





'''simple docstring'''






import math as m
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase=0.001						):





      """simple docstring"""


      return (f(a + h						) - f(a - h						)) / (2 * h)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase=0							,			lowerCAmelCase=1_00							,			lowerCAmelCase=0.0_001							,			lowerCAmelCase=1e-6							,			lowerCAmelCase=False						):





      """simple docstring"""


      _lowerCAmelCase							  =							xa  # set the initial guess
      _lowerCAmelCase							  =							[a]
      _lowerCAmelCase							  =							abs(f(lowerCAmelCase						)						)
      _lowerCAmelCase							  =							lambda lowerCAmelCase						: calc_derivative(lowerCAmelCase							,			lowerCAmelCase							,			h=lowerCAmelCase						)  # noqa: E731  Derivative of f(x)
      for _ in range(lowerCAmelCase						):
            if fa(lowerCAmelCase						) == 0:
                  raise ValueError("""No converging solution found"""						)
            _lowerCAmelCase							  =							a - f(lowerCAmelCase						) / fa(lowerCAmelCase						)  # Calculate the next estimate
            if logsteps:
                  steps.append(lowerCAmelCase						)
            if error < maxerror:
                  break
      else:
            raise ValueError("""Iteration limit reached, no converging solution found"""						)
      if logsteps:
            # If logstep is true, then log intermediate steps
            return a, error, steps
      return a, error


if __name__ == "__main__":
      from matplotlib import pyplot as plt

      A__       :     Tuple          =lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
      A__       :     int          =newton_raphson(
          f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
      )
      plt.plot([abs(f(x)) for x in steps])
      plt.xlabel('''step''')
      plt.ylabel('''error''')
      plt.show()
      print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")






'''simple docstring'''






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     CustomConfig




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				Tuple						)       ->   Optional[Any]:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.hidden_size    ,    config.hidden_size						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Dict:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int						)       ->   Any:
            pass




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     NoSuperInitConfig




      def __init__(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.attribute    ,    config.attribute						)




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Dict						)       ->   Any:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Dict:
            pass






