# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data (deprecated).

This module and all its submodules are deprecated.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import gzip
import os

import numpy
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin

from tensorflow.python.framework import dtypes
from tensorflow.python.framework import random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'

def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]

@deprecated(None, 'Please use tf.data to implement this functionality.')
def _extract_images(f):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].

Args:
f: A file object that can be passed into a gzip reader.

Returns:
data: A 4D uint8 numpy array [index, y, x, depth].

Raises:
ValueError: If the bytestream does not start with 2051.

"""
print('Extracting', f.name)
with gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
   raise ValueError('Invalid magic number %d in MNIST image file: %s' %
                  (magic, f.name))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data

@deprecated(None, 'Please use tf.one_hot on tensors.')
def _dense_to_one_hot(labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot

@deprecated(None, 'Please use tf.data to implement this functionality.')
def _extract_labels(f, one_hot=False, num_classes=10):
"""Extract the labels into a 1D uint8 numpy array [index].

Args:
f: A file object that can be passed into a gzip reader.
one_hot: Does one hot encoding for the result.
num_classes: Number of classes for the one hot encoding.

Returns:
labels: a 1D uint8 numpy array.

Raises:
ValueError: If the bystream doesn't start with 2049.
"""
print('Extracting', f.name)
with gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
   raise ValueError('Invalid magic number %d in MNIST label file: %s' %
                  (magic, f.name))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
   return _dense_to_one_hot(labels, num_classes)
return labels

## Instructions Please work these problems on your own. You may...