





"""simple docstring"""


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCamelCase__						= [
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

lowerCamelCase__						= [
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

lowerCamelCase__						= []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
  # loop over downblocks/upblocks

  for j in range(2):
    # loop over resnets/attentions for downblocks
    lowerCamelCase__						= f"""down_blocks.{i}.resnets.{j}."""
    lowerCamelCase__						= f"""input_blocks.{3*i + j + 1}.0."""
    unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

    if i < 3:
      # no attention layers in down_blocks.3
      lowerCamelCase__						= f"""down_blocks.{i}.attentions.{j}."""
      lowerCamelCase__						= f"""input_blocks.{3*i + j + 1}.1."""
      unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

  for j in range(3):
    # loop over resnets/attentions for upblocks
    lowerCamelCase__						= f"""up_blocks.{i}.resnets.{j}."""
    lowerCamelCase__						= f"""output_blocks.{3*i + j}.0."""
    unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

    if i > 0:
      # no attention layers in up_blocks.0
      lowerCamelCase__						= f"""up_blocks.{i}.attentions.{j}."""
      lowerCamelCase__						= f"""output_blocks.{3*i + j}.1."""
      unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

  if i < 3:
    # no downsample in down_blocks.3
    lowerCamelCase__						= f"""down_blocks.{i}.downsamplers.0.conv."""
    lowerCamelCase__						= f"""input_blocks.{3*(i+1)}.0.op."""
    unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

    # no upsample in up_blocks.3
    lowerCamelCase__						= f"""up_blocks.{i}.upsamplers.0."""
    lowerCamelCase__						= f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
    unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase__						= "mid_block.attentions.0."
lowerCamelCase__						= "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
  lowerCamelCase__						= f"""mid_block.resnets.{j}."""
  lowerCamelCase__						= f"""middle_block.{2*j}."""
  unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))




def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							{k: k for k in unet_state_dict.keys()}
      for sd_name, hf_name in unet_conversion_map:
            _UpperCamelCase   :						List[Any]    =							sd_name
      for k, v in mapping.items():
            if "resnets" in k:
                  for sd_part, hf_part in unet_conversion_map_resnet:
                        _UpperCamelCase   :						Any    =							v.replace(lowercase_		,lowercase_      )
                  _UpperCamelCase   :						Dict    =							v
      for k, v in mapping.items():
            for sd_part, hf_part in unet_conversion_map_layer:
                  _UpperCamelCase   :						int    =							v.replace(lowercase_		,lowercase_      )
            _UpperCamelCase   :						Union[str, Any]    =							v
      _UpperCamelCase   :						str    =							{v: unet_state_dict[k] for k, v in mapping.items()}
      return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCamelCase__						= [
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
  # down_blocks have two resnets
  for j in range(2):
    lowerCamelCase__						= f"""encoder.down_blocks.{i}.resnets.{j}."""
    lowerCamelCase__						= f"""encoder.down.{i}.block.{j}."""
    vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

  if i < 3:
    lowerCamelCase__						= f"""down_blocks.{i}.downsamplers.0."""
    lowerCamelCase__						= f"""down.{i}.downsample."""
    vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

    lowerCamelCase__						= f"""up_blocks.{i}.upsamplers.0."""
    lowerCamelCase__						= f"""up.{3-i}.upsample."""
    vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

  # up_blocks have three resnets
  # also, up blocks in hf are numbered in reverse from sd
  for j in range(3):
    lowerCamelCase__						= f"""decoder.up_blocks.{i}.resnets.{j}."""
    lowerCamelCase__						= f"""decoder.up.{3-i}.block.{j}."""
    vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
  lowerCamelCase__						= f"""mid_block.resnets.{i}."""
  lowerCamelCase__						= f"""mid.block_{i+1}."""
  vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase__						= [
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]




def        lowercase__     (       lowercase_      )       ->		Any:




      """simple docstring"""

      return w.reshape(*w.shape		,1		,1      )




