






'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A    	=   {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A    	=   ['''MaskFormerFeatureExtractor''']
						__A    	=   ['''MaskFormerImageProcessor''']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A    	=   [
						    '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MaskFormerForInstanceSegmentation''',
						    '''MaskFormerModel''',
						    '''MaskFormerPreTrainedModel''',
						]
						__A    	=   [
						    '''MaskFormerSwinBackbone''',
						    '''MaskFormerSwinModel''',
						    '''MaskFormerSwinPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
						from .configuration_maskformer_swin import MaskFormerSwinConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_maskformer import MaskFormerFeatureExtractor
												from .image_processing_maskformer import MaskFormerImageProcessor
						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_maskformer import (
												    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MaskFormerForInstanceSegmentation,
												    MaskFormerModel,
												    MaskFormerPreTrainedModel,
												)
												from .modeling_maskformer_swin import (
												    MaskFormerSwinBackbone,
												    MaskFormerSwinModel,
												    MaskFormerSwinPreTrainedModel,
												)


else:
						import sys

						__A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure)







'''simple docstring'''






__A    	=   {str(digit): digit**5 for digit in range(1_0)}




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> int:







   """simple docstring"""
   return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A  )  )




def        _SCREAMING_SNAKE_CASE  (				)    -> 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())