modeling.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 The Google AI Language Team Authors and 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.nn import TransformerEncoderLayer, TransformerEncoder
from paddle.nn.layer.transformer import _convert_attention_mask

from .. import PretrainedModel, register_base_model

__all__ = [
    'ElectraModel', 'ElectraPretrainedModel', 'ElectraForTotalPretraining',
    'ElectraDiscriminator', 'ElectraGenerator', 'ElectraClassificationHead',
    'ElectraForSequenceClassification', 'ElectraForTokenClassification',
    'ElectraPretrainingCriterion', 'ElectraForMultipleChoice',
    'ElectraForQuestionAnswering', 'ElectraForMaskedLM',
    'ElectraForPretraining', 'ErnieHealthForTotalPretraining',
    'ErnieHealthPretrainingCriterion', 'ErnieHealthDiscriminator'
]


def get_activation(activation_string):
    if activation_string in ACT2FN:
        return ACT2FN[activation_string]
    else:
        raise KeyError("function {} not found in ACT2FN mapping {}".format(
            activation_string, list(ACT2FN.keys())))


def mish(x):
    return x * F.tanh(F.softplus(x))


def linear_act(x):
    return x


def swish(x):
    return x * F.sigmoid(x)


ACT2FN = {
    "relu": F.relu,
    "gelu": F.gelu,
    "tanh": F.tanh,
    "sigmoid": F.sigmoid,
    "mish": mish,
    "linear": linear_act,
    "swish": swish,
}


class TransformerEncoderLayerPro(TransformerEncoderLayer):

    def __init__(self,
                 d_model,
                 nhead,
                 dim_feedforward,
                 dropout=0.1,
                 activation="relu",
                 attn_dropout=None,
                 act_dropout=None,
                 normalize_before=False,
                 weight_attr=None,
                 bias_attr=None):
        super(TransformerEncoderLayerPro,
              self).__init__(d_model, nhead, dim_feedforward, dropout,
                             activation, attn_dropout, act_dropout,
                             normalize_before, weight_attr, bias_attr)

    def forward(self, src, src_mask=None, cache=None, output_attentions=False):
        self.self_attn.need_weights = output_attentions
        src_mask = _convert_attention_mask(src_mask, src.dtype)
        attentions = None

        residual = src
        if self.normalize_before:
            src = self.norm1(src)
        if cache is None:
            src = self.self_attn(src, src, src, src_mask)
            if output_attentions:
                src, attentions = src
        else:
            output = self.self_attn(src, src, src, src_mask, cache)
            if output_attentions:
                src, attentions, incremental_cache = output
            else:
                src, incremental_cache = output

        src = residual + self.dropout1(src)
        if not self.normalize_before:
            src = self.norm1(src)

        residual = src
        if self.normalize_before:
            src = self.norm2(src)
        src = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = residual + self.dropout2(src)
        if not self.normalize_before:
            src = self.norm2(src)
        if output_attentions:
            src = (src, attentions)
        return src if cache is None else (src, incremental_cache)


class TransformerEncoderPro(TransformerEncoder):

    def __init__(self, encoder_layer, num_layers, norm=None):
        super(TransformerEncoderPro, self).__init__(encoder_layer, num_layers,
                                                    norm)

    def forward(self,
                src,
                src_mask=None,
                cache=None,
                output_attentions=False,
                output_hidden_states=False):
        src_mask = _convert_attention_mask(src_mask, src.dtype)

        output = src
        new_caches = []
        all_attentions = []
        all_hidden_states = []
        for i, mod in enumerate(self.layers):
            if cache is None:
                output = mod(output, src_mask=src_mask)
            else:
                output, new_cache = mod(output,
                                        src_mask=src_mask,
                                        cache=cache[i])
                new_caches.append(new_cache)
            if output_attentions:
                all_attentions.append(output[1])
                output = output[0]
            if output_hidden_states:
                all_hidden_states.append(output)

        if self.norm is not None:
            output = self.norm(output)

        if output_attentions or output_hidden_states:
            output = (output, all_attentions, all_hidden_states)

        return output if cache is None else (output, new_caches)


class ElectraEmbeddings(nn.Layer):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, vocab_size, embedding_size, hidden_dropout_prob,
                 max_position_embeddings, type_vocab_size, layer_norm_eps):
        super(ElectraEmbeddings, 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=layer_norm_eps)
        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
        position_ids = position_ids.astype("int64")

        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 ElectraDiscriminatorPredictions(nn.Layer):
    """Prediction layer for the discriminator, made up of two dense layers."""

    def __init__(self, hidden_size, hidden_act):
        super(ElectraDiscriminatorPredictions, 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 ElectraGeneratorPredictions(nn.Layer):
    """Prediction layer for the generator, made up of two dense layers."""

    def __init__(self, embedding_size, hidden_size, hidden_act):
        super(ElectraGeneratorPredictions, 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 ElectraPretrainedModel(PretrainedModel):
    """
    An abstract class for pretrained Electra models. It provides Electra 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 = "electra"

