='L')

        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)


class FashionMNIST_truncated(data.Dataset):

    def __init__(self, root, 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__()

    def __build_truncated_dataset__(self):

        mnist_dataobj = FashionMNIST(self.root, self.train, self.transform, self.target_transform, self.download)

        data = mnist_dataobj.data
        target = mnist_dataobj.targets

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

        return data, target

    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]

        img = Image.fromarray(img.numpy(), mode='L')

        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)


class CIFAR10_truncated(data.Dataset):

    def __init__(self, root, 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 = True

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

    def __build_truncated_dataset__(self):

        cifar_dataobj = CIFAR10(self.root, self.train, self.transform, self.target_transform, self.download)

        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):
        img, target = self.data[index], self.target[index]
        target = target.astype(np.int64)

        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)
