





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					:				str				=			0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_A					:				int				=			[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]



class 					__snake_case	:


    '''simple docstring'''







    def __init__(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			WATERMARK_BITS
        SCREAMING_SNAKE_CASE__											=			WatermarkEncoder()

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




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        if images.shape[-1] < 2_56:
            return images

        SCREAMING_SNAKE_CASE__											=			(2_55 * (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 / 2_55 - 0.5)   ,				min=-1.0   ,				max=1.0     )
        return images







'''simple docstring'''




import requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''title''': (
			        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
			        '''capacitance, volumetric capacitance, and energy density'''
			    ),
			    '''journal''': '''Chem. Mater.''',
			    '''volume''': 30,
			    '''pages''': '''3979-3990''',
			    '''year''': 2018,
			    '''hl''': '''en''',
			}
			print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))



