code: 
  
  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      )      ->     int:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			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      ):
          __UpperCAmelCase									=			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
         A_		:			List[str]										=   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_		:			Tuple										=   [
             'pip:./',
             'transformers',
             'datasets',
             'evaluate',
             'tqdm',
             'scipy',
             'scikit-learn',
             'tensorboard',
             'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
         ]
         A_		:			Union[str, Any]										=   rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')
  
         # Define train args/config, run train function
         A_		:			int										=   argparse.Namespace(cpu=False, mixed_precision='fp16')
         A_		:			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'])
  
  
  
  
  
style_context: 
  
  import copy
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto.configuration_auto import AutoConfig
  
  
  A_		:			int										=   logging.get_logger(__name__)
  class 					A_   (      _a						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       "speech-encoder-decoder"
      a__     							=       True
  
  
      def __init__(self     ,      **lowercase__  )     ->      Optional[int]:
          super().__init__(**lowercase__  )
          if "encoder" not in kwargs or "decoder" not in kwargs:
              raise ValueError(
                  F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                  F''' `decoder` sub-configurations are passed, but only {kwargs}'''  )
  
          __UpperCAmelCase									=			kwargs.pop('''encoder'''  )
          __UpperCAmelCase									=			encoder_config.pop('''model_type'''  )
          __UpperCAmelCase									=			kwargs.pop('''decoder'''  )
          __UpperCAmelCase									=			decoder_config.pop('''model_type'''  )
  
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			AutoConfig.for_model(lowercase__     ,      **lowercase__  )
          __UpperCAmelCase									=			True
  
  
      @classmethod
      def      lowerCAmelCase_						(cls     ,      lowercase__     ,      lowercase__     ,      **lowercase__  )     ->      PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''  )
          __UpperCAmelCase									=			True
          __UpperCAmelCase									=			True
  
          return cls(encoder=encoder_config.to_dict()     ,      decoder=decoder_config.to_dict()     ,      **lowercase__  )
  
  
  
  
  
  
      def      lowerCAmelCase_						(self  )     ->      Optional[Any]:
          __UpperCAmelCase									=			copy.deepcopy(self.__dict__  )
          __UpperCAmelCase									=			self.encoder.to_dict()
          __UpperCAmelCase									=			self.decoder.to_dict()
          __UpperCAmelCase									=			self.__class__.model_type
          return output
  
  
  
  
  