"""Native-answer scoring for the public panels; Python standard library only.

score(record, answers) -> {prediction, correct}; invalid answers raise ValueError.
answers maps question IDs to {'choice': label}, {'noul': probability}, or {'score': number}.
Only task.question_ids are read. No model calls are made.
"""
import math


def number(x):
    if isinstance(x, bool) or not isinstance(x, (int, float)) or not math.isfinite(x):
        raise ValueError('Expected a finite numeric model output')
    return float(x)


def score(record, answers):
    task = record['task']
    qids = task['question_ids']
    mode = task['mode']
    try:
        if mode == 'choice':
            if len(qids) != 1:
                raise ValueError('Choice expects one scoring target')
            pred = answers[qids[0]]['choice']
            options = record['clean']['questions'][qids[0]]['criteria']
            if pred not in options:
                raise ValueError('Choice label is not in the options')
        elif mode == 'score':
            if len(qids) != 1:
                raise ValueError('Score expects one scoring target')
            pred = number(answers[qids[0]]['score'])
            return {'prediction': pred, 'correct': abs(pred - record['gt']) <= 0.5}
        elif mode == 'noul':
            probs = [number(answers[q]['noul']) for q in qids]
            if any(p < 0 or p > 1 for p in probs):
                raise ValueError('Noul probability must be in [0,1]')
            labels = task['labels']
            if 'positive_label' in task:
                if len(probs) != 1 or len(labels) != 2:
                    raise ValueError('Binary Noul expects one output and two labels')
                positive = task['positive_label']
                negative = next(v for v in labels if v != positive)
                pred = positive if probs[0] >= 0.5 else negative
            else:
                if len(probs) != len(labels):
                    raise ValueError('Multi-Noul question and label counts differ')
                total = sum(probs)
                norm = [p / total for p in probs] if total else [1 / len(probs)] * len(probs)
                pred = labels[max(range(len(labels)), key=lambda i: norm[i])]
        else:
            raise ValueError('Unknown mode')
    except (KeyError, TypeError, StopIteration) as error:
        raise ValueError('Missing or malformed target model answer') from error
    return {'prediction': pred, 'correct': pred == record['gt']}