text: # Copyright 2017 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 array operations."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops
  from tensorflow.python.platform import test
  
  
  class ArrayOpTest(test.TestCase):
  
    @test_util.deprecated_graph_mode_only
    def testGatherGradHasPartialStaticShape(self):
      # Create a tensor with an unknown dim 1.
      x = random_ops.random_normal([4, 10, 10])
      x = array_ops.gather(
          x,
          array_ops.reshape(array_ops.where_v2(x[0, :, 0] > 0.5), [-1]),
          axis=1)
      self.assertAllEqual(x.shape.as_list(), [4, None, 10])
  
      a = array_ops.gather(array_ops.gather(x, [0, 1]), [0, 1])
      b = array_ops.gather(array_ops.gather(x, [2, 3], axis=2), [0, 1])
      grad_a = ops.convert_to_tensor(gradients.gradients(a, x)[0])
      grad_b = ops.convert_to_tensor(gradients.gradients(b, x)[0])
  
      # We make sure that the representation of the shapes are correct; the shape
      # equality check will always eval to false due to the shapes being partial.
      self.assertAllEqual(grad_a.shape.as_list(), [None, None, 10])
      self.assertAllEqual(grad_b.shape.as_list(), [4, None, 10])
  
    @test_util.deprecated_graph_mode_only
    def testReshapeShapeInference(self):
      # Create a tensor with an unknown dim 1.
      x = random_ops.random_normal([4, 10, 10])
      x = array_ops.gather(
          x,
          array_ops.reshape(array_ops.where_v2(x[0, :, 0] > 0.5), [-1]),
          axis=1)
      self.assertAllEqual(x.shape.as_list(), [4, None, 10])
      a = array_ops.reshape(x, array_ops.shape(x))
      self.assertAllEqual(a.shape.as_list(), [4, None, 10])
      b = array_ops.reshape(x, math_ops.cast(array_ops.shape(x), dtypes.int64))
      self.assertAllEqual(b.shape.as_list(), [4, None, 10])
  
      # We do not shape-infer across a tf.cast into anything that's not tf.int32
      # or tf.int64, since they might end up mangling the shape.
      c = array_ops.reshape(
          x,
          math_ops.cast(
              math_ops.cast(array_ops.shape(x), dtypes.float32), dtypes.int32))
      self.assertAllEqual(c.shape.as_list(), [None, None, None])
  
  
  if __name__ == "__main__":
    test.main()
  