tensorflow--tensorflow/tensorflow/python/trackable/data_structures.py

"""Trackable data structures."""
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import collections
import copy
import sys

try:
  import wrapt
except ImportError:
  # Fall back to the build-time dependency if the system package is not available.
  from .....third_party import wrapt  # pylint: disable=relative-beyond-top-level

from tensorflow.python.trackable import base
from tensorflow.python.trackable import layer_utils
from tensorflow.python.util.compat import collections_abc
from tensorflow.python.util.tf_export import tf_export


class NoDependency:
  """Allows attribute assignment to `Trackable` objects with no dependency.

  Example usage:
  ```python
  obj = Trackable()
  obj.has_dependency = tf.Variable(0., name="dep")
  obj.no_dependency = NoDependency(tf.Variable(1., name="nodep"))
  assert obj.no_dependency.name == "nodep:0"
  ```

  `obj` in this example has a dependency on the variable "dep", and both
  attributes contain un-wrapped `Variable` objects.

  `NoDependency` also works with `tf.keras.Model`, but only for checkpoint
  dependencies: wrapping a `Layer` in `NoDependency` will assign the (unwrapped)
  `Layer` to the attribute without a checkpoint dependency, but the `Model` will
  still track the `Layer` (so it will appear in `Model.layers`, and its
  variables will appear in `Model.variables`).
  """

  __slots__ = ["value"]

  def __init__(self, value):
    self.value = value


def _should_wrap_tuple(t):
  """Determine if a tuple has any trackable components."""
  # pylint: disable=unidiomatic-typecheck
  # Exact type checking to avoid mucking up custom logic in list/dict
  # subclasses, e.g. collections.Counter.
  for element in t:
    if isinstance(element, NoDependency):
      return True  # We should remove the NoDependency object from the tuple.
    if isinstance(element, base.Trackable):
      return True
    if type(element) == dict:
      return True
    if type(element) == collections.OrderedDict:
      return True
    if type(element) == list:
      return True
    if isinstance(element, tuple) and _should_wrap_tuple(element):
      return True
  # There are no trackable elements or data structures. Tuples are immutable, so
  # mutation isn't a concern. Don't wrap.
  return False
  # pylint: enable=unidiomatic-typecheck


@tf_export("__internal__.tracking.wrap", v1=[])
def wrap_or_unwrap(value):
  """Wraps input value into trackable data structures.

  This is mostly useful for containers like list, dict, etc, which could contain
  trackable objects in it. Wrapped data structure will be tracked when
  associated with a `tf.Module`, so that save model/checkpoint can properly
  track the dependency.

  It will also unwrap NoDependency objects.

  Args:
    value: the input object to be wrapped.

  Returns:
    Wrapped trackable data structure.
  """
  # pylint: disable=unidiomatic-typecheck
  # Exact type checking to avoid mucking up custom logic in list/dict
  # subclasses, e.g. collections.Counter.
  if isinstance(value, NoDependency):
    return value.value
  if isinstance(value, base.Trackable):
    return value  # Skip conversion for already trackable objects.
  elif type(value) == dict:
    return _DictWrapper(value)
  elif type(value) == collections.OrderedDict:
    return _DictWrapper(value)
  elif type(value) == list:
    return ListWrapper(value)
  elif isinstance(value, tuple) and _should_wrap_tuple(value):
    # There are trackable elements or data structures. Wrap the tuple.
    return _TupleWrapper(value)
  else:
    return value
  # pylint: enable=unidiomatic-typecheck


@tf_export("__internal__.tracking.sticky_attribute_assignment", v1=[])
def sticky_attribute_assignment(trackable, name, value):
  """Adds dependencies, generally called from __setattr__.

  This behavior is shared between Trackable and Model.

  Respects NoDependency indicators, but otherwise makes trackable objects
  out of common data structures and tracks objects by their attribute names.

  Args:
    trackable: The object to add dependencies to (generally the one having
      an attribute assigned).
    name: The attribute name being assigned.
    value: The value being assigned. Not necessarily a trackable object.

  Returns:
    The value which should be stored in the attribute (unwrapped from a
    NoDependency object if necessary).
  """
  if isinstance(value, NoDependency):
    add_dependency = False
  else:
    add_dependency = True
  value = wrap_or_unwrap(value)
  if not add_dependency:
    return value
  if isinstance(value, base.Trackable):
    trackable._track_trackable(  # pylint: disable=protected-access
        value, name=name,
        # Allow the user to switch the Trackable which is tracked by this
        # name, since assigning a new variable to an attribute has
        # historically been fine (e.g. Adam did this).
        overwrite=True)
  return value


class _UntrackableError(ValueError):

  def __init__(self, value):  # pylint: disable=super-init-not-called
    self._value = value

  def __str__(self):
    return ("Only trackable objects (such as Layers or Optimizers) may be "
            f"stored in a List object. Got {self._value}, which does not "
            "inherit from Trackable.")


