"""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 					_A		:

      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=0.0_2						,			__lowerCAmelCase=3						,			__lowerCAmelCase=4						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



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

      def 							A__			(		self		):







            """simple docstring"""



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

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

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

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

      def 							A__			(		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 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MPNetModel(config=a_		)
            model.to(a_		)
            model.eval()
            lowercase								=      model(a_						,			a_		)
            lowercase								=      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						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MPNetForQuestionAnswering(config=a_		)
            model.to(a_		)
            model.eval()
            lowercase								=      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						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.num_labels
            lowercase								=      MPNetForTokenClassification(config=a_		)
            model.to(a_		)
            model.eval()
            lowercase								=      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		):







            """simple docstring"""



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






@require_torch
class 					_A		(			_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase      ):
      snake_case__     :  Optional[int]				    =      (
          (
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetModel,
          )
          if is_torch_available()
          else ()
      )
      snake_case__     :  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 {}
      )
      snake_case__     :  List[Any]				    =      False
      snake_case__     :  Union[str, Any]				    =      True

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      MPNetModelTester(self		)
            lowercase								=      ConfigTester(self						,			config_class=a_						,			hidden_size=37		)

      def 							A__			(		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_model(*a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_for_sequence_classification(*a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_for_multiple_choice(*a_		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_for_token_classification(*a_		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            lowercase								=      MPNetModel.from_pretrained("""microsoft/mpnet-base"""		)
            lowercase								=      torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
            lowercase								=      model(a_		)[0]
            lowercase								=      torch.Size((1, 11, 768)		)
            self.assertEqual(output.shape						,			a_		)
            lowercase								=      torch.tensor(
                [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]		)
            # compare the actual values for a slice.
            self.assertTrue(torch.allclose(output[:, :3, :3]						,			a_						,			atol=1E-4		)		)










"""simple docstring"""






import functools






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	Any		 =			len(lowerCAmelCase__			)
			a__  :	Optional[int]		 =			len(lowerCAmelCase__			)

			@functools.cache
			def min_distance(lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						a__  :	List[Any]		 =			int(worda[indexa] != worda[indexa]			)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1							,			lowerCAmelCase__			)							,			1 + min_distance(lowerCAmelCase__							,			indexa + 1			)							,			diff + min_distance(indexa + 1							,			indexa + 1			)							,			)

			return min_distance(0							,			0			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()