"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Optional[int]           =   {
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"dinat"

     _UpperCamelCase							:			Dict							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=4				,     a__=3				,     a__=64				,     a__=[3, 4, 6, 5]				,     a__=[2, 4, 8, 16]				,     a__=7				,     a__=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]				,     a__=3.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=0.0_2				,     a__=1e-5				,     a__=0.0				,     a__=None				,     a__=None				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       List[str]  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Tuple  			=     depths
       _lowerCAmelCase						:       Tuple  			=     len(a__  )
       _lowerCAmelCase						:       str  			=     num_heads
       _lowerCAmelCase						:       List[str]  			=     kernel_size
       _lowerCAmelCase						:       Any  			=     dilations
       _lowerCAmelCase						:       List[Any]  			=     mlp_ratio
       _lowerCAmelCase						:       List[Any]  			=     qkv_bias
       _lowerCAmelCase						:       Any  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Union[str, Any]  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       Optional[Any]  			=     drop_path_rate
       _lowerCAmelCase						:       Optional[int]  			=     hidden_act
       _lowerCAmelCase						:       Any  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Optional[int]  			=     layer_scale_init_value
       _lowerCAmelCase						:       List[str]  			=     ["""stem"""] + [F"stage{idx}" for idx in range(1				,     len(a__  ) + 1  )]
       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     get_aligned_output_features_output_indices(
           out_features=a__				,     out_indices=a__				,     stage_names=self.stage_names  )





"""simple docstring"""




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     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       int	)       ->      List[str]:
  _lowerCAmelCase						:       Tuple  			=     k_size // 2
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						:       Union[str, Any]  			=     1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase	) + square(_lowerCamelCase	)) / (2 * square(_lowerCamelCase	))	)
  return g





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int	)       ->      Dict:
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     image.shape[0], image.shape[1]
  # dst image height and width
  _lowerCAmelCase						:       Optional[int]  			=     height - k_size + 1
  _lowerCAmelCase						:       Dict  			=     width - k_size + 1

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

  #  turn the kernel into shape(k*k, 1)
  _lowerCAmelCase						:       List[Any]  			=     gen_gaussian_kernel(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     ravel(_lowerCamelCase	)

  # reshape and get the dst image
  _lowerCAmelCase						:       int  			=     dot(_lowerCamelCase			,_lowerCamelCase	).reshape(_lowerCamelCase			,_lowerCamelCase	).astype(_lowerCamelCase	)

  return dst


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

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




