


import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax





@require_flax
class     __a     :
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  Optional[int]         =  self.prepare_init_args_and_inputs_for_common()

        lowercase__:  Optional[Any]         =  self.model_class(**lowerCAmelCase__					)
        lowercase__:  Dict         =  model.init(inputs_dict['prng_key']							,					inputs_dict['sample']					)
        jax.lax.stop_gradient(lowerCAmelCase__					)

        lowercase__:  Optional[int]         =  model.apply(lowerCAmelCase__							,					inputs_dict['sample']					)

        if isinstance(lowerCAmelCase__							,					lowerCAmelCase__					):
               lowercase__:  int         =  output.sample

        self.assertIsNotNone(lowerCAmelCase__					)
        lowercase__:  Optional[Any]         =  inputs_dict['sample'].shape
        self.assertEqual(output.shape							,					lowerCAmelCase__							,					'Input and output shapes do not match'					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__     ,		lowercase__:  int         =  self.prepare_init_args_and_inputs_for_common()

        lowercase__:  int         =  16
        lowercase__:  Optional[int]         =  (16, 32)

        lowercase__:  Union[str, Any]         =  self.model_class(**lowerCAmelCase__					)
        lowercase__:  str         =  model.init(inputs_dict['prng_key']							,					inputs_dict['sample']					)
        jax.lax.stop_gradient(lowerCAmelCase__					)

        lowercase__:  List[Any]         =  model.apply(lowerCAmelCase__							,					inputs_dict['sample']					)

        if isinstance(lowerCAmelCase__							,					lowerCAmelCase__					):
               lowercase__:  Any         =  output.sample

        self.assertIsNotNone(lowerCAmelCase__					)
        lowercase__:  Dict         =  inputs_dict['sample'].shape
        self.assertEqual(output.shape							,					lowerCAmelCase__							,					'Input and output shapes do not match'					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  Dict         =  'kwargs' in inspect.signature(self.model_class.__init__					).parameters
        lowercase__:  Dict         =  len(self.model_class._deprecated_kwargs					) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
               raise ValueError(
                   F'{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'
                   ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
                   ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
                   ' [<deprecated_argument>]`'					)

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
               raise ValueError(
                   F'{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'
                   ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
                   F' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'
                   ' from `_deprecated_kwargs = [<deprecated_argument>]`'					)






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

from digital_image_processing.filters.convolve import img_convolve




def   snake_case_	(    snake_case	)    ->							List[str]:
       lowercase__:  Any         =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
       lowercase__:  Optional[Any]         =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

       lowercase__:  Optional[int]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       lowercase__:  List[Any]         =  np.abs(img_convolve(snake_case     ,				snake_case	)	)
       # modify the pix within [0, 255]
       lowercase__:  Optional[Any]         =  dst_x * 2_55 / np.max(snake_case	)
       lowercase__:  Dict         =  dst_y * 2_55 / np.max(snake_case	)

       lowercase__:  Tuple         =  np.sqrt((np.square(snake_case	)) + (np.square(snake_case	))	)
       lowercase__:  str         =  dst_xy * 2_55 / np.max(snake_case	)
       lowercase__:  Tuple         =  dst_xy.astype(np.uinta	)

       lowercase__:  List[Any]         =  np.arctana(snake_case     ,				snake_case	)
       return dst, theta


if __name__ == "__main__":
 # read original image
 __lowerCAmelCase									=				imread('''../image_data/lena.jpg''')
 # turn image in gray scale value
 __lowerCAmelCase									=				cvtColor(img, COLOR_BGR2GRAY)

 __lowerCAmelCase			,__lowerCAmelCase									=				sobel_filter(gray)

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


