



'''simple docstring'''





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A):



     """simple docstring"""



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

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

     _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(__A) > -1:
               try:
                    _a											=						curr_layer.__getattr__(__A)
                    if len(__A) > 0:
                         _a											=						layer_infos.pop(0)
                    elif len(__A) == 0:
                         break
               except Exception:
                    if len(__A) > 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(__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											=						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(__A						,			__A).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(__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)








'''simple docstring'''





# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


lowercase_	 =						open  # noqa: we just need to have a builtin inside this module to test it properly



