





'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import numpy
   import tensorflow as tf

   from transformers import (
       TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
       TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
       BertConfig,
       DPRConfig,
       TFDPRContextEncoder,
       TFDPRQuestionEncoder,
       TFDPRReader,
   )
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						Tuple			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						List[Any]=13			,					lowerCAmelCase__  :						List[Any]=7			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						Union[str, Any]=True			,					lowerCAmelCase__  :						Tuple=True			,					lowerCAmelCase__  :						Dict=True			,					lowerCAmelCase__  :						Optional[Any]=99			,					lowerCAmelCase__  :						Optional[Any]=32			,					lowerCAmelCase__  :						Tuple=2			,					lowerCAmelCase__  :						Union[str, Any]=4			,					lowerCAmelCase__  :						str=37			,					lowerCAmelCase__  :						Optional[int]="gelu"			,					lowerCAmelCase__  :						List[Any]=0.1			,					lowerCAmelCase__  :						str=0.1			,					lowerCAmelCase__  :						Optional[int]=512			,					lowerCAmelCase__  :						int=16			,					lowerCAmelCase__  :						List[Any]=2			,					lowerCAmelCase__  :						Optional[int]=0.02			,					lowerCAmelCase__  :						Union[str, Any]=3			,					lowerCAmelCase__  :						Optional[int]=4			,					lowerCAmelCase__  :						Dict=None			,					lowerCAmelCase__  :						Union[str, Any]=0			,					)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			seq_length
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			use_input_mask
            _UpperCamelCase							       =			use_token_type_ids
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			vocab_size
            _UpperCamelCase							       =			hidden_size
            _UpperCamelCase							       =			num_hidden_layers
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			intermediate_size
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			hidden_dropout_prob
            _UpperCamelCase							       =			attention_probs_dropout_prob
            _UpperCamelCase							       =			max_position_embeddings
            _UpperCamelCase							       =			type_vocab_size
            _UpperCamelCase							       =			type_sequence_label_size
            _UpperCamelCase							       =			initializer_range
            _UpperCamelCase							       =			num_labels
            _UpperCamelCase							       =			num_choices
            _UpperCamelCase							       =			scope
            _UpperCamelCase							       =			projection_dim





       def snake_case__		(				self  :						int	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size	)

            _UpperCamelCase							       =			None
            if self.use_input_mask:
                 # follow test_modeling_tf_ctrl.py
                 _UpperCamelCase							       =			random_attention_mask([self.batch_size, self.seq_length]	)

            _UpperCamelCase							       =			None
            if self.use_token_type_ids:
                 _UpperCamelCase							       =			ids_tensor([self.batch_size, self.seq_length]			,					self.type_vocab_size	)

            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None
            if self.use_labels:
                 _UpperCamelCase							       =			ids_tensor([self.batch_size]			,					self.type_sequence_label_size	)
                 _UpperCamelCase							       =			ids_tensor([self.batch_size, self.seq_length]			,					self.num_labels	)
                 _UpperCamelCase							       =			ids_tensor([self.batch_size]			,					self.num_choices	)

            _UpperCamelCase							       =			BertConfig(
                vocab_size=self.vocab_size			,					hidden_size=self.hidden_size			,					num_hidden_layers=self.num_hidden_layers			,					num_attention_heads=self.num_attention_heads			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					max_position_embeddings=self.max_position_embeddings			,					type_vocab_size=self.type_vocab_size			,					is_decoder=lowerCAmelCase__			,					initializer_range=self.initializer_range			,					)
            _UpperCamelCase							       =			DPRConfig(projection_dim=self.projection_dim			,					**config.to_dict()	)

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





       def snake_case__		(				self  :						str			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Union[str, Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			TFDPRContextEncoder(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					attention_mask=lowerCAmelCase__			,					token_type_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					token_type_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.pooler_output.shape			,					(self.batch_size, self.projection_dim or self.hidden_size)	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			TFDPRQuestionEncoder(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					attention_mask=lowerCAmelCase__			,					token_type_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					token_type_ids=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.pooler_output.shape			,					(self.batch_size, self.projection_dim or self.hidden_size)	)





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Union[str, Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			TFDPRReader(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					attention_mask=lowerCAmelCase__	)

            self.parent.assertEqual(result.start_logits.shape			,					(self.batch_size, self.seq_length)	)
            self.parent.assertEqual(result.end_logits.shape			,					(self.batch_size, self.seq_length)	)
            self.parent.assertEqual(result.relevance_logits.shape			,					(self.batch_size,)	)







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

            '''simple docstring'''




            _UpperCamelCase							       =			self.prepare_config_and_inputs()
            (
                (
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							
            )							       =			config_and_inputs
            _UpperCamelCase							       =			{'''input_ids''': input_ids}
            return config, inputs_dict

@require_tf
class 							__lowerCAmelCase	(       __magic_name__		,		__magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	str									=       (
           (
               TFDPRContextEncoder,
               TFDPRQuestionEncoder,
               TFDPRReader,
           )
           if is_tf_available()
           else ()
       )
       _snake_case      :	List[Any]									=       {'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

