"""simple docstring"""





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				int  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			StableDiffusionPipeline.from_pretrained(_lowerCAmelCase     ,				torch_dtype=torch.floataa  )

       # load LoRA weight from .safetensors
       lowercase__    :	Optional[Any]     					=			load_file(_lowerCAmelCase  )

       lowercase__    :	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:
                     lowercase__    :	Any     					=			key.split('.'  )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'  )[-1].split('_'  )
                     lowercase__    :	Dict     					=			pipeline.text_encoder
              else:
                     lowercase__    :	List[str]     					=			key.split('.'  )[0].split(LORA_PREFIX_UNET + '_'  )[-1].split('_'  )
                     lowercase__    :	Optional[Any]     					=			pipeline.unet

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

              lowercase__    :	Tuple     					=			[]
              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:
                     lowercase__    :	Optional[int]     					=			state_dict[pair_keys[0]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
                     lowercase__    :	Dict     					=			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:
                     lowercase__    :	Union[str, Any]     					=			state_dict[pair_keys[0]].to(torch.floataa  )
                     lowercase__    :	Union[str, 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     :		Union[str, Any]											=					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=0.7_5, 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     :		Optional[Any]											=					parser.parse_args()

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

       _UpperCamelCase     :		Any											=					convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

       _UpperCamelCase     :		List[str]											=					pipe.to(args.device)
       pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


"""simple docstring"""





import math





def        a_  (   _lowerCAmelCase						:				int = 100  ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			sum(i * i for i in range(1     ,				n + 1  )  )
       lowercase__    :	str     					=			int(math.pow(sum(range(1     ,				n + 1  )  )     ,				2  )  )
       return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')

