


'''simple docstring'''







import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def _UpperCamelCase	(					*__A   )							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__  =    torch.cuda.device_count()
      print(F'''Device count: {num_processes}'''   )
      with patch_environment(
          world_size=__A				,      master_addr="127.0.01"				,      master_port="29500"				,      mixed_precision=args[1].mixed_precision   ):
            UpperCamelCase__  =    PrepareForLaunch(__A				,      distributed_type="MULTI_GPU"   )
            torch.multiprocessing.start_processes(__A				,      args=__A				,      nprocs=__A				,      start_method="spawn"   )


if __name__ == "__main__":
  # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
  # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

  # on-demand GPU
  # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
  a__  : Union[str, Any]				=	rh.cluster(name='rh-cluster', instance_type='V100:4', provider='cheapest', use_spot=False)  # multi GPU
  gpu.up_if_not()

  # on-prem GPU
  # gpu = rh.cluster(
  #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
  #       )

  # Set up remote function
  a__  : Optional[int]				=	[
      'pip:./',
      'transformers',
      'datasets',
      'evaluate',
      'tqdm',
      'scipy',
      'scikit-learn',
      'tensorboard',
      'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
  ]
  a__  : Any				=	rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

  # Define train args/config, run train function
  a__  : Optional[Any]				=	argparse.Namespace(cpu=False, mixed_precision='fp16')
  a__  : Any				=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
  launch_train_gpu(config, train_args, stream_logs=True)

  # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
  # gpu.install_packages(reqs)
  # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])









'''simple docstring'''







from itertools import permutations







def _UpperCamelCase	(					)							->		Optional[int]:


      '''simple docstring'''



      UpperCamelCase__  =    list(map("".join				,      permutations("0123456789"   )   )   )
      return result[999999]


if __name__ == "__main__":
  print(solution())





