



"""simple docstring"""







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

import yaml







class 				UpperCamelCase__       (     yaml.SafeLoader  ):



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    List[Any]								=			[self.constructed_objects[key_node] for key_node, _ in node.value]
        lowerCAmelCase_       :    str								=			[tuple(SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	) else key for key in keys]
        lowerCAmelCase_       :    Optional[Any]								=			Counter(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			[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    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict=False	):
        lowerCAmelCase_       :    int								=			super().construct_mapping(SCREAMING_SNAKE_CASE_   ,      deep=SCREAMING_SNAKE_CASE_	)
        self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE_	)
        return mapping




def     UpperCamelCase_   (   lowerCAmelCase__				:    str						)						->       Tuple[Optional[str], str]:

  """simple docstring"""




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

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







class 				UpperCamelCase__       (     __UpperCamelCase  ):
      # class attributes
      _SCREAMING_SNAKE_CASE      =					{"""train_eval_index"""}  # train-eval-index in the YAML metadata



      @classmethod
      def 					SCREAMING_SNAKE_CASE__			(				cls    :				str   ,      SCREAMING_SNAKE_CASE_    :				Path	):
        with open(SCREAMING_SNAKE_CASE_   ,      encoding='utf-8'	) as readme_file:
          lowerCAmelCase_       :    List[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	):
        if path.exists():
          with open(SCREAMING_SNAKE_CASE_   ,      encoding='utf-8'	) as readme_file:
            lowerCAmelCase_       :    Any								=			readme_file.read()
        else:
          lowerCAmelCase_       :    Any								=			None
        lowerCAmelCase_       :    List[str]								=			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    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[str] = None	):
        if readme_content is not None:
          lowerCAmelCase_       :    int								=			_split_yaml_from_readme(SCREAMING_SNAKE_CASE_	)
          lowerCAmelCase_       :    Dict								=			'---\n' + self.to_yaml_string() + '---\n' + content
        else:
          lowerCAmelCase_       :    Optional[Any]								=			'---\n' + self.to_yaml_string() + '---\n'
        return full_content



      @classmethod
      def 					SCREAMING_SNAKE_CASE__			(				cls    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				str	):
        lowerCAmelCase_       :    Any								=			yaml.load(SCREAMING_SNAKE_CASE_   ,      Loader=_NoDuplicateSafeLoader	) or {}

        # Convert the YAML keys to DatasetMetadata fields
        lowerCAmelCase_       :    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    :				Optional[Any]	):
        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'	)




lowercase__		:    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

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

     lowercase__		:    Optional[int]				   =     Path(args.readme_filepath)
     lowercase__		:    Union[str, Any]				   =     DatasetMetadata.from_readme(readme_filepath)
     print(dataset_metadata)
     dataset_metadata.to_readme(readme_filepath)










"""simple docstring"""







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase__		:    List[Any]				   =     logging.getLogger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""masked_bert"""



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_0_5_2_2   ,      SCREAMING_SNAKE_CASE_    :				int=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0_7_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.02   ,      SCREAMING_SNAKE_CASE_    :				Any=1E-12   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="topK"   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="constant"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			vocab_size
        lowerCAmelCase_       :    str								=			hidden_size
        lowerCAmelCase_       :    Optional[int]								=			num_hidden_layers
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_dropout_prob
        lowerCAmelCase_       :    str								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Any								=			max_position_embeddings
        lowerCAmelCase_       :    Dict								=			type_vocab_size
        lowerCAmelCase_       :    Tuple								=			initializer_range
        lowerCAmelCase_       :    List[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			pruning_method
        lowerCAmelCase_       :    Optional[Any]								=			mask_init
        lowerCAmelCase_       :    int								=			mask_scale





