# 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.
# ==============================================================================


"""Callbacks: utilities called at certain points during model training."""

import collections
import copy
import csv
import json
import os
import re
import sys
import time

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

from keras import backend
from keras.distribute import distributed_file_utils
from keras.distribute import worker_training_state
from keras.optimizers import optimizer
from keras.optimizers.schedules import learning_rate_schedule
from keras.utils import generic_utils
from keras.utils import io_utils
from keras.utils import tf_utils
from keras.utils import version_utils
from keras.utils.data_utils import Sequence
from keras.utils.generic_utils import Progbar
from keras.utils.mode_keys import ModeKeys

# isort: off
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.util import deprecation
from tensorflow.python.util.tf_export import keras_export
from tensorflow.tools.docs import doc_controls

try:
    import requests
except ImportError:
    requests = None


# Note: `configure_callbacks` is only used in TF1.
def configure_callbacks(
    callbacks,
    model,
    do_validation=False,
    batch_size=None,
    epochs=None,
    steps_per_epoch=None,
    samples=None,
    verbose=1,
    count_mode="steps",
    mode=ModeKeys.TRAIN,
):
    """Configures callbacks for use in various training loops.

    Args:
        callbacks: List of Callbacks.
        model: Model being trained.
        do_validation: Whether or not validation loop will be run.
        batch_size: Number of samples per batch.
        epochs: Number of epoch to train.
        steps_per_epoch: Number of batches to run per training epoch.
        samples: Number of training samples.
        verbose: int, 0 or 1. Keras logging verbosity to pass to ProgbarLogger.
        count_mode: One of 'steps' or 'samples'. Per-batch or per-sample count.
        mode: String. One of ModeKeys.TRAIN, ModeKeys.TEST, or ModeKeys.PREDICT.
          Which loop mode to configure callbacks for.

    Returns:
        Instance of CallbackList used to control all Callbacks.
    """
    # Check if callbacks have already been configured.
    if isinstance(callbacks, CallbackList):
        return callbacks

    if not callbacks:
        callbacks = []

    # Add additional callbacks during training.
    if mode == ModeKeys.TRAIN:
        model.history = History()
        callbacks = [BaseLogger()] + (callbacks or []) + [model.history]
        if verbose:
            callbacks.append(ProgbarLogger(count_mode))
    callback_list = CallbackList(callbacks)

    # Set callback model
    callback_model = model._get_callback_model()
    callback_list.set_model(callback_model)

    set_callback_parameters(
        callback_list,
        model,
        do_validation=do_validation,
        batch_size=batch_size,
        epochs=epochs,
        steps_per_epoch=steps_per_epoch,
        samples=samples,
        verbose=verbose,
        mode=mode,
    )

    callback_list.model.stop_training = False
    return callback_list


def set_callback_parameters(
    callback_list,
    model,
    do_validation=False,
    batch_size=None,
    epochs=None,
    steps_per_epoch=None,
    samples=None,
    verbose=1,
    mode=ModeKeys.TRAIN,
):
    """Sets callback parameters.

    Args:
        callback_list: CallbackList instance.
        model: Model being trained.
        do_validation: Whether or not validation loop will be run.
        batch_size: Number of samples per batch.
        epochs: Number of epoch to train.
        steps_per_epoch: Number of batches to run per training epoch.
        samples: Number of training samples.
        verbose: int, 0 or 1. Keras logging verbosity to pass to ProgbarLogger.
        mode: String. One of ModeKeys.TRAIN, ModeKeys.TEST, or ModeKeys.PREDICT.
          Which loop mode to configure callbacks for.
    """
    metric_names = None
    for cbk in callback_list:
        if isinstance(cbk, (BaseLogger, ProgbarLogger)):
            if not metric_names:
                metric_names = model.metrics_names
            cbk.stateful_metrics = metric_names[1:]  # Exclude `loss`

    # Set callback parameters
    callback_metrics = []
    # When we have deferred build scenario with iterator input, we will compile
    # when we standardize first batch of data.
    if mode != ModeKeys.PREDICT:
        if not metric_names:
            metric_names = model.metrics_names
        callback_metrics = copy.copy(metric_names)
        if do_validation:
            callback_metrics += ["val_" + n for n in metric_names]
    callback_params = {
        "batch_size": batch_size,
        "epochs": epochs,
        "steps": steps_per_epoch,
        "samples": samples,
        "verbose": verbose,
        "do_validation": do_validation,
        "metrics": callback_metrics,
    }
    callback_list.set_params(callback_params)


def _is_generator_like(data):
    """Checks if data is a generator, Sequence, or Iterator."""
    return (
        hasattr(data, "__next__")
        or hasattr(data, "next")
        or isinstance(
            data, (Sequence, tf.compat.v1.data.Iterator, tf.data.Iterator)
        )
    )


def make_logs(model, logs, outputs, mode, prefix=""):
    """Computes logs for sending to `on_batch_end` methods."""
    metric_names = model.metrics_names
    if mode in {ModeKeys.TRAIN, ModeKeys.TEST} and metric_names:
        for label, output in zip(metric_names, outputs):
            logs[prefix + label] = output
    else:
        logs["outputs"] = outputs
    return logs


@keras_export("keras.callbacks.CallbackList")
class CallbackList:
    """Container abstracting a list of callbacks."""

    def __init__(
        self,
        callbacks=None,
        add_history=False,
        add_progbar=False,
        model=None,
        **params,
    ):
        """Container for `Callback` instances.

        This object wraps a list of `Callback` instances, making it possible
        to call them all at once via a single endpoint
        (e.g. `callback_list.on_epoch_end(...)`).

        Args:
          callbacks: List of `Callback` instances.
          add_history: Whether a `History` callback should be added, if one does
            not already exist in the `callbacks` list.
          add_progbar: Whether a `ProgbarLogger` callback should be added, if
            one does not already exist in the `callbacks` list.
          model: The `Model` these callbacks are used with.
          **params: If provided, parameters will be passed to each `Callback`
            via `Callback.set_params`.
        """
        self.callbacks = tf.nest.flatten(callbacks) if callbacks else []
        self._add_default_callbacks(add_history, add_progbar)

        if model:
            self.set_model(model)
        if params:
            self.set_params(params)

        # Performance optimization: determines if batch hooks need to be called.

        self._supports_tf_logs = all(
            getattr(cb, "_supports_tf_logs", False) for cb in self.callbacks
        )
        self._batch_hooks_support_tf_logs = all(
            getattr(cb, "_supports_tf_logs", False)
            for cb in self.callbacks
            if cb._implements_train_batch_hooks()
            or cb._implements_test_batch_hooks()
            or cb._implements_predict_batch_hooks()
        )

        self._should_call_train_batch_hooks = any(
            cb._implements_train_batch_hooks() for cb in self.callbacks
        )
        self._should_call_test_batch_hooks = any(
            cb._implements_test_batch_hooks() for cb in self.callbacks
        )
        self._should_call_predict_batch_hooks = any(
            cb._implements_predict_batch_hooks() for cb in self.callbacks
        )

        self._disallow_batch_hooks_in_ps_strategy()

        # Performance check: Check batch hooks for slowness compared to batch
        # time.  Only run check for custom callbacks (i.e. not present in this
        # file).
        self._check_timing = any(
            cbk.__class__.__name__ not in globals() for cbk in self.callbacks
        )
        self._num_batches_for_timing_check = 5
        self._hook_times = {}
        self._batch_start_time = None
        self._batch_times = []

    def _add_default_callbacks(self, add_history, add_progbar):
        """Adds `Callback`s that are always present."""
        self._progbar = None
        self._history = None

        for cb in self.callbacks:
            if isinstance(cb, ProgbarLogger):
                self._progbar = cb
            elif isinstance(cb, History):
                self._history = cb

        if self._history is None and add_history:
            self._history = History()
            self.callbacks.append(self._history)

        if self._progbar is None and add_progbar:
            self._progbar = ProgbarLogger(count_mode="steps")
            self.callbacks.append(self._progbar)

    def _process_logs(self, logs, is_batch_hook=False):
        """Turns tensors into numpy arrays or Python scalars if necessary."""
        if logs is None:
            return {}
        if self._supports_tf_logs:
            return logs
        if is_batch_hook and self._batch_hooks_support_tf_logs:
            return logs
        return tf_utils.sync_to_numpy_or_python_type(logs)

    def append(self, callback):
        self.callbacks.append(callback)

    def set_params(self, params):
        self.params = params
        for callback in self.callbacks:
            callback.set_params(params)

    def set_model(self, model):
        self.model = model
        if self._history:
            model.history = self._history
        for callback in self.callbacks:
            callback.set_model(model)

    def _call_batch_hook(self, mode, hook, batch, logs=None):
        """Helper function for all batch_{begin | end} methods."""
        if not self.callbacks:
            return

        if hook == "begin":
            self._call_batch_begin_hook(mode, batch, logs)
        elif hook == "end":
            self._call_batch_end_hook(mode, batch, logs)
        else:
            raise ValueError(
                f"Unrecognized hook: {hook}. "
                'Expected values are ["begin", "end"]'
            )

    def _call_batch_begin_hook(self, mode, batch, logs):
        """Helper function for `on_*_batch_begin` methods."""
        hook_name = f"on_{mode}_batch_begin"
        self._call_batch_hook_helper(hook_name, batch, logs)

        if self._check_timing:
            self._batch_start_time = time.time()

    def _call_batch_end_hook(self, mode, batch, logs):
        """Helper function for `on_*_batch_end` methods."""
        hook_name = f"on_{mode}_batch_end"

        if self._check_timing and batch >= 1:
            batch_time = time.time() - self._batch_start_time
            self._batch_times.append(batch_time)

        self._call_batch_hook_helper(hook_name, batch, logs)

        if len(self._batch_times) >= self._num_batches_for_timing_check:
            end_hook_name = hook_name
            begin_hook_name = f"on_{mode}_batch_begin"
            avg_batch_time = sum(self._batch_times) / len(self._batch_times)
            avg_end_hook_time = sum(self._hook_times[end_hook_name]) / len(
                self._hook_times[end_hook_name]
            )
            avg_begin_hook_time = sum(self._hook_times[begin_hook_name]) / len(
                self._hook_times[begin_hook_name]
            )

            threshold_time = 1.0 * avg_batch_time
            warning_msg = (
                "Callback method `{hook}` is slow compared to "
                "the batch time (batch time: {batch_time:.4f}s vs "
                "`{hook}` time: {hook_time:.4f}s). Check your callbacks."
            )
            if avg_begin_hook_time > threshold_time:
                logging.warning(
                    warning_msg.format(
                        hook=begin_hook_name,
                        batch_time=avg_batch_time,
                        hook_time=avg_begin_hook_time,
                    )
                )
            if avg_end_hook_time > threshold_time:
                logging.warning(
                    warning_msg.format(
                        hook=end_hook_name,
                        batch_time=avg_batch_time,
                        hook_time=avg_end_hook_time,
                    )
                )
            self._check_timing = False
            self._batch_start_time = None
            self._batch_times = []
            self._hook_times = {}

    def _call_batch_hook_helper(self, hook_name, batch, logs):
        """Helper function for `on_*_batch_*` methods."""
        if self._check_timing:
            start_time = time.time()

        logs = self._process_logs(logs, is_batch_hook=True)
        for callback in self.callbacks:
            hook = getattr(callback, hook_name)
            hook(batch, logs)

        if self._check_timing:
            if hook_name not in self._hook_times:
                self._hook_times[hook_name] = []
            self._hook_times[hook_name].append(time.time() - start_time)

    def _call_begin_hook(self, mode):
        """Helper function for on_{train|test|predict}_begin methods."""
        if mode == ModeKeys.TRAIN:
            self.on_train_begin()
        elif mode == ModeKeys.TEST:
            self.on_test_begin()
        else:
            self.on_predict_begin()

    def _call_end_hook(self, mode):
        """Helper function for on_{train|test|predict}_end methods."""
        if mode == ModeKeys.TRAIN:
            self.on_train_end()
        elif mode == ModeKeys.TEST:
            self.on_test_end()
        else:
            self.on_predict_end()

    def on_batch_begin(self, batch, logs=None):
        if self._should_call_train_batch_hooks:
            self._call_batch_hook(ModeKeys.TRAIN, "begin", batch, logs=logs)

    def on_batch_end(self, batch, logs=None):
        if self._should_call_train_batch_hooks:
            self._call_batch_hook(ModeKeys.TRAIN, "end", batch, logs=logs)

    def on_epoch_begin(self, epoch, logs=None):
        """Calls the `on_epoch_begin` methods of its callbacks.

        This function should only be called during TRAIN mode.

        Args:
            epoch: Integer, index of epoch.
            logs: Dict. Currently no data is passed to this argument for this
               method but that may change in the future.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_epoch_begin(epoch, logs)

    def on_epoch_end(self, epoch, logs=None):
        """Calls the `on_epoch_end` methods of its callbacks.

