import unittest
import six
import tensorflow as tf
import numpy as np
import GPflow
from GPflow import settings
float_type = settings.dtypes.float_type
np_float_type = np.float32 if float_type is tf.float32 else np.float64


class TestSetup(object):
    def __init__(self, likelihood, Y, tolerance):
        self.likelihood, self.Y, self.tolerance = likelihood, Y, tolerance
        self.is_analytic = six.get_unbound_function(likelihood.predict_density) is not\
            six.get_unbound_function(GPflow.likelihoods.Likelihood.predict_density)


def getTestSetups(includeMultiClass=True, addNonStandardLinks=False):
    test_setups = []
    rng = np.random.RandomState(1)
    for likelihoodClass in GPflow.likelihoods.Likelihood.__subclasses__():
        if likelihoodClass == GPflow.likelihoods.Ordinal:
            test_setups.append(TestSetup(likelihoodClass(np.array([-1, 1])), rng.randint(0, 3, (10, 2)), 1e-6))
        elif likelihoodClass == GPflow.likelihoods.SwitchedLikelihood:
            continue  # switched likelihood tested separately
        elif (likelihoodClass == GPflow.likelihoods.MultiClass):
            if includeMultiClass:
                sample = rng.randn(10, 2)
                # Multiclass needs a less tight tolerance due to presence of clipping.
                tolerance = 1e-3
                test_setups.append(TestSetup(likelihoodClass(2),  np.argmax(sample, 1).reshape(-1, 1), tolerance))
        else:
            # most likelihoods follow this standard:
            test_setups.append(TestSetup(likelihoodClass(), rng.rand(10, 2).astype(np_float_type), 1e-6))

    if addNonStandardLinks:
        test_setups.append(TestSetup(GPflow.likelihoods.Poisson(invlink=tf.square),
                                     rng.rand(10, 2).astype(np_float_type), 1e-6))
        test_setups.append(TestSetup(GPflow.likelihoods.Exponential(invlink=tf.square),
                                     rng.rand(10, 2).astype(np_float_type), 1e-6))
        test_setups.append(TestSetup(GPflow.likelihoods.Gamma(invlink=tf.square),
                                     rng.rand(10, 2).astype(np_float_type), 1e-6))

        def sigmoid(x):
            return 1./(1 + tf.exp(-x))
        test_setups.append(TestSetup(GPflow.likelihoods.Bernoulli(invlink=sigmoid),
                                     rng.rand(10, 2).astype(np_float_type), 1e-6))
    return test_setups


class TestPredictConditional(unittest.TestCase):
    """
    Here we make sure that the conditional_mean and contitional_var functions
    give the same result as the predict_mean_and_var function if the prediction
    has no uncertainty.
    """
    def setUp(self):
        tf.reset_default_graph()
        self.test_setups = getTestSetups(addNonStandardLinks=True)
        self.x = tf.placeholder(float_type)
        for test_setup in self.test_setups:
            test_setup.likelihood.make_tf_array(self.x)

        self.F = tf.placeholder(float_type)
        rng = np.random.RandomState(0)
        self.F_data = rng.randn(10, 2).astype(np_float_type)

    def test_mean(self):
        for test_setup in self.test_setups:
            l = test_setup.likelihood
            with l.tf_mode():
                mu1 = tf.Session().run(l.conditional_mean(self.F),
                                       feed_dict={self.x: l.get_free_state(), self.F: self.F_data})
                mu2, _ = tf.Session().run(l.predict_mean_and_var(self.F, self.F * 0),
                                          feed_dict={self.x: l.get_free_state(), self.F: self.F_data})
            self.assertTrue(np.allclose(mu1, mu2, test_setup.tolerance, test_setup.tolerance))

    def test_variance(self):
        for test_setup in self.test_setups:
            l = test_setup.likelihood
            with l.tf_mode():
                v1 = tf.Session().run(l.conditional_variance(self.F),
                                      feed_dict={self.x: l.get_free_state(), self.F: self.F_data})
                v2 = tf.Session().run(l.predict_mean_and_var(self.F, self.F * 0)[1],
                                      feed_dict={self.x: l.get_free_state(), self.F: self.F_data})
            self.assertTrue(np.allclose(v1, v2, atol=test_setup.tolerance))

    def test_var_exp(self):
        """
        Here we make sure that the variational_expectations gives the same result
        as logp if the latent function has no uncertainty.
        """
        for test_setup in self.test_setups:
            l = test_setup.likelihood
            y = test_setup.Y
            with l.tf_mode():
                r1 = tf.Session().run(l.logp(self.F, y), feed_dict={self.x: l.get_free_state(), self.F: self.F_data})
                r2 = tf.Session().run(l.variational_expectations(self.F, self.F * 0, test_setup.Y),
                                      feed_dict={self.x: l.get_free_state(), self.F: self.F_data})
            self.assertTrue(np.allclose(r1, r2, test_setup.tolerance, test_setup.tolerance))


