






'''simple docstring'''



__UpperCamelCase      :       Optional[int]      			=      {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	dict,					SCREAMING_SNAKE_CASE__:	List[str],					SCREAMING_SNAKE_CASE__:	List[str]    )  ->			list[str]:

   """simple docstring"""
   __a				     =    set()
   # keep track of all the paths to be checked
   __a				     =    [[start]]

   # return path if start is goal
   if start == goal:
      return [start]

   # keeps looping until all possible paths have been checked
   while queue:
      # pop the first path from the queue
      __a				     =    queue.pop(0    )
      # get the last node from the path
      __a				     =    path[-1]
      if node not in explored:
         __a				     =    graph[node]
         # go through all neighbour nodes, construct a new path and
         # push it into the queue
         for neighbour in neighbours:
            __a				     =    list(SCREAMING_SNAKE_CASE__    )
            new_path.append(SCREAMING_SNAKE_CASE__    )
            queue.append(SCREAMING_SNAKE_CASE__    )
            # return path if neighbour is goal
            if neighbour == goal:
               return new_path

            # mark node as explored
         explored.add(SCREAMING_SNAKE_CASE__    )

    # in case there's no path between the 2 nodes
   return []



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	dict,					SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	Optional[int]    )  ->			int:

   """simple docstring"""
   if not graph or start not in graph or target not in graph:
      return -1
   if start == target:
      return 0
   __a				     =    [start]
   __a				     =    set(SCREAMING_SNAKE_CASE__    )
   # Keep tab on distances from `start` node.
   __a				     =    {start: 0, target: -1}
   while queue:
      __a				     =    queue.pop(0    )
      if node == target:
         __a				     =    (
             dist[node] if dist[target] == -1 else min(dist[target],					dist[node]    )
         )
      for adjacent in graph[node]:
         if adjacent not in visited:
            visited.add(SCREAMING_SNAKE_CASE__    )
            queue.append(SCREAMING_SNAKE_CASE__    )
            __a				     =    dist[node] + 1
   return dist[target]


if __name__ == "__main__":
 print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
 print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4







'''simple docstring'''



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
 import torch

if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm


__UpperCamelCase      :       Optional[Any]      			=      logging.get_logger(__name__)
@dataclass
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=[
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]




      def __init__(  self ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
               __a				     =    deprecated_arg[3:]
               setattr(self ,   lowerCamelCase ,   not kwargs.pop(lowerCamelCase      )      )
               logger.warning(
                   F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                   F""" {positive_arg}={kwargs[positive_arg]}"""      )

         __a				     =    kwargs.pop('torchscript' ,   self.torchscript      )
         __a				     =    kwargs.pop('torch_xla_tpu_print_metrics' ,   self.torch_xla_tpu_print_metrics      )
         __a				     =    kwargs.pop('fp16_opt_level' ,   self.fpaa_opt_level      )
         super().__init__(**lowerCamelCase      )

      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Trace the models using torchscript"}     )
      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Print Xla/PyTorch tpu metrics"}     )
      __a  					=field(
          default="O1"      ,      metadata={
              "help": (
                  "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
                  "See details at https://nvidia.github.io/apex/amp.html"
              )
          }      ,      )




      @cached_property
      def  __UpperCamelCase							(  self      )							->Tuple["torch.device", int]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         logger.info('PyTorch: setting up devices'      )
         if not self.cuda:
            __a				     =    torch.device('cpu'      )
            __a				     =    0
         elif is_torch_tpu_available():
            __a				     =    xm.xla_device()
            __a				     =    0
         else:
            __a				     =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
            __a				     =    torch.cuda.device_count()
         return device, n_gpu




      @property
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         return is_torch_tpu_available() and self.tpu




      @property
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()




      @property
      def  __UpperCamelCase							(  self      )							->"torch.device":



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[0]




      @property
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[1]






      @property
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         return self.n_gpu > 0