code: 
  
  
  from __future__ import annotations
  
  from PIL import Image
  
  # Define the first generation of cells
  # fmt: off
  A_    :					Any             =[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return [int(UpperCAmelCase__      ) for c in F"{ruleset:08}"[:8]]
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		len(cells[0]      )  # 31
  						a_					=		[]
  						for i in range(UpperCAmelCase__      ):
  												# Get the neighbors of each cell
  												# Handle neighbours outside bounds by using 0 as their value
  												a_					=		0 if i == 0 else cells[time][i - 1]
  												a_					=		0 if i == population - 1 else cells[time][i + 1]
  												# Define a new cell and add it to the new generation
  												a_					=		7 - int(F"{left_neighbor}{cells[time][i]}{right_neighbor}"       ,    2      )
  												next_generation.append(rule[situation]      )
  						return next_generation
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		Image.new("""RGB"""       ,    (len(cells[0]      ), len(UpperCAmelCase__      ))      )
  						a_					=		img.load()
  						# Generates image
  						for w in range(img.width      ):
  												for h in range(img.height      ):
  																		a_					=		255 - int(255 * cells[h][w]      )
  																		a_					=		(color, color, color)
  						return img
  
  
  if __name__ == "__main__":
  	A_    :					Union[str, Any]             =bin(int(input("""Rule:\n""").strip()))[2:]
  	A_    :					Any             =format_ruleset(int(rule_num))
  	for time in range(16):
  		CELLS.append(new_generation(CELLS, rule, time))
  	A_    :					Union[str, Any]             =generate_image(CELLS)
  	# Uncomment to save the image
  	# img.save(f"rule_{rule_num}.png")
  	img.show()
style_context: 
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		MPNetTokenizer
  							snake_case_        =		MPNetTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		True
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
  																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		"""UNwant\u00E9d,running"""
  													a_					=		"""unwanted, running"""
  													return input_text, output_text
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class(self.vocab_file		)
  
  													a_					=		tokenizer.tokenize("""UNwant\u00E9d,running"""		)
  													self.assertListEqual(_UpperCAmelCase			,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
  													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)			,    [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)
  
  													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
  													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)
  
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													assert encoded_sentence == [0] + text + [2]
  													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]