'''
Dataset Concstruction
Code based on https://github.com/FedML-AI/FedML
'''


import logging
import numpy as np
import torch.utils.data as data
from torchvision.datasets import CIFAR10
from torchvision.datasets import CIFAR100
from torchvision.datasets import DatasetFolder, ImageFolder

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

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

class CIFAR_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):
        print("download = " + str(self.download))
        if "cifar100" in self.root:
            cifar_dataobj = CIFAR100(self.root, self.train, self.transform, self.target_transform, self.download)
        else:
            cifar_dataobj = CIFAR10(self.root, self.train, self.transform, self.target_transform, self.download)


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

# Imagenet
class ImageFolder_custom(DatasetFolder):
    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

        if self.train:
            imagefolder_obj = ImageFolder(self.root+'/train', self.transform)
        else:
            imagefolder_obj = ImageFolder(self.root+'/val', self.transform)
        self.loader = imagefolder_obj.loader
        if self.dataidxs is not None:
            self.samples = np.array(imagefolder_obj.samples)[self.dataidxs]
        else:
            self.samples = np.array(imagefolder_obj.samples)
        self.target = self.samples[:,1].astype(np.int64)

    def __getitem__(self, index):
        path = self.samples[index][0]
        target = self.samples[index][1]
        target = int(target)
        sample = self.loader(path)
        if self.transform is not None:
            sample = self.transform(sample)
        if self.target_transform is not None:
            target = self.target_transform(target)

        return sample, target

    def __len__(self):
        if self.dataidxs is None:
            return len(self.samples)
        else:
            return len(self.dataidxs)