





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      Dict						)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_				,	torch_dtype=torch.floataa						)

   # load LoRA weight from .safetensors
   _UpperCAmelCase           =   load_file(SCREAMING_SNAKE_CASE_						)

   _UpperCAmelCase           =   []

   # 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:
         _UpperCAmelCase           =   key.split('''.'''						)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''						)[-1].split('''_'''						)
         _UpperCAmelCase           =   pipeline.text_encoder
      else:
         _UpperCAmelCase           =   key.split('''.'''						)[0].split(LORA_PREFIX_UNET + '''_'''						)[-1].split('''_'''						)
         _UpperCAmelCase           =   pipeline.unet

      # find the target layer
      _UpperCAmelCase           =   layer_infos.pop(0						)
      while len(SCREAMING_SNAKE_CASE_						) > -1:
         try:
            _UpperCAmelCase           =   curr_layer.__getattr__(SCREAMING_SNAKE_CASE_						)
            if len(SCREAMING_SNAKE_CASE_						) > 0:
               _UpperCAmelCase           =   layer_infos.pop(0						)
            elif len(SCREAMING_SNAKE_CASE_						) == 0:
               break
         except Exception:
            if len(SCREAMING_SNAKE_CASE_						) > 0:
               temp_name += "_" + layer_infos.pop(0						)
            else:
               _UpperCAmelCase           =   layer_infos.pop(0						)

      _UpperCAmelCase           =   []
      if "lora_down" in key:
         pair_keys.append(key.replace('''lora_down'''				,	'''lora_up'''						)						)
         pair_keys.append(SCREAMING_SNAKE_CASE_						)
      else:
         pair_keys.append(SCREAMING_SNAKE_CASE_						)
         pair_keys.append(key.replace('''lora_up'''				,	'''lora_down'''						)						)

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

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

   return pipeline


if __name__ == "__main__":
   UpperCAmelCase_           =	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_           =	parser.parse_args()

   UpperCAmelCase_           =	args.base_model_path
   UpperCAmelCase_           =	args.checkpoint_path
   UpperCAmelCase_           =	args.dump_path
   UpperCAmelCase_           =	args.lora_prefix_unet
   UpperCAmelCase_           =	args.lora_prefix_text_encoder
   UpperCAmelCase_           =	args.alpha

   UpperCAmelCase_           =	convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

   UpperCAmelCase_           =	pipe.to(args.device)
   pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






from ..utils import DummyObject, requires_backends




class 			__UpperCamelCase    (							metaclass=A__    ):
     __A	:  str							  =   ["""torch""", """scipy"""]





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(self				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)