"""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''')


def      a      (			A__	:   str      ,   A__	:   bool = False     )    ->      str:





      """simple docstring"""



      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected string as input, found {type(A__     )}'''
            raise ValueError(A__     )
      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected boolean as use_pascal parameter, found {type(A__     )}'''
            raise ValueError(A__     )

      _lowercase	       =input_str.split('_'     )

      _lowercase	       =0 if use_pascal else 1

      _lowercase	       =words[start_index:]

      _lowercase	       =[word[0].upper() + word[1:] for word in words_to_capitalize]

      _lowercase	       ='' if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
  from doctest import testmod

  testmod()


