import gc

from estimator_new import *
from sage.all import oo, save
from math import log2
import gc
from multiprocessing import *


def old_models(security_level, sd, logq=32):
    """
    Use the old model as a starting point for the data gathering step
    :param security_level: the security level under consideration
    :param sd            : the standard deviation of the LWE error distribution Xe
    :param logq          : the (base 2 log) value of the LWE modulus q
    """

    def evaluate_model(a, b, stddev=sd):
        return (stddev - b)/a

    models = dict()

    models["80"] = (-0.04049295502947623, 1.1288318226557081 + logq)
    models["96"] = (-0.03416314056943681, 1.4704806061716345 + logq)
    models["112"] = (-0.02970984362676178, 1.7848907787798667 + logq)
    models["128"] = (-0.026361288425133814, 2.0014671315214696 + logq)
    models["144"] = (-0.023744534465622812, 2.1710601038230712 + logq)
    models["160"] = (-0.021667220727651954, 2.3565507936475476 + logq)
    models["176"] = (-0.019947662046189942, 2.5109588704235803 + logq)
    models["192"] = (-0.018552804646747204, 2.7168913723130816 + logq)
    models["208"] = (-0.017291091126923574, 2.7956961446214326 + logq)
    models["224"] = (-0.016257546811508806, 2.9582401000615226 + logq)
    models["240"] = (-0.015329741032015766, 3.0744579055889782 + logq)
    models["256"] = (-0.014530554319171845, 3.2094375376751745 + logq)

    (a, b) = models["{}".format(security_level)]
    n_est = evaluate_model(a, b, sd)

    return round(n_est)


def estimate(params, red_cost_model=RC.BDGL16, skip=("arora-gb", "bkw")):
    """
    Retrieve an estimate using the Lattice Estimator, for a given set of input parameters
    :param params: the input LWE parameters
    :param red_cost_model: the lattice reduction cost model
    :param skip: attacks to skip
    """

    est = LWE.estimate(params, red_cost_model=red_cost_model, deny_list=skip)
    return est


def get_security_level(est, dp=2):
    """
    Get the security level lambda from a Lattice Estimator output
    :param est: the Lattice Estimator output
    :param dp: the number of decimal places to consider
    """
    attack_costs = []
    # note: key does not need to be specified est vs est.keys()
    for key in est:
        attack_costs.append(est[key]["rop"])
    # get the security level correct to 'dp' decimal places
    security_level = round(log2(min(attack_costs)), dp)
    return security_level


def inequality(x, y):
    """ A utility function which compresses the conditions x < y and x > y into a single condition via a multiplier
    :param x: the LHS of the inequality
    :param y: the RHS of the inequality
    """
    if x <= y:
        return 1

    if x > y:
        return -1


def automated_param_select_n(params, target_security=128):
    """ A function used to generate the smallest value of n which allows for
    target_security bits of security, for the input values of (params.Xe.stddev,params.q)
    :param params: the standard deviation of the error
    :param target_security: the target number of bits of security, 128 is default

    EXAMPLE:
    sage: X = automated_param_select_n(Kyber512, target_security = 128)
    sage: X
    456
    """

    # get an initial estimate
    # costs = estimate(params)
    # security_level = get_security_level(costs, 2)
    # determine if we are above or below the target security level
    # z = inequality(security_level, target_security)

    # get an estimate based on the prev. model
    print("n = {}".format(params.n))
    n_start = old_models(target_security, log2(params.Xe.stddev), log2(params.q))
    # TODO -- is this how we want to deal with the small n issue? Shouldn't the model have this baked in?
    # we want to start no lower than n = 450
    n_start = max(n_start, 450)

    #if n_start > 1024:
        # we only consider powers-of-two for now, in this range
        # n_log = log2(n_start)
        # n_start = 2**round(n_log)

    print("n_start = {}".format(n_start))
    params = params.updated(n=n_start)
    print(params)
    #
    costs2 = estimate(params)
    security_level = get_security_level(costs2, 2)
    costs2 = None
    z = inequality(security_level, target_security)

    # we keep n > 2 * target_security as a rough baseline for mitm security (on binary key guessing)
    while z * security_level < z * target_security:
        # if params.n > 1024:
        # we only need to consider powers-of-two in this case
        # TODO: fill in this case! For n > 1024 we only need to consider every 256
        params = params.updated(n = params.n + z * 8)
        costs = estimate(params)
        security_level = get_security_level(costs, 2)
        # try none with delete, try none without delete
        # test the list of objects that are in memory before end of program
        costs = None

        if -1 * params.Xe.stddev > 0:
            print("target security level is unatainable")
            break

