


'''simple docstring'''




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


_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		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  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Tuple			      = """donut-swin"""

	_lowerCamelCase   :					Optional[int]			      = {
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}



	def __init__(					self ,					snake_case__=224 ,					snake_case__=4 ,					snake_case__=3 ,					snake_case__=96 ,					snake_case__=[2, 2, 6, 2] ,					snake_case__=[3, 6, 12, 24] ,					snake_case__=7 ,					snake_case__=4.0 ,					snake_case__=True ,					snake_case__=0.0 ,					snake_case__=0.0 ,					snake_case__=0.1 ,					snake_case__="gelu" ,					snake_case__=False ,					snake_case__=0.0_2 ,					snake_case__=1e-5 ,					**snake_case__ ,					):
		super().__init__(**snake_case__    )

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





'''simple docstring'''




_lowerCAmelCase  :		List[str]		     =	{str(digit): digit**5 for digit in range(10)}

def 			__UpperCamelCase    (    _A    :				int				)    ->      int:




	"""simple docstring"""





	return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_A				)				)

def 			__UpperCamelCase    (    )    ->      int:




	"""simple docstring"""





	return sum(
	    number
	    for number in range(10_00					,   1_00_00_00				)
	    if number == digits_fifth_powers_sum(_A				)				)


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