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

@test
def test_PCG64_integers(seed, low, expected, high=None, size=None, e=False):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_integers(1234, 5, 4)
test_PCG64_integers(5678, 987.4, 43)
test_PCG64_integers(5678, 98, 137, high=987)
test_PCG64_integers(74, 89, 17, e=True)
test_PCG64_integers(74, -1218, -958, high=98)
test_PCG64_integers(74, -5, np.array([6, 48, 17]), 55, size=3)
test_PCG64_integers(74, -5, np.array([7, 49, 17]), 55, size=3, e=True)
test_PCG64_integers(5678, np.array([7, 49, 17]), np.array([10, 75, 43]), 78)
test_PCG64_integers(5678, -99, np.array([-92, 74, -13]),
                    np.array([77, 89, 101]))
test_PCG64_integers(5678, np.array([7, 49, 17]), np.array([10, 85, 53]),
                    np.array([77, 89, 101]))

@test
def test_PCG64_random(seed, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    if size is None:
        assert gen.random() == expected
    else:
        assert (round(gen.random(size), 8) == round(expected, 8)).all()

test_PCG64_random(0, 0.6369616873214543)
test_PCG64_random(1, 0.5118216247002567)
test_PCG64_random(1234, 0.9766997666981422)
test_PCG64_random(
    1234567,
    np.array([0.99365208, 0.00776825, 0.23769419, 0.77242113, 0.30128085]),
    size=5)
test_PCG64_random(74,
                  np.array([[0.89437827, 0.52072095, 0.67640414],
                            [0.66919894, 0.56483809, 0.5245347]]),
                  size=(2, 3))

@test
def test_PCG64_choice(seed,
                      a,
                      expected,
                      size=None,
                      r=True,
                      p=None,
                      axis=0,
                      shuffle=True):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_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([4.78, 7.99, 8.31]), 3)
test_PCG64_choice(
    318, np.array([5, 4, 2, 6, 7, 8, 2, 12, 1, 55, 33, 7, 78, 15, 43]), 4)
test_PCG64_choice(318, np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),
                  np.array([11, 2, 12]), 3, False)
test_PCG64_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
    ]), -7.99)
test_PCG64_choice(318, np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11,
                                                                   12]]),
                  np.array([[1, 2, 3], [10, 11, 12]]), 2)
test_PCG64_choice(74, np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11,
                                                                  12]]),
                  np.array([1, 2, 3]))
test_PCG64_choice(74, 1, 0)
test_PCG64_choice(74, np.array([13, -4]), -4, p=np.array([0.3, 0.7]))
test_PCG64_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_PCG64_choice(74,
                  np.array([13, -4, 3, 18]),
                  3,
                  p=np.array([0.5, 0.3, 0.1, 0.1]),
                  shuffle=True)

@test
def test_PCG64_bytes(seed, length, expected):
    gen = rnd.Generator(rnd.PCG64(seed))
    check = gen.bytes(length) == expected
    assert check

test_PCG64_bytes(555, 10, "\xbe^\x97g\x10\xa4;7z\x95")

@test
def test_PCG64_shuffle(seed, x, expected, axis=0):
    gen = rnd.Generator(rnd.PCG64(seed))
    gen.shuffle(x, axis)
    assert (x == expected).all()

test_PCG64_shuffle(876, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
                   np.array([6, 3, 5, 9, 7, 8, 1, 2, 4, 0]))
test_PCG64_shuffle(111, np.array([5.43, 6.56, 1.22, 4.3221, 7.88, 0.9352]),
                   np.array([0.9352, 6.56, 5.43, 7.88, 1.22, 4.3221]))
test_PCG64_shuffle(1234, np.array([3.14]), np.array([3.14]))
test_PCG64_shuffle(74,
                   np.arange(9).reshape((3, 3)),
                   np.array([[6, 7, 8], [3, 4, 5], [0, 1, 2]]))
test_PCG64_shuffle(74,
                   np.arange(9).reshape((3, 3)),
                   np.array([[2, 1, 0], [5, 4, 3], [8, 7, 6]]),
                   axis=1)

