






"""simple docstring"""






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	StableDiffusionPipeline.from_pretrained(__a					,       torch_dtype=torch.floataa							)

    # load LoRA weight from .safetensors
    SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	load_file(__a							)

    SCREAMING_SNAKE_CASE_   :						List[str]							=	[]

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

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

        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	[]
        if "lora_down" in key:
            pair_keys.append(key.replace('''lora_down'''					,       '''lora_up'''							)							)
            pair_keys.append(__a							)
        else:
            pair_keys.append(__a							)
            pair_keys.append(key.replace('''lora_up'''					,       '''lora_down'''							)							)

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

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

    return pipeline


if __name__ == "__main__":
  UpperCAmelCase_     :				List[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=0.7_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_     :				List[Any]         =				parser.parse_args()

  UpperCAmelCase_     :				Union[str, Any]         =				args.base_model_path
  UpperCAmelCase_     :				Any         =				args.checkpoint_path
  UpperCAmelCase_     :				Optional[Any]         =				args.dump_path
  UpperCAmelCase_     :				List[Any]         =				args.lora_prefix_unet
  UpperCAmelCase_     :				Tuple         =				args.lora_prefix_text_encoder
  UpperCAmelCase_     :				Union[str, Any]         =				args.alpha

  UpperCAmelCase_     :				Dict         =				convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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










"""simple docstring"""






from __future__ import annotations



def       _A    (__a					,       __a					,       __a							)				-> int | float:
    """simple docstring"""






    if len(__a							) == 0:
        raise ValueError('''find_max() arg is an empty sequence'''							)
    if (
        left >= len(__a							)
        or left < -len(__a							)
        or right >= len(__a							)
        or right < -len(__a							)
    ):
        raise IndexError('''list index out of range'''							)
    if left == right:
        return nums[left]
    SCREAMING_SNAKE_CASE_   :						str							=	(left + right) >> 1  # the middle
    SCREAMING_SNAKE_CASE_   :						int							=	find_max(__a					,       __a					,       __a							)  # find max in range[left, mid]
    SCREAMING_SNAKE_CASE_   :						str							=	find_max(__a					,       mid + 1					,       __a							)  # find max in range[mid + 1, right]

    return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)


