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 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_=13		,		snake_case_=7		,		snake_case_=True		,		snake_case_=True		,		snake_case_=False		,		snake_case_=True		,		snake_case_=99		,		snake_case_=64		,		snake_case_=2		,		snake_case_=4		,		snake_case_=64		,		snake_case_="gelu"		,		snake_case_=0.1		,		snake_case_=0.1		,		snake_case_=512		,		snake_case_=16		,		snake_case_=2		,		snake_case_=0.02		,		snake_case_=3		,		snake_case_=4		,		snake_case_=None		,		)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: str	     =parent
  											SCREAMING_SNAKE_CASE			: Dict	     =batch_size
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =seq_length
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =is_training
  											SCREAMING_SNAKE_CASE			: Any	     =use_input_mask
  											SCREAMING_SNAKE_CASE			: List[Any]	     =use_token_type_ids
  											SCREAMING_SNAKE_CASE			: List[Any]	     =use_labels
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =vocab_size
  											SCREAMING_SNAKE_CASE			: int	     =hidden_size
  											SCREAMING_SNAKE_CASE			: List[str]	     =num_hidden_layers
  											SCREAMING_SNAKE_CASE			: str	     =num_attention_heads
  											SCREAMING_SNAKE_CASE			: List[str]	     =intermediate_size
  											SCREAMING_SNAKE_CASE			: Any	     =hidden_act
  											SCREAMING_SNAKE_CASE			: str	     =hidden_dropout_prob
  											SCREAMING_SNAKE_CASE			: Tuple	     =attention_probs_dropout_prob
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =max_position_embeddings
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =type_vocab_size
  											SCREAMING_SNAKE_CASE			: List[str]	     =type_sequence_label_size
  											SCREAMING_SNAKE_CASE			: int	     =initializer_range
  											SCREAMING_SNAKE_CASE			: List[Any]	     =num_labels
  											SCREAMING_SNAKE_CASE			: int	     =num_choices
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =scope
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: str	     =ids_tensor([self.batch_size, self.seq_length]		,		self.vocab_size							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =None
  											if self.use_input_mask:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =random_attention_mask([self.batch_size, self.seq_length]							)
  
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: Tuple	     =None
  											SCREAMING_SNAKE_CASE			: List[str]	     =None
  											if self.use_labels:
  																		SCREAMING_SNAKE_CASE			: Dict	     =ids_tensor([self.batch_size]		,		self.type_sequence_label_size							)
  																		SCREAMING_SNAKE_CASE			: int	     =ids_tensor([self.batch_size, self.seq_length]		,		self.num_labels							)
  																		SCREAMING_SNAKE_CASE			: Dict	     =ids_tensor([self.batch_size]		,		self.num_choices							)
  
  											SCREAMING_SNAKE_CASE			: int	     =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		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Union[str, Any]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =TFMPNetModel(config=snake_case_							)
  											SCREAMING_SNAKE_CASE			: Tuple	     ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
  											SCREAMING_SNAKE_CASE			: List[str]	     =model(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Any	     =[input_ids, input_mask]
  											SCREAMING_SNAKE_CASE			: Dict	     =model(snake_case_							)
  											self.parent.assertEqual(result.last_hidden_state.shape		,		(self.batch_size, self.seq_length, self.hidden_size)							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      int:
  											SCREAMING_SNAKE_CASE			: Any	     =TFMPNetForMaskedLM(config=snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =model(snake_case_							)
  											self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.vocab_size)							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      str:
  											SCREAMING_SNAKE_CASE			: int	     =TFMPNetForQuestionAnswering(config=snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     ={
  											    '''input_ids''': input_ids,
  											    '''attention_mask''': input_mask,
  											}
  											SCREAMING_SNAKE_CASE			: Tuple	     =model(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		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.num_labels
  											SCREAMING_SNAKE_CASE			: List[Any]	     =TFMPNetForSequenceClassification(snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =model(snake_case_							)
  											self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels)							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Any	     =self.num_choices
  											SCREAMING_SNAKE_CASE			: List[str]	     =TFMPNetForMultipleChoice(snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =tf.tile(tf.expand_dims(snake_case_		,		1							)		,		(1, self.num_choices, 1)							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =tf.tile(tf.expand_dims(snake_case_		,		1							)		,		(1, self.num_choices, 1)							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     ={
  											    '''input_ids''': multiple_choice_inputs_ids,
  											    '''attention_mask''': multiple_choice_input_mask,
  											}
  											SCREAMING_SNAKE_CASE			: Any	     =model(snake_case_							)
  											self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_choices)							)
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =self.num_labels
  											SCREAMING_SNAKE_CASE			: List[Any]	     =TFMPNetForTokenClassification(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
  											SCREAMING_SNAKE_CASE			: Any	     =model(snake_case_							)
  											self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.seq_length, self.num_labels)							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.prepare_config_and_inputs()
  											((SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE)     ,							(SCREAMING_SNAKE_CASE))			: Union[str, Any]	     =config_and_inputs
  											SCREAMING_SNAKE_CASE			: List[Any]	     ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
  											return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class 							_lowerCAmelCase		(     UpperCamelCase__  ,   UpperCamelCase__  ,   unittest.TestCase	):
  				lowerCamelCase__									=    (
  				    (
  				        TFMPNetForMaskedLM,
  				        TFMPNetForMultipleChoice,
  				        TFMPNetForQuestionAnswering,
  				        TFMPNetForSequenceClassification,
  				        TFMPNetForTokenClassification,
  				        TFMPNetModel,
  				    )
  				    if is_tf_available()
  				    else ()
  				)
  				lowerCamelCase__									=    (
  				    {
  				        'feature-extraction': TFMPNetModel,
  				        'fill-mask': TFMPNetForMaskedLM,
  				        'question-answering': TFMPNetForQuestionAnswering,
  				        'text-classification': TFMPNetForSequenceClassification,
  				        'token-classification': TFMPNetForTokenClassification,
  				        'zero-shot': TFMPNetForSequenceClassification,
  				    }
  				    if is_tf_available()
  				    else {}
  				)
  				lowerCamelCase__									=    False
  				lowerCamelCase__									=    False
  
