'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
  )

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				str						,					A						:				List[Any]						,					A						:				Optional[int]=13						,					A						:				Optional[Any]=7						,					A						:				Optional[Any]=True						,					A						:				List[str]=True						,					A						:				Optional[Any]=True						,					A						:				str=True						,					A						:				Optional[int]=99						,					A						:				int=32						,					A						:				Union[str, Any]=2						,					A						:				List[Any]=4						,					A						:				Dict=37						,					A						:				Union[str, Any]="gelu"						,					A						:				Optional[Any]=0.1						,					A						:				Optional[int]=0.1						,					A						:				List[str]=512						,					A						:				Optional[int]=16						,					A						:				int=2						,					A						:				Optional[Any]=0.02						,					A						:				List[str]=False						,					A						:				Dict=True						,					A						:				Any="None"						,					A						:				List[str]=3						,					A						:				str=4						,					A						:				List[Any]=None						,					):
             _UpperCAmelCase     :  Union[str, Any]							      =    parent
             _UpperCAmelCase     :  str							      =    batch_size
             _UpperCAmelCase     :  Any							      =    seq_length
             _UpperCAmelCase     :  Optional[Any]							      =    is_training
             _UpperCAmelCase     :  Any							      =    use_input_mask
             _UpperCAmelCase     :  Union[str, Any]							      =    use_token_type_ids
             _UpperCAmelCase     :  Optional[Any]							      =    use_labels
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_size
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  Optional[int]							      =    num_hidden_layers
             _UpperCAmelCase     :  List[Any]							      =    num_attention_heads
             _UpperCAmelCase     :  Tuple							      =    intermediate_size
             _UpperCAmelCase     :  Union[str, Any]							      =    hidden_act
             _UpperCAmelCase     :  Tuple							      =    hidden_dropout_prob
             _UpperCAmelCase     :  List[str]							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  int							      =    max_position_embeddings
             _UpperCAmelCase     :  Union[str, Any]							      =    type_vocab_size
             _UpperCAmelCase     :  int							      =    type_sequence_label_size
             _UpperCAmelCase     :  int							      =    initializer_range
             _UpperCAmelCase     :  List[str]							      =    num_labels
             _UpperCAmelCase     :  Union[str, Any]							      =    num_choices
             _UpperCAmelCase     :  List[str]							      =    relative_attention
             _UpperCAmelCase     :  List[Any]							      =    position_biased_input
             _UpperCAmelCase     :  List[str]							      =    pos_att_type
             _UpperCAmelCase     :  Dict							      =    scope




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

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

             _UpperCAmelCase     :  Any							      =    None
             if self.use_token_type_ids:
                    _UpperCAmelCase     :  List[str]							      =    ids_tensor([self.batch_size, self.seq_length]						,					self.type_vocab_size       )

             _UpperCAmelCase     :  int							      =    None
             _UpperCAmelCase     :  Any							      =    None
             _UpperCAmelCase     :  Optional[int]							      =    None
             if self.use_labels:
                    _UpperCAmelCase     :  Tuple							      =    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							      =    DebertaVaConfig(
                 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						,					type_vocab_size=self.type_vocab_size						,					relative_attention=self.relative_attention						,					position_biased_input=self.position_biased_input						,					initializer_range=self.initializer_range						,					return_dict=A						,					)

             return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




      def      _A	(       self						:				Union[str, Any]						,					A						:				Tuple						,					A						:				List[str]						,					A						:				Tuple						,					A						:				List[Any]						,					A						:				Any						,					A						:				Any						,					A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    TFDebertaVaModel(config=A       )
             _UpperCAmelCase     :  List[Any]							      =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

             _UpperCAmelCase     :  int							      =    [input_ids, input_mask]
             _UpperCAmelCase     :  List[Any]							      =    model(A       )

             _UpperCAmelCase     :  Tuple							      =    model(A       )

             self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)       )




      def      _A	(       self						:				Optional[Any]						,					A						:				str						,					A						:				Union[str, Any]						,					A						:				str						,					A						:				List[str]						,					A						:				Any						,					A						:				List[Any]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    TFDebertaVaForMaskedLM(config=A       )
             _UpperCAmelCase     :  int							      =    {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
             }
             _UpperCAmelCase     :  List[str]							      =    model(A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)       )




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

             _UpperCAmelCase     :  int							      =    model(A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)       )




      def      _A	(       self						:				Tuple						,					A						:				List[Any]						,					A						:				List[Any]						,					A						:				List[Any]						,					A						:				Union[str, Any]						,					A						:				Any						,					A						:				Optional[int]						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Any							      =    self.num_labels
             _UpperCAmelCase     :  Optional[Any]							      =    TFDebertaVaForTokenClassification(config=A       )
             _UpperCAmelCase     :  Optional[int]							      =    {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
             }
             _UpperCAmelCase     :  Any							      =    model(A       )
             self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)       )




      def      _A	(       self						:				Any						,					A						:				Any						,					A						:				Tuple						,					A						:				Dict						,					A						:				Any						,					A						:				int						,					A						:				Optional[int]						,					A						:				Any       ):
             _UpperCAmelCase     :  Tuple							      =    TFDebertaVaForQuestionAnswering(config=A       )
             _UpperCAmelCase     :  Tuple							      =    {
                 "input_ids": input_ids,
                 "attention_mask": input_mask,
                 "token_type_ids": token_type_ids,
             }

             _UpperCAmelCase     :  Optional[int]							      =    model(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						:				Dict       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.prepare_config_and_inputs()
             (
                 (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  (
                 _UpperCAmelCase
             )      ,  
             )     :  Union[str, Any]							      =    config_and_inputs
             _UpperCAmelCase     :  Union[str, Any]							      =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict

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

      __UpperCamelCase:  Optional[int]						=						(
          (
              TFDebertaVaModel,
              TFDebertaVaForMaskedLM,
              TFDebertaVaForQuestionAnswering,
              TFDebertaVaForSequenceClassification,
              TFDebertaVaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      __UpperCamelCase:  int						=						(
          {
              "feature-extraction": TFDebertaVaModel,
              "fill-mask": TFDebertaVaForMaskedLM,
              "question-answering": TFDebertaVaForQuestionAnswering,
              "text-classification": TFDebertaVaForSequenceClassification,
              "token-classification": TFDebertaVaForTokenClassification,
              "zero-shot": TFDebertaVaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      __UpperCamelCase:  str						=						False
      __UpperCamelCase:  int						=						False




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




      def      _A	(       self						:				Optional[int]       ):
             self.config_tester.run_common_tests()




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




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




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  str							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*A       )




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




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





      @slow
      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  int							      =    TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"       )
             self.assertIsNotNone(A       )

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

      @unittest.skip(reason="Model not available yet"       )
      def      _A	(       self						:				Dict       ):
             pass





      @slow
      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Tuple							      =    TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"       )
             _UpperCAmelCase     :  List[Any]							      =    tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       )
             _UpperCAmelCase     :  Optional[int]							      =    tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
             _UpperCAmelCase     :  List[Any]							      =    model(A						,					attention_mask=A       )[0]

             _UpperCAmelCase     :  Any							      =    tf.constant(
                 [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]       )
             tf.debugging.assert_near(output[:, 1:4, 1:4]						,					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"""))

