






'''simple docstring'''




import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowerCamelCase_												=					3

def 					__lowercase						(			__lowercase       )       ->						int:

      '''simple docstring'''


      print("Generating primitive root of p"       )
      while True:
            _A			     =	random.randrange(3   ,  __lowercase       )
            if pow(__lowercase   ,  2   ,  __lowercase       ) == 1:
                  continue
            if pow(__lowercase   ,  __lowercase   ,  __lowercase       ) == 1:
                  continue
            return g

def 					__lowercase						(			__lowercase       )       ->						tuple[tuple[int, int, int, int], tuple[int, int]]:

      '''simple docstring'''


      print("Generating prime p..."       )
      _A			     =	rabin_miller.generate_large_prime(__lowercase       )  # select large prime number.
      _A			     =	primitive_root(__lowercase       )  # one primitive root on modulo p.
      _A			     =	random.randrange(3   ,  __lowercase       )  # private_key -> have to be greater than 2 for safety.
      _A			     =	cryptomath.find_mod_inverse(pow(__lowercase   ,  __lowercase   ,  __lowercase       )   ,  __lowercase       )

      _A			     =	(key_size, e_a, e_a, p)
      _A			     =	(key_size, d)

      return public_key, private_key

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						None:

      '''simple docstring'''


      if os.path.exists(F'''{name}_pubkey.txt'''       ) or os.path.exists(F'''{name}_privkey.txt'''       ):
            print("\nWARNING:"       )
            print(
                F'''"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'''
                "Use a different name or delete these files and re-run this program."       )
            sys.exit()

      _A     ,	_A			     =	generate_key(__lowercase       )
      print(F'''\nWriting public key to file {name}_pubkey.txt...'''       )
      with open(F'''{name}_pubkey.txt'''   ,  "w"       ) as fo:
            fo.write(F'''{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'''       )

      print(F'''Writing private key to file {name}_privkey.txt...'''       )
      with open(F'''{name}_privkey.txt'''   ,  "w"       ) as fo:
            fo.write(F'''{private_key[0]},{private_key[1]}'''       )

def 					__lowercase						(			)       ->						None:

      '''simple docstring'''


      print("Making key files..."       )
      make_key_files("elgamal"   ,  2048       )
      print("Key files generation successful"       )


if __name__ == "__main__":
    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 			_lowercase				(  *__snake_case	)						->   Optional[Any]:
			with open(__snake_case					,"r"	) as fh:
						fcntl.flock(__snake_case					,fcntl.LOCK_EX	)
						try:
									print(*__snake_case	)
						finally:
									fcntl.flock(__snake_case					,fcntl.LOCK_UN	)


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

__snake_case							:   str      =    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							:   Tuple      =    dist.get_rank()
	__snake_case							:   Any      =    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