





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 					A__   :





      def __init__( self       ,							A_       ,							A_=13       ,							A_=7       ,							A_=True       ,							A_=True       ,							A_=False       ,							A_=True       ,							A_=99       ,							A_=64       ,							A_=5       ,							A_=4       ,							A_=64       ,							A_="gelu"       ,							A_=0.1       ,							A_=0.1       ,							A_=512       ,							A_=16       ,							A_=2       ,							A_=0.02       ,							A_=3       ,							A_=4       ,							A_=None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   parent
       UpperCamelCase	:					str       	=   batch_size
       UpperCamelCase	:					List[str]       	=   seq_length
       UpperCamelCase	:					Union[str, Any]       	=   is_training
       UpperCamelCase	:					Any       	=   use_input_mask
       UpperCamelCase	:					Union[str, Any]       	=   use_token_type_ids
       UpperCamelCase	:					Any       	=   use_labels
       UpperCamelCase	:					Optional[Any]       	=   vocab_size
       UpperCamelCase	:					Optional[Any]       	=   hidden_size
       UpperCamelCase	:					List[str]       	=   num_hidden_layers
       UpperCamelCase	:					Optional[int]       	=   num_attention_heads
       UpperCamelCase	:					Tuple       	=   intermediate_size
       UpperCamelCase	:					Optional[int]       	=   hidden_act
       UpperCamelCase	:					Optional[Any]       	=   hidden_dropout_prob
       UpperCamelCase	:					str       	=   attention_probs_dropout_prob
       UpperCamelCase	:					Union[str, Any]       	=   max_position_embeddings
       UpperCamelCase	:					List[Any]       	=   type_vocab_size
       UpperCamelCase	:					Dict       	=   type_sequence_label_size
       UpperCamelCase	:					Optional[int]       	=   initializer_range
       UpperCamelCase	:					Tuple       	=   num_labels
       UpperCamelCase	:					Union[str, Any]       	=   num_choices
       UpperCamelCase	:					List[str]       	=   scope





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size   )

       UpperCamelCase	:					Tuple       	=   None
       if self.use_input_mask:
        UpperCamelCase	:					str       	=   random_attention_mask([self.batch_size, self.seq_length]   )

       UpperCamelCase	:					Any       	=   None
       UpperCamelCase	:					List[str]       	=   None
       UpperCamelCase	:					Optional[Any]       	=   None
       if self.use_labels:
        UpperCamelCase	:					Any       	=   ids_tensor([self.batch_size]       ,							self.type_sequence_label_size   )
        UpperCamelCase	:					List[str]       	=   ids_tensor([self.batch_size, self.seq_length]       ,							self.num_labels   )
        UpperCamelCase	:					int       	=   ids_tensor([self.batch_size]       ,							self.num_choices   )

       UpperCamelCase	:					Tuple       	=   self.get_config()
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





      def        __UpperCamelCase( self   ):
       '''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       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   MPNetModel(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					str       	=   model(A_       ,							A_   )
       UpperCamelCase	:					List[str]       	=   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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   MPNetForQuestionAnswering(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					List[Any]       	=   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        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self.num_labels
       UpperCamelCase	:					Tuple       	=   MPNetForSequenceClassification(A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Union[str, Any]       	=   model(A_       ,							attention_mask=A_       ,							labels=A_   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.num_choices
       UpperCamelCase	:					str       	=   MPNetForMultipleChoice(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					Union[str, Any]       	=   input_ids.unsqueeze(1   ).expand(-1       ,							self.num_choices       ,							-1   ).contiguous()
       UpperCamelCase	:					int       	=   input_mask.unsqueeze(1   ).expand(-1       ,							self.num_choices       ,							-1   ).contiguous()
       UpperCamelCase	:					List[str]       	=   model(
           A_       ,							attention_mask=A_       ,							labels=A_       ,							)
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_choices)   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.num_labels
       UpperCamelCase	:					int       	=   MPNetForTokenClassification(config=A_   )
       model.to(A_   )
       model.eval()
       UpperCamelCase	:					str       	=   model(A_       ,							attention_mask=A_       ,							labels=A_   )
       self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.num_labels)   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.prepare_config_and_inputs()
       ((UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase)	,    (UpperCamelCase))	:					Optional[int]       	=   config_and_inputs
       UpperCamelCase	:					Optional[int]       	=   {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   MPNetModelTester(self   )
       UpperCamelCase	:					Union[str, Any]       	=   ConfigTester(self       ,							config_class=A_       ,							hidden_size=37   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.config_tester.run_common_tests()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*A_   )



@require_torch
class 					A__   (       unittest.TestCase			):





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   MPNetModel.from_pretrained("microsoft/mpnet-base"   )
       UpperCamelCase	:					Union[str, Any]       	=   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
       UpperCamelCase	:					Dict       	=   model(A_   )[0]
       UpperCamelCase	:					Tuple       	=   torch.Size((1, 11, 768)   )
       self.assertEqual(output.shape       ,							A_   )
       UpperCamelCase	:					List[Any]       	=   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]       ,							A_       ,							atol=1e-4   )   )






'''simple docstring'''







import os





def 						snake_case_							():
    UpperCAmelCase			       =		os.path.join(os.path.dirname(_a     )    ,   '''num.txt'''     )
    with open(_a     ) as file_hand:
        return str(sum(int(_a     ) for line in file_hand     )     )[:1_0]


if __name__ == "__main__":
   print(solution())




