'''simple docstring'''


import importlib

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


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[Any]     ,					lowerCamelCase_ :							Dict=False       ):



     """simple docstring"""





     UpperCAmelCase_							:       Union[str, Any]       =				OmegaConf.load(lowerCamelCase_       )
     if display:
          print(yaml.dump(OmegaConf.to_container(lowerCamelCase_       )       )       )
     return config


def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							int=None     ,					lowerCamelCase_ :							List[str]=None       ):



     """simple docstring"""





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


def  _lowerCamelCase   (				lowerCamelCase_ :							Optional[int]     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     UpperCAmelCase_       ,      UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				model.encode(lowerCamelCase_       )
     print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''       )
     UpperCAmelCase_							:       Optional[int]       =				model.decode(lowerCamelCase_       )
     return xrec


def  _lowerCamelCase   (				lowerCamelCase_ :							Tuple     ,					lowerCamelCase_ :							int=False       ):



     """simple docstring"""





     UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				string.rsplit('.'     ,					1       )
     if reload:
          UpperCAmelCase_							:       Optional[int]       =				importlib.import_module(lowerCamelCase_       )
          importlib.reload(lowerCamelCase_       )
     return getattr(importlib.import_module(lowerCamelCase_     ,					package=lowerCamelCase_       )     ,					cls       )


def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]       ):



     """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  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Union[str, Any]     ,					lowerCamelCase_ :							Union[str, Any]=True     ,					lowerCamelCase_ :							Union[str, Any]=True       ):



     """simple docstring"""





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





def  _lowerCamelCase   (				lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							List[Any]     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     if ckpt:
          UpperCAmelCase_							:       int       =				torch.load(lowerCamelCase_     ,					map_location='cpu'       )
          UpperCAmelCase_							:       str       =				pl_sd['global_step']
          print(F'''loaded model from global step {global_step}.'''       )
     else:
          UpperCAmelCase_							:       int       =				{'state_dict': None}
          UpperCAmelCase_							:       Optional[int]       =				None
     UpperCAmelCase_							:       Union[str, Any]       =				load_model_from_config(config.model     ,					pl_sd['state_dict']     ,					gpu=lowerCamelCase_     ,					eval_mode=lowerCamelCase_       )['model']
     return model, global_step







'''simple docstring'''


snake_case__					:      Optional[Any]				      =  tuple[float, float, float]
snake_case__					:      Tuple				      =  tuple[float, float, float]


def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Any       =				end_pointa[0] - end_pointa[0]
     UpperCAmelCase_							:       Optional[Any]       =				end_pointa[1] - end_pointa[1]
     UpperCAmelCase_							:       Any       =				end_pointa[2] - end_pointa[2]
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							Vectorad       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[Any]       =				ab[1] * ac[2] - ab[2] * ac[1]  # *i
     UpperCAmelCase_							:       Optional[Any]       =				(ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
     UpperCAmelCase_							:       Dict       =				ab[0] * ac[1] - ab[1] * ac[0]  # *k
     return (x, y, z)


def  _lowerCamelCase   (				lowerCamelCase_ :							Vectorad     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     return tuple(round(lowerCamelCase_     ,					lowerCamelCase_       ) for x in vector       ) == (0, 0, 0)





def  _lowerCamelCase   (				lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							Pointad     ,					lowerCamelCase_ :							int = 10       ):



     """simple docstring"""





     UpperCAmelCase_							:       List[str]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				create_vector(lowerCamelCase_     ,					lowerCamelCase_       )
     return is_zero_vector(get_ad_vectors_cross(lowerCamelCase_     ,					lowerCamelCase_       )     ,					lowerCamelCase_       )






