






"""simple docstring"""


import heapq


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase   :list[list]						=			[]

						# 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(a__     ,      [-1 * len(a__ ), (key, value)] )

						# chosen_vertices = set of chosen vertices
						lowerCAmelCase   :Tuple						=			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
												lowerCAmelCase   :Tuple						=			heapq.heappop(a__ )[1][0]
												chosen_vertices.add(a__ )

												# 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]:
																								lowerCAmelCase   :Tuple						=			elem[1][1].index(a__ )
																								del elem[1][1][index]
																								elem[0] += 1
        # re-order the queue
												heapq.heapify(a__ )
						return chosen_vertices


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__SCREAMING_SNAKE_CASE   =   {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)}""")





import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__						:						Optional[int]										=OmegaConf.load(lowerCamelCase )
				if display:
								print(yaml.dump(OmegaConf.to_container(lowerCamelCase ) ) )
				return config




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=None ):
				if conf_path is None:
								__magic_name__						:						List[str]										="""./model_checkpoints/vqgan_only.yaml"""
				__magic_name__						:						Dict										=load_config(lowerCamelCase						,  display=lowerCamelCase )
				__magic_name__						:						Tuple										=VQModel(**config.model.params )
				if ckpt_path is None:
								__magic_name__						:						Optional[Any]										="""./model_checkpoints/vqgan_only.pt"""
				__magic_name__						:						Tuple										=torch.load(lowerCamelCase						,  map_location=lowerCamelCase )
				if ".ckpt" in ckpt_path:
								__magic_name__						:						Any										=sd["""state_dict"""]
				model.load_state_dict(lowerCamelCase						,  strict=lowerCamelCase )
				model.to(lowerCamelCase )
				del sd
				return model




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__					,			__magic_name__					,			__magic_name__						:						Optional[Any]										=model.encode(lowerCamelCase )
				print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}" )
				__magic_name__						:						List[Any]										=model.decode(lowerCamelCase )
				return xrec




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=False ):
				__magic_name__					,			__magic_name__						:						Optional[int]										=string.rsplit("""."""						,  1 )
				if reload:
								__magic_name__						:						Optional[int]										=importlib.import_module(lowerCamelCase )
								importlib.reload(lowerCamelCase )
				return getattr(importlib.import_module(lowerCamelCase						,  package=lowerCamelCase )						,  cls )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if "target" not in config:
								raise KeyError("""Expected key `target` to instantiate.""" )
				return get_obj_from_str(config["""target"""] )(**config.get("""params"""						,  {} ) )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=True						,  lowerCamelCase=True ):
				__magic_name__						:						str										=instantiate_from_config(lowerCamelCase )
				if sd is not None:
								model.load_state_dict(lowerCamelCase )
				if gpu:
								model.cuda()
				if eval_mode:
								model.eval()
				return {"model": model}




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				# load the specified checkpoint
				if ckpt:
								__magic_name__						:						str										=torch.load(lowerCamelCase						,  map_location="""cpu""" )
								__magic_name__						:						Any										=pl_sd["""global_step"""]
								print(F"loaded model from global step {global_step}." )
				else:
								__magic_name__						:						List[Any]										={"""state_dict""": None}
								__magic_name__						:						Optional[Any]										=None
				__magic_name__						:						Tuple										=load_model_from_config(config.model						,  pl_sd["""state_dict"""]						,  gpu=lowerCamelCase						,  eval_mode=lowerCamelCase )["""model"""]
				return model, global_step




