





"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def 					A__				(			*UpperCamelCase       ):
							with open(UpperCamelCase					,  "r"       ) as fh:
														fcntl.flock(UpperCamelCase					,  fcntl.LOCK_EX       )
														try:
																					print(*UpperCamelCase       )
														finally:
																					fcntl.flock(UpperCamelCase					,  fcntl.LOCK_UN       )


_snake_case     :		Optional[int]       						= int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
_snake_case     :		str       						= torch.device('cuda', local_rank)
_snake_case     :		Tuple       						= socket.gethostname()

_snake_case     :		Optional[Any]       						= F"""[{hostname}-{local_rank}]"""

try:
		# test distributed
		dist.init_process_group('nccl')
		dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
		dist.barrier()

		# test cuda is available and can allocate memory
		torch.cuda.is_available()
		torch.ones(1).cuda(local_rank)

		# global rank
		_snake_case     :		Tuple       						= dist.get_rank()
		_snake_case     :		int       						= dist.get_world_size()

		printflock(F"""{gpu} is OK (global rank: {rank}/{world_size})""")

		dist.barrier()
		if rank == 0:
				printflock(F"""pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}""")

except Exception:
		printflock(F"""{gpu} is broken""")
		raise









"""simple docstring"""
class 					_UpperCAmelCase   :


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Tuple		):
												A		=  name
												A		=  val


					def __str__(					self			:str		):
												return f"{self.__class__.__name__}({self.name}, {self.val})"


					def __lt__(					self			:List[Any]     ,       __UpperCamelCase			:Union[str, Any]		):
												return self.val < other.val







class 					_UpperCAmelCase   :


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  {}
												A		=  {}
												A		=  self.build_heap(__UpperCamelCase		)


					def __getitem__(					self			:int     ,       __UpperCamelCase			:Optional[int]		):
												return self.get_value(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:str		):
												return (idx - 1) // 2


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Optional[Any]		):
												return idx * 2 + 1


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:Optional[int]		):
												return idx * 2 + 2


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:str		):
												return self.heap_dict[key]


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  len(__UpperCamelCase		) - 1
												A		=  self.get_parent_idx(__UpperCamelCase		)

												for idx, i in enumerate(__UpperCamelCase		):
																			A		=  idx
																			A		=  i.val

												for i in range(__UpperCamelCase     ,       -1     ,       -1		):
																			self.sift_down(__UpperCamelCase     ,       __UpperCamelCase		)
												return array


					def 	lowerCamelCase       (					self			:str     ,       __UpperCamelCase			:Optional[Any]     ,       __UpperCamelCase			:Dict		):
												while True:
																			A		=  self.get_left_child_idx(__UpperCamelCase		)  # noqa: E741
																			A		=  self.get_right_child_idx(__UpperCamelCase		)

																			A		=  idx
																			if l < len(__UpperCamelCase		) and array[l] < array[idx]:
																										A		=  l
																			if r < len(__UpperCamelCase		) and array[r] < array[smallest]:
																										A		=  r

																			if smallest != idx:
																										A,	A		=  array[smallest], array[idx]
																										(
																										    (
																										    A
																										),	(
																										    A
																										),	
																										)		=  (
																										    self.idx_of_element[array[smallest]],
																										    self.idx_of_element[array[idx]],
																										)
																										A		=  smallest
																			else:
																										break


					def 	lowerCamelCase       (					self			:Optional[Any]     ,       __UpperCamelCase			:Optional[int]		):
												A		=  self.get_parent_idx(__UpperCamelCase		)
												while p >= 0 and self.heap[p] > self.heap[idx]:
																			A,	A		=  self.heap[idx], self.heap[p]
																			A,	A		=  (
																			    self.idx_of_element[self.heap[idx]],
																			    self.idx_of_element[self.heap[p]],
																			)
																			A		=  p
																			A		=  self.get_parent_idx(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												return self.heap[0]


					def 	lowerCamelCase       (					self			:Tuple		):
												A,	A		=  self.heap[-1], self.heap[0]
												A,	A		=  (
												    self.idx_of_element[self.heap[-1]],
												    self.idx_of_element[self.heap[0]],
												)

												A		=  self.heap.pop()
												del self.idx_of_element[x]
												self.sift_down(0     ,       self.heap		)
												return x


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:Optional[int]		):
												self.heap.append(__UpperCamelCase		)
												A		=  len(self.heap		) - 1
												A		=  node.val
												self.sift_up(len(self.heap		) - 1		)


					def 	lowerCamelCase       (					self			:Tuple		):
												return len(self.heap		) == 0


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Dict		):
												assert (
												    self.heap[self.idx_of_element[node]].val > new_value
												), "newValue must be less that current value"
												A		=  new_value
												A		=  new_value
												self.sift_up(self.idx_of_element[node]		)







_snake_case     :		Optional[int]       						= Node('R', -1)
_snake_case     :		Tuple       						= Node('B', 6)
_snake_case     :		Tuple       						= Node('A', 3)
_snake_case     :		Optional[int]       						= Node('X', 1)
_snake_case     :		List[Any]       						= Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_snake_case     :		Tuple       						= MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
		print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()


