






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__=100					,     lowerCAmelCase__=0.0_001					,     lowerCAmelCase__=1E-6					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		__A    =							xa  # set the initial guess
		__A    =							[a]
		__A    =							abs(f(lowerCAmelCase__	)	)
		__A    =							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"	)
				__A    =							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

					snake_case_   :	Tuple							      =lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
					snake_case_      ,	snake_case_      ,	snake_case_   :	str							      =newton_raphson(
					    f, xa=10, maxiter=1000, 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'''




from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available

def       __UpperCamelCase     (     _A     :					List[str]					)     ->   Any:




	"""simple docstring"""





	if is_torch_available():
		import torch

		if isinstance(_A       , torch.Tensor					):
			return True

	return isinstance(_A       , np.ndarray					)






class 			lowerCAmelCase					(					a				):



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     List[str]   				=			fields(self     )

		# Safety and consistency checks
		if not len(snake_case__     ):
			raise ValueError(f"{self.__class__.__name__} has no fields."     )

		lowerCAmelCase       :     List[str]   				=			getattr(self			,       class_fields[0].name     )
		lowerCAmelCase       :     List[Any]   				=			all(getattr(self			,       field.name     ) is None for field in class_fields[1:]     )

		if other_fields_are_none and isinstance(snake_case__			,       snake_case__     ):
			for key, value in first_field.items():
				lowerCAmelCase       :     Optional[int]   				=			value
		else:
			for field in class_fields:
				lowerCAmelCase       :     str   				=			getattr(self			,       field.name     )
				if v is not None:
					lowerCAmelCase       :     Optional[Any]   				=			v



	def __delitem__(       self			,       *snake_case__			,       **snake_case__     ):
		raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."     )



	def 					lowercase  (       self			,       *snake_case__			,       **snake_case__     ):
		raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."     )



	def 					lowercase  (       self			,       *snake_case__			,       **snake_case__     ):
		raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance."     )



	def 					lowercase  (       self			,       *snake_case__			,       **snake_case__     ):
		raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance."     )



	def __getitem__(       self			,       snake_case__     ):
		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			dict(self.items()     )
			return inner_dict[k]
		else:
			return self.to_tuple()[k]



	def __setattr__(       self			,       snake_case__			,       snake_case__     ):
		if name in self.keys() and value is not None:
			# Don't call self.__setitem__ to avoid recursion errors
			super().__setitem__(snake_case__			,       snake_case__     )
		super().__setattr__(snake_case__			,       snake_case__     )



	def __setitem__(       self			,       snake_case__			,       snake_case__     ):
		# Will raise a KeyException if needed
		super().__setitem__(snake_case__			,       snake_case__     )
		# Don't call self.__setattr__ to avoid recursion errors
		super().__setattr__(snake_case__			,       snake_case__     )



	def 					lowercase  (       self     ):
		return tuple(self[k] for k in self.keys()     )
