





'''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.
lowercase__   :				str      =   {'LayoutLMv2Config', 'LayoutLMv3Config'}


@is_pipeline_test
class 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):
     lowerCAmelCase		      =	MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
     lowerCAmelCase		      =	TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






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

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

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

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

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

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

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

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

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

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

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

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

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

          self.run_entailment_id(__lowerCamelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	zero_shot_classifier.model.config
          __A						:			List[str]								=	config.labelaid
          __A						:			int								=	zero_shot_classifier.entailment_id

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

          __A						:			Union[str, Any]								=	{'entailment': 0, 'neutral': 1, 'contradiction': 2}
          self.assertEqual(zero_shot_classifier.entailment_id						,     0)

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

          __A						:			Optional[int]								=	{'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
          self.assertEqual(zero_shot_classifier.entailment_id						,     2)

          __A						:			Union[str, Any]								=	original_labelaid
          self.assertEqual(__lowerCamelCase						,     zero_shot_classifier.entailment_id)


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          self.assertEqual(
              nested_simplify(__lowerCamelCase)						,     {
                  '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):

          '''simple docstring'''






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

          self.assertEqual(
              nested_simplify(__lowerCamelCase)						,     {
                  '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):

          '''simple docstring'''






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

          self.assertEqual(
              nested_simplify(__lowerCamelCase)						,     {
                  'sequence': 'Who are you voting for in 2020?',
                  'labels': ['politics', 'public health', 'science'],
                  'scores': [0.976, 0.015, 0.009],
              }						,     )
          __A						:			List[str]								=	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=__lowerCamelCase						,     )
          self.assertEqual(
              nested_simplify(__lowerCamelCase)						,     {
                  '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):

          '''simple docstring'''






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

          self.assertEqual(
              nested_simplify(__lowerCamelCase)						,     {
                  'sequence': 'Who are you voting for in 2020?',
                  'labels': ['politics', 'public health', 'science'],
                  'scores': [0.976, 0.015, 0.009],
              }						,     )
          __A						:			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=__lowerCamelCase						,     )
          self.assertEqual(
              nested_simplify(__lowerCamelCase)						,     {
                  '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'''




from __future__ import annotations






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]      )					->					None:
     create_state_space_tree(__snake_case			,							[]			,							0			,							[0 for i in range(len(__snake_case      )      )]      )






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]			,							__snake_case  :			list[int | str]			,							__snake_case  :			int			,							__snake_case  :			list[int]			,							)					->					None:

     if index == len(__snake_case      ):
          print(__snake_case      )
          return

     for i in range(len(__snake_case      )      ):
          if not index_used[i]:
               current_sequence.append(sequence[i]      )
               __A						:			Any								=	True
               create_state_space_tree(__snake_case			,							__snake_case			,							index + 1			,							__snake_case      )
               current_sequence.pop()
               __A						:			Any								=	False
lowercase__   :				list[int | str]      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

lowercase__   :				list[int | str]      =   ["A", "B", "C"]







generate_all_permutations(sequence_a)