


'''simple docstring'''



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Tuple				)	->						int:
     # load base model
     __lowerCamelCase  :      List[Any]														=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase_ ,       torch_dtype=torch.floataa				)

     # load LoRA weight from .safetensors
     __lowerCamelCase  :      List[str]														=						load_file(UpperCAmelCase_				)

     __lowerCamelCase  :      Dict														=						[]

     # 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:
               __lowerCamelCase  :      Optional[int]														=						key.split('.'				)[0].split(LORA_PREFIX_TEXT_ENCODER + '_'				)[-1].split('_'				)
               __lowerCamelCase  :      Union[str, Any]														=						pipeline.text_encoder
          else:
               __lowerCamelCase  :      int														=						key.split('.'				)[0].split(LORA_PREFIX_UNET + '_'				)[-1].split('_'				)
               __lowerCamelCase  :      Any														=						pipeline.unet

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

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

          # update weight
          if len(state_dict[pair_keys[0]].shape				) == 4:
               __lowerCamelCase  :      List[Any]														=						state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
               __lowerCamelCase  :      Optional[int]														=						state_dict[pair_keys[1]].squeeze(3				).squeeze(2				).to(torch.floataa				)
               curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_ ,       UpperCAmelCase_				).unsqueeze(2				).unsqueeze(3				)
          else:
               __lowerCamelCase  :      Optional[int]														=						state_dict[pair_keys[0]].to(torch.floataa				)
               __lowerCamelCase  :      Any														=						state_dict[pair_keys[1]].to(torch.floataa				)
               curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_ ,       UpperCAmelCase_				)

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

     return pipeline


if __name__ == "__main__":
      A__						:   List[str]				=    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=0.7_5, 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.)""")

      A__						:   Optional[int]				=    parser.parse_args()

      A__						:   Tuple				=    args.base_model_path
      A__						:   List[Any]				=    args.checkpoint_path
      A__						:   Union[str, Any]				=    args.dump_path
      A__						:   List[Any]				=    args.lora_prefix_unet
      A__						:   Tuple				=    args.lora_prefix_text_encoder
      A__						:   Tuple				=    args.alpha

      A__						:   Tuple				=    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

      A__						:   List[Any]				=    pipe.to(args.device)
      pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)










'''simple docstring'''



from __future__ import annotations

A__						:   int				=    10



def      UpperCAmelCase__   (      UpperCAmelCase_       :							list[int]				)	->						list[int]:
     __lowerCamelCase  :      List[Any]														=						1
     __lowerCamelCase  :      Any														=						max(UpperCAmelCase_				)
     while placement <= max_digit:
          # declare and initialize empty buckets
          __lowerCamelCase  :      list[list]														=						[[] for _ in range(UpperCAmelCase_				)]
          # split list_of_ints between the buckets
          for i in list_of_ints:
               __lowerCamelCase  :      List[Any]														=						int((i / placement) % RADIX				)
               buckets[tmp].append(UpperCAmelCase_				)
          # put each buckets' contents into list_of_ints
          __lowerCamelCase  :      Tuple														=						0
          for b in range(UpperCAmelCase_				):
               for i in buckets[b]:
                    __lowerCamelCase  :      List[Any]														=						i
                    a += 1
        # move to next
          placement *= RADIX
     return list_of_ints


if __name__ == "__main__":
      import doctest

      doctest.testmod()






