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
A  :	Union[str, Any]         =							0B101_100_111_110_110_010_010_000_011_110_111_011_000_110_011_110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
A  :	Any         =							[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 	lowerCamelCase       :
  """simple docstring"""







  def __init__(				self			:							Union[str, Any] )	->						Optional[int]:
        SCREAMING_SNAKE_CASE_    =	WATERMARK_BITS
        SCREAMING_SNAKE_CASE_    =	WatermarkEncoder()

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







  def  __A  (				self			:							Any	,  __magic_name__			:							torch.FloatTensor )	->						Optional[Any]:


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

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

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

        SCREAMING_SNAKE_CASE_    =	torch.from_numpy(np.array(_UpperCAmelCase ) ).permute(0	,  3	,  1	,  2 )

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






from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    float:
		"""simple docstring"""




		if donor_conc <= 0:
				raise ValueError("""Donor concentration should be positive"""	)
		elif acceptor_conc <= 0:
				raise ValueError("""Acceptor concentration should be positive"""	)
		elif intrinsic_conc <= 0:
				raise ValueError("""Intrinsic concentration should be positive"""	)
		elif donor_conc <= intrinsic_conc:
				raise ValueError(
				    """Donor concentration should be greater than intrinsic concentration"""	)
		elif acceptor_conc <= intrinsic_conc:
				raise ValueError(
				    """Acceptor concentration should be greater than intrinsic concentration"""	)
		else:
				return (
				    Boltzmann
				    * T
				    * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
				    / physical_constants["electron volt"][0]
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

