





import os
from pathlib import Path






def 	lowerCamelCase_		(							)						->		Tuple:






				"""simple docstring"""


				from torch.utils.cpp_extension import load

				snake_case_		:       List[str]           =    Path(_UpperCamelCase					).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
				snake_case_		:       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'''					,							_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



from __future__ import annotations
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Dict			,a__    :   Union[str, Any]			,a__    :   Any       )					->				Optional[int]:  # noqa: E741
	while r - l > 1:
		__A :			Any		    =			(l + r) // 2
		if v[m] >= key:
			__A :			Optional[int]		    =			m
		else:
			__A :			List[Any]		    =			m  # noqa: E741
	return r
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]       )					->				int:
	if len(a__       ) == 0:
		return 0

	__A :			str		    =			[0] * len(a__       )
	__A :			List[str]		    =			1

	__A :			List[Any]		    =			v[0]

	for i in range(1			,len(a__       )       ):
		if v[i] < tail[0]:
			__A :			int		    =			v[i]
		elif v[i] > tail[length - 1]:
			__A :			Union[str, Any]		    =			v[i]
			length += 1
		else:
			__A :			Any		    =			v[i]

	return length


if __name__ == "__main__":
			import doctest

			doctest.testmod()





