text: # 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.kernels.edit_distance_op."""
  
  import numpy as np
  
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.platform import test
  
  
  def ConstantOf(x):
    x = np.asarray(x)
    # Convert to int64 if it's not a string or unicode
    if x.dtype.char not in "SU":
      x = np.asarray(x, dtype=np.int64)
    return constant_op.constant(x)
  
  
  class EditDistanceTest(test.TestCase):
  
    def _testEditDistanceST(self,
                            hypothesis_st,
                            truth_st,
                            normalize,
                            expected_output,
                            expected_shape,
                            expected_err_re=None):
      edit_distance = array_ops.edit_distance(
          hypothesis=hypothesis_st, truth=truth_st, normalize=normalize)
  
      if expected_err_re is None:
        self.assertEqual(edit_distance.get_shape(), expected_shape)
        output = self.evaluate(edit_distance)
        self.assertAllClose(output, expected_output)
      else:
        with self.assertRaisesOpError(expected_err_re):
          self.evaluate(edit_distance)
  
    def _testEditDistance(self,
                          hypothesis,
                          truth,
                          normalize,
                          expected_output,
                          expected_err_re=None):
      # Shape inference figures out the shape from the shape variables
      # Explicit tuple() needed since zip returns an iterator in Python 3.
      expected_shape = [
          max(h, t) for h, t in tuple(zip(hypothesis[2], truth[2]))[:-1]
      ]
  
      # SparseTensorValue inputs.
      with ops.Graph().as_default() as g, self.session(g):
        # hypothesis and truth are (index, value, shape) tuples
        self._testEditDistanceST(
            hypothesis_st=sparse_tensor.SparseTensorValue(
                *[ConstantOf(x) for x in hypothesis]),
            truth_st=sparse_tensor.SparseTensorValue(
                *[ConstantOf(x) for x in truth]),
            normalize=normalize,
            expected_output=expected_output,
            expected_shape=expected_shape,
            expected_err_re=expected_err_re)
  
      # SparseTensor inputs.
      with ops.Graph().as_default() as g, self.session(g):
        # hypothesis and truth are (index, value, shape) tuples
        self._testEditDistanceST(
            hypothesis_st=sparse_tensor.SparseTensor(
                *[ConstantOf(x) for x in hypothesis]),
            truth_st=sparse_tensor.SparseTensor(*[ConstantOf(x) for x in truth]),
            normalize=normalize,
            expected_output=expected_output,
            expected_shape=expected_shape,
            expected_err_re=expected_err_re)
  
    def testEditDistanceNormalized(self):
      hypothesis_indices = [[0, 0], [0, 1], [1, 0], [1, 1]]
      hypothesis_values = [0, 1, 1, -1]
      hypothesis_shape = [2, 2]
      truth_indices = [[0, 0], [1, 0], [1, 1]]
      truth_values = [0, 1, 1]
      truth_shape = [2, 2]
      expected_output = [1.0, 0.5]
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=True,
          expected_output=expected_output)
  
    def testEditDistanceUnnormalized(self):
      hypothesis_indices = [[0, 0], [1, 0], [1, 1]]
      hypothesis_values = [10, 10, 11]
      hypothesis_shape = [2, 2]
      truth_indices = [[0, 0], [0, 1], [1, 0], [1, 1]]
      truth_values = [1, 2, 1, -1]
      truth_shape = [2, 3]
      expected_output = [2.0, 2.0]
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=False,
          expected_output=expected_output)
  
    def testEditDistanceProperDistance(self):
      # In this case, the values are individual characters stored in the
      # SparseTensor (type DT_STRING)
      hypothesis_indices = ([[0, i] for i, _ in enumerate("algorithm")] +
                            [[1, i] for i, _ in enumerate("altruistic")])
      hypothesis_values = [x for x in "algorithm"] + [x for x in "altruistic"]
      hypothesis_shape = [2, 11]
      truth_indices = ([[0, i] for i, _ in enumerate("altruistic")] +
                       [[1, i] for i, _ in enumerate("algorithm")])
      truth_values = [x for x in "altruistic"] + [x for x in "algorithm"]
      truth_shape = [2, 11]
      expected_unnormalized = [6.0, 6.0]
      expected_normalized = [6.0 / len("altruistic"), 6.0 / len("algorithm")]
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=False,
          expected_output=expected_unnormalized)
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=True,
          expected_output=expected_normalized)
  
    def testEditDistance3D(self):
      hypothesis_indices = [[0, 0, 0], [1, 0, 0]]
      hypothesis_values = [0, 1]
      hypothesis_shape = [2, 1, 1]
      truth_indices = [[0, 1, 0], [1, 0, 0], [1, 1, 0]]
      truth_values = [0, 1, 1]
      truth_shape = [2, 2, 1]
      expected_output = [
          [np.inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
          [0.0, 1.0]
      ]  # (1,0): match,    (1,1): no hypothesis
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=True,
          expected_output=expected_output)
  
    def testEditDistanceZeroLengthHypothesis(self):
      hypothesis_indices = np.empty((0, 2), dtype=np.int64)
      hypothesis_values = []
      hypothesis_shape = [1, 0]
      truth_indices = [[0, 0]]
      truth_values = [0]
      truth_shape = [1, 1]
      expected_output = [1.0]
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=True,
          expected_output=expected_output)
  
    def testEditDistanceZeroLengthTruth(self):
      hypothesis_indices = [[0, 0]]
      hypothesis_values = [0]
      hypothesis_shape = [1, 1]
      truth_indices = np.empty((0, 2), dtype=np.int64)
      truth_values = []
      truth_shape = [1, 0]
      expected_output = [np.inf]  # Normalized, loss is 1/0 = inf
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=True,
          expected_output=expected_output)
  
    def testEditDistanceZeroLengthHypothesisAndTruth(self):
      hypothesis_indices = np.empty((0, 2), dtype=np.int64)
      hypothesis_values = []
      hypothesis_shape = [1, 0]
      truth_indices = np.empty((0, 2), dtype=np.int64)
      truth_values = []
      truth_shape = [1, 0]
      expected_output = [0]  # Normalized is 0 because of exact match
  
      self._testEditDistance(
          hypothesis=(hypothesis_indices, hypothesis_values, hypothesis_shape),
          truth=(truth_indices, truth_values, truth_shape),
          normalize=True,
          expected_output=expected_output)
  
  
  if __name__ == "__main__":
    test.main()
  