


'''simple docstring'''



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    Tuple							)					->		Any:
     __lowerCamelCase    ,	__lowerCamelCase       :     List[Any]        =		image.shape
     __lowerCamelCase       :     Tuple        =		cols - block_size[1] + 1
     __lowerCamelCase       :     Union[str, Any]        =		rows - block_size[0] + 1
     __lowerCamelCase       :     Tuple        =		zeros((dst_height * dst_width, block_size[1] * block_size[0])							)
     __lowerCamelCase       :     int        =		0
     for i in range(0				,   UpperCAmelCase_							):
          for j in range(0				,   UpperCAmelCase_							):
               __lowerCamelCase       :     List[str]        =		ravel(image[i : i + block_size[0], j : j + block_size[1]]							)
               __lowerCamelCase       :     Optional[int]        =		window
               row += 1

     return image_array



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Any				,   UpperCAmelCase_   :    List[Any]							)					->		str:
     __lowerCamelCase    ,	__lowerCamelCase       :     int        =		image.shape[0], image.shape[1]
     __lowerCamelCase       :     Dict        =		filter_kernel.shape[0]
     __lowerCamelCase       :     Dict        =		k_size // 2
     # Pads image with the edge values of array.
     __lowerCamelCase       :     Union[str, Any]        =		pad(UpperCAmelCase_				,   UpperCAmelCase_				,   mode='edge'							)

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __lowerCamelCase       :     List[str]        =		imacol(UpperCAmelCase_				,   (k_size, k_size)							)

     #  turn the kernel into shape(k*k, 1)
     __lowerCamelCase       :     List[str]        =		ravel(UpperCAmelCase_							)
     # reshape and get the dst image
     __lowerCamelCase       :     Dict        =		dot(UpperCAmelCase_				,   UpperCAmelCase_							).reshape(UpperCAmelCase_				,   UpperCAmelCase_							)
     return dst


if __name__ == "__main__":
      # read original image
      A__		: Optional[int]												=  imread(R"""../image_data/lena.jpg""")
      # turn image in gray scale value
      A__		: Optional[int]												=  cvtColor(img, COLOR_BGR2GRAY)
      # Laplace operator
      A__		: str												=  array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
      A__		: List[Any]												=  img_convolve(gray, Laplace_kernel).astype(uinta)
      imshow("""Laplacian""", out)
      waitKey(0)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__		: str												=  {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: List[str]												=  [
          """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """HubertForCTC""",
          """HubertForSequenceClassification""",
          """HubertModel""",
          """HubertPreTrainedModel""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: Optional[int]												=  [
          """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFHubertForCTC""",
          """TFHubertModel""",
          """TFHubertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_hubert import (
                HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                HubertForCTC,
                HubertForSequenceClassification,
                HubertModel,
                HubertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_hubert import (
                TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFHubertForCTC,
                TFHubertModel,
                TFHubertPreTrainedModel,
            )


else:
      import sys

      A__		: Optional[int]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






