# Copyright 2016 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 ShapeUtil."""

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

import numpy as np

from tensorflow.contrib.distributions.python.ops.shape import _DistributionShape
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import test

_empty_shape = np.array([], dtype=np.int32)


def _eval(x):
  if hasattr(x, "__iter__"):
    return [x.eval() for x in x]
  return x.eval()


def _constant(x):
  if hasattr(x, "__iter__"):
    return [tensor_util.constant_value(x) for x in x]
  return tensor_util.constant_value(x)


class MakeBatchReadyTest(test.TestCase):

  def setUp(self):
    self._rng = np.random.RandomState(42)

  def _random_sample(self, sample_shape, dtype=np.float32):
    return self._rng.random_sample(sample_shape).astype(dtype)

  def _get_expected(self, x, batch_ndims, event_ndims, expand_batch_dim):
    # Cast as int32 array explicitly, since an empty x.shape defaults
    # to float64, and we can't index as float64 in numpy 1.12+.
    x_shape = np.array(x.shape, dtype=np.int32)
    n = x.ndim - batch_ndims - event_ndims
    sample_shape = x_shape[:n]
    y = np.reshape(x, np.concatenate([[-1], x_shape[n:]], 0))
    y = np.transpose(y, np.roll(np.arange(y.ndim), -1))
    if event_ndims == 0:
      y = y[..., np.newaxis, :]
    if batch_ndims == 0 and expand_batch_dim:
      y = y[np.newaxis, ...]
    return y, sample_shape

  def _build_graph(self, x, batch_ndims, event_ndims, expand_batch_dim):
    shaper = _DistributionShape(batch_ndims=batch_ndims,
                                event_ndims=event_ndims)
    y, sample_shape = shaper.make_batch_of_event_sample_matrices(
        x, expand_batch_dim=expand_batch_dim)
    should_be_x_value = shaper.undo_make_batch_of_event_sample_matrices(
        y, sample_shape, expand_batch_dim=expand_batch_dim)
    return y, sample_shape, should_be_x_value

  def _test_dynamic(self, x, batch_ndims, event_ndims, expand_batch_dim=True):
    with self.test_session() as sess:
      x_pl = array_ops.placeholder(x.dtype)
      batch_ndims_pl = array_ops.placeholder(dtypes.int32)
      event_ndims_pl = array_ops.placeholder(dtypes.int32)
      [y_, sample_shape_, should_be_x_value_] = sess.run(
          self._build_graph(
              x_pl, batch_ndims_pl, event_ndims_pl, expand_batch_dim),
          feed_dict={
              x_pl: x,
              batch_ndims_pl: batch_ndims,
              event_ndims_pl: event_ndims})
    expected_y, expected_sample_shape = self._get_expected(
        x, batch_ndims, event_ndims, expand_batch_dim)
    self.assertAllEqual(expected_sample_shape, sample_shape_)
    self.assertAllEqual(expected_y, y_)
    self.assertAllEqual(x, should_be_x_value_)

  def _test_static(self, x, batch_ndims, event_ndims, expand_batch_dim):
    with self.test_session() as sess:
      [y_, sample_shape_, should_be_x_value_] = sess.run(
          self._build_graph(x, batch_ndims, event_ndims, expand_batch_dim))
    expected_y, expected_sample_shape = self._get_expected(
        x, batch_ndims, event_ndims, expand_batch_dim)
    self.assertAllEqual(expected_sample_shape, sample_shape_)
    self.assertAllEqual(expected_y, y_)
    self.assertAllEqual(x, should_be_x_value_)

  # Group 1a: Static scalar input.

  def testStaticScalarNdims00ExpandNo(self):
    self._test_static(x=self._random_sample([]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=False)

  def testStaticScalarNdims00ExpandYes(self):
    self._test_static(x=self._random_sample([]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=True)

  def testStaticScalarNdims01ExpandNo(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([]),
                        batch_ndims=0,
                        event_ndims=1,
                        expand_batch_dim=False)

  def testStaticScalarNdims01ExpandYes(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([]),
                        batch_ndims=0,
                        event_ndims=1,
                        expand_batch_dim=True)

  def testStaticScalarNdims10ExpandNo(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([]),
                        batch_ndims=1,
                        event_ndims=0,
                        expand_batch_dim=False)

  def testStaticScalarNdims10ExpandYes(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([]),
                        batch_ndims=1,
                        event_ndims=0,
                        expand_batch_dim=True)

  def testStaticScalarNdims11ExpandNo(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([]),
                        batch_ndims=1,
                        event_ndims=1,
                        expand_batch_dim=False)

  def testStaticScalarNdims11ExpandYes(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([]),
                        batch_ndims=1,
                        event_ndims=1,
                        expand_batch_dim=True)

