text: # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
  # Copyright 2021 The HuggingFace Inc. team.
  #
  # Licensed under the Apache License, Version 2.0 (the "License"
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # 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 paddle
  import paddle.nn as nn
  import paddle.nn.functional as F
  from paddle import tensor
  from paddle.nn import Layer
  from ..electra.modeling import get_activation
  from .. import PretrainedModel, register_base_model
  
  __all__ = [
      "ConvBertModel", "ConvBertPretrainedModel", "ConvBertForTotalPretraining",
      "ConvBertDiscriminator", "ConvBertGenerator", "ConvBertClassificationHead",
      "ConvBertForSequenceClassification", "ConvBertForTokenClassification",
      "ConvBertPretrainingCriterion", "ConvBertForQuestionAnswering",
      "ConvBertForMultipleChoice", "ConvBertForPretraining"
  ]
  dtype_float = paddle.get_default_dtype()
  
  
  def _convert_attention_mask(attn_mask, dtype):
      if attn_mask is not None and attn_mask.dtype != dtype:
          attn_mask_dtype = attn_mask.dtype
          if attn_mask_dtype in [
                  paddle.bool, paddle.int8, paddle.int16, paddle.int32,
                  paddle.int64
          ]:
              attn_mask = (paddle.cast(attn_mask, dtype) - 1.0) * 1e9
          else:
              attn_mask = paddle.cast(attn_mask, dtype)
      return attn_mask
  
  
  class GroupedLinear(nn.Layer):
      def __init__(self, input_size, output_size, num_groups):
          super().__init__()
          self.input_size = input_size
          self.output_size = output_size
          self.num_groups = num_groups
          self.group_in_dim = self.input_size // self.num_groups
          self.group_out_dim = self.output_size // self.num_groups
          self.weight = paddle.create_parameter(
              shape=[self.num_groups, self.group_in_dim, self.group_out_dim],
              dtype=dtype_float)
          self.bias = paddle.create_parameter(
              shape=[output_size], dtype=dtype_float, is_bias=True)
  
      def forward(self, hidden_states):
          batch_size = hidden_states.shape[0]
          x = tensor.reshape(hidden_states,
                             [-1, self.num_groups, self.group_in_dim])
          x = tensor.transpose(x, perm=[1, 0, 2])
          x = tensor.matmul(x, self.weight)
          x = tensor.transpose(x, perm=[1, 0, 2])
          x = tensor.reshape(x, [batch_size, -1, self.output_size])
          x = x + self.bias
          return x
  
  
  class SeparableConv1D(nn.Layer):
      """This class implements separable convolution, i.e. a depthwise and a pointwise layer"""
  
      def __init__(self, input_filters, output_filters, kernel_size):
          super().__init__()
          self.depthwise = nn.Conv1D(
              input_filters,
              input_filters,
              kernel_size=kernel_size,
              groups=input_filters,
              padding=kernel_size // 2,
              bias_attr=False,
              data_format="NLC", )
          self.pointwise = nn.Conv1D(
              input_filters,
              output_filters,
              kernel_size=1,
              bias_attr=False,
              data_format="NLC", )
          self.bias = paddle.create_parameter(
              shape=[output_filters], dtype=dtype_float, is_bias=True)
  
      def forward(self, hidden_states):
          x = self.depthwise(hidden_states)
          x = self.pointwise(x) + self.bias
          return x
  
  
  class MultiHeadAttentionWithConv(Layer):
      def __init__(
              self,
              embed_dim,
              num_heads,
              dropout=0.,
              kdim=None,
              vdim=None,
              need_weights=False,
              conv_kernel_size=None,
              head_ratio=None, ):
          super(MultiHeadAttentionWithConv, self).__init__()
          self.embed_dim = embed_dim
          self.kdim = kdim if kdim is not None else embed_dim
          self.vdim = vdim if vdim is not None else embed_dim
          self.need_weights = need_weights
          self.head_dim = embed_dim // num_heads
          self.scale = self.head_dim**-0.5
          assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
  
          new_num_attention_heads = num_heads // head_ratio
          if num_heads // head_ratio < 1:
              self.num_heads = 1
              self.conv_type = "noconv"
          else:
              self.num_heads = new_num_attention_heads
              self.conv_type = "sdconv"
  
          self.all_head_size = self.num_heads * self.head_dim
  
          self.dropout = nn.Dropout(dropout)
          self.q_proj = nn.Linear(embed_dim, self.all_head_size)
          self.k_proj = nn.Linear(self.kdim, self.all_head_size)
          self.v_proj = nn.Linear(self.vdim, self.all_head_size)
          self.out_proj = nn.Linear(embed_dim, embed_dim)
  
          if self.conv_type == "sdconv":
              self.conv_kernel_size = conv_kernel_size
              self.key_conv_attn_layer = SeparableConv1D(
                  embed_dim, self.all_head_size, self.conv_kernel_size)
              self.conv_kernel_layer = nn.Linear(
                  self.all_head_size, self.num_heads * self.conv_kernel_size)
              self.conv_out_layer = nn.Linear(embed_dim, self.all_head_size)
              self.unfold = nn.Unfold(
                  kernel_sizes=[self.conv_kernel_size, 1],
                  paddings=[(self.conv_kernel_size - 1) // 2, 0], )
  
      def forward(self, query, key=None, value=None, attn_mask=None, cache=None):
          key = query if key is None else key
          value = query if value is None else value
  
          q = self.q_proj(query)
          k = self.k_proj(key)
          v = self.v_proj(value)
  
          if self.conv_type == "sdconv":
              mixed_key_conv_attn_layer = self.key_conv_attn_layer(query)
              conv_attn_layer = mixed_key_conv_attn_layer * q
              batch_size = q.shape[0]
              # conv_kernel_layer
              conv_kernel_layer = self.conv_kernel_layer(conv_attn_layer)
              conv_kernel_layer = tensor.reshape(
                  conv_kernel_layer, shape=[-1, self.conv_kernel_size, 1])
              conv_kernel_layer = F.softmax(conv_kernel_layer, axis=1)
              # conv_out
              conv_out_layer = self.conv_out_layer(query)
              conv_out_layer = tensor.reshape(
                  conv_out_layer, [batch_size, -1, self.all_head_size, 1])
              conv_out_layer = tensor.transpose(conv_out_layer, perm=[0, 2, 1, 3])
              conv_out_layer = self.unfold(conv_out_layer)
              conv_out_layer = tensor.transpose(conv_out_layer, perm=[0, 2, 1])
              conv_out_layer = tensor.reshape(
                  conv_out_layer,
                  shape=[-1, self.head_dim, self.conv_kernel_size])
              conv_out_layer = tensor.matmul(conv_out_layer, conv_kernel_layer)
              conv_out = tensor.reshape(
                  conv_out_layer,
                  shape=[batch_size, -1, self.num_heads, self.head_dim])
  
