





'''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 	_lowerCAmelCase      (  __snake_case  :			List[Any]			,							__snake_case  :			Dict      )					->					Union[str, Any]:
     __A						:			str								=	checkpoint

     __A						:			Optional[int]								=	{}

     __A						:			Optional[Any]								=	vae_state_dict['encoder.conv_in.weight']
     __A						:			Dict								=	vae_state_dict['encoder.conv_in.bias']
     __A						:			Optional[Any]								=	vae_state_dict['encoder.conv_out.weight']
     __A						:			Dict								=	vae_state_dict['encoder.conv_out.bias']
     __A						:			Dict								=	vae_state_dict['encoder.norm_out.weight']
     __A						:			Optional[Any]								=	vae_state_dict['encoder.norm_out.bias']

     __A						:			str								=	vae_state_dict['decoder.conv_in.weight']
     __A						:			List[Any]								=	vae_state_dict['decoder.conv_in.bias']
     __A						:			Tuple								=	vae_state_dict['decoder.conv_out.weight']
     __A						:			Optional[Any]								=	vae_state_dict['decoder.conv_out.bias']
     __A						:			Any								=	vae_state_dict['decoder.norm_out.weight']
     __A						:			Union[str, Any]								=	vae_state_dict['decoder.norm_out.bias']

     __A						:			Optional[Any]								=	vae_state_dict['quant_conv.weight']
     __A						:			List[str]								=	vae_state_dict['quant_conv.bias']
     __A						:			List[str]								=	vae_state_dict['post_quant_conv.weight']
     __A						:			List[str]								=	vae_state_dict['post_quant_conv.bias']

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

     # Retrieves the keys for the decoder up blocks only
     __A						:			List[Any]								=	len({'.'.join(layer.split('.'      )[:3]      ) for layer in vae_state_dict if 'decoder.up' in layer}      )
     __A						:			Optional[int]								=	{
         layer_id: [key for key in vae_state_dict if f'up.{layer_id}' in key] for layer_id in range(__snake_case      )
     }

     for i in range(__snake_case      ):
          __A						:			Any								=	[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						:			Any								=	vae_state_dict.pop(
                   f'encoder.down.{i}.downsample.conv.weight'      )
               __A						:			List[Any]								=	vae_state_dict.pop(
                   f'encoder.down.{i}.downsample.conv.bias'      )

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

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

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

     __A						:			Union[str, Any]								=	[key for key in vae_state_dict if 'encoder.mid.attn' in key]
     __A						:			Tuple								=	renew_vae_attention_paths(__snake_case      )
     __A						:			Tuple								=	{'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
     assign_to_checkpoint(__snake_case			,							__snake_case			,							__snake_case			,							additional_replacements=[meta_path]			,							config=__snake_case      )
     conv_attn_to_linear(__snake_case      )

     for i in range(__snake_case      ):
          __A						:			Dict								=	num_up_blocks - 1 - i
          __A						:			Optional[int]								=	[
              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						:			Dict								=	vae_state_dict[
                   f'decoder.up.{block_id}.upsample.conv.weight'
               ]
               __A						:			Union[str, Any]								=	vae_state_dict[
                   f'decoder.up.{block_id}.upsample.conv.bias'
               ]

          __A						:			int								=	renew_vae_resnet_paths(__snake_case      )
          __A						:			List[Any]								=	{'old': f'up.{block_id}.block', 'new': f'up_blocks.{i}.resnets'}
          assign_to_checkpoint(__snake_case			,							__snake_case			,							__snake_case			,							additional_replacements=[meta_path]			,							config=__snake_case      )

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

          __A						:			List[Any]								=	renew_vae_resnet_paths(__snake_case      )
          __A						:			List[str]								=	{'old': f'mid.block_{i}', 'new': f'mid_block.resnets.{i - 1}'}
          assign_to_checkpoint(__snake_case			,							__snake_case			,							__snake_case			,							additional_replacements=[meta_path]			,							config=__snake_case      )

     __A						:			List[Any]								=	[key for key in vae_state_dict if 'decoder.mid.attn' in key]
     __A						:			Optional[Any]								=	renew_vae_attention_paths(__snake_case      )
     __A						:			int								=	{'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
     assign_to_checkpoint(__snake_case			,							__snake_case			,							__snake_case			,							additional_replacements=[meta_path]			,							config=__snake_case      )
     conv_attn_to_linear(__snake_case      )
     return new_checkpoint






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str			,							)					->					Dict:
     # Only support V1
     __A						:			Union[str, Any]								=	requests.get(
         ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'      )
     __A						:			List[Any]								=	io.BytesIO(r.content      )

     __A						:			List[Any]								=	OmegaConf.load(__snake_case      )
     __A						:			List[str]								=	5_12
     __A						:			Dict								=	'cuda' if torch.cuda.is_available() else 'cpu'
     if checkpoint_path.endswith('safetensors'      ):
          from safetensors import safe_open

          __A						:			List[Any]								=	{}
          with safe_open(__snake_case			,							framework='pt'			,							device='cpu'      ) as f:
               for key in f.keys():
                    __A						:			Any								=	f.get_tensor(__snake_case      )
     else:
          __A						:			Optional[int]								=	torch.load(__snake_case			,							map_location=__snake_case      )['state_dict']

     # Convert the VAE model.
     __A						:			Optional[Any]								=	create_vae_diffusers_config(__snake_case			,							image_size=__snake_case      )
     __A						:			Any								=	custom_convert_ldm_vae_checkpoint(__snake_case			,							__snake_case      )

     __A						:			int								=	AutoencoderKL(**__snake_case      )
     vae.load_state_dict(__snake_case      )
     vae.save_pretrained(__snake_case      )


if __name__ == "__main__":
  lowercase__   :				Union[str, 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.''')

  lowercase__   :				Any      =   parser.parse_args()

  vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






'''simple docstring'''




import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
  lowercase__   :				Dict      =   {
      '''linear''': PIL.Image.Resampling.BILINEAR,
      '''bilinear''': PIL.Image.Resampling.BILINEAR,
      '''bicubic''': PIL.Image.Resampling.BICUBIC,
      '''lanczos''': PIL.Image.Resampling.LANCZOS,
      '''nearest''': PIL.Image.Resampling.NEAREST,
  }
else:
  lowercase__   :				Any      =   {
      '''linear''': PIL.Image.LINEAR,
      '''bilinear''': PIL.Image.BILINEAR,
      '''bicubic''': PIL.Image.BICUBIC,
      '''lanczos''': PIL.Image.LANCZOS,
      '''nearest''': PIL.Image.NEAREST,
  }






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Dict								=	(images / 2 + 0.5).clamp(0			,							1      )
     __A						:			str								=	images.cpu().permute(0			,							2			,							3			,							1      ).float().numpy()
     __A						:			Dict								=	numpy_to_pil(__snake_case      )
     return images







def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     if images.ndim == 3:
          __A						:			List[Any]								=	images[None, ...]
     __A						:			List[str]								=	(images * 2_55).round().astype('uint8'      )
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          __A						:			str								=	[Image.fromarray(image.squeeze()			,							mode='L'      ) for image in images]
     else:
          __A						:			str								=	[Image.fromarray(__snake_case      ) for image in images]

     return pil_images