# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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

import numpy as np

# pylint: disable=g-importing-member
from tensorflow.contrib.distributions.python.ops.bijectors.sinh_arcsinh import SinhArcsinh
from tensorflow.python.ops.distributions.bijector_test_util import assert_bijective_and_finite
from tensorflow.python.ops.distributions.bijector_test_util import assert_scalar_congruency
from tensorflow.python.platform import test

# pylint: enable=g-importing-member


class SinhArcsinhBijectorTest(test.TestCase):
  """Tests correctness of the power transformation."""

  def testBijectorVersusNumpyRewriteOfBasicFunctions(self):
    with self.cached_session():
      skewness = 0.2
      tailweight = 2.0
      bijector = 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, bijector.forward(x).eval())
      self.assertAllClose(x, bijector.inverse(y).eval())
      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),
          bijector.inverse_log_det_jacobian(y, event_ndims=1).eval())
      self.assertAllClose(
          -bijector.inverse_log_det_jacobian(y, event_ndims=1).eval(),
          bijector.forward_log_det_jacobian(x, event_ndims=1).eval(),
          rtol=1e-4,
          atol=0.)

  def testLargerTailWeightPutsMoreWeightInTails(self):
    with self.cached_session():
      # Will broadcast together to shape [3, 2].
      x = [-1., 1.]
      tailweight = [[0.5], [1.0], [2.0]]
      bijector = SinhArcsinh(tailweight=tailweight, validate_args=True)
      y = bijector.forward(x).eval()

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

      # 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):
    with self.cached_session():
      bijector = SinhArcsinh(skewness=1.0, tailweight=0.5, validate_args=True)
      assert_scalar_congruency(bijector, lower_x=-2., upper_x=2.0, rtol=0.05)

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

  def testBijectiveAndFiniteSkewnessNeg1Tailweight0p5(self):
    with self.cached_session():
      bijector = 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)
      assert_bijective_and_finite(bijector, x, x, event_ndims=0, rtol=1e-3)

  def testBijectiveAndFiniteSkewness1Tailweight3(self):
    with self.cached_session():
      bijector = 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)
      assert_bijective_and_finite(
          bijector, x, x, event_ndims=0, rtol=1e-3)

  def testBijectorEndpoints(self):
    with self.cached_session():
      for dtype in (np.float32, np.float64):
        bijector = 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.
        assert_bijective_and_finite(
            bijector, bounds, bounds, event_ndims=0, atol=2e-6)

  def testBijectorOverRange(self):
    with self.cached_session():
      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 = SinhArcsinh(
            skewness=skewness, tailweight=tailweight, validate_args=True)

        self.assertAllClose(y, bijector.forward(x).eval(), rtol=1e-4, atol=0.)
        self.assertAllClose(x, bijector.inverse(y).eval(), 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) and
        # below test fails due to overflow error giving inf. So 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),
              bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(),
              rtol=1e-4,
              atol=0.)
        self.assertAllClose(
            -bijector.inverse_log_det_jacobian(y, event_ndims=0).eval(),
            bijector.forward_log_det_jacobian(x, event_ndims=0).eval(),
            rtol=1e-4,
            atol=0.)

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

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


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