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# # The Annotated Transformer Tutorial - Reimplimentation of Original
#
# This is a reimplementation of the famous [Annotated Transformer](http://nlp.seas.harvard.edu/2018/04/03/attention.html) that I did to better understand the transformer.

# ## Imports & Inits

# %load_ext autoreload
# %autoreload 2

# +
import pdb

import numpy as np
np.set_printoptions(precision=4)

import math, copy, time

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("darkgrid")
sns.set_context(context="talk")
# %matplotlib inline

import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable


# -

# ## Model Architecture

def clones(module, N):
  """
  Produce N identitcal layers.
  """
  return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])


# +
class EncoderDecoder(nn.Module):
  """
  A standard Encoder-Decoder architecture. Base for this and
  many other models. 
  """
  def __ini__(self, encoder, decoder, src_embed, tgt_embed, generator):
    super(EncoderDecoder, self).__ini__()
    self.encoder = encoder 
    self.decoder = decoder 
    self.src_embed = src_embed 
    self.tgt_embed = tgt_embed
    self.generator = generator
    
  def forward(self, src, tgt, src_mask, tgt_mask):
    """
    Take in and process masked source and target sequences. 
    """
    return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)
  
  def encode(self, src, src_mask):
    return self.encoder(self.src_embed(src), src_mask)
  
  def decode(self, memory, src_mask, tgt, tgt_mask):
    return self.deocder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)

class Generator(nn.Module):
  """
  Define standard linear + softmax generation step.
  """
  def __init__(self, d_model, vocab):
    super(Generator, self).__init__()
    self.proj = nn.Linear(d_model, vocab)
    
  def forward(self, x):
    return F.log_softmax(self.proj(x), dim=-1)


# -

class LayerNorm(nn.Module):
  """
  Construct a layernorm module.
  """
  def __init__(self, features, eps=1e-6):
    super(LayerNorm, self).__init__()
    self.a_2 = nn.Parameter(torch.ones(features))
    self.b_2 = nn.Parameter(torch.zeros(features))
    self.eps = eps
    
  def forward(self, x):
    mean = x.mean(-1, keepdim=True)
    std = x.std(-1, keepdim=True)
    return self.a_2 * (x - mean) / (std + self.eps) + self.b_2


class SublayerConnection(nn.Module):
  """
  A residual connection followed by a layer norm.
  Note for code simplicity the norm is first as opposed to last.
  """
  def __init__(self, size, dropout):
    super(SublayerConnection, self).__init__()
    self.norm = LayerNorm(size)
    self.dropout = nn.Dropout(dropout)
    
  def forward(self, x, sublayer):
    """
    Apply residual connection to any subplayer with the same size.
    """
    return x + self.dropout(subplayer(self.norm(x)))  


# ## Encoder

# +
class Encoder(nn.Module):
  """
  Core encoder is a stack of N layers.
  """
  def __init__(self, layer, N):
    super(Encoder, self).__init__()
    self.layers = clones(layer, N)
    self.norm = LayerNorm(layer.size)
    
  def forward(self, x, mask):
    """
    Pass the input (and mask) through each layer in turn.
    """
    for layer in self.layers:
      x = layer(x, mask)
      
    return self.norm(x)

class EncoderLayer(nn.Module):
  """
  Encoder is made up of self-attn and feed forward.
  """
  def __init__(self, size, self_attn, feed_forward, dropout):
    super(EncoderLayer, self).__init__()
    self.self_attn = self_attn
    self.feed_forward = feed_forward
    self.subplayer = clones(SublayerConnection(size, dropout), 2)
    self.size = size
    
  def forward(self, x, mask):
    x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
    return self.sublayer[1](x, self.feed_forward)