@tf_export("__internal__.tracking.TrackableDataStructure", v1=[])
class TrackableDataStructure(base.Trackable):
  """Base class for data structures which contain trackable objects."""

  def __init__(self):
    # Attributes prefixed with "_self_" for compatibility with
    # wrapt.ObjectProxy. All additional attrs MUST conform to this pattern, as
    # extending `__slots__` on a subclass of ObjectProxy breaks in a variety of
    # ways.
    self._self_trainable = True
    self._self_extra_variables = []
    self._self_attribute_sentinel = layer_utils.AttributeSentinel(True)

  @property
  def _attribute_sentinel(self):
    return self._self_attribute_sentinel

  @property
  def trainable(self):
    return self._self_trainable

  @trainable.setter
  def trainable(self, value):
    self._self_trainable = value

  def _track_value(self, value, name):
    """Add a dependency on `value`."""
    # pylint: disable=g-import-not-at-top
    from tensorflow.python.ops import variables
    value = sticky_attribute_assignment(
        trackable=self, value=value, name=name)
    if isinstance(value, variables.Variable):
      self._self_extra_variables.append(value)
    if not isinstance(value, base.Trackable):
      raise _UntrackableError(value)
    if hasattr(value, "_use_resource_variables"):
      # In subclassed models, legacy layers (tf.layers) must always use
      # resource variables.
      value._use_resource_variables = True  # pylint: disable=protected-access
    value_attribute_sentinel = getattr(value, "_attribute_sentinel", None)
    if value_attribute_sentinel:
      value_attribute_sentinel.add_parent(self._attribute_sentinel)
    return value

  @property
  def _values(self):
    """An iterable/sequence which may contain trackable objects."""
    raise NotImplementedError("Abstract method")

  @property
  def _layers(self):
    """All Layers and Layer containers, including empty containers."""
    # Filter objects on demand so that wrapper objects use values from the thing
    # they're wrapping if out of sync.
    collected = []
    for obj in self._values:
      if (isinstance(obj, TrackableDataStructure)
          or layer_utils.is_layer(obj)
          or layer_utils.has_weights(obj)):
        collected.append(obj)
    return collected

  @property
  def layers(self):
    return list(layer_utils.filter_empty_layer_containers(self._layers))

  @property
  def trainable_weights(self):
    if not self._self_trainable:
      return []
    trainable_variables = []
    for obj in self._values:
      if isinstance(obj, base.Trackable) and hasattr(
          obj, "trainable_variables"):
        trainable_variables += obj.trainable_variables
    trainable_extra_variables = [
        v for v in self._self_extra_variables if v.trainable
    ]
    return trainable_variables + trainable_extra_variables

  @property
  def non_trainable_weights(self):
    trainable_extra_variables = [
        v for v in self._self_extra_variables if v.trainable
    ]
    non_trainable_extra_variables = [
        v for v in self._self_extra_variables if not v.trainable
    ]
    non_trainable_variables = []
    for obj in self._values:
      if isinstance(obj, base.Trackable) and hasattr(
          obj, "non_trainable_variables"):
        non_trainable_variables += obj.non_trainable_variables

    if not self._self_trainable:
      # Return order is all trainable vars, then all non-trainable vars.
      trainable_variables = []
      for obj in self._values:
        if isinstance(obj, base.Trackable) and hasattr(
            obj, "trainable_variables"):
          trainable_variables += obj.trainable_variables

      non_trainable_variables = (
          trainable_variables + trainable_extra_variables +
          non_trainable_variables + non_trainable_extra_variables)
    else:
      non_trainable_variables = (
          non_trainable_variables + non_trainable_extra_variables)

    return non_trainable_variables

  @property
  def weights(self):
    return self.trainable_weights + self.non_trainable_weights

  @property
  def trainable_variables(self):
    return self.trainable_weights

  @property
  def non_trainable_variables(self):
    return self.non_trainable_weights

  @property
  def variables(self):
    return self.weights

  @property
  def updates(self):
    """Aggregate updates from any `Layer` instances."""
    # Updates and conditional losses are forwarded as-is rather than being
    # filtered based on inputs, since this is just a container and won't ever
    # have any inputs.
    aggregated = []
    for layer in self.layers:
      if hasattr(layer, "updates"):
        aggregated += layer.updates
    return aggregated

  @property
  def losses(self):
    """Aggregate losses from any `Layer` instances."""
    aggregated = []
    for layer in self.layers:
      if hasattr(layer, "losses"):
        aggregated += layer.losses
    return aggregated

  def __hash__(self):
    # Support object-identity hashing, so these structures can be used as keys
    # in sets/dicts.
    return id(self)

  def __eq__(self, other):
    # Similar to Tensors, trackable data structures use object-identity
    # equality to support set/dict membership.
    return self is other


class List(TrackableDataStructure, collections_abc.Sequence):
  """An append-only sequence type which is trackable.

