def post_unify_check(self,
lvar_map,
lvar=None,
value=None,
old_state=None):
for lv_key, constraints in list(self.lvar_constraints.items()):
lv = reify(lv_key, lvar_map)
constraints_rf = reify(tuple(constraints), lvar_map)
for cs in constraints_rf:
s = unify(lv, cs, {})
if s is not False and not s:
# They already unify, but with no unground logic variables,
# so we have an immediate violation of the constraint.
return False
elif s is False:
# They don't unify and have no unground logic variables, so
# the constraint is immediately satisfied and there's no
# reason to continue checking this constraint.
constraints.discard(cs)
else:
# They unify when/if the unifications in `s` are made, so
# let's add these as new constraints.
for k, v in s.items():
self.add(k, v)
if len(constraints) == 0:
# This logic variable has no more unground constraints, so
# remove it.
del self.lvar_constraints[lv_key]
return True
def nullo_goal(s):
nonlocal args, default_ConsNull
if refs is not None:
refs_rf = reify(refs, s)
else:
refs_rf = ()
args_rf = reify(args, s)
arg_null_types = set(
# Get an empty instance of the type
type(a) for a in args_rf + refs_rf
# `ConsPair` and `ConsNull` types that are not literally `ConsPair`s
if isinstance(a, (ConsPair,
ConsNull)) and not issubclass(type(a), ConsPair))
try:
null_type = arg_null_types.pop()
except KeyError:
null_type = default_ConsNull
if len(arg_null_types) > 0 and any(a != null_type
for a in arg_null_types):
# Mismatching null types: fail.
return
g = lall(*[eq(a, null_type()) for a in args_rf])
yield from g(s)
def test_basic_unify_reify():
# Test reification with manually constructed replacements
a = tf.compat.v1.placeholder(tf.float64, name='a')
x_l = var('x_l')
a_reif = reify(x_l, {x_l: mt(a)})
assert a_reif.obj is not None
# Confirm that identity is preserved (i.e. that the underlying object
# was properly tracked and not unnecessarily reconstructed)
assert a == a_reif.reify()
test_expr = mt.add(tf.constant(1, dtype=tf.float64),
mt.mul(tf.constant(2, dtype=tf.float64),
x_l))
test_reify_res = reify(test_expr, {x_l: a})
test_base_res = test_reify_res.reify()
assert isinstance(test_base_res, tf.Tensor)
with tf.Graph().as_default():
a = tf.compat.v1.placeholder(tf.float64, name='a')
expected_res = tf.add(tf.constant(1, dtype=tf.float64),
tf.multiply(tf.constant(2, dtype=tf.float64), a))
assert_ops_equal(test_base_res, expected_res)
# Simply make sure that unification succeeds
meta_expected_res = mt(expected_res)
s_test = unify(test_expr, meta_expected_res, {})
assert len(s_test) == 3
assert reify(test_expr, s_test) == meta_expected_res
def post_unify_check(self,
lvar_map,
lvar=None,
value=None,
old_state=None):
for lv_key, constraints in list(self.lvar_constraints.items()):
lv = reify(lv_key, lvar_map)
is_lv_ground = self.constraint_isground(lv, lvar_map) or isground(
lv, lvar_map)
if not is_lv_ground:
# This constraint isn't ready to be checked
continue
# if is_lv_ground and not self.cterm_type_check(lv):
# self.lvar_constraints[lv_key]
# return False
constraint_grps = groupby(lambda x: isground(x, lvar_map),
reify(iter(constraints), lvar_map))
constraints_unground = constraint_grps.get(False, ())
constraints_ground = constraint_grps.get(True, ())
if len(constraints_ground) > 0 and not all(
self.cparam_type_check(c) for c in constraints_ground):
# Some constraint parameters aren't the correct type, so fail.
# del self.lvar_constraints[lv_key]
return False
assert constraints_unground or constraints_ground
if is_lv_ground and len(constraints_unground) == 0:
if self.require_all_constraints and any(
not self.constraint_check(lv, t)
for t in constraints_ground):
return False
elif not self.require_all_constraints and not any(
self.constraint_check(lv, t)
for t in constraints_ground):
