'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							42
				UpperCAmelCase__							=							42





				def __init__(						self					:						int       ,		lowercase__					:						UNetaDModel       ,		lowercase__					:						ScoreSdeVeScheduler					)							->Optional[int]:


										'''simple docstring'''




										super().__init__()
										self.register_modules(unet=lowercase__       ,		scheduler=lowercase__					)







				@torch.no_grad()
				def __call__(						self					:						Any       ,		lowercase__					:						int = 1       ,		lowercase__					:						int = 2_000       ,		lowercase__					:						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		lowercase__					:						Optional[str] = "pil"       ,		lowercase__					:						bool = True       ,		**lowercase__					:						str       ,		)							->Union[ImagePipelineOutput, Tuple]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						self.unet.config.sample_size
										_UpperCamelCase			:			Tuple   			=						(batch_size, 3, img_size, img_size)

										_UpperCamelCase			:			List[str]   			=						self.unet

										_UpperCamelCase			:			List[Any]   			=						randn_tensor(lowercase__       ,		generator=lowercase__					) * self.scheduler.init_noise_sigma
										_UpperCamelCase			:			List[Any]   			=						sample.to(self.device					)

										self.scheduler.set_timesteps(lowercase__					)
										self.scheduler.set_sigmas(lowercase__					)

										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
																_UpperCamelCase			:			Tuple   			=						self.scheduler.sigmas[i] * torch.ones(shape[0]       ,		device=self.device					)

																# correction step
																for _ in range(self.scheduler.config.correct_steps					):
																						_UpperCamelCase			:			int   			=						self.unet(lowercase__       ,		lowercase__					).sample
																						_UpperCamelCase			:			int   			=						self.scheduler.step_correct(lowercase__       ,		lowercase__       ,		generator=lowercase__					).prev_sample

																# prediction step
																_UpperCamelCase			:			List[Any]   			=						model(lowercase__       ,		lowercase__					).sample
																_UpperCamelCase			:			Optional[int]   			=						self.scheduler.step_pred(lowercase__       ,		lowercase__       ,		lowercase__       ,		generator=lowercase__					)

																_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						output.prev_sample, output.prev_sample_mean

										_UpperCamelCase			:			int   			=						sample_mean.clamp(0       ,		1					)
										_UpperCamelCase			:			Union[str, Any]   			=						sample.cpu().permute(0       ,		2       ,		3       ,		1					).numpy()
										if output_type == "pil":
																_UpperCamelCase			:			int   			=						self.numpy_to_pil(lowercase__					)

										if not return_dict:
																return (sample,)

										return ImagePipelineOutput(images=lowercase__					)



'''simple docstring'''





import requests
from bsa import BeautifulSoup
def 						__A							(     UpperCAmelCase     ,UpperCAmelCase					)     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Dict   			=						BeautifulSoup(requests.get(UpperCAmelCase     ,params=UpperCAmelCase					).content     ,"html.parser"					)
						_UpperCamelCase			:			Union[str, Any]   			=						soup.find("div"     ,attrs={"class": "gs_ri"}					)
						_UpperCamelCase			:			Optional[Any]   			=						div.find("div"     ,attrs={"class": "gs_fl"}					).find_all("a"					)
						return anchors[2].get_text()


if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[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))