  # Group 1b: Dynamic scalar input.
  def testDynamicScalar3Ndims00ExpandNo(self):
    self._test_dynamic(x=self._random_sample([]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=False)

  def testDynamicScalar3Ndims00ExpandYes(self):
    self._test_dynamic(x=self._random_sample([]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=True)

  def testDynamicScalarNdims01ExpandNo(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([]),
                         batch_ndims=0,
                         event_ndims=1,
                         expand_batch_dim=False)

  def testDynamicScalarNdims01ExpandYes(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([]),
                         batch_ndims=0,
                         event_ndims=1,
                         expand_batch_dim=True)

  def testDynamicScalarNdims10ExpandNo(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([]),
                         batch_ndims=1,
                         event_ndims=0,
                         expand_batch_dim=False)

  def testDynamicScalarNdims10ExpandYes(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([]),
                         batch_ndims=1,
                         event_ndims=0,
                         expand_batch_dim=True)

  def testDynamicScalarNdims11ExpandNo(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([]),
                         batch_ndims=1,
                         event_ndims=1,
                         expand_batch_dim=False)

  def testDynamicScalarNdims11ExpandYes(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([]),
                         batch_ndims=1,
                         event_ndims=1,
                         expand_batch_dim=True)

  # Group 2a: Static vector input.

  def testStaticVectorNdims00ExpandNo(self):
    self._test_static(x=self._random_sample([3]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=False)

  def testStaticVectorNdims00ExpandYes(self):
    self._test_static(x=self._random_sample([3]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=True)

  def testStaticVectorNdims01ExpandNo(self):
    self._test_static(x=self._random_sample([3]),
                      batch_ndims=0,
                      event_ndims=1,
                      expand_batch_dim=False)

  def testStaticVectorNdims01ExpandYes(self):
    self._test_static(x=self._random_sample([3]),
                      batch_ndims=0,
                      event_ndims=1,
                      expand_batch_dim=True)

  def testStaticVectorNdims10ExpandNo(self):
    self._test_static(x=self._random_sample([3]),
                      batch_ndims=1,
                      event_ndims=0,
                      expand_batch_dim=False)

  def testStaticVectorNdims10ExpandYes(self):
    self._test_static(x=self._random_sample([3]),
                      batch_ndims=1,
                      event_ndims=0,
                      expand_batch_dim=True)

  def testStaticVectorNdims11ExpandNo(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([3]),
                        batch_ndims=1,
                        event_ndims=1,
                        expand_batch_dim=False)

  def testStaticVectorNdims11ExpandYes(self):
    with self.assertRaises(ValueError):
      self._test_static(x=self._random_sample([3]),
                        batch_ndims=1,
                        event_ndims=1,
                        expand_batch_dim=True)

  # Group 2b: Dynamic vector input.

  def testDynamicVectorNdims00ExpandNo(self):
    self._test_dynamic(x=self._random_sample([3]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=False)

  def testDynamicVectorNdims00ExpandYes(self):
    self._test_dynamic(x=self._random_sample([3]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=True)

  def testDynamicVectorNdims01ExpandNo(self):
    self._test_dynamic(x=self._random_sample([3]),
                       batch_ndims=0,
                       event_ndims=1,
                       expand_batch_dim=False)

  def testDynamicVectorNdims01ExpandYes(self):
    self._test_dynamic(x=self._random_sample([3]),
                       batch_ndims=0,
                       event_ndims=1,
                       expand_batch_dim=True)

  def testDynamicVectorNdims10ExpandNo(self):
    self._test_dynamic(x=self._random_sample([3]),
                       batch_ndims=1,
                       event_ndims=0,
                       expand_batch_dim=False)

  def testDynamicVectorNdims10ExpandYes(self):
    self._test_dynamic(x=self._random_sample([3]),
                       batch_ndims=1,
                       event_ndims=0,
                       expand_batch_dim=True)

  def testDynamicVectorNdims11ExpandNo(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([3]),
                         batch_ndims=1,
                         event_ndims=1,
                         expand_batch_dim=False)

  def testDynamicVectorNdims11ExpandYes(self):
    with self.assertRaisesOpError(""):
      self._test_dynamic(x=self._random_sample([3]),
                         batch_ndims=1,
                         event_ndims=1,
                         expand_batch_dim=True)