          q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
          q = tensor.transpose(x=q, perm=[0, 2, 1, 3])
          k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
          k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
          v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
          v = tensor.transpose(x=v, perm=[0, 2, 1, 3])
  
          product = tensor.matmul(x=q, y=k, transpose_y=True) * self.scale
          if attn_mask is not None:
              attn_mask = _convert_attention_mask(attn_mask, product.dtype)
              product = product + attn_mask
  
          weights = F.softmax(product)
          weights = self.dropout(weights)
          out = tensor.matmul(weights, v)
  
          # combine heads
          out = tensor.transpose(out, perm=[0, 2, 1, 3])
          if self.conv_type == "sdconv":
              out = tensor.concat([out, conv_out], axis=2)
          out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
  
          # project to output
          out = self.out_proj(out)
  
          outs = [out]
          if self.need_weights:
              outs.append(weights)
          if cache is not None:
              outs.append(cache)
          return out if len(outs) == 1 else tuple(outs)
  
  
  class TransformerEncoderLayerWithConv(nn.TransformerEncoderLayer):
      def __init__(self,
                   d_model,
                   nhead,
                   dim_feedforward,
                   dropout=0.1,
                   activation="relu",
                   attn_dropout=None,
                   act_dropout=None,
                   normalize_before=False,
                   conv_kernel_size=None,
                   head_ratio=None,
                   num_groups=None,
                   **kwargs):
          super().__init__(
              d_model,
              nhead,
              dim_feedforward,
              dropout=dropout,
              activation=activation,
              attn_dropout=attn_dropout,
              act_dropout=act_dropout,
              normalize_before=normalize_before)
          self.self_attn = MultiHeadAttentionWithConv(
              d_model,
              nhead,
              dropout=attn_dropout,
              conv_kernel_size=conv_kernel_size,
              head_ratio=head_ratio, )
          if num_groups > 1:
              self.linear1 = GroupedLinear(
                  d_model, dim_feedforward, num_groups=num_groups)
              self.linear2 = GroupedLinear(
                  dim_feedforward, d_model, num_groups=num_groups)
          self._config.update({
              "conv_kernel_size": conv_kernel_size,
              "head_ratio": head_ratio,
              "num_groups": num_groups
          })
  
  
  class ConvBertEmbeddings(nn.Layer):
      """
      Include embeddings from word, position and token_type embeddings
      """
  
      def __init__(
              self,
              vocab_size,
              embedding_size,
              hidden_dropout_prob,
              max_position_embeddings,
              type_vocab_size, ):
          super(ConvBertEmbeddings, self).__init__()
          self.word_embeddings = nn.Embedding(vocab_size, embedding_size)
          self.position_embeddings = nn.Embedding(max_position_embeddings,
                                                  embedding_size)
          self.token_type_embeddings = nn.Embedding(type_vocab_size,
                                                    embedding_size)
  
          self.layer_norm = nn.LayerNorm(embedding_size, epsilon=1e-12)
          self.dropout = nn.Dropout(hidden_dropout_prob)
  
      def forward(self, input_ids, token_type_ids=None, position_ids=None):
          if position_ids is None:
              ones = paddle.ones_like(input_ids, dtype="int64")
              seq_length = paddle.cumsum(ones, axis=-1)
              position_ids = seq_length - ones
              position_ids.stop_gradient = True
  
          if token_type_ids is None:
              token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
  
          input_embeddings = self.word_embeddings(input_ids)
          position_embeddings = self.position_embeddings(position_ids)
          token_type_embeddings = self.token_type_embeddings(token_type_ids)
  
          embeddings = input_embeddings + position_embeddings + token_type_embeddings
          embeddings = self.layer_norm(embeddings)
          embeddings = self.dropout(embeddings)
          return embeddings
  
  
  class ConvBertDiscriminatorPredictions(nn.Layer):
      """
      Prediction layer for the discriminator.
      """
  
      def __init__(self, hidden_size, hidden_act):
          super(ConvBertDiscriminatorPredictions, self).__init__()
  
          self.dense = nn.Linear(hidden_size, hidden_size)
          self.dense_prediction = nn.Linear(hidden_size, 1)
          self.act = get_activation(hidden_act)
  
      def forward(self, discriminator_hidden_states):
          hidden_states = self.dense(discriminator_hidden_states)
          hidden_states = self.act(hidden_states)
          logits = self.dense_prediction(hidden_states).squeeze()
  
          return logits
  
  
  class ConvBertGeneratorPredictions(nn.Layer):
      """
      Prediction layer for the generator.
      """
  
      def __init__(self, embedding_size, hidden_size, hidden_act):
          super(ConvBertGeneratorPredictions, self).__init__()
  
          self.layer_norm = nn.LayerNorm(embedding_size)
          self.dense = nn.Linear(hidden_size, embedding_size)
          self.act = get_activation(hidden_act)
  
      def forward(self, generator_hidden_states):
          hidden_states = self.dense(generator_hidden_states)
          hidden_states = self.act(hidden_states)
          hidden_states = self.layer_norm(hidden_states)
  
          return hidden_states
  
  
  class ConvBertPretrainedModel(PretrainedModel):
      """
      An abstract class for pretrained ConvBert models. It provides ConvBert related
      `model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
      `pretrained_resource_files_map`, `base_model_prefix` for downloading and
      loading pretrained models.
      See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
      """
  
      base_model_prefix = "convbert"
      model_config_file = "model_config.json"
  
      # pretrained general configuration
      gen_weight = 1.0
      disc_weight = 50.0
      tie_word_embeddings = True
      untied_generator_embeddings = False
      use_softmax_sample = True
  
