"""simple docstring"""






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

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

						return image_array

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Dict		,			_SCREAMING_SNAKE_CASE	:Union[str, Any]  )				-> int:
						a_		,					a_							:			Optional[int]     		=					image.shape[0], image.shape[1]
						a_							:			List[Any]     		=					filter_kernel.shape[0]
						a_							:			Tuple     		=					k_size // 2
						# Pads image with the edge values of array.
						a_							:			Optional[int]     		=					pad(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			mode="edge"  )

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

						#  turn the kernel into shape(k*k, 1)
						a_							:			Optional[Any]     		=					ravel(_SCREAMING_SNAKE_CASE  )
						# reshape and get the dst image
						a_							:			List[Any]     		=					dot(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  ).reshape(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
						return dst


if __name__ == "__main__":
				# read original image
				UpperCamelCase     					=     imread(R'../image_data/lena.jpg')
				# turn image in gray scale value
				UpperCamelCase     					=     cvtColor(img, COLOR_BGR2GRAY)
				# Laplace operator
				UpperCamelCase     					=     array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
				UpperCamelCase     					=     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_torch_available, is_vision_available


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




