Baseline diff vs clean upstream/main (182 pre-existing environmental
failures on both sides) showed exactly 6 PR-caused failures, all the same
mechanism: tests construct ContextCompressor under a
get_model_context_length mock and read threshold_percent/threshold_tokens
after the with block, or build via object.__new__ and assign
context_length AFTER the derived budgets (the setter now resets the
lazily-cached budgets).
- test_compression_small_ctx_threshold_floor: resolve inside _make()
- test_cjk_token_estimation: resolve inside mock
- test_per_model_compression_threshold: resolve inside mock (2 tests)
- test_pre_compress_memory_context: assign context_length before
threshold/tail/summary budgets; add summary_target_ratio
The salvaged estimator ran a per-character Python loop on every
estimate_tokens_rough() call — a ~28,000,000x slowdown vs (len+3)//4 on a
1MB ASCII tool output (measured ~3.0s per call). Gate it:
- str.isascii() O(1) fast path keeps pure-ASCII text bit-identical to the
classic (len+3)//4 rule at ~1.3x baseline cost (0.23us vs 0.17us per
1MB call).
- Non-ASCII text counts dense CJK chars via a compiled character-class
regex in C (len(text) - len(re.sub(''))): ~352ms/1MB hangul vs ~2.1s
for the per-char loop.
- Non-ASCII-but-non-CJK text (accents, Cyrillic, emoji) keeps the classic
rule.
Also: parity tests against the per-char reference implementation, and
updated two stale expectations that encoded the old behavior (CJK now
counted ~1 token/char; short string content now ceil-divided instead of
floored to 0). The continuity test now detects merged-into-tail summaries
via _is_context_summary_content.
