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 the experimental input pipeline ops."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import functools
  import os
  import warnings
  
  from absl.testing import parameterized
  import numpy as np
  
  from tensorflow.core.protobuf import cluster_pb2
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.compat import compat as forward_compat
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.data.ops import readers
  from tensorflow.python.data.util import structure
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import functional_ops
  from tensorflow.python.ops import gen_dataset_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import io_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.ops import script_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  from tensorflow.python.training import checkpoint_management
  from tensorflow.python.training import server_lib
  from tensorflow.python.training.checkpointable import util as checkpointable_utils
  from tensorflow.python.util import compat
  
  
  class IteratorTest(test.TestCase, parameterized.TestCase):
  
    def testNoGradients(self):
      component = constant_op.constant([1.])
      side = constant_op.constant(0.)
      add = lambda x: x + side
      dataset = dataset_ops.Dataset.from_tensor_slices(component).map(add)
      value = dataset.make_one_shot_iterator().get_next()
      self.assertIsNone(gradients_impl.gradients(value, component)[0])
      self.assertIsNone(gradients_impl.gradients(value, side)[0])
      self.assertIsNone(gradients_impl.gradients(value, [component, side])[0])
  
    def testCapturingStateInOneShotRaisesException(self):
      var = variables.Variable(37.0, name="myvar")
      dataset = (
          dataset_ops.Dataset.from_tensor_slices([0.0, 1.0, 2.0])
          .map(lambda x: x + var))
      with self.assertRaisesRegexp(
          ValueError, r"`Dataset.make_one_shot_iterator\(\)` does not support "
          "datasets that capture stateful objects.+myvar"):
        dataset.make_one_shot_iterator()
  
    def testOneShotIterator(self):
      components = (np.arange(7),
                    np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
                    np.array(37.0) * np.arange(7))
  
      def _map_fn(x, y, z):
        return math_ops.square(x), math_ops.square(y), math_ops.square(z)
  
      iterator = (
          dataset_ops.Dataset.from_tensor_slices(components).map(_map_fn)
          .repeat(14).make_one_shot_iterator())
      get_next = iterator.get_next()
  
      self.assertEqual([c.shape[1:] for c in components],
                       [t.shape for t in get_next])
  
      with self.cached_session() as sess:
        for _ in range(14):
          for i in range(7):
            result = self.evaluate(get_next)
            for component, result_component in zip(components, result):
              self.assertAllEqual(component[i]**2, result_component)
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testOneShotIteratorCaptureByValue(self):
      components = (np.arange(7),
                    np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
                    np.array(37.0) * np.arange(7))
      tensor_components = tuple([ops.convert_to_tensor(c) for c in components])
  
      def _map_fn(x, y, z):
        return math_ops.square(x), math_ops.square(y), math_ops.square(z)
  
      iterator = (
          dataset_ops.Dataset.from_tensor_slices(tensor_components)
          .map(_map_fn).repeat(14).make_one_shot_iterator())
      get_next = iterator.get_next()
  
      self.assertEqual([c.shape[1:] for c in components],
                       [t.shape for t in get_next])
  
      with self.cached_session() as sess:
        for _ in range(14):
          for i in range(7):
            result = self.evaluate(get_next)
            for component, result_component in zip(components, result):
              self.assertAllEqual(component[i]**2, result_component)
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testOneShotIteratorInsideContainer(self):
      components = (np.arange(7),
                    np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
                    np.array(37.0) * np.arange(7))
  
      def within_container():
  
        def _map_fn(x, y, z):
          return math_ops.square(x), math_ops.square(y), math_ops.square(z)
  
        iterator = (
            dataset_ops.Dataset.from_tensor_slices(components)
            .map(_map_fn).repeat(14).make_one_shot_iterator())
        return iterator.get_next()
  
      server = server_lib.Server.create_local_server()
  
      # Create two iterators within unique containers, and run them to
      # make sure that the resources aren't shared.
      #
      # The test below would fail if cname were the same across both
      # sessions.
      for j in range(2):
        with session.Session(server.target) as sess:
          cname = "iteration%d" % j
          with ops.container(cname):
            get_next = within_container()
  
          for _ in range(14):
            for i in range(7):
              result = self.evaluate(get_next)
              for component, result_component in zip(components, result):
                self.assertAllEqual(component[i]**2, result_component)
          with self.assertRaises(errors.OutOfRangeError):
            sess.run(get_next)
  
    def testOneShotIteratorNonBlocking(self):
      dataset = dataset_ops.Dataset.from_tensors([1, 2, 3]).map(lambda x: x * x)
      iterator = dataset.make_one_shot_iterator()
      next_element = iterator.get_next()
  
