import cProfile

import timeit

from . import generate, reconstruct

from .tests.test_shamira import TestShamira

def measure(args):

	secret = "1234567890123456"

	shares = generate(secret, args.k, args.n)

	symbols = globals()

	symbols.update(locals())

	time = timeit.timeit("""generate(secret, args.k, args.n)""", number=1, globals=symbols)

	print("The generation took {0:.3}s, {1:.3}s per byte.".format(time, time/16))

	time = timeit.timeit("""reconstruct(*shares)""", number=1, globals=symbols)

	print("The reconstruction took {0:.3}s, {1:.3}s per byte.".format(time, time/16))

def profile(args):

	t = TestShamira()

def build_subparsers(parent):

	parent.set_defaults(func=lambda _: parent.error("missing command"))

	subparsers = parent.add_subparsers()

	profile_parser = subparsers.add_parser("profile")

	profile_parser.set_defaults(func=profile)

	measure_parser = subparsers.add_parser("measure")

	measure_parser.add_argument("-k", type=int, required=True)

	measure_parser.add_argument("-n", type=int, required=True)

	measure_parser.set_defaults(func=measure)

import math

import cmath

import itertools

from .gf256 import gfmul, gfpow

# divisors of 255 and their factors in natural numbers

DIVISORS = [3, 5, 15, 17, 51, 85, 255]

FACTORS = {3: [3], 5: [5], 15: [3, 5], 17: [17], 51: [3, 17], 85: [5, 17], 255: [3, 5, 17]}

# values of n-th square roots in GF256

SQUARE_ROOTS = {3: 189, 5: 12, 15: 225, 17: 53, 51: 51, 85: 15, 255: 3}

def ceil_size(n):

	assert n <= DIVISORS[-1]

	for (i, ni) in enumerate(DIVISORS):

		if ni >= n:

			break

	return ni

def precompute_x(n):

	"""Return a geometric sequence [1, w, w**2, ..., w**(n-1)], where w**n==1.

	This can be done only for certain values of n."""

	assert n in SQUARE_ROOTS, n

	w = SQUARE_ROOTS[n]  # primitive N-th square root of 1

	return list(itertools.accumulate([1]+[w]*(n-1), gfmul))

def complex_dft(p):

	"""Quadratic formula from the definition. The basic case in complex numbers."""

	N = len(p)

	w = cmath.exp(-2*math.pi*1j/N)  # primitive N-th square root of 1

	y = [0]*N

	for k in range(N):

		xk = w**k

		for n in range(N):

			y[k] += p[n] * xk**n

	return y

def dft(p):

	"""Quadratic formula from the definition. In GF256."""

	N = len(p)

	x = precompute_x(N)

	y = [0]*N

	for k in range(N):

		for n in range(N):

			y[k] ^= gfmul(p[n], gfpow(x[k], n))

	return y

def compute_inverse(N1, N2):

	for i in range(N2):

		if N1*i % N2 == 1:

			return i

	raise ValueError("Failed to find an inverse to {0} mod {1}.".format(N1, N2))

def prime_fft(p, divisors, basic_dft=dft):

	"""https://en.wikipedia.org/wiki/Prime-factor_FFT_algorithm"""

	if len(divisors) == 1:

		return basic_dft(p)

	N = len(p)

	N1 = divisors[0]

	N2 = N//N1

	N1_inv = compute_inverse(N1, N2)

	N2_inv = compute_inverse(N2, N1)

	ys = []

	for n1 in range(N1):  # compute rows

		p_ = [p[(n2*N1+n1*N2) % N] for n2 in range(N2)]

		ys.append(prime_fft(p_, divisors[1:], basic_dft))

	for k2 in range(N2):  # compute cols

		p_ = [row[k2] for row in ys]

		y_ = basic_dft(p_)

		for (yi, row) in zip(y_, ys):  # update col

			row[k2] = yi

	# remap and output

	res = [0]*N

	for k1 in range(N1):

		for k2 in range(N2):

			res[(k1*N2*N2_inv+k2*N1*N1_inv) % N] = ys[k1][k2]

	return res

def evaluate(coefs, n):

	ni = ceil_size(n)

	extended_coefs = coefs + [0]*(ni-len(coefs))

	ys = prime_fft(extended_coefs, FACTORS[ni])

	return ys[:n]

# GNU GPLv3, see LICENSE

"""Arithmetic operations on Galois Field 2**8. See https://en.wikipedia.org/wiki/Finite_field_arithmetic"""

import operator

def _gfmul(a, b):

	"""Basic multiplication. Russian peasant algorithm."""

	res = 0

	while a and b:

		if b&1: res ^= a

		if a&0x80: a = 0xff&(a<<1)^0x1b

		else: a <<= 1

		b >>= 1

	return res

g = 3  # generator

E = [None]*256  # exponentials

L = [None]*256  # logarithms

acc = 1

for i in range(256):

	E[i] = acc

	L[acc] = i

	acc = _gfmul(acc, g)

L[1] = 0

INV = [E[255-L[i]] if i!=0 else None for i in range(256)]  # multiplicative inverse

def gfmul(a, b):

	"""Fast multiplication. Basic multiplication is expensive. a*b==g**(log(a)+log(b))"""

	assert 0<=a<=255, 0<=b<=255

	if a==0 or b==0: return 0

	t = L[a]+L[b]

	if t>255: t -= 255

	return E[t]

def gfpow(x, k):

	"""Compute x**k."""

	i = 1

	res = 1

	while i <= k:

		if k&i:

			res = gfmul(res, x)

		x = gfmul(x, x)

		i <<= 1

	return res

def evaluate(coefs, x):

	"""Evaluate polynomial's value at x.

	:param coefs: [an, ..., a1, a0]."""

	res = 0

	for a in coefs:  # Horner's rule

		res = gfmul(res, x)

		res ^= a

	return res

def get_constant_coef(weights, y_coords):

	"""Compute constant polynomial coefficient given the points.

	See https://en.wikipedia.org/wiki/Shamir's_Secret_Sharing#Computationally_Efficient_Approach"""

	return reduce(

		operator.xor,

		map(lambda ab: gfmul(*ab), zip(weights, y_coords))

	)

def compute_weights(x_coords):

	assert x_coords

	res = [

			reduce(

				gfmul,

				(gfmul(xj, INV[xj^xi]) for xj in x_coords if xi!=xj),

				1

			) for xi in x_coords

	]

	return res