# 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 SinhArcsinh Bijector."""

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.python.framework import test_util  # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top


@test_util.run_all_in_graph_and_eager_modes
class SinhArcsinhBijectorTest(tf.test.TestCase):
  """Tests correctness of the power transformation."""

  def testBijectorVersusNumpyRewriteOfBasicFunctions(self):
    skewness = 0.2
    tailweight = 2.0
    bijector = tfb.SinhArcsinh(
        skewness=skewness, tailweight=tailweight, validate_args=True)
    self.assertEqual("SinhArcsinh", bijector.name)
    x = np.array([[[-2.01], [2.], [1e-4]]]).astype(np.float32)
    y = np.sinh((np.arcsinh(x) + skewness) * tailweight)
    self.assertAllClose(y, self.evaluate(bijector.forward(x)))
    self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
    self.assertAllClose(
        np.sum(
            np.log(np.cosh(np.arcsinh(y) / tailweight - skewness)) -
            np.log(tailweight) - np.log(np.sqrt(y**2 + 1)),
            axis=-1),
        self.evaluate(bijector.inverse_log_det_jacobian(y, 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 testLargerTailWeightPutsMoreWeightInTails(self):
    # Will broadcast together to shape [3, 2].
    x = [-1., 1.]
    tailweight = [[0.5], [1.0], [2.0]]
    bijector = tfb.SinhArcsinh(tailweight=tailweight, validate_args=True)
    y = self.evaluate(bijector.forward(x))

    # x = -1, 1 should be mapped to points symmetric about 0
    self.assertAllClose(y[:, 0], -1. * y[:, 1])

    # forward(1) should increase as tailweight increases, since higher
    # tailweight should map 1 to a larger number.
    forward_1 = y[:, 1]  # The positive values of y.
    self.assertLess(forward_1[0], forward_1[1])
    self.assertLess(forward_1[1], forward_1[2])

  def testSkew(self):
    # Will broadcast together to shape [3, 2].
    x = [-1., 1.]
    skewness = [[-1.], [0.], [1.]]
    bijector = tfb.SinhArcsinh(skewness=skewness, validate_args=True)
    y = self.evaluate(bijector.forward(x))

    # For skew < 0, |forward(-1)| > |forward(1)|
    self.assertGreater(np.abs(y[0, 0]), np.abs(y[0, 1]))

    # For skew = 0, |forward(-1)| = |forward(1)|
    self.assertAllClose(np.abs(y[1, 0]), np.abs(y[1, 1]))

    # For skew > 0, |forward(-1)| < |forward(1)|
    self.assertLess(np.abs(y[2, 0]), np.abs(y[2, 1]))

  def testScalarCongruencySkewness1Tailweight0p5(self):
    bijector = tfb.SinhArcsinh(
        skewness=1.0, tailweight=0.5, validate_args=True)
    bijector_test_util.assert_scalar_congruency(
        bijector, lower_x=-2., upper_x=2.0, eval_func=self.evaluate, rtol=0.05)

  def testScalarCongruencySkewnessNeg1Tailweight1p5(self):
    bijector = tfb.SinhArcsinh(
        skewness=-1.0, tailweight=1.5, validate_args=True)
    bijector_test_util.assert_scalar_congruency(
        bijector, lower_x=-2., upper_x=2.0, eval_func=self.evaluate, rtol=0.05)

  def testBijectiveAndFiniteSkewnessNeg1Tailweight0p5(self):
    bijector = tfb.SinhArcsinh(
        skewness=-1., tailweight=0.5, validate_args=True)
    x = np.concatenate((-np.logspace(-2, 10, 1000), [0], np.logspace(
        -2, 10, 1000))).astype(np.float32)
    bijector_test_util.assert_bijective_and_finite(
        bijector, x, x, eval_func=self.evaluate, event_ndims=0, rtol=1e-3)

