text: # Tencent is pleased to support the open source community by making ncnn available.
  #
  # Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
  #
  # Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
  # in compliance with the License. You may obtain a copy of the License at
  #
  # https://opensource.org/licenses/BSD-3-Clause
  #
  # Unless required by applicable law or agreed to in writing, software distributed
  # under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
  # CONDITIONS OF ANY KIND, either express or implied. See the License for the
  # specific language governing permissions and limitations under the License.
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  class Model(nn.Module):
      def __init__(self):
          super(Model, self).__init__()
  
          self.lstm_0_0 = nn.LSTM(input_size=32, hidden_size=16)
          self.lstm_0_1 = nn.LSTM(input_size=16, hidden_size=16, num_layers=3, bias=False)
          self.lstm_0_2 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
          self.lstm_0_3 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
          self.lstm_0_4 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, bidirectional=True)
  
          self.lstm_1_0 = nn.LSTM(input_size=25, hidden_size=16, batch_first=True)
          self.lstm_1_1 = nn.LSTM(input_size=16, hidden_size=16, num_layers=3, bias=False, batch_first=True)
          self.lstm_1_2 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bidirectional=True)
          self.lstm_1_3 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bidirectional=True)
          self.lstm_1_4 = nn.LSTM(input_size=16, hidden_size=16, num_layers=4, bias=True, batch_first=True, bidirectional=True)
  
      def forward(self, x, y):
          x = x.permute(1, 0, 2)
  
          x0, _ = self.lstm_0_0(x)
          x1, _ = self.lstm_0_1(x0)
          x2, (h0, c0) = self.lstm_0_2(x1)
          x3, (h1, c1) = self.lstm_0_3(x1, (h0, c0))
          x4, _ = self.lstm_0_4(x1, (h1, c1))
  
          y0, _ = self.lstm_1_0(y)
          y1, _ = self.lstm_1_1(y0)
          y2, (h2, c2) = self.lstm_1_2(y1)
          y3, (h3, c3) = self.lstm_1_3(y1, (h2, c2))
          y4, _ = self.lstm_1_4(y1, (h3, c3))
  
          x2 = x2.permute(1, 0, 2)
          x3 = x3.permute(1, 0, 2)
          x4 = x4.permute(1, 0, 2)
  
          return x2, x3, x4, y2, y3, y4
  
  def test():
      net = Model().half().float()
      net.eval()
  
      torch.manual_seed(0)
      x = torch.rand(1, 10, 32)
      y = torch.rand(1, 12, 25)
  
      a = net(x, y)
  
      # export torchscript
      mod = torch.jit.trace(net, (x, y))
      mod.save("test_nn_LSTM.pt")
  
      # torchscript to pnnx
      import os
      os.system("../../src/pnnx test_nn_LSTM.pt inputshape=[1,10,32],[1,12,25]")
  
      # ncnn inference
      import test_nn_LSTM_ncnn
      b = test_nn_LSTM_ncnn.test_inference()
  
      for a0, b0 in zip(a, b):
          if not torch.allclose(a0, b0, 1e-3, 1e-3):
              return False
      return True
  
  if __name__ == "__main__":
      if test():
          exit(0)
      else:
          exit(1)
  