



'''simple docstring'''





import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowercase_		       =  [
    # (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"),
]

lowercase_		       =  [
    # (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"),
]

lowercase_		       =  []
# 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
    lowercase_		       =  F"""down_blocks.{i}.resnets.{j}."""
    lowercase_		       =  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
      lowercase_		       =  F"""down_blocks.{i}.attentions.{j}."""
      lowercase_		       =  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
    lowercase_		       =  F"""up_blocks.{i}.resnets.{j}."""
    lowercase_		       =  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
      lowercase_		       =  F"""up_blocks.{i}.attentions.{j}."""
      lowercase_		       =  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
    lowercase_		       =  F"""down_blocks.{i}.downsamplers.0.conv."""
    lowercase_		       =  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
    lowercase_		       =  F"""up_blocks.{i}.upsamplers.0."""
    lowercase_		       =  F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
    unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

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

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




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  {k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          _a      =  sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    _a      =  v.replace(__A  ,	__A)
               _a      =  v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               _a      =  v.replace(__A  ,	__A)
          _a      =  v
     _a      =  {v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


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

lowercase_		       =  [
    # (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):
    lowercase_		       =  F"""encoder.down_blocks.{i}.resnets.{j}."""
    lowercase_		       =  F"""encoder.down.{i}.block.{j}."""
    vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

    lowercase_		       =  F"""up_blocks.{i}.upsamplers.0."""
    lowercase_		       =  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):
    lowercase_		       =  F"""decoder.up_blocks.{i}.resnets.{j}."""
    lowercase_		       =  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):
  lowercase_		       =  F"""mid_block.resnets.{i}."""
  lowercase_		       =  F"""mid.block_{i+1}."""
  vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


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




def lowerCAmelCase  (__A):



     """simple docstring"""



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




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  {k: k for k in vae_state_dict.keys()}
     for k, v in mapping.items():
          for sd_part, hf_part in vae_conversion_map:
               _a      =  v.replace(__A  ,	__A)
          _a      =  v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    _a      =  v.replace(__A  ,	__A)
               _a      =  v
     _a      =  {v: vae_state_dict[k] for k, v in mapping.items()}
     _a      =  ['''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''')
                    _a      =  reshape_weight_for_sd(__A)
     return new_state_dict


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


lowercase_		       =  [
    # (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"),
]
lowercase_		       =  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase_		       =  re.compile("|".join(protected.keys()))

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




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  {}
     _a      =  {}
     _a      =  {}
     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''')
          ):
               _a      =  k[: -len('''.q_proj.weight''')]
               _a      =  k[-len('''q_proj.weight''')]
               if k_pre not in capture_qkv_weight:
                    _a      =  [None, None, None]
               _a      =  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''')
          ):
               _a      =  k[: -len('''.q_proj.bias''')]
               _a      =  k[-len('''q_proj.bias''')]
               if k_pre not in capture_qkv_bias:
                    _a      =  [None, None, None]
               _a      =  v
               continue

          _a      =  textenc_pattern.sub(lambda __A: protected[re.escape(m.group(0))]  ,	__A)
          _a      =  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''')
          _a      =  textenc_pattern.sub(lambda __A: protected[re.escape(m.group(0))]  ,	__A)
          _a      =  torch.cat(__A)

     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''')
          _a      =  textenc_pattern.sub(lambda __A: protected[re.escape(m.group(0))]  ,	__A)
          _a      =  torch.cat(__A)

     return new_state_dict




def lowerCAmelCase  (__A):



     """simple docstring"""



     return text_enc_dict


if __name__ == "__main__":
  lowercase_		       =  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."
  )

  lowercase_		       =  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
  lowercase_		       =  osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
  lowercase_		       =  osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
  lowercase_		       =  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):
    lowercase_		       =  load_file(unet_path, device="cpu")
  else:
    lowercase_		       =  osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
    lowercase_		       =  torch.load(unet_path, map_location="cpu")

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

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

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

  # Convert the VAE model
  lowercase_		       =  convert_vae_state_dict(vae_state_dict)
  lowercase_		       =  {"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
  lowercase_		       =  "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
    lowercase_		       =  {"transformer." + k: v for k, v in text_enc_dict.items()}
    lowercase_		       =  convert_text_enc_state_dict_vaa(text_enc_dict)
    lowercase_		       =  {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
  else:
    lowercase_		       =  convert_text_enc_state_dict(text_enc_dict)
    lowercase_		       =  {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

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

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








'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



