"""Tests for module da on Domain Adaptation """

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

import numpy as np
from numpy.testing import assert_allclose, assert_equal
import pytest

import ot
from ot.datasets import make_data_classif
from ot.utils import unif

try:  # test if cudamat installed
    import sklearn  # noqa: F401
    nosklearn = False
except ImportError:
    nosklearn = True


def test_class_jax_tf():
    backends = []
    from ot.backend import jax, tf
    if jax:
        backends.append(ot.backend.JaxBackend())
    if tf:
        backends.append(ot.backend.TensorflowBackend())

    for nx in backends:
        ns = 150
        nt = 200

        Xs, ys = make_data_classif('3gauss', ns)
        Xt, yt = make_data_classif('3gauss2', nt)

        Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

        otda = ot.da.SinkhornLpl1Transport()

        with pytest.raises(TypeError):
            otda.fit(Xs=Xs, ys=ys, Xt=Xt)


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
@pytest.mark.parametrize("class_to_test", [ot.da.EMDTransport, ot.da.SinkhornTransport, ot.da.SinkhornLpl1Transport, ot.da.SinkhornL1l2Transport, ot.da.SinkhornL1l2Transport])
def test_log_da(nx, class_to_test):

    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

    otda = class_to_test(log=True)

    # test its computed
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
    assert hasattr(otda, "log_")


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_sinkhorn_lpl1_transport_class(nx):
    """test_sinkhorn_transport
    """

    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

    otda = ot.da.SinkhornLpl1Transport()

    # test its computed
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
    assert hasattr(otda, "cost_")
    assert hasattr(otda, "coupling_")

    # test dimensions of coupling
    assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))

    # test margin constraints
    mu_s = unif(ns)
    mu_t = unif(nt)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3)

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    assert_equal(transp_Xs.shape, Xs.shape)

    Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0])
    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # test inverse transform
    transp_Xt = otda.inverse_transform(Xt=Xt)
    assert_equal(transp_Xt.shape, Xt.shape)

    Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0])
    transp_Xt_new = otda.inverse_transform(Xt=Xt_new)

    # check that the oos method is working
    assert_equal(transp_Xt_new.shape, Xt_new.shape)

    # test fit_transform
    transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt)
    assert_equal(transp_Xs.shape, Xs.shape)

    # check label propagation
    transp_yt = otda.transform_labels(ys)
    assert_equal(transp_yt.shape[0], yt.shape[0])
    assert_equal(transp_yt.shape[1], len(np.unique(ys)))

    # check inverse label propagation
    transp_ys = otda.inverse_transform_labels(yt)
    assert_equal(transp_ys.shape[0], ys.shape[0])
    assert_equal(transp_ys.shape[1], len(np.unique(yt)))

    # test unsupervised vs semi-supervised mode
    otda_unsup = ot.da.SinkhornLpl1Transport()
    otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
    n_unsup = nx.sum(otda_unsup.cost_)

    otda_semi = ot.da.SinkhornLpl1Transport()
    otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
    assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    n_semisup = nx.sum(otda_semi.cost_)

    # check that the cost matrix norms are indeed different
    assert n_unsup != n_semisup, "semisupervised mode not working"

    # check that the coupling forbids mass transport between labeled source
    # and labeled target samples
    mass_semi = nx.sum(
        otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max])
    assert mass_semi == 0, "semisupervised mode not working"


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_sinkhorn_l1l2_transport_class(nx):
    """test_sinkhorn_transport
    """

    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

    otda = ot.da.SinkhornL1l2Transport()

    # test its computed
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
    assert hasattr(otda, "cost_")
    assert hasattr(otda, "coupling_")
    assert hasattr(otda, "log_")

    # test dimensions of coupling
    assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))

    # test margin constraints
    mu_s = unif(ns)
    mu_t = unif(nt)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3)

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    assert_equal(transp_Xs.shape, Xs.shape)

    Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0])
    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # test inverse transform
    transp_Xt = otda.inverse_transform(Xt=Xt)
    assert_equal(transp_Xt.shape, Xt.shape)

