



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






def     a(      A       :    int ,     A       :    int ,     A       :    int ,     A       :    int ,     A       :    int ,     A       :    int       )		->       np.ndarray:
					"""simple docstring"""





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

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

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

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

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

															# fill kernel
															a					     =   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: int      			=		imread("../image_data/lena.jpg")
				# turn image in gray scale value
				_lowercase: List[str]      			=		cvtColor(img, COLOR_BGR2GRAY)

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

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

				waitKey(0)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase: Union[str, Any]      			=		{
    "configuration_bridgetower": [
        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BridgeTowerConfig",
        "BridgeTowerTextConfig",
        "BridgeTowerVisionConfig",
    ],
    "processing_bridgetower": ["BridgeTowerProcessor"],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: Dict      			=		["BridgeTowerImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase: int      			=		[
				    "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BridgeTowerForContrastiveLearning",
				    "BridgeTowerForImageAndTextRetrieval",
				    "BridgeTowerForMaskedLM",
				    "BridgeTowerModel",
				    "BridgeTowerPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_bridgetower import (
				    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BridgeTowerConfig,
				    BridgeTowerTextConfig,
				    BridgeTowerVisionConfig,
				)
				from .processing_bridgetower import BridgeTowerProcessor

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_bridgetower import BridgeTowerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bridgetower import (
								    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BridgeTowerForContrastiveLearning,
								    BridgeTowerForImageAndTextRetrieval,
								    BridgeTowerForMaskedLM,
								    BridgeTowerModel,
								    BridgeTowerPreTrainedModel,
								)


else:
				import sys

				_lowercase: Optional[Any]      			=		_LazyModule(__name__, globals()["__file__"], _import_structure)
