import multiprocessing


class DDConfig:
    """
    An object holding global configuration.
    """

    def __init__(self,
                 c=0.01,
                 n_train=10_700_000,
                 n=10_700_000,
                 n_check=10_700_000,
                 n_final=200_000_000,
                 prediction_batch_size=10_000,
                 training_batch_size=100_000,
                 n_processes=multiprocessing.cpu_count(),
                 confidence=0.9):
        """
        Args:
            c: the probability lower bound (DP-Sniper avoids estimating probabilities below c)
            n_train: number of input samples used to train the machine learning attack model
            n: number of samples used to select the probability threshold during attack optimization
            n_check: number of samples used to estimate probabilities P[M(a) in S] approximately
            n_final: number of samples used to estimate probabilities P[M(a) in S] with high precision
            prediction_batch_size: number of samples in a single batch for prediction
            training_batch_size: number of samples in a single batch for training
            n_processes: number of processes to use for parallelization
            confidence: requested confidence for the computed lower bound on epsilon

        Note:
            Small batch sizes lead to higher runtime, while large batch sizes require more memory
        """
        self.c = c
        self.n_train = n_train
        self.n = n
        self.n_check = n_check
        self.n_final = n_final
        self.prediction_batch_size = prediction_batch_size
        self.training_batch_size = training_batch_size
        self.n_processes = n_processes
        self.confidence = confidence

        if self.n_check >= 10000 and self.n_check % 1000 != 0\
                or self.n_final >= 10000 and self.n_final % 1000 != 0:
            raise ValueError("Sample sizes >= 10000 must be multiples of 1000 for numerical stability")

    def __str__(self):
        s = "{"
        d = dir(self)
        for name in d:
            if not name.startswith('__'):
                s = s + "{}={}, ".format(name, str(getattr(self, name)))
        return s + "}"

39615cefee70ce8365a45d2cfb73516342e91de5
<mask token>


class DDConfig:
    <mask token>

    def __init__(self, c=0.01, n_train=10700000, n=10700000, n_check=
        10700000, n_final=200000000, prediction_batch_size=10000,
        training_batch_size=100000, n_processes=multiprocessing.cpu_count(),
        confidence=0.9):
        """
        Args:
            c: the probability lower bound (DP-Sniper avoids estimating probabilities below c)
            n_train: number of input samples used to train the machine learning attack model
            n: number of samples used to select the probability threshold during attack optimization
            n_check: number of samples used to estimate probabilities P[M(a) in S] approximately
            n_final: number of samples used to estimate probabilities P[M(a) in S] with high precision
            prediction_batch_size: number of samples in a single batch for prediction
            training_batch_size: number of samples in a single batch for training
            n_processes: number of processes to use for parallelization
            confidence: requested confidence for the computed lower bound on epsilon

        Note:
            Small batch sizes lead to higher runtime, while large batch sizes require more memory
        """
        self.c = c
        self.n_train = n_train
        self.n = n
        self.n_check = n_check
        self.n_final = n_final
        self.prediction_batch_size = prediction_batch_size
        self.training_batch_size = training_batch_size
        self.n_processes = n_processes
        self.confidence = confidence
        if (self.n_check >= 10000 and self.n_check % 1000 != 0 or self.
            n_final >= 10000 and self.n_final % 1000 != 0):
            raise ValueError(
                'Sample sizes >= 10000 must be multiples of 1000 for numerical stability'
                )
    <mask token>

<mask token>


class DDConfig:
    <mask token>

    def __init__(self, c=0.01, n_train=10700000, n=10700000, n_check=
        10700000, n_final=200000000, prediction_batch_size=10000,
        training_batch_size=100000, n_processes=multiprocessing.cpu_count(),
        confidence=0.9):
        """
        Args:
            c: the probability lower bound (DP-Sniper avoids estimating probabilities below c)
            n_train: number of input samples used to train the machine learning attack model
            n: number of samples used to select the probability threshold during attack optimization
            n_check: number of samples used to estimate probabilities P[M(a) in S] approximately
            n_final: number of samples used to estimate probabilities P[M(a) in S] with high precision
            prediction_batch_size: number of samples in a single batch for prediction
            training_batch_size: number of samples in a single batch for training
            n_processes: number of processes to use for parallelization
            confidence: requested confidence for the computed lower bound on epsilon

        Note:
            Small batch sizes lead to higher runtime, while large batch sizes require more memory
        """
        self.c = c
        self.n_train = n_train
        self.n = n
        self.n_check = n_check
        self.n_final = n_final
        self.prediction_batch_size = prediction_batch_size
        self.training_batch_size = training_batch_size
        self.n_processes = n_processes
        self.confidence = confidence
        if (self.n_check >= 10000 and self.n_check % 1000 != 0 or self.
            n_final >= 10000 and self.n_final % 1000 != 0):
            raise ValueError(
                'Sample sizes >= 10000 must be multiples of 1000 for numerical stability'
                )

    def __str__(self):
        s = '{'
        d = dir(self)
        for name in d:
            if not name.startswith('__'):
                s = s + '{}={}, '.format(name, str(getattr(self, name)))
        return s + '}'

