import torch
import torch.nn as nn
import math

class LayerNormalization(nn.Module):
    
    def __init__(self, eps: float=10**-6) -> None:
        super().__init__()
        self.eps = eps
        self.alpha = nn. Parameter(torch.ones (1)) #alpha is a learnable parameter
        self.bias = nn. Parameter(torch.zeros(1)) #·bias is a learnable parameter
        
    def forward(self,x):
        #x: (batch, seq_len, hidden_size)
        #Keep the dimension for broadcasting
        mean = x.mean (dim = -1, keepdim = True) # (batch, seq_len, 1)
        #Keep the dimension for broadcasting
        std = x.std (dim = -1, keepdim = True) # (batch, seq_len, ∙1)
        #eps is to prevent dividing by zero or when std is very small
        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.linear_1 = nn.Linear(d_model, d_ff) # w1 and b1
        self.dropout = nn. Dropout (dropout)
        self.linear_2= nn.Linear(d_ff, d_model) # w2 and b2
        
    def forward(self, x):
        # (batch, seq_len, d_model) --> (batch, seq_len, d_ff) --> (batch, seq_len, d_model)
        return self.linear_2(self.dropout (torch.relu(self.linear_1(x))))
    
    
class InputEmbeddings(nn.Module):
    
    def __init__(self, d_model: int, vocab_size: int) -> None:
        super().__init__()
        self.d_model=d_model
        self.vocab_size = vocab_size
        self.embedding = nn. Embedding (vocab_size, d_model)
        
    def forward(self,x):
        #· (batch, seq_len) --> (batch, seq_len, d_model)
        # Multiply by sqrt(d_model) to scale the embeddings according to the paper
        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.d_model = d_model
        self.seq_len = seq_len
        self.dropout = nn.Dropout(dropout) # Create a matrix of shape (seq_len, d_model) 
        pe = torch.zeros(seq_len, d_model) # Create a vector of shape (seq_len) 
        position = torch.arange(0, seq_len, dtype=torch.float).unsqueeze(1) # (seq_len, 1) 
        # Create a vector of shape (d_model) 
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        # Apply sine to even indices 
        pe[:, 0::2] = torch.sin(position * div_term) # sin(position * (10000 ** (2i / d_model)) 
        # Apply cosine to odd indices 
        pe[:, 1::2] = torch.cos(position * div_term) # cos(position * (10000 ** (2i / d_model))
        # Add a batch dimension to the positional encoding
        pe = pe.unsqueeze(0) # (1, seq_len, d_model) 
        # Register the positional encoding as a buffer 
        self.register_buffer('pe', pe) 
        
    def forward(self, x): 
        x = x + (self.pe[:, :x.shape[1], :]).requires_grad_(False) # (batch, seq_len, d_model) 
        return self.dropout(x)
    
class ResidualConnection(nn.Module):
    
    def __init__(self, dropout: float) -> None: 
        super().__init__() 
        self.dropout = nn.Dropout(dropout)
        self.norm = LayerNormalization()
        
    def forward(self, x, sublayer): 
        return x + self.dropout(sublayer(self.norm(x))) 
    
    
class MultiHeadAttentionBlock(nn.Module): 
    
    def __init__(self, d_model: int, h: int, dropout: float) -> None: 
        super().__init__() 
        self.d_model = d_model  # Embedding vector size 
        self.h = h # Number of heads 
        # Make sure d_model is divisible by h
        assert d_model % h == 0, "d_model is not divisible by h"
        
        self.d_k = d_model // h # Dimension of vector seen by each head
        self.w_q = nn.Linear(d_model, d_model, bias=False) # Wq 
        self.w_k = nn.Linear(d_model, d_model, bias=False) # Wk 
        self.w_v = nn.Linear(d_model, d_model, bias=False) # Wv 
        self.w_o = nn.Linear(d_model, d_model, bias=False) # Wo 
        self.dropout = nn.Dropout(dropout)
        
    @staticmethod
    def attention(query, key, value, mask, dropout: nn.Dropout):
        d_k = query.shape[-1]
        # Just apply the formula from the paper
        # (batch, h, seq_len, d_k) --> (batch, h, seq_len, seq_len)
        attention_scores = (query @ key.transpose(-2, -1)) / math.sqrt(d_k)
        if mask is not None:
            # Write a very low value (indicating -inf) to the positions where mask == 0
            _MASKING_VALUE = -1e9 if attention_scores.dtype == torch.float32 else -1e+4
            attention_scores.masked_fill_(mask == 0, _MASKING_VALUE)
        attention_scores = attention_scores.softmax(dim=-1) # (batch, h, seq_len, seq_len) # Apply soft
        if dropout is not None:
            attention_scores = dropout(attention_scores)
        # (batch, h, seq_len, seq_len) --> (batch, h, seq_len, d_k)
        # return attention scores which can be used for visualization
        return (attention_scores @ value), attention_scores
    
    def forward(self, q, k, v, mask):
        query = self.w_q(q) # (batch, seq_len, d_model) --> (batch, seq_len, d_model)
        key = self.w_k(k) # (batch, seq_len, d_model) --> (batch, seq_len, d_model)
        value = self.w_v(v) # (batch, seq_len, d_model) --> (batch, seq_len, d_model)
        
