


'''simple docstring'''


import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCamelCase__					=   """src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase__					=   direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCamelCase__					=   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCamelCase__					=   re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCamelCase__					=   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase__					=   [
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				int	    =					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"					,       _lowercase      )
		return [m.group(0      ) for m in matches]






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[Any]	    =					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		_UpperCAmelCase						:				Tuple	    =					{
		    config.replace("Config"					,       ""      ): model_type for model_type, config in config_maping_names.items()
		}

		# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
		_UpperCAmelCase						:				Union[str, Any]	    =					collections.defaultdict(_lowercase      )
		_UpperCAmelCase						:				Optional[Any]	    =					collections.defaultdict(_lowercase      )
		_UpperCAmelCase						:				Optional[Any]	    =					collections.defaultdict(_lowercase      )

		# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
		for attr_name in dir(_lowercase      ):
				_UpperCAmelCase						:				Tuple	    =					None
				if _re_tf_models.match(_lowercase      ) is not None:
						_UpperCAmelCase						:				Tuple	    =					tf_models
						_UpperCAmelCase						:				Dict	    =					_re_tf_models.match(_lowercase      ).groups()[0]
				elif _re_flax_models.match(_lowercase      ) is not None:
						_UpperCAmelCase						:				Optional[Any]	    =					flax_models
						_UpperCAmelCase						:				str	    =					_re_flax_models.match(_lowercase      ).groups()[0]
				elif _re_pt_models.match(_lowercase      ) is not None:
						_UpperCAmelCase						:				Any	    =					pt_models
						_UpperCAmelCase						:				Optional[Any]	    =					_re_pt_models.match(_lowercase      ).groups()[0]

				if lookup_dict is not None:
						while len(_lowercase      ) > 0:
								if attr_name in model_prefix_to_model_type:
										_UpperCAmelCase						:				Optional[int]	    =					True
										break
								# Try again after removing the last word in the name
								_UpperCAmelCase						:				List[Any]	    =					"""""".join(camel_case_split(_lowercase      )[:-1]      )

		_UpperCAmelCase						:				Tuple	    =					set(list(pt_models.keys()      ) + list(tf_models.keys()      ) + list(flax_models.keys()      )      )
		_UpperCAmelCase						:				Tuple	    =					list(_lowercase      )
		all_models.sort()

		_UpperCAmelCase						:				Optional[int]	    =					{"""model_type""": all_models}
		_UpperCAmelCase						:				Optional[Any]	    =					[pt_models[t] for t in all_models]
		_UpperCAmelCase						:				List[str]	    =					[tf_models[t] for t in all_models]
		_UpperCAmelCase						:				List[Any]	    =					[flax_models[t] for t in all_models]

		# Now let's use the auto-mapping names to make sure
		_UpperCAmelCase						:				Optional[int]	    =					{}
		for t in all_models:
				if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
						_UpperCAmelCase						:				Tuple	    =					"""AutoProcessor"""
				elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
						_UpperCAmelCase						:				Optional[Any]	    =					"""AutoTokenizer"""
				elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
						_UpperCAmelCase						:				Any	    =					"""AutoFeatureExtractor"""
				else:
						# Default to AutoTokenizer if a model has nothing, for backward compatibility.
						_UpperCAmelCase						:				Optional[int]	    =					"""AutoTokenizer"""

		_UpperCAmelCase						:				List[Any]	    =					[processors[t] for t in all_models]

		return pd.DataFrame(_lowercase      )






