import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
a_    					=       {'LayoutLMv2Config', 'LayoutLMv3Config'}





@is_pipeline_test
class 		_lowercase						(					unittest.TestCase       ):
 lowercase				     =   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 lowercase				     =   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  lowercase				     =   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  lowercase				     =   {
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Tuple   ,      snake_case	:							Dict			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					ZeroShotClassificationPipeline(
      model=snake_case   ,      tokenizer=snake_case   ,      candidate_labels=['polics', 'health']			)
  return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							int			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						int =					classifier('Who are you voting for in 2020?'   ,      candidate_labels='politics'			)
  self.assertEqual(snake_case   ,      {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			)], 'scores': [ANY(snake_case			)]}			)

  # No kwarg
  UpperCamelCase_					:						Dict =					classifier('Who are you voting for in 2020?'   ,      ['politics']			)
  self.assertEqual(snake_case   ,      {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			)], 'scores': [ANY(snake_case			)]}			)

  UpperCamelCase_					:						Optional[int] =					classifier('Who are you voting for in 2020?'   ,      candidate_labels=['politics']			)
  self.assertEqual(snake_case   ,      {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			)], 'scores': [ANY(snake_case			)]}			)

  UpperCamelCase_					:						List[str] =					classifier('Who are you voting for in 2020?'   ,      candidate_labels='politics, public health'			)
  self.assertEqual(
      snake_case   ,      {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			), ANY(snake_case			)], 'scores': [ANY(snake_case			), ANY(snake_case			)]}			)
  self.assertAlmostEqual(sum(nested_simplify(outputs['scores']			)			)   ,      1.0			)

  UpperCamelCase_					:						Optional[Any] =					classifier('Who are you voting for in 2020?'   ,      candidate_labels=['politics', 'public health']			)
  self.assertEqual(
      snake_case   ,      {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			), ANY(snake_case			)], 'scores': [ANY(snake_case			), ANY(snake_case			)]}			)
  self.assertAlmostEqual(sum(nested_simplify(outputs['scores']			)			)   ,      1.0			)

  UpperCamelCase_					:						Tuple =					classifier(
      'Who are you voting for in 2020?'   ,      candidate_labels='politics'   ,      hypothesis_template='This text is about {}'			)
  self.assertEqual(snake_case   ,      {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			)], 'scores': [ANY(snake_case			)]}			)

  # https://github.com/huggingface/transformers/issues/13846
  UpperCamelCase_					:						List[str] =					classifier(['I am happy']   ,      ['positive', 'negative']			)
  self.assertEqual(
      snake_case   ,      [
          {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			), ANY(snake_case			)], 'scores': [ANY(snake_case			), ANY(snake_case			)]}
          for i in range(1			)
      ]   ,      )
  UpperCamelCase_					:						Any =					classifier(['I am happy', 'I am sad']   ,      ['positive', 'negative']			)
  self.assertEqual(
      snake_case   ,      [
          {'sequence': ANY(snake_case			), 'labels': [ANY(snake_case			), ANY(snake_case			)], 'scores': [ANY(snake_case			), ANY(snake_case			)]}
          for i in range(2			)
      ]   ,      )

  with self.assertRaises(snake_case			):
   classifier(''   ,      candidate_labels='politics'			)

  with self.assertRaises(snake_case			):
   classifier(snake_case   ,      candidate_labels='politics'			)

  with self.assertRaises(snake_case			):
   classifier('Who are you voting for in 2020?'   ,      candidate_labels=''			)

  with self.assertRaises(snake_case			):
   classifier('Who are you voting for in 2020?'   ,      candidate_labels=snake_case			)

  with self.assertRaises(snake_case			):
   classifier(
       'Who are you voting for in 2020?'   ,      candidate_labels='politics'   ,      hypothesis_template='Not formatting template'   ,      )

  with self.assertRaises(snake_case			):
   classifier(
       'Who are you voting for in 2020?'   ,      candidate_labels='politics'   ,      hypothesis_template=snake_case   ,      )

  self.run_entailment_id(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int   ,      snake_case	:							Pipeline			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					zero_shot_classifier.model.config
  UpperCamelCase_					:						int =					config.labelaid
  UpperCamelCase_					:						str =					zero_shot_classifier.entailment_id

  UpperCamelCase_					:						Union[str, Any] =					{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
  self.assertEqual(zero_shot_classifier.entailment_id   ,      -1			)

  UpperCamelCase_					:						Dict =					{'entailment': 0, 'neutral': 1, 'contradiction': 2}
  self.assertEqual(zero_shot_classifier.entailment_id   ,      0			)

  UpperCamelCase_					:						Union[str, Any] =					{'ENTAIL': 0, 'NON-ENTAIL': 1}
  self.assertEqual(zero_shot_classifier.entailment_id   ,      0			)

  UpperCamelCase_					:						List[str] =					{'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
  self.assertEqual(zero_shot_classifier.entailment_id   ,      2			)

  UpperCamelCase_					:						List[Any] =					original_labelaid
  self.assertEqual(snake_case   ,      zero_shot_classifier.entailment_id			)

 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						str =					pipeline(
      'zero-shot-classification'   ,      model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,      framework='pt'   ,      )
  # There was a regression in 4.10 for this
  # Adding a test so we don't make the mistake again.
  # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
  zero_shot_classifier(
      'Who are you voting for in 2020?' * 1_0_0   ,      candidate_labels=['politics', 'public health', 'science']			)