       _snake_case      :	Optional[int]									=       False
       _snake_case      :	Any									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	Any									=       False





       def snake_case__		(				self  :						Dict	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			TFDPRModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					hidden_size=37	)





       def snake_case__		(				self  :						Optional[Any]	)       ->	str:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       def snake_case__		(				self  :						Optional[int]	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_dpr_context_encoder(*lowerCAmelCase__	)





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

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_dpr_question_encoder(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_dpr_reader(*lowerCAmelCase__	)







       @slow
       def snake_case__		(				self  :						List[str]	)       ->	Any:

            '''simple docstring'''




            for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			TFDPRContextEncoder.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)

            for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			TFDPRContextEncoder.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)

            for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			TFDPRQuestionEncoder.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)

            for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			TFDPRReader.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)

@require_tf
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





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

            '''simple docstring'''




            _UpperCamelCase							       =			TFDPRQuestionEncoder.from_pretrained('''facebook/dpr-question_encoder-single-nq-base'''	)

            _UpperCamelCase							       =			tf.constant(
                [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]	)  # [CLS] hello, is my dog cute? [SEP]
            _UpperCamelCase							       =			model(lowerCAmelCase__	)[0]  # embedding shape = (1, 768)
            # compare the actual values for a slice.
            _UpperCamelCase							       =			tf.constant(
                [
                    [
                        0.03236253,
                        0.12753335,
                        0.16818509,
                        0.00279786,
                        0.3896933,
                        0.24264945,
                        0.2178971,
                        -0.02335227,
                        -0.08481959,
                        -0.14324117,
                    ]
                ]	)
            self.assertTrue(numpy.allclose(output[:, :10].numpy()			,					expected_slice.numpy()			,					atol=1e-4	)	)










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
A       :   Optional[Any]					= '''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
A       :   str					= re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
A       :   Optional[Any]					= 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.
A       :   Any					= re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
A       :   str					= [
    ('''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   (				__a  :List[str]       )						->							Optional[int]:






		"""simple docstring"""

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







def   __lowerCamelCase   (				)						->							Dict:






		"""simple docstring"""

		A__     =  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		A__     =  {
		    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.
		A__     =  collections.defaultdict(__a       )
		A__     =  collections.defaultdict(__a       )
		A__     =  collections.defaultdict(__a       )

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

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

		A__     =  set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
		A__     =  list(__a       )
		all_models.sort()

		A__     =  {"""model_type""": all_models}
		A__     =  [pt_models[t] for t in all_models]
		A__     =  [tf_models[t] for t in all_models]
		A__     =  [flax_models[t] for t in all_models]

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

		A__     =  [processors[t] for t in all_models]

		return pd.DataFrame(__a       )







def   __lowerCamelCase   (				__a  :Union[str, Any]       )						->							List[str]:






		"""simple docstring"""

		A__     =  [
		    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:
				A__     =  [model_mapping, F'TF_{model_mapping}', F'FLAX_{model_mapping}']
				A__     =  [auto_class, F'TF_{auto_class}', F'Flax_{auto_class}']
				# Loop through all three frameworks
				for module, cls, mapping in zip(__a       ,	__a       ,	__a       ):
						# The type of pipeline may not exist in this framework
						if not hasattr(__a       ,	__a       ):
								continue
						# First extract all model_names
						A__     =  []
						for name in getattr(__a       ,	__a       ).values():
								if isinstance(__a       ,	__a       ):
										model_names.append(__a       )
								else:
										model_names.extend(list(__a       )       )

            # 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   (				__a  :Dict       ,	__a  :Optional[Any]       )						->							Any:






		"""simple docstring"""

		A__     =  get_frameworks_table()
		A__     =  Dataset.from_pandas(__a       )

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

		# Sort the model classes to avoid some nondeterministic updates to create false update commits.
		A__     =  sorted(table.keys()       )
		A__     =  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],
		    }       )
		A__     =  Dataset.from_pandas(__a       )

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

				if commit_sha is not None:
						A__     =  (
						    F'Update with commit {commit_sha}\n\nSee: '
						    F'https://github.com/huggingface/transformers/commit/{commit_sha}'
						)
				else:
						A__     =  """Update"""

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







def   __lowerCamelCase   (				)						->							Dict:






		"""simple docstring"""

		A__     =  {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
		A__     =  transformers_module.pipelines.SUPPORTED_TASKS
		A__     =  []
		for key in pipeline_tasks:
				if key not in in_table:
						A__     =  pipeline_tasks[key]["""pt"""]
						if isinstance(__a       ,	(list, tuple)       ):
								A__     =  model[0]
						A__     =  model.__name__
						if model not in in_table.values():
								missing.append(__a       )

		if len(__a       ) > 0:
				A__     =  """, """.join(__a       )
				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__":
					A       :   Tuple					= 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.''')
					A       :   Optional[int]					= parser.parse_args()

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



