

'''simple docstring'''



import importlib

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







def   a__			(    a__ ,       a__=False		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = OmegaConf.load(a__		)
     if display:
          print(yaml.dump(OmegaConf.to_container(a__		)		)		)
     return config







def   a__			(    a__ ,       a__=None ,       a__=None		):




     """simple docstring"""


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







def   a__			(    a__ ,       a__		):




     """simple docstring"""


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







def   a__			(    a__ ,       a__=False		):




     """simple docstring"""


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







def   a__			(    a__		):




     """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   a__			(    a__ ,       a__ ,       a__=True ,       a__=True		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 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   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


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









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = None
      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """simple docstring"""




      if text is None and images is None:
           raise ValueError("""You have to specify either text or images. Both cannot be none."""     )

      if text is not None:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if images is not None:
           __SCREAMING_SNAKE_CASE      = self.image_processor(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE      = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






