




'''simple docstring'''

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




def    snake_case__     (  a       ,      a	)			->       Optional[Any]:





			'''simple docstring'''






			snake_case__    ,			snake_case__          =						image.shape
			snake_case__          =						cols - block_size[1] + 1
			snake_case__          =						rows - block_size[0] + 1
			snake_case__          =						zeros((dst_height * dst_width, block_size[1] * block_size[0])	)
			snake_case__          =						0
			for i in range(0       ,      a	):
						for j in range(0       ,      a	):
									snake_case__          =						ravel(image[i : i + block_size[0], j : j + block_size[1]]	)
									snake_case__          =						window
									row += 1

			return image_array




def    snake_case__     (  a       ,      a	)			->       Any:





			'''simple docstring'''






			snake_case__    ,			snake_case__          =						image.shape[0], image.shape[1]
			snake_case__          =						filter_kernel.shape[0]
			snake_case__          =						k_size // 2
			# Pads image with the edge values of array.
			snake_case__          =						pad(a       ,      a       ,      mode="""edge"""	)

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

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


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

from typing import TYPE_CHECKING

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


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

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