


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    UpperCAmelCase_	(							_UpperCAmelCase):
							'''simple docstring'''
							__UpperCamelCase      :     Optional[int]							=					(EulerDiscreteScheduler,)
							__UpperCamelCase      :     str							=					10






							def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														UpperCamelCase    :	Optional[Any]   	=		{
														    '''num_train_timesteps''': 1_100,
														    '''beta_start''': 0.0_001,
														    '''beta_end''': 0.02,
														    '''beta_schedule''': '''linear''',
														}

														config.update(**A_						)
														return config






							def  _lowercase   (     self						):




														"""simple docstring"""




														for timesteps in [10, 50, 100, 1_000]:
																					self.check_over_configs(num_train_timesteps=A_						)






							def  _lowercase   (     self						):




														"""simple docstring"""




														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=A_ ,					beta_end=A_						)






							def  _lowercase   (     self						):




														"""simple docstring"""




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






							def  _lowercase   (     self						):




														"""simple docstring"""




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






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	List[str]   	=		self.scheduler_classes[0]
														UpperCamelCase    :	int   	=		self.get_scheduler_config()
														UpperCamelCase    :	Tuple   	=		scheduler_class(**A_						)

														scheduler.set_timesteps(self.num_inference_steps						)

														UpperCamelCase    :	Union[str, Any]   	=		torch.manual_seed(0						)

														UpperCamelCase    :	Dict   	=		self.dummy_model()
														UpperCamelCase    :	List[Any]   	=		self.dummy_sample_deter * scheduler.init_noise_sigma
														UpperCamelCase    :	Dict   	=		sample.to(A_						)

														for i, t in enumerate(scheduler.timesteps						):
																					UpperCamelCase    :	int   	=		scheduler.scale_model_input(A_ ,					A_						)

																					UpperCamelCase    :	Optional[int]   	=		model(A_ ,					A_						)

																					UpperCamelCase    :	Optional[int]   	=		scheduler.step(A_ ,					A_ ,					A_ ,					generator=A_						)
																					UpperCamelCase    :	Dict   	=		output.prev_sample

														UpperCamelCase    :	str   	=		torch.sum(torch.abs(A_						)						)
														UpperCamelCase    :	int   	=		torch.mean(torch.abs(A_						)						)

														assert abs(result_sum.item() - 10.0_807						) < 1e-2
														assert abs(result_mean.item() - 0.0_131						) < 1e-3






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	List[str]   	=		self.scheduler_classes[0]
														UpperCamelCase    :	str   	=		self.get_scheduler_config(prediction_type='''v_prediction'''						)
														UpperCamelCase    :	List[str]   	=		scheduler_class(**A_						)

														scheduler.set_timesteps(self.num_inference_steps						)

														UpperCamelCase    :	Optional[Any]   	=		torch.manual_seed(0						)

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

														for i, t in enumerate(scheduler.timesteps						):
																					UpperCamelCase    :	List[Any]   	=		scheduler.scale_model_input(A_ ,					A_						)

																					UpperCamelCase    :	int   	=		model(A_ ,					A_						)

																					UpperCamelCase    :	Dict   	=		scheduler.step(A_ ,					A_ ,					A_ ,					generator=A_						)
																					UpperCamelCase    :	Dict   	=		output.prev_sample

														UpperCamelCase    :	int   	=		torch.sum(torch.abs(A_						)						)
														UpperCamelCase    :	str   	=		torch.mean(torch.abs(A_						)						)

														assert abs(result_sum.item() - 0.0_002						) < 1e-2
														assert abs(result_mean.item() - 2.2676e-06						) < 1e-3






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	str   	=		self.scheduler_classes[0]
														UpperCamelCase    :	int   	=		self.get_scheduler_config()
														UpperCamelCase    :	Dict   	=		scheduler_class(**A_						)

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

														UpperCamelCase    :	List[str]   	=		torch.manual_seed(0						)

														UpperCamelCase    :	Any   	=		self.dummy_model()
														UpperCamelCase    :	Dict   	=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
														UpperCamelCase    :	List[Any]   	=		sample.to(A_						)

														for t in scheduler.timesteps:
																					UpperCamelCase    :	Any   	=		scheduler.scale_model_input(A_ ,					A_						)

