# 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 typing import Optional, Tuple

import paddle
import paddle.nn as nn
from paddle import Tensor

from paddlenlp.utils.env import CONFIG_NAME

from .. import PretrainedModel, register_base_model
from ..model_outputs import (
    BaseModelOutputWithPoolingAndCrossAttentions,
    MultipleChoiceModelOutput,
    QuestionAnsweringModelOutput,
    SequenceClassifierOutput,
    TokenClassifierOutput,
    tuple_output,
)
from .configuration import (
    ERNIE_M_PRETRAINED_INIT_CONFIGURATION,
    ERNIE_M_PRETRAINED_RESOURCE_FILES_MAP,
    ErnieMConfig,
)

__all__ = [
    "ErnieMModel",
    "ErnieMPretrainedModel",
    "ErnieMForSequenceClassification",
    "ErnieMForTokenClassification",
    "ErnieMForQuestionAnswering",
    "ErnieMForMultipleChoice",
    "UIEM",
]


class ErnieMEmbeddings(nn.Layer):
    r"""
    Include embeddings from word, position.
    """

    def __init__(self, config: ErnieMConfig):
        super(ErnieMEmbeddings, self).__init__()

        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings, 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,
        position_ids: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        past_key_values_length: int = 0,
    ):

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if position_ids is None:
            input_shape = inputs_embeds.shape[:-1]
            # maybe need use shape op to unify static graph and dynamic graph
            ones = paddle.ones(input_shape, dtype="int64")
            seq_length = paddle.cumsum(ones, axis=1)
            position_ids = seq_length - ones

            if past_key_values_length > 0:
                position_ids = position_ids + past_key_values_length

            position_ids.stop_gradient = True

        position_ids += 2

        position_embeddings = self.position_embeddings(position_ids)
        embeddings = inputs_embeds + position_embeddings
        embeddings = self.layer_norm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class ErnieMPooler(nn.Layer):
    def __init__(self, config: ErnieMConfig):
        super(ErnieMPooler, 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 ErnieMPretrainedModel(PretrainedModel):
    r"""
    An abstract class for pretrained ERNIE-M models. It provides ERNIE-M related
    `model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
    `pretrained_resource_files_map`, `base_model_prefix` for downloading and
    loading pretrained models.
    Refer to :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.

    """

    model_config_file = CONFIG_NAME
    config_class = ErnieMConfig
    resource_files_names = {"model_state": "model_state.pdparams"}

    pretrained_init_configuration = ERNIE_M_PRETRAINED_INIT_CONFIGURATION
    pretrained_resource_files_map = ERNIE_M_PRETRAINED_RESOURCE_FILES_MAP
    base_model_prefix = "ernie_m"

    def _init_weights(self, layer):
        """Initialization hook"""
        if isinstance(layer, (nn.Linear, nn.Embedding)):
            # only support dygraph, use truncated_normal and make it inplace
            # and configurable later
            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,
                    )
                )


@register_base_model
class ErnieMModel(ErnieMPretrainedModel):
    r"""
    The bare ERNIE-M 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:
        config (:class:`ErnieMConfig`):
            An instance of ErnieMConfig used to construct ErnieMModel.
    """

    def __init__(self, config: ErnieMConfig):
        super(ErnieMModel, self).__init__(config)
        self.pad_token_id = config.pad_token_id
        self.initializer_range = config.initializer_range
        self.embeddings = ErnieMEmbeddings(config)
        encoder_layer = nn.TransformerEncoderLayer(
            config.hidden_size,
            config.num_attention_heads,
            dim_feedforward=4 * config.hidden_size,
            dropout=config.hidden_dropout_prob,
            activation=config.hidden_act,
            attn_dropout=config.attention_probs_dropout_prob,
            act_dropout=0,
            normalize_before=False,
        )
        self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
        self.pooler = ErnieMPooler(config)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        position_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
        use_cache: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        r"""
        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].
            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].
                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`.
            past_key_values (tuple(tuple(Tensor)), optional):
                The length of tuple equals to the number of layers, and each inner
                tuple haves 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`)
                which contains precomputed key and value hidden states of the attention blocks.
                If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
                don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
                `input_ids` of shape `(batch_size, sequence_length)`.
            use_cache (`bool`, optional):
                If set to `True`, `past_key_values` key value states are returned.
                Defaults to `None`.
            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`.
            tuple: Returns tuple (``sequence_output``, ``pooled_output``).

