"""simple docstring"""






from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class  _A						(       lowerCAmelCase	):

      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            return 0.0






def 			UpperCAmelCase__       (				lowerCAmelCase__     :np.ndarray					,		lowerCAmelCase__     :int		) ->    tuple[int | float, int | float]:
      '''simple docstring'''


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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :FilterType					,		lowerCAmelCase__     :int		) ->    None:
      '''simple docstring'''


      lowercase														=	5_1_2
      lowercase														=	[1] + [0] * (size - 1)
      lowercase														=	[filter_type.process(lowerCAmelCase__		) for item in inputs]

      lowercase														=	[0] * (samplerate - size)  # zero-padding
      outputs += filler
      lowercase														=	np.abs(np.fft.fft(lowerCAmelCase__		)		)
      lowercase														=	2_0 * np.logaa(lowerCAmelCase__		)

      # 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
      lowercase														=	get_bounds(lowerCAmelCase__					,		lowerCAmelCase__		)
      plt.ylim(max([-8_0, bounds[0]]		)					,		min([8_0, bounds[1]]		)		)
      plt.ylabel("""Gain (dB)"""		)

      plt.plot(lowerCAmelCase__		)
      plt.show()






def 			UpperCAmelCase__       (				lowerCAmelCase__     :FilterType					,		lowerCAmelCase__     :int		) ->    None:
      '''simple docstring'''


      lowercase														=	5_1_2
      lowercase														=	[1] + [0] * (size - 1)
      lowercase														=	[filter_type.process(lowerCAmelCase__		) for item in inputs]

      lowercase														=	[0] * (samplerate - size)  # zero-padding
      outputs += filler
      lowercase														=	np.angle(np.fft.fft(lowerCAmelCase__		)		)

      # 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(lowerCAmelCase__					,		-2 * pi		)		)
      plt.show()




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



