'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

A_     :						str    							=TypeVar('''T''')



def snake_case_      (					__snake_case  :							int)			->					int:
   return (position - 1) // 2



def snake_case_      (					__snake_case  :							int)			->					int:
   return (2 * position) + 1



def snake_case_      (					__snake_case  :							int)			->					int:
   return (2 * position) + 2


class     __UpperCAmelCase       ( Generic[T]		):





  def __init__(	self ):
     lowerCAmelCase_			=      []
     lowerCAmelCase_			=      {}
     lowerCAmelCase_			=      0





  def __len__(	self ):
     return self.elements





  def __repr__(	self ):
     return str(self.heap )





  def 						UpperCAmelCase_	(	self ):
     # Check if the priority queue is empty
     return self.elements == 0





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     # Add an element with given priority to the queue
     self.heap.append((elem, weight) )
     lowerCAmelCase_			=      self.elements
     self.elements += 1
     self._bubble_up(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     # Remove and return the element with lowest weight (highest priority)
     if self.elements > 1:
        self._swap_nodes(0    ,     self.elements - 1 )
     lowerCAmelCase_	,lowerCAmelCase_			=      self.heap.pop()
     del self.position_map[elem]
     self.elements -= 1
     if self.elements > 0:
        lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[0]
        self._bubble_down(_lowerCamelCase )
     return elem





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     # Update the weight of the given key
     lowerCAmelCase_			=      self.position_map[elem]
     lowerCAmelCase_			=      (elem, weight)
     if position > 0:
        lowerCAmelCase_			=      get_parent_position(_lowerCamelCase )
        lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[parent_position]
        if parent_weight > weight:
           self._bubble_up(_lowerCamelCase )
        else:
           self._bubble_down(_lowerCamelCase )
     else:
        self._bubble_down(_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # Place a node at the proper position (upward movement) [to be used internally
     # only]
     lowerCAmelCase_			=      self.position_map[elem]
     if curr_pos == 0:
        return None
     lowerCAmelCase_			=      get_parent_position(_lowerCamelCase )
     lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[curr_pos]
     lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[parent_position]
     if parent_weight > weight:
        self._swap_nodes(_lowerCamelCase    ,     _lowerCamelCase )
        return self._bubble_up(_lowerCamelCase )
     return None





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # Place a node at the proper position (downward movement) [to be used
     # internally only]
     lowerCAmelCase_			=      self.position_map[elem]
     lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[curr_pos]
     lowerCAmelCase_			=      get_child_left_position(_lowerCamelCase )
     lowerCAmelCase_			=      get_child_right_position(_lowerCamelCase )
     if child_left_position < self.elements and child_right_position < self.elements:
        lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[child_left_position]
        lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
           self._swap_nodes(_lowerCamelCase    ,     _lowerCamelCase )
           return self._bubble_down(_lowerCamelCase )
     if child_left_position < self.elements:
        lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[child_left_position]
        if child_left_weight < weight:
           self._swap_nodes(_lowerCamelCase    ,     _lowerCamelCase )
           return self._bubble_down(_lowerCamelCase )
     else:
        return None
     if child_right_position < self.elements:
        lowerCAmelCase_	,lowerCAmelCase_			=      self.heap[child_right_position]
        if child_right_weight < weight:
           self._swap_nodes(_lowerCamelCase    ,     _lowerCamelCase )
           return self._bubble_down(_lowerCamelCase )
     return None







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     # Swap the nodes at the given positions
     lowerCAmelCase_			=      self.heap[nodea_pos][0]
     lowerCAmelCase_			=      self.heap[nodea_pos][0]
     lowerCAmelCase_	,lowerCAmelCase_			=      (
         self.heap[nodea_pos],
         self.heap[nodea_pos],
     )
     lowerCAmelCase_			=      nodea_pos
     lowerCAmelCase_			=      nodea_pos






class     __UpperCAmelCase       ( Generic[T]		):





  def __init__(	self ):
     lowerCAmelCase_			=      {}
     lowerCAmelCase_			=      0





  def __repr__(	self ):
     return str(self.connections )





  def __len__(	self ):
     return self.nodes





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     # Add a node in the graph if it is not in the graph
     if node not in self.connections:
        lowerCAmelCase_			=      {}
        self.nodes += 1







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     # Add an edge between 2 nodes in the graph
     self.add_node(_lowerCamelCase )
     self.add_node(_lowerCamelCase )
     lowerCAmelCase_			=      weight
     lowerCAmelCase_			=      weight






def snake_case_      (					__snake_case  :							GraphUndirectedWeighted[T]	,   )			->					tuple[dict[T, int], dict[T, T | None]]:
   lowerCAmelCase_			=      {node: maxsize for node in graph.connections}
   lowerCAmelCase_			=      {node: None for node in graph.connections}

   lowerCAmelCase_			=      MinPriorityQueue()
   for node, weight in dist.items():
      priority_queue.push(__snake_case	,   __snake_case)

   if priority_queue.is_empty():
      return dist, parent

   # initialization
   lowerCAmelCase_			=      priority_queue.extract_min()
   lowerCAmelCase_			=      0
   for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
         lowerCAmelCase_			=      dist[node] + graph.connections[node][neighbour]
         priority_queue.update_key(__snake_case	,   dist[neighbour])
         lowerCAmelCase_			=      node

    # running prim's algorithm
   while not priority_queue.is_empty():
      lowerCAmelCase_			=      priority_queue.extract_min()
      for neighbour in graph.connections[node]:
         if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            lowerCAmelCase_			=      dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(__snake_case	,   dist[neighbour])
            lowerCAmelCase_			=      node
   return dist, parent






'''simple docstring'''

import importlib

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



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]=None	,   __snake_case  :							str=None)			->					str:
   if conf_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.yaml'''
   lowerCAmelCase_			=      load_config(__snake_case	,   display=__snake_case)
   lowerCAmelCase_			=      VQModel(**config.model.params)
   if ckpt_path is None:
      lowerCAmelCase_			=      '''./model_checkpoints/vqgan_only.pt'''
   lowerCAmelCase_			=      torch.load(__snake_case	,   map_location=__snake_case)
   if ".ckpt" in ckpt_path:
      lowerCAmelCase_			=      sd['''state_dict''']
   model.load_state_dict(__snake_case	,   strict=__snake_case)
   model.to(__snake_case)
   del sd
   return model



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



def snake_case_      (					__snake_case  :							Tuple)			->					Union[str, Any]:
   if "target" not in config:
      raise KeyError('''Expected key `target` to instantiate.''')
   return get_obj_from_str(config['''target'''])(**config.get('''params'''	,   {}))



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Optional[int]	,   __snake_case  :							Dict	,   __snake_case  :							str)			->					Any:
   # load the specified checkpoint
   if ckpt:
      lowerCAmelCase_			=      torch.load(__snake_case	,   map_location='''cpu''')
      lowerCAmelCase_			=      pl_sd['''global_step''']
      print(F'''loaded model from global step {global_step}.''')
   else:
      lowerCAmelCase_			=      {'''state_dict''': None}
      lowerCAmelCase_			=      None
   lowerCAmelCase_			=      load_model_from_config(config.model	,   pl_sd['''state_dict''']	,   gpu=__snake_case	,   eval_mode=__snake_case)['''model''']
   return model, global_step