      # Create a session with a single thread to ensure that the
      # one-shot iterator initializer does not deadlock.
      config = config_pb2.ConfigProto(
          inter_op_parallelism_threads=1, use_per_session_threads=True)
      with session.Session(config=config) as sess:
        self.assertAllEqual([1, 4, 9], self.evaluate(next_element))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(next_element)
  
      # Test with multiple threads invoking the one-shot iterator concurrently.
      with session.Session(config=config) as sess:
        results = []
  
        def consumer_thread():
          try:
            results.append(sess.run(next_element))
          except errors.OutOfRangeError:
            results.append(None)
  
        num_threads = 8
        threads = [
            self.checkedThread(consumer_thread) for _ in range(num_threads)
        ]
        for t in threads:
          t.start()
        for t in threads:
          t.join()
  
        self.assertEqual(num_threads, len(results))
        self.assertEqual(num_threads - 1,
                         len([None for r in results if r is None]))
        self.assertAllEqual([[1, 4, 9]], [r for r in results if r is not None])
  
    def testOneShotIteratorInitializerFails(self):
      # Define a dataset whose initialization will always fail.
      dataset = dataset_ops.Dataset.from_tensors(
          array_ops.check_numerics(
              constant_op.constant(1.0) / constant_op.constant(0.0), "oops"))
      iterator = dataset.make_one_shot_iterator()
      next_element = iterator.get_next()
  
      with self.cached_session() as sess:
        with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
          sess.run(next_element)
  
        # Test that subsequent attempts to use the iterator also fail.
        with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
          sess.run(next_element)
  
      with self.cached_session() as sess:
  
        def consumer_thread():
          with self.assertRaisesRegexp(errors.InvalidArgumentError, "oops"):
            sess.run(next_element)
  
        num_threads = 8
        threads = [
            self.checkedThread(consumer_thread) for _ in range(num_threads)
        ]
        for t in threads:
          t.start()
        for t in threads:
          t.join()
  
    def testSimpleSharedResource(self):
      components = (np.array(1, dtype=np.int64),
                    np.array([1, 2, 3], dtype=np.int64),
                    np.array(37.0, dtype=np.float64))
  
      server = server_lib.Server.create_local_server()
  
      # Create two non-overlapping sessions that share the same iterator
      # resource on the same server, and verify that an action of the
      # first session (initializing the iterator) is visible in the
      # second session.
      with ops.Graph().as_default():
        iterator = (
            dataset_ops.Dataset.from_tensors(components)
            .map(lambda x, y, z: (x, y, z)).make_initializable_iterator(
                shared_name="shared_iterator"))
        init_op = iterator.initializer
        get_next = iterator.get_next()
  
        with session.Session(server.target) as sess:
          self.evaluate(init_op)
          results = self.evaluate(get_next)
          for component, result_component in zip(components, results):
            self.assertAllEqual(component, result_component)
          with self.assertRaises(errors.OutOfRangeError):
            sess.run(get_next)
  
          # Re-initialize the iterator in the first session.
          self.evaluate(init_op)
  
      with ops.Graph().as_default():
        # Re-define the iterator manually, without defining any of the
        # functions in this graph, to ensure that we are not
        # accidentally redefining functions with the same names in the
        # new graph.
        iterator = iterator_ops.Iterator.from_structure(
            shared_name="shared_iterator",
            output_types=(dtypes.int64, dtypes.int64, dtypes.float64),
            output_shapes=([], [3], []))
        get_next = iterator.get_next()
  
        with session.Session(server.target) as sess:
          # Use the iterator without re-initializing in the second session.
          results = self.evaluate(get_next)
          for component, result_component in zip(components, results):
            self.assertAllEqual(component, result_component)
          with self.assertRaises(errors.OutOfRangeError):
            sess.run(get_next)
  
    def testNotInitializedError(self):
      components = (np.array(1), np.array([1, 2, 3]), np.array(37.0))
      iterator = (
          dataset_ops.Dataset.from_tensors(components)
          .make_initializable_iterator())
      get_next = iterator.get_next()
  
      with self.cached_session() as sess:
        with self.assertRaisesRegexp(errors.FailedPreconditionError,
                                     "iterator has not been initialized"):
          sess.run(get_next)
  
