



import cva
import numpy as np







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						try:
												return int(image[x_coordinate][y_coordinate] >= center	)
						except (IndexError, TypeError):
												return 0







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     image[x_coordinate][y_coordinate]
						UpperCAmelCase_     							=     [1, 2, 4, 8, 16, 32, 64, 128]

						# skip get_neighbors_pixel if center is null
						if center is None:
												return 0

						# Starting from the top right, assigning value to pixels clockwise
						UpperCAmelCase_     							=     [
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					x_coordinate - 1							,					y_coordinate + 1							,					_SCREAMING_SNAKE_CASE	),
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					y_coordinate + 1							,					_SCREAMING_SNAKE_CASE	),
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					x_coordinate - 1							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	),
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					x_coordinate + 1							,					y_coordinate + 1							,					_SCREAMING_SNAKE_CASE	),
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					x_coordinate + 1							,					_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	),
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					x_coordinate + 1							,					y_coordinate - 1							,					_SCREAMING_SNAKE_CASE	),
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE							,					y_coordinate - 1							,					_SCREAMING_SNAKE_CASE	),
						    get_neighbors_pixel(_SCREAMING_SNAKE_CASE							,					x_coordinate - 1							,					y_coordinate - 1							,					_SCREAMING_SNAKE_CASE	),
						]

						# Converting the binary value to decimal.
						return sum(
						    binary_value * power for binary_value, power in zip(_SCREAMING_SNAKE_CASE							,					_SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
							# Reading the image and converting it to grayscale.
							UpperCamelCase_	       =   cva.imread(
							    "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
							)

							# Create a numpy array as the same height and width of read image
							UpperCamelCase_	       =   np.zeros((image.shape[0], image.shape[1]))

							# Iterating through the image and calculating the
							# local binary pattern value for each pixel.
							for i in range(0, image.shape[0]):
														for j in range(0, image.shape[1]):
																					UpperCamelCase_	       =   local_binary_value(image, i, j)

							cva.imshow("local binary pattern", lbp_image)
							cva.waitKey(0)
							cva.destroyAllWindows()








'''simple docstring'''
import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class  _snake_case						(__SCREAMING_SNAKE_CASE):
			__A   :     Optional[int]          =(KDPMaAncestralDiscreteScheduler,)
			__A   :     List[Any]          =10






			def 						UpperCamelCase__				(   self       ,**_snake_case			):
				UpperCAmelCase_			:	List[Any]      =   {
				    "num_train_timesteps": 11_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**_snake_case			)
				return config






			def 						UpperCamelCase__				(   self			):
				for timesteps in [10, 50, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,[0.0002, 0.002, 0.02]			):
					self.check_over_configs(beta_start=_snake_case       ,beta_end=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_snake_case			)






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	str      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	str      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	str      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	Optional[Any]      =   sample.to(_snake_case			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Tuple      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Union[str, Any]      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Optional[Any]      =   output.prev_sample

				UpperCAmelCase_			:	Dict      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	List[Any]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3877			) < 1E-2
				assert abs(result_mean.item() - 18.0331			) < 5E-3






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






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Any      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Any      =   self.get_scheduler_config(prediction_type="v_prediction"			)
				UpperCAmelCase_			:	Dict      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps			)

				UpperCAmelCase_			:	Union[str, Any]      =   self.dummy_model()
				UpperCAmelCase_			:	List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
				UpperCAmelCase_			:	int      =   sample.to(_snake_case			)

				UpperCAmelCase_			:	str      =   torch.manual_seed(0			)

				for i, t in enumerate(scheduler.timesteps			):
					UpperCAmelCase_			:	Dict      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	str      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Optional[Any]      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	Any      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Tuple      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 328.9970			) < 1E-2
				assert abs(result_mean.item() - 0.4284			) < 1E-3






			def 						UpperCamelCase__				(   self			):
				if torch_device == "mps":
					return
				UpperCAmelCase_			:	Optional[Any]      =   self.scheduler_classes[0]
				UpperCAmelCase_			:	Union[str, Any]      =   self.get_scheduler_config()
				UpperCAmelCase_			:	str      =   scheduler_class(**_snake_case			)

				scheduler.set_timesteps(self.num_inference_steps       ,device=_snake_case			)
				UpperCAmelCase_			:	Dict      =   torch.manual_seed(0			)

				UpperCAmelCase_			:	List[str]      =   self.dummy_model()
				UpperCAmelCase_			:	str      =   self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

				for t in scheduler.timesteps:
					UpperCAmelCase_			:	Union[str, Any]      =   scheduler.scale_model_input(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	Tuple      =   model(_snake_case       ,_snake_case			)

					UpperCAmelCase_			:	int      =   scheduler.step(_snake_case       ,_snake_case       ,_snake_case       ,generator=_snake_case			)
					UpperCAmelCase_			:	List[str]      =   output.prev_sample

				UpperCAmelCase_			:	Any      =   torch.sum(torch.abs(_snake_case			)			)
				UpperCAmelCase_			:	Optional[int]      =   torch.mean(torch.abs(_snake_case			)			)

				assert abs(result_sum.item() - 13849.3818			) < 1E-1
				assert abs(result_mean.item() - 18.0331			) < 1E-3




