



"""simple docstring"""




import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							__a    ):

		# prepare kernel
		# the kernel size have to be odd
		if (ksize % 2) == 0:
				SCREAMING_SNAKE_CASE_						 = ksize + 1
		SCREAMING_SNAKE_CASE_						 = np.zeros((ksize, ksize),							dtype=np.floataa    )

		# each value
		for y in range(__a    ):
				for x in range(__a    ):
						# distance from center
						SCREAMING_SNAKE_CASE_						 = x - ksize // 2
						SCREAMING_SNAKE_CASE_						 = y - ksize // 2

						# degree to radiant
						SCREAMING_SNAKE_CASE_						 = theta / 180 * np.pi
						SCREAMING_SNAKE_CASE_						 = np.cos(_theta    )
						SCREAMING_SNAKE_CASE_						 = np.sin(_theta    )

						# get kernel x
						SCREAMING_SNAKE_CASE_						 = cos_theta * px + sin_theta * py

						# get kernel y
						SCREAMING_SNAKE_CASE_						 = -sin_theta * px + cos_theta * py

						# fill kernel
						SCREAMING_SNAKE_CASE_						 = np.exp(
						    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)    ) * np.cos(2 * np.pi * _x / lambd + psi    )

		return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						lowerCAmelCase__							=				imread('../image_data/lena.jpg')
						# turn image in gray scale value
						lowerCAmelCase__							=				cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						lowerCAmelCase__							=				np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												lowerCAmelCase__							=				gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						lowerCAmelCase__							=				out / out.max() * 255
						lowerCAmelCase__							=				out.astype(np.uinta)

						imshow('Original', gray)
						imshow('Gabor filter with 20x20 mask and 6 directions', out)

						waitKey(0)

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index