    # 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 = {
        "electra-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
        },
        "electra-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
        },
        "electra-large": {
            "attention_probs_dropout_prob": 0.1,
            "embedding_size": 1024,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "hidden_size": 1024,
            "initializer_range": 0.02,
            "intermediate_size": 4096,
            "max_position_embeddings": 512,
            "num_attention_heads": 16,
            "num_hidden_layers": 24,
            "pad_token_id": 0,
            "type_vocab_size": 2,
            "vocab_size": 30522
        },
        "chinese-electra-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": 21128
        },
        "chinese-electra-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": 21128
        },
        "ernie-health-chinese": {
            "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": 22608,
            "layer_norm_eps": 1e-5
        },
    }
    pretrained_resource_files_map = {
        "model_state": {
            "electra-small":
            "https://bj.bcebos.com/paddlenlp/models/transformers/electra/electra-small.pdparams",
            "electra-base":
            "https://bj.bcebos.com/paddlenlp/models/transformers/electra/electra-base.pdparams",
            "electra-large":
            "https://bj.bcebos.com/paddlenlp/models/transformers/electra/electra-large.pdparams",
            "chinese-electra-small":
            "https://bj.bcebos.com/paddlenlp/models/transformers/chinese-electra-small/chinese-electra-small.pdparams",
            "chinese-electra-base":
            "https://bj.bcebos.com/paddlenlp/models/transformers/chinese-electra-base/chinese-electra-base.pdparams",
            "ernie-health-chinese":
            "https://paddlenlp.bj.bcebos.com/models/transformers/ernie-health-chinese/ernie-health-chinese.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)):
            layer.weight.set_value(
                paddle.tensor.normal(mean=0.0,
                                     std=self.initializer_range if hasattr(
                                         self, "initializer_range") else
                                     self.electra.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 = getattr(self, "layer_norm_eps", 1e-12)
        if isinstance(layer, nn.Linear) 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 ElectraModel(ElectraPretrainedModel):
    """
    The bare Electra 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 `ElectraModel`. 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 `ElectraModel`.
        embedding_size (int, optional):
            Dimensionality of the embedding layer.
        hidden_size (int, optional):
            Dimensionality of the encoder layer and pooler layer.
        num_hidden_layers (int, optional):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (int, optional):
            Number of attention heads for each attention layer in the Transformer encoder.
        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`.
        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.
        hidden_dropout_prob (float, optional):
            The dropout probability for all fully connected layers in the embeddings and encoder.
        attention_probs_dropout_prob (float, optional):
            The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.
        max_position_embeddings (int, optional):
            The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
            sequence.
        type_vocab_size (int, optional):
            The vocabulary size of `token_type_ids`.

        initializer_range (float, optional):
            The standard deviation of the normal initializer.

            .. note::
                A normal_initializer initializes weight matrices as normal distributions.
                See :meth:`ElectraPretrainedModel.init_weights()` for how weights are initialized in `ElectraModel`.

        pad_token_id (int, optional):
            The index of padding token in the token vocabulary.
    """

    def __init__(self,
                 vocab_size,
                 embedding_size,
                 hidden_size,
                 num_hidden_layers,
                 num_attention_heads,
                 intermediate_size,
                 hidden_act,
                 hidden_dropout_prob,
                 attention_probs_dropout_prob,
                 max_position_embeddings,
                 type_vocab_size,
                 initializer_range,
                 pad_token_id,
                 layer_norm_eps=1e-12):
        super(ElectraModel, self).__init__()
        self.pad_token_id = pad_token_id
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.embeddings = ElectraEmbeddings(vocab_size, embedding_size,
                                            hidden_dropout_prob,
                                            max_position_embeddings,
                                            type_vocab_size, layer_norm_eps)

        if embedding_size != hidden_size:
            self.embeddings_project = nn.Linear(embedding_size, hidden_size)

        encoder_layer = TransformerEncoderLayerPro(
            hidden_size,
            num_attention_heads,
            intermediate_size,
            dropout=hidden_dropout_prob,
            activation=hidden_act,
            attn_dropout=attention_probs_dropout_prob,
            act_dropout=0)
        self.encoder = TransformerEncoderPro(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,
                output_attentions=False,
                output_hidden_states=False):
        r'''
        The ElectraModel 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.
                When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.
                When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
                When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.
                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 `encoder_outputs`, which is the output at the last layer of the model.
            Its data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ElectraModel, ElectraTokenizer

                tokenizer = ElectraTokenizer.from_pretrained('electra-small')
                model = ElectraModel.from_pretrained('electra-small')

                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(
                    paddle.get_default_dtype()) * -1e4,
                axis=[1, 2])
        else:
            if attention_mask.ndim == 2:
                attention_mask = attention_mask.unsqueeze(axis=[1, 2])

        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)

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states)

        return encoder_outputs


class ElectraDiscriminator(ElectraPretrainedModel):
    """
    The Electra Discriminator can detect the tokens that are replaced by the Electra Generator.

    Args:
         electra (:class:`ElectraModel`):
             An instance of :class:`ElectraModel`.

