



'''simple docstring'''
import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class _A	(     lowercase_     ):
		_SCREAMING_SNAKE_CASE       :     Union[str, Any]       =      (LMSDiscreteScheduler,)
		_SCREAMING_SNAKE_CASE       :     str       =      10





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

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

					config.update(**UpperCAmelCase_					)
					return config





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

					'''simple docstring'''







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





		def 				__A							(  self					)							->  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					)							->  int:

					'''simple docstring'''







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





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

					'''simple docstring'''







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





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

					'''simple docstring'''







					for t in [0, 500, 800]:
								self.check_over_forward(time_step=UpperCAmelCase_					)





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

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

					scheduler.set_timesteps(self.num_inference_steps					)

					__UpperCAmelCase :   Optional[Any]    					=  self.dummy_model()
					__UpperCAmelCase :   Tuple    					=  self.dummy_sample_deter * scheduler.init_noise_sigma

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

								__UpperCAmelCase :   Tuple    					=  model(UpperCAmelCase_			,							UpperCAmelCase_					)

								__UpperCAmelCase :   int    					=  scheduler.step(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_					)
								__UpperCAmelCase :   List[Any]    					=  output.prev_sample

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

					assert abs(result_sum.item() - 1_006.388					) < 1E-2
					assert abs(result_mean.item() - 1.31					) < 1E-3





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

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

					scheduler.set_timesteps(self.num_inference_steps					)

					__UpperCAmelCase :   Dict    					=  self.dummy_model()
					__UpperCAmelCase :   Optional[Any]    					=  self.dummy_sample_deter * scheduler.init_noise_sigma

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

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

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

					__UpperCAmelCase :   Union[str, Any]    					=  torch.sum(torch.abs(UpperCAmelCase_					)					)
					__UpperCAmelCase :   Tuple    					=  torch.mean(torch.abs(UpperCAmelCase_					)					)

					assert abs(result_sum.item() - 0.0017					) < 1E-2
					assert abs(result_mean.item() - 2.2676E-06					) < 1E-3





		def 				__A							(  self					)							->  Tuple:

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

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

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

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

								__UpperCAmelCase :   Optional[int]    					=  model(UpperCAmelCase_			,							UpperCAmelCase_					)

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

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

					assert abs(result_sum.item() - 1_006.388					) < 1E-2
					assert abs(result_mean.item() - 1.31					) < 1E-3





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

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

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

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

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

								__UpperCAmelCase :   Any    					=  model(UpperCAmelCase_			,							UpperCAmelCase_					)

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

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

					assert abs(result_sum.item() - 3_812.9_927					) < 2E-2
					assert abs(result_mean.item() - 4.9648					) < 1E-3









UpperCAmelCase__			:   Optional[int]      			=    [0.4_85, 0.4_56, 0.4_06]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.2_29, 0.2_24, 0.2_25]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   Optional[Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   str      			=    [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
UpperCAmelCase__			:   Dict      			=    [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
