


'''simple docstring'''



import importlib

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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							str=False				)	->						Tuple:
     __lowerCamelCase  :      Optional[Any]														=						OmegaConf.load(UpperCAmelCase_				)
     if display:
          print(yaml.dump(OmegaConf.to_container(UpperCAmelCase_				)				)				)
     return config



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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Tuple				)	->						Optional[int]:
     __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						model.encode(UpperCAmelCase_				)
     print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'				)
     __lowerCamelCase  :      Optional[int]														=						model.decode(UpperCAmelCase_				)
     return xrec



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Dict=False				)	->						Optional[int]:
     __lowerCamelCase							,			__lowerCamelCase  :      Any														=						string.rsplit('.' ,       1				)
     if reload:
          __lowerCamelCase  :      Optional[Any]														=						importlib.import_module(UpperCAmelCase_				)
          importlib.reload(UpperCAmelCase_				)
     return getattr(importlib.import_module(UpperCAmelCase_ ,       package=UpperCAmelCase_				) ,       cls				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any]				)	->						Optional[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__   (      UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							List[Any]=True ,       UpperCAmelCase_       :							int=True				)	->						Dict:
     __lowerCamelCase  :      Optional[Any]														=						instantiate_from_config(UpperCAmelCase_				)
     if sd is not None:
          model.load_state_dict(UpperCAmelCase_				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							List[str]				)	->						Any:
     # load the specified checkpoint
     if ckpt:
          __lowerCamelCase  :      int														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)
          __lowerCamelCase  :      Union[str, Any]														=						pl_sd['global_step']
          print(F'loaded model from global step {global_step}.'				)
     else:
          __lowerCamelCase  :      Any														=						{'state_dict': None}
          __lowerCamelCase  :      str														=						None
     __lowerCamelCase  :      str														=						load_model_from_config(config.model ,       pl_sd['state_dict'] ,       gpu=UpperCAmelCase_ ,       eval_mode=UpperCAmelCase_				)['model']
     return model, global_step










'''simple docstring'''



from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
      from ..ta.tokenization_ta import TaTokenizer
else:
      from ...utils.dummy_sentencepiece_objects import TaTokenizer

A__						:   int				=    TaTokenizer

if is_tokenizers_available():
      from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
      from ...utils.dummy_tokenizers_objects import TaTokenizerFast

A__						:   Dict				=    TaTokenizerFast

A__						:   Dict				=    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   Any				=    [
          """MT5EncoderModel""",
          """MT5ForConditionalGeneration""",
          """MT5ForQuestionAnswering""",
          """MT5Model""",
          """MT5PreTrainedModel""",
          """MT5Stack""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__						:   str				=    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
      from .configuration_mta import MTaConfig, MTaOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mta import (
                MTaEncoderModel,
                MTaForConditionalGeneration,
                MTaForQuestionAnswering,
                MTaModel,
                MTaPreTrainedModel,
                MTaStack,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
      import sys

      A__						:   Union[str, Any]				=    _LazyModule(
          __name__,
          globals()["""__file__"""],
          _import_structure,
          extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
          module_spec=__spec__,
      )






