


import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				"Usage of script: script_name <size_of_canvas:int>"

SCREAMING_SNAKE_CASE :   List[Any]							      =				[0] * 100 + [1] * 10
random.shuffle(choice)



def        UpperCamelCase_(   lowerCamelCase_				)					->		Optional[Any]:
    _lowercase :							Dict   	=	[[False for i in range(lowerCamelCase_				)] for j in range(lowerCamelCase_				)]
    return canvas



def        UpperCamelCase_(   lowerCamelCase_				)					->		Union[str, Any]:
    for i, row in enumerate(lowerCamelCase_				):
        for j, _ in enumerate(lowerCamelCase_				):
            _lowercase :							List[str]   	=	bool(random.getrandbits(1				)				)



def        UpperCamelCase_(   lowerCamelCase_				)					->		Union[str, Any]:
    _lowercase :							Dict   	=	np.array(lowerCamelCase_				)
    _lowercase :							Tuple   	=	np.array(create_canvas(current_canvas.shape[0]				)				)
    for r, row in enumerate(lowerCamelCase_				):
        for c, pt in enumerate(lowerCamelCase_				):
            _lowercase :							str   	=	__judge_point(
                lowerCamelCase_							,				current_canvas[r - 1 : r + 2, c - 1 : c + 2]				)

    _lowercase :							List[str]   	=	next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    _lowercase :							str   	=	current_canvas.tolist()
    return return_canvas



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Tuple:
    _lowercase :							Tuple   	=	0
    _lowercase :							List[Any]   	=	0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    _lowercase :							int   	=	pt
    if pt:
        if alive < 2:
            _lowercase :							Tuple   	=	False
        elif alive == 2 or alive == 3:
            _lowercase :							Dict   	=	True
        elif alive > 3:
            _lowercase :							Optional[Any]   	=	False
    else:
        if alive == 3:
            _lowercase :							Optional[int]   	=	True

    return state


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise Exception(usage_doc)

 SCREAMING_SNAKE_CASE :   Dict							      =				int(sys.argv[1])
 # main working structure of this module.
 SCREAMING_SNAKE_CASE :   List[str]							      =				create_canvas(canvas_size)
 seed(c)
 SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE :   Optional[int]							      =				plt.subplots()
 fig.show()
 SCREAMING_SNAKE_CASE :   List[str]							      =				ListedColormap(["w", "k"])
 try:
  while True:
   SCREAMING_SNAKE_CASE :   List[Any]							      =				run(c)
   ax.matshow(c, cmap=cmap)
   fig.canvas.draw()
   ax.cla()
 except KeyboardInterrupt:
  # do nothing.
  pass




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 _lowerCamelCase(		unittest.TestCase     ):
       def __init__(      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	parent




       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           return {}



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							int   	=	'<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>'

    _lowercase :							List[Any]   	=	'\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 _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Tuple									=    MarkupLMFeatureExtractor if is_bsa_available() else None
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Tuple   	=	MarkupLMFeatureExtractionTester(self)
       @property
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           return self.feature_extract_tester.prepare_feat_extract_dict()




       def     UpperCamelCase  (      self) ->     List[str]:




           """simple docstring"""

           _lowercase :							int   	=	self.feature_extraction_class()

           # Test not batched input
           _lowercase :							List[Any]   	=	get_html_strings()[0]
           _lowercase :							Optional[Any]   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							str   	=	[['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']]
           _lowercase :							Dict   	=	[['/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
           _lowercase :							Optional[int]   	=	get_html_strings()
           _lowercase :							Tuple   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							List[Any]   	=	expected_nodes + [['My First Heading', 'My first paragraph.']]
           _lowercase :							List[str]   	=	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)


