



'''simple docstring'''





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

import yaml






class __A    (						yaml.SafeLoader		):



	'''simple docstring'''







	def       a__						(self				,     A )					->  Union[str, Any]:


						"""simple docstring"""



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



	def       a__						(self				,     A				,     A=False )					->  Optional[int]:


						"""simple docstring"""



						_a											=						super().construct_mapping(__UpperCamelCase				,     deep=__UpperCamelCase )
						self._check_no_duplicates_on_constructed_node(__UpperCamelCase )
						return mapping





def lowerCAmelCase						(__A):



					"""simple docstring"""



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

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






class __A    (						A		):



	'''simple docstring'''







	__lowerCamelCase					: str    = {'train_eval_index'}  # train-eval-index in the YAML metadata



	@classmethod
	def       a__						(cls				,     A )					->  int:


						"""simple docstring"""



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



	def       a__						(self				,     A )					->  Optional[Any]:


						"""simple docstring"""



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



	def       a__						(self				,     A = None )					->  Union[str, Any]:


						"""simple docstring"""



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



	@classmethod
	def       a__						(cls				,     A )					->  Union[str, Any]:


						"""simple docstring"""



						_a											=						yaml.load(__UpperCamelCase				,     Loader=_NoDuplicateSafeLoader ) or {}

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



	def       a__						(self )					->  List[Any]:


						"""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=__UpperCamelCase				,     allow_unicode=__UpperCamelCase				,     encoding='''utf-8'''				,     ).decode('''utf-8''' )





lowercase_	 =						{
    "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_	 =						ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
		ap.add_argument("readme_filepath")
		lowercase_	 =						ap.parse_args()

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








'''simple docstring'''





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						set()
     # To detect a back edge, keep track of vertices currently in the recursion stack
     _a											=						set()
     return any(
         node not in visited and depth_first_search(__A						,			__A						,			__A						,			__A)
         for node in graph)




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     visited.add(__A)
     rec_stk.add(__A)

     for node in graph[vertex]:
          if node not in visited:
               if depth_first_search(__A						,			__A						,			__A						,			__A):
                    return True
          elif node in rec_stk:
               return True

    # The node needs to be removed from recursion stack before function ends
     rec_stk.remove(__A)
     return False


if __name__ == "__main__":
  from doctest import testmod

  testmod()



