# Copyright 2015 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 tensorflow.ops.reverse_sequence_op."""

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

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.platform import test


class ReverseSequenceTest(test.TestCase):

  def _testReverseSequence(self,
                           x,
                           batch_axis,
                           seq_axis,
                           seq_lengths,
                           truth,
                           use_gpu=False,
                           expected_err_re=None):
    with self.test_session(use_gpu=use_gpu):
      ans = array_ops.reverse_sequence(
          x, batch_axis=batch_axis, seq_axis=seq_axis, seq_lengths=seq_lengths)
      if expected_err_re is None:
        tf_ans = ans.eval()
        self.assertAllClose(tf_ans, truth, atol=1e-10)
        self.assertShapeEqual(truth, ans)
      else:
        with self.assertRaisesOpError(expected_err_re):
          ans.eval()

  def _testBothReverseSequence(self,
                               x,
                               batch_axis,
                               seq_axis,
                               seq_lengths,
                               truth,
                               expected_err_re=None):
    self._testReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth, True,
                              expected_err_re)
    self._testReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth,
                              False, expected_err_re)

  def _testBasic(self, dtype, len_dtype=np.int64):
    x = np.asarray(
        [[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]],
         [[17, 18, 19, 20], [21, 22, 23, 24]]],
        dtype=dtype)
    x = x.reshape(3, 2, 4, 1, 1)
    x = x.transpose([2, 1, 0, 3, 4])  # permute axes 0 <=> 2

    # reverse dim 2 up to (0:3, none, 0:4) along dim=0
    seq_lengths = np.asarray([3, 0, 4], dtype=len_dtype)

    truth_orig = np.asarray(
        [
            [[3, 2, 1, 4], [7, 6, 5, 8]],  # reverse 0:3
            [[9, 10, 11, 12], [13, 14, 15, 16]],  # reverse none
            [[20, 19, 18, 17], [24, 23, 22, 21]]
        ],  # reverse 0:4 (all)
        dtype=dtype)
    truth_orig = truth_orig.reshape(3, 2, 4, 1, 1)
    truth = truth_orig.transpose([2, 1, 0, 3, 4])  # permute axes 0 <=> 2

    seq_axis = 0  # permute seq_axis and batch_axis (originally 2 and 0, resp.)
    batch_axis = 2
    self._testBothReverseSequence(x, batch_axis, seq_axis, seq_lengths, truth)

  def testSeqLengthInt32(self):
    self._testBasic(np.float32, np.int32)

  def testFloatBasic(self):
    self._testBasic(np.float32)

  def testDoubleBasic(self):
    self._testBasic(np.float64)

  def testInt32Basic(self):
    self._testBasic(np.int32)

  def testInt64Basic(self):
    self._testBasic(np.int64)

  def testComplex64Basic(self):
    self._testBasic(np.complex64)

  def testComplex128Basic(self):
    self._testBasic(np.complex128)

  def testFloatReverseSequenceGrad(self):
    x = np.asarray(
        [[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]],
         [[17, 18, 19, 20], [21, 22, 23, 24]]],
        dtype=np.float)
    x = x.reshape(3, 2, 4, 1, 1)
    x = x.transpose([2, 1, 0, 3, 4])  # transpose axes 0 <=> 2

    # reverse dim 0 up to (0:3, none, 0:4) along dim=2
    seq_axis = 0
    batch_axis = 2
    seq_lengths = np.asarray([3, 0, 4], dtype=np.int64)

    with self.test_session():
      input_t = constant_op.constant(x, shape=x.shape)
      seq_lengths_t = constant_op.constant(seq_lengths, shape=seq_lengths.shape)
      reverse_sequence_out = array_ops.reverse_sequence(
          input_t,
          batch_axis=batch_axis,
          seq_axis=seq_axis,
          seq_lengths=seq_lengths_t)
      err = gradient_checker.compute_gradient_error(
          input_t, x.shape, reverse_sequence_out, x.shape, x_init_value=x)
    print("ReverseSequence gradient error = %g" % err)
    self.assertLess(err, 1e-8)

  def testShapeFunctionEdgeCases(self):
    t = array_ops.reverse_sequence(
        array_ops.placeholder(
            dtypes.float32, shape=None),
        seq_lengths=array_ops.placeholder(
            dtypes.int64, shape=(32,)),
        batch_axis=0,
        seq_axis=1)
    self.assertIs(t.get_shape().ndims, None)

    # Batch size mismatched between input and seq_lengths.
    with self.assertRaises(ValueError):
      array_ops.reverse_sequence(
          array_ops.placeholder(
              dtypes.float32, shape=(32, 2, 3)),
          seq_lengths=array_ops.placeholder(
              dtypes.int64, shape=(33,)),
          seq_axis=3)

    # seq_axis out of bounds.
    with self.assertRaisesRegexp(ValueError, "seq_dim must be < input rank"):
      array_ops.reverse_sequence(
          array_ops.placeholder(
              dtypes.float32, shape=(32, 2, 3)),
          seq_lengths=array_ops.placeholder(
              dtypes.int64, shape=(32,)),
          seq_axis=3)

    # batch_axis out of bounds.
    with self.assertRaisesRegexp(ValueError, "batch_dim must be < input rank"):
      array_ops.reverse_sequence(
          array_ops.placeholder(
              dtypes.float32, shape=(32, 2, 3)),
          seq_lengths=array_ops.placeholder(
              dtypes.int64, shape=(32,)),
          seq_axis=0,
          batch_axis=3)

    with self.test_session():
      inputs = array_ops.placeholder(dtypes.float32, shape=(32, 2, 3))
      seq_lengths = array_ops.placeholder(dtypes.int64, shape=(32,))
      output = array_ops.reverse_sequence(
          inputs, seq_lengths=seq_lengths,
          seq_axis=0)  # batch_axis default is 0
      with self.assertRaisesOpError("batch_dim == seq_dim"):
        output.eval(feed_dict={
            inputs: np.random.rand(32, 2, 3),
            seq_lengths: xrange(32)
        })


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