text: # 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 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 GumbelCDFTest(test_util.TestCase):
    """Tests correctness of the Gumbel bijector."""
  
    def testBijector(self):
      loc = 0.3
      scale = 5.
      bijector = tfb.GumbelCDF(loc=loc, scale=scale, validate_args=True)
      self.assertStartsWith(bijector.name, "gumbel")
      x = np.array([[[-3.], [0.], [0.5], [4.2], [12.]]], dtype=np.float32)
      # Gumbel distribution
      gumbel_dist = stats.gumbel_r(loc=loc, scale=scale)
      y = gumbel_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(gumbel_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.GumbelCDF(loc=0.3, scale=20.), lower_x=1., upper_x=100.,
          eval_func=self.evaluate, rtol=0.05)
  
    def testBijectiveAndFinite(self):
      bijector = tfb.GumbelCDF(loc=0., scale=3.0, validate_args=True)
      x = np.linspace(-10., 10., 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 testVariableScale(self):
      x = tf.Variable(1.)
      b = tfb.GumbelCDF(loc=0., scale=x, validate_args=True)
      self.evaluate(x.initializer)
      self.assertIs(x, b.scale)
      self.assertEqual((), self.evaluate(b.forward(-3.)).shape)
      with self.assertRaisesOpError("Argument `scale` must be positive."):
        with tf.control_dependencies([x.assign(-1.)]):
          self.evaluate(b.forward(-3.))
  
  
  if __name__ == "__main__":
    tf.test.main()
  