


from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return 0.0




def      a	(							SCREAMING_SNAKE_CASE_      :		np.ndarray							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	List[str]   	=		min([-2_0, np.min(fft_results[1 : samplerate // 2 - 1]       )]       )
       UpperCamelCase    :	str   	=		max([2_0, np.max(fft_results[1 : samplerate // 2 - 1]       )]       )
       return lowest, highest


def      a	(							SCREAMING_SNAKE_CASE_      :		FilterType							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	List[Any]   	=		5_1_2
       UpperCamelCase    :	str   	=		[1] + [0] * (size - 1)
       UpperCamelCase    :	Dict   	=		[filter_type.process(SCREAMING_SNAKE_CASE_       ) for item in inputs]

       UpperCamelCase    :	int   	=		[0] * (samplerate - size)  # zero-padding
       outputs += filler
       UpperCamelCase    :	int   	=		np.abs(np.fft.fft(SCREAMING_SNAKE_CASE_       )       )
       UpperCamelCase    :	str   	=		2_0 * np.logaa(SCREAMING_SNAKE_CASE_       )

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(2_4							,			samplerate / 2 - 1       )
       plt.xlabel('''Frequency (Hz)'''       )
       plt.xscale('''log'''       )

       # Display within reasonable bounds
       UpperCamelCase    :	str   	=		get_bounds(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
       plt.ylim(max([-8_0, bounds[0]]       )							,			min([8_0, bounds[1]]       )       )
       plt.ylabel('''Gain (dB)'''       )

       plt.plot(SCREAMING_SNAKE_CASE_       )
       plt.show()


def      a	(							SCREAMING_SNAKE_CASE_      :		FilterType							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		5_1_2
       UpperCamelCase    :	Union[str, Any]   	=		[1] + [0] * (size - 1)
       UpperCamelCase    :	str   	=		[filter_type.process(SCREAMING_SNAKE_CASE_       ) for item in inputs]

       UpperCamelCase    :	List[str]   	=		[0] * (samplerate - size)  # zero-padding
       outputs += filler
       UpperCamelCase    :	List[str]   	=		np.angle(np.fft.fft(SCREAMING_SNAKE_CASE_       )       )

       # Frequencies on log scale from 24 to nyquist frequency
       plt.xlim(2_4							,			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(SCREAMING_SNAKE_CASE_							,			-2 * pi       )       )
       plt.show()





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