        This function should only be called during TRAIN mode.

        Args:
            epoch: Integer, index of epoch.
            logs: Dict, metric results for this training epoch, and for the
              validation epoch if validation is performed. Validation result
              keys are prefixed with `val_`.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_epoch_end(epoch, logs)

    def on_train_batch_begin(self, batch, logs=None):
        """Calls the `on_train_batch_begin` methods of its callbacks.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict, contains the return value of `model.train_step`.
              Typically, the values of the `Model`'s metrics are returned.
              Example: `{'loss': 0.2, 'accuracy': 0.7}`.
        """
        if self._should_call_train_batch_hooks:
            self._call_batch_hook(ModeKeys.TRAIN, "begin", batch, logs=logs)

    def on_train_batch_end(self, batch, logs=None):
        """Calls the `on_train_batch_end` methods of its callbacks.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Aggregated metric results up until this batch.
        """
        if self._should_call_train_batch_hooks:
            self._call_batch_hook(ModeKeys.TRAIN, "end", batch, logs=logs)

    def on_test_batch_begin(self, batch, logs=None):
        """Calls the `on_test_batch_begin` methods of its callbacks.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict, contains the return value of `model.test_step`.
              Typically, the values of the `Model`'s metrics are returned.
              Example: `{'loss': 0.2, 'accuracy': 0.7}`.
        """
        if self._should_call_test_batch_hooks:
            self._call_batch_hook(ModeKeys.TEST, "begin", batch, logs=logs)

    def on_test_batch_end(self, batch, logs=None):
        """Calls the `on_test_batch_end` methods of its callbacks.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Aggregated metric results up until this batch.
        """
        if self._should_call_test_batch_hooks:
            self._call_batch_hook(ModeKeys.TEST, "end", batch, logs=logs)

    def on_predict_batch_begin(self, batch, logs=None):
        """Calls the `on_predict_batch_begin` methods of its callbacks.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict, contains the return value of `model.predict_step`,
              it typically returns a dict with a key 'outputs' containing
              the model's outputs.
        """
        if self._should_call_predict_batch_hooks:
            self._call_batch_hook(ModeKeys.PREDICT, "begin", batch, logs=logs)

    def on_predict_batch_end(self, batch, logs=None):
        """Calls the `on_predict_batch_end` methods of its callbacks.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Aggregated metric results up until this batch.
        """
        if self._should_call_predict_batch_hooks:
            self._call_batch_hook(ModeKeys.PREDICT, "end", batch, logs=logs)

    def on_train_begin(self, logs=None):
        """Calls the `on_train_begin` methods of its callbacks.

        Args:
            logs: Dict. Currently, no data is passed via this argument
              for this method, but that may change in the future.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_train_begin(logs)

    def on_train_end(self, logs=None):
        """Calls the `on_train_end` methods of its callbacks.

        Args:
            logs: Dict. Currently, no data is passed via this argument
              for this method, but that may change in the future.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_train_end(logs)

    def on_test_begin(self, logs=None):
        """Calls the `on_test_begin` methods of its callbacks.

        Args:
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_test_begin(logs)

    def on_test_end(self, logs=None):
        """Calls the `on_test_end` methods of its callbacks.

        Args:
            logs: Dict. Currently, no data is passed via this argument
              for this method, but that may change in the future.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_test_end(logs)

    def on_predict_begin(self, logs=None):
        """Calls the 'on_predict_begin` methods of its callbacks.

        Args:
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_predict_begin(logs)

    def on_predict_end(self, logs=None):
        """Calls the `on_predict_end` methods of its callbacks.

        Args:
            logs: Dict. Currently, no data is passed via this argument
              for this method, but that may change in the future.
        """
        logs = self._process_logs(logs)
        for callback in self.callbacks:
            callback.on_predict_end(logs)

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

    def _disallow_batch_hooks_in_ps_strategy(self):
        """Error out if batch-level callbacks are passed with PSStrategy."""

        strategy = tf.distribute.get_strategy()
        if strategy._should_use_with_coordinator:
            unsupported_callbacks = []
            for cb in self.callbacks:
                # These Callbacks can accept RemoteValues directly.
                if getattr(cb, "_supports_tf_logs", False):
                    continue
                if (
                    cb._implements_train_batch_hooks()
                    or cb._implements_test_batch_hooks()
                    or cb._implements_predict_batch_hooks()
                ):
                    unsupported_callbacks.append(cb)
            if unsupported_callbacks:
                raise ValueError(
                    "Batch-level `Callback`s are not supported with "
                    "`ParameterServerStrategy`. Found unsupported "
                    f"callbacks: {unsupported_callbacks}"
                )

    def make_logs(self, model, logs, outputs, mode, prefix=""):
        """Computes logs for sending to `on_batch_end` methods."""
        if not self.callbacks:
            return logs

        return make_logs(model, logs, outputs, mode, prefix=prefix)


@keras_export("keras.callbacks.Callback")
class Callback:
    """Abstract base class used to build new callbacks.

    Callbacks can be passed to keras methods such as `fit`, `evaluate`, and
    `predict` in order to hook into the various stages of the model training and
    inference lifecycle.

    To create a custom callback, subclass `keras.callbacks.Callback` and
    override the method associated with the stage of interest. See
    https://www.tensorflow.org/guide/keras/custom_callback for more information.

    Example:

    >>> training_finished = False
    >>> class MyCallback(tf.keras.callbacks.Callback):
    ...   def on_train_end(self, logs=None):
    ...     global training_finished
    ...     training_finished = True
    >>> model = tf.keras.Sequential([
    ...     tf.keras.layers.Dense(1, input_shape=(1,))])
    >>> model.compile(loss='mean_squared_error')
    >>> model.fit(tf.constant([[1.0]]), tf.constant([[1.0]]),
    ...           callbacks=[MyCallback()])
    >>> assert training_finished == True

    If you want to use `Callback` objects in a custom training loop:

    1. You should pack all your callbacks into a single `callbacks.CallbackList`
       so they can all be called together.
    2. You will need to manually call all the `on_*` methods at the appropriate
       locations in your loop. Like this:

    Example:
    ```python
       callbacks =  tf.keras.callbacks.CallbackList([...])
       callbacks.append(...)
       callbacks.on_train_begin(...)
       for epoch in range(EPOCHS):
         callbacks.on_epoch_begin(epoch)
         for i, data in dataset.enumerate():
           callbacks.on_train_batch_begin(i)
           batch_logs = model.train_step(data)
           callbacks.on_train_batch_end(i, batch_logs)
         epoch_logs = ...
         callbacks.on_epoch_end(epoch, epoch_logs)
       final_logs=...
       callbacks.on_train_end(final_logs)
    ```

    Attributes:
        params: Dict. Training parameters
            (eg. verbosity, batch size, number of epochs...).
        model: Instance of `keras.models.Model`.
            Reference of the model being trained.

    The `logs` dictionary that callback methods
    take as argument will contain keys for quantities relevant to
    the current batch or epoch (see method-specific docstrings).
    """

    def __init__(self):
        self.validation_data = None
        self.model = None
        # Whether this Callback should only run on the chief worker in a
        # Multi-Worker setting.
        # TODO(omalleyt): Make this attr public once solution is stable.
        self._chief_worker_only = None
        self._supports_tf_logs = False

    def set_params(self, params):
        self.params = params

    def set_model(self, model):
        self.model = model

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_batch_begin(self, batch, logs=None):
        """A backwards compatibility alias for `on_train_batch_begin`."""

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_batch_end(self, batch, logs=None):
        """A backwards compatibility alias for `on_train_batch_end`."""

    @doc_controls.for_subclass_implementers
    def on_epoch_begin(self, epoch, logs=None):
        """Called at the start of an epoch.

        Subclasses should override for any actions to run. This function should
        only be called during TRAIN mode.

        Args:
            epoch: Integer, index of epoch.
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    def on_epoch_end(self, epoch, logs=None):
        """Called at the end of an epoch.

        Subclasses should override for any actions to run. This function should
        only be called during TRAIN mode.

        Args:
            epoch: Integer, index of epoch.
            logs: Dict, metric results for this training epoch, and for the
              validation epoch if validation is performed. Validation result
              keys are prefixed with `val_`. For training epoch, the values of
              the `Model`'s metrics are returned. Example:
              `{'loss': 0.2, 'accuracy': 0.7}`.
        """

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_train_batch_begin(self, batch, logs=None):
        """Called at the beginning of a training batch in `fit` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `tf.keras.Model` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """
        # For backwards compatibility.
        self.on_batch_begin(batch, logs=logs)

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_train_batch_end(self, batch, logs=None):
        """Called at the end of a training batch in `fit` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `tf.keras.Model` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Aggregated metric results up until this batch.
        """
        # For backwards compatibility.
        self.on_batch_end(batch, logs=logs)

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_test_batch_begin(self, batch, logs=None):
        """Called at the beginning of a batch in `evaluate` methods.

        Also called at the beginning of a validation batch in the `fit`
        methods, if validation data is provided.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `tf.keras.Model` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_test_batch_end(self, batch, logs=None):
        """Called at the end of a batch in `evaluate` methods.

        Also called at the end of a validation batch in the `fit`
        methods, if validation data is provided.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `tf.keras.Model` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Aggregated metric results up until this batch.
        """

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_predict_batch_begin(self, batch, logs=None):
        """Called at the beginning of a batch in `predict` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `tf.keras.Model` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    @generic_utils.default
    def on_predict_batch_end(self, batch, logs=None):
        """Called at the end of a batch in `predict` methods.

        Subclasses should override for any actions to run.

        Note that if the `steps_per_execution` argument to `compile` in
        `tf.keras.Model` is set to `N`, this method will only be called every
        `N` batches.

        Args:
            batch: Integer, index of batch within the current epoch.
            logs: Dict. Aggregated metric results up until this batch.
        """

    @doc_controls.for_subclass_implementers
    def on_train_begin(self, logs=None):
        """Called at the beginning of training.

        Subclasses should override for any actions to run.

        Args:
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    def on_train_end(self, logs=None):
        """Called at the end of training.

        Subclasses should override for any actions to run.

        Args:
            logs: Dict. Currently the output of the last call to
              `on_epoch_end()` is passed to this argument for this method but
              that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    def on_test_begin(self, logs=None):
        """Called at the beginning of evaluation or validation.

        Subclasses should override for any actions to run.

        Args:
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    def on_test_end(self, logs=None):
        """Called at the end of evaluation or validation.

        Subclasses should override for any actions to run.

        Args:
            logs: Dict. Currently the output of the last call to
              `on_test_batch_end()` is passed to this argument for this method
              but that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    def on_predict_begin(self, logs=None):
        """Called at the beginning of prediction.

        Subclasses should override for any actions to run.

        Args:
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """

    @doc_controls.for_subclass_implementers
    def on_predict_end(self, logs=None):
        """Called at the end of prediction.

        Subclasses should override for any actions to run.

        Args:
            logs: Dict. Currently no data is passed to this argument for this
              method but that may change in the future.
        """

    def _implements_train_batch_hooks(self):
        """Determines if this Callback should be called for each train batch."""
        return (
            not generic_utils.is_default(self.on_batch_begin)
            or not generic_utils.is_default(self.on_batch_end)
            or not generic_utils.is_default(self.on_train_batch_begin)
            or not generic_utils.is_default(self.on_train_batch_end)
        )

    def _implements_test_batch_hooks(self):
        """Determines if this Callback should be called for each test batch."""
        return not generic_utils.is_default(
            self.on_test_batch_begin
        ) or not generic_utils.is_default(self.on_test_batch_end)

    def _implements_predict_batch_hooks(self):
        """Determines if this Callback should be called for each predict
        batch."""
        return not generic_utils.is_default(
            self.on_predict_batch_begin
        ) or not generic_utils.is_default(self.on_predict_batch_end)


@keras_export("keras.callbacks.BaseLogger")
class BaseLogger(Callback):
    """Callback that accumulates epoch averages of metrics.

    This callback is automatically applied to every Keras model.

