



import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment







def 							A     (						*__UpperCAmelCase      )			->			List[Any]:



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

							# Define train args/config, run train function
							UpperCamelCase_      			=  argparse.Namespace(cpu=False, mixed_precision="fp16")
							UpperCamelCase_      			=  {"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'])










import random







class 				a_    :

						def __init__(       self      :Union[str, Any]  ,		_lowercase      :float  ,		_lowercase      :float)						->   None:
												UpperCAmelCase_					=	x
												UpperCAmelCase_					=	y

						def       __a			(       self      :str)						->   bool:
												return (self.x**2 + self.y**2) <= 1




						@classmethod
						def       __a			(       cls      :Optional[Any])						->   str:
												return cls(x=random.random()  ,		y=random.random())







def 							A     (						__UpperCAmelCase      )			->			float:



						'''simple docstring'''






						if number_of_simulations < 1:
												raise ValueError('''At least one simulation is necessary to estimate PI.'''      )

						UpperCAmelCase_					=	0
						for _ in range(__UpperCAmelCase      ):
												UpperCAmelCase_					=	Point.random_unit_square()

												if random_point.is_in_unit_circle():
																		number_in_unit_circle += 1

						return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							UpperCamelCase_      			=  "Please enter the desired number of Monte Carlo simulations: "
							UpperCamelCase_      			=  estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")





