




'''simple docstring'''






from collections.abc import Callable

A_ 			=		Callable[[float], float]  # type alias for a real -> real function






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			)						->      float:
      snake_case__					:      List[str]  			=						float(__SCREAMING_SNAKE_CASE		)
      while True:
            try:
                  snake_case__					:      int  			=						prev_guess - function(__SCREAMING_SNAKE_CASE		) / derivative(__SCREAMING_SNAKE_CASE		)
            except ZeroDivisionError:
                  raise ZeroDivisionError('Could not find root'		) from None
            if abs(prev_guess - next_guess		) < 10**-5:
                  return next_guess
            snake_case__					:      str  			=						next_guess






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      float:
      return (x**3) - (2 * x) - 5






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      float:
      return 3 * (x**2) - 2


if __name__ == "__main__":
      print(newton(f, fa, 3))






'''simple docstring'''






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A_ 			=		{
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		["ErnieMTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		[
          "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ErnieMForMultipleChoice",
          "ErnieMForQuestionAnswering",
          "ErnieMForSequenceClassification",
          "ErnieMForTokenClassification",
          "ErnieMModel",
          "ErnieMPreTrainedModel",
          "ErnieMForInformationExtraction",
      ]


if TYPE_CHECKING:
      from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ernie_m import ErnieMTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie_m import (
                ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieMForInformationExtraction,
                ErnieMForMultipleChoice,
                ErnieMForQuestionAnswering,
                ErnieMForSequenceClassification,
                ErnieMForTokenClassification,
                ErnieMModel,
                ErnieMPreTrainedModel,
            )


else:
      import sys

      A_ 			=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
