



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment

def 			lowerCAmelCase_  (						*_lowercase       :     Tuple)							-> int:







			"""simple docstring"""
			a__     :		Optional[Any]		 =						torch.cuda.device_count()
			print(F'''Device count: {num_processes}''')
			with patch_environment(
			    world_size=_lowercase		,     master_addr="""127.0.01"""		,     master_port="""29500"""		,     mixed_precision=args[1].mixed_precision):
						a__     :		Optional[int]		 =						PrepareForLaunch(_lowercase		,     distributed_type="""MULTI_GPU""")
						torch.multiprocessing.start_processes(_lowercase		,     args=_lowercase		,     nprocs=_lowercase		,     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
		_lowercase	:   Optional[int]    	=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
		_lowercase	:   List[Any]    	=[
		    "pip:./",
		    "transformers",
		    "datasets",
		    "evaluate",
		    "tqdm",
		    "scipy",
		    "scikit-learn",
		    "tensorboard",
		    "torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117",
		]
		_lowercase	:   Optional[Any]    	=rh.function(fn=launch_train, system=gpu, reqs=reqs, name="train_bert_glue")

		# Define train args/config, run train function
		_lowercase	:   str    	=argparse.Namespace(cpu=False, mixed_precision="fp16")
		_lowercase	:   Dict    	={"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

def 			lowerCAmelCase_  (						_lowercase       :     list[int]		,     _lowercase       :     int)							-> list[int]:







			"""simple docstring"""
			a__     :		dict[int, int]		 =						{}
			for index, val in enumerate(_lowercase):
						a__     :		str		 =						target - val
						if compl in chk_map:
									return [chk_map[compl], index]
						a__     :		str		 =						index
			return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'{two_sum([2, 7, 11, 15], 9) = }')




