




'''simple docstring'''



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any] ,						_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Optional[int] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  Tuple						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	StableDiffusionPipeline.from_pretrained(_lowerCamelCase ,						torch_dtype=torch.floataa						)

     # load LoRA weight from .safetensors
     __snake_case            =	load_file(_lowerCamelCase						)

     __snake_case            =	[]

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

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

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

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

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

     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_							:	List[str]	     =		parser.parse_args()

     UpperCAmelCase_							:	int	     =		args.base_model_path
     UpperCAmelCase_							:	Dict	     =		args.checkpoint_path
     UpperCAmelCase_							:	Dict	     =		args.dump_path
     UpperCAmelCase_							:	str	     =		args.lora_prefix_unet
     UpperCAmelCase_							:	Dict	     =		args.lora_prefix_text_encoder
     UpperCAmelCase_							:	Dict	     =		args.alpha

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

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












'''simple docstring'''

from ....utils import logging


a    :							Optional[int]										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   Any=None			,							lowercase_       :   List[Any]=2048   ):
									snake_case_		     =							config.__dict__
									snake_case_		     =							modal_hidden_size
									if num_labels:
														snake_case_		     =							num_labels

