


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

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




def    snake_case_     (  snake_case     )  ->				Optional[Any]:
       lowercase__:   Tuple  			=   numpy.dtype(numpy.uintaa     ).newbyteorder('>'     )
       return numpy.frombuffer(bytestream.read(4     )			,							dtype=__lowerCamelCase     )[0]




@deprecated(__lowerCamelCase			,							'Please use tf.data to implement this functionality.'     )
def    snake_case_     (  snake_case     )  ->				str:
       print('Extracting'			,							f.name     )
       with gzip.GzipFile(fileobj=__lowerCamelCase     ) as bytestream:
              lowercase__:   Union[str, Any]  			=   _readaa(__lowerCamelCase     )
              if magic != 20_51:
                     raise ValueError(
                         'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)     )
              lowercase__:   int  			=   _readaa(__lowerCamelCase     )
              lowercase__:   int  			=   _readaa(__lowerCamelCase     )
              lowercase__:   Tuple  			=   _readaa(__lowerCamelCase     )
              lowercase__:   Optional[Any]  			=   bytestream.read(rows * cols * num_images     )
              lowercase__:   List[str]  			=   numpy.frombuffer(__lowerCamelCase			,							dtype=numpy.uinta     )
              lowercase__:   Dict  			=   data.reshape(__lowerCamelCase			,							__lowerCamelCase			,							__lowerCamelCase			,							1     )
              return data




@deprecated(__lowerCamelCase			,							'Please use tf.one_hot on tensors.'     )
def    snake_case_     (  snake_case			,							snake_case     )  ->				Optional[Any]:
       lowercase__:   int  			=   labels_dense.shape[0]
       lowercase__:   Any  			=   numpy.arange(__lowerCamelCase     ) * num_classes
       lowercase__:   Optional[int]  			=   numpy.zeros((num_labels, num_classes)     )
       lowercase__:   List[Any]  			=   1
       return labels_one_hot




@deprecated(__lowerCamelCase			,							'Please use tf.data to implement this functionality.'     )
def    snake_case_     (  snake_case			,							snake_case=False			,							snake_case=10     )  ->				Tuple:
       print('Extracting'			,							f.name     )
       with gzip.GzipFile(fileobj=__lowerCamelCase     ) as bytestream:
              lowercase__:   List[Any]  			=   _readaa(__lowerCamelCase     )
              if magic != 20_49:
                     raise ValueError(
                         'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)     )
              lowercase__:   List[str]  			=   _readaa(__lowerCamelCase     )
              lowercase__:   Tuple  			=   bytestream.read(__lowerCamelCase     )
              lowercase__:   int  			=   numpy.frombuffer(__lowerCamelCase			,							dtype=numpy.uinta     )
              if one_hot:
                     return _dense_to_one_hot(__lowerCamelCase			,							__lowerCamelCase     )
              return labels





class   __a			:
 @deprecated(
     lowerCAmelCase__				, 'Please use alternatives such as official/mnist/_DataSet.py'
     ' from tensorflow/models.'				, )
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__=False				, lowerCAmelCase__=False				, lowerCAmelCase__=dtypes.floataa				, lowerCAmelCase__=True				, lowerCAmelCase__=None				, )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   random_seed.get_seed(lowerCAmelCase__ )
        # 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(lowerCAmelCase__ ).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__:   List[str]  			=   10_000
               lowercase__:   int  			=   one_hot
        else:
               assert (
                   images.shape[0] == labels.shape[0]
               ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
               lowercase__:   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
                      lowercase__:   Optional[int]  			=   images.reshape(
                          images.shape[0]				, images.shape[1] * images.shape[2] )
               if dtype == dtypes.floataa:
                      # Convert from [0, 255] -> [0.0, 1.0].
                      lowercase__:   Any  			=   images.astype(numpy.floataa )
                      lowercase__:   Optional[int]  			=   numpy.multiply(lowerCAmelCase__				, 1.0 / 255.0 )
        lowercase__:   Tuple  			=   images
        lowercase__:   Any  			=   labels
        lowercase__:   Dict  			=   0
        lowercase__:   Optional[Any]  			=   0
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return self._images
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        return self._labels
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        return self._num_examples
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        return self._epochs_completed
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__=False				, lowerCAmelCase__=True )					->				Dict:




        '''simple docstring'''




