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


import tensorflow.compat.v2 as tf

from keras.src.optimizers.legacy import optimizer_v2

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


@keras_export(
    "keras.optimizers.legacy.Ftrl",
    v1=["keras.optimizers.Ftrl", "keras.optimizers.legacy.Ftrl"],
)
class Ftrl(optimizer_v2.OptimizerV2):
    r"""Optimizer that implements the FTRL algorithm.

    "Follow The Regularized Leader" (FTRL) is an optimization algorithm
    developed at Google for click-through rate prediction in the early 2010s. It
    is most suitable for shallow models with large and sparse feature spaces.
    The algorithm is described by
    [McMahan et al., 2013](https://research.google.com/pubs/archive/41159.pdf).
    The Keras version has support for both online L2 regularization
    (the L2 regularization described in the paper
    above) and shrinkage-type L2 regularization
    (which is the addition of an L2 penalty to the loss function).

    Initialization:

    ```python
    n = 0
    sigma = 0
    z = 0
    ```

    Update rule for one variable `w`:

    ```python
    prev_n = n
    n = n + g ** 2
    sigma = (sqrt(n) - sqrt(prev_n)) / lr
    z = z + g - sigma * w
    if abs(z) < lambda_1:
      w = 0
    else:
      w = (sgn(z) * lambda_1 - z) / ((beta + sqrt(n)) / alpha + lambda_2)
    ```

    Notation:

    - `lr` is the learning rate
    - `g` is the gradient for the variable
    - `lambda_1` is the L1 regularization strength
    - `lambda_2` is the L2 regularization strength

    Check the documentation for the `l2_shrinkage_regularization_strength`
    parameter for more details when shrinkage is enabled, in which case gradient
    is replaced with a gradient with shrinkage.

    Args:
      learning_rate: A `Tensor`, floating point value, or a schedule that is a
        `tf.keras.optimizers.schedules.LearningRateSchedule`. The learning rate.
      learning_rate_power: A float value, must be less or equal to zero.
        Controls how the learning rate decreases during training. Use zero for
        a fixed learning rate.
      initial_accumulator_value: The starting value for accumulators.
        Only zero or positive values are allowed.
      l1_regularization_strength: A float value, must be greater than or
        equal to zero. Defaults to `0.0`.
      l2_regularization_strength: A float value, must be greater than or
        equal to zero. Defaults to `0.0`.
      name: Optional name prefix for the operations created when applying
        gradients.  Defaults to `"Ftrl"`.
      l2_shrinkage_regularization_strength: A float value, must be greater than
        or equal to zero. This differs from L2 above in that the L2 above is a
        stabilization penalty, whereas this L2 shrinkage is a magnitude penalty.
        When input is sparse shrinkage will only happen on the active weights.
      beta: A float value, representing the beta value from the paper.
        Defaults to `0.0`.
      **kwargs: keyword arguments. Allowed arguments are `clipvalue`,
        `clipnorm`, `global_clipnorm`.
        If `clipvalue` (float) is set, the gradient of each weight
        is clipped to be no higher than this value.
        If `clipnorm` (float) is set, the gradient of each weight
        is individually clipped so that its norm is no higher than this value.
        If `global_clipnorm` (float) is set the gradient of all weights is
        clipped so that their global norm is no higher than this value.

    Reference:
      - [McMahan et al., 2013](
        https://research.google.com/pubs/archive/41159.pdf)
    """

    def __init__(
        self,
        learning_rate=0.001,
        learning_rate_power=-0.5,
        initial_accumulator_value=0.1,
        l1_regularization_strength=0.0,
        l2_regularization_strength=0.0,
        name="Ftrl",
        l2_shrinkage_regularization_strength=0.0,
        beta=0.0,
        **kwargs,
    ):
        super().__init__(name, **kwargs)

        if initial_accumulator_value < 0.0:
            raise ValueError(
                "`initial_accumulator_value` needs to be "
                "positive or zero. Received: "
                f"initial_accumulator_value={initial_accumulator_value}."
            )
        if learning_rate_power > 0.0:
            raise ValueError(
                "`learning_rate_power` needs to be "
                "negative or zero. Received: "
                f"learning_rate_power={learning_rate_power}."
            )
        if l1_regularization_strength < 0.0:
            raise ValueError(
                "`l1_regularization_strength` needs to be positive or zero. "
                "Received: l1_regularization_strength="
                f"{l1_regularization_strength}."
            )
        if l2_regularization_strength < 0.0:
            raise ValueError(
                "`l2_regularization_strength` needs to be positive or zero. "
                "Received: l2_regularization_strength="
                f"{l2_regularization_strength}."
            )
        if l2_shrinkage_regularization_strength < 0.0:
            raise ValueError(
                "`l2_shrinkage_regularization_strength` needs to be positive "
                "or zero. Received: l2_shrinkage_regularization_strength"
                f"={l2_shrinkage_regularization_strength}."
            )

        self._set_hyper("learning_rate", learning_rate)
        self._set_hyper("decay", self._initial_decay)
        self._set_hyper("learning_rate_power", learning_rate_power)
        self._set_hyper(
            "l1_regularization_strength", l1_regularization_strength
        )
        self._set_hyper(
            "l2_regularization_strength", l2_regularization_strength
        )
        self._set_hyper("beta", beta)
        self._initial_accumulator_value = initial_accumulator_value
        self._l2_shrinkage_regularization_strength = (
            l2_shrinkage_regularization_strength
        )

