


'''simple docstring'''





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      snake_case_ =   StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE      ,      torch_dtype=torch.floataa      )

      # load LoRA weight from .safetensors
      snake_case_ =   load_file(SCREAMING_SNAKE_CASE      )

      snake_case_ =   []

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

            # find the target layer
            snake_case_ =   layer_infos.pop(0      )
            while len(SCREAMING_SNAKE_CASE      ) > -1:
                  try:
                        snake_case_ =   curr_layer.__getattr__(SCREAMING_SNAKE_CASE      )
                        if len(SCREAMING_SNAKE_CASE      ) > 0:
                              snake_case_ =   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:
                              snake_case_ =   layer_infos.pop(0      )

            snake_case_ =   []
            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:
                  snake_case_ =   state_dict[pair_keys[0]].squeeze(3      ).squeeze(2      ).to(torch.floataa      )
                  snake_case_ =   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:
                  snake_case_ =   state_dict[pair_keys[0]].to(torch.floataa      )
                  snake_case_ =   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)



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))