import torch
from functools import reduce
from .optimizer import Optimizer


class SdLBFGS(Optimizer):
    """PyTorch Implementation of SdLBFGS algorithm [1].

    Code is adopted from LBFGS in PyTorch and modified by
    Huidong Liu (h.d.liew@gmail.com) and Yingkai Li (yingkaili2023@u.northwestern.edu)

    .. warning::
        This optimizer doesn't support per-parameter options and parameter
        groups (there can be only one).

    .. warning::
        Right now all parameters have to be on a single device. This will be
        improved in the future.

    .. note::
        This is a very memory intensive optimizer (it requires additional
        ``param_bytes * (history_size + 1)`` bytes). If it doesn't fit in memory
        try reducing the history size, or use a different algorithm.

    Arguments:
        lr (float): learning rate (default: 1).
        lr_decay (bool): whether to perform learning rate decay (default: True).
        weight_decay (float): weight decay (default: 0).
        max_iter (int): maximal number of iterations per optimization step
            (default: 1).
        max_eval (int): maximal number of function evaluations per optimization
            step (default: max_iter * 1.25).
        tolerance_grad (float): termination tolerance on first order optimality
            (default: 1e-5).
        tolerance_change (float): termination tolerance on function
            value/parameter changes (default: 1e-9).
        history_size (int): update history size (default: 100).

    [1] Wang, Xiao, et al. "Stochastic quasi-Newton methods for nonconvex stochastic optimization."
    SIAM Journal on Optimization 27.2 (2017): 927-956.
    """

    def __init__(self, params, lr=1, lr_decay=True, weight_decay=0, max_iter=1, max_eval=None,
                 tolerance_grad=1e-5, tolerance_change=1e-9, history_size=100,
                 line_search_fn=None):
        if max_eval is None:
            max_eval = max_iter * 5 // 4
        defaults = dict(lr=lr, lr_decay=lr_decay, weight_decay=weight_decay, max_iter=max_iter,
                        max_eval=max_eval,
                        tolerance_grad=tolerance_grad, tolerance_change=tolerance_change,
                        history_size=history_size, line_search_fn=line_search_fn)
        super(SdLBFGS, self).__init__(params, defaults)

        if len(self.param_groups) != 1:
            raise ValueError("SdLBFGS doesn't support per-parameter options "
                             "(parameter groups)")

        self._params = self.param_groups[0]['params']
        self._numel_cache = None

    def _numel(self):
        if self._numel_cache is None:
            self._numel_cache = reduce(lambda total, p: total + p.numel(), self._params, 0)
        return self._numel_cache

    def _gather_flat_grad(self):
        views = []
        for p in self._params:
            if p.grad is None:
                view = p.data.new(p.data.numel()).zero_()
            elif p.grad.data.is_sparse:
                view = p.grad.data.to_dense().view(-1)
            else:
                view = p.grad.data.view(-1)
            views.append(view)
        return torch.cat(views, 0)

    def _add_grad(self, step_size, update):
        offset = 0
        for p in self._params:
            numel = p.numel()
            # view as to avoid deprecated pointwise semantics
            p.data.add_(step_size, update[offset:offset + numel].view_as(p.data))
            offset += numel
        assert offset == self._numel()

    def _add_weight_decay(self, weight_decay, update):
        offset = 0
        for p in self._params:
            numel = p.numel()
            update[offset:offset + numel].add_(weight_decay, p.data.view(-1))
            offset += numel
        return update

    def step(self, closure):
        """Performs a single optimization step.

        Arguments:
            closure (callable): A closure that reevaluates the model
                and returns the loss.
        """
        assert len(self.param_groups) == 1

        group = self.param_groups[0]
        lr = group['lr']
        lr_decay = group['lr_decay']
        weight_decay = group['weight_decay']
        max_iter = group['max_iter']
        max_eval = group['max_eval']
        tolerance_grad = group['tolerance_grad']
        tolerance_change = group['tolerance_change']
        line_search_fn = group['line_search_fn']
        history_size = group['history_size']

        state = self.state['global_state']
        state.setdefault('func_evals', 0)
        state.setdefault('n_iter', 0)