    # check label propagation
    transp_yt = otda.transform_labels(ys)
    assert_equal(transp_yt.shape[0], yt.shape[0])
    assert_equal(transp_yt.shape[1], len(np.unique(ys)))

    # check inverse label propagation
    transp_ys = otda.inverse_transform_labels(yt)
    assert_equal(transp_ys.shape[0], ys.shape[0])
    assert_equal(transp_ys.shape[1], len(np.unique(yt)))

    Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0])
    transp_Xt_new = otda.inverse_transform(Xt=Xt_new)

    # check that the oos method is working
    assert_equal(transp_Xt_new.shape, Xt_new.shape)

    # test fit_transform
    transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt)
    assert_equal(transp_Xs.shape, Xs.shape)

    # test unsupervised vs semi-supervised mode
    otda_unsup = ot.da.SinkhornL1l2Transport()
    otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
    n_unsup = nx.sum(otda_unsup.cost_)

    otda_semi = ot.da.SinkhornL1l2Transport()
    otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
    assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    n_semisup = nx.sum(otda_semi.cost_)

    # check that the cost matrix norms are indeed different
    assert n_unsup != n_semisup, "semisupervised mode not working"

    # check that the coupling forbids mass transport between labeled source
    # and labeled target samples
    mass_semi = nx.sum(
        otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max])
    mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]
    assert_allclose(nx.to_numpy(mass_semi), np.zeros(list(mass_semi.shape)),
                    rtol=1e-9, atol=1e-9)

    # check everything runs well with log=True
    otda = ot.da.SinkhornL1l2Transport(log=True)
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
    assert len(otda.log_.keys()) != 0


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_sinkhorn_transport_class(nx):
    """test_sinkhorn_transport
    """

    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

    otda = ot.da.SinkhornTransport()

    # test its computed
    otda.fit(Xs=Xs, Xt=Xt)
    assert hasattr(otda, "cost_")
    assert hasattr(otda, "coupling_")
    assert hasattr(otda, "log_")

    # test dimensions of coupling
    assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))

    # test margin constraints
    mu_s = unif(ns)
    mu_t = unif(nt)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3)

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    assert_equal(transp_Xs.shape, Xs.shape)

    Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0])
    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # test inverse transform
    transp_Xt = otda.inverse_transform(Xt=Xt)
    assert_equal(transp_Xt.shape, Xt.shape)

    # check label propagation
    transp_yt = otda.transform_labels(ys)
    assert_equal(transp_yt.shape[0], yt.shape[0])
    assert_equal(transp_yt.shape[1], len(np.unique(ys)))

    # check inverse label propagation
    transp_ys = otda.inverse_transform_labels(yt)
    assert_equal(transp_ys.shape[0], ys.shape[0])
    assert_equal(transp_ys.shape[1], len(np.unique(yt)))

    Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0])
    transp_Xt_new = otda.inverse_transform(Xt=Xt_new)

    # check that the oos method is working
    assert_equal(transp_Xt_new.shape, Xt_new.shape)

    # test fit_transform
    transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt)
    assert_equal(transp_Xs.shape, Xs.shape)

    # test unsupervised vs semi-supervised mode
    otda_unsup = ot.da.SinkhornTransport()
    otda_unsup.fit(Xs=Xs, Xt=Xt)
    n_unsup = nx.sum(otda_unsup.cost_)

    otda_semi = ot.da.SinkhornTransport()
    otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
    assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    n_semisup = nx.sum(otda_semi.cost_)

    # check that the cost matrix norms are indeed different
    assert n_unsup != n_semisup, "semisupervised mode not working"

    # check that the coupling forbids mass transport between labeled source
    # and labeled target samples
    mass_semi = nx.sum(
        otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max])
    assert mass_semi == 0, "semisupervised mode not working"

    # check everything runs well with log=True
    otda = ot.da.SinkhornTransport(log=True)
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
    assert len(otda.log_.keys()) != 0


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_unbalanced_sinkhorn_transport_class(nx):
    """test_sinkhorn_transport
    """

    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

    otda = ot.da.UnbalancedSinkhornTransport()

