xref: /external/tensorflow/tensorflow/python/training/tracking/util.py

"""Utilities for saving/loading Trackable objects."""
# 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import abc
import collections
import functools
import os
import threading
import time
import weakref

import six

from tensorflow.core.protobuf import trackable_object_graph_pb2
from tensorflow.python.client import session as session_lib
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
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 tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.lib.io import file_io
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_io_ops as io_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.saved_model import utils_impl
from tensorflow.python.saved_model.pywrap_saved_model import metrics
from tensorflow.python.training import checkpoint_management
from tensorflow.python.training import py_checkpoint_reader
from tensorflow.python.training import saver as v1_saver_lib
from tensorflow.python.training.saving import checkpoint_options
from tensorflow.python.training.saving import functional_saver
from tensorflow.python.training.saving import saveable_object_util
from tensorflow.python.training.tracking import base
from tensorflow.python.training.tracking import data_structures
from tensorflow.python.training.tracking import graph_view as graph_view_lib
from tensorflow.python.training.tracking import tracking
from tensorflow.python.util import compat
from tensorflow.python.util import deprecation
from tensorflow.python.util import object_identity
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util import tf_inspect
from tensorflow.python.util.tf_export import tf_export

# The callable that provide Keras default session that is needed for saving.
_SESSION_PROVIDER = None

# Captures the timestamp of the first Checkpoint instantiation or end of a write
# operation. Can be accessed by multiple Checkpoint instances.
_END_TIME_OF_LAST_WRITE = None
_END_TIME_OF_LAST_WRITE_LOCK = threading.Lock()

# API labels for cell names used in checkpoint metrics.
_CHECKPOINT_V1 = "checkpoint_v1"
_CHECKPOINT_V2 = "checkpoint_v2"


def _get_duration_microseconds(start_time_seconds, end_time_seconds):
  if end_time_seconds < start_time_seconds:
    # Avoid returning negative value in case of clock skew.
    return 0
  return round((end_time_seconds - start_time_seconds) * 1000000)


@tf_export("__internal__.tracking.register_session_provider", v1=[])
def register_session_provider(session_provider):
  global _SESSION_PROVIDER
  # TODO(scottzhu): Change it back to only allow one time setting for session
  # provider once we finished the keras repo split.
  # if _SESSION_PROVIDER is None:
  _SESSION_PROVIDER = session_provider


def get_session():
  # Prefer TF's default session since get_session from Keras has side-effects.
  session = ops.get_default_session()
  if session is None:
    global _SESSION_PROVIDER
    if _SESSION_PROVIDER is not None:
      session = _SESSION_PROVIDER()  # pylint: disable=not-callable
  return session


class _ObjectGraphProtoPrettyPrinter(object):
  """Lazily traverses an object graph proto to pretty print names.

  If no calls to `node_names` are made this object has no performance
  overhead. On the other hand, it will only traverse the object graph once, so
  repeated naming is cheap after the first.
  """

  __slots__ = ["_object_graph_proto", "_node_name_cache"]

  def __init__(self, object_graph_proto):
    self._object_graph_proto = object_graph_proto
    self._node_name_cache = None

  @property
  def node_names(self):
    """Lazily creates a mapping from node id to ("path", "to", "root")."""
    if self._node_name_cache is not None:
      return self._node_name_cache
    path_to_root = {}
    path_to_root[0] = ("(root)",)
    to_visit = collections.deque([0])
    while to_visit:
      node_id = to_visit.popleft()
      obj = self._object_graph_proto.nodes[node_id]
      for child in obj.children:
        if child.node_id not in path_to_root:
          path_to_root[child.node_id] = (
              path_to_root[node_id] + (child.local_name,))
          to_visit.append(child.node_id)

    node_names = {}
    for node_id, path_to_root in path_to_root.items():
      node_names[node_id] = ".".join(path_to_root)

    for node_id, node in enumerate(self._object_graph_proto.nodes):
      for slot_reference in node.slot_variables:
        node_names[slot_reference.slot_variable_node_id] = (
            "{}'s state '{}' for {}".format(
                node_names[node_id], slot_reference.slot_name,
                node_names[slot_reference.original_variable_node_id]))
    self._node_name_cache = node_names
    return node_names


class _CheckpointRestoreCoordinatorDeleter(object):
  """Deleter to avoid overriding _CheckpointRestoreCoordinator.__del__()."""

  __slots__ = [
      "expect_partial", "object_graph_proto", "matched_proto_ids",
      "unused_attributes"
  ]

  def __init__(self, expect_partial, object_graph_proto, matched_proto_ids,
               unused_attributes):
    self.expect_partial = expect_partial
    self.object_graph_proto = object_graph_proto
    self.matched_proto_ids = matched_proto_ids
    self.unused_attributes = unused_attributes

  def set_expect_partial(self, expect_partial):
    self.expect_partial = expect_partial

  def __del__(self):
    if self.expect_partial:
      return
    if logging is None:
      # The logging module may have been unloaded when __del__ is called.
      log_fn = print
    else:
      log_fn = logging.warning
    printed_warning = False
    pretty_printer = _ObjectGraphProtoPrettyPrinter(self.object_graph_proto)
    for node_id in range(len(self.object_graph_proto.nodes)):
      if node_id not in self.matched_proto_ids:
        log_fn("Unresolved object in checkpoint: {}"
               .format(pretty_printer.node_names[node_id]))
        printed_warning = True
    for node_id, attribute_name in self.unused_attributes.items():
      log_fn(("Unused attribute in object {}: {}"
              .format(pretty_printer.node_names[node_id], attribute_name)))
      printed_warning = True
    if printed_warning:
      log_fn(
          "A checkpoint was restored (e.g. tf.train.Checkpoint.restore or "
          "tf.keras.Model.load_weights) but not all checkpointed values were "
          "used. See above for specific issues. Use expect_partial() on the "
          "load status object, e.g. "
          "tf.train.Checkpoint.restore(...).expect_partial(), to silence these "
          "warnings, or use assert_consumed() to make the check explicit. See "
          "https://www.tensorflow.org/guide/checkpoint#loading_mechanics"
          " for details.")


class _CheckpointRestoreCoordinator(object):
  """Holds the status of an object-based checkpoint load."""

  def __init__(self, object_graph_proto, save_path, save_path_tensor,
               restore_op_cache, graph_view, options):
    """Specify the checkpoint being loaded.

    Args:
      object_graph_proto: The TrackableObjectGraph protocol buffer associated
        with this checkpoint.
      save_path: A string, the path to the checkpoint, as returned by
        `tf.train.latest_checkpoint`.
      save_path_tensor: A string `Tensor` which contains or will be fed the save
        path.
      restore_op_cache: A dictionary shared between
        `_CheckpointRestoreCoordinator`s for the same Python objects, used to
        look up restore ops by name to avoid re-creating them across multiple
        `restore()` calls.
      graph_view: A graph_view_lib.ObjectGraphView object for the restored
        objects.
      options: A CheckpointOptions object.
    """
    self.options = options
    self.object_graph_proto = object_graph_proto
    self.restore_uid = ops.uid()
    # Maps from proto ids to lists of attributes which were in the checkpoint
    # but not loaded into any object, for error checking.
    self.unused_attributes = {}
    # Dictionary mapping from an id in the protocol buffer flat array to
    # Trackable Python objects. This mapping may be deferred if a
    # checkpoint is restored before all dependencies have been tracked. Uses
    # weak references so that partial restorations don't create reference cycles
    # (as objects with deferred dependencies will generally have references to
    # this object).
    self.object_by_proto_id = weakref.WeakValueDictionary()
    self.matched_proto_ids = set()
    # A set of all Python objects we've seen as dependencies, even if we didn't
    # use them (for example because of inconsistent references when
    # loading). Used to make status assertions fail when loading checkpoints
    # that don't quite match.
    self.all_python_objects = object_identity.ObjectIdentityWeakSet()
    self.save_path_tensor = save_path_tensor
    self.save_path_string = save_path
    reader = py_checkpoint_reader.NewCheckpointReader(save_path)
    self.dtype_map = reader.get_variable_to_dtype_map()
    self.shape_map = reader.get_variable_to_shape_map()
    # A NewCheckpointReader for the most recent checkpoint, for streaming Python
    # state restoration.
    # When graph building, contains a list of ops to run to restore objects from
    # this checkpoint.
    self.restore_ops = []
    self.restore_ops_by_name = restore_op_cache
    self.graph_view = graph_view
    self.new_restore_ops_callback = None
    # A mapping from optimizer proto ids to lists of slot variables to be
    # restored when the optimizer is tracked. Only includes slot variables whose
    # regular variables have already been created, and only for optimizer
    # objects which have not yet been created/tracked.
    self.deferred_slot_restorations = {}
    # A mapping from variable proto ids to lists of slot variables to be
    # restored when the variable is created/tracked. These get shifted over to
    # deferred_slot_restorations if the optimizer hasn't been created when that
    # happens.
    self.slot_restorations = {}
    # Controls whether errors are printed in __del__ if some objects did not
    # match.
    self.expect_partial_attr = False
    for node_index, node in enumerate(self.object_graph_proto.nodes):
      for slot_reference in node.slot_variables:
        # `node` refers to an `Optimizer`, since only these have slot variables.
        self.slot_restorations.setdefault(
            slot_reference.original_variable_node_id, []).append(
                base._SlotVariableRestoration(  # pylint: disable=protected-access
                    optimizer_id=node_index,
                    slot_variable_id=slot_reference.slot_variable_node_id,
                    slot_name=slot_reference.slot_name))

    self._deleter = _CheckpointRestoreCoordinatorDeleter(
        self.expect_partial_attr,
        self.object_graph_proto,
        self.matched_proto_ids,
        self.unused_attributes)

  @property
  def expect_partial(self):
    return self.expect_partial_attr

  @expect_partial.setter
  def expect_partial(self, expect_partial):
    self.expect_partial_attr = expect_partial
    self._deleter.set_expect_partial(expect_partial)

  def new_restore_ops(self, new_ops):
    self.restore_ops.extend(new_ops)
    if self.new_restore_ops_callback:
      self.new_restore_ops_callback(new_ops)  # pylint: disable=not-callable

  def restore_saveables(self, tensor_saveables, python_saveables):
    """Run or build restore operations for SaveableObjects.

    Args:
      tensor_saveables: `SaveableObject`s which correspond to Tensors.
      python_saveables: `PythonStateSaveable`s which correspond to Python
        values.

