


import requests
from bsa import BeautifulSoup






def 		_A		(		_lowercase					,  _lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase =			BeautifulSoup(requests.get(_lowercase					,  params=_lowercase ).content					,  'html.parser' )
     __UpperCamelCase =			soup.find('div'					,  attrs={'class': 'gs_ri'} )
     __UpperCamelCase =			div.find('div'					,  attrs={'class': 'gs_fl'} ).find_all('a' )
     return anchors[2].get_text()


if __name__ == "__main__":
    __snake_case   							=						{
        '''title''': (
            '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
            '''capacitance, volumetric capacitance, and energy density'''
        ),
        '''journal''': '''Chem. Mater.''',
        '''volume''': 3_0,
        '''pages''': '''3979-3990''',
        '''year''': 2_0_1_8,
        '''hl''': '''en''',
    }
    print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))




import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


__A							=logging.get_logger(__name__)




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					CLIPConfig

	lowerCAmelCase__   =					['CLIPEncoderLayer']



	def __init__(       self		,    lowercase					)							->			int:
					super().__init__(lowercase					)

					lowerCamelCase_         =     CLIPVisionModelWithProjection(config.vision_config					)

					lowerCamelCase_         =     nn.Linear(config.vision_config.projection_dim		,    1					)
					lowerCamelCase_         =     nn.Linear(config.vision_config.projection_dim		,    1					)



	@torch.no_grad()
	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase=0.5		,    lowercase=0.5					)							->			int:
					lowerCamelCase_         =     self.vision_model(lowercase					)[0]

					lowerCamelCase_         =     self.p_head(lowercase					)
					lowerCamelCase_         =     nsfw_detected.flatten()
					lowerCamelCase_         =     nsfw_detected > p_threshold
					lowerCamelCase_         =     nsfw_detected.tolist()

					if any(lowercase					):
									logger.warning(
									    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
									    " Try again with a different prompt and/or seed."					)

					for idx, nsfw_detected_ in enumerate(lowercase					):
									if nsfw_detected_:
													lowerCamelCase_         =     np.zeros(images[idx].shape					)

					lowerCamelCase_         =     self.w_head(lowercase					)
					lowerCamelCase_         =     watermark_detected.flatten()
					lowerCamelCase_         =     watermark_detected > w_threshold
					lowerCamelCase_         =     watermark_detected.tolist()

					if any(lowercase					):
									logger.warning(
									    "Potential watermarked content was detected in one or more images. A black image will be returned instead."
									    " Try again with a different prompt and/or seed."					)

					for idx, watermark_detected_ in enumerate(lowercase					):
									if watermark_detected_:
													lowerCamelCase_         =     np.zeros(images[idx].shape					)

					return images, nsfw_detected, watermark_detected


