
'''simple docstring'''



class       A				:



			def __init__(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							)      ->		Optional[int]:




									'''simple docstring'''



									lowercase__							=							name
									lowercase__							=							value
									lowercase__							=							weight



			def __repr__(       self							)      ->		int:




									'''simple docstring'''



									return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''



			def A__			(       self							)      ->		Any:




									'''simple docstring'''



									return self.value



			def A__			(       self							)      ->		str:




									'''simple docstring'''



									return self.name



			def A__			(       self							)      ->		Dict:




									'''simple docstring'''



									return self.weight



			def A__			(       self							)      ->		Any:




									'''simple docstring'''



									return self.value / self.weight





def 			_A		(				lowercase__		,		lowercase__		,		lowercase__						):
						lowercase__							=							[]
						for i in range(len(UpperCAmelCase_						)						):
												menu.append(Things(name[i]		,		value[i]		,		weight[i]						)						)
						return menu





def 			_A		(				lowercase__		,		lowercase__		,		lowercase__						):
						lowercase__							=							sorted(UpperCAmelCase_		,		key=UpperCAmelCase_		,		reverse=UpperCAmelCase_						)
						lowercase__							=							[]
						lowercase__							=							0.0, 0.0
						for i in range(len(UpperCAmelCase_						)						):
												if (total_cost + items_copy[i].get_weight()) <= max_cost:
																		result.append(items_copy[i]						)
																		total_cost += items_copy[i].get_weight()
																		total_value += items_copy[i].get_value()
						return (result, total_value)





def 			_A		(				):
						pass


if __name__ == "__main__":
				import doctest

				doctest.testmod()











'''simple docstring'''
import os
from pathlib import Path






def A__       (	):
						from torch.utils.cpp_extension import load

						_UpperCamelCase   :						Optional[Any]												=  Path(UpperCAmelCase_				).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
						_UpperCamelCase   :						Tuple												=  [
						    root / filename
						    for filename in [
						        'vision.cpp',
						        os.path.join('cpu'    ,    'ms_deform_attn_cpu.cpp'				),
						        os.path.join('cuda'    ,    'ms_deform_attn_cuda.cu'				),
						    ]
						]

						load(
						    'MultiScaleDeformableAttention'    ,    UpperCAmelCase_    ,    with_cuda=UpperCAmelCase_    ,    extra_include_paths=[str(UpperCAmelCase_				)]    ,    extra_cflags=['-DWITH_CUDA=1']    ,    extra_cuda_cflags=[
						        '-DCUDA_HAS_FP16=1',
						        '-D__CUDA_NO_HALF_OPERATORS__',
						        '-D__CUDA_NO_HALF_CONVERSIONS__',
						        '-D__CUDA_NO_HALF2_OPERATORS__',
						    ]    ,    )

						import MultiScaleDeformableAttention as MSDA

						return MSDA


