






import collections
import gzip
import os
import urllib

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

__snake_case      :      Optional[Any]             =       collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__snake_case      :      List[str]             =       """https://storage.googleapis.com/cvdf-datasets/mnist/"""







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]						)					->				List[Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  numpy.dtype(numpy.uintaa						).newbyteorder('>'						)
						return numpy.frombuffer(bytestream.read(4						)      ,    dtype=UpperCamelCase_						)[0]







@deprecated(UpperCamelCase_      ,    'Please use tf.data to implement this functionality.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Optional[int]:

						"""simple docstring"""



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







@deprecated(UpperCamelCase_      ,    'Please use tf.one_hot on tensors.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  labels_dense.shape[0]
						lowerCAmelCase__        =  numpy.arange(UpperCamelCase_						) * num_classes
						lowerCAmelCase__        =  numpy.zeros((num_labels, num_classes)						)
						lowerCAmelCase__        =  1
						return labels_one_hot







@deprecated(UpperCamelCase_      ,    'Please use tf.data to implement this functionality.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Dict=False      ,    UpperCamelCase_    :   Any=10						)					->				Any:

						"""simple docstring"""



						print('Extracting'      ,    f.name						)
						with gzip.GzipFile(fileobj=UpperCamelCase_						) as bytestream:
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												if magic != 2049:
																		raise ValueError(
																		    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)						)
												lowerCAmelCase__        =  _readaa(UpperCamelCase_						)
												lowerCAmelCase__        =  bytestream.read(UpperCamelCase_						)
												lowerCAmelCase__        =  numpy.frombuffer(UpperCamelCase_      ,    dtype=numpy.uinta						)
												if one_hot:
																		return _dense_to_one_hot(UpperCamelCase_      ,    UpperCamelCase_						)
												return labels


class       __SCREAMING_SNAKE_CASE   :






						@deprecated(
						    _UpperCamelCase       ,  'Please use alternatives such as official/mnist/_DataSet.py'
						    ' from tensorflow/models.'       ,  )
						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=False       ,  _UpperCamelCase=False       ,  _UpperCamelCase=dtypes.floataa       ,  _UpperCamelCase=True       ,  _UpperCamelCase=None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__  ,    lowerCAmelCase__        =  random_seed.get_seed(_UpperCamelCase				)
												# If op level seed is not set, use whatever graph level seed is returned
												numpy.random.seed(seeda if seed is None else seeda				)
												lowerCAmelCase__        =  dtypes.as_dtype(_UpperCamelCase				).base_dtype
												if dtype not in (dtypes.uinta, dtypes.floataa):
																		raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype				)
												if fake_data:
																		lowerCAmelCase__        =  1_00_00
																		lowerCAmelCase__        =  one_hot
												else:
																		assert (
																		    images.shape[0] == labels.shape[0]
																		), F"images.shape: {images.shape} labels.shape: {labels.shape}"
																		lowerCAmelCase__        =  images.shape[0]

																		# Convert shape from [num examples, rows, columns, depth]
																		# to [num examples, rows*columns] (assuming depth == 1)
																		if reshape:
																								assert images.shape[3] == 1
																								lowerCAmelCase__        =  images.reshape(
																								    images.shape[0]       ,  images.shape[1] * images.shape[2]				)
																		if dtype == dtypes.floataa:
																								# Convert from [0, 255] -> [0.0, 1.0].
																								lowerCAmelCase__        =  images.astype(numpy.floataa				)
																								lowerCAmelCase__        =  numpy.multiply(_UpperCamelCase       ,  1.0 / 2_55.0				)
												lowerCAmelCase__        =  images
												lowerCAmelCase__        =  labels
												lowerCAmelCase__        =  0
												lowerCAmelCase__        =  0






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._images






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._labels






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._num_examples






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self._epochs_completed







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=False       ,  _UpperCamelCase=True				):






												"""simple docstring"""
												if fake_data:
																		lowerCAmelCase__        =  [1] * 7_84
																		lowerCAmelCase__        =  [1] + [0] * 9 if self.one_hot else 0
																		return (
																		    [fake_image for _ in range(_UpperCamelCase				)],
																		    [fake_label for _ in range(_UpperCamelCase				)],
																		)
												lowerCAmelCase__        =  self._index_in_epoch
												# Shuffle for the first epoch
												if self._epochs_completed == 0 and start == 0 and shuffle:
																		lowerCAmelCase__        =  numpy.arange(self._num_examples				)
																		numpy.random.shuffle(_UpperCamelCase				)
																		lowerCAmelCase__        =  self.images[perma]
																		lowerCAmelCase__        =  self.labels[perma]
												# Go to the next epoch
												if start + batch_size > self._num_examples:
																		# Finished epoch
																		self._epochs_completed += 1
																		# Get the rest examples in this epoch
																		lowerCAmelCase__        =  self._num_examples - start
																		lowerCAmelCase__        =  self._images[start : self._num_examples]
																		lowerCAmelCase__        =  self._labels[start : self._num_examples]
																		# Shuffle the data
																		if shuffle:
																								lowerCAmelCase__        =  numpy.arange(self._num_examples				)
																								numpy.random.shuffle(_UpperCamelCase				)
																								lowerCAmelCase__        =  self.images[perm]
																								lowerCAmelCase__        =  self.labels[perm]
																		# Start next epoch
																		lowerCAmelCase__        =  0
																		lowerCAmelCase__        =  batch_size - rest_num_examples
																		lowerCAmelCase__        =  self._index_in_epoch
																		lowerCAmelCase__        =  self._images[start:end]
																		lowerCAmelCase__        =  self._labels[start:end]
																		return (
																		    numpy.concatenate((images_rest_part, images_new_part)       ,  axis=0				),
																		    numpy.concatenate((labels_rest_part, labels_new_part)       ,  axis=0				),
																		)
												else:
																		self._index_in_epoch += batch_size
																		lowerCAmelCase__        =  self._index_in_epoch
																		return self._images[start:end], self._labels[start:end]







