




def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	float:



       """simple docstring"""

       return 10 - x * x







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	float:



       """simple docstring"""

       if equation(lowercase_						) * equation(lowercase_						) >= 0:
              raise ValueError('''Wrong space!'''						)

       A__							=   a
       while (b - a) >= 0.01:
              # Find middle point
              A__							=   (a + b) / 2
              # Check if middle point is root
              if equation(lowercase_						) == 0.0:
                     break
              # Decide the side to repeat the steps
              if equation(lowercase_						) * equation(lowercase_						) < 0:
                     A__							=   c
              else:
                     A__							=   c
       return c


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(bisection(-2, 5))
  print(bisection(0, 6))











from typing import TYPE_CHECKING

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


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

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


