

#
# 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      snake_case_				(*__A					:						List[Any]					)      ->     Optional[int]:







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


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

__UpperCAmelCase								=			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
   __UpperCAmelCase								=			dist.get_rank()
   __UpperCAmelCase								=			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





from __future__ import annotations





def      snake_case_				(__A					:						list[float] ,       __A					:						list[float]					)      ->     float:
   __lowerCAmelCase  :      Union[str, Any]    =						sorted(numsa + numsa					)
   __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[int]    =						divmod(len(__A					) ,       2					)
   if mod == 1:
      return all_numbers[div]
   else:
      return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of first array: """).split()]
   __UpperCAmelCase								=			[float(x) for x in input("""Enter the elements of second array: """).split()]
   print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')


