



"""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__	)			->      List[str]:
		# load base model
		a_     :      Optional[int] 						=     StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__, torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		a_     :      Optional[Any] 						=     load_file(SCREAMING_SNAKE_CASE__	)

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

				# find the target layer
				a_     :      Dict 						=     layer_infos.pop(0	)
				while len(SCREAMING_SNAKE_CASE__	) > -1:
						try:
								a_     :      str 						=     curr_layer.__getattr__(SCREAMING_SNAKE_CASE__	)
								if len(SCREAMING_SNAKE_CASE__	) > 0:
										a_     :      Tuple 						=     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:
										a_     :      List[str] 						=     layer_infos.pop(0	)

				a_     :      Optional[int] 						=     []
				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:
						a_     :      Union[str, Any] 						=     state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						a_     :      Dict 						=     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:
						a_     :      str 						=     state_dict[pair_keys[0]].to(torch.floataa	)
						a_     :      Tuple 						=     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__":
					SCREAMING_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=0.75, 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_				= parser.parse_args()

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

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

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




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(a - b	) for a, b in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_SNAKE_CASE__	):
				raise ValueError("Both points must be in the same n-dimensional space"	)

		return float(sum(abs(x - y	) for x, y in zip(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()