@test
def test_PCG64_permutation(seed, x, expected, axis=0):
    gen = rnd.Generator(rnd.PCG64(seed))
    assert (gen.permutation(x, axis) == expected).all()

test_PCG64_permutation(12345, 10, np.array([4, 8, 1, 3, 7, 9, 6, 0, 2, 5]))
test_PCG64_permutation(1234,
                       np.array([1.124, 4.7532, 9.1246, 12.53243, 15.64324]),
                       np.array([1.124, 15.64324, 4.7532, 12.53243, 9.1246]))
test_PCG64_permutation(
    4321,
    np.arange(24).reshape(3, 8),
    np.array([[16, 17, 18, 19, 20, 21, 22, 23], [8, 9, 10, 11, 12, 13, 14, 15],
              [0, 1, 2, 3, 4, 5, 6, 7]]))
test_PCG64_permutation(74,
                       np.arange(9).reshape((3, 3)),
                       np.array([[2, 1, 0], [5, 4, 3], [8, 7, 6]]),
                       axis=1)

@test
def test_PCG64_permuted(seed, x, expected, axis=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    arr = gen.permuted(x, axis)
    assert (arr == expected).all()

test_PCG64_permuted(4321, np.array([1.124, 4.7532, 9.1246, 12.53243,
                                    15.64324]),
                    np.array([1.124, 4.7532, 9.1246, 12.53243, 15.64324]))
test_PCG64_permuted(
    4321,
    np.arange(24).reshape(3, 8),
    np.array([[17, 6, 2, 12, 8, 10, 23, 7], [11, 13, 4, 19, 22, 5, 18, 21],
              [14, 1, 9, 0, 16, 3, 15, 20]]))

@test
def test_PCG64_beta(seed, a, b, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_beta(4321, -7, 0, error)
test_PCG64_beta(4321, 0.2, 0.1, 2.2810297427691828e-12)
test_PCG64_beta(4321, 10, 14, 0.4269214782557806)
test_PCG64_beta(4321, 167, 2041,
                np.array([0.07569048, 0.08132162, 0.07432054, 0.07364077]), 4)
test_PCG64_beta(139, 14.32, 4, 0.7753363939635708)
test_PCG64_beta(457, 12.87, 9.21, 0.5055315480345436)
test_PCG64_beta(
    457, np.array([12.87, 1.32, 4.532, 1.34, 8.432]), 9.21,
    np.array([0.50553155, 0.01362657, 0.34461015, 0.17758410, 0.47101973]))
test_PCG64_beta(
    457, 32.14, np.array([9.21, 0.21, 14.32, 41.21, 5.55]),
    np.array([0.72323361, 0.99185115, 0.76192197, 0.44454895, 0.86813848]))
test_PCG64_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([0.50553155, 0.36824135, 0.22202680, 0.05385470, 0.61055151]))

@test
def test_PCG64_binomial(seed, n, p, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_binomial(139, -2, .33, 0)
test_PCG64_binomial(139, 28400, .66, 18748)
test_PCG64_binomial(139, 14.76, .33, 4)
test_PCG64_binomial(139, 0, .33, 0)
test_PCG64_binomial(139, 14, 0, 0)
test_PCG64_binomial(147, 10, .5, np.array([9, 6, 6, 5, 6, 4, 3, 6, 5, 3]), 10)
test_PCG64_binomial(457, np.array([12, 1, 4, 7, 8]), 0.11,
                    np.array([1, 0, 0, 1, 0]))
test_PCG64_binomial(457, 12, np.array([0.11, 0.21, 0.32, 0.43]),
                    np.array([1, 4, 2, 6]))
test_PCG64_binomial(457, np.array([12, 1, 4, 7, 8]),
                    np.array([0.11, 0.21, 0.32, 0.43, 0.55]),
                    np.array([1, 1, 0, 3, 5]))

