





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


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	{
    """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 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Union[str, Any]		     =      "donut-swin"

     __snake_case   :     Any		     =      {
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(							self						:   Any						,lowerCamelCase__						:   Union[str, Any]=224						,lowerCamelCase__						:   Union[str, Any]=4						,lowerCamelCase__						:   Union[str, Any]=3						,lowerCamelCase__						:   Tuple=96						,lowerCamelCase__						:   Any=[2, 2, 6, 2]						,lowerCamelCase__						:   Optional[Any]=[3, 6, 12, 24]						,lowerCamelCase__						:   int=7						,lowerCamelCase__						:   Union[str, Any]=4.0						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   Any=0.0						,lowerCamelCase__						:   List[str]=0.0						,lowerCamelCase__						:   List[str]=0.1						,lowerCamelCase__						:   Optional[int]="gelu"						,lowerCamelCase__						:   int=False						,lowerCamelCase__						:   Union[str, Any]=0.02						,lowerCamelCase__						:   Optional[int]=1e-5						,**lowerCamelCase__						:   Union[str, Any]						,)					->		List[Any]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embed_dim
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						num_heads
           SCREAMING_SNAKE_CASE		=						window_size
           SCREAMING_SNAKE_CASE		=						mlp_ratio
           SCREAMING_SNAKE_CASE		=						qkv_bias
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						drop_path_rate
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						use_absolute_embeddings
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						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
           SCREAMING_SNAKE_CASE		=						int(embed_dim * 2 ** (len(lowerCamelCase__	) - 1)	)









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 1			,						_SCREAMING_SNAKE_CASE = 10_00     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						1
      SCREAMING_SNAKE_CASE		=						0

      for divide_by_number in range(_SCREAMING_SNAKE_CASE			,						digit + 1     ):
            SCREAMING_SNAKE_CASE		=						[]
            SCREAMING_SNAKE_CASE		=						numerator
            for _ in range(1			,						digit + 1     ):
                  if now_divide in has_been_divided:
                        if longest_list_length < len(_SCREAMING_SNAKE_CASE     ):
                              SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     )
                              SCREAMING_SNAKE_CASE		=						divide_by_number
                  else:
                        has_been_divided.append(_SCREAMING_SNAKE_CASE     )
                        SCREAMING_SNAKE_CASE		=						now_divide * 10 % divide_by_number

      return the_digit


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()


