# Copyright 2018 The TensorFlow Probability 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.
# ============================================================================
"""Tests for Kumaraswamy Bijector."""

# Dependency imports
import numpy as np

import tensorflow.compat.v2 as tf
from tensorflow_probability.python import bijectors as tfb

from tensorflow_probability.python.bijectors import bijector_test_util
from tensorflow_probability.python.internal import test_util


@test_util.test_all_tf_execution_regimes
class KumaraswamyTest(test_util.TestCase):
  """Tests correctness of the Kumaraswamy bijector."""

  def testBijector(self):
    a = 2.
    b = 0.3
    bijector = tfb.KumaraswamyCDF(
        concentration1=a, concentration0=b, validate_args=True)
    self.assertStartsWith(bijector.name, 'kumaraswamy')
    x = np.array([[[0.1], [0.2], [0.3], [0.4], [0.5]]], dtype=np.float32)
    # Kumaraswamy cdf. This is the same as forward(x).
    y = 1. - (1. - x ** a) ** b
    self.assertAllClose(y, self.evaluate(bijector.forward(x)))
    self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
    kumaraswamy_log_pdf = (np.log(a) + np.log(b) + (a - 1) * np.log(x) +
                           (b - 1) * np.log1p(-x ** a))

    self.assertAllClose(
        np.squeeze(kumaraswamy_log_pdf, axis=-1),
        self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)))
    self.assertAllClose(
        self.evaluate(-bijector.forward_log_det_jacobian(x, event_ndims=1)),
        self.evaluate(bijector.inverse_log_det_jacobian(y, event_ndims=1)),
        rtol=1e-4,
        atol=0.)

  def testBijectorConcentration1LogDetJacobianFiniteAtZero(self):
    # When concentration = 1., forward_log_det_jacobian should be finite at
    # zero.
    concentration0 = np.logspace(0.1, 10., num=20).astype(np.float32)
    bijector = tfb.KumaraswamyCDF(
        concentration1=1., concentration0=concentration0)
    ildj = self.evaluate(
        bijector.inverse_log_det_jacobian(0., event_ndims=0))
    self.assertAllEqual(np.ones_like(ildj, dtype=np.bool_), np.isfinite(ildj))

  def testScalarCongruency(self):
    bijector_test_util.assert_scalar_congruency(
        tfb.KumaraswamyCDF(concentration1=0.5, concentration0=1.1),
        lower_x=0.,
        upper_x=1.,
        eval_func=self.evaluate,
        n=int(10e3),
        rtol=0.05)

  def testBijectiveAndFinite(self):
    concentration1 = 1.2
    concentration0 = 2.
    bijector = tfb.KumaraswamyCDF(
        concentration1=concentration1,
        concentration0=concentration0,
        validate_args=True)
    # Omitting the endpoints 0 and 1, since idlj will be infinity at these
    # endpoints.
    x = np.linspace(.01, 0.99, num=10).astype(np.float32)
    y = 1 - (1 - x ** concentration1) ** concentration0
    bijector_test_util.assert_bijective_and_finite(
        bijector, x, y, eval_func=self.evaluate, event_ndims=0,
        rtol=1e-3)

  @test_util.jax_disable_variable_test
  def testVariableConcentration1(self):
    x = tf.Variable(1.)
    b = tfb.KumaraswamyCDF(
        concentration0=1., concentration1=x, validate_args=True)
    self.evaluate(x.initializer)
    self.assertIs(x, b.concentration1)
    self.assertEqual((), self.evaluate(b.forward(1.)).shape)
    with self.assertRaisesOpError(
        'Argument `concentration1` must be positive.'):
      with tf.control_dependencies([x.assign(-1.)]):
        self.evaluate(b.forward(1.))

  @test_util.jax_disable_variable_test
  def testVariableConcentration0(self):
    x = tf.Variable(1.)
    b = tfb.KumaraswamyCDF(
        concentration0=x, concentration1=1., validate_args=True)
    self.evaluate(x.initializer)
    self.assertIs(x, b.concentration0)
    self.assertEqual((), self.evaluate(b.forward(1.)).shape)
    with self.assertRaisesOpError(
        'Argument `concentration0` must be positive.'):
      with tf.control_dependencies([x.assign(-1.)]):
        self.evaluate(b.forward(1.))

  def testShapeGetterRaisesException(self):
    x = tf.Variable(-1.)
    self.evaluate(x.initializer)
    with self.assertRaisesOpError(
        'Argument `concentration1` must be positive.'):
      b = tfb.KumaraswamyCDF(concentration0=1.,
                             concentration1=x,
                             validate_args=True)
      self.evaluate(b.forward_event_shape_tensor([1, 2, 3]))
    with self.assertRaisesOpError(
        'Argument `concentration0` must be positive.'):
      b = tfb.KumaraswamyCDF(concentration0=x,
                             concentration1=1.,
                             validate_args=True)
      self.evaluate(b.forward_event_shape_tensor(tf.constant([1, 2, 3])))

  @test_util.numpy_disable_gradient_test
  @test_util.jax_disable_variable_test
  def testGradient(self):
    x = tf.Variable(1.)
    y = tf.Variable(2.)
    b = tfb.KumaraswamyCDF(
        concentration0=x, concentration1=y, validate_args=True)
    with tf.GradientTape() as tape:
      loss = b.forward(1.)
    g = tape.gradient(loss, b.trainable_variables)
    self.assertLen(g, 2)
    self.assertAllNotNone(g)


if __name__ == '__main__':
  test_util.main()
