






'''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
SCREAMING_SNAKE_CASE						=	'src/transformers'


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


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE						=	re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE						=	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.
SCREAMING_SNAKE_CASE						=	re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE						=	[
    ('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 							lowercase_				(    __A :       Tuple			)       ->      Optional[int]:



	"""simple docstring"""




	lowercase					:   Dict				  =re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''					,      __A			)
	return [m.group(0			) for m in matches]


def 							lowercase_				(    )       ->      List[Any]:



	"""simple docstring"""




	lowercase					:   List[str]				  =transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	lowercase					:   Optional[Any]				  ={
	    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.
	lowercase					:   Optional[Any]				  =collections.defaultdict(__A			)
	lowercase					:   List[str]				  =collections.defaultdict(__A			)
	lowercase					:   Optional[Any]				  =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			):
		lowercase					:   Tuple				  =None
		if _re_tf_models.match(__A			) is not None:
			lowercase					:   Tuple				  =tf_models
			lowercase					:   str				  =_re_tf_models.match(__A			).groups()[0]
		elif _re_flax_models.match(__A			) is not None:
			lowercase					:   int				  =flax_models
			lowercase					:   Dict				  =_re_flax_models.match(__A			).groups()[0]
		elif _re_pt_models.match(__A			) is not None:
			lowercase					:   int				  =pt_models
			lowercase					:   int				  =_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:
					lowercase					:   Optional[int]				  =True
					break
				# Try again after removing the last word in the name
				lowercase					:   Tuple				  =''.join(camel_case_split(__A			)[:-1]			)

	lowercase					:   Optional[Any]				  =set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
	lowercase					:   List[Any]				  =list(__A			)
	all_models.sort()

	lowercase					:   List[Any]				  ={'model_type': all_models}
	lowercase					:   str				  =[pt_models[t] for t in all_models]
	lowercase					:   List[str]				  =[tf_models[t] for t in all_models]
	lowercase					:   Optional[int]				  =[flax_models[t] for t in all_models]

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

	lowercase					:   Optional[int]				  =[processors[t] for t in all_models]

	return pd.DataFrame(__A			)


def 							lowercase_				(    __A :       Union[str, Any]			)       ->      Optional[int]:



	"""simple docstring"""




	lowercase					:   str				  =[
	    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:
		lowercase					:   Any				  =[model_mapping, F'TF_{model_mapping}', F'FLAX_{model_mapping}']
		lowercase					:   Optional[Any]				  =[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
			lowercase					:   Any				  =[]
			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 							lowercase_				(    __A :       Optional[Any]					,      __A :       List[Any]			)       ->      Dict:



	"""simple docstring"""




	lowercase					:   Optional[Any]				  =get_frameworks_table()
	lowercase					:   Optional[Any]				  =Dataset.from_pandas(__A			)

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

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	lowercase					:   Tuple				  =sorted(table.keys()			)
	lowercase					:   Tuple				  =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],
	    }			)
	lowercase					:   Any				  =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:
			lowercase					:   int				  =(
			    F'Update with commit {commit_sha}\n\nSee: '
			    F'https://github.com/huggingface/transformers/commit/{commit_sha}'
			)
		else:
			lowercase					:   Tuple				  ='Update'

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


def 							lowercase_				(    )       ->      Optional[int]:



	"""simple docstring"""




	lowercase					:   Optional[int]				  ={tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
	lowercase					:   Any				  =transformers_module.pipelines.SUPPORTED_TASKS
	lowercase					:   List[Any]				  =[]
	for key in pipeline_tasks:
		if key not in in_table:
			lowercase					:   str				  =pipeline_tasks[key]['pt']
			if isinstance(__A					,      (list, tuple)			):
				lowercase					:   List[str]				  =model[0]
			lowercase					:   Dict				  =model.__name__
			if model not in in_table.values():
				missing.append(__A			)

	if len(__A			) > 0:
		lowercase					:   Union[str, Any]				  =', '.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__":
						SCREAMING_SNAKE_CASE						=	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.')
						SCREAMING_SNAKE_CASE						=	parser.parse_args()

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











'''simple docstring'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



