


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass







@dataclass
class     _lowercase				:


       """simple docstring"""




       A__      		=    42
       A__      		=    42







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Tuple      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       list[list[Edge]]					=						[[] for _ in range(__lowerCamelCase )]
             lowerCamelCase__	:       int					=						size





       def __getitem__(     self							:    Dict      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             return iter(self._graph[vertex] )





       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return self._size





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if weight not in (0, 1):
                   raise ValueError("Edge weight must be either 0 or 1." )

             if to_vertex < 0 or to_vertex >= self.size:
                   raise ValueError("Vertex indexes must be in [0; size)." )

             self._graph[from_vertex].append(Edge(__lowerCamelCase      ,   __lowerCamelCase ) )





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						deque([start_vertex] )
             lowerCamelCase__	:       list[int | None]					=						[None] * self.size
             lowerCamelCase__	:       Union[str, Any]					=						0

             while queue:
                   lowerCamelCase__	:       str					=						queue.popleft()
                   lowerCamelCase__	:       str					=						distances[current_vertex]
                   if current_distance is None:
                         continue

                   for edge in self[current_vertex]:
                         lowerCamelCase__	:       int					=						current_distance + edge.weight
                         lowerCamelCase__	:       int					=						distances[edge.destination_vertex]
                         if (
                             isinstance(__lowerCamelCase      ,   __lowerCamelCase )
                             and new_distance >= dest_vertex_distance
                         ):
                               continue
                         lowerCamelCase__	:       List[str]					=						new_distance
                         if edge.weight == 0:
                               queue.appendleft(edge.destination_vertex )
                         else:
                               queue.append(edge.destination_vertex )

             if distances[finish_vertex] is None:
                   raise ValueError("No path from start_vertex to finish_vertex." )

             return distances[finish_vertex]


if __name__ == "__main__":
    import doctest

    doctest.testmod()






import os
from pathlib import Path

def    lowercase_	(	):

      """simple docstring"""




      from torch.utils.cpp_extension import load

      lowerCamelCase__	:       Any					=						Path(_A		).resolve().parent.parent.parent / "kernels" / "deformable_detr"
      lowerCamelCase__	:       Optional[int]					=						[
          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