  Maintains checkpoint dependencies on its contents (which must also be
  trackable), and forwards any `Layer` metadata such as updates and losses.

  Note that `List` is purely a container. It lets a `tf.keras.Model` or
  other trackable object know about its contents, but does not call any
  `Layer` instances which are added to it. To indicate a sequence of `Layer`
  instances which should be called sequentially, use `tf.keras.Sequential`.

  Example usage:
  ```python
  class HasList(tf.keras.Model):

    def __init__(self):
      super().__init__()
      self.layer_list = List([layers.Dense(3)])
      self.layer_list.append(layers.Dense(4))

    def call(self, x):
      aggregation = 0.
      for l in self.layer_list:
        x = l(x)
        aggregation += tf.reduce_sum(x)
      return aggregation
  ```

  This kind of wrapping is necessary because `Trackable` objects do not
  (yet) deeply inspect regular Python data structures, so for example assigning
  a regular list (`self.layer_list = [layers.Dense(3)]`) does not create a
  checkpoint dependency and does not add the `Layer` instance's weights to its
  parent `Model`.
  """

  def __init__(self, *args, **kwargs):
    """Construct a new sequence. Arguments are passed to `list()`."""
    super().__init__()
    self._storage = self._make_storage(*args, **kwargs)
    for index, element in enumerate(self._storage):
      self._storage[index] = self._track_value(
          element, name=self._name_element(index))

  def copy(self):
    return type(self)(copy.copy(self._storage))

  def __copy__(self):
    return self.copy()

  def __deepcopy__(self, memo):
    return type(self)(copy.deepcopy(self._storage, memo))

  def _make_storage(self, *args, **kwargs):
    """Determines the backing storage (overridden in subclasses)."""
    return list(*args, **kwargs)

  def _name_element(self, index):
    return "%d" % (index,)

  @property
  def _values(self):
    """Collect values for TrackableDataStructure."""
    return self

  def append(self, value):
    """Add a new trackable value."""
    value = self._track_value(value, self._name_element(len(self._storage)))
    self._storage.append(value)

  def extend(self, values):
    """Add a sequence of trackable values."""
    for value in values:
      self.append(value)

  def __iadd__(self, values):
    self.extend(values)
    return self

  def __add__(self, other):
    return self._storage + getattr(other, "_storage", other)

  def __imul__(self, y):
    if y <= 0:
      raise ValueError(
          f"List only supports append, multiplying in place by {y} removes "
          "elements.")

    n = len(self._storage)
    for _ in range(y - 1):
      for i in range(n):
        self.append(self._storage[i])

    return self

  def __mul__(self, n):
    return self._storage * n

  def __rmul__(self, n):
    return self * n

  def __radd__(self, other):
    return other + self._storage

  def __getitem__(self, key):
    return self._storage[key]

  def __getslice__(self, i, j):
    return self._storage[slice(i, j)]

  def __len__(self):
    return len(self._storage)

  def __repr__(self):
    return "List(%s)" % (repr(self._storage),)

  def __sizeof__(self):
    return super().__sizeof__() + sys.getsizeof(self._storage)


# TODO(tomhennigan) Update to collections.UserList?
# TODO(allenl): Try switching this to wrapt.ObjectProxy again when we drop
# Python 3.4 support (may still be tricky).
class ListWrapper(
    List,
    collections_abc.MutableSequence,
    # Shadowed, but there for isinstance checks.
    list):
  """Wraps the built-in `list` to support restore-on-create for variables.

  Unlike `List`, this sequence type is mutable in the same ways built-in lists
  are. Instead of throwing an error immediately like `List`, it records
  problematic mutations (e.g. assigning a new element to a position already
  occupied, meaning both elements get the same names at different times) and
  refuses to save.

  On assignment to an attribute of a Model or Trackable object, Python
  lists are replaced with ListWrapper. Wrapping a list in a
  `NoDependency` object prevents this.
  """

  def __init__(self, wrapped_list):
    """Construct a new list wrapper.

