from __future__ import annotations

from typing import Dict, List

import numpy as np

from sudoku4x4_11empty.shared_multi_output_policy import (
    compute_set_precision_recall,
    parse_values_json,
    stage_i_consistent_values,
)


def triangular_number(n: int) -> float:
    nn = max(0, int(n))
    return float(nn * (nn + 1) // 2)


def score_prediction_text(
    *,
    text: str,
    grid: np.ndarray,
    solved: np.ndarray,
    target_cell: tuple[int, int],
    stage_i: int,
    reward_good_value: float,
    penalty_bad_value: float,
    penalty_malformed: float,
    penalty_empty: float,
    penalty_singleton: float,
) -> Dict[str, float | List[int] | str]:
    parsed = parse_values_json(text)
    target_values = stage_i_consistent_values(grid, target_cell=target_cell, stage_i=stage_i)
    solved_value = int(np.asarray(solved, dtype=int).reshape(4, 4)[int(target_cell[0]), int(target_cell[1])])
    singleton_penalty = 0.0 if int(stage_i) >= 2 else float(penalty_singleton)

    if not parsed.parse_ok:
        return {
            'reward': -float(penalty_malformed),
            'parse_ok': 0.0,
            'strict_canonical': 0.0,
            'num_predicted_values': 0.0,
            'num_i_consistent_values': 0.0,
            'num_non_i_consistent_values': 0.0,
            'includes_ground_truth': 0.0,
            'value_precision': 0.0,
            'value_recall': 0.0,
            'exact_set_match': 0.0,
            'predicted_values': [],
            'target_values': [int(v) for v in target_values],
            'format_error': 'parse_failed',
        }

    predicted_values = [int(v) for v in parsed.values]
    target_set = set(int(v) for v in target_values)
    num_good = sum(1 for v in predicted_values if v in target_set)
    num_bad = sum(1 for v in predicted_values if v not in target_set)

    reward = triangular_number(num_good) * float(reward_good_value) - float(num_bad) * float(penalty_bad_value)
    if not predicted_values:
        reward -= float(penalty_empty)
    if len(predicted_values) == 1 and len(target_values) > 1:
        reward -= singleton_penalty

    precision, recall = compute_set_precision_recall(predicted_values, target_values)
    return {
        'reward': float(reward),
        'parse_ok': 1.0,
        'strict_canonical': 1.0 if parsed.strict_canonical else 0.0,
        'num_predicted_values': float(len(predicted_values)),
        'num_i_consistent_values': float(num_good),
        'num_non_i_consistent_values': float(num_bad),
        'includes_ground_truth': 1.0 if solved_value in predicted_values else 0.0,
        'value_precision': float(precision),
        'value_recall': float(recall),
        'exact_set_match': 1.0 if set(predicted_values) == target_set else 0.0,
        'predicted_values': predicted_values,
        'target_values': [int(v) for v in target_values],
        'format_error': '',
    }