






'''simple docstring'''





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 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

    def __init__(					self     :     Dict				,     __lowerCamelCase     :     List[str]     )							->							Optional[int]:

     '''simple docstring'''




     lowerCamelCase__  =     parent




    def    a__				(					self     :     List[Any]     )							->							List[Any]:

     '''simple docstring'''




     return {}






def    lowerCamelCase_     (    )     ->   str:
 lowerCamelCase__  =     "<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>"

 lowerCamelCase__  =     "\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 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							MarkupLMFeatureExtractor if is_bsa_available() else None

    def    a__				(					self     :     List[Any]     )							->							Any:

     '''simple docstring'''




     lowerCamelCase__  =     MarkupLMFeatureExtractionTester(self     )

    @property
    def    a__				(					self     :     List[Any]     )							->							Optional[int]:

     '''simple docstring'''




     return self.feature_extract_tester.prepare_feat_extract_dict()




    def    a__				(					self     :     List[Any]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     self.feature_extraction_class()

     # Test not batched input
     lowerCamelCase__  =     get_html_strings()[0]
     lowerCamelCase__  =     feature_extractor(__lowerCamelCase     )

     # fmt: off
     lowerCamelCase__  =     [["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"]]
     lowerCamelCase__  =     [["/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				,     __lowerCamelCase     )
     self.assertEqual(encoding.xpaths				,     __lowerCamelCase     )

     # Test batched
     lowerCamelCase__  =     get_html_strings()
     lowerCamelCase__  =     feature_extractor(__lowerCamelCase     )

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

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

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









'''simple docstring'''





from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     cva.getAffineTransform(lowercase__				,     lowercase__)
 return cva.warpAffine(lowercase__				,     lowercase__				,     (rows, cols))


if __name__ == "__main__":
 # read original image
 __A							:							Any  							=		cva.imread(
     str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
 )
 # turn image in gray scale value
 __A							:							int  							=		cva.cvtColor(image, cva.COLOR_BGR2GRAY)
 # get image shape
 __A							,			__A							:							Dict  							=		gray_img.shape

 # set different points to rotate image
 __A							:							int  							=		np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
 __A							:							Any  							=		np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
 __A							:							str  							=		np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
 __A							:							Optional[int]  							=		np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

 # add all rotated images in a list
 __A							:							List[Any]  							=		[
     gray_img,
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
     get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
 ]

 # plot different image rotations
 __A							:							int  							=		plt.figure(1)
 __A							:							int  							=		["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
 for i, image in enumerate(images):
  plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
  plt.title(titles[i])
  plt.axis("""off""")
  plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
 plt.show()

