

import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												(SCREAMING_SNAKE_CASE_):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
	import doctest

	doctest.testmod()









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

import torch_xla.distributed.xla_multiprocessing as xmp







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= 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     A_   (					):
						SCREAMING_SNAKE_CASE_:				List[str]							= parse_args()

						# Import training_script as a module.
						SCREAMING_SNAKE_CASE_:				Tuple							= Path(args.training_script					)
						sys.path.append(str(script_fpath.parent.resolve()					)					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= script_fpath.stem
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= importlib.import_module(_UpperCAmelCase					)

						# Patch sys.argv
						SCREAMING_SNAKE_CASE_:				int							= [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()






