






"""simple docstring"""
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class     UpperCamelCase_ (_UpperCAmelCase  ):
	__magic_name__			   =       (DDPMScheduler,)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			**lowerCAmelCase_      :				List[Any]		)		->    str:
				UpperCAmelCase_  :				Any      =      {
				    "num_train_timesteps": 1_000,
				    "beta_start": 0.0_0_0_1,
				    "beta_end": 0.0_2,
				    "beta_schedule": "linear",
				    "variance_type": "fixed_small",
				    "clip_sample": True,
				}

				config.update(**__UpperCamelCase		)
				return config





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






				for timesteps in [1, 5, 100, 1_000]:
							self.check_over_configs(num_train_timesteps=__UpperCamelCase		)





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






				for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]			,			[0.0_0_2, 0.0_2, 0.2, 2]		):
							self.check_over_configs(beta_start=__UpperCamelCase			,			beta_end=__UpperCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Tuple:






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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Optional[Any]:






				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=__UpperCamelCase		)





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






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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[str]:
				self.check_over_configs(thresholding=__UpperCamelCase		)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=__UpperCamelCase			,			prediction_type=__UpperCamelCase			,			sample_max_value=__UpperCamelCase			,			)





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






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





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    int:






				for t in [0, 500, 999]:
							self.check_over_forward(time_step=__UpperCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    int:
				UpperCAmelCase_  :				Dict      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				str      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Any      =      scheduler_class(**__UpperCamelCase		)

				assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0_9_7_9		)		) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.0_2		)		) < 1e-5





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Any:
				UpperCAmelCase_  :				Dict      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				int      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Dict      =      scheduler_class(**__UpperCamelCase		)

				UpperCAmelCase_  :				Tuple      =      len(__UpperCamelCase		)

				UpperCAmelCase_  :				Any      =      self.dummy_model()
				UpperCAmelCase_  :				Dict      =      self.dummy_sample_deter
				UpperCAmelCase_  :				Optional[int]      =      torch.manual_seed(0		)

				for t in reversed(range(__UpperCamelCase		)		):
							# 1. predict noise residual
							UpperCAmelCase_  :				Optional[Any]      =      model(__UpperCamelCase			,			__UpperCamelCase		)

							# 2. predict previous mean of sample x_t-1
							UpperCAmelCase_  :				Dict      =      scheduler.step(__UpperCamelCase			,			__UpperCamelCase			,			__UpperCamelCase			,			generator=__UpperCamelCase		).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							UpperCAmelCase_  :				Union[str, Any]      =      pred_prev_sample

				UpperCAmelCase_  :				Dict      =      torch.sum(torch.abs(__UpperCamelCase		)		)
				UpperCAmelCase_  :				Dict      =      torch.mean(torch.abs(__UpperCamelCase		)		)

				assert abs(result_sum.item() - 258.9_606		) < 1e-2
				assert abs(result_mean.item() - 0.3_3_7_2		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Dict:
				UpperCAmelCase_  :				Union[str, Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Any      =      self.get_scheduler_config(prediction_type="v_prediction"		)
				UpperCAmelCase_  :				Optional[Any]      =      scheduler_class(**__UpperCamelCase		)

				UpperCAmelCase_  :				int      =      len(__UpperCamelCase		)

				UpperCAmelCase_  :				List[str]      =      self.dummy_model()
				UpperCAmelCase_  :				Optional[int]      =      self.dummy_sample_deter
				UpperCAmelCase_  :				Any      =      torch.manual_seed(0		)

				for t in reversed(range(__UpperCamelCase		)		):
							# 1. predict noise residual
							UpperCAmelCase_  :				List[Any]      =      model(__UpperCamelCase			,			__UpperCamelCase		)

							# 2. predict previous mean of sample x_t-1
							UpperCAmelCase_  :				str      =      scheduler.step(__UpperCamelCase			,			__UpperCamelCase			,			__UpperCamelCase			,			generator=__UpperCamelCase		).prev_sample

							# if t > 0:
							#     noise = self.dummy_sample_deter
							#     variance = scheduler.get_variance(t) ** (0.5) * noise
							#
							# sample = pred_prev_sample + variance
							UpperCAmelCase_  :				Optional[int]      =      pred_prev_sample

				UpperCAmelCase_  :				Any      =      torch.sum(torch.abs(__UpperCamelCase		)		)
				UpperCAmelCase_  :				Dict      =      torch.mean(torch.abs(__UpperCamelCase		)		)

				assert abs(result_sum.item() - 202.0_296		) < 1e-2
				assert abs(result_mean.item() - 0.2_6_3_1		) < 1e-3





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[int]:
				UpperCAmelCase_  :				List[Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Tuple      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Tuple      =      scheduler_class(**__UpperCamelCase		)

				UpperCAmelCase_  :				Dict      =      [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=__UpperCamelCase		)

				UpperCAmelCase_  :				Union[str, Any]      =      scheduler.timesteps

				for i, timestep in enumerate(__UpperCamelCase		):
							if i == len(__UpperCamelCase		) - 1:
										UpperCAmelCase_  :				str      =      -1
							else:
										UpperCAmelCase_  :				Any      =      timesteps[i + 1]

							UpperCAmelCase_  :				Union[str, Any]      =      scheduler.previous_timestep(__UpperCamelCase		)
							UpperCAmelCase_  :				Dict      =      prev_t.item()

							self.assertEqual(__UpperCamelCase			,			__UpperCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Tuple:
				UpperCAmelCase_  :				Tuple      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Optional[int]      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Union[str, Any]      =      scheduler_class(**__UpperCamelCase		)

				UpperCAmelCase_  :				List[str]      =      [100, 87, 50, 51, 0]

				with self.assertRaises(__UpperCamelCase			,			msg="`custom_timesteps` must be in descending order."		):
							scheduler.set_timesteps(timesteps=__UpperCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Union[str, Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				int      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Tuple      =      scheduler_class(**__UpperCamelCase		)

				UpperCAmelCase_  :				Optional[Any]      =      [100, 87, 50, 1, 0]
				UpperCAmelCase_  :				Any      =      len(__UpperCamelCase		)

				with self.assertRaises(__UpperCamelCase			,			msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
							scheduler.set_timesteps(num_inference_steps=__UpperCamelCase			,			timesteps=__UpperCamelCase		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      self.scheduler_classes[0]
				UpperCAmelCase_  :				Union[str, Any]      =      self.get_scheduler_config()
				UpperCAmelCase_  :				Any      =      scheduler_class(**__UpperCamelCase		)

				UpperCAmelCase_  :				Union[str, Any]      =      [scheduler.config.num_train_timesteps]

				with self.assertRaises(
				    __UpperCamelCase			,			msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"			,			):
							scheduler.set_timesteps(timesteps=__UpperCamelCase		)











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

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      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
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

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

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

			waitKey(0)



