






"""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"""



def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					List[Any]      )		->List[Any]:
						A__						:  Union[str, Any]		  =  [1]
						for i in range(2,						UpperCAmelCase__      ):
												factorials.append(factorials[-1] * i      )
						assert 0 <= k < factorials[-1] * n, "k out of bounds"

						A__						:  Optional[int]		  =  []
						A__						:  List[str]		  =  list(range(UpperCAmelCase__      )      )

						# Find permutation
						while factorials:
												A__						:  Optional[int]		  =  factorials.pop()
												A__  ,  A__						:  Optional[int]		  =  divmod(UpperCAmelCase__,						UpperCAmelCase__      )
												permutation.append(elements[number]      )
												elements.remove(elements[number]      )
						permutation.append(elements[0]      )

						return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()






