
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def 							__SCREAMING_SNAKE_CASE   (		SCREAMING_SNAKE_CASE       :      Dict    ,							SCREAMING_SNAKE_CASE       :      Optional[Any]    ,							SCREAMING_SNAKE_CASE       :      List[Any]    ,							SCREAMING_SNAKE_CASE       :      Union[str, Any]    ,							SCREAMING_SNAKE_CASE       :      str			)     ->						Optional[int]:
				__lowercase								=					StableDiffusionPipeline.from_pretrained(A_    ,							torch_dtype=torch.floataa			)

				# load LoRA weight from .safetensors
				__lowercase								=					load_file(A_			)

				__lowercase								=					[]

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

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

								__lowercase								=					[]
								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:
												__lowercase								=					state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
												__lowercase								=					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:
												__lowercase								=					state_dict[pair_keys[0]].to(torch.floataa			)
												__lowercase								=					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__":
							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"""







import unittest

from knapsack import knapsack as k






class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    List[Any]):
      a						:		str      							=			0
      a						:		Optional[int]      							=			[0]
      a						:		Union[str, Any]      							=			[0]
      a						:		Any      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 0)

      a						:		List[str]      							=			[60]
      a						:		str      							=			[10]
      a						:		Optional[int]      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 0)






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Any      							=			3
      a						:		str      							=			[1, 2, 3]
      a						:		Tuple      							=			[3, 2, 1]
      a						:		Any      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 5)






    def 	__snake_case							(					self				:    Tuple):
      a						:		int      							=			50
      a						:		List[Any]      							=			[60, 100, 120]
      a						:		Optional[int]      							=			[10, 20, 30]
      a						:		str      							=			len(__UpperCAmelCase)
      self.assertEqual(k.knapsack(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)				, 220)


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






