




'''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")






from decimal import Decimal, getcontext
from math import ceil, factorial







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          raise TypeError('''Undefined for non-integers''' )
     elif precision < 1:
          raise ValueError('''Undefined for non-natural numbers''' )

     UpperCAmelCase_     :				Tuple  =     precision
     UpperCAmelCase_     :				Optional[Any]  =     ceil(precision / 14 )
     UpperCAmelCase_     :				int  =     426880 * Decimal(10005 ).sqrt()
     UpperCAmelCase_     :				Tuple  =     1
     UpperCAmelCase_     :				List[Any]  =     13591409
     UpperCAmelCase_     :				Optional[Any]  =     Decimal(_lowercase )
     for k in range(1					,		_lowercase ):
          UpperCAmelCase_     :				List[str]  =     factorial(6 * k ) // (factorial(3 * k ) * factorial(_lowercase ) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
     return str(constant_term / partial_sum )[:-1]


if __name__ == "__main__":
     __a							     =					50
     print(F"""The first {n} digits of pi is: {pi(n)}""")