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

import yaml





class 							A__		(	yaml.SafeLoader):
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	List[Any]							=      [self.constructed_objects[key_node] for key_node, _ in node.value]
       lowerCamelCase		:	Optional[int]							=      [tuple(__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else key for key in keys]
       lowerCamelCase		:	Any							=      Counter(__magic_name__      )
       lowerCamelCase		:	str							=      [key for key in counter if counter[key] > 1]
       if duplicate_keys:
        raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__=False      ):
       lowerCamelCase		:	Dict							=      super().construct_mapping(__magic_name__    ,  deep=__magic_name__      )
       self._check_no_duplicates_on_constructed_node(__magic_name__      )
       return mapping





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

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





class 							A__		(	__SCREAMING_SNAKE_CASE):
      # class attributes
      _UpperCAmelCase				:    Tuple           =			{"""train_eval_index"""}  # train-eval-index in the YAML metadata
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__      ):
       with open(__magic_name__    ,  encoding="""utf-8"""      ) as readme_file:
        lowerCamelCase			,    lowerCamelCase		:	Dict							=      _split_yaml_from_readme(readme_file.read()      )
       if yaml_string is not None:
        return cls.from_yaml_string(__magic_name__      )
       else:
        return cls()
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if path.exists():
        with open(__magic_name__    ,  encoding="""utf-8"""      ) as readme_file:
         lowerCamelCase		:	List[str]							=      readme_file.read()
       else:
        lowerCamelCase		:	Tuple							=      None
       lowerCamelCase		:	Optional[int]							=      self._to_readme(__magic_name__      )
       with open(__magic_name__    ,  """w"""    ,  encoding="""utf-8"""      ) as readme_file:
        readme_file.write(__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__ = None      ):
       if readme_content is not None:
        lowerCamelCase			,    lowerCamelCase		:	List[str]							=      _split_yaml_from_readme(__magic_name__      )
        lowerCamelCase		:	Any							=      """---\n""" + self.to_yaml_string() + """---\n""" + content
       else:
        lowerCamelCase		:	List[Any]							=      """---\n""" + self.to_yaml_string() + """---\n"""
       return full_content
      @classmethod
      def 						UpperCamelCase__							(      cls    ,  __magic_name__      ):
       lowerCamelCase		:	Optional[Any]							=      yaml.load(__magic_name__    ,  Loader=_NoDuplicateSafeLoader      ) or {}

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






      def 						UpperCamelCase__							(      self      ):
       return yaml.safe_dump(
           {
               (key.replace("""_"""    ,  """-"""      ) if key in self._FIELDS_WITH_DASHES else key): value
               for key, value in self.items()
           }    ,  sort_keys=__magic_name__    ,  allow_unicode=__magic_name__    ,  encoding="""utf-8"""    ,  ).decode("""utf-8"""      )


_lowerCamelCase					   ={
    """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

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

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






def     _a					(						lowerCamelCase = 100_0000							):
 lowerCamelCase		:	Any							=      set(range(3,	lowerCamelCase,	2							)							)
 primes.add(2							)
 for p in range(3,	lowerCamelCase,	2							):
  if p not in primes:
   continue
  primes.difference_update(set(range(p * p,	lowerCamelCase,	lowerCamelCase							)							)							)

 lowerCamelCase		:	Any							=      [float(lowerCamelCase							) for n in range(limit + 1							)]

 for p in primes:
  for n in range(lowerCamelCase,	limit + 1,	lowerCamelCase							):
   phi[n] *= 1 - 1 / p

 return int(sum(phi[2:]							)							)


if __name__ == "__main__":
      print(f'''{solution() = }''')




