from typing import Optional

from overrides import overrides
import torch

from allennlp.common.checks import ConfigurationError
from allennlp.training.metrics.metric import Metric


@Metric.register("sequence_accuracy")
class SequenceAccuracy(Metric):
    """
    Sequence Top-K accuracy. Assumes integer labels, with
    each item to be classified having a single correct class.
    """
    def __init__(self) -> None:
        self.correct_count = 0.0
        self.total_count = 0.0

    def __call__(self,
                 predictions: torch.Tensor,
                 gold_labels: torch.Tensor,
                 mask: Optional[torch.Tensor] = None):
        """
        Parameters
        ----------
        predictions : ``torch.Tensor``, required.
            A tensor of predictions of shape (batch_size, k, sequence_length).
        gold_labels : ``torch.Tensor``, required.
            A tensor of integer class label of shape (batch_size, sequence_length).
        mask: ``torch.Tensor``, optional (default = None).
            A masking tensor the same size as ``gold_labels``.
        """
        predictions, gold_labels, mask = self.unwrap_to_tensors(predictions, gold_labels, mask)

        # Some sanity checks.
        if gold_labels.dim() != predictions.dim() - 1:
            raise ConfigurationError("gold_labels must have dimension == predictions.dim() - 1 but "
                                     "found tensor of shape: {}".format(gold_labels.size()))
        if mask is not None and mask.size() != gold_labels.size():
            raise ConfigurationError("mask must have the same size as predictions but "
                                     "found tensor of shape: {}".format(mask.size()))

        k = predictions.size()[1]
        expanded_size = list(gold_labels.size())
        expanded_size.insert(1, k)
        expanded_gold = gold_labels.unsqueeze(1).expand(expanded_size)

        if mask is not None:
            expanded_mask = mask.unsqueeze(1).expand(expanded_size)
            masked_gold = expanded_mask * expanded_gold
            masked_predictions = expanded_mask * predictions
        else:
            masked_gold = expanded_gold
            masked_predictions = predictions

        eqs = masked_gold.eq(masked_predictions)
        matches_per_question = eqs.min(dim=2)[0]
        some_match = matches_per_question.max(dim=1)[0]
        correct = some_match.sum().item()

        self.total_count += predictions.size()[0]
        self.correct_count += correct

    def get_metric(self, reset: bool = False):
        """
        Returns
        -------
        The accumulated accuracy.
        """
        if self.total_count > 0:
            accuracy = float(self.correct_count) / float(self.total_count)
        else:
            accuracy = 0

        if reset:
            self.reset()
        return accuracy

    @overrides
    def reset(self):
        self.correct_count = 0.0
        self.total_count = 0.0
