# Copyright (c) OpenMMLab. All rights reserved.
import warnings

import torch
import torch.nn as nn

from ..registry import LOSSES
from .cross_entropy_loss import CrossEntropyLoss
from .utils import convert_to_one_hot


@LOSSES.register_module()
class LabelSmoothLoss(nn.Module):
    r"""Intializer for the label smoothed cross entropy loss.

    Refers to `Rethinking the Inception Architecture for Computer Vision
    <https://arxiv.org/abs/1512.00567>`_

    This decreases gap between output scores and encourages generalization.
    Labels provided to forward can be one-hot like vectors (NxC) or class
    indices (Nx1).
    And this accepts linear combination of one-hot like labels from mixup or
    cutmix except multi-label task.

    Args:
        label_smooth_val (float): The degree of label smoothing.
        num_classes (int, optional): Number of classes. Defaults to None.
        mode (str): Refers to notes, Options are 'original', 'classy_vision',
            'multi_label', 'mix_decouple'. Defaults to 'classy_vision'.
        reduction (str): The method used to reduce the loss.
            Options are "none", "mean" and "sum". Defaults to 'mean'.
        loss_weight (float):  Weight of the loss. Defaults to 1.0.

    Notes:
        if the mode is "original", this will use the same label smooth method
        as the original paper as:

        .. math::
            (1-\epsilon)\delta_{k, y} + \frac{\epsilon}{K}

        where epsilon is the `label_smooth_val`, K is the num_classes and
        delta(k,y) is Dirac delta, which equals 1 for k=y and 0 otherwise.

        if the mode is "classy_vision", this will use the same label smooth
        method as the facebookresearch/ClassyVision repo as:

        .. math::
            \frac{\delta_{k, y} + \epsilon/K}{1+\epsilon}

        if the mode is "multi_label", this will accept labels from multi-label
        task and smoothing them as:

        .. math::
            (1-2\epsilon)\delta_{k, y} + \epsilon
    """

    def __init__(self,
                 label_smooth_val,
                 num_classes=None,
                 mode=None,
                 reduction='mean',
                 loss_weight=1.0,
                 **kwargs):
        super().__init__()
        self.num_classes = num_classes
        self.loss_weight = loss_weight

        assert (isinstance(label_smooth_val, float)
                and 0 <= label_smooth_val < 1), \
            f'LabelSmoothLoss accepts a float label_smooth_val ' \
            f'over [0, 1), but gets {label_smooth_val}'
        self.label_smooth_val = label_smooth_val

        accept_reduction = {'none', 'mean', 'sum'}
        assert reduction in accept_reduction, \
            f'LabelSmoothLoss supports reduction {accept_reduction}, ' \
            f'but gets {mode}.'
        self.reduction = reduction
        self.post_process = "softmax"

        if mode is None:
            warnings.warn(
                'LabelSmoothLoss mode is not set, use "classy_vision" '
                'by default. The default value will be changed to '
                '"original" recently. Please set mode manually if want '
                'to keep "classy_vision".', UserWarning)
            mode = 'classy_vision'

        accept_mode = {'original', 'classy_vision', 'multi_label', 'mix_decouple'}
        assert mode in accept_mode, \
            f'LabelSmoothLoss supports mode {accept_mode}, but gets {mode}.'
        self.mode = mode

        self._eps = label_smooth_val
        if mode == 'classy_vision':
            self._eps = label_smooth_val / (1 + label_smooth_val)
        if mode == 'multi_label':
            self.ce = CrossEntropyLoss(use_sigmoid=True)
            self.smooth_label = self.multilabel_smooth_label
            self.post_process = "sigmoid"  # multi-label classification
        elif mode == 'mix_decouple':
            self.ce = CrossEntropyLoss(use_soft=True, use_sigmoid=False, use_mix_decouple=True)
            self.smooth_label = self.original_smooth_label
        else:
            self.ce = CrossEntropyLoss(use_soft=True)
            self.smooth_label = self.original_smooth_label

    def generate_one_hot_like_label(self, label):
        """This function takes one-hot or index label vectors and computes one-
        hot like label vectors (float)"""
        # check if targets are inputted as class integers
        if label.dim() == 1 or (label.dim() == 2 and label.shape[1] == 1):
            label = convert_to_one_hot(label.view(-1), self.num_classes)
        return label.float()

    def original_smooth_label(self, one_hot_like_label):
        assert self.num_classes > 0
        smooth_label = one_hot_like_label * (1 - self._eps)
        smooth_label += self._eps / self.num_classes
        return smooth_label

    def multilabel_smooth_label(self, one_hot_like_label):
        assert self.num_classes > 0
        smooth_label = torch.full_like(one_hot_like_label, self._eps)
        smooth_label.masked_fill_(one_hot_like_label > 0, 1 - self._eps)
        return smooth_label

    def forward(self,
                cls_score,
                label,
                weight=None,
                avg_factor=None,
                reduction_override=None,
                **kwargs):
        r"""Label smooth loss.

        Args:
            pred (torch.Tensor): The prediction with shape (N, \*).
            label (torch.Tensor): The ground truth label of the prediction
                with shape (N, \*).
            weight (torch.Tensor, optional): Sample-wise loss weight with shape
                (N, \*). Dafaults to None.
            avg_factor (int, optional): Average factor that is used to average
                the loss. Defaults to None.
            reduction_override (str, optional): The method used to reduce the
                loss into a scalar. Options are "none", "mean" and "sum".
                Defaults to None.

        Returns:
            torch.Tensor: Loss.
        """
        if self.num_classes is None:
            assert cls_score.dim() == 2
            self.num_classes = cls_score.shape[1]
        else:
            assert self.num_classes == cls_score.shape[1], \
                f'num_classes should equal to cls_score.shape[1], ' \
                f'but got num_classes: {self.num_classes} and ' \
                f'cls_score.shape[1]: {cls_score.shape[1]}'

        one_hot_like_label = self.generate_one_hot_like_label(label=label)
        assert one_hot_like_label.shape == cls_score.shape, \
            f'LabelSmoothLoss requires output and target ' \
            f'to be same shape, but got output.shape: {cls_score.shape} ' \
            f'and target.shape: {one_hot_like_label.shape}'

        smoothed_label = self.smooth_label(one_hot_like_label)
        return self.ce.forward(
            cls_score,
            smoothed_label,
            weight=weight,
            avg_factor=avg_factor,
            reduction_override=reduction_override,
            **kwargs)
