


'''simple docstring'''
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Dict:
    # load base model
    UpperCAmelCase__			:		Any													=				StableDiffusionPipeline.from_pretrained(lowerCAmelCase__  ,					torch_dtype=torch.floataa						)

    # load LoRA weight from .safetensors
    UpperCAmelCase__			:		Any													=				load_file(lowerCAmelCase__						)

    UpperCAmelCase__			:		Optional[Any]													=				[]

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

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

        UpperCAmelCase__			:		Optional[Any]													=				[]
        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:
            UpperCAmelCase__			:		Union[str, Any]													=				state_dict[pair_keys[0]].squeeze(3						).squeeze(2						).to(torch.floataa						)
            UpperCAmelCase__			:		Union[str, Any]													=				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:
            UpperCAmelCase__			:		int													=				state_dict[pair_keys[0]].to(torch.floataa						)
            UpperCAmelCase__			:		Optional[Any]													=				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__    					=      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)




'''simple docstring'''
def 					a__       (					lowerCAmelCase__						)    ->	int:
    if a < 0:
        raise ValueError('''Input value must be a positive integer'''						)
    elif isinstance(lowerCAmelCase__  ,					lowerCAmelCase__						):
        raise TypeError('''Input value must be a \'int\' type'''						)
    return bin(lowerCAmelCase__						).count('''1'''						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
