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 			a      :
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase=13	,     __UpperCamelCase=7	,     __UpperCamelCase=True	,     __UpperCamelCase=True	,     __UpperCamelCase=True	,     __UpperCamelCase=True	,     __UpperCamelCase=99	,     __UpperCamelCase=32	,     __UpperCamelCase=2	,     __UpperCamelCase=4	,     __UpperCamelCase=37	,     __UpperCamelCase="gelu"	,     __UpperCamelCase=0.1	,     __UpperCamelCase=0.1	,     __UpperCamelCase=5_12	,     __UpperCamelCase=16	,     __UpperCamelCase=2	,     __UpperCamelCase=0.02	,     __UpperCamelCase=3	,     __UpperCamelCase=4	,     __UpperCamelCase=None	,     )->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__     :							Any   				=parent
  						A__     :							Optional[Any]   				=13
  						A__     :							Dict   				=7
  						A__     :							Any   				=True
  						A__     :							Optional[Any]   				=True
  						A__     :							str   				=True
  						A__     :							Dict   				=True
  						A__     :							Dict   				=99
  						A__     :							Optional[Any]   				=32
  						A__     :							Optional[int]   				=2
  						A__     :							Optional[int]   				=4
  						A__     :							str   				=37
  						A__     :							Dict   				='''gelu'''
  						A__     :							Tuple   				=0.1
  						A__     :							Optional[int]   				=0.1
  						A__     :							Optional[Any]   				=5_12
  						A__     :							List[str]   				=16
  						A__     :							str   				=False
  						A__     :							str   				=-1
  						A__     :							Union[str, Any]   				=True
  						A__     :							int   				=2
  						A__     :							Tuple   				=0.02
  						A__     :							Optional[Any]   				=3
  						A__     :							Any   				=4
  						A__     :							Optional[Any]   				=None
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=ids_tensor([self.batch_size, self.seq_length]	,     self.vocab_size							)
  
  						A__     :							Dict   				=None
  						if self.use_input_mask:
  										A__     :							Union[str, Any]   				=random_attention_mask([self.batch_size, self.seq_length]							)
  
  						A__     :							str   				=None
  						if self.use_token_type_ids:
  										A__     :							int   				=ids_tensor([self.batch_size, self.seq_length]	,     self.type_vocab_size							)
  
  						A__     :							Optional[int]   				=None
  						A__     :							int   				=None
  						A__     :							List[str]   				=None
  						if self.use_labels:
  										A__     :							Any   				=ids_tensor([self.batch_size]	,     self.type_sequence_label_size							)
  										A__     :							str   				=ids_tensor([self.batch_size, self.seq_length]	,     self.num_labels							)
  
  						A__     :							List[Any]   				=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	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=TFDebertaModel(config=UpperCamelCase__							)
  						A__     :							Optional[Any]   				={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  
  						A__     :							Dict   				=[input_ids, input_mask]
  						A__     :							Optional[Any]   				=model(UpperCamelCase__							)
  
  						A__     :							List[str]   				=model(UpperCamelCase__							)
  
  						self.parent.assertEqual(result.last_hidden_state.shape	,     (self.batch_size, self.seq_length, self.hidden_size)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=TFDebertaForMaskedLM(config=UpperCamelCase__							)
  						A__     :							str   				={
  						    '''input_ids''': input_ids,
  						    '''attention_mask''': input_mask,
  						    '''token_type_ids''': token_type_ids,
  						}
  						A__     :							Any   				=model(UpperCamelCase__							)
  						self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.seq_length, self.vocab_size)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=self.num_labels
  						A__     :							Optional[int]   				=TFDebertaForSequenceClassification(config=UpperCamelCase__							)
  						A__     :							str   				={
  						    '''input_ids''': input_ids,
  						    '''attention_mask''': input_mask,
  						    '''token_type_ids''': token_type_ids,
  						}
  
