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   snake_case_       (							*_a     :							int			):
        snake_case_						       =							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			):
              snake_case_						       =							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           =   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           =   [
           "pip:./",
           "transformers",
           "datasets",
           "evaluate",
           "tqdm",
           "scipy",
           "scikit-learn",
           "tensorboard",
           "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
       ]
       A           =   rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")
  
       # Define train args/config, run train function
       A           =   argparse.Namespace(cpu=False, mixed_precision="fp16")
       A           =   {"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'''
  
  
  
  
  
  from __future__ import annotations
  
  import random
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= 6
  
    def __init__(self  : List[Any]    )   ->       Optional[Any]:
          snake_case_						       =							list(range(1	, Dice.NUM_SIDES + 1    )    )
  
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       int:
          return random.choice(self.sides    )
  
  def   snake_case_       (							_a     :							int ,					_a     :							int = 2			):
        snake_case_						       =							[Dice() for i in range(_a			)]
        snake_case_						       =							[0] * (len(_a			) * Dice.NUM_SIDES + 1)
        for _ in range(_a			):
              count_of_sum[sum(dice.roll() for dice in dices			)] += 1
        snake_case_						       =							[round((count * 100) / num_throws ,					2			) for count in count_of_sum]
        return probability[num_dice:]  # remove probability of sums that never appear
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  