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:
            is_complex = torch.is_complex(param)
            if is_complex:
                grad = torch.view_as_real(grad)
                state_sum = torch.view_as_real(state_sum)
                param = torch.view_as_real(param)
            state_sum.addcmul_(grad, grad, value=1)
            std = state_sum.sqrt().add_(eps)
            param.addcdiv_(grad, std, value=-clr)
            if is_complex:
                param = torch.view_as_complex(param)
                state_sum = torch.view_as_complex(state_sum)




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,
         maximize: bool):
    r"""Functional API that performs Adam algorithm computation.

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

    for i, param in enumerate(params):

        grad = grads[i] if not maximize else -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.conj(), 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,
          maximize: bool):
    r"""Functional API that performs AdamW algorithm computation.

    See :class:`~torch.optim.AdamW` for details.
    """
    for i, param in enumerate(params):
        grad = grads[i] if not maximize else -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,
        maximize: 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

        alpha = lr if maximize else -lr
        param.add_(d_p, alpha=alpha)


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)

        if torch.is_complex(param):
            square_avg = torch.view_as_real(square_avg)
            acc_delta = torch.view_as_real(acc_delta)
            grad = torch.view_as_real(grad)

        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)
        acc_delta.mul_(rho).addcmul_(delta, delta, value=1 - rho)
        if torch.is_complex(param):
            delta = torch.view_as_complex(delta)
        param.add_(delta, alpha=-lr)

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)


def rprop(params: List[Tensor],
          grads: List[Tensor],
          prevs: List[Tensor],
          step_sizes: List[Tensor],
          *,
          step_size_min: float,
          step_size_max: float,
          etaminus: float,
          etaplus: float):
    r"""Functional API that performs rprop algorithm computation.

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

    for i, param in enumerate(params):
        grad = grads[i]
        prev = prevs[i]
        step_size = step_sizes[i]

        sign = grad.mul(prev).sign()
        sign[sign.gt(0)] = etaplus
        sign[sign.lt(0)] = etaminus
        sign[sign.eq(0)] = 1

        # update stepsizes with step size updates
        step_size.mul_(sign).clamp_(step_size_min, step_size_max)

        # for dir<0, dfdx=0
        # for dir>=0 dfdx=dfdx
        grad = grad.clone(memory_format=torch.preserve_format)
        grad[sign.eq(etaminus)] = 0

        # update parameters
        param.addcmul_(grad.sign(), step_size, value=-1)

        prev.copy_(grad)


def adamax(params: List[Tensor],
           grads: List[Tensor],
           exp_avgs: List[Tensor],
           exp_infs: List[Tensor],
           state_steps: List[int],
           *,
           eps: float,
           beta1: float,
           beta2: float,
           lr: float,
           weight_decay: float):
    r"""Functional API that performs adamax algorithm computation.

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

    for i, param in enumerate(params):
        grad = grads[i]
        exp_avg = exp_avgs[i]
        exp_inf = exp_infs[i]
        step = state_steps[i]

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

        # Update biased first moment estimate.
        exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
        # Update the exponentially weighted infinity norm.
        norm_buf = torch.cat([
            exp_inf.mul_(beta2).unsqueeze(0),
            grad.abs().add_(eps).unsqueeze_(0)
        ], 0)
        torch.amax(norm_buf, 0, keepdim=False, out=exp_inf)

        bias_correction = 1 - beta1 ** step
        clr = lr / bias_correction

        param.addcdiv_(exp_avg, exp_inf, value=-clr)


def asgd(params: List[Tensor],
         grads: List[Tensor],
         axs: List[Tensor],
         mus: List[float],
         etas: List[float],
         *,
         weight_decay: float,
         lambd: float):
    r"""Functional API that performs asgd algorithm computation.

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

    for i, param in enumerate(params):
        grad = grads[i]
        mu = mus[i]
        ax = axs[i]
        eta = etas[i]

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

        # decay term
        param.mul_(1 - lambd * eta)

        # update parameter
        param.add_(grad, alpha=-eta)

        # averaging
        if mu != 1:
            ax.add_(param.sub(ax).mul(mu))
        else:
            ax.copy_(param)


def nadam(params: List[Tensor],
          grads: List[Tensor],
          exp_avgs: List[Tensor],
          exp_avg_sqs: List[Tensor],
          mu_products: List[float],
          state_steps: List[int],
          *,
          beta1: float,
          beta2: float,
          lr: float,
          weight_decay: float,
          momentum_decay: float,
          eps: float):
    r"""Functional API that performs NAdam algorithm computation.

