


import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):

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

 # each value
 for y in range(SCREAMING_SNAKE_CASE__						):
  for x in range(SCREAMING_SNAKE_CASE__						):
   # distance from center
   __lowerCamelCase		:							Optional[int]		 =						x - ksize // 2
   __lowerCamelCase		:							Union[str, Any]		 =						y - ksize // 2

   # degree to radiant
   __lowerCamelCase		:							List[str]		 =						theta / 180 * np.pi
   __lowerCamelCase		:							Union[str, Any]		 =						np.cos(_theta						)
   __lowerCamelCase		:							Any		 =						np.sin(_theta						)

   # get kernel x
   __lowerCamelCase		:							List[Any]		 =						cos_theta * px + sin_theta * py

   # get kernel y
   __lowerCamelCase		:							Optional[int]		 =						-sin_theta * px + cos_theta * py

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

       # Apply multiple Kernel to detect edges
       lowercase_	   =     np.zeros(gray.shape[:2])
       for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
              lowercase_	   =     gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
              out += filteraD(gray, CV_8UC3, kernel_aa)
       lowercase_	   =     out / out.max() * 2_5_5
       lowercase_	   =     out.astype(np.uinta)

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

       waitKey(0)





import datasets


lowercase_	   =     '\\n@InProceedings{conneau2018xnli,\n  author = "Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin",\n  title = "XNLI: Evaluating Cross-lingual Sentence Representations",\n  booktitle = "Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing",\n  year = "2018",\n  publisher = "Association for Computational Linguistics",\n  location = "Brussels, Belgium",\n}\n'

lowercase_	   =     '\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'

lowercase_	   =     '\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    \'accuracy\': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric("xnli")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return (preds == labels).mean()



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,				_KWARGS_DESCRIPTION   )
class 	A_ (	datasets.Metric   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Union[str, Any]    ):
       return datasets.MetricInfo(
           description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
               {
                   'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'    ),
                   'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'    ),
               }    )						,				codebase_urls=[]						,				reference_urls=[]						,				format='numpy'						,				)




      def 					_snake_case     (    self:	int						,				a:	Optional[Any]						,				a:	Optional[Any]    ):
       return {"accuracy": simple_accuracy(a						,				a    )}

