


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

__UpperCAmelCase    :Any				       =     {
    "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
    # See all ViT models at https://huggingface.co/models?filter=vit
}



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Dict							     =     "vit"

     def __init__(       self  :	Any							,				snake_case  :	str=768							,				snake_case  :	int=12							,				snake_case  :	int=12							,				snake_case  :	Optional[Any]=3072							,				snake_case  :	Optional[Any]="gelu"							,				snake_case  :	Optional[int]=0.0							,				snake_case  :	List[str]=0.0							,				snake_case  :	str=0.02							,				snake_case  :	Tuple=1E-12							,				snake_case  :	List[str]=224							,				snake_case  :	Tuple=16							,				snake_case  :	Optional[int]=3							,				snake_case  :	Optional[int]=True							,				snake_case  :	Any=16							,				**snake_case  :	Optional[Any]							,				)		->							Optional[Any]:
          super().__init__(**snake_case    )

          __UpperCAmelCase			:				List[Any]													=    hidden_size
          __UpperCAmelCase			:				Any													=    num_hidden_layers
          __UpperCAmelCase			:				Optional[int]													=    num_attention_heads
          __UpperCAmelCase			:				List[str]													=    intermediate_size
          __UpperCAmelCase			:				List[Any]													=    hidden_act
          __UpperCAmelCase			:				Any													=    hidden_dropout_prob
          __UpperCAmelCase			:				int													=    attention_probs_dropout_prob
          __UpperCAmelCase			:				Union[str, Any]													=    initializer_range
          __UpperCAmelCase			:				Dict													=    layer_norm_eps
          __UpperCAmelCase			:				Optional[int]													=    image_size
          __UpperCAmelCase			:				Optional[Any]													=    patch_size
          __UpperCAmelCase			:				List[Any]													=    num_channels
          __UpperCAmelCase			:				Union[str, Any]													=    qkv_bias
          __UpperCAmelCase			:				Dict													=    encoder_stride



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     version.parse("1.11" )

     @property
     def 				lowerCamelCase__				(       self  :	Tuple    )		->							Mapping[str, Mapping[int, str]]:
          return OrderedDict(
              [
                  ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ]    )






     @property
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							float:
          return 1E-4



'''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 			_a			(		_lowercase      :			Optional[Any]    ,      _lowercase      :			List[Any]						):





     '''simple docstring'''






     __UpperCAmelCase			:				Dict													=    k_size // 2
     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __UpperCAmelCase			:				Optional[int]													=    1 / (2 * pi * sigma) * exp(-(square(_lowercase						) + square(_lowercase						)) / (2 * square(_lowercase						))						)
     return g




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			List[str]    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    image.shape[0], image.shape[1]
     # dst image height and width
     __UpperCAmelCase			:				Any													=    height - k_size + 1
     __UpperCAmelCase			:				str													=    width - k_size + 1

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

     #  turn the kernel into shape(k*k, 1)
     __UpperCAmelCase			:				str													=    gen_gaussian_kernel(_lowercase    ,      _lowercase						)
     __UpperCAmelCase			:				Union[str, Any]													=    ravel(_lowercase						)

     # reshape and get the dst image
     __UpperCAmelCase			:				Optional[int]													=    dot(_lowercase    ,      _lowercase						).reshape(_lowercase    ,      _lowercase						).astype(_lowercase						)

     return dst


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

      # get values with two different mask size
      __UpperCAmelCase    :Tuple				       =     gaussian_filter(gray, 3, sigma=1)
      __UpperCAmelCase    :Optional[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()