class OneCycle(object):
    def __init__(self, nb, max_lr, momentum_vals=(0.95, 0.85), prcnt= 10 , div=10):
        self.nb = nb
        self.div = div
        self.step_len =  int(self.nb * (1- prcnt/100)/2)
        self.high_lr = max_lr
        self.low_mom = momentum_vals[1]
        self.high_mom = momentum_vals[0]
        self.prcnt = prcnt
        self.iteration = 0
        self.lrs = []
        self.moms = []

    def calc(self):
        self.iteration += 1
        lr = self.calc_lr()
        mom = self.calc_mom()
        return (lr, mom)

    def calc_lr(self):
        if self.iteration==self.nb:
            self.iteration = 0
            self.lrs.append(self.high_lr/self.div)
            return self.high_lr/self.div
        if self.iteration > 2 * self.step_len:
            ratio = (self.iteration - 2 * self.step_len) / (self.nb - 2 * self.step_len)
            lr = self.high_lr * ( 1 - 0.99 * ratio)/self.div
        elif self.iteration > self.step_len:
            ratio = 1- (self.iteration -self.step_len)/self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        else :
            ratio = self.iteration/self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        self.lrs.append(lr)
        return lr

    def calc_mom(self):
        if self.iteration==self.nb:
            self.iteration = 0
            self.moms.append(self.high_mom)
            return self.high_mom
        if self.iteration > 2 * self.step_len:
            mom = self.high_mom
        elif self.iteration > self.step_len:
            ratio = (self.iteration -self.step_len)/self.step_len
            mom = self.low_mom + ratio * (self.high_mom - self.low_mom)
        else :
            ratio = self.iteration/self.step_len
            mom = self.high_mom - ratio * (self.high_mom - self.low_mom)
        self.moms.append(mom)
        return mom
def update_lr(optimizer, lr):
    for g in optimizer.param_groups:
        g['lr'] = lr
def update_mom(optimizer, mom):
    for g in optimizer.param_groups:
        g['momentum'] = mom

efa5ba1594735dcbee34accc5458980ea45fabcc
class OneCycle(object):
    <mask token>
    <mask token>

    def calc_lr(self):
        if self.iteration == self.nb:
            self.iteration = 0
            self.lrs.append(self.high_lr / self.div)
            return self.high_lr / self.div
        if self.iteration > 2 * self.step_len:
            ratio = (self.iteration - 2 * self.step_len) / (self.nb - 2 *
                self.step_len)
            lr = self.high_lr * (1 - 0.99 * ratio) / self.div
        elif self.iteration > self.step_len:
            ratio = 1 - (self.iteration - self.step_len) / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        else:
            ratio = self.iteration / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        self.lrs.append(lr)
        return lr
    <mask token>


<mask token>

class OneCycle(object):

    def __init__(self, nb, max_lr, momentum_vals=(0.95, 0.85), prcnt=10, div=10
        ):
        self.nb = nb
        self.div = div
        self.step_len = int(self.nb * (1 - prcnt / 100) / 2)
        self.high_lr = max_lr
        self.low_mom = momentum_vals[1]
        self.high_mom = momentum_vals[0]
        self.prcnt = prcnt
        self.iteration = 0
        self.lrs = []
        self.moms = []

    def calc(self):
        self.iteration += 1
        lr = self.calc_lr()
        mom = self.calc_mom()
        return lr, mom

    def calc_lr(self):
        if self.iteration == self.nb:
            self.iteration = 0
            self.lrs.append(self.high_lr / self.div)
            return self.high_lr / self.div
        if self.iteration > 2 * self.step_len:
            ratio = (self.iteration - 2 * self.step_len) / (self.nb - 2 *
                self.step_len)
            lr = self.high_lr * (1 - 0.99 * ratio) / self.div
        elif self.iteration > self.step_len:
            ratio = 1 - (self.iteration - self.step_len) / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        else:
            ratio = self.iteration / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        self.lrs.append(lr)
        return lr

    def calc_mom(self):
        if self.iteration == self.nb:
            self.iteration = 0
            self.moms.append(self.high_mom)
            return self.high_mom
        if self.iteration > 2 * self.step_len:
            mom = self.high_mom
        elif self.iteration > self.step_len:
            ratio = (self.iteration - self.step_len) / self.step_len
            mom = self.low_mom + ratio * (self.high_mom - self.low_mom)
        else:
            ratio = self.iteration / self.step_len
            mom = self.high_mom - ratio * (self.high_mom - self.low_mom)
        self.moms.append(mom)
        return mom


