from sympy.core.numbers import oo
from sympy.core.symbol import Symbol
from sympy.functions.elementary.exponential import exp
from sympy.sets.sets import Interval
from sympy.external import import_module
from sympy.stats import Beta, Chi, Normal, Gamma, Exponential, LogNormal, Pareto, ChiSquared, Uniform, sample, \
    BetaPrime, Cauchy, GammaInverse, GaussianInverse, StudentT, Weibull, density, ContinuousRV, FDistribution, \
    Gumbel, Laplace, Logistic, Rayleigh, Triangular
from sympy.testing.pytest import skip, raises


def test_sample_numpy():
    distribs_numpy = [
        Beta("B", 1, 1),
        Normal("N", 0, 1),
        Gamma("G", 2, 7),
        Exponential("E", 2),
        LogNormal("LN", 0, 1),
        Pareto("P", 1, 1),
        ChiSquared("CS", 2),
        Uniform("U", 0, 1),
        FDistribution("FD", 1, 2),
        Gumbel("GB", 1, 2),
        Laplace("L", 1, 2),
        Logistic("LO", 1, 2),
        Rayleigh("R", 1),
        Triangular("T", 1, 2, 2),
    ]
    size = 3
    numpy = import_module('numpy')
    if not numpy:
        skip('Numpy is not installed. Abort tests for _sample_numpy.')
    else:
        for X in distribs_numpy:
            samps = sample(X, size=size, library='numpy')
            for sam in samps:
                assert sam in X.pspace.domain.set
        raises(NotImplementedError,
               lambda: sample(Chi("C", 1), library='numpy'))
    raises(NotImplementedError,
           lambda: Chi("C", 1).pspace.distribution.sample(library='tensorflow'))


def test_sample_scipy():
    distribs_scipy = [
        Beta("B", 1, 1),
        BetaPrime("BP", 1, 1),
        Cauchy("C", 1, 1),
        Chi("C", 1),
        Normal("N", 0, 1),
        Gamma("G", 2, 7),
        GammaInverse("GI", 1, 1),
        GaussianInverse("GUI", 1, 1),
        Exponential("E", 2),
        LogNormal("LN", 0, 1),
        Pareto("P", 1, 1),
        StudentT("S", 2),
        ChiSquared("CS", 2),
        Uniform("U", 0, 1)
    ]
    size = 3
    scipy = import_module('scipy')
    if not scipy:
        skip('Scipy is not installed. Abort tests for _sample_scipy.')
    else:
        for X in distribs_scipy:
            samps = sample(X, size=size, library='scipy')
            samps2 = sample(X, size=(2, 2), library='scipy')
            for sam in samps:
                assert sam in X.pspace.domain.set
            for i in range(2):
                for j in range(2):
                    assert samps2[i][j] in X.pspace.domain.set


def test_sample_pymc():
    distribs_pymc = [
        Beta("B", 1, 1),
        Cauchy("C", 1, 1),
        Normal("N", 0, 1),
        Gamma("G", 2, 7),
        GaussianInverse("GI", 1, 1),
        Exponential("E", 2),
        LogNormal("LN", 0, 1),
        Pareto("P", 1, 1),
        ChiSquared("CS", 2),
        Uniform("U", 0, 1)
    ]
    size = 3
    pymc = import_module('pymc')
    if not pymc:
        skip('PyMC is not installed. Abort tests for _sample_pymc.')
    else:
        for X in distribs_pymc:
            samps = sample(X, size=size, library='pymc')
            for sam in samps:
                assert sam in X.pspace.domain.set
        raises(NotImplementedError,
               lambda: sample(Chi("C", 1), library='pymc'))


def METHOD_NAME():
    scipy = import_module('scipy')
    if not scipy:
        skip('Scipy not installed. Abort tests for sampling of gamma inverse.')
    X = GammaInverse("x", 1, 1)
    assert sample(X) in X.pspace.domain.set


def test_lognormal_sampling():
    # Right now, only density function and sampling works
    scipy = import_module('scipy')
    if not scipy:
        skip('Scipy is not installed. Abort tests')
    for i in range(3):
        X = LogNormal('x', i, 1)
        assert sample(X) in X.pspace.domain.set

    size = 5
    samps = sample(X, size=size)
    for samp in samps:
        assert samp in X.pspace.domain.set


def test_sampling_gaussian_inverse():
    scipy = import_module('scipy')
    if not scipy:
        skip('Scipy not installed. Abort tests for sampling of Gaussian inverse.')
    X = GaussianInverse("x", 1, 1)
    assert sample(X, library='scipy') in X.pspace.domain.set


def test_prefab_sampling():
    scipy = import_module('scipy')
    if not scipy:
        skip('Scipy is not installed. Abort tests')
    N = Normal('X', 0, 1)
    L = LogNormal('L', 0, 1)
    E = Exponential('Ex', 1)
    P = Pareto('P', 1, 3)
    W = Weibull('W', 1, 1)
    U = Uniform('U', 0, 1)
    B = Beta('B', 2, 5)
    G = Gamma('G', 1, 3)

    variables = [N, L, E, P, W, U, B, G]
    niter = 10
    size = 5
    for var in variables:
        for _ in range(niter):
            assert sample(var) in var.pspace.domain.set
            samps = sample(var, size=size)
            for samp in samps:
                assert samp in var.pspace.domain.set


def test_sample_continuous():
    z = Symbol('z')
    Z = ContinuousRV(z, exp(-z), set=Interval(0, oo))
    assert density(Z)(-1) == 0

    scipy = import_module('scipy')
    if not scipy:
        skip('Scipy is not installed. Abort tests')
    assert sample(Z) in Z.pspace.domain.set
    sym, val = list(Z.pspace.sample().items())[0]
    assert sym == Z and val in Interval(0, oo)

    libraries = ['scipy', 'numpy', 'pymc']
    for lib in libraries:
        try:
            imported_lib = import_module(lib)
            if imported_lib:
                s0, s1, s2 = [], [], []
                s0 = sample(Z, size=10, library=lib, seed=0)
                s1 = sample(Z, size=10, library=lib, seed=0)
                s2 = sample(Z, size=10, library=lib, seed=1)
                assert all(s0 == s1)
                assert all(s1 != s2)
        except NotImplementedError:
            continue