text: # 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.
  # ==============================================================================
  """tensor_util tests."""
  
  # pylint: disable=unused-import
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import re
  
  import numpy as np
  
  from tensorflow.contrib.framework.python.framework import tensor_util
  from tensorflow.contrib.framework.python.ops import variables as variables_lib2
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import variables as variables_lib
  from tensorflow.python.platform import test
  
  
  class LocalVariabletest(test.TestCase):
  
    def test_local_variable(self):
      with self.test_session() as sess:
        self.assertEquals([], variables_lib.local_variables())
        value0 = 42
        variables_lib2.local_variable(value0)
        value1 = 43
        variables_lib2.local_variable(value1)
        variables = variables_lib.local_variables()
        self.assertEquals(2, len(variables))
        self.assertRaises(errors_impl.OpError, sess.run, variables)
        variables_lib.initialize_variables(variables).run()
        self.assertAllEqual(set([value0, value1]), set(sess.run(variables)))
  
  
  class ReduceSumNTest(test.TestCase):
  
    def test_reduce_sum_n(self):
      with self.test_session():
        a = constant_op.constant(1)
        b = constant_op.constant([2])
        c = constant_op.constant([[3, 4], [5, 6]])
        self.assertEqual(21, tensor_util.reduce_sum_n([a, b, c]).eval())
  
  
  class AssertScalarIntTest(test.TestCase):
  
    def test_assert_scalar_int(self):
      tensor_util.assert_scalar_int(constant_op.constant(3, dtype=dtypes.int32))
      tensor_util.assert_scalar_int(constant_op.constant(3, dtype=dtypes.int64))
      tensor_util.assert_scalar_int(3)
      with self.assertRaisesRegexp(ValueError, "Expected integer"):
        tensor_util.assert_scalar_int(
            constant_op.constant(
                3, dtype=dtypes.float32))
      with self.assertRaisesRegexp(ValueError, "Expected scalar"):
        tensor_util.assert_scalar_int(
            constant_op.constant(
                [3, 4], dtype=dtypes.int32))
  
  
  class WithShapeTest(test.TestCase):
  
    def _assert_with_shape(self, tensor, expected_value, expected_shape,
                           unexpected_shapes):
      for unexpected_shape in unexpected_shapes:
        self.assertRaises(ValueError, tensor_util.with_shape, unexpected_shape,
                          tensor)
        pattern = (
            r"\[Wrong shape for %s \[expected\] \[actual\].\] \[%s\] \[%s\]" %
            (tensor.name, " ".join([str(dim) for dim in unexpected_shape]),
             " ".join([str(dim) for dim in expected_shape])))
        self.assertRaisesRegexp(errors_impl.OpError,
                                re.compile(pattern),
                                tensor_util.with_shape(
                                    constant_op.constant(unexpected_shape),
                                    tensor).eval)
        expected_placeholder = array_ops.placeholder(dtypes.float32)
        self.assertRaisesRegexp(errors_impl.OpError,
                                re.compile(pattern),
                                tensor_util.with_same_shape(expected_placeholder,
                                                            tensor).eval,
                                {expected_placeholder: np.ones(unexpected_shape)})
  
      self.assertIs(tensor, tensor_util.with_shape(expected_shape, tensor))
      self.assertIs(
          tensor,
          tensor_util.with_same_shape(
              constant_op.constant(
                  1, shape=expected_shape), tensor))
      tensor_with_shape = tensor_util.with_shape(
          constant_op.constant(expected_shape), tensor)
      np.testing.assert_array_equal(expected_value, tensor_with_shape.eval())
      tensor_with_same_shape = tensor_util.with_same_shape(expected_placeholder,
                                                           tensor)
      np.testing.assert_array_equal(expected_value,
                                    tensor_with_same_shape.eval({
                                        expected_placeholder:
                                            np.ones(expected_shape)
                                    }))
  
    def test_with_shape_invalid_expected_shape(self):
      with self.test_session():
        self.assertRaisesRegexp(ValueError, "Invalid rank",
                                tensor_util.with_shape, [[1], [2]],
                                constant_op.constant(1.0))
  
    def test_with_shape_invalid_type(self):
      with self.test_session():
        self.assertRaisesRegexp(ValueError, "Invalid dtype",
                                tensor_util.with_shape, [1.1],
                                constant_op.constant([1.0]))
        self.assertRaisesRegexp(ValueError, "Invalid dtype",
                                tensor_util.with_shape,
                                np.array([1.1]), constant_op.constant(1.0))
        self.assertRaisesRegexp(ValueError, "Invalid dtype",
                                tensor_util.with_shape,
                                constant_op.constant(np.array([1.1])),
                                constant_op.constant(1.0))
  
