






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class        _SCREAMING_SNAKE_CASE			(     _lowerCamelCase ):
	snake_case__				:	Optional[Any]	      =    (EulerAncestralDiscreteScheduler,)
	snake_case__				:	List[str]	      =    1_0





	def 		_A					(						self							:   str					,    **__lowerCamelCase							:   Union[str, Any]      ):
		UpperCamelCase	:Optional[Any]		=      {
		    """num_train_timesteps""": 1_100,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		}

		config.update(**lowerCAmelCase_      )
		return config





	def 		_A					(						self							:   Union[str, Any]      ):
		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_      )





	def 		_A					(						self							:   Union[str, Any]      ):
		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=lowerCAmelCase_					,    beta_end=lowerCAmelCase_      )





	def 		_A					(						self							:   Optional[Any]      ):
		for schedule in ["linear", "scaled_linear"]:
			self.check_over_configs(beta_schedule=lowerCAmelCase_      )





	def 		_A					(						self							:   str      ):
		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=lowerCAmelCase_      )





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:Dict		=      self.scheduler_classes[0]
		UpperCamelCase	:List[str]		=      self.get_scheduler_config()
		UpperCamelCase	:int		=      scheduler_class(**lowerCAmelCase_      )

		scheduler.set_timesteps(self.num_inference_steps      )

		UpperCamelCase	:Any		=      torch.manual_seed(0      )

		UpperCamelCase	:str		=      self.dummy_model()
		UpperCamelCase	:Tuple		=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		UpperCamelCase	:List[Any]		=      sample.to(lowerCAmelCase_      )

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

			UpperCamelCase	:Tuple		=      model(lowerCAmelCase_					,    lowerCAmelCase_      )

			UpperCamelCase	:Any		=      scheduler.step(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    generator=lowerCAmelCase_      )
			UpperCamelCase	:str		=      output.prev_sample

		UpperCamelCase	:Tuple		=      torch.sum(torch.abs(lowerCAmelCase_      )      )
		UpperCamelCase	:Any		=      torch.mean(torch.abs(lowerCAmelCase_      )      )

		assert abs(result_sum.item() - 152.3_192      ) < 1E-2
		assert abs(result_mean.item() - 0.1983      ) < 1E-3





	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Dict		=      self.scheduler_classes[0]
		UpperCamelCase	:str		=      self.get_scheduler_config(prediction_type="""v_prediction"""      )
		UpperCamelCase	:Any		=      scheduler_class(**lowerCAmelCase_      )

		scheduler.set_timesteps(self.num_inference_steps      )

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

		UpperCamelCase	:List[str]		=      self.dummy_model()
		UpperCamelCase	:Dict		=      self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCamelCase	:int		=      sample.to(lowerCAmelCase_      )

		for i, t in enumerate(scheduler.timesteps      ):
			UpperCamelCase	:Dict		=      scheduler.scale_model_input(lowerCAmelCase_					,    lowerCAmelCase_      )

			UpperCamelCase	:Tuple		=      model(lowerCAmelCase_					,    lowerCAmelCase_      )

			UpperCamelCase	:Any		=      scheduler.step(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    generator=lowerCAmelCase_      )
			UpperCamelCase	:Optional[int]		=      output.prev_sample

		UpperCamelCase	:Optional[Any]		=      torch.sum(torch.abs(lowerCAmelCase_      )      )
		UpperCamelCase	:List[str]		=      torch.mean(torch.abs(lowerCAmelCase_      )      )

		assert abs(result_sum.item() - 108.4_439      ) < 1E-2
		assert abs(result_mean.item() - 0.1412      ) < 1E-3





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Tuple		=      self.scheduler_classes[0]
		UpperCamelCase	:Any		=      self.get_scheduler_config()
		UpperCamelCase	:Any		=      scheduler_class(**lowerCAmelCase_      )

		scheduler.set_timesteps(self.num_inference_steps					,    device=lowerCAmelCase_      )
		UpperCamelCase	:Tuple		=      torch.manual_seed(0      )

		UpperCamelCase	:int		=      self.dummy_model()
		UpperCamelCase	:Dict		=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
		UpperCamelCase	:int		=      sample.to(lowerCAmelCase_      )

		for t in scheduler.timesteps:
			UpperCamelCase	:Any		=      scheduler.scale_model_input(lowerCAmelCase_					,    lowerCAmelCase_      )

			UpperCamelCase	:int		=      model(lowerCAmelCase_					,    lowerCAmelCase_      )

			UpperCamelCase	:Tuple		=      scheduler.step(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    generator=lowerCAmelCase_      )
			UpperCamelCase	:str		=      output.prev_sample

		UpperCamelCase	:Any		=      torch.sum(torch.abs(lowerCAmelCase_      )      )
		UpperCamelCase	:Optional[Any]		=      torch.mean(torch.abs(lowerCAmelCase_      )      )

		assert abs(result_sum.item() - 152.3_192      ) < 1E-2
		assert abs(result_mean.item() - 0.1983      ) < 1E-3







import math
from timeit import timeit





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			int:



					'''simple docstring'''




					_snake_case						=					0
					_snake_case						=					abs(UpperCamelCase__    )
					while True:
										_snake_case						=					n // 10
										digits += 1
										if n == 0:
															break
					return digits





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			int:



					'''simple docstring'''




					return 1 if n == 0 else math.floor(math.log(abs(UpperCamelCase__    )							,     10    ) + 1    )





def     lowerCamelCase__  (  UpperCamelCase__      :							int    )      ->			int:



					'''simple docstring'''




					return len(str(abs(UpperCamelCase__    )    )    )





def     lowerCamelCase__  (  )      ->			None:



					'''simple docstring'''




					from collections.abc import Callable

					def benchmark_a_function(UpperCamelCase__      :							Callable							,     UpperCamelCase__      :							int    ) -> None:
										_snake_case						=					F'''{func.__name__}({value})'''
										_snake_case						=					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()