    # test its computed
    otda.fit(Xs=Xs, Xt=Xt)
    assert hasattr(otda, "cost_")
    assert hasattr(otda, "coupling_")
    assert hasattr(otda, "log_")

    # test dimensions of coupling
    assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    assert_equal(transp_Xs.shape, Xs.shape)

    # check label propagation
    transp_yt = otda.transform_labels(ys)
    assert_equal(transp_yt.shape[0], yt.shape[0])
    assert_equal(transp_yt.shape[1], len(np.unique(ys)))

    # check inverse label propagation
    transp_ys = otda.inverse_transform_labels(yt)
    assert_equal(transp_ys.shape[0], ys.shape[0])
    assert_equal(transp_ys.shape[1], len(np.unique(yt)))

    Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0])
    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # test inverse transform
    transp_Xt = otda.inverse_transform(Xt=Xt)
    assert_equal(transp_Xt.shape, Xt.shape)

    Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0])
    transp_Xt_new = otda.inverse_transform(Xt=Xt_new)

    # check that the oos method is working
    assert_equal(transp_Xt_new.shape, Xt_new.shape)

    # test fit_transform
    transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt)
    assert_equal(transp_Xs.shape, Xs.shape)

    # test unsupervised vs semi-supervised mode
    otda_unsup = ot.da.SinkhornTransport()
    otda_unsup.fit(Xs=Xs, Xt=Xt)
    n_unsup = nx.sum(otda_unsup.cost_)

    otda_semi = ot.da.SinkhornTransport()
    otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
    assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    n_semisup = nx.sum(otda_semi.cost_)

    # check that the cost matrix norms are indeed different
    assert n_unsup != n_semisup, "semisupervised mode not working"

    # check everything runs well with log=True
    otda = ot.da.SinkhornTransport(log=True)
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)
    assert len(otda.log_.keys()) != 0


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_emd_transport_class(nx):
    """test_sinkhorn_transport
    """

    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

    otda = ot.da.EMDTransport()

    # test its computed
    otda.fit(Xs=Xs, Xt=Xt)
    assert hasattr(otda, "cost_")
    assert hasattr(otda, "coupling_")

    # test dimensions of coupling
    assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))

    # test margin constraints
    mu_s = unif(ns)
    mu_t = unif(nt)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3)

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    assert_equal(transp_Xs.shape, Xs.shape)

    Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0])
    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # test inverse transform
    transp_Xt = otda.inverse_transform(Xt=Xt)
    assert_equal(transp_Xt.shape, Xt.shape)

    # check label propagation
    transp_yt = otda.transform_labels(ys)
    assert_equal(transp_yt.shape[0], yt.shape[0])
    assert_equal(transp_yt.shape[1], len(np.unique(ys)))

    # check inverse label propagation
    transp_ys = otda.inverse_transform_labels(yt)
    assert_equal(transp_ys.shape[0], ys.shape[0])
    assert_equal(transp_ys.shape[1], len(np.unique(yt)))

    Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0])
    transp_Xt_new = otda.inverse_transform(Xt=Xt_new)

    # check that the oos method is working
    assert_equal(transp_Xt_new.shape, Xt_new.shape)

    # test fit_transform
    transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt)
    assert_equal(transp_Xs.shape, Xs.shape)

    # test unsupervised vs semi-supervised mode
    otda_unsup = ot.da.EMDTransport()
    otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt)
    n_unsup = nx.sum(otda_unsup.cost_)

    otda_semi = ot.da.EMDTransport()
    otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt)
    assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0])))
    n_semisup = nx.sum(otda_semi.cost_)

    # check that the cost matrix norms are indeed different
    assert n_unsup != n_semisup, "semisupervised mode not working"

    # check that the coupling forbids mass transport between labeled source
    # and labeled target samples
    mass_semi = nx.sum(
        otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max])
    mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]

    # we need to use a small tolerance here, otherwise the test breaks
    assert_allclose(nx.to_numpy(mass_semi), np.zeros(list(mass_semi.shape)),
                    rtol=1e-2, atol=1e-2)


