


"""simple docstring"""






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline


def __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      Optional[int]						,					lowerCamelCase_  :      Tuple						,					lowerCamelCase_  :      str  ):
				# load base model
				SCREAMING_SNAKE_CASE__     							=				StableDiffusionPipeline.from_pretrained(lowerCamelCase_						,					torch_dtype=torch.floataa  )

				# load LoRA weight from .safetensors
				SCREAMING_SNAKE_CASE__     							=				load_file(lowerCamelCase_  )

				SCREAMING_SNAKE_CASE__     							=				[]

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

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

								SCREAMING_SNAKE_CASE__     							=				[]
								if "lora_down" in key:
												pair_keys.append(key.replace("lora_down"						,					"lora_up"  )  )
												pair_keys.append(lowerCamelCase_  )
								else:
												pair_keys.append(lowerCamelCase_  )
												pair_keys.append(key.replace("lora_up"						,					"lora_down"  )  )

								# update weight
								if len(state_dict[pair_keys[0]].shape  ) == 4:
												SCREAMING_SNAKE_CASE__     							=				state_dict[pair_keys[0]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
												SCREAMING_SNAKE_CASE__     							=				state_dict[pair_keys[1]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
												curr_layer.weight.data += alpha * torch.mm(lowerCamelCase_						,					lowerCamelCase_  ).unsqueeze(2  ).unsqueeze(3  )
								else:
												SCREAMING_SNAKE_CASE__     							=				state_dict[pair_keys[0]].to(torch.floataa  )
												SCREAMING_SNAKE_CASE__     							=				state_dict[pair_keys[1]].to(torch.floataa  )
												curr_layer.weight.data += alpha * torch.mm(lowerCamelCase_						,					lowerCamelCase_  )

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

				return pipeline


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

	_lowerCamelCase     =  parser.parse_args()

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

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

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




"""simple docstring"""






import heapq


def __lowercase     (      lowerCamelCase_  :      dict  ):
				SCREAMING_SNAKE_CASE__     							=				[]

				# for each node and his adjacency list add them and the rank of the node to queue
				# using heapq module the queue will be filled like a Priority Queue
				# heapq works with a min priority queue, so I used -1*len(v) to build it
				for key, value in graph.items():
								# O(log(n))
								heapq.heappush(lowerCamelCase_						,					[-1 * len(lowerCamelCase_  ), (key, value)]  )

				# chosen_vertices = set of chosen vertices
				SCREAMING_SNAKE_CASE__     							=				set()

				# while queue isn't empty and there are still edges
				#   (queue[0][0] is the rank of the node with max rank)
				while queue and queue[0][0] != 0:
								# extract vertex with max rank from queue and add it to chosen_vertices
								SCREAMING_SNAKE_CASE__     							=				heapq.heappop(lowerCamelCase_  )[1][0]
								chosen_vertices.add(lowerCamelCase_  )

								# Remove all arcs adjacent to argmax
								for elem in queue:
												# if v haven't adjacent node, skip
												if elem[0] == 0:
																continue
												# if argmax is reachable from elem
												# remove argmax from elem's adjacent list and update his rank
												if argmax in elem[1][1]:
																SCREAMING_SNAKE_CASE__     							=				elem[1][1].index(lowerCamelCase_  )
																del elem[1][1][index]
																elem[0] += 1
        # re-order the queue
								heapq.heapify(lowerCamelCase_  )
				return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCamelCase     =  {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
