

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

import yaml

class    _lowercase				(					yaml.SafeLoader			):

   def     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    List[Any]       ) ->			Optional[int]:
         """simple docstring"""





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





   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    Any=False       ) ->			str:
         """simple docstring"""





         A_							       =		super().construct_mapping(lowerCamelCase__			,						deep=lowerCamelCase__       )
         self._check_no_duplicates_on_constructed_node(lowerCamelCase__       )
         return mapping







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''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(SCREAMING_SNAKE_CASE   )

class    _lowercase				(					__lowerCamelCase			):
   # class attributes
   _lowercase     :       Union[str, Any]  					=    {'train_eval_index'}  # train-eval-index in the YAML metadata

   @classmethod
   def     UpperCamelCase					(				cls	:    Any			,						lowerCamelCase__	:    Path       ) ->			"DatasetMetadata":
         """simple docstring"""





         with open(lowerCamelCase__			,						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(lowerCamelCase__       )
         else:
               return cls()

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Path       ) ->			Tuple:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Optional[str] = None       ) ->			str:
         """simple docstring"""





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

   @classmethod
   def     UpperCamelCase					(				cls	:    str			,						lowerCamelCase__	:    str       ) ->			"DatasetMetadata":
         """simple docstring"""





         A_							       =		yaml.load(lowerCamelCase__			,						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(**lowerCamelCase__       )





   def     UpperCamelCase					(				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				      =						{
    """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)








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 1000   ):
      '''simple docstring'''







      return sum(2 * a * ((a - 1) // 2) for a in range(3		,				n + 1   )   )


if __name__ == "__main__":
      print(solution())