 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					pipeline(
      'zero-shot-classification'   ,      model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,      framework='pt'   ,      )
  UpperCamelCase_					:						Union[str, Any] =					zero_shot_classifier(
      'Who are you voting for in 2020?'   ,      candidate_labels=['politics', 'public health', 'science']			)

  self.assertEqual(
      nested_simplify(snake_case			)   ,      {
          'sequence': 'Who are you voting for in 2020?',
          'labels': ['science', 'public health', 'politics'],
          'scores': [0.333, 0.333, 0.333],
      }   ,      )

 @require_tf
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					pipeline(
      'zero-shot-classification'   ,      model='sshleifer/tiny-distilbert-base-cased-distilled-squad'   ,      framework='tf'   ,      )
  UpperCamelCase_					:						List[Any] =					zero_shot_classifier(
      'Who are you voting for in 2020?'   ,      candidate_labels=['politics', 'public health', 'science']			)

  self.assertEqual(
      nested_simplify(snake_case			)   ,      {
          'sequence': 'Who are you voting for in 2020?',
          'labels': ['science', 'public health', 'politics'],
          'scores': [0.333, 0.333, 0.333],
      }   ,      )

 @slow
 @require_torch
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						int =					pipeline('zero-shot-classification'   ,      model='roberta-large-mnli'   ,      framework='pt'			)
  UpperCamelCase_					:						Optional[Any] =					zero_shot_classifier(
      'Who are you voting for in 2020?'   ,      candidate_labels=['politics', 'public health', 'science']			)

  self.assertEqual(
      nested_simplify(snake_case			)   ,      {
          'sequence': 'Who are you voting for in 2020?',
          'labels': ['politics', 'public health', 'science'],
          'scores': [0.976, 0.015, 0.009],
      }   ,      )
  UpperCamelCase_					:						Any =					zero_shot_classifier(
      'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
      ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
      ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
      ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
      ' machine translation tasks show these models to be superior in quality while being more parallelizable'
      ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
      ' English-to-German translation task, improving over the existing best results, including ensembles by'
      ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
      ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
      ' fraction of the training costs of the best models from the literature. We show that the Transformer'
      ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
      ' large and limited training data.'   ,      candidate_labels=['machine learning', 'statistics', 'translation', 'vision']   ,      multi_label=snake_case   ,      )
  self.assertEqual(
      nested_simplify(snake_case			)   ,      {
          'sequence': (
              'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
              ' networks in an encoder-decoder configuration. The best performing models also connect the'
              ' encoder and decoder through an attention mechanism. We propose a new simple network'
              ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
              ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
              ' superior in quality while being more parallelizable and requiring significantly less time to'
              ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
              ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
              ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
              ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
              ' costs of the best models from the literature. We show that the Transformer generalizes well to'
              ' other tasks by applying it successfully to English constituency parsing both with large and'
              ' limited training data.'
          ),
          'labels': ['translation', 'machine learning', 'vision', 'statistics'],
          'scores': [0.817, 0.713, 0.018, 0.018],
      }   ,      )


 @slow
 @require_tf
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					pipeline('zero-shot-classification'   ,      model='roberta-large-mnli'   ,      framework='tf'			)
  UpperCamelCase_					:						Optional[Any] =					zero_shot_classifier(
      'Who are you voting for in 2020?'   ,      candidate_labels=['politics', 'public health', 'science']			)

  self.assertEqual(
      nested_simplify(snake_case			)   ,      {
          'sequence': 'Who are you voting for in 2020?',
          'labels': ['politics', 'public health', 'science'],
          'scores': [0.976, 0.015, 0.009],
      }   ,      )
  UpperCamelCase_					:						Union[str, Any] =					zero_shot_classifier(
      'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
      ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
      ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
      ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
      ' machine translation tasks show these models to be superior in quality while being more parallelizable'
      ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
      ' English-to-German translation task, improving over the existing best results, including ensembles by'
      ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
      ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
      ' fraction of the training costs of the best models from the literature. We show that the Transformer'
      ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
      ' large and limited training data.'   ,      candidate_labels=['machine learning', 'statistics', 'translation', 'vision']   ,      multi_label=snake_case   ,      )
  self.assertEqual(
      nested_simplify(snake_case			)   ,      {
          'sequence': (
              'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
              ' networks in an encoder-decoder configuration. The best performing models also connect the'
              ' encoder and decoder through an attention mechanism. We propose a new simple network'
              ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
              ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
              ' superior in quality while being more parallelizable and requiring significantly less time to'
              ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
              ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
              ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
              ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
              ' costs of the best models from the literature. We show that the Transformer generalizes well to'
              ' other tasks by applying it successfully to English constituency parsing both with large and'
              ' limited training data.'
          ),
          'labels': ['translation', 'machine learning', 'vision', 'statistics'],
          'scores': [0.817, 0.713, 0.018, 0.018],
      }   ,      )




def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     str	):

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

  return "".join(lowerCamelCase	)

 # matching characters
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						str =					get_matched_characters(lowerCamelCase  ,  lowerCamelCase	)
 UpperCamelCase_					:						Union[str, Any] =					len(lowerCamelCase	)

 # transposition
 UpperCamelCase_					:						int =					(
     len([(ca, ca) for ca, ca in zip(lowerCamelCase  ,  lowerCamelCase	) if ca != ca]	) // 2
 )

 if not match_count:
  UpperCamelCase_					:						Union[str, Any] =					0.0
 else:
  UpperCamelCase_					:						str =					(
      1
      / 3
      * (
          match_count / len(lowerCamelCase	)
          + match_count / len(lowerCamelCase	)
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 UpperCamelCase_					:						Dict =					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'))



