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 	a__				:
  
  
  
  
  
     '''simple docstring'''
  
     def __init__(     self       : int ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : int=13 ,						lowerCAmelCase_       : Dict=7 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : Dict=True ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=99 ,						lowerCAmelCase_       : Tuple=32 ,						lowerCAmelCase_       : List[str]=2 ,						lowerCAmelCase_       : List[Any]=4 ,						lowerCAmelCase_       : Optional[int]=37 ,						lowerCAmelCase_       : Dict="gelu" ,						lowerCAmelCase_       : Union[str, Any]=0.1 ,						lowerCAmelCase_       : List[str]=0.1 ,						lowerCAmelCase_       : int=512 ,						lowerCAmelCase_       : Optional[int]=16 ,						lowerCAmelCase_       : Any=2 ,						lowerCAmelCase_       : int=0.02 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : Tuple=4 ,						lowerCAmelCase_       : List[Any]=None ,						)			->		Optional[int]:
        __A=  parent
        __A=  13
        __A=  7
        __A=  True
        __A=  True
        __A=  True
        __A=  True
        __A=  99
        __A=  32
        __A=  2
        __A=  4
        __A=  37
        __A=  '''gelu'''
        __A=  0.1
        __A=  0.1
        __A=  512
        __A=  16
        __A=  False
        __A=  -1
        __A=  True
        __A=  2
        __A=  0.02
        __A=  3
        __A=  4
        __A=  None
     def 				lowerCAmelCase    (     self       : Any  )			->		List[str]:
        __A=  ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size  )
  
        __A=  None
        if self.use_input_mask:
           __A=  random_attention_mask([self.batch_size, self.seq_length]  )
  
        __A=  None
        if self.use_token_type_ids:
           __A=  ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size  )
  
        __A=  None
        __A=  None
        __A=  None
        if self.use_labels:
           __A=  ids_tensor([self.batch_size] ,						self.type_sequence_label_size  )
           __A=  ids_tensor([self.batch_size, self.seq_length] ,						self.num_labels  )
  
        __A=  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=UpperCamelCase__ ,						)
  
        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Optional[Any]  )			->		List[str]:
        __A=  TFDebertaModel(config=UpperCamelCase__  )
        __A=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  
        __A=  [input_ids, input_mask]
        __A=  model(UpperCamelCase__  )
  
        __A=  model(UpperCamelCase__  )
  
        self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)  )
     def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Any  )			->		int:
        __A=  TFDebertaForMaskedLM(config=UpperCamelCase__  )
        __A=  {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        __A=  model(UpperCamelCase__  )
        self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)  )
     def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Any  )			->		Optional[int]:
        __A=  self.num_labels
        __A=  TFDebertaForSequenceClassification(config=UpperCamelCase__  )
        __A=  {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
  
        __A=  model(UpperCamelCase__  )
        self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)  )
     def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Optional[int]  )			->		Optional[Any]:
        __A=  self.num_labels
        __A=  TFDebertaForTokenClassification(config=UpperCamelCase__  )
        __A=  {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        __A=  model(UpperCamelCase__  )
        self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)  )
     def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : List[str] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Dict  )			->		Optional[Any]:
        __A=  TFDebertaForQuestionAnswering(config=UpperCamelCase__  )
        __A=  {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
  
        __A=  model(UpperCamelCase__  )
        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       : List[Any]  )			->		str:
        __A=  self.prepare_config_and_inputs()
        (
            __A
        )=  config_and_inputs
        __A=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict
  
  
  @require_tf
  class 	a__				(				__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase      ):
  
  
  
  
  
     '''simple docstring'''
  
     A	:							List[Any]							= (
         (
             TFDebertaModel,
             TFDebertaForMaskedLM,
             TFDebertaForQuestionAnswering,
             TFDebertaForSequenceClassification,
             TFDebertaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     A	:							Union[str, Any]							= (
         {
             '''feature-extraction''': TFDebertaModel,
             '''fill-mask''': TFDebertaForMaskedLM,
             '''question-answering''': TFDebertaForQuestionAnswering,
             '''text-classification''': TFDebertaForSequenceClassification,
             '''token-classification''': TFDebertaForTokenClassification,
             '''zero-shot''': TFDebertaForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
  
