



#
# 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 		UpperCamelCase	(   *snake_case__       :    str	)	->     List[str]:
   with open(snake_case__       ,     'r'	) as fh:
      fcntl.flock(snake_case__       ,     fcntl.LOCK_EX	)
      try:
         print(*snake_case__	)
      finally:
         fcntl.flock(snake_case__       ,     fcntl.LOCK_UN	)


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

__UpperCAmelCase          =       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
    __UpperCAmelCase          =       dist.get_rank()
    __UpperCAmelCase          =       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








import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Optional[int]:
   monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings'       ,     set()	)
@pytest.fixture
def 		UpperCamelCase	(   snake_case__       :    str	)	->     Any:



   class 				lowerCAmelCase_		:
          def __init__(			self,			SCREAMING_SNAKE_CASE_       )     ->		Optional[int]:
             UpperCamelCase    :  Tuple							=	metric_id






   class 				lowerCAmelCase_		:
          UpperCAmelCase__				:					Optional[int]        =  [MetricMock(a__							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]
          def 					snake_case_       (			self       )     ->		str:
             return self._metrics






   monkeypatch.setattr('datasets.inspect.huggingface_hub'       ,     HfhMock()	)


@pytest.mark.parametrize(
    'func, args'       ,     [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))]	)
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Any       ,     snake_case__       :    List[str]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   if "tmp_path" in args:
      UpperCamelCase    :  Dict							=	tuple(arg if arg != 'tmp_path' else tmp_path for arg in args	)
   with pytest.warns(snake_case__       ,     match='https://huggingface.co/docs/evaluate'	):
      func(*snake_case__	)



