






import argparse
import copy

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   {}

				with open(snake_case			) as f:
								for line in f:
												if line.split()[0] not in dict_of_neighbours:
																__magic_name__		:Tuple   =   []
																_list.append([line.split()[1], line.split()[2]]			)
																__magic_name__		:Dict   =   _list
												else:
																dict_of_neighbours[line.split()[0]].append(
																    [line.split()[1], line.split()[2]]			)
												if line.split()[1] not in dict_of_neighbours:
																__magic_name__		:Optional[Any]   =   []
																_list.append([line.split()[0], line.split()[2]]			)
																__magic_name__		:Optional[Any]   =   _list
												else:
																dict_of_neighbours[line.split()[1]].append(
																    [line.split()[0], line.split()[2]]			)

				return dict_of_neighbours

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				with open(snake_case			) as f:
								__magic_name__		:Optional[Any]   =   f.read(1			)
				__magic_name__		:List[Any]   =   start_node

				__magic_name__		:int   =   []

				__magic_name__		:str   =   start_node

				__magic_name__		:Optional[Any]   =   0
				while visiting not in first_solution:
								__magic_name__		:List[str]   =   1_0_0_0_0
								for k in dict_of_neighbours[visiting]:
												if int(k[1]			) < int(snake_case			) and k[0] not in first_solution:
																__magic_name__		:Optional[Any]   =   k[1]
																__magic_name__		:int   =   k[0]

								first_solution.append(snake_case			)
								__magic_name__		:Dict   =   distance_of_first_solution + int(snake_case			)
								__magic_name__		:int   =   best_node

				first_solution.append(snake_case			)

				__magic_name__		:Tuple   =   0
				for k in dict_of_neighbours[first_solution[-2]]:
								if k[0] == start_node:
												break
								position += 1

				__magic_name__		:Optional[int]   =   (
				    distance_of_first_solution
				    + int(dict_of_neighbours[first_solution[-2]][position][1]			)
				    - 1_0_0_0_0
				)
				return first_solution, distance_of_first_solution

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   []

				for n in solution[1:-1]:
								__magic_name__		:List[str]   =   solution.index(snake_case			)
								for kn in solution[1:-1]:
												__magic_name__		:str   =   solution.index(snake_case			)
												if n == kn:
																continue

												__magic_name__		:Union[str, Any]   =   copy.deepcopy(snake_case			)
												__magic_name__		:List[str]   =   kn
												__magic_name__		:Optional[int]   =   n

												__magic_name__		:Any   =   0

												for k in _tmp[:-1]:
																__magic_name__		:Tuple   =   _tmp[_tmp.index(snake_case			) + 1]
																for i in dict_of_neighbours[k]:
																				if i[0] == next_node:
																								__magic_name__		:List[str]   =   distance + int(i[1]			)
												_tmp.append(snake_case			)

												if _tmp not in neighborhood_of_solution:
																neighborhood_of_solution.append(_tmp			)

				__magic_name__		:Optional[int]   =   len(neighborhood_of_solution[0]			) - 1

				neighborhood_of_solution.sort(key=lambda snake_case			: x[index_of_last_item_in_the_list]			)
				return neighborhood_of_solution

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Union[str, Any]   =   1
				__magic_name__		:Dict   =   first_solution
				__magic_name__		:Optional[Any]   =   []
				__magic_name__		:Optional[Any]   =   distance_of_first_solution
				__magic_name__		:str   =   solution

				while count <= iters:
								__magic_name__		:Dict   =   find_neighborhood(snake_case,	snake_case			)
								__magic_name__		:Any   =   0
								__magic_name__		:Union[str, Any]   =   neighborhood[index_of_best_solution]
								__magic_name__		:Union[str, Any]   =   len(snake_case			) - 1

								__magic_name__		:str   =   False
								while not found:
												__magic_name__		:List[str]   =   0
												while i < len(snake_case			):
																if best_solution[i] != solution[i]:
																				__magic_name__		:List[str]   =   best_solution[i]
																				__magic_name__		:Any   =   solution[i]
																				break
																__magic_name__		:Any   =   i + 1

												if [first_exchange_node, second_exchange_node] not in tabu_list and [
												    second_exchange_node,
												    first_exchange_node,
												] not in tabu_list:
																tabu_list.append([first_exchange_node, second_exchange_node]			)
																__magic_name__		:List[str]   =   True
																__magic_name__		:List[str]   =   best_solution[:-1]
																__magic_name__		:int   =   neighborhood[index_of_best_solution][best_cost_index]
																if cost < best_cost:
																				__magic_name__		:Union[str, Any]   =   cost
																				__magic_name__		:List[str]   =   solution
												else:
																__magic_name__		:Optional[int]   =   index_of_best_solution + 1
																__magic_name__		:Optional[Any]   =   neighborhood[index_of_best_solution]

								if len(snake_case			) >= size:
												tabu_list.pop(0			)

								__magic_name__		:List[Any]   =   count + 1

				return best_solution_ever, best_cost

def   __lowercase       (			snake_case=None			):







				"""simple docstring"""






				__magic_name__		:str   =   generate_neighbours(args.File			)

				__magic_name__  ,					__magic_name__		:List[str]   =   generate_first_solution(
				    args.File,	snake_case			)

				__magic_name__  ,					__magic_name__		:List[Any]   =   tabu_search(
				    snake_case,	snake_case,	snake_case,	args.Iterations,	args.Size,	)

				print(f'''Best solution: {best_sol}, with total distance: {best_cost}.'''			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      List[str]				=		argparse.ArgumentParser(description="""Tabu Search""")
					parser.add_argument(
					    """-f""",
					    """--File""",
					    type=str,
					    help="""Path to the file containing the data""",
					    required=True,
					)
					parser.add_argument(
					    """-i""",
					    """--Iterations""",
					    type=int,
					    help="""How many iterations the algorithm should perform""",
					    required=True,
					)
					parser.add_argument(
					    """-s""", """--Size""", type=int, help="""Size of the tabu list""", required=True
					)

					# Pass the arguments to main method
					main(parser.parse_args())










import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		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(__magic_name__	) > -1:
						try:
								lowercase__	     =						curr_layer.__getattr__(__magic_name__	)
								if len(__magic_name__	) > 0:
										lowercase__	     =						layer_infos.pop(0	)
								elif len(__magic_name__	) == 0:
										break
						except Exception:
								if len(__magic_name__	) > 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(__magic_name__	)
				else:
						pair_keys.append(__magic_name__	)
						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(__magic_name__       ,       __magic_name__	).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(__magic_name__       ,       __magic_name__	)

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

		return pipeline


if __name__ == "__main__":
	A			:						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.)')

	A			:						str        =  parser.parse_args()

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

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

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

