




from __future__ import annotations

from queue import Queue







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(   self			:							Optional[Any]     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							{}

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      str:






         """simple docstring"""



         for i in self.vertices:
             print(__lowerCamelCase    ,					""" : """    ,					""" -> """.join([str(__lowerCamelCase     ) for j in self.vertices[i]]     )     )

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any    ,					A			:							Dict    ,					A			:							str     )      ->      Union[str, Any]:






         """simple docstring"""



         if from_vertex in self.vertices:
             self.vertices[from_vertex].append(__lowerCamelCase     )
         else:
             a__  			=							[to_vertex]






     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]    ,					A			:							Dict     )      ->      List[str]:






         """simple docstring"""
         a__  			=							set()

         # create a first in first out queue to store all the vertices for BFS
         a__  			=							Queue()

         # mark the source node as visited and enqueue it
         visited.add(__lowerCamelCase     )
         queue.put(__lowerCamelCase     )

         while not queue.empty():
             a__  			=							queue.get()

             # loop through all adjacent vertex and enqueue it if not yet visited
             for adjacent_vertex in self.vertices[vertex]:
                 if adjacent_vertex not in visited:
                     queue.put(__lowerCamelCase     )
                     visited.add(__lowerCamelCase     )
         return visited


if __name__ == "__main__":
 from doctest import testmod

 testmod(verbose=True)

 SCREAMING_SNAKE_CASE_    :       Any       =							Graph()
 g.add_edge(0, 1)
 g.add_edge(0, 2)
 g.add_edge(1, 2)
 g.add_edge(2, 0)
 g.add_edge(2, 3)
 g.add_edge(3, 3)

 g.print_graph()
 # 0  :  1 -> 2
 # 1  :  2
 # 2  :  0 -> 3
 # 3  :  3

 assert sorted(g.bfs(2)) == [0, 1, 2, 3]







"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )						)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'  ,       type=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

     # positional
     parser.add_argument(
         'training_script'  ,       type=_SCREAMING_SNAKE_CASE  ,       help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )  ,       )

     # rest from the training program
     parser.add_argument('training_script_args'  ,       nargs=_SCREAMING_SNAKE_CASE						)

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

     # Import training_script as a module.
     __A					:					Dict								=		Path(args.training_script						)
     sys.path.append(str(script_fpath.parent.resolve()						)						)
     __A					:					List[Any]								=		script_fpath.stem
     __A					:					Optional[Any]								=		importlib.import_module(_SCREAMING_SNAKE_CASE						)

     # Patch sys.argv
     __A					:					Union[str, Any]								=		[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores						)]

     xmp.spawn(mod._mp_fn  ,       args=()  ,       nprocs=args.num_cores						)


if __name__ == "__main__":
       main()



