






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

import yaml







class 				UpperCAmelCase_  (		yaml.SafeLoader		):




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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=False		):
          UpperCAmelCase__      :  Dict				      =			super().construct_mapping(_lowerCAmelCase	,						deep=_lowerCAmelCase		)
          self._check_no_duplicates_on_constructed_node(_lowerCAmelCase		)
          return mapping





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Tuple[Optional[str], str]:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[int]				      =			list(readme_content.splitlines()       )
     if full_content and full_content[0] == "---" and "---" in full_content[1:]:
          UpperCAmelCase__      :  Dict				      =			full_content[1:].index("""---"""       ) + 1
          UpperCAmelCase__      :  Dict				      =			"""\n""".join(full_content[1:sep_idx]       )
          return yamlblock, "\n".join(full_content[sep_idx + 1 :]       )

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







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     # class attributes
     __lowerCamelCase      				=  {'train_eval_index'}  # train-eval-index in the YAML metadata




     @classmethod
     def   __UpperCAmelCase  (				cls	,						_lowerCAmelCase		):
          with open(_lowerCAmelCase	,						encoding="""utf-8"""		) as readme_file:
               UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			_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	,						_lowerCAmelCase		):
          if path.exists():
               with open(_lowerCAmelCase	,						encoding="""utf-8"""		) as readme_file:
                    UpperCAmelCase__      :  Tuple				      =			readme_file.read()
          else:
               UpperCAmelCase__      :  Optional[Any]				      =			None
          UpperCAmelCase__      :  Union[str, Any]				      =			self._to_readme(_lowerCAmelCase		)
          with open(_lowerCAmelCase	,						"""w"""	,						encoding="""utf-8"""		) as readme_file:
               readme_file.write(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase = None		):
          if readme_content is not None:
               UpperCAmelCase__      ,		UpperCAmelCase__      :  Optional[Any]				      =			_split_yaml_from_readme(_lowerCAmelCase		)
               UpperCAmelCase__      :  Dict				      =			"""---\n""" + self.to_yaml_string() + """---\n""" + content
          else:
               UpperCAmelCase__      :  Optional[Any]				      =			"""---\n""" + self.to_yaml_string() + """---\n"""
          return full_content




     @classmethod
     def   __UpperCAmelCase  (				cls	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  str				      =			yaml.load(_lowerCAmelCase	,						Loader=_NoDuplicateSafeLoader		) or {}

          # Convert the YAML keys to DatasetMetadata fields
          UpperCAmelCase__      :  Tuple				      =			{
              (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		):
          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"""		)



SCREAMING_SNAKE_CASE__		:							Tuple 				=				{
    """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__		:							str 				=				ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
      ap.add_argument("""readme_filepath""")
      SCREAMING_SNAKE_CASE__		:							int 				=				ap.parse_args()

      SCREAMING_SNAKE_CASE__		:							Dict 				=				Path(args.readme_filepath)
      SCREAMING_SNAKE_CASE__		:							int 				=				DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)












from math import isqrt


def 					_a							(					lowerCAmelCase )->	bool:
		return all(number % divisor != 0 for divisor in range(2    ,						isqrt(lowerCAmelCase ) + 1 ) )


def 					_a							(					lowerCAmelCase = 10**6 )->	int:
		SCREAMING_SNAKE_CASE_					      =							0
		SCREAMING_SNAKE_CASE_					      =							1
		SCREAMING_SNAKE_CASE_					      =							7
		while prime_candidate < max_prime:
				primes_count += is_prime(lowerCAmelCase )

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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