






'''simple docstring'''



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

import yaml
class UpperCAmelCase_					(    yaml.SafeLoader				):




      """simple docstring"""




      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Union[str, Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Dict=False		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   str				  =super().construct_mapping(UpperCAmelCase     ,      deep=UpperCAmelCase		)
       self._check_no_duplicates_on_constructed_node(UpperCAmelCase		)
       return mapping





def 							lowercase_				(    __A :       str			)       ->      Tuple[Optional[str], str]:



 """simple docstring"""




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

 return None, "\n".join(__A			)
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			{'''train_eval_index'''}  # train-eval-index in the YAML metadata






      @classmethod
      def 				A__      ( cls			:		Optional[Any]     ,      UpperCAmelCase			:		Path		)  ->  "DatasetMetadata":


       '''simple docstring'''

       with open(UpperCAmelCase     ,      encoding='''utf-8'''		) as readme_file:
        lowercase  ,		lowercase					:   List[str]				  =_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			:		Dict     ,      UpperCAmelCase			:		Path		)  ->  int:


       '''simple docstring'''

       if path.exists():
        with open(UpperCAmelCase     ,      encoding='''utf-8'''		) as readme_file:
         lowercase					:   Tuple				  =readme_file.read()
       else:
        lowercase					:   str				  =None
       lowercase					:   Dict				  =self._to_readme(UpperCAmelCase		)
       with open(UpperCAmelCase     ,      '''w'''     ,      encoding='''utf-8'''		) as readme_file:
        readme_file.write(UpperCAmelCase		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Optional[str] = None		)  ->  str:


       '''simple docstring'''

       if readme_content is not None:
        lowercase  ,		lowercase					:   List[Any]				  =_split_yaml_from_readme(UpperCAmelCase		)
        lowercase					:   Optional[Any]				  ='''---\n''' + self.to_yaml_string() + '''---\n''' + content
       else:
        lowercase					:   Tuple				  ='''---\n''' + self.to_yaml_string() + '''---\n'''
       return full_content






      @classmethod
      def 				A__      ( cls			:		List[str]     ,      UpperCAmelCase			:		str		)  ->  "DatasetMetadata":


       '''simple docstring'''

       lowercase					:   Dict				  =yaml.load(UpperCAmelCase     ,      Loader=_NoDuplicateSafeLoader		) or {}

       # Convert the YAML keys to DatasetMetadata fields
       lowercase					:   Tuple				  ={
           (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			:		Tuple		)  ->  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=UpperCAmelCase     ,      allow_unicode=UpperCAmelCase     ,      encoding='''utf-8'''     ,      ).decode('''utf-8'''		)





SCREAMING_SNAKE_CASE						=	{
    '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

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

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











'''simple docstring'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



