#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

import math

import torch
from pytext.optimizer.optimizers import Optimizer
from torch.optim import Optimizer as PT_Optimizer


class RAdam(Optimizer, PT_Optimizer):
    """Implements rectified adam as derived in the following paper:
    "On the Variance of the Adaptive Learning Rate and Beyond"
    (https://arxiv.org/abs/1908.03265)

    This code is mostly a direct copy-paste of the code provided by the authors here:
    https://github.com/LiyuanLucasLiu/RAdam/blob/master/radam.py
    """

    class Config(Optimizer.Config):
        lr: float = 0.001
        weight_decay: float = 0.00001
        eps: float = 1e-8

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay}
        self.buffer = [[None, None, None] for ind in range(10)]
        PT_Optimizer.__init__(self, params, defaults)

    def __setstate__(self, state):
        super(RAdam, self).__setstate__(state)

    def step(self, closure=None, **kwargs):
        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

    @classmethod
    def from_config(cls, config: Config, model: torch.nn.Module):
        return cls(
            model.parameters(),
            lr=config.lr,
            weight_decay=config.weight_decay,
            eps=config.eps,
        )
