






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       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]      ,					__lowercase :      int     )    ->   Union[str, Any]:





    """simple docstring"""

    __A       =						k_size // 2
    __A     ,						__A       =						mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    __A       =						1 / (2 * pi * sigma) * exp(-(square(__lowercase     ) + square(__lowercase     )) / (2 * square(__lowercase     ))     )
    return g



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      int      ,					__lowercase :      Dict      ,					__lowercase :      Any     )    ->   Any:





    """simple docstring"""

    __A     ,						__A       =						image.shape[0], image.shape[1]
    # dst image height and width
    __A       =						height - k_size + 1
    __A       =						width - k_size + 1

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

    #  turn the kernel into shape(k*k, 1)
    __A       =						gen_gaussian_kernel(__lowercase      ,					__lowercase     )
    __A       =						ravel(__lowercase     )

    # reshape and get the dst image
    __A       =						dot(__lowercase      ,					__lowercase     ).reshape(__lowercase      ,					__lowercase     ).astype(__lowercase     )

    return dst


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

  # get values with two different mask size
  __a   :							List[str]						= gaussian_filter(gray, 3, sigma=1)
  __a   :							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()












import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


__a   :							Any						= logging.get_logger(__name__)


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Tuple			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         warnings.warn(
             """The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
             """ use DeiTImageProcessor instead."""			,						UpperCamelCase_			,						)
         super().__init__(*UpperCamelCase_			,						**UpperCamelCase_					)