    def test_with_shape_0(self):
      with self.test_session():
        value = 42
        shape = [0]
        unexpected_shapes = [[1], [2], [1, 1]]
        self._assert_with_shape(
            constant_op.constant(
                value, shape=shape),
            value,
            shape,
            unexpected_shapes)
  
    def test_with_shape_1(self):
      with self.test_session():
        value = [42]
        shape = [1]
        unexpected_shapes = [[0], [2], [1, 1]]
        self._assert_with_shape(
            constant_op.constant(
                value, shape=shape),
            value,
            shape,
            unexpected_shapes)
  
    def test_with_shape_2(self):
      with self.test_session():
        value = [42, 43]
        shape = [2]
        unexpected_shapes = [[0], [1], [2, 1]]
        self._assert_with_shape(
            constant_op.constant(
                value, shape=shape),
            value,
            shape,
            unexpected_shapes)
  
    def test_with_shape_2x2(self):
      with self.test_session():
        value = [[42, 43], [44, 45]]
        shape = [2, 2]
        unexpected_shapes = [[0], [1], [2, 1]]
        self._assert_with_shape(
            constant_op.constant(
                value, shape=shape),
            value,
            shape,
            unexpected_shapes)
  
    def test_with_shape_none(self):
      with self.test_session():
        tensor_no_shape = array_ops.placeholder(dtypes.float32)
  
        compatible_shape = [2, 2]
        with_present_2x2 = tensor_util.with_shape(compatible_shape,
                                                  tensor_no_shape)
        self.assertEquals(compatible_shape, with_present_2x2.get_shape().dims)
        with_future_2x2 = tensor_util.with_shape(
            constant_op.constant(compatible_shape), tensor_no_shape)
  
        array_2x2 = [[42.0, 43.0], [44.0, 45.0]]
        for tensor_2x2 in [with_present_2x2, with_future_2x2]:
          np.testing.assert_array_equal(array_2x2,
                                        tensor_2x2.eval({
                                            tensor_no_shape: array_2x2
                                        }))
          self.assertRaisesRegexp(errors_impl.OpError, "Wrong shape",
                                  tensor_2x2.eval,
                                  {tensor_no_shape: [42.0, 43.0]})
          self.assertRaisesRegexp(errors_impl.OpError, "Wrong shape",
                                  tensor_2x2.eval, {tensor_no_shape: [42.0]})
  
    def test_with_shape_partial(self):
      with self.test_session():
        tensor_partial_shape = array_ops.placeholder(dtypes.float32)
        tensor_partial_shape.set_shape([None, 2])
  
        for incompatible_shape in [[0], [1]]:
          self.assertRaisesRegexp(
              ValueError, r"Shapes \(\?, 2\) and \([01],\) are not compatible",
              tensor_util.with_shape, incompatible_shape, tensor_partial_shape)
        for incompatible_shape in [[1, 2, 1]]:
          self.assertRaisesRegexp(ValueError, "Dimensions must be equal",
                                  tensor_util.with_shape, incompatible_shape,
                                  tensor_partial_shape)
        for incompatible_shape in [[2, 1]]:
          self.assertRaisesRegexp(
              ValueError, r"Shapes \(\?, 2\) and \(2, 1\) are not compatible",
              tensor_util.with_shape, incompatible_shape, tensor_partial_shape)
  
        compatible_shape = [2, 2]
        with_present_2x2 = tensor_util.with_shape(compatible_shape,
                                                  tensor_partial_shape)
        self.assertEquals(compatible_shape, with_present_2x2.get_shape().dims)
        with_future_2x2 = tensor_util.with_shape(
            constant_op.constant(compatible_shape), tensor_partial_shape)
  
        array_2x2 = [[42.0, 43.0], [44.0, 45.0]]
        for tensor_2x2 in [with_present_2x2, with_future_2x2]:
          np.testing.assert_array_equal(array_2x2,
                                        tensor_2x2.eval({
                                            tensor_partial_shape: array_2x2
                                        }))
          self.assertRaises(ValueError, tensor_2x2.eval,
                            {tensor_partial_shape: [42.0, 43.0]})
          self.assertRaises(ValueError, tensor_2x2.eval,
                            {tensor_partial_shape: [42.0]})
  