    def _create_slots(self, var_list):
        # Create the "accum" and "linear" slots.
        for var in var_list:
            dtype = var.dtype.base_dtype
            init = tf.compat.v1.constant_initializer(
                self._initial_accumulator_value, dtype=dtype
            )
            self.add_slot(var, "accumulator", init)
            self.add_slot(var, "linear")

    def _prepare_local(self, var_device, var_dtype, apply_state):
        super()._prepare_local(var_device, var_dtype, apply_state)
        apply_state[(var_device, var_dtype)].update(
            dict(
                learning_rate_power=tf.identity(
                    self._get_hyper("learning_rate_power", var_dtype)
                ),
                l1_regularization_strength=tf.identity(
                    self._get_hyper("l1_regularization_strength", var_dtype)
                ),
                l2_regularization_strength=tf.identity(
                    self._get_hyper("l2_regularization_strength", var_dtype)
                ),
                beta=tf.identity(self._get_hyper("beta", var_dtype)),
                l2_shrinkage_regularization_strength=tf.cast(
                    self._l2_shrinkage_regularization_strength, var_dtype
                ),
            )
        )

    def _resource_apply_dense(self, grad, var, apply_state=None):
        var_device, var_dtype = var.device, var.dtype.base_dtype
        coefficients = (apply_state or {}).get(
            (var_device, var_dtype)
        ) or self._fallback_apply_state(var_device, var_dtype)

        # Adjust L2 regularization strength to include beta to avoid the
        # underlying TensorFlow ops needing to include it.
        adjusted_l2_regularization_strength = coefficients[
            "l2_regularization_strength"
        ] + coefficients["beta"] / (2.0 * coefficients["lr_t"])

        accum = self.get_slot(var, "accumulator")
        linear = self.get_slot(var, "linear")

        if self._l2_shrinkage_regularization_strength <= 0.0:
            return tf.raw_ops.ResourceApplyFtrl(
                var=var.handle,
                accum=accum.handle,
                linear=linear.handle,
                grad=grad,
                lr=coefficients["lr_t"],
                l1=coefficients["l1_regularization_strength"],
                l2=adjusted_l2_regularization_strength,
                lr_power=coefficients["learning_rate_power"],
                use_locking=self._use_locking,
            )
        else:
            return tf.raw_ops.ResourceApplyFtrlV2(
                var=var.handle,
                accum=accum.handle,
                linear=linear.handle,
                grad=grad,
                lr=coefficients["lr_t"],
                l1=coefficients["l1_regularization_strength"],
                l2=adjusted_l2_regularization_strength,
                l2_shrinkage=coefficients[
                    "l2_shrinkage_regularization_strength"
                ],
                lr_power=coefficients["learning_rate_power"],
                use_locking=self._use_locking,
            )

    def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
        var_device, var_dtype = var.device, var.dtype.base_dtype
        coefficients = (apply_state or {}).get(
            (var_device, var_dtype)
        ) or self._fallback_apply_state(var_device, var_dtype)

        # Adjust L2 regularization strength to include beta to avoid the
        # underlying TensorFlow ops needing to include it.
        adjusted_l2_regularization_strength = coefficients[
            "l2_regularization_strength"
        ] + coefficients["beta"] / (2.0 * coefficients["lr_t"])

        accum = self.get_slot(var, "accumulator")
        linear = self.get_slot(var, "linear")

        if self._l2_shrinkage_regularization_strength <= 0.0:
            return tf.raw_ops.ResourceSparseApplyFtrl(
                var=var.handle,
                accum=accum.handle,
                linear=linear.handle,
                grad=grad,
                indices=indices,
                lr=coefficients["lr_t"],
                l1=coefficients["l1_regularization_strength"],
                l2=adjusted_l2_regularization_strength,
                lr_power=coefficients["learning_rate_power"],
                use_locking=self._use_locking,
            )
        else:
            return tf.raw_ops.ResourceSparseApplyFtrlV2(
                var=var.handle,
                accum=accum.handle,
                linear=linear.handle,
                grad=grad,
                indices=indices,
                lr=coefficients["lr_t"],
                l1=coefficients["l1_regularization_strength"],
                l2=adjusted_l2_regularization_strength,
                l2_shrinkage=coefficients[
                    "l2_shrinkage_regularization_strength"
                ],
                lr_power=coefficients["learning_rate_power"],
                use_locking=self._use_locking,
            )

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "learning_rate": self._serialize_hyperparameter(
                    "learning_rate"
                ),
                "decay": self._initial_decay,
                "initial_accumulator_value": self._initial_accumulator_value,
                "learning_rate_power": self._serialize_hyperparameter(
                    "learning_rate_power"
                ),
                "l1_regularization_strength": self._serialize_hyperparameter(
                    "l1_regularization_strength"
                ),
                "l2_regularization_strength": self._serialize_hyperparameter(
                    "l2_regularization_strength"
                ),
                "beta": self._serialize_hyperparameter("beta"),
                "l2_shrinkage_regularization_strength": self._l2_shrinkage_regularization_strength,  # noqa: E501
            }
        )
        return config

