import math

import torch

from torch import optim

from torch.optim.optimizer import Optimizer

class RAdam(Optimizer):

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

        if not 0.0 <= lr:

            raise ValueError("Invalid learning rate: {}".format(lr))

        if not 0.0 <= eps:

            raise ValueError("Invalid epsilon value: {}".format(eps))

        if not 0.0 <= betas[0] < 1.0:

            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))

        if not 0.0 <= betas[1] < 1.0:

            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))

        self.degenerated_to_sgd = degenerated_to_sgd

        if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):

            for param in params:

                if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):

                    param['buffer'] = [[None, None, None] for _ in range(10)]

        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, buffer=[[None, None, None] for _ in range(10)])

        super(RAdam, self).__init__(params, defaults)

    def __setstate__(self, state):

        super(RAdam, self).__setstate__(state)

    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_(grad, grad, value = 1 - beta2)

                exp_avg.mul_(beta1).add_(grad, alpha = 1 - beta1)

                state['step'] += 1

                buffered = group['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 = 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'])

                    elif self.degenerated_to_sgd:

                        step_size = 1.0 / (1 - beta1 ** state['step'])

                    else:

                        step_size = -1

                    buffered[2] = step_size

                # more conservative since it's an approximated value

                if N_sma >= 5:

                    if group['weight_decay'] != 0:

                        p_data_fp32.add_(p_data_fp32, alpha = -group['weight_decay'] * group['lr'])

                    denom = exp_avg_sq.sqrt().add_(group['eps'])

                    p_data_fp32.addcdiv_(exp_avg, denom, value = -step_size * group['lr'])

                    p.data.copy_(p_data_fp32)

                elif step_size > 0:

                    if group['weight_decay'] != 0:

                        p_data_fp32.add_(p_data_fp32, alpha = -group['weight_decay'] * group['lr'])

                    p_data_fp32.add_(exp_avg, alpha = -step_size * group['lr'])

                    p.data.copy_(p_data_fp32)

        return loss

def get_optimizer(name, params, lr, wd=0):

    if name == "RAdam":

        return RAdam(params, lr=lr, weight_decay=wd)

    elif name == "Adam":

        return optim.Adam(params, lr=lr, weight_decay=wd)

    elif name == "SGD":

        return optim.SGD(params, lr=lr, momentum=0.9, weight_decay=wd)

    else:

        raise ValueError("Invalid optimizer name")