    Returns:
      When graph building, a list of restore operations, either cached or newly
      created, to restore `tensor_saveables`.
    """
    restore_ops = []
    # Eagerly run restorations for Python state.
    reader = None
    for saveable in python_saveables:
      if reader is None:
        # Lazily create the NewCheckpointReader, since this requires file access
        # and we may not have any Python saveables.
        reader = py_checkpoint_reader.NewCheckpointReader(self.save_path_string)
      spec_names = [spec.name for spec in saveable.specs]
      saveable.python_restore([reader.get_tensor(name) for name in spec_names])

    # If we have new SaveableObjects, extract and cache restore ops.
    if tensor_saveables:
      validated_saveables = saveable_object_util.validate_and_slice_inputs(
          tensor_saveables)
      validated_names = set(saveable.name for saveable in validated_saveables)
      if set(tensor_saveables.keys()) != validated_names:
        raise AssertionError(
            ("Saveable keys changed when validating. Got back %s, was "
             "expecting %s") % (tensor_saveables.keys(), validated_names))
      new_restore_ops = functional_saver.MultiDeviceSaver(
          validated_saveables).restore(self.save_path_tensor, self.options)
      if not context.executing_eagerly():
        for name, restore_op in sorted(new_restore_ops.items()):
          restore_ops.append(restore_op)
          assert name not in self.restore_ops_by_name
          self.restore_ops_by_name[name] = restore_op
    return restore_ops


class _NameBasedRestoreCoordinator(object):
  """Keeps the status of a name-based checkpoint restore."""

  def __init__(self, save_path, dtype_map=None):
    self.save_path = save_path
    self.dtype_map = dtype_map
    # A map from trackable objects to unused attribute names. We don't have
    # proto IDs when doing a name-based restore, so the map keys differ from
    # those in _CheckpointRestoreCoordinator.
    self.unused_attributes = object_identity.ObjectIdentityWeakKeyDictionary()
    self.restore_uid = ops.uid()

  def globally_named_object_attributes(self, trackable):
    """Create globally named SaveableObjects from attributes.

    If an object's attribute has no global name specified (default construction
    for the SaveableObject factory), records the failure in
    `self.unused_attributes` (which can then be used to make status assertions
    fail; see `NameBasedSaverStatus`).

    Args:
      trackable: An object to save.

    Yields:
      SaveableObjects for `trackable`'s attributes.
    """
    for attribute_name, saveable_factory in (
        trackable._gather_saveables_for_checkpoint().items()):  # pylint: disable=protected-access
      if callable(saveable_factory):
        try:
          # This saveable object factory does not have a default name= argument,
          # which means there's no way to save/restore it using a name-based
          # checkpoint. Ignore the error now and make sure assert_consumed()
          # fails.
          saveable = saveable_factory()
        except TypeError:
          # Even if we can't name this object, we should construct it and check
          # whether it's optional to restore it. If it's optional we don't need
          # to make assertions fail.
          if not saveable_factory("").optional_restore:
            self.unused_attributes.setdefault(trackable,
                                              []).append(attribute_name)
          continue
      else:
        saveable = saveable_factory
      names_to_saveables = saveable_object_util.op_list_to_dict(
          [saveable], convert_variable_to_tensor=False)
      for name, op in names_to_saveables.items():
        for saveable_object in saveable_object_util.saveable_objects_for_op(
            op=op, name=name):
          yield saveable_object

  def eager_restore(self, trackable):
    """Runs restore ops for `trackable`'s attributes."""
    # When graph building, we don't add any restore ops to the graph until
    # run_restore_ops/initialize_or_restore on the status object for name-based
    # checkpoints.
    assert context.executing_eagerly()
    for saveable in self.globally_named_object_attributes(trackable):
      restored_tensors = []
      tensor_missing = False
      for spec in saveable.specs:
        if spec.name in self.dtype_map:
          with ops.device("cpu:0"):
            restored, = io_ops.restore_v2(
                prefix=self.save_path,
                tensor_names=[spec.name],
                shape_and_slices=[""],
                dtypes=[self.dtype_map[spec.name]],
                name="%s_checkpoint_read" % (spec.name,))
          restored_tensors.append(array_ops.identity(restored))
        else:
          tensor_missing = True

      if tensor_missing:
        # Record that this variable didn't match so assertions will fail.
        self.unused_attributes.setdefault(trackable, []).append(saveable.name)
      else:
        # Ignores values missing from the checkpoint, as with object-based
        # restore. Status assertions can be used to check exact matches,
        # although it's unlikely to ever happen for name-based checkpoints.
        saveable.restore(
            restored_tensors=restored_tensors, restored_shapes=None)


# TODO(allenl): If this ends up in a public API, consider adding LINT.If Change
# or consolidating the implementation with get_variable.
def _default_getter(name,
                    shape,
                    dtype,
                    initializer=None,
                    partition_info=None,
                    **kwargs):
  """A pared-down version of get_variable which does not reuse variables."""
  dtype = dtypes.as_dtype(dtype)
  shape_object = tensor_shape.as_shape(shape)
  with ops.init_scope():
    if initializer is None:
      initializer, initializing_from_value = (
          variable_scope._get_default_variable_store()._get_default_initializer(  # pylint: disable=protected-access
              name=name,
              shape=shape_object,
              dtype=dtype))
    else:
      initializing_from_value = not callable(initializer)
    # Same logic as get_variable
    variable_dtype = dtype.base_dtype
    if initializing_from_value:
      if shape is not None:
        raise ValueError("If initializer is a constant, do not specify shape.")
      initial_value = initializer
    else:
      # Instantiate initializer if provided initializer is a type object.
      if isinstance(initializer, type(init_ops.Initializer)):
        initializer = initializer(dtype=dtype)
      shape_list = None if shape is None else shape_object.as_list()
      if "partition_info" in tf_inspect.getargspec(initializer).args:
        initial_value = functools.partial(initializer,
                                          shape_list,
                                          dtype=dtype,
                                          partition_info=partition_info)
      else:
        initial_value = functools.partial(initializer,
                                          shape_list,
                                          dtype=dtype)

    return variables.VariableV1(
        initial_value=initial_value,
        name=name,
        dtype=variable_dtype,
        use_resource=True,
        **kwargs)


def add_variable(trackable,
                 name,
                 shape=None,
                 dtype=dtypes.float32,
                 initializer=None,
                 trainable=True):
  """Add a variable to a Trackable with no scope influence."""
  return trackable._add_variable_with_custom_getter(  # pylint: disable=protected-access
      name=name,
      shape=shape,
      dtype=dtype,
      initializer=initializer,
      getter=_default_getter,
      trainable=trainable)


def object_metadata(save_path):
  """Retrieves information about the objects in a checkpoint.

  Example usage:

  ```python
  object_graph = tf.contrib.checkpoint.object_metadata(
      tf.train.latest_checkpoint(checkpoint_directory))
  ckpt_variable_names = set()
  for node in object_graph.nodes:
    for attribute in node.attributes:
      ckpt_variable_names.add(attribute.full_name)
  ```

  Args:
    save_path: The path to the checkpoint, as returned by `save` or
      `tf.train.latest_checkpoint`.

  Returns:
    A parsed `tf.contrib.checkpoint.TrackableObjectGraph` protocol buffer.
  Raises:
    ValueError: If an object graph was not found in the checkpoint.
  """
  reader = py_checkpoint_reader.NewCheckpointReader(save_path)
  try:
    object_graph_string = reader.get_tensor(base.OBJECT_GRAPH_PROTO_KEY)
  except errors_impl.NotFoundError:
    raise ValueError(
        ('The specified checkpoint "%s" does not appear to be object-based (it '
         'is missing the key "%s"). Likely it was created with a name-based '
         "saver and does not contain an object dependency graph.") %
        (save_path, base.OBJECT_GRAPH_PROTO_KEY))
  object_graph_proto = (trackable_object_graph_pb2.TrackableObjectGraph())
  object_graph_proto.ParseFromString(object_graph_string)
  return object_graph_proto


def list_objects(root_trackable):
  """Traverse the object graph and list all accessible objects.

  Looks for `Trackable` objects which are dependencies of
  `root_trackable`. Includes slot variables only if the variable they are
  slotting for and the optimizer are dependencies of `root_trackable`
  (i.e. if they would be saved with a checkpoint).

  Args:
    root_trackable: A `Trackable` object whose dependencies should be flattened.

  Returns:
    A flat list of objects.
  """
  return graph_view_lib.ObjectGraphView(root_trackable).list_objects()


def gather_initializers(root_trackable):
  """Traverse the object graph and find initialization ops.

  Looks for `Trackable` objects which are dependencies of
  `root_trackable` and which have an `initializer` property. Includes
  initializers for slot variables only if the variable they are slotting for and
  the optimizer are dependencies of `root_trackable` (i.e. if they would be
  saved with a checkpoint).

  Args:
    root_trackable: A `Trackable` object to gather initializers for.

  Returns:
    A list of initialization ops.
  """
  trackable_objects = list_objects(root_trackable)
  return [
      c.initializer
      for c in trackable_objects
      if hasattr(c, "initializer") and c.initializer is not None
  ]


@tf_contextlib.contextmanager
def capture_dependencies(template):
  """Capture variables created within this scope as `Template` dependencies.

  Requires that `template.variable_scope` is active.

  This scope is intended as a compatibility measure, allowing a trackable
  object to add dependencies on variables created in a block of code which is
  not aware of object-based saving (and instead uses variable names
  heavily). This is how `Template` objects add dependencies on variables and
  sub-`Template`s. Where possible, use `tf.compat.v1.make_template` directly.

  Args:
    template: The `Template` object to register dependencies with.

  Yields:
    None (when used as a context manager).
  """
  name_prefix = template.variable_scope.name

  def _trackable_custom_creator(next_creator,
                                name,
                                initial_value,
                                trackable_parent=None,
                                **kwargs):
    """A variable creation hook which adds Trackable dependencies.

    Set for example during a `Template`'s first wrapped function
    execution. Ensures that (a) `template` depends on any trackable
    objects using their own `capture_dependencies` scope inside this scope which
    create variables, and (b) that any variables not in a more deeply nested
    scope are added as dependencies directly.

    The `trackable_parent` argument is passed between custom creators but
    ignored when the variable object itself is created. This argument indicates
    (if not `None`) that a more deeply nested scope has already added the
    variable as a dependency, and that parent scopes should add a dependency on
    that object rather than on the variable directly.

    Args:
      next_creator: See `variable_scope.variable_creator_scope`; the next
        creator in the chain.
      name: The (full, scope-influenced) name of the variable. The `name_prefix`
        itself is stripped for the purposes of object-based dependency tracking,
        but scopes opened within this scope are respected.
      initial_value: See `variable_scope.variable_creator_scope`. Taken
        explicitly so the argument can be re-named and used with
        `Trackable._add_variable_with_custom_getter`.
      trackable_parent: If not None, a more deeply nested trackable object and
        its name prefix which were passed to `capture_dependencies` to add a
        dependency on (rather than depending on the variable directly).
      **kwargs: Passed through to the next creator.