<mask token>


class DDConfig:
    """
    An object holding global configuration.
    """

    def __init__(self, c=0.01, n_train=10700000, n=10700000, n_check=
        10700000, n_final=200000000, prediction_batch_size=10000,
        training_batch_size=100000, n_processes=multiprocessing.cpu_count(),
        confidence=0.9):
        """
        Args:
            c: the probability lower bound (DP-Sniper avoids estimating probabilities below c)
            n_train: number of input samples used to train the machine learning attack model
            n: number of samples used to select the probability threshold during attack optimization
            n_check: number of samples used to estimate probabilities P[M(a) in S] approximately
            n_final: number of samples used to estimate probabilities P[M(a) in S] with high precision
            prediction_batch_size: number of samples in a single batch for prediction
            training_batch_size: number of samples in a single batch for training
            n_processes: number of processes to use for parallelization
            confidence: requested confidence for the computed lower bound on epsilon

        Note:
            Small batch sizes lead to higher runtime, while large batch sizes require more memory
        """
        self.c = c
        self.n_train = n_train
        self.n = n
        self.n_check = n_check
        self.n_final = n_final
        self.prediction_batch_size = prediction_batch_size
        self.training_batch_size = training_batch_size
        self.n_processes = n_processes
        self.confidence = confidence
        if (self.n_check >= 10000 and self.n_check % 1000 != 0 or self.
            n_final >= 10000 and self.n_final % 1000 != 0):
            raise ValueError(
                'Sample sizes >= 10000 must be multiples of 1000 for numerical stability'
                )

    def __str__(self):
        s = '{'
        d = dir(self)
        for name in d:
            if not name.startswith('__'):
                s = s + '{}={}, '.format(name, str(getattr(self, name)))
        return s + '}'

import multiprocessing


class DDConfig:
    """
    An object holding global configuration.
    """

    def __init__(self, c=0.01, n_train=10700000, n=10700000, n_check=
        10700000, n_final=200000000, prediction_batch_size=10000,
        training_batch_size=100000, n_processes=multiprocessing.cpu_count(),
        confidence=0.9):
        """
        Args:
            c: the probability lower bound (DP-Sniper avoids estimating probabilities below c)
            n_train: number of input samples used to train the machine learning attack model
            n: number of samples used to select the probability threshold during attack optimization
            n_check: number of samples used to estimate probabilities P[M(a) in S] approximately
            n_final: number of samples used to estimate probabilities P[M(a) in S] with high precision
            prediction_batch_size: number of samples in a single batch for prediction
            training_batch_size: number of samples in a single batch for training
            n_processes: number of processes to use for parallelization
            confidence: requested confidence for the computed lower bound on epsilon

        Note:
            Small batch sizes lead to higher runtime, while large batch sizes require more memory
        """
        self.c = c
        self.n_train = n_train
        self.n = n
        self.n_check = n_check
        self.n_final = n_final
        self.prediction_batch_size = prediction_batch_size
        self.training_batch_size = training_batch_size
        self.n_processes = n_processes
        self.confidence = confidence
        if (self.n_check >= 10000 and self.n_check % 1000 != 0 or self.
            n_final >= 10000 and self.n_final % 1000 != 0):
            raise ValueError(
                'Sample sizes >= 10000 must be multiples of 1000 for numerical stability'
                )

    def __str__(self):
        s = '{'
        d = dir(self)
        for name in d:
            if not name.startswith('__'):
                s = s + '{}={}, '.format(name, str(getattr(self, name)))
        return s + '}'

import multiprocessing


class DDConfig:
    """
    An object holding global configuration.
    """

    def __init__(self,
                 c=0.01,
                 n_train=10_700_000,
                 n=10_700_000,
                 n_check=10_700_000,
                 n_final=200_000_000,
                 prediction_batch_size=10_000,
                 training_batch_size=100_000,
                 n_processes=multiprocessing.cpu_count(),
                 confidence=0.9):
        """
        Args:
            c: the probability lower bound (DP-Sniper avoids estimating probabilities below c)
            n_train: number of input samples used to train the machine learning attack model
            n: number of samples used to select the probability threshold during attack optimization
            n_check: number of samples used to estimate probabilities P[M(a) in S] approximately
            n_final: number of samples used to estimate probabilities P[M(a) in S] with high precision
            prediction_batch_size: number of samples in a single batch for prediction
            training_batch_size: number of samples in a single batch for training
            n_processes: number of processes to use for parallelization
            confidence: requested confidence for the computed lower bound on epsilon

        Note:
            Small batch sizes lead to higher runtime, while large batch sizes require more memory
        """
        self.c = c
        self.n_train = n_train
        self.n = n
        self.n_check = n_check
        self.n_final = n_final
        self.prediction_batch_size = prediction_batch_size
        self.training_batch_size = training_batch_size
        self.n_processes = n_processes
        self.confidence = confidence

        if self.n_check >= 10000 and self.n_check % 1000 != 0\
                or self.n_final >= 10000 and self.n_final % 1000 != 0:
            raise ValueError("Sample sizes >= 10000 must be multiples of 1000 for numerical stability")

    def __str__(self):
        s = "{"
        d = dir(self)
        for name in d:
            if not name.startswith('__'):
                s = s + "{}={}, ".format(name, str(getattr(self, name)))
        return s + "}"
