code: 
  
  
  
  '''simple docstring'''
  
  
  
  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'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  a__						:			Optional[int]										=			{
      'configuration_bark': [
          'BARK_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'BarkCoarseConfig',
          'BarkConfig',
          'BarkFineConfig',
          'BarkSemanticConfig',
      ],
      'processing_bark': ['BarkProcessor'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a__						:			Any										=			[
       'BARK_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BarkFineModel',
       'BarkSemanticModel',
       'BarkCoarseModel',
       'BarkModel',
       'BarkPreTrainedModel',
       'BarkCausalModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_bark import (
       BARK_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BarkCoarseConfig,
       BarkConfig,
       BarkFineConfig,
       BarkSemanticConfig,
   )
   from .processing_bark import BarkProcessor
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_bark import (
        BARK_PRETRAINED_MODEL_ARCHIVE_LIST,
        BarkCausalModel,
        BarkCoarseModel,
        BarkFineModel,
        BarkModel,
        BarkPreTrainedModel,
        BarkSemanticModel,
    )
  
  else:
   import sys
  
   a__						:			Tuple										=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  