



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  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