      # model init configuration
      pretrained_init_configuration = {
          "convbert-base": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 768,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 768,
              "initializer_range": 0.02,
              "intermediate_size": 3072,
              "max_position_embeddings": 512,
              "num_attention_heads": 12,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 1,
          },
          "convbert-medium-small": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 128,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 384,
              "initializer_range": 0.02,
              "intermediate_size": 1536,
              "max_position_embeddings": 512,
              "num_attention_heads": 8,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 2,
          },
          "convbert-small": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 128,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 256,
              "initializer_range": 0.02,
              "intermediate_size": 1024,
              "max_position_embeddings": 512,
              "num_attention_heads": 4,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 1,
          },
      }
      resource_files_names = {"model_state": "model_state.pdparams"}
      pretrained_resource_files_map = {
          "model_state": {
              "convbert-base":
              "http://bj.bcebos.com/paddlenlp/models/transformers/convbert/convbert-base/model_state.pdparams",
              "convbert-medium-small":
              "http://bj.bcebos.com/paddlenlp/models/transformers/convbert/convbert-medium-small/model_state.pdparams",
              "convbert-small":
              "http://bj.bcebos.com/paddlenlp/models/transformers/convbert/convbert-small/model_state.pdparams",
          }
      }
  
      def init_weights(self):
          """
          Initializes and tie weights if needed.
          """
          # Initialize weights
          self.apply(self._init_weights)
          # Tie weights if needed
          self.tie_weights()
  
      def tie_weights(self):
          """
          Tie the weights between the input embeddings and the output embeddings.
          """
          if hasattr(self, "get_output_embeddings") and hasattr(
                  self, "get_input_embeddings"):
              output_embeddings = self.get_output_embeddings()
              if output_embeddings is not None:
                  self._tie_or_clone_weights(output_embeddings,
                                             self.get_input_embeddings())
  
      def _init_weights(self, layer):
          """ Initialize the weights """
          if isinstance(layer, (nn.Linear, nn.Embedding, GroupedLinear)):
              layer.weight.set_value(
                  paddle.tensor.normal(
                      mean=0.0,
                      std=self.initializer_range
                      if hasattr(self, "initializer_range") else
                      self.convbert.config["initializer_range"],
                      shape=layer.weight.shape, ))
          elif isinstance(layer, nn.LayerNorm):
              layer.bias.set_value(paddle.zeros_like(layer.bias))
              layer.weight.set_value(paddle.full_like(layer.weight, 1.0))
              layer._epsilon = 1e-12
          elif isinstance(layer, SeparableConv1D):
              layer.depthwise.weight.set_value(
                  paddle.tensor.normal(
                      mean=0.0,
                      std=self.initializer_range
                      if hasattr(self, "initializer_range") else
                      self.convbert.config["initializer_range"],
                      shape=layer.depthwise.weight.shape, ))
              layer.pointwise.weight.set_value(
                  paddle.tensor.normal(
                      mean=0.0,
                      std=self.initializer_range
                      if hasattr(self, "initializer_range") else
                      self.convbert.config["initializer_range"],
                      shape=layer.pointwise.weight.shape, ))
  
          if isinstance(layer, (nn.Linear, GroupedLinear,
                                SeparableConv1D)) and layer.bias is not None:
              layer.bias.set_value(paddle.zeros_like(layer.bias))
  
      def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
          """ Tie or clone layer weights """
          if output_embeddings.weight.shape == input_embeddings.weight.shape:
              output_embeddings.weight = input_embeddings.weight
          elif output_embeddings.weight.shape == input_embeddings.weight.t(
          ).shape:
              output_embeddings.weight.set_value(input_embeddings.weight.t())
          else:
              raise ValueError(
                  "when tie input/output embeddings, the shape of output embeddings: {}"
                  "should be equal to shape of input embeddings: {}"
                  "or should be equal to the shape of transpose input embeddings: {}".
                  format(
                      output_embeddings.weight.shape,
                      input_embeddings.weight.shape,
                      input_embeddings.weight.t().shape, ))
          if getattr(output_embeddings, "bias", None) is not None:
              if output_embeddings.weight.shape[
                      -1] != output_embeddings.bias.shape[0]:
                  raise ValueError(
                      "the weight lase shape: {} of output_embeddings is not equal to the bias shape: {}"
                      "please check output_embeddings configuration".format(
                          output_embeddings.weight.shape[-1],
                          output_embeddings.bias.shape[0], ))
  
  
  @register_base_model
  class ConvBertModel(ConvBertPretrainedModel):
      """
      The bare ConvBert Model transformer outputting raw hidden-states.
  
      This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
      Refer to the superclass documentation for the generic methods.
  
      This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
      /docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer
      and refer to the Paddle documentation for all matter related to general usage and behavior.
  
      Args:
          vocab_size (int):
              Vocabulary size of `inputs_ids` in `ConvBertModel`. Also is the vocab size of token embedding matrix.
              Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `ConvBertModel`.
          embedding_size (int, optional):
              Dimensionality of the embedding layer. Defaults to `768`.
          hidden_size (int, optional):
              Dimensionality of the encoder layer and pooler layer. Defaults to `768`.
          num_hidden_layers (int, optional):
              Number of hidden layers in the Transformer encoder. Defaults to `12`.
          num_attention_heads (int, optional):
              Number of attention heads for each attention layer in the Transformer encoder.
              Defaults to `12`.
          intermediate_size (int, optional):
              Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors
              to ff layers are firstly projected from `hidden_size` to `intermediate_size`,
              and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`.
              Defaults to `3072`.
          hidden_act (str, optional):
              The non-linear activation function in the feed-forward layer.
              ``"gelu"``, ``"relu"`` and any other paddle supported activation functions
              are supported. Defaults to `"gelu"`.
          hidden_dropout_prob (float, optional):
              The dropout probability for all fully connected layers in the embeddings and encoder.
              Defaults to `0.1`.
          attention_probs_dropout_prob (float, optional):
              The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.
              Defaults to `0.1`.
          max_position_embeddings (int, optional):
              The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
              sequence. Defaults to `512`.
          type_vocab_size (int, optional):
              The vocabulary size of `token_type_ids`.
              Defaults to `2`.
  
          initializer_range (float, optional):
              The standard deviation of the normal initializer.
              Defaults to 0.02.
  
