



import numpy as np
import qiskit






def     a(      A       :    int = 8 ,     A       :    int | None = None       )		->       List[str]:
					"""simple docstring"""





					a					     =   np.random.default_rng(seed=_SCREAMING_SNAKE_CASE       )

					# Roughly 25% of the qubits will contribute to the key.
					# So we take more than we need.
					a					     =   6 * key_len
					# Measurement basis for Alice's qubits.
					a					     =   rng.integers(2 ,     size=_SCREAMING_SNAKE_CASE       )
					# The set of states Alice will prepare.
					a					     =   rng.integers(2 ,     size=_SCREAMING_SNAKE_CASE       )
					# Measurement basis for Bob's qubits.
					a					     =   rng.integers(2 ,     size=_SCREAMING_SNAKE_CASE       )

					# Quantum Circuit to simulate BB84
					a					     =   qiskit.QuantumCircuit(_SCREAMING_SNAKE_CASE ,     name="BB84"       )

					# Alice prepares her qubits according to rules above.
					for index, _ in enumerate(_SCREAMING_SNAKE_CASE       ):
										if alice_state[index] == 1:
															bbaa_circ.x(_SCREAMING_SNAKE_CASE       )
										if alice_basis[index] == 1:
															bbaa_circ.h(_SCREAMING_SNAKE_CASE       )
					bbaa_circ.barrier()

					# Bob measures the received qubits according to rules above.
					for index, _ in enumerate(_SCREAMING_SNAKE_CASE       ):
										if bob_basis[index] == 1:
															bbaa_circ.h(_SCREAMING_SNAKE_CASE       )

					bbaa_circ.barrier()
					bbaa_circ.measure_all()

					# Simulate the quantum circuit.
					a					     =   qiskit.Aer.get_backend("aer_simulator"       )
					# We only need to run one shot because the key is unique.
					# Multiple shots will produce the same key.
					a					     =   qiskit.execute(_SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     shots=1 ,     seed_simulator=_SCREAMING_SNAKE_CASE       )
					# Returns the result of measurement.
					a					     =   job.result().get_counts(_SCREAMING_SNAKE_CASE       ).most_frequent()

					# Extracting the generated key from the simulation results.
					# Only keep measurement results where Alice and Bob chose the same basis.
					a					     =   ''.join(
					    [
					        result_bit
					        for alice_basis_bit, bob_basis_bit, result_bit in zip(
					            _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE ,     _SCREAMING_SNAKE_CASE       )
					        if alice_basis_bit == bob_basis_bit
					    ]       )

					# Get final key. Pad with 0 if too short, otherwise truncate.
					a					     =   gen_key[:key_len] if len(_SCREAMING_SNAKE_CASE       ) >= key_len else gen_key.ljust(_SCREAMING_SNAKE_CASE ,     "0"       )
					return key


if __name__ == "__main__":
				print(F"""The generated key is : {bbaa(8, seed=0)}""")
				from doctest import testmod

				testmod()




'''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							(*_SCREAMING_SNAKE_CASE      :   int   ):
					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	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[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
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, 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



