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gh-138281: Remove unused topsort and bump minimal version in peg_generator (#138487)
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2 changed files with 2 additions and 50 deletions
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@ -187,7 +187,7 @@ def main() -> None:
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if __name__ == "__main__":
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if sys.version_info < (3, 8): # noqa: UP036
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print("ERROR: using pegen requires at least Python 3.8!", file=sys.stderr)
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if sys.version_info < (3, 10): # noqa: UP036
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print("ERROR: using pegen requires at least Python 3.10!", file=sys.stderr)
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sys.exit(1)
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main()
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@ -49,54 +49,6 @@ def dfs(v: str) -> Iterator[set[str]]:
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yield from dfs(v)
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def topsort(
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data: dict[Set[str], set[Set[str]]]
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) -> Iterable[Set[Set[str]]]:
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"""Topological sort.
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Args:
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data: A map from SCCs (represented as frozen sets of strings) to
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sets of SCCs, its dependencies. NOTE: This data structure
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is modified in place -- for normalization purposes,
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self-dependencies are removed and entries representing
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orphans are added.
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Returns:
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An iterator yielding sets of SCCs that have an equivalent
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ordering. NOTE: The algorithm doesn't care about the internal
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structure of SCCs.
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Example:
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Suppose the input has the following structure:
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{A: {B, C}, B: {D}, C: {D}}
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This is normalized to:
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{A: {B, C}, B: {D}, C: {D}, D: {}}
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The algorithm will yield the following values:
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{D}
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{B, C}
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{A}
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From https://code.activestate.com/recipes/577413-topological-sort/history/1/.
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"""
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# TODO: Use a faster algorithm?
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for k, v in data.items():
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v.discard(k) # Ignore self dependencies.
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for item in set.union(*data.values()) - set(data.keys()):
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data[item] = set()
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while True:
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ready = {item for item, dep in data.items() if not dep}
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if not ready:
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break
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yield ready
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data = {item: (dep - ready) for item, dep in data.items() if item not in ready}
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assert not data, f"A cyclic dependency exists amongst {data}"
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def find_cycles_in_scc(
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graph: dict[str, Set[str]], scc: Set[str], start: str
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) -> Iterable[list[str]]:
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