

'''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        __lowercase    (  *UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					'r'      ) as fh:
										fcntl.flock(UpperCAmelCase__		,					fcntl.LOCK_EX      )
										try:
															print(*UpperCAmelCase__      )
										finally:
															fcntl.flock(UpperCAmelCase__		,					fcntl.LOCK_UN      )


lowerCamelCase      				=     int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
lowerCamelCase      				=     torch.device('''cuda''', local_rank)
lowerCamelCase      				=     socket.gethostname()

lowerCamelCase      				=     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
		lowerCamelCase      				=     dist.get_rank()
		lowerCamelCase      				=     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









def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert x is not None
					assert y is not None

					__lowerCAmelCase					=							len(UpperCAmelCase__      )
					__lowerCAmelCase					=							len(UpperCAmelCase__      )

					# declaring the array for storing the dp values
					__lowerCAmelCase					=							[[0] * (n + 1) for _ in range(m + 1      )]  # noqa: E741

					for i in range(1		,					m + 1      ):
										for j in range(1		,					n + 1      ):
															__lowerCAmelCase					=							1 if x[i - 1] == y[j - 1] else 0

															__lowerCAmelCase					=							max(l[i - 1][j]		,					l[i][j - 1]		,					l[i - 1][j - 1] + match      )

					__lowerCAmelCase					=							''
					__lowerCAmelCase,	__lowerCAmelCase					=							m, n
					while i > 0 and j > 0:
										__lowerCAmelCase					=							1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				__lowerCAmelCase					=							x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
		lowerCamelCase      				=     '''AGGTAB'''
		lowerCamelCase      				=     '''GXTXAYB'''
		lowerCamelCase      				=     4
		lowerCamelCase      				=     '''GTAB'''

		lowerCamelCase				,				lowerCamelCase      				=     longest_common_subsequence(a, b)
		print('''len =''', ln, ''', sub-sequence =''', subseq)
		import doctest

		doctest.testmod()






