

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       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    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(_SCREAMING_SNAKE_CASE       ) + square(_SCREAMING_SNAKE_CASE       )) / (2 * square(_SCREAMING_SNAKE_CASE       ))       )
    return g


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    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(_SCREAMING_SNAKE_CASE       ) ,   range(_SCREAMING_SNAKE_CASE       )       ):
        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(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						ravel(_SCREAMING_SNAKE_CASE       )

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

    return dst


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

       # get values with two different mask size
       UpperCamelCase__  :      Dict			=    gaussian_filter(gray, 3, sigma=1)
       UpperCamelCase__  :      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()





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


UpperCamelCase__  :      str			=    logging.get_logger(__name__)

UpperCamelCase__  :      Optional[int]			=    {
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}



class       __snake_case  (    lowerCAmelCase__       ):
  __lowerCAmelCase     :	Optional[int]						 =  'dpr'

  def __init__(					self						,		_A=30522						,		_A=768						,		_A=12						,		_A=12						,		_A=3072						,		_A="gelu"						,		_A=0.1						,		_A=0.1						,		_A=512						,		_A=2						,		_A=0.0_2						,		_A=1E-12						,		_A=0						,		_A="absolute"						,		_A = 0						,		**_A						,		):
      super().__init__(pad_token_id=_A						,		**_A)

      SCREAMING_SNAKE_CASE_									=						vocab_size
      SCREAMING_SNAKE_CASE_									=						hidden_size
      SCREAMING_SNAKE_CASE_									=						num_hidden_layers
      SCREAMING_SNAKE_CASE_									=						num_attention_heads
      SCREAMING_SNAKE_CASE_									=						hidden_act
      SCREAMING_SNAKE_CASE_									=						intermediate_size
      SCREAMING_SNAKE_CASE_									=						hidden_dropout_prob
      SCREAMING_SNAKE_CASE_									=						attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_									=						max_position_embeddings
      SCREAMING_SNAKE_CASE_									=						type_vocab_size
      SCREAMING_SNAKE_CASE_									=						initializer_range
      SCREAMING_SNAKE_CASE_									=						layer_norm_eps
      SCREAMING_SNAKE_CASE_									=						projection_dim
      SCREAMING_SNAKE_CASE_									=						position_embedding_type


