





'''simple docstring'''




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			Any			,							__snake_case  :			Dict			,							__snake_case  :			Dict			,							__snake_case  :			Any      )					->					Union[str, Any]:
     # load base model
     __A						:			Optional[Any]								=	StableDiffusionPipeline.from_pretrained(_UpperCAmelCase			,							torch_dtype=torch.floataa      )

     # load LoRA weight from .safetensors
     __A						:			List[Any]								=	load_file(_UpperCAmelCase      )

     __A						:			Optional[int]								=	[]

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

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

          __A						:			Tuple								=	[]
          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:
               __A						:			Optional[Any]								=	state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
               __A						:			List[str]								=	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:
               __A						:			str								=	state_dict[pair_keys[0]].to(torch.floataa      )
               __A						:			Tuple								=	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__":
  lowercase__   :				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=0.75, 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.)''')

  lowercase__   :				str      =   parser.parse_args()

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

  lowercase__   :				int      =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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






'''simple docstring'''




def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			str      )					->					float:

     def get_matched_characters(__snake_case  :			str			,							__snake_case  :			str      ) -> str:
          __A						:			Optional[int]								=	[]
          __A						:			Optional[Any]								=	min(len(_stra      )			,							len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               __A						:			str								=	int(max(0			,							i - limit      )      )
               __A						:			str								=	int(min(i + limit + 1			,							len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(__snake_case      )
                    __A						:			Dict								=	f'{_stra[0:_stra.index(__snake_case      )]} {_stra[_stra.index(__snake_case      ) + 1:]}'

          return "".join(__snake_case      )

     # matching characters
     __A						:			Any								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	get_matched_characters(__snake_case			,							__snake_case      )
     __A						:			str								=	len(__snake_case      )

     # transposition
     __A						:			Tuple								=	(
         len([(ca, ca) for ca, ca in zip(__snake_case			,							__snake_case      ) if ca != ca]      ) // 2
     )

     if not match_count:
          __A						:			int								=	0.0
     else:
          __A						:			Union[str, Any]								=	(
              1
              / 3
              * (
                  match_count / len(__snake_case      )
                  + match_count / len(__snake_case      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     __A						:			Tuple								=	0
     for ca, ca in zip(stra[:4]			,							stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler('''hello''', '''world'''))