    def testReinitializableIterator(self):
      dataset_3 = dataset_ops.Dataset.from_tensors(
          constant_op.constant([1, 2, 3]))
      dataset_4 = dataset_ops.Dataset.from_tensors(
          constant_op.constant([4, 5, 6, 7]))
      iterator = iterator_ops.Iterator.from_structure(dataset_3.output_types,
                                                      [None])
  
      dataset_3_init_op = iterator.make_initializer(dataset_3)
      dataset_4_init_op = iterator.make_initializer(dataset_4)
      get_next = iterator.get_next()
  
      self.assertEqual(dataset_3.output_types, iterator.output_types)
      self.assertEqual(dataset_4.output_types, iterator.output_types)
      self.assertEqual([None], iterator.output_shapes.as_list())
  
      with self.cached_session() as sess:
        # The iterator is initially uninitialized.
        with self.assertRaises(errors.FailedPreconditionError):
          sess.run(get_next)
  
        # Initialize with one dataset.
        self.evaluate(dataset_3_init_op)
        self.assertAllEqual([1, 2, 3], self.evaluate(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
        # Initialize with a different dataset.
        self.evaluate(dataset_4_init_op)
        self.assertAllEqual([4, 5, 6, 7], self.evaluate(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
        # Reinitialize with the first dataset.
        self.evaluate(dataset_3_init_op)
        self.assertAllEqual([1, 2, 3], self.evaluate(get_next))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(get_next)
  
    def testReinitializableIteratorWithFunctions(self):
  
      def g():
        for i in range(10):
          yield i
  
      iterator = iterator_ops.Iterator.from_structure(dtypes.int64, [])
      next_element = iterator.get_next()
  
      with self.cached_session() as sess:
        dataset_1 = dataset_ops.Dataset.from_generator(
            g, output_types=dtypes.int64)
        sess.run(iterator.make_initializer(dataset_1))
        for expected in range(10):
          self.assertEqual(expected, self.evaluate(next_element))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(next_element)
  
        dataset_2 = dataset_ops.Dataset.from_generator(
            g, output_types=dtypes.int64)
        sess.run(iterator.make_initializer(dataset_2))
        for expected in range(10):
          self.assertEqual(expected, self.evaluate(next_element))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(next_element)
  
    def testReinitializableIteratorStaticErrors(self):
      # Non-matching structure for types and shapes.
      with self.assertRaises(TypeError):
        iterator = iterator_ops.Iterator.from_structure(
            (dtypes.int64, dtypes.float64), [None])
  
      # Test validation of dataset argument.
      iterator = iterator_ops.Iterator.from_structure((dtypes.int64,
                                                       dtypes.float64))
  
      # Incompatible structure.
      with self.assertRaises(ValueError):
        iterator.make_initializer(
            dataset_ops.Dataset.from_tensors(((constant_op.constant(
                [1, 2, 3], dtype=dtypes.int64),), (constant_op.constant(
                    [4., 5., 6., 7.], dtype=dtypes.float64),))))
  
      # Incompatible types.
      with self.assertRaises(TypeError):
        iterator.make_initializer(
            dataset_ops.Dataset.from_tensors(
                (constant_op.constant([1, 2, 3], dtype=dtypes.int32),
                 constant_op.constant([4., 5., 6., 7.], dtype=dtypes.float32))))
  
      # Incompatible shapes.
      iterator = iterator_ops.Iterator.from_structure(
          (dtypes.int64, dtypes.float64), ([None], []))
      with self.assertRaises(TypeError):
        iterator.make_initializer(
            dataset_ops.Dataset.from_tensors(
                (constant_op.constant([1, 2, 3], dtype=dtypes.int64),
                 constant_op.constant([4., 5., 6., 7.], dtype=dtypes.float64))))
  
    def testIteratorStringHandle(self):
      dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
      dataset_4 = dataset_ops.Dataset.from_tensor_slices([10, 20, 30, 40])
  
      iterator_3 = dataset_3.make_one_shot_iterator()
      iterator_4 = dataset_4.make_one_shot_iterator()
  
      handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
      feedable_iterator = iterator_ops.Iterator.from_string_handle(
          handle_placeholder, dataset_3.output_types, dataset_3.output_shapes)
      next_element = feedable_iterator.get_next()
  
      self.assertEqual(dataset_3.output_types, feedable_iterator.output_types)
      self.assertEqual(dataset_4.output_types, feedable_iterator.output_types)
      self.assertEqual([], feedable_iterator.output_shapes)
  
