"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	UpperCAmelCase__					(     SCREAMING_SNAKE_CASE__      ):


	"""simple docstring"""
	lowerCAmelCase__     :       str       =				(EulerDiscreteScheduler,)
	lowerCAmelCase__     :       Any       =				10







	def    A     (      self							,  **_SCREAMING_SNAKE_CASE			)     ->						List[Any]:
							a_				:      Optional[int]  		=       {
							    """num_train_timesteps""": 1_1_0_0,
							    """beta_start""": 0.0_0_0_1,
							    """beta_end""": 0.0_2,
							    """beta_schedule""": """linear""",
							}

							config.update(**__lowerCAmelCase			)
							return config







	def    A     (      self			)     ->						List[str]:
							for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=__lowerCAmelCase			)







	def    A     (      self			)     ->						Tuple:
							for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]							,  [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
													self.check_over_configs(beta_start=__lowerCAmelCase							,  beta_end=__lowerCAmelCase			)







	def    A     (      self			)     ->						Tuple:
							for schedule in ["linear", "scaled_linear"]:
													self.check_over_configs(beta_schedule=__lowerCAmelCase			)







	def    A     (      self			)     ->						Any:
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__lowerCAmelCase			)







	def    A     (      self			)     ->						Optional[Any]:
							a_				:      int  		=       self.scheduler_classes[0]
							a_				:      List[Any]  		=       self.get_scheduler_config()
							a_				:      str  		=       scheduler_class(**__lowerCAmelCase			)

							scheduler.set_timesteps(self.num_inference_steps			)

							a_				:      int  		=       torch.manual_seed(0			)

							a_				:      List[str]  		=       self.dummy_model()
							a_				:      Tuple  		=       self.dummy_sample_deter * scheduler.init_noise_sigma
							a_				:      Dict  		=       sample.to(__lowerCAmelCase			)

							for i, t in enumerate(scheduler.timesteps			):
													a_				:      str  		=       scheduler.scale_model_input(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      Any  		=       model(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      Optional[Any]  		=       scheduler.step(__lowerCAmelCase							,  __lowerCAmelCase							,  __lowerCAmelCase							,  generator=__lowerCAmelCase			)
													a_				:      Any  		=       output.prev_sample

							a_				:      str  		=       torch.sum(torch.abs(__lowerCAmelCase			)			)
							a_				:      Optional[Any]  		=       torch.mean(torch.abs(__lowerCAmelCase			)			)

							assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
							assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3







	def    A     (      self			)     ->						List[Any]:
							a_				:      int  		=       self.scheduler_classes[0]
							a_				:      str  		=       self.get_scheduler_config(prediction_type="v_prediction"			)
							a_				:      List[Any]  		=       scheduler_class(**__lowerCAmelCase			)

							scheduler.set_timesteps(self.num_inference_steps			)

							a_				:      int  		=       torch.manual_seed(0			)

							a_				:      Optional[Any]  		=       self.dummy_model()
							a_				:      Union[str, Any]  		=       self.dummy_sample_deter * scheduler.init_noise_sigma
							a_				:      str  		=       sample.to(__lowerCAmelCase			)

							for i, t in enumerate(scheduler.timesteps			):
													a_				:      Optional[Any]  		=       scheduler.scale_model_input(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      int  		=       model(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      Dict  		=       scheduler.step(__lowerCAmelCase							,  __lowerCAmelCase							,  __lowerCAmelCase							,  generator=__lowerCAmelCase			)
													a_				:      Tuple  		=       output.prev_sample

							a_				:      List[str]  		=       torch.sum(torch.abs(__lowerCAmelCase			)			)
							a_				:      List[Any]  		=       torch.mean(torch.abs(__lowerCAmelCase			)			)

							assert abs(result_sum.item() - 0.0_0_0_2			) < 1E-2
							assert abs(result_mean.item() - 2.26_76E-06			) < 1E-3







	def    A     (      self			)     ->						Union[str, Any]:
							a_				:      str  		=       self.scheduler_classes[0]
							a_				:      Dict  		=       self.get_scheduler_config()
							a_				:      List[str]  		=       scheduler_class(**__lowerCAmelCase			)

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

							a_				:      Tuple  		=       torch.manual_seed(0			)

							a_				:      List[str]  		=       self.dummy_model()
							a_				:      Optional[int]  		=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							a_				:      Optional[int]  		=       sample.to(__lowerCAmelCase			)

							for t in scheduler.timesteps:
													a_				:      List[str]  		=       scheduler.scale_model_input(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      List[str]  		=       model(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      int  		=       scheduler.step(__lowerCAmelCase							,  __lowerCAmelCase							,  __lowerCAmelCase							,  generator=__lowerCAmelCase			)
													a_				:      Tuple  		=       output.prev_sample

							a_				:      List[Any]  		=       torch.sum(torch.abs(__lowerCAmelCase			)			)
							a_				:      Optional[int]  		=       torch.mean(torch.abs(__lowerCAmelCase			)			)

							assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
							assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3







	def    A     (      self			)     ->						Union[str, Any]:
							a_				:      Tuple  		=       self.scheduler_classes[0]
							a_				:      Tuple  		=       self.get_scheduler_config()
							a_				:      str  		=       scheduler_class(**__lowerCAmelCase							,  use_karras_sigmas=__lowerCAmelCase			)

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

							a_				:      Dict  		=       torch.manual_seed(0			)

							a_				:      Optional[Any]  		=       self.dummy_model()
							a_				:      List[Any]  		=       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							a_				:      Union[str, Any]  		=       sample.to(__lowerCAmelCase			)

							for t in scheduler.timesteps:
													a_				:      str  		=       scheduler.scale_model_input(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      Tuple  		=       model(__lowerCAmelCase							,  __lowerCAmelCase			)

													a_				:      Optional[int]  		=       scheduler.step(__lowerCAmelCase							,  __lowerCAmelCase							,  __lowerCAmelCase							,  generator=__lowerCAmelCase			)
													a_				:      Union[str, Any]  		=       output.prev_sample

							a_				:      List[str]  		=       torch.sum(torch.abs(__lowerCAmelCase			)			)
							a_				:      Union[str, Any]  		=       torch.mean(torch.abs(__lowerCAmelCase			)			)

							assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9			) < 1E-2
							assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3			) < 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     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

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

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

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

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






