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# coding: utf-8
# pylint: disable=
"""Dataset container."""
__all__ = ['MNIST', 'FashionMNIST', 'CIFAR10', 'CIFAR100',
           'ImageRecordDataset', 'ImageFolderDataset']

import os
import gzip
import tarfile
import struct
import warnings
import numpy as np

from .. import dataset
from ...utils import download, check_sha1, _get_repo_file_url
from .... import nd, image, recordio, base


class MNIST(dataset._DownloadedDataset):
    """MNIST handwritten digits dataset from http://yann.lecun.com/exdb/mnist

    Each sample is an image (in 3D NDArray) with shape (28, 28, 1).

    Parameters
    ----------
    root : str, default $MXNET_HOME/datasets/mnist
        Path to temp folder for storing data.
    train : bool, default True
        Whether to load the training or testing set.
    transform : function, default None
        A user defined callback that transforms each sample. For example::

        transform=lambda data, label: (data.astype(np.float32)/255, label)

    """
    def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'mnist'),
                 train=True, transform=None):
        self._train = train
        self._train_data = ('train-images-idx3-ubyte.gz',
                            '6c95f4b05d2bf285e1bfb0e7960c31bd3b3f8a7d')
        self._train_label = ('train-labels-idx1-ubyte.gz',
                             '2a80914081dc54586dbdf242f9805a6b8d2a15fc')
        self._test_data = ('t10k-images-idx3-ubyte.gz',
                           'c3a25af1f52dad7f726cce8cacb138654b760d48')
        self._test_label = ('t10k-labels-idx1-ubyte.gz',
                            '763e7fa3757d93b0cdec073cef058b2004252c17')
        self._namespace = 'mnist'
        super(MNIST, self).__init__(root, transform)

    def _get_data(self):
        if self._train:
            data, label = self._train_data, self._train_label
        else:
            data, label = self._test_data, self._test_label

        namespace = 'gluon/dataset/'+self._namespace
        data_file = download(_get_repo_file_url(namespace, data[0]),
                             path=self._root,
                             sha1_hash=data[1])
        label_file = download(_get_repo_file_url(namespace, label[0]),
                              path=self._root,
                              sha1_hash=label[1])

        with gzip.open(label_file, 'rb') as fin:
            struct.unpack(">II", fin.read(8))
            label = np.frombuffer(fin.read(), dtype=np.uint8).astype(np.int32)

        with gzip.open(data_file, 'rb') as fin:
            struct.unpack(">IIII", fin.read(16))
            data = np.frombuffer(fin.read(), dtype=np.uint8)
            data = data.reshape(len(label), 28, 28, 1)

        self._data = nd.array(data, dtype=data.dtype)
        self._label = label


class FashionMNIST(MNIST):
    """A dataset of Zalando's article images consisting of fashion products,
    a drop-in replacement of the original MNIST dataset from
    https://github.com/zalandoresearch/fashion-mnist

    Each sample is an image (in 3D NDArray) with shape (28, 28, 1).

    Parameters
    ----------
    root : str, default $MXNET_HOME/datasets/fashion-mnist'
        Path to temp folder for storing data.
    train : bool, default True
        Whether to load the training or testing set.
    transform : function, default None
        A user defined callback that transforms each sample. For example::

        transform=lambda data, label: (data.astype(np.float32)/255, label)

    """
    def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'fashion-mnist'),
                 train=True, transform=None):
        self._train = train
        self._train_data = ('train-images-idx3-ubyte.gz',
                            '0cf37b0d40ed5169c6b3aba31069a9770ac9043d')
        self._train_label = ('train-labels-idx1-ubyte.gz',
                             '236021d52f1e40852b06a4c3008d8de8aef1e40b')
        self._test_data = ('t10k-images-idx3-ubyte.gz',
                           '626ed6a7c06dd17c0eec72fa3be1740f146a2863')
        self._test_label = ('t10k-labels-idx1-ubyte.gz',
                            '17f9ab60e7257a1620f4ad76bbbaf857c3920701')
        self._namespace = 'fashion-mnist'
        super(MNIST, self).__init__(root, transform) # pylint: disable=bad-super-call


class CIFAR10(dataset._DownloadedDataset):
    """CIFAR10 image classification dataset from https://www.cs.toronto.edu/~kriz/cifar.html

    Each sample is an image (in 3D NDArray) with shape (32, 32, 1).

    Parameters
    ----------
    root : str, default $MXNET_HOME/datasets/cifar10
        Path to temp folder for storing data.
    train : bool, default True
        Whether to load the training or testing set.
    transform : function, default None
        A user defined callback that transforms each sample. For example::

        transform=lambda data, label: (data.astype(np.float32)/255, label)

    """
    def __init__(self, root=os.path.join(base.data_dir(), 'datasets', 'cifar10'),
                 train=True, transform=None):
        self._train = train
        self._archive_file = ('cifar-10-binary.tar.gz', 'fab780a1e191a7eda0f345501ccd62d20f7ed891')
        self._train_data = [('data_batch_1.bin', 'aadd24acce27caa71bf4b10992e9e7b2d74c2540'),
                            ('data_batch_2.bin', 'c0ba65cce70568cd57b4e03e9ac8d2a5367c1795'),
                            ('data_batch_3.bin', '1dd00a74ab1d17a6e7d73e185b69dbf31242f295'),
                            ('data_batch_4.bin', 'aab85764eb3584312d3c7f65fd2fd016e36a258e'),
                            ('data_batch_5.bin', '26e2849e66a845b7f1e4614ae70f4889ae604628')]
        self._test_data = [('test_batch.bin', '67eb016db431130d61cd03c7ad570b013799c88c')]
        self._namespace = 'cifar10'
        super(CIFAR10, self).__init__(root, transform)