


'''simple docstring'''

from typing import Union

import fire
import torch
from tqdm import tqdm






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[Any] = "cpu"      ,  __lowerCamelCase		: Optional[Any] = None				)      ->     Any:







     '''simple docstring'''






     _a						=		torch.load(A_      ,  map_location=A_				)
     for k, v in tqdm(state_dict.items()				):
          if not isinstance(A_      ,  torch.Tensor				):
               raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"				)
          _a						=		v.half()
     if save_path is None:  # overwrite src_path
          _a						=		src_path
     torch.save(A_      ,  A_				)


if __name__ == "__main__":
   fire.Fire(convert)




'''simple docstring'''

from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__				     =		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				     =		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

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

