# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import annotations

from dataclasses import dataclass
from typing import Optional, Tuple

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import Tensor
from paddle.nn import Layer

from paddlenlp.layers.crf import LinearChainCrf, LinearChainCrfLoss
from paddlenlp.transformers.model_outputs import ModelOutput, TokenClassifierOutput
from paddlenlp.utils.tools import compare_version

from .configuration import (
    ERNIE_CTM_PRETRAINED_INIT_CONFIGURATION,
    ERNIE_CTM_PRETRAINED_RESOURCE_FILES_MAP,
    ErnieCtmConfig,
)

if compare_version(paddle.version.full_version, "2.2.0") >= 0:
    # paddle.text.ViterbiDecoder is supported by paddle after version 2.2.0
    from paddle.text import ViterbiDecoder
else:
    from paddlenlp.layers.crf import ViterbiDecoder

from .. import PretrainedModel, register_base_model

__all__ = [
    "ErnieCtmPretrainedModel",
    "ErnieCtmModel",
    "ErnieCtmWordtagModel",
    "ErnieCtmNptagModel",
    "ErnieCtmForTokenClassification",
]


@dataclass
class ErnieCtmModelOutput(ModelOutput):
    """
    Base class for model's outputs, with potential hidden states and attentions.

    Args:
        last_hidden_state (`paddle.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.
        pooler_output (`paddle.Tensor` of shape :obj:`(batch_size, hidden_size)`):
            Last layer hidden-state of the first token of the sequence (classification token) further processed by a
            Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
            prediction (classification) objective during pretraining.
        content_output
        hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `paddle.Tensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.

    """

    last_hidden_state: paddle.Tensor = None
    pooler_output: paddle.Tensor = None
    content_output: paddle.Tensor = None
    hidden_states: Optional[Tuple[paddle.Tensor]] = None
    attentions: Optional[Tuple[paddle.Tensor]] = None


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

    def __init__(self, config: ErnieCtmConfig):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
        self.layer_norm = nn.LayerNorm(config.embedding_size, epsilon=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.cls_num = config.cls_num

    def forward(self, input_ids, token_type_ids=None, position_ids=None, inputs_embeds=None):
        if position_ids is None:

            content_len = input_ids.shape[1] - self.cls_num
            position_ids = paddle.concat(
                [
                    paddle.zeros(shape=[self.cls_num], dtype="int64"),
                    paddle.linspace(1, content_len, content_len, dtype="int64"),
                ]
            )
            position_ids.stop_gradient = True

        if token_type_ids is None:
            token_type_ids = paddle.zeros_like(input_ids, dtype="int64")

        if input_ids is not None:
            inputs_embeds = self.word_embeddings(input_ids)

        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = inputs_embeds + token_type_embeddings + position_embeddings

        embeddings = self.layer_norm(embeddings)
        embeddings = self.dropout(embeddings)

        return embeddings


class ErnieCtmPooler(Layer):
    """ """

    def __init__(self, hidden_size):
        super().__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = nn.Tanh()

    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 ErnieCtmPretrainedModel(PretrainedModel):
    """
    An abstract class for pretrained ErnieCtm models. It provides ErnieCtm 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.
    """

    model_config_file = "model_config.json"
    config_class = ErnieCtmConfig
    resource_files_names = {"model_state": "model_state.pdparams"}

    base_model_prefix = "ernie_ctm"

    pretrained_init_configuration = ERNIE_CTM_PRETRAINED_INIT_CONFIGURATION
    pretrained_resource_files_map = ERNIE_CTM_PRETRAINED_RESOURCE_FILES_MAP

    def _init_weights(self, layer):
        # Initialize weights
        if isinstance(layer, (nn.Linear, nn.Embedding)):
            # In the dygraph mode, use the `set_value` to reset the parameter directly,
            # and reset the `state_dict` to update parameter in static mode.
            if isinstance(layer.weight, paddle.Tensor):
                layer.weight.set_value(
                    paddle.tensor.normal(
                        mean=0.0,
                        std=self.initializer_range
                        if hasattr(self, "initializer_range")
                        else self.ernie_ctm.config["initializer_range"],
                        shape=layer.weight.shape,
                    )
                )
        elif isinstance(layer, nn.LayerNorm):
            layer._epsilon = 1e-12


