




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment



def 							_A   (						*lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

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

     # Define train args/config, run train function
     UpperCamelCase								=     argparse.Namespace(cpu=False, mixed_precision='fp16')
     UpperCamelCase								=     {'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'])


"""simple docstring"""

import doctest

import projectq
from projectq.ops import H, Measure




def 			lowercase__  (    lowerCAmelCase :    projectq.cengines._main.MainEngine       )       ->					int:






    """simple docstring"""




    UpperCAmelCase  	=    quantum_engine.allocate_qubit()
    H | qubit
    Measure | qubit
    return int(lowerCAmelCase       )


if __name__ == "__main__":
   doctest.testmod()

   # initialises a new quantum backend
   SCREAMING_SNAKE_CASE_			    =  projectq.MainEngine()

   # Generate a list of 10 random numbers
   SCREAMING_SNAKE_CASE_			    =  [get_random_number(quantum_engine) for _ in range(10)]

   # Flushes the quantum engine from memory
   quantum_engine.flush()

   print('''Random numbers''', random_numbers_list)



