





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=StableDiffusionPipeline.from_pretrained(lowercase__  ,    torch_dtype=torch.floataa  )

   # load LoRA weight from .safetensors
   UpperCAmelCase_											=load_file(lowercase__  )

   UpperCAmelCase_											=[]

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

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

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

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

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

   return pipeline


if __name__ == "__main__":
     __lowercase  :   Union[str, Any]						      =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  :   List[Any]						      =parser.parse_args()

     __lowercase  :   Dict						      =args.base_model_path
     __lowercase  :   Tuple						      =args.checkpoint_path
     __lowercase  :   Optional[int]						      =args.dump_path
     __lowercase  :   List[str]						      =args.lora_prefix_unet
     __lowercase  :   Tuple						      =args.lora_prefix_text_encoder
     __lowercase  :   Dict						      =args.alpha

     __lowercase  :   Any						      =convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

     __lowercase  :   Optional[Any]						      =pipe.to(args.device)
     pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)











import sys
import turtle




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    ):






   '''simple docstring'''






   my_pen.up()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.down()
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )
   my_pen.goto(vertexa[0]  ,    vertexa[1]  )

   if depth == 0:
      return

   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )
   triangle(lowercase__  ,    get_mid(lowercase__  ,    lowercase__  )  ,    get_mid(lowercase__  ,    lowercase__  )  ,    depth - 1  )


if __name__ == "__main__":
     if len(sys.argv) != 2:
          raise ValueError(
              """Correct format for using this script: """
              """python fractals.py <int:depth_for_fractal>"""
          )
     __lowercase  :   Any						      =turtle.Turtle()
     my_pen.ht()
     my_pen.speed(5)
     my_pen.pencolor("""red""")

     __lowercase  :   str						      =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
     triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))




