# Copyright 2016 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.from __future__ import print_function

from __future__ import print_function
import GPflow
import tensorflow as tf
import numpy as np
import unittest

rng = np.random.RandomState(0)


class Quadratic(GPflow.model.Model):
    def __init__(self):
        GPflow.model.Model.__init__(self)
        self.x = GPflow.param.Param(rng.randn(10))

    def build_likelihood(self):
        return -tf.reduce_sum(tf.square(self.x))


class TestOptimize(unittest.TestCase):
    def setUp(self):
        tf.reset_default_graph()
        rng = np.random.RandomState(0)

        class Quadratic(GPflow.model.Model):
            def __init__(self):
                GPflow.model.Model.__init__(self)
                self.x = GPflow.param.Param(rng.randn(10))

            def build_likelihood(self):
                return -tf.reduce_sum(tf.square(self.x))

        self.m = Quadratic()

    def test_adam(self):
        o = tf.train.AdamOptimizer()
        self.m.optimize(o, maxiter=5000)
        self.assertTrue(self.m.x.value.max() < 1e-2)

    def test_lbfgsb(self):
        self.m.optimize(disp=False)
        self.assertTrue(self.m.x.value.max() < 1e-6)

    def test_feval_counter(self):
        self.m._compile()
        self.m.num_fevals = 0
        for _ in range(10):
            self.m._objective(self.m.get_free_state())
        self.assertTrue(self.m.num_fevals == 10)


class TestNeedsRecompile(unittest.TestCase):
    def setUp(self):
        self.m = GPflow.model.Model()
        self.m.p = GPflow.param.Param(1.0)

    def test_fix(self):
        self.m._needs_recompile = False
        self.m.p.fixed = True
        self.assertTrue(self.m._needs_recompile)

    def test_replace_param(self):
        self.m._needs_recompile = False
        new_p = GPflow.param.Param(3.0)
        self.m.p = new_p
        self.assertTrue(self.m._needs_recompile)

    def test_set_prior(self):
        self.m._needs_recompile = False
        self.m.p.prior = GPflow.priors.Gaussian(0, 1)
        self.assertTrue(self.m._needs_recompile)

    def test_set_transform(self):
        self.m._needs_recompile = False
        self.m.p.transform = GPflow.transforms.Identity()
        self.assertTrue(self.m._needs_recompile)

    def test_replacement(self):
        m = GPflow.model.Model()
        m.p = GPflow.param.Parameterized()
        m.p.p = GPflow.param.Param(1.0)
        m._needs_recompile = False
        # replace Parameterized
        new_p = GPflow.param.Parameterized()
        new_p.p = GPflow.param.Param(1.0)
        m.p = new_p
        self.assertTrue(m._needs_recompile is True)


class TestRecompileNestedInHierarchy(unittest.TestCase):
    def setUp(self):
        tf.reset_default_graph()
        m1 = Quadratic()
        m1.p = GPflow.param.Param(3.0)
        m1._compile()
        m2 = Quadratic()
        m2._compile()
        self.m = GPflow.param.ParamList([m1, m2])

    def _validate_hierarchy_after_change(self):
        self.assertFalse(hasattr(self.m, '_needs_recompile'))
        self.assertTrue(self.m[0]._needs_recompile)
        self.assertFalse(self.m[1]._needs_recompile)
        self.m[0].optimize(maxiter=10)
        self.m[1].optimize(maxiter=10)
        self.assertFalse(hasattr(self.m, '_needs_recompile'))
        self.assertFalse(self.m[0]._needs_recompile)
        self.assertFalse(self.m[1]._needs_recompile)

    def test_fix(self):
        self.assertFalse(hasattr(self.m, '_needs_recompile'))
        self.m[0].p.fixed = True
        self._validate_hierarchy_after_change()

    def test_replace_param(self):
        self.assertFalse(hasattr(self.m, '_needs_recompile'))
        new_p = GPflow.param.Param(3.0)
        self.m[0].x = new_p
        self._validate_hierarchy_after_change()

    def test_set_prior(self):
        self.assertFalse(hasattr(self.m, '_needs_recompile'))
        self.m[0].x.prior = GPflow.priors.Gaussian(0, 1)
        self._validate_hierarchy_after_change()

    def test_set_transform(self):
        self.assertFalse(hasattr(self.m, '_needs_recompile'))
        self.m[0].x.transform = GPflow.transforms.Identity()
        self._validate_hierarchy_after_change()


