def testWatchingOutputSlotWithoutOutgoingEdge(self):
    """Test watching output slots not attached to any outgoing edges."""

    with session.Session() as sess:
      u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
      u = constant_op.constant(u_init_val, shape=[2, 2], name="u")

      # Create a control edge from a node with an output: From u to z.
      # Node u will get executed only because of the control edge. The output
      # tensor u:0 is not attached to any outgoing edge in the graph. This test
      # checks that the debugger can watch such a tensor.
      with ops.control_dependencies([u]):
        z = control_flow_ops.no_op(name="z")

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls=self._debug_urls())

      run_metadata = config_pb2.RunMetadata()
      sess.run(z, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)

      # Assert that the DebugIdentity watch on u works properly.
      self.assertEqual(1, len(dump.dumped_tensor_data))
      datum = dump.dumped_tensor_data[0]
      self.assertEqual("u", datum.node_name)
      self.assertEqual(0, datum.output_slot)
      self.assertEqual("DebugIdentity", datum.debug_op)
      self.assertAllClose([[5.0, 3.0], [-1.0, 0.0]], datum.get_tensor())

  def testDebugNumericSummaryOnInitializedTensorGivesCorrectResult(self):
    with session.Session() as sess:
      a = variables.Variable(
          [
              np.nan, np.nan, 0.0, 0.0, 0.0, -1.0, -3.0, 3.0, 7.0, -np.inf,
              -np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.nan, np.nan
          ],
          dtype=np.float32,
          name="numeric_summary/a")
      b = variables.Variable(
          [0.0] * 18, dtype=np.float32, name="numeric_summary/b")
      c = math_ops.add(a, b, name="numeric_summary/c")

      sess.run(variables.global_variables_initializer())

      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary"],
          debug_urls=self._debug_urls())

      sess.run(c, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)
      self.assertTrue(dump.loaded_partition_graphs())

      self.assertAllClose([[
          1.0, 18.0, 2.0, 2.0, 3.0, 2.0, 5.0, 4.0, -3.0, 7.0, 0.85714286,
          8.97959184
      ]], dump.get_tensors("numeric_summary/a/read", 0, "DebugNumericSummary"))

  def testDebugNumericSummaryOnUninitializedTensorGivesCorrectResult(self):
    with session.Session() as sess:
      a = variables.Variable(
          [42], dtype=np.float32, name="numeric_summary_uninit/a")

      run_metadata = config_pb2.RunMetadata()
      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugNumericSummary"],
          debug_urls=self._debug_urls())

      sess.run(a.initializer, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)
      self.assertTrue(dump.loaded_partition_graphs())

      # DebugNumericSummary output should reflect the uninitialized state of
      # the watched tensor.
      numeric_summary = dump.get_tensors(
          "numeric_summary_uninit/a", 0, "DebugNumericSummary")[0]
      self.assertAllClose(
          [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], numeric_summary[0:8])
      self.assertTrue(np.isinf(numeric_summary[8]))
      self.assertGreater(numeric_summary[8], 0.0)
      self.assertTrue(np.isinf(numeric_summary[9]))
      self.assertLess(numeric_summary[9], 0.0)
      self.assertTrue(np.isnan(numeric_summary[10]))
      self.assertTrue(np.isnan(numeric_summary[11]))

  def testRunWithError(self):
    """Test the debug tensor dumping when error occurs in graph runtime."""

    with session.Session() as sess:
      ph = tf.placeholder(tf.float32, name="mismatch/ph")
      x = tf.transpose(ph, name="mismatch/x")
      m = constant_op.constant(
          np.array(
              [[1.0, 2.0]], dtype=np.float32), name="mismatch/m")
      y = math_ops.matmul(m, x, name="mismatch/y")

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls=self._debug_urls())

      with self.assertRaises(errors.OpError):
        sess.run(y,
                 options=run_options,
                 feed_dict={ph: np.array([[-3.0], [0.0]])})

      dump = debug_data.DebugDumpDir(self._dump_root)
      self.assertFalse(dump.loaded_partition_graphs())

      m_dumps = dump.watch_key_to_data("mismatch/m:0:DebugIdentity")
      self.assertEqual(1, len(m_dumps))
      self.assertAllClose(np.array([[1.0, 2.0]]), m_dumps[0].get_tensor())

      x_dumps = dump.watch_key_to_data("mismatch/x:0:DebugIdentity")
      self.assertEqual(1, len(x_dumps))
      self.assertAllClose(np.array([[-3.0, 0.0]]), x_dumps[0].get_tensor())

  def testWatchGraph_allNodes(self):
    debug_utils.watch_graph(
        self._run_options,
        self._graph,
        debug_ops=["DebugIdentity", "DebugNanCount"],
        debug_urls="file:///tmp/tfdbg_1")

    self.assertEqual(self._expected_num_nodes,
                     len(self._run_options.debug_tensor_watch_opts))

