





"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Tuple								=     (LMSDiscreteScheduler,)
			__lowerCAmelCase    :							Optional[int]								=     10







			def      _a			(			self	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		Dict							      = {
									    """num_train_timesteps""": 1_1_0_0,
									    """beta_start""": 0.00_01,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**_A						)
									return config







			def      _a			(			self						):






									'''simple docstring'''







									for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
															self.check_over_configs(num_train_timesteps=_A						)







			def      _a			(			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=_A	,      beta_end=_A						)







			def      _a			(			self						):






									'''simple docstring'''







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







			def      _a			(			self						):






									'''simple docstring'''







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







			def      _a			(			self						):






									'''simple docstring'''







									for t in [0, 5_0_0, 8_0_0]:
															self.check_over_forward(time_step=_A						)







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		int							      = self.scheduler_classes[0]
									UpperCamelCase							:		List[Any]							      = self.get_scheduler_config()
									UpperCamelCase							:		Any							      = scheduler_class(**_A						)

									scheduler.set_timesteps(self.num_inference_steps						)

									UpperCamelCase							:		Union[str, Any]							      = self.dummy_model()
									UpperCamelCase							:		Optional[Any]							      = self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(scheduler.timesteps						):
															UpperCamelCase							:		Any							      = scheduler.scale_model_input(_A	,      _A						)

															UpperCamelCase							:		Optional[Any]							      = model(_A	,      _A						)

															UpperCamelCase							:		List[Any]							      = scheduler.step(_A	,      _A	,      _A						)
															UpperCamelCase							:		int							      = output.prev_sample

									UpperCamelCase							:		int							      = torch.sum(torch.abs(_A						)						)
									UpperCamelCase							:		Optional[Any]							      = torch.mean(torch.abs(_A						)						)

									assert abs(result_sum.item() - 10_06.3_88						) < 1e-2
									assert abs(result_mean.item() - 1.31						) < 1e-3







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = self.scheduler_classes[0]
									UpperCamelCase							:		Union[str, Any]							      = self.get_scheduler_config(prediction_type="""v_prediction"""						)
									UpperCamelCase							:		Any							      = scheduler_class(**_A						)

									scheduler.set_timesteps(self.num_inference_steps						)

									UpperCamelCase							:		Union[str, Any]							      = self.dummy_model()
									UpperCamelCase							:		Tuple							      = self.dummy_sample_deter * scheduler.init_noise_sigma

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

															UpperCamelCase							:		int							      = model(_A	,      _A						)

															UpperCamelCase							:		List[str]							      = scheduler.step(_A	,      _A	,      _A						)
															UpperCamelCase							:		Any							      = output.prev_sample

									UpperCamelCase							:		str							      = torch.sum(torch.abs(_A						)						)
									UpperCamelCase							:		int							      = torch.mean(torch.abs(_A						)						)

									assert abs(result_sum.item() - 0.00_17						) < 1e-2
									assert abs(result_mean.item() - 2.2_6_7_6e-0_6						) < 1e-3







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		int							      = self.scheduler_classes[0]
									UpperCamelCase							:		Optional[Any]							      = self.get_scheduler_config()
									UpperCamelCase							:		str							      = scheduler_class(**_A						)

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

									UpperCamelCase							:		str							      = self.dummy_model()
									UpperCamelCase							:		Any							      = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									UpperCamelCase							:		Dict							      = sample.to(_A						)

									for i, t in enumerate(scheduler.timesteps						):
															UpperCamelCase							:		List[str]							      = scheduler.scale_model_input(_A	,      _A						)

															UpperCamelCase							:		Any							      = model(_A	,      _A						)

															UpperCamelCase							:		List[Any]							      = scheduler.step(_A	,      _A	,      _A						)
															UpperCamelCase							:		int							      = output.prev_sample

									UpperCamelCase							:		Optional[Any]							      = torch.sum(torch.abs(_A						)						)
									UpperCamelCase							:		int							      = torch.mean(torch.abs(_A						)						)

									assert abs(result_sum.item() - 10_06.3_88						) < 1e-2
									assert abs(result_mean.item() - 1.31						) < 1e-3







			def      _a			(			self						):






									'''simple docstring'''







									UpperCamelCase							:		Tuple							      = self.scheduler_classes[0]
									UpperCamelCase							:		List[Any]							      = self.get_scheduler_config()
									UpperCamelCase							:		str							      = scheduler_class(**_A	,      use_karras_sigmas=_A						)

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

									UpperCamelCase							:		str							      = self.dummy_model()
									UpperCamelCase							:		Tuple							      = self.dummy_sample_deter.to(_A						) * scheduler.init_noise_sigma
									UpperCamelCase							:		Optional[Any]							      = sample.to(_A						)

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

															UpperCamelCase							:		str							      = model(_A	,      _A						)

															UpperCamelCase							:		Dict							      = scheduler.step(_A	,      _A	,      _A						)
															UpperCamelCase							:		List[str]							      = output.prev_sample

									UpperCamelCase							:		int							      = torch.sum(torch.abs(_A						)						)
									UpperCamelCase							:		List[Any]							      = torch.mean(torch.abs(_A						)						)

									assert abs(result_sum.item() - 38_12.99_27						) < 2e-2
									assert abs(result_mean.item() - 4.96_48						) < 1e-3










"""simple docstring"""


from transformers import PretrainedConfig






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Optional[int]								=     """custom"""







			def __init__(			self	,      _A=1	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		str							      = attribute
									super().__init__(**_A						)






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Any								=     """custom"""







			def __init__(			self	,      _A=1	,      **_A						):






									'''simple docstring'''







									UpperCamelCase							:		Any							      = attribute



