code: 
  
  
  import argparse
  
  import runhouse as rh
  import torch
  from nlp_example import training_function
  
  from accelerate.utils import PrepareForLaunch, patch_environment
  
  def   UpperCamelCase__   (    *UpperCAmelCase_			)						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Any           =			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			):
                _lowercase					:				Any           =			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
     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'])
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  UpperCamelCase__				       =			{
      'configuration_wav2vec2_conformer': [
          'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'Wav2Vec2ConformerConfig',
      ],
  }
  
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			[
         'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Wav2Vec2ConformerForAudioFrameClassification',
         'Wav2Vec2ConformerForCTC',
         'Wav2Vec2ConformerForPreTraining',
         'Wav2Vec2ConformerForSequenceClassification',
         'Wav2Vec2ConformerForXVector',
         'Wav2Vec2ConformerModel',
         'Wav2Vec2ConformerPreTrainedModel',
     ]
  
  if TYPE_CHECKING:
     from .configuration_wavaveca_conformer import (
         WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         WavaVecaConformerConfig,
     )
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_wavaveca_conformer import (
            WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            WavaVecaConformerForAudioFrameClassification,
            WavaVecaConformerForCTC,
            WavaVecaConformerForPreTraining,
            WavaVecaConformerForSequenceClassification,
            WavaVecaConformerForXVector,
            WavaVecaConformerModel,
            WavaVecaConformerPreTrainedModel,
        )
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)