# 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.
# ==============================================================================
"""Contains private utilities used mainly by the base Layer class."""

import functools
import threading

import tensorflow.compat.v1 as tf1
import tensorflow.compat.v2 as tf

from keras.src import backend
from keras.src.dtensor import dtensor_api as dtensor
from keras.src.utils import control_flow_util
from keras.src.utils import tf_inspect
from keras.src.utils import tf_utils

# isort: off
from tensorflow.python.util.tf_export import keras_export

_call_context = threading.local()


def create_mean_metric(value, name=None):
    # import keras will import base_layer and then this module, and metric
    # relies on base_layer, which result into a cyclic dependency.
    from keras.src import metrics as metrics_module

    metric_obj = metrics_module.Mean(name=name, dtype=value.dtype)
    return metric_obj, metric_obj(value)


def infer_init_val_and_dtype(initializer, dtype, shape, layout=None):
    if initializer is not None and not callable(initializer):
        init_val = initializer
        variable_dtype = None
    else:
        # Instantiate initializer if provided initializer is a type object.
        if tf_inspect.isclass(initializer):
            initializer = initializer()
        if layout:
            init_val = functools.partial(
                initializer, shape, dtype=dtype, layout=layout
            )
        else:
            init_val = functools.partial(initializer, shape, dtype=dtype)
        variable_dtype = dtype.base_dtype
    return init_val, variable_dtype


def make_variable(
    name,
    shape=None,
    dtype=tf.float32,
    initializer=None,
    trainable=None,
    caching_device=None,
    validate_shape=True,
    constraint=None,
    use_resource=None,
    collections=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.VariableAggregation.NONE,
    partitioner=None,
    layout=None,
    experimental_enable_variable_lifting=True,
):
    """Util to create a variable (relies on `variable_scope.variable`).

    Some reuse-related technicalities prevent us from using
    `variable_scope.get_variable()` directly, so we use a subcomponent
    that has fewer constraints (`variable_scope.variable()`).

    In the longer term, it seems like a similar "default variable creator"
    method should exist in `Trackable` instead. When this happens, we can get
    rid of this temporary solution.

    TODO(fchollet): remove this method when no longer needed.

    Args:
      name: Variable name.
      shape: Variable shape.
      dtype: The type of the variable. Defaults to `self.dtype` or `float32`.
      initializer: Initializer instance (callable).
      trainable: Whether the variable should be part of the layer's
        "trainable_variables" (e.g. variables, biases)
        or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
        Note, if the current variable scope is marked as non-trainable
        then this parameter is ignored and any added variables are also
        marked as non-trainable. `trainable` becomes `True` unless
        `synchronization` is set to `ON_READ`. Defaults to `None`.
      caching_device: Passed to `tf.Variable`.
      validate_shape: Passed to `tf.Variable`.
      constraint: Constraint instance (callable).
      use_resource: Whether to use a `ResourceVariable`.
      collections: List of graph collections keys. The new variable is added to
        these collections. Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
      synchronization: Indicates when a distributed a variable will be
        aggregated. Accepted values are constants defined in the class
        `tf.VariableSynchronization`. By default the synchronization is set to
        `AUTO` and the current `DistributionStrategy` chooses
        when to synchronize. If `synchronization` is set to `ON_READ`,
        `trainable` must not be set to `True`.
      aggregation: Indicates how a distributed variable will be aggregated.
        Accepted values are constants defined in the class
        `tf.VariableAggregation`.
      partitioner: Not handled at this time.
      layout: the optional DTensor layout, used for creating DVariable.

    Returns:
      Variable instance.
    """
    init_val, variable_dtype = infer_init_val_and_dtype(
        initializer, dtype, shape, layout
    )
    variable_shape = tf.TensorShape(shape)

    if use_resource is None:
        use_resource = True

    if layout is None:
        # In theory, in `use_resource` is True and `collections` is empty
        # (that is to say, in TF2), we can use tf.Variable.
        # However, this breaks legacy (Estimator) checkpoints because
        # it changes variable names. Remove this when V1 is fully deprecated.
        return tf1.Variable(
            initial_value=init_val,
            name=name,
            trainable=trainable,
            caching_device=caching_device,
            dtype=variable_dtype,
            validate_shape=validate_shape,
            constraint=constraint,
            use_resource=use_resource,
            collections=collections,
            synchronization=synchronization,
            aggregation=aggregation,
            shape=variable_shape if variable_shape else None,
            experimental_enable_variable_lifting=experimental_enable_variable_lifting,  # noqa: E501
        )
    else:
        return dtensor.DVariable(
            initial_value=init_val,
            name=name,
            trainable=trainable,
            caching_device=caching_device,
            dtype=variable_dtype,
            validate_shape=validate_shape,
            constraint=constraint,
            collections=collections,
            synchronization=synchronization,
            aggregation=aggregation,
            shape=variable_shape if variable_shape else None,
        )


def collect_previous_mask(input_tensors):
    """Retrieves the output mask(s) of the previous node.

