






import math

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		int=0							)	->     Optional[Any]:  # a graph with Node 0,1,...,N-1


												"""simple docstring"""

												__lowercase					:							Any			=   n
												__lowercase					:							Optional[int]			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # adjacency matrix for weight
												__lowercase					:							Dict			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # dp[i][j] stores minimum distance from i to j



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   w



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												for k in range(0						,   self.n							):
																			for i in range(0						,   self.n							):
																										for j in range(0						,   self.n							):
																																	__lowercase					:							List[str]			=   min(self.dp[i][j]						,   self.dp[i][k] + self.dp[k][j]							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		str							)	->     int:


												"""simple docstring"""

												return self.dp[u][v]


if __name__ == "__main__":
	lowerCamelCase       :       int        =				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)







lowerCamelCase       :       Dict        =				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
	from .utils import rich