code: 
  """simple docstring"""
  import argparse
  
  import runhouse as rh
  import torch
  from nlp_example import training_function
  
  from accelerate.utils import PrepareForLaunch, patch_environment
  
  
  
  
  def     __lowerCamelCase  (		*UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						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 =						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
  			__magic_name__		       =				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
  			__magic_name__		       =				[
  			    'pip:./',
  			    'transformers',
  			    'datasets',
  			    'evaluate',
  			    'tqdm',
  			    'scipy',
  			    'scikit-learn',
  			    'tensorboard',
  			    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
  			]
  			__magic_name__		       =				rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')
  
  			# Define train args/config, run train function
  			__magic_name__		       =				argparse.Namespace(cpu=False, mixed_precision='''fp16''')
  			__magic_name__		       =				{'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: 
  """simple docstring"""
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if not isinstance(UpperCamelCase__				,     UpperCamelCase__      ):
  		raise TypeError("recursive_lucas_number accepts only integer arguments."      )
  	if n_th_number == 0:
  		return 2
  	if n_th_number == 1:
  		return 1
  
  	return recursive_lucas_number(n_th_number - 1      ) + recursive_lucas_number(
  	    n_th_number - 2      )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	if not isinstance(UpperCamelCase__				,     UpperCamelCase__      ):
  		raise TypeError("dynamic_lucas_number accepts only integer arguments."      )
  	_UpperCAmelCase ,      _UpperCAmelCase =						2, 1
  	for _ in range(UpperCamelCase__      ):
  		_UpperCAmelCase ,      _UpperCAmelCase =						b, a + b
  	return a
  
  
  if __name__ == "__main__":
  			from doctest import testmod
  
  			testmod()
  			__magic_name__		       =				int(input('''Enter the number of terms in lucas series:\n''').strip())
  			print('''Using recursive function to calculate lucas series:''')
  			print(''' '''.join(str(recursive_lucas_number(i)) for i in range(n)))
  			print('''\nUsing dynamic function to calculate lucas series:''')
  			print(''' '''.join(str(dynamic_lucas_number(i)) for i in range(n)))
  
  
  
  
  