





'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase       :		Dict		=				{
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		List[str]		=				['MPNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase       :		Union[str, Any]		=				[
       '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:
   UpperCAmelCase       :		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

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









'''simple docstring'''

from __future__ import annotations




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  sorted(lowerCAmelCase_						)
      _snake_case		:						Any         =  len(lowerCAmelCase_						)
      _snake_case		:						List[str]         =  length >> 1
      return (
          (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
          if length % 2 == 0
          else sorted_list[mid_index]
      )




def  _a     (				):





      """simple docstring"""



      import doctest

      doctest.testmod()


if __name__ == "__main__":
   main()


