import numpy.random as rnd
from numpy import *
import numpy as np

@test
def test_Philox_integers(seed, low, expected, high=None, size=None, e=False):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(low, int)
                or isinstance(low, float)) and (high is None or isinstance(
                    high, int) or isinstance(high, float)):
            assert gen.integers(low, high, endpoint=e) == expected
        else:
            assert (gen.integers(low, high, endpoint=e) == expected).all()
    else:
        assert (gen.integers(low, high, size, endpoint=e) == expected).all()

test_Philox_integers(74, 5, 0)
test_Philox_integers(74, 987.4, 105)
test_Philox_integers(74, 98, 193, high=987)
test_Philox_integers(74, 89, 9, e=True)
test_Philox_integers(74, -1218, -1077, high=98)
test_Philox_integers(74, -5, np.array([1, 19, 13]), 55, size=3)
test_Philox_integers(74, -5, np.array([1, 19, 14]), 55, size=3, e=True)
test_Philox_integers(74, np.array([7, 49, 17]), np.array([14, 60, 36]), 78)
test_Philox_integers(74, -99, np.array([-81, -23, -36]),
                     np.array([77, 89, 101]))
test_Philox_integers(74, np.array([7, 49, 17]), np.array([14, 65, 43]),
                     np.array([77, 89, 101]))

@test
def test_Philox_random(seed, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        assert gen.random() == expected
    else:
        assert (round(gen.random(size), 8) == round(expected, 8)).all()

test_Philox_random(0, 0.014067035665647709)
test_Philox_random(1, 0.0668020093396563)
test_Philox_random(1234, 0.5572569371365311)
test_Philox_random(
    1234,
    np.array([0.55725694, 0.22373624, 0.29333458, 0.57975576, 0.81181516]),
    size=5)
test_Philox_random(74,
                   np.array([[0.40856838, 0.47485863, 0.99389396],
                             [0.15559785, 0.87641347, 0.49119767]]),
                   size=(2, 3))

@test
def test_Philox_choice(seed,
                       a,
                       expected,
                       size=None,
                       r=True,
                       p=None,
                       axis=0,
                       shuffle=True):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(a, int):
            assert gen.choice(a, size, r, p, axis, shuffle) == expected
        elif staticlen(a.shape) == 1:
            assert gen.choice(a, size, r, p, axis, shuffle) == expected
        else:
            assert (round(gen.choice(a, size, r, p, axis, shuffle),
                          8) == expected).all()
    else:
        assert (round(gen.choice(a, size, r, p, axis, shuffle),
                      8) == expected).all()

test_Philox_choice(
    621,
    np.array([
        1.34, 2.15, 3.21, 4.78, 5.55, 6.42, 7.99, 8.31, 9.61, 10.75, 11.33,
        12.93
    ]), np.array([7.99, 5.55, 11.33]), 3)
test_Philox_choice(
    318, np.array([5, 4, 2, 6, 7, 8, 2, 12, 1, 55, 33, 7, 78, 15, 43]), 43)
test_Philox_choice(318, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
                   np.array([6, 10, 12]), 3, False)
test_Philox_choice(
    4589,
    np.array([
        -1.34, -2.15, -3.21, -4.78, -5.55, -6.42, -7.99, -8.31, -9.61, -10.75,
        -11.33, -12.93
    ]), -8.31)
test_Philox_choice(318,
                   np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]),
                   np.array([[10, 11, 12], [7, 8, 9]]), 2)
test_Philox_choice(74, np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11,
                                                                   12]]),
                   np.array([1, 2, 3]))
test_Philox_choice(74, 1, 0)
test_Philox_choice(74, np.array([13, -4]), -4, p=np.array([0.3, 0.7]))
test_Philox_choice(74,
                   np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]),
                   np.array([[1], [4], [7], [10]]),
                   axis=1)
test_Philox_choice(74,
                   np.array([13, -4, 3, 18]),
                   13,
                   p=np.array([0.5, 0.3, 0.1, 0.1]),
                   shuffle=True)

@test
def test_Philox_bytes(seed, length, expected):
    gen = rnd.Generator(rnd.Philox(seed))
    check = gen.bytes(length) == expected
    assert check

test_Philox_bytes(555, 10, "\x86\xb5\xa3\xf8\xa9d\xdb\xa0,\xd2")

@test
def test_Philox_shuffle(seed, x, expected, axis=0):
    gen = rnd.Generator(rnd.Philox(seed))
    gen.shuffle(x, axis)
    assert (x == expected).all()

test_Philox_shuffle(876, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
                    np.array([2, 8, 9, 0, 3, 4, 7, 1, 6, 5]))
test_Philox_shuffle(111, np.array([5.43, 6.56, 1.22, 4.3221, 7.88, 0.9352]),
                    np.array([7.88, 1.22, 6.56, 0.9352, 4.3221, 5.43]))
test_Philox_shuffle(1234, np.array([3.14]), np.array([3.14]))
test_Philox_shuffle(74,
                    np.arange(9).reshape((3, 3)),
                    np.array([[3, 4, 5], [6, 7, 8], [0, 1, 2]]))
test_Philox_shuffle(74,
                    np.arange(9).reshape((3, 3)),
                    np.array([[1, 2, 0], [4, 5, 3], [7, 8, 6]]),
                    axis=1)