  
  class RemoveSqueezableDimensionsTest(test.TestCase):
  
    def testRemoveSqueezableDimensions(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=False,
          predictions_have_extra_dim=False,
          labels_have_static_shape=False,
          labels_have_extra_dim=False)
  
    def testRemoveSqueezableDimensions_extraLabelDim(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=False,
          predictions_have_extra_dim=False,
          labels_have_static_shape=False,
          labels_have_extra_dim=True)
  
    def testRemoveSqueezableDimensions_staticLabel(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=False,
          predictions_have_extra_dim=False,
          labels_have_static_shape=True,
          labels_have_extra_dim=False)
  
    def testRemoveSqueezableDimensions_staticLabel_extraLabelDim(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=False,
          predictions_have_extra_dim=False,
          labels_have_static_shape=True,
          labels_have_extra_dim=True)
  
    def testRemoveSqueezableDimensions_extraPredictionDim(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=False,
          predictions_have_extra_dim=True,
          labels_have_static_shape=False,
          labels_have_extra_dim=False)
  
    def testRemoveSqueezableDimensions_extraPredictionDim_staticLabel(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=False,
          predictions_have_extra_dim=True,
          labels_have_static_shape=True,
          labels_have_extra_dim=False)
  
    def testRemoveSqueezableDimensions_staticPrediction(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=True,
          predictions_have_extra_dim=False,
          labels_have_static_shape=False,
          labels_have_extra_dim=False)
  
    def testRemoveSqueezableDimensions_staticPrediction_extraLabelDim(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=True,
          predictions_have_extra_dim=False,
          labels_have_static_shape=False,
          labels_have_extra_dim=True)
  
    def testRemoveSqueezableDimensions_static(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=True,
          predictions_have_extra_dim=False,
          labels_have_static_shape=True,
          labels_have_extra_dim=False)
  
    def testRemoveSqueezableDimensions_static_extraLabelDim(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=True,
          predictions_have_extra_dim=False,
          labels_have_static_shape=True,
          labels_have_extra_dim=True)
  
    def testRemoveSqueezableDimensions_staticPrediction_extraPredictionDim(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=True,
          predictions_have_extra_dim=True,
          labels_have_static_shape=False,
          labels_have_extra_dim=False)
  
    def testRemoveSqueezableDimensions_static_extraPredictionDim(self):
      self._testRemoveSqueezableDimensions(
          predictions_have_static_shape=True,
          predictions_have_extra_dim=True,
          labels_have_static_shape=True,
          labels_have_extra_dim=False)
  
    # TODO(ptucker): Replace this with parameterized test.
    def _testRemoveSqueezableDimensions(self, predictions_have_static_shape,
                                        predictions_have_extra_dim,
                                        labels_have_static_shape,
                                        labels_have_extra_dim):
      assert not (predictions_have_extra_dim and labels_have_extra_dim)
      predictions_value = (0, 1, 1, 0, 0, 1, 0)
      labels_value = (0, 0, 1, 1, 0, 0, 0)
  
      input_predictions_value = ([[p] for p in predictions_value] if
                                 predictions_have_extra_dim else
                                 predictions_value)
      input_labels_value = ([[l] for l in labels_value] if labels_have_extra_dim
                            else labels_value)
  
      with ops.Graph().as_default() as g:
        feed_dict = {}
        if predictions_have_static_shape:
          predictions = constant_op.constant(
              input_predictions_value, dtype=dtypes.int32)
        else:
          predictions = array_ops.placeholder(
              dtype=dtypes.int32, name="predictions")
          feed_dict[predictions] = input_predictions_value
        if labels_have_static_shape:
          labels = constant_op.constant(input_labels_value, dtype=dtypes.int32)
        else:
          labels = array_ops.placeholder(dtype=dtypes.int32, name="labels")
          feed_dict[labels] = input_labels_value
  
        squeezed_predictions, squeezed_labels = (
            tensor_util.remove_squeezable_dimensions(predictions, labels))
        with self.test_session(g):
          variables_lib.local_variables_initializer().run()
          self.assertAllClose(
              predictions_value, squeezed_predictions.eval(feed_dict=feed_dict))
          self.assertAllClose(
              labels_value, squeezed_labels.eval(feed_dict=feed_dict))
  
  
  if __name__ == "__main__":
    test.main()
  