<mask token>

class OneCycle(object):

    def __init__(self, nb, max_lr, momentum_vals=(0.95, 0.85), prcnt=10, div=10
        ):
        self.nb = nb
        self.div = div
        self.step_len = int(self.nb * (1 - prcnt / 100) / 2)
        self.high_lr = max_lr
        self.low_mom = momentum_vals[1]
        self.high_mom = momentum_vals[0]
        self.prcnt = prcnt
        self.iteration = 0
        self.lrs = []
        self.moms = []

    def calc(self):
        self.iteration += 1
        lr = self.calc_lr()
        mom = self.calc_mom()
        return lr, mom

    def calc_lr(self):
        if self.iteration == self.nb:
            self.iteration = 0
            self.lrs.append(self.high_lr / self.div)
            return self.high_lr / self.div
        if self.iteration > 2 * self.step_len:
            ratio = (self.iteration - 2 * self.step_len) / (self.nb - 2 *
                self.step_len)
            lr = self.high_lr * (1 - 0.99 * ratio) / self.div
        elif self.iteration > self.step_len:
            ratio = 1 - (self.iteration - self.step_len) / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        else:
            ratio = self.iteration / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        self.lrs.append(lr)
        return lr

    def calc_mom(self):
        if self.iteration == self.nb:
            self.iteration = 0
            self.moms.append(self.high_mom)
            return self.high_mom
        if self.iteration > 2 * self.step_len:
            mom = self.high_mom
        elif self.iteration > self.step_len:
            ratio = (self.iteration - self.step_len) / self.step_len
            mom = self.low_mom + ratio * (self.high_mom - self.low_mom)
        else:
            ratio = self.iteration / self.step_len
            mom = self.high_mom - ratio * (self.high_mom - self.low_mom)
        self.moms.append(mom)
        return mom


<mask token>


def update_mom(optimizer, mom):
    for g in optimizer.param_groups:
        g['momentum'] = mom

class OneCycle(object):

    def __init__(self, nb, max_lr, momentum_vals=(0.95, 0.85), prcnt=10, div=10
        ):
        self.nb = nb
        self.div = div
        self.step_len = int(self.nb * (1 - prcnt / 100) / 2)
        self.high_lr = max_lr
        self.low_mom = momentum_vals[1]
        self.high_mom = momentum_vals[0]
        self.prcnt = prcnt
        self.iteration = 0
        self.lrs = []
        self.moms = []

    def calc(self):
        self.iteration += 1
        lr = self.calc_lr()
        mom = self.calc_mom()
        return lr, mom

    def calc_lr(self):
        if self.iteration == self.nb:
            self.iteration = 0
            self.lrs.append(self.high_lr / self.div)
            return self.high_lr / self.div
        if self.iteration > 2 * self.step_len:
            ratio = (self.iteration - 2 * self.step_len) / (self.nb - 2 *
                self.step_len)
            lr = self.high_lr * (1 - 0.99 * ratio) / self.div
        elif self.iteration > self.step_len:
            ratio = 1 - (self.iteration - self.step_len) / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        else:
            ratio = self.iteration / self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        self.lrs.append(lr)
        return lr

    def calc_mom(self):
        if self.iteration == self.nb:
            self.iteration = 0
            self.moms.append(self.high_mom)
            return self.high_mom
        if self.iteration > 2 * self.step_len:
            mom = self.high_mom
        elif self.iteration > self.step_len:
            ratio = (self.iteration - self.step_len) / self.step_len
            mom = self.low_mom + ratio * (self.high_mom - self.low_mom)
        else:
            ratio = self.iteration / self.step_len
            mom = self.high_mom - ratio * (self.high_mom - self.low_mom)
        self.moms.append(mom)
        return mom


def update_lr(optimizer, lr):
    for g in optimizer.param_groups:
        g['lr'] = lr


def update_mom(optimizer, mom):
    for g in optimizer.param_groups:
        g['momentum'] = mom

class OneCycle(object):
    def __init__(self, nb, max_lr, momentum_vals=(0.95, 0.85), prcnt= 10 , div=10):
        self.nb = nb
        self.div = div
        self.step_len =  int(self.nb * (1- prcnt/100)/2)
        self.high_lr = max_lr
        self.low_mom = momentum_vals[1]
        self.high_mom = momentum_vals[0]
        self.prcnt = prcnt
        self.iteration = 0
        self.lrs = []
        self.moms = []

    def calc(self):
        self.iteration += 1
        lr = self.calc_lr()
        mom = self.calc_mom()
        return (lr, mom)

    def calc_lr(self):
        if self.iteration==self.nb:
            self.iteration = 0
            self.lrs.append(self.high_lr/self.div)
            return self.high_lr/self.div
        if self.iteration > 2 * self.step_len:
            ratio = (self.iteration - 2 * self.step_len) / (self.nb - 2 * self.step_len)
            lr = self.high_lr * ( 1 - 0.99 * ratio)/self.div
        elif self.iteration > self.step_len:
            ratio = 1- (self.iteration -self.step_len)/self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        else :
            ratio = self.iteration/self.step_len
            lr = self.high_lr * (1 + ratio * (self.div - 1)) / self.div
        self.lrs.append(lr)
        return lr

    def calc_mom(self):
        if self.iteration==self.nb:
            self.iteration = 0
            self.moms.append(self.high_mom)
            return self.high_mom
        if self.iteration > 2 * self.step_len:
            mom = self.high_mom
        elif self.iteration > self.step_len:
            ratio = (self.iteration -self.step_len)/self.step_len
            mom = self.low_mom + ratio * (self.high_mom - self.low_mom)
        else :
            ratio = self.iteration/self.step_len
            mom = self.high_mom - ratio * (self.high_mom - self.low_mom)
        self.moms.append(mom)
        return mom
def update_lr(optimizer, lr):
    for g in optimizer.param_groups:
        g['lr'] = lr
def update_mom(optimizer, mom):
    for g in optimizer.param_groups:
        g['momentum'] = mom
