# 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.
# ============================================================================
"""Exp Tests."""

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

# 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 ExpBijectorTest(test_util.TestCase):
  """Tests correctness of the Y = g(X) = exp(X) transformation."""

  def testBijector(self):
    bijector = tfb.Exp()
    self.assertStartsWith(bijector.name, 'exp')
    x = [[[1.], [2.]]]
    y = np.exp(x)
    self.assertAllClose(y, self.evaluate(bijector.forward(x)))
    self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
    self.assertAllClose(
        -np.squeeze(np.log(y), axis=-1),
        self.evaluate(bijector.inverse_log_det_jacobian(
            y, event_ndims=1)))
    self.assertAllClose(
        self.evaluate(-bijector.inverse_log_det_jacobian(
            np.exp(x), event_ndims=1)),
        self.evaluate(bijector.forward_log_det_jacobian(
            x, event_ndims=1)))

  def testScalarCongruency(self):
    bijector = tfb.Exp()
    bijector_test_util.assert_scalar_congruency(
        bijector, lower_x=-2., upper_x=1.5, eval_func=self.evaluate,
        rtol=0.05)

  def testBijectiveAndFinite(self):
    bijector = tfb.Exp()
    x = np.linspace(-10, 10, num=10).astype(np.float32)
    y = np.logspace(-10, 10, num=10).astype(np.float32)
    bijector_test_util.assert_bijective_and_finite(
        bijector, x, y, eval_func=self.evaluate, event_ndims=0)

  @test_util.numpy_disable_gradient_test
  def testJacobian(self):
    bijector = tfb.Exp()
    x = tf.constant([22.])
    fldj = bijector.forward_log_det_jacobian(x, event_ndims=0)
    fldj_theoretical = bijector_test_util.get_fldj_theoretical(
        bijector, x, event_ndims=0)
    fldj_, fldj_theoretical_ = self.evaluate([fldj, fldj_theoretical])
    self.assertAllClose(fldj_, fldj_theoretical_)

    x = np.expand_dims(np.linspace(-1, 1, num=10), -1)
    fldj = bijector.forward_log_det_jacobian(x, event_ndims=1)
    fldj_theoretical = bijector_test_util.get_fldj_theoretical(
        bijector, x, event_ndims=1)
    fldj_, fldj_theoretical_ = self.evaluate([fldj, fldj_theoretical])
    self.assertAllClose(fldj_, fldj_theoretical_)


@test_util.test_all_tf_execution_regimes
class LogBijectorTest(test_util.TestCase):

  def testBijectorIsInvertExp(self):
    x = np.linspace(1., 10., num=200)
    log = tfb.Log()
    invert_exp = tfb.Invert(tfb.Exp())
    self.assertAllClose(
        self.evaluate(log.forward(x)),
        self.evaluate(invert_exp.forward(x)))
    self.assertAllClose(
        self.evaluate(log.inverse(x)),
        self.evaluate(invert_exp.inverse(x)))
    self.assertAllClose(
        self.evaluate(log.forward_log_det_jacobian(x, event_ndims=1)),
        self.evaluate(invert_exp.forward_log_det_jacobian(x, event_ndims=1)))
    self.assertAllClose(
        self.evaluate(log.inverse_log_det_jacobian(x, event_ndims=1)),
        self.evaluate(invert_exp.inverse_log_det_jacobian(x, event_ndims=1)))


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