

'''simple docstring'''

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def 					__UpperCamelCase				(						lowercase__    :	Union[str, Any],       lowercase__    :	Union[str, Any],       lowercase__    :	Tuple,       lowercase__    :	Optional[int],       lowercase__    :	Union[str, Any]			):






     '''simple docstring'''





     __lowercase      		=StableDiffusionPipeline.from_pretrained(lowercase__,       torch_dtype=torch.floataa			)

     # load LoRA weight from .safetensors
     __lowercase      		=load_file(lowercase__			)

     __lowercase      		=[]

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

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

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

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

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

     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'''

from __future__ import annotations

def 					__UpperCamelCase				(						lowercase__    :	float,       lowercase__    :	float,       lowercase__    :	float			):






     '''simple docstring'''





     if (voltage, current, resistance).count(0			) != 1:
          raise ValueError('One and only one argument must be 0'			)
     if resistance < 0:
          raise ValueError('Resistance cannot be negative'			)
     if voltage == 0:
          return {"voltage": float(current * resistance			)}
     elif current == 0:
          return {"current": voltage / resistance}
     elif resistance == 0:
          return {"resistance": voltage / current}
     else:
          raise ValueError('Exactly one argument must be 0'			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


