


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

import torch_xla.distributed.xla_multiprocessing as xmp

def        lowerCAmelCase__ (			)  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase 		=			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=a_			,  default=1			,  help='Number of TPU cores to use (1 or 8).'       )

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

							return parser.parse_args()

def        lowerCAmelCase__ (			)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase 		=			parse_args()

							# Import training_script as a module.
							_UpperCAmelCase 		=			Path(args.training_script       )
							sys.path.append(str(script_fpath.parent.resolve()       )       )
							_UpperCAmelCase 		=			script_fpath.stem
							_UpperCAmelCase 		=			importlib.import_module(a_       )

							# Patch sys.argv
							_UpperCAmelCase 		=			[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()


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