																					UpperCamelCase    :	Optional[Any]   	=		model(A_ ,					A_						)

																					UpperCamelCase    :	List[str]   	=		scheduler.step(A_ ,					A_ ,					A_ ,					generator=A_						)
																					UpperCamelCase    :	Optional[Any]   	=		output.prev_sample

														UpperCamelCase    :	Tuple   	=		torch.sum(torch.abs(A_						)						)
														UpperCamelCase    :	Optional[int]   	=		torch.mean(torch.abs(A_						)						)

														assert abs(result_sum.item() - 10.0_807						) < 1e-2
														assert abs(result_mean.item() - 0.0_131						) < 1e-3






							def  _lowercase   (     self						):




														"""simple docstring"""




														UpperCamelCase    :	Union[str, Any]   	=		self.scheduler_classes[0]
														UpperCamelCase    :	List[str]   	=		self.get_scheduler_config()
														UpperCamelCase    :	Optional[Any]   	=		scheduler_class(**A_ ,					use_karras_sigmas=A_						)

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

														UpperCamelCase    :	str   	=		torch.manual_seed(0						)

														UpperCamelCase    :	Any   	=		self.dummy_model()
														UpperCamelCase    :	Optional[int]   	=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
														UpperCamelCase    :	Union[str, Any]   	=		sample.to(A_						)

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

																					UpperCamelCase    :	Optional[Any]   	=		model(A_ ,					A_						)

																					UpperCamelCase    :	Any   	=		scheduler.step(A_ ,					A_ ,					A_ ,					generator=A_						)
																					UpperCamelCase    :	Any   	=		output.prev_sample

														UpperCamelCase    :	int   	=		torch.sum(torch.abs(A_						)						)
														UpperCamelCase    :	int   	=		torch.mean(torch.abs(A_						)						)

														assert abs(result_sum.item() - 124.52_299_499_511_719						) < 1e-2
														assert abs(result_mean.item() - 0.16_213_932_633_399_963						) < 1e-3





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		k_size // 2
       UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCamelCase    :	Dict   	=		1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE_       ) + square(SCREAMING_SNAKE_CASE_       )) / (2 * square(SCREAMING_SNAKE_CASE_       ))       )
       return g


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase	, UpperCamelCase    :	Tuple   	=		image.shape[0], image.shape[1]
       # dst image height and width
       UpperCamelCase    :	str   	=		height - k_size + 1
       UpperCamelCase    :	Optional[int]   	=		width - k_size + 1

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       UpperCamelCase    :	List[Any]   	=		zeros((dst_height * dst_width, k_size * k_size)       )
       UpperCamelCase    :	Tuple   	=		0
       for i, j in product(range(SCREAMING_SNAKE_CASE_       )							,			range(SCREAMING_SNAKE_CASE_       )       ):
              UpperCamelCase    :	Dict   	=		ravel(image[i : i + k_size, j : j + k_size]       )
              UpperCamelCase    :	Dict   	=		window
              row += 1

       #  turn the kernel into shape(k*k, 1)
       UpperCamelCase    :	Optional[int]   	=		gen_gaussian_kernel(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	List[Any]   	=		ravel(SCREAMING_SNAKE_CASE_       )

       # reshape and get the dst image
       UpperCamelCase    :	Optional[int]   	=		dot(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).reshape(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).astype(SCREAMING_SNAKE_CASE_       )

       return dst


if __name__ == "__main__":
  # read original image
  __UpperCAmelCase : Union[str, Any] 			=				imread(r"../image_data/lena.jpg")
  # turn image in gray scale value
  __UpperCAmelCase : Optional[Any] 			=				cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  __UpperCAmelCase : Optional[int] 			=				gaussian_filter(gray, 3, sigma=1)
  __UpperCAmelCase : List[Any] 			=				gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow("gaussian filter with 3x3 mask", gaussianaxa)
  imshow("gaussian filter with 5x5 mask", gaussianaxa)
  waitKey()

