



'''simple docstring'''




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info



def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							return {key.lstrip("""-"""		): value for key, value in zip(unknown_args[::2]	,				unknown_args[1::2]		)}



def       __lowerCamelCase  (					)      ->			Optional[int]:
							UpperCAmelCase     :		Dict							     =		ArgumentParser(
							    """HuggingFace Datasets CLI tool"""	,				usage="""datasets-cli <command> [<args>]"""	,				allow_abbrev=_lowercase		)
							UpperCAmelCase     :		List[str]							     =		parser.add_subparsers(help="""datasets-cli command helpers"""		)
							set_verbosity_info()

							# Register commands
							ConvertCommand.register_subcommand(_lowercase		)
							EnvironmentCommand.register_subcommand(_lowercase		)
							TestCommand.register_subcommand(_lowercase		)
							RunBeamCommand.register_subcommand(_lowercase		)
							DummyDataCommand.register_subcommand(_lowercase		)

							# Parse args
							UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		parser.parse_known_args()
							if not hasattr(_lowercase	,				"""func"""		):
														parser.print_help()
														exit(1		)
							UpperCAmelCase     :		int							     =		parse_unknown_args(_lowercase		)

							# Run
							UpperCAmelCase     :		Optional[Any]							     =		args.func(_lowercase	,				**_lowercase		)
							service.run()


if __name__ == "__main__":
							main()






'''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.
a   :			Tuple					  =    {"""LayoutLMv2Config""", """LayoutLMv3Config"""}

@is_pipeline_test
class 			UpperCamelCase_ (							unittest.TestCase			):
	lowercase          = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	lowercase          = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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





	def  _lowercase(  self		,		A		,		A		,		A    )			->			Dict:
								UpperCAmelCase     :		Union[str, Any]							     =		ZeroShotClassificationPipeline(
								    model=A		,		tokenizer=A		,		candidate_labels=["""polics""", """health"""]    )
								return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]





	def  _lowercase(  self		,		A		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		Dict							     =		classifier("""Who are you voting for in 2020?"""		,		candidate_labels="""politics"""    )
								self.assertEqual(A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    )], """scores""": [ANY(A    )]}    )

								# No kwarg
								UpperCAmelCase     :		Optional[int]							     =		classifier("""Who are you voting for in 2020?"""		,		["""politics"""]    )
								self.assertEqual(A		,		{"""sequence""": ANY(A    ), """labels""": [ANY(A    )], """scores""": [ANY(A    )]}    )

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

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

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

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

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

								with self.assertRaises(A    ):
															classifier(""""""		,		candidate_labels="""politics"""    )

								with self.assertRaises(A    ):
															classifier(A		,		candidate_labels="""politics"""    )

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

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

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

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

								self.run_entailment_id(A    )





	def  _lowercase(  self		,		A    )			->			Any:
								UpperCAmelCase     :		Tuple							     =		zero_shot_classifier.model.config
								UpperCAmelCase     :		Union[str, Any]							     =		config.labelaid
								UpperCAmelCase     :		Tuple							     =		zero_shot_classifier.entailment_id

								UpperCAmelCase     :		Any							     =		{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
								self.assertEqual(zero_shot_classifier.entailment_id		,		-1    )

								UpperCAmelCase     :		Optional[Any]							     =		{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
								self.assertEqual(zero_shot_classifier.entailment_id		,		0    )

								UpperCAmelCase     :		Any							     =		{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
								self.assertEqual(zero_shot_classifier.entailment_id		,		0    )

								UpperCAmelCase     :		List[str]							     =		{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
								self.assertEqual(zero_shot_classifier.entailment_id		,		2    )

								UpperCAmelCase     :		Tuple							     =		original_labelaid
								self.assertEqual(A		,		zero_shot_classifier.entailment_id    )





	@require_torch
	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     :		int							     =		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  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[int]							     =		pipeline(
								    """zero-shot-classification"""		,		model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""		,		framework="""pt"""		,		)
								UpperCAmelCase     :		Union[str, Any]							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""science""", """public health""", """politics"""],
								        """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
								    }		,		)





	@require_tf
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		pipeline(
								    """zero-shot-classification"""		,		model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""		,		framework="""tf"""		,		)
								UpperCAmelCase     :		List[Any]							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""science""", """public health""", """politics"""],
								        """scores""": [0.3_3_3, 0.3_3_3, 0.3_3_3],
								    }		,		)





	@slow
	@require_torch
	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		Optional[int]							     =		pipeline("""zero-shot-classification"""		,		model="""roberta-large-mnli"""		,		framework="""pt"""    )
								UpperCAmelCase     :		Optional[int]							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""politics""", """public health""", """science"""],
								        """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
								    }		,		)
								UpperCAmelCase     :		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=A		,		)
								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """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_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
								    }		,		)






	@slow
	@require_tf
	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		int							     =		pipeline("""zero-shot-classification"""		,		model="""roberta-large-mnli"""		,		framework="""tf"""    )
								UpperCAmelCase     :		Tuple							     =		zero_shot_classifier(
								    """Who are you voting for in 2020?"""		,		candidate_labels=["""politics""", """public health""", """science"""]    )

								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """sequence""": """Who are you voting for in 2020?""",
								        """labels""": ["""politics""", """public health""", """science"""],
								        """scores""": [0.9_7_6, 0.0_1_5, 0.0_0_9],
								    }		,		)
								UpperCAmelCase     :		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=A		,		)
								self.assertEqual(
								    nested_simplify(A    )		,		{
								        """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_1_7, 0.7_1_3, 0.0_1_8, 0.0_1_8],
								    }		,		)

