





snake_case 				=       [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int     )      ->						int:
  _lowercase							= 0
  while number:
    # Increased Speed Slightly by checking every 5 digits together.
    sum_of_digits_squared += DIGITS_SQUARED[number % 10_0000]
    number //= 10_0000

  return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
snake_case 				=       [None] * 1_0_0_0_0_0_0_0
snake_case 				=       True
snake_case 				=       False

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int     )      ->						bool:

  if CHAINS[number - 1] is not None:
    return CHAINS[number - 1]  # type: ignore

  _lowercase							= chain(next_number(snake_case__     )     )
  _lowercase							= number_chain

  while number < 1000_0000:
    _lowercase							= number_chain
    number *= 10

  return number_chain

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int = 1000_0000     )      ->						int:
  for i in range(1			,							snake_case__     ):
    if CHAINS[i] is None:
      chain(i + 1     )

  return CHAINS[:number].count(snake_case__     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(F"""{solution() = }""")






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

import yaml





class 				A_				(      yaml.SafeLoader	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Dict						,__A	:	List[Any]  )						->					Any:
         _lowercase							= [self.constructed_objects[key_node] for key_node, _ in node.value]
         _lowercase							= [tuple(__A  ) if isinstance(__A						,__A  ) else key for key in keys]
         _lowercase							= Counter(__A  )
         _lowercase							= [key for key in counter if counter[key] > 1]
         if duplicate_keys:
           raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int						,__A	:	Dict=False  )						->					Union[str, Any]:
         _lowercase							= super().construct_mapping(__A						,deep=__A  )
         self._check_no_duplicates_on_constructed_node(__A  )
         return mapping

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str     )      ->						Tuple[Optional[str], str]:
  _lowercase							= list(readme_content.splitlines()     )
  if full_content and full_content[0] == "---" and "---" in full_content[1:]:
    _lowercase							= full_content[1:].index('---'     ) + 1
    _lowercase							= '\n'.join(full_content[1:sep_idx]     )
    return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

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





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  {'''train_eval_index'''}  # train-eval-index in the YAML metadata







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	Path  )						->					"DatasetMetadata":
         with open(__A						,encoding='utf-8'  ) as readme_file:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(readme_file.read()  )
         if yaml_string is not None:
           return cls.from_yaml_string(__A  )
         else:
           return cls()







       def    __UpperCAmelCase (    self	:	Union[str, Any]						,__A	:	Path  )						->					Optional[Any]:
         if path.exists():
           with open(__A						,encoding='utf-8'  ) as readme_file:
             _lowercase							= readme_file.read()
         else:
           _lowercase							= None
         _lowercase							= self._to_readme(__A  )
         with open(__A						,'w'						,encoding='utf-8'  ) as readme_file:
           readme_file.write(__A  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Optional[str] = None  )						->					str:
         if readme_content is not None:
           _lowercase						,		_lowercase							= _split_yaml_from_readme(__A  )
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n' + content
         else:
           _lowercase							= '---\n' + self.to_yaml_string() + '---\n'
         return full_content







       @classmethod
       def    __UpperCAmelCase (    cls	:	List[Any]						,__A	:	str  )						->					"DatasetMetadata":
         _lowercase							= yaml.load(__A						,Loader=_NoDuplicateSafeLoader  ) or {}

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







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					str:
         return yaml.safe_dump(
             {
                 (key.replace('_'						,'-'  ) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }						,sort_keys=__A						,allow_unicode=__A						,encoding='utf-8'						,).decode('utf-8'  )
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

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

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