    Args:
        stateful_metrics: Iterable of string names of metrics that
            should *not* be averaged over an epoch.
            Metrics in this list will be logged as-is in `on_epoch_end`.
            All others will be averaged in `on_epoch_end`.
    """

    def __init__(self, stateful_metrics=None):
        super().__init__()
        self.stateful_metrics = set(stateful_metrics or [])

    def on_epoch_begin(self, epoch, logs=None):
        self.seen = 0
        self.totals = {}

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}
        batch_size = logs.get("size", 0)
        # In case of distribution strategy we can potentially run multiple steps
        # at the same time, we should account for that in the `seen`
        # calculation.
        num_steps = logs.get("num_steps", 1)
        self.seen += batch_size * num_steps

        for k, v in logs.items():
            if k in self.stateful_metrics:
                self.totals[k] = v
            else:
                if k in self.totals:
                    self.totals[k] += v * batch_size
                else:
                    self.totals[k] = v * batch_size

    def on_epoch_end(self, epoch, logs=None):
        if logs is not None:
            for k in self.params["metrics"]:
                if k in self.totals:
                    # Make value available to next callbacks.
                    if k in self.stateful_metrics:
                        logs[k] = self.totals[k]
                    else:
                        logs[k] = self.totals[k] / self.seen


@keras_export("keras.callbacks.TerminateOnNaN")
class TerminateOnNaN(Callback):
    """Callback that terminates training when a NaN loss is encountered."""

    def __init__(self):
        super().__init__()
        self._supports_tf_logs = True

    def on_batch_end(self, batch, logs=None):
        logs = logs or {}
        loss = logs.get("loss")
        if loss is not None:
            loss = tf_utils.sync_to_numpy_or_python_type(loss)
            if np.isnan(loss) or np.isinf(loss):
                io_utils.print_msg(
                    f"Batch {batch}: Invalid loss, terminating training"
                )
                self.model.stop_training = True


@keras_export("keras.callbacks.ProgbarLogger")
class ProgbarLogger(Callback):
    """Callback that prints metrics to stdout.

    Args:
        count_mode: One of `"steps"` or `"samples"`.
            Whether the progress bar should
            count samples seen or steps (batches) seen.
        stateful_metrics: Iterable of string names of metrics that
            should *not* be averaged over an epoch.
            Metrics in this list will be logged as-is.
            All others will be averaged over time (e.g. loss, etc).
            If not provided, defaults to the `Model`'s metrics.

    Raises:
        ValueError: In case of invalid `count_mode`.
    """

    def __init__(self, count_mode: str = "samples", stateful_metrics=None):
        super().__init__()
        self._supports_tf_logs = True
        if count_mode == "samples":
            self.use_steps = False
        elif count_mode == "steps":
            self.use_steps = True
        else:
            raise ValueError(
                f"Unknown `count_mode`: {count_mode}. "
                'Expected values are ["samples", "steps"]'
            )
        # Defaults to all Model's metrics except for loss.
        self.stateful_metrics = (
            set(stateful_metrics) if stateful_metrics else set()
        )

        self.seen = 0
        self.progbar = None
        self.target = None
        self.verbose = 1
        self.epochs = 1

        self._train_step, self._test_step, self._predict_step = None, None, None
        self._call_batch_hooks = True

        self._called_in_fit = False

    def set_params(self, params):
        self.verbose = params["verbose"]
        self.epochs = params["epochs"]
        if self.use_steps and "steps" in params:
            self.target = params["steps"]
        elif not self.use_steps and "samples" in params:
            self.target = params["samples"]
        else:
            self.target = (
                None  # Will be inferred at the end of the first epoch.
            )

        self._call_batch_hooks = self.verbose == 1
        if self.target is None:
            try:
                self._train_step = self.model._train_counter
                self._test_step = self.model._test_counter
                self._predict_step = self.model._predict_counter
            except AttributeError:
                self._call_batch_hooks = True

    def on_train_begin(self, logs=None):
        # When this logger is called inside `fit`, validation is silent.
        self._called_in_fit = True

    def on_test_begin(self, logs=None):
        if not self._called_in_fit:
            self._reset_progbar()
            self._maybe_init_progbar()

    def on_predict_begin(self, logs=None):
        self._reset_progbar()
        self._maybe_init_progbar()

    def on_epoch_begin(self, epoch, logs=None):
        self._reset_progbar()
        self._maybe_init_progbar()
        if self.verbose and self.epochs > 1:
            io_utils.print_msg(f"Epoch {epoch + 1}/{self.epochs}")

    def on_train_batch_end(self, batch, logs=None):
        self._batch_update_progbar(batch, logs)

    def on_test_batch_end(self, batch, logs=None):
        if not self._called_in_fit:
            self._batch_update_progbar(batch, logs)

    def on_predict_batch_end(self, batch, logs=None):
        # Don't pass prediction results.
        self._batch_update_progbar(batch, None)

    def on_epoch_end(self, epoch, logs=None):
        self._finalize_progbar(logs, self._train_step)

    def on_test_end(self, logs=None):
        if not self._called_in_fit:
            self._finalize_progbar(logs, self._test_step)

    def on_predict_end(self, logs=None):
        self._finalize_progbar(logs, self._predict_step)

    def _reset_progbar(self):
        self.seen = 0
        self.progbar = None

    def _maybe_init_progbar(self):
        """Instantiate a `Progbar` if not yet, and update the stateful
        metrics."""
        # TODO(rchao): Legacy TF1 code path may use list for
        # `self.stateful_metrics`. Remove "cast to set" when TF1 support is
        # dropped.
        self.stateful_metrics = set(self.stateful_metrics)

        if self.model:
            # Update the existing stateful metrics as `self.model.metrics` may
            # contain updated metrics after `MetricsContainer` is built in the
            # first train step.
            self.stateful_metrics = self.stateful_metrics.union(
                set(m.name for m in self.model.metrics)
            )

        if self.progbar is None:
            self.progbar = Progbar(
                target=self.target,
                verbose=self.verbose,
                stateful_metrics=self.stateful_metrics,
                unit_name="step" if self.use_steps else "sample",
            )

        self.progbar._update_stateful_metrics(self.stateful_metrics)

    def _implements_train_batch_hooks(self):
        return self._call_batch_hooks

    def _implements_test_batch_hooks(self):
        return self._call_batch_hooks

    def _implements_predict_batch_hooks(self):
        return self._call_batch_hooks

    def _batch_update_progbar(self, batch, logs=None):
        """Updates the progbar."""
        logs = logs or {}
        self._maybe_init_progbar()
        if self.use_steps:
            self.seen = batch + 1  # One-indexed.
        else:
            # v1 path only.
            logs = copy.copy(logs)
            batch_size = logs.pop("size", 0)
            num_steps = logs.pop("num_steps", 1)
            logs.pop("batch", None)
            add_seen = num_steps * batch_size
            self.seen += add_seen

        if self.verbose == 1:
            # Only block async when verbose = 1.
            logs = tf_utils.sync_to_numpy_or_python_type(logs)
            self.progbar.update(self.seen, list(logs.items()), finalize=False)

    def _finalize_progbar(self, logs, counter):
        logs = tf_utils.sync_to_numpy_or_python_type(logs or {})
        if self.target is None:
            if counter is not None:
                counter = counter.numpy()
                if not self.use_steps:
                    counter *= logs.get("size", 1)
            self.target = counter or self.seen
            self.progbar.target = self.target
        self.progbar.update(self.target, list(logs.items()), finalize=True)


@keras_export("keras.callbacks.History")
class History(Callback):
    """Callback that records events into a `History` object.

    This callback is automatically applied to
    every Keras model. The `History` object
    gets returned by the `fit` method of models.

    Example:

    >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
    >>> model.compile(tf.keras.optimizers.SGD(), loss='mse')
    >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
    ...                     epochs=10, verbose=1)
    >>> print(history.params)
    {'verbose': 1, 'epochs': 10, 'steps': 1}
    >>> # check the keys of history object
    >>> print(history.history.keys())
    dict_keys(['loss'])

    """

    def __init__(self):
        super().__init__()
        self.history = {}

    def on_train_begin(self, logs=None):
        self.epoch = []

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        self.epoch.append(epoch)
        for k, v in logs.items():
            self.history.setdefault(k, []).append(v)

        # Set the history attribute on the model after the epoch ends. This will
        # make sure that the state which is set is the latest one.
        self.model.history = self


@keras_export("keras.callbacks.ModelCheckpoint")
class ModelCheckpoint(Callback):
    """Callback to save the Keras model or model weights at some frequency.

    `ModelCheckpoint` callback is used in conjunction with training using
    `model.fit()` to save a model or weights (in a checkpoint file) at some
    interval, so the model or weights can be loaded later to continue the
    training from the state saved.

    A few options this callback provides include:

    - Whether to only keep the model that has achieved the "best performance" so
      far, or whether to save the model at the end of every epoch regardless of
      performance.
    - Definition of 'best'; which quantity to monitor and whether it should be
      maximized or minimized.
    - The frequency it should save at. Currently, the callback supports saving
      at the end of every epoch, or after a fixed number of training batches.
    - Whether only weights are saved, or the whole model is saved.

    Note: If you get `WARNING:tensorflow:Can save best model only with <name>
    available, skipping` see the description of the `monitor` argument for
    details on how to get this right.

    Example:

    ```python
    model.compile(loss=..., optimizer=...,
                  metrics=['accuracy'])

    EPOCHS = 10
    checkpoint_filepath = '/tmp/checkpoint'
    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
        filepath=checkpoint_filepath,
        save_weights_only=True,
        monitor='val_accuracy',
        mode='max',
        save_best_only=True)

    # Model weights are saved at the end of every epoch, if it's the best seen
    # so far.
    model.fit(epochs=EPOCHS, callbacks=[model_checkpoint_callback])

    # The model weights (that are considered the best) are loaded into the
    # model.
    model.load_weights(checkpoint_filepath)
    ```

    Args:
        filepath: string or `PathLike`, path to save the model file. e.g.
          filepath = os.path.join(working_dir, 'ckpt', file_name). `filepath`
          can contain named formatting options, which will be filled the value
          of `epoch` and keys in `logs` (passed in `on_epoch_end`). For example:
          if `filepath` is `weights.{epoch:02d}-{val_loss:.2f}.hdf5`, then the
          model checkpoints will be saved with the epoch number and the
          validation loss in the filename. The directory of the filepath should
          not be reused by any other callbacks to avoid conflicts.
        monitor: The metric name to monitor. Typically the metrics are set by
          the `Model.compile` method. Note:

          * Prefix the name with `"val_`" to monitor validation metrics.
          * Use `"loss"` or "`val_loss`" to monitor the model's total loss.
          * If you specify metrics as strings, like `"accuracy"`, pass the same
            string (with or without the `"val_"` prefix).
          * If you pass `metrics.Metric` objects, `monitor` should be set to
            `metric.name`
          * If you're not sure about the metric names you can check the contents
            of the `history.history` dictionary returned by
            `history = model.fit()`
          * Multi-output models set additional prefixes on the metric names.

        verbose: Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1
          displays messages when the callback takes an action.
        save_best_only: if `save_best_only=True`, it only saves when the model
          is considered the "best" and the latest best model according to the
          quantity monitored will not be overwritten. If `filepath` doesn't
          contain formatting options like `{epoch}` then `filepath` will be
          overwritten by each new better model.
        mode: one of {'auto', 'min', 'max'}. If `save_best_only=True`, the
          decision to overwrite the current save file is made based on either
          the maximization or the minimization of the monitored quantity.
          For `val_acc`, this should be `max`, for `val_loss` this should be
          `min`, etc. In `auto` mode, the mode is set to `max` if the quantities
          monitored are 'acc' or start with 'fmeasure' and are set to `min` for
          the rest of the quantities.
        save_weights_only: if True, then only the model's weights will be saved
          (`model.save_weights(filepath)`), else the full model is saved
          (`model.save(filepath)`).
        save_freq: `'epoch'` or integer. When using `'epoch'`, the callback
          saves the model after each epoch. When using integer, the callback
          saves the model at end of this many batches. If the `Model` is
          compiled with `steps_per_execution=N`, then the saving criteria will
          be checked every Nth batch. Note that if the saving isn't aligned to
          epochs, the monitored metric may potentially be less reliable (it
          could reflect as little as 1 batch, since the metrics get reset every
          epoch). Defaults to `'epoch'`.
        options: Optional `tf.train.CheckpointOptions` object if
          `save_weights_only` is true or optional `tf.saved_model.SaveOptions`
          object if `save_weights_only` is false.
        initial_value_threshold: Floating point initial "best" value of the
          metric to be monitored. Only applies if `save_best_value=True`. Only
          overwrites the model weights already saved if the performance of
          current model is better than this value.
        **kwargs: Additional arguments for backwards compatibility. Possible key
          is `period`.
    """

    def __init__(
        self,
        filepath,
        monitor: str = "val_loss",
        verbose: int = 0,
        save_best_only: bool = False,
        save_weights_only: bool = False,
        mode: str = "auto",
        save_freq="epoch",
        options=None,
        initial_value_threshold=None,
        **kwargs,
    ):
        super().__init__()
        self._supports_tf_logs = True
        self.monitor = monitor
        self.verbose = verbose
        self.filepath = io_utils.path_to_string(filepath)
        self.save_best_only = save_best_only
        self.save_weights_only = save_weights_only
        self.save_freq = save_freq
        self.epochs_since_last_save = 0
        self._batches_seen_since_last_saving = 0
        self._last_batch_seen = 0
        self.best = initial_value_threshold

        if save_weights_only:
            if options is None or isinstance(
                options, tf.train.CheckpointOptions
            ):
                self._options = options or tf.train.CheckpointOptions()
            else:
                raise TypeError(
                    "If save_weights_only is True, then `options` must be "
                    "either None or a tf.train.CheckpointOptions. "
                    f"Got {options}."
                )
        else:
            if options is None or isinstance(
                options, tf.saved_model.SaveOptions
            ):
                self._options = options or tf.saved_model.SaveOptions()
            else:
                raise TypeError(
                    "If save_weights_only is False, then `options` must be "
                    "either None or a tf.saved_model.SaveOptions. "
                    f"Got {options}."
                )

