# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2020 Huawei Technologies Co., Ltd.
# Copyright 2018 The Google AI Language Team Authors, The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.

import copy
import math
from dataclasses import dataclass
from typing import Optional, Tuple

import numpy as np
import paddle
import paddle.nn as nn
from paddle import Tensor

from paddlenlp.transformers import PretrainedModel, register_base_model

from ...utils.env import CONFIG_NAME
from ..activations import ACT2FN
from ..model_outputs import (
    BaseModelOutputWithPoolingAndCrossAttentions,
    ModelOutput,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
)
from .configuration import (
    NEZHA_PRETRAINED_INIT_CONFIGURATION,
    NEZHA_PRETRAINED_RESOURCE_FILES_MAP,
    NeZhaConfig,
)

__all__ = [
    "NeZhaModel",
    "NeZhaPretrainedModel",
    "NeZhaForPretraining",
    "NeZhaForSequenceClassification",
    "NeZhaForTokenClassification",
    "NeZhaForQuestionAnswering",
    "NeZhaForMultipleChoice",
]


class NeZhaAttention(nn.Layer):
    def __init__(self, config: NeZhaConfig):
        super(NeZhaAttention, self).__init__()
        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                "The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                "heads ({config.num_attention_heads})"
            )
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.relative_positions_embeddings = self.generate_relative_positions_embeddings(
            length=512, depth=self.attention_head_size, max_relative_position=config.max_relative_position
        )
        self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)

        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
        self.output_dropout = nn.Dropout(config.hidden_dropout_prob)

    def generate_relative_positions_embeddings(self, length, depth, max_relative_position=127):
        vocab_size = max_relative_position * 2 + 1
        range_vec = paddle.arange(length)
        range_mat = paddle.tile(range_vec, repeat_times=[length]).reshape((length, length))
        distance_mat = range_mat - paddle.t(range_mat)
        distance_mat_clipped = paddle.clip(
            distance_mat.astype("float32"), -max_relative_position, max_relative_position
        )
        final_mat = distance_mat_clipped + max_relative_position
        embeddings_table = np.zeros([vocab_size, depth])

        for pos in range(vocab_size):
            for i in range(depth // 2):
                embeddings_table[pos, 2 * i] = np.sin(pos / np.power(10000, 2 * i / depth))
                embeddings_table[pos, 2 * i + 1] = np.cos(pos / np.power(10000, 2 * i / depth))

        embeddings_table_tensor = paddle.to_tensor(embeddings_table, dtype="float32")
        flat_relative_positions_matrix = final_mat.reshape((-1,))
        one_hot_relative_positions_matrix = paddle.nn.functional.one_hot(
            flat_relative_positions_matrix.astype("int64"), num_classes=vocab_size
        )
        embeddings = paddle.matmul(one_hot_relative_positions_matrix, embeddings_table_tensor)
        my_shape = final_mat.shape
        my_shape.append(depth)
        embeddings = embeddings.reshape(my_shape)
        return embeddings

    def transpose_for_scores(self, x):
        new_x_shape = x.shape[:-1] + [self.num_attention_heads, self.attention_head_size]
        x = x.reshape(new_x_shape)
        return x.transpose((0, 2, 1, 3))

    def forward(self, hidden_states, attention_mask):
        mixed_query_layer = self.query(hidden_states)
        mixed_key_layer = self.key(hidden_states)
        mixed_value_layer = self.value(hidden_states)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
        value_layer = self.transpose_for_scores(mixed_value_layer)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = paddle.matmul(query_layer, key_layer.transpose((0, 1, 3, 2)))
        batch_size, num_attention_heads, from_seq_length, to_seq_length = attention_scores.shape

        relations_keys = self.relative_positions_embeddings.detach().clone()[:to_seq_length, :to_seq_length, :]

        query_layer_t = query_layer.transpose((2, 0, 1, 3))
        query_layer_r = query_layer_t.reshape(
            (from_seq_length, batch_size * num_attention_heads, self.attention_head_size)
        )
        key_position_scores = paddle.matmul(query_layer_r, relations_keys.transpose((0, 2, 1)))
        key_position_scores_r = key_position_scores.reshape(
            (from_seq_length, batch_size, num_attention_heads, from_seq_length)
        )
        key_position_scores_r_t = key_position_scores_r.transpose((1, 2, 0, 3))
        attention_scores = attention_scores + key_position_scores_r_t
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        attention_scores = attention_scores + attention_mask

