text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from utils.display import *
  from utils.dsp import *
  
  
  class WaveRNN(nn.Module) :
      def __init__(self, hidden_size=896, quantisation=256) :
          super(WaveRNN, self).__init__()
          
          self.hidden_size = hidden_size
          self.split_size = hidden_size // 2
          
          # The main matmul
          self.R = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
          
          # Output fc layers
          self.O1 = nn.Linear(self.split_size, self.split_size)
          self.O2 = nn.Linear(self.split_size, quantisation)
          self.O3 = nn.Linear(self.split_size, self.split_size)
          self.O4 = nn.Linear(self.split_size, quantisation)
          
          # Input fc layers
          self.I_coarse = nn.Linear(2, 3 * self.split_size, bias=False)
          self.I_fine = nn.Linear(3, 3 * self.split_size, bias=False)
  
          # biases for the gates
          self.bias_u = nn.Parameter(torch.zeros(self.hidden_size))
          self.bias_r = nn.Parameter(torch.zeros(self.hidden_size))
          self.bias_e = nn.Parameter(torch.zeros(self.hidden_size))
          
          # display num params
          self.num_params()
  
          
      def forward(self, prev_y, prev_hidden, current_coarse) :
          
          # Main matmul - the projection is split 3 ways
          R_hidden = self.R(prev_hidden)
          R_u, R_r, R_e, = torch.split(R_hidden, self.hidden_size, dim=1)
          
          # Project the prev input 
          coarse_input_proj = self.I_coarse(prev_y)
          I_coarse_u, I_coarse_r, I_coarse_e = \
              torch.split(coarse_input_proj, self.split_size, dim=1)
          
          # Project the prev input and current coarse sample
          fine_input = torch.cat([prev_y, current_coarse], dim=1)
          fine_input_proj = self.I_fine(fine_input)
          I_fine_u, I_fine_r, I_fine_e = \
              torch.split(fine_input_proj, self.split_size, dim=1)
          
          # concatenate for the gates
          I_u = torch.cat([I_coarse_u, I_fine_u], dim=1)
          I_r = torch.cat([I_coarse_r, I_fine_r], dim=1)
          I_e = torch.cat([I_coarse_e, I_fine_e], dim=1)
          
          # Compute all gates for coarse and fine 
          u = F.sigmoid(R_u + I_u + self.bias_u)
          r = F.sigmoid(R_r + I_r + self.bias_r)
          e = F.tanh(r * R_e + I_e + self.bias_e)
          hidden = u * prev_hidden + (1. - u) * e
          
          # Split the hidden state
          hidden_coarse, hidden_fine = torch.split(hidden, self.split_size, dim=1)
          
          # Compute outputs 
          out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
          out_fine = self.O4(F.relu(self.O3(hidden_fine)))
  
          return out_coarse, out_fine, hidden
      
          
      def generate(self, seq_len):
          with torch.no_grad():
              # First split up the biases for the gates 
              b_coarse_u, b_fine_u = torch.split(self.bias_u, self.split_size)
              b_coarse_r, b_fine_r = torch.split(self.bias_r, self.split_size)
              b_coarse_e, b_fine_e = torch.split(self.bias_e, self.split_size)
  
              # Lists for the two output seqs
              c_outputs, f_outputs = [], []
  
              # Some initial inputs
              out_coarse = torch.LongTensor([0]).cuda()
              out_fine = torch.LongTensor([0]).cuda()
  
              # We'll meed a hidden state
              hidden = self.init_hidden()
  
              # Need a clock for display
              start = time.time()
  
              # Loop for generation
              for i in range(seq_len) :
  
                  # Split into two hidden states
                  hidden_coarse, hidden_fine = \
                      torch.split(hidden, self.split_size, dim=1)
  
                  # Scale and concat previous predictions
                  out_coarse = out_coarse.unsqueeze(0).float() / 127.5 - 1.
                  out_fine = out_fine.unsqueeze(0).float() / 127.5 - 1.
                  prev_outputs = torch.cat([out_coarse, out_fine], dim=1)
  
                  # Project input 
                  coarse_input_proj = self.I_coarse(prev_outputs)
                  I_coarse_u, I_coarse_r, I_coarse_e = \
                      torch.split(coarse_input_proj, self.split_size, dim=1)
  
                  # Project hidden state and split 6 ways
                  R_hidden = self.R(hidden)
                  R_coarse_u , R_fine_u, \
                  R_coarse_r, R_fine_r, \
                  R_coarse_e, R_fine_e = torch.split(R_hidden, self.split_size, dim=1)
  
                  # Compute the coarse gates
                  u = F.sigmoid(R_coarse_u + I_coarse_u + b_coarse_u)
                  r = F.sigmoid(R_coarse_r + I_coarse_r + b_coarse_r)
                  e = F.tanh(r * R_coarse_e + I_coarse_e + b_coarse_e)
                  hidden_coarse = u * hidden_coarse + (1. - u) * e
  
                  # Compute the coarse output
                  out_coarse = self.O2(F.relu(self.O1(hidden_coarse)))
                  posterior = F.softmax(out_coarse, dim=1)
                  distrib = torch.distributions.Categorical(posterior)
                  out_coarse = distrib.sample()
                  c_outputs.append(out_coarse)
  
                  # Project the [prev outputs and predicted coarse sample]
                  coarse_pred = out_coarse.float() / 127.5 - 1.
                  fine_input = torch.cat([prev_outputs, coarse_pred.unsqueeze(0)], dim=1)
                  fine_input_proj = self.I_fine(fine_input)
                  I_fine_u, I_fine_r, I_fine_e = \
                      torch.split(fine_input_proj, self.split_size, dim=1)
  
                  # Compute the fine gates
                  u = F.sigmoid(R_fine_u + I_fine_u + b_fine_u)
                  r = F.sigmoid(R_fine_r + I_fine_r + b_fine_r)
                  e = F.tanh(r * R_fine_e + I_fine_e + b_fine_e)
                  hidden_fine = u * hidden_fine + (1. - u) * e
  
                  # Compute the fine output
                  out_fine = self.O4(F.relu(self.O3(hidden_fine)))
                  posterior = F.softmax(out_fine, dim=1)
                  distrib = torch.distributions.Categorical(posterior)
                  out_fine = distrib.sample()
                  f_outputs.append(out_fine)
  
                  # Put the hidden state back together
                  hidden = torch.cat([hidden_coarse, hidden_fine], dim=1)
  
                  # Display progress
                  speed = (i + 1) / (time.time() - start)
                  stream('Gen: %i/%i -- Speed: %i',  (i + 1, seq_len, speed))
  
              coarse = torch.stack(c_outputs).squeeze(1).cpu().data.numpy()
              fine = torch.stack(f_outputs).squeeze(1).cpu().data.numpy()        
              output = combine_signal(coarse, fine)
          
          return output, coarse, fine
  
      def init_hidden(self, batch_size=1) :
          return torch.zeros(batch_size, self.hidden_size).cuda()
      
      def num_params(self) :
          parameters = filter(lambda p: p.requires_grad, self.parameters())
          parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
          print('Trainable Parameters: %.3f million' % parameters)
  