






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_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__":
				__A      =				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      =				parser.parse_args()

				__A      =				args.base_model_path
				__A      =				args.checkpoint_path
				__A      =				args.dump_path
				__A      =				args.lora_prefix_unet
				__A      =				args.lora_prefix_text_encoder
				__A      =				args.alpha

				__A      =				convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

				__A      =				pipe.to(args.device)
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



