






"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase    :   Any   			=						{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Tuple   			=						[
       'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TapasForMaskedLM',
       'TapasForQuestionAnswering',
       'TapasForSequenceClassification',
       'TapasModel',
       'TapasPreTrainedModel',
       'load_tf_weights_in_tapas',
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Tuple   			=						[
       'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFTapasForMaskedLM',
       'TFTapasForQuestionAnswering',
       'TFTapasForSequenceClassification',
       'TFTapasModel',
       'TFTapasPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase    :   Optional[int]   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












"""simple docstring"""


def       snake_case  (A_				:int				):







  '''simple docstring'''

  if isinstance(A_     ,      A_				):
    raise TypeError('\'float\' object cannot be interpreted as an integer'				)
  if isinstance(A_     ,      A_				):
    raise TypeError('\'str\' object cannot be interpreted as an integer'				)

  if num == 0:
    return "0b0"

  a					:      List[Any]    =       False

  if num < 0:
    a					:      Optional[int]    =       True
    a					:      Dict    =       -num

  a					:      list[int]    =       []
  while num > 0:
    binary.insert(0     ,      num % 2				)
    num >>= 1

  if negative:
    return "-0b" + "".join(str(A_				) for e in binary				)

  return "0b" + "".join(str(A_				) for e in binary				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()




