
"""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'])






"""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)))