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

    for i, param in enumerate(params):
        grad = grads[i]
        exp_avg = exp_avgs[i]
        exp_avg_sq = exp_avg_sqs[i]
        mu_product = mu_products[i]
        step = state_steps[i]

        bias_correction2 = 1 - beta2 ** step

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

        # calculate the momentum cache \mu^{t} and \mu^{t+1}
        mu = beta1 * (1. - 0.5 * (0.96 ** (step * momentum_decay)))
        mu_next = beta1 * (1. - 0.5 * (0.96 ** ((step + 1) * momentum_decay)))
        mu_product = mu_product * mu
        mu_product_next = mu_product * mu * mu_next

        # 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)

        denom = exp_avg_sq.div(bias_correction2).sqrt().add_(eps)
        param.addcdiv_(grad, denom, value=-lr * (1. - mu) / (1. - mu_product))
        param.addcdiv_(exp_avg, denom, value=-lr * mu_next / (1. - mu_product_next))


def radam(params: List[Tensor],
          grads: List[Tensor],
          exp_avgs: List[Tensor],
          exp_avg_sqs: List[Tensor],
          state_steps: List[int],
          *,
          beta1: float,
          beta2: float,
          lr: float,
          weight_decay: float,
          eps: float):
    r"""Functional API that performs RAdam algorithm computation.

    See :class:`~torch.optim.RAdam` 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)

        # correcting bias for the first moving moment
        bias_corrected_exp_avg = exp_avg / bias_correction1

        # maximum length of the approximated SMA
        rho_inf = 2 / (1 - beta2) - 1
        # compute the length of the approximated SMA
        rho_t = rho_inf - 2 * step * (beta2 ** step) / bias_correction2

        if rho_t > 5.:
            # Compute the variance rectification term and update parameters accordingly
            rect = math.sqrt((rho_t - 4) * (rho_t - 2) * rho_inf / ((rho_inf - 4) * (rho_inf - 2) * rho_t))
            adaptive_lr = math.sqrt(bias_correction2) / exp_avg_sq.sqrt().add_(eps)

            param.add_(bias_corrected_exp_avg * lr * adaptive_lr * rect, alpha=-1.0)
        else:
            param.add_(bias_corrected_exp_avg * lr, alpha=-1.0)


def sparse_adam(params: List[Tensor],
                grads: List[Tensor],
                exp_avgs: List[Tensor],
                exp_avg_sqs: List[Tensor],
                state_steps: List[int],
                *,
                eps: float,
                beta1: float,
                beta2: float,
                lr: float):
    r"""Functional API that performs Sparse Adam algorithm computation.

    See :class:`~torch.optim.SparseAdam` for details.
    """
    for i, param in enumerate(params):
        grad = grads[i]
        grad = grad.coalesce()  # the update is non-linear so indices must be unique
        grad_indices = grad._indices()
        grad_values = grad._values()
        size = grad.size()

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


        def make_sparse(values):
            constructor = grad.new
            if grad_indices.dim() == 0 or values.dim() == 0:
                return constructor().resize_as_(grad)
            return constructor(grad_indices, values, size)

        # Decay the first and second moment running average coefficient
        #      old <- b * old + (1 - b) * new
        # <==> old += (1 - b) * (new - old)
        old_exp_avg_values = exp_avg.sparse_mask(grad)._values()
        exp_avg_update_values = grad_values.sub(old_exp_avg_values).mul_(1 - beta1)
        exp_avg.add_(make_sparse(exp_avg_update_values))
        old_exp_avg_sq_values = exp_avg_sq.sparse_mask(grad)._values()
        exp_avg_sq_update_values = grad_values.pow(2).sub_(old_exp_avg_sq_values).mul_(1 - beta2)
        exp_avg_sq.add_(make_sparse(exp_avg_sq_update_values))

        # Dense addition again is intended, avoiding another sparse_mask
        numer = exp_avg_update_values.add_(old_exp_avg_values)
        exp_avg_sq_update_values.add_(old_exp_avg_sq_values)
        denom = exp_avg_sq_update_values.sqrt_().add_(eps)
        del exp_avg_update_values, exp_avg_sq_update_values

        bias_correction1 = 1 - beta1 ** step
        bias_correction2 = 1 - beta2 ** step
        step_size = lr * math.sqrt(bias_correction2) / bias_correction1

        param.add_(make_sparse(-step_size * numer.div_(denom)))