  # Group 3a: Static matrix input.

  def testStaticMatrixNdims00ExpandNo(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=False)

  def testStaticMatrixNdims00ExpandYes(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=True)

  def testStaticMatrixNdims01ExpandNo(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=0,
                      event_ndims=1,
                      expand_batch_dim=False)

  def testStaticMatrixNdims01ExpandYes(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=0,
                      event_ndims=1,
                      expand_batch_dim=True)

  def testStaticMatrixNdims10ExpandNo(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=1,
                      event_ndims=0,
                      expand_batch_dim=False)

  def testStaticMatrixNdims10ExpandYes(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=1,
                      event_ndims=0,
                      expand_batch_dim=True)

  def testStaticMatrixNdims11ExpandNo(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=1,
                      event_ndims=1,
                      expand_batch_dim=False)

  def testStaticMatrixNdims11ExpandYes(self):
    self._test_static(x=self._random_sample([2, 3]),
                      batch_ndims=1,
                      event_ndims=1,
                      expand_batch_dim=True)

  # Group 3b: Dynamic matrix input.

  def testDynamicMatrixNdims00ExpandNo(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=False)

  def testDynamicMatrixNdims00ExpandYes(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=True)

  def testDynamicMatrixNdims01ExpandNo(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=0,
                       event_ndims=1,
                       expand_batch_dim=False)

  def testDynamicMatrixNdims01ExpandYes(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=0,
                       event_ndims=1,
                       expand_batch_dim=True)

  def testDynamicMatrixNdims10ExpandNo(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=1,
                       event_ndims=0,
                       expand_batch_dim=False)

  def testDynamicMatrixNdims10ExpandYes(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=1,
                       event_ndims=0,
                       expand_batch_dim=True)

  def testDynamicMatrixNdims11ExpandNo(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=1,
                       event_ndims=1,
                       expand_batch_dim=False)

  def testDynamicMatrixNdims11ExpandYes(self):
    self._test_dynamic(x=self._random_sample([2, 3]),
                       batch_ndims=1,
                       event_ndims=1,
                       expand_batch_dim=True)

  # Group 4a: Static tensor input.

  def testStaticTensorNdims00ExpandNo(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=False)

  def testStaticTensorNdims00ExpandYes(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=0,
                      event_ndims=0,
                      expand_batch_dim=True)

  def testStaticTensorNdims01ExpandNo(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=0,
                      event_ndims=1,
                      expand_batch_dim=False)

  def testStaticTensorNdims01ExpandYes(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=0,
                      event_ndims=1,
                      expand_batch_dim=True)

  def testStaticTensorNdims10ExpandNo(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=1,
                      event_ndims=0,
                      expand_batch_dim=False)

  def testStaticTensorNdims10ExpandYes(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=1,
                      event_ndims=0,
                      expand_batch_dim=True)

  def testStaticTensorNdims11ExpandNo(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=1,
                      event_ndims=1,
                      expand_batch_dim=False)

  def testStaticTensorNdims11ExpandYes(self):
    self._test_static(x=self._random_sample([4, 1, 2, 3]),
                      batch_ndims=1,
                      event_ndims=1,
                      expand_batch_dim=True)

  # Group 4b: Dynamic tensor input.

  def testDynamicTensorNdims00ExpandNo(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=False)

  def testDynamicTensorNdims00ExpandYes(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=0,
                       event_ndims=0,
                       expand_batch_dim=True)

  def testDynamicTensorNdims01ExpandNo(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=0,
                       event_ndims=1,
                       expand_batch_dim=False)

  def testDynamicTensorNdims01ExpandYes(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=0,
                       event_ndims=1,
                       expand_batch_dim=True)

  def testDynamicTensorNdims10ExpandNo(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=1,
                       event_ndims=0,
                       expand_batch_dim=False)

  def testDynamicTensorNdims10ExpandYes(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=1,
                       event_ndims=0,
                       expand_batch_dim=True)

  def testDynamicTensorNdims11ExpandNo(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=1,
                       event_ndims=1,
                       expand_batch_dim=False)

  def testDynamicTensorNdims11ExpandYes(self):
    self._test_dynamic(x=self._random_sample([4, 1, 2, 3]),
                       batch_ndims=1,
                       event_ndims=1,
                       expand_batch_dim=True)


class DistributionShapeTest(test.TestCase):

  def setUp(self):
    self._rng = np.random.RandomState(42)

  def _random_sample(self, sample_shape, dtype=dtypes.float64):
    return self._rng.random_sample(sample_shape).astype(dtype.as_numpy_dtype())

  def _assertNdArrayEqual(self, expected, actual):
    """Helper which properly compares two np.ndarray-like objects.

