'''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 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				str						,					A						:				str						,					A						:				Optional[Any]=13						,					A						:				Union[str, Any]=7						,					A						:				List[str]=True						,					A						:				Union[str, Any]=True						,					A						:				Optional[int]=False						,					A						:				Any=True						,					A						:				Optional[int]=99						,					A						:				str=64						,					A						:				Any=5						,					A						:				int=4						,					A						:				List[str]=64						,					A						:				Optional[int]="gelu"						,					A						:				Tuple=0.1						,					A						:				Optional[Any]=0.1						,					A						:				Dict=512						,					A						:				List[Any]=16						,					A						:				Any=2						,					A						:				Optional[int]=0.02						,					A						:				Any=3						,					A						:				Any=4						,					A						:				Optional[int]=None						,					):
             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Dict							      =    batch_size
             _UpperCAmelCase     :  Optional[int]							      =    seq_length
             _UpperCAmelCase     :  int							      =    is_training
             _UpperCAmelCase     :  Optional[Any]							      =    use_input_mask
             _UpperCAmelCase     :  Optional[int]							      =    use_token_type_ids
             _UpperCAmelCase     :  List[str]							      =    use_labels
             _UpperCAmelCase     :  Optional[int]							      =    vocab_size
             _UpperCAmelCase     :  int							      =    hidden_size
             _UpperCAmelCase     :  List[Any]							      =    num_hidden_layers
             _UpperCAmelCase     :  List[str]							      =    num_attention_heads
             _UpperCAmelCase     :  Union[str, Any]							      =    intermediate_size
             _UpperCAmelCase     :  str							      =    hidden_act
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Dict							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  int							      =    max_position_embeddings
             _UpperCAmelCase     :  int							      =    type_vocab_size
             _UpperCAmelCase     :  Optional[int]							      =    type_sequence_label_size
             _UpperCAmelCase     :  Any							      =    initializer_range
             _UpperCAmelCase     :  Dict							      =    num_labels
             _UpperCAmelCase     :  str							      =    num_choices
             _UpperCAmelCase     :  List[str]							      =    scope




      def      _A	(       self						:				Optional[int]       ):
             return MPNetConfig.from_pretrained("microsoft/mpnet-base"       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size       )

             _UpperCAmelCase     :  List[str]							      =    None
             if self.use_input_mask:
                    _UpperCAmelCase     :  Any							      =    random_attention_mask([self.batch_size, self.seq_length]       )

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

             _UpperCAmelCase     :  List[str]							      =    self.get_config()
             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




      def      _A	(       self						:				List[Any]       ):
             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      _A	(       self						:				Union[str, Any]						,					A						:				Union[str, Any]						,					A						:				Optional[int]						,					A						:				List[str]						,					A						:				Optional[int]						,					A						:				Any						,					A						:				str       ):
             _UpperCAmelCase     :  Dict							      =    MPNetModel(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Optional[int]							      =    model(A						,					A       )
             _UpperCAmelCase     :  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      _A	(       self						:				int						,					A						:				int						,					A						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				Any						,					A						:				List[Any]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  Any							      =    MPNetForQuestionAnswering(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  List[str]							      =    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      _A	(       self						:				List[str]						,					A						:				str						,					A						:				Optional[Any]						,					A						:				int						,					A						:				List[Any]						,					A						:				Union[str, Any]						,					A						:				int       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.num_labels
             _UpperCAmelCase     :  Tuple							      =    MPNetForSequenceClassification(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.num_labels)       )




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




      def      _A	(       self						:				Optional[Any]						,					A						:				List[Any]						,					A						:				List[Any]						,					A						:				int						,					A						:				List[Any]						,					A						:				Any						,					A						:				Dict       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.num_labels
             _UpperCAmelCase     :  Union[str, Any]							      =    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      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.prepare_config_and_inputs()
             ((_UpperCAmelCase)      ,  (_UpperCAmelCase)      ,  (_UpperCAmelCase)      ,  (_UpperCAmelCase)      ,  (_UpperCAmelCase)      ,  (_UpperCAmelCase))     :  Optional[Any]							      =    config_and_inputs
             _UpperCAmelCase     :  Dict							      =    {"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict

@require_torch
class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Any						=						(
          (
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetModel,
          )
          if is_torch_available()
          else ()
      )
      __UpperCamelCase:  List[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:  Union[str, Any]						=						False
      __UpperCamelCase:  Optional[int]						=						True




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    MPNetModelTester(self       )
             _UpperCAmelCase     :  Any							      =    ConfigTester(self						,					config_class=A						,					hidden_size=37       )




      def      _A	(       self						:				Union[str, Any]       ):
             self.config_tester.run_common_tests()




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  List[str]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_model(*A       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_sequence_classification(*A       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_multiple_choice(*A       )




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_token_classification(*A       )





      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  int							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_question_answering(*A       )

@require_torch
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      @slow
      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[int]							      =    MPNetModel.from_pretrained("microsoft/mpnet-base"       )
             _UpperCAmelCase     :  Optional[Any]							      =    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
             _UpperCAmelCase     :  Dict							      =    model(A       )[0]
             _UpperCAmelCase     :  List[str]							      =    torch.Size((1, 11, 768)       )
             self.assertEqual(output.shape						,					A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.tensor(
                 [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]       )
             # compare the actual values for a slice.
             self.assertTrue(torch.allclose(output[:, :3, :3]						,					A						,					atol=1E-4       )       )


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    float:


       """simple docstring"""







       def get_matched_characters(_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    ) -> str:
              _UpperCAmelCase     :  Tuple							      =    []
              _UpperCAmelCase     :  Dict							      =    min(len(_stra    )			,	len(_stra    )    ) // 2
              for i, l in enumerate(_stra    ):
                     _UpperCAmelCase     :  int							      =    int(max(0			,	i - limit    )    )
                     _UpperCAmelCase     :  Any							      =    int(min(i + limit + 1			,	len(_stra    )    )    )
                     if l in _stra[left:right]:
                            matched.append(_UpperCAmelCase    )
                            _UpperCAmelCase     :  List[Any]							      =    F"""{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}"""

              return "".join(_UpperCAmelCase    )

       # matching characters
       _UpperCAmelCase     :  Union[str, Any]							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    len(_UpperCAmelCase    )

       # transposition
       _UpperCAmelCase     :  Optional[Any]							      =    (
           len([(ca, ca) for ca, ca in zip(_UpperCAmelCase			,	_UpperCAmelCase    ) if ca != ca]    ) // 2
       )

       if not match_count:
              _UpperCAmelCase     :  Dict							      =    0.0
       else:
              _UpperCAmelCase     :  Optional[int]							      =    (
                  1
                  / 3
                  * (
                      match_count / len(_UpperCAmelCase    )
                      + match_count / len(_UpperCAmelCase    )
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       _UpperCAmelCase     :  str							      =    0
       for ca, ca in zip(stra[:4]			,	stra[:4]    ):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))

