# Copyright 2015 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.
# ==============================================================================
"""V1 Training-related part of the Keras engine."""
import collections
import warnings

import numpy as np
import tensorflow.compat.v2 as tf

from keras.src import backend
from keras.src import losses
from keras.src import metrics as metrics_module
from keras.src import optimizers
from keras.src.distribute import distributed_training_utils
from keras.src.distribute import distributed_training_utils_v1
from keras.src.engine import base_layer
from keras.src.engine import training as training_lib
from keras.src.engine import training_arrays_v1
from keras.src.engine import training_distributed_v1
from keras.src.engine import training_eager_v1
from keras.src.engine import training_generator_v1
from keras.src.engine import training_utils
from keras.src.engine import training_utils_v1
from keras.src.mixed_precision import loss_scale_optimizer
from keras.src.optimizers import optimizer_v1
from keras.src.optimizers.legacy import optimizer_v2
from keras.src.saving.legacy import saving_utils
from keras.src.saving.legacy.saved_model import model_serialization
from keras.src.utils import data_utils
from keras.src.utils import layer_utils
from keras.src.utils import losses_utils
from keras.src.utils import tf_inspect
from keras.src.utils import tf_utils
from keras.src.utils.mode_keys import ModeKeys

# isort: off
from tensorflow.python.platform import tf_logging as logging

try:
    from scipy.sparse import issparse
except ImportError:
    issparse = None


class Model(training_lib.Model):
    """A model groups layers into an object with training & inference features.

    There are two ways to instantiate a `Model`:

    1 - With the "functional API", where you start from `Input`,
    you chain layer calls to specify the model's forward pass,
    and finally you create your model from inputs and outputs:

    ```python
    import tensorflow as tf

    inputs = tf.keras.Input(shape=(3,))
    x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
    outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    ```

    2 - By subclassing the `Model` class: in that case, you should define your
    layers in `__init__` and you should implement the model's forward pass
    in `call`.

    ```python
    import tensorflow as tf

    class MyModel(tf.keras.Model):

      def __init__(self):
        super().__init__()
        self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)

      def call(self, inputs):
        x = self.dense1(inputs)
        return self.dense2(x)

    model = MyModel()
    ```

    If you subclass `Model`, you can optionally have
    a `training` argument (boolean) in `call`, which you can use to specify
    a different behavior in training and inference:

    ```python
    import tensorflow as tf

    class MyModel(tf.keras.Model):

      def __init__(self):
        super().__init__()
        self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
        self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
        self.dropout = tf.keras.layers.Dropout(0.5)

      def call(self, inputs, training=False):
        x = self.dense1(inputs)
        if training:
          x = self.dropout(x, training=training)
        return self.dense2(x)

    model = MyModel()
    ```
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # initializing _distribution_strategy here since it is possible to call
        # predict on a model without compiling it.
        self._distribution_strategy = None
        self._compile_time_distribution_strategy = None
        if (
            tf.compat.v1.executing_eagerly_outside_functions()
            and tf.distribute.has_strategy()
        ):
            self._set_strategy(tf.distribute.get_strategy())

        # This flag is used to track if the user is using the deprecated path of
        # passing distribution strategy to compile rather than creating the
        # model under distribution strategy scope.
        self._compile_distribution = False

        self._run_eagerly = None
        self._experimental_run_tf_function = (
            tf.compat.v1.executing_eagerly_outside_functions()
        )

        self._v1_compile_was_called = False

    def _init_batch_counters(self):
        pass  # Batch counters should not be created in legacy graph mode.

    @tf.__internal__.tracking.no_automatic_dependency_tracking
    def _set_strategy(self, strategy):
        self._compile_time_distribution_strategy = strategy

    def get_weights(self):
        """Retrieves the weights of the model.

        Returns:
            A flat list of Numpy arrays.
        """
        strategy = (
            self._distribution_strategy
            or self._compile_time_distribution_strategy
        )
        if strategy:
            with strategy.scope():
                return base_layer.Layer.get_weights(self)
        return base_layer.Layer.get_weights(self)

    def load_weights(self, filepath, by_name=False, skip_mismatch=False):
        """Loads all layer weights, either from a TensorFlow or an HDF5 file.

        If `by_name` is False weights are loaded based on the network's
        topology. This means the architecture should be the same as when the
        weights were saved.  Note that layers that don't have weights are not
        taken into account in the topological ordering, so adding or removing
        layers is fine as long as they don't have weights.

        If `by_name` is True, weights are loaded into layers only if they share
        the same name. This is useful for fine-tuning or transfer-learning
        models where some of the layers have changed.

        Only topological loading (`by_name=False`) is supported when loading
        weights from the TensorFlow format. Note that topological loading
        differs slightly between TensorFlow and HDF5 formats for user-defined
        classes inheriting from `tf.keras.Model`: HDF5 loads based on a
        flattened list of weights, while the TensorFlow format loads based on
        the object-local names of attributes to which layers are assigned in the
        `Model`'s constructor.

        Args:
            filepath: String, path to the weights file to load. For weight files
                in TensorFlow format, this is the file prefix (the same as was
                passed to `save_weights`).
            by_name: Boolean, whether to load weights by name or by topological
                order. Only topological loading is supported for weight files in
                TensorFlow format.
            skip_mismatch: Boolean, whether to skip loading of layers where
                there is a mismatch in the number of weights, or a mismatch in
                the shape of the weight (only valid when `by_name=True`).

        Returns:
            When loading a weight file in TensorFlow format, returns the same
            status object as `tf.train.Checkpoint.restore`. When graph building,
            restore ops are run automatically as soon as the network is built
            (on first call for user-defined classes inheriting from `Model`,
            immediately if it is already built).

            When loading weights in HDF5 format, returns `None`.

        Raises:
            ImportError: If h5py is not available and the weight file is in HDF5
                format.
            ValueError: If `skip_mismatch` is set to `True` when `by_name` is
              `False`.
        """
        if backend.is_tpu_strategy(self._distribution_strategy):
            if self._distribution_strategy.extended.steps_per_run > 1 and (
                not saving_utils.is_hdf5_filepath(filepath)
            ):
                raise ValueError(
                    "Load weights is not yet supported with TPUStrategy "
                    "with steps_per_run greater than 1."
                )
        return super().load_weights(
            filepath, by_name=by_name, skip_mismatch=skip_mismatch
        )

    @tf.__internal__.tracking.no_automatic_dependency_tracking
    def compile(
        self,
        optimizer="rmsprop",
        loss=None,
        metrics=None,
        loss_weights=None,
        sample_weight_mode=None,
        weighted_metrics=None,
        target_tensors=None,
        distribute=None,
        **kwargs,
    ):
        """Configures the model for training.

        Args:
            optimizer: String (name of optimizer) or optimizer instance.
                See `tf.keras.optimizers`.
            loss: String (name of objective function), objective function or
                `tf.keras.losses.Loss` instance. See `tf.keras.losses`. An
                objective function is any callable with the signature
                `scalar_loss = fn(y_true, y_pred)`. If the model has multiple
                outputs, you can use a different loss on each output by passing
                a dictionary or a list of losses. The loss value that will be
                minimized by the model will then be the sum of all individual
                losses.
            metrics: List of metrics to be evaluated by the model during
                training and testing. Typically you will use
                `metrics=['accuracy']`.  To specify different metrics for
                different outputs of a multi-output model, you could also pass a
                dictionary, such as `metrics={'output_a': 'accuracy',
                'output_b': ['accuracy', 'mse']}`.  You can also pass a list
                (len = len(outputs)) of lists of metrics such as
                `metrics=[['accuracy'], ['accuracy', 'mse']]` or
                `metrics=['accuracy', ['accuracy', 'mse']]`.
            loss_weights: Optional list or dictionary specifying scalar
                coefficients (Python floats) to weight the loss contributions
                of different model outputs.
                The loss value that will be minimized by the model
                will then be the *weighted sum* of all individual losses,
                weighted by the `loss_weights` coefficients.
                If a list, it is expected to have a 1:1 mapping
                to the model's outputs. If a tensor, it is expected to map
                output names (strings) to scalar coefficients.
            sample_weight_mode: If you need to do timestep-wise
                sample weighting (2D weights), set this to `"temporal"`.
                `None` becomes sample-wise weights (1D).
                If the model has multiple outputs, you can use a different
                `sample_weight_mode` on each output by passing a
                dictionary or a list of modes. Defaults to `None`.
            weighted_metrics: List of metrics to be evaluated and weighted
                by sample_weight or class_weight during training and testing.
            target_tensors: By default, Keras will create placeholders for the
                model's target, which will be fed with the target data during
                training. If instead you would like to use your own
                target tensors (in turn, Keras will not expect external
                Numpy data for these targets at training time), you
                can specify them via the `target_tensors` argument. It can be
                a single tensor (for a single-output model), a list of tensors,
                or a dict mapping output names to target tensors.
            distribute: NOT SUPPORTED IN TF 2.0, please create and compile the
                model under distribution strategy scope instead of passing it to
                compile.
            **kwargs: Any additional arguments.

        Raises:
            ValueError: In case of invalid arguments for
                `optimizer`, `loss`, `metrics` or `sample_weight_mode`.
        """
        self._assert_built_as_v1()
        self._run_eagerly = kwargs.pop("run_eagerly", None)
        self._experimental_run_tf_function = kwargs.pop(
            "experimental_run_tf_function", True
        )
        self._v1_compile_was_called = True

        # Prepare Session arguments (legacy).
        kwargs.pop("cloning", None)  # Legacy DistStrat argument, never used.
        self._from_serialized = kwargs.pop("from_serialized", False)
        allowed_kwargs = {"feed_dict", "fetches", "options", "run_metadata"}
        unknown_kwargs = set(kwargs.keys()) - allowed_kwargs
        if unknown_kwargs:
            raise TypeError(
                f"Invalid keyword argument(s) in `compile`: {unknown_kwargs}"
            )
        self._function_kwargs = kwargs
        if self._function_kwargs:
            self._experimental_run_tf_function = False
            if self.run_eagerly:
                raise ValueError(
                    "Session keyword arguments are not supported "
                    "when `run_eagerly=True`. You passed the following "
                    "Session arguments: %s" % (self._function_kwargs,)
                )

        self._set_optimizer(optimizer)
        is_any_keras_optimizer_v1 = any(
            (
                isinstance(opt, optimizer_v1.Optimizer)
                and not isinstance(opt, optimizer_v1.TFOptimizer)
            )
            for opt in tf.nest.flatten(self.optimizer)
        )

        if (
            is_any_keras_optimizer_v1
            and tf.compat.v1.executing_eagerly_outside_functions()
        ):
            raise ValueError(
                "`tf.compat.v1.keras` Optimizer (",
                optimizer,
                ") is "
                "not supported when eager execution is enabled. Use a "
                "`tf.keras` Optimizer instead, or disable eager "
                "execution.",
            )

        if (
            target_tensors is not None
        ) or not tf.compat.v1.executing_eagerly_outside_functions():
            # Fallback out of things that aren't supported with v2 loops
            self._experimental_run_tf_function = False

        if distribute is not None:
            if (
                tf.__internal__.tf2.enabled()
                or self._experimental_run_tf_function
            ):
                raise ValueError(
                    "Distribute argument in compile is not available in TF 2.0 "
                    "please create the model under the distribution strategy "
                    "scope."
                )
            logging.warning(
                "Distribute argument in compile is deprecated please "
                "create the model under the distribution strategy scope."
            )
            self._distribution_strategy = distribute
            self._compile_distribution = True
        else:
            if tf.distribute.has_strategy():
                # When the user builds the model in the DS scope and cross
                # replica context we want distribution strategy to be set but
                # when building the replica copies of the models internally we
                # should not be compiling with distribution strategy and use the
                # default compilation path.
                if tf.distribute.in_cross_replica_context():
                    self._distribution_strategy = tf.distribute.get_strategy()

        if isinstance(
            self._distribution_strategy,
            tf.compat.v1.distribute.experimental.ParameterServerStrategy,
        ):
            raise NotImplementedError(
                "`tf.compat.v1.distribute.experimental.ParameterServerStrategy`"
                " currently only works with the tf.Estimator API"
            )

        if isinstance(
            self._distribution_strategy,
            tf.distribute.experimental.ParameterServerStrategy,
        ):
            raise NotImplementedError(
                "`tf.distribute.experimental.ParameterServerStrategy` is only "
                "supported in TF2."
            )

        if not self._experimental_run_tf_function:
            self._validate_compile_param_for_distribution_strategy(
                self.run_eagerly,
                sample_weight_mode,
                target_tensors,
                weighted_metrics,
            )
        # We've disabled automatic dependency tracking for this method, but do
        # want to add a checkpoint dependency on the optimizer if it's
        # trackable.
        if isinstance(self.optimizer, tf.__internal__.tracking.Trackable):
            self._track_trackable(
                self.optimizer, name="optimizer", overwrite=True
            )
        self.loss = loss or {}
        self.loss_weights = loss_weights
        self.sample_weight_mode = sample_weight_mode
        self._compile_metrics = metrics or []
        self._compile_weighted_metrics = weighted_metrics
        if self.run_eagerly and target_tensors is not None:
            raise ValueError(
                "target_tensors argument is not supported when "
                "running a model eagerly."
            )

        # _training_endpoints contains a list of _TrainingEndpoint object, which
        # has all the model output/target/loss and related metadata.
        self._training_endpoints = []

        # Used to freeze the behavior of the Model once `compile` has been
        # called.
        self._compiled_trainable_state = self._get_trainable_state()

        # Set tf.distribute.Strategy specific parameters.
        self._distributed_model_cache = {}
        self._distributed_function_cache = {}

        # Clear any `_eager_losses` that was added.
        self._clear_losses()

        if (
            not tf.executing_eagerly()
            and self._distribution_strategy is not None
        ):
            # Ensures a Session is created and configured correctly for
            # Distribution Strategy.
            backend.configure_and_create_distributed_session(
                self._distribution_strategy
            )
        # Initialize model metric attributes.
        self._init_metric_attributes()
        if not self.built or not self.inputs or not self.outputs:
            # Model is not compilable because it does not know its number of
            # inputs and outputs, nor their shapes and names. We will compile
            # after the first time the model gets called on training data.
            return
        self._is_compiled = True
        base_layer.keras_api_gauge.get_cell("compile").set(True)