        # evaluate initial f(x) and df/dx
        orig_loss = closure()
        loss = orig_loss.data[0]
        current_evals = 1
        state['func_evals'] += 1

        flat_grad = self._gather_flat_grad()
        abs_grad_sum = flat_grad.abs().sum()

        if abs_grad_sum <= tolerance_grad:
            return loss

        # variables cached in state (for tracing)
        d = state.get('d')
        t = state.get('t')
        old_dirs = state.get('old_dirs')
        old_stps = state.get('old_stps')
        H_diag = state.get('H_diag')
        prev_flat_grad = state.get('prev_flat_grad')
        prev_loss = state.get('prev_loss')

        n_iter = 0
        # optimize for a max of max_iter iterations
        while n_iter < max_iter:
            # keep track of nb of iterations
            n_iter += 1
            state['n_iter'] += 1

            ############################################################
            # compute gradient descent direction
            ############################################################
            if state['n_iter'] == 1:
                d = flat_grad.neg()
                old_dirs = []
                old_stps = []
                H_diag = 1
            else:
                # do Sdlbfgs update (update memory)
                y = flat_grad.sub(prev_flat_grad)
                s = d.mul(t)
                ys = y.dot(s)  # y*s

                # update scale of initial Hessian approximation
                gamma = 1
                H_diag = 1 / gamma
                sT_H_inv_s = gamma * s.dot(s)

                if ys < 0.25 * sT_H_inv_s:
                    theta = 0.75 * sT_H_inv_s / (sT_H_inv_s - ys)
                else:
                    theta = 1
                y_bar = theta * y + (1 - theta) * gamma * s

                # updating memory
                if len(old_dirs) == history_size:
                    # shift history by one (limited-memory)
                    old_dirs.pop(0)
                    old_stps.pop(0)

                # store new direction/step
                old_dirs.append(s)
                old_stps.append(y_bar)

                # compute the approximate (SdL-BFGS) inverse Hessian
                # multiplied by the gradient
                num_old = len(old_dirs)

                if 'ro' not in state:
                    state['ro'] = [None] * history_size
                    state['al'] = [None] * history_size
                ro = state['ro']
                al = state['al']

                for i in range(num_old):
                    ro[i] = 1. / old_stps[i].dot(old_dirs[i])

                # iteration in SdL-BFGS loop collapsed to use just one buffer
                q = flat_grad.neg()
                for i in range(num_old - 1, -1, -1):
                    al[i] = old_dirs[i].dot(q) * ro[i]
                    q.add_(-al[i], old_stps[i])

                # multiply by initial Hessian
                # r/d is the final direction
                d = r = torch.mul(q, H_diag)
                for i in range(num_old):
                    be_i = old_stps[i].dot(r) * ro[i]
                    r.add_(al[i] - be_i, old_dirs[i])

            if prev_flat_grad is None:
                prev_flat_grad = flat_grad.clone()
            else:
                prev_flat_grad.copy_(flat_grad)
            prev_loss = loss

            ############################################################
            # compute step size
            ############################################################
            # reset initial guess for step size
            if weight_decay > 0:
                d = self._add_weight_decay(weight_decay, d)

            d = d / d.norm()

            if lr_decay:
                t = lr / (state['n_iter'] ** 0.5)
            else:
                if state['n_iter'] == 1:
                    t = min(1., 1. / abs_grad_sum) * lr
                else:
                    t = lr

            # directional derivative
            gtd = flat_grad.dot(d)  # g * d

            # optional line search: user function
            ls_func_evals = 0
            if line_search_fn is not None:
                # perform line search, using user function
                raise RuntimeError("line search function is not supported yet")
            else:
                # no line search, simply move with fixed-step
                self._add_grad(t, d)
                if n_iter != max_iter:
                    # re-evaluate function only if not in last iteration
                    # the reason we do this: in a stochastic setting,
                    # no use to re-evaluate that function here
                    loss = closure().data[0]
                    flat_grad = self._gather_flat_grad()
                    abs_grad_sum = flat_grad.abs().sum()
                    ls_func_evals = 1

            # update func eval
            current_evals += ls_func_evals
            state['func_evals'] += ls_func_evals

            ############################################################
            # check conditions
            ############################################################
            if n_iter == max_iter:
                break

            if current_evals >= max_eval:
                break

            if abs_grad_sum <= tolerance_grad:
                break

            if gtd > -tolerance_change:
                break

            if d.mul(t).abs_().sum() <= tolerance_change:
                break

            if abs(loss - prev_loss) < tolerance_change:
                break

        state['d'] = d
        state['t'] = t
        state['old_dirs'] = old_dirs
        state['old_stps'] = old_stps
        state['H_diag'] = H_diag
        state['prev_flat_grad'] = prev_flat_grad
        state['prev_loss'] = prev_loss

        return orig_loss