class TestQuadrature(unittest.TestCase):
    """
    Where quadratre methods have been overwritten, make sure the new code
     does something close to the quadrature
    """
    def setUp(self):
        tf.reset_default_graph()

        self.rng = np.random.RandomState()
        self.Fmu, self.Fvar, self.Y = self.rng.randn(3, 10, 2).astype(np_float_type)
        self.Fvar = 0.01 * self.Fvar ** 2
        self.test_setups = getTestSetups(includeMultiClass=False)

    def test_var_exp(self):
        # get all the likelihoods where variational expectations has been overwritten
        for test_setup in self.test_setups:
            if not test_setup.is_analytic:
                continue
            l = test_setup.likelihood
            y = test_setup.Y
            x_data = l.get_free_state()
            x = tf.placeholder(float_type)
            l.make_tf_array(x)
            # 'build' the functions
            with l.tf_mode():
                F1 = l.variational_expectations(self.Fmu, self.Fvar, y)
                F2 = GPflow.likelihoods.Likelihood.variational_expectations(l, self.Fmu, self.Fvar, y)
            # compile and run the functions:
            F1 = tf.Session().run(F1, feed_dict={x: x_data})
            F2 = tf.Session().run(F2, feed_dict={x: x_data})
            self.assertTrue(np.allclose(F1, F2, test_setup.tolerance, test_setup.tolerance))

    def test_pred_density(self):
        # get all the likelihoods where predict_density  has been overwritten.
        for test_setup in self.test_setups:
            if not test_setup.is_analytic:
                continue
            l = test_setup.likelihood
            y = test_setup.Y
            x_data = l.get_free_state()
            # make parameters if needed
            x = tf.placeholder(float_type)
            l.make_tf_array(x)
            # 'build' the functions
            with l.tf_mode():
                F1 = l.predict_density(self.Fmu, self.Fvar, y)
                F2 = GPflow.likelihoods.Likelihood.predict_density(l, self.Fmu, self.Fvar, y)
            # compile and run the functions:
            F1 = tf.Session().run(F1, feed_dict={x: x_data})
            F2 = tf.Session().run(F2, feed_dict={x: x_data})
            self.assertTrue(np.allclose(F1, F2, test_setup.tolerance, test_setup.tolerance))


class TestRobustMaxMulticlass(unittest.TestCase):
    """
    Some specialized tests to the multiclass likelihood with RobustMax inverse link function.
    """
    def setUp(self):
        tf.reset_default_graph()

    def testSymmetric(self):
        """
        This test is based on the observation that for
        symmetric inputs the class predictions must have equal probability.
        """
        nClasses = 5
        nPoints = 10
        tolerance = 1e-4
        epsilon = 1e-3
        F = tf.placeholder(float_type)
        x = tf.placeholder(float_type)
        F_data = np.ones((nPoints, nClasses))
        l = GPflow.likelihoods.MultiClass(nClasses)
        l.invlink.epsilon = epsilon
        rng = np.random.RandomState(1)
        Y = rng.randint(nClasses, size=(nPoints, 1))
        with l.tf_mode():
            mu, _ = tf.Session().run(l.predict_mean_and_var(F, F), feed_dict={x: l.get_free_state(), F: F_data})
            pred = tf.Session().run(l.predict_density(F, F, Y), feed_dict={x: l.get_free_state(), F: F_data})
            variational_expectations = tf.Session().run(l.variational_expectations(F, F, Y),
                                                        feed_dict={x: l.get_free_state(), F: F_data})
        expected_mu = (1./nClasses * (1. - epsilon) + (1. - 1./nClasses) * epsilon / (nClasses - 1)) * np.ones((nPoints, 1))
        self.assertTrue(np.allclose(mu, expected_mu, tolerance, tolerance))
        expected_log_denisty = np.log(expected_mu)
        self.assertTrue(np.allclose(pred, expected_log_denisty, 1e-3, 1e-3))
        validation_variational_expectation = 1./nClasses * np.log(1. - epsilon) + \
            (1. - 1./nClasses) * np.log(epsilon / (nClasses - 1))
        self.assertTrue(np.allclose(variational_expectations,
                                    np.ones((nPoints, 1)) * validation_variational_expectation,
                                    tolerance, tolerance))

    def testPredictDensity(self):
        tol = 1e-4
        num_points = 100
        mock_prob = 0.73

        class MockRobustMax(GPflow.likelihoods.RobustMax):
            def prob_is_largest(self, Y, Fmu, Fvar, gh_x, gh_w):
                return tf.ones((num_points, 1)) * mock_prob

        epsilon = 0.231
        num_classes = 5
        l = GPflow.likelihoods.MultiClass(num_classes, invlink=MockRobustMax(num_classes, epsilon))

