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_  : List[Any]								=					{'LayoutLMv2Config', 'LayoutLMv3Config'}






@is_pipeline_test
class 		_lowerCamelCase		( unittest.TestCase     ):
    __a						=   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    __a						=   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        __a						=   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        __a						=   {
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: str=   ZeroShotClassificationPipeline(
         model=lowerCAmelCase			,			tokenizer=lowerCAmelCase			,			candidate_labels=['''polics''', '''health'''] )
     return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     SCREAMING_SNAKE_CASE__: List[Any]=   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
     SCREAMING_SNAKE_CASE__: int=   classifier('''Who are you voting for in 2020?'''			,			['''politics'''] )
     self.assertEqual(lowerCAmelCase			,			{'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase )]} )

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

     SCREAMING_SNAKE_CASE__: Dict=   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 )

     SCREAMING_SNAKE_CASE__: Optional[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 )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   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
     SCREAMING_SNAKE_CASE__: str=   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 )
         ]			,			)
     SCREAMING_SNAKE_CASE__: 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     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   zero_shot_classifier.model.config
     SCREAMING_SNAKE_CASE__: str=   config.labelaid
     SCREAMING_SNAKE_CASE__: int=   zero_shot_classifier.entailment_id

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

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

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

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

     SCREAMING_SNAKE_CASE__: Dict=   original_labelaid
     self.assertEqual(lowerCAmelCase			,			zero_shot_classifier.entailment_id )
    @require_torch
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: Union[str, 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     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[int]=   pipeline(
         '''zero-shot-classification'''			,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''			,			framework='''pt'''			,			)
     SCREAMING_SNAKE_CASE__: 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     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   pipeline(
         '''zero-shot-classification'''			,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''			,			framework='''tf'''			,			)
     SCREAMING_SNAKE_CASE__: List[str]=   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     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: int=   pipeline('''zero-shot-classification'''			,			model='''roberta-large-mnli'''			,			framework='''pt''' )
     SCREAMING_SNAKE_CASE__: List[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],
         }			,			)
     SCREAMING_SNAKE_CASE__: Dict=   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     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Any=   pipeline('''zero-shot-classification'''			,			model='''roberta-large-mnli'''			,			framework='''tf''' )
     SCREAMING_SNAKE_CASE__: Tuple=   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],
         }			,			)
     SCREAMING_SNAKE_CASE__: 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],
         }			,			)





import inspect
import unittest






class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[Any]:




											'''simple docstring'''

											try:
																	import diffusers  # noqa: F401
											except ImportError:
																	assert False






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Any:




											'''simple docstring'''

											import diffusers
											from diffusers.dependency_versions_table import deps

											__snake_case				=		inspect.getmembers(SCREAMING_SNAKE_CASE  ,     inspect.isclass    )

											for cls_name, cls_module in all_classes:
																	if "dummy_" in cls_module.__module__:
																							for backend in cls_module._backends:
																													if backend == "k_diffusion":
																																			__snake_case				=		"k-diffusion"
																													elif backend == "invisible_watermark":
																																			__snake_case				=		"invisible-watermark"
																													assert backend in deps, f'''{backend} is not in the deps table!'''