        # Deprecated field `load_weights_on_restart` is for loading the
        # checkpoint file from `filepath` at the start of `model.fit()`
        # TODO(rchao): Remove the arg during next breaking release.
        if "load_weights_on_restart" in kwargs:
            self.load_weights_on_restart = kwargs["load_weights_on_restart"]
            logging.warning(
                "`load_weights_on_restart` argument is deprecated. "
                "Please use `model.load_weights()` for loading weights "
                "before the start of `model.fit()`."
            )
        else:
            self.load_weights_on_restart = False

        # Deprecated field `period` is for the number of epochs between which
        # the model is saved.
        if "period" in kwargs:
            self.period = kwargs["period"]
            logging.warning(
                "`period` argument is deprecated. Please use `save_freq` "
                "to specify the frequency in number of batches seen."
            )
        else:
            self.period = 1

        if mode not in ["auto", "min", "max"]:
            logging.warning(
                "ModelCheckpoint mode %s is unknown, fallback to auto mode.",
                mode,
            )
            mode = "auto"

        if mode == "min":
            self.monitor_op = np.less
            if self.best is None:
                self.best = np.Inf
        elif mode == "max":
            self.monitor_op = np.greater
            if self.best is None:
                self.best = -np.Inf
        else:
            if "acc" in self.monitor or self.monitor.startswith("fmeasure"):
                self.monitor_op = np.greater
                if self.best is None:
                    self.best = -np.Inf
            else:
                self.monitor_op = np.less
                if self.best is None:
                    self.best = np.Inf

        if self.save_freq != "epoch" and not isinstance(self.save_freq, int):
            raise ValueError(
                f"Unrecognized save_freq: {self.save_freq}. "
                'Expected save_freq are "epoch" or integer'
            )

        # Only the chief worker writes model checkpoints, but all workers
        # restore checkpoint at on_train_begin().
        self._chief_worker_only = False

    def on_train_begin(self, logs=None):
        if self.load_weights_on_restart:
            filepath_to_load = (
                self._get_most_recently_modified_file_matching_pattern(
                    self.filepath
                )
            )
            if filepath_to_load is not None and self._checkpoint_exists(
                filepath_to_load
            ):
                try:
                    # `filepath` may contain placeholders such as `{epoch:02d}`,
                    # and thus it attempts to load the most recently modified
                    # file with file name matching the pattern.
                    self.model.load_weights(filepath_to_load)
                except (IOError, ValueError) as e:
                    raise ValueError(
                        f"Error loading file from {filepath_to_load}. "
                        f"Reason: {e}"
                    )

    def _implements_train_batch_hooks(self):
        # Only call batch hooks when saving on batch
        return self.save_freq != "epoch"

    def on_train_batch_end(self, batch, logs=None):
        if self._should_save_on_batch(batch):
            self._save_model(epoch=self._current_epoch, batch=batch, logs=logs)

    def on_epoch_begin(self, epoch, logs=None):
        self._current_epoch = epoch

    def on_epoch_end(self, epoch, logs=None):
        self.epochs_since_last_save += 1

        if self.save_freq == "epoch":
            self._save_model(epoch=epoch, batch=None, logs=logs)

    def _should_save_on_batch(self, batch):
        """Handles batch-level saving logic, supports steps_per_execution."""
        if self.save_freq == "epoch":
            return False

        if batch <= self._last_batch_seen:  # New epoch.
            add_batches = batch + 1  # batches are zero-indexed.
        else:
            add_batches = batch - self._last_batch_seen
        self._batches_seen_since_last_saving += add_batches
        self._last_batch_seen = batch

        if self._batches_seen_since_last_saving >= self.save_freq:
            self._batches_seen_since_last_saving = 0
            return True
        return False

    def _save_model(self, epoch, batch, logs):
        """Saves the model.

        Args:
            epoch: the epoch this iteration is in.
            batch: the batch this iteration is in. `None` if the `save_freq`
              is set to `epoch`.
            logs: the `logs` dict passed in to `on_batch_end` or `on_epoch_end`.
        """
        logs = logs or {}

        if (
            isinstance(self.save_freq, int)
            or self.epochs_since_last_save >= self.period
        ):
            # Block only when saving interval is reached.
            logs = tf_utils.sync_to_numpy_or_python_type(logs)
            self.epochs_since_last_save = 0
            filepath = self._get_file_path(epoch, batch, logs)

            # Create host directory if it doesn't exist.
            dirname = os.path.dirname(filepath)
            if dirname and not tf.io.gfile.exists(dirname):
                tf.io.gfile.makedirs(dirname)

            try:
                if self.save_best_only:
                    current = logs.get(self.monitor)
                    if current is None:
                        logging.warning(
                            "Can save best model only with %s available, "
                            "skipping.",
                            self.monitor,
                        )
                    else:
                        if self.monitor_op(current, self.best):
                            if self.verbose > 0:
                                io_utils.print_msg(
                                    f"\nEpoch {epoch + 1}: {self.monitor} "
                                    "improved "
                                    f"from {self.best:.5f} to {current:.5f}, "
                                    f"saving model to {filepath}"
                                )
                            self.best = current
                            if self.save_weights_only:
                                self.model.save_weights(
                                    filepath,
                                    overwrite=True,
                                    options=self._options,
                                )
                            else:
                                self.model.save(
                                    filepath,
                                    overwrite=True,
                                    options=self._options,
                                )
                        else:
                            if self.verbose > 0:
                                io_utils.print_msg(
                                    f"\nEpoch {epoch + 1}: "
                                    f"{self.monitor} did not improve "
                                    f"from {self.best:.5f}"
                                )
                else:
                    if self.verbose > 0:
                        io_utils.print_msg(
                            f"\nEpoch {epoch + 1}: saving model to {filepath}"
                        )
                    if self.save_weights_only:
                        self.model.save_weights(
                            filepath, overwrite=True, options=self._options
                        )
                    else:
                        self.model.save(
                            filepath, overwrite=True, options=self._options
                        )

                self._maybe_remove_file()
            except IsADirectoryError:  # h5py 3.x
                raise IOError(
                    "Please specify a non-directory filepath for "
                    "ModelCheckpoint. Filepath used is an existing "
                    f"directory: {filepath}"
                )
            except IOError as e:  # h5py 2.x
                # `e.errno` appears to be `None` so checking the content of
                # `e.args[0]`.
                if "is a directory" in str(e.args[0]).lower():
                    raise IOError(
                        "Please specify a non-directory filepath for "
                        "ModelCheckpoint. Filepath used is an existing "
                        f"directory: f{filepath}"
                    )
                # Re-throw the error for any other causes.
                raise e

    def _get_file_path(self, epoch, batch, logs):
        """Returns the file path for checkpoint."""

        try:
            # `filepath` may contain placeholders such as
            # `{epoch:02d}`,`{batch:02d}` and `{mape:.2f}`. A mismatch between
            # logged metrics and the path's placeholders can cause formatting to
            # fail.
            if batch is None or "batch" in logs:
                file_path = self.filepath.format(epoch=epoch + 1, **logs)
            else:
                file_path = self.filepath.format(
                    epoch=epoch + 1, batch=batch + 1, **logs
                )
        except KeyError as e:
            raise KeyError(
                f'Failed to format this callback filepath: "{self.filepath}". '
                f"Reason: {e}"
            )
        self._write_filepath = distributed_file_utils.write_filepath(
            file_path, self.model.distribute_strategy
        )
        return self._write_filepath

    def _maybe_remove_file(self):
        # Remove the checkpoint directory in multi-worker training where this
        # worker should not checkpoint. It is a dummy directory previously saved
        # for sync distributed training.
        distributed_file_utils.remove_temp_dir_with_filepath(
            self._write_filepath, self.model.distribute_strategy
        )

    def _checkpoint_exists(self, filepath):
        """Returns whether the checkpoint `filepath` refers to exists."""
        if filepath.endswith(".h5"):
            return tf.io.gfile.exists(filepath)
        tf_saved_model_exists = tf.io.gfile.exists(filepath)
        tf_weights_only_checkpoint_exists = tf.io.gfile.exists(
            filepath + ".index"
        )
        return tf_saved_model_exists or tf_weights_only_checkpoint_exists

    def _get_most_recently_modified_file_matching_pattern(self, pattern):
        """Returns the most recently modified filepath matching pattern.

        Pattern may contain python formatting placeholder. If
        `tf.train.latest_checkpoint()` does not return None, use that;
        otherwise, check for most recently modified one that matches the
        pattern.

        In the rare case where there are more than one pattern-matching file
        having the same modified time that is most recent among all, return the
        filepath that is largest (by `>` operator, lexicographically using the
        numeric equivalents). This provides a tie-breaker when multiple files
        are most recent. Note that a larger `filepath` can sometimes indicate a
        later time of modification (for instance, when epoch/batch is used as
        formatting option), but not necessarily (when accuracy or loss is used).
        The tie-breaker is put in the logic as best effort to return the most
        recent, and to avoid undeterministic result.

        Modified time of a file is obtained with `os.path.getmtime()`.

        This utility function is best demonstrated via an example:

        ```python
        file_pattern = 'f.batch{batch:02d}epoch{epoch:02d}.h5'
        test_dir = self.get_temp_dir()
        path_pattern = os.path.join(test_dir, file_pattern)
        file_paths = [
            os.path.join(test_dir, file_name) for file_name in
            ['f.batch03epoch02.h5',
             'f.batch02epoch02.h5', 'f.batch01epoch01.h5']
        ]
        for file_path in file_paths:
          # Write something to each of the files
        self.assertEqual(
            _get_most_recently_modified_file_matching_pattern(path_pattern),
            file_paths[-1])
        ```

        Args:
            pattern: The file pattern that may optionally contain python
                placeholder such as `{epoch:02d}`.

        Returns:
            The most recently modified file's full filepath matching `pattern`.
            If `pattern` does not contain any placeholder, this returns the
            filepath that exactly matches `pattern`. Returns `None` if no match
            is found.
        """
        dir_name = os.path.dirname(pattern)
        base_name = os.path.basename(pattern)
        base_name_regex = "^" + re.sub(r"{.*}", r".*", base_name) + "$"

        # If tf.train.latest_checkpoint tells us there exists a latest
        # checkpoint, use that as it is more robust than `os.path.getmtime()`.
        latest_tf_checkpoint = tf.train.latest_checkpoint(dir_name)
        if latest_tf_checkpoint is not None and re.match(
            base_name_regex, os.path.basename(latest_tf_checkpoint)
        ):
            return latest_tf_checkpoint

        latest_mod_time = 0
        file_path_with_latest_mod_time = None
        n_file_with_latest_mod_time = 0
        file_path_with_largest_file_name = None

        if tf.io.gfile.exists(dir_name):
            for file_name in os.listdir(dir_name):
                # Only consider if `file_name` matches the pattern.
                if re.match(base_name_regex, file_name):
                    file_path = os.path.join(dir_name, file_name)
                    mod_time = os.path.getmtime(file_path)
                    if (
                        file_path_with_largest_file_name is None
                        or file_path > file_path_with_largest_file_name
                    ):
                        file_path_with_largest_file_name = file_path
                    if mod_time > latest_mod_time:
                        latest_mod_time = mod_time
                        file_path_with_latest_mod_time = file_path
                        # In the case a file with later modified time is found,
                        # reset the counter for the number of files with latest
                        # modified time.
                        n_file_with_latest_mod_time = 1
                    elif mod_time == latest_mod_time:
                        # In the case a file has modified time tied with the
                        # most recent, increment the counter for the number of
                        # files with latest modified time by 1.
                        n_file_with_latest_mod_time += 1

        if n_file_with_latest_mod_time == 1:
            # Return the sole file that has most recent modified time.
            return file_path_with_latest_mod_time
        else:
            # If there are more than one file having latest modified time,
            # return the file path with the largest file name.
            return file_path_with_largest_file_name


@keras_export("keras.callbacks.BackupAndRestore", v1=[])
class BackupAndRestore(Callback):
    """Callback to back up and restore the training state.

    `BackupAndRestore` callback is intended to recover training from an
    interruption that has happened in the middle of a `Model.fit` execution, by
    backing up the training states in a temporary checkpoint file (with the help
    of a `tf.train.CheckpointManager`), at the end of each epoch. Each backup
    overwrites the previously written checkpoint file, so at any given time
    there is at most one such checkpoint file for backup/restoring purpose.