     """

    def __init__(self, electra):
        super(ElectraDiscriminator, self).__init__()

        self.electra = electra
        self.discriminator_predictions = ElectraDiscriminatorPredictions(
            self.electra.config["hidden_size"],
            self.electra.config["hidden_act"])
        self.init_weights()

    def forward(self,
                input_ids,
                token_type_ids=None,
                position_ids=None,
                attention_mask=None):
        r"""

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel`.
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel`.
            position_ids (Tensor, optional):
                See :class:`ElectraModel`.
            attention_mask (Tensor, optional):
                See :class:`ElectraModel`.

        Returns:
            Tensor: Returns tensor `logits`, the prediction result of replaced tokens.
            Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length],
            if batch_size=1, its shape is [sequence_length].

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ElectraDiscriminator, ElectraTokenizer

                tokenizer = ElectraTokenizer.from_pretrained('electra-small')
                model = ElectraDiscriminator.from_pretrained('electra-small')

                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.electra(input_ids, token_type_ids,
                                                     position_ids,
                                                     attention_mask)

        logits = self.discriminator_predictions(discriminator_sequence_output)

        return logits


class ElectraGenerator(ElectraPretrainedModel):
    """
    The Electra Generator will replace some tokens of the given sequence, it is trained as
    a masked language model.

    Args:
         electra (:class:`ElectraModel`):
             An instance of :class:`ElectraModel`.
     """

    def __init__(self, electra):
        super(ElectraGenerator, self).__init__()

        self.electra = electra
        self.generator_predictions = ElectraGeneratorPredictions(
            self.electra.config["embedding_size"],
            self.electra.config["hidden_size"],
            self.electra.config["hidden_act"])

        if not self.tie_word_embeddings:
            self.generator_lm_head = nn.Linear(
                self.electra.config["embedding_size"],
                self.electra.config["vocab_size"])
        else:
            self.generator_lm_head_bias = self.create_parameter(
                shape=[self.electra.config["vocab_size"]],
                dtype=paddle.get_default_dtype(),
                is_bias=True)
        self.init_weights()

    def get_input_embeddings(self):
        return self.electra.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.electra.embeddings.word_embeddings = value

    def forward(self,
                input_ids=None,
                token_type_ids=None,
                position_ids=None,
                attention_mask=None):
        r"""

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel`.
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel`.
            position_ids (Tensor, optional):
                See :class:`ElectraModel`.
            attention_mask (Tensor, optional):
                See :class:`ElectraModel`.

        Returns:
            Tensor: Returns tensor `prediction_scores`, the scores of Electra Generator.
            Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size].

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ElectraGenerator, ElectraTokenizer

                tokenizer = ElectraTokenizer.from_pretrained('electra-small')
                model = ElectraGenerator.from_pretrained('electra-small')

                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.electra(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 ElectraClassificationHead(nn.Layer):
    """
    Perform sentence-level classification tasks.

    Args:
        hidden_size (int):
            Dimensionality of the embedding layer.
        hidden_dropout_prob (float):
            The dropout probability for all fully connected layers.
        num_classes (int):
            The number of classes.
        activation (str):
            The activation function name between layers.

    """

    def __init__(self, hidden_size, hidden_dropout_prob, num_classes,
                 activation):
        super(ElectraClassificationHead, 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(activation)

    def forward(self, features, **kwargs):
        r"""
         The ElectraClassificationHead forward method, overrides the __call__() special method.

         Args:
             features(Tensor):
                Input sequence, usually the `sequence_output` of electra model.
                Its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].

         Returns:
             Tensor: Returns a tensor of the input text classification logits.
             Shape as `[batch_size, num_classes]` and dtype as float32.
         """
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x)
        x = self.dense(x)
        x = self.act(x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x


class ErnieHealthDiscriminator(ElectraPretrainedModel):
    """
    The Discriminators in ERNIE-Health (https://arxiv.org/abs/2110.07244), including
        - token-level Replaced Token Detection (RTD) task
        - token-level Multi-Token Selection (MTS) task
        - sequence-level Contrastive Sequence Prediction (CSP) task.

    Args:
         electra (:class:`ElectraModel`):
             An instance of :class:`ElectraModel`.

     """

    def __init__(self, electra):
        super(ErnieHealthDiscriminator, self).__init__()

        self.electra = electra
        self.discriminator_rtd = ElectraDiscriminatorPredictions(
            self.electra.config["hidden_size"],
            self.electra.config["hidden_act"])

        self.discriminator_mts = nn.Linear(self.electra.config["hidden_size"],
                                           self.electra.config["hidden_size"])
        self.activation_mts = get_activation(self.electra.config["hidden_act"])
        self.bias_mts = nn.Embedding(self.electra.config["vocab_size"], 1)

        self.discriminator_csp = ElectraClassificationHead(
            self.electra.config["hidden_size"],
            self.electra.config["hidden_dropout_prob"],
            num_classes=128,
            activation='gelu')

        self.init_weights()

    def forward(self,
                input_ids,
                candidate_ids,
                token_type_ids=None,
                position_ids=None,
                attention_mask=None):
        r"""

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel`.
            candidate_ids (Tensor):
                The candidate indices of input sequence tokens in the vocabulary for MTS task.
                Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel`.
            position_ids (Tensor, optional):
                See :class:`ElectraModel`.
            attention_mask (Tensor, optional):
                See :class:`ElectraModel`.