# -

# ## Decoder

class Decoder(nn.Module):
  """
  Generic N layer decoder with masking.
  """
  def __init__(self, layer, N):
    super(Decoder, self).__init__()
    self.layers = clones(layer, N)
    self.norm = LayerNorm(layer.size)
    
  def forward(self, x, memory, src_mask, tgt_mask):
    """
    Pass the input (and mask) through each layer in turn.
    """
    for layer in self.layers:
      x = layer(x, memory, src_mask, tgt_mask)
      
    return self.norm(x)


class DecoderLayer(nn.Module):
  """
  Decoder is made of self-attn, src-attn, and feed forward.
  """
  def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
    super(DecoderLayer, self).__init__()
    self.size = size
    self.self_attn = self_attn
    self.src_attn = src_attn
    self.feed_forward = feed_forward
    self.sublayer = clones(SublayerConnection(size, dropout), 3)
    
  def forward(self, x, memory, src_mask, tgt_mask):
    m = memory
    x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
    x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
    return self.sublayer[2](x, self.feed_forward)


def subsequent_mask(size):
  """
  Mask out subsequent positions.
  """
  attn_shape = (1, size, size)
  subsq_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
  return torch.from_numpy(subsq_mask) == 0


fig, ax = plt.subplots(1,1,figsize=(5,5))
ax.imshow(subsequent_mask(20)[0])


# ## Attention

def attention(query, key, value, mask=None, dropout=None):
  """
  Compute 'Scaled Dot Product Attention'
  """
  d_k = query.size(-1)
  scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
  if mask is not None:
    scores = scores.maksed_fill(mask == 0, -1e9)
  p_attn = F.softmax(scores, dim=-1)
  if dropout is not None:
    p_attn = dropout(p_attn)
    
  return torch.matmul(p_attn, value), p_attn


class MultiHeadAttention(nn.Module):
  def __init__(self, h, d_model, dropout=0.1):
    """
    Take in the model size and number of heads.
    """
    super(MultiHeadAttention, self).__init__()
    assert d_model % h == 0 # assume d_v = d_k
    self.d_k = d_model // h
    self.h = h
    self.linears = clones(nn.Linear(d_model, d_model), 4)
    self.attn = None
    self.dropout = nn.Dropout(dropout)

  def forward(self, query, key, value, mask=None):
    if mask is not None:
      mask = mask.unsqueeze(1) # same maks is applied to all h heads
    
    nbatchs = query.size(0)
    
    # 1) Do all the linear projections in a batch from d_model => h x d_k
    query, key, value = [l(x).view(nbatchs, -1, self.h, self.d_k).transpose(1, 2) for l,x in zip(self.linears, (query, key, value))]
    
    # 2) Apply attention on all the projected vectors in a batch
    x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
    
    # 3) "concat" using view and apply a final linear
    x = x.transpose(1, 2).contiguous().view(nbatchs, -1, self.h * self.d_k)
    
    return self.linears[-1](x)    


# ## Feedforward, Embeddings, and Positional Encoding

class PositionWiseFeedForward(nn.Module):
  def __init__(self, d_model, d_ff, dropout=0.1):
    super(PositionWiseFeedForward, self).__init__()
    self.w_1 = nn.Linear(d_model, d_ff)
    self.w_2 = nn.Linear(d_ff, d_model)
    self.dropout = nn.Dropout(dropout)
    
  def forward(self, x):
    return self.w_2(self.dropout(F.relu(self.w_1(x))))


class Embeddings(nn.Module):
  def __init__(self, d_model, vocab):
    super(Embeddings, self).__init__()
    self.lut = nn.Embedding(vocab, d_model)
    self.d_model = d_model
    
  def forward(self, x):
    return self.lut(x) * math.sqrt(self.d_model)


class PositionalEncoding(nn.Module):
  "Implement the PE function."
  def __init__(self, d_model, dropout, max_len=5000):
    super(PositionalEncoding, self).__init__()
    self.dropout = nn.Dropout(dropout)

    # Compute the positional encodings once in log space.
    pe = torch.zeros(max_len, d_model)
    position = torch.arange(0, max_len).unsqueeze(1)
    div_term = torch.exp(torch.arange(0, d_model, 2) * -(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 + Variable(self.pe[:, :x.size(1)], 
                     requires_grad=False)
    return self.dropout(x)


fig, ax = plt.subplots(1,1,figsize=(15,5))
pe = PositionalEncoding(20, 0)
y = pe.forward(Variable(torch.zeros(1, 100, 20)))
ax.plot(np.arange(100), y[0, :, 4:8].data.numpy())
ax.legend(["dim %d"%p for p in [4,5,6,7]])

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