import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

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 AsymmetricLossOptimizedMod(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-5, disable_torch_grad_focal_loss=False):
        super().__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

        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:
                with torch.no_grad():
                    # if self.disable_torch_grad_focal_loss:
                    #     torch._C.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._C.set_grad_enabled(True)
                self.loss *= self.asymmetric_w
            else:
                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)   
                self.loss *= self.asymmetric_w         
        _loss = - self.loss.sum() / x.size(0)
        _loss = _loss / y.size(1) * 1000

        return _loss


class AsymmetricLossOptimized2(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, reduction='sum'):
        super().__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
        self.reduction = reduction

        # 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

        loss = -self.loss.sum(dim=1)
        if self.reduction == 'sum':
            loss = loss.sum()
        elif self.reduction == 'mean':
            loss = loss.mean()
        elif self.reduction is None:
            loss = loss
        else:
            raise ValueError

        return loss