    # Verify that each of the nodes in the graph with output tensors in the
    # graph have debug tensor watch.
    node_names = self._verify_watches(self._run_options.debug_tensor_watch_opts,
                                      0, ["DebugIdentity", "DebugNanCount"],
                                      ["file:///tmp/tfdbg_1"])

    # Verify the node names.
    self.assertTrue("a1_init" in node_names)
    self.assertTrue("a1" in node_names)
    self.assertTrue("a1/Assign" in node_names)
    self.assertTrue("a1/read" in node_names)

    self.assertTrue("b_init" in node_names)
    self.assertTrue("b" in node_names)
    self.assertTrue("b/Assign" in node_names)
    self.assertTrue("b/read" in node_names)

    self.assertTrue("c" in node_names)
    self.assertTrue("p1" in node_names)
    self.assertTrue("s" in node_names)

  def setUpClass(cls):
    cls._dump_root = tempfile.mkdtemp()

    with session.Session() as sess:
      # 2400 elements should exceed the default threshold (2000).
      x = constant_op.constant(np.zeros([300, 8]), name="large_tensors/x")

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls="file://%s" % cls._dump_root)

      # Invoke Session.run().
      run_metadata = config_pb2.RunMetadata()
      sess.run(x, options=run_options, run_metadata=run_metadata)

    cls._debug_dump = debug_data.DebugDumpDir(
        cls._dump_root, partition_graphs=run_metadata.partition_graphs)

    # Construct the analyzer.
    cls._analyzer = analyzer_cli.DebugAnalyzer(cls._debug_dump)

    # Construct the handler registry.
    cls._registry = debugger_cli_common.CommandHandlerRegistry()

    # Register command handler.
    cls._registry.register_command_handler(
        "print_tensor",
        cls._analyzer.print_tensor,
        cls._analyzer.get_help("print_tensor"),
        prefix_aliases=["pt"])

  def _session_run_for_graph_structure_lookup(self):
    with session.Session() as sess:
      u_name = "testDumpGraphStructureLookup/u"
      v_name = "testDumpGraphStructureLookup/v"
      w_name = "testDumpGraphStructureLookup/w"

      u_init = constant_op.constant([2.0, 4.0])
      u = variables.Variable(u_init, name=u_name)
      v = math_ops.add(u, u, name=v_name)
      w = math_ops.add(v, v, name=w_name)

      u.initializer.run()

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls=self._debug_urls())

      run_metadata = config_pb2.RunMetadata()
      sess.run(w, options=run_options, run_metadata=run_metadata)

    self.assertEqual(self._expected_partition_graph_count,
                     len(run_metadata.partition_graphs))

    dump = debug_data.DebugDumpDir(
        self._dump_root, partition_graphs=run_metadata.partition_graphs)

    return u_name, v_name, w_name, dump

  def _decorate_run_options(self,
                            run_options,
                            debug_urls,
                            debug_ops="DebugIdentity",
                            node_name_regex_whitelist=None,
                            op_type_regex_whitelist=None):
    """Modify a RunOptions object for debug tensor watching.

    Specifies request for outputting partition graphs. Adds
    debug_tensor_watch_opts with proper debug URLs.

    Args:
      run_options: (RunOptions) the modified RunOptions object.
      debug_urls: (list of str) debug URLs to be entered in run_options.
        debug_tensor_watch_opts.
      debug_ops: (str or list of str) debug op(s) to be used by the debugger.
      node_name_regex_whitelist: Regular-expression whitelist for node
        name.
      op_type_regex_whitelist: Regular-expression whitelist for op type.
    """

    run_options.output_partition_graphs = True
    debug_utils.watch_graph(
        run_options,
        self._sess.graph,
        debug_urls=debug_urls,
        debug_ops=debug_ops,
        node_name_regex_whitelist=node_name_regex_whitelist,
        op_type_regex_whitelist=op_type_regex_whitelist)