@test
def test_PCG64_chisquare(seed, df, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_chisquare(457, 12.12, 11.12088340114576)
test_PCG64_chisquare(457, 24.12, np.array([22.97619518, 30.58538001]), 2)
test_PCG64_chisquare(
    457, np.array([23, 42, 34, 17]),
    np.array([21.86780618, 50.59339837, 22.36475303, 15.23606778]))
test_PCG64_chisquare(
    457, np.array([274.23, 325.42, 352.34, 825.17]),
    np.array([271.92587239, 349.58892133, 312.74784926, 816.55165763]))

@test
def test_PCG64_dirichlet(seed, alpha, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    assert (round(gen.dirichlet(alpha, size), 8) == round(expected, 8)).all()

test_PCG64_dirichlet(
    457, np.array([278.23, 325.42, 352.34, 825.17]),
    np.array([0.15699856, 0.19433008, 0.18390472, 0.46476664]))
test_PCG64_dirichlet(
    874, np.array([10, 5, 3]),
    np.array([[0.70264987, 0.16185327, 0.13549686],
              [0.35554736, 0.40690167, 0.23755097]]), 2)
#test_PCG64_dirichlet(1234, np.array([]), np.array([]))
test_PCG64_dirichlet(74, np.array([0.01, 0.05]),
                     np.array([0.86854699, 0.13145301]))

@test
def test_PCG64_exponential(seed, scale, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_exponential(74, 1, 1.693462457917779)
test_PCG64_exponential(874, 3, 0.7277035588087964)
test_PCG64_exponential(874, 724.952, 175.85005012185152)
test_PCG64_exponential(
    874, np.array([278.23, 325.42, 352.34, 825.17]),
    np.array([67.48965372, 681.9977866, 102.20548868, 71.49681695]))
test_PCG64_exponential(
    874, 724.952,
    np.array([
        175.85005012, 1519.31552882, 210.29140441, 62.81343292, 679.83990463,
        351.19474068, 1802.19789426
    ]), 7)

@test
def test_PCG64_f(seed, dfnum, dfden, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_f(874, 12, 4.21, 3.428479552488479)
test_PCG64_f(
    874, np.array([14, 15, 16, 17, 18]), 4.21,
    np.array([3.34933794, 4.77314128, 0.96677047, 2.93878710, 0.50349151]))
test_PCG64_f(
    874, 2557, 0.92,
    np.array([14.97080143, 0.35262364, 1.69187013, 0.88598733, 41.64594068]),
    5)
test_PCG64_f(874, 7343, np.array([12.52, 0.92, 231.52]),
             np.array([1.53233839, 0.3528156, 0.94834774]))

@test
def test_PCG64_gamma(seed, shape, expected, scale=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_gamma(318, 564, 575.499699177782)
test_PCG64_gamma(318, 564, 3165.2483454778007, 5.5)
test_PCG64_gamma(318, 564, np.array([3165.24834548, 1172.04010906]),
                 np.array([5.5, 2.2]))
test_PCG64_gamma(
    318, 564,
    np.array([2373.03560208, 2196.74145777, 2459.45823200, 2409.61200879]),
    4.123435, 4)
test_PCG64_gamma(
    318, np.array([19, 17, 45, 52, 21]),
    np.array(
        [178.11061929, 100.74725160, 463.44439870, 495.50634389,
         194.23053272]), 8.527)
test_PCG64_gamma(
    318, np.array([19, 17, 45, 52, 21]),
    np.array(
        [22.97662498, 51.98638525, 108.70045707, 441.63811581, 224.82178468]),
    np.array([1.1, 4.4, 2, 7.6, 9.87]))

@test
def test_PCG64_geometric(seed, p, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_geometric(457, 0.35, 2)
test_PCG64_geometric(457, 0.2, 8)
test_PCG64_geometric(2000, 1, 1)
test_PCG64_geometric(12654, 0.5, np.array([2., 1., 2., 1.]), 4)
test_PCG64_geometric(12654, np.array([0.01, 0.6, 0.45, 0.33]),
                     np.array([93, 1, 4, 2]))

