


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 	a_      (    SCREAMING_SNAKE_CASE__					:      List[str]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		={k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              _lowerCamelCase       :		List[str]		=sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            _lowerCamelCase       :		Any		=v.replace(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
                     _lowerCamelCase       :		Any		=v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     _lowerCamelCase       :		List[str]		=v.replace(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		int		=v
       _lowerCamelCase       :		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 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]     ):
       '''simple docstring'''





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




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[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:
                     _lowerCamelCase       :		Dict		=v.replace(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Any		=v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            _lowerCamelCase       :		str		=v.replace(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
                     _lowerCamelCase       :		Any		=v
       _lowerCamelCase       :		List[Any]		={v: vae_state_dict[k] for k, v in mapping.items()}
       _lowerCamelCase       :		Any		=['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'''     )
                            _lowerCamelCase       :		Union[str, Any]		=reshape_weight_for_sd(SCREAMING_SNAKE_CASE__     )
       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 	a_      (    SCREAMING_SNAKE_CASE__					:      Dict     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		={}
       _lowerCamelCase       :		Dict		={}
       _lowerCamelCase       :		Optional[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'     )
              ):
                     _lowerCamelCase       :		Union[str, Any]		=k[: -len('.q_proj.weight'     )]
                     _lowerCamelCase       :		Optional[int]		=k[-len('q_proj.weight'     )]
                     if k_pre not in capture_qkv_weight:
                            _lowerCamelCase       :		Optional[int]		=[None, None, None]
                     _lowerCamelCase       :		Union[str, Any]		=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'     )
              ):
                     _lowerCamelCase       :		List[Any]		=k[: -len('.q_proj.bias'     )]
                     _lowerCamelCase       :		int		=k[-len('q_proj.bias'     )]
                     if k_pre not in capture_qkv_bias:
                            _lowerCamelCase       :		Union[str, Any]		=[None, None, None]
                     _lowerCamelCase       :		Tuple		=v
                     continue

              _lowerCamelCase       :		Optional[int]		=textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE__     : protected[re.escape(m.group(0     )     )]		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Tuple		=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'     )
              _lowerCamelCase       :		Optional[int]		=textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE__     : protected[re.escape(m.group(0     )     )]		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Dict		=torch.cat(SCREAMING_SNAKE_CASE__     )

       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'     )
              _lowerCamelCase       :		int		=textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE__     : protected[re.escape(m.group(0     )     )]		,			SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Any		=torch.cat(SCREAMING_SNAKE_CASE__     )

       return new_state_dict




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''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)






# Lint as: python3
import itertools
import os
import re


lowerCamelCase											=       re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase											=       re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase											=       re.compile(R'(?<!_)_(?!_)')
lowerCamelCase											=       re.compile(R'(_{2,})')

lowerCamelCase											=       R'^\w+(\.\w+)*$'

lowerCamelCase											=       R'<>:/\|?*'




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=_uppercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		List[Any]		=_lowercase_uppercase_re.sub(r'\1_\2'		,			SCREAMING_SNAKE_CASE__     )
       return name.lower()




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=_single_underscore_re.split(SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Tuple		=[_multiple_underscores_re.split(SCREAMING_SNAKE_CASE__     ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__     ) if n != ''     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[Any]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       return camelcase_to_snakecase(SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Union[str, Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[int]     ):
       '''simple docstring'''





       if os.path.basename(SCREAMING_SNAKE_CASE__     ) != name:
              raise ValueError(F'''Should be a dataset name, not a path: {name}'''     )
       if not re.match(_split_re		,			SCREAMING_SNAKE_CASE__     ):
              raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''     )
       return F'''{filename_prefix_for_name(SCREAMING_SNAKE_CASE__     )}-{split}'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Optional[int]		,			SCREAMING_SNAKE_CASE__					:      List[Any]		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]		,			SCREAMING_SNAKE_CASE__					:      int=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Union[str, Any]		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       if filetype_suffix:
              prefix += F'''.{filetype_suffix}'''
       _lowerCamelCase       :		Optional[Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       return F'''{filepath}*'''




def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple		,			SCREAMING_SNAKE_CASE__					:      List[str]		,			SCREAMING_SNAKE_CASE__					:      str		,			SCREAMING_SNAKE_CASE__					:      List[str]=None		,			SCREAMING_SNAKE_CASE__					:      Optional[Any]=None     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		=filename_prefix_for_split(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
       _lowerCamelCase       :		Union[str, Any]		=os.path.join(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )

       if shard_lengths:
              _lowerCamelCase       :		Union[str, Any]		=len(SCREAMING_SNAKE_CASE__     )
              _lowerCamelCase       :		Optional[int]		=[F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(SCREAMING_SNAKE_CASE__     )]
              if filetype_suffix:
                     _lowerCamelCase       :		List[Any]		=[filename + F'''.{filetype_suffix}''' for filename in filenames]
              return filenames
       else:
              _lowerCamelCase       :		Any		=prefix
              if filetype_suffix:
                     filename += F'''.{filetype_suffix}'''
              return [filename]


