# Modified from https://github.com/HobbitLong/SupContrast

from __future__ import print_function

import torch
import torch.nn as nn
import numpy


class SupConLoss(nn.Module):
    def __init__(self, temperature=0.07, contrast_mode='all',
                 base_temperature=0.07,device=None):
        super(SupConLoss, self).__init__()
        self.temperature = temperature
        self.contrast_mode = contrast_mode
        self.base_temperature = base_temperature
        self.device =device

    def forward(self, features, labels=None, gt_labels=None, mask=None, isCleans=None):
        """Compute loss for model.
        Args:
            features: hidden vector of shape [bsz, n_views, ...].
            labels: label of shape [bsz].
            gt_labels: ground-truth label of shape [bsz].
            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric.
            isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean.
        Returns:
            A loss scalar.
        """
        if self.device is None:
            device = (torch.device('cuda') if features.is_cuda else torch.device('cpu'))
        else:
            device = self.device

        if len(features.shape) < 3:
            raise ValueError('`features` needs to be [bsz, n_views, ...],'
                             'at least 3 dimensions are required')
        if len(features.shape) > 3:
            features = features.view(features.shape[0], features.shape[1], -1)

        batch_size = features.shape[0]
        if labels is not None and mask is not None:
            raise ValueError('Cannot define both `labels` and `mask`')
        elif labels is None and mask is None: # SimCLR (contrastive learning)
            mask = torch.eye(batch_size, dtype=torch.float32).to(device)
        elif labels is not None: # SupCon (supervised contrastive learning)
            labels = labels.contiguous().view(-1, 1)
            if labels.shape[0] != batch_size:
                raise ValueError('Num of labels does not match num of features')
                # set the positives of each sample as its own augmented version and the augmented versions of samples with the same label
            mask = torch.eq(labels, labels.T).float().to(device) # mask: positive==1
        else:
            mask = mask.float().to(device)

        contrast_count = features.shape[1]
        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
        if self.contrast_mode == 'one':
            anchor_feature = features[:, 0]
            anchor_count = 1
        elif self.contrast_mode == 'all':
            anchor_feature = contrast_feature
            anchor_count = contrast_count
        else:
            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))

        # compute logits
        anchor_dot_contrast = torch.div(
            torch.matmul(anchor_feature, contrast_feature.T),
            self.temperature)
        # for numerical stability
        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
        logits = anchor_dot_contrast - logits_max.detach()

        # tile mask
        mask = mask.repeat(anchor_count, contrast_count)
        # mask-out self-contrast cases
        logits_mask = torch.scatter(
            torch.ones_like(mask),
            1,
            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
            0
        )
        mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i.

        # compute log_prob
        exp_logits = torch.exp(logits) * logits_mask
        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))

        # compute mean of log-likelihood over positive
        mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
        
        # loss
        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
        loss = loss.view(anchor_count, batch_size).mean()

        return loss


class SupConLoss_Consistency(nn.Module):
    def __init__(self, temperature=0.07, contrast_mode='all',
                 base_temperature=0.07,device=None):
        super(SupConLoss_Consistency, self).__init__()
        self.temperature = temperature
        self.contrast_mode = contrast_mode
        self.base_temperature = base_temperature
        self.device =device
        

    def forward(self, features, labels=None, flags=None, mask=None):
        """Compute loss for model.
        Args:
            features: hidden vector of shape [bsz, n_views, ...].
            labels: label of shape [bsz].
            gt_labels: ground-truth label of shape [bsz].
            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j is the positive of sample i. Can be asymmetric.
            isCleans: is-clean sign of shape [bsz], isCleans{i}=1 if sample i is genuinely clean.
        Returns:
            A loss scalar.
        """
        if self.device is None:
            device = (torch.device('cuda') if features.is_cuda else torch.device('cpu'))
        else:
            device = self.device
                  
        if len(features.shape) < 3:
            raise ValueError('`features` needs to be [bsz, n_views, ...],'
                             'at least 3 dimensions are required')
        if len(features.shape) > 3:
            features = features.view(features.shape[0], features.shape[1], -1)

        batch_size = features.shape[0]
        if labels is not None and mask is not None:
            raise ValueError('Cannot define both `labels` and `mask`')
        elif labels is None and mask is None: # SimCLR (contrastive learning)
            mask = torch.eye(batch_size, dtype=torch.float32).to(device)
        elif labels is not None: # SS-CTL (semi-supervised contrastive learning)
            labels = labels.contiguous().view(-1, 1)
            if labels.shape[0] != batch_size:
                raise ValueError('Num of labels does not match num of features')
            # set the positive of a poisoned sample / an uncertain sample as its own augmented version
            # set the positives of a clean sample as its own augmented version and the augmented versions of samples with the same label                        
            mask = torch.eq(labels, labels.T).float().to(device)
            nonclean_idx = torch.where(flags!=0)[0] # poisoned samples and uncertain samples
            mask[nonclean_idx, :] = 0
            mask[nonclean_idx, nonclean_idx] = 1
        else:
            mask = mask.float().to(device)

        contrast_count = features.shape[1]
        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
        if self.contrast_mode == 'one':
            anchor_feature = features[:, 0]
            anchor_count = 1
        elif self.contrast_mode == 'all':
            anchor_feature = contrast_feature
            anchor_count = contrast_count
        else:
            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))

        # compute logits
        anchor_dot_contrast = torch.div(
            torch.matmul(anchor_feature, contrast_feature.T),
            self.temperature)
        # for numerical stability
        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
        logits = anchor_dot_contrast - logits_max.detach()

        # tile mask
        mask = mask.repeat(anchor_count, contrast_count)
        # isCleans_mask = isCleans_mask.repeat(anchor_count, contrast_count)
        # mask-out self-contrast cases
        logits_mask = torch.scatter(
            torch.ones_like(mask),
            1,
            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
            0
        )
        mask = mask * logits_mask # mask_{i,j}=1 if sample j is the positive of sample i.

        # compute log_prob
        exp_logits = torch.exp(logits) * logits_mask
        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))

        # compute mean of log-likelihood over positive
        mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)

        # loss
        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos 
        loss = loss.view(anchor_count, batch_size).mean() 

        return loss
