





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def  A					(				*UpperCAmelCase					):
					_snake_case							:       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					):
										_snake_case							:       Dict												= 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
							__lowerCAmelCase			: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
							__lowerCAmelCase			:Any	 =     [
							    'pip:./',
							    'transformers',
							    'datasets',
							    'evaluate',
							    'tqdm',
							    'scipy',
							    'scikit-learn',
							    'tensorboard',
							    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
							]
							__lowerCAmelCase			:int	 =     rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

							# Define train args/config, run train function
							__lowerCAmelCase			:Union[str, Any]	 =     argparse.Namespace(cpu=False, mixed_precision='fp16')
							__lowerCAmelCase			:str	 =     {'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 __future__ import annotations

from collections.abc import Generator







def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					) or n < 1:
										raise Exception("Sequence only defined for positive integers"					)

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_snake_case							:       Any												= 3 * n + 1
										yield n







def  A					(				):
					_snake_case							:       Dict												= 43
					_snake_case							:       List[str]												= tuple(collatz_sequence(UpperCAmelCase					)					)
					print(UpperCAmelCase					)
					print(F"""Collatz sequence from {n} took {len(UpperCAmelCase					)} steps."""					)


if __name__ == "__main__":
							main()