        # (batch, seq_len, d_model) --> (batch, seq_len, h, d_k) --> (batch, h, seq_len, d_k)
        query = query.view(query.shape[0], query.shape[1], self.h, self.d_k).transpose(1, 2)
        key = key.view(key.shape[0], key.shape[1], self.h, self.d_k).transpose(1, 2)
        value = value.view(value.shape[0], value.shape[1], self.h, self.d_k).transpose(1, 2)
        
        # Calculate attention
        x, self.attention_scores = MultiHeadAttentionBlock.attention(query, key, value, mask, self.dropout)
        
        # Combine all the heads together
        # (batch, h, seq_len, d_k) --> (batch, seq_len, h, d_k) --> (batch, seq_len, d_model)
        x = x.transpose(1, 2).contiguous().view(x.shape[0], -1, self.h * self.d_k)
        
        # Multiply by Wo
        # (batch, seq_len, d_model) --> (batch, seq_len, d_model)
        return self.w_o(x)
    
class EncoderBlock(nn.Module):
    def __init__(self, self_attention_block: MultiHeadAttentionBlock, feed_forward_block: FeedForwardBlock,  dropout: float) -> None : 
        super().__init__()
        self.self_attention_block = self_attention_block
        self.feed_forward_block = feed_forward_block
        self.residual_connections = nn.ModuleList([ResidualConnection(dropout) for _ in range(2)])
        
    def forward(self, x, src_mask):
        x = self.residual_connections[0](x, lambda x: self.self_attention_block(x, x, x, src_mask))
        x = self.residual_connections[1](x, self.feed_forward_block)
        return x
    
class Encoder(nn.Module):
    def __init__(self, layers: nn.ModuleList) -> None:
        super().__init__()
        self.layers = layers
        self.norm = LayerNormalization()
        
    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, self_attention_block: MultiHeadAttentionBlock, cross_attention_block: MultiHeadAttentionBlock, feed_forward_block: FeedForwardBlock, dropout: float ) -> None:
        super().__init__()
        self.self_attention_block = self_attention_block
        self.cross_attention_block = cross_attention_block
        self.feed_forward_block = feed_forward_block
        self.residual_connections = nn.ModuleList([ResidualConnection(dropout) for _ in range(3)])
        
    def forward(self, x, encoder_output, src_mask, tgt_mask):
        x = self.residual_connections[0](x, lambda x: self.self_attention_block(x, x, x, tgt_mask))
        x = self.residual_connections[1](x, lambda x: self.cross_attention_block(x, encoder_output, encoder_output, src_mask))
        x = self.residual_connections[2](x, self.feed_forward_block)
        return x

class Decoder(nn.Module):
    def __init__(self, layers: nn.ModuleList) -> None:
        super().__init__()
        self.layers = layers
        self.norm = LayerNormalization()
        
    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:
        #- (batch, seq_len, d_model) ---> (batch, seq_len, vocab_size)
        return torch.log_softmax(self.proj(x), dim = -1)

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):
        #- (batch, seq_len, d_model)
        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):
        #- (batch, -seq_len, -d_model)
        tgt = self.tgt_embed(tgt)
        tgt = self.tgt_pos(tgt)
        return self.decoder(tgt, encoder_output, src_mask, tgt_mask)
    
    def project(self, x):
        # (batch, -seq_len, -vocab_size)
        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, N: int=6, h: int=8, dropout: float=0.1, d_ff: int=2048) -> Transformer:
        
        # Create the embedding: layers
        src_embed = InputEmbeddings(d_model, src_vocab_size)
        tgt_embed = InputEmbeddings(d_model, tgt_vocab_size)
        
        # Create the positional encoding layers
        src_pos = PositionalEncoding(d_model, src_seq_len, dropout)
        tgt_pos = PositionalEncoding(d_model, tgt_seq_len, dropout)
        
        # Create the encoder blocks
        encoder_blocks = []
        for _ in range(N // 2):
            encoder_self_attention_block = MultiHeadAttentionBlock(d_model, h, dropout)
            feed_forward_block = FeedForwardBlock(d_model, d_ff, dropout)
            encoder_block = EncoderBlock(encoder_self_attention_block, feed_forward_block, dropout)
            encoder_blocks.append(encoder_block)
            
        #Create the decoder blocks
        decoder_blocks = []
        for _ in range(N // 2):
            decoder_self_attention_block = MultiHeadAttentionBlock(d_model, h, dropout)
            decoder_cross_attention_block = MultiHeadAttentionBlock(d_model, h, dropout)
            feed_forward_block = FeedForwardBlock(d_model, d_ff, dropout)
            decoder_block = DecoderBlock(decoder_self_attention_block, decoder_cross_attention_block, feed_forward_block, dropout)
            decoder_blocks.append(decoder_block)

        e1, e2, e3 = encoder_blocks
        d1, d2, d3 = decoder_blocks

        encoder_blocks1 = [e1, e2, e3, e3, e2, e1]
        decoder_blocks1 = [d1, d2, d3, d3, d2, d1]
            
        # Create the encoder and decoder
        encoder = Encoder(nn.ModuleList (encoder_blocks1))
        decoder = Decoder(nn.ModuleList(decoder_blocks1))
        
        # Create the projection layer
        projection_layer = ProjectionLayer(d_model, tgt_vocab_size)
        
        # Create the transformer
        transformer = Transformer(encoder, decoder, src_embed, tgt_embed, src_pos, tgt_pos, projection_layer)
        
        # Initialize the parameters
        for p in transformer.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)
                
        return transformer