              .. note::
                  A normal_initializer initializes weight matrices as normal distributions.
                  See :meth:`ConvBertPretrainedModel.init_weights()` for how weights are initialized in `ConvBertModel`.
  
          pad_token_id (int, optional):
              The index of padding token in the token vocabulary.
              Defaults to `0`.
  
          conv_kernel_size (int, optional):
              The size of the convolutional kernel.
              Defaults to `9`.
  
          head_ratio (int, optional):
              Ratio gamma to reduce the number of attention heads.
              Defaults to `2`.
  
          num_groups (int, optional):
              The number of groups for grouped linear layers for ConvBert model.
              Defaults to `1`.
  
      """
  
      def __init__(self,
                   vocab_size,
                   embedding_size=768,
                   hidden_size=768,
                   num_hidden_layers=12,
                   num_attention_heads=12,
                   intermediate_size=3072,
                   hidden_act="gelu",
                   hidden_dropout_prob=0.1,
                   attention_probs_dropout_prob=0.1,
                   max_position_embeddings=512,
                   type_vocab_size=2,
                   initializer_range=0.02,
                   pad_token_id=0,
                   conv_kernel_size=9,
                   head_ratio=2,
                   num_groups=1):
          super(ConvBertModel, self).__init__()
          self.pad_token_id = pad_token_id
          self.initializer_range = initializer_range
          self.embeddings = ConvBertEmbeddings(
              vocab_size,
              embedding_size,
              hidden_dropout_prob,
              max_position_embeddings,
              type_vocab_size, )
  
          if embedding_size != hidden_size:
              self.embeddings_project = nn.Linear(embedding_size, hidden_size)
  
          encoder_layer = TransformerEncoderLayerWithConv(
              hidden_size,
              num_attention_heads,
              intermediate_size,
              dropout=hidden_dropout_prob,
              activation=hidden_act,
              attn_dropout=attention_probs_dropout_prob,
              act_dropout=0,
              conv_kernel_size=conv_kernel_size,
              head_ratio=head_ratio,
              num_groups=num_groups, )
          self.encoder = nn.TransformerEncoder(encoder_layer, num_hidden_layers)
  
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.embeddings.word_embeddings
  
      def set_input_embeddings(self, value):
          self.embeddings.word_embeddings = value
  
      def forward(self,
                  input_ids,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
          r'''
          The ConvBertModel forward method, overrides the `__call__()` special method.
  
          Args:
              input_ids (Tensor):
                  Indices of input sequence tokens in the vocabulary. They are
                  numerical representations of tokens that build the input sequence.
                  Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
              token_type_ids (Tensor, optional):
                  Segment token indices to indicate different portions of the inputs.
                  Selected in the range ``[0, type_vocab_size - 1]``.
                  If `type_vocab_size` is 2, which means the inputs have two portions.
                  Indices can either be 0 or 1:
  
                  - 0 corresponds to a *sentence A* token,
                  - 1 corresponds to a *sentence B* token.
  
                  Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
                  Defaults to `None`, which means we don't add segment embeddings.
              position_ids(Tensor, optional):
                  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
                  max_position_embeddings - 1]``.
                  Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
              attention_mask (Tensor, optional):
                  Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
                  usually the paddings or the subsequent positions.
                  Its data type can be int, float and bool.
                  If its data type is int, the values should be either 0 or 1.
  
                  - **1** for tokens that **not masked**,
                  - **0** for tokens that **masked**.
  
                  It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
                  Defaults to `None`, which means nothing needed to be prevented attention to.
  
          Returns:
              Tensor: Returns Tensor `sequence_output`, sequence of hidden-states at the last layer of the model.
              Shape as `[batch_size, sequence_length, hidden_size]` and dtype as float32.
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import ConvBertModel, ConvBertTokenizer
  
                  tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
                  model = ConvBertModel.from_pretrained('convbert-base')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  output = model(**inputs)
          '''
          if attention_mask is None:
              attention_mask = paddle.unsqueeze(
                  (input_ids == self.pad_token_id).astype(dtype_float) * -1e4,
                  axis=[1, 2])
          else:
              attention_mask = paddle.unsqueeze(
                  attention_mask, axis=[1, 2]).astype(dtype_float)
              attention_mask = (1.0 - attention_mask) * -1e4
  
          embedding_output = self.embeddings(
              input_ids=input_ids,
              position_ids=position_ids,
              token_type_ids=token_type_ids, )
  
          if hasattr(self, "embeddings_project"):
              embedding_output = self.embeddings_project(embedding_output)
  
          sequence_output = self.encoder(embedding_output, attention_mask)
  
          return sequence_output
  
  
  class ConvBertDiscriminator(ConvBertPretrainedModel):
      """
      ConvBert Model with a discriminator prediction head on top.
  
      Args:
          convbert (:class:`ConvBertModel`):
              An instance of ConvBertModel.
      """
  
      def __init__(self, convbert):
          super(ConvBertDiscriminator, self).__init__()
  
          self.convbert = convbert
          self.discriminator_predictions = ConvBertDiscriminatorPredictions(
              self.convbert.config["hidden_size"],
              self.convbert.config["hidden_act"])
          self.init_weights()
  
      def forward(self,
                  input_ids,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
          r'''
          The ConvBertDiscriminator forward method, overrides the `__call__()` special method.
  
          Args:
              input_ids (Tensor):
                  Indices of input sequence tokens in the vocabulary. They are
                  numerical representations of tokens that build the input sequence.
                  Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
              token_type_ids (Tensor, optional):
                  Segment token indices to indicate different portions of the inputs.
                  Selected in the range ``[0, type_vocab_size - 1]``.
                  If `type_vocab_size` is 2, which means the inputs have two portions.
                  Indices can either be 0 or 1:
  
                  - 0 corresponds to a *sentence A* token,
                  - 1 corresponds to a *sentence B* token.
  
                  Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
                  Defaults to `None`, which means we don't add segment embeddings.
              position_ids(Tensor, optional):
                  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
                  max_position_embeddings - 1]``.
                  Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
              attention_mask (Tensor, optional):
                  Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
                  usually the paddings or the subsequent positions.
                  Its data type can be int, float and bool.
                  If its data type is int, the values should be either 0 or 1.
  
