


import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MPNetForMaskedLM,
       MPNetForMultipleChoice,
       MPNetForQuestionAnswering,
       MPNetForSequenceClassification,
       MPNetForTokenClassification,
       MPNetModel,
   )
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:str     ,		_a:Optional[Any]     ,		_a:Any=13     ,		_a:List[str]=7     ,		_a:str=True     ,		_a:Dict=True     ,		_a:Any=False     ,		_a:Dict=True     ,		_a:int=99     ,		_a:List[str]=64     ,		_a:Union[str, Any]=5     ,		_a:Optional[int]=4     ,		_a:int=64     ,		_a:List[str]="gelu"     ,		_a:List[Any]=0.1     ,		_a:Dict=0.1     ,		_a:str=5_12     ,		_a:Optional[int]=16     ,		_a:Tuple=2     ,		_a:int=0.02     ,		_a:Dict=3     ,		_a:Tuple=4     ,		_a:Tuple=None     ,		):
       snake_case__						= parent
       snake_case__						= batch_size
       snake_case__						= seq_length
       snake_case__						= is_training
       snake_case__						= use_input_mask
       snake_case__						= use_token_type_ids
       snake_case__						= use_labels
       snake_case__						= vocab_size
       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= intermediate_size
       snake_case__						= hidden_act
       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= max_position_embeddings
       snake_case__						= type_vocab_size
       snake_case__						= type_sequence_label_size
       snake_case__						= initializer_range
       snake_case__						= num_labels
       snake_case__						= num_choices
       snake_case__						= scope

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size   )

       snake_case__						= None
       if self.use_input_mask:
         snake_case__						= random_attention_mask([self.batch_size, self.seq_length]   )

       snake_case__						= None
       snake_case__						= None
       snake_case__						= None
       if self.use_labels:
         snake_case__						= ids_tensor([self.batch_size]     ,		self.type_sequence_label_size   )
         snake_case__						= ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels   )
         snake_case__						= ids_tensor([self.batch_size]     ,		self.num_choices   )

       snake_case__						= self.get_config()
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       return 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     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:Dict     ,		_a:Dict     ,		_a:Optional[Any]     ,		_a:Dict     ,		_a:Optional[int]     ,		_a:int   ):
       snake_case__						= MPNetModel(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a     ,		_a   )
       snake_case__						= model(_a   )
       self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)   )
       self.parent.assertEqual(result.pooler_output.shape     ,		(self.batch_size, self.hidden_size)   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:str     ,		_a:Optional[Any]     ,		_a:Tuple     ,		_a:Tuple     ,		_a:Tuple     ,		_a:Optional[int]   ):
       snake_case__						= MPNetForQuestionAnswering(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(
           _a     ,		attention_mask=_a     ,		start_positions=_a     ,		end_positions=_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 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:int     ,		_a:List[Any]     ,		_a:Union[str, Any]     ,		_a:Optional[int]     ,		_a:Tuple     ,		_a:List[str]   ):
       snake_case__						= self.num_labels
       snake_case__						= MPNetForSequenceClassification(_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a     ,		attention_mask=_a     ,		labels=_a   )
       self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:List[Any]     ,		_a:List[Any]     ,		_a:Any     ,		_a:int     ,		_a:str     ,		_a:Tuple   ):
       snake_case__						= self.num_choices
       snake_case__						= MPNetForMultipleChoice(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= input_ids.unsqueeze(1   ).expand(-1     ,		self.num_choices     ,		-1   ).contiguous()
       snake_case__						= input_mask.unsqueeze(1   ).expand(-1     ,		self.num_choices     ,		-1   ).contiguous()
       snake_case__						= model(
           _a     ,		attention_mask=_a     ,		labels=_a     ,		)
       self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices)   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:int     ,		_a:Optional[Any]     ,		_a:Optional[Any]     ,		_a:Union[str, Any]     ,		_a:List[Any]     ,		_a:List[Any]   ):
       snake_case__						= self.num_labels
       snake_case__						= MPNetForTokenClassification(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a     ,		attention_mask=_a     ,		labels=_a   )
       self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels)   )







     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.prepare_config_and_inputs()
       ((snake_case__)				,				(snake_case__)				,				(snake_case__)				,				(snake_case__)				,				(snake_case__)				,				(snake_case__))						= config_and_inputs
       snake_case__						= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict




@require_torch
class 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       (
         (
             MPNetForMaskedLM,
             MPNetForMultipleChoice,
             MPNetForQuestionAnswering,
             MPNetForSequenceClassification,
             MPNetForTokenClassification,
             MPNetModel,
         )
         if is_torch_available()
         else ()
     )
     __lowercase				:							int	   =       (
         {
             'feature-extraction': MPNetModel,
             'fill-mask': MPNetForMaskedLM,
             'question-answering': MPNetForQuestionAnswering,
             'text-classification': MPNetForSequenceClassification,
             'token-classification': MPNetForTokenClassification,
             'zero-shot': MPNetForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __lowercase				:							Tuple	   =       False
     __lowercase				:							Any	   =       True

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= MPNetModelTester(self   )
       snake_case__						= ConfigTester(self     ,		config_class=_a     ,		hidden_size=37   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       self.config_tester.run_common_tests()

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*_a   )




@require_torch
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= MPNetModel.from_pretrained('''microsoft/mpnet-base'''   )
       snake_case__						= torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]   )
       snake_case__						= model(_a   )[0]
       snake_case__						= torch.Size((1, 11, 7_68)   )
       self.assertEqual(output.shape     ,		_a   )
       snake_case__						= torch.tensor(
           [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]   )
       # compare the actual values for a slice.
       self.assertTrue(torch.allclose(output[:, :3, :3]     ,		_a     ,		atol=1e-4   )   )





lowerCamelCase__							:						List[str]							     =   """Alexander Joslin"""

import operator as op

from .stack import Stack







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				int:
  snake_case__						= {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

  snake_case__						= Stack()
  snake_case__						= Stack()

  for i in equation:
    if i.isdigit():
      # RULE 1
      operand_stack.push(int(__lowerCAmelCase				)				)
    elif i in operators:
      # RULE 2
      operator_stack.push(__lowerCAmelCase				)
    elif i == ")":
      # RULE 4
      snake_case__						= operator_stack.peek()
      operator_stack.pop()
      snake_case__						= operand_stack.peek()
      operand_stack.pop()
      snake_case__						= operand_stack.peek()
      operand_stack.pop()

      snake_case__						= operators[opr](__lowerCAmelCase							,   __lowerCAmelCase				)
      operand_stack.push(__lowerCAmelCase				)

    # RULE 5
  return operand_stack.peek()


if __name__ == "__main__":
   lowerCamelCase__							:						Optional[Any]							     =   """(5 + ((4 * 2) * (2 + 3)))"""
   # answer = 45
   print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")

