
'''simple docstring'''
from typing import Union

import fire
import torch
from tqdm import tqdm

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = "cpu",		__SCREAMING_SNAKE_CASE = None       ):
    """simple docstring"""






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


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






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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