    Args:
      wrapped_list: The initial value of the data structure. A shallow copy may
        be maintained for error checking. `wrapped_list` itself should not be
        modified directly after constructing the `ListWrapper`, and if changes
        are detected the `ListWrapper` will throw an exception on save.
    """
    # Monotonic flags which indicate this object would not be restored properly,
    # and therefore should throw an error on save to avoid giving the impression
    # that restoring it will work.
    self._non_append_mutation_value = False
    self._external_modification_value = False
    super().__init__(wrapped_list)
    self._last_wrapped_list_snapshot = list(self._storage)

  @property
  def _non_append_mutation(self):
    return self._non_append_mutation_value

  @_non_append_mutation.setter
  def _non_append_mutation(self, value):
    # Trackable only cares that a mutation occurred at some point; when
    # attempting to save it checks whether a mutation occurred and the object is
    # in a "dirty" state but otherwise the specifics of how it got to that state
    # are ignored. By contrast, the attribute cache needs to signal the mutation
    # immediately since a caller could query the value of an attribute (And
    # should not hit the cached value since the mutation may have affected the
    # result.)
    self._attribute_sentinel.invalidate_all()
    self._non_append_mutation_value = value

  @property
  def _external_modification(self):
    return self._external_modification_value

  @_external_modification.setter
  def _external_modification(self, value):
    # Invalidate for the same reason as `_non_append_mutation`
    self._attribute_sentinel.invalidate_all()
    self._external_modification_value = value

  # pylint: disable=protected-access
  def __copy__(self):
    copied = super().__copy__()
    copied._non_append_mutation = self._non_append_mutation
    copied._external_modification = self._external_modification
    return copied

  def __deepcopy__(self, memo):
    copied = super().__deepcopy__(memo)
    copied._non_append_mutation = self._non_append_mutation
    copied._external_modification = self._external_modification
    return copied
  # pylint: enable=protected-access

  def __reduce_ex__(self, protocol):
    return (self.__class__,
            (self._storage,))

  def _make_storage(self, wrapped_list):
    """Use the user's original list for storage."""
    return wrapped_list

  def _check_external_modification(self):
    """Checks for any changes to the wrapped list not through the wrapper."""
    if self._external_modification or self._non_append_mutation:
      return
    if self._storage != self._last_wrapped_list_snapshot:
      self._external_modification = True
      self._last_wrapped_list_snapshot = None

  def _update_snapshot(self):
    """Acknowledges tracked changes to the wrapped list."""

    # Mutation tracking for attributes reuses the same infrastructure as
    # Trackable mutation tracking.
    self._attribute_sentinel.invalidate_all()
    if self._external_modification or self._non_append_mutation:
      return
    self._last_wrapped_list_snapshot = list(self._storage)

  def _trackable_children(self, save_type=base.SaveType.CHECKPOINT, **kwargs):
    self._check_external_modification()
    if self._non_append_mutation:
      raise ValueError(
          f"Unable to save the object {self} (a list wrapper constructed to "
          "track trackable TensorFlow objects). A list element was replaced "
          "(__setitem__, __setslice__), deleted (__delitem__, __delslice__), "
          "or moved (sort). In order to support restoration on object "
          "creation, tracking is exclusively for append-only data structures."
          "\n\nIf you don't need this list checkpointed, wrap it in a "
          "non-trackable object; it will be subsequently ignored.")
    if self._external_modification:
      raise ValueError(
          f"Unable to save the object {self} (a list wrapper constructed to "
          "track trackable TensorFlow objects). The wrapped list was modified "
          f"outside the wrapper (its final value was {self._storage}, its value"
          " when a checkpoint dependency was added was "
          f"{self._last_wrapped_list_snapshot}), which breaks "
          "restoration on object creation.\n\nIf you don't need this list "
          "checkpointed, wrap it in a NoDependency object; it will be "
          "subsequently ignored.")
    children = super()._trackable_children(save_type, **kwargs)

    if save_type == base.SaveType.SAVEDMODEL:
      # Add functions to be serialized.
      children.update({
          str(key): value
          for key, value in enumerate(self)
          if _is_function(value)
      })

    return children

  def _has_mutation_or_trackable(self):
    """Short-circuits a check for trackables if there's already a mutation."""
    if self._non_append_mutation:
      return True
    return any(isinstance(element, base.Trackable) for element in self._storage)

  def __delitem__(self, key):
    self._check_external_modification()
    if self._has_mutation_or_trackable():
      self._non_append_mutation = True
    del self._storage[key]
    self._update_snapshot()

  def __setitem__(self, key, value):
    self._check_external_modification()

