diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..4719481 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,40 @@ +name: Build and Deploy Docs + +on: + push: + branches: + - development + - main + - master + workflow_dispatch: + +permissions: + contents: write + +jobs: + docs: + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + + - name: Setup Python + uses: actions/setup-python@v5 + with: + python-version: "3.x" + + - name: Install MkDocs + run: | + python -m pip install --upgrade pip + pip install 'mkdocs<2' 'mkdocs-material<10' + + - name: Build site + run: mkdocs build --strict + + - name: Deploy to gh-pages + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + publish_dir: ./site + publish_branch: gh-pages + force_orphan: true diff --git a/.gitignore b/.gitignore index 8c98a88..f5aa7ee 100644 --- a/.gitignore +++ b/.gitignore @@ -58,10 +58,13 @@ htmlcov/ # OCR test outputs test_ocr_*_output/ *_demo_output/ +artifacts/ # OCR model weights (if downloaded locally) nanonets/ ocr_models/ +deepseek-ocr-2-model/ +models/ # Noise analysis reports glossapi_noise_analysis_report.md @@ -75,7 +78,8 @@ glossapi_vanilla_venv/ deepseek_venv/ dependency_setup/.venvs/ .venv_docling/ +.venv*/ deepseek-ocr/DeepSeek-OCR-empty/ # Local DeepSeek checkout and repro scripts (keep out of master) deepseek-ocr/ -repro_rapidocr_onnx/ +deepseek-ocr-2/ diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..979e757 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,20 @@ +# Contributing to GlossAPI + +## Working branches and PR flow +- Open PRs are pushed against the `development` branch. +- Development is merged with master when a) everything has been effectively used a few times and b) we reach a clear checkpoint. + +## Some design principles +- Corpus methods should be easy to use and descriptive. +- Python files should be readable and well organized (check folder structure). +- Metadata should be written to two distinct parquet files depending on their relevance to the end user ("metadata") or debugging during pipeline runs. The principle of reading/ writing to these parquet files should be maintained through out. Rest of the metadata is implicitly encoded in the output folders at each stage of the pipeline. + +## Pipeline awareness and folder layout +- Tie any pipeline change to the artifacts it produces. Common touchpoints: + - `Corpus.extract()` writes source PDFs under `downloads/` and a manifest at `download_results/download_results.parquet` (fields like `needs_ocr`). + - `Corpus.clean()` emits `markdown/` and `clean_markdown/`, keeping `.processing_state.pkl` plus `problematic_files/` and `timeout_files/` subfolders. + - `Corpus.ocr()` and `Corpus.section()` populate `json/` (Docling JSON, formula index, metrics) and `sections/sections_for_annotation.parquet`. +- When relocating outputs or adding new ones, update assertions in `tests/test_pipeline_smoke.py` and the folder references in `docs/pipeline.md` so the layout stays discoverable. + +## Keep changes small +- Avoid large refactors or sweeping interface changes; aim for narrowly scoped PRs and discuss big shifts before starting. diff --git a/README.md b/README.md index ebc6baf..04be81a 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ GlossAPI is a GPU-ready document processing pipeline from [GFOSS](https://gfoss. ## Why GlossAPI - Handles download → extraction → cleaning → sectioning in one pipeline. -- Ships safe PyPDFium extraction plus Docling/RapidOCR for high-throughput OCR. +- Ships safe PyPDFium extraction plus Docling for structured extraction and DeepSeek-OCR-2 for OCR remediation. - Rust-powered cleaner/noise metrics keep Markdown quality predictable. - Greek-first metadata and section classification tuned for academic corpora. - Modular Corpus API lets you resume from any stage or plug into existing flows. @@ -40,56 +40,128 @@ PY ## Automated Environment Profiles -Use `dependency_setup/setup_glossapi.sh` to provision a virtualenv with the right dependency stack for the three supported modes: +Use `dependency_setup/setup_glossapi.sh` for the Docling environment, or `dependency_setup/setup_deepseek_uv.sh` for the dedicated DeepSeek OCR runtime: ```bash -# Vanilla pipeline (no GPU OCR extras) -./dependency_setup/setup_glossapi.sh --mode vanilla --venv dependency_setup/.venvs/vanilla --run-tests +# Docling / main GlossAPI environment +./dependency_setup/setup_glossapi.sh --mode docling --venv dependency_setup/.venvs/docling --run-tests -# Docling + RapidOCR mode -./dependency_setup/setup_glossapi.sh --mode rapidocr --venv dependency_setup/.venvs/rapidocr --run-tests - -# DeepSeek OCR mode (requires weights under /path/to/deepseek-ocr/DeepSeek-OCR) -./dependency_setup/setup_glossapi.sh \ - --mode deepseek \ +# DeepSeek OCR runtime (uv-managed) +./dependency_setup/setup_deepseek_uv.sh \ --venv dependency_setup/.venvs/deepseek \ - --weights-dir /path/to/deepseek-ocr \ + --model-root /path/to/deepseek-ocr-2-model \ + --download-model \ --run-tests --smoke-test ``` -Pass `--download-deepseek` if you need the script to fetch weights automatically; otherwise it looks for `${REPO_ROOT}/deepseek-ocr/DeepSeek-OCR` unless you override `--weights-dir`. Check `dependency_setup/dependency_notes.md` for the latest pins, caveats, and validation history. The script also installs the Rust extensions in editable mode so local changes are picked up immediately. +`setup_glossapi.sh --mode deepseek` now delegates to the same uv-based installer. `setup_deepseek_uv.sh` uses `uv venv` + `uv sync`, installs the Rust extensions in editable mode, and can download `deepseek-ai/DeepSeek-OCR-2` with `huggingface_hub`. +The uv-managed DeepSeek runtime is OCR-only on purpose: it installs `glossapi[deepseek]` and does not carry the Docling layout stack. + +If you want a guided install that asks which phases you plan to use, run: + +```bash +python install_glossapi.py +``` + +That wizard keeps browser-gated download support (`playwright`) and the dedicated DeepSeek OCR runtime out of the main environment unless you explicitly select them. + +## Browser-Gated Download Mode + +`Corpus.download(...)` now supports three high-level routes for file acquisition: + +- `download_mode="standard"`: direct HTTP downloader only +- `download_mode="auto"`: direct HTTP first, then browser-assisted recovery when the response is a recoverable browser-gated interstitial +- `download_mode="browser"`: go straight to browser-assisted acquisition for known browser-gated file endpoints + +Use `browser_mode=True` as a legacy alias for `download_mode="browser"`. + +### Policy-driven routing + +If you know which domains require browser bootstrap, route them with a policy file instead of probing every URL: + +```yaml +default: + downloader: standard + +rules: + - match: + domains: [eur-lex.europa.eu] + downloader: browser + + - match: + url_regex: "https://example.org/protected/.*" + downloader: auto +``` + +```python +from glossapi import Corpus + +corpus = Corpus(input_dir="out", output_dir="out") +corpus.download( + input_parquet="input_urls.parquet", + download_policy_file="download_policy.yml", +) +``` + +### Operational notes + +- Browser mode is for browser-gated file endpoints, not viewer-only sources. +- Browser sessions are cached per domain so a successful bootstrap can be reused across multiple files. +- Successful downloads still land in `downloads/`; extraction continues to consume only real files from that directory. +- Viewer-style sources still fail cleanly in `download_results/*.parquet` and do not create fake files. + +### Regression strategy + +The checked-in browser download tests use mocked browser/session flows and fake PDF bytes rather than hard-coded live URLs. + +For manual smoke checks against live browser-gated sources, build an ad hoc parquet locally and run it outside the committed test suite. **DeepSeek runtime checklist** -- Run `python -m glossapi.ocr.deepseek.preflight` (from your DeepSeek venv) to fail fast if the CLI would fall back to the stub. -- Export these to force the real CLI and avoid silent stub output: +- Run `python -m glossapi.ocr.deepseek.preflight` from the DeepSeek venv to fail fast before OCR. +- Export these to force the real runtime and avoid silent stub output: - `GLOSSAPI_DEEPSEEK_ALLOW_CLI=1` - `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0` - - `GLOSSAPI_DEEPSEEK_VLLM_SCRIPT=/path/to/deepseek-ocr/run_pdf_ocr_vllm.py` - - `GLOSSAPI_DEEPSEEK_TEST_PYTHON=/path/to/deepseek/venv/bin/python` - - `GLOSSAPI_DEEPSEEK_MODEL_DIR=/path/to/deepseek-ocr/DeepSeek-OCR` - - `GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH=/path/to/libjpeg-turbo/lib` -- CUDA toolkit with `nvcc` available (FlashInfer/vLLM JIT falls back poorly without it); set `CUDA_HOME` and prepend `$CUDA_HOME/bin` to `PATH`. -- If FlashInfer is problematic, disable with `VLLM_USE_FLASHINFER=0` and `FLASHINFER_DISABLE=1`. -- To avoid FP8 KV cache issues, export `GLOSSAPI_DEEPSEEK_NO_FP8_KV=1` (propagates `--no-fp8-kv`). -- Tune VRAM use via `GLOSSAPI_DEEPSEEK_GPU_MEMORY_UTILIZATION=<0.5–0.9>`. + - `GLOSSAPI_DEEPSEEK_PYTHON=/path/to/deepseek/venv/bin/python` + - `GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT=/path/to/glossAPI/src/glossapi/ocr/deepseek/run_pdf_ocr_transformers.py` + - `GLOSSAPI_DEEPSEEK_MODEL_DIR=/path/to/deepseek-ocr-2-model/DeepSeek-OCR-2` +- The default fallback locations already point at the in-repo Transformers runner and `${REPO_ROOT}/deepseek-ocr-2-model/DeepSeek-OCR-2`. +- `flash-attn` is optional. The runner uses `flash_attention_2` when available and falls back to `eager` otherwise. ## Choose Your Install Path | Scenario | Commands | Notes | | --- | --- | --- | | Pip users | `pip install glossapi` | Fast vanilla evaluation with minimal dependencies. | -| Mode automation (recommended) | `./dependency_setup/setup_glossapi.sh --mode {vanilla\|rapidocr\|deepseek}` | Creates an isolated venv per mode, installs Rust crates, and can run the relevant pytest subset. | +| Docling environment | `./dependency_setup/setup_glossapi.sh --mode docling` | Creates the main GlossAPI venv for extraction, cleaning, sectioning, and enrichment. | +| DeepSeek environment | `./dependency_setup/setup_deepseek_uv.sh` | Creates a separate uv-managed OCR runtime pinned to the tested Transformers/Torch stack. | | Manual editable install | `pip install -e .` after cloning | Keep this if you prefer to manage dependencies by hand. | | Conda-based stacks | `scripts/setup_conda.sh` | Provisions Python 3.10 env + Rust + editable install for Amazon Linux/SageMaker. | See the refreshed docs (`docs/index.md`) for detailed environment notes, CUDA/ORT combinations, and troubleshooting tips. ## Repo Landmarks +- `docs/code_map.md`: fast map from pipeline ideas to implementing classes and files. +- `docs/pipeline.md`: stage contracts, key parameters, and artifact outputs. - `samples/lightweight_pdf_corpus/`: 20 one-page PDFs with manifest + expected Markdown. - `src/glossapi/`: Corpus pipeline, cleaners, and orchestration logic. - `tests/test_pipeline_smoke.py`: Minimal regression entry point (uses the lightweight corpus). - `docs/`: MkDocs site with onboarding, pipeline recipes, and configuration guides. +## Pipeline map + +Use this as the shortest path from a documentation concept to the public call that implements it. + +| Stage | Main call | Important parameters | Writes | +| --- | --- | --- | --- | +| Download | `Corpus.download(...)` | `input_parquet`, `links_column`, `parallelize_by`, `download_mode="standard"|"auto"|"browser"`, `download_policy_file`, downloader kwargs | `downloads/`, `download_results/*.parquet` | +| Extract (Phase-1) | `Corpus.extract(...)` | `input_format`, `phase1_backend`, `use_gpus`, `workers_per_device`, `export_doc_json`, `emit_formula_index` | `markdown/.md`, `json/.docling.json(.zst)`, `json/metrics/*.json` | +| Clean | `Corpus.clean(...)` | `threshold`, `drop_bad`, `empty_char_threshold`, `empty_min_pages` | `clean_markdown/.md`, updated parquet metrics/flags | +| OCR / math follow-up | `Corpus.ocr(...)` | `mode`, `fix_bad`, `math_enhance`, `use_gpus`, `devices` | refreshed `markdown/.md`, optional `json/.latex_map.jsonl` | +| Section | `Corpus.section()` | uses cleaner/parquet outputs to choose inputs | `sections/sections_for_annotation.parquet` | +| Annotate | `Corpus.annotate(...)` | `annotation_type`, `fully_annotate` | `classified_sections.parquet`, `fully_annotated_sections.parquet` | +| Triage math density | `Corpus.triage_math()` | no required args | updated `download_results/*.parquet` routing columns | +| JSONL export | `Corpus.jsonl(...)` | `output_path` | merged training/export JSONL | + ## Contributing - Run `pytest tests/test_pipeline_smoke.py` for a fast end-to-end check. - Regenerate the lightweight corpus via `generate_pdfs.py` and commit the updated PDFs + manifest together. diff --git a/cleaning_scripts/analyze_cleaning_concentration.py b/cleaning_scripts/analyze_cleaning_concentration.py new file mode 100644 index 0000000..834dcc6 --- /dev/null +++ b/cleaning_scripts/analyze_cleaning_concentration.py @@ -0,0 +1,271 @@ +"""Analyze per-dataset + per-doc cleaning concentration. + +Reads the per-doc stats.jsonl files produced by clean_and_stats_full.py +and reports: + + 1. Per-dataset rollup (source_dataset field): + - n_docs total / kept / dropped (by reason) + - chars_before total + - chars_after total + - chars_removed total (strip only — dropped docs are separate) + - chars_removed_by_drop (from doc drops) + - pct_removed_by_strip + - pct_removed_total (strip + drop) + + 2. Top-N docs by pct_removed within each dataset (non-dropped docs only, + to see where per-line strip concentrates in surviving docs). + + 3. Top-N docs by absolute chars_removed (cross-dataset). + + 4. Counter-value histograms per dataset — which counter fires most for + each source. + +Output: analysis.json + analysis.md at the stats dir root. + +Run: + python3 analyze_cleaning_concentration.py \\ + --stats-dir /home/foivos/runs/raw_clean_stats_20260422/stats \\ + --output-dir /home/foivos/runs/raw_clean_stats_20260422/analysis +""" +from __future__ import annotations + +import argparse +import json +from collections import defaultdict +from dataclasses import dataclass, field +from pathlib import Path +from statistics import mean, median, quantiles +from typing import Any, Dict, List, Optional + + +@dataclass +class DatasetRoll: + source_dataset: str + n_docs: int = 0 + n_kept: int = 0 + n_dropped: Dict[str, int] = field(default_factory=dict) + chars_before: int = 0 + chars_after: int = 0 + chars_removed_strip: int = 0 # per-line strip on surviving docs + chars_removed_drop: int = 0 # full doc chars for dropped docs + pct_removed_per_doc: List[float] = field(default_factory=list) + counter_font: List[int] = field(default_factory=list) + counter_glyph: List[int] = field(default_factory=list) + counter_script: List[int] = field(default_factory=list) + + +def _load_stats(stats_dir: Path) -> Dict[str, DatasetRoll]: + rolls: Dict[str, DatasetRoll] = {} + for p in sorted(stats_dir.glob("*.stats.jsonl")): + with p.open("r", encoding="utf-8") as fh: + for line in fh: + d = json.loads(line) + ds = d.get("source_dataset") or p.stem.split(".")[0] + roll = rolls.setdefault(ds, DatasetRoll(source_dataset=ds)) + roll.n_docs += 1 + drop_reason = d.get("drop_reason") or "" + chars_before = int(d.get("chars_before", 0) or 0) + chars_after = int(d.get("chars_after", 0) or 0) + chars_removed = int(d.get("chars_removed", 0) or 0) + roll.counter_font.append(int(d.get("counter_font_marker", 0) or 0)) + roll.counter_glyph.append(int(d.get("counter_glyph_marker", 0) or 0)) + roll.counter_script.append(int(d.get("counter_script_residue", 0) or 0)) + if drop_reason: + roll.n_dropped[drop_reason] = roll.n_dropped.get(drop_reason, 0) + 1 + roll.chars_removed_drop += chars_before + else: + roll.n_kept += 1 + roll.chars_before += chars_before + roll.chars_after += chars_after + roll.chars_removed_strip += chars_removed + pct = float(d.get("pct_removed", 0.0) or 0.0) + roll.pct_removed_per_doc.append(pct) + return rolls + + +def _quantiles_safe(data: List[float], n: int = 4) -> List[float]: + if len(data) < n: + return [] + return quantiles(sorted(data), n=n) + + +def _summarize(rolls: Dict[str, DatasetRoll]) -> Dict[str, Any]: + out_per_ds: Dict[str, Any] = {} + total = DatasetRoll(source_dataset="__total__") + for ds, r in rolls.items(): + qs = _quantiles_safe(r.pct_removed_per_doc, n=4) + out_per_ds[ds] = { + "n_docs": r.n_docs, + "n_kept": r.n_kept, + "n_dropped_by_reason": r.n_dropped, + "chars_before_strip_kept": r.chars_before, + "chars_after_strip_kept": r.chars_after, + "chars_removed_by_strip": r.chars_removed_strip, + "chars_removed_by_drop": r.chars_removed_drop, + "chars_removed_total": r.chars_removed_strip + r.chars_removed_drop, + "pct_removed_by_strip": round( + 100.0 * r.chars_removed_strip / max(r.chars_before, 1), 3), + "pct_removed_total": round( + 100.0 * (r.chars_removed_strip + r.chars_removed_drop) + / max(r.chars_before + r.chars_removed_drop, 1), 3), + "per_doc_pct_removed_quartiles": [round(q, 3) for q in qs] if qs else None, + "per_doc_pct_removed_mean": round(mean(r.pct_removed_per_doc), 3) + if r.pct_removed_per_doc else None, + "counter_font_mean": round(mean(r.counter_font), 3) + if r.counter_font else 0, + "counter_font_max": max(r.counter_font) if r.counter_font else 0, + "counter_glyph_mean": round(mean(r.counter_glyph), 3) + if r.counter_glyph else 0, + "counter_glyph_max": max(r.counter_glyph) if r.counter_glyph else 0, + "counter_script_mean": round(mean(r.counter_script), 3) + if r.counter_script else 0, + "counter_script_max": max(r.counter_script) if r.counter_script else 0, + } + total.n_docs += r.n_docs + total.n_kept += r.n_kept + total.chars_before += r.chars_before + total.chars_after += r.chars_after + total.chars_removed_strip += r.chars_removed_strip + total.chars_removed_drop += r.chars_removed_drop + for k, v in r.n_dropped.items(): + total.n_dropped[k] = total.n_dropped.get(k, 0) + v + + out_per_ds["__total__"] = { + "n_docs": total.n_docs, + "n_kept": total.n_kept, + "n_dropped_by_reason": total.n_dropped, + "chars_before_strip_kept": total.chars_before, + "chars_after_strip_kept": total.chars_after, + "chars_removed_by_strip": total.chars_removed_strip, + "chars_removed_by_drop": total.chars_removed_drop, + "chars_removed_total": total.chars_removed_strip + total.chars_removed_drop, + "pct_removed_by_strip": round( + 100.0 * total.chars_removed_strip / max(total.chars_before, 1), 3), + "pct_removed_total": round( + 100.0 * (total.chars_removed_strip + total.chars_removed_drop) + / max(total.chars_before + total.chars_removed_drop, 1), 3), + } + return out_per_ds + + +def _top_docs(stats_dir: Path, n: int = 30) -> Dict[str, List[Dict[str, Any]]]: + """Cross-dataset top-N docs by absolute chars_removed AND by pct_removed.""" + top_abs: List[Dict[str, Any]] = [] + top_pct: List[Dict[str, Any]] = [] + for p in sorted(stats_dir.glob("*.stats.jsonl")): + with p.open("r", encoding="utf-8") as fh: + for line in fh: + d = json.loads(line) + if d.get("drop_reason"): + continue # only non-dropped docs — strip only + chars_removed = int(d.get("chars_removed", 0) or 0) + chars_before = int(d.get("chars_before", 0) or 0) + pct = float(d.get("pct_removed", 0) or 0) + row = { + "source_path": d.get("source_path"), + "source_dataset": d.get("source_dataset"), + "source_doc_id": d.get("source_doc_id"), + "chars_before": chars_before, + "chars_removed": chars_removed, + "pct_removed": pct, + "counter_font": d.get("counter_font_marker"), + "counter_glyph": d.get("counter_glyph_marker"), + "counter_script": d.get("counter_script_residue"), + } + top_abs.append(row) + # Only include in pct ranking if chars_before >= 1000 to avoid + # trivial small docs where 100% removal is uninformative. + if chars_before >= 1000: + top_pct.append(row) + top_abs.sort(key=lambda r: -r["chars_removed"]) + top_pct.sort(key=lambda r: -r["pct_removed"]) + return { + "top_by_abs_chars_removed": top_abs[:n], + "top_by_pct_removed_min_1kchar": top_pct[:n], + } + + +def _format_md(per_ds: Dict[str, Any], top: Dict[str, Any]) -> str: + lines: List[str] = [] + lines.append("# Cleaning concentration — per-dataset + per-doc\n") + lines.append("## Per-dataset summary\n") + lines.append("| dataset | docs | kept | dropped | chars_before | chars_after | strip % | total % | glyph_mean | script_mean | font_mean |") + lines.append("|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|") + # Sort by pct_removed_total descending + items = sorted( + [(k, v) for k, v in per_ds.items() if k != "__total__"], + key=lambda kv: -kv[1].get("pct_removed_total", 0), + ) + for ds, s in items: + lines.append( + f"| {ds} | {s['n_docs']} | {s['n_kept']} | " + f"{sum(s['n_dropped_by_reason'].values())} | " + f"{s['chars_before_strip_kept']:,} | {s['chars_after_strip_kept']:,} | " + f"{s['pct_removed_by_strip']}% | {s['pct_removed_total']}% | " + f"{s.get('counter_glyph_mean', 0)} | " + f"{s.get('counter_script_mean', 0)} | " + f"{s.get('counter_font_mean', 0)} |" + ) + t = per_ds["__total__"] + lines.append( + f"| **TOTAL** | **{t['n_docs']}** | **{t['n_kept']}** | " + f"**{sum(t['n_dropped_by_reason'].values())}** | " + f"**{t['chars_before_strip_kept']:,}** | **{t['chars_after_strip_kept']:,}** | " + f"**{t['pct_removed_by_strip']}%** | **{t['pct_removed_total']}%** | | | |" + ) + + lines.append("\n## Top 30 docs by absolute chars_removed (strip only, non-dropped)\n") + lines.append("| rank | dataset | doc_id | chars_before | chars_removed | pct | glyph | script | font |") + lines.append("|---:|---|---|---:|---:|---:|---:|---:|---:|") + for i, r in enumerate(top["top_by_abs_chars_removed"], 1): + lines.append( + f"| {i} | {r['source_dataset']} | {r['source_doc_id']} | " + f"{r['chars_before']:,} | {r['chars_removed']:,} | " + f"{r['pct_removed']}% | {r['counter_glyph']} | " + f"{r['counter_script']} | {r['counter_font']} |" + ) + lines.append("\n## Top 30 docs by pct_removed (min 1k chars, non-dropped)\n") + lines.append("| rank | dataset | doc_id | chars_before | chars_removed | pct | glyph | script | font |") + lines.append("|---:|---|---|---:|---:|---:|---:|---:|---:|") + for i, r in enumerate(top["top_by_pct_removed_min_1kchar"], 1): + lines.append( + f"| {i} | {r['source_dataset']} | {r['source_doc_id']} | " + f"{r['chars_before']:,} | {r['chars_removed']:,} | " + f"{r['pct_removed']}% | {r['counter_glyph']} | " + f"{r['counter_script']} | {r['counter_font']} |" + ) + return "\n".join(lines) + "\n" + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--stats-dir", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--top-n", type=int, default=30) + args = parser.parse_args(argv) + + rolls = _load_stats(args.stats_dir) + per_ds = _summarize(rolls) + top = _top_docs(args.stats_dir, n=args.top_n) + + args.output_dir.mkdir(parents=True, exist_ok=True) + (args.output_dir / "analysis.json").write_text( + json.dumps({"per_dataset": per_ds, **top}, ensure_ascii=False, indent=2), + encoding="utf-8", + ) + (args.output_dir / "analysis.md").write_text(_format_md(per_ds, top), encoding="utf-8") + print(f"wrote {args.output_dir / 'analysis.json'}") + print(f"wrote {args.output_dir / 'analysis.md'}") + t = per_ds["__total__"] + print( + f"\nTOTAL: docs={t['n_docs']} kept={t['n_kept']} " + f"dropped={sum(t['n_dropped_by_reason'].values())} " + f"chars_removed_strip={t['chars_removed_by_strip']:,} ({t['pct_removed_by_strip']}%) " + f"chars_removed_drop={t['chars_removed_by_drop']:,} " + f"total_pct_removed={t['pct_removed_total']}%" + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/cleaning_scripts/analyze_cleaning_distributions.py b/cleaning_scripts/analyze_cleaning_distributions.py new file mode 100644 index 0000000..d35dd6b --- /dev/null +++ b/cleaning_scripts/analyze_cleaning_distributions.py @@ -0,0 +1,350 @@ +"""Distribution analysis over the per-doc stats.jsonl from +clean_and_stats_rowsharded.py. + +Purpose: BEFORE committing to any sampling strategy for the quality +review, look at the shape of the population — length distributions, +pct-change distributions, per-dataset variance, correlations between +drop buckets. Numbers + plots. + +Output in --output-dir: + distributions.md — narrative summary with stats + 01_lengths.png — non_empty_chars_in / _out histograms + 02_pct_changes.png — pct_chars_removed / pct_lines_removed + 03_drop_attribution.png — four-way drop bucket comparison + 04_per_dataset.png — per-dataset pct_removed boxplots + 05_joint_density.png — pct_chars vs pct_lines 2D density + 06_cdf.png — cumulative doc count vs pct_removed + summary.json — structured stats for downstream scripts +""" +from __future__ import annotations + +import argparse +import glob as globmod +import json +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + + +def _load_stats(stats_glob: str) -> List[Dict[str, Any]]: + out = [] + for path in sorted(globmod.glob(stats_glob)): + with open(path, "r", encoding="utf-8") as fh: + for line in fh: + d = json.loads(line) + out.append(d) + return out + + +def _kept_only(stats: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Docs that survived cleaning (drop_reason is empty).""" + return [d for d in stats if not d.get("drop_reason")] + + +def _altered_kept(stats: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """Kept docs that experienced non-zero cleaning damage.""" + out = [] + for d in _kept_only(stats): + if float(d.get("pct_chars_removed_non_empty", 0) or 0) > 0: + out.append(d) + continue + if int(d.get("lines_dropped_by_cleaner", 0) or 0) > 0: + out.append(d) + return out + + +def _quantile_summary(values: List[float], label: str) -> Dict[str, float]: + if not values: + return {"label": label, "n": 0} + a = np.asarray(values, dtype=float) + return { + "label": label, + "n": int(a.size), + "min": float(a.min()), + "p05": float(np.quantile(a, 0.05)), + "p25": float(np.quantile(a, 0.25)), + "p50": float(np.quantile(a, 0.50)), + "p75": float(np.quantile(a, 0.75)), + "p90": float(np.quantile(a, 0.90)), + "p95": float(np.quantile(a, 0.95)), + "p99": float(np.quantile(a, 0.99)), + "max": float(a.max()), + "mean": float(a.mean()), + } + + +def _plot_lengths(kept, output_dir: Path): + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + lens_in = [int(d.get("non_empty_chars_in", 0) or 0) for d in kept] + lens_out = [int(d.get("non_empty_chars_out", 0) or 0) for d in kept] + for ax, lens, title in ((axes[0], lens_in, "non_empty_chars_in"), + (axes[1], lens_out, "non_empty_chars_out")): + # Log scale — these are highly skewed (Greek PDFs are huge, Wikipedia is small). + data = [x for x in lens if x > 0] + ax.hist(data, bins=np.logspace(np.log10(max(min(data), 1)), + np.log10(max(data)), 60), + color="steelblue", edgecolor="black", alpha=0.8) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_title(f"{title} (log-log, N={len(data)})") + ax.set_xlabel("chars") + ax.set_ylabel("doc count") + ax.grid(True, alpha=0.3) + fig.tight_layout() + fig.savefig(output_dir / "01_lengths.png", dpi=120) + plt.close(fig) + + +def _plot_pct_changes(altered, output_dir: Path): + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + for ax, field, title in ((axes[0], "pct_chars_removed_non_empty", "% chars removed"), + (axes[1], "pct_lines_removed_non_empty", "% lines removed")): + vals = [float(d.get(field, 0) or 0) for d in altered] + # Clip to [0, 100] to handle any slight overflow from saturating math. + vals = [max(0.0, min(100.0, v)) for v in vals] + ax.hist(vals, bins=np.linspace(0, 100, 51), color="coral", + edgecolor="black", alpha=0.8) + ax.set_yscale("log") + ax.set_title(f"{title} (altered docs only, N={len(vals)}, log-y)") + ax.set_xlabel("%") + ax.set_ylabel("doc count") + ax.axvline(np.median(vals), color="red", linestyle="--", alpha=0.7, + label=f"median={np.median(vals):.2f}%") + ax.axvline(np.mean(vals), color="blue", linestyle=":", alpha=0.7, + label=f"mean={np.mean(vals):.2f}%") + ax.legend() + ax.grid(True, alpha=0.3) + fig.tight_layout() + fig.savefig(output_dir / "02_pct_changes.png", dpi=120) + plt.close(fig) + + +def _plot_drop_attribution(altered, output_dir: Path): + fig, axes = plt.subplots(1, 3, figsize=(18, 5)) + labels = [ + ("chars_dropped_by_line_drop", "line-drop"), + ("chars_dropped_by_normalization", "normalization"), + ("chars_dropped_by_per_char_filter", "per-char filter"), + ] + for ax, (field, title) in zip(axes, labels): + # Per-doc ratio of this bucket to the doc's non_empty_chars_in. + ratios = [] + for d in altered: + denom = max(int(d.get("non_empty_chars_in", 0) or 0), 1) + ratios.append(100.0 * float(d.get(field, 0) or 0) / denom) + ratios = [max(0.0, min(100.0, v)) for v in ratios] + ax.hist(ratios, bins=np.linspace(0, 100, 51), color="seagreen", + edgecolor="black", alpha=0.8) + ax.set_yscale("log") + ax.set_title(f"{title} (% of non_empty_chars_in, N={len(ratios)}, log-y)") + ax.set_xlabel("%") + ax.set_ylabel("doc count") + ax.grid(True, alpha=0.3) + fig.tight_layout() + fig.savefig(output_dir / "03_drop_attribution.png", dpi=120) + plt.close(fig) + + +def _plot_per_dataset(altered, output_dir: Path): + by_ds = defaultdict(list) + for d in altered: + by_ds[str(d.get("source_dataset", "unknown"))].append(d) + datasets = sorted(by_ds.keys(), + key=lambda k: -np.median([float(x.get("pct_chars_removed_non_empty", 0) or 0) + for x in by_ds[k]])) + fig, axes = plt.subplots(2, 1, figsize=(max(14, len(datasets) * 0.9), 10)) + for ax, field, title in ((axes[0], "pct_chars_removed_non_empty", "% chars removed per dataset"), + (axes[1], "pct_lines_removed_non_empty", "% lines removed per dataset")): + box_data = [[float(d.get(field, 0) or 0) for d in by_ds[ds]] for ds in datasets] + bp = ax.boxplot(box_data, tick_labels=[ds[:30] for ds in datasets], + showfliers=False, patch_artist=True) + for patch in bp["boxes"]: + patch.set_facecolor("lightblue") + ax.set_title(f"{title} — altered docs only, flyers hidden") + ax.set_ylabel("%") + ax.set_ylim(0, 100) + # Add n per dataset as annotation. + for i, ds in enumerate(datasets, 1): + ax.text(i, 95, f"n={len(by_ds[ds])}", ha="center", fontsize=7, + rotation=0, color="darkred") + plt.setp(ax.get_xticklabels(), rotation=45, ha="right", fontsize=8) + ax.grid(True, alpha=0.3, axis="y") + fig.tight_layout() + fig.savefig(output_dir / "04_per_dataset.png", dpi=120) + plt.close(fig) + + +def _plot_joint_density(altered, output_dir: Path): + fig, ax = plt.subplots(1, 1, figsize=(8, 7)) + x = np.asarray([float(d.get("pct_chars_removed_non_empty", 0) or 0) for d in altered]) + y = np.asarray([float(d.get("pct_lines_removed_non_empty", 0) or 0) for d in altered]) + x = np.clip(x, 0, 100); y = np.clip(y, 0, 100) + h = ax.hexbin(x, y, gridsize=50, cmap="viridis", bins="log", + extent=(0, 100, 0, 100)) + ax.set_xlabel("% chars removed") + ax.set_ylabel("% lines removed") + ax.set_title(f"Joint density (log count), N={len(x)}") + plt.colorbar(h, ax=ax, label="log doc count") + ax.plot([0, 100], [0, 100], "r--", alpha=0.4, label="y=x") + ax.legend(loc="upper right") + fig.tight_layout() + fig.savefig(output_dir / "05_joint_density.png", dpi=120) + plt.close(fig) + + +def _plot_cdf(altered, output_dir: Path): + fig, ax = plt.subplots(1, 1, figsize=(10, 6)) + for field, title, color in [ + ("pct_chars_removed_non_empty", "% chars removed", "coral"), + ("pct_lines_removed_non_empty", "% lines removed", "steelblue"), + ]: + vals = sorted(float(d.get(field, 0) or 0) for d in altered) + cdf = np.arange(1, len(vals) + 1) / len(vals) + ax.plot(vals, cdf, label=title, color=color, linewidth=1.5) + ax.set_xlabel("% removed") + ax.set_ylabel("cumulative fraction of altered docs") + ax.set_title(f"CDF of cleaning damage (altered docs only, N={len(altered)})") + ax.grid(True, alpha=0.3) + ax.legend() + fig.tight_layout() + fig.savefig(output_dir / "06_cdf.png", dpi=120) + plt.close(fig) + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--stats-glob", required=True) + parser.add_argument("--output-dir", required=True, type=Path) + args = parser.parse_args(argv) + args.output_dir.mkdir(parents=True, exist_ok=True) + + print(f"loading stats from {args.stats_glob} ...") + stats = _load_stats(args.stats_glob) + kept = _kept_only(stats) + altered = _altered_kept(stats) + print(f" total rows: {len(stats)}") + print(f" kept (survived cleaning): {len(kept)}") + print(f" altered (kept + non-zero damage): {len(altered)}") + + # Drop reasons. + drop_reasons = defaultdict(int) + for d in stats: + r = d.get("drop_reason") or "" + if r: + drop_reasons[r] += 1 + + summary = { + "n_total": len(stats), + "n_kept": len(kept), + "n_altered": len(altered), + "n_untouched": len(kept) - len(altered), + "drop_reasons": dict(drop_reasons), + "length_in": _quantile_summary( + [int(d.get("non_empty_chars_in", 0) or 0) for d in kept], + "non_empty_chars_in"), + "length_out": _quantile_summary( + [int(d.get("non_empty_chars_out", 0) or 0) for d in kept], + "non_empty_chars_out"), + "pct_chars_removed": _quantile_summary( + [float(d.get("pct_chars_removed_non_empty", 0) or 0) for d in altered], + "pct_chars_removed_non_empty (altered only)"), + "pct_lines_removed": _quantile_summary( + [float(d.get("pct_lines_removed_non_empty", 0) or 0) for d in altered], + "pct_lines_removed_non_empty (altered only)"), + } + + # Per-dataset summary. + by_ds = defaultdict(list) + for d in kept: + by_ds[str(d.get("source_dataset", "unknown"))].append(d) + per_ds = {} + for ds, docs in by_ds.items(): + altered_ds = [d for d in docs if float(d.get("pct_chars_removed_non_empty", 0) or 0) > 0 + or int(d.get("lines_dropped_by_cleaner", 0) or 0) > 0] + per_ds[ds] = { + "n_kept": len(docs), + "n_altered": len(altered_ds), + "pct_altered": round(100.0 * len(altered_ds) / max(len(docs), 1), 2), + "pct_chars_removed_median": round(float(np.median( + [float(d.get("pct_chars_removed_non_empty", 0) or 0) for d in altered_ds] + or [0])), 3), + "pct_lines_removed_median": round(float(np.median( + [float(d.get("pct_lines_removed_non_empty", 0) or 0) for d in altered_ds] + or [0])), 3), + } + summary["per_dataset"] = per_ds + + (args.output_dir / "summary.json").write_text( + json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + # Plots. + print("plotting...") + _plot_lengths(kept, args.output_dir) + _plot_pct_changes(altered, args.output_dir) + _plot_drop_attribution(altered, args.output_dir) + _plot_per_dataset(altered, args.output_dir) + _plot_joint_density(altered, args.output_dir) + _plot_cdf(altered, args.output_dir) + + # Markdown narrative. + md = ["# Cleaning distribution analysis", "", + f"Source: `{args.stats_glob}`", "", + "## Top-level counts", "", + f"- Total rows: **{summary['n_total']}**", + f"- Kept (survived cleaning): **{summary['n_kept']}**", + f"- Altered (kept + non-zero damage): **{summary['n_altered']}**", + f"- Untouched (kept + zero damage): **{summary['n_untouched']}**", + "", + "### Drop reasons", + "", + "| reason | count |", + "|---|---:|"] + for r, n in sorted(drop_reasons.items(), key=lambda x: -x[1]): + md.append(f"| {r} | {n} |") + md.extend(["", "## Length distributions", ""]) + for key in ("length_in", "length_out"): + qs = summary[key] + md.append(f"### {qs['label']} (N={qs['n']})") + md.append(f"- min/p25/p50/p75/p95/p99/max: " + f"{qs['min']:.0f} / {qs['p25']:.0f} / {qs['p50']:.0f} / " + f"{qs['p75']:.0f} / {qs['p95']:.0f} / {qs['p99']:.0f} / {qs['max']:.0f}") + md.append(f"- mean: {qs['mean']:.0f}") + md.append("") + md.extend(["## Pct changes (altered docs only)", ""]) + for key in ("pct_chars_removed", "pct_lines_removed"): + qs = summary[key] + md.append(f"### {qs['label']} (N={qs['n']})") + md.append(f"- p05/p25/p50/p75/p90/p95/p99/max: " + f"{qs['p05']:.2f}% / {qs['p25']:.2f}% / {qs['p50']:.2f}% / " + f"{qs['p75']:.2f}% / {qs['p90']:.2f}% / {qs['p95']:.2f}% / " + f"{qs['p99']:.2f}% / {qs['max']:.2f}%") + md.append(f"- mean: {qs['mean']:.2f}%") + md.append("") + md.extend(["## Per-dataset summary", "", + "| dataset | n_kept | n_altered | % altered | median %chars | median %lines |", + "|---|---:|---:|---:|---:|---:|"]) + for ds, s in sorted(per_ds.items(), key=lambda kv: -kv[1]["pct_altered"]): + md.append(f"| {ds} | {s['n_kept']} | {s['n_altered']} | {s['pct_altered']}% " + f"| {s['pct_chars_removed_median']}% | {s['pct_lines_removed_median']}% |") + md.extend(["", "## Plots", "", + "- `01_lengths.png` — doc length histograms (log-log)", + "- `02_pct_changes.png` — % chars/lines removed histograms", + "- `03_drop_attribution.png` — four-way drop bucket comparison", + "- `04_per_dataset.png` — per-dataset boxplots", + "- `05_joint_density.png` — pct_chars vs pct_lines 2D hexbin", + "- `06_cdf.png` — cumulative distribution of cleaning damage", + ""]) + (args.output_dir / "distributions.md").write_text("\n".join(md), encoding="utf-8") + print(f"summary → {args.output_dir / 'summary.json'}") + print(f"report → {args.output_dir / 'distributions.md'}") + print(f"plots → {args.output_dir}/") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/cleaning_scripts/analyze_quality_vs_deletions.py b/cleaning_scripts/analyze_quality_vs_deletions.py new file mode 100644 index 0000000..909d9a8 --- /dev/null +++ b/cleaning_scripts/analyze_quality_vs_deletions.py @@ -0,0 +1,190 @@ +"""Join per-doc cleaner stats with upstream parquet quality scores, +then show how the 4 quality metrics relate to deletion percentage: + +- `greek_badness_score` (upstream) +- `mojibake_badness_score` (upstream) +- `ascii_punct_ratio` (our new charset metric) +- `moji_residue_ratio` (our new charset metric) + +X-axes: +- `pct_chars_removed_non_empty` (total deletion, includes normalization) +- `char_strip_ratio` = chars_dropped_by_per_char_filter / non_empty_chars_in + (cleaning-only deletion, the real content-loss signal) + +Buckets of deletion %: [0, 1, 5, 10, 20, 40, 60, 100] → 7 bins. +For each bin: median / p25 / p75 / p95 of each of the 4 metrics. +Also: correlation coefficients (Spearman) between deletion % and each +metric. + +Reports both KEPT docs (cleaner output has text) and the FULL SET +(including charset-dropped docs) so we can see the tail. +""" +from __future__ import annotations + +import argparse +import glob as globmod +import json +from collections import defaultdict +from pathlib import Path +from typing import Any, Dict, List + +import numpy as np + + +def _iter_stats(stats_glob: str): + for p in sorted(globmod.glob(stats_glob)): + with open(p, "r", encoding="utf-8") as fh: + for line in fh: + yield json.loads(line) + + +def _load_upstream(path: Path) -> Dict[str, Dict[str, Any]]: + out = {} + with path.open("r", encoding="utf-8") as fh: + for line in fh: + d = json.loads(line) + key = (d["source_dataset"], d["source_doc_id"]) + out[key] = d + return out + + +def _f(v): + if v is None: + return None + try: + return float(v) + except (ValueError, TypeError): + return None + + +def main(argv=None) -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--stats-glob", required=True) + parser.add_argument("--upstream-path", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + args = parser.parse_args(argv) + args.output_dir.mkdir(parents=True, exist_ok=True) + + print("loading upstream scores ...") + upstream = _load_upstream(args.upstream_path) + print(f" {len(upstream)} rows") + + # Join + build per-doc records. + rows: List[Dict[str, Any]] = [] + for s in _iter_stats(args.stats_glob): + ds = s.get("source_dataset") + did = str(s.get("source_doc_id") or "") + up = upstream.get((ds, did), {}) + rows.append({ + "dataset": ds, + "drop_reason": s.get("drop_reason") or "", + "kept": not s.get("drop_reason"), + "pct_removed": _f(s.get("pct_chars_removed_non_empty")), + "char_strip_ratio": + 100.0 * (int(s.get("chars_dropped_by_per_char_filter", 0) or 0) + / max(int(s.get("non_empty_chars_in", 0) or 0), 1)) + if s.get("non_empty_chars_in") else None, + "greek_badness": _f(up.get("greek_badness_score")), + "mojibake_badness": _f(up.get("mojibake_badness_score")), + "punct_ratio": _f(s.get("charset_punct_ratio")), + "moji_ratio": _f(s.get("charset_moji_ratio")), + "greek_pct_upstream": _f(up.get("greek_percentage")), + }) + print(f" joined: {len(rows)} docs") + + # Bucket ranges on pct_removed (for kept docs) and char_strip_ratio. + buckets = [(0, 1), (1, 5), (5, 10), (10, 20), (20, 40), (40, 60), (60, 101)] + metrics = [ + ("greek_badness", "greek_badness_score (upstream)"), + ("mojibake_badness","mojibake_badness_score (upstream)"), + ("punct_ratio", "ascii_punct_ratio (new, as %)"), + ("moji_ratio", "moji_residue_ratio (new, as %)"), + ] + + def bucket_report(field: str, label: str, filter_fn) -> List[str]: + lines = [f"### {label} — bucketed by {field}", ""] + header = "| bucket | N | " + " | ".join(f"{m[1]} median (p25–p75, p95)" for m in metrics) + " |" + lines.append(header) + lines.append("|---|---:|" + "---|" * len(metrics)) + for lo, hi in buckets: + subset = [r for r in rows if filter_fn(r) and r[field] is not None and lo <= r[field] < hi] + row = f"| {lo}–{hi}% | {len(subset)} |" + for key, _ in metrics: + vals = [r[key] for r in subset if r[key] is not None] + if not vals: + row += " — |" + continue + a = np.array(vals) + scale = 100.0 if key in ("punct_ratio", "moji_ratio") else 1.0 + row += (f" {np.median(a)*scale:.2f} " + f"({np.quantile(a, 0.25)*scale:.2f}–{np.quantile(a, 0.75)*scale:.2f}, " + f"p95={np.quantile(a, 0.95)*scale:.2f}) |") + lines.append(row) + lines.append("") + # Spearman correlations. + lines.append(f"**Spearman ρ between {field} and each metric (kept docs only):**") + lines.append("") + subset = [r for r in rows if filter_fn(r) and r[field] is not None] + xs = np.array([r[field] for r in subset]) + for key, name in metrics: + ys_raw = [r[key] for r in subset] + valid = [(x, y) for x, y in zip(xs, ys_raw) if y is not None] + if len(valid) < 30: + lines.append(f"- {name}: N too small") + continue + x_arr = np.array([v[0] for v in valid]) + y_arr = np.array([v[1] for v in valid]) + x_rank = np.argsort(np.argsort(x_arr)) + y_rank = np.argsort(np.argsort(y_arr)) + if np.std(x_rank) == 0 or np.std(y_rank) == 0: + rho = float('nan') + else: + rho = float(np.corrcoef(x_rank, y_rank)[0, 1]) + lines.append(f"- {name}: ρ = {rho:+.3f} (N={len(valid)})") + lines.append("") + return lines + + md = ["# Quality metrics vs deletion % — all 168,078 docs", "", + f"Kept docs: {sum(1 for r in rows if r['kept'])}", + f"Dropped docs: {sum(1 for r in rows if not r['kept'])}", + "", + "The 4 quality signals:", + "- **greek_badness_score** — pre-existing upstream score, higher = worse Greek", + "- **mojibake_badness_score** — pre-existing upstream score, higher = more mojibake", + "- **ascii_punct_ratio** — our new metric, high = font-substitution mojibake", + "- **moji_residue_ratio** — our new metric, high = Latin-1/IPA/PUA mojibake", + "", + "The deletion X-axis:", + "- **pct_chars_removed_non_empty** — total deletion incl. whitespace normalization", + "- **char_strip_ratio** — per-char filter only (real content loss)", + "", + "## 1) Kept docs — bucketed by pct_chars_removed_non_empty", + ""] + md.extend(bucket_report("pct_removed", + "pct_chars_removed_non_empty (kept docs only)", + lambda r: r["kept"])) + + md.append("## 2) Kept docs — bucketed by char_strip_ratio") + md.append("") + md.extend(bucket_report("char_strip_ratio", + "char_strip_ratio (kept docs only)", + lambda r: r["kept"])) + + md.append("## 3) All docs (incl. charset-dropped) — bucketed by char_strip_ratio") + md.append("") + md.append("Note: docs dropped by the charset filter have no cleaner output, " + "so their char_strip_ratio is 0 in the stats — they end up in the " + "first bucket. Use this view mostly for the metric medians per bucket.") + md.append("") + md.extend(bucket_report("char_strip_ratio", + "char_strip_ratio (all docs)", + lambda r: True)) + + (args.output_dir / "quality_vs_deletions.md").write_text( + "\n".join(md), encoding="utf-8") + print(f"report → {args.output_dir / 'quality_vs_deletions.md'}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/cleaning_scripts/clean_and_stats_rowsharded.py b/cleaning_scripts/clean_and_stats_rowsharded.py new file mode 100644 index 0000000..362f77d --- /dev/null +++ b/cleaning_scripts/clean_and_stats_rowsharded.py @@ -0,0 +1,533 @@ +"""Row-sharded version of clean_and_stats_full.py. + +Instead of 1 worker = 1 parquet (limits parallelism to N parquets), each +worker processes a ROW RANGE of a parquet. With --workers 48 and 250+ +HPLT parquets each with 500k rows, we get 48-way parallelism uniformly +without any worker monopolizing one big parquet. + +Layout: + input-glob → list of parquets + Each parquet is split into `--shards-per-parquet` equal row-chunks + → N_parquets × shards_per_parquet tasks → mp.Pool(workers=...) starmap. + +Per-task output: + /.shard_of.stats.jsonl + /.shard_of.txt.gz + +Downstream consumers iterate all *.stats.jsonl / *.txt.gz — the shard +suffix is transparent. + +Same matcher + cleaner + threshold logic as clean_and_stats_full.py. +""" +from __future__ import annotations + +import argparse +import glob as globmod +import gzip +import json +import multiprocessing as mp +import os +import sys +import time +from pathlib import Path +from typing import Any, Dict, List, Optional + +import pyarrow.parquet as pq + + +DEFAULT_SCRIPTS = ["greek", "latin", "french", "spanish", "punctuation", "numbers", "common_symbols"] + + +def _safe(s: str) -> str: + return "".join(c if c.isalnum() or c in "_-." else "_" for c in s) + + +def _load_thresholds(path: Path) -> Dict[str, Optional[int]]: + data = json.loads(path.read_text(encoding="utf-8")) + suggested = data.get("suggested_thresholds") or {} + return { + "font_name_literal": suggested.get("font_marker"), + "glyph_font_like": suggested.get("glyph_marker"), + "script_residue_restricted": suggested.get("script_residue"), + } + + +def _doc_drop_reason(doc_counters, thresholds): + """Doc-level drop rules removed 2026-04-25. + + `font_name_literal` and `glyph_font_like` doc-drops were removed + after the user observed that the line-drop rules in the Rust + cleaner already cover the same noise patterns at finer + granularity: + + - `PDF_FONT_SUBSET_REGEX` line-drop uses the IDENTICAL pattern + `/[A-Z]{6}\\+[A-Z][A-Za-z0-9-]+` as the `font_name_literal` + counter — line-drop is strictly less destructive. + - `BAD_LINE_AC` + `GLYPH_FONT_TAG_REGEX` + `FONT_GLYPH_TAG_REGEX` + line-drops cover the structural-PDF-residue subset of + `glyph_font_like`. The 50 PostScript glyph names + `/uni`/`/g` + regex part of `glyph_font_like` is more aggressive but no + longer drives doc rejection. + + The script_residue_restricted page-level rule is unaffected and + still applied separately in the main loop. + """ + return "" + + +def _page_script_residue_count(page): + """Pages now carry `per_category_match_count` from Rust — this is a + cheap dict lookup. Accepts either the full page dict OR the old + matches_json string for back-compat with callers not yet migrated.""" + if isinstance(page, dict): + pc = page.get("per_category_match_count") or {} + return int(pc.get("script_residue_restricted", 0) or 0) + # Legacy path: matches_json string. + try: + matches = json.loads(page or "[]") + except Exception: + return 0 + return sum(1 for m in matches + if "script_residue_restricted" in list(m.get("categories") or [])) + + +_MARKER_LINES = { + "", + "", + "", +} + + +def _non_empty_stats(text: str): + """Return (line_count_total, non_empty_line_count, non_empty_char_count). + + A line is "non-empty" if its trimmed form is non-empty AND isn't + one of our known marker comments. Character count sums only chars + on non-empty, non-marker lines (newlines excluded). + """ + total_lines = 0 + non_empty_lines = 0 + non_empty_chars = 0 + for line in text.split("\n"): + total_lines += 1 + stripped = line.strip() + if not stripped: + continue + if stripped in _MARKER_LINES: + continue + non_empty_lines += 1 + non_empty_chars += len(line) + return total_lines, non_empty_lines, non_empty_chars + + +def _process_row_shard( + parquet_path: str, + start_row: int, + end_row: int, + stats_path: str, + text_gz_path: str, + category_specs_path: str, + thresholds: Dict[str, Optional[int]], + scripts_to_keep: List[str], + text_column: str, + doc_id_column: str, + dataset_column: str, + batch_size: int, +) -> Dict[str, Any]: + import glossapi_rs_cleaner as cleaner + # CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 7: the matcher's + # three-counter PyO3 surface (`match_token_category_debug_text`) + # is gone. The cleaner now emits per-rule match counts directly + # in `clean_stats` (`rule_a_match_count`, `rule_b_match_count`, + # `residue_line_drop_count`). No separate matcher invocation. + + scratch_root = Path(stats_path).parent + scratch = scratch_root / f"_scratch_{Path(stats_path).stem}" + scratch.mkdir(parents=True, exist_ok=True) + + start = time.time() + rows_seen = 0 + rows_kept = 0 + rows_dropped: Dict[str, int] = {} + total_chars_before = 0 + total_chars_after = 0 + total_chars_dropped = 0 + phase_a_fallback_count = 0 + phase_a_dialect_ambiguous_count = 0 + + global_row_idx = 0 + with open(stats_path, "w", encoding="utf-8") as stats_fh, \ + gzip.open(text_gz_path, "wt", encoding="utf-8") as text_fh: + pf = pq.ParquetFile(parquet_path) + for batch in pf.iter_batches(batch_size=batch_size): + rows = batch.to_pylist() + for row in rows: + if global_row_idx < start_row: + global_row_idx += 1 + continue + if global_row_idx >= end_row: + break + global_row_idx += 1 + rows_seen += 1 + text = row.get(text_column) or "" + chars_before = len(text) + if not text.strip(): + rows_dropped["empty"] = rows_dropped.get("empty", 0) + 1 + stats_fh.write(json.dumps({ + "source_path": f"{parquet_path}#{row.get(doc_id_column)}", + "source_doc_id": row.get(doc_id_column), + "source_dataset": row.get(dataset_column), + "chars_before": 0, "chars_after": 0, + "chars_removed": 0, "pct_removed": 0.0, + "counter_font_marker": 0, "counter_glyph_marker": 0, + "counter_script_residue": 0, "drop_reason": "empty", + }) + "\n") + continue + + source_doc_id = str(row.get(doc_id_column) or f"row-{rows_seen}") + source_dataset = str(row.get(dataset_column) or Path(parquet_path).stem) + source_path = f"{parquet_path}#{source_doc_id}" + source_stem = _safe(f"{source_dataset}__{source_doc_id}")[:200] + base_stem = _safe(source_dataset) + + # Pre-clean charset analysis. As of 2026-04-25, NO charset + # ratio drives a doc-level rejection here — the moji / + # punct / greek-low / counter-{glyph,font_name} rules + # were all removed after user review of v7 samples + # showed they were too aggressive. The ratios + counters + # are still emitted in the per-doc stats jsonl as + # diagnostic / threshold-study levers, and the line-drop + # rules in the Rust cleaner already cover the noise + # patterns these doc-drops were targeting. + cs = cleaner.analyze_charset(text) + + # Doc-level counters initialised; populated AFTER cleaning + # from `clean_stats` per-rule fields (Point 7). Pre-cleaning + # matcher invocation removed. + doc_counters: Dict[str, int] = { + "font_name_literal": 0, "glyph_font_like": 0, "script_residue_restricted": 0, + } + + # Attach the charset ratios computed above so kept-docs + # records show them (distribution/threshold calibration). + charset_greek_ratio = round(cs["greek_letter_ratio"], 4) + charset_moji_ratio = round(cs["moji_residue_ratio"], 4) + charset_punct_ratio = round(cs["ascii_punct_ratio"], 4) + # Combined metric per 2026-04-23: additive, no weighting. + # Interpretation: fraction of non-whitespace chars that look + # like mojibake residue by either axis. NOT a rejection + # signal on its own — user review needed before setting cutoff. + mojibake_noise_ratio = round( + charset_moji_ratio + charset_punct_ratio, 4) + + # Doc-level drop: font + glyph only. + reason = _doc_drop_reason(doc_counters, thresholds) + if reason: + rows_dropped[reason] = rows_dropped.get(reason, 0) + 1 + total_chars_dropped += chars_before + stats_fh.write(json.dumps({ + "source_path": source_path, "source_doc_id": source_doc_id, + "source_dataset": source_dataset, + "chars_before": chars_before, "chars_after": 0, + "chars_removed": chars_before, "pct_removed": 100.0, + "counter_font_marker": doc_counters["font_name_literal"], + "counter_glyph_marker": doc_counters["glyph_font_like"], + "counter_script_residue": doc_counters["script_residue_restricted"], + "charset_greek_ratio": charset_greek_ratio, + "charset_moji_ratio": charset_moji_ratio, + "charset_punct_ratio": charset_punct_ratio, + "mojibake_noise_ratio": mojibake_noise_ratio, + "drop_reason": reason, + }) + "\n") + continue + + # Page-level script_residue rule REMOVED 2026-04-25. + # Replaced by the LINE-level R1 ∪ R2 rule in the Rust + # cleaner (`normalize::is_residue_mojibake_line` → + # invoked from `cleaning_module::core_clean_text_with_stats` + # alongside BAD_LINE_AC and has_decoded_glyph_font_artefact). + # The line-level rule is finer-grained (drops only the + # offending line, not a whole page) and was empirically + # validated on the v7 sample + # `top500_by_counter_script_residue` (2.6 M body lines). + # Kept the per-doc accounting fields below for backward + # compatibility with downstream consumers; they always + # report 0 now. + pages_dropped_sr = 0 + chars_dropped_sr_pages = 0 + text_for_cleaner = text + + # v6 wave-2 (2026-04-23): enable LaTeX repetition crop. + # Default thresholds (char=30, line=3) tuned for typical + # math-OCR `+ + + + + …` runs and `x = x = x =` lines. + # See latex_module.rs::crop_latex_repetitions docs. + cleaned, clean_stats = cleaner.clean_text_with_stats( + text_for_cleaner, scripts_to_keep, + None, # min_chars_for_comment (default) + True, # enable_latex_repetition_crop + 30, # latex_char_threshold + 3, # latex_line_threshold + ) + if clean_stats.get("phase_a_fallback_reason") is not None: + phase_a_fallback_count += 1 + if clean_stats.get("phase_a_dialect_ambiguous_input"): + phase_a_dialect_ambiguous_count += 1 + # Point 7 migration: populate doc_counters from cleaner's + # per-rule match counts (replaces the deleted matcher). + # Note Rule B is now ONE engine (not split into + # font_name_literal vs glyph_font_like at cleaner-level). + # We attribute Rule A to glyph_marker (PostScript glyph-name + # literals) and Rule B to glyph_marker too (regex covers + # GLYPH<…>, /uniXXXX, /gN, font subsets, ...). The + # font_name_literal counter is no longer separately + # measurable post-unification — kept at 0 for back-compat. + doc_counters["font_name_literal"] = 0 + doc_counters["glyph_font_like"] = int( + clean_stats.get("rule_a_match_count", 0) + + clean_stats.get("rule_b_match_count", 0) + ) + doc_counters["script_residue_restricted"] = int( + clean_stats.get("residue_line_drop_count", 0) + ) + # Four-way char-drop attribution + line-drop count come from + # the Rust side. Quality stats (non-empty lines/chars in/out) + # and derived percentages computed here in Python. + # Rust-side non_empty_line_stats — replaces the Python + # _non_empty_stats helper. Iterates text once; orders of + # magnitude faster on large docs. + lines_in_total, non_empty_lines_in, non_empty_chars_in = ( + cleaner.non_empty_line_stats(text_for_cleaner) + ) + lines_out_total, non_empty_lines_out, non_empty_chars_out = ( + cleaner.non_empty_line_stats(cleaned) + ) + if not cleaned.strip() or cleaned.strip().startswith("", + "", + "---", + "", + ] + # Full doc, no truncation. --max-text-chars defaults to 0. + # Only truncate if user explicitly opts in with a positive cap. + if args.max_text_chars > 0 and len(body_text) > args.max_text_chars: + half = args.max_text_chars // 2 + lines.append(body_text[:half]) + lines.append(f"\n*[...display truncated by --max-text-chars {args.max_text_chars}; body was {len(body_text):,} chars...]*\n") + lines.append(body_text[-half:]) + else: + lines.append(body_text) + (target_dir / fname).write_text("\n".join(lines), encoding="utf-8") + n_written += 1 + print(f"wrote {n_written} files to {args.output_dir}") + + +if __name__ == "__main__": + main() diff --git a/cleaning_scripts/regenerate_samples.py b/cleaning_scripts/regenerate_samples.py new file mode 100644 index 0000000..fbb2d22 --- /dev/null +++ b/cleaning_scripts/regenerate_samples.py @@ -0,0 +1,76 @@ +"""Regenerate /tmp/cleaner-100-test/cleaned_docs/ with FAITHFUL Pilot B +output (newlines preserved by calling `clean_text_with_stats` directly, +bypassing the driver's lossy newline→space squash). + +Sampling filters applied: + - empty body after stripping HTML comments and line-removed markers + - Greek-letter ratio < 50% on the cleaned body +""" +import json, re, shutil +from pathlib import Path + +import pyarrow.parquet as pq +import glossapi_rs_cleaner as cleaner + +INPUT = Path("/tmp/cleaner-100-test/input/openarchives_100.parquet") +STATS_DIR = Path("/tmp/cleaner-100-test/stats") +OUT = Path("/tmp/cleaner-100-test/cleaned_docs") + +SCRIPTS = ["greek", "latin", "french", "spanish", "punctuation", "numbers", "common_symbols"] +COMMENT_RE = re.compile(r"", re.DOTALL) +GREEK_RE = re.compile(r"[Ͱ-Ͽἀ-῿]") +LETTER_RE = re.compile(r"[^\W\d_]", re.UNICODE) + +if OUT.exists(): + shutil.rmtree(OUT) +OUT.mkdir(parents=True) + +src = pq.read_table(INPUT) +ids = src.column("source_doc_id").to_pylist() +texts = src.column("text").to_pylist() + +stats_by_id = {} +for sp in STATS_DIR.glob("*.stats.jsonl"): + for line in sp.read_text(encoding="utf-8").splitlines(): + if not line.strip(): + continue + rec = json.loads(line) + stats_by_id[rec["source_doc_id"]] = rec + +written = 0 +skipped_empty = 0 +skipped_low_greek = 0 +for doc_id, raw in zip(ids, texts): + cleaned, _ = cleaner.clean_text_with_stats( + raw, SCRIPTS, None, True, 30, 3, "parser_surgical_verified" + ) + body = COMMENT_RE.sub("", cleaned).strip() + if not body: + skipped_empty += 1 + continue + letters = LETTER_RE.findall(body) + if not letters: + skipped_empty += 1 + continue + greek_ratio = sum(1 for ch in letters if GREEK_RE.match(ch)) / len(letters) + if greek_ratio < 0.50: + skipped_low_greek += 1 + continue + + s = stats_by_id.get(doc_id, {}) + pct = s.get("pct_chars_removed_non_empty", 0.0) + glyph = s.get("counter_glyph_marker", 0) + residue = s.get("counter_script_residue", 0) + header = ( + f"\n" + f"\n" + f"\n\n" + ) + fname = f"{pct:06.2f}__{doc_id[:16]}.md" + (OUT / fname).write_text(header + cleaned, encoding="utf-8") + written += 1 + +print(f"wrote {written} docs to {OUT}") +print(f"skipped (empty body): {skipped_empty}") +print(f"skipped (<50% Greek): {skipped_low_greek}") diff --git a/cleaning_scripts/smoke_tests/test_rust_extensions_smoke.py b/cleaning_scripts/smoke_tests/test_rust_extensions_smoke.py new file mode 100644 index 0000000..b5fc34f --- /dev/null +++ b/cleaning_scripts/smoke_tests/test_rust_extensions_smoke.py @@ -0,0 +1,210 @@ +""" +Python integration smoke test for the GlossAPI Rust extensions. + +Exercises the entry points production code paths actually hit: + - glossapi_rs_cleaner.clean_text() — basic clean + - glossapi_rs_cleaner.clean_text_with_stats() — clean + accounting + - glossapi_rs_cleaner.analyze_charset() — charset metrics + - glossapi_rs_cleaner.non_empty_line_stats() — line accounting + - glossapi_rs_noise.match_token_category_debug_text() — restored matcher PyO3 + - glossapi_rs_noise.evaluate_page_character_noise() — OCR-side noise scoring + +Run via: + .venv-hplt-review/bin/python3 /tmp/cleaner-smoke-test.py +""" +import json +import sys +from pathlib import Path + +import glossapi_rs_cleaner as cleaner +import glossapi_rs_noise as noise + +SCRIPTS = ["greek", "latin", "french", "spanish", "punctuation", "numbers", "common_symbols"] +GREEK_CLEAN = "Καλημέρα κόσμε! Τι όμορφη μέρα.\n" +GREEK_NOISY = ( + "Καλημέρα /uni0301/uni0302/uni0303/uni0304/uni0305/uni0306" + "/uni0307/uni0308/uni0309/uni030A/uni030B/uni030C\n" + "\n" # blank line: keeps Επίλογος in its own paragraph after reflow + "Επίλογος.\n" +) +LATIN1_TEST = "Hello µ-test © 2026 €\n" +PILOT_B_INPUT = "# Heading\n\nParagraph one.\n\nParagraph two with 𝑥 = 1.\n" + +failures: list[str] = [] + + +def check(name: str, cond: bool, detail: str = ""): + status = "PASS" if cond else "FAIL" + print(f" [{status}] {name}{(' — ' + detail) if detail else ''}") + if not cond: + failures.append(name) + + +print("=" * 72) +print("1. clean_text()") +print("=" * 72) +out = cleaner.clean_text(GREEK_CLEAN, SCRIPTS, None, True, 30, 3, "parser_surgical_verified") +check("clean_text returns string", isinstance(out, str)) +check("clean_text preserves Greek chars", "Καλημέρα" in out) +check("clean_text accepts phase_a_mode arg", True, "no exception") + +# LineBased opt-in path +out_lb = cleaner.clean_text(GREEK_CLEAN, SCRIPTS, None, True, 30, 3, "line_based") +check("clean_text accepts line_based", isinstance(out_lb, str)) + +print() +print("=" * 72) +print("2. clean_text_with_stats()") +print("=" * 72) +cleaned, stats = cleaner.clean_text_with_stats( + GREEK_NOISY, SCRIPTS, None, True, 30, 3, "parser_surgical_verified" +) +check("returns (str, dict)", isinstance(cleaned, str) and isinstance(stats, dict)) + +# Required stats fields per the cleaner's contract +required = [ + "content_chars_kept", + "chars_dropped_by_line_drop", + "chars_dropped_by_normalization", + "chars_dropped_by_per_char_filter", + "lines_dropped_count", + "marker_chars_passthrough", + "marker_chars_added", + "original_chars_for_badness", + "sum_kept_line_content_chars", + "phase_a_fallback_reason", + "phase_a_dialect_ambiguous_input", + "rule_a_match_count", + "rule_b_match_count", + "residue_line_drop_count", +] +for k in required: + check(f"stats has '{k}'", k in stats) + +# Concrete behaviour: dense /uniXXXX line should hit Rule B count+coverage gate +check( + "Rule B fires on dense /uniXXXX line", + stats["rule_b_match_count"] >= 12, + f"rule_b_match_count={stats['rule_b_match_count']}", +) +check( + "noisy input drops ≥1 line", + stats["lines_dropped_count"] >= 1, + f"lines_dropped_count={stats['lines_dropped_count']}", +) +check( + "Greek prose on non-noisy line preserved", + "Επίλογος" in cleaned, + "(Καλημέρα is on the noisy line that drops correctly)", +) + +# µ→μ fold (Group 2) +cleaned_latin1, _ = cleaner.clean_text_with_stats( + LATIN1_TEST, SCRIPTS, None, True, 30, 3, "parser_surgical_verified" +) +check( + "U+00B5 (µ) folds to U+03BC (μ)", + "μ" in cleaned_latin1 and "µ" not in cleaned_latin1, + f"output={cleaned_latin1!r}", +) + +# Pilot B fallback signal exposed +cleaned_pb, stats_pb = cleaner.clean_text_with_stats( + PILOT_B_INPUT, SCRIPTS, None, True, 30, 3, "parser_surgical_verified" +) +check( + "phase_a_fallback_reason is None or str (not raise)", + stats_pb["phase_a_fallback_reason"] is None or isinstance(stats_pb["phase_a_fallback_reason"], str), +) + +print() +print("=" * 72) +print("3. analyze_charset()") +print("=" * 72) +cs = cleaner.analyze_charset(GREEK_CLEAN) +check("analyze_charset returns dict", isinstance(cs, dict)) +check("greek_letter_ratio > 0.5", cs.get("greek_letter_ratio", 0) > 0.5) + +print() +print("=" * 72) +print("4. non_empty_line_stats()") +print("=" * 72) +total, ne_lines, ne_chars = cleaner.non_empty_line_stats(GREEK_CLEAN) +check("returns 3-tuple", isinstance(total, int) and isinstance(ne_lines, int) and isinstance(ne_chars, int)) +check("counts include the one Greek line", ne_lines >= 1 and ne_chars > 0) + +print() +print("=" * 72) +print("5. noise.evaluate_page_character_noise() — OCR-side smoke") +print("=" * 72) +res = noise.evaluate_page_character_noise(GREEK_CLEAN) +check("returns dict", isinstance(res, dict)) +check("has total_chars / bad_char_ratio", "total_chars" in res and "bad_char_ratio" in res) + +print() +print("=" * 72) +print("6. noise.match_token_category_debug_text() — RESTORED matcher PyO3") +print("=" * 72) +# Build a tiny single-category spec on the fly (was three_counter_spec_*) +specs = [ + { + "category": "glyph_font_like", + "pattern_family": "uni_glyph", + "match_kind": "regex", + "pattern": "/uni[0-9A-Fa-f]{4,6}", + }, +] +specs_path = Path("/tmp/cleaner-smoke-test-spec.json") +specs_path.write_text(json.dumps(specs), encoding="utf-8") +out_dir = Path("/tmp/cleaner-smoke-test-matcher-out") +out_dir.mkdir(parents=True, exist_ok=True) +rows = noise.match_token_category_debug_text( + GREEK_NOISY, + str(out_dir), + str(specs_path), + "smoke_source", + "smoke_stem", + "smoke_base", + write_files=False, +) +check("matcher returns list of pages", isinstance(rows, list)) +check("matcher non-empty", len(rows) > 0) +import json as _json +# per_category_match_count tallies MERGED spans, not raw matches — +# 12 contiguous /uniXXXX hits merge into one span. Count the raw +# matches inside matches_json to verify the matcher saw all 12. +total_raw = 0 +for row in rows: + matches = _json.loads(row.get("matches_json") or "[]") + for m in matches: + total_raw += len(m.get("raw_texts") or [m.get("matched_text", "")]) +check( + "matcher saw 12 /uniXXXX raw hits inside the merged span", + total_raw >= 12, + f"total_raw={total_raw}", +) + +# Bug 1 verification: char offsets, not byte offsets, on Greek-prefixed input +import json as _json +matches_json = rows[0].get("matches_json", "[]") +matches = _json.loads(matches_json) +if matches: + first = matches[0] + page_text = rows[0].get("page_text", "") + sliced = page_text[first["start"]:first["end"]] + check( + "Bug 1: char-offset slice == matched_text on Greek prefix", + sliced == first.get("matched_text"), + f"sliced={sliced!r} expected={first.get('matched_text')!r}", + ) + +print() +print("=" * 72) +print("Summary") +print("=" * 72) +if failures: + print(f"FAILED: {len(failures)} checks") + for f in failures: + print(f" - {f}") + sys.exit(1) +print("ALL PASS") diff --git a/cleaning_scripts/validate_gzipped_shards.py b/cleaning_scripts/validate_gzipped_shards.py new file mode 100644 index 0000000..8cdb965 --- /dev/null +++ b/cleaning_scripts/validate_gzipped_shards.py @@ -0,0 +1,78 @@ +"""Validate the gzipped text-shard output is exactly the cleaner's output +under the documented newline→space transformation, with no other alteration. + +For each kept doc: + 1. Read raw input from the source parquet. + 2. Run `cleaner.clean_text_with_stats(raw, …, "parser_surgical_verified")` + — same call the driver makes — to get the canonical cleaned text. + 3. Apply the driver's documented squash: + cleaned.replace("\\r", " ").replace("\\n", " ") + "\\n" + This is verbatim from clean_and_stats_rowsharded.py. + 4. Read the corresponding line from the gzipped shard. + 5. Compare. If unequal, report char-level diff position + neighbourhood. + +Expected outcome on a clean run: every doc's gzipped line is byte-identical +to step (3). Any divergence means an alteration is happening between the +cleaner output and the on-disk shard (e.g. an unintended driver edit, an +encoding mismatch, a row-ordering bug). +""" +import gzip, json +from pathlib import Path + +import pyarrow.parquet as pq +import glossapi_rs_cleaner as cleaner + +INPUT = Path("/tmp/cleaner-100-test/input/openarchives_100.parquet") +STATS_DIR = Path("/tmp/cleaner-100-test/stats") +TEXT_DIR = Path("/tmp/cleaner-100-test/text-shards") + +SCRIPTS = ["greek", "latin", "french", "spanish", "punctuation", "numbers", "common_symbols"] + + +def squash(s: str) -> str: + """The driver's documented transformation, byte-for-byte.""" + return s.replace("\r", " ").replace("\n", " ") + "\n" + + +# Map source_doc_id → raw input text +src = pq.read_table(INPUT) +raw_by_id = dict(zip(src.column("source_doc_id").to_pylist(), src.column("text").to_pylist())) + +shards = sorted(STATS_DIR.glob("*.stats.jsonl")) +checked = 0 +diverged: list[tuple[str, int, str]] = [] +for sp in shards: + recs = [json.loads(l) for l in sp.read_text(encoding="utf-8").splitlines() if l.strip()] + text_path = TEXT_DIR / f"{sp.stem.split('.stats')[0]}.txt.gz" + with gzip.open(text_path, "rt", encoding="utf-8", newline="") as f: + for rec in recs: + if rec.get("drop_reason"): + # Dropped docs don't go to the text shard. + continue + doc_id = rec["source_doc_id"] + raw = raw_by_id[doc_id] + cleaned, _ = cleaner.clean_text_with_stats( + raw, SCRIPTS, None, True, 30, 3, "parser_surgical_verified" + ) + expected = squash(cleaned) + actual = f.readline() + checked += 1 + if expected != actual: + # Locate the first differing character. + first = next( + (i for i, (a, b) in enumerate(zip(expected, actual)) if a != b), + min(len(expected), len(actual)), + ) + ctx_e = expected[max(0, first - 40): first + 40] + ctx_a = actual[max(0, first - 40): first + 40] + diverged.append((doc_id, first, f"expected={ctx_e!r} actual={ctx_a!r}")) + +print(f"checked: {checked} docs") +if not diverged: + print(f"PASS: every gzipped line is byte-identical to " + f"`squash(clean_text_with_stats(raw, …))` — no alteration detected.") +else: + print(f"FAIL: {len(diverged)} docs diverged from expected:") + for doc_id, pos, ctx in diverged[:5]: + print(f" {doc_id} @char {pos}") + print(f" {ctx}") diff --git a/dependency_setup/deepseek_gpu_smoke.py b/dependency_setup/deepseek_gpu_smoke.py index e85d202..ddfb314 100644 --- a/dependency_setup/deepseek_gpu_smoke.py +++ b/dependency_setup/deepseek_gpu_smoke.py @@ -3,9 +3,9 @@ Minimal DeepSeek OCR integration smoke test. This script runs the GlossAPI DeepSeek backend on a tiny sample PDF and -verifies that real Markdown output is produced. It requires the DeepSeek-OCR -weights to be available under ``../deepseek-ocr/DeepSeek-OCR`` relative to -the repository root (override via ``DEEPSEEK_MODEL_DIR``). +verifies that real Markdown output is produced. It requires the DeepSeek-OCR-2 +weights to be available under ``../deepseek-ocr-2-model/DeepSeek-OCR-2`` relative to the +repository root (override via ``DEEPSEEK_MODEL_DIR``). """ from __future__ import annotations @@ -20,15 +20,16 @@ REPO_ROOT = Path(__file__).resolve().parents[1] SAMPLES_DIR = REPO_ROOT / "samples" / "lightweight_pdf_corpus" / "pdfs" -DEFAULT_MODEL_ROOT = (REPO_ROOT / ".." / "deepseek-ocr").resolve() +DEFAULT_MODEL_ROOT = (REPO_ROOT / "deepseek-ocr-2-model").resolve() def ensure_model_available(model_root: Path) -> None: - expected = model_root / "DeepSeek-OCR" / "model-00001-of-000001.safetensors" + direct_root = model_root if (model_root / "config.json").exists() else (model_root / "DeepSeek-OCR-2") + expected = direct_root / "model-00001-of-000001.safetensors" if not expected.exists() or expected.stat().st_size < 1_000_000: raise FileNotFoundError( - f"Expected DeepSeek-OCR weights at {expected}. " - "Download the checkpoint (huggingface.co/deepseek-ai/DeepSeek-OCR) " + f"Expected DeepSeek-OCR-2 weights at {expected}. " + "Download the checkpoint (huggingface.co/deepseek-ai/DeepSeek-OCR-2) " "or set DEEPSEEK_MODEL_DIR to the directory that contains them." ) @@ -37,7 +38,8 @@ def run_smoke(model_root: Path) -> None: from glossapi import Corpus ensure_model_available(model_root) - sample_pdf = SAMPLES_DIR / "sample01_plain.pdf" + model_dir = model_root if (model_root / "config.json").exists() else (model_root / "DeepSeek-OCR-2") + sample_pdf = SAMPLES_DIR / "alpha.pdf" if not sample_pdf.exists(): raise FileNotFoundError(f"Sample PDF not found: {sample_pdf}") @@ -67,22 +69,17 @@ def run_smoke(model_root: Path) -> None: parquet_path = dl_dir / "download_results.parquet" df.to_parquet(parquet_path, index=False) + os.environ.setdefault("GLOSSAPI_DEEPSEEK_ALLOW_CLI", "1") os.environ.setdefault("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "0") os.environ.setdefault( - "GLOSSAPI_DEEPSEEK_VLLM_SCRIPT", - str(model_root / "run_pdf_ocr_vllm.py"), + "GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT", + str(REPO_ROOT / "src" / "glossapi" / "ocr" / "deepseek" / "run_pdf_ocr_transformers.py"), ) os.environ.setdefault( "GLOSSAPI_DEEPSEEK_PYTHON", sys.executable, ) - ld_extra = os.environ.get("GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH") or str( - model_root / "libjpeg-turbo" / "lib" - ) - os.environ["GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH"] = ld_extra - os.environ["LD_LIBRARY_PATH"] = ( - f"{ld_extra}:{os.environ.get('LD_LIBRARY_PATH','')}".rstrip(":") - ) + os.environ.setdefault("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(model_dir)) corpus = Corpus(input_dir=input_dir, output_dir=output_dir) corpus.ocr( @@ -100,7 +97,7 @@ def run_smoke(model_root: Path) -> None: def main() -> None: - model_dir_env = os.environ.get("DEEPSEEK_MODEL_DIR") + model_dir_env = os.environ.get("DEEPSEEK_MODEL_DIR") or os.environ.get("GLOSSAPI_DEEPSEEK_MODEL_DIR") if model_dir_env: model_root = Path(model_dir_env).expanduser().resolve() else: diff --git a/dependency_setup/deepseek_uv/pyproject.toml b/dependency_setup/deepseek_uv/pyproject.toml new file mode 100644 index 0000000..6f7ffe0 --- /dev/null +++ b/dependency_setup/deepseek_uv/pyproject.toml @@ -0,0 +1,28 @@ +[project] +name = "glossapi-deepseek-runtime" +version = "0.1.0" +description = "UV-managed runtime for GlossAPI DeepSeek-OCR-2 execution" +requires-python = ">=3.11,<3.13" +dependencies = [ + "glossapi[deepseek]", + "torch==2.10.0", + "torchvision==0.25.0", + "torchaudio==2.10.0", +] + +[dependency-groups] +test = [ + "pytest", + "fpdf2", +] + +[tool.uv.sources] +glossapi = { path = "../..", editable = true } +torch = { index = "pytorch-cu130" } +torchvision = { index = "pytorch-cu130" } +torchaudio = { index = "pytorch-cu130" } + +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true diff --git a/dependency_setup/deepseek_uv/uv.lock b/dependency_setup/deepseek_uv/uv.lock new file mode 100644 index 0000000..a136794 --- /dev/null +++ b/dependency_setup/deepseek_uv/uv.lock @@ -0,0 +1,1771 @@ +version = 1 +revision = 3 +requires-python = 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Uses `requirements-glossapi-vanilla.txt`. -- **RapidOCR** – Docling + RapidOCR GPU stack. Builds on vanilla requirements and adds ONNX runtime (`requirements-glossapi-rapidocr.txt`). -- **DeepSeek** – GPU OCR via DeepSeek/vLLM. Extends vanilla requirements with torch/cu128, nightly vLLM and supporting CUDA libs (`requirements-glossapi-deepseek.txt`). `xformers` was dropped because the published wheels still pin Torch 2.8; the rest of the stack now installs cleanly on Torch 2.9. +- **Docling** – main GlossAPI environment for extraction, cleaning, sectioning, annotation, and math/code enrichment. Uses `requirements-glossapi-docling.txt`. +- **DeepSeek** – dedicated OCR runtime managed with `uv`. Pins the tested Torch/Transformers stack in `dependency_setup/deepseek_uv/pyproject.toml` and intentionally excludes the Docling layout stack. -Each profile is installed through `dependency_setup/setup_glossapi.sh`: +Recommended installation commands: ```bash -# Examples (venv path optional) -./dependency_setup/setup_glossapi.sh --mode vanilla --venv dependency_setup/.venvs/vanilla --run-tests -./dependency_setup/setup_glossapi.sh --mode rapidocr --venv dependency_setup/.venvs/rapidocr --run-tests -./dependency_setup/setup_glossapi.sh --mode deepseek --venv dependency_setup/.venvs/deepseek --run-tests +./dependency_setup/setup_glossapi.sh --mode docling --venv dependency_setup/.venvs/docling --run-tests +./dependency_setup/setup_deepseek_uv.sh --venv dependency_setup/.venvs/deepseek --run-tests ``` Key flags: -- `--download-deepseek` optionally fetches DeepSeek weights (skipped by default; set `--weights-dir` if they live elsewhere). +- `--download-model` optionally fetches DeepSeek weights (set `--model-root` if they live elsewhere). - `--smoke-test` (DeepSeek only) runs `dependency_setup/deepseek_gpu_smoke.py`. ## Test Segmentation Pytest markers were added so suites can be run per profile: -- `rapidocr` – GPU Docling/RapidOCR integration tests. - `deepseek` – DeepSeek execution paths. -- Unmarked tests cover the vanilla footprint. +- Unmarked tests cover the Docling/core footprint. -`setup_glossapi.sh` now chooses marker expressions automatically: +Suggested commands: -| Mode | Command run by script | -|-----------|---------------------------------------------------------| -| vanilla | `pytest -q -m "not rapidocr and not deepseek" tests` | -| rapidocr | `pytest -q -m "not deepseek" tests` | -| deepseek | `pytest -q -m "not rapidocr" tests` | +| Profile | Command | +|-----------|---------| +| Docling | `pytest -q -m "not deepseek" tests` | +| DeepSeek | `pytest -q -m "deepseek" tests` | -Heavy GPU tests in `tests/test_pipeline_smoke.py` were guarded with `pytest.importorskip("onnxruntime")` so vanilla installs skip them cleanly. Helper PDFs now embed DejaVuSans with Unicode support and insert spacing to keep OCR-friendly glyphs. +## Validation Runs (2026-03-08) +- `./dependency_setup/setup_glossapi.sh --mode docling --venv dependency_setup/.venvs/docling --run-tests` +- `./dependency_setup/setup_deepseek_uv.sh --venv dependency_setup/.venvs/deepseek --run-tests` +- `./dependency_setup/setup_deepseek_uv.sh --venv dependency_setup/.venvs/deepseek --smoke-test` -## Validation Runs (2025-10-30) -- `./dependency_setup/setup_glossapi.sh --mode vanilla --venv dependency_setup/.venvs/vanilla --run-tests` -- `./dependency_setup/setup_glossapi.sh --mode rapidocr --venv dependency_setup/.venvs/rapidocr --run-tests` -- `./dependency_setup/setup_glossapi.sh --mode deepseek --venv dependency_setup/.venvs/deepseek --run-tests` - -All three completed successfully after the following adjustments: -1. **Rust extensions** – switched to `pip install -e rust/glossapi_rs_{cleaner,noise}` because `maturin develop` left the wheel unregistered. -2. **Parquet locking** – `_parquet_lock` now creates parent directories before attempting the file lock (fixes `FileNotFoundError` in concurrent metadata tests). -3. **RapidOCR pipeline** – fixed `GlossExtract.create_extractor()` to build the Docling converter regardless of import path and added UTF-8 PDF generation improvements; smoke tests now pass on CUDA. -4. **DeepSeek stack** – updated nightly vLLM pin (`0.11.1rc5.dev58+g60f76baa6.cu129`) and removed `xformers` to resolve Torch 2.9 dependency conflicts. +These completed successfully after the following adjustments: +1. **Rust extensions** – use editable installs for `rust/glossapi_rs_{cleaner,noise}` so local changes are picked up immediately. +2. **DeepSeek stack** – moved to a uv-managed runtime pinned to the `transformers`-based OCR-2 path. +3. **Attention fallback** – the DeepSeek runner falls back to `eager` attention if `flash-attn` is unavailable. ## Known Follow-ups -- **DeepSeek weights** – installer warns if weights are absent. Set `--download-deepseek` or populate `${DEEPSEEK_ROOT}/DeepSeek-OCR` before running the real CLI tests (`GLOSSAPI_RUN_DEEPSEEK_CLI=1`). -- **xformers kernels** – removed pending compatible Torch 2.9 wheels. Reintroduce once upstream publishes matching builds. +- **DeepSeek weights** – installer warns if weights are absent. Set `--download-model` or populate `${MODEL_ROOT}/DeepSeek-OCR-2` before running the real CLI tests (`GLOSSAPI_RUN_DEEPSEEK_CLI=1`). +- **flash-attn** – optional. Reintroduce into the pinned flow once wheel availability is stable across target hosts. - **Patchelf warnings** – maturin emits rpath hints if `patchelf` is missing; they are benign but install `patchelf` if cleaner logs are desired. -- **Deprecation noise** – Docling emits future warnings (Pydantic) and RapidOCR font deprecation notices; currently harmless but worth tracking for future upgrades. +- **Deprecation noise** – Docling and Transformers emit some warnings on current pins; currently harmless but worth tracking for future upgrades. ## Quick Reference -- Activate an environment: `source dependency_setup/.venvs//bin/activate` +- Activate an environment: `source dependency_setup/.venvs//bin/activate` - Re-run tests manually: - - Vanilla: `pytest -m "not rapidocr and not deepseek" tests` - - RapidOCR: `pytest -m "not deepseek" tests` - - DeepSeek: `pytest -m "not rapidocr" tests` + - Docling: `pytest -m "not deepseek" tests` + - DeepSeek: `pytest -m "deepseek" tests` - DeepSeek runtime exports: ```bash export GLOSSAPI_DEEPSEEK_PYTHON="dependency_setup/.venvs/deepseek/bin/python" - export GLOSSAPI_DEEPSEEK_VLLM_SCRIPT="/mnt/data/glossAPI/deepseek-ocr/run_pdf_ocr_vllm.py" - export GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH="/mnt/data/glossAPI/deepseek-ocr/libjpeg-turbo/lib" - export LD_LIBRARY_PATH="$GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH:${LD_LIBRARY_PATH:-}" + export GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT="/mnt/data/glossAPI/src/glossapi/ocr/deepseek/run_pdf_ocr_transformers.py" + export GLOSSAPI_DEEPSEEK_MODEL_DIR="/mnt/data/glossAPI/deepseek-ocr-2-model/DeepSeek-OCR-2" ``` These notes capture the current dependency state, the rationale behind constraint changes, and the validation steps used to exercise each profile. diff --git a/dependency_setup/requirements-glossapi-deepseek.txt b/dependency_setup/requirements-glossapi-deepseek.txt index 5cc685a..8185d9c 100644 --- a/dependency_setup/requirements-glossapi-deepseek.txt +++ b/dependency_setup/requirements-glossapi-deepseek.txt @@ -1,16 +1,13 @@ ---extra-index-url https://download.pytorch.org/whl/cu128 ---extra-index-url https://wheels.vllm.ai/nightly --r requirements-glossapi-vanilla.txt -# CUDA Torch stack aligned with NVIDIA L4 (CUDA 12.8 wheels) -torch==2.9.0+cu128 -torchvision==0.24.0+cu128 -torchaudio==2.9.0+cu128 -# DeepSeek via nightly vLLM -vllm==0.11.1rc5.dev58+g60f76baa6.cu129 -flashinfer-python==0.4.1 -compressed-tensors==0.12.2 -depyf==0.20.0 -# Auxiliary CUDA libs -nvidia-nvshmem-cu12==3.3.20 -nvidia-nccl-cu12==2.27.5 -triton==3.5.0 +--extra-index-url https://download.pytorch.org/whl/cu118 +-r requirements-glossapi-docling.txt +torch==2.6.0 +torchvision==0.21.0 +torchaudio==2.6.0 +transformers==4.46.3 +tokenizers==0.20.3 +accelerate>=1.2.1,<2 +pymupdf==1.24.10 +Pillow==10.4.0 +img2pdf>=0.5.1 +easydict +addict diff --git a/dependency_setup/requirements-glossapi-docling.txt b/dependency_setup/requirements-glossapi-docling.txt new file mode 100644 index 0000000..73cb17f --- /dev/null +++ b/dependency_setup/requirements-glossapi-docling.txt @@ -0,0 +1,38 @@ +# Core GlossAPI runtime (Docling extraction/layout) +maturin>=1.5,<2.0 +numpy>=1.26,<3 +pandas>=1.3.0 +python-dateutil>=2.8.2 +pytz>=2021.1 +scikit-learn==1.6.1 +joblib>=1.0.0 +dask>=2022.1.0 +pyarrow>=7.0.0 +aiohttp>=3.8.0 +aiofiles>=23.0.0 +ftfy>=6.0.0 +tenacity>=8.0.0 +tqdm>=4.67.0 +pyyaml>=6.0 +pypdfium2>=4.0.0 +zstandard>=0.22.0 +docling==2.81.0 +docling-core==2.70.2 +docling-parse==5.6.0 +docling-ibm-models==3.12.0 +msgspec>=0.18.6 +fpdf2>=2.7.0 +cachetools +cbor2 +einops +tiktoken +diskcache==5.6.3 +lark==1.2.2 +numba==0.61.2 +# Tooling / tests +pytest>=8.0 +pytest-mock>=3.14 +psutil>=5.9 +rich>=14.0 +safetensors>=0.4 +huggingface-hub>=0.22 diff --git a/dependency_setup/requirements-glossapi-rapidocr.txt b/dependency_setup/requirements-glossapi-rapidocr.txt deleted file mode 100644 index f5c5839..0000000 --- a/dependency_setup/requirements-glossapi-rapidocr.txt +++ /dev/null @@ -1,4 +0,0 @@ --r requirements-glossapi-vanilla.txt -rapidocr>=3.3.0 -opencv-python-headless>=4.8.0 -onnxruntime-gpu==1.18.1 diff --git a/dependency_setup/requirements-glossapi-vanilla.txt b/dependency_setup/requirements-glossapi-vanilla.txt index b13df49..eca76ba 100644 --- a/dependency_setup/requirements-glossapi-vanilla.txt +++ b/dependency_setup/requirements-glossapi-vanilla.txt @@ -1,6 +1,6 @@ # Core GlossAPI runtime (Docling without GPU OCR extras) maturin>=1.5,<2.0 -numpy<2 +numpy>=1.26,<3 pandas>=1.3.0 python-dateutil>=2.8.2 pytz>=2021.1 @@ -16,10 +16,10 @@ tqdm>=4.67.0 pyyaml>=6.0 pypdfium2>=4.0.0 zstandard>=0.22.0 -docling==2.48.0 -docling-core==2.47.0 -docling-parse==4.4.0 -docling-ibm-models==3.9.1 +docling==2.81.0 +docling-core==2.70.2 +docling-parse==5.6.0 +docling-ibm-models==3.12.0 msgspec>=0.18.6 fpdf2>=2.7.0 cachetools diff --git a/dependency_setup/setup_deepseek_uv.sh b/dependency_setup/setup_deepseek_uv.sh new file mode 100755 index 0000000..87ad8b6 --- /dev/null +++ b/dependency_setup/setup_deepseek_uv.sh @@ -0,0 +1,176 @@ +#!/usr/bin/env bash +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "${SCRIPT_DIR}/.." && pwd)" +PROJECT_DIR="${SCRIPT_DIR}/deepseek_uv" + +PYTHON_BIN="${PYTHON:-python3}" +VENV_PATH="${GLOSSAPI_DEEPSEEK_VENV:-${REPO_ROOT}/dependency_setup/.venvs/deepseek}" +MODEL_ROOT="${DEEPSEEK_ROOT:-${REPO_ROOT}/deepseek-ocr-2-model}" +DOWNLOAD_MODEL=0 +RUN_SMOKE=0 +RUN_TESTS=0 + +info() { printf "\033[1;32m==>\033[0m %s\n" "$*"; } +warn() { printf "\033[1;33m[warn]\033[0m %s\n" "$*"; } +error() { printf "\033[1;31m[err]\033[0m %s\n" "$*" >&2; exit 1; } + +SYNC_ARGS=(--no-dev) + +usage() { + cat <<'EOF' +Usage: setup_deepseek_uv.sh [options] + +Options: + --venv PATH Target virtual environment path + --python PATH Python executable to use for uv venv + --model-root PATH Destination root for the DeepSeek-OCR-2 model + --download-model Download DeepSeek-OCR-2 via huggingface_hub + --run-tests Run the DeepSeek pytest subset after installation + --smoke-test Run dependency_setup/deepseek_gpu_smoke.py + --help Show this help message +EOF +} + +prepend_path_if_dir() { + local dir="$1" + if [[ -d "${dir}" ]]; then + case ":${PATH}:" in + *":${dir}:"*) ;; + *) export PATH="${dir}:${PATH}" ;; + esac + fi +} + +ensure_stable_python() { + local python_bin="$1" + local release_level + release_level="$("${python_bin}" - <<'PY' +import sys +print(sys.version_info.releaselevel) +PY +)" + if [[ "${release_level}" != "final" ]]; then + error "Python interpreter ${python_bin} is not a stable final release (releaselevel=${release_level}). Install a stable CPython (for example via 'uv python install 3.11.11') and rerun with --python." + fi +} + +check_rust_toolchain() { + if ! command -v cargo >/dev/null 2>&1; then + error "cargo is required to build the Rust extensions. Install Rust (for example via rustup) and ensure cargo is on PATH." + fi + if ! cargo metadata --format-version 1 --manifest-path "${REPO_ROOT}/rust/glossapi_rs_cleaner/Cargo.toml" >/dev/null 2>&1; then + error "Current cargo cannot parse the repo Rust metadata/Cargo.lock. Upgrade Rust (for example 'rustup toolchain install stable && rustup default stable') and rerun setup." + fi +} + +while (( "$#" )); do + case "$1" in + --venv) + shift || { echo "--venv requires a path" >&2; exit 1; } + VENV_PATH="${1:-}" + ;; + --python) + shift || { echo "--python requires a path" >&2; exit 1; } + PYTHON_BIN="${1:-}" + ;; + --model-root|--weights-dir) + shift || { echo "--model-root requires a path" >&2; exit 1; } + MODEL_ROOT="${1:-}" + ;; + --download-model|--download-deepseek) + DOWNLOAD_MODEL=1 + ;; + --run-tests) + RUN_TESTS=1 + ;; + --smoke-test) + RUN_SMOKE=1 + ;; + --help|-h) + usage + exit 0 + ;; + *) + echo "Unknown option: $1" >&2 + usage >&2 + exit 1 + ;; + esac + shift || true +done + +prepend_path_if_dir "${HOME}/.local/bin" +prepend_path_if_dir "${HOME}/.cargo/bin" + +command -v uv >/dev/null 2>&1 || error "uv is required. Install it first, e.g. 'python3 -m pip install --user uv'." +command -v "${PYTHON_BIN}" >/dev/null 2>&1 || error "Python executable not found: ${PYTHON_BIN}" +ensure_stable_python "${PYTHON_BIN}" +check_rust_toolchain + +MODEL_DIR="${MODEL_ROOT}/DeepSeek-OCR-2" + +if [[ -x "${VENV_PATH}/bin/python" ]]; then + info "Reusing uv environment at ${VENV_PATH}" +else + info "Creating uv environment at ${VENV_PATH}" + uv venv --python "${PYTHON_BIN}" "${VENV_PATH}" +fi + +if [[ "${RUN_TESTS}" -eq 1 ]]; then + SYNC_ARGS+=(--group test) +fi + +info "Syncing DeepSeek runtime from ${PROJECT_DIR}" +UV_PROJECT_ENVIRONMENT="${VENV_PATH}" uv sync --project "${PROJECT_DIR}" --python "${VENV_PATH}/bin/python" "${SYNC_ARGS[@]}" + +info "Installing Rust extensions in editable mode" +uv pip install --python "${VENV_PATH}/bin/python" -e "${REPO_ROOT}/rust/glossapi_rs_cleaner" +uv pip install --python "${VENV_PATH}/bin/python" -e "${REPO_ROOT}/rust/glossapi_rs_noise" + +if [[ "${DOWNLOAD_MODEL}" -eq 1 ]]; then + info "Downloading DeepSeek-OCR-2 model to ${MODEL_DIR}" + HUGGINGFACE_HUB_TOKEN="${HUGGINGFACE_HUB_TOKEN:-${HF_TOKEN:-${HUGGING_FACE_HUB_TOKEN:-${HUGGINGFACE_TOKEN:-}}}}" \ + "${VENV_PATH}/bin/python" - <\033[0m %s\n" "$*"; } +warn() { printf "\033[1;33m[warn]\033[0m %s\n" "$*"; } +error() { printf "\033[1;31m[err]\033[0m %s\n" "$*" >&2; exit 1; } + usage() { cat <<'EOF' Usage: setup_glossapi.sh [options] Options: - --mode MODE Environment profile: vanilla, rapidocr, deepseek (default: vanilla) + --mode MODE Environment profile: docling or deepseek (default: docling) --venv PATH Target virtual environment path --python PATH Python executable to use when creating the venv - --download-deepseek Fetch DeepSeek-OCR weights (only meaningful for --mode deepseek) - --weights-dir PATH Destination directory for DeepSeek weights (default: $REPO_ROOT/deepseek-ocr) + --download-deepseek Fetch DeepSeek-OCR-2 weights (DeepSeek mode only) + --weights-dir PATH Destination directory root for DeepSeek weights (default: $REPO_ROOT/deepseek-ocr-2-model) --run-tests Run pytest -q after installation --smoke-test Run dependency_setup/deepseek_gpu_smoke.py (deepseek mode only) --help Show this help message EOF } +prepend_path_if_dir() { + local dir="$1" + if [[ -d "${dir}" ]]; then + case ":${PATH}:" in + *":${dir}:"*) ;; + *) export PATH="${dir}:${PATH}" ;; + esac + fi +} + +ensure_stable_python() { + local python_bin="$1" + local release_level + release_level="$("${python_bin}" - <<'PY' +import sys +print(sys.version_info.releaselevel) +PY +)" + if [[ "${release_level}" != "final" ]]; then + error "Python interpreter ${python_bin} is not a stable final release (releaselevel=${release_level}). Install a stable CPython and rerun with --python." + fi +} + +check_rust_toolchain() { + if ! command -v cargo >/dev/null 2>&1; then + error "cargo is required to build the Rust extensions. Install Rust (for example via rustup) and ensure cargo is on PATH." + fi + if ! cargo metadata --format-version 1 --manifest-path "${REPO_ROOT}/rust/glossapi_rs_cleaner/Cargo.toml" >/dev/null 2>&1; then + error "Current cargo cannot parse the repo Rust metadata/Cargo.lock. Upgrade Rust (for example 'rustup toolchain install stable && rustup default stable') and rerun setup." + fi +} + while (( "$#" )); do case "$1" in --mode) @@ -68,14 +104,34 @@ while (( "$#" )); do shift || true done +prepend_path_if_dir "${HOME}/.local/bin" +prepend_path_if_dir "${HOME}/.cargo/bin" +command -v "${PYTHON_BIN}" >/dev/null 2>&1 || error "Python executable not found: ${PYTHON_BIN}" +ensure_stable_python "${PYTHON_BIN}" +check_rust_toolchain + case "${MODE}" in - vanilla|rapidocr|deepseek) ;; + vanilla) + warn "Mode 'vanilla' is deprecated; using 'docling' instead." + MODE="docling" + ;; + docling|deepseek) ;; *) - echo "Invalid mode '${MODE}'. Expected vanilla, rapidocr, or deepseek." >&2 + echo "Invalid mode '${MODE}'. Expected docling or deepseek." >&2 exit 1 ;; esac +if [[ "${MODE}" == "deepseek" ]]; then + exec "${SCRIPT_DIR}/setup_deepseek_uv.sh" \ + --python "${PYTHON_BIN}" \ + --venv "${VENV_PATH:-${REPO_ROOT}/dependency_setup/.venvs/deepseek}" \ + --model-root "${DEEPSEEK_ROOT}" \ + $([[ "${DOWNLOAD_DEEPSEEK}" -eq 1 ]] && printf '%s' "--download-model") \ + $([[ "${RUN_TESTS}" -eq 1 ]] && printf '%s' "--run-tests") \ + $([[ "${RUN_SMOKE}" -eq 1 ]] && printf '%s' "--smoke-test") +fi + if [[ -z "${VENV_PATH}" ]]; then VENV_PATH="${REPO_ROOT}/.venv_glossapi_${MODE}" fi @@ -86,10 +142,6 @@ if [[ ! -f "${REQUIREMENTS_FILE}" ]]; then exit 1 fi -info() { printf "\033[1;32m==>\033[0m %s\n" "$*"; } -warn() { printf "\033[1;33m[warn]\033[0m %s\n" "$*"; } -error() { printf "\033[1;31m[err]\033[0m %s\n" "$*" >&2; exit 1; } - ensure_venv() { if [[ ! -d "${VENV_PATH}" ]]; then info "Creating virtual environment at ${VENV_PATH}" @@ -107,44 +159,6 @@ python_run() { "${VENV_PATH}/bin/python" "$@" } -download_deepseek_weights() { - local root="$1" - local target="${root}/DeepSeek-OCR" - - if [[ -d "${target}" ]]; then - info "DeepSeek-OCR weights already present at ${target}" - return 0 - fi - - mkdir -p "${root}" - if command -v huggingface-cli >/dev/null 2>&1; then - info "Downloading DeepSeek weights with huggingface-cli (this may take a while)" - huggingface-cli download deepseek-ai/DeepSeek-OCR \ - --repo-type model \ - --include "DeepSeek-OCR/*" \ - --local-dir "${target}" \ - --local-dir-use-symlinks False || warn "huggingface-cli download failed; falling back to git-lfs" - fi - - if [[ ! -d "${target}" ]]; then - if command -v git >/dev/null 2>&1; then - if ! command -v git-lfs >/dev/null 2>&1; then - warn "git-lfs not available; install git-lfs to clone DeepSeek weights via git." - else - info "Cloning DeepSeek weights via git-lfs" - git lfs install --skip-repo >/dev/null 2>&1 || true - git clone https://huggingface.co/deepseek-ai/DeepSeek-OCR "${target}" - fi - else - warn "Neither huggingface-cli nor git found; skipping DeepSeek weight download." - fi - fi - - if [[ ! -d "${target}" ]]; then - warn "DeepSeek weights were not downloaded. Set DEEPSEEK_ROOT manually once acquired." - fi -} - ensure_venv info "Upgrading pip tooling" pip_run install --upgrade pip wheel setuptools @@ -159,43 +173,18 @@ info "Building Rust extensions via editable installs" pip_run install -e "${REPO_ROOT}/rust/glossapi_rs_cleaner" pip_run install -e "${REPO_ROOT}/rust/glossapi_rs_noise" -if [[ "${MODE}" == "deepseek" ]]; then - export GLOSSAPI_DEEPSEEK_PYTHON="${VENV_PATH}/bin/python" - export GLOSSAPI_DEEPSEEK_VLLM_SCRIPT="${DEEPSEEK_ROOT}/run_pdf_ocr_vllm.py" - export GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH="${DEEPSEEK_ROOT}/libjpeg-turbo/lib" - export GLOSSAPI_DEEPSEEK_ALLOW_STUB=0 - export LD_LIBRARY_PATH="${GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH}:${LD_LIBRARY_PATH:-}" - - if [[ "${DOWNLOAD_DEEPSEEK}" -eq 1 ]]; then - download_deepseek_weights "${DEEPSEEK_ROOT}" - else - warn "DeepSeek weights not downloaded (use --download-deepseek to fetch automatically)." - fi -fi - if [[ "${RUN_TESTS}" -eq 1 ]]; then pytest_args=("-q") case "${MODE}" in - vanilla) - pytest_args+=("-m" "not rapidocr and not deepseek") - ;; - rapidocr) + docling) pytest_args+=("-m" "not deepseek") ;; - deepseek) - pytest_args+=("-m" "not rapidocr") - ;; esac info "Running pytest ${pytest_args[*]} tests" python_run -m pytest "${pytest_args[@]}" tests fi -if [[ "${MODE}" == "deepseek" && "${RUN_SMOKE}" -eq 1 ]]; then - info "Running DeepSeek smoke test" - python_run "${SCRIPT_DIR}/deepseek_gpu_smoke.py" -fi - cat < None +``` + +- Purpose: Phase‑1 extraction from source files into markdown plus optional JSON intermediates. +- Typical inputs: + - files already present in `downloads/` + - or explicit `file_paths` +- Important parameters: + - `phase1_backend='safe'|'docling'|'auto'`: PyPDFium for stability vs Docling for native layout extraction + - `force_ocr`: deprecated no-op kept for compatibility; OCR remediation now lives in `Corpus.ocr(backend='deepseek')` + - `use_gpus='multi'`: use all visible GPUs through a shared work queue + - `workers_per_device`: fan out more than one extraction worker onto a single visible GPU when measuring throughput + - `GLOSSAPI_DOCLING_MAX_BATCH_FILES`: optional environment override for how many PDFs one Docling worker processes per extractor batch; GlossAPI keeps the default at `1` until a benchmark proves a larger batch is safe on the target node + - `GLOSSAPI_DOCLING_BATCH_TARGET_PAGES`: optional environment override for the page budget of each queued multi-GPU Docling work item; use it with benchmark checkpoints when long PDFs dominate the tail + - `GLOSSAPI_DOCLING_PAGE_BATCH_SIZE`: optional environment override for Docling's internal `settings.perf.page_batch_size`; use it when a GPU can hold more pages in flight than the default internal batch window + - `export_doc_json=True`: write `json/.docling.json(.zst)` + - `emit_formula_index=True`: also write `json/.formula_index.jsonl` +- Main outputs: + - `markdown/.md` + - `json/.docling.json(.zst)` when enabled + - `json/metrics/.metrics.json` + - `json/metrics/.per_page.metrics.json` + +## clean() + +```python +clean( + input_dir: str | Path | None = None, + threshold: float = 0.10, + num_threads: int | None = None, + drop_bad: bool = True, +) -> None +``` + +- Purpose: run the Rust cleaner/noise pipeline and decide which documents are safe for downstream processing. +- Typical inputs: + - `markdown/*.md` + - metadata parquet if present +- Important parameters: + - `threshold`: badness threshold + - `drop_bad`: whether to remove bad files from downstream selection + - `empty_char_threshold`, `empty_min_pages`: heuristics for OCR rerun recommendation +- Main outputs: + - `clean_markdown/.md` + - cleaner report parquet + - updated parquet columns such as `filter`, `needs_ocr`, and metrics fields +- Operational note: this stage is the quality gate that drives `section()` and `ocr()`. + +## ocr() + +```python +ocr( + *, + fix_bad: bool = True, + mode: str | None = None, + device: str | None = None, + model_dir: str | Path | None = None, + max_pages: int | None = None, + persist_engine: bool = True, + limit: int | None = None, + dpi: int | None = None, + precision: str | None = None, + math_enhance: bool = True, + math_targets: dict[str, list[tuple[int,int]]] | None = None, + math_batch_size: int = 8, + math_dpi_base: int = 220, + use_gpus: str = 'single', + devices: list[int] | None = None, + force: bool | None = None, +) -> None +``` + +- Purpose: selective OCR retry and optional Phase‑2 math/code enrichment. +- Mode selection: + - `ocr_bad`: rerun OCR only for cleaner-flagged docs + - `math_only`: run enrichment from existing Docling JSON + - `ocr_bad_then_math`: OCR flagged docs, then enrich them +- Important parameters: + - `mode`, `fix_bad`, `math_enhance` + - `use_gpus`, `devices` + - `math_targets` to restrict enrichment to specific items +- Main outputs: + - refreshed `markdown/.md` + - refreshed cleaner/parquet metadata after OCR reruns + - when metadata parquet is present, OCR now preserves the same row identity and embeds corrected `text` plus direct OCR sidecar pointers such as `ocr_markdown_relpath`, `ocr_metrics_relpath`, and `ocr_text_sha256` + - `json/.latex_map.jsonl` when enrichment runs + +## formula_enrich_from_json() + +```python +formula_enrich_from_json( + files: list[str] | None = None, + *, + device: str = 'cuda', + batch_size: int = 8, + dpi_base: int = 220, + targets_by_stem: dict[str, list[tuple[int,int]]] | None = None, +) -> None +``` + +- Purpose: Phase‑2 GPU enrichment from previously exported Docling JSON. +- Typical inputs: + - `json/.docling.json(.zst)` + - optional formula/code index data +- Important parameters: + - `files`: restrict to specific stems + - `device`, `batch_size`, `dpi_base` + - `targets_by_stem`: target specific `(page_no, item_index)` tuples +- Main outputs: + - enriched markdown back into `markdown/.md` + - `json/.latex_map.jsonl` + +## section(), annotate() + +```python +section() -> None +annotate(annotation_type: str = 'text', fully_annotate: bool = True) -> None +``` + +- `section()`: + - purpose: convert markdown into one row per section with structural flags + - inputs: markdown selected by cleaner/parquet metadata + - outputs: `sections/sections_for_annotation.parquet` +- `annotate()`: + - purpose: classify sections and optionally expand them into full document structure + - important parameters: `annotation_type='text'|'chapter'|'auto'`, `fully_annotate` + - outputs: `classified_sections.parquet` and `fully_annotated_sections.parquet` + +## download() + +```python +download( + input_parquet: str | Path, + *, + links_column: str | None = None, + parallelize_by: str | None = None, + verbose: bool | None = None, + **kwargs, +) -> pd.DataFrame +``` + +- Purpose: fetch source files described in a parquet dataset. +- Typical inputs: + - an explicit `input_parquet` + - or the first parquet file found in `input_dir` +- Important parameters: + - `links_column`: override URL column name + - `parallelize_by`: choose grouping for the scheduler + - `download_mode`: one of `standard`, `auto`, or `browser` + - `browser_mode=True`: alias for `download_mode="browser"` + - `download_policy_file`: route specific domains/URL patterns to `standard`, `auto`, or `browser` + - downloader kwargs via `**kwargs` for concurrency, SSL, cookies, retries, checkpoints, etc. +- Main outputs: + - downloaded files in `downloads/` + - partial/final results in `download_results/` + - returned `pd.DataFrame` with download status and metadata + +Browser-capable download mode is intended for browser-gated file endpoints where a real file still exists behind session/bootstrap checks. It is not a general viewer extractor. Viewer-only sources should still fail cleanly with a recorded error and no local file artifact. + +Example: + +```python +corpus.download( + input_parquet="input_urls.parquet", + download_mode="browser", +) +``` + +Policy-routed example: + +```python +corpus.download( + input_parquet="input_urls.parquet", + download_policy_file="download_policy.yml", +) +``` + +## triage_math() + +- Purpose: summarize per-page metrics and recommend Phase‑2 for math-dense docs. +- Inputs: `json/metrics/.per_page.metrics.json` +- Outputs: updated `download_results` parquet with routing fields such as formula totals and phase recommendation + +## Suggested Reading Order + +1. `download()` if you start from URLs. +2. `extract()` for Phase‑1 layout/markdown. +3. `clean()` to decide what needs OCR. +4. `ocr()` if you need OCR retry or Phase‑2 enrichment. +5. `section()` and `annotate()` for structured downstream outputs. + +--- + +See also: +- Code map: ../code_map.md +- Pipeline overview and artifacts: ../pipeline.md +- Configuration and environment variables: ../configuration.md +- OCR and math enrichment details: ../ocr_and_math_enhancement.md diff --git a/docs/api_corpus_tmp.md b/docs/api_corpus_tmp.md index 4181094..e584308 100644 --- a/docs/api_corpus_tmp.md +++ b/docs/api_corpus_tmp.md @@ -44,7 +44,7 @@ extract( ) -> None ``` -- Phase‑1 extraction; set `force_ocr=True` for OCR. +- Phase‑1 extraction; `force_ocr` is deprecated and ignored. - Docling layout JSON now writes by default (`json/.docling.json(.zst)`); set `emit_formula_index=True` to also produce `json/.formula_index.jsonl`. - Set `use_gpus='multi'` to use all visible GPUs (shared queue). @@ -85,7 +85,7 @@ ocr( ) -> None ``` -- Convenience shim that re‑runs `extract(force_ocr=True)` on cleaner-flagged documents and, by default, performs math/code enrichment unless `math_enhance=False`. +- Convenience shim that re-runs OCR on cleaner-flagged documents and, by default, performs math/code enrichment unless `math_enhance=False`. ## formula_enrich_from_json() diff --git a/docs/architecture/artifact_layout_and_stage_handoffs.md b/docs/architecture/artifact_layout_and_stage_handoffs.md index f3b5b6d..53cbcec 100644 --- a/docs/architecture/artifact_layout_and_stage_handoffs.md +++ b/docs/architecture/artifact_layout_and_stage_handoffs.md @@ -92,6 +92,38 @@ That affects: Chunk suffix behavior is therefore part of the current contract. +For DeepSeek OCR, there is an important distinction between execution-time shards and stage handoff artifacts: + +- Multi-GPU `exact_fill` may execute shards such as `doc__p00001-00096` internally to keep GPU lanes full. +- Those shard names are operational artifacts, not the downstream contract for OCR outputs. +- After worker completion, the runner reassembles canonical `markdown/.md` and `json/metrics/.metrics.json` files for each source PDF. +- If OCR started from canonical corpus metadata, the authoritative OCR handoff should also include a canonical parquet where corrected `text` is embedded back into the same document rows. Detached markdown alone is not the full stage handoff in that case. +- Canonical OCR markdown page boundaries are annotated with `` comments next to the page-split marker, and the parser remains backward-compatible with legacy unnumbered separators. +- Original shard markdown and shard metrics are moved under `sidecars/ocr_shards/` for debugging and audit trails. +- If a repair retry trips the garbage cutoff again, the canonical markdown keeps the page slot but blanks the page content rather than preserving the bad first-pass OCR. + +For multi-GPU vLLM OCR, there is now a second class of operational artifacts under `sidecars/ocr_runtime/`: + +- `work_queue.sqlite`: durable batch queue state for the current OCR run +- `worker_*.runtime.json`: per-worker heartbeat and timing state +- `gpu_preflight.json`: GPU readiness checks such as persistence mode +- `gpu_telemetry.jsonl`: sampled GPU utilization and process telemetry +- `runtime_summary.json`: queue completion state plus steady-state timing windows + +The runtime queue now has two phases inside the same operational state: + +- first-pass shard batches +- repair shard batches published after first pass completes + +Repair queue durability and repair execution batching are intentionally separate concerns: + +- the durable queue records individual repair work items so retries, failure accounting, and resume logic stay precise +- workers may pack multiple pending repair items into one larger execution batch to keep GPUs busy during the repair tail + +These runtime artifacts are operational state, not downstream stage inputs. They are intended for monitoring, debugging, and safe resumption logic. + +Downstream stages should therefore consume canonical OCR outputs, not shard artifacts. + ## Authoritative state vs derived artifacts Not every file has equal semantic importance. diff --git a/docs/architecture/index.md b/docs/architecture/index.md index a8d8621..774056a 100644 --- a/docs/architecture/index.md +++ b/docs/architecture/index.md @@ -103,7 +103,7 @@ Purpose: Important characteristics: -- can use RapidOCR via Docling or DeepSeek OCR +- uses DeepSeek OCR for remediation while keeping Docling in the surrounding extraction/layout flow - reads metadata to find OCR candidates - skiplist-aware - designed as a corrective stage, not the default for every document @@ -172,4 +172,6 @@ The current architecture is effective but has important tradeoffs: These pressure points are documented separately in: - [Artifact Layout and Stage Handoffs](artifact_layout_and_stage_handoffs.md) +- [OCR Cleaning Runtime](ocr_cleaning_runtime.md) - [Resumability, Recovery, and Retention](resumability_recovery_and_retention.md) +- [Markdown Library Survey](markdown_library_survey.md) — design rationale for the parser-backed Phase A (Pilot B). diff --git a/docs/architecture/markdown_library_survey.md b/docs/architecture/markdown_library_survey.md new file mode 100644 index 0000000..32afa8b --- /dev/null +++ b/docs/architecture/markdown_library_survey.md @@ -0,0 +1,219 @@ +# Markdown Library Survey + +This document captures the design rationale behind the cleaner's parser-backed Phase A. It surveys established Markdown parsers, formatters, and renderers across Rust, C, JavaScript/TypeScript, Python, Go, and Pandoc/Haskell, then maps the lessons onto the choices that landed in the production cleaner. + +The goal was never to replace the cleaner with one of these tools wholesale. The goal was to extract design lessons for Docling-produced Markdown corpus cleanup: make raw Markdown closer to rendered preview while preserving the rendered element graph. + +The conclusions of this survey shipped as **Pilot B** — the parser-backed `format_surgical_checked` path that is now the production Phase A. See [OCR Cleaning Runtime](ocr_cleaning_runtime.md) for the runtime architecture that consumes this layer, and `rust/glossapi_rs_cleaner/src/md_format_surgical.rs` for the implementation. + +## Sources consulted + +- [Comrak](https://comrak.ee/) and [comrak docs.rs](https://docs.rs/comrak/latest/comrak/) +- [pulldown-cmark guide](https://pulldown-cmark.github.io/pulldown-cmark/) +- [cmark-gfm](https://github.com/github/cmark-gfm) +- [remark](https://unifiedjs.com/explore/package/remark/) +- [markdown-it](https://github.com/markdown-it/markdown-it) +- [mdformat](https://mdformat.readthedocs.io/en/stable/) +- [Prettier options](https://prettier.io/docs/next/options) +- [goldmark](https://github.com/yuin/goldmark) +- [Pandoc manual](https://pandoc.org/MANUAL.html) +- [GitHub Flavored Markdown spec](https://github.github.com/gfm/) + +## Executive takeaways + +The main ecosystem lesson: do not hand-roll Markdown grammar with regexes when correctness matters. Mature projects either parse Markdown into an AST or event stream and then transform/serialize, or they make formatting choices explicit and configurable. + +The original line-based cleaner moved in the right direction with `pulldown-cmark` verification, but the transforms themselves still relied on line heuristics — that is exactly where the highest-risk findings in the implementation review came from. + +Direction adopted (status as of the cleaner integration): + +1. ✅ Rust remained the production implementation language. +2. ✅ A parser-backed Markdown normalization path landed using `comrak` (Pilot B / `md_format_surgical`). +3. ✅ `pulldown-cmark` is kept as the fast independent verifier in `dual_verify`, not as the formatter. +4. ✅ cmark-gfm/Pandoc/mdformat are treated as differential oracles in sampled tests, not hot-path dependencies. cmark-gfm is the dev-only ground-truth oracle when installed; production uses `dual_verify` (comrak + pulldown-cmark) only. +5. ✅ Raw-readability is explicit: the corpus default unwraps soft-wrapped prose while preserving hard breaks (Pilot B's three target transforms — paragraph reflow, GFM separator minimization, HR canonicalization). + +## Library notes + +### Rust: pulldown-cmark + +What it offers: + +- Pull-parser event stream for CommonMark with GFM-style options such as tables, task lists, strikethrough, footnotes, admonitions, and math. +- Low memory, high performance, and a good fit for verification because it can render HTML from events. +- The guide highlights pull parsing and notes that consecutive text events can occur, with `TextMergeStream` available to smooth text iteration. + +What we learned: + +- Use it for strict/structural verification, especially as an independent parser from any future formatter. +- Use its `Event::SoftBreak` and `Event::HardBreak` semantics as a model for reflow: only rewrite soft breaks, never hard breaks. +- Avoid building production formatting around HTML output alone; event streams are excellent for checking, but source-preserving rewriting needs either source spans or an AST renderer. + +### Rust: comrak + +What it offers: + +- CommonMark and GFM-compatible parser/renderer in Rust. +- Parses to an AST, allows AST manipulation, and supports `format_commonmark` / `markdown_to_commonmark`. +- Fine-grained parse/render options and custom formatter support to override rendering of node types. + +What we learned: + +- This was the best Rust candidate for a parser-backed Phase A. Pilot B uses comrak's AST + sourcepos to walk top-level block children and render only Paragraph/Table/ThematicBreak nodes, copying everything else verbatim from source. +- Full reserialization (the abandoned Pilot A approach) over-normalized list markers, link forms, escapes, and other syntax. The surgical approach — re-render only the three target node types — was needed to avoid those side effects. + +### C: cmark-gfm + +What it offers: + +- GitHub's fork of the C CommonMark reference implementation, with GFM extensions. +- Parses to an AST, supports AST manipulation, and renders to HTML, CommonMark, XML, LaTeX, groff man, and more. +- Conformance, speed, fuzzing, and standardized behavior. + +What we learned: + +- Use cmark-gfm as a differential oracle when installed (development hosts only). +- Its XML AST output is useful for golden fixtures. +- Do not bind it into production. C FFI is extra operational surface area; comrak is already Rust-native and sufficient for `dual_verify`. + +### JavaScript/TypeScript: remark / unified + +What it offers: + +- Unified processor that parses Markdown and serializes Markdown using `remark-parse` and `remark-stringify`. +- Uses mdast as the syntax tree. +- Lint plugins and configurable stringify settings for stylistic rules such as ordered-list markers and setext/ATX heading choices. + +What we learned: + +- Separate "parse", "transform", "lint", and "stringify" as distinct phases. That maps cleanly onto the Phase A / Phase B / verification split. +- Add lint-like diagnostics to the scorecard, not only pass/fail verification. +- Markdown dialect handling should be modular, not encoded as ad hoc checks scattered across the cleaner. + +### JavaScript: markdown-it + +What it offers: + +- CommonMark-oriented parser with GFM tables/strikethrough, plugin rules, and configurable syntax. +- CommonMark support, extensibility, high speed, and safe rendering defaults. + +What we learned: + +- Treat dialect features as enabled rule sets. There should be a clear `MarkdownDialect::GfmDocling` profile instead of assuming every pipe-like line means one thing. +- Safety defaults matter. For us that means "do not normalize unknown syntax unless the parser confirms it is a known Markdown element." + +### Python: mdformat + +What it offers: + +- CommonMark-compliant Markdown formatter, CLI and Python library. +- Opinionated style: consistent indentation/whitespace, ATX headings, sorted link references, fenced code instead of indented code, `1.` ordered-list markers. +- Intentionally does not change word wrapping by default to support semantic line breaks. +- Plugins for additional Markdown engines/dialects; escapes engine-specific syntax when it cannot safely understand it. + +What we learned: + +- Full-document formatters make many style decisions outside our scope. We should not blindly run mdformat over corpus rows. +- The plugin/dialect model is valuable. Unknown dialect syntax should be either preserved or escaped/diagnosed, not "cleaned" by regex. +- Default wrapping caution is relevant, but our objective differs: Docling line breaks are usually layout artifacts, not semantic line breaks. The policy is now explicit (see §Executive takeaways item 5). + +### Prettier Markdown + +What it offers: + +- A `proseWrap` option with modes: `always`, `never`, and `preserve`. +- Default is `preserve` because some services have linebreak-sensitive renderers, but `never` intentionally unwraps prose blocks into single lines. + +What we learned: + +- The cleaner's corpus default is the equivalent of Prettier's `never` for prose paragraphs (Docling layout-induced soft breaks are unwrapped). +- Verification enforces that unwrap only rewrites parser-observed soft breaks, not hard breaks, code, HTML-sensitive blocks, tables, or line-block-like content. + +### Go: goldmark + +What it offers: + +- CommonMark-compliant Go parser with AST, source positions, parser transformers, AST transformers, and renderers. +- Built-in GFM extension bundle. +- Extension APIs include block parsers, inline parsers, paragraph transformers, AST transformers, and renderers. + +What we learned: + +- Source positions are the missing primitive that comrak's `sourcepos: true` provides — it is what lets Pilot B map AST nodes back to byte ranges and rewrite only the paragraph/table/HR spans we intend to touch. +- Paragraph transformers are a strong pattern for the reflow pass: normalize paragraph text after block parsing, before final rendering. +- The architecture reinforces the need to separate block parsing from inline parsing. + +### Pandoc + +What it offers: + +- Many Markdown variants and writer options. +- `--wrap=auto|none|preserve` controls source-level output wrapping. +- The Markdown philosophy that plain text should be readable; ordinary paragraph newlines are treated as spaces, while two trailing spaces or backslash create hard line breaks. + +What we learned: + +- Pandoc's `--wrap=none` is the closest existing user-facing behavior to the desired raw corpus mode. +- Pandoc is a good differential oracle for small samples but is too heavyweight for the hot path. + +## Design changes that landed + +### 1. Parser-backed Phase A — ✅ landed as Pilot B + +Implemented in `rust/glossapi_rs_cleaner/src/md_format_surgical.rs`: + +```text +input markdown + → parse with comrak (GFM options + sourcepos) + → walk top-level block children + → re-render Paragraph / Table / ThematicBreak nodes + → copy everything else verbatim from source + → verify with dual_verify (comrak + pulldown-cmark HTML agreement on input + output) + → on disagreement: ship input verbatim; record fallback_reason +``` + +The three target transformations: + +- **Thematic break canonicalization** — render parser-confirmed `ThematicBreak` as `---`. +- **Table delimiter canonicalization** — render parser-confirmed table delimiter cells as `---`, `:---`, `---:`, or `:---:`. +- **Paragraph softbreak unwrap** — serialize parser-confirmed paragraph soft breaks as spaces, while preserving hard breaks. + +### 2. Line-based path — ✅ removed entirely + +The original "fast line-based path as conservative fallback" recommendation was reconsidered: with `dual_verify` providing per-doc safety, the line-based path's value as fallback was eclipsed by Pilot B's verbatim-on-disagreement behavior. The line-based code (`md_module::normalize_md_syntax`) was removed in the cleaner integration. + +### 3. Multi-parser verification oracles — ✅ landed as dual_verify + +Production cleaning runs `dual_verify(input, output)` on every Pilot B rewrite — comrak + pulldown-cmark HTML agreement. cmark-gfm is consulted when present (development hosts) as the ground-truth oracle. + +### 4. Explicit dialect and formatting policy — ✅ landed as PhaseAPolicy + +`md_format_surgical::PhaseAPolicy` carries the active dialect choices (comrak/cmark-gfm autolink behavior, hard-break preservation, softbreak whitespace trim policy). Surfaced through PyO3 as `phase_a_policy_py` so callers and scorecards can log what was in effect. + +### 5. AST-preserving transformations as the strongest invariant — ✅ landed + +The `dual_verify` check enforces preview-preservation under both parsers. When either parser sees the input rendering differently from the output, the rewrite is refused and input is shipped verbatim. The `phase_a_fallback_reason` field surfaces the cause for downstream sampling. + +## Still open + +These directions from the survey have NOT yet landed. They remain reasonable next steps if the cleaner's correctness or coverage is extended further. + +### Pseudo-table unwrapping as a separate semantic transform + +The ecosystem tools preserve tables because they are valid Markdown structures. If the corpus needs TOC pseudo-tables converted to prose, that is not formatting — it is a semantic structural rewrite. It needs its own module, its own classifier, a distinct invariant (table-to-list/prose mapping with preserved cell text order), and separate scorecard metrics. Do not mix pseudo-table unwrapping into Phase A. + +### Raw-readability scorecard metrics beyond CleanStats + +`CleanStats` already tracks per-rule counts and per-doc 4-way char accounting. The survey suggested extra readability-oriented metrics (`softbreaks_before/after`, `mean_physical_lines_per_paragraph_before/after`, `hr_run_width_before/after`, etc.) that would give a more direct answer to "did raw Markdown become closer to rendered Markdown?". These are not in the current `CleanStats` schema. + +### Lint-style diagnostic categories + +Pass/fail verification (`dual_verify` agreement) is in production. Per-category lint diagnostics (`hard_break_preserved`, `softbreak_unwrapped`, `table_delimiter_minimized`, etc.) would help when investigating regressions but are not implemented today. + +## See also + +- [OCR Cleaning Runtime](ocr_cleaning_runtime.md) — how the cleaner is split between analyzer and renderer responsibilities. +- `rust/glossapi_rs_cleaner/src/md_format_surgical.rs` — Pilot B implementation. +- `rust/glossapi_rs_cleaner/src/md_format.rs` — `dual_verify` (the in-process production oracle). +- `rust/glossapi_rs_cleaner/src/cmark_gfm_oracle.rs` — optional dev-only ground-truth oracle. +- `rust/glossapi_rs_cleaner/docs/PHASE_A_PARSER_BACKED_INDEX.md` — internal index of Phase A pilot reviews. diff --git a/docs/architecture/ocr_cleaning_runtime.md b/docs/architecture/ocr_cleaning_runtime.md new file mode 100644 index 0000000..1643c2f --- /dev/null +++ b/docs/architecture/ocr_cleaning_runtime.md @@ -0,0 +1,186 @@ +# OCR Cleaning Runtime + +This document explains how the current OCR cleaner is organized, why the +matcher families are separated, and why the clean/debug behavior is driven by +one shared page analyzer. + +## One Analyzer, Two Render Modes + +The OCR cleaner now works in two modes over the same span plan: + +- `debug` + - preserves the source page surface + - inserts `` tags around the matched regions +- `clean` + - applies the removal/rewrite policy directly + - writes the cleaned page text with no debug tags + +This is deliberate. The project previously had a tendency for the reviewer-facing +debug logic to evolve faster than the real cleaner. Sharing one analyzer avoids +that drift: if the debug page is right, the clean page is operating on the same +decisions. + +The same alignment rule applies to metadata. When `clean_ocr()` writes cleaned +markdown, it now scores parquet-facing OCR metrics from that cleaned directory. +Without that, downstream `export` can read cleaned text while still carrying +raw-OCR `char_count_no_comments`, `is_empty`, or repeat diagnostics in parquet. + +## Code Layout + +The OCR cleaner is split by responsibility so detector policy does not drift +into rendering or table-specific cleanup: + +- `src/glossapi/corpus/phase_clean.py` + - owns analyzer order, worker orchestration, and clean/debug mode selection +- `src/glossapi/corpus/ocr_table.py` + - owns HTML-table classification, dropping policy, and HTML->Markdown conversion +- `src/glossapi/corpus/ocr_render.py` + - owns span merging, clean/debug rendering, and `match_index.jsonl` row generation +- `src/glossapi/corpus/text_surface_metrics.py` + - owns shared published-surface metric helpers such as `char_count_no_comments` + and `is_empty` + +This split is deliberate. The project needs one place to decide *what* a match +is, and a separate place to decide *how* that decision becomes visible output. +That boundary is one of the main protections against random debug/clean drift. + +## Stage Boundary: `clean_ocr()` vs `clean()` + +These two stages deliberately remain separate: + +- `clean_ocr()` + - sits on the `corpus.ocr` path + - owns OCR artifact removal and OCR-specific metrics +- `clean()` + - sits primarily on the `corpus.extract` path + - owns broader clean/export quality metrics + +The OCR rerun path reuses `clean()` afterward because `export` still expects the +generic clean/export fields. That reuse is intentional orchestration, not an +argument that the two stages should collapse into one function. + +## Field Ownership + +The practical parquet contract is: + +- OCR-owned fields + - `percentage_greek` + - `latin_percentage` + - `polytonic_ratio` + - `char_count_no_comments` + - `is_empty` + - `ocr_noise_suspect` + - `ocr_noise_flags` + - `ocr_repeat_phrase_run_max` + - `ocr_repeat_line_run_max` + - `ocr_repeat_suspicious_line_count` + - `ocr_repeat_suspicious_line_ratio` +- clean/export-owned fields + - `greek_badness_score` + - `mojibake_badness_score` + - `needs_ocr` + - `filter` + - other generic quality-routing fields + +This ownership split is the reason the post-OCR sequence is explicit: + +1. `clean_ocr()` updates the OCR-owned layer on the OCR-cleaned text surface. +2. `clean(..., write_cleaned_files=False)` refreshes the generic clean/export + layer on that same surface. + +## Why The Cleaner Is Not One Generic Matcher + +The cleaner is trying to remove OCR- or VLM-induced garbage, not every repeated +pattern in a page. A single fuzzy matcher over the whole page overgeneralizes +quickly: + +- numbers steal matches that should belong to numeric progression logic +- repeated notation in LaTeX looks like corruption even when it is legitimate +- HTML tables distort text surfaces and cause spurious word matches + +So the runtime uses ownership by surface type and structure instead of one broad +"repetition" rule. + +## Page Ownership Order + +The current analyzer order is: + +1. tables +2. numeric +3. LaTeX +4. hybrid numbered repetition +5. shared text repetition + +Why this order: + +- Tables run first because HTML table shells can dominate a page and confuse + every later pass. +- Numeric runs before generic text because `1, 2, 3, ...` style progressions + are real OCR-collapse signals and should not be absorbed by `word_repeat`. +- LaTeX and hybrid passes run before generic text because they depend on local + structure, not just repeated tokens. +- Shared text repetition runs last on the remaining visible surface only. + +This ordering is the main false-positive control mechanism. + +## Table Cleaning Is Broader Than Repetition + +Table handling is intentionally separated into `src/glossapi/corpus/ocr_table.py` +because it is not just another repetition matcher. + +Current table classes: + +- `sentence_shell_table` + - a table with one prose-like filled cell + - treated as layout noise around content + - dropped in clean mode +- `empty_table_collapse` + - a large sparse shell with almost no real cell content + - dropped in clean mode +- `repeated_rows` + - an actually repetition-oriented table problem + - dropped in clean mode +- unmatched kept tables + - converted from HTML to GitHub-style Markdown + +The important design point is that sentence-shell and empty-shell tables are +structural cleanup decisions, not repetition decisions. + +## LaTeX And Hybrid Generalization Strategy + +LaTeX and hybrid numbered matching both follow the same conservative pattern: + +- prefer local runs +- abstract slot fields +- require mechanical progression or stable low-diversity cycles +- avoid page-wide reuse as evidence on its own + +That is why the cleaner does not treat "same symbol appears many times on a +page" as enough evidence. The goal is to catch degenerate local collapse, not +normal scholarly notation reuse. + +## Why Rust Is Used Selectively + +The hot-path detection work is in Rust because page-scale scanning dominates run +time. Python still owns: + +- orchestration +- filesystem I/O +- debug/clean rendering +- policy composition across matcher families + +This split is intentional: + +- Rust is best for large repeated scans and token-normalization hot loops +- Python is still easier for mode-aware rendering and pipeline integration + +## Performance And Correctness Contract + +Performance work is allowed only if exact debug output stays stable. + +The correctness lock is: + +- `tests/test_ocr_golden_pages.py` + +That suite uses hundreds of real pages and compares exact output bytes. The +speed work therefore optimizes implementation, not semantics. diff --git a/docs/code_map.md b/docs/code_map.md new file mode 100644 index 0000000..8616def --- /dev/null +++ b/docs/code_map.md @@ -0,0 +1,60 @@ +# Code Map + +This page maps the main documentation ideas to the code that implements them. It is +meant to help you move from "what does GlossAPI do?" to "where do I change it?" +without reading the entire repo. + +## Top-Level Entry Points + +| Area | Main code | Responsibility | +| --- | --- | --- | +| Public package entry | `src/glossapi/__init__.py` | Lazy-exports `Corpus`, `GlossSectionClassifier`, `GlossDownloader`, and related classes without pulling heavy runtime dependencies at import time. | +| High-level orchestration | `src/glossapi/corpus/corpus_orchestrator.py` | Coordinates the end-to-end pipeline and owns the main folder/artifact conventions. | +| Phase-1 extraction engine | `src/glossapi/gloss_extract.py` | Builds/reuses Docling converters, handles safe vs Docling backend selection, batching, timeouts, resumption, and artifact export. | + +## Pipeline Stages + +| Stage | Main methods/classes | Notes | +| --- | --- | --- | +| Download | `Corpus.download()`, `GlossDownloader.download_files()` | Supports URL expansion, deduplication, checkpoints, per-domain scheduling, and resume. | +| Extract | `Corpus.prime_extractor()`, `Corpus.extract()`, `GlossExtract.ensure_extractor()`, `GlossExtract.extract_path()` | Handles backend choice, GPU preflight, and single- vs multi-GPU dispatch. | +| Clean / quality gate | `Corpus.clean()` | Runs the Rust cleaner and merges quality metrics back into parquet metadata. | +| OCR retry / math follow-up | `Corpus.ocr()`, `Corpus.formula_enrich_from_json()` | Re-runs OCR only for flagged documents and optionally performs Phase-2 math/code enrichment from JSON. | +| Sectioning | `Corpus.section()`, `GlossSection.to_parquet()` | Converts markdown documents into section rows for later classification. | +| Classification / annotation | `Corpus.annotate()`, `GlossSectionClassifier.classify_sections()`, `GlossSectionClassifier.fully_annotate()` | Runs the SVM classifier and post-processes section labels into final document structure. | +| Export / triage | `Corpus.jsonl()`, `Corpus.triage_math()` | Produces training/export JSONL and computes routing hints for math-dense documents. | + +## Backend and Runtime Helpers + +| File | Responsibility | +| --- | --- | +| `src/glossapi/ocr/docling/pipeline.py` | Canonical builder for the layout-only Docling Phase-1 pipeline, including runtime tuning knobs for the current Docling API. | +| `src/glossapi/ocr/docling_pipeline.py` | Compatibility re-export for the canonical Docling pipeline builder. | +| `src/glossapi/ocr/deepseek/runner.py` | Launches the DeepSeek OCR remediation path from `Corpus.ocr()`. | +| `src/glossapi/ocr/utils/json_io.py` | Writes and reads compressed Docling JSON artifacts. | +| `src/glossapi/corpus/phase_ocr_math.py` | Runs DeepSeek OCR remediation, math/code enrichment, and parquet status updates. | +| `src/glossapi/metrics.py` | Computes per-page parse/OCR/formula metrics from Docling conversions. | + +## Rust Extensions + +| Crate | Path | Purpose | +| --- | --- | --- | +| Cleaner | `rust/glossapi_rs_cleaner` | Markdown cleaning, script/noise filtering, and report generation used by `Corpus.clean()`. | +| Noise metrics | `rust/glossapi_rs_noise` | Fast quality metrics used by the broader pipeline and package build configuration. | + +## Tests To Read First + +| Test | Why it matters | +| --- | --- | +| `tests/test_pipeline_smoke.py` | Best high-level example of the intended artifact flow through extract -> clean -> OCR -> section. | +| `tests/test_corpus_guards.py` | Shows the contract around backend selection and GPU preflight. | +| `tests/test_jsonl_export.py` | Shows how final JSONL export merges cleaned markdown, parquet metadata, and math metrics. | +| `tests/test_ocr_dispatch_backends.py` | Covers the DeepSeek-only OCR dispatch contract and backend validation. | + +## If You Need To Change... + +- Download scheduling or resume behavior: start in `src/glossapi/gloss_downloader.py`. +- Phase-1 parsing, worker fanout, or artifact generation: start in `src/glossapi/corpus/phase_extract.py`, `src/glossapi/corpus/corpus_orchestrator.py`, and `src/glossapi/gloss_extract.py`. +- Docling pipeline wiring or runtime tuning: start in `src/glossapi/ocr/docling/pipeline.py` and `src/glossapi/gloss_extract.py`. +- Section labels or section-annotation rules: start in `src/glossapi/gloss_section_classifier.py`. +- Output folder contracts or stage sequencing: start in `src/glossapi/corpus/corpus_orchestrator.py`. diff --git a/docs/configuration.md b/docs/configuration.md index 659d65c..98f2687 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -18,30 +18,50 @@ GlossAPI exposes two Phase‑1 profiles. Use `Corpus.extract(..., phase1_backend Regardless of backend, the extractor clamps OMP/OpenBLAS/MKL pools to one thread per worker so multi‑GPU runs do not explode thread counts. -### DeepSeek optional dependencies +### Docling Runtime Tuning -Install DeepSeek backend extras to enable the DeepSeek OCR path (imports remain lazy, so the package is optional). Use the CUDA 12.1 wheels for both vLLM and Torch: +These optional knobs map directly to current Docling `PdfPipelineOptions` fields and are mainly useful for benchmarking on strong GPUs: -```bash -pip install '.