        Returns:
            Tensor: Returns list of tensors, the prediction results of RTD, MTS and CSP.
            The logits' data type should be float32 and if batch_size > 1,
                - the shape of `logits_rtd` is [batch_size, sequence_length],
                - the shape of `logits_mts` is [batch_size, sequence_length, num_candidate],
                - the shape of `logits_csp` is [batch_size, 128].
            If batch_size=1, the shapes are [sequence_length], [sequence_length, num_cadidate],
            [128], separately.

        """

        discriminator_sequence_output = self.electra(input_ids, token_type_ids,
                                                     position_ids,
                                                     attention_mask)

        logits_rtd = self.discriminator_rtd(discriminator_sequence_output)

        cands_embs = self.electra.embeddings.word_embeddings(candidate_ids)
        hidden_mts = self.discriminator_mts(discriminator_sequence_output)
        hidden_mts = self.activation_mts(hidden_mts)
        hidden_mts = paddle.matmul(hidden_mts.unsqueeze(2),
                                   cands_embs,
                                   transpose_y=True).squeeze(2)
        logits_mts = paddle.add(hidden_mts,
                                self.bias_mts(candidate_ids).squeeze(3))

        logits_csp = self.discriminator_csp(discriminator_sequence_output)

        return logits_rtd, logits_mts, logits_csp


class ElectraForSequenceClassification(ElectraPretrainedModel):
    """
    Electra Model with a linear layer on top of the output layer,
    designed for sequence classification/regression tasks like GLUE tasks.

    Args:
        electra (:class:`ElectraModel`):
            An instance of ElectraModel.
        num_classes (int, optional):
            The number of classes. Defaults to `2`.
        dropout (float, optional):
            The dropout probability for output of Electra.
            If None, use the same value as `hidden_dropout_prob` of `ElectraModel`
            instance `electra`. Defaults to None.
        activation (str, optional):
            The activation function name for classifier.
            Defaults to "gelu".
        layer_norm_eps (float, optional):
            The epsilon to initialize nn.LayerNorm layers.
            Defaults to 1e-12.
    """

    def __init__(self, electra, num_classes=2, dropout=None, activation="gelu"):
        super(ElectraForSequenceClassification, self).__init__()
        self.num_classes = num_classes
        self.electra = electra
        self.classifier = ElectraClassificationHead(
            hidden_size=self.electra.config["hidden_size"],
            hidden_dropout_prob=dropout if dropout is not None else
            self.electra.config["hidden_dropout_prob"],
            num_classes=self.num_classes,
            activation=activation)
        self.init_weights()

    def forward(self,
                input_ids=None,
                token_type_ids=None,
                position_ids=None,
                attention_mask=None):
        r"""
        The ElectraForSequenceClassification forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel`.
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel`.
            position_ids(Tensor, optional):
                See :class:`ElectraModel`.
            attention_mask (list, optional):
                See :class:`ElectraModel`.

        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 ElectraForSequenceClassification
                from paddlenlp.transformers import ElectraTokenizer

                tokenizer = ElectraTokenizer.from_pretrained('electra-small')
                model = ElectraForSequenceClassification.from_pretrained('electra-small')

                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.electra(input_ids, token_type_ids, position_ids,
                                       attention_mask)

        logits = self.classifier(sequence_output)

        return logits


class ElectraForTokenClassification(ElectraPretrainedModel):
    """
    Electra  Model with a linear layer on top of the hidden-states output layer,
    designed for token classification tasks like NER tasks.

    Args:
        electra (:class:`ElectraModel`):
            An instance of ElectraModel.
        num_classes (int, optional):
            The number of classes. Defaults to `2`.
        dropout (float, optional):
            The dropout probability for output of Electra.
            If None, use the same value as `hidden_dropout_prob` of `ElectraModel`
            instance `electra`. Defaults to None.
    """

    def __init__(self, electra, num_classes=2, dropout=None):
        super(ElectraForTokenClassification, self).__init__()
        self.num_classes = num_classes
        self.electra = electra
        self.dropout = nn.Dropout(dropout if dropout is not None else self.
                                  electra.config["hidden_dropout_prob"])
        self.classifier = nn.Linear(self.electra.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 ElectraForTokenClassification forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel`.
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel`.
            position_ids(Tensor, optional):
                See :class:`ElectraModel`.
            attention_mask (list, optional):
                See :class:`ElectraModel`.

        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 ElectraForTokenClassification
                from paddlenlp.transformers import ElectraTokenizer

                tokenizer = ElectraTokenizer.from_pretrained('electra-small')
                model = ElectraForTokenClassification.from_pretrained('electra-small')

                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.electra(input_ids, token_type_ids, position_ids,
                                       attention_mask)

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        return logits


class ElectraForTotalPretraining(ElectraPretrainedModel):
    """
    Electra Model for pretraining tasks.

    Args:
        generator (:class:`ElectraGenerator`):
            An instance of :class:`ElectraGenerator`.
        discriminator (:class:`ElectraDiscriminator`):
            An instance of :class:`ElectraDiscriminator`.

