





'''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

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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase_					:    Optional[int]      	=			'https://storage.googleapis.com/cvdf-datasets/mnist/'






def  _lowerCamelCase				( lowercase						:      Tuple						)	->	int:
				_a								= numpy.dtype(numpy.uintaa						).newbyteorder(">"						)
				return numpy.frombuffer(bytestream.read(4						)    ,							dtype=lowercase						)[0]






@deprecated(lowercase    ,							"Please use tf.data to implement this functionality."						)
def  _lowerCamelCase				( lowercase						:      Any						)	->	Dict:
				print("Extracting"    ,							f.name						)
				with gzip.GzipFile(fileobj=lowercase						) as bytestream:
								_a								= _readaa(lowercase						)
								if magic != 2051:
												raise ValueError(
												    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)						)
								_a								= _readaa(lowercase						)
								_a								= _readaa(lowercase						)
								_a								= _readaa(lowercase						)
								_a								= bytestream.read(rows * cols * num_images						)
								_a								= numpy.frombuffer(lowercase    ,							dtype=numpy.uinta						)
								_a								= data.reshape(lowercase    ,							lowercase    ,							lowercase    ,							1						)
								return data






@deprecated(lowercase    ,							"Please use tf.one_hot on tensors."						)
def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= labels_dense.shape[0]
				_a								= numpy.arange(lowercase						) * num_classes
				_a								= numpy.zeros((num_labels, num_classes)						)
				_a								= 1
				return labels_one_hot






@deprecated(lowercase    ,							"Please use tf.data to implement this functionality."						)
def  _lowerCamelCase				( lowercase						:      Any    ,							lowercase						:      int=False    ,							lowercase						:      Optional[int]=10						)	->	List[Any]:
				print("Extracting"    ,							f.name						)
				with gzip.GzipFile(fileobj=lowercase						) as bytestream:
								_a								= _readaa(lowercase						)
								if magic != 2049:
												raise ValueError(
												    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)						)
								_a								= _readaa(lowercase						)
								_a								= bytestream.read(lowercase						)
								_a								= numpy.frombuffer(lowercase    ,							dtype=numpy.uinta						)
								if one_hot:
												return _dense_to_one_hot(lowercase    ,							lowercase						)
								return labels
class 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				@deprecated(
				    __a					,				"Please use alternatives such as official/mnist/_DataSet.py"
				    " from tensorflow/models."					,				)
				def __init__(  self     :					str					,				__a     :					str					,				__a     :					Optional[Any]					,				__a     :					List[str]=False					,				__a     :					int=False					,				__a     :					Tuple=dtypes.floataa					,				__a     :					Optional[int]=True					,				__a     :					Any=None					,				):
								_a	,					_a								= random_seed.get_seed(__a					)
								# 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(__a					).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_00_00
												_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(__a					,				1.0 / 255.0					)
								_a								= images
								_a								= labels
								_a								= 0
								_a								= 0




				@property
				def   UpperCamelCase__   (  self     :					int					):
								return self._images




				@property
				def   UpperCamelCase__   (  self     :					str					):
								return self._labels




				@property
				def   UpperCamelCase__   (  self     :					Dict					):
								return self._num_examples




				@property
				def   UpperCamelCase__   (  self     :					Any					):
								return self._epochs_completed




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				__a     :					Optional[int]					,				__a     :					Optional[int]=False					,				__a     :					Optional[Any]=True					):
								if fake_data:
												_a								= [1] * 7_84
												_a								= [1] + [0] * 9 if self.one_hot else 0
												return (
												    [fake_image for _ in range(__a					)],
												    [fake_label for _ in range(__a					)],
												)
								_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(__a					)
												_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(__a					)
																_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(lowercase    ,							"Please write your own downloading logic."						)
def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      List[Any]    ,							lowercase						:      Optional[Any]						)	->	int:
				if not gfile.Exists(lowercase						):
								gfile.MakeDirs(lowercase						)
				_a								= os.path.join(lowercase    ,							lowercase						)
				if not gfile.Exists(lowercase						):
								urllib.request.urlretrieve(lowercase    ,							lowercase						)  # noqa: S310
								with gfile.GFile(lowercase						) as f:
												_a								= f.size()
								print("Successfully downloaded"    ,							lowercase    ,							lowercase    ,							"bytes."						)
				return filepath






@deprecated(
    lowercase    ,							"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"						)
def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      List[Any]=False    ,							lowercase						:      str=False    ,							lowercase						:      Optional[Any]=dtypes.floataa    ,							lowercase						:      Optional[int]=True    ,							lowercase						:      List[str]=5000    ,							lowercase						:      str=None    ,							lowercase						:      List[str]=DEFAULT_SOURCE_URL    ,							)	->	Optional[int]:
				if fake_data:

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

								_a								= fake()
								_a								= fake()
								_a								= fake()
								return _Datasets(train=lowercase    ,							validation=lowercase    ,							test=lowercase						)

				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(
				    lowercase    ,							lowercase    ,							source_url + train_images_file						)
				with gfile.Open(lowercase    ,							"rb"						) as f:
								_a								= _extract_images(lowercase						)

				_a								= _maybe_download(
				    lowercase    ,							lowercase    ,							source_url + train_labels_file						)
				with gfile.Open(lowercase    ,							"rb"						) as f:
								_a								= _extract_labels(lowercase    ,							one_hot=lowercase						)

				_a								= _maybe_download(
				    lowercase    ,							lowercase    ,							source_url + test_images_file						)
				with gfile.Open(lowercase    ,							"rb"						) as f:
								_a								= _extract_images(lowercase						)

				_a								= _maybe_download(
				    lowercase    ,							lowercase    ,							source_url + test_labels_file						)
				with gfile.Open(lowercase    ,							"rb"						) as f:
								_a								= _extract_labels(lowercase    ,							one_hot=lowercase						)

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

				_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(lowercase    ,							lowercase    ,							**lowercase						)
				_a								= _DataSet(lowercase    ,							lowercase    ,							**lowercase						)
				_a								= _DataSet(lowercase    ,							lowercase    ,							**lowercase						)

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







'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      bytes						)	->	str:
				return "".join([hex(lowercase						)[2:].zfill(2						).upper() for byte in list(lowercase						)]						)






def  _lowerCamelCase				( lowercase						:      str						)	->	bytes:
				# Check data validity, following RFC3548
				# https://www.ietf.org/rfc/rfc3548.txt
				if (len(lowercase						) % 2) != 0:
								raise ValueError(
								    "Base16 encoded data is invalid:\nData does not have an even number of hex digits."						)
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(lowercase						) <= set("0123456789ABCDEF"						):
								raise ValueError(
								    "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."						)
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1]    ,							16						) for i in range(0    ,							len(lowercase						)    ,							2						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
