



"""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		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				Any:
  _snake_case   	=      checkpoint

  _snake_case   	=      {}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
     snake_case   					=				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   					=				parser.parse_args()

     vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


snake_case   					=				{'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				['''HerbertTokenizerFast''']


if TYPE_CHECKING:
     from .tokenization_herbert import HerbertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_herbert_fast import HerbertTokenizerFast

else:
     import sys

     snake_case   					=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