    """
    pretrained_init_configuration = {
        "electra-small-generator": {
            "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
        },
        "electra-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
        },
        "electra-large-generator": {
            "attention_probs_dropout_prob": 0.1,
            "embedding_size": 1024,
            "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": 24,
            "pad_token_id": 0,
            "type_vocab_size": 2,
            "vocab_size": 30522
        },
        "electra-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
        },
        "electra-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
        },
        "electra-large-discriminator": {
            "attention_probs_dropout_prob": 0.1,
            "embedding_size": 1024,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "hidden_size": 1024,
            "initializer_range": 0.02,
            "intermediate_size": 4096,
            "max_position_embeddings": 512,
            "num_attention_heads": 16,
            "num_hidden_layers": 24,
            "pad_token_id": 0,
            "type_vocab_size": 2,
            "vocab_size": 30522
        },
    }

    def __init__(self, generator, discriminator):
        super(ElectraForTotalPretraining, self).__init__()

        self.generator = generator
        self.discriminator = discriminator
        self.initializer_range = discriminator.electra.initializer_range
        self.init_weights()

    def get_input_embeddings(self):
        if not self.untied_generator_embeddings:
            return self.generator.electra.embeddings.word_embeddings
        else:
            return None

    def get_output_embeddings(self):
        if not self.untied_generator_embeddings:
            return self.discriminator.electra.embeddings.word_embeddings
        else:
            return None

    def get_discriminator_inputs(self, inputs, raw_inputs, generator_logits,
                                 generator_labels, use_softmax_sample):
        # get generator token result
        sampled_tokens = (self.sample_from_softmax(
            generator_logits, use_softmax_sample)).detach()
        sampled_tokids = paddle.argmax(sampled_tokens, axis=-1)
        # update token only at mask position
        # generator_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(generator_labels)
        mask_positions = paddle.ones_like(generator_labels)
        mask_positions = paddle.where(generator_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=paddle.get_default_dtype())
            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 [B, L]"
        B, L = shape
        N = positions.shape[1]
        assert (
            N == L), "the dimension of inputs and mask should be same as [B, L]"

        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,
                generator_labels=None):
        r"""
        The ElectraForPretraining forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel`.
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel`.
            position_ids(Tensor, optional):
                See :class:`ElectraModel`.
            attention_mask (list, optional):
                See :class:`ElectraModel`.
            raw_input_ids(Tensor, optional):
                Raw inputs used to get discriminator labels.
                Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
            generator_labels(Tensor, optional):
                Labels to compute the discriminator inputs.
                Its data type should be int64 and its shape is [batch_size, sequence_length].
                The value for unmasked tokens should be -100 and value for masked tokens should be 0.

        Returns:
            tuple: Returns tuple (generator_logits, disc_logits, disc_labels, attention_mask).

            With the fields:

            - `generator_logits` (Tensor):
                The scores of Electra Generator.
                Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size].

            - `disc_logits` (Tensor):
                The the prediction result of replaced tokens.
                Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length],
                if batch_size=1, its shape is [sequence_length].

            - `disc_labels` (Tensor):
                The labels of electra discriminator. Its data type should be int32,
                and its shape is [batch_size, sequence_length].

            - `attention_mask` (Tensor):
                See :class:`ElectraModel`. Its data type should be bool.

        """

        assert (
            generator_labels is not None
        ), "generator_labels should not be None, please check DataCollatorForLanguageModeling"

        generator_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, generator_logits, generator_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.electra.config["pad_token_id"])
        else:
            attention_mask = attention_mask.astype("bool")

        return generator_logits, disc_logits, disc_labels, attention_mask


class ElectraPooler(nn.Layer):

    def __init__(self, hidden_size, pool_act="gelu"):
        super(ElectraPooler, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = get_activation(pool_act)
        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)
        pooled_output = self.activation(pooled_output)
        return pooled_output


class ErnieHealthForTotalPretraining(ElectraForTotalPretraining):
    """
    ERNIE-Health Model for pretraining task.

    Args:
        generator (:class:`ElectraGenerator`):
            An instance of :class:`ElectraGenerator`.
        discriminator (:class:`ErnieHealthDiscriminator):
            An instance of :class:`ErnieHealthDiscriminator`.
    """
    pretrained_init_configuration = {
        "ernie-health-chinese-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": 22608,
            "layer_norm_eps": 1e-12
        },
        "ernie-health-chinese-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": 22608,
            "layer_norm_eps": 1e-12
        }
    }

    def get_discriminator_inputs_ernie_health(self, inputs, raw_inputs,
                                              generator_logits,
                                              generator_labels,
                                              use_softmax_sample):
        updated_inputs, labels, sampled_tokids = self.get_discriminator_inputs(
            inputs, raw_inputs, generator_logits, generator_labels,
            use_softmax_sample)

        # Get negative samples to construct candidates.
        neg_samples_ids = self.sample_negatives_from_softmax(
            generator_logits, raw_inputs, use_softmax_sample)
        candidate_ids = paddle.concat(
            [raw_inputs.unsqueeze(2), neg_samples_ids], axis=2).detach()
        return updated_inputs, labels, sampled_tokids, candidate_ids

    def sample_negatives_from_softmax(self,
                                      logits,
                                      raw_inputs,
                                      use_softmax_sample=True):
        r"""
        Sample K=5 non-original negative samples for candidate set.

