






"""simple docstring"""






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

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

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



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



@deprecated(UpperCamelCase__       ,'Please use tf.one_hot on tensors.'				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       labels_dense.shape[0]
				A__							 =       numpy.arange(UpperCamelCase__				) * num_classes
				A__							 =       numpy.zeros((num_labels, num_classes)				)
				A__							 =       1
				return labels_one_hot



@deprecated(UpperCamelCase__       ,'Please use tf.data to implement this functionality.'				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=False       ,lowerCAmelCase__=10				):
				print('Extracting'       ,f.name				)
				with gzip.GzipFile(fileobj=UpperCamelCase__				) as bytestream:
								A__							 =       _readaa(UpperCamelCase__				)
								if magic != 2049:
												raise ValueError(
												    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
								A__							 =       _readaa(UpperCamelCase__				)
								A__							 =       bytestream.read(UpperCamelCase__				)
								A__							 =       numpy.frombuffer(UpperCamelCase__       ,dtype=numpy.uinta				)
								if one_hot:
												return _dense_to_one_hot(UpperCamelCase__       ,UpperCamelCase__				)
								return labels





class       snake_case_       :


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


					"""simple docstring"""

					A__ ,			A__							 =       random_seed.get_seed(_lowercase						)
					# If op level seed is not set, use whatever graph level seed is returned
					numpy.random.seed(seeda if seed is None else seeda						)
					A__							 =       dtypes.as_dtype(_lowercase						).base_dtype
					if dtype not in (dtypes.uinta, dtypes.floataa):
									raise TypeError('Invalid image dtype %r, expected uint8 or float32' % dtype						)
					if fake_data:
									A__							 =       1_0000
									A__							 =       one_hot
					else:
									assert (
									    images.shape[0] == labels.shape[0]
									), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
									A__							 =       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
													A__							 =       images.reshape(
													    images.shape[0]  ,					images.shape[1] * images.shape[2]						)
									if dtype == dtypes.floataa:
													# Convert from [0, 255] -> [0.0, 1.0].
													A__							 =       images.astype(numpy.floataa						)
													A__							 =       numpy.multiply(_lowercase  ,					1.0 / 255.0						)
					A__							 =       images
					A__							 =       labels
					A__							 =       0
					A__							 =       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  ,					__a  ,					__a=False  ,					__a=True						):


					"""simple docstring"""

					if fake_data:
									A__							 =       [1] * 784
									A__							 =       [1] + [0] * 9 if self.one_hot else 0
									return (
									    [fake_image for _ in range(_lowercase						)],
									    [fake_label for _ in range(_lowercase						)],
									)
					A__							 =       self._index_in_epoch
					# Shuffle for the first epoch
					if self._epochs_completed == 0 and start == 0 and shuffle:
									A__							 =       numpy.arange(self._num_examples						)
									numpy.random.shuffle(_lowercase						)
									A__							 =       self.images[perma]
									A__							 =       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
									A__							 =       self._num_examples - start
									A__							 =       self._images[start : self._num_examples]
									A__							 =       self._labels[start : self._num_examples]
									# Shuffle the data
									if shuffle:
													A__							 =       numpy.arange(self._num_examples						)
													numpy.random.shuffle(_lowercase						)
													A__							 =       self.images[perm]
													A__							 =       self.labels[perm]
									# Start next epoch
									A__							 =       0
									A__							 =       batch_size - rest_num_examples
									A__							 =       self._index_in_epoch
									A__							 =       self._images[start:end]
									A__							 =       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
									A__							 =       self._index_in_epoch
									return self._images[start:end], self._labels[start:end]





@deprecated(UpperCamelCase__       ,'Please write your own downloading logic.'				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):





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







@deprecated(
    UpperCamelCase__       ,'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'				)
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__=False       ,lowerCAmelCase__=False       ,lowerCAmelCase__=dtypes.floataa       ,lowerCAmelCase__=True       ,lowerCAmelCase__=5000       ,lowerCAmelCase__=None       ,lowerCAmelCase__=DEFAULT_SOURCE_URL       ,):





				if fake_data:

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

								A__							 =       fake()
								A__							 =       fake()
								A__							 =       fake()
								return _Datasets(train=UpperCamelCase__       ,validation=UpperCamelCase__       ,test=UpperCamelCase__				)

				if not source_url:  # empty string check
								A__							 =       DEFAULT_SOURCE_URL

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

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

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

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

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

				if not 0 <= validation_size <= len(UpperCamelCase__				):
								A__							 =       (
								    'Validation size should be between 0 and '
								    f'''{len(UpperCamelCase__				)}. Received: {validation_size}.'''
								)
								raise ValueError(UpperCamelCase__				)

				A__							 =       train_images[:validation_size]
				A__							 =       train_labels[:validation_size]
				A__							 =       train_images[validation_size:]
				A__							 =       train_labels[validation_size:]

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

				A__							 =       _DataSet(UpperCamelCase__       ,UpperCamelCase__       ,**UpperCamelCase__				)
				A__							 =       _DataSet(UpperCamelCase__       ,UpperCamelCase__       ,**UpperCamelCase__				)
				A__							 =       _DataSet(UpperCamelCase__       ,UpperCamelCase__       ,**UpperCamelCase__				)

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










"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				return 1 if input_a == input_a else 0



def  __lowerCamelCase					(					):
				assert xnor_gate(0       ,0				) == 1
				assert xnor_gate(0       ,1				) == 0
				assert xnor_gate(1       ,0				) == 0
				assert xnor_gate(1       ,1				) == 1


if __name__ == "__main__":
					print(xnor_gate(0, 0))
					print(xnor_gate(0, 1))
					print(xnor_gate(1, 0))
					print(xnor_gate(1, 1))


