
"""simple docstring"""





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


_a														=		logging.get_logger(__name__)

_a														=		{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     _UpperCAmelCase(    lowerCamelCase	):
		lowercase__						=      'fnet'



		def __init__(					self				,		__a=3_20_00				,		__a=7_68				,		__a=12				,		__a=30_72				,		__a="gelu_new"				,		__a=0.1				,		__a=5_12				,		__a=4				,		__a=0.02				,		__a=1e-12				,		__a=False				,		__a=5_12				,		__a=3				,		__a=1				,		__a=2				,		**__a				,		)       ->       List[Any]:






							'''simple docstring'''
							super().__init__(pad_token_id=__a				,		bos_token_id=__a				,		eos_token_id=__a				,		**__a)

							_UpperCamelCase							 =  vocab_size
							_UpperCamelCase							 =  max_position_embeddings
							_UpperCamelCase							 =  hidden_size
							_UpperCamelCase							 =  num_hidden_layers
							_UpperCamelCase							 =  intermediate_size
							_UpperCamelCase							 =  hidden_act
							_UpperCamelCase							 =  hidden_dropout_prob
							_UpperCamelCase							 =  initializer_range
							_UpperCamelCase							 =  type_vocab_size
							_UpperCamelCase							 =  layer_norm_eps
							_UpperCamelCase							 =  use_tpu_fourier_optimizations
							_UpperCamelCase							 =  tpu_short_seq_length








"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

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

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

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

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

															# fill kernel
															_UpperCamelCase							 =  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
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

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

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

				waitKey(0)






