import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						a__							(   __snake_case		):
			A__				:   Optional[int]								=			(LMSDiscreteScheduler,)
			A__				:   List[str]								=			10



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    List[str]:
								__a							=   {
								    'num_train_timesteps': 1_1_0_0,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**UpperCAmelCase    )
								return config



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]      ,						[0.0_002, 0.002, 0.02]    ):
													self.check_over_configs(beta_start=UpperCAmelCase      ,						beta_end=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								for t in [0, 5_0_0, 8_0_0]:
													self.check_over_forward(time_step=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase    )

								scheduler.set_timesteps(self.num_inference_steps    )

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps    ):
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   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 __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config(prediction_type='v_prediction'    )
								__a							=   scheduler_class(**UpperCAmelCase    )

								scheduler.set_timesteps(self.num_inference_steps    )

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

								for i, t in enumerate(scheduler.timesteps    ):
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert abs(result_sum.item() - 0.0_017    ) < 1e-2
								assert abs(result_mean.item() - 2.2676e-06    ) < 1e-3



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase    )

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

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								__a							=   sample.to(UpperCAmelCase    )

								for i, t in enumerate(scheduler.timesteps    ):
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   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 __SCREAMING_SNAKE_CASE							(						self    ) ->    List[str]:
								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase      ,						use_karras_sigmas=UpperCAmelCase    )

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

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter.to(UpperCAmelCase    ) * scheduler.init_noise_sigma
								__a							=   sample.to(UpperCAmelCase    )

								for t in scheduler.timesteps:
													__a							=   scheduler.scale_model_input(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
													__a							=   output.prev_sample

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert abs(result_sum.item() - 3_812.9_927    ) < 2e-2
								assert abs(result_mean.item() - 4.9_648    ) < 1e-3







'''simple docstring'''
from transformers import PretrainedConfig
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[Any]          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute
				super().__init__(**_snake_case			)




class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Dict          ="custom"






			def __init__(   self       ,_snake_case=1       ,**_snake_case			):
				UpperCAmelCase_			:	Optional[int]      =   attribute