        Returns:
            Tensor: Returns tensor `neg_samples_ids`, a tensor of the negative
            samples of original inputs.
            Shape as ` [batch_size, sequence_length, K, vocab_size]` and dtype
            as `int64`.
        """
        K = 5
        # Initialize excluded_ids by original inputs in one-hot encoding.
        # Its shape is [batch_size, sequence_length, vocab_size].
        excluded_ids = F.one_hot(raw_inputs, logits.shape[-1]) * -10000
        neg_sample_one_hot = None
        neg_samples_ids = None
        for sample in range(K):
            # Update excluded_ids.
            if neg_sample_one_hot is not None:
                excluded_ids = excluded_ids + neg_sample_one_hot * -10000
            if use_softmax_sample:
                uniform_noise = paddle.rand(logits.shape,
                                            dtype=paddle.get_default_dtype())
                gumbel_noise = -paddle.log(-paddle.log(uniform_noise + 1e-9) +
                                           1e-9)
            else:
                gumbel_noise = paddle.zeros_like(logits)
            sampled_ids = paddle.argmax(F.softmax(logits + gumbel_noise +
                                                  excluded_ids),
                                        axis=-1)
            # One-hot encoding of sample_ids.
            neg_sample_one_hot = F.one_hot(sampled_ids, logits.shape[-1])
            if neg_samples_ids is None:
                neg_samples_ids = sampled_ids.unsqueeze(2)
            else:
                neg_samples_ids = paddle.concat(
                    [neg_samples_ids, sampled_ids.unsqueeze(2)], axis=2)
        return neg_samples_ids

    def forward(self,
                input_ids=None,
                token_type_ids=None,
                position_ids=None,
                attention_mask=None,
                raw_input_ids=None,
                generator_labels=None):
        assert (
            generator_labels is not None
        ), "generator_labels should not be None, please check DataCollator"

        generator_logits = self.generator(input_ids, token_type_ids,
                                          position_ids, attention_mask)

        disc_input_list = self.get_discriminator_inputs_ernie_health(
            input_ids, raw_input_ids, generator_logits, generator_labels,
            self.use_softmax_sample)
        disc_inputs, disc_labels, _, disc_candidates = disc_input_list

        logits_rtd, logits_mts, logits_csp = self.discriminator(
            disc_inputs, disc_candidates, token_type_ids, position_ids,
            attention_mask)

        if attention_mask is None:
            pad_id = self.generator.electra.pad_token_id
            attention_mask = (input_ids != pad_id)
        else:
            attention_mask = attention_mask.astype('bool')

        return generator_logits, logits_rtd, logits_mts, logits_csp, disc_labels, attention_mask


class ElectraForMultipleChoice(ElectraPretrainedModel):
    """
    Electra Model with a linear layer on top of the hidden-states output layer,
    designed for multiple choice tasks like RocStories/SWAG tasks.

    Args:
        electra (:class:`ElectraModel`):
            An instance of ElectraModel.
        num_choices (int, optional):
            The number of choices. Defaults to `2`.
        dropout (float, optional):
            The dropout probability for output of Electra.
            If None, use the same value as `hidden_dropout_prob` of `ElectraModel`
            instance `electra`. Defaults to None.
    """

    def __init__(self, electra, num_choices=2, dropout=None):
        super(ElectraForMultipleChoice, self).__init__()
        self.num_choices = num_choices
        self.electra = electra
        self.sequence_summary = ElectraPooler(
            self.electra.config["hidden_size"], pool_act="gelu")
        self.dropout = nn.Dropout(dropout if dropout is not None else self.
                                  electra.config["hidden_dropout_prob"])
        self.classifier = nn.Linear(self.electra.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 ElectraForMultipleChoice forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel` and shape as [batch_size, num_choice, sequence_length].
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel` and shape as [batch_size, num_choice, sequence_length].
            position_ids(Tensor, optional):
                See :class:`ElectraModel` and shape as [batch_size, num_choice, sequence_length].
            attention_mask (list, optional):
                See :class:`ElectraModel` 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 ElectraForMultipleChoice, ElectraTokenizer
                from paddlenlp.data import Pad, Dict

                tokenizer = ElectraTokenizer.from_pretrained('electra-small')
                model = ElectraForMultipleChoice.from_pretrained('electra-small', num_choices=2)

                data = [
                    {
                        "question": "how do you turn on an ipad screen?",
                        "answer1": "press the volume button.",
                        "answer2": "press the lock button.",
                        "label": 1,
                    },
                    {
                        "question": "how do you indent something?",
                        "answer1": "leave a space before starting the writing",
                        "answer2": "press the spacebar",
                        "label": 0,
                    },
                ]

                text = []
                text_pair = []
                for d in data:
                    text.append(d["question"])
                    text_pair.append(d["answer1"])
                    text.append(d["question"])
                    text_pair.append(d["answer2"])

                inputs = tokenizer(text, text_pair)
                batchify_fn = lambda samples, fn=Dict(
                    {
                        "input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),  # input_ids
                        "token_type_ids": Pad(
                            axis=0, pad_val=tokenizer.pad_token_type_id
                        ),  # token_type_ids
                    }
                ): fn(samples)
                inputs = batchify_fn(inputs)

                reshaped_logits = model(
                    input_ids=paddle.to_tensor(inputs[0], dtype="int64"),
                    token_type_ids=paddle.to_tensor(inputs[1], dtype="int64"),
                )
                print(reshaped_logits.shape)
                # [2, 2]

