





"""simple docstring"""


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class 	__a   (  lowerCAmelCase__		):
     SCREAMING_SNAKE_CASE__ :      Dict       		=			(DDIMScheduler,)
     SCREAMING_SNAKE_CASE__ :      Union[str, Any]       		=			(("eta", 0.0), ("num_inference_steps", 50))



     def 						snake_case_			(       self					,     **a__   ):
            _lowerCamelCase    	=					{
                'num_train_timesteps': 10_00,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**a__   )
            return config



     def 						snake_case_			(       self					,     **a__   ):
            _lowerCamelCase    	=					self.scheduler_classes[0]
            _lowerCamelCase    	=					self.get_scheduler_config(**a__   )
            _lowerCamelCase    	=					scheduler_class(**a__   )

            _lowerCamelCase	,	_lowerCamelCase    	=					10, 0.0

            _lowerCamelCase    	=					self.dummy_model()
            _lowerCamelCase    	=					self.dummy_sample_deter

            scheduler.set_timesteps(a__   )

            for t in scheduler.timesteps:
                   _lowerCamelCase    	=					model(a__					,     a__   )
                   _lowerCamelCase    	=					scheduler.step(a__					,     a__					,     a__					,     a__   ).prev_sample

            return sample



     def 						snake_case_			(       self   ):
            for timesteps in [1_00, 5_00, 10_00]:
                   self.check_over_configs(num_train_timesteps=a__   )



     def 						snake_case_			(       self   ):
            for steps_offset in [0, 1]:
                   self.check_over_configs(steps_offset=a__   )

            _lowerCamelCase    	=					self.scheduler_classes[0]
            _lowerCamelCase    	=					self.get_scheduler_config(steps_offset=1   )
            _lowerCamelCase    	=					scheduler_class(**a__   )
            scheduler.set_timesteps(5   )
            assert torch.equal(scheduler.timesteps					,     torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]   )   )



     def 						snake_case_			(       self   ):
            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,     [0.002, 0.02, 0.2, 2]   ):
                   self.check_over_configs(beta_start=a__					,     beta_end=a__   )



     def 						snake_case_			(       self   ):
            for schedule in ["linear", "squaredcos_cap_v2"]:
                   self.check_over_configs(beta_schedule=a__   )



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



     def 						snake_case_			(       self   ):
            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=a__   )



     def 						snake_case_			(       self   ):
            for timestep_spacing in ["trailing", "leading"]:
                   self.check_over_configs(timestep_spacing=a__   )



     def 						snake_case_			(       self   ):
            for rescale_betas_zero_snr in [True, False]:
                   self.check_over_configs(rescale_betas_zero_snr=a__   )



     def 						snake_case_			(       self   ):
            self.check_over_configs(thresholding=a__   )
            for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "v_prediction"]:
                          self.check_over_configs(
                              thresholding=a__					,     prediction_type=a__					,     sample_max_value=a__					,     )



     def 						snake_case_			(       self   ):
            for t in [1, 10, 49]:
                   self.check_over_forward(time_step=a__   )



     def 						snake_case_			(       self   ):
            for t, num_inference_steps in zip([1, 10, 50]					,     [10, 50, 5_00]   ):
                   self.check_over_forward(time_step=a__					,     num_inference_steps=a__   )



     def 						snake_case_			(       self   ):
            for t, eta in zip([1, 10, 49]					,     [0.0, 0.5, 1.0]   ):
                   self.check_over_forward(time_step=a__					,     eta=a__   )



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.scheduler_classes[0]
            _lowerCamelCase    	=					self.get_scheduler_config()
            _lowerCamelCase    	=					scheduler_class(**a__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0					,     0   ) - 0.0   )   ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_20					,     4_00   ) - 0.14771   )   ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_80					,     9_60   ) - 0.32460   )   ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(0					,     0   ) - 0.0   )   ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_87					,     4_86   ) - 0.00979   )   ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_99					,     9_98   ) - 0.02   )   ) < 1e-5



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.full_loop()

            _lowerCamelCase    	=					torch.sum(torch.abs(a__   )   )
            _lowerCamelCase    	=					torch.mean(torch.abs(a__   )   )

            assert abs(result_sum.item() - 172.0067   ) < 1e-2
            assert abs(result_mean.item() - 0.223967   ) < 1e-3



