





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def      __snake_case						(					*_lowerCAmelCase : Any	) ->						List[str]:
   A_					:				int          =			torch.cuda.device_count()
   print(f"Device count: {num_processes}"	)
   with patch_environment(
       world_size=_lowerCAmelCase				,				master_addr="127.0.01"				,				master_port="29500"				,				mixed_precision=args[1].mixed_precision	):
      A_					:				Tuple          =			PrepareForLaunch(_lowerCAmelCase				,				distributed_type="MULTI_GPU"	)
      torch.multiprocessing.start_processes(_lowerCAmelCase				,				args=_lowerCAmelCase				,				nprocs=_lowerCAmelCase				,				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
 _lowerCAmelCase							:      int						=	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
 _lowerCAmelCase							:      int						=	[
     '''pip:./''',
     '''transformers''',
     '''datasets''',
     '''evaluate''',
     '''tqdm''',
     '''scipy''',
     '''scikit-learn''',
     '''tensorboard''',
     '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
 ]
 _lowerCAmelCase							:      Optional[int]						=	rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

 # Define train args/config, run train function
 _lowerCAmelCase							:      List[Any]						=	argparse.Namespace(cpu=False, mixed_precision='''fp16''')
 _lowerCAmelCase							:      Any						=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 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'])








from __future__ import annotations

def      __snake_case						(					_lowerCAmelCase : int	) ->						list[int]:
   assert (
       isinstance(_lowerCAmelCase				,				_lowerCAmelCase	) and mask > 0
   ), f"mask needs to be positive integer, your input {mask}"
   A_					:				Optional[Any]          =			[]
   A_					:				Optional[int]          =			mask

   while submask:
      all_submasks.append(_lowerCAmelCase	)
      A_					:				List[str]          =			(submask - 1) & mask

   return all_submasks


if __name__ == "__main__":
 import doctest

 doctest.testmod()
