



'''simple docstring'''


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







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      int						):

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

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

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

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

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

																		# fill kernel
																		lowercase        =							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
						lowercase_      :				List[Any]   					=    imread('''../image_data/lena.jpg''')
						# turn image in gray scale value
						lowercase_      :				str   					=    cvtColor(img, COLOR_BGR2GRAY)

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


lowercase_      :				Any   					=    logging.get_logger(__name__)

lowercase_      :				str   					=    {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''vit_msn'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-06 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    List[Any]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias


