





'''simple docstring'''

from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class     __lowerCAmelCase					(   yaml.SafeLoader			):






							"""simple docstring"""





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

												'''simple docstring'''




												_UpperCamelCase       =	[self.constructed_objects[key_node] for key_node, _ in node.value]
												_UpperCamelCase       =	[tuple(lowerCAmelCase__					) if isinstance(lowerCAmelCase__	,			lowerCAmelCase__					) else key for key in keys]
												_UpperCamelCase       =	Counter(lowerCAmelCase__					)
												_UpperCamelCase       =	[key for key in counter if counter[key] > 1]
												if duplicate_keys:
																	raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""					)







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

												'''simple docstring'''




												_UpperCamelCase       =	super().construct_mapping(lowerCAmelCase__	,			deep=lowerCAmelCase__					)
												self._check_no_duplicates_on_constructed_node(lowerCAmelCase__					)
												return mapping







def  a__    (    lowercase :					str				)		->		Tuple[Optional[str], str]:





					"""simple docstring"""

					_UpperCamelCase       =	list(readme_content.splitlines()				)
					if full_content and full_content[0] == "---" and "---" in full_content[1:]:
										_UpperCamelCase       =	full_content[1:].index('''---'''				) + 1
										_UpperCamelCase       =	'''\n'''.join(full_content[1:sep_idx]				)
										return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

					return None, "\n".join(lowercase				)
class     __lowerCAmelCase					(   __magic_name__			):






							"""simple docstring"""
							_snake_case     :					Optional[int]						    =   {'train_eval_index'}  # train-eval-index in the YAML metadata





							@classmethod
							def snake_case__						(							cls       :		Tuple	,			lowerCAmelCase__       :		Path					)   ->					"DatasetMetadata":

												'''simple docstring'''




												with open(lowerCAmelCase__	,			encoding='''utf-8'''					) as readme_file:
																	_UpperCamelCase   ,    _UpperCamelCase       =	_split_yaml_from_readme(readme_file.read()					)
												if yaml_string is not None:
																	return cls.from_yaml_string(lowerCAmelCase__					)
												else:
																	return cls()





							def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		Path					)   ->					Optional[Any]:

												'''simple docstring'''




												if path.exists():
																	with open(lowerCAmelCase__	,			encoding='''utf-8'''					) as readme_file:
																						_UpperCamelCase       =	readme_file.read()
												else:
																	_UpperCamelCase       =	None
												_UpperCamelCase       =	self._to_readme(lowerCAmelCase__					)
												with open(lowerCAmelCase__	,			'''w'''	,			encoding='''utf-8'''					) as readme_file:
																	readme_file.write(lowerCAmelCase__					)





							def snake_case__						(							self       :		Any	,			lowerCAmelCase__       :		Optional[str] = None					)   ->					str:

												'''simple docstring'''




												if readme_content is not None:
																	_UpperCamelCase   ,    _UpperCamelCase       =	_split_yaml_from_readme(lowerCAmelCase__					)
																	_UpperCamelCase       =	'''---\n''' + self.to_yaml_string() + '''---\n''' + content
												else:
																	_UpperCamelCase       =	'''---\n''' + self.to_yaml_string() + '''---\n'''
												return full_content





							@classmethod
							def snake_case__						(							cls       :		Optional[Any]	,			lowerCAmelCase__       :		str					)   ->					"DatasetMetadata":

												'''simple docstring'''




												_UpperCamelCase       =	yaml.load(lowerCAmelCase__	,			Loader=_NoDuplicateSafeLoader					) or {}

												# Convert the YAML keys to DatasetMetadata fields
												_UpperCamelCase       =	{
												    (key.replace('''-'''	,			'''_'''					) if key.replace('''-'''	,			'''_'''					) in cls._FIELDS_WITH_DASHES else key): value
												    for key, value in metadata_dict.items()
												}
												return cls(**lowerCAmelCase__					)







							def snake_case__						(							self       :		Any					)   ->					str:

												'''simple docstring'''




												return yaml.safe_dump(
												    {
												        (key.replace('''_'''	,			'''-'''					) if key in self._FIELDS_WITH_DASHES else key): value
												        for key, value in self.items()
												    }	,			sort_keys=lowerCAmelCase__	,			allow_unicode=lowerCAmelCase__	,			encoding='''utf-8'''	,			).decode('''utf-8'''					)

lowercase__						:      Any	=    {
    'image-classification': [],
    'translation': [],
    'image-segmentation': [],
    'fill-mask': [],
    'automatic-speech-recognition': [],
    'token-classification': [],
    'sentence-similarity': [],
    'audio-classification': [],
    'question-answering': [],
    'summarization': [],
    'zero-shot-classification': [],
    'table-to-text': [],
    'feature-extraction': [],
    'other': [],
    'multiple-choice': [],
    'text-classification': [],
    'text-to-image': [],
    'text2text-generation': [],
    'zero-shot-image-classification': [],
    'tabular-classification': [],
    'tabular-regression': [],
    'image-to-image': [],
    'tabular-to-text': [],
    'unconditional-image-generation': [],
    'text-retrieval': [],
    'text-to-speech': [],
    'object-detection': [],
    'audio-to-audio': [],
    'text-generation': [],
    'conversational': [],
    'table-question-answering': [],
    'visual-question-answering': [],
    'image-to-text': [],
    'reinforcement-learning': [],
    'voice-activity-detection': [],
    'time-series-forecasting': [],
    'document-question-answering': [],
}


if __name__ == "__main__":
			from argparse import ArgumentParser

			lowercase__						:      Dict	=    ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
			ap.add_argument('readme_filepath')
			lowercase__						:      int	=    ap.parse_args()

			lowercase__						:      List[str]	=    Path(args.readme_filepath)
			lowercase__						:      Any	=    DatasetMetadata.from_readme(readme_filepath)
			print(dataset_metadata)
			dataset_metadata.to_readme(readme_filepath)










'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       def __init__(							self       :		List[Any]	,			*lowerCAmelCase__       :		List[str]	,			**lowerCAmelCase__       :		List[str]					)   ->					None:

            '''simple docstring'''




            warnings.warn(
                '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                ''' use OwlViTImageProcessor instead.'''	,			lowerCAmelCase__	,			)
            super().__init__(*lowerCAmelCase__	,			**lowerCAmelCase__					)



