



'''simple docstring'''

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 	a__     (				_lowercase	):
	__magic_name__		:    Optional[Any]	    =     (EulerDiscreteScheduler,)
	__magic_name__		:    List[Any]	    =     10


	def 				lowercase__  (self : int,					**__UpperCAmelCase : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= {
					    '''num_train_timesteps''': 1100,
					    '''beta_start''': 0.0001,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**__UpperCAmelCase      )
					return config


	def 				lowercase__  (self : Any      )     ->			Any:

					"""simple docstring"""

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


	def 				lowercase__  (self : Optional[int]      )     ->			Union[str, 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=__UpperCAmelCase,					beta_end=__UpperCAmelCase      )


	def 				lowercase__  (self : Any      )     ->			str:

					"""simple docstring"""

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


	def 				lowercase__  (self : str      )     ->			Optional[Any]:

					"""simple docstring"""

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


	def 				lowercase__  (self : Any      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Any      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= scheduler_class(**__UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.dummy_model()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.dummy_sample_deter * scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						str      					= sample.to(__UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
									SCREAMING_SNAKE_CASE  :						Any      					= scheduler.scale_model_input(__UpperCAmelCase,					__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= scheduler.step(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						str      					= output.prev_sample

					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

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


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						Dict      					= self.get_scheduler_config(prediction_type='''v_prediction'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= scheduler_class(**__UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.dummy_model()
					SCREAMING_SNAKE_CASE  :						Any      					= self.dummy_sample_deter * scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						str      					= sample.to(__UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= scheduler.scale_model_input(__UpperCAmelCase,					__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						str      					= scheduler.step(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= output.prev_sample

					SCREAMING_SNAKE_CASE  :						Tuple      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

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


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						str      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= scheduler_class(**__UpperCAmelCase      )

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

					SCREAMING_SNAKE_CASE  :						Any      					= torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.dummy_model()
					SCREAMING_SNAKE_CASE  :						Dict      					= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					SCREAMING_SNAKE_CASE  :						int      					= sample.to(__UpperCAmelCase      )

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

									SCREAMING_SNAKE_CASE  :						Tuple      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						Dict      					= scheduler.step(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						str      					= output.prev_sample

					SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

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






	def 				lowercase__  (self : Any      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.scheduler_classes[0]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_scheduler_config()
					SCREAMING_SNAKE_CASE  :						Dict      					= scheduler_class(**__UpperCAmelCase,					use_karras_sigmas=__UpperCAmelCase      )

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

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.manual_seed(0      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.dummy_model()
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					SCREAMING_SNAKE_CASE  :						List[str]      					= sample.to(__UpperCAmelCase      )

					for t in scheduler.timesteps:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= scheduler.scale_model_input(__UpperCAmelCase,					__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(__UpperCAmelCase,					__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						str      					= scheduler.step(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= output.prev_sample

					SCREAMING_SNAKE_CASE  :						int      					= torch.sum(torch.abs(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						int      					= torch.mean(torch.abs(__UpperCAmelCase      )      )

					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 typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 	a__     (				_lowercase	):
	__magic_name__		:    UNetaDModel
	__magic_name__		:    ScoreSdeVeScheduler


	def __init__(self : List[str],					__UpperCAmelCase : UNetaDModel,					__UpperCAmelCase : ScoreSdeVeScheduler      )     ->			Tuple:

					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=__UpperCAmelCase,					scheduler=__UpperCAmelCase      )






	@torch.no_grad()
	def __call__(self : int,					__UpperCAmelCase : int = 1,					__UpperCAmelCase : int = 2000,					__UpperCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None,					__UpperCAmelCase : Optional[str] = "pil",					__UpperCAmelCase : bool = True,					**__UpperCAmelCase : List[str],					)     ->			Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.unet.config.sample_size
					SCREAMING_SNAKE_CASE  :						Dict      					= (batch_size, 3, img_size, img_size)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.unet

					SCREAMING_SNAKE_CASE  :						str      					= randn_tensor(__UpperCAmelCase,					generator=__UpperCAmelCase      ) * self.scheduler.init_noise_sigma
					SCREAMING_SNAKE_CASE  :						Any      					= sample.to(self.device      )

					self.scheduler.set_timesteps(__UpperCAmelCase      )
					self.scheduler.set_sigmas(__UpperCAmelCase      )

					for i, t in enumerate(self.progress_bar(self.scheduler.timesteps      )      ):
									SCREAMING_SNAKE_CASE  :						int      					= self.scheduler.sigmas[i] * torch.ones(shape[0],					device=self.device      )

									# correction step
									for _ in range(self.scheduler.config.correct_steps      ):
													SCREAMING_SNAKE_CASE  :						Any      					= self.unet(__UpperCAmelCase,					__UpperCAmelCase      ).sample
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.scheduler.step_correct(__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      ).prev_sample

									# prediction step
									SCREAMING_SNAKE_CASE  :						str      					= model(__UpperCAmelCase,					__UpperCAmelCase      ).sample
									SCREAMING_SNAKE_CASE  :						Dict      					= self.scheduler.step_pred(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					generator=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= output.prev_sample, output.prev_sample_mean

					SCREAMING_SNAKE_CASE  :						Dict      					= sample_mean.clamp(0,					1      )
					SCREAMING_SNAKE_CASE  :						str      					= sample.cpu().permute(0,					2,					3,					1      ).numpy()
					if output_type == "pil":
									SCREAMING_SNAKE_CASE  :						int      					= self.numpy_to_pil(__UpperCAmelCase      )

					if not return_dict:
									return (sample,)

					return ImagePipelineOutput(images=__UpperCAmelCase      )




