






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_UpperCamelCase (      lowerCamelCase__	):







							'''simple docstring'''
							_A  				=  (EulerAncestralDiscreteScheduler,)
							_A  				=  1_0







							def 		_UpperCAmelCase	(		self  :				Dict		,				**SCREAMING_SNAKE_CASE_  :				Optional[int]		):
													_a     =			{
													    'num_train_timesteps': 1_1_0_0,
													    'beta_start': 0.0001,
													    'beta_end': 0.02,
													    'beta_schedule': 'linear',
													}

													config.update(**SCREAMING_SNAKE_CASE_		)
													return config







							def 		_UpperCAmelCase	(		self  :				List[str]		):
													for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																			self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				Tuple		):
													for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,				[0.0002, 0.002, 0.02]		):
																			self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_		,				beta_end=SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				List[str]		):
													for schedule in ["linear", "scaled_linear"]:
																			self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				List[str]		):
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_		)







							def 		_UpperCAmelCase	(		self  :				Tuple		):
													_a     =			self.scheduler_classes[0]
													_a     =			self.get_scheduler_config()
													_a     =			scheduler_class(**SCREAMING_SNAKE_CASE_		)

													scheduler.set_timesteps(self.num_inference_steps		)

													_a     =			torch.manual_seed(0		)

													_a     =			self.dummy_model()
													_a     =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													_a     =			sample.to(SCREAMING_SNAKE_CASE_		)

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

																			_a     =			model(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)

																			_a     =			scheduler.step(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		)
																			_a     =			output.prev_sample

													_a     =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
													_a     =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)

													assert abs(result_sum.item() - 152.3192		) < 1e-2
													assert abs(result_mean.item() - 0.1983		) < 1e-3







							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
													_a     =			self.scheduler_classes[0]
													_a     =			self.get_scheduler_config(prediction_type='v_prediction'		)
													_a     =			scheduler_class(**SCREAMING_SNAKE_CASE_		)

													scheduler.set_timesteps(self.num_inference_steps		)

													_a     =			torch.manual_seed(0		)

													_a     =			self.dummy_model()
													_a     =			self.dummy_sample_deter * scheduler.init_noise_sigma
													_a     =			sample.to(SCREAMING_SNAKE_CASE_		)

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

																			_a     =			model(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)

																			_a     =			scheduler.step(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		)
																			_a     =			output.prev_sample

													_a     =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
													_a     =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)

													assert abs(result_sum.item() - 108.4439		) < 1e-2
													assert abs(result_mean.item() - 0.1412		) < 1e-3







							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		):
													_a     =			self.scheduler_classes[0]
													_a     =			self.get_scheduler_config()
													_a     =			scheduler_class(**SCREAMING_SNAKE_CASE_		)

													scheduler.set_timesteps(self.num_inference_steps		,				device=SCREAMING_SNAKE_CASE_		)
													_a     =			torch.manual_seed(0		)

													_a     =			self.dummy_model()
													_a     =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													_a     =			sample.to(SCREAMING_SNAKE_CASE_		)

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

																			_a     =			model(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)

																			_a     =			scheduler.step(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		)
																			_a     =			output.prev_sample

													_a     =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_		)		)
													_a     =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_		)		)

													assert abs(result_sum.item() - 152.3192		) < 1e-2
													assert abs(result_mean.item() - 0.1983		) < 1e-3






import math


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
						_a     =			math.log(2							,     10				) * number
						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)

						return difference


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
						_a     =			90
						_a     =			0

						_a     =			math.log(1.23							,     10				)
						_a     =			math.log(1.24							,     10				)
						_a     =			0

						while position < number:
												_a     =			log_difference(_UpperCAmelCase				)

												if difference >= upper_limit:
																		power_iterator += 93

												elif difference < lower_limit:
																		power_iterator += 196

												else:
																		_a     =			power_iterator
																		power_iterator += 196
																		position += 1

						return previous_power


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		print(f"""{solution() = }""")