    This function checks for exact equality so is probably only suitable for
    integers or powers of 2.

    Args:
      expected: np.ndarray. Ground-truth value.
      actual: np.ndarray.  Observed value.
    """
    expected = np.asarray(expected)
    actual = np.asarray(actual)
    self.assertEqual(expected.shape, actual.shape,
                     "Shape mismatch: expected %s, got %s." %
                     (expected.shape, actual.shape))
    actual_item = actual.flat
    for expected_item in expected.flat:
      self.assertAllEqual(expected_item, next(actual_item))

  def testDistributionShapeGetNdimsStatic(self):
    with self.test_session():
      shaper = _DistributionShape(batch_ndims=0, event_ndims=0)
      x = 1
      self.assertEqual(0, shaper.get_sample_ndims(x).eval())
      self.assertEqual(0, shaper.batch_ndims.eval())
      self.assertEqual(0, shaper.event_ndims.eval())

      shaper = _DistributionShape(batch_ndims=1, event_ndims=1)
      x = self._random_sample((1, 2, 3))
      self.assertAllEqual(3, shaper.get_ndims(x).eval())
      self.assertEqual(1, shaper.get_sample_ndims(x).eval())
      self.assertEqual(1, shaper.batch_ndims.eval())
      self.assertEqual(1, shaper.event_ndims.eval())

      x += self._random_sample((1, 2, 3))
      self.assertAllEqual(3, shaper.get_ndims(x).eval())
      self.assertEqual(1, shaper.get_sample_ndims(x).eval())
      self.assertEqual(1, shaper.batch_ndims.eval())
      self.assertEqual(1, shaper.event_ndims.eval())

      # Test ndims functions work, even despite unfed Tensors.
      y = array_ops.placeholder(dtypes.float32, shape=(1024, None, 1024))
      self.assertEqual(3, shaper.get_ndims(y).eval())
      self.assertEqual(1, shaper.get_sample_ndims(y).eval())
      self.assertEqual(1, shaper.batch_ndims.eval())
      self.assertEqual(1, shaper.event_ndims.eval())

  def testDistributionShapeGetNdimsDynamic(self):
    with self.test_session() as sess:
      batch_ndims = array_ops.placeholder(dtypes.int32)
      event_ndims = array_ops.placeholder(dtypes.int32)
      shaper = _DistributionShape(
          batch_ndims=batch_ndims, event_ndims=event_ndims)
      y = array_ops.placeholder(dtypes.float32)
      y_value = np.ones((4, 2), dtype=y.dtype.as_numpy_dtype())
      feed_dict = {y: y_value, batch_ndims: 1, event_ndims: 1}
      self.assertEqual(2, sess.run(shaper.get_ndims(y), feed_dict=feed_dict))

  def testDistributionShapeGetDimsStatic(self):
    with self.test_session():
      shaper = _DistributionShape(batch_ndims=0, event_ndims=0)
      shaper = _DistributionShape(batch_ndims=0, event_ndims=0)
      x = 1
      self.assertAllEqual((_empty_shape, _empty_shape, _empty_shape),
                          _constant(shaper.get_dims(x)))
      shaper = _DistributionShape(batch_ndims=1, event_ndims=2)
      x += self._random_sample((1, 1, 2, 2))
      self._assertNdArrayEqual(([0], [1], [2, 3]),
                               _constant(shaper.get_dims(x)))
      x += x
      self._assertNdArrayEqual(([0], [1], [2, 3]),
                               _constant(shaper.get_dims(x)))

  def testDistributionShapeGetDimsDynamic(self):
    with self.test_session() as sess:
      # Works for static {batch,event}_ndims despite unfed input.
      shaper = _DistributionShape(batch_ndims=1, event_ndims=2)
      y = array_ops.placeholder(dtypes.float32, shape=(10, None, 5, 5))
      self._assertNdArrayEqual([[0], [1], [2, 3]], _eval(shaper.get_dims(y)))