    Returns:
      The output of `next_creator`: the fetched/created variable object.
    """

    def _call_next_creator_renaming_initializer(initializer, **inner_kwargs):
      inner_kwargs.pop("name")  # Ignored; this is the scope-stripped name which
      # we don't want to propagate.
      return next_creator(initial_value=initializer, name=name, **inner_kwargs)

    if name is not None and name.startswith(name_prefix):
      scope_stripped_name = name[len(name_prefix) + 1:]
      if not trackable_parent:
        return template._add_variable_with_custom_getter(  # pylint: disable=protected-access
            initializer=initial_value,
            name=scope_stripped_name,
            getter=_call_next_creator_renaming_initializer,
            # Disable error checking for Trackable. Exceptions are instead
            # raised if necessary when the object-based saver tries to
            # save/restore the object.
            overwrite=True,
            trackable_parent=(template, name_prefix),
            **kwargs)
      else:
        parent_object, parent_name_prefix = trackable_parent
        template._track_trackable(  # pylint: disable=protected-access
            parent_object,
            name=parent_name_prefix[len(name_prefix) + 1:],
            overwrite=True)
    return next_creator(
        name=name,
        initial_value=initial_value,
        trackable_parent=(template, name_prefix),
        **kwargs)

  with variable_scope.variable_creator_scope(_trackable_custom_creator):
    yield


class _LoadStatus(object):
  """Abstract base for load status callbacks."""

  @abc.abstractmethod
  def assert_consumed(self):
    """Raises an exception unless a non-trivial restoration has completed."""
    pass

  @abc.abstractmethod
  def assert_existing_objects_matched(self):
    """Raises an exception unless existing Python objects have been matched."""
    pass

  @abc.abstractmethod
  def assert_nontrivial_match(self):
    """Raises an exception if only the root object matched."""
    pass

  @abc.abstractmethod
  def run_restore_ops(self, session=None):
    """Runs restore ops from the checkpoint. Requires a valid checkpoint."""
    pass

  @abc.abstractmethod
  def initialize_or_restore(self, session=None):
    """Runs restore ops from the checkpoint, or initializes variables."""
    pass

  def expect_partial(self):
    """Silence warnings about incomplete checkpoint restores."""
    return self


@tf_export("__internal__.tracking.streaming_restore", v1=[])
def streaming_restore(status, session=None):
  """When graph building, runs restore ops as soon as they come in.

  Args:
    status: A _LoadStatus objects from an object-based saver's restore().
      Streaming restore from name-based checkpoints is not currently supported.
    session: A session to run new restore ops in.
  """
  if context.executing_eagerly():
    # Streaming restore is the default/only behavior when executing eagerly.
    return
  if session is None:
    session = get_session()
  if isinstance(status, NameBasedSaverStatus):
    raise NotImplementedError(
        "Streaming restore not supported from name-based checkpoints when "
        "graph building. File a feature request if this limitation bothers "
        "you. As a workaround, consider either using tf.train.Checkpoint to "
        "load name-based checkpoints or enabling eager execution.")
  status.run_restore_ops(session=session)
  # pylint: disable=protected-access
  status._checkpoint.new_restore_ops_callback = (
      lambda ops: session.run(ops, feed_dict=status._feed_dict))
  # pylint: enable=protected-access


def _objects_with_attributes(full_list):
  """Filters out objects with no direct variable dependencies for assertions."""
  return [o for o in full_list if o._gather_saveables_for_checkpoint()]  # pylint: disable=protected-access


class CheckpointLoadStatus(_LoadStatus):
  """Checks the status of checkpoint loading and manages restore ops.

  Returned from `Saver.restore`. Since `restore` may defer the loading of values
  in the checkpoint which don't yet have corresponding Python objects,
  `CheckpointLoadStatus` provides a callback to verify that checkpoint loading
  is complete (`assert_consumed`).

  When graph building, `restore` does not run restore ops itself since their
  creation may be deferred. The `run_restore_ops` method must be called once all
  Python objects with values to restore have been created and added to the
  dependency graph (this does not necessarily have to be the whole checkpoint;
  calling `run_restore_ops` while `assert_consumed` fails is supported and will
  partially restore the checkpoint).

  See `Saver.restore` for usage examples.
  """

  def __init__(self, checkpoint, feed_dict, graph_view):
    self._checkpoint = checkpoint
    self._feed_dict = feed_dict
    self._graph_view = graph_view
    # Keep a reference to the root, since graph_view might only have a weakref.
    self._root = graph_view.root

  def assert_consumed(self):
    """Asserts that all objects in the checkpoint have been created/matched.

    Returns:
      `self` for chaining.
    Raises:
      AssertionError: If there are any Python objects in the dependency graph
        which have not been restored from this checkpoint or a later `restore`,
        or if there are any checkpointed values which have not been matched to
        Python objects.
    """
    pretty_printer = _ObjectGraphProtoPrettyPrinter(
        self._checkpoint.object_graph_proto)
    self.assert_existing_objects_matched()
    for node_id, node in enumerate(self._checkpoint.object_graph_proto.nodes):
      if not node.attributes:
        # Only raise exceptions for the nodes with attributes themselves. Either
        # they're ultimately not important, or they have a child with an
        # attribute.
        continue
      trackable = self._checkpoint.object_by_proto_id.get(node_id, None)
      if trackable is None:
        raise AssertionError("Unresolved object in checkpoint {}: {}"
                             .format(pretty_printer.node_names[node_id], node))
    if self._checkpoint.slot_restorations:
      # Sanity check; this collection should be clear if everything has been
      # restored.
      raise AssertionError("Unresolved slot restorations: %s" %
                           (self._checkpoint.slot_restorations,))
    if self._checkpoint.unused_attributes:
      unused_attribute_messages = []
      for node_id, attribute in six.iteritems(
          self._checkpoint.unused_attributes):
        obj = self._checkpoint.object_by_proto_id[node_id]
        unused_attribute_messages.append(
            "{} ({}): {}"
            .format(pretty_printer.node_names[node_id], obj, attribute))
      raise AssertionError(
          ("Unused attributes in these objects (the attributes exist in the "
           "checkpoint but were not restored):\n{}")
          .format("\n".join(unused_attribute_messages)))
    return self

  def assert_existing_objects_matched(self):
    """Asserts that trackable Python objects have been matched.

    Note that this is a weaker assertion than `assert_consumed`. It will only
    fail for existing Python objects which are (transitive) dependencies of the
    root object and which do not have an entry in the checkpoint.

    It will not fail, for example, if a `tf.keras.Layer` object has not yet been
    built and so has not created any `tf.Variable` objects.

    Returns:
      `self` for chaining.

    Raises:
      AssertionError: If a Python object exists in the transitive dependencies
        of the root object but does not have a value in the checkpoint.
    """
    for node_id, node in enumerate(self._checkpoint.object_graph_proto.nodes):
      trackable = self._checkpoint.object_by_proto_id.get(node_id, None)
      if (trackable is not None and
          trackable._update_uid < self._checkpoint.restore_uid):  # pylint: disable=protected-access
        raise AssertionError("Object not assigned a value from checkpoint: %s" %
                             (node,))
    for trackable_object in self._graph_view.list_objects():
      # Remove data structures that do not contain any variables from
      # restoration checks.
      if (isinstance(trackable_object,
                     data_structures.TrackableDataStructure) and
          not trackable_object._checkpoint_dependencies):
        continue
      self._checkpoint.all_python_objects.add(trackable_object)
    unused_python_objects = (
        object_identity.ObjectIdentitySet(
            _objects_with_attributes(
                self._checkpoint.all_python_objects)) -
        object_identity.ObjectIdentitySet(
            self._checkpoint.object_by_proto_id.values()))
    if unused_python_objects:
      raise AssertionError(
          ("Some Python objects were not bound to checkpointed values, likely "
           "due to changes in the Python program: %s") %
          (list(unused_python_objects),))
    return self

  def assert_nontrivial_match(self):
    """Raises an exception if only the root object matched."""
    for trackable_object in self._graph_view.list_objects():
      self._checkpoint.all_python_objects.add(trackable_object)
    if len(self._checkpoint.object_by_proto_id) <= 1:
      unused_python_objects = (
          object_identity.ObjectIdentitySet(
              _objects_with_attributes(self._checkpoint.all_python_objects))
          - object_identity.ObjectIdentitySet(
              self._checkpoint.object_by_proto_id.values()))
      if unused_python_objects:
        raise AssertionError(
            ("Nothing except the root object matched a checkpointed value. "
             "Typically this means that the checkpoint does not match the "
             "Python program. The following objects have no matching "
             "checkpointed value: %s") % (list(unused_python_objects),))
      else:
        raise AssertionError(
            "Nothing to load. No dependencies have been added to %s yet." %
            (self._graph_view.root,))
    return self

  def run_restore_ops(self, session=None):
    """Run operations to restore objects in the dependency graph."""
    if context.executing_eagerly():
      return  # Run eagerly
    if session is None:
      session = get_session()
    session.run(self._checkpoint.restore_ops, feed_dict=self._feed_dict)

  def initialize_or_restore(self, session=None):
    """Run operations to initialize or restore objects in the dependency graph.

    Any objects in the dependency graph which have initializers but are not in
    the checkpoint will have those initializers run, unless those variables are
    being restored by a later call to `tf.train.Checkpoint.restore()`.

    This method has a sibling in `InitializationOnlyStatus` which instead
    initializes variables. That type is returned if no checkpoint is specified
    in `Saver.restore`.

    Args:
      session: The session to run init/restore ops in. If `None`, uses the
        default session.
    """
    if context.executing_eagerly():
      return  # Initialization and restoration ops are run eagerly
    if session is None:
      session = get_session()
    all_objects = self._graph_view.list_objects()
    already_initialized_objects = object_identity.ObjectIdentitySet(
        self._checkpoint.object_by_proto_id.values())
    initializers_for_non_restored_variables = [
        c.initializer for c in all_objects
        if hasattr(c, "initializer")
        and c not in already_initialized_objects
        and (getattr(c, "_update_uid", self._checkpoint.restore_uid - 1)
             < self._checkpoint.restore_uid)]
    self.run_restore_ops(session=session)
    session.run(initializers_for_non_restored_variables)

  def expect_partial(self):
    """Silence warnings about incomplete checkpoint restores."""
    self._checkpoint.expect_partial = True
    return self


class InitializationOnlyStatus(_LoadStatus):
  """Returned from `Saver.restore` when no checkpoint has been specified.