                  - **1** for tokens that **not masked**,
                  - **0** for tokens that **masked**.
  
                  It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
                  Defaults to `None`, which means nothing needed to be prevented attention to.
  
          Returns:
              Tensor: Returns tensor `logits`, a tensor of the discriminator prediction logits.
              Shape as `[batch_size, sequence_length]` and dtype as float32.
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import ConvBertDiscriminatorPredictions, ConvBertTokenizer
  
                  tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
                  model = ConvBertDiscriminatorPredictions.from_pretrained('convbert-base')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  logits = model(**inputs)
          '''
  
          discriminator_sequence_output = self.convbert(
              input_ids, token_type_ids, position_ids, attention_mask)
  
          logits = self.discriminator_predictions(discriminator_sequence_output)
  
          return logits
  
  
  class ConvBertGenerator(ConvBertPretrainedModel):
      """
      ConvBert Model with a generator prediction head on top.
  
      Args:
          convbert (:class:`ConvBertModel`):
              An instance of ConvBertModel.
      """
  
      def __init__(self, convbert):
          super(ConvBertGenerator, self).__init__()
  
          self.convbert = convbert
          self.generator_predictions = ConvBertGeneratorPredictions(
              self.convbert.config["embedding_size"],
              self.convbert.config["hidden_size"],
              self.convbert.config["hidden_act"], )
  
          if not self.tie_word_embeddings:
              self.generator_lm_head = nn.Linear(
                  self.convbert.config["embedding_size"],
                  self.convbert.config["vocab_size"])
          else:
              self.generator_lm_head_bias = paddle.create_parameter(
                  shape=[self.convbert.config["vocab_size"]],
                  dtype=dtype_float,
                  is_bias=True, )
          self.init_weights()
  
      def get_input_embeddings(self):
          return self.convbert.embeddings.word_embeddings
  
      def forward(
              self,
              input_ids=None,
              token_type_ids=None,
              position_ids=None,
              attention_mask=None, ):
          r'''
          The ConvBertGenerator forward method, overrides the `__call__()` special method.
  
          Args:
              input_ids (Tensor):
                  Indices of input sequence tokens in the vocabulary. They are
                  numerical representations of tokens that build the input sequence.
                  Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
              token_type_ids (Tensor, optional):
                  Segment token indices to indicate different portions of the inputs.
                  Selected in the range ``[0, type_vocab_size - 1]``.
                  If `type_vocab_size` is 2, which means the inputs have two portions.
                  Indices can either be 0 or 1:
  
                  - 0 corresponds to a *sentence A* token,
                  - 1 corresponds to a *sentence B* token.
  
                  Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
                  Defaults to `None`, which means we don't add segment embeddings.
              position_ids(Tensor, optional):
                  Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
                  max_position_embeddings - 1]``.
                  Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
              attention_mask (Tensor, optional):
                  Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
                  usually the paddings or the subsequent positions.
                  Its data type can be int, float and bool.
                  If its data type is int, the values should be either 0 or 1.
  
                  - **1** for tokens that **not masked**,
                  - **0** for tokens that **masked**.
  
                  It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
                  Defaults to `None`, which means nothing needed to be prevented attention to.
  
          Returns:
              Tensor: Returns tensor `prediction_scores`, a tensor of the generator prediction scores.
              Shape as `[batch_size, sequence_length, vocab_size]` and dtype as float32.
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import ConvBertGenerator, ConvBertTokenizer
  
                  tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
                  model = ConvBertGenerator.from_pretrained('convbert-base')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  prediction_scores = model(**inputs)
          '''
  
          generator_sequence_output = self.convbert(input_ids, token_type_ids,
                                                    position_ids, attention_mask)
  
          prediction_scores = self.generator_predictions(
              generator_sequence_output)
          if not self.tie_word_embeddings:
              prediction_scores = self.generator_lm_head(prediction_scores)
          else:
              prediction_scores = paddle.add(
                  paddle.matmul(
                      prediction_scores,
                      self.get_input_embeddings().weight,
                      transpose_y=True, ),
                  self.generator_lm_head_bias, )
  
          return prediction_scores
  
  
  class ConvBertClassificationHead(nn.Layer):
      """
      ConvBert head for sentence-level classification tasks.
      """
  
      def __init__(self, hidden_size, hidden_dropout_prob, num_classes):
          super(ConvBertClassificationHead, self).__init__()
          self.dense = nn.Linear(hidden_size, hidden_size)
          self.dropout = nn.Dropout(hidden_dropout_prob)
          self.out_proj = nn.Linear(hidden_size, num_classes)
          self.act = get_activation("gelu")
  
      def forward(self, features, **kwargs):
          x = features[:, 0, :]  # take [CLS] token
          x = self.dropout(x)
          x = self.dense(x)
          x = self.act(x)  # ConvBert paper used gelu here
          x = self.dropout(x)
          x = self.out_proj(x)
          return x
  
  
  class ConvBertForSequenceClassification(ConvBertPretrainedModel):
      """
      ConvBert Model with a linear layer on top of the output layer,
      designed for sequence classification/regression tasks like GLUE tasks.
  
      Args:
          convbert (:class:`ConvBertModel`):
              An instance of ConvBertModel.
          num_classes (int, optional):
              The number of classes. Defaults to `2`.
          dropout (float, optional):
              The dropout probability for output of ConvBert.
              If None, use the same value as `hidden_dropout_prob` of `ConvBertModel`
              instance `convbert`. Defaults to None.
      """
  
      def __init__(self, convbert, num_classes=2, dropout=None):
          super(ConvBertForSequenceClassification, self).__init__()
          self.num_classes = num_classes
          self.convbert = convbert
          self.classifier = ConvBertClassificationHead(
              hidden_size=self.convbert.config["hidden_size"],
              hidden_dropout_prob=dropout if dropout is not None else
              self.convbert.config["hidden_dropout_prob"],
              num_classes=self.num_classes, )
  
          self.init_weights()
  
      def forward(
              self,
              input_ids=None,
              token_type_ids=None,
              position_ids=None,
              attention_mask=None, ):
          r"""
          The ConvBertForSequenceClassification forward method, overrides the __call__() special method.
  
