
"""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__			(					*lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     UpperCAmelCase__				:			Dict							=    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		):
          UpperCAmelCase__				:			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
    A__   :						Any 							=     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__   :						Union[str, Any] 							=     [
        """pip:./""",
        """transformers""",
        """datasets""",
        """evaluate""",
        """tqdm""",
        """scipy""",
        """scikit-learn""",
        """tensorboard""",
        """torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117""",
    ]
    A__   :						Dict 							=     rh.function(fn=launch_train, system=gpu, reqs=reqs, name="""train_bert_glue""")

    # Define train args/config, run train function
    A__   :						Tuple 							=     argparse.Namespace(cpu=False, mixed_precision="""fp16""")
    A__   :						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'])





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if number != int(lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if number < 0:
          raise ValueError("factorial() not defined for negative values"		)
     UpperCAmelCase__				:			int							=    1
     for i in range(1							,     number + 1		):
          value *= i
     return value



def 				a__			(					lowerCAmelCase						:							int		):






     '''simple docstring'''
     if not isinstance(lowerCAmelCase							,     lowerCAmelCase		):
          raise ValueError("factorial() only accepts integral values"		)
     if n < 0:
          raise ValueError("factorial() not defined for negative values"		)
     return 1 if n == 0 or n == 1 else n * factorial(n - 1		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    A__   :						Any 							=     int(input("""Enter a positive integer: """).strip() or 0)
    print(f"""factorial{n} is {factorial(n)}""")