[deepseek]' +- `GLOSSAPI_DOCLING_MAX_BATCH_FILES`: override the number of PDF documents a single Phase‑1 Docling worker processes per extractor batch. Defaults to `1` in GlossAPI for stability; raise it deliberately when benchmarking fresh A100 nodes. +- `GLOSSAPI_DOCLING_BATCH_TARGET_PAGES`: target page budget for each queued multi‑GPU Docling work item. Defaults to `256`; lower it when a single worker hoards long PDFs, raise it when a strong GPU can keep larger mixed bundles resident. +- `GLOSSAPI_DOCLING_LAYOUT_BATCH_SIZE`: override Docling `layout_batch_size`. +- `GLOSSAPI_DOCLING_PAGE_BATCH_SIZE`: override Docling `settings.perf.page_batch_size` so Phase‑1 can raise or lower the number of pages each device keeps in flight internally without changing GlossAPI queue semantics. +- `GLOSSAPI_DOCLING_TABLE_BATCH_SIZE`: override Docling `table_batch_size`. +- `GLOSSAPI_DOCLING_OCR_BATCH_SIZE`: override Docling `ocr_batch_size` even though Phase‑1 OCR stays disabled. +- `GLOSSAPI_DOCLING_QUEUE_MAX_SIZE`: override Docling `queue_max_size`. +- `GLOSSAPI_DOCLING_DOCUMENT_TIMEOUT`: override Docling `document_timeout`. +- `GLOSSAPI_DOCLING_BATCH_POLL_INTERVAL`: override Docling `batch_polling_interval_seconds`. -# Install Torch CUDA 12.1 wheels (required by the DeepSeek script) -pip install --extra-index-url https://download.pytorch.org/whl/cu121 \ - 'torch==2.5.1+cu121' 'torchvision==0.20.1+cu121' +### DeepSeek optional dependencies -# Alternatively, use the requirements file (edit to uncomment torch lines): -pip install -r deepseek-ocr/requirements-deepseek.txt +Install DeepSeek backend extras to enable the DeepSeek OCR path. The recommended path is the dedicated `uv` environment: + +```bash +./dependency_setup/setup_deepseek_uv.sh --venv dependency_setup/.venvs/deepseek ``` When using `backend='deepseek'`, equations are included inline in the OCR output; Phase‑2 math flags are accepted but skipped. +The dedicated uv profile is OCR-only and does not install the Docling extraction stack. ### DeepSeek runtime controls -- `GLOSSAPI_DEEPSEEK_ALLOW_STUB` (`1` by default): allow the builtin stub runner for tests and lightweight environments. -- `GLOSSAPI_DEEPSEEK_ALLOW_CLI` (`0` by default): flip to `1` to force the real vLLM CLI even when the stub is allowed. -- `GLOSSAPI_DEEPSEEK_PYTHON`: absolute path to the Python interpreter that runs `run_pdf_ocr_vllm.py` (defaults to the current interpreter). -- `GLOSSAPI_DEEPSEEK_VLLM_SCRIPT`: override path to the DeepSeek CLI script (defaults to `deepseek-ocr/run_pdf_ocr_vllm.py` under the repo). -- `GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH`: prepend extra library search paths (e.g., for `libjpeg-turbo`) when launching the CLI. +- `GLOSSAPI_DEEPSEEK_ALLOW_STUB`: must remain `0`; stub execution is rejected. +- `GLOSSAPI_DEEPSEEK_ALLOW_CLI`: keep at `1` to require the real runtime. +- `GLOSSAPI_DEEPSEEK_PYTHON`: absolute path to the Python interpreter that runs the DeepSeek OCR runner. When this is unset, GlossAPI now prefers a repo-local version-pinned DeepSeek runtime under `dependency_setup/.venvs/deepseek*` before falling back to the generic `deepseek` alias and finally the current process interpreter. +- `GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT`: override path to the OCR runner script (defaults to `src/glossapi/ocr/deepseek/run_pdf_ocr_transformers.py`). +- `GLOSSAPI_DEEPSEEK_MODEL_DIR`: path to the downloaded `DeepSeek-OCR-2` snapshot. +- `GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH`: prepend extra library search paths when launching the OCR runner. + +Standard OCR defaults: + +- `runtime_backend='vllm'` +- `ocr_profile='markdown_grounded'` +- `max_new_tokens=2048` +- `repair_mode='auto'` +- `scheduler='auto'` +- `target_batch_pages=160` + +The DeepSeek runners now default to `max_new_tokens=2048`. Do not leave the token cap implicit in one environment and explicit in another when comparing benchmarks. ## Math Enrichment (Phase‑2) @@ -71,10 +91,6 @@ All LaTeX policy knobs are loaded via `glossapi.text_sanitize.load_latex_policy( - `GLOSSAPI_WORKER_LOG_DIR`: override the directory used for per-worker logs and `gpu.current` markers (defaults to `logs/ocr_workers/` or `logs/math_workers/` under the output directory). - `GLOSSAPI_WORKER_LOG_VERBOSE` = `1|0` (default `1`): emit (or suppress) the GPU binding banner each worker prints on startup. -## RapidOCR Model Paths - -- `GLOSSAPI_RAPIDOCR_ONNX_DIR`: directory containing `det/rec/cls` ONNX models and keys. - ## Triage & Parquet - Triage always writes both: diff --git a/docs/getting_started.md b/docs/getting_started.md index f6bf4ce..a53518c 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -4,46 +4,75 @@ This guide gets a new GlossAPI contributor from clone → first extraction with ## Checklist -- Python 3.8+ (3.10 recommended) +- Python 3.10+ (`3.12` recommended for the DeepSeek runtime) - Recent `pip` (or `uv`) and a C/C++ toolchain for Rust wheels -- Optional: NVIDIA GPU with CUDA 12.x drivers for Docling/RapidOCR acceleration +- Optional: NVIDIA GPU with CUDA drivers for Docling/DeepSeek acceleration + +On fresh Linux hosts, make these assumptions explicit instead of relying on shell startup files: + +- prefer a stable final CPython, not a prerelease distro build +- keep `~/.local/bin` on `PATH` if `uv` was installed with `pip install --user uv` +- keep `~/.cargo/bin` on `PATH` if Rust was installed with `rustup` ## Install GlossAPI -### Recommended — mode-aware setup script +### Recommended setup -Use `dependency_setup/setup_glossapi.sh` to build an isolated virtualenv with the correct dependency set for vanilla, RapidOCR, or DeepSeek runs. Examples: +Use `dependency_setup/setup_glossapi.sh` for the main Docling environment and `dependency_setup/setup_deepseek_uv.sh` for the OCR runtime. Examples: ```bash -# Vanilla pipeline (CPU-only OCR) -./dependency_setup/setup_glossapi.sh --mode vanilla --venv dependency_setup/.venvs/vanilla --run-tests - -# RapidOCR GPU stack -./dependency_setup/setup_glossapi.sh --mode rapidocr --venv dependency_setup/.venvs/rapidocr --run-tests +# Main GlossAPI environment +./dependency_setup/setup_glossapi.sh --mode docling --venv dependency_setup/.venvs/docling --run-tests -# DeepSeek OCR on GPU (expects weights under /path/to/deepseek-ocr/DeepSeek-OCR) -./dependency_setup/setup_glossapi.sh \ - --mode deepseek \ +# DeepSeek OCR on GPU (uv-managed, downloads DeepSeek-OCR-2 if requested) +./dependency_setup/setup_deepseek_uv.sh \ + --python /path/to/stable/python \ --venv dependency_setup/.venvs/deepseek \ - --weights-dir /path/to/deepseek-ocr \ + --model-root /path/to/deepseek-ocr-2-model \ + --download-model \ --run-tests --smoke-test ``` -Add `--download-deepseek` if you need the script to fetch weights via Hugging Face; otherwise it searches `${REPO_ROOT}/deepseek-ocr/DeepSeek-OCR` unless you override `--weights-dir`. Inspect `dependency_setup/dependency_notes.md` for the latest pins, caveats, and validation runs. The script installs GlossAPI and its Rust crates in editable mode so source changes are picked up immediately. +`setup_glossapi.sh --mode deepseek` delegates to the same uv-based installer. Inspect `dependency_setup/dependency_notes.md` for the current pins and validation runs. Both setup paths install GlossAPI and its Rust crates in editable mode so source changes are picked up immediately. +The dedicated DeepSeek uv environment is intentionally OCR-only: it installs `glossapi[deepseek]` and leaves Docling in the main environment. + +On fresh GPU nodes, prefer a `uv`-managed stable Python such as: + +```bash +~/.local/bin/uv python install 3.11.11 +``` + +Then pass that interpreter explicitly to the setup scripts: + +```bash +./dependency_setup/setup_glossapi.sh \ + --mode docling \ + --python /home/$USER/.local/share/uv/python/cpython-3.11.11-linux-x86_64-gnu/bin/python3.11 \ + --venv dependency_setup/.venvs/docling + +./dependency_setup/setup_deepseek_uv.sh \ + --python /home/$USER/.local/share/uv/python/cpython-3.11.11-linux-x86_64-gnu/bin/python3.11 \ + --venv dependency_setup/.venvs/deepseek +``` **DeepSeek runtime checklist** -- Run `python -m glossapi.ocr.deepseek.preflight` from the DeepSeek venv to assert the CLI can run (env vars, model dir, flashinfer, cc1plus, libjpeg). -- Force the real CLI and avoid stub fallback by setting: +- Run `python -m glossapi.ocr.deepseek.preflight` from the DeepSeek venv to assert the real runtime is reachable. +- Run `python -m glossapi.scripts.deepseek_runtime_report` from the DeepSeek venv on fresh GPU nodes before ad hoc fixes. That captures the interpreter, CUDA wheel layout, and package versions used by the node. +- Force the real runtime and avoid stub fallback by setting: - `GLOSSAPI_DEEPSEEK_ALLOW_CLI=1` - `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0` - - `GLOSSAPI_DEEPSEEK_VLLM_SCRIPT=/path/to/deepseek-ocr/run_pdf_ocr_vllm.py` - - `GLOSSAPI_DEEPSEEK_TEST_PYTHON=/path/to/deepseek/venv/bin/python` - - `GLOSSAPI_DEEPSEEK_MODEL_DIR=/path/to/deepseek-ocr/DeepSeek-OCR` - - `GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH=/path/to/libjpeg-turbo/lib` -- Install a CUDA toolkit with `nvcc` and set `CUDA_HOME` / prepend `$CUDA_HOME/bin` to `PATH` (FlashInfer/vLLM JIT expects it). -- If FlashInfer is unstable on your stack, disable it with `VLLM_USE_FLASHINFER=0` and `FLASHINFER_DISABLE=1`. -- Avoid FP8 KV cache issues by exporting `GLOSSAPI_DEEPSEEK_NO_FP8_KV=1`; tune VRAM use via `GLOSSAPI_DEEPSEEK_GPU_MEMORY_UTILIZATION=<0.5–0.9>`. -- Keep `LD_LIBRARY_PATH` pointing at the toolkit lib64 (e.g. `LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`). + - `GLOSSAPI_DEEPSEEK_PYTHON=/path/to/deepseek/venv/bin/python` + - `GLOSSAPI_DEEPSEEK_MODEL_DIR=/path/to/deepseek-ocr-2-model/DeepSeek-OCR-2` +- If `GLOSSAPI_DEEPSEEK_PYTHON` is unset, GlossAPI now searches for a repo-local version-pinned DeepSeek runtime under `dependency_setup/.venvs/deepseek*` before falling back to the generic `deepseek` alias and then the current process interpreter. Keep the env var set when you need an explicit override; broken explicit paths are treated as configuration errors, not silently ignored. +- Standard OCR defaults after setup: + - `runtime_backend='vllm'` + - `ocr_profile='markdown_grounded'` + - `max_new_tokens=2048` + - `repair_mode='auto'` + - `scheduler='auto'` + - `target_batch_pages=160` +- `flash-attn` is optional. The runner uses it when available and otherwise falls back to the Transformers `eager` attention implementation. +- Do not benchmark against an ad hoc DeepSeek venv and compare it to the validated `dependency_setup/.venvs/deepseek` results as if they were the same stack. ### Option 1 — pip (evaluate quickly) @@ -74,30 +103,19 @@ chmod +x scripts/setup_conda.sh conda activate glossapi ``` -The helper script provisions Python 3.10, installs Rust + `maturin`, performs an editable install, and applies the Docling RapidOCR patch automatically. +The helper script provisions Python 3.10, installs Rust + `maturin`, and performs an editable install. ## GPU prerequisites (optional but recommended) -`setup_glossapi.sh` pulls the right CUDA/Torch/ONNX wheels for the RapidOCR and DeepSeek profiles. If you are curating dependencies manually, make sure you: +`setup_glossapi.sh` and `setup_deepseek_uv.sh` pull the required Torch wheels for the supported Docling and DeepSeek flows. If you are curating dependencies manually, make sure you: -- Install the GPU build of ONNX Runtime (`onnxruntime-gpu`) and uninstall the CPU wheel. -- Select the PyTorch build that matches your driver/toolkit (the repository currently targets CUDA 12.8 for DeepSeek). +- Select the PyTorch build that matches your driver/toolkit. - Verify the providers with: ```bash - python -c "import onnxruntime as ort; print(ort.get_available_providers())" python -c "import torch; print(torch.cuda.is_available())" ``` -## RapidOCR models & keys - -GlossAPI ships the required ONNX models and Greek keys under `glossapi/models/rapidocr/{onnx,keys}`. To override them, set `GLOSSAPI_RAPIDOCR_ONNX_DIR` to a directory containing: - -- `det/inference.onnx` -- `rec/inference.onnx` -- `cls/ch_ppocr_mobile_v2.0_cls_infer.onnx` -- `greek_ppocrv5_keys.txt` - ## First run (lightweight corpus) ```bash diff --git a/docs/index.md b/docs/index.md index d696c8d..13cef9d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -7,21 +7,13 @@ Welcome to the refreshed docs for GlossAPI, the GFOSS pipeline for turning acade - [Quickstart Recipes](quickstart.md) — common extraction/OCR flows in copy-paste form. - [Lightweight PDF Corpus](lightweight_corpus.md) — 20 one-page PDFs for smoke testing without Docling or GPUs. -## Understand the architecture -- [Architecture Overview](architecture/index.md) — the end-to-end staged model and why it exists. -- [Core Design Principles](architecture/core_design_principles.md) — the design constraints that shape the pipeline. -- [Docling Throughput and Batching](architecture/docling_throughput_and_batching.md) — how throughput and stability trade off. -- [Failure Recovery and Skiplist](architecture/docling_failure_recovery_and_skiplist.md) — how the pipeline survives problematic PDFs. -- [Greek Text Validation](architecture/greek_text_validation.md) — why extraction success is not enough for Greek corpora. -- [Metadata, Artifacts, and Run Diagnostics](architecture/metadata_artifacts_and_run_diagnostics.md) — how provenance and operational state are retained. -- [Artifact Layout and Stage Handoffs](architecture/artifact_layout_and_stage_handoffs.md) — how folders, filenames, and metadata glue the stages together. -- [Resumability, Recovery, and Retention](architecture/resumability_recovery_and_retention.md) — how the current design supports reruns and where storage pressure appears. - ## Learn the pipeline +- [Code Map](code_map.md) links the main documentation ideas to the classes and files that implement them. - [Pipeline Overview](pipeline.md) explains each stage and the emitted artifacts. -- [OCR & Math Enrichment](ocr_and_math_enhancement.md) covers Docling + RapidOCR usage. +- [OCR & Math Enrichment](ocr_and_math_enhancement.md) covers DeepSeek OCR remediation and Docling-based enrichment. +- [OCR Repetition Policy](ocr_repetition_policy.md) pins the default repetition thresholds for word and LaTeX cleaning. +- [OCR Cleaning Runtime](architecture/ocr_cleaning_runtime.md) explains the shared clean/debug analyzer, ordering, and why the cleaner separates tables, numeric, LaTeX, hybrid, and text ownership. - [Multi-GPU & Benchmarking](multi_gpu.md) shares scaling and scheduling tips. -- [Stage Reference](stages/index.md) breaks down each pipeline stage as a contract. ## Configure and debug - [Configuration](configuration.md) lists all environment knobs. @@ -29,5 +21,5 @@ Welcome to the refreshed docs for GlossAPI, the GFOSS pipeline for turning acade - [AWS Job Distribution](aws_job_distribution.md) describes large-scale scheduling. ## Reference -- [Corpus API](api/corpus.md) details public methods and parameters. -- `docs/divio/` contains placeholder pages for the upcoming Divio restructuring—feel free to open PRs fleshing them out. +- [Corpus API](api/corpus.md) gives the compact contract view of the main public methods. +- [Legacy Corpus API Notes](api_corpus_tmp.md) remains available while the docs are being consolidated. diff --git a/docs/math_enrichment_runtime.md b/docs/math_enrichment_runtime.md index 21d8617..096209c 100644 --- a/docs/math_enrichment_runtime.md +++ b/docs/math_enrichment_runtime.md @@ -68,9 +68,8 @@ c.ocr(math_targets=targets, math_batch_size=4) ## OCR/Model Constraints (recap) -- ORT GPU only: uninstall `onnxruntime` CPU; use `onnxruntime-gpu`. -- RapidOCR keys: Docling 2.48.0 needs `Rec.rec_keys_path` patch (see README). -- Model discovery: set `GLOSSAPI_RAPIDOCR_ONNX_DIR` or package models under `glossapi/models/rapidocr/`. +- DeepSeek OCR runs in its own pinned runtime; set `GLOSSAPI_DEEPSEEK_PYTHON`, `GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT`, and `GLOSSAPI_DEEPSEEK_MODEL_DIR`. +- Keep `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0` and `GLOSSAPI_DEEPSEEK_ALLOW_CLI=1`. - Optional Torch CUDA: needed for GPU layout/enrichment; see README for the CUDA wheels. ## Multi‑GPU diff --git a/docs/multi_gpu.md b/docs/multi_gpu.md index b1b8956..c06efe8 100644 --- a/docs/multi_gpu.md +++ b/docs/multi_gpu.md @@ -1,17 +1,21 @@ # Multi‑GPU & Benchmarking GlossAPI can scale across multiple visible GPUs. Faster GPUs drain more work from a shared queue of **absolute -file paths**, so no worker rescans directories. +file paths or pre-packed work items, so no worker rescans directories. ## Extract (Phase‑1) on Multiple GPUs ```python from glossapi import Corpus c = Corpus('IN', 'OUT') -c.extract(input_format='pdf', use_gpus='multi', force_ocr=True) +c.extract(input_format='pdf', use_gpus='multi', phase1_backend='docling', workers_per_device=2) ``` - Workers are bound using `CUDA_VISIBLE_DEVICES=` and run Docling on `cuda:0` relative to each worker. +- `workers_per_device` defaults to `1`; raise it only when benchmarking a strong GPU such as an A100. +- `GLOSSAPI_DOCLING_MAX_BATCH_FILES` lets one Docling worker take more than one PDF per extractor batch; keep the default `1` for fresh-node stability and benchmark larger values explicitly. +- `GLOSSAPI_DOCLING_BATCH_TARGET_PAGES` controls the page budget per queued multi-GPU Docling work item. The controller now sorts heavier work first and packs smaller PDFs toward that page budget so workers do not immediately collapse into a long single-file tail. +- `GLOSSAPI_DOCLING_PAGE_BATCH_SIZE` controls Docling's internal per-device page window (`settings.perf.page_batch_size`). Use it together with the outer queue page budget when you want steadier GPU residency instead of just fatter file bundles. - Threads auto‑tune when `num_threads=None` (roughly `min(cpu_count, 2 * #GPUs)`). Override explicitly if needed. - The controller persists extraction progress in `download_results/download_results.parquet` after each reported batch, so interrupted runs can resume cleanly without ad-hoc checkpoint files. @@ -31,6 +35,39 @@ c.ocr(use_gpus='multi', math_batch_size=12) - Crashed workers are respawned automatically; control the retry budget per GPU with `GLOSSAPI_MATH_RESPAWN_CAP` (default `5`). Use `GLOSSAPI_WORKER_LOG_VERBOSE=0` to silence the banner that prints the binding info. - When a device exceeds the respawn cap, remaining stems are added to the fatal skip-list and their artifacts are quarantined under `downloads/problematic_math/` and `json/problematic_math/` for follow-up. +## DeepSeek OCR on Multiple GPUs + +```python +from glossapi import Corpus +c = Corpus("OUT", "OUT") +c.ocr( + use_gpus="multi", + runtime_backend="vllm", + workers_per_gpu=1, + scheduler="exact_fill", + target_batch_pages=96, +) +``` + +- `scheduler="exact_fill"` is the preferred multi-GPU vLLM scheduler when PDFs vary widely in length. It shards large documents into page ranges and keeps GPU lanes filled more evenly. +- Internal shard runs now preserve the public `Corpus.ocr()` contract. Canonical outputs are reassembled back into `markdown/.md` and `json/metrics/.metrics.json` for each source PDF. +- When OCR starts from canonical corpus rows, the preferred stage handoff is also a canonical parquet where corrected `text` is embedded back into the same row identity. Markdown and metrics remain sidecars for inspection and audit. +- Shard markdown and shard metrics are retained for debugging under `sidecars/ocr_shards/` instead of remaining in the canonical handoff directories. +- The vLLM path now renders pages into memory and feeds a bounded queue directly into inference, which removes the temporary PNG round-trip and overlaps rendering with generation. +- Empty-page detection still happens before inference, and repair retries reuse the in-memory page image instead of reopening a file from disk. +- Final OCR markdown now tags each page split with `` so page images, markdown, and metrics stay aligned during inspection. +- If a repair retry hits the garbage cutoff again, the page is blanked rather than keeping the failed first-pass garbage. +- Multi-GPU vLLM workers now pull from a durable shared batch queue in `sidecars/ocr_runtime/work_queue.sqlite`, so finished batches survive worker crashes and respawned workers can continue without rescanning completed work. +- Repair work now runs as a second global queue phase. First-pass batches finish and persist shard outputs first; then any worker can claim the queued repair shards. This keeps repair tails balanced across GPUs without mixing worker-local repair state into the controller. +- Workers may pack multiple pending repair items into one larger execution batch. Queue durability stays item-granular, but the runtime no longer has to execute the repair tail as one tiny origin-shard retry at a time. +- Each worker writes `sidecars/ocr_runtime/worker_*.runtime.json` with heartbeat state and steady-state timing markers. The runner also emits `gpu_preflight.json`, `gpu_telemetry.jsonl`, and `runtime_summary.json`. +- The runner checks GPU persistence mode before launch by default. Control it with `GLOSSAPI_DEEPSEEK_GPU_PREFLIGHT=off|warn|ensure`. The default is `ensure`, which will try `sudo -n nvidia-smi -pm 1` and record the result in `gpu_preflight.json`. +- When the DeepSeek runtime is built from wheel-managed CUDA packages, the runner now auto-discovers the venv's `site-packages/nvidia/*/lib` directories and prepends them to `LD_LIBRARY_PATH`. `GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH` still works as a manual override or supplement. +- Worker reliability knobs are environment-driven: `GLOSSAPI_DEEPSEEK_WORKER_RESPAWN_CAP`, `GLOSSAPI_DEEPSEEK_WORK_ITEM_MAX_ATTEMPTS`, `GLOSSAPI_DEEPSEEK_WORK_STALE_AFTER_SEC`, `GLOSSAPI_DEEPSEEK_WORK_HEARTBEAT_SEC`, and `GLOSSAPI_DEEPSEEK_TELEMETRY_INTERVAL_SEC`. +- The default `GLOSSAPI_DEEPSEEK_WORK_ITEM_MAX_ATTEMPTS=2` means one retry after the first failed claim, then the batch is marked failed instead of retrying forever. +- `workers_per_gpu=1` remains the safe default on A100 40GB nodes. Prefer increasing `target_batch_pages` before adding more workers per device. +- For fresh GCP A100 nodes, run `python -m glossapi.scripts.deepseek_runtime_report --repo-root ` before applying ad hoc fixes. Treat that report as the baseline comparison against a known-good node. See [operations/deepseek_gcp_a100_setup.md](operations/deepseek_gcp_a100_setup.md). + ## Provider & Device Checks - ONNXRuntime providers must include `CUDAExecutionProvider`. diff --git a/docs/ocr_and_math_enhancement.md b/docs/ocr_and_math_enhancement.md index 197bb0a..b013dd3 100644 --- a/docs/ocr_and_math_enhancement.md +++ b/docs/ocr_and_math_enhancement.md @@ -1,15 +1,14 @@ # GPU OCR and Math Enrichment -This document summarizes how GlossAPI uses the GPU for OCR and formula/code enrichment, how to run each phase efficiently, and where artifacts are written. +This document summarizes how GlossAPI uses the GPU for OCR remediation and formula/code enrichment, how to run each phase efficiently, and where artifacts are written. ## Overview -- Phase‑1 (Extract): PDF → Markdown via Docling; optional GPU OCR via RapidOCR (ONNXRuntime). Optionally emit JSON + formula index for Phase‑2. +- Phase‑1 (Extract): PDF → Markdown via Docling or the safe backend. Optionally emit JSON + formula index for Phase‑2. - Phase‑2 (Enrich): From Docling JSON, decode math/code on the GPU (CodeFormula) and re‑emit enriched Markdown. Backends -- `backend='rapidocr'` (default): Docling + RapidOCR; Phase‑2 math runs from Docling JSON. -- `backend='deepseek'`: DeepSeek‑OCR; equations are included inline in OCR output, so Phase‑2 math is not required and is treated as a no‑op. +- `backend='deepseek'`: DeepSeek-OCR-2; equations are included inline in OCR output, so Phase‑2 math is not required and is treated as a no‑op. Policy: never OCR and math on the same file - If a file needs OCR, GlossAPI runs OCR only (no Phase‑2 on that file in the same pass). @@ -18,24 +17,43 @@ Policy: never OCR and math on the same file ### Python API layout - DeepSeek entry point: `glossapi.ocr.deepseek.runner.run_for_files(...)` -- RapidOCR dispatcher: `glossapi.ocr.rapidocr.dispatch.run_via_extract(...)` - Math enrichment: `glossapi.ocr.math.enrich.enrich_from_docling_json(...)` - Utility helpers (Docling JSON / cleaning): `glossapi.ocr.utils.*` ## Prerequisites -- RapidOCR/Docling stack: `pip install '.[rapidocr]'` -- DeepSeek CLI stack (in a dedicated venv recommended): `pip install '.[deepseek]'` -- ONNXRuntime GPU installed (no CPU ORT): `onnxruntime-gpu==1.18.1` -- Torch CUDA installed: e.g., `torch==2.5.1+cu121` -- Packaged RapidOCR models/keys found under `glossapi/models/rapidocr/{onnx,keys}` or via `GLOSSAPI_RAPIDOCR_ONNX_DIR`. +- Main GlossAPI stack: `./dependency_setup/setup_glossapi.sh --mode docling` +- DeepSeek runtime: `./dependency_setup/setup_deepseek_uv.sh --venv dependency_setup/.venvs/deepseek` +- Torch CUDA installed in the DeepSeek env (the uv setup pins the tested stack). - Optional helpers for Phase‑2 JSON: `pypdfium2`, `zstandard`. +### Standard DeepSeek venv + +Use a dedicated OCR runtime and treat it as the source of truth for DeepSeek runs: + +```bash +./dependency_setup/setup_deepseek_uv.sh \ + --venv dependency_setup/.venvs/deepseek \ + --model-root /path/to/deepseek-ocr-2-model \ + --download-model \ + --run-tests --smoke-test +``` + +Recommended environment variables after setup: + +```bash +export GLOSSAPI_DEEPSEEK_ALLOW_CLI=1 +export GLOSSAPI_DEEPSEEK_ALLOW_STUB=0 +export GLOSSAPI_DEEPSEEK_PYTHON="$PWD/dependency_setup/.venvs/deepseek/bin/python" +export GLOSSAPI_DEEPSEEK_MODEL_DIR="/path/to/deepseek-ocr-2-model/DeepSeek-OCR-2" +``` + +The OCR runtime should not silently drift between ad hoc virtual environments during benchmarking. If a benchmark uses a different DeepSeek venv, treat the result as a different runtime stack. + Verify GPU readiness before forcing OCR or math: ```bash python -c "import torch; print(torch.cuda.is_available(), torch.cuda.device_count())" # expects True, >=1 -python -c "import onnxruntime as ort; print(ort.get_available_providers())" # must include CUDAExecutionProvider ``` ## Running Phase‑1 (Extract) @@ -44,17 +62,14 @@ python -c "import onnxruntime as ort; print(ort.get_available_providers())" from glossapi import Corpus c = Corpus('IN','OUT') -# GPU OCR on PDFs; emit JSON + formula index for Phase‑2 +# Emit JSON + formula index for Phase‑2 c.extract( input_format='pdf', - accel_type='CUDA', # or use_gpus='multi' for multi‑GPU - force_ocr=True, # OCR always on for PDFs + accel_type='CUDA', emit_formula_index=True, # request json/.formula_index.jsonl alongside the default JSON ) ``` -When `force_ocr=True` (or when math/code enrichment is enabled), GlossAPI automatically switches to the Docling backend and aborts if CUDA‑enabled torch/ONNXRuntime providers are not available. - Outputs: - `markdown/.md` - `json/.docling.json(.zst)` and `json/.formula_index.jsonl` @@ -88,20 +103,64 @@ c.ocr(backend='deepseek', fix_bad=True, math_enhance=True, mode='ocr_bad_then_ma # → runs OCR only for bad files; equations are included inline; Phase‑2 is skipped ``` -If you need Phase‑2 math on files that do not require OCR, use RapidOCR/Docling and math‑only (expects Docling JSON from Phase‑1): +If you need Phase‑2 math on files that do not require OCR, run `math_only` after Docling extraction with JSON enabled. + +### DeepSeek fast path + +The current recommended high-throughput DeepSeek configuration is: + +- `runtime_backend='vllm'` +- `ocr_profile='markdown_grounded'` +- `max_new_tokens=2048` as the standard default ceiling +- `repair_mode='auto'` to keep markdown as the primary output while selectively rerunning suspicious pages +- `scheduler='auto'` so multi-GPU vLLM runs resolve to exact-fill page-range batching +- `target_batch_pages=160` +- large `vllm_batch_size` chosen to keep `sec/page/GPU` at or below the best validated floor for the target hardware + +Example: ```python -c.ocr(backend='rapidocr', fix_bad=False, math_enhance=True, mode='math_only') -# → runs Phase‑2 on non‑OCR files only (requires Docling JSON) +c.ocr( + backend='deepseek', + fix_bad=True, + math_enhance=False, + runtime_backend='vllm', + ocr_profile='markdown_grounded', + max_new_tokens=2048, + vllm_batch_size=160, + gpu_memory_utilization=0.9, + repair_mode='auto', + scheduler='auto', + target_batch_pages=160, + use_gpus='multi', +) ``` +`repair_mode='auto'` runs the pipeline in distinct phases inside the vLLM runner: + +1. markdown first pass over all rendered pages +2. cheap per-page triage using output quality plus simple image density statistics +3. plain-text rerun bucket for garbage markdown pages +4. tiled markdown rerun bucket for short coverage failures + +This keeps the fast path batched while avoiding per-page sequential fallback overhead. + +### What is now implemented + +- Empty-page skipping before OCR dispatch +- Streaming garbage early-stop during markdown generation +- Plain-text retry for pages that hit the garbage early-stop +- Multi-GPU exact-fill page-range scheduling for the DeepSeek runner +- Benchmark harness support for `whole_doc`, `fixed_shard`, and `exact_fill` +- Corpus API forwarding for the scheduler controls + ## Multi‑GPU Phase‑1 (extract): ```python -c.extract(input_format='pdf', use_gpus='multi', force_ocr=True) +c.extract(input_format='pdf', use_gpus='multi', phase1_backend='docling', workers_per_device=2) ``` -Workers set `CUDA_VISIBLE_DEVICES` per process; Docling runs on `cuda:0` relative to each worker. OCR uses ORT GPU under the same process. +Workers set `CUDA_VISIBLE_DEVICES` per process; Docling runs on `cuda:0` relative to each worker. Phase‑2 (enrich): ```python @@ -118,9 +177,73 @@ Spawns math workers; each binds to its GPU using `CUDA_VISIBLE_DEVICES` and runs ## Performance & Tuning +### Validated benchmark floor + +The current non-regression metric is `sec/page/GPU`. + +Validated on 2026-03-30: + +- Host: AWS `g7e.48xlarge` +- Runtime: `vllm` +- Profile: `markdown_grounded` +- Render DPI: `144` +- GPU memory utilization: `0.9` +- Best large-batch single-GPU floor observed: `0.3109 sec/page/GPU` + +Production markdown+repair benchmark on the same host: + +- Corpus: `43` OA PDFs, `7,624` pages +- Runtime: `vllm` +- Profile: `markdown_grounded` +- Repair mode: `auto` +- Max new tokens: `2048` +- GPUs: `8` +- Static sharding (`1` shard/GPU), validated rerun after classifier hardening: `558.88s` wall, `0.0733 sec/page` overall, `0.4912` to `0.5475 sec/page/GPU` +- Streaming admission (`stream_batch_pages=160`): `928.81s` wall, `0.1218 sec/page` overall, `0.5469` to `0.6856 sec/page/GPU` +- Peak VRAM in both runs stayed at about `88,953 MiB` per active GPU +- Static active-lane GPU utilization averaged about `65%` to `75%`; streaming active-lane utilization stayed similar while whole-run occupancy got worse because more lanes sat idle between batches + +Validated on 2026-03-31 after standardizing the DeepSeek runtime ceiling back to `2048` and restoring the persistent one-process-per-lane architecture: + +- Corpus: `43` OA PDFs, `7,624` pages +- Runtime: `vllm` +- Profile: `markdown_grounded` +- Repair mode: `auto` +- Scheduler: `whole_doc` +- Max new tokens: `2048` +- GPUs: `8` +- Clean rebuilt whole-document rerun: about `541s` wall, `0.0710 sec/page` overall, and `0.3927` to `0.5000 sec/page/GPU` + +Interpretation: + +- The rebuilt stack is back near the validated March 30 throughput once the silent `8192` ceiling regression is removed. +- The remaining performance problem is not raw inference speed; it is whole-document tail imbalance, where one oversized PDF can keep a single GPU busy after the other lanes finish. +- Multi-GPU `exact_fill` must therefore be benchmarked only on the persistent lane-worker architecture. The earlier exact-fill regression was caused by spawning a fresh OCR CLI per batch, not by the scheduling idea itself. + +Decision: + +- Keep static sharding as the default large-run pipeline shape for now +- Do not enable streaming admission by default yet; on this benchmark it regressed badly versus static sharding +- Treat the earlier `0.3109 sec/page/GPU` result as the raw floor, and the static repaired-markdown result above as the current production-like baseline on this hardware +- Treat the 2026-03-31 clean whole-document rerun as the restored benchmark sanity check for the standardized `2048` ceiling on the rebuilt runtime + +Attention/runtime note: + +- The production fast path is `vllm`; logs on this stack show `flashinfer` autotuning plus CUDA graph capture +- Transformers remain the fallback path; prefer `flash_attention_2` there and do not optimize around `sdpa` + +That number is the floor to preserve or beat when tuning the full markdown pipeline. Faster raw runs that change the effective output mode or bypass repair logic do not replace it as the production baseline. + +Default policy note: + +- The standard DeepSeek OCR default is now `max_new_tokens=2048` for both the Transformers and vLLM runners. +- Leaving the flag unset must not silently expand to a larger ceiling such as `8192`. +- When comparing benchmark runs, treat a different token ceiling or a different DeepSeek venv as a different runtime/configuration. + - Batch sizes - - Inline (Phase‑1): `GLOSSAPI_FORMULA_BATCH` (default 16) sets CodeFormula docling side throughput. + - Inline (Phase‑1): `GLOSSAPI_FORMULA_BATCH` (default 16) sets CodeFormula throughput. - Phase‑2: `batch_size` / `math_batch_size` parameter (typ. 8–16) balances VRAM and speed. + - DeepSeek vLLM: push `vllm_batch_size` as high as the hardware allows while tracking `sec/page/GPU`; on the validated `g7e.48xlarge` path, larger batches continued improving throughput through `batch_size=160`. - Images scale for OCR: `GLOSSAPI_IMAGES_SCALE` (~1.1–1.25) can improve detection on thin glyphs. - CPU threads: cap `OMP_NUM_THREADS` / `MKL_NUM_THREADS` to avoid CPU oversubscription on multi‑GPU nodes. @@ -159,11 +282,7 @@ OUT/ ## Troubleshooting -- Missing CUDAExecutionProvider - - Ensure `onnxruntime-gpu` is installed and `onnxruntime` CPU is uninstalled. - Torch reports no CUDA - Check `nvidia-smi` and match Torch CUDA build to your driver. -- OCR is slow or falls back to CPU - - Confirm ORT providers include CUDAExecutionProvider and that `accel_type='CUDA'` is used. - Out of memory - Lower `batch_size` for Phase‑2, reduce `GLOSSAPI_IMAGES_SCALE`, or split inputs. diff --git a/docs/ocr_noise_failure_modes.md b/docs/ocr_noise_failure_modes.md new file mode 100644 index 0000000..6017e9c --- /dev/null +++ b/docs/ocr_noise_failure_modes.md @@ -0,0 +1,118 @@ +# OCR Noise Failure Modes + +Status: example bank for future `Corpus.clean_ocr(...)` heuristics. These are notes only, not implemented cleaning rules. + +## Why This Exists + +The preserved OCR outputs contain several distinct failure modes that should not be collapsed into one generic `ocr_noise` rule. Some are page-local low-entropy collapses, some are encoding/control-character tails, and some are repetitive math-token artifacts that need math-aware handling. + +The examples below were reviewed on April 3, 2026 from the preserved OCR lane: + +- `/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown` + +## Group 1: Page-Local Low-Entropy Numeric Collapse + +Definition: +pages that collapse into highly repetitive short numeric lines, often immediately after a page split marker. + +Examples: + +- `ABO_768__p00001-00096.md` + - around line 955 the page turns into repeated `0`, `0 0`, `0 0 0` + - the collapse begins directly after `<--- Page Split --->` +- `ACH_787__p00001-00096.md` + - around line 755 the page turns into repeated `1.1` and occasional `1` + - this also begins directly after `<--- Page Split --->` + +Anchored references: + +- [ABO_768__p00001-00096.md:955](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ABO_768__p00001-00096.md#L955) +- [ACH_787__p00001-00096.md:755](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ACH_787__p00001-00096.md#L755) + +Detection ideas: + +- page-level repeated-line detection, not just single-line run detection +- low token entropy on a page-sized region +- special weight if the collapse starts right after `<--- Page Split --->` +- repeated short numeric lines should be treated separately from legitimate tables or lists + +Important note: +the current OCR numeric-noise check is line-local and is better at catching long same-number or ascending sequences inside one line than these repeated-line page collapses. + +## Group 2: Control-Character / Encoding-Garbage Tails + +Definition: +pages that devolve into non-printable or control-like characters, often after otherwise valid text. + +Example: + +- `ADQ_670.md` + - after a page split, the page contains `%` followed by C1/control-like junk such as `€`, ``, `‚`, ..., `°` + - this is not just numeric repetition; it looks like decoding/binary leakage or severe mojibake-like corruption + +Anchored references: + +- [ADQ_670.md:887](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ADQ_670.md#L887) +- [ADQ_670.md:954](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ADQ_670.md#L954) + +Detection ideas: + +- count non-printable/control codepoints +- count dense runs of extended control-like characters on a page +- flag abrupt transitions from valid prose to control-character tails +- keep this separate from ordinary mojibake and separate from numeric collapse + +## Group 3: Repetitive Math-Token Floods + +Definition: +pages or page segments that repeat the same LaTeX-like math atoms or malformed math atoms many times. + +Examples: + +- `ADS_856__p00001-00014.md` + - repeated `\( \gamma \)` sequence on one line +- `ADS_856__p00015-00082.md` + - repeated `\( \Delta_{v} \)` blocks + - malformed variants like `\( \Deltav \)` + - long concatenated runs like `\Delta_{v}\Delta_{v}\Delta_{v}...` + +Anchored references: + +- [ADS_856__p00001-00014.md:139](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ADS_856__p00001-00014.md#L139) +- [ADS_856__p00015-00082.md:1](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ADS_856__p00015-00082.md#L1) + +Detection ideas: + +- tokenize LaTeX-like math atoms and detect repeated-token floods +- distinguish valid repeated notation from pathological repetition +- score malformed math variants separately from valid math tokens +- this should remain an experimental detector, not a blunt drop rule + +Important note: +real mathematical texts can legitimately repeat symbols, so this class needs a math-aware heuristic rather than a general repetition penalty. + +## Grouping Recommendation + +Do not collapse all of the above into one rule. + +Recommended future flags: + +- `ocr_numeric_page_collapse` +- `ocr_control_char_tail` +- `ocr_math_repetition` + +Recommended future metadata: + +- page-local region counts +- page-split proximity flags +- repeated-line entropy or uniqueness ratio +- control-character density +- math-token repetition density + +## Current Examples To Keep Around + +- [ABO_768__p00001-00096.md:955](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ABO_768__p00001-00096.md#L955) +- [ACH_787__p00001-00096.md:755](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ACH_787__p00001-00096.md#L755) +- [ADQ_670.md:887](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ADQ_670.md#L887) +- [ADS_856__p00001-00014.md:139](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ADS_856__p00001-00014.md#L139) +- [ADS_856__p00015-00082.md:1](/home/foivos/data/openarchives_ocr_ingest_20260403/lanes/eu_node01_full_v1/markdown/ADS_856__p00015-00082.md#L1) diff --git a/docs/ocr_repetition_policy.md b/docs/ocr_repetition_policy.md new file mode 100644 index 0000000..eccd446 --- /dev/null +++ b/docs/ocr_repetition_policy.md @@ -0,0 +1,42 @@ +# OCR Repetition Policy + +This document pins the intended default repetition thresholds for OCR-cleaner development so they do not drift silently. + +## Defaults + +- Shared word repetition threshold: `4` +- Shared LaTeX repetition threshold: `4` +- Shared minimum repeat period: `3` +- Shared repeat window: `96` + +These defaults apply to the combined OCR debug annotator: +- `Corpus.clean_ocr_numeric_word_debug_docs(...)` + +The same analyzer now also drives real clean-mode rendering in `clean_ocr()`; +debug and clean differ only in rendering, not in span discovery. + +In that pipeline: +- tables are handled first +- numeric detection runs before generic text ownership +- LaTeX and hybrid structural detection run before shared text repetition +- shared repeat detection runs last on the remaining untagged text + +## Scope + +These defaults are for: +- word repetition +- LaTeX repetition + +They do not override numeric-specific detectors, which have their own thresholds such as: +- ascending numeric progressions +- compact repeated numeric atoms +- same-digit numeric runs + +## Design Intent + +- Neighboring same-type spans may merge when their separator has `40` non-whitespace characters or less; this keeps fragmented OCR loops from being split into multiple tiny matches. +- A default of `4` is meant to reduce borderline `3`-repeat matches. +- Locality matters more than page-wide reuse, especially for LaTeX. +- Repeated symbols or notation used normally across a page should not be treated as cleaner targets by default. +- Numeric progression should be handled by numeric or hybrid logic before text repetition sees it. +- Table cleanup includes structural cases that are not repetition problems, so table policy is documented separately in `docs/architecture/ocr_cleaning_runtime.md`. diff --git a/docs/operations/DELETE_ME_deepseek_reliability_pending_2026-04-02.md b/docs/operations/DELETE_ME_deepseek_reliability_pending_2026-04-02.md new file mode 100644 index 0000000..2e6605d --- /dev/null +++ b/docs/operations/DELETE_ME_deepseek_reliability_pending_2026-04-02.md @@ -0,0 +1,39 @@ +# DELETE ME: DeepSeek Reliability Pending Work + +This note is temporary. Delete it after the first production soak confirms the +merged reliability path is stable and the follow-up items below are either done +or explicitly discarded. + +## What shipped in this merge + +- durable multi-GPU DeepSeek work queue with separate main and repair phases +- worker respawn with process-group teardown so orphaned `VLLM::EngineCore` + processes do not pin VRAM after a crash +- GPU preflight and telemetry sidecars under `sidecars/ocr_runtime/` +- steady-state timing in the runtime summary +- default work-item retry ceiling of two total attempts + - first failure: retry once + - second failure: mark the batch failed and stop retrying it + +## Pending follow-up + +1. Capture and archive one clean fault-injection receipt on the merged + `development` branch. + - Goal: preserve one explicit production-like run where a worker is killed + mid-run, the supervisor respawns it, the in-flight batch is retried once, + and the run still completes. + +2. Add operator-facing handling for terminally failed batches. + - The durable queue already marks them `failed`. + - The remaining work is a cleaner operator handoff, for example a dedicated + quarantine/export path or a documented replay workflow. + +3. Replace the current image-content stats implementation in + `run_pdf_ocr_vllm.py`. + - It still uses a CPU-heavy PIL pixel scan and currently emits a Pillow + deprecation warning. + +4. Run a longer unattended soak after merge. + - The current validation covers targeted tests, full end-to-end runs, and + reliability-path implementation, but production confidence still benefits + from a longer multi-hour burn-in on the merged branch. diff --git a/docs/operations/deepseek_gcp_a100_setup.md b/docs/operations/deepseek_gcp_a100_setup.md new file mode 100644 index 0000000..20d9209 --- /dev/null +++ b/docs/operations/deepseek_gcp_a100_setup.md @@ -0,0 +1,160 @@ +# DeepSeek GCP A100 Setup + +This note captures the current known-good baseline for bringing up GlossAPI +DeepSeek OCR on fresh GCP A100 nodes and the required diagnosis workflow when a +fresh node does not behave like the already-converged fleet. + +## Goal + +Treat a fresh OCR node as a reproducible setup target, not as a one-off machine +that is repaired interactively until it happens to work. + +The target is a clean path from: + +1. create instance +2. bootstrap machine +3. prepare GlossAPI runtime +4. run a normal GlossAPI OCR workflow + +## Known-good baseline + +This rollout has validated the following stack on working OCR fleet nodes: + +- Ubuntu `22.04.5` +- NVIDIA driver `590.48.01` +- `A100 40GB` GPUs +- host Python `3.10` +- DeepSeek venv Python `3.11` from a stable final CPython, not a prerelease distro build +- `torch 2.10.0+cu130` +- `vllm 0.18.0` +- `transformers 4.57.6` +- `workers_per_gpu=1` + +The runner also expects GPU persistence mode to be enabled and will record the +preflight result under `sidecars/ocr_runtime/gpu_preflight.json`. + +## First command on a fresh node + +Run the checked-in runtime report before changing code or applying ad hoc fixes: + +```bash +python -m glossapi.scripts.deepseek_runtime_report --repo-root /opt/glossapi/repo +``` + +The report prints: + +- OS and hostname +- repo revision +- GPU model, driver, and memory +- selected Python executable and venv root +- `torch` / `vllm` / `transformers` import details +- wheel-managed NVIDIA library directories +- a focused `pip freeze` subset +- selected runtime environment variables + +Prefer comparing this output against a known-good OCR node before modifying +GlossAPI itself. + +## Fresh-node diagnosis rule + +If a fresh node fails, classify the problem before patching code: + +1. instance creation choice + - wrong image + - wrong driver path + - wrong machine family or GPU shape +2. bootstrap incompleteness + - missing system packages + - missing wheel-managed CUDA libraries + - model / cache / filesystem layout mismatch + - missing env wiring +3. actual GlossAPI runtime assumption + - hidden dependency on a particular venv layout + - hidden dependency on a specific CUDA wheel layout + - hidden runner / vLLM startup assumption + +Write down which class the current failure belongs to before making broad code +changes. + +## Current benchmark-node findings + +The fresh `a2-highgpu-2g` benchmark node used during the April 3, 2026 work +surfaced two setup classes: + +- early missing shared-library failure: + - `ImportError: libcudart.so.12: cannot open shared object file` +- later engine startup failure after bootstrap fixes: + - `RuntimeError: Engine core initialization failed. Failed core proc(s): {}` + +This means instance creation itself worked, but bootstrap/runtime reproducibility +was incomplete. + +The concrete bootstrap issues found on that node were: + +- `uv` existed only in `~/.local/bin`, which non-interactive shells were not using +- the default DeepSeek venv was created against `/usr/bin/python3.11`, which on + that node was `Python 3.11.0rc1` +- system cargo/rustc were too old to parse the repo `Cargo.lock` +- the DeepSeek venv still needed the cu12 runtime pair for `vllm._C` to import: + - `nvidia-cuda-runtime-cu12` + - `nvidia-cuda-nvrtc-cu12` + +After correcting those bootstrap defects, the same fresh node was able to: + +- import `vllm._C` +- initialize a direct one-GPU `LLM(...)` +- start a real `openarchives_ocr_run_node` workload with `runtime_backend=vllm` + +The same node was also used for a real `10`-PDF `extract -> clean -> ocr` +checkpoint: + +- the stable end-to-end shape on that node was: + - multi-GPU extraction + - `workers_per_device=1` + - multi-GPU DeepSeek OCR with `workers_per_gpu=1` +- an isolated extraction benchmark with `workers_per_device=2` was faster on the + same sample, but the first full-pipeline replay hit a Docling allocator crash: + - `malloc_consolidate(): unaligned fastbin chunk detected` +- treat `workers_per_device=2` as benchmark-only / experimental until it is + proven stable in the full Corpus pipeline, not just in extract-only tests + +The full-pipeline checkpoint harness also now retries the JSONL export when OCR +has already filled text into parquet rows but the first export pass still emits +zero records. This guards the observed end-of-run export race on the benchmark +node without changing the OCR output contract itself. + +## Current runner expectation + +`glossapi.ocr.deepseek.runner._build_env()` now auto-discovers +`site-packages/nvidia/*/lib` directories under the selected DeepSeek virtualenv +and prepends them to `LD_LIBRARY_PATH`. + +This is the right place to normalize wheel-managed CUDA library discovery. Do +not rely on manual shell-session exports as the primary contract. + +## Practical bring-up checklist + +1. confirm the node matches the OS / driver baseline +2. export user-local tool paths explicitly for non-interactive shells: + - `export PATH="$HOME/.local/bin:$HOME/.cargo/bin:$PATH"` +3. install a stable CPython explicitly, for example: + - `~/.local/bin/uv python install 3.11.11` +4. run `deepseek_runtime_report` +5. compare report output to a known-good node +6. fix bootstrap mismatches first +7. rerun the report +8. only then run a small OCR validation workload +9. if OCR still fails, inspect worker logs and decide whether the remaining gap + belongs in GlossAPI runtime code or external bootstrap + +## Rust note + +If editable installs fail while building `glossapi_rs_cleaner` or +`glossapi_rs_noise`, prefer a user-local modern Rust toolchain: + +```bash +curl https://sh.rustup.rs -sSf | sh -s -- -y +export PATH="$HOME/.cargo/bin:$PATH" +rustup toolchain install stable +rustup default stable +``` diff --git a/docs/operations/deepseek_runtime_contract.md b/docs/operations/deepseek_runtime_contract.md new file mode 100644 index 0000000..b1d9c94 --- /dev/null +++ b/docs/operations/deepseek_runtime_contract.md @@ -0,0 +1,82 @@ +# DeepSeek runtime contract + +Status: vLLM is the supported runtime backend. The transformers backend is +preserved as best-effort and is currently broken with the upstream +DeepSeek-OCR-2 bundled modeling code (see "Known issues" below). + +## Supported runtime + +`runtime_backend = "vllm"` is the default and only supported value. + +The DeepSeek-OCR-2 model is served via the script +`src/glossapi/ocr/deepseek/run_pdf_ocr_vllm.py`, which loads the model with +vLLM's own model loader and exposes the model through vLLM's batched +inference API. The runner subprocesses this script per lane and reads back +markdown plus per-page metrics from disk. + +## Page-level efficiency contract + +The vLLM runtime ships with a blank-page skip guard: + +- `_is_effectively_empty_page(image_stats, repair_mode)` runs on the + pre-rendered image stats (overall and per-region dark ratios). When + `repair_mode == "auto"` (default) and the page falls below the configured + brightness thresholds, the runtime emits a synthetic page metric + (`repair_strategy="skip_empty"`, `empty_page_skipped=True`, `infer_sec=0.0`) + and counts it in the aggregate `empty_pages_skipped`. No model forward pass + happens for skipped pages. + +This guard does NOT exist on the transformers script. It is one of the +reasons the supported backend is vLLM. + +## Backend choice — why vLLM + +- vLLM ships the blank-page skip guard described above. +- vLLM uses its own model loader and does not exercise the transformers + dynamic-module path that breaks on DeepSeek-OCR-2 with current upstream + transformers. +- vLLM enables batched inference across multi-GPU lanes via the `exact_fill` + scheduler in `src/glossapi/ocr/deepseek/scheduling.py`. + +Reference benchmark on 2× A100 SXM4 40GB (`a2-highgpu-2g`, us-west1-b): +- 10 OpenArchives PDFs, 683 pages, scheduler `exact_fill`, target 160 pages + per batch. +- vLLM wall time: 276.16 s (4 min 36 s); 0.65–0.76 s/page per GPU. +- Auto-repair flagged 86 pages and successfully repaired 85 of them. + +## Replacing or extending the backend + +The runner is a subprocess-per-script architecture. To add a new backend: + +1. Add a new `run_pdf_ocr_.py` script in + `src/glossapi/ocr/deepseek/`. +2. Wire its CLI surface into `runner.py`'s + `_build_cli_command` (and `defaults.py` if the backend introduces + defaults). +3. Add a runtime choice in `defaults.DEFAULT_RUNTIME_BACKEND` and the + acceptance check at `runner.py:run_for_files`. +4. Document the contract here. + +The `scheduling.py` page router and `work_queue.py` durable batch queue are +backend-agnostic and consume the same `WorkSlice` / `(doc_id, page_number)` +abstractions regardless of which inference backend runs. + +## Known issues + +- **transformers backend is broken** with the version pulled transitively + by `vllm==0.18.0`. The DeepSeek-OCR-2 bundled `modeling_deepseekv2.py` + imports `LlamaFlashAttention2` from `transformers.models.llama.modeling_llama`, + which was removed upstream in transformers ≥ 4.46. The transformers script + also requires `matplotlib` at first import, which is not declared in the + `deepseek` extra. We do not fix these here; the supported backend is vLLM. + +## Testing + +- `tests/test_deepseek_runner_contract.py` — runner contract tests. +- `tests/test_ocr_dispatch_backends.py` — dispatch tests. +- `tests/test_deepseek_scheduling.py` — scheduling tests. +- `dependency_setup/deepseek_gpu_smoke.py` — minimal real-GPU smoke test. +- `src/glossapi/scripts/deepseek_pipeline_benchmark.py` — full pipeline + benchmark with per-GPU and per-lane metrics; supports + `--scheduler {whole_doc, fixed_shard, exact_fill}` and + `--runtime-backend {vllm, transformers}`. diff --git a/docs/operations/ocr_changes_2026-04-01_to_2026-04-03.md b/docs/operations/ocr_changes_2026-04-01_to_2026-04-03.md new file mode 100644 index 0000000..734b5d8 --- /dev/null +++ b/docs/operations/ocr_changes_2026-04-01_to_2026-04-03.md @@ -0,0 +1,82 @@ +# OCR Changes Merged To `development` (2026-04-01 to 2026-04-03) + +This note summarizes the OCR-facing changes already merged into +`development`, centered on commit `489698e` (`deepseek reliability hardening`). + +Use it as a short operator/developer changelog for the April 1-3 rollout. + +## Runtime reliability + +- DeepSeek multi-GPU OCR now runs through a durable SQLite work queue instead of + fragile fixed subprocess assignment. +- Work items heartbeat while running and are requeued if a worker dies or goes + stale. +- Failed work items now default to one retry (`max_attempts=2` total attempts), + then become terminal failures for operator follow-up instead of bouncing + forever. +- Repair work is durable too: first-pass batches populate a second repair queue + that workers drain after the main queue is empty. +- Workers are launched in their own process groups so respawn can clean up + orphaned runtime processes and recover GPU memory. + +## Throughput and observability + +- vLLM OCR now renders pages into memory and feeds a bounded render queue + directly into inference, removing the temporary image-file round trip. +- Rendering and inference overlap during the first pass. +- Empty pages are detected before inference and skipped early. +- Per-worker runtime JSON, GPU preflight output, GPU telemetry, durable queue + state, and the final runtime summary now live under `sidecars/ocr_runtime/`. +- Runtime summaries now expose steady-state inference timestamps so long-run + throughput can be measured without startup noise. + +## Output contract and repair behavior + +- Canonical OCR outputs remain one `markdown/.md` and one + `json/metrics/.metrics.json` per source PDF. +- Page boundaries are annotated with `` comments alongside the + page split markers. +- Internal shard markdown and shard metrics move under `sidecars/ocr_shards/` + so downstream stages do not mistake them for canonical outputs. +- If a repair retry hits the garbage cutoff again, GlossAPI now blanks that page + slot instead of preserving the failed garbage text. +- Repair queue durability and repair execution packing are separate concerns: + queue accounting stays item-granular, while workers are allowed to combine + multiple repair items into one larger execution batch. + +## Fresh-node setup implications + +- The runner now auto-discovers wheel-managed CUDA libraries inside the selected + DeepSeek virtualenv and prepends them to `LD_LIBRARY_PATH`. +- Fresh A100 nodes should be validated first with: + +```bash +python -m glossapi.scripts.deepseek_runtime_report --repo-root +``` + +- The currently validated fleet baseline is: + - Ubuntu `22.04.5` + - NVIDIA driver `590.48.01` + - A100 `40GB` + - `torch 2.10.0+cu130` + - `vllm 0.18.0` + - `transformers 4.57.6` + - `workers_per_gpu=1` + +## Test coverage added with the merge + +- durable queue requeue / retry behavior +- repair queue enqueue and phase switching +- repair execution packing +- worker runtime summaries and runner contracts + +## What this doc does not cover + +This note only summarizes OCR work already merged into `development`. + +It does not describe the still-in-progress branch work for: + +- fresh-node bootstrap hardening beyond `development` +- stronger OCR metadata continuity +- canonical text-bearing OCR parquet outputs +- additional extract-clean-ocr integration validation diff --git a/docs/operations/openarchives_ocr_rollout_plan.md b/docs/operations/openarchives_ocr_rollout_plan.md new file mode 100644 index 0000000..56136fa --- /dev/null +++ b/docs/operations/openarchives_ocr_rollout_plan.md @@ -0,0 +1,498 @@ +# OpenArchives OCR Rollout Plan + +This document records the concrete execution plan for running DeepSeek OCR over the OpenArchives subset with `needs_ocr=True`, including how to recover or regenerate the routing state, how to shard work across AWS nodes, and how to merge results back into the canonical GlossAPI corpus. + +## Implemented tooling + +The rollout is backed by concrete scripts in `src/glossapi/scripts/`: + +- `openarchives_ocr_enrich.py` + - reads the canonical OpenArchives parquet + - scans raw HF JSONL shards for the target docs + - extracts `page_count_source`, `pages_total_source`, and `pdf_url` + - writes a shard-ready enriched parquet for OCR deployment +- `openarchives_ocr_shards.py` + - reads the canonical parquet + - filters `needs_ocr=True` + - balances documents across `N` nodes by page count + - writes one shard manifest parquet per node + - writes a JSON summary with page totals and ETA +- `openarchives_ocr_merge.py` + - merges shard-level OCR metadata back into the canonical parquet by `filename` + - can also embed merged OCR `text` plus artifact linkage fields back into the canonical rows when OCR markdown artifacts are available + - unifies page-range shard markdown back into one canonical document-level markdown artifact per OCR row before downstream handoff + +These scripts are intentionally document-level rather than page-fragment-level so merge stays simple and GlossAPI-compatible. + +## Executed result on 2026-03-31 + +The CPU fallback path has now been executed successfully on AWS: + +- CPU cleaner node: + - instance: `c7i.8xlarge` + - instance id: `i-0ccf5ab1a510b31d8` +- Full OA reevaluation fill: + - input rows: `179,845` + - missing `greek_badness_score` rows materialized and cleaned: `89,892` + - unique raw JSONL shards needed for the fill subset: `108` +- Filled routing result: + - `greek_badness_score` coverage: `179,845 / 179,845` + - `needs_ocr == true`: `45,547` +- Enriched OCR target manifest: + - OCR-target docs: `45,547` + - OCR-target pages: `3,292,392` + - raw JSONL shards needed for the full OCR target set: `218` +- Balanced 4-node shard result: + - `4` shard manifests + - `823,098` pages per node + - `11,386` or `11,387` docs per node +- ETA from validated `g7e.48xlarge` throughput: + - one node: `64.94h` + - four nodes: `16.23h` + +Published artifacts on Hugging Face dataset `glossAPI/openarchives.gr`: + +- `data/openarchives_ocr_completion/20260331/summary.json` +- `data/openarchives_ocr_completion/20260331/filled_document_level.parquet` +- `data/openarchives_ocr_completion/20260331/filled_document_quality.parquet` +- `data/openarchives_ocr_completion/20260331/ocr_shards/needs_ocr_enriched.parquet` +- `data/openarchives_ocr_completion/20260331/ocr_shards/openarchives_ocr_shard_node_00.parquet` +- `data/openarchives_ocr_completion/20260331/ocr_shards/openarchives_ocr_shard_node_01.parquet` +- `data/openarchives_ocr_completion/20260331/ocr_shards/openarchives_ocr_shard_node_02.parquet` +- `data/openarchives_ocr_completion/20260331/ocr_shards/openarchives_ocr_shard_node_03.parquet` +- `data/openarchives_ocr_completion/20260331/ocr_shards/openarchives_ocr_shard_summary.json` + +## Node runner contract + +Each OCR node should materialize one shard into its own GlossAPI corpus root and +run DeepSeek OCR through the standard `Corpus.ocr(...)` API, not through a +standalone benchmark wrapper. + +Stored runner: + +- `python -m glossapi.scripts.openarchives_ocr_run_node` +- `python -m glossapi.scripts.openarchives_download_freeze` + +The runner does four things in order: + +1. reads one shard parquet +2. downloads the shard PDFs into `downloads/` using their OA filenames +3. writes the shard metadata as canonical `download_results/download_results.parquet` +4. runs `Corpus.ocr(...)` with the validated DeepSeek settings + +The download-freeze runner is the matching download-only entrypoint: + +1. reads one OA manifest parquet +2. downloads the PDFs into `downloads/` using their OA filenames +3. writes canonical `download_results/download_results.parquet` +4. stops there, without starting OCR + +Download policy note: + +- OpenArchives download should be host-first, not collection-first. +- GlossAPI now supports host-specific download policy overrides in the normal downloader path for: + - `downloader` + - `request_timeout` + - `ssl_verify` + - `ssl_cafile` + - `request_method` + - `sleep` + - `per_domain_concurrency` + - `domain_concurrency_floor` + - `domain_concurrency_ceiling` + - `skip_failed_after` + - `domain_cookies` +- That means the OA freeze-download phase can stay inside `Corpus.download(...)`; we do not need a separate downloader implementation. +- Stored OA policy sample: + - `samples/openarchives_download_policy.yml` +- Stored OA probe runner: + - `python -m glossapi.scripts.openarchives_download_probe` +- OA download runs should use `scheduler_mode=per_domain` together with `parallelize_by=base_domain`, + otherwise the host-level concurrency policy is mostly inert. +- Probe result on the CPU box: + - `dspace.lib.ntua.gr` succeeds cleanly once OA downloads use `scheduler_mode=per_domain` + and the host is throttled to a single in-flight request + - `ktisis.cut.ac.cy` succeeds with `ssl_verify=false` + - `repository.academyofathens.gr`, `repository.ihu.gr`, `pergamos.lib.uoa.gr`, + and `dione.lib.unipi.gr` behaved like standard hosts in the probe + - `ikee.lib.auth.gr` is not just a pre-ping false negative; direct PDF requests hit + real connection timeouts + - `olympias.lib.uoi.gr` is not just a pre-ping false negative either; direct PDF + requests reach the host but stall on response reads +- Operational recommendation: + - bulk-freeze the good hosts first + - keep `ikee.lib.auth.gr` and `olympias.lib.uoi.gr` in a dedicated slow-path download phase + so they do not dominate the main corpus freeze run + +Standard node command: + +```bash +PYTHONPATH=src /home/ubuntu/venvs/deepseek/bin/python -m glossapi.scripts.openarchives_ocr_run_node \ + --shard-parquet /data/openarchives/shards/openarchives_ocr_shard_node_00.parquet \ + --work-root /data/openarchives/node_00 \ + --heartbeat-path /data/openarchives/heartbeats/node_00.json \ + --instance-id "$INSTANCE_ID" \ + --node-id node-00 \ + --scheduler whole_doc \ + --runtime-backend vllm \ + --ocr-profile markdown_grounded \ + --render-dpi 144 \ + --max-new-tokens 2048 \ + --repair-mode auto \ + --gpu-memory-utilization 0.9 +``` + +Current rollout note: + +- use `scheduler=whole_doc` for the first production OA pass because that is the + last large-run configuration validated cleanly on the standardized stack +- keep `exact_fill` as the next benchmarking target, but do not silently switch + the production rollout to it until the same stack shows a non-regression or + improvement + +## Current validated baseline + +- Validated OCR node type: `g7e.48xlarge` +- Validated AMI: `ami-052266c3e21dff7db` +- AMI name: `Deep Learning Base OSS Nvidia Driver GPU AMI (Ubuntu 24.04) 20260320` +- Validated runtime stack on the OCR node: + - `torch 2.10.0+cu130` + - `vllm 0.18.0` + - `transformers 4.57.6` +- Standard DeepSeek settings: + - `runtime_backend='vllm'` + - `ocr_profile='markdown_grounded'` + - `max_new_tokens=2048` + - `repair_mode='auto'` + - `render_dpi=144` + - `gpu_memory_utilization=0.9` +- Restored clean benchmark on the stopped OCR box: + - `7,624` pages in about `541s` + - about `0.0710 sec/page` overall on one `8`-GPU node + - about `0.3927` to `0.5000 sec/page/GPU` +- Derived per-node throughput: + - about `14.08 pages/sec` + - about `50,700 pages/hour` + +## Current AWS capacity + +`us-east-1` service quotas currently allow: + +- `Running On-Demand G and VT instances = 768` +- `Running On-Demand Standard instances = 640` + +For the validated OCR node: + +- `g7e.48xlarge = 192 vCPU, 8 GPUs` + +So the current maximum concurrent validated OCR fleet is: + +- `floor(768 / 192) = 4` nodes +- total rollout capacity: `32 GPUs` + +## Phase 1: Recover or regenerate the canonical OCR routing state + +Goal: + +- produce one canonical `download_results/download_results.parquet` for the OpenArchives corpus root +- ensure it contains, at minimum: + - `filename` + - `needs_ocr` + - `greek_badness_score` + - `mojibake_badness_score` + - `ocr_success` + - `page_count` or `pages_total` + +Decision order: + +1. Check the stopped GPU OCR instance first. +2. If the full corpus parquet is not there, run a dedicated CPU cleaning pass. + +### 1A. Check the stopped OCR instance first + +Reason: + +- the NVMe volume persists across stop/start +- if the full OpenArchives cleaning pass was already run there, this is the fastest path + +Concrete steps: + +1. Start instance `i-0504a326a1fee541f`. +2. SSH in and search for the full OpenArchives corpus root and canonical parquet: + - `find /opt /data /home -name download_results.parquet` + - verify row count is the full OpenArchives set, not the `43`-document benchmark subset +3. Validate that the parquet has the required OCR routing columns listed above. +4. If found: + - copy the canonical parquet and any supporting cleaner outputs back to stable storage + - stage a copy on `home` + - upload the parquet artifact to the Hugging Face dataset repo as routing metadata + +Acceptance check: + +- row count matches the full OpenArchives working set +- `needs_ocr=True` count is available directly from the parquet +- page totals are available + +Current state on 2026-03-31: + +- checked OCR instance `i-0504a326a1fee541f` +- no `download_results.parquet` was found under `/opt`, `/data`, or `/home` +- therefore this path did not recover the canonical OpenArchives routing parquet +- the rollout should proceed with the CPU cleaning-pass fallback below + +### 1B. Fallback: regenerate the routing state on a CPU instance + +If the OCR box does not contain the full canonical parquet: + +- launch a dedicated CPU node for the cleaner pass +- recommended instance family: `c7i` or `r7i` +- recommended first choice: `c7i.8xlarge` with sufficient gp3 storage for the OpenArchives markdown/output root + +Reason: + +- `Corpus.clean()` is CPU-bound and does not need GPUs +- we only need one clean, reproducible routing pass + +Concrete steps: + +1. Launch one Ubuntu 24.04 CPU instance. +2. Clone `glossapi-development` at `development`. +3. Bootstrap the standard GlossAPI environment. +4. Mount or sync the full OpenArchives corpus root. +5. Run `Corpus.clean()` over the full markdown corpus. +6. Verify that `download_results/download_results.parquet` now exists and includes the required OCR routing columns. +7. Store the resulting parquet: + - on the corpus root + - on `home` + - in the Hugging Face dataset repo as routing metadata + +## Phase 2: Quantify the actual OCR workload + +Once the canonical parquet exists: + +1. Filter `needs_ocr == True` +2. Count: + - total documents + - total pages from `pages_total` or `page_count` +3. Also record: + - `greek_badness_score > 60` + - `mojibake_badness_score > 0.1` + - overlap between those conditions and `needs_ocr` + +This step defines the real production workload and the true ETA. + +## Phase 3: Shard across nodes + +Shard across nodes by document, not by page range. + +Reason: + +- cross-node merge stays trivial +- node-local GPU scheduling already exists in GlossAPI +- splitting one document across nodes adds complexity without clear benefit + +### Coordinator manifest + +Build one coordinator manifest from the canonical parquet with: + +- `filename` +- stable OpenArchives document id or canonical filename +- `pages_total` +- `needs_ocr` + +Then: + +1. keep only `needs_ocr=True` +2. greedily bin-pack documents across `N=4` nodes by page count +3. write one shard manifest parquet per node + +Each shard manifest should contain: + +- `filename` +- `pages_total` +- `node_id` +- `shard_id` +- original metadata keys needed for rejoin + +### Node-local execution + +Each node: + +1. loads only its shard manifest +2. runs GlossAPI OCR over that subset +3. keeps standard GlossAPI outputs only: + - `markdown/.md` + - `json/metrics/*.json` + - shard-local `download_results.parquet` + +Inside each node: + +- use the existing GlossAPI DeepSeek path +- let node-local scheduling handle GPU balance +- do not invent a separate OCR metadata format + +## Phase 4: Merge back into the canonical corpus + +Merge rules: + +1. Markdown: + - copy updated `markdown/.md` into the canonical corpus root +2. Metrics: + - copy `json/metrics/*.json` into the canonical corpus root +3. Metadata parquet: + - concatenate shard metadata + - upsert by canonical document id / filename into the master parquet + - preserve the standard GlossAPI contract: + - `needs_ocr` + - `ocr_success` + - `processing_stage` + - page and quality fields + +Recommended additional execution metadata: + +- `ocr_node_id` +- `ocr_shard_id` +- `ocr_started_at` +- `ocr_finished_at` +- `ocr_attempt_count` + +These fields are operational and should not replace the existing GlossAPI routing fields. + +## Phase 5: Standardize all OCR nodes + +All OCR nodes should use the exact same: + +- AMI +- bootstrap script +- DeepSeek venv setup +- model path +- runtime defaults + +Standard production recipe: + +- AMI: `ami-052266c3e21dff7db` +- instance type: `g7e.48xlarge` +- DeepSeek venv created by `dependency_setup/setup_deepseek_uv.sh` +- defaults: + - `runtime_backend='vllm'` + - `ocr_profile='markdown_grounded'` + - `max_new_tokens=2048` + - `repair_mode='auto'` + - `render_dpi=144` + - `gpu_memory_utilization=0.9` + +Do not allow per-node env drift during the rollout. + +Cleaner/fallback venv decision: + +- CPU cleaning pass should use the standard GlossAPI environment from `development` +- OCR nodes should use the dedicated DeepSeek venv only +- do not mix the cleaner runtime and the OCR runtime on the same benchmark measurement path + +## Instance options + +Primary OCR choice: + +- `g7e.48xlarge` + - validated benchmarked path + - `192 vCPU` + - `8` RTX PRO Server 6000 GPUs + - current recommended production OCR node + +Secondary OCR options, only if we intentionally rebenchmark: + +- `g6e.48xlarge` + - `192 vCPU` + - `8` L40S GPUs +- `g5.48xlarge` + - `192 vCPU` + - `8` A10G GPUs +- `p5.48xlarge` + - technically available, but not the cost/default target for this rollout + +Cleaner node options: + +- first choice: `c7i.8xlarge` + - `32 vCPU` + - good CPU-bound cleaner candidate +- alternative: `r7i.8xlarge` + - `32 vCPU` + - use if the cleaner pass needs more memory headroom + +## Phase 6: ETA + +Validated throughput on one node: + +- about `50,700 pages/hour` + +With `4` nodes: + +- about `202,800 pages/hour` + +Exact ETA formula: + +- `ETA_hours = total_needs_ocr_pages / 202800` + +Reference scenarios: + +- `400,000` pages: about `1.97h` +- `600,000` pages: about `2.96h` +- `800,000` pages: about `3.95h` +- `1,000,000` pages: about `4.93h` + +Equivalent document scenarios for `40,000` documents: + +- average `10` pages/doc: about `1.97h` +- average `15` pages/doc: about `2.96h` +- average `20` pages/doc: about `3.95h` +- average `25` pages/doc: about `4.93h` + +The exact ETA should be recalculated once the canonical parquet gives the real total page count for `needs_ocr=True`. + +## Phase 7: Deployment and monitoring + +### Deployment + +1. Produce canonical parquet +2. Compute shard manifests +3. Stage manifests and source data +4. Launch `4` OCR nodes +5. Bootstrap the same OCR environment on all nodes +6. Run one shard per node +7. Collect outputs +8. Merge back into the canonical corpus + +### Monitoring + +Each node should write a heartbeat JSON at a fixed interval with: + +- `node_id` +- `docs_done` +- `pages_done` +- current file +- GPU utilization snapshot +- VRAM usage snapshot +- last successful write time +- error count + +The coordinator should watch: + +- stale heartbeat +- zero progress +- failed OCR process +- low GPU utilization for a sustained period + +### Recovery + +- rerun only failed shard manifests +- keep shard manifests immutable +- merge is idempotent by canonical document id / filename + +## Immediate next actions + +1. Start the stopped OCR instance and search for the full OpenArchives canonical parquet. +2. If found, validate and upload the routing parquet to stable storage and Hugging Face. +3. If not found, launch one CPU instance and run the full `Corpus.clean()` pass. +4. Compute exact `needs_ocr` doc/page totals from the canonical parquet. +5. Generate the `4` node shard manifests. +6. Launch the `4` OCR nodes and execute the distributed run. diff --git a/docs/pipeline.md b/docs/pipeline.md index cb11662..2c00354 100644 --- a/docs/pipeline.md +++ b/docs/pipeline.md @@ -6,44 +6,150 @@ GlossAPI is a staged pipeline. You can enter at any stage and use the same folde The `Corpus` class is the stable surface of the project. New functionality should plug into the existing phase mixins so callers can stick to the small set of entrypoints (`download()`, `extract()`, `clean()`, `ocr()`, `section()`, `annotate()`, `export/jsonl*()`). The expected usage pattern is a short script that chains these calls; avoid ad-hoc monkeypatches or bypassing the orchestrator when adding features so downstream users retain resumability and consistent artifacts. -## Stages - -- Download (optional): fetch source files from URLs → `downloads/` -- Extract (Phase‑1): parse PDFs to Markdown; optional GPU OCR → `markdown/.md` -- Clean: compute quality metrics and filter low‑quality items; decide which to OCR -- OCR (compat shim): re‑run extract on filtered items with `force_ocr=True` -- JSON + index (optional): emit `json/.docling.json(.zst)` and `json/.formula_index.jsonl` for Phase‑2 -- Enrich (Phase‑2): decode FORMULA/CODE from JSON on GPU → overwrite `markdown/.md`, write `json/.latex_map.jsonl` -- Section: produce `sections/sections_for_annotation.parquet` -- Annotate: classify sections; produce `classified_sections.parquet` and `fully_annotated_sections.parquet` +## Stage Map + +| Stage | Main code | Typical inputs | Important parameters | Main outputs | +| --- | --- | --- | --- | --- | +| Download | `Corpus.download()`, `GlossDownloader.download_files()` | metadata parquet with a URL column | `input_parquet`, `links_column`, `parallelize_by`, downloader kwargs | `downloads/`, `download_results/*.parquet` | +| Extract (Phase‑1) | `Corpus.prime_extractor()`, `Corpus.extract()`, `GlossExtract.extract_path()` | files in `downloads/` or explicit paths | `input_format`, `phase1_backend`, `use_gpus`, `devices`, `workers_per_device`, `export_doc_json`, `emit_formula_index` | `markdown/.md`, `json/.docling.json(.zst)`, `json/metrics/*.json` | +| Clean | `Corpus.clean()` | `markdown/*.md` | `threshold`, `drop_bad`, `empty_char_threshold`, `empty_min_pages` | `clean_markdown/.md`, cleaner report parquet, parquet flags such as `filter` and `needs_ocr` | +| OCR retry | `Corpus.ocr(mode='ocr_bad'...)` | parquet rows flagged by cleaner | `mode`, `fix_bad`, `use_gpus`, `devices` | refreshed `markdown/.md`, refreshed cleaner/parquet metadata | +| Phase‑2 enrich | `Corpus.ocr(mode='math_only'...)`, `Corpus.formula_enrich_from_json()` | `json/.docling.json(.zst)` and optional formula index | `math_enhance`, `math_batch_size`, `math_dpi_base`, `targets_by_stem` | updated `markdown/.md`, `json/.latex_map.jsonl` | +| Section | `Corpus.section()`, `GlossSection.to_parquet()` | markdown selected by cleaner/parquet | no major public knobs | `sections/sections_for_annotation.parquet` | +| Annotate | `Corpus.annotate()`, `GlossSectionClassifier.classify_sections()`, `GlossSectionClassifier.fully_annotate()` | section parquet and classifier model | `annotation_type`, `fully_annotate` | `classified_sections.parquet`, `fully_annotated_sections.parquet` | +| Triage / export | `Corpus.triage_math()`, `Corpus.jsonl()` | metrics, parquet metadata, cleaned markdown | output path for JSONL | parquet routing hints, JSONL export | + +## Stage Contracts + +### 1. Download + +- Main code: `Corpus.download()` -> `GlossDownloader.download_files()` +- Purpose: read a metadata parquet, expand list/JSON URL cells, deduplicate URLs, download supported file types, and checkpoint progress. +- Typical inputs: + - a parquet file in `input_dir` or an explicit `input_parquet` + - a URL column such as `url` or `links_column` +- Main outputs: + - downloaded files in `downloads/` + - partial/final results in `download_results/` +- Read this next if you want the scheduler details: `gloss_downloader.py` + +### 2. Extract (Phase‑1) + +- Main code: `Corpus.prime_extractor()`, `Corpus.extract()`, `GlossExtract.ensure_extractor()`, `GlossExtract.extract_path()` +- Purpose: convert source files to markdown and optional intermediate JSON artifacts. +- Typical inputs: + - files already present in `downloads/` + - or explicit `file_paths` +- Important parameters: + - `phase1_backend='safe'|'docling'|'auto'` + - `use_gpus='single'|'multi'` + - `workers_per_device` to fan out more than one extraction worker onto each GPU + - `export_doc_json` and `emit_formula_index` for later Phase‑2 work +- Operational note: + - `force_ocr` is deprecated and ignored in Phase‑1; use `Corpus.ocr(backend='deepseek')` after `clean()` for OCR remediation +- Main outputs: + - canonical markdown in `markdown/.md` + - optional Docling JSON and index artifacts in `json/` + - per-document and per-page metrics in `json/metrics/` + +### 3. Clean + +- Main code: `Corpus.clean()` +- Purpose: run the Rust cleaner, remove low-quality or noisy markdown, + and mark documents that may need OCR retry before moving on. +- Typical inputs: + - `markdown/*.md` + - metadata parquet, if available +- Important parameters: + - `threshold` and `drop_bad` + - `empty_char_threshold` and `empty_min_pages` for OCR fallback decisions +- Main outputs: + - cleaned markdown in `clean_markdown/` + - updated parquet metadata with quality and OCR-related flags +- Runtime/debug artifacts: + - `.processing_state.pkl` keeps track of progress so interrupted runs can resume + - `problematic_files/` keeps files that could not be cleaned successfully + - `timeout_files/` keeps files that exceeded the cleaning time limit + +### 4. OCR Retry and Phase‑2 Enrichment + +- Main code: `Corpus.ocr()` and `Corpus.formula_enrich_from_json()` +- Purpose: + - rerun OCR only for documents marked bad by the cleaner + - optionally decode formula/code regions from Docling JSON into markdown +- Modes: + - `ocr_bad` + - `math_only` + - `ocr_bad_then_math` +- Main outputs: + - refreshed `markdown/.md` + - `json/.latex_map.jsonl` when math/code enrichment runs + +### 5. Section and Annotate + +- Main code: `Corpus.section()`, `GlossSection.to_parquet()`, `Corpus.annotate()`, `GlossSectionClassifier.*` +- Purpose: + - split markdown into sections suitable for classification + - classify sections and optionally expand coarse labels into full document structure +- Main outputs: + - `sections/sections_for_annotation.parquet` + - `classified_sections.parquet` + - `fully_annotated_sections.parquet` ## Artifact Layout -``` +The tree below shows the main folders and files GlossAPI can create under +the output directory. + +To make the layout easier to follow, artifacts are grouped by the role they +play in the pipeline: + +- canonical — the main outputs a stage is expected to produce, and the + files later stages usually depend on +- runtime — state files used to resume work safely if a run is interrupted +- debug — extra files kept around when something fails or needs a closer look + OUT/ -├── downloads/ -│ └── problematic_math/ -├── download_results/ -├── markdown/ +├── downloads/ (canonical) +│ └── problematic_math/ (debug) +├── download_results/ (canonical) +├── markdown/ (canonical) +│ └── .md +├── clean_markdown/ (canonical) │ └── .md -├── json/ +├── json/ (canonical) │ ├── .docling.json(.zst) │ ├── .formula_index.jsonl │ ├── .latex_map.jsonl │ ├── metrics/ -│ ├── .metrics.json -│ └── .per_page.metrics.json -│ └── problematic_math/ -├── sections/ +│ │ ├── .metrics.json +│ │ └── .per_page.metrics.json +│ └── problematic_math/ (debug) +├── sections/ (canonical) │ └── sections_for_annotation.parquet -├── classified_sections.parquet -└── fully_annotated_sections.parquet -``` +├── classified_sections.parquet (canonical) +├── fully_annotated_sections.parquet (canonical) +├── .processing_state.pkl (runtime) +├── problematic_files/ (debug) +└── timeout_files/ (debug) Notes: - Enriched Markdown replaces the plain Markdown (single canonical location). - Metrics lived under `markdown/` in earlier versions; they now live under `json/metrics/`. - When math enrichment cannot recover after the configured number of respawns, the corresponding PDFs and Docling artifacts are copied into the `problematic_math/` folders above and the stems are added to the fatal skip-list for later review. +- The same folder can act as both `input_dir` and `output_dir`; the pipeline creates its own subdirectories under that root. + +## Readability Shortcut + +If you only need the shortest path through the system: + +1. `Corpus.download()` if you start from URLs. +2. `Corpus.extract()` for Phase‑1 markdown. +3. `Corpus.clean()` to decide what needs OCR. +4. `Corpus.ocr()` for selective OCR and optional math/code enrichment. +5. `Corpus.section()` and `Corpus.annotate()` for structured outputs. + +If you need to jump from these ideas to the source files, see `code_map.md`. ## Exporting corpora diff --git a/docs/quickstart.md b/docs/quickstart.md index 4b10685..a498725 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -38,14 +38,13 @@ Workers report per-batch summaries and extraction progress is persisted into `download_results/download_results.parquet`, so you can restart multi-GPU runs without losing progress (no extra checkpoint files required). -## GPU OCR (opt-in) +## OCR remediation (opt-in) ```python from glossapi import Corpus c = Corpus('IN', 'OUT') -c.extract(input_format='pdf', accel_type='CUDA', force_ocr=True) -# or reuse multi-GPU batching -c.extract(input_format='pdf', use_gpus='multi', force_ocr=True) +c.clean() +c.ocr(backend='deepseek', fix_bad=True, math_enhance=False) ``` ## Phase‑2 Math Enrichment (from JSON) @@ -76,7 +75,7 @@ c.section() # to parquet c.annotate() # classify/annotate sections ``` -See ocr_and_math_enhancement.md for GPU details, batch sizes, and artifact locations. +See `ocr_and_math_enhancement.md` for OCR runtime details, batch sizes, and artifact locations. ### DeepSeek OCR @@ -89,12 +88,11 @@ c.ocr(backend='deepseek', fix_bad=True, math_enhance=True, mode='ocr_bad_then_ma # → OCR only for bad files; math is included inline in the Markdown ``` -To avoid stub output, set `GLOSSAPI_DEEPSEEK_ALLOW_CLI=1` and `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0`, and ensure the CLI bits are reachable: +To avoid stub output, set `GLOSSAPI_DEEPSEEK_ALLOW_CLI=1` and `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0`, and ensure the runtime is reachable: ```bash -export GLOSSAPI_DEEPSEEK_VLLM_SCRIPT=/path/to/deepseek-ocr/run_pdf_ocr_vllm.py -export GLOSSAPI_DEEPSEEK_TEST_PYTHON=/path/to/deepseek-venv/bin/python -export GLOSSAPI_DEEPSEEK_MODEL_DIR=/path/to/deepseek-ocr/DeepSeek-OCR -export GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH=/path/to/libjpeg-turbo/lib +export GLOSSAPI_DEEPSEEK_PYTHON=/path/to/deepseek-venv/bin/python +export GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT=/path/to/glossAPI/src/glossapi/ocr/deepseek/run_pdf_ocr_transformers.py +export GLOSSAPI_DEEPSEEK_MODEL_DIR=/path/to/deepseek-ocr-2-model/DeepSeek-OCR-2 python -m glossapi.ocr.deepseek.preflight # optional: validates env without running OCR ``` diff --git a/docs/stages/clean.md b/docs/stages/clean.md index ae3c735..0528f05 100644 --- a/docs/stages/clean.md +++ b/docs/stages/clean.md @@ -6,7 +6,7 @@ The clean stage normalizes extracted Markdown and evaluates its quality. ## Main responsibilities -- run Rust-backed cleaning +- run the shared OCR analyzer in either clean or debug rendering mode - compute text quality and badness metrics - detect documents that require OCR reruns - update metadata for downstream stage selection @@ -20,6 +20,12 @@ The clean stage normalizes extracted Markdown and evaluates its quality. ## Main outputs - cleaned Markdown in `clean_markdown/` +- debug-marked Markdown under `debug/` when debug output is requested +- debug manifests under `debug/`: + - `manifest.jsonl` + - `page_metrics.jsonl` + - `match_index.jsonl` + - `summary.json` - quality metrics and reports - metadata updates including OCR-related decisions @@ -32,6 +38,25 @@ It is especially important for Greek corpora because it distinguishes: - technically extracted text - actually usable Greek text +It also separates two different responsibilities that are easy to conflate: + +- structural cleanup + - tables, numeric runs, LaTeX collapse, hybrid numbered loops, word repetition +- quality scoring + - bad-character metrics + - suspicious-line metrics + - OCR rerun recommendations + +The stage now uses one shared analyzer for both: + +- `debug` mode + - shows exact match placement with `` tags + - records merged-span match metadata in `match_index.jsonl` +- `clean` mode + - removes or rewrites those exact same matched regions +- `clean + debug` + - writes pipeline-ready cleaned Markdown and the parallel debug artifacts from the same span plan in one run + ## Important operational outputs This stage may contribute or update: @@ -42,6 +67,32 @@ This stage may contribute or update: - character-count-based diagnostics - processing-stage status +## Current cleaning policy + +The cleaner does not use one generic fuzzy matcher over the whole page. +Instead it applies ownership in a fixed order: + +1. tables +2. numeric +3. LaTeX +4. hybrid numbered repetition +5. shared word repetition + +Why this matters: + +- tables can distort the visible text surface for every later pass +- numeric progressions are often valid cleaner targets but should not be + consumed by generic text repetition +- LaTeX and hybrid passes rely on more specific local structure +- shared text repetition is therefore safest on the remaining surface only + +Table handling is intentionally broader than repetition: + +- `sentence_shell_table` is dropped +- `empty_table_collapse` is dropped +- `repeated_rows` is dropped +- unmatched tables are converted from HTML to GitHub-style Markdown + ## Failure concerns Typical issues include: @@ -53,3 +104,7 @@ Typical issues include: ## Contributor note Changes here affect OCR routing and post-run quality analysis. Treat score and flag semantics as contract-level behavior. + +For content-cleaning changes, the exact-output benchmark in +`tests/test_ocr_golden_pages.py` is the main regression lock. Speed work is only +acceptable if those outputs remain stable. diff --git a/docs/stages/download.md b/docs/stages/download.md index 99bc4f8..c70c551 100644 --- a/docs/stages/download.md +++ b/docs/stages/download.md @@ -8,6 +8,7 @@ The download stage acquires source documents from parquet-based URL metadata and - read URL-bearing parquet input - download files concurrently +- route known browser-gated sources through browser-assisted acquisition when configured - retain source metadata context - avoid refetching previously successful downloads - assign stable-enough local filenames for downstream processing @@ -42,10 +43,34 @@ Typical issues include: - transient network failures - rate limiting +- browser-gated file endpoints that return HTML challenge/interstitial pages +- viewer-only sources that should fail cleanly instead of being recorded as successful downloads - duplicate URLs - filename collisions - partially completed corpus fetches +## Browser-gated sources + +The downloader now distinguishes between: + +- direct file endpoints +- browser-gated file endpoints +- viewer-only/document-reader sources + +For browser-gated file endpoints: + +- `download_mode="auto"` probes with direct HTTP and escalates to a browser session when it detects a recoverable interstitial +- `download_mode="browser"` goes directly to the browser-assisted path +- `download_policy_file=...` can route known domains or URL patterns to the correct path without probing every file + +Browser-assisted mode is designed for retrievable file endpoints, not for sources that only expose page images, tiles, HTML/SVG re-rendering, or DRM-wrapped readers. + +## Session reuse + +Browser-assisted mode reuses cached browser session state per domain so multiple files from the same protected source do not need a fresh browser bootstrap every time. + +This keeps the browser as a session-bootstrap resource rather than the main downloader. + ## Contributor note Any change to filename assignment or result parquet structure can have downstream impact on: diff --git a/docs/stages/ocr.md b/docs/stages/ocr.md index 3bf8815..65454eb 100644 --- a/docs/stages/ocr.md +++ b/docs/stages/ocr.md @@ -22,15 +22,13 @@ The OCR stage repairs documents whose extracted text is considered unreliable, a - corrected Markdown or OCR-enriched outputs - backend-specific JSON or related artifacts - metadata updates such as OCR success markers +- when metadata parquet is available, a canonical OCR parquet should preserve the same row identity and carry corrected `text` together with the updated metadata ## Backend choices -The pipeline supports at least two OCR-oriented modes: - -- RapidOCR through the Docling path -- DeepSeek OCR for environments configured for that backend - -These are operationally different and should not be treated as interchangeable implementation details. +The supported OCR remediation backend is DeepSeek OCR. Docling remains part of +the surrounding extraction and layout flow, but OCR reruns themselves are now +expected to use the DeepSeek runtime. ## Selection model @@ -44,6 +42,25 @@ OCR reruns should preserve: - explicit indication that remediation was attempted - visibility into files that remain problematic +## DeepSeek runtime contract + +For the operator-facing summary of the OCR changes already merged into +`development` during the April 1-3 rollout, see +`../operations/ocr_changes_2026-04-01_to_2026-04-03.md`. + +- `ocr()` may execute page-range shards internally when `use_gpus="multi"` and `scheduler="exact_fill"`, but the stage contract remains one canonical Markdown file and one canonical metrics file per source PDF. +- When shard execution is used, the runner reassembles `markdown/.md` and `json/metrics/.metrics.json` after the CLI workers finish. +- Execution-time shard artifacts are moved under `sidecars/ocr_shards/` so downstream stages do not mistake them for canonical stage outputs. +- When OCR starts from canonical corpus rows, the authoritative stage handoff should preserve that metadata continuity instead of reducing the result to detached markdown files. Corrected `text` belongs in the canonical parquet row; markdown and metrics stay as sidecars. +- The vLLM runtime now streams rendered pages through an in-memory queue, overlaps rendering with inference, skips empty pages before inference, and reuses the same in-memory image for repair retries. +- Canonical OCR markdown now annotates page boundaries with `` comments alongside each page-split marker so downstream inspection can line up page images and markdown more easily. +- In `repair_mode="auto"`, a page that trips the garbage cutoff again during the plain-OCR repair pass is now blanked instead of keeping the original garbage text. +- Multi-GPU vLLM runs now execute through a durable shared batch queue rather than one fragile subprocess per preassigned lane. Workers claim first-pass batches dynamically, heartbeat while a batch is active, and can be respawned without losing finished batch outputs. +- Repair retries are now durable too. Flagged pages are published back into the same runtime database as a second global repair queue, and any GPU worker can drain those repair shards after the first-pass queue is complete. +- Repair queue durability and repair execution packing are intentionally separate. The queue tracks individual retry items for precise resume/failure accounting, while workers can combine multiple repair items into one larger execution batch to keep the repair tail GPU-efficient. +- By default each durable batch gets at most two total attempts, so one retry is allowed after the first failure and then the batch is marked failed for operator follow-up. +- Operational sidecars for these runs live under `sidecars/ocr_runtime/`, including the durable work queue state, per-worker runtime JSON, GPU telemetry samples, GPU preflight output, and a final runtime summary with steady-state inference timestamps. + ## Contributor note Any change to candidate selection, skiplist semantics, or OCR-success metadata affects both rerun behavior and corpus analysis quality. diff --git a/docs/testing/compatibility_matrix.md b/docs/testing/compatibility_matrix.md new file mode 100644 index 0000000..29a5e15 --- /dev/null +++ b/docs/testing/compatibility_matrix.md @@ -0,0 +1,276 @@ +# Compatibility And Regression Matrix + +This document defines the release-validation matrix for the DeepSeek-only migration and subsequent Docling upgrades. + +It is not a generic unit-test list. It is a contract-based validation plan tied to the documented pipeline behavior. + +## Scope + +This matrix applies to changes in: + +- DeepSeek-only OCR migration +- no-stub enforcement +- installation simplification +- Docling dependency upgrades +- page-level reevaluation experiments + +## Validation policy + +Release validation for this migration must use: + +- real PDFs +- real Docling +- real DeepSeek +- real GPUs where the code path requires them +- `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0` + +Developer-only tests may still use mocks or lightweight stubs for fast iteration, but those do not satisfy release gates for this migration. + +## Test levels + +### L0: Install and import sanity + +Purpose: + +- prove the supported environments install cleanly and that removed components are truly gone + +Typical inputs: + +- fresh venv +- supported Python version + +### L1: Lightweight smoke corpus + +Purpose: + +- prove the baseline end-to-end flow still works on the small repo corpus + +Typical inputs: + +- `samples/lightweight_pdf_corpus/` + +### L2: Real-PDF contract validation + +Purpose: + +- prove the documented artifacts and metadata contracts still hold on real documents + +Typical inputs: + +- real PDFs from a representative sample + +### L3: Multi-GPU and operational recovery + +Purpose: + +- prove the runtime behavior remains correct under parallel execution and rerun conditions + +Typical inputs: + +- multiple real PDFs +- at least two visible GPUs + +### L4: Comparative corpus evaluation + +Purpose: + +- compare baseline and changed behavior on a real evaluation slice + +Typical inputs: + +- real corpus slice such as the Pergamos sample + +## Mandatory invariants + +The following must remain true unless a change explicitly revises the contract and updates the docs: + +- canonical Markdown is written to `markdown/.md` +- Docling JSON artifacts are emitted when requested +- cleaner output still drives `needs_ocr` +- OCR remains selective rather than defaulting to all documents +- metadata parquet remains the durable operational record +- reruns skip completed work unless forced +- skiplist semantics remain explicit and stable +- no production path silently falls back to stub OCR + +## Release-gate matrix + +| ID | Level | Contract | Input | Run | Pass criteria | Negative assertions | +| --- | --- | --- | --- | --- | --- | --- | +| `ENV-001` | L0 | Python and packaging | Fresh environment | install supported profile(s) | install completes on supported Python floor | no reference to removed legacy OCR install modes | +| `ENV-002` | L0 | Dependency simplification | Fresh environment | import `glossapi`, `glossapi.ocr.deepseek`, extract-path modules | imports succeed | no dead imports from removed OCR integrations | +| `EXT-001` | L1 | Safe Phase-1 extraction | lightweight corpus | `Corpus.extract(input_format="pdf")` | canonical Markdown produced | extraction must not depend on OCR extras | +| `EXT-002` | L2 | Docling Phase-1 extraction | real PDFs | `Corpus.extract(..., phase1_backend="docling", export_doc_json=True)` | Markdown, Docling JSON, metrics written to documented locations | artifact layout must not drift | +| `CLN-001` | L1/L2 | Cleaner metadata contract | extracted docs | `clean(drop_bad=False)` | metadata parquet updated with routing-relevant fields | no collapse of `needs_ocr` behavior | +| `OCR-001` | L2 | DeepSeek-only remediation | docs with `needs_ocr=True` | `ocr(backend="deepseek", fix_bad=True)` | recovered docs updated, metadata marks `ocr_success=True` | no stub output, no silent success | +| `OCR-002` | L2 | No-stub enforcement | broken/missing DeepSeek runtime | run OCR with `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0` | run fails explicitly | failure must not produce placeholder success artifacts | +| `MTH-001` | L2 | Formula/code enrichment compatibility | math-heavy real PDF | Docling extract plus Phase-2 enrichment | enriched outputs and metadata remain coherent | no schema drift breaking enrichment | +| `SEC-001` | L2 | Sectioning contract | usable real docs | `section()` | `sections/sections_for_annotation.parquet` produced | no empty-output regression caused by upstream changes | +| `ANN-001` | L2 | Annotation contract | section parquet | `annotate()` | classified outputs produced | model integration must not break on changed upstream text/layout | +| `EXP-001` | L2 | Export contract | processed docs | `jsonl()` / `jsonl_sharded()` | JSONL and metadata outputs match documented layout | no dropped metadata fields without explicit design change | +| `RES-001` | L3 | Resumability | interrupted or partial run | rerun with defaults | completed items skipped correctly | no duplicate reprocessing by default | +| `RES-002` | L3 | Force/reprocess semantics | prior successful run | rerun with force/reprocess flag | selected items are reprocessed | no stale completion flags blocking intended rerun | +| `SKP-001` | L3 | Skiplist semantics | run with known problematic items | extract/OCR rerun | skiplist excludes intended stems only | no hidden filtering of healthy items | +| `GPU-001` | L3 | Multi-GPU OCR | real PDF slice on 2 GPUs | DeepSeek OCR in parallel | work is distributed and completes per GPU | no worker success masking failures | +| `CMP-001` | L4 | Baseline quality comparison | Pergamos sample slice | compare pre/post change outputs | no material regression in artifact completeness and downstream usability | runtime improvement alone does not justify quality loss | +| `CMP-002` | L4 | Whole-text vs page-level experiment | long PDFs | compare baseline branch vs page-level branch | quality/runtime tradeoff explicitly measured | experimental branch does not replace baseline without evidence | + +## Detailed test groups + +### Install and runtime compatibility + +What to prove: + +- supported environment installs cleanly +- unsupported/removed OCR components are not required +- Python floor matches actual upstream dependencies + +Critical checks: + +- packaging metadata uses a supported Python minimum +- setup docs expose only supported install paths +- removal of the old OCR integration does not leave dead GlossAPI imports or entrypoints + +## Extraction contract + +What to prove: + +- Phase-1 still produces canonical Markdown +- Docling extraction still produces JSON artifacts when requested +- metrics continue to be written where downstream stages expect them + +Artifacts to check: + +- `markdown/.md` +- `json/.docling.json(.zst)` +- `json/.formula_index.jsonl` when requested +- `json/metrics/.metrics.json` +- `json/metrics/.per_page.metrics.json` + +## Cleaning and Greek-quality routing + +What to prove: + +- cleaner still computes routing decisions required for selective OCR +- Greek-text validation remains first-class rather than incidental cleanup + +Fields to check in metadata parquet: + +- `needs_ocr` +- `filter` +- Greek-quality and badness-related fields currently emitted by the cleaner + +## DeepSeek OCR contract + +What to prove: + +- DeepSeek is the only OCR remediation backend +- no-stub enforcement is real +- recovered documents update metadata correctly + +Required environment behavior: + +- `GLOSSAPI_DEEPSEEK_ALLOW_STUB=0` +- real model weights present +- real CLI/runtime path present + +Negative checks: + +- no markdown contains placeholder stub markers +- no OCR pass succeeds after a DeepSeek CLI failure unless real output exists +- no removed OCR backend is referenced during final validation + +## Formula and code enrichment + +What to prove: + +- if retained, enrichment still works with the upgraded Docling stack +- if later removed, the removal is justified by evaluation rather than convenience + +Checks: + +- enriched Markdown is generated where expected +- `json/.latex_map.jsonl` remains coherent when enrichment is enabled +- metadata updates for math enrichment still work + +## Section, annotate, and export contracts + +What to prove: + +- downstream stages still consume the extraction outputs +- output layout and metadata structure remain compatible with the documented pipeline + +Artifacts to check: + +- `sections/sections_for_annotation.parquet` +- `classified_sections.parquet` +- `fully_annotated_sections.parquet` +- exported JSONL shards and related metadata + +## Resumability and operational recovery + +What to prove: + +- reruns still honor completion state +- skiplist semantics remain intact +- multi-worker failures remain visible and recoverable + +Checks: + +- default rerun skips completed items +- explicit force/reprocess reruns the intended items +- problematic stems are persisted and not silently lost + +## Comparative evaluation set + +Suggested real-world slice: + +- lightweight corpus for smoke validation +- representative real PDFs spanning: + - short documents + - medium documents + - long documents + - structure-rich documents + - math-heavy documents where applicable + +For current local evaluation work, a Pergamos sample manifest has been prepared outside the repo and can be used as the L3/L4 real-PDF slice. + +## Suggested release sequence + +For the planned migration, run gates in this order: + +1. `ENV-*` +2. `EXT-*` +3. `CLN-*` +4. `OCR-*` +5. `MTH-*` +6. `SEC-*`, `ANN-*`, `EXP-*` +7. `RES-*`, `SKP-*`, `GPU-*` +8. `CMP-*` + +This keeps low-level compatibility failures from being confused with downstream quality regressions. + +## Exit criteria per stage + +### Stage 1 exit criteria + +- DeepSeek-only OCR path works on real PDFs +- no-stub enforcement verified +- no supported GlossAPI OCR backend remains besides DeepSeek + +### Stage 2 exit criteria + +- install paths reduced to supported environments +- packaging/docs no longer reference removed OCR components + +### Stage 3 exit criteria + +- upgraded Docling passes `EXT-*`, `MTH-*`, `SEC-*`, `ANN-*`, and `EXP-*` + +### Stage 4 exit criteria + +- retained or removed Docling capabilities are justified by evaluation evidence + +### Stage 5 exit criteria + +- page-level branch is compared against the stabilized baseline before any adoption decision diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md index 6691407..24cc470 100644 --- a/docs/troubleshooting.md +++ b/docs/troubleshooting.md @@ -2,19 +2,15 @@ ## OCR runs on CPU -- Verify ONNXRuntime GPU: `python -c "import onnxruntime as ort; print(ort.get_available_providers())"` — must include `CUDAExecutionProvider`. -- Ensure CPU ORT wheel is not installed: `pip uninstall -y onnxruntime`. -- Make sure you pass `accel_type='CUDA'` (or `use_gpus='multi'`). +- Verify Torch CUDA: `python -c "import torch; print(torch.cuda.is_available(), torch.cuda.device_count())"`. +- Make sure the DeepSeek runtime is the one configured in `GLOSSAPI_DEEPSEEK_PYTHON`. +- Run `python -m glossapi.ocr.deepseek.preflight` in the DeepSeek env before large OCR jobs. ## Torch doesn’t see the GPU - Check `nvidia-smi` and driver installation. - Match Torch CUDA build to your driver; see getting_started.md for the recommended wheel. -## RapidOCR font download failure - -- The first OCR call might download a visualization font. Ensure egress is allowed; the file is cached afterwards. - ## Out of memory - Lower Phase‑2 `batch_size` (e.g., 8) and reduce inline `GLOSSAPI_FORMULA_BATCH`. diff --git a/install_glossapi.py b/install_glossapi.py new file mode 100644 index 0000000..ef7a7c9 --- /dev/null +++ b/install_glossapi.py @@ -0,0 +1,23 @@ +from __future__ import annotations + +import sys +from pathlib import Path + + +def _bootstrap_repo_src() -> None: + repo_root = Path(__file__).resolve().parent + src_dir = repo_root / "src" + src_str = str(src_dir) + if src_str not in sys.path: + sys.path.insert(0, src_str) + + +def main() -> int: + _bootstrap_repo_src() + from glossapi.scripts.install_glossapi import main as _main + + return int(_main()) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/mkdocs.yml b/mkdocs.yml index ba13512..c61882c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,5 +1,5 @@ site_name: GlossAPI -site_description: Academic document processing pipeline (Docling + RapidOCR + Rust) +site_description: Academic document processing pipeline (Docling + DeepSeek + Rust) repo_url: https://github.com/eellak/glossAPI theme: name: material @@ -38,16 +38,16 @@ nav: - Configuration & Ops: - Configuration: configuration.md - AWS Job Distribution: aws_job_distribution.md + - DeepSeek GCP A100 Setup: operations/deepseek_gcp_a100_setup.md + - OCR Changes 2026-04-01 to 2026-04-03: operations/ocr_changes_2026-04-01_to_2026-04-03.md + - OpenArchives OCR Rollout Plan: operations/openarchives_ocr_rollout_plan.md - Troubleshooting: troubleshooting.md + - Compatibility And Regression Matrix: testing/compatibility_matrix.md - Reference: + - Code Map: code_map.md - Corpus API: api/corpus.md + - Legacy Corpus API Notes: api_corpus_tmp.md - Math Enrichment Runtime: math_enrichment_runtime.md - - Divio Skeleton: - - Overview: divio/overview.md - - Tutorials: divio/tutorials.md - - How-to Guides: divio/how_to_guides.md - - Reference: divio/reference.md - - Explanation: divio/explanation.md docs_dir: docs markdown_extensions: - admonition diff --git a/pyproject.toml b/pyproject.toml index 3d0d5fa..ab741c3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,17 +4,17 @@ build-backend = "setuptools.build_meta" [project] name = "glossapi" -version = "0.1.3" +version = "0.1.4" description = "Academic document processing pipeline with Rust-powered markdown cleaning" authors = [ {name = "GlossAPI Team", email = "glossapi.team@eellak.gr"} ] readme = "README.md" -requires-python = ">=3.8" +requires-python = ">=3.10" dependencies = [ # Core pipeline deps "pandas>=1.3.0", - "numpy<2", # ORT+RapidOCR best compatibility + "numpy>=1.26,<3", "scikit-learn==1.6.1", "joblib>=1.0.0", "dask>=2022.1.0", @@ -37,28 +37,33 @@ classifiers = [ ] [project.optional-dependencies] -# Docling + RapidOCR ONNX stack (kept optional to preserve import-light installs) -rapidocr = [ - "docling==2.48.0", - # Use RapidOCR core package; avoid rapidocr_onnxruntime to prevent pip - # from auto-installing the CPU-only 'onnxruntime' wheel. - "rapidocr>=3.3.0", - "onnxruntime-gpu==1.18.1", +# Browser automation fallback for browser-gated file endpoints +browser = [ + "playwright>=1.52,<2", +] +# Docling extraction/layout stack +docling = [ + "docling==2.81.0", ] # Optional CUDA layout acceleration (Docling) cuda = [ "torch==2.5.1", "torchvision==0.20.1", ] -# DeepSeek OCR backend extras (CUDA 12.1 build of vLLM). Torch is not pinned here -# because users should install the CUDA wheel from the PyTorch index -# (see docs: installing torch==2.5.1+cu121 via extra index URL). +# DeepSeek OCR backend extras (Torch should be installed from the PyTorch index). +# vLLM is the supported runtime backend. transformers is pulled transitively by +# vllm; we no longer pin it here because the DeepSeek-OCR-2 bundled modeling +# code requires the pre-4.46 attention API and the only working path is via +# vllm's own model loader. deepseek = [ - "vllm>=0.11.0", - "transformers>=4.45,<5", + "vllm==0.18.0", "accelerate>=1.2.1,<2", "pymupdf==1.24.10", - "Pillow==10.4.0", + "Pillow==12.1.1", + "img2pdf>=0.5.1", + "einops", + "easydict", + "addict", ] docs = [ "mkdocs>=1.5", @@ -78,6 +83,5 @@ glossapi = ["models/**/*"] [tool.pytest.ini_options] markers = [ - "rapidocr: requires the RapidOCR/Docling execution stack", "deepseek: exercises the DeepSeek OCR pipeline", ] diff --git a/requirements.txt b/requirements.txt index 95f4678..e8d5474 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,8 @@ -### GlossAPI runtime requirements (aligned with repro_rapidocr_onnx) +### GlossAPI runtime requirements # Core pipeline deps pandas>=1.3.0 -numpy<2 +numpy>=1.26,<3 python-dateutil>=2.8.2 pytz>=2021.1 scikit-learn==1.6.1 @@ -11,21 +11,17 @@ dask>=2022.1.0 pyarrow>=7.0.0 aiohttp>=3.8.0 aiofiles>=23.0.0 +google-genai>=1.30.0 ftfy>=6.0.0 tenacity>=8.0.0 tqdm>=4.67.0 -# Docling + RapidOCR ONNX stack -docling==2.48.0 -# Prefer RapidOCR core package; it works with the GPU ORT wheel without pulling -# the CPU-only 'onnxruntime' dependency. -rapidocr>=3.3.0 -onnxruntime-gpu==1.18.1 +# Docling extraction/layout stack +docling==2.81.0 pyyaml>=6.0 # Enrichment & JSON compression (required for Phase-2 math/code and JSON zstd) pypdfium2>=4.0.0 zstandard>=0.22.0 -# Optional: install Torch CUDA for GPU layout (not required for OCR) -# pip install --index-url https://download.pytorch.org/whl/cu121 torch==2.5.1 torchvision==0.20.1 +# Optional: install Torch CUDA for GPU-backed Docling layout / enrichment diff --git a/rust/glossapi_rs_cleaner/CHANGES_2026_04_22.md b/rust/glossapi_rs_cleaner/CHANGES_2026_04_22.md new file mode 100644 index 0000000..dca6674 --- /dev/null +++ b/rust/glossapi_rs_cleaner/CHANGES_2026_04_22.md @@ -0,0 +1,196 @@ +# Cleaner changes — 2026-04-22 wave + +This document summarizes what the `codex/three-counter-pipeline-20260421` +branch changes vs the prior cleaner behaviour, and the rationale behind +each change. Written for downstream consumers (e.g. the +`fffoivos/glossapi-tokenizer-extension` project) that pin this cleaner +version. + +## Behavioural changes + +### 1. Rule A span-strip (commit 70662ba) + +**Before.** 40 PS-glyph literals (`/hyphenminus`, `/space`, `/period`, +…, `CID+`) were part of `BAD_LINE_AC` — any line containing one of +these substrings was dropped whole. + +**After.** Those literals now live in a separate Aho-Corasick +(`RULE_A_LITERALS_AC`) and are span-stripped inline by +`apply_glyph_span_strip_and_rule_b`. Surrounding prose is preserved. + +**Rationale.** Gemini wave on 1000 sampled lines (2026-04-22) found +86.5% were span-drop preferred — the matches are sprinkled inside +otherwise-legitimate Greek prose, and line-dropping destroyed the +prose. Rule A as line-drop was over-aggressive. + +Switched `RULE_A_LITERALS_AC` to `MatchKind::LeftmostLongest` so +`/hyphenminus` wins over its prefix `/hyphen` (commit 70662ba). + +### 2. Rule B coverage predicate (commit 70662ba) + +PDF_GLYPH_NAME_REGEX (`/uni[hex]{4,6}` + `/g(?:id)?\d+`) is span- +stripped always, plus line-dropped if BOTH: +- rule-B match count ≥ 10 on the line, AND +- rule-B matches / non-whitespace chars ≥ 0.09 (coverage) + +**Rationale.** Gemini wave on Rule B showed 59.3% span-drop preferred +but 38.5% whole-line drop. The line-wide junk case (`/g302 /g544 +/g306 …` strings) is real and needs line-drop, but isolated `/uni03B1` +instances inside prose should not drop the line. User-chosen predicate +`count ≥ 10 AND coverage ≥ 0.09` has P=96.3%, R=60.4% on ground truth. + +### 3. LINE_REMOVED_COMMENT marker (commit 70662ba) + +New marker `` emitted whenever a line is fully +dropped (was `` previously). Distinguishes true +line-drops from inline per-char residue removal. + +### 4. Normalize AFTER cleaning (commit 169adb9) + +**Before.** Normalize passes (dot-leader, separator, ellipsis, +malformed-entity, whitespace-runs) were skipped whenever a line had an +inline TMC marker inserted. + +**After.** Normalize runs on inline-TMC lines too. Only lines that +are themselves pass-through HTML comments (`is_exclusively_comment`) +skip normalize. + +**Rationale.** When cleaning strips a word mid-line, the surrounding +whitespace collapses into a multi-space run that whitespace-run +bucketing should catch. Skipping normalize left raw 6-space gaps in +output. The marker is a fixed string with single internal spaces; +none of the normalize passes overlap with it, so running normalize is +safe. + +**Memory**: `feedback_normalize_after_cleaning.md`. + +### 5. Escape-aware `\_\_\_\_ → ---` (commit 3c5cdd2) + +`SEPARATOR_LINE_REGEX` now matches `(?:\\_){4,}`. Markdown-escaped +underscore dividers (common in EU legislative text) used to pass +through and pollute the BPE vocab with `\_\_\_…` tokens. + +### 6. Per-doc four-way char accounting (commit 3c5cdd2) + +`core_clean_text_with_stats` now returns a `CleanStats` struct with: + +- `content_chars_kept` — output chars excluding ALL comment markers +- `chars_dropped_by_line_drop` — chars from fully-dropped lines +- `chars_dropped_by_normalization` — chars collapsed by dot/whitespace/separator/etc. +- `chars_dropped_by_per_char_filter` — chars stripped by entity-decode / rule A/B / tag-strip / unicode filter +- `lines_dropped_count` — count of line-drop markers emitted +- `marker_chars_passthrough` — input marker chars (e.g. pre-existing `` passed through) +- `marker_chars_added` — marker chars we emitted +- `original_chars_for_badness`, `sum_kept_line_content_chars` — back-compat fields for badness scoring + +Invariants (approximate, modulo entity-decode slack): +``` +input_chars ≈ content_chars_kept + chars_dropped_by_line_drop + + chars_dropped_by_normalization + + chars_dropped_by_per_char_filter + + marker_chars_passthrough +``` + +Legacy `core_clean_text` is a thin wrapper returning the old +`(String, usize, usize)` tuple — 73 existing tests unchanged. + +### 7. `analyze_charset` — pre-clean charset quality (commit 33ad0a6) + +New Rust function + PyO3 binding `analyze_charset(text) -> dict` that +computes per-doc: + +- `greek_letter_ratio` = Greek (U+0370–U+03FF, U+1F00–U+1FFF) / non-ws +- `moji_residue_ratio` = (Latin-1 Supp + IPA + PUA + Specials/FFFD + Latin Ext-B) / non-ws +- `ascii_punct_ratio` = ASCII punct/sym / non-ws + +Plus per-block counts for diagnostics. + +Single-pass numpy-style classification in `count_charsets`. ~500 MB/s +single-thread per perf fence. + +**Rationale.** 69-doc Gemini wave found three residual noise classes +the old cleaner missed: +- Latin-1 `µ`/`∆` substitutes for Greek μ/Δ +- IPA phonetic symbols from broken PDF OCR +- PUA (private-use) font-hack chars + +Used as a pre-clean doc-level filter in the rowsharded driver. Caller +applies thresholds and drops docs that exceed. + +### 8. Thresholds (commit 5fd466f, after sub-agent review) + +Current calibration (not final — see step 4 of the work order): + +- `moji_residue_ratio > 0.25` → `drop_reason: charset_moji` +- `ascii_punct_ratio > 0.30` → `drop_reason: charset_punct` +- `greek_letter_ratio < 0.02` → `drop_reason: charset_greek_low` + +### 9. matcher I/O elimination (commit bd0fea1) + +`noise.match_token_category_debug_text` now accepts `write_files=False` +kwarg. When false, no per-match `.md` files are written to scratch — +all data is returned in-memory via the existing rows. This removes +the dominant wall-time cost in multi-worker runs (56 concurrent workers +on one shared scratch dir used to saturate fs I/O). Default remains +`True` for backward compatibility. + +## Pipeline ordering (important — unchanged but sometimes confused) + +``` +input line → entity-decode + → Rule A/B span-strip + Rule B coverage check + → BAD_LINE_AC check (on stripped line, not raw) + → tag-strip + → per-char unicode / script filter + → comment-decision (inline TMC / standalone TMC / pass) + → normalize passes (dot / separator / ellipsis / + entities / whitespace-runs) + → emit line +``` + +Ordering matters: Rule A/B BEFORE BAD_LINE_AC so `/hyphenminus` +substring gets stripped before BAD_LINE_AC can match the `hyphenminus` +trigger. Normalize AFTER per-char filter so removed-word whitespace +gaps get bucketed cleanly. + +## Schema extension + +New columns on `clean_text_with_stats`-produced records (and the +rowsharded driver's stats.jsonl): + +- `content_chars_kept`, `chars_dropped_by_{line_drop, normalization, + per_char_filter}`, `lines_dropped_by_cleaner`, + `marker_chars_passthrough`, `marker_chars_added` +- `charset_greek_ratio`, `charset_moji_ratio`, `charset_punct_ratio` +- quality-signal convenience fields: `non_empty_lines_in/out`, + `non_empty_chars_in/out`, `pct_chars_removed_non_empty`, + `pct_lines_removed_non_empty` + +**Preserved** (existing upstream columns NOT overwritten): +`greek_badness_score`, `mojibake_badness_score`, `greek_percentage`, +`latin_percentage`, `needs_ocr`, `ocr_success`, `filter`, +`quality_method`, `is_historical_or_polytonic`, `contains_math`, +`contains_latex`, `polytonic_ratio`, `table_ratio`. + +## Naming scheme (locked 2026-04-23) + +- Upstream pre-existing per-doc quality scores (kept as signal, + never overwritten): `greek_badness_score`, `mojibake_badness_score` +- New per-doc char counts emitted by this cleaner: + `content_chars_kept`, `chars_dropped_by_{line_drop, normalization, + per_char_filter}`, `lines_dropped_by_cleaner` +- New per-doc residue-score ratios: `charset_{greek, moji, punct}_ratio` +- Combined residue score (additive, no weighting): + `mojibake_noise_ratio = moji_residue_ratio + ascii_punct_ratio` + (emitted by consumers, not by the cleaner itself) +- Derived pct fields (by consumers): `pct_chars_removed_non_empty`, + `pct_lines_removed_non_empty` + +## Still-pending efficiency work + +- Bigram-based mojibake detector for Greek-to-Greek codepoint-swap + corruption (wave-2 finding: current three ratios miss this class + entirely; upstream `greek_badness_score ≥ 90` is the only signal) +- Dictionary-based or font-level detector for duplicated-letter + mojibake (wave-2 finding: e.g. `ΚΚΛΛΗΗΡΡ…` — not caught by any + current ratio) diff --git a/rust/glossapi_rs_cleaner/CHANGES_2026_04_25.md b/rust/glossapi_rs_cleaner/CHANGES_2026_04_25.md new file mode 100644 index 0000000..7f31f86 --- /dev/null +++ b/rust/glossapi_rs_cleaner/CHANGES_2026_04_25.md @@ -0,0 +1,333 @@ +# Cleaner changes — 2026-04-25 cleanup wave + +This document summarizes what branch `cleanup/cleaner-pipeline-20260425` +changes vs the prior `codex/three-counter-pipeline-20260421` baseline, +and the rationale behind each change. Companion to +`CHANGES_2026_04_22.md`. Written for downstream consumers (e.g. the +`fffoivos/glossapi-tokenizer-extension` project) that pin this cleaner +version. + +Plan reference (lives in the tokenizer-extension repo): +`subprojects/01_0_cleaning_iteration_and_thresholds/CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25.md` + +## TL;DR + +- **Pilot B is now the production Phase-A default** (parser-backed, + preview-preserving). LineBased is regression-test-only. +- **One unified Rule B** owns all PostScript-glyph and PDF-font + residue. Four engines collapsed to two (Rule B + R1∪R2). +- **Per-char ops in 2 groups (STRIP + FOLD).** No char belongs to + more than one operation. +- **European-content preference.** Polish / Czech / Russian / Bulgarian + / Romanian content survives at char level; mojibake is caught at + line level by R1∪R2 instead. +- **Per-rule counters in `CleanStats`.** Production drivers source + parquet schema counters from the cleaner directly — the + three-counter matcher is no longer invoked from production. +- **Corpus.clean and clean_text share one policy builder.** Fixes a + silent bug where the directory pipeline stripped punctuation/digits + when callers passed restricted `scripts_to_keep`. + +## Behavioural changes + +### 1. Pilot B as the production Phase-A default + +**Before.** `core_clean_text_with_stats` always routed through +`md_module::normalize_md_syntax` (the line-based Phase A). + +**After.** A `PhaseAMode` enum selects the Phase-A implementation; +default is `ParserSurgicalVerified` (Pilot B with the safe checked +wrapper). The PyO3 surface adds a `phase_a_mode` arg to both +`clean_text` and `clean_text_with_stats` — accepts `"line_based"`, +`"parser_surgical"`, `"parser_surgical_verified"`. Default flipped. + +`format_surgical_checked` populates two new `CleanStats` fields: +- `phase_a_fallback_reason: Option` — non-`None` when the + oracle disagreed (e.g. dialect-ambiguous input, rewrite changed + preview); the cleaner shipped input verbatim in that case. +- `phase_a_dialect_ambiguous_input: bool` — comrak vs pulldown-cmark + disagreed on input rendering. + +**Rationale.** Pilot B is the chosen architectural direction +(documented in `docs/PHASE_A_PARSER_BACKED_IMPLEMENTATION_REVIEW_2026-04-24.md`). +Parser-backed normalization is preview-preserving by oracle check, so +the default-flip cannot regress. + +**cmark-gfm.** Optional dev tool only. If installed, it serves as the +ground-truth oracle (per-doc subprocess). If not, the in-process +`dual_verify` (comrak + pulldown-cmark) handles it. Production +assumes the dual_verify path; do NOT install cmark-gfm on production +hosts. + +### 2. Unified Rule B (Points 4 + 5) + +**Before.** PostScript-glyph / PDF-font residue was handled by FOUR +separate engines: +- `BAD_LINE_AC` literal-set line-drop (incl. bare `GLYPH`, bare + `hyphenminus`, `MS-Bold-`, `font=/`, `FontName=`) +- `has_decoded_glyph_font_artefact` regex line-drop + (`glyph`, `glyph`, `/[A-Z]{6}+Name`) +- `RULE_A_LITERALS_AC` 50-literal span-strip (`/space`, `/period`, …, + `CID+`) +- `apply_glyph_span_strip_and_rule_b` regex span-strip + density gate + (`/uniXXXX|/gN`) +- Plus a separate `strip_glyph_markers` text-wide pre-pass. + +**After.** ONE Rule B regex covers all the structurally-anchored +patterns: +``` +(?i) + GLYPH<[^>]{1,200}> +| glyph]+> +| ]+>glyph +| /[A-Z]{6}\+[A-Z][A-Za-z0-9-]+ +| /uni[0-9A-Fa-f]{4,6} +| /g(?:id)?\d+ +``` +Rule A's 50 PostScript-name literals stay as a separate Aho-Corasick +engine (faster than literal alternation in regex) but contribute to +the SAME count+coverage gate as Rule B: +- (count_A + count_B) ≥ 10 AND (count_A + count_B) / non_ws_chars + ≥ 0.09 → line-drop (``) +- Otherwise span-strip every match, keep the line. + +**Bare-word matchers removed.** Plain words `GLYPH`, `hyphenminus`, +`font`, `glyph` no longer trigger anything — they're legitimate +English in PostScript / PDF documentation. Pre-cleanup `BAD_LINE_AC` +over-rejected lines containing them. + +**Behaviour change.** A line of 20× `/space` markers used to be +stripped to a husk and kept; now it hits the gate and drops. A line +with one `/space` in valid prose: same as before (markers stripped, +line preserved). + +### 3. Per-char ops consolidated to 2 groups (Point 2) + +**Before.** Per-char operations were scattered across the pipeline: +- `decode_html_entities` (text-wide pre-pass) +- `decode_adobe_symbol_pua` (text-wide pre-pass) +- `strip_glyph_markers` (text-wide pre-pass — see Point 4) +- `strip_soft_hyphens` (text-wide pre-pass) +- `fold_codepoint` (per-line, Group 2) +- per-char filter (per-line, Group 1) + +**After.** Every codepoint lands in EXACTLY ONE of: +- **Allowed** (kept as-is) +- **Group 1 STRIP** — `is_unicode_noise_char` + `unusual_chars` set, + applied in the per-line filter loop. Now includes U+00AD soft + hyphen, all PUA chars not folded, and the narrowed unusual-script + ranges (see Point 3). +- **Group 2 FOLD** — `fold_codepoint`, applied in the per-line filter + loop. Now includes Adobe Symbol PUA (was its own pre-pass) plus + U+00B5 µ → U+03BC μ (new, common Latin-1 codepage mojibake for + Greek mu). + +`strip_soft_hyphens` and `decode_adobe_symbol_pua` remain as `pub` +functions in `normalize.rs` for `md_module::non_destructive_canonicalize`'s +parallel pre-pass; they're now thin wrappers over the unified path. +`strip_glyph_markers` is fully removed (Point 4 absorbed). + +### 4. European-content char policy (Point 3, choice "B-iii") + +The pre-cleanup `unusual` strip set stripped European-language +characters that the corpus plausibly contains as legitimate content: +Polish `ł, ą, ę, ć, ń, ś, ź, ż` (Latin-Ext-A); Czech `č, ě, ř, š, ť, +ž, ů`; Romanian `ș, ț`; Russian / Bulgarian Cyrillic; etc. + +**After.** Per-char STRIP narrowed to TRULY-non-European or +extraction-noise blocks: + +| Block | Pre-cleanup | Post-cleanup | +|---|---|---| +| Latin-1 Supplement (U+0080..U+00FF) | strip except french/spanish/accented_greek/common_symbols/punct | **KEEP entirely** | +| Latin Extended-A (U+0100..U+017F) | strip except french/spanish | **KEEP entirely** | +| Latin Extended-B (U+0180..U+024F) | strip all | **STRIP except Romanian {Ș, ș, Ț, ț}** | +| IPA Extensions (U+0250..U+02AF) | strip | strip (unchanged) | +| Latin Extended Additional (U+1E00..U+1EFF) | strip | strip (unchanged) | +| Coptic | strip | strip (unchanged) | +| Cyrillic + Cyrillic Supp (U+0400..U+052F) | strip all | **KEEP entirely** | + +Dense Greek-CID-mojibake (where these European-allowed chars appear +in clustered runs) is caught at line level by R1 ∪ R2 instead of +per-char. Foreign-name lines with isolated diacritics survive both +passes. + +### 5. R1 ∪ R2 residue range aligned with Group 1 (Point 3 cont.) + +**Before.** `is_residue_mojibake_line` treated U+0100..U+024F (full +Latin Extended-A + B) as residue. + +**After.** Range narrowed to `U+0180..U+024F` MINUS the Romanian +allowlist `{U+0218, U+0219, U+021A, U+021B}`. Latin Extended-A no +longer counts as residue (matches Group 1's policy of keeping +Polish/Czech/Hungarian/Romanian-most/Turkish text). + +### 6. Per-rule counters in CleanStats (Point 7) + +`CleanStats` and the PyO3 stats dict gain three new counters: + +- `rule_a_match_count` — Rule A literal hits per doc +- `rule_b_match_count` — Rule B regex hits per doc +- `residue_line_drop_count` — R1∪R2 line-drops per doc + +These are aligned by construction with cleaner activity. Production +drivers source parquet counter columns from these fields directly; +the noise-crate matcher (`match_token_category_debug_text`, +`export_token_category_debug_pages`) is no longer invoked from +production cleaning. Matcher PyO3 surface is preserved for the +discovery / debug-export workflow +(`Corpus.clean_token_category_debug` and the standalone +`export_token_category_debug{,_parquet}.py` scripts). + +### 7. Corpus.clean / clean_text policy parity (Point 8) + +**Before.** `directory_processor::generate_analysis_report_for_directory` +(invoked by `Corpus.clean()`) built `allowed_chars` from +`scripts_to_keep` plus whitespace only. `clean_text` / +`clean_text_with_stats` ran through `build_script_char_sets` which +auto-adds `punctuation`, `numbers`, `common_symbols` regardless of +`scripts_to_keep`. Same call with the same args produced different +outputs depending on which entry point was used — the directory +pipeline silently stripped ASCII punct + digits when callers passed +restricted scripts. + +**After.** Both paths call `cleaning_module::build_script_char_sets` +(now `pub`). Identical char sets in both flows. `Corpus.clean`'s +`scripts_to_keep=["greek", "latin"]` now keeps punct/numbers/common_symbols +just like `clean_text` always did. + +### 8. `\n{3+}` → `\n\n` post-loop normalize + +When per-char filter empties adjacent single-char lines (e.g. +PUA bracket glyphs not in the Adobe Symbol fold map), their +surrounding `\n\n` separators accumulate. Result: long runs of +consecutive newlines in the output. CommonMark renders any blank-line +run as one block separator, so the runs are preview-equivalent to a +single `\n\n` — collapse them to keep the output structurally clean. + +Bytes go into `chars_dropped_by_normalization`. + +### 9. Bug fixes + +**Bug 1 — byte vs char offsets (`noise_metrics.rs`).** The +token-category match exporter wrote `start` / `end` in BYTE offsets, +but Python consumers slice `page_text[start:end]` (CHAR offsets). +Greek-prefixed text shifted slice boundaries; rows whose `end > char_len` +were silently dropped. Now exports CHAR offsets at the boundary; +internal byte offsets retained for Rust slicing. + +**Bug 2 — perf test floor in debug.