


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
							from .camera import create_pan_cameras
							from .pipeline_shap_e import ShapEPipeline
							from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
							from .renderer import (
							    BoundingBoxVolume,
							    ImportanceRaySampler,
							    MLPNeRFModelOutput,
							    MLPNeRSTFModel,
							    ShapEParamsProjModel,
							    ShapERenderer,
							    StratifiedRaySampler,
							    VoidNeRFModel,
							)





def 	__a     (      A__			:  int       ):
    if not isinstance(A__			,  A__       ):
        raise ValueError("Input must be an integer"       )
    if input_num <= 0:
        raise ValueError("Input must be positive"       )
    return sum(
        divisor for divisor in range(1			,  input_num // 2 + 1       ) if input_num % divisor == 0       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()