




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Any							=					(HeunDiscreteScheduler,)
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					10






   def  _a						(	self   :       Optional[Any]						,					**UpperCamelCase_   :       str     )->			Any:
     _lowercase: List[str]						     =    {
         'num_train_timesteps': 11_00,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
     }

     config.update(**UpperCamelCase_     )
     return config






   def  _a						(	self   :       Dict     )->			int:
     for timesteps in [10, 50, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_     )






   def  _a						(	self   :       Dict     )->			str:
     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=UpperCamelCase_						,					beta_end=UpperCamelCase_     )






   def  _a						(	self   :       List[str]     )->			List[str]:
     for schedule in ["linear", "scaled_linear", "exp"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_     )






   def  _a						(	self   :       Union[str, Any]     )->			Optional[Any]:
     for clip_sample_range in [1.0, 2.0, 3.0]:
       self.check_over_configs(clip_sample_range=UpperCamelCase_						,					clip_sample=UpperCamelCase_     )






   def  _a						(	self   :       Dict     )->			Tuple:
     for prediction_type in ["epsilon", "v_prediction", "sample"]:
       self.check_over_configs(prediction_type=UpperCamelCase_     )






   def  _a						(	self   :       Dict     )->			Any:
     _lowercase: Optional[int]						     =    self.scheduler_classes[0]
     _lowercase: int						     =    self.get_scheduler_config()
     _lowercase: str						     =    scheduler_class(**UpperCamelCase_     )

     scheduler.set_timesteps(self.num_inference_steps     )

     _lowercase: Any						     =    self.dummy_model()
     _lowercase: Any						     =    self.dummy_sample_deter * scheduler.init_noise_sigma
     _lowercase: Dict						     =    sample.to(UpperCamelCase_     )

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

       _lowercase: Optional[Any]						     =    model(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Dict						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
       _lowercase: Optional[Any]						     =    output.prev_sample

     _lowercase: str						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: Optional[int]						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 0.1_233     ) < 1E-2
       assert abs(result_mean.item() - 0.0_002     ) < 1E-3
     else:
       # CUDA
       assert abs(result_sum.item() - 0.1_233     ) < 1E-2
       assert abs(result_mean.item() - 0.0_002     ) < 1E-3






   def  _a						(	self   :       Optional[Any]     )->			Optional[int]:
     _lowercase: Any						     =    self.scheduler_classes[0]
     _lowercase: int						     =    self.get_scheduler_config(prediction_type='v_prediction'     )
     _lowercase: List[Any]						     =    scheduler_class(**UpperCamelCase_     )

     scheduler.set_timesteps(self.num_inference_steps     )

     _lowercase: str						     =    self.dummy_model()
     _lowercase: Tuple						     =    self.dummy_sample_deter * scheduler.init_noise_sigma
     _lowercase: Optional[Any]						     =    sample.to(UpperCamelCase_     )

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

       _lowercase: Optional[int]						     =    model(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Optional[Any]						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
       _lowercase: List[str]						     =    output.prev_sample

     _lowercase: List[Any]						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: int						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     if torch_device in ["cpu", "mps"]:
       assert abs(result_sum.item() - 4.6_934E-07     ) < 1E-2
       assert abs(result_mean.item() - 6.1_112E-10     ) < 1E-3
     else:
       # CUDA
       assert abs(result_sum.item() - 4.693_428_650_170_972E-07     ) < 1E-2
       assert abs(result_mean.item() - 0.0_002     ) < 1E-3






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: int						     =    self.scheduler_classes[0]
     _lowercase: Optional[Any]						     =    self.get_scheduler_config()
     _lowercase: List[str]						     =    scheduler_class(**UpperCamelCase_     )

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

     _lowercase: int						     =    self.dummy_model()
     _lowercase: Tuple						     =    self.dummy_sample_deter.to(UpperCamelCase_     ) * scheduler.init_noise_sigma

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

       _lowercase: str						     =    model(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Any						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
       _lowercase: Optional[int]						     =    output.prev_sample

     _lowercase: Union[str, Any]						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: Tuple						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     if str(UpperCamelCase_     ).startswith('cpu'     ):
       # The following sum varies between 148 and 156 on mps. Why?
       assert abs(result_sum.item() - 0.1_233     ) < 1E-2
       assert abs(result_mean.item() - 0.0_002     ) < 1E-3
     elif str(UpperCamelCase_     ).startswith('mps'     ):
       # Larger tolerance on mps
       assert abs(result_mean.item() - 0.0_002     ) < 1E-2
     else:
       # CUDA
       assert abs(result_sum.item() - 0.1_233     ) < 1E-2
       assert abs(result_mean.item() - 0.0_002     ) < 1E-3






   def  _a						(	self   :       int     )->			Tuple:
     _lowercase: int						     =    self.scheduler_classes[0]
     _lowercase: List[Any]						     =    self.get_scheduler_config()
     _lowercase: int						     =    scheduler_class(**UpperCamelCase_						,					use_karras_sigmas=UpperCamelCase_     )

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

     _lowercase: Dict						     =    self.dummy_model()
     _lowercase: List[str]						     =    self.dummy_sample_deter.to(UpperCamelCase_     ) * scheduler.init_noise_sigma
     _lowercase: Optional[int]						     =    sample.to(UpperCamelCase_     )

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

       _lowercase: List[str]						     =    model(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Optional[int]						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
       _lowercase: Optional[int]						     =    output.prev_sample

     _lowercase: Any						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: Optional[int]						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 0.00_015     ) < 1E-2
     assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07     ) < 1E-2






from cva import destroyAllWindows, imread, imshow, waitKey


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase     ,					_lowercase: Tuple						     =    img.shape[0], img.shape[1]

  def to_grayscale(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							):
    return 0.2_126 * red + 0.587 * green + 0.114 * blue

  def normalize(UpperCamelCase							):
    return min(UpperCamelCase					,     255							)

  for i in range(UpperCamelCase							):
    for j in range(UpperCamelCase							):
      _lowercase: Any						     =    int(to_grayscale(*img[i][j]							)							)
      _lowercase: str						     =    [
          normalize(UpperCamelCase							),
          normalize(greyscale + factor							),
          normalize(greyscale + 2 * factor							),
      ]

  return img


if __name__ == "__main__":
   # read original image
   UpperCAmelCase__						:       Optional[Any]							=    {
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()
