






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

lowerCamelCase     :							int    =[
    # (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     :							str    =[
    # (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     :							Optional[int]    =[]
# 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     :							List[str]    =F"""down_blocks.{i}.resnets.{j}."""
  lowerCamelCase     :							str    =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     :							Optional[int]    =F"""down_blocks.{i}.attentions.{j}."""
   lowerCamelCase     :							Optional[Any]    =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     :							Optional[Any]    =F"""up_blocks.{i}.resnets.{j}."""
  lowerCamelCase     :							Optional[Any]    =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     :							Tuple    =F"""up_blocks.{i}.attentions.{j}."""
   lowerCamelCase     :							int    =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     :							Optional[Any]    =F"""down_blocks.{i}.downsamplers.0.conv."""
  lowerCamelCase     :							Union[str, Any]    =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     :							int    =F"""up_blocks.{i}.upsamplers.0."""
  lowerCamelCase     :							Union[str, Any]    =F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
  unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase     :							int    ='''mid_block.attentions.0.'''
lowerCamelCase     :							Optional[Any]    ='''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
 lowerCamelCase     :							List[str]    =F"""mid_block.resnets.{j}."""
 lowerCamelCase     :							Optional[int]    =F"""middle_block.{2*j}."""
 unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Optional[int]:
     # buyer beware: this is a *brittle* function,
     # and correct output requires that all of these pieces interact in
     # the exact order in which I have arranged them.
     UpperCamelCase__      :       List[str]	    =  {k: k for k in unet_state_dict.keys()}
     for sd_name, hf_name in unet_conversion_map:
          UpperCamelCase__      :       List[str]	    =  sd_name
     for k, v in mapping.items():
          if "resnets" in k:
               for sd_part, hf_part in unet_conversion_map_resnet:
                    UpperCamelCase__      :       List[Any]	    =  v.replace(__lowerCAmelCase ,   __lowerCAmelCase      )
               UpperCamelCase__      :       str	    =  v
     for k, v in mapping.items():
          for sd_part, hf_part in unet_conversion_map_layer:
               UpperCamelCase__      :       str	    =  v.replace(__lowerCAmelCase ,   __lowerCAmelCase      )
          UpperCamelCase__      :       Any	    =  v
     UpperCamelCase__      :       int	    =  {v: unet_state_dict[k] for k, v in mapping.items()}
     return new_state_dict


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

lowerCamelCase     :							Optional[Any]    =[
    # (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     :							List[str]    =F"""encoder.down_blocks.{i}.resnets.{j}."""
  lowerCamelCase     :							Optional[int]    =F"""encoder.down.{i}.block.{j}."""
  vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

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

  lowerCamelCase     :							str    =F"""up_blocks.{i}.upsamplers.0."""
  lowerCamelCase     :							List[str]    =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     :							Union[str, Any]    =F"""decoder.up_blocks.{i}.resnets.{j}."""
  lowerCamelCase     :							Tuple    =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     :							List[Any]    =F"""mid_block.resnets.{i}."""
 lowerCamelCase     :							List[str]    =F"""mid.block_{i+1}."""
 vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase     :							Union[str, Any]    =[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
     # convert HF linear weights to SD conv2d weights
     return w.reshape(*w.shape ,   1 ,   1      )







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Dict:
     UpperCamelCase__      :       Dict	    =  {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(__lowerCAmelCase ,   __lowerCAmelCase      )
          UpperCamelCase__      :       List[str]	    =  v
     for k, v in mapping.items():
          if "attentions" in k:
               for sd_part, hf_part in vae_conversion_map_attn:
                    UpperCamelCase__      :       List[str]	    =  v.replace(__lowerCAmelCase ,   __lowerCAmelCase      )
               UpperCamelCase__      :       Union[str, Any]	    =  v
     UpperCamelCase__      :       Tuple	    =  {v: vae_state_dict[k] for k, v in mapping.items()}
     UpperCamelCase__      :       List[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__      :       Any	    =  reshape_weight_for_sd(__lowerCAmelCase      )
     return new_state_dict


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


lowerCamelCase     :							Any    =[
    # (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     :							Optional[Any]    ={re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase     :							List[Any]    =re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase     :							Optional[int]    ={'''q''': 0, '''k''': 1, '''v''': 2}