        """
        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.electra(input_ids, token_type_ids, position_ids,
                                       attention_mask)
        pooled_output = self.sequence_summary(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 ElectraPretrainingCriterion(paddle.nn.Layer):
    '''

    Args:
        vocab_size(int):
            Vocabulary size of `inputs_ids` in `ElectraModel`. Defines the number of different tokens that can
            be represented by the `inputs_ids` passed when calling `ElectraModel`.
        gen_weight(float):
            The weight of the Electra Generator.
        disc_weight(float):
            The weight of the Electra Discriminator.

    '''

    def __init__(self, vocab_size, gen_weight, disc_weight):
        super(ElectraPretrainingCriterion, 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):
        """
        Args:
            generator_prediction_scores(Tensor):
                The scores of masked token prediction. Its data type should be float32.
                and its shape is [batch_size, sequence_length, vocab_size].
            discriminator_prediction_scores(Tensor):
                The scores of masked token prediction. Its data type should be float32.
                and its shape is [batch_size, sequence_length] or [sequence length] if batch_size=1.
            generator_labels(Tensor):
                The labels of the generator, its dimensionality is equal to `generator_prediction_scores`.
                Its data type should be int64 and its shape is [batch_size, sequence_size, 1].
            discriminator_labels(Tensor):
                The labels of the discriminator, its dimensionality is equal to `discriminator_prediction_scores`.
                The labels should be numbers between 0 and 1.
                Its data type should be float32 and its shape is [batch_size, sequence_size] or [sequence length] if batch_size=1.
            attention_mask(Tensor):
                See :class:`ElectraModel`.

        Returns:
            Tensor: The pretraining loss, equals to weighted generator loss plus the weighted discriminator loss.
            Its data type should be float32 and its shape is [1].

        """
        # 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(
            paddle.get_default_dtype())
        mask_positions = paddle.ones_like(generator_labels).astype(
            paddle.get_default_dtype())
        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(paddle.get_default_dtype()))
        if attention_mask is not None:
            umask_positions = paddle.ones_like(discriminator_labels).astype(
                paddle.get_default_dtype())
            mask_positions = paddle.zeros_like(discriminator_labels).astype(
                paddle.get_default_dtype())
            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(
                paddle.get_default_dtype())
            disc_loss = disc_loss.sum() / total_positions.sum()

        return self.gen_weight * gen_loss + self.disc_weight * disc_loss


class ErnieHealthPretrainingCriterion(paddle.nn.Layer):
    '''

    Args:
        vocab_size(int):
            Vocabulary size of `inputs_ids` in `ElectraModel`. Defines the number of different tokens that can
            be represented by the `inputs_ids` passed when calling `ElectraModel`.
        gen_weight(float):
            The weight of the Electra Generator.
        disc_weight(float):
            The weight of the Electra Discriminator.

    '''

    def __init__(self, vocab_size, gen_weight):
        super(ErnieHealthPretrainingCriterion, self).__init__()

        self.vocab_size = vocab_size
        self.gen_weight = gen_weight
        self.rtd_weight = 50.0
        self.mts_weight = 20.0
        self.csp_weight = 1.0
        self.gen_loss_fct = nn.CrossEntropyLoss(reduction='none')
        self.disc_rtd_loss_fct = nn.BCEWithLogitsLoss(reduction='none')
        self.disc_csp_loss_fct = nn.CrossEntropyLoss(reduction='none')
        self.disc_mts_loss_fct = nn.CrossEntropyLoss(reduction='none')
        self.temperature = 0.07

    def forward(self, generator_logits, generator_labels, logits_rtd,
                logits_mts, logits_csp, discriminator_labels, attention_mask):
        """
        Args:
            generator_logits(Tensor):
                The scores of masked token prediction. Its data type should be float32.
                and its shape is [batch_size, sequence_length, vocab_size].
            generator_labels(Tensor):
                The labels of the generator, its dimensionality is equal to `generator_prediction_scores`.
                Its data type should be int64 and its shape is [batch_size, sequence_size, 1].
            logits_rtd(Tensor):
                The scores of masked token prediction. Its data type should be float32.
                and its shape is [batch_size, sequence_length] or [sequence length] if batch_size=1.
            discriminator_labels(Tensor):
                The labels of the discriminator, its dimensionality is equal to `discriminator_prediction_scores`.
                The labels should be numbers between 0 and 1.
                Its data type should be float32 and its shape is [batch_size, sequence_size] or [sequence length] if batch_size=1.
            attention_mask(Tensor):
                See :class:`ElectraModel`.

        Returns:
            Tensor: The pretraining loss, equals to weighted generator loss plus the weighted discriminator loss.
            Its data type should be float32 and its shape is [1].

