import math
import random
from typing import Optional


def clamp_duration_bias(duration_bias: float, duration_bias_range: Optional[tuple]) -> float:
    """Clamp duration bias to [0,1] or workout-type specific range."""
    if not duration_bias_range:
        return max(0.0, min(1.0, duration_bias))
    low, high = duration_bias_range
    return max(float(low), min(float(high), duration_bias))


def plan_superset_modalities(
    *,
    num_supersets: int,
    duration_bias: float,
    duration_bias_range: Optional[tuple],
    is_strength_workout: bool,
    rng=random,
) -> list[bool]:
    """Plan per-superset modality (True=duration, False=reps)."""
    if num_supersets <= 0:
        return []
    if is_strength_workout:
        return [False] * num_supersets

    if duration_bias_range:
        low, high = duration_bias_range
        target_bias = (float(low) + float(high)) / 2.0
        min_duration_sets = max(0, math.ceil(num_supersets * float(low)))
        max_duration_sets = min(num_supersets, math.floor(num_supersets * float(high)))
    else:
        target_bias = max(0.0, min(1.0, duration_bias))
        min_duration_sets = max(0, math.floor(num_supersets * max(0.0, target_bias - 0.15)))
        max_duration_sets = min(num_supersets, math.ceil(num_supersets * min(1.0, target_bias + 0.15)))

    duration_sets = int(round(num_supersets * target_bias))
    duration_sets = max(min_duration_sets, min(max_duration_sets, duration_sets))

    if num_supersets > 1 and duration_sets == num_supersets and max_duration_sets < num_supersets:
        duration_sets = max_duration_sets
    if num_supersets > 1 and duration_sets == 0 and min_duration_sets > 0:
        duration_sets = min_duration_sets

    modalities = [False] * num_supersets
    if duration_sets > 0:
        positions = list(range(num_supersets))
        rng.shuffle(positions)
        for idx in positions[:duration_sets]:
            modalities[idx] = True
    return modalities