

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 			a__     (		UpperCAmelCase							:			int				, UpperCAmelCase							:			int			)  ->						Dict:
  UpperCAmelCase       :							Any  = checkpoint

  UpperCAmelCase       :							List[str]  = {}

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

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

  UpperCAmelCase       :							Any  = vae_state_dict['quant_conv.weight']
  UpperCAmelCase       :							Union[str, Any]  = vae_state_dict['quant_conv.bias']
  UpperCAmelCase       :							Tuple  = vae_state_dict['post_quant_conv.weight']
  UpperCAmelCase       :							List[Any]  = vae_state_dict['post_quant_conv.bias']

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

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

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

    UpperCAmelCase       :							Optional[Any]  = renew_vae_resnet_paths(_lowerCAmelCase			)
    UpperCAmelCase       :							Dict  = {'old': f'''mid.block_{i}''', 'new': f'''mid_block.resnets.{i - 1}'''}
    assign_to_checkpoint(_lowerCAmelCase				, _lowerCAmelCase				, _lowerCAmelCase				, additional_replacements=[meta_path]				, config=_lowerCAmelCase			)

  UpperCAmelCase       :							Optional[Any]  = [key for key in vae_state_dict if 'encoder.mid.attn' in key]
  UpperCAmelCase       :							int  = renew_vae_attention_paths(_lowerCAmelCase			)
  UpperCAmelCase       :							Optional[Any]  = {'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			):
    UpperCAmelCase       :							Optional[Any]  = num_up_blocks - 1 - i
    UpperCAmelCase       :							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:
      UpperCAmelCase       :							Optional[int]  = vae_state_dict[
          f'''decoder.up.{block_id}.upsample.conv.weight'''
      ]
      UpperCAmelCase       :							Optional[Any]  = vae_state_dict[
          f'''decoder.up.{block_id}.upsample.conv.bias'''
      ]

    UpperCAmelCase       :							Tuple  = renew_vae_resnet_paths(_lowerCAmelCase			)
    UpperCAmelCase       :							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			)

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

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

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

  UpperCAmelCase       :							Any  = OmegaConf.load(_lowerCAmelCase			)
  UpperCAmelCase       :							List[Any]  = 512
  UpperCAmelCase       :							Dict  = 'cuda' if torch.cuda.is_available() else 'cpu'
  if checkpoint_path.endswith('''safetensors'''			):
    from safetensors import safe_open

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

  # Convert the VAE model.
  UpperCAmelCase       :							int  = create_vae_diffusers_config(_lowerCAmelCase				, image_size=_lowerCAmelCase			)
  UpperCAmelCase       :							int  = custom_convert_ldm_vae_checkpoint(_lowerCAmelCase				, _lowerCAmelCase			)

  UpperCAmelCase       :							Any  = AutoencoderKL(**_lowerCAmelCase			)
  vae.load_state_dict(_lowerCAmelCase			)
  vae.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
     _lowerCamelCase				:   Tuple					   =							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.")

     _lowerCamelCase				:   List[str]					   =							parser.parse_args()

     vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

def        a_     (							_lowerCAmelCase       )      ->      str:
  __lowerCamelCase       ,__lowerCamelCase       :       List[Any]           = image.size
  __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __lowerCamelCase       :       Optional[Any]           = image.resize((w, h)       ,resample=PIL_INTERPOLATION['lanczos']       )
  __lowerCamelCase       :       List[Any]           = np.array(_lowerCAmelCase       ).astype(np.floataa       ) / 255.0
  __lowerCamelCase       :       Optional[Any]           = image[None].transpose(0       ,3       ,1       ,2       )
  __lowerCamelCase       :       int           = torch.from_numpy(_lowerCAmelCase       )
  return 2.0 * image - 1.0






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	str			,	_a      :	VQModel			,	_a      :	UNetaDModel			,	_a      :	Union[
          DDIMScheduler,
          PNDMScheduler,
          LMSDiscreteScheduler,
          EulerDiscreteScheduler,
          EulerAncestralDiscreteScheduler,
          DPMSolverMultistepScheduler,
      ]			,	)					->					Optional[Any]:
    super().__init__()
    self.register_modules(vqvae=_a			,	unet=_a			,	scheduler=_a   )




  @torch.no_grad()
  def __call__( self      :	List[Any]			,	_a      :	Union[torch.Tensor, PIL.Image.Image] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[int] = 100			,	_a      :	Optional[float] = 0.0			,	_a      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	)					->					Union[Tuple, ImagePipelineOutput]:
    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       Any           = 1
    elif isinstance(_a			,	torch.Tensor   ):
      __lowerCamelCase       :       Any           = image.shape[0]
    else:
      raise ValueError(f'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a   )}'   )

    if isinstance(_a			,	PIL.Image.Image   ):
      __lowerCamelCase       :       List[str]           = preprocess(_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = image.shape[-2:]

    # in_channels should be 6: 3 for latents, 3 for low resolution image
    __lowerCamelCase       :       Union[str, Any]           = (batch_size, self.unet.config.in_channels // 2, height, width)
    __lowerCamelCase       :       Tuple           = next(self.unet.parameters()   ).dtype

    __lowerCamelCase       :       Optional[int]           = randn_tensor(_a			,	generator=_a			,	device=self.device			,	dtype=_a   )

    __lowerCamelCase       :       Optional[int]           = image.to(device=self.device			,	dtype=_a   )

    # set timesteps and move to the correct device
    self.scheduler.set_timesteps(_a			,	device=self.device   )
    __lowerCamelCase       :       Union[str, Any]           = self.scheduler.timesteps

    # scale the initial noise by the standard deviation required by the scheduler
    __lowerCamelCase       :       List[Any]           = latents * self.scheduler.init_noise_sigma

    # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
    # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
    # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
    # and should be between [0, 1]
    __lowerCamelCase       :       int           = 'eta' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
    __lowerCamelCase       :       List[str]           = {}
    if accepts_eta:
      __lowerCamelCase       :       str           = eta

    for t in self.progress_bar(_a   ):
      # concat latents and low resolution image in the channel dimension.
      __lowerCamelCase       :       str           = torch.cat([latents, image]			,	dim=1   )
      __lowerCamelCase       :       Union[str, Any]           = self.scheduler.scale_model_input(_a			,	_a   )
      # predict the noise residual
      __lowerCamelCase       :       Optional[int]           = self.unet(_a			,	_a   ).sample
      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a			,	**_a   ).prev_sample

    # decode the image latents with the VQVAE
    __lowerCamelCase       :       Union[str, Any]           = self.vqvae.decode(_a   ).sample
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_a			,	-1.0			,	1.0   )
    __lowerCamelCase       :       List[str]           = image / 2 + 0.5
    __lowerCamelCase       :       Optional[Any]           = image.cpu().permute(0			,	2			,	3			,	1   ).numpy()

    if output_type == "pil":
      __lowerCamelCase       :       Tuple           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






