



"""simple docstring"""






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


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       str        =						"donut-swin"

  snake_case__    :       List[Any]        =						{
      "num_attention_heads": "num_heads",
      "num_hidden_layers": "num_layers",
  }




  def __init__(			self					:						str						, __lowerCAmelCase					:						str=2_2_4						, __lowerCAmelCase					:						Tuple=4						, __lowerCAmelCase					:						Union[str, Any]=3						, __lowerCAmelCase					:						Tuple=9_6						, __lowerCAmelCase					:						Optional[int]=[2, 2, 6, 2]						, __lowerCAmelCase					:						List[str]=[3, 6, 1_2, 2_4]						, __lowerCAmelCase					:						List[str]=7						, __lowerCAmelCase					:						str=4.0						, __lowerCAmelCase					:						str=True						, __lowerCAmelCase					:						Tuple=0.0						, __lowerCAmelCase					:						Union[str, Any]=0.0						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Any="gelu"						, __lowerCAmelCase					:						str=False						, __lowerCAmelCase					:						str=0.02						, __lowerCAmelCase					:						List[str]=1E-5						, **__lowerCAmelCase					:						Any						, ):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			image_size
         _lowerCamelCase						:    List[Any]         =			patch_size
         _lowerCamelCase						:    Any         =			num_channels
         _lowerCamelCase						:    str         =			embed_dim
         _lowerCamelCase						:    List[str]         =			depths
         _lowerCamelCase						:    Union[str, Any]         =			len(__lowerCAmelCase		)
         _lowerCamelCase						:    List[str]         =			num_heads
         _lowerCamelCase						:    str         =			window_size
         _lowerCamelCase						:    List[str]         =			mlp_ratio
         _lowerCamelCase						:    int         =			qkv_bias
         _lowerCamelCase						:    int         =			hidden_dropout_prob
         _lowerCamelCase						:    Optional[int]         =			attention_probs_dropout_prob
         _lowerCamelCase						:    Tuple         =			drop_path_rate
         _lowerCamelCase						:    int         =			hidden_act
         _lowerCamelCase						:    List[str]         =			use_absolute_embeddings
         _lowerCamelCase						:    List[str]         =			layer_norm_eps
         _lowerCamelCase						:    str         =			initializer_range
         # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
         # this indicates the channel dimension after the last stage of the model
         _lowerCamelCase						:    List[Any]         =			int(embed_dim * 2 ** (len(__lowerCAmelCase		) - 1)		)







"""simple docstring"""






def 	snake_case_  (				A_ :		float						):


       '''simple docstring'''





       if edge <= 0 or not isinstance(A_,    A_						):
              raise ValueError('''Length must be a positive.'''						)
       return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)






def 	snake_case_  (				A_ :		float						):


       '''simple docstring'''





       if edge <= 0 or not isinstance(A_,    A_						):
              raise ValueError('''Length must be a positive.'''						)
       return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


