



"""simple docstring"""






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

import yaml






class 	__snake_case	(     yaml.SafeLoader):




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			[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						:    Any         =			Counter(SCREAMING_SNAKE_CASE_		)
         _lowerCamelCase						:    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					:						Any						, __lowerCAmelCase					:						Optional[Any]						, __lowerCAmelCase					:						Dict=False		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			super().construct_mapping(SCREAMING_SNAKE_CASE_						, deep=SCREAMING_SNAKE_CASE_		)
         self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE_		)
         return mapping






def 	snake_case_  (				A_ :		str						):


       '''simple docstring'''





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

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






class 	__snake_case	(     a__):
  # class attributes
  snake_case__    :       List[Any]        =						{"train_eval_index"}  # train-eval-index in the YAML metadata




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Optional[int]						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         with open(SCREAMING_SNAKE_CASE_						, encoding='''utf-8'''		) as readme_file:
                _lowerCamelCase						:    Optional[int]         =			_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					:						Dict						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         if path.exists():
                with open(SCREAMING_SNAKE_CASE_						, encoding='''utf-8'''		) as readme_file:
                       _lowerCamelCase						:    str         =			readme_file.read()
         else:
                _lowerCamelCase						:    str         =			None
         _lowerCamelCase						:    Dict         =			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]						, __lowerCAmelCase					:						Union[str, Any] = None		):






         """simple docstring"""






         if readme_content is not None:
                _lowerCamelCase						:    Union[str, Any]         =			_split_yaml_from_readme(SCREAMING_SNAKE_CASE_		)
                _lowerCamelCase						:    Union[str, Any]         =			'---\n' + self.to_yaml_string() + '---\n' + content
         else:
                _lowerCamelCase						:    Any         =			'---\n' + self.to_yaml_string() + '---\n'
         return full_content




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Optional[int]						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			yaml.load(SCREAMING_SNAKE_CASE_						, Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         _lowerCamelCase						:    Optional[Any]         =			{
             (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					:						Tuple		):






         """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=SCREAMING_SNAKE_CASE_						, allow_unicode=SCREAMING_SNAKE_CASE_						, encoding='''utf-8'''						, ).decode('''utf-8'''		)


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

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







"""simple docstring"""






from torch import nn






class 	__snake_case	(     nn.Module):




  def __init__(			self					:						Optional[Any]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase						:    List[str]         =			class_size
         _lowerCamelCase						:    List[str]         =			embed_size
         # self.mlp1 = nn.Linear(embed_size, embed_size)
         # self.mlp2 = (nn.Linear(embed_size, class_size))
         _lowerCamelCase						:    Optional[int]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			self.mlp(__lowerCAmelCase		)
         return logits


