# Copyright 2019 the GPflow authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import numpy as np
import pytest
import tensorflow as tf

import gpflow
from gpflow.base import AnyNDArray
from gpflow.config import default_float

rng = np.random.RandomState(0)


class Datum:
    n_inputs = 1
    n_outputs = 2
    n_outputs_c = 1

    X: AnyNDArray = rng.rand(20, n_inputs) * 10
    Y = np.sin(X) + 0.9 * np.cos(X * 1.6) + rng.randn(*X.shape) * 0.8
    Y = np.tile(Y, n_outputs)  # identical columns
    Xtest: AnyNDArray = rng.rand(10, n_outputs) * 10
    data = (X, Y)

    # for classification:
    Yc = Y[:, :n_outputs_c]
    cdata = (X, Yc)


def test_vgp() -> None:
    X = tf.Variable(
        tf.zeros((1, Datum.n_inputs), dtype=default_float()), shape=(None, None), trainable=False
    )
    Y = tf.Variable(
        tf.zeros((1, Datum.n_outputs), dtype=default_float()), shape=(None, None), trainable=False
    )

    model = gpflow.models.VGP(
        (X, Y),
        gpflow.kernels.SquaredExponential(),
        gpflow.likelihoods.Gaussian(),
        num_latent_gps=Datum.n_outputs,
    )

    @tf.function
    def model_closure() -> tf.Tensor:
        return -model.elbo()

    model_closure()  # Trigger compilation.

    gpflow.models.vgp.update_vgp_data(model, (Datum.X, Datum.Y))
    opt = gpflow.optimizers.Scipy()

    # simply test whether it runs without erroring...:
    opt.minimize(
        model_closure,
        variables=model.trainable_variables,
        options=dict(maxiter=3),
        compile=True,
    )


@pytest.mark.parametrize("whiten", [True, False])
@pytest.mark.parametrize("q_diag", [True, False])
def test_svgp(whiten: bool, q_diag: bool) -> None:
    model = gpflow.models.SVGP(
        gpflow.kernels.SquaredExponential(),
        gpflow.likelihoods.Gaussian(),
        inducing_variable=Datum.X.copy(),
        q_diag=q_diag,
        whiten=whiten,
        mean_function=gpflow.mean_functions.Constant(),
        num_latent_gps=Datum.n_outputs,
    )
    gpflow.set_trainable(model.inducing_variable, False)

    # test with explicitly unknown shapes:
    tensor_spec = tf.TensorSpec(shape=None, dtype=default_float())
    elbo = tf.function(
        model.elbo,
        input_signature=[(tensor_spec, tensor_spec)],
    )

    @tf.function
    def model_closure() -> tf.Tensor:
        return -elbo(Datum.data)

    model_closure()  # Trigger compilation.

    opt = gpflow.optimizers.Scipy()

    # simply test whether it runs without erroring...:
    opt.minimize(
        model_closure,
        variables=model.trainable_variables,
        options=dict(maxiter=3),
        compile=True,
    )


def test_vgp_multiclass() -> None:
    X = tf.Variable(
        tf.zeros((1, Datum.n_inputs), dtype=default_float()), shape=(None, None), trainable=False
    )
    Yc = tf.Variable(
        tf.zeros((1, Datum.n_outputs_c), dtype=default_float()), shape=(None, None), trainable=False
    )

    num_classes = 3
    model = gpflow.models.VGP(
        (X, Yc),
        gpflow.kernels.SquaredExponential(),
        gpflow.likelihoods.MultiClass(num_classes=num_classes),
        num_latent_gps=num_classes,
    )

    @tf.function
    def model_closure() -> tf.Tensor:
        return -model.elbo()

    model_closure()  # Trigger compilation.

    gpflow.models.vgp.update_vgp_data(model, (Datum.X, Datum.Yc))
    opt = gpflow.optimizers.Scipy()

    # simply test whether it runs without erroring...:
    opt.minimize(
        model_closure,
        variables=model.trainable_variables,
        options=dict(maxiter=3),
        compile=True,
    )


def test_svgp_multiclass() -> None:
    num_classes = 3
    model = gpflow.models.SVGP(
        gpflow.kernels.SquaredExponential(),
        gpflow.likelihoods.MultiClass(num_classes=num_classes),
        inducing_variable=Datum.X.copy(),
        num_latent_gps=num_classes,
    )
    gpflow.set_trainable(model.inducing_variable, False)

    # test with explicitly unknown shapes:
    tensor_spec = tf.TensorSpec(shape=None, dtype=default_float())
    elbo = tf.function(
        model.elbo,
        input_signature=[(tensor_spec, tensor_spec)],
    )

    @tf.function
    def model_closure() -> tf.Tensor:
        return -elbo(Datum.cdata)

    model_closure()  # Trigger compilation.

    opt = gpflow.optimizers.Scipy()

    # simply test whether it runs without erroring...:
    opt.minimize(
        model_closure,
        variables=model.trainable_variables,
        options=dict(maxiter=3),
        compile=True,
    )