        # Prepare list of loss functions, same size of model outputs.
        self.loss_functions = training_utils_v1.prepare_loss_functions(
            self.loss, self.output_names
        )

        target_tensors = self._process_target_tensor_for_compile(target_tensors)

        for o, n, l, t in zip(
            self.outputs, self.output_names, self.loss_functions, target_tensors
        ):
            endpoint = _TrainingEndpoint(o, n, l)
            endpoint.create_training_target(t, run_eagerly=self.run_eagerly)
            self._training_endpoints.append(endpoint)

        # Prepare list loss weights, same size of model outputs.
        training_utils_v1.prepare_loss_weights(
            self._training_endpoints, loss_weights
        )

        # Initialization for Eager mode execution.
        if self.run_eagerly:
            self._compile_eagerly(metrics, weighted_metrics, sample_weight_mode)
            return

        with backend.get_graph().as_default():
            # Save all metric attributes per output of the model.
            self._cache_output_metric_attributes(metrics, weighted_metrics)

            # Set metric attributes on model.
            self._set_metric_attributes()

            # Invoke metric functions (unweighted) for all the outputs.
            self._handle_metrics(
                self.outputs,
                targets=self._targets,
                skip_target_masks=self._prepare_skip_target_masks(),
                masks=self._prepare_output_masks(),
            )

            # Prepare sample weight modes. List with the same length as model
            # outputs.
            training_utils_v1.prepare_sample_weight_modes(
                self._training_endpoints, sample_weight_mode
            )

            # Creates the model loss and weighted metrics sub-graphs.
            self._compile_weights_loss_and_weighted_metrics()

            # Functions for train, test and predict will
            # be compiled lazily when required.
            # This saves time when the user is not using all functions.
            self.train_function = None
            self.test_function = None
            self.predict_function = None

            # Collected trainable weights, sorted in topological order.
            self._collected_trainable_weights = self.trainable_weights

            # Validate all variables were correctly created in distribution
            # scope.
            if self._distribution_strategy and not self._compile_distribution:
                for v in self.variables:
                    strategy = self._distribution_strategy
                    if not strategy.extended.variable_created_in_scope(v):
                        raise ValueError(
                            "Variable (%s) was not created in the distribution "
                            "strategy scope of (%s). It is most likely due to "
                            "not all layers or the model or optimizer being "
                            "created outside the distribution strategy scope. "
                            "Try to make sure your code looks similar "
                            "to the following.\n"
                            "with strategy.scope():\n"
                            "  model=_create_model()\n"
                            "  model.compile(...)" % (v, strategy)
                        )

    @tf.__internal__.tracking.no_automatic_dependency_tracking
    def _init_distributed_function_cache_if_not_compiled(self):
        if not hasattr(self, "_distributed_function_cache"):
            self._distributed_function_cache = {}

    @property
    def metrics(self):
        """Returns the model's metrics added using `compile`, `add_metric`
        APIs."""
        metrics = []
        if self._is_compiled:
            if not hasattr(self, "_v1_compile_was_called"):
                # See b/155687393 for more details, the model is created as a v2
                # instance but converted to v1. Fallback to use base Model to
                # retrieve the metrics.
                return super().metrics
            metrics += self._compile_metric_functions
        metrics.extend(self._metrics)
        metrics.extend(
            _get_metrics_from_layers(
                list(self._flatten_layers(include_self=False, recursive=False))
            )
        )
        return metrics

    @property
    def metrics_names(self):
        """Returns the model's display labels for all outputs."""

        # This property includes all output names including `loss` and
        # per-output losses for backward compatibility.
        metrics_names = ["loss"]
        if self._is_compiled:
            if not hasattr(self, "_v1_compile_was_called"):
                # See b/155687393 for more details, the model is created as a v2
                # instance but converted to v1. Fallback to use base Model to
                # retrieve the metrics name
                return super().metrics_names

            # Add output loss metric names to the metric names list.
            if len(self._training_endpoints) > 1:
                metrics_names.extend(
                    [
                        e.loss_name()
                        for e in self._training_endpoints
                        if not e.should_skip_target()
                    ]
                )

        # Add all metric names.
        metrics_names += [m.name for m in self.metrics]
        return metrics_names

    @property
    def run_eagerly(self):
        """Settable attribute indicating whether the model should run eagerly.

        Running eagerly means that your model will be run step by step,
        like Python code. Your model might run slower, but it should become
        easier for you to debug it by stepping into individual layer calls.

        By default, we will attempt to compile your model to a static graph to
        deliver the best execution performance.

        Returns:
          Boolean, whether the model should run eagerly.
        """
        if self._run_eagerly is True and not tf.executing_eagerly():
            raise ValueError(
                "You can only set `run_eagerly=True` if eager execution "
                "is enabled."
            )
        if not self.dynamic:
            if self._run_eagerly is None:
                # Respect `tf.config.run_functions_eagerly` unless
                # `run_eagerly` was explicitly passed to `compile`.
                return tf.config.functions_run_eagerly()
            else:
                return self._run_eagerly
        else:
            if not tf.executing_eagerly():
                raise ValueError(
                    "Your model contains layers that can only be "
                    "successfully run in eager execution (layers "
                    "constructed with `dynamic=True`). "
                    "You must enable eager execution with "
                    "`tf.enable_eager_execution()`."
                )
            if self._run_eagerly is False:
                # TODO(fchollet): consider using py_func to enable this.
                raise ValueError(
                    "Your model contains layers that can only be "
                    "successfully run in eager execution (layers "
                    "constructed with `dynamic=True`). "
                    "You cannot set `run_eagerly=False`."
                )
            return tf.executing_eagerly()

    @run_eagerly.setter
    def run_eagerly(self, value):
        self._run_eagerly = value

    def _select_training_loop(self, inputs):
        """Select training loop for fit/eval/predict based on the inputs."""
        # TODO(kaftan) or TODO(scottzhu): This check should eventually be nicely
        # integrated into the data adapters in the v2 loop. We can't do this yet
        # because we currently have to fall back for unhandled data types.
        if isinstance(inputs, (tf.compat.v1.data.Iterator, tf.data.Iterator)):
            raise ValueError(
                "For performance reasons Keras `fit`, `evaluate` and"
                "`predict` accept tf.data `Datasets` as input but not "
                "iterators that have been manually generated from "
                "Datasets by users. Please directly pass in the "
                "original `Dataset` object instead of passing in "
                "`iter(dataset)`."
            )

        # Case 1: distribution strategy.
        if self._distribution_strategy:
            if self._in_multi_worker_mode():
                return training_distributed_v1.DistributionMultiWorkerTrainingLoop(  # noqa: E501
                    training_distributed_v1.DistributionSingleWorkerTrainingLoop()  # noqa: E501
                )
            else:
                return (
                    training_distributed_v1.DistributionSingleWorkerTrainingLoop()  # noqa: E501
                )

        # Case 2: generator-like. Input is Python generator, or Sequence object,
        # or a non-distributed Dataset or iterator in eager execution.
        if data_utils.is_generator_or_sequence(inputs):
            return training_generator_v1.GeneratorOrSequenceTrainingLoop()
        if training_utils_v1.is_eager_dataset_or_iterator(inputs):
            return training_generator_v1.EagerDatasetOrIteratorTrainingLoop()

        # Case 3: Symbolic tensors or Numpy array-like.
        # This includes Datasets and iterators in graph mode (since they
        # generate symbolic tensors).
        if self.run_eagerly:
            return training_generator_v1.GeneratorLikeTrainingLoop()
        else:
            return training_arrays_v1.ArrayLikeTrainingLoop()

    def fit(
        self,
        x=None,
        y=None,
        batch_size=None,
        epochs=1,
        verbose=1,
        callbacks=None,
        validation_split=0.0,
        validation_data=None,
        shuffle=True,
        class_weight=None,
        sample_weight=None,
        initial_epoch=0,
        steps_per_epoch=None,
        validation_steps=None,
        validation_freq=1,
        max_queue_size=10,
        workers=1,
        use_multiprocessing=False,
        **kwargs,
    ):
        """Trains the model for a fixed number of epochs (dataset iterations).

        Args:
            x: Input data. It could be:
              - A Numpy array (or array-like), or a list of arrays
                (in case the model has multiple inputs).
              - A TensorFlow tensor, or a list of tensors
                (in case the model has multiple inputs).
              - A dict mapping input names to the corresponding array/tensors,
                if the model has named inputs.
              - A `tf.data` dataset. Should return a tuple
                of either `(inputs, targets)` or
                `(inputs, targets, sample_weights)`.
              - A generator or `keras.utils.Sequence` returning `(inputs,
                targets)` or `(inputs, targets, sample weights)`.
            y: Target data. Like the input data `x`,
              it could be either Numpy array(s) or TensorFlow tensor(s).
              It should be consistent with `x` (you cannot have Numpy inputs and
              tensor targets, or inversely). If `x` is a dataset, generator,
              or `keras.utils.Sequence` instance, `y` should
              not be specified (since targets will be obtained from `x`).
            batch_size: Integer or `None`.
                Number of samples per gradient update.
                If unspecified, `batch_size` will default to 32.
                Do not specify the `batch_size` if your data is in the
                form of symbolic tensors, datasets,
                generators, or `keras.utils.Sequence` instances (since they
                generate batches).
            epochs: Integer. Number of epochs to train the model.
                An epoch is an iteration over the entire `x` and `y`
                data provided.
                Note that in conjunction with `initial_epoch`,
                `epochs` is to be understood as "final epoch".
                The model is not trained for a number of iterations
                given by `epochs`, but merely until the epoch
                of index `epochs` is reached.
            verbose: 0, 1, or 2. Verbosity mode.
                0 = silent, 1 = progress bar, 2 = one line per epoch.
                Note that the progress bar is not particularly useful when
                logged to a file, so verbose=2 is recommended when not running
                interactively (eg, in a production environment).
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during training.
                See `tf.keras.callbacks`.
            validation_split: Float between 0 and 1.
                Fraction of the training data to be used as validation data.
                The model will set apart this fraction of the training data,
                will not train on it, and will evaluate
                the loss and any model metrics
                on this data at the end of each epoch.
                The validation data is selected from the last samples
                in the `x` and `y` data provided, before shuffling. This
                argument is not supported when `x` is a dataset, generator or
               `keras.utils.Sequence` instance.
            validation_data: Data on which to evaluate
                the loss and any model metrics at the end of each epoch.
                The model will not be trained on this data.
                `validation_data` will override `validation_split`.
                `validation_data` could be:
                  - tuple `(x_val, y_val)` of Numpy arrays or tensors
                  - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays
                  - dataset
                For the first two cases, `batch_size` must be provided.
                For the last case, `validation_steps` could be provided.
            shuffle: Boolean (whether to shuffle the training data
                before each epoch) or str (for 'batch').
                'batch' is a special option for dealing with the
                limitations of HDF5 data; it shuffles in batch-sized chunks.
                Has no effect when `steps_per_epoch` is not `None`.
            class_weight: Optional dictionary mapping class indices (integers)
                to a weight (float) value, used for weighting the loss function
                (during training only).
                This can be useful to tell the model to
                "pay more attention" to samples from
                an under-represented class.
            sample_weight: Optional Numpy array of weights for
                the training samples, used for weighting the loss function
                (during training only). You can either pass a flat (1D)
                Numpy array with the same length as the input samples
                (1:1 mapping between weights and samples),
                or in the case of temporal data,
                you can pass a 2D array with shape
                `(samples, sequence_length)`,
                to apply a different weight to every timestep of every sample.
                In this case you should make sure to specify
                `sample_weight_mode="temporal"` in `compile()`. This argument is
                not supported when `x` is a dataset, generator, or
                `keras.utils.Sequence` instance, instead provide the
                sample_weights as the third element of `x`.
            initial_epoch: Integer.
                Epoch at which to start training
                (useful for resuming a previous training run).
            steps_per_epoch: Integer or `None`.
                Total number of steps (batches of samples)
                before declaring one epoch finished and starting the
                next epoch. When training with input tensors such as
                TensorFlow data tensors, the default `None` is equal to
                the number of samples in your dataset divided by
                the batch size, or 1 if that cannot be determined. If x is a
                `tf.data` dataset, and 'steps_per_epoch'
                is None, the epoch will run until the input dataset is
                exhausted.  This argument is not supported with array inputs.
            validation_steps: Only relevant if `validation_data` is provided and
                is a `tf.data` dataset. Total number of steps (batches of
                samples) to draw before stopping when performing validation at
                the end of every epoch. If 'validation_steps' is None,
                validation will run until the `validation_data` dataset is
                exhausted. In the case of a infinite dataset, it will run into a
                infinite loop.  If 'validation_steps' is specified and only part
                of the dataset will be consumed, the evaluation will start from
                the beginning of the dataset at each epoch. This ensures that
                the same validation samples are used every time.
            validation_freq: Only relevant if validation data is provided.
                Integer or `collections.abc.Container` instance (e.g. list,
                tuple, etc.).  If an integer, specifies how many training epochs
                to run before a new validation run is performed, e.g.
                `validation_freq=2` runs validation every 2 epochs. If a
                Container, specifies the epochs on which to run validation, e.g.
                `validation_freq=[1, 2, 10]` runs validation at the end of the
                1st, 2nd, and 10th epochs.
            max_queue_size: Integer. Used for generator or
                `keras.utils.Sequence` input only. Maximum size for the
                generator queue.  If unspecified, `max_queue_size` will default
                to 10.
            workers: Integer. Used for generator or `keras.utils.Sequence` input
                only. Maximum number of processes to spin up
                when using process-based threading. If unspecified, `workers`
                will default to 1. If 0, will execute the generator on the main
                thread.
            use_multiprocessing: Boolean. Used for generator or
                `keras.utils.Sequence` input only. If `True`, use process-based
                threading. If unspecified, `use_multiprocessing` will default to
                `False`. Note that because this implementation relies on
                multiprocessing, you should not pass non-picklable arguments to
                the generator as they can't be passed easily to children
                processes.
            **kwargs: Used for backwards compatibility.

        Returns:
            A `History` object. Its `History.history` attribute is
            a record of training loss values and metrics values
            at successive epochs, as well as validation loss values
            and validation metrics values (if applicable).

