






"""simple docstring"""







import math


class    __UpperCAmelCase:




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	0.0
												lowercase__    :				Optional[int]=	0.0
												for i in range(len(snake_case__      )      ):
																		da += math.pow((sample[i] - weights[0][i])	,  2      )
																		da += math.pow((sample[i] - weights[1][i])	,  2      )
																		return 0 if da > da else 1
												return 0




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												for i in range(len(snake_case__      )      ):
																		weights[j][i] += alpha * (sample[i] - weights[j][i])
												return weights





def 						lowercase__()    ->None:




						"""simple docstring"""



						lowercase__    :				Dict=	[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

						# weight initialization ( n, C )
						lowercase__    :				Optional[int]=	[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

						# training
						lowercase__    :				str=	SelfOrganizingMap()
						lowercase__    :				Dict=	3
						lowercase__    :				Dict=	0.5

						for _ in range(A						):
												for j in range(len(A						)						):
																		# training sample
																		lowercase__    :				Optional[Any]=	training_samples[j]

																		# Compute the winning vector
																		lowercase__    :				List[Any]=	self_organizing_map.get_winner(A ,		A						)

																		# Update the winning vector
																		lowercase__    :				List[str]=	self_organizing_map.update(A ,		A ,		A ,		A						)

    # classify test sample
						lowercase__    :				List[Any]=	[0, 0, 0, 1]
						lowercase__    :				Tuple=	self_organizing_map.get_winner(A ,		A						)

						# results
						print(f'''Clusters that the test sample belongs to : {winner}'''						)
						print(f'''Weights that have been trained : {weights}'''						)


# running the main() function
if __name__ == "__main__":
			main()














"""simple docstring"""







import os
from pathlib import Path





def 						lowercase__()    ->List[Any]:




						"""simple docstring"""



						from torch.utils.cpp_extension import load

						lowercase__    :				Any=	Path(A						).resolve().parent.parent.parent / "kernels" / "deformable_detr"
						lowercase__    :				Any=	[
						    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" ,		A ,		with_cuda=A ,		extra_include_paths=[str(A						)] ,		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