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
@pytest.mark.parametrize("kernel", ["linear", "gaussian"])
@pytest.mark.parametrize("bias", ["unbiased", "biased"])
def test_mapping_transport_class(nx, kernel, bias):
    """test_mapping_transport
    """

    ns = 20
    nt = 30

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)
    Xs_new, _ = make_data_classif('3gauss', ns + 1)

    Xs, Xt, Xs_new = nx.from_numpy(Xs, Xt, Xs_new)

    # Mapping tests
    bias = bias == "biased"
    otda = ot.da.MappingTransport(kernel=kernel, bias=bias)
    otda.fit(Xs=Xs, Xt=Xt)
    assert hasattr(otda, "coupling_")
    assert hasattr(otda, "mapping_")
    assert hasattr(otda, "log_")

    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))
    S = Xs.shape[0] if kernel == "gaussian" else Xs.shape[1]  # if linear
    if bias:
        S += 1
    assert_equal(otda.mapping_.shape, ((S, Xt.shape[1])))

    # test margin constraints
    mu_s = unif(ns)
    mu_t = unif(nt)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3)

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    assert_equal(transp_Xs.shape, Xs.shape)

    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # check everything runs well with log=True
    otda = ot.da.MappingTransport(kernel=kernel, bias=bias, log=True)
    otda.fit(Xs=Xs, Xt=Xt)
    assert len(otda.log_.keys()) != 0


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_mapping_transport_class_specific_seed(nx):
    # check that it does not crash when derphi is very close to 0
    ns = 20
    nt = 30
    np.random.seed(39)
    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)
    otda = ot.da.MappingTransport(kernel="gaussian", bias=False)
    otda.fit(Xs=nx.from_numpy(Xs), Xt=nx.from_numpy(Xt))
    np.random.seed(None)


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_linear_mapping_class(nx):
    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xsb, Xtb = nx.from_numpy(Xs, Xt)

    otmap = ot.da.LinearTransport()

    otmap.fit(Xs=Xsb, Xt=Xtb)
    assert hasattr(otmap, "A_")
    assert hasattr(otmap, "B_")
    assert hasattr(otmap, "A1_")
    assert hasattr(otmap, "B1_")

    Xst = nx.to_numpy(otmap.transform(Xs=Xsb))

    Ct = np.cov(Xt.T)
    Cst = np.cov(Xst.T)

    np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2)


@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_jcpot_transport_class(nx):
    """test_jcpot_transport
    """

    ns1 = 50
    ns2 = 50
    nt = 50

    Xs1, ys1 = make_data_classif('3gauss', ns1)
    Xs2, ys2 = make_data_classif('3gauss', ns2)

    Xt, yt = make_data_classif('3gauss2', nt)

    Xs1, ys1, Xs2, ys2, Xt, yt = nx.from_numpy(Xs1, ys1, Xs2, ys2, Xt, yt)

    Xs = [Xs1, Xs2]
    ys = [ys1, ys2]

    otda = ot.da.JCPOTTransport(reg_e=1, max_iter=10000, tol=1e-9, verbose=True, log=True)

    # test its computed
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)

    assert hasattr(otda, "coupling_")
    assert hasattr(otda, "proportions_")
    assert hasattr(otda, "log_")

    # test dimensions of coupling
    for i, xs in enumerate(Xs):
        assert_equal(otda.coupling_[i].shape, ((xs.shape[0], Xt.shape[0])))

    # test all margin constraints
    mu_t = unif(nt)

    for i in range(len(Xs)):
        # test margin constraints w.r.t. uniform target weights for each coupling matrix
        assert_allclose(
            nx.to_numpy(nx.sum(otda.coupling_[i], axis=0)), mu_t, rtol=1e-3, atol=1e-3)

        # test margin constraints w.r.t. modified source weights for each source domain

        assert_allclose(
            nx.to_numpy(
                nx.dot(otda.log_['D1'][i], nx.sum(otda.coupling_[i], axis=1))
            ),
            nx.to_numpy(otda.proportions_),
            rtol=1e-3,
            atol=1e-3
        )

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)]

