



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_       (   A_			):







	'''simple docstring'''




	A_       :     Union[str, Any]									=		(EulerAncestralDiscreteScheduler,)
	A_       :     str									=		10

	def 				_A			(	self   ,       **lowerCamelCase       ):



				'''simple docstring'''


				a__					=  {
				    """num_train_timesteps""": 1100,
				    """beta_start""": 0.0_0_0_1,
				    """beta_end""": 0.0_2,
				    """beta_schedule""": """linear""",
				}

				config.update(**lowerCamelCase       )
				return config

	def 				_A			(	self       ):



				'''simple docstring'''


				for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=lowerCamelCase       )

	def 				_A			(	self       ):



				'''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=lowerCamelCase   ,       beta_end=lowerCamelCase       )

	def 				_A			(	self       ):



				'''simple docstring'''


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

	def 				_A			(	self       ):



				'''simple docstring'''


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

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.scheduler_classes[0]
				a__					=  self.get_scheduler_config()
				a__					=  scheduler_class(**lowerCamelCase       )

				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(lowerCamelCase       )

				for i, t in enumerate(scheduler.timesteps       ):
							a__					=  scheduler.scale_model_input(lowerCamelCase   ,       lowerCamelCase       )

							a__					=  model(lowerCamelCase   ,       lowerCamelCase       )

							a__					=  scheduler.step(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       generator=lowerCamelCase       )
							a__					=  output.prev_sample

				a__					=  torch.sum(torch.abs(lowerCamelCase       )       )
				a__					=  torch.mean(torch.abs(lowerCamelCase       )       )

				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 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.scheduler_classes[0]
				a__					=  self.get_scheduler_config(prediction_type="""v_prediction"""       )
				a__					=  scheduler_class(**lowerCamelCase       )

				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(lowerCamelCase       )

				for i, t in enumerate(scheduler.timesteps       ):
							a__					=  scheduler.scale_model_input(lowerCamelCase   ,       lowerCamelCase       )

							a__					=  model(lowerCamelCase   ,       lowerCamelCase       )

							a__					=  scheduler.step(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       generator=lowerCamelCase       )
							a__					=  output.prev_sample

				a__					=  torch.sum(torch.abs(lowerCamelCase       )       )
				a__					=  torch.mean(torch.abs(lowerCamelCase       )       )

				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 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.scheduler_classes[0]
				a__					=  self.get_scheduler_config()
				a__					=  scheduler_class(**lowerCamelCase       )

				scheduler.set_timesteps(self.num_inference_steps   ,       device=lowerCamelCase       )
				a__					=  torch.manual_seed(0       )

				a__					=  self.dummy_model()
				a__					=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
				a__					=  sample.to(lowerCamelCase       )

				for t in scheduler.timesteps:
							a__					=  scheduler.scale_model_input(lowerCamelCase   ,       lowerCamelCase       )

							a__					=  model(lowerCamelCase   ,       lowerCamelCase       )

							a__					=  scheduler.step(lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase   ,       generator=lowerCamelCase       )
							a__					=  output.prev_sample

				a__					=  torch.sum(torch.abs(lowerCamelCase       )       )
				a__					=  torch.mean(torch.abs(lowerCamelCase       )       )

				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					(  lowercase__						:					int = 998001	):



			'''simple docstring'''





			for number in range(n - 1				,					9999				,					-1	):
						a__					=  str(lowercase__	)

						# checks whether 'str_number' is a palindrome.
						if str_number == str_number[::-1]:
									a__					=  999

									# if 'number' is a product of two 3-digit numbers
									# then number is the answer otherwise fetch next number.
									while divisor != 99:
												if (number % divisor == 0) and (len(str(number // divisor	)	) == 3.0):
															return number
												divisor -= 1
			raise ValueError("""That number is larger than our acceptable range."""	)


if __name__ == "__main__":
	print(f'''{solution() = }''')

