




import requests
from bsa import BeautifulSoup



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


if __name__ == "__main__":
		UpperCAmelCase_						={
		    """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))









# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_=20_00       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=1E-3						):
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		torch.linspace(1       , self.config.sampling_eps       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None						):
											if self.timesteps is None:
																		raise ValueError(
																		    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											lowerCAmelCase   =		(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											lowerCAmelCase   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
											lowerCAmelCase   =		std.flatten()
											while len(std.shape						) < len(score.shape						):
																		lowerCAmelCase   =		std.unsqueeze(-1						)
											lowerCAmelCase   =		-score / std

											# compute
											lowerCAmelCase   =		-1.0 / len(self.timesteps						)

											lowerCAmelCase   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											lowerCAmelCase   =		beta_t.flatten()
											while len(beta_t.shape						) < len(x.shape						):
																		lowerCAmelCase   =		beta_t.unsqueeze(-1						)
											lowerCAmelCase   =		-0.5 * beta_t * x

											lowerCAmelCase   =		torch.sqrt(UpperCAmelCase_						)
											lowerCAmelCase   =		drift - diffusion**2 * score
											lowerCAmelCase   =		x + drift * dt

											# add noise
											lowerCAmelCase   =		randn_tensor(x.shape       , layout=x.layout       , generator=UpperCAmelCase_       , device=x.device       , dtype=x.dtype						)
											lowerCAmelCase   =		x_mean + diffusion * math.sqrt(-dt						) * noise

											return x, x_mean






				def __len__( self						):
											return self.config.num_train_timesteps