      with self.cached_session() as sess:
        iterator_3_handle = sess.run(iterator_3.string_handle())
        iterator_4_handle = sess.run(iterator_4.string_handle())
  
        self.assertEqual(10,
                         sess.run(
                             next_element,
                             feed_dict={handle_placeholder: iterator_4_handle}))
        self.assertEqual(1,
                         sess.run(
                             next_element,
                             feed_dict={handle_placeholder: iterator_3_handle}))
        self.assertEqual(20,
                         sess.run(
                             next_element,
                             feed_dict={handle_placeholder: iterator_4_handle}))
        self.assertEqual(2,
                         sess.run(
                             next_element,
                             feed_dict={handle_placeholder: iterator_3_handle}))
        self.assertEqual(30,
                         sess.run(
                             next_element,
                             feed_dict={handle_placeholder: iterator_4_handle}))
        self.assertEqual(3,
                         sess.run(
                             next_element,
                             feed_dict={handle_placeholder: iterator_3_handle}))
        self.assertEqual(40,
                         sess.run(
                             next_element,
                             feed_dict={handle_placeholder: iterator_4_handle}))
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(
              next_element, feed_dict={handle_placeholder: iterator_3_handle})
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(
              next_element, feed_dict={handle_placeholder: iterator_4_handle})
  
    def testIteratorStringHandleFuture(self):
      with forward_compat.forward_compatibility_horizon(2018, 8, 4):
        dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
        dataset_4 = dataset_ops.Dataset.from_tensor_slices([10, 20, 30, 40])
  
        iterator_3 = dataset_3.make_one_shot_iterator()
        iterator_4 = dataset_4.make_one_shot_iterator()
  
        handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
        feedable_iterator = iterator_ops.Iterator.from_string_handle(
            handle_placeholder, dataset_3.output_types, dataset_3.output_shapes)
        next_element = feedable_iterator.get_next()
  
        self.assertEqual(dataset_3.output_types, feedable_iterator.output_types)
        self.assertEqual(dataset_4.output_types, feedable_iterator.output_types)
        self.assertEqual([], feedable_iterator.output_shapes)
  
        with self.cached_session() as sess:
          iterator_3_handle = sess.run(iterator_3.string_handle())
          iterator_4_handle = sess.run(iterator_4.string_handle())
  
          self.assertEqual(
              10,
              sess.run(
                  next_element,
                  feed_dict={handle_placeholder: iterator_4_handle}))
          self.assertEqual(
              1,
              sess.run(
                  next_element,
                  feed_dict={handle_placeholder: iterator_3_handle}))
          self.assertEqual(
              20,
              sess.run(
                  next_element,
                  feed_dict={handle_placeholder: iterator_4_handle}))
          self.assertEqual(
              2,
              sess.run(
                  next_element,
                  feed_dict={handle_placeholder: iterator_3_handle}))
          self.assertEqual(
              30,
              sess.run(
                  next_element,
                  feed_dict={handle_placeholder: iterator_4_handle}))
          self.assertEqual(
              3,
              sess.run(
                  next_element,
                  feed_dict={handle_placeholder: iterator_3_handle}))
          self.assertEqual(
              40,
              sess.run(
                  next_element,
                  feed_dict={handle_placeholder: iterator_4_handle}))
          with self.assertRaises(errors.OutOfRangeError):
            sess.run(
                next_element, feed_dict={handle_placeholder: iterator_3_handle})
          with self.assertRaises(errors.OutOfRangeError):
            sess.run(
                next_element, feed_dict={handle_placeholder: iterator_4_handle})
  
    def testIteratorStringHandleReuseTensorObject(self):
      dataset = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
      one_shot_iterator = dataset.make_one_shot_iterator()
      initializable_iterator = dataset.make_initializable_iterator()
      structure_iterator = iterator_ops.Iterator.from_structure(
          dataset.output_types)
  
      created_ops = len(ops.get_default_graph().get_operations())
  
      self.assertIs(one_shot_iterator.string_handle(),
                    one_shot_iterator.string_handle())
      self.assertIs(initializable_iterator.string_handle(),
                    initializable_iterator.string_handle())
      self.assertIs(structure_iterator.string_handle(),
                    structure_iterator.string_handle())
  
      # Assert that getting the (default) string handle creates no ops.
      self.assertEqual(created_ops, len(ops.get_default_graph().get_operations()))
  