  def before_run(self, run_context):
    if not self._wrapper_initialized:
      dumping_wrapper.DumpingDebugWrapperSession.__init__(
          self,
          run_context.session,
          self._session_root,
          watch_fn=self._watch_fn,
          log_usage=self._log_usage)
      self._wrapper_initialized = True

    self._run_call_count += 1

    (debug_urls, debug_ops, node_name_regex_whitelist,
     op_type_regex_whitelist) = self._prepare_run_watch_config(
         run_context.original_args.fetches, run_context.original_args.feed_dict)
    run_options = config_pb2.RunOptions()
    debug_utils.watch_graph(
        run_options,
        run_context.session.graph,
        debug_urls=debug_urls,
        debug_ops=debug_ops,
        node_name_regex_whitelist=node_name_regex_whitelist,
        op_type_regex_whitelist=op_type_regex_whitelist)

    run_args = session_run_hook.SessionRunArgs(
        None, feed_dict=None, options=run_options)
    return run_args

    def testDumpCausalityCheck(self):
        with session.Session() as sess:
            u_name = "testDumpCausalityCheck/u"
            v_name = "testDumpCausalityCheck/v"
            w_name = "testDumpCausalityCheck/w"

            u_init = constant_op.constant([2.0, 4.0])
            u = variables.Variable(u_init, name=u_name)
            v = math_ops.add(u, u, name=v_name)
            w = math_ops.add(v, v, name=w_name)

            u.initializer.run()

            run_options = config_pb2.RunOptions(output_partition_graphs=True)
            debug_utils.watch_graph(
                run_options, sess.graph, debug_ops=["DebugIdentity"], debug_urls="file://%s" % self._dump_root
            )

            run_metadata = config_pb2.RunMetadata()
            sess.run(w, options=run_options, run_metadata=run_metadata)

            self.assertEqual(self._expected_partition_graph_count, len(run_metadata.partition_graphs))

            # First, loading the original dump without supplying the
            # partition_graphs should not cause a RuntimeError, validation occurs
            # only with partition_graphs loaded.
            debug_data.DebugDumpDir(self._dump_root)

            # Now, loading the original dump with partition graphs supplied should
            # succeed. The validation should pass quietly.
            dump = debug_data.DebugDumpDir(self._dump_root, partition_graphs=run_metadata.partition_graphs)

            # Get the dump file names and compute their timestamps.
            self.assertEqual(1, len(dump.get_tensor_file_paths(u_name, 0, "DebugIdentity")))
            u_file_path = dump.get_tensor_file_paths(u_name, 0, "DebugIdentity")[0]

            self.assertEqual(1, len(dump.get_tensor_file_paths(v_name, 0, "DebugIdentity")))
            v_file_path = dump.get_tensor_file_paths(v_name, 0, "DebugIdentity")[0]

            u_timestamp = int(u_file_path[u_file_path.rindex("_") + 1 :])
            v_timestamp = int(v_file_path[v_file_path.rindex("_") + 1 :])

            # Swap the time stamps
            new_u_file_path = u_file_path[: u_file_path.rindex("_")] + "_%d" % v_timestamp
            new_v_file_path = v_file_path[: v_file_path.rindex("_")] + "_%d" % u_timestamp

            os.rename(u_file_path, new_u_file_path)
            os.rename(v_file_path, new_v_file_path)

            # Load the dump directory again. Now a ValueError is expected to be
            # raised due to the timestamp swap.
            with self.assertRaisesRegexp(ValueError, "Causality violated"):
                dump = debug_data.DebugDumpDir(self._dump_root, partition_graphs=run_metadata.partition_graphs)

            # Loading the dump directory with kwarg "validate" set explicitly to
            # False should get rid of the error.
            dump = debug_data.DebugDumpDir(
                self._dump_root, partition_graphs=run_metadata.partition_graphs, validate=False
            )

  def setUpClass(cls):
    cls._dump_root = tempfile.mkdtemp()

    cls._is_gpu_available = test.is_gpu_available()
    if cls._is_gpu_available:
      cls._main_device = "/job:localhost/replica:0/task:0/gpu:0"
    else:
      cls._main_device = "/job:localhost/replica:0/task:0/cpu:0"

    with session.Session() as sess:
      u_init_val = np.array([[5.0, 3.0], [-1.0, 0.0]])
      v_init_val = np.array([[2.0], [-1.0]])

      u_name = "simple_mul_add/u"
      v_name = "simple_mul_add/v"

      u_init = constant_op.constant(u_init_val, shape=[2, 2])
      u = variables.Variable(u_init, name=u_name)
      v_init = constant_op.constant(v_init_val, shape=[2, 1])
      v = variables.Variable(v_init, name=v_name)

      w = math_ops.matmul(u, v, name="simple_mul_add/matmul")

      x = math_ops.add(w, w, name="simple_mul_add/add")

      u.initializer.run()
      v.initializer.run()

