text: # -*- coding:utf-8 -*-
  """
  Author:
      Weichen Shen,wcshen1994@163.com
  Reference:
      [1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf)
  """
  
  import torch
  import torch.nn as nn
  
  from .basemodel import BaseModel
  from ..inputs import combined_dnn_input
  from ..layers import DNN, concat_fun, InnerProductLayer, OutterProductLayer
  
  
  class PNN(BaseModel):
      """Instantiates the Product-based Neural Network architecture.
  
      :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
      :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
      :param l2_reg_embedding: float . L2 regularizer strength applied to embedding vector
      :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
      :param init_std: float,to use as the initialize std of embedding vector
      :param seed: integer ,to use as random seed.
      :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
      :param dnn_activation: Activation function to use in DNN
      :param use_inner: bool,whether use inner-product or not.
      :param use_outter: bool,whether use outter-product or not.
      :param kernel_type: str,kernel_type used in outter-product,can be ``'mat'`` , ``'vec'`` or ``'num'``
      :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
      :param device: str, ``"cpu"`` or ``"cuda:0"``
      :return: A PyTorch model instance.
      
      """
  
      def __init__(self, dnn_feature_columns, dnn_hidden_units=(128, 128), l2_reg_embedding=1e-5, l2_reg_dnn=0,
                   init_std=0.0001, seed=1024, dnn_dropout=0, dnn_activation='relu', use_inner=True, use_outter=False,
                   kernel_type='mat', task='binary', device='cpu', ):
  
          super(PNN, self).__init__([], dnn_feature_columns,
                                    dnn_hidden_units=dnn_hidden_units,
                                    l2_reg_embedding=l2_reg_embedding, l2_reg_dnn=l2_reg_dnn,
                                    l2_reg_linear=0, init_std=init_std, seed=seed,
                                    dnn_dropout=dnn_dropout, dnn_activation=dnn_activation,
                                    task=task, device=device)
  
          if kernel_type not in ['mat', 'vec', 'num']:
              raise ValueError("kernel_type must be mat,vec or num")
  
          self.use_inner = use_inner
          self.use_outter = use_outter
          self.kernel_type = kernel_type
          self.task = task
  
          product_out_dim = 0
          num_inputs = self.compute_input_dim(dnn_feature_columns, include_dense=False, feature_group=True)
          num_pairs = int(num_inputs * (num_inputs - 1) / 2)
  
          if self.use_inner:
              product_out_dim += num_pairs
              self.innerproduct = InnerProductLayer(device=device)
  
          if self.use_outter:
              product_out_dim += num_pairs
              self.outterproduct = OutterProductLayer(
                  num_inputs, self.embedding_size, kernel_type=kernel_type, device=device)
  
          self.dnn = DNN(product_out_dim + self.compute_input_dim(dnn_feature_columns), dnn_hidden_units,
                         activation=dnn_activation, l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout, use_bn=False,
                         init_std=init_std, device=device)
  
          self.dnn_linear = nn.Linear(
              dnn_hidden_units[-1], 1, bias=False).to(device)
          self.add_regularization_loss(
              filter(lambda x: 'weight' in x[0] and 'bn' not in x[0], self.dnn.named_parameters()), l2_reg_dnn)
          self.add_regularization_loss(self.dnn_linear.weight, l2_reg_dnn)
  
          self.to(device)
  
      def forward(self, X):
  
          sparse_embedding_list, dense_value_list = self.input_from_feature_columns(X, self.dnn_feature_columns,
                                                                                    self.embedding_dict)
          linear_signal = torch.flatten(
              concat_fun(sparse_embedding_list), start_dim=1)
  
          if self.use_inner:
              inner_product = torch.flatten(
                  self.innerproduct(sparse_embedding_list), start_dim=1)
  
          if self.use_outter:
              outer_product = self.outterproduct(sparse_embedding_list)
  
          if self.use_outter and self.use_inner:
              product_layer = torch.cat(
                  [linear_signal, inner_product, outer_product], dim=1)
          elif self.use_outter:
              product_layer = torch.cat([linear_signal, outer_product], dim=1)
          elif self.use_inner:
              product_layer = torch.cat([linear_signal, inner_product], dim=1)
          else:
              product_layer = linear_signal
  
          dnn_input = combined_dnn_input([product_layer], dense_value_list)
          dnn_output = self.dnn(dnn_input)
          dnn_logit = self.dnn_linear(dnn_output)
          logit = dnn_logit
  
          y_pred = self.out(logit)
  
          return y_pred
  