



'''simple docstring'''
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 							lowercase__			(							lowerCamelCase__     ):
	lowercase__						       =    (EulerAncestralDiscreteScheduler,)
	lowercase__						       =    10





	def 			UpperCamelCase_						(				self      :       Dict				,**lowerCamelCase__      :       Optional[int]				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[int]				       =     {
							    'num_train_timesteps': 1100,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							}

							config.update(**SCREAMING_SNAKE_CASE_				)
							return config





	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							for timesteps in [10, 50, 100, 1000]:
													self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_				)





	def 			UpperCamelCase_						(				self      :       Tuple				):







							'''simple docstring'''
							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]				,[0.0_0_0_2, 0.0_0_2, 0.0_2]				):
													self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_				,beta_end=SCREAMING_SNAKE_CASE_				)





	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_				)





	def 			UpperCamelCase_						(				self      :       List[str]				):







							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_				)





	def 			UpperCamelCase_						(				self      :       Tuple				):







							'''simple docstring'''
							_UpperCamelCase      :	Optional[Any]				       =     self.scheduler_classes[0]
							_UpperCamelCase      :	Union[str, Any]				       =     self.get_scheduler_config()
							_UpperCamelCase      :	Any				       =     scheduler_class(**SCREAMING_SNAKE_CASE_				)

							scheduler.set_timesteps(self.num_inference_steps				)

							_UpperCamelCase      :	Optional[int]				       =     torch.manual_seed(0				)

							_UpperCamelCase      :	str				       =     self.dummy_model()
							_UpperCamelCase      :	Union[str, Any]				       =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							_UpperCamelCase      :	str				       =     sample.to(SCREAMING_SNAKE_CASE_				)

							for i, t in enumerate(scheduler.timesteps				):
													_UpperCamelCase      :	Union[str, Any]				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)

													_UpperCamelCase      :	Tuple				       =     model(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)

													_UpperCamelCase      :	Any				       =     scheduler.step(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,generator=SCREAMING_SNAKE_CASE_				)
													_UpperCamelCase      :	List[Any]				       =     output.prev_sample

							_UpperCamelCase      :	Optional[Any]				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_				)				)
							_UpperCamelCase      :	str				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_				)				)

							assert abs(result_sum.item() - 1_5_2.3_1_9_2				) < 1E-2
							assert abs(result_mean.item() - 0.1_9_8_3				) < 1E-3





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     self.scheduler_classes[0]
							_UpperCamelCase      :	Dict				       =     self.get_scheduler_config(prediction_type='v_prediction'				)
							_UpperCamelCase      :	int				       =     scheduler_class(**SCREAMING_SNAKE_CASE_				)

							scheduler.set_timesteps(self.num_inference_steps				)

							_UpperCamelCase      :	List[str]				       =     torch.manual_seed(0				)

							_UpperCamelCase      :	int				       =     self.dummy_model()
							_UpperCamelCase      :	List[str]				       =     self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase      :	int				       =     sample.to(SCREAMING_SNAKE_CASE_				)

							for i, t in enumerate(scheduler.timesteps				):
													_UpperCamelCase      :	str				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)

													_UpperCamelCase      :	Union[str, Any]				       =     model(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)

													_UpperCamelCase      :	str				       =     scheduler.step(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,generator=SCREAMING_SNAKE_CASE_				)
													_UpperCamelCase      :	str				       =     output.prev_sample

							_UpperCamelCase      :	Any				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_				)				)
							_UpperCamelCase      :	List[Any]				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_				)				)

							assert abs(result_sum.item() - 1_0_8.4_4_3_9				) < 1E-2
							assert abs(result_mean.item() - 0.1_4_1_2				) < 1E-3





	def 			UpperCamelCase_						(				self      :       Union[str, Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	Tuple				       =     self.scheduler_classes[0]
							_UpperCamelCase      :	Tuple				       =     self.get_scheduler_config()
							_UpperCamelCase      :	str				       =     scheduler_class(**SCREAMING_SNAKE_CASE_				)

							scheduler.set_timesteps(self.num_inference_steps				,device=SCREAMING_SNAKE_CASE_				)
							_UpperCamelCase      :	Tuple				       =     torch.manual_seed(0				)

							_UpperCamelCase      :	str				       =     self.dummy_model()
							_UpperCamelCase      :	str				       =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							_UpperCamelCase      :	Any				       =     sample.to(SCREAMING_SNAKE_CASE_				)

							for t in scheduler.timesteps:
													_UpperCamelCase      :	int				       =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)

													_UpperCamelCase      :	Optional[int]				       =     model(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)

													_UpperCamelCase      :	int				       =     scheduler.step(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,generator=SCREAMING_SNAKE_CASE_				)
													_UpperCamelCase      :	Any				       =     output.prev_sample

							_UpperCamelCase      :	Dict				       =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_				)				)
							_UpperCamelCase      :	Union[str, Any]				       =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_				)				)

							assert abs(result_sum.item() - 1_5_2.3_1_9_2				) < 1E-2
							assert abs(result_mean.item() - 0.1_9_8_3				) < 1E-3







'''simple docstring'''
import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment






def A__					(  *UpperCAmelCase_							):
						_UpperCamelCase      :	str				       =     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      :	Optional[int]				       =     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[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
					snake_case_				:			List[Any]						 =						[
					    'pip:./',
					    'transformers',
					    'datasets',
					    'evaluate',
					    'tqdm',
					    'scipy',
					    'scikit-learn',
					    'tensorboard',
					    'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
					]
					snake_case_				:			Union[str, Any]						 =						rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

					# Define train args/config, run train function
					snake_case_				:			List[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'])


