






'''simple docstring'''







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest

@require_torchsde
class _lowercase     (    _lowercase		):
       a    				=	(DPMSolverSDEScheduler,)
       a    				=	10




       def 		lowerCamelCase_    (							self:    Any      ,							**UpperCamelCase__:    Union[str, Any]					):
              lowerCamelCase__ :     Optional[Any]	  =	{
                  """num_train_timesteps""": 1_100,
                  """beta_start""": 0.0_001,
                  """beta_end""": 0.02,
                  """beta_schedule""": """linear""",
                  """noise_sampler_seed""": 0,
              }

              config.update(**UpperCamelCase__					)
              return config




       def 		lowerCamelCase_    (							self:    str					):
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=UpperCamelCase__					)




       def 		lowerCamelCase_    (							self:    str					):
              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=UpperCamelCase__      ,							beta_end=UpperCamelCase__					)




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




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




       def 		lowerCamelCase_    (							self:    List[str]					):
              lowerCamelCase__ :     int	  =	self.scheduler_classes[0]
              lowerCamelCase__ :     int	  =	self.get_scheduler_config()
              lowerCamelCase__ :     str	  =	scheduler_class(**UpperCamelCase__					)

              scheduler.set_timesteps(self.num_inference_steps					)

              lowerCamelCase__ :     Dict	  =	self.dummy_model()
              lowerCamelCase__ :     Any	  =	self.dummy_sample_deter * scheduler.init_noise_sigma
              lowerCamelCase__ :     Optional[int]	  =	sample.to(UpperCamelCase__					)

              for i, t in enumerate(scheduler.timesteps					):
                     lowerCamelCase__ :     Any	  =	scheduler.scale_model_input(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Dict	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Optional[int]	  =	scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
                     lowerCamelCase__ :     Any	  =	output.prev_sample

              lowerCamelCase__ :     Dict	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
              lowerCamelCase__ :     str	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.47_821_044_921_875					) < 1e-2
                     assert abs(result_mean.item() - 0.2_178_705_964_565_277					) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_352_111_816_406					) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_906_892_299_652					) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562					) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326					) < 1e-3




       def 		lowerCamelCase_    (							self:    Optional[Any]					):
              lowerCamelCase__ :     str	  =	self.scheduler_classes[0]
              lowerCamelCase__ :     Optional[Any]	  =	self.get_scheduler_config(prediction_type="""v_prediction"""					)
              lowerCamelCase__ :     int	  =	scheduler_class(**UpperCamelCase__					)

              scheduler.set_timesteps(self.num_inference_steps					)

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

              for i, t in enumerate(scheduler.timesteps					):
                     lowerCamelCase__ :     Union[str, Any]	  =	scheduler.scale_model_input(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Optional[Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Tuple	  =	scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
                     lowerCamelCase__ :     Tuple	  =	output.prev_sample

              lowerCamelCase__ :     int	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
              lowerCamelCase__ :     Optional[int]	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 124.77_149_200_439_453					) < 1e-2
                     assert abs(result_mean.item() - 0.16_226_289_014_816_284					) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 128.1_663_360_595_703					) < 1e-2
                     assert abs(result_mean.item() - 0.16_688_326_001_167_297					) < 1e-3
              else:
                     assert abs(result_sum.item() - 119.8_487_548_828_125					) < 1e-2
                     assert abs(result_mean.item() - 0.1_560_530_662_536_621					) < 1e-3




       def 		lowerCamelCase_    (							self:    List[str]					):
              lowerCamelCase__ :     List[Any]	  =	self.scheduler_classes[0]
              lowerCamelCase__ :     str	  =	self.get_scheduler_config()
              lowerCamelCase__ :     Union[str, Any]	  =	scheduler_class(**UpperCamelCase__					)

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

              lowerCamelCase__ :     Dict	  =	self.dummy_model()
              lowerCamelCase__ :     int	  =	self.dummy_sample_deter.to(UpperCamelCase__					) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     lowerCamelCase__ :     Dict	  =	scheduler.scale_model_input(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Union[str, Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Optional[int]	  =	scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
                     lowerCamelCase__ :     Union[str, Any]	  =	output.prev_sample

              lowerCamelCase__ :     Any	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
              lowerCamelCase__ :     List[Any]	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.46_957_397_460_938					) < 1e-2
                     assert abs(result_mean.item() - 0.21_805_934_607_982_635					) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_353_637_695_312					) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_908_382_415_771					) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562					) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326					) < 1e-3




       def 		lowerCamelCase_    (							self:    Tuple					):
              lowerCamelCase__ :     List[str]	  =	self.scheduler_classes[0]
              lowerCamelCase__ :     Any	  =	self.get_scheduler_config()
              lowerCamelCase__ :     Optional[int]	  =	scheduler_class(**UpperCamelCase__      ,							use_karras_sigmas=UpperCamelCase__					)

