


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_A		       =						{'LayoutLMv2Config', 'LayoutLMv3Config'}






@is_pipeline_test
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple      =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    UpperCAmelCase__						:		Any      =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        UpperCAmelCase__						:		Tuple      =  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        UpperCAmelCase__						:		List[str]      =  {
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }






    def _a					(			self					,   A_					,   A_					,   A_    )  ->       List[str]:
         __UpperCamelCase    							=ZeroShotClassificationPipeline(
             model=A_					,   tokenizer=A_					,   candidate_labels=['polics', 'health']    )
         return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]






    def _a					(			self					,   A_					,   A_    )  ->       Any:
         __UpperCamelCase    							=classifier('Who are you voting for in 2020?'					,   candidate_labels='politics'    )
         self.assertEqual(A_					,   {'sequence': ANY(A_    ), 'labels': [ANY(A_    )], 'scores': [ANY(A_    )]}    )

         # No kwarg
         __UpperCamelCase    							=classifier('Who are you voting for in 2020?'					,   ['politics']    )
         self.assertEqual(A_					,   {'sequence': ANY(A_    ), 'labels': [ANY(A_    )], 'scores': [ANY(A_    )]}    )

         __UpperCamelCase    							=classifier('Who are you voting for in 2020?'					,   candidate_labels=['politics']    )
         self.assertEqual(A_					,   {'sequence': ANY(A_    ), 'labels': [ANY(A_    )], 'scores': [ANY(A_    )]}    )

         __UpperCamelCase    							=classifier('Who are you voting for in 2020?'					,   candidate_labels='politics, public health'    )
         self.assertEqual(
             A_					,   {'sequence': ANY(A_    ), 'labels': [ANY(A_    ), ANY(A_    )], 'scores': [ANY(A_    ), ANY(A_    )]}    )
         self.assertAlmostEqual(sum(nested_simplify(outputs['scores']    )    )					,   1.0    )

         __UpperCamelCase    							=classifier('Who are you voting for in 2020?'					,   candidate_labels=['politics', 'public health']    )
         self.assertEqual(
             A_					,   {'sequence': ANY(A_    ), 'labels': [ANY(A_    ), ANY(A_    )], 'scores': [ANY(A_    ), ANY(A_    )]}    )
         self.assertAlmostEqual(sum(nested_simplify(outputs['scores']    )    )					,   1.0    )

         __UpperCamelCase    							=classifier(
             'Who are you voting for in 2020?'					,   candidate_labels='politics'					,   hypothesis_template='This text is about {}'    )
         self.assertEqual(A_					,   {'sequence': ANY(A_    ), 'labels': [ANY(A_    )], 'scores': [ANY(A_    )]}    )

         # https://github.com/huggingface/transformers/issues/13846
         __UpperCamelCase    							=classifier(['I am happy']					,   ['positive', 'negative']    )
         self.assertEqual(
             A_					,   [
                 {'sequence': ANY(A_    ), 'labels': [ANY(A_    ), ANY(A_    )], 'scores': [ANY(A_    ), ANY(A_    )]}
                 for i in range(1    )
             ]					,   )
         __UpperCamelCase    							=classifier(['I am happy', 'I am sad']					,   ['positive', 'negative']    )
         self.assertEqual(
             A_					,   [
                 {'sequence': ANY(A_    ), 'labels': [ANY(A_    ), ANY(A_    )], 'scores': [ANY(A_    ), ANY(A_    )]}
                 for i in range(2    )
             ]					,   )

         with self.assertRaises(A_    ):
              classifier(''					,   candidate_labels='politics'    )

         with self.assertRaises(A_    ):
              classifier(A_					,   candidate_labels='politics'    )

         with self.assertRaises(A_    ):
              classifier('Who are you voting for in 2020?'					,   candidate_labels=''    )

         with self.assertRaises(A_    ):
              classifier('Who are you voting for in 2020?'					,   candidate_labels=A_    )

         with self.assertRaises(A_    ):
              classifier(
                  'Who are you voting for in 2020?'					,   candidate_labels='politics'					,   hypothesis_template='Not formatting template'					,   )

         with self.assertRaises(A_    ):
              classifier(
                  'Who are you voting for in 2020?'					,   candidate_labels='politics'					,   hypothesis_template=A_					,   )

         self.run_entailment_id(A_    )






    def _a					(			self					,   A_    )  ->       Dict:
         __UpperCamelCase    							=zero_shot_classifier.model.config
         __UpperCamelCase    							=config.labelaid
         __UpperCamelCase    							=zero_shot_classifier.entailment_id

         __UpperCamelCase    							={'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
         self.assertEqual(zero_shot_classifier.entailment_id					,   -1    )

         __UpperCamelCase    							={'entailment': 0, 'neutral': 1, 'contradiction': 2}
         self.assertEqual(zero_shot_classifier.entailment_id					,   0    )

         __UpperCamelCase    							={'ENTAIL': 0, 'NON-ENTAIL': 1}
         self.assertEqual(zero_shot_classifier.entailment_id					,   0    )

