#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
from collections import defaultdict
from typing import Optional

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

from .optimizers import Optimizer


class MADGRAD(Optimizer, PT_Optimizer):
    """
    `MADGRAD Optimizer`: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic
    Optimization.
    Paper: https://arxiv.org/abs/2101.11075

    Implementation has been copied over from the original author
    (https://github.com/facebookresearch/madgrad/blob/master/madgrad/madgrad.py)
    """

    class Config(Optimizer.Config):
        lr: float = 1e-3
        eps: float = 1e-6
        momentum: float = 0.9
        weight_decay: float = 0.0

    r"""
    Arguments:
        params (iterable):
            Iterable of parameters to optimize or dicts defining parameter groups.
        lr (float):
            Learning rate (default: 1e-2).
        momentum (float):
            Momentum value in  the range [0,1) (default: 0.9).
        weight_decay (float):
            Weight decay, i.e. a L2 penalty (default: 0).
        eps (float):
            Term added to the denominator outside of the root operation to improve numerical stability. (default: 1e-6).
    """

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

    def __init__(
        self,
        params,
        lr: float = 1e-2,
        momentum: float = 0.9,
        weight_decay: float = 0,
        eps: float = 1e-6,
        k: int = 0,
    ):
        if momentum < 0 or momentum >= 1:
            raise ValueError(f"Momentum {momentum} must be in the range [0,1]")
        if lr <= 0:
            raise ValueError(f"Learning rate {lr} must be positive")
        if weight_decay < 0:
            raise ValueError(f"Weight decay {weight_decay} must be non-negative")
        if eps < 0:
            raise ValueError("Eps must be non-negative")

        defaults = {
            "lr": lr,
            "eps": eps,
            "momentum": momentum,
            "weight_decay": weight_decay,
            "k": k,
        }

        self.momentum = momentum

        PT_Optimizer.__init__(self, params, defaults)

        self.initialize_state()

    def initialize_state(self):
        for group in self.param_groups:
            for p in group["params"]:
                if p not in self.state:
                    state = self.state[p]
                    state["grad_sum_sq"] = torch.zeros_like(p.data).detach()
                    state["s"] = torch.zeros_like(p.data).detach()
                    if self.momentum != 0:
                        state["x0"] = torch.clone(p.data).detach()

    @property
    def supports_memory_efficient_fp16(self) -> bool:
        return False

    @property
    def supports_flat_params(self) -> bool:
        return True

    def step(self, closure=None, **kwargs) -> Optional[float]:
        """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:
            eps = group["eps"]
            k = group["k"]
            lr = group["lr"] + eps
            decay = group["weight_decay"]
            momentum = group["momentum"]

            ck = 1 - momentum
            lamb = lr * math.pow(k + 1, 0.5)

            for p in group["params"]:
                if p.grad is None:
                    continue
                grad = p.grad.data
                state = self.state[p]

                if momentum != 0.0 and grad.is_sparse:
                    raise RuntimeError(
                        "momentum != 0 is not compatible with sparse gradients"
                    )

                grad_sum_sq = state["grad_sum_sq"]
                s = state["s"]

                # Apply weight decay
                if decay != 0:
                    if grad.is_sparse:
                        raise RuntimeError(
                            "weight_decay option is not compatible with sparse gradients"
                        )

                    grad.add_(p.data, alpha=decay)

                if grad.is_sparse:
                    grad = grad.coalesce()
                    grad_val = grad._values()

                    p_masked = p.sparse_mask(grad)
                    grad_sum_sq_masked = grad_sum_sq.sparse_mask(grad)
                    s_masked = s.sparse_mask(grad)

                    # Compute x_0 from other known quantities
                    rms_masked_vals = grad_sum_sq_masked._values().pow(1 / 3).add_(eps)
                    x0_masked_vals = p_masked._values().addcdiv(
                        s_masked._values(), rms_masked_vals, value=1
                    )

                    # Dense + sparse op
                    grad_sq = grad * grad
                    grad_sum_sq.add_(grad_sq, alpha=lamb)
                    grad_sum_sq_masked.add_(grad_sq, alpha=lamb)

                    rms_masked_vals = grad_sum_sq_masked._values().pow_(1 / 3).add_(eps)

                    s.add_(grad, alpha=lamb)
                    s_masked._values().add_(grad_val, alpha=lamb)

                    # update masked copy of p
                    p_kp1_masked_vals = x0_masked_vals.addcdiv(
                        s_masked._values(), rms_masked_vals, value=-1
                    )
                    # Copy updated masked p to dense p using an add operation
                    p_masked._values().add_(p_kp1_masked_vals, alpha=-1)
                    p.data.add_(p_masked, alpha=-1)
                else:
                    if momentum == 0:
                        # Compute x_0 from other known quantities
                        rms = grad_sum_sq.pow(1 / 3).add_(eps)
                        x0 = p.data.addcdiv(s, rms, value=1)
                    else:
                        x0 = state["x0"]

                    # Accumulate second moments
                    grad_sum_sq.addcmul_(grad, grad, value=lamb)
                    rms = grad_sum_sq.pow(1 / 3).add_(eps)

                    # Update s
                    s.data.add_(grad, alpha=lamb)

                    # Step
                    if momentum == 0:
                        p.data.copy_(x0.addcdiv(s, rms, value=-1))
                    else:
                        z = x0.addcdiv(s, rms, value=-1)

                        # p is a moving average of z
                        p.data.mul_(1 - ck).add_(z, alpha=ck)

            group["k"] = group["k"] + 1
        return loss

    def add_param_group(self, param_group):
        r"""Add a param group to the :class:`Optimizer` s `param_groups`.

        This can be useful when fine tuning a pre-trained network as frozen
        layers can be made trainable and added to the :class:`Optimizer` as
        training progresses.

        Args:
            param_group (dict): Specifies what Tensors should be optimized along
            with group specific optimization options.
        """
        super().add_param_group(param_group)
        self.initialize_state()

    def reset_param_groups(self):
        self.param_groups = []
        self.state = defaultdict(dict)

    def clip_grad_norm(self, max_norm, model=None):
        return Optimizer.clip_grad_norm(self, max_norm, model)
