'''simple docstring'''

from __future__ import annotations

from collections import Counter
from random import random


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      {}





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      {}





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     if nodea not in self.connections:
        self.add_node(_lowerCamelCase )
     if nodea not in self.connections:
        self.add_node(_lowerCamelCase )
     lowerCAmelCase_			=      probability





  def 						UpperCAmelCase_	(	self ):
     return list(self.connections )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      0
     lowerCAmelCase_			=      random()

     for dest in self.connections[node]:
        current_probability += self.connections[node][dest]
        if current_probability > random_value:
           return dest
     return ""






def snake_case_      (					__snake_case  :							str	,   __snake_case  :							list[tuple[str, str, float]]	,   __snake_case  :							int)			->					dict[str, int]:
   lowerCAmelCase_			=      MarkovChainGraphUndirectedUnweighted()

   for nodea, nodea, probability in transitions:
      graph.add_transition_probability(__snake_case	,   __snake_case	,   __snake_case)

   lowerCAmelCase_			=      Counter(graph.get_nodes())
   lowerCAmelCase_			=      start

   for _ in range(__snake_case):
      lowerCAmelCase_			=      graph.transition(__snake_case)
      visited[node] += 1

   return visited


if __name__ == "__main__":
 import doctest

 doctest.testmod()






'''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





