'''simple docstring'''






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__a										=					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')
__a										=					parser.parse_args()


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

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

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

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

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

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

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




"""simple docstring"""
from __future__ import annotations

__a      =				10
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			1
						snake_case_ :List[str]						=			max(_lowercase	)
						while placement <= max_digit:
												# declare and initialize empty buckets
												snake_case_ :list[list]						=			[[] for _ in range(_lowercase	)]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		snake_case_ :Any						=			int((i / placement) % RADIX	)
																		buckets[tmp].append(_lowercase	)
												# put each buckets' contents into list_of_ints
												snake_case_ :Optional[Any]						=			0
												for b in range(_lowercase	):
																		for i in buckets[b]:
																								snake_case_ :Union[str, Any]						=			i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()
