"""Legacy single-constraint patching helpers kept for backward compatibility."""

from __future__ import annotations

from typing import List

import numpy as np


def patch_global_threshold(metric: np.ndarray, threshold: float) -> List[List[int]]:
    """
    Patch when metric exceeds threshold.

    Args:
        metric: Entropy or perplexity values
        threshold: Threshold value

    Returns:
        List of patches (each patch is list of token indices)
    """
    patches = []
    current_patch = []

    for i, value in enumerate(metric):
        current_patch.append(i)
        if value > threshold:
            patches.append(current_patch)
            current_patch = []

    if current_patch:
        patches.append(current_patch)

    return patches


def patch_additive_monotonic(metric: np.ndarray, threshold: float) -> List[List[int]]:
    """Patch when metric increase exceeds threshold."""
    patches = []
    current_patch = [0]

    for i in range(1, len(metric)):
        delta = metric[i] - metric[i - 1]

        if delta > threshold:
            patches.append(current_patch)
            current_patch = [i]
        else:
            current_patch.append(i)

    if current_patch:
        patches.append(current_patch)

    return patches


def patch_multiplicative_monotonic(metric: np.ndarray, threshold: float) -> List[List[int]]:
    """Patch when metric ratio exceeds threshold."""
    patches = []
    current_patch = [0]

    for i in range(1, len(metric)):
        if metric[i - 1] > 1e-6:
            ratio = metric[i] / metric[i - 1]
        else:
            ratio = 1.0

        if ratio > threshold:
            patches.append(current_patch)
            current_patch = [i]
        else:
            current_patch.append(i)

    if current_patch:
        patches.append(current_patch)

    return patches


def patch_additive_min(metric: np.ndarray, threshold: float) -> List[List[int]]:
    """Patch when metric increase from minimum in current patch exceeds threshold."""
    patches = []
    current_patch = [0]
    patch_min = metric[0]

    for i in range(1, len(metric)):
        delta = metric[i] - patch_min

        if delta > threshold:
            patches.append(current_patch)
            current_patch = [i]
            patch_min = metric[i]
        else:
            current_patch.append(i)
            patch_min = min(patch_min, metric[i])

    if current_patch:
        patches.append(current_patch)

    return patches


def patch_multiplicative_min(metric: np.ndarray, threshold: float) -> List[List[int]]:
    """Patch when metric ratio to minimum in current patch exceeds threshold."""
    patches = []
    current_patch = [0]
    patch_min = metric[0]

    for i in range(1, len(metric)):
        if patch_min > 1e-6:
            ratio = metric[i] / patch_min
        else:
            ratio = 1.0

        if ratio > threshold:
            patches.append(current_patch)
            current_patch = [i]
            patch_min = metric[i]
        else:
            current_patch.append(i)
            patch_min = min(patch_min, metric[i])

    if current_patch:
        patches.append(current_patch)

    return patches