




import math
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  return math.pow(__lowerCAmelCase   , 2				) - a
def 		a_							( __lowerCAmelCase				):
  return 2 * x
def 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      2.0

  while start <= a:
    lowerCAmelCase__										=      math.pow(__lowerCAmelCase   , 2				)

  return start
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase = 99_99   , __lowerCAmelCase = 0.00_000_000_000_001				):

  if a < 0:
    raise ValueError('''math domain error'''				)

  lowerCAmelCase__										=      get_initial_point(__lowerCAmelCase				)

  for _ in range(__lowerCAmelCase				):
    lowerCAmelCase__										=      value
    lowerCAmelCase__										=      value - fx(__lowerCAmelCase   , __lowerCAmelCase				) / fx_derivative(__lowerCAmelCase				)
    if abs(prev_value - value				) < tolerance:
      return value

  return value


if __name__ == "__main__":
      from doctest import testmod

      testmod()







from typing import TYPE_CHECKING

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


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

