code: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import runhouse as rh
  import torch
  from nlp_example import training_function
  
  from accelerate.utils import PrepareForLaunch, patch_environment
  
  
  
  
  
  
  
  def _lowercase   (  *__A       ):
  
  
  
   '''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__						:			Optional[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
   a__						:			Optional[int]										=			[
       'pip:./',
       'transformers',
       'datasets',
       'evaluate',
       'tqdm',
       'scipy',
       'scikit-learn',
       'tensorboard',
       'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
   ]
   a__						:			Optional[Any]										=			rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')
  
   # Define train args/config, run train function
   a__						:			List[str]										=			argparse.Namespace(cpu=False, mixed_precision='fp16')
   a__						:			Dict										=			{'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'])
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  1
   while b > 0:
    if b & 1:
     res *= a
  
    a *= a
    b >>= 1
  
   return res
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  1
   while b > 0:
    if b & 1:
     __UpperCamelCase				      =  ((res % c) * (a % c)) % c
  
    a *= a
    b >>= 1
  
   return res
  
  
  
  
  
  