


'''simple docstring'''


import math
import unittest






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      ) and (
		    number >= 0
		), "'number' must been an int and positive"

		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5					,       int(math.sqrt(__lowerCAmelCase      ) + 1      )					,       6      ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






class 		lowerCAmelCase__	(	unittest.TestCase		):




			def 							lowerCAmelCase__			(						self     :   List[str]       )      ->Dict:



					'''simple docstring'''







					self.assertTrue(is_prime(2       )       )
					self.assertTrue(is_prime(3       )       )
					self.assertTrue(is_prime(5       )       )
					self.assertTrue(is_prime(7       )       )
					self.assertTrue(is_prime(11       )       )
					self.assertTrue(is_prime(13       )       )
					self.assertTrue(is_prime(17       )       )
					self.assertTrue(is_prime(19       )       )
					self.assertTrue(is_prime(23       )       )
					self.assertTrue(is_prime(29       )       )






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



					'''simple docstring'''







					with self.assertRaises(lowerCamelCase__       ):
							is_prime(-19       )
					self.assertFalse(
					    is_prime(0       )		,     "Zero doesn't have any positive factors, primes must have exactly two."		,     )
					self.assertFalse(
					    is_prime(1       )		,     "One only has 1 positive factor, primes must have exactly two."		,     )
					self.assertFalse(is_prime(2 * 2       )       )
					self.assertFalse(is_prime(2 * 3       )       )
					self.assertFalse(is_prime(3 * 3       )       )
					self.assertFalse(is_prime(3 * 5       )       )
					self.assertFalse(is_prime(3 * 5 * 7       )       )


if __name__ == "__main__":
						unittest.main()







'''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        __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



