


"""simple docstring"""






import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class     A(     lowerCamelCase__		):

  """simple docstring"""





  A         =       (VQDiffusionScheduler,)
  def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  List[str]:

     """simple docstring"""



     _UpperCamelCase				:Optional[Any]				    =  {
         '''num_vec_classes''': 40_97,
         '''num_train_timesteps''': 1_00,
     }

     config.update(**SCREAMING_SNAKE_CASE__      )
     return config
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  str:

     """simple docstring"""



     _UpperCamelCase				:Any				    =  4
     _UpperCamelCase				:Optional[Any]				    =  8
     _UpperCamelCase				:List[str]				    =  8

     _UpperCamelCase				:Optional[Any]				    =  torch.randint(0				,							SCREAMING_SNAKE_CASE__				,							(batch_size, height * width)      )

     return sample
  @property
  def      _UpperCamelCase(   self      )			->  Optional[int]:

     """simple docstring"""



     assert False
  def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__      )			->  int:

     """simple docstring"""



     def model(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__      ):
        _UpperCamelCase				:Optional[int]				    =  sample.shape
        _UpperCamelCase				:int				    =  torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
        _UpperCamelCase				:List[str]				    =  F.log_softmax(logits.double()				,							dim=1      ).float()
        return return_value

     return model
  def      _UpperCamelCase(   self      )			->  List[str]:

     """simple docstring"""



     for timesteps in [2, 5, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__      )
  def      _UpperCamelCase(   self      )			->  Tuple:

     """simple docstring"""



     for num_vec_classes in [5, 1_00, 10_00, 40_00]:
        self.check_over_configs(num_vec_classes=SCREAMING_SNAKE_CASE__      )
  def      _UpperCamelCase(   self      )			->  List[str]:

     """simple docstring"""



     for t in [0, 50, 99]:
        self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__      )





  def      _UpperCamelCase(   self      )			->  Any:

     """simple docstring"""



     pass





"""simple docstring"""






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

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


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

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

					# show result images
					imshow("""sobel filter""", sobel_grad)
					imshow("""sobel theta""", sobel_theta)
					waitKey(0)

