





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def   __lowerCamelCase   (				__a  :Dict       ,	__a  :str       ,	__a  :List[str]       ,	__a  :Any       ,	__a  :Optional[int]       )						->							List[Any]:






		"""simple docstring"""

		A__     =  StableDiffusionPipeline.from_pretrained(__a       ,	torch_dtype=torch.floataa       )

		# load LoRA weight from .safetensors
		A__     =  load_file(__a       )

		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(__a       ) > -1:
						try:
								A__     =  curr_layer.__getattr__(__a       )
								if len(__a       ) > 0:
										A__     =  layer_infos.pop(0       )
								elif len(__a       ) == 0:
										break
						except Exception:
								if len(__a       ) > 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(__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:
						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(__a       ,	__a       ).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(__a       ,	__a       )

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

		return pipeline


if __name__ == "__main__":
					A       :   Tuple					= 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       :   Optional[Any]					= parser.parse_args()

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

					A       :   List[Any]					= convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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










class     A     :
			'''simple docstring'''




			def __init__(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  {}






			def   a_			(     self  :       Any  )      ->      None:







					"""simple docstring"""






					print(self.vertex  )
					for i in self.vertex:
							print(__lowerCAmelCase				,      """ -> """				,      """ -> """.join([str(__lowerCAmelCase  ) for j in self.vertex[i]]  )  )






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int  )      ->      None:







					"""simple docstring"""






					if from_vertex in self.vertex:
							self.vertex[from_vertex].append(__lowerCAmelCase  )
					else:
							# else make a new vertex
							A__     =  [to_vertex]






			def   a_			(     self  :       List[str]  )      ->      None:







					"""simple docstring"""






					A__     =  [False] * len(self.vertex  )

					# call the recursive helper function
					for i in range(len(self.vertex  )  ):
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       list  )      ->      None:







					"""simple docstring"""






					A__     =  True

					print(__lowerCAmelCase				,      end=""" """  )

					# Recur for all the vertices that are adjacent to this node
					for i in self.vertex:
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )


if __name__ == "__main__":
					A       :   Tuple					= Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print('''DFS:''')
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3



