import torch

import torch.nn as nn

class AsymmetricLoss(nn.Module):

    def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=True):

        super(AsymmetricLoss, self).__init__()

        self.gamma_neg = gamma_neg

        self.gamma_pos = gamma_pos

        self.clip = clip

        self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss

        self.eps = eps

    def forward(self, x, y):

        """"

        Parameters

        ----------

        x: input logits

        y: targets (multi-label binarized vector)

        """

        # Calculating Probabilities

        x_sigmoid = torch.sigmoid(x)

        xs_pos = x_sigmoid

        xs_neg = 1 - x_sigmoid

        # Asymmetric Clipping

        if self.clip is not None and self.clip > 0:

            xs_neg = (xs_neg + self.clip).clamp(max=1)

        # Basic CE calculation

        los_pos = y * torch.log(xs_pos.clamp(min=self.eps))

        los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))

        loss = los_pos + los_neg

        # Asymmetric Focusing

        if self.gamma_neg > 0 or self.gamma_pos > 0:

            if self.disable_torch_grad_focal_loss:

                torch.set_grad_enabled(False)

            pt0 = xs_pos * y

            pt1 = xs_neg * (1 - y)  # pt = p if t > 0 else 1-p

            pt = pt0 + pt1

            one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)

            one_sided_w = torch.pow(1 - pt, one_sided_gamma)

            if self.disable_torch_grad_focal_loss:

                torch.set_grad_enabled(True)

            loss *= one_sided_w

        return -loss.sum()

class AsymmetricLossOptimized(nn.Module):

    ''' Notice - optimized version, minimizes memory allocation and gpu uploading,

    favors inplace operations'''

    def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):

        super(AsymmetricLossOptimized, self).__init__()

        self.gamma_neg = gamma_neg

        self.gamma_pos = gamma_pos

        self.clip = clip

        self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss

        self.eps = eps

        # prevent memory allocation and gpu uploading every iteration, and encourages inplace operations

        self.targets = self.anti_targets = self.xs_pos = self.xs_neg = self.asymmetric_w = self.loss = None

    def forward(self, x, y):

        """"

        Parameters

        ----------

        x: input logits

        y: targets (multi-label binarized vector)

        """

        self.targets = y

        self.anti_targets = 1 - y

        # Calculating Probabilities

        self.xs_pos = torch.sigmoid(x)

        self.xs_neg = 1.0 - self.xs_pos

        # Asymmetric Clipping

        if self.clip is not None and self.clip > 0:

            self.xs_neg.add_(self.clip).clamp_(max=1)

        # Basic CE calculation

        self.loss = self.targets * torch.log(self.xs_pos.clamp(min=self.eps))

        self.loss.add_(self.anti_targets * torch.log(self.xs_neg.clamp(min=self.eps)))

        # Asymmetric Focusing

        if self.gamma_neg > 0 or self.gamma_pos > 0:

            if self.disable_torch_grad_focal_loss:

                torch.set_grad_enabled(False)

            self.xs_pos = self.xs_pos * self.targets

            self.xs_neg = self.xs_neg * self.anti_targets

            self.asymmetric_w = torch.pow(1 - self.xs_pos - self.xs_neg,

                                          self.gamma_pos * self.targets + self.gamma_neg * self.anti_targets)

            if self.disable_torch_grad_focal_loss:

                torch.set_grad_enabled(True)

            self.loss *= self.asymmetric_w

        return -self.loss.sum()

class ASLSingleLabel(nn.Module):

    '''

    This loss is intended for single-label classification problems

    '''

    def __init__(self, gamma_pos=0, gamma_neg=4, eps: float = 0.1, reduction='mean'):

        super(ASLSingleLabel, self).__init__()

        self.eps = eps

        self.logsoftmax = nn.LogSoftmax(dim=-1)

        self.targets_classes = []

        self.gamma_pos = gamma_pos

        self.gamma_neg = gamma_neg

        self.reduction = reduction

    def forward(self, inputs, target):

        '''

        "input" dimensions: - (batch_size,number_classes)

        "target" dimensions: - (batch_size)

        '''

        num_classes = inputs.size()[-1]

        log_preds = self.logsoftmax(inputs)

        self.targets_classes = torch.zeros_like(inputs).scatter_(1, target.long().unsqueeze(1), 1)

        # ASL weights

        targets = self.targets_classes

        anti_targets = 1 - targets

        xs_pos = torch.exp(log_preds)

        xs_neg = 1 - xs_pos

        xs_pos = xs_pos * targets

        xs_neg = xs_neg * anti_targets

        asymmetric_w = torch.pow(1 - xs_pos - xs_neg,

                                 self.gamma_pos * targets + self.gamma_neg * anti_targets)

        log_preds = log_preds * asymmetric_w

        if self.eps > 0:  # label smoothing

            self.targets_classes = self.targets_classes.mul(1 - self.eps).add(self.eps / num_classes)

        # loss calculation

        loss = - self.targets_classes.mul(log_preds)

        loss = loss.sum(dim=-1)

        if self.reduction == 'mean':

            loss = loss.mean()

        return loss