@test
def test_Philox_permutation(seed, x, expected, axis=0):
    gen = rnd.Generator(rnd.Philox(seed))
    assert (gen.permutation(x, axis) == expected).all()

test_Philox_permutation(12345, 10, np.array([8, 9, 6, 4, 3, 2, 7, 1, 5, 0]))
test_Philox_permutation(1234,
                        np.array([1.124, 4.7532, 9.1246, 12.53243, 15.64324]),
                        np.array([12.53243, 15.64324, 9.1246, 1.124, 4.7532]))
test_Philox_permutation(
    4321,
    np.arange(24).reshape(3, 8),
    np.array([[8, 9, 10, 11, 12, 13, 14, 15], [16, 17, 18, 19, 20, 21, 22, 23],
              [0, 1, 2, 3, 4, 5, 6, 7]]))
test_Philox_permutation(74,
                        np.arange(9).reshape((3, 3)),
                        np.array([[1, 2, 0], [4, 5, 3], [7, 8, 6]]),
                        axis=1)

@test
def test_Philox_permuted(seed, x, expected, axis=None):
    gen = rnd.Generator(rnd.Philox(seed))
    arr = gen.permuted(x, axis)
    assert (arr == expected).all()

test_Philox_permuted(4321,
                     np.array([1.124, 4.7532, 9.1246, 12.53243, 15.64324]),
                     np.array([15.64324, 4.7532, 9.1246, 1.124, 12.53243]))
test_Philox_permuted(
    4321,
    np.arange(24).reshape(3, 8),
    np.array([[14, 21, 10, 17, 16, 12, 19, 5], [13, 20, 6, 0, 7, 15, 4, 9],
              [18, 22, 1, 23, 3, 2, 8, 11]]))

@test
def test_Philox_beta(seed, a, b, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(a, int) or isinstance(a, float)) and (isinstance(
                b, int) or isinstance(b, float)):
            assert gen.beta(a, b) == expected
        else:
            assert (round(gen.beta(a, b), 8) == expected).all()
    else:
        assert (round(gen.beta(a, b, size), 8) == expected).all()

#test_Philox_beta(4321, -7, 0, error)
test_Philox_beta(4321, 0.2, 0.1, 0.0001674971842261576)
test_Philox_beta(4321, 10, 14, 0.3991628907366655)
test_Philox_beta(4321, 167, 2041,
                 np.array([0.07344472, 0.08315303, 0.08115929, 0.07195913]), 4)
test_Philox_beta(139, 14.32, 4, 0.828761259380857)
test_Philox_beta(457, 12.87, 9.21, 0.53629054859836)
test_Philox_beta(
    457, np.array([12.87, 1.32, 4.532, 1.34, 8.432]), 9.21,
    np.array([0.53629055, 0.31970199, 0.61013405, 0.06910402, 0.58929108]))
test_Philox_beta(
    457, 32.14, np.array([9.21, 0.21, 14.32, 41.21, 5.55]),
    np.array([0.78367830, 0.99976162, 0.83007856, 0.44624419, 0.89121140]))
test_Philox_beta(
    457, np.array([12.87, 1.32, 4.532, 1.34, 8.432]),
    np.array([9.21, 0.21, 14.32, 41.21, 5.55]),
    np.array([0.53629055, 0.99558555, 0.45404014, 0.01310910, 0.71423699]))

@test
def test_Philox_binomial(seed, n, p, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(n, int) or isinstance(n, float)) and (isinstance(
                p, int) or isinstance(p, float)):
            assert gen.binomial(n, p) == expected
        else:
            assert (gen.binomial(n, p) == expected).all()
    else:
        assert (gen.binomial(n, p, size) == expected).all()

#test_Philox_binomial(139, -2, .33, error)
test_Philox_binomial(139, 28400, .66, 18735)
test_Philox_binomial(139, 14.76, .33, 8)
test_Philox_binomial(139, 0, .33, 0)
test_Philox_binomial(139, 14, 0, 0)
test_Philox_binomial(147, 10, .5, np.array([5, 4, 5, 4, 3, 7, 7, 4, 2, 4]), 10)
test_Philox_binomial(457, np.array([12, 1, 4, 7, 8]), 0.11,
                     np.array([2, 0, 0, 0, 1]))
test_Philox_binomial(457, 12, np.array([0.11, 0.21, 0.32, 0.43]),
                     np.array([2, 1, 3, 4]))
test_Philox_binomial(457, np.array([12, 1, 4, 7, 8]),
                     np.array([0.11, 0.21, 0.32, 0.43, 0.55]),
                     np.array([2, 0, 1, 2, 4]))

@test
def test_Philox_chisquare(seed, df, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(df, float) or isinstance(df, int)):
            assert gen.chisquare(df) == expected
        else:
            assert (round(gen.chisquare(df), 8) == expected).all()
    else:
        assert (round(gen.chisquare(df, size), 8) == expected).all()

test_Philox_chisquare(457, 12.12, 4.987326814746572)
test_Philox_chisquare(457, 24.12, np.array([13.45189670, 17.54542500]), 2)
test_Philox_chisquare(
    457, np.array([23, 42, 34, 17]),
    np.array([12.61672185, 33.30496035, 39.938353210, 7.36364561]))
test_Philox_chisquare(
    457, np.array([274.23, 325.42, 352.34, 825.17]),
    np.array([234.89812766, 301.20489918, 372.23635244, 745.94706704]))

