#
# Copyright 2022- IBM Inc. All rights reserved
# SPDX-License-Identifier: Apache2.0
#
import torch
from PIL import Image
import os
import os.path
import numpy as np
import pickle

import torchvision.transforms as transforms

from torchvision.datasets.vision import VisionDataset
from torchvision.datasets.utils import check_integrity , download_and_extract_archive


class CIFAR10(VisionDataset):
    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
    Args:
        root (string): Root directory of dataset where directory
            ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
        train (bool, optional): If True, creates dataset from training set, otherwise
            creates from test set.
        transform (callable, optional): A function/transform that takes in an PIL image
            and returns a transformed version. E.g, ``transforms.RandomCrop``
        target_transform (callable, optional): A function/transform that takes in the
            target and transforms it.
        download (bool, optional): If true, downloads the dataset from the internet and
            puts it in root directory. If dataset is already downloaded, it is not
            downloaded again.
    """
    base_folder = 'cifar-10-batches-py'
    url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
    filename = "cifar-10-python.tar.gz"
    tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
    train_list = [
        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
        ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
    ]

    test_list = [
        ['test_batch', '40351d587109b95175f43aff81a1287e'],
    ]
    meta = {
        'filename': 'batches.meta',
        'key': 'label_names',
        'md5': '5ff9c542aee3614f3951f8cda6e48888',
    }

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

        super(CIFAR10, self).__init__(root, transform=transform,
                                      target_transform=target_transform)
        self.root = os.path.expanduser(root)
        self.train = train  # training set or test set

        if download:
            self.download()

        if not self._check_integrity():
            raise RuntimeError('Dataset not found or corrupted.' +
                               ' You can use download=True to download it')

        # if self.train:
        #     downloaded_list = self.train_list
        # else:
        #     downloaded_list = self.test_list

        if self.train:
            downloaded_list = self.train_list
            self.transform = transforms.Compose([
                transforms.RandomCrop(32, padding=4),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276])
            ])
        else:
            downloaded_list = self.test_list
            self.transform = transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276])
            ])

        self.data = []
        self.targets = []

        # now load the picked numpy arrays
        for file_name, checksum in downloaded_list:
            file_path = os.path.join(self.root, self.base_folder, file_name)
            with open(file_path, 'rb') as f:
                entry = pickle.load(f, encoding='latin1')
                self.data.append(entry['data'])
                if 'labels' in entry:
                    self.targets.extend(entry['labels'])
                else:
                    self.targets.extend(entry['fine_labels'])

        self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
        self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC

        self.targets = np.asarray(self.targets)

        if base_sess:
            # label_sel=[]
            # for i in index:
            #     label_sel.append(labels[int(i)])
            # cls_ind = list(set(label_sel))
            self.data, self.targets = self.SelectfromDefault(self.data, self.targets, index)
        else:  # new Class session
            if train:
                self.data, self.targets = self.NewClassSelector(self.data, self.targets, index)
            else:
                self.data, self.targets = self.SelectfromDefault(self.data, self.targets, index)

        self._load_meta()

    def SelectfromDefault(self, data, targets, index):
        data_tmp = []
        targets_tmp = []
        for i in index:
            ind_cl = np.where(i == targets)[0]
            if data_tmp == []:
                data_tmp = data[ind_cl]
                targets_tmp = targets[ind_cl]
            else:
                data_tmp = np.vstack((data_tmp, data[ind_cl]))
                targets_tmp = np.hstack((targets_tmp, targets[ind_cl]))

        return data_tmp, targets_tmp

    def NewClassSelector(self, data, targets, index):
        data_tmp = []
        targets_tmp = []
        ind_list = [int(i) for i in index]
        ind_np = np.array(ind_list)
        index = ind_np.reshape((5,5))
        for i in index:
            ind_cl = i
            if data_tmp == []:
                data_tmp = data[ind_cl]
                targets_tmp = targets[ind_cl]
            else:
                data_tmp = np.vstack((data_tmp, data[ind_cl]))
                targets_tmp = np.hstack((targets_tmp, targets[ind_cl]))

        return data_tmp, targets_tmp

    def _load_meta(self):
        path = os.path.join(self.root, self.base_folder, self.meta['filename'])
        if not check_integrity(path, self.meta['md5']):
            raise RuntimeError('Dataset metadata file not found or corrupted.' +
                               ' You can use download=True to download it')
        with open(path, 'rb') as infile:
            data = pickle.load(infile, encoding='latin1')
            self.classes = data[self.meta['key']]
        self.class_to_idx = {_class: i for i, _class in enumerate(self.classes)}

    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.targets[index]

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

        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)

    def _check_integrity(self):
        root = self.root
        for fentry in (self.train_list + self.test_list):
            filename, md5 = fentry[0], fentry[1]
            fpath = os.path.join(root, self.base_folder, filename)
            if not check_integrity(fpath, md5):
                return False
        return True

    def download(self):
        if self._check_integrity():
            print('Files already downloaded and verified')
            return
        download_and_extract_archive(self.url, self.root, filename=self.filename, md5=self.tgz_md5)

    def extra_repr(self):
        return "Split: {}".format("Train" if self.train is True else "Test")


class CIFAR100(CIFAR10):
    """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
    This is a subclass of the `CIFAR10` Dataset.
    """
    base_folder = 'cifar-100-python'
    url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
    filename = "cifar-100-python.tar.gz"
    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
    train_list = [
        ['train', '16019d7e3df5f24257cddd939b257f8d'],
    ]

    test_list = [
        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
    ]
    meta = {
        'filename': 'meta',
        'key': 'fine_label_names',
        'md5': '7973b15100ade9c7d40fb424638fde48',
    }

if __name__ == "__main__":
    dataroot = '~/dataloader/data/'
    batch_size_base = 128
    txt_path = "../../data/index_list/cifar100/session_2.txt"
    # class_index = open(txt_path).read().splitlines()
    class_index = np.arange(60)
    trainset = CIFAR100(root=dataroot, train=True, download=True, transform=None, index=class_index,
                        base_sess=True)
    testset = CIFAR100(root=dataroot, train=False, download=False,index=class_index, base_sess=True)
    cls = np.unique(trainset.targets)
    trainloader = torch.utils.data.DataLoader(dataset=trainset, batch_size=batch_size_base, shuffle=True, num_workers=8,
                                              pin_memory=True)
    testloader = torch.utils.data.DataLoader(
        dataset=testset, batch_size=100, shuffle=False, num_workers=8, pin_memory=True)
    print(testloader.dataset.data.shape)