            With the fields:

            - `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].

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ErnieMModel, ErnieMTokenizer

                tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
                model = ErnieMModel.from_pretrained('ernie-m-base')

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

        # init the default bool value
        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
        use_cache = use_cache if use_cache is not None else False

        past_key_values_length = 0
        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]

        if attention_mask is None:
            # TODO(linjieccc): fix attention mask after uie-m related models updated
            attention_mask = paddle.unsqueeze(
                (input_ids == 0).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
            )
            if past_key_values is not None:
                batch_size = past_key_values[0][0].shape[0]
                past_mask = paddle.zeros([batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
                attention_mask = paddle.concat([past_mask, attention_mask], axis=-1)

        # 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,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )

        self.encoder._use_cache = use_cache  # To be consistent with HF
        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask,
            cache=past_key_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if isinstance(encoder_outputs, type(embedding_output)):
            sequence_output = encoder_outputs
            pooled_output = self.pooler(sequence_output)
            return (sequence_output, pooled_output)

        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output)
        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class ErnieMForSequenceClassification(ErnieMPretrainedModel):
    r"""
    Ernie-M Model with a linear layer on top of the output layer,
    designed for sequence classification/regression tasks like GLUE tasks.

    Args:
        config (:class:`ErnieMConfig`):
            An instance of ErnieMConfig used to construct ErnieMForSequenceClassification.
    """

    def __init__(self, config: ErnieMConfig):
        super(ErnieMForSequenceClassification, self).__init__(config)
        self.ernie_m = ErnieMModel(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, config.num_labels)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        position_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        labels: 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"""
        Args:
            input_ids (Tensor):
                See :class:`ErnieMModel`.
            position_ids (Tensor, optional):
                See :class:`ErnieMModel`.
            attention_mask (Tensor, optional):
                See :class:`ErnieMModel`.
            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).
            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.SequenceClassifierOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.

        Returns:
            An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` 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.SequenceClassifierOutput`.

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ErnieMForSequenceClassification, ErnieMTokenizer

                tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
                model = ErnieMForSequenceClassification.from_pretrained('ernie-m-base')

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

        """
        outputs = self.ernie_m(
            input_ids,
            position_ids=position_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 = self.dropout(outputs[1])
        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 tuple_output(output, loss)

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


class ErnieMForQuestionAnswering(ErnieMPretrainedModel):
    """
    Ernie-M 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:
        config (:class:`ErnieMConfig`):
            An instance of ErnieMConfig used to construct ErnieMForQuestionAnswering.
    """

    def __init__(self, config: ErnieMConfig):
        super(ErnieMForQuestionAnswering, self).__init__(config)
        self.ernie_m = ErnieMModel(config)
        self.classifier = nn.Linear(config.hidden_size, 2)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        position_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        start_positions: Optional[Tensor] = None,
        end_positions: 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"""
        Args:
            input_ids (Tensor):
                See :class:`ErnieMModel`.
            position_ids (Tensor, optional):
                See :class:`ErnieMModel`.
            attention_mask (Tensor, optional):
                See :class:`ErnieMModel`.
            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.
            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.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 ErnieMForQuestionAnswering, ErnieMTokenizer

                tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
                model = ErnieMForQuestionAnswering.from_pretrained('ernie-m-base')

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

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

        logits = self.classifier(outputs[0])
        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 start_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

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return tuple_output(output, total_loss)

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


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

    Args:
        config (:class:`ErnieMConfig`):
            An instance of ErnieMConfig used to construct ErnieMForTokenClassification.
    """

    def __init__(self, config: ErnieMConfig):
        super(ErnieMForTokenClassification, self).__init__(config)
        self.ernie_m = ErnieMModel(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, config.num_labels)

    def forward(
        self,
        input_ids: Optional[Tensor] = None,
        position_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        labels: 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"""
        Args:
            input_ids (Tensor):
                See :class:`ErnieMModel`.
            position_ids (Tensor, optional):
                See :class:`ErnieMModel`.
            attention_mask (Tensor, optional):
                See :class:`ErnieMModel`.
            labels (Tensor of shape `(batch_size, sequence_length)`, optional):
                Labels for computing the token classification loss. Indices should be in `[0, ..., num_labels - 1]`.
            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.TokenClassifierOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.

