
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__							(					a		,		a      )      ->   Union[str, Any]:
   _A:  List[Any]					=       checkpoint

   _A:  Tuple					=       {}

   _A:  Dict					=       vae_state_dict["""encoder.conv_in.weight"""]
   _A:  Optional[Any]					=       vae_state_dict["""encoder.conv_in.bias"""]
   _A:  Dict					=       vae_state_dict["""encoder.conv_out.weight"""]
   _A:  Union[str, Any]					=       vae_state_dict["""encoder.conv_out.bias"""]
   _A:  List[Any]					=       vae_state_dict["""encoder.norm_out.weight"""]
   _A:  Tuple					=       vae_state_dict["""encoder.norm_out.bias"""]

   _A:  Dict					=       vae_state_dict["""decoder.conv_in.weight"""]
   _A:  Tuple					=       vae_state_dict["""decoder.conv_in.bias"""]
   _A:  Optional[int]					=       vae_state_dict["""decoder.conv_out.weight"""]
   _A:  Optional[int]					=       vae_state_dict["""decoder.conv_out.bias"""]
   _A:  Optional[Any]					=       vae_state_dict["""decoder.norm_out.weight"""]
   _A:  Union[str, Any]					=       vae_state_dict["""decoder.norm_out.bias"""]

   _A:  Optional[int]					=       vae_state_dict["""quant_conv.weight"""]
   _A:  int					=       vae_state_dict["""quant_conv.bias"""]
   _A:  Union[str, Any]					=       vae_state_dict["""post_quant_conv.weight"""]
   _A:  Any					=       vae_state_dict["""post_quant_conv.bias"""]

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

   # Retrieves the keys for the decoder up blocks only
   _A:  Dict					=       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(__lowerCamelCase      )
   }

   for i in range(__lowerCamelCase      ):
      _A:  List[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:  Optional[Any]					=       vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.weight"""      )
         _A:  int					=       vae_state_dict.pop(
             f"""encoder.down.{i}.downsample.conv.bias"""      )

      _A:  Optional[int]					=       renew_vae_resnet_paths(__lowerCamelCase      )
      _A:  Optional[Any]					=       {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		additional_replacements=[meta_path]		,		config=__lowerCamelCase      )

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

      _A:  Tuple					=       renew_vae_resnet_paths(__lowerCamelCase      )
      _A:  Tuple					=       {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		additional_replacements=[meta_path]		,		config=__lowerCamelCase      )

   _A:  List[Any]					=       [key for key in vae_state_dict if """encoder.mid.attn""" in key]
   _A:  str					=       renew_vae_attention_paths(__lowerCamelCase      )
   _A:  List[str]					=       {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
   assign_to_checkpoint(__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		additional_replacements=[meta_path]		,		config=__lowerCamelCase      )
   conv_attn_to_linear(__lowerCamelCase      )

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

      _A:  Dict					=       renew_vae_resnet_paths(__lowerCamelCase      )
      _A:  Optional[Any]					=       {"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
      assign_to_checkpoint(__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		additional_replacements=[meta_path]		,		config=__lowerCamelCase      )

   _A:  Tuple					=       [key for key in vae_state_dict if """decoder.mid.block""" in key]
   _A:  Union[str, Any]					=       2
   for i in range(1		,		num_mid_res_blocks + 1      ):
      _A:  Dict					=       [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

      _A:  List[Any]					=       renew_vae_resnet_paths(__lowerCamelCase      )
      _A:  int					=       {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
      assign_to_checkpoint(__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		additional_replacements=[meta_path]		,		config=__lowerCamelCase      )

   _A:  Dict					=       [key for key in vae_state_dict if """decoder.mid.attn""" in key]
   _A:  List[Any]					=       renew_vae_attention_paths(__lowerCamelCase      )
   _A:  List[str]					=       {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
   assign_to_checkpoint(__lowerCamelCase		,		__lowerCamelCase		,		__lowerCamelCase		,		additional_replacements=[meta_path]		,		config=__lowerCamelCase      )
   conv_attn_to_linear(__lowerCamelCase      )
   return new_checkpoint






def   lowerCamelCase__							(					a		,		a		,		)      ->   Union[str, Any]:
   # Only support V1
   _A:  Optional[int]					=       requests.get(
       ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'''      )
   _A:  Optional[int]					=       io.BytesIO(r.content      )

   _A:  Dict					=       OmegaConf.load(__lowerCamelCase      )
   _A:  str					=       5_12
   _A:  Any					=       """cuda""" if torch.cuda.is_available() else """cpu"""
   if checkpoint_path.endswith('''safetensors'''      ):
      from safetensors import safe_open

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

   # Convert the VAE model.
   _A:  Optional[int]					=       create_vae_diffusers_config(__lowerCamelCase		,		image_size=__lowerCamelCase      )
   _A:  Union[str, Any]					=       custom_convert_ldm_vae_checkpoint(__lowerCamelCase		,		__lowerCamelCase      )

   _A:  Optional[int]					=       AutoencoderKL(**__lowerCamelCase      )
   vae.load_state_dict(__lowerCamelCase      )
   vae.save_pretrained(__lowerCamelCase      )


if __name__ == "__main__":
   UpperCAmelCase__		:     List[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.')

   UpperCAmelCase__		:     Optional[int]     			=      parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)











def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Tuple           =  [1]
  for i in range(2  ,		__lowerCamelCase		):
    factorials.append(factorials[-1] * i		)
  assert 0 <= k < factorials[-1] * n, "k out of bounds"

  __UpperCAmelCase							:	Optional[Any]           =  []
  __UpperCAmelCase							:	str           =  list(range(__lowerCamelCase		)		)

  # Find permutation
  while factorials:
    __UpperCAmelCase							:	Any           =  factorials.pop()
    __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  divmod(__lowerCamelCase  ,		__lowerCamelCase		)
    permutation.append(elements[number]		)
    elements.remove(elements[number]		)
  permutation.append(elements[0]		)

  return permutation


if __name__ == "__main__":
       import doctest

       doctest.testmod()


