



'''simple docstring'''



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowerCamelCase	 =       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	 =       parser.parse_args()


lowerCamelCase	 =       """cpu"""
lowerCamelCase	 =       """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

lowerCamelCase	 =       """path-to-your-trained-model"""
lowerCamelCase	 =       StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		lowerCamelCase	 =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowerCamelCase	 =       pipe.to(device)

# to channels last
lowerCamelCase	 =       pipe.unet.to(memory_format=torch.channels_last)
lowerCamelCase	 =       pipe.vae.to(memory_format=torch.channels_last)
lowerCamelCase	 =       pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowerCamelCase	 =       pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowerCamelCase	 =       torch.randn(2, 4, 64, 64)
lowerCamelCase	 =       torch.rand(1) * 999
lowerCamelCase	 =       torch.randn(2, 77, 768)
lowerCamelCase	 =       (sample, timestep, encoder_hidden_status)
try:
		lowerCamelCase	 =       ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		lowerCamelCase	 =       ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowerCamelCase	 =       ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowerCamelCase	 =       ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		lowerCamelCase	 =       ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowerCamelCase	 =       666
lowerCamelCase	 =       torch.Generator(device).manual_seed(seed)
lowerCamelCase	 =       {"""generator""": generator}
if args.steps is not None:
		lowerCamelCase	 =       args.steps

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

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








'''simple docstring'''



import string



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=''
							for i in sequence:
														__lowercase       	=ord(_lowerCAmelCase					)
														if 65 <= extract <= 90:
																					output += chr(155 - extract					)
														elif 97 <= extract <= 122:
																					output += chr(219 - extract					)
														else:
																					output += i
							return output



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=string.ascii_letters
							__lowercase       	=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(_lowerCAmelCase					)] if c in letters else c for c in sequence					)



def   _A       (   ):



							"""simple docstring"""



							from timeit import timeit

							print('Running performance benchmarks...'					)
							__lowercase       	='from string import printable ; from __main__ import atbash, atbash_slow'
							print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)
							print(f""">      atbash(): {timeit('atbash(printable)'  ,   setup=_lowerCAmelCase					)} seconds"""					)


if __name__ == "__main__":
		for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
				print(f"{example} encrypted in atbash: {atbash(example)}")
		benchmark()



