from __future__ import annotations

import numpy as np
import torch


def irregular_batch(
    batch: int,
    length: int,
    seed: int,
    *,
    large_gaps: bool,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    rng = np.random.default_rng(seed)
    low, high = ((0.12, 0.40) if large_gaps else (0.02, 0.12))
    delta_time = rng.uniform(low, high, size=(batch, length))
    time = np.cumsum(delta_time, axis=1)
    amplitudes = rng.uniform(0.5, 1.5, size=(batch, 3, 1))
    frequencies = rng.uniform(
        np.array([0.7, 1.7, 3.5])[None, :, None],
        np.array([1.1, 2.3, 5.0])[None, :, None],
        size=(batch, 3, 1),
    )
    phases = rng.uniform(0, 2 * np.pi, size=(batch, 3, 1))
    values = (
        amplitudes
        * np.sin(frequencies * time[:, None, :] + phases)
        * np.array([1.0, 0.5, 0.2])[None, :, None]
    ).sum(axis=1)
    values += rng.normal(0, 0.01, values.shape)
    current = np.concatenate([np.zeros((batch, 1)), values[:, :-1]], axis=1)
    inputs = np.stack([current, delta_time], axis=2).astype(np.float32)
    return (
        torch.from_numpy(inputs),
        torch.from_numpy(values.astype(np.float32)).unsqueeze(2),
        torch.from_numpy(time.astype(np.float32)),
    )