"""simple docstring"""




import importlib

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




def        lowerCAmelCase_			(					__A,  __A=False							)					->    int:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A=None,  __A=None							)					->    Tuple:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A							)					->    List[Any]:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A,  __A=False							)					->    Union[str, Any]:







      '''simple docstring'''


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




def        lowerCAmelCase_			(					__A							)					->    Optional[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_			(					__A,  __A,  __A=True,  __A=True							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  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        lowerCAmelCase_			(					__A,  __A,  __A,  __A							)					->    List[Any]:







      '''simple docstring'''


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




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