@test
def test_PCG64_gumbel(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_gumbel(1234, -1.3242306166629008)
test_PCG64_gumbel(1234, 10.6757693833371, 12)
test_PCG64_gumbel(1234, -161.55613523287388, scale=122)
test_PCG64_gumbel(1234,
                  np.array([-1.32423062, 0.73740935, -0.94279743]),
                  size=3)
test_PCG64_gumbel(1234,
                  np.array(
                      [-0.52423062, 1.90740935, -0.81279743, 266.64272045]),
                  loc=np.array([0.8, 1.17, 0.13, 265.45]))
test_PCG64_gumbel(1234,
                  np.array(
                      [-15.62592128, 37.73323651, -56.69040968, 7.69304687]),
                  scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_PCG64_hypergeometric(seed, ngood, nbad, nsample, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_hypergeometric(1234, 11, 12, 9, 4)
test_PCG64_hypergeometric(1234, 100, 300, 200, 49)
#test_PCG64_hypergeometric(1234, 100.31, 300.55, 200.2, 49)
test_PCG64_hypergeometric(1234, 100, 300, np.array([200, 201, 222, 221]),
                          np.array([49, 52, 62, 53]))
test_PCG64_hypergeometric(1234, 100, np.array([301, 303, 304, 344, 355]), 200,
                          np.array([49, 43, 52, 47, 41]))
test_PCG64_hypergeometric(1234, np.array([100, 111, 112, 122]), 300, 200,
                          np.array([49, 52, 48, 60]))
test_PCG64_hypergeometric(1234, 100, 700, 450, np.array([57, 63, 58, 54, 54]),
                          5)

@test
def test_PCG64_laplace(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_laplace(1234, 3.0661447249453975)
test_PCG64_laplace(1234, 15.066144724945397, 12)
test_PCG64_laplace(1234, 374.0696564433385, scale=122)
test_PCG64_laplace(1234,
                   np.array([3.06614472, -0.27392189, 1.87400564]),
                   size=3)
test_PCG64_laplace(1234,
                   np.array([3.86614472, 0.89607811, 2.00400564,
                             264.80256176]),
                   loc=np.array([0.8, 1.17, 0.13, 265.45]))
test_PCG64_laplace(1234,
                   np.array(
                       [36.18050775, -14.01658291, 112.68395923, -4.17597664]),
                   scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_PCG64_logistic(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_logistic(1234, 3.7357159301091363)
test_PCG64_logistic(1234, 15.735715930109137, 12)
test_PCG64_logistic(1234, 455.7573434733146, scale=122)
test_PCG64_logistic(1234,
                    np.array([3.73571593, -0.48871751, 2.48729352]),
                    size=3)
test_PCG64_logistic(1234,
                    np.array(
                        [4.53571593, 0.68128249, 2.61729352, 264.41280935]),
                    loc=np.array([0.8, 1.17, 0.13, 265.45]))
test_PCG64_logistic(
    1234,
    np.array([44.08144798, -25.00767521, 149.56095922, -6.68987968]),
    scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_PCG64_lognormal(seed, expected, mean=0.0, sigma=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_lognormal(1234, 0.20112336451311477)
test_PCG64_lognormal(1234, 32733.791240820276, 12)
test_PCG64_lognormal(1234, 0.04045060775307524, sigma=2)
test_PCG64_lognormal(1234,
                     np.array([0.20112336, 1.06619892, 2.09780445]),
                     size=3)
test_PCG64_lognormal(1234,
                     np.array(
                         [0.44760828, 3.43528508, 2.38903925, 271.13563493]),
                     mean=np.array([0.8, 1.17, 0.13, 5.45]))
test_PCG64_lognormal(1234,
                     np.array(
                         [0.05574842, 26.57557603, 1.10110681, 2.67618908]),
                     sigma=np.array([1.8, 51.17, 0.13, 6.45]))