    if isinstance(key, slice):
      # Note: this is quite inefficient, but the list API supports a broad range
      # of slice setters (e.g. truncate, extend, replace) and imitating this
      # for a range of Python versions is non-trivial.
      storage_copy = list(self._storage)
      self._storage[key] = value

      len_before = len(storage_copy)
      len_now = len(self._storage)
      for i in range(max(len_before, len_now)):
        value_now = self._storage[i] if i < len_now else None
        value_before = storage_copy[i] if i < len_before else None

        if isinstance(value_before, base.Trackable):
          self._non_append_mutation = True

        if value_now is not None and value_now != value_before:
          self._storage[i] = self._track_value(self._storage[i],
                                               self._name_element(i))

    else:
      if isinstance(self._storage[key], base.Trackable):
        self._non_append_mutation = True
      self._storage[key] = self._track_value(value, self._name_element(key))

    self._update_snapshot()

  def append(self, value):
    """Add a new trackable value."""
    self._check_external_modification()
    super().append(value)
    self._update_snapshot()

  def extend(self, values):
    """Add a sequence of trackable values."""
    self._check_external_modification()
    super().extend(values)
    self._update_snapshot()

  def __imul__(self, y):
    if y <= 0:
      self._check_external_modification()
      if self._has_mutation_or_trackable():
        self._non_append_mutation = True
      self._storage *= y
      self._update_snapshot()
      return self

    # Relies on super() calling append, which updates the snapshot.
    return super().__imul__(y)

  def __eq__(self, other):
    return self._storage == getattr(other, "_storage", other)

  def __ne__(self, other):
    return self._storage != getattr(other, "_storage", other)

  def __lt__(self, other):
    return self._storage < getattr(other, "_storage", other)

  def __le__(self, other):
    return self._storage <= getattr(other, "_storage", other)

  def __gt__(self, other):
    return self._storage > getattr(other, "_storage", other)

  def __ge__(self, other):
    return self._storage >= getattr(other, "_storage", other)

  def __hash__(self):
    # List wrappers need to compare like regular lists, and so like regular
    # lists they don't belong in hash tables.
    raise TypeError("unhashable type: 'ListWrapper'")

  def insert(self, index, obj):
    self._check_external_modification()
    if (self._has_mutation_or_trackable() or isinstance(obj, base.Trackable)):
      self._non_append_mutation = True
    self._storage.insert(index, obj)
    self._update_snapshot()

  def sort(self):
    self._check_external_modification()
    if self._has_mutation_or_trackable():
      self._non_append_mutation = True
    self._storage.sort()
    self._update_snapshot()

  def __setslice__(self, i, j, y):
    self.__setitem__(slice(i, j), y)

  def __delslice__(self, i, j):
    self._check_external_modification()
    if self._has_mutation_or_trackable():
      self._non_append_mutation = True
    del self._storage[slice(i, j)]
    self._update_snapshot()

  def _track_value(self, value, name):
    """Allows storage of non-trackable objects."""
    try:
      value = super()._track_value(value=value, name=name)
    except ValueError:
      # Even if this value isn't trackable, we need to make sure
      # NoDependency objects get unwrapped.
      value = sticky_attribute_assignment(
          trackable=self, value=value, name=name)
    return value

  def __repr__(self):
    return "ListWrapper(%s)" % (repr(self._storage),)


class Mapping(TrackableDataStructure, collections_abc.Mapping):
  """An append-only trackable mapping data structure with string keys.

  Maintains checkpoint dependencies on its contents (which must also be
  trackable), named based on its keys.

  Note that once a key has been added, it may not be deleted or replaced.
  """

  def __init__(self, *args, **kwargs):
    """Construct a new sequence. Arguments are passed to `dict()`."""
    super().__init__()
    self._storage = self._make_storage(*args, **kwargs)
    self._storage.update(
        {key: self._track_value(
            value, name=self._name_element(key))
         for key, value in self._storage.items()})

  def __copy__(self):
    return type(self)(copy.copy(self._storage))

  def __deepcopy__(self, memo):
    return type(self)(copy.deepcopy(self._storage, memo))

  def _make_storage(self, *args, **kwargs):
    return dict(*args, **kwargs)

