


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        snake_case_   (unittest.TestCase      ):
					UpperCAmelCase__					:    Union[str, Any]          =			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
					UpperCAmelCase__					:    List[str]          =			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

					if model_mapping is not None:
										UpperCAmelCase__					:    str          =			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
					if tf_model_mapping is not None:
										UpperCAmelCase__					:    List[str]          =			{
										    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
										}
					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :Optional[Any]			,__snake_case   :int			,__snake_case   :Tuple      )					->    str:
												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  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :Dict			,__snake_case   :Union[str, Any]      )					->    Optional[int]:
												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  lowerCamelCase__(      self   :Optional[Any]			,__snake_case   :Pipeline      )					->    Tuple:
												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  lowerCamelCase__(      self   :Tuple      )					->    int:
												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?' * 1_00			,candidate_labels=['politics', 'public health', 'science']      )
					@require_torch
					def  lowerCamelCase__(      self   :int      )					->    Optional[int]:
												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': [0.3_33, 0.3_33, 0.3_33],
												    }			,)
					@require_tf
					def  lowerCamelCase__(      self   :Tuple      )					->    Tuple:
												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': [0.3_33, 0.3_33, 0.3_33],
												    }			,)
					@slow
					@require_torch
					def  lowerCamelCase__(      self   :Dict      )					->    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(__snake_case      )			,{
												        'sequence': 'Who are you voting for in 2020?',
												        'labels': ['politics', 'public health', 'science'],
												        'scores': [0.9_76, 0.0_15, 0.0_09],
												    }			,)
												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': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
												    }			,)



					@slow
					@require_tf
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Tuple:
												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': [0.9_76, 0.0_15, 0.0_09],
												    }			,)
												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': [0.8_17, 0.7_13, 0.0_18, 0.0_18],
												    }			,)








def 							__lowercase   ( __lowerCAmelCase							:				list[int]   ):
							a__             =     []
							if len(__lowerCAmelCase   ) == 1:
														return [nums.copy()]
							for _ in range(len(__lowerCAmelCase   )   ):
														a__             =     nums.pop(0   )
														a__             =     permute(__lowerCAmelCase   )
														for perm in permutations:
																					perm.append(__lowerCAmelCase   )
														result.extend(__lowerCAmelCase   )
														nums.append(__lowerCAmelCase   )
							return result






def 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):

							def backtrack(__lowerCAmelCase							:				Any   ):
														if start == len(__lowerCAmelCase   ) - 1:
																					output.append(nums[:]   )
														else:
																					for i in range(__lowerCAmelCase					,    len(__lowerCAmelCase   )   ):
																												a__      ,					a__             =     nums[i], nums[start]
																												backtrack(start + 1   )
																												a__      ,					a__             =     nums[i], nums[start]  # backtrack

							a__             =     []
							backtrack(0   )
							return output


if __name__ == "__main__":
						import doctest

						# use res to print the data in permute2 function
						snake_case							:       Optional[Any]     			=      permutea([1, 2, 3])
						print(res)
						doctest.testmod()




