"""https://github.com/Z-T-WANG/LaProp-Optimizer/blob/master/laprop.py"""
from torch.optim import Optimizer
import math
import torch


class LaProp(Optimizer):
    def __init__(
        self,
        params,
        lr=4e-4,
        betas=(0.9, 0.999),
        eps=1e-15,
        weight_decay=0,
        amsgrad=False,
        centered=False,
    ):

        self.steps_before_using_centered = 10

        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]))
        defaults = dict(
            lr=lr,
            betas=betas,
            eps=eps,
            weight_decay=weight_decay,
            amsgrad=amsgrad,
            centered=centered,
        )
        super(LaProp, self).__init__(params, defaults)

    def step(self, closure=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.
        """
        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
                if grad.is_sparse:
                    raise RuntimeError(
                        "Adam does not support sparse gradients, please consider SparseAdam instead"
                    )
                amsgrad = group["amsgrad"]
                centered = group["centered"]

                state = self.state[p]

                # State initialization
                if len(state) == 0:
                    state["step"] = 0
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(p.data)
                    # Exponential moving average of learning rates
                    state["exp_avg_lr_1"] = 0.0
                    state["exp_avg_lr_2"] = 0.0
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros_like(p.data)
                    if centered:
                        # Exponential moving average of gradient values as calculated by beta2
                        state["exp_mean_avg_beta2"] = torch.zeros_like(p.data)
                    if amsgrad:
                        # Maintains max of all exp. moving avg. of sq. grad. values
                        state["max_exp_avg_sq"] = torch.zeros_like(p.data)

                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
                if centered:
                    exp_mean_avg_beta2 = state["exp_mean_avg_beta2"]
                if amsgrad:
                    max_exp_avg_sq = state["max_exp_avg_sq"]
                beta1, beta2 = group["betas"]

                state["step"] += 1

                # Decay the first and second moment running average coefficient
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)

                state["exp_avg_lr_1"] = (
                    state["exp_avg_lr_1"] * beta1 + (1 - beta1) * group["lr"]
                )
                state["exp_avg_lr_2"] = state["exp_avg_lr_2"] * beta2 + (1 - beta2)

                bias_correction1 = (
                    state["exp_avg_lr_1"] / group["lr"] if group["lr"] != 0.0 else 1.0
                )  # 1 - beta1 ** state['step']
                step_size = 1 / bias_correction1

                bias_correction2 = state["exp_avg_lr_2"]

                denom = exp_avg_sq
                if centered:
                    exp_mean_avg_beta2.mul_(beta2).add_(1 - beta2, grad)
                    if state["step"] > self.steps_before_using_centered:
                        mean = exp_mean_avg_beta2**2
                        denom = denom - mean

                if amsgrad:
                    if not (
                        centered and state["step"] <= self.steps_before_using_centered
                    ):
                        # Maintains the maximum of all (centered) 2nd moment running avg. till now
                        torch.max(max_exp_avg_sq, denom, out=max_exp_avg_sq)
                        # Use the max. for normalizing running avg. of gradient
                        denom = max_exp_avg_sq

                denom = denom.div(bias_correction2).sqrt_().add_(group["eps"])
                step_of_this_grad = grad / denom
                exp_avg.mul_(beta1).add_((1 - beta1) * group["lr"], step_of_this_grad)

                p.data.add_(-step_size, exp_avg)
                if group["weight_decay"] != 0:
                    p.data.add_(-group["weight_decay"], p.data)

        return loss