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   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: int=13     ,  UpperCamelCase__							: Dict=7     ,  UpperCamelCase__							: str=True     ,  UpperCamelCase__							: str=True     ,  UpperCamelCase__							: Dict=True     ,  UpperCamelCase__							: str=True     ,  UpperCamelCase__							: List[Any]=99     ,  UpperCamelCase__							: Tuple=32     ,  UpperCamelCase__							: List[str]=2     ,  UpperCamelCase__							: List[Any]=4     ,  UpperCamelCase__							: Optional[int]=37     ,  UpperCamelCase__							: Dict="gelu"     ,  UpperCamelCase__							: Union[str, Any]=0.1     ,  UpperCamelCase__							: List[str]=0.1     ,  UpperCamelCase__							: int=512     ,  UpperCamelCase__							: Optional[int]=16     ,  UpperCamelCase__							: Any=2     ,  UpperCamelCase__							: int=0.02     ,  UpperCamelCase__							: Optional[Any]=3     ,  UpperCamelCase__							: Tuple=4     ,  UpperCamelCase__							: List[Any]=None     ,  )	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	parent
  				snake_case     :					Optional[Any]     =	13
  				snake_case     :					Dict     =	7
  				snake_case     :					Any     =	True
  				snake_case     :					Optional[Any]     =	True
  				snake_case     :					str     =	True
  				snake_case     :					Dict     =	True
  				snake_case     :					Dict     =	99
  				snake_case     :					Optional[Any]     =	32
  				snake_case     :					Optional[int]     =	2
  				snake_case     :					Optional[int]     =	4
  				snake_case     :					str     =	37
  				snake_case     :					Dict     =	'''gelu'''
  				snake_case     :					Tuple     =	0.1
  				snake_case     :					Optional[int]     =	0.1
  				snake_case     :					Optional[Any]     =	512
  				snake_case     :					List[str]     =	16
  				snake_case     :					str     =	False
  				snake_case     :					str     =	-1
  				snake_case     :					Union[str, Any]     =	True
  				snake_case     :					int     =	2
  				snake_case     :					Tuple     =	0.02
  				snake_case     :					Optional[Any]     =	3
  				snake_case     :					Any     =	4
  				snake_case     :					Optional[Any]     =	None
  
  
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)
  
  				snake_case     :					Dict     =	None
  				if self.use_input_mask:
  						snake_case     :					Union[str, Any]     =	random_attention_mask([self.batch_size, self.seq_length]			)
  
  				snake_case     :					str     =	None
  				if self.use_token_type_ids:
  						snake_case     :					int     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size			)
  
  				snake_case     :					Optional[int]     =	None
  				snake_case     :					int     =	None
  				snake_case     :					List[str]     =	None
  				if self.use_labels:
  						snake_case     :					Any     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
  						snake_case     :					str     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels			)
  
  				snake_case     :					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							: int     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: Optional[Any]			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	TFDebertaModel(config=UpperCamelCase__			)
  				snake_case     :					Optional[Any]     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  
  				snake_case     :					Dict     =	[input_ids, input_mask]
  				snake_case     :					Optional[Any]     =	model(UpperCamelCase__			)
  
  				snake_case     :					List[str]     =	model(UpperCamelCase__			)
  
  				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Any			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	TFDebertaForMaskedLM(config=UpperCamelCase__			)
  				snake_case     :					str     =	{
  				    '''input_ids''': input_ids,
  				    '''attention_mask''': input_mask,
  				    '''token_type_ids''': token_type_ids,
  				}
  				snake_case     :					Any     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Any			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	self.num_labels
  				snake_case     :					Optional[int]     =	TFDebertaForSequenceClassification(config=UpperCamelCase__			)
  				snake_case     :					str     =	{
  				    '''input_ids''': input_ids,
  				    '''attention_mask''': input_mask,
  				    '''token_type_ids''': token_type_ids,
  				}
  
  				snake_case     :					List[Any]     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Optional[int]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.num_labels
  				snake_case     :					int     =	TFDebertaForTokenClassification(config=UpperCamelCase__			)
  				snake_case     :					List[Any]     =	{
  				    '''input_ids''': input_ids,
  				    '''attention_mask''': input_mask,
  				    '''token_type_ids''': token_type_ids,
  				}
  				snake_case     :					int     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Dict			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	TFDebertaForQuestionAnswering(config=UpperCamelCase__			)
  				snake_case     :					Optional[Any]     =	{
  				    '''input_ids''': input_ids,
  				    '''attention_mask''': input_mask,
  				    '''token_type_ids''': token_type_ids,
  				}
  