  						A__     :							List[Any]   				=model(UpperCamelCase__							)
  						self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.num_labels)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=self.num_labels
  						A__     :							int   				=TFDebertaForTokenClassification(config=UpperCamelCase__							)
  						A__     :							List[Any]   				={
  						    '''input_ids''': input_ids,
  						    '''attention_mask''': input_mask,
  						    '''token_type_ids''': token_type_ids,
  						}
  						A__     :							int   				=model(UpperCamelCase__							)
  						self.parent.assertEqual(result.logits.shape	,     (self.batch_size, self.seq_length, self.num_labels)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=TFDebertaForQuestionAnswering(config=UpperCamelCase__							)
  						A__     :							Optional[Any]   				={
  						    '''input_ids''': input_ids,
  						    '''attention_mask''': input_mask,
  						    '''token_type_ids''': token_type_ids,
  						}
  
  						A__     :							List[Any]   				=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							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							Optional[Any]   				=self.prepare_config_and_inputs()
  						(
  						    A__
  						)     :							Optional[int]   				=config_and_inputs
  						A__     :							int   				={'''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   ):
  		__lowercase													=		(
  		    (
  		        TFDebertaModel,
  		        TFDebertaForMaskedLM,
  		        TFDebertaForQuestionAnswering,
  		        TFDebertaForSequenceClassification,
  		        TFDebertaForTokenClassification,
  		    )
  		    if is_tf_available()
  		    else ()
  		)
  		__lowercase													=		(
  		    {
  		        """feature-extraction""": TFDebertaModel,
  		        """fill-mask""": TFDebertaForMaskedLM,
  		        """question-answering""": TFDebertaForQuestionAnswering,
  		        """text-classification""": TFDebertaForSequenceClassification,
  		        """token-classification""": TFDebertaForTokenClassification,
  		        """zero-shot""": TFDebertaForSequenceClassification,
  		    }
  		    if is_tf_available()
  		    else {}
  		)
  
  		__lowercase													=		False
  		__lowercase													=		False
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=TFDebertaModelTester(self							)
  						A__     :							Tuple   				=ConfigTester(self	,     config_class=UpperCamelCase__	,     hidden_size=37							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						self.config_tester.run_common_tests()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							str   				=self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*UpperCamelCase__							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							Optional[int]   				=self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__							)
  
  
  
  
  
  
  		@slow
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''							)
  						self.assertIsNotNone(UpperCamelCase__							)
  
  
  
  
  
  
  
  @require_tf
  class 			a      (      unittest.TestCase   ):
  
  
  
  
  
  
  		@unittest.skip(reason='''Model not available yet'''							)
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  		@slow
  		def       lowerCAmelCase_  (  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''							)
  						A__     :							List[Any]   				=tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]							)
  						A__     :							str   				=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
  						A__     :							Tuple   				=model(UpperCamelCase__	,     attention_mask=UpperCamelCase__							)[0]
  
  						A__     :							Optional[int]   				=tf.constant(
  						    [
  						        [
  						            [-0.5985_5896, -0.8055_2566, -0.846_2135],
  						            [1.448_4025, -0.9348_3794, -0.8059_3085],
  						            [0.312_2741, 0.0031_6059, -1.413_1377],
  						        ]
  						    ]							)
  						tf.debugging.assert_near(output[:, 1:4, 1:4]	,     UpperCamelCase__	,     atol=1E-4							)
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
  )
  
  
  __lowerCamelCase       :       str				   =			{
      "configuration_align": [
          "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "AlignConfig",
          "AlignTextConfig",
          "AlignVisionConfig",
      ],
      "processing_align": ["AlignProcessor"],
  }
  
  try:
  						if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  						pass
  else:
  						__lowerCamelCase       :       Dict				   =			[
  						    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
  						    "AlignModel",
  						    "AlignPreTrainedModel",
  						    "AlignTextModel",
  						    "AlignVisionModel",
  						]
  
  if TYPE_CHECKING:
  						from .configuration_align import (
  						    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
  						    AlignConfig,
  						    AlignTextConfig,
  						    AlignVisionConfig,
  						)
  						from .processing_align import AlignProcessor
  
  						try:
  												if not is_torch_available():
  																		raise OptionalDependencyNotAvailable()
  						except OptionalDependencyNotAvailable:
  												pass
  						else:
  												from .modeling_align import (
  												    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
  												    AlignModel,
  												    AlignPreTrainedModel,
  												    AlignTextModel,
  												    AlignVisionModel,
  												)
  
  else:
  						import sys
  
  						__lowerCamelCase       :       List[str]				   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  