




"""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 					lowercase      (    A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  a						:		str											=      k_size // 2
  a						:		Any											=      mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  a						:		str											=      1 / (2 * pi * sigma) * exp(-(square(A_	) + square(A_	)) / (2 * square(A_	))	)
  return g


def 					lowercase      (    A_  ,       A_  ,       A_	)->				List[str]:
  '''simple docstring'''







  a						:		Tuple											=      image.shape[0], image.shape[1]
  # dst image height and width
  a						:		Optional[Any]											=      height - k_size + 1
  a						:		Dict											=      width - k_size + 1

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

  #  turn the kernel into shape(k*k, 1)
  a						:		str											=      gen_gaussian_kernel(A_  ,       A_	)
  a						:		Tuple											=      ravel(A_	)

  # reshape and get the dst image
  a						:		List[Any]											=      dot(A_  ,       A_	).reshape(A_  ,       A_	).astype(A_	)

  return dst


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

 # get values with two different mask size
 __lowercase    =				gaussian_filter(gray, 3, sigma=1)
 __lowercase    =				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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase    =				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowercase    =				[
     """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """InstructBlipQFormerModel""",
     """InstructBlipPreTrainedModel""",
     """InstructBlipForConditionalGeneration""",
     """InstructBlipVisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_instructblip import (
     INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
     InstructBlipConfig,
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
 from .processing_instructblip import InstructBlipProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_instructblip import (
      INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
      InstructBlipForConditionalGeneration,
      InstructBlipPreTrainedModel,
      InstructBlipQFormerModel,
      InstructBlipVisionModel,
  )

else:
 import sys

 __lowercase    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






