






'''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

A:      int  				=				collections.namedtuple("_Datasets", ["train", "validation", "test"])

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






def 	_UpperCAmelCase    (  a		:	List[Any]							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   str        =			numpy.dtype(numpy.uintaa							).newbyteorder('>'							)
						return numpy.frombuffer(bytestream.read(4							)						,			dtype=a							)[0]






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





						"""simple docstring"""
						print('Extracting'						,			f.name							)
						with gzip.GzipFile(fileobj=a							) as bytestream:
												lowercase_      :   List[str]        =			_readaa(a							)
												if magic != 2_0_5_1:
																		raise ValueError(
																		    'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)							)
												lowercase_      :   Optional[int]        =			_readaa(a							)
												lowercase_      :   Tuple        =			_readaa(a							)
												lowercase_      :   Optional[Any]        =			_readaa(a							)
												lowercase_      :   Tuple        =			bytestream.read(rows * cols * num_images							)
												lowercase_      :   Optional[int]        =			numpy.frombuffer(a						,			dtype=numpy.uinta							)
												lowercase_      :   Optional[Any]        =			data.reshape(a						,			a						,			a						,			1							)
												return data






@deprecated(a						,			'Please use tf.one_hot on tensors.'							)
def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	List[str]							)			->			Any:





						"""simple docstring"""
						lowercase_      :   Dict        =			labels_dense.shape[0]
						lowercase_      :   List[Any]        =			numpy.arange(a							) * num_classes
						lowercase_      :   Union[str, Any]        =			numpy.zeros((num_labels, num_classes)							)
						lowercase_      :   List[str]        =			1
						return labels_one_hot






@deprecated(a						,			'Please use tf.data to implement this functionality.'							)
def 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	str=False						,			a		:	Tuple=1_0							)			->			str:





						"""simple docstring"""
						print('Extracting'						,			f.name							)
						with gzip.GzipFile(fileobj=a							) as bytestream:
												lowercase_      :   int        =			_readaa(a							)
												if magic != 2_0_4_9:
																		raise ValueError(
																		    'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)							)
												lowercase_      :   Tuple        =			_readaa(a							)
												lowercase_      :   Union[str, Any]        =			bytestream.read(a							)
												lowercase_      :   int        =			numpy.frombuffer(a						,			dtype=numpy.uinta							)
												if one_hot:
																		return _dense_to_one_hot(a						,			a							)
												return labels






class 							__magic_name__     :







	"""simple docstring"""



	@deprecated(
	    _lowercase  ,      'Please use alternatives such as official/mnist/_DataSet.py'
	    ' from tensorflow/models.'  ,      )
	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase=False  ,      _lowercase=False  ,      _lowercase=dtypes.floataa  ,      _lowercase=True  ,      _lowercase=None  ,      )						->  int:
							lowercase_  ,      lowercase_      :   Union[str, Any]        =			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       )
							lowercase_      :   Optional[int]        =			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:
													lowercase_      :   Optional[int]        =			1_0000
													lowercase_      :   int        =			one_hot
							else:
													assert (
													    images.shape[0] == labels.shape[0]
													), f"images.shape: {images.shape} labels.shape: {labels.shape}"
													lowercase_      :   Any        =			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
																			lowercase_      :   Optional[Any]        =			images.reshape(
																			    images.shape[0]  ,      images.shape[1] * images.shape[2]       )
													if dtype == dtypes.floataa:
																			# Convert from [0, 255] -> [0.0, 1.0].
																			lowercase_      :   Union[str, Any]        =			images.astype(numpy.floataa       )
																			lowercase_      :   str        =			numpy.multiply(_lowercase  ,      1.0 / 2_55.0       )
							lowercase_      :   Dict        =			images
							lowercase_      :   Union[str, Any]        =			labels
							lowercase_      :   int        =			0
							lowercase_      :   List[Any]        =			0
	@property
	def        lowerCamelCase__		(							self       )						->  Tuple:
							return self._images
	@property
	def        lowerCamelCase__		(							self       )						->  Tuple:
							return self._labels
	@property
	def        lowerCamelCase__		(							self       )						->  Dict:
							return self._num_examples
	@property
	def        lowerCamelCase__		(							self       )						->  Optional[Any]:
							return self._epochs_completed







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase=False  ,      _lowercase=True       )						->  int:
							if fake_data:
													lowercase_      :   str        =			[1] * 784
													lowercase_      :   Union[str, Any]        =			[1] + [0] * 9 if self.one_hot else 0
													return (
													    [fake_image for _ in range(_lowercase       )],
													    [fake_label for _ in range(_lowercase       )],
													)
							lowercase_      :   str        =			self._index_in_epoch
							# Shuffle for the first epoch
							if self._epochs_completed == 0 and start == 0 and shuffle:
													lowercase_      :   List[Any]        =			numpy.arange(self._num_examples       )
													numpy.random.shuffle(_lowercase       )
													lowercase_      :   Optional[Any]        =			self.images[perma]
													lowercase_      :   Optional[Any]        =			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
													lowercase_      :   List[str]        =			self._num_examples - start
													lowercase_      :   List[str]        =			self._images[start : self._num_examples]
													lowercase_      :   Union[str, Any]        =			self._labels[start : self._num_examples]
													# Shuffle the data
													if shuffle:
																			lowercase_      :   int        =			numpy.arange(self._num_examples       )
																			numpy.random.shuffle(_lowercase       )
																			lowercase_      :   List[str]        =			self.images[perm]
																			lowercase_      :   Dict        =			self.labels[perm]
													# Start next epoch
													lowercase_      :   List[str]        =			0
													lowercase_      :   int        =			batch_size - rest_num_examples
													lowercase_      :   Dict        =			self._index_in_epoch
													lowercase_      :   Union[str, Any]        =			self._images[start:end]
													lowercase_      :   List[Any]        =			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
													lowercase_      :   Any        =			self._index_in_epoch
													return self._images[start:end], self._labels[start:end]