        F = tf.placeholder(float_type)
        y = tf.placeholder(float_type)
        F_data = np.ones((num_points, num_classes))
        rng = np.random.RandomState(1)
        Y_data = rng.randint(num_classes, size=(num_points, 1))

        with l.tf_mode():
            pred = tf.Session().run(l.predict_density(F, F, y), feed_dict={F: F_data, y: Y_data})

        expected_prediction = -0.5499780059
        #^ evaluated on calculator: log( (1-\epsilon) * 0.73 + (1-0.73) * \epsilon/(num_classes -1))

        self.assertTrue(np.allclose(pred, expected_prediction, tol, tol))






class TestMulticlassIndexFix(unittest.TestCase):
    """
    A regression test for a bug in multiclass likelihood.
    """
    def setUp(self):
        tf.reset_default_graph()

    def testA(self):
        mu, var = tf.placeholder(float_type), tf.placeholder(float_type)
        Y = tf.placeholder(tf.int32)
        lik = GPflow.likelihoods.MultiClass(3)
        ve = lik.variational_expectations(mu, var, Y)
        tf.gradients(tf.reduce_sum(ve), mu)


class TestSwitchedLikelihood(unittest.TestCase):
    """
    SwitchedLikelihood is saparately tested here.
    Here, we make sure the partition-stictch works fine.
    """
    def setUp(self):
        rng = np.random.RandomState(1)
        self.Y_list = [rng.randn(3, 2),  rng.randn(4, 2),  rng.randn(5, 2)]
        self.F_list = [rng.randn(3, 2),  rng.randn(4, 2),  rng.randn(5, 2)]
        self.Fvar_list = [np.exp(rng.randn(3, 2)),  np.exp(rng.randn(4, 2)),  np.exp(rng.randn(5, 2))]
        self.Y_label = [np.ones((3, 1))*0, np.ones((4, 1))*1, np.ones((5, 1))*2]
        self.Y_perm = list(range(3+4+5))
        rng.shuffle(self.Y_perm)

        # shuffle the original data
        self.Y_sw = np.hstack([np.concatenate(self.Y_list), np.concatenate(self.Y_label)])[self.Y_perm, :]
        self.F_sw = np.concatenate(self.F_list)[self.Y_perm, :]
        self.Fvar_sw = np.concatenate(self.Fvar_list)[self.Y_perm, :]
        # likelihoods
        self.likelihoods = [GPflow.likelihoods.Gaussian(),
                            GPflow.likelihoods.Gaussian(),
                            GPflow.likelihoods.Gaussian()]
        for lik in self.likelihoods:
            lik.variance = np.exp(rng.randn(1))
        self.switched_likelihood = GPflow.likelihoods.SwitchedLikelihood(self.likelihoods)

        # initialize switched likelihood
        self.sess = tf.Session()
        free_array = self.switched_likelihood.get_free_state()
        self.switched_likelihood.make_tf_array(free_array)
        self.sess.run(tf.global_variables_initializer())

    def test_logp(self):
        # switchedlikelihood
        with self.switched_likelihood.tf_mode():
            switched_rslt = self.sess.run(self.switched_likelihood.logp(self.F_sw, self.Y_sw))
        # likelihood
        rslts = []
        for lik, y, f in zip(self.likelihoods, self.Y_list, self.F_list):
            with lik.tf_mode():
                rslts.append(self.sess.run(lik.logp(f, y)))

        self.assertTrue(np.allclose(switched_rslt, np.concatenate(rslts)[self.Y_perm, :]))
        self.sess.close()

    def test_predict_density(self):
        # switchedlikelihood
        with self.switched_likelihood.tf_mode():
            switched_rslt = self.sess.run(
                self.switched_likelihood.predict_density(self.F_sw, self.Fvar_sw, self.Y_sw))
        # likelihood
        rslts = []
        for lik, y, f, fvar in zip(self.likelihoods, self.Y_list, self.F_list, self.Fvar_list):
            self.sess.run(tf.global_variables_initializer())
            with lik.tf_mode():
                rslts.append(self.sess.run(lik.predict_density(f, fvar, y)))

        self.assertTrue(np.allclose(switched_rslt, np.concatenate(rslts)[self.Y_perm, :]))
        self.sess.close()

    def test_variational_expectations(self):
        # switchedlikelihood
        with self.switched_likelihood.tf_mode():
            switched_rslt = self.sess.run(
                self.switched_likelihood.variational_expectations(self.F_sw, self.Fvar_sw, self.Y_sw))
        # likelihood
        rslts = []
        for lik, y, f, fvar in zip(self.likelihoods, self.Y_list, self.F_list, self.Fvar_list):
            self.sess.run(tf.global_variables_initializer())
            with lik.tf_mode():
                rslts.append(self.sess.run(lik.variational_expectations(f, fvar, y)))

        self.assertTrue(np.allclose(switched_rslt, np.concatenate(rslts)[self.Y_perm, :]))
        self.sess.close()

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