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					[
		    transformers_module.models.auto.modeling_auto,
		    transformers_module.models.auto.modeling_tf_auto,
		    transformers_module.models.auto.modeling_flax_auto,
		]
		for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
				_UpperCAmelCase						:				str	    =					[model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
				_UpperCAmelCase						:				Optional[Any]	    =					[auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
				# Loop through all three frameworks
				for module, cls, mapping in zip(_lowercase					,       _lowercase					,       _lowercase      ):
						# The type of pipeline may not exist in this framework
						if not hasattr(_lowercase					,       _lowercase      ):
								continue
						# First extract all model_names
						_UpperCAmelCase						:				Tuple	    =					[]
						for name in getattr(_lowercase					,       _lowercase      ).values():
								if isinstance(_lowercase					,       _lowercase      ):
										model_names.append(_lowercase      )
								else:
										model_names.extend(list(_lowercase      )      )

            # Add pipeline tag and auto model class for those models
						table.update({model_name: (pipeline_tag, cls) for model_name in model_names}      )

		return table






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Tuple	    =					get_frameworks_table()
		_UpperCAmelCase						:				str	    =					Dataset.from_pandas(_lowercase      )

		_UpperCAmelCase						:				Optional[Any]	    =					hf_hub_download(
		    "huggingface/transformers-metadata"					,       "pipeline_tags.json"					,       repo_type="dataset"					,       token=_lowercase      )
		_UpperCAmelCase						:				Dict	    =					Dataset.from_json(_lowercase      )
		_UpperCAmelCase						:				Optional[int]	    =					{
		    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
		    for i in range(len(_lowercase      )      )
		}
		_UpperCAmelCase						:				Dict	    =					update_pipeline_and_auto_class_table(_lowercase      )

		# Sort the model classes to avoid some nondeterministic updates to create false update commits.
		_UpperCAmelCase						:				Dict	    =					sorted(table.keys()      )
		_UpperCAmelCase						:				Optional[int]	    =					pd.DataFrame(
		    {
		        "model_class": model_classes,
		        "pipeline_tag": [table[m][0] for m in model_classes],
		        "auto_class": [table[m][1] for m in model_classes],
		    }      )
		_UpperCAmelCase						:				int	    =					Dataset.from_pandas(_lowercase      )

		with tempfile.TemporaryDirectory() as tmp_dir:
				frameworks_dataset.to_json(os.path.join(_lowercase					,       "frameworks.json"      )      )
				tags_dataset.to_json(os.path.join(_lowercase					,       "pipeline_tags.json"      )      )

				if commit_sha is not None:
						_UpperCAmelCase						:				Union[str, Any]	    =					(
						    F"""Update with commit {commit_sha}\n\nSee: """
						    F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
						)
				else:
						_UpperCAmelCase						:				Union[str, Any]	    =					"""Update"""

				upload_folder(
				    repo_id="huggingface/transformers-metadata"					,       folder_path=_lowercase					,       repo_type="dataset"					,       token=_lowercase					,       commit_message=_lowercase					,       )






def        __lowerCAmelCase       ():
		_UpperCAmelCase						:				Optional[int]	    =					{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
		_UpperCAmelCase						:				List[str]	    =					transformers_module.pipelines.SUPPORTED_TASKS
		_UpperCAmelCase						:				List[str]	    =					[]
		for key in pipeline_tasks:
				if key not in in_table:
						_UpperCAmelCase						:				Tuple	    =					pipeline_tasks[key]["""pt"""]
						if isinstance(_lowercase					,       (list, tuple)      ):
								_UpperCAmelCase						:				List[str]	    =					model[0]
						_UpperCAmelCase						:				List[str]	    =					model.__name__
						if model not in in_table.values():
								missing.append(_lowercase      )

		if len(_lowercase      ) > 0:
				_UpperCAmelCase						:				Any	    =					""", """.join(_lowercase      )
				raise ValueError(
				    "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
				    F"""`utils/update_metadata.py`: {msg}. Please add them!"""      )


if __name__ == "__main__":
						lowerCamelCase__					=   argparse.ArgumentParser()
						parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
						parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
						parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
						lowerCamelCase__					=   parser.parse_args()

						if args.check_only:
												check_pipeline_tags()
						else:
												update_metadata(args.token, args.commit_sha)








'''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],
								    }		,		)