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls="file://%s" % cls._dump_root)

      # Invoke Session.run().
      run_metadata = config_pb2.RunMetadata()
      sess.run(x, options=run_options, run_metadata=run_metadata)

    debug_dump = debug_data.DebugDumpDir(
        cls._dump_root, partition_graphs=run_metadata.partition_graphs)

    # Construct the analyzer.
    analyzer = analyzer_cli.DebugAnalyzer(debug_dump)

    # Construct the handler registry.
    cls._registry = debugger_cli_common.CommandHandlerRegistry()

    # Register command handlers.
    cls._registry.register_command_handler(
        "list_tensors",
        analyzer.list_tensors,
        analyzer.get_help("list_tensors"),
        prefix_aliases=["lt"])
    cls._registry.register_command_handler(
        "node_info",
        analyzer.node_info,
        analyzer.get_help("node_info"),
        prefix_aliases=["ni"])

  def testFindNodesWithBadTensorValues(self):
    with session.Session() as sess:
      u_name = "testFindNodesWithBadTensorValues/u"
      v_name = "testFindNodesWithBadTensorValues/v"
      w_name = "testFindNodesWithBadTensorValues/w"
      x_name = "testFindNodesWithBadTensorValues/x"
      y_name = "testFindNodesWithBadTensorValues/y"
      z_name = "testFindNodesWithBadTensorValues/z"

      u_init = constant_op.constant([2.0, 4.0])
      u = variables.Variable(u_init, name=u_name)
      v_init = constant_op.constant([2.0, 1.0])
      v = variables.Variable(v_init, name=v_name)

      # Expected output: [0.0, 3.0]
      w = math_ops.sub(u, v, name=w_name)

      # Expected output: [inf, 1.3333]
      x = math_ops.div(u, w, name=x_name)

      # Expected output: [nan, 4.0]
      y = math_ops.mul(w, x, name=y_name)

      z = math_ops.mul(y, y, name=z_name)

      u.initializer.run()
      v.initializer.run()

      run_options = config_pb2.RunOptions()
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls="file://%s" % self._dump_root)

      run_metadata = config_pb2.RunMetadata()
      sess.run(z, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(self._dump_root)

      def has_bad_value(_, tensor):
        return np.any(np.isnan(tensor)) or np.any(np.isinf(tensor))

      # Find all "offending tensors".
      bad_data = dump.find(has_bad_value)

      # Verify that the nodes with bad values are caught through running find
      # on the debug dump.
      self.assertEqual(3, len(bad_data))
      self.assertEqual(x_name, bad_data[0].node_name)
      self.assertEqual(y_name, bad_data[1].node_name)
      self.assertEqual(z_name, bad_data[2].node_name)

      # Test first_n kwarg of find(): Find the first offending tensor.
      first_bad_datum = dump.find(has_bad_value, first_n=1)

      self.assertEqual(1, len(first_bad_datum))
      self.assertEqual(x_name, first_bad_datum[0].node_name)

  def _decorate_options_for_debug(self, options, graph):
    """Modify RunOptions.debug_options.debug_tensor_watch_opts for debugging.

    Args:
      options: (config_pb2.RunOptions) The RunOptions instance to be modified.
      graph: A TensorFlow Graph object.
    """

    debug_utils.watch_graph(
        options, graph, debug_urls=self._get_run_debug_urls())
    options.output_partition_graphs = True

  def testWatchGraph_opTypeWhitelist(self):
    debug_utils.watch_graph(
        self._run_options,
        self._graph,
        debug_urls="file:///tmp/tfdbg_1",
        op_type_regex_whitelist="(Variable|MatMul)")

    node_names = self._verify_watches(self._run_options.debug_tensor_watch_opts,
                                      0, ["DebugIdentity"],
                                      ["file:///tmp/tfdbg_1"])
    self.assertEqual(sorted(["a1", "b", "p1"]), sorted(node_names))

  def testWatchingVariableUpdateOpsSeesUpdatedValues(self):
    """Watch output slots on Variable-updating ops, with no emitted edges."""

