

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE		,							torch_dtype=torch.floataa				)

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

     _lowerCAmelCase   :					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:
               _lowerCAmelCase   :					Any        =    key.split("."				)[0].split(LORA_PREFIX_TEXT_ENCODER + "_"				)[-1].split("_"				)
               _lowerCAmelCase   :					Optional[Any]        =    pipeline.text_encoder
          else:
               _lowerCAmelCase   :					int        =    key.split("."				)[0].split(LORA_PREFIX_UNET + "_"				)[-1].split("_"				)
               _lowerCAmelCase   :					int        =    pipeline.unet

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

          _lowerCAmelCase   :					List[str]        =    []
          if "lora_down" in key:
               pair_keys.append(key.replace("lora_down"		,							"lora_up"				)				)
               pair_keys.append(SCREAMING_SNAKE_CASE				)
          else:
               pair_keys.append(SCREAMING_SNAKE_CASE				)
               pair_keys.append(key.replace("lora_up"		,							"lora_down"				)				)

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

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

     return pipeline


if __name__ == "__main__":
  __UpperCAmelCase    				=     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.)')

  __UpperCAmelCase    				=     parser.parse_args()

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

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

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









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




      warnings.warn(
          "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
          " Please use ChineseCLIPImageProcessor instead."     ,						A_     ,						)
      super().__init__(*A_     ,						**A_       )






