import numpy as np
import torch
from . import metric


class ConfusionMatrix(metric.Metric):
    """Constructs a confusion matrix for a multi-class classification problems.
    Does not support multi-label, multi-class problems.
    Keyword arguments:
    - num_classes (int): number of classes in the classification problem.
    - normalized (boolean, optional): Determines whether or not the confusion
    matrix is normalized or not. Default: False.
    Modified from: https://github.com/pytorch/tnt/blob/master/torchnet/meter/confusionmeter.py
    """

    def __init__(self, num_classes, normalized=False):
        super().__init__()

        self.conf = np.ndarray((num_classes, num_classes), dtype=np.int32)
        self.normalized = normalized
        self.num_classes = num_classes
        self.reset()

    def reset(self):
        self.conf.fill(0)

    def add(self, predicted, target):
        """Computes the confusion matrix
        The shape of the confusion matrix is K x K, where K is the number
        of classes.
        Keyword arguments:
        - predicted (Tensor or numpy.ndarray): Can be an N x K tensor/array of
        predicted scores obtained from the model for N examples and K classes,
        or an N-tensor/array of integer values between 0 and K-1.
        - target (Tensor or numpy.ndarray): Can be an N x K tensor/array of
        ground-truth classes for N examples and K classes, or an N-tensor/array
        of integer values between 0 and K-1.
        """
        # If target and/or predicted are tensors, convert them to numpy arrays
        if torch.is_tensor(predicted):
            predicted = predicted.cpu().numpy()
        if torch.is_tensor(target):
            target = target.cpu().numpy()

        assert predicted.shape[0] == target.shape[0], \
            'number of targets and predicted outputs do not match'

        if np.ndim(predicted) != 1:
            assert predicted.shape[1] == self.num_classes, \
                'number of predictions does not match size of confusion matrix'
            predicted = np.argmax(predicted, 1)
        else:
            assert (predicted.max() < self.num_classes) and (predicted.min() >= 0), \
                'predicted values are not between 0 and k-1'

        if np.ndim(target) != 1:
            assert target.shape[1] == self.num_classes, \
                'Onehot target does not match size of confusion matrix'
            assert (target >= 0).all() and (target <= 1).all(), \
                'in one-hot encoding, target values should be 0 or 1'
            assert (target.sum(1) == 1).all(), \
                'multi-label setting is not supported'
            target = np.argmax(target, 1)
        else:
            assert (target.max() < self.num_classes) and (target.min() >= 0), \
                'target values are not between 0 and k-1'

        # hack for bincounting 2 arrays together
        x = predicted + self.num_classes * target
        bincount_2d = np.bincount(
            x.astype(np.int32), minlength=self.num_classes**2)
        assert bincount_2d.size == self.num_classes**2
        conf = bincount_2d.reshape((self.num_classes, self.num_classes))

        self.conf += conf

    def value(self):
        """
        Returns:
            Confustion matrix of K rows and K columns, where rows corresponds
            to ground-truth targets and columns corresponds to predicted
            targets.
        """
        if self.normalized:
            conf = self.conf.astype(np.float32)
            return conf / conf.sum(1).clip(min=1e-12)[:, None]
        else:
            return self.conf