r"""Functional interface"""
import math
import torch
from torch import Tensor
from typing import List, Optional

# TODO: use foreach API in optim._functional to do all the computation

def _make_sparse(grad, grad_indices, values):
    size = grad.size()
    if grad_indices.numel() == 0 or values.numel() == 0:
        return torch.empty_like(grad)
    return torch.sparse_coo_tensor(grad_indices, values, size)


def adagrad(params: List[Tensor],
            grads: List[Tensor],
            state_sums: List[Tensor],
            state_steps: List[int],
            lr: float,
            weight_decay: float,
            lr_decay: float,
            eps: float):
    r"""Functional API that performs Adagrad algorithm computation.

    See :class:`~torch.optim.Adagrad` for details.
    """

    for (param, grad, state_sum, step) in zip(params, grads, state_sums, state_steps):
        if weight_decay != 0:
            if grad.is_sparse:
                raise RuntimeError("weight_decay option is not compatible with sparse gradients")
            grad = grad.add(param, alpha=weight_decay)

        clr = lr / (1 + (step - 1) * lr_decay)

        if grad.is_sparse:
            grad = grad.coalesce()  # the update is non-linear so indices must be unique
            grad_indices = grad._indices()
            grad_values = grad._values()
            size = grad.size()

            state_sum.add_(_make_sparse(grad, grad_indices, grad_values.pow(2)))
            std = state_sum.sparse_mask(grad)
            std_values = std._values().sqrt_().add_(eps)
            param.add_(_make_sparse(grad, grad_indices, grad_values / std_values), alpha=-clr)
        else:
            state_sum.addcmul_(grad, grad, value=1)
            std = state_sum.sqrt().add_(eps)
            param.addcdiv_(grad, std, value=-clr)


def adam(params: List[Tensor],
         grads: List[Tensor],
         exp_avgs: List[Tensor],
         exp_avg_sqs: List[Tensor],
         max_exp_avg_sqs: List[Tensor],
         state_steps: List[int],
         amsgrad: bool,
         beta1: float,
         beta2: float,
         lr: float,
         weight_decay: float,
         eps: float):
    r"""Functional API that performs Adam algorithm computation.

    See :class:`~torch.optim.Adam` for details.
    """

    for i, param in enumerate(params):

        grad = grads[i]
        exp_avg = exp_avgs[i]
        exp_avg_sq = exp_avg_sqs[i]
        step = state_steps[i]

        bias_correction1 = 1 - beta1 ** step
        bias_correction2 = 1 - beta2 ** step

        if weight_decay != 0:
            grad = grad.add(param, alpha=weight_decay)

        # Decay the first and second moment running average coefficient
        exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
        if amsgrad:
            # Maintains the maximum of all 2nd moment running avg. till now
            torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])
            # Use the max. for normalizing running avg. of gradient
            denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)
        else:
            denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)

        step_size = lr / bias_correction1

        param.addcdiv_(exp_avg, denom, value=-step_size)


def adamw(params: List[Tensor],
          grads: List[Tensor],
          exp_avgs: List[Tensor],
          exp_avg_sqs: List[Tensor],
          max_exp_avg_sqs: List[Tensor],
          state_steps: List[int],
          amsgrad: bool,
          beta1: float,
          beta2: float,
          lr: float,
          weight_decay: float,
          eps: float):
    r"""Functional API that performs AdamW algorithm computation.

    See :class:`~torch.optim.AdamW` for details.
    """
    for i, param in enumerate(params):
        grad = grads[i]
        exp_avg = exp_avgs[i]
        exp_avg_sq = exp_avg_sqs[i]
        step = state_steps[i]

        # Perform stepweight decay
        param.mul_(1 - lr * weight_decay)

        bias_correction1 = 1 - beta1 ** step
        bias_correction2 = 1 - beta2 ** step

        # Decay the first and second moment running average coefficient
        exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
        if amsgrad:
            # Maintains the maximum of all 2nd moment running avg. till now
            torch.maximum(max_exp_avg_sqs[i], exp_avg_sq, out=max_exp_avg_sqs[i])
            # Use the max. for normalizing running avg. of gradient
            denom = (max_exp_avg_sqs[i].sqrt() / math.sqrt(bias_correction2)).add_(eps)
        else:
            denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)

        step_size = lr / bias_correction1

        param.addcdiv_(exp_avg, denom, value=-step_size)


def sgd(params: List[Tensor],
        d_p_list: List[Tensor],
        momentum_buffer_list: List[Optional[Tensor]],
        weight_decay: float,
        momentum: float,
        lr: float,
        dampening: float,
        nesterov: bool):
    r"""Functional API that performs SGD algorithm computation.

    See :class:`~torch.optim.SGD` for details.
    """

    for i, param in enumerate(params):

        d_p = d_p_list[i]
        if weight_decay != 0:
            d_p = d_p.add(param, alpha=weight_decay)

        if momentum != 0:
            buf = momentum_buffer_list[i]

            if buf is None:
                buf = torch.clone(d_p).detach()
                momentum_buffer_list[i] = buf
            else:
                buf.mul_(momentum).add_(d_p, alpha=1 - dampening)

            if nesterov:
                d_p = d_p.add(buf, alpha=momentum)
            else:
                d_p = buf

        param.add_(d_p, alpha=-lr)


def adadelta(params: List[Tensor],
             grads: List[Tensor],
             square_avgs: List[Tensor],
             acc_deltas: List[Tensor],
             lr: float,
             rho: float,
             eps: float,
             weight_decay: float):
    r"""Functional API that performs Adadelta algorithm computation.

    See :class:`~torch.optim.Adadelta` for details.
    """

    for (param, grad, square_avg, acc_delta) in zip(params, grads, square_avgs, acc_deltas):
        if weight_decay != 0:
            grad = grad.add(param, alpha=weight_decay)

        square_avg.mul_(rho).addcmul_(grad, grad, value=1 - rho)
        std = square_avg.add(eps).sqrt_()
        delta = acc_delta.add(eps).sqrt_().div_(std).mul_(grad)
        param.add_(delta, alpha=-lr)
        acc_delta.mul_(rho).addcmul_(delta, delta, value=1 - rho)


def rmsprop(params: List[Tensor],
            grads: List[Tensor],
            square_avgs: List[Tensor],
            grad_avgs: List[Tensor],
            momentum_buffer_list: List[Tensor],
            lr: float,
            alpha: float,
            eps: float,
            weight_decay: float,
            momentum: float,
            centered: bool):
    r"""Functional API that performs rmsprop algorithm computation.

    See :class:`~torch.optim.RMSProp` for details.
    """

    for i, param in enumerate(params):
        grad = grads[i]
        square_avg = square_avgs[i]

        if weight_decay != 0:
            grad = grad.add(param, alpha=weight_decay)

        square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha)

        if centered:
            grad_avg = grad_avgs[i]
            grad_avg.mul_(alpha).add_(grad, alpha=1 - alpha)
            avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).sqrt_().add_(eps)
        else:
            avg = square_avg.sqrt().add_(eps)

        if momentum > 0:
            buf = momentum_buffer_list[i]
            buf.mul_(momentum).addcdiv_(grad, avg)
            param.add_(buf, alpha=-lr)
        else:
            param.addcdiv_(grad, avg, value=-lr)