


'''simple docstring'''



import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[Any]   ):  # picklable for multiprocessing
					return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def   lowerCamelCase							():
					with parallel_backend('spark'   ):
										assert ParallelBackendConfig.backend_name == "spark"

					__a			:       Any					    =  [1, 2, 3]
					with pytest.raises(_SCREAMING_SNAKE_CASE   ):
										with parallel_backend('unsupported backend'   ):
															map_nested(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      num_proc=2   )

					with pytest.raises(_SCREAMING_SNAKE_CASE   ):
										with parallel_backend('unsupported backend'   ):
															map_nested(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      num_proc=-1   )






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'						,      [2, -1]   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  [1, 2]
					__a			:       List[str]					    =  {'a': 1, 'b': 2}
					__a			:       Optional[Any]					    =  {'a': [1, 2], 'b': [3, 4]}
					__a			:       Union[str, Any]					    =  {'a': {'1': 1}, 'b': 2}
					__a			:       int					    =  {'a': 1, 'b': 2, 'c': 3, 'd': 4}
					__a			:       List[Any]					    =  [2, 3]
					__a			:       List[Any]					    =  {'a': 2, 'b': 3}
					__a			:       int					    =  {'a': [2, 3], 'b': [4, 5]}
					__a			:       str					    =  {'a': {'1': 2}, 'b': 3}
					__a			:       List[Any]					    =  {'a': 2, 'b': 3, 'c': 4, 'd': 5}

					with parallel_backend('spark'   ):
										assert map_nested(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      num_proc=_SCREAMING_SNAKE_CASE   ) == expected_map_nested_sa
										assert map_nested(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      num_proc=_SCREAMING_SNAKE_CASE   ) == expected_map_nested_sa
										assert map_nested(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      num_proc=_SCREAMING_SNAKE_CASE   ) == expected_map_nested_sa
										assert map_nested(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      num_proc=_SCREAMING_SNAKE_CASE   ) == expected_map_nested_sa
										assert map_nested(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      num_proc=_SCREAMING_SNAKE_CASE   ) == expected_map_nested_sa







'''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



