

"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     str=False				)							->				str:





    '''simple docstring'''






    A__   				=       OmegaConf.load(a__				)
    if display:
        print(yaml.dump(OmegaConf.to_container(a__				)				)				)
    return config
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]=None   ,      SCREAMING_SNAKE_CASE__      :     str=None				)							->				List[str]:





    '''simple docstring'''






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





    '''simple docstring'''






    A__				,						A__				,						A__   				=       model.encode(a__				)
    print(f'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'				)
    A__   				=       model.decode(a__				)
    return xrec
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     List[Any]=False				)							->				Any:





    '''simple docstring'''






    A__				,						A__   				=       string.rsplit('.'   ,      1				)
    if reload:
        A__   				=       importlib.import_module(a__				)
        importlib.reload(a__				)
    return getattr(importlib.import_module(a__   ,      package=a__				)   ,      cls				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]				)							->				Dict:





    '''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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     Dict=True   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]=True				)							->				str:





    '''simple docstring'''






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

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				Tuple:





    '''simple docstring'''






    if ckpt:
        A__   				=       torch.load(a__   ,      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=a__   ,      eval_mode=a__				)['model']
    return model, global_step




from __future__ import annotations

from typing import Any
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list				)							->				int:





    '''simple docstring'''






    if not postfix_notation:
        return 0

    A__   				=       {'+', '-', '*', '/'}
    A__   				=       []

    for token in postfix_notation:
        if token in operations:
            A__				,						A__   				=       stack.pop(), stack.pop()
            if token == "+":
                stack.append(a + b				)
            elif token == "-":
                stack.append(a - b				)
            elif token == "*":
                stack.append(a * b				)
            else:
                if a * b < 0 and a % b != 0:
                    stack.append(a // b + 1				)
                else:
                    stack.append(a // b				)
        else:
            stack.append(int(SCREAMING_SNAKE_CASE__				)				)

    return stack.pop()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