  @property
  def _values(self):
    """Collect values for TrackableDataStructure."""
    # Sort items deterministically by key
    ordered = list(zip(*sorted(self.items(), key=lambda it: it[0])))
    if ordered:
      return ordered[1]
    return []

  def _name_element(self, key):
    if not isinstance(key, str):
      raise TypeError(
          f"Mapping accepts only string keys, but got a key {repr(key)}.")
    return str(key)

  def __setitem__(self, key, value):
    name = self._name_element(key)
    value = self._track_value(value, name=name)
    current_value = self._storage.setdefault(key, value)
    if current_value is not value:
      raise ValueError(
          "Mappings are an append-only data structure. Tried to overwrite the "
          f"key '{key}' with value {value}, but it already contains "
          f"{current_value}")

  def update(self, *args, **kwargs):
    for key, value in dict(*args, **kwargs).items():
      self[key] = value

  def __getitem__(self, key):
    return self._storage[key]

  def __len__(self):
    return len(self._storage)

  def __repr__(self):
    return "Mapping(%s)" % (repr(self._storage),)

  def __iter__(self):
    return iter(self._storage)


class _DictWrapper(TrackableDataStructure, wrapt.ObjectProxy):
  """Wraps built-in dicts to support restore-on-create for variables.

  _DictWrapper is to Mapping as ListWrapper is to List. Unlike Mapping,
  _DictWrapper allows non-string keys and values and arbitrary mutations (delete
  keys, reassign values). Like ListWrapper, these mutations mean that
  _DictWrapper will raise an exception on save.
  """

  def __init__(self, wrapped_dict=None):
    if wrapped_dict is None:
      # Allow zero-argument construction, e.g. from session.run's re-wrapping.
      wrapped_dict = {}
    if not isinstance(wrapped_dict, collections_abc.Mapping):
      # Allow construction from a sequence, e.g. from nest.pack_sequence_as.
      wrapped_dict = dict(wrapped_dict)
    wrapt.ObjectProxy.__init__(self, wrapped_dict)
    TrackableDataStructure.__init__(self)
    self._self_non_string_key = False
    self._self_external_modification = False
    self.__wrapped__.update(
        {key: self._track_value(
            value, name=self._name_element(key))
         for key, value in self.__wrapped__.items()})
    self._update_snapshot()

  def __reduce_ex__(self, protocol):
    return (self.__class__,
            (self.__wrapped__,))

  def __getattribute__(self, name):
    if name == "__dict__":
      # Returns __dict__ from wrapt.ObjectProxy
      try:
        return object.__getattribute__(self, "__dict__")
      except (AttributeError, TypeError):
        return {}
    if (hasattr(type(self), name)
        and isinstance(getattr(type(self), name), property)):
      # Bypass ObjectProxy for properties. Whether this workaround is necessary
      # appears to depend on the Python version but not the wrapt version: 3.4
      # in particular seems to look up properties on the wrapped object instead
      # of the wrapper without this logic.
      return object.__getattribute__(self, name)
    else:
      # Raise TypeError as AttributeError to fix breakage in wrapt 1.15 for
      # `__getattribute__` as suggested in discussion with library author in
      # GitHub https://github.com/GrahamDumpleton/wrapt/issues/231
      try:
        return super().__getattribute__(name)
      except TypeError as e:
        raise AttributeError from e

  def copy(self):
    return copy.copy(self)

  # pylint: disable=protected-access
  def __copy__(self):
    copied = _DictWrapper(copy.copy(self.__wrapped__))
    copied._self_external_modification = self._self_external_modification
    copied._self_non_string_key = self._self_non_string_key
    return copied

  def __deepcopy__(self, memo):
    copied = _DictWrapper(copy.deepcopy(self.__wrapped__, memo))
    copied._self_external_modification = self._self_external_modification
    copied._self_non_string_key = self._self_non_string_key
    return copied
  # pylint: enable=protected-access

  @property
  def _values(self):
    """Collect values for TrackableDataStructure."""
    # Sort items deterministically by key
    ordered = list(zip(*sorted(self.items(), key=lambda it: it[0])))
    if ordered:
      return ordered[1]
    return []

  def _trackable_children(self, save_type=base.SaveType.CHECKPOINT, **kwargs):
    """Check that the object is saveable before listing its dependencies."""
    self._check_self_external_modification()
    if self._self_non_string_key:
      raise ValueError(
          f"Unable to save the object {self} (a dictionary wrapper constructed "
          "automatically on attribute assignment). The wrapped dictionary "
          "contains a non-string key which maps to a trackable object or "
          "mutable data structure.\n\nIf you don't need this dictionary "
          "checkpointed, wrap it in a non-trackable "
          "object; it will be subsequently ignored.")
    if self._self_external_modification:
      raise ValueError(
          f"Unable to save the object {self} (a dictionary wrapper constructed "
          "automatically on attribute assignment). The wrapped dictionary was "
          f"modified outside the wrapper (its final value was {self}, its value"
          " when a checkpoint dependency was added was "
          f"{self._self_last_wrapped_dict_snapshot}), which breaks "
          "restoration on object creation.\n\nIf you don't need this "
          "dictionary checkpointed, wrap it in a "
          "non-trackable object; it will be subsequently ignored.")
    assert not self._dirty  # Any reason for dirtiness should have an exception.
    children = super()._trackable_children(save_type, **kwargs)