  
  
  
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Tuple	     =TFMPNetModelTester(self							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =ConfigTester(self		,		config_class=snake_case_		,		hidden_size=37							)
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											self.config_tester.run_common_tests()
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_mpnet_model(*snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_mpnet_for_masked_lm(*snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Any	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case_							)
  
  
  
  
  
  
  
  				@slow
  				def     __a		( self							)    ->      List[str]:
  											for model_name in ["microsoft/mpnet-base"]:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =TFMPNetModel.from_pretrained(snake_case_							)
  																		self.assertIsNotNone(snake_case_							)
  
  
  
  
  
  
  
  @require_tf
  class 							_lowerCAmelCase		(     unittest.TestCase	):
  
  
  
  
  				@slow
  				def     __a		( self							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Any	     =TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =tf.constant([[0, 1, 2, 3, 4, 5]]							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =model(snake_case_							)[0]
  
  											SCREAMING_SNAKE_CASE			: Any	     =[1, 6, 768]
  											self.assertEqual(output.shape		,		snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =tf.constant(
  											    [
  											        [
  											            [-0.106_7172, 0.0821_6473, 0.002_4543],
  											            [-0.0346_5879, 0.835_4118, -0.0325_2288],
  											            [-0.0656_9476, -0.1242_4111, -0.049_4436],
  											        ]
  											    ]							)
  											tf.debugging.assert_near(output[:, :3, :3]		,		snake_case_		,		atol=1E-4							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import Any
  
  import numpy as np
  
  
  class 							_lowerCAmelCase		:
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_							)    ->      None:
  											# Check if RHS is negative
  											if np.any(tableau[:, -1]		,		where=tableau[:, -1] < 0							):
  																		raise ValueError('''RHS must be > 0'''							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Tuple	     =tableau.shape
  
  											# Number of decision variables x1, x2, x3...
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =n_vars
  
  											# Number of artificial variables to be minimised
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(np.where(tableau[self.n_vars : -1] == -1							)[0]							)
  
  											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =(self.n_art_vars > 0) + 1
  
  											# Number of slack variables added to make inequalities into equalities
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.n_rows - self.n_stages
  
  											# Objectives for each stage
  											SCREAMING_SNAKE_CASE			: str	     =['''max''']
  
  											# In two stage simplex, first minimise then maximise
  											if self.n_art_vars:
  																		self.objectives.append('''min'''							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =['''''']
  
  											# Index of current pivot row and column
  											SCREAMING_SNAKE_CASE			: str	     =None
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =None
  
  											# Does objective row only contain (non)-negative values?
  											SCREAMING_SNAKE_CASE			: Dict	     =False
  
  
  
  
  				@staticmethod
  				def     __a		( *snake_case_							)    ->      list[str]:
  											if len(snake_case_							) != 3:
  																		raise ValueError('''Must provide n_vars, n_slack, and n_art_vars'''							)
  
  											if not all(x >= 0 and isinstance(snake_case_		,		snake_case_							) for x in args							):
  																		raise ValueError('''All arguments must be non-negative integers'''							)
  
  											# decision | slack | artificial
  											SCREAMING_SNAKE_CASE			: Any	     =['''x''', '''s''', '''a''']
  											SCREAMING_SNAKE_CASE			: int	     =[]
  											for i in range(3							):
  																		for j in range(args[i]							):
  																									titles.append(string_starts[i] + str(j + 1							)							)
  											titles.append('''RHS'''							)
  											return titles
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      tuple[Any, Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.objectives[-1]
  
