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					:	Tuple       =  '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase					:	Union[str, Any]       =  direct_transformers_import(TRANSFORMERS_PATH)


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCamelCase					:	Any       =  [
    ('''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       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







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




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {
		    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.
		SCREAMING_SNAKE_CASE_  :		Any				      =    collections.defaultdict(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    collections.defaultdict(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    collections.defaultdict(lowerCAmelCase	)

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

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

		SCREAMING_SNAKE_CASE_  :		int				      =    set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    list(lowerCAmelCase	)
		all_models.sort()

		SCREAMING_SNAKE_CASE_  :		Dict				      =    {"model_type": all_models}
		SCREAMING_SNAKE_CASE_  :		str				      =    [pt_models[t] for t in all_models]
		SCREAMING_SNAKE_CASE_  :		Any				      =    [tf_models[t] for t in all_models]
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    [flax_models[t] for t in all_models]

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

		SCREAMING_SNAKE_CASE_  :		int				      =    [processors[t] for t in all_models]

		return pd.DataFrame(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







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

            # 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       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    get_frameworks_table()
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    Dataset.from_pandas(lowerCAmelCase	)

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

		# Sort the model classes to avoid some nondeterministic updates to create false update commits.
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    sorted(table.keys()	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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],
		    }	)
		SCREAMING_SNAKE_CASE_  :		Dict				      =    Dataset.from_pandas(lowerCAmelCase	)

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

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

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




def       _snake_case	(   ):
		"""simple docstring"""







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

		if len(lowerCAmelCase	) > 0:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    ", ".join(lowerCAmelCase	)
				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					:	Any       =  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					:	List[Any]       =  parser.parse_args()

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

from functools import lru_cache




@lru_cache
def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		if num < 0:
				raise ValueError("Number should not be negative."	)

		return 1 if num in (0, 1) else num * factorial(num - 1	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
