





"""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       __UpperCamelCase  (*_SCREAMING_SNAKE_CASE		)    ->       str:
  with open(_SCREAMING_SNAKE_CASE		,						'r'		) as fh:
    fcntl.flock(_SCREAMING_SNAKE_CASE		,						fcntl.LOCK_EX		)
    try:
      print(*_SCREAMING_SNAKE_CASE		)
    finally:
      fcntl.flock(_SCREAMING_SNAKE_CASE		,						fcntl.LOCK_UN		)


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

lowercase_          =		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
      lowercase_          =		dist.get_rank()
      lowercase_          =		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









class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Dict	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  [
         [],
         [],
         [],
     ]


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    int      ,					a      :    int	)->       None:





     """simple docstring"""







     try:
       if len(self.queues[priority]	) >= 100:
         raise OverflowError('Maximum queue size is 100'	)
       self.queues[priority].append(a	)
     except IndexError:
       raise ValueError('Valid priorities are 0, 1, and 2'	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       int:





     """simple docstring"""







     for queue in self.queues:
       if queue:
         return queue.pop(0	)
     raise UnderFlowError('All queues are empty'	)


   def __str__(   self      :    Dict	)->       str:





     """simple docstring"""







     return "\n".join(f"""Priority {i}: {q}""" for i, q in enumerate(self.queues	)	)






class     SCREAMING_SNAKE_CASE       :


   def __init__(   self      :    Optional[Any]	)->       Any:





     """simple docstring"""







     lowercase__           =  []


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     if len(self.queue	) == 100:
       raise OverFlowError('Maximum queue size is 100'	)
     self.queue.append(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       int:





     """simple docstring"""







     if not self.queue:
       raise UnderFlowError('The queue is empty'	)
     else:
       lowercase__           =  min(self.queue	)
       self.queue.remove(a	)
       return data


   def __str__(   self      :    Union[str, Any]	)->       str:





     """simple docstring"""







     return str(self.queue	)






def       __UpperCamelCase  ()    ->       str:
  lowercase__           =  FixedPriorityQueue()
  fpq.enqueue(0		,						10		)
  fpq.enqueue(1		,						70		)
  fpq.enqueue(0		,						100		)
  fpq.enqueue(2		,						1		)
  fpq.enqueue(2		,						5		)
  fpq.enqueue(1		,						7		)
  fpq.enqueue(2		,						4		)
  fpq.enqueue(1		,						64		)
  fpq.enqueue(0		,						128		)
  print(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)
  print(fpq.dequeue()		)




def       __UpperCamelCase  ()    ->       List[str]:
  lowercase__           =  ElementPriorityQueue()
  epq.enqueue(10		)
  epq.enqueue(70		)
  epq.enqueue(100		)
  epq.enqueue(1		)
  epq.enqueue(5		)
  epq.enqueue(7		)
  epq.enqueue(4		)
  epq.enqueue(64		)
  epq.enqueue(128		)
  print(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(_SCREAMING_SNAKE_CASE		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)
  print(epq.dequeue()		)


if __name__ == "__main__":
      fixed_priority_queue()
      element_priority_queue()