      # Specifying an explicit name will create a new op.
      handle_with_name = one_shot_iterator.string_handle(name="foo")
      self.assertEqual("foo", handle_with_name.op.name)
      self.assertIsNot(one_shot_iterator.string_handle(), handle_with_name)
  
      handle_with_same_name = one_shot_iterator.string_handle(name="foo")
      self.assertEqual("foo_1", handle_with_same_name.op.name)
      self.assertIsNot(handle_with_name, handle_with_same_name)
  
    def testIteratorStringHandleError(self):
      dataset_int_scalar = (
          dataset_ops.Dataset.from_tensor_slices([1, 2, 3]).repeat())
      dataset_float_vector = (dataset_ops.Dataset.from_tensors([1.0, 2.0, 3.0]))
  
      handle_placeholder = array_ops.placeholder(dtypes.string, shape=[])
  
      feedable_int_scalar = iterator_ops.Iterator.from_string_handle(
          handle_placeholder, dtypes.int32, [])
      feedable_int_vector = iterator_ops.Iterator.from_string_handle(
          handle_placeholder, dtypes.int32, [None])
      feedable_int_any = iterator_ops.Iterator.from_string_handle(
          handle_placeholder, dtypes.int32)
  
      with self.cached_session() as sess:
        handle_int_scalar = sess.run(
            dataset_int_scalar.make_one_shot_iterator().string_handle())
        handle_float_vector = sess.run(
            dataset_float_vector.make_one_shot_iterator().string_handle())
  
        self.assertEqual(1,
                         sess.run(
                             feedable_int_scalar.get_next(),
                             feed_dict={handle_placeholder: handle_int_scalar}))
  
        self.assertEqual(2,
                         sess.run(
                             feedable_int_any.get_next(),
                             feed_dict={handle_placeholder: handle_int_scalar}))
  
        with self.assertRaises(errors.InvalidArgumentError):
          print(sess.run(
              feedable_int_vector.get_next(),
              feed_dict={handle_placeholder: handle_int_scalar}))
  
        with self.assertRaises(errors.InvalidArgumentError):
          print(sess.run(
              feedable_int_vector.get_next(),
              feed_dict={handle_placeholder: handle_float_vector}))
  
    def testRemoteIteratorUsingRemoteCallOpDirectSession(self):
      worker_config = config_pb2.ConfigProto()
      worker_config.device_count["CPU"] = 3
  
      with ops.device("/job:localhost/replica:0/task:0/cpu:1"):
        dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
        iterator_3 = dataset_3.make_one_shot_iterator()
        iterator_3_handle = iterator_3.string_handle()
  
      @function.Defun(dtypes.string)
      def _remote_fn(h):
        remote_iterator = iterator_ops.Iterator.from_string_handle(
            h, dataset_3.output_types, dataset_3.output_shapes)
        return remote_iterator.get_next()
  
      with ops.device("/job:localhost/replica:0/task:0/cpu:0"):
        target_placeholder = array_ops.placeholder(dtypes.string, shape=[])
        remote_op = functional_ops.remote_call(
            args=[iterator_3_handle],
            Tout=[dtypes.int32],
            f=_remote_fn,
            target=target_placeholder)
  
      with self.session(config=worker_config) as sess:
        elem = sess.run(
            remote_op,
            feed_dict={
                target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
            })
        self.assertEqual(elem, [1])
        # Fails when target is cpu:2 where the resource is not located.
        with self.assertRaises(errors.InvalidArgumentError):
          sess.run(
              remote_op,
              feed_dict={
                  target_placeholder: "/job:localhost/replica:0/task:0/cpu:2"
              })
        elem = sess.run(
            remote_op,
            feed_dict={
                target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
            })
        self.assertEqual(elem, [2])
        elem = sess.run(
            remote_op,
            feed_dict={
                target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
            })
        self.assertEqual(elem, [3])
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(
              remote_op,
              feed_dict={
                  target_placeholder: "/job:localhost/replica:0/task:0/cpu:1"
              })
  
    def testRemoteIteratorUsingRemoteCallOpMultiWorkers(self):
      s1 = server_lib.Server.create_local_server()
      s2 = server_lib.Server.create_local_server()
      s3 = server_lib.Server.create_local_server()
  
      cluster_def = cluster_pb2.ClusterDef()
      workers = cluster_def.job.add()
      workers.name = "worker"
      workers.tasks[0] = s1.target[len("grpc://"):]
      workers.tasks[1] = s2.target[len("grpc://"):]
      client = cluster_def.job.add()
      client.name = "client"
      client.tasks[0] = s3.target[len("grpc://"):]
      config = config_pb2.ConfigProto(cluster_def=cluster_def)
  
      worker_devices = [
          "/job:worker/replica:0/task:%d/cpu:0" % i for i in range(2)
      ]
      itr_handles = []
      for device in worker_devices:
        with ops.device(device):
          src = dataset_ops.Dataset.from_tensor_slices([device])
          itr = src.make_one_shot_iterator()
          itr_handles.append(itr.string_handle())
  
      targets = dataset_ops.Dataset.from_tensor_slices(worker_devices)
      handles = dataset_ops.Dataset.from_tensor_slices(itr_handles)
  
