


'''simple docstring'''






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						(LMSDiscreteScheduler,)
 _lowerCAmelCase			       =						1_0
 def 				__UpperCAmelCase		(			self   ,					**__magic_name__    )       ->				Any:
      _a         =				{
          "num_train_timesteps": 11_00,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**A_    )
      return config
 def 				__UpperCAmelCase		(			self    )       ->				List[str]:




      for timesteps in [10, 50, 1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=A_    )
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:




      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=A_   ,					beta_end=A_    )
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:




      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_    )
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:




      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_    )
 def 				__UpperCAmelCase		(			self    )       ->				str:




      for t in [0, 5_00, 8_00]:
           self.check_over_forward(time_step=A_    )
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:
      _a         =				self.scheduler_classes[0]
      _a         =				self.get_scheduler_config()
      _a         =				scheduler_class(**A_    )

      scheduler.set_timesteps(self.num_inference_steps    )

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

      for i, t in enumerate(scheduler.timesteps    ):
           _a         =				scheduler.scale_model_input(A_   ,					A_    )

           _a         =				model(A_   ,					A_    )

           _a         =				scheduler.step(A_   ,					A_   ,					A_    )
           _a         =				output.prev_sample

      _a         =				torch.sum(torch.abs(A_    )    )
      _a         =				torch.mean(torch.abs(A_    )    )

      assert abs(result_sum.item() - 1_0_0_6.3_8_8    ) < 1e-2
      assert abs(result_mean.item() - 1.3_1    ) < 1e-3
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      _a         =				self.scheduler_classes[0]
      _a         =				self.get_scheduler_config(prediction_type='v_prediction'    )
      _a         =				scheduler_class(**A_    )

      scheduler.set_timesteps(self.num_inference_steps    )

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

      for i, t in enumerate(scheduler.timesteps    ):
           _a         =				scheduler.scale_model_input(A_   ,					A_    )

           _a         =				model(A_   ,					A_    )

           _a         =				scheduler.step(A_   ,					A_   ,					A_    )
           _a         =				output.prev_sample

      _a         =				torch.sum(torch.abs(A_    )    )
      _a         =				torch.mean(torch.abs(A_    )    )

      assert abs(result_sum.item() - 0.0_0_1_7    ) < 1e-2
      assert abs(result_mean.item() - 2.2676e-06    ) < 1e-3
 def 				__UpperCAmelCase		(			self    )       ->				str:
      _a         =				self.scheduler_classes[0]
      _a         =				self.get_scheduler_config()
      _a         =				scheduler_class(**A_    )

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

      _a         =				self.dummy_model()
      _a         =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _a         =				sample.to(A_    )

      for i, t in enumerate(scheduler.timesteps    ):
           _a         =				scheduler.scale_model_input(A_   ,					A_    )

           _a         =				model(A_   ,					A_    )

           _a         =				scheduler.step(A_   ,					A_   ,					A_    )
           _a         =				output.prev_sample

      _a         =				torch.sum(torch.abs(A_    )    )
      _a         =				torch.mean(torch.abs(A_    )    )

      assert abs(result_sum.item() - 1_0_0_6.3_8_8    ) < 1e-2
      assert abs(result_mean.item() - 1.3_1    ) < 1e-3




 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      _a         =				self.scheduler_classes[0]
      _a         =				self.get_scheduler_config()
      _a         =				scheduler_class(**A_   ,					use_karras_sigmas=A_    )

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

      _a         =				self.dummy_model()
      _a         =				self.dummy_sample_deter.to(A_    ) * scheduler.init_noise_sigma
      _a         =				sample.to(A_    )

      for t in scheduler.timesteps:
           _a         =				scheduler.scale_model_input(A_   ,					A_    )

           _a         =				model(A_   ,					A_    )

           _a         =				scheduler.step(A_   ,					A_   ,					A_    )
           _a         =				output.prev_sample

      _a         =				torch.sum(torch.abs(A_    )    )
      _a         =				torch.mean(torch.abs(A_    )    )

      assert abs(result_sum.item() - 3_8_1_2.9_9_2_7    ) < 2e-2
      assert abs(result_mean.item() - 4.9_6_4_8    ) < 1e-3




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






