import torch
import torch.nn as nn
import math

class LayerNormalization(nn.Module):
    def __init__(self, features: int, eps: float = 1e-6) -> None:
        super().__init__()
        self.eps = eps
        self.alpha = nn.Parameter(torch.ones(features))
        self.bias = nn.Parameter(torch.zeros(features))

    def forward(self, x):
        mean = x.mean(dim=-1, keepdim=True)
        std = x.std(dim=-1, keepdim=True)
        return self.alpha * (x - mean) / (std + self.eps) + self.bias

class FeedForwardBlock(nn.Module):
    def __init__(self, d_model: int, d_ff: int, dropout: float) -> None:
        super().__init__()
        self.fc1 = nn.Linear(d_model, d_ff)
        self.dropout = nn.Dropout(dropout)
        self.fc2 = nn.Linear(d_ff, d_model)

    def forward(self, x):
        return self.fc2(self.dropout(torch.relu(self.fc1(x))))

class InputEmbeddings(nn.Module):
    def __init__(self, d_model: int, vocab_size: int) -> None:
        super().__init__()
        self.d_model = d_model
        self.embedding = nn.Embedding(vocab_size, d_model)

    def forward(self, x):
        return self.embedding(x) * math.sqrt(self.d_model)

class PositionalEncoding(nn.Module):
    def __init__(self, d_model: int, seq_len: int, dropout: float) -> None:
        super().__init__()
        self.dropout = nn.Dropout(dropout)
        pe = torch.zeros(seq_len, d_model)
        position = torch.arange(0, seq_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:, :x.shape[1], :].requires_grad_(False)
        return self.dropout(x)

class ResidualConnection(nn.Module):
    def __init__(self, features: int, dropout: float) -> None:
        super().__init__()
        self.dropout = nn.Dropout(dropout)
        self.norm = LayerNormalization(features)

    def forward(self, x, sublayer):
        return x + self.dropout(sublayer(self.norm(x)))

class MultiHeadAttentionBlock(nn.Module):
    def __init__(self, d_model: int, num_heads: int, dropout: float) -> None:
        super().__init__()
        self.num_heads = num_heads
        self.d_k = d_model // num_heads
        self.w_q = nn.Linear(d_model, d_model, bias=False)
        self.w_k = nn.Linear(d_model, d_model, bias=False)
        self.w_v = nn.Linear(d_model, d_model, bias=False)
        self.w_o = nn.Linear(d_model, d_model, bias=False)
        self.dropout = nn.Dropout(dropout)

    @staticmethod
    def attention(query, key, value, mask, dropout: nn.Dropout):
        d_k = query.shape[-1]
        scores = (query @ key.transpose(-2, -1)) / math.sqrt(d_k)
        if mask is not None:
            scores.masked_fill_(mask == 0, -1e9)
        scores = scores.softmax(dim=-1)
        if dropout is not None:
            scores = dropout(scores)
        return scores @ value, scores

    def forward(self, q, k, v, mask):
        query = self.w_q(q)
        key = self.w_k(k)
        value = self.w_v(v)
        query = query.view(query.shape[0], query.shape[1], self.num_heads, self.d_k).transpose(1, 2)
        key = key.view(key.shape[0], key.shape[1], self.num_heads, self.d_k).transpose(1, 2)
        value = value.view(value.shape[0], value.shape[1], self.num_heads, self.d_k).transpose(1, 2)
        x, self.attention_scores = MultiHeadAttentionBlock.attention(query, key, value, mask, self.dropout)
        x = x.transpose(1, 2).contiguous().view(x.shape[0], -1, self.num_heads * self.d_k)
        return self.w_o(x)

class EncoderBlock(nn.Module):
    def __init__(self, features: int, self_attention: MultiHeadAttentionBlock, feed_forward: FeedForwardBlock, dropout: float) -> None:
        super().__init__()
        self.self_attention = self_attention
        self.feed_forward = feed_forward
        self.residuals = nn.ModuleList([ResidualConnection(features, dropout) for _ in range(2)])

    def forward(self, x, src_mask):
        x = self.residuals[0](x, lambda x: self.self_attention(x, x, x, src_mask))
        x = self.residuals[1](x, self.feed_forward)
        return x

class Encoder(nn.Module):
    def __init__(self, features: int, layers: nn.ModuleList) -> None:
        super().__init__()
        self.layers = layers
        self.norm = LayerNormalization(features)