@test
def test_Philox_dirichlet(seed, alpha, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    assert (round(gen.dirichlet(alpha, size), 8) == round(expected, 8)).all()

test_Philox_dirichlet(
    457, np.array([278.23, 325.42, 352.34, 825.17]),
    np.array([0.14758409, 0.18205838, 0.21640768, 0.45394985]))
test_Philox_dirichlet(
    874, np.array([10, 5, 3]),
    np.array([[0.63748511, 0.22927703, 0.13323785],
              [0.38843058, 0.41643801, 0.19513141]]), 2)
#test_Philox_dirichlet(1234, np.array([]), np.array([]))
test_Philox_dirichlet(74, np.array([0.01, 0.05]),
                      np.array([3.93720652e-33, 1.00000000]))

@test
def test_Philox_exponential(seed, scale, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(scale, float) or isinstance(scale, int)):
            assert gen.exponential(scale) == expected
        else:
            assert (round(gen.exponential(scale), 8) == expected).all()
    else:
        assert (round(gen.exponential(scale, size), 8) == expected).all()

test_Philox_exponential(74, 1, 0.677350334467786)
test_Philox_exponential(874, 3, 2.0459069098380795)
test_Philox_exponential(874, 724.952, 494.39476870031183)
test_Philox_exponential(
    874, np.array([278.23, 325.42, 352.34, 825.17]),
    np.array([189.74422651, 10.21851605, 301.34973289, 110.59639221]))
test_Philox_exponential(
    874, 724.952,
    np.array([
        494.39476870, 22.76422361, 620.03772368, 97.16431247, 1148.46168532,
        1852.86020050, 2203.97929230
    ]), 7)

@test
def test_Philox_f(seed, dfnum, dfden, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(dfnum, float)
                or isinstance(dfnum, int)) and (isinstance(dfden, float)
                                                or isinstance(dfden, int)):
            assert gen.f(dfnum, dfden) == expected
        else:
            assert (round(gen.f(dfnum, dfden), 8) == expected).all()
    else:
        assert (round(gen.f(dfnum, dfden, size), 8) == expected).all()

test_Philox_f(874, 12, 4.21, 1.5606440016521452)
test_Philox_f(
    874, np.array([14, 15, 16, 17, 18]), 4.21,
    np.array([1.51159613, 2.12468474, 1.13710348, 2.75465007, 2.48772155]))
test_Philox_f(
    874, 2557, 0.92,
    np.array([0.640902440, 1.79388925, 18.24367928, 20.97090336, 14.06117768]),
    5)
test_Philox_f(874, 7343, np.array([12.52, 0.92, 231.52]),
              np.array([0.95066689, 1.78861180, 0.96449548]))

@test
def test_Philox_gamma(seed, shape, expected, scale=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(shape, float)
                or isinstance(shape, int)) and (isinstance(scale, float)
                                                or isinstance(scale, int)):
            assert gen.gamma(shape, scale) == expected
        else:
            assert (round(gen.gamma(shape, scale), 8) == expected).all()
    else:
        assert (round(gen.gamma(shape, scale, size), 8) == expected).all()

test_Philox_gamma(318, 564, 569.4970306954932)
test_Philox_gamma(318, 564, 3132.2336688252126, 5.5)
test_Philox_gamma(318, 564, np.array([3132.23366883, 1304.85485375]),
                  np.array([5.5, 2.2]))
test_Philox_gamma(
    318, 564,
    np.array([2348.28398877, 2445.67462449, 2219.17838471, 2465.85102355]),
    4.123435, 4)
test_Philox_gamma(
    318, np.array([19, 17, 45, 52, 21]),
    np.array(
        [168.35816137, 188.93243525, 322.08095866, 533.31906573,
         175.15726538]), 8.527)
test_Philox_gamma(
    318, np.array([19, 17, 45, 52, 21]),
    np.array(
        [21.71853847, 97.49064326, 75.54379234, 475.34008438, 202.74448332]),
    np.array([1.1, 4.4, 2, 7.6, 9.87]))

@test
def test_Philox_geometric(seed, p, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(p, float) or isinstance(p, int):
            assert gen.geometric(p) == expected
        else:
            assert (gen.geometric(p) == expected).all()
    else:
        assert (gen.geometric(p, size) == expected).all()

test_Philox_geometric(457, 0.35, 3)
test_Philox_geometric(457, 0.2, 6)
test_Philox_geometric(2000, 1, 1)
test_Philox_geometric(12654, 0.5, np.array([1., 1., 1., 1.]), 4)
test_Philox_geometric(12654, np.array([0.01, 0.6, 0.45, 0.33]),
                      np.array([102, 1, 2, 2]))