          Args:
              input_ids (Tensor):
                  See :class:`ConvBertModel`.
              token_type_ids (Tensor, optional):
                  See :class:`ConvBertModel`.
              position_ids(Tensor, optional):
                  See :class:`ConvBertModel`.
              attention_mask (list, optional):
                  See :class:`ConvBertModel`.
  
          Returns:
              Tensor: Returns tensor `logits`, a tensor of the input text classification logits.
              Shape as `[batch_size, num_classes]` and dtype as float32.
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import ConvBertForSequenceClassification, ConvBertTokenizer
  
                  tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
                  model = ConvBertForSequenceClassification.from_pretrained('convbert-base')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  logits = model(**inputs)
          """
  
          sequence_output = self.convbert(input_ids, token_type_ids, position_ids,
                                          attention_mask)
  
          logits = self.classifier(sequence_output)
  
          return logits
  
  
  class ConvBertForTokenClassification(ConvBertPretrainedModel):
      """
      ConvBert Model with a linear layer on top of the hidden-states output layer,
      designed for token classification tasks like NER tasks.
  
      Args:
          convbert (:class:`ConvBertModel`):
              An instance of ConvBertModel.
          num_classes (int, optional):
              The number of classes. Defaults to `2`.
          dropout (float, optional):
              The dropout probability for output of ConvBert.
              If None, use the same value as `hidden_dropout_prob` of `ConvBertModel`
              instance `convbert`. Defaults to None.
      """
  
      def __init__(self, convbert, num_classes=2, dropout=None):
          super(ConvBertForTokenClassification, self).__init__()
          self.num_classes = num_classes
          self.convbert = convbert
          self.dropout = nn.Dropout(dropout if dropout is not None else
                                    self.convbert.config["hidden_dropout_prob"])
          self.classifier = nn.Linear(self.convbert.config["hidden_size"],
                                      self.num_classes)
          self.init_weights()
  
      def forward(
              self,
              input_ids=None,
              token_type_ids=None,
              position_ids=None,
              attention_mask=None, ):
          r"""
          The ConvBertForTokenClassification forward method, overrides the __call__() special method.
  
          Args:
              input_ids (Tensor):
                  See :class:`ConvBertModel`.
              token_type_ids (Tensor, optional):
                  See :class:`ConvBertModel`.
              position_ids(Tensor, optional):
                  See :class:`ConvBertModel`.
              attention_mask (list, optional):
                  See :class:`ConvBertModel`.
  
          Returns:
              Tensor: Returns tensor `logits`, a tensor of the input token classification logits.
              Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`.
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import ConvBertForTokenClassification, ConvBertTokenizer
  
                  tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
                  model = ConvBertForTokenClassification.from_pretrained('convbert-base')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  logits = model(**inputs)
          """
  
          sequence_output = self.convbert(input_ids, token_type_ids, position_ids,
                                          attention_mask)
  
          sequence_output = self.dropout(sequence_output)
          logits = self.classifier(sequence_output)
  
          return logits
  
  
  class ConvBertForTotalPretraining(ConvBertPretrainedModel):
      """
      Combine generator with discriminator for Replaced Token Detection (RTD) pretraining.
      """
      pretrained_init_configuration = {
          "convbert-base-generator": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 768,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 256,
              "initializer_range": 0.02,
              "intermediate_size": 1024,
              "max_position_embeddings": 512,
              "num_attention_heads": 4,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 1,
          },
          "convbert-medium-small-generator": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 128,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 96,
              "initializer_range": 0.02,
              "intermediate_size": 384,
              "max_position_embeddings": 512,
              "num_attention_heads": 2,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 2,
          },
          "convbert-small-generator": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 128,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 64,
              "initializer_range": 0.02,
              "intermediate_size": 256,
              "max_position_embeddings": 512,
              "num_attention_heads": 1,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 1,
          },
          "convbert-base-discriminator": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 768,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 768,
              "initializer_range": 0.02,
              "intermediate_size": 3072,
              "max_position_embeddings": 512,
              "num_attention_heads": 12,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 1,
          },
          "convbert-medium-small-discriminator": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 128,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 384,
              "initializer_range": 0.02,
              "intermediate_size": 1536,
              "max_position_embeddings": 512,
              "num_attention_heads": 8,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 2,
          },
          "convbert-small-discriminator": {
              "attention_probs_dropout_prob": 0.1,
              "embedding_size": 128,
              "hidden_act": "gelu",
              "hidden_dropout_prob": 0.1,
              "hidden_size": 256,
              "initializer_range": 0.02,
              "intermediate_size": 1024,
              "max_position_embeddings": 512,
              "num_attention_heads": 4,
              "num_hidden_layers": 12,
              "pad_token_id": 0,
              "type_vocab_size": 2,
              "vocab_size": 30522,
              "conv_kernel_size": 9,
              "head_ratio": 2,
              "num_groups": 1,
          },
      }
  
      def __init__(self, generator, discriminator):
          super(ConvBertForTotalPretraining, self).__init__()
  
          self.generator = generator
          self.discriminator = discriminator
          self.initializer_range = discriminator.convbert.initializer_range
          self.init_weights()
  
      def get_input_embeddings(self):
          if not self.untied_generator_embeddings:
              return self.generator.convbert.embeddings.word_embeddings
          else:
              return None
  
      def get_output_embeddings(self):
          if not self.untied_generator_embeddings:
              return self.discriminator.convbert.embeddings.word_embeddings
          else:
              return None
  
      def get_discriminator_inputs(self, inputs, raw_inputs, gen_logits,
                                   gen_labels, use_softmax_sample):
          """Sample from the generator to create discriminator input."""
          # get generator token result
          sampled_tokens = (
              self.sample_from_softmax(gen_logits, use_softmax_sample)).detach()
          sampled_tokids = paddle.argmax(sampled_tokens, axis=-1)
          # update token only at mask position
          # gen_labels : [B, L], L contains -100(unmasked) or token value(masked)
          # mask_positions : [B, L], L contains 0(unmasked) or 1(masked)
          umask_positions = paddle.zeros_like(gen_labels)
          mask_positions = paddle.ones_like(gen_labels)
          mask_positions = paddle.where(gen_labels == -100, umask_positions,
                                        mask_positions)
          updated_inputs = self.update_inputs(inputs, sampled_tokids,
                                              mask_positions)
          # use inputs and updated_input to get discriminator labels
          labels = mask_positions * (paddle.ones_like(inputs) - paddle.equal(
              updated_inputs, raw_inputs).astype("int32"))
          return updated_inputs, labels, sampled_tokids
  
