


"""simple docstring"""







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_  ,   A_       )->      Optional[int]:
  '''simple docstring'''







  a						:		Any      							=			StableDiffusionPipeline.from_pretrained(A_  ,   torch_dtype=torch.floataa       )

  # load LoRA weight from .safetensors
  a						:		str      							=			load_file(A_       )

  a						:		Dict      							=			[]

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

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

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

 __lowercase    					=      parser.parse_args()

 __lowercase    					=      args.base_model_path
 __lowercase    					=      args.checkpoint_path
 __lowercase    					=      args.dump_path
 __lowercase    					=      args.lora_prefix_unet
 __lowercase    					=      args.lora_prefix_text_encoder
 __lowercase    					=      args.alpha

 __lowercase    					=      convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 __lowercase    					=      pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Tuple:





    '''simple docstring'''






    A__          =   0
    A__          =   len(SCREAMING_SNAKE_CASE__      ) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            else:
                return None

        A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
            return None

        A__          =   sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                A__          =   left
                A__          =   point
            elif point > right:
                A__          =   right
                A__          =   point
            else:
                if item < current_item:
                    A__          =   point - 1
                else:
                    A__          =   point + 1
    return None
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      str:





    '''simple docstring'''






    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        else:
            return None

    A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
        return None

    if sorted_collection[point] == item:
        return point
    elif point < left:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    elif point > right:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    else:
        if sorted_collection[point] > item:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point - 1      )
        else:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point + 1    ,			SCREAMING_SNAKE_CASE__      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      Tuple:





    '''simple docstring'''






    if collection != sorted(SCREAMING_SNAKE_CASE__      ):
        raise ValueError('Collection must be ascending sorted'      )
    return True


if __name__ == "__main__":
     import sys
     lowercase_       		=  0
     if debug == 1:
          lowercase_       		=  [10, 30, 40, 45, 50, 66, 77, 93]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("Sequence must be ascending sorted to apply interpolation search")
          lowercase_       		=  67

     lowercase_       		=  interpolation_search(collection, target)
     if result is not None:
          print(f"""{target} found at positions: {result}""")
     else:
          print("Not found")

