





import math
from collections.abc import Callable




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Dict      )      ->	List[Any]:
    UpperCamelCase__							:				float          =      xa
    UpperCamelCase__							:				float          =      xa
    while True:
        if x_n == x_na or function(__UpperCAmelCase      ) == function(__UpperCAmelCase      ):
            raise ZeroDivisionError('''float division by zero, could not find root'''      )
        UpperCamelCase__							:				float          =      x_na - (
            function(__UpperCAmelCase      ) / ((function(__UpperCAmelCase      ) - function(__UpperCAmelCase      )) / (x_na - x_n))
        )
        if abs(x_na - x_na      ) < 10**-5:
            return x_na
        UpperCamelCase__							:				Tuple          =      x_na
        UpperCamelCase__							:				Optional[int]          =      x_na




def 		lowerCAmelCase_    (  __UpperCAmelCase:			List[Any]      )      ->	List[Any]:
    return math.pow(__UpperCAmelCase		,   3      ) - (2 * x) - 5


if __name__ == "__main__":
  print(intersection(f, 3, 3.5))


'''simple docstring'''

from typing import TYPE_CHECKING

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


__a: Dict          =   {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CTRLForSequenceClassification""",
      """CTRLLMHeadModel""",
      """CTRLModel""",
      """CTRLPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a: int          =   [
      """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFCTRLForSequenceClassification""",
      """TFCTRLLMHeadModel""",
      """TFCTRLModel""",
      """TFCTRLPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .tokenization_ctrl import CTRLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ctrl import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
        CTRLPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
        TFCTRLPreTrainedModel,
    )

else:
  import sys

  __a: Dict          =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

