'''simple docstring'''




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



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

 return image_array



def 						__snake_case (       lowerCAmelCase       :     List[str]      ,  lowerCAmelCase       :     str			):
 __UpperCAmelCase  ,			__UpperCAmelCase         =					image.shape[0], image.shape[1]
 __UpperCAmelCase         =					filter_kernel.shape[0]
 __UpperCAmelCase         =					k_size // 2
 # Pads image with the edge values of array.
 __UpperCAmelCase         =					pad(lowerCAmelCase      ,  lowerCAmelCase      ,  mode='edge'			)

 # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
 __UpperCAmelCase         =					imacol(lowerCAmelCase      ,  (k_size, k_size)			)

 #  turn the kernel into shape(k*k, 1)
 __UpperCAmelCase         =					ravel(lowerCAmelCase			)
 # reshape and get the dst image
 __UpperCAmelCase         =					dot(lowerCAmelCase      ,  lowerCAmelCase			).reshape(lowerCAmelCase      ,  lowerCAmelCase			)
 return dst


if __name__ == "__main__":
     # read original image
     _UpperCamelCase				:		Union[str, Any]       			=		imread(R'../image_data/lena.jpg')
     # turn image in gray scale value
     _UpperCamelCase				:		Optional[int]       			=		cvtColor(img, COLOR_BGR2GRAY)
     # Laplace operator
     _UpperCamelCase				:		int       			=		array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
     _UpperCamelCase				:		Tuple       			=		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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



