


'''simple docstring'''



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Union[str, Any]  		=	(PNDMScheduler,)
				__magic_name__    :				Any  		=	(("num_inference_steps", 50),)




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

										"""simple docstring"""




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

										config.update(**lowerCAmelCase				)
										return config




				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			Optional[int]=0				,   **lowerCAmelCase      :			Optional[int]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     dict(self.forward_default_kwargs				)
										UpperCAmelCase					    =     kwargs.pop('''num_inference_steps'''				,   lowerCAmelCase				)
										UpperCAmelCase					    =     self.dummy_sample
										UpperCAmelCase					    =     0.1 * sample
										UpperCAmelCase					    =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase					    =     self.get_scheduler_config(**lowerCAmelCase				)
																UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)
																scheduler.set_timesteps(lowerCAmelCase				)
																# copy over dummy past residuals
																UpperCAmelCase					    =     dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(lowerCAmelCase				)
																						UpperCAmelCase					    =     scheduler_class.from_pretrained(lowerCAmelCase				)
																						new_scheduler.set_timesteps(lowerCAmelCase				)
																						# copy over dummy past residuals
																						UpperCAmelCase					    =     dummy_past_residuals[:]

																UpperCAmelCase					    =     scheduler.step_prk(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample
																UpperCAmelCase					    =     new_scheduler.step_prk(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample

																assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

																UpperCAmelCase					    =     scheduler.step_plms(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample
																UpperCAmelCase					    =     new_scheduler.step_plms(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample

																assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"




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

										"""simple docstring"""




										pass




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Dict=0				,   **lowerCAmelCase      :			List[str]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     dict(self.forward_default_kwargs				)
										UpperCAmelCase					    =     kwargs.pop('''num_inference_steps'''				,   lowerCAmelCase				)
										UpperCAmelCase					    =     self.dummy_sample
										UpperCAmelCase					    =     0.1 * sample
										UpperCAmelCase					    =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase					    =     self.get_scheduler_config()
																UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)
																scheduler.set_timesteps(lowerCAmelCase				)

																# copy over dummy past residuals (must be after setting timesteps)
																UpperCAmelCase					    =     dummy_past_residuals[:]

																with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(lowerCAmelCase				)
																						UpperCAmelCase					    =     scheduler_class.from_pretrained(lowerCAmelCase				)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(lowerCAmelCase				)

																						# copy over dummy past residual (must be after setting timesteps)
																						UpperCAmelCase					    =     dummy_past_residuals[:]

																UpperCAmelCase					    =     scheduler.step_prk(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample
																UpperCAmelCase					    =     new_scheduler.step_prk(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample

																assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

																UpperCAmelCase					    =     scheduler.step_plms(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample
																UpperCAmelCase					    =     new_scheduler.step_plms(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample

																assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"




				def 					a__(		self      :			Any				,   **lowerCAmelCase      :			Tuple				)->					Any:

										"""simple docstring"""




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

										UpperCAmelCase					    =     10
										UpperCAmelCase					    =     self.dummy_model()
										UpperCAmelCase					    =     self.dummy_sample_deter
										scheduler.set_timesteps(lowerCAmelCase				)

										for i, t in enumerate(scheduler.prk_timesteps				):
																UpperCAmelCase					    =     model(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     scheduler.step_prk(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps				):
																UpperCAmelCase					    =     model(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     scheduler.step_plms(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				).prev_sample

										return sample




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

										"""simple docstring"""




										UpperCAmelCase					    =     dict(self.forward_default_kwargs				)

										UpperCAmelCase					    =     kwargs.pop('''num_inference_steps'''				,   lowerCAmelCase				)

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase					    =     self.get_scheduler_config()
																UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)

																UpperCAmelCase					    =     self.dummy_sample
																UpperCAmelCase					    =     0.1 * sample

																if num_inference_steps is not None and hasattr(lowerCAmelCase				,   '''set_timesteps'''				):
																						scheduler.set_timesteps(lowerCAmelCase				)
																elif num_inference_steps is not None and not hasattr(lowerCAmelCase				,   '''set_timesteps'''				):
																						UpperCAmelCase					    =     num_inference_steps

																# copy over dummy past residuals (must be done after set_timesteps)
																UpperCAmelCase					    =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																UpperCAmelCase					    =     dummy_past_residuals[:]

																UpperCAmelCase					    =     scheduler.step_prk(lowerCAmelCase				,   0				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample
																UpperCAmelCase					    =     scheduler.step_prk(lowerCAmelCase				,   1				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample

																self.assertEqual(output_a.shape				,   sample.shape				)
																self.assertEqual(output_a.shape				,   output_a.shape				)

																UpperCAmelCase					    =     scheduler.step_plms(lowerCAmelCase				,   0				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample
																UpperCAmelCase					    =     scheduler.step_plms(lowerCAmelCase				,   1				,   lowerCAmelCase				,   **lowerCAmelCase				).prev_sample

																self.assertEqual(output_a.shape				,   sample.shape				)
																self.assertEqual(output_a.shape				,   output_a.shape				)




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

										"""simple docstring"""




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




				def 					a__(		self      :			Dict				)->					Tuple:

										"""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(10				)
										assert torch.equal(
										    scheduler.timesteps				,   torch.LongTensor(
										        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)				,   )




				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




										for beta_start, beta_end in zip([0.0001, 0.001]				,   [0.002, 0.02]				):
																self.check_over_configs(beta_start=lowerCAmelCase				,   beta_end=lowerCAmelCase				)




				def 					a__(		self      :			int				)->					Any:

										"""simple docstring"""




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




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

										"""simple docstring"""




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




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

										"""simple docstring"""




										for t in [1, 5, 10]:
																self.check_over_forward(time_step=lowerCAmelCase				)




				def 					a__(		self      :			str				)->					Tuple:

										"""simple docstring"""




										for t, num_inference_steps in zip([1, 5, 10]				,   [10, 50, 100]				):
																self.check_over_forward(num_inference_steps=lowerCAmelCase				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     27

										for scheduler_class in self.scheduler_classes:
																UpperCAmelCase					    =     self.dummy_sample
																UpperCAmelCase					    =     0.1 * sample

																UpperCAmelCase					    =     self.get_scheduler_config()
																UpperCAmelCase					    =     scheduler_class(**lowerCAmelCase				)

																scheduler.set_timesteps(lowerCAmelCase				)

																# before power of 3 fix, would error on first step, so we only need to do two
																for i, t in enumerate(scheduler.prk_timesteps[:2]				):
																						UpperCAmelCase					    =     scheduler.step_prk(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				).prev_sample




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

										"""simple docstring"""




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

																scheduler.step_plms(self.dummy_sample				,   1				,   self.dummy_sample				).prev_sample




				def 					a__(		self      :			str				)->					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() - 198.1318				) < 1E-2
										assert abs(result_mean.item() - 0.2580				) < 1E-3




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

										"""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() - 67.3986				) < 1E-2
										assert abs(result_mean.item() - 0.0878				) < 1E-3




				def 					a__(		self      :			List[Any]				)->					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() - 230.0399				) < 1E-2
										assert abs(result_mean.item() - 0.2995				) < 1E-3





				def 					a__(		self      :			Tuple				)->					Optional[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() - 186.9482				) < 1E-2
										assert abs(result_mean.item() - 0.2434				) < 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)