return False
# The instance and constraint parameters are all ground and the
# constraint is satisfied, so, since nothing should change from
# here on, we can remove the constraint.
del self.lvar_constraints[lv_key]
# Some types are unground, so we continue checking until they are
return True
def allgoal(s):
for i, g in enumerate(goals):
try:
goal = goaleval(reify(g, s))
except EarlyGoalError:
continue
other_goals = tuple(goals[:i] + goals[i + 1:])
return unique(interleave(
goaleval(reify((lallfirst, ) + other_goals, ss))(ss)
for ss in goal(s)),
key=dicthash)
else:
raise EarlyGoalError()
def allgoal(s):
for i, g in enumerate(goals):
try:
goal = goaleval(reify(g, s))
except EarlyGoalError:
continue
other_goals = tuple(goals[:i] + goals[i + 1:])
return unique(
interleave(
goaleval(reify((lallfirst, ) + other_goals, ss))(ss)
for ss in goal(s)),
key=dicthash)
else:
raise EarlyGoalError()
def isinstanceo_goal(S):
nonlocal u, u_type
u_rf, u_type_rf = reify((u, u_type), S)
if not isground(u_rf, S) or not isground(u_type_rf, S):
if not isinstance(S, ConstrainedState):
S = ConstrainedState(S)
cs = S.constraints.setdefault(IsinstanceStore, IsinstanceStore())
try:
cs.add(u_rf, u_type_rf)
except TypeError:
# If the instance object can't be hashed, we can simply use a
# logic variable to uniquely identify it.
u_lv = var()
S[u_lv] = u_rf
cs.add(u_lv, u_type_rf)
if cs.post_unify_check(S.data, u_rf, u_type_rf):
yield S
# elif isground(u_type, S):
# yield from lany(eq(u_type, u_t) for u_t in type(u).mro())(S)
elif (isinstance(u_type_rf, type)
# or (
# isinstance(u_type, Iterable)
# and all(isinstance(t, type) for t in u_type)
# )
) and isinstance(u_rf, u_type_rf):
yield S
def typeo_goal(S):
nonlocal u, u_type
u_rf, u_type_rf = reify((u, u_type), S)
if not isground(u_rf, S) or not isground(u_type_rf, S):
if not isinstance(S, ConstrainedState):
S = ConstrainedState(S)
cs = S.constraints.setdefault(TypeStore, TypeStore())
try:
cs.add(u_rf, u_type_rf)
except TypeError:
# If the instance object can't be hashed, we can simply use a
# logic variable to uniquely identify it.
u_lv = var()
S[u_lv] = u_rf
cs.add(u_lv, u_type_rf)
if cs.post_unify_check(S.data, u_rf, u_type_rf):
yield S
elif isinstance(u_type_rf, type) and type(u_rf) == u_type_rf:
yield S
def neq_goal(S):
nonlocal u, v
u_rf, v_rf = reify((u, v), S)
# Get the unground logic variables that would unify the two objects;
# these are all the logic variables that we can't let unify.
s_uv = unify(u_rf, v_rf, {})
if s_uv is False:
# They don't unify and have no unground logic variables, so the
# constraint is immediately satisfied.
yield S
return
elif not s_uv:
# They already unify, but with no unground logic variables, so we
# have an immediate violation of the constraint.
return
if not isinstance(S, ConstrainedState):
S = ConstrainedState(S)
cs = S.constraints.setdefault(DisequalityStore, DisequalityStore())
for lvar, obj in s_uv.items():
cs.add(lvar, obj)
# We need to check the current state for validity.
if cs.post_unify_check(S.data):
yield S
def appendo_goal(S):
nonlocal lst, s, out
l_rf, s_rf, out_rf = reify((lst, s, out), S)
a, d, res = var(prefix="a"), var(prefix="d"), var(prefix="res")
_nullo = partial(nullo, default_ConsNull=default_ConsNull)
g = conde(
[
# All empty
_nullo(s_rf, l_rf, out_rf),
],
[
# `lst` is empty
conso(a, d, out_rf),
eq(s_rf, out_rf),
_nullo(l_rf, refs=(s_rf, out_rf)),
],
[
conso(a, d, l_rf),
conso(a, res, out_rf),
appendo(d, s_rf, res, default_ConsNull=default_ConsNull),
],
)
yield from g(S)
def goal(substitution):
newx, newy = reify((x, y), substitution)
def apply_constrain(oldvar, newvar):
if hasrange(oldvar):
newvar = RangedVar.new_from_intersection(oldvar, newvar)
if newvar:
yield temp_assoc(substitution, oldvar, newvar)
else:
yield temp_assoc(substitution, oldvar, newvar)
if isvar(newx):
if isvar(newy):
raise EarlyGoalError('two vars in comparison')
elif isinstance(newy, Number):
oldvar = newx
newvar = RangedVar(RealRange([(newy, np.inf)]))
yield from apply_constrain(oldvar, newvar)
else:
raise EarlyGoalError('Invalid constant type')
elif isinstance(newx, Number):
if isvar(newy):
oldvar = newy
newvar = RangedVar(RealRange([(-np.inf, newx)]))
yield from apply_constrain(oldvar, newvar)
elif isinstance(newy, Number):
if newx > newy:
yield substitution
else:
raise EarlyGoalError('Invalid constant type')
else:
raise EarlyGoalError('Invalid constant type')
def walko_goal(s):
nonlocal goal, rator_goal, graph_in, graph_out, null_type, map_rel
graph_in_rf, graph_out_rf = reify((graph_in, graph_out), s)
rator_in, rands_in, rator_out, rands_out = var(), var(), var(), var()
_walko = partial(walko,
goal,
rator_goal=rator_goal,
null_type=null_type,
map_rel=map_rel)
