
'''simple docstring'''







from __future__ import annotations







class 		snake_case_       :



   """simple docstring"""





   def __init__( self			,   UpperCamelCase):
          lowerCamelCase__				   =      vertices
          lowerCamelCase__				   =      [[0] * vertices for _ in range(UpperCamelCase)]







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          print("Vertex \t Distance from Source")
          for vertex in range(self.vertices):
                 print(UpperCamelCase			,   "\t\t"			,   distances_from_source[vertex])







   def     __UpperCAmelCase      ( self			,   UpperCamelCase			,   UpperCamelCase):
          lowerCamelCase__				   =      1E7
          lowerCamelCase__				   =      0

          # Search not nearest vertex not in the shortest path tree
          for vertex in range(self.vertices):
                 if distances_from_source[vertex] < minimum and visited[vertex] is False:
                        lowerCamelCase__				   =      distances_from_source[vertex]
                        lowerCamelCase__				   =      vertex
          return min_index







   def     __UpperCAmelCase      ( self			,   UpperCamelCase):
          lowerCamelCase__				   =      [int(1E7)] * self.vertices  # distances from the source
          lowerCamelCase__				   =      0
          lowerCamelCase__				   =      [False] * self.vertices

          for _ in range(self.vertices):
                 lowerCamelCase__				   =      self.minimum_distance(UpperCamelCase			,   UpperCamelCase)
                 lowerCamelCase__				   =      True

                 # Update dist value of the adjacent vertices
                 # of the picked vertex only if the current
                 # distance is greater than new distance and
                 # the vertex in not in the shortest path tree
                 for v in range(self.vertices):
                        if (
                            self.graph[u][v] > 0
                            and visited[v] is False
                            and distances[v] > distances[u] + self.graph[u][v]
                        ):
                               lowerCamelCase__				   =      distances[u] + self.graph[u][v]

          self.print_solution(UpperCamelCase)


if __name__ == "__main__":
    lowerCAmelCase_      =   Graph(9)
    lowerCAmelCase_      =   [
        [0, 4, 0, 0, 0, 0, 0, 8, 0],
        [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
        [0, 8, 0, 7, 0, 4, 0, 0, 2],
        [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
        [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
        [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
        [0, 0, 0, 0, 0, 2, 0, 1, 6],
        [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
        [0, 0, 2, 0, 0, 0, 6, 7, 0],
    ]
    graph.dijkstra(0)



'''simple docstring'''







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase_      =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







    def   lowerCAmelCase(  cls    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "cpu"
                     if device != "mps":
                            logger.info(
                                f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
                                f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
                                f""" slighly speed up this function by passing a generator that was created on the {device} device."""      )
              elif gen_device_type != device.type and gen_device_type == "cuda":
                     raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""      )

       if isinstance(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






       if is_torch_version("<"      , "2.0.0"      ) or not hasattr(a__      , "_dynamo"      ):
              return False
       return isinstance(a__      , torch._dynamo.eval_frame.OptimizedModule      )

