"""Unit tests for SamplingParams seq_seed handling."""

import pytest
import torch  # noqa: F401  Load torch's shared libraries before deepinfer.

from deepinfer.sampling_params import SamplingParams

pytestmark = [pytest.mark.ci, pytest.mark.ci_npu]


def test_seq_seed_none_is_random_uint64() -> None:
    params = SamplingParams()
    assert 0 <= params.seq_seed < 2**64


def test_seq_seed_valid_is_preserved() -> None:
    assert SamplingParams(seq_seed=42).seq_seed == 42
    assert SamplingParams(seq_seed=2**64 - 1).seq_seed == 2**64 - 1


def test_seq_seed_wraps_to_uint64() -> None:
    assert SamplingParams(seq_seed=2**64 + 5).seq_seed == 5


def test_seq_seed_negative_raises() -> None:
    with pytest.raises(ValueError, match="seq_seed"):
        SamplingParams(seq_seed=-1)
    with pytest.raises(ValueError, match="seq_seed"):
        SamplingParams(seq_seed=-(2**64) + 5)


def test_seq_seed_wrong_type_raises() -> None:
    with pytest.raises(TypeError, match="seq_seed"):
        SamplingParams(seq_seed="42")  # type: ignore[arg-type]
    with pytest.raises(TypeError, match="seq_seed"):
        SamplingParams(seq_seed=1.5)  # type: ignore[arg-type]