g = conde(
# TODO: Use `Zzz`, if needed.
[
goal(graph_in_rf, graph_out_rf),
],
[
lall(
applyo(rator_in, rands_in, graph_in_rf),
applyo(rator_out, rands_out, graph_out_rf),
rator_goal(rator_in, rator_out),
map_rel(_walko, rands_in, rands_out, null_type=null_type),
) if rator_goal is not None else map_rel(
_walko, graph_in_rf, graph_out_rf, null_type=null_type),
],
)
yield from g(s)
def goalify_goal(S):
"""
This is the goal that's generated.
Parameters
----------
S: Mapping
The miniKanren state (e.g. unification mappings/`dict`).
Yields
------
miniKanren states.
"""
nonlocal args
# 2. If you only want to confirm something in/about the state, `S`, then
# simply `yield` it if the condition(s) are met:
args_rf = reify(args, S)
if func(*args_rf) == expected_result:
yield S
else:
# If the condition isn't met, end the stream by returning/not
# `yield`ing anything.
return
def _proofs_of(cls, term):
# If term contains any variables then we rename them here so that they
# don't collide with variable names used in this proof.
term = rename_variables(term, '__parent__.')
for rule in cls.get_rules():
variables = unify(term, rule.conclusion)
if variables is False:
continue
if not rule.premises:
yield Proof(rule, variables)
continue
# If we reach here it means that there are premises to prove.
reified_premises = [reify(x, variables) for x in rule.premises]
for premise_proofs in cls._proofs_of_many(reified_premises):
candiate_variables = variables
for (premise, premise_proof) in zip(reified_premises,
premise_proofs):
candiate_variables = unify(premise,
premise_proof.conclusion,
candiate_variables)
if candiate_variables is False:
break
else:
yield Proof(rule, candiate_variables, premise_proofs)
def rembero_goal(s):
nonlocal x, lst, o
x_rf, l_rf, o_rf = reify((x, lst, o), s)
l_car, l_cdr, r = var(), var(), var()
g = conde(
[
nullo(l_rf, o_rf, default_ConsNull=default_ConsNull),
],
[
conso(l_car, l_cdr, l_rf),
eq(x_rf, l_car),
eq(l_cdr, o_rf),
],
[
conso(l_car, l_cdr, l_rf),
neq(l_car, x),
conso(l_car, r, o_rf),
rembero(x_rf, l_cdr, r, default_ConsNull=default_ConsNull),
],
)
yield from g(s)
def membero_goal(S):
nonlocal x, ls
x_rf, ls_rf = reify((x, ls), S)
a, d = var(), var()
g = lall(conso(a, d, ls), conde([eq(a, x)], [membero(x, d)]))
yield from g(S)
def test_map_anyo_misc():
q_lv = var("q")
res = run(0, q_lv, map_anyo(eq, [1, 2, 3], [1, 2, 3]))
# TODO: Remove duplicate results
assert len(res) == 7
res = run(0, q_lv, map_anyo(eq, [1, 2, 3], [1, 3, 3]))
assert len(res) == 0
def one_to_threeo(x, y):
return conde([eq(x, 1), eq(y, 3)])
res = run(0, q_lv, map_anyo(one_to_threeo, [1, 2, 4, 1, 4, 1, 1], q_lv))
assert res[0] == [3, 2, 4, 3, 4, 3, 3]
assert (len(
run(4, q_lv, map_anyo(math_reduceo, [etuple(mul, 2, var("x"))],
q_lv))) == 0)
test_res = run(4, q_lv, map_anyo(math_reduceo, [etuple(add, 2, 2), 1],
q_lv))
assert test_res == ([etuple(mul, 2, 2), 1], )
test_res = run(4, q_lv, map_anyo(math_reduceo, [1, etuple(add, 2, 2)],
