


import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       DebertaVaForMaskedLM,
       DebertaVaForMultipleChoice,
       DebertaVaForQuestionAnswering,
       DebertaVaForSequenceClassification,
       DebertaVaForTokenClassification,
       DebertaVaModel,
   )
   from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_=13					,   A_=7					,   A_=True					,   A_=True					,   A_=True					,   A_=True					,   A_=99					,   A_=32					,   A_=5					,   A_=4					,   A_=37					,   A_="gelu"					,   A_=0.1					,   A_=0.1					,   A_=512					,   A_=16					,   A_=2					,   A_=0.02					,   A_=False					,   A_=True					,   A_="None"					,   A_=3					,   A_=4					,   A_=None					,   )  ->       Optional[int]:
         __UpperCamelCase    							=parent
         __UpperCamelCase    							=batch_size
         __UpperCamelCase    							=seq_length
         __UpperCamelCase    							=is_training
         __UpperCamelCase    							=use_input_mask
         __UpperCamelCase    							=use_token_type_ids
         __UpperCamelCase    							=use_labels
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=hidden_size
         __UpperCamelCase    							=num_hidden_layers
         __UpperCamelCase    							=num_attention_heads
         __UpperCamelCase    							=intermediate_size
         __UpperCamelCase    							=hidden_act
         __UpperCamelCase    							=hidden_dropout_prob
         __UpperCamelCase    							=attention_probs_dropout_prob
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=type_vocab_size
         __UpperCamelCase    							=type_sequence_label_size
         __UpperCamelCase    							=initializer_range
         __UpperCamelCase    							=num_labels
         __UpperCamelCase    							=num_choices
         __UpperCamelCase    							=relative_attention
         __UpperCamelCase    							=position_biased_input
         __UpperCamelCase    							=pos_att_type
         __UpperCamelCase    							=scope






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size    )

         __UpperCamelCase    							=None
         if self.use_input_mask:
              __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2    )

         __UpperCamelCase    							=None
         if self.use_token_type_ids:
              __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.type_vocab_size    )

         __UpperCamelCase    							=None
         __UpperCamelCase    							=None
         __UpperCamelCase    							=None
         if self.use_labels:
              __UpperCamelCase    							=ids_tensor([self.batch_size]					,   self.type_sequence_label_size    )
              __UpperCamelCase    							=ids_tensor([self.batch_size, self.seq_length]					,   self.num_labels    )
              __UpperCamelCase    							=ids_tensor([self.batch_size]					,   self.num_choices    )

         __UpperCamelCase    							=self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






    def _a					(			self    )  ->       str:
         return DebertaVaConfig(
             vocab_size=self.vocab_size					,   hidden_size=self.hidden_size					,   num_hidden_layers=self.num_hidden_layers					,   num_attention_heads=self.num_attention_heads					,   intermediate_size=self.intermediate_size					,   hidden_act=self.hidden_act					,   hidden_dropout_prob=self.hidden_dropout_prob					,   attention_probs_dropout_prob=self.attention_probs_dropout_prob					,   max_position_embeddings=self.max_position_embeddings					,   type_vocab_size=self.type_vocab_size					,   initializer_range=self.initializer_range					,   relative_attention=self.relative_attention					,   position_biased_input=self.position_biased_input					,   pos_att_type=self.pos_att_type					,   )






    def _a					(			self					,   A_    )  ->       Tuple:
         self.parent.assertListEqual(list(result.loss.size()    )					,   []    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Tuple:
         __UpperCamelCase    							=DebertaVaModel(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   token_type_ids=A_    )[0]
         __UpperCamelCase    							=model(A_					,   token_type_ids=A_    )[0]
         __UpperCamelCase    							=model(A_    )[0]

         self.parent.assertListEqual(list(sequence_output.size()    )					,   [self.batch_size, self.seq_length, self.hidden_size]    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Optional[Any]:
         __UpperCamelCase    							=DebertaVaForMaskedLM(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   token_type_ids=A_					,   labels=A_    )

