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kronos-venv/lib/python3.12/site-packages/sympy/utilities/tests/test_enumerative.py

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+import string
+from itertools import zip_longest
+
+from sympy.utilities.enumerative import (
+    list_visitor,
+    MultisetPartitionTraverser,
+    multiset_partitions_taocp
+    )
+from sympy.utilities.iterables import _set_partitions
+
+# first some functions only useful as test scaffolding - these provide
+# straightforward, but slow reference implementations against which to
+# compare the real versions, and also a comparison to verify that
+# different versions are giving identical results.
+
+def part_range_filter(partition_iterator, lb, ub):
+    """
+    Filters (on the number of parts) a multiset partition enumeration
+
+    Arguments
+    =========
+
+    lb, and ub are a range (in the Python slice sense) on the lpart
+    variable returned from a multiset partition enumeration.  Recall
+    that lpart is 0-based (it points to the topmost part on the part
+    stack), so if you want to return parts of sizes 2,3,4,5 you would
+    use lb=1 and ub=5.
+    """
+    for state in partition_iterator:
+        f, lpart, pstack = state
+        if lpart >= lb and lpart < ub:
+            yield state
+
+def multiset_partitions_baseline(multiplicities, components):
+    """Enumerates partitions of a multiset
+
+    Parameters
+    ==========
+
+    multiplicities
+         list of integer multiplicities of the components of the multiset.
+
+    components
+         the components (elements) themselves
+
+    Returns
+    =======
+
+    Set of partitions.  Each partition is tuple of parts, and each
+    part is a tuple of components (with repeats to indicate
+    multiplicity)
+
+    Notes
+    =====
+
+    Multiset partitions can be created as equivalence classes of set
+    partitions, and this function does just that.  This approach is
+    slow and memory intensive compared to the more advanced algorithms
+    available, but the code is simple and easy to understand.  Hence
+    this routine is strictly for testing -- to provide a
+    straightforward baseline against which to regress the production
+    versions.  (This code is a simplified version of an earlier
+    production implementation.)
+    """
+
+    canon = []                  # list of components with repeats
+    for ct, elem in zip(multiplicities, components):
+        canon.extend([elem]*ct)
+
+    # accumulate the multiset partitions in a set to eliminate dups
+    cache = set()
+    n = len(canon)
+    for nc, q in _set_partitions(n):
+        rv = [[] for i in range(nc)]
+        for i in range(n):
+            rv[q[i]].append(canon[i])
+        canonical = tuple(
+            sorted([tuple(p) for p in rv]))
+        cache.add(canonical)
+    return cache
+
+
+def compare_multiset_w_baseline(multiplicities):
+    """
+    Enumerates the partitions of multiset with AOCP algorithm and
+    baseline implementation, and compare the results.
+
+    """
+    letters = string.ascii_lowercase
+    bl_partitions = multiset_partitions_baseline(multiplicities, letters)
+
+    # The partitions returned by the different algorithms may have
+    # their parts in different orders.  Also, they generate partitions
+    # in different orders.  Hence the sorting, and set comparison.
+
+    aocp_partitions = set()
+    for state in multiset_partitions_taocp(multiplicities):
+        p1 = tuple(sorted(
+                [tuple(p) for p in list_visitor(state, letters)]))
+        aocp_partitions.add(p1)
+
+    assert bl_partitions == aocp_partitions
+
+def compare_multiset_states(s1, s2):
+    """compare for equality two instances of multiset partition states
+
+    This is useful for comparing different versions of the algorithm
+    to verify correctness."""
+    # Comparison is physical, the only use of semantics is to ignore
+    # trash off the top of the stack.
+    f1, lpart1, pstack1 = s1
+    f2, lpart2, pstack2 = s2
+
+    if (lpart1 == lpart2) and (f1[0:lpart1+1] == f2[0:lpart2+1]):
+        if pstack1[0:f1[lpart1+1]] == pstack2[0:f2[lpart2+1]]:
+            return True
+    return False
+
+def test_multiset_partitions_taocp():
+    """Compares the output of multiset_partitions_taocp with a baseline
+    (set partition based) implementation."""
+
+    # Test cases should not be too large, since the baseline
+    # implementation is fairly slow.
+    multiplicities = [2,2]
+    compare_multiset_w_baseline(multiplicities)
+
+    multiplicities = [4,3,1]
+    compare_multiset_w_baseline(multiplicities)
+
+def test_multiset_partitions_versions():
+    """Compares Knuth-based versions of multiset_partitions"""
+    multiplicities = [5,2,2,1]
+    m = MultisetPartitionTraverser()
+    for s1, s2 in zip_longest(m.enum_all(multiplicities),
+                              multiset_partitions_taocp(multiplicities)):
+        assert compare_multiset_states(s1, s2)
+
+def subrange_exercise(mult, lb, ub):
+    """Compare filter-based and more optimized subrange implementations
+
+    Helper for tests, called with both small and larger multisets.
+    """
+    m = MultisetPartitionTraverser()
+    assert m.count_partitions(mult) == \
+        m.count_partitions_slow(mult)
+
+    # Note - multiple traversals from the same
+    # MultisetPartitionTraverser object cannot execute at the same
+    # time, hence make several instances here.
+    ma = MultisetPartitionTraverser()
+    mc = MultisetPartitionTraverser()
+    md = MultisetPartitionTraverser()
+
+    #  Several paths to compute just the size two partitions
+    a_it = ma.enum_range(mult, lb, ub)
+    b_it = part_range_filter(multiset_partitions_taocp(mult), lb, ub)
+    c_it = part_range_filter(mc.enum_small(mult, ub), lb, sum(mult))
+    d_it = part_range_filter(md.enum_large(mult, lb), 0, ub)
+
+    for sa, sb, sc, sd in zip_longest(a_it, b_it, c_it, d_it):
+        assert compare_multiset_states(sa, sb)
+        assert compare_multiset_states(sa, sc)
+        assert compare_multiset_states(sa, sd)
+
+def test_subrange():
+    # Quick, but doesn't hit some of the corner cases
+    mult = [4,4,2,1] # mississippi
+    lb = 1
+    ub = 2
+    subrange_exercise(mult, lb, ub)
+
+
+def test_subrange_large():
+    # takes a second or so, depending on cpu, Python version, etc.
+    mult = [6,3,2,1]
+    lb = 4
+    ub = 7
+    subrange_exercise(mult, lb, ub)