import random
import sys

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

UpperCAmelCase_								=							'''Usage of script: script_name <size_of_canvas:int>'''

UpperCAmelCase_								=							[0] * 100 + [1] * 10
random.shuffle(choice)

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       _A=	[[False for i in range(lowerCamelCase_   )] for j in range(lowerCamelCase_   )]
       return canvas

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       for i, row in enumerate(lowerCamelCase_   ):
              for j, _ in enumerate(lowerCamelCase_   ):
                     _A=	bool(random.getrandbits(1   )   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       _A=	np.array(lowerCamelCase_   )
       _A=	np.array(create_canvas(current_canvas.shape[0]   )   )
       for r, row in enumerate(lowerCamelCase_   ):
              for c, pt in enumerate(lowerCamelCase_   ):
                     _A=	__judge_point(
                         lowerCamelCase_       ,					current_canvas[r - 1 : r + 2, c - 1 : c + 2]   )

       _A=	next_gen_canvas
       del next_gen_canvas  # cleaning memory as we move on.
       _A=	current_canvas.tolist()
       return return_canvas

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       _A=	0
       _A=	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.
       _A=	pt
       if pt:
              if alive < 2:
                     _A=	False
              elif alive == 2 or alive == 3:
                     _A=	True
              elif alive > 3:
                     _A=	False
       else:
              if alive == 3:
                     _A=	True

       return state


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

      UpperCAmelCase_								=							int(sys.argv[1])
      # main working structure of this module.
      UpperCAmelCase_								=							create_canvas(canvas_size)
      seed(c)
      UpperCAmelCase_		,    UpperCAmelCase_								=							plt.subplots()
      fig.show()
      UpperCAmelCase_								=							ListedColormap(['''w''', '''k'''])
      try:
            while True:
                  UpperCAmelCase_								=							run(c)
                  ax.matshow(c, cmap=cmap)
                  fig.canvas.draw()
                  ax.cla()
      except KeyboardInterrupt:
            # do nothing.
            pass
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.nn.Linear(10				,      10       )
            _A=	torch.optim.SGD(model.parameters()				,      0.1       )
            _A=	Accelerator()
            _A=	accelerator.prepare(lowerCAmelCase__       )
            try:
                   pickle.loads(pickle.dumps(lowerCAmelCase__       )       )
            except Exception as e:
                   self.fail(f"Accelerated optimizer pickling failed with {e}"       )
            AcceleratorState._reset_state()