from contextlib import contextmanager

@contextmanager
def temp_seed(seed: int):
    # Сохраняем состояния
    py_state = random.getstate()
    try:
        import numpy as np
        np_state = np.random.get_state()
    except Exception:
        np = None
        np_state = None
    try:
        import torch
        torch_state = torch.random.get_rng_state()
        torch_cuda_state = torch.cuda.get_rng_state_all() if torch.cuda.is_available() else None
    except Exception:
        torch = None
        torch_state = torch_cuda_state = None

    # Устанавливаем семена
    random.seed(seed)
    if np: np.random.seed(seed)
    if torch:
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(seed)
    try:
        yield
    finally:
        # Восстанавливаем состояния
        random.setstate(py_state)
        if np_state is not None: np.random.set_state(np_state)
        if torch_state is not None: torch.random.set_rng_state(torch_state)
        if torch_cuda_state is not None and torch.cuda.is_available():
            torch.cuda.set_rng_state_all(torch_cuda_state)

# Пример использования
if __name__ == "__main__":
    import numpy as np

    with temp_seed(123):
        print("Блок A:", random.random(), np.random.randint(0, 10))
    with temp_seed(123):
        print("Блок B:", random.random(), np.random.randint(0, 10))  # те же значения
    print("Вне блока:", random.random())  # обычный глобальный поток случайности