




"""simple docstring"""

import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Union[str, Any]        =       (KDPMaAncestralDiscreteScheduler,)
			a__	: Optional[int]        =       10


			def 							lowerCamelCase_			(       self			,	**SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			{
					    "num_train_timesteps": 1_100,
					    "beta_start": 0.00_01,
					    "beta_end": 0.02,
					    "beta_schedule": "linear",
					}

					config.update(**SCREAMING_SNAKE_CASE							)
					return config


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					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=SCREAMING_SNAKE_CASE			,	beta_end=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if torch_device == "mps":
							return
					snake_case						:  Dict        =			self.scheduler_classes[0]
					snake_case						:  Optional[int]        =			self.get_scheduler_config()
					snake_case						:  Optional[Any]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

					scheduler.set_timesteps(self.num_inference_steps							)

					snake_case						:  str        =			torch.manual_seed(0							)

					snake_case						:  Any        =			self.dummy_model()
					snake_case						:  str        =			self.dummy_sample_deter * scheduler.init_noise_sigma
					snake_case						:  str        =			sample.to(SCREAMING_SNAKE_CASE							)

					for i, t in enumerate(scheduler.timesteps							):
							snake_case						:  List[Any]        =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  Optional[Any]        =			model(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  List[str]        =			scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)
							snake_case						:  Any        =			output.prev_sample

					snake_case						:  List[str]        =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE							)							)
					snake_case						:  str        =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE							)							)

					assert abs(result_sum.item() - 13_849.3_877							) < 1E-2
					assert abs(result_mean.item() - 18.03_31							) < 5E-3


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if torch_device == "mps":
							return
					snake_case						:  Dict        =			self.scheduler_classes[0]
					snake_case						:  List[str]        =			self.get_scheduler_config(prediction_type="v_prediction"							)
					snake_case						:  Optional[int]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

					scheduler.set_timesteps(self.num_inference_steps							)

					snake_case						:  Tuple        =			self.dummy_model()
					snake_case						:  Optional[int]        =			self.dummy_sample_deter * scheduler.init_noise_sigma
					snake_case						:  Optional[Any]        =			sample.to(SCREAMING_SNAKE_CASE							)

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

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

							snake_case						:  Any        =			model(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  Optional[int]        =			scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			output.prev_sample

					snake_case						:  int        =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE							)							)
					snake_case						:  Union[str, Any]        =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE							)							)

					assert abs(result_sum.item() - 328.9_970							) < 1E-2
					assert abs(result_mean.item() - 0.42_84							) < 1E-3



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if torch_device == "mps":
							return
					snake_case						:  List[str]        =			self.scheduler_classes[0]
					snake_case						:  List[str]        =			self.get_scheduler_config()
					snake_case						:  List[Any]        =			scheduler_class(**SCREAMING_SNAKE_CASE							)

					scheduler.set_timesteps(self.num_inference_steps			,	device=SCREAMING_SNAKE_CASE							)
					snake_case						:  Dict        =			torch.manual_seed(0							)

					snake_case						:  Optional[int]        =			self.dummy_model()
					snake_case						:  Union[str, Any]        =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE							) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
							snake_case						:  List[str]        =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  Optional[int]        =			model(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							)

							snake_case						:  List[str]        =			scheduler.step(SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	generator=SCREAMING_SNAKE_CASE							)
							snake_case						:  str        =			output.prev_sample

					snake_case						:  Union[str, Any]        =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE							)							)
					snake_case						:  Dict        =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE							)							)

					assert abs(result_sum.item() - 13_849.3_818							) < 1E-1
					assert abs(result_mean.item() - 18.03_31							) < 1E-3







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       set    ,       lowercase__					:       set				):
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_a={set_a}] is not a set'''
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_b={set_b}] is not a set'''

		return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
							from doctest import testmod

							testmod()

