import torch
import torch.nn as nn
import torch.nn.functional as F
import math


class FocalLoss(nn.Module):
    def __init__(self, alpha=1, gamma=2, reduction='mean'):
        super(FocalLoss, self).__init__()
        self.alpha = alpha
        self.gamma = gamma
        self.reduction = reduction

    def forward(self, inputs, targets):
        ce_loss = F.cross_entropy(inputs, targets, reduction='none')
        pt = torch.exp(-ce_loss)
        focal_loss = self.alpha * (1 - pt) ** self.gamma * ce_loss
        if self.reduction == 'mean': return focal_loss.mean()
        return focal_loss.sum()

class ArcMarginProduct(nn.Module):
    def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):
        super(ArcMarginProduct, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.s = s
        self.m = m
        self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))
        nn.init.xavier_uniform_(self.weight)

        self.easy_margin = easy_margin
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.th = math.cos(math.pi - m)
        self.mm = math.sin(math.pi - m) * m

    def forward(self, input, label):
        cosine = F.linear(F.normalize(input), F.normalize(self.weight))
        sine = torch.sqrt((1.0 - torch.pow(cosine, 2)).clamp(0, 1))
        phi = cosine * self.cos_m - sine * self.sin_m
        if self.easy_margin:
            phi = torch.where(cosine > 0, phi, cosine)
        else:
            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
        
        one_hot = torch.zeros(cosine.size(), device=input.device)
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
        output *= self.s
        return output

    def predict(self, input):
        return F.linear(F.normalize(input), F.normalize(self.weight)) * self.s


class DualStreamTransformer(nn.Module):
    def __init__(self, feat_num_1, feat_num_2, d_model=64, num_classes=3, dropout=0.3):
        super().__init__()
        # Stream 1: CCMQ
        self.feat_tokenizers_1 = nn.ModuleList([nn.Linear(1, d_model) for _ in range(feat_num_1)])
        enc_layer_1 = nn.TransformerEncoderLayer(d_model=d_model, nhead=4, dim_feedforward=128, dropout=dropout, batch_first=True)
        self.encoder_1 = nn.TransformerEncoder(enc_layer_1, num_layers=2)
        self.cls_token_1 = nn.Parameter(torch.zeros(1, 1, d_model))
        
        # Stream 2: OSDI
        self.feat_tokenizers_2 = nn.ModuleList([nn.Linear(1, d_model) for _ in range(feat_num_2)])
        enc_layer_2 = nn.TransformerEncoderLayer(d_model=d_model, nhead=4, dim_feedforward=128, dropout=dropout, batch_first=True)
        self.encoder_2 = nn.TransformerEncoder(enc_layer_2, num_layers=2)
        self.cls_token_2 = nn.Parameter(torch.zeros(1, 1, d_model))

        # Fusion
        self.fusion = nn.Sequential(
            nn.Linear(d_model * 2, d_model),
            nn.LayerNorm(d_model),
            nn.ReLU(),
            nn.Dropout(dropout)
        )

    def forward_stream(self, x, tokenizers, encoder, cls_token):
        batch_size = x.size(0)
        tokens = []
        for i, tokenizer in enumerate(tokenizers):
            val = x[:, i].unsqueeze(1)
            tokens.append(tokenizer(val))
        x_emb = torch.stack(tokens, dim=1)
        cls_tokens = cls_token.expand(batch_size, -1, -1)
        x_emb = torch.cat((cls_tokens, x_emb), dim=1)
        x_out = encoder(x_emb)
        return x_out[:, 0, :] 

    def forward(self, x1, x2):
        feat_1 = self.forward_stream(x1, self.feat_tokenizers_1, self.encoder_1, self.cls_token_1)
        feat_2 = self.forward_stream(x2, self.feat_tokenizers_2, self.encoder_2, self.cls_token_2)
        combined = torch.cat((feat_1, feat_2), dim=1)
        return self.fusion(combined)