@test
def test_PCG64_logseries(seed, p, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_logseries(457, 0.35, 1)
test_PCG64_logseries(2000, 0, 1)
test_PCG64_logseries(457, 0.5, np.array([2., 3., 2., 1.]), 4)
test_PCG64_logseries(457, np.array([0.01, 0.6, 0.45, 0.33]),
                     np.array([1, 1, 3, 2]))

@test
def test_PCG64_multinomial(seed, n, pvals, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    assert (round(gen.multinomial(n, pvals, size), 8) == expected).all()

test_PCG64_multinomial(457, 20, [1 / 6.], np.array([20]))
test_PCG64_multinomial(457, 20.6, [1 / 6.], np.array([20]))
test_PCG64_multinomial(457, 20, [1 / 6.] * 6, np.array([3, 5, 1, 4, 2, 5]))
test_PCG64_multinomial(
    457, 20, [1 / 6] * 6,
    np.array([[3, 5, 1, 4, 2, 5], [5, 4, 3, 6, 0, 2], [4, 2, 2, 3, 6, 3]]), 3)
#test_PCG64_multinomial(457, 1, np.array([[.1, .5, .4 ], [.3, .7, .0]]), np.array([[0, 0, 1], [0, 1, 0]]))
#test_PCG64_multinomial(457, -1, [1/6.], 0)

@test
def test_PCG64_multivariate_hypergeometric(seed,
                                           colors,
                                           nsample,
                                           expected,
                                           size=None,
                                           method='marginals'):
    gen = rnd.Generator(rnd.PCG64(seed))
    assert (gen.multivariate_hypergeometric(colors, nsample, size,
                                            method) == expected).all()

test_PCG64_multivariate_hypergeometric(457, np.array([16, 8, 4]), 15,
                                       np.array([8, 5, 2]))
test_PCG64_multivariate_hypergeometric(457,
                                       np.array([16, 8, 4]),
                                       15,
                                       np.array([[8, 5, 2], [9, 4, 2],
                                                 [8, 5, 2]]),
                                       size=3)
test_PCG64_multivariate_hypergeometric(457, np.array([16, 8, 4]), 0,
                                       np.array([0, 0, 0]))
test_PCG64_multivariate_hypergeometric(457,
                                       np.array([16, 8, 4]),
                                       8,
                                       np.array([4, 3, 1]),
                                       method='count')

@test
def test_PCG64_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.PCG64(seed))
    assert (round(
        gen.multivariate_normal(mean,
                                cov,
                                size,
                                check_valid=check_valid,
                                tol=tol,
                                method=m), 8) == expected).all()

test_PCG64_multivariate_normal(1234, (1, 2), np.array([[1, 0], [0, 1]]),
                               np.array([-0.60383681, 2.06409991]))
test_PCG64_multivariate_normal(5432, (1, 2),
                               np.array([[1, 0], [0, 1]]),
                               np.array([1.46547576, 1.68314218]),
                               m='cholesky')

@test
def test_PCG64_negative_binomial(seed, n, p, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_negative_binomial(139, 28400, .66, 14407)
test_PCG64_negative_binomial(139, 14.76, .33, 17)
#test_PCG64_negative_binomial(139, 14, 0, error)
test_PCG64_negative_binomial(147, 10, .5,
                             np.array([6, 1, 7, 14, 6, 8, 10, 5, 8, 12]), 10)
test_PCG64_negative_binomial(457, np.array([12, 1, 4, 7, 8]), 0.11,
                             np.array([75, 4, 8, 40, 64]))
test_PCG64_negative_binomial(457, 12, np.array([0.11, 0.21, 0.32, 0.43]),
                             np.array([[75, 30, 19, 28]]))

