






'''simple docstring'''




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class    __lowercase       (__lowerCamelCase     ):
	_lowerCamelCase														=	(EulerDiscreteScheduler,)
	_lowerCamelCase														=	10

	def   __UpperCamelCase				(   self :    Any	,       **UpperCAmelCase_ :    Optional[int]):
			UpperCamelCase__					:       str       =       {
			    'num_train_timesteps': 1_100,
			    'beta_start': 0.00_01,
			    'beta_end': 0.02,
			    'beta_schedule': 'linear',
			}

			config.update(**lowerCamelCase__)
			return config

	def   __UpperCamelCase				(   self :    Optional[int]):
			for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase__)

	def   __UpperCamelCase				(   self :    Dict):
			for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]	,       [0.00_02, 0.0_02, 0.02]):
					self.check_over_configs(beta_start=lowerCamelCase__	,       beta_end=lowerCamelCase__)

	def   __UpperCamelCase				(   self :    Any):
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowerCamelCase__)

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

	def   __UpperCamelCase				(   self :    List[str]):
			UpperCamelCase__					:       Any       =       self.scheduler_classes[0]
			UpperCamelCase__					:       List[str]       =       self.get_scheduler_config()
			UpperCamelCase__					:       int       =       scheduler_class(**lowerCamelCase__)

			scheduler.set_timesteps(self.num_inference_steps)

			UpperCamelCase__					:       Optional[Any]       =       torch.manual_seed(0)

			UpperCamelCase__					:       List[str]       =       self.dummy_model()
			UpperCamelCase__					:       Dict       =       self.dummy_sample_deter * scheduler.init_noise_sigma
			UpperCamelCase__					:       Tuple       =       sample.to(lowerCamelCase__)

			for i, t in enumerate(scheduler.timesteps):
					UpperCamelCase__					:       int       =       scheduler.scale_model_input(lowerCamelCase__	,       lowerCamelCase__)

					UpperCamelCase__					:       Dict       =       model(lowerCamelCase__	,       lowerCamelCase__)

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

			UpperCamelCase__					:       List[Any]       =       torch.sum(torch.abs(lowerCamelCase__))
			UpperCamelCase__					:       List[Any]       =       torch.mean(torch.abs(lowerCamelCase__))

			assert abs(result_sum.item() - 10.08_07) < 1e-2
			assert abs(result_mean.item() - 0.01_31) < 1e-3

	def   __UpperCamelCase				(   self :    Optional[int]):
			UpperCamelCase__					:       Tuple       =       self.scheduler_classes[0]
			UpperCamelCase__					:       List[str]       =       self.get_scheduler_config(prediction_type='v_prediction')
			UpperCamelCase__					:       List[Any]       =       scheduler_class(**lowerCamelCase__)

			scheduler.set_timesteps(self.num_inference_steps)

			UpperCamelCase__					:       int       =       torch.manual_seed(0)

			UpperCamelCase__					:       str       =       self.dummy_model()
			UpperCamelCase__					:       List[str]       =       self.dummy_sample_deter * scheduler.init_noise_sigma
			UpperCamelCase__					:       Tuple       =       sample.to(lowerCamelCase__)

			for i, t in enumerate(scheduler.timesteps):
					UpperCamelCase__					:       int       =       scheduler.scale_model_input(lowerCamelCase__	,       lowerCamelCase__)

					UpperCamelCase__					:       int       =       model(lowerCamelCase__	,       lowerCamelCase__)

					UpperCamelCase__					:       str       =       scheduler.step(lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       generator=lowerCamelCase__)
					UpperCamelCase__					:       List[str]       =       output.prev_sample

			UpperCamelCase__					:       Dict       =       torch.sum(torch.abs(lowerCamelCase__))
			UpperCamelCase__					:       Any       =       torch.mean(torch.abs(lowerCamelCase__))

			assert abs(result_sum.item() - 0.00_02) < 1e-2
			assert abs(result_mean.item() - 2.2_676e-06) < 1e-3

	def   __UpperCamelCase				(   self :    Any):
			UpperCamelCase__					:       Dict       =       self.scheduler_classes[0]
			UpperCamelCase__					:       int       =       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__					:       Optional[int]       =       self.dummy_model()
			UpperCamelCase__					:       str       =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			UpperCamelCase__					:       Tuple       =       sample.to(lowerCamelCase__)

			for t in scheduler.timesteps:
					UpperCamelCase__					:       Optional[Any]       =       scheduler.scale_model_input(lowerCamelCase__	,       lowerCamelCase__)

					UpperCamelCase__					:       List[Any]       =       model(lowerCamelCase__	,       lowerCamelCase__)

					UpperCamelCase__					:       List[str]       =       scheduler.step(lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       generator=lowerCamelCase__)
					UpperCamelCase__					:       Tuple       =       output.prev_sample

			UpperCamelCase__					:       List[str]       =       torch.sum(torch.abs(lowerCamelCase__))
			UpperCamelCase__					:       Any       =       torch.mean(torch.abs(lowerCamelCase__))

			assert abs(result_sum.item() - 10.08_07) < 1e-2
			assert abs(result_mean.item() - 0.01_31) < 1e-3





	def   __UpperCamelCase				(   self :    Optional[Any]):
			UpperCamelCase__					:       Optional[int]       =       self.scheduler_classes[0]
			UpperCamelCase__					:       List[str]       =       self.get_scheduler_config()
			UpperCamelCase__					:       Dict       =       scheduler_class(**lowerCamelCase__	,       use_karras_sigmas=lowerCamelCase__)

			scheduler.set_timesteps(self.num_inference_steps	,       device=lowerCamelCase__)

			UpperCamelCase__					:       Any       =       torch.manual_seed(0)

			UpperCamelCase__					:       str       =       self.dummy_model()
			UpperCamelCase__					:       Optional[int]       =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			UpperCamelCase__					:       Any       =       sample.to(lowerCamelCase__)

			for t in scheduler.timesteps:
					UpperCamelCase__					:       Optional[Any]       =       scheduler.scale_model_input(lowerCamelCase__	,       lowerCamelCase__)

					UpperCamelCase__					:       Optional[int]       =       model(lowerCamelCase__	,       lowerCamelCase__)

					UpperCamelCase__					:       List[str]       =       scheduler.step(lowerCamelCase__	,       lowerCamelCase__	,       lowerCamelCase__	,       generator=lowerCamelCase__)
					UpperCamelCase__					:       Optional[int]       =       output.prev_sample

			UpperCamelCase__					:       int       =       torch.sum(torch.abs(lowerCamelCase__))
			UpperCamelCase__					:       Tuple       =       torch.mean(torch.abs(lowerCamelCase__))

			assert abs(result_sum.item() - 124.52_299_499_511_719) < 1e-2
			assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63) < 1e-3










def      lowerCamelCase_						(			)	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       0
				for i in range(1					,	1001 ):
								total += i**i
				return str(UpperCamelCase__ )[-10:]


if __name__ == "__main__":
					print(solution())


