




'''simple docstring'''


import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def 						snake_case_       (UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    Tuple ):





							'''simple docstring'''


							_a      	=							checkpoint

							_a      	=							{}

							_a      	=							vae_state_dict['''encoder.conv_in.weight''']
							_a      	=							vae_state_dict['''encoder.conv_in.bias''']
							_a      	=							vae_state_dict['''encoder.conv_out.weight''']
							_a      	=							vae_state_dict['''encoder.conv_out.bias''']
							_a      	=							vae_state_dict['''encoder.norm_out.weight''']
							_a      	=							vae_state_dict['''encoder.norm_out.bias''']

							_a      	=							vae_state_dict['''decoder.conv_in.weight''']
							_a      	=							vae_state_dict['''decoder.conv_in.bias''']
							_a      	=							vae_state_dict['''decoder.conv_out.weight''']
							_a      	=							vae_state_dict['''decoder.conv_out.bias''']
							_a      	=							vae_state_dict['''decoder.norm_out.weight''']
							_a      	=							vae_state_dict['''decoder.norm_out.bias''']

							_a      	=							vae_state_dict['''quant_conv.weight''']
							_a      	=							vae_state_dict['''quant_conv.bias''']
							_a      	=							vae_state_dict['''post_quant_conv.weight''']
							_a      	=							vae_state_dict['''post_quant_conv.bias''']

							# Retrieves the keys for the encoder down blocks only
							_a      	=							len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''encoder.down''' in layer} )
							_a      	=							{
							    layer_id: [key for key in vae_state_dict if f'down.{layer_id}' in key] for layer_id in range(UpperCamelCase )
							}

							# Retrieves the keys for the decoder up blocks only
							_a      	=							len({'''.'''.join(layer.split('''.''' )[:3] ) for layer in vae_state_dict if '''decoder.up''' in layer} )
							_a      	=							{
							    layer_id: [key for key in vae_state_dict if f'up.{layer_id}' in key] for layer_id in range(UpperCamelCase )
							}

							for i in range(UpperCamelCase ):
														_a      	=							[key for key in down_blocks[i] if f'down.{i}' in key and f'down.{i}.downsample' not in key]

														if f'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
																					_a      	=							vae_state_dict.pop(
																					    f'encoder.down.{i}.downsample.conv.weight' )
																					_a      	=							vae_state_dict.pop(
																					    f'encoder.down.{i}.downsample.conv.bias' )

														_a      	=							renew_vae_resnet_paths(UpperCamelCase )
														_a      	=							{'''old''': f'down.{i}.block', '''new''': f'down_blocks.{i}.resnets'}
														assign_to_checkpoint(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		additional_replacements=[meta_path]					,		config=UpperCamelCase )

							_a      	=							[key for key in vae_state_dict if '''encoder.mid.block''' in key]
							_a      	=							2
							for i in range(1					,		num_mid_res_blocks + 1 ):
														_a      	=							[key for key in mid_resnets if f'encoder.mid.block_{i}' in key]

														_a      	=							renew_vae_resnet_paths(UpperCamelCase )
														_a      	=							{'''old''': f'mid.block_{i}', '''new''': f'mid_block.resnets.{i - 1}'}
														assign_to_checkpoint(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		additional_replacements=[meta_path]					,		config=UpperCamelCase )

							_a      	=							[key for key in vae_state_dict if '''encoder.mid.attn''' in key]
							_a      	=							renew_vae_attention_paths(UpperCamelCase )
							_a      	=							{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
							assign_to_checkpoint(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		additional_replacements=[meta_path]					,		config=UpperCamelCase )
							conv_attn_to_linear(UpperCamelCase )

							for i in range(UpperCamelCase ):
														_a      	=							num_up_blocks - 1 - i
														_a      	=							[
														    key for key in up_blocks[block_id] if f'up.{block_id}' in key and f'up.{block_id}.upsample' not in key
														]

														if f'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
																					_a      	=							vae_state_dict[
																					    f'decoder.up.{block_id}.upsample.conv.weight'
																					]
																					_a      	=							vae_state_dict[
																					    f'decoder.up.{block_id}.upsample.conv.bias'
																					]

														_a      	=							renew_vae_resnet_paths(UpperCamelCase )
														_a      	=							{'''old''': f'up.{block_id}.block', '''new''': f'up_blocks.{i}.resnets'}
														assign_to_checkpoint(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		additional_replacements=[meta_path]					,		config=UpperCamelCase )

							_a      	=							[key for key in vae_state_dict if '''decoder.mid.block''' in key]
							_a      	=							2
							for i in range(1					,		num_mid_res_blocks + 1 ):
														_a      	=							[key for key in mid_resnets if f'decoder.mid.block_{i}' in key]

														_a      	=							renew_vae_resnet_paths(UpperCamelCase )
														_a      	=							{'''old''': f'mid.block_{i}', '''new''': f'mid_block.resnets.{i - 1}'}
														assign_to_checkpoint(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		additional_replacements=[meta_path]					,		config=UpperCamelCase )

							_a      	=							[key for key in vae_state_dict if '''decoder.mid.attn''' in key]
							_a      	=							renew_vae_attention_paths(UpperCamelCase )
							_a      	=							{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
							assign_to_checkpoint(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		additional_replacements=[meta_path]					,		config=UpperCamelCase )
							conv_attn_to_linear(UpperCamelCase )
							return new_checkpoint
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		):





							'''simple docstring'''


							_a      	=							requests.get(
							    ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
							_a      	=							io.BytesIO(r.content )

							_a      	=							OmegaConf.load(UpperCamelCase )
							_a      	=							512
							_a      	=							'''cuda''' if torch.cuda.is_available() else '''cpu'''
							if checkpoint_path.endswith('''safetensors''' ):
														from safetensors import safe_open

														_a      	=							{}
														with safe_open(UpperCamelCase					,		framework='''pt'''					,		device='''cpu''' ) as f:
																					for key in f.keys():
																												_a      	=							f.get_tensor(UpperCamelCase )
							else:
														_a      	=							torch.load(UpperCamelCase					,		map_location=UpperCamelCase )['''state_dict''']

							# Convert the VAE model.
							_a      	=							create_vae_diffusers_config(UpperCamelCase					,		image_size=UpperCamelCase )
							_a      	=							custom_convert_ldm_vae_checkpoint(UpperCamelCase					,		UpperCamelCase )

							_a      	=							AutoencoderKL(**UpperCamelCase )
							vae.load_state_dict(UpperCamelCase )
							vae.save_pretrained(UpperCamelCase )


if __name__ == "__main__":
				_snake_case : Any  				=						argparse.ArgumentParser()

				parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

				_snake_case : Tuple  				=						parser.parse_args()

				vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)







'''simple docstring'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    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))
