



"""simple docstring"""







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      UpperCAmelCase__		(      unittest.TestCase     ):
		lowerCAmelCase_       :	List[Any] 							=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
		lowerCAmelCase_       :	str 							=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

		if model_mapping is not None:
				lowerCAmelCase_       :	List[str] 							=     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
		if tf_model_mapping is not None:
				lowerCAmelCase_       :	str 							=     {
				    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
				}



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






									'''simple docstring'''

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



		def 					A_		( self  :    str      ,							snake_case  :    int      ,							snake_case  :    str				)			->						Dict:






									'''simple docstring'''

									A         =					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
									A         =					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				)]}				)

									A         =					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				)]}				)

									A         =					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				)

									A         =					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				)

									A         =					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
									A         =					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				)
									    ]      ,							)
									A         =					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 					A_		( self  :    Optional[int]      ,							snake_case  :    Pipeline				)			->						Union[str, Any]:






									'''simple docstring'''

									A         =					zero_shot_classifier.model.config
									A         =					config.labelaid
									A         =					zero_shot_classifier.entailment_id

									A         =					{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
									self.assertEqual(zero_shot_classifier.entailment_id      ,							-1				)

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

									A         =					{'ENTAIL': 0, 'NON-ENTAIL': 1}
									self.assertEqual(zero_shot_classifier.entailment_id      ,							0				)

									A         =					{'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
									self.assertEqual(zero_shot_classifier.entailment_id      ,							2				)

									A         =					original_labelaid
									self.assertEqual(snake_case      ,							zero_shot_classifier.entailment_id				)



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






									'''simple docstring'''

									A         =					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?' * 100      ,							candidate_labels=['politics', 'public health', 'science']				)



		@require_torch
		def 					A_		( self  :    int				)			->						int:






									'''simple docstring'''

									A         =					pipeline(
									    'zero-shot-classification'      ,							model='sshleifer/tiny-distilbert-base-cased-distilled-squad'      ,							framework='pt'      ,							)
									A         =					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 					A_		( self  :    str				)			->						Optional[int]:






									'''simple docstring'''

									A         =					pipeline(
									    'zero-shot-classification'      ,							model='sshleifer/tiny-distilbert-base-cased-distilled-squad'      ,							framework='tf'      ,							)
									A         =					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 					A_		( self  :    str				)			->						Any:






									'''simple docstring'''

									A         =					pipeline('zero-shot-classification'      ,							model='roberta-large-mnli'      ,							framework='pt'				)
									A         =					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],
									    }      ,							)
									A         =					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 					A_		( self  :    List[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					pipeline('zero-shot-classification'      ,							model='roberta-large-mnli'      ,							framework='tf'				)
									A         =					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],
									    }      ,							)
									A         =					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],
									    }      ,							)





"""simple docstring"""







def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							float:

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

														return "".join(lowerCamelCase__			)

							# matching characters
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					get_matched_characters(lowerCamelCase__				,      lowerCamelCase__			)
							A         =					len(lowerCamelCase__			)

							# transposition
							A         =					(
							    len([(ca, ca) for ca, ca in zip(lowerCamelCase__				,      lowerCamelCase__			) if ca != ca]			) // 2
							)

							if not match_count:
														A         =					0.0
							else:
														A         =					(
														    1
														    / 3
														    * (
														        match_count / len(lowerCamelCase__			)
														        + match_count / len(lowerCamelCase__			)
														        + (match_count - transpositions) / match_count
														    )
														)

							# common prefix up to 4 characters
							A         =					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'))