        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(axis=-1)(attention_scores)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.attention_dropout(attention_probs)

        context_layer = paddle.matmul(attention_probs, value_layer)

        relations_values = self.relative_positions_embeddings.clone()[:to_seq_length, :to_seq_length, :]
        attention_probs_t = attention_probs.transpose((2, 0, 1, 3))
        attentions_probs_r = attention_probs_t.reshape(
            (from_seq_length, batch_size * num_attention_heads, to_seq_length)
        )
        value_position_scores = paddle.matmul(attentions_probs_r, relations_values)
        value_position_scores_r = value_position_scores.reshape(
            (from_seq_length, batch_size, num_attention_heads, self.attention_head_size)
        )
        value_position_scores_r_t = value_position_scores_r.transpose((1, 2, 0, 3))
        context_layer = context_layer + value_position_scores_r_t

        context_layer = context_layer.transpose((0, 2, 1, 3))
        new_context_layer_shape = context_layer.shape[:-2] + [self.all_head_size]
        context_layer = context_layer.reshape(new_context_layer_shape)

        projected_context_layer = self.dense(context_layer)
        projected_context_layer_dropout = self.output_dropout(projected_context_layer)
        layer_normed_context_layer = self.layer_norm(hidden_states + projected_context_layer_dropout)

        return layer_normed_context_layer, attention_scores


class NeZhaLayer(nn.Layer):
    def __init__(self, config: NeZhaConfig):
        super(NeZhaLayer, self).__init__()
        self.seq_len_dim = 1
        self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
        self.attention = NeZhaAttention(config)
        self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
        self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
        self.activation = ACT2FN[config.hidden_act]
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(self, hidden_states, attention_mask=None):
        attention_output, layer_att = self.attention(hidden_states, attention_mask)

        ffn_output = self.ffn(attention_output)
        ffn_output = self.activation(ffn_output)
        ffn_output = self.ffn_output(ffn_output)

        ffn_output_dropout = self.dropout(ffn_output)
        hidden_states = self.layer_norm(ffn_output_dropout + attention_output)

        return hidden_states, layer_att


class NeZhaEncoder(nn.Layer):
    def __init__(self, config: NeZhaConfig):
        super(NeZhaEncoder, self).__init__()
        layer = NeZhaLayer(config)
        self.layer = nn.LayerList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])

    def forward(self, hidden_states, attention_mask):
        all_encoder_layers = []
        all_encoder_att = []
        for i, layer_module in enumerate(self.layer):
            all_encoder_layers.append(hidden_states)
            hidden_states, layer_att = layer_module(all_encoder_layers[i], attention_mask)
            all_encoder_att.append(layer_att)
        all_encoder_layers.append(hidden_states)
        return all_encoder_layers, all_encoder_att


class NeZhaEmbeddings(nn.Layer):
    def __init__(self, config: NeZhaConfig):
        super(NeZhaEmbeddings, self).__init__()
        self.use_relative_position = config.use_relative_position

        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)

        if not self.use_relative_position:
            self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)

        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.layer_norm = nn.LayerNorm(config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
    ):
        if input_ids is not None:
            inputs_embeds = self.word_embeddings(input_ids)

        input_shape = inputs_embeds.shape[:-1]

        ones = paddle.ones(input_shape, dtype="int64")
        seq_length = paddle.cumsum(ones, axis=1)
        position_ids = seq_length - ones
        position_ids.stop_gradient = True

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

        embeddings = inputs_embeds

        if not self.use_relative_position:
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings

        token_type_embeddings = self.token_type_embeddings(token_type_ids)

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

        return embeddings


class NeZhaPooler(nn.Layer):
    def __init__(self, config: NeZhaConfig):
        super(NeZhaPooler, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.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 NeZhaPretrainedModel(PretrainedModel):
    """
    An abstract class for pretrained NeZha models. It provides NeZha 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 = CONFIG_NAME
    config_class = NeZhaConfig
    resource_files_names = {"model_state": "model_state.pdparams"}
    base_model_prefix = "nezha"

    pretrained_init_configuration = NEZHA_PRETRAINED_INIT_CONFIGURATION
    pretrained_resource_files_map = NEZHA_PRETRAINED_RESOURCE_FILES_MAP

    def _init_weights(self, layer):
        """Initialization hook"""
        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.config.initializer_range,
                        shape=layer.weight.shape,
                    )
                )
        elif isinstance(layer, nn.LayerNorm):
            layer._epsilon = 1e-12


