chore: import upstream snapshot with attribution

tests/test_word_count_cache.py

@@ -0,0 +1,52 @@
+"""#1656 — word counts are cached against each file's stat signature so
+detect() doesn't re-parse every unchanged PDF/docx on each run just to size
+the corpus.
+"""
+from __future__ import annotations
+
+from pathlib import Path
+
+from graphify import cache
+
+
+def test_word_count_cached_until_file_changes(tmp_path, monkeypatch):
+    # Isolate the stat index to this tmp root.
+    monkeypatch.setattr(cache, "_stat_index", {})
+    monkeypatch.setattr(cache, "_stat_index_root", None)
+
+    f = tmp_path / "doc.txt"
+    f.write_text("one two three four five")
+
+    calls = {"n": 0}
+    def compute(p: Path) -> int:
+        calls["n"] += 1
+        return len(p.read_text().split())
+
+    assert cache.cached_word_count(f, tmp_path, compute) == 5
+    assert calls["n"] == 1
+    # Second call, file unchanged → served from cache, compute NOT re-run.
+    assert cache.cached_word_count(f, tmp_path, compute) == 5
+    assert calls["n"] == 1
+
+    # Change the file → recompute.
+    f.write_text("only three words now")  # 4 words
+    assert cache.cached_word_count(f, tmp_path, compute) == 4
+    assert calls["n"] == 2
+
+
+def test_word_count_augments_existing_hash_entry(tmp_path, monkeypatch):
+    # cached_word_count must not clobber a hash already stored for the file.
+    monkeypatch.setattr(cache, "_stat_index", {})
+    monkeypatch.setattr(cache, "_stat_index_root", None)
+
+    f = tmp_path / "m.py"
+    f.write_text("x = 1\n")  # -> ["x", "=", "1"] == 3 tokens
+    h = cache.file_hash(f, tmp_path)
+    assert h
+    wc = cache.cached_word_count(f, tmp_path, lambda p: len(p.read_text().split()))
+    assert wc == 3
+    # The hash entry survives alongside the word_count.
+    assert cache.file_hash(f, tmp_path) == h
+    key = str(cache._normalize_path(f).resolve())
+    entry = cache._stat_index[key]
+    assert entry.get("hash") == h and entry.get("word_count") == 3