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

"""simple docstring"""




from __future__ import annotations



def 							_lowerCamelCase			(			__a,				__a = None     ):
		SCREAMING_SNAKE_CASE_    			=			word_bank or []
		# create a table
		SCREAMING_SNAKE_CASE_    			=			len(__a     ) + 1

		SCREAMING_SNAKE_CASE_    			=			[]
		for _ in range(__a     ):
				table.append([]     )
		# seed value
		SCREAMING_SNAKE_CASE_    			=			[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(__a     ):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(__a     )] == word:
										SCREAMING_SNAKE_CASE_    			=			[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(__a     )] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(__a     )]:
				combination.reverse()

		return table[len(__a     )]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)