



'''simple docstring'''


import importlib

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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ):


     '''simple docstring'''







     a_       		=OmegaConf.load(lowercase__       )
     if display:
          print(yaml.dump(OmegaConf.to_container(lowercase__       )       )       )
     return config






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=None       ,	lowercase__=None       ):


     '''simple docstring'''







     if conf_path is None:
          a_       		="./model_checkpoints/vqgan_only.yaml"
     a_       		=load_config(lowercase__       ,	display=lowercase__       )
     a_       		=VQModel(**config.model.params       )
     if ckpt_path is None:
          a_       		="./model_checkpoints/vqgan_only.pt"
     a_       		=torch.load(lowercase__       ,	map_location=lowercase__       )
     if ".ckpt" in ckpt_path:
          a_       		=sd["state_dict"]
     model.load_state_dict(lowercase__       ,	strict=lowercase__       )
     model.to(lowercase__       )
     del sd
     return model






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_		,						a_		,						a_       		=model.encode(lowercase__       )
     print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""       )
     a_       		=model.decode(lowercase__       )
     return xrec






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__=False       ):


     '''simple docstring'''







     a_		,						a_       		=string.rsplit("."       ,	1       )
     if reload:
          a_       		=importlib.import_module(lowercase__       )
          importlib.reload(lowercase__       )
     return getattr(importlib.import_module(lowercase__       ,	package=lowercase__       )       ,	cls       )






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__=True       ,	lowercase__=True       ):


     '''simple docstring'''







     a_       		=instantiate_from_config(lowercase__       )
     if sd is not None:
          model.load_state_dict(lowercase__       )
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     if ckpt:
          a_       		=torch.load(lowercase__       ,	map_location="cpu"       )
          a_       		=pl_sd["global_step"]
          print(F"""loaded model from global step {global_step}."""       )
     else:
          a_       		={"state_dict": None}
          a_       		=None
     a_       		=load_model_from_config(config.model       ,	pl_sd["state_dict"]       ,	gpu=lowercase__       ,	eval_mode=lowercase__       )["model"]
     return model, global_step







'''simple docstring'''


from collections.abc import Sequence






def  UpperCAmelCase_       (		lowercase__ = None       ):


     '''simple docstring'''







     if nums is None or not nums:
          raise ValueError("Input sequence should not be empty"       )

     a_       		=nums[0]
     for i in range(1       ,	len(lowercase__       )       ):
          a_       		=nums[i]
          a_       		=max(lowercase__       ,	ans + num       ,	lowercase__       )

     return ans


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Try on a sample input from the user
  lowercase							       =			int(input('''Enter number of elements : ''').strip())
  lowercase							       =			list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n]
  print(max_subsequence_sum(array))


