code: 
  
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
  				    TFMPNetForMaskedLM,
  				    TFMPNetForMultipleChoice,
  				    TFMPNetForQuestionAnswering,
  				    TFMPNetForSequenceClassification,
  				    TFMPNetForTokenClassification,
  				    TFMPNetModel,
  				)
  
  
  
  class 			a_					:
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=13			,    SCREAMING_SNAKE_CASE=7			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=False			,    SCREAMING_SNAKE_CASE=True			,    SCREAMING_SNAKE_CASE=99			,    SCREAMING_SNAKE_CASE=64			,    SCREAMING_SNAKE_CASE=2			,    SCREAMING_SNAKE_CASE=4			,    SCREAMING_SNAKE_CASE=64			,    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=2			,    SCREAMING_SNAKE_CASE=0.0_2			,    SCREAMING_SNAKE_CASE=3			,    SCREAMING_SNAKE_CASE=4			,    SCREAMING_SNAKE_CASE=None			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	parent
  				SCREAMING_SNAKE_CASE_			=	batch_size
  				SCREAMING_SNAKE_CASE_			=	seq_length
  				SCREAMING_SNAKE_CASE_			=	is_training
  				SCREAMING_SNAKE_CASE_			=	use_input_mask
  				SCREAMING_SNAKE_CASE_			=	use_token_type_ids
  				SCREAMING_SNAKE_CASE_			=	use_labels
  				SCREAMING_SNAKE_CASE_			=	vocab_size
  				SCREAMING_SNAKE_CASE_			=	hidden_size
  				SCREAMING_SNAKE_CASE_			=	num_hidden_layers
  				SCREAMING_SNAKE_CASE_			=	num_attention_heads
  				SCREAMING_SNAKE_CASE_			=	intermediate_size
  				SCREAMING_SNAKE_CASE_			=	hidden_act
  				SCREAMING_SNAKE_CASE_			=	hidden_dropout_prob
  				SCREAMING_SNAKE_CASE_			=	attention_probs_dropout_prob
  				SCREAMING_SNAKE_CASE_			=	max_position_embeddings
  				SCREAMING_SNAKE_CASE_			=	type_vocab_size
  				SCREAMING_SNAKE_CASE_			=	type_sequence_label_size
  				SCREAMING_SNAKE_CASE_			=	initializer_range
  				SCREAMING_SNAKE_CASE_			=	num_labels
  				SCREAMING_SNAKE_CASE_			=	num_choices
  				SCREAMING_SNAKE_CASE_			=	scope
  
  
  
  
  
  	def 	A_(       self			)     ->				List[str]:
  
  
  
  
  				"""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
  				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_			=	ids_tensor([self.batch_size]			,    self.num_choices			)
  
  				SCREAMING_SNAKE_CASE_			=	MPNetConfig(
  				    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			,    initializer_range=self.initializer_range			,    )
  				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	TFMPNetModel(config=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	{'input_ids': input_ids, 'attention_mask': input_mask}
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	[input_ids, input_mask]
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  				self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				int:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	TFMPNetForMaskedLM(config=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	{'input_ids': input_ids, 'attention_mask': input_mask}
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  				self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	TFMPNetForQuestionAnswering(config=SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	{
  				    'input_ids': input_ids,
  				    'attention_mask': input_mask,
  				}
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  				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			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.num_labels
  				SCREAMING_SNAKE_CASE_			=	TFMPNetForSequenceClassification(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	{'input_ids': input_ids, 'attention_mask': input_mask}
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  				self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels)			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.num_choices
  				SCREAMING_SNAKE_CASE_			=	TFMPNetForMultipleChoice(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE			,    1			)			,    (1, self.num_choices, 1)			)
  				SCREAMING_SNAKE_CASE_			=	tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE			,    1			)			,    (1, self.num_choices, 1)			)
  				SCREAMING_SNAKE_CASE_			=	{
  				    'input_ids': multiple_choice_inputs_ids,
  				    'attention_mask': multiple_choice_input_mask,
  				}
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  				self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_choices)			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.num_labels
  				SCREAMING_SNAKE_CASE_			=	TFMPNetForTokenClassification(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	{'input_ids': input_ids, 'attention_mask': input_mask}
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)
  				self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.num_labels)			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""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_))			=	config_and_inputs
  				SCREAMING_SNAKE_CASE_			=	{'input_ids': input_ids, 'attention_mask': input_mask}
  				return config, inputs_dict
  