    if save_type == base.SaveType.SAVEDMODEL:
      # Add functions to be serialized.
      children.update(
          {key: value for key, value in self.items() if _is_function(value)})

    return children

  @property
  def _dirty(self):
    """Check if there has already been a mutation which prevents saving."""
    return (self._self_external_modification
            or self._self_non_string_key)

  def _check_self_external_modification(self):
    """Checks for any changes to the wrapped dict not through the wrapper."""
    if self._dirty:
      return
    if self != self._self_last_wrapped_dict_snapshot:
      self._self_external_modification = True
      self._self_last_wrapped_dict_snapshot = None

  def _update_snapshot(self):
    """Acknowledges tracked changes to the wrapped dict."""
    self._attribute_sentinel.invalidate_all()
    if self._dirty:
      return
    self._self_last_wrapped_dict_snapshot = dict(self)

  def _track_value(self, value, name):
    """Allows storage of non-trackable objects."""
    if isinstance(name, str):
      string_key = True
    else:
      name = "-non_string_key"
      string_key = False
    try:
      no_dependency = isinstance(value, NoDependency)
      value = super()._track_value(value=value, name=name)
      if not (string_key or no_dependency):
        # A non-string key maps to a trackable value. This data structure
        # is not saveable.
        self._self_non_string_key = True
      return value
    except ValueError:
      # Even if this value isn't trackable, we need to make sure
      # NoDependency objects get unwrapped.
      return sticky_attribute_assignment(
          trackable=self, value=value, name=name)

  def _name_element(self, key):
    """Tells TrackableDataStructure to use keys as names as-is."""
    return key

  def __setitem__(self, key, value):
    """Allow any modifications, but possibly mark the wrapper as unsaveable."""
    self._check_self_external_modification()
    self._maybe_initialize_trackable()
    no_dep = isinstance(value, NoDependency)
    if isinstance(key, str):
      value = self._track_value(value, name=key)
    else:
      value = wrap_or_unwrap(value)
      if not no_dep and isinstance(value, base.Trackable):
        # Non-string keys are OK as long as we have no reason to add a
        # dependency on the value (either because the value is not
        # trackable, or because it was wrapped in a NoDependency object).
        self._self_non_string_key = True
    self.__wrapped__[key] = value

    self._update_snapshot()

  def __delitem__(self, key):
    self._check_self_external_modification()
    del self.__wrapped__[key]
    self._update_snapshot()

  def __repr__(self):
    return "DictWrapper(%s)" % (repr(self.__wrapped__),)

  def __hash__(self):
    raise TypeError("unhashable type: 'DictWrapper'")

  def __eq__(self, other):
    # Override the TrackableDataStructure "== -> is" forwarding and go back to
    # the wrapt implementation.
    return self.__wrapped__ == other

  def update(self, *args, **kwargs):
    for key, value in dict(*args, **kwargs).items():
      self[key] = value


class _TupleWrapper(TrackableDataStructure, wrapt.ObjectProxy):
  """Trackable wrapper for tuples and namedtuples."""

  def __init__(self, original_wrapped_tuple=()):
    add_dependency = []
    substituted_wrapped_tuple = []
    for element in original_wrapped_tuple:
      if isinstance(element, NoDependency):
        add_dependency.append(False)
      else:
        add_dependency.append(True)
      substituted_wrapped_tuple.append(wrap_or_unwrap(element))
    try:
      fields = original_wrapped_tuple._fields
    except AttributeError:
      # Not a namedtuple
      is_namedtuple = False
    else:
      is_namedtuple = True
    original_type = type(original_wrapped_tuple)
    # Flag to poison saving if we can't re-construct a namedtupled because its
    # __new__ takes different keyword arguments than its _fields.
    self._self_tuple_is_constructable = True
    if is_namedtuple:
      try:
        # NamedTuples take N arguments, unlike tuple which takes a sequence.
        substituted_wrapped_tuple = original_type(
            **dict(zip(fields, substituted_wrapped_tuple)))
      except TypeError:
        wrapt.ObjectProxy.__init__(self, original_wrapped_tuple)
        TrackableDataStructure.__init__(self)
        self._self_tuple_is_constructable = False
        return
    else:
      substituted_wrapped_tuple = original_type(substituted_wrapped_tuple)
    wrapt.ObjectProxy.__init__(self, substituted_wrapped_tuple)
    TrackableDataStructure.__init__(self)

    if is_namedtuple:
      # For namedtuples, also track by names for compatibility with
      # dictionaries.
      for name, should_depend, element in zip(
          fields, add_dependency, substituted_wrapped_tuple):
        if should_depend:
          self._track_value(element, name=name)

