


'''simple docstring'''



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Optional[int]  		=	(DDIMParallelScheduler,)
				__magic_name__    :				Any  		=	(("eta", 0.0), ("num_inference_steps", 50))




				def 					a__(		self      :			List[Any]				,   **lowerCAmelCase      :			Optional[int]				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     {
										    '''num_train_timesteps''': 1000,
										    '''beta_start''': 0.0001,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''clip_sample''': True,
										}

										config.update(**lowerCAmelCase				)
										return config




				def 					a__(		self      :			Union[str, Any]				,   **lowerCAmelCase      :			Optional[Any]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.scheduler_classes[0]
										UpperCAmelCase					    =     self.get_scheduler_config(**lowerCAmelCase				)
										UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)

										UpperCAmelCase    ,    UpperCAmelCase					    =     10, 0.0

										UpperCAmelCase					    =     self.dummy_model()
										UpperCAmelCase					    =     self.dummy_sample_deter

										scheduler.set_timesteps(lowerCAmelCase				)

										for t in scheduler.timesteps:
																UpperCAmelCase					    =     model(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     scheduler.step(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				).prev_sample

										return sample




				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										for timesteps in [100, 500, 1000]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase				)




				def 					a__(		self      :			Optional[int]				)->					List[Any]:

										"""simple docstring"""




										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=lowerCAmelCase				)

										UpperCAmelCase					    =     self.scheduler_classes[0]
										UpperCAmelCase					    =     self.get_scheduler_config(steps_offset=1				)
										UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)
										scheduler.set_timesteps(5				)
										assert torch.equal(scheduler.timesteps				,   torch.LongTensor([801, 601, 401, 201, 1]				)				)




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										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=lowerCAmelCase				,   beta_end=lowerCAmelCase				)




				def 					a__(		self      :			str				)->					List[Any]:

										"""simple docstring"""




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




				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




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




				def 					a__(		self      :			List[Any]				)->					Any:

										"""simple docstring"""




										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=lowerCAmelCase				)




				def 					a__(		self      :			Union[str, Any]				)->					List[str]:

										"""simple docstring"""




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




				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase				)




				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




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




				def 					a__(		self      :			Union[str, Any]				)->					Tuple:

										"""simple docstring"""




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




				def 					a__(		self      :			Tuple				)->					Dict:

										"""simple docstring"""




										for t, num_inference_steps in zip([1, 10, 50]				,   [10, 50, 500]				):
																self.check_over_forward(time_step=lowerCAmelCase				,   num_inference_steps=lowerCAmelCase				)




				def 					a__(		self      :			Dict				)->					Tuple:

										"""simple docstring"""




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




				def 					a__(		self      :			Optional[int]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.scheduler_classes[0]
										UpperCAmelCase					    =     self.get_scheduler_config()
										UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)

										assert torch.sum(torch.abs(scheduler._get_variance(0				,   0				) - 0.0				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(420				,   400				) - 0.14771				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(980				,   960				) - 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(487				,   486				) - 0.00979				)				) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999				,   998				) - 0.02				)				) < 1E-5




				def 					a__(		self      :			str				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.scheduler_classes[0]
										UpperCAmelCase					    =     self.get_scheduler_config()
										UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)

										UpperCAmelCase    ,    UpperCAmelCase					    =     10, 0.0
										scheduler.set_timesteps(lowerCAmelCase				)

										UpperCAmelCase					    =     self.dummy_model()
										UpperCAmelCase					    =     self.dummy_sample_deter
										UpperCAmelCase					    =     self.dummy_sample_deter + 0.1
										UpperCAmelCase					    =     self.dummy_sample_deter - 0.1

										UpperCAmelCase					    =     samplea.shape[0]
										UpperCAmelCase					    =     torch.stack([samplea, samplea, samplea]				,   dim=0				)
										UpperCAmelCase					    =     torch.arange(lowerCAmelCase				)[0:3, None].repeat(1				,   lowerCAmelCase				)