@test
def test_Philox_gumbel(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(loc, float) or isinstance(loc, int)) and (isinstance(
                scale, float) or isinstance(scale, int)):
            assert gen.gumbel(loc, scale) == expected
        else:
            assert (round(gen.gumbel(loc, scale), 8) == expected).all()
    else:
        assert (round(gen.gumbel(loc, scale, size), 8) == expected).all()

test_Philox_gumbel(1234, 0.20485472765439966)
test_Philox_gumbel(1234, 12.2048547276544, 12)
test_Philox_gumbel(1234, 24.992276773836757, scale=122)
test_Philox_gumbel(1234,
                   np.array([0.20485473, 1.37332713, 1.05786017]),
                   size=3)
test_Philox_gumbel(1234,
                   np.array([1.00485473, 2.54332713, 1.18786017,
                             265.59280948]),
                   loc=np.array([0.8, 1.17, 0.13, 265.45]))
test_Philox_gumbel(1234,
                   np.array([2.41728579, 70.27314942, 63.60913187,
                             0.92112117]),
                   scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_Philox_hypergeometric(seed,
                               ngood,
                               nbad,
                               nsample,
                               expected,
                               size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(ngood, int) or isinstance(ngood, float)) and (
                isinstance(nbad, int) or isinstance(nbad, float)) and (
                    isinstance(nsample, int) or isinstance(nsample, float)):
            assert gen.hypergeometric(ngood, nbad, nsample) == expected
        else:
            assert (gen.hypergeometric(ngood, nbad, nsample) == expected).all()
    else:
        assert (gen.hypergeometric(ngood, nbad, nsample,
                                   size) == expected).all()

test_Philox_hypergeometric(1234, 11, 12, 9, 4)
test_Philox_hypergeometric(1234, 100, 300, 200, 46)
#test_Philox_hypergeometric(1234, 100.31, 300.55, 200.2, 46)
test_Philox_hypergeometric(1234, 100, 300, np.array([200, 201, 222, 221]),
                           np.array([46, 48, 54, 46]))
test_Philox_hypergeometric(1234, 100, np.array([301, 303, 304, 344, 355]), 200,
                           np.array([46, 52, 51, 55, 47]))
test_Philox_hypergeometric(1234, np.array([100, 111, 112, 122]), 300, 200,
                           np.array([46, 56, 56, 68]))
test_Philox_hypergeometric(1234, 100, 700, 450, np.array([61, 54, 54, 46, 64]),
                           5)

@test
def test_Philox_laplace(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(loc, float) or isinstance(loc, int)) and (isinstance(
                scale, float) or isinstance(scale, int)):
            assert gen.laplace(loc, scale) == expected
        else:
            assert (round(gen.laplace(loc, scale), 8) == expected).all()
    else:
        assert (round(gen.laplace(loc, scale, size), 8) == expected).all()

test_Philox_laplace(1234, 0.12161849017455835)
test_Philox_laplace(1234, 12.121618490174559, 12)
test_Philox_laplace(1234, 14.83745580129612, scale=122)
test_Philox_laplace(1234,
                    np.array([0.12161849, -0.80414025, -0.53329424]),
                    size=3)
test_Philox_laplace(1234,
                    np.array(
                        [0.92161849, 0.36585975, -0.40329424, 265.62377203]),
                    loc=np.array([0.8, 1.17, 0.13, 265.45]))
test_Philox_laplace(1234,
                    np.array(
                        [1.43509818, -41.14785663, -32.06698241, 1.12082962]),
                    scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_Philox_logistic(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(loc, float) or isinstance(loc, int)) and (isinstance(
                scale, float) or isinstance(scale, int)):
            assert gen.logistic(loc, scale) == expected
        else:
            assert (round(gen.logistic(loc, scale), 8) == expected).all()
    else:
        assert (round(gen.logistic(loc, scale, size), 8) == expected).all()

test_Philox_logistic(1234, 0.23003681281754018)
test_Philox_logistic(1234, 12.23003681281754, 12)
test_Philox_logistic(1234, 28.064491163739902, scale=122)
test_Philox_logistic(1234,
                     np.array([0.23003681, -1.24402451, -0.87924346]),
                     size=3)
test_Philox_logistic(1234,
                     np.array(
                         [1.03003681, -0.07402451, -0.74924346, 25.77177085]),
                     loc=np.array([0.8, 1.17, 0.13, 25.45]))
test_Philox_logistic(1234,
                     np.array(
                         [2.71443439, -63.65673441, -52.86890896, 2.07542198]),
                     scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_Philox_lognormal(seed, expected, mean=0.0, sigma=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(mean, float) or isinstance(mean, int)) and (isinstance(
                sigma, float) or isinstance(sigma, int)):
            assert gen.lognormal(mean, sigma) == expected
        else:
            assert (round(gen.lognormal(mean, sigma), 8) == expected).all()
    else:
        assert (round(gen.lognormal(mean, sigma, size), 8) == expected).all()

test_Philox_lognormal(1234, 0.46906380361126687)
test_Philox_lognormal(1234, 76342.3815189564, 12)
test_Philox_lognormal(1234, 0.22002085185826914, sigma=2)
test_Philox_lognormal(1234,
                      np.array([0.4690638, 5.02772598, 1.96860722]),
                      size=3)
test_Philox_lognormal(1234,
                      np.array(
                          [1.04392069, 16.19929610, 2.24190578, 668.12967098]),
                      mean=np.array([0.8, 1.17, 0.13, 5.45]))
test_Philox_lognormal(1234,
                      np.array(
                          [0.25598673, 33.26414553, 1.69607119, 4.61355770]),
                      sigma=np.array([1.8, 2.17, 0.78, 1.45]))

