
'''simple docstring'''



def 			_A		(				lowercase__		,		lowercase__						):
						_enforce_args(UpperCAmelCase_		,		UpperCAmelCase_						)
						if n == 0:
												return 0
						lowercase__							=							float("""-inf"""						)
						for i in range(1		,		n + 1						):
												lowercase__							=							max(
												    UpperCAmelCase_		,		prices[i - 1] + naive_cut_rod_recursive(n - i		,		UpperCAmelCase_						)						)

						return max_revue





def 			_A		(				lowercase__		,		lowercase__						):
						_enforce_args(UpperCAmelCase_		,		UpperCAmelCase_						)
						lowercase__							=							[float("""-inf"""						) for _ in range(n + 1						)]
						return _top_down_cut_rod_recursive(UpperCAmelCase_		,		UpperCAmelCase_		,		UpperCAmelCase_						)





def 			_A		(				lowercase__		,		lowercase__		,		lowercase__						):
						if max_rev[n] >= 0:
												return max_rev[n]
						elif n == 0:
												return 0
						else:
												lowercase__							=							float("""-inf"""						)
												for i in range(1		,		n + 1						):
																		lowercase__							=							max(
																		    UpperCAmelCase_		,		prices[i - 1] + _top_down_cut_rod_recursive(n - i		,		UpperCAmelCase_		,		UpperCAmelCase_						)		,		)

												lowercase__							=							max_revenue

						return max_rev[n]





def 			_A		(				lowercase__		,		lowercase__						):
						_enforce_args(UpperCAmelCase_		,		UpperCAmelCase_						)

						# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
						# length 0.
						lowercase__							=							[float("""-inf"""						) for _ in range(n + 1						)]
						lowercase__							=							0

						for i in range(1		,		n + 1						):
												lowercase__							=							max_rev[i]
												for j in range(1		,		i + 1						):
																		lowercase__							=							max(UpperCAmelCase_		,		prices[j - 1] + max_rev[i - j]						)

												lowercase__							=							max_revenue_i

						return max_rev[n]





def 			_A		(				lowercase__		,		lowercase__						):
						if n < 0:
												lowercase__							=							f'''n must be greater than or equal to 0. Got n = {n}'''
												raise ValueError(UpperCAmelCase_						)

						if n > len(UpperCAmelCase_						):
												lowercase__							=							(
												    'Each integral piece of rod must have a corresponding price. '
												    f'''Got n = {n} but length of prices = {len(UpperCAmelCase_						)}'''
												)
												raise ValueError(UpperCAmelCase_						)





def 			_A		(				):
						lowercase__							=							[6, 10, 12, 15, 20, 23]
						lowercase__							=							len(UpperCAmelCase_						)

						# the best revenue comes from cutting the rod into 6 pieces, each
						# of length 1 resulting in a revenue of 6 * 6 = 36.
						lowercase__							=							36

						lowercase__							=							top_down_cut_rod(UpperCAmelCase_		,		UpperCAmelCase_						)
						lowercase__							=							bottom_up_cut_rod(UpperCAmelCase_		,		UpperCAmelCase_						)
						lowercase__							=							naive_cut_rod_recursive(UpperCAmelCase_		,		UpperCAmelCase_						)

						assert expected_max_revenue == max_rev_top_down
						assert max_rev_top_down == max_rev_bottom_up
						assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
				main()











'''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


