


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  (				SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



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

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







 """simple docstring"""



 UpperCamelCase__       ,				UpperCamelCase__					:    Dict          =   image.shape[0], image.shape[1]
 # dst image height and width
 UpperCamelCase__					:    Dict          =   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__					:    Any          =   zeros((dst_height * dst_width, k_size * k_size)     )
 UpperCamelCase__					:    Dict          =   0
 for i, j in product(range(SCREAMING_SNAKE_CASE     )    , range(SCREAMING_SNAKE_CASE     )     ):
  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__					:    Optional[Any]          =   gen_gaussian_kernel(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Any          =   ravel(SCREAMING_SNAKE_CASE     )

 # reshape and get the dst image
 UpperCamelCase__					:    List[Any]          =   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       :       Optional[Any]						       =				imread(r"../image_data/lena.jpg")
       # turn image in gray scale value
       __UpperCamelCase       :       Dict						       =				cvtColor(img, COLOR_BGR2GRAY)

       # get values with two different mask size
       __UpperCamelCase       :       List[Any]						       =				gaussian_filter(gray, 3, sigma=1)
       __UpperCamelCase       :       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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__UpperCamelCase       :       Dict						       =				{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       __UpperCamelCase       :       Tuple						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