         __UpperCamelCase    							={'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
         self.assertEqual(zero_shot_classifier.entailment_id					,   2    )

         __UpperCamelCase    							=original_labelaid
         self.assertEqual(A_					,   zero_shot_classifier.entailment_id    )






    @require_torch
    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=pipeline(
             'zero-shot-classification'					,   model='sshleifer/tiny-distilbert-base-cased-distilled-squad'					,   framework='pt'					,   )
         # There was a regression in 4.10 for this
         # Adding a test so we don't make the mistake again.
         # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
         zero_shot_classifier(
             'Who are you voting for in 2020?' * 100					,   candidate_labels=['politics', 'public health', 'science']    )






    @require_torch
    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=pipeline(
             'zero-shot-classification'					,   model='sshleifer/tiny-distilbert-base-cased-distilled-squad'					,   framework='pt'					,   )
         __UpperCamelCase    							=zero_shot_classifier(
             'Who are you voting for in 2020?'					,   candidate_labels=['politics', 'public health', 'science']    )

         self.assertEqual(
             nested_simplify(A_    )					,   {
                 'sequence': 'Who are you voting for in 2020?',
                 'labels': ['science', 'public health', 'politics'],
                 'scores': [0.333, 0.333, 0.333],
             }					,   )






    @require_tf
    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=pipeline(
             'zero-shot-classification'					,   model='sshleifer/tiny-distilbert-base-cased-distilled-squad'					,   framework='tf'					,   )
         __UpperCamelCase    							=zero_shot_classifier(
             'Who are you voting for in 2020?'					,   candidate_labels=['politics', 'public health', 'science']    )

         self.assertEqual(
             nested_simplify(A_    )					,   {
                 'sequence': 'Who are you voting for in 2020?',
                 'labels': ['science', 'public health', 'politics'],
                 'scores': [0.333, 0.333, 0.333],
             }					,   )






    @slow
    @require_torch
    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=pipeline('zero-shot-classification'					,   model='roberta-large-mnli'					,   framework='pt'    )
         __UpperCamelCase    							=zero_shot_classifier(
             'Who are you voting for in 2020?'					,   candidate_labels=['politics', 'public health', 'science']    )

         self.assertEqual(
             nested_simplify(A_    )					,   {
                 'sequence': 'Who are you voting for in 2020?',
                 'labels': ['politics', 'public health', 'science'],
                 'scores': [0.976, 0.015, 0.009],
             }					,   )
         __UpperCamelCase    							=zero_shot_classifier(
             'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
             ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
             ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
             ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
             ' machine translation tasks show these models to be superior in quality while being more parallelizable'
             ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
             ' English-to-German translation task, improving over the existing best results, including ensembles by'
             ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
             ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
             ' fraction of the training costs of the best models from the literature. We show that the Transformer'
             ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
             ' large and limited training data.'					,   candidate_labels=['machine learning', 'statistics', 'translation', 'vision']					,   multi_label=A_					,   )
         self.assertEqual(
             nested_simplify(A_    )					,   {
                 'sequence': (
                     'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                     ' networks in an encoder-decoder configuration. The best performing models also connect the'
                     ' encoder and decoder through an attention mechanism. We propose a new simple network'
                     ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                     ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                     ' superior in quality while being more parallelizable and requiring significantly less time to'
                     ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                     ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                     ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                     ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                     ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                     ' other tasks by applying it successfully to English constituency parsing both with large and'
                     ' limited training data.'
                 ),
                 'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                 'scores': [0.817, 0.713, 0.018, 0.018],
             }					,   )






    @slow
    @require_tf
    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=pipeline('zero-shot-classification'					,   model='roberta-large-mnli'					,   framework='tf'    )
         __UpperCamelCase    							=zero_shot_classifier(
             'Who are you voting for in 2020?'					,   candidate_labels=['politics', 'public health', 'science']    )

         self.assertEqual(
             nested_simplify(A_    )					,   {
                 'sequence': 'Who are you voting for in 2020?',
                 'labels': ['politics', 'public health', 'science'],
                 'scores': [0.976, 0.015, 0.009],
             }					,   )
         __UpperCamelCase    							=zero_shot_classifier(
             'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
             ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
             ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
             ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
             ' machine translation tasks show these models to be superior in quality while being more parallelizable'
             ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
             ' English-to-German translation task, improving over the existing best results, including ensembles by'
             ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
             ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
             ' fraction of the training costs of the best models from the literature. We show that the Transformer'
             ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
             ' large and limited training data.'					,   candidate_labels=['machine learning', 'statistics', 'translation', 'vision']					,   multi_label=A_					,   )
         self.assertEqual(
             nested_simplify(A_    )					,   {
                 'sequence': (
                     'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                     ' networks in an encoder-decoder configuration. The best performing models also connect the'
                     ' encoder and decoder through an attention mechanism. We propose a new simple network'
                     ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                     ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                     ' superior in quality while being more parallelizable and requiring significantly less time to'
                     ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                     ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                     ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                     ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                     ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                     ' other tasks by applying it successfully to English constituency parsing both with large and'
                     ' limited training data.'
                 ),
                 'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                 'scores': [0.817, 0.713, 0.018, 0.018],
             }					,   )












'''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 )