    Args:
        input_tensors: An arbitrary structure of Tensors.

    Returns:
        A mask tensor or list of mask tensors.
    """

    def _collect_previous_mask(x):
        return getattr(x, "_keras_mask", None)

    return tf.nest.map_structure(_collect_previous_mask, input_tensors)


def have_all_keras_metadata(tensors):
    return all(hasattr(x, "_keras_history") for x in tf.nest.flatten(tensors))


def generate_placeholders_from_shape(shape):
    return tf1.placeholder(shape=shape, dtype=backend.floatx())


def create_keras_history(tensors):
    """Wraps TensorFlow Operations for compatibility with the Functional API.

    This method checks to see if a Tensor in `tensors` is missing Keras metadata
    and has its origin in a Keras `Input` Layer. If so, this method will replace
    the raw TensorFlow Operations that created this tensor with
    `TensorFlowOpLayer` instances that create identical operations.

    Any Tensors not originating from a Keras `Input` Layer will be treated as
    constants when constructing `TensorFlowOpLayer` instances.

    Args:
      tensors: A structure of Tensors, some of which come from raw TensorFlow
        operations and need to have Keras metadata assigned to them.

    Returns:
      created_layers: List. The `TensorFlowOpLayer` instances created to wrap
        the raw Tensorflow operations.
    """
    _, created_layers = _create_keras_history_helper(tensors, set(), [])
    return created_layers


# Unsafe Internal attribute.
# If True, Keras will not evaluate the constant-foldable inputs to tf op
# layers in TF1 graphs. This *might* speed up model construction time in
# certain settings, but it means
# the models will not be serializable/deserializable via get_config
# (Only via Savedmodels). It may also change the semantics of whether
# generated random numbers are generated once and re-used, or recomputed
# each time.
# Note: This path triggers for TPUEstimators / xla compiled graphs regardless
# of this setting.
_UNSAFE_GRAPH_OP_LAYER_CREATION = False


def _create_keras_history_helper(tensors, processed_ops, created_layers):
    """Helper method for `create_keras_history`.

    Args:
      tensors: A structure of Tensors for which to create Keras metadata.
      processed_ops: Set. TensorFlow operations that have already been wrapped
        in `TensorFlowOpLayer` instances.
      created_layers: List. The `TensorFlowOpLayer` instances created.

    Returns:
      Tuple. First element is the updated set of TensorFlow Operations that
      have been wrapped in `TensorFlowOpLayer` instances. Second element is
      a list of the `TensorFlowOpLayer` instances created.
    """
    if tf1.executing_eagerly_outside_functions():
        raise ValueError(
            "`create_keras_history` should only be called if eager is disabled!"
        )
    # Import of `base_layer` needed in order to create `TensorFlowOpLayer`.
    # Cannot be imported at top because of circular dependencies.
    # TODO(omalleyt): Resolve circular dependency.
    from keras.src.engine import base_layer