      # Works for deferred {batch,event}_ndims.
      batch_ndims = array_ops.placeholder(dtypes.int32)
      event_ndims = array_ops.placeholder(dtypes.int32)
      shaper = _DistributionShape(
          batch_ndims=batch_ndims, event_ndims=event_ndims)
      y = array_ops.placeholder(dtypes.float32)
      y_value = self._random_sample((10, 3, 5, 5), dtype=y.dtype)
      feed_dict = {y: y_value, batch_ndims: 1, event_ndims: 2}
      self._assertNdArrayEqual(
          ([0], [1], [2, 3]), sess.run(shaper.get_dims(y), feed_dict=feed_dict))

  def testDistributionShapeGetShapeStatic(self):
    with self.test_session():
      shaper = _DistributionShape(batch_ndims=0, event_ndims=0)
      self.assertAllEqual((_empty_shape, _empty_shape, _empty_shape),
                          _constant(shaper.get_shape(1.)))
      self._assertNdArrayEqual(([1], _empty_shape, _empty_shape),
                               _constant(shaper.get_shape(np.ones(1))))
      self._assertNdArrayEqual(([2, 2], _empty_shape, _empty_shape),
                               _constant(shaper.get_shape(np.ones((2, 2)))))
      self._assertNdArrayEqual(([3, 2, 1], _empty_shape, _empty_shape),
                               _constant(shaper.get_shape(np.ones((3, 2, 1)))))

      shaper = _DistributionShape(batch_ndims=0, event_ndims=1)
      with self.assertRaisesRegexp(ValueError, "expected .* <= ndims"):
        shaper.get_shape(1.)
      self._assertNdArrayEqual((_empty_shape, _empty_shape, [1]),
                               _constant(shaper.get_shape(np.ones(1))))
      self._assertNdArrayEqual(([2], _empty_shape, [2]),
                               _constant(shaper.get_shape(np.ones((2, 2)))))
      self._assertNdArrayEqual(([3, 2], _empty_shape, [1]),
                               _constant(shaper.get_shape(np.ones((3, 2, 1)))))

      shaper = _DistributionShape(batch_ndims=1, event_ndims=0)
      with self.assertRaisesRegexp(ValueError, "expected .* <= ndims"):
        shaper.get_shape(1.)
      self._assertNdArrayEqual((_empty_shape, [1], _empty_shape),
                               _constant(shaper.get_shape(np.ones(1))))
      self._assertNdArrayEqual(([2], [2], _empty_shape),
                               _constant(shaper.get_shape(np.ones((2, 2)))))
      self._assertNdArrayEqual(([3, 2], [1], _empty_shape),
                               _constant(shaper.get_shape(np.ones((3, 2, 1)))))

      shaper = _DistributionShape(batch_ndims=1, event_ndims=1)
      with self.assertRaisesRegexp(ValueError, "expected .* <= ndims"):
        shaper.get_shape(1.)
      with self.assertRaisesRegexp(ValueError, "expected .* <= ndims"):
        shaper.get_shape(np.ones(1))
      self._assertNdArrayEqual((_empty_shape, [2], [2]),
                               _constant(shaper.get_shape(np.ones((2, 2)))))
      self._assertNdArrayEqual(([3], [2], [1]),
                               _constant(shaper.get_shape(np.ones((3, 2, 1)))))

  def testDistributionShapeGetShapeDynamic(self):
    with self.test_session() as sess:
      # Works for static ndims despite unknown static shape.
      shaper = _DistributionShape(batch_ndims=1, event_ndims=1)
      y = array_ops.placeholder(dtypes.int32, shape=(None, None, 2))
      y_value = np.ones((3, 4, 2), dtype=y.dtype.as_numpy_dtype())
      self._assertNdArrayEqual(
          ([3], [4], [2]),
          sess.run(shaper.get_shape(y), feed_dict={y: y_value}))

      shaper = _DistributionShape(batch_ndims=0, event_ndims=1)
      y = array_ops.placeholder(dtypes.int32, shape=(None, None))
      y_value = np.ones((3, 2), dtype=y.dtype.as_numpy_dtype())
      self._assertNdArrayEqual(
          ([3], _empty_shape, [2]),
          sess.run(shaper.get_shape(y), feed_dict={y: y_value}))

      # Works for deferred {batch,event}_ndims.
      batch_ndims = array_ops.placeholder(dtypes.int32)
      event_ndims = array_ops.placeholder(dtypes.int32)
      shaper = _DistributionShape(
          batch_ndims=batch_ndims, event_ndims=event_ndims)
      y = array_ops.placeholder(dtypes.float32)
      y_value = self._random_sample((3, 4, 2), dtype=y.dtype)
      feed_dict = {y: y_value, batch_ndims: 1, event_ndims: 1}
      self._assertNdArrayEqual(
          ([3], [4], [2]), sess.run(shaper.get_shape(y), feed_dict=feed_dict))

      y_value = self._random_sample((3, 2), dtype=y.dtype)
      feed_dict = {y: y_value, batch_ndims: 0, event_ndims: 1}
      self._assertNdArrayEqual(
          ([3], _empty_shape, [2]),
          sess.run(shaper.get_shape(y), feed_dict=feed_dict))

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