@test
def test_PCG64_noncentral_chisquare(seed, df, nonc, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_noncentral_chisquare(457, 0.1, 0.2, 0.061552060427800606)
test_PCG64_noncentral_chisquare(457, 5, 0, 4.130095429396157)
test_PCG64_noncentral_chisquare(457, 99, 7, 109.29213518232945)
test_PCG64_noncentral_chisquare(
    457, 14.2, 0.5, np.array([14.93361759, 16.34617163, 11.73437051]), 3)
test_PCG64_noncentral_chisquare(457, np.array([8, 7.1, 45.3]), 0.6,
                                np.array([9.06347808, 8.17895956, 41.9335875]))
test_PCG64_noncentral_chisquare(
    457, 0.6, np.array([8, 7.1, 45.3]),
    np.array([2.07706000, 16.39779665, 54.01862096]))

@test
def test_PCG64_noncentral_f(seed, dfnum, dfden, nonc, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_noncentral_f(874, 3, 20, 3, 0.9479155644253658)
test_PCG64_noncentral_f(874, 3, 20, 0, 2.563595388305448)
test_PCG64_noncentral_f(
    874, np.array([14, 15, 16, 17, 18]), 4.21, 6.7,
    np.array([1.41231957, 1.18112963, 2.53826028, 1.35162988, 4.92394815]))
test_PCG64_noncentral_f(
    874, 27, 5.92, 3.1,
    np.array([1.16488560, 0.84874875, 1.91535224, 1.22666518, 2.94041668]), 5)
test_PCG64_noncentral_f(
    874, 27, np.array([5.92, 8.13, 9.53, 33.14]), 3.1,
    np.array([1.16488560, 0.86567470, 1.67032973, 1.38249851]))
test_PCG64_noncentral_f(
    874, 743, 8.24, np.array([0.11, 0.21, 0.32, 0.43]),
    np.array([0.95109146, 0.79144400, 1.53122749, 0.87951980]))

@test
def test_PCG64_normal(seed, expected, loc=0.0, scale=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_normal(1234, -1.6038368053963015)
test_PCG64_normal(1234, 10.396163194603698, 12)
test_PCG64_normal(1234, -195.66809025834877, scale=122)
test_PCG64_normal(1234,
                  np.array([-1.60383681, 0.06409991, 0.74089130]),
                  size=3)
test_PCG64_normal(1234,
                  np.array([-0.80383681, 1.23409991, 0.87089130,
                            265.60261919]),
                  loc=np.array([0.8, 1.17, 0.13, 265.45]))
test_PCG64_normal(1234,
                  np.array([-18.92527430, 3.27999260, 44.54979362,
                            0.98439380]),
                  scale=np.array([11.8, 51.17, 60.13, 6.45]))

@test
def test_PCG64_pareto(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_pareto(1234, 3, 0.66181450621784)
test_PCG64_pareto(
    1234, 5,
    np.array([0.35628053, 0.15364923, 0.43703957, 0.17654679, 0.21708714]), 5)
test_PCG64_pareto(
    1234, np.array([3, 13, 4, 23.2, 5.6]),
    np.array([0.66181451, 0.05651224, 0.57338827, 0.03566071, 0.19173602]))

@test
def test_PCG64_poisson(seed, expected, lam=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_poisson(1234, 0, 0)
test_PCG64_poisson(1234, 2)
test_PCG64_poisson(1234, 4, 3)
test_PCG64_poisson(1234, 21, 14.65)
test_PCG64_poisson(1234, np.array([46., 11., 8., 78., 18.]),
                   np.array([35.62, 9.23, 9.57, 76.79, 21.74]))
test_PCG64_poisson(1234, np.array([875., 809., 801., 904., 819.]), 824.14, 5)

@test
def test_PCG64_power(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    if size is None:
        if isinstance(a, float) or isinstance(a, int):
            assert np.isclose(gen.power(a), expected)
        else:
            assert (round(gen.power(a), 8) == expected).all()
    else:
        assert (round(gen.power(a, size), 8) == expected).all()

test_PCG64_power(1234, 1, 0.7821024420483678)
test_PCG64_power(1234, 45.11, 0.9945665870769975)
test_PCG64_power(1234, 6, np.array([0.95986600, 0.89402992, 0.97074617]), 3)
test_PCG64_power(
    1234, np.array([5.4, 1.1, 78, 19.34, 99999999999999]),
    np.array(
        array([0.95550730, 0.54280964, 0.99771874, 0.97014457, 1.00000000])))

