import torch
import math

from torch.optim import Adam
from torch.optim.optimizer import Optimizer
from utils.class_registry import ClassRegistry

optimizers = ClassRegistry()


@optimizers.add_to_registry("adam", stop_args=("self", "params"))
class Adam(Adam):
    def __init__(
        self,
        params,
        lr=1e-4,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
    ):
        super().__init__(params, lr, tuple(betas), eps, weight_decay, amsgrad)


@optimizers.add_to_registry(name="ranger", stop_args=("self", "params"))
class Ranger(Optimizer):
    def __init__(
        self,
        params,
        lr=1e-4,  # lr
        alpha=0.5,
        k=6,
        N_sma_threshhold=5,  # Ranger options
        betas=(0.95, 0.999),
        eps=1e-5,
        weight_decay=0,  # Adam options
        use_gc=True,
        gc_conv_only=False
        # Gradient centralization on or off, applied to conv layers only or conv + fc layers
    ):

        # parameter checks
        assert params is not None
        if not 0.0 <= alpha <= 1.0:
            raise ValueError(f"Invalid slow update rate: {alpha}")
        if not 1 <= k:
            raise ValueError(f"Invalid lookahead steps: {k}")
        if not lr > 0:
            raise ValueError(f"Invalid Learning Rate: {lr}")
        if not eps > 0:
            raise ValueError(f"Invalid eps: {eps}")

        # parameter comments:
        # beta1 (momentum) of .95 seems to work better than .90...
        # N_sma_threshold of 5 seems better in testing than 4.
        # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.

        # prep defaults and init torch.optim base
        betas = tuple(betas)
        defaults = dict(
            lr=lr,
            alpha=alpha,
            k=k,
            step_counter=0,
            betas=betas,
            N_sma_threshhold=N_sma_threshhold,
            eps=eps,
            weight_decay=weight_decay,
        )
        super().__init__(params, defaults)

        # adjustable threshold
        self.N_sma_threshhold = N_sma_threshhold

        # look ahead params

        self.alpha = alpha
        self.k = k

        # radam buffer for state
        self.radam_buffer = [[None, None, None] for ind in range(10)]

        # gc on or off
        self.use_gc = use_gc

        # level of gradient centralization
        self.gc_gradient_threshold = 3 if gc_conv_only else 1

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

    def step(self, closure=None):
        loss = None

        # Evaluate averages and grad, update param tensors
        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(
                        "Ranger optimizer does not support sparse gradients"
                    )

                p_data_fp32 = p.data.float()

                state = self.state[p]  # get state dict for this param

                if (
                    len(state) == 0
                ):  # if first time to run...init dictionary with our desired entries
                    # if self.first_run_check==0:
                    # self.first_run_check=1
                    # print("Initializing slow buffer...should not see this at load from saved model!")
                    state["step"] = 0
                    state["exp_avg"] = torch.zeros_like(p_data_fp32)
                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)

                    # look ahead weight storage now in state dict
                    state["slow_buffer"] = torch.empty_like(p.data)
                    state["slow_buffer"].copy_(p.data)

                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)

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

                # GC operation for Conv layers and FC layers
                if grad.dim() > self.gc_gradient_threshold:
                    grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))

                state["step"] += 1

                # compute variance mov avg
                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
                # compute mean moving avg
                exp_avg.mul_(beta1).add_(1 - beta1, grad)

                buffered = self.radam_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
                    if N_sma > self.N_sma_threshhold:
                        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"])
                    else:
                        step_size = 1.0 / (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)

                # apply lr
                if N_sma > self.N_sma_threshhold:
                    denom = exp_avg_sq.sqrt().add_(group["eps"])
                    p_data_fp32.addcdiv_(-step_size * group["lr"], exp_avg, denom)
                else:
                    p_data_fp32.add_(-step_size * group["lr"], exp_avg)

                p.data.copy_(p_data_fp32)

                # integrated look ahead...
                # we do it at the param level instead of group level
                if state["step"] % group["k"] == 0:
                    slow_p = state["slow_buffer"]  # get access to slow param tensor
                    slow_p.add_(
                        self.alpha, p.data - slow_p
                    )  # (fast weights - slow weights) * alpha
                    p.data.copy_(
                        slow_p
                    )  # copy interpolated weights to RAdam param tensor

        return loss