@test
def test_Philox_logseries(seed, p, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(p, float) or isinstance(p, int):
            assert gen.logseries(p) == expected
        else:
            assert (gen.logseries(p) == expected).all()
    else:
        assert (gen.logseries(p, size) == expected).all()

test_Philox_logseries(457, 0.35, 1)
test_Philox_logseries(2000, 0, 1)
test_Philox_logseries(457, 0.5, np.array([1., 2., 2., 2.]), 4)
test_Philox_logseries(457, np.array([0.01, 0.6, 0.45, 0.33]),
                      np.array([1, 2, 1, 1]))

@test
def test_Philox_multinomial(seed, n, pvals, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    assert (round(gen.multinomial(n, pvals, size), 8) == expected).all()

test_Philox_multinomial(457, 20, [1 / 6.], np.array([20]))
test_Philox_multinomial(457, 20.6, [1 / 6.], np.array([20]))
test_Philox_multinomial(457, 20, [1 / 6.] * 6, np.array([4, 1, 3, 3, 5, 4]))
test_Philox_multinomial(
    457, 20, [1 / 6] * 6,
    np.array([[4, 1, 3, 3, 5, 4], [2, 3, 2, 8, 2, 3], [2, 5, 1, 5, 2, 5]]), 3)
#test_Philox_multinomial(457, 1, np.array([[.1, .5, .4 ], [.3, .7, .0]]), np.array([[0, 1, 0], [0, 1, 0]]))
#test_Philox_multinomial(457, -1, [1/6.], error)

@test
def test_Philox_multivariate_hypergeometric(seed,
                                            colors,
                                            nsample,
                                            expected,
                                            size=None,
                                            method='marginals'):
    gen = rnd.Generator(rnd.Philox(seed))
    assert (gen.multivariate_hypergeometric(colors, nsample, size,
                                            method) == expected).all()

test_Philox_multivariate_hypergeometric(457, np.array([16, 8, 4]), 15,
                                        np.array([7, 4, 4]))
test_Philox_multivariate_hypergeometric(457,
                                        np.array([16, 8, 4]),
                                        15,
                                        np.array([[7, 4, 4], [6, 7, 2],
                                                  [6, 6, 3]]),
                                        size=3)
test_Philox_multivariate_hypergeometric(457, np.array([16, 8, 4]), 0,
                                        np.array([0, 0, 0]))
test_Philox_multivariate_hypergeometric(457,
                                        np.array([16, 8, 4]),
                                        8,
                                        np.array([6, 1, 1]),
                                        method='count')

@test
def test_Philox_multivariate_normal(seed,
                                    mean,
                                    cov,
                                    expected,
                                    size=None,
                                    check_valid: Static[str] = 'warn',
                                    tol: float = 1e-8,
                                    m: Static[str] = 'svd'):
    gen = rnd.Generator(rnd.Philox(seed))
    assert (round(
        gen.multivariate_normal(mean,
                                cov,
                                size,
                                check_valid=check_valid,
                                tol=tol,
                                method=m), 8) == expected).all()

test_Philox_multivariate_normal(1234, (1, 2), np.array([[1, 0], [0, 1]]),
                                np.array([0.24298352, 3.61496779]))
test_Philox_multivariate_normal(5432, (1, 2),
                                np.array([[1, 0], [0, 1]]),
                                np.array([1.31546928, 1.83957609]),
                                m='cholesky')

@test
def test_Philox_negative_binomial(seed, n, p, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(n, int) or isinstance(n, float)) and (isinstance(
                p, int) or isinstance(p, float)):
            assert gen.negative_binomial(n, p) == expected
        else:
            assert (gen.negative_binomial(n, p) == expected).all()
    else:
        assert (gen.negative_binomial(n, p, size) == expected).all()

test_Philox_negative_binomial(139, 28400, .66, 14627)
test_Philox_negative_binomial(139, 14.76, .33, 27)
#test_Philox_negative_binomial(139, 14, 0, error)
test_Philox_negative_binomial(147, 10, .5, np.array([6, 10, 21]), 3)
test_Philox_negative_binomial(457, np.array([12, 1, 4, 7, 8]), 0.11,
                              np.array([51, 10, 23, 42, 65]))
test_Philox_negative_binomial(457, 12, np.array([0.11, 0.21, 0.32, 0.43]),
                              np.array([[51, 51, 27, 9]]))

