torch/fx/experimental/unification/multipledispatch/conflict.py

from .utils import _toposort, groupby
from .variadic import isvariadic

__all__ = ["AmbiguityWarning", "supercedes", "consistent", "ambiguous", "ambiguities", "super_signature",
           "edge", "ordering"]

class AmbiguityWarning(Warning):
    pass


def supercedes(a, b):
    """ A is consistent and strictly more specific than B """
    if len(a) < len(b):
        # only case is if a is empty and b is variadic
        return not a and len(b) == 1 and isvariadic(b[-1])
    elif len(a) == len(b):
        return all(map(issubclass, a, b))
    else:
        # len(a) > len(b)
        p1 = 0
        p2 = 0
        while p1 < len(a) and p2 < len(b):
            cur_a = a[p1]
            cur_b = b[p2]
            if not (isvariadic(cur_a) or isvariadic(cur_b)):
                if not issubclass(cur_a, cur_b):
                    return False
                p1 += 1
                p2 += 1
            elif isvariadic(cur_a):
                assert p1 == len(a) - 1
                return p2 == len(b) - 1 and issubclass(cur_a, cur_b)
            elif isvariadic(cur_b):
                assert p2 == len(b) - 1
                if not issubclass(cur_a, cur_b):
                    return False
                p1 += 1
        return p2 == len(b) - 1 and p1 == len(a)


def consistent(a, b):
    """ It is possible for an argument list to satisfy both A and B """

    # Need to check for empty args
    if not a:
        return not b or isvariadic(b[0])
    if not b:
        return not a or isvariadic(a[0])

    # Non-empty args check for mutual subclasses
    if len(a) == len(b):
        return all(issubclass(aa, bb) or issubclass(bb, aa)
                   for aa, bb in zip(a, b))
    else:
        p1 = 0
        p2 = 0
        while p1 < len(a) and p2 < len(b):
            cur_a = a[p1]
            cur_b = b[p2]
            if not issubclass(cur_b, cur_a) and not issubclass(cur_a, cur_b):
                return False
            if not (isvariadic(cur_a) or isvariadic(cur_b)):
                p1 += 1
                p2 += 1
            elif isvariadic(cur_a):
                p2 += 1
            elif isvariadic(cur_b):
                p1 += 1
        # We only need to check for variadic ends
        # Variadic types are guaranteed to be the last element
        return (isvariadic(cur_a) and p2 == len(b) or
                isvariadic(cur_b) and p1 == len(a))


def ambiguous(a, b):
    """ A is consistent with B but neither is strictly more specific """
    return consistent(a, b) and not (supercedes(a, b) or supercedes(b, a))


def ambiguities(signatures):
    """ All signature pairs such that A is ambiguous with B """
    signatures = list(map(tuple, signatures))
    return set((a, b) for a in signatures for b in signatures
               if hash(a) < hash(b)
               and ambiguous(a, b)
               and not any(supercedes(c, a) and supercedes(c, b)
                           for c in signatures))


def super_signature(signatures):
    """ A signature that would break ambiguities """
    n = len(signatures[0])
    assert all(len(s) == n for s in signatures)

    return [max([type.mro(sig[i]) for sig in signatures], key=len)[0]
            for i in range(n)]


def edge(a, b, tie_breaker=hash):
    """ A should be checked before B
    Tie broken by tie_breaker, defaults to ``hash``
    """
    # A either supercedes B and B does not supercede A or if B does then call
    # tie_breaker
    return supercedes(a, b) and (not supercedes(b, a) or tie_breaker(a) > tie_breaker(b))


def ordering(signatures):
    """ A sane ordering of signatures to check, first to last
    Topoological sort of edges as given by ``edge`` and ``supercedes``
    """
    signatures = list(map(tuple, signatures))
    edges = [(a, b) for a in signatures for b in signatures if edge(a, b)]
    edges = groupby(lambda x: x[0], edges)
    for s in signatures:
        if s not in edges:
            edges[s] = []
    edges = dict((k, [b for a, b in v]) for k, v in edges.items())  # type: ignore[assignment, attr-defined]
    return _toposort(edges)