# modified from https://github.com/LiyuanLucasLiu/RAdam

import math

import torch
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 lr < 0.0:
            raise ValueError("Invalid learning rate: {}".format(lr))
        if eps < 0.0:
            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().__init__(params, defaults)

    def __setstate__(self, state):  # pylint: disable=useless-super-delegation
        super().__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