
"""simple docstring"""





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_  (__a					, __a					):

					'''simple docstring'''





					A_								=    k_size // 2
					A_			,     A_								=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					A_								=    1 / (2 * pi * sigma) * exp(-(square(__a					) + square(__a					)) / (2 * square(__a					))					)
					return g
def 				A_  (__a					, __a					, __a					):

					'''simple docstring'''





					A_			,     A_								=    image.shape[0], image.shape[1]
					# dst image height and width
					A_								=    height - k_size + 1
					A_								=    width - k_size + 1

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

					#  turn the kernel into shape(k*k, 1)
					A_								=    gen_gaussian_kernel(__a					, __a					)
					A_								=    ravel(__a					)

					# reshape and get the dst image
					A_								=    dot(__a					, __a					).reshape(__a					, __a					).astype(__a					)

					return dst


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

		# get values with two different mask size
		UpperCamelCase_       :    str						      =			gaussian_filter(gray, 3, sigma=1)
		UpperCamelCase_       :    str						      =			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()






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_       :    Dict						      =			{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    int						      =			['''PLBartTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Optional[int]						      =			[
		    '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PLBartForCausalLM''',
		    '''PLBartForConditionalGeneration''',
		    '''PLBartForSequenceClassification''',
		    '''PLBartModel''',
		    '''PLBartPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_plbart import PLBartTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_plbart import (
				    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PLBartForCausalLM,
				    PLBartForConditionalGeneration,
				    PLBartForSequenceClassification,
				    PLBartModel,
				    PLBartPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_       :    Optional[int]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