q_lv))
assert test_res == ([1, etuple(mul, 2, 2)], )
test_res = run(4, q_lv, map_anyo(math_reduceo, q_lv, var("z")))
assert all(isinstance(r, list) for r in test_res)
test_res = run(4, q_lv, map_anyo(math_reduceo, q_lv, var("z"), tuple))
assert all(isinstance(r, tuple) for r in test_res)
x, y, z = var(), var(), var()
def test_bin(a, b):
return conde([eq(a, 1), eq(b, 2)])
res = run(10, (x, y), map_anyo(test_bin, x, y, null_type=tuple))
exp_res_form = (
((1, ), (2, )),
((x, 1), (x, 2)),
((1, 1), (2, 2)),
((x, y, 1), (x, y, 2)),
((1, x), (2, x)),
((x, 1, 1), (x, 2, 2)),
((1, 1, 1), (2, 2, 2)),
((x, y, z, 1), (x, y, z, 2)),
((1, x, 1), (2, x, 2)),
((x, 1, y), (x, 2, y)),
)
for a, b in zip(res, exp_res_form):
s = unify(a, b)
assert s is not False
assert all(isvar(i) for i in reify((x, y, z), s))
def seq_apply_anyo_sub_goal(s):
nonlocal i_any, null_type
l_in_rf, l_out_rf = reify((l_in, l_out), s)
i_car, i_cdr = var(), var()
o_car, o_cdr = var(), var()
conde_branches = []
if i_any or (isvar(l_in_rf) and isvar(l_out_rf)):
# Consider terminating the sequences when we've had at least
# one successful goal or when both sequences are logic variables.
conde_branches.append([eq(l_in_rf, null_type), eq(l_in_rf, l_out_rf)])
# Extract the CAR and CDR of each argument sequence; this is how we
# iterate through elements of the two sequences.
cons_parts_branch = [
goaleval(conso(i_car, i_cdr, l_in_rf)),
goaleval(conso(o_car, o_cdr, l_out_rf)),
]
conde_branches.append(cons_parts_branch)
conde_relation_branches = []
relation_branch = None
if not skip_cars:
relation_branch = [
# This case tries the relation continues on.
relation(i_car, o_car),
# In this conde clause, we can tell future calls to
# seq_apply_anyo that we've had at least one successful
# application of the relation (otherwise, this clause
# would fail due to the above goal).
_seq_apply_anyo(relation, i_cdr, o_cdr, True, null_type),
]
conde_relation_branches.append(relation_branch)
base_branch = [
# This is the "base" case; it is used when, for example,
# the given relation isn't satisfied.
eq(i_car, o_car),
_seq_apply_anyo(relation, i_cdr, o_cdr, i_any, null_type),
]
conde_relation_branches.append(base_branch)
cons_parts_branch.append(conde(*conde_relation_branches))
g = conde(*conde_branches)
g = goaleval(g)
yield from g(s)
def test_objects():
fact(commutative, Add)
fact(associative, Add)
assert tuple(goaleval(eq_assoccomm(add(1, 2, 3), add(3, 1, 2)))({}))
assert tuple(goaleval(eq_assoccomm(add(1, 2, 3), add(3, 1, 2)))({}))
x = var('x')
assert reify(x, tuple(goaleval(eq_assoccomm(
add(1, 2, 3), add(1, 2, x)))({}))[0]) == 3
assert reify(x, next(goaleval(eq_assoccomm(
add(1, 2, 3), add(x, 2, 1)))({}))) == 3
v = add(1, 2, 3)
with variables(v):
x = add(5, 6)
assert reify(v, next(goaleval(eq_assoccomm(v, x))({}))) == x
def test_sexp_unify_reify():
"""Make sure we can unify and reify etuples/S-exps."""
