


'''simple docstring'''
import importlib

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


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       List[str]=False      ):
 __A	:		Union[str, Any]	  =      OmegaConf.load(UpperCamelCase__      )
 if display:
  print(yaml.dump(OmegaConf.to_container(UpperCamelCase__      )      )      )
 return config


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


def       _lowercase							(					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Optional[Any]      ):
 __A  ,__A  ,__A	:		List[str]	  =      model.encode(UpperCamelCase__      )
 print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""      )
 __A	:		Any	  =      model.decode(UpperCamelCase__      )
 return xrec


def       _lowercase							(					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       str=False      ):
 __A  ,__A	:		Dict	  =      string.rsplit('.',					1      )
 if reload:
  __A	:		str	  =      importlib.import_module(UpperCamelCase__      )
  importlib.reload(UpperCamelCase__      )
 return getattr(importlib.import_module(UpperCamelCase__,					package=UpperCamelCase__      ),					cls      )


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


def       _lowercase							(					UpperCamelCase__							:       Optional[Any],					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       str=True,					UpperCamelCase__							:       Optional[Any]=True      ):
 __A	:		Optional[int]	  =      instantiate_from_config(UpperCamelCase__      )
 if sd is not None:
  model.load_state_dict(UpperCamelCase__      )
 if gpu:
  model.cuda()
 if eval_mode:
  model.eval()
 return {"model": model}


def       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Union[str, Any],					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       Optional[int]      ):
 # load the specified checkpoint
 if ckpt:
  __A	:		Optional[int]	  =      torch.load(UpperCamelCase__,					map_location='cpu'      )
  __A	:		Union[str, Any]	  =      pl_sd['global_step']
  print(f"""loaded model from global step {global_step}."""      )
 else:
  __A	:		List[str]	  =      {'state_dict': None}
  __A	:		Tuple	  =      None
 __A	:		Any	  =      load_model_from_config(config.model,					pl_sd['state_dict'],					gpu=UpperCamelCase__,					eval_mode=UpperCamelCase__      )['model']
 return model, global_step










'''simple docstring'''
def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int      ):
 return 1 if input_a == input_a else 0


def       _lowercase							(					):
 assert xnor_gate(0,					0      ) == 1
 assert xnor_gate(0,					1      ) == 0
 assert xnor_gate(1,					0      ) == 0
 assert xnor_gate(1,					1      ) == 1


if __name__ == "__main__":
 print(xnor_gate(0, 0))
 print(xnor_gate(0, 1))
 print(xnor_gate(1, 0))
 print(xnor_gate(1, 1))