      def sample_from_softmax(self, logits, use_softmax_sample=True):
          if use_softmax_sample:
              # uniform_noise = paddle.uniform(logits.shape, dtype="float32", min=0, max=1)
              uniform_noise = paddle.rand(logits.shape, dtype=dtype_float)
              gumbel_noise = -paddle.log(-paddle.log(uniform_noise + 1e-9) + 1e-9)
          else:
              gumbel_noise = paddle.zeros_like(logits)
          # softmax_sample equal to sampled_tokids.unsqueeze(-1)
          softmax_sample = paddle.argmax(
              F.softmax(logits + gumbel_noise), axis=-1)
          # one hot
          return F.one_hot(softmax_sample, logits.shape[-1])
  
      def update_inputs(self, sequence, updates, positions):
          shape = sequence.shape
          assert len(
              shape
          ) == 2, "the dimension of inputs should be [batch_size, sequence_length]"
          B, L = shape
          N = positions.shape[1]
          assert N == L, "the dimension of inputs and mask should be same as [batch_size, sequence_length]"
  
          updated_sequence = ((paddle.ones_like(sequence) - positions) * sequence
                              ) + (positions * updates)
  
          return updated_sequence
  
      def forward(
              self,
              input_ids=None,
              token_type_ids=None,
              position_ids=None,
              attention_mask=None,
              raw_input_ids=None,
              gen_labels=None, ):
          r"""
  
          Args:
              input_ids (Tensor):
                  See :class:`ConvBertModel`.
              token_type_ids (Tensor, optional):
                  See :class:`ConvBertModel`.
              position_ids (Tensor, optional):
                  See :class:`ConvBertModel`.
              attention_mask (Tensor, optional):
                  See :class:`ConvBertModel`.
              raw_input_ids(Tensor, optional):
                  The raw input_ids. Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
              gen_labels(Tensor, optional):
                  The generator labels. Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
  
          Returns:
              tuple: Returns tuple (``gen_logits``, ``disc_logits``, ``disc_labels``, ``attention_mask``).
  
              With the fields:
  
              - `gen_logits` (Tensor):
                  a tensor of the generator prediction logits. Shape as `[batch_size, sequence_length, vocab_size]` and dtype as float32.
  
              - `disc_logits` (Tensor):
                  a tensor of the discriminator prediction logits. Shape as `[batch_size, sequence_length]` and dtype as float32.
  
              - `disc_labels` (Tensor):
                  a tensor of the discriminator prediction labels. Shape as `[batch_size, sequence_length]` and dtype as int64.
  
              - `attention_mask` (Tensor):
                  See :class:`ConvBertModel`.
          """
  
          assert (gen_labels is not None), "gen_labels should not be None"
  
          gen_logits = self.generator(input_ids, token_type_ids, position_ids,
                                      attention_mask)
  
          (
              disc_inputs,
              disc_labels,
              generator_predict_tokens, ) = self.get_discriminator_inputs(
                  input_ids, raw_input_ids, gen_logits, gen_labels,
                  self.use_softmax_sample)
  
          disc_logits = self.discriminator(disc_inputs, token_type_ids,
                                           position_ids, attention_mask)
  
          if attention_mask is None:
              attention_mask = (
                  input_ids != self.discriminator.convbert.config["pad_token_id"])
          else:
              attention_mask = attention_mask.astype("bool")
  
          return gen_logits, disc_logits, disc_labels, attention_mask
  
  
  class ConvBertPretrainingCriterion(nn.Layer):
      """
  
      Args:
          vocab_size(int):
              Vocabulary size of `inputs_ids` in `ConvBertModel`. Defines the number of different tokens that can
              be represented by the `inputs_ids` passed when calling `ConvBertModel`.
          gen_weight(float):
              This is the generator weight.
          disc_weight(float):
              This is the discriminator weight.
  
      """
  
      def __init__(self, vocab_size, gen_weight, disc_weight):
          super(ConvBertPretrainingCriterion, self).__init__()
  
          self.vocab_size = vocab_size
          self.gen_weight = gen_weight
          self.disc_weight = disc_weight
          self.gen_loss_fct = nn.CrossEntropyLoss(reduction="none")
          self.disc_loss_fct = nn.BCEWithLogitsLoss(reduction="none")
  
      def forward(
              self,
              generator_prediction_scores,
              discriminator_prediction_scores,
              generator_labels,
              discriminator_labels,
              attention_mask, ):
          # generator loss
          gen_loss = self.gen_loss_fct(
              paddle.reshape(generator_prediction_scores, [-1, self.vocab_size]),
              paddle.reshape(generator_labels, [-1]), )
          # todo: we can remove 4 lines after when CrossEntropyLoss(reduction='mean') improved
          umask_positions = paddle.zeros_like(generator_labels).astype(
              dtype_float)
          mask_positions = paddle.ones_like(generator_labels).astype(dtype_float)
          mask_positions = paddle.where(generator_labels == -100, umask_positions,
                                        mask_positions)
          if mask_positions.sum() == 0:
              gen_loss = paddle.to_tensor([0.0])
          else:
              gen_loss = gen_loss.sum() / mask_positions.sum()
  
          # discriminator loss
          seq_length = discriminator_labels.shape[1]
          disc_loss = self.disc_loss_fct(
              paddle.reshape(discriminator_prediction_scores, [-1, seq_length]),
              discriminator_labels.astype(dtype_float), )
          if attention_mask is not None:
              umask_positions = paddle.ones_like(discriminator_labels).astype(
                  dtype_float)
              mask_positions = paddle.zeros_like(discriminator_labels).astype(
                  dtype_float)
              use_disc_loss = paddle.where(attention_mask, disc_loss,
                                           mask_positions)
              umask_positions = paddle.where(attention_mask, umask_positions,
                                             mask_positions)
              disc_loss = use_disc_loss.sum() / umask_positions.sum()
          else:
              total_positions = paddle.ones_like(discriminator_labels).astype(
                  dtype_float)
              disc_loss = disc_loss.sum() / total_positions.sum()
  
          return self.gen_weight * gen_loss + self.disc_weight * disc_loss
  
  
  class ConvBertPooler(Layer):
      def __init__(self, hidden_size, pool_act="tanh"):
          super(ConvBertPooler, self).__init__()
          self.dense = nn.Linear(hidden_size, hidden_size)
          self.activation = nn.Tanh()
          self.pool_act = pool_act
  
      def forward(self, hidden_states):
          # We "pool" the model by simply taking the hidden state corresponding
          # to the first token.
          first_token_tensor = hidden_states[:, 0]
          pooled_output = self.dense(first_token_tensor)
          if self.pool_act == "tanh":
              pooled_output = self.activation(pooled_output)
          return pooled_output
  
  
  class ConvBertForMultipleChoice(ConvBertPretrainedModel):
      """
      ConvBert Model with a linear layer on top of the hidden-states output layer,
      designed for multiple choice tasks like RocStories/SWAG tasks .
  