  Objects of this type have the same `assert_consumed` method as
  `CheckpointLoadStatus`, but it always fails. However,
  `initialize_or_restore` works on objects of both types, and will
  initialize variables in `InitializationOnlyStatus` objects or restore them
  otherwise.
  """

  def __init__(self, graph_view, restore_uid):
    self._restore_uid = restore_uid
    self._graph_view = graph_view
    # Keep a reference to the root, since graph_view might only have a weakref.
    self._root = graph_view.root

  def assert_consumed(self):
    """Assertion for consistency with `CheckpointLoadStatus`. Always fails."""
    raise AssertionError(
        "No checkpoint specified (save_path=None); nothing is being restored.")

  def assert_existing_objects_matched(self):
    """Assertion for consistency with `CheckpointLoadStatus`. Always fails."""
    raise AssertionError(
        "No checkpoint specified (save_path=None); nothing is being restored.")

  def assert_nontrivial_match(self):
    """Assertion for consistency with `CheckpointLoadStatus`. Always fails."""
    raise AssertionError(
        "No checkpoint specified (save_path=None); nothing is being restored.")

  def run_restore_ops(self, session=None):
    """For consistency with `CheckpointLoadStatus`.

    Use `initialize_or_restore` for initializing if no checkpoint was passed
    to `Saver.restore` and restoring otherwise.

    Args:
      session: Not used.
    """
    raise AssertionError(
        "No checkpoint specified, so no restore ops are available "
        "(save_path=None to Saver.restore).")

  def initialize_or_restore(self, session=None):
    """Runs initialization ops for variables.

    Objects which would be saved by `Saver.save` will be initialized, unless
    those variables are being restored by a later call to
    `tf.train.Checkpoint.restore()`.

    This method does nothing when executing eagerly (initializers get run
    eagerly).

    Args:
      session: The session to run initialization ops in. If `None`, uses the
        default session.
    """
    if context.executing_eagerly():
      return  # run eagerly
    if session is None:
      session = get_session()
    trackable_objects = self._graph_view.list_objects()
    initializers = [
        c.initializer for c in trackable_objects
        if hasattr(c, "initializer") and c.initializer is not None
        and (getattr(c, "_update_uid", self._restore_uid - 1)
             < self._restore_uid)]
    session.run(initializers)


_DEPRECATED_RESTORE_INSTRUCTIONS = (
    "Restoring a name-based tf.train.Saver checkpoint using the object-based "
    "restore API. This mode uses global names to match variables, and so is "
    "somewhat fragile. It also adds new restore ops to the graph each time it "
    "is called when graph building. Prefer re-encoding training checkpoints in "
    "the object-based format: run save() on the object-based saver (the same "
    "one this message is coming from) and use that checkpoint in the future.")


class NameBasedSaverStatus(_LoadStatus):
  """Status for loading a name-based training checkpoint."""

  # Ideally this deprecation decorator would be on the class, but that
  # interferes with isinstance checks.
  @deprecation.deprecated(
      date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS)
  def __init__(self, checkpoint, graph_view):
    self._checkpoint = checkpoint
    self._graph_view = graph_view
    self._optionally_restored = []
    # Keep a reference to the root, since graph_view might only have a weakref.
    self._root = graph_view.root

  def add_to_optionally_restored(self, var):
    """Add a variable to the list of optionally restored variables.

    There are situations where certain variables should be ignored in assertions
    such as assert_existing_objects_matched(). One example is that of a
    checkpoint saved with train.Saver(), and restored with train.Checkpoint():
    it is possible for the train.Saver() checkpoint to be missing the internal
    `save_counter` variable, which we want to ignore on restore.

    Args:
      var: The variable to treat as optionally restored.
    """
    self._optionally_restored.append(var)

  def assert_consumed(self):
    """Raises an exception if any variables are unmatched."""
    unused_attributes = list(self._checkpoint.unused_attributes.items())
    unused_attributes = [
        a for a in unused_attributes
        if all(a[0] is not x for x in self._optionally_restored)
    ]
    if unused_attributes:
      unused_attribute_strings = [
          "\n    {}: {}".format(obj, attributes)
          for obj, attributes in unused_attributes
      ]
      raise AssertionError(
          "Some objects had attributes which were not restored:{}".format(
              "".join(unused_attribute_strings)))
    for trackable in self._graph_view.list_objects():
      # pylint: disable=protected-access
      trackable._maybe_initialize_trackable()
      if trackable._update_uid < self._checkpoint.restore_uid:
        raise AssertionError("Object not restored: %s" % (trackable,))
      # pylint: enable=protected-access
    return self

  def assert_existing_objects_matched(self):
    """Raises an exception if currently created objects are unmatched."""
    # For name-based checkpoints there's no object information in the
    # checkpoint, so there's no distinction between
    # assert_existing_objects_matched and assert_consumed (and both are less
    # useful since we don't touch Python objects or Python state).
    return self.assert_consumed()

  def assert_nontrivial_match(self):
    """Raises an exception if currently created objects are unmatched."""
    # For name-based checkpoints there's no object information in the
    # checkpoint, so there's no distinction between
    # assert_nontrivial_match and assert_consumed (and both are less
    # useful since we don't touch Python objects or Python state).
    return self.assert_consumed()

  def _gather_saveable_objects(self):
    """Walk the object graph, using global names for SaveableObjects."""
    objects = self._graph_view.list_objects()
    saveable_objects = []
    for trackable in objects:
      # pylint: disable=protected-access
      trackable._maybe_initialize_trackable()
      if trackable._update_uid < self._checkpoint.restore_uid:
        trackable._update_uid = self._checkpoint.restore_uid
      else:
        continue
      # pylint: enable=protected-access
      saveable_objects.extend(
          self._checkpoint.globally_named_object_attributes(trackable))
    return saveable_objects

  def run_restore_ops(self, session=None):
    """Load the name-based checkpoint using a new `tf.compat.v1.train.Saver`."""
    if context.executing_eagerly():
      return  # Nothing to do, variables are restored on creation.
    if session is None:
      session = get_session()
    with ops.device("/cpu:0"):
      saveables = self._gather_saveable_objects()
      v1_saver_lib.Saver(saveables).restore(
          sess=session, save_path=self._checkpoint.save_path)

  def initialize_or_restore(self, session=None):
    """Alias for `run_restore_ops`."""
    self.run_restore_ops(session=session)


class _SessionWithFeedDictAdditions(session_lib.SessionInterface):
  """Pretends to be a session, inserts extra feeds on run()."""

  def __init__(self, session, feed_additions):
    self._wrapped_session = session
    self._feed_additions = feed_additions

  def run(self, fetches, feed_dict=None, **kwargs):
    if feed_dict is None:
      feed_dict = {}
    else:
      feed_dict = feed_dict.copy()
    feed_dict.update(self._feed_additions)
    return self._wrapped_session.run(
        fetches=fetches, feed_dict=feed_dict, **kwargs)


@tf_export("__internal__.tracking.TrackableSaver", v1=[])
class TrackableSaver(object):
  """Saves and restores a `Trackable` object and its dependencies.

  See `Trackable` for details of dependency management. `Saver` wraps
  `tf.compat.v1.train.Saver` for saving, including extra information about the
  graph of
  dependencies between Python objects. When restoring, it uses this information
  about the save-time dependency graph to more robustly match objects with their
  checkpointed values. When executing eagerly, it supports restoring variables
  on object creation (see `Saver.restore`).

  Values in a checkpoint are mapped to `Trackable` Python objects
  (`Variable`s, `Optimizer`s, `Layer`s) based on the names provided when the
  checkpoint was written. To avoid breaking existing checkpoints when modifying
  a class, dependency names (the names of attributes to which `Trackable`
  objects are assigned) may not change. These names are local to objects, in
  contrast to the `Variable.name`-based save/restore from
  `tf.compat.v1.train.Saver`, and
  so allow additional program transformations.
  """

  def __init__(self, graph_view):
    """Configure saving.

    Args:
      graph_view: A `GraphView` object containing a description of the object
        graph to save.
    """
    # The file prefix placeholder is created lazily when graph building (and not
    # at all when executing eagerly) to avoid creating ops in the constructor
    # (when they may never be necessary).
    self._file_prefix_placeholder = None

    # Op caching for save
    self._object_graph_feed_tensor = None
    self._last_save_object_graph = None
    self._file_prefix_feed_tensor = None
    self._cached_save_operation = None

    # Op caching for restore, shared between _CheckpointRestoreCoordinators
    self._restore_op_cache = {}
    self._graph_view = graph_view

  def _gather_saveables(self, object_graph_tensor=None):
    """Wraps _serialize_object_graph to include the object graph proto."""
    (named_saveable_objects, graph_proto,
     feed_additions) = self._graph_view.serialize_object_graph()
    if object_graph_tensor is None:
      with ops.device("/cpu:0"):
        object_graph_tensor = constant_op.constant(
            graph_proto.SerializeToString(), dtype=dtypes.string)
    else:
      feed_additions.update(
          {object_graph_tensor: graph_proto.SerializeToString()})
    assert base.OBJECT_GRAPH_PROTO_KEY not in named_saveable_objects
    named_saveable_objects.append(
        base.NoRestoreSaveable(
            tensor=object_graph_tensor, name=base.OBJECT_GRAPH_PROTO_KEY))
    return named_saveable_objects, graph_proto, feed_additions

  def _save_cached_when_graph_building(self,
                                       file_prefix,
                                       object_graph_tensor,
                                       options):
    """Create or retrieve save ops.

    Args:
      file_prefix: The prefix for saved checkpoint files.
      object_graph_tensor: A `Tensor` to which the current object graph will be
        fed.
      options: `CheckpointOptions` object.

    Returns:
      A two-element tuple with a filename tensor and a feed_dict of tensors to
      feed when running it (if graph building). The feed dict contains the
      current object graph and any Python state to be saved in the
      checkpoint. When executing eagerly only the first argument is meaningful.
    """
    (named_saveable_objects, graph_proto,
     feed_additions) = self._gather_saveables(
         object_graph_tensor=object_graph_tensor)
    if (self._last_save_object_graph != graph_proto
        # When executing eagerly, we need to re-create SaveableObjects each time
        # save() is called so they pick up new Tensors passed to their
        # constructors. That means the Saver needs to be copied with a new
        # var_list.
        or context.executing_eagerly() or ops.inside_function()):
      saver = functional_saver.MultiDeviceSaver(named_saveable_objects)
      save_op = saver.save(file_prefix, options=options)
      with ops.device("/cpu:0"):
        with ops.control_dependencies([save_op]):
          self._cached_save_operation = array_ops.identity(file_prefix)
      self._last_save_object_graph = graph_proto
    return self._cached_save_operation, feed_additions

  def save(self, file_prefix, checkpoint_number=None, session=None,
           options=None):
    """Save a training checkpoint.

    The saved checkpoint includes variables created by this object and any
    Trackable objects it depends on at the time `Saver.save()` is called.

