



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A:   Tuple								=		logging.get_logger(__name__)

A:   List[Any]								=		{
    "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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Union[str, Any]						=						'donut-swin'

   __lowerCAmelCase  :   int						=						{
       'num_attention_heads': 'num_heads',
       'num_hidden_layers': 'num_layers',
   }

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE=224      ,     _SCREAMING_SNAKE_CASE=4      ,     _SCREAMING_SNAKE_CASE=3      ,     _SCREAMING_SNAKE_CASE=96      ,     _SCREAMING_SNAKE_CASE=[2, 2, 6, 2]      ,     _SCREAMING_SNAKE_CASE=[3, 6, 12, 24]      ,     _SCREAMING_SNAKE_CASE=7      ,     _SCREAMING_SNAKE_CASE=4.0      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.0      ,     _SCREAMING_SNAKE_CASE=0.1      ,     _SCREAMING_SNAKE_CASE="gelu"      ,     _SCREAMING_SNAKE_CASE=False      ,     _SCREAMING_SNAKE_CASE=0.02      ,     _SCREAMING_SNAKE_CASE=1E-5      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Any:





    '''simple docstring'''




    super().__init__(**_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Any	=	image_size
    UpperCAmelCase	:					Optional[Any]	=	patch_size
    UpperCAmelCase	:					Any	=	num_channels
    UpperCAmelCase	:					int	=	embed_dim
    UpperCAmelCase	:					Tuple	=	depths
    UpperCAmelCase	:					Any	=	len(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[int]	=	num_heads
    UpperCAmelCase	:					Optional[int]	=	window_size
    UpperCAmelCase	:					List[str]	=	mlp_ratio
    UpperCAmelCase	:					Any	=	qkv_bias
    UpperCAmelCase	:					int	=	hidden_dropout_prob
    UpperCAmelCase	:					List[str]	=	attention_probs_dropout_prob
    UpperCAmelCase	:					List[str]	=	drop_path_rate
    UpperCAmelCase	:					List[str]	=	hidden_act
    UpperCAmelCase	:					Any	=	use_absolute_embeddings
    UpperCAmelCase	:					List[Any]	=	layer_norm_eps
    UpperCAmelCase	:					Tuple	=	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
    UpperCAmelCase	:					Optional[int]	=	int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE						) - 1)						)





"""simple docstring"""
from math import isqrt, loga

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					Any	=	[True] * max_number
 for i in range(2			,		isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2			,		UpperCamelCase			,		UpperCamelCase						):
    UpperCAmelCase	:					str	=	False

 return [i for i in range(2			,		UpperCamelCase						) if is_prime[i]]

def       _snake_case						(						UpperCamelCase						:		int = 800800			,		UpperCamelCase						:		int = 800800						):
 UpperCAmelCase	:					Union[str, Any]	=	degree * loga(UpperCamelCase						)
 UpperCAmelCase	:					int	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Union[str, Any]	=	calculate_prime_numbers(UpperCamelCase						)

 UpperCAmelCase	:					Dict	=	0
 UpperCAmelCase	:					Optional[int]	=	0
 UpperCAmelCase	:					Dict	=	len(UpperCamelCase						) - 1
 while left < right:
  while (
      prime_numbers[right] * loga(prime_numbers[left]						)
      + prime_numbers[left] * loga(prime_numbers[right]						)
      > upper_bound
  ):
   right -= 1
  hybrid_integers_count += right - left
  left += 1

 return hybrid_integers_count


if __name__ == "__main__":
      print(f"""{solution() = }""")
