





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load base model
   a__						:    Union[str, Any]   		=	StableDiffusionPipeline.from_pretrained(lowerCamelCase				,			torch_dtype=torch.floataa )

   # load LoRA weight from .safetensors
   a__						:    str   		=	load_file(lowerCamelCase )

   a__						:    List[str]   		=	[]

   # 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__						:    Union[str, Any]   		=	key.split("." )[0].split(LORA_PREFIX_TEXT_ENCODER + "_" )[-1].split("_" )
         a__						:    List[Any]   		=	pipeline.text_encoder
      else:
         a__						:    str   		=	key.split("." )[0].split(LORA_PREFIX_UNET + "_" )[-1].split("_" )
         a__						:    Optional[int]   		=	pipeline.unet

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

      a__						:    int   		=	[]
      if "lora_down" in key:
         pair_keys.append(key.replace("lora_down"				,			"lora_up" ) )
         pair_keys.append(lowerCamelCase )
      else:
         pair_keys.append(lowerCamelCase )
         pair_keys.append(key.replace("lora_up"				,			"lora_down" ) )

      # update weight
      if len(state_dict[pair_keys[0]].shape ) == 4:
         a__						:    List[Any]   		=	state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
         a__						:    List[Any]   		=	state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
         curr_layer.weight.data += alpha * torch.mm(lowerCamelCase				,			lowerCamelCase ).unsqueeze(2 ).unsqueeze(3 )
      else:
         a__						:    Optional[int]   		=	state_dict[pair_keys[0]].to(torch.floataa )
         a__						:    Dict   		=	state_dict[pair_keys[1]].to(torch.floataa )
         curr_layer.weight.data += alpha * torch.mm(lowerCamelCase				,			lowerCamelCase )

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

   return pipeline


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      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.)""")

  SCREAMING_SNAKE_CASE__							:      str         =    parser.parse_args()

  SCREAMING_SNAKE_CASE__							:      Any         =    args.base_model_path
  SCREAMING_SNAKE_CASE__							:      List[Any]         =    args.checkpoint_path
  SCREAMING_SNAKE_CASE__							:      List[Any]         =    args.dump_path
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    args.lora_prefix_unet
  SCREAMING_SNAKE_CASE__							:      Tuple         =    args.lora_prefix_text_encoder
  SCREAMING_SNAKE_CASE__							:      Any         =    args.alpha

  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  SCREAMING_SNAKE_CASE__							:      int         =    pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








def    _A	(   lowerCamelCase = 200 ):
   a__						:    List[str]   		=	[1, 2, 5, 10, 20, 50, 100, 200]
   a__						:    Dict   		=	[0] * (pence + 1)
   a__						:    int   		=	1  # base case: 1 way to make 0 pence

   for coin in coins:
      for i in range(lowerCamelCase				,			pence + 1				,			1 ):
         number_of_ways[i] += number_of_ways[i - coin]
   return number_of_ways[pence]


if __name__ == "__main__":
  assert solution(2_0_0) == 7_3_6_8_2