    Args:
      file_prefix: A prefix to use for the checkpoint filenames
        (/path/to/directory/and_a_prefix). Names are generated based on this
        prefix and `checkpoint_number`, if provided.
      checkpoint_number: An integer variable or Tensor, used to number
        checkpoints. Typically this value is saved along with other variables in
        training checkpoints, which will happen automatically if it was created
        by `root_trackable` or one of its dependencies (via
        `Trackable._add_variable`).
      session: The session to evaluate variables in. Ignored when executing
        eagerly. If not provided when graph building, the default session is
        used.
      options: Optional `tf.train.CheckpointOptions` object.

    Returns:
      The full path to the checkpoint.
    """
    options = options or checkpoint_options.CheckpointOptions()
    feed_dict = {}
    use_session = (not context.executing_eagerly() and
                   not ops.inside_function())
    if checkpoint_number:
      file_prefix = "%s-%d" % (file_prefix, checkpoint_number)
    if use_session:
      if self._object_graph_feed_tensor is None:
        with ops.device("/cpu:0"):
          self._object_graph_feed_tensor = constant_op.constant(
              "", dtype=dtypes.string)
          self._file_prefix_feed_tensor = constant_op.constant(
              "", dtype=dtypes.string)
      object_graph_tensor = self._object_graph_feed_tensor
      file_prefix_tensor = self._file_prefix_feed_tensor
      feed_dict[file_prefix_tensor] = file_prefix
    else:
      with ops.device("/cpu:0"):
        file_prefix_tensor = constant_op.constant(
            file_prefix, dtype=dtypes.string)
      object_graph_tensor = None

    file_io.recursive_create_dir(os.path.dirname(file_prefix))
    save_path, new_feed_additions = self._save_cached_when_graph_building(
        file_prefix_tensor, object_graph_tensor, options)
    if new_feed_additions:
      feed_dict.update(new_feed_additions)
    if not use_session:
      session = None
    elif session is None:
      session = get_session()

    if session:
      return session.run(save_path, feed_dict=feed_dict)
    else:
      return save_path

  def restore(self, save_path, options=None):
    """Restore a training checkpoint.

    Restores `root_trackable` and any objects that it tracks
    (transitive). Either assigns values immediately if variables to restore have
    been created already, or defers restoration until the variables are
    created. Dependencies added to the `root_trackable` passed to the
    constructor after this call will be matched if they have a corresponding
    object in the checkpoint.

    When building a graph, restorations are added to the graph but not run.

    ```python
    saver = Saver(root)
    saver.restore(path)
    ```

    To ensure that loading is complete and no more assignments will take place
    you can use the `assert_consumed()` method of the status object returned
    by the `restore` call.

    The assert will raise an exception unless every object was matched and all
    checkpointed values have a matching variable object.

    ```python
    saver = Saver(root)
    saver.restore(path).assert_consumed()
    ```

    When graph building, `assert_consumed()` indicates that all of the restore
    ops which will be created for this checkpoint have been created. They can be
    run via the `run_restore_ops()` function of the status object:

    ```python
    saver.restore(path).assert_consumed().run_restore_ops()
    ```

    If the checkpoint has not been consumed completely, then the list of restore
    ops will grow as more objects are added to the dependency graph.

    Name-based `tf.compat.v1.train.Saver` checkpoints can be loaded using this
    method. There is no deferred loading, and names are used to match
    variables. No restore ops are created/run until `run_restore_ops()` or
    `initialize_or_restore()` are called on the returned status object, even
    when executing eagerly. Re-encode name-based checkpoints using this
    object-based `Saver.save` as soon as possible.

    Args:
      save_path: The path to the checkpoint, as returned by `save` or
        `tf.train.latest_checkpoint`. If None (as when there is no latest
        checkpoint for `tf.train.latest_checkpoint` to return), returns an
        object which may run initializers for objects in the dependency graph.
        If the checkpoint was written by the name-based
        `tf.compat.v1.train.Saver`, names are used to match variables.
      options: Optional `tf.train.CheckpointOptions` object.

    Returns:
      A load status object, which can be used to make assertions about the
      status of checkpoint restoration and run initialization/restore ops
      (of type `CheckpointLoadStatus`, or `InitializationOnlyStatus` if
      `save_path` is `None`).

      If `save_path` points to a name-based checkpoint, a `NameBasedSaverStatus`
      object is returned which runs restore ops from a name-based saver.
    """
    options = options or checkpoint_options.CheckpointOptions()
    if save_path is None:
      return InitializationOnlyStatus(self._graph_view, ops.uid())
    reader = py_checkpoint_reader.NewCheckpointReader(save_path)
    graph_building = not context.executing_eagerly()
    if graph_building:
      dtype_map = None
    else:
      dtype_map = reader.get_variable_to_dtype_map()
    try:
      object_graph_string = reader.get_tensor(base.OBJECT_GRAPH_PROTO_KEY)
    except errors_impl.NotFoundError:
      # The object graph proto does not exist in this checkpoint. Try the
      # name-based compatibility mode.
      restore_coordinator = _NameBasedRestoreCoordinator(
          save_path=save_path,
          dtype_map=dtype_map)
      if not graph_building:
        for existing_trackable in self._graph_view.list_objects():
          # pylint: disable=protected-access
          existing_trackable._maybe_initialize_trackable()
          existing_trackable._name_based_restores.add(restore_coordinator)
          existing_trackable._name_based_attribute_restore(restore_coordinator)
          # pylint: enable=protected-access
      return NameBasedSaverStatus(
          restore_coordinator,
          graph_view=self._graph_view)

    if graph_building:
      if self._file_prefix_placeholder is None:
        with ops.device("/cpu:0"):
          self._file_prefix_placeholder = constant_op.constant("model")
      file_prefix_tensor = self._file_prefix_placeholder
      file_prefix_feed_dict = {self._file_prefix_placeholder: save_path}
    else:
      with ops.device("/cpu:0"):
        file_prefix_tensor = constant_op.constant(save_path)
      file_prefix_feed_dict = None
    object_graph_proto = (trackable_object_graph_pb2.TrackableObjectGraph())
    object_graph_proto.ParseFromString(object_graph_string)
    checkpoint = _CheckpointRestoreCoordinator(
        object_graph_proto=object_graph_proto,
        save_path=save_path,
        save_path_tensor=file_prefix_tensor,
        restore_op_cache=self._restore_op_cache,
        graph_view=self._graph_view,
        options=options)
    base.CheckpointPosition(
        checkpoint=checkpoint, proto_id=0).restore(self._graph_view.root)

    # Attached dependencies are not attached to the root, so should be restored
    # separately.
    if self._graph_view.attached_dependencies:
      for ref in self._graph_view.attached_dependencies:
        if ref.name == "root":
          # Root dependency is automatically added to attached dependencies --
          # this can be ignored since it maps back to the root object.
          continue
        proto_id = None
        # Find proto ID of attached dependency (if it is in the proto).
        for proto_ref in object_graph_proto.nodes[0].children:
          if proto_ref.local_name == ref.name:
            proto_id = proto_ref.node_id
            break

        if proto_id in checkpoint.object_by_proto_id:
          # Object has already been restored. This can happen when there's an
          # indirect connection from the attached object to the root.
          continue

        base.CheckpointPosition(
            checkpoint=checkpoint, proto_id=proto_id).restore(ref.ref)

    load_status = CheckpointLoadStatus(
        checkpoint,
        graph_view=self._graph_view,
        feed_dict=file_prefix_feed_dict)
    return load_status


def frozen_saver(root_trackable):
  """Creates a static `tf.compat.v1.train.Saver` from a trackable object.

  The returned `Saver` saves object-based checkpoints, but these checkpoints
  will no longer reflect structural changes to the object graph, only changes to
  the values of `Variable`s added as dependencies of the root object before
  `freeze` was called.

  `restore` works on the returned `Saver`, but requires that the object graph of
  the checkpoint being loaded exactly matches the object graph when `freeze` was
  called. This is in contrast the object-based restore performed by
  `tf.train.Checkpoint` which attempts a fuzzy matching between a checkpoint's
  object graph and the current Python object graph.

  Args:
    root_trackable: A trackable object to save.

  Returns:
    A saver which saves object-based checkpoints for the object graph frozen at
    the time `frozen_saver` was called.
  """
  named_saveable_objects = graph_view_lib.ObjectGraphView(
      root_trackable).frozen_saveable_objects()
  return functional_saver.MultiDeviceSaver(named_saveable_objects)


def saver_with_op_caching(obj, attached_dependencies=None):
  if context.executing_eagerly():
    saveables_cache = None
  else:
    saveables_cache = object_identity.ObjectIdentityWeakKeyDictionary()
  return TrackableSaver(
      graph_view_lib.ObjectGraphView(
          weakref.ref(obj), saveables_cache=saveables_cache,
          attached_dependencies=attached_dependencies))


def _assert_trackable(obj):
  if not isinstance(
      obj, (base.Trackable, def_function.Function)):
    raise ValueError(
        "`Checkpoint` was expecting a trackable object (an object "
        "derived from `TrackableBase`), got {}. If you believe this "
        "object should be trackable (i.e. it is part of the "
        "TensorFlow Python API and manages state), please open an issue."
        .format(obj))


# Mentions graph building / Sessions. The v2 version is below.
@tf_export(v1=["train.Checkpoint"])
class CheckpointV1(tracking.AutoTrackable):
  """Groups trackable objects, saving and restoring them.

  `Checkpoint`'s constructor accepts keyword arguments whose values are types
  that contain trackable state, such as `tf.compat.v1.train.Optimizer`
  implementations, `tf.Variable`, `tf.keras.Layer` implementations, or
  `tf.keras.Model` implementations. It saves these values with a checkpoint, and
  maintains a `save_counter` for numbering checkpoints.

  Example usage when graph building:

  ```python
  import tensorflow as tf
  import os

  checkpoint_directory = "/tmp/training_checkpoints"
  checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")

  checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
  status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
  train_op = optimizer.minimize( ... )
  status.assert_consumed()  # Optional sanity checks.
  with tf.compat.v1.Session() as session:
    # Use the Session to restore variables, or initialize them if
    # tf.train.latest_checkpoint returned None.
    status.initialize_or_restore(session)
    for _ in range(num_training_steps):
      session.run(train_op)
    checkpoint.save(file_prefix=checkpoint_prefix)
  ```

  Example usage with eager execution enabled:

  ```python
  import tensorflow as tf
  import os

  tf.compat.v1.enable_eager_execution()

  checkpoint_directory = "/tmp/training_checkpoints"
  checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")

  checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
  status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
  for _ in range(num_training_steps):
    optimizer.minimize( ... )  # Variables will be restored on creation.
  status.assert_consumed()  # Optional sanity checks.
  checkpoint.save(file_prefix=checkpoint_prefix)
  ```

  `Checkpoint.save` and `Checkpoint.restore` write and read object-based
  checkpoints, in contrast to `tf.compat.v1.train.Saver` which writes and reads
  `variable.name` based checkpoints. Object-based checkpointing saves a graph of
  dependencies between Python objects (`Layer`s, `Optimizer`s, `Variable`s,
  etc.) with named edges, and this graph is used to match variables when
  restoring a checkpoint. It can be more robust to changes in the Python
  program, and helps to support restore-on-create for variables when executing
  eagerly. Prefer `tf.train.Checkpoint` over `tf.compat.v1.train.Saver` for new
  code.

