import torch
import math
import copy

class Embedder(torch.nn.Module):
    def __init__(self, vocab_size, d_model):
        super().__init__()
        self.embed = torch.nn.Embedding(vocab_size, d_model)
    def forward(self, x):
        return self.embed(x)
    
class PositionalEncoder(torch.nn.Module):
    def __init__(self, d_model, dropout=0.1, max_seq_len = 80):
        super().__init__()
        self.dropout = torch.nn.Dropout(p=dropout)

        position = torch.arange(max_seq_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
        pe = torch.zeros(max_seq_len, 1, d_model)
        pe[:, 0, 0::2] = torch.sin(position * div_term)
        pe[:, 0, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe) # notifies PyTorch that this value should be saved like a model parameter but should not have gradients
    
    def forward(self, x):
        x = x + self.pe[:x.size(0)]
        return self.dropout(x)
    
class MultiHeadAttention(torch.nn.Module):
    def __init__(self, heads, d_model, dropout = 0.1):
        super().__init__()
        
        self.d_model = d_model
        self.d_k = d_model // heads
        self.h = heads
        
        self.q_linear = torch.nn.Linear(d_model, d_model)
        self.v_linear = torch.nn.Linear(d_model, d_model)
        self.k_linear = torch.nn.Linear(d_model, d_model)
        self.dropout = torch.nn.Dropout(dropout)
        self.out = torch.nn.Linear(d_model, d_model)
    
    def forward(self, q, k, v, mask=None):
        bs = q.size(0)
        
        # perform linear operation and split into h heads
        
        k = self.k_linear(k).view(bs, -1, self.h, self.d_k)
        q = self.q_linear(q).view(bs, -1, self.h, self.d_k)
        v = self.v_linear(v).view(bs, -1, self.h, self.d_k)
        
        # transpose to get dimensions bs * h * sl * d_model
       
        k = k.transpose(1,2)
        q = q.transpose(1,2)
        v = v.transpose(1,2)
        
        # calculate attention using function we will define next
        scores = attention(q, k, v, self.d_k, mask, self.dropout)
        
        # concatenate heads and put through final linear layer
        concat = scores.transpose(1,2).contiguous().view(bs, -1, self.d_model)
        
        output = self.out(concat)
    
        return output
    
def attention(q, k, v, d_k, mask=None, dropout=None):
    scores = torch.matmul(q, k.transpose(-2, -1)) /  math.sqrt(d_k)
    if mask is not None:
        mask = mask.unsqueeze(1)
        scores = scores.masked_fill(mask == 0, -1e9)
    scores = torch.nn.functional.softmax(scores, dim=-1)
    
    if dropout is not None:
        scores = dropout(scores)
        
    output = torch.matmul(scores, v)
    return output

class FeedForward(torch.nn.Module):
    def __init__(self, d_model, d_ff=2048, dropout = 0.1):
        super().__init__() 
        # We set d_ff as a default to 2048
        self.linear_1 = torch.nn.Linear(d_model, d_ff)
        self.dropout = torch.nn.Dropout(dropout)
        self.linear_2 = torch.nn.Linear(d_ff, d_model)
    def forward(self, x):
        x = self.dropout(torch.nn.functional.relu(self.linear_1(x)))
        x = self.linear_2(x)
        return x
    
class Norm(torch.nn.Module):
    def __init__(self, d_model, eps = 1e-6):
        super().__init__()
    
        self.size = d_model
        # create two learnable parameters to calibrate normalization
        self.alpha = torch.nn.Parameter(torch.ones(self.size))
        self.bias = torch.nn.Parameter(torch.zeros(self.size))
        self.eps = eps
        
    def forward(self, x):
        norm = self.alpha * (x - x.mean(dim=-1, keepdim=True)) / (x.std(dim=-1, keepdim=True) + self.eps) + self.bias
        return norm
    
# encoder layer with one multi-head attention layer and one # feed-forward layer
class EncoderLayer(torch.nn.Module):
    def __init__(self, d_model, heads, dropout = 0.1):
        super().__init__()
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        self.attn = MultiHeadAttention(heads, d_model)
        self.ff = FeedForward(d_model)
        self.dropout_1 = torch.nn.Dropout(dropout)
        self.dropout_2 = torch.nn.Dropout(dropout)
        
    def forward(self, x, mask):
        x2 = self.norm_1(x)
        x = x + self.dropout_1(self.attn(x2,x2,x2,mask))
        x2 = self.norm_2(x)
        x = x + self.dropout_2(self.ff(x2))
        return x
    
# build a decoder layer with two multi-head attention layers and
# one feed-forward layer
class DecoderLayer(torch.nn.Module):
    def __init__(self, d_model, heads, dropout=0.1):
        super().__init__()
        self.norm_1 = Norm(d_model)
        self.norm_2 = Norm(d_model)
        self.norm_3 = Norm(d_model)
        
        self.dropout_1 = torch.nn.Dropout(dropout)
        self.dropout_2 = torch.nn.Dropout(dropout)
        self.dropout_3 = torch.nn.Dropout(dropout)
        
        self.attn_1 = MultiHeadAttention(heads, d_model)
        self.attn_2 = MultiHeadAttention(heads, d_model)
        self.ff = FeedForward(d_model)
    def forward(self, x, e_outputs, src_mask, trg_mask):
        x2 = self.norm_1(x)
        x = x + self.dropout_1(self.attn_1(x2, x2, x2, trg_mask))
        x2 = self.norm_2(x)
        x = x + self.dropout_2(self.attn_2(x2, e_outputs, e_outputs,
        src_mask))
        x2 = self.norm_3(x)
        x = x + self.dropout_3(self.ff(x2))
        return x
# generate multiple layers
def get_clones(module, N):
    return torch.nn.ModuleList([copy.deepcopy(module) for i in range(N)])

class Encoder(torch.nn.Module):
    def __init__(self, vocab_size, d_model, N, heads):
        super().__init__()
        self.N = N
        self.embed = Embedder(vocab_size, d_model)
        self.pe = PositionalEncoder(d_model)
        self.layers = get_clones(EncoderLayer(d_model, heads), N)
        self.norm = Norm(d_model)
    def forward(self, src, mask):
        x = self.embed(src)
        x = self.pe(x)
        for i in range(self.N):
            x = self.layers[i](x, mask)
        return self.norm(x)
    
class Decoder(torch.nn.Module):
    def __init__(self, vocab_size, d_model, N, heads):
        super().__init__()
        self.N = N
        self.embed = Embedder(vocab_size, d_model)
        self.pe = PositionalEncoder(d_model)
        self.layers = get_clones(DecoderLayer(d_model, heads), N)
        self.norm = Norm(d_model)
    def forward(self, trg, e_outputs, src_mask, trg_mask):
        x = self.embed(trg)
        x = self.pe(x)
        for i in range(self.N):
            x = self.layers[i](x, e_outputs, src_mask, trg_mask)
        return self.norm(x)
    
class Transformer(torch.nn.Module):
    def __init__(self, src_vocab, trg_vocab, d_model, N, heads):
        super().__init__()
        self.encoder = Encoder(src_vocab, d_model, N, heads)
        self.decoder = Decoder(trg_vocab, d_model, N, heads)
        self.out = torch.nn.Linear(d_model, trg_vocab)
    def forward(self, src, trg, src_mask, trg_mask):
        e_outputs = self.encoder(src, src_mask)
        d_output = self.decoder(trg, e_outputs, src_mask, trg_mask)
        output = self.out(d_output)
        return output