    # Track by index as well, for compatibility with lists.
    for index, (should_depend, element) in enumerate(
        zip(add_dependency, substituted_wrapped_tuple)):
      if should_depend:
        self._track_value(element, name="%d" % (index,))

  @property
  def _values(self):
    """Collect values for TrackableDataStructure."""
    return self

  def _track_value(self, value, name):
    """Allows storage of non-trackable objects."""
    try:
      value = super()._track_value(value=value, name=name)
    except ValueError:
      # Even if this value isn't trackable, we need to make sure
      # NoDependency objects get unwrapped.
      value = sticky_attribute_assignment(
          trackable=self, value=value, name=name)
    return value

  def __repr__(self):
    return "_TupleWrapper(%s)" % (repr(self.__wrapped__),)

  def __hash__(self):
    # Override the TrackableDataStructure hash forwarding and go back to
    # the wrapt implementation.
    return hash(self.__wrapped__)

  def __eq__(self, other):
    # Override the TrackableDataStructure "== -> is" forwarding and go back to
    # the wrapt implementation.
    return self.__wrapped__ == other

  def __copy__(self):
    return _TupleWrapper(copy.copy(self.__wrapped__))

  def __deepcopy__(self, memo):
    return _TupleWrapper(copy.deepcopy(self.__wrapped__, memo))

  @property
  def __dict__(self):
    # Python 3.12 inspect._check_instance() method only expects and handles
    # AttributeError but TypeError was raised when the method looks for
    # `__dict__` on the data structure proxy wrapper. Thus we overrides the
    # `__dict__` property and forwarding the `__dict__` lookup to the underlying
    # wrapped TrackalbeDataStructure. AttributeError will be raised when
    # TrackableDataStructure does not support `__dict__` and thus will be
    # handled properly.
    return self.__wrapped__.__dict__

  def __reduce_ex__(self, protocol):
    return (self.__class__,
            (self.__wrapped__,))

  # imul and iadd are the only tuple-relevant in-place operators. They need to
  # be special-cased to avoid mutating the original proxy object.
  def __imul__(self, y):
    """Avoid running self.__wrapped__ *= y, which mutates `self`."""
    return self.__wrapped__ * y

  def __iadd__(self, y):
    """Avoid running self.__wrapped__ += y, which mutates `self`."""
    return self.__wrapped__ + y

  def _trackable_children(self, save_type=base.SaveType.CHECKPOINT, **kwargs):
    if not self._self_tuple_is_constructable:
      raise ValueError(
          f"Unable to save because the namedtuple {self.__wrapped__} is not "
          "constructable from its _fields (i.e. __new__ is overridden). "
          f"Expected keyword arguments {self.__wrapped__._fields}. If you do "
          "not need to save this object, consider wrapping it in a custom "
          "object that does not inherit from tuple.")
    return super()._trackable_children(save_type, **kwargs)

  def __getattribute__(self, name):
    if name != "__wrapped__" and hasattr(self.__wrapped__, name):
      # Prefer attributes on the wrapped object when they conflict with
      # attributes on the wrapper object.
      return getattr(self.__wrapped__, name)

    if (hasattr(type(self), name)
        and isinstance(getattr(type(self), name), property)):
      # Bypass ObjectProxy for properties. Whether this workaround is necessary
      # appears to depend on the Python version but not the wrapt version: 3.4
      # in particular seems to look up properties on the wrapped object instead
      # of the wrapper without this logic.
      return object.__getattribute__(self, name)
    else:
      return super().__getattribute__(name)


def _is_function(x):
  # pylint: disable=g-import-not-at-top
  from tensorflow.python.eager import def_function
  from tensorflow.python.eager import function as defun
  return isinstance(x, (def_function.Function, defun.ConcreteFunction))


def set_list_item(list_object, index_string, value):
  item_index = int(index_string)
  if len(list_object) <= item_index:
    list_object.extend([None] * (1 + item_index - len(list_object)))
  list_object[item_index] = value


def set_tuple_item(list_object, index_string, value):
  try:
    item_index = int(index_string)
  except ValueError:
    # Ignore namedtuple fields.
    return
  if len(list_object) <= item_index:
    list_object.extend([None] * (1 + item_index - len(list_object)))
  list_object[item_index] = value