    def forward(self, x, mask):
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)

class DecoderBlock(nn.Module):
    def __init__(self, features: int, self_attention: MultiHeadAttentionBlock, cross_attention: MultiHeadAttentionBlock, feed_forward: FeedForwardBlock, dropout: float) -> None:
        super().__init__()
        self.self_attention = self_attention
        self.cross_attention = cross_attention
        self.feed_forward = feed_forward
        self.residuals = nn.ModuleList([ResidualConnection(features, dropout) for _ in range(3)])

    def forward(self, x, encoder_output, src_mask, tgt_mask):
        x = self.residuals[0](x, lambda x: self.self_attention(x, x, x, tgt_mask))
        x = self.residuals[1](x, lambda x: self.cross_attention(x, encoder_output, encoder_output, src_mask))
        x = self.residuals[2](x, self.feed_forward)
        return x

class Decoder(nn.Module):
    def __init__(self, features: int, layers: nn.ModuleList) -> None:
        super().__init__()
        self.layers = layers
        self.norm = LayerNormalization(features)

    def forward(self, x, encoder_output, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, encoder_output, src_mask, tgt_mask)
        return self.norm(x)

class ProjectionLayer(nn.Module):
    def __init__(self, d_model, vocab_size) -> None:
        super().__init__()
        self.proj = nn.Linear(d_model, vocab_size)

    def forward(self, x) -> None:
        return self.proj(x)

class Transformer(nn.Module):
    def __init__(self, encoder: Encoder, decoder: Decoder, src_embed: InputEmbeddings, tgt_embed: InputEmbeddings, src_pos: PositionalEncoding, tgt_pos: PositionalEncoding, projection_layer: ProjectionLayer) -> None:
        super().__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.src_embed = src_embed
        self.tgt_embed = tgt_embed
        self.src_pos = src_pos
        self.tgt_pos = tgt_pos
        self.projection_layer = projection_layer

    def encode(self, src, src_mask):
        src = self.src_embed(src)
        src = self.src_pos(src)
        return self.encoder(src, src_mask)

    def decode(self, encoder_output: torch.Tensor, src_mask: torch.Tensor, tgt: torch.Tensor, tgt_mask: torch.Tensor):
        tgt = self.tgt_embed(tgt)
        tgt = self.tgt_pos(tgt)
        return self.decoder(tgt, encoder_output, src_mask, tgt_mask)

    def project(self, x):
        return self.projection_layer(x)

def build_transformer(src_vocab_size: int, tgt_vocab_size: int, src_seq_len: int, tgt_seq_len: int, d_model: int = 512, num_layers: int = 6, num_heads: int = 8, dropout: float = 0.1, d_ff: int = 2048) -> Transformer:
    src_embed = InputEmbeddings(d_model, src_vocab_size)
    tgt_embed = InputEmbeddings(d_model, tgt_vocab_size)
    src_pos = PositionalEncoding(d_model, src_seq_len, dropout)
    tgt_pos = PositionalEncoding(d_model, tgt_seq_len, dropout)

    encoder_blocks = []
    for _ in range(num_layers):
        self_attention = MultiHeadAttentionBlock(d_model, num_heads, dropout)
        feed_forward = FeedForwardBlock(d_model, d_ff, dropout)
        encoder_block = EncoderBlock(d_model, self_attention, feed_forward, dropout)
        encoder_blocks.append(encoder_block)

    decoder_blocks = []
    for _ in range(num_layers):
        self_attention = MultiHeadAttentionBlock(d_model, num_heads, dropout)
        cross_attention = MultiHeadAttentionBlock(d_model, num_heads, dropout)
        feed_forward = FeedForwardBlock(d_model, d_ff, dropout)
        decoder_block = DecoderBlock(d_model, self_attention, cross_attention, feed_forward, dropout)
        decoder_blocks.append(decoder_block)

    encoder = Encoder(d_model, nn.ModuleList(encoder_blocks))
    decoder = Decoder(d_model, nn.ModuleList(decoder_blocks))
    projection_layer = ProjectionLayer(d_model, tgt_vocab_size)
    transformer = Transformer(encoder, decoder, src_embed, tgt_embed, src_pos, tgt_pos, projection_layer)

    for p in transformer.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform_(p)

    return transformer