



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 	lowerCamelCase__ (		_a   ):
						_lowerCAmelCase								= (DDIMScheduler,)
						_lowerCAmelCase								= (('''eta''', 0.0), ('''num_inference_steps''', 50))


						def   _lowerCamelCase			( self						:  Any      ,       **_a						:  int			):
													a__:       Union[str, Any]											={
													    "num_train_timesteps": 1_0_0_0,
													    "beta_start": 0.0_0_0_1,
													    "beta_end": 0.0_2,
													    "beta_schedule": "linear",
													    "clip_sample": True,
													}

													config.update(**_a			)
													return config


						def   _lowerCamelCase			( self						:  Tuple      ,       **_a						:  Any			):
													a__:       Union[str, Any]											=self.scheduler_classes[0]
													a__:       List[str]											=self.get_scheduler_config(**_a			)
													a__:       int											=scheduler_class(**_a			)

													a__:       Tuple											=1_0, 0.0

													a__:       Dict											=self.dummy_model()
													a__:       Optional[Any]											=self.dummy_sample_deter

													scheduler.set_timesteps(_a			)

													for t in scheduler.timesteps:
																				a__:       str											=model(_a      ,       _a			)
																				a__:       List[Any]											=scheduler.step(_a      ,       _a      ,       _a      ,       _a			).prev_sample

													return sample


						def   _lowerCamelCase			( self						:  int			):
													for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
																				self.check_over_configs(num_train_timesteps=_a			)


						def   _lowerCamelCase			( self						:  int			):
													for steps_offset in [0, 1]:
																				self.check_over_configs(steps_offset=_a			)

													a__:       str											=self.scheduler_classes[0]
													a__:       Dict											=self.get_scheduler_config(steps_offset=1			)
													a__:       Any											=scheduler_class(**_a			)
													scheduler.set_timesteps(5			)
													assert torch.equal(scheduler.timesteps      ,       torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]			)			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]			):
																				self.check_over_configs(beta_start=_a      ,       beta_end=_a			)


						def   _lowerCamelCase			( self						:  int			):
													for schedule in ["linear", "squaredcos_cap_v2"]:
																				self.check_over_configs(beta_schedule=_a			)


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=_a			)


						def   _lowerCamelCase			( self						:  Union[str, Any]			):
													for clip_sample in [True, False]:
																				self.check_over_configs(clip_sample=_a			)


						def   _lowerCamelCase			( self						:  List[Any]			):
													for timestep_spacing in ["trailing", "leading"]:
																				self.check_over_configs(timestep_spacing=_a			)


						def   _lowerCamelCase			( self						:  List[str]			):
													for rescale_betas_zero_snr in [True, False]:
																				self.check_over_configs(rescale_betas_zero_snr=_a			)


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													self.check_over_configs(thresholding=_a			)
													for threshold in [0.5, 1.0, 2.0]:
																				for prediction_type in ["epsilon", "v_prediction"]:
																											self.check_over_configs(
																											    thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )


						def   _lowerCamelCase			( self						:  str			):
													for t in [1, 1_0, 4_9]:
																				self.check_over_forward(time_step=_a			)


						def   _lowerCamelCase			( self						:  Optional[int]			):
													for t, num_inference_steps in zip([1, 1_0, 5_0]      ,       [1_0, 5_0, 5_0_0]			):
																				self.check_over_forward(time_step=_a      ,       num_inference_steps=_a			)


						def   _lowerCamelCase			( self						:  str			):
													for t, eta in zip([1, 1_0, 4_9]      ,       [0.0, 0.5, 1.0]			):
																				self.check_over_forward(time_step=_a      ,       eta=_a			)


						def   _lowerCamelCase			( self						:  Any			):
													a__:       Any											=self.scheduler_classes[0]
													a__:       Optional[int]											=self.get_scheduler_config()
													a__:       Any											=scheduler_class(**_a			)

													assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0			) - 0.0			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_2_0      ,       4_0_0			) - 0.1_4_7_7_1			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_8_0      ,       9_6_0			) - 0.3_2_4_6_0			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0      ,       0			) - 0.0			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ,       4_8_6			) - 0.0_0_9_7_9			)			) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ,       9_9_8			) - 0.0_2			)			) < 1e-5


						def   _lowerCamelCase			( self						:  Optional[Any]			):
													a__:       Tuple											=self.full_loop()

													a__:       Optional[int]											=torch.sum(torch.abs(_a			)			)
													a__:       Optional[int]											=torch.mean(torch.abs(_a			)			)

													assert abs(result_sum.item() - 1_7_2.0_0_6_7			) < 1e-2
													assert abs(result_mean.item() - 0.2_2_3_9_6_7			) < 1e-3


						def   _lowerCamelCase			( self						:  Any			):
													a__:       Union[str, Any]											=self.full_loop(prediction_type="v_prediction"			)

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

													assert abs(result_sum.item() - 5_2.5_3_0_2			) < 1e-2
													assert abs(result_mean.item() - 0.0_6_8_4			) < 1e-3


						def   _lowerCamelCase			( self						:  Any			):
													# We specify different beta, so that the first alpha is 0.99
													a__:       List[Any]											=self.full_loop(set_alpha_to_one=_a      ,       beta_start=0.0_1			)
													a__:       Optional[int]											=torch.sum(torch.abs(_a			)			)
													a__:       Any											=torch.mean(torch.abs(_a			)			)

													assert abs(result_sum.item() - 1_4_9.8_2_9_5			) < 1e-2
													assert abs(result_mean.item() - 0.1_9_5_1			) < 1e-3







						def   _lowerCamelCase			( self						:  Tuple			):
													# We specify different beta, so that the first alpha is 0.99
													a__:       Any											=self.full_loop(set_alpha_to_one=_a      ,       beta_start=0.0_1			)
													a__:       str											=torch.sum(torch.abs(_a			)			)
													a__:       Union[str, Any]											=torch.mean(torch.abs(_a			)			)

													assert abs(result_sum.item() - 1_4_9.0_7_8_4			) < 1e-2
													assert abs(result_mean.item() - 0.1_9_4_1			) < 1e-3











from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
		from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline





