





'''simple docstring'''


from __future__ import annotations



class 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   str   ,   _lowerCAmelCase    :   str   ):
			A					=   vertices
			A					=   [[0] * vertices for _ in range(_lowerCAmelCase   )]







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   int   ):
			print("""Vertex \t Distance from Source"""   )
			for vertex in range(self.vertices   ):
				print(_lowerCAmelCase   ,   """\t\t"""   ,   distances_from_source[vertex]   )







		def 							A  (self    :   Optional[int]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[Any]   ):
			A					=   1e7
			A					=   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:
					A					=   distances_from_source[vertex]
					A					=   vertex
			return min_index







		def 							A  (self    :   Dict   ,   _lowerCAmelCase    :   Optional[Any]   ):
			A					=   [int(1e7   )] * self.vertices  # distances from the source
			A					=   0
			A					=   [False] * self.vertices

			for _ in range(self.vertices   ):
				A					=   self.minimum_distance(_lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   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]
					):
						A					=   distances[u] + self.graph[u][v]

			self.print_solution(_lowerCAmelCase   )


if __name__ == "__main__":
				_lowerCamelCase					:						str					     =							Graph(9)
				_lowerCamelCase					:						Tuple					     =							[
				    [0, 4, 0, 0, 0, 0, 0, 8, 0],
				    [4, 0, 8, 0, 0, 0, 0, 11, 0],
				    [0, 8, 0, 7, 0, 4, 0, 0, 2],
				    [0, 0, 7, 0, 9, 14, 0, 0, 0],
				    [0, 0, 0, 9, 0, 10, 0, 0, 0],
				    [0, 0, 4, 14, 10, 0, 2, 0, 0],
				    [0, 0, 0, 0, 0, 2, 0, 1, 6],
				    [8, 11, 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      )

