# Original source taken from https://github.com/LiyuanLucasLiu/RAdam
#
# Copyright 2019 Liyuan Liu
#
#   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.
import math

import torch

# pylint: disable=no-name-in-module
from torch.optim.optimizer import Optimizer


class RAdam(Optimizer):
    """RAdam optimizer"""

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
                 weight_decay=0):
        """
        Init

        :param params: parameters to optimize
        :param lr: learning rate
        :param betas: beta
        :param eps: numerical precision
        :param weight_decay: weight decay weight
        """
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        self.buffer = [[None, None, None] for _ in range(10)]
        super().__init__(params, defaults)

    def step(self, closure=None):

        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:

            for p in group['params']:
                if p.grad is None:
                    continue
                grad = p.grad.data.float()
                if grad.is_sparse:
                    raise RuntimeError(
                        'RAdam does not support sparse gradients'
                    )

                p_data_fp32 = p.data.float()

                state = self.state[p]

                if len(state) == 0:
                    state['step'] = 0
                    state['exp_avg'] = torch.zeros_like(p_data_fp32)
                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
                else:
                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
                    state['exp_avg_sq'] = (
                        state['exp_avg_sq'].type_as(p_data_fp32)
                    )

                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
                beta1, beta2 = group['betas']

                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                state['step'] += 1
                buffered = self.buffer[int(state['step'] % 10)]
                if state['step'] == buffered[0]:
                    N_sma, step_size = buffered[1], buffered[2]
                else:
                    buffered[0] = state['step']
                    beta2_t = beta2 ** state['step']
                    N_sma_max = 2 / (1 - beta2) - 1
                    N_sma = (
                        N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
                    )
                    buffered[1] = N_sma

                    # more conservative since it's an approximated value
                    if N_sma >= 5:
                        step_size = (
                            group['lr'] *
                            math.sqrt(
                                (1 - beta2_t) * (N_sma - 4) /
                                (N_sma_max - 4) * (N_sma - 2) /
                                N_sma * N_sma_max / (N_sma_max - 2)
                            ) / (1 - beta1 ** state['step'])
                        )
                    else:
                        step_size = group['lr'] / (1 - beta1 ** state['step'])
                    buffered[2] = step_size

                if group['weight_decay'] != 0:
                    p_data_fp32.add_(
                        -group['weight_decay'] * group['lr'], p_data_fp32
                    )

                # more conservative since it's an approximated value
                if N_sma >= 5:
                    denom = exp_avg_sq.sqrt().add_(group['eps'])
                    p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
                else:
                    p_data_fp32.add_(-step_size, exp_avg)

                p.data.copy_(p_data_fp32)

        return loss
