text: # Copyright 2018 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 the experimental input pipeline ops."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from tensorflow.contrib.data.python.ops import optimization
  from tensorflow.core.framework import graph_pb2
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.framework import errors
  from tensorflow.python.platform import test
  
  
  class OptimizeDatasetTest(test.TestCase):
  
    def testDefaultOptimizations(self):
      dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch(
          10).apply(optimization.optimize())
      iterator = dataset.make_one_shot_iterator()
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        graph = graph_pb2.GraphDef().FromString(
            sess.run(dataset._as_serialized_graph()))
        self.assertTrue(
            all([node.op != "MapAndBatchDatasetV2" for node in graph.node]))
        self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testEmptyOptimizations(self):
      dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch(
          10).apply(optimization.optimize([]))
      iterator = dataset.make_one_shot_iterator()
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        graph = graph_pb2.GraphDef().FromString(
            sess.run(dataset._as_serialized_graph()))
        self.assertTrue(
            all([node.op != "MapAndBatchDatasetV2" for node in graph.node]))
        self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testOptimization(self):
      dataset = dataset_ops.Dataset.range(10).map(lambda x: x * x).batch(
          10).apply(optimization.optimize(["map_and_batch_fusion"]))
      iterator = dataset.make_one_shot_iterator()
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        graph = graph_pb2.GraphDef().FromString(
            sess.run(dataset._as_serialized_graph()))
        self.assertTrue(
            any([node.op == "MapAndBatchDatasetV2" for node in graph.node]))
        self.assertAllEqual([x * x for x in range(10)], sess.run(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testFunctionLibraryDefinitionModification(self):
      dataset = dataset_ops.Dataset.from_tensors(0).map(lambda x: x).apply(
          optimization.optimize(["_test_only_function_rename"]))
      iterator = dataset.make_one_shot_iterator()
      get_next = iterator.get_next()
  
      with self.test_session() as sess:
        with self.assertRaisesRegexp(errors.NotFoundError,
                                     "Function .* is not defined."):
          sess.run(get_next)
  
  
  if __name__ == "__main__":
    test.main()
  