





import random
import sys

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

_lowerCAmelCase				:   Optional[Any]						       =						'''Usage of script: script_name <size_of_canvas:int>'''

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

def   __snake_case    (       _lowerCAmelCase			:			int						)			->    list[list[bool]]:
   A_       :					Union[str, Any]        =      [[False for i in range(_lowerCAmelCase						)] for j in range(_lowerCAmelCase						)]
   return canvas

def   __snake_case    (       _lowerCAmelCase			:			list[list[bool]]						)			->    None:
   for i, row in enumerate(_lowerCAmelCase						):
      for j, _ in enumerate(_lowerCAmelCase						):
         A_       :					str        =      bool(random.getrandbits(1						)						)

def   __snake_case    (       _lowerCAmelCase			:			list[list[bool]]						)			->    list[list[bool]]:
   A_       :					str        =      np.array(_lowerCAmelCase						)
   A_       :					Tuple        =      np.array(create_canvas(current_canvas.shape[0]						)						)
   for r, row in enumerate(_lowerCAmelCase						):
      for c, pt in enumerate(_lowerCAmelCase						):
         A_       :					Optional[Any]        =      __judge_point(
             _lowerCAmelCase					,					current_canvas[r - 1 : r + 2, c - 1 : c + 2]						)

   A_       :					List[Any]        =      next_gen_canvas
   del next_gen_canvas  # cleaning memory as we move on.
   A_       :					list[list[bool]]        =      current_canvas.tolist()
   return return_canvas

def   __snake_case    (       _lowerCAmelCase			:			bool					,					_lowerCAmelCase			:			list[list[bool]]						)			->    bool:
   A_       :					List[str]        =      0
   A_       :					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.
   A_       :					Dict        =      pt
   if pt:
      if alive < 2:
         A_       :					List[Any]        =      False
      elif alive == 2 or alive == 3:
         A_       :					Union[str, Any]        =      True
      elif alive > 3:
         A_       :					Optional[int]        =      False
   else:
      if alive == 3:
         A_       :					Optional[int]        =      True

   return state


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

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







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCAmelCase				:   Any						       =						argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
_lowerCAmelCase				:   Tuple						       =						parser.parse_args()


_lowerCAmelCase				:   Any						       =						'''cpu'''
_lowerCAmelCase				:   Dict						       =						'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

_lowerCAmelCase				:   Union[str, Any]						       =						'''path-to-your-trained-model'''
_lowerCAmelCase				:   Tuple						       =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
 _lowerCAmelCase				:   List[str]						       =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowerCAmelCase				:   List[Any]						       =						pipe.to(device)

# to channels last
_lowerCAmelCase				:   Any						       =						pipe.unet.to(memory_format=torch.channels_last)
_lowerCAmelCase				:   int						       =						pipe.vae.to(memory_format=torch.channels_last)
_lowerCAmelCase				:   Optional[Any]						       =						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
 _lowerCAmelCase				:   Optional[int]						       =						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_lowerCAmelCase				:   Tuple						       =						torch.randn(2, 4, 64, 64)
_lowerCAmelCase				:   List[str]						       =						torch.rand(1) * 999
_lowerCAmelCase				:   List[Any]						       =						torch.randn(2, 77, 768)
_lowerCAmelCase				:   int						       =						(sample, timestep, encoder_hidden_status)
try:
 _lowerCAmelCase				:   Tuple						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
 _lowerCAmelCase				:   Optional[Any]						       =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowerCAmelCase				:   Dict						       =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
 _lowerCAmelCase				:   Tuple						       =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowerCAmelCase				:   Dict						       =						666
_lowerCAmelCase				:   Tuple						       =						torch.Generator(device).manual_seed(seed)
_lowerCAmelCase				:   Tuple						       =						{'''generator''': generator}
if args.steps is not None:
 _lowerCAmelCase				:   Tuple						       =						args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
 _lowerCAmelCase				:   int						       =						pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')
