"""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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Tuple:
    _lowerCamelCase	: Optional[int]       =       checkpoint

    _lowerCamelCase	: Union[str, Any]       =       {}

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

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

    _lowerCamelCase	: str       =       vae_state_dict['''quant_conv.weight''']
    _lowerCamelCase	: Tuple       =       vae_state_dict['''quant_conv.bias''']
    _lowerCamelCase	: int       =       vae_state_dict['''post_quant_conv.weight''']
    _lowerCamelCase	: Optional[Any]       =       vae_state_dict['''post_quant_conv.bias''']

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

        _lowerCamelCase	: Tuple       =       renew_vae_resnet_paths(UpperCamelCase__ )
        _lowerCamelCase	: Tuple       =       {'''old''': F'''down.{i}.block''', '''new''': F'''down_blocks.{i}.resnets'''}
        assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[meta_path]						,	config=UpperCamelCase__ )

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

        _lowerCamelCase	: Optional[int]       =       renew_vae_resnet_paths(UpperCamelCase__ )
        _lowerCamelCase	: List[str]       =       {'''old''': F'''mid.block_{i}''', '''new''': F'''mid_block.resnets.{i - 1}'''}
        assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[meta_path]						,	config=UpperCamelCase__ )

    _lowerCamelCase	: Optional[Any]       =       [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
    _lowerCamelCase	: Tuple       =       renew_vae_attention_paths(UpperCamelCase__ )
    _lowerCamelCase	: List[str]       =       {'''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__ ):
        _lowerCamelCase	: Optional[int]       =       num_up_blocks - 1 - i
        _lowerCamelCase	: List[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:
            _lowerCamelCase	: List[Any]       =       vae_state_dict[
                F'''decoder.up.{block_id}.upsample.conv.weight'''
            ]
            _lowerCamelCase	: Optional[Any]       =       vae_state_dict[
                F'''decoder.up.{block_id}.upsample.conv.bias'''
            ]

        _lowerCamelCase	: Union[str, Any]       =       renew_vae_resnet_paths(UpperCamelCase__ )
        _lowerCamelCase	: Optional[int]       =       {'''old''': F'''up.{block_id}.block''', '''new''': F'''up_blocks.{i}.resnets'''}
        assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[meta_path]						,	config=UpperCamelCase__ )

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

        _lowerCamelCase	: List[Any]       =       renew_vae_resnet_paths(UpperCamelCase__ )
        _lowerCamelCase	: Optional[Any]       =       {'''old''': F'''mid.block_{i}''', '''new''': F'''mid_block.resnets.{i - 1}'''}
        assign_to_checkpoint(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	additional_replacements=[meta_path]						,	config=UpperCamelCase__ )

    _lowerCamelCase	: int       =       [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
    _lowerCamelCase	: Dict       =       renew_vae_attention_paths(UpperCamelCase__ )
    _lowerCamelCase	: Optional[Any]       =       {'''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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	)      ->Dict:
    _lowerCamelCase	: Optional[Any]       =       requests.get(
        ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml''' )
    _lowerCamelCase	: Any       =       io.BytesIO(r.content )

    _lowerCamelCase	: Union[str, Any]       =       OmegaConf.load(UpperCamelCase__ )
    _lowerCamelCase	: Optional[Any]       =       512
    _lowerCamelCase	: Any       =       '''cuda''' if torch.cuda.is_available() else '''cpu'''
    if checkpoint_path.endswith('''safetensors''' ):
        from safetensors import safe_open

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

    # Convert the VAE model.
    _lowerCamelCase	: int       =       create_vae_diffusers_config(UpperCamelCase__						,	image_size=UpperCamelCase__ )
    _lowerCamelCase	: List[str]       =       custom_convert_ldm_vae_checkpoint(UpperCamelCase__						,	UpperCamelCase__ )

    _lowerCamelCase	: Tuple       =       AutoencoderKL(**UpperCamelCase__ )
    vae.load_state_dict(UpperCamelCase__ )
    vae.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__   :      Optional[int]							=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.')

  SCREAMING_SNAKE_CASE__   :      str							=parser.parse_args()

  vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






_lowercase							:   Dict						=						{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}



