

'''simple docstring'''


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

import torch_xla.distributed.xla_multiprocessing as xmp







def 						SCREAMING_SNAKE_CASE(  )    ->    List[str]:
	UpperCAmelCase_		:      str       	=					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=UpperCamelCase   ,default=1   ,help='Number of TPU cores to use (1 or 8).'  )

	# positional
	parser.add_argument(
	    'training_script'   ,type=UpperCamelCase   ,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=UpperCamelCase  )

	return parser.parse_args()







def 						SCREAMING_SNAKE_CASE(  )    ->    Tuple:
	UpperCAmelCase_		:      Tuple       	=					parse_args()

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

	# Patch sys.argv
	UpperCAmelCase_		:      List[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()









'''simple docstring'''


import math



class 					lowercase						:


							def __init__(			self    ,			_snake_case=0)							->				Union[str, Any]:  # a graph with Node 0,1,...,N-1
								UpperCAmelCase_		:      Tuple       	=					n
								UpperCAmelCase_		:      Optional[Any]       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # adjacency matrix for weight
								UpperCAmelCase_		:      Tuple       	=					[
								    [math.inf for j in range(0    ,			_snake_case)] for i in range(0    ,			_snake_case)
								]  # dp[i][j] stores minimum distance from i to j


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				List[Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					w


							def 						_snake_case							(			self)							->				str:
								for k in range(0    ,			self.n):
									for i in range(0    ,			self.n):
										for j in range(0    ,			self.n):
											UpperCAmelCase_		:      Optional[int]       	=					min(self.dp[i][j]    ,			self.dp[i][k] + self.dp[k][j])





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				str:
								return self.dp[u][v]


if __name__ == "__main__":
						lowerCAmelCase__										= Graph(5)
						graph.add_edge(0, 2, 9)
						graph.add_edge(0, 4, 10)
						graph.add_edge(1, 3, 5)
						graph.add_edge(2, 3, 7)
						graph.add_edge(3, 0, 10)
						graph.add_edge(3, 1, 2)
						graph.add_edge(3, 2, 1)
						graph.add_edge(3, 4, 6)
						graph.add_edge(4, 1, 3)
						graph.add_edge(4, 2, 4)
						graph.add_edge(4, 3, 9)
						graph.floyd_warshall()
						graph.show_min(1, 4)
						graph.show_min(0, 3)






