






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.
_a      	=  {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}





@is_pipeline_test
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	UpperCamelCase__								=      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

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

							_UpperCAmelCase									=			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    )

							_UpperCAmelCase									=			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    )

							_UpperCAmelCase									=			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
							_UpperCAmelCase									=			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    )
							    ]       ,    )
							_UpperCAmelCase									=			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       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			zero_shot_classifier.model.config
							_UpperCAmelCase									=			config.labelaid
							_UpperCAmelCase									=			zero_shot_classifier.entailment_id

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

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

							_UpperCAmelCase									=			{'ENTAIL': 0, 'NON-ENTAIL': 1}
							self.assertEqual(zero_shot_classifier.entailment_id       ,    0    )

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

							_UpperCAmelCase									=			original_labelaid
							self.assertEqual(UpperCAmelCase       ,    zero_shot_classifier.entailment_id    )

	@require_torch
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			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    ):
							"""simple docstring"""







							_UpperCAmelCase									=			pipeline(
							    'zero-shot-classification'       ,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'       ,    framework='pt'       ,    )
							_UpperCAmelCase									=			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_33, 0.3_33, 0.3_33],
							    }       ,    )

	@require_tf
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			pipeline(
							    'zero-shot-classification'       ,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'       ,    framework='tf'       ,    )
							_UpperCAmelCase									=			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_33, 0.3_33, 0.3_33],
							    }       ,    )

	@slow
	@require_torch
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			pipeline('zero-shot-classification'       ,    model='roberta-large-mnli'       ,    framework='pt'    )
							_UpperCAmelCase									=			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_76, 0.0_15, 0.0_09],
							    }       ,    )
							_UpperCAmelCase									=			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_17, 0.7_13, 0.0_18, 0.0_18],
							    }       ,    )

	@slow
	@require_tf
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			pipeline('zero-shot-classification'       ,    model='roberta-large-mnli'       ,    framework='tf'    )
							_UpperCAmelCase									=			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_76, 0.0_15, 0.0_09],
							    }       ,    )
							_UpperCAmelCase									=			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_17, 0.7_13, 0.0_18, 0.0_18],
							    }       ,    )








from __future__ import annotations


def __A    (      __lowerCAmelCase	)->  list[int]:





						"""simple docstring"""



						_UpperCAmelCase									=			2
						_UpperCAmelCase									=			[]
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(__lowerCAmelCase	)
						if n > 1:
												factors.append(__lowerCAmelCase	)
						return factors


if __name__ == "__main__":
		import doctest

		doctest.testmod()
