





'''simple docstring'''






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






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

		# load LoRA weight from .safetensors
		lowerCAmelCase     :    Any						=   load_file(a_	)

		lowerCAmelCase     :    Optional[Any]						=   []

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

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

				lowerCAmelCase     :    List[Any]						=   []
				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:
						lowerCAmelCase     :    Optional[int]						=   state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						lowerCAmelCase     :    Optional[int]						=   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:
						lowerCAmelCase     :    int						=   state_dict[pair_keys[0]].to(torch.floataa	)
						lowerCAmelCase     :    Tuple						=   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__":
					lowerCAmelCase       		=     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.)""")

					lowerCAmelCase       		=     parser.parse_args()

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

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

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










'''simple docstring'''






def      __A						(							a_						:  int	):
		if not isinstance(a_      ,a_	):
				lowerCAmelCase     :    Dict						=   f'''Input value of [number={number}] must be an integer'''
				raise TypeError(a_	)
		if number < 0:
				return False
		lowerCAmelCase     :    Dict						=   number * number
		while number > 0:
				if number % 1_0 != number_square % 1_0:
						return False
				number //= 1_0
				number_square //= 1_0
		return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()