  				snake_case     :					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							: List[Any]			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	self.prepare_config_and_inputs()
  				(
  				    (
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,
  				)     :					Optional[int]     =	config_and_inputs
  				snake_case     :					int     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	(
  		    (
  		        TFDebertaModel,
  		        TFDebertaForMaskedLM,
  		        TFDebertaForQuestionAnswering,
  		        TFDebertaForSequenceClassification,
  		        TFDebertaForTokenClassification,
  		    )
  		    if is_tf_available()
  		    else ()
  		)
  		lowerCamelCase           =	(
  		    {
  		        """feature-extraction""": TFDebertaModel,
  		        """fill-mask""": TFDebertaForMaskedLM,
  		        """question-answering""": TFDebertaForQuestionAnswering,
  		        """text-classification""": TFDebertaForSequenceClassification,
  		        """token-classification""": TFDebertaForTokenClassification,
  		        """zero-shot""": TFDebertaForSequenceClassification,
  		    }
  		    if is_tf_available()
  		    else {}
  		)
  
  		lowerCamelCase           =	False
  		lowerCamelCase           =	False
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	TFDebertaModelTester(self			)
  				snake_case     :					Tuple     =	ConfigTester(self     ,  config_class=UpperCamelCase__     ,  hidden_size=37			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: str			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	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]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__			)
  
  
  
  		@slow
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''			)
  				self.assertIsNotNone(UpperCamelCase__			)
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		@unittest.skip(reason='''Model not available yet'''			)
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				pass
  
  
  
  		@slow
  		def 			lowerCAmelCase							(			self							: str			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''			)
  				snake_case     :					List[Any]     =	tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]			)
  				snake_case     :					str     =	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
  				snake_case     :					Tuple     =	model(UpperCamelCase__     ,  attention_mask=UpperCamelCase__			)[0]
  
  				snake_case     :					Optional[int]     =	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]     ,  UpperCamelCase__     ,  atol=1e-4			)
  
  
  
style_context: 
  '''simple docstring'''
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: List[str]			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	AutoTokenizer.from_pretrained(UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: str			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Any     =	None
  						for l in x:
  								snake_case     :					str     =	self.tokenizer(
  								    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  								snake_case     :					Tuple     =	torch.tensor([len(UpperCamelCase__			)]			)
  								if d is not None:
  										for k in d.keys():
  												snake_case     :					int     =	torch.cat((d[k], t[k])     ,  0			)
  								else:
  										snake_case     :					List[str]     =	t
  
  						snake_case     :					Union[str, Any]     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''			)			)
  						snake_case     :					int     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''			)			)
  
  				elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Optional[int]     =	self.tokenizer(
  						    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  
  				else:
  						raise Exception(
  						    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
  						    ''' strings` for supports are supported.'''			)
  
  				return d
  
  
  
  		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any=0.70			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	[]
  				for idx in range(len(W_query['''input_ids''']			)			):
  						snake_case     :					Optional[int]     =	range(p_start.shape[1]			)
  
  						snake_case     :					str     =	[]
  						for start_id in start_indexes:
  								for end_id in end_indexes:
  										if start_id < end_id:
  												output.append(
  												    (
  												        start_id,
  												        end_id,
  												        p_start[idx][start_id].item(),
  												        p_end[idx][end_id].item(),
  												    )			)
  
  						output.sort(key=lambda UpperCamelCase__			: (tup[2] * tup[3])     ,  reverse=UpperCamelCase__			)
  						snake_case     :					int     =	[]
  						for k in range(len(UpperCamelCase__			)			):
  								if output[k][2] * output[k][3] >= thresh:
  										snake_case ,snake_case     :					Tuple     =	output[k][0], output[k][1]
  										snake_case     :					str     =	self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]			)
  										temp.append((decoded, output[k][2] * output[k][3])			)
  
  						final_outputs.append(UpperCamelCase__			)
  
  				return final_outputs
  
  
  