





'''simple docstring'''

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

import yaml







class __a			( yaml.SafeLoader      ):





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      List[Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      Optional[Any]=False     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super().construct_mapping(lowerCamelCase					,deep=lowerCamelCase     )
      self._check_no_duplicates_on_constructed_node(lowerCamelCase     )
      return mapping





def     __magic_name__							(				__UpperCAmelCase   )    ->       Tuple[Optional[str], str]:

   '''simple docstring'''







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

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







class __a			( _snake_case      ):
   # class attributes
   __UpperCamelCase :			Union[str, Any]						   =						{'train_eval_index'}  # train-eval-index in the YAML metadata





   @classmethod
   def   UpperCAmelCase__  (       cls					:      str					,lowerCamelCase					:      Path     ):

      '''simple docstring'''

      with open(lowerCamelCase					,encoding="""utf-8"""     ) as readme_file:
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							_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					:      Optional[Any]					,lowerCamelCase					:      Path     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Optional[str] = None     ):

      '''simple docstring'''

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





   @classmethod
   def   UpperCAmelCase__  (       cls					:      Optional[int]					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							yaml.load(lowerCamelCase					,Loader=_NoDuplicateSafeLoader     ) or {}

      # Convert the YAML keys to DatasetMetadata fields
      __SCREAMING_SNAKE_CASE   				=							{
          (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					:      int     ):

      '''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"""     )





a   	=    {
    "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

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

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











from __future__ import annotations

from typing import Any

def lowercase    (  SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    []       ,    0 )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			if index == len(SCREAMING_SNAKE_CASE ):
						print(SCREAMING_SNAKE_CASE )
						return

			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.append(sequence[index] )
			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.pop()


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     list[Any]		    =       [3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(["A", "B", "C"])
				generate_all_subsequences(seq)
