

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					__lowercase      (     _A						,    _A						,    _A						,    _A						,    _A			)     ->						str:
      # load base model
      SCREAMING_SNAKE_CASE :      Optional[int]       =   StableDiffusionPipeline.from_pretrained(_A						,    torch_dtype=torch.floataa			)

      # load LoRA weight from .safetensors
      SCREAMING_SNAKE_CASE :      Tuple       =   load_file(_A			)

      SCREAMING_SNAKE_CASE :      List[str]       =   []

      # directly update weight in diffusers model
      for key in state_dict:
            # it is suggested to print out the key, it usually will be something like below
            # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

            # as we have set the alpha beforehand, so just skip
            if ".alpha" in key or key in visited:
                  continue

            if "text" in key:
                  SCREAMING_SNAKE_CASE :      List[Any]       =   key.split("""."""			)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""			)[-1].split("""_"""			)
                  SCREAMING_SNAKE_CASE :      int       =   pipeline.text_encoder
            else:
                  SCREAMING_SNAKE_CASE :      Any       =   key.split("""."""			)[0].split(LORA_PREFIX_UNET + """_"""			)[-1].split("""_"""			)
                  SCREAMING_SNAKE_CASE :      Dict       =   pipeline.unet

            # find the target layer
            SCREAMING_SNAKE_CASE :      List[str]       =   layer_infos.pop(0			)
            while len(_A			) > -1:
                  try:
                        SCREAMING_SNAKE_CASE :      Optional[int]       =   curr_layer.__getattr__(_A			)
                        if len(_A			) > 0:
                              SCREAMING_SNAKE_CASE :      List[Any]       =   layer_infos.pop(0			)
                        elif len(_A			) == 0:
                              break
                  except Exception:
                        if len(_A			) > 0:
                              temp_name += "_" + layer_infos.pop(0			)
                        else:
                              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   layer_infos.pop(0			)

            SCREAMING_SNAKE_CASE :      Optional[int]       =   []
            if "lora_down" in key:
                  pair_keys.append(key.replace("""lora_down"""						,    """lora_up"""			)			)
                  pair_keys.append(_A			)
            else:
                  pair_keys.append(_A			)
                  pair_keys.append(key.replace("""lora_up"""						,    """lora_down"""			)			)

            # update weight
            if len(state_dict[pair_keys[0]].shape			) == 4:
                  SCREAMING_SNAKE_CASE :      str       =   state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
                  SCREAMING_SNAKE_CASE :      Tuple       =   state_dict[pair_keys[1]].squeeze(3			).squeeze(2			).to(torch.floataa			)
                  curr_layer.weight.data += alpha * torch.mm(_A						,    _A			).unsqueeze(2			).unsqueeze(3			)
            else:
                  SCREAMING_SNAKE_CASE :      List[str]       =   state_dict[pair_keys[0]].to(torch.floataa			)
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   state_dict[pair_keys[1]].to(torch.floataa			)
                  curr_layer.weight.data += alpha * torch.mm(_A						,    _A			)

            # update visited list
            for item in pair_keys:
                  visited.append(_A			)

      return pipeline


if __name__ == "__main__":
  UpperCAmelCase__ :     Dict				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
  )
  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
  parser.add_argument(
      """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
  )
  parser.add_argument(
      """--lora_prefix_text_encoder""",
      default="""lora_te""",
      type=str,
      help="""The prefix of text encoder weight in safetensors""",
  )
  parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
  parser.add_argument(
      """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
  )
  parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

  UpperCAmelCase__ :     Dict				    =    parser.parse_args()

  UpperCAmelCase__ :     int				    =    args.base_model_path
  UpperCAmelCase__ :     Any				    =    args.checkpoint_path
  UpperCAmelCase__ :     int				    =    args.dump_path
  UpperCAmelCase__ :     Optional[Any]				    =    args.lora_prefix_unet
  UpperCAmelCase__ :     int				    =    args.lora_prefix_text_encoder
  UpperCAmelCase__ :     str				    =    args.alpha

  UpperCAmelCase__ :     Optional[Any]				    =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  UpperCAmelCase__ :     int				    =    pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







def 					__lowercase      (     _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   """"""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 					__lowercase      (     _A			)     ->						dict[str, str]:
      SCREAMING_SNAKE_CASE :      int       =   [chr(i + 65			) for i in range(26			)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE :      List[str]       =   remove_duplicates(key.upper()			)
      SCREAMING_SNAKE_CASE :      Dict       =   len(_A			)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE :      int       =   {alphabet[i]: char for i, char in enumerate(_A			)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_A			)						,    26			):
            SCREAMING_SNAKE_CASE :      List[Any]       =   alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE :      str       =   alphabet[i - offset]
            SCREAMING_SNAKE_CASE :      List[Any]       =   char
      return cipher_alphabet






def 					__lowercase      (     _A						,    _A			)     ->						str:
      return "".join(cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      List[Any]       =   input("""Enter message to encode or decode: """			).strip()
      SCREAMING_SNAKE_CASE :      str       =   input("""Enter keyword: """			).strip()
      SCREAMING_SNAKE_CASE :      Dict       =   input("""Encipher or decipher? E/D:"""			).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE :      int       =   {"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   create_cipher_map(_A			)
      print(func(_A						,    _A			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