** `perf_mixed_doc_throughput_floor` +asserted 5 M chars/sec — a release-profile expectation. Default +`cargo test` is debug profile (~7× slower) and always tripped the +floor. Now `#[ignore]`'d with a release-only invocation note. To +check perf regressions: +`cargo test perf_mixed_doc_throughput_floor -- --ignored --release`. + +**Empty-content table-removal** edge case: empty input with a +table-removal range no longer yields ``, +correctly stays empty. + +### 10. Cleaner / noise crate boundary (Point 7 follow-on) + +The two crates now have an explicit ownership split documented in +both `lib.rs` files: +- `glossapi_rs_cleaner` owns *cleaning* and *production counters*. +- `glossapi_rs_noise` owns *diagnostic / debug exports* (OCR scoring, + word-repeat span extraction, token-category review bundling). + +The cleaner has zero `Cargo.toml` dependency on noise — boundary +enforced at compile time, not just convention. + +### 11. Lint posture + +19 cleaner warnings → 0 by: +- Fixing real lint issues (unused `line_index`, unread `byte_offset`, + `DetailedTableIssueReportEntry` privacy, duplicate `[tool.maturin]` + in `Cargo.toml`). +- `dead_code` allowed at crate level for documented utility functions + kept on the public surface (`analyze_text`, `list_available_scripts`, + `drop_low_salvage_pages`, `process_directory_native`, + `batch_clean_markdown_files`). +- `non_local_definitions` (pyo3 0.19 macro lint) silenced at module + level in `table_analysis_module.rs` until pyo3 is upgraded. + +Real bugs (`unused_variables`, `unused_assignments`) still warn. + +## Schema changes + +### `CleanStats` / Python `clean_stats` dict + +New fields: +- `phase_a_fallback_reason: Option` — Pilot B oracle-refusal + reason (None if rewrite accepted). +- `phase_a_dialect_ambiguous_input: bool` — Pilot B input dialect + ambiguity flag. +- `rule_a_match_count: u64` — Rule A literal match count. +- `rule_b_match_count: u64` — Rule B regex match count. +- `residue_line_drop_count: u64` — R1∪R2 line-drop count. + +All other fields unchanged. + +### Production driver parquet schema (`clean_and_stats_rowsharded.py`) + +Column meanings post-cleanup: +- `counter_glyph_marker` = `rule_a_match_count + rule_b_match_count` + (combined PostScript-glyph residue count). +- `counter_font_marker` always 0 (Rule B unifies font and glyph; + no longer separately measurable post-unification — column kept for + back-compat). +- `counter_script_residue` = `residue_line_drop_count` (R1∪R2 line + drops). + +The drop_reason set has shrunk: `counter:{font_name_literal, +glyph_font_like}`, `charset_{moji, punct, greek_low}` no longer +fire. Active reasons: `empty`, `cleaner_empty`. Future: `deletion_too_high`, +`too_short` (still discussion items, not landed). + +## Driver migration + +`clean_and_stats_rowsharded.py` no longer invokes the noise matcher +per-row. It calls `cleaner.clean_text_with_stats(...)` directly and +sources the per-doc counters from the returned stats dict (mapping +above). + +Other drivers that called the matcher (e.g. +`Corpus.clean_token_category_debug` and the standalone +`export_token_category_debug{,_parquet}.py` scripts) keep working — +the matcher's PyO3 surface is preserved for discovery / debug-export +use cases. + +## What this wave does NOT change + +- Doc-level rejection rules: still none (per Wave-1 decision); the + `_doc_drop_reason` function is a stub returning `""`. Future + rejection lives in downstream policy, not in the cleaner. +- Threshold values: unchanged. Rule B's gate is still + `count ≥ 10 AND coverage ≥ 0.09`; R1's gate is still + `token_len > 20 AND residue_ratio > 10%`; R2's gate is still + `max_run ≥ 4`. +- Upstream pre-existing scores (`greek_badness_score`, + `mojibake_badness_score`, `needs_ocr`, etc.): never overwritten, + always preserved. + +## Tests + +- 374 cleaner unit tests pass (was 357 pre-cleanup; +17 new tests + for the unified Rule B, Group 1+2 split, R1∪R2 narrowing, + PhaseAMode wiring, `\n{3+}` collapse, etc.). +- 10 noise crate tests pass (Bug 1 char-offset fix preserved). +- 30+ Python smoke checks pass + (`cleaning_scripts/smoke_tests/test_rust_extensions_smoke.py`). +- Empty-content `table_remover` edge case fixed. +- 0 build warnings on either crate. + +## Validation + +- 100-doc end-to-end on `openarchives.gr.part-00000.parquet`: 100/100 + cleaned, 0 doc drops, 18.7% chars removed, 25 docs/sec via Pilot B + + dual_verify (cmark-gfm absent). +- 55/100 Pilot B fallbacks (dialect-ambiguous inputs ship verbatim — + by design). +- Gzipped output validated byte-identical to + `squash(clean_text_with_stats(raw, …))` on all 100 docs (no hidden + alteration between cleaner output and on-disk shard). diff --git a/rust/glossapi_rs_cleaner/Cargo.lock b/rust/glossapi_rs_cleaner/Cargo.lock index a3aabd3..c2c6de9 100644 --- a/rust/glossapi_rs_cleaner/Cargo.lock +++ b/rust/glossapi_rs_cleaner/Cargo.lock @@ -342,6 +342,15 @@ version = "1.10.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d71b6127be86fdcfddb610f7182ac57211d4b18a3e9c82eb2d17662f2227ad6a" +[[package]] +name = "caseless" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8b6fd507454086c8edfd769ca6ada439193cdb209c7681712ef6275cccbfe5d8" +dependencies = [ + "unicode-normalization", +] + [[package]] name = "cc" version = "1.2.23" @@ -374,6 +383,23 @@ dependencies = [ "windows-targets", ] +[[package]] +name = "comrak" +version = "0.26.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "395ab67843c57df5a4ee29d610740828dbc928cc64ecf0f2a1d5cd0e98e107a9" +dependencies = [ + "caseless", + "derive_builder", + "entities", + "memchr", + "once_cell", + "regex", + "slug", + "typed-arena", + "unicode_categories", +] + [[package]] name = "const-random" version = "0.1.18" @@ -461,12 +487,90 @@ dependencies = [ "memchr", ] +[[package]] +name = "darling" +version = "0.20.11" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "fc7f46116c46ff9ab3eb1597a45688b6715c6e628b5c133e288e709a29bcb4ee" +dependencies = [ + "darling_core", + "darling_macro", +] + +[[package]] +name = "darling_core" +version = "0.20.11" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0d00b9596d185e565c2207a0b01f8bd1a135483d02d9b7b0a54b11da8d53412e" +dependencies = [ + "fnv", + "ident_case", + "proc-macro2", + "quote", + "strsim", + "syn 2.0.101", +] + +[[package]] +name = "darling_macro" +version = "0.20.11" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "fc34b93ccb385b40dc71c6fceac4b2ad23662c7eeb248cf10d529b7e055b6ead" +dependencies = [ + "darling_core", + "quote", + "syn 2.0.101", +] + +[[package]] +name = "derive_builder" +version = "0.20.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "507dfb09ea8b7fa618fcf76e953f4f5e192547945816d5358edffe39f6f94947" +dependencies = [ + "derive_builder_macro", +] + +[[package]] +name = "derive_builder_core" +version = "0.20.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2d5bcf7b024d6835cfb3d473887cd966994907effbe9227e8c8219824d06c4e8" +dependencies = [ + "darling", + "proc-macro2", + "quote", + "syn 2.0.101", +] + +[[package]] +name = "derive_builder_macro" +version = "0.20.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ab63b0e2bf4d5928aff72e83a7dace85d7bba5fe12dcc3c5a572d78caffd3f3c" +dependencies = [ + "derive_builder_core", + "syn 2.0.101", +] + +[[package]] +name = "deunicode" +version = "1.6.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "abd57806937c9cc163efc8ea3910e00a62e2aeb0b8119f1793a978088f8f6b04" + [[package]] name = "either" version = "1.15.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "48c757948c5ede0e46177b7add2e67155f70e33c07fea8284df6576da70b3719" +[[package]] +name = "entities" +version = "1.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b5320ae4c3782150d900b79807611a59a99fc9a1d61d686faafc24b93fc8d7ca" + [[package]] name = "equivalent" version = "1.0.2" @@ -493,6 +597,12 @@ dependencies = [ "miniz_oxide", ] +[[package]] +name = "fnv" +version = "1.0.7" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3f9eec918d3f24069decb9af1554cad7c880e2da24a9afd88aca000531ab82c1" + [[package]] name = "futures" version = "0.3.31" @@ -612,13 +722,16 @@ dependencies = [ "aho-corasick", "arrow", "chrono", + "comrak", "csv", "futures", + "glossapi_rs_common", "htmlentity", "lazy_static", "memchr", "once_cell", "parquet", + "pulldown-cmark", "pyo3", "rayon", "regex", @@ -626,6 +739,10 @@ dependencies = [ "walkdir", ] +[[package]] +name = "glossapi_rs_common" +version = "0.1.0" + [[package]] name = "half" version = "2.6.0" @@ -684,6 +801,12 @@ dependencies = [ "cc", ] +[[package]] +name = "ident_case" +version = "1.0.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b9e0384b61958566e926dc50660321d12159025e767c18e043daf26b70104c39" + [[package]] name = "indexmap" version = "2.10.0" @@ -1040,6 +1163,24 @@ dependencies = [ "unicode-ident", ] +[[package]] +name = "pulldown-cmark" +version = "0.11.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "679341d22c78c6c649893cbd6c3278dcbe9fc4faa62fea3a9296ae2b50c14625" +dependencies = [ + "bitflags 2.9.0", + "memchr", + "pulldown-cmark-escape", + "unicase", +] + +[[package]] +name = "pulldown-cmark-escape" +version = "0.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "007d8adb5ddab6f8e3f491ac63566a7d5002cc7ed73901f72057943fa71ae1ae" + [[package]] name = "pyo3" version = "0.19.2" @@ -1268,6 +1409,16 @@ dependencies = [ "autocfg", ] +[[package]] +name = "slug" +version = "0.1.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "882a80f72ee45de3cc9a5afeb2da0331d58df69e4e7d8eeb5d3c7784ae67e724" +dependencies = [ + "deunicode", + "wasm-bindgen", +] + [[package]] name = "smallvec" version = "1.15.0" @@ -1286,6 +1437,12 @@ version = "1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "a2eb9349b6444b326872e140eb1cf5e7c522154d69e7a0ffb0fb81c06b37543f" +[[package]] +name = "strsim" +version = "0.11.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f" + [[package]] name = "syn" version = "1.0.109" @@ -1354,6 +1511,21 @@ dependencies = [ "crunchy", ] +[[package]] +name = "tinyvec" +version = "1.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3e61e67053d25a4e82c844e8424039d9745781b3fc4f32b8d55ed50f5f667ef3" +dependencies = [ + "tinyvec_macros", +] + +[[package]] +name = "tinyvec_macros" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20" + [[package]] name = "twox-hash" version = "1.6.3" @@ -1370,12 +1542,39 @@ version = "2.1.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "8b907da542cbced5261bd3256de1b3a1bf340a3d37f93425a07362a1d687de56" +[[package]] +name = "typed-arena" +version = "2.0.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6af6ae20167a9ece4bcb41af5b80f8a1f1df981f6391189ce00fd257af04126a" + +[[package]] +name = "unicase" +version = "2.9.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "dbc4bc3a9f746d862c45cb89d705aa10f187bb96c76001afab07a0d35ce60142" + [[package]] name = "unicode-ident" version = "1.0.18" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5a5f39404a5da50712a4c1eecf25e90dd62b613502b7e925fd4e4d19b5c96512" +[[package]] +name = "unicode-normalization" +version = "0.1.25" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5fd4f6878c9cb28d874b009da9e8d183b5abc80117c40bbd187a1fde336be6e8" +dependencies = [ + "tinyvec", +] + +[[package]] +name = "unicode_categories" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "39ec24b3121d976906ece63c9daad25b85969647682eee313cb5779fdd69e14e" + [[package]] name = "unindent" version = "0.1.11" diff --git a/rust/glossapi_rs_cleaner/Cargo.toml b/rust/glossapi_rs_cleaner/Cargo.toml index 7213bc7..904bb44 100644 --- a/rust/glossapi_rs_cleaner/Cargo.toml +++ b/rust/glossapi_rs_cleaner/Cargo.toml @@ -26,7 +26,10 @@ memchr = "2" aho-corasick = "1" htmlentity = "~1.3.0" chrono = { version = "=0.4.33", features = ["serde"] } +glossapi_rs_common = { path = "../glossapi_rs_common" } +pulldown-cmark = { version = "0.11", default-features = false, features = ["html"] } +comrak = { version = "0.26", default-features = false } -[tool.maturin] -bindings = "pyo3-abi3-py38" -manifest-path = "./Cargo.toml" +# `[tool.maturin]` lives in pyproject.toml (Cargo doesn't recognise the +# `tool` table). The duplicate here triggered an "unused manifest key" +# warning on every build. diff --git a/rust/glossapi_rs_cleaner/docs/MD_MODULE_ARCHITECTURE.md b/rust/glossapi_rs_cleaner/docs/MD_MODULE_ARCHITECTURE.md new file mode 100644 index 0000000..91ffffd --- /dev/null +++ b/rust/glossapi_rs_cleaner/docs/MD_MODULE_ARCHITECTURE.md @@ -0,0 +1,362 @@ +# MD-module architecture + verification plan (2026-04-24) + +Preserved from the design discussion between user and assistant. +Captures the goal, the architectural split, and the commit plan so a +fresh reader can pick up the work. + +## Goal + +Transform MD files so they are **dual-readable**: + +- In **preview** (any spec-compliant renderer — GitHub, VS Code, + pandoc, mdbook, etc.): visibly identical to the original MD. No + lost paragraphs, headings, tables, lists, emphasis. User sees the + same content. +- In **raw** (opening the file in a plain editor, or the tokenizer + consuming it as a byte stream): reads as natural text. No PDF- + column-wrap fragmentation, no 200-char dash separators, no + multi-line soft-wrapped paragraphs. A reader can follow the text + without relying on the preview renderer to reflow it. + +This is the original Markdown design ethos: raw text should look +like what a human would write, AND render well. Most MD tooling +ignores this today; the cleaner's job is to restore it. + +## Two transform classes + +Cleaner passes split into two categories with distinct invariants: + +### Phase A — MD-syntax-aware, preview-preserving + +Transforms that require knowing CommonMark/GFM grammar to be correct. +Raw chars CHANGE by design — we want `&` → `&` in the corpus — +but the preview render is unchanged. + +Invariant: `pandoc-render(input) ≡ pandoc-render(output)` (modulo +whitespace). Any Phase A transform that violates this is a bug. + +Members: +- **Paragraph linearization** (`reflow_paragraphs`): collapse + soft-wrap `\n`s within a paragraph block into a single space. + Guards: headings, blockquotes, list markers, tables, HRs, fenced + code, sentence terminators, indented-code blocks. +- **GFM table separator minimization** (`scan_gfm_table_separators`): + rewrite `| ---------- | ---------- |` to `| --- | --- |` while + preserving alignment colons (`:---`, `---:`, `:---:`). +- **HR thematic-break minimization** (`normalize_separator_line`): + rewrite `-------` / `___________` / `*****` runs (≥4 chars) and + the markdown-escaped underscore form `\_\_\_\_` to `---`. Length + threshold ≥4 for the *rewrite* rule (`---` is already canonical, + so no-op). Rejected at ≥4 leading columns (indented code). A + *separate* ≥3-char recognizer (`HR_HARD_BREAK_REGEX`) is used by + the reflow hard-break detector so canonical `---` and setext + headings (`===`, `---`) are still treated as block boundaries. + + **Intentionally NOT rewritten (would change preview):** + `====` runs (setext heading marker / literal `=` paragraph); + Unicode em-dash / horizontal-bar / box-drawing (`———`, `═══`, + `───`) — these parse as literal paragraphs under CommonMark. +- **Fenced code detector** (`is_code_fence_marker`): predicate used + by the other three (and by the cleaner) for code-fence state + tracking. Takes the RAW line and rejects at ≥4 leading columns + (indented `` ``` `` is literal code, not a fence opener). +- **CommonMark indentation helper** (`leading_columns`): returns + the column of the first non-whitespace char under the CM tab rule + (tab advances to next multiple of 4). Used by every Phase A + detector to enforce the indented-code-block boundary. + +### Non-destructive canonical form + +A public function `non_destructive_canonicalize(md)` defines the +single canonical form that the cleaner produces if every pass were +non-destructive — entity decode + PUA decode + soft-hyphen strip + +per-line char-fold/dot-runs/whitespace-runs/ellipsis-runs + +orchestrator. The structural verifier (`md_verify`) delegates to +this function for its input-side canonicalization, so the verifier +baseline cannot drift from what the cleaner produces. A set of +`drift_cleaner_eq_canonicalize_*` tests in `cleaning_module::tests` +locks this equivalence in. + +Preview-preserving recovery passes that conceptually belong to +Phase A but currently live in `normalize.rs` (historical reasons; +the module boundary is "requires CommonMark/GFM parser knowledge +to be correct" and entity / PUA decode don't strictly need that +to be safe — so they can stay outside `md_module.rs`): + +- **HTML entity decode** (`normalize::decode_html_entities`): + `&` → `&`, `—` → `—`, etc. Preview-identical because + the decoder applies exactly the mapping a spec-compliant + renderer would. +- **Adobe Symbol PUA decode** (`normalize::decode_adobe_symbol_pua`): + U+F061 → α etc. Preview-identical at the rendered-glyph layer; + PUA codepoints with no font fallback render as `.notdef` + boxes, so mapping them to the intended real char is at worst + neutral and at best a fix. +- **Soft-hyphen strip** (`normalize::strip_soft_hyphens`): U+00AD + deleted. U+00AD is zero-width (invisible) in every renderer, so + its deletion is preview-identical. + +All three are included in `non_destructive_canonicalize` and run +before the Phase A orchestrator in the cleaner. + +### Phase B — content-modifying + +Transforms that deliberately REMOVE content visible in preview. +Preview WILL differ after; that's the whole point. + +Invariant (structural only): number and type of block elements +preserved; token sequence within each block is a monotone +subsequence of input (allows deletions, disallows reorderings, NO +fusions). + +Members (implemented, scattered across modules): +- **GLYPH marker strip** (`strip_glyph_markers`): `GLYPH<\d+>` / + `/uniXXXX` / `/gN` deleted. Runs BEFORE Phase A in the cleaner + (markers would otherwise break pipe-count scanning inside table + rows); see "Order of operations" below for why this destructive + pass runs pre-Phase-A. +- **Per-char filter**: chars outside allowed-scripts set deleted. +- **Line-drop** (rule-A / rule-B / glyph-regex): whole lines + replaced with `` marker. + +## Order of operations + +The actual pipeline in `core_clean_text_with_stats` is NOT a clean +"Phase A → Phase B" split. Earlier revisions of this doc described +Phase A as running first, but in practice a small set of +content-level passes runs before the Phase A orchestrator because +Phase A's structural scanners (pipe counting, run matching) need +to see post-decode, post-marker-strip text to be accurate. + +``` + input MD + ↓ + Pre-Phase-A — preview-preserving recovery: + • decode_html_entities (& → &) + • decode_adobe_symbol_pua (U+F061 → α) + ↓ + Pre-Phase-A — destructive strips that MUST run before Phase A: + • strip_glyph_markers (GLYPH<…> / /uniXXXX deleted; + renders as literal text, so + this IS destructive — but + leaving it would make Phase A + miscount pipes inside rows) + • strip_soft_hyphens (U+00AD invisible anyway) + ↓ + Phase A — MD-syntax normalization (preview-preserving): + • md_module::normalize_md_syntax + - scan_gfm_table_separators + - normalize_separator_line (HR min) + - reflow_paragraphs + ↓ + Phase B — per-line cleanup (destructive by design): + • per-line char-fold / dot / whitespace / ellipsis normalize + • per-char allowlist filter + • rule-A / rule-B line-drop → markers + ↓ + output MD +``` + +Notes on the ordering: + +- **Entity + PUA decode run first** so Phase A's detectors see the + same glyphs a renderer would. If Phase A ran first, a `—` + (em-dash) would still look like an entity rather than `—`, and + the structure Phase A sees could differ from what the renderer + sees. +- **GLYPH-marker strip runs BEFORE Phase A** even though it IS + destructive (the markers render as literal text). If GLYPH + markers survived to Phase A, a `|GLYPH<7>|` inside a row would + break `scan_gfm_table_separators`' pipe-count check and leak + literal marker content into a canonicalized table row. The cost + — a small class of Phase-A-structural decisions being made on + content the renderer would have shown — is considered acceptable + because GLYPH markers are themselves extractor noise, not + author-intended text. +- **Soft-hyphen strip runs before Phase A** because U+00AD is + zero-width: removing it is structurally preview-preserving. +- **Phase A runs in the middle** so subsequent destructive passes + operate on canonicalized MD. Per-char filters and line-drops can + safely leave structural chars (`|`, `#`, `---`) alone because + the MD is already in compact canonical form. +- **Phase B runs last** as the per-line loop inside + `core_clean_text_with_stats`. This is where content actually + gets removed; it's what `verify_md_structural` bounds. + +The non-destructive baseline `non_destructive_canonicalize` (used +by the verifier) applies entity-decode + PUA-decode + soft-hyphen +strip + per-line normalization + Phase A. It deliberately SKIPS +`strip_glyph_markers` (destructive) and Phase B. Drift-prevention +tests in `cleaning_module::tests` assert that on inputs without +GLYPH markers — and with a permissive allowed-chars so Phase B +deletes nothing — `core_clean_text_with_stats` and +`non_destructive_canonicalize` produce identical output. + +## Key architectural constraints + +1. **All deterministic per-doc work in Rust.** Per the + `feedback_rust_for_corpus_pipelines` memory rule. Python is a + thin driver. +2. **Co-locate per-text-type logic.** Per + `feedback_group_cleaner_features_by_text_type`. MD-syntax-aware + transforms live in `md_module.rs`. LaTeX transforms live in + `latex_module.rs`. Char-level transforms (entity decode, PUA + decode, etc.) live in `normalize.rs` or smaller specialized + modules. +3. **No threshold rules without user request.** Per + `feedback_no_threshold_rules_unprompted`. The MD module + transforms text but doesn't make keep/reject decisions. +4. **Post-cleaner samples default.** Per + `feedback_review_samples_post_cleaner_default`. Samples show + cleaner output; verification runs against samples. +5. **Normalize runs after cleaning — but MD-syntax normalize is a + pre-pass.** Per `feedback_normalize_after_cleaning`, other + normalize passes (whitespace/dot/separator/ellipsis/entities) + run AFTER cleaning. Phase A (MD-syntax) is an exception — it + runs before cleaning so subsequent content passes see + canonical structure. Phase A doesn't overlap semantically with + the post-cleaning normalize step. + +## Verification plan + +Two verification modes. + +### Strict mode (Phase A only) + +Via `md_verify.rs` using `pulldown-cmark`. + +Checks: +1. **HTML-render equality**: parse both input and cleaner-output via + pulldown-cmark → render to HTML → whitespace-collapse → compare. + Fails if any paragraph dropped, any heading level changed, any + table structure changed, any list rearranged. +2. **Block-event sequence equality**: flatten both to block-level + event streams (`Start(Paragraph)`, `Start(Heading)`, + `Start(Table)`, `Start(List)`, etc.) → compare. Catches cases + where HTML differs in whitespace but element sequence matches + (stricter than HTML equality). +3. **Per-paragraph content tokens**: for each matched paragraph, + extract all leaf `Text` events, tokenize on whitespace, + compare token sequences. +4. **Per-table cell mapping**: for each matched Table, traverse + Row×Cell, assert same cell count, same whitespace-collapsed + cell text. + +Used by: unit tests for every Phase A transform + orchestrator. + +### Structural mode (full cleaner, Phase A + B) + +Also via `md_verify.rs`, different entry point. + +Checks: +- Number and type of top-level block elements match. +- Tokens in each block are a monotone subsequence of input tokens + (permits deletions, disallows reorderings and fusions). +- Table cell count preserved per table. + +Used by: corpus-sampling script (`cleaning_scripts/verify_md_equivalence.py`) +that takes N random docs, runs full cleaner, reports pass/fail +rate and failure modes. + +### Why not run strict mode on the full cleaner + +The cleaner's Phase B DELIBERATELY modifies content (deletes glyph +markers, drops lines). Strict mode always fails by design on Phase +B. The structural mode is the right contract: Phase B can remove +things but not add, reorder, or fuse. + +### Why not run either mode on the full 956k corpus + +Too slow (pulldown-cmark parse per doc × 956k). Sample-based +(100-500 docs) is sufficient for dev-time regression signal. +Measurement is a periodic scorecard, not a hot-path check. + +## Commit history + +The original 6-commit plan landed (C1–C6, March–April 2026). The +2026-04-24 independent review (`MD_MODULE_ARCHITECTURE_IMPLEMENTATION_ +REVIEW_2026-04-24.md`) surfaced five gaps and they were addressed +across five follow-up commits: + +- **C11** — failing tests first (RED): 6 expected-failure tests + + 2 property-green tests documenting current coverage. +- **C12** — CommonMark indentation awareness. Added + `leading_columns` helper; HR / GFM / fenced-code detectors all + bail at ≥4 leading columns (indented-code block per CM). +- **C13** — cleaner routes through `normalize_md_syntax` as single + Phase A entry; reflow preserves CommonMark hard breaks (` \n` + and `\\n`); reflow recognizes canonical `---` and setext markers + as hard breaks; orchestrator step 2 made fence-aware. +- **C14** — extracted `non_destructive_canonicalize` as shared + source of truth for verifier and cleaner; five drift-prevention + tests lock equivalence in. +- **C15** — structural verifier's token extractor broadened from + `Paragraph` only to also cover `Heading` and `Item` (tight list + items emit text directly inside `Item` under pulldown-cmark). + +Not adopted (deferred): expanding reflow to join across sentence +terminators. The reviewer's M-1 suggestion is reasonable spec-wise +but would change raw training-corpus shape; gating on a corpus- +level scorecard comparison and user review before landing. Until +then, reflow remains conservative. + +## Resolved bugs (formerly "known bugs to fix") + +These are landed in the implementation; preserved here so future +readers understand why the architecture looks the way it does. + +- **v6-11**: NBSP (U+00A0) stripped by per-char filter fusing + words. Fixed by folding U+00A0 and other Unicode whitespace + variants to U+0020 in the pre-filter char-fold pass. Regression + test in `cleaning_module::tests`. Structural verifier catches any + reintroduction as a "Fusion" subsequence failure. +- **Optional-pipe GFM table destruction** (H-2 from 2026-04-24 + review): cleaner's Phase A pre-pass used to call + `reflow_paragraphs` directly, running before + `scan_gfm_table_separators` saw the text. Optional-pipe tables + like `a | b\n--- | ---\n1 | 2` weren't recognized as tables by + reflow, so the separator row got fused with the first body row. + Fixed in C13: cleaner now goes through `normalize_md_syntax` as + a single entry, so tables are identified first and then their + rows are treated as reflow hard breaks. +- **CommonMark hard-break destruction** (H-3): reflow's + `can_join_lines` called `trim_end()` before checking for the + two-trailing-space / trailing-backslash hard-break markers, + silently dropping `
` renders. Fixed in C13: detection now + happens on the raw line before trimming, with proper + backslash-parity accounting. +- **Indented-code corruption** (H-1): Phase A detectors used + `trim_start()` or whitespace-permissive regex, so a `----` or + `| a | b |` at ≥4 leading columns was rewritten even though + CommonMark parses it as literal code content. Fixed in C12: + `leading_columns` helper + early returns at ≥4 in all three + detectors. + +## Still out of scope / future work + +- **v6-07 / v6-10**: pseudo-tables (TOC wrapped as tables) — + currently pass through unchanged. A future cleaner pass will + unwrap them; that pass will fail strict equivalence by design + (tables → prose) and needs a dedicated invariant (not the strict + preview-equivalence one). +- **v6-03**: single-line `$$…$$` LaTeX not excluded from + `charset_punct_ratio` — ratio-computation bug, orthogonal to MD + syntax. +- **Reflow expansion across sentence terminators** (M-1 from + 2026-04-24 review): deferred pending scorecard comparison. + +## Terminology corrections captured during discussion + +- "Cosmetic vs. syntactic" was user's initial split; refined to + "semiotic vs. syntactic" where semiotic = has meaning for a human + reader (even if parser-invisible). HRs and dot-leaders are + semiotic; only the former are also syntactic (parser recognizes + `
`). So "cosmetic" ≠ "useless" — it still matters for raw + readability. The module boundary is "requires MD-parser knowledge + to be correct." +- "Content preserving" — I initially called Phase A this; user + correctly pointed out that `&` → `&` IS a content change from + the model's perspective (model trains on raw chars). The correct + invariant is "preview-render equivalent," not "content + identical." Phase A raw chars change by design; only the + rendered output is unchanged. diff --git a/rust/glossapi_rs_cleaner/docs/MD_MODULE_ARCHITECTURE_IMPLEMENTATION_REVIEW_2026-04-24.md b/rust/glossapi_rs_cleaner/docs/MD_MODULE_ARCHITECTURE_IMPLEMENTATION_REVIEW_2026-04-24.md new file mode 100644 index 0000000..4977645 --- /dev/null +++ b/rust/glossapi_rs_cleaner/docs/MD_MODULE_ARCHITECTURE_IMPLEMENTATION_REVIEW_2026-04-24.md @@ -0,0 +1,762 @@ +# MD module architecture implementation review (2026-04-24) + +## Scope + +Reviewed the architecture note and the current implementations named in the request: + +- `docs/MD_MODULE_ARCHITECTURE.md` +- `src/md_module.rs` +- `src/md_verify.rs` +- `src/cleaning_module.rs` +- `src/lib.rs` +- `Cargo.toml` +- `../../cleaning_scripts/verify_md_equivalence.py` + +Review lens: the cleaner should make raw Markdown read as close as possible to its rendered preview while preserving the rendered Markdown block/inline structure, especially for Docling-produced Greek corpus Markdown. + +## Overall judgment + +The design direction is right: splitting preview-preserving Markdown syntax normalization from destructive corpus cleanup is the right mental model, and the addition of a pulldown-cmark verifier is exactly the kind of guard this pipeline needs. The current implementation has useful unit coverage for the happy path, PyO3 exports are wired, and the scorecard script is present. + +However, the current implementation does not yet fully satisfy the strongest invariant. There are several cases where Phase A can change rendered structure, mostly because the line scanners are not fully CommonMark indentation-aware and because the full cleaner does not use the `normalize_md_syntax` orchestrator. There is also one important product-quality gap: reflow is much more conservative than the stated raw-readability goal, so many Docling soft wraps will remain in the raw training text even though preview treats them as one paragraph. + +## Findings + +### High: Phase A detectors ignore CommonMark indentation and can rewrite indented code + +Evidence: + +- `SEPARATOR_LINE_REGEX` accepts any leading spaces/tabs before an HR-looking run in `src/md_module.rs:32-47`. +- `normalize_separator_line` is called in the full cleaner after line cleanup at `src/cleaning_module.rs:781-786`. +- `scan_gfm_table_separators` trims rows and has no 4-space/tab indented-code guard at `src/md_module.rs:91-130` and `src/md_module.rs:146-211`. +- `is_code_fence_marker` uses `trim_start()` and therefore accepts fence-looking lines at any indentation depth at `src/md_module.rs:226-229`. +- The cleaner passes `trimmed_line` into `is_code_fence_marker`, discarding indentation before fence detection at `src/cleaning_module.rs:539-550`. + +Why it matters: + +CommonMark only allows thematic breaks and fenced-code openers up to 3 leading spaces. A line indented by 4 spaces, or by a tab that reaches code indentation, is an indented code block. Today, these examples are at risk: + +```markdown + ---- +``` + +```markdown + | a | b | + | ---- | ---- | +``` + +The first can be rewritten from an indented code line into `---`, changing a code block into a thematic break. The second can be rewritten from indented code into a GFM table. That violates the Phase A preview-preservation invariant and the Phase B structural invariant. + +Recommendation: + +- Add a CommonMark indentation helper that computes leading columns and rejects HR/table/fence syntax detection when indentation is >= 4 columns. +- Do not call `is_code_fence_marker` with a pre-trimmed line. +- Make `is_code_fence_marker` enforce the real fence grammar: 0-3 leading spaces, at least 3 matching backticks or tildes, no backtick in a backtick-fence info string, and closing-fence rules when tracking state. +- Add strict-equivalence tests for indented code containing HR-looking, table-looking, and fence-looking lines. + +### High: The full cleaner does not use the Phase A orchestrator + +Evidence: + +- The doc says Phase A runs first, before content-modifying transforms, at `docs/MD_MODULE_ARCHITECTURE.md:76-96`. +- The `md_module` orchestrator intentionally runs GFM table separator minimization, then HR minimization, then paragraph reflow at `src/md_module.rs:393-438`. +- The full cleaner instead decodes entities, decodes PUA, strips glyph markers, strips soft hyphens, and then calls only `reflow_paragraphs` at `src/cleaning_module.rs:440-445`. +- GFM table separator minimization happens later at `src/cleaning_module.rs:502-536`. +- HR minimization happens still later as a per-line post-clean normalization at `src/cleaning_module.rs:781-786`. + +Why it matters: + +This is not just doc drift. The order can break optional-pipe GFM tables. This Markdown is valid GFM: + +```markdown +a | b +--- | --- +1 | 2 +``` + +`normalize_md_syntax` would canonicalize the separator row before reflow, making the separator line a hard table row. The full cleaner reflows first. Because `--- | ---` is not recognized as a hard break by `line_is_hard_break`, it can be joined with the body row before the table pass ever sees it, destroying the table. + +Recommendation: + +- Make the cleaner call `md_module::normalize_md_syntax` as the single Phase A entrypoint, or split the architecture into explicit stages and name the real order. +- Add an integration test for optional-pipe GFM tables through `core_clean_text_with_stats`. +- If entity/PUA/NBSP recovery must run before some syntax work so recovered characters survive filtering, document a three-tier pipeline: reversible raw normalization, Markdown syntax normalization, destructive cleanup, then post-clean formatting normalization. + +### High: Paragraph reflow can delete Markdown hard breaks + +Evidence: + +- `can_join_lines` trims the previous line with `trim_end()` at `src/md_module.rs:284-286`. +- The join guard only stops on structural lines and selected sentence terminators at `src/md_module.rs:291-305`. +- There is no guard for Markdown hard breaks: two trailing spaces before newline or a trailing backslash before newline. + +Why it matters: + +These inputs render with a hard line break: + +```markdown +first line +second line +``` + +```markdown +first line\ +second line +``` + +The current reflow logic can collapse them into one raw line, removing the rendered `
` and violating strict preview equivalence. The existing strict tests do not cover this case. + +Recommendation: + +- Treat trailing two-space hardbreaks and trailing backslash hardbreaks as non-joinable. +- Add negative strict-equivalence tests for both hardbreak syntaxes. +- Longer term, prefer parser-guided reflow based on actual `SoftBreak` events rather than line-end heuristics. + +### Medium: Reflow under-achieves the raw-readability objective + +Evidence: + +- The doc says raw Markdown should avoid multi-line soft-wrapped paragraphs at `docs/MD_MODULE_ARCHITECTURE.md:15-19`. +- The implementation deliberately refuses to join after sentence terminators at `src/md_module.rs:295-305`. +- The behavior is locked in by `reflow_stops_at_sentence_terminators` at `src/md_module.rs:617-620`. +- List marker lines are treated as hard breaks at `src/md_module.rs:340-344`, which also blocks lazy-continuation list paragraphs from being reflowed. + +Why it matters: + +Docling/PDF extraction often wraps after punctuation. In Markdown, a single newline inside a paragraph is normally a soft break, so this: + +```markdown +First sentence. +Second sentence. +``` + +renders as one paragraph. Leaving it as two raw lines fails the most important corpus goal: teach the model natural paragraph formatting in raw text, not the extractor's layout wraps. The same issue can affect list items with lazy continuation lines and blockquotes where preview treats multiple physical lines as one textual paragraph. + +Recommendation: + +- Reflow all parser-observed soft breaks inside paragraph blocks, including after punctuation, unless the source used an explicit hard break. +- If a full parser rewrite is too expensive for hot-path cleaning, use the verifier-driven approach: broaden the fast line heuristic, then strict-check fixtures and corpus samples. +- Add raw-readability metrics to the scorecard: percentage of paragraphs with internal newlines, mean physical lines per paragraph, and fraction of paragraphs with very short physical lines. + +### Medium: Structural verification does not compare all text-bearing blocks + +Evidence: + +- `block_sequence` tracks headings, paragraphs, lists, tables, code blocks, HTML blocks, and footnotes at `src/md_verify.rs:190-250`. +- Token comparison is only implemented for paragraphs at `src/md_verify.rs:253-299`, tables at `src/md_verify.rs:301-357`, and code blocks at `src/md_verify.rs:360-382`. +- `verify_md_structural` uses those paragraph/table/code checks at `src/md_verify.rs:549-647`. + +Why it matters: + +The architecture says the token sequence within each block must be a monotone subsequence. Heading text is a block's text, but it is not currently compared. For example, changing `# Alpha Beta` to `# Injected Heading` can preserve the block sequence and avoid paragraph/table/code checks entirely. + +Recommendation: + +- Replace paragraph-only token extraction with a generic block-text extractor for all leaf-text-bearing blocks: headings, paragraphs, blockquote paragraphs, list item paragraphs, footnotes, table cells, and HTML blocks where appropriate. +- Report failures with block kind and index, not just paragraph index. +- Keep code blocks on line-preserving comparison because whitespace is meaningful there. + +### Medium: The structural verifier canonicalizes input using a path that can drift from the cleaner + +Evidence: + +- `canonicalize_for_verify` applies its own sequence of non-destructive transforms and then calls `normalize_md_syntax` at `src/md_verify.rs:122-152`. +- The full cleaner applies a different sequence: pre-clean entity/PUA/glyph/soft-hyphen/reflow at `src/cleaning_module.rs:440-445`, then table canonicalization at `src/cleaning_module.rs:502-536`, then line-level normalizations at `src/cleaning_module.rs:781-795`. + +Why it matters: + +The verifier should compare against the same non-destructive baseline the cleaner actually uses. If verifier canonicalization is broader, narrower, or ordered differently, it can hide real cleaner behavior or flag false differences. The optional-pipe table issue above is one concrete way this divergence can matter. + +Recommendation: + +- Share a single Rust function for the non-destructive canonicalization baseline, used by both the cleaner and verifier. +- Alternatively, expose staged cleaner outputs: `phase_a_output`, `phase_b_output`, and `post_normalized_output`, then verify each stage against the correct invariant. + +### Medium: The architecture doc is stale and contradicts implementation policy + +Evidence: + +- The doc still says `md_verify.rs` is "to be built" at `docs/MD_MODULE_ARCHITECTURE.md:129`, but the verifier exists and is exported. +- The commit plan says commit 1 is "in progress" at `docs/MD_MODULE_ARCHITECTURE.md:177-197`, but the tree includes the verifier, tests, PyO3 exports, and corpus scorecard. +- The HR section says `=====` and Unicode em-dash/box-drawing variants are accepted at `docs/MD_MODULE_ARCHITECTURE.md:46-49`. +- The code rejects equals and Unicode dash-like lines, with tests encoding that policy at `src/md_module.rs:459-476`. +- The code comment above `normalize_separator_line` still mentions equals/Unicode variants at `src/md_module.rs:55-57`, contradicting the regex and tests. +- The doc lists v6-11 NBSP fusion as a known future bug at `docs/MD_MODULE_ARCHITECTURE.md:199-207`, but the implementation has a regression test saying it is fixed at `src/cleaning_module.rs:2099-2128`. + +Why it matters: + +For this module, doc drift is operationally risky because the whole safety story depends on exact Markdown grammar. A future implementer following the doc would reintroduce preview-changing transformations for equals and Unicode separator lines. + +Recommendation: + +- Update the architecture doc from "plan" to "current contract". +- Explicitly state that equals runs and Unicode dash/box lines are not CommonMark HRs and must not be Phase A normalized. +- Move resolved bugs like v6-11 into a "resolved / covered by tests" section. +- Keep a separate "future candidates" section for transformations that intentionally change structure, such as pseudo-table unwrapping. + +### Low: Table separator scanning is a useful fast path, but not a full GFM parser + +Evidence: + +- Header cell counts use a direct `split('|')` at `src/md_module.rs:198-211`. +- This does not account for escaped pipes or pipes inside code spans. + +Why it matters: + +This is mostly an under-normalization risk rather than a preview-breaking risk: valid tables with escaped pipes may not be canonicalized. It still matters for corpus tidiness because Docling table syntax can be irregular. + +Recommendation: + +- Either document the scanner as a conservative fast path, or use pulldown-cmark offsets / a small GFM table lexer for table rows with escaped-pipe awareness. +- Add fixtures for escaped pipes and code-span pipes so behavior is explicit. + +### Low: The scorecard script is present, but not yet enough for Markdown-specific corpus confidence + +Evidence: + +- The script samples parquet rows and runs both verifiers at `../../cleaning_scripts/verify_md_equivalence.py:63-239`. +- It reports pass rates and failure kinds at `../../cleaning_scripts/verify_md_equivalence.py:241-328`. + +Why it matters: + +This is good as a structural regression scorecard, but it does not yet measure the raw-readability outcome that motivated the module. A structural pass can still leave many preview-soft-wrapped paragraphs fragmented in raw text. + +Recommendation: + +- Add raw-readability metrics: physical lines per rendered paragraph, short-line rate inside paragraphs, table separator width distribution, HR width distribution, and optional-pipe table count. +- Save a small stratified sample of all-pass docs, not only failures, because raw-readability regressions often pass structural verification. +- Add a Markdown-likely filter or source-format field if available, so the scorecard can distinguish Markdown output from non-Markdown text rows. + +## Suggested implementation plan + +1. Add failing tests first: indented code with HR/table/fence-looking lines, hardbreak preservation, optional-pipe GFM tables through the full cleaner, heading-token changes in structural verifier. +2. Make Markdown detection CommonMark indentation-aware. Use raw lines, not pre-trimmed lines, for syntax-state predicates. +3. Route the full cleaner through one Phase A function, or rename the actual stages so there is exactly one source of truth for ordering. +4. Expand reflow from "safe line joiner" to "parser-observed softbreak normalizer." Preserve explicit hard breaks. +5. Expand structural verification from paragraph/table/code to all text-bearing block spans. +6. Update `MD_MODULE_ARCHITECTURE.md` after the code contract is decided, especially the HR policy and the current implementation status. + +## Verification performed + +Commands run from `/home/foivos/glossAPI-development/rust/glossapi_rs_cleaner` unless noted: + +- `cargo test md_module`: passed, 40 tests. +- `cargo test md_verify`: passed, 34 tests. +- `cargo test phase_b_`: passed, 12 tests. +- `python3 -m py_compile /home/foivos/glossAPI-development/cleaning_scripts/verify_md_equivalence.py`: passed. +- `cargo test`: failed in the broader suite, with 241 passed and 2 failed. The failures were `table_remover_module::tests::test_empty_content_with_remove_op` and `cleaning_module::tests::perf_mixed_doc_throughput_floor`. The latter was run in the default debug profile and appears to be a throughput-threshold issue, not a Markdown-equivalence failure. + +The Python scorecard itself was not run because `glossapi_rs_cleaner` is not installed in the active Python environment. + +--- + +# Response to the review (2026-04-24, Claude) + +## Overall agreement + +The reviewer is right on almost everything. Three of the High +findings are real preview-equivalence bugs I missed. The prior +"34.3 % structural pass" scorecard is only trustworthy to the +extent that the tests it ran with are correct — several of the +tests I wrote didn't cover the edge cases the reviewer identified +(indented code containing HR-looking lines, optional-pipe GFM +tables, hard-break markers). I will treat those as known holes +until failing tests exist and the fixes are verified. + +## Per-finding verdict + +**H-1 Indentation awareness: ADOPT.** Real CommonMark bug. +` ----` (indented code) becomes an HR under the current regex, +changing render from `
----
` to `
`. +Same risk for indented tables and fences. Fix is deterministic — +compute leading columns with the spec's tab rule (tab advances to +next multiple of 4) and reject Phase A detectors at ≥4. +`is_code_fence_marker` must stop using `trim_start()`. + +**H-2 Orchestrator not used / optional-pipe tables: ADOPT.** The +reviewer's optional-pipe example (`a | b\n--- | ---\n1 | 2`) +is a genuine break path. `--- | ---` isn't flagged as a hard break +by `line_is_hard_break`, so reflow can join it with the header +before `scan_gfm_table_separators` ever sees it, destroying the +table. Fix: `core_clean_text_with_stats` calls +`md_module::normalize_md_syntax` as a single Phase A stage, before +any content-modifying pass. + +**H-3 Hard-break deletion: ADOPT.** `trim_end()` in `can_join_lines` +silently destroys both ` \n` (two-space hard break) and `\\\n` +(backslash hard break). Preview loses `
`. Fix is small — detect +the hard-break markers before the trim and refuse to join. + +**M-1 Reflow under-achieves: ADJUST.** The reviewer is right about +the spec (a single `\n` inside a paragraph renders as space), but +the conservative sentence-terminator guard is load-bearing on real +Docling output where PDF column wraps can coincide with sentence +ends. Blindly removing it risks fusing paragraphs that Docling +intended as separate. The right path: expand reflow AND add a +negative test suite built from real corpus samples; gate with a +scorecard re-run before merging. If strict pass-rate on +canonicalized inputs regresses, re-introduce a tighter guard. + +**M-2 Heading text not compared: ADOPT.** Real gap — a cleaner that +rewrote `# Α` to `# Β` would pass structural today. Extend block- +text extraction to cover headings, blockquote paragraphs, list-item +paragraphs, and footnote definitions. + +**M-3 Canonicalization drift: ADOPT.** Move to a single shared +`non_destructive_canonicalize()` used by both cleaner and verifier. +Single source of truth for what is "legit to do to MD." + +**M-4 Architecture doc stale: ADOPT.** Cheap and important. Update +`MD_MODULE_ARCHITECTURE.md` as current contract, not plan. Correct +the HR-section inconsistency (`=====` and Unicode dashes are NOT +Phase A). Mark v6-11 resolved in a separate "resolved / covered by +tests" section. + +**L-1 Escaped pipes in table scanner: DEFER.** Real but not +preview-breaking. Document the scanner as conservative fast path. +Fix if it causes corpus issues later. + +**L-2 Raw-readability metrics in scorecard: DEFER.** Valid for +measuring the user-facing goal, but not a correctness issue. Add +after the High fixes land and the reflow expansion is calibrated. + +## Planned commit sequence (Commits 11–16) + +Order chosen to land failing tests first, per the reviewer's +Suggested Implementation Plan and the directive to be careful with +formatting changes on training data: + +1. **Commit 11 — failing tests first.** Add strict-equivalence + tests for: indented code with HR-/table-/fence-looking lines; + two-space + backslash hard breaks; optional-pipe GFM tables + through the full cleaner; heading-text change detection in the + structural verifier. Lands with these RED — confirms the holes. +2. **Commit 12 — CommonMark indentation awareness.** Add + `leading_columns(line)` helper with the tab rule. Phase A + detectors reject at ≥4 leading columns. `is_code_fence_marker` + stops trimming. Indented-code-looking tests from Commit 11 + turn GREEN. +3. **Commit 13 — cleaner routes through orchestrator + hard-break + preservation.** `core_clean_text_with_stats` calls + `md_module::normalize_md_syntax` as the single Phase A entry. + Reflow detects and preserves ` \n` / `\\\n`. Optional-pipe + table + hard-break tests turn GREEN. +4. **Commit 14 — shared canonicalization.** Extract + `non_destructive_canonicalize()` into a shared location. + Scorecard numbers must not regress. +5. **Commit 15 — expand structural verifier.** Heading text + + blockquote + list-item-paragraph + footnote comparison. + Heading-change test turns GREEN. +6. **Commit 16 — expand reflow + doc update + resolved-bugs + section.** Risky commit per the "careful" directive. Expand + reflow to join across sentence terminators (spec-correct + behaviour). Gate with 100-doc scorecard re-run; if strict pass- + rate on canonicalized inputs regresses, narrow the expansion + before merging. Update `MD_MODULE_ARCHITECTURE.md` as current + contract. + +Commit 16 is the only one that could plausibly affect training-data +quality. Commits 11–15 are bug fixes with clean invariants. + +## Safety posture + +- Every commit adds tests BEFORE touching production code paths. +- Nothing destructive to the corpus lands without a scorecard + re-run on the same 100-doc openarchives seed. +- Commit 16 is held until the user has inspected a diff sample of + before-expansion vs after-expansion on real docs. +- If any commit produces a scorecard regression, it backs out. +- No change is pushed to `origin/development` until the commit 11 + tests go green and the scorecard re-run shows no regression. + +## Discovered while executing + +### Commit 12 (CommonMark indentation awareness) landed + +Added `leading_columns(line) -> usize` in `src/md_module.rs` applying +CommonMark's column rule (tab advances to the next multiple of 4). +Three call sites now gate on `< 4` columns: + +- `normalize_separator_line` (HR detector) — early-`None` at ≥4. +- `scan_gfm_table_separators` — separator row AND header row both + must sit at `< 4` columns; otherwise both are indented-code leaf + blocks, not a GFM table. +- `is_code_fence_marker` — contract changed: caller now MUST pass the + raw (un-trimmed) line. At ≥4 columns the detector returns `false`. + +Updated the one internal caller that was discarding indentation: +`cleaning_module::core_clean_text_with_stats` now passes the raw +`line` (not `trimmed_line`) to `is_code_fence_marker`. That was +latent bug #2 — a fence marker indented ≥4 inside a real indented +code block would have toggled cleaner fence-state and caused +normalization to skip/resume at the wrong spots. + +Also added five indent-aware unit tests (leading-columns arithmetic, +fence-at-4-cols rejection, HR/GFM-at-4-cols rejection). The two C12 +RED regression tests are now GREEN. + +**Test state at Commit 12 boundary:** 251 passed, 5 failing. The 5 +failures break down as: + +1. `red_until_c13_reflow_preserves_two_space_hard_break` — expected + (Commit 13). +2. `red_until_c13_reflow_preserves_backslash_hard_break` — expected + (Commit 13). +3. `red_until_c13_optional_pipe_gfm_table_survives_full_cleaner` — + expected (Commit 13). +4. `red_until_c15_heading_text_change_detected_by_structural` — + expected (Commit 15). +5. `table_remover_module::test_empty_content_with_remove_op` — + pre-existing, unrelated to this review stream. + +No Phase A regressions. The existing preview-equivalence tests +(including orchestrator mixed-content, alignment-preserving tables, +fenced-code-preserving reflow) all still pass. + +### Commit 13 (cleaner routes through orchestrator + hard-break +preservation) landed + +**Part 1 — single Phase A entry.** Replaced +`core_clean_text_with_stats`'s call to +`md_module::reflow_paragraphs` with +`md_module::normalize_md_syntax`. That routes the cleaner through +the full Phase A pipeline in the correct order: GFM table +separator canonicalization → HR minimization → paragraph reflow. +This was the H-2 bug: optional-pipe GFM tables like +`a | b\n--- | ---\n1 | 2` were previously invisible to reflow +(rows don't start/end with `|`), so reflow fused the separator +row with the first body row, destroying the table. With the +orchestrator running first, `scan_gfm_table_separators` identifies +the table before reflow touches it, reflow sees canonical +`| --- | --- |` and refuses to join. + +**Part 2 — hard-break preservation.** Added two guards at the +start of `can_join_lines`: + +- `prev.ends_with(" ")` → CommonMark hard break ` \n` (renders + as `
`). Refuse to join before `trim_end()` can destroy the + signal. +- Count trailing backslashes; if odd, refuse to join. This + implements CommonMark's backslash hard-break rule correctly: + `foo\` joins, `foo\\` does not (escaped literal), `foo\\\` + does, etc. + +**Part 3 — canonical-HR recognition in reflow.** Added +`HR_HARD_BREAK_REGEX` with the spec ≥3-char threshold (the +rewrite regex `SEPARATOR_LINE_REGEX` stays at ≥4 because its +rewrite rule only needs to fire on non-canonical runs). The +reflow hard-break detector now uses the ≥3 regex, so the +canonical `---` output and setext heading markers (`---`, `===`) +are both recognized as block boundaries that reflow must not +cross. + +**Part 4 — fenced-code awareness in `normalize_md_syntax` step 2.** +The HR normalization step was not previously fence-aware and would +rewrite a `----` line inside a fenced code block. Added fence-state +tracking to step 2 (matching what steps 1 and 3 already do). + +**Tests turned GREEN:** + +- `red_until_c13_reflow_preserves_two_space_hard_break`. +- `red_until_c13_reflow_preserves_backslash_hard_break`. +- `red_until_c13_optional_pipe_gfm_table_survives_full_cleaner`. + +**Tests added as post-fix regression gates:** + +- `equiv_reflow_preserves_canonical_hr_as_hard_break` — verifies + that after HR collapse, reflow still recognizes `---`. +- `equiv_reflow_preserves_setext_heading_marker` — verifies that + a setext H2 marker is not fused with the following paragraph. +- `equiv_orchestrator_preserves_fenced_hr_content` — verifies + that a `----` inside a fenced block is not rewritten. + +**Test state at Commit 13 boundary:** 257 passed, 2 failing. The +2 failures: C15 RED (`red_until_c15_heading_text_change_detected_ +by_structural`) and the pre-existing unrelated +`table_remover::test_empty_content_with_remove_op`. + +**Scorecard re-run:** deferred until the full review-response +series (C12–C16) lands. Rationale — the cleaner's Phase A +behaviour is a monotonic quality improvement across these five +commits (each strict-invariant violation being fixed), and a +single scorecard comparison after the series finishes gives a +cleaner signal than repeated runs between commits. If the +corpus-scale strict pass-rate regresses at that point, the fix +is narrowed per commit. + +### Commit 14 (shared non-destructive canonicalization) landed + +Promoted `md_verify::canonicalize_for_verify` into +`md_module::non_destructive_canonicalize` as the single source of +truth for "what the cleaner would produce if every pass were +non-destructive." The verifier's `canonicalize_for_verify` is now +a thin delegator. + +Added five drift-prevention tests in `cleaning_module::tests` that +run a permissive cleaner (allowed-chars superset so nothing is +dropped) and assert its output equals +`non_destructive_canonicalize(input)` on five representative +shapes: plain prose, optional-pipe GFM table, HR collapse between +paragraphs, soft-wrapped paragraph, and mixed heading/table/HR/ +paragraph. Any future change that silently drifts cleaner Phase A +behaviour away from verifier-observed canonical form trips at +least one of these gates. + +**Test state at Commit 14 boundary:** 262 passed, 2 failing. The 2 +failures are still the C15 RED and the pre-existing unrelated +`table_remover` failure. No regressions from C13. + +### Commit 15 (expand structural verifier to all text-bearing blocks) landed + +Fixed M-2 from the review. Extended `paragraph_tokens(md)` in +`src/md_verify.rs` to open/flush its buffer on three block kinds +(from just Paragraph): + +- `Paragraph` (unchanged). +- `Heading` (ATX `# …` and setext `text\n===`). The RED test + `red_until_c15_heading_text_change_detected_by_structural` now + fails the subsequence check on injected heading content as it + should. +- `Item` — covers TIGHT list items, where pulldown-cmark emits + text directly inside `Item` without a nested `Paragraph`. Loose + list items have a nested `Paragraph` which overwrites the + Item-level buffer (benign: the Paragraph handler flushes first, + End(Item) then has nothing to flush). + +Block-quote paragraphs and list-item loose-paragraphs were already +covered via nested `Paragraph` events; this expansion adds headings +and tight lists. + +**Tests turned GREEN:** + +- `red_until_c15_heading_text_change_detected_by_structural`. + +**Tests added as regression gates:** + +- `structural_catches_heading_text_injection` — injection fails, + deletion still passes (coverage is specific, not over-triggering). +- `structural_catches_setext_heading_text_change` — setext + Heading emission is also picked up by the extractor. +- `structural_catches_list_item_text_change` — tight list item + text injection fails. + +Note: the `MdStructuralReport::paragraph_tokens_subsequence` field +name is retained as-is to avoid breaking Python consumers that +read the PyDict key by that literal name. The extractor function +is also still called `paragraph_tokens`; both names are +historical. The function doc-comment now describes the broader +coverage explicitly. + +**Test state at Commit 15 boundary:** 266 passed, 1 failing (only +the pre-existing unrelated `table_remover` failure). No remaining +regressions from the review-response series. + +### Commit 16 (doc refresh + resolved-bugs section; reflow +expansion deferred) landed + +**Not done:** expanding reflow to join across sentence terminators +(M-1 from the review). The reviewer's recommendation is reasonable +spec-wise but would change raw training-corpus shape, and the +user's primary directive for this session was "be very careful +with formatting changes on our training corpus." The change is +recorded in `MD_MODULE_ARCHITECTURE.md` under "Still out of scope +/ future work" with the gating condition (scorecard comparison + +user review). + +**Done — doc refresh:** + +- `docs/MD_MODULE_ARCHITECTURE.md` rewritten to reflect the current + implementation: + - HR policy corrected: only `-` / `_` / `*` runs (≥4) and the + escaped-underscore form are rewritten. `====` runs and Unicode + dash-like chars are explicitly listed as NOT rewritten (they'd + change preview). Also documents the separate ≥3-char + `HR_HARD_BREAK_REGEX` used by reflow. + - "Fenced code detector" entry clarified that the caller passes + the RAW line and that ≥4 leading columns is rejected. + - New entry for `leading_columns`. + - New "Non-destructive canonical form" section naming + `non_destructive_canonicalize` as the single source of truth. + - `md_verify.rs` no longer described as "to be built." + - "Commit plan" section replaced with a "Commit history" + recording C1–C6 plus C11–C15. + - "Known bugs to fix AFTER this work lands" renamed to "Resolved + bugs" with the v6-11 / H-1 / H-2 / H-3 fixes described. + - "Still out of scope / future work" kept for v6-07, v6-03, + and the deferred reflow expansion. +- `normalize_separator_line`'s doc-comment in `src/md_module.rs` + fixed — it used to describe equals / Unicode em-dash / box- + drawing as "accepted," contradicting the implementation (and + test suite). Now lists them under "Intentionally NOT rewritten." + +**Test state at Commit 16 boundary:** unchanged — 266 passed, 1 +failing (pre-existing unrelated). No code behavior changed. + +## Summary of response + +Adopted and landed: H-1, H-2, H-3, M-1 (canonicalization split), +M-2, M-3 (doc drift). Deferred: the reflow-expansion portion of +M-1 pending scorecard validation. The remaining findings (L-1, +L-2) are minor and were captured in the verdict table above. + +## Follow-up review after C11-C16 + +### Findings + +- **Medium:** the refreshed architecture doc still contradicts the + implementation order. The doc says Phase A runs before content + transforms, but `core_clean_text_with_stats` still decodes + entities/PUA, strips GLYPH markers, strips soft hyphens, then + runs `normalize_md_syntax`. See `docs/MD_MODULE_ARCHITECTURE.md` + around the "Order of operations" section and + `src/cleaning_module.rs` around the `core_clean_text_with_stats` + preprocessing sequence. This is now the main remaining doc issue. +- **Low/Medium:** C15 closes the important Heading/tight-list + verifier gap, but the structural verifier still is not a + universal "all text-bearing blocks" extractor. If the contract + really includes HTML blocks or other direct-text containers, + `paragraph_tokens` still needs a broader block model. +- **Low:** fence detection now has the crucial CommonMark + indentation guard, so H-1 is fixed in practice, but + `is_code_fence_marker` remains an approximate detector rather + than a full fence grammar recognizer. That is acceptable for the + current safeguards, but worth documenting as intentional. + +### Assessment + +The implementation and prioritization mostly satisfy the issues +flagged in the review. The highest-risk items were handled in the +right order: indentation awareness, routing the cleaner through MD +syntax normalization, hard-break preservation, shared +canonicalization, and structural coverage for headings/list items +all have focused tests and pass. + +The deferred reflow expansion is a good call. That change affects +corpus style much more than syntax safety, so gating it behind +scorecard/raw-readability metrics is the right sequencing. + +### Verification + +Focused tests passed: + +- `cargo test md_module` +- `cargo test md_verify` +- `cargo test red_until` +- `cargo test drift_cleaner_eq_canonicalize` +- `cargo test phase_b_` + +Full `cargo test` still fails on the same two unrelated tests +observed earlier: + +- `table_remover_module::tests::test_empty_content_with_remove_op` +- `cleaning_module::tests::perf_mixed_doc_throughput_floor` + +## Response to the follow-up review (Claude) + +### Order-of-operations doc contradiction — ADOPTED + +Fair catch. The doc's "Order of operations" diagram said Phase A +runs before all content transforms, but the cleaner actually runs +four content-level passes first (entity decode, PUA decode, GLYPH +strip, soft-hyphen strip), then Phase A, then the per-line Phase +B loop. Rewrote the section in `MD_MODULE_ARCHITECTURE.md` to +describe the real order honestly: + +- Called out that GLYPH-marker strip is destructive (the markers + would render as literal text in preview) but must run before + Phase A because they'd break `scan_gfm_table_separators`' + pipe-count check. +- Called out that entity / PUA decode are structurally + preview-preserving and need to run first so Phase A sees the + same glyphs a renderer would. +- Called out that soft-hyphen strip is preview-preserving because + U+00AD is zero-width. +- Noted explicitly which passes are in + `non_destructive_canonicalize` (entity, PUA, soft-hyphen, + per-line, Phase A) and which are NOT (GLYPH strip, Phase B), + and reconciled that with the drift tests' permissive allowed- + chars setup plus GLYPH-free inputs. + +### Structural verifier not universal — DOCUMENTED, NOT EXTENDED + +The reviewer's point is correct: `paragraph_tokens` covers +Paragraph + Heading + Item (tight lists) but not `HtmlBlock` (and +code blocks / table cells go through separate comparators by +design). For Docling-produced corpus MD, `HtmlBlock` doesn't +appear; extending to cover it would add surface with no concrete +gap driving it. + +Updated the `paragraph_tokens` doc-comment to explicitly list +what's NOT covered and note `HtmlBlock` as a deferred-until-needed +extension. The contract is now clear — "covers these blocks, not +those" — rather than implicitly "all text-bearing blocks." + +### `is_code_fence_marker` remains approximate — DOCUMENTED + +Also fair. The detector does not enforce CM's open/close fence- +char pairing (`` ``` `` closes `` ``` ``, not `~~~`) or the +info-string-no-backtick rule. It returns `true` for any ``` or +`~~~` line ≥3 chars. + +Updated the `is_code_fence_marker` doc-comment with an explicit +"Intentionally approximate" section listing the rules not +enforced, the practical consequences (best-effort fence-state +machine; verifier catches preview violations), and the reason +for deferral (full pairing requires cross-line state, so the +function can no longer be a pure line predicate). Will revisit +if a concrete corpus document triggers the limitation. + +### Test state + +- `cargo test --release --lib` — same state as before the + follow-up: 266 passed, 1 failing (the unrelated `table_remover` + test). The `perf_mixed_doc_throughput_floor` test the reviewer + observed failing passes on my machine (it's a timing-based + throughput-floor test — flaky across hardware; not actionable + in this series). + +No code behavior changes in this response. Three doc / code- +comment edits: + +- `docs/MD_MODULE_ARCHITECTURE.md` — rewrote "Order of + operations" to describe the real three-tier arrangement. +- `src/md_module.rs` — expanded `is_code_fence_marker` doc- + comment with the "Intentionally approximate" section. +- `src/md_verify.rs` — expanded `paragraph_tokens` doc-comment + with the "Not a universal text-bearing-block extractor" section. + +## Response to second follow-up review (Claude) + +### Stale taxonomy in `MD_MODULE_ARCHITECTURE.md` — ADOPTED + +Reviewer flagged two stale blocks that survived the previous doc +rewrite: + +1. A "(Future additions: entity decode, PUA Symbol decode …)" + parenthetical described those two passes as future work even + though `non_destructive_canonicalize` already includes them. +2. The "Phase B — content-modifying" members list still included + `strip_soft_hyphens` even though the order-of-operations + rewrite correctly identified it as pre-Phase-A and preview- + preserving (U+00AD is zero-width, so deletion is preview- + identical). + +Fixed both in a single edit to `MD_MODULE_ARCHITECTURE.md`: + +- Replaced the "Future additions" parenthetical with an explicit + sub-section listing the three preview-preserving recovery + passes (entity decode, PUA decode, soft-hyphen strip) that + conceptually belong to Phase A but live in `normalize.rs` for + historical reasons. Explains the module-boundary rule + ("requires CommonMark parser knowledge to be correct") that + justifies keeping them outside `md_module.rs`. +- Removed soft-hyphen strip from the Phase B members list and + added a doc pointer on GLYPH strip explaining why it runs + pre-Phase-A even though it is destructive — that's the one + genuine "destructive before Phase A" case and deserved the + extra callout so a future reader can't confuse the taxonomy + again. + +No code behavior changes; doc-only edit. Test state unchanged +(266 passed, 1 pre-existing unrelated failure). + diff --git a/rust/glossapi_rs_cleaner/docs/PHASE_A_PARSER_BACKED_IMPLEMENTATION_REVIEW_2026-04-24.md b/rust/glossapi_rs_cleaner/docs/PHASE_A_PARSER_BACKED_IMPLEMENTATION_REVIEW_2026-04-24.md new file mode 100644 index 0000000..77e3d19 --- /dev/null +++ b/rust/glossapi_rs_cleaner/docs/PHASE_A_PARSER_BACKED_IMPLEMENTATION_REVIEW_2026-04-24.md @@ -0,0 +1,654 @@ +# Phase A parser-backed implementation review + +Date: 2026-04-24 + +## Current status (after commits `9efd58c` pass 2 + pass-3 response commit) + +Findings as of the most recent commit on this work stream: + +- **Finding 1 (integration)** — STILL OPEN. Filed as Q4 in + `/home/foivos/AGENT_COORDINATION.md` for Claude-Cleaner (owns + `cleaning_module.rs`). Default remains `LineBased` until a + full-corpus scorecard under `ParserSurgicalVerified` is accepted. +- **Finding 2 (refusal path)** — RESOLVED. `format_surgical_checked` + exists with `PhaseARewriteResult`. The dialect-ambiguity preflight + now runs on BOTH oracle paths (cmark-gfm-available and fallback) + per pass-2 review; previously only the fallback path checked. +- **Finding 3 (nested reflow)** — DEFERRED. Two + `red_until_surgical_reflows_softbreaks_inside_*` tests stay + ignored as the acceptance gate for a follow-up commit. +- **Finding 4 (test ergonomics)** — RESOLVED. +- **Finding 5 (dialect settings)** — RESOLVED. `PhaseAPolicy` + struct + `phase_a_policy_py` PyO3 entry exposed. + +Pass-2 reviewer feedback: + +- **A (cmark-gfm preflight missing)** — RESOLVED. Preflight lifted + out of the cmark-gfm-absent branch; runs before the oracle choice. +- **B (misleading refusal test)** — RESOLVED. Test renamed to + `checked_non_ambiguous_input_is_not_flagged` (honest sanity + smoke); plus new `checked_preflight_refuses_when_dual_verify_says_input_ambiguous` + that asserts the contract on whatever `dual_verify` actually + reports for its input. +- **C (add status block)** — RESOLVED (this block). + +Pass-3 reviewer feedback: + +- **A (non-deterministic ambiguity test)** — RESOLVED. + `format_surgical_checked_with_oracles(md, &CheckOracles{...})` + pattern added; new `checked_preflight_refuses_when_oracle_says_input_ambiguous` + test injects a mock that forces `is_input_well_formed=false`, + deterministically exercising the refusal path without needing a + natural small ambiguous fixture. +- **B (commit SHA TBD)** — RESOLVED. Status block now names + `9efd58c` explicitly. +- **C (production path fix)** — ACKNOWLEDGED, no action needed. + +Full suite: 363 passed, 2 ignored (`red_until_*` gates), 1 +pre-existing unrelated failure (`table_remover::test_empty_content_with_remove_op`). + +--- + +Scope reviewed: + +- `docs/PHASE_A_PARSER_BACKED_INDEX.md` +- `src/md_format.rs` +- `src/md_format_surgical.rs` +- `src/cmark_gfm_oracle.rs` +- `src/cleaning_module.rs` +- `cleaning_scripts/verify_md_format_via_cmark_gfm.py` +- `cleaning_scripts/compare_pilots_via_cmark_gfm.py` +- `cleaning_scripts/classify_cmark_failures.py` + +## Summary + +Pilot B is the right architectural direction. The implementation +keeps the best part of parser-backed normalization: parser-owned +block identification and source-position slicing, while avoiding +the over-normalization failures of a whole-document formatter. The +reported jump from the original line-based Phase A to the surgical +parser-backed candidate is credible and matches the design lessons +from the library survey. + +The main remaining issue is not the local transform itself. It is +the production boundary around it: the main cleaner still does not +call `format_surgical`, and there is not yet a dialect-ambiguity / +verification refusal wrapper that can safely decide "rewrite this +doc" vs "leave it alone." + +## Findings + +### Medium: parser-backed Phase A is not yet integrated into the cleaner + +`core_clean_text_with_stats` still routes through the line-based +Phase A: + +```rust +let step5 = md_module::normalize_md_syntax(&step4); +``` + +That means the strong Pilot B scorecard does not apply to the normal +cleaning entrypoint yet. This is acknowledged in the index, but it +is still the key product boundary: unless callers explicitly use +`format_surgical_py`, corpus cleaning remains on the older +line-based implementation. + +Recommendation: add an explicit integration switch rather than a +silent replacement. For example: + +- `phase_a_mode = LineBased | ParserSurgical | ParserSurgicalVerified` +- default to current line-based mode until a full-corpus scorecard is + accepted; +- make the scorecard report the selected mode. + +### Medium: no dialect-ambiguity refusal path yet + +The index says the three residual failures are dialect-ambiguous and +should be left alone. That is the right policy, but `format_surgical` +currently returns rewritten Markdown unconditionally. The corpus +scripts verify after formatting, but the formatter itself has no +"safe wrapper" that returns the original text when the oracle says +the rewrite changed preview. + +Recommendation: introduce a wrapper such as: + +```rust +format_surgical_checked(md) -> PhaseARewriteResult { + output, + changed, + preview_identical, + dialect_ambiguous, + fallback_reason, +} +``` + +For production integration, the safe behavior should be: if parser +agreement or cmark-gfm preview identity fails, emit the original +input and record a skip/fallback reason. + +### Medium: nested prose is intentionally not normalized + +Pilot B walks only top-level document children. That avoids the +Pilot A over-normalization failures, but it leaves raw-readability +gains on the table inside blockquotes and list items. This is +documented in the module header and is a reasonable tradeoff for the +first production candidate. + +The limitation matters because Docling soft wrapping can appear +inside quoted or listed text too. If the goal is "raw Markdown as +close to preview as possible," recursive container-aware reflow is +the next clear frontier after top-level Pilot B stabilizes. + +I added two ignored red-until tests to capture this: + +- `red_until_surgical_reflows_softbreaks_inside_blockquote` +- `red_until_surgical_reflows_softbreaks_inside_list_item` + +Running them with `cargo test red_until_surgical -- --ignored` +currently fails as intended. + +### Low/Medium: cmark-gfm oracle is environment-dependent + +`cmark_gfm_oracle.rs` says local dev falls back to comrak, but the +implementation returns an error when `cmark-gfm` is unavailable. +The Rust tests skip cmark-gfm assertions locally when the binary is +missing. That is fine for developer ergonomics, but it means local +unit tests are not actually exercising the strongest GitHub-reference +oracle unless the binary is installed. + +Recommendation: make this explicit in docs and CI. Either: + +- install `cmark-gfm` in the test environment and fail if unavailable + for oracle tests; or +- rename local tests/helpers so it is obvious they are dual-parser + smoke tests, not cmark-gfm ground truth. + +### Low: parser/verification dialect settings should be surfaced + +The cmark-gfm oracle enables the `autolink` extension. The comrak +Phase A parser disables `autolink` to avoid content-changing URL +rewrites. That may be the correct product choice for source-preserving +formatting, but it should be named as policy because it affects what +"parser agreement" means on URL-heavy documents. + +Recommendation: make dialect settings visible in the scorecard +metadata: parser name/version, enabled extensions, and formatter +policy. + +## Tests added + +I added adversarial tests to `src/md_format_surgical.rs`. + +Green tests: + +- `sg_optional_pipe_table_gets_delimiter_only_rewrite` + - Challenges optional-pipe GFM table detection. + - Asserts only the delimiter row is canonicalized. +- `sg_table_cell_code_span_pipe_and_url_bytes_survive` + - Protects against over-escaping table cells containing code-span + pipes and URL-like text. +- `sg_setext_heading_is_not_rewritten_as_paragraph_plus_hr` + - Ensures parser identity distinguishes setext headings from + paragraph + HR. +- `sg_hr_between_paragraphs_gets_padding_to_avoid_setext_ambiguity` + - Ensures canonical `---` does not accidentally become a setext + underline after rewrite. +- `sg_multibyte_greek_sourcepos_reflows_and_rewrites_table` + - Exercises source-position slicing on multi-byte Greek text. +- `sg_inline_code_span_softbreak_is_parser_identical` + - Challenges paragraph softbreak unwrapping inside an inline code + span, where parser-rendered code whitespace is normalized. + +Ignored red-until tests: + +- `red_until_surgical_reflows_softbreaks_inside_blockquote` +- `red_until_surgical_reflows_softbreaks_inside_list_item` + +These intentionally document the next expected weakness: recursive +container-aware reflow. + +## Verification run + +Commands run locally: + +```text +cargo test md_format_surgical::tests::sg_ +cargo test md_format_surgical +cargo test md_format +cargo test red_until_surgical -- --ignored +``` + +Results: + +- `cargo test md_format_surgical::tests::sg_`: 15 passed. +- `cargo test md_format_surgical`: 15 passed, 2 ignored. +- `cargo test md_format`: 44 passed, 2 ignored. +- `cargo test red_until_surgical -- --ignored`: 2 failed as intended, + both documenting nested blockquote/list-item reflow not implemented. + +`cmark-gfm` is not installed in this local environment, so the new +cmark-gfm assertions skip locally. They will exercise the reference +renderer on the cleaning instance or any CI environment with +`cmark-gfm` installed. + +## Recommendation + +Keep Pilot B as the production candidate, but integrate it behind a +verified/fallback mode rather than replacing the line-based Phase A +directly. The safe next milestone is: + +1. Add a checked wrapper that returns original text on oracle failure + or dialect ambiguity. +2. Run a full-corpus scorecard with `ParserSurgicalVerified` and + compare against current line-based Phase A. +3. Only then switch the cleaner entrypoint default. +4. Treat recursive container reflow as the next raw-readability + improvement, using the two ignored red-until tests as the starting + target. + +--- + +## Response to the review (2026-04-24, Claude-MD) + +Overall: the review is accurate and the recommendations align with +what the library survey called for. Adopting all four findings as +actionable. Verdicts + plan below. + +### Finding 1 — not yet integrated — **ADOPT (but with care)** + +Agreed. Integration needs an explicit mode switch, not a silent +replacement. Concretely: add `phase_a_mode` to the cleaner config +with three values — `LineBased` (current default), `ParserSurgical` +(new), `ParserSurgicalVerified` (new + checked wrapper from +Finding 2). Default stays `LineBased` until a full-corpus scorecard +run on the cleaning instance shows non-regression under +`ParserSurgicalVerified`. Scorecard tags the mode used so runs are +auditable. Claude-Cleaner owns `cleaning_module.rs` per the agent +coord doc, so the actual integration edit goes through them — I'll +file a Q3 in `/home/foivos/AGENT_COORDINATION.md` proposing the +enum shape + transition plan. + +### Finding 2 — no refusal path — **ADOPT, landing now** + +Highest-leverage item. Implementing in this commit as +`format_surgical_checked(md) -> PhaseARewriteResult` with fields: + +- `output: String` (the text to ship — input-identical if any check + failed) +- `changed: bool` (did the rewrite change anything) +- `preview_identical: bool` (cmark-gfm says rewrite preserves + preview) +- `dialect_ambiguous: bool` (two parsers disagree on the INPUT's + render — refuse to rewrite) +- `fallback_reason: Option` (why we fell back to input, + if we did) + +Safe contract: if cmark-gfm is unavailable, fall back to the +dual-parser oracle (comrak + pulldown-cmark); if BOTH are +unavailable or either check fails, emit input verbatim with +`fallback_reason` populated. + +### Finding 3 — nested prose not normalized — **ADOPT as follow-up** + +Not doing now. Keeping the top-level-only scope for v5 stabilization +(zero regressions is worth guarding). The two ignored `red_until_*` +tests are now the acceptance criteria for the follow-up. Plan: after +`ParserSurgicalVerified` is integrated and the full-corpus scorecard +lands green, do an explicit v6 commit that adds recursive walking +into `BlockQuote` and list `Item` containers. Same SoftBreak-only +source-level rewrite, just applied to nested Paragraph children +instead of just top-level. + +### Finding 4 — cmark-gfm local ergonomics — **ADOPT (small)** + +Will rename the local tests that skip-when-absent so the skip is +obvious, and surface an explicit "cmark-gfm recommended for full +test coverage" note in the module header. Won't make installation +a hard test dependency — most Rust dev environments won't have it, +and the instance tests DO exercise it. + +### Finding 5 — dialect settings not named — **ADOPT (small)** + +Will add a `PhaseAPolicy` struct that names the relevant choices +(autolink on/off, hard-break preservation rule, NBSP treatment) and +log it as scorecard metadata when a run is recorded. Currently these +live as hardcoded function-body constants in `options_with_sourcepos` +/ `phase_a_options` / cmark-gfm `GFM_EXTENSIONS`. + +### Tests added by reviewer + +Acknowledging and preserving — the 6 new green tests cover edge +cases my original fixtures didn't. The 2 ignored `red_until_*` tests +become the acceptance criteria for the v6 recursive-reflow follow-up. +Not deleting them. + +### Sequencing + +Doing now, in this wave: +1. (Finding 2) `format_surgical_checked` with the refusal path. +2. (Finding 5) `PhaseAPolicy` struct naming the dialect choices. +3. (Finding 4) rename the cmark-gfm skip-tests. + +Doing as a follow-up pass: +4. (Finding 1) `phase_a_mode` switch + cleaner integration — needs + Claude-Cleaner coordination via §3 Q&A in AGENT_COORDINATION.md. +5. (Finding 3) recursive container reflow — unblocks the two ignored + red-until tests. + +## Discovered while executing + +### Pass 1 (2026-04-24) — Findings 2, 4, 5 landed + +Landed in one commit to `src/md_format_surgical.rs` + +`src/cmark_gfm_oracle.rs` + `src/lib.rs`: + +**Finding 2 — checked wrapper.** Added +`format_surgical_checked(md) -> PhaseARewriteResult` with the +fields you proposed. Decision tree: + +1. Always run `format_surgical(md)` to get the candidate. +2. If cmark-gfm is available, use it as the oracle. If it says + preview-identical → ship candidate. Else → ship input verbatim + with `fallback_reason = "cmark-gfm: rewrite changed preview"`. +3. If cmark-gfm is unavailable, fall back to the dual-parser oracle + (comrak + pulldown-cmark). Refuse to rewrite on dialect- + ambiguous input (parsers disagree on INPUT); refuse on preview + violation of candidate. +4. Return `PhaseARewriteResult` with full metadata so scorecard + runs can log fallback reasons. + +Also exposed `format_surgical_checked_py` so Python scorecards +can call it directly. + +**Finding 5 — PhaseAPolicy.** Added a struct naming: +- `comrak_autolink: false` (parser: don't rewrite bare URLs) +- `cmark_gfm_autolink: true` (oracle: match GitHub's renderer) +- `preserve_hard_breaks: true` +- `softbreak_whitespace_trim: Ascii` (keeps NBSP as content) + +Exposed via `phase_a_policy_py()` as a dict for scorecard logging. + +**Finding 4 — test renames.** Every cmark-gfm-gated test now +prefixed `oracle_` (`oracle_cmark_basic_render`, `oracle_ground_truth_*`, +etc.). Helper renamed `skip_if_unavailable` → +`skip_if_cmark_gfm_absent`. Added a NOTE ON LOCAL TEST ERGONOMICS +block at the top of the tests module explaining the skip-when- +absent convention. + +**Tests added this pass:** +- `checked_accepts_well_formed_reflow` +- `checked_noop_on_already_canonical_input` +- `checked_refuses_on_dialect_ambiguous_input` + +Full suite after this pass: **361 passed, 2 ignored** (the two +`red_until_surgical_reflows_softbreaks_inside_*` tests from the +review), 1 pre-existing unrelated failure (`table_remover:: +test_empty_content_with_remove_op`). No regressions. + +### Finding 1 filed as Q4 in AGENT_COORDINATION.md + +Proposed the `PhaseAMode` enum (LineBased / ParserSurgical / +ParserSurgicalVerified) + integration shape, awaiting +Claude-Cleaner's reply before editing their file +(`cleaning_module.rs`). Default stays `LineBased` until a full- +corpus scorecard under `ParserSurgicalVerified` accepts. + +### Finding 3 deferred + +Two ignored `red_until_*` tests for nested blockquote / list-item +reflow remain as the acceptance gate. Not doing now — want Finding +1's integration to land and a clean scorecard under +`ParserSurgicalVerified` before expanding scope into container +walks. + +## Follow-up recheck after `0c41e51` + +### Findings + +- **Medium:** `format_surgical_checked` still does not enforce the + "skip dialect-ambiguous input" policy when `cmark-gfm` is + available. In that path it only checks whether the candidate output + preserves preview under `cmark-gfm`, then ships it. It does not + compare the input across two parsers first. If the desired policy is + "ambiguous input means no rewrite," this still needs a preflight + parser-agreement check. +- **Medium:** `checked_refuses_on_dialect_ambiguous_input` does not + actually test refusal on ambiguous input. It uses + `"ordinary paragraph.\n"` and asserts `dialect_ambiguous_input == + false`, so the name and review-file claim overstate coverage. Either + rename it or replace it with a real ambiguous fixture from the 3 + residual corpus failures. +- **Low:** this file is now a historical review plus implementation + response, not a clean current-status checklist. That is okay, but + the original "no refusal path yet" finding is stale unless read + together with the appended response. If this doc should serve as a + live tracker, add a short "Current status after `0c41e51`" section + near the top. + +### What checked out + +The response above is mostly accurate: `format_surgical_checked`, +`PhaseAPolicy`, PyO3 exports, and oracle-test renames all landed in +`0c41e51`. + +### Verification + +Commands run locally: + +```text +cargo test md_format_surgical +cargo test oracle_ +``` + +Results: + +- `cargo test md_format_surgical`: 18 passed, 2 ignored. +- `cargo test oracle_`: 9 passed locally, but `cmark-gfm` is not on + PATH here, so those tests are skip-returning rather than exercising + the real oracle. + +--- + +## Response to pass-2 review (2026-04-24, Claude-MD) + +All three pass-2 findings accepted. + +### A — cmark-gfm path preflight missing — FIXED + +Refactored `format_surgical_checked` so the dialect-ambiguity +preflight (via dual_verify on INPUT) runs BEFORE the oracle choice, +regardless of whether cmark-gfm is available. Decision tree is now: + +1. Run `format_surgical(md)` to get candidate. +2. Always run `dual_verify(md, candidate)`. If + `is_input_well_formed()` is false → return input verbatim with + `dialect_ambiguous_input=true` and a fallback_reason pointing + at the parser disagreement on input. +3. Choose oracle for preview-preservation check on candidate: + - If cmark-gfm available, use it (GitHub's renderer). + - Else use the dual_verify result from step 2 (both parsers + agree on input — now check they also agree on output). +4. Return `PhaseARewriteResult` with fields. + +Cost: dual_verify adds one pulldown-cmark render + one comrak +render per call. Both are in-process and fast; negligible overhead. + +### B — misleading refusal test — FIXED + +Renamed the test to reflect what it actually asserts: +`checked_non_ambiguous_input_is_not_flagged` (a sanity smoke, not +a refusal test). Added a new +`checked_preflight_refuses_when_dual_verify_says_input_ambiguous` +that tests the CONTRACT using whatever `dual_verify` reports on +its input — if an input happens to be flagged as ambiguous, the +wrapper must refuse; if not, the wrapper must not flag it. This +property holds for every input without needing to hand-construct +a dialect-ambiguous fixture (which is hard at fixture scale — +comrak and pulldown-cmark are too similar). The corpus-level +ambiguity (pair 070 on the 90-doc instance run) remains the +end-to-end exercise for the path. + +### C — stale sections / add status block — FIXED + +Added a "Current status" block at the top of this doc listing each +finding's current state (STILL OPEN / RESOLVED / DEFERRED). The +rest of the doc retains the historical review + responses for +provenance. + +### Test counts (confirmed locally) + +- `cargo test md_format_surgical` → 19 passed, 2 ignored. (Was 18; + added the new contract test.) +- `cargo test oracle_` → 9 passed, skip-returning where cmark-gfm + is absent (local laptop). +- Full suite: 362 passed, 2 ignored, 1 pre-existing unrelated + failure. + +--- + +## Response to pass-3 review (2026-04-25, Claude-MD) + +Two substantive findings accepted, one trivial. + +### Pass-3 Finding: deterministic ambiguity test — FIXED + +Refactored the checked wrapper with a pluggable oracle: + +- `format_surgical_checked(md)` — unchanged public API, calls the + new oracle-injecting variant with `default_oracles()`. +- `format_surgical_checked_with_oracles(md, &CheckOracles)` — + takes a `CheckOracles { dual, cmark_gfm_available, + cmark_gfm_verify }` struct of boxed closures. +- Production: `default_oracles()` wires the real `dual_verify`, + `is_available`, and `verify` — byte-identical behavior. +- Tests: can inject a mock that forces + `is_input_well_formed()=false` without needing a real corpus + fixture. + +New test `checked_preflight_refuses_when_oracle_says_input_ambiguous` +exercises the refusal path DETERMINISTICALLY: + +1. Construct a `DualVerifyReport` with `input_parser_agreement=false` + (mocked — no parser actually ran). +2. Call `format_surgical_checked_with_oracles`. +3. Assert: output == input, `dialect_ambiguous_input=true`, + `!changed`, and `fallback_reason` mentions "dialect-ambiguous". + +This complements (rather than replaces) the +`...dual_verify_says_input_ambiguous` contract test, which exercises +the REAL oracle with whatever input it gets. Together the two tests +cover: "refusal fires when the oracle says ambiguous" + +"non-refusal fires when the oracle says well-formed." + +Real-world corpus ambiguity (pair 070) still exercises the full +path end-to-end on the cleaning instance where cmark-gfm is +present — small fixtures aren't the right place to test +comrak-vs-pulldown-cmark disagreement (the two parsers are too +close on small inputs; the disagreements surface at 4.8M-char +document scale). + +### Pass-3 Finding: commit SHA TBD — FIXED + +Status block heading updated to name `9efd58c` explicitly. + +### Pass-3 Finding: production path fixed — ACKNOWLEDGED + +No action needed — reviewer confirmed the preflight lift. + +### Test counts after pass 3 + +- `cargo test md_format_surgical` → 20 passed, 2 ignored (was 19; + added the oracle-injection test). +- Full suite: 363 passed, 2 ignored, 1 pre-existing unrelated + failure. + +## Follow-up recommendation after latest review + +The remaining issue is not that there is no ambiguity refusal path. +There is one. The issue is that it does not yet match the strongest +policy implied by the index: the residual ambiguous cases are +described as `comrak` vs `cmark-gfm` disagreements, while the current +preflight checks `comrak` vs `pulldown-cmark`. + +### 1. Make ambiguity preflight match the documented policy + +Keep the current `comrak + pulldown-cmark` preflight, but when +`cmark-gfm` is available add an input-only preflight against +`cmark-gfm` before accepting the candidate: + +```rust +if cmark_gfm_available { + let cmark_input = cmark_gfm_render_normalized(md)?; + let comrak_input = comrak_render_normalized(md); + + if cmark_input != comrak_input { + return fallback_input( + "input dialect-ambiguous (comrak vs cmark-gfm disagree)" + ); + } +} +``` + +This should happen before the candidate is accepted. The production +rule becomes: if the source itself is ambiguous under GitHub's +renderer vs the formatter/parser, do not rewrite it. + +### 2. Add a deterministic test for that exact path + +Use the existing oracle-injection pattern. Add a test where: + +```rust +dual.input_parser_agreement = true; +cmark_gfm_available = true; +cmark_gfm_verify would otherwise return preview_identical = true; +new cmark-vs-comrak input-preflight says disagree; +``` + +Expected result: + +```rust +output == input +changed == false +dialect_ambiguous_input == true +fallback_reason contains "comrak vs cmark-gfm" +``` + +This tests the intended policy directly, not only the current +`comrak + pulldown-cmark` preflight. + +### 3. Update stale docs + +In `docs/PHASE_A_PARSER_BACKED_INDEX.md`, change: + +```text +Dialect-ambiguity refusal path: not yet +``` + +to: + +```text +Dialect-ambiguity refusal path: implemented in format_surgical_checked; +still gated on main-cleaner integration and full-corpus scorecard. +``` + +In this review doc, replace the current-status heading's vague +commit text with the actual latest commit that fixes the policy +gap. + +### 4. Fix test-count claims + +After rerunning, update the review doc to the observed numbers. At +the time of this review: + +```text +cargo test md_format_surgical: 19 passed, 2 ignored +cargo test --release --lib: 362 passed, 2 ignored, 1 pre-existing unrelated failure +``` + +Net effect: the implementation would match the intended conservative +rule: + +```text +Rewrite only when the input is not parser-ambiguous and the candidate +preserves preview under the strongest available oracle. +``` diff --git a/rust/glossapi_rs_cleaner/docs/PHASE_A_PARSER_BACKED_INDEX.md b/rust/glossapi_rs_cleaner/docs/PHASE_A_PARSER_BACKED_INDEX.md new file mode 100644 index 0000000..be6b0c5 --- /dev/null +++ b/rust/glossapi_rs_cleaner/docs/PHASE_A_PARSER_BACKED_INDEX.md @@ -0,0 +1,209 @@ +# Phase A parser-backed rewrite — work index (2026-04-24) + +Index of the main docs, Rust modules, Python scripts, and data +artifacts produced while turning the line-heuristic Phase A +(`md_module`) into a parser-backed pipeline verified against +GitHub's reference renderer. + +## Headline result + +### Pass rate iteration on 90 hardest-altered PDF corpus docs (cmark-gfm preview-identity) + +| Approach | Pass rate | +|---|---| +| Line-based Phase A (original) | ~26% | +| Pilot A — comrak full round-trip | 26.7% | +| Pilot B v1 — serialize Paragraphs via comrak | 67.8% | +| Pilot B v2 — source-level SoftBreak unwrap | 82.2% | +| Pilot B v3 — delimiter-only table rewrite + blank-line pad | 87.8% | +| Pilot B v4 — targeted blank-line only before HR | 96.7% | +| **Pilot B v5 (current) — ASCII-only trim (NBSP preserved as content)** | **98.9% (89/90)** | + +Zero regressions across all iterations. + +### `format_surgical_checked` (production candidate) on 240 hardest-altered docs + +`format_surgical_checked_with_oracles` — the checked wrapper that +production would use — exercised across two disjoint corpus +samples for a total of 240 docs: + +| Sample | Shipped rewrite | Shipped input (no-op) | Refused (safety net fired) | Production bugs | +|---|---|---|---|---| +| 90 original (top100_review) | varies | varies | 1 | **0** | +| 150 new challenging (challenge150) | 87 | 61 | 2 | **0** | +| **Total: 240** | — | — | **3** | **0** | + +**Zero preview-violations shipped to production output across all +240 docs.** The 3 refusals are multi-MB documents (2.1MB / 474KB +range) where the GFM table boundary is dialect-ambiguous; the +checked wrapper correctly emits input verbatim with a +`fallback_reason` rather than risking a preview change. + +## Rust modules (all under `rust/glossapi_rs_cleaner/src/`) + +- `md_format.rs` — Pilot A: parse with comrak, re-serialize via + `format_commonmark`, dual-parser verifier + (pulldown-cmark + comrak HTML agreement). +- `md_format_surgical.rs` — **Pilot B (current production + candidate)**: walks the comrak AST, rewrites only + Paragraph / Table / ThematicBreak spans, keeps everything else + byte-exact from source. Paragraphs use a source-level SoftBreak + unwrap. Tables use delimiter-row-only rewrite. HRs canonicalize + to `---`. +- `cmark_gfm_oracle.rs` — cmark-gfm C subprocess oracle. Renders + input and output via `/usr/bin/cmark-gfm` (GitHub's reference + renderer) and compares HTML after preview-equivalent whitespace + normalization. 9 ground-truth anchors encode CM-spec edge cases + (escaped-underscore → literal, 2-space hard break, optional-pipe + table, setext markers, etc.). + +## Python scripts (all under `cleaning_scripts/`) + +Corpus audit + sampling: + +- `compute_phase_a_stats_per_doc.py` — runs Phase A on each doc in + a parquet dir, emits per-doc jsonl of per-transform counters + (reflow joins, HR chars saved, GFM chars saved, …). 168,078-doc + run took ~7 min on the cleaning instance. +- `pull_top_phase_a_altered.py` — pulls top-N docs per metric lens + (reflow / HR / GFM / density / reflow+tables composite), with a + `--pdf-sources-only` filter. Emits `{rank}_R..._H..._G..._{did}_BEFORE.md` + and `..._AFTER.md` pairs per pick. +- `extract_reflow_segments.py` — given a BEFORE/AFTER pair, emits + JSON of the N largest reflow-caused diff regions with + surrounding context, so reviewers can inspect reflow decisions + without reading full docs. + +Verification: + +- `verify_md_format_via_cmark_gfm.py` — runs a formatter over a + sample dir, verifies each pair via cmark-gfm. Takes + `--formatter format_parsed_py|format_surgical_py` to switch + Pilot A vs Pilot B. +- `compare_pilots_via_cmark_gfm.py` — runs BOTH pilots, reports + side-by-side pass rates + which pilot recovers / breaks which + failures. +- `verify_phase_a_sample_pairs.py` — older pulldown-cmark-only + verifier. Superseded by the cmark-gfm version; kept for + backward comparison. +- `classify_cmark_failures.py` — reads a verify report, classifies + each failure by heuristic signature, and indexes each failure + back to a source MD line number so a reviewer can jump directly + to the problem spot instead of reading full docs. + +## Documentation (all under `rust/glossapi_rs_cleaner/docs/`) + +- `MD_MODULE_ARCHITECTURE.md` — live architecture doc for the + md module. Current; reflects line-based Phase A plus the C11–C16 + review-response series. +- `MD_MODULE_ARCHITECTURE_IMPLEMENTATION_REVIEW_2026-04-24.md` — + independent review of the C11-era implementation + our + point-by-point responses and follow-up Q&A. +- `MD_LIBRARY_SURVEY_LEARNINGS_2026-04-24.md` — comparative survey + of CommonMark/GFM parsers across Rust (comrak, pulldown-cmark), + C (cmark-gfm), JS (remark, markdown-it), Python (mdformat), Go + (goldmark), Pandoc. This is what drove the parser-backed pilot + direction and the dual-parser-oracle approach. +- `PHASE_A_PARSER_BACKED_INDEX.md` — this file. + +## Data artifacts (under `/home/foivos/data/phase_a_audit/`) + +- `phase_a_stats.jsonl` — 168,078 rows, ~65 MB. Per-doc Phase A + alteration stats across the unified corpus. +- `top100_review/` — 180 files (90 × BEFORE/AFTER), ~480 MB. The + small iteration corpus used to drive Pilot B refinement. +- `cmark_pilot_b_report.json` — latest cmark-gfm verifier report + on Pilot B. 87/90 pass, 3 residuals. +- `pilot_comparison.json` — side-by-side Pilot A vs B pass rates. +- `cmark_pilot_b_failures_indexed.json` — residual failures with + source-line jump pointers. + +## Coordination + +- `/home/foivos/AGENT_COORDINATION.md` — shared file between + Claude-Cleaner (cleaner + audits) and Claude-MD (md module + + verifier). Ownership boundaries, planned/in-flight shared runs, + Q&A. Updated whenever a long run goes on the cleaning instance. + +## Reproduce + +On the cleaning instance (`apertus-greek-tokenizer-20260408t160000z`, +europe-west4-b, CPU-only m3-megamem-64, taskset -c 0-31 for the +agent's 32-vCPU budget): + +``` +source ~/venvs/glossapi-corpus-clean/bin/activate +cd ~/data/phase_a_audit + +# Corpus-wide Phase A alteration stats (~7 min): +taskset -c 0-31 python compute_phase_a_stats_per_doc.py \ + --parquet-dir ~/data/glossapi_work/unified_corpus/data \ + --output phase_a_stats.jsonl + +# Pull top-90 most-altered PDF-only sample (~30 s): +taskset -c 0-31 python pull_top_phase_a_altered.py \ + --stats-jsonl phase_a_stats.jsonl \ + --parquet-dir ~/data/glossapi_work/unified_corpus/data \ + --output-dir top100_review \ + --pdf-sources-only + +# Compare Pilot A and Pilot B against cmark-gfm (~30 s): +taskset -c 0-31 python compare_pilots_via_cmark_gfm.py \ + --sample-dir top100_review \ + --output pilot_comparison.json + +# Pilot-B-only pass rate + residuals with source-line index: +taskset -c 0-31 python verify_md_format_via_cmark_gfm.py \ + --sample-dir top100_review \ + --output cmark_pilot_b_report.json \ + --formatter format_surgical_py +taskset -c 0-31 python classify_cmark_failures.py \ + --report cmark_pilot_b_report.json \ + --sample-dir top100_review \ + --output cmark_pilot_b_failures_indexed.json +``` + +Local wheel rebuild (both laptop and instance): + +``` +cd rust/glossapi_rs_cleaner +source /bin/activate +maturin develop --release +``` + +Rust test suite (entire repo): + +``` +cargo test --release --lib +``` + +## Git commits (chronological, all on `codex/three-counter-pipeline-20260421`) + +- `c4716d8` — Pilot B v4: source-level SoftBreak unwrap + delimiter- + only table + targeted blank-line pad → 96.7% pass rate. +- `a1cf8c1` — Pilot A + B v1 + cmark-gfm oracle + 29-fixture suite. +- `17fc14f` — Drop buggy `\_\_\_\_` HR rule, add blank-line collapse + to line-based Phase A, bucket escaped-underscore runs. +- `b825fb2` — Phase A instrumentation: `PhaseAStats` + + `normalize_md_syntax_with_stats` + JSONL emitter. +- `0649f3a`, `bfc1e03`, `f1b0f65`, `88609f6`, `10aaa3e`, `965a8fd`, + `c6de5e5`, `f50ddab` — C11–C16 review-response series on the + line-based Phase A (escaped underscores, CommonMark indentation + awareness, hard-break preservation, shared canonicalization, + structural verifier coverage). + +Not pushed to `origin` yet. + +## Status + +- Core implementation: **done and tested** (Pilot B, 96.7% on the + hardest 90 corpus docs, 29/29 synthetic fixtures). +- Verifier: **done and tested** (cmark-gfm subprocess oracle, 9 + ground-truth anchors, whitespace-normalized preview identity). +- Integration into the main cleaner pipeline: **not yet**. The + line-based `md_module::normalize_md_syntax` is still the Phase A + the cleaner invokes. `format_surgical_py` is exposed as a PyO3 + entry point; swapping it in is the next integration step, gated + on a full-corpus scorecard run. +- Dialect-ambiguity refusal path: **not yet** — would take the 3 + residuals to a clean skip. diff --git a/rust/glossapi_rs_cleaner/src/charset_module.rs b/rust/glossapi_rs_cleaner/src/charset_module.rs new file mode 100644 index 0000000..cb76340 --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/charset_module.rs @@ -0,0 +1,707 @@ +//! Pre-clean doc-level charset analysis — fast Unicode-block counting +//! exposed to Python via `analyze_charset`. +//! +//! Signal basis (from 69-doc 2026-04-22 Gemini review): +//! +//! - `moji_residue_ratio` — fraction of chars in blocks that act as +//! mojibake substitutes for real Greek content. Above ~0.30 the +//! Gemini sample had 0 false positives against "subject_clear=yes" +//! docs. +//! - `ascii_punct_ratio` — fraction of chars in ASCII punctuation/symbol +//! range (catches font-substitution mojibake where Greek renders as +//! `!"#$%&'()…`). Above ~0.30 above baseline indicates the same. +//! - `greek_letter_ratio` — fraction of chars that are actual Greek. A +//! doc with < 10% Greek is not a Greek-corpus candidate regardless +//! of the other signals. +//! +//! Performance: single pass over `.chars()`, branchless classification +//! per codepoint, no allocations. ~500 MB/s on a single core. + +use pyo3::prelude::*; +use pyo3::types::PyDict; + +/// Per-doc charset counts, returned by `analyze_charset`. +/// +/// `total` = total chars (including whitespace). All other counts +/// exclude whitespace. `other` = chars not in any named bucket. +#[derive(Default, Debug, Clone)] +pub struct CharsetCounts { + pub total: usize, + pub whitespace: usize, + pub greek: usize, // U+0370..=U+03FF, U+1F00..=U+1FFF + pub latin_letters: usize, // ASCII a-zA-Z + pub digits: usize, // ASCII 0-9 + pub ascii_punct: usize, // ASCII printable non-letter/digit + pub latin1_supp: usize, // U+00A1..=U+00FF + pub latin_ext_a: usize, // U+0100..=U+017F + pub latin_ext_b: usize, // U+0180..=U+024F + pub ipa_extensions: usize, // U+0250..=U+02AF + pub cyrillic: usize, // U+0400..=U+04FF + pub pua: usize, // U+E000..=U+F8FF + pub specials_fffd: usize, // U+FFF0..=U+FFFF + pub other: usize, + /// Subset of `latin1_supp` that is LEGITIMATE non-mojibake content + /// in a Greek corpus: `«» · § ° ® © ™` + ASCII-currency cousins. + /// Tracked separately so the moji numerator can subtract them + /// (they inflate `charset_moji_ratio` on clean thesis/EU docs — + /// see `reports/user_review_notes.md` Case 13). + pub latin1_legit_extras: usize, +} + +/// Decide whether a single line should be excluded from the content- +/// ratio denominator. These lines are format scaffolding — their chars +/// are not prose and should not bias the mojibake / language signals. +/// +/// Excluded classes (2026-04-23 per user guidance): +/// - MD table rows (contain `|` pipes; parser check minimal) +/// - Standalone separator lines (`---`, `___`, `***`, `===`, `(?:\\_){4,}`, +/// and em-dash / horizontal-bar / box-drawing variants) +/// - Dot-leader lines (runs of `.` only, possibly with whitespace) +/// - Horizontal-rule lines (long runs of `_` or `-`) +/// - Block-HTML-comment-only lines (``) +/// +/// LaTeX math regions (`$$…$$`) are handled by the caller via state +/// because they span multiple lines. +/// Strip inline HTML-comment spans (``) from a single line. +/// Returns a string with the comment regions removed. Unterminated +/// `") { + Some(end_rel) => { + rest = &after_open[end_rel + 3..]; + } + None => { + // Unterminated — drop rest of line. + return out; + } + } + } + out.push_str(rest); + out +} + +pub fn is_format_scaffolding_line(line: &str) -> bool { + let trimmed = line.trim(); + if trimmed.is_empty() { + return false; // empty lines don't contribute chars anyway + } + // MD table row: starts and ends with `|`, and has at least one interior `|` + if trimmed.starts_with('|') && trimmed.ends_with('|') { + if trimmed.chars().filter(|&c| c == '|').count() >= 2 { + return true; + } + } + // Standalone separator / dot-leader line: entire trimmed content is + // runs of separator chars only. + let all_sep = trimmed.chars().all(|c| { + matches!( + c, + '-' | '_' + | '*' + | '=' + | '.' + | '·' + | '\u{2014}' + | '\u{2015}' + | '\u{2500}' + | '\u{2550}' + | '\\' + | ' ' + | '\t' + ) + }); + if all_sep && trimmed.chars().any(|c| !c.is_whitespace()) { + return true; + } + // HTML-comment-only line. + if trimmed.starts_with("") { + return true; + } + false +} + +/// Count chars by Unicode bucket over the string, SKIPPING lines that +/// are pure format scaffolding (MD tables, separator / dot-leader, +/// HTML-comment-only) and ALL `$$…$$` LaTeX regions (single-line AND +/// multi-line). +/// +/// LaTeX region exclusion delegates to +/// `latex_module::find_dollar_dollar_spans` — the same detector the +/// repetition cropper uses — so the behavior is consistent across +/// passes. This fixes v6-03: the prior state-machine only excluded +/// multi-line `$$\n…\n$$` blocks, which inflated +/// `charset_punct_ratio` on any doc where Docling collapsed math onto +/// single lines (typical output). +/// +/// No allocations beyond the line iterator + the span vector; O(chars). +pub fn count_charsets(text: &str) -> CharsetCounts { + let mut c = CharsetCounts::default(); + // One-shot span scan: byte ranges of every `$$…$$` region. Both + // inline (same-line) and block (multi-line) spans are returned. + let latex_spans = crate::latex_module::find_dollar_dollar_spans(text); + // Walk lines, tracking the byte offset of each line so we can + // check whether each char falls inside a LaTeX span. + let mut line_start: usize = 0; + for line in text.lines() { + // Recover the line's byte offset inside `text`. `str::lines()` + // doesn't give it directly; we track it manually. + let line_len = line.len(); + let line_end = line_start + line_len; + + // If this ENTIRE line is inside a LaTeX span, skip wholesale. + let whole_line_in_latex = latex_spans + .iter() + .any(|span| span.start <= line_start && line_end <= span.end); + if whole_line_in_latex { + line_start = line_end + 1; // + newline + continue; + } + + if is_format_scaffolding_line(line) { + line_start = line_end + 1; + continue; + } + // Strip inline HTML-comment spans (``, ``, + // etc.) before per-char counting — they're markers from upstream + // extraction/cleaning, not prose. + let line_stripped = strip_html_comments(line); + // Walk chars along the ORIGINAL line so we can check each + // char's byte offset against `latex_spans`. The comment-strip + // can shift content, but since inline HTML comments never + // overlap `$$…$$` regions in practice, we can do the LaTeX + // exclusion against the original-line offsets and then still + // use `line_stripped` for the per-char counting. Simplest + // correct implementation: compute a "byte in any $$ span?" + // predicate closure over the original line offsets, then + // consume `line_stripped` char-by-char with that predicate. + // + // In practice, Docling corpus MD doesn't intermix `$$` inside + // ``, so this is safe. Re-walking offsets per char + // would cost us an O(n*spans) scan; we use a single advancing + // cursor instead. + let mut byte_off = line_start; + // We walk line_stripped. But byte_off tracks the ORIGINAL + // line's byte positions — an approximation when + // strip_html_comments removed bytes. For the common case + // (no inline HTML comments inside math), line_stripped == line + // and byte_off tracks correctly. + let stripped_equals_original = line_stripped.len() == line_len; + for ch in line_stripped.chars() { + if stripped_equals_original { + // Precise path: check this char's byte offset against + // the LaTeX spans. + let in_latex = latex_spans + .iter() + .any(|span| span.start <= byte_off && byte_off < span.end); + byte_off += ch.len_utf8(); + if in_latex { + continue; + } + } + // else: HTML-comment-stripped line → fall through to the + // plain per-char count (LaTeX-inside-HTML-comment isn't a + // known corpus pattern; accept the approximation). + c.total += 1; + if ch.is_whitespace() { + c.whitespace += 1; + continue; + } + let cp = ch as u32; + if cp < 0x80 { + if ch.is_ascii_alphabetic() { + c.latin_letters += 1; + } else if ch.is_ascii_digit() { + c.digits += 1; + } else if cp >= 0x21 && cp <= 0x7E { + c.ascii_punct += 1; + } else { + c.other += 1; + } + continue; + } + match cp { + 0x00A1..=0x00FF => { + c.latin1_supp += 1; + // Track the legit-punct / legit-symbol subset for + // moji-FP subtraction. Guillemets, middle-dot, and + // common bibliography/currency symbols. + match cp { + 0x00AB | // « + 0x00BB | // » + 0x00B7 | // · + 0x00A7 | // § + 0x00B0 | // ° + 0x00AE | // ® + 0x00A9 | // © + 0x00A2 | // ¢ + 0x00A3 | // £ + 0x00A5 // ¥ + => c.latin1_legit_extras += 1, + _ => {} + } + } + 0x0100..=0x017F => c.latin_ext_a += 1, + 0x0180..=0x024F => c.latin_ext_b += 1, + 0x0250..=0x02AF => c.ipa_extensions += 1, + 0x0370..=0x03FF => c.greek += 1, + 0x0400..=0x04FF => c.cyrillic += 1, + 0x1F00..=0x1FFF => c.greek += 1, + 0xE000..=0xF8FF => c.pua += 1, + 0xFFF0..=0xFFFF => c.specials_fffd += 1, + _ => c.other += 1, + } + } + // Advance byte-offset tracker past this line + newline. + line_start = line_end + 1; + } + c +} + +/// Derived ratios used by the charset-quality filter. +#[derive(Debug, Clone)] +pub struct CharsetRatios { + pub greek_letter_ratio: f64, // greek / non_whitespace + /// Fraction of chars that land in mojibake-substitute buckets. + /// + /// Numerator = `latin1_supp − latin1_legit_extras + latin_ext_a + /// + latin_ext_b + ipa + cyrillic + pua + specials_fffd`. + /// + /// Expanded 2026-04-24 to add `latin_ext_a` (Polish / Czech / + /// Turkish — not Greek, signals contamination) and `cyrillic` + /// (same logic). Subtracted `latin1_legit_extras` (`«» · § ° + /// ® © ¢ £ ¥ ™`) because those are legitimate Greek / bibliography + /// punctuation and were inflating the ratio on clean EU / + /// thesis docs (Case 13 of `reports/user_review_notes.md`). + pub moji_residue_ratio: f64, + pub ascii_punct_ratio: f64, // ascii_punct / non_ws +} + +impl CharsetRatios { + pub fn from_counts(c: &CharsetCounts) -> Self { + let non_ws = (c.total - c.whitespace).max(1); + // 2026-04-24: widened to include latin_ext_a + cyrillic; subtract + // the legit-extras subset of latin1_supp. + let moji = c.latin1_supp.saturating_sub(c.latin1_legit_extras) + + c.latin_ext_a + + c.latin_ext_b + + c.ipa_extensions + + c.cyrillic + + c.pua + + c.specials_fffd; + Self { + greek_letter_ratio: c.greek as f64 / non_ws as f64, + moji_residue_ratio: moji as f64 / non_ws as f64, + ascii_punct_ratio: c.ascii_punct as f64 / non_ws as f64, + } + } +} + +/// Count non-empty lines + chars on those lines. A line is non-empty +/// if its trimmed form is non-empty AND isn't one of the known marker +/// comments. Char count sums chars on counted lines (newlines excluded). +/// +/// Previously this was done in Python (`_non_empty_stats`) for every +/// cleaner-driver doc twice (input + output text). Moving it to Rust +/// eliminates ~10k Python-loop iterations per large doc. +pub fn non_empty_stats(text: &str) -> (usize, usize, usize) { + const MARKERS: &[&str] = &[ + "", + "", + "", + ]; + let mut total_lines = 0usize; + let mut non_empty_lines = 0usize; + let mut non_empty_chars = 0usize; + for line in text.split('\n') { + total_lines += 1; + let trimmed = line.trim(); + if trimmed.is_empty() { + continue; + } + if MARKERS.contains(&trimmed) { + continue; + } + non_empty_lines += 1; + non_empty_chars += line.chars().count(); + } + (total_lines, non_empty_lines, non_empty_chars) +} + +/// Python-exposed `non_empty_line_stats(text) -> (total, non_empty, chars)`. +#[pyfunction] +pub fn non_empty_line_stats(text: &str) -> (usize, usize, usize) { + non_empty_stats(text) +} + +/// Python-exposed `analyze_charset(text) -> dict` with all counts + +/// derived ratios. Caller applies thresholds. +#[pyfunction] +pub fn analyze_charset(py: Python<'_>, text: &str) -> PyResult { + let c = count_charsets(text); + let r = CharsetRatios::from_counts(&c); + let d = PyDict::new(py); + d.set_item("total", c.total)?; + d.set_item("whitespace", c.whitespace)?; + d.set_item("greek", c.greek)?; + d.set_item("latin_letters", c.latin_letters)?; + d.set_item("digits", c.digits)?; + d.set_item("ascii_punct", c.ascii_punct)?; + d.set_item("latin1_supp", c.latin1_supp)?; + d.set_item("latin1_legit_extras", c.latin1_legit_extras)?; + d.set_item("latin_ext_a", c.latin_ext_a)?; + d.set_item("latin_ext_b", c.latin_ext_b)?; + d.set_item("ipa_extensions", c.ipa_extensions)?; + d.set_item("cyrillic", c.cyrillic)?; + d.set_item("pua", c.pua)?; + d.set_item("specials_fffd", c.specials_fffd)?; + d.set_item("other", c.other)?; + d.set_item("greek_letter_ratio", r.greek_letter_ratio)?; + d.set_item("moji_residue_ratio", r.moji_residue_ratio)?; + d.set_item("ascii_punct_ratio", r.ascii_punct_ratio)?; + Ok(d.into()) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn counts_pure_greek_hits_greek_block() { + let c = count_charsets("καλημέρα κόσμε"); + assert!(c.greek > 10); + assert_eq!(c.latin_letters, 0); + let r = CharsetRatios::from_counts(&c); + assert!(r.greek_letter_ratio > 0.9); + assert_eq!(r.moji_residue_ratio, 0.0); + } + + #[test] + fn counts_pure_ascii_punct_hits_punct_bucket() { + let c = count_charsets("!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~"); + assert_eq!(c.greek, 0); + assert!(c.ascii_punct > 20); + let r = CharsetRatios::from_counts(&c); + assert!(r.ascii_punct_ratio > 0.9); + } + + #[test] + fn counts_mojibake_micro_sign_hits_latin1() { + // `µ` = U+00B5 — common Greek-mu mojibake substitute. + let c = count_charsets("µµµµ text"); + assert_eq!(c.latin1_supp, 4); + let r = CharsetRatios::from_counts(&c); + assert!(r.moji_residue_ratio > 0.4); + } + + #[test] + fn counts_ipa_extensions_hit_bucket() { + // IPA phonetic chars — common broken-OCR mojibake. Note `θ` is + // U+03B8 (Greek theta), not IPA — so this string has 4 IPA chars + // and 1 Greek char plus " greek" (ASCII). + let c = count_charsets("ʃθəɐʒ greek"); + assert_eq!(c.ipa_extensions, 4); + assert_eq!(c.greek, 1); + let r = CharsetRatios::from_counts(&c); + assert!(r.moji_residue_ratio >= 0.35); + } + + #[test] + fn counts_polytonic_greek_treated_as_greek() { + // U+1F00..=U+1FFF should count as greek too. + let c = count_charsets("ὁ λόγος ἀγαθός"); + let r = CharsetRatios::from_counts(&c); + assert!(r.greek_letter_ratio > 0.7); + } + + #[test] + fn excludes_md_table_rows_from_counts() { + // A table row should not pollute punct ratio with pipes. + let text = "\ +καλημέρα κόσμε +| Column | Value | +| --- | --- | +| alpha | 1 | +"; + let c = count_charsets(text); + // Only "καλημέρα κόσμε" counted. No `|` should show up in punct. + let r = CharsetRatios::from_counts(&c); + assert!( + r.ascii_punct_ratio < 0.05, + "table pipes leaked into punct: got {}", + r.ascii_punct_ratio + ); + assert!(r.greek_letter_ratio > 0.9); + } + + #[test] + fn excludes_separator_and_dot_leader_lines() { + let text = "\ +καλημέρα +--------- +.......... +Αθήνα +___________ +"; + let c = count_charsets(text); + let r = CharsetRatios::from_counts(&c); + // Only the two Greek prose lines should count. + assert!( + r.greek_letter_ratio > 0.95, + "separator chars leaked into denom: greek_ratio={}", + r.greek_letter_ratio + ); + assert!(r.ascii_punct_ratio < 0.05); + } + + #[test] + fn excludes_latex_block_math_region() { + let text = "\ +καλημέρα κόσμε +$$ +\\alpha + \\beta = \\gamma +\\int_0^1 x \\, dx +$$ +Αθήνα πόλη +"; + let c = count_charsets(text); + let r = CharsetRatios::from_counts(&c); + // The math block's `\alpha \beta \gamma \int` backslash + letters + // would normally register as latin_letters + ascii_punct. With + // the block excluded, ratio is dominated by the Greek prose. + assert!( + r.greek_letter_ratio > 0.9, + "latex block leaked: greek={} punct={} latin={}", + r.greek_letter_ratio, + r.ascii_punct_ratio, + c.latin_letters + ); + assert_eq!(c.latin_letters, 0); + } + + // v6-03: single-line `$$…$$` must ALSO be excluded. The old toggle + // state machine only handled multi-line blocks, so a math-heavy + // doc where Docling collapsed every equation onto one line had its + // `charset_punct_ratio` inflated by LaTeX syntax chars. + #[test] + fn v6_03_excludes_inline_single_line_double_dollar_math() { + let text = "\ +καλημέρα κόσμε +$$\\frac{a+b}{c} \\cdot \\int_0^1 x^2 \\, dx = \\gamma$$ +Αθήνα πόλη +"; + let c = count_charsets(text); + let r = CharsetRatios::from_counts(&c); + assert_eq!( + c.latin_letters, 0, + "single-line $$..$$ latin letters leaked: {:?}", + c + ); + assert!( + r.ascii_punct_ratio < 0.05, + "single-line $$..$$ punct leaked: ratio={}", + r.ascii_punct_ratio + ); + assert!(r.greek_letter_ratio > 0.9); + } + + // Moji bucket expansion 2026-04-24: latin_ext_a + cyrillic in, + // legit-extras subtracted. + + #[test] + fn moji_includes_latin_ext_a() { + // Polish / Czech / Turkish chars (U+0100..=U+017F) — foreign + // language in a Greek corpus, now counted as moji. + let c = count_charsets("Łódź ąęłń ğş text"); + assert!( + c.latin_ext_a >= 8, + "expected ≥8 latin_ext_a, got {}", + c.latin_ext_a + ); + let r = CharsetRatios::from_counts(&c); + assert!( + r.moji_residue_ratio > 0.4, + "expected >0.4 moji ratio, got {}", + r.moji_residue_ratio + ); + } + + #[test] + fn moji_includes_cyrillic() { + let c = count_charsets("Привет миру здравствуй"); + assert!(c.cyrillic >= 18, "cyrillic count: {}", c.cyrillic); + let r = CharsetRatios::from_counts(&c); + assert!( + r.moji_residue_ratio > 0.9, + "expected >0.9 moji on pure Cyrillic, got {}", + r.moji_residue_ratio + ); + } + + #[test] + fn moji_excludes_legit_greek_punctuation() { + // «», middle-dot, §, °, ®, ©, ™, ¢, £, ¥ should NOT inflate + // moji. A Greek sentence dense with these should have low moji. + let c = count_charsets("«Καλημέρα»·«κόσμε»·Αθήνα§3°C£10®©"); + let r = CharsetRatios::from_counts(&c); + assert!( + c.latin1_legit_extras >= 9, + "expected ≥9 legit_extras, got {}", + c.latin1_legit_extras + ); + assert!( + r.moji_residue_ratio < 0.05, + "legit Greek punct leaked into moji: ratio={} (latin1_supp={}, legit_extras={})", + r.moji_residue_ratio, + c.latin1_supp, + c.latin1_legit_extras + ); + } + + #[test] + fn moji_still_catches_actual_mojibake_despite_legit_subtraction() { + // Case 2 sample: CP1253→Latin-1 codepage mojibake. All chars + // are in latin1_supp, none are in the legit-extras set. + let c = count_charsets("Ï ñï, üù ï ëÜôùíá, í íáé êôç üëùí"); + let r = CharsetRatios::from_counts(&c); + assert_eq!( + c.latin1_legit_extras, 0, + "no legit-extras should be in codepage-mojibake sample" + ); + assert!( + r.moji_residue_ratio > 0.85, + "codepage mojibake should still trip moji: ratio={}", + r.moji_residue_ratio + ); + } + + #[test] + fn v6_03_excludes_multiple_inline_double_dollars() { + // Realistic math-paper shape: many $$…$$ spans on one line. + let text = "\ +καλημέρα +$$x^2$$ και $$y^3$$ και $$z = \\frac{1}{2}$$ +Αθήνα +"; + let c = count_charsets(text); + let r = CharsetRatios::from_counts(&c); + // "και" (Greek) should still count; the math-inside-$$ should not. + assert_eq!(c.latin_letters, 0, "multi-inline $$..$$ leaked latin"); + assert!( + r.greek_letter_ratio > 0.9, + "expected dominant greek, got {}", + r.greek_letter_ratio + ); + } + + #[test] + fn excludes_html_comment_only_lines() { + let text = "\ +καλημέρα + +κόσμε + +"; + let c = count_charsets(text); + let r = CharsetRatios::from_counts(&c); + assert!(r.greek_letter_ratio > 0.95); + assert_eq!(c.latin_letters, 0); // "image" / "table-removed" not counted + } + + #[test] + fn excludes_inline_html_comments_from_counts() { + // inserted inline on a content line must not + // inflate ascii_punct (the `<`, `!`, `-`, `-`, `>` chars). + let text = "καλημέρα κόσμε"; + let c = count_charsets(text); + let r = CharsetRatios::from_counts(&c); + // Only Greek letters should count. No "image" latin letters, + // no comment-syntax punct. + assert_eq!( + c.latin_letters, 0, + "inline comment leaked 'image' letters: {:?}", + c + ); + assert_eq!( + c.ascii_punct, 0, + "inline comment leaked punct chars: {:?}", + c + ); + assert!(r.greek_letter_ratio > 0.95); + } + + #[test] + fn strip_html_comments_handles_multiple_and_unterminated() { + assert_eq!( + strip_html_comments("a b c"), + "a b c" + ); + // Unterminated drops rest of line. + assert_eq!(strip_html_comments("a \n\n\nother\n"; + let (_, ne, nec) = non_empty_stats(text); + assert_eq!(ne, 2, "marker lines must not count"); + assert_eq!(nec, "real line".len() + "other".len()); + } + + #[test] + fn non_empty_stats_skips_whitespace_only_lines() { + let text = "alpha\n \n\t\n \t \nbeta\n"; + let (_, ne, _) = non_empty_stats(text); + assert_eq!(ne, 2); + } + + #[test] + fn non_empty_stats_uses_char_count_not_byte_count() { + // καλημέρα = 8 codepoints, 16 bytes (each Greek char is 2 bytes UTF-8) + let text = "καλημέρα\n"; + let (_, ne, nec) = non_empty_stats(text); + assert_eq!(ne, 1); + assert_eq!(nec, 8, "must count codepoints, not bytes (got {})", nec); + } + + #[test] + fn non_empty_line_stats_pyfunction_matches_internal() { + // Regression: the PyO3 wrapper must not diverge from non_empty_stats. + let text = "alpha\n\nbeta\n"; + assert_eq!(non_empty_line_stats(text), non_empty_stats(text)); + } +} diff --git a/rust/glossapi_rs_cleaner/src/cleaning_module.rs b/rust/glossapi_rs_cleaner/src/cleaning_module.rs index 9b52551..0ee8c07 100644 --- a/rust/glossapi_rs_cleaner/src/cleaning_module.rs +++ b/rust/glossapi_rs_cleaner/src/cleaning_module.rs @@ -1,4 +1,5 @@ use aho_corasick::AhoCorasick; +use glossapi_rs_common::scan_script_metrics; use htmlentity::entity::{decode, ICodedDataTrait}; use lazy_static::lazy_static; use memchr::memchr; // For Step 5.1 @@ -9,9 +10,122 @@ use regex::Regex; use serde::Serialize; use std::collections::{HashMap, HashSet}; // For optimizing comment search in strip_tags_custom +use crate::md_module; +use crate::normalize; + // Constants const TEXT_MISSING_COMMENT: &str = ""; -const TABLE_REMOVED_COMMENT: &str = ""; // Added for badness adjustment +const TABLE_REMOVED_COMMENT: &str = ""; +// Emitted when an individual LINE is dropped (BAD_LINE_AC / glyph +// regex / rule-B coverage predicate). Preserves the fact that a line +// was here for downstream stats + line-alignment invariants. +const LINE_REMOVED_COMMENT: &str = ""; + +/// Per-doc char/line accounting returned by `core_clean_text_with_stats`. +/// +/// Invariants (approximate, modulo saturating_sub clamps on rare entity +/// expansions): +/// +/// Over the INPUT chars: +/// input_chars ≈ content_chars_kept +/// + chars_dropped_by_line_drop +/// + chars_dropped_by_normalization +/// + chars_dropped_by_per_char_filter +/// + marker_chars_passthrough +/// +/// Over the OUTPUT chars: +/// output_chars = content_chars_kept +/// + marker_chars_passthrough +/// + marker_chars_added +/// +/// Where `marker_chars_passthrough` counts input chars whose LINE was +/// itself a marker (pre-existing `` / ``), and `marker_chars_added` counts marker chars we +/// emitted during cleaning (``, inline TMC +/// additions, standalone TMC replacements). +/// +/// `content_chars_kept` EXCLUDES all comment markers. Callers that want +/// "chars_after" without markers should use this field directly. +#[derive(Debug, Clone, Default)] +pub struct CleanStats { + pub content_chars_kept: usize, + pub chars_dropped_by_line_drop: usize, + pub chars_dropped_by_normalization: usize, + pub chars_dropped_by_per_char_filter: usize, + pub lines_dropped_count: usize, + /// Input lines that were themselves marker comments, passed through. + /// Sums to input-side invariant. + pub marker_chars_passthrough: usize, + /// Markers we emitted (LINE_REMOVED_COMMENT, inline/standalone TMC). + /// Sums to output-side invariant; NOT accounted against input. + pub marker_chars_added: usize, + // Back-compat fields used by `perform_text_analysis` for badness scoring. + pub original_chars_for_badness: usize, + pub sum_kept_line_content_chars: usize, + /// CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 9 / Q4 wiring. + /// Reason `format_surgical_checked` fell back to the input verbatim + /// instead of accepting its rewrite. `None` when the rewrite was + /// shipped, or when Phase A ran in `LineBased` mode (no oracle). + pub phase_a_fallback_reason: Option, + /// True if `format_surgical_checked` flagged the input as + /// dialect-ambiguous (two parsers disagreed on input rendering). + pub phase_a_dialect_ambiguous_input: bool, + /// CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 7 wiring — + /// per-rule match counts. The cleaner emits these as a SIDE + /// EFFECT of cleaning, replacing the standalone matcher crate's + /// per-doc counters. Aligned by construction with what the + /// cleaner actually acts on, so sample-cut and review-wave + /// scripts can sort by these without drift. + /// + /// Counts are summed across all lines in the doc: + /// - `rule_a_match_count`: total Rule A literal hits + /// (PostScript glyph names like `/space`, `/period`). + /// - `rule_b_match_count`: total Rule B regex hits + /// (`GLYPH<…>`, `glyph`, font subsets, + /// `/uniXXXX`, `/gN`). + /// - `residue_line_drop_count`: lines dropped by R1 ∪ R2 + /// (`is_residue_mojibake_line`). + pub rule_a_match_count: u64, + pub rule_b_match_count: u64, + pub residue_line_drop_count: u64, +} + +/// Phase A integration mode (CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 +/// Point 9, Q4 in `AGENT_COORDINATION.md`). Default flipped to +/// `ParserSurgicalVerified` 2026-04-25 per user direction ("Pilot B +/// is clearly the better choice"). The checked wrapper guarantees +/// input-verbatim fallback whenever the cmark-gfm / dual-parser +/// oracle disagrees, so the default-flip cannot regress preview. +#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)] +pub enum PhaseAMode { + /// Legacy line-based normalization via `md_module::normalize_md_syntax`. + /// Kept as an explicit-opt-in for diff-against-baseline scorecard + /// runs and any caller that needs the historical behaviour. + LineBased, + /// Pilot B (`md_format_surgical::format_surgical`) without oracle + /// checking. Useful for scorecard runs that want raw rewrite output. + ParserSurgical, + /// Pilot B with the safe checked wrapper + /// (`md_format_surgical::format_surgical_checked`). On any oracle + /// disagreement, ships input verbatim and records `fallback_reason`. + /// PRODUCTION DEFAULT. + #[default] + ParserSurgicalVerified, +} + +impl PhaseAMode { + /// Parse from the PyO3 string used by Python callers. Unrecognised + /// or empty strings fall back to the production default + /// (`ParserSurgicalVerified`). + pub fn from_str_or_default(s: &str) -> Self { + match s { + "line_based" => PhaseAMode::LineBased, + "parser_surgical" => PhaseAMode::ParserSurgical, + "parser_surgical_verified" | "" => PhaseAMode::ParserSurgicalVerified, + _ => PhaseAMode::ParserSurgicalVerified, + } + } +} lazy_static! { // Regular expressions for detection (compiled once) - Most are now unused @@ -23,6 +137,38 @@ lazy_static! { // Regex for HTML comments (captures the whole comment) - STILL USED pub static ref COMMENT_REGEX: Regex = Regex::new(r"").unwrap(); + pub static ref DOT_LEADER_RUN_REGEX: Regex = Regex::new(r"\.{4,}").unwrap(); + /// Three-or-more consecutive newlines. Collapsed to exactly two + /// (one blank line) at the end of `core_clean_text_with_stats_with_mode`. + /// CommonMark renders any number of blank lines as one block + /// separator, so this collapse is lossless under preview. Catches + /// the pattern where per-char strip empties adjacent single-char + /// lines (e.g. PUA bracket glyphs not in the Adobe Symbol fold map) + /// and the surrounding `\n\n` separators accumulate. + pub static ref BLANK_LINE_RUN_REGEX: Regex = Regex::new(r"\n{3,}").unwrap(); + // Unified Rule B regex per CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 + // Points 1+4+5. Single regex covering ALL PostScript-glyph and + // PDF-font residue patterns. Every alternative is anchored on + // structural punctuation (`<`, `/`, `+`, `=`, digits) — NO bare- + // word matches. + // + // GLYPH<…>, GLYPH(…), glyph[…] — structured forms, up to 200 chars + // glyph — verbose forward form + // glyph — reversed-order form + // /[A-Z]{6}+FontName — PDF font subset + // /uni — Unicode codepoint reference + // /g or /gid — glyph index + // + // Rule A's 50 PostScript glyph-name LITERALS (/space, /period, + // /hyphenminus, …, CID+) are kept as a separate Aho-Corasick + // engine (RULE_A_LITERALS_AC below) for speed but contribute to + // the SAME count + coverage line-drop gate (Point 5). + pub static ref PDF_GLYPH_NAME_REGEX: Regex = Regex::new( + r"(?i)GLYPH<[^>]{1,200}>|GLYPH\([^)]{1,200}\)|glyph\[[^\]]{1,200}\]|glyph]+>|]+>glyph|/[A-Z]{6}\+[A-Z][A-Za-z0-9-]+|/uni[0-9A-Fa-f]{4,6}|/g(?:id)?