    tensor_list = tf.nest.flatten(tensors)
    sparse_ops = []
    ragged_tensors = []
    for tensor in tensor_list:
        if getattr(tensor, "_keras_history", None) is not None:
            continue
        if isinstance(tensor, (tf.SparseTensor, tf1.SparseTensorValue)):
            sparse_ops.append(tensor.op)
            continue
        if tf_utils.is_ragged(tensor):
            # Ragged tensors don't have an op property
            ragged_tensors.append(tensor)
            continue
        op = tensor.op  # The Op that created this Tensor.
        if op not in processed_ops:
            # Recursively set `_keras_history`.
            op_inputs = list(op.inputs)
            constants = {}
            layer_inputs = []
            for i, op_input in enumerate(op_inputs):
                if uses_keras_history(op_input):
                    layer_inputs.append(op_input)
                else:
                    # Treat any value not originating from a `keras.Input` as
                    # a constant. Variables cannot be supported.
                    ds_with_session = (
                        tf.distribute.in_cross_replica_context()
                        and not tf1.executing_eagerly_outside_functions()
                    )
                    using_xla = control_flow_util.GraphOrParentsInXlaContext(
                        tf1.get_default_graph()
                    )
                    if (
                        ds_with_session
                        or using_xla
                        or _UNSAFE_GRAPH_OP_LAYER_CREATION
                    ):
                        # In Legacy Graph mode, evaluating here makes Session be
                        # configured improperly. The downside of this is that
                        # saving via `get_config` breaks, but SavedModel still
                        # works.
                        constants[i] = op_input
                    else:
                        with tf.init_scope():
                            constants[i] = backend.function([], op_input)([])
            layer_inputs = unnest_if_single_tensor(layer_inputs)
            processed_ops, created_layers = _create_keras_history_helper(
                layer_inputs, processed_ops, created_layers
            )
            name = op.name
            node_def = op.node_def.SerializeToString()
            op_layer = base_layer.TensorFlowOpLayer(
                node_def, constants=constants, name=name
            )
            created_layers.append(op_layer)
            op_layer._set_connectivity_metadata(
                args=(layer_inputs,), kwargs={}, outputs=op.outputs
            )
            processed_ops.update([op])
    if sparse_ops or ragged_tensors:
        lambda_example = """
    weights_mult = lambda x: tf.sparse.sparse_dense_matmul(x, weights)
    output = tf.keras.layers.Lambda(weights_mult)(input)
    """
        raise ValueError(
            "Tensorflow ops that generate ragged or sparse tensor "
            "outputs are currently not supported by Keras automatic "
            "op wrapping. Please wrap these ops in a Lambda layer: "
            "\n\n```\n{example}\n```\n"
            "Sparse ops encountered: {sparse_ops}\n"
            "Ragged tensors encountered: {ragged_tensors}\n".format(
                example=lambda_example,
                sparse_ops=str(sparse_ops),
                ragged_tensors=str(ragged_tensors),
            )
        )
    return processed_ops, created_layers


def unnest_if_single_tensor(input_tensors):
    # Preserve compatibility with older configs
    flat_input_tensors = tf.nest.flatten(input_tensors)
    # If this is a single element but not a dict, unwrap. If this is a dict,
    # assume the first layer expects a dict (as is the case with a
    # DenseFeatures layer); pass through.
    if not isinstance(input_tensors, dict) and len(flat_input_tensors) == 1:
        input_tensors = flat_input_tensors[0]
    return input_tensors


def needs_keras_history(tensors, ignore_call_context=False):
    """Check if any Tensors need to be wrapped in TensorFlowOpLayers.

    This will never return True inside a sublayer, because sublayers
    do not need to create Keras History. Otherwise, this returns True
    if one or more of `tensors` originates from a `keras.Input` and
    does not have `_keras_history` set.

    Args:
      tensors: An arbitrary nested structure of Tensors.
      ignore_call_context: Whether to ignore the check of if currently
        outside of a `call` context. This is `True` when creating
        KerasHistory inside `Node`, where we always know that Tensors
        are being used with the Functional API.

    Returns:
      Bool, whether at least one Tensor needs to be wrapped.
    """
    input_tensors = tf.nest.flatten(tensors)
    if call_context().in_call and not ignore_call_context:
        return False
    if all(
        getattr(tensor, "_keras_history", None) is not None
        for tensor in input_tensors
    ):
        # KerasHistory already set.
        return False
    return uses_keras_history(tensors)


def is_in_keras_graph():
    """Returns if currently executing inside of a Keras graph."""
    return call_context().in_keras_graph


def is_in_eager_or_tf_function():
    """Returns if in eager mode or inside of a tf.function."""
    return tf.executing_eagerly() or is_in_tf_function()


def is_in_tf_function():
    """Returns if inside of a tf.function."""
    # Check if running in V1 graph mode.
    if not tf1.executing_eagerly_outside_functions():
        return False
    if not tf.inside_function():
        return False
    # Check if inside Keras FuncGraph.
    if is_in_keras_graph():
        return False
    # Check for a v1 `wrap_function` FuncGraph.
    graph = tf1.get_default_graph()
    if getattr(graph, "name", False) and graph.name.startswith(
        "wrapped_function"
    ):
        return False
    return True


def uses_keras_history(tensors):
    """Check if at least one Tensor originates from a `keras.Input`.