    with session.Session() as sess:
      u_init = constant_op.constant(10.0)
      u = variables.Variable(u_init, name="gdo/u")
      v_init = constant_op.constant(20.0)
      v = variables.Variable(v_init, name="gdo/v")

      w = math_ops.multiply(u, v, name="gdo/w")
      # gdo stands for GradientDescentOptimizer.

      train_op = gradient_descent.GradientDescentOptimizer(
          learning_rate=0.1).minimize(
              w, name="gdo/train")

      u.initializer.run()
      v.initializer.run()

      run_options = config_pb2.RunOptions(output_partition_graphs=True)
      debug_utils.watch_graph(
          run_options,
          sess.graph,
          debug_ops=["DebugIdentity"],
          debug_urls=self._debug_urls())

      run_metadata = config_pb2.RunMetadata()
      sess.run(train_op, options=run_options, run_metadata=run_metadata)

      dump = debug_data.DebugDumpDir(
          self._dump_root, partition_graphs=run_metadata.partition_graphs)

      update_u_data = dump.watch_key_to_data(
          "gdo/train/update_gdo/u/ApplyGradientDescent:0:DebugIdentity")
      self.assertEqual(1, len(update_u_data))

      # Gradient descent on u: w = u * v, so dw / du = v.
      # Updated value of u should be:
      #   10.0 - learning_rate * v = 10.0 - 0.1 * 20.0 = 8.0
      self.assertAllClose(8.0, update_u_data[0].get_tensor())

      update_v_data = dump.watch_key_to_data(
          "gdo/train/update_gdo/v/ApplyGradientDescent:0:DebugIdentity")
      self.assertEqual(1, len(update_v_data))

      # Gradient descent on u: w = u * v, so dw / dv = u.
      # Updated value of u should be:
      #   20.0 - learning_rate * u = 20.0 - 0.1 * 10.0 = 19.0
      self.assertAllClose(19.0, update_v_data[0].get_tensor())

      # Verify that the Variables u and v are updated properly.
      self.assertAllClose(8.0, sess.run(u))
      self.assertAllClose(19.0, sess.run(v))

  def testWatchGraph_nodeNameAndOpTypeWhitelists(self):
    debug_utils.watch_graph(
        self._run_options,
        self._graph,
        debug_urls="file:///tmp/tfdbg_1",
        node_name_regex_whitelist="([a-z]+1$)",
        op_type_regex_whitelist="(MatMul)")

    node_names = self._verify_watches(self._run_options.debug_tensor_watch_opts,
                                      0, ["DebugIdentity"],
                                      ["file:///tmp/tfdbg_1"])
    self.assertEqual(["p1"], node_names)

    def testWatchingUnconnectedOutputTensor(self):
        """Watch an output slot not emitting any edges.

    (Not even control edges from the node.)
    """

        with session.Session() as sess:
            x_init = constant_op.constant([2, 2, 3, 5, 5])
            x = variables.Variable(x_init, name="unconnected/x")

            # The UniqueOp (tf.unique) has two output slots. Use only slot 0 in the
            # graph. Let the debugger watch the unused slot 1.
            unique_x, _ = tf.unique(x, name="unconnected/unique_x")
            y = tf.add(unique_x, [0, 1, 2], name="unconnected/y")

            x.initializer.run()

            # Verify that only slot 0 of unique_x has recipients, while slot 1 of the
            # same node does not have recipients.
            unique_x_slot_0_recipients = []
            unique_x_slot_1_recipients = []
            for op in sess.graph.get_operations():
                for inp in op.inputs:
                    if inp.name == "unconnected/unique_x:0":
                        unique_x_slot_0_recipients.append(op.name)
                    elif inp.name == "unconnected/unique_x:1":
                        unique_x_slot_1_recipients.append(op.name)

            self.assertEqual(["unconnected/y"], unique_x_slot_0_recipients)
            self.assertEqual([], unique_x_slot_1_recipients)

            run_options = config_pb2.RunOptions(output_partition_graphs=True)
            debug_utils.watch_graph(run_options, sess.graph, debug_ops=["DebugIdentity"], debug_urls=self._debug_urls())

            run_metadata = config_pb2.RunMetadata()
            result = sess.run(y, options=run_options, run_metadata=run_metadata)
            self.assertAllClose([2, 4, 7], result)

            dump = debug_data.DebugDumpDir(self._dump_root, partition_graphs=run_metadata.partition_graphs)

