from keras.src.api_export import keras_export
from keras.src.optimizers import adam
from keras.src.optimizers import optimizer


@keras_export(["keras.optimizers.AdamW"])
class AdamW(adam.Adam):
    """Optimizer that implements the AdamW algorithm.

    AdamW optimization is a stochastic gradient descent method that is based on
    adaptive estimation of first-order and second-order moments with an added
    method to decay weights per the techniques discussed in the paper,
    'Decoupled Weight Decay Regularization' by
    [Loshchilov, Hutter et al., 2019](https://arxiv.org/abs/1711.05101).

    According to
    [Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
    the underlying Adam method is "*computationally
    efficient, has little memory requirement, invariant to diagonal rescaling of
    gradients, and is well suited for problems that are large in terms of
    data/parameters*".

    Args:
        learning_rate: A float, a
            `keras.optimizers.schedules.LearningRateSchedule` instance, or
            a callable that takes no arguments and returns the actual value to
            use. The learning rate. Defaults to `0.001`.
        beta_1: A float value or a constant float tensor, or a callable
            that takes no arguments and returns the actual value to use. The
            exponential decay rate for the 1st moment estimates.
            Defaults to `0.9`.
        beta_2: A float value or a constant float tensor, or a callable
            that takes no arguments and returns the actual value to use. The
            exponential decay rate for the 2nd moment estimates.
            Defaults to `0.999`.
        epsilon: A small constant for numerical stability. This epsilon is
            "epsilon hat" in the Kingma and Ba paper (in the formula just
            before Section 2.1), not the epsilon in Algorithm 1 of the paper.
            Defaults to 1e-7.
        amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm
            from the paper "On the Convergence of Adam and beyond".
            Defaults to `False`.
        {{base_optimizer_keyword_args}}

    References:

    - [Loshchilov et al., 2019](https://arxiv.org/abs/1711.05101)
    - [Kingma et al., 2014](http://arxiv.org/abs/1412.6980) for `adam`
    - [Reddi et al., 2018](
        https://openreview.net/pdf?id=ryQu7f-RZ) for `amsgrad`.
    """

    def __init__(
        self,
        learning_rate=0.001,
        weight_decay=0.004,
        beta_1=0.9,
        beta_2=0.999,
        epsilon=1e-7,
        amsgrad=False,
        clipnorm=None,
        clipvalue=None,
        global_clipnorm=None,
        use_ema=False,
        ema_momentum=0.99,
        ema_overwrite_frequency=None,
        loss_scale_factor=None,
        gradient_accumulation_steps=None,
        name="adamw",
        **kwargs,
    ):
        super().__init__(
            learning_rate=learning_rate,
            beta_1=beta_1,
            beta_2=beta_2,
            epsilon=epsilon,
            amsgrad=amsgrad,
            name=name,
            weight_decay=weight_decay,
            clipnorm=clipnorm,
            clipvalue=clipvalue,
            global_clipnorm=global_clipnorm,
            use_ema=use_ema,
            ema_momentum=ema_momentum,
            ema_overwrite_frequency=ema_overwrite_frequency,
            loss_scale_factor=loss_scale_factor,
            gradient_accumulation_steps=gradient_accumulation_steps,
            **kwargs,
        )

        if self.weight_decay is None:
            raise ValueError(
                "Argument `weight_decay` must be a float. Received: "
                "weight_decay=None"
            )


AdamW.__doc__ = AdamW.__doc__.replace(
    "{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
)

from keras.src.api_export import keras_export
from keras.src.optimizers import adam
from keras.src.optimizers import optimizer


@keras_export(["keras.optimizers.AdamW"])
class AdamW(adam.Adam):
    """Optimizer that implements the AdamW algorithm.

    AdamW optimization is a stochastic gradient descent method that is based on
    adaptive estimation of first-order and second-order moments with an added
    method to decay weights per the techniques discussed in the paper,
    'Decoupled Weight Decay Regularization' by
    [Loshchilov, Hutter et al., 2019](https://arxiv.org/abs/1711.05101).

    According to
    [Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
    the underying Adam method is "*computationally
    efficient, has little memory requirement, invariant to diagonal rescaling of
    gradients, and is well suited for problems that are large in terms of
    data/parameters*".

    Args:
        learning_rate: A float, a
            `keras.optimizers.schedules.LearningRateSchedule` instance, or
            a callable that takes no arguments and returns the actual value to
            use. The learning rate. Defaults to `0.001`.
        beta_1: A float value or a constant float tensor, or a callable
            that takes no arguments and returns the actual value to use. The
            exponential decay rate for the 1st moment estimates.
            Defaults to `0.9`.
        beta_2: A float value or a constant float tensor, or a callable
            that takes no arguments and returns the actual value to use. The
            exponential decay rate for the 2nd moment estimates.
            Defaults to `0.999`.
        epsilon: A small constant for numerical stability. This epsilon is
            "epsilon hat" in the Kingma and Ba paper (in the formula just
            before Section 2.1), not the epsilon in Algorithm 1 of the paper.
            Defaults to 1e-7.
        amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm
            from the paper "On the Convergence of Adam and beyond".
            Defaults to `False`.
        {{base_optimizer_keyword_args}}

    References:

    - [Loshchilov et al., 2019](https://arxiv.org/abs/1711.05101)
    - [Kingma et al., 2014](http://arxiv.org/abs/1412.6980) for `adam`
    - [Reddi et al., 2018](
        https://openreview.net/pdf?id=ryQu7f-RZ) for `amsgrad`.
    """

    def __init__(
        self,
        learning_rate=0.001,
        weight_decay=0.004,
        beta_1=0.9,
        beta_2=0.999,
        epsilon=1e-7,
        amsgrad=False,
        clipnorm=None,
        clipvalue=None,
        global_clipnorm=None,
        use_ema=False,
        ema_momentum=0.99,
        ema_overwrite_frequency=None,
        loss_scale_factor=None,
        gradient_accumulation_steps=None,
        name="adamw",
        **kwargs,
    ):
        super().__init__(
            learning_rate=learning_rate,
            beta_1=beta_1,
            beta_2=beta_2,
            epsilon=epsilon,
            amsgrad=amsgrad,
            name=name,
            weight_decay=weight_decay,
            clipnorm=clipnorm,
            clipvalue=clipvalue,
            global_clipnorm=global_clipnorm,
            use_ema=use_ema,
            ema_momentum=ema_momentum,
            ema_overwrite_frequency=ema_overwrite_frequency,
            loss_scale_factor=loss_scale_factor,
            gradient_accumulation_steps=gradient_accumulation_steps,
            **kwargs,
        )

        if self.weight_decay is None:
            raise ValueError(
                "Argument `weight_decay` must be a float. Received: "
                "weight_decay=None"
            )


AdamW.__doc__ = AdamW.__doc__.replace(
    "{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
)
