



'''simple docstring'''




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

import yaml



class    lowerCamelCase__				(			yaml.SafeLoader						):






      """simple docstring"""




      def lowerCAmelCase_					(       self							:     Tuple			,a__							:     List[str]   ):
             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 lowerCAmelCase_					(       self							:     str			,a__							:     Any			,a__							:     Optional[Any]=False   ):
             a__								= super().construct_mapping(__lowerCamelCase			,deep=__lowerCamelCase   )
             self._check_no_duplicates_on_constructed_node(__lowerCamelCase   )
             return mapping






def 		_lowerCAmelCase				(_lowercase			):

       """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(lowerCAmelCase_			)



class    lowerCamelCase__				(			__SCREAMING_SNAKE_CASE						):






      """simple docstring"""
      # class attributes
      UpperCamelCase__			 =     {'''train_eval_index'''}  # train-eval-index in the YAML metadata




      @classmethod
      def lowerCAmelCase_					(       cls							:     str			,a__							:     Path   ):
             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 lowerCAmelCase_					(       self							:     List[Any]			,a__							:     Path   ):
             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 lowerCAmelCase_					(       self							:     Union[str, Any]			,a__							:     Optional[str] = None   ):
             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 lowerCAmelCase_					(       cls							:     Tuple			,a__							:     str   ):
             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 lowerCAmelCase_					(       self							:     Dict   ):
             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"   )
UpperCamelCase_       :  List[Any]					=  {
    """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

    UpperCamelCase_       :  List[Any]					=  ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
    ap.add_argument("""readme_filepath""")
    UpperCamelCase_       :  int					=  ap.parse_args()

    UpperCamelCase_       :  str					=  Path(args.readme_filepath)
    UpperCamelCase_       :  Tuple					=  DatasetMetadata.from_readme(readme_filepath)
    print(dataset_metadata)
    dataset_metadata.to_readme(readme_filepath)





"""simple docstring"""






from itertools import product




def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				list[int]:
  _snake_case   	=      sides_number
  _snake_case   	=      max_face_number * dice_number
  _snake_case   	=      [0] * (max_total + 1)

  _snake_case   	=      1
  _snake_case   	=      range(lowerCAmelCase_  ,						max_face_number + 1	)
  for dice_numbers in product(lowerCAmelCase_  ,						repeat=lowerCAmelCase_	):
    _snake_case   	=      sum(lowerCAmelCase_	)
    totals_frequencies[total] += 1

  return totals_frequencies




def 			snake_case		(		)						->				float:
  _snake_case   	=      total_frequency_distribution(
      sides_number=4  ,						dice_number=9	)
  _snake_case   	=      total_frequency_distribution(
      sides_number=6  ,						dice_number=6	)

  _snake_case   	=      0
  _snake_case   	=      9
  _snake_case   	=      4 * 9
  _snake_case   	=      6
  for peter_total in range(lowerCAmelCase_  ,						max_peter_total + 1	):
    peter_wins_count += peter_totals_frequencies[peter_total] * sum(
        colin_totals_frequencies[min_colin_total:peter_total]	)

  _snake_case   	=      (4**9) * (6**6)
  _snake_case   	=      peter_wins_count / total_games_number

  _snake_case   	=      round(lowerCAmelCase_  ,						ndigits=7	)

  return rounded_peter_win_probability


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