@register_base_model
class NeZhaModel(NeZhaPretrainedModel):
    """
    The bare NeZha 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 `DistilBertModel`. Defines the number of different tokens that can
            be represented by the `inputs_ids` passed when calling `DistilBertModel`.
        hidden_size (int, optional):
            Dimensionality of the embedding layer, 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_act (str, optional):
            The non-linear activation function in the feed-forward layer.
            ``"gelu"``, ``"relu"`` and any other paddle supported activation functions
            are supported. Defaults to `"gelu"`.
        hidden_dropout_prob (float, optional):
            The dropout probability for all fully connected layers in the embeddings and encoder.
            Defaults to `0.1`.
        attention_probs_dropout_prob (float, optional):
            The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.
            Defaults to `0.1`.
        max_position_embeddings (int, optional):
            The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
            sequence. Defaults to `512`.
        type_vocab_size (int, optional):
            The vocabulary size of `token_type_ids`.
            Defaults to `16`.
        initializer_range (float, optional):
            The standard deviation of the normal initializer.
            Defaults to `0.02`.

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

        max_relative_embeddings (int, optional):
            The maximum value of the dimensionality of relative encoding, which dictates the maximum supported
            relative distance of two sentences.
            Defaults to `64`.
        layer_norm_eps (float, optional):
            The small value added to the variance in `LayerNorm` to prevent division by zero.
            Defaults to `1e-12`.
        use_relative_position (bool, optional):
            Whether or not to use relative position embedding. Defaults to `True`.

    """

    def __init__(self, config: NeZhaConfig):
        super(NeZhaModel, self).__init__(config)
        self.initializer_range = config.initializer_range

        self.embeddings = NeZhaEmbeddings(config)

        self.encoder = NeZhaEncoder(config)

        self.pooler = NeZhaPooler(config)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        The NeZhaModel 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.
            attention_mask (Tensor, optional):
                Mask used in multi-head attention to avoid performing attention 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 NeZha, 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):
                If you want to control how to convert `inputs_ids` indices into associated vectors, you can
                pass an embedded representation directly instead of passing `inputs_ids`.
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `False`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `False`.
            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 `False`.

        Returns:
            An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if
            `return_dict=True`. Otherwise it returns a tuple of tensors corresponding
            to ordered and not None (depending on the input arguments) fields of
            :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`.

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import NeZhaModel, NeZhaTokenizer

                tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
                model = NeZhaModel.from_pretrained('nezha-base-chinese')

                inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
                output = model(**inputs)
        """
        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.")

        output_attentions = output_attentions if output_attentions is not None else False
        output_hidden_states = output_hidden_states if output_hidden_states is not None else False
        return_dict = return_dict if return_dict is not None else False

        if attention_mask is None:
            attention_mask = paddle.ones_like(input_ids)
        if token_type_ids is None:
            token_type_ids = paddle.zeros_like(input_ids)

        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0

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

        encoder_outputs = self.encoder(embedding_output, extended_attention_mask)
        encoder_hidden_outputs, encoder_att_outputs = encoder_outputs

        sequence_output = encoder_hidden_outputs[-1]
        pooled_output = self.pooler(sequence_output)

        if not return_dict:
            outputs = (sequence_output, pooled_output)
            if output_hidden_states:
                outputs += (encoder_hidden_outputs,)
            if output_attentions:
                outputs += (encoder_att_outputs,)
            return outputs
        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_hidden_outputs if output_hidden_states else None,
            attentions=encoder_att_outputs if output_attentions else None,
        )

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

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


class NeZhaLMPredictionHead(nn.Layer):
    def __init__(self, config: NeZhaConfig, embedding_weights=None):
        super(NeZhaLMPredictionHead, self).__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.activation = ACT2FN[config.hidden_act]
        self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)

        self.decoder_weight = embedding_weights
        self.decoder_bias = self.create_parameter(
            shape=[config.vocab_size], dtype=self.decoder_weight.dtype, is_bias=True
        )

    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 = paddle.tensor.matmul(hidden_states, self.decoder_weight, transpose_y=True) + self.decoder_bias

        return hidden_states


class NeZhaPretrainingHeads(nn.Layer):
    """
    Perform language modeling task and next sentence classification task.

