from typing import List
from typing import Any


def _flatten_non_tensor_optim_state(
    state_name: str,
    non_tensors: List[Any],
    unflat_param_names: List[str],
) -> Any:
    """
    Flattens the non-tensor optimizer state given by the values ``non_tensors``
    for the state ``state_name`` for a single flattened parameter corresponding
    to the unflattened parameter names ``unflat_param_names`` by enforcing that
    all values are the same and using that common value.

    See the note in :func:`_flatten_zero_dim_tensor_optim_state`.

    Args:
        state_name (str): Optimizer state name.
        non_tensors (List[Any]): Non-tensor optimizer state for the unflattened
            parameters corresponding to the single flattened parameter.
        unflat_param_names (List[str]): A :class:`list` of unflattened
            parameter names corresponding to the single flattened parameter.

    Returns:
        Any: A non-tensor giving the value of the state ``state_name`` for all
        unflattened parameters corresponding to the names
        ``unflat_param_names``.
    """
    non_none_non_tensors = [nt for nt in non_tensors if nt is not None]
    # Enforce that all have the same value (same type already checked)
    non_tensor_set = set(non_tensors)
    if len(non_none_non_tensors) != len(non_tensors) or \
            len(non_tensor_set) != 1:
        raise ValueError(
            "All unflattened parameters comprising a single flattened "
            "parameter must have scalar state with the same value and dtype "
            f"but got values {non_tensor_set} for state {state_name} and  "
            f"unflattened parameter names {unflat_param_names}"
        )
    non_tensor = next(iter(non_tensor_set))
    return non_tensor