@test
def test_Philox_noncentral_chisquare(seed, df, nonc, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(df, int) or isinstance(df, float)) and (isinstance(
                nonc, int) or isinstance(nonc, float)):
            assert gen.noncentral_chisquare(df, nonc) == expected
        else:
            assert (round(gen.noncentral_chisquare(df, nonc),
                          8) == expected).all()
    else:
        assert (round(gen.noncentral_chisquare(df, nonc, size),
                      8) == expected).all()

test_Philox_noncentral_chisquare(457, 0.1, 0.2, 1.3412155895288272e-19)
test_Philox_noncentral_chisquare(457, 5, 0, 0.9667491310888645)
test_Philox_noncentral_chisquare(457, 99, 7, 77.93117603431178)
test_Philox_noncentral_chisquare(
    457, 14.2, 0.5, np.array([5.74888694, 17.17099047, 7.53364203]), 3)
test_Philox_noncentral_chisquare(
    457, np.array([8, 7.1, 45.3]), 0.6,
    np.array([1.97336093, 9.18958927, 30.29737961]))
test_Philox_noncentral_chisquare(
    457, 0.6, np.array([8, 7.1, 45.3]),
    np.array([8.95858437, 1.28791705, 46.52231840]))

@test
def test_Philox_noncentral_f(seed, dfnum, dfden, nonc, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(dfnum, float) or isinstance(dfnum, int)) and (
                isinstance(dfden, int)
                or isinstance(dfden, float)) and (isinstance(nonc, int)
                                                  or isinstance(nonc, float)):
            assert gen.noncentral_f(dfnum, dfden, nonc) == expected
        else:
            assert (round(gen.noncentral_f(dfnum, dfden, nonc),
                          8) == expected).all()
    else:
        assert (round(gen.noncentral_f(dfnum, dfden, nonc, size),
                      8) == expected).all()

test_Philox_noncentral_f(874, 3, 20, 3, 2.792477829917526)
test_Philox_noncentral_f(874, 3, 20, 0, 2.2362415189878746)
test_Philox_noncentral_f(
    874, np.array([14, 15, 16, 17, 18]), 4.21, 6.7,
    np.array([1.04484935, 1.21683455, 1.13983240, 3.66570463, 0.57133496]))
test_Philox_noncentral_f(
    874, 27, 5.92, 3.1,
    np.array([0.82984208, 1.01215833, 0.94357663, 2.19740092, 0.48883215]), 5)
test_Philox_noncentral_f(
    874, 27, np.array([5.92, 8.13, 9.53, 33.14]), 3.1,
    np.array([0.82984208, 1.09159907, 0.97830503, 1.43588432]))
test_Philox_noncentral_f(
    874, 743, 8.24, np.array([0.11, 0.21, 0.32, 0.43]),
    np.array([0.62498462, 0.65443537, 0.81711437, 1.64557267]))

@test
def test_Philox_normal(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(loc, float) or isinstance(loc, int)) and (isinstance(
                scale, float) or isinstance(scale, int)):
            assert gen.normal(loc, scale) == expected
        else:
            assert (round(gen.normal(loc, scale), 8) == expected).all()
    else:
        assert (round(gen.normal(loc, scale, size), 8) == expected).all()

test_Philox_normal(1234, -0.7570164779736382)
test_Philox_normal(1234, 11.242983522026362, 12)
test_Philox_normal(1234, -92.35601031278387, scale=122)
test_Philox_normal(1234,
                   np.array([-0.75701648, 1.61496779, 0.67732630]),
                   size=3)
test_Philox_normal(1234,
                   np.array([0.04298352, 2.78496779, 0.80732630, 26.50448227]),
                   loc=np.array([0.8, 1.17, 0.13, 25.45]))
test_Philox_normal(1234,
                   np.array(
                       [-8.93279444, 82.63790185, 40.72763043, 6.80141066]),
                   scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_Philox_pareto(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(a, float) or isinstance(a, int):
            assert gen.pareto(a) == expected
        else:
            assert (round(gen.pareto(a), 8) == expected).all()
    else:
        assert (round(gen.pareto(a, size), 8) == expected).all()

test_Philox_pareto(1234, 3, 0.5653481922908028)
test_Philox_pareto(
    1234, 5,
    np.array([0.30847845, 0.09324970, 0.02876564, 0.04935884, 0.94079746]), 5)
test_Philox_pareto(
    1234, np.array([3, 13, 4, 23.2, 5.6]),
    np.array([0.56534819, 0.03488493, 0.03608542, 0.01043758, 0.80769515]))

@test
def test_Philox_poisson(seed, expected, lam=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(lam, float) or isinstance(lam, int):
            assert gen.poisson(lam) == expected
        else:
            assert (round(gen.poisson(lam), 8) == expected).all()
    else:
        assert (round(gen.poisson(lam, size), 8) == expected).all()

test_Philox_poisson(1234, 0, 0)
test_Philox_poisson(1234, 1)
test_Philox_poisson(1234, 2, 3)
test_Philox_poisson(1234, 15, 14.65)
test_Philox_poisson(1234, np.array([37., 9., 11., 67., 27.]),
                    np.array([35.62, 9.23, 9.57, 76.79, 21.74]))
test_Philox_poisson(1234, np.array([829., 806., 853., 800., 821.]), 824.14, 5)

@test
def test_Philox_power(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(a, float) or isinstance(a, int):
            assert gen.power(a) == expected
        else:
            assert (round(gen.power(a), 8) == expected).all()
    else:
        assert (round(gen.power(a, size), 8) == expected).all()

test_Philox_power(1234, 1, 0.7392843317079527)
test_Philox_power(1234, 45.11, 0.9933260132890823)
test_Philox_power(1234, 6, np.array([0.95090088, 0.84330415, 0.71374101]), 3)
test_Philox_power(
    1234, np.array([5.4, 1.1, 78, 19.34, 99999999999999]),
    np.array(
        array([0.94559645, 0.39470958, 0.97439242, 0.92339234, 1.00000000])))