    Args:
        hidden_size (int):
            See :class:`NeZhaModel`.
        vocab_size (int):
            See :class:`NeZhaModel`.
        hidden_act (str):
            Activation function used in the language modeling task.
        embedding_weights (Tensor, optional):
            Decoding weights used to map hidden_states to logits of the masked token prediction.
            Its data type should be float32 and its shape is [vocab_size, hidden_size].
            Defaults to `None`, which means use the same weights of the embedding layer.

    """

    def __init__(self, config: NeZhaConfig, embedding_weights=None):
        super(NeZhaPretrainingHeads, self).__init__()
        self.predictions = NeZhaLMPredictionHead(config=config, embedding_weights=embedding_weights)
        self.seq_relationship = nn.Linear(config.hidden_size, 2)

    def forward(self, sequence_output, pooled_output):
        """
        Args:
            sequence_output(Tensor):
                Sequence of hidden-states at the last layer of the model.
                It's data type should be float32 and its shape is [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].

        Returns:
            tuple: Returns tuple (``prediction_scores``, ``seq_relationship_score``).

            With the fields:

            - `prediction_scores` (Tensor):
                The scores of masked token prediction. Its data type should be float32.
                If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size].
                Otherwise, its shape is [batch_size, mask_token_num, vocab_size].

            - `seq_relationship_score` (Tensor):
                The scores of next sentence prediction.
                Its data type should be float32 and its shape is [batch_size, 2].

        """
        prediction_scores = self.predictions(sequence_output)
        seq_relationship_score = self.seq_relationship(pooled_output)
        return prediction_scores, seq_relationship_score


@dataclass
class NeZhaForPreTrainingOutput(ModelOutput):
    """
    Output type of [`NeZhaForPreTraining`].

    Args:
        loss (*optional*, returned when `labels` is provided, `paddle.Tensor` of shape `(1,)`):
            Total loss as the sum of the masked language modeling loss and the next sequence prediction
            (classification) loss.
        prediction_logits (`paddle.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        seq_relationship_logits (`paddle.Tensor` of shape `(batch_size, 2)`):
            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
            before SoftMax).
        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 + 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 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.
    """

    loss: Optional[paddle.Tensor] = None
    prediction_logits: paddle.Tensor = None
    seq_relationship_logits: paddle.Tensor = None
    hidden_states: Optional[Tuple[paddle.Tensor]] = None
    attentions: Optional[Tuple[paddle.Tensor]] = None


class NeZhaForPretraining(NeZhaPretrainedModel):
    """
    NeZha Model with pretraining tasks on top.

    Args:
        nezha (:class:`NeZhaModel`):
            An instance of :class:`NeZhaModel`.

    """

    def __init__(self, config: NeZhaConfig):
        super(NeZhaForPretraining, self).__init__(config)
        self.nezha = NeZhaModel(config)
        self.cls = NeZhaPretrainingHeads(
            config,
            self.nezha.embeddings.word_embeddings.weight,
        )

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        masked_lm_labels: Optional[Tensor] = None,
        next_sentence_label: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""

        Args:
            input_ids (Tensor):
                See :class:`NeZhaModel`.
            token_type_ids (Tensor, optional):
                See :class:`NeZhaModel`.
            attention_mask (Tensor, optional):
                See :class:`NeZhaModel`.
            inputs_embeds(Tensor, optional):
                See :class:`NeZhaModel`.
            masked_lm_labels (Tensor, optional):
                The labels of the masked language modeling, its dimensionality is equal to `prediction_scores`.
                Its data type should be int64 and its shape is [batch_size, sequence_length, 1].
            next_sentence_label (Tensor, optional):
                The labels of the next sentence prediction task, the dimensionality of `next_sentence_labels`
                is equal to `seq_relation_labels`. Its data type should be int64 and its shape is [batch_size, 1].
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `False`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `False`.
            return_dict (bool, optional):
                Whether to return a :class:`~paddlenlp.transformers.nezha.NeZhaForPreTrainingOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.