            # Assert that the connected slot (slot 0) is dumped properly.
            unique_x_slot_0_dumps = dump.watch_key_to_data("unconnected/unique_x:0:DebugIdentity")
            self.assertEqual(1, len(unique_x_slot_0_dumps))
            self.assertEqual("unconnected/unique_x", unique_x_slot_0_dumps[0].node_name)
            self.assertEqual(0, unique_x_slot_0_dumps[0].output_slot)
            self.assertAllClose([2, 3, 5], unique_x_slot_0_dumps[0].get_tensor())

            # Assert that the unconnected slot (slot 1) is dumped properly.
            unique_x_slot_1_dumps = dump.watch_key_to_data("unconnected/unique_x:1:DebugIdentity")
            self.assertEqual(1, len(unique_x_slot_1_dumps))
            self.assertEqual("unconnected/unique_x", unique_x_slot_1_dumps[0].node_name)
            self.assertEqual(1, unique_x_slot_1_dumps[0].output_slot)
            self.assertAllClose([0, 0, 1, 2, 2], unique_x_slot_1_dumps[0].get_tensor())

  def testWatchGraph_nodeNameWhitelist(self):
    debug_utils.watch_graph(
        self._run_options,
        self._graph,
        debug_urls="file:///tmp/tfdbg_1",
        node_name_regex_whitelist="(a1$|a1_init$|a1/.*|p1$)")

    node_names = self._verify_watches(self._run_options.debug_tensor_watch_opts,
                                      0, ["DebugIdentity"],
                                      ["file:///tmp/tfdbg_1"])
    self.assertEqual(
        sorted(["a1_init", "a1", "a1/Assign", "a1/read", "p1"]),
        sorted(node_names))

    def setUpClass(cls):
        cls._dump_root = tempfile.mkdtemp()

        cls._is_gpu_available = test.is_gpu_available()
        if cls._is_gpu_available:
            cls._main_device = "/job:localhost/replica:0/task:0/gpu:0"
        else:
            cls._main_device = "/job:localhost/replica:0/task:0/cpu:0"

        with session.Session() as sess:
            x_init_val = np.array([5.0, 3.0])
            x_init = constant_op.constant(x_init_val, shape=[2])
            x = variables.Variable(x_init, name="control_deps/x")

            y = math_ops.add(x, x, name="control_deps/y")
            y = control_flow_ops.with_dependencies([x], y, name="control_deps/ctrl_dep_y")

            z = math_ops.mul(x, y, name="control_deps/z")

            z = control_flow_ops.with_dependencies([x, y], z, name="control_deps/ctrl_dep_z")

            x.initializer.run()

            run_options = config_pb2.RunOptions(output_partition_graphs=True)
            debug_utils.watch_graph(
                run_options, sess.graph, debug_ops=["DebugIdentity"], debug_urls="file://%s" % cls._dump_root
            )

            # Invoke Session.run().
            run_metadata = config_pb2.RunMetadata()
            sess.run(z, options=run_options, run_metadata=run_metadata)

        debug_dump = debug_data.DebugDumpDir(cls._dump_root, partition_graphs=run_metadata.partition_graphs)

        # Construct the analyzer.
        analyzer = analyzer_cli.DebugAnalyzer(debug_dump)

        # Construct the handler registry.
        cls._registry = debugger_cli_common.CommandHandlerRegistry()

        # Register command handlers.
        cls._registry.register_command_handler(
            "node_info", analyzer.node_info, analyzer.get_help("node_info"), prefix_aliases=["ni"]
        )
        cls._registry.register_command_handler(
            "list_inputs", analyzer.list_inputs, analyzer.get_help("list_inputs"), prefix_aliases=["li"]
        )
        cls._registry.register_command_handler(
            "list_outputs", analyzer.list_outputs, analyzer.get_help("list_outputs"), prefix_aliases=["lo"]
        )

  def _decorate_run_options(self, run_options, debug_urls):
    """Modify a RunOptions object for debug tensor watching.

    Specifies request for outputting partition graphs. Adds
    debug_tensor_watch_opts with proper debug URLs.

    Args:
      run_options: (RunOptions) the modified RunOptions object.
      debug_urls: (list of str) debug URLs to be entered in run_options.
        debug_tensor_watch_opts.
    """

    run_options.output_partition_graphs = True
    debug_utils.watch_graph(
        run_options, self._sess.graph, debug_urls=debug_urls)