      @function.Defun(dtypes.string)
      def loading_func(h):
        remote_itr = iterator_ops.Iterator.from_string_handle(
            h, itr.output_types, itr.output_shapes)
        return remote_itr.get_next()
  
      def map_fn(target, handle):
        return functional_ops.remote_call(
            args=[handle], Tout=[dtypes.string], f=loading_func, target=target)
  
      with ops.device("/job:client"):
        client_dataset = dataset_ops.Dataset.zip((targets, handles)).map(map_fn)
        itr = client_dataset.make_initializable_iterator()
        n = itr.get_next()
  
      with session.Session(s3.target, config=config) as sess:
        self.evaluate(itr.initializer)
        expected_values = worker_devices
        for expected in expected_values:
          self.assertEqual((compat.as_bytes(expected),), self.evaluate(n))
  
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(n)
  
    def testRemoteIteratorUsingRemoteCallOpDirectSessionGPUCPU(self):
      if not test_util.is_gpu_available():
        self.skipTest("No GPU available")
  
      with ops.device("/job:localhost/replica:0/task:0/cpu:0"):
        dataset_3 = dataset_ops.Dataset.from_tensor_slices([1, 2, 3])
        iterator_3 = dataset_3.make_one_shot_iterator()
        iterator_3_handle = iterator_3.string_handle()
  
      def _encode_raw(byte_array):
        return bytes(bytearray(byte_array))
  
      @function.Defun(dtypes.uint8)
      def _remote_fn(h):
        handle = script_ops.py_func(_encode_raw, [h], dtypes.string)
        remote_iterator = iterator_ops.Iterator.from_string_handle(
            handle, dataset_3.output_types, dataset_3.output_shapes)
        return remote_iterator.get_next()
  
      with ops.device("/job:localhost/replica:0/task:0/device:GPU:0"):
        target_placeholder = array_ops.placeholder(dtypes.string, shape=[])
        iterator_3_handle_uint8 = parsing_ops.decode_raw(
            bytes=iterator_3_handle, out_type=dtypes.uint8)
        remote_op = functional_ops.remote_call(
            args=[iterator_3_handle_uint8],
            Tout=[dtypes.int32],
            f=_remote_fn,
            target=target_placeholder)
  
      with self.cached_session() as sess:
        elem = sess.run(
            remote_op,
            feed_dict={
                target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
            })
        self.assertEqual(elem, [1])
        elem = sess.run(
            remote_op,
            feed_dict={
                target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
            })
        self.assertEqual(elem, [2])
        elem = sess.run(
            remote_op,
            feed_dict={
                target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
            })
        self.assertEqual(elem, [3])
        with self.assertRaises(errors.OutOfRangeError):
          sess.run(
              remote_op,
              feed_dict={
                  target_placeholder: "/job:localhost/replica:0/task:0/cpu:0"
              })
  
    def testIncorrectIteratorRestore(self):
  
      def _path():
        return os.path.join(self.get_temp_dir(), "iterator")
  
      def _save_op(iterator_resource):
        iterator_state_variant = gen_dataset_ops.serialize_iterator(
            iterator_resource)
        save_op = io_ops.write_file(
            _path(), parsing_ops.serialize_tensor(iterator_state_variant))
        return save_op
  
      def _restore_op(iterator_resource):
        iterator_state_variant = parsing_ops.parse_tensor(
            io_ops.read_file(_path()), dtypes.variant)
        restore_op = gen_dataset_ops.deserialize_iterator(iterator_resource,
                                                          iterator_state_variant)
        return restore_op
  
      def _build_range_dataset_graph():
        start = 1
        stop = 10
        iterator = dataset_ops.Dataset.range(start,
                                             stop).make_initializable_iterator()
        init_op = iterator.initializer
        get_next = iterator.get_next()
        save_op = _save_op(iterator._iterator_resource)
        restore_op = _restore_op(iterator._iterator_resource)
        return init_op, get_next, save_op, restore_op
  
      def _build_reader_dataset_graph():
        filenames = ["test"]  # Does not exist but we don't care in this test.
        iterator = readers.FixedLengthRecordDataset(
            filenames, 1, 0, 0).make_initializable_iterator()
        init_op = iterator.initializer
        get_next_op = iterator.get_next()
        save_op = _save_op(iterator._iterator_resource)
        restore_op = _restore_op(iterator._iterator_resource)
        return init_op, get_next_op, save_op, restore_op
  
