

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


SCREAMING_SNAKE_CASE		:Dict						=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE		:Dict						=     {
    """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 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:int										=      """donut-swin"""

     UpperCamelCase_	:str										=      {
         """num_attention_heads""": """num_heads""",
         """num_hidden_layers""": """num_layers""",
     }






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

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



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_0_0_0_0_0_0			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[i - 1 for i in range(limit + 1			)]

    for i in range(2			,						limit + 1			):
        if phi[i] == i - 1:
            for j in range(2 * i			,						limit + 1			,						SCREAMING_SNAKE_CASE_			):
                phi[j] -= phi[j] // i

    return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
 print(solution())