@test
def test_Philox_rayleigh(seed, expected, scale=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(scale, float) or isinstance(scale, int):
            assert gen.rayleigh(scale) == expected
        else:
            assert (round(gen.rayleigh(scale), 8) == expected).all()
    else:
        assert (round(gen.rayleigh(scale, size), 8) == expected).all()

#test_Philox_rayleigh(1234, error, -1)
test_Philox_rayleigh(1234, 1.6397102542808777)
test_Philox_rayleigh(1234, 4.919130762842633, 3)
test_Philox_rayleigh(1234, 24.021755225214857, 14.65)
test_Philox_rayleigh(1234,
                     np.array([1.63971025, 0.94421734, 0.53253802,
                               0.69411344]),
                     size=4)
test_Philox_rayleigh(
    1234,
    np.array([8.85443537, 1.03863908, 41.53796574, 13.42415392, 144.71897760]),
    np.array([5.4, 1.1, 78, 19.34, 56.2]))

@test
def test_Philox_standard_cauchy(seed, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        assert gen.standard_cauchy() == expected
    else:
        assert (round(gen.standard_cauchy(size), 8) == round(expected,
                                                             8)).all()

test_Philox_standard_cauchy(1234, -0.46875020188678784)
test_Philox_standard_cauchy(
    1234, np.array([-0.46875020, 0.64233067, -0.70952735, -18.92416824]), 4)

@test
def test_Philox_standard_exponential(seed, expected, size=None, m='zig'):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        assert gen.standard_exponential(method=m) == expected
    else:
        assert (round(gen.standard_exponential(size, method=m),
                      8) == expected).all()

test_Philox_standard_exponential(1234, 1.34432485899693)
test_Philox_standard_exponential(
    1234, np.array([1.34432486, 0.44577319, 0.14179837, 0.24089673]), 4)
test_Philox_standard_exponential(1234, 0.8147656707345036, m='inv')

@test
def test_Philox_standard_gamma(seed, shape, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(shape, float) or isinstance(shape, int):
            assert gen.standard_gamma(shape) == expected
        else:
            assert (round(gen.standard_gamma(shape), 8) == expected).all()
    else:
        assert (round(gen.standard_gamma(shape, size), 8) == expected).all()

test_Philox_standard_gamma(318, 1, 0.15235906502561086)
test_Philox_standard_gamma(1234, 0, 0)
test_Philox_standard_gamma(1234, 0.5, 0.3145070093753639)
test_Philox_standard_gamma(1234, 3, 1.6116492009757368)
test_Philox_standard_gamma(
    1234, 0.5, np.array([0.31450701, 0.08604517, 0.97731355, 0.05278714]), 4)
test_Philox_standard_gamma(
    1234, np.array([4.5, 2, 7.7, 81.15]),
    np.array([2.80456639, 2.70292948, 5.81000297, 62.55816392]))

@test
def test_Philox_standard_normal(seed, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        assert gen.standard_normal(size) == expected
    else:
        assert (round(gen.standard_normal(size), 8) == expected).all()

test_Philox_standard_normal(1234, -0.7570164779736382)
test_Philox_standard_normal(1234,
                            np.array([-0.75701648, 1.61496779, 0.67732630]), 3)

@test
def test_Philox_standard_t(seed, df, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if isinstance(df, float) or isinstance(df, int):
            assert gen.standard_t(df) == expected
        else:
            assert (round(gen.standard_t(df), 8) == expected).all()
    else:
        assert (round(gen.standard_t(df, size), 8) == expected).all()

test_Philox_standard_t(1234, 3, -0.4679940382376999)
test_Philox_standard_t(1234, 1, -2.3925113365078903)
test_Philox_standard_t(1234, 0.5, -7.561408056322522)
test_Philox_standard_t(1234, 0.5,
                       np.array([-7.56140806, 0.78219204, -1.00719952]), 3)
test_Philox_standard_t(1234, np.array([99, 5.6, 11.43]),
                       np.array([-0.67980828, 1.38799933, -2.21798311]))

@test
def test_Philox_triangular(seed, left, mode, right, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(left, int) or isinstance(left, float)) and (isinstance(
                mode, int) or isinstance(mode, float)) and (isinstance(
                    right, int) or isinstance(right, float)):
            assert gen.triangular(left, mode, right) == expected
        else:
            assert (round(gen.triangular(left, mode, right),
                          8) == expected).all()
    else:
        assert (round(gen.triangular(left, mode, right, size),
                      8) == expected).all()

test_Philox_triangular(1234, -3, 0, 8, 1.758094078569811)
test_Philox_triangular(1234, np.array([-1, -4.2, -3.2]), -0.2, 77,
                       np.array([25.36646799, 7.24243359, 10.85412666]))
test_Philox_triangular(
    1234, -4, np.array([0.1, 0.5, 3, 6, 7.9]), 8,
    np.array([1.52141664, -0.52411783, 0.96387999, 4.34090471, 6.76694962]))
test_Philox_triangular(1234, -77, 0, np.array([0.1, 11, 789]),
                       np.array([-19.48246129, -38.06368345, 94.12871014]))
test_Philox_triangular(
    1234,
    2,
    10,
    53,
    np.array([21.84016148, 11.74049890, 13.63355147, 22.64220666]),
    size=4)

