





import warnings

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

class SCREAMING_SNAKE_CASE_ (					_UpperCamelCase   ):





					"""simple docstring"""


					A__				 =    ["image_processor", "tokenizer"]
					A__				 =    "ViTImageProcessor"
					A__				 =    ("CLIPTokenizer", "CLIPTokenizerFast")





					def __init__( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       None
										if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."	,  _UpperCAmelCase	,  )
															lowerCamelCase__       =       kwargs.pop("feature_extractor"	)

										lowerCamelCase__       =       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__(_UpperCAmelCase	,  _UpperCAmelCase	)





					def __call__( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  **_lowerCAmelCase	):
										if text is None and visual_prompt is None and images is None:
															raise ValueError("You have to specify either text, visual prompt or images."	)

										if text is not None and visual_prompt is not None:
															raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."	)

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

										if visual_prompt is not None:
															lowerCamelCase__       =       self.image_processor(_UpperCAmelCase	,  return_tensors=_UpperCAmelCase	,  **_UpperCAmelCase	)

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

										if visual_prompt is not None and images is not None:
															lowerCamelCase__       =       {
															    '''pixel_values''': image_features.pixel_values,
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
										elif text is not None and images is not None:
															lowerCamelCase__       =       image_features.pixel_values
															return encoding
										elif text is not None:
															return encoding
										elif visual_prompt is not None:
															lowerCamelCase__       =       {
															    '''conditional_pixel_values''': prompt_features.pixel_values,
															}
															return encoding
										else:
															return BatchEncoding(data=dict(**_UpperCAmelCase	)	,  tensor_type=_UpperCAmelCase	)





					def 					__magic_name__ ( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										return self.tokenizer.batch_decode(*_UpperCAmelCase	,  **_UpperCAmelCase	)





					def 					__magic_name__ ( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										return self.tokenizer.decode(*_UpperCAmelCase	,  **_UpperCAmelCase	)





					@property
					def 					__magic_name__ ( self	):
										warnings.warn(
										    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,  _UpperCAmelCase	,  )
										return self.image_processor_class






					@property
					def 					__magic_name__ ( self	):
										warnings.warn(
										    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,  _UpperCAmelCase	,  )
										return self.image_processor








import importlib

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





def    __UpperCamelCase      (    a,     a=False) ->Optional[Any]:
					lowerCamelCase__       =       OmegaConf.load(a)
					if display:
										print(yaml.dump(OmegaConf.to_container(a)))
					return config





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





def    __UpperCamelCase      (    a,     a) ->int:
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       model.encode(a)
					print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
					lowerCamelCase__       =       model.decode(a)
					return xrec





def    __UpperCamelCase      (    a,     a=False) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       string.rsplit(".",     1)
					if reload:
										lowerCamelCase__       =       importlib.import_module(a)
										importlib.reload(a)
					return getattr(importlib.import_module(a,     package=a),     cls)





def    __UpperCamelCase      (    a) ->int:
					if "target" not in config:
										raise KeyError("Expected key `target` to instantiate.")
					return get_obj_from_str(config["target"])(**config.get("params",     {}))





def    __UpperCamelCase      (    a,     a,     a=True,     a=True) ->Optional[Any]:
					lowerCamelCase__       =       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    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					# load the specified checkpoint
					if ckpt:
										lowerCamelCase__       =       torch.load(a,     map_location="cpu")
										lowerCamelCase__       =       pl_sd["global_step"]
										print(f"loaded model from global step {global_step}.")
					else:
										lowerCamelCase__       =       {"state_dict": None}
										lowerCamelCase__       =       None
					lowerCamelCase__       =       load_model_from_config(config.model,     pl_sd["state_dict"],     gpu=a,     eval_mode=a)["model"]
					return model, global_step