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





						"""simple docstring"""
						if not gfile.Exists(a							):
												gfile.MakeDirs(a							)
						lowercase_      :   Tuple        =			os.path.join(a						,			a							)
						if not gfile.Exists(a							):
												urllib.request.urlretrieve(a						,			a							)  # noqa: S310
												with gfile.GFile(a							) as f:
																		lowercase_      :   str        =			f.size()
												print('Successfully downloaded'						,			a						,			a						,			'bytes.'							)
						return filepath






@deprecated(
    a						,			'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'							)
def 	_UpperCAmelCase    (  a		:	Union[str, Any]						,			a		:	int=False						,			a		:	Dict=False						,			a		:	List[Any]=dtypes.floataa						,			a		:	List[str]=True						,			a		:	Tuple=5_0_0_0						,			a		:	Any=None						,			a		:	Optional[int]=DEFAULT_SOURCE_URL						,			)			->			Optional[int]:





						"""simple docstring"""
						if fake_data:

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

												lowercase_      :   Tuple        =			fake()
												lowercase_      :   Tuple        =			fake()
												lowercase_      :   Tuple        =			fake()
												return _Datasets(train=a						,			validation=a						,			test=a							)

						if not source_url:  # empty string check
												lowercase_      :   Dict        =			DEFAULT_SOURCE_URL

						lowercase_      :   List[Any]        =			'train-images-idx3-ubyte.gz'
						lowercase_      :   str        =			'train-labels-idx1-ubyte.gz'
						lowercase_      :   Optional[int]        =			't10k-images-idx3-ubyte.gz'
						lowercase_      :   Optional[Any]        =			't10k-labels-idx1-ubyte.gz'

						lowercase_      :   Tuple        =			_maybe_download(
						    a						,			a						,			source_url + train_images_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   Optional[int]        =			_extract_images(a							)

						lowercase_      :   Optional[int]        =			_maybe_download(
						    a						,			a						,			source_url + train_labels_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   Optional[Any]        =			_extract_labels(a						,			one_hot=a							)

						lowercase_      :   Dict        =			_maybe_download(
						    a						,			a						,			source_url + test_images_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   Optional[Any]        =			_extract_images(a							)

						lowercase_      :   List[str]        =			_maybe_download(
						    a						,			a						,			source_url + test_labels_file							)
						with gfile.Open(a						,			'rb'							) as f:
												lowercase_      :   int        =			_extract_labels(a						,			one_hot=a							)

						if not 0 <= validation_size <= len(a							):
												lowercase_      :   List[str]        =			(
												    'Validation size should be between 0 and '
												    f"{len(a							)}. Received: {validation_size}."
												)
												raise ValueError(a							)

						lowercase_      :   List[Any]        =			train_images[:validation_size]
						lowercase_      :   List[str]        =			train_labels[:validation_size]
						lowercase_      :   Any        =			train_images[validation_size:]
						lowercase_      :   Optional[int]        =			train_labels[validation_size:]

						lowercase_      :   Optional[int]        =			{'dtype': dtype, 'reshape': reshape, 'seed': seed}

						lowercase_      :   Optional[int]        =			_DataSet(a						,			a						,			**a							)
						lowercase_      :   List[Any]        =			_DataSet(a						,			a						,			**a							)
						lowercase_      :   Optional[Any]        =			_DataSet(a						,			a						,			**a							)

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








'''simple docstring'''

class 							__magic_name__     :







	"""simple docstring"""



	def __init__(							self  ,      _lowercase       )						->  Union[str, Any]:
							lowercase_      :   Dict        =			n
							lowercase_      :   Dict        =			[None] * self.n
							lowercase_      :   Tuple        =			0  # index of the first element
							lowercase_      :   List[Any]        =			0
							lowercase_      :   List[Any]        =			0
	def __len__(							self       )						->  int:
							return self.size
	def        lowerCamelCase__		(							self       )						->  bool:
							return self.size == 0
	def        lowerCamelCase__		(							self       )						->  List[Any]:
							return False if self.is_empty() else self.array[self.front]
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Any:
							if self.size >= self.n:
													raise Exception('QUEUE IS FULL'       )

							lowercase_      :   Tuple        =			data
							lowercase_      :   List[Any]        =			(self.rear + 1) % self.n
							self.size += 1
							return self







	def        lowerCamelCase__		(							self       )						->  Any:
							if self.size == 0:
													raise Exception('UNDERFLOW'       )

							lowercase_      :   Dict        =			self.array[self.front]
							lowercase_      :   Tuple        =			None
							lowercase_      :   int        =			(self.front + 1) % self.n
							self.size -= 1
							return temp
