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

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, n_heads):
        super(MultiHeadAttention, self).__init__()
        assert d_model % n_heads == 0, "d_model must be divisible by n_heads"
        
        self.d_model = d_model
        self.n_heads = n_heads
        self.d_k = d_model // n_heads
        
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
        self.W_o = nn.Linear(d_model, d_model)
    
    def forward(self, Q, K, V, mask=None):
        batch_size = Q.size(0)
        
        Q = self.W_q(Q).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2)
        K = self.W_k(K).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2)
        V = self.W_v(V).view(batch_size, -1, self.n_heads, self.d_k).transpose(1, 2)
        
        scores = torch.matmul(Q, K.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.d_k, dtype=torch.float32))
        
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)
        
        attention = F.softmax(scores, dim=-1)
        output = torch.matmul(attention, V)
        
        output = output.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
        output = self.W_o(output)
        
        return output, attention

class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff):
        super(PositionwiseFeedForward, self).__init__()
        self.linear1 = nn.Linear(d_model, d_ff)
        self.linear2 = nn.Linear(d_ff, d_model)
    
    def forward(self, x):
        return self.linear2(F.relu(self.linear1(x)))

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=5000):
        super(PositionalEncoding, self).__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-torch.log(torch.tensor(10000.0)) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)
    
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return x

class EncoderLayer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout=0.1):
        super(EncoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(d_model, n_heads)
        self.feed_forward = PositionwiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
    
    def forward(self, x, mask=None):
        attn_output, _ = self.self_attn(x, x, x, mask)
        x = self.norm1(x + self.dropout1(attn_output))
        ff_output = self.feed_forward(x)
        x = self.norm2(x + self.dropout2(ff_output))
        return x

class DecoderLayer(nn.Module):
    def __init__(self, d_model, n_heads, d_ff, dropout=0.1):
        super(DecoderLayer, self).__init__()
        self.self_attn = MultiHeadAttention(d_model, n_heads)
        self.src_attn = MultiHeadAttention(d_model, n_heads)
        self.feed_forward = PositionwiseFeedForward(d_model, d_ff)
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)
    
    def forward(self, x, memory, src_mask=None, tgt_mask=None):
        attn1, _ = self.self_attn(x, x, x, tgt_mask)
        x = self.norm1(x + self.dropout1(attn1))
        attn2, _ = self.src_attn(x, memory, memory, src_mask)
        x = self.norm2(x + self.dropout2(attn2))
        ff_output = self.feed_forward(x)
        x = self.norm3(x + self.dropout3(ff_output))
        return x

class Transformer(nn.Module):
    def __init__(self, src_vocab_size, tgt_vocab_size, d_model, n_heads, d_ff, n_layers, dropout=0.1):
        super(Transformer, self).__init__()
        self.encoder = nn.ModuleList([EncoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_layers)])
        self.decoder = nn.ModuleList([DecoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_layers)])
        self.src_embed = nn.Sequential(nn.Embedding(src_vocab_size, d_model), PositionalEncoding(d_model))
        self.tgt_embed = nn.Sequential(nn.Embedding(tgt_vocab_size, d_model), PositionalEncoding(d_model))
        self.out = nn.Linear(d_model, tgt_vocab_size)
    
    def forward(self, src, tgt, src_mask=None, tgt_mask=None):
        enc_output = self.src_embed(src)
        for layer in self.encoder:
            enc_output = layer(enc_output, src_mask)
        
        dec_output = self.tgt_embed(tgt)
        for layer in self.decoder:
            dec_output = layer(dec_output, enc_output, src_mask, tgt_mask)
        
        output = self.out(dec_output)
        return output

# Example usage
src_vocab_size = 1000
tgt_vocab_size = 1000
d_model = 512
n_heads = 8
d_ff = 2048
n_layers = 6
dropout = 0.1

model = Transformer(src_vocab_size, tgt_vocab_size, d_model, n_heads, d_ff, n_layers, dropout)
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