@register_base_model
class ErnieCtmModel(ErnieCtmPretrainedModel):
    """
    The bare ErnieCtm 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/zh/api/paddle/nn/Layer_cn.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 `ErnieCtmModel`. 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 `ErnieCtmModel`.
        embedding_size (int, optional):
            Dimensionality of the embedding layer.
            Defaults to `128`.
        hidden_size (int, optional):
            Dimensionality of the encoder layers and the 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_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 the `token_type_ids`.
            Defaults to `16`.
        initializer_range (float, optional):
            The standard deviation of the normal initializer for initializing all weight matrices.
            Defaults to `0.02`.
        pad_token_id (int, optional):
            The index of padding token in the token vocabulary.
            Defaults to `0`.
        use_content_summary (`bool`, optional):
            Whether or not to add content summary tokens.
            Defaults to `True`.
        content_summary_index (int, optional):
            The number of the content summary tokens. Only valid when use_content_summary is True.
            Defaults to `1`.
        cls_num (int, optional):
            The number of the CLS tokens. Only valid when use_content_summary is True.
            Defaults to `2`.
    """

    def __init__(self, config: ErnieCtmConfig):
        super(ErnieCtmModel, self).__init__(config)

        self.config = config
        self.pad_token_id = config.pad_token_id
        self.content_summary_index = config.content_summary_index
        self.initializer_range = config.initializer_range
        self.embeddings = ErnieCtmEmbeddings(config)
        self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)

        def construct_encoder_layer():
            encoder_layer = nn.TransformerEncoderLayer(
                config.hidden_size,
                config.num_attention_heads,
                config.intermediate_size,
                dropout=config.hidden_dropout_prob,
                activation="gelu",
                attn_dropout=config.attention_probs_dropout_prob,
                act_dropout=0,
            )
            encoder_layer.activation = nn.GELU(approximate=True)
            return encoder_layer

        self.encoder = nn.TransformerEncoder(construct_encoder_layer(), config.num_hidden_layers)
        self.pooler = ErnieCtmPooler(config.hidden_size)

        self.use_content_summary = config.use_content_summary
        self.content_summary_index = config.content_summary_index
        if config.use_content_summary is True:
            self.feature_fuse = nn.Linear(config.hidden_size * 2, config.intermediate_size)
            self.feature_output = nn.Linear(config.intermediate_size, config.hidden_size)

    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=None,
        token_type_ids=None,
        position_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        content_clone=False,
        output_hidden_states=None,
        output_attentions=None,
        return_dict=None,
    ):
        r"""
        The ErnieCtmModel 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.
                It's data type should be `int64` and 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]`.
                For example, its shape can be
                [batch_size, sequence_length], [batch_size, sequence_length, sequence_length],
                [batch_size, num_attention_heads, sequence_length, sequence_length].
                We use whole-word-mask in ERNIE, so the whole word will have the same value.
                For example, "使用" as a word, "使" and "用" will have the same value.
                Defaults to `None`, which means nothing needed to be prevented attention to.
            inputs_embeds (Tensor, optional):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation
                of shape `(batch_size, sequence_length, hidden_size)`. This is useful if you want more control over
                how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
                Default to None.
            content_clone (bool, optional):
                Whether the `content_output` is clone from `sequence_output`. If set to `True`, the content_output is
                clone from sequence_output, which may cause the classification task impact on the sequence labeling
                task.
                Defaults to `False`.
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `None`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `None`. (currently not supported)
            return_dict (bool, optional):
                Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
                will be a tuple of tensors. Defaults to `None`.

        Returns:
            tuple: Returns tuple (``sequence_output``, ``pooled_output``, ``content_output``).

            With the fields:

            - `sequence_output` (Tensor):
                Sequence of 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].

