





'''simple docstring'''

import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


a        =						logging.get_logger(__name__)







class __a (   _snake_case			):





   def __init__(					self				:			Optional[Any]				,lowerCamelCase				:			Union[List[ControlNetModel], Tuple[ControlNetModel]]    ):

      '''simple docstring'''

      super().__init__()
      __SCREAMING_SNAKE_CASE      							=       nn.ModuleList(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[int]				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			Union[torch.Tensor, float, int]				,lowerCamelCase				:			torch.Tensor				,lowerCamelCase				:			List[torch.tensor]				,lowerCamelCase				:			List[float]				,lowerCamelCase				:			Optional[torch.Tensor] = None				,lowerCamelCase				:			Optional[torch.Tensor] = None				,lowerCamelCase				:			Optional[torch.Tensor] = None				,lowerCamelCase				:			Optional[Dict[str, Any]] = None				,lowerCamelCase				:			bool = False				,lowerCamelCase				:			bool = True				,):

      '''simple docstring'''

      for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase				,lowerCamelCase				,self.nets    )    ):
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       controlnet(
             lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,lowerCamelCase				,)

         # merge samples
         if i == 0:
            __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       down_samples, mid_sample
         else:
            __SCREAMING_SNAKE_CASE      							=       [
                samples_prev + samples_curr
                for samples_prev, samples_curr in zip(lowerCamelCase				,lowerCamelCase    )
            ]
            mid_block_res_sample += mid_sample

      return down_block_res_samples, mid_block_res_sample





   def      UpperCAmelCase__     (					self				:			List[str]				,lowerCamelCase				:			Union[str, os.PathLike]				,lowerCamelCase				:			bool = True				,lowerCamelCase				:			Callable = None				,lowerCamelCase				:			bool = False				,lowerCamelCase				:			Optional[str] = None				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       0
      __SCREAMING_SNAKE_CASE      							=       save_directory
      for controlnet in self.nets:
         controlnet.save_pretrained(
             lowerCamelCase				,is_main_process=lowerCamelCase				,save_function=lowerCamelCase				,safe_serialization=lowerCamelCase				,variant=lowerCamelCase				,)

         idx += 1
         __SCREAMING_SNAKE_CASE      							=       model_path_to_save + f"""_{idx}"""





   @classmethod
   def      UpperCAmelCase__     (					cls				:			List[Any]				,lowerCamelCase				:			Optional[Union[str, os.PathLike]]				,**lowerCamelCase				:			str    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       0
      __SCREAMING_SNAKE_CASE      							=       []

      # load controlnet and append to list until no controlnet directory exists anymore
      # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
      # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
      __SCREAMING_SNAKE_CASE      							=       pretrained_model_path
      while os.path.isdir(lowerCamelCase    ):
         __SCREAMING_SNAKE_CASE      							=       ControlNetModel.from_pretrained(lowerCamelCase				,**lowerCamelCase    )
         controlnets.append(lowerCamelCase    )

         idx += 1
         __SCREAMING_SNAKE_CASE      							=       pretrained_model_path + f"""_{idx}"""

      logger.info(f"""{len(lowerCamelCase    )} controlnets loaded from {pretrained_model_path}."""    )

      if len(lowerCamelCase    ) == 0:
         raise ValueError(
             f"""No ControlNets found under {os.path.dirname(lowerCamelCase    )}. Expected at least {pretrained_model_path + '_0'}."""    )

      return cls(lowerCamelCase    )












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       BeautifulSoup(requests.get(__UpperCAmelCase							,	params=__UpperCAmelCase      ).content							,	"""html.parser"""      )
   __SCREAMING_SNAKE_CASE      							=       soup.find("""div"""							,	attrs={"""class""": """gs_ri"""}      )
   __SCREAMING_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": 30,
        "pages": "3979-3990",
        "year": 2018,
        "hl": "en",
    }
    print(get_citation("https://scholar.google.com/scholar_lookup", params=params))





