


'''simple docstring'''




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline





def 	a_					( lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Tuple	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Any	, lowerCamelCase	:     Dict				):
   # load base model
   lowerCAmelCase     =			StableDiffusionPipeline.from_pretrained(lowerCamelCase	, torch_dtype=torch.floataa				)

   # load LoRA weight from .safetensors
   lowerCAmelCase     =			load_file(lowerCamelCase				)

   lowerCAmelCase     =			[]

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

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

      lowerCAmelCase     =			[]
      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:
         lowerCAmelCase     =			state_dict[pair_keys[0]].squeeze(3				).squeeze(2				).to(torch.floataa				)
         lowerCAmelCase     =			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:
         lowerCAmelCase     =			state_dict[pair_keys[0]].to(torch.floataa				)
         lowerCAmelCase     =			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__":
       __snake_case    						=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=0.7_5, 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.)""")

       __snake_case    						=parser.parse_args()

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

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

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







def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[0 for i in range(r + 1    )]
				# nc0 = 1
				lowerCamelCase_						=			1
				for i in range(1	, n + 1    ):
								# to compute current row from previous row.
								lowerCamelCase_						=			min(lowerCamelCase__	, lowerCamelCase__    )
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]





print(binomial_coefficient(n=1_0, r=5))


