



"""simple docstring"""






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

import yaml



class     UpperCAmelCase    (	yaml.SafeLoader  ):



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      [self.constructed_objects[key_node] for key_node, _ in node.value]
     _snake_case   	=      [tuple(__lowerCamelCase				) if isinstance(__lowerCamelCase							,       __lowerCamelCase				) else key for key in keys]
     _snake_case   	=      Counter(__lowerCamelCase				)
     _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    :	str							,       __lowerCamelCase    :	Any							,       __lowerCamelCase    :	Optional[Any]=False				):



     """simple docstring"""
     _snake_case   	=      super().construct_mapping(__lowerCamelCase							,       deep=__lowerCamelCase				)
     self._check_no_duplicates_on_constructed_node(__lowerCamelCase				)
     return mapping




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

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



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   # class attributes
   A__		:	int           =  {'''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:
       _snake_case	,     _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    :	List[Any]							,       __lowerCamelCase    :	Path				):



     """simple docstring"""
     if path.exists():
       with open(__lowerCamelCase							,       encoding='''utf-8'''				) as readme_file:
         _snake_case   	=      readme_file.read()
     else:
       _snake_case   	=      None
     _snake_case   	=      self._to_readme(__lowerCamelCase				)
     with open(__lowerCamelCase							,       '''w'''							,       encoding='''utf-8'''				) as readme_file:
       readme_file.write(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	Optional[str] = None				):



     """simple docstring"""
     if readme_content is not None:
       _snake_case	,     _snake_case   	=      _split_yaml_from_readme(__lowerCamelCase				)
       _snake_case   	=      '''---\n''' + self.to_yaml_string() + '''---\n''' + content
     else:
       _snake_case   	=      '''---\n''' + self.to_yaml_string() + '''---\n'''
     return full_content



   @classmethod
   def    __UpperCAmelCase    (       cls    :	Tuple							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     _snake_case   	=      yaml.load(__lowerCamelCase							,       Loader=_NoDuplicateSafeLoader				) or {}

     # Convert the YAML keys to DatasetMetadata fields
     _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    :	Dict				):



     """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'''				)

snake_case   					=				{
    '''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

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

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







'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  Any   =		num_attention_heads
         A			:  Optional[Any]   =		intermediate_size
         A			:  Union[str, Any]   =		hidden_act
         A			:  Tuple   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Tuple   =		initializer_range
         A			:  List[Any]   =		layer_norm_eps
         A			:  Union[str, Any]   =		qkv_bias

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




