





'''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  (*__lowerCamelCase  :     Dict     )   ->    Optional[Any]:
			with open(__lowerCamelCase					,  "r"     ) as fh:
						fcntl.flock(__lowerCamelCase					,  fcntl.LOCK_EX     )
						try:
									print(*__lowerCamelCase     )
						finally:
									fcntl.flock(__lowerCamelCase					,  fcntl.LOCK_UN     )


lowerCAmelCase_   :   int       =					int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
lowerCAmelCase_   :   List[Any]       =					torch.device("cuda", local_rank)
lowerCAmelCase_   :   List[str]       =					socket.gethostname()

lowerCAmelCase_   :   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
		lowerCAmelCase_   :   Union[str, Any]       =					dist.get_rank()
		lowerCAmelCase_   :   Dict       =					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'''


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCAmelCase_   :   List[Any]       =					[
    "EAGER",
    "AOT_EAGER",
    "INDUCTOR",
    "NVFUSER",
    "AOT_NVFUSER",
    "AOT_CUDAGRAPHS",
    "OFI",
    "FX2TRT",
    "ONNXRT",
    "IPEX",
]







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     List[Any]=None     )   ->    Any:
			a__     = True
			while ask_again:
						a__     = input(__lowerCamelCase     )
						try:
									if default is not None and len(__lowerCamelCase     ) == 0:
												return default
									return convert_value(__lowerCamelCase     ) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]=[]					,  __lowerCamelCase  :     str=None					,  __lowerCamelCase  :     List[Any]=0     )   ->    List[Any]:
			a__     = BulletMenu(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = menu.run(default_choice=__lowerCamelCase     )
			return convert_value(__lowerCamelCase     ) if convert_value is not None else result







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Optional[int]:
			a__     = int(__lowerCamelCase     )
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return DynamoBackend(DYNAMO_BACKENDS[value]     ).value







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    str:
			a__     = int(__lowerCamelCase     )
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    List[Any]:
			return {"yes": True, "no": False}[value.lower()]






class       UpperCamelCase__  (		argparse.RawDescriptionHelpFormatter     ):







	def 						__a   (				self			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = super()._format_usage(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				a__     = usage.replace("<command> [<args>] " , ""		)
				return usage