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

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import ErnieMForTokenClassification, ErnieMTokenizer

                tokenizer = ErnieMTokenizer.from_pretrained('ernie-m-base')
                model = ErnieMForTokenClassification.from_pretrained('ernie-m-base')

                inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                logits = model(**inputs)
        """
        outputs = self.ernie_m(
            input_ids,
            position_ids=position_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 = self.dropout(outputs[0])
        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 tuple_output(output, loss)

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


class ErnieMForMultipleChoice(ErnieMPretrainedModel):
    """
    ERNIE-M with a linear layer on top of the hidden-states output layer,
    designed for multiple choice tasks like RocStories/SWAG tasks.

    Args:
        config (:class:`ErnieMConfig`):
            An instance of ErnieMConfig used to construct ErnieMForMultipleChoice.
    """

    def __init__(self, config: ErnieMConfig):
        super(ErnieMForMultipleChoice, self).__init__(config)
        self.ernie_m = ErnieMModel(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,
        position_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        labels: 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 ErnieMForMultipleChoice forward method, overrides the __call__() special method.
        Args:
            input_ids (Tensor):
                See :class:`ErnieMModel` and shape as [batch_size, num_choice, sequence_length].
            position_ids(Tensor, optional):
                See :class:`ErnieMModel` and shape as [batch_size, num_choice, sequence_length].
            attention_mask (list, optional):
                See :class:`ErnieMModel` and shape as [batch_size, num_choice, sequence_length].
            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)
            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.MultipleChoiceModelOutput` object. If
                `False`, the output will be a tuple of tensors. Defaults to `False`.
        Returns:
            An instance of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` 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.MultipleChoiceModelOutput`.
        """
        # input_ids: [bs, num_choice, seq_l]
        input_ids = input_ids.reshape(shape=(-1, input_ids.shape[-1]))  # flat_input_ids: [bs*num_choice,seq_l]

        if position_ids is not None:
            position_ids = position_ids.reshape(shape=(-1, position_ids.shape[-1]))

        if attention_mask is not None:
            attention_mask = attention_mask.reshape(shape=(-1, attention_mask.shape[-1]))

        outputs = self.ernie_m(
            input_ids,
            position_ids=position_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 = self.dropout(outputs[1])

        logits = self.classifier(pooled_output)  # logits: (bs*num_choice,1)
        reshaped_logits = logits.reshape(shape=(-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 tuple_output(output, loss)

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


class UIEM(ErnieMPretrainedModel):
    """
    Ernie-M Model with two linear layer on top of the hidden-states
    output to compute `start_prob` and `end_prob`,
    designed for Universal Information Extraction.

    Args:
        config (:class:`ErnieMConfig`):
            An instance of ErnieMConfig used to construct UIEM.
    """

    def __init__(self, config: ErnieMConfig):
        super(UIEM, self).__init__(config)
        self.ernie_m = ErnieMModel(config)
        self.linear_start = paddle.nn.Linear(config.hidden_size, 1)
        self.linear_end = paddle.nn.Linear(config.hidden_size, 1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, input_ids, position_ids=None, attention_mask=None):
        r"""
        Args:
            input_ids (Tensor):
                See :class:`ErnieMModel`.
            position_ids (Tensor, optional):
                See :class:`ErnieMModel`.
            attention_mask (Tensor, optional):
                See :class:`ErnieMModel`.

        Example:
            .. code-block::

                import paddle
                from paddlenlp.transformers import UIEM, ErnieMTokenizer

                tokenizer = ErnieMTokenizer.from_pretrained('uie-m-base')
                model = UIEM.from_pretrained('uie-m-base')

                inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
                inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
                start_prob, end_prob = model(**inputs)
        """
        sequence_output, _ = self.ernie_m(
            input_ids=input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
        )
        start_logits = self.linear_start(sequence_output)
        start_logits = paddle.squeeze(start_logits, -1)
        start_prob = self.sigmoid(start_logits)
        end_logits = self.linear_end(sequence_output)
        end_logits = paddle.squeeze(end_logits, -1)
        end_prob = self.sigmoid(end_logits)
        # TODO: add return dict support
        return start_prob, end_prob
