






"""simple docstring"""





from ...processing_utils import ProcessorMixin






class      a						(					SCREAMING_SNAKE_CASE__		):
    SCREAMING_SNAKE_CASE						:     int						= ["""image_processor""", """feature_extractor"""]
    SCREAMING_SNAKE_CASE						:     List[Any]						= """TvltImageProcessor"""
    SCREAMING_SNAKE_CASE						:     Dict						= """TvltFeatureExtractor"""






    def __init__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		int:
      super().__init__(image_processor=__SCREAMING_SNAKE_CASE       ,						feature_extractor=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						image_processor
      lowerCamelCase_         =						feature_extractor






    def __call__(       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=None       ,						__SCREAMING_SNAKE_CASE     :							int=None       ,						__SCREAMING_SNAKE_CASE     :							str=None       ,						__SCREAMING_SNAKE_CASE     :							Any=False       ,						__SCREAMING_SNAKE_CASE     :							int=False       ,						*__SCREAMING_SNAKE_CASE     :							int       ,						**__SCREAMING_SNAKE_CASE     :							Any       ,						)     ->		List[Any]:

      if images is None and audio is None:
        raise ValueError('You need to specify either an `images` or `audio` input to process.'					)

      lowerCamelCase_         =						None
      if images is not None:
        lowerCamelCase_         =						self.image_processor(__SCREAMING_SNAKE_CASE       ,						mask_pixel=__SCREAMING_SNAKE_CASE       ,						*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)
      if images_mixed is not None:
        lowerCamelCase_         =						self.image_processor(__SCREAMING_SNAKE_CASE       ,						is_mixed=__SCREAMING_SNAKE_CASE       ,						*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)
      if audio is not None:
        lowerCamelCase_         =						self.feature_extractor(
            __SCREAMING_SNAKE_CASE       ,						*__SCREAMING_SNAKE_CASE       ,						sampling_rate=__SCREAMING_SNAKE_CASE       ,						mask_audio=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						{}
      if audio is not None:
        output_dict.update(__SCREAMING_SNAKE_CASE					)
      if images is not None:
        output_dict.update(__SCREAMING_SNAKE_CASE					)
      if images_mixed_dict is not None:
        output_dict.update(__SCREAMING_SNAKE_CASE					)
      return output_dict






    @property
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		int:
      lowerCamelCase_         =						self.image_processor.model_input_names
      lowerCamelCase_         =						self.feature_extractor.model_input_names
      return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names					)					)





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     k_size // 2
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      SCREAMING_SNAKE_CASE_      =     1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
      return g


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     image.shape[0], image.shape[1]
      # dst image height and width
      SCREAMING_SNAKE_CASE_      =     height - k_size + 1
      SCREAMING_SNAKE_CASE_      =     width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      SCREAMING_SNAKE_CASE_      =     zeros((dst_height * dst_width, k_size * k_size) )
      SCREAMING_SNAKE_CASE_      =     0
      for i, j in product(range(__UpperCamelCase )   ,					range(__UpperCamelCase ) ):
            SCREAMING_SNAKE_CASE_      =     ravel(image[i : i + k_size, j : j + k_size] )
            SCREAMING_SNAKE_CASE_      =     window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      SCREAMING_SNAKE_CASE_      =     gen_gaussian_kernel(__UpperCamelCase   ,					__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ravel(__UpperCamelCase )

      # reshape and get the dst image
      SCREAMING_SNAKE_CASE_      =     dot(__UpperCamelCase   ,					__UpperCamelCase ).reshape(__UpperCamelCase   ,					__UpperCamelCase ).astype(__UpperCamelCase )

      return dst


if __name__ == "__main__":
      # read original image
      A   :     Tuple       =				imread(r"../image_data/lena.jpg")
      # turn image in gray scale value
      A   :     Optional[int]       =				cvtColor(img, COLOR_BGR2GRAY)

      # get values with two different mask size
      A   :     Tuple       =				gaussian_filter(gray, 3, sigma=1)
      A   :     Optional[int]       =				gaussian_filter(gray, 5, sigma=0.8)

      # show result images
      imshow("gaussian filter with 3x3 mask", gaussianaxa)
      imshow("gaussian filter with 5x5 mask", gaussianaxa)
      waitKey()