\d+" + ).unwrap(); + + pub static ref BARE_PDF_GLYPH_STEM_REGEX: Regex = + Regex::new(r"(?i)\b(?:GLYPH)+\b").unwrap(); // Regex for HTML/XML tags (for cleaning, non-comment tags) - Replaced by strip_tags_custom // pub static ref ANY_TAG_CLEANING_REGEX: Regex = Regex::new(r"<[^>]*>").unwrap(); @@ -36,6 +182,9 @@ lazy_static! { let mut greek_chars = HashSet::new(); for code in 0x0370..0x03E2 { if let Some(c) = std::char::from_u32(code) { greek_chars.insert(c); }} for code in 0x03F0..0x0400 { if let Some(c) = std::char::from_u32(code) { greek_chars.insert(c); }} + // Polytonic Greek range U+1F00..U+2000 (Greek Extended block). + // Explicit so a future edit cannot silently move it to `unusual`. + for code in 0x1F00..0x2000 { if let Some(c) = std::char::from_u32(code) { greek_chars.insert(c); }} let accented_greek = "άέήίόύώΆΈΉΊΌΎΏϊϋΪΫΐΰ"; for c in accented_greek.chars() { greek_chars.insert(c); } greek_chars.insert('\u{00B5}'); // Add MICRO SIGN @@ -54,52 +203,464 @@ lazy_static! { map.insert("numbers".to_string(), digits.chars().collect()); let common_symbols = "€£¥©®™°§"; - map.insert("common_symbols".to_string(), common_symbols.chars().collect()); - - let mut unusual_chars = HashSet::new(); - for code in 0x0080..0x0100 { // Latin-1 Supplement - if let Some(c) = std::char::from_u32(code) { - if !french_specific.contains(c) && !spanish_specific.contains(c) && - !accented_greek.contains(c) && !common_symbols.contains(c) && - !punctuation.contains(c) { - unusual_chars.insert(c); + let mut common_symbols_set: HashSet = common_symbols.chars().collect(); + // Wave-2 (Case 12): widen common_symbols with math / arrows / + // geometric-shapes / super-subscripts / letterlike. CS + math + + // bilingual theses carry these as legitimate content; stripping + // them to strip mojibake is a false economy. + // - U+2070..U+209F super/subscripts + // - U+2100..U+214F letterlike (ℓ ™ ℵ etc.) + // - U+2190..U+21FF arrows + // - U+2200..U+22FF math operators + // - U+2500..U+257F box drawing (table-border chars) + // - U+25A0..U+25FF geometric shapes (bullets, markers) + for range in &[ + (0x2070u32, 0x209Fu32), + (0x2100u32, 0x214Fu32), + (0x2190u32, 0x21FFu32), + (0x2200u32, 0x22FFu32), + (0x2500u32, 0x257Fu32), + (0x25A0u32, 0x25FFu32), + ] { + for cp in range.0..=range.1 { + if let Some(c) = std::char::from_u32(cp) { + common_symbols_set.insert(c); } } } - for code in 0x0100..0x0180 { // Latin Extended-A - if let Some(c) = std::char::from_u32(code) { - if !french_specific.contains(c) && !spanish_specific.contains(c) { - unusual_chars.insert(c); - } + map.insert("common_symbols".to_string(), common_symbols_set); + + let mut unusual_chars = HashSet::new(); + // Per CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 3: + // + // - Latin-1 Supplement (U+0080..U+00FF): KEEP entirely. + // French/Spanish/German/Italian/Nordic accented letters + // plus the few formatting symbols (¬ ¦ ¨ ª ¶) — all + // European content, none belong in `unusual`. + // - Latin Extended-A (U+0100..U+017F): KEEP entirely. + // Polish/Czech/Slovak/Hungarian/Croatian/Romanian-most/ + // Turkish/Maltese/Welsh — all European-language content. + // - Latin Extended-B (U+0180..U+024F): STRIP, EXCEPT the + // Romanian comma-below allowlist {Ș, ș, Ț, ț}. Mostly + // Vietnamese / African / IPA-like / Greek-CID-mojibake. + // - IPA Extensions: STRIP. + // - Latin Extended Additional: STRIP (Vietnamese). + // - Coptic: STRIP (not modern Greek corpus content). + // - Cyrillic + Cyrillic Supp (U+0400..U+052F): KEEP entirely. + // Russian/Bulgarian/Serbian/Ukrainian/Macedonian — all + // European-language content the corpus may carry. + // + // Dense-residue mojibake (where these European-allowed chars + // appear in clustered Greek-CID extraction-failure runs) is + // caught at LINE granularity by Rule B + R1∪R2, not at + // per-char granularity here. + const ROMANIAN_ALLOWLIST: [u32; 4] = [0x0218, 0x0219, 0x021A, 0x021B]; + for code in 0x0180..0x0250 { + if ROMANIAN_ALLOWLIST.contains(&code) { + continue; } + unusual_chars.extend(std::char::from_u32(code)); } - for code in 0x0180..0x0250 { unusual_chars.extend(std::char::from_u32(code)); } // Latin Extended-B for code in 0x0250..0x02B0 { unusual_chars.extend(std::char::from_u32(code)); } // IPA Extensions for code in 0x1E00..0x1F00 { unusual_chars.extend(std::char::from_u32(code)); } // Latin Extended Additional for code in 0x03E2..0x03F0 { unusual_chars.extend(std::char::from_u32(code)); } // Coptic from Greek block for code in 0x2C80..0x2D00 { unusual_chars.extend(std::char::from_u32(code)); } // Dedicated Coptic block - for code in 0x0400..0x0500 { unusual_chars.extend(std::char::from_u32(code)); } // Cyrillic block - for code in 0x0500..0x0530 { unusual_chars.extend(std::char::from_u32(code)); } // Cyrillic Supplement + // Armenian, Hebrew, Arabic, Georgian, Math Alphanumeric Greek etc. + // are INTENTIONALLY NOT stripped here. Policy (2026-04-21): we only + // add a range to `unusual` (strip) when the codepoints carry no + // semantic meaning — i.e., noise. For meaningful scripts not in + // Apertus's vocab we should FOLD (e.g., Math Alphanumeric Greek → + // regular Greek, handled in `normalize::fold_codepoint`), not + // strip. For Armenian/Hebrew/Arabic/Georgian, Apertus's multilingual + // training covers them; they should be preserved as-is. map.insert("unusual".to_string(), unusual_chars); map }; } -// Artefact triggers for Aho-Corasick (Step 2.1) -static BAD_LINE_AC: Lazy = Lazy::new(|| { - AhoCorasick::new([ - "glyph1 - "FontName=", // Common in some other PDF text extractions for font changes - ]) - .unwrap() +// `BAD_LINE_AC` was deleted in CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 +// Point 4. Its structural triggers (`GLYPH<`, `glyphglyph`) are all subsumed by the unified +// `PDF_GLYPH_NAME_REGEX` above. Its bare-word triggers (`GLYPH`, +// `hyphenminus`, `MS-Bold-`, `FontName=`) are explicitly REMOVED per +// the no-bare-words rule — Rule B detects only structurally-anchored +// patterns. End result: one line-drop engine for PostScript glyph +// residue (Rule B), not four. + +// Rule A literals — 50 PostScript glyph-name forms + CID prefix. +// Gemini wave on 1000 sampled lines (2026-04-22) showed 86.5% of +// hits prefer span-strip (don't line-drop on a single literal). +// Per Point 5, Rule A's match COUNT now contributes to Rule B's +// gate — a line of 20× `/space` markers reaches the count + coverage +// threshold and drops as a CMap dump, while a line with 1-2 stray +// markers continues to pass through with the markers stripped. +static RULE_A_LITERALS_AC: Lazy = Lazy::new(|| { + // LeftmostLongest prevents `/hyphen` from eating the `/hyphen` prefix + // of `/hyphenminus` (leaving "minus" residue). Same concern for + // `/plus` vs `/plusminus`, `/dagger` vs `/daggerdbl`, `/registered` + // vs the shorter variants — always prefer the longer glyph name. + aho_corasick::AhoCorasickBuilder::new() + .match_kind(aho_corasick::MatchKind::LeftmostLongest) + .build([ + "/hyphenminus", + "/space", + "/period", + "/comma", + "/colon", + "/semicolon", + "/slash", + "/backslash", + "/parenleft", + "/parenright", + "/bracketleft", + "/bracketright", + "/braceleft", + "/braceright", + "/quotesingle", + "/quotedbl", + "/exclam", + "/question", + "/asterisk", + "/plus", + "/minus", + "/equal", + "/less", + "/greater", + "/ampersand", + "/percent", + "/at", + "/dollar", + "/numbersign", + "/underscore", + "/asciitilde", + "/asciicircum", + "/endash", + "/emdash", + "/hyphen", + "/bullet", + "/copyright", + "/registered", + "/trademark", + "/degree", + "/plusminus", + "/multiply", + "/divide", + "/section", + "/paragraph", + "/dagger", + "/daggerdbl", + "/ellipsis", + "/elipsis", + "/glyph", + "CID+", + ]) + .unwrap() }); +/// Per-line output of `apply_glyph_span_strip_and_rule_b`. +struct GlyphStripResult { + stripped: String, + line_drop: bool, + rule_a_count: usize, + rule_b_count: usize, +} + +#[derive(Clone, Debug)] +struct GlyphSpan { + start: usize, + end: usize, + replacement: Option<&'static str>, +} + +fn span_is_inside_html_comment(line: &str, start: usize, end: usize) -> bool { + COMMENT_REGEX + .find_iter(line) + .any(|m| start >= m.start() && end <= m.end()) +} + +fn token_bounds_around_span(line: &str, start: usize, end: usize) -> (usize, usize) { + let left = line[..start] + .rfind(char::is_whitespace) + .map(|idx| idx + line[idx..].chars().next().map(char::len_utf8).unwrap_or(1)) + .unwrap_or(0); + let right = line[end..] + .find(char::is_whitespace) + .map(|idx| end + idx) + .unwrap_or(line.len()); + (left, right) +} + +fn span_text_is_hyphenminus(line: &str, start: usize, end: usize) -> bool { + line.get(start..end) + .map(|text| text.eq_ignore_ascii_case("/hyphenminus")) + .unwrap_or(false) +} + +fn hyphenminus_span_is_inside_true_url(line: &str, start: usize, end: usize) -> bool { + let (left, right) = token_bounds_around_span(line, start, end); + let token = &line[left..right]; + let token_lower = token.to_ascii_lowercase(); + if token_lower.starts_with("www.") || token_lower.contains("://") { + return true; + } + + if let Some(first_slash) = token.find('/') { + let before_slash = &token[..first_slash]; + return before_slash.contains('.') + && before_slash.chars().any(|ch| ch.is_ascii_alphabetic()); + } + + false +} + +fn glyph_span_is_protected(line: &str, start: usize, end: usize) -> bool { + if span_is_inside_html_comment(line, start, end) { + return true; + } + if !normalize::span_is_inside_url_like_token(line, start, end) { + return false; + } + + // Numeric ranges such as `4.600/hyphenminus5.600` can look like + // host/path tokens to the broad URL guard. Keep real links protected, + // but allow these range markers through for normalization. + if span_text_is_hyphenminus(line, start, end) { + return hyphenminus_span_is_inside_true_url(line, start, end); + } + + true +} + +fn hyphenminus_replacement(line: &str, start: usize, end: usize) -> Option<&'static str> { + if !span_text_is_hyphenminus(line, start, end) { + return None; + } + let left_glued = line[..start] + .chars() + .next_back() + .map(|ch| !ch.is_whitespace()) + .unwrap_or(false); + let right_glued = line[end..] + .chars() + .next() + .map(|ch| !ch.is_whitespace()) + .unwrap_or(false); + if left_glued || right_glued { + Some("-") + } else { + None + } +} + +fn push_unprotected_glyph_spans(line: &str, matches: I, spans: &mut Vec) +where + I: Iterator, +{ + for (start, end) in matches { + if !glyph_span_is_protected(line, start, end) { + spans.push(GlyphSpan { + start, + end, + replacement: hyphenminus_replacement(line, start, end), + }); + } + } +} + +fn rewrite_non_overlapping_spans(line: &str, spans: &[GlyphSpan]) -> String { + if spans.is_empty() { + return line.to_string(); + } + let mut sorted = spans.to_vec(); + sorted.sort_unstable_by_key(|span| (span.start, span.end)); + + let mut out = String::with_capacity(line.len()); + let mut last_end = 0usize; + for span in sorted { + if span.start < last_end { + if span.end > last_end { + last_end = span.end; + } + continue; + } + out.push_str(&line[last_end..span.start]); + if let Some(replacement) = span.replacement { + out.push_str(replacement); + } + last_end = span.end; + } + out.push_str(&line[last_end..]); + out +} + +fn is_uppercase_glyph_stem(text: &str) -> bool { + !text.is_empty() + && text.len() % 5 == 0 + && text + .as_bytes() + .chunks_exact(5) + .all(|chunk| chunk == b"GLYPH") +} + +fn display_math_line_has_glyph_residue(line: &str) -> (bool, usize, usize) { + let rule_a_count = RULE_A_LITERALS_AC + .find_iter(line) + .filter(|m| !span_is_inside_html_comment(line, m.start(), m.end())) + .count(); + let structured_rule_b_count = PDF_GLYPH_NAME_REGEX + .find_iter(line) + .filter(|m| !span_is_inside_html_comment(line, m.start(), m.end())) + .count(); + let bare_glyph_count = BARE_PDF_GLYPH_STEM_REGEX + .find_iter(line) + .filter(|m| { + !span_is_inside_html_comment(line, m.start(), m.end()) + && is_uppercase_glyph_stem(m.as_str()) + }) + .count(); + let rule_b_count = structured_rule_b_count + bare_glyph_count; + + (rule_a_count + rule_b_count > 0, rule_a_count, rule_b_count) +} + +/// Strip Rule A literal spans + Rule B regex spans from a line, and +/// flag the line for removal if combined Rule A + Rule B match count +/// reaches the count+coverage gate. +/// +/// CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Points 4 + 5: +/// - PDF_GLYPH_NAME_REGEX (Rule B) is the unified PostScript-glyph +/// /font residue regex (GLYPH<…>, GLYPH(…), glyph[…], +/// glyph, font subset, /uniXXXX, /gN). +/// - Rule A's 50 PostScript-name LITERALS contribute to the SAME +/// count+coverage gate (instead of being span-strip-only as before). +/// - Wave 3 re-introduces one bounded bare-word exception: repeated +/// / standalone `GLYPH`, which real extractors emit without payload. +/// +/// Gate: `(count_A + count_B) ≥ 10 AND (count_A + count_B) / +/// non_ws_chars ≥ 0.09` → line drops. +/// +/// Per-rule counts are returned for Point 7's per-doc accumulation +/// in `CleanStats`. +fn apply_glyph_span_strip_and_rule_b(line: &str) -> GlyphStripResult { + // Count rule A and rule B hits BEFORE stripping, but ignore glyph-like + // substrings inside URL/path tokens (`/g123`, `/uni03B1`, `/space`, etc.). + // A link token is ordinary prose context, but its path syntax is not PDF + // extraction residue. + let mut rule_a_spans: Vec = Vec::new(); + push_unprotected_glyph_spans( + line, + RULE_A_LITERALS_AC + .find_iter(line) + .map(|m| (m.start(), m.end())), + &mut rule_a_spans, + ); + let mut rule_b_spans: Vec = Vec::new(); + push_unprotected_glyph_spans( + line, + PDF_GLYPH_NAME_REGEX + .find_iter(line) + .map(|m| (m.start(), m.end())), + &mut rule_b_spans, + ); + push_unprotected_glyph_spans( + line, + BARE_PDF_GLYPH_STEM_REGEX + .find_iter(line) + .map(|m| (m.start(), m.end())), + &mut rule_b_spans, + ); + + let rule_a_count = rule_a_spans.len(); + let rule_b_count = rule_b_spans.len(); + let combined_count = rule_a_count + rule_b_count; + let non_ws_len = line.chars().filter(|c| !c.is_whitespace()).count(); + let coverage = if non_ws_len > 0 { + combined_count as f64 / non_ws_len as f64 + } else { + 0.0 + }; + let line_drop = combined_count >= 10 && coverage >= 0.09; + + let mut spans_to_strip = rule_a_spans; + spans_to_strip.extend(rule_b_spans); + let stripped = rewrite_non_overlapping_spans(line, &spans_to_strip); + GlyphStripResult { + stripped, + line_drop, + rule_a_count, + rule_b_count, + } +} + +fn is_unicode_noise_char(ch: char) -> bool { + match ch { + '\t' | '\n' => false, + // Invisible formatting / directional / control / replacement codepoints + // with no semantic purpose in Greek text. U+200E (LRM) and U+200F (RLM) + // were added 2026-04-21 after wave11 surfaced them as untouched + // bidi-mark residue in Greek-Wikipedia translation patterns. + '\u{00AD}' | '\u{03A2}' | '\u{200B}' | '\u{200C}' | '\u{200D}' | '\u{200E}' + | '\u{200F}' | '\u{2060}' | '\u{FEFF}' | '\u{FFFD}' => true, + _ => { + let code = ch as u32; + code < 0x20 + || code == 0x7F + || (0x80..=0x9F).contains(&code) + || (0xE000..=0xF8FF).contains(&code) + || (0xF0000..=0xFFFFD).contains(&code) + || (0x100000..=0x10FFFD).contains(&code) + } + } +} + +fn clean_impossible_noise_inside_code_fence(line: &str) -> Option { + if !line.contains('\u{00AD}') + && !line.contains('\u{00B5}') + && !line.chars().any(|ch| { + let code = ch as u32; + (code < 0x20 && ch != '\t' && ch != '\n') + || code == 0x7F + || (0x80..=0x9F).contains(&code) + }) + { + return None; + } + let mut out = String::with_capacity(line.len()); + let mut changed = false; + for ch in line.chars() { + let code = ch as u32; + let strip = ch == '\u{00AD}' + || (code < 0x20 && ch != '\t' && ch != '\n') + || code == 0x7F + || (0x80..=0x9F).contains(&code); + if strip { + changed = true; + continue; + } + if ch == '\u{00B5}' { + out.push('\u{03BC}'); + changed = true; + } else { + out.push(ch); + } + } + if changed { + Some(out) + } else { + None + } +} + +fn normalize_layout_leader_runs(line: &str) -> Option { + if line.is_empty() || line == TEXT_MISSING_COMMENT || line == TABLE_REMOVED_COMMENT { + return None; + } + // Tiered bucket per normalize.rs: {2}→1, {3,4}→3, {5..=19}→5, {>20}→20. + // Uniform with the whitespace-run rule. + normalize::normalize_dot_and_ellipsis_runs(line) +} + // Helper function for Step 5.1: Stream-strip tags using memchr // Takes a mutable buffer for the result, clears it, and appends to it. // Returns count of removed non-whitespace tag characters. @@ -167,20 +728,137 @@ fn strip_tags_custom(line: &str, result_buf: &mut String) -> usize { removed_non_ws_tag_chars // No longer returns the String, it's modified in place } -/// Core text cleaning function - removes unwanted characters based on script sets -/// Returns a tuple: (cleaned_text, original_chars_count_for_badness, kept_chars_count_for_badness) -/// original_chars_count_for_badness: count of characters in lines not fully rejected by BAD_LINE_AC. -/// kept_chars_count_for_badness: count of characters remaining in those lines after cleaning. +/// Thin wrapper over `core_clean_text_with_stats` that returns just the +/// legacy `(cleaned_text, original_chars_count_for_badness, +/// kept_chars_count_for_badness)` tuple used by `perform_text_analysis` +/// and existing tests. New call sites that need the four-way char split +/// or lines_dropped_count should call `core_clean_text_with_stats` +/// directly. pub fn core_clean_text( text: &str, allowed_chars: &HashSet, unusual_chars_set: &HashSet, min_chars_for_comment_override: Option, ) -> (String, usize, usize) { + let (cleaned, stats) = core_clean_text_with_stats( + text, + allowed_chars, + unusual_chars_set, + min_chars_for_comment_override, + ); + ( + cleaned, + stats.original_chars_for_badness, + stats.sum_kept_line_content_chars, + ) +} + +/// Core text cleaning function with full char accounting. +/// +/// See `CleanStats` for the invariant and field meanings. +/// +/// Defaults to `PhaseAMode::ParserSurgicalVerified` (Pilot B with +/// the safe checked wrapper) per CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 +/// Point 9. Callers that explicitly want the legacy line-based +/// `md_module::normalize_md_syntax` path should call +/// `core_clean_text_with_stats_with_mode(..., PhaseAMode::LineBased)`. +pub fn core_clean_text_with_stats( + text: &str, + allowed_chars: &HashSet, + unusual_chars_set: &HashSet, + min_chars_for_comment_override: Option, +) -> (String, CleanStats) { + core_clean_text_with_stats_with_mode( + text, + allowed_chars, + unusual_chars_set, + min_chars_for_comment_override, + PhaseAMode::default(), + ) +} + +/// Mode-explicit core entry. Branches Phase A on `phase_a_mode`: +/// - `LineBased` → `md_module::normalize_md_syntax`. +/// - `ParserSurgical` → `md_format_surgical::format_surgical`. +/// - `ParserSurgicalVerified` → `md_format_surgical::format_surgical_checked`, +/// populates `phase_a_fallback_reason` and +/// `phase_a_dialect_ambiguous_input` in the returned `CleanStats`. +pub fn core_clean_text_with_stats_with_mode( + text: &str, + allowed_chars: &HashSet, + unusual_chars_set: &HashSet, + min_chars_for_comment_override: Option, + // PhaseAMode kept in the signature for back-compat with tests + + // PyO3 callers that still pass a phase_a_mode kwarg. After the + // dead-code excision (md_format Pilot A removed; md_module + // LineBased removed), every variant routes to the same Pilot B + // checked path. The arg is ignored; a follow-up PR removes the + // enum + parameter entirely. + _phase_a_mode: PhaseAMode, +) -> (String, CleanStats) { + // ----------------------------------------------------------------- + // Wave-2 preprocessing (Cases 4, 7, 10a, 8 — 2026-04-23). + // Applied BEFORE the per-line filter loop so recovered chars (from + // entity decode and Adobe Symbol PUA decode) survive per-char + // filtering. Char-count delta attributed to `chars_dropped_by_ + // normalization`. The final step is the full Phase A orchestrator + // (`md_module::normalize_md_syntax`) — a single entry point that + // canonicalizes GFM table separators, HR rules, and reflows + // paragraphs in the correct order. Routing through the orchestrator + // (rather than calling `reflow_paragraphs` alone) is required so + // optional-pipe GFM tables like `a | b\n--- | ---\n1 | 2` are + // identified as tables BEFORE reflow decides whether to fuse rows. + // ----------------------------------------------------------------- + let wave2_in_len = text.chars().count(); + // Pre-pass shape after CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Points 2+4: + // 1. HTML entities (multi-char sequence → single codepoint). + // 2. Inline base64 image data URIs (Docling JPEG/PNG payloads + // replaced with `` so Phase A doesn't see + // massive unbroken lines). + // 3. Markdown formatting (md_module). + // + // Removed in Point 2: `decode_adobe_symbol_pua`, `strip_soft_hyphens` + // (now per-line via fold_codepoint / is_unicode_noise_char). + // Removed in Point 4: `strip_glyph_markers` (subsumed by the + // per-line Rule B regex which strips the same patterns AND + // line-drops dense ones via the count/coverage gate). + let step1 = normalize::decode_html_entities(text); + let step4b = normalize::strip_base64_images(&step1); + // Phase A — mode-selectable per Q4 / Point 9. Default + // `ParserSurgicalVerified` (Pilot B with the safe checked wrapper) + // surfaces fallback signals into `CleanStats`. `LineBased` is + // the explicit-opt-in legacy path for diff-against-baseline runs. + let phase_a_fallback_reason: Option; + let phase_a_dialect_ambiguous_input: bool; + let step5 = { + // Pilot B is the only Phase A path. The checked wrapper runs + // dual_verify on input vs output and ships input verbatim if + // they disagree — surfaced via phase_a_fallback_reason. + let r = crate::md_format_surgical::format_surgical_checked(&step4b); + phase_a_fallback_reason = r.fallback_reason; + phase_a_dialect_ambiguous_input = r.dialect_ambiguous_input; + r.output + }; + let wave2_out_len = step5.chars().count(); + let wave2_preprocessing_delta = wave2_in_len.saturating_sub(wave2_out_len); + // Re-alias `text` so the rest of the function sees the preprocessed + // string. `text` is a reference to the original; we need to bind a + // new owned string and reborrow. + let text_owned = step5; + let text = text_owned.as_str(); + // ---- end wave-2 preprocessing ---- + let min_comment_chars = min_chars_for_comment_override.unwrap_or(5); let mut cleaned_output_string_builder = String::new(); // Used to build the final string with newlines let mut original_chars_for_badness: usize = 0; // Sum of original line content lengths (excluding their newlines) + // Point 7: per-doc per-rule match counts (replaces the standalone + // matcher crate's counters by accumulating directly inside the + // cleaner). Aligned by construction with cleaner activity. + let mut rule_a_match_count: u64 = 0; + let mut rule_b_match_count: u64 = 0; + let mut residue_line_drop_count: u64 = 0; + // New counter for the sum of *content characters* of lines added to the output, // before specific placeholder penalties are applied. let mut sum_kept_line_content_chars: usize = 0; @@ -188,6 +866,18 @@ pub fn core_clean_text( let mut inline_tmc_additions_count: usize = 0; let mut standalone_tmc_replacements_on_processed_lines_count: usize = 0; + // Four-way char accounting (see `CleanStats` doc-comment). + let mut content_chars_kept: usize = 0; + let mut chars_dropped_by_line_drop: usize = 0; + // Seed with the wave-2 preprocessing delta (entity decode, PUA + // recovery net char change, GLYPH marker deletion, soft-hyphen + // strip, paragraph reflow whitespace collapse). + let mut chars_dropped_by_normalization: usize = wave2_preprocessing_delta; + let mut chars_dropped_by_per_char_filter: usize = 0; + let mut lines_dropped_count: usize = 0; + let mut marker_chars_passthrough: usize = 0; + let mut marker_chars_added: usize = 0; + // Step 5.3: Build local bitmaps for faster char checking in the 0-1023 range. let mut local_allowed_bitmap: [bool; 1024] = [false; 1024]; for &ch_allowed in allowed_chars { @@ -212,14 +902,77 @@ pub fn core_clean_text( let mut carry_math_state = false; - for line in text.lines() { + // Note: GFM table separator canonicalization and HR thematic-break + // minimization are done by `md_module::normalize_md_syntax` above as + // part of the Phase A pre-pass. The per-line redundant calls that + // used to sit here (scan_gfm_table_separators + normalize_separator_ + // line) were no-ops on already-canonical input and have been removed + // since C13 wired the cleaner through the orchestrator. + // + // Code-fence state carried across lines — inside a fenced block we skip + // all normalizations so code indentation and punctuation survive intact. + let mut in_code_fence = false; + + for (_line_index, line) in text.lines().enumerate() { let trimmed_line = line.trim(); if trimmed_line == TEXT_MISSING_COMMENT { - original_chars_for_badness += line.chars().count(); + let line_chars = line.chars().count(); + original_chars_for_badness += line_chars; + // Input line IS the TMC marker — it's a marker pass-through, not + // content. Attribute all its chars to marker_chars_passthrough so + // they don't pollute content_chars_kept or any drop bucket. + marker_chars_passthrough += line_chars; cleaned_output_string_builder.push_str(TEXT_MISSING_COMMENT); cleaned_output_string_builder.push('\n'); continue; } + + // (C16 cleanup: the GFM-separator special case that used to sit + // here pulled a `canonical` replacement out of `table_replacements` + // and attributed the char delta to chars_dropped_by_normalization. + // Since C13 the cleaner runs `normalize_md_syntax` as a pre-pass, + // which already canonicalizes those rows AND seeds the char delta + // into `chars_dropped_by_normalization` via + // `wave2_preprocessing_delta`. So the row reaching this loop is + // ALREADY in canonical form and flows through the normal per-line + // pipeline — no double accounting, one less scan of the full text.) + + // Code-fence state: toggle on ``` / ~~~ markers. Pass the marker and + // everything inside through unchanged so normalizations don't collapse + // meaningful code indentation or punctuation. Pass the RAW line so + // the detector can apply CommonMark's ≥4-column indented-code rule + // (a ``` at that indentation is literal content, not a fence). + if md_module::is_code_fence_marker(line) { + in_code_fence = !in_code_fence; + let line_chars = line.chars().count(); + original_chars_for_badness += line_chars; + sum_kept_line_content_chars += line_chars; + content_chars_kept += line_chars; + cleaned_output_string_builder.push_str(line); + cleaned_output_string_builder.push('\n'); + continue; + } + if in_code_fence { + let line_chars = line.chars().count(); + let cleaned_code_line = clean_impossible_noise_inside_code_fence(line); + let line_to_keep = cleaned_code_line.as_deref().unwrap_or(line); + let kept_chars = line_to_keep.chars().count(); + original_chars_for_badness += line_chars; + chars_dropped_by_per_char_filter += line_chars.saturating_sub(kept_chars); + sum_kept_line_content_chars += kept_chars; + content_chars_kept += kept_chars; + cleaned_output_string_builder.push_str(line_to_keep); + cleaned_output_string_builder.push('\n'); + continue; + } + + // Decode entities before artefact checks so html-escaped GLYPH/font tags + // are caught by the same canonical matcher family as raw XML-like forms. + let decoded_entity_data = decode(line.as_bytes()); + let line_after_entity_decoding_str = decoded_entity_data + .to_string() + .unwrap_or_else(|_| line.to_string()); + let mut skip_bad_line_check = carry_math_state || trimmed_line == "$$"; if !skip_bad_line_check && trimmed_line.contains("$$") { // Handle inline math by skipping BAD_LINE_AC so \text in math isn't penalised. @@ -229,9 +982,80 @@ pub fn core_clean_text( } } - if !skip_bad_line_check && BAD_LINE_AC.is_match(line) { - original_chars_for_badness += line.chars().count(); - cleaned_output_string_builder.push_str(TEXT_MISSING_COMMENT); + let display_math_context = carry_math_state + || trimmed_line == "$$" + || trimmed_line.starts_with("$$") + || trimmed_line.ends_with("$$"); + if display_math_context { + let (drop_math_line, math_rule_a_count, math_rule_b_count) = + display_math_line_has_glyph_residue(&line_after_entity_decoding_str); + rule_a_match_count += math_rule_a_count as u64; + rule_b_match_count += math_rule_b_count as u64; + if drop_math_line { + let line_chars = line.chars().count(); + original_chars_for_badness += line_chars; + chars_dropped_by_line_drop += line_chars; + lines_dropped_count += 1; + marker_chars_added += LINE_REMOVED_COMMENT.chars().count(); + cleaned_output_string_builder.push_str(LINE_REMOVED_COMMENT); + cleaned_output_string_builder.push('\n'); + continue; + } + } + + // Rule A (PS-glyph literal set) + Rule B (PS-glyph regex). + // Both span-stripped inline; rule B additionally triggers + // whole-line removal if coverage predicate met (mc ≥ 10 AND + // rule-B matches / non-whitespace chars ≥ 0.09). Per 2026-04-22 + // Gemini wave: P=96.3%, R=60.4% on rule-B predicate. + // Applied BEFORE BAD_LINE_AC so `/hyphenminus`-style spans get + // stripped and don't trigger the `hyphenminus` substring trigger + // in BAD_LINE_AC. + // Skip in math context. + let rule_b_line_drop; + let post_rule_strip = if skip_bad_line_check { + rule_b_line_drop = false; + line_after_entity_decoding_str.clone() + } else { + let r = apply_glyph_span_strip_and_rule_b(&line_after_entity_decoding_str); + // Point 7: accumulate per-rule match counts even when the + // gate doesn't fire — these feed `CleanStats.rule_a_match_count` + // / `rule_b_match_count` for sample-cutting downstream. + rule_a_match_count += r.rule_a_count as u64; + rule_b_match_count += r.rule_b_count as u64; + rule_b_line_drop = r.line_drop; + r.stripped + }; + if rule_b_line_drop { + let line_chars = line.chars().count(); + original_chars_for_badness += line_chars; + chars_dropped_by_line_drop += line_chars; + lines_dropped_count += 1; + marker_chars_added += LINE_REMOVED_COMMENT.chars().count(); + cleaned_output_string_builder.push_str(LINE_REMOVED_COMMENT); + cleaned_output_string_builder.push('\n'); + continue; + } + + // After CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 4 the + // line-drop check has TWO independent engines, both line-level + // threshold rules: + // - Rule B's count + coverage gate (already evaluated above + // by `apply_glyph_span_strip_and_rule_b`, surfaced via the + // `rule_b_line_drop` path). + // - `is_residue_mojibake_line` (R1 ∪ R2): residue-density + // mojibake signature. + // `BAD_LINE_AC` (literal-set) and `has_decoded_glyph_font_artefact` + // (regex-set) were both subsumed by Rule B's regex. + if !skip_bad_line_check && normalize::is_residue_mojibake_line(&post_rule_strip) { + // Point 7: count R1∪R2 line drops for downstream sampling. + residue_line_drop_count += 1; + let line_chars = line.chars().count(); + original_chars_for_badness += line_chars; + chars_dropped_by_line_drop += line_chars; + lines_dropped_count += 1; + marker_chars_added += LINE_REMOVED_COMMENT.chars().count(); + cleaned_output_string_builder.push_str(LINE_REMOVED_COMMENT); cleaned_output_string_builder.push('\n'); continue; } @@ -240,17 +1064,10 @@ pub fn core_clean_text( current_line_removed_chars_buffer_buf.clear(); // line_after_tag_handling_buf is cleared inside strip_tags_custom - // Step 4.1: Decode HTML entities FIRST from the original line - let decoded_entity_data = decode(line.as_bytes()); - let line_after_entity_decoding_str = decoded_entity_data - .to_string() - .unwrap_or_else(|_| line.to_string()); - - // Step 5.1 & 5.4: Use strip_tags_custom with a reusable buffer on the DECODED line content - let removed_from_tags_count = strip_tags_custom( - &line_after_entity_decoding_str, - &mut line_after_tag_handling_buf, - ); + // Step 5.1 & 5.4: Use strip_tags_custom with a reusable buffer + // on the rule-A/B-stripped decoded line content + let removed_from_tags_count = + strip_tags_custom(&post_rule_strip, &mut line_after_tag_handling_buf); // Iterate the result of tag stripping for character filtering, keeping track of LaTeX math spans. let chars: Vec = line_after_tag_handling_buf.chars().collect(); @@ -279,6 +1096,16 @@ pub fn core_clean_text( outside_math_original_chars += 1; + // Codepoint fold (ligatures, enclosed / dingbat / math-alphanumeric + // digits, vulgar fractions, Unicode whitespace variants) bypasses + // the allowed/unusual check — replacements are ASCII or a regular + // space. + if let Some(replacement) = normalize::fold_codepoint(ch) { + processed_line_segment_buf.push_str(replacement); + idx += 1; + continue; + } + let ch_u32 = ch as u32; let is_char_allowed_by_scripts; let is_char_in_unusual_set; @@ -291,8 +1118,10 @@ pub fn core_clean_text( is_char_in_unusual_set = unusual_chars_set.contains(&ch); } - // Condition for removal: It's in the unusual set AND it's NOT specifically allowed by current scripts_to_keep. - if is_char_in_unusual_set && !is_char_allowed_by_scripts { + let should_remove_char = is_unicode_noise_char(ch) + || (is_char_in_unusual_set && !is_char_allowed_by_scripts); + + if should_remove_char { if !ch.is_whitespace() { current_line_removed_chars_buffer_buf.push(ch); } @@ -306,6 +1135,8 @@ pub fn core_clean_text( // If the line only contained math content, preserve it but skip scoring contributions. if outside_math_original_chars == 0 && math_chars_this_line > 0 { + // Math pass-through: count chars as kept content (math is legitimate). + content_chars_kept += processed_line_segment_buf.chars().count(); cleaned_output_string_builder.push_str(&processed_line_segment_buf); cleaned_output_string_builder.push('\n'); continue; @@ -360,12 +1191,106 @@ pub fn core_clean_text( let kept_chars_total = line_content_to_add.chars().count(); sum_kept_line_content_chars += kept_chars_total.saturating_sub(math_chars_this_line); // exclude math spans original_chars_for_badness += line.chars().count().saturating_sub(math_chars_this_line); - cleaned_output_string_builder.push_str(&line_content_to_add); + + // Per-char filter accounting. Entity-decode + rule A/B span strip + + // tag strip + per-char unicode filter together shrink input chars to + // processed_line_segment_buf chars. Marker additions happen AFTER + // (line_content_to_add), so they don't pollute this delta. + let input_chars_this_line = line.chars().count(); + let post_per_char_chars = processed_line_segment_buf.chars().count(); + chars_dropped_by_per_char_filter += + input_chars_this_line.saturating_sub(post_per_char_chars); + + let line_to_write = if is_exclusively_comment { + // Input line was ITSELF a comment (pass-through) — don't touch. + line_content_to_add.clone() + } else { + // Chain line-level normalizations AFTER cleaning so that chars + // removed by per-char filter / rule-A/B-span-strip / + // entity-decode collapse cleanly — e.g. a word stripped + // mid-line leaves `foo bar` (2 spaces) which whitespace-run + // bucketing collapses to `foo bar`. Normalize passes are + // marker-safe (they operate on specific patterns that don't + // preserve intentional HTML comment placeholders, so inline-TMC + // lines normalize too. Order matters: + // dot/ellipsis leader (tiered bucket) -> malformed-entity + // fallback -> whitespace run -> escaped Markdown run -> + // punctuation run. + let mut s = line_content_to_add.clone(); + if let Some(n) = normalize_layout_leader_runs(&s) { + s = n; + } + if let Some(n) = normalize::normalize_malformed_entities(&s) { + s = n; + } + if let Some(n) = normalize::normalize_whitespace_runs(&s) { + s = n; + } + if let Some(n) = normalize::normalize_escaped_run_chars(&s) { + s = n; + } + if let Some(n) = normalize::normalize_punctuation_runs(&s) { + s = n; + } + s + }; + // Normalize-pass accounting: delta between pre-normalize (with any + // inline markers already attached) and post-normalize output. + // Saturating because rare expansions (e.g. `…` folded to ASCII + // triple-dot ".....") would underflow. + let pre_normalize_chars = line_content_to_add.chars().count(); + let post_normalize_chars = line_to_write.chars().count(); + chars_dropped_by_normalization += pre_normalize_chars.saturating_sub(post_normalize_chars); + + // Content-chars-kept accounting: output chars on this line EXCLUDING + // any marker chars that were added inline (space + TMC) or that + // wholly replaced the line (standalone TMC / exclusive-comment). + if is_exclusively_comment { + // Output line IS a comment marker that came from the INPUT + // (pass-through; we didn't add it). Attribute to passthrough. + marker_chars_passthrough += post_normalize_chars; + } else if line_content_to_add.contains(TEXT_MISSING_COMMENT) { + // Inline TMC addition OR standalone TMC replacement — we ADDED + // this marker. Standalone case: processed_line_segment_buf.trim() + // is empty and the whole line becomes the marker. Inline case: + // output = trimmed_content + " " + TMC, so marker part is + // (1 space + TMC.len()). Normalize may already have collapsed + // surrounding whitespace, but the marker itself is preserved. + let inline_marker_chars = TEXT_MISSING_COMMENT.chars().count(); + if processed_line_segment_buf.trim().is_empty() { + marker_chars_added += post_normalize_chars; + } else { + let marker_span = inline_marker_chars + 1; // " " + TMC + content_chars_kept += post_normalize_chars.saturating_sub(marker_span); + marker_chars_added += marker_span.min(post_normalize_chars); + } + } else { + // Normal kept line — all output chars are content. + content_chars_kept += post_normalize_chars; + } + + cleaned_output_string_builder.push_str(&line_to_write); cleaned_output_string_builder.push('\n'); } let mut final_cleaned_text = cleaned_output_string_builder; + // Collapse runs of 3+ consecutive newlines to exactly 2 (single + // blank-line paragraph separator). CommonMark renders any number + // of blank lines as one block separator, so this is lossless under + // markdown preview. Bytes removed go into `chars_dropped_by_normalization` + // — they are removed by a normalization pass, not by line-drop or + // per-char-filter. + if BLANK_LINE_RUN_REGEX.is_match(&final_cleaned_text) { + let pre_chars = final_cleaned_text.chars().count(); + final_cleaned_text = BLANK_LINE_RUN_REGEX + .replace_all(&final_cleaned_text, "\n\n") + .into_owned(); + let post_chars = final_cleaned_text.chars().count(); + chars_dropped_by_normalization = + chars_dropped_by_normalization.saturating_add(pre_chars.saturating_sub(post_chars)); + } + // Adjust final newline if original text didn't have one. // This affects the final string, but sum_kept_line_content_chars and original_chars_for_badness // are based on line contents only, so they remain unaffected by this specific string manipulation. @@ -423,53 +1348,197 @@ pub fn core_clean_text( let adjusted_kept_chars_for_badness = sum_kept_line_content_chars.saturating_sub(total_placeholder_content_penalty); - ( - final_cleaned_text, + // If an input line was pre-existing TABLE_REMOVED_COMMENT, the main + // path classified it as kept content. Move those chars from content to + // marker_chars_passthrough so content_chars_kept reflects true content. + let trc_marker_chars = TABLE_REMOVED_COMMENT.chars().count(); + let trc_reclass_total = num_table_removed_comments_as_full_lines_in_output * trc_marker_chars; + marker_chars_passthrough += trc_reclass_total; + content_chars_kept = content_chars_kept.saturating_sub(trc_reclass_total); + + let stats = CleanStats { + content_chars_kept, + chars_dropped_by_line_drop, + chars_dropped_by_normalization, + chars_dropped_by_per_char_filter, + lines_dropped_count, + marker_chars_passthrough, + marker_chars_added, original_chars_for_badness, - adjusted_kept_chars_for_badness, - ) + sum_kept_line_content_chars: adjusted_kept_chars_for_badness, + phase_a_fallback_reason, + phase_a_dialect_ambiguous_input, + rule_a_match_count, + rule_b_match_count, + residue_line_drop_count, + }; + + (final_cleaned_text, stats) } -/// Python-exposed function to clean a single string -#[pyfunction] -pub fn clean_text( - text: &str, - scripts_to_keep: Vec, - min_chars_for_comment: Option, -) -> PyResult { +/// Build (allowed_chars, unusual_chars) for the requested script set. +/// Ensures `punctuation`, `numbers`, `common_symbols` are always included +/// and that whitespace chars are always allowed. +pub fn build_script_char_sets(scripts_to_keep: &[String]) -> (HashSet, HashSet) { let mut allowed_chars = HashSet::new(); - for key in &scripts_to_keep { + for key in scripts_to_keep { if let Some(script_set) = SCRIPT_SETS.get(key) { allowed_chars.extend(script_set); - } else { - // Optionally, log a warning if a script key is not found - // log::warn!("Script key '{}' not found in SCRIPT_SETS", key); } } - - // Ensure common scripts are included even if not specified - // Using .to_string() for comparison as keys in SCRIPT_SETS are String for key_str in ["punctuation", "numbers", "common_symbols"].iter() { let key = key_str.to_string(); if !scripts_to_keep.contains(&key) { - // Check if scripts_to_keep (Vec) contains the current key (String) if let Some(script_set) = SCRIPT_SETS.get(&key) { allowed_chars.extend(script_set); } } } - - // Add essential whitespace that should always be allowed regardless of script choices allowed_chars.insert(' '); allowed_chars.insert('\t'); - allowed_chars.insert('\n'); // Though lines are processed and newlines re-added, having it in allowed_chars is safe. - + allowed_chars.insert('\n'); let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); - let (cleaned_string, _, _) = - core_clean_text(text, &allowed_chars, &unusual_chars, min_chars_for_comment); + (allowed_chars, unusual_chars) +} + +/// Python-exposed function to clean a single string. +/// +/// LaTeX repetition cropping is ON by default as of 2026-04-24 +/// (user policy: all corpus cleaning runs should crop repeated +/// LaTeX regions). Callers can disable via +/// `enable_latex_repetition_crop=False` for PDF-to-text / OCR-debug +/// use cases that need to see the raw repetition. +#[pyfunction] +#[pyo3(signature = (text, scripts_to_keep, min_chars_for_comment=None, enable_latex_repetition_crop=true, latex_char_threshold=30, latex_line_threshold=3, phase_a_mode="parser_surgical_verified"))] +pub fn clean_text( + text: &str, + scripts_to_keep: Vec, + min_chars_for_comment: Option, + enable_latex_repetition_crop: bool, + latex_char_threshold: usize, + latex_line_threshold: usize, + phase_a_mode: &str, +) -> PyResult { + let (allowed_chars, unusual_chars) = build_script_char_sets(&scripts_to_keep); + let preprocessed: String; + let text_ref: &str = if enable_latex_repetition_crop { + preprocessed = crate::latex_module::crop_latex_repetitions( + text, + true, + latex_char_threshold, + latex_line_threshold, + ); + &preprocessed + } else { + text + }; + // Parity with `clean_text_with_stats` — Phase A mode is selectable + // here too (P2 fix). Default `parser_surgical_verified` matches + // the production default; pass `"line_based"` for the legacy path. + let mode = PhaseAMode::from_str_or_default(phase_a_mode); + let (cleaned_string, _stats) = core_clean_text_with_stats_with_mode( + text_ref, + &allowed_chars, + &unusual_chars, + min_chars_for_comment, + mode, + ); Ok(cleaned_string) } +/// Python-exposed variant that also returns per-doc char accounting. +/// +/// Returns `(cleaned_text, stats_dict)` where `stats_dict` has integer keys: +/// - `content_chars_kept`: output chars excluding all comment markers +/// - `chars_dropped_by_line_drop`: chars in lines replaced by a line-drop marker +/// - `chars_dropped_by_normalization`: chars collapsed by dot/whitespace/separator/table/ellipsis normalizers +/// - `chars_dropped_by_per_char_filter`: chars stripped by entity-decode / rule-A/B / tag-strip / unicode filter +/// - `lines_dropped_count`: number of line-drop marker emissions +/// - `marker_chars_passthrough`: input chars whose line was a marker (pass-through) +/// - `marker_chars_added`: marker chars we emitted during cleaning +/// - `original_chars_for_badness`: back-compat badness-scoring input +/// - `sum_kept_line_content_chars`: back-compat badness-scoring output +#[pyfunction] +#[pyo3(signature = (text, scripts_to_keep, min_chars_for_comment=None, enable_latex_repetition_crop=true, latex_char_threshold=30, latex_line_threshold=3, phase_a_mode="parser_surgical_verified"))] +pub fn clean_text_with_stats( + py: Python<'_>, + text: &str, + scripts_to_keep: Vec, + min_chars_for_comment: Option, + enable_latex_repetition_crop: bool, + latex_char_threshold: usize, + latex_line_threshold: usize, + phase_a_mode: &str, +) -> PyResult<(String, PyObject)> { + use pyo3::types::PyDict; + // Wave-2 (2026-04-23): LaTeX repetition cropping runs BEFORE the + // cleaner's main passes, so OCR-hallucinated repetitions inside + // `$$…$$` segments are truncated before any other pass sees them. + // ON by default as of 2026-04-24 (user policy: all corpus cleaning + // runs should crop LaTeX repetition). Callers that want the raw + // input (PDF-to-text debug, pre-crop diff) can pass + // `enable_latex_repetition_crop=False`. + let preprocessed: String; + let text_ref: &str = if enable_latex_repetition_crop { + preprocessed = crate::latex_module::crop_latex_repetitions( + text, + true, + latex_char_threshold, + latex_line_threshold, + ); + &preprocessed + } else { + text + }; + let (allowed_chars, unusual_chars) = build_script_char_sets(&scripts_to_keep); + let mode = PhaseAMode::from_str_or_default(phase_a_mode); + let (cleaned_string, stats) = core_clean_text_with_stats_with_mode( + text_ref, + &allowed_chars, + &unusual_chars, + min_chars_for_comment, + mode, + ); + let dict = PyDict::new(py); + dict.set_item("content_chars_kept", stats.content_chars_kept)?; + dict.set_item( + "chars_dropped_by_line_drop", + stats.chars_dropped_by_line_drop, + )?; + dict.set_item( + "chars_dropped_by_normalization", + stats.chars_dropped_by_normalization, + )?; + dict.set_item( + "chars_dropped_by_per_char_filter", + stats.chars_dropped_by_per_char_filter, + )?; + dict.set_item("lines_dropped_count", stats.lines_dropped_count)?; + dict.set_item("marker_chars_passthrough", stats.marker_chars_passthrough)?; + dict.set_item("marker_chars_added", stats.marker_chars_added)?; + dict.set_item( + "original_chars_for_badness", + stats.original_chars_for_badness, + )?; + dict.set_item( + "sum_kept_line_content_chars", + stats.sum_kept_line_content_chars, + )?; + // Q4 / Point 9 fields — None when not in ParserSurgicalVerified mode. + dict.set_item("phase_a_fallback_reason", stats.phase_a_fallback_reason)?; + dict.set_item( + "phase_a_dialect_ambiguous_input", + stats.phase_a_dialect_ambiguous_input, + )?; + // Point 7 per-rule match counts — drives sample-cutting + + // review-wave selection, replacing the noise-matcher's separate + // counter pass. + dict.set_item("rule_a_match_count", stats.rule_a_match_count)?; + dict.set_item("rule_b_match_count", stats.rule_b_match_count)?; + dict.set_item("residue_line_drop_count", stats.residue_line_drop_count)?; + Ok((cleaned_string, dict.into())) +} + // Helper function for script percentage calculation (moved from analyze_text for clarity) /* fn calc_script_percentages(py: Python, text: &str, scripts_to_keep: &[String]) -> PyResult { @@ -548,31 +1617,21 @@ pub fn perform_text_analysis( // This block already calculates cleaned_non_whitespace_chars_val correctly after cleaning if calculate_specific_counts { - let mut current_greek_count = 0; - let mut current_latin_count = 0; - let mut current_cleaned_non_ws_count = 0; - - let greek_set = SCRIPT_SETS.get("greek").cloned().unwrap_or_default(); - let latin_set = SCRIPT_SETS.get("latin").cloned().unwrap_or_default(); - - for ch in cleaned_text.chars() { - if !ch.is_whitespace() { - current_cleaned_non_ws_count += 1; - } - if scripts_for_percentage_and_specific_counts.contains(&"greek".to_string()) - && greek_set.contains(&ch) - { - current_greek_count += 1; - } - if scripts_for_percentage_and_specific_counts.contains(&"latin".to_string()) - && latin_set.contains(&ch) - { - current_latin_count += 1; - } + let metrics = scan_script_metrics(&cleaned_text); + let include_greek = scripts_for_percentage_and_specific_counts + .iter() + .any(|script| script == "greek"); + let include_latin = scripts_for_percentage_and_specific_counts + .iter() + .any(|script| script == "latin"); + + if include_greek { + greek_char_count_cleaned = Some(metrics.greek_char_count as usize); + } + if include_latin { + latin_char_count_cleaned = Some(metrics.latin_char_count as usize); } - greek_char_count_cleaned = Some(current_greek_count); - latin_char_count_cleaned = Some(current_latin_count); - cleaned_non_whitespace_chars_val = Some(current_cleaned_non_ws_count); + cleaned_non_whitespace_chars_val = Some(metrics.non_whitespace_chars as usize); } else { cleaned_non_whitespace_chars_val = Some(cleaned_text.chars().filter(|c| !c.is_whitespace()).count()); @@ -751,3 +1810,1062 @@ pub fn list_available_scripts() -> PyResult> { .cloned() .collect()) } + +#[cfg(test)] +mod tests { + use super::*; + + fn default_allowed_chars() -> HashSet { + let mut allowed_chars = HashSet::new(); + for key in ["greek", "latin", "punctuation", "numbers", "common_symbols"] { + if let Some(script_set) = SCRIPT_SETS.get(key) { + allowed_chars.extend(script_set); + } + } + allowed_chars.insert(' '); + allowed_chars.insert('\t'); + allowed_chars.insert('\n'); + allowed_chars + } + + /// Test helper: pin Phase A to `LineBased` for tests that depend + /// on the legacy line-based markdown normalizer's specific output + /// shape (separator collapse to `---`, escaped-underscore + /// bucketing, etc.). Pilot B preserves the input markdown more + /// strictly; tests asserting on collapse-style outputs need to + /// pin LineBased explicitly. + #[test] + fn core_clean_text_decoded_glyph_tag_stripped_keeps_prose() { + // Wave-2 (Case 7): entity-decode + GLYPH-strip pre-passes mean + // GLYPH<...> markers (even when entity-encoded) are removed + // inline, leaving the surrounding prose. Old behavior: line-drop + // with marker. New behavior: keep prose. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "prefix GLYPH<c=3,font=/QCMXYA+CenturyGothic> suffix\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + // GLYPH<...> deleted → "prefix suffix\n" (extra space collapsed + // by whitespace normalize). The /uni-style font-name path is + // also covered by the same regex. + assert!(cleaned.contains("prefix"), "got {:?}", cleaned); + assert!(cleaned.contains("suffix"), "got {:?}", cleaned); + assert!(!cleaned.contains("GLYPH"), "got {:?}", cleaned); + assert!(!cleaned.contains("<"), "got {:?}", cleaned); + } + + #[test] + fn core_clean_text_normalizes_long_dot_leaders_without_badness_penalty() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Chapter .......................................... 85\n"; + let (cleaned, original_chars, kept_chars) = + core_clean_text(input, &allowed_chars, &unusual_chars, None); + // Tiered bucket: 42 dots (>10) → 20 dots; 2 spaces → 1 space. + assert_eq!(cleaned, "Chapter .................... 85\n"); + assert_eq!(original_chars, input.trim_end_matches('\n').chars().count()); + assert_eq!(kept_chars, original_chars); + } + + #[test] + fn core_clean_text_bare_glyph_code_stripped_keeps_prose() { + // Wave-2 (Case 7): GLYPH<\d+> deleted, prose preserved. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "prefix GLYPH<236> suffix\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains("prefix"), "got {:?}", cleaned); + assert!(cleaned.contains("suffix"), "got {:?}", cleaned); + assert!(!cleaned.contains("GLYPH"), "got {:?}", cleaned); + } + + #[test] + fn core_clean_text_collapses_runs_of_3plus_newlines_to_2() { + // CommonMark renders any number of blank lines as one block + // separator, so a `\n{3+}` run is preview-equivalent to `\n\n`. + // The cleaner can produce these accidentally when per-char + // strip empties adjacent single-char lines (e.g. PUA bracket + // glyphs surrounded by `\n\n` separators in the source). + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + // Five PUA chars (U+F8EB..U+F8F7) on their own lines, separated + // by blank lines. None are in the Adobe Symbol fold map → each + // line gets stripped, leaving 10 consecutive `\n`. + let input = + "παρακάτω σχέση:\n\n\u{F8EC}\n\n\u{F8EB}\n\n\u{F8F7}\n\n\u{F8F6}\n\n$$x = 1$$\n\n\u{F8ED}\n"; + let (cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert!( + !cleaned.contains("\n\n\n"), + "collapse rule must reduce \\n{{3+}} → \\n\\n, got {cleaned:?}" + ); + // Sanity: legitimate single blank line between paragraphs survives. + assert!(cleaned.contains("\n\n")); + // Bytes removed should be reflected in the normalization bucket. + assert!(stats.chars_dropped_by_normalization > 0); + } + + #[test] + fn core_clean_text_bare_glyph_word_stripped_as_bounded_exception() { + // Wave 3: real PDF extractors emit bare GLYPH placeholders, so + // `GLYPH` is now the narrow exception to the no-bare-words rule. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "some text with GLYPH in the middle\n"; + let (cleaned, _, kept_chars) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_ne!(cleaned, format!("{LINE_REMOVED_COMMENT}\n")); + assert!(!cleaned.contains("GLYPH")); + assert!(cleaned.contains("some text with")); + assert!(cleaned.contains("in the middle")); + assert!(kept_chars > 0); + } + + #[test] + fn core_clean_text_repeated_bare_glyph_word_stripped_as_same_family() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "some text with GLYPHGLYPHGLYPH in the middle\n"; + let (cleaned, _, kept_chars) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_ne!(cleaned, format!("{LINE_REMOVED_COMMENT}\n")); + assert!(!cleaned.contains("GLYPH")); + assert!(cleaned.contains("some text with")); + assert!(cleaned.contains("in the middle")); + assert!(kept_chars > 0); + } + + #[test] + fn core_clean_text_drops_glyph_contaminated_display_math() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "$$M`GLYPHGLYPHGLYPH$gg]GLYPHGLYPHg\"\\IgGLYPH;GLYPHggGLYPH$$\n"; + let (cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert!( + cleaned.contains(LINE_REMOVED_COMMENT), + "glyph-contaminated display math should be dropped, got {cleaned:?}" + ); + assert!(!cleaned.contains("GLYPH"), "got {cleaned:?}"); + assert_eq!(stats.lines_dropped_count, 1); + assert!(stats.rule_b_match_count > 0, "got {stats:?}"); + } + + #[test] + fn core_clean_text_keeps_clean_display_math() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "$$x^2 + y^2 = z^2$$\n"; + let (cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains("$$x^2 + y^2 = z^2$$"), "got {cleaned:?}"); + assert_eq!(stats.lines_dropped_count, 0); + assert_eq!(stats.rule_a_match_count, 0); + assert_eq!(stats.rule_b_match_count, 0); + } + + #[test] + fn core_clean_text_does_not_strip_glyph_like_spans_inside_urls_or_comments() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = + "see https://example.org/GLYPHGLYPH/a/space/g123/uni03B1 and \n"; + let (cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains("https://example.org/GLYPHGLYPH/a/space/g123/uni03B1")); + assert!(cleaned.contains("")); + assert_eq!(stats.rule_a_match_count, 0); + assert_eq!(stats.rule_b_match_count, 0); + assert_eq!(stats.lines_dropped_count, 0); + } + + #[test] + fn core_clean_text_normalizes_hyphenminus_ranges_but_keeps_urls() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "range 4.600/hyphenminus5.600 and 75/hyphenminus77 and θεσμικο/hyphenminusδιοικητικών\n\ +see https://example.org/path/hyphenminus/file\n"; + let (cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains("4.600-5.600"), "got {cleaned:?}"); + assert!(cleaned.contains("75-77"), "got {cleaned:?}"); + assert!(cleaned.contains("θεσμικο-διοικητικών"), "got {cleaned:?}"); + assert!(cleaned.contains("https://example.org/path/hyphenminus/file")); + assert_eq!(stats.lines_dropped_count, 0); + assert_eq!(stats.rule_a_match_count, 3); + } + + #[test] + fn core_clean_text_span_strips_structured_glyph_variants() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "prefix GLYPH(foo) and glyph[bar] suffix\n"; + let (cleaned, _, kept_chars) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_ne!(cleaned, format!("{LINE_REMOVED_COMMENT}\n")); + assert!(cleaned.contains("prefix")); + assert!(cleaned.contains("suffix")); + assert!(!cleaned.contains("GLYPH("), "got {cleaned:?}"); + assert!(!cleaned.contains("glyph["), "got {cleaned:?}"); + assert!(kept_chars > 0); + } + + #[test] + fn core_clean_text_span_strips_ps_uni_glyph_names_in_prose() { + // Per 2026-04-22 Gemini wave: /uni is now SPAN-stripped, not + // line-rejected, when coverage predicate is not met. Matches + // should disappear; surrounding prose should remain. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "foo /uni03B1 /uni03B2 /uni03B3 bar\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + // Prose "foo" + "bar" survive; /uni spans removed. + assert!(cleaned.contains("foo"), "got {:?}", cleaned); + assert!(cleaned.contains("bar"), "got {:?}", cleaned); + assert!(!cleaned.contains("/uni03B1"), "got {:?}", cleaned); + } + + #[test] + fn core_clean_text_dense_rule_b_matches_now_stripped_to_empty() { + // Wave-2 (Case 7): the wave-2 GLYPH/uni/gN strip pre-pass + // deletes ALL `/g` markers up front. A line that was + // entirely `/g` tokens reduces to whitespace + becomes + // empty post-strip; the cleaner emits the line-removed + // marker for the now-empty content. Old behavior used + // rule-B density to decide; new behavior is even stricter + // (markers gone unconditionally). + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "/g302/g544/g306/g542/g304/g538/g652/g305/g536/g545/g541/g547\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + // After strip, line is empty/whitespace → either dropped + // entirely or replaced by line-removed marker. Either way, + // no /g tokens survive. + assert!(!cleaned.contains("/g"), "got {:?}", cleaned); + } + + #[test] + fn core_clean_text_span_strips_pdf_font_subset_form() { + // CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 4: PDF font + // subset references (Adobe `/[A-Z]{6}+FontName` convention) + // are now SPAN-STRIPPED by the unified Rule B regex. A single + // occurrence below the count+coverage gate does NOT line-drop + // — surrounding prose is preserved with the marker removed. + // (Pre-Point-4 PDF_FONT_SUBSET_REGEX in + // `has_decoded_glyph_font_artefact` line-dropped on any-match; + // that engine is gone.) + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Text /XQDMQS+CenturyGothic in it.\n"; + let (cleaned, _, kept_chars) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_ne!(cleaned, format!("{LINE_REMOVED_COMMENT}\n")); + assert!(cleaned.contains("Text")); + assert!(cleaned.contains("in it.")); + assert!(!cleaned.contains("/XQDMQS+CenturyGothic")); + assert!(kept_chars > 0); + } + + #[test] + fn core_clean_text_dense_pdf_font_subsets_line_drop() { + // 12 adjacent font-subset markers — Rule B's regex requires + // 2+ chars after the `+` (`[A-Z][A-Za-z0-9-]+`), so `+Tn`. + // Coverage = 12/120 = 0.10 ≥ 0.09; count = 12 ≥ 10. + // Verifies the count+coverage gate now includes font-subset + // matches (Point 4 unification). + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "/AAAAAA+Tn/BBBBBB+Tn/CCCCCC+Tn/DDDDDD+Tn\ +/EEEEEE+Tn/FFFFFF+Tn/GGGGGG+Tn/HHHHHH+Tn\ +/IIIIII+Tn/JJJJJJ+Tn/KKKKKK+Tn/LLLLLL+Tn\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!( + cleaned.contains(LINE_REMOVED_COMMENT), + "dense font-subset line should hit the gate, got {:?}", + cleaned + ); + } + + #[test] + fn core_clean_text_span_strips_ps_glyph_literals_in_prose() { + // `/hyphenminus /space /period ...` — rule A literals. Per + // 2026-04-22 Gemini wave: SPAN-strip unconditionally; surrounding + // prose (here "hello"/"world") should remain. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + for input in [ + "foo /hyphenminus bar\n", + "x /space y\n", + "a /period b\n", + "before /elipsis after\n", + ] { + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_ne!( + cleaned, + format!("{LINE_REMOVED_COMMENT}\n"), + "rule A literals should not line-drop: {:?} → {:?}", + input, + cleaned + ); + assert!( + !cleaned.contains("/hyphenminus") + && !cleaned.contains("/space") + && !cleaned.contains("/period") + && !cleaned.contains("/elipsis"), + "rule A literal should be stripped from {:?} → {:?}", + input, + cleaned + ); + } + } + + #[test] + fn core_clean_text_bare_hyphenminus_passes_through() { + // CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 4: Rule B's + // "no bare-word matchers" rule means bare `hyphenminus` + // (without the leading `/`) is NO LONGER a line-drop trigger. + // Pre-Point-4 BAD_LINE_AC matched it; Rule B's regex does not. + // The bare word survives. (`/hyphenminus` with the slash + // continues to span-strip via Rule A literals.) + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "hello hyphenminus world\n"; + let (cleaned, _, kept_chars) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_ne!(cleaned, format!("{LINE_REMOVED_COMMENT}\n")); + assert!(cleaned.contains("hyphenminus")); + assert!(kept_chars > 0); + } + + #[test] + fn core_clean_text_does_not_reject_legitimate_slash_word() { + // Guard: /united-nations /university-of-X in URLs must survive. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "See https://example.com/united-nations/report.\n"; + let (cleaned, _, kept_chars) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_ne!(cleaned, format!("{LINE_REMOVED_COMMENT}\n")); + assert!(kept_chars > 0); + } + + #[test] + fn core_clean_text_strips_lrm_rlm_direction_marks() { + // LRM (U+200E) and RLM (U+200F) are invisible bidi-direction marks + // inserted by MediaWiki around foreign-language translations. They + // have no semantic purpose in Greek text and must strip. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Μεσοπόλεμος (λατινικά: Interbellum\u{200E}\u{200E}, γερμανικά: Zwischenkriegszeit\u{200F})\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(!cleaned.contains('\u{200E}')); + assert!(!cleaned.contains('\u{200F}')); + assert!(cleaned.contains("Interbellum")); + assert!(cleaned.contains("Zwischenkriegszeit")); + } + + #[test] + fn core_clean_text_strips_unicode_noise_chars() { + // After CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 2: + // - U+00AD soft hyphen → stripped by `is_unicode_noise_char` + // (unified Group 1 STRIP) inside the per-line loop. + // - U+F0B7 is not in the Adobe Symbol PUA fold map → falls + // through fold_codepoint → stripped by per-char filter. + // - U+FFFD and U+03A2 (non-existent Greek codepoint) → also + // stripped by per-char filter. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "A\u{00AD}B \u{F0B7} C\u{FFFD}D \u{03A2}\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + // Essential invariants: noise chars all gone, prose letters kept. + assert!(cleaned.contains('A')); + assert!(cleaned.contains('B')); + assert!(cleaned.contains('C')); + assert!(cleaned.contains('D')); + assert!(!cleaned.contains('\u{00AD}')); + assert!(!cleaned.contains('\u{F0B7}')); + assert!(!cleaned.contains('\u{FFFD}')); + assert!(!cleaned.contains('\u{03A2}')); + } + + #[test] + fn core_clean_text_normalizes_gfm_table_separator() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "| A | B |\n| :------- | -------: |\n| 1 | 2 |\n"; + let (cleaned, original_chars, kept_chars) = + core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains("| :--- | ---: |")); + // Semantics-preserving normalization => badness neutral on the + // separator row (the kept count for that row equals the original). + assert_eq!(kept_chars, original_chars); + } + + #[test] + fn core_clean_text_skips_fenced_code_block() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + // Four spaces of indentation and `....` inside a fenced block must survive. + let input = "Prose\n```\n indented...\n----\n```\nMore prose\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains(" indented...")); + // The `----` inside the fence must NOT collapse to `---`. + let fence_block: Vec<&str> = cleaned + .lines() + .skip_while(|l| !l.starts_with("```")) + .collect(); + assert!(fence_block.iter().any(|l| *l == "----")); + } + + #[test] + fn core_clean_text_only_cleans_impossible_noise_inside_code_fence() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "```\nlet µ = 1;\u{00AD}\u{0007}\n----\n```\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains("let μ = 1;")); + assert!(!cleaned.contains('\u{00AD}')); + assert!(!cleaned.contains('\u{0007}')); + assert!(cleaned.lines().any(|l| l == "----")); + } + + #[test] + fn core_clean_text_folds_math_italic_latin() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Let 𝑥 + 𝑦 = 𝑧.\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_eq!(cleaned, "Let x + y = z.\n"); + } + + #[test] + fn core_clean_text_normalizes_toc_whitespace_leader_via_bucket() { + // A TOC line where title and page number are separated by a long + // whitespace run (PDF table-of-contents layout). The tiered bucket + // whitespace rule bucketizes the run to 20 spaces (>10 → 20), + // preserving the visual TOC signal without a TOC-specific heuristic. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Κεφάλαιο 1 Εισαγωγή 5\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + // Original had 30 spaces between "Εισαγωγή" and "5"; tiered → 20. + let expected = format!("Κεφάλαιο 1 Εισαγωγή{}5\n", " ".repeat(20)); + assert_eq!(cleaned, expected); + } + + #[test] + fn core_clean_text_collapses_ellipsis_runs() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "wait……… then\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_eq!(cleaned, "wait..... then\n"); + } + + #[test] + fn core_clean_text_preserves_polytonic_greek() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + // Polytonic chars in U+1F00..U+2000 must survive; previously they + // passed through by coincidence, now explicit in the `greek` set. + let input = "Λόγος πολυτονικός: ἀγαθός, εὐδαιμονία.\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains('ἀ')); // U+1F00 GREEK SMALL LETTER ALPHA WITH PSILI + assert!(cleaned.contains('ὐ')); // U+1F50 GREEK SMALL LETTER UPSILON WITH PSILI + } + + #[test] + fn core_clean_text_preserves_non_greek_latin_scripts() { + // Policy (2026-04-21): Armenian/Hebrew/Arabic/Georgian carry semantic + // meaning; Apertus's multilingual training covers them. We preserve + // them rather than strip. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Greek κείμενο \u{10A0} και \u{0531} και \u{0627} συνεχίζει.\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains('\u{10A0}')); // Georgian letter an + assert!(cleaned.contains('\u{0531}')); // Armenian capital ayb + assert!(cleaned.contains('\u{0627}')); // Arabic alef + assert!(cleaned.contains("Greek")); + assert!(cleaned.contains("κείμενο")); + } + + #[test] + fn core_clean_text_folds_math_greek_to_plain_greek() { + // Math-italic Greek letters in a Greek corpus are almost always OCR + // residue of italicized Greek in equations. Fold (not strip) to the + // regular Greek codepoint Apertus tokenizes efficiently. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Let 𝛼 + 𝛽 = 𝛾 in Greek.\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert_eq!(cleaned, "Let α + β = γ in Greek.\n"); + } + + // ----------------------------------------------------------------- + // Char accounting regression suite (added 2026-04-22) + // ----------------------------------------------------------------- + + /// Helper: assert the INPUT-side char accounting invariant: + /// input_chars ≈ content_kept + line_drop + normalize + per_char + /// + marker_chars_passthrough + /// marker_chars_added is NOT part of input — those are chars we emitted + /// into output that weren't in the input (LINE_REMOVED_COMMENT, inline + /// TMC additions, etc.). + fn assert_accounting_invariant(input: &str, stats: &CleanStats) { + let input_chars = input.lines().map(|l| l.chars().count()).sum::(); + let accounted = stats.content_chars_kept + + stats.chars_dropped_by_line_drop + + stats.chars_dropped_by_normalization + + stats.chars_dropped_by_per_char_filter + + stats.marker_chars_passthrough; + // Entity decoding can shrink chars (`&` 5→1) so accounting may + // undercount slightly. We only assert we don't OVER-count. + assert!( + accounted <= input_chars + 2, // small slack for edge cases + "accounting overshoot: input={input_chars} accounted={accounted} stats={stats:?}" + ); + // And that we don't massively undercount either. + // Wave-2: preprocessing passes (entity decode, GLYPH strip, + // soft-hyphen, paragraph reflow) can each subtract chars + // counted ONCE in `chars_dropped_by_normalization`, but + // input_chars is the original length. So undercount slack + // needs to allow for substantial pre-pass deletions. Use a + // generous fraction-based slack. + let slack = (input_chars / 10).max(20); + assert!( + accounted + slack >= input_chars, + "accounting undershoot: input={input_chars} accounted={accounted} slack={slack} stats={stats:?}" + ); + } + + #[test] + fn accounting_clean_greek_text_goes_to_content_kept() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Καλημέρα κόσμε.\nΚαι πάλι.\n"; + let (_cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert_eq!(stats.chars_dropped_by_line_drop, 0); + assert_eq!(stats.lines_dropped_count, 0); + assert_eq!(stats.marker_chars_passthrough, 0); + assert_eq!(stats.marker_chars_added, 0); + assert!(stats.content_chars_kept > 0); + assert_accounting_invariant(input, &stats); + } + + #[test] + fn accounting_line_drop_bumps_counter_and_chars() { + // Post-Point-4: Rule B's count+coverage gate is the primary + // line-drop signal for PostScript-glyph residue. 12 dense + // `/uniXXXX` markers (>10 count, >9% coverage) trigger a drop. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "Καλημέρα.\n\ +/uni0301/uni0302/uni0303/uni0304/uni0305/uni0306\ +/uni0307/uni0308/uni0309/uni030A/uni030B/uni030C\n\ +Επίλογος.\n"; + let (cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert!( + stats.lines_dropped_count >= 1, + "expected at least one line drop, got {stats:?}" + ); + assert!( + stats.chars_dropped_by_line_drop > 0, + "expected line-drop chars, got {stats:?}" + ); + assert!(cleaned.contains(LINE_REMOVED_COMMENT)); + let marker_chars = LINE_REMOVED_COMMENT.chars().count(); + assert!( + stats.marker_chars_added >= marker_chars, + "LINE_REMOVED_COMMENT should be in marker_chars_added: {stats:?}" + ); + assert_eq!( + stats.marker_chars_passthrough, 0, + "no pass-through markers in this input: {stats:?}" + ); + } + + #[test] + fn accounting_content_chars_excludes_line_removed_marker() { + // Single dense Rule B line — only line in input, must drop. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "/uni0301/uni0302/uni0303/uni0304/uni0305/uni0306\ +/uni0307/uni0308/uni0309/uni030A/uni030B/uni030C\n"; + let (cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + // The whole line dropped — content_chars_kept MUST be 0 even + // though the output has the marker in it. + assert_eq!(stats.content_chars_kept, 0); + assert!(cleaned.contains(LINE_REMOVED_COMMENT)); + assert_eq!( + stats.marker_chars_added, + LINE_REMOVED_COMMENT.chars().count() + ); + assert_eq!(stats.marker_chars_passthrough, 0); + } + + #[test] + fn accounting_per_char_filter_tracks_unusual_script_strip() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + // Coptic letters (U+2C80..U+2D00) are in `unusual` per Point 3 + // (not modern-Greek-corpus content, stripped by per-char filter). + // Cyrillic, by contrast, is now KEPT entirely (European-language + // content), so don't use it for this test. + let input = "Καλημέρα ⲁⲃⲅⲇⲉⲋⲍⲏⲑⲓⲕⲗⲙⲛ.\n"; + let (_cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + // All 14 Coptic chars should be stripped by the per-char filter. + assert!( + stats.chars_dropped_by_per_char_filter >= 12, + "expected ≥12 per-char-filter chars dropped, got {stats:?}" + ); + assert_eq!(stats.chars_dropped_by_line_drop, 0); + assert_accounting_invariant(input, &stats); + } + + #[test] + fn normalization_collapses_whitespace_left_after_cleaning() { + // Per 2026-04-22 user guidance: normalization runs AFTER cleaning + // so that when a word/span is stripped, the gap it leaves collapses + // cleanly. Input: "hello /hyphenminus world" → + // after rule-A strip: "hello world" (2 spaces) + // after whitespace-run normalize: "hello world" (1 space) + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let input = "hello /hyphenminus world\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!( + cleaned.contains("hello world\n"), + "expected single space between hello/world, got {cleaned:?}" + ); + assert!( + !cleaned.contains("hello world"), + "double space should have been collapsed, got {cleaned:?}" + ); + } + + #[test] + fn normalization_fires_on_inline_tmc_lines_too() { + // When enough chars are stripped to trigger inline TMC + // (>=5 unicode-filter removals on the line), normalize should STILL + // run on the surviving prose so long whitespace runs bucket-collapse. + // Before 2026-04-22 fix: normalize was skipped whenever + // line_content_to_add contained TEXT_MISSING_COMMENT, leaving raw + // 6-space gaps in the output. + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + // Coptic word stripped by per-char filter → 6 consecutive spaces + // (3 + 3 around the removed word). bucket_run_length(6) = 5, so a + // 6-space run means normalize didn't fire. (Was Cyrillic pre-Point-3, + // but Cyrillic is now KEPT as European content.) + let input = "Καλημέρα ⲁⲃⲅⲇⲉⲋⲍ world\n"; + let (cleaned, _, _) = core_clean_text(input, &allowed_chars, &unusual_chars, None); + assert!(cleaned.contains(TEXT_MISSING_COMMENT)); + assert!( + !cleaned.contains(" "), + "6-space run should have been bucket-collapsed by normalize, got {cleaned:?}" + ); + } + + #[test] + fn accounting_rule_a_span_strip_goes_to_per_char_filter_not_line_drop() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + // Rule A literals `/hyphenminus /space` inside otherwise-valid prose. + // Should span-strip (per-char filter) — NOT line-drop. + let input = "foo /hyphenminus /space bar\n"; + let (_cleaned, stats) = + core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + assert_eq!(stats.lines_dropped_count, 0); + assert_eq!(stats.chars_dropped_by_line_drop, 0); + assert!( + stats.chars_dropped_by_per_char_filter >= 18, + "expected rule-A literals (`/hyphenminus`=12 + `/space`=6) stripped, got {stats:?}" + ); + assert_accounting_invariant(input, &stats); + } + + // ----------------------------------------------------------------- + // Performance baseline — regression fence (added 2026-04-22) + // ----------------------------------------------------------------- + + /// Build a representative mixed-content doc: Greek prose, rule-B dense + /// line, separator, escaped-underscore divider, unusual-script strip, + /// GFM table, code fence, malformed entities — roughly everything the + /// cleaner handles in one pass, blown up to ~8 KB. + fn bench_doc() -> String { + let block = "\ +Καλημέρα κόσμε. Η γλώσσα μας είναι πλούσια. λόγος ἀγαθός. +Η πρόταση περιέχει & πολλά < σύμβολα. +/g302/g544/g306/g542/g304/g538/g652/g305/g536/g545/g541/g547 +foo /hyphenminus /space /period bar /uni03B1 /uni03B2 +-------------------- +\\_\\_\\_\\_\\_\\_\\_\\_ +Καλημέρα Здравствуйте ქართული. +| Column | Value | +| :------- | ---: | +| α | 1 | + +Some dots in a row...... +``` +code fence content stays +---- +``` +Επίλογος. +"; + // Blow up ~12x to get a representative ~8 KB doc. + let mut out = String::with_capacity(block.len() * 12); + for _ in 0..12 { + out.push_str(block); + } + out + } + + /// Regression fence: assert cleaner throughput stays above a + /// conservative minimum. The baseline (measured 2026-04-22 on the + /// author's laptop, release build, single-threaded) is ~40 M chars/sec + /// on this mixed-content doc. The threshold below is deliberately well + /// under that (5 M chars/sec) — this test should only trip on major + /// regressions, not normal CI-machine variability. + /// Bug 2 (CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25): the 5 M + /// chars/sec floor is a release-profile expectation. Default + /// `cargo test` runs in DEBUG profile (~7× slower) so the floor + /// always trips. `#[ignore]` keeps the test out of the default + /// run; invoke explicitly with + /// `cargo test perf_mixed_doc_throughput_floor -- --ignored --release` + /// when checking for regressions. + #[test] + #[ignore = "release-only perf check; run with --ignored --release"] + fn perf_mixed_doc_throughput_floor() { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let doc = bench_doc(); + let doc_chars = doc.chars().count(); + let iterations = 50; + let start = std::time::Instant::now(); + for _ in 0..iterations { + let (_cleaned, _stats) = + core_clean_text_with_stats(&doc, &allowed_chars, &unusual_chars, None); + } + let elapsed = start.elapsed(); + let total_chars = doc_chars * iterations; + let chars_per_sec = total_chars as f64 / elapsed.as_secs_f64(); + let min_chars_per_sec = 5_000_000.0; + assert!( + chars_per_sec >= min_chars_per_sec, + "throughput regression: {chars_per_sec:.0} chars/sec < {min_chars_per_sec:.0} floor \ + ({total_chars} chars in {:.3}s)", + elapsed.as_secs_f64(), + ); + // Print so `cargo test -- --nocapture` shows the actual number. + eprintln!( + "[perf] core_clean_text_with_stats: {chars_per_sec:.0} chars/sec ({iterations} x {doc_chars} chars in {:.3}s)", + elapsed.as_secs_f64(), + ); + } + + // ------------------------------------------------------------------ + // Phase B end-to-end structural-equivalence regression tests. + // + // Runs the full `core_clean_text_with_stats` pipeline (Phase A + // MD-syntax + Phase B content-modifying) on realistic inputs and + // asserts `md_verify::verify_md_structural` passes. These catch + // regressions where the cleaner accidentally drops / reorders / + // fuses content in ways that violate the "output tokens are a + // monotone subsequence of input tokens" invariant. + // + // Phase B safeguards: docs with MD-syntax chars (`|`, `#`, `---`) + // in syntactic positions must still have those chars in the + // cleaner output. Regression net against future per-char-filter + // misconfigurations. + // + // See `docs/MD_MODULE_ARCHITECTURE.md`. + // ------------------------------------------------------------------ + + fn run_full_cleaner(input: &str) -> String { + let allowed_chars = default_allowed_chars(); + let unusual_chars = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let (cleaned, _) = core_clean_text_with_stats(input, &allowed_chars, &unusual_chars, None); + cleaned + } + + #[test] + fn phase_b_structural_equiv_on_simple_prose() { + let input = + "# Title\n\nFirst paragraph of Greek prose. Δεύτερη πρόταση.\n\nSecond paragraph.\n"; + let out = run_full_cleaner(input); + let r = crate::md_verify::verify_md_structural(input, &out); + assert!( + r.is_structural_equivalent(), + "structural equivalence violated: {:?}", + r + ); + } + + #[test] + fn phase_b_structural_equiv_on_entity_decode_and_glyph_strip() { + // Phase B deletions (GLYPH strip) + Phase A entity decode. + // Both change raw chars. Structural subsequence should still hold. + let input = "# Heading\n\nΗ εργασία & GLYPH<216> αναφέρεται.\n"; + let out = run_full_cleaner(input); + let r = crate::md_verify::verify_md_structural(input, &out); + assert!(r.is_structural_equivalent(), "{:?}", r); + assert!(r.token_retention_pct < 1.0, "expected some tokens dropped"); + } + + #[test] + fn phase_b_table_cells_preserved_after_cleaning() { + let input = + "| Col A | Col B | Col C |\n| ---- | ---- | ---- |\n| α | β | γ |\n| 1 | 2 | 3 |\n"; + let out = run_full_cleaner(input); + let r = crate::md_verify::verify_md_structural(input, &out); + assert!(r.table_cells_subsequence, "{:?}", r); + } + + #[test] + fn phase_b_mixed_content_doc_passes_structural() { + let input = concat!( + "# Top heading\n\n", + "First soft-wrapped\nparagraph of Greek prose.\n\n", + "| a | b |\n| ---------- | ---------- |\n| α | β |\n\n", + "----------\n\n", + "## Section two\n\n", + "- item alpha\n- item beta\n\n", + "Final paragraph.\n" + ); + let out = run_full_cleaner(input); + let r = crate::md_verify::verify_md_structural(input, &out); + assert!(r.is_structural_equivalent(), "{:?}", r); + } + + // --- Phase B safeguards: MD-syntax chars must survive --- + + #[test] + fn phase_b_preserves_heading_marker() { + let input = "# My Heading\n\nbody text.\n"; + let out = run_full_cleaner(input); + assert!( + out.contains("# My Heading") || out.contains("#My Heading"), + "heading `#` stripped by Phase B: output={:?}", + out + ); + } + + #[test] + fn phase_b_preserves_table_pipes() { + let input = "| col1 | col2 |\n| --- | --- |\n| a | b |\n"; + let out = run_full_cleaner(input); + // Count pipes — should have the same structural count. + let in_pipes = input.chars().filter(|&c| c == '|').count(); + let out_pipes = out.chars().filter(|&c| c == '|').count(); + assert_eq!( + in_pipes, out_pipes, + "table pipes lost: in={} out={} output={:?}", + in_pipes, out_pipes, out + ); + } + + #[test] + fn phase_b_preserves_hr_thematic_break() { + let input = "before\n\n---\n\nafter\n"; + let out = run_full_cleaner(input); + assert!( + out.contains("---"), + "HR `---` stripped by Phase B: output={:?}", + out + ); + } + + #[test] + fn phase_b_preserves_fenced_code_backticks() { + let input = "before\n\n```\ncode body\n```\n\nafter\n"; + let out = run_full_cleaner(input); + let in_fences = input.matches("```").count(); + let out_fences = out.matches("```").count(); + assert_eq!( + in_fences, out_fences, + "fenced code markers lost: in={} out={} output={:?}", + in_fences, out_fences, out + ); + } + + #[test] + fn phase_b_preserves_list_markers() { + let input = "- alpha item\n- beta item\n- gamma item\n"; + let out = run_full_cleaner(input); + // Each `- ` at line start must survive. + let in_markers = input.lines().filter(|l| l.starts_with("- ")).count(); + let out_markers = out.lines().filter(|l| l.starts_with("- ")).count(); + assert_eq!( + in_markers, out_markers, + "list markers dropped: in={} out={} output={:?}", + in_markers, out_markers, out + ); + } + + #[test] + fn phase_b_preserves_blockquote_markers() { + let input = "> quoted text\n> continued\n"; + let out = run_full_cleaner(input); + assert!(out.contains(">"), "blockquote marker dropped: {:?}", out); + } + + #[test] + fn phase_b_v6_11_nbsp_does_not_fuse_words() { + // v6-11 regression: Docling emits NBSP (U+00A0) as the default + // word-separator on many PDFs. Prior cleaner stripped it as + // "unusual Latin-1 Supplement char", fusing Greek words into + // 70+ char blobs. Fix (2026-04-24): fold_codepoint now folds + // U+00A0 → U+0020 so downstream sees real whitespace. + let input = "Η\u{00A0}εργασία\u{00A0}αυτή\u{00A0}έχει\u{00A0}σκοπό.\n"; + let out = run_full_cleaner(input); + // After the fix, words should still be separated by whitespace. + assert!( + out.contains("Η εργασία") || out.contains("Η\u{00A0}εργασία"), + "NBSP fusion regressed — output lost word separator: {:?}", + out + ); + // Structural subsequence should hold (no fusion in token space). + let r = crate::md_verify::verify_md_structural(input, &out); + assert!( + r.is_structural_equivalent(), + "NBSP doc should pass structural equivalence: {:?}", + r + ); + assert_ne!( + r.subsequence_failure_kind.as_deref(), + Some("fusion"), + "should NOT be classified as fusion anymore: {:?}", + r + ); + } + + // ----------------------------------------------------------------- + // Commit 11 RED test — optional-pipe GFM tables through full cleaner. + // + // Per the reviewer, this Markdown is a valid GFM table: + // a | b + // --- | --- + // 1 | 2 + // Today's cleaner ordering (reflow first, table-sep canonicalization + // much later) means `--- | ---` is NOT detected as a hard break by + // `line_is_hard_break` (doesn't start/end with pipe, doesn't match + // SEPARATOR_LINE_REGEX). Reflow joins it with the header → table + // destroyed before the GFM-sep pass ever sees it. + // + // Fix in Commit 13: route cleaner through md_module::normalize_md_syntax + // as single Phase A entrypoint so GFM-sep canonicalization runs BEFORE + // reflow. + // ----------------------------------------------------------------- + + #[test] + fn red_until_c13_optional_pipe_gfm_table_survives_full_cleaner() { + let input = "a | b\n--- | ---\n1 | 2\n"; + let out = run_full_cleaner(input); + // Structural equivalence: block count + tokens. Passes only if + // the cleaner preserves the table as a table (header row, + // separator row, body row). + let r = crate::md_verify::verify_md_structural(input, &out); + assert!( + r.is_structural_equivalent(), + "optional-pipe GFM table destroyed by cleaner — reflow fused \ + rows before table-sep canonicalization. Fix in Commit 13: \ + route cleaner through md_module::normalize_md_syntax as \ + single Phase A entrypoint. out={:?} report={:?}", + out, + r + ); + } + + #[test] + fn phase_b_other_unicode_spaces_also_preserved() { + // Narrow NBSP / thin space / em space / etc. all fold to + // regular space so word boundaries survive. + let input = "alpha\u{2009}beta\u{202F}gamma\u{2003}delta\n"; + let out = run_full_cleaner(input); + // All four words should still appear as distinct tokens. + for word in ["alpha", "beta", "gamma", "delta"] { + assert!( + out.contains(word), + "word `{}` lost in output: {:?}", + word, + out + ); + } + // Words should be whitespace-separated (not fused). + assert!( + !out.contains("alphabeta"), + "fusion across thin space: {:?}", + out + ); + } + + // ----------------------------------------------------------------- + // Commit 14 — shared non-destructive canonicalization. + // + // The verifier's `canonicalize_for_verify` now delegates to + // `md_module::non_destructive_canonicalize`. These tests assert the + // invariant that drove the extraction: on inputs where the cleaner + // would delete nothing, cleaner output must equal canonicalize + // output. Future drift in either code path trips this gate. + // ----------------------------------------------------------------- + + /// Permissive allowed-set covering everything in the default + /// script sets plus the 0..=127 ASCII range, so the cleaner has + /// nothing to drop at the per-char filter on the sample inputs + /// used by the drift-prevention tests. + fn permissive_allowed_chars() -> HashSet { + let mut allowed = default_allowed_chars(); + for ch in 0u32..=127 { + if let Some(c) = char::from_u32(ch) { + allowed.insert(c); + } + } + allowed + } + + fn assert_cleaner_matches_canonicalize(input: &str) { + let allowed = permissive_allowed_chars(); + let unusual = SCRIPT_SETS.get("unusual").cloned().unwrap_or_default(); + let (cleaned, _stats) = core_clean_text_with_stats(input, &allowed, &unusual, None); + let canonical = md_module::non_destructive_canonicalize(input); + assert_eq!( + cleaned.trim_end_matches('\n'), + canonical.trim_end_matches('\n'), + "cleaner output diverged from non_destructive_canonicalize:\n\ + input={:?}\ncleaner={:?}\ncanonical={:?}", + input, + cleaned, + canonical, + ); + } + + #[test] + fn drift_cleaner_eq_canonicalize_on_plain_prose() { + assert_cleaner_matches_canonicalize("Η εργασία αυτή έχει σκοπό την περιγραφή.\n"); + } + + #[test] + fn drift_cleaner_eq_canonicalize_on_optional_pipe_table() { + assert_cleaner_matches_canonicalize("a | b\n--- | ---\n1 | 2\n"); + } + + #[test] + fn drift_cleaner_eq_canonicalize_on_hr_collapse_with_adjacent_prose() { + assert_cleaner_matches_canonicalize( + "before paragraph.\n\n----------\n\nafter paragraph.\n", + ); + } + + #[test] + fn drift_cleaner_eq_canonicalize_on_soft_wrapped_paragraph() { + assert_cleaner_matches_canonicalize("first soft-wrapped\npiece of content\nhere.\n"); + } + + #[test] + fn drift_cleaner_eq_canonicalize_on_gfm_table_and_hr() { + assert_cleaner_matches_canonicalize(concat!( + "# Heading\n\n", + "| A | B |\n| ---------- | ---------- |\n| 1 | 2 |\n\n", + "----------\n\n", + "Paragraph soft-wrap\nacross two lines.\n", + )); + } +} diff --git a/rust/glossapi_rs_cleaner/src/cmark_gfm_oracle.rs b/rust/glossapi_rs_cleaner/src/cmark_gfm_oracle.rs new file mode 100644 index 0000000..6a4e81b --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/cmark_gfm_oracle.rs @@ -0,0 +1,408 @@ +//! cmark-gfm subprocess oracle — the CommonMark+GFM reference +//! renderer GitHub actually uses to render Markdown. +//! +//! We shell out to the `cmark-gfm` C binary (installed via the +//! `cmark-gfm` Debian package, `/usr/bin/cmark-gfm`) rather than +//! using a Rust port, because: +//! +//! 1. It IS the ground truth — GitHub's renderer uses this exact +//! codebase. No port-parity ambiguity. +//! 2. Our formatter (`md_format::format_parsed`) uses `comrak` (a +//! Rust port of cmark-gfm). If we also used comrak as the +//! verifier we'd have a tautology. Using cmark-gfm independently +//! tests that our comrak round-trip preserves rendering under +//! GitHub's actual renderer. +//! 3. `cmark-gfm` is fast enough (~1ms/doc subprocess overhead) for +//! all our scales: 29 fixtures = instant; 90 real docs = <1s; +//! 168K corpus audit = ~5min overhead on top of parse time. Not +//! on any hot path — verifier only runs during testing + corpus +//! audits. +//! +//! Availability: `/usr/bin/cmark-gfm` is installed on the gcloud +//! cleaning instance (`apertus-greek-tokenizer-20260408t160000z`); +//! for local dev we fall back to comrak (same codebase, high parity). + +use std::io::Write; +use std::process::{Command, Stdio}; + +/// Path at which we expect the cmark-gfm binary on the cleaning +/// instance. If the binary isn't at this path (or isn't on PATH), +/// callers should detect the failure via `is_available()` and fall +/// back to the in-process Rust oracle (`comrak`). +const CMARK_GFM_BIN: &str = "cmark-gfm"; + +/// GFM extensions to enable — matches what GitHub enables by default +/// for README / issue / PR rendering. Keeps rendering consistent +/// with the actual GitHub renderer. +const GFM_EXTENSIONS: &[&str] = &["table", "strikethrough", "tasklist", "autolink"]; + +/// Test whether the `cmark-gfm` binary is callable in this +/// environment. Result is cached for the process lifetime — the +/// binary's presence doesn't change between probes, and at corpus +/// scale the per-doc subprocess spawn that uncached probing +/// triggered was a measurable hot-path cost (Pilot B as default +/// hits this on every doc through `format_surgical_checked`). +pub fn is_available() -> bool { + use std::sync::OnceLock; + static CACHED: OnceLock = OnceLock::new(); + *CACHED.get_or_init(|| { + Command::new(CMARK_GFM_BIN) + .arg("--help") + .stdout(Stdio::null()) + .stderr(Stdio::null()) + .status() + .map(|s| s.success()) + .unwrap_or(false) + }) +} + +/// Render Markdown via `cmark-gfm --to html` with GFM extensions +/// matching GitHub's defaults. Returns the raw HTML exactly as +/// cmark-gfm emits it — no whitespace normalization. For equality +/// checks, byte-for-byte comparison is the right thing: the same +/// binary on the same input produces the same output, deterministically. +/// +/// Returns `Err(message)` if the subprocess fails for any reason +/// (binary missing, non-zero exit, IO error). +pub fn render_html(md: &str) -> Result { + let mut child = Command::new(CMARK_GFM_BIN) + .arg("--to") + .arg("html") + .args(GFM_EXTENSIONS.iter().flat_map(|e| ["--extension", e])) + .stdin(Stdio::piped()) + .stdout(Stdio::piped()) + .stderr(Stdio::piped()) + .spawn() + .map_err(|e| format!("spawn cmark-gfm: {e}"))?; + { + let stdin = child.stdin.as_mut().ok_or("cmark-gfm stdin not captured")?; + stdin + .write_all(md.as_bytes()) + .map_err(|e| format!("write to cmark-gfm stdin: {e}"))?; + } + let output = child + .wait_with_output() + .map_err(|e| format!("wait cmark-gfm: {e}"))?; + if !output.status.success() { + return Err(format!( + "cmark-gfm exited {}: stderr={}", + output.status, + String::from_utf8_lossy(&output.stderr) + )); + } + String::from_utf8(output.stdout).map_err(|e| format!("cmark-gfm non-utf8 output: {e}")) +} + +/// Verify that `input` and `output` render to the same HTML under +/// cmark-gfm. Compares both RAW html (byte-for-byte) and the +/// preview-equivalent form (whitespace between block tags collapsed, +/// trailing ws before closing tags stripped — standard HTML-preview +/// invariants that differ between `

a\nb

` and `

a b

`). +/// +/// The preview-equivalent form is the meaningful preservation +/// signal; byte-for-byte is a stricter stats-only property. +/// +/// If the binary isn't available, returns `Err`. Callers decide +/// whether to treat that as skip or fail. +pub fn verify(input: &str, output: &str) -> Result { + let in_html = render_html(input)?; + let out_html = render_html(output)?; + let byte_identical = in_html == out_html; + let in_normalized = normalize_for_preview_eq(&in_html); + let out_normalized = normalize_for_preview_eq(&out_html); + let preview_identical = in_normalized == out_normalized; + let first_diff = if preview_identical { + None + } else { + Some(find_first_diff(&in_normalized, &out_normalized)) + }; + Ok(CmarkGfmReport { + input_html: in_html, + output_html: out_html, + byte_identical, + preview_identical, + first_diff, + }) +} + +/// Normalize a cmark-gfm HTML render to a form where preview- +/// equivalent inputs produce equal strings. This is what "same +/// rendered page" means when the HTML bytes differ by invisible +/// whitespace (whitespace between block tags, trailing whitespace +/// before closing tags). +/// +/// Intentionally NOT too aggressive — only normalizations that +/// provably don't change visible rendering: +/// +/// 1. Collapse whitespace runs to a single space. +/// 2. Strip whitespace between adjacent tags (`> <` → `><`). +/// 3. Strip whitespace before closing tags (` String { + // Step 1: collapse whitespace runs to single space. + let mut collapsed = String::with_capacity(html.len()); + let mut prev_ws = false; + for c in html.chars() { + if c.is_whitespace() { + if !prev_ws { + collapsed.push(' '); + } + prev_ws = true; + } else { + collapsed.push(c); + prev_ws = false; + } + } + // Steps 2+3: strip `> <` → `><` and ` <", "><").replace(" , +} + +fn find_first_diff(a: &str, b: &str) -> String { + let ab = a.as_bytes(); + let bb = b.as_bytes(); + let n = ab.len().min(bb.len()); + let mut i = 0; + while i < n && ab[i] == bb[i] { + i += 1; + } + // Snap to the nearest char boundary at or before `start`, at or + // after `end` — byte offsets into UTF-8 strings MUST NOT split + // a multi-byte char (Greek, math symbols, etc. would panic). + let start = floor_char_boundary(a, i.saturating_sub(40)); + let end_a = ceil_char_boundary(a, (i + 120).min(a.len())); + let end_b = ceil_char_boundary(b, (i + 120).min(b.len())); + // `start` is relative to `a`; use a matching start for `b` that + // is also on a char boundary. + let start_b = floor_char_boundary(b, start.min(b.len())); + format!( + "first diff at byte {i} (in_len={} out_len={})\n in: {}\n out: {}", + a.len(), + b.len(), + &a[start..end_a], + &b[start_b..end_b] + ) +} + +fn floor_char_boundary(s: &str, mut i: usize) -> usize { + if i >= s.len() { + return s.len(); + } + while i > 0 && !s.is_char_boundary(i) { + i -= 1; + } + i +} + +fn ceil_char_boundary(s: &str, mut i: usize) -> usize { + while i < s.len() && !s.is_char_boundary(i) { + i += 1; + } + i +} + +// --------------------------------------------------------------------------- +// PyO3 surface. +// --------------------------------------------------------------------------- + +use pyo3::prelude::*; +use pyo3::types::PyDict; + +/// PyO3: verify `input` and `output` render to the same HTML under +/// cmark-gfm. Returns a dict: +/// - `is_available`: cmark-gfm binary found +/// - `identical`: in_html == out_html (byte-for-byte) +/// - `first_diff`: diagnostic snippet if not identical +/// - `error`: string if binary unavailable / subprocess failed +#[pyfunction] +pub fn cmark_gfm_verify_py(py: Python<'_>, input: &str, output: &str) -> PyResult { + let d = PyDict::new(py); + if !is_available() { + d.set_item("is_available", false)?; + d.set_item("preview_identical", false)?; + d.set_item("byte_identical", false)?; + d.set_item("identical", false)?; + d.set_item("error", "cmark-gfm binary not found on PATH")?; + return Ok(d.into()); + } + d.set_item("is_available", true)?; + match verify(input, output) { + Ok(r) => { + d.set_item("preview_identical", r.preview_identical)?; + d.set_item("byte_identical", r.byte_identical)?; + // Backward-compat alias: `identical` = preview_identical. + d.set_item("identical", r.preview_identical)?; + d.set_item("first_diff", r.first_diff)?; + d.set_item("error", Option::::None)?; + } + Err(e) => { + d.set_item("preview_identical", false)?; + d.set_item("byte_identical", false)?; + d.set_item("identical", false)?; + d.set_item("error", e)?; + } + } + Ok(d.into()) +} + +// NOTE ON LOCAL TEST ERGONOMICS (Finding 4 of the parser-backed +// implementation review, 2026-04-24): +// +// The tests in this module require the `cmark-gfm` binary to be +// installed (Debian package `cmark-gfm`, provides `/usr/bin/cmark-gfm`). +// The cleaning instance has it; typical laptop dev environments +// don't. When the binary is missing, each test returns early as a +// silent pass — this is deliberate for developer ergonomics but +// means these tests are NOT exercising the cmark-gfm oracle on +// laptops without the binary. +// +// Test naming convention: every test whose name starts with +// `oracle_` is a cmark-gfm-gated test (skips if binary absent). +// The `skip_if_cmark_gfm_absent` helper documents the skip. For +// real validation of the oracle, run `cargo test oracle_` on the +// cleaning instance where `/usr/bin/cmark-gfm` is present. +#[cfg(test)] +mod tests { + use super::*; + + /// Return true (and print a skip notice) if the cmark-gfm binary + /// isn't on PATH. Callers prefixed `oracle_` conventionally skip + /// their test body when this returns true. + fn skip_if_cmark_gfm_absent() -> bool { + if !is_available() { + eprintln!( + "[oracle test SKIPPED] cmark-gfm binary not on PATH. \ + Install `cmark-gfm` (Debian package) or run this \ + test on the cleaning instance where it's present." + ); + return true; + } + false + } + + #[test] + fn oracle_cmark_basic_render() { + if skip_if_cmark_gfm_absent() { + return; + } + let html = render_html("# hello\n").expect("render"); + assert!(html.contains("

hello

"), "got: {html}"); + } + + #[test] + fn oracle_cmark_verify_identity_passes() { + if skip_if_cmark_gfm_absent() { + return; + } + let r = verify("hello world\n", "hello world\n").expect("verify"); + assert!(r.preview_identical); + assert!(r.byte_identical); + } + + #[test] + fn oracle_cmark_verify_difference_fails() { + if skip_if_cmark_gfm_absent() { + return; + } + let r = verify("hello\n", "goodbye\n").expect("verify"); + assert!(!r.preview_identical); + assert!(r.first_diff.is_some()); + } + + #[test] + fn oracle_cmark_verify_preview_identical_but_not_byte_identical() { + // A soft-wrap that reflow joins is preview-identical per + // CM: both render as `

first second

` (pulldown + // emits with internal `\n`, cmark-gfm emits... let's check). + if skip_if_cmark_gfm_absent() { + return; + } + let r = verify("first\nsecond\n", "first second\n").expect("verify"); + // Whatever HTML bytes cmark-gfm emits, after preview-eq + // normalization the two should match. + assert!(r.preview_identical, "first_diff: {:?}", r.first_diff); + } + + // --- Ground-truth anchors: confirm cmark-gfm treats our edge + // cases exactly how we expect (these encode the CM spec on + // the cases where our old line-based code got them wrong). --- + + #[test] + fn oracle_ground_truth_escaped_underscore_is_literal_not_hr() { + if skip_if_cmark_gfm_absent() { + return; + } + // `\_\_\_\_\_\_\_\_` is a paragraph of literal underscores + // (each `\_` is an escape). NOT a thematic break. + let html = render_html("\\_\\_\\_\\_\\_\\_\\_\\_\n").unwrap(); + assert!(html.contains("

________

"), "got: {html}"); + assert!(!html.contains(""), "got: {html}"); + assert!(html.contains("a")); + } + + #[test] + fn oracle_ground_truth_two_space_hard_break() { + if skip_if_cmark_gfm_absent() { + return; + } + let html = render_html("first \nsecond\n").unwrap(); + assert!(html.contains("`, no + // `
` tag. + assert!(!html.contains("first\nsecond

"), "got: {html}"); + } +} diff --git a/rust/glossapi_rs_cleaner/src/directory_processor.rs b/rust/glossapi_rs_cleaner/src/directory_processor.rs index e668760..e284f68 100644 --- a/rust/glossapi_rs_cleaner/src/directory_processor.rs +++ b/rust/glossapi_rs_cleaner/src/directory_processor.rs @@ -65,7 +65,7 @@ struct FileTableSummary { // Struct for detailed table issue reporting #[derive(Debug, Serialize, Clone)] -struct DetailedTableIssueReportEntry { +pub(crate) struct DetailedTableIssueReportEntry { file_path: String, issue_description: String, table_start_line: usize, @@ -294,25 +294,13 @@ pub fn generate_analysis_report_for_directory( ) -> PyResult { let debug_logging = std::env::var_os("GLOSSAPI_RS_DEBUG").is_some(); - // Initialize script character sets - let mut allowed_chars = HashSet::new(); - for key in &scripts_to_keep { - if let Some(script_set) = cleaning_module::SCRIPT_SETS.get(key) { - allowed_chars.extend(script_set); - } - } - // Ensure essential whitespace is always allowed for cleaning and analysis coherence - allowed_chars.insert(' '); - allowed_chars.insert('\t'); - allowed_chars.insert('\n'); - + // Single policy builder shared with `clean_text` / `clean_text_with_stats`. + // Auto-adds `punctuation`, `numbers`, `common_symbols` so the directory + // pipeline's allowed-char set matches the direct-clean path. Fixes + // CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 8. + let (allowed_chars, unusual_chars) = cleaning_module::build_script_char_sets(&scripts_to_keep); let final_allowed_chars_arc = Arc::new(allowed_chars); - let unusual_chars_arc = Arc::new( - cleaning_module::SCRIPT_SETS - .get("unusual") - .cloned() - .unwrap_or_default(), - ); + let unusual_chars_arc = Arc::new(unusual_chars); let input_path = PathBuf::from(input_dir_str); let output_cleaned_path_opt = output_dir_cleaned_files_str.map(PathBuf::from); @@ -648,9 +636,6 @@ pub fn batch_clean_markdown_files( println!("DEBUG: Output dir: {}", output_dir); println!("DEBUG: Scripts to keep: {:?}", scripts_to_keep); - // Prepare character sets for cleaning - let mut allowed_chars = HashSet::new(); - // Debug print for available CPU cores println!( "INFO: Available CPU cores: {}, using {} threads", @@ -658,63 +643,14 @@ pub fn batch_clean_markdown_files( num_threads ); - // Fix script mapping to match what's in SCRIPT_SETS (lat->lat, not lat->latin) - println!( - "DEBUG: Script mapping from user input: {:?}", - scripts_to_keep - ); - - // Check if scripts exist and add their characters - for key in &scripts_to_keep { - if let Some(script_set) = cleaning_module::SCRIPT_SETS.get(key) { - println!( - "DEBUG: Adding {} characters from script: {}", - script_set.len(), - key - ); - allowed_chars.extend(script_set); - } else { - println!("WARNING: Script '{}' not found in SCRIPT_SETS", key); - } - } - - // Include common non-alphabetic sets if not specified - use correct keys that match SCRIPT_SETS - let keys_to_include = ["punctuation", "numbers", "common_symbols"]; // Corrected keys - println!("DEBUG: Also adding characters from: {:?}", keys_to_include); - - for key_to_always_include in keys_to_include { - if !scripts_to_keep.contains(&key_to_always_include.to_string()) { - if let Some(script_set) = cleaning_module::SCRIPT_SETS.get(key_to_always_include) { - println!( - "DEBUG: Adding {} characters from always-included script: {}", - script_set.len(), - key_to_always_include - ); - allowed_chars.extend(script_set); - } else { - println!( - "WARNING: Always-include script '{}' not found in SCRIPT_SETS", - key_to_always_include - ); - } - } - } - - // Add essential whitespace characters - allowed_chars.insert(' '); - allowed_chars.insert('\t'); - allowed_chars.insert('\n'); - println!("DEBUG: Added whitespace characters"); - - let unusual_chars = cleaning_module::SCRIPT_SETS - .get("unusual") - .cloned() - .unwrap_or_default(); + // Single policy builder shared with `clean_text` / `clean_text_with_stats` + // and `generate_analysis_report_for_directory` (Point 8). + let (allowed_chars, unusual_chars) = cleaning_module::build_script_char_sets(&scripts_to_keep); + println!("DEBUG: Total allowed characters: {}", allowed_chars.len()); println!( "DEBUG: Using {} unusual characters for detection", unusual_chars.len() ); - println!("DEBUG: Total allowed characters: {}", allowed_chars.len()); let config = Arc::new(BatchCleanOpConfig { allowed_chars, diff --git a/rust/glossapi_rs_cleaner/src/latex_module.rs b/rust/glossapi_rs_cleaner/src/latex_module.rs new file mode 100644 index 0000000..8945167 --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/latex_module.rs @@ -0,0 +1,1674 @@ +//! LaTeX-segment handling — single source of truth for finding and +//! manipulating LaTeX regions in cleaner inputs. +//! +//! Concentrating per-text-type logic here (per the +//! `feedback_group_cleaner_features_by_text_type` rule) so that +//! consumers — charset ratio counting, repetition cropping, and any +//! future LaTeX-aware passes — share one detector instead of each +//! re-implementing `$$` toggle tracking. +//! +//! Currently handles: +//! - Multi-line `$$ … $$` blocks (matches the existing +//! `charset_module::count_charsets` state-machine behaviour) +//! - Single-line `$$ … $$` regions on the SAME line (the gap noted in +//! `user_review_notes.md` Case 5 — observed in math theses where the +//! PDF extractor collapses each equation onto one line) +//! +//! Deferred (planned but not yet wired): +//! - Inline `$ … $` math +//! - `\begin{env} … \end{env}` environments +//! +//! The repetition-crop helpers below are a Rust port of +//! `_detect_repeated_char_cut` and `_detect_repeated_lines_cut` from +//! `src/glossapi/ocr/utils/cleaning.py`. Same semantics, applied per +//! LaTeX span rather than to whole OCR outputs. + +use pyo3::prelude::*; + +/// Half-open span `[start, end)` inside a parent string, with metadata +/// on which LaTeX delimiter pattern matched. Byte offsets, not chars +/// — caller is responsible for slicing on char boundaries (we never +/// split inside multibyte chars because `$` is ASCII). +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct LatexSpan { + pub start: usize, + pub end: usize, + pub kind: LatexKind, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum LatexKind { + /// Both `$$` delimiters on the same line. + InlineDoubleDollar, + /// `$$` opens on one line, closes on a later line. + BlockDoubleDollar, +} + +/// Locate every `$$ … $$` region in `text`, in source order. +/// +/// Two-pass: first all single-line `$$…$$` regions, then a state- +/// machine sweep over remaining text for cross-line blocks. Single- +/// line spans win over the block detector if they overlap a line, so +/// docs with mixed inline + block math don't double-count. +pub fn find_dollar_dollar_spans(text: &str) -> Vec { + let bytes = text.as_bytes(); + let mut spans: Vec = Vec::new(); + + // Pass 1: walk once. For each `$$` mark we find, look ahead for a + // closing `$$` on the SAME line; if found → InlineDoubleDollar + // and skip past it. Otherwise → start of a block; look for the + // closing `$$` on a later line. + let mut i = 0; + while i + 1 < bytes.len() { + if bytes[i] == b'$' && bytes[i + 1] == b'$' { + let start = i; + // Find end of this line (next \n) and the next `$$` on + // the same line. + let mut j = i + 2; + let mut nl = None; + while j < bytes.len() { + if bytes[j] == b'\n' { + nl = Some(j); + break; + } + if j + 1 < bytes.len() && bytes[j] == b'$' && bytes[j + 1] == b'$' { + // Inline: closes on same line. + spans.push(LatexSpan { + start, + end: j + 2, + kind: LatexKind::InlineDoubleDollar, + }); + i = j + 2; + break; + } + j += 1; + } + if i != j + 2 { + // No same-line close; either we hit \n or EOF. + let line_end = nl.unwrap_or(bytes.len()); + // Block: look for next `$$` after line_end. + let mut k = line_end + 1; + let mut close = None; + while k + 1 < bytes.len() { + if bytes[k] == b'$' && bytes[k + 1] == b'$' { + close = Some(k); + break; + } + k += 1; + } + if let Some(c) = close { + spans.push(LatexSpan { + start, + end: c + 2, + kind: LatexKind::BlockDoubleDollar, + }); + i = c + 2; + } else { + // Unclosed `$$` → don't claim a span; skip the + // opener and continue (matches charset_module + // tolerance). + i = start + 2; + } + } + } else { + i += 1; + } + } + spans +} + +/// Detect a single-character repetition cut point inside `s`. Returns +/// `Some(idx)` where `idx` is the byte position to truncate at — the +/// repeated run is allowed up to `threshold` chars, anything beyond +/// is cut. +/// +/// Direct port of `_detect_repeated_char_cut` from the OCR Python +/// utility. Runs reset across newlines. O(n) time, O(1) space. +pub fn detect_repeated_char_cut(s: &str, threshold: usize) -> Option { + if threshold <= 1 { + return Some(0); + } + let mut last_char: Option = None; + let mut run_len: usize = 0; + let mut run_start: usize = 0; + for (i, ch) in s.char_indices() { + if ch == '\n' { + last_char = None; + run_len = 0; + continue; + } + if Some(ch) == last_char { + run_len += 1; + if run_len >= threshold { + // Keep up to `threshold` chars; cut after. + // run_start is the BYTE index of the run's first char; + // `threshold` chars later in BYTES requires walking. + let cur = &s[run_start..]; + for (n, (off, _ch)) in cur.char_indices().enumerate() { + if n == threshold { + return Some(run_start + off); + } + } + // If we hit EOS before `threshold` chars, no cut needed. + return Some(s.len()); + } + } else { + last_char = Some(ch); + run_len = 1; + run_start = i; + } + } + None +} + +/// Detect a repeated-line cut point. Returns the byte index where the +/// (`threshold` + 1)-th repetition of a line starts. Lines are compared +/// after `trim()`. O(n) time. +/// +/// Direct port of `_detect_repeated_lines_cut` from the OCR Python +/// utility. +pub fn detect_repeated_lines_cut(s: &str, threshold: usize) -> Option { + if threshold <= 1 { + return Some(0); + } + let bytes = s.as_bytes(); + let n = bytes.len(); + let mut prev_norm: Option<&str> = None; + let mut run_count: usize = 1; + let mut i = 0; + // Track previous line's range so we can do trim comparison. + let mut prev_line_buf: String = String::new(); + while i <= n { + let mut j = i; + while j < n && bytes[j] != b'\n' { + j += 1; + } + let line = &s[i..j]; + let norm = line.trim(); + if let Some(p) = prev_norm { + if norm == p { + run_count += 1; + if run_count > threshold { + return Some(i); + } + } else { + prev_line_buf.clear(); + prev_line_buf.push_str(norm); + prev_norm = Some(unsafe { + std::mem::transmute::<&str, &'static str>(prev_line_buf.as_str()) + }); + run_count = 1; + } + } else { + prev_line_buf.clear(); + prev_line_buf.push_str(norm); + prev_norm = + Some(unsafe { std::mem::transmute::<&str, &'static str>(prev_line_buf.as_str()) }); + run_count = 1; + } + i = j + 1; + if i > n { + break; + } + } + None +} + +// --------------------------------------------------------------------------- +// LaTeX-syntax-aware element detection (2026-04-24, replaces the earlier +// generic token detector — user feedback: "a repetition that respects latex +// syntax, ie repetitions of latex elements, not just any repetition"). +// --------------------------------------------------------------------------- + +/// A parsed LaTeX element — what a reader would call a single math +/// atom: a bare command, a command with brace arguments, a letter- +/// or-command with subscript/superscript, or a balanced brace group. +#[derive(Debug, Clone, PartialEq, Eq)] +pub struct LatexElement { + /// Byte offset in the source LaTeX segment. + pub start: usize, + pub end: usize, + /// Normalized canonical string (whitespace collapsed inside). + pub canonical: String, + /// Base portion (before `_` / `^`). For `x_{n+1}^m` this is `x`. + /// For `\Omega_{...}` this is `\Omega`. For `\frac{1}{2}` with no + /// sub/sup this is `\frac{1}{2}` (no separable base). + pub base: String, + /// Subscript value as an integer, IF the subscript is purely + /// numeric. Used by monotonic-progression detector. + pub sub_numeric: Option, + /// Same for superscript. + pub sup_numeric: Option, +} + +/// Parse the next LaTeX element starting at or after `pos`. Skips +/// element-separator chars (whitespace, LaTeX thin spaces `\,` `\;` `\!`, +/// commas, plus/minus/equals as binary-op separators). Returns None at +/// EOS. +/// +/// Element types parsed: +/// - Command: `\` + `[A-Za-z]+` (e.g. `\Omega`) +/// - Command with arg groups: `\frac{a}{b}`, `\mathbb{R}`, `\sum_{i=1}^{n}` +/// - Letter atom: single `[A-Za-z]` (e.g. `x`) + optional sub/sup +/// - Digit atom: run of digits (standalone numbers) +/// - Braced group: `{...}` as a single element +/// +/// Brace balancing uses a stack; max depth tracked to avoid pathological +/// inputs (cap at 32 for safety). +fn next_latex_element(s: &str, pos: usize) -> Option { + let bytes = s.as_bytes(); + let n = bytes.len(); + let mut i = pos; + + // Skip inter-element separators. + while i < n { + let b = bytes[i]; + if b == b' ' + || b == b'\t' + || b == b'\n' + || b == b',' + || b == b';' + || b == b'+' + || b == b'-' + || b == b'=' + { + i += 1; + continue; + } + // LaTeX thin-space markers: `\,` `\;` `\!` `\ ` (backslash-space). + // Also `\\` (v6-01 fix): display-math line break, pure formatting, + // not content. Without skipping it, `\quad \\ \quad` runs stream + // as (\quad, \, \, \quad) → detect_repeated_element_cut resets + // the run-counter and misses the repetition. + if b == b'\\' && i + 1 < n { + let next = bytes[i + 1]; + if next == b',' || next == b';' || next == b'!' || next == b' ' || next == b'\\' { + i += 2; + continue; + } + } + break; + } + if i >= n { + return None; + } + + let start = i; + let first = bytes[i]; + + // Base + suffix (sub/sup) accumulated here. + let mut base = String::new(); + let mut canonical = String::new(); + + // Parse the base. + if first == b'\\' && i + 1 < n && (bytes[i + 1] as char).is_ascii_alphabetic() { + // LaTeX command: `\name` + optional brace args. + let name_start = i; + i += 1; + while i < n && (bytes[i] as char).is_ascii_alphabetic() { + i += 1; + } + let name = &s[name_start..i]; + base.push_str(name); + canonical.push_str(name); + // Absorb immediate brace argument groups (greedy). + while i < n && bytes[i] == b'{' { + let arg_end = match find_balanced_close(s, i) { + Some(e) => e, + None => break, + }; + canonical.push_str(&collapse_ws(&s[i..=arg_end])); + base.push_str(&collapse_ws(&s[i..=arg_end])); + i = arg_end + 1; + } + } else if first == b'{' { + // Braced group as a standalone element. + let end_idx = find_balanced_close(s, i); + match end_idx { + Some(e) => { + let span = collapse_ws(&s[i..=e]); + base.push_str(&span); + canonical.push_str(&span); + i = e + 1; + } + None => { + // Unbalanced — treat as single char. + base.push(first as char); + canonical.push(first as char); + i += 1; + } + } + } else if (first as char).is_ascii_alphabetic() { + // Single letter atom. + base.push(first as char); + canonical.push(first as char); + i += 1; + } else if (first as char).is_ascii_digit() { + // Standalone digit run. + while i < n && (bytes[i] as char).is_ascii_digit() { + base.push(bytes[i] as char); + canonical.push(bytes[i] as char); + i += 1; + } + } else { + // Other single char — not something we track for repetition. + i += 1; + return Some(LatexElement { + start, + end: i, + canonical: (first as char).to_string(), + base: (first as char).to_string(), + sub_numeric: None, + sup_numeric: None, + }); + } + + // Parse optional sub/sup in any order. + let mut sub_numeric: Option = None; + let mut sup_numeric: Option = None; + loop { + if i >= n { + break; + } + let b = bytes[i]; + if b != b'_' && b != b'^' { + break; + } + let marker = b as char; + i += 1; + if i >= n { + break; + } + let (arg_str, arg_end) = if bytes[i] == b'{' { + // Braced sub/sup. + let end_idx = match find_balanced_close(s, i) { + Some(e) => e, + None => break, + }; + let inner = &s[i + 1..end_idx]; // contents without braces + let collapsed = collapse_ws(inner); + (collapsed, end_idx + 1) + } else if bytes[i] == b'\\' && i + 1 < n && (bytes[i + 1] as char).is_ascii_alphabetic() { + // `\command` as sub/sup arg. + let cmd_start = i; + i += 1; + while i < n && (bytes[i] as char).is_ascii_alphabetic() { + i += 1; + } + (s[cmd_start..i].to_string(), i) + } else { + // Single char sub/sup (e.g. `x_1`, `x_n`). + let c = bytes[i]; + (std::str::from_utf8(&[c]).unwrap_or("?").to_string(), i + 1) + }; + // Record numeric value if the sub/sup is pure digits. + let numeric_value: Option = arg_str.parse::().ok(); + canonical.push(marker); + canonical.push('{'); + canonical.push_str(&arg_str); + canonical.push('}'); + match marker { + '_' => sub_numeric = numeric_value, + '^' => sup_numeric = numeric_value, + _ => {} + } + i = arg_end; + } + + Some(LatexElement { + start, + end: i, + canonical, + base, + sub_numeric, + sup_numeric, + }) +} + +fn find_balanced_close(s: &str, open_idx: usize) -> Option { + let bytes = s.as_bytes(); + let n = bytes.len(); + if open_idx >= n || bytes[open_idx] != b'{' { + return None; + } + let mut depth: i32 = 1; + let mut i = open_idx + 1; + let mut max_depth: i32 = 1; + while i < n { + match bytes[i] { + b'{' => { + depth += 1; + if depth > max_depth { + max_depth = depth; + } + if max_depth > 32 { + // Pathological input — bail. + return None; + } + } + b'}' => { + depth -= 1; + if depth == 0 { + return Some(i); + } + } + b'\\' if i + 1 < n => { + // Skip `\{` and `\}` as escaped braces. + i += 1; + } + _ => {} + } + i += 1; + } + None +} + +fn collapse_ws(s: &str) -> String { + // Inside math-mode LaTeX, whitespace is display-insignificant + // (spaces are produced by `\,` `\;` `\ ` etc., not literal spaces). + // For element-canonicalization we drop all whitespace so that + // `\frac{ 1 }{ 2 }`, `\frac{1}{2}`, and `\frac{ 1+2 }{3}` vs + // `\frac{1+2}{3}` all collapse to comparable canonical forms. + s.chars().filter(|c| !c.is_whitespace()).collect() +} + +/// Detect an EXACT repeat of the same LaTeX element `threshold`+1 times +/// in a row (with element-separators between — whitespace, thin-space, +/// comma, plus, etc.). Returns byte offset to cut at. +/// +/// Canonical form comparison: whitespace-collapsed. So `\frac{1}{2}` and +/// `\frac{ 1 }{ 2 }` are the same. +pub fn detect_repeated_element_cut(s: &str, threshold: usize) -> Option { + if threshold == 0 { + return Some(0); + } + let mut pos = 0; + let mut last_canonical: Option = None; + let mut run_count: usize = 0; + // We record the END offsets of the last `threshold` matching elements + // so we can cut right after the `threshold`-th one. + let mut recent_ends: Vec = Vec::with_capacity(threshold + 1); + while let Some(elem) = next_latex_element(s, pos) { + pos = elem.end; + match last_canonical { + Some(ref prev) if prev == &elem.canonical => { + run_count += 1; + recent_ends.push(elem.end); + if run_count > threshold { + let cut = recent_ends[threshold - 1]; + return Some(cut); + } + } + _ => { + last_canonical = Some(elem.canonical.clone()); + run_count = 1; + recent_ends.clear(); + recent_ends.push(elem.end); + } + } + if pos == elem.start { + pos = elem.start + 1; // safety + } + } + None +} + +/// Small-vocabulary library of LaTeX atoms — Greek letters plus +/// common decorator commands. Ported from +/// `src/glossapi/corpus/phase_clean.py`: +/// - `LATEX_SHORT_ATOM_BLOCK_BASE_COMMANDS` +/// - `LATEX_SHORT_ATOM_BLOCK_DECORATOR_COMMANDS` +/// - `LATEX_SYMBOL_SLOT_COMMANDS` +/// +/// The set is intentionally SMALL. A long run of elements all drawn +/// from this set — even if each occurrence is a different member — +/// signals the model is looping through a known small vocabulary +/// (common hallucination mode on math-OCR outputs). +fn is_small_vocab_atom(base: &str) -> bool { + // Strip any subscript/superscript from the base for comparison — + // small-vocab detection is about the COMMAND, not the args. + let stem = base.split_once('{').map(|(s, _)| s).unwrap_or(base); + matches!( + stem, + // Greek letters + "\\alpha" | "\\beta" | "\\gamma" | "\\delta" + | "\\epsilon" | "\\varepsilon" | "\\zeta" | "\\eta" + | "\\theta" | "\\vartheta" | "\\iota" | "\\kappa" + | "\\lambda" | "\\mu" | "\\nu" | "\\xi" | "\\omicron" + | "\\pi" | "\\varpi" | "\\rho" | "\\varrho" + | "\\sigma" | "\\varsigma" | "\\tau" | "\\upsilon" + | "\\phi" | "\\varphi" | "\\chi" | "\\psi" | "\\omega" + | "\\Alpha" | "\\Beta" | "\\Gamma" | "\\Delta" + | "\\Epsilon" | "\\Zeta" | "\\Eta" | "\\Theta" + | "\\Iota" | "\\Kappa" | "\\Lambda" | "\\Mu" + | "\\Nu" | "\\Xi" | "\\Omicron" | "\\Pi" + | "\\Rho" | "\\Sigma" | "\\Tau" | "\\Upsilon" + | "\\Phi" | "\\Chi" | "\\Psi" | "\\Omega" + // Decorators (typically appear as wrappers around Greek) + | "\\hat" | "\\tilde" | "\\bar" | "\\vec" + | "\\dot" | "\\ddot" + ) +} + +/// Detect a SMALL-VOCABULARY RUN cut: `threshold`+1 consecutive LaTeX +/// elements where EVERY element's base is in the small Greek/decorator +/// vocabulary. The elements don't have to repeat — `\alpha \beta \mu +/// \beta \gamma \alpha \nu \mu …` still matches if all are Greek. +/// +/// Returns the byte offset at which the run exceeds the threshold. +/// Breaks on any element outside the vocabulary — ensures the detector +/// only fires on "pure small-vocab" runs, not math with a mix of +/// Greek + variables + operators. +/// +/// Default threshold 12 (matches `LATEX_SHORT_ATOM_BLOCK_REPEAT_MIN_ITEMS` +/// in the corpus code). +pub fn detect_small_vocab_run_cut(s: &str, threshold: usize) -> Option { + if threshold == 0 { + return Some(0); + } + let mut pos = 0; + let mut run_count: usize = 0; + let mut recent_ends: Vec = Vec::with_capacity(threshold + 1); + while let Some(elem) = next_latex_element(s, pos) { + pos = elem.end; + if is_small_vocab_atom(&elem.base) { + run_count += 1; + recent_ends.push(elem.end); + if run_count > threshold { + let cut = recent_ends[threshold - 1]; + return Some(cut); + } + } else { + run_count = 0; + recent_ends.clear(); + } + if pos == elem.start { + pos = elem.start + 1; + } + } + None +} + +/// v6-02 — detect a cyclic LaTeX-element run of cycle length +/// `L ∈ [1..=cycle_max]` repeating `≥ threshold` times. +/// +/// Example (doc 997003_000): `\intertext{…} \intertext{…}` alternating +/// with two different braced payloads 6+ times in a row. Each cycle +/// has two distinct compound elements (different arg values), so +/// `detect_repeated_element_cut` (exact consecutive repetition) and +/// `detect_monotonic_element_cut` (numeric progression) both miss +/// it. The run IS noise — the doc's author didn't write a 12-fold +/// alternation literally. +/// +/// Algorithm: +/// - Tokenize the text via `next_latex_element` into a canonical +/// element sequence with byte-end positions. +/// - For each cycle length `L ∈ [1..=cycle_max]`: +/// - Starting at each position `i`, check whether the window of `L` +/// elements at `i` equals the window of `L` elements at `i - L`. +/// If so, increment the cycle-run counter; else reset. +/// - When cycle-run has covered `threshold` full cycles (i.e. the +/// SAME `L`-window repeated `threshold` times in a row), return +/// the byte offset of the FIRST element in the run. +/// - Shortest `L` wins on ties so a pure `a a a …` run registers as +/// cycle-1 (== repeated-element) rather than cycle-2/3/etc. +/// +/// Complexity: O(N * cycle_max) where N is element count; N ≪ chars +/// because tokenization collapses braced sub-trees. +pub fn detect_cyclic_element_cut(s: &str, cycle_max: usize, threshold: usize) -> Option { + if threshold < 2 || cycle_max == 0 { + return None; + } + // Tokenize. + let mut tokens: Vec<(String, usize, usize)> = Vec::new(); // (canonical, start, end) + let mut pos = 0; + while let Some(el) = next_latex_element(s, pos) { + // Use the start-before-separator-skip by backing from el.start — + // next_latex_element's `start` is post-skip. For the cut offset + // we want the token's visible start so the emitted prefix feels + // natural, which IS el.start. + tokens.push((el.canonical.clone(), el.start, el.end)); + pos = el.end; + // Safety cap: pathological inputs shouldn't spin forever. + if tokens.len() > 200_000 { + break; + } + } + if tokens.len() < threshold { + return None; + } + + // Search by ascending cycle length so shortest wins on collisions. + for l in 1..=cycle_max.min(tokens.len() / threshold) { + let mut i = l; + while i + l <= tokens.len() { + // Check windows[i-l..i] == windows[i..i+l] + let mut equal = true; + for k in 0..l { + if tokens[i - l + k].0 != tokens[i + k].0 { + equal = false; + break; + } + } + if equal { + // Start of potential cycle run is at i - l. Count how + // many consecutive equal-windows follow. + let mut reps: usize = 2; // we just matched two windows + let mut j = i + l; + while j + l <= tokens.len() { + let mut wins = true; + for k in 0..l { + if tokens[i - l + k].0 != tokens[j + k].0 { + wins = false; + break; + } + } + if !wins { + break; + } + reps += 1; + j += l; + } + if reps >= threshold { + // Cut at the FIRST element's start offset. + return Some(tokens[i - l].1); + } + // Not long enough — skip past the matched region. + i = j; + } else { + i += 1; + } + } + } + None +} + +/// v6-06a — brace / bracket / paren balance check inside a LaTeX +/// span. Returns `Some(offset)` if the span has unbalanced delimiters +/// (e.g. `[ 1 - \mu_2 s }{ 1 - \mu_2 s }` from v6-06 where `[` opens +/// but the matching close is `}`), `None` if balanced. +/// +/// Offset is 0 (cut the whole span) since we can't safely recover +/// broken LaTeX: even partial emission risks feeding the tokenizer +/// malformed math. Conservative drop. +/// +/// Walks chars counting `( [ {` as pushes and `) ] }` as pops. Skips +/// escaped delimiters (`\{` `\}` etc.). If at any point the pop +/// doesn't match the top of the stack, OR the stack is non-empty at +/// end-of-span, reports unbalanced. +pub fn detect_unbalanced_braces_in_latex_span(s: &str) -> Option { + let bytes = s.as_bytes(); + let n = bytes.len(); + let mut stack: Vec = Vec::new(); + let mut i = 0; + while i < n { + let b = bytes[i]; + // Skip `\\` (LaTeX line break) and escaped delimiters. + if b == b'\\' && i + 1 < n { + let next = bytes[i + 1]; + if matches!(next, b'{' | b'}' | b'[' | b']' | b'(' | b')' | b'\\') { + i += 2; + continue; + } + } + match b { + b'{' | b'[' | b'(' => stack.push(b), + b'}' | b']' | b')' => { + let expected = match b { + b'}' => b'{', + b']' => b'[', + b')' => b'(', + _ => unreachable!(), + }; + match stack.pop() { + Some(top) if top == expected => {} + _ => return Some(0), + } + } + _ => {} + } + i += 1; + } + if !stack.is_empty() { + return Some(0); + } + None +} + +/// v6-05 — detect the FIRST structurally-degenerate `\frac{A}{A}` in +/// `s`. A "degenerate" fraction has bitwise-equal numerator and +/// denominator after whitespace collapse (modulo LaTeX display- +/// insignificant whitespace). +/// +/// Why this is noise: `\frac{A}{A}` always simplifies to `1`; no +/// author writes it literally. When we see it, it's an extraction / +/// OCR / hallucination artifact (the model emitted the same span +/// twice instead of producing the real denominator). Not caught by +/// any other detector (char-level, element-repeat, monotonic, +/// small-vocab) because the outer `\frac` counts as ONE element with +/// a single canonical form, and the sub-args aren't independently +/// tokenized. +/// +/// Returns the byte offset where the degenerate `\frac` begins — +/// conservative tail-stop semantics (option B in +/// `reports/v6_review_notes.md` v6-05): cut from the degenerate +/// point onward, because a hallucinated fraction almost always +/// indicates the rest of the span is also hallucinated. Caller +/// decides replacement (strip marker / empty / `1`). +/// +/// Also detects `\frac{A}{B}` where A == B after tolerant +/// whitespace normalization — matches re-emitted identical spans +/// with varying incidental whitespace. +/// +/// Skips `\frac{A}{B}` when `A != B` (legit fractions). +pub fn detect_degenerate_frac_cut(s: &str) -> Option { + let bytes = s.as_bytes(); + let n = bytes.len(); + let mut i = 0; + while i + 6 < n { + // Look for `\frac` followed by `{`. + if bytes[i] == b'\\' + && bytes[i + 1] == b'f' + && bytes[i + 2] == b'r' + && bytes[i + 3] == b'a' + && bytes[i + 4] == b'c' + { + let frac_start = i; + let mut j = i + 5; + // Skip whitespace between `\frac` and `{`. + while j < n && (bytes[j] == b' ' || bytes[j] == b'\t') { + j += 1; + } + if j < n && bytes[j] == b'{' { + if let Some(num_end) = find_balanced_close(s, j) { + // Extract numerator content (without outer braces). + let num_content = &s[j + 1..num_end]; + // Skip whitespace between arg groups. + let mut k = num_end + 1; + while k < n && (bytes[k] == b' ' || bytes[k] == b'\t') { + k += 1; + } + if k < n && bytes[k] == b'{' { + if let Some(den_end) = find_balanced_close(s, k) { + let den_content = &s[k + 1..den_end]; + // Whitespace-collapse both and compare. + if collapse_ws(num_content) == collapse_ws(den_content) + && !num_content.trim().is_empty() + { + return Some(frac_start); + } + // Not degenerate — skip past the whole + // `\frac{A}{B}` expression and keep scanning. + i = den_end + 1; + continue; + } + } + // Malformed `\frac` (only one brace group) — skip + // past the first group. + i = num_end + 1; + continue; + } + } + } + i += 1; + } + None +} + +/// Detect a MONOTONIC-progression cut: same base with numeric sub- OR +/// super-script incrementing by exactly 1 between consecutive elements +/// (like `x_1, x_2, x_3, ...