      Args:
          convbert (:class:`ConvBertModel`):
              An instance of ConvBertModel.
          num_choices (int, optional):
              The number of choices. Defaults to `2`.
          dropout (float, optional):
              The dropout probability for output of ConvBert.
              If None, use the same value as `hidden_dropout_prob` of `ConvBertModel`
              instance `convbert`. Defaults to None.
      """
  
      def __init__(self, convbert, num_choices=2, dropout=None):
          super(ConvBertForMultipleChoice, self).__init__()
          self.num_choices = num_choices
          self.convbert = convbert
          self.pooler = ConvBertPooler(self.convbert.config["hidden_size"])
          self.dropout = nn.Dropout(dropout if dropout is not None else
                                    self.convbert.config["hidden_dropout_prob"])
          self.classifier = nn.Linear(self.convbert.config["hidden_size"], 1)
          self.init_weights()
  
      def forward(self,
                  input_ids=None,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
          r"""
          The ConvBertForMultipleChoice forward method, overrides the __call__() special method.
  
          Args:
              input_ids (Tensor):
                  See :class:`ConvBertModel` and shape as [batch_size,num_choice, sequence_length].
              token_type_ids (Tensor, optional):
                  See :class:`ConvBertModel` and shape as [batch_size,num_choice, sequence_length].
              position_ids(Tensor, optional):
                  See :class:`ConvBertModel` and shape as [batch_size,num_choice, sequence_length].
              attention_mask (list, optional):
                  See :class:`ConvBertModel` and shape as [batch_size,num_choice, sequence_length].
  
          Returns:
              Tensor: Returns tensor `reshaped_logits`, a tensor of the multiple choice classification logits.
              Shape as `[batch_size, num_choice]` and dtype as `float32`.
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import ConvBertForMultipleChoice, ConvBertTokenizer
  
                  tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
                  model = ConvBertForMultipleChoice.from_pretrained('convbert-base')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  logits = model(**inputs)
          """
          input_ids = input_ids.reshape(
              (-1, input_ids.shape[-1]))  # flat_input_ids: [bs*num_choice,seq_l]
  
          if token_type_ids is not None:
              token_type_ids = token_type_ids.reshape(
                  (-1, token_type_ids.shape[-1]))
          if position_ids is not None:
              position_ids = position_ids.reshape((-1, position_ids.shape[-1]))
          if attention_mask is not None:
              attention_mask = attention_mask.reshape(
                  (-1, attention_mask.shape[-1]))
  
          sequence_output = self.convbert(input_ids, token_type_ids, position_ids,
                                          attention_mask)
          pooled_output = self.pooler(sequence_output)
          pooled_output = self.dropout(pooled_output)
          logits = self.classifier(pooled_output)  # logits: (bs*num_choice,1)
          reshaped_logits = logits.reshape(
              (-1, self.num_choices))  # logits: (bs, num_choice)
  
          return reshaped_logits
  
  
  class ConvBertForQuestionAnswering(ConvBertPretrainedModel):
      """
      ConvBert Model with a linear layer on top of the hidden-states output to compute `span_start_logits`
      and `span_end_logits`, designed for question-answering tasks like SQuAD.
  
      Args:
          convbert (:class:`ConvBertModel`):
              An instance of ConvBertModel.
              
      """
  
      def __init__(self, convbert):
          super(ConvBertForQuestionAnswering, self).__init__()
          self.convbert = convbert
          self.classifier = nn.Linear(self.convbert.config["hidden_size"], 2)
          self.init_weights()
  
      def forward(self,
                  input_ids,
                  token_type_ids=None,
                  position_ids=None,
                  attention_mask=None):
          r"""
          The ConvBertForQuestionAnswering forward method, overrides the __call__() special method.
  
          Args:
              input_ids (Tensor):
                  See :class:`ConvBertModel`.
              token_type_ids (Tensor, optional):
                  See :class:`ConvBertModel`.
              position_ids(Tensor, optional):
                  See :class:`ConvBertModel`.
              attention_mask (list, optional):
                  See :class:`ConvBertModel`.
          Returns:
              tuple: Returns tuple (`start_logits`, `end_logits`).
  
              With the fields:
  
              - `start_logits` (Tensor):
                  A tensor of the input token classification logits, indicates the start position of the labelled span.
                  Its data type should be float32 and its shape is [batch_size, sequence_length].
  
              - `end_logits` (Tensor):
                  A tensor of the input token classification logits, indicates the end position of the labelled span.
                  Its data type should be float32 and its shape is [batch_size, sequence_length].
  
          Example:
              .. code-block::
  
                  import paddle
                  from paddlenlp.transformers import ConvBertForQuestionAnswering, ConvBertTokenizer
  
                  tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
                  model = ConvBertForQuestionAnswering.from_pretrained('convbert-base')
  
                  inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                  inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                  outputs = model(**inputs)
  
                  start_logits = outputs[0]
                  end_logits  = outputs[1]
  
          """
          sequence_output = self.convbert(
              input_ids,
              token_type_ids,
              position_ids=position_ids,
              attention_mask=attention_mask)
          logits = self.classifier(sequence_output)
          logits = paddle.transpose(logits, perm=[2, 0, 1])
          start_logits, end_logits = paddle.unstack(x=logits, axis=0)
  
          return start_logits, end_logits
  
  
  ConvBertForPretraining = ConvBertForTotalPretraining
  