text: ''' Define the sublayers in encoder/decoder layer '''
  import numpy as np
  import torch.nn as nn
  import torch.nn.functional as F
  from models.Transformer.Modules import ScaledDotProductAttention
  
  __author__ = "Yu-Hsiang Huang"
  
  class MultiHeadAttention(nn.Module):
      ''' Multi-Head Attention module '''
  
      def __init__(self, n_head, d_model, d_k, d_v, dropout=0.1):
          super().__init__()
  
          self.n_head = n_head
          self.d_k = d_k
          self.d_v = d_v
  
          self.w_qs = nn.Linear(d_model, n_head * d_k)
          self.w_ks = nn.Linear(d_model, n_head * d_k)
          self.w_vs = nn.Linear(d_model, n_head * d_v)
          nn.init.normal_(self.w_qs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
          nn.init.normal_(self.w_ks.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_k)))
          nn.init.normal_(self.w_vs.weight, mean=0, std=np.sqrt(2.0 / (d_model + d_v)))
  
          self.attention = ScaledDotProductAttention(temperature=np.power(d_k, 0.5))
          self.layer_norm = nn.LayerNorm(d_model)
  
          self.fc = nn.Linear(n_head * d_v, d_model)
          nn.init.xavier_normal_(self.fc.weight)
  
          self.dropout = nn.Dropout(dropout)
  
  
      def forward(self, q, k, v, mask=None):
  
          d_k, d_v, n_head = self.d_k, self.d_v, self.n_head
  
          sz_b, len_q, _ = q.size()
          sz_b, len_k, _ = k.size()
          sz_b, len_v, _ = v.size()
  
          residual = q
  
          q = self.w_qs(q).view(sz_b, len_q, n_head, d_k)
          k = self.w_ks(k).view(sz_b, len_k, n_head, d_k)
          v = self.w_vs(v).view(sz_b, len_v, n_head, d_v)
  
          q = q.permute(2, 0, 1, 3).contiguous().view(-1, len_q, d_k) # (n*b) x lq x dk
          k = k.permute(2, 0, 1, 3).contiguous().view(-1, len_k, d_k) # (n*b) x lk x dk
          v = v.permute(2, 0, 1, 3).contiguous().view(-1, len_v, d_v) # (n*b) x lv x dv
  
          mask = mask.repeat(n_head, 1, 1) # (n*b) x .. x ..
          output, attn = self.attention(q, k, v, mask=mask)
  
          output = output.view(n_head, sz_b, len_q, d_v)
          output = output.permute(1, 2, 0, 3).contiguous().view(sz_b, len_q, -1) # b x lq x (n*dv)
  
          output = self.dropout(self.fc(output))
          output = self.layer_norm(output + residual)
  
          return output, attn
  
  class PositionwiseFeedForward(nn.Module):
      ''' A two-feed-forward-layer module '''
  
      def __init__(self, d_in, d_hid, dropout=0.1):
          super().__init__()
          self.w_1 = nn.Conv1d(d_in, d_hid, 1) # position-wise
          self.w_2 = nn.Conv1d(d_hid, d_in, 1) # position-wise
          self.layer_norm = nn.LayerNorm(d_in)
          self.dropout = nn.Dropout(dropout)
  
      def forward(self, x):
          residual = x
          output = x.transpose(1, 2)
          output = self.w_2(F.relu(self.w_1(output)))
          output = output.transpose(1, 2)
          output = self.dropout(output)
          output = self.layer_norm(output + residual)
          return output
  