





def _A       (   __magic_name__	):
   return "".join([hex(__magic_name__	)[2:].zfill(2	).upper() for byte in list(__magic_name__	)]	)


def _A       (   __magic_name__	):
   # Check data validity, following RFC3548
   # https://www.ietf.org/rfc/rfc3548.txt
   if (len(__magic_name__	) % 2) != 0:
      raise ValueError(
          "Base16 encoded data is invalid:\nData does not have an even number of hex digits."	)
   # Check the character set - the standard base16 alphabet
   # is uppercase according to RFC3548 section 6
   if not set(__magic_name__	) <= set("0123456789ABCDEF"	):
      raise ValueError(
          "Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters."	)
   # For every two hexadecimal digits (= a byte), turn it into an integer.
   # Then, string the result together into bytes, and return it.
   return bytes(int(data[i] + data[i + 1]  , 16	) for i in range(0  , len(__magic_name__	)  , 2	)	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case           =			{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case           =			[
      """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """AutoformerForPrediction""",
      """AutoformerModel""",
      """AutoformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

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






