


from __future__ import annotations






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if len(SCREAMING_SNAKE_CASE_	) <= 1 or n <= 1:
    return

  insert_next(SCREAMING_SNAKE_CASE_      ,						n - 1	)
  rec_insertion_sort(SCREAMING_SNAKE_CASE_      ,						n - 1	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if index >= len(SCREAMING_SNAKE_CASE_	) or collection[index - 1] <= collection[index]:
    return

  # Swaps adjacent elements since they are not in ascending order
  lowerCamelCase    ,	lowerCamelCase :					Optional[int]    =				(
      collection[index],
      collection[index - 1],
  )

  insert_next(SCREAMING_SNAKE_CASE_      ,						index + 1	)


if __name__ == "__main__":
   _snake_case             =     input('''Enter integers separated by spaces: ''')
   _snake_case             =     [int(num) for num in numbers.split()]
   rec_insertion_sort(number_list, len(number_list))
   print(number_list)







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case             =     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''')
_snake_case             =     parser.parse_args()


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

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

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

# optimize with ipex
_snake_case             =     torch.randn(2, 4, 64, 64)
_snake_case             =     torch.rand(1) * 9_99
_snake_case             =     torch.randn(2, 77, 7_68)
_snake_case             =     (sample, timestep, encoder_hidden_status)
try:
   _snake_case             =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   _snake_case             =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case             =     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case             =     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   _snake_case             =     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case             =     6_66
_snake_case             =     torch.Generator(device).manual_seed(seed)
_snake_case             =     {'''generator''': generator}
if args.steps is not None:
   _snake_case             =     args.steps

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

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



