


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       int     ):
							a__    =  StableDiffusionPipeline.from_pretrained(_UpperCamelCase      ,					torch_dtype=torch.floataa     )

							# load LoRA weight from .safetensors
							a__    =  load_file(_UpperCamelCase     )

							a__    =  []

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

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

														a__    =  []
														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:
																					a__    =  state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
																					a__    =  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:
																					a__    =  state_dict[pair_keys[0]].to(torch.floataa     )
																					a__    =  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							: Dict      =   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							: Tuple      =   parser.parse_args()

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

			snake_case							: str      =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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








import unittest

from knapsack import greedy_knapsack as kp







class  snake_case_     (unittest.TestCase   ):
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					Union[str, Any]:
												a__    =  [10, 20, 30, 40, 50, 60]
												a__    =  [2, 4, 6, 8, 10, 12]
												a__    =  1_00
												self.assertEqual(kp.calc_profit(__snake_case    ,__snake_case    ,__snake_case   )    ,2_10   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[int]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					int:
												self.assertRaisesRegex(__snake_case    ,'Weight can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					List[str]:
												self.assertRaisesRegex(__snake_case    ,'Profit can not be negative.'   )
					def 				lowerCamelCase__(   self     :str   )      ->					Optional[Any]:
												self.assertRaisesRegex(__snake_case    ,'max_weight must greater than zero.'   )



					def 				lowerCamelCase__(   self     :int   )      ->					List[Any]:
												self.assertRaisesRegex(
												    __snake_case    ,'The length of profit and weight must be same.'   )


if __name__ == "__main__":
						unittest.main()




