






'''simple docstring'''

import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 	_UpperCAmelCase    (  a		:	List[str]						,			a		:	Optional[int]						,			a		:	List[str]						,			a		:	Optional[Any]						,			a		:	List[str]							)			->			Dict:





						"""simple docstring"""
						# load base model
						lowercase_      :   Union[str, Any]        =			StableDiffusionPipeline.from_pretrained(__lowerCAmelCase						,			torch_dtype=torch.floataa							)

						# load LoRA weight from .safetensors
						lowercase_      :   Union[str, Any]        =			load_file(__lowerCAmelCase							)

						lowercase_      :   Optional[int]        =			[]

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

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

												lowercase_      :   Union[str, Any]        =			[]
												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:
																		lowercase_      :   List[str]        =			state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
																		lowercase_      :   Optional[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:
																		lowercase_      :   Optional[int]        =			state_dict[pair_keys[0]].to(torch.floataa							)
																		lowercase_      :   Any        =			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__":
						A:      Optional[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.)")

						A:      int  				=				parser.parse_args()

						A:      List[Any]  				=				args.base_model_path
						A:      Dict  				=				args.checkpoint_path
						A:      List[str]  				=				args.dump_path
						A:      List[Any]  				=				args.lora_prefix_unet
						A:      Optional[Any]  				=				args.lora_prefix_text_encoder
						A:      Optional[Any]  				=				args.alpha

						A:      Optional[int]  				=				convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						A:      Tuple  				=				pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)








'''simple docstring'''

from ..utils import DummyObject, requires_backends






class 							__magic_name__     (	metaclass=UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      str        =  ['transformers', 'torch', 'note_seq']
	def __init__(							self  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(self  ,      ['transformers', 'torch', 'note_seq']       )
	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  List[str]:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )







	@classmethod
	def        lowerCamelCase__		(							cls  ,      *_lowercase  ,      **_lowercase       )						->  Dict:
							requires_backends(cls  ,      ['transformers', 'torch', 'note_seq']       )