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							{k: k for k in vae_state_dict.keys()}
      for k, v in mapping.items():
            for sd_part, hf_part in vae_conversion_map:
                  _UpperCamelCase   :						int    =							v.replace(lowercase_		,lowercase_      )
            _UpperCamelCase   :						int    =							v
      for k, v in mapping.items():
            if "attentions" in k:
                  for sd_part, hf_part in vae_conversion_map_attn:
                        _UpperCamelCase   :						Tuple    =							v.replace(lowercase_		,lowercase_      )
                  _UpperCamelCase   :						Union[str, Any]    =							v
      _UpperCamelCase   :						List[Any]    =							{v: vae_state_dict[k] for k, v in mapping.items()}
      _UpperCamelCase   :						str    =							["q", "k", "v", "proj_out"]
      for k, v in new_state_dict.items():
            for weight_name in weights_to_convert:
                  if F'''mid.attn_1.{weight_name}.weight''' in k:
                        print(F'''Reshaping {k} for SD format'''      )
                        _UpperCamelCase   :						Union[str, Any]    =							reshape_weight_for_sd(lowercase_      )
      return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCamelCase__						= [
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
lowerCamelCase__						= {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase__						= re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase__						= {"q": 0, "k": 1, "v": 2}




def        lowercase__     (       lowercase_      )       ->		int:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							{}
      _UpperCamelCase   :						Any    =							{}
      _UpperCamelCase   :						int    =							{}
      for k, v in text_enc_dict.items():
            if (
                k.endswith(".self_attn.q_proj.weight"      )
                or k.endswith(".self_attn.k_proj.weight"      )
                or k.endswith(".self_attn.v_proj.weight"      )
            ):
                  _UpperCamelCase   :						Dict    =							k[: -len(".q_proj.weight"      )]
                  _UpperCamelCase   :						Optional[Any]    =							k[-len("q_proj.weight"      )]
                  if k_pre not in capture_qkv_weight:
                        _UpperCamelCase   :						str    =							[None, None, None]
                  _UpperCamelCase   :						Tuple    =							v
                  continue

            if (
                k.endswith(".self_attn.q_proj.bias"      )
                or k.endswith(".self_attn.k_proj.bias"      )
                or k.endswith(".self_attn.v_proj.bias"      )
            ):
                  _UpperCamelCase   :						Dict    =							k[: -len(".q_proj.bias"      )]
                  _UpperCamelCase   :						Optional[int]    =							k[-len("q_proj.bias"      )]
                  if k_pre not in capture_qkv_bias:
                        _UpperCamelCase   :						List[str]    =							[None, None, None]
                  _UpperCamelCase   :						Optional[Any]    =							v
                  continue

            _UpperCamelCase   :						str    =							textenc_pattern.sub(lambda lowercase_      : protected[re.escape(m.group(0      )      )]		,lowercase_      )
            _UpperCamelCase   :						str    =							v

      for k_pre, tensors in capture_qkv_weight.items():
            if None in tensors:
                  raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
            _UpperCamelCase   :						Dict    =							textenc_pattern.sub(lambda lowercase_      : protected[re.escape(m.group(0      )      )]		,lowercase_      )
            _UpperCamelCase   :						Dict    =							torch.cat(lowercase_      )

      for k_pre, tensors in capture_qkv_bias.items():
            if None in tensors:
                  raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"      )
            _UpperCamelCase   :						Union[str, Any]    =							textenc_pattern.sub(lambda lowercase_      : protected[re.escape(m.group(0      )      )]		,lowercase_      )
            _UpperCamelCase   :						str    =							torch.cat(lowercase_      )

      return new_state_dict




def        lowercase__     (       lowercase_      )       ->		Dict:




      """simple docstring"""

      return text_enc_dict


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()

  parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
  parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
  parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
  parser.add_argument(
      "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
  )

  lowerCamelCase__						= parser.parse_args()

  assert args.model_path is not None, "Must provide a model path!"

  assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

  # Path for safetensors
  lowerCamelCase__						= osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
  lowerCamelCase__						= osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
  lowerCamelCase__						= osp.join(args.model_path, "text_encoder", "model.safetensors")