    If training restarts before completion, the training state (which includes
    the `Model` weights and epoch number) is restored to the most recently saved
    state at the beginning of a new `Model.fit` run. At the completion of a
    `Model.fit` run, the temporary checkpoint file is deleted.

    Note that the user is responsible to bring jobs back after the interruption.
    This callback is important for the backup and restore mechanism for fault
    tolerance purpose, and the model to be restored from a previous checkpoint
    is expected to be the same as the one used to back up. If user changes
    arguments passed to compile or fit, the checkpoint saved for fault tolerance
    can become invalid.

    Note:

    1. This callback is not compatible with eager execution disabled.
    2. A checkpoint is saved at the end of each epoch. After restoring,
    `Model.fit` redoes any partial work during the unfinished epoch in which the
    training got restarted (so the work done before the interruption doesn't
    affect the final model state).
    3. This works for both single worker and multi-worker modes. When
    `Model.fit` is used with `tf.distribute`, it supports
    `tf.distribute.MirroredStrategy`,
    `tf.distribute.MultiWorkerMirroredStrategy`, `tf.distribute.TPUStrategy`,
    and `tf.distribute.experimental.ParameterServerStrategy`.

    Example:

    >>> class InterruptingCallback(tf.keras.callbacks.Callback):
    ...   def on_epoch_begin(self, epoch, logs=None):
    ...     if epoch == 4:
    ...       raise RuntimeError('Interrupting!')
    >>> callback = tf.keras.callbacks.BackupAndRestore(backup_dir="/tmp/backup")
    >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
    >>> model.compile(tf.keras.optimizers.SGD(), loss='mse')
    >>> try:
    ...   model.fit(np.arange(100).reshape(5, 20), np.zeros(5), epochs=10,
    ...             batch_size=1, callbacks=[callback, InterruptingCallback()],
    ...             verbose=0)
    ... except:
    ...   pass
    >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
    ...                     epochs=10, batch_size=1, callbacks=[callback],
    ...                     verbose=0)
    >>> # Only 6 more epochs are run, since first training got interrupted at
    >>> # zero-indexed epoch 4, second training will continue from 4 to 9.
    >>> len(history.history['loss'])
    6

    Besides the option to save at the end of every epoch or every N steps, if
    you are doing distributed training with
    `tf.distribute.MultiWorkerMirroredStrategy` on Google Cloud Platform or
    Google Borg, you can also use the `save_before_preemption` argument
    to enable saving a checkpoint right before a worker gets preempted
    by other jobs and training gets interrupted. See
    `tf.distribute.experimental.PreemptionCheckpointHandler` for more details.

    Args:
        backup_dir: String, path to store the checkpoint.
          e.g. `backup_dir = os.path.join(working_dir, 'backup')`.
          This is the directory in which the system stores temporary files to
          recover the model from jobs terminated unexpectedly. The directory
          cannot be reused elsewhere to store other files, e.g. by the
          `BackupAndRestore` callback of another training run,
          or by another callback
          (e.g. `ModelCheckpoint`) of the same training.
        save_freq: `'epoch'`, integer, or `False`. When set to `'epoch'`
          the callback saves the checkpoint at the end of each epoch.
          When set to an integer, the callback saves the checkpoint every
          `save_freq` batches. Set `save_freq` to `False` if only using
          preemption checkpointing (with `save_before_preemption=True`).
        delete_checkpoint: Boolean, default to True. This `BackupAndRestore`
          callback works by saving a checkpoint to back up the training state.
          If `delete_checkpoint=True`, the checkpoint will be deleted after
          training is finished. Use `False` if you'd like to keep the checkpoint
          for future usage.
        save_before_preemption: A boolean value instructing whether to turn on
          the automatic checkpoint saving for preemption/maintenance events.
          This only supports
          `tf.distribute.MultiWorkerMirroredStrategy` on Google Cloud Platform
          or Google Borg for now.
    """

    def __init__(
        self,
        backup_dir,
        save_freq="epoch",
        delete_checkpoint=True,
        save_before_preemption=False,
    ):
        super().__init__()
        self.backup_dir = backup_dir
        self._supports_tf_logs = True
        self._supported_strategies = (
            tf.distribute.MirroredStrategy,
            tf.distribute.MultiWorkerMirroredStrategy,
            tf.distribute.experimental.TPUStrategy,
            tf.distribute.TPUStrategy,
            tf.distribute.experimental.ParameterServerStrategy,
        )
        self.save_freq = save_freq
        self.delete_checkpoint = delete_checkpoint
        self.save_before_preemption = save_before_preemption
        self._batches_count = 0
        self._current_epoch = 0

        if not tf.executing_eagerly():
            if tf.inside_function():
                raise ValueError(
                    "This Callback's method contains Python state and "
                    "should be called outside of `tf.function`s."
                )
            else:  # Legacy graph mode:
                raise ValueError(
                    "BackupAndRestore only supports eager mode. In graph "
                    "mode, consider using ModelCheckpoint to manually save "
                    "and restore weights with `model.load_weights()` and by "
                    "providing `initial_epoch` in `model.fit()` for fault "
                    "tolerance."
                )
        if (not save_freq) and (not save_before_preemption):
            raise ValueError(
                "Either `save_freq` or `save_before_preemption` " "must be set."
            )

        # Only the chief worker writes model checkpoints, but all workers
        # restore checkpoint at on_train_begin().
        self._chief_worker_only = False

    def on_train_begin(self, logs=None):
        # TrainingState is used to manage the training state needed for
        # failure-recovery of a worker in training.

        if self.model._distribution_strategy and not isinstance(
            self.model.distribute_strategy, self._supported_strategies
        ):
            raise NotImplementedError(
                f"{type(self.model.distribute_strategy)} is not supported yet. "
                "Currently BackupAndRestore callback "
                "only supports empty strategy, "
                "MirroredStrategy, MultiWorkerMirroredStrategy and TPUStrategy."
            )
        self.model._training_state = worker_training_state.WorkerTrainingState(
            self.model,
            self.backup_dir,
            self.save_freq,
            self.save_before_preemption,
        )
        self._training_state = self.model._training_state
        self._training_state.restore()

    def on_train_batch_begin(self, batch, logs=None):
        self._training_state._ckpt_saved_batch.assign(batch)

    def on_train_batch_end(self, batch, logs=None):
        self._training_state.backup_if_preempted()
        if self.save_freq and self.save_freq != "epoch":
            self._batches_count += 1
            if self._batches_count >= self.save_freq:
                self._batches_count = 0
                self._backup(epoch=self._current_epoch, batch=batch)

    def _implements_train_batch_hooks(self):
        return self.save_freq != "epoch"

    def on_train_end(self, logs=None):
        if self.delete_checkpoint:
            # On exit of training, delete the training state backup file saved
            # for the purpose of worker recovery unless the user opts out.
            self._training_state.delete_backup()
        # Clean up the training state.
        del self._training_state
        del self.model._training_state

    def on_epoch_begin(self, epoch, logs=None):
        self._training_state._ckpt_saved_epoch.assign(epoch)
        self._current_epoch = epoch

    def on_epoch_end(self, epoch, logs=None):
        # Back up the model and current epoch for possible future recovery.
        if self.save_freq == "epoch":
            self._backup(epoch=epoch)

    def _backup(self, epoch, batch=0):
        self._training_state.back_up(epoch=epoch, batch=batch)


@keras_export("keras.callbacks.experimental.BackupAndRestore", v1=[])
@deprecation.deprecated_endpoints(
    "keras.callbacks.experimental.BackupAndRestore"
)
class BackupAndRestoreExperimental(BackupAndRestore):
    """Deprecated. Please use `tf.keras.callbacks.BackupAndRestore` instead.

    Caution: `tf.keras.callbacks.experimental.BackupAndRestore` endpoint is
      deprecated and will be removed in a future release. Please use
      `tf.keras.callbacks.BackupAndRestore`.
    """

    def __init__(self, *args, **kwargs):
        logging.warning(
            "`tf.keras.callbacks.experimental.BackupAndRestore` endpoint is "
            "deprecated and will be removed in a future release. Please use "
            "`tf.keras.callbacks.BackupAndRestore`."
        )
        super().__init__(*args, **kwargs)


@keras_export("keras.callbacks.EarlyStopping")
class EarlyStopping(Callback):
    """Stop training when a monitored metric has stopped improving.

    Assuming the goal of a training is to minimize the loss. With this, the
    metric to be monitored would be `'loss'`, and mode would be `'min'`. A
    `model.fit()` training loop will check at end of every epoch whether
    the loss is no longer decreasing, considering the `min_delta` and
    `patience` if applicable. Once it's found no longer decreasing,
    `model.stop_training` is marked True and the training terminates.

    The quantity to be monitored needs to be available in `logs` dict.
    To make it so, pass the loss or metrics at `model.compile()`.

    Args:
      monitor: Quantity to be monitored.
      min_delta: Minimum change in the monitored quantity
          to qualify as an improvement, i.e. an absolute
          change of less than min_delta, will count as no
          improvement.
      patience: Number of epochs with no improvement
          after which training will be stopped.
      verbose: Verbosity mode, 0 or 1. Mode 0 is silent, and mode 1
          displays messages when the callback takes an action.
      mode: One of `{"auto", "min", "max"}`. In `min` mode,
          training will stop when the quantity
          monitored has stopped decreasing; in `"max"`
          mode it will stop when the quantity
          monitored has stopped increasing; in `"auto"`
          mode, the direction is automatically inferred
          from the name of the monitored quantity.
      baseline: Baseline value for the monitored quantity.
          Training will stop if the model doesn't show improvement over the
          baseline.
      restore_best_weights: Whether to restore model weights from
          the epoch with the best value of the monitored quantity.
          If False, the model weights obtained at the last step of
          training are used. An epoch will be restored regardless
          of the performance relative to the `baseline`. If no epoch
          improves on `baseline`, training will run for `patience`
          epochs and restore weights from the best epoch in that set.
      start_from_epoch: Number of epochs to wait before starting
          to monitor improvement. This allows for a warm-up period in which
          no improvement is expected and thus training will not be stopped.


    Example:

    >>> callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
    >>> # This callback will stop the training when there is no improvement in
    >>> # the loss for three consecutive epochs.
    >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
    >>> model.compile(tf.keras.optimizers.SGD(), loss='mse')
    >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
    ...                     epochs=10, batch_size=1, callbacks=[callback],
    ...                     verbose=0)
    >>> len(history.history['loss'])  # Only 4 epochs are run.
    4
    """

    def __init__(
        self,
        monitor="val_loss",
        min_delta=0,
        patience=0,
        verbose=0,
        mode="auto",
        baseline=None,
        restore_best_weights=False,
        start_from_epoch=0,
    ):
        super().__init__()

        self.monitor = monitor
        self.patience = patience
        self.verbose = verbose
        self.baseline = baseline
        self.min_delta = abs(min_delta)
        self.wait = 0
        self.stopped_epoch = 0
        self.restore_best_weights = restore_best_weights
        self.best_weights = None
        self.start_from_epoch = start_from_epoch

        if mode not in ["auto", "min", "max"]:
            logging.warning(
                "EarlyStopping mode %s is unknown, fallback to auto mode.",
                mode,
            )
            mode = "auto"

        if mode == "min":
            self.monitor_op = np.less
        elif mode == "max":
            self.monitor_op = np.greater
        else:
            if (
                self.monitor.endswith("acc")
                or self.monitor.endswith("accuracy")
                or self.monitor.endswith("auc")
            ):
                self.monitor_op = np.greater
            else:
                self.monitor_op = np.less

        if self.monitor_op == np.greater:
            self.min_delta *= 1
        else:
            self.min_delta *= -1

    def on_train_begin(self, logs=None):
        # Allow instances to be re-used
        self.wait = 0
        self.stopped_epoch = 0
        self.best = np.Inf if self.monitor_op == np.less else -np.Inf
        self.best_weights = None
        self.best_epoch = 0

    def on_epoch_end(self, epoch, logs=None):
        current = self.get_monitor_value(logs)
        if current is None or epoch < self.start_from_epoch:
            # If no monitor value exists or still in initial warm-up stage.
            return
        if self.restore_best_weights and self.best_weights is None:
            # Restore the weights after first epoch if no progress is ever made.
            self.best_weights = self.model.get_weights()

        self.wait += 1
        if self._is_improvement(current, self.best):
            self.best = current
            self.best_epoch = epoch
            if self.restore_best_weights:
                self.best_weights = self.model.get_weights()
            # Only restart wait if we beat both the baseline and our previous
            # best.
            if self.baseline is None or self._is_improvement(
                current, self.baseline
            ):
                self.wait = 0
            return

        # Only check after the first epoch.
        if self.wait >= self.patience and epoch > 0:
            self.stopped_epoch = epoch
            self.model.stop_training = True
            if self.restore_best_weights and self.best_weights is not None:
                if self.verbose > 0:
                    io_utils.print_msg(
                        "Restoring model weights from "
                        "the end of the best epoch: "
                        f"{self.best_epoch + 1}."
                    )
                self.model.set_weights(self.best_weights)

    def on_train_end(self, logs=None):
        if self.stopped_epoch > 0 and self.verbose > 0:
            io_utils.print_msg(
                f"Epoch {self.stopped_epoch + 1}: early stopping"
            )

    def get_monitor_value(self, logs):
        logs = logs or {}
        monitor_value = logs.get(self.monitor)
        if monitor_value is None:
            logging.warning(
                "Early stopping conditioned on metric `%s` "
                "which is not available. Available metrics are: %s",
                self.monitor,
                ",".join(list(logs.keys())),
            )
        return monitor_value

    def _is_improvement(self, monitor_value, reference_value):
        return self.monitor_op(monitor_value - self.min_delta, reference_value)


@keras_export("keras.callbacks.RemoteMonitor")
class RemoteMonitor(Callback):
    """Callback used to stream events to a server.