@test
def test_Philox_uniform(seed, expected, low=0.0, high=1.0, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    result = round(gen.uniform(low, high, size), 8)
    if size is None:
        if (isinstance(low, int) or isinstance(low, float)) and (isinstance(
                high, int) or isinstance(high, float)):
            assert result == round(expected, 8)
        else:
            assert (result == expected).all()
    else:
        assert (result == expected).all()

test_Philox_uniform(1234, 10.572569371365311, 5, 15)
test_Philox_uniform(1234, 0.601531243422878, 0.1)
test_Philox_uniform(1234, 67.9853463306568, high=122)
test_Philox_uniform(1234,
                    np.array([0.55725694, 0.22373624, 0.29333458]),
                    size=3)
test_Philox_uniform(1234,
                    np.array(
                        [25.25365940, 12.39692198, 17.44004855, 29.03071891]),
                    low=np.array([0.4, 3., 6., 7.]),
                    high=45.)
test_Philox_uniform(
    1234,
    np.array([31.20638848, 3.57977980, 12.90672144, 1.79724286, 0.16236303]),
    high=np.array([56., 16., 44., 3.1, 0.2]))

@test
def test_Philox_vonmises(seed, mu, kappa, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(mu, float) or isinstance(mu, int)) and (isinstance(
                kappa, float) or isinstance(kappa, int)):
            assert gen.vonmises(mu, kappa) == expected
        else:
            assert (round(gen.vonmises(mu, kappa), 8) == expected).all()
    else:
        assert (round(gen.vonmises(mu, kappa, size), 8) == expected).all()

test_Philox_vonmises(1234, 0, 4, -0.581040612761238)
test_Philox_vonmises(1234, 0.1, 0.2, -1.4566350588891002)
test_Philox_vonmises(1234, -1, 0, 0.3597559461503576)
test_Philox_vonmises(1234,
                     0,
                     5,
                     np.array([-0.52295031, 0.56024213, -0.16818327]),
                     size=3)
test_Philox_vonmises(
    1234, np.array([43.09, 18.62, 42, 16.94]), 5,
    np.array([-1.41524746, 0.33068621, -2.15048042, -1.76019478]))
test_Philox_vonmises(
    1234, 4, np.array([0, 0.1, 7.67, 99]),
    np.array([0.35975595, -1.64210258, -2.41918436, -2.24951773]))

@test
def test_Philox_wald(seed, mean, scale, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(mean, float) or isinstance(mean, int)) and (isinstance(
                scale, float) or isinstance(scale, int)):
            assert gen.wald(mean, scale) == expected
        else:
            assert (round(gen.wald(mean, scale), 8) == expected).all()
    else:
        assert (round(gen.wald(mean, scale, size), 8) == expected).all()

test_Philox_wald(1234, 0.1, 0.1, 0.04771066895204586)
test_Philox_wald(1234, 3, 2, 1.2236223738845495)
test_Philox_wald(1234, 3, 2, np.array([1.22362237, 1.33799709, 1.42908304]), 3)
test_Philox_wald(1234, np.array([0.1, 0.5, 1, 4]), 0.5,
                 np.array([0.07139433, 0.25713553, 0.42732171, 0.09772811]))
test_Philox_wald(1234, 0.5, np.array([0.1, 0.5, 1, 4]),
                 np.array([0.10751922, 0.25713553, 0.77217339, 1.07087709]))
test_Philox_wald(1234, np.array([0.1, 0.5, 1, 4]), np.array([3, 2, 1, 8]),
                 np.array([0.08710099, 0.35693113, 1.84037068, 0.95321118]))

@test
def test_Philox_weibull(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(a, float) or isinstance(a, int)):
            assert gen.weibull(a) == expected
        else:
            assert (round(gen.weibull(a), 8) == expected).all()
    else:
        assert (round(gen.weibull(a, size), 8) == expected).all()

test_Philox_weibull(1234, 0, 0)
test_Philox_weibull(1234, 0.1, 19.277126441209486)
test_Philox_weibull(1234, 1, 1.34432485899693)
test_Philox_weibull(1234, 1, np.array([1.34432486, 0.44577319, 0.14179837]), 3)
test_Philox_weibull(1234, np.array([17.8, 4.32]),
                    np.array([1.01676207, 0.82942358]))

@test
def test_Philox_zipf(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.Philox(seed))
    if size is None:
        if (isinstance(a, float) or isinstance(a, int)):
            assert gen.zipf(a) == expected
        else:
            assert (gen.zipf(a) == expected).all()
    else:
        assert (gen.zipf(a, size) == expected).all()

test_Philox_zipf(1234, 1.1, 3455)
test_Philox_zipf(1234, 3, 1)
test_Philox_zipf(1234, 45, np.array([1, 1, 1]), 3)
test_Philox_zipf(1234, np.array([17.8, 4.32]), np.array([1, 1]))