


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:					Optional[Any]          =							{
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:					Optional[Any]          =							["MPNetTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:					int          =							[
       "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MPNetForMaskedLM",
       "MPNetForMultipleChoice",
       "MPNetForQuestionAnswering",
       "MPNetForSequenceClassification",
       "MPNetForTokenClassification",
       "MPNetLayer",
       "MPNetModel",
       "MPNetPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:					Tuple          =							[
       "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFMPNetEmbeddings",
       "TFMPNetForMaskedLM",
       "TFMPNetForMultipleChoice",
       "TFMPNetForQuestionAnswering",
       "TFMPNetForSequenceClassification",
       "TFMPNetForTokenClassification",
       "TFMPNetMainLayer",
       "TFMPNetModel",
       "TFMPNetPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
   from .tokenization_mpnet import MPNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mpnet_fast import MPNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mpnet import (
          MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetLayer,
          MPNetModel,
          MPNetPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mpnet import (
          TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMPNetEmbeddings,
          TFMPNetForMaskedLM,
          TFMPNetForMultipleChoice,
          TFMPNetForQuestionAnswering,
          TFMPNetForSequenceClassification,
          TFMPNetForTokenClassification,
          TFMPNetMainLayer,
          TFMPNetModel,
          TFMPNetPreTrainedModel,
      )

else:
   import sys

   _A		:					List[str]          =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








def   A		(__A  :       int			)  -> int:







     """simple docstring"""


     if not isinstance(__A  ,							__A			):
          UpperCAmelCase_		=				F"""Input value of [number={number}] must be an integer"""
          raise TypeError(__A			)
     if number < 1:
          raise ValueError('''Input must be a positive integer'''			)
     return number * (2 * number - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