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





@deprecated(__lowerCamelCase			,							'Please write your own downloading logic.'     )
def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				List[str]:
       if not gfile.Exists(__lowerCamelCase     ):
              gfile.MakeDirs(__lowerCamelCase     )
       lowercase__:   Dict  			=   os.path.join(__lowerCamelCase			,							__lowerCamelCase     )
       if not gfile.Exists(__lowerCamelCase     ):
              urllib.request.urlretrieve(__lowerCamelCase			,							__lowerCamelCase     )  # noqa: S310
              with gfile.GFile(__lowerCamelCase     ) as f:
                     lowercase__:   List[str]  			=   f.size()
              print('Successfully downloaded'			,							__lowerCamelCase			,							__lowerCamelCase			,							'bytes.'     )
       return filepath




@deprecated(
    __lowerCamelCase			,							'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'     )
def    snake_case_     (  snake_case			,							snake_case=False			,							snake_case=False			,							snake_case=dtypes.floataa			,							snake_case=True			,							snake_case=50_00			,							snake_case=None			,							snake_case=DEFAULT_SOURCE_URL			,							)  ->				Optional[int]:
       if fake_data:

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

              lowercase__:   int  			=   fake()
              lowercase__:   Optional[Any]  			=   fake()
              lowercase__:   Tuple  			=   fake()
              return _Datasets(train=__lowerCamelCase			,							validation=__lowerCamelCase			,							test=__lowerCamelCase     )

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

       lowercase__:   Optional[int]  			=   "train-images-idx3-ubyte.gz"
       lowercase__:   Optional[int]  			=   "train-labels-idx1-ubyte.gz"
       lowercase__:   Optional[Any]  			=   "t10k-images-idx3-ubyte.gz"
       lowercase__:   int  			=   "t10k-labels-idx1-ubyte.gz"

       lowercase__:   Tuple  			=   _maybe_download(
           __lowerCamelCase			,							__lowerCamelCase			,							source_url + train_images_file     )
       with gfile.Open(__lowerCamelCase			,							'rb'     ) as f:
              lowercase__:   Any  			=   _extract_images(__lowerCamelCase     )

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

       lowercase__:   Any  			=   _maybe_download(
           __lowerCamelCase			,							__lowerCamelCase			,							source_url + test_images_file     )
       with gfile.Open(__lowerCamelCase			,							'rb'     ) as f:
              lowercase__:   Union[str, Any]  			=   _extract_images(__lowerCamelCase     )

       lowercase__:   int  			=   _maybe_download(
           __lowerCamelCase			,							__lowerCamelCase			,							source_url + test_labels_file     )
       with gfile.Open(__lowerCamelCase			,							'rb'     ) as f:
              lowercase__:   Union[str, Any]  			=   _extract_labels(__lowerCamelCase			,							one_hot=__lowerCamelCase     )

       if not 0 <= validation_size <= len(__lowerCamelCase     ):
              lowercase__:   Union[str, Any]  			=   (
                  "Validation size should be between 0 and "
                  f'{len(__lowerCamelCase     )}. Received: {validation_size}.'
              )
              raise ValueError(__lowerCamelCase     )

       lowercase__:   int  			=   train_images[:validation_size]
       lowercase__:   Optional[int]  			=   train_labels[:validation_size]
       lowercase__:   List[str]  			=   train_images[validation_size:]
       lowercase__:   int  			=   train_labels[validation_size:]

       lowercase__:   Dict  			=   {"dtype": dtype, "reshape": reshape, "seed": seed}

       lowercase__:   List[str]  			=   _DataSet(__lowerCamelCase			,							__lowerCamelCase			,							**__lowerCamelCase     )
       lowercase__:   str  			=   _DataSet(__lowerCamelCase			,							__lowerCamelCase			,							**__lowerCamelCase     )
       lowercase__:   List[str]  			=   _DataSet(__lowerCamelCase			,							__lowerCamelCase			,							**__lowerCamelCase     )