        Raises:
            RuntimeError: If the model was never compiled.
            ValueError: In case of mismatch between the provided input data
                and what the model expects.
        """
        self._assert_built_as_v1()
        base_layer.keras_api_gauge.get_cell("fit").set(True)
        # Legacy support
        if "nb_epoch" in kwargs:
            logging.warning(
                "The `nb_epoch` argument in `fit` has been renamed `epochs`."
            )
            epochs = kwargs.pop("nb_epoch")
        if kwargs:
            raise TypeError("Unrecognized keyword arguments: " + str(kwargs))
        self._assert_compile_was_called()
        self._check_call_args("fit")

        func = self._select_training_loop(x)
        return func.fit(
            self,
            x=x,
            y=y,
            batch_size=batch_size,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            validation_split=validation_split,
            validation_data=validation_data,
            shuffle=shuffle,
            class_weight=class_weight,
            sample_weight=sample_weight,
            initial_epoch=initial_epoch,
            steps_per_epoch=steps_per_epoch,
            validation_steps=validation_steps,
            validation_freq=validation_freq,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )

    def evaluate(
        self,
        x=None,
        y=None,
        batch_size=None,
        verbose=1,
        sample_weight=None,
        steps=None,
        callbacks=None,
        max_queue_size=10,
        workers=1,
        use_multiprocessing=False,
    ):
        """Returns the loss value & metrics values for the model in test mode.

        Computation is done in batches (see the `batch_size` arg.)

        Args:
            x: Input data. It could be:
              - A Numpy array (or array-like), or a list of arrays
                (in case the model has multiple inputs).
              - A TensorFlow tensor, or a list of tensors
                (in case the model has multiple inputs).
              - A dict mapping input names to the corresponding array/tensors,
                if the model has named inputs.
              - A `tf.data` dataset.
              - A generator or `keras.utils.Sequence` instance.
            y: Target data. Like the input data `x`,
              it could be either Numpy array(s) or TensorFlow tensor(s).
              It should be consistent with `x` (you cannot have Numpy inputs and
              tensor targets, or inversely).
              If `x` is a dataset, generator or
              `keras.utils.Sequence` instance, `y` should not be specified
              (since targets will be obtained from the iterator/dataset).
            batch_size: Integer or `None`.
                Number of samples per batch of computation.
                If unspecified, `batch_size` will default to 32.
                Do not specify the `batch_size` if your data is in the
                form of symbolic tensors, dataset,
                generators, or `keras.utils.Sequence` instances (since they
                generate batches).
            verbose: 0 or 1. Verbosity mode.
                0 = silent, 1 = progress bar.
            sample_weight: Optional Numpy array of weights for
                the test samples, used for weighting the loss function.
                You can either pass a flat (1D)
                Numpy array with the same length as the input samples
                (1:1 mapping between weights and samples),
                or in the case of temporal data,
                you can pass a 2D array with shape
                `(samples, sequence_length)`,
                to apply a different weight to every timestep of every sample.
                In this case you should make sure to specify
                `sample_weight_mode="temporal"` in `compile()`. This argument is
                not supported when `x` is a dataset, instead pass sample weights
                as the third element of `x`.
            steps: Integer or `None`.
                Total number of steps (batches of samples)
                before declaring the evaluation round finished.
                Ignored with the default value of `None`.
                If x is a `tf.data` dataset and `steps` is
                None, 'evaluate' will run until the dataset is exhausted.
                This argument is not supported with array inputs.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during evaluation.
                See [callbacks](/api_docs/python/tf/keras/callbacks).
            max_queue_size: Integer. Used for generator or
                `keras.utils.Sequence` input only. Maximum size for the
                generator queue.  If unspecified, `max_queue_size` will default
                to 10.
            workers: Integer. Used for generator or `keras.utils.Sequence` input
                only. Maximum number of processes to spin up when using
                process-based threading. If unspecified, `workers` will default
                to 1. If 0, will execute the generator on the main thread.
            use_multiprocessing: Boolean. Used for generator or
                `keras.utils.Sequence` input only. If `True`, use process-based
                threading. If unspecified, `use_multiprocessing` will default to
                `False`. Note that because this implementation relies on
                multiprocessing, you should not pass non-picklable arguments to
                the generator as they can't be passed easily to children
                processes.

        Returns:
            Scalar test loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.

        Raises:
            ValueError: in case of invalid arguments.
        """
        self._assert_built_as_v1()
        base_layer.keras_api_gauge.get_cell("evaluate").set(True)
        self._assert_compile_was_called()
        self._check_call_args("evaluate")

        func = self._select_training_loop(x)
        return func.evaluate(
            self,
            x=x,
            y=y,
            batch_size=batch_size,
            verbose=verbose,
            sample_weight=sample_weight,
            steps=steps,
            callbacks=callbacks,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )

    def predict(
        self,
        x,
        batch_size=None,
        verbose=0,
        steps=None,
        callbacks=None,
        max_queue_size=10,
        workers=1,
        use_multiprocessing=False,
    ):
        """Generates output predictions for the input samples.

        Computation is done in batches (see the `batch_size` arg.)

        Args:
            x: Input samples. It could be:
              - A Numpy array (or array-like), or a list of arrays
                (in case the model has multiple inputs).
              - A TensorFlow tensor, or a list of tensors
                (in case the model has multiple inputs).
              - A `tf.data` dataset.
              - A generator or `keras.utils.Sequence` instance.
            batch_size: Integer or `None`.
                Number of samples per batch of computation.
                If unspecified, `batch_size` will default to 32.
                Do not specify the `batch_size` if your data is in the
                form of symbolic tensors, dataset,
                generators, or `keras.utils.Sequence` instances (since they
                generate batches).
            verbose: Verbosity mode, 0 or 1.
            steps: Total number of steps (batches of samples)
                before declaring the prediction round finished.
                Ignored with the default value of `None`. If x is a `tf.data`
                dataset and `steps` is None, `predict` will
                run until the input dataset is exhausted.
            callbacks: List of `keras.callbacks.Callback` instances.
                List of callbacks to apply during prediction.
                See [callbacks](/api_docs/python/tf/keras/callbacks).
            max_queue_size: Integer. Used for generator or
                `keras.utils.Sequence` input only. Maximum size for the
                generator queue. If unspecified, `max_queue_size` will default
                to 10.
            workers: Integer. Used for generator or `keras.utils.Sequence` input
                only. Maximum number of processes to spin up when using
                process-based threading. If unspecified, `workers` will default
                to 1. If 0, will execute the generator on the main thread.
            use_multiprocessing: Boolean. Used for generator or
                `keras.utils.Sequence` input only. If `True`, use process-based
                threading. If unspecified, `use_multiprocessing` will default to
                `False`. Note that because this implementation relies on
                multiprocessing, you should not pass non-picklable arguments to
                the generator as they can't be passed easily to children
                processes.


        Returns:
            Numpy array(s) of predictions.

        Raises:
            ValueError: In case of mismatch between the provided
                input data and the model's expectations,
                or in case a stateful model receives a number of samples
                that is not a multiple of the batch size.
        """
        self._assert_built_as_v1()
        base_layer.keras_api_gauge.get_cell("predict").set(True)
        self._check_call_args("predict")

        func = self._select_training_loop(x)
        return func.predict(
            self,
            x=x,
            batch_size=batch_size,
            verbose=verbose,
            steps=steps,
            callbacks=callbacks,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
        )

    def reset_metrics(self):
        """Resets the state of metrics."""
        metrics = self._get_training_eval_metrics()
        for m in metrics:
            m.reset_state()

        # Reset metrics on all the distributed (cloned) models.
        if self._distribution_strategy:
            distributed_training_utils_v1._reset_metrics(self)

    def train_on_batch(
        self,
        x,
        y=None,
        sample_weight=None,
        class_weight=None,
        reset_metrics=True,
    ):
        """Runs a single gradient update on a single batch of data.

        Args:
            x: Input data. It could be:
              - A Numpy array (or array-like), or a list of arrays
                  (in case the model has multiple inputs).
              - A TensorFlow tensor, or a list of tensors
                  (in case the model has multiple inputs).
              - A dict mapping input names to the corresponding array/tensors,
                  if the model has named inputs.
              - A `tf.data` dataset.
            y: Target data. Like the input data `x`, it could be either Numpy
              array(s) or TensorFlow tensor(s). It should be consistent with `x`
              (you cannot have Numpy inputs and tensor targets, or inversely).
              If `x` is a dataset, `y` should not be specified
              (since targets will be obtained from the iterator).
            sample_weight: Optional array of the same length as x, containing
              weights to apply to the model's loss for each sample. In the case
              of temporal data, you can pass a 2D array with shape (samples,
              sequence_length), to apply a different weight to every timestep of
              every sample. In this case you should make sure to specify
              sample_weight_mode="temporal" in compile(). This argument is not
              supported when `x` is a dataset.
            class_weight: Optional dictionary mapping class indices (integers)
              to a weight (float) to apply to the model's loss for the samples
              from this class during training. This can be useful to tell the
              model to "pay more attention" to samples from an under-represented
              class.
            reset_metrics: If `True`, the metrics returned will be only for this
              batch. If `False`, the metrics will be statefully accumulated
              across batches.

        Returns:
            Scalar training loss
            (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.

        Raises:
          ValueError: In case of invalid user-provided arguments.
        """
        self._assert_compile_was_called()
        self._check_call_args("train_on_batch")

        # If at this point we are in the replica context, then it is okay to
        # execute the Eager code path.  The expected way to get here is to call
        # `fit` that calls `train_on_batch` on each replica.
        if (
            self._distribution_strategy
            and tf.distribute.in_cross_replica_context()
        ):
            raise NotImplementedError(
                "`train_on_batch` is not supported for models "
                "distributed with tf.distribute.Strategy."
            )
        # Validate and standardize user data.
        x, y, sample_weights = self._standardize_user_data(
            x,
            y,
            sample_weight=sample_weight,
            class_weight=class_weight,
            extract_tensors_from_dataset=True,
        )

        # If `self._distribution_strategy` is True, then we are in a replica
        # context at this point because of the check above.  `train_on_batch` is
        # being run for each replica by `self._distribution_strategy` and the
        # same code path as Eager is expected to be taken.
        if self.run_eagerly or self._distribution_strategy:
            output_dict = training_eager_v1.train_on_batch(
                self,
                x,
                y,
                sample_weights=sample_weights,
                output_loss_metrics=self._output_loss_metrics,
            )
            outputs = (
                output_dict["total_loss"]
                + output_dict["output_losses"]
                + output_dict["metrics"]
            )
            outputs = [_non_none_constant_value(v) for v in outputs]
        else:
            x = training_utils_v1.ModelInputs(x).as_list()
            ins = x + list(y or []) + list(sample_weights or [])

            if not isinstance(backend.symbolic_learning_phase(), int):
                ins += [True]  # Add learning phase value.

            self._update_sample_weight_modes(sample_weights=sample_weights)
            self._make_train_function()
            outputs = self.train_function(ins)

        if reset_metrics:
            self.reset_metrics()

        if len(outputs) == 1:
            return outputs[0]
        return outputs

    def test_on_batch(self, x, y=None, sample_weight=None, reset_metrics=True):
        """Test the model on a single batch of samples.

        Args:
            x: Input data. It could be:
              - A Numpy array (or array-like), or a list of arrays
                (in case the model has multiple inputs).
              - A TensorFlow tensor, or a list of tensors
                (in case the model has multiple inputs).
              - A dict mapping input names to the corresponding array/tensors,
                if the model has named inputs.
              - A `tf.data` dataset.
            y: Target data. Like the input data `x`,
              it could be either Numpy array(s) or TensorFlow tensor(s).
              It should be consistent with `x` (you cannot have Numpy inputs and
              tensor targets, or inversely). If `x` is a dataset `y` should
              not be specified (since targets will be obtained from the
              iterator).
            sample_weight: Optional array of the same length as x, containing
                weights to apply to the model's loss for each sample.
                In the case of temporal data, you can pass a 2D array
                with shape (samples, sequence_length),
                to apply a different weight to every timestep of every sample.
                In this case you should make sure to specify
                sample_weight_mode="temporal" in compile(). This argument is not
                supported when `x` is a dataset.
            reset_metrics: If `True`, the metrics returned will be only for this
              batch. If `False`, the metrics will be statefully accumulated
              across batches.

        Returns:
            Scalar test loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs
            and/or metrics). The attribute `model.metrics_names` will give you
            the display labels for the scalar outputs.

        Raises:
            ValueError: In case of invalid user-provided arguments.
        """
        self._assert_compile_was_called()
        self._check_call_args("test_on_batch")

        if (
            self._distribution_strategy
            and tf.distribute.in_cross_replica_context()
        ):
            raise NotImplementedError(
                "`test_on_batch` is not supported for models "
                "distributed with tf.distribute.Strategy."
            )
        # Validate and standardize user data.
        x, y, sample_weights = self._standardize_user_data(
            x, y, sample_weight=sample_weight, extract_tensors_from_dataset=True
        )

        # If `self._distribution_strategy` is True, then we are in a replica
        # context at this point.
        if self.run_eagerly or self._distribution_strategy:
            output_dict = training_eager_v1.test_on_batch(
                self,
                x,
                y,
                sample_weights=sample_weights,
                output_loss_metrics=self._output_loss_metrics,
            )
            outputs = (
                output_dict["total_loss"]
                + output_dict["output_losses"]
                + output_dict["metrics"]
            )
            outputs = [_non_none_constant_value(v) for v in outputs]
        else:
            x = training_utils_v1.ModelInputs(x).as_list()
            inputs = x + list(y or []) + list(sample_weights or [])

            self._update_sample_weight_modes(sample_weights=sample_weights)
            self._make_test_function()
            outputs = self.test_function(inputs)

        if reset_metrics:
            self.reset_metrics()

        if len(outputs) == 1:
            return outputs[0]
        return outputs

    def predict_on_batch(self, x):
        """Returns predictions for a single batch of samples.

        Args:
            x: Input data. It could be:
              - A Numpy array (or array-like), or a list of arrays
                (in case the model has multiple inputs).
              - A TensorFlow tensor, or a list of tensors
                (in case the model has multiple inputs).
              - A `tf.data` dataset.

        Returns:
            Numpy array(s) of predictions.

        Raises:
            ValueError: In case of mismatch between given number of inputs and
              expectations of the model.
        """
        self._check_call_args("predict_on_batch")

        if (
            self._distribution_strategy
            and tf.distribute.in_cross_replica_context()
        ):
            raise NotImplementedError(
                "`predict_on_batch` is not supported for models distributed "
                "with tf.distribute.Strategy."
            )
        # Validate and standardize user data.
        inputs, _, _ = self._standardize_user_data(
            x, extract_tensors_from_dataset=True
        )
        # If `self._distribution_strategy` is True, then we are in a replica
        # context at this point.
        if self.run_eagerly or self._distribution_strategy:
            inputs = training_utils_v1.cast_if_floating_dtype(inputs)
            if isinstance(inputs, collections.abc.Sequence):
                # Unwrap lists with only one input, as we do when training on
                # batch
                if len(inputs) == 1:
                    inputs = inputs[0]

            return self(inputs)

        self._make_predict_function()
        outputs = self.predict_function(inputs)

        if len(outputs) == 1:
            return outputs[0]
        return outputs

    def fit_generator(
        self,
        generator,
        steps_per_epoch=None,
        epochs=1,
        verbose=1,
        callbacks=None,
        validation_data=None,
        validation_steps=None,
        validation_freq=1,
        class_weight=None,
        max_queue_size=10,
        workers=1,
        use_multiprocessing=False,
        shuffle=True,
        initial_epoch=0,
    ):
        """Fits the model on data yielded batch-by-batch by a Python generator.

