

'''simple docstring'''







from typing import TYPE_CHECKING

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


__lowerCamelCase						    =      {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      ['''ConvBertTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ConvBertForMaskedLM''',
          '''ConvBertForMultipleChoice''',
          '''ConvBertForQuestionAnswering''',
          '''ConvBertForSequenceClassification''',
          '''ConvBertForTokenClassification''',
          '''ConvBertLayer''',
          '''ConvBertModel''',
          '''ConvBertPreTrainedModel''',
          '''load_tf_weights_in_convbert''',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFConvBertForMaskedLM''',
          '''TFConvBertForMultipleChoice''',
          '''TFConvBertForQuestionAnswering''',
          '''TFConvBertForSequenceClassification''',
          '''TFConvBertForTokenClassification''',
          '''TFConvBertLayer''',
          '''TFConvBertModel''',
          '''TFConvBertPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
      from .tokenization_convbert import ConvBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_convbert_fast import ConvBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_convbert import (
                CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                ConvBertForMaskedLM,
                ConvBertForMultipleChoice,
                ConvBertForQuestionAnswering,
                ConvBertForSequenceClassification,
                ConvBertForTokenClassification,
                ConvBertLayer,
                ConvBertModel,
                ConvBertPreTrainedModel,
                load_tf_weights_in_convbert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_convbert import (
                TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFConvBertForMaskedLM,
                TFConvBertForMultipleChoice,
                TFConvBertForQuestionAnswering,
                TFConvBertForSequenceClassification,
                TFConvBertForTokenClassification,
                TFConvBertLayer,
                TFConvBertModel,
                TFConvBertPreTrainedModel,
            )


else:
      import sys

      __lowerCamelCase						    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     float:

       if edge <= 0 or not isinstance(UpperCAmelCase__,    UpperCAmelCase__			):
              raise ValueError("""Length must be a positive."""			)
       return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     float:

       if edge <= 0 or not isinstance(UpperCAmelCase__,    UpperCAmelCase__			):
              raise ValueError("""Length must be a positive."""			)
       return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
