





__lowercase  :   Optional[Any]							      =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}
   UpperCAmelCase_											=0
   UpperCAmelCase_											=0
   while place < len(lowercase__  ):
      if (place + 1 < len(lowercase__  )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   for arabic, roman in ROMAN:
      ((UpperCAmelCase_)						,       (UpperCAmelCase_))											=divmod(lowercase__  ,    lowercase__  )
      result.append(roman * factor  )
      if number == 0:
         break
   return "".join(lowercase__  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()











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

import yaml

class      A       (	yaml.SafeLoader						):



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int       )						->    Optional[int]:
         '''simple docstring'''

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






      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Optional[Any]=False       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=super().construct_mapping(_lowerCAmelCase		,  deep=_lowerCAmelCase       )
         self._check_no_duplicates_on_constructed_node(_lowerCAmelCase       )
         return mapping




def     a__ (					lowercase__  ):






   '''simple docstring'''






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

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

class      A       (	__lowercase						):
      # class attributes
      _snake_case							     ={'''train_eval_index'''}  # train-eval-index in the YAML metadata



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Any		,  _lowerCAmelCase:						Path       )						->    "DatasetMetadata":
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						Path       )						->    List[str]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    str:
         '''simple docstring'''

         if readme_content is not None:
            UpperCAmelCase_						,       UpperCAmelCase_											=_split_yaml_from_readme(_lowerCAmelCase       )
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n" + content
         else:
            UpperCAmelCase_											="---\n" + self.to_yaml_string() + "---\n"
         return full_content



      @classmethod
      def 	lowerCAmelCase__    (	cls:						Optional[Any]		,  _lowerCAmelCase:						str       )						->    "DatasetMetadata":
         '''simple docstring'''

         UpperCAmelCase_											=yaml.load(_lowerCAmelCase		,  Loader=_NoDuplicateSafeLoader       ) or {}

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






      def 	lowerCAmelCase__    (	self:						int       )						->    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  :   int						      ={
    """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  :   List[Any]						      =ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
     ap.add_argument("""readme_filepath""")
     __lowercase  :   int						      =ap.parse_args()

     __lowercase  :   Optional[int]						      =Path(args.readme_filepath)
     __lowercase  :   Tuple						      =DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)




