


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

import yaml
class 				SCREAMING_SNAKE_CASE__						(       yaml.SafeLoader    ):
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  )	->    Any:
             a_      :	str							=						[self.constructed_objects[key_node] for key_node, _ in node.value]
             a_      :	Tuple							=						[tuple(SCREAMING_SNAKE_CASE__  ) if isinstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ) else key for key in keys]
             a_      :	List[str]							=						Counter(SCREAMING_SNAKE_CASE__  )
             a_      :	Any							=						[key for key in counter if counter[key] > 1]
             if duplicate_keys:
                    raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""  )

      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  )	->    List[str]:
             a_      :	List[Any]							=						super().construct_mapping(SCREAMING_SNAKE_CASE__  ,							deep=SCREAMING_SNAKE_CASE__  )
             self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE__  )
             return mapping







def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	Tuple[Optional[str], str]:

       """simple docstring"""

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

       return None, "\n".join(__A       )
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      # class attributes
      snake_case__       :  int       =		{'''train_eval_index'''}  # train-eval-index in the YAML metadata
      @classmethod
      def  SCREAMING_SNAKE_CASE							( cls       : Dict  ,							SCREAMING_SNAKE_CASE__       : Path  )	->    "DatasetMetadata":
             with open(SCREAMING_SNAKE_CASE__  ,							encoding='utf-8'  ) as readme_file:
                    a_							,	a_      :	Any							=						_split_yaml_from_readme(readme_file.read()  )
             if yaml_string is not None:
                    return cls.from_yaml_string(SCREAMING_SNAKE_CASE__  )
             else:
                    return cls()
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Path  )	->    Tuple:
             if path.exists():
                    with open(SCREAMING_SNAKE_CASE__  ,							encoding='utf-8'  ) as readme_file:
                           a_      :	Dict							=						readme_file.read()
             else:
                    a_      :	Optional[Any]							=						None
             a_      :	Tuple							=						self._to_readme(SCREAMING_SNAKE_CASE__  )
             with open(SCREAMING_SNAKE_CASE__  ,							'w'  ,							encoding='utf-8'  ) as readme_file:
                    readme_file.write(SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[str] = None  )	->    str:
             if readme_content is not None:
                    a_							,	a_      :	Tuple							=						_split_yaml_from_readme(SCREAMING_SNAKE_CASE__  )
                    a_      :	Union[str, Any]							=						'---\n' + self.to_yaml_string() + '---\n' + content
             else:
                    a_      :	Optional[int]							=						'---\n' + self.to_yaml_string() + '---\n'
             return full_content
      @classmethod
      def  SCREAMING_SNAKE_CASE							( cls       : Any  ,							SCREAMING_SNAKE_CASE__       : str  )	->    "DatasetMetadata":
             a_      :	Tuple							=						yaml.load(SCREAMING_SNAKE_CASE__  ,							Loader=_NoDuplicateSafeLoader  ) or {}

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

      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    str:
             return yaml.safe_dump(
                 {
                     (key.replace('_'  ,							'-'  ) if key in self._FIELDS_WITH_DASHES else key): value
                     for key, value in self.items()
                 }  ,							sort_keys=SCREAMING_SNAKE_CASE__  ,							allow_unicode=SCREAMING_SNAKE_CASE__  ,							encoding='utf-8'  ,							).decode('utf-8'  )
UpperCAmelCase_   :				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_   :				str						      = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
   ap.add_argument('readme_filepath')
   UpperCAmelCase_   :				List[Any]						      = ap.parse_args()

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






from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_   :				Dict						      = [
    'python',
    'tqdm',
    'regex',
    'requests',
    'packaging',
    'filelock',
    'numpy',
    'tokenizers',
    'huggingface-hub',
    'safetensors',
    'accelerate',
    'pyyaml',
]

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed
      elif pkg == "accelerate":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_accelerate_available

         # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
         # Transformers with PyTorch
         if not is_accelerate_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(F'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int=None       )			->	int:

       """simple docstring"""

       require_version(deps[pkg]						,					__A       )