    Requires the `requests` library.
    Events are sent to `root + '/publish/epoch/end/'` by default. Calls are
    HTTP POST, with a `data` argument which is a
    JSON-encoded dictionary of event data.
    If `send_as_json=True`, the content type of the request will be
    `"application/json"`.
    Otherwise the serialized JSON will be sent within a form.

    Args:
      root: String; root url of the target server.
      path: String; path relative to `root` to which the events will be sent.
      field: String; JSON field under which the data will be stored.
          The field is used only if the payload is sent within a form
          (i.e. send_as_json is set to False).
      headers: Dictionary; optional custom HTTP headers.
      send_as_json: Boolean; whether the request should be
          sent as `"application/json"`.
    """

    def __init__(
        self,
        root="http://localhost:9000",
        path="/publish/epoch/end/",
        field="data",
        headers=None,
        send_as_json=False,
    ):
        super().__init__()

        self.root = root
        self.path = path
        self.field = field
        self.headers = headers
        self.send_as_json = send_as_json

    def on_epoch_end(self, epoch, logs=None):
        if requests is None:
            raise ImportError("RemoteMonitor requires the `requests` library.")
        logs = logs or {}
        send = {}
        send["epoch"] = epoch
        for k, v in logs.items():
            # np.ndarray and np.generic are not scalar types
            # therefore we must unwrap their scalar values and
            # pass to the json-serializable dict 'send'
            if isinstance(v, (np.ndarray, np.generic)):
                send[k] = v.item()
            else:
                send[k] = v
        try:
            if self.send_as_json:
                requests.post(
                    self.root + self.path, json=send, headers=self.headers
                )
            else:
                requests.post(
                    self.root + self.path,
                    {self.field: json.dumps(send)},
                    headers=self.headers,
                )
        except requests.exceptions.RequestException:
            logging.warning(
                "Warning: could not reach RemoteMonitor root server at "
                + str(self.root)
            )


@keras_export("keras.callbacks.LearningRateScheduler")
class LearningRateScheduler(Callback):
    """Learning rate scheduler.

    At the beginning of every epoch, this callback gets the updated learning
    rate value from `schedule` function provided at `__init__`, with the current
    epoch and current learning rate, and applies the updated learning rate on
    the optimizer.

    Args:
      schedule: a function that takes an epoch index (integer, indexed from 0)
          and current learning rate (float) as inputs and returns a new
          learning rate as output (float).
      verbose: int. 0: quiet, 1: update messages.

    Example:

    >>> # This function keeps the initial learning rate for the first ten epochs
    >>> # and decreases it exponentially after that.
    >>> def scheduler(epoch, lr):
    ...   if epoch < 10:
    ...     return lr
    ...   else:
    ...     return lr * tf.math.exp(-0.1)
    >>>
    >>> model = tf.keras.models.Sequential([tf.keras.layers.Dense(10)])
    >>> model.compile(tf.keras.optimizers.SGD(), loss='mse')
    >>> round(model.optimizer.lr.numpy(), 5)
    0.01

    >>> callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
    >>> history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
    ...                     epochs=15, callbacks=[callback], verbose=0)
    >>> round(model.optimizer.lr.numpy(), 5)
    0.00607

    """

    def __init__(self, schedule, verbose=0):
        super().__init__()
        self.schedule = schedule
        self.verbose = verbose

    def on_epoch_begin(self, epoch, logs=None):
        if not hasattr(self.model.optimizer, "lr"):
            raise ValueError('Optimizer must have a "lr" attribute.')
        try:  # new API
            lr = float(backend.get_value(self.model.optimizer.lr))
            lr = self.schedule(epoch, lr)
        except TypeError:  # Support for old API for backward compatibility
            lr = self.schedule(epoch)
        if not isinstance(lr, (tf.Tensor, float, np.float32, np.float64)):
            raise ValueError(
                'The output of the "schedule" function '
                f"should be float. Got: {lr}"
            )
        if isinstance(lr, tf.Tensor) and not lr.dtype.is_floating:
            raise ValueError(
                f"The dtype of `lr` Tensor should be float. Got: {lr.dtype}"
            )
        backend.set_value(self.model.optimizer.lr, backend.get_value(lr))
        if self.verbose > 0:
            io_utils.print_msg(
                f"\nEpoch {epoch + 1}: LearningRateScheduler setting learning "
                f"rate to {lr}."
            )

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        logs["lr"] = backend.get_value(self.model.optimizer.lr)


def keras_model_summary(name, data, step=None):
    """Writes a Keras model as JSON to as a Summary.

    Writing the Keras model configuration allows the TensorBoard graph plugin to
    render a conceptual graph, as opposed to graph of ops. In case the model
    fails to serialize as JSON, it ignores and returns False.

    Args:
      name: A name for this summary. The summary tag used for TensorBoard will
        be this name prefixed by any active name scopes.
      data: A Keras Model to write.
      step: Explicit `int64`-castable monotonic step value for this summary. If
        omitted, this defaults to `tf.summary.experimental.get_step()`, which
        must not be None.

    Returns:
      True on success, or False if no summary was written because no default
      summary writer was available.

    Raises:
      ValueError: if a default writer exists, but no step was provided and
        `tf.summary.experimental.get_step()` is None.
    """
    summary_metadata = tf.compat.v1.SummaryMetadata()
    # Hard coding a plugin name. Please refer to go/tb-plugin-name-hardcode for
    # the rationale.
    summary_metadata.plugin_data.plugin_name = "graph_keras_model"
    # version number = 1
    summary_metadata.plugin_data.content = b"1"

    try:
        json_string = data.to_json()
    except Exception as exc:
        # An exception should not break a model code.
        logging.warning(
            "Model failed to serialize as JSON. Ignoring... %s", exc
        )
        return False

    with tf.summary.experimental.summary_scope(
        name, "graph_keras_model", [data, step]
    ) as (tag, _):
        with tf.device("cpu:0"):
            tensor = tf.constant(json_string, dtype=tf.string)
        return tf.summary.write(
            tag=tag, tensor=tensor, step=step, metadata=summary_metadata
        )


@keras_export("keras.callbacks.TensorBoard", v1=[])
class TensorBoard(Callback, version_utils.TensorBoardVersionSelector):

    """Enable visualizations for TensorBoard.

    TensorBoard is a visualization tool provided with TensorFlow.

    This callback logs events for TensorBoard, including:

    * Metrics summary plots
    * Training graph visualization
    * Weight histograms
    * Sampled profiling

    When used in `Model.evaluate`, in addition to epoch summaries, there will be
    a summary that records evaluation metrics vs `Model.optimizer.iterations`
    written. The metric names will be prepended with `evaluation`, with
    `Model.optimizer.iterations` being the step in the visualized TensorBoard.

    If you have installed TensorFlow with pip, you should be able
    to launch TensorBoard from the command line:

    ```
    tensorboard --logdir=path_to_your_logs
    ```

    You can find more information about TensorBoard
    [here](https://www.tensorflow.org/get_started/summaries_and_tensorboard).

    Args:
        log_dir: the path of the directory where to save the log files to be
          parsed by TensorBoard. e.g. log_dir = os.path.join(working_dir,
          'logs') This directory should not be reused by any other callbacks.
        histogram_freq: frequency (in epochs) at which to compute
          weight histograms for the layers of the model. If set to 0, histograms
          won't be computed. Validation data (or split) must be specified for
          histogram visualizations.
        write_graph: whether to visualize the graph in TensorBoard. The log file
          can become quite large when write_graph is set to True.
        write_images: whether to write model weights to visualize as image in
          TensorBoard.
        write_steps_per_second: whether to log the training steps per second
          into TensorBoard. This supports both epoch and batch frequency
          logging.
        update_freq: `'batch'` or `'epoch'` or integer. When using `'epoch'`,
          writes the losses and metrics to TensorBoard after every epoch.
          If using an integer, let's say `1000`, all metrics and losses
          (including custom ones added by `Model.compile`) will be logged to
          TensorBoard every 1000 batches. `'batch'` is a synonym for `1`,
          meaning that they will be written every batch.
          Note however that writing too frequently to TensorBoard can slow down
          your training, especially when used with `tf.distribute.Strategy` as
          it will incur additional synchronization overhead.
          Use with `ParameterServerStrategy` is not supported.
          Batch-level summary writing is also available via `train_step`
          override. Please see
          [TensorBoard Scalars tutorial](https://www.tensorflow.org/tensorboard/scalars_and_keras#batch-level_logging)  # noqa: E501
          for more details.
        profile_batch: Profile the batch(es) to sample compute characteristics.
          profile_batch must be a non-negative integer or a tuple of integers.
          A pair of positive integers signify a range of batches to profile.
          By default, profiling is disabled.
        embeddings_freq: frequency (in epochs) at which embedding layers will be
          visualized. If set to 0, embeddings won't be visualized.
        embeddings_metadata: Dictionary which maps embedding layer names to the
          filename of a file in which to save metadata for the embedding layer.
          In case the same metadata file is to be
          used for all embedding layers, a single filename can be passed.

    Examples:

    Basic usage:

    ```python
    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs")
    model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
    # Then run the tensorboard command to view the visualizations.
    ```

    Custom batch-level summaries in a subclassed Model:

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

      def build(self, _):
        self.dense = tf.keras.layers.Dense(10)

      def call(self, x):
        outputs = self.dense(x)
        tf.summary.histogram('outputs', outputs)
        return outputs

    model = MyModel()
    model.compile('sgd', 'mse')

    # Make sure to set `update_freq=N` to log a batch-level summary every N
    # batches.  In addition to any `tf.summary` contained in `Model.call`,
    # metrics added in `Model.compile` will be logged every N batches.
    tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq=1)
    model.fit(x_train, y_train, callbacks=[tb_callback])
    ```

    Custom batch-level summaries in a Functional API Model:

    ```python
    def my_summary(x):
      tf.summary.histogram('x', x)
      return x

    inputs = tf.keras.Input(10)
    x = tf.keras.layers.Dense(10)(inputs)
    outputs = tf.keras.layers.Lambda(my_summary)(x)
    model = tf.keras.Model(inputs, outputs)
    model.compile('sgd', 'mse')

    # Make sure to set `update_freq=N` to log a batch-level summary every N
    # batches. In addition to any `tf.summary` contained in `Model.call`,
    # metrics added in `Model.compile` will be logged every N batches.
    tb_callback = tf.keras.callbacks.TensorBoard('./logs', update_freq=1)
    model.fit(x_train, y_train, callbacks=[tb_callback])
    ```

    Profiling:

    ```python
    # Profile a single batch, e.g. the 5th batch.
    tensorboard_callback = tf.keras.callbacks.TensorBoard(
        log_dir='./logs', profile_batch=5)
    model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])

    # Profile a range of batches, e.g. from 10 to 20.
    tensorboard_callback = tf.keras.callbacks.TensorBoard(
        log_dir='./logs', profile_batch=(10,20))
    model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
    ```
    """

    def __init__(
        self,
        log_dir="logs",
        histogram_freq=0,
        write_graph=True,
        write_images=False,
        write_steps_per_second=False,
        update_freq="epoch",
        profile_batch=0,
        embeddings_freq=0,
        embeddings_metadata=None,
        **kwargs,
    ):
        super().__init__()
        self._supports_tf_logs = True
        self._validate_kwargs(kwargs)

        self.log_dir = io_utils.path_to_string(log_dir)
        self.histogram_freq = histogram_freq
        self.write_graph = write_graph
        self.write_images = write_images
        self.write_steps_per_second = write_steps_per_second
        self.update_freq = 1 if update_freq == "batch" else update_freq
        self.embeddings_freq = embeddings_freq
        self.embeddings_metadata = embeddings_metadata
        self._init_profile_batch(profile_batch)
        self._global_train_batch = 0
        self._previous_epoch_iterations = 0
        self._train_accumulated_time = 0
        self._batch_start_time = 0

