from typing import Dict, Optional, List, Any

import numpy
import torch
import torch.nn.functional as F
from allennlp.common.checks import check_dimensions_match, ConfigurationError
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import Seq2SeqEncoder, TimeDistributed, TextFieldEmbedder
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from allennlp.nn.util import get_text_field_mask, sequence_cross_entropy_with_logits
from allennlp.training.metrics import CategoricalAccuracy, SpanBasedF1Measure
from overrides import overrides
from torch.nn.modules.linear import Linear


@Model.register("my_simple_tagger")
class SimpleTagger(Model):
    """
    This ``SimpleTagger`` simply encodes a sequence of text with a stacked ``Seq2SeqEncoder``, then
    predicts a tag for each token in the sequence.

    Parameters
    ----------
    vocab : ``Vocabulary``, required
        A Vocabulary, required in order to compute sizes for input/output projections.
    text_field_embedder : ``TextFieldEmbedder``, required
        Used to embed the ``tokens`` ``TextField`` we get as input to the model.
    encoder : ``Seq2SeqEncoder``
        The encoder (with its own internal stacking) that we will use in between embedding tokens
        and predicting output tags.
    calculate_span_f1 : ``bool``, optional (default=``None``)
        Calculate span-level F1 metrics during training. If this is ``True``, then
        ``label_encoding`` is required. If ``None`` and
        label_encoding is specified, this is set to ``True``.
        If ``None`` and label_encoding is not specified, it defaults
        to ``False``.
    label_encoding : ``str``, optional (default=``None``)
        Label encoding to use when calculating span f1.
        Valid options are "BIO", "BIOUL", "IOB1", "BMES".
        Required if ``calculate_span_f1`` is true.
    label_namespace : ``str``, optional (default=``labels``)
        This is needed to compute the SpanBasedF1Measure metric, if desired.
        Unless you did something unusual, the default value should be what you want.
    verbose_metrics : ``bool``, optional (default = False)
        If true, metrics will be returned per label class in addition
        to the overall statistics.
    initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
        Used to initialize the model parameters.
    regularizer : ``RegularizerApplicator``, optional (default=``None``)
        If provided, will be used to calculate the regularization penalty during training.
    """

    def __init__(self, vocab: Vocabulary,
                 text_field_embedder: TextFieldEmbedder,
                 encoder: Seq2SeqEncoder,
                 calculate_span_f1: bool = None,
                 label_encoding: Optional[str] = None,
                 label_namespace: str = "labels",
                 verbose_metrics: bool = False,
                 initializer: InitializerApplicator = InitializerApplicator(),
                 regularizer: Optional[RegularizerApplicator] = None) -> None:
        super(SimpleTagger, self).__init__(vocab, regularizer)

        self.label_namespace = label_namespace
        self.text_field_embedder = text_field_embedder
        self.num_classes = self.vocab.get_vocab_size(label_namespace)
        self.encoder = encoder
        self._verbose_metrics = verbose_metrics
        self.tag_projection_layer = TimeDistributed(Linear(self.encoder.get_output_dim(),
                                                           self.num_classes))

        check_dimensions_match(text_field_embedder.get_output_dim(), encoder.get_input_dim(),
                               "text field embedding dim", "encoder input dim")

        # We keep calculate_span_f1 as a constructor argument for API consistency with
        # the CrfTagger, even it is redundant in this class
        # (label_encoding serves the same purpose).
        if calculate_span_f1 and not label_encoding:
            raise ConfigurationError("calculate_span_f1 is True, but "
                                     "no label_encoding was specified.")
        self.metrics = {
            "accuracy": CategoricalAccuracy(),
            "accuracy3": CategoricalAccuracy(top_k=3)
        }

        if calculate_span_f1 or label_encoding:
            self._f1_metric = SpanBasedF1Measure(vocab,
                                                 tag_namespace=label_namespace,
                                                 label_encoding=label_encoding)
        else:
            self._f1_metric = None

        initializer(self)

    @overrides
    def forward(self,  # type: ignore
                tokens: Dict[str, torch.LongTensor],
                tags: torch.LongTensor = None,
                metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
        # pylint: disable=arguments-differ
        """
        Parameters
        ----------
        tokens : Dict[str, torch.LongTensor], required
            The output of ``TextField.as_array()``, which should typically be passed directly to a
            ``TextFieldEmbedder``. This output is a dictionary mapping keys to ``TokenIndexer``
            tensors.  At its most basic, using a ``SingleIdTokenIndexer`` this is: ``{"tokens":
            Tensor(batch_size, num_tokens)}``. This dictionary will have the same keys as were used
            for the ``TokenIndexers`` when you created the ``TextField`` representing your
            sequence.  The dictionary is designed to be passed directly to a ``TextFieldEmbedder``,
            which knows how to combine different word representations into a single vector per
            token in your input.
        tags : torch.LongTensor, optional (default = None)
            A torch tensor representing the sequence of integer gold class labels of shape
            ``(batch_size, num_tokens)``.
        metadata : ``List[Dict[str, Any]]``, optional, (default = None)
            metadata containing the original words in the sentence to be tagged under a 'words' key.

        Returns
        -------
        An output dictionary consisting of:
        logits : torch.FloatTensor
            A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
            unnormalised log probabilities of the tag classes.
        class_probabilities : torch.FloatTensor
            A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
            a distribution of the tag classes per word.
        loss : torch.FloatTensor, optional
            A scalar loss to be optimised.

        """
        embedded_text_input = self.text_field_embedder(tokens)
        batch_size, sequence_length, _ = embedded_text_input.size()
        mask = get_text_field_mask(tokens)
        encoded_text = self.encoder(embedded_text_input, mask)

        logits = self.tag_projection_layer(encoded_text)
        reshaped_log_probs = logits.view(-1, self.num_classes)
        class_probabilities = F.softmax(reshaped_log_probs, dim=-1).view([batch_size,
                                                                          sequence_length,
                                                                          self.num_classes])

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

        if tags is not None:
            loss = sequence_cross_entropy_with_logits(logits, tags, mask)
            for metric in self.metrics.values():
                metric(logits, tags, mask.float())
            if self._f1_metric is not None:
                self._f1_metric(logits, tags, mask.float())
            output_dict["loss"] = loss

        if metadata is not None:
            output_dict["words"] = [x["words"] for x in metadata]
        return output_dict

    @overrides
    def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
        """
        Does a simple position-wise argmax over each token, converts indices to string labels, and
        adds a ``"tags"`` key to the dictionary with the result.
        """
        all_predictions = output_dict['class_probabilities']
        all_predictions = all_predictions.cpu().data.numpy()
        if all_predictions.ndim == 3:
            predictions_list = [all_predictions[i] for i in range(all_predictions.shape[0])]
        else:
            predictions_list = [all_predictions]
        all_tags = []
        for predictions in predictions_list:
            argmax_indices = numpy.argmax(predictions, axis=-1)
            tags = [self.vocab.get_token_from_index(x, namespace=self.label_namespace)
                    for x in argmax_indices]
            all_tags.append(tags)
        output_dict['tags'] = all_tags
        return output_dict

    @overrides
    def get_metrics(self, reset: bool = False) -> Dict[str, float]:
        metrics_to_return = {metric_name: metric.get_metric(reset) for
                             metric_name, metric in self.metrics.items()}

        if self._f1_metric is not None:
            f1_dict = self._f1_metric.get_metric(reset=reset)
            if self._verbose_metrics:
                metrics_to_return.update(f1_dict)
            else:
                metrics_to_return.update({
                    x: y for x, y in f1_dict.items() if
                    "overall" in x})
        return metrics_to_return