    # final estimate (we went too far in the above loop)
    if security_level < target_security:
        # we make n larger
        print("we make n larger")
        params = params.updated(n=params.n + 8)
        costs = estimate(params)
        security_level = get_security_level(costs, 2)

    print("the finalised parameters are n = {}, log2(sd) = {}, log2(q) = {}, with a security level of {}-bits".format(params.n,
                                                                                                                      log2(params.Xe.stddev),
                                                                                                                      log2(params.q),
                                                                                                                      security_level))

    # final sanity check so we don't return insecure (or inf) parameters
    # TODO: figure out inf in new estimator
    # or security_level == oo:
    if security_level < target_security:
        params.updated(n=None)

    return params


def generate_parameter_matrix(params_in, sd_range, target_security_levels=[128], name="v0.sobj"):
    """
    :param sd_range: a tuple (sd_min, sd_max) giving the values of sd for which to generate parameters
    :param params: the standard deviation of the LWE error
    :param target_security: the target number of bits of security, 128 is default

    EXAMPLE:
    sage: X = generate_parameter_matrix()
    sage: X
    """

    results = dict()

    # grab min and max value/s of n
    (sd_min, sd_max) = sd_range

    for lam in target_security_levels:
        results["{}".format(lam)] = []
        for sd in range(sd_min, sd_max + 1):
            Xe_new = nd.NoiseDistribution.DiscreteGaussian(2**sd)
            params_out = automated_param_select_n(params_in.updated(Xe=Xe_new), target_security=lam)
            results["{}".format(lam)].append((params_out.n, params_out.q, params_out.Xe.stddev))
            save(results, "{}.sobj".format(name))
            del(params_out)
            gc.collect()
    return results


def generate_parameter_matrix_para(params_in, sd_range, target_security_levels=[128], name="v0.sobj"):
    """
    :param sd_range: a tuple (sd_min, sd_max) giving the values of sd for which to generate parameters
    :param params: the standard deviation of the LWE error
    :param target_security: the target number of bits of security, 128 is default

    EXAMPLE:
    sage: X = generate_parameter_matrix()
    sage: X
    """
    if __name__ == "__main__":

        results = dict()

        def test_memory(x):
            print("doing job...")
            print(x)
            y = LWE.estimate(x, deny_list=("arora-gb", "bkw"))
            return y

        # grab min and max value/s of n
        (sd_min, sd_max) = sd_range
        print(sd_range)

        for lam in target_security_levels:
            results["{}".format(lam)] = []
            names = range(sd_min, sd_max + 1)
            procs = []
            proc = Process(target=automated_param_select_n)
            procs.append(proc)
            proc.start()
            p = Pool(1)
            for name in names:
                proc = Process(target=test_memory, args=(name,))
                procs.append(proc)
                proc.start()
                proc.join()
                Xe_new = nd.NoiseDistribution.DiscreteGaussian(2**sd)
                params_out = automated_param_select_n(params_in.updated(Xe=Xe_new), target_security=lam)
                results["{}".format(lam)].append((params_out.n, params_out.q, params_out.Xe.stddev))
                save(results, "{}.sobj".format(name))
                params_out = None
                del(params_out)
                gc.collect()
    return results

# what we run

def generate_zama_curves64(sd_range=[2, 56], target_security_levels=[256], name="default", pools = 1):
    if __name__ == '__main__':

        D = ND.DiscreteGaussian
        vals = sd_range
        p = Pool(pools)
        procs = []
        for val in vals:
            init_params = LWE.Parameters(n=1024, q=2 ** 64, Xs=D(0.50, -0.50), Xe=D(2**55), m=oo, tag='TFHE_DEFAULT')
            proc = Process(target=generate_parameter_matrix, args=(init_params, [val, val + 1], target_security_levels, name))
            procs.append(proc)
            proc.start()

    return "done"

import sys
a = int(sys.argv[1])
print("input arg is {}".format(a))

D = ND.DiscreteGaussian
init_params = LWE.Parameters(n=1024, q=2 ** 32, Xs=ND.UniformMod(2), Xe=D(131072.00), m=oo, tag='TFHE_DEFAULT')
#automated_param_select_n(init_params, target_security=128)
#automated_param_select_n(init_params, target_security=192)
generate_zama_curves64(sd_range=[50, 53], target_security_levels=[a], name="{}".format("testing"))









#if __name__ == "__main__":
#    D = ND.DiscreteGaussian
#    params = LWE.Parameters(n=1024, q=2 ** 64, Xs=D(0.50, -0.50), Xe=D(2**57), m=oo, tag='TFHE_DEFAULT')
#
#    names = [params, params.updated(n=761), params.updated(q=2**65), params.updated(n=762)]
#    procs = []
#    proc = Process(target=print_func)
#    procs.append(proc)
#    proc.start()
#    p = Pool(1)
#
#    for name in names:
#        proc = Process(target=test_memory, args=(name,))
#        procs.append(proc)
#        proc.start()
#        proc.join()
#
#    for proc in procs:
#        proc.join()