# Copyright 2022 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
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# ==============================================================================
"""Adamax optimizer implementation."""

import tensorflow.compat.v2 as tf

from keras.optimizers import optimizer
from keras.saving.object_registration import register_keras_serializable

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


@register_keras_serializable()
@keras_export(
    "keras.optimizers.experimental.Adamax", "keras.optimizers.Adamax", v1=[]
)
class Adamax(optimizer.Optimizer):
    """Optimizer that implements the Adamax algorithm.

    Adamax, a variant of Adam based on the infinity norm, is a first-order
    gradient-based optimization method. Due to its capability of adjusting the
    learning rate based on data characteristics, it is suited to learn
    time-variant process, e.g., speech data with dynamically changed noise
    conditions. Default parameters follow those provided in the paper (see
    references below).

    Initialization:

    ```python
    m = 0  # Initialize initial 1st moment vector
    u = 0  # Initialize the exponentially weighted infinity norm
    t = 0  # Initialize timestep
    ```

    The update rule for parameter `w` with gradient `g` is described at the end
    of section 7.1 of the paper (see the referenece section):

    ```python
    t += 1
    m = beta1 * m + (1 - beta) * g
    u = max(beta2 * u, abs(g))
    current_lr = learning_rate / (1 - beta1 ** t)
    w = w - current_lr * m / (u + epsilon)
    ```

    Args:
      learning_rate: A `tf.Tensor`, floating point value, a schedule that is a
        `tf.keras.optimizers.schedules.LearningRateSchedule`, 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. The exponential decay
        rate for the 1st moment estimates.
      beta_2: A float value or a constant float tensor. The exponential decay
        rate for the exponentially weighted infinity norm.
      epsilon: A small constant for numerical stability.
      {{base_optimizer_keyword_args}}

    Reference:
      - [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
    """

    def __init__(
        self,
        learning_rate=0.001,
        beta_1=0.9,
        beta_2=0.999,
        epsilon=1e-7,
        weight_decay=None,
        clipnorm=None,
        clipvalue=None,
        global_clipnorm=None,
        use_ema=False,
        ema_momentum=0.99,
        ema_overwrite_frequency=None,
        jit_compile=True,
        name="Adamax",
        **kwargs
    ):
        super().__init__(
            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,
            jit_compile=jit_compile,
            **kwargs
        )
        self._learning_rate = self._build_learning_rate(learning_rate)
        self.beta_1 = beta_1
        self.beta_2 = beta_2
        self.epsilon = epsilon

    def build(self, var_list):
        """Initialize optimizer variables.

        Adamax optimizer has 2 types of variables: momentums (denoted as m),
        exponentially weighted infinity norm (denoted as u).

        Args:
          var_list: list of model variables to build Adamax variables on.
        """
        super().build(var_list)
        if hasattr(self, "_built") and self._built:
            return
        self._built = True
        self._m = []
        self._u = []
        for var in var_list:
            self._m.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name="m"
                )
            )
            self._u.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name="u"
                )
            )

    def update_step(self, gradient, variable):
        """Update step given gradient and the associated model variable."""
        lr = tf.cast(self.learning_rate, variable.dtype)
        local_step = tf.cast(self.iterations + 1, variable.dtype)
        beta_1_power = tf.pow(tf.cast(self.beta_1, variable.dtype), local_step)

        var_key = self._var_key(variable)
        m = self._m[self._index_dict[var_key]]
        u = self._u[self._index_dict[var_key]]

        if isinstance(gradient, tf.IndexedSlices):
            # Sparse gradients.
            indices = gradient.indices
            m.assign_add(-m * (1 - self.beta_1))
            m.scatter_add(
                tf.IndexedSlices(gradient.values * (1 - self.beta_1), indices)
            )
            u.assign(u * self.beta_2)
            u_slice = tf.gather(u, indices)
            u_slice_incremental = (
                tf.maximum(u_slice, tf.abs(gradient.values)) - u_slice
            )
            u.scatter_add(tf.IndexedSlices(u_slice_incremental, indices))
            variable.assign_sub(
                (lr * m) / ((1 - beta_1_power) * (u + self.epsilon))
            )
        else:
            # Dense gradients.
            m.assign_add((gradient - m) * (1 - self.beta_1))
            u.assign(tf.maximum(self.beta_2 * u, tf.abs(gradient)))
            variable.assign_sub(
                (lr * m) / ((1 - beta_1_power) * (u + self.epsilon))
            )

    def get_config(self):
        config = super().get_config()

        config.update(
            {
                "learning_rate": self._serialize_hyperparameter(
                    self._learning_rate
                ),
                "beta_1": self.beta_1,
                "beta_2": self.beta_2,
                "epsilon": self.epsilon,
            }
        )
        return config


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