# from fastai.vision import *
import torch
import torch.nn as nn
import torch.nn.functional as F

class MultiLosses(nn.Module):
    def __init__(self, one_hot=True):
        super().__init__()
        self.ce = SoftCrossEntropyLoss() if one_hot else torch.nn.CrossEntropyLoss()
        self.bce = torch.nn.BCELoss()

    @property
    def last_losses(self):
        return self.losses

    def _flatten(self, sources, lengths):
        return torch.cat([t[:l] for t, l in zip(sources, lengths)])

    def _merge_list(self, all_res):
        if not isinstance(all_res, (list, tuple)):
            return all_res
        def merge(items):
            if isinstance(items[0], torch.Tensor): return torch.cat(items, dim=0)
            else: return items[0]
        res = dict()
        for key in all_res[0].keys():
            items = [r[key] for r in all_res]
            res[key] = merge(items)
        return res

    def _ce_loss(self, output, gt_labels, gt_lengths, idx=None, record=True):
        loss_name = output.get('name')
        pt_logits, weight = output['logits'], output['loss_weight']

        assert pt_logits.shape[0] % gt_labels.shape[0] == 0
        iter_size = pt_logits.shape[0] // gt_labels.shape[0]
        if iter_size > 1:
            gt_labels = gt_labels.repeat(3, 1, 1)
            gt_lengths = gt_lengths.repeat(3)
        flat_gt_labels = self._flatten(gt_labels, gt_lengths)
        flat_pt_logits = self._flatten(pt_logits, gt_lengths)

        nll = output.get('nll')
        if nll is not None:
            loss = self.ce(flat_pt_logits, flat_gt_labels, softmax=False) * weight
        else:
            loss = self.ce(flat_pt_logits, flat_gt_labels) * weight
        if record and loss_name is not None: self.losses[f'{loss_name}_loss'] = loss

        return loss

    def forward(self, outputs, *args):
        self.losses = {}
        if isinstance(outputs, (tuple, list)):
            outputs = [self._merge_list(o) for o in outputs]
            # return sum([self._ce_loss(o, *args) for o in outputs if o['loss_weight'] > 0.])
            return torch.mean(torch.stack([self._ce_loss(o, *args) for o in outputs if o['loss_weight'] > 0.]))
        else:
            return self._ce_loss(outputs, *args, record=False)


class SoftCrossEntropyLoss(nn.Module):
    def __init__(self, reduction="mean"):
        super().__init__()
        self.reduction = reduction

    def forward(self, input, target, softmax=True):
        if softmax: log_prob = F.log_softmax(input, dim=-1)
        else: log_prob = torch.log(input)
        loss = -(target * log_prob).sum(dim=-1)
        if self.reduction == "mean": return loss.mean()
        elif self.reduction == "sum": return loss.sum()
        else: return loss
