code: 
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import DebertaConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import tensorflow as tf
  
   from transformers import (
       TFDebertaForMaskedLM,
       TFDebertaForQuestionAnswering,
       TFDebertaForSequenceClassification,
       TFDebertaForTokenClassification,
       TFDebertaModel,
   )
  class        UpperCAmelCase__  :
  
  
  
   def __init__(  self		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=True		,	lowercase=9_9		,	lowercase=3_2		,	lowercase=2		,	lowercase=4		,	lowercase=3_7		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=5_1_2		,	lowercase=1_6		,	lowercase=2		,	lowercase=0.02		,	lowercase=3		,	lowercase=4		,	lowercase=None		,	)  ->							List[str]:
    __UpperCamelCase				      =  parent
    __UpperCamelCase				      =  1_3
    __UpperCamelCase				      =  7
    __UpperCamelCase				      =  True
    __UpperCamelCase				      =  True
    __UpperCamelCase				      =  True
    __UpperCamelCase				      =  True
    __UpperCamelCase				      =  9_9
    __UpperCamelCase				      =  3_2
    __UpperCamelCase				      =  2
    __UpperCamelCase				      =  4
    __UpperCamelCase				      =  3_7
    __UpperCamelCase				      =  """gelu"""
    __UpperCamelCase				      =  0.1
    __UpperCamelCase				      =  0.1
    __UpperCamelCase				      =  5_1_2
    __UpperCamelCase				      =  1_6
    __UpperCamelCase				      =  False
    __UpperCamelCase				      =  -1
    __UpperCamelCase				      =  True
    __UpperCamelCase				      =  2
    __UpperCamelCase				      =  0.02
    __UpperCamelCase				      =  3
    __UpperCamelCase				      =  4
    __UpperCamelCase				      =  None
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    __UpperCamelCase				      =  ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)
  
    __UpperCamelCase				      =  None
    if self.use_input_mask:
     __UpperCamelCase				      =  random_attention_mask([self.batch_size, self.seq_length]			)
  
    __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				      =  DebertaConfig(
        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		,	relative_attention=self.relative_attention		,	max_relative_positions=self.max_relative_positions		,	position_biased_input=self.position_biased_input		,	initializer_range=self.initializer_range		,	return_dict=lowercase		,	)
  
    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							str:
    __UpperCamelCase				      =  TFDebertaModel(config=lowercase			)
    __UpperCamelCase				      =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  
    __UpperCamelCase				      =  [input_ids, input_mask]
    __UpperCamelCase				      =  model(lowercase			)
  
    __UpperCamelCase				      =  model(lowercase			)
  
    self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							List[str]:
    __UpperCamelCase				      =  TFDebertaForMaskedLM(config=lowercase			)
    __UpperCamelCase				      =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Optional[Any]:
    __UpperCamelCase				      =  self.num_labels
    __UpperCamelCase				      =  TFDebertaForSequenceClassification(config=lowercase			)
    __UpperCamelCase				      =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
  
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							Dict:
    __UpperCamelCase				      =  self.num_labels
    __UpperCamelCase				      =  TFDebertaForTokenClassification(config=lowercase			)
    __UpperCamelCase				      =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    __UpperCamelCase				      =  model(lowercase			)
    self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.seq_length, self.num_labels)			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase			)  ->							str:
    __UpperCamelCase				      =  TFDebertaForQuestionAnswering(config=lowercase			)
    __UpperCamelCase				      =  {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
  
    __UpperCamelCase				      =  model(lowercase			)
    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    __lowerCamelCase				(  self			)  ->							Dict:
    __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_tf
  class        UpperCAmelCase__  (    UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase):
   __SCREAMING_SNAKE_CASE					      =				(
       (
           TFDebertaModel,
           TFDebertaForMaskedLM,
           TFDebertaForQuestionAnswering,
           TFDebertaForSequenceClassification,
           TFDebertaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   __SCREAMING_SNAKE_CASE					      =				(
       {
           '''feature-extraction''': TFDebertaModel,
           '''fill-mask''': TFDebertaForMaskedLM,
           '''question-answering''': TFDebertaForQuestionAnswering,
           '''text-classification''': TFDebertaForSequenceClassification,
           '''token-classification''': TFDebertaForTokenClassification,
           '''zero-shot''': TFDebertaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
  
   __SCREAMING_SNAKE_CASE					      =				False
   __SCREAMING_SNAKE_CASE					      =				False
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  TFDebertaModelTester(self			)
    __UpperCamelCase				      =  ConfigTester(self		,	config_class=lowercase		,	hidden_size=3_7			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    self.config_tester.run_common_tests()
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							str:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
    __UpperCamelCase				      =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*lowercase			)
  
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Dict:
    __UpperCamelCase				      =  TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""			)
    self.assertIsNotNone(lowercase			)
  
  
  @require_tf
  class        UpperCAmelCase__  (    unittest.TestCase):
  
  
  
   @unittest.skip(reason="""Model not available yet"""			)
   def    __lowerCamelCase				(  self			)  ->							int:
    pass
  
  
  
  
   @slow
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    __UpperCamelCase				      =  TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""			)
    __UpperCamelCase				      =  tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
    __UpperCamelCase				      =  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
    __UpperCamelCase				      =  model(lowercase		,	attention_mask=lowercase			)[0]
  
    __UpperCamelCase				      =  tf.constant(
        [
            [
                [-0.59_855_896, -0.80_552_566, -0.8_462_135],
                [1.4_484_025, -0.93_483_794, -0.80_593_085],
                [0.3_122_741, 0.00_316_059, -1.4_131_377],
            ]
        ]			)
    tf.debugging.assert_near(output[:, 1:4, 1:4]		,	lowercase		,	atol=1E-4			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text.txt"""
   __UpperCamelCase				      =  textwrap.dedent(
       """\
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
  
          Second paragraph:
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
          """       )
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
   __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  
  
  @require_pil
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines()[0]
   __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
   __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
   assert pa_table.schema.field("""image"""       ).type == Image()()
   __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read()
   if sample_by == "line":
    __UpperCamelCase				      =  expected_content.splitlines()
   elif sample_by == "paragraph":
    __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
   elif sample_by == "document":
    __UpperCamelCase				      =  [expected_content]
   __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  