"""simple docstring"""
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







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

 # Define train args/config, run train function
 lowercase__   			=      argparse.Namespace(cpu=False, mixed_precision='fp16')
 lowercase__   			=      {'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 .stack import Stack


def   A		(__A  :       str			)  -> bool:







     """simple docstring"""


     UpperCAmelCase_		=				Stack()
     UpperCAmelCase_		=				{'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__A			)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
    from doctest import testmod

    testmod()

    snake_case_					:					Dict       						=			["((()))", "((())", "(()))"]
    print("Balanced parentheses demonstration:\n")
    for example in examples:
        snake_case_					:					Optional[int]       						=			"" if balanced_parentheses(example) else "not "
        print(f"{example} is {not_str}balanced")






