





import importlib

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






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Tuple=False			)	->			List[str]:
     lowerCAmelCase_					:       List[Any]    =		OmegaConf.load(__snake_case			)
     if display:
          print(yaml.dump(OmegaConf.to_container(__snake_case			)			)			)
     return config






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Dict=None					,   _a   :					Tuple=None			)	->			Optional[int]:


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






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					Optional[Any]			)	->			List[str]:
     lowerCAmelCase_					:       Union[str, Any]    =		model.encode(__snake_case			)
     print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'			)
     lowerCAmelCase_					:       str    =		model.decode(__snake_case			)
     return xrec






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					Dict=False			)	->			Tuple:
     lowerCAmelCase_					:       List[Any]    =		string.rsplit("""."""					,   1			)
     if reload:
          lowerCAmelCase_					:       Optional[Any]    =		importlib.import_module(__snake_case			)
          importlib.reload(__snake_case			)
     return getattr(importlib.import_module(__snake_case					,   package=__snake_case			)					,   cls			)






def 							_lowerCAmelCase (  _a   :					Optional[Any]			)	->			Tuple:


     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   :					Tuple					,   _a   :					List[Any]					,   _a   :					Optional[Any]=True					,   _a   :					Any=True			)	->			int:
     lowerCAmelCase_					:       str    =		instantiate_from_config(__snake_case			)
     if sd is not None:
          model.load_state_dict(__snake_case			)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def 							_lowerCAmelCase (  _a   :					List[str]					,   _a   :					Tuple					,   _a   :					Union[str, Any]					,   _a   :					Optional[Any]			)	->			Tuple:


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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					:   int 							=						{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Optional[int] 							=						["""PLBartTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						[
          """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PLBartForCausalLM""",
          """PLBartForConditionalGeneration""",
          """PLBartForSequenceClassification""",
          """PLBartModel""",
          """PLBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_plbart import PLBartTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_plbart import (
                PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                PLBartForCausalLM,
                PLBartForConditionalGeneration,
                PLBartForSequenceClassification,
                PLBartModel,
                PLBartPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   List[str] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