  def testBijectiveAndFiniteSkewness1Tailweight3(self):
    bijector = tfb.SinhArcsinh(skewness=1., tailweight=3., validate_args=True)
    x = np.concatenate((-np.logspace(-2, 5, 1000), [0], np.logspace(
        -2, 5, 1000))).astype(np.float32)
    bijector_test_util.assert_bijective_and_finite(
        bijector, x, x, eval_func=self.evaluate, event_ndims=0, rtol=1e-3)

  def testBijectorEndpoints(self):
    for dtype in (np.float32, np.float64):
      bijector = tfb.SinhArcsinh(
          skewness=dtype(0.), tailweight=dtype(1.), validate_args=True)
      bounds = np.array(
          [np.finfo(dtype).min, np.finfo(dtype).max], dtype=dtype)
      # Note that the above bijector is the identity bijector. Hence, the
      # log_det_jacobian will be 0. Because of this we use atol.
      bijector_test_util.assert_bijective_and_finite(
          bijector, bounds, bounds, eval_func=self.evaluate, event_ndims=0,
          atol=2e-6)

  def testBijectorOverRange(self):
    for dtype in (np.float32, np.float64):
      skewness = np.array([1.2, 5.], dtype=dtype)
      tailweight = np.array([2., 10.], dtype=dtype)
      # The inverse will be defined up to where sinh is valid, which is
      # arcsinh(np.finfo(dtype).max).
      log_boundary = np.log(
          np.sinh(np.arcsinh(np.finfo(dtype).max) / tailweight - skewness))
      x = np.array([
          np.logspace(-2, log_boundary[0], base=np.e, num=1000),
          np.logspace(-2, log_boundary[1], base=np.e, num=1000)
      ], dtype=dtype)
      # Ensure broadcasting works.
      x = np.swapaxes(x, 0, 1)

      y = np.sinh((np.arcsinh(x) + skewness) * tailweight)
      bijector = tfb.SinhArcsinh(
          skewness=skewness, tailweight=tailweight, validate_args=True)

      self.assertAllClose(
          y, self.evaluate(bijector.forward(x)), rtol=1e-4, atol=0.)
      self.assertAllClose(
          x, self.evaluate(bijector.inverse(y)), rtol=1e-4, atol=0.)

      # On IBM PPC systems, longdouble (np.float128) is same as double except
      # that it can have more precision. Type double being of 8 bytes, can't
      # hold square of max of float64 (which is also 8 bytes).
      # Below test fails due to overflow error giving inf. This check avoids
      # that error by skipping square calculation and corresponding assert.

      if (np.amax(y) <= np.sqrt(np.finfo(np.float128).max) and
          np.fabs(np.amin(y)) <= np.sqrt(np.fabs(np.finfo(np.float128).min))):

        # Do the numpy calculation in float128 to avoid inf/nan.
        y_float128 = np.float128(y)
        self.assertAllClose(
            np.log(np.cosh(
                np.arcsinh(y_float128) / tailweight - skewness) / np.sqrt(
                    y_float128**2 + 1)) -
            np.log(tailweight),
            self.evaluate(
                bijector.inverse_log_det_jacobian(y, event_ndims=0)),
            rtol=1e-4,
            atol=0.)
      self.assertAllClose(
          self.evaluate(-bijector.inverse_log_det_jacobian(y, event_ndims=0)),
          self.evaluate(bijector.forward_log_det_jacobian(x, event_ndims=0)),
          rtol=1e-4,
          atol=0.)

  def testZeroTailweightRaises(self):
    with self.assertRaisesOpError("not positive"):
      self.evaluate(
          tfb.SinhArcsinh(tailweight=0., validate_args=True).forward(1.0))

  def testDefaultDtypeIsFloat32(self):
    bijector = tfb.SinhArcsinh()
    self.assertEqual(bijector.tailweight.dtype, np.float32)
    self.assertEqual(bijector.skewness.dtype, np.float32)


if __name__ == "__main__":
  tf.test.main()