  `Checkpoint` objects have dependencies on the objects passed as keyword
  arguments to their constructors, and each dependency is given a name that is
  identical to the name of the keyword argument for which it was created.
  TensorFlow classes like `Layer`s and `Optimizer`s will automatically add
  dependencies on their variables (e.g. "kernel" and "bias" for
  `tf.keras.layers.Dense`). Inheriting from `tf.keras.Model` makes managing
  dependencies easy in user-defined classes, since `Model` hooks into attribute
  assignment. For example:

  ```python
  class Regress(tf.keras.Model):

    def __init__(self):
      super(Regress, self).__init__()
      self.input_transform = tf.keras.layers.Dense(10)
      # ...

    def call(self, inputs):
      x = self.input_transform(inputs)
      # ...
  ```

  This `Model` has a dependency named "input_transform" on its `Dense` layer,
  which in turn depends on its variables. As a result, saving an instance of
  `Regress` using `tf.train.Checkpoint` will also save all the variables created
  by the `Dense` layer.

  When variables are assigned to multiple workers, each worker writes its own
  section of the checkpoint. These sections are then merged/re-indexed to behave
  as a single checkpoint. This avoids copying all variables to one worker, but
  does require that all workers see a common filesystem.

  While `tf.keras.Model.save_weights` and `tf.train.Checkpoint.save` save in the
  same format, note that the root of the resulting checkpoint is the object the
  save method is attached to. This means saving a `tf.keras.Model` using
  `save_weights` and loading into a `tf.train.Checkpoint` with a `Model`
  attached (or vice versa) will not match the `Model`'s variables. See the
  [guide to training
  checkpoints](https://www.tensorflow.org/guide/checkpoint) for
  details. Prefer `tf.train.Checkpoint` over `tf.keras.Model.save_weights` for
  training checkpoints.

  Attributes:
    save_counter: Incremented when `save()` is called. Used to number
      checkpoints.
  """

  def __init__(self, **kwargs):
    """Group objects into a training checkpoint.

    Args:
      **kwargs: Keyword arguments are set as attributes of this object, and are
        saved with the checkpoint. Values must be trackable objects.

    Raises:
      ValueError: If objects in `kwargs` are not trackable.
    """
    super(CheckpointV1, self).__init__()
    global _END_TIME_OF_LAST_WRITE
    with _END_TIME_OF_LAST_WRITE_LOCK:
      if _END_TIME_OF_LAST_WRITE is None:
        _END_TIME_OF_LAST_WRITE = time.time()

    for k, v in sorted(kwargs.items(), key=lambda item: item[0]):
      setattr(self, k, v)
      if not isinstance(
          getattr(self, k), (base.Trackable, def_function.Function)):
        raise ValueError(
            ("`Checkpoint` was expecting a trackable object (an object "
             "derived from `TrackableBase`), got %s. If you believe this "
             "object should be trackable (i.e. it is part of the "
             "TensorFlow Python API and manages state), please open an issue.")
            % (v,))
    self._save_counter = None  # Created lazily for restore-on-create.
    self._save_assign_op = None
    self._saver = saver_with_op_caching(self)

  def _maybe_create_save_counter(self):
    """Create a save counter if it does not yet exist."""
    if self._save_counter is None:
      # Initialized to 0 and incremented before saving.
      with ops.device("/cpu:0"):
        # add_variable creates a dependency named "save_counter"; NoDependency
        # prevents creating a second dependency named "_save_counter".
        self._save_counter = data_structures.NoDependency(
            add_variable(
                self,
                name="save_counter",
                initializer=0,
                dtype=dtypes.int64,
                trainable=False))

  def write(self, file_prefix, session=None):
    """Writes a training checkpoint.

    The checkpoint includes variables created by this object and any
    trackable objects it depends on at the time `Checkpoint.write()` is
    called.

    `write` does not number checkpoints, increment `save_counter`, or update the
    metadata used by `tf.train.latest_checkpoint`. It is primarily intended for
    use by higher level checkpoint management utilities. `save` provides a very
    basic implementation of these features.

    Args:
      file_prefix: A prefix to use for the checkpoint filenames
        (/path/to/directory/and_a_prefix).
      session: The session to evaluate variables in. Ignored when executing
        eagerly. If not provided when graph building, the default session is
        used.

    Returns:
      The full path to the checkpoint (i.e. `file_prefix`).
    """
    start_time = time.time()
    output = self._saver.save(file_prefix=file_prefix, session=session)
    end_time = time.time()

    metrics.AddCheckpointWriteDuration(
        api_label=_CHECKPOINT_V1,
        microseconds=_get_duration_microseconds(start_time, end_time))

    global _END_TIME_OF_LAST_WRITE
    with _END_TIME_OF_LAST_WRITE_LOCK:
      metrics.AddTrainingTimeSaved(
          api_label=_CHECKPOINT_V1,
          microseconds=_get_duration_microseconds(_END_TIME_OF_LAST_WRITE,
                                                  end_time))
      _END_TIME_OF_LAST_WRITE = end_time

    if tensor_util.is_tf_type(output):
      if context.executing_eagerly():
        return compat.as_str(output.numpy())
      else:
        # Function building
        return output
    else:
      # Graph + Session, so we already session.ran it.
      return compat.as_str(output)

  @property
  def save_counter(self):
    """An integer variable which starts at zero and is incremented on save.

    Used to number checkpoints.

    Returns:
      The save counter variable.
    """
    self._maybe_create_save_counter()
    return self._save_counter

  def save(self, file_prefix, session=None):
    """Saves a training checkpoint and provides basic checkpoint management.

    The saved checkpoint includes variables created by this object and any
    trackable objects it depends on at the time `Checkpoint.save()` is
    called.

    `save` is a basic convenience wrapper around the `write` method,
    sequentially numbering checkpoints using `save_counter` and updating the
    metadata used by `tf.train.latest_checkpoint`. More advanced checkpoint
    management, for example garbage collection and custom numbering, may be
    provided by other utilities which also wrap `write`
    (`tf.train.CheckpointManager` for example).

    Args:
      file_prefix: A prefix to use for the checkpoint filenames
        (/path/to/directory/and_a_prefix). Names are generated based on this
        prefix and `Checkpoint.save_counter`.
      session: The session to evaluate variables in. Ignored when executing
        eagerly. If not provided when graph building, the default session is
        used.

    Returns:
      The full path to the checkpoint.
    """
    graph_building = not context.executing_eagerly()
    if graph_building:
      if ops.inside_function():
        raise NotImplementedError(
            "Calling tf.train.Checkpoint.save() from a function is not "
            "supported, as save() modifies saving metadata in ways not "
            "supported by TensorFlow Operations. Consider using "
            "tf.train.Checkpoint.write(), a lower-level API which does not "
            "update metadata. tf.train.latest_checkpoint and related APIs will "
            "not see this checkpoint.")
      if session is None:
        session = get_session()
      if self._save_counter is None:
        # When graph building, if this is a new save counter variable then it
        # needs to be initialized before assign_add. This is only an issue if
        # restore() has not been called first.
        session.run(self.save_counter.initializer)
    if not graph_building or self._save_assign_op is None:
      with ops.colocate_with(self.save_counter):
        assign_op = self.save_counter.assign_add(1, read_value=True)
      if graph_building:
        self._save_assign_op = data_structures.NoDependency(assign_op)
    if graph_building:
      checkpoint_number = session.run(self._save_assign_op)
    else:
      checkpoint_number = assign_op.numpy()
    file_path = self.write(
        "%s-%d" % (file_prefix, checkpoint_number), session=session)
    checkpoint_management.update_checkpoint_state_internal(
        save_dir=os.path.dirname(file_prefix),
        model_checkpoint_path=file_path,
        all_model_checkpoint_paths=[file_path],
        save_relative_paths=True)
    return file_path

  def restore(self, save_path):
    """Restore a training checkpoint.

    Restores this `Checkpoint` and any objects it depends on.

    When executing eagerly, either assigns values immediately if variables to
    restore have been created already, or defers restoration until the variables
    are created. Dependencies added after this call will be matched if they have
    a corresponding object in the checkpoint (the restore request will queue in
    any trackable object waiting for the expected dependency to be added).

    When graph building, restoration ops are added to the graph but not run
    immediately.

    ```python
    checkpoint = tf.train.Checkpoint( ... )
    checkpoint.restore(path)
    ```

    To ensure that loading is complete and no more assignments will take place,
    you can use the `assert_consumed()` method of the status object returned by
    `restore`.
    The assert will raise an exception if any Python objects in the dependency
    graph were not found in the checkpoint, or if any checkpointed values do not
    have a matching Python object:

    ```python
    checkpoint = tf.train.Checkpoint( ... )
    checkpoint.restore(path).assert_consumed()
    ```

    When graph building, `assert_consumed()` indicates that all of the restore
    ops that will be created for this checkpoint have been created. They can be
    run via the `run_restore_ops()` method of the status object:

    ```python
    checkpoint.restore(path).assert_consumed().run_restore_ops()
    ```

    If the checkpoint has not been consumed completely, then the list of restore
    ops will grow as more objects are added to the dependency graph.

    Name-based `tf.compat.v1.train.Saver` checkpoints can be loaded using this
    method. Names are used to match variables. No restore ops are created/run
    until `run_restore_ops()` or `initialize_or_restore()` are called on the
    returned status object when graph building, but there is restore-on-creation
    when executing eagerly. Re-encode name-based checkpoints using
    `tf.train.Checkpoint.save` as soon as possible.

    Args:
      save_path: The path to the checkpoint, as returned by `save` or
        `tf.train.latest_checkpoint`. If None (as when there is no latest
        checkpoint for `tf.train.latest_checkpoint` to return), returns an
        object which may run initializers for objects in the dependency graph.
        If the checkpoint was written by the name-based
        `tf.compat.v1.train.Saver`, names are used to match variables.

    Returns:
      A load status object, which can be used to make assertions about the
      status of a checkpoint restoration and run initialization/restore ops.