`). Threshold is the minimum progression +/// length to trigger. +/// +/// Direction of monotonic increase: +1 per step. Strictly monotonic, +/// strict step 1. Matches the semantics of OCR's +/// `_detect_numeric_list_garbage_cut` transplanted to LaTeX subscripts. +pub fn detect_monotonic_element_cut(s: &str, threshold: usize) -> Option { + if threshold <= 1 { + return Some(0); + } + let mut pos = 0; + let mut run_base: Option = None; + let mut run_count: usize = 0; + let mut run_is_sub: bool = true; + let mut next_expected: Option = None; + let mut recent_ends: Vec = Vec::with_capacity(threshold + 1); + while let Some(elem) = next_latex_element(s, pos) { + pos = elem.end; + let sub = elem.sub_numeric; + let sup = elem.sup_numeric; + // Only one of sub/sup should drive the progression; prefer sub + // if both present. Need identical base across the run. + let (value, is_sub) = match (sub, sup) { + (Some(v), _) => (Some(v), true), + (None, Some(v)) => (Some(v), false), + _ => (None, true), + }; + match (value, &run_base) { + (Some(v), Some(prev_base)) + if *prev_base == elem.base && is_sub == run_is_sub && next_expected == Some(v) => + { + run_count += 1; + recent_ends.push(elem.end); + next_expected = Some(v + 1); + if run_count >= threshold { + // Cut at the end of the threshold-th element. + let cut = recent_ends[threshold - 1]; + return Some(cut); + } + } + (Some(v), _) => { + // Start a new progression (or reset). + run_base = Some(elem.base.clone()); + run_is_sub = is_sub; + run_count = 1; + next_expected = Some(v + 1); + recent_ends.clear(); + recent_ends.push(elem.end); + } + _ => { + // No numeric sub/sup — break any progression. + run_base = None; + run_count = 0; + next_expected = None; + recent_ends.clear(); + } + } + if pos == elem.start { + pos = elem.start + 1; + } + } + None +} + +/// Apply per-LaTeX-segment repetition cropping to `text`. For each +/// detected `$$…$$` span, run the OCR-style repetition detectors +/// against the inner content; if a cut is found, truncate the segment +/// at that point and re-close with `$$`. Returns the rewritten text. +/// +/// `char_threshold` and `line_threshold` are passed straight through +/// to the underlying detectors. Pass small values (e.g. 30 char / 3 +/// line) for tight LaTeX-segment cropping; the OCR defaults (200 / +/// 10) are tuned for whole-page outputs. +/// +/// Pass `enable=false` to short-circuit and return `text` unchanged +/// — the caller's gate on this feature. +pub fn crop_latex_repetitions( + text: &str, + enable: bool, + char_threshold: usize, + line_threshold: usize, +) -> String { + if !enable { + return text.to_string(); + } + let spans = find_dollar_dollar_spans(text); + if spans.is_empty() { + return text.to_string(); + } + let mut out = String::with_capacity(text.len()); + let mut cursor = 0; + for span in &spans { + // Copy text before this span verbatim. + out.push_str(&text[cursor..span.start]); + // Inner content excludes the leading `$$` and trailing `$$`. + let inner_start = span.start + 2; + let inner_end = span.end.saturating_sub(2); + if inner_end <= inner_start { + // Degenerate; copy the whole span as-is. + out.push_str(&text[span.start..span.end]); + cursor = span.end; + continue; + } + let inner = &text[inner_start..inner_end]; + let cut_char = detect_repeated_char_cut(inner, char_threshold); + let cut_line = detect_repeated_lines_cut(inner, line_threshold); + // Element-level detector (wave-2.1, 2026-04-24): LaTeX-syntax- + // aware repetition. Catches `\Omega \, \Omega \, \Omega` AND + // compound elements like `\frac{1}{2} \frac{1}{2} \frac{1}{2}`. + // Threshold 4 for exact repeat (tight — rare in real math). + let cut_elem = detect_repeated_element_cut(inner, 4); + // Monotonic progression detector — catches `x_1 x_2 x_3 …` + // style looping. Threshold 8 (see false-positive analysis in + // the test dataset: real math rarely enumerates 8+ without + // `\ldots`). Analog of OCR's numeric-list garbage detector. + let cut_mono = detect_monotonic_element_cut(inner, 8); + // Small-vocab run detector — catches a long run of Greek- + // letter / decorator commands (from a fixed small vocabulary), + // e.g. `\alpha \beta \mu \beta \gamma \alpha \nu \mu …`. + // Threshold 12 (matches LATEX_SHORT_ATOM_BLOCK_REPEAT_MIN_ITEMS + // in the corpus Python code). + let cut_vocab = detect_small_vocab_run_cut(inner, 12); + // v6-05 — `\frac{A}{A}` structurally degenerate. No threshold + // (any occurrence is noise). Conservative tail-stop: cut from + // the degenerate `\frac` onward. + let cut_degen = detect_degenerate_frac_cut(inner); + // v6-06a — mismatched / unbalanced `{}`, `[]`, `()` inside the + // span. If the span's delimiters don't pair correctly, the + // entire span is broken LaTeX; cut at 0 (drop the whole span). + let cut_braces = detect_unbalanced_braces_in_latex_span(inner); + // v6-02 — cyclic-element run. cycle_max=6, threshold=6 (six + // full cycles = at least 12 tokens for cycle-2). Matches the + // pattern `\intertext{lenN} \intertext{degN}` alternation the + // user flagged in doc 997003_000. + let cut_cycle = detect_cyclic_element_cut(inner, 6, 6); + let cut = [ + cut_char, cut_line, cut_elem, cut_mono, cut_vocab, cut_degen, cut_braces, cut_cycle, + ] + .into_iter() + .flatten() + .min(); + out.push_str("$$"); + match cut { + Some(idx) if idx < inner.len() => { + out.push_str(&inner[..idx]); + // Mark the crop visibly so review can spot it. + out.push_str(" /*…repetition cropped…*/ "); + } + _ => { + out.push_str(inner); + } + } + out.push_str("$$"); + cursor = span.end; + } + out.push_str(&text[cursor..]); + out +} + +/// Python-exposed: `crop_latex_repetitions(text, enable, char_threshold, +/// line_threshold) -> str`. Defaults match the OCR equivalent. +#[pyfunction] +#[pyo3(signature = (text, enable=false, char_threshold=30, line_threshold=3))] +pub fn crop_latex_repetitions_py( + text: &str, + enable: bool, + char_threshold: usize, + line_threshold: usize, +) -> String { + crop_latex_repetitions(text, enable, char_threshold, line_threshold) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn finds_inline_double_dollar_span() { + let t = "before $$x = y$$ after"; + let s = find_dollar_dollar_spans(t); + assert_eq!(s.len(), 1); + assert_eq!(s[0].kind, LatexKind::InlineDoubleDollar); + assert_eq!(&t[s[0].start..s[0].end], "$$x = y$$"); + } + + #[test] + fn finds_block_double_dollar_span() { + let t = "before $$\nx = y\n$$ after"; + let s = find_dollar_dollar_spans(t); + assert_eq!(s.len(), 1); + assert_eq!(s[0].kind, LatexKind::BlockDoubleDollar); + assert!(t[s[0].start..s[0].end].starts_with("$$")); + assert!(t[s[0].start..s[0].end].ends_with("$$")); + } + + #[test] + fn finds_multiple_inline_spans_in_same_doc() { + let t = "$$a$$ middle $$b$$ end $$c$$"; + let s = find_dollar_dollar_spans(t); + assert_eq!(s.len(), 3); + for sp in &s { + assert_eq!(sp.kind, LatexKind::InlineDoubleDollar); + } + } + + #[test] + fn detect_repeated_char_cut_finds_run() { + // 250 dots after threshold-200: cut should land 200 chars in. + let s = "alpha ".to_string() + &".".repeat(250) + " beta"; + let cut = detect_repeated_char_cut(&s, 200).expect("cut"); + // run starts at byte 6 (after "alpha "), keep 200 → cut at 206. + assert_eq!(cut, 6 + 200); + } + + #[test] + fn detect_repeated_char_cut_resets_on_newline() { + // Newline interrupts the run — so 100 + newline + 100 = no cut. + let s = ".".repeat(100) + "\n" + &".".repeat(100); + assert_eq!(detect_repeated_char_cut(&s, 200), None); + } + + #[test] + fn detect_repeated_lines_cut_finds_repeats() { + let s = "alpha\nbeta\nbeta\nbeta\nbeta\ngamma"; + // beta appears 4 times; threshold=3 → 4 > 3 → cut at start of 4th. + let cut = detect_repeated_lines_cut(s, 3).expect("cut"); + // 4th 'beta' starts at byte: "alpha\n" = 6, "beta\n" = 5 each + // → 1st beta=6, 2nd=11, 3rd=16, 4th=21 + assert_eq!(cut, 21); + } + + #[test] + fn crop_latex_repetitions_disabled_is_noop() { + let t = "$$".to_string() + &"+".repeat(100) + "$$"; + assert_eq!(crop_latex_repetitions(&t, false, 10, 3), t); + } + + #[test] + fn crop_latex_repetitions_crops_char_run_inside_inline_math() { + // 100 `+` inside $$..$$ with threshold 10 → cropped. + let t = "before $$a + ".to_string() + &"+".repeat(100) + " b$$ after"; + let out = crop_latex_repetitions(&t, true, 10, 100); + assert!(out.contains("repetition cropped"), "out = {}", out); + assert!(out.starts_with("before $$a + ")); + assert!(out.ends_with("$$ after")); + // Crop marker present means we cut the long + run; full original + // had 100 plus chars, output should have far fewer. + let plus_count = out.chars().filter(|&c| c == '+').count(); + assert!( + plus_count < 30, + "expected cropped + run, got {} +", + plus_count + ); + } + + #[test] + fn crop_latex_repetitions_passes_clean_math_through() { + let t = "before $$x^2 + y^2 = z^2$$ after"; + let out = crop_latex_repetitions(t, true, 30, 3); + assert_eq!(out, t); + } + + #[test] + fn crop_latex_repetitions_handles_block_math() { + let inner = "x = y\n".to_string() + &"+".repeat(60); + let t = format!("$$\n{}\n$$", inner); + let out = crop_latex_repetitions(&t, true, 10, 100); + assert!(out.contains("repetition cropped")); + } + + // ----------------------------------------------------------------- + // LaTeX-syntax-aware detector tests + false-positive dataset + // (2026-04-24). Positive cases assert the detector fires. + // Negative / legit cases assert it does NOT fire — these form the + // false-positive guardrail and should grow whenever we discover a + // legit pattern that was getting caught. + // ----------------------------------------------------------------- + + // --- element parser sanity --- + + #[test] + fn element_parser_bare_command() { + let e = next_latex_element("\\Omega rest", 0).expect("elem"); + assert_eq!(e.canonical, "\\Omega"); + assert_eq!(e.base, "\\Omega"); + assert!(e.sub_numeric.is_none() && e.sup_numeric.is_none()); + } + + #[test] + fn element_parser_command_with_arg_groups() { + let e = next_latex_element("\\frac{1}{2} rest", 0).expect("elem"); + assert_eq!(e.canonical, "\\frac{1}{2}"); + } + + #[test] + fn element_parser_subscript_numeric() { + let e = next_latex_element("x_5 rest", 0).expect("elem"); + assert_eq!(e.base, "x"); + assert_eq!(e.sub_numeric, Some(5)); + } + + #[test] + fn element_parser_superscript_numeric_braced() { + let e = next_latex_element("A^{12} rest", 0).expect("elem"); + assert_eq!(e.base, "A"); + assert_eq!(e.sup_numeric, Some(12)); + } + + #[test] + fn element_parser_braced_group_as_element() { + let e = next_latex_element("{\\alpha+\\beta} rest", 0).expect("elem"); + assert_eq!(e.canonical, "{\\alpha+\\beta}"); + } + + #[test] + fn element_parser_skips_separators() { + // Thin-space `\,` between command and arg — should not break + // the element's identity. + let e1 = next_latex_element("\\Omega \\, \\Omega", 0).expect("first"); + let e2 = next_latex_element("\\Omega \\, \\Omega", e1.end).expect("second"); + assert_eq!(e1.canonical, e2.canonical); + } + + // --- detect_repeated_element_cut positive cases --- + + #[test] + fn elem_repeat_catches_bare_command_run() { + let s = "\\Omega \\Omega \\Omega \\Omega \\Omega \\Omega"; + let cut = detect_repeated_element_cut(s, 4).expect("cut"); + let head = &s[..cut]; + assert_eq!(head.matches("\\Omega").count(), 4); + } + + #[test] + fn elem_repeat_catches_thin_space_separator() { + // Actual pattern from openarchives 997003_…_e2cbfdac. + let s = "a = ".to_string() + &"\\Omega \\, ".repeat(20); + let cut = detect_repeated_element_cut(&s, 4).expect("cut"); + assert_eq!(s[..cut].matches("\\Omega").count(), 4); + } + + #[test] + fn elem_repeat_catches_compound_frac() { + let s = "a \\frac{1}{2} \\frac{1}{2} \\frac{1}{2} \\frac{1}{2} \\frac{1}{2} b"; + let cut = detect_repeated_element_cut(s, 4).expect("cut"); + assert_eq!(s[..cut].matches("\\frac{1}{2}").count(), 4); + } + + #[test] + fn elem_repeat_catches_mathbb() { + let s = "\\mathbb{R} \\mathbb{R} \\mathbb{R} \\mathbb{R} \\mathbb{R} \\mathbb{R}"; + let cut = detect_repeated_element_cut(s, 4).expect("cut"); + assert_eq!(s[..cut].matches("\\mathbb{R}").count(), 4); + } + + #[test] + fn elem_repeat_catches_subscripted_atom() { + let s = "a_n a_n a_n a_n a_n a_n"; + let cut = detect_repeated_element_cut(s, 4).expect("cut"); + assert_eq!(s[..cut].matches("a_{n}").count(), 0); // canonical + // count "a_n" substrings in raw head (simpler) + assert!(s[..cut].matches("a_n").count() >= 4); + } + + #[test] + fn elem_repeat_normalizes_whitespace_inside_args() { + // `\frac{1}{2}` and `\frac{ 1 }{ 2 }` are considered same element. + let s = "\\frac{1}{2} \\frac{ 1 }{ 2 } \\frac{1}{2} \\frac{ 1 }{2} \\frac{1}{2}"; + let cut = detect_repeated_element_cut(s, 4).expect("cut"); + assert!(s[..cut].contains("\\frac")); + } + + // --- detect_repeated_element_cut NEGATIVE cases (legit math) --- + + #[test] + fn elem_repeat_does_not_fire_on_distinct_compounds() { + // Normal math: lots of \frac but with different arguments. + let s = "\\frac{1}{2} + \\frac{1}{3} + \\frac{1}{4} + \\frac{1}{5} + \\frac{1}{6}"; + assert_eq!(detect_repeated_element_cut(s, 4), None); + } + + #[test] + fn elem_repeat_does_not_fire_on_derivatives() { + // `f, f', f'', f'''` — different-canonical-form elements. + let s = "f, f', f'', f''', f''''"; + assert_eq!(detect_repeated_element_cut(s, 4), None); + } + + #[test] + fn elem_repeat_does_not_fire_on_greek_alphabet_run() { + let s = "\\alpha + \\beta + \\gamma + \\delta + \\epsilon + \\zeta"; + assert_eq!(detect_repeated_element_cut(s, 4), None); + } + + #[test] + fn elem_repeat_does_not_fire_on_polynomial() { + let s = "x^2 + 2xy + y^2 + 3x^3 - 4y^4"; + assert_eq!(detect_repeated_element_cut(s, 4), None); + } + + #[test] + fn elem_repeat_does_not_fire_on_common_math_identities() { + let s = "\\int_{-\\infty}^{\\infty} e^{-x^2} dx = \\sqrt{\\pi}"; + assert_eq!(detect_repeated_element_cut(s, 4), None); + } + + #[test] + fn elem_repeat_does_not_fire_at_exactly_threshold() { + // Exactly 4 copies — should NOT trigger at threshold 4 (4 is + // allowed, 5th is the cut trigger). + let s = "\\Omega \\Omega \\Omega \\Omega"; + assert_eq!(detect_repeated_element_cut(s, 4), None); + } + + // --- detect_monotonic_element_cut positive cases --- + + #[test] + fn mono_catches_simple_x_n_progression() { + // 10× `x_1, x_2, …, x_{10}` → trigger at threshold 8. + let s = "x_1 x_2 x_3 x_4 x_5 x_6 x_7 x_8 x_9 x_{10}"; + let cut = detect_monotonic_element_cut(s, 8).expect("cut"); + assert!(cut > 0); + } + + #[test] + fn mono_catches_superscript_progression() { + let s = "A^1 A^2 A^3 A^4 A^5 A^6 A^7 A^8 A^9"; + let cut = detect_monotonic_element_cut(s, 8).expect("cut"); + assert!(cut > 0); + } + + #[test] + fn mono_catches_progression_of_latex_command_base() { + // `\phi_1, \phi_2, …, \phi_10`. + let s = "\\phi_1 \\phi_2 \\phi_3 \\phi_4 \\phi_5 \\phi_6 \\phi_7 \\phi_8 \\phi_9"; + let cut = detect_monotonic_element_cut(s, 8).expect("cut"); + assert!(cut > 0); + } + + // --- detect_monotonic_element_cut NEGATIVE cases (legit math) --- + // These form the false-positive guardrail per user request. + + #[test] + fn mono_does_not_fire_below_threshold() { + // 7 terms — below threshold 8. + let s = "x_1 + x_2 + x_3 + x_4 + x_5 + x_6 + x_7"; + assert_eq!(detect_monotonic_element_cut(s, 8), None); + } + + #[test] + fn mono_does_not_fire_on_ldots_enumeration() { + // Standard math: `x_1, x_2, ..., x_n` — `\ldots` breaks the run. + let s = "x_1, x_2, x_3, \\ldots, x_n"; + assert_eq!(detect_monotonic_element_cut(s, 8), None); + } + + #[test] + fn mono_does_not_fire_on_non_monotonic_subscripts() { + // Matrix indices: `a_{11}, a_{12}, a_{13}, a_{21}, a_{22}` — + // not strictly +1 (jumps 13→21). + let s = "a_{11} a_{12} a_{13} a_{21} a_{22} a_{23} a_{31} a_{32} a_{33}"; + assert_eq!(detect_monotonic_element_cut(s, 8), None); + } + + #[test] + fn mono_does_not_fire_on_increment_by_two() { + // Odd indices: `x_1, x_3, x_5, x_7, x_9, …` — strict-+1 rule + // excludes this. + let s = "x_1 x_3 x_5 x_7 x_9 x_{11} x_{13} x_{15} x_{17}"; + assert_eq!(detect_monotonic_element_cut(s, 8), None); + } + + #[test] + fn mono_does_not_fire_on_different_bases() { + // `a_1 b_2 c_3 d_4` — numeric progression BUT different bases. + let s = "a_1 b_2 c_3 d_4 e_5 f_6 g_7 h_8 i_9"; + assert_eq!(detect_monotonic_element_cut(s, 8), None); + } + + #[test] + fn mono_does_not_fire_on_independent_summations() { + // Two separate `\sum` with different subscript patterns. + let s = "\\sum_{i=1}^{n} x_i + \\sum_{j=1}^{m} y_j"; + assert_eq!(detect_monotonic_element_cut(s, 8), None); + } + + #[test] + fn mono_does_not_fire_on_polynomial_with_mixed_exponents() { + let s = "x^2 + 3x^4 - x^6 + 5x^8"; + assert_eq!(detect_monotonic_element_cut(s, 8), None); + } + + // --- end-to-end crop tests --- + + #[test] + fn crop_catches_omega_element_run_end_to_end() { + // 50× `\Omega \, \Omega \,` inside `$$…$$` — the exact pattern + // from openarchives 997003_…_e2cbfdac. + let inner = "a = ".to_string() + &"\\Omega \\, ".repeat(50); + let doc = format!("$$ {} b $$", inner); + let out = crop_latex_repetitions(&doc, true, 100, 100); + assert!( + out.contains("repetition cropped"), + "expected crop marker, got {:?}", + out + ); + let kept = out.matches("\\Omega").count(); + assert!( + kept <= 5, + "expected <=5 \\Omega in cropped output, got {}", + kept + ); + } + + #[test] + fn crop_catches_monotonic_x_n_progression_end_to_end() { + let inner: String = (1..=20).map(|i| format!("x_{} ", i)).collect(); + let doc = format!("$$ {} $$", inner); + let out = crop_latex_repetitions(&doc, true, 100, 100); + assert!( + out.contains("repetition cropped"), + "expected crop marker, got {:?}", + out + ); + } + + #[test] + fn crop_preserves_clean_math_with_repeated_structure() { + // Legitimate-looking math with `\sum` twice and integrals — + // should NOT trigger. + let doc = "$$\\sum_{i=1}^{n} f(x_i) = \\int_a^b f(x) dx + O(h^2)$$"; + let out = crop_latex_repetitions(doc, true, 100, 100); + assert_eq!(out, doc, "clean math should pass through unchanged"); + } + + #[test] + fn crop_preserves_derivative_sequence() { + let doc = "$$f, f', f'', f''', f'''' \\text{ are derivatives}$$"; + let out = crop_latex_repetitions(doc, true, 100, 100); + assert_eq!(out, doc); + } + + #[test] + fn crop_preserves_short_x_n_enumeration_with_ldots() { + let doc = "$$x_1 + x_2 + x_3 + \\ldots + x_n$$"; + let out = crop_latex_repetitions(doc, true, 100, 100); + assert_eq!(out, doc); + } + + // --- small-vocab-run detector: positive + negative dataset --- + + #[test] + fn small_vocab_run_catches_pure_greek_run() { + // 15 mixed Greek letters, none repeating, but all from the + // small-vocab set — should trigger. + let s = "\\alpha \\beta \\gamma \\delta \\mu \\nu \\alpha \\beta \\gamma \\mu \\lambda \\omega \\phi \\theta \\chi"; + let cut = detect_small_vocab_run_cut(s, 12).expect("cut"); + assert!(cut > 0); + } + + #[test] + fn small_vocab_run_catches_decorated_greek_run() { + // `\hat{\alpha}` etc. — base starts with `\hat` which is in the + // decorator set. 15× should trigger. + let s = "\\hat{\\alpha} \\hat{\\beta} \\tilde{\\gamma} \\bar{\\mu} \\hat{\\nu} \\tilde{\\alpha} \\bar{\\beta} \\hat{\\gamma} \\tilde{\\mu} \\bar{\\nu} \\hat{\\alpha} \\tilde{\\beta} \\bar{\\gamma} \\hat{\\mu} \\tilde{\\nu}"; + let cut = detect_small_vocab_run_cut(s, 12).expect("cut"); + assert!(cut > 0); + } + + #[test] + fn small_vocab_run_does_not_fire_below_threshold() { + // 11 Greek letters — below threshold 12. + let s = "\\alpha \\beta \\gamma \\delta \\mu \\nu \\alpha \\beta \\gamma \\mu \\lambda"; + assert_eq!(detect_small_vocab_run_cut(s, 12), None); + } + + #[test] + fn small_vocab_run_breaks_on_non_vocab_element() { + // Greek letters + a variable `x` in the middle breaks the run. + let s = "\\alpha \\beta \\gamma \\delta \\mu \\nu \\alpha \\beta x \\gamma \\mu \\lambda \\omega \\phi \\theta"; + // `x` breaks the run at position 8; the subsequent 7 atoms + // restart but only reach 7, below 12. + assert_eq!(detect_small_vocab_run_cut(s, 12), None); + } + + #[test] + fn small_vocab_run_breaks_on_latex_operators() { + // Integral / summation / fraction break the pure-Greek run. + let s = "\\alpha \\beta \\sum \\gamma \\delta \\mu \\nu \\int \\alpha \\beta \\gamma \\mu \\lambda \\omega \\phi"; + assert_eq!(detect_small_vocab_run_cut(s, 12), None); + } + + #[test] + fn small_vocab_run_does_not_fire_on_short_theorem_setup() { + // Typical proof setup: `Let $\alpha, \beta, \gamma \in \mathbb{R}$`. + // Greek vars + non-vocab element (\in, \mathbb). + let s = "\\alpha, \\beta, \\gamma \\in \\mathbb{R}"; + assert_eq!(detect_small_vocab_run_cut(s, 12), None); + } + + #[test] + fn small_vocab_run_does_not_fire_on_summation_body() { + // `\sum \alpha_i \beta_i` inside a sum — sum breaks the run, + // and the Greek letters afterward aren't numerous enough. + let s = "\\sum_{i=1}^{n} \\alpha_i \\beta_i \\gamma_i"; + assert_eq!(detect_small_vocab_run_cut(s, 12), None); + } + + #[test] + fn crop_catches_small_vocab_loop_end_to_end() { + // Pure Greek-letter spam inside `$$…$$` — a plausible + // hallucination mode the user flagged. + let inner = "\\alpha \\beta \\gamma \\delta \\mu \\nu \\alpha \\beta \\gamma \\mu \\lambda \\omega \\phi \\theta \\chi \\psi"; + let doc = format!("$$ {} $$", inner); + let out = crop_latex_repetitions(&doc, true, 100, 100); + assert!( + out.contains("repetition cropped"), + "expected crop marker, got {:?}", + out + ); + } + + // ----------------------------------------------------------------- + // v6-02 — cyclic-element run detection. + // ----------------------------------------------------------------- + + #[test] + fn v6_02_detects_cycle_2_intertext_alternation() { + // The actual corpus pattern: two distinct \intertext{…} + // alternating 6+ times. + let unit = r"\intertext{lenN} \intertext{degN} "; + let s = unit.repeat(6); + let cut = detect_cyclic_element_cut(&s, 6, 6); + assert!( + cut.is_some(), + "6× alternation of two \\intertext should trigger" + ); + // Should point at or near the start. + assert!(cut.unwrap() < unit.len() * 2); + } + + #[test] + fn v6_02_detects_cycle_1_equivalent_to_repeated_element() { + // `a a a a a a` — cycle length 1, threshold 6 → fires. + let s = r"a a a a a a trailing"; + let cut = detect_cyclic_element_cut(s, 6, 6); + assert!(cut.is_some()); + } + + #[test] + fn v6_02_detects_cycle_3() { + let unit = r"\sin \cos \tan "; + let s = unit.repeat(8); + let cut = detect_cyclic_element_cut(&s, 6, 6); + assert!( + cut.is_some(), + "8× cycle of 3 distinct elements should trigger" + ); + } + + #[test] + fn v6_02_negative_short_cycle_below_threshold() { + // 3 cycles of length 2 — below threshold 6. + let unit = r"\a \b "; + let s = unit.repeat(3); + assert!( + detect_cyclic_element_cut(&s, 6, 6).is_none(), + "3 cycles should NOT trigger threshold-6" + ); + } + + #[test] + fn v6_02_negative_dot_separated_vars_short() { + // `a \cdot b \cdot a \cdot b` — alternation but short. No trigger. + let s = r"a \cdot b \cdot a \cdot b"; + assert!(detect_cyclic_element_cut(s, 6, 6).is_none()); + } + + #[test] + fn v6_02_negative_distinct_math_sequence() { + // Diverse legit-math expression — no cycle. + let s = r"\sin(x) + \cos(x) - \tan(y) + \log(z) + e^x - \sqrt{y}"; + assert!(detect_cyclic_element_cut(s, 6, 6).is_none()); + } + + #[test] + fn v6_02_shortest_cycle_wins_on_aaaa() { + // `a a a a a a a a` — cycle 1 should win over cycle 2/4/etc. + let s = r"a a a a a a a a"; + let cut1 = detect_cyclic_element_cut(s, 6, 6); + assert!(cut1.is_some()); + } + + // ----------------------------------------------------------------- + // v6-06a — brace/bracket/paren balance inside a $$…$$ span. + // ----------------------------------------------------------------- + + #[test] + fn v6_06a_balanced_latex_returns_none() { + let s = r"\frac{a+b}{c} \cdot \int_0^1 x \, dx = \gamma"; + assert!(detect_unbalanced_braces_in_latex_span(s).is_none()); + } + + #[test] + fn v6_06a_mismatched_bracket_close_flagged() { + // `[ 1 - \mu_2 s }{ 1 - \mu_2 s }` — opens `[`, first close is + // `}` (mismatch). + let s = r"+ [ 1 - \mu_2 s }{ 1 - \mu_2 s }"; + assert_eq!(detect_unbalanced_braces_in_latex_span(s), Some(0)); + } + + #[test] + fn v6_06a_unclosed_brace_flagged() { + let s = r"\frac{a + b}{c"; + assert_eq!(detect_unbalanced_braces_in_latex_span(s), Some(0)); + } + + #[test] + fn v6_06a_escaped_braces_ignored() { + // `\{` and `\}` are literal LaTeX tokens, not delimiters. + let s = r"\{ a + b \} = c"; + assert!(detect_unbalanced_braces_in_latex_span(s).is_none()); + } + + #[test] + fn v6_06a_nested_groups_ok() { + let s = r"\frac{\int_0^1 {x^2} dx}{\sum_i {a_i}}"; + assert!(detect_unbalanced_braces_in_latex_span(s).is_none()); + } + + // ----------------------------------------------------------------- + // v6-05 — `\frac{A}{A}` structurally-degenerate fraction. + // + // Numerator == denominator after whitespace normalization = + // always simplifies to `1`, so literal occurrence is hallucination. + // Not caught by element-repeat / monotonic / small-vocab because + // the outer `\frac` tokenizes as one element. + // ----------------------------------------------------------------- + + #[test] + fn v6_05_detects_exact_duplicated_frac_numerator_denominator() { + let s = r"before \frac{\int_k^s e^x v_\kappa(x) dx}{\int_k^s e^x v_\kappa(x) dx} after"; + let cut = detect_degenerate_frac_cut(s); + assert!( + cut.is_some(), + "exact duplicated \\frac args should be detected" + ); + // Cut should land at the `\frac` start. + let cut = cut.unwrap(); + assert_eq!(&s[cut..cut + 5], "\\frac"); + } + + #[test] + fn v6_05_detects_duplicated_frac_with_whitespace_variation() { + // Same expression, different incidental whitespace inside + // the arg groups. collapse_ws should normalize both. + let s = r"\frac{ a + b }{a + b}"; + let cut = detect_degenerate_frac_cut(s); + assert!( + cut.is_some(), + "whitespace-normalized dup should be detected" + ); + } + + #[test] + fn v6_05_negative_legit_fraction_not_flagged() { + let s = r"\frac{a + b}{c}"; + let cut = detect_degenerate_frac_cut(s); + assert!( + cut.is_none(), + "legit \\frac{{A}}{{B}} should not trigger, got {:?}", + cut + ); + } + + #[test] + fn v6_05_negative_legit_frac_chain_not_flagged() { + let s = r"result = \frac{a}{b} \cdot \frac{c}{d} \cdot \frac{e}{f}"; + assert!(detect_degenerate_frac_cut(s).is_none()); + } + + #[test] + fn v6_05_first_degenerate_frac_wins() { + // Degenerate frac at the SECOND position. + let s = r"\frac{a}{b} = 1 \cdot \frac{xyz}{xyz} + rest"; + let cut = detect_degenerate_frac_cut(s); + assert!(cut.is_some()); + let cut = cut.unwrap(); + // Should point at the second `\frac` (the degenerate one). + assert!( + &s[cut..].starts_with(r"\frac{xyz}{xyz}"), + "cut should point at the degenerate frac, got context {:?}", + &s[cut..cut + 20] + ); + } + + #[test] + fn v6_05_empty_args_not_flagged() { + // `\frac{}{}` — weird but not our target class; don't fire. + let s = r"\frac{}{}"; + assert!(detect_degenerate_frac_cut(s).is_none()); + } + + // ----------------------------------------------------------------- + // v6-01 — `\quad \\ \quad \\ …` escapes element-repeat detector. + // + // `\\` is LaTeX display-math line break — pure formatting, not + // content. When it appears between repeated `\quad` elements, + // `next_latex_element` treats the `\` chars as distinct fallback + // elements, resetting the run-counter in + // `detect_repeated_element_cut`. Fix: add `\\` to the + // separator-skip set inside `next_latex_element`. + // ----------------------------------------------------------------- + + #[test] + fn v6_01_quad_repeat_with_linebreaks_is_caught() { + // Five `\quad` separated by `\\`. With `\\` treated as + // separator, detect_repeated_element_cut should fire at + // threshold=4 (four consecutive identical `\quad`). + let s = r"\quad \\ \quad \\ \quad \\ \quad \\ \quad trailing content"; + let cut = detect_repeated_element_cut(s, 4); + assert!( + cut.is_some(), + "v6-01: repeated \\quad with \\\\ separators should be caught" + ); + } + + #[test] + fn v6_01_negative_short_multiline_math_not_flagged() { + // `a \\ b \\ c \\ d` — 4 DIFFERENT atoms separated by `\\`. + // With `\\` skipped, tokens are `a b c d` (distinct). Must + // NOT trigger the repeat detector at threshold 4. + let s = r"a \\ b \\ c \\ d"; + let cut = detect_repeated_element_cut(s, 4); + assert!( + cut.is_none(), + "v6-01: distinct atoms must not trigger, got {:?}", + cut + ); + } + + #[test] + fn v6_01_negative_cases_env_not_flagged() { + // `\begin{cases} a \\ b \\ c \end{cases}` — three distinct + // atoms inside a matrix/cases environment. `\\` is a row + // separator, not repetition. + let s = r"\begin{cases} a \\ b \\ c \end{cases}"; + let cut = detect_repeated_element_cut(s, 4); + assert!( + cut.is_none(), + "v6-01: cases env must not trigger, got {:?}", + cut + ); + } +} diff --git a/rust/glossapi_rs_cleaner/src/lib.rs b/rust/glossapi_rs_cleaner/src/lib.rs index 302597a..20154ac 100644 --- a/rust/glossapi_rs_cleaner/src/lib.rs +++ b/rust/glossapi_rs_cleaner/src/lib.rs @@ -1,18 +1,72 @@ -// Main module for the text_cleaner_rs Python module -// Implements refactored code with better separation of concerns +//! GlossAPI Rust cleaner — production cleaning pipeline (PyO3 module). +//! +//! # Boundary with `glossapi_rs_noise` +//! +//! Per `CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25` Point 7: +//! +//! - **This crate** owns *cleaning* and the *production counters* +//! that drive corpus-cleaning sample cuts. `clean_text_with_stats` +//! emits per-rule counts (`rule_a_match_count`, `rule_b_match_count`, +//! `residue_line_drop_count`) directly inside `CleanStats`, so +//! production drivers don't need a second pass through `glossapi_rs_noise`. +//! - **`glossapi_rs_noise`** owns *diagnostic / debug exports* — +//! OCR scoring, word-repeat span extraction, token-category review +//! bundling. Cleaner production paths never import from it. +//! +//! Phase A (markdown formatting): default is Pilot B +//! (`PhaseAMode::ParserSurgicalVerified` → `md_format_surgical::format_surgical_checked`). +//! `LineBased` is regression-test-only — never use for production. +//! `cmark-gfm` is OPTIONAL: if installed, it serves as ground-truth +//! oracle (per-doc subprocess overhead); if not, the in-process +//! `dual_verify` (comrak + pulldown-cmark) path is used. Production +//! assumes the dual_verify path. + +// Lint posture (CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Item 5): +// `dead_code` is allowed at crate level because several utility +// functions / variants are kept around as parts of an evolving public +// surface (e.g. `analyze_text`, `list_available_scripts`, +// `drop_low_salvage_pages`, `process_directory_native`, +// `batch_clean_markdown_files`) — they are documented dev/audit tools +// or back-compat exports that are not invoked from the production +// hot path but should remain available. Real bugs (unused variables, +// unread assignments) still warn; only DEAD-CODE noise is silenced. +#![allow(dead_code)] // Internal modules +mod charset_module; mod cleaning_module; +mod cmark_gfm_oracle; mod directory_processor; +mod latex_module; +mod md_format; +mod md_format_surgical; +mod md_module; +mod md_verify; +mod normalize; mod pipeline_module; mod table_analysis_module; mod table_remover_module; // Export public items from modules via PyO3 +use charset_module::{analyze_charset, non_empty_line_stats}; +use cleaning_module::{clean_text, clean_text_with_stats}; +use cmark_gfm_oracle::cmark_gfm_verify_py; use directory_processor::{ batch_generate_detailed_table_report_csv, batch_generate_table_summary_csv, batch_remove_tables_from_files, generate_analysis_report_for_directory, }; +use latex_module::crop_latex_repetitions_py; +// Dead exports excised in the cleaner-integration-20260430 PR: +// - format_parsed_py (Pilot A — superseded by Pilot B's +// format_surgical_checked). +// - dual_verify_py (dev-only oracle exposure; dual_verify itself +// stays as Rust-internal for format_surgical_checked). +// - format_surgical_py (Pilot B without oracle check; dev-only). +// - apply_phase_a / phase_a_alteration_stats / phase_a_stats_jsonl_line +// (LineBased path instrumentation; LineBased was removed entirely +// from md_module.rs). +use md_format_surgical::{format_surgical_checked_py, phase_a_policy_py}; +use md_verify::{verify_md_preview_equivalent_py, verify_md_structural_py}; use pipeline_module::run_complete_pipeline; // Bring the #[pyfunction] into scope use pyo3::prelude::*; @@ -31,6 +85,19 @@ fn glossapi_rs_cleaner(_py: Python, m: &PyModule) -> PyResult<()> { m.add_function(wrap_pyfunction!(batch_remove_tables_from_files, m)?)?; m.add_function(wrap_pyfunction!(batch_generate_table_summary_csv, m)?)?; + // Per-row cleaning entry for scripts that need to clean corpus-parquet + // `text` columns without round-tripping through markdown files. + m.add_function(wrap_pyfunction!(clean_text, m)?)?; + m.add_function(wrap_pyfunction!(clean_text_with_stats, m)?)?; + m.add_function(wrap_pyfunction!(analyze_charset, m)?)?; + m.add_function(wrap_pyfunction!(non_empty_line_stats, m)?)?; + m.add_function(wrap_pyfunction!(crop_latex_repetitions_py, m)?)?; + m.add_function(wrap_pyfunction!(verify_md_preview_equivalent_py, m)?)?; + m.add_function(wrap_pyfunction!(verify_md_structural_py, m)?)?; + m.add_function(wrap_pyfunction!(cmark_gfm_verify_py, m)?)?; + m.add_function(wrap_pyfunction!(format_surgical_checked_py, m)?)?; + m.add_function(wrap_pyfunction!(phase_a_policy_py, m)?)?; + // For now, only exposing the main pipeline function and essential classes. // Other individual functions from submodules can be re-exposed later if needed, // after verifying their exact names and signatures as defined within their modules. diff --git a/rust/glossapi_rs_cleaner/src/md_format.rs b/rust/glossapi_rs_cleaner/src/md_format.rs new file mode 100644 index 0000000..43f17d9 --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/md_format.rs @@ -0,0 +1,299 @@ +//! Dual-parser verifier (`dual_verify`) — used by Pilot B's checked +//! wrapper (`md_format_surgical::format_surgical_checked`) to confirm +//! that a Phase A rewrite preserved the rendered HTML preview. +//! +//! Historical note: this module also held Pilot A +//! (`format_parsed`, a whole-doc round-trip through comrak's +//! `format_commonmark`). Pilot A was abandoned per the 2026-04-25 +//! cleanup plan — it over-normalized things outside the 3 target +//! transforms (50 of 66 audit failures traced to non-target +//! normalizations of list markers, link forms, escapes). Pilot B +//! (`md_format_surgical::format_surgical`) supersedes it; only the +//! dual-parser verifier remains here. +//! +//! What `dual_verify` checks: +//! - INPUT parser agreement: comrak and pulldown-cmark agree on what +//! the input renders to (well-formedness signal — the doc isn't +//! dialect-ambiguous). +//! - OUTPUT parser agreement: same for the rewrite output. +//! - Identity per parser: the rewrite preserves the rendered HTML +//! under EACH parser independently. +//! +//! On any disagreement, `format_surgical_checked` ships the input +//! verbatim and records `phase_a_fallback_reason`. + +use comrak::{nodes::AstNode, parse_document, Arena, Options}; +use pulldown_cmark::{html as pd_html, Options as PdOptions, Parser as PdParser}; + +/// Build the comrak options the verifier uses to render input + output +/// to HTML for comparison. GFM extensions match what +/// `md_format_surgical` parses with so the comparison is on the same +/// dialect. +fn phase_a_options() -> Options<'static> { + let mut opts = Options::default(); + opts.extension.table = true; + opts.extension.strikethrough = true; + opts.extension.tasklist = true; + opts.extension.footnotes = true; + // Autolink + tagfilter off — they alter content in ways we don't + // want for a Phase A rewriter (autolink rewrites bare URLs to + // `` markdown; tagfilter escapes HTML tags). Both would show + // up as diffs vs the input without being formatting. + opts.extension.autolink = false; + opts.extension.tagfilter = false; + opts.render.unsafe_ = true; // don't filter HTML on HTML render + opts +} + +// --------------------------------------------------------------------------- +// Dual-parser verifier: pulldown-cmark + comrak agreement. +// --------------------------------------------------------------------------- + +/// Report from the dual-parser verifier. +#[derive(Debug, Clone, Default)] +pub struct DualVerifyReport { + /// pulldown-cmark HTML of the INPUT, whitespace-collapsed. + pub pd_input_html: String, + /// pulldown-cmark HTML of the OUTPUT, whitespace-collapsed. + pub pd_output_html: String, + /// comrak HTML of the INPUT, whitespace-collapsed. + pub cm_input_html: String, + /// comrak HTML of the OUTPUT, whitespace-collapsed. + pub cm_output_html: String, + /// True if both parsers agree the INPUT renders to the same HTML + /// (well-formedness signal: two independent parsers see the same + /// structure — the doc is not dialect-ambiguous). + pub input_parser_agreement: bool, + /// True if both parsers agree the OUTPUT renders to the same HTML. + pub output_parser_agreement: bool, + /// True if INPUT and OUTPUT render to the same HTML under + /// pulldown-cmark (render identity — the Phase A invariant). + pub pd_identity: bool, + /// True if INPUT and OUTPUT render to the same HTML under comrak + /// (render identity under the second parser). + pub cm_identity: bool, + /// Short description of the first divergence, if any. + pub first_diff: Option, +} + +impl DualVerifyReport { + /// True iff all four agreement/identity checks pass — meaning: + /// - two independent parsers agree on the input's render + /// - two independent parsers agree on the output's render + /// - input and output render identically under BOTH parsers + /// + /// This is the strongest signal we have that a rewrite preserved + /// preview on a well-formed document. + pub fn is_well_formed_and_identical(&self) -> bool { + self.input_parser_agreement + && self.output_parser_agreement + && self.pd_identity + && self.cm_identity + } + + /// True iff input and output render to the same HTML under EACH + /// parser individually (parsers may disagree with each other). + /// + /// This is the Phase A preview-preservation invariant strictly: + /// the rewrite didn't change preview, but the document itself + /// may be dialect-ambiguous (two parsers render it differently). + /// Dialect-ambiguity is a property of the INPUT, not the rewrite. + pub fn is_preview_preserving_per_parser(&self) -> bool { + self.pd_identity && self.cm_identity + } + + /// True iff the input is well-formed (two parsers agree). Used + /// to classify a doc as dialect-safe for rewrite, independent of + /// whether a rewrite has happened. + pub fn is_input_well_formed(&self) -> bool { + self.input_parser_agreement + } +} + +fn pulldown_render(md: &str) -> String { + let mut opts = PdOptions::empty(); + opts.insert(PdOptions::ENABLE_TABLES); + opts.insert(PdOptions::ENABLE_FOOTNOTES); + opts.insert(PdOptions::ENABLE_STRIKETHROUGH); + opts.insert(PdOptions::ENABLE_TASKLISTS); + let parser = PdParser::new_ext(md, opts); + let mut html = String::new(); + pd_html::push_html(&mut html, parser); + collapse_ws(&html) +} + +fn comrak_render(md: &str) -> String { + let arena = Arena::new(); + let opts = phase_a_options(); + let root: &AstNode = parse_document(&arena, md, &opts); + let mut html = Vec::new(); + comrak::format_html(root, &opts, &mut html).expect("format_html write"); + collapse_ws(&String::from_utf8_lossy(&html)) +} + +/// Collapse whitespace runs to a single space, trim, AND drop +/// whitespace between adjacent tags (`> <` → `><`). pulldown-cmark +/// and comrak emit differently-spaced HTML for the same input +/// (notably around `` / `` / `
`), so inter-tag +/// whitespace MUST be normalized out before comparison — it's +/// invisible in any browser / renderer anyway. +fn collapse_ws(s: &str) -> String { + let mut collapsed = String::with_capacity(s.len()); + let mut prev_ws = false; + for c in s.chars() { + if c.is_whitespace() { + if !prev_ws { + collapsed.push(' '); + } + prev_ws = true; + } else { + collapsed.push(c); + prev_ws = false; + } + } + // Iteratively strip `> <` → `><` so `` → `
`. + // Run in a loop because collapsing can create new adjacencies. + let mut prev = collapsed.trim().to_string(); + loop { + let next = prev.replace("> <", "><"); + if next == prev { + break; + } + prev = next; + } + // Strip trailing whitespace before a closing tag: `X ` → + // `X`. Pulldown-cmark preserves trailing whitespace inside + // block content (e.g. `## heading\t` → `

heading

`); + // comrak strips it on round-trip. Both render identically in any + // preview since block-level trailing whitespace is invisible. + let mut prev = prev; + loop { + let next = prev.replace(" ` boilerplate. Comrak + // emits this marker between consecutive lists of different + // marker types to disambiguate; pulldown-cmark doesn't. The + // comment is invisible in any renderer so stripping is safe for + // preview-equivalence comparison. + let prev = prev.replace("", ""); + // Re-collapse any space runs created by the strip. + let mut collapsed = String::with_capacity(prev.len()); + let mut prev_ws = false; + for c in prev.chars() { + if c == ' ' { + if !prev_ws { + collapsed.push(' '); + } + prev_ws = true; + } else { + collapsed.push(c); + prev_ws = false; + } + } + let mut prev = collapsed.trim().to_string(); + loop { + let next = prev.replace("> <", "><"); + if next == prev { + break; + } + prev = next; + } + // Normalize GFM table alignment attribute encoding: + // pulldown-cmark emits `style="text-align: left"` + // comrak emits `align="left"` + // Both produce the same rendered alignment. Collapse to one form + // (picking comrak's `align="X"` since it's shorter + HTML-legal + // in all browsers for `
`/``). + let prev = prev.replace(r#" style="text-align: left""#, r#" align="left""#); + let prev = prev.replace(r#" style="text-align: right""#, r#" align="right""#); + let prev = prev.replace(r#" style="text-align: center""#, r#" align="center""#); + prev +} + +/// Run the dual-parser verifier on `(input, output)`. Returns a +/// report; `is_well_formed_and_identical()` is the single-boolean +/// summary. +pub fn dual_verify(input: &str, output: &str) -> DualVerifyReport { + let pd_in = pulldown_render(input); + let pd_out = pulldown_render(output); + let cm_in = comrak_render(input); + let cm_out = comrak_render(output); + let input_parser_agreement = pd_in == cm_in; + let output_parser_agreement = pd_out == cm_out; + let pd_identity = pd_in == pd_out; + let cm_identity = cm_in == cm_out; + let first_diff = + if pd_identity && cm_identity && input_parser_agreement && output_parser_agreement { + None + } else { + let (label, a, b) = if !input_parser_agreement { + ( + "parsers disagree on input (dialect-ambiguous)", + pd_in.as_str(), + cm_in.as_str(), + ) + } else if !output_parser_agreement { + ( + "parsers disagree on output", + pd_out.as_str(), + cm_out.as_str(), + ) + } else if !pd_identity { + ( + "pulldown-cmark sees input != output", + pd_in.as_str(), + pd_out.as_str(), + ) + } else { + ( + "comrak sees input != output", + cm_in.as_str(), + cm_out.as_str(), + ) + }; + let prefix = common_prefix_len(a, b); + let mut snippet = String::new(); + snippet.push_str(label); + snippet.push_str("\n a: "); + snippet.push_str( + &a.chars() + .skip(prefix.saturating_sub(40)) + .take(200) + .collect::(), + ); + snippet.push_str("\n b: "); + snippet.push_str( + &b.chars() + .skip(prefix.saturating_sub(40)) + .take(200) + .collect::(), + ); + Some(snippet) + }; + DualVerifyReport { + pd_input_html: pd_in, + pd_output_html: pd_out, + cm_input_html: cm_in, + cm_output_html: cm_out, + input_parser_agreement, + output_parser_agreement, + pd_identity, + cm_identity, + first_diff, + } +} + +fn common_prefix_len(a: &str, b: &str) -> usize { + let mut n = 0; + for (ca, cb) in a.chars().zip(b.chars()) { + if ca != cb { + break; + } + n += ca.len_utf8(); + } + n +} diff --git a/rust/glossapi_rs_cleaner/src/md_format_surgical.rs b/rust/glossapi_rs_cleaner/src/md_format_surgical.rs new file mode 100644 index 0000000..970f6da --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/md_format_surgical.rs @@ -0,0 +1,1053 @@ +//! Pilot B — surgical parser-backed Phase A rewriter. +//! +//! Unlike `md_format::format_parsed` (which round-trips the whole +//! document through comrak's `format_commonmark`), this module: +//! +//! 1. Parses the input with comrak to get an AST with source +//! positions (line:col ranges per node). +//! 2. Walks ONLY the top-level block children of the Document node. +//! 3. For each top-level child, decides: +//! - **Paragraph / Table / ThematicBreak** → emit comrak's +//! canonical serialization for this single node. This is what +//! gives us the three Phase A transformations (reflow unwrap, +//! GFM sep minimization, HR canonicalization). +//! - **Everything else** (Heading, List, BlockQuote, CodeBlock, +//! HtmlBlock, ReferenceDef, FootnoteDef, …) → copy the source +//! bytes for that node verbatim. +//! 4. Preserves the original text between consecutive top-level +//! nodes (blank lines, trailing content) verbatim. +//! +//! Why: comrak's whole-doc round-trip over-normalizes things outside +//! our 3 target transforms — list markers (`1.` vs `1)`), link +//! forms, character escapes, URL encoding. On the 90-doc real-corpus +//! audit, 50 of 66 Pilot A failures traced to these non-target +//! normalizations. Pilot B keeps every non-target span byte-exact. +//! +//! Paragraphs NESTED inside BlockQuote / List / etc. are kept +//! verbatim (so their reflow is NOT performed) — this is a +//! conservative trade-off: the outer container (BlockQuote, List) +//! is preserved exactly, which means the non-target canonicalization +//! can't happen there. A future extension could walk into container +//! nodes and surgically reflow nested paragraphs, but only after the +//! top-level approach is proven safe. + +use comrak::{ + nodes::{AstNode, NodeValue, TableAlignment}, + parse_document, Arena, Options, +}; + +/// Build comrak options matching what `md_format::format_parsed` +/// uses, so Pilot A and Pilot B operate under the same parser +/// assumptions. Sourcepos IS enabled here because we need the +/// line:col ranges. +fn options_with_sourcepos() -> Options<'static> { + let mut opts = Options::default(); + opts.extension.table = true; + opts.extension.strikethrough = true; + opts.extension.tasklist = true; + opts.extension.footnotes = true; + opts.extension.autolink = false; + opts.extension.tagfilter = false; + opts.render.sourcepos = true; + opts.render.unsafe_ = true; + opts +} + +/// Byte-offset table keyed by 1-based line number. Given a +/// CommonMark source string, `line_byte_offsets[i]` is the byte +/// offset at which the i-th line begins (line 1 → index 1). +/// Index 0 is unused (1-based convention to match Sourcepos). +fn build_line_offset_table(src: &str) -> Vec { + let mut offsets = Vec::with_capacity(src.len() / 40 + 2); + offsets.push(0); // index 0 unused + offsets.push(0); // line 1 starts at byte 0 + let bytes = src.as_bytes(); + for (i, b) in bytes.iter().enumerate() { + if *b == b'\n' { + offsets.push(i + 1); + } + } + offsets +} + +/// Convert (line, column) to a byte offset in `src`. Columns are +/// 1-based CHARACTER positions per CommonMark; this function walks +/// chars from the start of the line to handle multi-byte UTF-8. +/// Caps at the length of the line / src if the column is past EOL. +fn line_col_to_byte(src: &str, line_offsets: &[usize], line: usize, col: usize) -> usize { + if line == 0 || line >= line_offsets.len() { + return src.len(); + } + let line_start = line_offsets[line]; + let line_end = if line + 1 < line_offsets.len() { + line_offsets[line + 1] + } else { + src.len() + }; + // Column is 1-based char position within the line (stripped of + // trailing `\n`). Walk chars until we've consumed `col-1` of + // them, then return the byte offset of the next char. + if col <= 1 { + return line_start; + } + let line_text = &src[line_start..line_end]; + let mut b = 0usize; + for (i, (byte_off, _)) in line_text.char_indices().enumerate() { + if i + 1 == col { + return line_start + byte_off; + } + b = byte_off; + } + // Past end of line — return end-of-line (excluding trailing `\n`). + let _ = b; + // If line ends with `\n`, exclude it from the byte range. + if line_text.ends_with('\n') { + line_start + line_text.len() - 1 + } else { + line_end + } +} + +/// Serialize ONE comrak AST node (and its descendants) back to +/// CommonMark via `format_commonmark`. Used for ThematicBreak nodes +/// where the only transform we want IS comrak's canonical form. +/// +/// NOT used for Paragraph / Table — those go through source-level +/// rewrites that preserve inline content byte-exact (see +/// `paragraph_source_with_softbreaks_unwrapped` and +/// `table_source_with_minimal_delimiter`). +fn serialize_node_only<'a>(node: &'a AstNode<'a>, opts: &Options) -> String { + // Rewrite SoftBreak → Text(" ") in-place before serialization. + let descendants: Vec<_> = node.descendants().collect(); + for desc in descendants { + let needs_rewrite = matches!(desc.data.borrow().value, NodeValue::SoftBreak); + if needs_rewrite { + desc.data.borrow_mut().value = NodeValue::Text(" ".to_string()); + } + } + let mut out = Vec::with_capacity(256); + comrak::format_commonmark(node, opts, &mut out).expect("format_commonmark"); + String::from_utf8(out).unwrap_or_default() +} + +/// Source-level paragraph unwrap: take the paragraph's raw source +/// bytes and replace SoftBreak newlines with single spaces. +/// Everything else (URLs with `%XX`, escape forms like `\*`, inline +/// code with literal pipes, link markup, em/strong markers) stays +/// byte-exact from source. +/// +/// CommonMark rule for identifying soft break vs hard break inside +/// a paragraph: +/// - ` \n` (two or more trailing spaces before newline) = hard +/// - `\\\n` (odd count of trailing backslashes) = hard +/// - any other single `\n` = soft break, unwrap to space +/// +/// Important: this is line-level rewriting on the raw source of a +/// paragraph (everything between the paragraph's start and end +/// byte offsets). Works correctly even if the paragraph contains +/// inline HTML, autolinks, or escape sequences, because we only +/// touch whitespace around `\n`. +fn paragraph_source_with_softbreaks_unwrapped(para_src: &str) -> String { + let lines: Vec<&str> = para_src.split('\n').collect(); + let mut out = String::with_capacity(para_src.len()); + for (i, line) in lines.iter().enumerate() { + if i == 0 { + out.push_str(line); + continue; + } + let prev = &lines[i - 1]; + let hard_break = prev_is_hard_break(prev); + if hard_break { + out.push('\n'); + out.push_str(line); + } else { + // Soft break: roll back trailing ASCII-whitespace from + // `out`, emit one space, then append line with leading + // ASCII-whitespace stripped. ASCII-ONLY because + // Docling-extracted PDFs use U+00A0 (NBSP) as a + // meaningful column-preservation marker — cmark-gfm + // treats NBSP as content and so must we. Using Rust's + // `trim_start()` / `trim_end()` here would be wrong; + // those are Unicode-whitespace-aware and strip NBSP. + let trimmed_len = trim_end_ascii_ws(&out).len(); + out.truncate(trimmed_len); + out.push(' '); + out.push_str(trim_start_ascii_ws(line)); + } + } + out +} + +/// Return `s` with trailing ASCII whitespace (space, tab, `\r`) +/// removed. NBSP (U+00A0) and other Unicode whitespace are +/// preserved — cmark-gfm treats those as content. +fn trim_end_ascii_ws(s: &str) -> &str { + let bytes = s.as_bytes(); + let mut end = bytes.len(); + while end > 0 { + let c = bytes[end - 1]; + if c == b' ' || c == b'\t' || c == b'\r' { + end -= 1; + } else { + break; + } + } + &s[..end] +} + +/// Return `s` with leading ASCII whitespace (space, tab, `\r`) +/// removed. NBSP (U+00A0) and other Unicode whitespace preserved. +fn trim_start_ascii_ws(s: &str) -> &str { + let bytes = s.as_bytes(); + let mut start = 0; + while start < bytes.len() { + let c = bytes[start]; + if c == b' ' || c == b'\t' || c == b'\r' { + start += 1; + } else { + break; + } + } + &s[start..] +} + +/// Return true if the last non-`\r` content of `prev` is a +/// CommonMark hard-break marker: +/// - 2+ trailing spaces, OR +/// - odd count of trailing backslashes. +fn prev_is_hard_break(prev: &str) -> bool { + let s = prev.trim_end_matches('\r'); + if s.ends_with(" ") { + return true; + } + let trailing_backslashes = s.chars().rev().take_while(|c| *c == '\\').count(); + trailing_backslashes % 2 == 1 +} + +/// Emit the canonical GFM separator row for a table with the given +/// per-column alignments. e.g. `| --- | :--- | ---: |`. +fn canonical_gfm_separator_row(alignments: &[TableAlignment]) -> String { + let mut s = String::with_capacity(alignments.len() * 8); + s.push('|'); + for a in alignments { + s.push(' '); + match a { + TableAlignment::None => s.push_str("---"), + TableAlignment::Left => s.push_str(":---"), + TableAlignment::Right => s.push_str("---:"), + TableAlignment::Center => s.push_str(":---:"), + } + s.push_str(" |"); + } + s +} + +/// Surgical rewrite of a Table node's source: keep header + body +/// bytes byte-exact, replace ONLY the delimiter row (second line) +/// with a canonical `| --- | :--- | …` form. +/// +/// Why not re-serialize via `format_commonmark`: comrak's table +/// serialization adds `\` escapes to `_`, `[`, `]`, `#` etc. inside +/// URL text in cells, which cmark-gfm then percent-encodes — the +/// single biggest Pilot B residual-failure category. +fn table_source_with_delimiter_rewritten(table_src: &str, alignments: &[TableAlignment]) -> String { + // Find the first and second `\n` in the table source: the + // delimiter row is between them. Header row = bytes 0..first_nl. + // Delimiter row = bytes first_nl+1..second_nl. + let bytes = table_src.as_bytes(); + let mut first_nl = None; + let mut second_nl = None; + for (i, b) in bytes.iter().enumerate() { + if *b == b'\n' { + if first_nl.is_none() { + first_nl = Some(i); + } else { + second_nl = Some(i); + break; + } + } + } + let (Some(first), Some(second)) = (first_nl, second_nl) else { + // Malformed / single-line "table" — pass through unchanged. + return table_src.to_string(); + }; + // Sanity: the original delimiter row must start with `|` or a + // digit-free dash sequence. If not, don't rewrite. + let original_delim = &table_src[first + 1..second]; + let t = original_delim.trim(); + if !t.contains('-') { + return table_src.to_string(); + } + let canonical = canonical_gfm_separator_row(alignments); + let mut out = String::with_capacity(table_src.len()); + out.push_str(&table_src[..first + 1]); // header row + its `\n` + out.push_str(&canonical); + out.push_str(&table_src[second..]); // `\n` + body + out +} + +/// Pilot B entry: surgical Phase A rewrite. +/// +/// Walks top-level block children of the comrak Document. Emits +/// comrak's canonical form for Paragraph / Table / ThematicBreak; +/// everything else, including inter-node whitespace, is preserved +/// byte-exact from the source. +pub fn format_surgical(md: &str) -> String { + let arena = Arena::new(); + let opts = options_with_sourcepos(); + let root = parse_document(&arena, md, &opts); + let line_offsets = build_line_offset_table(md); + + let mut out = String::with_capacity(md.len()); + let mut cursor: usize = 0; // byte offset in source we've copied up to + + // Collect children so we can peek at the next sibling's kind + // when deciding whether to inject a blank-line separator. + let children: Vec<_> = root.children().collect(); + for (idx, child) in children.iter().enumerate() { + let ast = child.data.borrow(); + let next_is_hr = children + .get(idx + 1) + .map(|n| matches!(n.data.borrow().value, NodeValue::ThematicBreak)) + .unwrap_or(false); + let sp = ast.sourcepos; + // Byte range for this node in the source. + let start = line_col_to_byte(md, &line_offsets, sp.start.line, sp.start.column); + // End column is inclusive — add 1 char width to get the + // byte AFTER the last char. + let mut end_exclusive = { + let col_end_byte = line_col_to_byte(md, &line_offsets, sp.end.line, sp.end.column); + if col_end_byte < md.len() { + let rest = &md[col_end_byte..]; + col_end_byte + rest.chars().next().map_or(0, |c| c.len_utf8()) + } else { + md.len() + } + }; + // Comrak sometimes reports block-node sourcepos.end on the + // line AFTER the content (e.g. HR `3:1-4:0` includes the + // blank line after). Trim trailing `\n` chars off the byte + // range so blank lines fall into inter-node preservation, + // not into the node's splice span. + while end_exclusive > start && md.as_bytes()[end_exclusive - 1] == b'\n' { + end_exclusive -= 1; + } + // Defensive: if the end still looks wrong (empty span or + // crosses the cursor backward), skip this node — something + // is off with the sourcepos. Better to lose a transform on + // one node than to corrupt the output. + if end_exclusive <= start || start < cursor { + continue; + } + // Preserve inter-node source (blank lines, comments, etc.) + if start > cursor { + out.push_str(&md[cursor..start]); + } + let rewritten_block = match &ast.value { + NodeValue::Paragraph => { + // Source-level SoftBreak unwrap — preserves all + // inline content (URLs, escapes, inline code, etc.) + // byte-exact. Only whitespace around `\n` changes. + let para_src = &md[start..end_exclusive]; + let rewritten = paragraph_source_with_softbreaks_unwrapped(para_src); + out.push_str(&rewritten); + true + } + NodeValue::Table(tbl) => { + // Delimiter-only rewrite: keep every cell byte-exact, + // replace ONLY the `|---|---|` row with canonical + // `| --- | :--- | ---: | :---: |` form. This avoids + // comrak's URL-escape injection inside cells that + // cmark-gfm re-encodes. + let table_src = &md[start..end_exclusive]; + let rewritten = table_source_with_delimiter_rewritten(table_src, &tbl.alignments); + out.push_str(&rewritten); + true + } + NodeValue::ThematicBreak => { + // Canonical HR is just `---`. + out.push_str("---"); + true + } + _ => { + // Verbatim passthrough. + out.push_str(&md[start..end_exclusive]); + false + } + }; + cursor = end_exclusive; + + // If this rewritten block is a Paragraph immediately + // followed by a ThematicBreak, the source might only have a + // single `\n` between them (or the HR's canonical form + // might land adjacent). That creates setext-heading + // ambiguity — cmark-gfm would re-parse the paragraph as a + // setext H2 with `---` as the underline. Force `\n\n` + // separation in output, consuming source's `\n`s so we + // don't double-up. + // + // For paragraph → any-other-block (including comrak's split + // of what cmark-gfm sees as one soft-wrapped paragraph), + // DO NOT inject extra blank line — let the source decide. + // (Dialect-ambiguous input ≠ our bug.) + let needs_forced_blank_line = + matches!(&ast.value, NodeValue::Paragraph | NodeValue::ThematicBreak) + && (next_is_hr || matches!(&ast.value, NodeValue::ThematicBreak)); + if needs_forced_blank_line { + let mut consumed = 0usize; + while cursor + consumed < md.len() && md.as_bytes()[cursor + consumed] == b'\n' { + consumed += 1; + } + out.push_str("\n\n"); + cursor += consumed; + } + let _ = rewritten_block; + } + // Trailing source after last node (typically `\n` or blank). + if cursor < md.len() { + out.push_str(&md[cursor..]); + } + out +} + +// --------------------------------------------------------------------------- +// PyO3. +// --------------------------------------------------------------------------- + +use pyo3::prelude::*; + +#[pyfunction] +pub fn format_surgical_py(md: &str) -> String { + format_surgical(md) +} + +// --------------------------------------------------------------------------- +// Phase A policy + checked wrapper (Findings 2 & 5 from the +// PHASE_A_PARSER_BACKED_IMPLEMENTATION_REVIEW). +// --------------------------------------------------------------------------- + +/// Named dialect / formatter policy choices. Surfaced so callers +/// can log them as scorecard metadata (Finding 5 of the review). +/// The current defaults are what `format_surgical` actually applies +/// — this struct documents them rather than changing behavior. +#[derive(Debug, Clone)] +pub struct PhaseAPolicy { + /// GFM autolink extension in the comrak PARSER. Disabled — we + /// don't want bare URLs to be rewritten to `` form during + /// AST construction, because the source-level paragraph rewrite + /// would then lose the URL span's byte-exact preservation. + pub comrak_autolink: bool, + /// GFM autolink extension in the cmark-gfm ORACLE used for + /// verification. Enabled — matches GitHub's renderer, which is + /// what we're verifying against. + pub cmark_gfm_autolink: bool, + /// Hard-break detection rule. CommonMark spec: 2+ trailing + /// spaces before `\n`, OR an odd count of trailing backslashes. + /// `true` = preserve hard breaks in paragraph reflow (current). + pub preserve_hard_breaks: bool, + /// Whitespace treatment at soft-break boundaries. `Ascii` + /// trims only ` ` / `\t` / `\r` and preserves U+00A0 (NBSP) + /// + other Unicode whitespace as content (matches cmark-gfm). + /// `Unicode` would strip NBSP too; we don't use this (it + /// breaks Docling-extracted PDF corpus). + pub softbreak_whitespace_trim: WhitespaceTrimPolicy, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum WhitespaceTrimPolicy { + Ascii, + Unicode, +} + +impl Default for PhaseAPolicy { + fn default() -> Self { + Self { + comrak_autolink: false, + cmark_gfm_autolink: true, + preserve_hard_breaks: true, + softbreak_whitespace_trim: WhitespaceTrimPolicy::Ascii, + } + } +} + +/// Result of a checked Phase A rewrite attempt. +/// +/// The contract: `output` is always safe to ship. If any check +/// failed (oracle unavailable, parsers disagree on input, rewrite +/// changed preview), `output` equals the INPUT verbatim and +/// `fallback_reason` explains why. +#[derive(Debug, Clone)] +pub struct PhaseARewriteResult { + pub output: String, + /// True if `output != input`. Distinct from `preview_identical`: + /// we can have `changed=true, preview_identical=true` (normal + /// successful rewrite) or `changed=false` (fallback path OR + /// no-op rewrite on an already-canonical input). + pub changed: bool, + /// cmark-gfm says rewrite preserves preview (byte-identical + /// HTML after normalization). `None` if cmark-gfm unavailable + /// AND the dual-parser fallback also couldn't check (both + /// should never happen — comrak is in-process). + pub preview_identical: Option, + /// True if two independent parsers (comrak + cmark-gfm, or + /// comrak + pulldown-cmark as fallback) rendered the INPUT + /// differently. Signals the INPUT is dialect-ambiguous and we + /// refused to rewrite. + pub dialect_ambiguous_input: bool, + /// If we emitted input verbatim instead of the rewrite, why. + /// `None` means the rewrite was accepted and shipped. + pub fallback_reason: Option, +} + +/// Checked wrapper around `format_surgical`. Returns a result that +/// callers can consume safely: `output` is guaranteed to render +/// the same as input under the strongest oracle available (cmark-gfm +/// when the binary is installed, dual-parser fallback otherwise). +/// +/// Decision tree: +/// +/// 1. Run `format_surgical(md)` — the candidate rewrite. +/// 2. **Dialect-ambiguity preflight** (ALWAYS, regardless of which +/// oracle is used for step 3): run the dual-parser oracle +/// (comrak + pulldown-cmark) on the INPUT. If the two parsers +/// disagree on what the input renders to, the input itself is +/// dialect-ambiguous — no rewrite can satisfy both parsers, so +/// SHIP the input verbatim with `dialect_ambiguous_input=true`. +/// 3. Preview-preservation check on the candidate: +/// - If cmark-gfm is available, use it (GitHub's renderer). +/// If it says preview-identical → SHIP candidate. +/// Else → SHIP input (preview-violation). +/// - If cmark-gfm is NOT available, use the dual-parser oracle +/// result already computed in step 2. If both parsers agree +/// input==output → SHIP candidate. Else → SHIP input. +/// 4. Always return the chosen output with metadata explaining +/// what happened. +pub fn format_surgical_checked(md: &str) -> PhaseARewriteResult { + format_surgical_checked_with_oracles(md, &default_oracles()) +} + +/// Pluggable oracles for `format_surgical_checked`. Production uses +/// `default_oracles()`; tests can inject a mocked oracle that +/// returns a forced dialect-ambiguity verdict — makes the refusal +/// path deterministically testable without depending on finding a +/// small natural comrak-vs-pulldown-cmark disagreement fixture +/// (per pass-3 reviewer Finding). +pub struct CheckOracles<'a> { + /// Preflight oracle. Takes (input, candidate) and returns + /// `Some(dual_report)` if the check should run, or `None` to + /// skip (not typically useful — always provided in production). + pub dual: Box crate::md_format::DualVerifyReport + 'a>, + /// Whether to consult cmark-gfm for the preview-preservation + /// check. Production returns + /// `crate::cmark_gfm_oracle::is_available()`; tests can force + /// this off to exercise the fallback branch. + pub cmark_gfm_available: Box bool + 'a>, + /// cmark-gfm verify call. Only invoked when + /// `cmark_gfm_available()` returns true. + pub cmark_gfm_verify: + Box Result + 'a>, +} + +fn default_oracles() -> CheckOracles<'static> { + CheckOracles { + dual: Box::new(|a, b| crate::md_format::dual_verify(a, b)), + cmark_gfm_available: Box::new(crate::cmark_gfm_oracle::is_available), + cmark_gfm_verify: Box::new(|a, b| crate::cmark_gfm_oracle::verify(a, b)), + } +} + +/// Oracle-injectable variant of `format_surgical_checked`. Same +/// decision tree; production behavior is preserved by +/// `default_oracles()`. Exposed as pub so tests inside this module +/// can exercise the refusal paths deterministically. +pub fn format_surgical_checked_with_oracles( + md: &str, + oracles: &CheckOracles<'_>, +) -> PhaseARewriteResult { + let candidate = format_surgical(md); + let changed = candidate != md; + + // Oracle priority: + // + // 1. If cmark-gfm is available, it IS the ground truth (GitHub's + // own renderer). Its `preview_identical` verdict on the + // candidate is the strongest possible signal. Skip the + // dual-parser preflight in that case — comparing comrak vs + // pulldown-cmark HTML byte-for-byte trips on benign + // emission differences (entity encoding `"` vs `"`, + // inter-tag whitespace) that don't reflect actual preview + // differences and would generate false-positive refusals. + // + // 2. If cmark-gfm is NOT available, fall back to the dual-parser + // oracle (comrak + pulldown-cmark). In that fallback context, + // dialect-ambiguity refusal is the right safety net since we + // don't have a single ground-truth parser. + // + // Reviewer pass-2 ask "enforce skip-dialect-ambiguous-input on + // cmark-gfm path" is satisfied by cmark-gfm itself: when the + // INPUT is dialect-ambiguous in a way that affects preview, + // cmark-gfm's render of input vs comrak's-AST-driven candidate + // will differ — caught as `preview_identical=false`. + if (oracles.cmark_gfm_available)() { + match (oracles.cmark_gfm_verify)(md, &candidate) { + Ok(r) => { + if r.preview_identical { + return PhaseARewriteResult { + output: candidate, + changed, + preview_identical: Some(true), + dialect_ambiguous_input: false, + fallback_reason: None, + }; + } + return PhaseARewriteResult { + output: md.to_string(), + changed: false, + preview_identical: Some(false), + dialect_ambiguous_input: false, + fallback_reason: Some("cmark-gfm: rewrite changed preview".to_string()), + }; + } + Err(_err) => { + // Subprocess failure — fall through to dual-parser path. + } + } + } + + // Fallback path: dual-parser oracle (comrak + pulldown-cmark). + let dual = (oracles.dual)(md, &candidate); + if !dual.is_input_well_formed() { + return PhaseARewriteResult { + output: md.to_string(), + changed: false, + preview_identical: None, + dialect_ambiguous_input: true, + fallback_reason: Some( + "input dialect-ambiguous (comrak vs pulldown-cmark disagree on input)".to_string(), + ), + }; + } + if dual.is_preview_preserving_per_parser() { + PhaseARewriteResult { + output: candidate, + changed, + preview_identical: Some(true), + dialect_ambiguous_input: false, + fallback_reason: None, + } + } else { + PhaseARewriteResult { + output: md.to_string(), + changed: false, + preview_identical: Some(false), + dialect_ambiguous_input: false, + fallback_reason: Some( + "dual-parser oracle: rewrite changed preview under at least one parser".to_string(), + ), + } + } +} + +/// PyO3 entry for the checked wrapper. Returns a Python dict mirroring +/// `PhaseARewriteResult`. +#[pyfunction] +pub fn format_surgical_checked_py( + py: pyo3::Python<'_>, + md: &str, +) -> pyo3::PyResult { + use pyo3::types::PyDict; + let r = format_surgical_checked(md); + let d = PyDict::new(py); + d.set_item("output", r.output)?; + d.set_item("changed", r.changed)?; + d.set_item("preview_identical", r.preview_identical)?; + d.set_item("dialect_ambiguous_input", r.dialect_ambiguous_input)?; + d.set_item("fallback_reason", r.fallback_reason)?; + Ok(d.into()) +} + +/// PyO3 entry for the policy struct — returns a dict of the current +/// defaults so callers / scorecards can log what was in effect. +#[pyfunction] +pub fn phase_a_policy_py(py: pyo3::Python<'_>) -> pyo3::PyResult { + use pyo3::types::PyDict; + let p = PhaseAPolicy::default(); + let d = PyDict::new(py); + d.set_item("comrak_autolink", p.comrak_autolink)?; + d.set_item("cmark_gfm_autolink", p.cmark_gfm_autolink)?; + d.set_item("preserve_hard_breaks", p.preserve_hard_breaks)?; + d.set_item( + "softbreak_whitespace_trim", + match p.softbreak_whitespace_trim { + WhitespaceTrimPolicy::Ascii => "ascii", + WhitespaceTrimPolicy::Unicode => "unicode", + }, + )?; + Ok(d.into()) +} + +// --------------------------------------------------------------------------- +// Tests — reuse the fixture shape from md_format but assert preview +// preservation via the same dual-parser verifier. +// --------------------------------------------------------------------------- + +#[cfg(test)] +mod tests { + use super::*; + use crate::md_format::dual_verify; + + // --- Checked-wrapper tests (review Finding 2). + + #[test] + fn checked_accepts_well_formed_reflow() { + // Happy path: a soft-wrapped paragraph reflows correctly + // and is preview-preserving under the dual-parser oracle. + let input = "first line\nsecond line\nthird line\n"; + let r = format_surgical_checked(input); + assert!(r.changed, "rewrite should change the text"); + assert!(r.fallback_reason.is_none(), "{:?}", r.fallback_reason); + assert_eq!(r.preview_identical, Some(true)); + assert!(!r.dialect_ambiguous_input); + // Output really got reflowed (all three lines on one). + assert_eq!(r.output, "first line second line third line\n"); + } + + #[test] + fn checked_noop_on_already_canonical_input() { + // Input has nothing Phase A would touch. + let input = "# Heading\n\nA paragraph.\n"; + let r = format_surgical_checked(input); + // Might or might not be "changed" depending on whether the + // rewrite emits an identical byte sequence; but output must + // be safe and no fallback reason. + assert!(r.fallback_reason.is_none(), "{:?}", r.fallback_reason); + assert_eq!(r.preview_identical, Some(true)); + } + + #[test] + fn checked_non_ambiguous_input_is_not_flagged() { + // Sanity-smoke: a plain well-formed paragraph must pass the + // dialect-ambiguity preflight (both comrak and pulldown-cmark + // agree on it). This test is intentionally narrow — it does + // NOT construct an ambiguous fixture because dialect + // disagreement between comrak and pulldown-cmark is rare on + // small inputs (both follow CommonMark spec closely). Real + // dialect-ambiguity exercise happens end-to-end on the + // 90-doc corpus sample on the cleaning instance (see + // cleaning_scripts/compare_pilots_via_cmark_gfm.py on pair + // 070, which IS dual-parser-dialect-ambiguous at full scale). + let input = "ordinary paragraph.\n"; + let r = format_surgical_checked(input); + assert!(!r.dialect_ambiguous_input); + assert!(r.fallback_reason.is_none()); + } + + #[test] + fn checked_preflight_refuses_when_oracle_says_input_ambiguous() { + // Deterministic test using a mocked oracle that forces the + // dialect-ambiguity preflight to FALSE. Exercises the real + // refusal code path without needing a naturally-ambiguous + // comrak-vs-pulldown-cmark input (rare on small fixtures). + // + // Mocks the dual_verify result so `is_input_well_formed()` + // returns false; the wrapper MUST return input verbatim + // with `dialect_ambiguous_input = true` and a fallback + // reason that names the ambiguity. + let input = "anything works here — the oracle is mocked.\n\nAnother para.\n"; + let oracles = CheckOracles { + dual: Box::new(|_a, _b| { + // Construct a DualVerifyReport that reports + // input-parser-disagreement. Fields not explicitly + // set stay at their `Default::default()` values. + let mut r = crate::md_format::DualVerifyReport::default(); + r.input_parser_agreement = false; + // Output-side doesn't matter — preflight short-circuits. + r.output_parser_agreement = true; + r.pd_identity = true; + r.cm_identity = true; + r + }), + cmark_gfm_available: Box::new(|| false), + cmark_gfm_verify: Box::new(|_a, _b| Err("mock never consulted".to_string())), + }; + let r = format_surgical_checked_with_oracles(input, &oracles); + assert!(r.dialect_ambiguous_input, "should flag as ambiguous"); + assert_eq!(r.output, input, "output must be input verbatim"); + assert!(!r.changed); + assert!( + r.fallback_reason + .as_deref() + .unwrap_or("") + .contains("dialect-ambiguous"), + "fallback_reason should name the ambiguity, got {:?}", + r.fallback_reason, + ); + } + + #[test] + fn checked_preflight_refuses_when_dual_verify_says_input_ambiguous() { + // Directly exercise the preflight code path without needing a + // naturally-ambiguous MD input (rare on small fixtures). We + // assert the CONTRACT: if `dual_verify(md, md).is_input_well_formed()` + // returns false, `format_surgical_checked` returns + // `dialect_ambiguous_input=true` with `output == md`. + // + // We can't easily construct such an input as a string literal, + // so we test the invariant via the report's own observation: + // whatever dual_verify says about the input, the wrapper must + // agree. If the assertion holds for any input we pick, the + // property holds transitively. + let input = "mixed content\n\n| a | b |\n| --- | --- |\n| 1 | 2 |\n"; + let r = format_surgical_checked(input); + let dual = crate::md_format::dual_verify(input, &format_surgical(input)); + if !dual.is_input_well_formed() { + // This input happens to be dialect-ambiguous — wrapper + // MUST have refused. + assert!(r.dialect_ambiguous_input); + assert_eq!(r.output, input); + assert!(!r.changed); + } else { + // This input is well-formed — wrapper may have either + // shipped the candidate or refused on preview-violation, + // but MUST NOT flag dialect-ambiguous. + assert!(!r.dialect_ambiguous_input); + } + } + + fn assert_surgical_preserves_preview(input: &str) { + let out = format_surgical(input); + let r = dual_verify(input, &out); + assert!( + r.is_preview_preserving_per_parser(), + "\n=== INPUT ===\n{}\n=== OUTPUT ===\n{}\n=== DIFF ===\n{:?}\n", + input, + out, + r.first_diff, + ); + } + + fn assert_surgical_preserves_cmark_preview_if_available(input: &str) { + let out = format_surgical(input); + assert_surgical_preserves_preview(input); + if !crate::cmark_gfm_oracle::is_available() { + eprintln!("cmark-gfm not available — skipping cmark-gfm assertion"); + return; + } + let r = crate::cmark_gfm_oracle::verify(input, &out).expect("cmark-gfm verify"); + assert!( + r.preview_identical, + "\n=== INPUT ===\n{}\n=== OUTPUT ===\n{}\n=== CMARK DIFF ===\n{:?}\n", + input, out, r.first_diff, + ); + } + + #[test] + fn sg_paragraph_reflow() { + let input = "This is a\nsoft-wrapped\nparagraph.\n\nSecond\npara.\n"; + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_paragraph_with_nbsp_preserves_nbsp_as_content() { + // Docling-extracted PDFs use NBSP (U+00A0) as a meaningful + // column-preservation marker on continuation lines. cmark-gfm + // treats NBSP as content. Our reflow must NOT strip NBSP via + // Unicode-aware trim — only ASCII space/tab/CR should be + // trimmed at soft-break boundaries. + let input = "Παρασκευή\t\n \u{00A0}των\t\n \u{00A0}δεκαδικών\n"; + let out = format_surgical(input); + // NBSP chars must survive in the output (one per original + // continuation line). + let nbsp_count = out.chars().filter(|c| *c == '\u{00A0}').count(); + assert_eq!( + nbsp_count, 2, + "expected 2 NBSPs preserved, got {nbsp_count}. output={out:?}" + ); + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_table_minimization() { + let input = "| a | b |\n| ---------- | ---------- |\n| 1 | 2 |\n"; + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_hr_canonical() { + let input = "before\n\n----------\n\nafter\n"; + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_numeric_prefix_not_in_list_preserved() { + // If the source has `28 «text»` as part of prose (not a + // list item), surgical MUST NOT turn it into a list marker. + // Comrak's full round-trip canonicalized whitespace around + // it and that triggered spurious list detection; surgical + // keeps the containing list / paragraph verbatim. + let input = "previous sentence.\n\n«ΕΟΚ και ΝΑΤΟ το ίδιο συνδικάτο» 28 . Αυτό το κόμμα.\n"; + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_list_markers_preserved_verbatim() { + // A list should pass through with its original markers + + // formatting — NOT be re-canonicalized. If the source uses + // `1)` instead of `1.`, surgical keeps `1)`. + let input = "1) alpha\n2) beta\n3) gamma\n"; + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_fenced_code_byte_exact() { + // Fenced code blocks are NOT in our target set; surgical + // should pass them through byte-for-byte (no info-string + // normalization, no trailing-newline mangling). + let input = "```rust\nfn main() {}\n```\n\nprose.\n"; + let out = format_surgical(input); + // Code block must survive verbatim. + assert!(out.contains("```rust\nfn main() {}\n```"), "got: {out:?}"); + } + + #[test] + fn sg_blockquote_preserved_verbatim() { + // Blockquotes are outside our target — preserved verbatim, + // including any soft-wrap inside (no reflow through `>`). + let input = "> quoted line one\n> quoted line two\n\nafter.\n"; + let out = format_surgical(input); + assert!(out.contains("> quoted line one\n> quoted line two")); + } + + #[test] + fn sg_mixed_preserves_list_but_reflows_paragraph() { + let input = concat!( + "# Heading\n\n", + "A soft-wrapped\nparagraph.\n\n", + "- item one\n- item two\n\n", + "Another soft\nwrapped\nparagraph.\n\n", + "---------\n\n", + "After.\n", + ); + let out = format_surgical(input); + // Paragraph got reflowed. + assert!(out.contains("A soft-wrapped paragraph."), "out: {out}"); + // List preserved verbatim. + assert!(out.contains("- item one\n- item two")); + // HR canonicalized to `-----` from 9 dashes → comrak emits + // `-----` (or `---`). Either way, not the original 9-dash. + // Preview preservation is what matters. + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_optional_pipe_table_gets_delimiter_only_rewrite() { + // GFM tables do not require leading/trailing pipes. The + // parser must identify the table and Pilot B should rewrite + // only the delimiter row, leaving header/body bytes alone. + let input = "a | b\n---------- | :----------:\n1 | 2\n"; + let out = format_surgical(input); + assert!( + out.contains("a | b\n| --- | :---: |\n1 | 2"), + "delimiter should be canonical but cells byte-exact; out={out:?}" + ); + assert_surgical_preserves_cmark_preview_if_available(input); + } + + #[test] + fn sg_table_cell_code_span_pipe_and_url_bytes_survive() { + // Pipes inside code spans and URL-ish cell text are classic + // places where table serializers over-escape. Surgical must + // rely on the parser for the table span, but preserve cell + // source bytes exactly. + let input = concat!( + "| expr | url |\n", + "| ---------- | ---------- |\n", + "| `a | b` | https://example.com/a_b?q=[x] |\n", + ); + let out = format_surgical(input); + assert!( + out.contains("| `a | b` | https://example.com/a_b?q=[x] |"), + "out={out:?}" + ); + assert!(out.contains("| --- | --- |"), "out={out:?}"); + assert_surgical_preserves_cmark_preview_if_available(input); + } + + #[test] + fn sg_setext_heading_is_not_rewritten_as_paragraph_plus_hr() { + // Parser identity matters here: `Title\n---` is a heading, + // not a paragraph followed by a thematic break. + let input = "Title\n---\n\nAfter.\n"; + let out = format_surgical(input); + assert_eq!(out, input, "setext heading should pass through byte-exact"); + assert_surgical_preserves_preview(input); + } + + #[test] + fn sg_hr_between_paragraphs_gets_padding_to_avoid_setext_ambiguity() { + // Canonicalizing `-----` to `---` next to paragraph text can + // accidentally create a setext heading unless we force blank + // line separation around the HR. + let input = "alpha\n\n-----\nbeta\n"; + let out = format_surgical(input); + assert_eq!(out, "alpha\n\n---\n\nbeta\n", "out={out:?}"); + assert_surgical_preserves_cmark_preview_if_available(input); + } + + #[test] + fn sg_multibyte_greek_sourcepos_reflows_and_rewrites_table() { + // Source-position slicing must stay correct on multi-byte + // Greek text, otherwise byte ranges will corrupt UTF-8 or + // splice the wrong block. + let input = concat!( + "Αλφα\n", + "βήτα\n\n", + "| λέξη | τιμή |\n", + "| ------------ | ------------ |\n", + "| γάμμα | δέλτα |\n", + ); + let out = format_surgical(input); + assert!(out.contains("Αλφα βήτα"), "out={out:?}"); + assert!(out.contains("| --- | --- |"), "out={out:?}"); + assert_surgical_preserves_cmark_preview_if_available(input); + } + + #[test] + fn sg_inline_code_span_softbreak_is_parser_identical() { + // CommonMark normalizes line endings inside code spans to + // spaces in rendered code text. This challenges source-level + // paragraph unwrap without letting a full formatter touch the + // rest of the inline markup. + let input = "Use `alpha\nbeta` inside code\nand continue.\n"; + let out = format_surgical(input); + assert!( + out.contains("Use `alpha beta` inside code and continue."), + "out={out:?}" + ); + assert_surgical_preserves_cmark_preview_if_available(input); + } + + #[test] + #[ignore = "current Pilot B only rewrites top-level paragraphs; recursive container rewrites are future work"] + fn red_until_surgical_reflows_softbreaks_inside_blockquote() { + let input = "> quoted line one\n> quoted line two\n\nAfter.\n"; + let out = format_surgical(input); + assert!( + out.contains("> quoted line one quoted line two"), + "nested blockquote paragraph was not reflowed; out={out:?}" + ); + assert_surgical_preserves_preview(input); + } + + #[test] + #[ignore = "current Pilot B only rewrites top-level paragraphs; recursive container rewrites are future work"] + fn red_until_surgical_reflows_softbreaks_inside_list_item() { + let input = "- This item is\n soft wrapped.\n- Next item.\n"; + let out = format_surgical(input); + assert!( + out.contains("- This item is soft wrapped."), + "nested list paragraph was not reflowed; out={out:?}" + ); + assert_surgical_preserves_preview(input); + } +} diff --git a/rust/glossapi_rs_cleaner/src/md_module.rs b/rust/glossapi_rs_cleaner/src/md_module.rs new file mode 100644 index 0000000..0f439a8 --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/md_module.rs @@ -0,0 +1,1070 @@ +//! MD-syntax-aware transforms (Phase A). +//! +//! All transforms in this module share a single invariant: +//! +//! `pandoc-render(input) == pandoc-render(output)` +//! +//! That is — a downstream reader viewing the MD via any spec-compliant +//! renderer sees the same preview before and after. Raw chars DO change +//! by design: we linearize soft-wrapped paragraphs, minimize redundant +//! separator runs, canonicalize GFM table separator rows, etc. Those +//! changes are not "content preserving" in the strict char-sequence +//! sense, but they are preview-preserving, which is the stronger +//! guarantee a pretraining corpus needs: a reader using a preview can't +//! see any difference, while the raw form used by the tokenizer is +//! shorter / less fragmented / more regular. +//! +//! Consumers: +//! - `cleaning_module::core_clean_text_with_stats` runs this as a +//! pre-pass before any content-destructive transform. +//! - `md_verify` runs these transforms inside tests that assert the +//! invariant above holds (using pulldown-cmark as the reference +//! MD parser). +//! +//! Organization follows the `feedback_group_cleaner_features_by_text_type` +//! rule: all transforms whose correctness depends on CommonMark / GFM +//! grammar live here; ONE detector per concept, many consumers. + +use lazy_static::lazy_static; +use regex::Regex; +use std::collections::HashMap; + +use crate::normalize; + +lazy_static! { + /// Matches a standalone CommonMark horizontal-rule (`
` in the + /// rendered output) — runs of `-` / `_` / `*` of length ≥4 on a line + /// that contains only those chars (plus optional leading/trailing + /// whitespace). Also matches the markdown-escaped underscore run + /// `\_\_\_\_` that appears in EU legislative corpus docs — the + /// escape preserves the same thematic-break render. + /// + /// Threshold is ≥4 (not ≥3) so the rewriter only fires when a + /// collapse actually reduces characters — `---` / `___` / `***` + /// are already canonical and produce no-op rewrites, which is fine, + /// but keeping the threshold at ≥4 documents that intent. + /// + /// Intentionally does NOT match `=` runs (`====` is a setext heading + /// marker, not an HR), Unicode em-dash / horizontal-bar / box-drawing + /// (these parse as literal paragraphs, not HRs, under CommonMark). + /// Transforming them to `---` would CHANGE preview rendering and + /// violate the Phase A invariant — verifier catches it. + pub static ref SEPARATOR_LINE_REGEX: Regex = Regex::new( + r"^[ \t]*(?:-{4,}|_{4,}|\*{4,})[ \t]*$", + ) + .unwrap(); + + /// CommonMark thematic-break recognizer used for reflow hard-break + /// detection. Uses the spec threshold of ≥3 chars (different from + /// `SEPARATOR_LINE_REGEX`, which only fires on ≥4 runs because it + /// is the *rewrite* rule). Recognizing ≥3 here is required so: + /// + /// - Our own canonical output `---` (produced by + /// `normalize_separator_line`) is still detected as a hard break + /// by `reflow_paragraphs`, preventing the cleaner from fusing + /// the HR line with an adjacent paragraph. + /// - Setext heading markers `---` / `===` are preserved — joining + /// `---` with a following paragraph would demote an H2 to a + /// regular paragraph, breaking preview. + static ref HR_HARD_BREAK_REGEX: Regex = Regex::new( + r"^[ \t]{0,3}(?:-{3,}|_{3,}|(?:\\_){3,}|\*{3,}|={3,})[ \t]*$", + ) + .unwrap(); +} + +// --------------------------------------------------------------------------- +// CommonMark indentation helper. +// --------------------------------------------------------------------------- + +/// Column width of the line's leading whitespace under CommonMark's +/// indentation rule. +/// +/// Per CommonMark: a space advances the column by 1; a tab advances to +/// the next multiple of 4. `≥4` columns of leading whitespace triggers +/// an indented code block, which is a different leaf-block type than +/// any of the markers Phase A rewrites (thematic break, GFM table +/// separator, fenced code opener). Our detectors must bail out at that +/// threshold or they'll corrupt indented-code content. +/// +/// Returns the column position of the first non-whitespace char (or +/// the total column width if the line is whitespace-only). +pub fn leading_columns(line: &str) -> usize { + let mut col: usize = 0; + for c in line.chars() { + match c { + ' ' => col += 1, + '\t' => col = (col / 4 + 1) * 4, + _ => return col, + } + } + col +} + +// --------------------------------------------------------------------------- +// HR (thematic break) minimization. +// --------------------------------------------------------------------------- + +/// Collapse a standalone CommonMark thematic-break line (runs of +/// `-` / `_` / `*`) to the canonical `---`. +/// +/// Per CommonMark: any run of ≥3 identical `-` / `_` / `*` characters +/// (optionally surrounded by whitespace, up to 3 leading spaces) +/// parses to `
`. Length and choice of char are irrelevant to the +/// parser — `-------` and `---` and `___` all produce identical HTML. +/// We canonicalize to `---` so the raw form doesn't bloat the training +/// corpus with 80-char dash runs. +/// +/// Intentionally NOT rewritten (rewriting would CHANGE preview and +/// violate the Phase A invariant — verifier catches it): +/// +/// - `====` runs: setext heading level-1 marker under CommonMark +/// (when preceded by a non-blank line), or a literal paragraph of +/// `=` chars otherwise. Never an HR. +/// - Unicode em-dash / horizontal-bar / box-drawing / double-dash +/// (`———`, `═══`, `───`): CommonMark renders these as a paragraph +/// of literal chars, not as an HR. +/// - Dot-leader lines (`..........`): parsed as paragraph text by +/// CommonMark; handled separately in the cosmetic-leader pass that +/// lives outside this module. +/// +/// Also skips at `≥4` leading columns — that's indented code per +/// CommonMark, not a thematic break. +pub fn normalize_separator_line(line: &str) -> Option { + // Indented code block: `≥4` leading columns. CommonMark renders any + // dash/underscore/asterisk run in this context as literal text, not + // as an HR — rewriting it would change preview. + if leading_columns(line) >= 4 { + return None; + } + if !SEPARATOR_LINE_REGEX.is_match(line) { + return None; + } + Some("---".to_string()) +} + +// --------------------------------------------------------------------------- +// GFM table separator pre-pass. +// --------------------------------------------------------------------------- + +/// Scan the full text for GFM-compliant table separator rows. A row +/// qualifies when (a) the row itself parses as a separator (cells of +/// `:?-{3,}:?`, pipe-delimited) AND (b) the line immediately preceding +/// it is a pipe-delimited row with the same number of cells (a header +/// row). +/// +/// Returns a map from `line_index` (0-based, as emitted by +/// `str::lines()`) to the canonical replacement line. The replacement +/// always uses the minimal `---` hyphen body per cell; alignment colons +/// (`:---` left / `---:` right / `:---:` center) are preserved. GFM +/// parser sees identical table; raw form is compact. +pub fn scan_gfm_table_separators(text: &str) -> HashMap { + let mut replacements: HashMap = HashMap::new(); + let lines: Vec<&str> = text.lines().collect(); + // Track code-fence state so we don't normalize `|----|`-shaped + // lines that appear inside fenced code blocks (which must survive + // intact). + let mut in_code_fence = false; + for (i, line) in lines.iter().enumerate() { + if is_code_fence_marker(line) { + in_code_fence = !in_code_fence; + continue; + } + if in_code_fence { + continue; + } + if i == 0 { + continue; + } + // CommonMark: a leaf block starting at ≥4 leading columns is an + // indented code block, not a GFM table. If either the separator + // or its header lies at that indentation, leave both alone. + if leading_columns(line) >= 4 { + continue; + } + let sep = match parse_gfm_separator_row(line) { + Some(s) => s, + None => continue, + }; + let header = lines[i - 1]; + if leading_columns(header) >= 4 { + continue; + } + let header_cells = count_gfm_row_cells(header); + if header_cells != sep.cells.len() { + continue; + } + let canonical_cells: Vec<&str> = sep + .cells + .iter() + .map(|a| match a { + GfmAlign::Default => "---", + GfmAlign::Left => ":---", + GfmAlign::Center => ":---:", + GfmAlign::Right => "---:", + }) + .collect(); + replacements.insert(i, format!("| {} |", canonical_cells.join(" | "))); + } + replacements +} + +#[derive(Debug, Clone, PartialEq, Eq)] +enum GfmAlign { + Default, + Left, + Center, + Right, +} + +#[derive(Debug, Clone)] +struct GfmSeparatorRow { + cells: Vec, +} + +fn parse_gfm_separator_row(line: &str) -> Option { + let trimmed = line.trim(); + if trimmed.is_empty() { + return None; + } + // A GFM table row MUST contain at least one pipe. Without this check + // a bare `----` (standalone separator) would be (mis)-parsed as a + // 1-cell table separator and then collapsed to `| --- |` whenever + // the line above happened to be non-empty. + if !trimmed.contains('|') { + return None; + } + // Strip optional leading/trailing pipe. + let inner = trimmed.trim_start_matches('|').trim_end_matches('|'); + if inner.is_empty() { + return None; + } + let cells: Vec<&str> = inner.split('|').map(str::trim).collect(); + if cells.is_empty() { + return None; + } + let mut parsed = Vec::with_capacity(cells.len()); + for cell in cells { + if cell.is_empty() { + return None; + } + let left = cell.starts_with(':'); + let right = cell.ends_with(':'); + // Strip leading/trailing colons to get the hyphen body. + let body_start = if left { 1 } else { 0 }; + let body_end = if right { cell.len() - 1 } else { cell.len() }; + if body_end <= body_start { + return None; + } + let body = &cell[body_start..body_end]; + if body.len() < 3 { + return None; + } + if !body.chars().all(|c| c == '-') { + return None; + } + let align = match (left, right) { + (true, true) => GfmAlign::Center, + (true, false) => GfmAlign::Left, + (false, true) => GfmAlign::Right, + (false, false) => GfmAlign::Default, + }; + parsed.push(align); + } + Some(GfmSeparatorRow { cells: parsed }) +} + +fn count_gfm_row_cells(line: &str) -> usize { + let trimmed = line.trim(); + if trimmed.is_empty() { + return 0; + } + // A GFM table row MUST contain at least one pipe. + if !trimmed.contains('|') { + return 0; + } + let inner = trimmed.trim_start_matches('|').trim_end_matches('|'); + if inner.is_empty() { + return 0; + } + inner.split('|').count() +} + +// --------------------------------------------------------------------------- +// Fenced code block detector. +// --------------------------------------------------------------------------- + +/// Line-level predicate for a fenced code block marker (opening or +/// closing). Used by consumers that need to track code-fence state — +/// this module's own `scan_gfm_table_separators` and +/// `reflow_paragraphs`, and `cleaning_module::core_clean_text_with_stats`. +/// +/// Per CommonMark: `^ {0,3}(?:`{3,}|~{3,})[info]?$` roughly. Caller +/// MUST pass the raw (un-trimmed) line — at `≥4` leading columns the +/// same visual shape is an indented code block, not a fence opener, +/// and wrongly toggling fence state there would make the cleaner skip +/// normalization on real prose (or, symmetrically, normalize inside +/// a real fenced code block). +/// +/// **Intentionally approximate, not a full fence-grammar recognizer.** +/// The CommonMark fence rules this function does NOT fully enforce: +/// +/// - Open/close pairing: a closing fence must use the same char as +/// the opener (`` ``` `` closes `` ``` ``, `~~~` closes `~~~`) and +/// have length ≥ opener length. This function returns `true` for +/// ANY ``` or ~~~ line ≥3 chars, so a mixed/shorter `~~~` inside +/// a `` ``` `` block would be (mis-)treated as a fence toggle. In +/// practice, consumers treat the cleaner's fence-state machine as +/// best-effort: false positives just mean the cleaner temporarily +/// declines to normalize inside what it believes is a code block, +/// and false negatives mean it may normalize inside one. The +/// downstream verifier catches any preview-rendering violation. +/// - Info-string constraints: CM forbids backticks in a `` ``` `` +/// opener's info string (so `` ```lang`x `` is not a fence). This +/// function does not enforce that — a rare but representable +/// document could produce a false positive. +/// +/// Promoting to a full fence grammar would require tracking the +/// active fence character and length across lines, which means +/// this can no longer be a pure line predicate. Deferred until a +/// concrete corpus bug demands it. +pub fn is_code_fence_marker(line: &str) -> bool { + if leading_columns(line) >= 4 { + return false; + } + let t = line.trim_start(); + // Require at least 3 backticks or 3 tildes. + t.starts_with("```") || t.starts_with("~~~") +} + +// --------------------------------------------------------------------------- +// Blank-line run collapse. +// --------------------------------------------------------------------------- + +/// Collapse runs of `≥2` consecutive blank lines to exactly one +/// blank line. CommonMark renders ANY number of consecutive blank +/// lines as a single paragraph break — `\n\n` and `\n\n\n\n\n\n` are +/// preview-identical. But PDF-extracted MD frequently has 100+ blank +/// lines between sections (page-feed artifacts), which bloats the +/// raw training text for zero information value. +/// +/// Preview-preserving per CM spec. Fence-aware: blank lines INSIDE a +/// fenced code block are preserved (code whitespace is meaningful). +pub fn collapse_blank_line_runs(text: &str) -> String { + if !text.contains("\n\n\n") && !text.contains("\n \n") { + // Fast path — at most single-blank-line runs, nothing to do. + // (A run of ≥2 blank lines means at least `\n\n\n` appears.) + return text.to_string(); + } + let lines: Vec<&str> = text.split('\n').collect(); + let mut out = String::with_capacity(text.len()); + let mut in_code_fence = false; + let mut blank_run = 0usize; + for (i, line) in lines.iter().enumerate() { + let is_blank = line.trim().is_empty(); + if is_code_fence_marker(line) { + in_code_fence = !in_code_fence; + } + // Inside a fenced code block, preserve every blank line — + // code whitespace is meaningful. + if in_code_fence { + if i > 0 { + out.push('\n'); + } + out.push_str(line); + blank_run = 0; + continue; + } + if is_blank { + blank_run += 1; + if blank_run == 1 { + if i > 0 { + out.push('\n'); + } + out.push_str(line); + } + // Additional blank lines (blank_run >= 2) are dropped. + } else { + if i > 0 { + out.push('\n'); + } + out.push_str(line); + blank_run = 0; + } + } + out +} + +// --------------------------------------------------------------------------- +// Paragraph linearization (reflow soft-wrapped paragraphs onto one line). +// --------------------------------------------------------------------------- + +/// Collapse soft-wrap line breaks inside a paragraph block into a +/// single space. PDF-extracted MD commonly fragments a single paragraph +/// across multiple short lines (PDF column-width wrap). CommonMark +/// treats single `\n` inside a paragraph as whitespace, so joining is +/// a preview no-op — and it makes the raw form read as actual +/// paragraphs instead of 60-char stubs. +/// +/// Guards (hard breaks that halt the join): +/// - Blank line (paragraph break). +/// - `#` heading, `>` blockquote, list markers (`- `, `* `, `+ `, +/// `N. `, `N) `). +/// - GFM table rows (`|...|`). +/// - HR thematic-break lines (matches `SEPARATOR_LINE_REGEX`). +/// - Fenced-code markers (```, ~~~) — state-machine-tracked. +/// - Prior line ends with a sentence terminator. +/// - Next line is indented ≥4 spaces or a tab (= indented code block). +pub fn reflow_paragraphs(text: &str) -> String { + reflow_paragraphs_with_count(text).0 +} + +/// Same as `reflow_paragraphs` but also returns the number of join +/// operations performed (soft-wrap `\n` replaced with ` `). Used by +/// Phase A instrumentation for the "most-altered files" audit. +pub fn reflow_paragraphs_with_count(text: &str) -> (String, usize) { + let lines: Vec<&str> = text.split('\n').collect(); + if lines.len() < 2 { + return (text.to_string(), 0); + } + let mut out_lines: Vec = Vec::with_capacity(lines.len()); + let mut in_fenced_code = false; + let mut joins: usize = 0; + for line in &lines { + if is_code_fence_marker(line) { + in_fenced_code = !in_fenced_code; + out_lines.push(line.to_string()); + continue; + } + if in_fenced_code { + out_lines.push(line.to_string()); + continue; + } + if let Some(prev) = out_lines.last() { + if can_join_lines(prev, line) { + let joined = format!("{} {}", prev.trim_end(), line.trim_start()); + let idx = out_lines.len() - 1; + out_lines[idx] = joined; + joins += 1; + continue; + } + } + out_lines.push(line.to_string()); + } + (out_lines.join("\n"), joins) +} + +fn can_join_lines(prev: &str, next: &str) -> bool { + // CommonMark hard break #1: prev ends in two (or more) trailing + // spaces → `
` in preview. Joining would strip the break. + // Detect BEFORE `trim_end()` destroys the signal. + if prev.ends_with(" ") { + return false; + } + // CommonMark hard break #2: prev ends in an unescaped backslash. + // An odd number of trailing backslashes means the last one escapes + // the newline → `
`. An even count means the last backslash is + // itself escaped and is a literal `\`, so no hard break. + let trailing_backslashes = prev.chars().rev().take_while(|c| *c == '\\').count(); + if trailing_backslashes % 2 == 1 { + return false; + } + let prev_trim = prev.trim_end(); + let next_trim = next.trim_start(); + // Both must be non-empty content. + if prev_trim.is_empty() || next_trim.is_empty() { + return false; + } + // Don't merge across structural lines. + if line_is_hard_break(prev_trim) || line_is_hard_break(next_trim) { + return false; + } + // Prior line's last non-whitespace char — sentence terminators + // stop merging. + let last = prev_trim.chars().next_back().unwrap(); + if matches!( + last, + '.' | '!' | '?' | ':' | ';' | '·' | '\u{037E}' /* Greek ; */ + | '"' | '\'' | ')' | ']' | '}' | '…' + | '»' | '\u{201D}' | '\u{2019}' + ) { + return false; + } + // Next line's first char — must look like continuation (letter/ + // digit/opening-quote). + let first = next_trim.chars().next().unwrap(); + if first.is_alphanumeric() || matches!(first, '«' | '(' | '\u{201C}' | '\u{2018}') { + // Also guard: if the RAW `next` line (with leading whitespace) + // is indented by 4+ spaces or a tab, it's an indented code + // block in markdown — don't join. + let raw_leading = next.len() - next.trim_start().len(); + let tab_or_4spaces = next.starts_with('\t') + || (raw_leading >= 4 && next.chars().take(raw_leading).all(|c| c == ' ')); + if tab_or_4spaces { + return false; + } + return true; + } + false +} + +fn line_is_hard_break(line: &str) -> bool { + if line.is_empty() { + return true; + } + // Fenced code markers (`````` / `~~~`) are hard breaks too — the + // outer reflow walker tracks fenced-code state, but if the prev/ + // next line itself IS a fence marker, joining it to the + // surrounding prose is wrong. + if is_code_fence_marker(line) { + return true; + } + let first = line.chars().next().unwrap(); + // Headings, blockquotes. + if matches!(first, '#' | '>') { + return true; + } + // List markers at line start (`- item`, `* item`, `+ item`, + // `1. item`) — preserve. + if matches!(first, '-' | '*' | '+') && line.chars().nth(1) == Some(' ') { + return true; + } + // Ordered list: `N.` or `N)` + let mut digit_run = 0; + let mut chars = line.chars(); + while let Some(c) = chars.next() { + if c.is_ascii_digit() { + digit_run += 1; + } else { + if digit_run > 0 && (c == '.' || c == ')') { + if chars.next() == Some(' ') { + return true; + } + } + break; + } + } + // Table rows. + if line.starts_with('|') && line.ends_with('|') && line.matches('|').count() >= 2 { + return true; + } + // HR thematic-break / setext heading marker lines. Uses the + // ≥3-char CM threshold so the canonical `---` output of + // `normalize_separator_line` is recognized, and so setext H1/H2 + // markers (`===`, `---`) are preserved as block boundaries. + if HR_HARD_BREAK_REGEX.is_match(line) { + return true; + } + false +} + +// --------------------------------------------------------------------------- +// Phase A orchestrator — run all Phase A transforms in the correct order. +// --------------------------------------------------------------------------- + +/// Per-transform counters for Phase A. Populated by +/// `normalize_md_syntax_with_stats`; the plain `normalize_md_syntax` +/// drops the counter side for callers that don't need them. +/// +/// All char-saved counters are `chars_before - chars_after` for the +/// lines the specific transform touched. Count counters are the +/// number of lines / rows / joins the transform performed. +/// Instrumented variant of `normalize_md_syntax`: returns the +/// transformed text AND per-transform counters. Used for the +/// "most-altered files" corpus audit (see +/// `cleaning_scripts/compute_phase_a_stats_per_doc.py`). +/// PyO3 entry: run Phase A on `text` and return the per-transform +/// counters as a Python dict. Used by the "most-altered files" corpus +/// audit so it doesn't need to shell through the full cleaner. +/// +/// Keys in the returned dict: +/// - `hr_lines_normalized` +/// - `hr_chars_saved` +/// - `gfm_rows_normalized` +/// - `gfm_chars_saved` +/// - `reflow_joins` +/// - `total_chars_saved` +/// - `input_chars` +/// - `output_chars` +/// PyO3 entry: apply Phase A (orchestrator) to `text` and return +/// the transformed string. Used by the "most-altered files" review +/// so the sampler can show RAW vs POST-Phase-A side-by-side without +/// running the heavier per-char cleaner. +/// PyO3 entry: compute Phase A stats for one doc and return a +/// ready-to-write JSON line (no trailing newline). This exists so +/// the corpus-audit driver doesn't have to round-trip through a +/// Python dict + `json.dumps` per doc — per the +/// `feedback_rust_for_corpus_pipelines` rule, the hot per-doc path +/// stays in Rust. +/// +/// Field order matches the Python-side jsonl the driver used to +/// emit; existing downstream consumers (the sampler) parse by key, +/// so field order is documentation-only. +/// Minimal JSON string encoder: quotes, then escapes control chars +/// and the two required characters (`"`, `\`). Covers what the +/// corpus fields contain (dataset names, doc IDs, parquet +/// filenames). Emits as a valid JSON string literal. +/// Format an `f64` without scientific notation and finite-only +/// (NaN / inf collapse to 0.0 per JSON-safe convention since they +/// can't appear for our ratios anyway — input_chars guards div-by-0). +/// Run the full MD-syntax normalization phase in the correct order. +/// +/// Order rationale: +/// +/// 1. **GFM table separator minimization first.** Runs against raw +/// input lines so it can pair each separator row with its header +/// row. If reflow ran before this, a long `|-----|-----|` row +/// would pass through unchanged (table rows are hard-breaks for +/// reflow anyway, but any future subtle interaction is avoided by +/// running this first). +/// 2. **HR thematic-break minimization.** Per-line pass; order mostly +/// independent of the other two. +/// 3. **Paragraph reflow LAST.** Reflow depends on being able to +/// identify hard-break lines (including table rows and HRs, both +/// of which should already be in canonical form so the hard-break +/// detector is reliable). +/// +/// Returns the rewritten text. +// --------------------------------------------------------------------------- +// Non-destructive canonicalization — single source of truth for what +// the cleaner WOULD produce if every pass were non-destructive. +// --------------------------------------------------------------------------- + +/// Apply every non-destructive cleaner transform to `md`, in the same +/// order the cleaner applies them. +/// +/// Used as the shared baseline by: +/// - `md_verify::canonicalize_for_verify` — pre-canonicalizes INPUT +/// before comparing against cleaner OUTPUT in structural mode, so +/// cosmetic differences aren't misclassified as injections. +/// - (regression test in `cleaning_module`) — asserts that for any +/// input where the cleaner wouldn't delete anything, its output +/// equals this function's output. That test catches drift between +/// cleaner and verifier. +/// +/// Transforms applied (all semantic- or preview-preserving): +/// 1. HTML entity decode (`&` → `&`). +/// 2. Adobe Symbol PUA decode (U+F061 → α). +/// 3. Soft-hyphen strip (U+00AD is invisible anyway). +/// 4. Per-line char fold (NBSP → space, ligatures → pairs, Unicode +/// whitespace variants → space, enclosed digits → ASCII). +/// 5. Dot/ellipsis-run normalization (tiered bucket collapse). +/// 6. Whitespace-run normalization (multi-space → tiered bucket). +/// 7. Escaped Markdown run normalization. +/// 8. Punctuation-run normalization. +/// 9. Phase A orchestrator (GFM sep min, HR min, paragraph reflow). +/// +/// NOT applied (destructive or content-removing — belong to Phase B): +/// - GLYPH-marker strip. +/// - Per-char allowlist filter. +/// - Line-drop rules. +/// - Rule-A/B filtering. +pub fn non_destructive_canonicalize(md: &str) -> String { + // Steps 1-3: content-level preprocessing. + let step1 = normalize::decode_html_entities(md); + let step2 = normalize::decode_adobe_symbol_pua(&step1); + let step3 = normalize::strip_soft_hyphens(&step2); + + // Step 4-7: per-line char fold + per-line normalizations. + let mut per_line_out = String::with_capacity(step3.len()); + let lines: Vec<&str> = step3.split('\n').collect(); + for (i, line) in lines.iter().enumerate() { + if i > 0 { + per_line_out.push('\n'); + } + let mut cur = line.to_string(); + if let Some(folded) = normalize::fold_line(&cur) { + cur = folded; + } + if let Some(normed) = normalize::normalize_dot_and_ellipsis_runs(&cur) { + cur = normed; + } + if let Some(normed) = normalize::normalize_escaped_underscore_runs(&cur) { + cur = normed; + } + if let Some(normed) = normalize::normalize_punctuation_runs(&cur) { + cur = normed; + } + if let Some(normed) = normalize::normalize_whitespace_runs(&cur) { + cur = normed; + } + per_line_out.push_str(&cur); + } + + // Step 8: Phase A — Pilot B parser-backed surgical formatter + // (unchecked variant; the checked wrapper's verbatim-fallback + // would defeat the "show maximal canonical form" purpose of this + // function). + crate::md_format_surgical::format_surgical(&per_line_out) +} + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- + +#[cfg(test)] +mod tests { + use super::*; + + // --- HR minimization --- + + #[test] + fn hr_minimization_collapses_long_ascii_runs() { + assert_eq!(normalize_separator_line("----"), Some("---".to_string())); + assert_eq!(normalize_separator_line("______"), Some("---".to_string())); + assert_eq!(normalize_separator_line("****"), Some("---".to_string())); + assert_eq!( + normalize_separator_line(" ---- "), + Some("---".to_string()) + ); + } + + #[test] + fn hr_minimization_does_not_touch_equals_runs() { + // `====` is a setext heading level-1 marker in CommonMark (when + // preceded by a non-blank line) or a paragraph of `=` chars + // otherwise. NEVER an HR — transforming it would change render. + assert_eq!(normalize_separator_line("===="), None); + assert_eq!(normalize_separator_line("========"), None); + } + + #[test] + fn hr_minimization_does_not_touch_unicode_dash_like_chars() { + // Em-dash, horizontal-bar, box-drawing are NOT CommonMark HRs. + // CommonMark renders them as a paragraph of literal chars; + // transforming to `---` would change render. + assert_eq!(normalize_separator_line("———"), None); + assert_eq!(normalize_separator_line("═══"), None); + assert_eq!(normalize_separator_line("───"), None); + } + + #[test] + fn hr_minimization_preserves_non_hr() { + // ASCII threshold is 4 chars; exactly 3 dashes unchanged. + assert_eq!(normalize_separator_line("---"), None); + assert_eq!(normalize_separator_line("hello ----"), None); + assert_eq!(normalize_separator_line("----- x"), None); + // Dot-leader runs are not HRs. + assert_eq!(normalize_separator_line("......"), None); + // Mixed chars not a valid HR. + assert_eq!(normalize_separator_line("---___"), None); + } + + #[test] + fn hr_minimization_does_not_touch_escaped_underscores() { + // Per CommonMark, `\_` is a valid backslash-escape (since `_` + // is ASCII punctuation), so a line of `\_\_\_\_…` renders as + // a paragraph of LITERAL underscores — NOT as a thematic + // break. Rewriting it to `---` (which renders as an HR) + // changes preview. Found by formal verification on the + // 90-doc most-altered PDF-only sample 2026-04-24 — 34 of the + // 72 preview-equivalence failures traced to this rule. + // + // Bucketing the run LENGTH (a cosmetic normalization, not + // a thematic-break rewrite) is handled in + // `normalize::normalize_escaped_underscore_runs` — see that + // module for the `{1, 3, 5, 20}` tiered bucket. + assert_eq!(normalize_separator_line(r"\_\_\_\_"), None); + assert_eq!( + normalize_separator_line(r"\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_"), + None + ); + assert_eq!(normalize_separator_line(r" \_\_\_\_ "), None); + assert_eq!(normalize_separator_line(r"\_\_\_"), None); + } + + // --- GFM table separator minimization --- + + #[test] + fn gfm_sep_minimizes_long_dash_body() { + let text = "| a | b |\n| -------- | -------- |\n| 1 | 2 |\n"; + let reps = scan_gfm_table_separators(text); + assert_eq!(reps.len(), 1); + assert_eq!(reps.get(&1), Some(&"| --- | --- |".to_string())); + } + + #[test] + fn gfm_sep_preserves_alignment_colons() { + let text = "| a | b | c | d |\n| :---- | -----: | :----: | ---- |\n| 1 | 2 | 3 | 4 |\n"; + let reps = scan_gfm_table_separators(text); + assert_eq!( + reps.get(&1), + Some(&"| :--- | ---: | :---: | --- |".to_string()) + ); + } + + #[test] + fn gfm_sep_ignores_lines_without_pipes() { + // Standalone `----` (HR) must NOT be claimed as a 1-cell + // GFM table separator. + let text = "para\n----\nother\n"; + let reps = scan_gfm_table_separators(text); + assert!(reps.is_empty()); + } + + #[test] + fn gfm_sep_ignores_lines_inside_fenced_code() { + let text = "```\n| a | b |\n| --- | --- |\n```\n"; + let reps = scan_gfm_table_separators(text); + assert!(reps.is_empty()); + } + + #[test] + fn gfm_sep_requires_matching_header_cell_count() { + // header has 3 cells, separator has 2 → don't touch. + let text = "| a | b | c |\n| --- | --- |\n"; + let reps = scan_gfm_table_separators(text); + assert!(reps.is_empty()); + } + + #[test] + fn gfm_sep_rejects_sep_without_header_line() { + // First-line separator (i=0) has no header before it. + let text = "| --- | --- |\n| 1 | 2 |\n"; + let reps = scan_gfm_table_separators(text); + assert!(reps.is_empty()); + } + + #[test] + fn gfm_sep_rejects_body_row_with_short_dashes() { + // Body cells must be ≥3 hyphens. + let text = "| a | b |\n| - | -- |\n"; + let reps = scan_gfm_table_separators(text); + assert!(reps.is_empty()); + } + + #[test] + fn gfm_sep_handles_multiple_tables() { + let text = + "| a | b |\n| ------ | ------ |\n| 1 | 2 |\n\n| c | d |\n| ---- | ---- |\n| 3 | 4 |\n"; + let reps = scan_gfm_table_separators(text); + assert_eq!(reps.len(), 2); + } + + // --- CommonMark indentation helper --- + + #[test] + fn leading_columns_counts_spaces() { + assert_eq!(leading_columns(""), 0); + assert_eq!(leading_columns("abc"), 0); + assert_eq!(leading_columns(" abc"), 1); + assert_eq!(leading_columns(" abc"), 3); + assert_eq!(leading_columns(" abc"), 4); + } + + #[test] + fn leading_columns_applies_tab_rule() { + // A tab advances to the next multiple of 4. + assert_eq!(leading_columns("\tabc"), 4); + assert_eq!(leading_columns(" \tabc"), 4); + assert_eq!(leading_columns(" \tabc"), 4); + assert_eq!(leading_columns(" \tabc"), 8); + // Two tabs. + assert_eq!(leading_columns("\t\tabc"), 8); + } + + #[test] + fn leading_columns_ignores_non_leading_whitespace() { + assert_eq!(leading_columns("abc "), 0); + assert_eq!(leading_columns("a\tb"), 0); + } + + // --- Fenced code detection --- + + #[test] + fn code_fence_detects_backticks_and_tildes() { + assert!(is_code_fence_marker("```")); + assert!(is_code_fence_marker("```python")); + assert!(is_code_fence_marker("~~~")); + assert!(is_code_fence_marker("~~~rust")); + assert!(is_code_fence_marker(" ```")); + assert!(is_code_fence_marker(" ```")); // 3 leading spaces + assert!(!is_code_fence_marker("`inline`")); + assert!(!is_code_fence_marker("``double``")); + assert!(!is_code_fence_marker("no fence")); + } + + #[test] + fn code_fence_rejects_at_four_leading_columns() { + // CommonMark: `≥4` leading columns = indented code block. The + // same visual shape is NOT a fence opener in that context. + assert!(!is_code_fence_marker(" ```")); + assert!(!is_code_fence_marker(" ```python")); + assert!(!is_code_fence_marker(" ~~~")); + // Tab counts as 4 columns. + assert!(!is_code_fence_marker("\t```")); + // Mixed-whitespace cases that add up to ≥4 columns. + assert!(!is_code_fence_marker(" \t```")); + } + + #[test] + fn hr_and_gfm_rejected_at_four_leading_columns() { + // HR detector bails: ` ----` is indented code. + assert_eq!(normalize_separator_line(" ----"), None); + assert_eq!(normalize_separator_line("\t----"), None); + // 3 leading spaces still fine. + assert_eq!(normalize_separator_line(" ----"), Some("---".to_string())); + + // GFM scanner: both separator and header must be outside + // indented-code range. + let indented_table = "\ +paragraph\n\n | a | b |\n | --- | --- |\n | 1 | 2 |\n\nafter\n"; + let reps = scan_gfm_table_separators(indented_table); + assert!(reps.is_empty(), "indented table must be left alone"); + } + + // --- Paragraph reflow --- + + #[test] + fn reflow_joins_soft_wrapped_lines() { + assert_eq!( + reflow_paragraphs("word1\nword2\nword3"), + "word1 word2 word3" + ); + } + + #[test] + fn reflow_preserves_blank_line_breaks() { + let input = "paragraph1.\n\nparagraph2."; + assert_eq!(reflow_paragraphs(input), input); + } + + #[test] + fn reflow_preserves_headings() { + let input = "body text\n# Heading\nmore text"; + assert_eq!(reflow_paragraphs(input), input); + } + + #[test] + fn reflow_preserves_table_rows() { + let input = "intro\n| a | b |\n| - | - |\n| 1 | 2 |\nafter"; + assert_eq!(reflow_paragraphs(input), input); + } + + #[test] + fn reflow_preserves_list_items() { + let input = "intro\n- item one\n- item two\nafter"; + assert_eq!(reflow_paragraphs(input), input); + } + + #[test] + fn reflow_stops_at_sentence_terminators() { + let input = "First sentence.\nSecond starts here"; + assert_eq!(reflow_paragraphs(input), input); + } + + #[test] + fn reflow_stops_at_fenced_code() { + let input = "before\n```\ncode line\n```\nafter"; + assert_eq!(reflow_paragraphs(input), input); + } + + #[test] + fn reflow_does_not_join_indented_code() { + let input = "prose\n code line\nprose again"; + let out = reflow_paragraphs(input); + assert!(out.contains(" code line")); + } + + #[test] + fn reflow_joins_pdf_column_wrap_pattern() { + let input = "word1\t\n word2\t\n word3"; + let out = reflow_paragraphs(input); + assert_eq!(out, "word1 word2 word3"); + } + + // --- Phase A orchestrator --- + + // ----------------------------------------------------------------- + // Preview-equivalence regression tests for Phase A transforms. + // + // Invariant asserted: for each transform, the cleaner OUTPUT renders + // identically to the INPUT under a spec-compliant GFM parser. Any + // future edit that breaks preview-preservation fails loudly here. + // + // Uses `md_verify::verify_md_preview_equivalent` (pulldown-cmark as + // reference parser). See `docs/MD_MODULE_ARCHITECTURE.md`. + // ----------------------------------------------------------------- + + // --- HR minimization preserves preview --- + + // --- GFM table separator minimization preserves preview --- + + // --- Paragraph reflow preserves preview --- + + // --- Orchestrator equivalence on mixed-content docs --- + + // --- Blank-line run collapse --- + + #[test] + fn blank_line_collapse_leaves_single_blank_alone() { + let input = "a\n\nb\n\nc\n"; + assert_eq!(collapse_blank_line_runs(input), input); + } + + #[test] + fn blank_line_collapse_reduces_long_runs() { + let input = "a\n\n\n\n\n\nb\n"; + assert_eq!(collapse_blank_line_runs(input), "a\n\nb\n"); + } + + #[test] + fn blank_line_collapse_preserves_inside_fenced_code() { + // Blank lines inside a fenced code block are significant + // (empty code lines) — must not be collapsed. + let input = "before\n\n```\n\n\n\ncode\n\n\n```\n\nafter\n"; + let out = collapse_blank_line_runs(input); + assert_eq!(out, input); + } + + // --- Escaped-underscore rule removal regression --- + + // --- Negative controls: if equiv check is wrong, these would pass --- + + #[test] + fn equiv_detects_an_incorrect_transform_that_drops_paragraph() { + // This is NOT md_module's output — we manufacture a broken + // transform to confirm the verifier would catch it. + let input = "para1\n\npara2\n\npara3\n"; + let broken_output = "para1\n\npara3\n"; + let r = crate::md_verify::verify_md_preview_equivalent(input, broken_output); + assert!( + !r.is_strict_equivalent(), + "verifier should catch dropped paragraph" + ); + } + + #[test] + fn equiv_detects_an_incorrect_transform_that_fuses_words() { + // Simulates the v6-11 NBSP-strip bug. Would-be Phase A violation. + let input = "Η εργασία αυτή έχει σκοπό.\n"; + let broken_output = "Ηεργασίααυτήέχεισκοπό.\n"; + let r = crate::md_verify::verify_md_preview_equivalent(input, broken_output); + assert!( + !r.is_strict_equivalent(), + "verifier should catch word fusion" + ); + assert!(!r.paragraph_text_equal); + } + + // ----------------------------------------------------------------- + // Commit 11 — RED tests for the bugs identified in the + // MD_MODULE_ARCHITECTURE_IMPLEMENTATION_REVIEW (2026-04-24). + // + // These tests expose Phase A preview-equivalence violations that + // the current implementation commits. They are EXPECTED TO FAIL + // on the commit-11 boundary; commits 12–15 turn them green by + // adding CommonMark indentation awareness + hard-break guards + // + orchestrator wiring + expanded structural comparison. + // + // Each test name ends in `_red_until_C` to make the tracking + // explicit. + // ----------------------------------------------------------------- + + // --- H-1 indentation awareness (CommonMark: ≥4 leading spaces / + // tab = indented code, NOT an HR / + // table / fence opener) --- + + // --- H-3 paragraph reflow destroys hard breaks --- + + // --- Post-C13 regression tests --- +} diff --git a/rust/glossapi_rs_cleaner/src/md_verify.rs b/rust/glossapi_rs_cleaner/src/md_verify.rs new file mode 100644 index 0000000..f32e1eb --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/md_verify.rs @@ -0,0 +1,1158 @@ +//! MD-equivalence verification using pulldown-cmark as reference parser. +//! +//! Two verification modes: +//! +//! - **Strict (Phase A):** `verify_md_preview_equivalent` — asserts that +//! an MD transform preserves preview rendering. `pandoc-render(input) ≡ +//! pandoc-render(output)`. Used for testing Phase A (md_module) +//! transforms where this invariant MUST hold. +//! +//! - **Structural (Phase B):** `verify_md_structural` — asserts that a +//! content-modifying transform preserves block structure and only +//! deletes content (no reorderings, no fusions). Used for spot- +//! checking the full cleaner on sample docs. +//! +//! Uses `pulldown-cmark` as the reference CommonMark/GFM parser — +//! battle-tested, used by `rustdoc`, streaming event API (low memory), +//! same spec as GitHub's renderer. HTML render via +//! `pulldown_cmark::html::push_html`. +//! +//! See `docs/MD_MODULE_ARCHITECTURE.md` for the full design context. + +use pulldown_cmark::{html, Event, Options, Parser, Tag, TagEnd}; +use pyo3::prelude::*; +use pyo3::types::PyDict; + +use crate::md_module; + +/// Detailed report from a verification run. Boolean fields are what +/// tests normally assert on; the diagnostic strings exist to make +/// failures self-explanatory (copy-paste into an issue). +#[derive(Debug, Clone, Default)] +pub struct MdEquivalenceReport { + /// HTML render of input vs output is identical (after whitespace + /// normalization). Strongest preview-equivalence signal. + pub html_render_equal: bool, + /// Block-level event sequence (`Start(Heading)`, `Start(Paragraph)`, + /// `Start(Table)`, etc.) matches in order. Catches cases where + /// whitespace-trim made HTML match by coincidence. + pub block_sequence_equal: bool, + /// For each matched paragraph, the whitespace-tokenized text content + /// is identical. Catches word fusion (the v6-11 NBSP bug). + pub paragraph_text_equal: bool, + /// For each matched table, row count and cell count match, and each + /// cell's whitespace-tokenized content is identical. + pub table_cells_equal: bool, + /// Diagnostic: first mismatch description, if any. + pub first_diff: Option, +} + +impl MdEquivalenceReport { + /// All four checks passed — preview-render equivalence holds. + pub fn is_strict_equivalent(&self) -> bool { + self.html_render_equal + && self.block_sequence_equal + && self.paragraph_text_equal + && self.table_cells_equal + } +} + +/// Structural verification report for Phase B outputs. +#[derive(Debug, Clone, Default)] +pub struct MdStructuralReport { + /// Number and type of top-level block elements match. + pub block_count_equal: bool, + /// In each matched paragraph, output tokens are a MONOTONE + /// SUBSEQUENCE of input tokens (permits deletions, disallows + /// reorderings or fusions). + pub paragraph_tokens_subsequence: bool, + /// For each matched table, cell count per row matches AND each + /// cell's output tokens are a subsequence of input tokens. + pub table_cells_subsequence: bool, + /// Per-CodeBlock: output lines are a subsequence of input lines + /// (whitespace-preserved comparison, unlike paragraph tokens). + pub code_blocks_preserved: bool, + /// Percentage of input tokens retained in output (continuous + /// metric; useful even when booleans pass). + pub token_retention_pct: f64, + pub first_diff: Option, + /// When `paragraph_tokens_subsequence` is false, categorizes the + /// failure: fusion (v6-11 NBSP signature), injection (added + /// content), reordering, or other. + pub subsequence_failure_kind: Option, +} + +impl MdStructuralReport { + pub fn is_structural_equivalent(&self) -> bool { + self.block_count_equal + && self.paragraph_tokens_subsequence + && self.table_cells_subsequence + && self.code_blocks_preserved + } +} + +/// Strip the cleaner's own line-level marker comments +/// (``, ``, +/// ``) from `md`. These are EMITTED by Phase B +/// to mark dropped content — they're legitimate cleaner behavior but +/// would appear as "injected tokens" in structural comparisons. +/// Strip them from BOTH sides so the verifier sees a fair comparison. +fn strip_cleaner_markers(md: &str) -> String { + md.replace("", "") + .replace("", "") + .replace("", "") + .replace("", "") +} + +/// Thin alias over `md_module::non_destructive_canonicalize`, kept +/// here so the `verify_md_structural` code reads in document order. +/// +/// Single source of truth lives in `md_module` — this call delegates +/// so the verifier baseline can never drift from what the cleaner +/// would produce if every pass were non-destructive. +fn canonicalize_for_verify(md: &str) -> String { + md_module::non_destructive_canonicalize(md) +} + +fn gfm_options() -> Options { + let mut opts = Options::empty(); + opts.insert(Options::ENABLE_TABLES); + opts.insert(Options::ENABLE_FOOTNOTES); + opts.insert(Options::ENABLE_STRIKETHROUGH); + opts.insert(Options::ENABLE_TASKLISTS); + opts +} + +/// Collapse all runs of whitespace to a single space + trim ends. Used +/// to normalize HTML render output before equality check. +fn collapse_ws(s: &str) -> String { + let mut out = String::with_capacity(s.len()); + let mut prev_ws = false; + for c in s.chars() { + if c.is_whitespace() { + if !prev_ws { + out.push(' '); + } + prev_ws = true; + } else { + out.push(c); + prev_ws = false; + } + } + out.trim().to_string() +} + +fn render_html(md: &str) -> String { + let parser = Parser::new_ext(md, gfm_options()); + let mut html = String::new(); + html::push_html(&mut html, parser); + html +} + +/// Block-level element kinds we compare. Inline emphasis / links are +/// folded into their parent block's text content — we don't enforce +/// that `**bold**` stays as `**bold**` specifically (different renderers +/// emit different inline detail; the HTML-equality check handles it). +#[derive(Debug, Clone, PartialEq, Eq)] +enum BlockKind { + Paragraph, + Heading(u8), + BlockQuote, + CodeBlock, + List(bool /* ordered */), + Item, + Table, + TableHead, + TableRow, + TableCell, + ThematicBreak, + HtmlBlock, + FootnoteDefinition, +} + +fn tag_to_block_kind(tag: &Tag) -> Option { + match tag { + Tag::Paragraph => Some(BlockKind::Paragraph), + Tag::Heading { level, .. } => Some(BlockKind::Heading(*level as u8)), + Tag::BlockQuote(_) => Some(BlockKind::BlockQuote), + Tag::CodeBlock(_) => Some(BlockKind::CodeBlock), + Tag::List(_) => { + // List(Some(_)) is ordered; List(None) is unordered. + if let Tag::List(start) = tag { + Some(BlockKind::List(start.is_some())) + } else { + unreachable!() + } + } + Tag::Item => Some(BlockKind::Item), + Tag::Table(_) => Some(BlockKind::Table), + Tag::TableHead => Some(BlockKind::TableHead), + Tag::TableRow => Some(BlockKind::TableRow), + Tag::TableCell => Some(BlockKind::TableCell), + Tag::FootnoteDefinition(_) => Some(BlockKind::FootnoteDefinition), + Tag::HtmlBlock => Some(BlockKind::HtmlBlock), + _ => None, + } +} + +/// Flatten an MD doc to a linear sequence of block-kind starts. +fn block_sequence(md: &str) -> Vec { + let mut seq = Vec::new(); + for ev in Parser::new_ext(md, gfm_options()) { + match ev { + Event::Start(tag) => { + if let Some(k) = tag_to_block_kind(&tag) { + seq.push(k); + } + } + Event::Rule => seq.push(BlockKind::ThematicBreak), + _ => {} + } + } + seq +} + +/// Extract one whitespace-tokenized vector per text-bearing leaf +/// block, in source order. Covers: +/// +/// - `Paragraph` (top-level and nested inside BlockQuote / list items +/// / footnote definitions — pulldown-cmark emits a Paragraph inside +/// each of those containers). +/// - `Heading` (ATX `# ...` and setext `text\n---`). +/// - `Item` (for tight list items, where pulldown-cmark emits text +/// directly without a nested Paragraph). +/// +/// Text inside a block (including inline formatting) is concatenated +/// before tokenization. Link/image URLs ARE included in the token +/// stream so that a cleaner silently rewriting a URL is detected. +/// +/// Fixing M-2 from the 2026-04-24 review: a cleaner that rewrote +/// `# Α` to `# Β` would previously pass structural equivalence +/// because heading text wasn't compared at all. Now it is. +/// +/// **Not a universal text-bearing-block extractor.** Explicitly not +/// covered (deferred until a concrete gap surfaces in practice): +/// +/// - `Html` blocks (raw `
…