        DEPRECATED:
          `Model.fit` now supports generators, so there is no longer any need to
          use this endpoint.
        """
        warnings.warn(
            "`model.fit_generator` is deprecated and "
            "will be removed in a future version. "
            "Please use `Model.fit`, which supports generators.",
            stacklevel=2,
        )
        return self.fit(
            generator,
            steps_per_epoch=steps_per_epoch,
            epochs=epochs,
            verbose=verbose,
            callbacks=callbacks,
            validation_data=validation_data,
            validation_steps=validation_steps,
            validation_freq=validation_freq,
            class_weight=class_weight,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
            shuffle=shuffle,
            initial_epoch=initial_epoch,
        )

    def evaluate_generator(
        self,
        generator,
        steps=None,
        callbacks=None,
        max_queue_size=10,
        workers=1,
        use_multiprocessing=False,
        verbose=0,
    ):
        """Evaluates the model on a data generator.

        DEPRECATED:
          `Model.evaluate` now supports generators, so there is no longer any
          need to use this endpoint.
        """
        warnings.warn(
            "`Model.evaluate_generator` is deprecated and "
            "will be removed in a future version. "
            "Please use `Model.evaluate`, which supports generators.",
            stacklevel=2,
        )
        self._check_call_args("evaluate_generator")

        return self.evaluate(
            generator,
            steps=steps,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
            verbose=verbose,
            callbacks=callbacks,
        )

    def predict_generator(
        self,
        generator,
        steps=None,
        callbacks=None,
        max_queue_size=10,
        workers=1,
        use_multiprocessing=False,
        verbose=0,
    ):
        """Generates predictions for the input samples from a data generator.

        DEPRECATED:
          `Model.predict` now supports generators, so there is no longer any
          need to use this endpoint.
        """
        warnings.warn(
            "`Model.predict_generator` is deprecated and "
            "will be removed in a future version. "
            "Please use `Model.predict`, which supports generators.",
            stacklevel=2,
        )
        return self.predict(
            generator,
            steps=steps,
            max_queue_size=max_queue_size,
            workers=workers,
            use_multiprocessing=use_multiprocessing,
            verbose=verbose,
            callbacks=callbacks,
        )

    def _check_call_args(self, method_name):
        """Check that `call` has only one positional arg."""
        # Always allow first arg, regardless of arg name.
        fullargspec = self._call_spec.full_argspec
        if fullargspec.defaults:
            positional_args = fullargspec.args[: -len(fullargspec.defaults)]
        else:
            positional_args = fullargspec.args
        if "training" in positional_args:
            positional_args.remove("training")

        # self and first arg can be positional.
        if len(positional_args) > 2:
            extra_args = positional_args[2:]
            raise ValueError(
                "Models passed to `"
                + method_name
                + "` can only have `training` "
                "and the first argument in `call` as positional arguments, "
                "found: " + str(extra_args) + "."
            )

    def _set_optimizer(self, optimizer):
        """Sets self.optimizer.

        Sets self.optimizer to `optimizer`, potentially wrapping it with a
        LossScaleOptimizer.

        Args:
          optimizer: The optimizer(s) to assign to self.optimizer.
        """
        if isinstance(optimizer, (list, tuple)):
            self.optimizer = [optimizers.get(opt) for opt in optimizer]
        else:
            self.optimizer = optimizers.get(optimizer)

        if self._dtype_policy.name == "mixed_float16" and not isinstance(
            self.optimizer, loss_scale_optimizer.LossScaleOptimizer
        ):
            if isinstance(self.optimizer, list):
                raise ValueError(
                    'When the "mixed_float16" dtype policy is used, you '
                    "can only pass a single optimizer. Using policy %s "
                    "and got optimizers: %s" % self._dtype_policy,
                    self.optimizer,
                )
            if not isinstance(self.optimizer, optimizer_v2.OptimizerV2):
                raise ValueError(
                    '"optimizer" must be an instance of '
                    "tf.keras.optimizers.legacy.Optimizer when a dype policy "
                    "with a loss scale is used, but got: %s. Using policy: "
                    "%s" % (self.optimizer, self._dtype_policy)
                )
            self.optimizer = loss_scale_optimizer.LossScaleOptimizer(
                self.optimizer
            )

    def _prepare_validation_data(
        self, validation_data, batch_size, validation_steps
    ):
        """Unpack and check the validation data."""
        (
            val_x,
            val_y,
            val_sample_weights,
        ) = training_utils_v1.unpack_validation_data(validation_data)
        return self._standardize_user_data(
            val_x,
            val_y,
            sample_weight=val_sample_weights,
            batch_size=batch_size,
            steps=validation_steps,
            steps_name="validation_steps",
        )

    def _validate_compile_param_for_distribution_strategy(
        self, run_eagerly, sample_weight_mode, target_tensors, weighted_metrics
    ):
        # Validate that arguments passed by the user to `compile` are supported
        # by tf.distribute.Strategy.
        if self._distribution_strategy:
            if sample_weight_mode:
                raise NotImplementedError(
                    "sample_weight_mode is not supported with "
                    "tf.distribute.Strategy."
                )
            if weighted_metrics:
                raise NotImplementedError(
                    "weighted_metrics is not supported with "
                    "tf.distribute.Strategy."
                )
            if target_tensors:
                raise ValueError(
                    "target_tensors is not supported with "
                    "tf.distribute.Strategy."
                )

            if run_eagerly:
                raise ValueError(
                    "We currently do not support enabling `run_eagerly` with "
                    "distribution strategy."
                )

            if distributed_training_utils_v1.is_distributing_by_cloning(
                self
            ) and (not self.built or not self.inputs or not self.outputs):
                raise ValueError(
                    "We currently do not support distribution strategy with a "
                    "`Sequential` model that is created without `input_shape`/"
                    "`input_dim` set in its first layer or a subclassed model."
                )

    def _process_target_tensor_for_compile(self, target_tensors):
        if self.run_eagerly:
            # target tensor is not supported with run_eagerly. Create a list
            # with None as placeholder for each output.
            return [None for _ in self.output_names]

        if target_tensors is not None and not (
            isinstance(target_tensors, list) and target_tensors == []
        ):
            if isinstance(target_tensors, list):
                if len(target_tensors) != len(self.outputs):
                    raise ValueError(
                        "When passing a list as `target_tensors`, "
                        "it should have one entry per model output. "
                        "The model has %s outputs, "
                        "but you passed target_tensors=%s"
                        % (len(self.outputs), target_tensors)
                    )
            elif isinstance(target_tensors, dict):
                unexpected_target_tensor_names = set(
                    target_tensors.keys()
                ).difference(self.output_names)
                if unexpected_target_tensor_names:
                    raise ValueError(
                        "Unknown entry in `target_tensors` dictionary: "
                        '"{name}". '
                        "Only expected the following keys: {keys}".format(
                            name=unexpected_target_tensor_names,
                            keys=str(self.output_names),
                        )
                    )
                tmp_target_tensors = []
                for name in self.output_names:
                    tmp_target_tensors.append(target_tensors.get(name, None))
                target_tensors = tmp_target_tensors
            elif tf.is_tensor(target_tensors):
                target_tensors = [target_tensors]
            else:
                raise TypeError(
                    "Expected `target_tensors` to be a list or tuple or "
                    "dict or a single tensor, but got:",
                    target_tensors,
                )
        else:
            # In case target tensor is empty or None, create a list with Nones
            # that has same length as self.output_names. With that, the None
            # check of target tensor can be skipped downstream.
            target_tensors = [None for _ in self.output_names]
        return target_tensors

    def _compile_eagerly(self, metrics, weighted_metrics, sample_weight_mode):
        # Prepare sample weight modes. List with the same length as model
        # outputs.
        training_utils_v1.prepare_sample_weight_modes(
            self._training_endpoints, sample_weight_mode
        )
        # Prepare sample weights.
        self._prepare_sample_weights()
        # Save all metric attributes per output of the model.
        self._cache_output_metric_attributes(metrics, weighted_metrics)
        self.total_loss = None
        # Set metric attributes on model.
        self._set_metric_attributes()

        self._collected_trainable_weights = self.trainable_weights

    def _update_sample_weight_modes(self, sample_weights=None):
        """Updates sample weight modes based on training/eval inputs.

        Sample weight placeholders will be created for all or no outputs
        based on whether sample_weight is provided for any output.

        If model contains `_sample_weight_modes` we check if the input
        `sample_weights` corresponds to the sample weight modes.
          1. Set sample weight mode to be 'temporal' for output i, if `compile`
            sample_weight_mode was set to `temporal` and sample weight inputs
            are given for one or more outputs.
          2. Set sample weight mode to be 'samplewise' for output i, if
            `compile` sample_weight_mode was not set and sample weight inputs
            are given for one or more outputs.
          3. Reset sample weight mode to None for output i if sample weight mode
            was set but there is no sample weight input.

        Args:
          sample_weights: List of sample weights of the same length as model
            outputs or None.
        """
        if not self._is_compiled:
            return
        if sample_weights and any(s is not None for s in sample_weights):
            for endpoint in self._training_endpoints:
                endpoint.sample_weight_mode = (
                    endpoint.sample_weight_mode or "samplewise"
                )
        else:
            for endpoint in self._training_endpoints:
                endpoint.sample_weight_mode = None

    def _recompile_weights_loss_and_weighted_metrics(self):
        if not self._is_compiled:
            return False
        recompile = any(
            e.sample_weights_mismatch() for e in self._training_endpoints
        )

        if recompile:
            self._compile_weights_loss_and_weighted_metrics()
        return recompile

    @tf.__internal__.tracking.no_automatic_dependency_tracking
    def _compile_weights_loss_and_weighted_metrics(self, sample_weights=None):
        """Compiles the model loss and weighted metric sub-graphs.

        This may be used to set graph tensors as sample weights (instead of
        creating placeholders). This functionality is necessary for
        `tf.keras.estimator.model_to_estimator`, which calls Keras models in a
        v1 graph, and creates iterator tensors for inputs, targets, and sample
        weights.

        Args:
          sample_weights: List of tensors to use as the sample weights. Must be
            the same length as the number of outputs. If left as `None`,
            placeholders are used instead.
        """
        with backend.get_graph().as_default():
            if sample_weights is not None:
                self._update_sample_weight_modes(sample_weights)
            self._prepare_sample_weights(sample_weights)

            masks = self._prepare_output_masks()

            # Compute weighted metrics.
            self._handle_metrics(
                self.outputs,
                targets=self._targets,
                skip_target_masks=self._prepare_skip_target_masks(),
                sample_weights=self.sample_weights,
                masks=masks,
                return_weighted_metrics=True,
            )

            # Compute total loss.
            # Used to keep track of the total loss value (stateless).
            # eg., total_loss = loss_weight_1 * output_1_loss_fn(...) +
            #                   loss_weight_2 * output_2_loss_fn(...) +
            #                   layer losses.
            self.total_loss = self._prepare_total_loss(masks)

    def _prepare_skip_target_masks(self):
        """Boolean mask for whether target in output list should be skipped.

        If the loss function corresponding to a model output is None, then this
        output will be skipped during total loss calculation and feed targets
        preparation.

        Returns:
          A boolean list for whether the corresponding target in the output list
          should be skipped during loss calculation.
        """
        return [l is None for l in self.loss_functions]

    def _prepare_output_masks(self):
        """Returns masks corresponding to model outputs."""
        return [getattr(x, "_keras_mask", None) for x in self.outputs]

    def _prepare_total_loss(self, masks):
        """Computes total loss from loss functions.

        Args:
            masks: List of mask values corresponding to each model output.

        Returns:
            A list of loss weights of python floats.

        Raises:
            TypeError: If model run_eagerly is True.
        """
        if self.run_eagerly:
            raise TypeError(
                "total loss can not be computed when compiled with "
                "run_eagerly = True."
            )
        loss_list = []
        with backend.name_scope("loss"):
            for endpoint, mask in zip(self._training_endpoints, masks):
                if endpoint.should_skip_target():
                    continue
                y_true = endpoint.training_target.target
                y_pred = endpoint.output
                loss_fn = endpoint.loss_fn
                loss_weight = endpoint.loss_weight
                loss_name = endpoint.loss_name()
                sample_weight = endpoint.sample_weight

                with backend.name_scope(loss_name):
                    if mask is not None:
                        mask = tf.cast(mask, y_pred.dtype)
                        # Update weights with mask.
                        if sample_weight is None:
                            sample_weight = mask
                        else:
                            # Update dimensions of weights to match with mask if
                            # possible.
                            (
                                mask,
                                _,
                                sample_weight,
                            ) = losses_utils.squeeze_or_expand_dimensions(
                                mask, sample_weight=sample_weight
                            )

                    if hasattr(loss_fn, "reduction"):
                        per_sample_losses = loss_fn.call(y_true, y_pred)
                        sample_weight = losses_utils.apply_valid_mask(
                            per_sample_losses,
                            sample_weight,
                            mask,
                            loss_fn.reduction,
                        )
                        weighted_losses = losses_utils.compute_weighted_loss(
                            per_sample_losses,
                            sample_weight=sample_weight,
                            reduction=losses_utils.ReductionV2.NONE,
                        )
                        loss_reduction = loss_fn.reduction

                        # `AUTO` loss reduction defaults to
                        # `SUM_OVER_BATCH_SIZE` for all compile use cases.
                        if loss_reduction == losses_utils.ReductionV2.AUTO:
                            loss_reduction = (
                                losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE
                            )

                        # Compute the stateless loss value.
                        output_loss = losses_utils.reduce_weighted_loss(
                            weighted_losses, reduction=loss_reduction
                        )
                    else:
                        # Compute the stateless loss value for a custom loss
                        # class.  Here we assume that the class takes care of
                        # loss reduction because if this class returns a vector
                        # value we cannot differentiate between use case where a
                        # custom optimizer expects a vector loss value vs
                        # unreduced per-sample loss value.
                        output_loss = loss_fn(
                            y_true, y_pred, sample_weight=sample_weight
                        )
                        loss_reduction = (
                            losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE
                        )

                if len(self.outputs) > 1:
                    # Keep track of stateful result tensor for the loss.
                    endpoint.output_loss_metric(output_loss)

