'''simple docstring'''

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     __UpperCAmelCase       :





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=13    ,     _lowerCamelCase=7    ,     _lowerCamelCase=True    ,     _lowerCamelCase=True    ,     _lowerCamelCase=False    ,     _lowerCamelCase=True    ,     _lowerCamelCase=99    ,     _lowerCamelCase=64    ,     _lowerCamelCase=5    ,     _lowerCamelCase=4    ,     _lowerCamelCase=64    ,     _lowerCamelCase="gelu"    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=0.1    ,     _lowerCamelCase=512    ,     _lowerCamelCase=16    ,     _lowerCamelCase=2    ,     _lowerCamelCase=0.02    ,     _lowerCamelCase=3    ,     _lowerCamelCase=4    ,     _lowerCamelCase=None    ,     ):
     lowerCAmelCase_			=      parent
     lowerCAmelCase_			=      batch_size
     lowerCAmelCase_			=      seq_length
     lowerCAmelCase_			=      is_training
     lowerCAmelCase_			=      use_input_mask
     lowerCAmelCase_			=      use_token_type_ids
     lowerCAmelCase_			=      use_labels
     lowerCAmelCase_			=      vocab_size
     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      num_hidden_layers
     lowerCAmelCase_			=      num_attention_heads
     lowerCAmelCase_			=      intermediate_size
     lowerCAmelCase_			=      hidden_act
     lowerCAmelCase_			=      hidden_dropout_prob
     lowerCAmelCase_			=      attention_probs_dropout_prob
     lowerCAmelCase_			=      max_position_embeddings
     lowerCAmelCase_			=      type_vocab_size
     lowerCAmelCase_			=      type_sequence_label_size
     lowerCAmelCase_			=      initializer_range
     lowerCAmelCase_			=      num_labels
     lowerCAmelCase_			=      num_choices
     lowerCAmelCase_			=      scope





  def 						UpperCAmelCase_	(	self ):
     return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size )

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

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

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





  def 						UpperCAmelCase_	(	self ):
     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    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      MPNetModel(config=__snake_case )
     model.to(__snake_case )
     model.eval()
     lowerCAmelCase_			=      model(__snake_case    ,     __snake_case )
     lowerCAmelCase_			=      model(__snake_case )
     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    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      MPNetForQuestionAnswering(config=__snake_case )
     model.to(__snake_case )
     model.eval()
     lowerCAmelCase_			=      model(
         __snake_case    ,     attention_mask=__snake_case    ,     start_positions=__snake_case    ,     end_positions=__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 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.num_labels
     lowerCAmelCase_			=      MPNetForSequenceClassification(__snake_case )
     model.to(__snake_case )
     model.eval()
     lowerCAmelCase_			=      model(__snake_case    ,     attention_mask=__snake_case    ,     labels=__snake_case )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.num_choices
     lowerCAmelCase_			=      MPNetForMultipleChoice(config=__snake_case )
     model.to(__snake_case )
     model.eval()
     lowerCAmelCase_			=      input_ids.unsqueeze(1 ).expand(-1    ,     self.num_choices    ,     -1 ).contiguous()
     lowerCAmelCase_			=      input_mask.unsqueeze(1 ).expand(-1    ,     self.num_choices    ,     -1 ).contiguous()
     lowerCAmelCase_			=      model(
         __snake_case    ,     attention_mask=__snake_case    ,     labels=__snake_case    ,     )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_choices) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      self.num_labels
     lowerCAmelCase_			=      MPNetForTokenClassification(config=__snake_case )
     model.to(__snake_case )
     model.eval()
     lowerCAmelCase_			=      model(__snake_case    ,     attention_mask=__snake_case    ,     labels=__snake_case )
     self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels) )







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






@require_torch
class     __UpperCAmelCase       ( _SCREAMING_SNAKE_CASE						,       _SCREAMING_SNAKE_CASE						,       unittest.TestCase		):
  __A :				Optional[Any]											= (
      (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
      if is_torch_available()
      else ()
  )
  __A :				Optional[int]											= (
      {
          """feature-extraction""": MPNetModel,
          """fill-mask""": MPNetForMaskedLM,
          """question-answering""": MPNetForQuestionAnswering,
          """text-classification""": MPNetForSequenceClassification,
          """token-classification""": MPNetForTokenClassification,
          """zero-shot""": MPNetForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  __A :				Optional[Any]											= False
  __A :				Optional[int]											= True





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      MPNetModelTester(self )
     lowerCAmelCase_			=      ConfigTester(self    ,     config_class=__snake_case    ,     hidden_size=37 )





  def 						UpperCAmelCase_	(	self ):
     self.config_tester.run_common_tests()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*__snake_case )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*__snake_case )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*__snake_case )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*__snake_case )







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*__snake_case )






@require_torch
class     __UpperCAmelCase       ( unittest.TestCase		):





  @slow
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
     lowerCAmelCase_			=      torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
     lowerCAmelCase_			=      model(__snake_case )[0]
     lowerCAmelCase_			=      torch.Size((1, 11, 768) )
     self.assertEqual(output.shape    ,     __snake_case )
     lowerCAmelCase_			=      torch.tensor(
         [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]] )
     # compare the actual values for a slice.
     self.assertTrue(torch.allclose(output[:, :3, :3]    ,     __snake_case    ,     atol=1E-4 ) )












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def      UpperCAmelCase_    (    )	->				Optional[Any]:
     '''simple docstring'''


     A_          =       HfArgumentParser(_UpperCAmelCase				)
     A_          =       parser.parse_args_into_dataclasses()[0]
     A_          =       TensorFlowBenchmark(args=_UpperCAmelCase				)
     try:
          A_          =       parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_          =       '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_          =       ''' '''.join(str(_UpperCAmelCase				).split(''' '''				)[:-1]				)
          A_          =       ''''''
          A_          =       eval(str(_UpperCAmelCase				).split(''' '''				)[-1]				)
          A_          =       []
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]				)
               else:
                    wrong_args.append(_UpperCAmelCase				)
          if len(_UpperCAmelCase				) > 0:
               A_          =       full_error_msg + begin_error_msg + str(_UpperCAmelCase				)
          raise ValueError(_UpperCAmelCase				)
     benchmark.run()


if __name__ == "__main__":
      main()



