"""Tests for module utils for timing and parallel computation """

# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License


import ot
import numpy as np
import sys


def test_parmap():

    n = 10

    def f(i):
        return 1.0 * i * i

    a = np.arange(n)

    l1 = list(map(f, a))

    l2 = list(ot.utils.parmap(f, a))

    np.testing.assert_allclose(l1, l2)


def test_tic_toc():

    import time

    ot.tic()
    time.sleep(0.5)
    t = ot.toc()
    t2 = ot.toq()

    # test timing
    np.testing.assert_allclose(0.5, t, rtol=1e-1, atol=1e-1)

    # test toc vs toq
    np.testing.assert_allclose(t, t2, rtol=1e-1, atol=1e-1)


def test_kernel():

    n = 100

    x = np.random.randn(n, 2)

    K = ot.utils.kernel(x, x)

    # gaussian kernel  has ones on the diagonal
    np.testing.assert_allclose(np.diag(K), np.ones(n))


def test_unif():

    n = 100

    u = ot.unif(n)

    np.testing.assert_allclose(1, np.sum(u))


def test_dist():

    n = 100

    x = np.random.randn(n, 2)

    D = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            D[i, j] = np.sum(np.square(x[i, :] - x[j, :]))

    D2 = ot.dist(x, x)
    D3 = ot.dist(x)

    # dist shoul return squared euclidean
    np.testing.assert_allclose(D, D2)
    np.testing.assert_allclose(D, D3)


def test_dist0():

    n = 100
    M = ot.utils.dist0(n, method='lin_square')

    # dist0 default to linear sampling with quadratic loss
    np.testing.assert_allclose(M[0, -1], (n - 1) * (n - 1))


def test_dots():

    n1, n2, n3, n4 = 100, 50, 200, 100

    A = np.random.randn(n1, n2)
    B = np.random.randn(n2, n3)
    C = np.random.randn(n3, n4)

    X1 = ot.utils.dots(A, B, C)

    X2 = A.dot(B.dot(C))

    np.testing.assert_allclose(X1, X2)


def test_clean_zeros():

    n = 100
    nz = 50
    nz2 = 20
    u1 = ot.unif(n)
    u1[:nz] = 0
    u1 = u1 / u1.sum()
    u2 = ot.unif(n)
    u2[:nz2] = 0
    u2 = u2 / u2.sum()

    M = ot.utils.dist0(n)

    a, b, M2 = ot.utils.clean_zeros(u1, u2, M)

    assert len(a) == n - nz
    assert len(b) == n - nz2


def test_cost_normalization():

    C = np.random.rand(10, 10)

    # does nothing
    M0 = ot.utils.cost_normalization(C)
    np.testing.assert_allclose(C, M0)

    M = ot.utils.cost_normalization(C, 'median')
    np.testing.assert_allclose(np.median(M), 1)

    M = ot.utils.cost_normalization(C, 'max')
    np.testing.assert_allclose(M.max(), 1)

    M = ot.utils.cost_normalization(C, 'log')
    np.testing.assert_allclose(M.max(), np.log(1 + C).max())

    M = ot.utils.cost_normalization(C, 'loglog')
    np.testing.assert_allclose(M.max(), np.log(1 + np.log(1 + C)).max())


def test_check_params():

    res1 = ot.utils.check_params(first='OK', second=20)
    assert res1 is True

    res0 = ot.utils.check_params(first='OK', second=None)
    assert res0 is False


def test_deprecated_func():

    @ot.utils.deprecated('deprecated text for fun')
    def fun():
        pass

    def fun2():
        pass

    @ot.utils.deprecated('deprecated text for class')
    class Class():
        pass

    if sys.version_info < (3, 5):
        print('Not tested')
    else:
        assert ot.utils._is_deprecated(fun) is True

        assert ot.utils._is_deprecated(fun2) is False


def test_BaseEstimator():

    class Class(ot.utils.BaseEstimator):

        def __init__(self, first='spam', second='eggs'):

            self.first = first
            self.second = second

    cl = Class()

    names = cl._get_param_names()
    assert 'first' in names
    assert 'second' in names

    params = cl.get_params()
    assert 'first' in params
    assert 'second' in params

    params['first'] = 'spam again'
    cl.set_params(**params)

    assert cl.first == 'spam again'