



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  _lowercase				(a_ ):
				'''simple docstring'''







				lowercase__				  =  (LMSDiscreteScheduler,)
				lowercase__				  =  10

				def  _lowerCamelCase   (	self       ,	**snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				{
									    "num_train_timesteps": 1100,
									    "beta_start": 0.0_001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**snake_case__      )
									return config

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for timesteps in [10, 50, 100, 1000]:
														self.check_over_configs(num_train_timesteps=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									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=snake_case__       ,	beta_end=snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.scheduler_classes[0]
									UpperCamelCase_											=				self.get_scheduler_config()
									UpperCamelCase_											=				scheduler_class(**snake_case__      )

									scheduler.set_timesteps(self.num_inference_steps      )

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

									for i, t in enumerate(scheduler.timesteps      ):
														UpperCamelCase_											=				scheduler.scale_model_input(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__      )
														UpperCamelCase_											=				output.prev_sample

									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.scheduler_classes[0]
									UpperCamelCase_											=				self.get_scheduler_config(prediction_type="v_prediction"      )
									UpperCamelCase_											=				scheduler_class(**snake_case__      )

									scheduler.set_timesteps(self.num_inference_steps      )

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

									for i, t in enumerate(scheduler.timesteps      ):
														UpperCamelCase_											=				scheduler.scale_model_input(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__      )
														UpperCamelCase_											=				output.prev_sample

									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

									assert abs(result_sum.item() - 0.0_017      ) < 1e-2
									assert abs(result_mean.item() - 2.2_676e-06      ) < 1e-3

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.scheduler_classes[0]
									UpperCamelCase_											=				self.get_scheduler_config()
									UpperCamelCase_											=				scheduler_class(**snake_case__      )

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

									UpperCamelCase_											=				self.dummy_model()
									UpperCamelCase_											=				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									UpperCamelCase_											=				sample.to(snake_case__      )

									for i, t in enumerate(scheduler.timesteps      ):
														UpperCamelCase_											=				scheduler.scale_model_input(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__      )
														UpperCamelCase_											=				output.prev_sample

									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

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

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.scheduler_classes[0]
									UpperCamelCase_											=				self.get_scheduler_config()
									UpperCamelCase_											=				scheduler_class(**snake_case__       ,	use_karras_sigmas=snake_case__      )

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

									UpperCamelCase_											=				self.dummy_model()
									UpperCamelCase_											=				self.dummy_sample_deter.to(snake_case__      ) * scheduler.init_noise_sigma
									UpperCamelCase_											=				sample.to(snake_case__      )

									for t in scheduler.timesteps:
														UpperCamelCase_											=				scheduler.scale_model_input(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				model(snake_case__       ,	snake_case__      )

														UpperCamelCase_											=				scheduler.step(snake_case__       ,	snake_case__       ,	snake_case__      )
														UpperCamelCase_											=				output.prev_sample

									UpperCamelCase_											=				torch.sum(torch.abs(snake_case__      )      )
									UpperCamelCase_											=				torch.mean(torch.abs(snake_case__      )      )

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