


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

    # Define train args/config, run train function
    __lowerCamelCase       				=	argparse.Namespace(cpu=False, mixed_precision='''fp16''')
    __lowerCamelCase       				=	{'''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__    (				UpperCamelCase_	:			int = 100	):
   UpperCAmelCase__  :List[str]            =			1
   UpperCAmelCase__  :Dict            =			0
   for i in range(1,      num + 1	):
      fact *= i

   for j in str(UpperCamelCase_	):
      result += int(UpperCamelCase_	)

   return result


if __name__ == "__main__":
    print(solution(int(input('''Enter the Number: ''').strip())))



