





_lowerCamelCase :    Union[str, Any]  							= '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_lowerCamelCase :    Tuple  							= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_lowerCamelCase :    int  							= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}













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

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




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						numpy.dtype(numpy.uintaa       ).newbyteorder(">"       )
							return numpy.frombuffer(bytestream.read(4       )			,				dtype=SCREAMING_SNAKE_CASE__       )[0]




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.data to implement this functionality."       )
def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    List[str]:

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      int     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_51:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      Dict     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      str     =						bytestream.read(rows * cols * num_images       )
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						numpy.frombuffer(SCREAMING_SNAKE_CASE__			,				dtype=numpy.uinta       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						data.reshape(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				1       )
														return data




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.one_hot on tensors."       )
def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Tuple       )	->    Union[str, Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						labels_dense.shape[0]
							SCREAMING_SNAKE_CASE__       :      List[str]     =						numpy.arange(SCREAMING_SNAKE_CASE__       ) * num_classes
							SCREAMING_SNAKE_CASE__       :      Dict     =						numpy.zeros((num_labels, num_classes)       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						1
							return labels_one_hot




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please use tf.data to implement this functionality."       )
def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]=False			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=10       )	->    Optional[int]:

							'''simple docstring'''






							print("Extracting"			,				f.name       )
							with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_readaa(SCREAMING_SNAKE_CASE__       )
														if magic != 20_49:
																					raise ValueError(
																					    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						_readaa(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						bytestream.read(SCREAMING_SNAKE_CASE__       )
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						numpy.frombuffer(SCREAMING_SNAKE_CASE__			,				dtype=numpy.uinta       )
														if one_hot:
																					return _dense_to_one_hot(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
														return labels


class 						lowerCamelCase       :







							"""simple docstring"""




							@deprecated(
							    _UpperCAmelCase,			"Please use alternatives such as official/mnist/_DataSet.py"
							    " from tensorflow/models.",			)
							def __init__( self     :					int,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					List[str]=False,			_UpperCAmelCase     :					Tuple=False,			_UpperCAmelCase     :					Union[str, Any]=dtypes.floataa,			_UpperCAmelCase     :					Optional[Any]=True,			_UpperCAmelCase     :					Optional[int]=None,			)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						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						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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:
																					SCREAMING_SNAKE_CASE__       :      str     =						1_0_0_0_0
																					SCREAMING_SNAKE_CASE__       :      str     =						one_hot
														else:
																					assert (
																					    images.shape[0] == labels.shape[0]
																					), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																					SCREAMING_SNAKE_CASE__       :      List[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
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						images.reshape(
																												    images.shape[0],			images.shape[1] * images.shape[2]						)
																					if dtype == dtypes.floataa:
																												# Convert from [0, 255] -> [0.0, 1.0].
																												SCREAMING_SNAKE_CASE__       :      Any     =						images.astype(numpy.floataa						)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						numpy.multiply(_UpperCAmelCase,			1.0 / 255.0						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						images
														SCREAMING_SNAKE_CASE__       :      str     =						labels
														SCREAMING_SNAKE_CASE__       :      Any     =						0
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0






							@property
							def  A_     ( self     :					List[Any]						)				->			int:





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






							@property
							def  A_     ( self     :					List[str]						)				->			Optional[int]:





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






							@property
							def  A_     ( self     :					Optional[Any]						)				->			Union[str, Any]:





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






							@property
							def  A_     ( self     :					List[Any]						)				->			Tuple:





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






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					List[Any]=False,			_UpperCAmelCase     :					str=True						)				->			Any:





														"""simple docstring"""
														if fake_data:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[1] * 7_8_4
																					SCREAMING_SNAKE_CASE__       :      Dict     =						[1] + [0] * 9 if self.one_hot else 0
																					return (
																					    [fake_image for _ in range(_UpperCAmelCase						)],
																					    [fake_label for _ in range(_UpperCAmelCase						)],
																					)
														SCREAMING_SNAKE_CASE__       :      str     =						self._index_in_epoch
														# Shuffle for the first epoch
														if self._epochs_completed == 0 and start == 0 and shuffle:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						numpy.arange(self._num_examples						)
																					numpy.random.shuffle(_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.images[perma]
																					SCREAMING_SNAKE_CASE__       :      Union[str, 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
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self._num_examples - start
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self._images[start : self._num_examples]
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._labels[start : self._num_examples]
																					# Shuffle the data
																					if shuffle:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						numpy.arange(self._num_examples						)
																												numpy.random.shuffle(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Dict     =						self.images[perm]
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.labels[perm]
																					# Start next epoch
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						batch_size - rest_num_examples
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self._index_in_epoch
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._images[start:end]
																					SCREAMING_SNAKE_CASE__       :      Optional[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
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self._index_in_epoch
																					return self._images[start:end], self._labels[start:end]