                # Scale output loss for distribution. For custom losses we
                # assume reduction was mean.
                if (
                    loss_reduction
                    == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE
                ):
                    output_loss = losses_utils.scale_loss_for_distribution(
                        output_loss
                    )

                loss_list.append(loss_weight * output_loss)
            if not loss_list and not self.losses:
                raise ValueError(
                    "The model cannot be compiled "
                    "because it has no loss to optimize."
                )

            # Add regularization penalties and other layer-specific losses.
            custom_losses = self.get_losses_for(None) + self.get_losses_for(
                self.inputs
            )
            if custom_losses:
                total_custom_loss = tf.add_n(
                    losses_utils.cast_losses_to_common_dtype(custom_losses)
                )
                loss_list.append(
                    losses_utils.scale_loss_for_distribution(total_custom_loss)
                )

            loss_list = losses_utils.cast_losses_to_common_dtype(loss_list)
            if loss_list:
                total_loss = tf.add_n(loss_list)
            else:
                total_loss = 0.0
        return total_loss

    def _get_callback_model(self):
        """Returns the Callback Model for this Model."""

        if hasattr(self, "_replicated_model") and self._replicated_model:
            # When using training_distributed, we set the callback model
            # to an instance of the `DistributedModel` that we create in
            # the `compile` call. The `DistributedModel` is initialized
            # with the first replicated model. We need to set the callback
            # model to a DistributedModel to allow us to override saving
            # and loading weights when we checkpoint the model during training.
            return self._replicated_model
        if hasattr(self, "callback_model") and self.callback_model:
            return self.callback_model
        return self

    @tf.__internal__.tracking.no_automatic_dependency_tracking
    def _make_callback_model(self, grouped_model):
        first_replicated_model = self._distribution_strategy.unwrap(
            grouped_model
        )[0]
        # We initialize the callback model with the first replicated model.
        self._replicated_model = DistributedCallbackModel(
            first_replicated_model
        )
        self._replicated_model.set_original_model(self)

    def _validate_or_infer_batch_size(self, batch_size, steps, x):
        """Validates that `batch_size` provided is consistent with InputLayer.

        It's possible that the user specified a static batch size in their
        InputLayer. If so, this method checks the provided `batch_size` and `x`
        arguments are consistent with this static batch size. Also, if
        `batch_size` is `None`, this method will attempt to infer the batch size
        from the static batch size of the InputLayer. Lastly, ValueError will be
        raised if `x` is a tf.data.Dataset and `batch_size` is specified as we
        expect users to provide batched datasets.

        Args:
          batch_size: The batch_size provided as an argument to
            fit/evaluate/predict.
          steps: The steps provided as an argument to fit/evaluate/predict.
          x: The data passed as `x` to fit/evaluate/predict.

        Returns:
          The validated batch_size, auto-inferred from the first layer if not
          provided.
        """
        if isinstance(
            x, (tf.compat.v1.data.Dataset, tf.data.Dataset, data_utils.Sequence)
        ) or tf_inspect.isgenerator(x):
            if batch_size is not None:
                raise ValueError(
                    "The `batch_size` argument must not be specified for the "
                    "given input type. Received input: "
                    "{}, batch_size: {}".format(x, batch_size)
                )
            return

        # Avoids the override in Sequential.layers which filters Input layers.
        # (Which are often the very layers that we're after.)
        layers = self._flatten_layers(include_self=False, recursive=False)
        first_layer = next(layers, None)
        if first_layer:
            # The per-replica static batch size.
            static_batch_size = training_utils.get_static_batch_size(
                first_layer
            )
            if static_batch_size is not None:

                # Determine number of times the user-supplied batch size will be
                # split.
                if (
                    self._distribution_strategy
                    and distributed_training_utils.global_batch_size_supported(
                        self._distribution_strategy
                    )
                ):
                    num_splits_for_ds = (
                        self._distribution_strategy.num_replicas_in_sync
                    )
                else:
                    num_splits_for_ds = 1

                # Check `batch_size` argument is consistent with InputLayer.
                if batch_size is not None:
                    if batch_size % num_splits_for_ds != 0:
                        raise ValueError(
                            "The `batch_size` argument ({}) must be divisible "
                            "the by number of replicas ({})".format(
                                batch_size, num_splits_for_ds
                            )
                        )
                    per_replica_batch_size = batch_size // num_splits_for_ds

                    if per_replica_batch_size != static_batch_size:
                        raise ValueError(
                            "The `batch_size` argument value {} is "
                            "incompatible with the specified batch size of "
                            "your Input Layer: {}".format(
                                per_replica_batch_size, static_batch_size
                            )
                        )

                # Check Dataset/Iterator batch size is consistent with
                # InputLayer.
                if isinstance(
                    x,
                    (
                        tf.data.Dataset,
                        tf.compat.v1.data.Iterator,
                        tf.data.Iterator,
                    ),
                ):
                    ds_batch_size = tf.compat.v1.Dimension(
                        tf.nest.flatten(tf.compat.v1.data.get_output_shapes(x))[
                            0
                        ][0]
                    ).value
                    if ds_batch_size is not None:
                        if ds_batch_size % num_splits_for_ds != 0:
                            raise ValueError(
                                "The batch output shape of your `Dataset` {} "
                                "cannot be divisible by number of "
                                "replicas {}".format(
                                    ds_batch_size, num_splits_for_ds
                                )
                            )

                        ds_per_replica_batch_size = (
                            ds_batch_size // num_splits_for_ds
                        )
                        if ds_per_replica_batch_size != static_batch_size:
                            raise ValueError(
                                "The batch output shape of your `Dataset` is "
                                "{}, which is incompatible with the specified "
                                "batch size of your Input Layer: {}".format(
                                    ds_per_replica_batch_size, static_batch_size
                                )
                            )

                # Set inferred batch size from the InputLayer.
                if steps is None:
                    batch_size = static_batch_size * num_splits_for_ds

        if batch_size is None and steps is None:
            # Backwards compatibility
            batch_size = 32
        return batch_size

    def _prepare_sample_weights(self, sample_weights=None):
        """Sets sample weight attribute on the model."""
        # List with the same length as model outputs.
        if sample_weights is not None:
            if len(sample_weights) != len(self._training_endpoints):
                raise ValueError(
                    "Provided sample weights must have same length as the "
                    "number of outputs. Expected: {}, got: {}.".format(
                        len(self._training_endpoints), len(sample_weights)
                    )
                )
        else:
            sample_weights = [None] * len(self._training_endpoints)
        for endpoint, weight in zip(self._training_endpoints, sample_weights):
            endpoint.populate_sample_weight(weight, endpoint.sample_weight_mode)

    def _cache_output_metric_attributes(self, metrics, weighted_metrics):
        """Caches metric name and function attributes for every model output."""
        output_shapes = []
        for output in self.outputs:
            if output is None or output.shape.rank is None:
                output_shapes.append(None)
            else:
                output_shapes.append(output.shape.as_list())
        self._per_output_metrics = (
            training_utils_v1.collect_per_output_metric_info(
                metrics,
                self.output_names,
                output_shapes,
                self.loss_functions,
                from_serialized=self._from_serialized,
            )
        )
        self._per_output_weighted_metrics = (
            training_utils_v1.collect_per_output_metric_info(
                weighted_metrics,
                self.output_names,
                output_shapes,
                self.loss_functions,
                from_serialized=self._from_serialized,
                is_weighted=True,
            )
        )

    def _add_unique_metric_name(self, metric_name, metric_fn, output_index):
        """Makes the metric name unique.

          If there are multiple outputs for which the metrics are calculated,
          the metric names have to be made unique by appending an integer.

        Args:
          metric_name: Metric name that corresponds to the metric specified by
            the user. For example: 'acc'.
          metric_fn: The Metric object.
          output_index: The index of the model output for which the metric name
            is being added.

        Returns:
          string, name of the model's unique metric name
        """
        # For multi-output models, prepend the output names to the metric name.
        if len(self.output_names) > 1:
            # If we're loading from an already-serialized model, we've already
            # prepended the output name, and we don't want to do it again.
            #
            # Alternatively, we may be receiving a stateless metric (e.g. the
            # string "accuracy") rather than a `Metric` object, in which case we
            # want to prepend the output name even if we are loading a
            # serialized model.
            if not getattr(metric_fn, "_from_serialized", False):
                metric_name = f"{self.output_names[output_index]}_{metric_name}"

        j = 1
        base_metric_name = metric_name
        while metric_name in self.metrics_names:
            metric_name = "%s_%d" % (base_metric_name, j)
            j += 1

        return metric_name

    def _init_metric_attributes(self):
        """Initialized model metric attributes."""
        # List of stateful metric functions. Used for resetting metric state
        # during training/eval.
        self._compile_metric_functions = []

    def _set_per_output_metric_attributes(self, metrics_dict, output_index):
        """Sets the metric attributes on the model for the given output.

        Args:
          metrics_dict: A dict with metric names as keys and metric fns as
            values.
          output_index: The index of the model output for which the metric
            attributes are added.

        Returns:
          Metrics dict updated with unique metric names as keys.
        """
        updated_metrics_dict = collections.OrderedDict()
        for metric_name, metric_fn in metrics_dict.items():
            metric_name = self._add_unique_metric_name(
                metric_name, metric_fn, output_index
            )

            # Update the name on the metric class to be the unique generated
            # name.
            metric_fn._name = metric_name
            updated_metrics_dict[metric_name] = metric_fn
            # Keep track of metric name and function.
            self._compile_metric_functions.append(metric_fn)
        return updated_metrics_dict

    def _set_metric_attributes(self):
        """Sets the metric attributes on the model for all the model outputs."""
        updated_per_output_metrics = []
        updated_per_output_weighted_metrics = []
        for i, endpoint in enumerate(self._training_endpoints):
            if endpoint.should_skip_target():
                updated_per_output_metrics.append(self._per_output_metrics[i])
                updated_per_output_weighted_metrics.append(
                    self._per_output_weighted_metrics[i]
                )
                continue
            updated_per_output_metrics.append(
                self._set_per_output_metric_attributes(
                    self._per_output_metrics[i], i
                )
            )
            updated_per_output_weighted_metrics.append(
                self._set_per_output_metric_attributes(
                    self._per_output_weighted_metrics[i], i
                )
            )

        # Create a metric wrapper for each output loss. This computes mean of an
        # output loss across mini-batches (irrespective of how we reduce within
        # a batch).
        if len(self._training_endpoints) > 1:
            for endpoint in self._training_endpoints:
                if not endpoint.should_skip_target():
                    endpoint.output_loss_metric = metrics_module.Mean(
                        name=endpoint.loss_name()
                    )

        self._per_output_metrics = updated_per_output_metrics
        self._per_output_weighted_metrics = updated_per_output_weighted_metrics

    def _handle_per_output_metrics(
        self, metrics_dict, y_true, y_pred, mask, weights=None
    ):
        """Calls metric functions for a single output.

        Args:
          metrics_dict: A dict with metric names as keys and metric fns as
            values.
          y_true: Target output.
          y_pred: Predicted output.
          mask: Computed mask value for the current output.
          weights: Weights to be applied on the current output.

        Returns:
          A list of metric result tensors.
        """
        metric_results = []
        for metric_name, metric_fn in metrics_dict.items():
            with backend.name_scope(metric_name):
                metric_result = training_utils_v1.call_metric_function(
                    metric_fn, y_true, y_pred, weights=weights, mask=mask
                )
                metric_results.append(metric_result)
        return metric_results

    def _handle_metrics(
        self,
        outputs,
        targets=None,
        skip_target_masks=None,
        sample_weights=None,
        masks=None,
        return_weighted_metrics=False,
        return_weighted_and_unweighted_metrics=False,
    ):
        """Handles calling metric functions.

        Args:
          outputs: List of outputs (predictions).
          targets: List of targets.
          skip_target_masks: Optional. List of boolean for whether the
            corresponding target should be ignored or not.
          sample_weights: Optional list of sample weight arrays.
          masks: List of computed output mask values.
          return_weighted_metrics: Flag that indicates whether weighted metrics
            should be computed instead of unweighted metrics. This flag is
            ignored when `return_weighted_and_unweighted_metrics` is enabled.
          return_weighted_and_unweighted_metrics: Flag that is used to indicate
            whether both weighted and unweighted metrics should be computed.
            When this is not enabled, we use `return_weighted_metrics` param to
            indicate whether weighted or unweighted metrics should be returned.

        Returns:
          A list of metric result tensors.
        """
        # TODO(scottzhu): Update this to use the new training_endpoints.
        # Currently the eager and graph logic is bit different.
        skip_target_masks = skip_target_masks or [False] * len(outputs)
        metric_results = []
        with backend.name_scope("metrics"):
            # Invoke all metrics added using `compile`.
            for i in range(len(outputs)):
                if skip_target_masks[i]:
                    continue
                output = outputs[i] if outputs else None
                target = targets[i] if targets else None
                output_mask = masks[i] if masks else None

                if (
                    return_weighted_and_unweighted_metrics
                    or not return_weighted_metrics
                ):
                    metric_results.extend(
                        self._handle_per_output_metrics(
                            self._per_output_metrics[i],
                            target,
                            output,
                            output_mask,
                        )
                    )
                if (
                    return_weighted_and_unweighted_metrics
                    or return_weighted_metrics
                ):
                    metric_results.extend(
                        self._handle_per_output_metrics(
                            self._per_output_weighted_metrics[i],
                            target,
                            output,
                            output_mask,
                            weights=sample_weights[i]
                            if sample_weights
                            else None,
                        )
                    )
        return metric_results

    def _check_trainable_weights_consistency(self):
        """Check trainable weights count consistency.

