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_						:
  
  						'''simple docstring'''
  
  
  
  
  						def __init__(					self   ,							_A   ,							_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=2   ,							_A=0.0_2   ,							_A=3   ,							_A=4   ,							_A=None   ,							):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      parent
  													__SCREAMING_SNAKE_CASE								=      13
  													__SCREAMING_SNAKE_CASE								=      7
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      99
  													__SCREAMING_SNAKE_CASE								=      32
  													__SCREAMING_SNAKE_CASE								=      2
  													__SCREAMING_SNAKE_CASE								=      4
  													__SCREAMING_SNAKE_CASE								=      37
  													__SCREAMING_SNAKE_CASE								=      'gelu'
  													__SCREAMING_SNAKE_CASE								=      0.1
  													__SCREAMING_SNAKE_CASE								=      0.1
  													__SCREAMING_SNAKE_CASE								=      512
  													__SCREAMING_SNAKE_CASE								=      16
  													__SCREAMING_SNAKE_CASE								=      False
  													__SCREAMING_SNAKE_CASE								=      -1
  													__SCREAMING_SNAKE_CASE								=      True
  													__SCREAMING_SNAKE_CASE								=      2
  													__SCREAMING_SNAKE_CASE								=      0.0_2
  													__SCREAMING_SNAKE_CASE								=      3
  													__SCREAMING_SNAKE_CASE								=      4
  													__SCREAMING_SNAKE_CASE								=      None
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size	)
  
  													__SCREAMING_SNAKE_CASE								=      None
  													if self.use_input_mask:
  																				__SCREAMING_SNAKE_CASE								=      random_attention_mask([self.batch_size, self.seq_length]	)
  
  													__SCREAMING_SNAKE_CASE								=      None
  													if self.use_token_type_ids:
  																				__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size, self.seq_length]   ,							self.type_vocab_size	)
  
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  													if self.use_labels:
  																				__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size]   ,							self.type_sequence_label_size	)
  																				__SCREAMING_SNAKE_CASE								=      ids_tensor([self.batch_size, self.seq_length]   ,							self.num_labels	)
  
  													__SCREAMING_SNAKE_CASE								=      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=_A   ,							)
  
  													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFDebertaModel(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
  
  													__SCREAMING_SNAKE_CASE								=      [input_ids, input_mask]
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  
  													self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFDebertaForMaskedLM(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {
  													    'input_ids': input_ids,
  													    'attention_mask': input_mask,
  													    'token_type_ids': token_type_ids,
  													}
  													__SCREAMING_SNAKE_CASE								=      model(_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	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.num_labels
  													__SCREAMING_SNAKE_CASE								=      TFDebertaForSequenceClassification(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {
  													    'input_ids': input_ids,
  													    'attention_mask': input_mask,
  													    'token_type_ids': token_type_ids,
  													}
  
  													__SCREAMING_SNAKE_CASE								=      model(_A	)
  													self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.num_labels
  													__SCREAMING_SNAKE_CASE								=      TFDebertaForTokenClassification(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {
  													    'input_ids': input_ids,
  													    'attention_mask': input_mask,
  													    'token_type_ids': token_type_ids,
  													}
  													__SCREAMING_SNAKE_CASE								=      model(_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	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFDebertaForQuestionAnswering(config=_A	)
  													__SCREAMING_SNAKE_CASE								=      {
  													    'input_ids': input_ids,
  													    'attention_mask': input_mask,
  													    'token_type_ids': token_type_ids,
  													}
  