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









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

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCAmelCase					:     str												='''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   lowercase_			:Tuple    			= numpy.dtype(numpy.uintaa ).newbyteorder(">" )
   return numpy.frombuffer(bytestream.read(4 )	,dtype=__lowerCamelCase )[0]
@deprecated(__lowerCamelCase	,"Please use tf.data to implement this functionality." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:Union[str, Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_51:
         raise ValueError(
             "Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:int    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= _readaa(__lowerCamelCase )
      lowercase_			:Optional[Any]    			= bytestream.read(rows * cols * num_images )
      lowercase_			:List[str]    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      lowercase_			:Dict    			= data.reshape(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,1 )
      return data
@deprecated(__lowerCamelCase	,"Please use tf.one_hot on tensors." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[Any]	,__lowerCamelCase					:					Tuple ):
   lowercase_			:int    			= labels_dense.shape[0]
   lowercase_			:Any    			= numpy.arange(__lowerCamelCase ) * num_classes
   lowercase_			:Optional[int]    			= numpy.zeros((num_labels, num_classes) )
   lowercase_			:List[Any]    			= 1
   return labels_one_hot
@deprecated(__lowerCamelCase	,"Please use tf.data to implement this functionality." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Tuple	,__lowerCamelCase					:					Optional[Any]=False	,__lowerCamelCase					:					List[str]=10 ):
   print("Extracting"	,f.name )
   with gzip.GzipFile(fileobj=__lowerCamelCase ) as bytestream:
      lowercase_			:List[Any]    			= _readaa(__lowerCamelCase )
      if magic != 20_49:
         raise ValueError(
             "Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
      lowercase_			:List[str]    			= _readaa(__lowerCamelCase )
      lowercase_			:Tuple    			= bytestream.read(__lowerCamelCase )
      lowercase_			:int    			= numpy.frombuffer(__lowerCamelCase	,dtype=numpy.uinta )
      if one_hot:
         return _dense_to_one_hot(__lowerCamelCase	,__lowerCamelCase )
      return labels
class       a_   :





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





        """simple docstring"""




        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_			:List[str]    			= 10_000
           lowercase_			:int    			= one_hot
        else:
           assert (
               images.shape[0] == labels.shape[0]
           ), F'images.shape: {images.shape} labels.shape: {labels.shape}'
           lowercase_			: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
              lowercase_			:Optional[int]    			= images.reshape(
                  images.shape[0]	,      images.shape[1] * images.shape[2]						)
           if dtype == dtypes.floataa:
              # Convert from [0, 255] -> [0.0, 1.0].
              lowercase_			:Any    			= images.astype(numpy.floataa						)
              lowercase_			:Optional[int]    			= numpy.multiply(lowercase	,      1.0 / 2_55.0						)
        lowercase_			:Tuple    			= images
        lowercase_			:Any    			= labels
        lowercase_			:Dict    			= 0
        lowercase_			:Optional[Any]    			= 0





     @property
     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        return self._images





     @property
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        return self._labels





     @property
     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        return self._num_examples





     @property
     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        return self._epochs_completed







     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					int	,      lowercase		:					List[Any]=False	,      lowercase		:					int=True						):





        """simple docstring"""




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







@deprecated(__lowerCamelCase	,"Please write your own downloading logic." )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					Any	,__lowerCamelCase					:					Dict ):
   if not gfile.Exists(__lowerCamelCase ):
      gfile.MakeDirs(__lowerCamelCase )
   lowercase_			:Dict    			= os.path.join(__lowerCamelCase	,__lowerCamelCase )
   if not gfile.Exists(__lowerCamelCase ):
      urllib.request.urlretrieve(__lowerCamelCase	,__lowerCamelCase )  # noqa: S310
      with gfile.GFile(__lowerCamelCase ) as f:
         lowercase_			:List[str]    			= f.size()
      print("Successfully downloaded"	,__lowerCamelCase	,__lowerCamelCase	,"bytes." )
   return filepath


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

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

      lowercase_			:int    			= fake()
      lowercase_			:Optional[Any]    			= fake()
      lowercase_			:Tuple    			= fake()
      return _Datasets(train=__lowerCamelCase	,validation=__lowerCamelCase	,test=__lowerCamelCase )

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

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

   lowercase_			:Tuple    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + train_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Any    			= _extract_images(__lowerCamelCase )

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

   lowercase_			:Any    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_images_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_images(__lowerCamelCase )

   lowercase_			:int    			= _maybe_download(
       __lowerCamelCase	,__lowerCamelCase	,source_url + test_labels_file )
   with gfile.Open(__lowerCamelCase	,"rb" ) as f:
      lowercase_			:Union[str, Any]    			= _extract_labels(__lowerCamelCase	,one_hot=__lowerCamelCase )

   if not 0 <= validation_size <= len(__lowerCamelCase ):
      lowercase_			:Union[str, Any]    			= (
          "Validation size should be between 0 and "
          F'{len(__lowerCamelCase )}. Received: {validation_size}.'
      )
      raise ValueError(__lowerCamelCase )

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

   lowercase_			:Dict    			= {"dtype": dtype, "reshape": reshape, "seed": seed}

   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:str    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )
   lowercase_			:List[str]    			= _DataSet(__lowerCamelCase	,__lowerCamelCase	,**__lowerCamelCase )

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