        This will raise a warning if `trainable_weights` and
        `_collected_trainable_weights` are inconsistent (i.e. have different
        number of parameters).
        Inconsistency will typically arise when one modifies `model.trainable`
        without calling `model.compile` again.
        """
        if not hasattr(self, "_collected_trainable_weights"):
            return

        if len(self.trainable_weights) != len(
            self._collected_trainable_weights
        ):
            logging.log_first_n(
                logging.WARN,
                "Discrepancy between trainable weights and collected"
                " trainable weights, did you set `model.trainable`"
                " without calling `model.compile` after ?",
                1,
            )

    def _make_train_function(self):
        has_recompiled = self._recompile_weights_loss_and_weighted_metrics()
        self._check_trainable_weights_consistency()
        if isinstance(self.optimizer, list):
            raise ValueError(
                "The `optimizer` in `compile` should be a single optimizer."
            )
        # If we have re-compiled the loss/weighted metric sub-graphs then create
        # train function even if one exists already. This is because
        # `_feed_sample_weights` list has been updated on re-compile.
        if getattr(self, "train_function", None) is None or has_recompiled:
            # Restore the compiled trainable state.
            current_trainable_state = self._get_trainable_state()
            self._set_trainable_state(self._compiled_trainable_state)

            inputs = (
                self._feed_inputs
                + self._feed_targets
                + self._feed_sample_weights
            )
            if not isinstance(backend.symbolic_learning_phase(), int):
                inputs += [backend.symbolic_learning_phase()]

            with backend.get_graph().as_default():
                with backend.name_scope("training"):
                    # Training updates
                    updates = self.optimizer.get_updates(
                        params=self._collected_trainable_weights,
                        loss=self.total_loss,
                    )
                    # Unconditional updates
                    updates += self.get_updates_for(None)
                    # Conditional updates relevant to this model
                    updates += self.get_updates_for(self.inputs)

                metrics = self._get_training_eval_metrics()
                metrics_tensors = [
                    m._call_result
                    for m in metrics
                    if hasattr(m, "_call_result")
                ]

            with backend.name_scope("training"):
                # Gets loss and metrics. Updates weights at each call.
                fn = backend.function(
                    inputs,
                    [self.total_loss] + metrics_tensors,
                    updates=updates,
                    name="train_function",
                    **self._function_kwargs,
                )
                setattr(self, "train_function", fn)

            # Restore the current trainable state
            self._set_trainable_state(current_trainable_state)

    def _make_test_function(self):
        has_recompiled = self._recompile_weights_loss_and_weighted_metrics()
        # If we have re-compiled the loss/weighted metric sub-graphs then create
        # test function even if one exists already. This is because
        # `_feed_sample_weights` list has been updated on re-compile.
        if getattr(self, "test_function", None) is None or has_recompiled:
            inputs = (
                self._feed_inputs
                + self._feed_targets
                + self._feed_sample_weights
            )

            with backend.get_graph().as_default():
                metrics = self._get_training_eval_metrics()
                metrics_tensors = [
                    m._call_result
                    for m in metrics
                    if hasattr(m, "_call_result")
                ]

            with backend.name_scope("evaluation"):
                updates = self.state_updates
                # Return loss and metrics, no gradient updates.
                # Does update the network states.
                fn = backend.function(
                    inputs,
                    [self.total_loss] + metrics_tensors,
                    updates=updates,
                    name="test_function",
                    **self._function_kwargs,
                )
                setattr(self, "test_function", fn)

    def _make_predict_function(self):
        if not hasattr(self, "predict_function"):
            self.predict_function = None
        if self.predict_function is None:
            inputs = self._feed_inputs
            # Gets network outputs. Does not update weights.
            # Does update the network states.
            kwargs = getattr(self, "_function_kwargs", {})
            with backend.name_scope(ModeKeys.PREDICT):
                self.predict_function = backend.function(
                    inputs,
                    self.outputs,
                    updates=self.state_updates,
                    name="predict_function",
                    **kwargs,
                )

    def _make_execution_function(self, mode):
        if mode == ModeKeys.TRAIN:
            self._make_train_function()
            return self.train_function
        if mode == ModeKeys.TEST:
            self._make_test_function()
            return self.test_function
        if mode == ModeKeys.PREDICT:
            self._make_predict_function()
            return self.predict_function

    def _distribution_standardize_user_data(
        self,
        x,
        y=None,
        sample_weight=None,
        class_weight=None,
        batch_size=None,
        validation_split=0.0,
        shuffle=False,
        epochs=1,
        allow_partial_batch=False,
    ):
        """Runs validation checks on input and target data passed by the user.

        This is called when using tf.distribute.Strategy to train, evaluate or
        serve the model.

        Args:
          x: Input data. A numpy array or `tf.data` dataset.
          y: Target data. A numpy array or None if x is a `tf.data` dataset.
          sample_weight: An optional sample-weight array passed by the user to
            weight the importance of each sample in `x`.
          class_weight: An optional class-weight array by the user to
            weight the importance of samples in `x` based on the class they
            belong to, as conveyed by `y`.
          batch_size: Integer batch size. If provided, it is used to run
            additional validation checks on stateful models.
          validation_split: Float between 0 and 1.
            Fraction of the training data to be used as validation data.
          shuffle: Boolean whether to shuffle the training data before each
            epoch.
          epochs: Integer epochs. If > 1, repeat the numpy training data epochs
            times when converting to training dataset.
          allow_partial_batch: Boolean whether to enforce that all batches have
            the same size.

        Returns:
          Dataset instance.

        Raises:
          ValueError: In case of invalid user-provided data.
          RuntimeError: If the model was never compiled.
        """
        if class_weight:
            raise NotImplementedError(
                "`class_weight` is currently not supported "
                "when using tf.distribute.Strategy."
            )

        if (
            sample_weight is not None
            and sample_weight.all()
            and backend.is_tpu_strategy(self._distribution_strategy)
        ):
            raise NotImplementedError(
                "`sample_weight` is currently not supported "
                "when using TPUStrategy."
            )

        # Validates `steps` and `shuffle` arguments right at the beginning
        # since we use it to construct the dataset object.
        # TODO(anjalisridhar): Remove this check once we refactor the
        # _standardize_user_data code path. This check is already present
        # elsewhere in the codebase.
        if isinstance(x, tf.data.Dataset):
            if shuffle:
                training_utils_v1.verify_dataset_shuffled(x)

        strategy = self._distribution_strategy
        with strategy.scope():
            # We should be sure to call get_session() inside the
            # strategy.scope() so the strategy can affect the session options.
            if tf.compat.v1.executing_eagerly_outside_functions():
                session = None
            else:
                session = backend.get_session()

            first_x_value = tf.nest.flatten(x)[0]
            if isinstance(first_x_value, np.ndarray):
                x = training_utils.list_to_tuple(x)
                if y is not None:
                    y = training_utils.list_to_tuple(y)
                    if sample_weight is not None:
                        sample_weight = training_utils.list_to_tuple(
                            sample_weight
                        )
                        in_tuple = (x, y, sample_weight)
                    else:
                        in_tuple = (x, y)
                else:
                    in_tuple = x

                ds = strategy.extended.experimental_make_numpy_dataset(
                    in_tuple, session=session
                )
                if shuffle:
                    # We want a buffer size that is larger than the batch size
                    # provided by the user and provides sufficient randomness.
                    # Note that larger numbers introduce more memory usage based
                    # on the size of each sample.
                    ds = ds.shuffle(max(1024, batch_size * 8))
                if epochs > 1:
                    ds = ds.repeat(epochs)

                # We need to use the drop_remainder argument to get a known
                # static input shape which is required for TPUs.
                drop_remainder = (
                    not allow_partial_batch
                    and strategy.extended.experimental_require_static_shapes
                )

                # TODO(b/131720208): We still drop remainder here if number of
                # examples is divisible by batch size, as sometimes dynamic
                # padder will time out with keras.metrics.CategoricalAccuracy()
                # metric.
                if backend.is_tpu_strategy(strategy) and not drop_remainder:
                    dataset_size = first_x_value.shape[0]
                    if dataset_size % batch_size == 0:
                        drop_remainder = True

                x = ds.batch(batch_size, drop_remainder=drop_remainder)
            else:
                assert isinstance(x, tf.data.Dataset)
                training_utils_v1.validate_dataset_input(
                    x, y, sample_weight, validation_split
                )
        return x

    def _standardize_user_data(
        self,
        x,
        y=None,
        sample_weight=None,
        class_weight=None,
        batch_size=None,
        check_steps=False,
        steps_name="steps",
        steps=None,
        validation_split=0.0,
        shuffle=False,
        extract_tensors_from_dataset=False,
    ):
        """Runs validation checks on input and target data passed by the user.

        Also standardizes the data to lists of arrays, in order.

        Also builds and compiles the model on the fly if it is a subclassed
        model that has never been called before (and thus has no
        inputs/outputs).

        This is a purely internal method, subject to refactoring at any time.

        Args:
          x: Input data. It could be:
            - A Numpy array (or array-like), or a list of arrays
              (in case the model has multiple inputs).
            - A TensorFlow tensor, or a list of tensors
              (in case the model has multiple inputs).
            - A dict mapping input names to the corresponding array/tensors,
              if the model has named inputs.
            - A `tf.data` dataset.
          y: Target data. Like the input data `x`,
            it could be either Numpy array(s) or TensorFlow tensor(s).
            It should be consistent with `x` (you cannot have Numpy inputs and
            tensor targets, or inversely). If `x` is a dataset, `y` should not
            be specified (since targets will be obtained from the iterator).
          sample_weight: An optional sample-weight array passed by the user to
            weight the importance of each sample in `x`.
          class_weight: An optional class-weight array by the user to
            weight the importance of samples in `x` based on the class they
            belong to, as conveyed by `y`. If both `sample_weight` and
            `class_weight` are provided, the weights are multiplied.
          batch_size: Integer batch size. If provided, it is used to run
            additional validation checks on stateful models.
          check_steps: boolean, True if we want to check for validity of `steps`
            and False, otherwise. For example, when we are standardizing one
            batch of data for train_on_batch/predict_on_batch/test_on_batch
            APIs, `steps` value is not required and we should not check for its
            validity in these cases.
          steps_name: The public API's parameter name for `steps`.
          steps: Integer or `None`. Total number of steps (batches of samples)
            to execute.
          validation_split: Float between 0 and 1.
            Fraction of the training data to be used as validation data.
          shuffle: Boolean whether to shuffle the training data before each
            epoch.
          extract_tensors_from_dataset: Boolean. When `x` is a dataset instance,
            this indicates whether to extract actual tensors from the dataset or
            instead output the dataset instance itself.
            Set to True when calling from `train_on_batch`/etc.

        Returns:
          A tuple of 3: inputs (arrays or dicts, depending on whether `x` was a
          dict or not), target arrays, sample-weight arrays.  If the model's
          input and targets are symbolic, these lists are empty (since the model
          takes no user-provided data, instead the data comes from the symbolic
          inputs/targets).

        Raises:
          ValueError: In case of invalid user-provided data.
          RuntimeError: If the model was never compiled.
        """
        if isinstance(x, (tf.compat.v1.data.Dataset, tf.data.Dataset)):
            # Graph mode dataset. We'll pass the dataset as-is (unless
            # `extract_tensors_from_dataset` is True, in which case we extract
            # the tensors from the dataset and we output them.
            training_utils_v1.validate_dataset_input(
                x, y, sample_weight, validation_split
            )
            if shuffle:
                training_utils_v1.verify_dataset_shuffled(x)

            is_dataset = True
            if extract_tensors_from_dataset:
                # We do this for `train_on_batch`/etc.
                (
                    x,
                    y,
                    sample_weight,
                ) = training_utils_v1.extract_tensors_from_dataset(x)
        elif isinstance(x, tf.compat.v1.data.Iterator):
            # Graph mode iterator. We extract the symbolic tensors.
            training_utils_v1.validate_dataset_input(
                x, y, sample_weight, validation_split
            )
            iterator = x
            x, y, sample_weight = training_utils_v1.unpack_iterator_input(
                iterator
            )
            is_dataset = True
        else:
            is_dataset = False

        # Validates `steps` argument based on x's type.
        if check_steps:
            training_utils_v1.check_steps_argument(x, steps, steps_name)

        # First, we build the model on the fly if necessary.
        if not self.inputs:
            all_inputs, y_input, dict_inputs = self._build_model_with_inputs(
                x, y
            )
            is_build_called = True
        else:
            all_inputs = []
            # Whether this is a subclassed model that expects dictionary inputs
            # rather than list inputs (e.g. FeatureColumn-based models).
            dict_inputs = isinstance(self.inputs, dict)
            is_build_called = False
            y_input = y

        # Second, we compile the model on the fly if necessary, mostly for
        # subclass models.
        is_compile_called = False
        if not self._is_compiled and self.optimizer:
            self._compile_from_inputs(all_inputs, y_input, x, y)
            is_compile_called = True

        # In graph mode, if we had just set inputs and targets as symbolic
        # tensors by invoking build and compile on the model respectively, we do
        # not have to feed anything to the model. Model already has input and
        # target data as part of the graph.  Note: in this case, `any` and `all`
        # are equivalent since we disallow mixed symbolic/value inputs.