@deprecated(SCREAMING_SNAKE_CASE__			,				"Please write your own downloading logic."       )
def _a (  SCREAMING_SNAKE_CASE__      :  Union[str, Any]			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]       )	->    str:

							'''simple docstring'''






							if not gfile.Exists(SCREAMING_SNAKE_CASE__       ):
														gfile.MakeDirs(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						os.path.join(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							if not gfile.Exists(SCREAMING_SNAKE_CASE__       ):
														urllib.request.urlretrieve(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )  # noqa: S310
														with gfile.GFile(SCREAMING_SNAKE_CASE__       ) as f:
																					SCREAMING_SNAKE_CASE__       :      Any     =						f.size()
														print("Successfully downloaded"			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				"bytes."       )
							return filepath







@deprecated(
    SCREAMING_SNAKE_CASE__			,				"Please use alternatives such as:" " tensorflow_datasets.load('mnist')"       )
def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  str=False			,				SCREAMING_SNAKE_CASE__      :  Any=False			,				SCREAMING_SNAKE_CASE__      :  List[Any]=dtypes.floataa			,				SCREAMING_SNAKE_CASE__      :  str=True			,				SCREAMING_SNAKE_CASE__      :  Dict=50_00			,				SCREAMING_SNAKE_CASE__      :  str=None			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=DEFAULT_SOURCE_URL			,				)	->    Any:

							'''simple docstring'''






							if fake_data:

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

														SCREAMING_SNAKE_CASE__       :      int     =						fake()
														SCREAMING_SNAKE_CASE__       :      List[str]     =						fake()
														SCREAMING_SNAKE_CASE__       :      Any     =						fake()
														return _Datasets(train=SCREAMING_SNAKE_CASE__			,				validation=SCREAMING_SNAKE_CASE__			,				test=SCREAMING_SNAKE_CASE__       )

							if not source_url:  # empty string check
														SCREAMING_SNAKE_CASE__       :      str     =						DEFAULT_SOURCE_URL

							SCREAMING_SNAKE_CASE__       :      Dict     =						"train-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"train-labels-idx1-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"t10k-images-idx3-ubyte.gz"
							SCREAMING_SNAKE_CASE__       :      str     =						"t10k-labels-idx1-ubyte.gz"

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + train_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Dict     =						_extract_images(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      List[Any]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + train_labels_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						_extract_labels(SCREAMING_SNAKE_CASE__			,				one_hot=SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + test_images_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      List[str]     =						_extract_images(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						_maybe_download(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				source_url + test_labels_file       )
							with gfile.Open(SCREAMING_SNAKE_CASE__			,				"rb"       ) as f:
														SCREAMING_SNAKE_CASE__       :      Any     =						_extract_labels(SCREAMING_SNAKE_CASE__			,				one_hot=SCREAMING_SNAKE_CASE__       )

							if not 0 <= validation_size <= len(SCREAMING_SNAKE_CASE__       ):
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						(
														    "Validation size should be between 0 and "
														    f'''{len(SCREAMING_SNAKE_CASE__       )}. Received: {validation_size}.'''
														)
														raise ValueError(SCREAMING_SNAKE_CASE__       )

							SCREAMING_SNAKE_CASE__       :      str     =						train_images[:validation_size]
							SCREAMING_SNAKE_CASE__       :      str     =						train_labels[:validation_size]
							SCREAMING_SNAKE_CASE__       :      Any     =						train_images[validation_size:]
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						train_labels[validation_size:]

							SCREAMING_SNAKE_CASE__       :      Any     =						{"dtype": dtype, "reshape": reshape, "seed": seed}

							SCREAMING_SNAKE_CASE__       :      Tuple     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						_DataSet(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )

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






