import torch
import torch.optim
from heavyball.utils import copy_stochastic_list_

from .utils import warmup, beta_debias, update_param_, StatefulOptimizer, promote


@torch.compile(mode='max-autotune-no-cudagraphs', fullgraph=True, dynamic=True)
def _compilable_step_(y, grad, exp_avg_sq, exp_avg, beta1, beta2, step, lr, eps, decay):
    g32, exp_avg32, exp_avg_sq32 = [list(map(promote, x)) for x in [grad, exp_avg, exp_avg_sq]]
    update_param_(y, exp_avg, lr, decay)

    beta1 = beta_debias(beta1, step)
    denom = torch._foreach_sqrt(exp_avg_sq32)
    torch._foreach_maximum_(denom, eps)
    torch._foreach_mul_(exp_avg32, beta1)
    [ea32.addcdiv_(g, d, value=1 - beta1) for ea32, g, d in zip(exp_avg32, g32, denom)]

    beta2 = beta_debias(beta2, step + 1)
    torch._foreach_mul_(exp_avg_sq32, beta2)
    [eas32.addcmul_(g, g, value=1 - beta2) for eas32, g in zip(exp_avg_sq32, g32)]

    copy_stochastic_list_(exp_avg, exp_avg32)
    copy_stochastic_list_(exp_avg_sq, exp_avg_sq32)


class ForeachADOPT(StatefulOptimizer):

    def __init__(self, params, lr=0.0025, betas=(0.9, 0.99), eps=1e-8, weight_decay=0, warmup_steps=0,
                 foreach: bool = True):
        defaults = dict(lr=lr, betas=betas, eps=eps, k=0, warmup_steps=warmup_steps, train_mode=True, weight_sum=0.0,
                        lr_max=-1.0, weight_decay=weight_decay)
        super().__init__(params, defaults, foreach)

    def _step(self, group):
        eps = group['eps']
        decay = group['weight_decay']
        k = group['k']

        if not group['train_mode']:
            raise Exception("Not in train mode!")

        active_p = [p for p in group['params'] if p.grad is not None]

        if not active_p:
            return

        for p in active_p:
            if 'exp_avg' not in self.state_(p):
                self.state_(p)['exp_avg'] = torch.zeros_like(p.data, dtype=torch.float32)
                self.state_(p)['exp_avg_sq'] = torch.zeros_like(p.data, dtype=torch.float32)

        y, grad, exp_avg_sq, exp_avg = zip(
            *[(p.data, p.grad, self.state_(p)['exp_avg_sq'], self.state_(p)['exp_avg']) for p in active_p])

        group['k'] = k + 1

        if k > 1:
            lr = -warmup(group['lr'], k - 1, group['warmup_steps'])
            lr = torch.empty((), dtype=torch.float32, device=y[0].device).fill_(lr)
            k = torch.empty((), dtype=torch.int32, device=y[0].device).fill_(k)
            _compilable_step_(y, grad, exp_avg_sq, exp_avg, group['betas'][0], group['betas'][1], k, lr, eps, decay)
            return

        grad = [promote(g) for g in grad]
        if k > 0:
            beta1 = beta_debias(group['betas'][0], k)
            denom = torch._foreach_sqrt(exp_avg_sq)
            torch._foreach_maximum_(denom, eps)
            torch._foreach_mul_(exp_avg, beta1)
            torch._foreach_addcdiv_(exp_avg, grad, denom, 1 - beta1)

        beta2 = beta_debias(group['betas'][1], k + 1)
        torch._foreach_mul_(exp_avg_sq, beta2)
        torch._foreach_addcmul_(exp_avg_sq, grad, grad, value=1 - beta2)
        del grad
