# -*- coding:utf-8 -*-
import torch
from torch._six import inf


class NativeScalerWithGradNormCount:
    state_dict_key = "amp_scaler"

    def __init__(self):
        self._scaler = torch.cuda.amp.GradScaler()

    def __call__(
        self,
        cfg,
        loss,
        optimizer,
        clip_grad=None,
        parameters=None,
        create_graph=False,
        update_grad=True,
        step=0,
    ):
        self._scaler.scale(loss).backward(create_graph=create_graph)

        if update_grad:
            if clip_grad is not None:
                assert parameters is not None
                self._scaler.unscale_(
                    optimizer
                )  # unscale the gradients of optimizer's assigned params in-place
                norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
            else:
                self._scaler.unscale_(optimizer)
                norm = get_grad_norm_(parameters)

            if cfg.TRAIN.optimizer != "SAM":
                self._scaler.step(optimizer)
            else:
                if step == 0:
                    optimizer.first_step(zero_grad=True)
                else:
                    self._scaler = optimizer.second_step(
                        zero_grad=False, scaler=self._scaler
                    )
            self._scaler.update()
        else:
            norm = None
        return norm

    def state_dict(self):
        return self._scaler.state_dict()

    def load_state_dict(self, state_dict):
        self._scaler.load_state_dict(state_dict)


def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    parameters = [p for p in parameters if p.grad is not None]
    norm_type = float(norm_type)
    if len(parameters) == 0:
        return torch.tensor(0.0)
    device = parameters[0].grad.device
    if norm_type == inf:
        total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
    else:
        total_norm = torch.norm(
            torch.stack(
                [torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]
            ),
            norm_type,
        )
    return total_norm