"""
Loss functions for frame prediction and representation learning
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math


class SSIMLoss(nn.Module):
    """
    Structural Similarity Index Measure Loss
    Based on: https://github.com/Po-Hsun-Su/pytorch-ssim
    """
    def __init__(self, window_size=11, size_average=True):
        super().__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = 3
        self.window = self.create_window(window_size, self.channel)

    def create_window(self, window_size, channel):
        def gaussian(window_size, sigma):
            gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
            return gauss/gauss.sum()
        
        _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
        _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
        window = _2D_window.expand(channel, 1, window_size, window_size).contiguous()
        return window

    def forward(self, img1, img2):
        # Ensure window is on correct device
        if self.window.device != img1.device:
            self.window = self.window.to(img1.device)
        
        mu1 = F.conv2d(img1, self.window, padding=self.window_size//2, groups=self.channel)
        mu2 = F.conv2d(img2, self.window, padding=self.window_size//2, groups=self.channel)
        
        mu1_sq = mu1.pow(2)
        mu2_sq = mu2.pow(2)
        mu1_mu2 = mu1 * mu2

        sigma1_sq = F.conv2d(img1*img1, self.window, padding=self.window_size//2, groups=self.channel) - mu1_sq
        sigma2_sq = F.conv2d(img2*img2, self.window, padding=self.window_size//2, groups=self.channel) - mu2_sq
        sigma12 = F.conv2d(img1*img2, self.window, padding=self.window_size//2, groups=self.channel) - mu1_mu2

        C1 = 0.01**2
        C2 = 0.03**2

        ssim_map = ((2*mu1_mu2 + C1)*(2*sigma12 + C2)) / ((mu1_sq + mu2_sq + C1)*(sigma1_sq + sigma2_sq + C2))
        
        if self.size_average:
            return 1 - ssim_map.mean()
        else:
            return 1 - ssim_map.mean(1).mean(1).mean(1)


class FramePredictionLoss(nn.Module):
    """
    Combined loss for frame prediction
    """
    def __init__(self, l1_weight=1.0, ssim_weight=0.1, use_ssim=True):
        super().__init__()
        self.l1_weight = l1_weight
        self.ssim_weight = ssim_weight
        self.use_ssim = use_ssim
        
        self.l1_loss = nn.L1Loss()
        if use_ssim:
            self.ssim_loss = SSIMLoss()
        
    def forward(self, pred, target):
        """
        Args:
            pred: predicted frame [B, 3, H, W] in range [-1, 1]
            target: target frame [B, 3, H, W] in range [-1, 1]
        Returns:
            total_loss, loss_dict
        """
        loss_dict = {}
        
        # L1 loss
        l1_loss = self.l1_loss(pred, target)
        loss_dict['l1'] = l1_loss
        total_loss = self.l1_weight * l1_loss
        
        # SSIM loss
        if self.use_ssim:
            ssim_loss = self.ssim_loss(pred, target)
            loss_dict['ssim'] = ssim_loss
            total_loss += self.ssim_weight * ssim_loss
        
        loss_dict['total'] = total_loss
        return total_loss, loss_dict


class ContrastiveLoss(nn.Module):
    """
    Contrastive loss for representation learning
    Positive pairs: representations from adjacent frames
    Negative pairs: representations from distant frames
    """
    def __init__(self, temperature=0.1, margin=1.0):
        super().__init__()
        self.temperature = temperature
        self.margin = margin
        self.cosine_similarity = nn.CosineSimilarity(dim=-1)
        
    def forward(self, representations, temporal_indices):
        """
        Args:
            representations: [B, D] representation vectors
            temporal_indices: [B] temporal indices of each sample
        Returns:
            contrastive_loss
        """
        batch_size = representations.size(0)
        
        # Compute similarity matrix
        sim_matrix = torch.matmul(representations, representations.T) / self.temperature
        
        # Create positive mask (adjacent frames)
        indices_expanded = temporal_indices.unsqueeze(0)
        diff = torch.abs(indices_expanded - indices_expanded.T)
        positive_mask = (diff == 1).float()
        
        # Create negative mask (distant frames)
        negative_mask = (diff > 2).float()
        
        # Positive loss
        pos_sim = sim_matrix * positive_mask
        pos_loss = -torch.log(torch.exp(pos_sim) / torch.exp(sim_matrix).sum(dim=-1, keepdim=True) + 1e-8)
        pos_loss = (pos_loss * positive_mask).sum() / (positive_mask.sum() + 1e-8)
        
        # Negative loss (push apart)
        neg_sim = sim_matrix * negative_mask
        neg_loss = torch.relu(neg_sim - self.margin).mean()
        
        return pos_loss + 0.1 * neg_loss


class MultiTaskLoss(nn.Module):
    """
    Multi-task loss combining frame prediction and representation learning
    """
    def __init__(self, frame_weight=1.0, contrastive_weight=0.1, 
                 l1_weight=1.0, ssim_weight=0.1, use_contrastive=True):
        super().__init__()
        self.frame_weight = frame_weight
        self.contrastive_weight = contrastive_weight
        self.use_contrastive = use_contrastive
        
        self.frame_loss = FramePredictionLoss(l1_weight=l1_weight, ssim_weight=ssim_weight)
        if use_contrastive:
            self.contrastive_loss = ContrastiveLoss()
        
    def forward(self, pred_frame, target_frame, representations=None, temporal_indices=None):
        """
        Args:
            pred_frame: predicted frame [B, 3, H, W]
            target_frame: target frame [B, 3, H, W]
            representations: [B, D] representation vectors (optional)
            temporal_indices: [B] temporal indices (optional)
        Returns:
            total_loss, loss_dict
        """
        loss_dict = {}
        
        # Frame prediction loss
        frame_loss, frame_loss_dict = self.frame_loss(pred_frame, target_frame)
        loss_dict.update({f'frame_{k}': v for k, v in frame_loss_dict.items()})
        total_loss = self.frame_weight * frame_loss
        
        # Contrastive loss (if representations provided)
        if self.use_contrastive and representations is not None and temporal_indices is not None:
            contrastive_loss = self.contrastive_loss(representations, temporal_indices)
            loss_dict['contrastive'] = contrastive_loss
            total_loss += self.contrastive_weight * contrastive_loss
        
        loss_dict['total'] = total_loss
        return total_loss, loss_dict