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     str:
     UpperCamelCase__      :       Union[str, Any]	    =  {}
     UpperCamelCase__      :       str	    =  {}
     UpperCamelCase__      :       Optional[Any]	    =  {}
     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__      :       List[str]	    =  k[: -len(".q_proj.weight"      )]
               UpperCamelCase__      :       Tuple	    =  k[-len("q_proj.weight"      )]
               if k_pre not in capture_qkv_weight:
                    UpperCamelCase__      :       Tuple	    =  [None, None, None]
               UpperCamelCase__      :       Optional[int]	    =  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__      :       List[Any]	    =  k[: -len(".q_proj.bias"      )]
               UpperCamelCase__      :       Optional[int]	    =  k[-len("q_proj.bias"      )]
               if k_pre not in capture_qkv_bias:
                    UpperCamelCase__      :       int	    =  [None, None, None]
               UpperCamelCase__      :       Optional[int]	    =  v
               continue

          UpperCamelCase__      :       Dict	    =  textenc_pattern.sub(lambda __lowerCAmelCase      : protected[re.escape(m.group(0      )      )] ,   __lowerCAmelCase      )
          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__      :       Any	    =  textenc_pattern.sub(lambda __lowerCAmelCase      : protected[re.escape(m.group(0      )      )] ,   __lowerCAmelCase      )
          UpperCamelCase__      :       List[Any]	    =  torch.cat(__lowerCAmelCase      )

     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__      :       List[str]	    =  textenc_pattern.sub(lambda __lowerCAmelCase      : protected[re.escape(m.group(0      )      )] ,   __lowerCAmelCase      )
          UpperCamelCase__      :       int	    =  torch.cat(__lowerCAmelCase      )

     return new_state_dict







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Tuple:
     return text_enc_dict


if __name__ == "__main__":
 lowerCamelCase     :							Optional[int]    =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     :							Tuple    =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     :							Tuple    =osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
 lowerCamelCase     :							List[Any]    =osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
 lowerCamelCase     :							List[str]    =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     :							Dict    =load_file(unet_path, device='''cpu''')
 else:
  lowerCamelCase     :							Dict    =osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
  lowerCamelCase     :							Any    =torch.load(unet_path, map_location='''cpu''')

 if osp.exists(vae_path):
  lowerCamelCase     :							List[str]    =load_file(vae_path, device='''cpu''')
 else:
  lowerCamelCase     :							str    =osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
  lowerCamelCase     :							str    =torch.load(vae_path, map_location='''cpu''')

 if osp.exists(text_enc_path):
  lowerCamelCase     :							List[str]    =load_file(text_enc_path, device='''cpu''')
 else:
  lowerCamelCase     :							List[Any]    =osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
  lowerCamelCase     :							int    =torch.load(text_enc_path, map_location='''cpu''')

 # Convert the UNet model
 lowerCamelCase     :							List[Any]    =convert_unet_state_dict(unet_state_dict)
 lowerCamelCase     :							List[str]    ={'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

 # Convert the VAE model
 lowerCamelCase     :							str    =convert_vae_state_dict(vae_state_dict)
 lowerCamelCase     :							str    ={'''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     :							str    ='''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     :							Any    ={'''transformer.''' + k: v for k, v in text_enc_dict.items()}
  lowerCamelCase     :							List[str]    =convert_text_enc_state_dict_vaa(text_enc_dict)
  lowerCamelCase     :							int    ={'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
 else:
  lowerCamelCase     :							Optional[Any]    =convert_text_enc_state_dict(text_enc_dict)
  lowerCamelCase     :							Dict    ={'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

 # Put together new checkpoint
 lowerCamelCase     :							Tuple    ={**unet_state_dict, **vae_state_dict, **text_enc_dict}
 if args.half:
  lowerCamelCase     :							Optional[int]    ={k: v.half() for k, v in state_dict.items()}

 if args.use_safetensors:
  save_file(state_dict, args.checkpoint_path)
 else:
  lowerCamelCase     :							Optional[int]    ={'''state_dict''': state_dict}
  torch.save(state_dict, args.checkpoint_path)







from __future__ import annotations







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase = None ,   __lowerCAmelCase = None      )			->     None:
     if start is None:
          UpperCamelCase__      :       Union[str, Any]	    =  0

     if end is None:
          UpperCamelCase__      :       List[Any]	    =  len(__lowerCAmelCase      ) - 1

     if start >= end:
          return

     UpperCamelCase__      :       Union[str, Any]	    =  (start + end) // 2

     slowsort(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )
     slowsort(__lowerCAmelCase ,   mid + 1 ,   __lowerCAmelCase      )

     if sequence[end] < sequence[mid]:
          UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  sequence[mid], sequence[end]

     slowsort(__lowerCAmelCase ,   __lowerCAmelCase ,   end - 1      )


if __name__ == "__main__":
 from doctest import testmod

 testmod()