# Copyright 2021 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.
# ==============================================================================
"""Nadam optimizer implementation."""

import tensorflow.compat.v2 as tf

from keras.src.optimizers import optimizer
from keras.src.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.Nadam", "keras.optimizers.Nadam", v1=[]
)
class Nadam(optimizer.Optimizer):
    r"""Optimizer that implements the Nadam algorithm.

    Much like Adam is essentially RMSprop with momentum, Nadam is Adam with
    Nesterov momentum.

    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, 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`.
      {{base_optimizer_keyword_args}}

    Reference:
      - [Dozat, 2015](http://cs229.stanford.edu/proj2015/054_report.pdf).

    """

    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="Nadam",
        **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.

        Nadam optimizer has 2 types of variables: momentums and velocities.

        Args:
          var_list: list of model variables to build Nadam variables on.
        """
        super().build(var_list)
        if getattr(self, "_built", False):
            return
        self._built = True
        self._momentums = []
        self._velocities = []
        self._u_product = tf.Variable(1.0, dtype=var_list[0].dtype)
        # Keep a counter on how many times of _u_product has been computed to
        # avoid duplicated computations.
        self._u_product_counter = 1

        for var in var_list:
            self._momentums.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name="m"
                )
            )
            self._velocities.append(
                self.add_variable_from_reference(
                    model_variable=var, variable_name="v"
                )
            )

    def update_step(self, gradient, variable):
        """Update step given gradient and the associated model variable."""
        var_dtype = variable.dtype
        lr = tf.cast(self.learning_rate, var_dtype)
        local_step = tf.cast(self.iterations + 1, var_dtype)
        next_step = tf.cast(self.iterations + 2, var_dtype)
        decay = tf.cast(0.96, var_dtype)
        beta_1 = tf.cast(self.beta_1, var_dtype)
        beta_2 = tf.cast(self.beta_2, var_dtype)
        u_t = beta_1 * (1.0 - 0.5 * (tf.pow(decay, local_step)))
        u_t_1 = beta_1 * (1.0 - 0.5 * (tf.pow(decay, next_step)))

        def get_cached_u_product():
            return self._u_product

        def compute_new_u_product():
            u_product_t = self._u_product * u_t
            self._u_product.assign(u_product_t)
            self._u_product_counter += 1
            return u_product_t

        u_product_t = tf.cond(
            self._u_product_counter == (self.iterations + 2),
            true_fn=get_cached_u_product,
            false_fn=compute_new_u_product,
        )
        u_product_t_1 = u_product_t * u_t_1
        beta_2_power = tf.pow(beta_2, local_step)

        var_key = self._var_key(variable)
        m = self._momentums[self._index_dict[var_key]]
        v = self._velocities[self._index_dict[var_key]]

        if isinstance(gradient, tf.IndexedSlices):
            # Sparse gradients.
            m.assign_add(-m * (1 - beta_1))
            m.scatter_add(
                tf.IndexedSlices(
                    gradient.values * (1 - beta_1), gradient.indices
                )
            )
            v.assign_add(-v * (1 - beta_2))
            v.scatter_add(
                tf.IndexedSlices(
                    tf.square(gradient.values) * (1 - beta_2), gradient.indices
                )
            )
            m_hat = u_t_1 * m / (1 - u_product_t_1) + (1 - u_t) * gradient / (
                1 - u_product_t
            )
            v_hat = v / (1 - beta_2_power)

            variable.assign_sub((m_hat * lr) / (tf.sqrt(v_hat) + self.epsilon))
        else:
            # Dense gradients.
            m.assign_add((gradient - m) * (1 - beta_1))
            v.assign_add((tf.square(gradient) - v) * (1 - beta_2))
            m_hat = u_t_1 * m / (1 - u_product_t_1) + (1 - u_t) * gradient / (
                1 - u_product_t
            )
            v_hat = v / (1 - beta_2_power)

            variable.assign_sub((m_hat * lr) / (tf.sqrt(v_hat) + 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


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

