





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class 	__lowerCAmelCase      (       _UpperCamelCase	):





   def 						_snake_case			(			self       ,      snake_case   )					->	float:
      """simple docstring"""





      return 0.0







def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Optional[int]   		=	min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
   a__						:    Any   		=	max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
   return lowest, highest






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Any   		=	512
   a__						:    Dict   		=	[1] + [0] * (size - 1)
   a__						:    List[str]   		=	[filter_type.process(lowerCamelCase ) for item in inputs]

   a__						:    Union[str, Any]   		=	[0] * (samplerate - size)  # zero-padding
   outputs += filler
   a__						:    Dict   		=	np.abs(np.fft.fft(lowerCamelCase ) )
   a__						:    Any   		=	20 * np.logaa(lowerCamelCase )

   # Frequencies on log scale from 24 to nyquist frequency
   plt.xlim(24				,			samplerate / 2 - 1 )
   plt.xlabel("Frequency (Hz)" )
   plt.xscale("log" )

   # Display within reasonable bounds
   a__						:    List[str]   		=	get_bounds(lowerCamelCase				,			lowerCamelCase )
   plt.ylim(max([-80, bounds[0]] )				,			min([80, bounds[1]] ) )
   plt.ylabel("Gain (dB)" )

   plt.plot(lowerCamelCase )
   plt.show()






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    str   		=	512
   a__						:    int   		=	[1] + [0] * (size - 1)
   a__						:    Union[str, Any]   		=	[filter_type.process(lowerCamelCase ) for item in inputs]

   a__						:    Any   		=	[0] * (samplerate - size)  # zero-padding
   outputs += filler
   a__						:    Optional[Any]   		=	np.angle(np.fft.fft(lowerCamelCase ) )

   # Frequencies on log scale from 24 to nyquist frequency
   plt.xlim(24				,			samplerate / 2 - 1 )
   plt.xlabel("Frequency (Hz)" )
   plt.xscale("log" )

   plt.ylim(-2 * pi				,			2 * pi )
   plt.ylabel("Phase shift (Radians)" )
   plt.plot(np.unwrap(lowerCamelCase				,			-2 * pi ) )
   plt.show()








import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      List[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""")
SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()


SCREAMING_SNAKE_CASE__							:      int         =    """cpu"""
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

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

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

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

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

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

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