  													__SCREAMING_SNAKE_CASE								=      model(_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	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.prepare_config_and_inputs()
  													(
  													    (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							, (
  													    __SCREAMING_SNAKE_CASE 
  													)							,  
  													)								=      config_and_inputs
  													__SCREAMING_SNAKE_CASE								=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
  													return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase_						(     snake_case_					,					snake_case_					,					unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				Dict       						=						(
  						    (
  						        TFDebertaModel,
  						        TFDebertaForMaskedLM,
  						        TFDebertaForQuestionAnswering,
  						        TFDebertaForSequenceClassification,
  						        TFDebertaForTokenClassification,
  						    )
  						    if is_tf_available()
  						    else ()
  						)
  						UpperCamelCase__    :				int       						=						(
  						    {
  						        '''feature-extraction''': TFDebertaModel,
  						        '''fill-mask''': TFDebertaForMaskedLM,
  						        '''question-answering''': TFDebertaForQuestionAnswering,
  						        '''text-classification''': TFDebertaForSequenceClassification,
  						        '''token-classification''': TFDebertaForTokenClassification,
  						        '''zero-shot''': TFDebertaForSequenceClassification,
  						    }
  						    if is_tf_available()
  						    else {}
  						)
  
  						UpperCamelCase__    :				Dict       						=						False
  						UpperCamelCase__    :				str       						=						False
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFDebertaModelTester(self	)
  													__SCREAMING_SNAKE_CASE								=      ConfigTester(self   ,							config_class=_A   ,							hidden_size=37	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													self.config_tester.run_common_tests()
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_model(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_for_masked_lm(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_for_question_answering(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_for_sequence_classification(*_A	)
  
  
  
  
  
  
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      self.model_tester.prepare_config_and_inputs()
  													self.model_tester.create_and_check_for_token_classification(*_A	)
  
  
  
  
  
  
  						@slow
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFDebertaModel.from_pretrained('kamalkraj/deberta-base'	)
  													self.assertIsNotNone(_A	)
  
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase_						(     unittest.TestCase ):
  
  						'''simple docstring'''
  
  
  
  
  						@unittest.skip(reason='Model not available yet'	)
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													pass
  
  
  
  
  
  
  						@slow
  						def  _A			(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      TFDebertaModel.from_pretrained('kamalkraj/deberta-base'	)
  													__SCREAMING_SNAKE_CASE								=      tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
  													__SCREAMING_SNAKE_CASE								=      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
  													__SCREAMING_SNAKE_CASE								=      model(_A   ,							attention_mask=_A	)[0]
  
  													__SCREAMING_SNAKE_CASE								=      tf.constant(
  													    [
  													        [
  													            [-0.5_9_8_5_5_8_9_6, -0.8_0_5_5_2_5_6_6, -0.8_4_6_2_1_3_5],
  													            [1.4_4_8_4_0_2_5, -0.9_3_4_8_3_7_9_4, -0.8_0_5_9_3_0_8_5],
  													            [0.3_1_2_2_7_4_1, 0.0_0_3_1_6_0_5_9, -1.4_1_3_1_3_7_7],
  													        ]
  													    ]	)
  													tf.debugging.assert_near(output[:, 1:4, 1:4]   ,							_A   ,							atol=1e-4	)
  
  
  
  
  
  
  
style_context: 
  
  
  
  from __future__ import annotations
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						def __init__(					self   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      value
  													__SCREAMING_SNAKE_CASE								=      None
  													__SCREAMING_SNAKE_CASE								=      None
  
  
  
  
  
  
  
  class      UpperCAmelCase_						:
  
  						'''simple docstring'''
  
  
  
  
  						UpperCamelCase__    :				int
  
  
  
  
  
  
  						def __init__(					self	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													__SCREAMING_SNAKE_CASE								=      0
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if node is None:
  																				return
  
  													if path_sum == self.target:
  																				self.paths += 1
  
  													if node.left:
  																				self.depth_first_search(node.left   ,							path_sum + node.left.value	)
  													if node.right:
  																				self.depth_first_search(node.right   ,							path_sum + node.right.value	)
  
  
  
  
  
  
  						def  _A			(					self   ,							_A   ,							_A = None	):
  
  
  
  
  
  
  
  													'''simple docstring'''
  
  													if node is None:
  																				return 0
  													if target is not None:
  																				__SCREAMING_SNAKE_CASE								=      target
  
  													self.depth_first_search(_A   ,							node.value	)
  													self.path_sum(node.left	)
  													self.path_sum(node.right	)
  
  													return self.paths
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  
  
  