






"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowercase				:				Union[str, 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")
_lowercase				:				int   		=		parser.parse_args()


_lowercase				:				List[str]   		=		"cpu"
_lowercase				:				str   		=		"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

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

# to channels last
_lowercase				:				str   		=		pipe.unet.to(memory_format=torch.channels_last)
_lowercase				:				List[str]   		=		pipe.vae.to(memory_format=torch.channels_last)
_lowercase				:				Union[str, Any]   		=		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		_lowercase				:				List[Any]   		=		pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_lowercase				:				int   		=		torch.randn(2, 4, 6_4, 6_4)
_lowercase				:				str   		=		torch.rand(1) * 9_9_9
_lowercase				:				Optional[int]   		=		torch.randn(2, 7_7, 7_6_8)
_lowercase				:				str   		=		(sample, timestep, encoder_hidden_status)
try:
		_lowercase				:				Optional[int]   		=		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		_lowercase				:				Dict   		=		ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase				:				Optional[int]   		=		ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_lowercase				:				Optional[Any]   		=		ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		_lowercase				:				str   		=		ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_lowercase				:				Tuple   		=		6_6_6
_lowercase				:				Any   		=		torch.Generator(device).manual_seed(seed)
_lowercase				:				Union[str, Any]   		=		{"generator": generator}
if args.steps is not None:
		_lowercase				:				Any   		=		args.steps

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

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









"""simple docstring"""






def   snake_case__     (	__lowerCamelCase       :					list[int]			):
			"""simple docstring"""
			if not numbers:
						return 0

			if not isinstance(__lowerCamelCase							,	(list, tuple)			) or not all(
			    isinstance(__lowerCamelCase							,	__lowerCamelCase			) for number in numbers			):
						raise ValueError('''numbers must be an iterable of integers'''			)

			lowerCamelCase__	:	Any				=numbers[0]

			for i in range(1							,	len(__lowerCamelCase			)			):
						# update the maximum and minimum subarray products
						lowerCamelCase__	:	Dict				=numbers[i]
						if number < 0:
									lowerCamelCase__			,     lowerCamelCase__	:	List[Any]				=min_till_now, max_till_now
						lowerCamelCase__	:	Optional[int]				=max(__lowerCamelCase							,	max_till_now * number			)
						lowerCamelCase__	:	Dict				=min(__lowerCamelCase							,	min_till_now * number			)

						# update the maximum product found till now
						lowerCamelCase__	:	Tuple				=max(__lowerCamelCase							,	__lowerCamelCase			)

			return max_prod