      The returned status object has the following methods:

      * `assert_consumed()`:
          Raises an exception if any variables are unmatched: either
          checkpointed values which don't have a matching Python object or
          Python objects in the dependency graph with no values in the
          checkpoint. This method returns the status object, and so may be
          chained with `initialize_or_restore` or `run_restore_ops`.

      * `assert_existing_objects_matched()`:
          Raises an exception if any existing Python objects in the dependency
          graph are unmatched. Unlike `assert_consumed`, this assertion will
          pass if values in the checkpoint have no corresponding Python
          objects. For example a `tf.keras.Layer` object which has not yet been
          built, and so has not created any variables, will pass this assertion
          but fail `assert_consumed`. Useful when loading part of a larger
          checkpoint into a new Python program, e.g. a training checkpoint with
          a `tf.compat.v1.train.Optimizer` was saved but only the state required
          for
          inference is being loaded. This method returns the status object, and
          so may be chained with `initialize_or_restore` or `run_restore_ops`.

      * `assert_nontrivial_match()`: Asserts that something aside from the root
          object was matched. This is a very weak assertion, but is useful for
          sanity checking in library code where objects may exist in the
          checkpoint which haven't been created in Python and some Python
          objects may not have a checkpointed value.

      * `expect_partial()`: Silence warnings about incomplete checkpoint
          restores. Warnings are otherwise printed for unused parts of the
          checkpoint file or object when the `Checkpoint` object is deleted
          (often at program shutdown).

      * `initialize_or_restore(session=None)`:
          When graph building, runs variable initializers if `save_path` is
          `None`, but otherwise runs restore operations. If no `session` is
          explicitly specified, the default session is used. No effect when
          executing eagerly (variables are initialized or restored eagerly).

      * `run_restore_ops(session=None)`:
          When graph building, runs restore operations. If no `session` is
          explicitly specified, the default session is used. No effect when
          executing eagerly (restore operations are run eagerly). May only be
          called when `save_path` is not `None`.
    """
    start_time = time.time()
    status = self._saver.restore(save_path=save_path)
    # Create the save counter now so it gets initialized with other variables
    # when graph building. Creating it earlier would lead to errors when using,
    # say, train.Saver() to save the model before initializing it.
    self._maybe_create_save_counter()
    if isinstance(status, NameBasedSaverStatus):
      status.add_to_optionally_restored(self.save_counter)

    metrics.AddCheckpointReadDuration(
        api_label=_CHECKPOINT_V1,
        microseconds=_get_duration_microseconds(start_time, time.time()))
    return status


@tf_export("train.Checkpoint", v1=[])
class Checkpoint(tracking.AutoTrackable):
  """Manages saving/restoring trackable values to disk.

  TensorFlow objects may contain trackable state, such as `tf.Variable`s,
  `tf.keras.optimizers.Optimizer` implementations, `tf.data.Dataset` iterators,
  `tf.keras.Layer` implementations, or  `tf.keras.Model` implementations.
  These are called **trackable objects**.

  A `Checkpoint` object can be constructed to save either a single or group of
  trackable objects to a checkpoint file. It maintains a `save_counter` for
  numbering checkpoints.

  Example:

  ```python
  model = tf.keras.Model(...)
  checkpoint = tf.train.Checkpoint(model)

  # Save a checkpoint to /tmp/training_checkpoints-{save_counter}. Every time
  # checkpoint.save is called, the save counter is increased.
  save_path = checkpoint.save('/tmp/training_checkpoints')

  # Restore the checkpointed values to the `model` object.
  checkpoint.restore(save_path)
  ```

  Example 2:

  ```python
  import tensorflow as tf
  import os

  checkpoint_directory = "/tmp/training_checkpoints"
  checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")

  # Create a Checkpoint that will manage two objects with trackable state,
  # one we name "optimizer" and the other we name "model".
  checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
  status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
  for _ in range(num_training_steps):
    optimizer.minimize( ... )  # Variables will be restored on creation.
  status.assert_consumed()  # Optional sanity checks.
  checkpoint.save(file_prefix=checkpoint_prefix)
  ```

  `Checkpoint.save()` and `Checkpoint.restore()` write and read object-based
  checkpoints, in contrast to TensorFlow 1.x's `tf.compat.v1.train.Saver` which
  writes and
  reads `variable.name` based checkpoints. Object-based checkpointing saves a
  graph of dependencies between Python objects (`Layer`s, `Optimizer`s,
  `Variable`s, etc.) with named edges, and this graph is used to match variables
  when restoring a checkpoint. It can be more robust to changes in the Python
  program, and helps to support restore-on-create for variables.

  `Checkpoint` objects have dependencies on the objects passed as keyword
  arguments to their constructors, and each dependency is given a name that is
  identical to the name of the keyword argument for which it was created.
  TensorFlow classes like `Layer`s and `Optimizer`s will automatically add
  dependencies on their own variables (e.g. "kernel" and "bias" for
  `tf.keras.layers.Dense`). Inheriting from `tf.keras.Model` makes managing
  dependencies easy in user-defined classes, since `Model` hooks into attribute
  assignment. For example:

  ```python
  class Regress(tf.keras.Model):

    def __init__(self):
      super(Regress, self).__init__()
      self.input_transform = tf.keras.layers.Dense(10)
      # ...

    def call(self, inputs):
      x = self.input_transform(inputs)
      # ...
  ```

  This `Model` has a dependency named "input_transform" on its `Dense` layer,
  which in turn depends on its variables. As a result, saving an instance of
  `Regress` using `tf.train.Checkpoint` will also save all the variables created
  by the `Dense` layer.

  When variables are assigned to multiple workers, each worker writes its own
  section of the checkpoint. These sections are then merged/re-indexed to behave
  as a single checkpoint. This avoids copying all variables to one worker, but
  does require that all workers see a common filesystem.

  This function differs slightly from the Keras Model `save_weights` function.
  `tf.keras.Model.save_weights` creates a checkpoint file with the name
  specified in `filepath`, while `tf.train.Checkpoint` numbers the checkpoints,
  using `filepath` as the prefix for the checkpoint file names. Aside from this,
  `model.save_weights()` and `tf.train.Checkpoint(model).save()` are equivalent.

  See the [guide to training
  checkpoints](https://www.tensorflow.org/guide/checkpoint) for
  details.

  Attributes:
    save_counter: Incremented when `save()` is called. Used to number
      checkpoints.
  """

  def __init__(self, root=None, **kwargs):
    """Creates a training checkpoint for a single or group of objects.

    Args:
      root: The root object to checkpoint.
      **kwargs: Keyword arguments are set as attributes of this object, and are
        saved with the checkpoint. Values must be trackable objects.

    Raises:
      ValueError: If `root` or the objects in `kwargs` are not trackable. A
        `ValueError` is also raised if the `root` object tracks different
        objects from the ones listed in attributes in kwargs (e.g.
        `root.child = A` and `tf.train.Checkpoint(root, child=B)` are
        incompatible).

    """
    super(Checkpoint, self).__init__()
    global _END_TIME_OF_LAST_WRITE
    with _END_TIME_OF_LAST_WRITE_LOCK:
      if _END_TIME_OF_LAST_WRITE is None:
        _END_TIME_OF_LAST_WRITE = time.time()

    saver_root = self
    attached_dependencies = None
    self._save_counter = None  # Created lazily for restore-on-create.
    self._save_assign_op = None

    if root:
      _assert_trackable(root)
      saver_root = root
      attached_dependencies = []

      # All keyword arguments (including root itself) are set as children
      # of root.
      kwargs["root"] = root
      root._maybe_initialize_trackable()

      self._save_counter = data_structures.NoDependency(
          root._lookup_dependency("save_counter"))
      self._root = data_structures.NoDependency(root)

    for k, v in sorted(kwargs.items(), key=lambda item: item[0]):
      setattr(self, k, v)

      # Call getattr instead of directly using v because setattr converts
      # v to a Trackable data structure when v is a list/dict/tuple.
      converted_v = getattr(self, k)
      _assert_trackable(converted_v)

      if root:
        # Make sure that root doesn't already have dependencies with these names
        child = root._lookup_dependency(k)
        if child is None:
          attached_dependencies.append(base.TrackableReference(k, converted_v))
        elif child != converted_v:
          raise ValueError(
              "Cannot create a Checkpoint with keyword argument {name} if "
              "root.{name} already exists.".format(name=k))

    self._saver = saver_with_op_caching(saver_root, attached_dependencies)
    self._attached_dependencies = data_structures.NoDependency(
        attached_dependencies)

  def _maybe_create_save_counter(self):
    """Create a save counter if it does not yet exist."""
    if self._save_counter is None:
      # Initialized to 0 and incremented before saving.
      with ops.device("/cpu:0"):
        # add_variable creates a dependency named "save_counter"; NoDependency
        # prevents creating a second dependency named "_save_counter".
        self._save_counter = data_structures.NoDependency(
            add_variable(
                self,
                name="save_counter",
                initializer=0,
                dtype=dtypes.int64,
                trainable=False))
        if self._attached_dependencies is not None:
          self._attached_dependencies.append(
              base.TrackableReference("save_counter", self._save_counter))
          # When loading a checkpoint, the save counter is created after
          # the checkpoint has been loaded, so it must be handled in a deferred
          # manner.
          restore = self.root._deferred_dependencies.pop("save_counter", ())  # pylint: disable=protected-access
          if restore:
            restore[0].restore(self._save_counter)

  def write(self, file_prefix, options=None):
    """Writes a training checkpoint.

    The checkpoint includes variables created by this object and any
    trackable objects it depends on at the time `Checkpoint.write()` is
    called.

    `write` does not number checkpoints, increment `save_counter`, or update the
    metadata used by `tf.train.latest_checkpoint`. It is primarily intended for
    use by higher level checkpoint management utilities. `save` provides a very
    basic implementation of these features.

    Checkpoints written with `write` must be read with `read`.

    Example usage:

    ```
    step = tf.Variable(0, name="step")
    checkpoint = tf.Checkpoint(step=step)
    checkpoint.write("/tmp/ckpt")

    # Later, read the checkpoint with read()
    checkpoint.read("/tmp/ckpt").assert_consumed()

    # You can also pass options to write() and read(). For example this
    # runs the IO ops on the localhost:
    options = tf.CheckpointOptions(experimental_io_device="/job:localhost")
    checkpoint.write("/tmp/ckpt", options=options)

    # Later, read the checkpoint with read()
    checkpoint.read("/tmp/ckpt", options=options).assert_consumed()
    ```

    Args:
      file_prefix: A prefix to use for the checkpoint filenames
        (/path/to/directory/and_a_prefix).
      options: Optional `tf.train.CheckpointOptions` object.