         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.vocab_size)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Dict:
         __UpperCamelCase    							=self.num_labels
         __UpperCamelCase    							=DebertaVaForSequenceClassification(A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   token_type_ids=A_					,   labels=A_    )
         self.parent.assertListEqual(list(result.logits.size()    )					,   [self.batch_size, self.num_labels]    )
         self.check_loss_output(A_    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=self.num_labels
         __UpperCamelCase    							=DebertaVaForTokenClassification(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(A_					,   attention_mask=A_					,   token_type_ids=A_					,   labels=A_    )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.seq_length, self.num_labels)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       Any:
         __UpperCamelCase    							=DebertaVaForQuestionAnswering(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=model(
             A_					,   attention_mask=A_					,   token_type_ids=A_					,   start_positions=A_					,   end_positions=A_					,   )
         self.parent.assertEqual(result.start_logits.shape					,   (self.batch_size, self.seq_length)    )
         self.parent.assertEqual(result.end_logits.shape					,   (self.batch_size, self.seq_length)    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int:
         __UpperCamelCase    							=DebertaVaForMultipleChoice(config=A_    )
         model.to(A_    )
         model.eval()
         __UpperCamelCase    							=input_ids.unsqueeze(1    ).expand(-1					,   self.num_choices					,   -1    ).contiguous()
         __UpperCamelCase    							=token_type_ids.unsqueeze(1    ).expand(-1					,   self.num_choices					,   -1    ).contiguous()
         __UpperCamelCase    							=input_mask.unsqueeze(1    ).expand(-1					,   self.num_choices					,   -1    ).contiguous()
         __UpperCamelCase    							=model(
             A_					,   attention_mask=A_					,   token_type_ids=A_					,   labels=A_					,   )
         self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_choices)    )






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=self.prepare_config_and_inputs()
         (
             (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      (
             __UpperCamelCase
         )							,      
         )    							=config_and_inputs
         __UpperCamelCase    							={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict






@require_torch
class     UpperCAmelCase__       (					A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  (
        (
            DebertaVaModel,
            DebertaVaForMaskedLM,
            DebertaVaForSequenceClassification,
            DebertaVaForTokenClassification,
            DebertaVaForQuestionAnswering,
            DebertaVaForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__						:		Union[str, Any]      =  (
        {
            "feature-extraction": DebertaVaModel,
            "fill-mask": DebertaVaForMaskedLM,
            "question-answering": DebertaVaForQuestionAnswering,
            "text-classification": DebertaVaForSequenceClassification,
            "token-classification": DebertaVaForTokenClassification,
            "zero-shot": DebertaVaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__						:		Tuple      =  True
    UpperCAmelCase__						:		List[Any]      =  False
    UpperCAmelCase__						:		int      =  False
    UpperCAmelCase__						:		Tuple      =  False
    UpperCAmelCase__						:		List[str]      =  False






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=DebertaVaModelTester(self    )
         __UpperCamelCase    							=ConfigTester(self					,   config_class=A_					,   hidden_size=37    )






    def _a					(			self    )  ->       Dict:
         self.config_tester.run_common_tests()






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*A_    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*A_    )






    def _a					(			self    )  ->       Any:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*A_    )






    def _a					(			self    )  ->       Optional[int]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*A_    )






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*A_    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_multiple_choice(*A_    )






    @slow
    def _a					(			self    )  ->       Tuple:
         for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase    							=DebertaVaModel.from_pretrained(A_    )
              self.assertIsNotNone(A_    )






@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    @unittest.skip(reason='Model not available yet'    )
    def _a					(			self    )  ->       int:
         pass






    @slow
    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'    )

         __UpperCamelCase    							=torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]    )
         __UpperCamelCase    							=torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
         with torch.no_grad():
              __UpperCamelCase    							=model(A_					,   attention_mask=A_    )[0]
         # compare the actual values for a slice.
         __UpperCamelCase    							=torch.tensor(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]    )
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,   A_					,   atol=1E-4    )					,   f'{output[:, 1:4, 1:4]}'    )