@deprecated(UpperCamelCase_      ,    'Please write your own downloading logic.'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   List[str]						)					->				Union[str, Any]:

						"""simple docstring"""



						if not gfile.Exists(UpperCamelCase_						):
												gfile.MakeDirs(UpperCamelCase_						)
						lowerCAmelCase__        =  os.path.join(UpperCamelCase_      ,    UpperCamelCase_						)
						if not gfile.Exists(UpperCamelCase_						):
												urllib.request.urlretrieve(UpperCamelCase_      ,    UpperCamelCase_						)  # noqa: S310
												with gfile.GFile(UpperCamelCase_						) as f:
																		lowerCAmelCase__        =  f.size()
												print('Successfully downloaded'      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    'bytes.'						)
						return filepath







@deprecated(
    UpperCamelCase_      ,    'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'						)
def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   List[str]=False      ,    UpperCamelCase_    :   Tuple=False      ,    UpperCamelCase_    :   Tuple=dtypes.floataa      ,    UpperCamelCase_    :   List[str]=True      ,    UpperCamelCase_    :   Dict=5000      ,    UpperCamelCase_    :   Optional[int]=None      ,    UpperCamelCase_    :   Dict=DEFAULT_SOURCE_URL      ,    )					->				Dict:

						"""simple docstring"""



						if fake_data:

												def fake():
																		return _DataSet(
																		    []      ,    []      ,    fake_data=UpperCamelCase_      ,    one_hot=UpperCamelCase_      ,    dtype=UpperCamelCase_      ,    seed=UpperCamelCase_						)

												lowerCAmelCase__        =  fake()
												lowerCAmelCase__        =  fake()
												lowerCAmelCase__        =  fake()
												return _Datasets(train=UpperCamelCase_      ,    validation=UpperCamelCase_      ,    test=UpperCamelCase_						)

						if not source_url:  # empty string check
												lowerCAmelCase__        =  DEFAULT_SOURCE_URL

						lowerCAmelCase__        =  'train-images-idx3-ubyte.gz'
						lowerCAmelCase__        =  'train-labels-idx1-ubyte.gz'
						lowerCAmelCase__        =  't10k-images-idx3-ubyte.gz'
						lowerCAmelCase__        =  't10k-labels-idx1-ubyte.gz'

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + train_images_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_images(UpperCamelCase_						)

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + train_labels_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_labels(UpperCamelCase_      ,    one_hot=UpperCamelCase_						)

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + test_images_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_images(UpperCamelCase_						)

						lowerCAmelCase__        =  _maybe_download(
						    UpperCamelCase_      ,    UpperCamelCase_      ,    source_url + test_labels_file						)
						with gfile.Open(UpperCamelCase_      ,    'rb'						) as f:
												lowerCAmelCase__        =  _extract_labels(UpperCamelCase_      ,    one_hot=UpperCamelCase_						)

						if not 0 <= validation_size <= len(UpperCamelCase_						):
												lowerCAmelCase__        =  (
												    'Validation size should be between 0 and '
												    F"{len(UpperCamelCase_						)}. Received: {validation_size}."
												)
												raise ValueError(UpperCamelCase_						)

						lowerCAmelCase__        =  train_images[:validation_size]
						lowerCAmelCase__        =  train_labels[:validation_size]
						lowerCAmelCase__        =  train_images[validation_size:]
						lowerCAmelCase__        =  train_labels[validation_size:]

						lowerCAmelCase__        =  {'dtype': dtype, 'reshape': reshape, 'seed': seed}

						lowerCAmelCase__        =  _DataSet(UpperCamelCase_      ,    UpperCamelCase_      ,    **UpperCamelCase_						)
						lowerCAmelCase__        =  _DataSet(UpperCamelCase_      ,    UpperCamelCase_      ,    **UpperCamelCase_						)
						lowerCAmelCase__        =  _DataSet(UpperCamelCase_      ,    UpperCamelCase_      ,    **UpperCamelCase_						)

						return _Datasets(train=UpperCamelCase_      ,    validation=UpperCamelCase_      ,    test=UpperCamelCase_						)














def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				str:

						"""simple docstring"""



						return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





