




'''simple docstring'''



UpperCamelCase_    	=				[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					Dict      ,__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[False] * len(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[s]
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						True

      while queue:
            SCREAMING_SNAKE_CASE   :      Tuple    					=						queue.pop(0    )
            for ind in range(len(graph[u]    )    ):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(__UpperCamelCase    )
                        SCREAMING_SNAKE_CASE   :      int    					=						True
                        SCREAMING_SNAKE_CASE   :      Any    					=						u

      return visited[t]




def   lowercase__(			__UpperCamelCase:					Any      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						[-1] * (len(__UpperCamelCase    ))
      SCREAMING_SNAKE_CASE   :      int    					=						0
      SCREAMING_SNAKE_CASE   :      Dict    					=						[]
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						[i[:] for i in graph]  # Record original cut, copy.
      while bfs(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      str    					=						float('Inf'    )
            SCREAMING_SNAKE_CASE   :      str    					=						sink

            while s != source:
                  # Find the minimum value in select path
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						min(__UpperCamelCase      ,graph[parent[s]][s]    )
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						parent[s]

            max_flow += path_flow
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						sink

            while v != source:
                  SCREAMING_SNAKE_CASE   :      Any    					=						parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  SCREAMING_SNAKE_CASE   :      Dict    					=						parent[v]

      for i in range(len(__UpperCamelCase    )    ):
            for j in range(len(graph[0]    )    ):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)    )

      return res


if __name__ == "__main__":
      print(mincut(test_graph, source=0, sink=5))








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




