






import math





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  0
	__lowercase             =  0
	while num > 0:
		__lowercase             =  num % 8
		__lowercase             =  octal + (remainder * math.floor(math.pow(10				,  lowerCamelCase					)					))
		counter += 1
		__lowercase             =  math.floor(num / 8					)  # basically /= 8 without remainder if any
		# This formatting removes trailing '.0' from `octal`.
	return F'0o{int(lowerCamelCase					)}'





def 				snake_case  (     ):




	'''simple docstring'''







	print("""\n2 in octal is:"""					)
	print(decimal_to_octal(2					)					)  # = 2
	print("""\n8 in octal is:"""					)
	print(decimal_to_octal(8					)					)  # = 10
	print("""\n65 in octal is:"""					)
	print(decimal_to_octal(65					)					)  # = 101
	print("""\n216 in octal is:"""					)
	print(decimal_to_octal(216					)					)  # = 330
	print("""\n512 in octal is:"""					)
	print(decimal_to_octal(512					)					)  # = 1000
	print("""\n"""					)


if __name__ == "__main__":
					main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__UpperCamelCase      :      Tuple     						=						{
    """configuration_swiftformer""": [
        """SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwiftFormerConfig""",
        """SwiftFormerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase      :      Optional[Any]     						=						[
						    """SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwiftFormerForImageClassification""",
						    """SwiftFormerModel""",
						    """SwiftFormerPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase      :      List[Any]     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

