






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







def       lowercase__	(					A_:						int  ,      A_:						int  ,      A_:						int  ,      A_:						int  ,      A_:						int  ,      A_:						int		)	->				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(A_		):
              for x in range(A_		):
                     # distance from center
                     __UpperCAmelCase     				=x - ksize // 2
                     __UpperCAmelCase     				=y - ksize // 2

                     # degree to radiant
                     __UpperCAmelCase     				=theta / 180 * 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, 1_20, 1_50]:
      __A					=	gabor_filter_kernel(10, 8, theta, 10, 0, 0)
      out += filteraD(gray, CV_8UC3, kernel_aa)
   __A					=	out / out.max() * 2_55
   __A					=	out.astype(np.uinta)

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

   waitKey(0)












"""simple docstring"""
from typing import Dict

from .base import GenericTensor, Pipeline



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	def  lowerCamelCase(self  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if tokenize_kwargs is None:
			A_		:    Dict						     = {}

		if truncation is not None:
			if "truncation" in tokenize_kwargs:
				raise ValueError(
				    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""				)
			A_		:    Optional[Any]						     = truncation

		A_		:    Optional[int]						     = tokenize_kwargs

		A_		:    Optional[Any]						     = {}
		if return_tensors is not None:
			A_		:    Optional[Any]						     = return_tensors

		return preprocess_params, {}, postprocess_params





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				):
		A_		:    List[Any]						     = self.framework
		A_		:    Optional[int]						     = self.tokenizer(lowerCAmelCase_  ,  return_tensors=lowerCAmelCase_  ,  **lowerCAmelCase_				)
		return model_inputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = self.model(**lowerCAmelCase_				)
		return model_outputs





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=False				):
		# [0] is the first available tensor, logits or last_hidden_state.
		if return_tensors:
			return model_outputs[0]
		if self.framework == "pt":
			return model_outputs[0].tolist()
		elif self.framework == "tf":
			return model_outputs[0].numpy().tolist()





	def __call__(self  ,  *lowerCAmelCase_  ,  **lowerCAmelCase_				):
		return super().__call__(*lowerCAmelCase_  ,  **lowerCAmelCase_				)

