text: from .base import ReleaseMechanism
  import numpy as np
  from relm import backend
  import secrets
  import math
  
  
  class LaplaceMechanism(ReleaseMechanism):
      """
      Secure implementation of the Laplace mechanism. This mechanism can be used once
      after which its privacy budget will be exhausted and it can no longer be used.
  
      Under the hood this mechanism samples exactly from a 64-bit fixed point
      Laplace mechanism bit by bit.
  
      Args:
          epsilon: the maximum privacy loss of the mechanism.
          sensitivity: the sensitivity of the query to which this mechanism will be applied.
          precision: number of fractional bits to use in the internal fixed point representation.
      """
  
      def __init__(self, epsilon, sensitivity, precision=35):
          super(LaplaceMechanism, self).__init__(epsilon)
          self.sensitivity = sensitivity
          self.precision = precision
          self.effective_epsilon = self.epsilon / (
              self.sensitivity + 2.0 ** -self.precision
          )
  
      def release(self, values):
          """
          Releases a differential private query response.
  
          Args:
              values: numpy array of the output of a query.
  
          Returns:
              A numpy array of perturbed values.
          """
  
          self._check_valid()
          self._is_valid = False
          self._update_accountant()
          args = (values, self.effective_epsilon, self.precision)
          return backend.laplace_mechanism(*args)
  
      @property
      def privacy_consumed(self):
          """
          Computes the privacy budget consumed by the mechanism so far.
          """
          if self._is_valid:
              return 0
          else:
              return self.epsilon
  
  
  class GeometricMechanism(ReleaseMechanism):
      """
      Secure implementation of the Geometric mechanism. This mechanism can be used once
      after which its privacy budget will be exhausted and it can no longer be used.
  
      Args:
          epsilon: the maximum privacy loss of the mechanism.
          sensitivity: the sensitivity of the query to which this mechanism will be applied.
      """
  
      def __init__(self, epsilon, sensitivity):
          super(GeometricMechanism, self).__init__(epsilon)
          self.sensitivity = sensitivity
          self.effective_epsilon = self.epsilon / self.sensitivity
  
      def release(self, values):
          """
          Releases a differential private query response.
  
          Args:
              values: numpy array of the output of a query.
  
          Returns:
              A numpy array of perturbed values.
          """
          self._check_valid()
          self._is_valid = False
          self._update_accountant()
          return backend.geometric_mechanism(values, self.effective_epsilon)
  
      @property
      def privacy_consumed(self):
          """
          Computes the privacy budget consumed by the mechanism so far.
          """
          if self._is_valid:
              return 0
          else:
              return self.epsilon
  
  
  class SnappingMechanism(ReleaseMechanism):
      """
      Secure implementation of the Snapping mechanism. This mechanism can be used once
      after which its privacy budget will be exhausted and it can no longer be used.
  
      Args:
          epsilon: the maximum privacy loss of the mechanism.
          B: the bound of the range to use for the snapping mechanism.
              B should ideally be larger than the range of outputs expected but the larger B is
              the less accurate the results.
      """
  
      def __init__(self, epsilon, B):
          lam = (1 + 2 ** (-49) * B) / epsilon
          if (B <= lam) or (B >= (2 ** 46 * lam)):
              raise ValueError()
          self.lam = lam
          self.quanta = 2 ** math.ceil(math.log2(self.lam))
          self.B = B
          super(SnappingMechanism, self).__init__(epsilon)
  
      def release(self, values):
          """
          Releases a differential private query response.
  
          Args:
              values: numpy array of the output of a query.
  
          Returns:
              A numpy array of perturbed values.
          """
          self._check_valid()
          args = (values, self.B, self.lam, self.quanta)
          release_values = backend.snapping(*args)
          self._is_valid = False
          self._update_accountant()
          return release_values
  
      @property
      def privacy_consumed(self):
          """
          Computes the privacy budget consumed by the mechanism so far.
          """
          if self._is_valid:
              return 0
          else:
              return self.epsilon
  
  
  class CauchyMechanism(ReleaseMechanism):
      """
      ***Insecure*** implementation of the Cauchy mechanism. This mechanism can be used
      once after which its privacy budget will be exhausted and it can no longer be used.
  
      Args:
          epsilon: the maximum privacy loss of the mechanism.
          beta: the smoothness parameter for the beta-smooth upper bound on the local
                sensitivity of the query to which this mechanism will be applied.
      """
  
      def __init__(self, epsilon, beta):
          super(CauchyMechanism, self).__init__(epsilon)
          self.beta = beta
          if self.beta > self.epsilon / 6.0:
              raise ValueError("beta must not be greater than epsilon/6.0.")
  
      def release(self, values, smooth_sensitivity):
          """
          Releases a differential private query response.
  
          Args:
              values: numpy array of the output of a query.
              smooth_senstivity: A beta-smooth upper bound on the local sensitivity of
                                 a query.
  
          Returns:
              A numpy array of perturbed values.
          """
  
          self._check_valid()
          self._is_valid = False
          self._update_accountant()
          effective_epsilon = self.epsilon / (6.0 * smooth_sensitivity)
          return backend.cauchy_mechanism(values, effective_epsilon)
  
      @property
      def privacy_consumed(self):
          """
          Computes the privacy budget consumed by the mechanism so far.
          """
          if self._is_valid:
              return 0
          else:
              return self.epsilon
  
  
  class ReportNoisyMax(ReleaseMechanism):
      """
      Secure implementation of the ReportNoisyMax mechanism. This mechanism can be used
      once after which its privacy budget will be exhausted and it can no longer be used.
  
      This mechanism adds Laplace noise to each of a set of counting queries and
      returns both the index of the largest perturbed value (the argmax) and the
      largest perturbed value.
  
      Args:
          epsilon: the maximum privacy loss of the mechanism.
          precision: number of fractional bits to use in the internal fixed point representation.
      """
  
      def __init__(self, epsilon, precision=35):
          super(ReportNoisyMax, self).__init__(epsilon)
          self.sensitivity = 1.0
          self.precision = precision
          self.effective_epsilon = self.epsilon / self.sensitivity
  
      def release(self, values):
          """
          Releases a differential private query response.
  
          Args:
              values: numpy array of the outputs of a collection of counting queries.
  
          Returns:
              A tuple containing the (argmax, max) of the perturbed values.
          """
          self._check_valid()
          self._is_valid = False
          self._update_accountant()
          args = (values, self.effective_epsilon, self.precision)
          perturbed_values = backend.laplace_mechanism(*args)
          valmax = np.max(perturbed_values)
          argmax = secrets.choice(np.where(perturbed_values == valmax)[0])
          return (argmax, valmax)
  
      @property
      def privacy_consumed(self):
          """
          Computes the privacy budget consumed by the mechanism so far.
          """
          if self._is_valid:
              return 0
          else:
              return self.epsilon
  