import torch
import torch.nn as nn
import math


class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=100):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        pos = torch.arange(0, max_len).unsqueeze(1)
        div = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(pos * div)
        pe[:, 1::2] = torch.cos(pos * div)
        self.register_buffer('pe', pe.unsqueeze(0))

    def forward(self, x):
        return x + self.pe[:, :x.size(1)].to(x.device)

class ScaledDotProductAttention(nn.Module):
    def forward(self, Q, K, V, mask=None):
        d_k = Q.size(-1)
        scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_k)
        attn = torch.softmax(scores, dim=-1)
        return torch.matmul(attn, V), attn

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        assert d_model % num_heads == 0
        self.d_k = d_model // num_heads
        self.num_heads = num_heads
        self.Q = nn.Linear(d_model, d_model)
        self.K = nn.Linear(d_model, d_model)
        self.V = nn.Linear(d_model, d_model)
        self.out = nn.Linear(d_model, d_model)
        self.attn = ScaledDotProductAttention()
        self.last_attn_weights = None

    def forward(self, q, k, v, mask=None):
        B = q.size(0)
        Q = self.Q(q).view(B, -1, self.num_heads, self.d_k).transpose(1, 2)
        K = self.K(k).view(B, -1, self.num_heads, self.d_k).transpose(1, 2)
        V = self.V(v).view(B, -1, self.num_heads, self.d_k).transpose(1, 2)
        out, attn = self.attn(Q, K, V)
        self.last_attn_weights = attn.detach().cpu()
        out = out.transpose(1, 2).contiguous().view(B, -1, self.num_heads * self.d_k)
        return self.out(out)

class FeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.ff = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.ReLU(),
            nn.Linear(d_ff, d_model)
        )

    def forward(self, x):
        return self.ff(x)

class EncoderLayer(nn.Module):
    def __init__(self, d_model, num_heads, d_ff):
        super().__init__()
        self.attn = MultiHeadAttention(d_model, num_heads)
        self.ff = FeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)

    def forward(self, x):
        x2 = self.attn(x, x, x)
        x = self.norm1(x + x2)
        x2 = self.ff(x)
        x = self.norm2(x + x2)
        return x