





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       SCREAMING_SNAKE_CASE__  (       _lowercase     : str   ,    _lowercase     : Union[str, Any]			)   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__					:      Optional[Any]      =		k_size // 2
		lowercase__   ,				lowercase__					:      Tuple      =		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		lowercase__					:      int      =		1 / (2 * pi * sigma) * exp(-(square(_lowercase			) + square(_lowercase			)) / (2 * square(_lowercase			))			)
		return g


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[str]   ,    _lowercase     : Optional[int]   ,    _lowercase     : str			)   ->							Optional[Any]:




		'''simple docstring'''
		lowercase__   ,				lowercase__					:      List[str]      =		image.shape[0], image.shape[1]
		# dst image height and width
		lowercase__					:      Tuple      =		height - k_size + 1
		lowercase__					:      int      =		width - k_size + 1

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

		#  turn the kernel into shape(k*k, 1)
		lowercase__					:      Tuple      =		gen_gaussian_kernel(_lowercase   ,    _lowercase			)
		lowercase__					:      int      =		ravel(_lowercase			)

		# reshape and get the dst image
		lowercase__					:      Optional[Any]      =		dot(_lowercase   ,    _lowercase			).reshape(_lowercase   ,    _lowercase			).astype(_lowercase			)

		return dst


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

			# get values with two different mask size
			__UpperCamelCase:	Optional[int] = gaussian_filter(gray, 3, sigma=1)
			__UpperCamelCase:	List[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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