        Returns:
            An instance of :class:`~paddlenlp.transformers.nezha.NeZhaForPreTrainingOutput` if `return_dict=True`.
            Otherwise it returns a tuple of tensors corresponding to ordered and
            not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.nezha.NeZhaForPreTrainingOutput`.
        """
        outputs = self.nezha(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output, pooled_output = outputs[0], outputs[1]
        prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)

        total_loss = None
        if masked_lm_labels is not None and next_sentence_label is not None:
            loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
            masked_lm_loss = loss_fct(
                prediction_scores.reshape((-1, self.nezha.config.vocab_size)), masked_lm_labels.reshape((-1,))
            )
            next_sentence_loss = loss_fct(seq_relationship_score.reshape((-1, 2)), next_sentence_label.reshape((-1,)))
            total_loss = masked_lm_loss + next_sentence_loss
        elif masked_lm_labels is not None:
            loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
            masked_lm_loss = loss_fct(
                prediction_scores.reshape((-1, self.nezha.config.vocab_size)), masked_lm_labels.reshape((-1,))
            )
            total_loss = masked_lm_loss

        if not return_dict:
            output = (prediction_scores, seq_relationship_score) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return NeZhaForPreTrainingOutput(
            loss=total_loss,
            prediction_logits=prediction_scores,
            seq_relationship_logits=seq_relationship_score,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class NeZhaForQuestionAnswering(NeZhaPretrainedModel):
    """
    NeZha 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:
        config (:class:`NeZhaConfig`):
            An instance of NeZhaConfig used to construct NeZhaForQuestionAnswering.
    """

    def __init__(self, config: NeZhaConfig):
        super(NeZhaForQuestionAnswering, self).__init__(config)
        self.nezha = NeZhaModel(config)
        self.classifier = nn.Linear(config.hidden_size, 2)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        start_positions: Optional[Tensor] = None,
        end_positions: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        The NeZhaForQuestionAnswering forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`NeZhaModel`.
            token_type_ids (Tensor, optional):
                See :class:`NeZhaModel`.
            attention_mask (Tensor, optional):
                See :class:`NeZhaModel`.
            inputs_embeds(Tensor, optional):
                See :class:`NeZhaModel`.
            start_positions (Tensor of shape `(batch_size,)`, optional):
                Labels for position (index) of the start of the labelled span for computing the token classification loss.
                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
                are not taken into account for computing the loss.
            end_positions (Tensor of shape `(batch_size,)`, optional):
                Labels for position (index) of the end of the labelled span for computing the token classification loss.
                Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
                are not taken into account for computing the loss.
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `False`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `False`.
            return_dict (bool, optional):
                Whether to return a :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.

        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 NeZhaForQuestionAnswering
                from paddlenlp.transformers import NeZhaTokenizer

                tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
                model = NeZhaForQuestionAnswering.from_pretrained('nezha-base-chinese')

                inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
                outputs = model(**inputs)

                start_logits = outputs[0]
                end_logits  =outputs[1]
        """
        outputs = self.nezha(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]

        logits = self.classifier(sequence_output)
        logits = paddle.transpose(logits, perm=[2, 0, 1])

        start_logits, end_logits = paddle.unstack(x=logits, axis=0)

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if start_positions.ndim > 1:
                start_positions = start_positions.squeeze(-1)
            if end_positions.ndim > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.shape[1]
            start_positions = start_positions.clip(0, ignored_index)
            end_positions = end_positions.clip(0, ignored_index)

            loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        output = (start_logits, end_logits)
        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


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

    Args:
        config (:class:`NeZhaConfig`):
            An instance of NeZhaConfig used to construct NeZhaForSequenceClassification.
    """

    def __init__(self, config: NeZhaConfig):
        super(NeZhaForSequenceClassification, self).__init__(config)
        self.nezha = NeZhaModel(config)
        self.num_labels = config.num_labels
        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, self.num_labels)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        labels: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        The NeZhaForSequenceClassification forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`NeZhaModel`.
            token_type_ids (Tensor, optional):
                See :class:`NeZhaModel`.
            attention_mask (Tensor, optional):
                See :class:`NeZhaModel`.
            inputs_embeds(Tensor, optional):
                See :class:`NeZhaModel`.
            labels (Tensor of shape `(batch_size,)`, optional):
                Labels for computing the sequence classification/regression loss.
                Indices should be in `[0, ..., num_labels - 1]`. If `num_labels == 1`
                a regression loss is computed (Mean-Square loss), If `num_labels > 1`
                a classification loss is computed (Cross-Entropy).
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `False`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `False`.
            return_dict (bool, optional):
                Whether to return a :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.

