



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 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Dict ):
		lowerCAmelCase_			:     Optional[Any]									= k_size // 2
		lowerCAmelCase_					,  lowerCAmelCase_			:     List[str]									= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowerCAmelCase_			:     List[str]									= 1 / (2 * pi * sigma) * exp(-(square(__UpperCamelCase ) + square(__UpperCamelCase )) / (2 * square(__UpperCamelCase )) )
		return g


def 			UpperCamelCase(  __UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[str] ):
		lowerCAmelCase_					,  lowerCAmelCase_			:     Union[str, Any]									= image.shape[0], image.shape[1]
		# dst image height and width
		lowerCAmelCase_			:     Union[str, Any]									= height - k_size + 1
		lowerCAmelCase_			:     Optional[Any]									= width - k_size + 1

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

		#  turn the kernel into shape(k*k, 1)
		lowerCAmelCase_			:     Dict									= gen_gaussian_kernel(__UpperCamelCase     ,__UpperCamelCase )
		lowerCAmelCase_			:     str									= ravel(__UpperCamelCase )

		# reshape and get the dst image
		lowerCAmelCase_			:     Dict									= dot(__UpperCamelCase     ,__UpperCamelCase ).reshape(__UpperCamelCase     ,__UpperCamelCase ).astype(__UpperCamelCase )

		return dst


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

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






'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "encoder-decoder"
       SCREAMING_SNAKE_CASE							=    True

       def __init__(						self	,   **__lowerCAmelCase )  ->  int:
        super().__init__(**__lowerCAmelCase )
        assert (
            "encoder" in kwargs and "decoder" in kwargs
        ), "Config has to be initialized with encoder and decoder config"
        lowercase__				:    Optional[int]  =					kwargs.pop('''encoder''' )
        lowercase__				:    Union[str, Any]  =					encoder_config.pop('''model_type''' )
        lowercase__				:    Any  =					kwargs.pop('''decoder''' )
        lowercase__				:    Any  =					decoder_config.pop('''model_type''' )

        from ..auto.configuration_auto import AutoConfig

        lowercase__				:    Union[str, Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Optional[Any]  =					AutoConfig.for_model(__lowerCAmelCase	,   **__lowerCAmelCase )
        lowercase__				:    Tuple  =					True

       @classmethod
       def _lowerCAmelCase(						cls	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  PretrainedConfig:
        logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''' )
        lowercase__				:    Union[str, Any]  =					True
        lowercase__				:    Any  =					True

        return cls(encoder=encoder_config.to_dict()	,   decoder=decoder_config.to_dict()	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Any  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    Optional[Any]  =					self.encoder.to_dict()
        lowercase__				:    Tuple  =					self.decoder.to_dict()
        lowercase__				:    Dict  =					self.__class__.model_type
        return output

