



'''simple docstring'''
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class _A	(     lowercase_     ):
		_SCREAMING_SNAKE_CASE       :     str       =      (EulerAncestralDiscreteScheduler,)
		_SCREAMING_SNAKE_CASE       :     str       =      10





		def 				__A							(  self			,							**__UpperCAmelCase					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  {
					    '''num_train_timesteps''': 1_100,
					    '''beta_start''': 0.0001,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**UpperCAmelCase_					)
					return config





		def 				__A							(  self					)							->  Dict:

					'''simple docstring'''







					for timesteps in [10, 50, 100, 1_000]:
								self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''







					for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,							[0.0002, 0.002, 0.02]					):
								self.check_over_configs(beta_start=UpperCAmelCase_			,							beta_end=UpperCAmelCase_					)





		def 				__A							(  self					)							->  Union[str, Any]:

					'''simple docstring'''







					for schedule in ["linear", "scaled_linear"]:
								self.check_over_configs(beta_schedule=UpperCAmelCase_					)





		def 				__A							(  self					)							->  int:

					'''simple docstring'''







					for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=UpperCAmelCase_					)





		def 				__A							(  self					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  self.scheduler_classes[0]
					__UpperCAmelCase :   Union[str, Any]    					=  self.get_scheduler_config()
					__UpperCAmelCase :   Tuple    					=  scheduler_class(**UpperCAmelCase_					)

					scheduler.set_timesteps(self.num_inference_steps					)

					__UpperCAmelCase :   Dict    					=  torch.manual_seed(0					)

					__UpperCAmelCase :   List[Any]    					=  self.dummy_model()
					__UpperCAmelCase :   int    					=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__UpperCAmelCase :   Any    					=  sample.to(UpperCAmelCase_					)

					for i, t in enumerate(scheduler.timesteps					):
								__UpperCAmelCase :   Tuple    					=  scheduler.scale_model_input(UpperCAmelCase_			,							UpperCAmelCase_					)

								__UpperCAmelCase :   str    					=  model(UpperCAmelCase_			,							UpperCAmelCase_					)

								__UpperCAmelCase :   str    					=  scheduler.step(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							generator=UpperCAmelCase_					)
								__UpperCAmelCase :   Optional[Any]    					=  output.prev_sample

					__UpperCAmelCase :   Optional[int]    					=  torch.sum(torch.abs(UpperCAmelCase_					)					)
					__UpperCAmelCase :   List[Any]    					=  torch.mean(torch.abs(UpperCAmelCase_					)					)

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





		def 				__A							(  self					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  self.scheduler_classes[0]
					__UpperCAmelCase :   List[Any]    					=  self.get_scheduler_config(prediction_type="""v_prediction"""					)
					__UpperCAmelCase :   List[Any]    					=  scheduler_class(**UpperCAmelCase_					)

					scheduler.set_timesteps(self.num_inference_steps					)

					__UpperCAmelCase :   Any    					=  torch.manual_seed(0					)

					__UpperCAmelCase :   Union[str, Any]    					=  self.dummy_model()
					__UpperCAmelCase :   Tuple    					=  self.dummy_sample_deter * scheduler.init_noise_sigma
					__UpperCAmelCase :   Optional[Any]    					=  sample.to(UpperCAmelCase_					)

					for i, t in enumerate(scheduler.timesteps					):
								__UpperCAmelCase :   int    					=  scheduler.scale_model_input(UpperCAmelCase_			,							UpperCAmelCase_					)

								__UpperCAmelCase :   List[str]    					=  model(UpperCAmelCase_			,							UpperCAmelCase_					)

								__UpperCAmelCase :   Union[str, Any]    					=  scheduler.step(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							generator=UpperCAmelCase_					)
								__UpperCAmelCase :   Dict    					=  output.prev_sample

					__UpperCAmelCase :   Optional[Any]    					=  torch.sum(torch.abs(UpperCAmelCase_					)					)
					__UpperCAmelCase :   Tuple    					=  torch.mean(torch.abs(UpperCAmelCase_					)					)

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





		def 				__A							(  self					)							->  Optional[Any]:

					'''simple docstring'''
					__UpperCAmelCase :   int    					=  self.scheduler_classes[0]
					__UpperCAmelCase :   Optional[Any]    					=  self.get_scheduler_config()
					__UpperCAmelCase :   List[Any]    					=  scheduler_class(**UpperCAmelCase_					)

					scheduler.set_timesteps(self.num_inference_steps			,							device=UpperCAmelCase_					)
					__UpperCAmelCase :   List[str]    					=  torch.manual_seed(0					)

					__UpperCAmelCase :   List[str]    					=  self.dummy_model()
					__UpperCAmelCase :   Dict    					=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					__UpperCAmelCase :   List[str]    					=  sample.to(UpperCAmelCase_					)

					for t in scheduler.timesteps:
								__UpperCAmelCase :   List[Any]    					=  scheduler.scale_model_input(UpperCAmelCase_			,							UpperCAmelCase_					)

								__UpperCAmelCase :   Tuple    					=  model(UpperCAmelCase_			,							UpperCAmelCase_					)

								__UpperCAmelCase :   Tuple    					=  scheduler.step(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							generator=UpperCAmelCase_					)
								__UpperCAmelCase :   Any    					=  output.prev_sample

					__UpperCAmelCase :   Optional[int]    					=  torch.sum(torch.abs(UpperCAmelCase_					)					)
					__UpperCAmelCase :   Optional[Any]    					=  torch.mean(torch.abs(UpperCAmelCase_					)					)

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









import math
from timeit import timeit


def 	_A	(   _UpperCamelCase			):
				_UpperCAmelCase						:  str     					=	0
				_UpperCAmelCase						:  Tuple     					=	abs(_UpperCamelCase			)
				while True:
								_UpperCAmelCase						:  str     					=	n // 10
								digits += 1
								if n == 0:
												break
				return digits


def 	_A	(   _UpperCamelCase			):
				return 1 if n == 0 else math.floor(math.log(abs(_UpperCamelCase			)      ,					10			) + 1			)


def 	_A	(   _UpperCamelCase			):
				return len(str(abs(_UpperCamelCase			)			)			)


def 	_A	(   ):
				from collections.abc import Callable

				def benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			) -> None:
								_UpperCAmelCase						:  List[str]     					=	F'''{func.__name__}({value})'''
								_UpperCAmelCase						:  Any     					=	timeit(F'''__main__.{call}'''      ,					setup='''import __main__'''			)
								print(F'''{call}: {func(_UpperCamelCase			)} -- {timing} seconds'''			)

				for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
								for func in (num_digits, num_digits_fast, num_digits_faster):
												benchmark_a_function(_UpperCamelCase      ,					_UpperCamelCase			)
								print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()
