
"""simple docstring"""





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class    _UpperCAmelCase(   __SCREAMING_SNAKE_CASE     ):
		lowercase__      =				(EulerDiscreteScheduler,)
		lowercase__      =				10



		def 						UpperCAmelCase							( self    ,		**__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  {
							    "num_train_timesteps": 11_00,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							}

							config.update(**_SCREAMING_SNAKE_CASE)
							return config



		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''

							for timesteps in [10, 50, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)



		def 						UpperCAmelCase							( self)	->	Optional[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=_SCREAMING_SNAKE_CASE    ,		beta_end=_SCREAMING_SNAKE_CASE)



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






							'''simple docstring'''

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



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






							'''simple docstring'''

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



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config()
							_UpperCamelCase										=  scheduler_class(**_SCREAMING_SNAKE_CASE)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase										=  torch.manual_seed(0)

							_UpperCamelCase										=  self.dummy_model()
							_UpperCamelCase										=  self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase										=  sample.to(_SCREAMING_SNAKE_CASE)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase										=  scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  model(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  scheduler.step(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		generator=_SCREAMING_SNAKE_CASE)
												_UpperCamelCase										=  output.prev_sample

							_UpperCamelCase										=  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
							_UpperCamelCase										=  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

							assert abs(result_sum.item() - 10.0807) < 1e-2
							assert abs(result_mean.item() - 0.0131) < 1e-3



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase										=  scheduler_class(**_SCREAMING_SNAKE_CASE)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase										=  torch.manual_seed(0)

							_UpperCamelCase										=  self.dummy_model()
							_UpperCamelCase										=  self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase										=  sample.to(_SCREAMING_SNAKE_CASE)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase										=  scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  model(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  scheduler.step(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		generator=_SCREAMING_SNAKE_CASE)
												_UpperCamelCase										=  output.prev_sample

							_UpperCamelCase										=  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
							_UpperCamelCase										=  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

							assert abs(result_sum.item() - 0.0002) < 1e-2
							assert abs(result_mean.item() - 2.2_676e-06) < 1e-3



		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config()
							_UpperCamelCase										=  scheduler_class(**_SCREAMING_SNAKE_CASE)

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

							_UpperCamelCase										=  torch.manual_seed(0)

							_UpperCamelCase										=  self.dummy_model()
							_UpperCamelCase										=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							_UpperCamelCase										=  sample.to(_SCREAMING_SNAKE_CASE)

							for t in scheduler.timesteps:
												_UpperCamelCase										=  scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  model(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  scheduler.step(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		generator=_SCREAMING_SNAKE_CASE)
												_UpperCamelCase										=  output.prev_sample

							_UpperCamelCase										=  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
							_UpperCamelCase										=  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

							assert abs(result_sum.item() - 10.0807) < 1e-2
							assert abs(result_mean.item() - 0.0131) < 1e-3




		def 						UpperCAmelCase							( self)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  self.scheduler_classes[0]
							_UpperCamelCase										=  self.get_scheduler_config()
							_UpperCamelCase										=  scheduler_class(**_SCREAMING_SNAKE_CASE    ,		use_karras_sigmas=_SCREAMING_SNAKE_CASE)

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

							_UpperCamelCase										=  torch.manual_seed(0)

							_UpperCamelCase										=  self.dummy_model()
							_UpperCamelCase										=  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							_UpperCamelCase										=  sample.to(_SCREAMING_SNAKE_CASE)

							for t in scheduler.timesteps:
												_UpperCamelCase										=  scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  model(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE)

												_UpperCamelCase										=  scheduler.step(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		generator=_SCREAMING_SNAKE_CASE)
												_UpperCamelCase										=  output.prev_sample

							_UpperCamelCase										=  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
							_UpperCamelCase										=  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

							assert abs(result_sum.item() - 124.52_2994_9951_1719) < 1e-2
							assert abs(result_mean.item() - 0.1_6213_9326_3339_9963) < 1e-3








"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