            - `pooled_output` (Tensor):
                The output of first token (`[CLS]`) in sequence.
                We "pool" the model by simply taking the hidden state corresponding to the first token.
                Its data type should be float32 and its shape is [batch_size, hidden_size].

            - `content_output` (Tensor):
                The output of content summary token (`[CLS1]` in sequence). Its data type should be float32 and
                has a shape of [batch_size, hidden_size].

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ErnieModel, ErnieTokenizer

                tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')
                model = ErnieModel.from_pretrained('ernie-1.0')

                inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                sequence_output, pooled_output, content_output = model(**inputs)

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        # check the variable of `input_ids` and `inputs_embeds`
        if input_ids is None and inputs_embeds is None:
            raise ValueError("You have to specify either input_ids or inputs_embeds")
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")

        if attention_mask is None:
            attention_mask = paddle.unsqueeze(
                (input_ids == self.pad_token_id).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
            )
        # For 2D attention_mask from tokenizer
        elif attention_mask.ndim == 2:
            attention_mask = paddle.unsqueeze(attention_mask, axis=[1, 2]).astype(paddle.get_default_dtype())
            attention_mask = (1.0 - attention_mask) * -1e4
        attention_mask.stop_gradient = True

        embedding_output = self.embeddings(
            input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
        )
        embedding_output = self.embedding_hidden_mapping_in(embedding_output)

        hidden_states = embedding_output

        encoder_output = self.encoder(
            hidden_states,
            src_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        # when `output_attentions` and `output_hidden_states` are False, it will return tensor object.
        encoder_output = (encoder_output,) if paddle.is_tensor(encoder_output) else encoder_output

        sequence_output = encoder_output[0]

        pooled_output = self.pooler(sequence_output)
        content_output = sequence_output[:, self.content_summary_index] if self.use_content_summary else None

        if self.use_content_summary is True:
            if content_clone is True:
                sequence_output = paddle.concat(
                    (
                        sequence_output,
                        sequence_output[:, self.content_summary_index]
                        .clone()
                        .unsqueeze([1])
                        .expand_as(sequence_output),
                    ),
                    2,
                )
            else:
                content_output = paddle.expand(
                    content_output.unsqueeze([1]),
                    shape=(sequence_output.shape[0], sequence_output.shape[1], sequence_output.shape[2]),
                )

                sequence_output = paddle.concat((sequence_output, content_output), 2)

            sequence_output = self.feature_fuse(sequence_output)

            sequence_output = self.feature_output(sequence_output)

        if not return_dict:
            return (
                sequence_output,
                pooled_output,
                content_output,
            ) + encoder_output[1:]

        return ErnieCtmModelOutput(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            content_output=content_output,
            hidden_states=encoder_output.hidden_states,
            attentions=encoder_output.attentions,
        )


class ErnieCtmWordtagModel(ErnieCtmPretrainedModel):
    """
    ErnieCtmWordtag Model with a token classification head on top (a crf layer on top of the hidden-states output) .
    e.g. for Named-Entity-Recognition (NER) tasks.

    Args:
        ernie_ctm (:clss:`ErnieCtmModel`):
            An instance of :class:`ErnieCtmModel`.
        num_tag (int):
            The number of different tags.
        crf_lr (float):
            The learning rate of the crf. Defaults to `100`.
    """

    def __init__(self, config: ErnieCtmConfig):
        super(ErnieCtmWordtagModel, self).__init__(config)
        self.num_tag = config.num_labels
        self.ernie_ctm = ErnieCtmModel(config)
        self.tag_classifier = nn.Linear(config.hidden_size, self.num_tag)
        self.crf = LinearChainCrf(self.num_tag, with_start_stop_tag=False)
        self.crf_loss = LinearChainCrfLoss(self.crf)
        self.viterbi_decoder = ViterbiDecoder(self.crf.transitions, False)