        # self.run_eagerly is not free to compute, so we want to reuse the
        # value.
        run_eagerly = self.run_eagerly

        if (
            not run_eagerly
            and is_build_called
            and is_compile_called
            and not is_dataset
            and any(_is_symbolic_tensor(v) for v in all_inputs)
        ):
            return [], [], None

        return self._standardize_tensors(
            x,
            y,
            sample_weight,
            run_eagerly=run_eagerly,
            dict_inputs=dict_inputs,
            is_dataset=is_dataset,
            class_weight=class_weight,
            batch_size=batch_size,
        )

    def _standardize_tensors(
        self,
        x,
        y,
        sample_weight,
        run_eagerly,
        dict_inputs,
        is_dataset,
        class_weight=None,
        batch_size=None,
    ):
        if run_eagerly:
            # In eager mode, do not do shape validation
            # since the network has no input nodes (placeholders) to be fed.
            feed_input_names = self.input_names
            feed_input_shapes = None
        elif not self._is_graph_network:
            # Case: symbolic-mode subclassed network. Do not do shape
            # validation.
            feed_input_names = self._feed_input_names
            feed_input_shapes = None
        else:
            # Case: symbolic-mode graph network.
            # In this case, we run extensive shape validation checks.
            feed_input_names = self._feed_input_names
            feed_input_shapes = self._feed_input_shapes

        # Standardize the inputs.
        if not isinstance(x, (tf.compat.v1.data.Dataset, tf.data.Dataset)):
            # TODO(fchollet): run static checks with dataset output shape(s).
            x = training_utils_v1.standardize_input_data(
                x,
                feed_input_names,
                feed_input_shapes,
                check_batch_axis=False,  # Don't enforce the batch size.
                exception_prefix="input",
            )

        # Get typespecs for the input data and sanitize it if necessary.
        # TODO(momernick): This should be capable of doing full input validation
        # at all times - validate that this is so and refactor the
        # standardization code.
        if isinstance(x, tf.data.Dataset):
            x_shapes = tf.data.experimental.get_structure(x)
            if isinstance(x_shapes, tuple):
                # If the output of a Dataset is a tuple, we assume it's either
                # of the form (x_data, y_data) or (x_data, y_data,
                # sample_weights). In either case, we only care about x_data
                # here.
                x_shapes = x_shapes[0]
        else:
            flat_inputs = tf.nest.flatten(x)
            flat_expected_inputs = tf.nest.flatten(self.inputs)
            converted_x = []
            for a, b in zip(flat_inputs, flat_expected_inputs):
                converted_x.append(_convert_scipy_sparse_tensor(a, b))
            x = tf.nest.pack_sequence_as(x, converted_x)

            # Convert ResourceVariables to tensors so nest.assert_same_structure
            # below won't fail with Variable and Tensor.
            x_tensors = tf_utils.convert_variables_to_tensors(x)
            x_shapes = tf.nest.map_structure(
                tf_utils.type_spec_from_value, x_tensors
            )

        flat_inputs = tf.nest.flatten(x_shapes)
        # Convert ResourceVariables to tensors so nest.assert_same_structure
        # below won't fail with Variable and Tensor.
        flat_expected_inputs = tf.nest.flatten(
            tf_utils.convert_variables_to_tensors(self.inputs)
        )
        for a, b in zip(flat_inputs, flat_expected_inputs):
            tf.nest.assert_same_structure(a, b, expand_composites=True)

        if y is not None:
            # Prepare self._sample_weight_modes. List with the same length as
            # model outputs.
            training_utils_v1.prepare_sample_weight_modes(
                self._training_endpoints, self.sample_weight_mode
            )
            feed_output_names = self._feed_output_names
            feed_sample_weight_modes = self._sample_weight_modes
            if not self._is_graph_network:
                feed_output_shapes = None
            else:
                feed_output_shapes = self._feed_output_shapes

            # Standardize the outputs.
            y = training_utils_v1.standardize_input_data(
                y,
                feed_output_names,
                # Don't enforce target shapes to match output shapes.
                # Precise checks will be run in
                # `check_loss_and_target_compatibility`.
                shapes=None,
                check_batch_axis=False,  # Don't enforce the batch size.
                exception_prefix="target",
            )

            # Generate sample-wise weight values given the `sample_weight` and
            # `class_weight` arguments.
            sample_weights = training_utils_v1.standardize_sample_weights(
                sample_weight, feed_output_names
            )
            class_weights = training_utils_v1.standardize_class_weights(
                class_weight, feed_output_names
            )

            sample_weights = [
                training_utils_v1.standardize_weights(ref, sw, cw, mode)
                for (ref, sw, cw, mode) in zip(
                    y, sample_weights, class_weights, feed_sample_weight_modes
                )
            ]
            # Check that all arrays have the same length.
            if not self._distribution_strategy:
                training_utils_v1.check_array_lengths(x, y, sample_weights)
                if self._is_graph_network and not run_eagerly:
                    # Additional checks to avoid users mistakenly using improper
                    # loss fns.
                    training_utils_v1.check_loss_and_target_compatibility(
                        y, self._feed_loss_fns, feed_output_shapes
                    )

            sample_weights, _, _ = training_utils.handle_partial_sample_weights(
                y, sample_weights, feed_sample_weight_modes, check_all_flat=True
            )
        else:
            y = []
            sample_weights = None

        if self.stateful and batch_size and not is_dataset:
            # Check that for stateful networks, number of samples is a multiple
            # of the static batch size.
            if x[0].shape[0] % batch_size != 0:
                raise ValueError(
                    "In a stateful network, "
                    "you should only pass inputs with "
                    "a number of samples that can be "
                    "divided by the batch size. Found: "
                    + str(x[0].shape[0])
                    + " samples"
                )

        # If dictionary inputs were provided, we return a dictionary as well.
        if dict_inputs and not isinstance(
            x, (tf.compat.v1.data.Dataset, tf.data.Dataset)
        ):
            x = dict(zip(feed_input_names, x))
        return x, y, sample_weights

    def _build_model_with_inputs(self, inputs, targets):
        """Build the model (set model inputs/outputs), mainly for subclass
        model."""
        processed_inputs = []
        is_dict_inputs = False
        orig_inputs = inputs
        # We need to use `inputs` to set the model inputs.
        # If input data is a dataset iterator in graph mode or if it is an eager
        # iterator and only one batch of samples is required, we fetch the data
        # tensors from the iterator and then standardize them.
        if isinstance(inputs, (tf.compat.v1.data.Dataset, tf.data.Dataset)):
            inputs, targets, _ = training_utils_v1.extract_tensors_from_dataset(
                inputs
            )
        # We type-check that `inputs` and `targets` are either single arrays
        # or lists of arrays, and extract a flat list of inputs from the passed
        # structure.
        training_utils_v1.validate_input_types(inputs, orig_inputs)

        if isinstance(inputs, (list, tuple)):
            processed_inputs += list(inputs)
        elif isinstance(inputs, dict):
            is_dict_inputs = True
            keys = sorted(inputs.keys())
            processed_inputs = [inputs[k] for k in keys]
        else:
            processed_inputs.append(inputs)
        # Now that we have a flat set of inputs, we make sure that none of them
        # are CompositeTensors or CompositeTensorValues of any type (or scipy
        # sparse arrays, which we treat as SparseTensor values). We cannot
        # safely infer input data from an arbitrary composite tensor, so we
        # don't try - users should explicitly add composite tensor inputs to
        # their subclassed models.
        for input_tensor in processed_inputs:
            if training_utils_v1.is_composite_or_composite_value(
                input_tensor
            ) and not isinstance(input_tensor, tf.Variable):
                # TODO(b/132691975): Document subclass-model CT input handling.
                raise ValueError(
                    "All SparseTensor and RaggedTensor inputs must be "
                    "explicitly declared using a keras.Input() with "
                    "sparse=True or ragged=True. We found an undeclared "
                    "input %s. For Sequential models, please add a "
                    "keras.Input() as your first Layer. For subclassed models, "
                    "please call self._set_inputs() on your input set, which "
                    "you can create using keras.Input() for each input to your "
                    "model." % (input_tensor,)
                )
        # Build the model using the retrieved inputs (value or symbolic).
        # If values are generated from a dataset, then in symbolic-mode
        # placeholders will be created to match the value shapes.
        if isinstance(
            orig_inputs,
            (
                tf.compat.v1.data.Dataset,
                tf.data.Dataset,
                tf.compat.v1.data.Iterator,
            ),
        ):
            if not self.inputs:
                # For subclassed models, a robust input spec is not available so
                # we must cast to the model dtype.
                inputs = training_utils_v1.cast_if_floating_dtype(
                    inputs, self.dtype
                )

            def create_tensor_spec(t):
                return tf.TensorSpec(t.shape, t.dtype)

            cast_inputs = tf.nest.map_structure(create_tensor_spec, inputs)
        elif training_utils_v1.has_tensors(inputs):
            cast_inputs = training_utils_v1.cast_if_floating_dtype(inputs)
        else:
            cast_inputs = inputs
        self._set_inputs(cast_inputs)
        return processed_inputs, targets, is_dict_inputs

    def _compile_from_inputs(
        self, all_inputs, target, orig_inputs, orig_target
    ):
        if target is not None:
            # We need to use `y` to set the model targets.
            if training_utils_v1.has_tensors(target):
                target = training_utils_v1.cast_if_floating_dtype_and_mismatch(
                    target, self.outputs
                )
            training_utils_v1.validate_input_types(
                target, orig_target, allow_dict=False, field_name="target"
            )
            if isinstance(target, (list, tuple)):
                all_inputs += list(target)
            else:
                all_inputs.append(target)
        # Type check that all inputs are *either* value *or* symbolic.
        # TODO(fchollet): this check could be removed in Eager mode?
        if any(tf.is_tensor(v) for v in all_inputs):
            if not all(tf.is_tensor(v) for v in all_inputs):
                raise ValueError(
                    "Do not pass inputs that mix Numpy arrays and "
                    "TensorFlow tensors. "
                    "You passed: x="
                    + str(orig_inputs)
                    + "; y="
                    + str(orig_target)
                )
        is_dataset = isinstance(
            orig_inputs,
            (
                tf.compat.v1.data.Dataset,
                tf.data.Dataset,
                tf.compat.v1.data.Iterator,
            ),
        )
        if is_dataset or tf.executing_eagerly():
            target_tensors = None
        else:
            # Handle target tensors if any passed.
            if target is not None:
                if not isinstance(target, (list, tuple)):
                    target = [target]
                target_tensors = [v for v in target if _is_symbolic_tensor(v)]
            else:
                target_tensors = None

        self.compile(
            optimizer=self.optimizer,
            loss=self.loss,
            metrics=self._compile_metrics,
            weighted_metrics=self._compile_weighted_metrics,
            loss_weights=self.loss_weights,
            target_tensors=target_tensors,
            sample_weight_mode=self.sample_weight_mode,
            run_eagerly=self.run_eagerly,
            experimental_run_tf_function=self._experimental_run_tf_function,
        )

    # TODO(omalleyt): Consider changing to a more descriptive function name.
    def _set_inputs(self, inputs, outputs=None, training=None):
        """Set model's input and output specs based on the input data received.

        This is to be used for Model subclasses, which do not know at
        instantiation time what their inputs look like.

        Args:
          inputs: Single array, or list of arrays. The arrays could be
            placeholders, Numpy arrays, data tensors, or TensorSpecs.
            - if placeholders: the model is built on top of these placeholders,
              and we expect Numpy data to be fed for them when calling
              `fit`/etc.
            - if Numpy data or TensorShapes: we create placeholders matching the
              TensorShapes or shapes of the Numpy arrays. We expect Numpy data
              to be fed for these placeholders when calling `fit`/etc.
            - if data tensors: the model is built on top of these tensors.
              We do not expect any Numpy data to be provided when calling
              `fit`/etc.
          outputs: None, a data tensor, or a list of tensors. If None, the
            outputs will be determined by invoking `self.call()`, otherwise the
            provided value will be used.
          training: Boolean or None. Only relevant in symbolic mode. Specifies
            whether to build the model's graph in inference mode (False),
            training mode (True), or using the Keras learning phase (None).
        Raises:
          ValueError: If dict inputs are passed to a Sequential Model where the
            first layer isn't FeatureLayer.
        """
        self._set_save_spec(inputs)
        inputs = self._set_input_attrs(inputs)

        if outputs is None:
            kwargs = {}
            if self._expects_training_arg:
                # In V2 mode, feeding `training=None` is not allowed because any
                # value explicitly passed by the user is respected, even
                # `None`.`
                if (
                    training is None
                    and not tf.compat.v1.executing_eagerly_outside_functions()
                ):
                    training = backend.learning_phase()
                if training is not None:
                    kwargs["training"] = training
            try:
                outputs = self(inputs, **kwargs)
            except NotImplementedError:
                # This Model or a submodel is dynamic and hasn't overridden
                # `compute_output_shape`.
                outputs = None

        self._set_output_attrs(outputs)

    @tf.__internal__.tracking.no_automatic_dependency_tracking
    def _set_input_attrs(self, inputs):
        """Sets attributes related to the inputs of the Model."""
        if self.inputs:
            raise ValueError("Model inputs are already set.")

        if self.__class__.__name__ == "Sequential" and not self.built:
            if tf.is_tensor(inputs):
                input_shape = (None,) + tuple(inputs.shape.as_list()[1:])
            elif isinstance(inputs, tf.TensorShape):
                input_shape = (None,) + tuple(inputs.as_list()[1:])
            elif isinstance(inputs, dict):
                # We assert that the first layer is a FeatureLayer.
                if not training_utils_v1.is_feature_layer(self.layers[0]):
                    raise ValueError(
                        "Passing a dictionary input to a Sequential Model "
                        "which doesn't have FeatureLayer as the first layer"
                        " is an error."
                    )
                input_shape = (None,)
            else:
                input_shape = (None,) + tuple(inputs.shape[1:])
            self._build_input_shape = input_shape

        # Cast inputs to the compute dtype. This is primarily used
        # when saving to determine the correct dtype in the input signature.
        inputs = self._maybe_cast_inputs(inputs)

        # On-the-fly setting of symbolic model inputs (either by using the
        # tensor provided, or by creating a placeholder if Numpy data was
        # provided).
        model_inputs = training_utils_v1.ModelInputs(inputs)
        inputs = model_inputs.get_symbolic_inputs()
        self.inputs = model_inputs.get_symbolic_inputs(
            return_single_as_list=True
        )
        self.input_names = model_inputs.get_input_names()

        self._feed_inputs = []
        self._feed_input_names = []
        self._feed_input_shapes = []

        for k, v in model_inputs.as_dict():
            if backend.is_placeholder(v):
                self._feed_input_names.append(k)
                self._feed_inputs.append(v)
                self._feed_input_shapes.append(backend.int_shape(v))

        return inputs

    @tf.__internal__.tracking.no_automatic_dependency_tracking
    def _set_output_attrs(self, outputs):
        """Sets attributes related to the outputs of the Model."""
        # NOTE(taylorrobie): This convention cannot be changed without updating
        # the data adapter since it assumes nest.flatten ordering.
        outputs = tf.nest.flatten(outputs)
        self.outputs = outputs
        self.output_names = training_utils_v1.generic_output_names(outputs)
        # TODO(scottzhu): Should we cleanup the self._training_endpoints here?
        self.built = True

    @property
    def _targets(self):
        """The output target tensors for the model."""
        return [
            e.training_target.target
            for e in self._training_endpoints
            if e.has_training_target()
        ]

    @property
    def _feed_targets(self):
        return [
            e.training_target.target
            for e in self._training_endpoints
            if e.has_feedable_training_target()
        ]

    @property
    def _feed_output_names(self):
        return [
            e.output_name
            for e in self._training_endpoints
            if e.has_feedable_training_target()
        ]

    @property
    def _feed_output_shapes(self):
        return [
            e.feed_output_shape
            for e in self._training_endpoints
            if e.has_feedable_training_target()
        ]

    @property
    def _feed_loss_fns(self):
        return [
            e.loss_fn
            for e in self._training_endpoints
            if e.has_feedable_training_target()
        ]

    @property
    def _loss_weights_list(self):
        return [e.loss_weight for e in self._training_endpoints]

    @property
    def _output_loss_metrics(self):
        if hasattr(self, "_training_endpoints"):
            return [
                e.output_loss_metric
                for e in self._training_endpoints
                if e.output_loss_metric is not None
            ]
        return None

    @property
    def sample_weights(self):
        return [e.sample_weight for e in self._training_endpoints]

    @property
    def _sample_weight_modes(self):
        return [e.sample_weight_mode for e in self._training_endpoints]

    @property
    def _feed_sample_weights(self):
        return [
            e.sample_weight
            for e in self._training_endpoints
            if e.sample_weight is not None
        ]

    def _maybe_load_initial_epoch_from_ckpt(self, initial_epoch, mode):
        """Maybe load 1st epoch from checkpoint, considering worker recovery.