  # Load models from safetensors if it exists, if it doesn't pytorch
  if osp.exists(unet_path):
    lowerCamelCase__						= load_file(unet_path, device="cpu")
  else:
    lowerCamelCase__						= osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
    lowerCamelCase__						= torch.load(unet_path, map_location="cpu")

  if osp.exists(vae_path):
    lowerCamelCase__						= load_file(vae_path, device="cpu")
  else:
    lowerCamelCase__						= osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
    lowerCamelCase__						= torch.load(vae_path, map_location="cpu")

  if osp.exists(text_enc_path):
    lowerCamelCase__						= load_file(text_enc_path, device="cpu")
  else:
    lowerCamelCase__						= osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
    lowerCamelCase__						= torch.load(text_enc_path, map_location="cpu")

  # Convert the UNet model
  lowerCamelCase__						= convert_unet_state_dict(unet_state_dict)
  lowerCamelCase__						= {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

  # Convert the VAE model
  lowerCamelCase__						= convert_vae_state_dict(vae_state_dict)
  lowerCamelCase__						= {"first_stage_model." + k: v for k, v in vae_state_dict.items()}

  # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
  lowerCamelCase__						= "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

  if is_vaa_model:
    # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
    lowerCamelCase__						= {"transformer." + k: v for k, v in text_enc_dict.items()}
    lowerCamelCase__						= convert_text_enc_state_dict_vaa(text_enc_dict)
    lowerCamelCase__						= {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
  else:
    lowerCamelCase__						= convert_text_enc_state_dict(text_enc_dict)
    lowerCamelCase__						= {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

  # Put together new checkpoint
  lowerCamelCase__						= {**unet_state_dict, **vae_state_dict, **text_enc_dict}
  if args.half:
    lowerCamelCase__						= {k: v.half() for k, v in state_dict.items()}

  if args.use_safetensors:
    save_file(state_dict, args.checkpoint_path)
  else:
    lowerCamelCase__						= {"state_dict": state_dict}
    torch.save(state_dict, args.checkpoint_path)







"""simple docstring"""


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase__						= False
lowerCamelCase__						= True
lowerCamelCase__						= False


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()

  parser.add_argument(
      "--repo_path",
      default=None,
      type=str,
      required=True,
      help="The config json file corresponding to the architecture.",
  )

  parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

  lowerCamelCase__						= parser.parse_args()

  lowerCamelCase__						= {
      "image_size": "sample_size",
      "num_res_blocks": "layers_per_block",
      "block_channels": "block_out_channels",
      "down_blocks": "down_block_types",
      "up_blocks": "up_block_types",
      "downscale_freq_shift": "freq_shift",
      "resnet_num_groups": "norm_num_groups",
      "resnet_act_fn": "act_fn",
      "resnet_eps": "norm_eps",
      "num_head_channels": "attention_head_dim",
  }

  lowerCamelCase__						= {
      "time_steps": "time_proj",
      "mid": "mid_block",
      "downsample_blocks": "down_blocks",
      "upsample_blocks": "up_blocks",
  }

  lowerCamelCase__						= "" if has_file(args.repo_path, "config.json") else "unet"

  with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
    lowerCamelCase__						= reader.read()
    lowerCamelCase__						= json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, "config.json"):
    lowerCamelCase__						= UNetaDModel(**config)
  else:
    lowerCamelCase__						= UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
    lowerCamelCase__						= class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  lowerCamelCase__						= dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        lowerCamelCase__						= config[key]
        del config[key]

    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["down_block_types"]]
    lowerCamelCase__						= [k.replace("UNetRes", "") for k in config["up_block_types"]]

  if do_only_weights:
    lowerCamelCase__						= torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

    lowerCamelCase__						= {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
        continue
      lowerCamelCase__						= False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(".")[0] == key:
          lowerCamelCase__						= param_value
          lowerCamelCase__						= True
      if not has_changed:
        lowerCamelCase__						= param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))
