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gh-138281: Remove unused topsort and bump minimal version in peg_generator #138487

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Merged
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Sep 3, 2025
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gh-138281: Remove unused topsort and bump minimal version in peg_generator #138487

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4 changes: 2 additions & 2 deletions Tools/peg_generator/pegen/__main__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,7 @@ def main() -> None:


if __name__ == "__main__":
if sys.version_info < (3, 8): # noqa: UP036
print("ERROR: using pegen requires at least Python 3.8!", file=sys.stderr)
if sys.version_info < (3, 10): # noqa: UP036
print("ERROR: using pegen requires at least Python 3.10!", file=sys.stderr)
sys.exit(1)
main()
48 changes: 0 additions & 48 deletions Tools/peg_generator/pegen/sccutils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,54 +49,6 @@ def dfs(v: str) -> Iterator[set[str]]:
yield from dfs(v)


def topsort(
data: dict[Set[str], set[Set[str]]]
) -> Iterable[Set[Set[str]]]:
"""Topological sort.

Args:
data: A map from SCCs (represented as frozen sets of strings) to
sets of SCCs, its dependencies. NOTE: This data structure
is modified in place -- for normalization purposes,
self-dependencies are removed and entries representing
orphans are added.

Returns:
An iterator yielding sets of SCCs that have an equivalent
ordering. NOTE: The algorithm doesn't care about the internal
structure of SCCs.

Example:
Suppose the input has the following structure:

{A: {B, C}, B: {D}, C: {D}}

This is normalized to:

{A: {B, C}, B: {D}, C: {D}, D: {}}

The algorithm will yield the following values:

{D}
{B, C}
{A}

From https://code.activestate.com/recipes/577413-topological-sort/history/1/.
"""
# TODO: Use a faster algorithm?
for k, v in data.items():
v.discard(k) # Ignore self dependencies.
for item in set.union(*data.values()) - set(data.keys()):
data[item] = set()
while True:
ready = {item for item, dep in data.items() if not dep}
if not ready:
break
yield ready
data = {item: (dep - ready) for item, dep in data.items() if item not in ready}
assert not data, f"A cyclic dependency exists amongst {data}"


def find_cycles_in_scc(
graph: dict[str, Set[str]], scc: Set[str], start: str
) -> Iterable[list[str]]:
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