





"""simple docstring"""


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       StableDiffusionPipeline.from_pretrained(a__		,torch_dtype=torch.floataa			)

      # load LoRA weight from .safetensors
      _UpperCamelCase      :	List[str]						=       load_file(a__			)

      _UpperCamelCase      :	int						=       []

      # 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      :	Optional[int]						=       key.split("."			)[0].split(LORA_PREFIX_TEXT_ENCODER + "_"			)[-1].split("_"			)
                  _UpperCamelCase      :	List[str]						=       pipeline.text_encoder
            else:
                  _UpperCamelCase      :	Any						=       key.split("."			)[0].split(LORA_PREFIX_UNET + "_"			)[-1].split("_"			)
                  _UpperCamelCase      :	Union[str, Any]						=       pipeline.unet

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

            _UpperCamelCase      :	Optional[int]						=       []
            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:
                  _UpperCamelCase      :	List[str]						=       state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
                  _UpperCamelCase      :	Optional[int]						=       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:
                  _UpperCamelCase      :	Any						=       state_dict[pair_keys[0]].to(torch.floataa			)
                  _UpperCamelCase      :	str						=       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__":
  lowerCamelCase__				  =   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.)")

  lowerCamelCase__				  =   parser.parse_args()

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

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

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







"""simple docstring"""


from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCamelCase__				  =   TypeVar("KEY")
lowerCamelCase__				  =   TypeVar("VAL")



@dataclass(frozen=_UpperCamelCase		,  slots=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						Generic[KEY, VAL]   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:KEY
  SCREAMING_SNAKE_CASE__							:VAL





class      __SCREAMING_SNAKE_CASE    (						_Item   ):







  '''simple docstring'''


  def __init__(		self   :       List[str]	)		->				None:
        super().__init__(__a					,   __a	)


  def __bool__(		self   :       Dict	)		->				bool:
        return False





lowerCamelCase__				  =   _DeletedItem()



class      __SCREAMING_SNAKE_CASE    (						MutableMapping[KEY, VAL]   ):







  '''simple docstring'''


  def __init__(		self   :       int					,   __a   :       int = 8					,   __a   :       float = 0.75	)		->				None:
        _UpperCamelCase      :	str						=       initial_block_size
        _UpperCamelCase      :	list[_Item | None]						=       [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        _UpperCamelCase      :	List[str]						=       capacity_factor
        _UpperCamelCase      :	Dict						=       0

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       KEY	)		->				int:
        return hash(__a	) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       int	)		->				int:
        return (ind + 1) % len(self._buckets	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				bool:
        _UpperCamelCase      :	List[Any]						=       self._buckets[ind]
        if not stored:
              _UpperCamelCase      :	Tuple						=       _Item(__a					,   __a	)
              self._len += 1
              return True
        elif stored.key == key:
              _UpperCamelCase      :	Union[str, Any]						=       _Item(__a					,   __a	)
              return True
        else:
              return False

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				bool:
        _UpperCamelCase      :	Any						=       len(self._buckets	) * self._capacity_factor
        return len(self	) >= int(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				bool:
        if len(self._buckets	) <= self._initial_block_size:
              return False
        _UpperCamelCase      :	List[str]						=       len(self._buckets	) * self._capacity_factor / 2
        return len(self	) < limit

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       int	)		->				None:
        _UpperCamelCase      :	Any						=       self._buckets
        _UpperCamelCase      :	List[Any]						=       [None] * new_size
        _UpperCamelCase      :	List[str]						=       0
        for item in old_buckets:
              if item:
                    self._add_item(item.key					,   item.val	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				None:
        self._resize(len(self._buckets	) * 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				None:
        self._resize(len(self._buckets	) // 2	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       KEY	)		->				Iterator[int]:
        _UpperCamelCase      :	str						=       self._get_bucket_index(__a	)
        for _ in range(len(self._buckets	)	):
              yield ind
              _UpperCamelCase      :	Tuple						=       self._get_next_ind(__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        for ind in self._iterate_buckets(__a	):
              if self._try_set(__a					,   __a					,   __a	):
                    break

  def __setitem__(		self   :       int					,   __a   :       KEY					,   __a   :       VAL	)		->				None:
        if self._is_full():
              self._size_up()

        self._add_item(__a					,   __a	)

  def __delitem__(		self   :       str					,   __a   :       KEY	)		->				None:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    raise KeyError(__a	)
              if item is _deleted:
                    continue
              if item.key == key:
                    _UpperCamelCase      :	List[Any]						=       _deleted
                    self._len -= 1
                    break
        if self._is_sparse():
              self._size_down()

  def __getitem__(		self   :       str					,   __a   :       KEY	)		->				VAL:
        for ind in self._iterate_buckets(__a	):
              _UpperCamelCase      :	Tuple						=       self._buckets[ind]
              if item is None:
                    break
              if item is _deleted:
                    continue
              if item.key == key:
                    return item.val
        raise KeyError(__a	)

  def __len__(		self   :       List[Any]	)		->				int:
        return self._len

  def __iter__(		self   :       List[str]	)		->				Iterator[KEY]:
        yield from (item.key for item in self._buckets if item)


  def __repr__(		self   :       List[str]	)		->				str:
        _UpperCamelCase      :	Optional[int]						=       " ,".join(
            F'''{item.key}: {item.val}''' for item in self._buckets if item	)
        return F'''HashMap({val_string})'''
