"""simple docstring"""






import importlib

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=False )  ->  str:
   lowercase__:   Dict					=      OmegaConf.load(__UpperCAmelCase )
   if display:
      print(yaml.dump(OmegaConf.to_container(__UpperCAmelCase ) ) )
   return config



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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   lowercase__, lowercase__, lowercase__:   Dict					=      model.encode(__UpperCAmelCase )
   print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}""" )
   lowercase__:   Union[str, Any]					=      model.decode(__UpperCAmelCase )
   return xrec



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=False )  ->  Any:
   lowercase__, lowercase__:   Optional[Any]					=      string.rsplit('''.'''       ,    1 )
   if reload:
      lowercase__:   Optional[int]					=      importlib.import_module(__UpperCAmelCase )
      importlib.reload(__UpperCAmelCase )
   return getattr(importlib.import_module(__UpperCAmelCase       ,    package=__UpperCAmelCase )       ,    cls )



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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=True       ,    __UpperCAmelCase=True )  ->  List[str]:
   lowercase__:   List[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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[Any]:
   # load the specified checkpoint
   if ckpt:
      lowercase__:   Optional[Any]					=      torch.load(__UpperCAmelCase       ,    map_location='''cpu''' )
      lowercase__:   int					=      pl_sd['''global_step''']
      print(F"""loaded model from global step {global_step}.""" )
   else:
      lowercase__:   int					=      {'''state_dict''': None}
      lowercase__:   Union[str, Any]					=      None
   lowercase__:   Tuple					=      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_sentencepiece_available,
    is_torch_available,
)


__A			   = {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
           "SpeechT5ForSpeechToText",
           "SpeechT5ForSpeechToSpeech",
           "SpeechT5ForTextToSpeech",
           "SpeechT5Model",
           "SpeechT5PreTrainedModel",
           "SpeechT5HifiGan",
       ]

if TYPE_CHECKING:
       from .configuration_speechta import (
           SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
           SpeechTaConfig,
           SpeechTaHifiGanConfig,
       )
       from .feature_extraction_speechta import SpeechTaFeatureExtractor
       from .processing_speechta import SpeechTaProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_speechta import SpeechTaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speechta import (
                  SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechTaForSpeechToSpeech,
                  SpeechTaForSpeechToText,
                  SpeechTaForTextToSpeech,
                  SpeechTaHifiGan,
                  SpeechTaModel,
                  SpeechTaPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
