


import random
import sys

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

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

__snake_case   							=						[0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)






def 		_A		(		_lowercase )					-> list[list[bool]]:


     """simple docstring"""


     __UpperCamelCase =			[[False for i in range(_lowercase )] for j in range(_lowercase )]
     return canvas






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


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






def 		_A		(		_lowercase )					-> list[list[bool]]:


     """simple docstring"""


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

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






def 		_A		(		_lowercase					,  _lowercase )					-> bool:


     """simple docstring"""


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

     return state


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

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






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()

    parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

    parser.add_argument(
        '''--txt2img_unclip''',
        default='''kakaobrain/karlo-v1-alpha''',
        type=str,
        required=False,
        help='''The pretrained txt2img unclip.''',
    )

    __snake_case   							=						parser.parse_args()

    __snake_case   							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

    __snake_case   							=						CLIPImageProcessor()
    __snake_case   							=						CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

    __snake_case   							=						UnCLIPImageVariationPipeline(
        decoder=txtaimg.decoder,
        text_encoder=txtaimg.text_encoder,
        tokenizer=txtaimg.tokenizer,
        text_proj=txtaimg.text_proj,
        feature_extractor=feature_extractor,
        image_encoder=image_encoder,
        super_res_first=txtaimg.super_res_first,
        super_res_last=txtaimg.super_res_last,
        decoder_scheduler=txtaimg.decoder_scheduler,
        super_res_scheduler=txtaimg.super_res_scheduler,
    )

    imgaimg.save_pretrained(args.dump_path)


