
'''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      __SCREAMING_SNAKE_CASE      :




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




 def      __magic_name__	(      self	:							str      )	->						List[str]:
       return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''      )




 def      __magic_name__	(      self	:							str      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Any									=ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size      )

       SCREAMING_SNAKE_CASE__     :				Any									=None
       if self.use_input_mask:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=random_attention_mask([self.batch_size, self.seq_length]      )

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

       SCREAMING_SNAKE_CASE__     :				Any									=self.get_config()
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




 def      __magic_name__	(      self	:							Any      )	->						Dict:
       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      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Optional[int]					,      __lowercase	:							Optional[int]					,      __lowercase	:							Tuple					,      __lowercase	:							Optional[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				int									=MPNetModel(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=model(__lowercase					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									=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      __magic_name__	(      self	:							int					,      __lowercase	:							List[Any]					,      __lowercase	:							int					,      __lowercase	:							List[str]					,      __lowercase	:							Optional[int]					,      __lowercase	:							int					,      __lowercase	:							List[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Any									=MPNetForQuestionAnswering(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Tuple									=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      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Any					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Tuple					,      __lowercase	:							Tuple					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[int]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.num_labels
       SCREAMING_SNAKE_CASE__     :				int									=MPNetForSequenceClassification(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=model(__lowercase					,      attention_mask=__lowercase					,      labels=__lowercase      )
       self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)      )




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




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Optional[int]					,      __lowercase	:							Any					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							int					,      __lowercase	:							Optional[int]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				Any									=self.num_labels
       SCREAMING_SNAKE_CASE__     :				Dict									=MPNetForTokenClassification(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				List[str]									=model(__lowercase					,      attention_mask=__lowercase					,      labels=__lowercase      )
       self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)      )




 def      __magic_name__	(      self	:							Tuple      )	->						str:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.prepare_config_and_inputs()
       ((SCREAMING_SNAKE_CASE__)		,	(SCREAMING_SNAKE_CASE__)		,	(SCREAMING_SNAKE_CASE__)		,	(SCREAMING_SNAKE_CASE__)		,	(SCREAMING_SNAKE_CASE__)		,	(SCREAMING_SNAKE_CASE__))     :				Union[str, Any]									=config_and_inputs
       SCREAMING_SNAKE_CASE__     :				int									={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class      __SCREAMING_SNAKE_CASE      (			lowercase__	,				lowercase__	,				unittest.TestCase				):
 snake_case_  							=							(
     (
         MPNetForMaskedLM,
         MPNetForMultipleChoice,
         MPNetForQuestionAnswering,
         MPNetForSequenceClassification,
         MPNetForTokenClassification,
         MPNetModel,
     )
     if is_torch_available()
     else ()
 )
 snake_case_  							=							(
     {
         """feature-extraction""": MPNetModel,
         """fill-mask""": MPNetForMaskedLM,
         """question-answering""": MPNetForQuestionAnswering,
         """text-classification""": MPNetForSequenceClassification,
         """token-classification""": MPNetForTokenClassification,
         """zero-shot""": MPNetForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 snake_case_  							=							False
 snake_case_  							=							True




 def      __magic_name__	(      self	:							List[Any]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=MPNetModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				Any									=ConfigTester(self					,      config_class=__lowercase					,      hidden_size=37      )




 def      __magic_name__	(      self	:							List[str]      )	->						Tuple:
       self.config_tester.run_common_tests()




 def      __magic_name__	(      self	:							Tuple      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*__lowercase      )




 def      __magic_name__	(      self	:							List[Any]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase      )




 def      __magic_name__	(      self	:							List[Any]      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase      )




 def      __magic_name__	(      self	:							Any      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase      )




 def      __magic_name__	(      self	:							int      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase      )






@require_torch
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 @slow
 def      __magic_name__	(      self	:							Dict      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				int									=MPNetModel.from_pretrained('''microsoft/mpnet-base'''      )
       SCREAMING_SNAKE_CASE__     :				Dict									=torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=model(__lowercase      )[0]
       SCREAMING_SNAKE_CASE__     :				str									=torch.Size((1, 11, 7_68)      )
       self.assertEqual(output.shape					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				int									=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      )      )







'''simple docstring'''
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
      SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      for i in range(UpperCamelCase__							):
            for j in range(m + 1							):
                  if dp[i][j]:
                        if j < m and a[i].upper() == b[j]:
                              SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
                        if a[i].islower():
                              SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      return dp[n][m]


if __name__ == "__main__":
  import doctest

  doctest.testmod()