  											# Find entries of highest magnitude in objective rows
  											SCREAMING_SNAKE_CASE			: str	     =(objective == '''min''') - (objective == '''max''')
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.argmax(sign * tableau[0, : self.n_vars]							)
  
  											# Choice is only valid if below 0 for maximise, and above for minimise
  											if sign * self.tableau[0, col_idx] <= 0:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =True
  																		return 0, 0
  
  											# Pivot row is chosen as having the lowest quotient when elements of
  											# the pivot column divide the right-hand side
  
  											# Slice excluding the objective rows
  											SCREAMING_SNAKE_CASE			: Tuple	     =slice(self.n_stages		,		self.n_rows							)
  
  											# RHS
  											SCREAMING_SNAKE_CASE			: Dict	     =tableau[s, -1]
  
  											# Elements of pivot column within slice
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[s, col_idx]
  
  											# Array filled with nans
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =np.full(self.n_rows - self.n_stages		,		np.nan							)
  
  											# If element in pivot column is greater than zeron_stages, return
  											# quotient or nan otherwise
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =np.divide(snake_case_		,		snake_case_		,		out=snake_case_		,		where=divisor > 0							)
  
  											# Arg of minimum quotient excluding the nan values. n_stages is added
  											# to compensate for earlier exclusion of objective columns
  											SCREAMING_SNAKE_CASE			: List[Any]	     =np.nanargmin(snake_case_							) + self.n_stages
  											return row_idx, col_idx
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      np.ndarray:
  											SCREAMING_SNAKE_CASE			: Tuple	     =tableau[row_idx].copy()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =piv_row[col_idx]
  
  											# Entry becomes 1
  											piv_row *= 1 / piv_val
  
  											# Variable in pivot column becomes basic, ie the only non-zero entry
  											for idx, coeff in enumerate(tableau[:, col_idx]							):
  																		tableau[idx] += -coeff * piv_row
  											SCREAMING_SNAKE_CASE			: Dict	     =piv_row
  											return tableau
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      np.ndarray:
  											self.objectives.pop()
  
  											if not self.objectives:
  																		return tableau
  
  											# Slice containing ids for artificial columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =slice(-self.n_art_vars - 1		,		-1							)
  
  											# Delete the artificial variable columns
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =np.delete(snake_case_		,		snake_case_		,		axis=1							)
  
  											# Delete the objective row of the first stage
  											SCREAMING_SNAKE_CASE			: str	     =np.delete(snake_case_		,		0		,		axis=0							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =1
  											self.n_rows -= 1
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
  											SCREAMING_SNAKE_CASE			: List[str]	     =False
  											return tableau
  
  
  
  
  				def     __a		( self							)    ->      dict[Any, Any]:
  											# Stop simplex algorithm from cycling.
  											for _ in range(100							):
  																		# Completion of each stage removes an objective. If both stages
  																		# are complete, then no objectives are left
  																		if not self.objectives:
  																									SCREAMING_SNAKE_CASE			: int	     =self.generate_col_titles(
  																									    self.n_vars		,		self.n_slack		,		self.n_art_vars							)
  
  																									# Find the values of each variable at optimal solution
  																									return self.interpret_tableau(self.tableau		,		self.col_titles							)
  
  																		SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =self.find_pivot(self.tableau							)
  
  																		# If there are no more negative values in objective row
  																		if self.stop_iter:
  																									# Delete artificial variable columns and rows. Update attributes
  																									SCREAMING_SNAKE_CASE			: Optional[int]	     =self.change_stage(self.tableau							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: Dict	     =self.pivot(self.tableau		,		snake_case_		,		snake_case_							)
  											return {}
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_							)    ->      dict[str, float]:
  											SCREAMING_SNAKE_CASE			: Any	     ={'''P''': abs(tableau[0, -1]							)}
  
  											for i in range(self.n_vars							):
  																		# Gives ids of nonzero entries in the ith column
  																		SCREAMING_SNAKE_CASE			: Any	     =np.nonzero(tableau[:, i]							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =len(nonzero[0]							)
  
  																		# First entry in the nonzero ids
  																		SCREAMING_SNAKE_CASE			: List[str]	     =nonzero[0][0]
  																		SCREAMING_SNAKE_CASE			: Any	     =tableau[nonzero_rowidx, i]
  
  																		# If there is only one nonzero value in column, which is one
  																		if n_nonzero == nonzero_val == 1:
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tableau[nonzero_rowidx, -1]
  																									SCREAMING_SNAKE_CASE			: Any	     =rhs_val
  
          # Check for basic variables
  											for title in col_titles:
  																		# Don't add RHS or slack variables to output dict
  																		if title[0] not in "R-s-a":
  																									output_dict.setdefault(snake_case_		,		0							)
  											return output_dict
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  
  
  