class KeyboardRaiser:
    """
    This wraps a function and makes it raise a KeyboardInterrupt after some number of calls
    """

    def __init__(self, iters_to_raise, f):
        self.iters_to_raise, self.f = iters_to_raise, f
        self.count = 0

    def __call__(self, *a, **kw):
        self.count += 1
        if self.count >= self.iters_to_raise:
            raise KeyboardInterrupt
        return self.f(*a, **kw)


class TestKeyboardCatching(unittest.TestCase):
    def setUp(self):
        tf.reset_default_graph()
        X = np.random.randn(1000, 3)
        Y = np.random.randn(1000, 3)
        Z = np.random.randn(100, 3)
        self.m = GPflow.sgpr.SGPR(X, Y, Z=Z, kern=GPflow.kernels.RBF(3))

    def test_optimize_np(self):
        x0 = self.m.get_free_state()
        self.m._compile()
        self.m._objective = KeyboardRaiser(15, self.m._objective)
        self.m.optimize(disp=0, maxiter=10000, ftol=0, gtol=0)
        x1 = self.m.get_free_state()
        self.assertFalse(np.allclose(x0, x1))

    def test_optimize_tf(self):
        x0 = self.m.get_free_state()
        callback = KeyboardRaiser(5, lambda x: None)
        o = tf.train.AdamOptimizer()
        self.m.optimize(o, maxiter=15, callback=callback)
        x1 = self.m.get_free_state()
        self.assertFalse(np.allclose(x0, x1))


class TestLikelihoodAutoflow(unittest.TestCase):
    def setUp(self):
        tf.reset_default_graph()
        X = np.random.randn(1000, 3)
        Y = np.random.randn(1000, 3)
        Z = np.random.randn(100, 3)
        self.m = GPflow.sgpr.SGPR(X, Y, Z=Z, kern=GPflow.kernels.RBF(3))

    def test_lik_and_prior(self):
        l0 = self.m.compute_log_likelihood()
        p0 = self.m.compute_log_prior()
        self.m.kern.variance.prior = GPflow.priors.Gamma(1.4, 1.6)
        l1 = self.m.compute_log_likelihood()
        p1 = self.m.compute_log_prior()

        self.assertTrue(p0 == 0.0)
        self.assertFalse(p0 == p1)
        self.assertTrue(l0 == l1)


class TestName(unittest.TestCase):
    def test_name(self):
        m = GPflow.model.Model(name='foo')
        assert m.name == 'foo'


class TestNoRecompileThroughNewModelInstance(unittest.TestCase):
    """ Regression tests for Bug #454 """

    def setUp(self):
        self.X = np.random.rand(10, 2)
        self.Y = np.random.rand(10, 1)

    def test_gpr(self):
        m1 = GPflow.gpr.GPR(self.X, self.Y, GPflow.kernels.Matern32(2))
        m1._compile()
        m2 = GPflow.gpr.GPR(self.X, self.Y, GPflow.kernels.Matern32(2))
        self.assertFalse(m1._needs_recompile)

    def test_sgpr(self):
        m1 = GPflow.sgpr.SGPR(self.X, self.Y, GPflow.kernels.Matern32(2), Z=self.X)
        m1._compile()
        m2 = GPflow.sgpr.SGPR(self.X, self.Y, GPflow.kernels.Matern32(2), Z=self.X)
        self.assertFalse(m1._needs_recompile)

    def test_gpmc(self):
        m1 = GPflow.gpmc.GPMC(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT())
        m1._compile()
        m2 = GPflow.gpmc.GPMC(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT())
        self.assertFalse(m1._needs_recompile)

    def test_sgpmc(self):
        m1 = GPflow.sgpmc.SGPMC(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT(),
                                Z=self.X)
        m1._compile()
        m2 = GPflow.sgpmc.SGPMC(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT(),
                                Z=self.X)
        self.assertFalse(m1._needs_recompile)

    def test_svgp(self):
        m1 = GPflow.svgp.SVGP(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT(),
                              Z=self.X)
        m1._compile()
        m2 = GPflow.svgp.SVGP(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT(),
                              Z=self.X)
        self.assertFalse(m1._needs_recompile)

    def test_vgp(self):
        m1 = GPflow.vgp.VGP(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT())
        m1._compile()
        m2 = GPflow.vgp.VGP(self.X, self.Y, GPflow.kernels.Matern32(2), likelihood=GPflow.likelihoods.StudentT())
        self.assertFalse(m1._needs_recompile)


if __name__ == "__main__":
    unittest.main()