for index in range(10):
    
    query_index, query_instance = learner.query(manager.unlabeld)
    ix = query_index[0]
    print(manager.remaining_sources[ix])
    label = (ix,int(input()))
    manager.add_labels(label)
             

import typing
from typing import List,Tuple,Any,Union
import numpy as np
Label = Tuple[int, Any]
LabelList = List[Label]
Sources =List[Any]
class ALManager():
    def __init__(self, features:np.ndarray, labels:LabelList=None, sources:Sources=None):
        '''

        :param features: An array of the features that will be used for AL.
        :param labels: Any prexesiting labels. Each label is a tuple(idx,label)
        :param source: A list of the original data
        '''
        if labels is None:
            labels = []
        self.features = features
        if labels is None:
            self.labels = []
        self.labels = labels
        self.labeled_mask = np.zeros(self.features.shape[0],dtype=bool)
        self.unlabeled_mask = np.ones(self.features.shape[0],dtype=bool)
        self._update_masks(self.labels)
        self.sources = np.array(sources if sources else [])
    def _update_masks(self,labels:Union[LabelList,Label]):

        for label in labels:
            self.labeled_mask[label[0]] = True
            self.unlabeled_mask[label[0]] = False
    def _offset_new_labes(self,labels:LabelList):
        if len(self.labels)==0:
            # Nothing to correct in this case
            return labels
        correctLabels: LabelList = []
        labeledIndices =self.labeled_mask.nonzero()[0]

        for label in labels:
            #The argmax trick on a bool condition returns the index of the first labeled item which came before the new one
            newLabel :Label = (np.argmax(labeledIndices<=label[0])+1+label[0],label[1])
            correctLabels.append(newLabel)
        return correctLabels
    def add_labels(self, labels:LabelList):
        if isinstance(labels,tuple): # if this is a single example
            labels :LabelList = [labels]
        elif isinstance(labels,list):
            pass
        else:
            raise Exception("Malformed input. Please add either a tuple (ix,label) or a list [(ix,label),..]")
        labels = self._offset_new_labes(labels)
        self._update_masks(labels)
        self.labels +=(labels)

    @property
    def unlabeld(self):
        '''

        :return: A view (in the numpy sense) of the features restricted to those that aren't labeled
        '''
        return self.features[self.unlabeled_mask]

    @property
    def labeled(self):
        '''
                :return: A view (in the numpy sense) of the features restricted to those that aren't labeled
        '''
        return self.features[self.labeled_mask]

    @property
    def remaining_sources(self):
        '''

        :return: The original inputs, masked so that only unlabeled ones are returned
        '''
        return self.sources[self.unlabeled_mask]