@test
def test_PCG64_rayleigh(seed, expected, scale=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_rayleigh(1234, error, -1)
test_PCG64_rayleigh(1234, 1.7456977082484888)
test_PCG64_rayleigh(1234, 5.237093124745466, 3)
test_PCG64_rayleigh(1234, 25.57447142584036, 14.65)
test_PCG64_rayleigh(1234,
                    np.array([1.74569771, 1.19553403, 1.90416686, 1.27508312]),
                    size=4)
test_PCG64_rayleigh(
    1234,
    np.array([9.42676762, 1.31508743, 148.52501510, 24.66010757, 78.77235752]),
    np.array([5.4, 1.1, 78, 19.34, 56.2]))

@test
def test_PCG64_standard_cauchy(seed, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    if size is None:
        assert gen.standard_cauchy() == expected
    else:
        assert (round(gen.standard_cauchy(size), 8) == round(expected,
                                                             8)).all()

test_PCG64_standard_cauchy(1234, -25.020888566279826)
test_PCG64_standard_cauchy(
    1234, np.array([-25.02088857, 4.85450931, 0.29650342, -1.56410960]), 4)

@test
def test_PCG64_standard_exponential(seed, expected, size=None, m='zig'):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_standard_exponential(1234, 1.5237302442920129)
test_PCG64_standard_exponential(
    1234, np.array([1.52373024, 0.71465080, 1.81292572, 0.81291848]), 4)
test_PCG64_standard_exponential(1234, 3.75929190550534, m='inv')

@test
def test_PCG64_standard_gamma(seed, shape, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_standard_gamma(318, 1, 1.5074447843195276)
test_PCG64_standard_gamma(1234, 0, 0)
test_PCG64_standard_gamma(1234, 0.5, 2.0649771071013165)
test_PCG64_standard_gamma(1234, 3, 0.8114738755097568)
test_PCG64_standard_gamma(
    1234, 0.5, np.array([2.06497711, 0.10182293, 0.05845094, 3.30020422]), 4)
test_PCG64_standard_gamma(
    1234, np.array([4.5, 2, 7.7, 81.15]),
    np.array([1.67542406, 2.81779395, 9.96848401, 68.23797589]))

@test
def test_PCG64_standard_normal(seed, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    if size is None:
        assert gen.standard_normal(size) == expected
    else:
        assert (round(gen.standard_normal(size), 8) == expected).all()

test_PCG64_standard_normal(1234, -1.6038368053963015)
test_PCG64_standard_normal(1234,
                           np.array([-1.60383681, 0.06409991, 0.74089130]), 3)

@test
def test_PCG64_standard_t(seed, df, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_standard_t(1234, 3, -1.7659223883243176)
test_PCG64_standard_t(1234, 1, -2.982894800105467)
test_PCG64_standard_t(1234, 0.5, -5.547734880855075)
test_PCG64_standard_t(1234, 0.5,
                      np.array([-5.54773488, 0.74943429, -7.28743863]), 3)
test_PCG64_standard_t(1234, np.array([99, 5.6, 11.43]),
                      np.array([-1.60193665, 0.12632228, -1.25887551]))

@test
def test_PCG64_triangular(seed, left, mode, right, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_triangular(1234, -3, 0, 8, 6.56807104556005)
test_PCG64_triangular(1234, np.array([-1, -4.2, -3.2]), -0.2, 77,
                      np.array([65.15497215, 14.66758622, 55.20054270]))
test_PCG64_triangular(
    1234, -4, np.array([0.1, 0.5, 3, 6, 7.9]), 8,
    np.array([6.51377589, 0.53123947, 5.85402098, 1.60384608, 2.75033776]))
test_PCG64_triangular(1234, -77, 0, np.array([0.1, 11, 789]),
                      np.array([-0.85294815, -26.24365753, 559.99377111]))
test_PCG64_triangular(
    1234,
    2,
    10,
    53,
    np.array([45.85175465, 16.13225321, 40.02614131, 12.76185250]),
    size=4)

