from typing import Dict

import torch
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import Seq2SeqEncoder, Seq2VecEncoder, TextFieldEmbedder
from allennlp.nn import InitializerApplicator
from allennlp.nn.util import get_text_field_mask
from allennlp.training.metrics import CategoricalAccuracy


@Model.register("basic_classifier")
class BasicClassifier(Model):
    """
    This ``Model`` implements a basic text classifier. After embedding the text into
    a text field, we will optionally encode the embeddings with a Seq2SeqEncoder and/or
    a Seq2VecEncoder and then pass the embeddings to a linear classification layer,
    which projects into the label space. If neither Seq2SeqEncoder nor Seq2VecEncoder is
    provided, we will pass the embedded text directly to the linear classification layer.

    Parameters
    ----------
    vocab : ``Vocabulary``
    text_field_embedder : ``TextFieldEmbedder``
        Used to embed the input text into a ``TextField``
    seq2seq_encoder : ``Seq2SeqEncoder``, optional (default=``None``)
        Optional Seq2Seq encoder layer for the input text.
    seq2vec_encoder : ``Seq2VecEncoder``
        Required Seq2Vec encoder layer. If `seq2seq_encoder` is provided, this encoder
        will pool its output. Otherwise, this encoder will operate directly on the output
        of the `text_field_embedder`.
    dropout : ``float``, optional (default = ``None``)
        Dropout percentage to use.
    num_labels: ``int``, optional (default = ``None``)
        Number of labels to project to in classification layer. By default, the classification layer will
        project to the size of the vocabulary namespace corresponding to labels.
    label_namespace: ``str``, optional (default = "labels")
        Vocabulary namespace corresponding to labels. By default, we use the "labels" namespace.
    initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
        If provided, will be used to initialize the model parameters.
    """
    def __init__(self,
                 vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 seq2vec_encoder: Seq2VecEncoder,
                 seq2seq_encoder: Seq2SeqEncoder = None,
                 dropout: float = None,
                 num_labels: int = None,
                 label_namespace: str = "labels",
                 initializer: InitializerApplicator = InitializerApplicator()) -> None:

        super().__init__(vocab)
        self._text_field_embedder = text_field_embedder

        if seq2seq_encoder:
            self._seq2seq_encoder = seq2seq_encoder
        else:
            self._seq2seq_encoder = None

        self._seq2vec_encoder = seq2vec_encoder
        self._classifier_input_dim = self._seq2vec_encoder.get_output_dim()

        if dropout:
            self._dropout = torch.nn.Dropout(dropout)
        else:
            self._dropout = None

        if num_labels:
            self._num_labels = num_labels
        else:
            self._num_labels = vocab.get_vocab_size(namespace=label_namespace)
        self._classification_layer = torch.nn.Linear(self._classifier_input_dim, self._num_labels)
        self._accuracy = CategoricalAccuracy()
        self._loss = torch.nn.CrossEntropyLoss()
        initializer(self)

    def forward(self,  # type: ignore
                tokens: Dict[str, torch.LongTensor],
                label: torch.IntTensor = None) -> Dict[str, torch.Tensor]:
        # pylint: disable=arguments-differ
        """
        Parameters
        ----------
        tokens : Dict[str, torch.LongTensor]
            From a ``TextField``
        label : torch.IntTensor, optional (default = None)
            From a ``LabelField``

        Returns
        -------
        An output dictionary consisting of:

        logits : torch.FloatTensor
            A tensor of shape ``(batch_size, num_labels)`` representing
            unnormalized log probabilities of the label.
        probs : torch.FloatTensor
            A tensor of shape ``(batch_size, num_labels)`` representing
            probabilities of the label.
        loss : torch.FloatTensor, optional
            A scalar loss to be optimised.
        """
        embedded_text = self._text_field_embedder(tokens)
        mask = get_text_field_mask(tokens).float()

        if self._seq2seq_encoder:
            embedded_text = self._seq2seq_encoder(embedded_text, mask=mask)

        embedded_text = self._seq2vec_encoder(embedded_text, mask=mask)

        if self._dropout:
            embedded_text = self._dropout(embedded_text)

        logits = self._classification_layer(embedded_text)
        probs = torch.nn.functional.softmax(logits, dim=-1)

        output_dict = {"logits": logits, "probs": probs}

        if label is not None:
            loss = self._loss(logits, label.long().view(-1))
            output_dict["loss"] = loss
            self._accuracy(logits, label)

        return output_dict

    def get_metrics(self, reset: bool = False) -> Dict[str, float]:
        metrics = {'accuracy': self._accuracy.get_metric(reset)}
        return metrics

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