    This is `True` if at least one Tensor has its origin in a `keras.Input`.
    Any Tensor that originates from a `keras.Input` will have a dependency
    Tensor with a `_keras_history` attribute attached. Tensors that have
    already been checked to not originate from a `keras.Input`
    are marked as `_keras_history_checked`.

    Args:
      tensors: An arbitrary nested structure of Tensors.

    Returns:
      Bool, whether at least one Tensor originates from a `keras.Input`.
    """
    checked_tensors = set()
    tensors_to_check = tf.nest.flatten(tensors)

    while tensors_to_check:
        new_tensors_to_check = []
        for tensor in tensors_to_check:
            if id(tensor) in checked_tensors:
                continue

            checked_tensors.add(id(tensor))

            if getattr(tensor, "_keras_history_checked", None) is not None:
                continue
            if getattr(tensor, "_keras_history", None) is not None:
                return True

            try:
                new_tensors_to_check.extend(tensor.op.inputs)
            except AttributeError:
                # In case `tensor` is a Variable created in an Eager context.
                pass

        tensors_to_check = new_tensors_to_check

    # Mark that these Tensors have been checked once for `_keras_history`,
    # and should not be checked again for performance reasons.
    mark_checked(tensors)
    return False


def mark_checked(tensors):
    """Marks that these Tensors should not be tracked.

    This prevents Layers from attempting to create TensorFlowOpLayers
    for these Tensors.

    Args:
      tensors: An arbitrary structure of Tensors.
    """

    def _mark_checked(tensor):
        tensor._keras_history_checked = True

    tf.nest.map_structure(_mark_checked, tensors)


def call_context():
    """Returns currently active `CallContext`."""
    call_ctx = getattr(_call_context, "call_context", None)
    if call_ctx is None:
        call_ctx = CallContext()
        _call_context.call_context = call_ctx
    return call_ctx


# Inject the call_context function to keras_deps to remove the dependency
# from TFLite to Keras.
tf.__internal__.register_call_context_function(call_context)


class CallContext:
    """Keeps track of properties currently inside a Layer/Model's `call`.

    Attributes:
      in_call: Whether currently inside the `call` of a Layer.
      layer: The `Layer` whose `call` is currently active.
      inputs: The inputs to the currently active `Layer`.
      build_graph: Whether currently inside a Graph or FuncGraph.
      training: Whether currently executing in training or inference mode.
      saving: Whether currently saving to SavedModel.
      frozen: Whether currently executing inside a `Layer` with `trainable` set
        to `False`.
      in_keras_graph: Whether executing inside the Keras Graph.
    """

    def __init__(self):
        # Handle `in_call` separately as it is the most-read attr and reading it
        # is on the hot path.
        self.in_call = False
        self._state = {
            "layer": None,
            "inputs": None,
            "build_graph": False,
            "training": None,
            "saving": None,
        }
        # TODO(b/150169018): This logic can be replaced after the Functional API
        # refactor.
        self._in_keras_graph = False

    def enter(self, layer, inputs, build_graph, training, saving=None):
        """Push a Layer and its inputs and state onto the current call context.

        Args:
          layer: The `Layer` whose `call` is currently active.
          inputs: The inputs to the currently active `Layer`.
          build_graph: Whether currently inside a Graph or FuncGraph.
          training: Whether currently executing in training or inference mode.
          saving: Whether currently saving to SavedModel.

        Returns:
          Context manager.
        """
        state = {
            "layer": layer,
            "inputs": inputs,
            "build_graph": build_graph,
            "training": training,
            "saving": saving,
        }
        return CallContextManager(self, state)

    @property
    def layer(self):
        return self._state["layer"]

    @property
    def inputs(self):
        return self._state["inputs"]

    @property
    def build_graph(self):
        return self._state["build_graph"]

    @property
    def training(self):
        return self._state["training"]

    @property
    def saving(self):
        return self._state["saving"]

    @property
    def frozen(self):
        layer = self._state["layer"]
        if not layer:
            return False
        return not layer.trainable

    @property
    def in_keras_graph(self):
        # Returns True even if in a subgraph of the Keras graph, such as those
        # created by control flow ops.
        if tf.executing_eagerly():
            return False
        return (
            self._in_keras_graph
            or getattr(backend.get_graph(), "name", None) == "keras_graph"
        )


class CallContextManager:
    """Context manager for `CallContext`."""

