





"""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       _SCREAMING_SNAKE_CASE		(	*_lowercase						:    str					) ->str:






	'''simple docstring'''






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


a						: Optional[int]     	=			int(os.environ['''LOCAL_RANK'''])
torch.cuda.set_device(local_rank)
a						: str     	=			torch.device('''cuda''', local_rank)
a						: List[str]     	=			socket.gethostname()

a						: Union[str, Any]     	=			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						: Optional[int]     	=			dist.get_rank()
		a						: str     	=			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"""




from decimal import Decimal, getcontext
from math import ceil, factorial






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int					) ->str:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("Undefined for non-integers"					)
	elif precision < 1:
		raise ValueError("Undefined for non-natural numbers"					)

	a      :		Tuple	=				precision
	a      :		str	=				ceil(precision / 14					)
	a      :		List[Any]	=				42_6880 * Decimal(1_0005					).sqrt()
	a      :		Union[str, Any]	=				1
	a      :		Dict	=				1359_1409
	a      :		Optional[int]	=				Decimal(_lowercase					)
	for k in range(1    ,       _lowercase					):
		a      :		int	=				factorial(6 * k					) // (factorial(3 * k					) * factorial(_lowercase					) ** 3)
		linear_term += 5_4514_0134
		exponential_term *= -26_2537_4126_4076_8000
		partial_sum += Decimal(multinomial_term * linear_term					) / exponential_term
	return str(constant_term / partial_sum					)[:-1]


if __name__ == "__main__":
		a						: Optional[Any]     	=			50
		print(F'''The first {n} digits of pi is: {pi(n)}''')


