


'''simple docstring'''




import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  (HeunDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Optional[int]									=  1_0
   def a     (	self					:			Dict					,      **SCREAMING_SNAKE_CASE_					:			Any			)   ->       Union[str, Any]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Any			)   ->       List[Any]:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Optional[int]			)   ->       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=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Optional[Any]			)   ->       List[Any]:
        for schedule in ["linear", "scaled_linear", "exp"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       List[str]:
        for clip_sample_range in [1.0, 2.0, 3.0]:
             self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_					,      clip_sample=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       Union[str, Any]:
        for prediction_type in ["epsilon", "v_prediction", "sample"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 0.1_2_3_3			) < 1e-2
             assert abs(result_mean.item() - 0.0_0_0_2			) < 1e-3
        else:
             # CUDA
             assert abs(result_sum.item() - 0.1_2_3_3			) < 1e-2
             assert abs(result_mean.item() - 0.0_0_0_2			) < 1e-3
   def a     (	self					:			Optional[Any]			)   ->       Any:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        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_0_0_2			) < 1e-3
   def a     (	self					:			Any			)   ->       Tuple:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

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

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        if str(SCREAMING_SNAKE_CASE_			).startswith('cpu'			):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 0.1_2_3_3			) < 1e-2
             assert abs(result_mean.item() - 0.0_0_0_2			) < 1e-3
        elif str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
             # Larger tolerance on mps
             assert abs(result_mean.item() - 0.0_0_0_2			) < 1e-2
        else:
             # CUDA
             assert abs(result_sum.item() - 0.1_2_3_3			) < 1e-2
             assert abs(result_mean.item() - 0.0_0_0_2			) < 1e-3





   def a     (	self					:			int			)   ->       List[str]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_					,      use_karras_sigmas=SCREAMING_SNAKE_CASE_			)

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

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 0.0_0_0_1_5			) < 1e-2
        assert abs(result_mean.item() - 1.9_869_554_535_034_695e-07			) < 1e-2








'''simple docstring'''




import numpy as np
from PIL import Image





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case			,					__snake_case			,					__snake_case    			=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b





def      _a		(lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     return (gray > 1_2_7) & (gray <= 2_5_5)





def      _a		(lowercase__ :      np.array       ,      lowercase__ :      np.array	) -> np.array:






     """simple docstring"""
     __snake_case    			=	np.zeros_like(lowercase__	)
     __snake_case    			=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

     # Copy image to padded image
     __snake_case    			=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]	):
          for y in range(image.shape[0]	):
               __snake_case    			=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __snake_case    			=	int(summation == 5	)
     return output


# kernel to be applied
_a      :      Tuple       				=      np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
       # read original image
       _a      :      Any       				=      np.array(Image.open(R"..\image_data\lena.jpg"))
       # Apply erosion operation to a binary image
       _a      :      List[Any]       				=      erosion(grayabinary(rgbagray(image)), structuring_element)
       # Save the output image
       _a      :      int       				=      Image.fromarray(output).convert("RGB")
       pil_img.save("result_erosion.png")