    Returns:
      The full path to the checkpoint (i.e. `file_prefix`).
    """
    start_time = time.time()
    options = options or checkpoint_options.CheckpointOptions()
    output = self._saver.save(file_prefix=file_prefix, options=options)
    end_time = time.time()

    metrics.AddCheckpointWriteDuration(
        api_label=_CHECKPOINT_V2,
        microseconds=_get_duration_microseconds(start_time, end_time))

    global _END_TIME_OF_LAST_WRITE
    with _END_TIME_OF_LAST_WRITE_LOCK:
      metrics.AddTrainingTimeSaved(
          api_label=_CHECKPOINT_V2,
          microseconds=_get_duration_microseconds(_END_TIME_OF_LAST_WRITE,
                                                  end_time))
      _END_TIME_OF_LAST_WRITE = end_time

    if tensor_util.is_tf_type(output):
      if context.executing_eagerly():
        return compat.as_str(output.numpy())
      else:
        # Function building
        return output
    else:
      # Graph + Session, so we already session.ran it.
      return compat.as_str(output)

  @property
  def save_counter(self):
    """An integer variable which starts at zero and is incremented on save.

    Used to number checkpoints.

    Returns:
      The save counter variable.
    """
    self._maybe_create_save_counter()
    return self._save_counter

  def save(self, file_prefix, options=None):
    """Saves a training checkpoint and provides basic checkpoint management.

    The saved checkpoint includes variables created by this object and any
    trackable objects it depends on at the time `Checkpoint.save()` is
    called.

    `save` is a basic convenience wrapper around the `write` method,
    sequentially numbering checkpoints using `save_counter` and updating the
    metadata used by `tf.train.latest_checkpoint`. More advanced checkpoint
    management, for example garbage collection and custom numbering, may be
    provided by other utilities which also wrap `write` and `read`.
    (`tf.train.CheckpointManager` for example).

    ```
    step = tf.Variable(0, name="step")
    checkpoint = tf.Checkpoint(step=step)
    checkpoint.save("/tmp/ckpt")

    # Later, read the checkpoint with restore()
    checkpoint.restore("/tmp/ckpt").assert_consumed()

    # You can also pass options to save() and restore(). For example this
    # runs the IO ops on the localhost:
    options = tf.CheckpointOptions(experimental_io_device="/job:localhost")
    checkpoint.save("/tmp/ckpt", options=options)

    # Later, read the checkpoint with restore()
    checkpoint.restore("/tmp/ckpt", options=options).assert_consumed()
    ```

    Args:
      file_prefix: A prefix to use for the checkpoint filenames
        (/path/to/directory/and_a_prefix). Names are generated based on this
        prefix and `Checkpoint.save_counter`.
      options: Optional `tf.train.CheckpointOptions` object.

    Returns:
      The full path to the checkpoint.
    """
    options = options or checkpoint_options.CheckpointOptions()
    graph_building = not context.executing_eagerly()
    if graph_building:
      if ops.inside_function():
        raise NotImplementedError(
            "Calling tf.train.Checkpoint.save() from a function is not "
            "supported, as save() modifies saving metadata in ways not "
            "supported by TensorFlow Operations. Consider using "
            "tf.train.Checkpoint.write(), a lower-level API which does not "
            "update metadata. tf.train.latest_checkpoint and related APIs will "
            "not see this checkpoint.")
      session = get_session()
      if self._save_counter is None:
        # When graph building, if this is a new save counter variable then it
        # needs to be initialized before assign_add. This is only an issue if
        # restore() has not been called first.
        session.run(self.save_counter.initializer)
    if not graph_building or self._save_assign_op is None:
      with ops.colocate_with(self.save_counter):
        assign_op = self.save_counter.assign_add(1, read_value=True)
      if graph_building:
        self._save_assign_op = data_structures.NoDependency(assign_op)
    if graph_building:
      checkpoint_number = session.run(self._save_assign_op)
    else:
      checkpoint_number = assign_op.numpy()
    file_path = self.write("%s-%d" % (file_prefix, checkpoint_number),
                           options=options)
    checkpoint_management.update_checkpoint_state_internal(
        save_dir=os.path.dirname(file_prefix),
        model_checkpoint_path=file_path,
        all_model_checkpoint_paths=[file_path],
        save_relative_paths=True)
    if not graph_building:
      context.async_wait()  # Ensure save operations have completed.
    return file_path

  def read(self, save_path, options=None):
    """Reads a training checkpoint written with `write`.

    Reads this `Checkpoint` and any objects it depends on.

    This method is just like `restore()` but does not expect the `save_counter`
    variable in the checkpoint. It only restores the objects that the checkpoint
    already depends on.

    The method is primarily intended for use by higher level checkpoint
    management utilities that use `write()` instead of `save()` and have their
    own mechanisms to number and track checkpoints.

    Example usage:

    ```python
    # Create a checkpoint with write()
    ckpt = tf.train.Checkpoint(v=tf.Variable(1.))
    path = ckpt.write('/tmp/my_checkpoint')

    # Later, load the checkpoint with read()
    # With restore() assert_consumed() would have failed.
    checkpoint.read(path).assert_consumed()

    # You can also pass options to read(). For example this
    # runs the IO ops on the localhost:
    options = tf.train.CheckpointOptions(
        experimental_io_device="/job:localhost")
    checkpoint.read(path, options=options)
    ```

    Args:
      save_path: The path to the checkpoint as returned by `write`.
      options: Optional `tf.train.CheckpointOptions` object.

    Returns:
      A load status object, which can be used to make assertions about the
      status of a checkpoint restoration.  See `restore` for details.
    """
    start_time = time.time()
    options = options or checkpoint_options.CheckpointOptions()
    result = self._saver.restore(save_path=save_path, options=options)
    metrics.AddCheckpointReadDuration(
        api_label=_CHECKPOINT_V2,
        microseconds=_get_duration_microseconds(start_time, time.time()))
    return result

  def restore(self, save_path, options=None):
    """Restores a training checkpoint.

    Restores this `Checkpoint` and any objects it depends on.

    This method is intended to be used to load checkpoints created by `save()`.
    For checkpoints created by `write()` use the `read()` method which does not
    expect the `save_counter` variable added by `save()`.

    `restore()` either assigns values immediately if variables to restore have
    been created already, or defers restoration until the variables are
    created. Dependencies added after this call will be matched if they have a
    corresponding object in the checkpoint (the restore request will queue in
    any trackable object waiting for the expected dependency to be added).

    ```python
    checkpoint = tf.train.Checkpoint( ... )
    checkpoint.restore(path)

    # You can additionally pass options to restore():
    options = tf.CheckpointOptions(experimental_io_device="/job:localhost")
    checkpoint.restore(path, options=options)
    ```

    To ensure that loading is complete and no more assignments will take place,
    use the `assert_consumed()` method of the status object returned by
    `restore()`:

    ```python
    checkpoint = tf.train.Checkpoint( ... )
    checkpoint.restore(path).assert_consumed()

    # You can additionally pass options to restore():
    options = tf.CheckpointOptions(experimental_io_device="/job:localhost")
    checkpoint.restore(path, options=options).assert_consumed()
    ```

    The assert will raise an error if any Python objects in the dependency graph
    were not found in the checkpoint, or if any checkpointed values do not have
    a matching Python object.

    Name-based `tf.compat.v1.train.Saver` checkpoints from TensorFlow 1.x can be
    loaded using this method. Names are used to match variables. Re-encode
    name-based checkpoints using `tf.train.Checkpoint.save` as soon as possible.

    **Loading from SavedModel checkpoints**

    To load values from a SavedModel, just pass the SavedModel directory
    to checkpoint.restore:

    ```python
    model = tf.keras.Model(...)
    tf.saved_model.save(model, path)  # or model.save(path, save_format='tf')

    checkpoint = tf.train.Checkpoint(model)
    checkpoint.restore(path).expect_partial()
    ```

    This example calls `expect_partial()` on the loaded status, since
    SavedModels saved from Keras often generates extra keys in the checkpoint.
    Otherwise, the program prints a lot of warnings about unused keys at exit
    time.

    Args:
      save_path: The path to the checkpoint, as returned by `save` or
        `tf.train.latest_checkpoint`. If the checkpoint was written by the
        name-based `tf.compat.v1.train.Saver`, names are used to match
        variables. This path may also be a SavedModel directory.
      options: Optional `tf.train.CheckpointOptions` object.

    Returns:
      A load status object, which can be used to make assertions about the
      status of a checkpoint restoration.

      The returned status object has the following methods:

      * `assert_consumed()`:
          Raises an exception if any variables are unmatched: either
          checkpointed values which don't have a matching Python object or
          Python objects in the dependency graph with no values in the
          checkpoint. This method returns the status object, and so may be
          chained with other assertions.

      * `assert_existing_objects_matched()`:
          Raises an exception if any existing Python objects in the dependency
          graph are unmatched. Unlike `assert_consumed`, this assertion will
          pass if values in the checkpoint have no corresponding Python
          objects. For example a `tf.keras.Layer` object which has not yet been
          built, and so has not created any variables, will pass this assertion
          but fail `assert_consumed`. Useful when loading part of a larger
          checkpoint into a new Python program, e.g. a training checkpoint with
          a `tf.compat.v1.train.Optimizer` was saved but only the state required
          for
          inference is being loaded. This method returns the status object, and
          so may be chained with other assertions.

      * `assert_nontrivial_match()`: Asserts that something aside from the root
          object was matched. This is a very weak assertion, but is useful for
          sanity checking in library code where objects may exist in the
          checkpoint which haven't been created in Python and some Python
          objects may not have a checkpointed value.

      * `expect_partial()`: Silence warnings about incomplete checkpoint
          restores. Warnings are otherwise printed for unused parts of the
          checkpoint file or object when the `Checkpoint` object is deleted
          (often at program shutdown).

    Raises:
      NotFoundError: if the a checkpoint or SavedModel cannot be found at
        `save_path`.
    """
    orig_save_path = save_path

    if save_path is not None and gfile.IsDirectory(save_path) and (
        (gfile.Exists(utils_impl.get_saved_model_pb_path(save_path)) or
         gfile.Exists(utils_impl.get_saved_model_pbtxt_path(save_path)))):
      save_path = utils_impl.get_variables_path(save_path)

    try:
      status = self.read(save_path, options=options)
      if context.executing_eagerly():
        context.async_wait()  # Ensure restore operations have completed.
    except errors_impl.NotFoundError as e:
      raise errors_impl.NotFoundError(
          None, None,
          "Failed to restore from checkpoint or SavedModel at {}: {}".format(
              orig_save_path, e.message))
    # Create the save counter now so it gets initialized with other variables
    # when graph building. Creating it earlier would lead to errors when using,
    # say, train.Saver() to save the model before initializing it.
    self._maybe_create_save_counter()
    if isinstance(status, NameBasedSaverStatus):
      status.add_to_optionally_restored(self.save_counter)
    return status