# 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.
# ==============================================================================
"""Adadelta optimizer implementation."""

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

from keras import backend_config
from keras.optimizers.optimizer_v2 import optimizer_v2

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


@keras_export("keras.optimizers.Adadelta")
class Adadelta(optimizer_v2.OptimizerV2):
    r"""Optimizer that implements the Adadelta algorithm.

    Adadelta optimization is a stochastic gradient descent method that is based
    on adaptive learning rate per dimension to address two drawbacks:

    - The continual decay of learning rates throughout training.
    - The need for a manually selected global learning rate.

    Adadelta is a more robust extension of Adagrad that adapts learning rates
    based on a moving window of gradient updates, instead of accumulating all
    past gradients. This way, Adadelta continues learning even when many updates
    have been done. Compared to Adagrad, in the original version of Adadelta you
    don't have to set an initial learning rate. In this version, the initial
    learning rate can be set, as in most other Keras optimizers.

    Args:
      learning_rate: Initial value for the learning rate:
        either a floating point value,
        or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance.
        Defaults to 0.001.
        Note that `Adadelta` tends to benefit from higher initial learning rate
        values compared to other optimizers.
        To match the exact form in the original paper, use 1.0.
      rho: A `Tensor` or a floating point value. The decay rate.
      epsilon: Small floating point value used to maintain numerical stability.
      name: Optional name prefix for the operations created when applying
        gradients.  Defaults to `"Adadelta"`.
      **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:
      - [Zeiler, 2012](http://arxiv.org/abs/1212.5701)
    """

    _HAS_AGGREGATE_GRAD = True

    def __init__(
        self,
        learning_rate=0.001,
        rho=0.95,
        epsilon=1e-7,
        name="Adadelta",
        **kwargs
    ):
        super().__init__(name, **kwargs)
        self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
        self._set_hyper("decay", self._initial_decay)
        self._set_hyper("rho", rho)
        self.epsilon = epsilon or backend_config.epsilon()

    def _create_slots(self, var_list):
        # Separate for-loops to respect the ordering of slot variables from v1.
        for v in var_list:
            self.add_slot(v, "accum_grad")
        for v in var_list:
            self.add_slot(v, "accum_var")

    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(
                epsilon=tf.convert_to_tensor(self.epsilon, var_dtype),
                rho=tf.identity(self._get_hyper("rho", var_dtype)),
            )
        )

    def set_weights(self, weights):
        params = self.weights
        # Override set_weights for backward compatibility of Keras V1 optimizer
        # since it does not include iteration at head of the weight list. Set
        # iteration to 0.
        if len(params) == len(weights) + 1:
            weights = [np.array(0)] + weights
        super().set_weights(weights)

    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)

        accum_grad = self.get_slot(var, "accum_grad")
        accum_var = self.get_slot(var, "accum_var")
        return tf.raw_ops.ResourceApplyAdadelta(
            var=var.handle,
            accum=accum_grad.handle,
            accum_update=accum_var.handle,
            lr=coefficients["lr_t"],
            rho=coefficients["rho"],
            epsilon=coefficients["epsilon"],
            grad=grad,
            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)

        accum_grad = self.get_slot(var, "accum_grad")
        accum_var = self.get_slot(var, "accum_var")
        return tf.raw_ops.ResourceSparseApplyAdadelta(
            var=var.handle,
            accum=accum_grad.handle,
            accum_update=accum_var.handle,
            lr=coefficients["lr_t"],
            rho=coefficients["rho"],
            epsilon=coefficients["epsilon"],
            grad=grad,
            indices=indices,
            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,
                "rho": self._serialize_hyperparameter("rho"),
                "epsilon": self.epsilon,
            }
        )
        return config