#
# 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 				__lowerCamelCase		(						*lowerCAmelCase__     ):
       with open(lowerCAmelCase__     ,  'r'     ) as fh:
              fcntl.flock(lowerCAmelCase__     ,  fcntl.LOCK_EX     )
              try:
                     print(*lowerCAmelCase__     )
              finally:
                     fcntl.flock(lowerCAmelCase__     ,  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 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = len(lowerCAmelCase__     )
       for i in range(lowerCAmelCase__     ):
              for j in range(i + 1     ,  lowerCAmelCase__     ):
                     if numbers[j] < numbers[i]:
                            lowerCAmelCase__     , lowerCAmelCase__					     = numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    lowerCAmelCase__     						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__     						=			[int(item) for item in user_input.split(',')]
    print(exchange_sort(unsorted))
