



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.
__snake_case     :List[str]			       ={'LayoutLMv2Config', 'LayoutLMv3Config'}



@is_pipeline_test
class        lowerCAmelCase__					(  unittest.TestCase       ):
				A_   :     Any								=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				A_   :     List[Any]								=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Dict       )		->     Any:
											A      						=    ZeroShotClassificationPipeline(
											    model=__UpperCamelCase						, tokenizer=__UpperCamelCase						, candidate_labels=['polics', 'health']       )
											return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  List[Any]       )		->     int:
											A      						=    classifier('Who are you voting for in 2020?'						, candidate_labels='politics'       )
											self.assertEqual(__UpperCamelCase						, {'sequence': ANY(__UpperCamelCase       ), 'labels': [ANY(__UpperCamelCase       )], 'scores': [ANY(__UpperCamelCase       )]}       )

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

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

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

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

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

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

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

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

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

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

											self.run_entailment_id(__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  Pipeline       )		->     Any:
											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(__UpperCamelCase						, zero_shot_classifier.entailment_id       )


				@require_torch
				def __UpperCamelCase						(							self      :  Tuple       )		->     Optional[Any]:
											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 __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											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(__UpperCamelCase       )						, {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['science', 'public health', 'politics'],
											        'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
											    }						, )


				@require_tf
				def __UpperCamelCase						(							self      :  int       )		->     Dict:
											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(__UpperCamelCase       )						, {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['science', 'public health', 'politics'],
											        'scores': [0.3_3_3, 0.3_3_3, 0.3_3_3],
											    }						, )


				@slow
				@require_torch
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[Any]:
											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(__UpperCamelCase       )						, {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['politics', 'public health', 'science'],
											        'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
											    }						, )
											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=__UpperCamelCase						, )
											self.assertEqual(
											    nested_simplify(__UpperCamelCase       )						, {
											        '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': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
											    }						, )


				@slow
				@require_tf
				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											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(__UpperCamelCase       )						, {
											        'sequence': 'Who are you voting for in 2020?',
											        'labels': ['politics', 'public health', 'science'],
											        'scores': [0.9_7_6, 0.0_1_5, 0.0_0_9],
											    }						, )
											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=__UpperCamelCase						, )
											self.assertEqual(
											    nested_simplify(__UpperCamelCase       )						, {
											        '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': [0.8_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
											    }						, )






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	int:
     if len(__snake_case     ) != len(__snake_case     ):
          raise ValueError('String lengths must match!'     )

     __A		:					Optional[Any]						=			0

     for chara, chara in zip(__snake_case					,				__snake_case     ):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
  import doctest

  doctest.testmod()