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