    Xs_new = nx.from_numpy(make_data_classif('3gauss', ns1 + 1)[0])
    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # check label propagation
    transp_yt = otda.transform_labels(ys)
    assert_equal(transp_yt.shape[0], yt.shape[0])
    assert_equal(transp_yt.shape[1], len(np.unique(nx.to_numpy(*ys))))

    # check inverse label propagation
    transp_ys = otda.inverse_transform_labels(yt)
    for x, y in zip(transp_ys, ys):
        assert_equal(x.shape[0], y.shape[0])
        assert_equal(x.shape[1], len(np.unique(nx.to_numpy(y))))


def test_jcpot_barycenter(nx):
    """test_jcpot_barycenter
    """

    ns1 = 50
    ns2 = 50
    nt = 50

    sigma = 0.1
    np.random.seed(1985)

    ps1 = .2
    ps2 = .9
    pt = .4

    Xs1, ys1 = make_data_classif('2gauss_prop', ns1, nz=sigma, p=ps1)
    Xs2, ys2 = make_data_classif('2gauss_prop', ns2, nz=sigma, p=ps2)
    Xt, _ = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt)

    Xs1b, ys1b, Xs2b, ys2b, Xtb = nx.from_numpy(Xs1, ys1, Xs2, ys2, Xt)

    Xsb = [Xs1b, Xs2b]
    ysb = [ys1b, ys2b]

    prop = ot.bregman.jcpot_barycenter(Xsb, ysb, Xtb, reg=.5, metric='sqeuclidean',
                                       numItermax=10000, stopThr=1e-9, verbose=False, log=False)

    np.testing.assert_allclose(nx.to_numpy(prop), [1 - pt, pt], rtol=1e-3, atol=1e-3)


@pytest.mark.skipif(nosklearn, reason="No sklearn available")
@pytest.skip_backend("jax")
@pytest.skip_backend("tf")
def test_emd_laplace_class(nx):
    """test_emd_laplace_transport
    """
    ns = 50
    nt = 50

    Xs, ys = make_data_classif('3gauss', ns)
    Xt, yt = make_data_classif('3gauss2', nt)

    Xs, ys, Xt, yt = nx.from_numpy(Xs, ys, Xt, yt)

    otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True)

    # test its computed
    otda.fit(Xs=Xs, ys=ys, Xt=Xt)

    assert hasattr(otda, "coupling_")
    assert hasattr(otda, "log_")

    # test dimensions of coupling
    assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0])))

    # test all margin constraints
    mu_s = unif(ns)
    mu_t = unif(nt)

    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=0)), mu_t, rtol=1e-3, atol=1e-3)
    assert_allclose(
        nx.to_numpy(nx.sum(otda.coupling_, axis=1)), mu_s, rtol=1e-3, atol=1e-3)

    # test transform
    transp_Xs = otda.transform(Xs=Xs)
    [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)]

    Xs_new = nx.from_numpy(make_data_classif('3gauss', ns + 1)[0])
    transp_Xs_new = otda.transform(Xs_new)

    # check that the oos method is working
    assert_equal(transp_Xs_new.shape, Xs_new.shape)

    # test inverse transform
    transp_Xt = otda.inverse_transform(Xt=Xt)
    assert_equal(transp_Xt.shape, Xt.shape)

    Xt_new = nx.from_numpy(make_data_classif('3gauss2', nt + 1)[0])
    transp_Xt_new = otda.inverse_transform(Xt=Xt_new)

    # check that the oos method is working
    assert_equal(transp_Xt_new.shape, Xt_new.shape)

    # test fit_transform
    transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt)
    assert_equal(transp_Xs.shape, Xs.shape)

    # check label propagation
    transp_yt = otda.transform_labels(ys)
    assert_equal(transp_yt.shape[0], yt.shape[0])
    assert_equal(transp_yt.shape[1], len(np.unique(nx.to_numpy(ys))))

    # check inverse label propagation
    transp_ys = otda.inverse_transform_labels(yt)
    assert_equal(transp_ys.shape[0], ys.shape[0])
    assert_equal(transp_ys.shape[1], len(np.unique(nx.to_numpy(yt))))