        # Lazily initialized in order to avoid creating event files when
        # not needed.
        self._writers = {}

        # Used to restore any existing `SummaryWriter` after training ends.
        self._prev_summary_state = []

    def _validate_kwargs(self, kwargs):
        """Handle arguments were supported in V1."""
        if kwargs.get("write_grads", False):
            logging.warning(
                "`write_grads` will be ignored in TensorFlow 2.0 "
                "for the `TensorBoard` Callback."
            )
        if kwargs.get("batch_size", False):
            logging.warning(
                "`batch_size` is no longer needed in the "
                "`TensorBoard` Callback and will be ignored "
                "in TensorFlow 2.0."
            )
        if kwargs.get("embeddings_layer_names", False):
            logging.warning(
                "`embeddings_layer_names` is not supported in "
                "TensorFlow 2.0. Instead, all `Embedding` layers "
                "will be visualized."
            )
        if kwargs.get("embeddings_data", False):
            logging.warning(
                "`embeddings_data` is not supported in TensorFlow "
                "2.0. Instead, all `Embedding` variables will be "
                "visualized."
            )

        supported_kwargs = {
            "write_grads",
            "embeddings_layer_names",
            "embeddings_data",
            "batch_size",
        }
        unrecognized_kwargs = set(kwargs.keys()) - supported_kwargs

        # Only allow kwargs that were supported in V1.
        if unrecognized_kwargs:
            raise ValueError(
                "Unrecognized arguments in `TensorBoard` Callback: "
                f"{unrecognized_kwargs}. "
                f"Supported kwargs are: {supported_kwargs}"
            )

    def set_model(self, model):
        """Sets Keras model and writes graph if specified."""
        self.model = model
        self._log_write_dir = self._get_log_write_dir()

        self._train_dir = os.path.join(self._log_write_dir, "train")
        self._train_step = self.model._train_counter

        self._val_dir = os.path.join(self._log_write_dir, "validation")
        self._val_step = self.model._test_counter

        self._writers = {}  # Resets writers.

        self._should_write_train_graph = False
        if self.write_graph:
            self._write_keras_model_summary()
            self._should_write_train_graph = True
        if self.embeddings_freq:
            self._configure_embeddings()

    @property
    def _train_writer(self):
        if "train" not in self._writers:
            self._writers["train"] = tf.summary.create_file_writer(
                self._train_dir
            )
        return self._writers["train"]

    @property
    def _val_writer(self):
        if "val" not in self._writers:
            self._writers["val"] = tf.summary.create_file_writer(self._val_dir)
        return self._writers["val"]

    def _get_log_write_dir(self):
        """For multi-worker, only chief should write, others write to '/tmp'."""
        return distributed_file_utils.write_dirpath(
            self.log_dir, self.model.distribute_strategy
        )

    def _delete_tmp_write_dir(self):
        """Deletes tmp write directories for multi-worker."""
        distributed_file_utils.remove_temp_dirpath(
            self.log_dir, self.model.distribute_strategy
        )

    def _write_keras_model_train_graph(self):
        """Writes Keras model train_function graph to TensorBoard."""
        with self._train_writer.as_default():
            with tf.summary.record_if(True):
                train_fn = self.model.train_tf_function
                # If the train_function is a `tf.function`, we can write out a
                # graph
                if hasattr(train_fn, "function_spec"):
                    # TODO(b/243822285): Use _variable_creation_fn directly.
                    if hasattr(train_fn, "_concrete_stateful_fn"):
                        tf.summary.graph(train_fn._concrete_stateful_fn.graph)
                    else:
                        tf.summary.graph(
                            train_fn._concrete_variable_creation_fn.graph
                        )

    def _write_keras_model_summary(self):
        """Writes Keras graph network summary to TensorBoard."""
        with self._train_writer.as_default():
            with tf.summary.record_if(True):
                summary_writable = (
                    self.model._is_graph_network
                    or self.model.__class__.__name__ == "Sequential"
                )
                if summary_writable:
                    keras_model_summary("keras", self.model, step=0)

    def _configure_embeddings(self):
        """Configure the Projector for embeddings."""
        # TODO(omalleyt): Add integration tests.
        from keras.layers import core
        from keras.protobuf import projector_config_pb2

        # isort: off
        from google.protobuf import text_format

        config = projector_config_pb2.ProjectorConfig()
        for layer in self.model.layers:
            if isinstance(layer, core.Embedding):
                embedding = config.embeddings.add()
                # Embeddings are always the first layer, so this naming should
                # be consistent in any keras models checkpoints.
                name = (
                    "layer_with_weights-0/embeddings/.ATTRIBUTES/VARIABLE_VALUE"
                )
                embedding.tensor_name = name

                if self.embeddings_metadata is not None:
                    if isinstance(self.embeddings_metadata, str):
                        embedding.metadata_path = self.embeddings_metadata
                    else:
                        if layer.name in self.embeddings_metadata.keys():
                            embedding.metadata_path = (
                                self.embeddings_metadata.pop(layer.name)
                            )

        if self.embeddings_metadata and not isinstance(
            self.embeddings_metadata, str
        ):
            raise ValueError(
                "Unrecognized `Embedding` layer names passed to "
                "`keras.callbacks.TensorBoard` `embeddings_metadata` "
                f"argument: {self.embeddings_metadata.keys()}"
            )

        config_pbtxt = text_format.MessageToString(config)
        path = os.path.join(self._log_write_dir, "projector_config.pbtxt")
        with tf.io.gfile.GFile(path, "w") as f:
            f.write(config_pbtxt)

    def _push_writer(self, writer, step):
        """Sets the default writer for custom batch-level summaries."""
        if self.update_freq == "epoch":
            return

        should_record = lambda: tf.equal(step % self.update_freq, 0)
        # TODO(b/151339474): Fix deadlock when not using .value() here.
        summary_context = (
            writer.as_default(step.value()),
            tf.summary.record_if(should_record),
        )
        self._prev_summary_state.append(summary_context)
        summary_context[0].__enter__()
        summary_context[1].__enter__()

    def _pop_writer(self):
        """Pops the current writer."""
        if self.update_freq == "epoch":
            return

        # See _push_writer for the content of the previous_context, which is
        # pair of context.
        previous_context = self._prev_summary_state.pop()
        previous_context[1].__exit__(*sys.exc_info())
        previous_context[0].__exit__(*sys.exc_info())

    def _close_writers(self):
        for writer in self._writers.values():
            writer.close()

    def _init_profile_batch(self, profile_batch):
        """Validate profile_batch value and set the range of batches to profile.

        Sets values of _start_batch and _stop_batch attributes,
        specifying the start and stop batch to profile.
        Setting `profile_batch=0` disables profiling.

        Args:
          profile_batch: The range of batches to profile. Should be a
            non-negative integer or a comma separated string of pair of positive
            integers. A pair of positive integers signify a range of batches to
            profile.

        Raises:
          ValueError: If profile_batch is not an integer or a comma separated
            pair of positive integers.

        """
        profile_batch_error_message = (
            "profile_batch must be a non-negative integer or "
            "2-tuple of positive "
            "integers. A pair of positive integers "
            "signifies a range of batches "
            f"to profile. Found: {profile_batch}"
        )

        # Support legacy way of specifying "start,stop" or "start" as str.
        if isinstance(profile_batch, str):
            profile_batch = str(profile_batch).split(",")
            profile_batch = tf.nest.map_structure(int, profile_batch)

        if isinstance(profile_batch, int):
            self._start_batch = profile_batch
            self._stop_batch = profile_batch
        elif (
            isinstance(profile_batch, (tuple, list)) and len(profile_batch) == 2
        ):
            self._start_batch, self._stop_batch = profile_batch
        else:
            raise ValueError(profile_batch_error_message)

        if self._start_batch < 0 or self._stop_batch < self._start_batch:
            raise ValueError(profile_batch_error_message)

        # True when the profiler was successfully started by this callback.
        # We track the status here to make sure callbacks do not interfere with
        # each other. The callback will only stop the profiler it started.
        self._profiler_started = False
        if self._start_batch > 0:
            # Warm up and improve the profiling accuracy.
            self._start_profiler(logdir="")
            self._stop_profiler(save=False)
        # True when a trace is running.
        self._is_tracing = False

        # Setting `profile_batch=0` disables profiling.
        self._should_trace = not (
            self._start_batch == 0 and self._stop_batch == 0
        )

    def on_train_begin(self, logs=None):
        self._global_train_batch = 0
        self._previous_epoch_iterations = 0
        self._push_writer(self._train_writer, self._train_step)

    def on_train_end(self, logs=None):
        self._pop_writer()

        if self._is_tracing:
            self._stop_trace()

        self._close_writers()
        self._delete_tmp_write_dir()

    def on_test_begin(self, logs=None):
        self._push_writer(self._val_writer, self._val_step)

    def on_test_end(self, logs=None):
        if self.model.optimizer and hasattr(self.model.optimizer, "iterations"):
            with tf.summary.record_if(True), self._val_writer.as_default():
                for name, value in logs.items():
                    tf.summary.scalar(
                        "evaluation_" + name + "_vs_iterations",
                        value,
                        step=self.model.optimizer.iterations.read_value(),
                    )
        self._pop_writer()

    def _implements_train_batch_hooks(self):
        # Only call batch hooks when tracing or write_steps_per_second are
        # enabled
        return self._should_trace or self.write_steps_per_second

    def on_train_batch_begin(self, batch, logs=None):
        self._global_train_batch += 1
        if self.write_steps_per_second:
            self._batch_start_time = time.time()
        if not self._should_trace:
            return

        if self._global_train_batch == self._start_batch:
            self._start_trace()

    def on_train_batch_end(self, batch, logs=None):
        if self._should_write_train_graph:
            self._write_keras_model_train_graph()
            self._should_write_train_graph = False
        if self.write_steps_per_second:
            batch_run_time = time.time() - self._batch_start_time
            tf.summary.scalar(
                "batch_steps_per_second",
                1.0 / batch_run_time,
                step=self._train_step,
            )

        # `logs` isn't necessarily always a dict. For example, when using
        # `tf.distribute.experimental.ParameterServerStrategy`, a
        # `tf.distribute.experimental.coordinator.RemoteValue` will be passed.
        # For now, we just disable `update_freq` in those cases.
        if isinstance(logs, dict):
            for name, value in logs.items():
                tf.summary.scalar("batch_" + name, value, step=self._train_step)

        if not self._should_trace:
            return

        if self._is_tracing and self._global_train_batch >= self._stop_batch:
            self._stop_trace()

    def on_epoch_begin(self, epoch, logs=None):
        # Keeps track of epoch for profiling.
        if self.write_steps_per_second:
            self._previous_epoch_iterations = (
                self.model.optimizer.iterations.numpy()
            )
            self._epoch_start_time = time.time()

    def on_epoch_end(self, epoch, logs=None):
        """Runs metrics and histogram summaries at epoch end."""
        self._log_epoch_metrics(epoch, logs)

        if self.histogram_freq and epoch % self.histogram_freq == 0:
            self._log_weights(epoch)

        if self.embeddings_freq and epoch % self.embeddings_freq == 0:
            self._log_embeddings(epoch)

    def _start_trace(self):
        tf.summary.trace_on(graph=True, profiler=False)
        self._start_profiler(logdir=self.log_dir)
        self._is_tracing = True

    def _stop_trace(self, batch=None):
        """Logs the trace graph to TensorBoard."""
        if batch is None:
            batch = self._stop_batch
        with self._train_writer.as_default():
            with tf.summary.record_if(True):
                # TODO(b/126388999): Remove step info in the summary name.
                tf.summary.trace_export(name="batch_%d" % batch, step=batch)
        self._stop_profiler()
        self._is_tracing = False

    def _collect_learning_rate(self, logs):
        if isinstance(self.model.optimizer, optimizer.Optimizer):
            lr_schedule = getattr(self.model.optimizer, "_learning_rate", None)
        else:
            lr_schedule = getattr(self.model.optimizer, "lr", None)
        if isinstance(lr_schedule, learning_rate_schedule.LearningRateSchedule):
            logs["learning_rate"] = lr_schedule(self.model.optimizer.iterations)
        return logs

    def _compute_steps_per_second(self):
        current_iteration = self.model.optimizer.iterations.numpy()
        time_since_epoch_begin = time.time() - self._epoch_start_time
        steps_per_second = (
            current_iteration - self._previous_epoch_iterations
        ) / time_since_epoch_begin
        return steps_per_second

    def _log_epoch_metrics(self, epoch, logs):
        """Writes epoch metrics out as scalar summaries.