    def __init__(self, call_ctx, state):
        self._call_ctx = call_ctx
        self._state = state
        self._build_graph = state["build_graph"]

    def __enter__(self):
        call_ctx = self._call_ctx
        self._prev_in_call = call_ctx.in_call
        self._prev_state = call_ctx._state

        call_ctx.in_call = True
        call_ctx._state = self._state

        # TODO(b/150169018): This logic can be removed after the Functional API
        # refactor.
        if self._build_graph:
            self._prev_in_keras_graph = call_ctx._in_keras_graph
            call_ctx._in_keras_graph = (
                call_ctx._in_keras_graph
                or getattr(backend.get_graph(), "name", None) == "keras_graph"
            )

    def __exit__(self, *exc_info):
        call_ctx = self._call_ctx
        call_ctx.in_call = self._prev_in_call
        call_ctx._state = self._prev_state

        if self._build_graph:
            call_ctx._in_keras_graph = self._prev_in_keras_graph


def training_arg_passed_to_call(argspec, args, kwargs):
    """Returns whether a user passed the `training` argument in `__call__`."""
    # `argspec.args` starts with ['self', 'inputs']
    full_args = dict(zip(argspec.args[2:], args))
    full_args.update(kwargs)
    return "training" in full_args and full_args["training"] is not None


def is_subclassed(layer):
    """Returns True if the object is a subclassed layer or subclassed model."""
    return (
        layer.__module__.find("keras.engine") == -1
        and layer.__module__.find("keras.layers") == -1
    )


def from_saved_model(layer):
    """Returns whether the layer is loaded from a SavedModel."""
    return layer.__module__.find("keras.saving.legacy.saved_model") != -1


def check_graph_consistency(tensor=None, method="add_loss", force_raise=False):
    """Checks that tensors passed to `add_*` method match the Keras graph.

    When one of the `add_*` method is called inside a V2 conditional branch, the
    underlying tensor gets created in a FuncGraph managed by control_flow_v2.
    We need to raise clear error messages in such cases.

    Args:
      tensor: Tensor to check, or `False` if it is known that an error
        should be raised.
      method: Caller method, one of {'add_metric', 'add_loss', 'add_update'}.
      force_raise: If an error should be raised regardless of `tensor`.

    Raises:
      RuntimeError: In case of an out-of-graph tensor.
    """
    if force_raise or (
        tf1.executing_eagerly_outside_functions()
        and hasattr(tensor, "graph")
        and tensor.graph.is_control_flow_graph
    ):
        if method == "activity_regularizer":
            bad_example = """
      class TestModel(tf.keras.Model):

        def __init__(self):
          super(TestModel, self).__init__(name='test_model')
          self.dense = tf.keras.layers.Dense(2, activity_regularizer='l2')

        def call(self, x, training=None):
          if training:
            return self.dense(x)
          else:
            return self.dense(x)
      """
            correct_example = """
      class TestModel(tf.keras.Model):

        def __init__(self):
          super(TestModel, self).__init__(name='test_model')
          self.dense = tf.keras.layers.Dense(2, activity_regularizer='l2')

        def call(self, x, training=None):
          return self.dense(x)
      """
            raise RuntimeError(
                "You are using a layer with `activity_regularizer` in a "
                f"control flow branch, e.g.:\n{bad_example}\nThis is currently "
                "not supported. Please move your call to the layer with "
                "`activity_regularizer` out of the control flow branch, "
                f"e.g.:\n{correct_example}\nYou can also resolve this by "
                "marking your outer model/layer dynamic (eager-only) by "
                "passing `dynamic=True` to the layer constructor. Any kind of "
                "control flow is supported with dynamic layers. Note that "
                "using `dynamic=True` requires you to implement static shape "
                "inference in the `compute_output_shape(input_shape)` "
                "method."
            )