      # Saving iterator for RangeDataset graph.
      with ops.Graph().as_default() as g:
        init_op, _, save_op, _ = _build_range_dataset_graph()
        with self.session(graph=g) as sess:
          self.evaluate(init_op)
          self.evaluate(save_op)
  
      # Attempt to restore the saved iterator into an IteratorResource of
      # incompatible type. An iterator of RangeDataset has output type int64,
      # while an iterator of FixedLengthRecordDataset has output type string.
      # So an InvalidArgumentError should be raised by
      # IteratorResource::set_iterator.
      with ops.Graph().as_default() as g:
        _, _, _, restore_op = _build_reader_dataset_graph()
        with self.session(graph=g) as sess:
          with self.assertRaises(errors.InvalidArgumentError):
            self.evaluate(restore_op)
  
    def testRepeatedGetNextWarning(self):
      iterator = dataset_ops.Dataset.range(10).make_one_shot_iterator()
      warnings.simplefilter("always")
      with warnings.catch_warnings(record=True) as w:
        for _ in range(100):
          iterator.get_next()
      self.assertEqual(100 - iterator_ops.GET_NEXT_CALL_WARNING_THRESHOLD, len(w))
      for warning in w:
        self.assertIn(
            iterator_ops.GET_NEXT_CALL_WARNING_MESSAGE, str(warning.message))
  
    def testEagerIteratorAsync(self):
      with context.eager_mode(), context.execution_mode(context.ASYNC):
        val = 0
        dataset = dataset_ops.Dataset.range(10)
        for foo in dataset:
          self.assertEqual(val, foo.numpy())
          val += 1
  
    # pylint: disable=g-long-lambda
    @parameterized.named_parameters(
        ("Tensor", lambda: constant_op.constant(37.0),
         structure.TensorStructure(dtypes.float32, []),
         ops.Tensor, dtypes.float32, []),
        ("SparseTensor", lambda: sparse_tensor.SparseTensor(
            indices=[[0]], values=constant_op.constant([0], dtype=dtypes.int32),
            dense_shape=[1]),
         structure.SparseTensorStructure(dtypes.int32, [1]),
         sparse_tensor.SparseTensor, dtypes.int32, [1]),
        ("Nest", lambda: {
            "a": constant_op.constant(37.0),
            "b": (constant_op.constant(["Foo"]), constant_op.constant("Bar"))},
         structure.NestedStructure({
             "a": structure.TensorStructure(dtypes.float32, []),
             "b": (structure.TensorStructure(dtypes.string, [1]),
                   structure.TensorStructure(dtypes.string, []))}),
         {"a": ops.Tensor, "b": (ops.Tensor, ops.Tensor)},
         {"a": dtypes.float32, "b": (dtypes.string, dtypes.string)},
         {"a": [], "b": ([1], [])}),
    )
    def testIteratorStructure(self, tf_value_fn, expected_element_structure,
                              expected_output_classes, expected_output_types,
                              expected_output_shapes):
      tf_value = tf_value_fn()
      iterator = dataset_ops.Dataset.from_tensors(
          tf_value).make_one_shot_iterator()
  
      self.assertTrue(expected_element_structure.is_compatible_with(
          iterator._element_structure))
      self.assertTrue(iterator._element_structure.is_compatible_with(
          expected_element_structure))
  
      self.assertEqual(expected_output_classes, iterator.output_classes)
      self.assertEqual(expected_output_types, iterator.output_types)
      self.assertEqual(expected_output_shapes, iterator.output_shapes)
  
    def testIteratorGetNextName(self):
      with ops.Graph().as_default():
        iterator = dataset_ops.Dataset.from_tensors(37.0).make_one_shot_iterator()
        next_element = iterator.get_next(name="overridden_name")
        self.assertEqual("overridden_name", next_element.op.name)
  
