# Copyright 2020 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 Bijector."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Dependency imports

import numpy as np
from scipy import stats
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 GompertzCDF(test_util.TestCase):
  """Tests correctness of the Gompertz bijector."""

  def testBijector(self):
    concentration = 0.3
    rate = 1.
    bijector = tfb.GompertzCDF(
        concentration=concentration, rate=rate, validate_args=True)
    self.assertStartsWith(bijector.name, "gompertz")
    x = np.array([[[0.1], [0.5], [1.4], [3.]]], dtype=np.float32)
    # Gompertz distribution
    gompertz_dist = stats.gompertz(c=concentration, scale=1. / rate)
    y = gompertz_dist.cdf(x).astype(np.float32)
    self.assertAllClose(y, self.evaluate(bijector.forward(x)))
    self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
    self.assertAllClose(
        np.squeeze(gompertz_dist.logpdf(x), axis=-1),
        self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)))
    self.assertAllClose(
        self.evaluate(-bijector.inverse_log_det_jacobian(y, event_ndims=1)),
        self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=1)),
        rtol=1e-4,
        atol=0.)

  def testScalarCongruency(self):
    bijector_test_util.assert_scalar_congruency(
        tfb.GompertzCDF(concentration=0.2, rate=0.2), lower_x=1., upper_x=10.,
        eval_func=self.evaluate, rtol=0.05)

  def testBijectiveAndFinite(self):
    bijector = tfb.GompertzCDF(concentration=1., rate=0.01, validate_args=True)
    x = np.logspace(-10, 2, num=10).astype(np.float32)
    y = np.linspace(0.01, 0.99, num=10).astype(np.float32)
    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 testVariableConcentration(self):
    x = tf.Variable(1.)
    b = tfb.GompertzCDF(concentration=x, rate=1., validate_args=True)
    self.evaluate(x.initializer)
    self.assertIs(x, b.concentration)
    self.assertEqual((), self.evaluate(b.forward(1.)).shape)
    with self.assertRaisesOpError("Argument `concentration` must be positive."):
      with tf.control_dependencies([x.assign(-1.)]):
        self.evaluate(b.forward(1.))

  @test_util.jax_disable_variable_test
  def testVariableRate(self):
    x = tf.Variable(1.)
    b = tfb.GompertzCDF(concentration=1., rate=x, validate_args=True)
    self.evaluate(x.initializer)
    self.assertIs(x, b.rate)
    self.assertEqual((), self.evaluate(b.forward(1.)).shape)
    with self.assertRaisesOpError("Argument `rate` must be positive."):
      with tf.control_dependencies([x.assign(-1.)]):
        self.evaluate(b.forward(1.))


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