        if method == "add_metric":
            bad_example = """
      def call(self, inputs, training=None):
        if training:
          metric = compute_metric(inputs)
          self.add_metric(metric, name='my_metric', aggregation='mean')
        return inputs
      """
            correct_example = """
      def call(self, inputs, training=None):
        if training:
          metric = compute_metric(inputs)
        else:
          metric = 0.
        self.add_metric(metric, name='my_metric', aggregation='mean')
        return inputs
      """
        elif method == "add_loss":
            bad_example = """
      def call(self, inputs, training=None):
        if training:
          loss = compute_loss(inputs)
          self.add_loss(loss)
        return inputs
      """
            correct_example = """
      def call(self, inputs, training=None):
        if training:
          loss = compute_loss(inputs)
        else:
          loss = 0.
        self.add_loss(loss)
        return inputs
      """
        else:
            bad_example = """
      def call(self, inputs, training=None):
        if training:
          self.add_update(self.w.assign_add(1))
        return inputs
      """
            correct_example = """
      def call(self, inputs, training=None):
        if training:
          increment = 1
        else:
          increment = 0
        self.add_update(self.w.assign_add(increment))
        return inputs
      """
        raise RuntimeError(
            "You are using the method `{method}` in a control flow branch "
            "in your layer, e.g.:\n{bad_example}\n"
            "This is not currently supported. "
            "Please move your call to {method} out of the control flow branch, "
            "e.g.:\n{correct_example}\n"
            "You can also resolve this by marking your layer "
            "as dynamic (eager-only) by passing "
            "`dynamic=True` to the layer constructor. "
            "Any kind of control flow is supported with dynamic layers. "
            "Note that using `dynamic=True` requires you "
            "to implement static shape inference "
            "in the `compute_output_shape(input_shape)` method.".format(
                method=method,
                bad_example=bad_example,
                correct_example=correct_example,
            )
        )


def mark_as_return(outputs, acd):
    """Marks `outputs` as the return values for automatic control deps."""

    def _mark_as_return(tensor):
        """Marks `tensor` as the return value for automatic control deps."""
        if not tf.is_tensor(tensor):
            return tensor

        return_tensor = acd.mark_as_return(tensor)
        if getattr(tensor, "_keras_mask", None) is not None:
            return_tensor._keras_mask = acd.mark_as_return(tensor._keras_mask)
        else:
            return_tensor._keras_mask = None

        # Handle TensorFlow Probability attached metadata.
        # TODO(b/132076537): Remove this once TFP uses `CompositeTensor`.
        if getattr(tensor, "_tfp_distribution", None) is not None:
            return_tensor._tfp_distribution = tensor._tfp_distribution

        return return_tensor

    return tf.nest.map_structure(_mark_as_return, outputs)


V2_DTYPE_BEHAVIOR = None


@keras_export(v1=["keras.layers.enable_v2_dtype_behavior"])
def enable_v2_dtype_behavior():
    """Enable the V2 dtype behavior for Keras layers.

    By default, the V2 dtype behavior is enabled in TensorFlow 2, so this
    function is only useful if `tf.compat.v1.disable_v2_behavior` has been
    called. Since mixed precision requires V2 dtype behavior to be enabled, this
    function allows you to use mixed precision in Keras layers if
    `disable_v2_behavior` has been called.

    When enabled, the dtype of Keras layers defaults to floatx (which is
    typically float32) instead of None. In addition, layers will automatically
    cast floating-point inputs to the layer's dtype.

    >>> x = tf.ones((4, 4, 4, 4), dtype='float64')
    >>> layer = tf.keras.layers.Conv2D(filters=4, kernel_size=2)
    >>> print(layer.dtype)  # float32 since V2 dtype behavior is enabled
    float32
    >>> y = layer(x)  # Layer casts inputs since V2 dtype behavior is enabled
    >>> print(y.dtype.name)
    float32

    A layer author can opt-out their layer from the automatic input casting by
    passing `autocast=False` to the base Layer's constructor. This disables the
    autocasting part of the V2 behavior for that layer, but not the defaulting
    to floatx part of the V2 behavior.

    When a global `tf.keras.mixed_precision.Policy` is set, a Keras layer's
    dtype will default to the global policy instead of floatx. Layers will
    automatically cast inputs to the policy's compute_dtype.
    """
    global V2_DTYPE_BEHAVIOR
    V2_DTYPE_BEHAVIOR = True


@keras_export(v1=["keras.layers.disable_v2_dtype_behavior"])
def disable_v2_dtype_behavior():
    """Disables the V2 dtype behavior for Keras layers.

    See `tf.compat.v1.keras.layers.enable_v2_dtype_behavior`.
    """
    global V2_DTYPE_BEHAVIOR
    V2_DTYPE_BEHAVIOR = False


def v2_dtype_behavior_enabled():
    """Returns True if the V2 dtype behavior is enabled."""
    if V2_DTYPE_BEHAVIOR is None:
        return tf.__internal__.tf2.enabled()
    return V2_DTYPE_BEHAVIOR


class TrackableWeightHandler:
    """Keras wrapper for handling Trackable object saving and restoring.

