






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

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







"""simple docstring"""



from __future__ import annotations




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					assert n > 1 and a > 0 and greatest_common_divisor(__A			,	__A			) == 1
					((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	__A			)  # Implemented below
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	__A			)  # Implemented below
					if b < 0:
										lowerCamelCase__				=(b % n + n) % n
					return b




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					lowerCamelCase__				=invert_modulo(__A			,	__A			)
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					assert a >= 0 and b >= 0

					if b == 0:
										lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=a, 1, 0
					else:
										((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	a % b			)
										lowerCamelCase__				=q
										lowerCamelCase__				=p - q * (a // b)

					assert a % d == 0 and b % d == 0
					assert d == a * x + b * y

					return (d, x, y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if b == 0:
										return (1, 0)
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	a % b			)
					lowerCamelCase__				=a // b
					return (y, x - k * y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if a < b:
										lowerCamelCase__				,     lowerCamelCase__				=b, a

					while a % b != 0:
										lowerCamelCase__				,     lowerCamelCase__				=b, a % b

					return b


if __name__ == "__main__":
						from doctest import testmod

						testmod(name="modular_division", verbose=True)
						testmod(name="modular_division2", verbose=True)
						testmod(name="invert_modulo", verbose=True)
						testmod(name="extended_gcd", verbose=True)
						testmod(name="extended_euclid", verbose=True)
						testmod(name="greatest_common_divisor", verbose=True)