        Args:
            epoch: Int. The global step to use for TensorBoard.
            logs: Dict. Keys are scalar summary names, values are scalars.
        """
        if not logs:
            return

        train_logs = {k: v for k, v in logs.items() if not k.startswith("val_")}
        val_logs = {k: v for k, v in logs.items() if k.startswith("val_")}
        train_logs = self._collect_learning_rate(train_logs)
        if self.write_steps_per_second:
            train_logs["steps_per_second"] = self._compute_steps_per_second()

        with tf.summary.record_if(True):
            if train_logs:
                with self._train_writer.as_default():
                    for name, value in train_logs.items():
                        tf.summary.scalar("epoch_" + name, value, step=epoch)
            if val_logs:
                with self._val_writer.as_default():
                    for name, value in val_logs.items():
                        name = name[4:]  # Remove 'val_' prefix.
                        tf.summary.scalar("epoch_" + name, value, step=epoch)

    def _log_weights(self, epoch):
        """Logs the weights of the Model to TensorBoard."""
        with self._train_writer.as_default():
            with tf.summary.record_if(True):
                for layer in self.model.layers:
                    for weight in layer.weights:
                        weight_name = weight.name.replace(":", "_")
                        # Add a suffix to prevent summary tag name collision.
                        histogram_weight_name = weight_name + "/histogram"
                        tf.summary.histogram(
                            histogram_weight_name, weight, step=epoch
                        )
                        if self.write_images:
                            # Add a suffix to prevent summary tag name
                            # collision.
                            image_weight_name = weight_name + "/image"
                            self._log_weight_as_image(
                                weight, image_weight_name, epoch
                            )
                self._train_writer.flush()

    def _log_weight_as_image(self, weight, weight_name, epoch):
        """Logs a weight as a TensorBoard image."""
        w_img = tf.squeeze(weight)
        shape = backend.int_shape(w_img)
        if len(shape) == 1:  # Bias case
            w_img = tf.reshape(w_img, [1, shape[0], 1, 1])
        elif len(shape) == 2:  # Dense layer kernel case
            if shape[0] > shape[1]:
                w_img = tf.transpose(w_img)
                shape = backend.int_shape(w_img)
            w_img = tf.reshape(w_img, [1, shape[0], shape[1], 1])
        elif len(shape) == 3:  # ConvNet case
            if backend.image_data_format() == "channels_last":
                # Switch to channels_first to display every kernel as a separate
                # image.
                w_img = tf.transpose(w_img, perm=[2, 0, 1])
                shape = backend.int_shape(w_img)
            w_img = tf.reshape(w_img, [shape[0], shape[1], shape[2], 1])

        shape = backend.int_shape(w_img)
        # Not possible to handle 3D convnets etc.
        if len(shape) == 4 and shape[-1] in [1, 3, 4]:
            tf.summary.image(weight_name, w_img, step=epoch)

    def _log_embeddings(self, epoch):
        embeddings_ckpt = os.path.join(
            self._log_write_dir,
            "train",
            f"keras_embedding.ckpt-{epoch}",
        )
        self.model.save_weights(embeddings_ckpt)

    def _start_profiler(self, logdir):
        """Starts the profiler if currently inactive.

        Args:
          logdir: Directory where profiler results will be saved.
        """
        if self._profiler_started:
            return
        try:
            tf.profiler.experimental.start(logdir=logdir)
            self._profiler_started = True
        except tf.errors.AlreadyExistsError as e:
            # Profiler errors should not be fatal.
            logging.error("Failed to start profiler: %s", e.message)

    def _stop_profiler(self, save=True):
        """Stops the profiler if currently active.

        Args:
          save: Whether to save the profiler results to TensorBoard.
        """
        if not self._profiler_started:
            return
        try:
            tf.profiler.experimental.stop(save=save)
        except tf.errors.UnavailableError as e:
            # Profiler errors should not be fatal.
            logging.error("Failed to stop profiler: %s", e.message)
        finally:
            self._profiler_started = False


@keras_export("keras.callbacks.ReduceLROnPlateau")
class ReduceLROnPlateau(Callback):
    """Reduce learning rate when a metric has stopped improving.

    Models often benefit from reducing the learning rate by a factor
    of 2-10 once learning stagnates. This callback monitors a
    quantity and if no improvement is seen for a 'patience' number
    of epochs, the learning rate is reduced.

    Example:

    ```python
    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
                                  patience=5, min_lr=0.001)
    model.fit(X_train, Y_train, callbacks=[reduce_lr])
    ```

    Args:
        monitor: quantity to be monitored.
        factor: factor by which the learning rate will be reduced.
          `new_lr = lr * factor`.
        patience: number of epochs with no improvement after which learning rate
          will be reduced.
        verbose: int. 0: quiet, 1: update messages.
        mode: one of `{'auto', 'min', 'max'}`. In `'min'` mode,
          the learning rate will be reduced when the
          quantity monitored has stopped decreasing; in `'max'` mode it will be
          reduced when the quantity monitored has stopped increasing; in
          `'auto'` mode, the direction is automatically inferred from the name
          of the monitored quantity.
        min_delta: threshold for measuring the new optimum, to only focus on
          significant changes.
        cooldown: number of epochs to wait before resuming normal operation
          after lr has been reduced.
        min_lr: lower bound on the learning rate.
    """

    def __init__(
        self,
        monitor="val_loss",
        factor=0.1,
        patience=10,
        verbose=0,
        mode="auto",
        min_delta=1e-4,
        cooldown=0,
        min_lr=0,
        **kwargs,
    ):
        super().__init__()

        self.monitor = monitor
        if factor >= 1.0:
            raise ValueError(
                "ReduceLROnPlateau does not support "
                f"a factor >= 1.0. Got {factor}"
            )
        if "epsilon" in kwargs:
            min_delta = kwargs.pop("epsilon")
            logging.warning(
                "`epsilon` argument is deprecated and "
                "will be removed, use `min_delta` instead."
            )
        self.factor = factor
        self.min_lr = min_lr
        self.min_delta = min_delta
        self.patience = patience
        self.verbose = verbose
        self.cooldown = cooldown
        self.cooldown_counter = 0  # Cooldown counter.
        self.wait = 0
        self.best = 0
        self.mode = mode
        self.monitor_op = None
        self._reset()

    def _reset(self):
        """Resets wait counter and cooldown counter."""
        if self.mode not in ["auto", "min", "max"]:
            logging.warning(
                "Learning rate reduction mode %s is unknown, "
                "fallback to auto mode.",
                self.mode,
            )
            self.mode = "auto"
        if self.mode == "min" or (
            self.mode == "auto" and "acc" not in self.monitor
        ):
            self.monitor_op = lambda a, b: np.less(a, b - self.min_delta)
            self.best = np.Inf
        else:
            self.monitor_op = lambda a, b: np.greater(a, b + self.min_delta)
            self.best = -np.Inf
        self.cooldown_counter = 0
        self.wait = 0

    def on_train_begin(self, logs=None):
        self._reset()

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        logs["lr"] = backend.get_value(self.model.optimizer.lr)
        current = logs.get(self.monitor)
        if current is None:
            logging.warning(
                "Learning rate reduction is conditioned on metric `%s` "
                "which is not available. Available metrics are: %s",
                self.monitor,
                ",".join(list(logs.keys())),
            )

        else:
            if self.in_cooldown():
                self.cooldown_counter -= 1
                self.wait = 0

            if self.monitor_op(current, self.best):
                self.best = current
                self.wait = 0
            elif not self.in_cooldown():
                self.wait += 1
                if self.wait >= self.patience:
                    old_lr = backend.get_value(self.model.optimizer.lr)
                    if old_lr > np.float32(self.min_lr):
                        new_lr = old_lr * self.factor
                        new_lr = max(new_lr, self.min_lr)
                        backend.set_value(self.model.optimizer.lr, new_lr)
                        if self.verbose > 0:
                            io_utils.print_msg(
                                f"\nEpoch {epoch +1}: "
                                "ReduceLROnPlateau reducing "
                                f"learning rate to {new_lr}."
                            )
                        self.cooldown_counter = self.cooldown
                        self.wait = 0

    def in_cooldown(self):
        return self.cooldown_counter > 0


@keras_export("keras.callbacks.CSVLogger")
class CSVLogger(Callback):
    """Callback that streams epoch results to a CSV file.

    Supports all values that can be represented as a string,
    including 1D iterables such as `np.ndarray`.

    Example:

    ```python
    csv_logger = CSVLogger('training.log')
    model.fit(X_train, Y_train, callbacks=[csv_logger])
    ```

    Args:
        filename: Filename of the CSV file, e.g. `'run/log.csv'`.
        separator: String used to separate elements in the CSV file.
        append: Boolean. True: append if file exists (useful for continuing
            training). False: overwrite existing file.
    """

    def __init__(self, filename, separator=",", append=False):
        self.sep = separator
        self.filename = io_utils.path_to_string(filename)
        self.append = append
        self.writer = None
        self.keys = None
        self.append_header = True
        super().__init__()

    def on_train_begin(self, logs=None):
        if self.append:
            if tf.io.gfile.exists(self.filename):
                with tf.io.gfile.GFile(self.filename, "r") as f:
                    self.append_header = not bool(len(f.readline()))
            mode = "a"
        else:
            mode = "w"
        self.csv_file = tf.io.gfile.GFile(self.filename, mode)

    def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}

        def handle_value(k):
            is_zero_dim_ndarray = isinstance(k, np.ndarray) and k.ndim == 0
            if isinstance(k, str):
                return k
            elif (
                isinstance(k, collections.abc.Iterable)
                and not is_zero_dim_ndarray
            ):
                return f"\"[{', '.join(map(str, k))}]\""
            else:
                return k

        if self.keys is None:
            self.keys = sorted(logs.keys())

        if self.model.stop_training:
            # We set NA so that csv parsers do not fail for this last epoch.
            logs = dict(
                (k, logs[k]) if k in logs else (k, "NA") for k in self.keys
            )

        if not self.writer:

            class CustomDialect(csv.excel):
                delimiter = self.sep

            fieldnames = ["epoch"] + self.keys

            self.writer = csv.DictWriter(
                self.csv_file, fieldnames=fieldnames, dialect=CustomDialect
            )
            if self.append_header:
                self.writer.writeheader()

        row_dict = collections.OrderedDict({"epoch": epoch})
        row_dict.update((key, handle_value(logs[key])) for key in self.keys)
        self.writer.writerow(row_dict)
        self.csv_file.flush()

    def on_train_end(self, logs=None):
        self.csv_file.close()
        self.writer = None


@keras_export("keras.callbacks.LambdaCallback")
class LambdaCallback(Callback):
    r"""Callback for creating simple, custom callbacks on-the-fly.

    This callback is constructed with anonymous functions that will be called
    at the appropriate time (during `Model.{fit | evaluate | predict}`).
    Note that the callbacks expects positional arguments, as:

    - `on_epoch_begin` and `on_epoch_end` expect two positional arguments:
      `epoch`, `logs`
    - `on_batch_begin` and `on_batch_end` expect two positional arguments:
      `batch`, `logs`
    - `on_train_begin` and `on_train_end` expect one positional argument:
      `logs`

    Args:
        on_epoch_begin: called at the beginning of every epoch.
        on_epoch_end: called at the end of every epoch.
        on_batch_begin: called at the beginning of every batch.
        on_batch_end: called at the end of every batch.
        on_train_begin: called at the beginning of model training.
        on_train_end: called at the end of model training.

    Example:

    ```python
    # Print the batch number at the beginning of every batch.
    batch_print_callback = LambdaCallback(
        on_batch_begin=lambda batch,logs: print(batch))

    # Stream the epoch loss to a file in JSON format. The file content
    # is not well-formed JSON but rather has a JSON object per line.
    import json
    json_log = open('loss_log.json', mode='wt', buffering=1)
    json_logging_callback = LambdaCallback(
        on_epoch_end=lambda epoch, logs: json_log.write(
            json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
        on_train_end=lambda logs: json_log.close()
    )

    # Terminate some processes after having finished model training.
    processes = ...
    cleanup_callback = LambdaCallback(
        on_train_end=lambda logs: [
            p.terminate() for p in processes if p.is_alive()])

    model.fit(...,
              callbacks=[batch_print_callback,
                         json_logging_callback,
                         cleanup_callback])
    ```
    """

    def __init__(
        self,
        on_epoch_begin=None,
        on_epoch_end=None,
        on_batch_begin=None,
        on_batch_end=None,
        on_train_begin=None,
        on_train_end=None,
        **kwargs,
    ):
        super().__init__()
        self.__dict__.update(kwargs)
        if on_epoch_begin is not None:
            self.on_epoch_begin = on_epoch_begin
        if on_epoch_end is not None:
            self.on_epoch_end = on_epoch_end
        if on_batch_begin is not None:
            self.on_batch_begin = on_batch_begin
        if on_batch_end is not None:
            self.on_batch_end = on_batch_end
        if on_train_begin is not None:
            self.on_train_begin = on_train_begin
        if on_train_end is not None:
            self.on_train_end = on_train_end
