





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_A					:				List[Any]				=			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					:				List[str]				=			parser.parse_args()


_A					:				Optional[Any]				=			'cpu'
_A					:				Optional[Any]				=			'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					:				Union[str, Any]				=			StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  _A					:				Optional[Any]				=			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_A					:				Optional[int]				=			pipe.to(device)

# to channels last
_A					:				int				=			pipe.unet.to(memory_format=torch.channels_last)
_A					:				Optional[Any]				=			pipe.vae.to(memory_format=torch.channels_last)
_A					:				Any				=			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					:				List[Any]				=			torch.randn(2, 4, 64, 64)
_A					:				Dict				=			torch.rand(1) * 9_99
_A					:				List[str]				=			torch.randn(2, 77, 7_68)
_A					:				Any				=			(sample, timestep, encoder_hidden_status)
try:
  _A					:				Union[str, Any]				=			ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  _A					:				Union[str, Any]				=			ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_A					:				Optional[Any]				=			ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_A					:				Optional[Any]				=			ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  _A					:				List[str]				=			ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

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

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

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





"""simple docstring"""






import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__snake_case				:  Optional[Any] =       pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__snake_case				:  int =       dataset.iloc[:, 1:2].values
__snake_case				:  Tuple =       dataset.iloc[:, 2].values


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


__snake_case				:  Tuple =       PolynomialFeatures(degree=4)
__snake_case				:  Any =       poly_reg.fit_transform(X)
__snake_case				:  List[str] =       LinearRegression()
pol_reg.fit(X_poly, y)


def      _lowercase							(    )		->  List[Any]:
			plt.scatter(__snake_case   ,__snake_case   ,color="red"      )
			plt.plot(__snake_case   ,pol_reg.predict(poly_reg.fit_transform(__snake_case      )      )   ,color="blue"      )
			plt.title("Truth or Bluff (Linear Regression)"      )
			plt.xlabel("Position level"      )
			plt.ylabel("Salary"      )
			plt.show()


if __name__ == "__main__":
	viz_polymonial()

	# Predicting a new result with Polymonial Regression
	pol_reg.predict(poly_reg.fit_transform([[5.5]]))
	# output should be 132148.43750003