


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					__a			:       List[str]					    =  0

					# if input_string is "aba" than new_input_string become "a|b|a"
					__a			:       int					    =  ''
					__a			:       Any					    =  ''

					# append each character + "|" in new_string for range(0, length-1)
					for i in input_string[: len(_SCREAMING_SNAKE_CASE   ) - 1]:
										new_input_string += i + "|"
					# append last character
					new_input_string += input_string[-1]

					# we will store the starting and ending of previous furthest ending palindromic
					# substring
					__a    ,				__a			:       Union[str, Any]					    =  0, 0

					# length[i] shows the length of palindromic substring with center i
					__a			:       int					    =  [1 for i in range(len(_SCREAMING_SNAKE_CASE   )   )]

					# for each character in new_string find corresponding palindromic string
					__a			:       Optional[int]					    =  0
					for j in range(len(_SCREAMING_SNAKE_CASE   )   ):
										__a			:       List[str]					    =  1 if j > r else min(length[l + r - j] // 2						,      r - j + 1   )
										while (
										    j - k >= 0
										    and j + k < len(_SCREAMING_SNAKE_CASE   )
										    and new_input_string[k + j] == new_input_string[j - k]
										):
															k += 1

										__a			:       List[Any]					    =  2 * k - 1

										# does this string is ending after the previously explored end (that is r) ?
										# if yes the update the new r to the last index of this
										if j + k - 1 > r:
															__a			:       List[Any]					    =  j - k + 1  # noqa: E741
															__a			:       Dict					    =  j + k - 1

										# update max_length and start position
										if max_length < length[j]:
															__a			:       Tuple					    =  length[j]
															__a			:       List[str]					    =  j

    # create that string
					__a			:       Optional[Any]					    =  new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
					for i in s:
										if i != "|":
															output_string += i

					return output_string


if __name__ == "__main__":
	import doctest

	doctest.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



