




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class     snake_case  (	lowercase_							):


   '''simple docstring'''







   UpperCamelCase__	:       Any     =      (DPMSolverMultistepScheduler,)
   UpperCamelCase__	:       List[str]     =      (("""num_inference_steps""", 25),)




   def 	UpperCAmelCase       (					self     :	Tuple						,    **lowerCamelCase_     :	int			)					->Optional[int]:
     '''simple docstring'''







     UpperCAmelCase__    						=   {
         """num_train_timesteps""": 1000,
         """beta_start""": 0.0001,
         """beta_end""": 0.02,
         """beta_schedule""": """linear""",
         """solver_order""": 2,
         """prediction_type""": """epsilon""",
         """thresholding""": False,
         """sample_max_value""": 1.0,
         """algorithm_type""": """dpmsolver++""",
         """solver_type""": """midpoint""",
         """lower_order_final""": False,
         """lambda_min_clipped""": -float("""inf"""			),
         """variance_type""": None,
     }

     config.update(**lowerCamelCase_			)
     return config




   def 	UpperCAmelCase       (					self     :	str						,    lowerCamelCase_     :	List[Any]=0						,    **lowerCamelCase_     :	Optional[Any]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   dict(self.forward_default_kwargs			)
     UpperCAmelCase__    						=   kwargs.pop("""num_inference_steps"""						,    lowerCamelCase_			)
     UpperCAmelCase__    						=   self.dummy_sample
     UpperCAmelCase__    						=   0.1 * sample
     UpperCAmelCase__    						=   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase__    						=   self.get_scheduler_config(**lowerCamelCase_			)
       UpperCAmelCase__    						=   scheduler_class(**lowerCamelCase_			)
       scheduler.set_timesteps(lowerCamelCase_			)
       # copy over dummy past residuals
       UpperCAmelCase__    						=   dummy_past_residuals[: scheduler.config.solver_order]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase_			)
         UpperCAmelCase__    						=   scheduler_class.from_pretrained(lowerCamelCase_			)
         new_scheduler.set_timesteps(lowerCamelCase_			)
         # copy over dummy past residuals
         UpperCAmelCase__    						=   dummy_past_residuals[: new_scheduler.config.solver_order]

       UpperCAmelCase__						,					UpperCAmelCase__    						=   sample, sample
       for t in range(lowerCamelCase_						,    time_step + scheduler.config.solver_order + 1			):
         UpperCAmelCase__    						=   scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			).prev_sample
         UpperCAmelCase__    						=   new_scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			).prev_sample

         assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"




   def 	UpperCAmelCase       (					self     :	List[str]			)					->Optional[Any]:
     '''simple docstring'''







     pass




   def 	UpperCAmelCase       (					self     :	Optional[int]						,    lowerCamelCase_     :	Any=0						,    **lowerCamelCase_     :	Optional[Any]			)					->List[str]:
     '''simple docstring'''







     UpperCAmelCase__    						=   dict(self.forward_default_kwargs			)
     UpperCAmelCase__    						=   kwargs.pop("""num_inference_steps"""						,    lowerCamelCase_			)
     UpperCAmelCase__    						=   self.dummy_sample
     UpperCAmelCase__    						=   0.1 * sample
     UpperCAmelCase__    						=   [residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase__    						=   self.get_scheduler_config()
       UpperCAmelCase__    						=   scheduler_class(**lowerCamelCase_			)
       scheduler.set_timesteps(lowerCamelCase_			)

       # copy over dummy past residuals (must be after setting timesteps)
       UpperCAmelCase__    						=   dummy_past_residuals[: scheduler.config.solver_order]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase_			)
         UpperCAmelCase__    						=   scheduler_class.from_pretrained(lowerCamelCase_			)
         # copy over dummy past residuals
         new_scheduler.set_timesteps(lowerCamelCase_			)

         # copy over dummy past residual (must be after setting timesteps)
         UpperCAmelCase__    						=   dummy_past_residuals[: new_scheduler.config.solver_order]

       UpperCAmelCase__    						=   scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			).prev_sample
       UpperCAmelCase__    						=   new_scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			).prev_sample

       assert torch.sum(torch.abs(output - new_output			)			) < 1E-5, "Scheduler outputs are not identical"




   def 	UpperCAmelCase       (					self     :	Any						,    lowerCamelCase_     :	str=None						,    **lowerCamelCase_     :	Tuple			)					->int:
     '''simple docstring'''







     if scheduler is None:
       UpperCAmelCase__    						=   self.scheduler_classes[0]
       UpperCAmelCase__    						=   self.get_scheduler_config(**lowerCamelCase_			)
       UpperCAmelCase__    						=   scheduler_class(**lowerCamelCase_			)

