






"""simple docstring"""



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

import yaml






class 							__SCREAMING_SNAKE_CASE						(				yaml.SafeLoader		):


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  [self.constructed_objects[key_node] for key_node, _ in node.value]
													A__						:  Union[str, Any]		  =  [tuple(snake_case							) if isinstance(snake_case			,    snake_case							) else key for key in keys]
													A__						:  Dict		  =  Counter(snake_case							)
													A__						:  Union[str, Any]		  =  [key for key in counter if counter[key] > 1]
													if duplicate_keys:
																			raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						Optional[Any]=False							):
													'''simple docstring'''





													A__						:  int		  =  super().construct_mapping(snake_case			,    deep=snake_case							)
													self._check_no_duplicates_on_constructed_node(snake_case							)
													return mapping




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->Tuple[Optional[str], str]:
						A__						:  Tuple		  =  list(readme_content.splitlines()      )
						if full_content and full_content[0] == "---" and "---" in full_content[1:]:
												A__						:  List[str]		  =  full_content[1:].index("""---"""      ) + 1
												A__						:  Optional[int]		  =  """\n""".join(full_content[1:sep_idx]      )
												return yamlblock, "\n".join(full_content[sep_idx + 1 :]      )

						return None, "\n".join(UpperCAmelCase__      )






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							# class attributes
							snake_case_     =							{'train_eval_index'}  # train-eval-index in the YAML metadata


							@classmethod
							def 						_UpperCamelCase       (			cls      :						List[Any]			,    snake_case      :						Path							):
													'''simple docstring'''





													with open(snake_case			,    encoding="""utf-8"""							) as readme_file:
																			A__  ,  A__						:  str		  =  _split_yaml_from_readme(readme_file.read()							)
													if yaml_string is not None:
																			return cls.from_yaml_string(snake_case							)
													else:
																			return cls()


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Path							):
													'''simple docstring'''





													if path.exists():
																			with open(snake_case			,    encoding="""utf-8"""							) as readme_file:
																									A__						:  List[str]		  =  readme_file.read()
													else:
																			A__						:  Union[str, Any]		  =  None
													A__						:  str		  =  self._to_readme(snake_case							)
													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as readme_file:
																			readme_file.write(snake_case							)


							def 						_UpperCamelCase       (			self      :						Tuple			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													if readme_content is not None:
																			A__  ,  A__						:  Optional[int]		  =  _split_yaml_from_readme(snake_case							)
																			A__						:  Tuple		  =  """---\n""" + self.to_yaml_string() + """---\n""" + content
													else:
																			A__						:  List[str]		  =  """---\n""" + self.to_yaml_string() + """---\n"""
													return full_content


							@classmethod
							def 						_UpperCamelCase       (			cls      :						Union[str, Any]			,    snake_case      :						str							):
													'''simple docstring'''





													A__						:  List[str]		  =  yaml.load(snake_case			,    Loader=_NoDuplicateSafeLoader							) or {}

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


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''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=snake_case			,    allow_unicode=snake_case			,    encoding="""utf-8"""			,    ).decode("""utf-8"""							)




A_    =							{
    '''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

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

						A_    =							Path(args.readme_filepath)
						A_    =							DatasetMetadata.from_readme(readme_filepath)
						print(dataset_metadata)
						dataset_metadata.to_readme(readme_filepath)














"""simple docstring"""



import baseaa




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->bytes:
						return baseaa.baaencode(string.encode("""utf-8"""      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					bytes      )		->str:
						return baseaa.baadecode(UpperCAmelCase__      ).decode("""utf-8"""      )


if __name__ == "__main__":
						A_    =							'''Hello World!'''
						A_    =							baseaa_encode(test)
						print(encoded)

						A_    =							baseaa_decode(encoded)
						print(decoded)