        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 NeZhaForSequenceClassification
                from paddlenlp.transformers import NeZhaTokenizer

                tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
                model = NeZhaForSequenceClassification.from_pretrained('nezha-base-chinese')

                inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
                output = model(**inputs)

                logits = outputs[0]

        """
        outputs = self.nezha(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output)

        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == paddle.int64 or labels.dtype == paddle.int32):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = paddle.nn.MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = paddle.nn.CrossEntropyLoss()
                loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = paddle.nn.BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)

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


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

    Args:
        config (:class:`NeZhaConfig`):
            An instance of NeZhaConfig used to construct NeZhaForSequenceClassification.
    """

    def __init__(self, config: NeZhaConfig):
        super(NeZhaForTokenClassification, self).__init__(config)
        self.nezha = NeZhaModel(config)
        self.num_labels = config.num_labels
        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, self.num_labels)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        labels: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        The NeZhaForTokenClassification forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`NeZhaModel`.
            token_type_ids (Tensor, optional):
                See :class:`NeZhaModel`.
            attention_mask (list, optional):
                See :class:`NeZhaModel`.
            inputs_embeds (Tensor, optional):
                See :class:`NeZhaModel`.
            labels (Tensor of shape `(batch_size, sequence_length)`, optional):
                Labels for computing the token classification loss. Indices should be in `[0, ..., num_labels - 1]`.
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `False`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `False`.
            return_dict (bool, optional):
                Whether to return a :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.

        Returns:
            An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` if `return_dict=True`.
            Otherwise it returns a tuple of tensors corresponding to ordered and
            not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput`.

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import NeZhaForTokenClassification
                from paddlenlp.transformers import NeZhaTokenizer

                tokenizer = NeZhaTokenizer.from_pretrained('nezha-base-chinese')
                model = NeZhaForTokenClassification.from_pretrained('nezha-base-chinese')

                inputs = tokenizer("欢迎使用百度飞浆!", return_tensors='pt')
                output = model(**inputs)

                logits = outputs[0]
        """
        outputs = self.nezha(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[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_labels)), labels.reshape((-1,)))

        if not return_dict:
            output = (logits,) + outputs[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=outputs.hidden_states,
            attentions=outputs.attentions,
        )


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

    Args:
        config (:class:`BertConfig`):
            An instance of BertConfig used to construct BertForMultipleChoice.
    """

    def __init__(self, config: NeZhaConfig):
        super(NeZhaForMultipleChoice, self).__init__(config)
        self.nezha = NeZhaModel(config)
        self.num_choices = config.num_choices
        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, 1)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        token_type_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        labels: Optional[Tensor] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        The NeZhaForMultipleChoice forward method, overrides the __call__() special method.

        Args:
            input_ids (Tensor):
                See :class:`NeZhaModel`.
            token_type_ids (Tensor, optional):
                See :class:`NeZhaModel`.
            attention_mask (list, optional):
                See :class:`NeZhaModel`.
            inputs_embeds (Tensor, optional):
                See :class:`NeZhaModel`.
            labels (Tensor of shape `(batch_size, )`, optional):
                Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
                num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
                `input_ids` above)
            output_hidden_states (bool, optional):
                Whether to return the hidden states of all layers.
                Defaults to `False`.
            output_attentions (bool, optional):
                Whether to return the attentions tensors of all attention layers.
                Defaults to `False`.
            return_dict (bool, optional):
                Whether to return a :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.

        Returns:
            Tensor: Returns tensor `reshaped_logits`, a tensor of the input multiple choice classification logits.
            Shape as `[batch_size, num_classes]` and dtype as `float32`.
        """

        # input_ids: [bs, num_choice, seq_l]
        if input_ids is not None:
            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 attention_mask is not None:
            attention_mask = attention_mask.reshape((-1, attention_mask.shape[-1]))
        if inputs_embeds is not None:
            inputs_embeds = inputs_embeds.reshape(shape=(-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]))

        outputs = self.nezha(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        pooled_output = outputs[1]
        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)

        loss = None
        if labels is not None:
            loss_fct = paddle.nn.CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)
        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