     UpperCAmelCase__    						=   10
     UpperCAmelCase__    						=   self.dummy_model()
     UpperCAmelCase__    						=   self.dummy_sample_deter
     scheduler.set_timesteps(lowerCamelCase_			)

     for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase__    						=   model(lowerCamelCase_						,    lowerCamelCase_			)
       UpperCAmelCase__    						=   scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			).prev_sample

     return sample




   def 	UpperCAmelCase       (					self     :	int			)					->Optional[Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   dict(self.forward_default_kwargs			)

     UpperCAmelCase__    						=   kwargs.pop("""num_inference_steps"""						,    lowerCamelCase_			)

     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase__    						=   self.get_scheduler_config()
       UpperCAmelCase__    						=   scheduler_class(**lowerCamelCase_			)

       UpperCAmelCase__    						=   self.dummy_sample
       UpperCAmelCase__    						=   0.1 * sample

       if num_inference_steps is not None and hasattr(lowerCamelCase_						,    """set_timesteps"""			):
         scheduler.set_timesteps(lowerCamelCase_			)
       elif num_inference_steps is not None and not hasattr(lowerCamelCase_						,    """set_timesteps"""			):
         UpperCAmelCase__    						=   num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       UpperCAmelCase__    						=   [residual + 0.2, residual + 0.15, residual + 0.10]
       UpperCAmelCase__    						=   dummy_past_residuals[: scheduler.config.solver_order]

       UpperCAmelCase__    						=   scheduler.timesteps[5]
       UpperCAmelCase__    						=   scheduler.timesteps[6]

       UpperCAmelCase__    						=   scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			).prev_sample
       UpperCAmelCase__    						=   scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    **lowerCamelCase_			).prev_sample

       self.assertEqual(output_a.shape						,    sample.shape			)
       self.assertEqual(output_a.shape						,    output_a.shape			)




   def 	UpperCAmelCase       (					self     :	Dict			)					->Any:
     '''simple docstring'''







     for timesteps in [25, 50, 100, 999, 1000]:
       self.check_over_configs(num_train_timesteps=lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	int			)					->Optional[Any]:
     '''simple docstring'''







     self.check_over_configs(thresholding=lowerCamelCase_			)
     for order in [1, 2, 3]:
       for solver_type in ["midpoint", "heun"]:
         for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 thresholding=lowerCamelCase_						,    prediction_type=lowerCamelCase_						,    sample_max_value=lowerCamelCase_						,    algorithm_type="""dpmsolver++"""						,    solver_order=lowerCamelCase_						,    solver_type=lowerCamelCase_						,    )




   def 	UpperCAmelCase       (					self     :	Optional[int]			)					->str:
     '''simple docstring'''







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




   def 	UpperCAmelCase       (					self     :	Any			)					->Any:
     '''simple docstring'''







     for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
       for solver_type in ["midpoint", "heun"]:
         for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
             if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
               if order == 3:
                 continue
             else:
               self.check_over_configs(
                   solver_order=lowerCamelCase_						,    solver_type=lowerCamelCase_						,    prediction_type=lowerCamelCase_						,    algorithm_type=lowerCamelCase_						,    )
             UpperCAmelCase__    						=   self.full_loop(
                 solver_order=lowerCamelCase_						,    solver_type=lowerCamelCase_						,    prediction_type=lowerCamelCase_						,    algorithm_type=lowerCamelCase_						,    )
             assert not torch.isnan(lowerCamelCase_			).any(), "Samples have nan numbers"




   def 	UpperCAmelCase       (					self     :	List[str]			)					->Any:
     '''simple docstring'''







     self.check_over_configs(lower_order_final=lowerCamelCase_			)
     self.check_over_configs(lower_order_final=lowerCamelCase_			)




   def 	UpperCAmelCase       (					self     :	Union[str, Any]			)					->Optional[int]:
     '''simple docstring'''







     self.check_over_configs(lambda_min_clipped=-float("""inf"""			)			)
     self.check_over_configs(lambda_min_clipped=-5.1			)




   def 	UpperCAmelCase       (					self     :	Optional[Any]			)					->Optional[Any]:
     '''simple docstring'''







     self.check_over_configs(variance_type=lowerCamelCase_			)
     self.check_over_configs(variance_type="""learned_range"""			)




   def 	UpperCAmelCase       (					self     :	List[Any]			)					->str:
     '''simple docstring'''







     for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
       self.check_over_forward(num_inference_steps=lowerCamelCase_						,    time_step=0			)




   def 	UpperCAmelCase       (					self     :	int			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.full_loop()
     UpperCAmelCase__    						=   torch.mean(torch.abs(lowerCamelCase_			)			)

