




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

_A     =			'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
						raise ImportWarning(
						    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
						)

if version.parse(pyarrow.__version__).major < 8:
						raise ImportWarning(
						    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
						    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
						)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

_A     =			concatenate_datasets
_A     =			DownloadConfig
_A     =			DownloadManager
_A     =			DownloadMode
_A     =			DownloadConfig
_A     =			DownloadMode
_A     =			DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager









#
# 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        a						(      *_UpperCAmelCase							:					List[str]						):







		'''simple docstring'''





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


__A   			=int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__A   			=torch.device("cuda", local_rank)
__A   			=socket.gethostname()

__A   			=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
				__A   			=dist.get_rank()
				__A   			=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