        Refer to tensorflow/python/keras/distribute/worker_training_state.py
        for more information.

        Args:
          initial_epoch: The original initial_epoch user passes in in `fit()`.
          mode: The mode for running `model.fit()`.

        Returns:
          If the training is recovering from previous failure under multi-worker
          training setting, return the epoch the training is supposed to
          continue at. Otherwise, return the `initial_epoch` the user passes in.
        """
        if self._training_state is not None:
            return self._training_state.maybe_load_initial_epoch_from_ckpt(
                initial_epoch, mode
            )
        return initial_epoch

    def _get_training_eval_metrics(self):
        """Returns all the metrics that are to be reported.

        This includes the output loss metrics, compile metrics/weighted metrics,
        add_metric metrics.
        """
        metrics = []
        metrics.extend(getattr(self, "_output_loss_metrics", None) or [])
        metrics.extend(getattr(self, "metrics", None) or [])
        return metrics

    def _assert_compile_was_called(self):
        # Checks whether `compile` has been called. If it has been called,
        # then the optimizer is set. This is different from whether the
        # model is compiled
        # (i.e. whether the model is built and its inputs/outputs are set).
        if not self._compile_was_called:
            raise RuntimeError(
                "You must compile your model before "
                "training/testing. "
                "Use `model.compile(optimizer, loss)`."
            )

    def _in_multi_worker_mode(self):
        """Method to infer if this `Model` is working in multi-worker settings.

        Multi-worker training refers to the setup where the training is
        distributed across multiple workers, as opposed to the case where
        only a local process performs the training. This function is
        used to infer for example whether or not a distribute coordinator
        should be run, and thus TensorFlow servers should be started for
        communication with other servers in the cluster, or whether or not
        saving/restoring checkpoints is relevant for preemption fault tolerance.

        Experimental. Signature and implementation are subject to change.

        Returns:
          Whether this model indicates it's working in multi-worker settings.
        """
        strategy = self._distribution_strategy

        # Otherwise, use the strategy whose scope this is in.
        if not strategy and tf.distribute.has_strategy():
            strategy = tf.distribute.get_strategy()
        return strategy and strategy.extended._in_multi_worker_mode()

    @property
    def _trackable_saved_model_saver(self):
        return model_serialization.ModelSavedModelSaver(self)

    def _get_compile_args(self, user_metrics=True):
        del user_metrics
        self._assert_compile_was_called()
        kwargs = {
            "loss": self.loss,
            "metrics": self._compile_metrics,
            "loss_weights": self.loss_weights,
            "sample_weight_mode": self.sample_weight_mode,
            "weighted_metrics": self._compile_weighted_metrics,
        }
        return kwargs

    @property
    def _compile_was_called(self):
        return self._v1_compile_was_called


class DistributedCallbackModel(Model):
    """Model that is used for callbacks with tf.distribute.Strategy."""

    def __init__(self, model):
        super().__init__()
        self.optimizer = model.optimizer

    def set_original_model(self, orig_model):
        self._original_model = orig_model

    def save_weights(self, filepath, overwrite=True, save_format=None):
        self._replicated_model.save_weights(
            filepath, overwrite=overwrite, save_format=save_format
        )

    def save(self, filepath, overwrite=True, include_optimizer=True):
        # save weights from the distributed model to the original model
        distributed_model_weights = self.get_weights()
        self._original_model.set_weights(distributed_model_weights)
        # TODO(anjalisridhar): Do we need to save the original model here?
        # Saving the first replicated model works as well.
        self._original_model.save(
            filepath, overwrite=True, include_optimizer=False
        )

    def load_weights(self, filepath, by_name=False):
        self._original_model.load_weights(filepath, by_name=False)
        # Copy the weights from the original model to each of the replicated
        # models.
        orig_model_weights = self._original_model.get_weights()
        distributed_training_utils_v1.set_weights(
            self._original_model._distribution_strategy,
            self,
            orig_model_weights,
        )

    def __getattr__(self, item):
        # Allowed attributes of the model that can be accessed by the user
        # during a callback.
        if item not in ("_setattr_tracking", "_layers"):
            logging.warning(
                "You are accessing attribute " + item + " of the "
                "DistributedCallbackModel that may not have been set "
                "correctly."
            )
        return super().__getattr__(item)


class _TrainingEndpoint:
    """A container for the training output/target and related entities.

    In the case of model with multiple outputs, there is a one-to-one mapping
    between model output (y_pred), model target (y_true), loss, metrics etc.
    By unifying these entities into one class, different entity can access
    information between each other, rather than currently access different list
    of attributes of the model.
    """

    def __init__(
        self,
        output,
        output_name,
        loss_fn,
        loss_weight=None,
        training_target=None,
        output_loss_metric=None,
        sample_weight=None,
        sample_weight_mode=None,
    ):
        """Initialize the _TrainingEndpoint.

        Note that the output and output_name should be stable as long as the
        model structure doesn't change. The training_target suppose to be
        mutable since the information is provided via `compile()`

        Args:
          output: the output tensor of the model.
          output_name: the unique name of the output tensor.
          loss_fn: the loss function for the output tensor.
          loss_weight: float, the weights for the loss.
          training_target: the _TrainingTarget for the model.
          output_loss_metric: the metric object for the loss function.
          sample_weight: the weights for how a sample is weighted during metric
            and loss calculation. Could be None.
          sample_weight_mode: string, 'temporal', 'samplewise' or None. The mode
            for how the sample_weight is populated.
        """
        self._output = output
        self._output_name = output_name
        self._loss_fn = loss_fn
        self._loss_weight = loss_weight
        self._training_target = training_target
        self._output_loss_metric = output_loss_metric
        self._sample_weight = sample_weight
        self._sample_weight_mode = sample_weight_mode

    @property
    def output(self):
        return self._output

    @property
    def output_name(self):
        return self._output_name

    @property
    def shape(self):
        return backend.int_shape(self.output)

    @property
    def loss_fn(self):
        return self._loss_fn

    @property
    def loss_weight(self):
        return self._loss_weight

    @loss_weight.setter
    def loss_weight(self, value):
        self._loss_weight = value

    @property
    def training_target(self):
        return self._training_target

    @training_target.setter
    def training_target(self, value):
        self._training_target = value

    def create_training_target(self, target, run_eagerly=False):
        """Create training_target instance and update the self.training_target.

        Note that the input target should just be a tensor or None, and
        corresponding training target will be created based on the output and
        loss_fn.

        Args:
          target: the target tensor for the current output. Could be None.
          run_eagerly: boolean, whether the model is in run_eagerly mode.

        Raises:
          ValueError if the training_target field for the current instance has
          already been populated.
        """
        if self.has_training_target():
            raise ValueError(
                "The training_target field for the _TrainingEndpoint "
                "instance has already been populated"
            )
        if run_eagerly:
            # When run_eagerly, the target tensor is ignored, and the None
            # placeholder is created instead.
            self.training_target = _TrainingTarget(
                None, feedable=True, skip_target_weights=False
            )
            return

        if self.should_skip_target():
            self.training_target = _TrainingTarget(None)
        else:
            if target is not None and not backend.is_placeholder(target):
                feedable = False
                skip_target_weights = True
            else:
                feedable = True
                skip_target_weights = False

            if target is None:
                target_dtype = losses.LABEL_DTYPES_FOR_LOSSES.get(
                    self.loss_fn, backend.dtype(self.output)
                )

                target = backend.placeholder(
                    ndim=len(self.shape),
                    name=self.output_name + "_target",
                    sparse=backend.is_sparse(self.output),
                    dtype=target_dtype,
                )

            self.training_target = _TrainingTarget(
                target,
                feedable=feedable,
                skip_target_weights=skip_target_weights,
            )

    @property
    def output_loss_metric(self):
        return self._output_loss_metric

    @output_loss_metric.setter
    def output_loss_metric(self, value):
        self._output_loss_metric = value

    @property
    def sample_weight(self):
        return self._sample_weight

    @sample_weight.setter
    def sample_weight(self, value):
        self._sample_weight = value

    @property
    def sample_weight_mode(self):
        return self._sample_weight_mode

    @sample_weight_mode.setter
    def sample_weight_mode(self, value):
        self._sample_weight_mode = value

    def should_skip_target(self):
        return self._loss_fn is None

    def should_skip_target_weights(self):
        return (
            self.should_skip_target()
            or self.training_target is None
            or self.training_target.skip_target_weights
        )

    def has_training_target(self):
        return self.training_target is not None

    def has_feedable_training_target(self):
        return (
            not self.should_skip_target()
            and self.training_target is not None
            and self.training_target.feedable
        )

    def loss_name(self):
        if self._loss_fn is not None:
            return self._output_name + "_loss"
        return None

    @property
    def feed_output_shape(self):
        """The output shape for the feedable target."""
        if not self.has_feedable_training_target():
            return None

        if (
            (
                isinstance(self.loss_fn, losses.LossFunctionWrapper)
                and self.loss_fn.fn == losses.sparse_categorical_crossentropy
            )
        ) or (isinstance(self.loss_fn, losses.SparseCategoricalCrossentropy)):
            if backend.image_data_format() == "channels_first":
                return (self.shape[0], 1) + self.shape[2:]
            else:
                return self.shape[:-1] + (1,)
        elif not isinstance(self.loss_fn, losses.Loss) or (
            isinstance(self.loss_fn, losses.LossFunctionWrapper)
            and (getattr(losses, self.loss_fn.fn.__name__, None) is None)
        ):
            # If the given loss is not an instance of the `Loss` class (custom
            # class) or if the loss function that is wrapped is not in the
            # `losses` module, then it is a user-defined loss and we make no
            # assumptions about it.
            return None
        else:
            return self.shape

    def sample_weights_mismatch(self):
        """Check if the sample weight and the mode match or not."""
        # If there is a mismatch between sample weight mode and the placeholders
        # created, then recompile the sub-graphs that depend on sample weights.
        return (
            self.sample_weight_mode is not None and self.sample_weight is None
        ) or (
            self.sample_weight_mode is None and self.sample_weight is not None
        )

    def populate_sample_weight(self, sample_weight, sample_weight_mode):
        """Populate the sample weight and based on the sample weight mode."""
        if sample_weight is None and (
            self.should_skip_target_weights()
            or sample_weight_mode is None
            or tf.executing_eagerly()
        ):
            self._sample_weight = None
            return

        assert sample_weight_mode in ["temporal", "samplewise"]
        if sample_weight_mode == "temporal":
            default_value = [[1.0]]
            shape = [None, None]
        else:
            # sample_weight_mode == 'samplewise'
            default_value = [1.0]
            shape = [None]

        if sample_weight is not None:
            if not sample_weight.shape.is_compatible_with(shape):
                raise ValueError(
                    "Received sample weight with shape {}. Expected shape "
                    "{}.".format(sample_weight.shape, shape)
                )
            self._sample_weight = sample_weight
        else:
            self._sample_weight = tf.compat.v1.placeholder_with_default(
                tf.constant(default_value, dtype=backend.floatx()),
                shape=shape,
                name=self.output_name + "_sample_weights",
            )


class _TrainingTarget:
    """Container for a target tensor (y_true) and its metadata (shape, loss...).

    Args:
      target: A target tensor for the model. It may be `None` if the
        output is excluded from loss computation. It is still kept as None
        since each output of the model should have a corresponding target. If
        the target is None, the rest of the attributes will be None as well.
      feedable: Boolean, whether the target is feedable (requires data to be
        passed in `fit` or `train_on_batch`), or not (model compiled with
        `target_tensors` argument).
      skip_target_weights: Boolean, whether the target should be skipped during
        weights calculation.
    """

    def __init__(self, target, feedable=False, skip_target_weights=True):
        self._target = target
        self._feedable = feedable
        self._skip_target_weights = skip_target_weights

    @property
    def target(self):
        return self._target

    @property
    def feedable(self):
        return self._feedable

    @property
    def skip_target_weights(self):
        return self._skip_target_weights


def _is_symbolic_tensor(x):
    return tf.is_tensor(x)


def _convert_scipy_sparse_tensor(value, expected_input):
    """Handle scipy sparse tensor conversions.

    This method takes a value 'value' and returns the proper conversion. If
    value is a scipy sparse tensor and the expected input is a dense tensor,
    we densify 'value'. If value is a scipy sparse tensor and the expected input
    is a TF SparseTensor, we convert 'value' to a SparseTensor. If 'value' is
    not a scipy sparse tensor, or scipy is not imported, we pass it through
    unchanged.

    Args:
      value: An object that may be a scipy sparse tensor
      expected_input: The expected input placeholder.

    Returns:
      The possibly-converted 'value'.
    """
    if issparse is not None and issparse(value):
        if backend.is_sparse(expected_input):
            sparse_coo = value.tocoo()
            row, col = sparse_coo.row, sparse_coo.col
            data, shape = sparse_coo.data, sparse_coo.shape
            indices = np.concatenate(
                (np.expand_dims(row, 1), np.expand_dims(col, 1)), 1
            )
            return tf.SparseTensor(indices, data, shape)
        else:
            if tf.compat.v1.executing_eagerly_outside_functions():
                # In TF2 we do not silently densify sparse matrices.
                raise ValueError(
                    "A SciPy sparse matrix was passed to a model "
                    "that expects dense inputs. Please densify your "
                    "inputs first, such as by calling `x.toarray()."
                )
            return value.toarray()
    else:
        return value


def _get_metrics_from_layers(layers):
    """Returns list of metrics from the given layers.

    This will not include the `compile` metrics of a model layer.

    Args:
      layers: List of layers.

    Returns:
      List of metrics.
    """
    metrics = []
    layers = layer_utils.filter_empty_layer_containers(layers)
    for layer in layers:
        if isinstance(layer, Model):
            # We cannot call 'metrics' on the model because we do not want to
            # include the metrics that were added in compile API of a nested
            # model.
            metrics.extend(layer._metrics)
            metrics.extend(_get_metrics_from_layers(layer.layers))
        else:
            metrics.extend(layer.metrics)
    return metrics


def _non_none_constant_value(v):
    constant_value = tf.get_static_value(v)
    return constant_value if constant_value is not None else v

