





'''simple docstring'''

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       StableDiffusionPipeline.from_pretrained(__UpperCAmelCase							,	torch_dtype=torch.floataa      )

   # load LoRA weight from .safetensors
   __SCREAMING_SNAKE_CASE      							=       load_file(__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       []

   # directly update weight in diffusers model
   for key in state_dict:
      # it is suggested to print out the key, it usually will be something like below
      # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

      # as we have set the alpha beforehand, so just skip
      if ".alpha" in key or key in visited:
         continue

      if "text" in key:
         __SCREAMING_SNAKE_CASE      							=       key.split("""."""      )[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""      )[-1].split("""_"""      )
         __SCREAMING_SNAKE_CASE      							=       pipeline.text_encoder
      else:
         __SCREAMING_SNAKE_CASE      							=       key.split("""."""      )[0].split(LORA_PREFIX_UNET + """_"""      )[-1].split("""_"""      )
         __SCREAMING_SNAKE_CASE      							=       pipeline.unet

      # find the target layer
      __SCREAMING_SNAKE_CASE      							=       layer_infos.pop(0      )
      while len(__UpperCAmelCase      ) > -1:
         try:
            __SCREAMING_SNAKE_CASE      							=       curr_layer.__getattr__(__UpperCAmelCase      )
            if len(__UpperCAmelCase      ) > 0:
               __SCREAMING_SNAKE_CASE      							=       layer_infos.pop(0      )
            elif len(__UpperCAmelCase      ) == 0:
               break
         except Exception:
            if len(__UpperCAmelCase      ) > 0:
               temp_name += "_" + layer_infos.pop(0      )
            else:
               __SCREAMING_SNAKE_CASE      							=       layer_infos.pop(0      )

      __SCREAMING_SNAKE_CASE      							=       []
      if "lora_down" in key:
         pair_keys.append(key.replace("""lora_down"""							,	"""lora_up"""      )      )
         pair_keys.append(__UpperCAmelCase      )
      else:
         pair_keys.append(__UpperCAmelCase      )
         pair_keys.append(key.replace("""lora_up"""							,	"""lora_down"""      )      )

      # update weight
      if len(state_dict[pair_keys[0]].shape      ) == 4:
         __SCREAMING_SNAKE_CASE      							=       state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
         __SCREAMING_SNAKE_CASE      							=       state_dict[pair_keys[1]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
         curr_layer.weight.data += alpha * torch.mm(__UpperCAmelCase							,	__UpperCAmelCase      ).unsqueeze(2      ).unsqueeze(3      )
      else:
         __SCREAMING_SNAKE_CASE      							=       state_dict[pair_keys[0]].to(torch.floataa      )
         __SCREAMING_SNAKE_CASE      							=       state_dict[pair_keys[1]].to(torch.floataa      )
         curr_layer.weight.data += alpha * torch.mm(__UpperCAmelCase							,	__UpperCAmelCase      )

      # update visited list
      for item in pair_keys:
         visited.append(__UpperCAmelCase      )

   return pipeline


if __name__ == "__main__":
    a        =						argparse.ArgumentParser()

    parser.add_argument(
        "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
    )
    parser.add_argument(
        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
    )
    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
    parser.add_argument(
        "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
    )
    parser.add_argument(
        "--lora_prefix_text_encoder",
        default="lora_te",
        type=str,
        help="The prefix of text encoder weight in safetensors",
    )
    parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
    parser.add_argument(
        "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
    )
    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

    a        =						parser.parse_args()

    a        =						args.base_model_path
    a        =						args.checkpoint_path
    a        =						args.dump_path
    a        =						args.lora_prefix_unet
    a        =						args.lora_prefix_text_encoder
    a        =						args.alpha

    a        =						convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

    a        =						pipe.to(args.device)
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)












'''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))





