import math
import warnings
from typing import Callable, Iterable, Tuple

import torch
from torch import nn
from torch.optim import Optimizer


class Grams(Optimizer):
    """
    Parameters:
        params (`Iterable[nn.parameter.Parameter]`):
            Iterable of parameters to optimize or dictionaries defining parameter groups.
        lr (`float`, *optional*, defaults to 0.001):
            The learning rate to use.
        betas (`Tuple[float,float]`, *optional*, defaults to `(0.9, 0.999)`):
            Adam's betas parameters (b1, b2).
        eps (`float`, *optional*, defaults to 1e-06):
            Adam's epsilon for numerical stability.
        weight_decay (`float`, *optional*, defaults to 0.0):
            Decoupled weight decay to apply.
        correct_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to correct bias in Adam (for instance, in Bert TF repository they use `False`).
    """

    def __init__(
        self,
        params: Iterable[nn.parameter.Parameter],
        lr: float = 1e-3,
        betas: Tuple[float, float] = (0.9, 0.999),
        eps: float = 1e-6,
        weight_decay: float = 0.0,
        correct_bias: bool = True,
    ):
        if lr < 0.0:
            raise ValueError(f"Invalid learning rate: {lr} - should be >= 0.0")
        if not 0.0 <= betas[0] < 1.0:
            raise ValueError(
                f"Invalid beta parameter: {betas[0]} - should be in [0.0, 1.0)"
            )
        if not 0.0 <= betas[1] < 1.0:
            raise ValueError(
                f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0)"
            )
        if not 0.0 <= eps:
            raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0")
        defaults = {
            "lr": lr,
            "betas": betas,
            "eps": eps,
            "weight_decay": weight_decay,
            "correct_bias": correct_bias,
        }
        super().__init__(params, defaults)
        self.init_lr = lr

    @torch.no_grad()
    def step(self, closure: Callable = 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 i, p in enumerate(group["params"]):
                if p.grad is None:
                    continue
                grad = p.grad
                state = self.state[p]

                if "step" not in state:
                    state["step"] = 0

                # State initialization
                if "exp_avg" not in state:
                    # Exponential moving average of gradient values
                    state["exp_avg"] = torch.zeros_like(grad)
                    # Exponential moving average of squared gradient values
                    state["exp_avg_sq"] = torch.zeros_like(grad)

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

                state["step"] += 1

                if group["weight_decay"] > 0.0:
                    p.add_(p, alpha=(-group["lr"] * group["weight_decay"]))

                # Decay the first and second moment running average coefficient
                # In-place operations to update the averages at the same time
                exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1))
                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
                denom = exp_avg_sq.sqrt().add_(group["eps"])

                step_size = group["lr"]
                if group["correct_bias"]:  # No bias correction for Bert
                    bias_correction1 = 1.0 - beta1 ** state["step"]
                    bias_correction2 = 1.0 - beta2 ** state["step"]
                    step_size = (
                        step_size * math.sqrt(bias_correction2) / bias_correction1
                    )

                grad.sign_().mul_(exp_avg.abs())
                p.addcdiv_(grad, denom, value=-step_size)

        return loss
