






import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def _UpperCAmelCase   (	a						:	Tuple ):
       snake_case__  , snake_case__	  =       image.size
       snake_case__  , snake_case__	  =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       snake_case__	  =       image.resize((w, h)  ,					resample=PIL_INTERPOLATION["""lanczos"""] )
       snake_case__	  =       np.array(a ).astype(np.floataa ) / 255.0
       snake_case__	  =       image[None].transpose(0  ,					3  ,					1  ,					2 )
       snake_case__	  =       torch.from_numpy(a )
       return 2.0 * image - 1.0



class    _lowerCAmelCase			(		lowercase_      ):



    """simple docstring"""



    def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    VQModel			,		UpperCamelCase__    :    UNetaDModel			,		UpperCamelCase__    :    Union[
            DDIMScheduler,
            PNDMScheduler,
            LMSDiscreteScheduler,
            EulerDiscreteScheduler,
            EulerAncestralDiscreteScheduler,
            DPMSolverMultistepScheduler,
        ]			,		):






           '''simple docstring'''







           super().__init__()
           self.register_modules(vqvae=UpperCamelCase__			,		unet=UpperCamelCase__			,		scheduler=UpperCamelCase__)



    @torch.no_grad()
    def __call__(      self    :    int			,		UpperCamelCase__    :    Union[torch.Tensor, PIL.Image.Image] = None			,		UpperCamelCase__    :    Optional[int] = 1			,		UpperCamelCase__    :    Optional[int] = 1_0_0			,		UpperCamelCase__    :    Optional[float] = 0.0			,		UpperCamelCase__    :    Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		UpperCamelCase__    :    Optional[str] = "pil"			,		UpperCamelCase__    :    bool = True			,		):






           '''simple docstring'''







           if isinstance(UpperCamelCase__			,		PIL.Image.Image):
                  snake_case__	  =       1
           elif isinstance(UpperCamelCase__			,		torch.Tensor):
                  snake_case__	  =       image.shape[0]
           else:
                  raise ValueError(F'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__)}''')

           if isinstance(UpperCamelCase__			,		PIL.Image.Image):
                  snake_case__	  =       preprocess(UpperCamelCase__)

           snake_case__  , snake_case__	  =       image.shape[-2:]

           # in_channels should be 6: 3 for latents, 3 for low resolution image
           snake_case__	  =       (batch_size, self.unet.config.in_channels // 2, height, width)
           snake_case__	  =       next(self.unet.parameters()).dtype

           snake_case__	  =       randn_tensor(UpperCamelCase__			,		generator=UpperCamelCase__			,		device=self.device			,		dtype=UpperCamelCase__)

           snake_case__	  =       image.to(device=self.device			,		dtype=UpperCamelCase__)

           # set timesteps and move to the correct device
           self.scheduler.set_timesteps(UpperCamelCase__			,		device=self.device)
           snake_case__	  =       self.scheduler.timesteps

           # scale the initial noise by the standard deviation required by the scheduler
           snake_case__	  =       latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           snake_case__	  =       """eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
           snake_case__	  =       {}
           if accepts_eta:
                  snake_case__	  =       eta

           for t in self.progress_bar(UpperCamelCase__):
                  # concat latents and low resolution image in the channel dimension.
                  snake_case__	  =       torch.cat([latents, image]			,		dim=1)
                  snake_case__	  =       self.scheduler.scale_model_input(UpperCamelCase__			,		UpperCamelCase__)
                  # predict the noise residual
                  snake_case__	  =       self.unet(UpperCamelCase__			,		UpperCamelCase__).sample
                  # compute the previous noisy sample x_t -> x_t-1
                  snake_case__	  =       self.scheduler.step(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		**UpperCamelCase__).prev_sample

           # decode the image latents with the VQVAE
           snake_case__	  =       self.vqvae.decode(UpperCamelCase__).sample
           snake_case__	  =       torch.clamp(UpperCamelCase__			,		-1.0			,		1.0)
           snake_case__	  =       image / 2 + 0.5
           snake_case__	  =       image.cpu().permute(0			,		2			,		3			,		1).numpy()

           if output_type == "pil":
                  snake_case__	  =       self.numpy_to_pil(UpperCamelCase__)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=UpperCamelCase__)








import requests
from bsa import BeautifulSoup

def _UpperCAmelCase   (	a						:	str  ,					a						:	dict ):
							snake_case__	  =       BeautifulSoup(requests.get(a  ,					params=a ).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__       =     {
	    """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))