    def forward(
        self,
        input_ids=None,
        token_type_ids=None,
        lengths=None,
        position_ids=None,
        attention_mask=None,
        inputs_embeds=None,
        tag_labels=None,
        output_hidden_states=None,
        output_attentions=None,
        return_dict=None,
        **kwargs
    ):
        r"""
        Args:
            input_ids (Tensor):
                See :class:`ErnieCtmModel`.
            token_type_ids (Tensor, optional):
                See :class:`ErnieCtmModel`.
            position_ids (Tensor, optional):
                See :class:`ErnieCtmModel`.
            attention_mask (Tensor, optional):
                See :class:`ErnieCtmModel`.
            inputs_embeds (Tensor, optional):
                See :class:`ErnieCtmModel`.
            lengths (Tensor, optional):
                The input length. Its dtype is int64 and has a shape of `[batch_size]`.
                Defaults to `None`.
            tag_labels (Tensor, optional):
                The input predicted tensor.
                Its dtype is float32 and has a shape of `[batch_size, sequence_length, num_tags]`.
                Defaults to `None`.
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `None`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `None`. (currently not supported)
            return_dict (bool, optional):
                Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
                will be a tuple of tensors. Defaults to `None`.


        Returns:
            tuple: Returns tuple (`seq_logits`, `cls_logits`).

            With the fields:

            - `seq_logits` (Tensor):
                A tensor of next sentence prediction logits.
                Its data type should be float32 and its shape is [batch_size, sequence_length, num_tag].

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ErnieCtmWordtagModel, ErnieCtmTokenizer

                tokenizer = ErnieCtmTokenizer.from_pretrained('ernie-ctm')
                model = ErnieCtmWordtagModel.from_pretrained('ernie-ctm', num_tag=2)

                inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                logits = model(**inputs)

        """
        # author want to keep the name of `tab_labels`, so add this code to keep style consistent with paddlenlp.
        tag_labels = kwargs.get("labels", tag_labels)

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        outputs = self.ernie_ctm(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        seq_logits = self.tag_classifier(sequence_output)
        loss = None

        if lengths is None:
            lengths = paddle.sum(input_ids != self.config.pad_token_id, axis=-1)

        if tag_labels is not None:
            crf_loss = self.crf_loss(seq_logits, lengths, tag_labels)
            seq_loss = F.cross_entropy(seq_logits.reshape((-1, self.num_tag)), tag_labels.reshape((-1,)))
            loss = crf_loss + seq_loss
            output = (loss, seq_logits)
        else:
            _, seq_logits = self.viterbi_decoder(seq_logits, lengths)
            output = (seq_logits,)

        if not return_dict:
            return output + outputs[1:]

        return TokenClassifierOutput(
            loss=loss, logits=seq_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )


class ErnieCtmMLMHead(Layer):
    def __init__(self, config: ErnieCtmConfig):
        super(ErnieCtmMLMHead, self).__init__()
        self.layer_norm = nn.LayerNorm(config.embedding_size)

        self.bias = self.create_parameter(
            [config.vocab_size], is_bias=True, default_initializer=nn.initializer.Constant(value=0.0)
        )
        self.dense = nn.Linear(config.hidden_size, config.embedding_size)
        self.decoder = nn.Linear(config.embedding_size, config.vocab_size)
        self.activation = nn.GELU(approximate=True)
        # Link bias
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.layer_norm(hidden_states)
        hidden_states = self.decoder(hidden_states)
        prediction_scores = hidden_states
        return prediction_scores


class ErnieCtmNptagModel(ErnieCtmPretrainedModel):
    r"""
    ErnieCtmNptag Model with a `masked language modeling` head on top.

    Args:
        ernie_ctm (:clss:`ErnieCtmModel`):
            An instance of :class:`ErnieCtmModel`.
    """

    def __init__(self, config: ErnieCtmConfig):
        super(ErnieCtmNptagModel, self).__init__(config)

        self.ernie_ctm = ErnieCtmModel(config)
        self.predictions = ErnieCtmMLMHead(config)

    def forward(
        self,
        input_ids=None,
        token_type_ids=None,
        attention_mask=None,
        position_ids=None,
        inputs_embeds=None,
        labels=None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        Args:
            input_ids (Tensor):
                See :class:`ErnieCtmModel`.
            token_type_ids (Tensor, optional):
                See :class:`ErnieCtmModel`.
            attention_mask (Tensor, optional):
                See :class:`ErnieCtmModel`.
            position_ids (Tensor, optional):
                See :class:`ErnieCtmModel`.
            inputs_embeds (Tensor, optional):
                See :class:`ErnieCtmModel`.
            output_hidden_states (bool, optional):
                See :class:`ErnieCtmModel`.
            output_attentions (bool, optional):
                See :class:`ErnieCtmModel`.
            return_dict (bool, optional):
                See :class:`ErnieCtmModel`.