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

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

              for t in scheduler.timesteps:
                     lowerCamelCase__ :     str	  =	scheduler.scale_model_input(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Optional[Any]	  =	model(UpperCamelCase__      ,							UpperCamelCase__					)

                     lowerCamelCase__ :     Optional[int]	  =	scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)
                     lowerCamelCase__ :     Union[str, Any]	  =	output.prev_sample

              lowerCamelCase__ :     Union[str, Any]	  =	torch.sum(torch.abs(UpperCamelCase__					)					)
              lowerCamelCase__ :     Tuple	  =	torch.mean(torch.abs(UpperCamelCase__					)					)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 176.66_974_135_742_188					) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811					) < 1e-2
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 177.63_653_564_453_125					) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811					) < 1e-2
              else:
                     assert abs(result_sum.item() - 170.3_135_223_388_672					) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811					) < 1e-2









'''simple docstring'''







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

_A     :      int   					=imread(r'''digital_image_processing/image_data/lena_small.jpg''')
_A     :      Optional[Any]   					=cvtColor(img, COLOR_BGR2GRAY)





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__ :     int	  =	cn.convert_to_negative(UpperCamelCase				)
							# assert negative_img array for at least one True
							assert negative_img.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							with Image.open("""digital_image_processing/image_data/lena_small.jpg"""				) as img:
														# Work around assertion for response
														assert str(cc.change_contrast(UpperCamelCase    ,      110				)				).startswith(
														    """<PIL.Image.Image image mode=RGB size=100x100 at"""				)





def 			SCREAMING_SNAKE_CASE_	()				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[int]	  =	canny.gen_gaussian_kernel(9    ,      sigma=1.4				)
							# Assert ambiguous array
							assert resp.all()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							lowerCamelCase__ :     int	  =	imread("""digital_image_processing/image_data/lena_small.jpg"""    ,      0				)
							# assert ambiguous array for all == True
							assert canny_img.all()
							lowerCamelCase__ :     Union[str, Any]	  =	canny.canny(UpperCamelCase				)
							# assert canny array for at least one True
							assert canny_array.any()





def 			SCREAMING_SNAKE_CASE_	()				->	str:
							assert gg.gaussian_filter(UpperCamelCase    ,      5    ,      sigma=0.9				).all()





def 			SCREAMING_SNAKE_CASE_	()				->	int:
							# laplace diagonals
							lowerCamelCase__ :     int	  =	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
							lowerCamelCase__ :     int	  =	conv.img_convolve(UpperCamelCase    ,      UpperCamelCase				).astype(UpperCamelCase				)
							assert res.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							assert med.median_filter(UpperCamelCase    ,      3				).any()





def 			SCREAMING_SNAKE_CASE_	()				->	Any:
							lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	sob.sobel_filter(UpperCamelCase				)
							assert grad.any() and theta.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Tuple:
							lowerCamelCase__ :     Union[str, Any]	  =	sp.make_sepia(UpperCamelCase    ,      20				)
							assert sepia.all()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"				)				->	Optional[Any]:
							lowerCamelCase__ :     Union[str, Any]	  =	bs.Burkes(imread(UpperCamelCase    ,      1				)    ,      120				)
							burkes.process()
							assert burkes.output_img.any()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    ,      )				->	Optional[Any]:
							lowerCamelCase__ :     int	  =	rs.NearestNeighbour(imread(UpperCamelCase    ,      1				)    ,      400    ,      200				)
							nn.process()
							assert nn.output.any()





def 			SCREAMING_SNAKE_CASE_	()				->	Optional[int]:
							lowerCamelCase__ :     Union[str, Any]	  =	"""digital_image_processing/image_data/lena.jpg"""

							# Reading the image and converting it to grayscale.
							lowerCamelCase__ :     Tuple	  =	imread(UpperCamelCase    ,      0				)

							# Test for get_neighbors_pixel function() return not None
							lowerCamelCase__ :     int	  =	0
							lowerCamelCase__ :     Dict	  =	0
							lowerCamelCase__ :     Optional[Any]	  =	image[x_coordinate][y_coordinate]

							lowerCamelCase__ :     str	  =	lbp.get_neighbors_pixel(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert neighbors_pixels is not None

							# Test for local_binary_pattern function()
							# Create a numpy array as the same height and width of read image
							lowerCamelCase__ :     List[str]	  =	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]				):
																					lowerCamelCase__ :     List[str]	  =	lbp.local_binary_value(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							assert lbp_image.any()