     assert abs(result_mean.item() - 0.3301			) < 1E-3




   def 	UpperCAmelCase       (					self     :	Optional[Any]			)					->Optional[int]:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.full_loop(thresholding=lowerCamelCase_						,    dynamic_thresholding_ratio=0.87						,    sample_max_value=0.5			)
     UpperCAmelCase__    						=   torch.mean(torch.abs(lowerCamelCase_			)			)

     assert abs(result_mean.item() - 1.1364			) < 1E-3




   def 	UpperCAmelCase       (					self     :	Dict			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.full_loop(prediction_type="""v_prediction"""			)
     UpperCAmelCase__    						=   torch.mean(torch.abs(lowerCamelCase_			)			)

     assert abs(result_mean.item() - 0.2251			) < 1E-3




   def 	UpperCAmelCase       (					self     :	Tuple			)					->str:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.full_loop(prediction_type="""v_prediction"""						,    use_karras_sigmas=lowerCamelCase_			)
     UpperCAmelCase__    						=   torch.mean(torch.abs(lowerCamelCase_			)			)

     assert abs(result_mean.item() - 0.2096			) < 1E-3




   def 	UpperCAmelCase       (					self     :	Union[str, Any]			)					->Any:
     '''simple docstring'''







     UpperCAmelCase__    						=   DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
     UpperCAmelCase__    						=   self.full_loop(scheduler=lowerCamelCase_			)
     UpperCAmelCase__    						=   torch.mean(torch.abs(lowerCamelCase_			)			)

     assert abs(result_mean.item() - 0.3301			) < 1E-3

     UpperCAmelCase__    						=   DPMSolverSinglestepScheduler.from_config(scheduler.config			)
     UpperCAmelCase__    						=   UniPCMultistepScheduler.from_config(scheduler.config			)
     UpperCAmelCase__    						=   DEISMultistepScheduler.from_config(scheduler.config			)
     UpperCAmelCase__    						=   DPMSolverMultistepScheduler.from_config(scheduler.config			)

     UpperCAmelCase__    						=   self.full_loop(scheduler=lowerCamelCase_			)
     UpperCAmelCase__    						=   torch.mean(torch.abs(lowerCamelCase_			)			)

     assert abs(result_mean.item() - 0.3301			) < 1E-3




   def 	UpperCAmelCase       (					self     :	List[Any]			)					->List[str]:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.scheduler_classes[0]
     UpperCAmelCase__    						=   self.get_scheduler_config(thresholding=lowerCamelCase_						,    dynamic_thresholding_ratio=0			)
     UpperCAmelCase__    						=   scheduler_class(**lowerCamelCase_			)

     UpperCAmelCase__    						=   10
     UpperCAmelCase__    						=   self.dummy_model()
     UpperCAmelCase__    						=   self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowerCamelCase_			)

     for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase__    						=   model(lowerCamelCase_						,    lowerCamelCase_			)
       UpperCAmelCase__    						=   scheduler.step(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			).prev_sample

     assert sample.dtype == torch.floataa







   def 	UpperCAmelCase       (					self     :	Tuple						,    **lowerCamelCase_     :	Tuple			)					->Optional[Any]:
     '''simple docstring'''







     for scheduler_class in self.scheduler_classes:
       UpperCAmelCase__    						=   self.get_scheduler_config(**lowerCamelCase_			)
       UpperCAmelCase__    						=   scheduler_class(**lowerCamelCase_			)

       scheduler.set_timesteps(scheduler.config.num_train_timesteps			)
       assert len(scheduler.timesteps.unique()			) == scheduler.num_inference_steps










from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase=3      ):
  UpperCAmelCase__    						=   int(mask / 2      )
  # copy image size
  UpperCAmelCase__    						=   zeros_like(_lowerCAmelCase      )
  for i in range(_lowerCAmelCase   ,     gray_img.shape[0] - bd      ):
    for j in range(_lowerCAmelCase   ,     gray_img.shape[1] - bd      ):
      # get mask according with mask
      UpperCAmelCase__    						=   ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
      # calculate mask median
      UpperCAmelCase__    						=   sort(_lowerCAmelCase      )[inta(divide((multiply(_lowerCAmelCase   ,     _lowerCAmelCase      ))   ,     2      ) + 1      )]
      UpperCAmelCase__    						=   median
  return median_img


if __name__ == "__main__":
   # read original image
   snake_case__ :						Tuple     =			imread('''../image_data/lena.jpg''')
   # turn image in gray scale value
   snake_case__ :						List[Any]     =			cvtColor(img, COLOR_BGR2GRAY)

   # get values with two different mask size
   snake_case__ :						str     =			median_filter(gray, 3)
   snake_case__ :						List[str]     =			median_filter(gray, 5)

   # show result images
   imshow('''median filter with 3x3 mask''', medianaxa)
   imshow('''median filter with 5x5 mask''', medianaxa)
   waitKey(0)