        Returns:
            tuple: Returns tensor `logits`, the scores of masked token prediction.
            Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size].

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ErnieCtmNptagModel, ErnieCtmTokenizer

                tokenizer = ErnieCtmTokenizer.from_pretrained('ernie-ctm')
                model = ErnieCtmNptagModel.from_pretrained('ernie-ctm')

                inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}

                logits = model(**inputs)
                print(logits.shape)
                # [1, 45, 23000]

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        outputs = self.ernie_ctm(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        logits = self.predictions(sequence_output)

        loss = None
        if labels is not None:
            loss = F.cross_entropy(logits.reshape([-1, self.config.vocab_size]), labels.reshape([-1]))

        if not return_dict:
            outputs = (logits,) + outputs[2:]
            return (loss,) + outputs if loss is not None else outputs

        return TokenClassifierOutput(
            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
        )


class ErnieCtmForTokenClassification(ErnieCtmPretrainedModel):
    r"""
    ERNIECtm Model with a linear layer on top of the hidden-states output layer,
    designed for token classification tasks like NER tasks.

    Args:
        ernie (`ErnieModel`):
            An instance of `ErnieModel`.
        num_tag (int, optional):
            The number of classes. Defaults to `2`.
        dropout (float, optional):
            The dropout probability for output of ERNIE.
            If None, use the same value as `hidden_dropout_prob`
            of `ErnieCtmModel` instance `ernie`. Defaults to `None`.
    """

    def __init__(self, config: ErnieCtmConfig):
        super(ErnieCtmForTokenClassification, self).__init__(config)
        self.num_tag = config.num_labels
        self.ernie_ctm = ErnieCtmModel(config)
        self.dropout = nn.Dropout(
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

    def forward(
        self,
        input_ids: Tensor,
        token_type_ids: Tensor | None = None,
        position_ids: Tensor | None = None,
        attention_mask: Tensor | None = None,
        inputs_embeds: Tensor | None = None,
        labels: Tensor | None = None,
        output_hidden_states: bool | None = None,
        output_attentions: bool | None = None,
        return_dict: bool | None = None,
    ):
        r"""
        Args:
            input_ids (Tensor):
                See :class:`ErnieCtmModel`.
            token_type_ids (Tensor, optional):
                See :class:`ErnieCtmModel`.
            position_ids (Tensor, optional):
                See :class:`ErnieCtmModel`.
            attention_mask (Tensor, optional):
                See :class:`ErnieCtmModel`.
            inputs_embeds (Tensor, optional):
                See :class:`ErnieCtmModel`.
            labels (Tensor, optional): labels for model to compute the loss

        Returns:
            Tensor: Returns tensor `logits`, a tensor of the input token classification logits.
            Shape as `[sequence_length, num_tag]` and dtype as `float32`.

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ErnieCtmForTokenClassification, ErnieCtmTokenizer

                tokenizer = ErnieCtmTokenizer.from_pretrained('ernie-ctm')
                model = ErnieCtmForTokenClassification.from_pretrained('ernie-ctm')

                inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                logits = model(**inputs)

        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions

        output = self.ernie_ctm(
            input_ids,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
            return_dict=return_dict,
        )

        sequence_output = output[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = paddle.nn.CrossEntropyLoss()
            loss = loss_fct(logits.reshape((-1, self.num_tag)), labels.reshape((-1,)))

        if not return_dict:
            output = (logits,) + output[2:]
            return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=output.hidden_states,
            attentions=output.attentions,
        )
