


import math





def 	lowercase__(		A     ,			A      ):
       snake_case__ :       str	   =							len(lowerCAmelCase_      )
       snake_case__ :       Dict	   =							int(math.floor(math.sqrt(lowerCAmelCase_      )      )      )
       snake_case__ :       Optional[int]	   =							0
       while arr[min(lowerCAmelCase_     ,			lowerCAmelCase_      ) - 1] < x:
              snake_case__ :       str	   =							step
              step += int(math.floor(math.sqrt(lowerCAmelCase_      )      )      )
              if prev >= n:
                     return -1

       while arr[prev] < x:
              snake_case__ :       List[Any]	   =							prev + 1
              if prev == min(lowerCAmelCase_     ,			lowerCAmelCase_      ):
                     return -1
       if arr[prev] == x:
              return prev
       return -1


if __name__ == "__main__":
 lowerCamelCase					:		Union[str, Any]            =						input('Enter numbers separated by a comma:\n').strip()
 lowerCamelCase					:		Optional[int]            =						[int(item) for item in user_input.split(',')]
 lowerCamelCase					:		int            =						int(input('Enter the number to be searched:\n'))
 lowerCamelCase					:		Any            =						jump_search(arr, x)
 if res == -1:
  print('Number not found!')
 else:
  print(F"""Number {x} is at index {res}""")










"""simple docstring"""






import os
from pathlib import Path




def 			snake_case		(		)						->				Tuple:
  from torch.utils.cpp_extension import load

  _snake_case   	=      Path(lowerCAmelCase_	).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
  _snake_case   	=      [
      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'''  ,						lowerCAmelCase_  ,						with_cuda=lowerCAmelCase_  ,						extra_include_paths=[str(lowerCAmelCase_	)]  ,						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





