



'''simple docstring'''
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						# load base model
						_UpperCamelCase :		Optional[int]			    =       StableDiffusionPipeline.from_pretrained(UpperCAmelCase_  ,  torch_dtype=torch.floataa							)

						# load LoRA weight from .safetensors
						_UpperCamelCase :		int			    =       load_file(UpperCAmelCase_							)

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

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

												_UpperCamelCase :		int			    =       []
												if "lora_down" in key:
																		pair_keys.append(key.replace('lora_down'  ,  'lora_up'							)							)
																		pair_keys.append(UpperCAmelCase_							)
												else:
																		pair_keys.append(UpperCAmelCase_							)
																		pair_keys.append(key.replace('lora_up'  ,  'lora_down'							)							)

												# update weight
												if len(state_dict[pair_keys[0]].shape							) == 4:
																		_UpperCamelCase :		List[str]			    =       state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
																		_UpperCamelCase :		str			    =       state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
																		curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_  ,  UpperCAmelCase_							).unsqueeze(2							).unsqueeze(3							)
												else:
																		_UpperCamelCase :		Any			    =       state_dict[pair_keys[0]].to(torch.floataa							)
																		_UpperCamelCase :		Tuple			    =       state_dict[pair_keys[1]].to(torch.floataa							)
																		curr_layer.weight.data += alpha * torch.mm(UpperCAmelCase_  ,  UpperCAmelCase_							)

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

						return pipeline


if __name__ == "__main__":
					snake_case_			:     Optional[int]  	=  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.)')

					snake_case_			:     Any  	=  parser.parse_args()

					snake_case_			:     List[str]  	=  args.base_model_path
					snake_case_			:     Tuple  	=  args.checkpoint_path
					snake_case_			:     Tuple  	=  args.dump_path
					snake_case_			:     Dict  	=  args.lora_prefix_unet
					snake_case_			:     Dict  	=  args.lora_prefix_text_encoder
					snake_case_			:     Optional[int]  	=  args.alpha

					snake_case_			:     Dict  	=  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

					snake_case_			:     Optional[Any]  	=  pipe.to(args.device)
					pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







'''simple docstring'''
from __future__ import annotations






def A__   (					UpperCAmelCase_							):
						if not nums:
												return 0
						_UpperCamelCase :		Any			    =       nums[0]
						_UpperCamelCase :		Optional[int]			    =       0
						for num in nums[1:]:
												_UpperCamelCase    ,      _UpperCamelCase :		Optional[Any]			    =       (
												    max_excluding + num,
												    max(UpperCAmelCase_  ,  UpperCAmelCase_							),
												)
						return max(UpperCAmelCase_  ,  UpperCAmelCase_							)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


