






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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):






   if (ksize % 2) == 0:
      UpperCamelCase_									=     ksize + 1
   UpperCamelCase_									=     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
         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
 snake_case__      :	List[str]       	=      imread("""../image_data/lena.jpg""")
 # turn image in gray scale value
 snake_case__      :	Any       	=      cvtColor(img, COLOR_BGR2GRAY)

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

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

 waitKey(0)













import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
 from transformers import MarkupLMFeatureExtractor


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     parent




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return {}






def     _snake_case    ():
   UpperCamelCase_									=     '<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

   UpperCamelCase_									=     '\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

   return [html_string_a, html_string_a]


@require_bsa
class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   MarkupLMFeatureExtractor if is_bsa_available() else None




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     MarkupLMFeatureExtractionTester(self )




  @property
  def    _UpperCAmelCase     (     self )			-> Any:
     return self.feature_extract_tester.prepare_feat_extract_dict()




  def    _UpperCAmelCase     (     self )			-> Dict:
     # Initialize feature_extractor
     UpperCamelCase_									=     self.feature_extraction_class()

     # Test not batched input
     UpperCamelCase_									=     get_html_strings()[0]
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
     UpperCamelCase_									=     [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
     # fmt: on

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )

     # Test batched
     UpperCamelCase_									=     get_html_strings()
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     expected_nodes + [['My First Heading', 'My first paragraph.']]
     UpperCamelCase_									=     expected_xpaths + [['/html/body/h1', '/html/body/p']]

     self.assertEqual(len(encoding.nodes )		,		2 )
     self.assertEqual(len(encoding.xpaths )		,		2 )

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )





