'''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       lowerCAmelCase_							(   snake_case_			:					Optional[int]   ,       snake_case_			:					List[Any]     )       -> Optional[int]:


     '''simple docstring'''




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





def       lowerCAmelCase_							(   snake_case_			:					Any   ,       snake_case_			:					str   ,       snake_case_			:					Optional[Any]     )       -> List[str]:


     '''simple docstring'''




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

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

     #  turn the kernel into shape(k*k, 1)
     UpperCAmelCase_			       =  gen_gaussian_kernel(snake_case_   ,       snake_case_     )
     UpperCAmelCase_			       =  ravel(snake_case_     )

     # reshape and get the dst image
     UpperCAmelCase_			       =  dot(snake_case_   ,       snake_case_     ).reshape(snake_case_   ,       snake_case_     ).astype(snake_case_     )

     return dst


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

    # get values with two different mask size
    SCREAMING_SNAKE_CASE_:	int    =gaussian_filter(gray, 3, sigma=1)
    SCREAMING_SNAKE_CASE_:	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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "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   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""dpr"""



					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						Dict=30_522      ,       _lowercase     :						Tuple=768      ,       _lowercase     :						int=12      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Union[str, Any]=3_072      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Dict=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						Union[str, Any]=512      ,       _lowercase     :						Optional[int]=2      ,       _lowercase     :						Union[str, Any]=0.02      ,       _lowercase     :						List[Any]=1E-12      ,       _lowercase     :						List[str]=0      ,       _lowercase     :						Dict="absolute"      ,       _lowercase     :						int = 0      ,       **_lowercase     :						int      ,       )    ->							int:
								super().__init__(pad_token_id=_lowercase      ,       **_lowercase)

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