        """
        # generator loss
        gen_loss = self.gen_loss_fct(
            paddle.reshape(generator_logits, [-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(
            paddle.get_default_dtype())
        mask_positions = paddle.ones_like(generator_labels).astype(
            paddle.get_default_dtype())
        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()

        # RTD discriminator loss
        seq_length = discriminator_labels.shape[1]
        rtd_labels = discriminator_labels

        disc_rtd_loss = self.disc_rtd_loss_fct(
            paddle.reshape(logits_rtd, [-1, seq_length]),
            rtd_labels.astype(logits_rtd.dtype))
        if attention_mask is not None:
            umask_positions = paddle.ones_like(rtd_labels).astype(
                paddle.get_default_dtype())
            mask_positions = paddle.zeros_like(rtd_labels).astype(
                paddle.get_default_dtype())
            umask_positions = paddle.where(attention_mask, umask_positions,
                                           mask_positions)
            # Mask has different meanings here. It denotes [mask] token in
            # generator and denotes [pad] token in discriminator.
            disc_rtd_loss = paddle.where(attention_mask, disc_rtd_loss,
                                         mask_positions)
            disc_rtd_loss = disc_rtd_loss.sum() / umask_positions.sum()
        else:
            total_positions = paddle.ones_like(rtd_labels).astype(
                paddle.get_default_dtype())
            disc_rtd_loss = disc_rtd_loss.sum() / total_positions.sum()

        # MTS discriminator loss
        replaced_positions = discriminator_labels.astype("bool")
        mts_labels = paddle.zeros([logits_mts.shape[0] * logits_mts.shape[1]],
                                  dtype=generator_labels.dtype).detach()
        disc_mts_loss = self.disc_mts_loss_fct(
            paddle.reshape(logits_mts, [-1, logits_mts.shape[-1]]), mts_labels)
        disc_mts_loss = paddle.reshape(disc_mts_loss, [-1, seq_length])
        original_positions = paddle.zeros_like(replaced_positions).astype(
            paddle.get_default_dtype())
        disc_mts_loss = paddle.where(replaced_positions, disc_mts_loss,
                                     original_positions)
        if discriminator_labels.sum() == 0:
            disc_mts_loss = paddle.to_tensor([0.0])
        else:
            disc_mts_loss = disc_mts_loss.sum() / discriminator_labels.sum()

        # CSP discriminator loss
        logits_csp = F.normalize(logits_csp, axis=-1)
        # Gather from all devices (split first)
        logit_csp_0, logit_csp_1 = paddle.split(logits_csp,
                                                num_or_sections=2,
                                                axis=0)
        if paddle.distributed.get_world_size() > 1:
            csp_list_0, csp_list_1 = [], []
            paddle.distributed.all_gather(csp_list_0, logit_csp_0)
            paddle.distributed.all_gather(csp_list_1, logit_csp_1)
            logit_csp_0 = paddle.concat(csp_list_0, axis=0)
            logit_csp_1 = paddle.concat(csp_list_1, axis=0)
        batch_size = logit_csp_0.shape[0]
        logits_csp = paddle.concat([logit_csp_0, logit_csp_1], axis=0)
        # Similarity matrix
        logits_csp = paddle.matmul(logits_csp, logits_csp, transpose_y=True)
        # Temperature scale
        logits_csp = logits_csp / self.temperature
        # Mask self-contrast
        mask = -1e4 * paddle.eye(logits_csp.shape[0])
        logits_csp = logits_csp + mask
        # Create labels for bundle
        csp_labels = paddle.concat([
            paddle.arange(batch_size, 2 * batch_size),
            paddle.arange(batch_size)
        ],
                                   axis=0)
        # Calculate SimCLR loss
        disc_csp_loss = self.disc_csp_loss_fct(logits_csp, csp_labels)
        disc_csp_loss = disc_csp_loss.sum() / (batch_size * 2)

        loss = self.gen_weight * gen_loss + self.rtd_weight * disc_rtd_loss + \
            self.mts_weight * disc_mts_loss + self.csp_weight * disc_csp_loss

        return loss, gen_loss, disc_rtd_loss, disc_mts_loss, disc_csp_loss


class ElectraForQuestionAnswering(ElectraPretrainedModel):
    """
    Electra 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:
        electra (:class:`ElectraModel`):
            An instance of ElectraModel.
            
    """

    def __init__(self, electra):
        super(ElectraForQuestionAnswering, self).__init__()
        self.electra = electra
        self.classifier = nn.Linear(self.electra.config["hidden_size"], 2)
        self.init_weights()

    def forward(self,
                input_ids,
                token_type_ids=None,
                position_ids=None,
                attention_mask=None):
        r"""
        The ElectraForQuestionAnswering forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`ElectraModel`.
            token_type_ids (Tensor, optional):
                See :class:`ElectraModel`.
            position_ids(Tensor, optional):
                See :class:`ElectraModel`.
            attention_mask (list, optional):
                See :class:`ElectraModel`.
        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 ElectraForQuestionAnswering, ElectraTokenizer

                tokenizer = ElectraTokenizer.from_pretrained('electra-small')
                model = ElectraForQuestionAnswering.from_pretrained('electra-small')

                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.electra(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


# ElectraForMaskedLM is the same as ElectraGenerator
ElectraForMaskedLM = ElectraGenerator
ElectraForPretraining = ElectraForTotalPretraining