
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment
def     UpperCAmelCase__							(       *__magic_name__						:     List[Any]  ):


     '''simple docstring'''

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

  # Define train args/config, run train function
  __SCREAMING_SNAKE_CASE			:   Optional[int]         =     argparse.Namespace(cpu=False, mixed_precision='fp16')
  __SCREAMING_SNAKE_CASE			:   Tuple         =     {'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 ..utils import requires_backends


__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     None


class 		__magic_name__   :





		def __init__(							self    :	Optional[int]						,  *lowerCamelCase__    :	List[str]						,  **lowerCamelCase__    :	Optional[Any]				):
							requires_backends(self						,  ['''detectron2''']				)





		@classmethod
		def       _A							(							cls    :	Dict						,  *lowerCamelCase__    :	List[str]						,  **lowerCamelCase__    :	Optional[Any]				):
							requires_backends(cls						,  ['''detectron2''']				)
