

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_       		=  {"LayoutLMv2Config", "LayoutLMv3Config"}


@is_pipeline_test
class  A						(							unittest.TestCase ):







    """simple docstring"""





    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 						snake_case__     (      self :       Any,lowercase_ :       Any,lowercase_ :       Union[str, Any],lowercase_ :       int		)->    List[str]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[str],lowercase_ :       str,lowercase_ :       int		)->    Optional[int]:





        '''simple docstring'''







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

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

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

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

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

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

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

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

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

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

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

        self.run_entailment_id(lowercase_		)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Pipeline		)->    List[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(lowercase_,zero_shot_classifier.entailment_id		)


    @require_torch
    def 						snake_case__     (      self :       Dict		)->    List[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?' * 1_0_0,candidate_labels=['politics', 'public health', 'science']		)


    @require_torch
    def 						snake_case__     (      self :       str		)->    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(lowercase_		),{
                'sequence': 'Who are you voting for in 2020?',
                'labels': ['science', 'public health', 'politics'],
                'scores': [0.333, 0.333, 0.333],
            },)


    @require_tf
    def 						snake_case__     (      self :       List[str]		)->    Tuple:





        '''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(lowercase_		),{
                'sequence': 'Who are you voting for in 2020?',
                'labels': ['science', 'public health', 'politics'],
                'scores': [0.333, 0.333, 0.333],
            },)


    @slow
    @require_torch
    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''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(lowercase_		),{
                '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=lowercase_,)
        self.assertEqual(
            nested_simplify(lowercase_		),{
                '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 						snake_case__     (      self :       Any		)->    int:





        '''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(lowercase_		),{
                '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=lowercase_,)
        self.assertEqual(
            nested_simplify(lowercase_		),{
                '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],
            },)




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

