






'''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, 6_4, 6_4)
__A    	=   torch.rand(1) * 9_9_9
__A    	=   torch.randn(2, 7_7, 7_6_8)
__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    	=   6_6_6
__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        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       list[float]  )    -> float:







   """simple docstring"""
   if discount_rate < 0:
      raise ValueError('Discount rate cannot be negative'  )
   if not cash_flows:
      raise ValueError('Cash flows list cannot be empty'  )
   __snake_case				:      List[str]     							= sum(
       cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(A  )  )
   return round(A  ,		ndigits=2  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()