# Unify `A . (x + y)`, for `x`, `y` logic variables
A = tf.compat.v1.placeholder(tf.float64,
name="A",
shape=tf.TensorShape([None, None]))
x = tf.compat.v1.placeholder(tf.float64,
name="x",
shape=tf.TensorShape([None, 1]))
y = tf.compat.v1.placeholder(tf.float64,
name="y",
shape=tf.TensorShape([None, 1]))
z = tf.matmul(A, tf.add(x, y))
z_sexp = etuplize(z, shallow=False)
# Let's just be sure that the original TF objects are preserved
assert z_sexp[1].reify() == A
assert z_sexp[2][1].reify() == x
assert z_sexp[2][2].reify() == y
A_lv, x_lv, y_lv = var(), var(), var()
dis_pat = etuple(
TFlowMetaOperator(mt.matmul.op_def, var()),
A_lv,
etuple(TFlowMetaOperator(mt.add.op_def, var()), x_lv, y_lv),
)
s = unify(dis_pat, z_sexp, {})
assert s[A_lv] == mt(A)
assert s[x_lv] == mt(x)
assert s[y_lv] == mt(y)
# Now, we construct a graph that reflects the distributive property and
# reify with the substitutions from the un-distributed form
out_pat = etuple(mt.add, etuple(mt.matmul, A_lv, x_lv),
etuple(mt.matmul, A_lv, y_lv))
z_dist = reify(out_pat, s)
# Evaluate the tuple-expression and get a meta object/graph
z_dist_mt = z_dist.eval_obj
# If all the logic variables were reified, we should be able to
# further reify the meta graph and get a concrete TF graph
z_dist_tf = z_dist_mt.reify()
assert isinstance(z_dist_tf, tf.Tensor)
# Check the first part of `A . x + A . y` (i.e. `A . x`)
assert z_dist_tf.op.inputs[0].op.inputs[0] == A
assert z_dist_tf.op.inputs[0].op.inputs[1] == x
# Now, the second, `A . y`
assert z_dist_tf.op.inputs[1].op.inputs[0] == A
assert z_dist_tf.op.inputs[1].op.inputs[1] == y
def itero_goal(S):
nonlocal lst, nullo_refs, default_ConsNull
l_rf = reify(lst, S)
c, d = var(), var()
g = conde(
[nullo(l_rf, refs=nullo_refs, default_ConsNull=default_ConsNull)],
[conso(c, d, l_rf),
itero(d, default_ConsNull=default_ConsNull)],
)
yield from g(S)
def apply_rule(graph, rule):
LHS, RHS = rule
matches = find_matches(graph, LHS)
# remove matched nodes except for inputs
remove = {n for match in matches for k, n in match.items() if k in LHS}
# generate names for nodes to be added to the graph
IDs = filter(lambda key: key not in graph, count(1))
add = [reify(reindex(RHS, union(dict(zip(RHS.keys(), IDs)), match)), match)
for match in matches]
return union({k: v for k, v in graph.items() if k not in remove}, *add)
def _proofs_of_many(cls, terms):
(first_term, *other_terms) = terms
for proof in cls._proofs_of(first_term):
if other_terms:
reified_other_terms = reify(other_terms,
proof.parent_variables)
for other_proofs in cls._proofs_of_many(reified_other_terms):
yield (proof, *other_proofs)
else:
yield (proof, )
def test_unification():
x, y, a, b = tt.dvectors("xyab")
x_s = tt.scalar("x_s")
y_s = tt.scalar("y_s")
c_tt = tt.constant(1, "c")
d_tt = tt.constant(2, "d")
x_l = var("x_l")
y_l = var("y_l")
assert a == reify(x_l, {x_l: a}).reify()
test_expr = mt.add(1, mt.mul(2, x_l))
test_reify_res = reify(test_expr, {x_l: a})
assert graph_equal(test_reify_res.reify(), 1 + 2 * a)
z = tt.add(b, a)
assert {x_l: z} == unify(x_l, z)
assert b == unify(mt.add(x_l, a), mt.add(b, a))[x_l].reify()
res = unify(mt.inv(mt.add(x_l, a)), mt.inv(mt.add(b, y_l)))
assert res[x_l].reify() == b
assert res[y_l].reify() == a
mt_expr_add = mt.add(x_l, y_l)
# The parameters are vectors
tt_expr_add_1 = tt.add(x, y)
assert graph_equal(
tt_expr_add_1,
reify(mt_expr_add, unify(mt_expr_add, tt_expr_add_1)).reify())
# The parameters are scalars
tt_expr_add_2 = tt.add(x_s, y_s)
assert graph_equal(
tt_expr_add_2,
reify(mt_expr_add, unify(mt_expr_add, tt_expr_add_2)).reify())
# The parameters are constants
tt_expr_add_3 = tt.add(c_tt, d_tt)