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

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_51:
               raise ValueError(
                   'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(rows * cols * num_images				)
          __UpperCamelCase    							=numpy.frombuffer(SCREAMING_SNAKE_CASE__			,     dtype=numpy.uinta				)
          __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=labels_dense.shape[0]
     __UpperCamelCase    							=numpy.arange(SCREAMING_SNAKE_CASE__				) * num_classes
     __UpperCamelCase    							=numpy.zeros((num_labels, num_classes)				)
     __UpperCamelCase    							=1
     return labels_one_hot

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please use tf.data to implement this functionality.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					Dict=False			,     SCREAMING_SNAKE_CASE__			:					str=10				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_49:
               raise ValueError(
                   'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=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     UpperCAmelCase__       :



    """simple docstring"""






    @deprecated(
        A_					,   'Please use alternatives such as official/mnist/_DataSet.py'
        ' from tensorflow/models.'					,   )
    def __init__(			self					,   A_					,   A_					,   A_=False					,   A_=False					,   A_=dtypes.floataa					,   A_=True					,   A_=None					,   )  ->       Optional[int]:
         __UpperCamelCase							,      __UpperCamelCase    							=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    )
         __UpperCamelCase    							=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:
              __UpperCamelCase    							=10000
              __UpperCamelCase    							=one_hot
         else:
              assert (
                  images.shape[0] == labels.shape[0]
              ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
              __UpperCamelCase    							=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
                   __UpperCamelCase    							=images.reshape(
                       images.shape[0]					,   images.shape[1] * images.shape[2]    )
              if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   __UpperCamelCase    							=images.astype(numpy.floataa    )
                   __UpperCamelCase    							=numpy.multiply(A_					,   1.0 / 255.0    )
         __UpperCamelCase    							=images
         __UpperCamelCase    							=labels
         __UpperCamelCase    							=0
         __UpperCamelCase    							=0






    @property
    def _a					(			self    )  ->       Tuple:
         return self._images






    @property
    def _a					(			self    )  ->       Union[str, Any]:
         return self._labels






    @property
    def _a					(			self    )  ->       Optional[Any]:
         return self._num_examples






    @property
    def _a					(			self    )  ->       List[str]:
         return self._epochs_completed






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

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please write your own downloading logic.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					str				):
     if not gfile.Exists(SCREAMING_SNAKE_CASE__				):
          gfile.MakeDirs(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=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:
               __UpperCamelCase    							=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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					int=False			,     SCREAMING_SNAKE_CASE__			:					str=False			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]=dtypes.floataa			,     SCREAMING_SNAKE_CASE__			:					Optional[int]=True			,     SCREAMING_SNAKE_CASE__			:					str=50_00			,     SCREAMING_SNAKE_CASE__			:					List[Any]=None			,     SCREAMING_SNAKE_CASE__			:					str=DEFAULT_SOURCE_URL			,     ):
     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__				)

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

     if not source_url:  # empty string check
          __UpperCamelCase    							=DEFAULT_SOURCE_URL

     __UpperCamelCase    							='train-images-idx3-ubyte.gz'
     __UpperCamelCase    							='train-labels-idx1-ubyte.gz'
     __UpperCamelCase    							='t10k-images-idx3-ubyte.gz'
     __UpperCamelCase    							='t10k-labels-idx1-ubyte.gz'

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + train_images_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_images(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + train_labels_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_labels(SCREAMING_SNAKE_CASE__			,     one_hot=SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + test_images_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_images(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + test_labels_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_labels(SCREAMING_SNAKE_CASE__			,     one_hot=SCREAMING_SNAKE_CASE__				)

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

     __UpperCamelCase    							=train_images[:validation_size]
     __UpperCamelCase    							=train_labels[:validation_size]
     __UpperCamelCase    							=train_images[validation_size:]
     __UpperCamelCase    							=train_labels[validation_size:]

     __UpperCamelCase    							={'dtype': dtype, 'reshape': reshape, 'seed': seed}

     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)

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