    This class handles Trackables in both V1 and V2 modes, ensuring that they
    can be saved and restored with the correct data and without adding
    additional ops on every save.

    Attributes:
      trackable: The trackable to wrap.
      num_tensors: The number of tensors that this trackable requires for
        saving.
    """

    def __init__(self, trackable):
        if not isinstance(trackable, tf.__internal__.tracking.Trackable):
            raise ValueError(f"{trackable} is not a Trackable object.")
        self._trackable = trackable
        self._distribute_strategy = tf.distribute.get_strategy()

        saveables = tf.__internal__.tracking.saveable_objects_from_trackable(
            trackable
        ).values()
        # 'Saveables' won't exist when we're passed a legacy TF1 table like
        # a StaticHashTable.
        if not saveables:
            self._num_tensors = 0
            self._setter = lambda weights: None
            self._getter = lambda: []

        elif len(saveables) == 1:
            saveable = list(saveables)[0]

            if tf1.executing_eagerly_outside_functions():
                # If we're in eager mode, we need to defer calling the
                # Trackable's saveable() callable until data export time.
                # However, it is safe to call the saveable as many times as we
                # want, so we will call it now to figure out how many tensors
                # this Trackable will produce.
                self._saveable = saveable
                self._num_tensors = len(self._saveable().specs)
                self._setter = lambda weights: self._saveable().restore(
                    weights, None
                )
                self._getter = lambda: [
                    spec.tensor for spec in self._saveable().specs
                ]
            else:
                # If we're in Graph mode, we need to evaluate the Saveable only
                # once and cache the resulting restore graph. Failing to do this
                # will result in new assignment ops being added to the graph
                # each time set_weights() is called.
                self._placeholder_tensors = []
                self._saveable = saveable()
                self._num_tensors = len(self._saveable.specs)
                for spec in self._saveable.specs:
                    tensor = spec.tensor
                    self._placeholder_tensors.append(
                        tf1.placeholder(tensor.dtype, tensor.shape)
                    )
                self._assign_op = self._saveable.restore(
                    self._placeholder_tensors, None
                )
                self._setter = self._set_weights_v1
                self._getter = lambda: [
                    spec.tensor for spec in self._saveable.specs
                ]
        else:
            raise ValueError(
                "Only Trackables with one Saveable are supported. "
                f"The Trackable {trackable} has {len(saveables)} Saveables."
            )

    @property
    def num_tensors(self):
        return self._num_tensors

    def set_weights(self, weights):
        if len(weights) != self._num_tensors:
            raise ValueError(
                f"Weight handler for trackable {self._trackable} received "
                "an incorrect number of weights: "
                f"expected {self._num_tensors} weights, "
                f"got {len(weights)} weights."
            )
        self._setter(weights)

    def get_tensors(self):
        return self._getter()

    def _set_weights_v1(self, weights):
        feed_dict = {}
        for idx, tensor in enumerate(weights):
            feed_dict[self._placeholder_tensors[idx]] = tensor
        backend.get_session().run(self._assign_op, feed_dict)


def no_ragged_support(inputs, layer_name):
    input_list = tf.nest.flatten(inputs)
    if any(isinstance(x, tf.RaggedTensor) for x in input_list):
        raise ValueError(
            f"Layer {layer_name} does not support RaggedTensors as input. "
            f"Inputs received: {inputs}. You can try converting your "
            "input to a dense (uniform) tensor."
        )


def is_split_variable(v):
    """Returns True if `v` is a PartitionedVariable or a ShardedVariable."""
    return not {clz.__name__ for clz in v.__class__.__mro__}.isdisjoint(
        {"PartitionedVariable", "ShardedVariable"}
    )


def has_weights(obj):
    obj_type = type(obj)
    return (
        hasattr(obj_type, "trainable_weights")
        and hasattr(obj_type, "non_trainable_weights")
        and not isinstance(obj, type)
    )


# TODO(kathywu): This is a temporary hack. When a network of layers is revived
# from SavedModel, only the top-level layer will have losses. This causes issues
# in eager mode because the child layers may have graph losses
# (thus model.losses returns a mix of Eager and graph tensors). To fix this,
# whenever eager losses are added to one layer, add eager losses to all
# child layers. This causes `.losses` to only return eager losses.
REVIVED_LOSS_PLACEHOLDER = (
    "This layer's losses have been added to the parent layer."
)

