





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



def 							lowerCAmelCase_				(    A_  ,A_):
    UpperCamelCase__:					Union[str, Any]												=      image.shape
    UpperCamelCase__:					Union[str, Any]												=      cols - block_size[1] + 1
    UpperCamelCase__:					str												=      rows - block_size[0] + 1
    UpperCamelCase__:					Optional[Any]												=      zeros((dst_height * dst_width, block_size[1] * block_size[0]))
    UpperCamelCase__:					Optional[Any]												=      0
    for i in range(0  ,A_):
        for j in range(0  ,A_):
            UpperCamelCase__:					Union[str, Any]												=      ravel(image[i : i + block_size[0], j : j + block_size[1]])
            UpperCamelCase__:					Optional[Any]												=      window
            row += 1

    return image_array



def 							lowerCAmelCase_				(    A_  ,A_):
    UpperCamelCase__:					List[str]												=      image.shape[0], image.shape[1]
    UpperCamelCase__:					Tuple												=      filter_kernel.shape[0]
    UpperCamelCase__:					List[str]												=      k_size // 2
    # Pads image with the edge values of array.
    UpperCamelCase__:					int												=      pad(A_  ,A_  ,mode="edge")

    # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
    UpperCamelCase__:					int												=      imacol(A_  ,(k_size, k_size))

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


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__:  List[str]       					=					{
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:  int       					=					[
       '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ConformerForAudioFrameClassification''',
       '''Wav2Vec2ConformerForCTC''',
       '''Wav2Vec2ConformerForPreTraining''',
       '''Wav2Vec2ConformerForSequenceClassification''',
       '''Wav2Vec2ConformerForXVector''',
       '''Wav2Vec2ConformerModel''',
       '''Wav2Vec2ConformerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   A__:  Dict       					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

