





import warnings

from .generation import TFGenerationMixin


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			warnings.warn(
			    '''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
			    '''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.'''  ,	SCREAMING_SNAKE_CASE  ,	)










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)



