





from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[str]  						=   k_size // 2
				_snake_case						,			_snake_case :			List[Any]  						=   mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				_snake_case :			int  						=   1 / (2 * pi * sigma) * exp(-(square(__lowerCAmelCase ) + square(__lowerCAmelCase )) / (2 * square(__lowerCAmelCase )) )
				return g

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case						,			_snake_case :			Optional[Any]  						=   image.shape[0], image.shape[1]
				# dst image height and width
				_snake_case :			int  						=   height - k_size + 1
				_snake_case :			Optional[Any]  						=   width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				_snake_case :			Union[str, Any]  						=   zeros((dst_height * dst_width, k_size * k_size) )
				_snake_case :			int  						=   0
				for i, j in product(range(__lowerCAmelCase )	,			range(__lowerCAmelCase ) ):
								_snake_case :			Optional[Any]  						=   ravel(image[i : i + k_size, j : j + k_size] )
								_snake_case :			List[str]  						=   window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				_snake_case :			List[str]  						=   gen_gaussian_kernel(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   ravel(__lowerCAmelCase )

				# reshape and get the dst image
				_snake_case :			Any  						=   dot(__lowerCAmelCase	,			__lowerCAmelCase ).reshape(__lowerCAmelCase	,			__lowerCAmelCase ).astype(__lowerCAmelCase )

				return dst


if __name__ == "__main__":
		# read original image
		lowercase_		:				List[Any]				=						imread(r'''../image_data/lena.jpg''')
		# turn image in gray scale value
		lowercase_		:				Tuple				=						cvtColor(img, COLOR_BGR2GRAY)

		# get values with two different mask size
		lowercase_		:				int				=						gaussian_filter(gray, 3, sigma=1)
		lowercase_		:				Union[str, Any]				=						gaussian_filter(gray, 5, sigma=0.8)

		# show result images
		imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
		imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
		waitKey()













from typing import TYPE_CHECKING

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


lowercase_		:				Any				=						{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Optional[Any]				=						[
		    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''CanineForMultipleChoice''',
		    '''CanineForQuestionAnswering''',
		    '''CanineForSequenceClassification''',
		    '''CanineForTokenClassification''',
		    '''CanineLayer''',
		    '''CanineModel''',
		    '''CaninePreTrainedModel''',
		    '''load_tf_weights_in_canine''',
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