     A	:							Tuple							= False
     A	:							Union[str, Any]							= False
     def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Optional[Any]:
        __A=  TFDebertaModelTester(self  )
        __A=  ConfigTester(self ,						config_class=UpperCamelCase__ ,						hidden_size=37  )
     def 				lowerCAmelCase    (     self       : Any  )			->		Dict:
        self.config_tester.run_common_tests()
     def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		List[Any]:
        __A=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCamelCase__  )
     def 				lowerCAmelCase    (     self       : str  )			->		str:
        __A=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__  )
     def 				lowerCAmelCase    (     self       : List[str]  )			->		Optional[int]:
        __A=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__  )
     def 				lowerCAmelCase    (     self       : List[str]  )			->		Union[str, Any]:
        __A=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__  )
     def 				lowerCAmelCase    (     self       : Tuple  )			->		Tuple:
        __A=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__  )
  
     @slow
     def 				lowerCAmelCase    (     self       : Tuple  )			->		Dict:
        __A=  TFDebertaModel.from_pretrained('kamalkraj/deberta-base'  )
        self.assertIsNotNone(UpperCamelCase__  )
  
  
  @require_tf
  class 	a__				(				unittest.TestCase      ):
  
  
  
  
  
     '''simple docstring'''
  
     @unittest.skip(reason='Model not available yet'  )
     def 				lowerCAmelCase    (     self       : Tuple  )			->		List[str]:
        pass
  
     @slow
     def 				lowerCAmelCase    (     self       : str  )			->		List[Any]:
        __A=  TFDebertaModel.from_pretrained('kamalkraj/deberta-base'  )
        __A=  tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]  )
        __A=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
        __A=  model(UpperCamelCase__ ,						attention_mask=UpperCamelCase__  )[0]
  
        __A=  tf.constant(
            [
                [
                    [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
                    [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
                    [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
                ]
            ]  )
        tf.debugging.assert_near(output[:, 1:4, 1:4] ,						UpperCamelCase__ ,						atol=1E-4  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''dandelin/vilt-b32-mlm''': '''https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= '''vilt'''
  			def __init__(     self       : str ,						lowerCAmelCase_       : Tuple=30_522 ,						lowerCAmelCase_       : List[Any]=2 ,						lowerCAmelCase_       : Optional[Any]=2 ,						lowerCAmelCase_       : Union[str, Any]=40 ,						lowerCAmelCase_       : str=768 ,						lowerCAmelCase_       : Tuple=12 ,						lowerCAmelCase_       : List[Any]=12 ,						lowerCAmelCase_       : int=3_072 ,						lowerCAmelCase_       : List[Any]="gelu" ,						lowerCAmelCase_       : int=0.0 ,						lowerCAmelCase_       : str=0.0 ,						lowerCAmelCase_       : Optional[Any]=0.02 ,						lowerCAmelCase_       : Union[str, Any]=1E-12 ,						lowerCAmelCase_       : Dict=384 ,						lowerCAmelCase_       : Optional[int]=32 ,						lowerCAmelCase_       : Optional[Any]=3 ,						lowerCAmelCase_       : str=True ,						lowerCAmelCase_       : List[Any]=-1 ,						lowerCAmelCase_       : Union[str, Any]=False ,						lowerCAmelCase_       : str=-1 ,						**lowerCAmelCase_       : Any ,						)			->		Tuple:
  						super().__init__(tie_word_embeddings=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  type_vocab_size
  						__A=  modality_type_vocab_size
  						__A=  max_position_embeddings
  
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  intermediate_size
  						__A=  hidden_act
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  initializer_range
  						__A=  layer_norm_eps
  
  						__A=  image_size
  						__A=  patch_size
  						__A=  num_channels
  						__A=  qkv_bias
  						__A=  max_image_length
  						__A=  num_images
  
  
  
  