										UpperCAmelCase					    =     model(samples.flatten(0				,   1				)				,   timesteps.flatten(0				,   1				)				)
										UpperCAmelCase					    =     scheduler.batch_step_no_noise(lowerCAmelCase				,   timesteps.flatten(0				,   1				)				,   samples.flatten(0				,   1				)				,   lowerCAmelCase				)

										UpperCAmelCase					    =     torch.sum(torch.abs(lowerCAmelCase				)				)
										UpperCAmelCase					    =     torch.mean(torch.abs(lowerCAmelCase				)				)

										assert abs(result_sum.item() - 1147.7904				) < 1E-2
										assert abs(result_mean.item() - 0.4982				) < 1E-3




				def 					a__(		self      :			Union[str, Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.full_loop()

										UpperCAmelCase					    =     torch.sum(torch.abs(lowerCAmelCase				)				)
										UpperCAmelCase					    =     torch.mean(torch.abs(lowerCAmelCase				)				)

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




				def 					a__(		self      :			Optional[Any]				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     self.full_loop(prediction_type='''v_prediction'''				)

										UpperCAmelCase					    =     torch.sum(torch.abs(lowerCAmelCase				)				)
										UpperCAmelCase					    =     torch.mean(torch.abs(lowerCAmelCase				)				)

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




				def 					a__(		self      :			Dict				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     self.full_loop(set_alpha_to_one=lowerCAmelCase				,   beta_start=0.01				)
										UpperCAmelCase					    =     torch.sum(torch.abs(lowerCAmelCase				)				)
										UpperCAmelCase					    =     torch.mean(torch.abs(lowerCAmelCase				)				)

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





				def 					a__(		self      :			Optional[int]				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     self.full_loop(set_alpha_to_one=lowerCAmelCase				,   beta_start=0.01				)
										UpperCAmelCase					    =     torch.sum(torch.abs(lowerCAmelCase				)				)
										UpperCAmelCase					    =     torch.mean(torch.abs(lowerCAmelCase				)				)

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









'''simple docstring'''



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey


def 				lowerCamelCase__				(	A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int					):




						'''simple docstring'''






						if (ksize % 2) == 0:
												UpperCAmelCase					    =     ksize + 1
						UpperCAmelCase					    =     np.zeros((ksize, ksize)				,       dtype=np.floataa					)

						# each value
						for y in range(A					):
												for x in range(A					):
																		# distance from center
																		UpperCAmelCase					    =     x - ksize // 2
																		UpperCAmelCase					    =     y - ksize // 2

																		# degree to radiant
																		UpperCAmelCase					    =     theta / 1_80 * np.pi
																		UpperCAmelCase					    =     np.cos(_theta					)
																		UpperCAmelCase					    =     np.sin(_theta					)

																		# get kernel x
																		UpperCAmelCase					    =     cos_theta * px + sin_theta * py

																		# get kernel y
																		UpperCAmelCase					    =     -sin_theta * px + cos_theta * py

																		# fill kernel
																		UpperCAmelCase					    =     np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

						return gabor


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# read original image
	_lowercase      :      Tuple            =					imread("""../image_data/lena.jpg""")
	# turn image in gray scale value
	_lowercase      :      int            =					cvtColor(img, COLOR_BGR2GRAY)

	# Apply multiple Kernel to detect edges
	_lowercase      :      List[str]            =					np.zeros(gray.shape[:2])
	for theta in [0, 30, 60, 90, 120, 150]:
		_lowercase      :      List[Any]            =					gabor_filter_kernel(10, 8, theta, 10, 0, 0)
		out += filteraD(gray, CV_8UC3, kernel_aa)
	_lowercase      :      Optional[int]            =					out / out.max() * 255
	_lowercase      :      Optional[int]            =					out.astype(np.uinta)

	imshow("""Original""", gray)
	imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

	waitKey(0)