assert graph_equal(
tt_expr_add_3,
reify(mt_expr_add, unify(mt_expr_add, tt_expr_add_3)).reify())
def test_objects():
fact(commutative, Add)
fact(associative, Add)
assert tuple(goaleval(eq_assoccomm(add(1, 2, 3), add(3, 1, 2)))({}))
assert tuple(goaleval(eq_assoccomm(add(1, 2, 3), add(3, 1, 2)))({}))
x = var('x')
assert reify(
x,
tuple(goaleval(eq_assoccomm(add(1, 2, 3), add(1, 2, x)))({}))[0]) == 3
assert reify(x,
next(goaleval(eq_assoccomm(add(1, 2, 3), add(x, 2,
1)))({}))) == 3
v = add(1, 2, 3)
with variables(v):
x = add(5, 6)
assert reify(v, next(goaleval(eq_assoccomm(v, x))({}))) == x
def goal(substitution):
args2 = reify(args, substitution)
subsets = [self.index[key] for key in enumerate(args) if key in self.index]
if subsets: # we are able to reduce the pool early
facts = intersection(*sorted(subsets, key=len))
else:
facts = self.facts
for fact in facts:
unified = unify(fact, args2, substitution)
if unified != False:
yield merge(unified, substitution)
def goal(s):
for gs in goalseqs:
if len(gs) == 1:
print(gs)
g = gs[0]
evaled_g = goaleval(reify(g, s))
print(evaled_g)
new_s = Stream(evaled_g(s))
if not new_s.empty():
return new_s
g, rgs = gs
print(g, rgs)
print('reify', s, reify(g, s))
evaled_g = goaleval(reify(g, s))
print(evaled_g)
new_s = Stream(evaled_g(s))
# new_s = Stream(goaleval(reify(g, s))(s))
if new_s.empty():
continue
return unique(interleave(goaleval(lall(rgs))(ss) for ss in new_s))
def test_operator():
s = unify(TFlowMetaOperator(var('a'), var('b')), mt.add)
assert s[var('a')] == mt.add.op_def
assert s[var('b')] == mt.add.node_def
add_mt = reify(TFlowMetaOperator(var('a'), var('b')), s)
assert add_mt == mt.add
assert unify(mt.mul, mt.matmul) is False
assert unify(mt.mul.op_def, mt.matmul.op_def) is False
def dbgo_goal(S):
nonlocal args
args = reify(args, S)
if msg is not None:
print(msg)
pprint(args)
import pdb
pdb.set_trace()
yield S
def test_unifiable_with_term():
add = Operator("add")
t = Node(add, (1, 2))
assert arguments(t) == (1, 2)
assert operator(t) == add
assert term(operator(t), arguments(t)) == t
x = var()
s = unify(Node(add, (1, x)), Node(add, (1, 2)), {})
assert s == {x: 2}
assert reify(Node(add, (1, x)), s) == Node(add, (1, 2))
def concat_goal(S):
nonlocal a, b, out
a_rf, b_rf, out_rf = reify((a, b, out), S)
if isinstance(a_rf, str) and isinstance(b_rf, str):
S_new = unify(out_rf, a_rf + b_rf, S)
if S_new is not False:
yield S_new
return
elif isinstance(a_rf, (Var, str)) and isinstance(b_rf, (Var, str)):
yield S
def goal(substitution):
args2 = reify(args, substitution)
subsets = [self.index[key] for key in enumerate(args)
if key in self.index]
if subsets: # we are able to reduce the pool early
facts = intersection(*sorted(subsets, key=len))
else:
facts = self.facts
for fact in facts:
unified = unify(fact, args2, substitution)
if unified != False:
yield merge(unified, substitution)
def ground_order_goal(S):
nonlocal in_args, out_args
in_args_rf, out_args_rf = reify((in_args, out_args), S)
S_new = unify(
list(out_args_rf)
if isinstance(out_args_rf, Sequence) else out_args_rf,
sorted(in_args_rf, key=partial(ground_order_key, S)),
S,
)
if S_new is not False:
yield S_new
def _nullo(s):
_s = reify(l, s)
if isvar(_s):
yield unify(_s, None, s)
elif is_null(_s):
yield s
def allgoal(s):
g = goaleval(reify(goals[0], s))
return unique(
interleave(goaleval(reify(
(lallgreedy, ) + tuple(goals[1:]), ss))(ss) for ss in g(s)),
key=dicthash)
def f(goals):
for goal in goals:
try:
yield goaleval(reify(goal, s))(s)
except EarlyGoalError:
pass
def reify_term(obj, s):
op, args = operator(obj), arguments(obj)
op = reify(op, s)
args = reify(args, s)
new = term(op, args)
return new