  
  
  
  @require_tf
  class 			a_					(    SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__     ,						unittest.TestCase    ):
  	A						       =   (
  	    (
  	        TFMPNetForMaskedLM,
  	        TFMPNetForMultipleChoice,
  	        TFMPNetForQuestionAnswering,
  	        TFMPNetForSequenceClassification,
  	        TFMPNetForTokenClassification,
  	        TFMPNetModel,
  	    )
  	    if is_tf_available()
  	    else ()
  	)
  	A						       =   (
  	    {
  	        '''feature-extraction''': TFMPNetModel,
  	        '''fill-mask''': TFMPNetForMaskedLM,
  	        '''question-answering''': TFMPNetForQuestionAnswering,
  	        '''text-classification''': TFMPNetForSequenceClassification,
  	        '''token-classification''': TFMPNetForTokenClassification,
  	        '''zero-shot''': TFMPNetForSequenceClassification,
  	    }
  	    if is_tf_available()
  	    else {}
  	)
  	A						       =   False
  	A						       =   False
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	TFMPNetModelTester(self			)
  				SCREAMING_SNAKE_CASE_			=	ConfigTester(self			,    config_class=SCREAMING_SNAKE_CASE			,    hidden_size=37			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  
  
  	def 	A_(       self			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_mpnet_model(*SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				List[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_mpnet_for_masked_lm(*SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_mpnet_for_question_answering(*SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_mpnet_for_sequence_classification(*SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_mpnet_for_multiple_choice(*SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			)     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_mpnet_for_token_classification(*SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	@slow
  	def 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				for model_name in ["microsoft/mpnet-base"]:
  							SCREAMING_SNAKE_CASE_			=	TFMPNetModel.from_pretrained(SCREAMING_SNAKE_CASE			)
  							self.assertIsNotNone(SCREAMING_SNAKE_CASE			)
  
  
  
  
  @require_tf
  class 			a_					(    unittest.TestCase    ):
  
  
  
  
  
  	@slow
  	def 	A_(       self			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	TFMPNetModel.from_pretrained('microsoft/mpnet-base'			)
  				SCREAMING_SNAKE_CASE_			=	tf.constant([[0, 1, 2, 3, 4, 5]]			)
  				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)[0]
  
  				SCREAMING_SNAKE_CASE_			=	[1, 6, 768]
  				self.assertEqual(output.shape			,    SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	tf.constant(
  				    [
  				        [
  				            [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
  				            [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
  				            [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
  				        ]
  				    ]			)
  				tf.debugging.assert_near(output[:, :3, :3]			,    SCREAMING_SNAKE_CASE			,    atol=1e-4			)
  
style_context: 
  
  
  
  
  from __future__ import annotations
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	m - 1, 0
  			while i >= 0 and j < n:
  						if key == mat[i][j]:
  									print(F'Key {key} found at row- {i + 1} column- {j + 1}'   )
  									return
  						if key < mat[i][j]:
  									i -= 1
  						else:
  									j += 1
  			print(F'Key {key} not found'   )
  
  def lowercase						(     ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  			SCREAMING_SNAKE_CASE_			=	int(input('Enter the element to be searched:'   )   )
  			print(SCREAMING_SNAKE_CASE   )
  			search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE					,						len(SCREAMING_SNAKE_CASE   )					,						len(mat[0]   )					,						SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  				main()
  