# coding: utf-8

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"""BERT models."""

__all__ = ['BERTClassifier', 'BERTRegression']

from mxnet.gluon import HybridBlock
from mxnet.gluon import nn

class BERTRegression(HybridBlock):
    """Model for sentence (pair) regression task with BERT.

    The model feeds token ids and token type ids into BERT to get the
    pooled BERT sequence representation, then apply a Dense layer for
    regression.

    Parameters
    ----------
    bert: BERTModel
        Bidirectional encoder with transformer.
    dropout : float or None, default 0.0.
        Dropout probability for the bert output.
    prefix : str or None
        See document of `mx.gluon.Block`.
    params : ParameterDict or None
        See document of `mx.gluon.Block`.
    """

    def __init__(self, bert, dropout=0.0, prefix=None, params=None):
        super(BERTRegression, self).__init__(prefix=prefix, params=params)
        self.bert = bert
        with self.name_scope():
            self.regression = nn.HybridSequential(prefix=prefix)
            if dropout:
                self.regression.add(nn.Dropout(rate=dropout))
            self.regression.add(nn.Dense(1))

    def hybrid_forward(self, F, inputs, token_types, valid_length=None):
        # pylint: disable=arguments-differ
        """Generate the unnormalized score for the given the input sequences.

        Parameters
        ----------
        inputs : NDArray, shape (batch_size, seq_length)
            Input words for the sequences.
        token_types : NDArray, shape (batch_size, seq_length)
            Token types for the sequences, used to indicate whether the word belongs to the
            first sentence or the second one.
        valid_length : NDArray or None, shape (batch_size)
            Valid length of the sequence. This is used to mask the padded tokens.

        Returns
        -------
        outputs : NDArray
            Shape (batch_size, num_classes)
        """
        _, pooler_out = self.bert(inputs, token_types, valid_length)
        return self.regression(pooler_out)


class BERTClassifier(HybridBlock):
    """Model for sentence (pair) classification task with BERT.

    The model feeds token ids and token type ids into BERT to get the
    pooled BERT sequence representation, then apply a Dense layer for
    classification.

    Parameters
    ----------
    bert: BERTModel
        Bidirectional encoder with transformer.
    num_classes : int, default is 2
        The number of target classes.
    dropout : float or None, default 0.0.
        Dropout probability for the bert output.
    prefix : str or None
        See document of `mx.gluon.Block`.
    params : ParameterDict or None
        See document of `mx.gluon.Block`.
    """

    def __init__(self,
                 bert,
                 num_classes=2,
                 dropout=0.0,
                 prefix=None,
                 params=None):
        super(BERTClassifier, self).__init__(prefix=prefix, params=params)
        self.bert = bert
        with self.name_scope():
            self.classifier = nn.HybridSequential(prefix=prefix)
            if dropout:
                self.classifier.add(nn.Dropout(rate=dropout))
            self.classifier.add(nn.Dense(units=num_classes))

    def hybrid_forward(self, F, inputs, token_types, valid_length=None):
        # pylint: disable=arguments-differ
        """Generate the unnormalized score for the given the input sequences.

        Parameters
        ----------
        inputs : NDArray, shape (batch_size, seq_length)
            Input words for the sequences.
        token_types : NDArray, shape (batch_size, seq_length)
            Token types for the sequences, used to indicate whether the word belongs to the
            first sentence or the second one.
        valid_length : NDArray or None, shape (batch_size)
            Valid length of the sequence. This is used to mask the padded tokens.

        Returns
        -------
        outputs : NDArray
            Shape (batch_size, num_classes)
        """
        _, pooler_out = self.bert(inputs, token_types, valid_length)
        return self.classifier(pooler_out)