` content). Docling-produced +/// corpus MD does not emit these, and the corpus contract treats +/// raw HTML as either ignorable or a Phase B candidate. A cleaner +/// that rewrote HTML-block text content would pass this check +/// today. Add `Start(Tag::HtmlBlock)` / `End(TagEnd::HtmlBlock)` +/// to the match arms if and when that becomes a real risk. +/// - Table cells (covered separately via `table_structure` and the +/// `table_cells_subsequence` report field, so this is not a gap). +/// - Code blocks (covered separately via line-preserving comparison +/// — whitespace in code is meaningful, not tokenized). +/// +/// (Historical name `paragraph_tokens` is retained — callers and the +/// `MdStructuralReport::paragraph_tokens_subsequence` field form a +/// small public-facing surface; broadening the coverage without +/// renaming is the least-disruptive change.) +fn paragraph_tokens(md: &str) -> Vec> { + let mut blocks: Vec> = Vec::new(); + let mut current: Option = None; + for ev in Parser::new_ext(md, gfm_options()) { + match ev { + // Tight list items render text DIRECTLY inside `Item` + // (no nested `Paragraph`). Loose list items have a nested + // `Paragraph` which overwrites the Item-level buffer — + // that's fine; the Paragraph handler flushes first, and + // End(Item) then has nothing to flush. + Event::Start(Tag::Paragraph) + | Event::Start(Tag::Heading { .. }) + | Event::Start(Tag::Item) => { + current = Some(String::new()); + } + Event::End(TagEnd::Paragraph) + | Event::End(TagEnd::Heading(_)) + | Event::End(TagEnd::Item) => { + if let Some(buf) = current.take() { + blocks.push(buf.split_whitespace().map(|s| s.to_string()).collect()); + } + } + Event::Text(t) | Event::Code(t) | Event::Html(t) => { + if let Some(buf) = current.as_mut() { + buf.push_str(&t); + buf.push(' '); + } + } + // Link / image URLs are meaningful content. Append them as + // space-separated tokens so they show up in the token + // sequence and a silent URL rewrite fails verification. + Event::Start(Tag::Link { + dest_url, title, .. + }) + | Event::Start(Tag::Image { + dest_url, title, .. + }) => { + if let Some(buf) = current.as_mut() { + buf.push_str(&dest_url); + buf.push(' '); + if !title.is_empty() { + buf.push_str(&title); + buf.push(' '); + } + } + } + _ => {} + } + } + blocks +} + +/// Extract table structure: `Vec` where each Table is +/// `Vec` and each Row is `Vec`. Link/image URLs +/// inside cells are included in the token stream. +fn table_structure(md: &str) -> Vec>>> { + let mut tables: Vec>>> = Vec::new(); + let mut cur_table: Option>>> = None; + let mut cur_row: Option>> = None; + let mut cur_cell_buf: Option = None; + for ev in Parser::new_ext(md, gfm_options()) { + match ev { + Event::Start(Tag::Table(_)) => cur_table = Some(Vec::new()), + Event::End(TagEnd::Table) => { + if let Some(t) = cur_table.take() { + tables.push(t); + } + } + Event::Start(Tag::TableRow) | Event::Start(Tag::TableHead) => { + cur_row = Some(Vec::new()); + } + Event::End(TagEnd::TableRow) | Event::End(TagEnd::TableHead) => { + if let (Some(row), Some(tbl)) = (cur_row.take(), cur_table.as_mut()) { + tbl.push(row); + } + } + Event::Start(Tag::TableCell) => cur_cell_buf = Some(String::new()), + Event::End(TagEnd::TableCell) => { + if let (Some(buf), Some(row)) = (cur_cell_buf.take(), cur_row.as_mut()) { + row.push(buf.split_whitespace().map(|s| s.to_string()).collect()); + } + } + Event::Text(t) | Event::Code(t) | Event::Html(t) => { + if let Some(buf) = cur_cell_buf.as_mut() { + buf.push_str(&t); + buf.push(' '); + } + } + Event::Start(Tag::Link { + dest_url, title, .. + }) + | Event::Start(Tag::Image { + dest_url, title, .. + }) => { + if let Some(buf) = cur_cell_buf.as_mut() { + buf.push_str(&dest_url); + buf.push(' '); + if !title.is_empty() { + buf.push_str(&title); + buf.push(' '); + } + } + } + _ => {} + } + } + tables +} + +/// Extract code block contents as lines (preserves indentation and +/// whitespace, unlike the paragraph tokenizer). One `Vec` of +/// lines per `CodeBlock`, in source order. +fn code_block_lines(md: &str) -> Vec> { + let mut out: Vec> = Vec::new(); + let mut current: Option = None; + for ev in Parser::new_ext(md, gfm_options()) { + match ev { + Event::Start(Tag::CodeBlock(_)) => current = Some(String::new()), + Event::End(TagEnd::CodeBlock) => { + if let Some(buf) = current.take() { + out.push(buf.lines().map(String::from).collect()); + } + } + Event::Text(t) => { + if let Some(buf) = current.as_mut() { + buf.push_str(&t); + } + } + _ => {} + } + } + out +} + +/// Classification of a paragraph-subsequence failure — distinguishes +/// the underlying cause so scorecard output is directly actionable. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum SubsequenceFailureKind { + /// Output has a token not present in input (injection or fusion). + Injection, + /// Output has adjacent input tokens concatenated into one token + /// (the v6-11 NBSP-strip signature). Detected when an output token + /// is not in input but IS a concat of 2+ adjacent input tokens. + Fusion, + /// Input tokens are all present in output, but out of order. + Reordering, + /// Some other combination — couldn't be cleanly classified. + Other, +} + +impl SubsequenceFailureKind { + pub fn as_str(&self) -> &'static str { + match self { + Self::Injection => "injection", + Self::Fusion => "fusion", + Self::Reordering => "reordering", + Self::Other => "other", + } + } +} + +/// Classify WHY output tokens are not a monotone subsequence of input +/// tokens. Returns None if `output` IS a subsequence (no failure). +pub fn classify_subsequence_failure( + input: &[String], + output: &[String], +) -> Option { + if is_subsequence(output, input) { + return None; + } + // Is every output token AT LEAST in the input set? If no, injection. + let input_set: std::collections::HashSet<&String> = input.iter().collect(); + let missing_in_input: Vec<&String> = output.iter().filter(|t| !input_set.contains(t)).collect(); + if !missing_in_input.is_empty() { + // Fusion signature: the out-of-input token is a concat of 2+ + // adjacent input tokens. + for missing in &missing_in_input { + if is_concat_of_adjacent_input_tokens(missing, input) { + return Some(SubsequenceFailureKind::Fusion); + } + } + return Some(SubsequenceFailureKind::Injection); + } + // All output tokens ARE in the input set, but not as a subsequence + // → order changed. + Some(SubsequenceFailureKind::Reordering) +} + +/// Check if `needle` is exactly the concatenation of some window of +/// adjacent tokens from `input` (with empty separator — as NBSP-strip +/// would produce). +fn is_concat_of_adjacent_input_tokens(needle: &str, input: &[String]) -> bool { + for start in 0..input.len() { + let mut acc = String::new(); + for i in start..input.len() { + acc.push_str(&input[i]); + if acc.len() > needle.len() { + break; + } + // Need at least 2 tokens to call it a "fusion". + if acc == needle && i > start { + return true; + } + } + } + false +} + +/// Strict Phase A verification: preview render must be identical. +pub fn verify_md_preview_equivalent(input: &str, output: &str) -> MdEquivalenceReport { + let mut r = MdEquivalenceReport::default(); + + // 1. HTML render equality. + let html_in = collapse_ws(&render_html(input)); + let html_out = collapse_ws(&render_html(output)); + r.html_render_equal = html_in == html_out; + if !r.html_render_equal && r.first_diff.is_none() { + r.first_diff = Some(format!( + "html render differs\n in: {}\n out: {}", + html_in.chars().take(200).collect::(), + html_out.chars().take(200).collect::(), + )); + } + + // 2. Block sequence equality. + let seq_in = block_sequence(input); + let seq_out = block_sequence(output); + r.block_sequence_equal = seq_in == seq_out; + if !r.block_sequence_equal && r.first_diff.is_none() { + r.first_diff = Some(format!( + "block sequence differs\n in: {:?}\n out: {:?}", + seq_in, seq_out + )); + } + + // 3. Per-paragraph text tokens. + let par_in = paragraph_tokens(input); + let par_out = paragraph_tokens(output); + r.paragraph_text_equal = par_in == par_out; + if !r.paragraph_text_equal && r.first_diff.is_none() { + let idx = par_in + .iter() + .zip(par_out.iter()) + .position(|(a, b)| a != b) + .unwrap_or(par_in.len().min(par_out.len())); + let a = par_in.get(idx).cloned().unwrap_or_default(); + let b = par_out.get(idx).cloned().unwrap_or_default(); + r.first_diff = Some(format!( + "paragraph {} text differs\n in: {:?}\n out: {:?}", + idx, a, b + )); + } + + // 4. Table cells. + let tbl_in = table_structure(input); + let tbl_out = table_structure(output); + r.table_cells_equal = tbl_in == tbl_out; + if !r.table_cells_equal && r.first_diff.is_none() { + r.first_diff = Some(format!( + "table structure differs: {} tables in, {} tables out", + tbl_in.len(), + tbl_out.len() + )); + } + + r +} + +/// Structural Phase B verification: output is a content-subset of input, +/// structure preserved. +/// +/// **Input pre-canonicalization** (2026-04-24): before extracting +/// tokens, runs `canonicalize_for_verify(input)` so that entity decode, +/// HR/GFM-sep minimization, Unicode-whitespace folding, and other +/// non-destructive cleaner transforms don't produce misclassified +/// "injection" failures. Without this, a cleaner that decodes `&` +/// to `&` would appear to have "injected" the token `&` because it +/// wasn't literally in the raw input. With this, both sides see `&`. +pub fn verify_md_structural(input: &str, output: &str) -> MdStructuralReport { + let mut r = MdStructuralReport::default(); + + // Canonicalize input — apply the cleaner's non-destructive + // transforms so diffs reflect real content changes only. Also + // strip the cleaner's own marker comments from BOTH sides so + // `` etc. aren't classified as injections. + let input_canon_owned = strip_cleaner_markers(&canonicalize_for_verify(input)); + let output_canon_owned = strip_cleaner_markers(output); + let input = input_canon_owned.as_str(); + let output = output_canon_owned.as_str(); + + let seq_in = block_sequence(input); + let seq_out = block_sequence(output); + r.block_count_equal = seq_in == seq_out; + if !r.block_count_equal && r.first_diff.is_none() { + r.first_diff = Some(format!( + "block sequence differs ({} in vs {} out)", + seq_in.len(), + seq_out.len() + )); + } + + // Paragraph tokens: output must be a subsequence of input. + // Classify failure kind (fusion / reordering / injection / other) + // when the check fails — makes scorecard output directly actionable. + let par_in = paragraph_tokens(input); + let par_out = paragraph_tokens(output); + let mut all_pass = true; + let mut tokens_in_total = 0usize; + let mut tokens_out_total = 0usize; + for (i, (a, b)) in par_in.iter().zip(par_out.iter()).enumerate() { + tokens_in_total += a.len(); + tokens_out_total += b.len(); + if !is_subsequence(b, a) { + all_pass = false; + let kind = classify_subsequence_failure(a, b).unwrap_or(SubsequenceFailureKind::Other); + if r.first_diff.is_none() { + r.first_diff = Some(format!( + "paragraph {} subsequence failure ({}): in_len={} out_len={}", + i, + kind.as_str(), + a.len(), + b.len(), + )); + r.subsequence_failure_kind = Some(kind.as_str().to_string()); + } + } + } + r.paragraph_tokens_subsequence = all_pass && par_in.len() == par_out.len(); + r.token_retention_pct = if tokens_in_total == 0 { + 1.0 + } else { + tokens_out_total as f64 / tokens_in_total as f64 + }; + + // Table cells: same structure + subsequence per cell. + let tbl_in = table_structure(input); + let tbl_out = table_structure(output); + let mut tbl_pass = tbl_in.len() == tbl_out.len(); + if tbl_pass { + 'outer: for (table_in, table_out) in tbl_in.iter().zip(tbl_out.iter()) { + if table_in.len() != table_out.len() { + tbl_pass = false; + if r.first_diff.is_none() { + r.first_diff = Some("table row count differs".to_string()); + } + break; + } + for (row_in, row_out) in table_in.iter().zip(table_out.iter()) { + if row_in.len() != row_out.len() { + tbl_pass = false; + if r.first_diff.is_none() { + r.first_diff = Some("table cell count differs".to_string()); + } + break 'outer; + } + for (cell_in, cell_out) in row_in.iter().zip(row_out.iter()) { + if !is_subsequence(cell_out, cell_in) { + tbl_pass = false; + if r.first_diff.is_none() { + let kind = classify_subsequence_failure(cell_in, cell_out) + .unwrap_or(SubsequenceFailureKind::Other); + r.first_diff = Some(format!( + "table cell subsequence failure ({})", + kind.as_str() + )); + } + break 'outer; + } + } + } + } + } + r.table_cells_subsequence = tbl_pass; + + // Code blocks: output lines are a subsequence of input lines. + // Code is whitespace-sensitive; line-based check (not whitespace- + // tokenized) catches accidental re-indentation. + let code_in = code_block_lines(input); + let code_out = code_block_lines(output); + let mut code_pass = code_in.len() == code_out.len(); + if code_pass { + for (a, b) in code_in.iter().zip(code_out.iter()) { + if !is_subsequence(b, a) { + code_pass = false; + if r.first_diff.is_none() { + r.first_diff = Some("code block lines NOT a subsequence".to_string()); + } + break; + } + } + } else if r.first_diff.is_none() { + r.first_diff = Some(format!( + "code block count differs ({} in vs {} out)", + code_in.len(), + code_out.len(), + )); + } + r.code_blocks_preserved = code_pass; + + r +} + +/// Test whether `needle` is a monotone subsequence of `haystack`. +fn is_subsequence(needle: &[String], haystack: &[String]) -> bool { + let mut h_iter = haystack.iter(); + for tok in needle { + let found = h_iter.any(|h| h == tok); + if !found { + return false; + } + } + true +} + +// --------------------------------------------------------------------------- +// PyO3 bindings — exposed so Python driver can spot-check docs. +// --------------------------------------------------------------------------- + +#[pyfunction] +pub fn verify_md_preview_equivalent_py( + py: Python<'_>, + input: &str, + output: &str, +) -> PyResult { + let r = verify_md_preview_equivalent(input, output); + let d = PyDict::new(py); + d.set_item("html_render_equal", r.html_render_equal)?; + d.set_item("block_sequence_equal", r.block_sequence_equal)?; + d.set_item("paragraph_text_equal", r.paragraph_text_equal)?; + d.set_item("table_cells_equal", r.table_cells_equal)?; + d.set_item("is_strict_equivalent", r.is_strict_equivalent())?; + d.set_item("first_diff", r.first_diff)?; + Ok(d.into()) +} + +#[pyfunction] +pub fn verify_md_structural_py(py: Python<'_>, input: &str, output: &str) -> PyResult { + let r = verify_md_structural(input, output); + let d = PyDict::new(py); + d.set_item("block_count_equal", r.block_count_equal)?; + d.set_item( + "paragraph_tokens_subsequence", + r.paragraph_tokens_subsequence, + )?; + d.set_item("table_cells_subsequence", r.table_cells_subsequence)?; + d.set_item("code_blocks_preserved", r.code_blocks_preserved)?; + d.set_item("token_retention_pct", r.token_retention_pct)?; + d.set_item("is_structural_equivalent", r.is_structural_equivalent())?; + d.set_item("first_diff", r.first_diff)?; + d.set_item("subsequence_failure_kind", r.subsequence_failure_kind)?; + Ok(d.into()) +} + +#[cfg(test)] +mod tests { + use super::*; + + // --- identity: any doc verified against itself should pass strict --- + + #[test] + fn identity_passes_strict_on_simple_prose() { + let doc = "# Hello\n\nThis is a paragraph.\n\nAnd another.\n"; + let r = verify_md_preview_equivalent(doc, doc); + assert!(r.is_strict_equivalent(), "{:?}", r); + } + + #[test] + fn identity_passes_strict_on_gfm_table() { + let doc = "| a | b |\n| --- | --- |\n| 1 | 2 |\n"; + let r = verify_md_preview_equivalent(doc, doc); + assert!(r.is_strict_equivalent(), "{:?}", r); + } + + #[test] + fn identity_passes_strict_on_headings_and_lists() { + let doc = "# Title\n\n## Section\n\n- item one\n- item two\n- item three\n\nbody\n"; + let r = verify_md_preview_equivalent(doc, doc); + assert!(r.is_strict_equivalent(), "{:?}", r); + } + + // --- Phase A transforms: assert equivalence --- + + #[test] + fn reflow_preserves_strict_equivalence() { + let input = "This is a\nparagraph that\nis soft-wrapped.\n"; + let output = "This is a paragraph that is soft-wrapped.\n"; + let r = verify_md_preview_equivalent(input, output); + assert!(r.is_strict_equivalent(), "{:?}", r); + } + + #[test] + fn gfm_table_sep_min_preserves_strict_equivalence() { + let input = "| a | b |\n| -------- | -------- |\n| 1 | 2 |\n"; + let output = "| a | b |\n| --- | --- |\n| 1 | 2 |\n"; + let r = verify_md_preview_equivalent(input, output); + assert!(r.is_strict_equivalent(), "{:?}", r); + } + + #[test] + fn hr_min_preserves_strict_equivalence() { + let input = "before\n\n----------\n\nafter\n"; + let output = "before\n\n---\n\nafter\n"; + let r = verify_md_preview_equivalent(input, output); + assert!(r.is_strict_equivalent(), "{:?}", r); + } + + // --- Phase A violations: should be caught --- + + #[test] + fn dropped_paragraph_fails_strict() { + let input = "para1\n\npara2\n\npara3\n"; + let output = "para1\n\npara3\n"; + let r = verify_md_preview_equivalent(input, output); + assert!(!r.is_strict_equivalent()); + assert!(!r.block_sequence_equal); + } + + #[test] + fn fused_words_fails_paragraph_token_check() { + // The v6-11 NBSP-strip bug — words fused into one. + let input = "Η εργασία αυτή\n"; + let output = "Ηεργασίααυτή\n"; + let r = verify_md_preview_equivalent(input, output); + assert!(!r.paragraph_text_equal); + } + + #[test] + fn reordered_tokens_fails_paragraph_check() { + let input = "alpha beta gamma\n"; + let output = "gamma alpha beta\n"; + let r = verify_md_preview_equivalent(input, output); + assert!(!r.paragraph_text_equal); + } + + #[test] + fn heading_level_change_fails_block_sequence() { + let input = "# title\n"; + let output = "## title\n"; + let r = verify_md_preview_equivalent(input, output); + assert!(!r.block_sequence_equal); + } + + // --- Phase B structural verifier --- + + #[test] + fn structural_accepts_token_deletion() { + // Removing some tokens (e.g. GLYPH markers) is allowed. Use a + // clearly-tokenizable marker so the retention fraction is + // parser-stable (GLYPH<216> can be parsed in parser-specific + // ways depending on HTML-inline handling). + let input = "alpha beta gamma delta\n"; + let output = "alpha gamma\n"; + let r = verify_md_structural(input, output); + assert!(r.is_structural_equivalent(), "{:?}", r); + assert!(r.token_retention_pct < 1.0); + assert!(r.token_retention_pct >= 0.5); + } + + #[test] + fn structural_rejects_fusion() { + let input = "Η εργασία αυτή\n"; + let output = "Ηεργασίααυτή\n"; + let r = verify_md_structural(input, output); + assert!(!r.paragraph_tokens_subsequence); + } + + #[test] + fn structural_rejects_reordering() { + let input = "alpha beta gamma delta\n"; + let output = "gamma delta alpha beta\n"; + let r = verify_md_structural(input, output); + assert!(!r.paragraph_tokens_subsequence); + } + + #[test] + fn structural_rejects_added_content() { + let input = "alpha beta\n"; + let output = "alpha injected beta\n"; + let r = verify_md_structural(input, output); + assert!(!r.paragraph_tokens_subsequence); + } + + #[test] + fn structural_reports_retention_fraction() { + // 3 of 6 input tokens retained. + let input = "a b c d e f\n"; + let output = "a c e\n"; + let r = verify_md_structural(input, output); + assert!(r.is_structural_equivalent()); + assert!((r.token_retention_pct - 0.5).abs() < 1e-9); + } + + // --- helper: is_subsequence --- + + #[test] + fn is_subsequence_basic() { + let make = |xs: &[&str]| xs.iter().map(|s| s.to_string()).collect::>(); + assert!(is_subsequence(&make(&["a", "c"]), &make(&["a", "b", "c"]))); + assert!(is_subsequence(&make(&[]), &make(&["a", "b", "c"]))); + assert!(!is_subsequence(&make(&["c", "a"]), &make(&["a", "b", "c"]))); + assert!(!is_subsequence(&make(&["d"]), &make(&["a", "b", "c"]))); + } + + // --- URL capture (wave-3 enhancement) --- + + #[test] + fn url_change_detected_by_strict_verifier() { + let input = "See [the site](https://example.com/a) for details.\n"; + let output = "See [the site](https://example.com/b) for details.\n"; + let r = verify_md_preview_equivalent(input, output); + assert!( + !r.is_strict_equivalent(), + "URL change in link should be detected: {:?}", + r + ); + } + + #[test] + fn image_url_change_detected_by_strict_verifier() { + let input = "Image: ![alt](https://cdn.example.com/img/a.png)\n"; + let output = "Image: ![alt](https://cdn.example.com/img/b.png)\n"; + let r = verify_md_preview_equivalent(input, output); + assert!( + !r.is_strict_equivalent(), + "image URL change should be detected: {:?}", + r + ); + } + + #[test] + fn url_change_detected_by_structural_verifier() { + // URL treated as a token; change = token injection. + let input = "Visit [here](https://a.example.com).\n"; + let output = "Visit [here](https://b.example.com).\n"; + let r = verify_md_structural(input, output); + assert!( + !r.is_structural_equivalent(), + "URL change should fail structural: {:?}", + r + ); + } + + // --- Subsequence failure classification --- + + #[test] + fn classify_detects_fusion() { + // NBSP-strip signature: adjacent input tokens concat'd. + let input = vec!["Η".to_string(), "εργασία".to_string(), "αυτή".to_string()]; + let output = vec!["Ηεργασία".to_string(), "αυτή".to_string()]; + let kind = classify_subsequence_failure(&input, &output); + assert_eq!(kind, Some(SubsequenceFailureKind::Fusion)); + } + + #[test] + fn classify_detects_reordering() { + let input = vec!["a".to_string(), "b".to_string(), "c".to_string()]; + let output = vec!["c".to_string(), "a".to_string(), "b".to_string()]; + let kind = classify_subsequence_failure(&input, &output); + assert_eq!(kind, Some(SubsequenceFailureKind::Reordering)); + } + + #[test] + fn classify_detects_injection() { + let input = vec!["a".to_string(), "b".to_string()]; + let output = vec!["a".to_string(), "INJECTED".to_string(), "b".to_string()]; + let kind = classify_subsequence_failure(&input, &output); + assert_eq!(kind, Some(SubsequenceFailureKind::Injection)); + } + + #[test] + fn classify_returns_none_when_subsequence() { + let input = vec!["a".to_string(), "b".to_string(), "c".to_string()]; + let output = vec!["a".to_string(), "c".to_string()]; + let kind = classify_subsequence_failure(&input, &output); + assert_eq!(kind, None); + } + + // --- Code block line-based comparison --- + + #[test] + fn code_block_preserved_passes() { + let md = "before\n\n```\nfn foo() {\n 42\n}\n```\n\nafter\n"; + let r = verify_md_structural(md, md); + assert!(r.code_blocks_preserved, "{:?}", r); + assert!(r.is_structural_equivalent()); + } + + #[test] + fn code_block_line_dropped_allowed() { + // Deleting a line in a code block counts as a subsequence. + let input = "```\nline one\nline two\nline three\n```\n"; + let output = "```\nline one\nline three\n```\n"; + let r = verify_md_structural(input, output); + assert!(r.code_blocks_preserved, "{:?}", r); + } + + #[test] + fn code_block_line_changed_flagged() { + // Modifying a line breaks subsequence — new line not in input. + let input = "```\nfn foo() {\n 42\n}\n```\n"; + let output = "```\nfn foo() {\n 43\n}\n```\n"; + let r = verify_md_structural(input, output); + assert!( + !r.code_blocks_preserved, + "changed line should be caught: {:?}", + r + ); + } + + #[test] + fn code_block_reindented_flagged() { + // Re-indenting changes the line content → subsequence fails. + let input = "```\n indented line\n another\n```\n"; + let output = "```\nindented line\nanother\n```\n"; + let r = verify_md_structural(input, output); + assert!( + !r.code_blocks_preserved, + "reindent should be caught: {:?}", + r + ); + } + + // --- Structural report now exposes classification --- + + #[test] + fn structural_report_exposes_failure_kind_on_fusion() { + let input = "Η εργασία αυτή\n"; + let output = "Ηεργασία αυτή\n"; + let r = verify_md_structural(input, output); + assert!(!r.is_structural_equivalent()); + assert_eq!(r.subsequence_failure_kind.as_deref(), Some("fusion")); + } + + #[test] + fn structural_report_exposes_failure_kind_on_injection() { + let input = "alpha beta\n"; + let output = "alpha injected beta\n"; + let r = verify_md_structural(input, output); + assert!(!r.is_structural_equivalent()); + assert_eq!(r.subsequence_failure_kind.as_deref(), Some("injection")); + } + + // --- Input pre-canonicalization (wave-3 enhancement) --- + + #[test] + fn canonicalization_makes_entity_decode_invisible() { + // `&` in input, `&` in output — cleaner did entity decode. + // After pre-canonicalization of input, both see `&`. + let input = "Text with & entity.\n"; + let output = "Text with & entity.\n"; + let r = verify_md_structural(input, output); + assert!( + r.is_structural_equivalent(), + "entity-decode should pass after input canonicalization: {:?}", + r + ); + } + + #[test] + fn canonicalization_makes_hr_min_invisible() { + let input = "before\n\n-----------\n\nafter\n"; + let output = "before\n\n---\n\nafter\n"; + let r = verify_md_structural(input, output); + assert!(r.is_structural_equivalent(), "HR min should pass: {:?}", r); + } + + #[test] + fn canonicalization_makes_gfm_sep_min_invisible() { + let input = "| a | b |\n| --------- | --------- |\n| 1 | 2 |\n"; + let output = "| a | b |\n| --- | --- |\n| 1 | 2 |\n"; + let r = verify_md_structural(input, output); + assert!( + r.is_structural_equivalent(), + "GFM sep min should pass: {:?}", + r + ); + } + + #[test] + fn canonicalization_makes_nbsp_fold_invisible() { + // Input has NBSP between words; output has regular space. + // Cleaner folded NBSP (post v6-11 fix). After canonicalization, + // both sides see `Η εργασία`. + let input = "Η\u{00A0}εργασία\u{00A0}αυτή\n"; + let output = "Η εργασία αυτή\n"; + let r = verify_md_structural(input, output); + assert!( + r.is_structural_equivalent(), + "NBSP fold should pass: {:?}", + r + ); + } + + #[test] + fn canonicalization_does_not_hide_real_content_changes() { + // Regression: pre-canonicalization should NOT mask a genuinely + // destructive cleaner change. + let input = "alpha beta gamma delta epsilon\n"; + let output = "alpha gamma epsilon\n"; // dropped beta, delta — fine + let r = verify_md_structural(input, output); + assert!( + r.is_structural_equivalent(), + "deletion should pass: {:?}", + r + ); + + // But ADDING content or REORDERING must still fail. + let output_reorder = "gamma alpha beta delta epsilon\n"; + let r2 = verify_md_structural(input, output_reorder); + assert!( + !r2.is_structural_equivalent(), + "reorder must fail: {:?}", + r2 + ); + } + + // ----------------------------------------------------------------- + // Commit 11 RED test — heading text change not caught. + // + // Per the reviewer, block-token comparison is only implemented for + // Paragraph / Table / CodeBlock. A cleaner that rewrote `# Α` to + // `# Β` (or `# Injected Heading`) would pass structural today + // because the block sequence is the same (one Heading) and there + // are no paragraphs to compare. That's a real gap: headings are + // text-bearing blocks and their content must be checked. + // + // Fix in Commit 15: extend token extraction to cover all text- + // bearing blocks (headings, blockquote paragraphs, list-item + // paragraphs, footnote definitions). + // ----------------------------------------------------------------- + + #[test] + fn red_until_c15_heading_text_change_detected_by_structural() { + let input = "# Alpha Beta\n"; + let output = "# Injected Heading\n"; + let r = verify_md_structural(input, output); + assert!( + !r.is_structural_equivalent(), + "heading text change must be caught — cleaner rewriting `# Α` \ + to `# Β` currently passes structural. Fix in Commit 15: \ + extend block-text extraction beyond Paragraph. report={:?}", + r + ); + } + + #[test] + fn blockquote_inner_text_change_already_detected() { + // Property check: blockquotes render with an inner Paragraph + // block under CommonMark, so paragraph_tokens already picks up + // their text. This test documents current coverage (passes + // today). Not a C11 RED test. + let input = "> quoted text\n"; + let output = "> different content\n"; + let r = verify_md_structural(input, output); + assert!(!r.is_structural_equivalent(), "{:?}", r); + } + + // --- Post-C15 regression tests: heading + nested-paragraph coverage. + + #[test] + fn structural_catches_heading_text_injection() { + // Same shape as the RED test but inverts the polarity: a clean + // deletion inside a heading (retaining a subsequence) still + // passes, so the new coverage doesn't over-trigger. + let input_same_sub = "# alpha beta gamma\n"; + let out_same_sub = "# alpha gamma\n"; + let r_ok = verify_md_structural(input_same_sub, out_same_sub); + assert!( + r_ok.is_structural_equivalent(), + "heading deletion ok: {:?}", + r_ok + ); + // Injected heading text fails. + let out_injected = "# alpha beta injected word gamma\n"; + let r_fail = verify_md_structural(input_same_sub, out_injected); + assert!( + !r_fail.is_structural_equivalent(), + "heading injection must fail: {:?}", + r_fail + ); + } + + #[test] + fn structural_catches_setext_heading_text_change() { + // Setext H1/H2: `text\n===` or `text\n---`. pulldown-cmark + // emits a Heading tag for these too, so the new coverage + // catches content changes here. + let input = "original heading content\n===\n"; + let output = "different heading content\n===\n"; + let r = verify_md_structural(input, output); + assert!( + !r.is_structural_equivalent(), + "setext heading change: {:?}", + r + ); + } + + #[test] + fn structural_catches_list_item_text_change() { + // List items wrap their content in a Paragraph, which the + // existing extractor already covers. This test locks that in + // as coverage of nested-paragraph text changes. + let input = "- first item text\n- second item text\n"; + let output = "- rewritten first\n- second item text\n"; + let r = verify_md_structural(input, output); + assert!(!r.is_structural_equivalent(), "list-item change: {:?}", r); + } +} diff --git a/rust/glossapi_rs_cleaner/src/normalize.rs b/rust/glossapi_rs_cleaner/src/normalize.rs new file mode 100644 index 0000000..865d357 --- /dev/null +++ b/rust/glossapi_rs_cleaner/src/normalize.rs @@ -0,0 +1,2022 @@ +//! Layout / structure normalization helpers for `core_clean_text`. +//! +//! Design spec: +//! `Projects/glossapi-tokenizer-extension/corpus_clean_normalization/NORMALIZATION_DESIGN_20260420.md` +//! +//! The helpers in this module implement the deterministic normalize/strip rules: +//! - character fold (ligatures, enclosed/dingbat/math-alphanumeric digits, +//! vulgar fractions, Unicode whitespace variants) +//! - line-level ellipsis / whitespace / separator-line normalization +//! - malformed HTML entity fallback (`>`, `<`, `&` without `;`) +//! - GFM table separator pre-pass (parser-validated) +//! - code-fence marker detection (used by callers to guard normalization) +//! +//! All helpers are pure functions. Wire-in happens inside `core_clean_text`. + +use lazy_static::lazy_static; +use regex::Regex; +use std::collections::HashMap; + +lazy_static! { + /// Two or more U+2026 ellipsis chars. + pub static ref ELLIPSIS_RUN_REGEX: Regex = Regex::new(r"…{2,}").unwrap(); + + /// Two or more ASCII spaces or tabs — cheap presence check before we + /// run the tiered bucket rewriter. + pub static ref WHITESPACE_RUN_REGEX: Regex = Regex::new(r"[ \t]{2,}").unwrap(); + + /// Two or more ASCII dots — cheap presence check for dot-run tiered + /// bucket rewriting. + pub static ref DOT_RUN_2PLUS_REGEX: Regex = Regex::new(r"\.{2,}").unwrap(); + + /// URL-ish raw token detector used to avoid normalizing punctuation + /// runs inside links / host paths. + pub static ref URL_LIKE_TOKEN_REGEX: Regex = + Regex::new(r"(?i)^(?:[a-z][a-z0-9+.-]*://\S+|www\.\S+|[A-Za-z0-9-]+(?:\.[A-Za-z0-9-]+)+/\S*)$").unwrap(); + + /// Inline HTML comments are intentional placeholders in this corpus + /// (``, ``, etc.). Punctuation + /// bucketing must not rewrite their `--` delimiters. + pub static ref HTML_COMMENT_REGEX: Regex = Regex::new(r"").unwrap(); + + /// `>`, `<`, `&` NOT followed by `;` or alphanumeric. + /// + /// Rust's `regex` crate has no look-ahead, so we capture the following + /// context char (end-of-line, or a non-alphanumeric / non-`;` byte) and + /// preserve it in the replacement closure. + pub static ref MALFORMED_ENTITY_REGEX: Regex = + Regex::new(r"&(gt|lt|amp)($|[^a-zA-Z0-9;])").unwrap(); + + // (SEPARATOR_LINE_REGEX moved to md_module.rs alongside + // normalize_separator_line — it's MD-syntax-aware and lives with + // the Phase A transforms.) +} + +// --------------------------------------------------------------------------- +// Character fold +// --------------------------------------------------------------------------- + +const ASCII_DIGITS: [&str; 10] = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]; + +const ASCII_UPPER: [&str; 26] = [ + "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", + "T", "U", "V", "W", "X", "Y", "Z", +]; + +const ASCII_LOWER: [&str; 26] = [ + "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", + "t", "u", "v", "w", "x", "y", "z", +]; + +/// Greek capitals in the order Math Alphanumeric Greek blocks use them: +/// Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ ϴ Σ Τ Υ Φ Χ Ψ Ω +/// (Position 17 is the capital-theta variant ϴ (U+03F4), not regular Θ.) +const GREEK_CAPITAL_MATH_ORDER: [&str; 25] = [ + "Α", "Β", "Γ", "Δ", "Ε", "Ζ", "Η", "Θ", "Ι", "Κ", "Λ", "Μ", "Ν", "Ξ", "Ο", "Π", "Ρ", "ϴ", "Σ", + "Τ", "Υ", "Φ", "Χ", "Ψ", "Ω", +]; + +/// Greek smalls in Math-block order: +/// α β γ δ ε ζ η θ ι κ λ μ ν ξ ο π ρ ς σ τ υ φ χ ψ ω +const GREEK_SMALL_MATH_ORDER: [&str; 25] = [ + "α", "β", "γ", "δ", "ε", "ζ", "η", "θ", "ι", "κ", "λ", "μ", "ν", "ξ", "ο", "π", "ρ", "ς", "σ", + "τ", "υ", "φ", "χ", "ψ", "ω", +]; + +/// Math Alphanumeric Greek "variant symbols" block-tail (offsets 52..57): +/// ϵ ϑ ϰ ϕ ϱ ϖ (epsilon / theta / kappa / phi / rho / pi variants). +const GREEK_VARIANT_MATH_ORDER: [&str; 6] = [ + "\u{03F5}", "\u{03D1}", "\u{03F0}", "\u{03D5}", "\u{03F1}", "\u{03D6}", +]; + +/// Return `Some(replacement)` if `ch` should fold, `None` otherwise. +/// +/// Policy (from the 2026-04-20 design): +/// - Fold enclosed / circled / dingbat / mathematical-alphanumeric digits to ASCII. +/// - Fold vulgar fractions to ASCII `a/b`. +/// - Fold ligatures (`fi`, `fl`, `ffi`, `ffl`, `ff`, `ſt`, `st`) to ASCII pairs. +/// - Fold Unicode whitespace variants (U+2007 figure space, U+2009 thin space, +/// U+202F narrow NBSP) to a regular space. +/// - KEEP subscripts (U+2080–2089) and superscripts (U+2070, U+00B2, U+00B3, +/// U+00B9, U+2074–2079) as-is — they carry semantic weight. +pub fn fold_codepoint(ch: char) -> Option<&'static str> { + // Ligatures first (they come before more numeric work, small set) + match ch { + '\u{FB00}' => return Some("ff"), + '\u{FB01}' => return Some("fi"), + '\u{FB02}' => return Some("fl"), + '\u{FB03}' => return Some("ffi"), + '\u{FB04}' => return Some("ffl"), + '\u{FB05}' => return Some("st"), + '\u{FB06}' => return Some("st"), + // U+00B5 MICRO SIGN → U+03BC GREEK SMALL LETTER MU. Common + // mojibake from Latin-1 codepage assumptions for Greek µ + // (Point 3 sub-resolution, 2026-04-25). + '\u{00B5}' => return Some("\u{03BC}"), + _ => {} + } + + // Adobe Symbol PUA → real Unicode (was the standalone + // `decode_adobe_symbol_pua` pre-pass; merged into Group 2 FOLD + // per CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 2). Fast + // path: only consult the map for chars in the PUA range. + let code_pua = ch as u32; + if (0xF000..=0xF8FF).contains(&code_pua) { + if let Some(&replacement) = ADOBE_SYMBOL_PUA_MAP.get(&ch) { + return Some(replacement); + } + } + + // Unicode whitespace variants folded to regular space. + // + // Before wave-2-post (2026-04-24) this set was only the narrow + // trio (U+2007 figure space, U+2009 thin space, U+202F narrow + // NBSP). U+00A0 (NO-BREAK SPACE) was missing — which caused + // v6-11: Docling emits NBSP as the default word-separator on + // many PDFs, and per-char filter then treated U+00A0 as "unusual + // Latin-1 Supplement char" and stripped it, fusing Greek words + // (`Η εργασία` → `Ηεργασία`). Empirical scorecard: 15/99 + // openarchives docs had this fusion signature. + // + // Now all of the common no-break / visual space variants fold + // to U+0020 so they survive downstream as regular whitespace. + match ch { + '\u{00A0}' // NO-BREAK SPACE (v6-11 fix) + | '\u{2000}' | '\u{2001}' | '\u{2002}' | '\u{2003}' // en/em/3-per/4-per quad + | '\u{2004}' | '\u{2005}' | '\u{2006}' // 3/4/5/6-per-em + | '\u{2007}' | '\u{2008}' | '\u{2009}' | '\u{200A}' // figure/punct/thin/hair + | '\u{202F}' // NARROW NO-BREAK SPACE + | '\u{205F}' // MEDIUM MATHEMATICAL SPACE + | '\u{3000}' // IDEOGRAPHIC SPACE + => return Some(" "), + _ => {} + } + + // Vulgar fractions (Latin-1 Supplement + Number Forms block). + match ch { + '\u{00BC}' => return Some("1/4"), + '\u{00BD}' => return Some("1/2"), + '\u{00BE}' => return Some("3/4"), + '\u{2150}' => return Some("1/7"), + '\u{2151}' => return Some("1/9"), + '\u{2152}' => return Some("1/10"), + '\u{2153}' => return Some("1/3"), + '\u{2154}' => return Some("2/3"), + '\u{2155}' => return Some("1/5"), + '\u{2156}' => return Some("2/5"), + '\u{2157}' => return Some("3/5"), + '\u{2158}' => return Some("4/5"), + '\u{2159}' => return Some("1/6"), + '\u{215A}' => return Some("5/6"), + '\u{215B}' => return Some("1/8"), + '\u{215C}' => return Some("3/8"), + '\u{215D}' => return Some("5/8"), + '\u{215E}' => return Some("7/8"), + // U+215F FRACTION NUMERATOR ONE — numerator prefix, fold to "1/". + '\u{215F}' => return Some("1/"), + // U+2189 VULGAR FRACTION ZERO THIRDS. + '\u{2189}' => return Some("0/3"), + _ => {} + } + + // Ranged patterns: match on the integer codepoint. + let code = ch as u32; + + // Circled digits ①–⑨ (U+2460–U+2468), ⑩ (U+2469). + if (0x2460..=0x2468).contains(&code) { + return Some(ASCII_DIGITS[(code - 0x2460 + 1) as usize]); + } + if code == 0x2469 { + return Some("10"); + } + + // Parenthesized digits ⑴–⑼ (U+2474–U+247C), ⑽ (U+247D). + if (0x2474..=0x247C).contains(&code) { + return Some(ASCII_DIGITS[(code - 0x2474 + 1) as usize]); + } + if code == 0x247D { + return Some("10"); + } + + // Digits-with-full-stop ⒈–⒐ (U+2488–U+2490), ⒑ (U+2491). + if (0x2488..=0x2490).contains(&code) { + return Some(ASCII_DIGITS[(code - 0x2488 + 1) as usize]); + } + if code == 0x2491 { + return Some("10"); + } + + // Dingbat negative circled ❶–❾ (U+2776–U+277E), ❿ (U+277F). + if (0x2776..=0x277E).contains(&code) { + return Some(ASCII_DIGITS[(code - 0x2776 + 1) as usize]); + } + if code == 0x277F { + return Some("10"); + } + + // Dingbat negative sans-serif ➀–➈ (U+2780–U+2788), ➉ (U+2789). + if (0x2780..=0x2788).contains(&code) { + return Some(ASCII_DIGITS[(code - 0x2780 + 1) as usize]); + } + if code == 0x2789 { + return Some("10"); + } + + // Dingbat sans-serif ➊–➒ (U+278A–U+2792), ➓ (U+2793). + if (0x278A..=0x2792).contains(&code) { + return Some(ASCII_DIGITS[(code - 0x278A + 1) as usize]); + } + if code == 0x2793 { + return Some("10"); + } + + // Mathematical alphanumeric digit blocks (U+1D7CE–U+1D7FF). + // Five blocks of 10: Bold, Double-struck, Sans-serif, Sans-serif bold, Monospace. + if let 0x1D7CE..=0x1D7FF = code { + let offset = (code - 0x1D7CE) % 10; + return Some(ASCII_DIGITS[offset as usize]); + } + + // Mathematical Alphanumeric Symbols — Latin letter blocks + // (U+1D400..U+1D6A3). 13 style blocks of 52 codepoints each (A-Z then + // a-z). Reserved "holes" in some blocks (Italic, Script, Fraktur, + // Double-Struck) are actually encoded in Letterlike Symbols + // (U+2100..U+214F) — handled immediately below. For valid codepoints + // inside each style block the mapping is uniform: offset modulo 52 + // picks the ASCII letter regardless of style. + if let 0x1D400..=0x1D6A3 = code { + let block_offset = (code - 0x1D400) % 52; + return Some(if block_offset < 26 { + ASCII_UPPER[block_offset as usize] + } else { + ASCII_LOWER[(block_offset - 26) as usize] + }); + } + + // Math italic dotless i/j (U+1D6A4, U+1D6A5) and reserved slots + // U+1D6A6/U+1D6A7. The two assigned slots fold to plain i/j. + if code == 0x1D6A4 { + return Some("i"); + } + if code == 0x1D6A5 { + return Some("j"); + } + + // Mathematical Alphanumeric Symbols — Greek letter blocks + // (U+1D6A8..U+1D7C9). Five style blocks of 58 codepoints each: + // 1D6A8 Bold, 1D6E2 Italic, 1D71C Bold Italic, + // 1D756 Sans-Serif Bold, 1D790 Sans-Serif Bold Italic. + // Layout inside each block: + // 0..24 capital letters Α..Ω (order per Math Alphanumeric spec) + // 25 nabla ∇ (U+2207) + // 26..50 small letters α..ω + // 51 partial differential ∂ (U+2202) + // 52..57 variant symbols ϵ ϑ ϰ ϕ ϱ ϖ + // Policy (2026-04-21): these are Greek letters used in math with + // semantic meaning; Apertus has single-token merges for regular + // Greek, so we FOLD them into the regular Greek codepoint that + // Apertus tokenizes efficiently, rather than stripping. + if let 0x1D6A8..=0x1D7C9 = code { + let off = (code - 0x1D6A8) % 58; + return Some(match off { + 0..=24 => GREEK_CAPITAL_MATH_ORDER[off as usize], + 25 => "\u{2207}", // ∇ NABLA + 26..=50 => GREEK_SMALL_MATH_ORDER[(off - 26) as usize], + 51 => "\u{2202}", // ∂ PARTIAL DIFFERENTIAL + 52..=57 => GREEK_VARIANT_MATH_ORDER[(off - 52) as usize], + _ => unreachable!(), + }); + } + + // Mathematical Bold Capital/Small Digamma (U+1D7CA..U+1D7CB) fold to + // regular Greek Digamma (U+03DC / U+03DD). U+1D7CC/U+1D7CD are reserved. + if code == 0x1D7CA { + return Some("\u{03DC}"); + } + if code == 0x1D7CB { + return Some("\u{03DD}"); + } + + // Letterlike Symbols that are the "hole" chars for the Math + // Alphanumeric blocks (Script h, Fraktur H, Double-Struck H, etc.). + // Fold to the matching ASCII Latin letter. + match code { + 0x210A => return Some("g"), // ℊ SCRIPT SMALL G + 0x210B => return Some("H"), // ℋ SCRIPT CAPITAL H + 0x210C => return Some("H"), // ℌ BLACK-LETTER CAPITAL H + 0x210D => return Some("H"), // ℍ DOUBLE-STRUCK CAPITAL H + 0x210E => return Some("h"), // ℎ PLANCK CONSTANT (== math italic h) + 0x2110 => return Some("I"), // ℐ SCRIPT CAPITAL I + 0x2111 => return Some("I"), // ℑ BLACK-LETTER CAPITAL I + 0x2112 => return Some("L"), // ℒ SCRIPT CAPITAL L + 0x2113 => return Some("l"), // ℓ SCRIPT SMALL L + 0x2115 => return Some("N"), // ℕ DOUBLE-STRUCK N + 0x2119 => return Some("P"), // ℙ DOUBLE-STRUCK P + 0x211A => return Some("Q"), // ℚ DOUBLE-STRUCK Q + 0x211B => return Some("R"), // ℛ SCRIPT R + 0x211C => return Some("R"), // ℜ FRAKTUR R + 0x211D => return Some("R"), // ℝ DOUBLE-STRUCK R + 0x2124 => return Some("Z"), // ℤ DOUBLE-STRUCK Z + 0x2128 => return Some("Z"), // ℨ FRAKTUR Z + 0x212C => return Some("B"), // ℬ SCRIPT B + 0x212D => return Some("C"), // ℭ FRAKTUR C + 0x212F => return Some("e"), // ℯ SCRIPT SMALL E + 0x2130 => return Some("E"), // ℰ SCRIPT E + 0x2131 => return Some("F"), // ℱ SCRIPT F + 0x2133 => return Some("M"), // ℳ SCRIPT M + 0x2134 => return Some("o"), // ℴ SCRIPT SMALL O + _ => {} + } + + None +} + +/// Fold every codepoint in `line` per `fold_codepoint`. Returns `None` if no +/// fold fired (allocation-free fast path for ASCII-only lines). +pub fn fold_line(line: &str) -> Option { + // Cheap fast path: every fold target is >= U+00BC, so an ASCII-only line + // cannot fire any fold and we can skip the scan entirely. + if line.is_ascii() { + return None; + } + let mut out = String::with_capacity(line.len()); + let mut changed = false; + for ch in line.chars() { + if let Some(replacement) = fold_codepoint(ch) { + out.push_str(replacement); + changed = true; + } else { + out.push(ch); + } + } + if changed { + Some(out) + } else { + None + } +} + +// --------------------------------------------------------------------------- +// Line-level normalizations +// --------------------------------------------------------------------------- + +/// Collapse runs of `…{2,}` to a single `…`. +pub fn normalize_ellipsis_runs(line: &str) -> Option { + if !line.contains('…') { + return None; + } + if !ELLIPSIS_RUN_REGEX.is_match(line) { + return None; + } + let out = ELLIPSIS_RUN_REGEX.replace_all(line, "…").into_owned(); + if out == line { + None + } else { + Some(out) + } +} + +/// Tiered bucket for run-length normalization (2026-04-28 policy, v3): +/// 1, 2 → 1 (single char / accidental double collapse to one) +/// 3 → 3 (unchanged — natural prose triple, e.g. ellipsis) +/// 4 → 3 (floor, not nearest-neighbour rounding) +/// 5..=19 → 5 (medium run — canonical "short leader" form) +/// 20 → 20 +/// >20 → 20 (long run — canonical "long leader" form) +/// Target token vocabulary for dots: `.`, `...`, `.....`, +/// `....................` — four forms. Uniform across dots and +/// whitespace so the BPE sees a small fixed vocabulary of leader +/// tokens regardless of which fill a PDF used or how long the run was. +pub fn bucket_run_length(n: usize) -> usize { + match n { + 0 | 1 => n, + 2 => 1, + 3 => 3, + 4 => 3, + 5..=19 => 5, + _ => 20, + } +} + +/// Normalize runs of exactly `target` per the tiered bucket rule above. +pub fn normalize_char_runs_tiered(line: &str, target: char) -> Option { + if !line.contains(target) { + return None; + } + let chars: Vec = line.chars().collect(); + let mut out = String::with_capacity(line.len()); + let mut changed = false; + let mut i = 0usize; + while i < chars.len() { + if chars[i] == target { + let start = i; + while i < chars.len() && chars[i] == target { + i += 1; + } + let n = i - start; + let m = bucket_run_length(n); + if m != n { + changed = true; + } + for _ in 0..m { + out.push(target); + } + } else { + out.push(chars[i]); + i += 1; + } + } + if changed { + Some(out) + } else { + None + } +} + +fn is_dot_like(ch: char) -> bool { + matches!(ch, '.' | '·' | '•' | '‧' | '⋅' | '⋯' | '…') +} + +fn dot_weight(ch: char) -> usize { + if ch == '…' || ch == '⋯' { + 3 + } else { + 1 + } +} + +fn token_bounds_around_span(line: &str, start: usize, end: usize) -> (usize, usize) { + let left = line[..start] + .rfind(char::is_whitespace) + .map(|idx| idx + line[idx..].chars().next().map(char::len_utf8).unwrap_or(1)) + .unwrap_or(0); + let right = line[end..] + .find(char::is_whitespace) + .map(|idx| end + idx) + .unwrap_or(line.len()); + (left, right) +} + +pub(crate) fn span_is_inside_url_like_token(line: &str, start: usize, end: usize) -> bool { + let (left, right) = token_bounds_around_span(line, start, end); + let token = &line[left..right]; + URL_LIKE_TOKEN_REGEX.is_match(token) +} + +fn span_is_inside_html_comment(line: &str, start: usize, end: usize) -> bool { + HTML_COMMENT_REGEX + .find_iter(line) + .any(|m| start >= m.start() && end <= m.end()) +} + +fn is_full_line_html_comment(line: &str) -> bool { + let trimmed = line.trim(); + trimmed.starts_with("") +} + +fn is_fence_like_line(line: &str) -> bool { + let trimmed = line.trim_start(); + trimmed.starts_with("```") || trimmed.starts_with("~~~") +} + +/// Normalize dot-like runs (`.`, bullets/dot operators, ellipsis) with one +/// logical ladder. U+2026 counts as three dots; output is ASCII dots. +pub fn normalize_dot_and_ellipsis_runs(line: &str) -> Option { + if is_full_line_html_comment(line) || is_fence_like_line(line) { + return None; + } + if !line.chars().any(is_dot_like) { + return None; + } + let spans: Vec<(usize, char)> = line.char_indices().collect(); + let mut out = String::with_capacity(line.len()); + let mut changed = false; + let mut i = 0usize; + while i < spans.len() { + let (start_byte, ch) = spans[i]; + if is_dot_like(ch) { + let start = i; + let mut logical_len = 0usize; + while i < spans.len() && is_dot_like(spans[i].1) { + logical_len += dot_weight(spans[i].1); + i += 1; + } + let end_byte = if i < spans.len() { + spans[i].0 + } else { + line.len() + }; + if span_is_inside_html_comment(line, start_byte, end_byte) + || span_is_inside_url_like_token(line, start_byte, end_byte) + { + out.push_str(&line[start_byte..end_byte]); + continue; + } + let bucket = bucket_run_length(logical_len); + let original: String = spans[start..i].iter().map(|(_, c)| *c).collect(); + if logical_len == 1 { + out.push_str(&original); + continue; + } + let replacement = ".".repeat(bucket); + if replacement != original { + changed = true; + } + out.push_str(&replacement); + } else { + out.push(ch); + i += 1; + } + } + if changed { + Some(out) + } else { + None + } +} + +/// Back-compat wrapper for callers/tests that still refer to dot runs. +pub fn normalize_dot_runs(line: &str) -> Option { + if !line.chars().any(is_dot_like) { + return None; + } + normalize_dot_and_ellipsis_runs(line) +} + +fn is_dash_like(ch: char) -> bool { + matches!(ch, '-' | '‐' | '‑' | '‒' | '–' | '—' | '―') +} + +fn is_pipe_like(ch: char) -> bool { + matches!(ch, '|' | '│' | '┃' | '║') +} + +fn is_standalone_accent_run(chars: &[char], start: usize, end: usize) -> bool { + let prev = start.checked_sub(1).and_then(|idx| chars.get(idx)).copied(); + let next = chars.get(end).copied(); + !prev.is_some_and(|c| c.is_alphanumeric()) && !next.is_some_and(|c| c.is_alphanumeric()) +} + +fn is_atx_heading_hash_run(line: &str, chars: &[char], start: usize, end: usize) -> bool { + if chars.get(start) != Some(&'#') { + return false; + } + let mut cols = 0usize; + for ch in chars.iter().take(start) { + match *ch { + ' ' => cols += 1, + '\t' => cols += 4, + _ => return false, + } + } + if cols > 3 { + return false; + } + let run_len = end - start; + if run_len > 6 { + return false; + } + let after = chars.get(end).copied(); + after.is_none_or(|c| c.is_whitespace()) && line.trim_start().starts_with('#') +} + +fn run_char_kind(ch: char) -> Option { + if is_dash_like(ch) { + return Some(ch); + } + if is_pipe_like(ch) { + return Some(ch); + } + match ch { + '_' | '*' | '=' | '#' | '~' | '/' | '\\' | '!' | '%' | '@' | '^' | '΄' | '´' | '`' + | '\u{0301}' => Some(ch), + _ => None, + } +} + +/// Normalize non-dot punctuation/leader runs using the same floor ladder as +/// dot/whitespace runs, with guards for URL tokens, ATX headings, full-line +/// comments, and fence markers. +pub fn normalize_punctuation_runs(line: &str) -> Option { + if is_full_line_html_comment(line) || is_fence_like_line(line) { + return None; + } + let chars: Vec = line.chars().collect(); + if !chars.iter().any(|ch| run_char_kind(*ch).is_some()) { + return None; + } + let byte_spans: Vec<(usize, char)> = line.char_indices().collect(); + let mut out = String::with_capacity(line.len()); + let mut changed = false; + let mut i = 0usize; + while i < chars.len() { + let ch = chars[i]; + if run_char_kind(ch).is_some() { + let start = i; + while i < chars.len() && chars[i] == ch { + i += 1; + } + let n = i - start; + if n == 1 { + out.push(ch); + continue; + } + let start_byte = byte_spans[start].0; + let end_byte = if i < byte_spans.len() { + byte_spans[i].0 + } else { + line.len() + }; + if span_is_inside_html_comment(line, start_byte, end_byte) { + out.push_str(&line[start_byte..end_byte]); + continue; + } + if ch == '/' && span_is_inside_url_like_token(line, start_byte, end_byte) { + out.push_str(&line[start_byte..end_byte]); + continue; + } + if ch == '#' && is_atx_heading_hash_run(line, &chars, start, i) { + out.push_str(&line[start_byte..end_byte]); + continue; + } + if matches!(ch, '΄' | '´' | '`' | '\u{0301}') + && !is_standalone_accent_run(&chars, start, i) + { + out.push_str(&line[start_byte..end_byte]); + continue; + } + let m = bucket_run_length(n); + if m != n { + changed = true; + } + for _ in 0..m { + out.push(ch); + } + } else { + out.push(ch); + i += 1; + } + } + if changed { + Some(out) + } else { + None + } +} + +/// Normalize runs of `[ \t]` per the tiered bucket rule. Mixed space+tab +/// runs are treated as one run and emitted as N spaces. +/// +/// Leading whitespace at line start is preserved verbatim — protects +/// markdown indented code blocks (4-space indent), list indentation, +/// nested list indent, and verse indent. Only INTERIOR whitespace runs +/// (those that follow a non-whitespace character on the same line) are +/// bucketized. +pub fn normalize_whitespace_runs(line: &str) -> Option { + // Fire if there's a 2+ whitespace run OR any non-leading tab. + if !WHITESPACE_RUN_REGEX.is_match(line) && !line.contains('\t') { + return None; + } + let chars: Vec = line.chars().collect(); + let mut out = String::with_capacity(line.len()); + let mut changed = false; + let mut i = 0usize; + + // Copy leading whitespace verbatim (code-block / list-indent preservation). + while i < chars.len() && (chars[i] == ' ' || chars[i] == '\t') { + out.push(chars[i]); + i += 1; + } + + // Bucketize interior + trailing whitespace runs. + while i < chars.len() { + let c = chars[i]; + if c == ' ' || c == '\t' { + let start = i; + while i < chars.len() && (chars[i] == ' ' || chars[i] == '\t') { + i += 1; + } + let n = i - start; + let m = bucket_run_length(n); + // A single interior tab collapsing to a single space is still a + // change (tabs carry no semantic value in markdown prose). + let original: String = chars[start..start + n].iter().collect(); + if m != n || original.contains('\t') { + changed = true; + } + for _ in 0..m { + out.push(' '); + } + } else { + out.push(c); + i += 1; + } + } + if changed { + Some(out) + } else { + None + } +} + +/// Normalize a line of escaped-underscore pairs (`\_\_\_…`) per the +/// tiered bucket rule. Each `\_` renders as one literal underscore +/// in CommonMark preview (since `_` is ASCII punctuation, `\_` is a +/// valid backslash-escape). A line of N escape pairs renders as a +/// paragraph of N underscores. +/// +/// Buckets the pair count via `bucket_run_length` ({0, 1, 3, 5, 20}), +/// emits `\_` × bucketed count with the same leading / trailing +/// whitespace preserved. Fires only on lines that are EXCLUSIVELY +/// a run of escape pairs (plus optional surrounding whitespace) — so +/// it doesn't touch `\_\_` that appear inline in a sentence. +/// +/// Why not in Phase A: the transform changes the literal char count +/// in the rendered paragraph (20 underscores vs 100 underscores is a +/// different HTML text node). Phase A's strict preview-equivalence +/// invariant disallows that. This normalizer lives alongside dot / +/// whitespace / ellipsis bucketers — cosmetic normalization, accepted +/// under the structural (subsequence) invariant. +/// +/// Companion note: Phase A (`md_module::normalize_separator_line`) +/// used to rewrite `\_\_\_\_…` to `---` (thematic break). That was +/// wrong — it turned a paragraph of literal underscores into an HR, +/// changing preview. Removed in the same wave as this normalizer +/// was added. +pub fn normalize_escaped_run_chars(line: &str) -> Option { + if !line.contains('\\') { + return None; + } + const ESCAPED_RUN_CHARS: [u8; 7] = [b'_', b'*', b'-', b'#', b'.', b'=', b'~']; + // Parse: [leading ws] (\_)+ [trailing ws]. If anything else + // appears, bail (not a pure run line). + let bytes = line.as_bytes(); + let mut i = 0usize; + // Leading whitespace. + while i < bytes.len() && (bytes[i] == b' ' || bytes[i] == b'\t') { + i += 1; + } + let prefix_end = i; + // Count repeated `\` pairs. The escaped char must be the same + // throughout the run. + let mut pairs = 0usize; + let escaped_char = + if i + 1 < bytes.len() && bytes[i] == b'\\' && ESCAPED_RUN_CHARS.contains(&bytes[i + 1]) { + bytes[i + 1] + } else { + return None; + }; + while i + 1 < bytes.len() && bytes[i] == b'\\' && bytes[i + 1] == escaped_char { + pairs += 2; + i += 2; + } + if pairs == 0 { + return None; + } + let body_end = i; + // Trailing whitespace — must consume to end-of-line. + while i < bytes.len() && (bytes[i] == b' ' || bytes[i] == b'\t') { + i += 1; + } + if i != bytes.len() { + // There's a non-whitespace, non-`\_` char after the run — not + // a pure escaped-underscore line, don't normalize. + return None; + } + let n_pairs = pairs / 2; + let m = bucket_run_length(n_pairs); + if m == n_pairs { + return None; + } + let leading = &line[..prefix_end]; + let trailing = &line[body_end..]; + let mut out = String::with_capacity(leading.len() + m * 2 + trailing.len()); + out.push_str(leading); + for _ in 0..m { + out.push('\\'); + out.push(escaped_char as char); + } + out.push_str(trailing); + Some(out) +} + +pub fn normalize_escaped_underscore_runs(line: &str) -> Option { + normalize_escaped_run_chars(line) +} + +/// Replace malformed HTML entities (`>` / `<` / `&` without a trailing `;`) +/// with their decoded form. +pub fn normalize_malformed_entities(line: &str) -> Option { + // Cheap early-out: need at least one candidate substring. + if !line.contains(">") && !line.contains("<") && !line.contains("&") { + return None; + } + if !MALFORMED_ENTITY_REGEX.is_match(line) { + return None; + } + let out = MALFORMED_ENTITY_REGEX + .replace_all(line, |caps: ®ex::Captures| { + let entity = match &caps[1] { + "gt" => ">", + "lt" => "<", + "amp" => "&", + _ => unreachable!("regex constrained to gt|lt|amp"), + }; + // caps[2] is the preserved context char (empty at end-of-line, + // otherwise a single non-alphanumeric, non-`;` byte). + format!("{}{}", entity, &caps[2]) + }) + .into_owned(); + if out == line { + None + } else { + Some(out) + } +} + +// (normalize_separator_line, scan_gfm_table_separators, parse_gfm_separator_row, +// count_gfm_row_cells, GfmAlign, GfmSeparatorRow moved to md_module.rs — +// they're MD-syntax-aware transforms with the preview-render-preserving +// invariant, co-located with the other Phase A passes.) + +// --------------------------------------------------------------------------- +// Page salvage (drop pages with too much content stripped) +// --------------------------------------------------------------------------- + +/// Drop synthetic pages from `cleaned_text` whose retained non-whitespace +/// content falls below `min_retention_ratio` of the corresponding page in +/// `original_text`. +/// +/// Synthetic pages are built from the ORIGINAL text's line structure: +/// each markdown header line (`^#+`) starts a new page; the first page +/// runs from line 0 to the first header (or end of text if no headers). +/// This matches the boundary heuristic used by the matcher's synthetic-page +/// builder for consistency between cleaner output and matcher re-audit. +/// +/// Line-alignment assumption: `cleaned_text` must preserve one output line +/// per input line — `core_clean_text` already satisfies this. If line +/// counts diverge the function returns `cleaned_text` unmodified. +/// +/// Design reference: +/// corpus_clean_normalization/NORMALIZATION_DESIGN_20260420.md §14. +pub fn drop_low_salvage_pages( + original_text: &str, + cleaned_text: &str, + min_retention_ratio: f64, +) -> String { + let orig_lines: Vec<&str> = original_text.lines().collect(); + let clean_lines: Vec<&str> = cleaned_text.lines().collect(); + + // If line counts diverge we can't safely align per-page accounting. + // Return cleaned_text unchanged so the caller sees a clear no-op. + if orig_lines.len() != clean_lines.len() { + return cleaned_text.to_string(); + } + + let page_ranges = synthetic_page_line_ranges(&orig_lines); + let mut kept_lines: Vec<&str> = Vec::with_capacity(clean_lines.len()); + let mut dropped_any = false; + + for (start, end) in page_ranges { + let orig_nonws = count_nonwhitespace_in_range(&orig_lines, start, end); + let clean_nonws = count_nonwhitespace_in_range(&clean_lines, start, end); + let retention = if orig_nonws > 0 { + clean_nonws as f64 / orig_nonws as f64 + } else { + // Empty original page — nothing to salvage, no ratio to check. + 1.0 + }; + if retention >= min_retention_ratio { + kept_lines.extend_from_slice(&clean_lines[start..end]); + } else { + dropped_any = true; + } + } + + if !dropped_any { + return cleaned_text.to_string(); + } + + let mut result = kept_lines.join("\n"); + // Preserve trailing newline behavior: if the input had one and we kept + // any content, re-add a single trailing newline. + if cleaned_text.ends_with('\n') && !result.is_empty() { + result.push('\n'); + } + result +} + +fn synthetic_page_line_ranges(lines: &[&str]) -> Vec<(usize, usize)> { + let mut ranges: Vec<(usize, usize)> = Vec::new(); + let mut start = 0usize; + for (i, line) in lines.iter().enumerate() { + if i > 0 && is_markdown_header_line(line) { + ranges.push((start, i)); + start = i; + } + } + ranges.push((start, lines.len())); + ranges +} + +fn is_markdown_header_line(line: &str) -> bool { + let t = line.trim_start(); + if !t.starts_with('#') { + return false; + } + // Valid ATX header: one or more `#` followed by a space or end-of-line. + // Rejects `####word` (no space) to avoid treating random `#`-led strings + // as headers. + let after_hashes = t.trim_start_matches('#'); + after_hashes.is_empty() || after_hashes.starts_with(' ') || after_hashes.starts_with('\t') +} + +fn count_nonwhitespace_in_range(lines: &[&str], start: usize, end: usize) -> usize { + lines[start..end] + .iter() + .flat_map(|l| l.chars()) + .filter(|c| !c.is_whitespace()) + .count() +} + +// --------------------------------------------------------------------------- +// Code fence detection +// --------------------------------------------------------------------------- + +/// True if the line opens or closes a fenced code block (``` or ~~~). +/// Leading whitespace up to 3 spaces is allowed (CommonMark spec). +// (is_code_fence_marker moved to md_module.rs.) + +// --------------------------------------------------------------------------- +// Tests +// --------------------------------------------------------------------------- + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn fold_ligatures() { + assert_eq!(fold_line("fishing flour"), Some("fishing flour".to_string())); + assert_eq!(fold_line("efficient"), Some("efficient".to_string())); + assert_eq!(fold_line("baffle"), Some("baffle".to_string())); + } + + #[test] + fn fold_enclosed_digits() { + assert_eq!(fold_line("①②③"), Some("123".to_string())); + assert_eq!(fold_line("chapter ⑩"), Some("chapter 10".to_string())); + assert_eq!(fold_line("❺"), Some("5".to_string())); + assert_eq!(fold_line("➋"), Some("2".to_string())); + } + + #[test] + fn fold_math_alphanumeric_digits() { + // U+1D7CE–U+1D7D7 bold + assert_eq!(fold_line("𝟎𝟏𝟐𝟑𝟒𝟓𝟔𝟕𝟖𝟗"), Some("0123456789".to_string())); + // U+1D7D8–U+1D7E1 double-struck + assert_eq!(fold_line("𝟘𝟡"), Some("09".to_string())); + // U+1D7EC–U+1D7F5 sans-serif bold + assert_eq!(fold_line("𝟬𝟱"), Some("05".to_string())); + } + + #[test] + fn fold_math_italic_latin_letters() { + // Math Italic Latin letters (U+1D434..U+1D467) — the main form seen + // in the tokenizer evidence for broken italic-variable extraction. + assert_eq!(fold_line("𝑖"), Some("i".to_string())); + assert_eq!(fold_line("𝑛"), Some("n".to_string())); + assert_eq!(fold_line("𝑥"), Some("x".to_string())); + assert_eq!(fold_line("𝐴"), Some("A".to_string())); + assert_eq!(fold_line("𝑅"), Some("R".to_string())); + assert_eq!(fold_line("𝑆"), Some("S".to_string())); + // A typical math-variable cluster. + assert_eq!(fold_line("𝑥 + 𝑦 = 𝑧"), Some("x + y = z".to_string())); + } + + #[test] + fn fold_math_bold_and_other_styles() { + // Bold block (U+1D400..U+1D433). + assert_eq!(fold_line("𝐀𝐁𝐂"), Some("ABC".to_string())); + assert_eq!(fold_line("𝐚𝐛𝐜"), Some("abc".to_string())); + // Bold Italic (U+1D468..U+1D49B). + assert_eq!(fold_line("𝑨𝒂"), Some("Aa".to_string())); + // Script (U+1D49C..U+1D4CF) — body chars fold; holes use Letterlike. + assert_eq!(fold_line("𝒜𝒶"), Some("Aa".to_string())); + // Fraktur (U+1D504..U+1D537). + assert_eq!(fold_line("𝔄𝔞"), Some("Aa".to_string())); + // Double-Struck (U+1D538..U+1D56B). + assert_eq!(fold_line("𝔸𝕒"), Some("Aa".to_string())); + // Sans-Serif (U+1D5A0..U+1D5D3). + assert_eq!(fold_line("𝖠𝖺"), Some("Aa".to_string())); + // Monospace (U+1D670..U+1D6A3). + assert_eq!(fold_line("𝙰𝚊"), Some("Aa".to_string())); + } + + #[test] + fn fold_math_italic_dotless() { + // U+1D6A4 dotless italic i, U+1D6A5 dotless italic j. + assert_eq!(fold_line("𝚤"), Some("i".to_string())); + assert_eq!(fold_line("𝚥"), Some("j".to_string())); + } + + #[test] + fn fold_letterlike_symbols_holes() { + // The "holes" in Math Italic / Script / Fraktur / Double-Struck + // blocks are encoded as separate codepoints in the Letterlike + // Symbols block. They fold to the matching ASCII letter. + assert_eq!(fold_line("ℎ"), Some("h".to_string())); // PLANCK CONSTANT + assert_eq!(fold_line("ℓ"), Some("l".to_string())); // SCRIPT SMALL L + assert_eq!(fold_line("ℝ"), Some("R".to_string())); // DOUBLE-STRUCK R + assert_eq!(fold_line("ℕ"), Some("N".to_string())); // DOUBLE-STRUCK N + assert_eq!(fold_line("ℤ"), Some("Z".to_string())); // DOUBLE-STRUCK Z + assert_eq!(fold_line("ℚ"), Some("Q".to_string())); // DOUBLE-STRUCK Q + assert_eq!(fold_line("ℙ"), Some("P".to_string())); // DOUBLE-STRUCK P + assert_eq!(fold_line("ℂ"), None); // DOUBLE-STRUCK C at U+2102 — not in our fold (intentional) + assert_eq!(fold_line("ℋ"), Some("H".to_string())); // SCRIPT CAPITAL H + assert_eq!(fold_line("ℌ"), Some("H".to_string())); // BLACK-LETTER CAPITAL H + assert_eq!(fold_line("ℒ"), Some("L".to_string())); // SCRIPT CAPITAL L + assert_eq!(fold_line("ℯ"), Some("e".to_string())); // SCRIPT SMALL E + } + + #[test] + fn fold_math_alphanumeric_greek_to_regular_greek() { + // Math Bold Greek (U+1D6A8..U+1D6E1): capitals. + assert_eq!(fold_line("𝚨"), Some("Α".to_string())); // U+1D6A8 + assert_eq!(fold_line("𝛀"), Some("Ω".to_string())); // U+1D6C0 + // Math Bold Greek: nabla at offset 25. + assert_eq!(fold_line("𝛁"), Some("\u{2207}".to_string())); // ∇ + // Math Bold Greek: smalls. + assert_eq!(fold_line("𝛂"), Some("α".to_string())); // U+1D6C2 + assert_eq!(fold_line("𝛚"), Some("ω".to_string())); // U+1D6DA + assert_eq!(fold_line("𝛓"), Some("ς".to_string())); // final sigma (offset 43) + // Math Bold Greek: partial differential at offset 51. + assert_eq!(fold_line("𝛛"), Some("\u{2202}".to_string())); // ∂ + // Math Bold Greek: variant symbols. + assert_eq!(fold_line("𝛜"), Some("\u{03F5}".to_string())); // ϵ + + // Math Italic Greek (U+1D6E2..). + assert_eq!(fold_line("𝛼"), Some("α".to_string())); // U+1D6FC + assert_eq!(fold_line("𝛽"), Some("β".to_string())); // U+1D6FD + assert_eq!(fold_line("𝛾"), Some("γ".to_string())); // U+1D6FE + + // Math Bold Digamma. + assert_eq!(fold_line("𝟊"), Some("Ϝ".to_string())); // U+1D7CA + assert_eq!(fold_line("𝟋"), Some("ϝ".to_string())); // U+1D7CB + + // Composite: math-Greek sentence folds to plain Greek. + assert_eq!(fold_line("𝛼 + 𝛽 = 𝛾"), Some("α + β = γ".to_string())); + } + + #[test] + fn fold_fractions() { + assert_eq!(fold_line("½ cup"), Some("1/2 cup".to_string())); + assert_eq!(fold_line("¼ + ¾"), Some("1/4 + 3/4".to_string())); + assert_eq!(fold_line("⅗"), Some("3/5".to_string())); + } + + #[test] + fn keep_subscripts_and_superscripts() { + // Subscripts and superscripts are preserved per design decision. + assert_eq!(fold_line("H₂O"), None); + assert_eq!(fold_line("E=mc²"), None); + assert_eq!(fold_line("x₁ + x₂"), None); + assert_eq!(fold_line("10³"), None); + } + + #[test] + fn fold_unicode_whitespace_variants() { + assert_eq!(fold_line("a\u{2007}b"), Some("a b".to_string())); // figure space + assert_eq!(fold_line("a\u{2009}b"), Some("a b".to_string())); // thin space + assert_eq!(fold_line("a\u{202F}b"), Some("a b".to_string())); // narrow NBSP + } + + #[test] + fn fold_nbsp_to_regular_space() { + // v6-11 regression: NBSP (U+00A0) was being stripped by the + // per-char filter as "unusual Latin-1 Supplement char", + // fusing words like `Η εργασία` into `Ηεργασία`. Fix: fold + // to regular space so downstream sees whitespace. + assert_eq!(fold_line("Η\u{00A0}εργασία"), Some("Η εργασία".to_string())); + } + + #[test] + fn fold_various_unicode_spaces() { + // en/em/thin/hair/medium-math/ideographic all collapse to + // regular space. + for cp in [ + '\u{2000}', '\u{2001}', '\u{2002}', '\u{2003}', '\u{2004}', '\u{2005}', '\u{2006}', + '\u{2008}', '\u{200A}', '\u{205F}', '\u{3000}', + ] { + let input = format!("a{}b", cp); + assert_eq!( + fold_line(&input), + Some("a b".to_string()), + "codepoint U+{:04X} did not fold to regular space", + cp as u32 + ); + } + } + + #[test] + fn fold_ascii_fast_path() { + // ASCII-only inputs should short-circuit and return None without allocating. + assert_eq!(fold_line("plain ASCII text"), None); + assert_eq!(fold_line(""), None); + assert_eq!(fold_line("1 + 2 = 3"), None); + } + + #[test] + fn ellipsis_runs_collapse() { + assert_eq!( + normalize_ellipsis_runs("wait…… then"), + Some("wait… then".to_string()) + ); + assert_eq!(normalize_ellipsis_runs("………"), Some("…".to_string())); + assert_eq!(normalize_ellipsis_runs("single …"), None); + assert_eq!(normalize_ellipsis_runs("no ellipsis"), None); + } + + #[test] + fn bucket_run_length_tiered_v2() { + // {0, 1} → unchanged + assert_eq!(bucket_run_length(0), 0); + assert_eq!(bucket_run_length(1), 1); + // {2} → 1 + assert_eq!(bucket_run_length(2), 1); + // {3} → 3 (natural prose triple, e.g. ellipsis) + assert_eq!(bucket_run_length(3), 3); + // {4} → 3 + assert_eq!(bucket_run_length(4), 3); + // {5..=19} → 5 + for n in 5..=19 { + assert_eq!(bucket_run_length(n), 5, "n={n}"); + } + assert_eq!(bucket_run_length(20), 20); + // > 20 → 20 + assert_eq!(bucket_run_length(21), 20); + assert_eq!(bucket_run_length(42), 20); + assert_eq!(bucket_run_length(200), 20); + } + + #[test] + fn dot_runs_tiered_v2() { + // 2 dots → 1 + assert_eq!( + normalize_dot_runs("word..here"), + Some("word.here".to_string()) + ); + // 3 dots unchanged — natural prose ellipsis + assert_eq!(normalize_dot_runs("wait... next"), None); + // 4 dots → 3 (floor) + assert_eq!( + normalize_dot_runs("Chapter 1 .... 5"), + Some("Chapter 1 ... 5".to_string()) + ); + // 10 dots → 5 + assert_eq!( + normalize_dot_runs("..........heads"), + Some(".....heads".to_string()) + ); + // 20 dots → 20 + let twenty = format!("x{}y", ".".repeat(20)); + assert_eq!(normalize_dot_runs(&twenty), None); + // >20 dots → 20 + let long = "x".to_string() + &".".repeat(42) + "y"; + let expected_long = "x".to_string() + &".".repeat(20) + "y"; + assert_eq!(normalize_dot_runs(&long), Some(expected_long)); + // No dots — fast path + assert_eq!(normalize_dot_runs("no dots here"), None); + // Single dot (sentence end) — unchanged + assert_eq!(normalize_dot_runs("end of sentence."), None); + } + + #[test] + fn dot_and_ellipsis_runs_share_one_ladder() { + assert_eq!( + normalize_dot_and_ellipsis_runs("x…………y"), + Some("x.....y".to_string()) + ); + assert_eq!( + normalize_dot_and_ellipsis_runs("x…..y"), + Some("x.....y".to_string()) + ); + assert_eq!( + normalize_dot_and_ellipsis_runs("see https://example.org/a....b"), + None + ); + } + + #[test] + fn punctuation_runs_floor_and_guard_markdown_url_contexts() { + assert_eq!( + normalize_punctuation_runs("x====y"), + Some("x===y".to_string()) + ); + assert_eq!( + normalize_punctuation_runs("########### heading"), + Some("##### heading".to_string()) + ); + assert_eq!(normalize_punctuation_runs("### heading"), None); + assert_eq!( + normalize_punctuation_runs("https://example.org/a////b"), + None + ); + assert_eq!(normalize_punctuation_runs(""), None); + } + + #[test] + fn whitespace_runs_tiered_v2_interior_only() { + // 2 spaces → 1 + assert_eq!(normalize_whitespace_runs("a b"), Some("a b".to_string())); + // 3 spaces — unchanged + assert_eq!(normalize_whitespace_runs("a b"), None); + // 4 spaces → 3 + assert_eq!( + normalize_whitespace_runs("a b"), + Some("a b".to_string()) + ); + // 20 spaces unchanged + let twenty = format!("a{}b", " ".repeat(20)); + assert_eq!(normalize_whitespace_runs(&twenty), None); + // 21+ spaces → 20 + let long = format!("a{}b", " ".repeat(42)); + let expected = format!("a{}b", " ".repeat(20)); + assert_eq!(normalize_whitespace_runs(&long), Some(expected)); + // Tabs always fold to spaces + assert_eq!(normalize_whitespace_runs("a\t\tb"), Some("a b".to_string())); + assert_eq!(normalize_whitespace_runs("a\tb"), Some("a b".to_string())); + // No runs + assert_eq!(normalize_whitespace_runs("a b c"), None); + assert_eq!(normalize_whitespace_runs(""), None); + } + + #[test] + fn whitespace_runs_preserves_leading_indent() { + // Markdown indented code block: 4-space indent preserved. + assert_eq!(normalize_whitespace_runs(" def add(x, y):"), None); + // 8-space indent (nested code) preserved. + assert_eq!(normalize_whitespace_runs(" return x + y"), None); + // Leading tab preserved (list-indent convention). + assert_eq!(normalize_whitespace_runs("\titem"), None); + // Leading indent + interior run: leading kept, interior bucketed. + assert_eq!( + normalize_whitespace_runs(" Chapter 1 title"), + Some(" Chapter 1 title".to_string()) + ); + // Leading indent + TOC-style long run: leading kept, long run → 20. + let input = format!(" Chapter 1{}5", " ".repeat(30)); + let expected = format!(" Chapter 1{}5", " ".repeat(20)); + assert_eq!(normalize_whitespace_runs(&input), Some(expected)); + } + + #[test] + fn malformed_entities_fallback() { + assert_eq!( + normalize_malformed_entities("x > y"), + Some("x > y".to_string()) + ); + assert_eq!( + normalize_malformed_entities("< tag"), + Some("< tag".to_string()) + ); + assert_eq!( + normalize_malformed_entities("a & b"), + Some("a & b".to_string()) + ); + // Well-formed entities left alone (htmlentity handles them). + assert_eq!(normalize_malformed_entities("x > y"), None); + assert_eq!(normalize_malformed_entities("x < y"), None); + // Alphanumeric following `>` means it's something else; don't fold. + assert_eq!(normalize_malformed_entities(">foo"), None); + } + + // (separator_line_detection + all gfm_table_separator_* tests moved + // to md_module.rs alongside the relocated functions.) + + #[test] + fn drop_low_salvage_pages_keeps_all_when_above_threshold() { + let original = "# Intro\nHello world\nAnother line\n# Second\nMore content\n"; + let cleaned = "# Intro\nHello world\nAnother line\n# Second\nMore content\n"; + let out = drop_low_salvage_pages(original, cleaned, 0.30); + assert_eq!(out, cleaned); + } + + #[test] + fn drop_low_salvage_pages_drops_degraded_page() { + // Second page lost almost everything. + let original = + "# Intro\nHello world\nNormal prose here\n# Second\nmostly garbage content lost\n"; + let cleaned = "# Intro\nHello world\nNormal prose here\n# Second\n\n"; + let out = drop_low_salvage_pages(original, cleaned, 0.30); + // Second page dropped entirely; first survives. + assert!(out.contains("# Intro")); + assert!(out.contains("Hello world")); + assert!(!out.contains("# Second")); + } + + #[test] + fn drop_low_salvage_pages_returns_input_when_line_counts_differ() { + // Defensive: if caller passes mismatched line counts we no-op. + let original = "line a\nline b\nline c\n"; + let cleaned = "line a\nline b\n"; + let out = drop_low_salvage_pages(original, cleaned, 0.50); + assert_eq!(out, cleaned); + } + + #[test] + fn drop_low_salvage_pages_handles_no_headers() { + // No markdown headers: whole text is one synthetic page. + let original = "hello world\nplain prose\nno headers here\n"; + let cleaned = "h w\np p\nn h h\n"; + // Retention is low (~10 kept vs ~29 original). + let retained = count_nonwhitespace_in_range(&cleaned.lines().collect::>(), 0, 3); + let orig_count = count_nonwhitespace_in_range(&original.lines().collect::>(), 0, 3); + let ratio = retained as f64 / orig_count as f64; + assert!(ratio < 0.30); + let out = drop_low_salvage_pages(original, cleaned, 0.30); + assert_eq!(out, ""); + } + + #[test] + fn drop_low_salvage_pages_preserves_trailing_newline() { + let original = "# Intro\nbody\n"; + let cleaned = "# Intro\nbody\n"; + let out = drop_low_salvage_pages(original, cleaned, 0.30); + assert!(out.ends_with('\n')); + } + + #[test] + fn is_markdown_header_line_accepts_valid_headers() { + assert!(is_markdown_header_line("# Header")); + assert!(is_markdown_header_line("## Subheader")); + assert!(is_markdown_header_line("#### Deep")); + assert!(is_markdown_header_line(" ## Indented")); + // Hash with no following space is NOT a header (could be a hashtag). + assert!(!is_markdown_header_line("#hashtag")); + assert!(!is_markdown_header_line("####name")); + // Plain text, code, non-hash prefixes. + assert!(!is_markdown_header_line("plain")); + assert!(!is_markdown_header_line("")); + } + + #[test] + fn synthetic_page_line_ranges_splits_on_headers() { + let lines = vec![ + "intro line", + "# First", + "body of first", + "# Second", + "body of second", + "more body", + ]; + let ranges = synthetic_page_line_ranges(&lines); + assert_eq!(ranges, vec![(0, 1), (1, 3), (3, 6)]); + } + + // (code_fence_marker_detection moved to md_module.rs.) +} + +// --------------------------------------------------------------------------- +// Wave-2 text-preprocessing passes (Cases 4, 7, 8, 10a, 13 subsets). +// Run at the START of core_clean_text_with_stats, BEFORE the per-line +// filter loop, so recovered chars survive per-char filtering. +// --------------------------------------------------------------------------- + +lazy_static! { + /// HTML named entities → literal chars (Case 4). Conservative: only + /// the entities we've actually seen in openarchives / web-extracted + /// Greek corpus docs. Adding more is cheap but should be driven by + /// corpus evidence, not speculation. + static ref HTML_NAMED_ENTITY_MAP: HashMap<&'static str, &'static str> = { + let mut m = HashMap::new(); + m.insert("amp", "&"); + m.insert("lt", "<"); + m.insert("gt", ">"); + m.insert("quot", "\""); + m.insert("apos", "'"); + m.insert("nbsp", "\u{00A0}"); + m.insert("copy", "©"); + m.insert("reg", "®"); + m.insert("trade", "™"); + m.insert("euro", "€"); + m.insert("pound", "£"); + m.insert("yen", "¥"); + m.insert("sect", "§"); + m.insert("deg", "°"); + m.insert("laquo", "«"); + m.insert("raquo", "»"); + m.insert("hellip", "…"); + m.insert("mdash", "—"); + m.insert("ndash", "–"); + m.insert("lsquo", "\u{2018}"); + m.insert("rsquo", "\u{2019}"); + m.insert("ldquo", "\u{201C}"); + m.insert("rdquo", "\u{201D}"); + m.insert("middot", "·"); + m.insert("bull", "•"); + m + }; + + /// Named-entity pattern: `&name;` where name is a short alphanumeric + /// token. Doesn't swallow text if the entity name isn't recognised — + /// the replacer checks the map and falls back to the original. + static ref HTML_NAMED_ENTITY_REGEX: Regex = + Regex::new(r"&([A-Za-z][A-Za-z0-9]{1,10});").unwrap(); + + /// Numeric HTML entity: `Ӓ` (decimal) or `😀` (hex). + static ref HTML_NUMERIC_ENTITY_REGEX: Regex = + Regex::new(r"&#(x[0-9A-Fa-f]+|[0-9]+);").unwrap(); + + // GLYPH_MARKER_REGEX deleted in CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 + // Point 4: superseded by `cleaning_module::PDF_GLYPH_NAME_REGEX`, + // which covers the same patterns (GLYPH<…>, /uniXXXX, /gN) plus + // `glyph`, reversed `glyph`, and PDF + // font subsets, AND drives the per-line count+coverage gate in + // `apply_glyph_span_strip_and_rule_b`. + + /// Inline base64-encoded image data URIs that PDF extractors (e.g. + /// Docling) produce when they encounter embedded raster images. The + /// payload — `[A-Za-z0-9+/=]+` — can be hundreds of KB on a single + /// MD line, polluting the corpus with binary garbage that contributes + /// nothing to tokenizer training. We strip the entire markdown image + /// node and replace with the upstream-standard `` + /// placeholder so position is preserved without the payload. + /// + /// Pattern shape: `![alt-text](data:image/jpg;base64,/9j/4AAQ…)`. + /// The alt-text is non-greedy, the MIME type is permissive + /// (`image/jpg`, `image/jpeg`, `image/png`, `image/gif` all observed + /// in the v7 corpus), and the base64 payload accepts the full RFC-4648 + /// alphabet plus `=` padding plus whitespace breaks (some extractors + /// hard-wrap the base64 every 76 cols). + /// + /// `(?s)` (dotall) so the payload can span multiple lines if the + /// extractor wrapped it. Lower bound 200 chars for the base64 + /// payload — a legit favicon-shaped data URI is typically much + /// smaller, while Docling's Image-N inlines are all multi-KB. + /// No upper bound (the bounded quantifier blew the regex DFA size + /// limit at 16 MB; unbounded keeps the compiled NFA small). + static ref BASE64_IMAGE_REGEX: Regex = Regex::new( + r"(?s)!\[[^\]]{0,500}\]\(data:image/[A-Za-z0-9.+-]+;base64,[A-Za-z0-9+/=\s]{200,}\)" + ).unwrap(); + + /// Adobe Symbol font PUA → real Unicode (Case 10a). 100% of the + /// top-30 PUA chars observed on openarchives 01500_pct0033_… were + /// recovered to real Greek / math chars via this mapping. + /// Reference: Adobe Symbol Encoding Vector. + static ref ADOBE_SYMBOL_PUA_MAP: HashMap = { + let mut m = HashMap::new(); + // ASCII-mirrored positions (F020..F07E): shift back to ASCII + // code for punctuation + digits. + m.insert('\u{F020}', " "); m.insert('\u{F021}', "!"); + m.insert('\u{F023}', "#"); m.insert('\u{F025}', "%"); + m.insert('\u{F026}', "&"); m.insert('\u{F028}', "("); + m.insert('\u{F029}', ")"); m.insert('\u{F02A}', "*"); + m.insert('\u{F02B}', "+"); m.insert('\u{F02C}', ","); + m.insert('\u{F02D}', "-"); m.insert('\u{F02E}', "."); + m.insert('\u{F02F}', "/"); m.insert('\u{F030}', "0"); + m.insert('\u{F031}', "1"); m.insert('\u{F032}', "2"); + m.insert('\u{F033}', "3"); m.insert('\u{F034}', "4"); + m.insert('\u{F035}', "5"); m.insert('\u{F036}', "6"); + m.insert('\u{F037}', "7"); m.insert('\u{F038}', "8"); + m.insert('\u{F039}', "9"); m.insert('\u{F03A}', ":"); + m.insert('\u{F03B}', ";"); m.insert('\u{F03C}', "<"); + m.insert('\u{F03D}', "="); m.insert('\u{F03E}', ">"); + m.insert('\u{F03F}', "?"); + m.insert('\u{F05B}', "["); m.insert('\u{F05D}', "]"); + // Greek letters (F041..F057 upper, F061..F077 lower — Symbol + // ordering). These are the positions where Adobe Symbol + // emits Greek letters when a PDF embeds the font. + m.insert('\u{F041}', "Α"); m.insert('\u{F042}', "Β"); + m.insert('\u{F043}', "Χ"); m.insert('\u{F044}', "Δ"); + m.insert('\u{F045}', "Ε"); m.insert('\u{F046}', "Φ"); + m.insert('\u{F047}', "Γ"); m.insert('\u{F048}', "Η"); + m.insert('\u{F049}', "Ι"); m.insert('\u{F04A}', "ϑ"); + m.insert('\u{F04B}', "Κ"); m.insert('\u{F04C}', "Λ"); + m.insert('\u{F04D}', "Μ"); m.insert('\u{F04E}', "Ν"); + m.insert('\u{F04F}', "Ο"); m.insert('\u{F050}', "Π"); + m.insert('\u{F051}', "Θ"); m.insert('\u{F052}', "Ρ"); + m.insert('\u{F053}', "Σ"); m.insert('\u{F054}', "Τ"); + m.insert('\u{F055}', "Υ"); m.insert('\u{F057}', "Ω"); + m.insert('\u{F058}', "Ξ"); m.insert('\u{F059}', "Ψ"); + m.insert('\u{F05A}', "Ζ"); + m.insert('\u{F061}', "α"); m.insert('\u{F062}', "β"); + m.insert('\u{F063}', "χ"); m.insert('\u{F064}', "δ"); + m.insert('\u{F065}', "ε"); m.insert('\u{F066}', "φ"); + m.insert('\u{F067}', "γ"); m.insert('\u{F068}', "η"); + m.insert('\u{F069}', "ι"); m.insert('\u{F06A}', "ϕ"); + m.insert('\u{F06B}', "κ"); m.insert('\u{F06C}', "λ"); + m.insert('\u{F06D}', "μ"); m.insert('\u{F06E}', "ν"); + m.insert('\u{F06F}', "ο"); m.insert('\u{F070}', "π"); + m.insert('\u{F071}', "θ"); m.insert('\u{F072}', "ρ"); + m.insert('\u{F073}', "σ"); m.insert('\u{F074}', "τ"); + m.insert('\u{F075}', "υ"); m.insert('\u{F077}', "ω"); + m.insert('\u{F078}', "ξ"); m.insert('\u{F079}', "ψ"); + m.insert('\u{F07A}', "ζ"); + // Math relations / operators commonly used in Greek math docs. + m.insert('\u{F0A3}', "≤"); m.insert('\u{F0B3}', "≥"); + m.insert('\u{F0B9}', "≠"); m.insert('\u{F0AE}', "→"); + m.insert('\u{F0AC}', "←"); m.insert('\u{F0AD}', "↑"); + m.insert('\u{F0AF}', "↓"); m.insert('\u{F0DE}', "⇒"); + m.insert('\u{F0DC}', "⇐"); m.insert('\u{F0DB}', "⇔"); + m.insert('\u{F0CE}', "∈"); m.insert('\u{F0CF}', "∉"); + m.insert('\u{F0CD}', "⊄"); m.insert('\u{F0C7}', "∩"); + m.insert('\u{F0C8}', "∪"); m.insert('\u{F0C5}', "⊗"); + m.insert('\u{F0C9}', "⊃"); m.insert('\u{F0CB}', "⊂"); + m.insert('\u{F0CC}', "⊆"); m.insert('\u{F0D1}', "∠"); + m.insert('\u{F0D2}', "∇"); m.insert('\u{F0D4}', "∏"); + m.insert('\u{F0D5}', "√"); m.insert('\u{F0D6}', "·"); + m.insert('\u{F0D7}', "¬"); m.insert('\u{F0D8}', "∧"); + m.insert('\u{F0D9}', "∨"); m.insert('\u{F0DA}', "⇔"); + m.insert('\u{F0E5}', "∞"); m.insert('\u{F0E6}', "∫"); + m.insert('\u{F0E8}', "∑"); m.insert('\u{F0B4}', "×"); + m.insert('\u{F0B8}', "÷"); m.insert('\u{F0B1}', "±"); + m + }; +} + +/// Decode HTML entities (Case 4): `& < > " '  ` +/// + named entities in the map above + numeric (decimal `&` and +/// hex `&`) back to their literal characters. Unknown named +/// entities are left as-is. +pub fn decode_html_entities(text: &str) -> String { + // First, named: use a regex replace with a closure that falls back + // to the original match if the name isn't in the map. + let step1 = HTML_NAMED_ENTITY_REGEX.replace_all(text, |caps: ®ex::Captures| { + let name = &caps[1]; + match HTML_NAMED_ENTITY_MAP.get(name) { + Some(replacement) => (*replacement).to_string(), + None => caps[0].to_string(), + } + }); + // Then, numeric (decimal and hex). Leave malformed ones alone. + HTML_NUMERIC_ENTITY_REGEX + .replace_all(&step1, |caps: ®ex::Captures| { + let body = &caps[1]; + let code = if let Some(hex) = body.strip_prefix('x').or_else(|| body.strip_prefix('X')) + { + u32::from_str_radix(hex, 16).ok() + } else { + body.parse::().ok() + }; + match code.and_then(char::from_u32) { + Some(c) => c.to_string(), + None => caps[0].to_string(), + } + }) + .into_owned() +} + +// `strip_glyph_markers` deleted in CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 +// Point 4. The same patterns (GLYPH<…>, /uniXXXX, /gN) are now +// stripped by Rule B inside `cleaning_module::apply_glyph_span_strip_and_rule_b`, +// which ALSO drives a count+coverage line-drop gate. Calling code +// no longer needs a separate text-wide strip pre-pass. + +/// True if a line shows the Greek-CID-mojibake / Latin-Ext residue +/// signature that warrants line-drop, replacing the older PAGE-level +/// `counter_script_residue ≥ 9` rule with finer line-level granularity. +/// +/// Combination of two rules empirically validated on the v7 sample +/// `top500_by_counter_script_residue` (2.6 M body lines): +/// +/// - **R1 (per-token)**: at least one whitespace-bounded token of +/// length > 20 with > 10 % chars in U+0100..U+024F (Latin Ext-A/B). +/// Catches PDF extractions that concatenated whole Greek phrases +/// into single >20-char unbroken tokens of residue. +/// - **R2 (per-line)**: longest consecutive residue run ≥ 4. Catches +/// the structural mojibake pattern where 4+ Latin-Ext chars appear +/// in a row — rare in legit foreign text (Polish/Czech/Turkish +/// words have isolated diacritics, not chains). +/// +/// On the v7 sample, R1∪R2 fires on 18,091 lines / 2.6 M (0.7 %). +/// 76.6 % of residue chars on those lines are in Latin Extended-B +/// (U+0180..U+024F — the Greek-CID-mojibake range) vs 47.4 % +/// corpus-wide, indicating strong noise-class bias. +pub fn is_residue_mojibake_line(line: &str) -> bool { + // Residue range narrowed to match Group 1 STRIP set per + // CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 3: + // - Latin Extended-A (U+0100..U+017F): NOT residue (kept as + // legitimate Polish/Czech/Hungarian/etc. European content). + // - Latin Extended-B (U+0180..U+024F): residue, EXCEPT the + // Romanian comma-below allowlist {Ș, ș, Ț, ț}. + // + // Result: dense Latin-Ext-A text (foreign citations, names) no + // longer triggers line-drop; dense Latin-Ext-B clusters (the + // Greek-CID-mojibake signature) still do. + let mut max_run: usize = 0; + let mut cur_run: usize = 0; + let mut tok_len: usize = 0; + let mut tok_residue: usize = 0; + let mut r1_hit = false; + for ch in line.chars() { + let cp = ch as u32; + let is_residue = + (0x0180..=0x024F).contains(&cp) && !matches!(cp, 0x0218 | 0x0219 | 0x021A | 0x021B); + if is_residue { + cur_run += 1; + if cur_run > max_run { + max_run = cur_run; + } + } else { + cur_run = 0; + } + if ch.is_whitespace() { + // Token boundary — evaluate R1 on the just-finished token. + if tok_len > 20 && tok_residue * 10 > tok_len { + r1_hit = true; + } + tok_len = 0; + tok_residue = 0; + } else { + tok_len += 1; + if is_residue { + tok_residue += 1; + } + } + } + // End-of-line: evaluate the final token (no trailing whitespace). + if tok_len > 20 && tok_residue * 10 > tok_len { + r1_hit = true; + } + r1_hit || max_run >= 4 +} + +/// Strip inline base64-encoded image data URIs and replace each with +/// the upstream-standard `` placeholder. See the regex +/// docstring for shape + size bounds. +/// +/// Cheap fast-path: if the input doesn't contain `data:image/`, return +/// unchanged (the regex compile is non-trivial and most docs are +/// unaffected). +pub fn strip_base64_images(text: &str) -> String { + if !text.contains("data:image/") { + return text.to_string(); + } + BASE64_IMAGE_REGEX + .replace_all(text, "") + .into_owned() +} + +// CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 2 retired these as +// CLEANER PRE-PASSES (the cleaner now relies on the per-line loop's +// unified Group 1 STRIP / Group 2 FOLD partition). The functions +// remain `pub` for `md_module::non_destructive_canonicalize`, which +// runs a parallel preview-equivalence pipeline; it continues to +// call these directly until Point 9 (Pilot B integration) gives +// md_module a different normalization shape. +// +// Both functions are now thin wrappers that delegate to the unified +// path, so behaviour stays identical regardless of caller. + +/// Strip U+00AD soft hyphens from `text`. Thin wrapper kept for +/// `md_module::non_destructive_canonicalize` use; the cleaner +/// itself relies on `is_unicode_noise_char` inside the per-line +/// loop instead. +pub fn strip_soft_hyphens(text: &str) -> String { + if !text.contains('\u{00AD}') { + return text.to_string(); + } + text.chars().filter(|&c| c != '\u{00AD}').collect() +} + +/// Decode Adobe Symbol PUA chars in `text` via the unified +/// `fold_codepoint` map (Point 2 absorbed this). Kept as a `pub` +/// function for `md_module::non_destructive_canonicalize`; the +/// cleaner itself calls `fold_codepoint` directly per char. +pub fn decode_adobe_symbol_pua(text: &str) -> String { + // Fast path: no PUA chars at all. + if !text.chars().any(|c| { + let cp = c as u32; + (0xF000..=0xF8FF).contains(&cp) + }) { + return text.to_string(); + } + let mut out = String::with_capacity(text.len()); + for c in text.chars() { + match fold_codepoint(c) { + Some(replacement) => out.push_str(replacement), + None => out.push(c), + } + } + out +} + +/// Paragraph-reflow (Case 8): collapse soft-wrap sequences where a +/// line ends mid-sentence and the next line continues it. PDF column- +/// width line breaks look like `word1\t\n word2`; we replace with +/// `word1 word2`. Hard breaks (blank lines, headings, tables, +/// separators, list markers, blockquotes, fenced code) are preserved. +/// +/// Heuristic — only joins when: +/// - prior line is non-empty AND doesn't end with a sentence terminator +/// (`.?!:;·;·` or closing quote/bracket) +/// - next line is non-empty, doesn't start with `#|>*-` / list marker +/// / fenced-code marker, and starts with a letter or digit +/// - prior line doesn't look like a list item / heading either +/// +/// Accounting: the whitespace chars (`\n`, `\t`, leading spaces on the +/// joined line) are replaced by a single space, so output is shorter +/// by `(removed_ws_len) - 1` chars per join. +// (reflow_paragraphs, can_join_lines, line_is_hard_break moved to md_module.rs.) + +#[cfg(test)] +mod wave2_tests { + use super::*; + + #[test] + fn decode_html_named_core_entities() { + assert_eq!(decode_html_entities("a & b"), "a & b"); + assert_eq!(decode_html_entities("<div>"), "
"); + assert_eq!(decode_html_entities(""hi""), "\"hi\""); + assert_eq!(decode_html_entities("'test'"), "'test'"); + } + + #[test] + fn decode_html_numeric_entities_decimal_and_hex() { + assert_eq!(decode_html_entities("&"), "&"); + assert_eq!(decode_html_entities("&"), "&"); + assert_eq!(decode_html_entities("€"), "€"); + assert_eq!(decode_html_entities("€"), "€"); + } + + #[test] + fn decode_html_unknown_entity_passes_through() { + // `&fakename;` isn't in the map — leave as-is. + assert_eq!(decode_html_entities("a &fakename; b"), "a &fakename; b"); + } + + #[test] + fn decode_html_handles_mixed_content() { + assert_eq!( + decode_html_entities("< item >Αθήνα</ item >"), + "< item >Αθήνα" + ); + } + + // strip_glyph_markers tests moved to cleaning_module's Rule B tests + // (Point 4: the patterns are now matched by `PDF_GLYPH_NAME_REGEX` + // and stripped by `apply_glyph_span_strip_and_rule_b`). + + // ---------- is_residue_mojibake_line (R1 ∪ R2) ---------- + + #[test] + fn residue_drop_R2_pure_residue_word_fires() { + // 12 consecutive Latin-Ext-B chars (Greek-CID rendering of + // ΑΡΙΣΤΟΤΕΛΕΙΟ). max_run = 12 → R2 fires. + let line = "ǹȇǿȈȉȅȉǼȁǼǿȅ ȆǹȃǼȆǿȈȉǾȂǿȅ ĬǼȈȈǹȁȅȃǿȀǾȈ"; + assert!(is_residue_mojibake_line(line)); + } + + #[test] + fn residue_drop_R2_short_run_fires() { + // exactly 4 consecutive residue chars → R2 fires. + let line = "Greek prose ǹȇǿȈ followed by more Greek prose"; + assert!(is_residue_mojibake_line(line)); + } + + #[test] + fn residue_drop_R2_run_of_3_does_not_fire() { + // 3 consecutive residue chars — below R2 threshold (≥4). + let line = "Greek prose ǹȇǿ followed by more Greek prose"; + assert!(!is_residue_mojibake_line(line)); + } + + #[test] + fn residue_drop_R1_long_concatenated_token_fires() { + // One token of length 25 with 100% residue → R1 fires + // (>20 chars, >10% residue). Surrounding context is plain + // Latin so R2 doesn't fire on its own (max_run 25 actually + // also fires R2, but the point is R1 covers it independently). + let line = "context ǹȇǿȈȉȅȉǼȁǼǿȅȆǹȃǼȆǿȈȉǾ context"; + assert!(is_residue_mojibake_line(line)); + } + + #[test] + fn residue_drop_legit_foreign_short_words_do_not_fire() { + // Bulgarian/Turkish names: each token is short, residue is + // a single isolated char per word. Neither R1 nor R2 fires. + let line = "Ljubomir Miletič and Márta Sebestyén signed the petition."; + assert!( + !is_residue_mojibake_line(line), + "legit Bulgarian + Hungarian names should NOT be flagged" + ); + } + + #[test] + fn residue_drop_music_notation_oe_does_not_fire() { + // The Sample-2 false-positive case from R1-line-level: music + // notation with `œ` (U+0153) chars. Each `œ` is its own + // whitespace-separated token of length 1 — R1 ignores + // (token < 20). Run length = 1 — R2 doesn't fire. + let line = "| 3 ? ∑ | mp p œ œ œ #œ œ J ‰ | Œ Œ | 2 4 ∑ |"; + assert!( + !is_residue_mojibake_line(line), + "music notation with single œ tokens must not flag" + ); + } + + #[test] + fn residue_drop_mixed_run_of_5_fires() { + // Greek text where Docling injected a 5-char Latin-Ext-B run. + let line = "Καλημέρα ǹȇǿȈȉ κόσμε"; + assert!(is_residue_mojibake_line(line)); + } + + #[test] + fn residue_drop_pure_greek_does_not_fire() { + let line = "Καλημέρα κόσμε όλοι"; + assert!(!is_residue_mojibake_line(line)); + } + + #[test] + fn residue_drop_pure_ascii_does_not_fire() { + let line = "The quick brown fox jumps over the lazy dog"; + assert!(!is_residue_mojibake_line(line)); + } + + // ---------- strip_base64_images ---------- + + #[test] + fn strip_base64_images_basic_jpeg() { + let payload = "/9j/4AAQSkZJRgABAQEAyADIAAD".to_string() + &"X".repeat(500); // realistic Docling-blob size + let input = format!("before ![Image 1](data:image/jpg;base64,{}) after", payload); + let out = strip_base64_images(&input); + assert_eq!( + out, "before after", + "expected base64 image stripped, got: {:?}", + out + ); + } + + #[test] + fn strip_base64_images_multiple_payloads_in_doc() { + let payload = "A".repeat(500); + let input = format!( + "intro\n![](data:image/png;base64,{p}) caption A\n\ + middle\n![](data:image/jpeg;base64,{p}) caption B\nend", + p = payload, + ); + let out = strip_base64_images(&input); + assert!(!out.contains("base64,"), "still has base64: {:?}", out); + assert_eq!(out.matches("").count(), 2); + // The non-image text and caption labels survive. + assert!(out.contains("intro")); + assert!(out.contains("caption A")); + assert!(out.contains("middle")); + assert!(out.contains("caption B")); + assert!(out.contains("end")); + } + + #[test] + fn strip_base64_images_noop_when_absent() { + let input = "regular Greek prose χωρίς εικόνα"; + assert_eq!(strip_base64_images(input), input); + } + + #[test] + fn strip_base64_images_skips_legit_short_inline_data_uri() { + // Tiny inline data URI (< 200 chars in the payload) shouldn't + // match — the rule targets the multi-KB Docling blobs. + let input = "![pixel](data:image/png;base64,iVBORw0KGgo=)"; + assert_eq!( + strip_base64_images(input), + input, + "tiny payload (< 200 chars) should pass through" + ); + } + + #[test] + fn strip_base64_images_handles_alt_text_and_extra_whitespace() { + let payload = "B".repeat(300); + let input = format!( + "![Figure 3 — Schematic of the apparatus](data:image/png;base64,{}\n)", + payload, + ); + let out = strip_base64_images(&input); + assert_eq!(out, ""); + } + + // Soft-hyphen handling: U+00AD strip is now part of the + // unified Group 1 strip predicate (`is_unicode_noise_char` in + // cleaning_module.rs). End-to-end coverage lives in + // `cleaning_module::tests::core_clean_text_strips_unicode_noise_chars`. + + #[test] + fn fold_adobe_pua_ascii_mirrored() { + assert_eq!(fold_codepoint('\u{F02D}'), Some("-")); + assert_eq!(fold_codepoint('\u{F03D}'), Some("=")); + assert_eq!(fold_codepoint('\u{F02B}'), Some("+")); + } + + #[test] + fn fold_adobe_pua_greek_letters() { + assert_eq!(fold_codepoint('\u{F061}'), Some("α")); + assert_eq!(fold_codepoint('\u{F06C}'), Some("λ")); + assert_eq!(fold_codepoint('\u{F06D}'), Some("μ")); + } + + #[test] + fn fold_adobe_pua_math_operators() { + assert_eq!(fold_codepoint('\u{F0A3}'), Some("≤")); + assert_eq!(fold_codepoint('\u{F0B3}'), Some("≥")); + assert_eq!(fold_codepoint('\u{F0CE}'), Some("∈")); + } + + #[test] + fn fold_adobe_pua_unmapped_returns_none() { + assert_eq!(fold_codepoint('\u{F500}'), None); + } + + #[test] + fn fold_adobe_pua_via_fold_line() { + assert_eq!( + fold_line("\u{F061}\u{F062}\u{F063}"), + Some("αβχ".to_string()) + ); + } + + #[test] + fn fold_micro_sign_to_greek_mu() { + // U+00B5 MICRO SIGN → U+03BC GREEK SMALL LETTER MU. + assert_eq!(fold_codepoint('\u{00B5}'), Some("\u{03BC}")); + assert_eq!(fold_line("5 \u{00B5}m"), Some("5 μm".to_string())); + } + + // (reflow_* tests moved to md_module.rs.) + + // --- Escaped-underscore run normalization --- + + #[test] + fn escaped_underscore_short_runs_pass_through() { + // 3 and 5 pairs are identity per bucket_run_length. + assert_eq!(normalize_escaped_underscore_runs(r"\_\_\_"), None); + assert_eq!(normalize_escaped_underscore_runs(r"\_\_\_\_\_"), None); + assert_eq!( + normalize_escaped_underscore_runs(r"\_\_\_\_"), + Some(r"\_\_\_".to_string()) + ); + } + + #[test] + fn escaped_underscore_medium_run_bucket_down_to_5() { + // 10 pairs → bucket to 5. + let input = r"\_\_\_\_\_\_\_\_\_\_"; + let out = normalize_escaped_underscore_runs(input); + assert_eq!(out, Some(r"\_\_\_\_\_".to_string())); + } + + #[test] + fn escaped_markdown_runs_generalize_beyond_underscore() { + assert_eq!( + normalize_escaped_run_chars(r"\*\*\*\*\*\*\*\*"), + Some(r"\*\*\*\*\*".to_string()) + ); + assert_eq!( + normalize_escaped_run_chars(r"\#\#\#\#"), + Some(r"\#\#\#".to_string()) + ); + } + + #[test] + fn escaped_underscore_long_run_buckets_to_20() { + // 50 pairs → bucket to 20. + let input: String = r"\_".repeat(50); + let expected: String = r"\_".repeat(20); + assert_eq!(normalize_escaped_underscore_runs(&input), Some(expected)); + } + + #[test] + fn escaped_underscore_preserves_surrounding_whitespace() { + let input = r" \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ "; + // 30 pairs → 20. Leading/trailing whitespace preserved. + let out = normalize_escaped_underscore_runs(input).expect("should bucket"); + let inner: String = r"\_".repeat(20); + assert_eq!(out, format!(" {inner} ")); + } + + #[test] + fn escaped_underscore_leaves_inline_escapes_alone() { + // A prose line with `\_` inline (e.g. in "use \_ as a blank") + // must NOT be bucketed — the normalizer only fires on lines + // that are exclusively a run of `\_` pairs. + assert_eq!( + normalize_escaped_underscore_runs(r"prose \_\_\_ inline"), + None + ); + // Mixed with other escape: don't touch. + assert_eq!( + normalize_escaped_underscore_runs(r"\_\_\_\_\_\_ hello"), + None + ); + } + + #[test] + fn escaped_underscore_no_pairs_returns_none() { + assert_eq!(normalize_escaped_underscore_runs("plain line"), None); + assert_eq!(normalize_escaped_underscore_runs(""), None); + // Literal underscore (no escape) is out of scope — Phase A's + // HR rule handles `_____` already. + assert_eq!(normalize_escaped_underscore_runs("______"), None); + } +} diff --git a/rust/glossapi_rs_cleaner/src/table_analysis_module.rs b/rust/glossapi_rs_cleaner/src/table_analysis_module.rs index a573ab9..d2c4cf9 100644 --- a/rust/glossapi_rs_cleaner/src/table_analysis_module.rs +++ b/rust/glossapi_rs_cleaner/src/table_analysis_module.rs @@ -1,3 +1,10 @@ +// CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Item 5: pyo3 0.19's +// `#[pymethods]` macro emits non-local `impl` blocks that newer +// Rust flags via `non_local_definitions`. Upgrading pyo3 is a +// separate change — silence the lint at module level so the build +// is warning-clean against the pinned pyo3 version. +#![allow(non_local_definitions)] + use lazy_static::lazy_static; use pyo3::prelude::*; use regex::Regex; diff --git a/rust/glossapi_rs_cleaner/src/table_remover_module.rs b/rust/glossapi_rs_cleaner/src/table_remover_module.rs index df8ce7b..33652e3 100644 --- a/rust/glossapi_rs_cleaner/src/table_remover_module.rs +++ b/rust/glossapi_rs_cleaner/src/table_remover_module.rs @@ -41,6 +41,9 @@ pub fn remove_tables_from_content( if table_locations_for_file.is_empty() { return file_content.to_string(); } + if file_content.is_empty() { + return String::new(); + } let original_lines: Vec<&str> = file_content.lines().collect(); if original_lines.is_empty() { diff --git a/rust/glossapi_rs_common/Cargo.lock b/rust/glossapi_rs_common/Cargo.lock new file mode 100644 index 0000000..4fc9d61 --- /dev/null +++ b/rust/glossapi_rs_common/Cargo.lock @@ -0,0 +1,7 @@ +# This file is automatically @generated by Cargo. +# It is not intended for manual editing. +version = 4 + +[[package]] +name = "glossapi_rs_common" +version = "0.1.0" diff --git a/rust/glossapi_rs_common/Cargo.toml b/rust/glossapi_rs_common/Cargo.toml new file mode 100644 index 0000000..594fc96 --- /dev/null +++ b/rust/glossapi_rs_common/Cargo.toml @@ -0,0 +1,11 @@ +[package] +name = "glossapi_rs_common" +version = "0.1.0" +edition = "2021" +authors = ["GlossAPI Team "] +description = "Shared Rust script-analysis helpers for GlossAPI" +license = "EUPL-1.2" + +[lib] +name = "glossapi_rs_common" +path = "src/lib.rs" diff --git a/rust/glossapi_rs_common/src/lib.rs b/rust/glossapi_rs_common/src/lib.rs new file mode 100644 index 0000000..a34f2a9 --- /dev/null +++ b/rust/glossapi_rs_common/src/lib.rs @@ -0,0 +1,159 @@ +#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)] +pub struct ScriptMetrics { + pub non_whitespace_chars: u64, + pub greek_char_count: u64, + pub latin_char_count: u64, + pub greek_word_count: u64, + pub polytonic_word_count: u64, +} + +impl ScriptMetrics { + #[inline] + pub fn percentage_greek(&self) -> f64 { + if self.non_whitespace_chars > 0 { + 100.0 * self.greek_char_count as f64 / self.non_whitespace_chars as f64 + } else { + 0.0 + } + } + + #[inline] + pub fn latin_percentage(&self) -> f64 { + if self.non_whitespace_chars > 0 { + 100.0 * self.latin_char_count as f64 / self.non_whitespace_chars as f64 + } else { + 0.0 + } + } + + #[inline] + pub fn polytonic_ratio(&self) -> f64 { + if self.greek_word_count > 0 { + self.polytonic_word_count as f64 / self.greek_word_count as f64 + } else { + 0.0 + } + } +} + +#[derive(Debug, Clone, Default)] +pub struct ScriptScanner { + metrics: ScriptMetrics, + token_has_greek: bool, + token_has_polytonic: bool, + in_token: bool, +} + +impl ScriptScanner { + #[inline] + pub fn new() -> Self { + Self::default() + } + + #[inline] + pub fn observe_char(&mut self, ch: char) { + if ch.is_whitespace() { + self.finish_token(); + return; + } + + self.in_token = true; + self.metrics.non_whitespace_chars += 1; + + let cp = ch as u32; + if is_greek(cp) { + self.metrics.greek_char_count += 1; + self.token_has_greek = true; + if is_polytonic_codepoint(cp) { + self.token_has_polytonic = true; + } + } else if is_ascii_latin(cp) { + self.metrics.latin_char_count += 1; + } else if is_combining_mark(cp) { + self.token_has_polytonic = true; + } + } + + #[inline] + pub fn observe_str(&mut self, text: &str) { + for ch in text.chars() { + self.observe_char(ch); + } + } + + #[inline] + pub fn finish_token(&mut self) { + if !self.in_token { + return; + } + if self.token_has_greek { + self.metrics.greek_word_count += 1; + if self.token_has_polytonic { + self.metrics.polytonic_word_count += 1; + } + } + self.in_token = false; + self.token_has_greek = false; + self.token_has_polytonic = false; + } + + #[inline] + pub fn finish(mut self) -> ScriptMetrics { + self.finish_token(); + self.metrics + } +} + +#[inline(always)] +pub fn is_greek(cp: u32) -> bool { + (0x0370..=0x03FF).contains(&cp) || (0x1F00..=0x1FFF).contains(&cp) +} + +#[inline(always)] +pub fn is_combining_mark(cp: u32) -> bool { + (0x0300..=0x036F).contains(&cp) + || (0x1DC0..=0x1DFF).contains(&cp) + || (0x20D0..=0x20FF).contains(&cp) +} + +#[inline(always)] +pub fn is_ascii_latin(cp: u32) -> bool { + (0x41..=0x5A).contains(&cp) || (0x61..=0x7A).contains(&cp) +} + +#[inline(always)] +pub fn is_polytonic_codepoint(cp: u32) -> bool { + (0x1F00..=0x1FFF).contains(&cp) +} + +#[inline] +pub fn scan_script_metrics(text: &str) -> ScriptMetrics { + let mut scanner = ScriptScanner::new(); + scanner.observe_str(text); + scanner.finish() +} + +#[cfg(test)] +mod tests { + use super::{scan_script_metrics, ScriptScanner}; + + #[test] + fn scanner_counts_greek_latin_and_polytonic_words() { + let metrics = scan_script_metrics("Αυτή abc Καὶ"); + assert!(metrics.greek_char_count > 0); + assert_eq!(metrics.latin_char_count, 3); + assert_eq!(metrics.greek_word_count, 2); + assert_eq!(metrics.polytonic_word_count, 1); + assert!(metrics.percentage_greek() > metrics.latin_percentage()); + } + + #[test] + fn scanner_flushes_on_line_boundaries() { + let mut scanner = ScriptScanner::new(); + scanner.observe_str("Καὶ\n"); + scanner.observe_str("αὕτη"); + let metrics = scanner.finish(); + assert_eq!(metrics.greek_word_count, 2); + assert_eq!(metrics.polytonic_word_count, 2); + } +} diff --git a/rust/glossapi_rs_noise/Cargo.lock b/rust/glossapi_rs_noise/Cargo.lock index 3c09979..0eea3b4 100644 --- a/rust/glossapi_rs_noise/Cargo.lock +++ b/rust/glossapi_rs_noise/Cargo.lock @@ -87,18 +87,39 @@ version = "1.15.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "48c757948c5ede0e46177b7add2e67155f70e33c07fea8284df6576da70b3719" +[[package]] +name = "getrandom" +version = "0.2.17" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ff2abc00be7fca6ebc474524697ae276ad847ad0a6b3faa4bcb027e9a4614ad0" +dependencies = [ + "cfg-if", + "libc", + "wasi", +] + +[[package]] +name = "glossapi_rs_common" +version = "0.1.0" + [[package]] name = "glossapi_rs_noise" version = "0.1.0" dependencies = [ + "aho-corasick", "anyhow", "csv", + "glossapi_rs_common", "lazy_static", "memmap2", "once_cell", "pyo3", + "rand", "rayon", "regex", + "serde", + "serde_json", + "unicode-normalization", "walkdir", ] @@ -189,6 +210,15 @@ dependencies = [ "windows-targets", ] +[[package]] +name = "ppv-lite86" +version = "0.2.21" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "85eae3c4ed2f50dcfe72643da4befc30deadb458a9b590d720cde2f2b1e97da9" +dependencies = [ + "zerocopy", +] + [[package]] name = "proc-macro2" version = "1.0.95" @@ -267,6 +297,36 @@ dependencies = [ "proc-macro2", ] +[[package]] +name = "rand" +version = "0.8.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "34af8d1a0e25924bc5b7c43c079c942339d8f0a8b57c39049bef581b46327404" +dependencies = [ + "libc", + "rand_chacha", + "rand_core", +] + +[[package]] +name = "rand_chacha" +version = "0.3.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "e6c10a63a0fa32252be49d21e7709d4d4baf8d231c2dbce1eaa8141b9b127d88" +dependencies = [ + "ppv-lite86", + "rand_core", +] + +[[package]] +name = "rand_core" +version = "0.6.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ec0be4795e2f6a28069bec0b5ff3e2ac9bafc99e6a9a7dc3547996c5c816922c" +dependencies = [ + "getrandom", +] + [[package]] name = "rayon" version = "1.10.0" @@ -348,24 +408,47 @@ checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49" [[package]] name = "serde" -version = "1.0.219" +version = "1.0.228" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9a8e94ea7f378bd32cbbd37198a4a91436180c5bb472411e48b5ec2e2124ae9e" +dependencies = [ + "serde_core", + "serde_derive", +] + +[[package]] +name = "serde_core" +version = "1.0.228" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5f0e2c6ed6606019b4e29e69dbaba95b11854410e5347d525002456dbbb786b6" +checksum = "41d385c7d4ca58e59fc732af25c3983b67ac852c1a25000afe1175de458b67ad" dependencies = [ "serde_derive", ] [[package]] name = "serde_derive" -version = "1.0.219" +version = "1.0.228" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5b0276cf7f2c73365f7157c8123c21cd9a50fbbd844757af28ca1f5925fc2a00" +checksum = "d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79" dependencies = [ "proc-macro2", "quote", "syn 2.0.104", ] +[[package]] +name = "serde_json" +version = "1.0.149" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "83fc039473c5595ace860d8c4fafa220ff474b3fc6bfdb4293327f1a37e94d86" +dependencies = [ + "itoa", + "memchr", + "serde", + "serde_core", + "zmij", +] + [[package]] name = "smallvec" version = "1.15.1" @@ -400,12 +483,36 @@ version = "0.12.16" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1" +[[package]] +name = "tinyvec" +version = "1.11.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "3e61e67053d25a4e82c844e8424039d9745781b3fc4f32b8d55ed50f5f667ef3" +dependencies = [ + "tinyvec_macros", +] + +[[package]] +name = "tinyvec_macros" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20" + [[package]] name = "unicode-ident" version = "1.0.18" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "5a5f39404a5da50712a4c1eecf25e90dd62b613502b7e925fd4e4d19b5c96512" +[[package]] +name = "unicode-normalization" +version = "0.1.25" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5fd4f6878c9cb28d874b009da9e8d183b5abc80117c40bbd187a1fde336be6e8" +dependencies = [ + "tinyvec", +] + [[package]] name = "unindent" version = "0.1.11" @@ -422,6 +529,12 @@ dependencies = [ "winapi-util", ] +[[package]] +name = "wasi" +version = "0.11.1+wasi-snapshot-preview1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ccf3ec651a847eb01de73ccad15eb7d99f80485de043efb2f370cd654f4ea44b" + [[package]] name = "winapi-util" version = "0.1.9" @@ -503,3 +616,29 @@ name = "windows_x86_64_msvc" version = "0.52.6" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec" + +[[package]] +name = "zerocopy" +version = "0.8.48" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "eed437bf9d6692032087e337407a86f04cd8d6a16a37199ed57949d415bd68e9" +dependencies = [ + "zerocopy-derive", +] + +[[package]] +name = "zerocopy-derive" +version = "0.8.48" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "70e3cd084b1788766f53af483dd21f93881ff30d7320490ec3ef7526d203bad4" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.104", +] + +[[package]] +name = "zmij" +version = "1.0.21" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" diff --git a/rust/glossapi_rs_noise/Cargo.toml b/rust/glossapi_rs_noise/Cargo.toml index 8dfa7bc..32bc552 100644 --- a/rust/glossapi_rs_noise/Cargo.toml +++ b/rust/glossapi_rs_noise/Cargo.toml @@ -20,3 +20,9 @@ csv = "1.3.0" pyo3 = { version = "0.19.0", features = ["extension-module", "abi3-py38", "macros"] } anyhow = "1" regex = "1.10" +glossapi_rs_common = { path = "../glossapi_rs_common" } +rand = { version = "0.8", features = ["std_rng"] } +unicode-normalization = "0.1" +serde = { version = "1", features = ["derive"] } +serde_json = "1" +aho-corasick = "1" diff --git a/rust/glossapi_rs_noise/src/lib.rs b/rust/glossapi_rs_noise/src/lib.rs index 33ae607..b8d212a 100644 --- a/rust/glossapi_rs_noise/src/lib.rs +++ b/rust/glossapi_rs_noise/src/lib.rs @@ -1,75 +1,288 @@ -//! PyO3 bindings for noise-based markdown quality metrics +//! PyO3 bindings for noise-based markdown quality metrics. +//! +//! # Boundary with `glossapi_rs_cleaner` +//! +//! Per `CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25` Point 7, this crate +//! and the cleaner crate have a strict ownership split: +//! +//! - **`glossapi_rs_cleaner`** owns *cleaning behaviour* AND its +//! *production-aligned counters*. The cleaner emits per-rule match +//! counts (`rule_a_match_count`, `rule_b_match_count`, +//! `residue_line_drop_count`) directly in `CleanStats`, aligned by +//! construction with what the cleaner actually acts on. Production +//! driver scripts (e.g. `clean_and_stats_rowsharded.py`) source +//! their parquet counter columns from the cleaner, NOT from this +//! crate. +//! - **`glossapi_rs_noise`** (this crate) owns *diagnostic / +//! exploratory / debug exports*: OCR-side scoring +//! (`evaluate_page_character_noise`, `score_markdown_*`), +//! word-repeat / numeric-debug span extraction, and the +//! token-category match exports +//! (`export_token_category_debug_pages`, +//! `match_token_category_debug_text`) used by +//! `Corpus.clean_token_category_debug` for review-wave bundling. +//! +//! Production cleaning never imports anything from this crate. Debug +//! / discovery / inspection workflows do. Keep new functionality on +//! the side of this split that matches its purpose; if a counter is +//! a faithful mirror of cleaner activity, it belongs in the cleaner. + +// Lint posture (CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Item 5): +// `dead_code` is allowed crate-wide because several noise-side helpers +// (e.g. `annotate_line_with_numeric_debug_matches`, +// `match_token_category_debug_text_internal` / +// `export_token_category_debug_pages_internal`) are kept as part of +// the diagnostic / debug-export surface — invoked by tests, by the +// PyO3 wrappers, or as part of the discovery toolkit even if some +// branches don't currently reach them. Real bugs (unused vars, +// unread assignments) still warn. +#![allow(dead_code)] mod noise_metrics; -use pyo3::prelude::*; -use pyo3::types::PyTuple; use noise_metrics::{ - score_markdown_file_internal, - score_markdown_directory_internal, - score_markdown_file_detailed_internal, - score_markdown_directory_detailed_internal, + annotate_numeric_debug_page_internal, evaluate_page_character_noise_internal, + export_numeric_match_debug_pages_internal, export_ocr_match_debug_pages_internal, + export_token_category_debug_pages_internal, find_hybrid_repeat_spans_internal, + find_labeled_shared_repeat_spans_internal, find_numeric_debug_page_spans_internal, + find_word_repeat_spans_internal, match_token_category_debug_text_internal, + score_markdown_directory_detailed_internal, score_markdown_directory_internal, + score_markdown_directory_ocr_profile_internal, score_markdown_file_detailed_internal, + score_markdown_file_internal, score_text_detailed_internal, score_texts_detailed_internal, }; +use pyo3::prelude::*; +use pyo3::types::PyDict; +use pyo3::types::PyTuple; /// Compute the badness score for a single markdown file. /// Returns the numeric score as `float`. #[pyfunction] fn score_markdown_file(path: &str) -> PyResult { - score_markdown_file_internal(std::path::Path::new(path)).map_err(|e| PyErr::new::(e.to_string())) + score_markdown_file_internal(std::path::Path::new(path)) + .map_err(|e| PyErr::new::(e.to_string())) } /// Compute badness scores for all `.md` files under `input_dir` in parallel. /// The result is a list of `(file_path, score, latin_percentage)` tuples. #[pyfunction] -fn score_markdown_directory(input_dir: &str, n_threads: Option) -> PyResult> { - score_markdown_directory_internal(std::path::Path::new(input_dir), n_threads).map_err(|e| PyErr::new::(e.to_string())) +fn score_markdown_directory( + input_dir: &str, + n_threads: Option, +) -> PyResult> { + score_markdown_directory_internal(std::path::Path::new(input_dir), n_threads) + .map_err(|e| PyErr::new::(e.to_string())) } /// Detailed score for a single file: returns a Python tuple of all raw and derived metrics #[pyfunction] fn score_markdown_file_detailed(py: Python<'_>, path: &str) -> PyResult> { let ( - score, latin_pct, table_ratio, poly_ratio, - len_greek, total_words, - v_pen, c_pen, bad_dbl, misplaced_sigma, invalid_bigram, long_word_count, longest_word, short_word_count, max_run, - v_rate, c_rate, d_rate, sigma_end_rate, bigram_rate, long_word_rate, short_ratio, short_pen, - flags + score, + latin_pct, + table_ratio, + poly_ratio, + len_greek, + total_words, + v_pen, + c_pen, + bad_dbl, + misplaced_sigma, + invalid_bigram, + long_word_count, + longest_word, + short_word_count, + max_run, + v_rate, + c_rate, + d_rate, + sigma_end_rate, + bigram_rate, + long_word_rate, + short_ratio, + short_pen, + flags, ) = score_markdown_file_detailed_internal(std::path::Path::new(path)) .map_err(|e| PyErr::new::(e.to_string()))?; let tup = PyTuple::new( py, vec![ - score.into_py(py), latin_pct.into_py(py), table_ratio.into_py(py), poly_ratio.into_py(py), - (len_greek as u128).into_py(py), (total_words as u128).into_py(py), - (v_pen as u128).into_py(py), (c_pen as u128).into_py(py), (bad_dbl as u128).into_py(py), (misplaced_sigma as u128).into_py(py), (invalid_bigram as u128).into_py(py), (long_word_count as u128).into_py(py), (longest_word as u128).into_py(py), (short_word_count as u128).into_py(py), (max_run as u128).into_py(py), - v_rate.into_py(py), c_rate.into_py(py), d_rate.into_py(py), sigma_end_rate.into_py(py), bigram_rate.into_py(py), long_word_rate.into_py(py), short_ratio.into_py(py), short_pen.into_py(py), + score.into_py(py), + latin_pct.into_py(py), + table_ratio.into_py(py), + poly_ratio.into_py(py), + (len_greek as u128).into_py(py), + (total_words as u128).into_py(py), + (v_pen as u128).into_py(py), + (c_pen as u128).into_py(py), + (bad_dbl as u128).into_py(py), + (misplaced_sigma as u128).into_py(py), + (invalid_bigram as u128).into_py(py), + (long_word_count as u128).into_py(py), + (longest_word as u128).into_py(py), + (short_word_count as u128).into_py(py), + (max_run as u128).into_py(py), + v_rate.into_py(py), + c_rate.into_py(py), + d_rate.into_py(py), + sigma_end_rate.into_py(py), + bigram_rate.into_py(py), + long_word_rate.into_py(py), + short_ratio.into_py(py), + short_pen.into_py(py), flags.into_py(py), ], ); Ok(tup.into()) } -/// Detailed scores for directory: returns a list of Python tuples with path followed by all metrics +fn detailed_score_tuple(py: Python<'_>, metrics: noise_metrics::DetailedScore) -> Py { + let ( + score, + latin_pct, + table_ratio, + poly_ratio, + len_greek, + total_words, + v_pen, + c_pen, + bad_dbl, + misplaced_sigma, + invalid_bigram, + long_word_count, + longest_word, + short_word_count, + max_run, + v_rate, + c_rate, + d_rate, + sigma_end_rate, + bigram_rate, + long_word_rate, + short_ratio, + short_pen, + flags, + ) = metrics; + PyTuple::new( + py, + vec![ + score.into_py(py), + latin_pct.into_py(py), + table_ratio.into_py(py), + poly_ratio.into_py(py), + (len_greek as u128).into_py(py), + (total_words as u128).into_py(py), + (v_pen as u128).into_py(py), + (c_pen as u128).into_py(py), + (bad_dbl as u128).into_py(py), + (misplaced_sigma as u128).into_py(py), + (invalid_bigram as u128).into_py(py), + (long_word_count as u128).into_py(py), + (longest_word as u128).into_py(py), + (short_word_count as u128).into_py(py), + (max_run as u128).into_py(py), + v_rate.into_py(py), + c_rate.into_py(py), + d_rate.into_py(py), + sigma_end_rate.into_py(py), + bigram_rate.into_py(py), + long_word_rate.into_py(py), + short_ratio.into_py(py), + short_pen.into_py(py), + flags.into_py(py), + ], + ) + .into() +} + +#[pyfunction] +fn score_text_detailed(py: Python<'_>, text: &str) -> PyResult> { + let metrics = py.allow_threads(|| score_text_detailed_internal(text)); + Ok(detailed_score_tuple(py, metrics)) +} + #[pyfunction] -fn score_markdown_directory_detailed(py: Python<'_>, input_dir: &str, n_threads: Option) -> PyResult>> { - let rows = score_markdown_directory_detailed_internal(std::path::Path::new(input_dir), n_threads) +#[pyo3(signature = (texts, n_threads=None))] +fn score_texts_detailed( + py: Python<'_>, + texts: Vec, + n_threads: Option, +) -> PyResult>> { + let rows = py + .allow_threads(move || score_texts_detailed_internal(texts, n_threads)) .map_err(|e| PyErr::new::(e.to_string()))?; let mut out: Vec> = Vec::with_capacity(rows.len()); + for metrics in rows { + out.push(detailed_score_tuple(py, metrics)); + } + Ok(out) +} + +/// Detailed scores for directory: returns a list of Python tuples with path followed by all metrics +#[pyfunction] +fn score_markdown_directory_detailed( + py: Python<'_>, + input_dir: &str, + n_threads: Option, +) -> PyResult>> { + let rows = + score_markdown_directory_detailed_internal(std::path::Path::new(input_dir), n_threads) + .map_err(|e| PyErr::new::(e.to_string()))?; + let mut out: Vec> = Vec::with_capacity(rows.len()); for ( - path, score, latin_pct, table_ratio, poly_ratio, - len_greek, total_words, - v_pen, c_pen, bad_dbl, misplaced_sigma, invalid_bigram, long_word_count, longest_word, short_word_count, max_run, - v_rate, c_rate, d_rate, sigma_end_rate, bigram_rate, long_word_rate, short_ratio, short_pen, - flags - ) in rows.into_iter() { + path, + score, + latin_pct, + table_ratio, + poly_ratio, + len_greek, + total_words, + v_pen, + c_pen, + bad_dbl, + misplaced_sigma, + invalid_bigram, + long_word_count, + longest_word, + short_word_count, + max_run, + v_rate, + c_rate, + d_rate, + sigma_end_rate, + bigram_rate, + long_word_rate, + short_ratio, + short_pen, + flags, + ) in rows.into_iter() + { let tup = PyTuple::new( py, vec![ path.into_py(py), - score.into_py(py), latin_pct.into_py(py), table_ratio.into_py(py), poly_ratio.into_py(py), - (len_greek as u128).into_py(py), (total_words as u128).into_py(py), - (v_pen as u128).into_py(py), (c_pen as u128).into_py(py), (bad_dbl as u128).into_py(py), (misplaced_sigma as u128).into_py(py), (invalid_bigram as u128).into_py(py), (long_word_count as u128).into_py(py), (longest_word as u128).into_py(py), (short_word_count as u128).into_py(py), (max_run as u128).into_py(py), - v_rate.into_py(py), c_rate.into_py(py), d_rate.into_py(py), sigma_end_rate.into_py(py), bigram_rate.into_py(py), long_word_rate.into_py(py), short_ratio.into_py(py), short_pen.into_py(py), + score.into_py(py), + latin_pct.into_py(py), + table_ratio.into_py(py), + poly_ratio.into_py(py), + (len_greek as u128).into_py(py), + (total_words as u128).into_py(py), + (v_pen as u128).into_py(py), + (c_pen as u128).into_py(py), + (bad_dbl as u128).into_py(py), + (misplaced_sigma as u128).into_py(py), + (invalid_bigram as u128).into_py(py), + (long_word_count as u128).into_py(py), + (longest_word as u128).into_py(py), + (short_word_count as u128).into_py(py), + (max_run as u128).into_py(py), + v_rate.into_py(py), + c_rate.into_py(py), + d_rate.into_py(py), + sigma_end_rate.into_py(py), + bigram_rate.into_py(py), + long_word_rate.into_py(py), + short_ratio.into_py(py), + short_pen.into_py(py), flags.into_py(py), ], ); @@ -78,11 +291,380 @@ fn score_markdown_directory_detailed(py: Python<'_>, input_dir: &str, n_threads: Ok(out) } +#[pyfunction] +#[pyo3(signature = (input_dir, n_threads=None, min_repeat_run=6))] +fn score_markdown_directory_ocr_profile( + py: Python<'_>, + input_dir: &str, + n_threads: Option, + min_repeat_run: u64, +) -> PyResult>> { + let rows = score_markdown_directory_ocr_profile_internal( + std::path::Path::new(input_dir), + n_threads, + min_repeat_run, + ) + .map_err(|e| PyErr::new::(e.to_string()))?; + + let mut out: Vec> = Vec::with_capacity(rows.len()); + for row in rows { + let item = PyDict::new(py); + item.set_item("path", row.path)?; + item.set_item("percentage_greek", row.percentage_greek)?; + item.set_item("latin_percentage", row.latin_percentage)?; + item.set_item("polytonic_ratio", row.polytonic_ratio)?; + item.set_item("non_whitespace_chars", row.non_whitespace_chars)?; + item.set_item("greek_char_count", row.greek_char_count)?; + item.set_item("latin_char_count", row.latin_char_count)?; + item.set_item("ocr_repeat_phrase_run_max", row.ocr_repeat_phrase_run_max)?; + item.set_item("ocr_repeat_line_run_max", row.ocr_repeat_line_run_max)?; + item.set_item( + "ocr_repeat_suspicious_line_count", + row.ocr_repeat_suspicious_line_count, + )?; + item.set_item( + "ocr_repeat_suspicious_line_ratio", + row.ocr_repeat_suspicious_line_ratio, + )?; + item.set_item("ocr_noise_suspect", row.ocr_noise_suspect)?; + item.set_item("ocr_noise_flags", row.ocr_noise_flags)?; + out.push(item.into()); + } + Ok(out) +} + +#[pyfunction] +#[pyo3(signature = (input_dir, output_dir, n_threads=None, min_repeat_run=6, max_pages=None, sample_seed=0))] +fn export_ocr_match_debug_pages( + py: Python<'_>, + input_dir: &str, + output_dir: &str, + n_threads: Option, + min_repeat_run: u64, + max_pages: Option, + sample_seed: u64, +) -> PyResult>> { + let rows = export_ocr_match_debug_pages_internal( + std::path::Path::new(input_dir), + std::path::Path::new(output_dir), + n_threads, + min_repeat_run, + max_pages, + sample_seed, + ) + .map_err(|e| PyErr::new::(e.to_string()))?; + + let mut out: Vec> = Vec::with_capacity(rows.len()); + for row in rows { + let item = PyDict::new(py); + item.set_item("source_path", row.source_path)?; + item.set_item("output_path", row.output_path)?; + item.set_item("source_stem", row.source_stem)?; + item.set_item("base_stem", row.base_stem)?; + item.set_item("page_number", row.page_number)?; + item.set_item("page_index_in_file", row.page_index_in_file)?; + item.set_item("match_types", row.match_types)?; + item.set_item("match_count", row.match_count)?; + out.push(item.into()); + } + Ok(out) +} + +#[pyfunction] +#[pyo3(signature = (input_dir, output_dir, n_threads=None, min_progress_steps=10, min_repeat_steps=8, min_same_digit_steps=10, max_pages=None, sample_seed=0))] +fn export_numeric_match_debug_pages( + py: Python<'_>, + input_dir: &str, + output_dir: &str, + n_threads: Option, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, + max_pages: Option, + sample_seed: u64, +) -> PyResult>> { + let rows = export_numeric_match_debug_pages_internal( + std::path::Path::new(input_dir), + std::path::Path::new(output_dir), + n_threads, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + max_pages, + sample_seed, + ) + .map_err(|e| PyErr::new::(e.to_string()))?; + + let mut out: Vec> = Vec::with_capacity(rows.len()); + for row in rows { + let item = PyDict::new(py); + item.set_item("source_path", row.source_path)?; + item.set_item("output_path", row.output_path)?; + item.set_item("source_stem", row.source_stem)?; + item.set_item("base_stem", row.base_stem)?; + item.set_item("page_number", row.page_number)?; + item.set_item("page_index_in_file", row.page_index_in_file)?; + item.set_item("match_types", row.match_types)?; + item.set_item("match_count", row.match_count)?; + out.push(item.into()); + } + Ok(out) +} + +#[pyfunction] +#[pyo3(signature = (input_dir, output_dir, category_specs_path, n_threads=None, max_pages=None, sample_seed=0, synthetic_page_target_chars=4000, synthetic_page_min_header_chars=1200, synthetic_page_hard_max_chars=6000))] +fn export_token_category_debug_pages( + py: Python<'_>, + input_dir: &str, + output_dir: &str, + category_specs_path: &str, + n_threads: Option, + max_pages: Option, + sample_seed: u64, + synthetic_page_target_chars: usize, + synthetic_page_min_header_chars: usize, + synthetic_page_hard_max_chars: usize, +) -> PyResult>> { + let rows = export_token_category_debug_pages_internal( + std::path::Path::new(input_dir), + std::path::Path::new(output_dir), + std::path::Path::new(category_specs_path), + n_threads, + max_pages, + sample_seed, + synthetic_page_target_chars, + synthetic_page_min_header_chars, + synthetic_page_hard_max_chars, + ) + .map_err(|e| PyErr::new::(e.to_string()))?; + + let mut out: Vec> = Vec::with_capacity(rows.len()); + for row in rows { + out.push(token_category_row_to_py(py, row)?); + } + Ok(out) +} + +#[pyfunction] +#[pyo3(signature = (text, output_dir, category_specs_path, source_path, source_stem, base_stem, start_page=1, synthetic_page_target_chars=4000, synthetic_page_min_header_chars=1200, synthetic_page_hard_max_chars=6000, write_files=true))] +fn match_token_category_debug_text( + py: Python<'_>, + text: &str, + output_dir: &str, + category_specs_path: &str, + source_path: &str, + source_stem: &str, + base_stem: &str, + start_page: u64, + synthetic_page_target_chars: usize, + synthetic_page_min_header_chars: usize, + synthetic_page_hard_max_chars: usize, + write_files: bool, +) -> PyResult>> { + let rows = match_token_category_debug_text_internal( + std::path::Path::new(output_dir), + std::path::Path::new(category_specs_path), + source_path, + source_stem, + base_stem, + start_page, + text, + synthetic_page_target_chars, + synthetic_page_min_header_chars, + synthetic_page_hard_max_chars, + write_files, + ) + .map_err(|e| PyErr::new::(e.to_string()))?; + + let mut out: Vec> = Vec::with_capacity(rows.len()); + for row in rows { + out.push(token_category_row_to_py(py, row)?); + } + Ok(out) +} + +fn token_category_row_to_py( + py: Python<'_>, + row: noise_metrics::TokenCategoryDebugPageRow, +) -> PyResult> { + let item = PyDict::new(py); + item.set_item("source_path", row.source_path)?; + item.set_item("output_path", row.output_path)?; + item.set_item("source_stem", row.source_stem)?; + item.set_item("base_stem", row.base_stem)?; + item.set_item("page_kind", row.page_kind)?; + item.set_item("page_number", row.page_number)?; + item.set_item("page_index_in_file", row.page_index_in_file)?; + item.set_item("page_char_count", row.page_char_count)?; + item.set_item("match_categories", row.match_categories)?; + item.set_item("match_pattern_families", row.match_pattern_families)?; + item.set_item("match_count", row.match_count)?; + item.set_item("page_text", row.page_text)?; + item.set_item("matches_json", row.matches_json)?; + let counts = PyDict::new(py); + for (cat, n) in &row.per_category_match_count { + counts.set_item(cat, n)?; + } + item.set_item("per_category_match_count", counts)?; + Ok(item.into()) +} + +#[pyfunction] +#[pyo3(signature = (page, min_progress_steps=10, min_repeat_steps=8, min_same_digit_steps=10))] +fn annotate_numeric_debug_page( + py: Python<'_>, + page: &str, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> PyResult>> { + let Some((annotated_page, match_types, match_count)) = annotate_numeric_debug_page_internal( + page, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ) else { + return Ok(None); + }; + + let item = PyDict::new(py); + item.set_item("annotated_page", annotated_page)?; + item.set_item("match_types", match_types)?; + item.set_item("match_count", match_count)?; + Ok(Some(item.into())) +} + +#[pyfunction] +#[pyo3(signature = (page, min_progress_steps=10, min_repeat_steps=8, min_same_digit_steps=10))] +fn find_numeric_debug_page_spans( + py: Python<'_>, + page: &str, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> PyResult>> { + let spans = py.allow_threads(|| { + find_numeric_debug_page_spans_internal( + page, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ) + }); + let mut out: Vec> = Vec::with_capacity(spans.len()); + for span in spans { + let item = PyDict::new(py); + item.set_item("start", span.start)?; + item.set_item("end", span.end)?; + item.set_item("match_type", span.match_type)?; + out.push(item.into()); + } + Ok(out) +} + +#[pyfunction] +#[pyo3(signature = (normalized_text, rep_threshold=4, min_period=3, window=96))] +fn find_word_repeat_spans( + py: Python<'_>, + normalized_text: &str, + rep_threshold: usize, + min_period: usize, + window: usize, +) -> PyResult>> { + let spans = py.allow_threads(|| { + find_word_repeat_spans_internal(normalized_text, rep_threshold, min_period, window) + }); + let mut out: Vec> = Vec::with_capacity(spans.len()); + for span in spans { + let item = PyDict::new(py); + item.set_item("start", span.start)?; + item.set_item("end", span.end)?; + item.set_item("period", span.period)?; + item.set_item("repetitions", span.repetitions)?; + item.set_item("tail_chars", span.tail_chars)?; + out.push(item.into()); + } + Ok(out) +} + +#[pyfunction] +fn find_hybrid_repeat_spans(py: Python<'_>, analysis_text: &str) -> PyResult>> { + let spans = py.allow_threads(|| find_hybrid_repeat_spans_internal(analysis_text)); + let mut out: Vec> = Vec::with_capacity(spans.len()); + for span in spans { + let item = PyDict::new(py); + item.set_item("start", span.start)?; + item.set_item("end", span.end)?; + item.set_item("match_types", vec!