

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,    lowercase_,    lowercase_=13,    lowercase_=7,    lowercase_=True,    lowercase_=True,    lowercase_=False,    lowercase_=True,    lowercase_=99,    lowercase_=64,    lowercase_=5,    lowercase_=4,    lowercase_=64,    lowercase_="gelu",    lowercase_=0.1,    lowercase_=0.1,    lowercase_=512,    lowercase_=16,    lowercase_=2,    lowercase_=0.02,    lowercase_=3,    lowercase_=4,    lowercase_=None,    )							->    Tuple:






     """simple docstring"""






     a__  =parent
     a__  =batch_size
     a__  =seq_length
     a__  =is_training
     a__  =use_input_mask
     a__  =use_token_type_ids
     a__  =use_labels
     a__  =vocab_size
     a__  =hidden_size
     a__  =num_hidden_layers
     a__  =num_attention_heads
     a__  =intermediate_size
     a__  =hidden_act
     a__  =hidden_dropout_prob
     a__  =attention_probs_dropout_prob
     a__  =max_position_embeddings
     a__  =type_vocab_size
     a__  =type_sequence_label_size
     a__  =initializer_range
     a__  =num_labels
     a__  =num_choices
     a__  =scope



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''       )



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =ids_tensor([self.batch_size, self.seq_length],    self.vocab_size       )

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

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

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



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     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  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    Optional[Any]:






     """simple docstring"""






     a__  =MPNetModel(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    lowercase_       )
     a__  =model(lowercase_       )
     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  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    List[Any]:






     """simple docstring"""






     a__  =MPNetForQuestionAnswering(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(
         lowercase_,    attention_mask=lowercase_,    start_positions=lowercase_,    end_positions=lowercase_,    )
     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  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    Dict:






     """simple docstring"""






     a__  =self.num_labels
     a__  =MPNetForSequenceClassification(lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    attention_mask=lowercase_,    labels=lowercase_       )
     self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    Any:






     """simple docstring"""






     a__  =self.num_choices
     a__  =MPNetForMultipleChoice(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =input_ids.unsqueeze(1       ).expand(-1,    self.num_choices,    -1       ).contiguous()
     a__  =input_mask.unsqueeze(1       ).expand(-1,    self.num_choices,    -1       ).contiguous()
     a__  =model(
         lowercase_,    attention_mask=lowercase_,    labels=lowercase_,    )
     self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_choices)       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    Any:






     """simple docstring"""






     a__  =self.num_labels
     a__  =MPNetForTokenClassification(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    attention_mask=lowercase_,    labels=lowercase_       )
     self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.num_labels)       )



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =self.prepare_config_and_inputs()
     ((a__),      (a__),      (a__),      (a__),      (a__),      (a__))  =config_and_inputs
     a__  ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
     return config, inputs_dict







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

   '''simple docstring'''




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



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     a__  =MPNetModelTester(self       )
     a__  =ConfigTester(self,    config_class=lowercase_,    hidden_size=37       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     self.config_tester.run_common_tests()



   def  _UpperCAmelCase (				self       )							->    Union[str, Any]:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase_       )







@require_torch
class 						__magic_name__			(							unittest.TestCase							):

   '''simple docstring'''




   @slow
   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






     a__  =MPNetModel.from_pretrained('''microsoft/mpnet-base'''       )
     a__  =torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
     a__  =model(lowercase_       )[0]
     a__  =torch.Size((1, 11, 768)       )
     self.assertEqual(output.shape,    lowercase_       )
     a__  =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],    lowercase_,    atol=1E-4       )       )



from __future__ import annotations




def 			UpperCAmelCase__				(			_A      : float	,      _A      : float	,      _A      : float	,      ):





  '''simple docstring'''







  if (stress, tangential_force, area).count(0							) != 1:
    raise ValueError('''You cannot supply more or less than 2 values'''							)
  elif stress < 0:
    raise ValueError('''Stress cannot be negative'''							)
  elif tangential_force < 0:
    raise ValueError('''Tangential Force cannot be negative'''							)
  elif area < 0:
    raise ValueError('''Area cannot be negative'''							)
  elif stress == 0:
    return (
        "stress",
        tangential_force / area,
    )
  elif tangential_force == 0:
    return (
        "tangential_force",
        stress * area,
    )
  else:
    return (
        "area",
        tangential_force / stress,
    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
