


'''simple docstring'''

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: Dict      ,  __lowerCamelCase		: Optional[Any]				)      ->     List[Any]:







     '''simple docstring'''






     _a						=		StableDiffusionPipeline.from_pretrained(__UpperCAmelCase      ,  torch_dtype=torch.floataa				)

     # load LoRA weight from .safetensors
     _a						=		load_file(__UpperCAmelCase				)

     _a						=		[]

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

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

          _a						=		[]
          if "lora_down" in key:
               pair_keys.append(key.replace("lora_down"      ,  "lora_up"				)				)
               pair_keys.append(__UpperCAmelCase				)
          else:
               pair_keys.append(__UpperCAmelCase				)
               pair_keys.append(key.replace("lora_up"      ,  "lora_down"				)				)

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

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

     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)





'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				     =		logging.get_logger(__name__)





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''encoder-decoder'''
     UpperCAmelCase 					=				True






     def __init__(			self     ,							**__UpperCamelCase		)       ->    List[Any]:
          super().__init__(**__UpperCamelCase		)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          _a						=		kwargs.pop("encoder"		)
          _a						=		encoder_config.pop("model_type"		)
          _a						=		kwargs.pop("decoder"		)
          _a						=		decoder_config.pop("model_type"		)

          from ..auto.configuration_auto import AutoConfig

          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		True






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    PretrainedConfig:
          logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
          _a						=		True
          _a						=		True

          return cls(encoder=encoder_config.to_dict()     ,							decoder=decoder_config.to_dict()     ,							**__UpperCamelCase		)






     def  a_					(			self		)       ->    Any:
          _a						=		copy.deepcopy(self.__dict__		)
          _a						=		self.encoder.to_dict()
          _a						=		self.decoder.to_dict()
          _a						=		self.__class__.model_type
          return output

