





"""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 					__A  (*_SCREAMING_SNAKE_CASE     )   ->Optional[Any]:





  """simple docstring"""


  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     )


__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







"""simple docstring"""





# Copyright 2023 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.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MgpstrModel""",
      """MgpstrPreTrainedModel""",
      """MgpstrForSceneTextRecognition""",
  ]

if TYPE_CHECKING:
  from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
  from .processing_mgp_str import MgpstrProcessor
  from .tokenization_mgp_str import MgpstrTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mgp_str import (
        MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
        MgpstrForSceneTextRecognition,
        MgpstrModel,
        MgpstrPreTrainedModel,
    )
else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
