





'''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  (   _lowerCAmelCase							,     _lowerCAmelCase     )->  Any:
 __UpperCAmelCase       = checkpoint

 __UpperCAmelCase       = {}

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

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

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

 # Retrieves the keys for the encoder down blocks only
 __UpperCAmelCase       = len({'.'.join(layer.split('.'     )[:3]     ) for layer in vae_state_dict if 'encoder.down' in layer}     )
 __UpperCAmelCase       = {
     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
 __UpperCAmelCase       = len({'.'.join(layer.split('.'     )[:3]     ) for layer in vae_state_dict if 'decoder.up' in layer}     )
 __UpperCAmelCase       = {
     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     ):
  __UpperCAmelCase       = [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:
   __UpperCAmelCase       = vae_state_dict.pop(
       F'encoder.down.{i}.downsample.conv.weight'     )
   __UpperCAmelCase       = vae_state_dict.pop(
       F'encoder.down.{i}.downsample.conv.bias'     )

  __UpperCAmelCase       = renew_vae_resnet_paths(_snake_case     )
  __UpperCAmelCase       = {'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     )

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

  __UpperCAmelCase       = renew_vae_resnet_paths(_snake_case     )
  __UpperCAmelCase       = {'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     )

 __UpperCAmelCase       = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
 __UpperCAmelCase       = renew_vae_attention_paths(_snake_case     )
 __UpperCAmelCase       = {'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     ):
  __UpperCAmelCase       = num_up_blocks - 1 - i
  __UpperCAmelCase       = [
      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:
   __UpperCAmelCase       = vae_state_dict[
       F'decoder.up.{block_id}.upsample.conv.weight'
   ]
   __UpperCAmelCase       = vae_state_dict[
       F'decoder.up.{block_id}.upsample.conv.bias'
   ]

  __UpperCAmelCase       = renew_vae_resnet_paths(_snake_case     )
  __UpperCAmelCase       = {'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     )

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

  __UpperCAmelCase       = renew_vae_resnet_paths(_snake_case     )
  __UpperCAmelCase       = {'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     )

 __UpperCAmelCase       = [key for key in vae_state_dict if 'decoder.mid.attn' in key]
 __UpperCAmelCase       = renew_vae_attention_paths(_snake_case     )
 __UpperCAmelCase       = {'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  (   _lowerCAmelCase							,     _lowerCAmelCase							,     )->  List[str]:
 # Only support V1
 __UpperCAmelCase       = requests.get(
     ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'     )
 __UpperCAmelCase       = io.BytesIO(r.content     )

 __UpperCAmelCase       = OmegaConf.load(_snake_case     )
 __UpperCAmelCase       = 5_12
 __UpperCAmelCase       = 'cuda' if torch.cuda.is_available() else 'cpu'
 if checkpoint_path.endswith('safetensors'     ):
  from safetensors import safe_open

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

 # Convert the VAE model.
 __UpperCAmelCase       = create_vae_diffusers_config(_snake_case							,     image_size=_snake_case     )
 __UpperCAmelCase       = custom_convert_ldm_vae_checkpoint(_snake_case							,     _snake_case     )

 __UpperCAmelCase       = AutoencoderKL(**_snake_case     )
 vae.load_state_dict(_snake_case     )
 vae.save_pretrained(_snake_case     )


if __name__ == "__main__":
    _A:       str						    =					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.""")

    _A:       List[Any]						    =					parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)







"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