  
  class IteratorCheckpointingTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes
    def testSaveRestoreOneShotIterator(self):
      checkpoint_directory = self.get_temp_dir()
      checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
      dataset = dataset_ops.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6]).map(
          math_ops.square).batch(2)
      iterator = dataset.make_one_shot_iterator()
      get_next = iterator.get_next if context.executing_eagerly(
      ) else functools.partial(self.evaluate, iterator.get_next())
      checkpoint = checkpointable_utils.Checkpoint(iterator=iterator)
      self.assertAllEqual([1, 4], get_next())
      save_path = checkpoint.save(checkpoint_prefix)
      self.assertAllEqual([9, 16], get_next())
      self.assertAllEqual([25, 36], get_next())
      checkpoint.restore(save_path).run_restore_ops()
      self.assertAllEqual([9, 16], get_next())
      self.assertAllEqual([25, 36], get_next())
      with self.assertRaises(errors.OutOfRangeError):
        get_next()
  
    @test_util.run_in_graph_and_eager_modes
    def testSaveRestoreMultipleIterator(self):
      checkpoint_directory = self.get_temp_dir()
      checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
      dataset = dataset_ops.Dataset.from_tensor_slices(
          [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
      dataset = dataset.map(math_ops.square).batch(2)
      iterator_1 = dataset.make_one_shot_iterator()
      get_next_1 = iterator_1.get_next if context.executing_eagerly(
      ) else functools.partial(self.evaluate, iterator_1.get_next())
      iterator_2 = dataset.make_one_shot_iterator()
      get_next_2 = iterator_2.get_next if context.executing_eagerly(
      ) else functools.partial(self.evaluate, iterator_2.get_next())
      dataset_2 = dataset_ops.Dataset.range(10)
      iterator_3 = dataset_2.make_one_shot_iterator()
      get_next_3 = iterator_3.get_next if context.executing_eagerly(
      ) else functools.partial(self.evaluate, iterator_3.get_next())
      checkpoint = checkpointable_utils.Checkpoint(
          iterator_1=iterator_1, iterator_2=iterator_2, iterator_3=iterator_3)
      self.assertAllEqual([1, 4], get_next_1())
      self.assertAllEqual(0, get_next_3())
      self.assertAllEqual(1, get_next_3())
      self.assertAllEqual(2, get_next_3())
      save_path = checkpoint.save(checkpoint_prefix)
      self.assertAllEqual([1, 4], get_next_2())
      self.assertAllEqual([9, 16], get_next_2())
      self.assertAllEqual(3, get_next_3())
      checkpoint.restore(save_path).run_restore_ops()
      self.assertAllEqual([9, 16], get_next_1())
      self.assertAllEqual([1, 4], get_next_2())
      self.assertAllEqual(3, get_next_3())
  
    @test_util.run_in_graph_and_eager_modes
    def testRestoreExhaustedIterator(self):
      checkpoint_directory = self.get_temp_dir()
      checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
      dataset = dataset_ops.Dataset.range(3)
      iterator = dataset.make_one_shot_iterator()
      get_next = iterator.get_next if context.executing_eagerly(
      ) else functools.partial(self.evaluate, iterator.get_next())
      checkpoint = checkpointable_utils.Checkpoint(iterator=iterator)
      self.assertAllEqual(0, get_next())
      self.assertAllEqual(1, get_next())
      save_path = checkpoint.save(checkpoint_prefix)
      self.assertAllEqual(2, get_next())
      checkpoint.restore(save_path).run_restore_ops()
      self.assertAllEqual(2, get_next())
      save_path = checkpoint.save(checkpoint_prefix)
      checkpoint.restore(save_path).run_restore_ops()
      with self.assertRaises(errors.OutOfRangeError):
        get_next()
  
    def testRestoreInReconstructedIteratorInitializable(self):
      checkpoint_directory = self.get_temp_dir()
      checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
      dataset = dataset_ops.Dataset.range(10)
      iterator = dataset.make_initializable_iterator()
      get_next = iterator.get_next()
      checkpoint = checkpointable_utils.Checkpoint(iterator=iterator)
      for i in range(5):
        with self.cached_session() as sess:
          checkpoint.restore(checkpoint_management.latest_checkpoint(
              checkpoint_directory)).initialize_or_restore(sess)
          for j in range(2):
            self.assertEqual(i * 2 + j, self.evaluate(get_next))
          checkpoint.save(file_prefix=checkpoint_prefix)
  
  
  if __name__ == "__main__":
    test.main()
  