



"""simple docstring"""







class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			pass






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			pass






class       UpperCamelCase__			:

			"""simple docstring"""


			def __init__(      self		)      ->	List[str]:
				A__											=					[
				    [],
				    [],
				    [],
				]


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				try:
					if len(self.queues[priority]		) >= 100:
						raise OverflowError("Maximum queue size is 100"		)
					self.queues[priority].append(SCREAMING_SNAKE_CASE__		)
				except IndexError:
					raise ValueError("Valid priorities are 0, 1, and 2"		)


			def   snake_case__	(      self		)      ->	int:
				for queue in self.queues:
					if queue:
						return queue.pop(0		)
				raise UnderFlowError("All queues are empty"		)







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






class       UpperCamelCase__			:

			"""simple docstring"""


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


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				if len(self.queue		) == 100:
					raise OverFlowError("Maximum queue size is 100"		)
				self.queue.append(SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	int:
				if not self.queue:
					raise UnderFlowError("The queue is empty"		)
				else:
					A__											=					min(self.queue		)
					self.queue.remove(SCREAMING_SNAKE_CASE__		)
					return data







			def __str__(      self		)      ->	str:
				return str(self.queue		)



def       _lowerCamelCase				(							)	-> Optional[Any]:






	"""simple docstring"""





	A__											=					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(UpperCAmelCase_     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )
	print(UpperCAmelCase_     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )
	print(fpq.dequeue()     )

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






	"""simple docstring"""





	A__											=					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(UpperCAmelCase_     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )
	print(UpperCAmelCase_     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )
	print(epq.dequeue()     )


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








#
# 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		(		*_lowercase )					-> Tuple:


     """simple docstring"""


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


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

__snake_case   							=						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
    __snake_case   							=						dist.get_rank()
    __snake_case   							=						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