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					self.full_loop(prediction_type='v_prediction'   )

            _lowerCamelCase    	=					torch.sum(torch.abs(a__   )   )
            _lowerCamelCase    	=					torch.mean(torch.abs(a__   )   )

            assert abs(result_sum.item() - 52.5302   ) < 1e-2
            assert abs(result_mean.item() - 0.0684   ) < 1e-3



     def 						snake_case_			(       self   ):
            # We specify different beta, so that the first alpha is 0.99
            _lowerCamelCase    	=					self.full_loop(set_alpha_to_one=a__					,     beta_start=0.01   )
            _lowerCamelCase    	=					torch.sum(torch.abs(a__   )   )
            _lowerCamelCase    	=					torch.mean(torch.abs(a__   )   )

            assert abs(result_sum.item() - 149.8295   ) < 1e-2
            assert abs(result_mean.item() - 0.1951   ) < 1e-3



     def 						snake_case_			(       self   ):
            # We specify different beta, so that the first alpha is 0.99
            _lowerCamelCase    	=					self.full_loop(set_alpha_to_one=a__					,     beta_start=0.01   )
            _lowerCamelCase    	=					torch.sum(torch.abs(a__   )   )
            _lowerCamelCase    	=					torch.mean(torch.abs(a__   )   )

            assert abs(result_sum.item() - 149.0784   ) < 1e-2
            assert abs(result_mean.item() - 0.1941   ) < 1e-3













"""simple docstring"""


import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A_     :      Any								=1_8_0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Any		)->		Tuple:
       _lowerCamelCase    	=					k_size // 2
       _lowerCamelCase	,	_lowerCamelCase    	=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _lowerCamelCase    	=					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(snake_case		) + np.square(snake_case		)) / (2 * np.square(snake_case		))		)
       )
       return g





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[int]						,  snake_case   :					int						,  snake_case   :					Dict		)->		Optional[Any]:
       _lowerCamelCase    	=					np.zeros(snake_case		)

       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     _lowerCamelCase    	=					gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row + 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            _lowerCamelCase    	=					sobel_grad[row - 1, col - 1]
                            _lowerCamelCase    	=					sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   _lowerCamelCase    	=					sobel_grad[row, col]

       return destination





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Any]						,  snake_case   :					List[Any]						,  snake_case   :					int						,  snake_case   :					int						,  snake_case   :					List[str]						,  snake_case   :					str		)->		Tuple:
       for row in range(1						,  image_shape[0] - 1		):
              for col in range(1						,  image_shape[1] - 1		):
                     if destination[row, col] >= threshold_high:
                            _lowerCamelCase    	=					strong
                     elif destination[row, col] <= threshold_low:
                            _lowerCamelCase    	=					0
                     else:
                            _lowerCamelCase    	=					weak





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Union[str, Any]						,  snake_case   :					Optional[int]						,  snake_case   :					Dict						,  snake_case   :					Tuple		)->		Optional[int]:
       for row in range(1						,  image_shape[0]		):
              for col in range(1						,  image_shape[1]		):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   _lowerCamelCase    	=					strong
                            else:
                                   _lowerCamelCase    	=					0





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Dict						,  snake_case   :					Union[str, Any]=15						,  snake_case   :					Optional[int]=30						,  snake_case   :					int=128						,  snake_case   :					str=255		)->		List[Any]:
       # gaussian_filter
       _lowerCamelCase    	=					img_convolve(snake_case						,  gen_gaussian_kernel(9						,  sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _lowerCamelCase	,	_lowerCamelCase    	=					sobel_filter(snake_case		)
       _lowerCamelCase    	=					PI + np.radadeg(snake_case		)

       _lowerCamelCase    	=					suppress_non_maximum(image.shape						,  snake_case						,  snake_case		)

       detect_high_low_threshold(
           image.shape						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case		)

       track_edge(image.shape						,  snake_case						,  snake_case						,  snake_case		)

       return destination


if __name__ == "__main__":
      # read original image in gray mode
      A_     :      Optional[Any]								=cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      A_     :      Optional[Any]								=canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)






