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


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

_a		       =							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
    _a		       =							dist.get_rank()
    _a		       =							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"""





def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    int(__lowerCamelCase  )
   if n_element < 1:
      UpperCAmelCase_					:					List[Any]			     =    ValueError("a should be a positive number"  )
      raise my_error

   UpperCAmelCase_					:					List[Any]			     =    [1]
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    (0, 0, 0)
   UpperCAmelCase_					:					Dict			     =    1
   while index < n_element:
      while hamming_list[i] * 2 <= hamming_list[-1]:
         i += 1
      while hamming_list[j] * 3 <= hamming_list[-1]:
         j += 1
      while hamming_list[k] * 5 <= hamming_list[-1]:
         k += 1
      hamming_list.append(
          min(hamming_list[i] * 2,		hamming_list[j] * 3,		hamming_list[k] * 5  )  )
      index += 1
   return hamming_list


if __name__ == "__main__":
    _a		       =							input('Enter the last number (nth term) of the Hamming Number Series: ')
    print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
    _a		       =							hamming(int(n))
    print('-----------------------------------------------------')
    print(f"""The list with nth numbers is: {hamming_numbers}""")
    print('-----------------------------------------------------')


