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.w3 = nn.Parameter(torch.rand(24))
          self.b3 = nn.Parameter(torch.rand(24))
          self.w4 = nn.Parameter(torch.rand(12, 16))
          self.b4 = nn.Parameter(torch.rand(12, 16))
          self.w5 = nn.Parameter(torch.rand(24))
          self.b5 = nn.Parameter(torch.rand(24))
  
      def forward(self, x, y, z, w0, b0, w1, b1, w2, b2):
          x = F.layer_norm(x, (24,), w0, b0)
          x = F.layer_norm(x, (12,24), None, None)
          x = F.layer_norm(x, (24,), self.w3, self.b3)
  
          y = F.layer_norm(y, (16,), None, None, eps=1e-3)
          y = F.layer_norm(y, (12,16), w1, b1)
          y = F.layer_norm(y, (12,16), self.w4, self.b4)
  
          z = F.layer_norm(z, (24,), w2, b2)
          z = F.layer_norm(z, (12,16,24), None, None, eps=1e-2)
          z = F.layer_norm(z, (24,), self.w5, self.b5)
          return x, y, z
  
  def test():
      net = Model()
      net.eval()
  
      torch.manual_seed(0)
      x = torch.rand(1, 12, 24)
      y = torch.rand(2, 3, 12, 16)
      z = torch.rand(1, 10, 12, 16, 24)
      w0 = torch.rand(24)
      b0 = torch.rand(24)
      w1 = torch.rand(12, 16)
      b1 = torch.rand(12, 16)
      w2 = torch.rand(24)
      b2 = torch.rand(24)
  
      a0, a1, a2 = net(x, y, z, w0, b0, w1, b1, w2, b2)
  
      # export torchscript
      mod = torch.jit.trace(net, (x, y, z, w0, b0, w1, b1, w2, b2))
      mod.save("test_F_layer_norm.pt")
  
      # torchscript to pnnx
      import os
      os.system("../src/pnnx test_F_layer_norm.pt inputshape=[1,12,24],[2,3,12,16],[1,10,12,16,24],[24],[24],[12,16],[12,16],[24],[24]")
  
      # pnnx inference
      import test_F_layer_norm_pnnx
      b0, b1, b2 = test_F_layer_norm_pnnx.test_inference()
  
      return torch.equal(a0, b0) and torch.equal(a1, b1) and torch.equal(a2, b2)
  
  if __name__ == "__main__":
      if test():
          exit(0)
      else:
          exit(1)
  