import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


a  :      str            =    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  :      int            =    parser.parse_args()


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

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

# to channels last
a  :      str            =    pipe.unet.to(memory_format=torch.channels_last)
a  :      Any            =    pipe.vae.to(memory_format=torch.channels_last)
a  :      str            =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
 a  :      Optional[Any]            =    pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
a  :      int            =    6_6_6
a  :      int            =    torch.Generator(device).manual_seed(seed)
a  :      Optional[int]            =    {"""generator""": generator}
if args.steps is not None:
 a  :      Optional[Any]            =    args.steps

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

# save image
image.save("""generated.png""")
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	[1]

   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	0, 0, 0
   lowerCAmelCase_:							Union[str, Any]       =	ugly_nums[ia] * 2
   lowerCAmelCase_:							str       =	ugly_nums[ia] * 3
   lowerCAmelCase_:							Dict       =	ugly_nums[ia] * 5

   for _ in range(1							,				lowercase					):
      lowerCAmelCase_:							Any       =	min(lowercase							,				lowercase							,				lowercase					)
      ugly_nums.append(lowercase					)
      if next_num == next_a:
         ia += 1
         lowerCAmelCase_:							str       =	ugly_nums[ia] * 2
      if next_num == next_a:
         ia += 1
         lowerCAmelCase_:							Optional[int]       =	ugly_nums[ia] * 3
      if next_num == next_a:
         ia += 1
         lowerCAmelCase_:							int       =	ugly_nums[ia] * 5
   return ugly_nums[-1]


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

 testmod(verbose=True)
 print(f'''{ugly_numbers(2_0_0) = }''')