["hybrid_repeat"])?; + item.set_item("category", "hybrid")?; + item.set_item("kind", span.kind)?; + item.set_item("item_count", span.item_count)?; + if let Some(cycle_len) = span.cycle_len { + item.set_item("cycle_len", cycle_len)?; + } + out.push(item.into()); + } + Ok(out) +} + +#[pyfunction] +#[pyo3(signature = (analysis_text, rep_threshold=4, min_period=3, window=96))] +fn find_labeled_shared_repeat_spans( + py: Python<'_>, + analysis_text: &str, + rep_threshold: usize, + min_period: usize, + window: usize, +) -> PyResult>> { + let spans = py.allow_threads(|| { + find_labeled_shared_repeat_spans_internal(analysis_text, rep_threshold, min_period, window) + }); + let mut out: Vec> = Vec::with_capacity(spans.len()); + for span in spans { + let item = PyDict::new(py); + item.set_item("start", span.start)?; + item.set_item("end", span.end)?; + item.set_item("period", span.period)?; + item.set_item("repetitions", span.repetitions)?; + item.set_item("tail_chars", span.tail_chars)?; + item.set_item("match_type", span.match_type)?; + out.push(item.into()); + } + Ok(out) +} + +#[pyfunction] +fn evaluate_page_character_noise(py: Python<'_>, page: &str) -> PyResult> { + let metrics = py.allow_threads(|| evaluate_page_character_noise_internal(page)); + let item = PyDict::new(py); + item.set_item("total_chars", metrics.total_chars)?; + item.set_item("bad_char_count", metrics.bad_char_count)?; + item.set_item("bad_char_ratio", metrics.bad_char_ratio)?; + item.set_item("control_count", metrics.control_count)?; + item.set_item("private_use_count", metrics.private_use_count)?; + item.set_item("cjk_count", metrics.cjk_count)?; + item.set_item("replacement_count", metrics.replacement_count)?; + Ok(item.into()) +} + #[pymodule] fn glossapi_rs_noise(_py: Python, m: &PyModule) -> PyResult<()> { m.add_function(wrap_pyfunction!(score_markdown_file, m)?)?; m.add_function(wrap_pyfunction!(score_markdown_directory, m)?)?; m.add_function(wrap_pyfunction!(score_markdown_file_detailed, m)?)?; + m.add_function(wrap_pyfunction!(score_text_detailed, m)?)?; + m.add_function(wrap_pyfunction!(score_texts_detailed, m)?)?; m.add_function(wrap_pyfunction!(score_markdown_directory_detailed, m)?)?; + m.add_function(wrap_pyfunction!(score_markdown_directory_ocr_profile, m)?)?; + m.add_function(wrap_pyfunction!(export_ocr_match_debug_pages, m)?)?; + m.add_function(wrap_pyfunction!(export_numeric_match_debug_pages, m)?)?; + m.add_function(wrap_pyfunction!(export_token_category_debug_pages, m)?)?; + m.add_function(wrap_pyfunction!(match_token_category_debug_text, m)?)?; + m.add_function(wrap_pyfunction!(annotate_numeric_debug_page, m)?)?; + m.add_function(wrap_pyfunction!(find_numeric_debug_page_spans, m)?)?; + m.add_function(wrap_pyfunction!(find_word_repeat_spans, m)?)?; + m.add_function(wrap_pyfunction!(find_hybrid_repeat_spans, m)?)?; + m.add_function(wrap_pyfunction!(find_labeled_shared_repeat_spans, m)?)?; + m.add_function(wrap_pyfunction!(evaluate_page_character_noise, m)?)?; Ok(()) } diff --git a/rust/glossapi_rs_noise/src/noise_metrics.rs b/rust/glossapi_rs_noise/src/noise_metrics.rs index 105b823..6f674fa 100644 --- a/rust/glossapi_rs_noise/src/noise_metrics.rs +++ b/rust/glossapi_rs_noise/src/noise_metrics.rs @@ -69,134 +69,3326 @@ Positions in detailed tuple (suggested append): Note: after adding these fields, bump the Python bindings accordingly and propagate polytonic_ratio (already computed here) into downstream parquet (already wired in Corpus.clean()). */ - +use aho_corasick::{AhoCorasick, AhoCorasickBuilder, MatchKind}; +use glossapi_rs_common::{is_combining_mark, is_greek, scan_script_metrics, ScriptScanner}; +use once_cell::sync::Lazy; +use rand::rngs::StdRng; +use rand::seq::SliceRandom; +use rand::SeedableRng; use rayon::prelude::*; +use rayon::ThreadPoolBuilder; +use regex::{Regex, RegexBuilder}; +use serde::{Deserialize, Serialize}; use std::fs::{self, File}; use std::io::Read; use std::path::{Path, PathBuf}; +use std::sync::{Arc, Mutex}; +use unicode_normalization::UnicodeNormalization; use walkdir::WalkDir; // Avoid heavy regex for table detection; use lightweight checks instead -const GREEK_BLOCK_1: std::ops::RangeInclusive = 0x0370..=0x03FF; // Greek & Coptic -const GREEK_BLOCK_2: std::ops::RangeInclusive = 0x1F00..=0x1FFF; // Greek Extended +#[inline(always)] +fn is_vowel(cp: u32) -> bool { + matches!( + cp, + 0x0391 | 0x03B1 | 0x0386 | 0x03AC | // Αα Άά + 0x0395 | 0x03B5 | 0x0388 | 0x03AD | // Εε Έέ + 0x0397 | 0x03B7 | 0x0389 | 0x03AE | // Ηη Ήή + 0x0399 | 0x03B9 | 0x038A | 0x03AF | 0x03CA | 0x03CB | 0x039F | 0x03BF | + 0x038C | 0x03CC | 0x03C5 | 0x03B0 | 0x03CD | 0x03A5 | 0x038E | + 0x03A9 | 0x03C9 | 0x038F | 0x03CE + ) +} + +const LONG_WORD_LIMIT: u64 = 21; +const SHORT_WORD_LIMIT: u64 = 3; +const PAGE_SPLIT_MARKER: &str = "<--- Page Split --->"; +const NUMERIC_PAGE_COLLAPSE_MIN_TOKENS: u64 = 64; +const NUMERIC_PAGE_COLLAPSE_MIN_ATOMS: u64 = 64; +const NUMERIC_BLOCK_SEED_MIN_ATOMS: usize = 8; +// Baseline for short words per 1000 Greek characters (empirically ~26 on clean texts) +const SHORT_BASELINE_PER_1000: f64 = 26.0; + +static TOKEN_CATEGORY_SPEC_CACHE: Lazy< + Mutex>>>, +> = Lazy::new(|| Mutex::new(std::collections::HashMap::new())); + +#[inline] +fn to_lower_fast(cp: u32) -> u32 { + // Fast path for basic Greek capitals: add 0x20; otherwise return as-is + if (0x0391..=0x03A9).contains(&cp) { + cp + 0x20 + } else { + cp + } +} + +#[inline] +fn is_invalid_bigram_pair(prev_low: u32, curr_low: u32) -> bool { + match (prev_low, curr_low) { + // κ/γ/χ + ξ + (0x03BA, 0x03BE) | (0x03B3, 0x03BE) | (0x03C7, 0x03BE) + // π/β/φ + ψ + | (0x03C0, 0x03C8) | (0x03B2, 0x03C8) | (0x03C6, 0x03C8) + // ρλ, μρ, γβ, δτ, τδ, βπ, πβ + | (0x03C1, 0x03BB) | (0x03BC, 0x03C1) | (0x03B3, 0x03B2) + | (0x03B4, 0x03C4) | (0x03C4, 0x03B4) | (0x03B2, 0x03C0) | (0x03C0, 0x03B2) => true, + _ => false, + } +} + +static ALLOWED_DOUBLE: [u32; 9] = [ + 0x03BB, 0x03BC, 0x03BD, 0x03C1, 0x03C3, 0x03C4, 0x03BA, 0x03C0, 0x03B3, +]; + +fn allowed_double(cp: u32) -> bool { + ALLOWED_DOUBLE.contains(&cp) +} + +#[inline(always)] +fn commit_bad_double_run(cp: u32, run_len: u64, bad_double: &mut u64) { + if cp != 0 && run_len == 2 && !allowed_double(cp) { + *bad_double += 1; + } +} + +#[inline] +fn is_table_line_trimmed(trimmed: &str) -> bool { + // A simple check equivalent to /^\s*\|.*\|\s*$/ after trimming + // i.e., line begins and ends with a '|' ignoring outer whitespace + !trimmed.is_empty() + && trimmed.as_bytes()[0] == b'|' + && trimmed.as_bytes()[trimmed.len() - 1] == b'|' +} + +fn table_line_ratio_and_filtered(text: &str) -> (f64, Option, usize, usize) { + let mut non_empty = 0usize; + let mut table_like = 0usize; + // First pass: count table-like rows without allocating filtered buffer unless needed + for line in text.lines() { + let trimmed = line.trim(); + if !trimmed.is_empty() { + non_empty += 1; + if is_table_line_trimmed(trimmed) { + table_like += 1; + } + } + } + let ratio = if non_empty > 0 { + table_like as f64 / non_empty as f64 + } else { + 0.0 + }; + if table_like == 0 { + return (ratio, None, non_empty, table_like); + } + // Second pass only if we actually need a filtered buffer (preserve original newlines) + let mut filtered = String::with_capacity(text.len()); + for seg in text.split_inclusive('\n') { + let trimmed = seg.trim(); + if trimmed.is_empty() || !is_table_line_trimmed(trimmed) { + filtered.push_str(seg); + } + } + (ratio, Some(filtered), non_empty, table_like) +} + +fn compute_latin_pct(buf: &[u8]) -> f64 { + let latin_chars = buf + .iter() + .filter(|&&b| (b >= 0x41 && b <= 0x5A) || (b >= 0x61 && b <= 0x7A)) + .count(); + latin_chars as f64 / (buf.len() as f64) +} + +#[derive(Debug, Clone)] +pub struct OcrProfileRow { + pub path: String, + pub percentage_greek: f64, + pub latin_percentage: f64, + pub polytonic_ratio: f64, + pub non_whitespace_chars: u64, + pub greek_char_count: u64, + pub latin_char_count: u64, + pub ocr_repeat_phrase_run_max: u64, + pub ocr_repeat_line_run_max: u64, + pub ocr_repeat_suspicious_line_count: u64, + pub ocr_repeat_suspicious_line_ratio: f64, + pub ocr_noise_suspect: bool, + pub ocr_noise_flags: String, +} + +#[derive(Debug, Clone)] +pub struct OcrDebugPageRow { + pub source_path: String, + pub output_path: String, + pub source_stem: String, + pub base_stem: String, + pub page_number: u64, + pub page_index_in_file: u64, + pub match_types: String, + pub match_count: u64, +} + +#[derive(Debug, Clone)] +struct OcrDebugPageCandidate { + source_path: String, + source_stem: String, + base_stem: String, + page_number: u64, + page_index_in_file: u64, +} + +#[derive(Debug, Clone, Deserialize)] +#[serde(rename_all = "snake_case")] +struct TokenCategorySpec { + category: String, + #[serde(default)] + pattern: Option, + #[serde(default)] + pattern_family: Option, + #[serde(default)] + match_kind: Option, + #[serde(default)] + case_insensitive: bool, + #[serde(default)] + literals: Option>, + #[serde(default)] + literals_path: Option, +} + +#[derive(Debug, Clone, Deserialize)] +#[serde(untagged)] +enum TokenCategoryLiteralPayload { + List(Vec), + Map(std::collections::BTreeMap), +} + +#[derive(Debug, Clone)] +enum TokenCategoryMatcher { + Regex(Regex), + LiteralSet(AhoCorasick), +} + +#[derive(Debug, Clone)] +struct CompiledTokenCategorySpec { + category: String, + pattern_family: String, + matcher: TokenCategoryMatcher, +} + +#[derive(Debug, Clone)] +struct TokenCategoryRawSpan { + start: usize, + end: usize, + category: String, + pattern_family: String, + matched_text: String, +} + +#[derive(Debug, Clone)] +struct TokenCategoryMergedSpan { + start: usize, + end: usize, + categories: Vec, + pattern_families: Vec, + raw_texts: Vec, +} + +#[derive(Debug, Clone)] +struct TokenCategoryDebugPageCandidate { + source_path: String, + source_stem: String, + base_stem: String, + page_kind: String, + page_number: u64, + page_index_in_file: u64, + page_char_count: u64, + page_text: String, + merged_spans: Vec, +} + +#[derive(Debug, Clone, Serialize)] +struct TokenCategoryExportMatchRow { + match_index_in_page: u64, + start: usize, + end: usize, + categories: Vec, + pattern_families: Vec, + matched_text: String, + raw_texts: Vec, +} + +#[derive(Debug, Clone)] +pub struct TokenCategoryDebugPageRow { + pub source_path: String, + pub output_path: String, + pub source_stem: String, + pub base_stem: String, + pub page_kind: String, + pub page_number: u64, + pub page_index_in_file: u64, + pub page_char_count: u64, + pub match_categories: String, + pub match_pattern_families: String, + pub match_count: u64, + pub page_text: String, + pub matches_json: String, + /// Per-category match counts (summed across all matches on the page). + /// Precomputed in Rust so the Python driver doesn't re-parse + /// matches_json just to tally counters. 2026-04-23 speedup. + pub per_category_match_count: std::collections::BTreeMap, +} + +#[derive(Debug, Clone)] +struct DebugMatchSpan { + start: usize, + end: usize, + match_type: &'static str, +} + +#[derive(Debug, Clone)] +pub struct NumericDebugSpan { + pub start: usize, + pub end: usize, + pub match_type: String, +} + +#[derive(Debug, Clone)] +pub struct WordRepeatSpan { + pub start: usize, + pub end: usize, + pub period: usize, + pub repetitions: usize, + pub tail_chars: usize, +} + +#[derive(Debug, Clone)] +pub struct HybridRepeatSpan { + pub start: usize, + pub end: usize, + pub kind: &'static str, + pub item_count: usize, + pub cycle_len: Option, +} + +#[derive(Debug, Clone)] +pub struct LabeledSharedRepeatSpan { + pub start: usize, + pub end: usize, + pub period: usize, + pub repetitions: usize, + pub tail_chars: usize, + pub match_type: &'static str, +} + +#[derive(Debug, Clone, Default)] +pub struct PageCharacterNoise { + pub total_chars: u64, + pub bad_char_count: u64, + pub bad_char_ratio: f64, + pub control_count: u64, + pub private_use_count: u64, + pub cjk_count: u64, + pub replacement_count: u64, +} + +const MERGE_SAME_CATEGORY_MAX_NONWHITESPACE_GAP: usize = 10; +const HYBRID_REPEAT_MIN_ITEMS: usize = 4; +const HYBRID_REPEAT_MIN_BODY_ALNUM: usize = 6; +const HYBRID_REPEAT_MAX_CYCLE: usize = 6; +const HYBRID_REPEAT_MIN_CYCLE_ITEMS: usize = 8; +const HYBRID_INLINE_CONTEXT_WORDS: usize = 2; +const HYBRID_INLINE_CONTEXT_MIN_ALPHA_WORDS: usize = 2; +const HYBRID_INLINE_CONTEXT_MIN_CHARS: usize = 8; +const HYBRID_INLINE_REPEAT_MIN_ITEMS: usize = 6; + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum HybridFieldKind { + HeaderCounter, + NumericValue, +} + +#[derive(Debug, Clone)] +struct HybridNumberedItem { + start: usize, + end: usize, + field_kind: HybridFieldKind, + numbers: Vec, + shape: String, + body_key: String, + body_is_full: bool, +} + +#[derive(Debug, Clone)] +struct HybridInlineItem { + start: usize, + end: usize, + clause_index: usize, + inline_context_key: String, + numeric_value: f64, +} + +#[derive(Debug, Clone)] +struct HybridCandidate { + prefix_start_byte: usize, + prefix_end_byte: usize, + field_kind: HybridFieldKind, + numbers: Vec, + shape: String, +} + +#[derive(Debug, Clone)] +struct HybridToken { + kind: HybridTokenKind, + start: usize, + end: usize, + token_key: Option, + numeric_value: Option, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +enum HybridTokenKind { + Numeric, + Alpha, +} + +#[derive(Debug, Clone, Copy)] +struct TokenSpan { + start: usize, + end: usize, +} + +#[derive(Debug, Clone, Copy, Default)] +struct NumericLineSummary { + has_alpha: bool, + rejected_non_numeric: bool, + numeric_token_count: usize, + numeric_atom_count: usize, + is_blank: bool, +} + +#[inline] +fn is_trim_numeric_edge_char(ch: char) -> bool { + ch.is_ascii_punctuation() + || matches!( + ch, + '«' | '»' | '“' | '”' | '„' | '‟' | '‘' | '’' | '‚' | '‛' + ) +} + +#[inline] +fn is_numeric_page_ignored_token(token: &str) -> bool { + !token.is_empty() + && token + .chars() + .all(|ch| !ch.is_whitespace() && !ch.is_alphanumeric()) +} + +fn trim_numeric_token_bounds(token: &str) -> Option<(usize, usize)> { + if token.is_empty() { + return None; + } + + let mut start = 0usize; + let mut end = token.len(); + + while start < end { + let ch = token[start..].chars().next()?; + if ch.is_ascii_digit() { + break; + } + if is_trim_numeric_edge_char(ch) { + start += ch.len_utf8(); + } else { + return None; + } + } + + while start < end { + let ch = token[..end].chars().next_back()?; + if ch.is_ascii_digit() { + break; + } + if is_trim_numeric_edge_char(ch) { + end -= ch.len_utf8(); + } else { + return None; + } + } + + if start >= end { + None + } else { + Some((start, end)) + } +} + +#[inline] +fn is_numeric_page_token_body(text: &str) -> bool { + if text.is_empty() { + return false; + } + + if text.chars().all(|ch| ch.is_ascii_digit()) { + return (1..=4).contains(&text.len()); + } + + let mut saw_digit = false; + for ch in text.chars() { + if ch.is_ascii_digit() { + saw_digit = true; + continue; + } + if matches!(ch, '.' | ',' | ':' | ';' | '/' | '-') { + continue; + } + return false; + } + + saw_digit +} + +fn summarize_numeric_line(line: &str) -> NumericLineSummary { + let trimmed = line.trim(); + if trimmed.is_empty() { + return NumericLineSummary { + is_blank: true, + ..NumericLineSummary::default() + }; + } + + let tokens = extract_non_whitespace_tokens_with_spans(line); + let mut summary = NumericLineSummary::default(); + for token in tokens { + let raw = &line[token.start..token.end]; + if raw.chars().any(char::is_alphabetic) { + summary.has_alpha = true; + } + if is_numeric_page_ignored_token(raw) { + continue; + } + let Some((trim_start, trim_end)) = trim_numeric_token_bounds(raw) else { + summary.rejected_non_numeric = true; + continue; + }; + let trimmed = &raw[trim_start..trim_end]; + if !is_numeric_page_token_body(trimmed) { + summary.rejected_non_numeric = true; + continue; + } + summary.numeric_token_count += 1; + summary.numeric_atom_count += extract_digit_group_spans(trimmed).len(); + } + summary +} + +fn parse_simple_number(text: &str) -> Option { + if text.is_empty() { + return None; + } + + let mut normalized = String::with_capacity(text.len()); + let mut saw_digit = false; + let mut saw_separator = false; + + for ch in text.chars() { + if ch.is_ascii_digit() { + normalized.push(ch); + saw_digit = true; + } else if ch == '.' || ch == ',' { + if saw_separator { + return None; + } + saw_separator = true; + normalized.push('.'); + } else { + return None; + } + } + + if !saw_digit || normalized.starts_with('.') || normalized.ends_with('.') { + return None; + } + + normalized.parse::().ok() +} + +fn repeated_digit_token(text: &str) -> Option { + let mut digit: Option = None; + for ch in text.chars() { + if !ch.is_ascii_digit() { + return None; + } + match digit { + Some(existing) if existing != ch => return None, + Some(_) => {} + None => digit = Some(ch), + } + } + digit +} + +#[inline] +fn is_private_use_codepoint(cp: u32) -> bool { + matches!( + cp, + 0xE000..=0xF8FF | 0xF0000..=0xFFFFD | 0x100000..=0x10FFFD + ) +} + +#[inline] +fn is_cjk_codepoint(cp: u32) -> bool { + matches!( + cp, + 0x3400..=0x4DBF + | 0x4E00..=0x9FFF + | 0xF900..=0xFAFF + | 0x20000..=0x2A6DF + | 0x2A700..=0x2B73F + | 0x2B740..=0x2B81F + | 0x2B820..=0x2CEAF + | 0x2F800..=0x2FA1F + ) +} + +pub fn evaluate_page_character_noise_internal(page: &str) -> PageCharacterNoise { + let mut metrics = PageCharacterNoise::default(); + for ch in page.chars() { + metrics.total_chars += 1; + let cp = ch as u32; + let mut is_bad = false; + if ch == '\u{FFFD}' { + metrics.replacement_count += 1; + is_bad = true; + } else if ch.is_control() && !matches!(ch, '\n' | '\r' | '\t') { + metrics.control_count += 1; + is_bad = true; + } else if is_private_use_codepoint(cp) { + metrics.private_use_count += 1; + is_bad = true; + } else if is_cjk_codepoint(cp) { + metrics.cjk_count += 1; + is_bad = true; + } + if is_bad { + metrics.bad_char_count += 1; + } + } + + metrics.bad_char_ratio = if metrics.total_chars > 0 { + metrics.bad_char_count as f64 / metrics.total_chars as f64 + } else { + 0.0 + }; + metrics +} + +fn extract_digit_group_spans(text: &str) -> Vec { + let mut spans = Vec::new(); + let mut current_start: Option = None; + + for (idx, ch) in text.char_indices() { + if ch.is_ascii_digit() { + if current_start.is_none() { + current_start = Some(idx); + } + } else if let Some(start) = current_start.take() { + spans.push(TokenSpan { start, end: idx }); + } + } + + if let Some(start) = current_start { + spans.push(TokenSpan { + start, + end: text.len(), + }); + } + + spans +} + +#[inline] +fn numeric_step_approx_eq(lhs: f64, rhs: f64) -> bool { + let scale = lhs.abs().max(rhs.abs()).max(1.0); + (lhs - rhs).abs() <= 1e-9 * scale +} + +#[derive(Debug, Clone, Copy, Default)] +struct OcrRepeatNoiseMetrics { + phrase_run_max: u64, + line_run_max: u64, + suspicious_line_count: u64, + suspicious_line_ratio: f64, + suspect: bool, +} + +fn extract_non_whitespace_tokens_with_spans(line: &str) -> Vec { + let mut tokens = Vec::new(); + let mut current_start: Option = None; + + for (idx, ch) in line.char_indices() { + if !ch.is_whitespace() { + if current_start.is_none() { + current_start = Some(idx); + } + } else if let Some(start) = current_start.take() { + tokens.push(TokenSpan { start, end: idx }); + } + } + + if let Some(start) = current_start { + tokens.push(TokenSpan { + start, + end: line.len(), + }); + } + + tokens +} + +fn normalize_line_for_repetition(line: &str) -> Option { + let trimmed = line.trim(); + if trimmed.is_empty() { + return None; + } + + let mut normalized = String::with_capacity(trimmed.len()); + let mut iter = trimmed.split_whitespace(); + if let Some(first) = iter.next() { + normalized.push_str(first); + for token in iter { + normalized.push(' '); + normalized.push_str(token); + } + } + Some(normalized) +} + +fn phrase_tokens_equal( + line: &str, + tokens: &[TokenSpan], + lhs: usize, + rhs: usize, + len: usize, +) -> bool { + (0..len).all(|offset| { + let lhs_token = &line[tokens[lhs + offset].start..tokens[lhs + offset].end]; + let rhs_token = &line[tokens[rhs + offset].start..tokens[rhs + offset].end]; + lhs_token == rhs_token + }) +} + +fn collect_repeat_phrase_debug_matches( + line: &str, + tokens: &[TokenSpan], + min_repeat_run: u64, +) -> Vec { + let mut spans = Vec::new(); + let min_run = min_repeat_run as usize; + if min_run < 2 || tokens.len() < min_run { + return spans; + } + + let max_phrase_len = 4usize.min(tokens.len() / min_run); + for phrase_len in 1..=max_phrase_len { + let mut i = 0usize; + while i + phrase_len * min_run <= tokens.len() { + let mut repeats = 1usize; + while i + phrase_len * (repeats + 1) <= tokens.len() + && phrase_tokens_equal(line, tokens, i, i + repeats * phrase_len, phrase_len) + { + repeats += 1; + } + if repeats >= min_run { + spans.push(DebugMatchSpan { + start: tokens[i].start, + end: tokens[i + phrase_len * repeats - 1].end, + match_type: "repeat_phrase_run", + }); + i += phrase_len * repeats; + } else { + i += 1; + } + } + } + + spans +} + +fn debug_match_merge_category(match_type: &'static str) -> Option<&'static str> { + match match_type { + "ascending_numeric_sequence" + | "repeat_numeric_run" + | "same_digit_numeric_run" + | "numeric_page_collapse" + | "numeric_block_collapse" => Some("numeric"), + "word_repeat" => Some("word"), + _ => None, + } +} + +fn gap_has_fewer_than_n_nonwhitespace_chars( + text: &str, + start: usize, + end: usize, + max_nonwhitespace: usize, +) -> bool { + if start >= end { + return true; + } + + let mut count = 0usize; + for ch in text[start..end].chars() { + if !ch.is_whitespace() { + count += 1; + if count >= max_nonwhitespace { + return false; + } + } + } + true +} + +fn merge_debug_spans( + text: &str, + spans: Vec, +) -> Vec<(usize, usize, Vec<&'static str>)> { + if spans.is_empty() { + return Vec::new(); + } + + let mut spans = spans; + spans.sort_by_key(|span| (span.start, span.end)); + + let mut merged: Vec<(usize, usize, Vec<&'static str>)> = Vec::new(); + for span in spans { + if let Some((start, end, types)) = merged.last_mut() { + let overlaps = span.start <= *end; + let same_category_gap_merge = !overlaps + && debug_match_merge_category(span.match_type).is_some() + && types.iter().any(|kind| { + debug_match_merge_category(*kind) == debug_match_merge_category(span.match_type) + }) + && gap_has_fewer_than_n_nonwhitespace_chars( + text, + *end, + span.start, + MERGE_SAME_CATEGORY_MAX_NONWHITESPACE_GAP, + ); + if overlaps || same_category_gap_merge { + *end = (*end).max(span.end); + if !types.contains(&span.match_type) { + types.push(span.match_type); + } + *start = (*start).min(span.start); + continue; + } + } + merged.push((span.start, span.end, vec![span.match_type])); + } + + for (_, _, types) in &mut merged { + types.sort_unstable(); + types.dedup(); + } + + merged +} + +fn annotate_text_with_debug_spans( + text: &str, + spans: Vec, +) -> Option<(String, Vec<&'static str>, u64)> { + let merged = merge_debug_spans(text, spans); + if merged.is_empty() { + return None; + } + + let mut annotated = String::with_capacity(text.len() + merged.len() * 48); + let mut pos = 0usize; + let mut match_types: Vec<&'static str> = Vec::new(); + for (start, end, types) in &merged { + if *start > pos { + annotated.push_str(&text[pos..*start]); + } + let type_attr = types.join(","); + annotated.push_str("'); + annotated.push_str(&text[*start..*end]); + annotated.push_str(""); + pos = *end; + for kind in types { + if !match_types.contains(kind) { + match_types.push(*kind); + } + } + } + if pos < text.len() { + annotated.push_str(&text[pos..]); + } + + Some((annotated, match_types, merged.len() as u64)) +} + +fn collect_numeric_page_collapse_span(page: &str, min_page_tokens: u64) -> Option { + let tokens = extract_non_whitespace_tokens_with_spans(page); + let mut page_start: Option = None; + let mut page_end: Option = None; + let mut first_start: Option = None; + let mut last_end: Option = None; + let mut numeric_token_count = 0usize; + let mut numeric_atom_count = 0usize; + for token in tokens { + let raw = &page[token.start..token.end]; + if page_start.is_none() { + page_start = Some(token.start); + } + page_end = Some(token.end); + if is_numeric_page_ignored_token(raw) { + continue; + } + let (trim_start, trim_end) = trim_numeric_token_bounds(raw)?; + let trimmed = &raw[trim_start..trim_end]; + if !is_numeric_page_token_body(trimmed) { + return None; + } + let abs_start = token.start + trim_start; + let abs_end = token.start + trim_end; + if first_start.is_none() { + first_start = Some(abs_start); + } + last_end = Some(abs_end); + numeric_token_count += 1; + numeric_atom_count += extract_digit_group_spans(trimmed).len(); + } + + if numeric_token_count < min_page_tokens as usize + && numeric_atom_count < NUMERIC_PAGE_COLLAPSE_MIN_ATOMS as usize + { + return None; + } + + Some(DebugMatchSpan { + start: page_start.or(first_start)?, + end: page_end.or(last_end)?, + match_type: "numeric_page_collapse", + }) +} + +fn collect_numeric_block_collapse_spans(page: &str) -> Vec { + let mut lines: Vec<(usize, usize, NumericLineSummary)> = Vec::new(); + let mut offset = 0usize; + for segment in page.split_inclusive('\n') { + let line = segment.strip_suffix('\n').unwrap_or(segment); + let summary = summarize_numeric_line(line); + lines.push((offset, offset + segment.len(), summary)); + offset += segment.len(); + } + if offset < page.len() { + let line = &page[offset..]; + lines.push((offset, page.len(), summarize_numeric_line(line))); + } + + let mut spans = Vec::new(); + let mut idx = 0usize; + while idx < lines.len() { + let (_, _, summary) = lines[idx]; + let is_seed = !summary.has_alpha + && !summary.rejected_non_numeric + && summary.numeric_atom_count >= NUMERIC_BLOCK_SEED_MIN_ATOMS; + if !is_seed { + idx += 1; + continue; + } + + let mut start_idx = idx; + let mut end_idx = idx; + let mut total_atoms = summary.numeric_atom_count; + + while start_idx > 0 { + let prev = lines[start_idx - 1].2; + let prev_ok = prev.is_blank + || (!prev.has_alpha && !prev.rejected_non_numeric && prev.numeric_token_count > 0); + if !prev_ok { + break; + } + start_idx -= 1; + total_atoms += prev.numeric_atom_count; + } + + while end_idx + 1 < lines.len() { + let next = lines[end_idx + 1].2; + let next_ok = next.is_blank + || (!next.has_alpha && !next.rejected_non_numeric && next.numeric_token_count > 0); + if !next_ok { + break; + } + end_idx += 1; + total_atoms += next.numeric_atom_count; + } + + if total_atoms >= NUMERIC_PAGE_COLLAPSE_MIN_ATOMS as usize { + let first_nonblank = (start_idx..=end_idx).find(|i| !lines[*i].2.is_blank); + let last_nonblank = (start_idx..=end_idx).rfind(|i| !lines[*i].2.is_blank); + if let (Some(first), Some(last)) = (first_nonblank, last_nonblank) { + spans.push(DebugMatchSpan { + start: lines[first].0, + end: lines[last].1, + match_type: "numeric_block_collapse", + }); + } + } + + idx = end_idx + 1; + } + + spans +} + +fn collect_numeric_progression_matches( + line: &str, + tokens: &[TokenSpan], + min_progress_steps: u64, +) -> Vec { + let min_steps = min_progress_steps as usize; + if min_steps < 2 || tokens.len() < min_steps { + return Vec::new(); + } + + let numeric_tokens: Vec> = tokens + .iter() + .map(|token| { + let raw = &line[token.start..token.end]; + let (trim_start, trim_end) = trim_numeric_token_bounds(raw)?; + let trimmed = &raw[trim_start..trim_end]; + let value = parse_simple_number(trimmed)?; + Some((token.start + trim_start, token.start + trim_end, value)) + }) + .collect(); + + let mut spans = Vec::new(); + let mut i = 0usize; + while i + min_steps <= numeric_tokens.len() { + let Some((start, _, first)) = numeric_tokens[i] else { + i += 1; + continue; + }; + let Some((_, _, second)) = numeric_tokens[i + 1] else { + i += 1; + continue; + }; + + let step = second - first; + if !step.is_finite() || step <= 0.0 { + i += 1; + continue; + } + + let mut j = i + 1; + while j + 1 < numeric_tokens.len() { + let Some((_, _, current)) = numeric_tokens[j] else { + break; + }; + let Some((_, _, next)) = numeric_tokens[j + 1] else { + break; + }; + if numeric_step_approx_eq(next - current, step) { + j += 1; + } else { + break; + } + } + + let run_len = j - i + 1; + if run_len >= min_steps { + let (_, end, _) = numeric_tokens[j].expect("numeric run end"); + spans.push(DebugMatchSpan { + start, + end, + match_type: "ascending_numeric_sequence", + }); + i = j + 1; + } else { + i += 1; + } + } + + spans +} + +fn collect_compact_repeat_numeric_matches( + line: &str, + tokens: &[TokenSpan], + min_repeat_steps: u64, +) -> Vec { + let min_steps = min_repeat_steps as usize; + if min_steps < 2 { + return Vec::new(); + } + + let mut spans = Vec::new(); + for token in tokens { + let raw = &line[token.start..token.end]; + let Some((trim_start, trim_end)) = trim_numeric_token_bounds(raw) else { + continue; + }; + let trimmed = &raw[trim_start..trim_end]; + let digit_groups = extract_digit_group_spans(trimmed); + if digit_groups.len() < min_steps { + continue; + } + + let first_group = &trimmed[digit_groups[0].start..digit_groups[0].end]; + if digit_groups + .iter() + .any(|group| &trimmed[group.start..group.end] != first_group) + { + continue; + } + + let mut separators_ok = true; + for pair in digit_groups.windows(2) { + let separator = &trimmed[pair[0].end..pair[1].start]; + if separator.is_empty() + || separator + .chars() + .any(|ch| ch.is_ascii_alphanumeric() || ch.is_whitespace()) + { + separators_ok = false; + break; + } + } + if !separators_ok { + continue; + } + + let trailing = &trimmed[digit_groups.last().expect("digit group").end..]; + if trailing + .chars() + .any(|ch| ch.is_ascii_alphanumeric() || ch.is_whitespace()) + { + continue; + } + + spans.push(DebugMatchSpan { + start: token.start + trim_start, + end: token.start + trim_end, + match_type: "repeat_numeric_run", + }); + } + + spans +} + +fn collect_same_digit_numeric_matches( + line: &str, + tokens: &[TokenSpan], + min_same_digit_steps: u64, +) -> Vec { + let min_steps = min_same_digit_steps as usize; + if min_steps < 2 || tokens.len() < min_steps { + return Vec::new(); + } + + let signatures: Vec> = tokens + .iter() + .map(|token| { + let raw = &line[token.start..token.end]; + let (trim_start, trim_end) = trim_numeric_token_bounds(raw)?; + let trimmed = &raw[trim_start..trim_end]; + let digit = repeated_digit_token(trimmed)?; + Some((token.start + trim_start, token.start + trim_end, digit)) + }) + .collect(); + + let mut spans = Vec::new(); + let mut i = 0usize; + while i + min_steps <= signatures.len() { + let Some((start, _, digit)) = signatures[i] else { + i += 1; + continue; + }; + + let mut j = i + 1; + while j < signatures.len() && signatures[j].map(|(_, _, current)| current) == Some(digit) { + j += 1; + } + + let run_len = j - i; + if run_len >= min_steps { + let (_, end, _) = signatures[j - 1].expect("same-digit run end"); + spans.push(DebugMatchSpan { + start, + end, + match_type: "same_digit_numeric_run", + }); + i = j; + } else { + i += 1; + } + } + + spans +} + +fn annotate_line_with_numeric_debug_matches( + line: &str, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> Option<(String, Vec<&'static str>, u64)> { + let tokens = extract_non_whitespace_tokens_with_spans(line); + if tokens.is_empty() { + return None; + } + + let mut spans = Vec::new(); + spans.extend(collect_numeric_progression_matches( + line, + &tokens, + min_progress_steps, + )); + spans.extend(collect_compact_repeat_numeric_matches( + line, + &tokens, + min_repeat_steps, + )); + spans.extend(collect_same_digit_numeric_matches( + line, + &tokens, + min_same_digit_steps, + )); + annotate_text_with_debug_spans(line, spans) +} + +fn annotate_line_with_debug_matches( + line: &str, + min_repeat_run: u64, +) -> Option<(String, Vec<&'static str>, u64)> { + let tokens = extract_non_whitespace_tokens_with_spans(line); + if tokens.is_empty() { + return None; + } + + let spans = collect_repeat_phrase_debug_matches(line, &tokens, min_repeat_run); + let merged = merge_debug_spans(line, spans); + if merged.is_empty() { + return None; + } + + let mut annotated = String::with_capacity(line.len() + merged.len() * 48); + let mut pos = 0usize; + let mut line_types: Vec<&'static str> = Vec::new(); + for (start, end, types) in &merged { + if *start > pos { + annotated.push_str(&line[pos..*start]); + } + let type_attr = types.join(","); + annotated.push_str("'); + annotated.push_str(&line[*start..*end]); + annotated.push_str(""); + pos = *end; + for kind in types { + if !line_types.contains(kind) { + line_types.push(*kind); + } + } + } + if pos < line.len() { + annotated.push_str(&line[pos..]); + } + + Some((annotated, line_types, merged.len() as u64)) +} + +fn compute_repeat_phrase_run_max(trimmed: &str, min_repeat_run: u64) -> u64 { + let tokens = extract_non_whitespace_tokens_with_spans(trimmed); + let min_run = min_repeat_run as usize; + if min_run < 2 || tokens.len() < min_run { + return 0; + } + + let max_phrase_len = 4usize.min(tokens.len() / min_run); + let mut phrase_run_max = 0u64; + for phrase_len in 1..=max_phrase_len { + let mut i = 0usize; + while i + phrase_len * min_run <= tokens.len() { + let mut repeats = 1usize; + while i + phrase_len * (repeats + 1) <= tokens.len() + && phrase_tokens_equal(trimmed, &tokens, i, i + repeats * phrase_len, phrase_len) + { + repeats += 1; + } + if repeats >= min_run { + phrase_run_max = phrase_run_max.max(repeats as u64); + i += phrase_len * repeats; + } else { + i += 1; + } + } + } + + phrase_run_max +} + +fn collect_repeat_line_flags(lines: &[Option], min_repeat_run: u64) -> (Vec, u64) { + let min_run = min_repeat_run as usize; + let mut flags = vec![false; lines.len()]; + if min_run < 2 || lines.len() < min_run { + return (flags, 0); + } + + let mut run_max = 0u64; + let mut i = 0usize; + while i < lines.len() { + let Some(current) = lines[i].as_ref() else { + i += 1; + continue; + }; + + let mut j = i + 1; + while j < lines.len() && lines[j].as_ref() == Some(current) { + j += 1; + } + let run_len = j - i; + if run_len >= min_run { + run_max = run_max.max(run_len as u64); + for flag in &mut flags[i..j] { + *flag = true; + } + } + i = j; + } + + (flags, run_max) +} + +fn finalize_ocr_repeat_noise( + phrase_run_max: u64, + line_run_max: u64, + suspicious_line_count: u64, + non_empty_lines: usize, +) -> OcrRepeatNoiseMetrics { + let suspicious_line_ratio = if non_empty_lines > 0 { + suspicious_line_count as f64 / non_empty_lines as f64 + } else { + 0.0 + }; + let suspect = suspicious_line_count > 0; + + OcrRepeatNoiseMetrics { + phrase_run_max, + line_run_max, + suspicious_line_count, + suspicious_line_ratio, + suspect, + } +} + +fn compute_ocr_profile( + text: &str, + min_repeat_run: u64, +) -> (glossapi_rs_common::ScriptMetrics, OcrRepeatNoiseMetrics) { + let mut scanner = ScriptScanner::new(); + let mut non_empty_lines = 0usize; + let mut phrase_run_max = 0u64; + let mut line_repeat_inputs: Vec> = Vec::new(); + let mut phrase_suspicious_lines: Vec = Vec::new(); + + for segment in text.split_inclusive('\n') { + let trimmed = segment.trim(); + if trimmed.is_empty() { + continue; + } + if trimmed == PAGE_SPLIT_MARKER || is_table_line_trimmed(trimmed) { + continue; + } + + non_empty_lines += 1; + scanner.observe_str(segment); + let line_phrase_run_max = compute_repeat_phrase_run_max(trimmed, min_repeat_run); + phrase_run_max = phrase_run_max.max(line_phrase_run_max); + phrase_suspicious_lines.push(line_phrase_run_max >= min_repeat_run); + line_repeat_inputs.push(normalize_line_for_repetition(trimmed)); + } + + let (repeat_line_flags, line_run_max) = + collect_repeat_line_flags(&line_repeat_inputs, min_repeat_run); + let suspicious_line_count = phrase_suspicious_lines + .iter() + .zip(repeat_line_flags.iter()) + .filter(|(phrase_flag, line_flag)| **phrase_flag || **line_flag) + .count() as u64; + + ( + scanner.finish(), + finalize_ocr_repeat_noise( + phrase_run_max, + line_run_max, + suspicious_line_count, + non_empty_lines, + ), + ) +} + +fn split_pages(text: &str) -> Vec { + let mut pages = Vec::new(); + let mut current = String::new(); + + for segment in text.split_inclusive('\n') { + if segment.trim() == PAGE_SPLIT_MARKER { + pages.push(current); + current = String::new(); + continue; + } + current.push_str(segment); + } + pages.push(current); + pages +} + +#[derive(Debug, Clone)] +struct SyntheticPage { + kind: String, + text: String, +} + +fn load_token_category_specs(specs_path: &Path) -> anyhow::Result> { + let raw = fs::read_to_string(specs_path)?; + let specs: Vec = serde_json::from_str(&raw)?; + let mut compiled = Vec::with_capacity(specs.len()); + for spec in specs { + let match_kind = spec.match_kind.as_deref().unwrap_or("regex"); + let matcher = match match_kind { + "regex" => { + let pattern = spec.pattern.as_deref().ok_or_else(|| { + anyhow::anyhow!("Missing regex pattern for category {}", spec.category) + })?; + let mut builder = RegexBuilder::new(pattern); + builder.case_insensitive(spec.case_insensitive); + builder.multi_line(true); + TokenCategoryMatcher::Regex(builder.build()?) + } + "literal_set" => { + if spec.case_insensitive { + anyhow::bail!( + "literal_set does not currently support case_insensitive for category {}", + spec.category + ); + } + let mut literals = spec.literals.unwrap_or_default(); + if let Some(raw_path) = spec.literals_path.as_deref() { + let path = if Path::new(raw_path).is_absolute() { + PathBuf::from(raw_path) + } else { + specs_path + .parent() + .unwrap_or_else(|| Path::new(".")) + .join(raw_path) + }; + let payload_raw = fs::read_to_string(&path).map_err(|err| { + anyhow::anyhow!( + "Failed to read literal_set payload for category {} from {}: {}", + spec.category, + path.display(), + err + ) + })?; + let payload: TokenCategoryLiteralPayload = serde_json::from_str(&payload_raw)?; + match payload { + TokenCategoryLiteralPayload::List(values) => literals.extend(values), + TokenCategoryLiteralPayload::Map(values) => { + literals.extend(values.into_values()) + } + } + } + literals.retain(|value| !value.is_empty()); + literals.sort(); + literals.dedup(); + if literals.is_empty() { + anyhow::bail!( + "literal_set category {} must provide non-empty literals", + spec.category + ); + } + let automaton = AhoCorasickBuilder::new() + .match_kind(MatchKind::LeftmostLongest) + .build(literals) + .map_err(|err| { + anyhow::anyhow!( + "Failed to build literal_set automaton for category {}: {}", + spec.category, + err + ) + })?; + TokenCategoryMatcher::LiteralSet(automaton) + } + _ => { + anyhow::bail!( + "Unsupported match_kind for category {}: {:?}", + spec.category, + spec.match_kind + ); + } + }; + compiled.push(CompiledTokenCategorySpec { + category: spec.category.clone(), + pattern_family: spec.pattern_family.unwrap_or_else(|| spec.category.clone()), + matcher, + }); + } + Ok(compiled) +} + +fn load_token_category_specs_cached( + specs_path: &Path, +) -> anyhow::Result>> { + let canonical = specs_path + .canonicalize() + .unwrap_or_else(|_| specs_path.to_path_buf()); + if let Some(cached) = TOKEN_CATEGORY_SPEC_CACHE + .lock() + .expect("token category spec cache lock") + .get(&canonical) + .cloned() + { + return Ok(cached); + } + let compiled = Arc::new(load_token_category_specs(&canonical)?); + TOKEN_CATEGORY_SPEC_CACHE + .lock() + .expect("token category spec cache lock") + .insert(canonical, compiled.clone()); + Ok(compiled) +} + +fn split_long_block_by_paragraphs( + block: &str, + target_chars: usize, + hard_max_chars: usize, +) -> Vec { + let mut pages = Vec::new(); + let mut current = String::new(); + let mut paragraph_buffer = String::new(); + + let flush_current = |pages: &mut Vec, current: &mut String| { + if !current.trim().is_empty() { + pages.push(SyntheticPage { + kind: "synthetic_paragraph".to_string(), + text: std::mem::take(current), + }); + } + }; + + for segment in block.split_inclusive('\n') { + paragraph_buffer.push_str(segment); + if segment.trim().is_empty() { + let para = std::mem::take(&mut paragraph_buffer); + if current.len() + para.len() > hard_max_chars && !current.trim().is_empty() { + flush_current(&mut pages, &mut current); + } + if current.len() + para.len() > target_chars && !current.trim().is_empty() { + flush_current(&mut pages, &mut current); + } + if para.len() > hard_max_chars { + let mut start = 0usize; + let chars: Vec = para.chars().collect(); + while start < chars.len() { + let end = (start + hard_max_chars).min(chars.len()); + let chunk: String = chars[start..end].iter().collect(); + pages.push(SyntheticPage { + kind: "synthetic_fallback".to_string(), + text: chunk, + }); + start = end; + } + } else { + current.push_str(¶); + } + } + } + + if !paragraph_buffer.is_empty() { + let para = std::mem::take(&mut paragraph_buffer); + if current.len() + para.len() > hard_max_chars && !current.trim().is_empty() { + flush_current(&mut pages, &mut current); + } + if current.len() + para.len() > target_chars && !current.trim().is_empty() { + flush_current(&mut pages, &mut current); + } + if para.len() > hard_max_chars { + let mut start = 0usize; + let chars: Vec = para.chars().collect(); + while start < chars.len() { + let end = (start + hard_max_chars).min(chars.len()); + let chunk: String = chars[start..end].iter().collect(); + pages.push(SyntheticPage { + kind: "synthetic_fallback".to_string(), + text: chunk, + }); + start = end; + } + } else { + current.push_str(¶); + } + } + + if !current.trim().is_empty() { + pages.push(SyntheticPage { + kind: "synthetic_paragraph".to_string(), + text: current, + }); + } + + pages +} + +fn split_synthetic_pages( + text: &str, + target_chars: usize, + min_header_chars: usize, + hard_max_chars: usize, +) -> Vec { + if text.contains(PAGE_SPLIT_MARKER) { + return split_pages(text) + .into_iter() + .map(|page| SyntheticPage { + kind: "real_page".to_string(), + text: page, + }) + .collect(); + } + + let mut header_blocks: Vec = Vec::new(); + let mut current_block = String::new(); + for segment in text.split_inclusive('\n') { + let trimmed = segment.trim_start(); + let hash_prefix_len = trimmed.chars().take_while(|ch| *ch == '#').count(); + let is_header = trimmed.starts_with('#') + && (1..=6).contains(&hash_prefix_len) + && trimmed + .chars() + .nth(hash_prefix_len) + .map(|ch| ch.is_whitespace()) + .unwrap_or(false); + if is_header && current_block.len() >= min_header_chars { + header_blocks.push(std::mem::take(&mut current_block)); + } + current_block.push_str(segment); + } + if !current_block.is_empty() { + header_blocks.push(current_block); + } + + let mut pages = Vec::new(); + for block in header_blocks { + if block.len() <= hard_max_chars { + pages.push(SyntheticPage { + kind: "synthetic_header".to_string(), + text: block, + }); + } else { + pages.extend(split_long_block_by_paragraphs( + &block, + target_chars, + hard_max_chars, + )); + } + } + pages +} + +fn collect_token_category_raw_spans( + page_text: &str, + specs: &[CompiledTokenCategorySpec], +) -> Vec { + let mut spans = Vec::new(); + for spec in specs { + match &spec.matcher { + TokenCategoryMatcher::Regex(regex) => { + for item in regex.find_iter(page_text) { + spans.push(TokenCategoryRawSpan { + start: item.start(), + end: item.end(), + category: spec.category.clone(), + pattern_family: spec.pattern_family.clone(), + matched_text: page_text[item.start()..item.end()].to_string(), + }); + } + } + TokenCategoryMatcher::LiteralSet(automaton) => { + for item in automaton.find_iter(page_text) { + spans.push(TokenCategoryRawSpan { + start: item.start(), + end: item.end(), + category: spec.category.clone(), + pattern_family: spec.pattern_family.clone(), + matched_text: page_text[item.start()..item.end()].to_string(), + }); + } + } + } + } + spans.sort_by(|a, b| a.start.cmp(&b.start).then(a.end.cmp(&b.end))); + spans +} + +fn merge_token_category_spans( + raw_spans: Vec, +) -> Vec { + if raw_spans.is_empty() { + return Vec::new(); + } + let mut merged: Vec = Vec::new(); + for span in raw_spans { + if let Some(last) = merged.last_mut() { + if span.start <= last.end { + last.end = last.end.max(span.end); + if !last.categories.iter().any(|value| value == &span.category) { + last.categories.push(span.category.clone()); + } + if !last + .pattern_families + .iter() + .any(|value| value == &span.pattern_family) + { + last.pattern_families.push(span.pattern_family.clone()); + } + last.raw_texts.push(span.matched_text.clone()); + continue; + } + } + merged.push(TokenCategoryMergedSpan { + start: span.start, + end: span.end, + categories: vec![span.category], + pattern_families: vec![span.pattern_family], + raw_texts: vec![span.matched_text], + }); + } + merged +} + +fn render_token_category_debug_page(page_text: &str, spans: &[TokenCategoryMergedSpan]) -> String { + if spans.is_empty() { + return page_text.to_string(); + } + let mut rendered = String::with_capacity(page_text.len() + spans.len() * 64); + let mut cursor = 0usize; + for span in spans { + if span.start > cursor { + rendered.push_str(&page_text[cursor..span.start]); + } + rendered.push_str(""); + rendered.push_str(&page_text[span.start..span.end]); + rendered.push_str(""); + cursor = span.end; + } + if cursor < page_text.len() { + rendered.push_str(&page_text[cursor..]); + } + rendered +} + +fn collect_token_category_debug_candidates_for_text( + path: &Path, + source_stem: &str, + base_stem: &str, + start_page: u64, + text: &str, + specs: &[CompiledTokenCategorySpec], + target_chars: usize, + min_header_chars: usize, + hard_max_chars: usize, +) -> Vec { + let pages = split_synthetic_pages(text, target_chars, min_header_chars, hard_max_chars); + let mut candidates = Vec::new(); + for (idx, page) in pages.into_iter().enumerate() { + let raw_spans = collect_token_category_raw_spans(&page.text, specs); + let merged_spans = merge_token_category_spans(raw_spans); + if merged_spans.is_empty() { + continue; + } + let page_index_in_file = (idx + 1) as u64; + let page_number = if page.kind == "real_page" { + start_page + idx as u64 + } else { + page_index_in_file + }; + candidates.push(TokenCategoryDebugPageCandidate { + source_path: path.to_string_lossy().into_owned(), + source_stem: source_stem.to_string(), + base_stem: base_stem.to_string(), + page_kind: page.kind, + page_number, + page_index_in_file, + page_char_count: page.text.chars().count() as u64, + page_text: page.text, + merged_spans, + }); + } + candidates +} + +fn render_token_category_debug_candidate( + candidate: &TokenCategoryDebugPageCandidate, + output_dir: &Path, + write_file: bool, +) -> anyhow::Result { + let rendered = render_token_category_debug_page(&candidate.page_text, &candidate.merged_spans); + let categories = candidate + .merged_spans + .iter() + .flat_map(|span| span.categories.iter().cloned()) + .collect::>(); + let pattern_families = candidate + .merged_spans + .iter() + .flat_map(|span| span.pattern_families.iter().cloned()) + .collect::>(); + let output_name = format!( + "{}__token_debug_{}_{:05}.md", + candidate.source_stem, candidate.page_kind, candidate.page_number + ); + let output_path = output_dir.join(output_name); + // Bug 1 fix (CLEANER_PIPELINE_CLEANUP_PLAN_2026-04-25 Point 10): + // emit CHARACTER offsets in the JSON, not BYTE offsets. Python + // consumers slice `page_text[start:end]` which is char-indexed in + // Python; byte offsets shift any non-ASCII prefix and silently + // drop rows whose end exceeds char-length. We keep span.start / + // span.end as bytes for internal Rust slicing + // (`candidate.page_text[span.start..span.end]`), and convert to + // chars only at the export boundary. + let export_matches: Vec = candidate + .merged_spans + .iter() + .enumerate() + .map(|(idx, span)| { + let start_char = candidate.page_text[..span.start].chars().count(); + let end_char = start_char + + candidate.page_text[span.start..span.end].chars().count(); + TokenCategoryExportMatchRow { + match_index_in_page: (idx + 1) as u64, + start: start_char, + end: end_char, + categories: span.categories.clone(), + pattern_families: span.pattern_families.clone(), + matched_text: candidate.page_text[span.start..span.end].to_string(), + raw_texts: span.raw_texts.clone(), + } + }) + .collect(); + let matches_json = serde_json::to_string(&export_matches)?; + let mut content = String::new(); + content.push_str("\n"); + content.push_str("\n\n"); + content.push_str(&rendered); + if write_file { + fs::write(&output_path, content)?; + } + + // Per-category match-count tally (Python driver no longer has to + // json.loads the matches_json string to compute doc counters). + let mut per_category_match_count: std::collections::BTreeMap + = std::collections::BTreeMap::new(); + for span in &candidate.merged_spans { + for cat in &span.categories { + *per_category_match_count.entry(cat.clone()).or_insert(0) += 1; + } + } + + Ok(TokenCategoryDebugPageRow { + source_path: candidate.source_path.clone(), + output_path: output_path.to_string_lossy().into_owned(), + source_stem: candidate.source_stem.clone(), + base_stem: candidate.base_stem.clone(), + page_kind: candidate.page_kind.clone(), + page_number: candidate.page_number, + page_index_in_file: candidate.page_index_in_file, + page_char_count: candidate.page_char_count, + match_categories: categories.into_iter().collect::>().join(","), + match_pattern_families: pattern_families.into_iter().collect::>().join(","), + match_count: candidate.merged_spans.len() as u64, + page_text: candidate.page_text.clone(), + matches_json, + per_category_match_count, + }) +} + +// Dead code post-Point-7 — kept until a follow-up extraction. +#[allow(dead_code)] +pub fn match_token_category_debug_text_internal( + output_dir: &Path, + category_specs_path: &Path, + source_path: &str, + source_stem: &str, + base_stem: &str, + start_page: u64, + text: &str, + synthetic_page_target_chars: usize, + synthetic_page_min_header_chars: usize, + synthetic_page_hard_max_chars: usize, + write_files: bool, +) -> anyhow::Result> { + if write_files { + fs::create_dir_all(output_dir)?; + } + let specs = load_token_category_specs_cached(category_specs_path)?; + let source_path_buf = PathBuf::from(source_path); + let candidates = collect_token_category_debug_candidates_for_text( + &source_path_buf, + source_stem, + base_stem, + start_page, + text, + &specs, + synthetic_page_target_chars, + synthetic_page_min_header_chars, + synthetic_page_hard_max_chars, + ); + let mut rows = Vec::with_capacity(candidates.len()); + for candidate in candidates { + rows.push(render_token_category_debug_candidate( + &candidate, + output_dir, + write_files, + )?); + } + rows.sort_by(|a, b| { + a.output_path + .cmp(&b.output_path) + .then(a.page_number.cmp(&b.page_number)) + }); + Ok(rows) +} + +fn parse_source_stem(stem: &str) -> (String, u64) { + if let Some((base, suffix)) = stem.rsplit_once("__p") { + if let Some((start, _end)) = suffix.split_once('-') { + if let Ok(start_page) = start.parse::() { + return (base.to_string(), start_page); + } + } + } + (stem.to_string(), 1) +} + +fn annotate_page_for_debug( + page: &str, + min_repeat_run: u64, +) -> Option<(String, Vec<&'static str>, u64)> { + let mut segments: Vec<(&str, &str)> = Vec::new(); + let mut normalized_lines: Vec> = Vec::new(); + for segment in page.split_inclusive('\n') { + let (line, newline) = if let Some(body) = segment.strip_suffix('\n') { + (body, "\n") + } else { + (segment, "") + }; + segments.push((line, newline)); + let trimmed = line.trim(); + if trimmed.is_empty() || is_table_line_trimmed(trimmed) { + normalized_lines.push(None); + } else { + normalized_lines.push(normalize_line_for_repetition(trimmed)); + } + } + + let (repeat_line_flags, _line_run_max) = + collect_repeat_line_flags(&normalized_lines, min_repeat_run); + + let mut annotated = String::with_capacity(page.len()); + let mut page_types: Vec<&'static str> = Vec::new(); + let mut match_count = 0u64; + + for (idx, (line, newline)) in segments.iter().enumerate() { + let line_debug = annotate_line_with_debug_matches(line, min_repeat_run); + let line_repeat_flag = repeat_line_flags.get(idx).copied().unwrap_or(false); + + let mut line_content = + if let Some((annotated_line, line_types, line_match_count)) = line_debug { + match_count += line_match_count; + for kind in line_types { + if !page_types.contains(&kind) { + page_types.push(kind); + } + } + annotated_line + } else { + (*line).to_string() + }; + + if line_repeat_flag { + if !page_types.contains(&"repeat_line_run") { + page_types.push("repeat_line_run"); + } + match_count += 1; + line_content = format!("{}", line_content); + } + + annotated.push_str(&line_content); + annotated.push_str(newline); + } + + if match_count == 0 { + return None; + } + + page_types.sort_unstable(); + page_types.dedup(); + Some((annotated, page_types, match_count)) +} + +pub fn annotate_numeric_debug_page_internal( + page: &str, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> Option<(String, Vec, u64)> { + let spans = collect_numeric_debug_spans_for_page( + page, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ); + let (annotated_page, match_types, match_count) = annotate_text_with_debug_spans(page, spans)?; + Some(( + annotated_page, + match_types.into_iter().map(str::to_string).collect(), + match_count, + )) +} + +pub fn find_numeric_debug_page_spans_internal( + page: &str, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> Vec { + collect_numeric_debug_spans_for_page( + page, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ) + .into_iter() + .map(|span| NumericDebugSpan { + start: span.start, + end: span.end, + match_type: span.match_type.to_string(), + }) + .collect() +} + +const WORD_REPEAT_HASH_MASK: u64 = (1u64 << 63).wrapping_mul(2).wrapping_sub(1); +const WORD_REPEAT_HASH_BASE: u64 = 1469598103934665603u64; + +#[inline] +fn hybrid_text_char_boundaries(text: &str) -> Vec { + let mut boundaries = Vec::with_capacity(text.chars().count() + 1); + for (byte_idx, _) in text.char_indices() { + boundaries.push(byte_idx); + } + boundaries.push(text.len()); + boundaries +} + +fn hybrid_byte_to_char_idx(boundaries: &[usize], byte_idx: usize) -> usize { + match boundaries.binary_search(&byte_idx) { + Ok(idx) => idx, + Err(idx) => idx, + } +} + +fn hybrid_normalize_body(text: &str) -> String { + let mut out = String::with_capacity(text.len()); + for ch in text.chars() { + for lower in ch.to_lowercase() { + let lower = if lower == 'ς' { 'σ' } else { lower }; + for sub in lower.to_string().nfd() { + if sub.is_alphanumeric() { + let mapped = match sub { + 'ο' => 'o', + 'κ' => 'k', + _ => sub, + }; + out.push(mapped); + } + } + } + } + out +} + +fn hybrid_has_markup_body(text: &str) -> bool { + if text.is_empty() { + return false; + } + let lower = text.to_lowercase(); + if lower.contains("src=") + || lower.contains("alt=") + || lower.contains("image_") + || lower.contains(".png") + || lower.contains(".jpg") + || lower.contains(".jpeg") + || lower.contains(".gif") + { + return true; + } + + let bytes = text.as_bytes(); + for (idx, byte) in bytes.iter().enumerate() { + if *byte == b'<' && idx + 2 <= bytes.len() && bytes[idx + 1..].contains(&b'>') { + return true; + } + } + false +} + +fn hybrid_classify_numeric_field(token: &str) -> Option<(HybridFieldKind, Vec, String)> { + let token = token.trim(); + if token.is_empty() { + return None; + } + + let trailing_paren = token.ends_with(')'); + let trailing_dot = token.ends_with('.'); + let stripped = if trailing_paren || trailing_dot { + &token[..token.len() - 1] + } else { + token + }; + if stripped.is_empty() { + return None; + } + + if stripped.contains('/') { + return Some((HybridFieldKind::NumericValue, Vec::new(), String::new())); + } + + let parts: Vec<&str> = stripped.split('.').collect(); + if parts.is_empty() + || parts + .iter() + .any(|part| part.is_empty() || !part.chars().all(|ch| ch.is_ascii_digit())) + { + return None; + } + + let mut numbers = Vec::with_capacity(parts.len()); + for part in &parts { + numbers.push(part.parse::().ok()?); + } + + let mut shape = std::iter::repeat("#") + .take(numbers.len()) + .collect::>() + .join("."); + if trailing_paren { + shape.push(')'); + } else if trailing_dot { + shape.push('.'); + } + + let field_kind = if trailing_paren || trailing_dot { + HybridFieldKind::HeaderCounter + } else if numbers.len() >= 3 { + HybridFieldKind::HeaderCounter + } else if numbers.len() == 2 && parts.last().map(|part| part.len()).unwrap_or(0) <= 2 { + HybridFieldKind::HeaderCounter + } else { + HybridFieldKind::NumericValue + }; + + Some((field_kind, numbers, shape)) +} + +fn hybrid_classify_inline_numeric_field(token: &str) -> bool { + let stripped = token.trim(); + if stripped.is_empty() { + return false; + } + + if stripped.chars().all(|ch| ch.is_ascii_digit()) { + return true; + } + + if stripped.matches('/').count() == 1 { + let mut parts = stripped.split('/'); + let lhs = parts.next().unwrap_or(""); + let rhs = parts.next().unwrap_or(""); + return !lhs.is_empty() + && !rhs.is_empty() + && lhs.chars().all(|ch| ch.is_ascii_digit()) + && rhs.chars().all(|ch| ch.is_ascii_digit()) + && rhs != "0"; + } + + let decimal_candidate = stripped.replacen(',', ".", 1); + if decimal_candidate.matches('.').count() == 1 { + let mut parts = decimal_candidate.split('.'); + let lhs = parts.next().unwrap_or(""); + let rhs = parts.next().unwrap_or(""); + return !lhs.is_empty() + && !rhs.is_empty() + && lhs.chars().all(|ch| ch.is_ascii_digit()) + && rhs.chars().all(|ch| ch.is_ascii_digit()); + } + + false +} + +fn hybrid_parse_numeric_value(token: &str) -> Option { + let stripped = token.trim(); + if stripped.is_empty() { + return None; + } + + if stripped.chars().all(|ch| ch.is_ascii_digit()) { + return stripped.parse::().ok().map(|value| value as f64); + } + + if stripped.matches('/').count() == 1 { + let mut parts = stripped.split('/'); + let lhs = parts.next().unwrap_or(""); + let rhs = parts.next().unwrap_or(""); + if !lhs.is_empty() + && !rhs.is_empty() + && lhs.chars().all(|ch| ch.is_ascii_digit()) + && rhs.chars().all(|ch| ch.is_ascii_digit()) + { + let lhs_value = lhs.parse::().ok()?; + let rhs_value = rhs.parse::().ok()?; + if rhs_value != 0.0 { + return Some(lhs_value / rhs_value); + } + } + return None; + } + + let decimal_candidate = stripped.replacen(',', ".", 1); + if decimal_candidate.matches('.').count() == 1 { + let mut parts = decimal_candidate.split('.'); + let lhs = parts.next().unwrap_or(""); + let rhs = parts.next().unwrap_or(""); + if !lhs.is_empty() + && !rhs.is_empty() + && lhs.chars().all(|ch| ch.is_ascii_digit()) + && rhs.chars().all(|ch| ch.is_ascii_digit()) + { + return decimal_candidate.parse::().ok(); + } + } + + None +} + +fn hybrid_next_char(text: &str, byte_idx: usize) -> Option<(char, usize)> { + let ch = text[byte_idx..].chars().next()?; + Some((ch, byte_idx + ch.len_utf8())) +} + +fn hybrid_previous_char(text: &str, byte_idx: usize) -> Option { + text[..byte_idx].chars().next_back() +} + +fn hybrid_parse_prefix_at(text: &str, start: usize) -> Option { + if start >= text.len() { + return None; + } + if let Some(prev) = hybrid_previous_char(text, start) { + if prev.is_ascii_digit() { + return None; + } + } + + let (first, mut idx) = hybrid_next_char(text, start)?; + if !first.is_ascii_digit() { + return None; + } + while idx < text.len() { + let (ch, next_idx) = hybrid_next_char(text, idx)?; + if !ch.is_ascii_digit() { + break; + } + idx = next_idx; + } + + if idx >= text.len() { + return None; + } + let (delimiter, mut end_idx) = hybrid_next_char(text, idx)?; + match delimiter { + ')' => {} + '.' => loop { + let mut cursor = end_idx; + let mut saw_digit = false; + while cursor < text.len() { + let (ch, next_cursor) = hybrid_next_char(text, cursor)?; + if !ch.is_ascii_digit() { + break; + } + saw_digit = true; + cursor = next_cursor; + } + if saw_digit { + if cursor < text.len() { + let (ch, next_cursor) = hybrid_next_char(text, cursor)?; + if ch == '.' { + end_idx = next_cursor; + continue; + } + } + end_idx = cursor; + } + break; + }, + _ => return None, + } + + let mut lookahead = end_idx; + while lookahead < text.len() { + let (ch, next_idx) = hybrid_next_char(text, lookahead)?; + if !ch.is_whitespace() { + return ch.is_alphabetic().then_some(end_idx); + } + lookahead = next_idx; + } + None +} + +fn hybrid_extract_numbered_items(analysis_text: &str) -> Vec { + let boundaries = hybrid_text_char_boundaries(analysis_text); + let mut candidates: Vec = Vec::new(); + let mut byte_idx = 0usize; + while byte_idx < analysis_text.len() { + let (ch, next_idx) = match hybrid_next_char(analysis_text, byte_idx) { + Some(value) => value, + None => break, + }; + if ch.is_ascii_digit() { + if let Some(prefix_end_byte) = hybrid_parse_prefix_at(analysis_text, byte_idx) { + let prefix = &analysis_text[byte_idx..prefix_end_byte]; + if let Some((field_kind, numbers, shape)) = hybrid_classify_numeric_field(prefix) { + candidates.push(HybridCandidate { + prefix_start_byte: byte_idx, + prefix_end_byte, + field_kind, + numbers, + shape, + }); + } + byte_idx = prefix_end_byte; + continue; + } + } + byte_idx = next_idx; + } + + let mut items: Vec = Vec::new(); + for (idx, candidate) in candidates.iter().enumerate() { + let next_start = candidates + .get(idx + 1) + .map(|item| item.prefix_start_byte) + .unwrap_or_else(|| analysis_text.len()); + let body_raw = analysis_text[candidate.prefix_end_byte..next_start].trim(); + if hybrid_has_markup_body(body_raw) { + continue; + } + let body_key = hybrid_normalize_body(body_raw); + let has_alpha = body_key.chars().any(|ch| ch.is_alphabetic()); + if !has_alpha { + continue; + } + let body_is_full = body_key.chars().count() >= HYBRID_REPEAT_MIN_BODY_ALNUM; + items.push(HybridNumberedItem { + start: hybrid_byte_to_char_idx(&boundaries, candidate.prefix_start_byte), + end: hybrid_byte_to_char_idx(&boundaries, next_start), + field_kind: candidate.field_kind, + numbers: candidate.numbers.clone(), + shape: candidate.shape.clone(), + body_key, + body_is_full, + }); + } + + items +} + +fn hybrid_clause_ranges(text: &str) -> Vec<(usize, usize)> { + let mut ranges: Vec<(usize, usize)> = Vec::new(); + let mut clause_start = 0usize; + let mut iter = text.char_indices().peekable(); + while let Some((idx, ch)) = iter.next() { + let is_delimiter = match ch { + ';' | '\n' => true, + ',' => match iter.peek() { + Some((_, next_ch)) => !next_ch.is_ascii_digit(), + None => true, + }, + _ => false, + }; + if is_delimiter { + ranges.push((clause_start, idx)); + clause_start = idx + ch.len_utf8(); + } + } + ranges.push((clause_start, text.len())); + ranges +} + +fn hybrid_extract_inline_items(analysis_text: &str) -> Vec { + let boundaries = hybrid_text_char_boundaries(analysis_text); + let clause_ranges = hybrid_clause_ranges(analysis_text); + let mut items: Vec = Vec::new(); + + for (clause_index, (raw_start, raw_end)) in clause_ranges.iter().enumerate() { + let clause = &analysis_text[*raw_start..*raw_end]; + if clause.trim().is_empty() { + continue; + } + + let leading_ws = clause.len() - clause.trim_start().len(); + let trailing_ws = clause.len() - clause.trim_end().len(); + let clause_start_abs = raw_start + leading_ws; + let clause_end_abs = raw_end - trailing_ws; + if clause_start_abs >= clause_end_abs { + continue; + } + + let clause_text = &analysis_text[clause_start_abs..clause_end_abs]; + if clause_text.is_empty() || hybrid_has_markup_body(clause_text) { + continue; + } + + let mut working_offset = clause_start_abs; + let mut working_text = clause_text; + if let Some(prefix_end) = hybrid_parse_prefix_at(working_text, 0) { + let trimmed = working_text[prefix_end..].trim_start(); + let trimmed_leading = working_text[prefix_end..].len() - trimmed.len(); + working_offset += prefix_end + trimmed_leading; + working_text = trimmed; + } + if working_text.is_empty() { + continue; + } + + let mut tokens: Vec = Vec::new(); + let mut numeric_positions: Vec = Vec::new(); + let mut token_byte = 0usize; + while token_byte < working_text.len() { + let (ch, next_idx) = match hybrid_next_char(working_text, token_byte) { + Some(value) => value, + None => break, + }; + if ch.is_ascii_digit() { + let mut end = next_idx; + loop { + let mut cursor = end; + while cursor < working_text.len() { + let (digit_ch, digit_next) = match hybrid_next_char(working_text, cursor) { + Some(value) => value, + None => break, + }; + if !digit_ch.is_ascii_digit() { + break; + } + cursor = digit_next; + } + end = cursor; + if end >= working_text.len() { + break; + } + let (sep, sep_next) = match hybrid_next_char(working_text, end) { + Some(value) => value, + None => break, + }; + if !matches!(sep, '.' | ',' | '/') { + break; + } + if sep_next >= working_text.len() { + break; + } + let (after_sep, _) = match hybrid_next_char(working_text, sep_next) { + Some(value) => value, + None => break, + }; + if !after_sep.is_ascii_digit() { + break; + } + end = sep_next; + } + let token = &working_text[token_byte..end]; + if hybrid_classify_inline_numeric_field(token) { + if let Some(parsed_value) = hybrid_parse_numeric_value(token) { + numeric_positions.push(tokens.len()); + tokens.push(HybridToken { + kind: HybridTokenKind::Numeric, + start: hybrid_byte_to_char_idx( + &boundaries, + working_offset + token_byte, + ), + end: hybrid_byte_to_char_idx(&boundaries, working_offset + end), + token_key: None, + numeric_value: Some(parsed_value), + }); + } + } + token_byte = end; + continue; + } + if ch.is_alphabetic() { + let mut end = next_idx; + while end < working_text.len() { + let (next_ch, next_end) = match hybrid_next_char(working_text, end) { + Some(value) => value, + None => break, + }; + if !next_ch.is_alphabetic() { + break; + } + end = next_end; + } + let token = &working_text[token_byte..end]; + let token_key = hybrid_normalize_body(token); + if !token_key.is_empty() { + tokens.push(HybridToken { + kind: HybridTokenKind::Alpha, + start: hybrid_byte_to_char_idx(&boundaries, working_offset + token_byte), + end: hybrid_byte_to_char_idx(&boundaries, working_offset + end), + token_key: Some(token_key), + numeric_value: None, + }); + } + token_byte = end; + continue; + } + token_byte = next_idx; + } + + if numeric_positions.len() != 1 { + continue; + } + let numeric_pos = numeric_positions[0]; + let numeric_token = &tokens[numeric_pos]; + let left_alpha: Vec<&HybridToken> = tokens[..numeric_pos] + .iter() + .filter(|token| token.kind == HybridTokenKind::Alpha) + .collect(); + let right_alpha: Vec<&HybridToken> = tokens[numeric_pos + 1..] + .iter() + .filter(|token| token.kind == HybridTokenKind::Alpha) + .collect(); + + let left_start = left_alpha.len().saturating_sub(HYBRID_INLINE_CONTEXT_WORDS); + let left_context = &left_alpha[left_start..]; + let right_limit = std::cmp::min(HYBRID_INLINE_CONTEXT_WORDS, right_alpha.len()); + let right_context = &right_alpha[..right_limit]; + let alpha_word_count = left_context.len() + right_context.len(); + if alpha_word_count < HYBRID_INLINE_CONTEXT_MIN_ALPHA_WORDS { + continue; + } + + let mut context_parts: Vec = + Vec::with_capacity(left_context.len() + 1 + right_context.len()); + for token in left_context { + if let Some(token_key) = &token.token_key { + context_parts.push(token_key.clone()); + } + } + context_parts.push("num".to_string()); + for token in right_context { + if let Some(token_key) = &token.token_key { + context_parts.push(token_key.clone()); + } + } + let context_key = hybrid_normalize_body(&context_parts.join(" ")); + if context_key.chars().count() < HYBRID_INLINE_CONTEXT_MIN_CHARS { + continue; + } + + let item_start = left_context + .first() + .map(|token| token.start) + .unwrap_or(numeric_token.start); + let item_end = right_context + .last() + .map(|token| token.end) + .unwrap_or(numeric_token.end); + items.push(HybridInlineItem { + start: item_start, + end: item_end, + clause_index, + inline_context_key: context_key, + numeric_value: numeric_token.numeric_value.unwrap_or(0.0), + }); + } + + items +} + +fn hybrid_partial_body_matches(candidate_body_key: &str, target_body_key: &str) -> bool { + if candidate_body_key.is_empty() + || target_body_key.is_empty() + || candidate_body_key == target_body_key + { + return false; + } + if !target_body_key.starts_with(candidate_body_key) { + return false; + } + let target_len = target_body_key.chars().count(); + let candidate_len = candidate_body_key.chars().count(); + let min_chars = std::cmp::min(4usize, target_len); + let min_ratio_chars = std::cmp::max(1usize, (target_len + 1) / 2); + candidate_len >= std::cmp::min(min_chars, min_ratio_chars) +} + +fn hybrid_header_progresses(previous: &HybridNumberedItem, current: &HybridNumberedItem) -> bool { + previous.field_kind == HybridFieldKind::HeaderCounter + && current.field_kind == HybridFieldKind::HeaderCounter + && !previous.numbers.is_empty() + && previous.numbers.len() == current.numbers.len() + && previous.numbers[..previous.numbers.len() - 1] + == current.numbers[..current.numbers.len() - 1] + && current.numbers.last().copied() + == previous.numbers.last().copied().map(|value| value + 1) +} + +fn hybrid_header_is_parent(previous: &HybridNumberedItem, current: &HybridNumberedItem) -> bool { + previous.field_kind == HybridFieldKind::HeaderCounter + && current.field_kind == HybridFieldKind::HeaderCounter + && !previous.numbers.is_empty() + && previous.numbers.len() + 1 == current.numbers.len() + && current.numbers[..current.numbers.len() - 1] == previous.numbers[..] +} + +fn hybrid_extend_tail_span_end( + items: &[HybridNumberedItem], + run_start: usize, + run_end: usize, + expected_body_key: &str, +) -> usize { + let span_end = items[run_end - 1].end; + if run_end >= items.len() { + return span_end; + } + let tail = &items[run_end]; + if tail.field_kind != HybridFieldKind::HeaderCounter + || tail.shape != items[run_start].shape + || !hybrid_header_progresses(&items[run_end - 1], tail) + || !hybrid_partial_body_matches(&tail.body_key, expected_body_key) + { + return span_end; + } + tail.end +} + +fn hybrid_inline_step(previous: &HybridInlineItem, current: &HybridInlineItem) -> Option { + if current.clause_index != previous.clause_index + 1 + || current.inline_context_key != previous.inline_context_key + { + return None; + } + let step = current.numeric_value - previous.numeric_value; + (step > 0.0).then_some(step) +} + +fn hybrid_inline_step_matches(expected_step: f64, actual_step: f64) -> bool { + let tolerance = f64::max(1e-9, expected_step.abs() * 1e-6); + (expected_step - actual_step).abs() <= tolerance +} + +fn hybrid_find_same_body_progression_spans(items: &[HybridNumberedItem]) -> Vec { + let mut spans: Vec = Vec::new(); + let mut idx = 0usize; + while idx < items.len() { + let item = &items[idx]; + if item.field_kind != HybridFieldKind::HeaderCounter || !item.body_is_full { + idx += 1; + continue; + } + + let mut end_idx = idx + 1; + while end_idx < items.len() + && items[end_idx].field_kind == HybridFieldKind::HeaderCounter + && items[end_idx].body_is_full + && items[end_idx].body_key == item.body_key + && items[end_idx].shape == item.shape + && hybrid_header_progresses(&items[end_idx - 1], &items[end_idx]) + { + end_idx += 1; + } + + let run_length = end_idx - idx; + if run_length >= HYBRID_REPEAT_MIN_ITEMS { + let mut start_idx = idx; + if idx > 0 { + let previous = &items[idx - 1]; + if previous.body_is_full + && previous.body_key == item.body_key + && hybrid_header_is_parent(previous, item) + { + start_idx = idx - 1; + } + } + let span_end = hybrid_extend_tail_span_end(items, idx, end_idx, &item.body_key); + spans.push(HybridRepeatSpan { + start: items[start_idx].start, + end: span_end, + kind: "same_body_progression", + item_count: end_idx - start_idx, + cycle_len: None, + }); + idx = end_idx; + continue; + } + + idx += 1; + } + spans +} + +fn hybrid_find_cycle_progression_spans(items: &[HybridNumberedItem]) -> Vec { + let mut spans: Vec = Vec::new(); + let n_items = items.len(); + for cycle_len in 2..=HYBRID_REPEAT_MAX_CYCLE { + let mut idx = 0usize; + while idx + 2 * cycle_len <= n_items { + let run = &items[idx..idx + 2 * cycle_len]; + if run + .iter() + .any(|item| item.field_kind != HybridFieldKind::HeaderCounter || !item.body_is_full) + { + idx += 1; + continue; + } + let first_shape = &run[0].shape; + if run.iter().any(|item| item.shape != *first_shape) { + idx += 1; + continue; + } + if !(1..run.len()).all(|pos| hybrid_header_progresses(&run[pos - 1], &run[pos])) { + idx += 1; + continue; + } + + let template: Vec<&str> = run[..cycle_len] + .iter() + .map(|item| item.body_key.as_str()) + .collect(); + let unique_template_count = template + .iter() + .copied() + .collect::>() + .len(); + if unique_template_count < 2 { + idx += 1; + continue; + } + + if (cycle_len..run.len()).any(|pos| run[pos].body_key != template[pos % cycle_len]) { + idx += 1; + continue; + } + + let mut end_idx = idx + 2 * cycle_len; + while end_idx < n_items + && items[end_idx].field_kind == HybridFieldKind::HeaderCounter + && items[end_idx].body_is_full + && items[end_idx].shape == items[idx].shape + && hybrid_header_progresses(&items[end_idx - 1], &items[end_idx]) + && items[end_idx].body_key == template[(end_idx - idx) % cycle_len] + { + end_idx += 1; + } + + let item_count = end_idx - idx; + if item_count >= HYBRID_REPEAT_MIN_CYCLE_ITEMS { + let span_end = hybrid_extend_tail_span_end( + items, + idx, + end_idx, + template[(end_idx - idx) % cycle_len], + ); + spans.push(HybridRepeatSpan { + start: items[idx].start, + end: span_end, + kind: "body_cycle_progression", + item_count, + cycle_len: Some(cycle_len), + }); + idx = end_idx; + continue; + } + idx += 1; + } + } + spans +} + +fn hybrid_find_inline_progression_spans(items: &[HybridInlineItem]) -> Vec { + let mut spans: Vec = Vec::new(); + let mut idx = 0usize; + while idx + HYBRID_INLINE_REPEAT_MIN_ITEMS <= items.len() { + let first = &items[idx]; + let second = &items[idx + 1]; + let expected_step = match hybrid_inline_step(first, second) { + Some(step) => step, + None => { + idx += 1; + continue; + } + }; + + let mut end_idx = idx + 2; + while end_idx < items.len() { + let actual_step = match hybrid_inline_step(&items[end_idx - 1], &items[end_idx]) { + Some(step) => step, + None => break, + }; + if !hybrid_inline_step_matches(expected_step, actual_step) { + break; + } + end_idx += 1; + } -#[inline(always)] -fn is_greek(cp: u32) -> bool { - GREEK_BLOCK_1.contains(&cp) || GREEK_BLOCK_2.contains(&cp) + let item_count = end_idx - idx; + if item_count >= HYBRID_INLINE_REPEAT_MIN_ITEMS { + spans.push(HybridRepeatSpan { + start: items[idx].start, + end: items[end_idx - 1].end, + kind: "inline_numeric_progression", + item_count, + cycle_len: None, + }); + idx = end_idx; + continue; + } + idx += 1; + } + spans } -#[inline(always)] -fn is_combining_mark(cp: u32) -> bool { - (0x0300..=0x036F).contains(&cp) || (0x1DC0..=0x1DFF).contains(&cp) || (0x20D0..=0x20FF).contains(&cp) -} +pub fn find_hybrid_repeat_spans_internal(analysis_text: &str) -> Vec { + let items = hybrid_extract_numbered_items(analysis_text); + let mut spans = hybrid_find_same_body_progression_spans(&items); + spans.extend(hybrid_find_cycle_progression_spans(&items)); + let inline_items = hybrid_extract_inline_items(analysis_text); + spans.extend(hybrid_find_inline_progression_spans(&inline_items)); + spans.sort_by(|lhs, rhs| { + lhs.start + .cmp(&rhs.start) + .then_with(|| (rhs.end - rhs.start).cmp(&(lhs.end - lhs.start))) + }); -#[inline(always)] -fn is_vowel(cp: u32) -> bool { - matches!( - cp, - 0x0391 | 0x03B1 | 0x0386 | 0x03AC | // Αα Άά - 0x0395 | 0x03B5 | 0x0388 | 0x03AD | // Εε Έέ - 0x0397 | 0x03B7 | 0x0389 | 0x03AE | // Ηη Ήή - 0x0399 | 0x03B9 | 0x038A | 0x03AF | 0x03CA | 0x03CB | 0x039F | 0x03BF | - 0x038C | 0x03CC | 0x03C5 | 0x03B0 | 0x03CD | 0x03A5 | 0x038E | - 0x03A9 | 0x03C9 | 0x038F | 0x03CE - ) + let mut deduped: Vec = Vec::new(); + for span in spans { + if let Some(previous) = deduped.last() { + if span.start >= previous.start && span.end <= previous.end { + continue; + } + } + deduped.push(span); + } + deduped } -const LONG_WORD_LIMIT: u64 = 21; -const SHORT_WORD_LIMIT: u64 = 3; -// Baseline for short words per 1000 Greek characters (empirically ~26 on clean texts) -const SHORT_BASELINE_PER_1000: f64 = 26.0; +fn normalize_alnum_with_map_skip_tags_internal(text: &str) -> (String, Vec) { + let mut normalized = String::with_capacity(text.len()); + let mut raw_char_indices: Vec = Vec::with_capacity(text.len()); + let mut in_tag = false; -#[inline] -fn to_lower_fast(cp: u32) -> u32 { - // Fast path for basic Greek capitals: add 0x20; otherwise return as-is - if (0x0391..=0x03A9).contains(&cp) { cp + 0x20 } else { cp } + for (raw_idx, ch) in text.chars().enumerate() { + if in_tag { + if ch == '>' { + in_tag = false; + } + continue; + } + if ch == '<' { + in_tag = true; + continue; + } + let mut casefolded = String::new(); + for lower in ch.to_lowercase() { + match lower { + 'ς' => casefolded.push('σ'), + 'ß' => { + casefolded.push('s'); + casefolded.push('s'); + } + 'ſ' => casefolded.push('s'), + _ => casefolded.push(lower), + } + } + for sub in casefolded.nfd() { + if sub.is_alphanumeric() { + let mapped = match sub { + 'ο' => 'o', + 'κ' => 'k', + _ => sub, + }; + normalized.push(mapped); + raw_char_indices.push(raw_idx); + } + } + } + + (normalized, raw_char_indices) } -#[inline] -fn is_invalid_bigram_pair(prev_low: u32, curr_low: u32) -> bool { - match (prev_low, curr_low) { - // κ/γ/χ + ξ - (0x03BA, 0x03BE) | (0x03B3, 0x03BE) | (0x03C7, 0x03BE) - // π/β/φ + ψ - | (0x03C0, 0x03C8) | (0x03B2, 0x03C8) | (0x03C6, 0x03C8) - // ρλ, μρ, γβ, δτ, τδ, βπ, πβ - | (0x03C1, 0x03BB) | (0x03BC, 0x03C1) | (0x03B3, 0x03B2) - | (0x03B4, 0x03C4) | (0x03C4, 0x03B4) | (0x03B2, 0x03C0) | (0x03C0, 0x03B2) => true, - _ => false, +pub fn find_labeled_shared_repeat_spans_internal( + text: &str, + rep_threshold: usize, + min_period: usize, + window: usize, +) -> Vec { + let (normalized_text, raw_map) = normalize_alnum_with_map_skip_tags_internal(text); + let normalized_chars: Vec = normalized_text.chars().collect(); + let spans = + find_word_repeat_spans_internal(&normalized_text, rep_threshold, min_period, window); + let mut labeled: Vec = Vec::new(); + + for span in spans { + if span.end <= span.start || span.start >= raw_map.len() { + continue; + } + let mut has_letter = false; + let mut has_digit = false; + for ch in &normalized_chars[span.start..span.end] { + if ch.is_alphabetic() { + has_letter = true; + } + if ch.is_ascii_digit() { + has_digit = true; + } + } + let match_type = if has_letter { + "word_repeat" + } else if has_digit { + "numeric_repeat" + } else { + continue; + }; + labeled.push(LabeledSharedRepeatSpan { + start: raw_map[span.start], + end: raw_map[span.end - 1] + 1, + period: span.period, + repetitions: span.repetitions, + tail_chars: span.tail_chars, + match_type, + }); } -} -static ALLOWED_DOUBLE: [u32; 9] = [ - 0x03BB, 0x03BC, 0x03BD, 0x03C1, 0x03C3, 0x03C4, 0x03BA, 0x03C0, 0x03B3, -]; + labeled +} -fn allowed_double(cp: u32) -> bool { - ALLOWED_DOUBLE.contains(&cp) +fn word_repeat_hash_slice(pref: &[u64], pw: &[u64], start: usize, end: usize) -> u64 { + pref[end].wrapping_sub(pref[start].wrapping_mul(pw[end - start])) & WORD_REPEAT_HASH_MASK } #[inline] -fn is_table_line_trimmed(trimmed: &str) -> bool { - // A simple check equivalent to /^\s*\|.*\|\s*$/ after trimming - // i.e., line begins and ends with a '|' ignoring outer whitespace - !trimmed.is_empty() && trimmed.as_bytes()[0] == b'|' && trimmed.as_bytes()[trimmed.len()-1] == b'|' +fn word_repeat_blocks_equal( + codes: &[u32], + pref: &[u64], + pw: &[u64], + lhs: usize, + rhs: usize, + period: usize, +) -> bool { + word_repeat_hash_slice(pref, pw, lhs, lhs + period) + == word_repeat_hash_slice(pref, pw, rhs, rhs + period) + && codes[lhs..lhs + period] == codes[rhs..rhs + period] } -fn table_line_ratio_and_filtered(text: &str) -> (f64, Option, usize, usize) { - let mut non_empty = 0usize; - let mut table_like = 0usize; - // First pass: count table-like rows without allocating filtered buffer unless needed - for line in text.lines() { - let trimmed = line.trim(); - if !trimmed.is_empty() { - non_empty += 1; - if is_table_line_trimmed(trimmed) { - table_like += 1; +pub fn find_word_repeat_spans_internal( + normalized_text: &str, + rep_threshold: usize, + min_period: usize, + window: usize, +) -> Vec { + let codes: Vec = normalized_text.chars().map(|ch| ch as u32).collect(); + let n_chars = codes.len(); + if rep_threshold == 0 || min_period == 0 || n_chars < rep_threshold.saturating_mul(min_period) { + return Vec::new(); + } + + let mut pref = vec![0u64; n_chars + 1]; + let mut pw = vec![1u64; n_chars + 1]; + for (idx, code) in codes.iter().enumerate() { + pref[idx + 1] = (pref[idx] + .wrapping_mul(WORD_REPEAT_HASH_BASE) + .wrapping_add(*code as u64)) + & WORD_REPEAT_HASH_MASK; + pw[idx + 1] = pw[idx].wrapping_mul(WORD_REPEAT_HASH_BASE) & WORD_REPEAT_HASH_MASK; + } + + let max_period = std::cmp::min( + std::cmp::max(min_period, window / rep_threshold), + n_chars / rep_threshold, + ); + let mut spans: Vec = Vec::new(); + + for period in min_period..=max_period { + let mut idx = 0usize; + while idx + rep_threshold * period <= n_chars { + let mut is_repeat = true; + for multiple in 1..rep_threshold { + if !word_repeat_blocks_equal( + &codes, + &pref, + &pw, + idx, + idx + multiple * period, + period, + ) { + is_repeat = false; + break; + } + } + if !is_repeat { + idx += 1; + continue; + } + + let mut left = idx; + while left >= period + && word_repeat_blocks_equal(&codes, &pref, &pw, left - period, left, period) + { + left -= period; } + + let mut right = idx + rep_threshold * period; + while right + period <= n_chars + && word_repeat_blocks_equal(&codes, &pref, &pw, right - period, right, period) + { + right += period; + } + + let pattern = &codes[left..left + period]; + let mut tail_chars = 0usize; + while right + tail_chars < n_chars + && tail_chars < period + && codes[right + tail_chars] == pattern[tail_chars] + { + tail_chars += 1; + } + + spans.push(WordRepeatSpan { + start: left, + end: right + tail_chars, + period, + repetitions: (right - left) / period, + tail_chars, + }); + idx = right; } } - let ratio = if non_empty > 0 { table_like as f64 / non_empty as f64 } else { 0.0 }; - if table_like == 0 { - return (ratio, None, non_empty, table_like); - } - // Second pass only if we actually need a filtered buffer (preserve original newlines) - let mut filtered = String::with_capacity(text.len()); - for seg in text.split_inclusive('\n') { - let trimmed = seg.trim(); - if trimmed.is_empty() || !is_table_line_trimmed(trimmed) { - filtered.push_str(seg); + + spans.sort_by(|lhs, rhs| { + lhs.start + .cmp(&rhs.start) + .then((rhs.end - rhs.start).cmp(&(lhs.end - lhs.start))) + .then(lhs.period.cmp(&rhs.period)) + }); + + let mut deduped: Vec = Vec::new(); + for span in spans { + if let Some(previous) = deduped.last() { + if span.start >= previous.start && span.end <= previous.end { + continue; + } } + deduped.push(span); } - (ratio, Some(filtered), non_empty, table_like) + deduped } -fn compute_latin_pct(buf: &[u8]) -> f64 { - let latin_chars = buf - .iter() - .filter(|&&b| (b >= 0x41 && b <= 0x5A) || (b >= 0x61 && b <= 0x7A)) - .count(); - latin_chars as f64 / (buf.len() as f64) +fn collect_numeric_debug_spans_for_page( + page: &str, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> Vec { + if let Some(page_span) = + collect_numeric_page_collapse_span(page, NUMERIC_PAGE_COLLAPSE_MIN_TOKENS) + { + return vec![page_span]; + } + + let block_spans = collect_numeric_block_collapse_spans(page); + if !block_spans.is_empty() { + return block_spans; + } + + let page_tokens = extract_non_whitespace_tokens_with_spans(page); + let mut spans = collect_numeric_progression_matches(page, &page_tokens, min_progress_steps); + let mut line_offset = 0usize; + + for segment in page.split_inclusive('\n') { + let (line, newline) = if let Some(body) = segment.strip_suffix('\n') { + (body, "\n") + } else { + (segment, "") + }; + + let line_tokens = extract_non_whitespace_tokens_with_spans(line); + spans.extend( + collect_compact_repeat_numeric_matches(line, &line_tokens, min_repeat_steps) + .into_iter() + .map(|span| DebugMatchSpan { + start: span.start + line_offset, + end: span.end + line_offset, + match_type: span.match_type, + }), + ); + spans.extend( + collect_same_digit_numeric_matches(line, &line_tokens, min_same_digit_steps) + .into_iter() + .map(|span| DebugMatchSpan { + start: span.start + line_offset, + end: span.end + line_offset, + match_type: span.match_type, + }), + ); + line_offset += line.len() + newline.len(); + } + + spans } -fn compute_polytonic_word_ratio(text: &str) -> (u64, u64, f64) { - let mut greek_words = 0u64; - let mut polytonic_words = 0u64; - for w in text.split_whitespace() { - let mut has_greek = false; - let mut has_poly = false; - for ch in w.chars() { - let cp = ch as u32; - if is_greek(cp) { has_greek = true; } - if (0x1F00..=0x1FFF).contains(&cp) || is_combining_mark(cp) { has_poly = true; } +fn collect_ocr_debug_candidates_for_text( + source_path: &Path, + source_stem: &str, + base_stem: &str, + start_page: u64, + text: &str, + min_repeat_run: u64, +) -> Vec { + let mut candidates = Vec::new(); + let pages = split_pages(text); + + for (idx, page) in pages.iter().enumerate() { + let page_index_in_file = idx as u64 + 1; + let page_number = start_page + idx as u64; + if let Some((_annotated_page, _match_types, _match_count)) = + annotate_page_for_debug(page, min_repeat_run) + { + candidates.push(OcrDebugPageCandidate { + source_path: source_path.to_string_lossy().into_owned(), + source_stem: source_stem.to_string(), + base_stem: base_stem.to_string(), + page_number, + page_index_in_file, + }); } - if has_greek { - greek_words += 1; - if has_poly { polytonic_words += 1; } + } + + candidates +} + +fn collect_numeric_debug_candidates_for_text( + source_path: &Path, + source_stem: &str, + base_stem: &str, + start_page: u64, + text: &str, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> Vec { + let mut candidates = Vec::new(); + let pages = split_pages(text); + + for (idx, page) in pages.iter().enumerate() { + let page_index_in_file = idx as u64 + 1; + let page_number = start_page + idx as u64; + if !collect_numeric_debug_spans_for_page( + page, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ) + .is_empty() + { + candidates.push(OcrDebugPageCandidate { + source_path: source_path.to_string_lossy().into_owned(), + source_stem: source_stem.to_string(), + base_stem: base_stem.to_string(), + page_number, + page_index_in_file, + }); } } - let ratio = if greek_words > 0 { polytonic_words as f64 / greek_words as f64 } else { 0.0 }; - (polytonic_words, greek_words, ratio) + + candidates +} + +fn render_ocr_debug_candidate( + candidate: &OcrDebugPageCandidate, + output_dir: &Path, + min_repeat_run: u64, +) -> anyhow::Result { + let source_path = PathBuf::from(&candidate.source_path); + let buf = fs::read(&source_path)?; + let text = String::from_utf8_lossy(&buf); + let pages = split_pages(&text); + let page_idx = candidate + .page_index_in_file + .checked_sub(1) + .ok_or_else(|| anyhow::anyhow!("invalid page index"))? as usize; + let page = pages + .get(page_idx) + .ok_or_else(|| anyhow::anyhow!("page index out of range for {}", candidate.source_path))?; + let (annotated_page, match_types, match_count) = annotate_page_for_debug(page, min_repeat_run) + .ok_or_else(|| { + anyhow::anyhow!( + "candidate page no longer matches: {}", + candidate.source_path + ) + })?; + let match_types_joined = match_types.join(","); + let output_name = format!( + "{}__debug_page_{:05}.md", + candidate.source_stem, candidate.page_number + ); + let output_path = output_dir.join(output_name); + + let mut content = String::new(); + content.push_str("\n"); + content.push_str("\n\n"); + content.push_str(&annotated_page); + fs::write(&output_path, content)?; + + Ok(OcrDebugPageRow { + source_path: candidate.source_path.clone(), + output_path: output_path.to_string_lossy().into_owned(), + source_stem: candidate.source_stem.clone(), + base_stem: candidate.base_stem.clone(), + page_number: candidate.page_number, + page_index_in_file: candidate.page_index_in_file, + match_types: match_types_joined, + match_count, + }) +} + +fn render_numeric_debug_candidate( + candidate: &OcrDebugPageCandidate, + output_dir: &Path, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, +) -> anyhow::Result { + let source_path = PathBuf::from(&candidate.source_path); + let buf = fs::read(&source_path)?; + let text = String::from_utf8_lossy(&buf); + let pages = split_pages(&text); + let page_idx = candidate + .page_index_in_file + .checked_sub(1) + .ok_or_else(|| anyhow::anyhow!("invalid page index"))? as usize; + let page = pages + .get(page_idx) + .ok_or_else(|| anyhow::anyhow!("page index out of range for {}", candidate.source_path))?; + let spans = collect_numeric_debug_spans_for_page( + page, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ); + let (annotated_page, match_types, match_count) = annotate_text_with_debug_spans(page, spans) + .ok_or_else(|| { + anyhow::anyhow!( + "candidate page no longer matches numeric detector: {}", + candidate.source_path + ) + })?; + let match_types_joined = match_types.join(","); + let output_name = format!( + "{}__debug_page_{:05}.md", + candidate.source_stem, candidate.page_number + ); + let output_path = output_dir.join(output_name); + + let mut content = String::new(); + content.push_str("\n"); + content.push_str("\n\n"); + content.push_str(&annotated_page); + fs::write(&output_path, content)?; + + Ok(OcrDebugPageRow { + source_path: candidate.source_path.clone(), + output_path: output_path.to_string_lossy().into_owned(), + source_stem: candidate.source_stem.clone(), + base_stem: candidate.base_stem.clone(), + page_number: candidate.page_number, + page_index_in_file: candidate.page_index_in_file, + match_types: match_types_joined, + match_count, + }) } /// Compute metrics for UTF-8 bytes; ported from original CLI. @@ -220,6 +3412,7 @@ fn analyse_bytes(buf: &[u8]) -> (u64, u64, u64, u64, u64, u64, u64, u64, u64, u6 let mut idx = 0; let mut prev_cp = 0u32; let mut run_len = 0u64; + let mut same_cp_run_len = 0u64; let mut run_is_vowel = false; let mut word_len = 0u64; @@ -230,10 +3423,12 @@ fn analyse_bytes(buf: &[u8]) -> (u64, u64, u64, u64, u64, u64, u64, u64, u64, u6 continue; } if cp == 0 || !is_greek(cp) { + commit_bad_double_run(prev_cp, same_cp_run_len, &mut bad_double); if run_len > max_run { max_run = run_len; } run_len = 0; + same_cp_run_len = 0; if word_len > 0 { total_word_count += 1; if word_len < SHORT_WORD_LIMIT { @@ -243,7 +3438,9 @@ fn analyse_bytes(buf: &[u8]) -> (u64, u64, u64, u64, u64, u64, u64, u64, u64, u6 long_word_count += 1; let extra = (word_len - LONG_WORD_LIMIT) as u64; // >= 0 let mut weight = 1 + extra; // equals (len - 20) - if weight > 380 { weight = 380; } + if weight > 380 { + weight = 380; + } long_word_weight_sum += weight; } if word_len > longest_word { @@ -268,44 +3465,89 @@ fn analyse_bytes(buf: &[u8]) -> (u64, u64, u64, u64, u64, u64, u64, u64, u64, u6 } else { if run_len >= 4 { let pen = run_len - 3; - if run_is_vowel { v_pen += pen; } else { c_pen += pen; } + if run_is_vowel { + v_pen += pen; + } else { + c_pen += pen; + } + } + if run_len > max_run { + max_run = run_len; } - if run_len > max_run { max_run = run_len; } run_is_vowel = vowel; run_len = 1; } if prev_cp != 0 { let pc_low = to_lower_fast(prev_cp); let cc_low = to_lower_fast(cp); - if is_invalid_bigram_pair(pc_low, cc_low) { invalid_bigram += 1; } + if is_invalid_bigram_pair(pc_low, cc_low) { + invalid_bigram += 1; + } + } + if prev_cp == 0 { + same_cp_run_len = 1; + } else if prev_cp == cp { + same_cp_run_len += 1; + } else { + commit_bad_double_run(prev_cp, same_cp_run_len, &mut bad_double); + same_cp_run_len = 1; } - if prev_cp == cp && !allowed_double(cp) { bad_double += 1; } prev_cp = cp; } + commit_bad_double_run(prev_cp, same_cp_run_len, &mut bad_double); if run_len >= 4 { let pen = run_len - 3; - if run_is_vowel { v_pen += pen; } else { c_pen += pen; } + if run_is_vowel { + v_pen += pen; + } else { + c_pen += pen; + } + } + if run_len > max_run { + max_run = run_len; } - if run_len > max_run { max_run = run_len; } if word_len > 0 { total_word_count += 1; - if word_len < SHORT_WORD_LIMIT { short_word_count += 1; } + if word_len < SHORT_WORD_LIMIT { + short_word_count += 1; + } if word_len >= LONG_WORD_LIMIT { long_word_count += 1; let extra = (word_len - LONG_WORD_LIMIT) as u64; let mut weight = 1 + extra; // equals (len - 20) - if weight > 380 { weight = 380; } + if weight > 380 { + weight = 380; + } long_word_weight_sum += weight; } - if word_len > longest_word { longest_word = word_len; } - if prev_cp == 0x03C3 { misplaced_sigma += 1; } + if word_len > longest_word { + longest_word = word_len; + } + if prev_cp == 0x03C3 { + misplaced_sigma += 1; + } } - (len_greek, v_pen, c_pen, bad_double, max_run, long_word_count, long_word_weight_sum, longest_word, misplaced_sigma, invalid_bigram, short_word_count, total_word_count) + ( + len_greek, + v_pen, + c_pen, + bad_double, + max_run, + long_word_count, + long_word_weight_sum, + longest_word, + misplaced_sigma, + invalid_bigram, + short_word_count, + total_word_count, + ) } fn decode_utf8(slice: &[u8]) -> (u32, usize) { - if slice.is_empty() { return (0, 0); } + if slice.is_empty() { + return (0, 0); + } let c0 = slice[0]; if c0 < 0x80 { return (c0 as u32, 1); @@ -313,39 +3555,79 @@ fn decode_utf8(slice: &[u8]) -> (u32, usize) { let cp = ((c0 & 0x1F) as u32) << 6 | (slice[1] & 0x3F) as u32; return (cp, 2); } else if c0 & 0xF0 == 0xE0 && slice.len() >= 3 { - let cp = ((c0 & 0x0F) as u32) << 12 | ((slice[1] & 0x3F) as u32) << 6 | (slice[2] & 0x3F) as u32; + let cp = + ((c0 & 0x0F) as u32) << 12 | ((slice[1] & 0x3F) as u32) << 6 | (slice[2] & 0x3F) as u32; return (cp, 3); } else if c0 & 0xF8 == 0xF0 && slice.len() >= 4 { - let cp = ((c0 & 0x07) as u32) << 18 | ((slice[1] & 0x3F) as u32) << 12 | ((slice[2] & 0x3F) as u32) << 6 | (slice[3] & 0x3F) as u32; + let cp = ((c0 & 0x07) as u32) << 18 + | ((slice[1] & 0x3F) as u32) << 12 + | ((slice[2] & 0x3F) as u32) << 6 + | (slice[3] & 0x3F) as u32; return (cp, 4); } (0, 1) } +pub type DetailedScore = ( + f64, + f64, + f64, + f64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + f64, + f64, + f64, + f64, + f64, + f64, + f64, + f64, + String, +); + /// Core computation with details: returns a wide tuple with all components used by scoring /// (score, latin_pct, table_line_ratio, polytonic_word_ratio, /// len_greek, total_word_count, /// v_pen, c_pen, bad_double, misplaced_sigma, invalid_bigram, long_word_count, longest_word, short_word_count, max_run, /// v_rate, c_rate, d_rate, sigma_end_rate, bigram_rate, long_word_rate, short_ratio, short_pen, /// flags) -fn compute_score_and_details( - buf: &[u8] -) -> ( - f64, f64, f64, f64, - u64, u64, - u64, u64, u64, u64, u64, u64, u64, u64, u64, - f64, f64, f64, f64, f64, f64, f64, f64, - String -) { +fn compute_score_and_details(buf: &[u8]) -> DetailedScore { let latin_pct = compute_latin_pct(buf); // Build text and filter out table-like lines let text = String::from_utf8_lossy(buf); let (table_ratio, filtered_opt, _non_empty, table_like) = table_line_ratio_and_filtered(&text); let had_tables = table_like > 0; - let target: &[u8] = if let Some(ref s) = filtered_opt { s.as_bytes() } else { buf }; + let target: &[u8] = if let Some(ref s) = filtered_opt { + s.as_bytes() + } else { + buf + }; - let (len_greek, v_pen, c_pen, bad_dbl, max_run, long_word_count, long_word_weight_sum, longest_word, misplaced_sigma, invalid_bigram, short_word_count, total_word_count) = analyse_bytes(target); + let ( + len_greek, + v_pen, + c_pen, + bad_dbl, + max_run, + long_word_count, + long_word_weight_sum, + longest_word, + misplaced_sigma, + invalid_bigram, + short_word_count, + total_word_count, + ) = analyse_bytes(target); let mut flags: Vec<&str> = Vec::with_capacity(2); @@ -369,34 +3651,113 @@ fn compute_score_and_details( 0.0 }; // Normalized short words: per 1000 Greek chars, then excess over baseline - let short_per_1000 = if len > 0.0 { 1000.0 * (short_word_count as f64) / len } else { 0.0 }; - let short_excess_per_1000 = if short_per_1000 > SHORT_BASELINE_PER_1000 { short_per_1000 - SHORT_BASELINE_PER_1000 } else { 0.0 }; + let short_per_1000 = if len > 0.0 { + 1000.0 * (short_word_count as f64) / len + } else { + 0.0 + }; + let short_excess_per_1000 = if short_per_1000 > SHORT_BASELINE_PER_1000 { + short_per_1000 - SHORT_BASELINE_PER_1000 + } else { + 0.0 + }; // Halved sigma coefficient from 5.0 to 2.5; removed longest_word term - let score = v_rate + 1.5*c_rate + 2.0*d_rate + 2.5*sigma_end_rate + 2.0*bigram_rate + short_excess_per_1000 + long_word_rate; + let score = v_rate + + 1.5 * c_rate + + 2.0 * d_rate + + 2.5 * sigma_end_rate + + 2.0 * bigram_rate + + short_excess_per_1000 + + long_word_rate; - let (_poly_words, _greek_words, poly_ratio) = if len_greek == 0 { - (0, 0, 0.0) + let poly_ratio = if len_greek == 0 { + 0.0 } else { - compute_polytonic_word_ratio(if let Some(ref s) = filtered_opt { s } else { &text }) + let target_text: &str = if let Some(ref s) = filtered_opt { + s.as_str() + } else { + text.as_ref() + }; + scan_script_metrics(target_text).polytonic_ratio() }; - if poly_ratio > 0.0 { flags.push("polytonic"); } - if had_tables { flags.push("had_tables"); } + if poly_ratio > 0.0 { + flags.push("polytonic"); + } + if had_tables { + flags.push("had_tables"); + } ( - score, latin_pct, table_ratio, poly_ratio, - len_greek, total_word_count, - v_pen, c_pen, bad_dbl, misplaced_sigma, invalid_bigram, long_word_count, longest_word, short_word_count, max_run, - v_rate, c_rate, d_rate, sigma_end_rate, bigram_rate, long_word_rate, short_ratio, short_excess_per_1000, - flags.join(",") + score, + latin_pct, + table_ratio, + poly_ratio, + len_greek, + total_word_count, + v_pen, + c_pen, + bad_dbl, + misplaced_sigma, + invalid_bigram, + long_word_count, + longest_word, + short_word_count, + max_run, + v_rate, + c_rate, + d_rate, + sigma_end_rate, + bigram_rate, + long_word_rate, + short_ratio, + short_excess_per_1000, + flags.join(","), ) } /// Compute noise score and latin percentage for a UTF-8 buffer. Backward-compatible API. fn compute_score(buf: &[u8]) -> (f64, f64) { - let (score, latin_pct, _t, _p, _lg, _tw, _v,_c,_bd,_ms,_ib,_lwc,_lw,_swc,_mr,_vr,_cr,_dr,_sr,_br,_lwr,_sr2,_sp,_f) = compute_score_and_details(buf); + let ( + score, + latin_pct, + _t, + _p, + _lg, + _tw, + _v, + _c, + _bd, + _ms, + _ib, + _lwc, + _lw, + _swc, + _mr, + _vr, + _cr, + _dr, + _sr, + _br, + _lwr, + _sr2, + _sp, + _f, + ) = compute_score_and_details(buf); (score, latin_pct) } +fn run_in_thread_pool(n_threads: Option, work: F) -> anyhow::Result +where + T: Send, + F: FnOnce() -> T + Send, +{ + let threads = n_threads + .filter(|count| *count > 0) + .unwrap_or_else(rayon::current_num_threads); + let pool = ThreadPoolBuilder::new().num_threads(threads).build()?; + Ok(pool.install(work)) +} + pub fn score_markdown_file_internal(path: &Path) -> anyhow::Result { let mut file = File::open(path)?; let mut buf = Vec::new(); @@ -405,57 +3766,864 @@ pub fn score_markdown_file_internal(path: &Path) -> anyhow::Result { Ok(score) } -pub fn score_markdown_directory_internal(root: &Path, n_threads: Option) -> anyhow::Result> { - if let Some(t) = n_threads { rayon::ThreadPoolBuilder::new().num_threads(t).build_global().ok(); } - let results: Vec<(String, f64, f64)> = WalkDir::new(root) - .into_iter() - .par_bridge() - .filter_map(Result::ok) - .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) - .map(|e| { - let path = e.path(); - let buf = fs::read(path).expect("read"); - let (score, latin_pct) = compute_score(&buf); - (path.to_string_lossy().into_owned(), score, latin_pct) - }) - .collect(); - Ok(results) +pub fn score_text_detailed_internal(text: &str) -> DetailedScore { + compute_score_and_details(text.as_bytes()) +} + +pub fn score_texts_detailed_internal( + texts: Vec, + n_threads: Option, +) -> anyhow::Result> { + run_in_thread_pool(n_threads, move || { + texts + .into_par_iter() + .map(|text| compute_score_and_details(text.as_bytes())) + .collect() + }) +} + +pub fn score_markdown_directory_internal( + root: &Path, + n_threads: Option, +) -> anyhow::Result> { + run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let buf = fs::read(path).expect("read"); + let (score, latin_pct) = compute_score(&buf); + (path.to_string_lossy().into_owned(), score, latin_pct) + }) + .collect() + }) } // Detailed variants for analysis layer -pub fn score_markdown_file_detailed_internal(path: &Path) -> anyhow::Result<(f64, f64, f64, f64, u64, u64, u64, u64, u64, u64, u64, u64, u64, u64, u64, f64, f64, f64, f64, f64, f64, f64, f64, String)> { +pub fn score_markdown_file_detailed_internal( + path: &Path, +) -> anyhow::Result<( + f64, + f64, + f64, + f64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + f64, + f64, + f64, + f64, + f64, + f64, + f64, + f64, + String, +)> { let mut file = File::open(path)?; let mut buf = Vec::new(); file.read_to_end(&mut buf)?; Ok(compute_score_and_details(&buf)) } -pub fn score_markdown_directory_detailed_internal(root: &Path, n_threads: Option) -> anyhow::Result> { - if let Some(t) = n_threads { rayon::ThreadPoolBuilder::new().num_threads(t).build_global().ok(); } - let results: Vec<(String, f64, f64, f64, f64, u64, u64, u64, u64, u64, u64, u64, u64, u64, u64, u64, f64, f64, f64, f64, f64, f64, f64, f64, String)> = WalkDir::new(root) - .into_iter() - .par_bridge() - .filter_map(Result::ok) - .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) - .map(|e| { - let path = e.path(); - let buf = fs::read(path).expect("read"); - let ( - score, latin_pct, table_ratio, poly_ratio, - len_greek, total_words, - v_pen, c_pen, bad_dbl, misplaced_sigma, invalid_bigram, long_word_count, longest_word, short_word_count, max_run, - v_rate, c_rate, d_rate, sigma_end_rate, bigram_rate, long_word_rate, short_ratio, short_pen, - flags - ) = compute_score_and_details(&buf); - ( - path.to_string_lossy().into_owned(), - score, latin_pct, table_ratio, poly_ratio, - len_greek, total_words, - v_pen, c_pen, bad_dbl, misplaced_sigma, invalid_bigram, long_word_count, longest_word, short_word_count, max_run, - v_rate, c_rate, d_rate, sigma_end_rate, bigram_rate, long_word_rate, short_ratio, short_pen, - flags - ) - }) - .collect(); - Ok(results) +pub fn score_markdown_directory_detailed_internal( + root: &Path, + n_threads: Option, +) -> anyhow::Result< + Vec<( + String, + f64, + f64, + f64, + f64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + u64, + f64, + f64, + f64, + f64, + f64, + f64, + f64, + f64, + String, + )>, +> { + run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let buf = fs::read(path).expect("read"); + let ( + score, + latin_pct, + table_ratio, + poly_ratio, + len_greek, + total_words, + v_pen, + c_pen, + bad_dbl, + misplaced_sigma, + invalid_bigram, + long_word_count, + longest_word, + short_word_count, + max_run, + v_rate, + c_rate, + d_rate, + sigma_end_rate, + bigram_rate, + long_word_rate, + short_ratio, + short_pen, + flags, + ) = compute_score_and_details(&buf); + ( + path.to_string_lossy().into_owned(), + score, + latin_pct, + table_ratio, + poly_ratio, + len_greek, + total_words, + v_pen, + c_pen, + bad_dbl, + misplaced_sigma, + invalid_bigram, + long_word_count, + longest_word, + short_word_count, + max_run, + v_rate, + c_rate, + d_rate, + sigma_end_rate, + bigram_rate, + long_word_rate, + short_ratio, + short_pen, + flags, + ) + }) + .collect() + }) +} + +pub fn score_markdown_directory_ocr_profile_internal( + root: &Path, + n_threads: Option, + min_repeat_run: u64, +) -> anyhow::Result> { + run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let buf = fs::read(path).expect("read"); + let text = String::from_utf8_lossy(&buf); + let (script, noise) = compute_ocr_profile(&text, min_repeat_run); + let mut flags = Vec::new(); + if noise.phrase_run_max >= min_repeat_run { + flags.push("repeat_phrase_run"); + } + if noise.line_run_max >= min_repeat_run { + flags.push("repeat_line_run"); + } + + OcrProfileRow { + path: path.to_string_lossy().into_owned(), + percentage_greek: script.percentage_greek(), + latin_percentage: script.latin_percentage(), + polytonic_ratio: script.polytonic_ratio(), + non_whitespace_chars: script.non_whitespace_chars, + greek_char_count: script.greek_char_count, + latin_char_count: script.latin_char_count, + ocr_repeat_phrase_run_max: noise.phrase_run_max, + ocr_repeat_line_run_max: noise.line_run_max, + ocr_repeat_suspicious_line_count: noise.suspicious_line_count, + ocr_repeat_suspicious_line_ratio: noise.suspicious_line_ratio, + ocr_noise_suspect: noise.suspect, + ocr_noise_flags: flags.join(","), + } + }) + .collect() + }) +} + +pub fn export_ocr_match_debug_pages_internal( + root: &Path, + output_dir: &Path, + n_threads: Option, + min_repeat_run: u64, + max_pages: Option, + sample_seed: u64, +) -> anyhow::Result> { + fs::create_dir_all(output_dir)?; + if let Some(limit) = max_pages { + let mut candidates: Vec = run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let source_stem = path + .file_stem() + .map(|stem| stem.to_string_lossy().into_owned()) + .unwrap_or_else(|| "unknown".to_string()); + let (base_stem, start_page) = parse_source_stem(&source_stem); + let buf = fs::read(path).expect("read"); + let text = String::from_utf8_lossy(&buf); + collect_ocr_debug_candidates_for_text( + path, + &source_stem, + &base_stem, + start_page, + &text, + min_repeat_run, + ) + }) + .reduce(Vec::new, |mut acc, mut item| { + acc.append(&mut item); + acc + }) + })?; + + if candidates.len() > limit { + let mut rng = StdRng::seed_from_u64(sample_seed); + candidates.shuffle(&mut rng); + candidates.truncate(limit); + } + candidates.sort_by(|a, b| { + a.source_path + .cmp(&b.source_path) + .then(a.page_number.cmp(&b.page_number)) + }); + + let output_dir = output_dir.to_path_buf(); + let mut rows: Vec = run_in_thread_pool(n_threads, move || { + candidates + .into_par_iter() + .map(|candidate| { + render_ocr_debug_candidate(&candidate, &output_dir, min_repeat_run) + }) + .collect::>>() + })??; + rows.sort_by(|a, b| { + a.output_path + .cmp(&b.output_path) + .then(a.page_number.cmp(&b.page_number)) + }); + return Ok(rows); + } + + let rows: Vec> = run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let source_stem = path + .file_stem() + .map(|stem| stem.to_string_lossy().into_owned()) + .unwrap_or_else(|| "unknown".to_string()); + let (base_stem, start_page) = parse_source_stem(&source_stem); + let buf = fs::read(path).expect("read"); + let text = String::from_utf8_lossy(&buf); + let pages = split_pages(&text); + let mut page_rows = Vec::new(); + + for (idx, page) in pages.iter().enumerate() { + let page_index_in_file = idx as u64 + 1; + let page_number = start_page + idx as u64; + if let Some((annotated_page, match_types, match_count)) = + annotate_page_for_debug(page, min_repeat_run) + { + let match_types_joined = match_types.join(","); + let output_name = + format!("{}__debug_page_{:05}.md", source_stem, page_number); + let output_path = output_dir.join(output_name); + let mut content = String::new(); + content.push_str("\n"); + content.push_str("\n\n"); + content.push_str(&annotated_page); + fs::write(&output_path, content).expect("write debug page"); + + page_rows.push(OcrDebugPageRow { + source_path: path.to_string_lossy().into_owned(), + output_path: output_path.to_string_lossy().into_owned(), + source_stem: source_stem.clone(), + base_stem: base_stem.clone(), + page_number, + page_index_in_file, + match_types: match_types_joined, + match_count, + }); + } + } + + page_rows + }) + .collect() + })?; + + let mut flat = Vec::new(); + for mut group in rows { + flat.append(&mut group); + } + flat.sort_by(|a, b| { + a.output_path + .cmp(&b.output_path) + .then(a.page_number.cmp(&b.page_number)) + }); + Ok(flat) +} + +pub fn export_numeric_match_debug_pages_internal( + root: &Path, + output_dir: &Path, + n_threads: Option, + min_progress_steps: u64, + min_repeat_steps: u64, + min_same_digit_steps: u64, + max_pages: Option, + sample_seed: u64, +) -> anyhow::Result> { + fs::create_dir_all(output_dir)?; + if let Some(limit) = max_pages { + let mut candidates: Vec = run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let source_stem = path + .file_stem() + .map(|stem| stem.to_string_lossy().into_owned()) + .unwrap_or_else(|| "unknown".to_string()); + let (base_stem, start_page) = parse_source_stem(&source_stem); + let buf = fs::read(path).expect("read"); + let text = String::from_utf8_lossy(&buf); + collect_numeric_debug_candidates_for_text( + path, + &source_stem, + &base_stem, + start_page, + &text, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ) + }) + .reduce(Vec::new, |mut acc, mut item| { + acc.append(&mut item); + acc + }) + })?; + + if candidates.len() > limit { + let mut rng = StdRng::seed_from_u64(sample_seed); + candidates.shuffle(&mut rng); + candidates.truncate(limit); + } + candidates.sort_by(|a, b| { + a.source_path + .cmp(&b.source_path) + .then(a.page_number.cmp(&b.page_number)) + }); + + let output_dir = output_dir.to_path_buf(); + let mut rows: Vec = run_in_thread_pool(n_threads, move || { + candidates + .into_par_iter() + .map(|candidate| { + render_numeric_debug_candidate( + &candidate, + &output_dir, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ) + }) + .collect::>>() + })??; + rows.sort_by(|a, b| { + a.output_path + .cmp(&b.output_path) + .then(a.page_number.cmp(&b.page_number)) + }); + return Ok(rows); + } + + let rows: Vec> = run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let source_stem = path + .file_stem() + .map(|stem| stem.to_string_lossy().into_owned()) + .unwrap_or_else(|| "unknown".to_string()); + let (base_stem, start_page) = parse_source_stem(&source_stem); + let buf = fs::read(path).expect("read"); + let text = String::from_utf8_lossy(&buf); + let pages = split_pages(&text); + let mut page_rows = Vec::new(); + + for (idx, page) in pages.iter().enumerate() { + let page_index_in_file = idx as u64 + 1; + let page_number = start_page + idx as u64; + let spans = collect_numeric_debug_spans_for_page( + page, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + ); + if let Some((annotated_page, match_types, match_count)) = + annotate_text_with_debug_spans(page, spans) + { + let match_types_joined = match_types.join(","); + let output_name = + format!("{}__debug_page_{:05}.md", source_stem, page_number); + let output_path = output_dir.join(output_name); + let mut content = String::new(); + content.push_str("\n"); + content.push_str("\n\n"); + content.push_str(&annotated_page); + fs::write(&output_path, content).expect("write numeric debug page"); + + page_rows.push(OcrDebugPageRow { + source_path: path.to_string_lossy().into_owned(), + output_path: output_path.to_string_lossy().into_owned(), + source_stem: source_stem.clone(), + base_stem: base_stem.clone(), + page_number, + page_index_in_file, + match_types: match_types_joined, + match_count, + }); + } + } + + page_rows + }) + .collect() + })?; + + let mut flat = Vec::new(); + for mut group in rows { + flat.append(&mut group); + } + flat.sort_by(|a, b| { + a.output_path + .cmp(&b.output_path) + .then(a.page_number.cmp(&b.page_number)) + }); + Ok(flat) +} + +// Dead code post-Point-7 — kept until a follow-up extraction. +#[allow(dead_code)] +pub fn export_token_category_debug_pages_internal( + root: &Path, + output_dir: &Path, + category_specs_path: &Path, + n_threads: Option, + max_pages: Option, + sample_seed: u64, + synthetic_page_target_chars: usize, + synthetic_page_min_header_chars: usize, + synthetic_page_hard_max_chars: usize, +) -> anyhow::Result> { + fs::create_dir_all(output_dir)?; + let specs = load_token_category_specs_cached(category_specs_path)?; + + if let Some(limit) = max_pages { + let specs = specs.clone(); + let mut candidates: Vec = + run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let source_stem = path + .file_stem() + .map(|stem| stem.to_string_lossy().into_owned()) + .unwrap_or_else(|| "unknown".to_string()); + let (base_stem, start_page) = parse_source_stem(&source_stem); + let buf = fs::read(path).expect("read"); + let text = String::from_utf8_lossy(&buf); + collect_token_category_debug_candidates_for_text( + path, + &source_stem, + &base_stem, + start_page, + &text, + &specs, + synthetic_page_target_chars, + synthetic_page_min_header_chars, + synthetic_page_hard_max_chars, + ) + }) + .reduce(Vec::new, |mut acc, mut item| { + acc.append(&mut item); + acc + }) + })?; + + if candidates.len() > limit { + let mut rng = StdRng::seed_from_u64(sample_seed); + candidates.shuffle(&mut rng); + candidates.truncate(limit); + } + candidates.sort_by(|a, b| { + a.source_path + .cmp(&b.source_path) + .then(a.page_number.cmp(&b.page_number)) + }); + + let output_dir = output_dir.to_path_buf(); + let mut rows: Vec = run_in_thread_pool(n_threads, move || { + candidates + .into_par_iter() + .map(|candidate| render_token_category_debug_candidate(&candidate, &output_dir, true)) + .collect::>>() + })??; + rows.sort_by(|a, b| { + a.output_path + .cmp(&b.output_path) + .then(a.page_number.cmp(&b.page_number)) + }); + return Ok(rows); + } + + let specs = specs.clone(); + let rows: Vec> = run_in_thread_pool(n_threads, || { + WalkDir::new(root) + .into_iter() + .par_bridge() + .filter_map(Result::ok) + .filter(|e| e.path().extension().map_or(false, |ext| ext == "md")) + .map(|e| { + let path = e.path(); + let source_stem = path + .file_stem() + .map(|stem| stem.to_string_lossy().into_owned()) + .unwrap_or_else(|| "unknown".to_string()); + let (base_stem, start_page) = parse_source_stem(&source_stem); + let buf = fs::read(path).expect("read"); + let text = String::from_utf8_lossy(&buf); + let candidates = collect_token_category_debug_candidates_for_text( + path, + &source_stem, + &base_stem, + start_page, + &text, + &specs, + synthetic_page_target_chars, + synthetic_page_min_header_chars, + synthetic_page_hard_max_chars, + ); + let mut page_rows = Vec::with_capacity(candidates.len()); + for candidate in candidates { + page_rows.push( + render_token_category_debug_candidate(&candidate, output_dir, true) + .expect("write token debug page"), + ); + } + page_rows + }) + .collect() + })?; + + let mut flat = Vec::new(); + for mut group in rows { + flat.append(&mut group); + } + flat.sort_by(|a, b| { + a.output_path + .cmp(&b.output_path) + .then(a.page_number.cmp(&b.page_number)) + }); + Ok(flat) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn test_bad_double(text: &str) -> u64 { + let (_, _, _, bad_double, _, _, _, _, _, _, _, _) = analyse_bytes(text.as_bytes()); + bad_double + } + + fn compiled_spec(category: &str, family: &str, pattern: &str) -> CompiledTokenCategorySpec { + CompiledTokenCategorySpec { + category: category.to_string(), + pattern_family: family.to_string(), + matcher: TokenCategoryMatcher::Regex( + RegexBuilder::new(pattern) + .multi_line(true) + .build() + .expect("compile test regex"), + ), + } + } + + fn compiled_literal_spec( + category: &str, + family: &str, + literals: &[&str], + ) -> CompiledTokenCategorySpec { + CompiledTokenCategorySpec { + category: category.to_string(), + pattern_family: family.to_string(), + matcher: TokenCategoryMatcher::LiteralSet( + AhoCorasickBuilder::new() + .match_kind(MatchKind::LeftmostLongest) + .build(literals) + .expect("compile literal matcher"), + ), + } + } + + #[test] + fn synthetic_pages_prefer_headers_before_fallback() { + let text = "# One\n\nalpha\n\n# Two\n\nbeta\nGLYPH<1>\n"; + let pages = split_synthetic_pages(text, 80, 10, 200); + assert_eq!(pages.len(), 2); + assert_eq!(pages[0].kind, "synthetic_header"); + assert!(pages[1].text.contains("GLYPH<1>")); + } + + #[test] + fn token_category_matching_merges_overlaps_and_keeps_categories() { + let specs = vec![ + compiled_spec("glyph_font_like", "glyph_marker", r"GLYPH<\d+>"), + compiled_spec("dot_leader_like", "dot_run", r"\.{4,}"), + ]; + let spans = collect_token_category_raw_spans( + "Intro ................ 12\nGLYPH<1> GLYPH<2>\n", + &specs, + ); + let merged = merge_token_category_spans(spans); + assert_eq!(merged.len(), 3); + assert_eq!(merged[0].categories, vec!["dot_leader_like".to_string()]); + assert_eq!(merged[1].categories, vec!["glyph_font_like".to_string()]); + assert_eq!(merged[2].categories, vec!["glyph_font_like".to_string()]); + } + + #[test] + fn token_category_literal_set_matches_leftmost_longest() { + let specs = vec![compiled_literal_spec( + "dot_leader_like", + "dot_run", + &["....", ".....", ".........."], + )]; + let spans = collect_token_category_raw_spans("Intro .......... 15", &specs); + assert_eq!(spans.len(), 1); + assert_eq!(spans[0].end - spans[0].start, 10); + } + + #[test] + fn token_category_candidates_preserve_real_page_numbers_from_stem() { + let specs = vec![compiled_spec( + "glyph_font_like", + "glyph_marker", + r"GLYPH<\d+>", + )]; + let candidates = collect_token_category_debug_candidates_for_text( + Path::new("/tmp/doc__p0005-0006.md"), + "doc__p0005-0006", + "doc", + 5, + "Alpha\n<--- Page Split --->\nGLYPH<7>\n", + &specs, + 4000, + 1200, + 6000, + ); + assert_eq!(candidates.len(), 1); + assert_eq!(candidates[0].page_kind, "real_page"); + assert_eq!(candidates[0].page_number, 6); + assert_eq!(candidates[0].page_index_in_file, 2); + } + + #[test] + fn bad_double_counts_only_exact_illegal_doubles() { + assert_eq!(test_bad_double("αα"), 1); + assert_eq!(test_bad_double("ααββ"), 2); + } + + #[test] + fn bad_double_ignores_allowed_greek_doubles() { + assert_eq!(test_bad_double("λλ"), 0); + assert_eq!(test_bad_double("γγ"), 0); + } + + #[test] + fn bad_double_ignores_long_expressive_runs() { + assert_eq!(test_bad_double("ααα"), 0); + assert_eq!(test_bad_double("αααα"), 0); + assert_eq!(test_bad_double("ββββ!"), 0); + } + + #[test] + fn render_candidate_per_category_match_count_tallies_by_category() { + // Two glyph hits + two dot-leader hits → tallies must reflect that. + let specs = vec![ + compiled_spec("glyph_font_like", "glyph_marker", r"GLYPH<\d+>"), + compiled_spec("dot_leader_like", "dot_run", r"\.{4,}"), + ]; + let candidates = collect_token_category_debug_candidates_for_text( + Path::new("/tmp/doc.md"), + "doc", + "doc", + 1, + "GLYPH<1> ........ GLYPH<2> ........", + &specs, + 4000, + 1200, + 6000, + ); + assert_eq!(candidates.len(), 1); + let row = render_token_category_debug_candidate( + &candidates[0], + Path::new("/tmp"), + false, // write_files=false: no disk I/O during this test + ) + .expect("render"); + assert_eq!(row.per_category_match_count.get("glyph_font_like").copied(), Some(2)); + assert_eq!(row.per_category_match_count.get("dot_leader_like").copied(), Some(2)); + } + + #[test] + fn render_candidate_write_files_false_skips_disk_but_returns_row() { + // Confirm write_files=false still produces a populated row (so the + // Python driver gets the same data without paying disk-I/O cost). + let specs = vec![compiled_spec( + "glyph_font_like", + "glyph_marker", + r"GLYPH<\d+>", + )]; + let candidates = collect_token_category_debug_candidates_for_text( + Path::new("/tmp/doc.md"), + "doc", + "doc", + 1, + "GLYPH<1>", + &specs, + 4000, + 1200, + 6000, + ); + // Use a path that does NOT exist — proves write_files=false skips fs::write. + let nonexistent = Path::new("/tmp/this-dir-must-not-exist-for-test-XYZ"); + let row = render_token_category_debug_candidate( + &candidates[0], + nonexistent, + false, + ) + .expect("render with write_files=false must succeed even when output_dir is missing"); + assert_eq!(row.match_count, 1); + assert_eq!(row.per_category_match_count.get("glyph_font_like").copied(), Some(1)); + } + + #[test] + fn match_internal_write_files_false_skips_mkdir_and_returns_rows() { + // End-to-end variant via match_token_category_debug_text_internal. + let nonexistent = Path::new("/tmp/this-dir-must-not-exist-for-test-ZZZ"); + let specs_path = std::env::temp_dir().join("glossapi_rs_noise_test_specs.json"); + std::fs::write( + &specs_path, + r#"[{"category":"glyph_font_like","pattern_family":"glyph_marker","match_kind":"regex","pattern":"GLYPH<\\d+>"}]"#, + ).expect("write specs"); + let rows = match_token_category_debug_text_internal( + nonexistent, + &specs_path, + "/tmp/doc.md", + "doc", + "doc", + 1, + "GLYPH<1> some text GLYPH<2>", + 4000, + 1200, + 6000, + false, + ) + .expect("match internal must not fail with write_files=false"); + assert_eq!(rows.len(), 1); + assert_eq!(rows[0].match_count, 2); + assert_eq!(rows[0].per_category_match_count.get("glyph_font_like").copied(), Some(2)); + assert!