
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment


def 		UpperCAmelCase    (			*a_		)	->			Union[str, Any]:







				"""simple docstring"""



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

				# Define train args/config, run train function
				SCREAMING_SNAKE_CASE							:List[str]											=       argparse.Namespace(cpu=False, mixed_precision='fp16')
				SCREAMING_SNAKE_CASE							: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 typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