@test
def test_PCG64_uniform(seed, expected, low=0.0, high=1.0, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_uniform(1234, 14.766997666981421, 5, 15)
test_PCG64_uniform(1234, 0.9790297900283279, 0.1)
test_PCG64_uniform(1234, 119.15737153717335, high=122)
test_PCG64_uniform(1234,
                   np.array([0.97669977, 0.38019574, 0.92324623]),
                   size=3)
test_PCG64_uniform(1234,
                   np.array(
                       [43.96080959, 18.96822087, 42.00660312, 16.94431211]),
                   low=np.array([0.4, 3., 6., 7.]),
                   high=45.)
test_PCG64_uniform(
    1234,
    np.array([54.69518694, 6.08313176, 40.62283429, 0.81124651, 0.06381941]),
    high=np.array([56., 16., 44., 3.1, 0.2]))

@test
def test_PCG64_vonmises(seed, mu, kappa, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(seed))
    if size is None:
        if (isinstance(mu, float) or isinstance(mu, int)) and (isinstance(
                kappa, float) or isinstance(kappa, int)):
            assert np.isclose(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_PCG64_vonmises(1234, 0, 4, -0.27173852171597135)
test_PCG64_vonmises(1234, 0.1, 0.2, -3.130535859602788)
test_PCG64_vonmises(1234, -1, 0, 2.9951929700537034)
test_PCG64_vonmises(1234,
                    0,
                    5,
                    np.array([-0.24351687, 0.19570655, 0.75861058]),
                    size=3)
test_PCG64_vonmises(
    1234, np.array([43.09, 18.62, 42, 16.94]), 5,
    np.array([-1.13581402, -0.03384937, -1.22368657, -1.74692310]))
test_PCG64_vonmises(
    1234, 4, np.array([0, 0.1, 7.67, 99]),
    np.array([2.99519297, 2.89617923, -2.48027841, -2.23901495]))

@test
def test_PCG64_wald(seed, mean, scale, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_wald(1234, 0.1, 0.1, 0.023030951620822665)
test_PCG64_wald(1234, 3, 2, 0.527940495568358)
test_PCG64_wald(1234, 3, 2, np.array([0.52794050, 1.24578624, 1.08842697]), 3)
test_PCG64_wald(1234, np.array([0.1, 0.5, 1, 4]), 0.5,
                np.array([0.04952415, 0.24218309, 0.31472446, 0.20571985]))
test_PCG64_wald(1234, 0.5, np.array([0.1, 0.5, 1, 4]),
                np.array([0.03379779, 0.24218309, 0.27395511, 0.29813439]))
test_PCG64_wald(1234, np.array([0.1, 0.5, 1, 4]), np.array([3, 2, 1, 8]),
                np.array([0.07469306, 0.34593420, 0.43217476, 1.46696504]))

@test
def test_PCG64_weibull(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_weibull(1234, 0, 0)
test_PCG64_weibull(1234, 0.1, 67.46536569583394)
test_PCG64_weibull(1234, 1, 1.5237302442920129)
test_PCG64_weibull(1234, 1, np.array([1.52373024, 0.71465080, 1.81292572]), 3)
test_PCG64_weibull(1234, np.array([17.8, 4.32]),
                   np.array([1.02394289, 0.92517830]))

@test
def test_PCG64_zipf(seed, a, expected, size=None):
    gen = rnd.Generator(rnd.PCG64(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_PCG64_zipf(1234, 1.1, 21202999555633256)
test_PCG64_zipf(1234, 3, 6)
test_PCG64_zipf(1234, 45, np.array([1, 1, 1]), 3)
test_PCG64_zipf(1234, np.array([17.8, 4.32]), np.array([1, 2]))