


import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment






def 		_A		(		_lowercase					,  _lowercase=()					,  _lowercase=None					,  _lowercase="no"					,  _lowercase="29500" )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			False
     __UpperCamelCase =			False
     if any(key.startswith('KAGGLE' ) for key in os.environ.keys() ):
          __UpperCamelCase =			True
     elif "IPython" in sys.modules:
          __UpperCamelCase =			'google.colab' in str(sys.modules['IPython'].get_ipython() )

     try:
          __UpperCamelCase =			PrecisionType(mixed_precision.lower() )
     except ValueError:
          raise ValueError(
              f'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.''' )

     if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'					,  _lowercase ) is not None):
          # TPU launch
          import torch_xla.distributed.xla_multiprocessing as xmp

          if len(AcceleratorState._shared_state ) > 0:
               raise ValueError(
                   'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
                   'your training function. Restart your notebook and make sure no cells initializes an '
                   '`Accelerator`.' )
          if num_processes is None:
               __UpperCamelCase =			8

          __UpperCamelCase =			PrepareForLaunch(_lowercase					,  distributed_type='TPU' )
          print(f'''Launching a training on {num_processes} TPU cores.''' )
          xmp.spawn(_lowercase					,  args=_lowercase					,  nprocs=_lowercase					,  start_method='fork' )
     elif in_colab:
          # No need for a distributed launch otherwise as it's either CPU or one GPU.
          if torch.cuda.is_available():
               print('Launching training on one GPU.' )
          else:
               print('Launching training on one CPU.' )
          function(*_lowercase )
     else:
          if num_processes is None:
               raise ValueError(
                   'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.' )

          if num_processes > 1:
               # Multi-GPU launch
               from torch.multiprocessing import start_processes
               from torch.multiprocessing.spawn import ProcessRaisedException

               if len(AcceleratorState._shared_state ) > 0:
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
                        'inside your training function. Restart your notebook and make sure no cells initializes an '
                        '`Accelerator`.' )

               if torch.cuda.is_initialized():
                    raise ValueError(
                        'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
                        'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
                        'function.' )

               # torch.distributed will expect a few environment variable to be here. We set the ones common to each
               # process here (the other ones will be set be the launcher).
               with patch_environment(
                   world_size=_lowercase					,  master_addr='127.0.01'					,  master_port=_lowercase					,  mixed_precision=_lowercase ):
                    __UpperCamelCase =			PrepareForLaunch(_lowercase					,  distributed_type='MULTI_GPU' )
                    print(f'''Launching training on {num_processes} GPUs.''' )
                    try:
                         start_processes(_lowercase					,  args=_lowercase					,  nprocs=_lowercase					,  start_method='fork' )
                    except ProcessRaisedException as e:
                         if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                              raise RuntimeError(
                                  'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
                                  'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
                                  'Please review your imports and test them when running the `notebook_launcher()` to identify '
                                  'which one is problematic.' ) from e

          else:
               # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
               if is_mps_available():
                    __UpperCamelCase =			'1'
                    print('Launching training on MPS.' )
               elif torch.cuda.is_available():
                    print('Launching training on one GPU.' )
               else:
                    print('Launching training on CPU.' )
               function(*_lowercase )






def 		_A		(		_lowercase					,  _lowercase=()					,  _lowercase=2 )					-> Dict:


     """simple docstring"""


     from torch.multiprocessing import start_processes

     with tempfile.NamedTemporaryFile() as tmp_file:
          # torch.distributed will expect a few environment variable to be here. We set the ones common to each
          # process here (the other ones will be set be the launcher).
          with patch_environment(
              world_size=_lowercase					,  master_addr='127.0.01'					,  master_port='29500'					,  accelerate_mixed_precision='no'					,  accelerate_debug_rdv_file=tmp_file.name					,  accelerate_use_cpu='yes'					,  ):
               __UpperCamelCase =			PrepareForLaunch(_lowercase					,  debug=_lowercase )
               start_processes(_lowercase					,  args=_lowercase					,  nprocs=_lowercase					,  start_method='fork' )






#
# 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		(		*_lowercase )					-> Tuple:


     """simple docstring"""


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


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

__snake_case   							=						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
    __snake_case   							=						dist.get_rank()
    __snake_case   							=						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