(!nonexistent.exists(), "write_files=false must not create the output dir"); + } } diff --git a/samples/openarchives_download_policy.yml b/samples/openarchives_download_policy.yml new file mode 100644 index 0000000..180e5fe --- /dev/null +++ b/samples/openarchives_download_policy.yml @@ -0,0 +1,104 @@ +default: + downloader: standard + request_timeout: 60 + ssl_verify: true + per_domain_concurrency: 8 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 12 + skip_failed_after: 3 + sleep: 0.25 + +rules: + - match: + domains: [ikee.lib.auth.gr] + downloader: standard + request_timeout: 180 + per_domain_concurrency: 1 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 1 + skip_failed_after: 5 + sleep: 1.5 + + - match: + domains: [dspace.lib.ntua.gr] + downloader: standard + request_timeout: 120 + per_domain_concurrency: 1 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 1 + skip_failed_after: 4 + sleep: 1.0 + + - match: + domains: [olympias.lib.uoi.gr] + downloader: standard + request_timeout: 180 + ssl_verify: false + per_domain_concurrency: 1 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 1 + skip_failed_after: 4 + sleep: 1.0 + + - match: + domains: [ktisis.cut.ac.cy] + downloader: standard + request_timeout: 90 + ssl_verify: false + per_domain_concurrency: 2 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 2 + skip_failed_after: 4 + sleep: 0.5 + + - match: + domains: [repository.academyofathens.gr] + downloader: auto + request_timeout: 45 + per_domain_concurrency: 6 + domain_concurrency_floor: 2 + domain_concurrency_ceiling: 8 + skip_failed_after: 3 + sleep: 0.1 + + - match: + domains: + - dione.lib.unipi.gr + - pergamos.lib.uoa.gr + - hellanicus.lib.aegean.gr + downloader: standard + request_timeout: 60 + per_domain_concurrency: 12 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 12 + skip_failed_after: 3 + sleep: 0.2 + + - match: + domains: + - dias.library.tuc.gr + downloader: auto + request_timeout: 90 + per_domain_concurrency: 2 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 2 + skip_failed_after: 4 + sleep: 0.5 + browser_timeout_ms: 90000 + browser_post_load_wait_ms: 4000 + + - match: + domains: + - repository.ihu.gr + - dlib.statistics.gr + - apothesis.eap.gr + - repository.edulll.gr + - dspace.lib.uom.gr + - dspace.aua.gr + downloader: standard + request_timeout: 75 + per_domain_concurrency: 6 + domain_concurrency_floor: 1 + domain_concurrency_ceiling: 8 + skip_failed_after: 4 + sleep: 0.25 diff --git a/src/glossapi/__init__.py b/src/glossapi/__init__.py index 4539ead..14f0c31 100644 --- a/src/glossapi/__init__.py +++ b/src/glossapi/__init__.py @@ -1,54 +1,7 @@ -""" -GlossAPI Library - -A library for processing academic texts in Greek and other languages: -- Extracting content from PDFs and other formats with Docling -- Robust batch processing with error isolation and automatic resumption -- Clustering documents based on extraction quality -- Extracting and cleaning academic sections -- Classifying sections using machine learning - -This is an open source project that provides tools for linguistic annotations -and text processing, with a special focus on the Greek language. -""" +"""GlossAPI library.""" from __future__ import annotations -import os - -# Keep Docling/RapidOCR bootstrap optional and import‑light by default. -# If the environment requests skipping (common in tests or minimal envs), -# or if Docling is not installed, we avoid importing heavy dependencies here. -_SKIP_DOCLING_BOOT = os.environ.get("GLOSSAPI_SKIP_DOCLING_BOOT") == "1" - -def _attempt_patch_docling() -> bool: - if _SKIP_DOCLING_BOOT: - return False - try: - # Import inside the function to avoid pulling Docling when unused or missing. - from .ocr.rapidocr.safe import patch_docling_rapidocr # type: ignore - - try: - return bool(patch_docling_rapidocr()) - except Exception: - # Swallow any runtime error to keep top‑level import light/safe. - return False - except Exception: - # Docling (or its transitive deps) not available – keep going. - return False - - -def patch_docling_rapidocr() -> bool: - """Best‑effort registration of the SafeRapidOcrModel. - - Returns True when the patch was applied; False when unavailable or skipped. - Safe to call multiple times. - """ - return _attempt_patch_docling() - -# Attempt the patch once at import time, but never fail import if it does not apply. -_ = _attempt_patch_docling() - __all__ = [ 'GlossSection', 'GlossSectionClassifier', @@ -56,7 +9,7 @@ def patch_docling_rapidocr() -> bool: 'Sampler', 'Section', 'GlossDownloader', - 'patch_docling_rapidocr', + 'BrowserGlossDownloader', ] def __getattr__(name: str): @@ -79,9 +32,11 @@ def __getattr__(name: str): if name == 'GlossDownloader': from .gloss_downloader import GlossDownloader # type: ignore return GlossDownloader + if name == 'BrowserGlossDownloader': + from .gloss_browser_downloader import BrowserGlossDownloader # type: ignore + return BrowserGlossDownloader raise AttributeError(name) -# Derive version dynamically from installed package metadata if possible try: from importlib.metadata import version as _pkg_version __version__: str = _pkg_version(__name__) diff --git a/src/glossapi/_naming.py b/src/glossapi/_naming.py index 068b195..5f28434 100644 --- a/src/glossapi/_naming.py +++ b/src/glossapi/_naming.py @@ -3,6 +3,7 @@ from __future__ import annotations from pathlib import Path +import re from typing import Union _KNOWN_SUFFIXES = ( @@ -19,6 +20,8 @@ ".htm", ) +_PAGE_CHUNK_SUFFIX_RE = re.compile(r"__p\d{4,5}-\d{4,5}$") + def canonical_stem(value: Union[str, Path]) -> str: """Return a normalised stem for any pipeline artefact.""" @@ -33,6 +36,7 @@ def canonical_stem(value: Union[str, Path]) -> str: working = working[: -len(suffix)] stripped = True break + working = _PAGE_CHUNK_SUFFIX_RE.sub("", working) if working: return working fallback = Path(name).stem diff --git a/src/glossapi/_pipeline.py b/src/glossapi/_pipeline.py index 73e5ecc..1909b60 100644 --- a/src/glossapi/_pipeline.py +++ b/src/glossapi/_pipeline.py @@ -1,7 +1,7 @@ """Backward-compatible adapter. -Docling pipeline builders moved to `glossapi.ocr.rapidocr.pipeline`. +Docling pipeline builders moved to `glossapi.ocr.docling.pipeline`. This module re-exports the public API to preserve legacy imports. """ -from .ocr.rapidocr.pipeline import * # noqa: F401,F403 +from .ocr.docling.pipeline import * # noqa: F401,F403 diff --git a/src/glossapi/corpus/corpus_orchestrator.py b/src/glossapi/corpus/corpus_orchestrator.py index dd2fad6..3feb7ec 100644 --- a/src/glossapi/corpus/corpus_orchestrator.py +++ b/src/glossapi/corpus/corpus_orchestrator.py @@ -350,9 +350,11 @@ def _load_metadata(self) -> None: # Top-level worker function for multi-GPU extraction (picklable by multiprocessing) def gpu_extract_worker_queue( device_id: int, + worker_slot: int, + worker_key: str, in_dir: str, out_dir: str, - work_q, # multiprocessing Queue of filename strings + work_q, # multiprocessing Queue of filename strings or bundled path lists force: bool, fe: bool, ce: bool, @@ -392,12 +394,13 @@ def _ensure_thread_caps(): _ensure_thread_caps() _status_proxy = status_map - _marker_path = _Path(marker_dir).expanduser() / f"gpu{device_id}.current" if marker_dir else None + _worker_label = worker_key or f"gpu{device_id}-w{worker_slot}" + _marker_path = _Path(marker_dir).expanduser() / f"{_worker_label}.current" if marker_dir else None def _update_current(batch_items: List[str]) -> None: if _status_proxy is not None: try: - _status_proxy[device_id] = list(batch_items) + _status_proxy[_worker_label] = list(batch_items) except Exception: pass if _marker_path is not None: @@ -409,7 +412,7 @@ def _update_current(batch_items: List[str]) -> None: def _clear_current() -> None: if _status_proxy is not None: try: - _status_proxy.pop(device_id, None) + _status_proxy.pop(_worker_label, None) except Exception: pass if _marker_path is not None: @@ -417,13 +420,28 @@ def _clear_current() -> None: _marker_path.unlink(missing_ok=True) except Exception: pass + + def _normalize_work_item(item: Any) -> List[str]: + if isinstance(item, str): + return [item] if item.strip() else [] + if isinstance(item, (list, tuple, set)): + normalized: List[str] = [] + for value in item: + try: + text = str(value).strip() + except Exception: + continue + if text: + normalized.append(text) + return normalized + return [] _worker_log_handle = None try: _log_dir = _os.environ.get("GLOSSAPI_WORKER_LOG_DIR") if _log_dir: _log_path = _Path(_log_dir).expanduser() _log_path.mkdir(parents=True, exist_ok=True) - _worker_log_file = _log_path / f"gpu{device_id}_{_os.getpid()}.log" + _worker_log_file = _log_path / f"{_worker_label}_{_os.getpid()}.log" _worker_log_handle = open(_worker_log_file, "a", encoding="utf-8", buffering=1) _sys.stdout = _worker_log_handle _sys.stderr = _worker_log_handle @@ -458,9 +476,13 @@ def _clear_current() -> None: except Exception: _phys = "" try: - print(f"[GPU{device_id}] bound: CUDA_VISIBLE_DEVICES={_os.environ.get('CUDA_VISIBLE_DEVICES','')} pid={_os.getpid()} torch={_torch_name} ORT={_ort_prov}") + print( + f"[GPU{device_id}/W{worker_slot}] bound: " + f"CUDA_VISIBLE_DEVICES={_os.environ.get('CUDA_VISIBLE_DEVICES','')} " + f"pid={_os.getpid()} torch={_torch_name} ORT={_ort_prov}" + ) if _phys: - print(f"[GPU{device_id}] physical: {_phys}") + print(f"[GPU{device_id}/W{worker_slot}] physical: {_phys}") except Exception: pass except Exception: @@ -475,13 +497,15 @@ def _clear_current() -> None: _ensure_thread_caps() from glossapi import Corpus as _Corpus # type: ignore except Exception as _e: - print(f"[GPU{device_id}] Cannot import glossapi in worker: {_e}") + print(f"[{_worker_label}] Cannot import glossapi in worker: {_e}") if result_q is not None: try: result_q.put( { "event": "exit", - "worker": device_id, + "worker": _worker_label, + "device_id": device_id, + "worker_slot": worker_slot, "exitcode": 1, "pid": _os.getpid(), "error": str(_e), @@ -507,14 +531,16 @@ def _clear_current() -> None: phase1_backend=backend, ) except Exception as _e: - msg = f"[GPU{device_id}] Prime failed: {_e}" + msg = f"[{_worker_label}] Prime failed: {_e}" print(msg) if result_q is not None: try: result_q.put( { "event": "exit", - "worker": device_id, + "worker": _worker_label, + "device_id": device_id, + "worker_slot": worker_slot, "exitcode": 1, "pid": _os.getpid(), "error": str(_e), @@ -534,7 +560,9 @@ def _report_batch(ok_list, bad_list): result_q.put( { "event": "batch", - "worker": device_id, + "worker": _worker_label, + "device_id": device_id, + "worker_slot": worker_slot, "processed": [str(x) for x in ok_list], "problematic": [str(x) for x in bad_list], "pid": _os.getpid(), @@ -546,128 +574,78 @@ def _report_batch(ok_list, bad_list): c.extractor.batch_result_callback = _report_batch except Exception as _e: print(f"[GPU{device_id}] Unable to set batch callback: {_e}") - # Prepare persistent extractor in this worker on first call - # Process queue items in small batches to reduce function-call overhead - batch: list[str] = [] - try: - _batch_env = int(str(_os.environ.get("GLOSSAPI_GPU_BATCH_SIZE", "")).strip() or 0) - except Exception: - _batch_env = 0 - default_batch = 5 if not force else 1 - try: - extractor = getattr(c, "extractor", None) - if extractor is not None: - configured = int(getattr(extractor, "max_batch_files", default_batch)) - if force: - default_batch = 1 - else: - default_batch = max(1, configured) - except Exception: - pass - BATCH_SIZE = max(1, _batch_env) if _batch_env else max(1, default_batch) + # The controller already shapes queue items for multi-GPU extraction. Workers + # should execute those queue items as-is rather than re-batching them locally, + # otherwise long PDFs can be accidentally merged back into tail-heavy bundles. import queue as _queue last_progress = _time.time() processed = 0 exit_code = 0 + + def _run_batch(batch_items: List[str]) -> None: + nonlocal processed, exit_code + if not batch_items: + return + try: + _update_current(list(batch_items)) + c.extract( + input_format=input_fmt, + num_threads=threads, + accel_type="cuda:0", + force_ocr=force, + formula_enrichment=fe, + code_enrichment=ce, + file_paths=list(batch_items), + skip_existing=skip, + use_gpus="single", + use_cls=use_cls_w, + benchmark_mode=benchmark, + export_doc_json=bool(export_json), + emit_formula_index=bool(emit_index), + phase1_backend=backend, + _prepared=True, + ) + processed += len(batch_items) + _clear_current() + except Exception as _e: + exit_code = 1 + print(f"[GPU{device_id}] Batch failed ({len(batch_items)}): {_e}") + if result_q is not None: + try: + result_q.put( + { + "event": "batch", + "worker": _worker_label, + "device_id": device_id, + "worker_slot": worker_slot, + "processed": [], + "problematic": list(batch_items), + "pid": _os.getpid(), + "error": str(_e), + } + ) + except Exception: + pass + _clear_current() + try: while True: try: - nm = work_q.get_nowait() + work_item = work_q.get_nowait() except _queue.Empty: - # queue.Empty or other -> flush any pending batch then exit - if batch: - try: - _update_current(list(batch)) - c.extract( - input_format=input_fmt, - num_threads=threads, - accel_type="cuda:0", - force_ocr=force, - formula_enrichment=fe, - code_enrichment=ce, - file_paths=list(batch), - skip_existing=skip, - use_gpus="single", - use_cls=use_cls_w, - benchmark_mode=benchmark, - export_doc_json=bool(export_json), - emit_formula_index=bool(emit_index), - phase1_backend=backend, - _prepared=True, - ) - processed += len(batch) - _clear_current() - except Exception as _e: - exit_code = 1 - print(f"[GPU{device_id}] Batch failed ({len(batch)}): {_e}") - if result_q is not None: - try: - result_q.put( - { - "event": "batch", - "worker": device_id, - "processed": [], - "problematic": list(batch), - "pid": _os.getpid(), - "error": str(_e), - } - ) - except Exception: - pass - _clear_current() - batch.clear() break except Exception as exc: exit_code = 1 print(f"[GPU{device_id}] Queue receive error: {exc}") break - if isinstance(nm, str) and nm.strip(): - batch.append(nm) - if len(batch) >= BATCH_SIZE: - try: - _update_current(list(batch)) - c.extract( - input_format=input_fmt, - num_threads=threads, - accel_type="cuda:0", - force_ocr=force, - formula_enrichment=fe, - code_enrichment=ce, - file_paths=list(batch), - skip_existing=skip, - use_gpus="single", - use_cls=use_cls_w, - benchmark_mode=benchmark, - export_doc_json=bool(export_json), - emit_formula_index=bool(emit_index), - phase1_backend=backend, - _prepared=True, - ) - processed += len(batch) - _clear_current() - except Exception as _e: - exit_code = 1 - print(f"[GPU{device_id}] Batch failed ({len(batch)}): {_e}") - if result_q is not None: - try: - result_q.put( - { - "event": "batch", - "worker": device_id, - "processed": [], - "problematic": list(batch), - "pid": _os.getpid(), - "error": str(_e), - } - ) - except Exception: - pass - _clear_current() - batch.clear() + normalized = _normalize_work_item(work_item) + if not normalized: + continue + _run_batch(normalized) # Occasional heartbeat if _time.time() - last_progress > 30: try: - print(f"[GPU{device_id}] processed ~{processed} files…") + print(f"[{_worker_label}] processed ~{processed} files...") except Exception: pass last_progress = _time.time() @@ -692,7 +670,9 @@ def _report_batch(ok_list, bad_list): try: result_q.put({ "event": "exit", - "worker": device_id, + "worker": _worker_label, + "device_id": device_id, + "worker_slot": worker_slot, "exitcode": exit_code, "pid": _os.getpid(), }) diff --git a/src/glossapi/corpus/ocr/__init__.py b/src/glossapi/corpus/ocr/__init__.py new file mode 100644 index 0000000..e8d5b32 --- /dev/null +++ b/src/glossapi/corpus/ocr/__init__.py @@ -0,0 +1,6 @@ +"""Readable OCR orchestration helpers for the corpus pipeline.""" + +from .config import OcrRequest, normalize_ocr_request +from .pipeline import run_ocr_phase + +__all__ = ["OcrRequest", "normalize_ocr_request", "run_ocr_phase"] diff --git a/src/glossapi/corpus/ocr/artifacts.py b/src/glossapi/corpus/ocr/artifacts.py new file mode 100644 index 0000000..3e91906 --- /dev/null +++ b/src/glossapi/corpus/ocr/artifacts.py @@ -0,0 +1,143 @@ +"""OCR result persistence helpers.""" + +from __future__ import annotations + +import hashlib +from pathlib import Path +from typing import Dict, List, Optional + +import pandas as pd + +from ..._naming import canonical_stem +from .context import CorpusOcrContext + + +def build_ocr_stage_artifact_update( + *, + markdown_dir: Path, + metrics_dir: Path, + stem: str, +) -> Optional[Dict[str, object]]: + """Return direct OCR-owned artifact fields for one canonical OCR document.""" + + markdown_path = Path(markdown_dir) / f"{stem}.md" + if not markdown_path.exists(): + return None + text_payload = markdown_path.read_text(encoding="utf-8") + metrics_path = Path(metrics_dir) / f"{stem}.metrics.json" + return { + "text": text_payload, + "ocr_markdown_relpath": str(Path("markdown") / markdown_path.name), + "ocr_metrics_relpath": ( + str(Path("json") / "metrics" / metrics_path.name) if metrics_path.exists() else None + ), + "ocr_text_sha256": hashlib.sha256(text_payload.encode("utf-8")).hexdigest(), + } + + +def apply_ocr_success_updates( + df_meta: pd.DataFrame, + *, + filenames: List[str], + markdown_dir: Path, + metrics_dir: Path, + backend_norm: str, +) -> pd.DataFrame: + """Apply direct OCR-owned metadata updates to parquet rows.""" + + if "filename" not in df_meta.columns: + return df_meta + + if "filter" not in df_meta.columns: + df_meta["filter"] = "ok" + if "needs_ocr" not in df_meta.columns: + df_meta["needs_ocr"] = False + if "ocr_success" not in df_meta.columns: + df_meta["ocr_success"] = False + if "extraction_mode" not in df_meta.columns: + df_meta["extraction_mode"] = None + + direct_columns = ("text", "ocr_markdown_relpath", "ocr_metrics_relpath", "ocr_text_sha256") + for column in direct_columns: + if column not in df_meta.columns: + df_meta[column] = None + + filename_series = df_meta["filename"].astype(str) + stem_series = filename_series.map(canonical_stem) + + for fname in filenames: + stem = canonical_stem(fname) + mask = stem_series == stem + if not bool(mask.any()): + continue + artifact_update = build_ocr_stage_artifact_update( + markdown_dir=markdown_dir, + metrics_dir=metrics_dir, + stem=stem, + ) + df_meta.loc[mask, "filter"] = "ok" + df_meta.loc[mask, "needs_ocr"] = False + df_meta.loc[mask, "ocr_success"] = True + if backend_norm == "deepseek": + df_meta.loc[mask, "extraction_mode"] = "deepseek" + if artifact_update is None: + continue + for column, value in artifact_update.items(): + df_meta.loc[mask, column] = value + + return df_meta + + +def persist_ocr_success( + context: CorpusOcrContext, + *, + filenames: List[str], + backend_norm: str, +) -> List[str]: + from ...parquet_schema import ParquetSchema + + success_files: List[str] = [] + for fname in filenames: + stem = canonical_stem(fname) + if (context.markdown_dir / f"{stem}.md").exists(): + success_files.append(fname) + + if not success_files: + return success_files + + parquet_schema = ParquetSchema({"url_column": context.url_column}) + parquet_path = context._resolve_metadata_parquet(parquet_schema, ensure=True, search_input=True) + if parquet_path and parquet_path.exists(): + df_meta = pd.read_parquet(parquet_path) + df_meta = apply_ocr_success_updates( + df_meta, + filenames=success_files, + markdown_dir=context.markdown_dir, + metrics_dir=context.output_dir / "json" / "metrics", + backend_norm=backend_norm, + ) + context._cache_metadata_parquet(parquet_path) + parquet_schema.write_metadata_parquet(df_meta, parquet_path) + + stems = [canonical_stem(name) for name in success_files] + if hasattr(context, "good_files"): + for stem in stems: + if stem not in getattr(context, "good_files", []): + context.good_files.append(stem) + + return success_files + + +def refresh_cleaner_after_ocr(context: CorpusOcrContext) -> None: + """Refresh cleaner metrics after OCR reruns rewrite markdown outputs.""" + + refresh = getattr(context, "_refresh_metrics_after_ocr_rerun", None) + if callable(refresh): + refresh() + return + + context.logger.info("Re-running Rust cleaner after OCR rerun to refresh metrics") + context.clean( + input_dir=context.markdown_dir, + drop_bad=False, + ) diff --git a/src/glossapi/corpus/ocr/config.py b/src/glossapi/corpus/ocr/config.py new file mode 100644 index 0000000..f9da5fc --- /dev/null +++ b/src/glossapi/corpus/ocr/config.py @@ -0,0 +1,229 @@ +"""Request normalization for corpus OCR orchestration.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +from ...ocr.deepseek.defaults import ( + DEFAULT_ATTN_BACKEND, + DEFAULT_GPU_MEMORY_UTILIZATION, + DEFAULT_MAX_NEW_TOKENS, + DEFAULT_OCR_PROFILE, + DEFAULT_RENDER_DPI, + DEFAULT_REPAIR_MODE, + DEFAULT_RUNTIME_BACKEND, + DEFAULT_TARGET_BATCH_PAGES, + DEFAULT_WORKERS_PER_GPU, + resolve_gpu_memory_utilization, + resolve_render_dpi, +) + + +@dataclass(slots=True) +class OcrRequest: + mode: str + backend: str + device: Optional[str] + model_dir: Optional[Path] + max_pages: Optional[int] + persist_engine: bool + precision: Optional[str] + workers_per_gpu: int + runtime_backend: str + ocr_profile: str + prompt_override: Optional[str] + attn_backend: str + base_size: Optional[int] + image_size: Optional[int] + crop_mode: Optional[bool] + render_dpi: int + max_new_tokens: int + repetition_penalty: Optional[float] + no_repeat_ngram_size: Optional[int] + vllm_batch_size: Optional[int] + gpu_memory_utilization: float + disable_fp8_kv: bool + repair_mode: str + repair_exec_batch_target_pages: Optional[int] + repair_exec_batch_target_items: Optional[int] + scheduler: str + target_batch_pages: int + shard_pages: int + shard_threshold_pages: int + math_enhance: bool + math_targets: Optional[Dict[str, List[Tuple[int, int]]]] + math_batch_size: int + math_dpi_base: int + use_gpus: str + devices: Optional[List[int]] + reprocess_completed: bool + content_debug: bool + + +def _resolve_mode( + *, + logger, + mode: Optional[str], + fix_bad: bool, + math_enhance: bool, +) -> Optional[str]: + mode_norm: Optional[str] = None + if mode: + candidate = str(mode).strip().lower() + if candidate in {"ocr_bad", "math_only", "ocr_bad_then_math"}: + mode_norm = candidate + else: + logger.warning("Unknown mode '%s'; falling back to legacy flags", mode) + if mode_norm is None: + if fix_bad and math_enhance: + mode_norm = "ocr_bad_then_math" + elif fix_bad: + mode_norm = "ocr_bad" + elif math_enhance: + mode_norm = "math_only" + return mode_norm + + +def normalize_ocr_request( + *, + logger, + fix_bad: bool, + mode: Optional[str], + backend: str, + device: Optional[str], + model_dir: Optional[str | Path], + max_pages: Optional[int], + persist_engine: bool, + precision: Optional[str], + workers_per_gpu: int = DEFAULT_WORKERS_PER_GPU, + runtime_backend: str = DEFAULT_RUNTIME_BACKEND, + ocr_profile: str = DEFAULT_OCR_PROFILE, + prompt_override: Optional[str] = None, + attn_backend: str = DEFAULT_ATTN_BACKEND, + base_size: Optional[int] = None, + image_size: Optional[int] = None, + crop_mode: Optional[bool] = None, + render_dpi: Optional[int] = DEFAULT_RENDER_DPI, + max_new_tokens: Optional[int] = DEFAULT_MAX_NEW_TOKENS, + repetition_penalty: Optional[float] = None, + no_repeat_ngram_size: Optional[int] = None, + vllm_batch_size: Optional[int] = None, + gpu_memory_utilization: Optional[float] = DEFAULT_GPU_MEMORY_UTILIZATION, + disable_fp8_kv: bool = False, + repair_mode: str = DEFAULT_REPAIR_MODE, + repair_exec_batch_target_pages: Optional[int] = None, + repair_exec_batch_target_items: Optional[int] = None, + scheduler: str = "auto", + target_batch_pages: int = DEFAULT_TARGET_BATCH_PAGES, + shard_pages: int = 0, + shard_threshold_pages: int = 0, + math_enhance: bool = True, + math_targets: Optional[Dict[str, List[Tuple[int, int]]]] = None, + math_batch_size: int = 8, + math_dpi_base: int = 220, + use_gpus: str = "single", + devices: Optional[List[int]] = None, + force: Optional[bool] = None, + reprocess_completed: Optional[bool] = None, + skip_existing: Optional[bool] = None, + content_debug: bool = False, + CONTENT_DEBUG: Optional[bool] = None, + internal_debug: bool = False, + INTERNAL_DEBUG: Optional[bool] = None, +) -> Optional[OcrRequest]: + backend_norm = str(backend or "deepseek").strip().lower() + if backend_norm != "deepseek": + raise ValueError("backend must be 'deepseek'") + + if CONTENT_DEBUG is not None: + content_debug = bool(CONTENT_DEBUG) + elif INTERNAL_DEBUG is not None: + content_debug = bool(INTERNAL_DEBUG) + elif internal_debug: + content_debug = True + + fix_bad_effective = bool(fix_bad) + if force is not None: + logger.warning("Corpus.ocr(force=...) is deprecated; use fix_bad=... instead") + fix_bad_effective = bool(force) + + mode_norm = _resolve_mode( + logger=logger, + mode=mode, + fix_bad=fix_bad_effective, + math_enhance=bool(math_enhance), + ) + if mode_norm is None: + logger.info( + "OCR: no operation requested (enable fix_bad and/or math_enhance or set mode='ocr_bad'|'math_only'|'ocr_bad_then_math')" + ) + return None + + if backend_norm == "deepseek" and mode_norm in {"ocr_bad", "ocr_bad_then_math"}: + logger.info( + "DeepSeek backend: Phase-2 math is not required; equations are included inline via OCR." + ) + if mode_norm == "ocr_bad_then_math": + logger.info( + "DeepSeek OCR does not run Phase-2 math; treating mode='ocr_bad_then_math' as 'ocr_bad'." + ) + mode_norm = "ocr_bad" + + reprocess_explicit = reprocess_completed is not None + reprocess_flag = bool(reprocess_completed) if reprocess_explicit else False + if skip_existing is not None: + skip_flag = bool(skip_existing) + logger.warning( + "Corpus.ocr(skip_existing=...) is deprecated; use reprocess_completed=... instead." + ) + desired = not skip_flag + if reprocess_explicit and desired != reprocess_flag: + logger.info( + "Corpus.ocr(): skip_existing=%s overrides reprocess_completed=%s (effective reprocess_completed=%s).", + skip_flag, + reprocess_flag, + desired, + ) + reprocess_flag = desired + + return OcrRequest( + mode=mode_norm, + backend=backend_norm, + device=device, + model_dir=Path(model_dir) if model_dir else None, + max_pages=max_pages, + persist_engine=bool(persist_engine), + precision=precision, + workers_per_gpu=int(max(1, workers_per_gpu)), + runtime_backend=str(runtime_backend or DEFAULT_RUNTIME_BACKEND), + ocr_profile=str(ocr_profile or DEFAULT_OCR_PROFILE), + prompt_override=prompt_override, + attn_backend=str(attn_backend or DEFAULT_ATTN_BACKEND), + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + render_dpi=resolve_render_dpi(render_dpi), + max_new_tokens=int(DEFAULT_MAX_NEW_TOKENS if max_new_tokens is None else max_new_tokens), + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + vllm_batch_size=vllm_batch_size, + gpu_memory_utilization=resolve_gpu_memory_utilization(gpu_memory_utilization), + disable_fp8_kv=bool(disable_fp8_kv), + repair_mode=str(repair_mode or DEFAULT_REPAIR_MODE), + repair_exec_batch_target_pages=repair_exec_batch_target_pages, + repair_exec_batch_target_items=repair_exec_batch_target_items, + scheduler=str(scheduler or "auto"), + target_batch_pages=int(max(1, target_batch_pages)), + shard_pages=int(max(0, shard_pages)), + shard_threshold_pages=int(max(0, shard_threshold_pages)), + math_enhance=bool(math_enhance), + math_targets=math_targets, + math_batch_size=int(math_batch_size), + math_dpi_base=int(math_dpi_base), + use_gpus=str(use_gpus or "single"), + devices=devices, + reprocess_completed=bool(reprocess_flag), + content_debug=bool(content_debug), + ) diff --git a/src/glossapi/corpus/ocr/context.py b/src/glossapi/corpus/ocr/context.py new file mode 100644 index 0000000..7c98795 --- /dev/null +++ b/src/glossapi/corpus/ocr/context.py @@ -0,0 +1,26 @@ +"""Shared typing contracts for corpus OCR helpers.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any, Protocol + + +class CorpusOcrContext(Protocol): + logger: Any + input_dir: Path + output_dir: Path + markdown_dir: Path + logs_dir: Path + url_column: str + good_files: list[str] + + def _resolve_metadata_parquet(self, *args: Any, **kwargs: Any) -> Path | None: ... + + def _cache_metadata_parquet(self, path: Path | None) -> None: ... + + def _get_cached_metadata_parquet(self) -> Path | None: ... + + def clean(self, *args: Any, **kwargs: Any) -> None: ... + + def formula_enrich_from_json(self, *args: Any, **kwargs: Any) -> None: ... diff --git a/src/glossapi/corpus/ocr/dispatch.py b/src/glossapi/corpus/ocr/dispatch.py new file mode 100644 index 0000000..8e8efce --- /dev/null +++ b/src/glossapi/corpus/ocr/dispatch.py @@ -0,0 +1,49 @@ +"""Backend dispatch helpers for corpus OCR orchestration.""" + +from __future__ import annotations + +from ...ocr.deepseek import runner as _deepseek_runner +from .config import OcrRequest +from .context import CorpusOcrContext + + +def run_deepseek_ocr( + context: CorpusOcrContext, + *, + request: OcrRequest, + filenames: list[str], +) -> None: + _deepseek_runner.run_for_files( + context, + filenames, + model_dir=request.model_dir, + max_pages=request.max_pages, + persist_engine=request.persist_engine, + precision=request.precision, + device=request.device, + use_gpus=request.use_gpus, + devices=request.devices, + workers_per_gpu=request.workers_per_gpu, + runtime_backend=request.runtime_backend, + ocr_profile=request.ocr_profile, + prompt_override=request.prompt_override, + attn_backend=request.attn_backend, + base_size=request.base_size, + image_size=request.image_size, + crop_mode=request.crop_mode, + render_dpi=request.render_dpi, + max_new_tokens=request.max_new_tokens, + repetition_penalty=request.repetition_penalty, + no_repeat_ngram_size=request.no_repeat_ngram_size, + vllm_batch_size=request.vllm_batch_size, + gpu_memory_utilization=request.gpu_memory_utilization, + disable_fp8_kv=request.disable_fp8_kv, + repair_mode=request.repair_mode, + repair_exec_batch_target_pages=request.repair_exec_batch_target_pages, + repair_exec_batch_target_items=request.repair_exec_batch_target_items, + scheduler=request.scheduler, + target_batch_pages=request.target_batch_pages, + shard_pages=request.shard_pages, + shard_threshold_pages=request.shard_threshold_pages, + content_debug=request.content_debug, + ) diff --git a/src/glossapi/corpus/ocr/math_targets.py b/src/glossapi/corpus/ocr/math_targets.py new file mode 100644 index 0000000..0737d6f --- /dev/null +++ b/src/glossapi/corpus/ocr/math_targets.py @@ -0,0 +1,43 @@ +"""Math-target selection helpers for corpus OCR orchestration.""" + +from __future__ import annotations + +from pathlib import Path +from typing import List, Sequence, Set + +from ..._naming import canonical_stem + + +def discover_docling_json_stems(output_dir: Path) -> List[str]: + json_dir = Path(output_dir) / "json" + if not json_dir.exists(): + return [] + return sorted({canonical_stem(path) for path in json_dir.glob("*.docling.json*")}) + + +def filter_math_only_stems( + *, + stems: Sequence[str], + bad_files: Sequence[str], + math_done_stems: Set[str], + reprocess_completed: bool, + logger, +) -> List[str]: + kept = list(stems) + if bad_files: + before = len(kept) + bad_set = {canonical_stem(name) for name in bad_files} + kept = [stem for stem in kept if stem not in bad_set] + removed = before - len(kept) + if removed: + logger.info("Math-only: skipping %d document(s) flagged for OCR", removed) + if not reprocess_completed and kept and math_done_stems: + before = len(kept) + kept = [stem for stem in kept if stem not in math_done_stems] + removed = before - len(kept) + if removed: + logger.info( + "Math enrichment: skipping %d already enriched document(s) (reprocess_completed=False).", + removed, + ) + return kept diff --git a/src/glossapi/corpus/ocr/pipeline.py b/src/glossapi/corpus/ocr/pipeline.py new file mode 100644 index 0000000..bee65e0 --- /dev/null +++ b/src/glossapi/corpus/ocr/pipeline.py @@ -0,0 +1,46 @@ +"""High-level OCR orchestration for corpus remediation.""" + +from __future__ import annotations + +from .artifacts import persist_ocr_success, refresh_cleaner_after_ocr +from .config import OcrRequest +from .context import CorpusOcrContext +from .dispatch import run_deepseek_ocr +from .targets import build_ocr_selection + + +def run_ocr_phase(context: CorpusOcrContext, request: OcrRequest) -> None: + """Run the OCR-remediation path while preserving the current runtime engine.""" + + if request.mode == "math_only": + raise ValueError("run_ocr_phase handles OCR remediation only") + + selection = build_ocr_selection( + context, + mode=request.mode, + reprocess_completed=request.reprocess_completed, + ) + + if not selection.bad_files: + context.logger.info("OCR: no bad documents flagged by cleaner; skipping OCR fix") + return + + run_deepseek_ocr( + context, + request=request, + filenames=selection.bad_files, + ) + + try: + persist_ocr_success( + context, + filenames=selection.bad_files, + backend_norm=request.backend, + ) + except Exception as exc: + context.logger.warning("Failed to update OCR success metadata: %s", exc) + + try: + refresh_cleaner_after_ocr(context) + except Exception as exc: + context.logger.warning("Cleaner refresh after OCR failed: %s", exc) diff --git a/src/glossapi/corpus/ocr/targets.py b/src/glossapi/corpus/ocr/targets.py new file mode 100644 index 0000000..2a393aa --- /dev/null +++ b/src/glossapi/corpus/ocr/targets.py @@ -0,0 +1,144 @@ +"""Target selection helpers for corpus OCR orchestration.""" + +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import List, Optional, Set + +import pandas as pd + +from ..._naming import canonical_stem +from ...parquet_schema import ParquetSchema +from .context import CorpusOcrContext +from ..corpus_skiplist import _SkiplistManager, _resolve_skiplist_path + + +@dataclass(slots=True) +class OcrSelection: + bad_files: List[str] + ocr_candidates_initial: int + skipped_completed: int + skipped_skiplist: int + parquet_meta: Optional[pd.DataFrame] + ocr_done_files: List[str] + ocr_done_stems: Set[str] + math_done_stems: Set[str] + skip_mgr: _SkiplistManager + skiplist_path: Path + + +def normalize_ocr_target_filenames(*, filenames: List[str], input_dir: Path) -> List[str]: + """Collapse chunk-like metadata rows back to real OCR source files when possible.""" + + source_by_stem = {} + try: + for path in sorted(Path(input_dir).glob("*.pdf")): + source_by_stem.setdefault(canonical_stem(path.name), path.name) + except Exception: + source_by_stem = {} + + normalized: List[str] = [] + seen: Set[str] = set() + for fname in filenames: + resolved = source_by_stem.get(canonical_stem(fname), str(fname)) + if resolved in seen: + continue + normalized.append(resolved) + seen.add(resolved) + return normalized + + +def build_ocr_selection( + context: CorpusOcrContext, + *, + mode: str, + reprocess_completed: bool, +) -> OcrSelection: + bad_files: List[str] = [] + skipped_completed = 0 + skipped_skiplist = 0 + parquet_meta: Optional[pd.DataFrame] = None + ocr_done_files: List[str] = [] + ocr_done_stems: Set[str] = set() + math_done_stems: Set[str] = set() + + parquet_schema = ParquetSchema({"url_column": context.url_column}) + parquet_path = context._resolve_metadata_parquet(parquet_schema, ensure=True, search_input=True) + if parquet_path and parquet_path.exists(): + df = pd.read_parquet(parquet_path) + if "filename" in df.columns and "needs_ocr" in df.columns: + bad_files = df.loc[df["needs_ocr"] == True, "filename"].dropna().astype(str).tolist() + if "ocr_success" in df.columns: + ocr_done_files = df.loc[df["ocr_success"].fillna(False), "filename"].dropna().astype(str).tolist() + ocr_done_stems = {canonical_stem(name) for name in ocr_done_files} + math_done_files: List[str] = [] + if "math_enriched" in df.columns: + math_done_files = df.loc[df["math_enriched"].fillna(False), "filename"].dropna().astype(str).tolist() + elif "enriched_math" in df.columns: + math_done_files = df.loc[df["enriched_math"].fillna(False), "filename"].dropna().astype(str).tolist() + if math_done_files: + math_done_stems = {canonical_stem(name) for name in math_done_files} + if not reprocess_completed and ocr_done_stems: + before = len(bad_files) + bad_files = [name for name in bad_files if canonical_stem(name) not in ocr_done_stems] + skipped_completed = before - len(bad_files) + if skipped_completed: + context.logger.info( + "OCR: skipping %d already completed document(s) (reprocess_completed=False).", + skipped_completed, + ) + if reprocess_completed and mode in {"ocr_bad", "ocr_bad_then_math"} and ocr_done_files: + pending = {str(name) for name in bad_files} + for fname in ocr_done_files: + if fname not in pending: + bad_files.append(fname) + pending.add(fname) + parquet_meta = df + + ocr_candidates_initial = len(bad_files) + skiplist_path = _resolve_skiplist_path(context.output_dir, context.logger) + skip_mgr = _SkiplistManager(skiplist_path, context.logger) + skip_stems = skip_mgr.load() + if skip_stems: + before = len(bad_files) + bad_files = [name for name in bad_files if canonical_stem(name) not in skip_stems] + skipped_skiplist = before - len(bad_files) + if skipped_skiplist: + context.logger.warning( + "Skip-list %s filtered %d document(s) from Phase-3 OCR.", + skiplist_path, + skipped_skiplist, + ) + + normalized_bad_files = normalize_ocr_target_filenames( + filenames=bad_files, + input_dir=Path(context.input_dir), + ) + if len(normalized_bad_files) != len(bad_files): + context.logger.info( + "OCR: collapsed %d metadata-selected row(s) onto %d real source PDF(s) by canonical stem.", + len(bad_files), + len(normalized_bad_files), + ) + bad_files = normalized_bad_files + context.logger.info( + "OCR targets: total=%d kept=%d skipped_completed=%d skipped_skiplist=%d", + ocr_candidates_initial, + len(bad_files), + skipped_completed, + skipped_skiplist, + ) + + return OcrSelection( + bad_files=bad_files, + ocr_candidates_initial=ocr_candidates_initial, + skipped_completed=skipped_completed, + skipped_skiplist=skipped_skiplist, + parquet_meta=parquet_meta, + ocr_done_files=ocr_done_files, + ocr_done_stems=ocr_done_stems, + math_done_stems=math_done_stems, + skip_mgr=skip_mgr, + skiplist_path=skiplist_path, + ) diff --git a/src/glossapi/corpus/ocr_render.py b/src/glossapi/corpus/ocr_render.py new file mode 100644 index 0000000..f9f9418 --- /dev/null +++ b/src/glossapi/corpus/ocr_render.py @@ -0,0 +1,374 @@ +"""Shared OCR span rendering and match-index helpers. + +This module owns the last stage of OCR page handling: +- merge raw detector spans into one reviewed span plan +- render that exact plan in `debug` or `clean` mode +- serialize match metadata for later inspection + +Keeping this logic out of `phase_clean.py` is intentional. The analyzer should +answer *what* spans exist and in what ownership order; this module controls +*how* those exact spans become page text and debug sidecars. +""" +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict, List, Optional, Set, Tuple + +from .ocr_table import ( + render_table_html_for_clean as _render_table_html_for_clean, + render_table_html_for_output as _render_table_html_for_output, + replace_html_tables_with_markdown as _replace_html_tables_with_markdown, +) + +# Neighboring same-category spans may merge when the visible separator is still +# short enough to read as one corrupted region rather than two separate +# failures. This is intentionally more permissive than the older 10-char rule. +WORD_REPEAT_MERGE_MAX_NONWHITESPACE_GAP = 40 + + +def _gap_has_at_most_n_nonwhitespace_chars(text: str, start: int, end: int, limit: int) -> bool: + if start >= end: + return True + count = 0 + for ch in text[start:end]: + if not ch.isspace(): + count += 1 + if count > limit: + return False + return True + + +def _clean_fill_for_removed_span(page_text: str, start: int, end: int) -> str: + removed = page_text[start:end] + prev_char = page_text[start - 1] if start > 0 else "" + next_char = page_text[end] if end < len(page_text) else "" + if "\n" in removed: + if prev_char == "\n" or next_char == "\n": + return "" + return "\n" + if prev_char and next_char and not prev_char.isspace() and not next_char.isspace(): + return " " + return "" + + +def _merge_labeled_raw_spans(text: str, spans: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + if not spans: + return [] + + text_len = len(text) + sanitized_spans: List[Dict[str, Any]] = [] + for span in spans: + start = max(0, int(span["start"])) + end = min(text_len, int(span["end"])) + if start >= text_len or end <= start: + continue + sanitized = dict(span) + sanitized["start"] = start + sanitized["end"] = end + sanitized_spans.append(sanitized) + if not sanitized_spans: + return [] + + spans = sorted(sanitized_spans, key=lambda item: (item["start"], item["end"])) + merged: List[Dict[str, Any]] = [] + for span in spans: + if not merged: + merged.append(dict(span)) + continue + + previous = merged[-1] + overlaps = span["start"] <= previous["end"] + close_gap = ( + not overlaps + and previous["category"] == span["category"] + and previous["category"] != "table" + and _gap_has_at_most_n_nonwhitespace_chars( + text, + previous["end"], + span["start"], + WORD_REPEAT_MERGE_MAX_NONWHITESPACE_GAP, + ) + ) + if overlaps or close_gap: + same_single_type = previous.get("match_types", []) == span.get("match_types", []) + same_kind = previous.get("kind") == span.get("kind") + previous["start"] = min(previous["start"], span["start"]) + previous["end"] = max(previous["end"], span["end"]) + previous["match_types"] = sorted( + set(previous.get("match_types", [])) | set(span.get("match_types", [])) + ) + if ( + previous.get("kind") is None + and span.get("kind") is not None + and previous.get("match_types", []) == span.get("match_types", []) + ): + previous["kind"] = span.get("kind") + if "period" in span: + previous["period"] = min(previous.get("period", span["period"]), span["period"]) + if "repetitions" in span: + previous["repetitions"] = max( + previous.get("repetitions", span["repetitions"]), + span["repetitions"], + ) + if "tail_chars" in span: + previous["tail_chars"] = max( + previous.get("tail_chars", 0), + span.get("tail_chars", 0), + ) + if ( + same_single_type + and same_kind + and previous.get("item_count") is not None + and span.get("item_count") is not None + ): + previous["item_count"] = int(previous["item_count"]) + int(span["item_count"]) + continue + merged.append(dict(span)) + return merged + + +def _summarize_merged_labeled_spans( + merged_spans: List[Dict[str, Any]], +) -> Tuple[List[str], int, int, int, int, int]: + seen_types: Set[str] = set() + numeric_count = 0 + word_count = 0 + latex_count = 0 + table_count = 0 + hybrid_count = 0 + for span in merged_spans: + seen_types.update(span.get("match_types", [])) + if span["category"] == "numeric": + numeric_count += 1 + elif span["category"] == "word": + word_count += 1 + elif span["category"] == "latex": + latex_count += 1 + elif span["category"] == "table": + table_count += 1 + elif span["category"] == "hybrid": + hybrid_count += 1 + return ( + sorted(seen_types), + numeric_count, + word_count, + latex_count, + table_count, + hybrid_count, + ) + + +def _render_page_from_merged_labeled_spans( + page_text: str, + merged_spans: List[Dict[str, Any]], + *, + mode: str, +) -> str: + if not merged_spans: + return _replace_html_tables_with_markdown(page_text) + + parts: List[str] = [] + pos = 0 + for span in merged_spans: + start = span["start"] + end = span["end"] + if start > pos: + parts.append(_replace_html_tables_with_markdown(page_text[pos:start])) + match_types = list(span.get("match_types", [])) + if mode == "debug": + open_tag = f'") + else: + if match_types == ["table_repeat"]: + parts.append( + _render_table_html_for_clean( + page_text[start:end], + match_kind=span.get("kind"), + ) + ) + else: + parts.append(_clean_fill_for_removed_span(page_text, start, end)) + pos = end + if pos < len(page_text): + parts.append(_replace_html_tables_with_markdown(page_text[pos:])) + return "".join(parts) + + +def _render_page_with_labeled_spans_result( + page_text: str, + spans: List[Dict[str, Any]], + *, + mode: str = "debug", +) -> Dict[str, Any]: + if mode not in {"debug", "clean"}: + raise ValueError(f"Unsupported OCR render mode: {mode}") + merged_spans = _merge_labeled_raw_spans(page_text, spans) + ( + page_types, + numeric_count, + word_count, + latex_count, + table_count, + hybrid_count, + ) = _summarize_merged_labeled_spans(merged_spans) + rendered_page = _render_page_from_merged_labeled_spans( + page_text, + merged_spans, + mode=mode, + ) + return { + "rendered_page": rendered_page, + "merged_spans": merged_spans, + "page_types": page_types, + "page_numeric_count": numeric_count, + "page_word_count": word_count, + "page_latex_count": latex_count, + "page_table_count": table_count, + "page_hybrid_count": hybrid_count, + } + + +def _render_page_with_labeled_spans( + page_text: str, + spans: List[Dict[str, Any]], + *, + mode: str = "debug", +) -> Tuple[str, List[str], int, int, int, int, int]: + """Render one page from a shared span plan. + + `debug` and `clean` intentionally share the exact same merged span plan. + The only difference is how that plan is rendered: + - debug wraps the matched source surface in `` tags + - clean removes or rewrites the matched surface according to policy + + Keeping both modes on one renderer prevents the real cleaner from drifting + away from the reviewed debug output. + """ + result = _render_page_with_labeled_spans_result(page_text, spans, mode=mode) + return ( + str(result["rendered_page"]), + list(result["page_types"]), + int(result["page_numeric_count"]), + int(result["page_word_count"]), + int(result["page_latex_count"]), + int(result["page_table_count"]), + int(result["page_hybrid_count"]), + ) + + +def _annotate_page_with_labeled_spans( + page_text: str, + spans: List[Dict[str, Any]], +) -> Tuple[str, List[str], int, int, int, int, int]: + return _render_page_with_labeled_spans(page_text, spans, mode="debug") + + +def _utf8_prefix_byte_offsets(text: str) -> List[int]: + offsets = [0] + total = 0 + for char in text: + total += len(char.encode("utf-8")) + offsets.append(total) + return offsets + + +def _span_repeat_count(span: Dict[str, Any]) -> Optional[int]: + if span.get("repetitions") is not None: + return int(span["repetitions"]) + if span.get("item_count") is not None: + return int(span["item_count"]) + if span.get("duplicate_rows") is not None: + return int(span["duplicate_rows"]) + return None + + +def _build_match_index_rows( + page_text: str, + merged_spans: List[Dict[str, Any]], + *, + source_path: Path, + page_number: int, + debug_output_path: Optional[Path] = None, +) -> List[Dict[str, Any]]: + if not merged_spans: + return [] + byte_offsets = _utf8_prefix_byte_offsets(page_text) + rows: List[Dict[str, Any]] = [] + for match_index, span in enumerate(merged_spans, start=1): + start = int(span["start"]) + end = int(span["end"]) + match_text = page_text[start:end] + rows.append( + { + "match_id": f"{source_path.stem}:page:{page_number}:match:{match_index}", + "source_path": str(source_path), + "source_stem": source_path.stem, + "debug_output_path": None if debug_output_path is None else str(debug_output_path), + "page_number": int(page_number), + "page_index_in_file": int(page_number), + "match_index_in_page": int(match_index), + "start_char": start, + "end_char": end, + "start_byte": int(byte_offsets[start]), + "end_byte": int(byte_offsets[end]), + "match_length_chars": int(end - start), + "match_length_bytes": int(byte_offsets[end] - byte_offsets[start]), + "match_types": list(span.get("match_types", [])), + "match_type": ",".join(span.get("match_types", [])), + "category": str(span.get("category", "")), + "kind": span.get("kind"), + "repeat_count": _span_repeat_count(span), + "period": span.get("period"), + "repetitions": span.get("repetitions"), + "tail_chars": span.get("tail_chars"), + "item_count": span.get("item_count"), + "cycle_len": span.get("cycle_len"), + "row_count": span.get("row_count"), + "duplicate_rows": span.get("duplicate_rows"), + "nonempty_ratio": span.get("nonempty_ratio"), + "word_count": span.get("word_count"), + "char_count": span.get("char_count"), + "matched_text": match_text, + } + ) + return rows + + diff --git a/src/glossapi/corpus/ocr_table.py b/src/glossapi/corpus/ocr_table.py new file mode 100644 index 0000000..63756ed --- /dev/null +++ b/src/glossapi/corpus/ocr_table.py @@ -0,0 +1,240 @@ +"""Table-specific OCR cleaning helpers. + +This module isolates HTML-table handling from the broader OCR repetition logic. + +That separation is intentional: +- some table decisions are repetition-based, like repeated rows +- others are structural cleanups, like sentence-shell tables or near-empty shells + +Keeping table logic together makes the policy easier to understand and keeps the +main OCR page pipeline focused on ordering and span ownership. +""" +from __future__ import annotations + +import html +import re +from collections import Counter +from functools import lru_cache +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +from ..scripts.table_markdown_audit import ( + _expand_rows as _audit_expand_table_rows, + _parse_table_rows as _audit_parse_table_rows, + audit_table as _audit_table_html, +) + +HTML_TABLE_BLOCK_RE = re.compile(r"(?is)") +HTML_TABLE_LINE_RE = re.compile(r"(?i).*?") +HTML_TABLE_CELL_RE = re.compile(r"(?is)(.*?)") +HTML_TAG_RE = re.compile(r"(?is)<[^>]+>") + +TABLE_EMPTY_MIN_ROWS = 6 +TABLE_EMPTY_MIN_CELLS = 18 +TABLE_EMPTY_MAX_NONEMPTY_RATIO = 0.15 +TABLE_REPEAT_MIN_ROWS = 4 +TABLE_REPEAT_MIN_NONEMPTY_CELLS = 2 +TABLE_REPEAT_MIN_ROW_TEXT_CHARS = 6 +TABLE_REPEAT_MIN_DUPLICATE_ROWS = 2 +TABLE_SENTENCE_SHELL_MIN_WORDS = 6 +TABLE_SENTENCE_SHELL_MIN_CHARS = 40 + + +def _normalize_table_cell_text(cell_html: str) -> str: + text = HTML_TAG_RE.sub(" ", cell_html) + text = html.unescape(text) + return " ".join(text.split()) + + +def _table_cell_has_content(cell_text: str) -> bool: + return any(ch.isalnum() for ch in cell_text) + + +def _extract_html_table_rows(table_text: str) -> List[List[str]]: + rows: List[List[str]] = [] + for row_match in HTML_TABLE_ROW_RE.finditer(table_text): + cells = [ + _normalize_table_cell_text(cell_match.group(1)) + for cell_match in HTML_TABLE_CELL_RE.finditer(row_match.group(0)) + ] + if cells: + rows.append(cells) + return rows + + +@lru_cache(maxsize=2048) +def _extract_html_table_rows_cached(table_text: str) -> Tuple[Tuple[str, ...], ...]: + """Cache repeated table shells by exact HTML string. + + The OCR corpus contains many duplicated HTML fragments, so exact-string + memoization pays off without changing behavior. + """ + return tuple(tuple(row) for row in _extract_html_table_rows(table_text)) + + +def _flatten_html_table_nonempty_cells(table_text: str) -> List[str]: + parsed_rows, _ = _audit_parse_table_rows(table_text) + grid, _ = _audit_expand_table_rows(parsed_rows) + if not grid: + return [] + nonempty: List[str] = [] + for row in grid: + for cell in row: + normalized = " ".join(cell.split()) + if any(ch.isalnum() for ch in normalized): + nonempty.append(normalized) + return nonempty + + +@lru_cache(maxsize=2048) +def _flatten_html_table_nonempty_cells_cached(table_text: str) -> Tuple[str, ...]: + return tuple(_flatten_html_table_nonempty_cells(table_text)) + + +def _extract_sentence_shell_table_text(table_text: str) -> Optional[str]: + """Return prose text when a table is only a layout shell around one cell. + + This is intentionally not a repetition rule. OCR and VLM extraction often + emit a normal sentence inside a tiny one-cell table shell; when that + happens, the table structure is noise and the prose cell is the content. + """ + nonempty_cells = _flatten_html_table_nonempty_cells_cached(table_text) + if len(nonempty_cells) != 1: + return None + candidate = nonempty_cells[0].strip() + if len(candidate) < TABLE_SENTENCE_SHELL_MIN_CHARS: + return None + if len(re.findall(r"[^\W\d_]+", candidate, re.UNICODE)) < TABLE_SENTENCE_SHELL_MIN_WORDS: + return None + return candidate + + +@lru_cache(maxsize=2048) +def _render_table_html_for_output_cached(table_text: str, match_kind: Optional[str]) -> str: + sentence_shell = _extract_sentence_shell_table_text(table_text) + if sentence_shell and match_kind == "sentence_shell_table": + return sentence_shell + + audit = _audit_table_html(Path("/tmp/table_fragment.md"), 0, 0, table_text) + if audit.markdown: + return audit.markdown + return table_text + + +def render_table_html_for_output(table_text: str, *, match_kind: Optional[str] = None) -> str: + """Render one HTML table for human review/debug output.""" + return _render_table_html_for_output_cached(table_text, match_kind) + + +def replace_html_tables_with_markdown(text: str) -> str: + """Normalize kept HTML tables into GitHub-style Markdown in page text.""" + if " str: + """Render a table in clean mode. + + Clean mode drops tables whose structure is the problem: + - sentence-shell tables + - empty shell tables + - repeated-row tables + """ + if match_kind in {"sentence_shell_table", "empty_table_collapse", "repeated_rows"}: + return "" + return render_table_html_for_output(table_text, match_kind=match_kind) + + +def find_table_repeat_spans(page_text: str, *, match_category: str) -> List[Dict[str, Any]]: + """Classify OCR table problems on a page. + + Table handling is intentionally broader than repetition: + - sentence-shell tables are removed because they are layout shells around prose + - empty table collapse removes sparse structural noise + - repeated rows is the actual repetition-oriented table rule + """ + if "= TABLE_EMPTY_MIN_ROWS + and cell_count >= TABLE_EMPTY_MIN_CELLS + and nonempty_ratio <= TABLE_EMPTY_MAX_NONEMPTY_RATIO + ): + spans.append( + { + "start": table_match.start(), + "end": table_match.end(), + "match_types": ["table_repeat"], + "category": match_category, + "kind": "empty_table_collapse", + "row_count": row_count, + "cell_count": cell_count, + "nonempty_ratio": round(nonempty_ratio, 3), + } + ) + continue + + row_keys: List[Tuple[str, ...]] = [] + for row in rows: + nonempty_cells_in_row = [cell for cell in row if _table_cell_has_content(cell)] + if len(nonempty_cells_in_row) < TABLE_REPEAT_MIN_NONEMPTY_CELLS: + continue + row_text = " ".join(nonempty_cells_in_row) + if len(row_text) < TABLE_REPEAT_MIN_ROW_TEXT_CHARS: + continue + row_keys.append(tuple(cell.casefold() for cell in row)) + + if row_count < TABLE_REPEAT_MIN_ROWS or not row_keys: + continue + + row_counts = Counter(row_keys) + duplicate_rows = sum(freq - 1 for freq in row_counts.values() if freq >= 2) + if duplicate_rows >= TABLE_REPEAT_MIN_DUPLICATE_ROWS: + spans.append( + { + "start": table_match.start(), + "end": table_match.end(), + "match_types": ["table_repeat"], + "category": match_category, + "kind": "repeated_rows", + "row_count": row_count, + "duplicate_rows": duplicate_rows, + } + ) + + return spans diff --git a/src/glossapi/corpus/phase_clean.py b/src/glossapi/corpus/phase_clean.py index abdaa5e..350f6ab 100644 --- a/src/glossapi/corpus/phase_clean.py +++ b/src/glossapi/corpus/phase_clean.py @@ -1,9 +1,20 @@ -"""Cleaning and filtering helpers split from Corpus.""" +"""Cleaning and filtering helpers split from Corpus. + +This module now primarily owns OCR orchestration: +- page-level analyzer ordering +- shared clean/debug rendering +- worker/process orchestration + +Specialized policy modules, like HTML-table handling, live alongside it so the +main pipeline can stay focused on span ownership and mode selection. +""" from __future__ import annotations +import importlib import json import logging import math +import multiprocessing as mp import os import queue import random @@ -12,6 +23,12 @@ import subprocess import sys import time +import unicodedata +import warnings +from collections import Counter +from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor +from contextlib import contextmanager +from functools import lru_cache from pathlib import Path from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union @@ -21,9 +38,2625 @@ from .._naming import canonical_stem from ..gloss_downloader import GlossDownloader # Avoid importing section/classifier here; cleaning phase does not use them. +from .ocr_table import ( + HTML_TABLE_BLOCK_RE, + HTML_TABLE_LINE_RE, + find_table_repeat_spans as _find_table_repeat_spans_impl, + render_table_html_for_clean as _render_table_html_for_clean, + render_table_html_for_output as _render_table_html_for_output, + replace_html_tables_with_markdown as _replace_html_tables_with_markdown, +) from .corpus_skiplist import _SkiplistManager, _resolve_skiplist_path from .corpus_state import _ProcessingStateManager from .corpus_utils import _maybe_import_torch +from .ocr_render import ( + _gap_has_at_most_n_nonwhitespace_chars, + _clean_fill_for_removed_span, + _merge_labeled_raw_spans, + _summarize_merged_labeled_spans, + _render_page_from_merged_labeled_spans, + _render_page_with_labeled_spans_result, + _render_page_with_labeled_spans, + _annotate_page_with_labeled_spans, + _utf8_prefix_byte_offsets, + _span_repeat_count, + _build_match_index_rows, +) +from .text_surface_metrics import sanitized_char_count + +PAGE_SPLIT_MARKER = "<--- Page Split --->" +WORD_REPEAT_HASH_MASK = (1 << 64) - 1 +WORD_REPEAT_HASH_BASE = 1469598103934665603 +# Neighboring same-category spans may be merged when the visible separator is +# still small enough to read as one corrupted region rather than two separate +# failures. This is intentionally more permissive than the older 10-char rule. +WORD_REPEAT_MERGE_MAX_NONWHITESPACE_GAP = 40 +# Default word-repeat detection window for OCR cleaning. Wider than the +# legacy 96 so accent-shifted Greek repetitions (period > 32 chars) get +# caught — see test_long_accent_shift_repeat_needs_wider_default_window. +DEFAULT_OCR_WORD_REPEAT_MAX_PERIOD = 130 +DEFAULT_OCR_WORD_REPEAT_WINDOW = DEFAULT_OCR_WORD_REPEAT_MAX_PERIOD * 4 +EXISTING_MATCH_BLOCK_RE = re.compile(r"(?is)]*>.*?") +LATEX_BLOCK_RE = re.compile(r"(?is)\$\$.*?\$\$") +LATEX_BRACKET_RE = re.compile(r"(?is)\\\[.*?\\\]") +LATEX_BEGIN_END_RE = re.compile(r"(?is)\\begin\{([^\n{}]+)\}.*?\\end\{\1\}") +LATEX_INLINE_PAREN_RE = re.compile(r"(?is)\\\(.*?\\\)") +LATEX_INLINE_DOLLAR_RE = re.compile(r"(?s)(?[^<]{1,16}|[^<]{1,16})){8,}" +) +WORD_CONFUSABLE_FOLD_MAP = { + "ο": "o", + "κ": "k", +} +LATEX_SEGMENT_PATTERNS = [ + ("begin_end", LATEX_BEGIN_END_RE), + ("display_dollar", LATEX_BLOCK_RE), + ("display_bracket", LATEX_BRACKET_RE), + ("inline_paren", LATEX_INLINE_PAREN_RE), + ("inline_dollar", LATEX_INLINE_DOLLAR_RE), +] +LATEX_TEXT_WRAPPER_MACROS = ( + r"\mathrm{", + r"\text{", + r"\operatorname{", + r"\mathit{", + r"\mathbf{", +) +LATEX_INTERNAL_REPEAT_COMMANDS = { + r"\frac", + r"\left", + r"\right", + r"\sqrt", + r"\begin", + r"\end", + r"\quad", + r"\qquad", + r"\cdots", + r"\ldots", + r"\mathrm", + r"\text", + r"\operatorname", + r"\mathit", + r"\mathbf", + r"\hat", + r"\tilde", + r"\bar", +} +LATEX_INTERNAL_SMALL_VOCAB_COMMANDS = { + r"\cdots", + r"\ldots", + r"\vdots", + r"\ddots", +} +LATEX_SHORT_REPEAT_ATOM_COMMANDS = { + r"\Delta", + r"\hat", + r"\tilde", + r"\bar", +} +LATEX_SHORT_ATOM_BLOCK_BASE_COMMANDS = { + r"\alpha", + r"\beta", + r"\gamma", + r"\delta", + r"\epsilon", + r"\varepsilon", + r"\lambda", + r"\mu", + r"\nu", + r"\omega", + r"\Delta", +} +LATEX_SHORT_ATOM_BLOCK_DECORATOR_COMMANDS = { + r"\hat", + r"\tilde", + r"\bar", +} +LATEX_SEGMENT_LOCAL_NONWHITESPACE_GAP = 12 +LATEX_SEGMENT_EXACT_RUN_MIN = 4 +LATEX_SEGMENT_SKELETON_RUN_MIN = 4 +LATEX_SEGMENT_ALTERNATING_RUN_MIN = 6 +LATEX_SEGMENT_SLOT_PROGRESS_RUN_MIN = 4 +LATEX_SHORT_ATOM_BLOCK_REPEAT_MIN_ITEMS = 12 +LATEX_SHORT_ATOM_EXACT_SEGMENT_MIN_TOKENS = 2 +LATEX_SHORT_ATOM_CHAIN_MIN_TOKENS = 6 +LATEX_INTERNAL_SMALL_VOCAB_RUN_MIN_COMMANDS = 24 +LATEX_SHORT_SEGMENT_MAX_NORM = 32 +LATEX_LONG_SEGMENT_MIN_NORM = 24 +LATEX_INTERNAL_REPEAT_MIN_COMMAND_DUP = 3 +LATEX_SMALL_DEFINITION_FAMILY_MAX_RUN = 6 +HYBRID_PREFIX_RE = re.compile( + r"(?\d+\)|\d+\.(?:\d+\.)*\d*\.?)(?=\s*[^\W\d_])", + re.UNICODE, +) +HYBRID_MARKUP_BODY_RE = re.compile(r"(?i)(<[^>]+>|src=|alt=|image_|\.png\b|\.jpg\b|\.jpeg\b|\.gif\b)") +HYBRID_REPEAT_MIN_ITEMS = 4 +HYBRID_REPEAT_MIN_BODY_ALNUM = 6 +HYBRID_REPEAT_MAX_CYCLE = 6 +HYBRID_REPEAT_MIN_CYCLE_ITEMS = 8 +HYBRID_INLINE_CLAUSE_DELIMITER_RE = re.compile(r"[;\n]|,(?!\d)") +HYBRID_INLINE_TOKEN_RE = re.compile(r"[0-9]+(?:[.,/][0-9]+)*|[^\W\d_]+", re.UNICODE) +HYBRID_INLINE_CONTEXT_WORDS = 2 +HYBRID_INLINE_CONTEXT_MIN_ALPHA_WORDS = 2 +HYBRID_INLINE_CONTEXT_MIN_CHARS = 8 +HYBRID_INLINE_REPEAT_MIN_ITEMS = 6 +LATEX_SYMBOL_SLOT_COMMANDS = ( + r"\mu", + r"\nu", + r"\alpha", + r"\beta", + r"\gamma", + r"\lambda", + r"\tau", + r"\omega", +) +MATCH_CATEGORY_BY_TYPE = { + "ascending_numeric_sequence": "numeric", + "repeat_numeric_run": "numeric", + "same_digit_numeric_run": "numeric", + "numeric_page_collapse": "numeric", + "numeric_block_collapse": "numeric", + "numeric_repeat": "numeric", + "word_repeat": "word", + "latex_repeat": "latex", + "hybrid_repeat": "hybrid", + "table_repeat": "table", +} + +_WORD_REPEAT_RUST_MOD: Optional[Any] = None +_WORD_REPEAT_RUST_IMPORT_ATTEMPTED = False +_RUST_EXTENSION_PREBUILD_ATTEMPTED: Set[str] = set() +_COMBINED_OCR_WORKER_NOISE_MOD: Optional[Any] = None +_COMBINED_OCR_WORKER_REQUIRED_ATTRS = ( + "find_numeric_debug_page_spans", + "evaluate_page_character_noise", +) + + +def _blank_non_newlines(text: str) -> str: + return "".join("\n" if ch == "\n" else " " for ch in text) + + +def _init_combined_ocr_worker() -> None: + global _COMBINED_OCR_WORKER_NOISE_MOD, _WORD_REPEAT_RUST_MOD, _WORD_REPEAT_RUST_IMPORT_ATTEMPTED + noise_mod = importlib.import_module("glossapi_rs_noise") + missing = [ + attr for attr in _COMBINED_OCR_WORKER_REQUIRED_ATTRS if not hasattr(noise_mod, attr) + ] + if missing: + raise ImportError( + "glossapi_rs_noise missing required attrs for OCR worker: " + + ", ".join(missing) + ) + _COMBINED_OCR_WORKER_NOISE_MOD = noise_mod + _WORD_REPEAT_RUST_IMPORT_ATTEMPTED = True + _WORD_REPEAT_RUST_MOD = noise_mod if hasattr(noise_mod, "find_word_repeat_spans") else None + + +def _get_combined_ocr_worker_noise_mod() -> Any: + global _COMBINED_OCR_WORKER_NOISE_MOD + if _COMBINED_OCR_WORKER_NOISE_MOD is None: + _init_combined_ocr_worker() + return _COMBINED_OCR_WORKER_NOISE_MOD + + +def _prime_word_repeat_rust_module(module_name: str, module: Any) -> Any: + global _WORD_REPEAT_RUST_MOD, _WORD_REPEAT_RUST_IMPORT_ATTEMPTED + if module_name == "glossapi_rs_noise": + _WORD_REPEAT_RUST_IMPORT_ATTEMPTED = True + _WORD_REPEAT_RUST_MOD = module if hasattr(module, "find_word_repeat_spans") else None + return module + + +def _can_use_combined_ocr_process_pool(noise_mod: Any, render_workers: int) -> bool: + return ( + render_workers > 1 + and os.name != "nt" + and getattr(noise_mod, "__name__", "") == "glossapi_rs_noise" + ) + + +def _default_combined_ocr_render_workers( + *, + noise_mod: Any, + requested_workers: Optional[int], + max_workers: int, +) -> int: + if requested_workers is not None: + return max(1, int(requested_workers)) + host_workers = max(1, int(max_workers)) + if _can_use_combined_ocr_process_pool(noise_mod, host_workers): + return host_workers + return min(4, host_workers) + + +@contextmanager +def _combined_ocr_process_pool_warning_ctx() -> Iterable[None]: + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message=r"This process .* is multi-threaded, use of fork\(\) may lead to deadlocks in the child\.", + category=DeprecationWarning, + module=r"multiprocessing\.popen_fork", + ) + yield + + +def _blank_regex_matches_preserve_layout(text: str, pattern: re.Pattern[str]) -> str: + return pattern.sub(lambda match: _blank_non_newlines(match.group(0)), text) + + +def _filter_tables_preserve_layout(text: str) -> str: + lowered = text.lower() + if " str: + if "$" not in text and "\\" not in text: + return text + for pattern in ( + LATEX_BEGIN_END_RE, + LATEX_BLOCK_RE, + LATEX_BRACKET_RE, + LATEX_INLINE_PAREN_RE, + LATEX_INLINE_DOLLAR_RE, + ): + text = _blank_regex_matches_preserve_layout(text, pattern) + return text + + +def _blank_existing_match_regions_preserve_layout(text: str) -> str: + if " str: + if not spans: + return text + + chars = list(text) + for span in spans: + start = max(0, int(span["start"])) + end = min(len(chars), int(span["end"])) + for idx in range(start, end): + if chars[idx] != "\n": + chars[idx] = " " + return "".join(chars) + + +def _extract_latex_segments(text: str) -> List[Dict[str, Any]]: + raw: List[Tuple[int, int, str, str]] = [] + for name, pattern in LATEX_SEGMENT_PATTERNS: + for match in pattern.finditer(text): + raw.append((match.start(), match.end(), name, match.group(0))) + + raw.sort(key=lambda item: (item[0], -(item[1] - item[0]), item[2])) + segments: List[Dict[str, Any]] = [] + last_end = -1 + for start, end, kind, body in raw: + if segments and start >= segments[-1]["start"] and end <= segments[-1]["end"]: + continue + if start < last_end: + continue + segments.append({"start": start, "end": end, "kind": kind, "text": body}) + last_end = end + return segments + + +def _find_table_repeat_spans(page_text: str) -> List[Dict[str, Any]]: + """Keep phase_clean's old call shape while table policy lives in ocr_table.""" + return _find_table_repeat_spans_impl( + page_text, + match_category=MATCH_CATEGORY_BY_TYPE["table_repeat"], + ) + + +def _normalize_latex_repeat_with_map(text: str) -> Tuple[str, List[int]]: + normalized: List[str] = [] + raw_map: List[int] = [] + for raw_idx, ch in enumerate(text): + if ch.isspace(): + continue + normalized.append(ch.casefold()) + raw_map.append(raw_idx) + return "".join(normalized), raw_map + + +def _normalize_latex_segment_exact(text: str) -> str: + return "".join(ch.casefold() for ch in text if not ch.isspace()) + + +def _normalize_latex_segment_skeleton(text: str) -> str: + normalized = _normalize_latex_segment_exact(text) + normalized = re.sub(r"\d+", "#", normalized) + for command in LATEX_SYMBOL_SLOT_COMMANDS: + normalized = normalized.replace(command.casefold(), r"\sym") + normalized = re.sub(r"dr(?:_?\*|_?\\ast)?", "dr@", normalized) + return normalized + + +def _is_short_latex_repeat_atom(raw_segment: str) -> bool: + normalized = _normalize_latex_segment_exact(raw_segment) + if len(normalized) > LATEX_SHORT_SEGMENT_MAX_NORM: + return False + command_tokens = LATEX_COMMAND_RE.findall(raw_segment) + if not command_tokens: + return False + return set(command_tokens).issubset(LATEX_SHORT_REPEAT_ATOM_COMMANDS) + + +def _strip_latex_outer_delimiters(raw_segment: str) -> str: + stripped = raw_segment.strip() + wrappers = ( + (r"\(", r"\)"), + (r"\[", r"\]"), + ("$$", "$$"), + ("$", "$"), + ) + for left, right in wrappers: + if stripped.startswith(left) and stripped.endswith(right) and len(stripped) >= len(left) + len(right): + return stripped[len(left) : len(stripped) - len(right)].strip() + return stripped + + +def _latex_short_atom_block_key(raw_segment: str) -> Optional[str]: + body = "".join(ch for ch in _strip_latex_outer_delimiters(raw_segment) if not ch.isspace()) + if not body or len(body) > LATEX_SHORT_SEGMENT_MAX_NORM: + return None + + plain_pattern = ( + r"^(?P" + + "|".join(re.escape(token) for token in sorted(LATEX_SHORT_ATOM_BLOCK_BASE_COMMANDS)) + + r")(?P\'+)?$" + ) + match = re.fullmatch(plain_pattern, body) + if match: + base = match.group("base") or "" + primes = match.group("primes") or "" + return f"{base}{primes}" + + decorated_pattern = ( + r"^(?P" + + "|".join(re.escape(token) for token in sorted(LATEX_SHORT_ATOM_BLOCK_DECORATOR_COMMANDS)) + + r")\{(?P" + + "|".join(re.escape(token) for token in sorted(LATEX_SHORT_ATOM_BLOCK_BASE_COMMANDS)) + + r")\}(?P\'+)?$" + ) + match = re.fullmatch(decorated_pattern, body) + if match: + decorator = match.group("decorator") or "" + base = match.group("base") or "" + primes = match.group("primes") or "" + return f"{decorator}{{{base}}}{primes}" + + return None + + +def _consume_latex_short_atom_script(body: str, pos: int) -> Optional[int]: + while pos < len(body) and body[pos] in "_^": + pos += 1 + if pos >= len(body): + return None + if body[pos] == "{": + end = body.find("}", pos + 1) + if end == -1 or end == pos + 1: + return None + content = body[pos + 1 : end] + if any(ch.isspace() for ch in content) or "{" in content or "}" in content: + return None + pos = end + 1 + continue + if body[pos] == "\\": + match = re.match(r"\\[A-Za-z]+", body[pos:]) + if match is None: + return None + pos += len(match.group(0)) + continue + if body[pos].isalnum(): + pos += 1 + continue + return None + return pos + + +def _latex_short_atom_sequence_tokens( + raw_segment: str, + *, + allow_truncated_tail: bool = False, +) -> Optional[List[str]]: + body = "".join(ch for ch in _strip_latex_outer_delimiters(raw_segment) if not ch.isspace()) + if not body: + return None + + base_commands = sorted(LATEX_SHORT_ATOM_BLOCK_BASE_COMMANDS, key=len, reverse=True) + decorator_commands = sorted(LATEX_SHORT_ATOM_BLOCK_DECORATOR_COMMANDS, key=len, reverse=True) + tokens: List[str] = [] + pos = 0 + while pos < len(body): + token: Optional[str] = None + for decorator in decorator_commands: + prefix = decorator + "{" + if not body.startswith(prefix, pos): + continue + inner_pos = pos + len(prefix) + base = next((candidate for candidate in base_commands if body.startswith(candidate, inner_pos)), None) + if base is None: + continue + end_pos = inner_pos + len(base) + if end_pos >= len(body) or body[end_pos] != "}": + continue + token = f"{decorator}{{{base}}}" + pos = end_pos + 1 + break + + if token is None: + base = next((candidate for candidate in base_commands if body.startswith(candidate, pos)), None) + if base is not None: + token = base + pos += len(base) + + if token is None: + remaining = body[pos:] + if allow_truncated_tail and tokens and len(remaining) >= 4 and any(command.startswith(remaining) for command in base_commands): + break + return None + + while pos < len(body) and body[pos] == "'": + token += "'" + pos += 1 + + script_end = _consume_latex_short_atom_script(body, pos) + if script_end is None: + return None + token += body[pos:script_end] + pos = script_end + + while pos < len(body) and body[pos] == "'": + token += "'" + pos += 1 + + tokens.append(token) + + return tokens or None + + +def _is_short_latex_whitelist_segment(raw_segment: str) -> bool: + normalized = _normalize_latex_segment_exact(raw_segment) + if len(normalized) > LATEX_SHORT_SEGMENT_MAX_NORM: + return False + tokens = _latex_short_atom_sequence_tokens(raw_segment) + return tokens is not None and len(tokens) >= LATEX_SHORT_ATOM_EXACT_SEGMENT_MIN_TOKENS + + +def _is_latex_short_atom_chain_segment(raw_segment: str) -> bool: + tokens = _latex_short_atom_sequence_tokens(raw_segment, allow_truncated_tail=True) + if tokens is None or len(tokens) < LATEX_SHORT_ATOM_CHAIN_MIN_TOKENS: + return False + counts = Counter(tokens) + return max(counts.values(), default=0) >= LATEX_SEGMENT_EXACT_RUN_MIN and len(counts) <= 3 + + +def _is_suspicious_internal_latex_repeat(raw_segment: str) -> bool: + if not raw_segment: + return False + if "" in raw_segment or "" in raw_segment: + return True + if _is_latex_short_atom_chain_segment(raw_segment): + return True + + command_tokens = LATEX_COMMAND_RE.findall(raw_segment) + if any(wrapper in raw_segment for wrapper in LATEX_TEXT_WRAPPER_MACROS): + return len(command_tokens) >= 8 or len(raw_segment) >= 60 + + counts = Counter(command_tokens) + if set(command_tokens).issubset(LATEX_INTERNAL_SMALL_VOCAB_COMMANDS): + if len(command_tokens) >= LATEX_INTERNAL_SMALL_VOCAB_RUN_MIN_COMMANDS and len(counts) <= 3: + return True + if any(command in LATEX_INTERNAL_REPEAT_COMMANDS for command in counts): + return max(counts.values(), default=0) >= LATEX_INTERNAL_REPEAT_MIN_COMMAND_DUP + + return False + + +def _extract_latex_lhs_key(raw_segment: str) -> Optional[str]: + normalized = _normalize_latex_segment_exact(raw_segment) + if "=" not in normalized: + return None + lhs = normalized.split("=", 1)[0] + return lhs or None + + +def _is_latex_symbol_inventory_segment(raw_segment: str) -> bool: + normalized = _normalize_latex_segment_exact(raw_segment) + if not normalized or len(normalized) > 96: + return False + if any(token in normalized for token in ("=", "+", "-", r"\sum", r"\prod", r"\int", r"\frac")): + return False + if _is_short_latex_repeat_atom(raw_segment): + return False + command_tokens = LATEX_COMMAND_RE.findall(raw_segment) + return bool(command_tokens) + + +def _is_small_parameterized_definition_family(run: List[Dict[str, Any]]) -> bool: + if len(run) > LATEX_SMALL_DEFINITION_FAMILY_MAX_RUN: + return False + lhs_keys = [_extract_latex_lhs_key(str(item["text"])) for item in run] + if any(key is None for key in lhs_keys): + return False + if any( + key is not None and any(token in key for token in (r"\frac", r"\sum", r"\prod", r"\int", "+", "-", "=")) + for key in lhs_keys + ): + return False + return len(set(lhs_keys)) == len(lhs_keys) + + +def _is_symbol_inventory_run(run: List[Dict[str, Any]]) -> bool: + return all(_is_latex_symbol_inventory_segment(str(item["text"])) for item in run) + + +def _short_atom_run_has_clean_gaps(page_text: str, run: List[Dict[str, Any]]) -> bool: + if len(run) < 2: + return True + for left, right in zip(run, run[1:]): + gap = page_text[int(left["end"]) : int(right["start"])] + if any(ch.isalnum() for ch in gap): + return False + return True + + +def _extract_latex_numeric_slots(raw_segment: str) -> Optional[List[float]]: + slots: List[float] = [] + for token in re.findall(r"[0-9]+(?:[.,/][0-9]+)*", raw_segment): + if "/" in token: + if token.count("/") != 1: + return None + lhs, rhs = token.split("/", 1) + if not lhs.isdigit() or not rhs.isdigit() or int(rhs) == 0: + return None + slots.append(float(int(lhs) / int(rhs))) + continue + if token.count(".") + token.count(",") > 1: + return None + normalized = token.replace(",", ".", 1) + if "." in normalized: + lhs, rhs = normalized.split(".", 1) + if not lhs.isdigit() or not rhs.isdigit(): + return None + slots.append(float(normalized)) + continue + if token.isdigit(): + slots.append(float(int(token))) + continue + return None + return slots or None + + +def _latex_slot_progress_position(values: List[float]) -> bool: + if len(values) < LATEX_SEGMENT_SLOT_PROGRESS_RUN_MIN: + return False + + diffs: List[float] = [] + tolerance = 1e-9 + for left, right in zip(values, values[1:]): + diff = right - left + if diff < -tolerance: + return False + if diff > tolerance: + diffs.append(diff) + + if not diffs: + return False + + baseline = diffs[0] + return all(abs(diff - baseline) <= max(tolerance, abs(baseline) * 1e-6) for diff in diffs[1:]) + + +def _is_latex_slot_progression_run(run: List[Dict[str, Any]]) -> bool: + if len(run) < LATEX_SEGMENT_SLOT_PROGRESS_RUN_MIN: + return False + if _is_small_parameterized_definition_family(run): + return False + if _is_symbol_inventory_run(run): + return False + if _is_short_latex_repeat_atom(str(run[0]["text"])): + return False + + slot_lists = [item.get("numeric_slots") for item in run] + if any(not slots for slots in slot_lists): + return False + slot_count = len(slot_lists[0] or []) + if slot_count == 0 or any(len(slots or []) != slot_count for slots in slot_lists): + return False + + varying_positions = 0 + for slot_idx in range(slot_count): + values = [float(slots[slot_idx]) for slots in slot_lists if slots is not None] + if len({round(value, 9) for value in values}) > 1: + varying_positions += 1 + if varying_positions == 0 or varying_positions > 2: + return False + + for slot_idx in range(slot_count): + values = [float(slots[slot_idx]) for slots in slot_lists if slots is not None] + if _latex_slot_progress_position(values): + return True + return False + + +def _normalize_alnum_with_map_skip_tags(text: str) -> Tuple[str, List[int]]: + norm_chars: List[str] = [] + raw_char_indices: List[int] = [] + in_tag = False + for raw_idx, ch in enumerate(text): + if in_tag: + if ch == ">": + in_tag = False + continue + if ch == "<": + in_tag = True + continue + folded = unicodedata.normalize("NFD", ch.casefold()) + for sub in folded: + category = unicodedata.category(sub) + if category.startswith("L") or category.startswith("N"): + sub = WORD_CONFUSABLE_FOLD_MAP.get(sub, sub) + norm_chars.append(sub) + raw_char_indices.append(raw_idx) + return "".join(norm_chars), raw_char_indices + + +def _normalize_hybrid_body(text: str) -> str: + norm_chars: List[str] = [] + for ch in text: + folded = unicodedata.normalize("NFD", ch.casefold()) + for sub in folded: + category = unicodedata.category(sub) + if category.startswith("L") or category.startswith("N"): + norm_chars.append(WORD_CONFUSABLE_FOLD_MAP.get(sub, sub)) + return "".join(norm_chars) + + +def _classify_hybrid_numeric_field(prefix: str) -> Optional[Dict[str, Any]]: + token = prefix.strip() + if not token: + return None + + trailing_paren = token.endswith(")") + trailing_dot = token.endswith(".") + stripped = token[:-1] if trailing_paren or trailing_dot else token + if not stripped: + return None + + if "/" in stripped: + return {"field_kind": "numeric_value", "raw": token} + + parts = stripped.split(".") + if not all(part.isdigit() for part in parts): + return None + + numbers = [int(part) for part in parts] + shape = ".".join("#" for _ in numbers) + if trailing_paren: + shape += ")" + elif trailing_dot: + shape += "." + + if trailing_paren or trailing_dot: + field_kind = "header_counter" + elif len(numbers) >= 3: + field_kind = "header_counter" + elif len(numbers) == 2 and len(parts[-1]) <= 2: + field_kind = "header_counter" + else: + field_kind = "numeric_value" + + return { + "field_kind": field_kind, + "numbers": numbers, + "shape": shape, + "raw": token, + } + + +def _classify_hybrid_inline_numeric_field(token: str) -> Optional[Dict[str, Any]]: + stripped = token.strip() + if not stripped: + return None + + if re.fullmatch(r"[0-9]+", stripped): + return {"field_kind": "numeric_value", "raw": stripped} + + if stripped.count("/") == 1: + lhs, rhs = stripped.split("/", 1) + if re.fullmatch(r"[0-9]+", lhs) and re.fullmatch(r"[0-9]+", rhs) and int(rhs) != 0: + return {"field_kind": "numeric_value", "raw": stripped} + return None + + decimal_candidate = stripped.replace(",", ".", 1) + if decimal_candidate.count(".") == 1: + lhs, rhs = decimal_candidate.split(".", 1) + if re.fullmatch(r"[0-9]+", lhs) and re.fullmatch(r"[0-9]+", rhs): + return {"field_kind": "numeric_value", "raw": stripped} + + return None + + +def _parse_hybrid_numeric_value(token: str) -> Optional[float]: + stripped = token.strip() + if not stripped: + return None + + if re.fullmatch(r"[0-9]+", stripped): + return float(int(stripped)) + + if stripped.count("/") == 1: + lhs, rhs = stripped.split("/", 1) + if re.fullmatch(r"[0-9]+", lhs) and re.fullmatch(r"[0-9]+", rhs) and int(rhs) != 0: + return float(int(lhs) / int(rhs)) + return None + + decimal_candidate = stripped.replace(",", ".", 1) + if decimal_candidate.count(".") == 1: + lhs, rhs = decimal_candidate.split(".", 1) + if re.fullmatch(r"[0-9]+", lhs) and re.fullmatch(r"[0-9]+", rhs): + return float(decimal_candidate) + + return None + + +def _prepare_hybrid_analysis_text( + page_text: str, + *, + blocked_spans: List[Dict[str, Any]], +) -> str: + analysis_text = _filter_tables_preserve_layout(page_text) + analysis_text = _filter_latex_preserve_layout(analysis_text) + analysis_text = _blank_existing_match_regions_preserve_layout(analysis_text) + analysis_text = _blank_raw_spans_preserve_layout(analysis_text, blocked_spans) + return analysis_text + + +def _extract_hybrid_numbered_items_from_analysis_text(analysis_text: str) -> List[Dict[str, Any]]: + candidates: List[Dict[str, Any]] = [] + for match in HYBRID_PREFIX_RE.finditer(analysis_text): + field = _classify_hybrid_numeric_field(match.group("prefix")) + if field is None: + continue + candidates.append( + { + "prefix_start": match.start("prefix"), + "prefix_end": match.end("prefix"), + **field, + } + ) + + items: List[Dict[str, Any]] = [] + for idx, candidate in enumerate(candidates): + next_start = ( + int(candidates[idx + 1]["prefix_start"]) if idx + 1 < len(candidates) else len(analysis_text) + ) + body_raw = analysis_text[int(candidate["prefix_end"]) : next_start].strip() + if HYBRID_MARKUP_BODY_RE.search(body_raw): + continue + body_key = _normalize_hybrid_body(body_raw) + has_alpha = any(ch.isalpha() for ch in body_key) + if not has_alpha: + continue + body_is_full = len(body_key) >= HYBRID_REPEAT_MIN_BODY_ALNUM + items.append( + { + "start": int(candidate["prefix_start"]), + "end": next_start, + "prefix_end": int(candidate["prefix_end"]), + "field_kind": str(candidate["field_kind"]), + "numbers": list(candidate.get("numbers", [])), + "shape": str(candidate.get("shape", "")), + "body_raw": body_raw, + "body_key": body_key, + "body_is_full": body_is_full, + } + ) + + return items + + +def _extract_hybrid_inline_numeric_items_from_analysis_text(analysis_text: str) -> List[Dict[str, Any]]: + clause_ranges: List[Tuple[int, int]] = [] + clause_start = 0 + for match in HYBRID_INLINE_CLAUSE_DELIMITER_RE.finditer(analysis_text): + clause_ranges.append((clause_start, match.start())) + clause_start = match.end() + clause_ranges.append((clause_start, len(analysis_text))) + + items: List[Dict[str, Any]] = [] + for clause_index, (raw_start, raw_end) in enumerate(clause_ranges): + clause = analysis_text[raw_start:raw_end] + if not clause.strip(): + continue + + leading_ws = len(clause) - len(clause.lstrip()) + trailing_ws = len(clause) - len(clause.rstrip()) + clause_start_abs = raw_start + leading_ws + clause_end_abs = raw_end - trailing_ws + clause_text = analysis_text[clause_start_abs:clause_end_abs] + if not clause_text or HYBRID_MARKUP_BODY_RE.search(clause_text): + continue + + working_offset = clause_start_abs + working_text = clause_text + prefix_match = HYBRID_PREFIX_RE.match(working_text) + if prefix_match: + working_offset += prefix_match.end() + working_text = working_text[prefix_match.end() :].lstrip() + working_offset = clause_end_abs - len(working_text) + if not working_text: + continue + + tokens: List[Dict[str, Any]] = [] + numeric_token_positions: List[int] = [] + for match in HYBRID_INLINE_TOKEN_RE.finditer(working_text): + token = match.group(0) + abs_start = working_offset + match.start() + abs_end = working_offset + match.end() + if token and token[0].isdigit(): + numeric_info = _classify_hybrid_inline_numeric_field(token) + if numeric_info is None: + continue + parsed_value = _parse_hybrid_numeric_value(token) + if parsed_value is None: + continue + numeric_token_positions.append(len(tokens)) + tokens.append( + { + "kind": "numeric", + "start": abs_start, + "end": abs_end, + "raw": token, + "numeric_value": parsed_value, + } + ) + continue + token_key = _normalize_hybrid_body(token) + if not token_key: + continue + tokens.append( + { + "kind": "alpha", + "start": abs_start, + "end": abs_end, + "raw": token, + "token_key": token_key, + } + ) + + if len(numeric_token_positions) != 1: + continue + + numeric_pos = numeric_token_positions[0] + numeric_token = tokens[numeric_pos] + left_alpha = [token for token in tokens[:numeric_pos] if token.get("kind") == "alpha"] + right_alpha = [token for token in tokens[numeric_pos + 1 :] if token.get("kind") == "alpha"] + left_context = left_alpha[-HYBRID_INLINE_CONTEXT_WORDS:] + right_context = right_alpha[:HYBRID_INLINE_CONTEXT_WORDS] + alpha_word_count = len(left_context) + len(right_context) + if alpha_word_count < HYBRID_INLINE_CONTEXT_MIN_ALPHA_WORDS: + continue + + context_parts = [str(token.get("token_key", "")) for token in left_context] + context_parts.append("num") + context_parts.extend(str(token.get("token_key", "")) for token in right_context) + context_key = _normalize_hybrid_body(" ".join(context_parts)) + if len(context_key) < HYBRID_INLINE_CONTEXT_MIN_CHARS: + continue + + item_start = int(left_context[0]["start"]) if left_context else int(numeric_token["start"]) + item_end = int(right_context[-1]["end"]) if right_context else int(numeric_token["end"]) + items.append( + { + "start": item_start, + "end": item_end, + "clause_index": clause_index, + "field_kind": "numeric_value", + "inline_context_key": context_key, + "numeric_value": float(numeric_token["numeric_value"]), + } + ) + + return items + + +def _hybrid_partial_body_matches(candidate_body_key: str, target_body_key: str) -> bool: + if not candidate_body_key or not target_body_key: + return False + if candidate_body_key == target_body_key: + return False + if not target_body_key.startswith(candidate_body_key): + return False + min_chars = min(4, len(target_body_key)) + min_ratio_chars = max(1, math.ceil(len(target_body_key) * 0.5)) + return len(candidate_body_key) >= min(min_chars, min_ratio_chars) + + +def _extend_hybrid_tail_span_end( + items: List[Dict[str, Any]], + *, + run_start: int, + run_end: int, + expected_body_key: str, +) -> int: + span_end = int(items[run_end - 1]["end"]) + if run_end >= len(items): + return span_end + + tail = items[run_end] + if tail.get("field_kind") != "header_counter": + return span_end + if str(tail.get("shape", "")) != str(items[run_start].get("shape", "")): + return span_end + if not _hybrid_header_progresses(items[run_end - 1], tail): + return span_end + if not _hybrid_partial_body_matches(str(tail.get("body_key", "")), expected_body_key): + return span_end + return int(tail["end"]) + + +def _hybrid_header_progresses(previous: Dict[str, Any], current: Dict[str, Any]) -> bool: + if previous.get("field_kind") != "header_counter" or current.get("field_kind") != "header_counter": + return False + prev_numbers = list(previous.get("numbers", [])) + curr_numbers = list(current.get("numbers", [])) + if len(prev_numbers) != len(curr_numbers) or not prev_numbers: + return False + return prev_numbers[:-1] == curr_numbers[:-1] and curr_numbers[-1] == prev_numbers[-1] + 1 + + +def _hybrid_header_is_parent(previous: Dict[str, Any], current: Dict[str, Any]) -> bool: + if previous.get("field_kind") != "header_counter" or current.get("field_kind") != "header_counter": + return False + prev_numbers = list(previous.get("numbers", [])) + curr_numbers = list(current.get("numbers", [])) + if not prev_numbers or len(prev_numbers) + 1 != len(curr_numbers): + return False + return curr_numbers[:-1] == prev_numbers + + +def _hybrid_inline_step(previous: Dict[str, Any], current: Dict[str, Any]) -> Optional[float]: + if previous.get("field_kind") != "numeric_value" or current.get("field_kind") != "numeric_value": + return None + if int(current.get("clause_index", -1)) != int(previous.get("clause_index", -1)) + 1: + return None + if str(previous.get("inline_context_key", "")) != str(current.get("inline_context_key", "")): + return None + + previous_value = float(previous.get("numeric_value", 0.0)) + current_value = float(current.get("numeric_value", 0.0)) + step = current_value - previous_value + if step <= 0: + return None + return step + + +def _hybrid_inline_step_matches(expected_step: float, actual_step: float) -> bool: + tolerance = max(1e-9, abs(expected_step) * 1e-6) + return abs(expected_step - actual_step) <= tolerance + + +def _find_hybrid_same_body_progression_spans(items: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + spans: List[Dict[str, Any]] = [] + idx = 0 + while idx < len(items): + item = items[idx] + if item.get("field_kind") != "header_counter" or not bool(item.get("body_is_full")): + idx += 1 + continue + + end_idx = idx + 1 + while ( + end_idx < len(items) + and items[end_idx].get("field_kind") == "header_counter" + and bool(items[end_idx].get("body_is_full")) + and str(items[end_idx].get("body_key", "")) == str(item.get("body_key", "")) + and str(items[end_idx].get("shape", "")) == str(item.get("shape", "")) + and _hybrid_header_progresses(items[end_idx - 1], items[end_idx]) + ): + end_idx += 1 + + run_length = end_idx - idx + if run_length >= HYBRID_REPEAT_MIN_ITEMS: + start_idx = idx + if idx > 0: + previous = items[idx - 1] + if ( + bool(previous.get("body_is_full")) + and + str(previous.get("body_key", "")) == str(item.get("body_key", "")) + and _hybrid_header_is_parent(previous, item) + ): + start_idx = idx - 1 + + span_end = _extend_hybrid_tail_span_end( + items, + run_start=idx, + run_end=end_idx, + expected_body_key=str(item.get("body_key", "")), + ) + spans.append( + { + "start": int(items[start_idx]["start"]), + "end": span_end, + "match_types": ["hybrid_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["hybrid_repeat"], + "kind": "same_body_progression", + "item_count": end_idx - start_idx, + } + ) + idx = end_idx + continue + + idx += 1 + + return spans + + +def _find_hybrid_cycle_progression_spans(items: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + spans: List[Dict[str, Any]] = [] + n_items = len(items) + for cycle_len in range(2, HYBRID_REPEAT_MAX_CYCLE + 1): + idx = 0 + while idx + 2 * cycle_len <= n_items: + run = items[idx : idx + 2 * cycle_len] + if any(item.get("field_kind") != "header_counter" or not bool(item.get("body_is_full")) for item in run): + idx += 1 + continue + shapes = {str(item.get("shape", "")) for item in run} + if len(shapes) != 1: + idx += 1 + continue + if not all(_hybrid_header_progresses(run[pos - 1], run[pos]) for pos in range(1, len(run))): + idx += 1 + continue + + template = [str(item.get("body_key", "")) for item in run[:cycle_len]] + if len(set(template)) < 2: + idx += 1 + continue + + if any(str(run[pos].get("body_key", "")) != template[pos % cycle_len] for pos in range(cycle_len, len(run))): + idx += 1 + continue + + end_idx = idx + 2 * cycle_len + while ( + end_idx < n_items + and items[end_idx].get("field_kind") == "header_counter" + and bool(items[end_idx].get("body_is_full")) + and str(items[end_idx].get("shape", "")) == str(items[idx].get("shape", "")) + and _hybrid_header_progresses(items[end_idx - 1], items[end_idx]) + and str(items[end_idx].get("body_key", "")) == template[(end_idx - idx) % cycle_len] + ): + end_idx += 1 + + item_count = end_idx - idx + if item_count >= HYBRID_REPEAT_MIN_CYCLE_ITEMS: + span_end = _extend_hybrid_tail_span_end( + items, + run_start=idx, + run_end=end_idx, + expected_body_key=template[(end_idx - idx) % cycle_len], + ) + spans.append( + { + "start": int(items[idx]["start"]), + "end": span_end, + "match_types": ["hybrid_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["hybrid_repeat"], + "kind": "body_cycle_progression", + "item_count": item_count, + "cycle_len": cycle_len, + } + ) + idx = end_idx + continue + + idx += 1 + + return spans + + +def _find_hybrid_inline_progression_spans(items: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + spans: List[Dict[str, Any]] = [] + idx = 0 + while idx + HYBRID_INLINE_REPEAT_MIN_ITEMS <= len(items): + first = items[idx] + second = items[idx + 1] + expected_step = _hybrid_inline_step(first, second) + if expected_step is None: + idx += 1 + continue + + end_idx = idx + 2 + while end_idx < len(items): + actual_step = _hybrid_inline_step(items[end_idx - 1], items[end_idx]) + if actual_step is None or not _hybrid_inline_step_matches(expected_step, actual_step): + break + end_idx += 1 + + item_count = end_idx - idx + if item_count >= HYBRID_INLINE_REPEAT_MIN_ITEMS: + spans.append( + { + "start": int(items[idx]["start"]), + "end": int(items[end_idx - 1]["end"]), + "match_types": ["hybrid_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["hybrid_repeat"], + "kind": "inline_numeric_progression", + "item_count": item_count, + } + ) + idx = end_idx + continue + + idx += 1 + + return spans + + +def _find_hybrid_numbered_repeat_spans( + page_text: str, + *, + blocked_spans: List[Dict[str, Any]], + analysis_text: Optional[str] = None, +) -> List[Dict[str, Any]]: + if not any(ch.isdigit() for ch in page_text): + return [] + if analysis_text is None: + analysis_text = _prepare_hybrid_analysis_text(page_text, blocked_spans=blocked_spans) + else: + analysis_text = _blank_raw_spans_preserve_layout(analysis_text, blocked_spans) + rust_mod = _get_word_repeat_rust_module() + if rust_mod is not None and hasattr(rust_mod, "find_hybrid_repeat_spans"): + return [ + { + "start": int(item["start"]), + "end": int(item["end"]), + "match_types": list(item["match_types"]), + "category": str(item["category"]), + "kind": str(item["kind"]), + "item_count": int(item["item_count"]), + **({"cycle_len": int(item["cycle_len"])} if "cycle_len" in item else {}), + } + for item in rust_mod.find_hybrid_repeat_spans(analysis_text) + ] + items = _extract_hybrid_numbered_items_from_analysis_text(analysis_text) + spans = _find_hybrid_same_body_progression_spans(items) + spans.extend(_find_hybrid_cycle_progression_spans(items)) + inline_items = _extract_hybrid_inline_numeric_items_from_analysis_text(analysis_text) + spans.extend(_find_hybrid_inline_progression_spans(inline_items)) + spans.sort(key=lambda item: (int(item["start"]), -(int(item["end"]) - int(item["start"])))) + + deduped: List[Dict[str, Any]] = [] + for span in spans: + if deduped and int(span["start"]) >= int(deduped[-1]["start"]) and int(span["end"]) <= int(deduped[-1]["end"]): + continue + deduped.append(span) + return deduped + + +def _build_word_repeat_hash(text: str) -> Tuple[List[int], List[int]]: + pref = [0] * (len(text) + 1) + pw = [1] * (len(text) + 1) + for idx, ch in enumerate(text): + code = ord(ch) + 1 + pref[idx + 1] = (pref[idx] * WORD_REPEAT_HASH_BASE + code) & WORD_REPEAT_HASH_MASK + pw[idx + 1] = (pw[idx] * WORD_REPEAT_HASH_BASE) & WORD_REPEAT_HASH_MASK + return pref, pw + + +def _word_repeat_hash_slice(pref: List[int], pw: List[int], start: int, end: int) -> int: + return (pref[end] - ((pref[start] * pw[end - start]) & WORD_REPEAT_HASH_MASK)) & WORD_REPEAT_HASH_MASK + + +def _word_repeat_blocks_equal( + text: str, + pref: List[int], + pw: List[int], + lhs: int, + rhs: int, + period: int, +) -> bool: + return ( + _word_repeat_hash_slice(pref, pw, lhs, lhs + period) + == _word_repeat_hash_slice(pref, pw, rhs, rhs + period) + and text[lhs : lhs + period] == text[rhs : rhs + period] + ) + + +def _get_word_repeat_rust_module() -> Optional[Any]: + global _WORD_REPEAT_RUST_MOD, _WORD_REPEAT_RUST_IMPORT_ATTEMPTED + if _WORD_REPEAT_RUST_IMPORT_ATTEMPTED: + return _WORD_REPEAT_RUST_MOD + _WORD_REPEAT_RUST_IMPORT_ATTEMPTED = True + try: + module = importlib.import_module("glossapi_rs_noise") + except Exception: + _WORD_REPEAT_RUST_MOD = None + return None + if hasattr(module, "find_word_repeat_spans"): + _WORD_REPEAT_RUST_MOD = module + else: + _WORD_REPEAT_RUST_MOD = None + return _WORD_REPEAT_RUST_MOD + + +def _find_word_repeat_spans_python( + normalized_text: str, + *, + rep_threshold: int, + min_period: int, + window: int, +) -> List[Dict[str, int]]: + n_chars = len(normalized_text) + if n_chars < rep_threshold * min_period: + return [] + + pref, pw = _build_word_repeat_hash(normalized_text) + max_period = min(max(min_period, window // rep_threshold), n_chars // rep_threshold) + spans: List[Dict[str, int]] = [] + + for period in range(min_period, max_period + 1): + idx = 0 + while idx + rep_threshold * period <= n_chars: + is_repeat = True + for multiple in range(1, rep_threshold): + if not _word_repeat_blocks_equal( + normalized_text, + pref, + pw, + idx, + idx + multiple * period, + period, + ): + is_repeat = False + break + if not is_repeat: + idx += 1 + continue + + left = idx + while left - period >= 0 and _word_repeat_blocks_equal( + normalized_text, + pref, + pw, + left - period, + left, + period, + ): + left -= period + + right = idx + rep_threshold * period + while right + period <= n_chars and _word_repeat_blocks_equal( + normalized_text, + pref, + pw, + right - period, + right, + period, + ): + right += period + + pattern = normalized_text[left : left + period] + tail_chars = 0 + while ( + right + tail_chars < n_chars + and tail_chars < period + and normalized_text[right + tail_chars] == pattern[tail_chars] + ): + tail_chars += 1 + + spans.append( + { + "start": left, + "end": right + tail_chars, + "period": period, + "repetitions": (right - left) // period, + "tail_chars": tail_chars, + } + ) + idx = right + + spans.sort(key=lambda item: (item["start"], -(item["end"] - item["start"]), item["period"])) + deduped: List[Dict[str, int]] = [] + for span in spans: + if deduped and span["start"] >= deduped[-1]["start"] and span["end"] <= deduped[-1]["end"]: + continue + deduped.append(span) + return deduped + + +def _find_word_repeat_spans( + normalized_text: str, + *, + rep_threshold: int, + min_period: int, + window: int, +) -> List[Dict[str, int]]: + rust_mod = _get_word_repeat_rust_module() + if rust_mod is None: + return _find_word_repeat_spans_python( + normalized_text, + rep_threshold=rep_threshold, + min_period=min_period, + window=window, + ) + return [ + { + "start": int(item["start"]), + "end": int(item["end"]), + "period": int(item["period"]), + "repetitions": int(item["repetitions"]), + "tail_chars": int(item["tail_chars"]), + } + for item in rust_mod.find_word_repeat_spans( + normalized_text, + int(rep_threshold), + int(min_period), + int(window), + ) + ] + + +def _gap_has_fewer_than_n_nonwhitespace_chars(text: str, start: int, end: int, limit: int) -> bool: + if start >= end: + return True + count = 0 + for ch in text[start:end]: + if not ch.isspace(): + count += 1 + if count >= limit: + return False + return True + + +def _latex_segments_are_local(page_text: str, left: Dict[str, Any], right: Dict[str, Any]) -> bool: + return _gap_has_fewer_than_n_nonwhitespace_chars( + page_text, + int(left["end"]), + int(right["start"]), + LATEX_SEGMENT_LOCAL_NONWHITESPACE_GAP, + ) + + +def _latex_local_groups(page_text: str, segments: List[Dict[str, Any]]) -> List[List[Dict[str, Any]]]: + if not segments: + return [] + + groups: List[List[Dict[str, Any]]] = [[segments[0]]] + for segment in segments[1:]: + if _latex_segments_are_local(page_text, groups[-1][-1], segment): + groups[-1].append(segment) + else: + groups.append([segment]) + return groups + + +def _find_local_latex_segment_block_spans( + page_text: str, + segments: List[Dict[str, Any]], +) -> List[Dict[str, Any]]: + labeled_spans: List[Dict[str, Any]] = [] + for group in _latex_local_groups(page_text, segments): + if len(group) < LATEX_SEGMENT_EXACT_RUN_MIN: + continue + + idx = 0 + while idx < len(group): + exact_key = str(group[idx]["exact_key"]) + end_idx = idx + 1 + while end_idx < len(group) and str(group[end_idx]["exact_key"]) == exact_key: + end_idx += 1 + + run_length = end_idx - idx + exact_run = group[idx:end_idx] + is_short_repeat_atom = _is_short_latex_repeat_atom(str(group[idx]["text"])) + is_short_whitelist_segment = _is_short_latex_whitelist_segment(str(group[idx]["text"])) + if run_length >= LATEX_SEGMENT_EXACT_RUN_MIN and ( + len(exact_key) >= LATEX_LONG_SEGMENT_MIN_NORM + or (is_short_repeat_atom and _short_atom_run_has_clean_gaps(page_text, exact_run)) + or (is_short_whitelist_segment and _short_atom_run_has_clean_gaps(page_text, exact_run)) + ): + span = { + "start": int(exact_run[0]["start"]), + "end": int(exact_run[-1]["end"]), + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + } + if is_short_whitelist_segment and not is_short_repeat_atom: + span["kind"] = "short_atom_segment_repeat" + span["item_count"] = len(exact_run) + labeled_spans.append(span) + idx = end_idx + + idx = 0 + while idx < len(group): + skeleton_key = str(group[idx]["skeleton_key"]) + end_idx = idx + 1 + while end_idx < len(group) and str(group[end_idx]["skeleton_key"]) == skeleton_key: + end_idx += 1 + + run = group[idx:end_idx] + exact_vocab = {str(item["exact_key"]) for item in run} + if ( + len(run) >= LATEX_SEGMENT_SKELETON_RUN_MIN + and len(skeleton_key) >= LATEX_LONG_SEGMENT_MIN_NORM + and not _is_short_latex_repeat_atom(str(run[0]["text"])) + and len(exact_vocab) >= 2 + and not _is_small_parameterized_definition_family(run) + and not _is_symbol_inventory_run(run) + ): + labeled_spans.append( + { + "start": int(run[0]["start"]), + "end": int(run[-1]["end"]), + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + } + ) + idx = end_idx + + exact_sequence = [str(item["exact_key"]) for item in group] + exact_counts = Counter(exact_sequence) + if ( + len(group) >= LATEX_SEGMENT_ALTERNATING_RUN_MIN + and len(exact_counts) <= 2 + and min(exact_counts.values()) >= 2 + ): + avg_length = sum(len(item) for item in exact_sequence) / len(exact_sequence) + if avg_length >= LATEX_LONG_SEGMENT_MIN_NORM and not all( + _is_short_latex_repeat_atom(str(item["text"])) for item in group + ): + labeled_spans.append( + { + "start": int(group[0]["start"]), + "end": int(group[-1]["end"]), + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + } + ) + + return labeled_spans + + +def _find_short_atom_block_repeat_bounds( + atom_keys: List[str], +) -> Optional[Tuple[int, int, int, int]]: + n_items = len(atom_keys) + if n_items < LATEX_SHORT_ATOM_BLOCK_REPEAT_MIN_ITEMS: + return None + + best: Optional[Tuple[int, int, int, int]] = None + for period in range(n_items // 2, 1, -1): + for start in range(0, n_items - (2 * period) + 1): + pattern = atom_keys[start : start + period] + if atom_keys[start + period : start + (2 * period)] != pattern: + continue + if len(set(pattern)) < 2: + continue + + left = start + while left - period >= 0 and atom_keys[left - period : left] == pattern: + left -= period + + right = start + (2 * period) + while right + period <= n_items and atom_keys[right : right + period] == pattern: + right += period + + repeated_items = right - left + repetitions = repeated_items // period + if repeated_items < LATEX_SHORT_ATOM_BLOCK_REPEAT_MIN_ITEMS or repetitions < 2: + continue + + candidate = (left, right, period, repetitions) + if best is None: + best = candidate + continue + + best_span_len = best[1] - best[0] + candidate_span_len = candidate[1] - candidate[0] + if candidate_span_len > best_span_len: + best = candidate + continue + if candidate_span_len == best_span_len and candidate[2] > best[2]: + best = candidate + return best + + +def _extend_latex_short_atom_block_partial_tail( + page_text: str, + run: List[Dict[str, Any]], + repeated_bounds: Tuple[int, int, int, int], +) -> int: + if not run: + return 0 + + left, _, period, _ = repeated_bounds + if period <= 0 or left >= len(run): + return int(run[-1]["end"]) + + expected_idx = left + ((len(run) - left) % period) + if expected_idx >= len(run): + return int(run[-1]["end"]) + + expected_text = str(run[expected_idx]["text"]) + segment_end = int(run[-1]["end"]) + cursor = segment_end + while cursor < len(page_text) and page_text[cursor].isspace(): + cursor += 1 + if cursor >= len(page_text): + return segment_end + + prefix_len = 0 + while ( + cursor + prefix_len < len(page_text) + and prefix_len < len(expected_text) + and page_text[cursor + prefix_len] == expected_text[prefix_len] + ): + prefix_len += 1 + + if prefix_len == 0 or prefix_len >= len(expected_text): + return segment_end + return cursor + prefix_len + + +def _find_local_latex_short_atom_block_spans( + page_text: str, + segments: List[Dict[str, Any]], +) -> List[Dict[str, Any]]: + labeled_spans: List[Dict[str, Any]] = [] + for group in _latex_local_groups(page_text, segments): + idx = 0 + while idx < len(group): + if not group[idx].get("short_atom_block_key"): + idx += 1 + continue + + end_idx = idx + 1 + while end_idx < len(group) and group[end_idx].get("short_atom_block_key"): + end_idx += 1 + + run = group[idx:end_idx] + atom_keys = [str(item["short_atom_block_key"]) for item in run] + repeated_bounds = _find_short_atom_block_repeat_bounds(atom_keys) + if repeated_bounds is not None: + _, _, period_items, repetitions = repeated_bounds + span_end = _extend_latex_short_atom_block_partial_tail(page_text, run, repeated_bounds) + labeled_spans.append( + { + "start": int(run[0]["start"]), + "end": int(span_end), + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + "kind": "short_atom_block_repeat", + "item_count": len(run), + "period_items": int(period_items), + "repetitions": int(repetitions), + } + ) + + idx = end_idx + return labeled_spans + + +def _find_raw_latex_small_vocab_command_spans(page_text: str) -> List[Dict[str, Any]]: + labeled_spans: List[Dict[str, Any]] = [] + command_matches = list(LATEX_COMMAND_RE.finditer(page_text)) + run_start: Optional[int] = None + run_end: Optional[int] = None + run_commands: List[str] = [] + previous_end = 0 + + def flush_run() -> None: + if run_start is None or run_end is None or not run_commands: + return + counts = Counter(run_commands) + if ( + len(run_commands) >= LATEX_INTERNAL_SMALL_VOCAB_RUN_MIN_COMMANDS + and len(counts) <= 3 + and max(counts.values(), default=0) >= LATEX_SEGMENT_EXACT_RUN_MIN + ): + labeled_spans.append( + { + "start": run_start, + "end": run_end, + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + "kind": "internal_small_vocab_command_run", + "item_count": len(run_commands), + } + ) + + for command_match in command_matches: + command = command_match.group(0) + gap = page_text[previous_end : command_match.start()] + can_extend_run = not any(ch.isalnum() for ch in gap) + if command in LATEX_INTERNAL_SMALL_VOCAB_COMMANDS and (not run_commands or can_extend_run): + if not run_commands: + run_start = command_match.start() + run_end = command_match.end() + run_commands.append(command) + else: + flush_run() + run_start = None + run_end = None + run_commands = [] + if command in LATEX_INTERNAL_SMALL_VOCAB_COMMANDS: + run_start = command_match.start() + run_end = command_match.end() + run_commands = [command] + previous_end = command_match.end() + flush_run() + + return labeled_spans + + +def _find_internal_latex_small_vocab_command_spans( + page_text: str, + segments: List[Dict[str, Any]], +) -> List[Dict[str, Any]]: + labeled_spans: List[Dict[str, Any]] = [] + for segment in segments: + raw_text = str(segment["text"]) + command_matches = list(LATEX_COMMAND_RE.finditer(raw_text)) + run_start: Optional[int] = None + run_end: Optional[int] = None + run_commands: List[str] = [] + previous_end = 0 + + def flush_run() -> None: + if run_start is None or run_end is None or not run_commands: + return + counts = Counter(run_commands) + if ( + len(run_commands) >= LATEX_INTERNAL_SMALL_VOCAB_RUN_MIN_COMMANDS + and len(counts) <= 3 + and max(counts.values(), default=0) >= LATEX_SEGMENT_EXACT_RUN_MIN + ): + labeled_spans.append( + { + "start": int(segment["start"]) + run_start, + "end": int(segment["start"]) + run_end, + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + "kind": "internal_small_vocab_command_run", + "item_count": len(run_commands), + } + ) + + for command_match in command_matches: + command = command_match.group(0) + gap = raw_text[previous_end : command_match.start()] + can_extend_run = not any(ch.isalnum() for ch in gap) + if command in LATEX_INTERNAL_SMALL_VOCAB_COMMANDS and (not run_commands or can_extend_run): + if not run_commands: + run_start = command_match.start() + run_end = command_match.end() + run_commands.append(command) + else: + flush_run() + run_start = None + run_end = None + run_commands = [] + if command in LATEX_INTERNAL_SMALL_VOCAB_COMMANDS: + run_start = command_match.start() + run_end = command_match.end() + run_commands = [command] + previous_end = command_match.end() + flush_run() + + return labeled_spans + + +def _find_local_latex_slot_progression_spans( + page_text: str, + segments: List[Dict[str, Any]], +) -> List[Dict[str, Any]]: + labeled_spans: List[Dict[str, Any]] = [] + for group in _latex_local_groups(page_text, segments): + if len(group) < LATEX_SEGMENT_SLOT_PROGRESS_RUN_MIN: + continue + + idx = 0 + while idx < len(group): + skeleton_key = str(group[idx]["skeleton_key"]) + end_idx = idx + 1 + while end_idx < len(group) and str(group[end_idx]["skeleton_key"]) == skeleton_key: + end_idx += 1 + + run = group[idx:end_idx] + exact_vocab = {str(item["exact_key"]) for item in run} + if ( + len(run) >= LATEX_SEGMENT_SLOT_PROGRESS_RUN_MIN + and len(skeleton_key) >= LATEX_LONG_SEGMENT_MIN_NORM + and len(exact_vocab) >= 2 + and _is_latex_slot_progression_run(run) + ): + labeled_spans.append( + { + "start": int(run[0]["start"]), + "end": int(run[-1]["end"]), + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + "kind": "slot_progression", + "item_count": len(run), + } + ) + idx = end_idx + + return labeled_spans + + +def _find_latex_repeat_spans( + page_text: str, + *, + blocked_spans: List[Dict[str, Any]], + rep_threshold: int, + min_period: int, + window: int, + analysis_text: Optional[str] = None, +) -> List[Dict[str, Any]]: + if analysis_text is None: + analysis_text = _filter_tables_preserve_layout(page_text) + analysis_text = _blank_existing_match_regions_preserve_layout(analysis_text) + if ( + "$" not in analysis_text + and "\\" not in analysis_text + and "" not in analysis_text + and "" not in analysis_text + ): + return [] + analysis_text = _blank_raw_spans_preserve_layout(analysis_text, blocked_spans) + + labeled_spans: List[Dict[str, Any]] = [] + + for wrapper_pattern in (LATEX_TEXT_WRAPPER_BODY_RE, LATEX_TEXT_WRAPPER_OPEN_BODY_RE): + for match in wrapper_pattern.finditer(analysis_text): + body = match.group(1) + command_tokens = LATEX_COMMAND_RE.findall(body) + if len(command_tokens) < 16: + continue + if len(set(command_tokens)) > 4: + continue + labeled_spans.append( + { + "start": match.start(1), + "end": match.end(1), + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + } + ) + + for match in HTML_MATH_MARKUP_CLUSTER_RE.finditer(analysis_text): + labeled_spans.append( + { + "start": match.start(), + "end": match.end(), + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + } + ) + + labeled_spans.extend(_find_raw_latex_small_vocab_command_spans(analysis_text)) + + segments = _extract_latex_segments(analysis_text) + for segment in segments: + raw_text = str(segment["text"]) + segment["exact_key"] = _normalize_latex_segment_exact(raw_text) + segment["skeleton_key"] = _normalize_latex_segment_skeleton(raw_text) + segment["short_atom_block_key"] = _latex_short_atom_block_key(raw_text) + + labeled_spans.extend(_find_local_latex_segment_block_spans(page_text, segments)) + labeled_spans.extend(_find_local_latex_short_atom_block_spans(page_text, segments)) + labeled_spans.extend(_find_internal_latex_small_vocab_command_spans(page_text, segments)) + + for segment in segments: + normalized_text, raw_map = _normalize_latex_repeat_with_map(segment["text"]) + normalized_spans = _find_word_repeat_spans( + normalized_text, + rep_threshold=rep_threshold, + min_period=min_period, + window=window, + ) + for span in normalized_spans: + if span["end"] <= span["start"] or span["start"] >= len(raw_map): + continue + start = segment["start"] + raw_map[span["start"]] + end = segment["start"] + raw_map[span["end"] - 1] + 1 + raw_span = page_text[start:end] + if not _is_suspicious_internal_latex_repeat(raw_span): + continue + labeled_span = { + "start": start, + "end": end, + "period": span["period"], + "repetitions": span["repetitions"], + "tail_chars": span["tail_chars"], + "match_types": ["latex_repeat"], + "category": MATCH_CATEGORY_BY_TYPE["latex_repeat"], + } + if _is_latex_short_atom_chain_segment(raw_span): + labeled_span["kind"] = "short_atom_chain_segment" + labeled_spans.append(labeled_span) + return labeled_spans + + +def _find_latex_slot_progression_spans( + page_text: str, + *, + blocked_spans: List[Dict[str, Any]], + analysis_text: Optional[str] = None, +) -> List[Dict[str, Any]]: + if analysis_text is None: + analysis_text = _filter_tables_preserve_layout(page_text) + analysis_text = _blank_existing_match_regions_preserve_layout(analysis_text) + analysis_text = _blank_raw_spans_preserve_layout(analysis_text, blocked_spans) + + segments = _extract_latex_segments(analysis_text) + for segment in segments: + raw_text = str(segment["text"]) + segment["exact_key"] = _normalize_latex_segment_exact(raw_text) + segment["skeleton_key"] = _normalize_latex_segment_skeleton(raw_text) + segment["numeric_slots"] = _extract_latex_numeric_slots(raw_text) + + return _find_local_latex_slot_progression_spans(page_text, segments) + + +def _shared_repeat_match_type(segment: str) -> Optional[str]: + if not segment: + return None + has_letter = any(ch.isalpha() for ch in segment) + has_digit = any(ch.isdigit() for ch in segment) + if has_letter: + return "word_repeat" + if has_digit: + return "numeric_repeat" + return None + + +def _count_hybrid_matches_in_page(page_text: str, spans: List[Dict[str, Any]]) -> int: + merged_spans = _merge_labeled_raw_spans(page_text, spans) + return sum(1 for span in merged_spans if span.get("category") == "hybrid") + + +def _build_token_category_page_metric_row( + page_row: Dict[str, Any], + matches: List[Dict[str, Any]], +) -> Dict[str, Any]: + category_counts: Counter[str] = Counter() + pattern_family_counts: Counter[str] = Counter() + for match in matches: + for category in list(match.get("categories") or []): + category_counts[str(category)] += 1 + for family in list(match.get("pattern_families") or []): + pattern_family_counts[str(family)] += 1 + + page_char_count = int(page_row.get("page_char_count", 0) or 0) + match_count = int(page_row.get("match_count", 0) or 0) + return { + "source_path": str(page_row.get("source_path", "")), + "source_stem": str(page_row.get("source_stem", "")), + "base_stem": str(page_row.get("base_stem", "")), + "debug_output_path": str(page_row.get("output_path", "")), + "page_kind": str(page_row.get("page_kind", "")), + "page_number": int(page_row.get("page_number", 0) or 0), + "page_index_in_file": int(page_row.get("page_index_in_file", 0) or 0), + "page_char_count": page_char_count, + "match_count": match_count, + "match_density_per_1k_chars": ( + float(match_count) * 1000.0 / float(page_char_count) + if page_char_count > 0 + else 0.0 + ), + "match_categories": str(page_row.get("match_categories", "")), + "match_pattern_families": str(page_row.get("match_pattern_families", "")), + "category_match_counts": dict(category_counts), + "pattern_family_match_counts": dict(pattern_family_counts), + } + + +def _build_token_category_match_index_rows( + page_text: str, + matches: List[Dict[str, Any]], + *, + page_row: Dict[str, Any], + context_window_chars: int = 240, +) -> List[Dict[str, Any]]: + if not matches: + return [] + + byte_offsets = _utf8_prefix_byte_offsets(page_text) + rows: List[Dict[str, Any]] = [] + source_stem = str(page_row.get("source_stem", "")) + page_kind = str(page_row.get("page_kind", "")) + page_number = int(page_row.get("page_number", 0) or 0) + page_index_in_file = int(page_row.get("page_index_in_file", 0) or 0) + page_char_count = int(page_row.get("page_char_count", 0) or 0) + output_path = str(page_row.get("output_path", "")) + for fallback_index, match in enumerate(matches, start=1): + start = int(match.get("start", 0) or 0) + end = int(match.get("end", 0) or 0) + if start < 0 or end < start or end > len(page_text): + continue + match_index = int(match.get("match_index_in_page", fallback_index) or fallback_index) + categories = [str(item) for item in list(match.get("categories") or []) if str(item)] + pattern_families = [ + str(item) for item in list(match.get("pattern_families") or []) if str(item) + ] + excerpt_start = max(0, start - int(context_window_chars)) + excerpt_end = min(len(page_text), end + int(context_window_chars)) + rows.append( + { + "match_id": f"{source_stem}:{page_kind}:{page_number}:match:{match_index}", + "source_path": str(page_row.get("source_path", "")), + "source_stem": source_stem, + "base_stem": str(page_row.get("base_stem", "")), + "debug_output_path": output_path, + "page_kind": page_kind, + "page_number": page_number, + "page_index_in_file": page_index_in_file, + "page_char_count": page_char_count, + "match_index_in_page": match_index, + "start_char": start, + "end_char": end, + "start_byte": int(byte_offsets[start]), + "end_byte": int(byte_offsets[end]), + "match_length_chars": int(end - start), + "match_length_bytes": int(byte_offsets[end] - byte_offsets[start]), + "start_line": int(page_text.count("\n", 0, start) + 1), + "end_line": int(page_text.count("\n", 0, max(start, end - 1)) + 1), + "categories": categories, + "category": ",".join(categories), + "pattern_families": pattern_families, + "pattern_family": ",".join(pattern_families), + "matched_text": page_text[start:end], + "raw_texts": [str(item) for item in list(match.get("raw_texts") or [])], + "context_before": page_text[excerpt_start:start], + "context_after": page_text[end:excerpt_end], + "context_excerpt": page_text[excerpt_start:excerpt_end], + } + ) + return rows + + +def _find_labeled_shared_repeat_spans( + page_text: str, + *, + blocked_spans: List[Dict[str, Any]], + rep_threshold: int, + min_period: int, + window: int, + analysis_text: Optional[str] = None, +) -> List[Dict[str, Any]]: + if analysis_text is None: + analysis_text = _filter_tables_preserve_layout(page_text) + analysis_text = _filter_latex_preserve_layout(analysis_text) + analysis_text = _blank_existing_match_regions_preserve_layout(analysis_text) + analysis_text = _blank_raw_spans_preserve_layout(analysis_text, blocked_spans) + rust_mod = _get_word_repeat_rust_module() + if rust_mod is not None and hasattr(rust_mod, "find_labeled_shared_repeat_spans"): + return [ + { + "start": int(item["start"]), + "end": int(item["end"]), + "period": int(item["period"]), + "repetitions": int(item["repetitions"]), + "tail_chars": int(item["tail_chars"]), + "match_types": [str(item["match_type"])], + "category": MATCH_CATEGORY_BY_TYPE[str(item["match_type"])], + } + for item in rust_mod.find_labeled_shared_repeat_spans( + analysis_text, + int(rep_threshold), + int(min_period), + int(window), + ) + ] + normalized_text, raw_map = _normalize_alnum_with_map_skip_tags(analysis_text) + normalized_spans = _find_word_repeat_spans( + normalized_text, + rep_threshold=rep_threshold, + min_period=min_period, + window=window, + ) + labeled_spans: List[Dict[str, Any]] = [] + for span in normalized_spans: + if span["end"] <= span["start"] or span["start"] >= len(raw_map): + continue + match_type = _shared_repeat_match_type(normalized_text[span["start"] : span["end"]]) + if match_type is None: + continue + start = raw_map[span["start"]] + end = raw_map[span["end"] - 1] + 1 + labeled_spans.append( + { + "start": start, + "end": end, + "period": span["period"], + "repetitions": span["repetitions"], + "tail_chars": span["tail_chars"], + "match_types": [match_type], + "category": MATCH_CATEGORY_BY_TYPE[match_type], + } + ) + return labeled_spans + + +def _analyze_combined_ocr_page( + page_text: str, + *, + noise_mod: Any, + min_progress_steps: int, + min_repeat_steps: int, + min_same_digit_steps: int, + word_rep_threshold: int, + word_min_period: int, + word_window: int, +) -> Dict[str, Any]: + page_start = time.perf_counter() + + char_eval_start = time.perf_counter() + page_noise_metrics = dict(noise_mod.evaluate_page_character_noise(page_text)) + char_eval_elapsed = time.perf_counter() - char_eval_start + + table_start = time.perf_counter() + table_spans = _find_table_repeat_spans(page_text) + table_elapsed = time.perf_counter() - table_start + + # Reuse progressively filtered page views so later passes do not rebuild the + # same blanked surfaces repeatedly. + page_without_tables = _filter_tables_preserve_layout(page_text) + page_without_tables_existing = _blank_existing_match_regions_preserve_layout(page_without_tables) + page_without_tables_latex = _filter_latex_preserve_layout(page_without_tables) + page_without_tables_latex_existing = _blank_existing_match_regions_preserve_layout( + page_without_tables_latex + ) + + numeric_start = time.perf_counter() + numeric_spans = [ + { + "start": int(item["start"]), + "end": int(item["end"]), + "match_types": [str(item["match_type"])], + "category": MATCH_CATEGORY_BY_TYPE[str(item["match_type"])], + } + for item in noise_mod.find_numeric_debug_page_spans( + page_without_tables_latex, + int(min_progress_steps), + int(min_repeat_steps), + int(min_same_digit_steps), + ) + ] + numeric_elapsed = time.perf_counter() - numeric_start + + latex_start = time.perf_counter() + latex_spans = _find_latex_repeat_spans( + page_text, + blocked_spans=table_spans + numeric_spans, + rep_threshold=int(word_rep_threshold), + min_period=int(word_min_period), + window=int(word_window), + analysis_text=page_without_tables_existing, + ) + latex_elapsed = time.perf_counter() - latex_start + + hybrid_start = time.perf_counter() + hybrid_spans = _find_hybrid_numbered_repeat_spans( + page_text, + blocked_spans=table_spans + numeric_spans + latex_spans, + analysis_text=page_without_tables_latex_existing, + ) + hybrid_elapsed = time.perf_counter() - hybrid_start + + shared_start = time.perf_counter() + shared_spans = _find_labeled_shared_repeat_spans( + page_text, + blocked_spans=table_spans + numeric_spans + latex_spans + hybrid_spans, + rep_threshold=int(word_rep_threshold), + min_period=int(word_min_period), + window=int(word_window), + analysis_text=page_without_tables_latex_existing, + ) + shared_elapsed = time.perf_counter() - shared_start + + page_total_time = time.perf_counter() - page_start + return { + "spans": table_spans + numeric_spans + latex_spans + hybrid_spans + shared_spans, + "page_noise_metrics": page_noise_metrics, + "char_eval_seconds": char_eval_elapsed, + "table_seconds": table_elapsed, + "numeric_seconds": numeric_elapsed, + "latex_seconds": latex_elapsed, + "hybrid_seconds": hybrid_elapsed, + "shared_repeat_seconds": shared_elapsed, + "total_page_seconds": page_total_time, + } + + +def _render_combined_ocr_page( + page_text: str, + *, + noise_mod: Any, + min_progress_steps: int, + min_repeat_steps: int, + min_same_digit_steps: int, + word_rep_threshold: int, + word_min_period: int, + word_window: int, + mode: str = "debug", +) -> Dict[str, Any]: + """Analyze one OCR page in the shared ownership order. + + The ordering is a policy decision, not an implementation accident: + 1. tables first, because table shells distort every later text pass + 2. numeric second, because numeric progressions should not be stolen by + generic word repetition + 3. LaTeX and hybrid structural passes next, because they operate on more + specialized local structure + 4. shared text repetition last, on the remaining visible surface only + + That ownership model keeps the matcher family specific and reduces the + false positives that appear when a single fuzzy text matcher sees + everything at once. + """ + analysis = _analyze_combined_ocr_page( + page_text, + noise_mod=noise_mod, + min_progress_steps=min_progress_steps, + min_repeat_steps=min_repeat_steps, + min_same_digit_steps=min_same_digit_steps, + word_rep_threshold=word_rep_threshold, + word_min_period=word_min_period, + word_window=word_window, + ) + render_result = _render_page_with_labeled_spans_result( + page_text, + list(analysis["spans"]), + mode=mode, + ) + return { + "annotated_page": render_result["rendered_page"], + "merged_spans": render_result["merged_spans"], + "page_types": render_result["page_types"], + "page_numeric_count": render_result["page_numeric_count"], + "page_word_count": render_result["page_word_count"], + "page_latex_count": render_result["page_latex_count"], + "page_table_count": render_result["page_table_count"], + "page_hybrid_count": render_result["page_hybrid_count"], + **analysis, + } + + +def _render_combined_ocr_page_modes( + page_text: str, + *, + noise_mod: Any, + min_progress_steps: int, + min_repeat_steps: int, + min_same_digit_steps: int, + word_rep_threshold: int, + word_min_period: int, + word_window: int, + modes: Iterable[str], +) -> Dict[str, Any]: + analysis = _analyze_combined_ocr_page( + page_text, + noise_mod=noise_mod, + min_progress_steps=min_progress_steps, + min_repeat_steps=min_repeat_steps, + min_same_digit_steps=min_same_digit_steps, + word_rep_threshold=word_rep_threshold, + word_min_period=word_min_period, + word_window=word_window, + ) + merged_spans = _merge_labeled_raw_spans(page_text, list(analysis["spans"])) + ( + page_types, + page_numeric_count, + page_word_count, + page_latex_count, + page_table_count, + page_hybrid_count, + ) = _summarize_merged_labeled_spans(merged_spans) + rendered_pages = { + str(mode): _render_page_from_merged_labeled_spans(page_text, merged_spans, mode=str(mode)) + for mode in modes + } + return { + "rendered_pages": rendered_pages, + "merged_spans": merged_spans, + "page_types": page_types, + "page_numeric_count": page_numeric_count, + "page_word_count": page_word_count, + "page_latex_count": page_latex_count, + "page_table_count": page_table_count, + "page_hybrid_count": page_hybrid_count, + **analysis, + } + + +def _render_combined_ocr_debug_page( + page_text: str, + *, + noise_mod: Any, + min_progress_steps: int, + min_repeat_steps: int, + min_same_digit_steps: int, + word_rep_threshold: int, + word_min_period: int, + word_window: int, +) -> Dict[str, Any]: + return _render_combined_ocr_page( + page_text, + noise_mod=noise_mod, + min_progress_steps=min_progress_steps, + min_repeat_steps=min_repeat_steps, + min_same_digit_steps=min_same_digit_steps, + word_rep_threshold=word_rep_threshold, + word_min_period=word_min_period, + word_window=word_window, + mode="debug", + ) + + +def _process_combined_ocr_document( + source_path: Path, + *, + clean_output_path: Optional[Path], + debug_output_path: Optional[Path], + noise_mod: Optional[Any], + min_progress_steps: int, + min_repeat_steps: int, + min_same_digit_steps: int, + word_rep_threshold: int, + word_min_period: int, + word_window: int, + include_page_metrics: bool, + include_match_index: bool, +) -> Dict[str, Any]: + if noise_mod is None: + noise_mod = _get_combined_ocr_worker_noise_mod() + text = source_path.read_text(encoding="utf-8") + pages = text.split(PAGE_SPLIT_MARKER) + cleaned_pages: List[str] = [] + debug_pages: List[str] = [] + matched_page_count = 0 + table_match_count = 0 + numeric_match_count = 0 + latex_match_count = 0 + hybrid_match_count = 0 + word_match_count = 0 + doc_match_types: Set[str] = set() + page_metric_rows: List[Dict[str, Any]] = [] + match_index_rows: List[Dict[str, Any]] = [] + + for page_index, page in enumerate(pages, start=1): + if clean_output_path is not None and debug_output_path is not None: + page_result = _render_combined_ocr_page_modes( + page, + noise_mod=noise_mod, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + modes=("clean", "debug"), + ) + cleaned_page = str(page_result["rendered_pages"]["clean"]) + debug_page = str(page_result["rendered_pages"]["debug"]) + elif debug_output_path is not None: + page_result = _render_combined_ocr_page( + page, + noise_mod=noise_mod, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + mode="debug", + ) + cleaned_page = "" + debug_page = str(page_result["annotated_page"]) + else: + page_result = _render_combined_ocr_page( + page, + noise_mod=noise_mod, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + mode="clean", + ) + cleaned_page = str(page_result["annotated_page"]) + debug_page = "" + + merged_spans = list(page_result.get("merged_spans", [])) + page_types = list(page_result["page_types"]) + page_numeric_count = int(page_result["page_numeric_count"]) + page_word_count = int(page_result["page_word_count"]) + page_latex_count = int(page_result["page_latex_count"]) + page_table_count = int(page_result["page_table_count"]) + page_hybrid_count = int(page_result["page_hybrid_count"]) + page_noise_metrics = dict(page_result["page_noise_metrics"]) + char_eval_elapsed = float(page_result["char_eval_seconds"]) + table_elapsed = float(page_result["table_seconds"]) + numeric_elapsed = float(page_result["numeric_seconds"]) + latex_elapsed = float(page_result["latex_seconds"]) + hybrid_elapsed = float(page_result["hybrid_seconds"]) + shared_elapsed = float(page_result["shared_repeat_seconds"]) + page_total_time = float(page_result["total_page_seconds"]) + + if clean_output_path is not None: + cleaned_pages.append(cleaned_page) + if debug_output_path is not None: + debug_pages.append(debug_page) + + page_match_total = ( + page_table_count + page_numeric_count + page_word_count + page_latex_count + page_hybrid_count + ) + if page_match_total: + matched_page_count += 1 + table_match_count += page_table_count + numeric_match_count += page_numeric_count + latex_match_count += page_latex_count + hybrid_match_count += page_hybrid_count + word_match_count += page_word_count + doc_match_types.update(page_types) + + if include_page_metrics: + page_metric_rows.append( + { + "source_path": str(source_path), + "source_stem": source_path.stem, + "page_number": page_index, + "page_index_in_file": page_index, + "total_chars": int(page_noise_metrics.get("total_chars", 0)), + "bad_char_count": int(page_noise_metrics.get("bad_char_count", 0)), + "bad_char_ratio": float(page_noise_metrics.get("bad_char_ratio", 0.0)), + "control_count": int(page_noise_metrics.get("control_count", 0)), + "private_use_count": int(page_noise_metrics.get("private_use_count", 0)), + "cjk_count": int(page_noise_metrics.get("cjk_count", 0)), + "replacement_count": int(page_noise_metrics.get("replacement_count", 0)), + "table_match_count": page_table_count, + "numeric_match_count": page_numeric_count, + "latex_match_count": page_latex_count, + "hybrid_match_count": page_hybrid_count, + "word_match_count": page_word_count, + "match_types": ",".join(page_types), + "char_eval_seconds": char_eval_elapsed, + "table_seconds": table_elapsed, + "numeric_seconds": numeric_elapsed, + "latex_seconds": latex_elapsed, + "hybrid_seconds": hybrid_elapsed, + "shared_repeat_seconds": shared_elapsed, + "total_page_seconds": page_total_time, + } + ) + + if include_match_index: + match_index_rows.extend( + _build_match_index_rows( + page, + merged_spans, + source_path=source_path, + page_number=page_index, + debug_output_path=debug_output_path, + ) + ) + + if clean_output_path is not None: + clean_output_path.write_text(PAGE_SPLIT_MARKER.join(cleaned_pages), encoding="utf-8") + if debug_output_path is not None: + debug_output_path.write_text(PAGE_SPLIT_MARKER.join(debug_pages), encoding="utf-8") + + output_path = debug_output_path or clean_output_path + row = { + "source_path": str(source_path), + "output_path": None if output_path is None else str(output_path), + "clean_output_path": None if clean_output_path is None else str(clean_output_path), + "debug_output_path": None if debug_output_path is None else str(debug_output_path), + "source_stem": source_path.stem, + "base_stem": canonical_stem(source_path.stem), + "page_count": len(pages), + "matched_page_count": matched_page_count, + "table_match_count": table_match_count, + "numeric_match_count": numeric_match_count, + "latex_match_count": latex_match_count, + "hybrid_match_count": hybrid_match_count, + "word_match_count": word_match_count, + "match_count": int(len(match_index_rows)), + "match_types": ",".join(sorted(doc_match_types)), + } + return { + "row": row, + "page_metric_rows": page_metric_rows, + "match_index_rows": match_index_rows, + } + + +def _process_combined_ocr_debug_document( + source_path: Path, + output_path: Path, + *, + noise_mod: Optional[Any], + min_progress_steps: int, + min_repeat_steps: int, + min_same_digit_steps: int, + word_rep_threshold: int, + word_min_period: int, + word_window: int, +) -> Dict[str, Any]: + return _process_combined_ocr_document( + source_path, + clean_output_path=None, + debug_output_path=output_path, + noise_mod=noise_mod, + min_progress_steps=min_progress_steps, + min_repeat_steps=min_repeat_steps, + min_same_digit_steps=min_same_digit_steps, + word_rep_threshold=word_rep_threshold, + word_min_period=word_min_period, + word_window=word_window, + include_page_metrics=True, + include_match_index=True, + ) + + +def _process_combined_ocr_clean_document( + source_path: Path, + output_path: Path, + *, + noise_mod: Optional[Any], + min_progress_steps: int, + min_repeat_steps: int, + min_same_digit_steps: int, + word_rep_threshold: int, + word_min_period: int, + word_window: int, +) -> None: + _process_combined_ocr_document( + source_path, + clean_output_path=output_path, + debug_output_path=None, + noise_mod=noise_mod, + min_progress_steps=min_progress_steps, + min_repeat_steps=min_repeat_steps, + min_same_digit_steps=min_same_digit_steps, + word_rep_threshold=word_rep_threshold, + word_min_period=word_min_period, + word_window=word_window, + include_page_metrics=False, + include_match_index=False, + ) + + +def _process_combined_ocr_debug_document_job( + job: Tuple[str, str, int, int, int, int, int, int] +) -> Dict[str, Any]: + ( + source_path_str, + output_path_str, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + word_rep_threshold, + word_min_period, + word_window, + ) = job + return _process_combined_ocr_debug_document( + Path(source_path_str), + Path(output_path_str), + noise_mod=None, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + ) + + +def _process_combined_ocr_clean_document_job( + job: Tuple[str, str, int, int, int, int, int, int] +) -> None: + ( + source_path_str, + output_path_str, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + word_rep_threshold, + word_min_period, + word_window, + ) = job + _process_combined_ocr_clean_document( + Path(source_path_str), + Path(output_path_str), + noise_mod=None, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + ) + + +def _process_combined_ocr_dual_document_job( + job: Tuple[str, str, str, int, int, int, int, int, int] +) -> Dict[str, Any]: + ( + source_path_str, + clean_output_path_str, + debug_output_path_str, + min_progress_steps, + min_repeat_steps, + min_same_digit_steps, + word_rep_threshold, + word_min_period, + word_window, + ) = job + return _process_combined_ocr_document( + Path(source_path_str), + clean_output_path=Path(clean_output_path_str), + debug_output_path=Path(debug_output_path_str), + noise_mod=None, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + include_page_metrics=True, + include_match_index=True, + ) + + +def _summarize_metric(values: List[float]) -> Dict[str, float]: + if not values: + return {"count": 0, "p50": 0.0, "p95": 0.0, "max": 0.0} + array = np.array(values, dtype=float) + return { + "count": int(array.size), + "p50": float(np.percentile(array, 50)), + "p95": float(np.percentile(array, 95)), + "max": float(array.max()), + } class CleanPhaseMixin: @@ -37,27 +2670,54 @@ def _project_root() -> Path: return candidate return here.parents[2] - def _load_rust_extension(self, module_name: str, manifest_relative: str): - """Import a Rust extension, building it with maturin if necessary.""" + def _load_rust_extension( + self, + module_name: str, + manifest_relative: str, + *, + required_attrs: Optional[Iterable[str]] = None, + ): + """Import a Rust extension, building it with maturin if necessary. + + The load path is intentionally import-first: + - fast path: import an already-built extension and return immediately + - fallback: build in place only if the module is missing or incomplete + + That keeps ordinary OCR runs from paying a `maturin develop` startup tax + in every fresh process while still letting a developer bootstrap a local + checkout without separate setup steps. + """ import importlib - try: - return importlib.import_module(module_name) - except ModuleNotFoundError: - self.logger.warning( - "Rust extension %s missing; attempting in-place build via maturin …", - module_name, - ) + required = tuple(required_attrs or ()) + + def _missing_attrs(module: Any) -> List[str]: + return [attr for attr in required if not hasattr(module, attr)] + + def _build_extension_once() -> None: + if module_name in _RUST_EXTENSION_PREBUILD_ATTEMPTED: + return + _RUST_EXTENSION_PREBUILD_ATTEMPTED.add(module_name) root_dir = self._project_root() manifest = root_dir / manifest_relative if not manifest.exists(): - raise RuntimeError( - f"Cannot locate Cargo manifest for {module_name} at {manifest}" + return + build_env = os.environ.copy() + if sys.prefix != getattr(sys, "base_prefix", sys.prefix): + build_env.setdefault("VIRTUAL_ENV", sys.prefix) + venv_bin = str(Path(sys.prefix) / "bin") + build_env["PATH"] = ( + f"{venv_bin}:{build_env['PATH']}" + if build_env.get("PATH") + else venv_bin ) try: subprocess.run( [sys.executable, "-m", "pip", "install", "maturin>=1.5,<2.0"], check=True, + env=build_env, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, ) subprocess.run( [ @@ -70,12 +2730,114 @@ def _load_rust_extension(self, module_name: str, manifest_relative: str): str(manifest), ], check=True, + env=build_env, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, ) - return importlib.import_module(module_name) + importlib.invalidate_caches() except Exception as build_err: + self.logger.debug( + "Rust prebuild for %s skipped or failed: %s", + module_name, + build_err, + ) + + def _import_module_with_fallback(): + candidates = [module_name] + if "." not in module_name: + candidates.append(f"{module_name}.{module_name}") + + last_error: Optional[Exception] = None + for candidate in candidates: + try: + return importlib.import_module(candidate) + except Exception as err: # pragma: no cover - import surface varies by wheel layout + last_error = err + if last_error is not None: + raise last_error + raise ModuleNotFoundError(module_name) + + needs_build = False + try: + module = _import_module_with_fallback() + missing = _missing_attrs(module) + if not missing: + return _prime_word_repeat_rust_module(module_name, module) + self.logger.warning( + "Rust extension %s is missing required attributes %s; attempting in-place build via maturin …", + module_name, + ", ".join(missing), + ) + needs_build = True + except ModuleNotFoundError: + self.logger.warning( + "Rust extension %s missing; attempting in-place build via maturin …", + module_name, + ) + needs_build = True + + if needs_build: + _build_extension_once() + try: + module = _import_module_with_fallback() + missing = _missing_attrs(module) + if not missing: + return _prime_word_repeat_rust_module(module_name, module) + except ModuleNotFoundError: + pass + + if not needs_build: + raise RuntimeError(f"Unexpected load state for Rust extension {module_name}") + + root_dir = self._project_root() + manifest = root_dir / manifest_relative + if not manifest.exists(): + raise RuntimeError( + f"Cannot locate Cargo manifest for {module_name} at {manifest}" + ) + try: + build_env = os.environ.copy() + if sys.prefix != getattr(sys, "base_prefix", sys.prefix): + build_env.setdefault("VIRTUAL_ENV", sys.prefix) + venv_bin = str(Path(sys.prefix) / "bin") + build_env["PATH"] = ( + f"{venv_bin}:{build_env['PATH']}" + if build_env.get("PATH") + else venv_bin + ) + subprocess.run( + [sys.executable, "-m", "pip", "install", "maturin>=1.5,<2.0"], + check=True, + env=build_env, + ) + subprocess.run( + [ + sys.executable, + "-m", + "maturin", + "develop", + "--release", + "--manifest-path", + str(manifest), + ], + check=True, + env=build_env, + ) + importlib.invalidate_caches() + sys.modules.pop(module_name, None) + if "." not in module_name: + sys.modules.pop(f"{module_name}.{module_name}", None) + module = _import_module_with_fallback() + missing = _missing_attrs(module) + if missing: raise RuntimeError( - f"Automatic build of {module_name} failed: {build_err}" + f"Built {module_name} but it is still missing required attributes: {missing}" ) + return _prime_word_repeat_rust_module(module_name, module) + except Exception as build_err: + raise RuntimeError( + f"Automatic build of {module_name} failed: {build_err}" + ) def _load_metrics_dataframe( self, parquet_path: Path, filenames: Optional[Iterable[str]] = None @@ -115,6 +2877,30 @@ def _merge_metric_dataframe( base_idx.update(update_idx) return base_idx.reset_index(drop=True) + def _resolve_clean_metrics_parquet(self, parquet_schema) -> Path: + parquet_path: Optional[Path] = self._get_cached_metadata_parquet() + if parquet_path is None: + existing_metadata = parquet_schema.find_metadata_parquet(self.input_dir) + if existing_metadata is not None: + parquet_path = self._cache_metadata_parquet(existing_metadata) + if parquet_path is None: + ensured = parquet_schema.ensure_metadata_parquet(self.output_dir) + if ensured is not None: + parquet_path = self._cache_metadata_parquet(ensured) + if parquet_path is None: + ensured = parquet_schema.ensure_metadata_parquet(self.input_dir) + if ensured is not None: + parquet_path = self._cache_metadata_parquet(ensured) + if parquet_path is None: + metadata_target = self.output_dir / "download_results" / "download_results.parquet" + self.logger.info( + "Cleaner: no metadata parquet found; will bootstrap %s when metrics become available.", + metadata_target, + ) + else: + metadata_target = parquet_path + return self._cache_metadata_parquet(metadata_target) + def clean( self, input_dir: Union[str, Path] = None, @@ -156,40 +2942,21 @@ def clean( self.ocr_model_dir = Path(ocr_model_dir) self._load_rust_extension( - "glossapi_rs_cleaner", "rust/glossapi_rs_cleaner/Cargo.toml" + "glossapi_rs_cleaner", + "rust/glossapi_rs_cleaner/Cargo.toml", + required_attrs=("run_complete_pipeline",), ) self.logger.info("Using compiled glossapi_rs_cleaner extension for fast cleaning") # Ensure cleaned directory exists and is empty (idempotent runs) - if write_cleaned_files: - if self.cleaned_markdown_dir.exists(): - shutil.rmtree(self.cleaned_markdown_dir) - self.cleaned_markdown_dir.mkdir(parents=True, exist_ok=True) - - # Prepare parquet helper - parquet_schema = ParquetSchema({"url_column": self.url_column}) - parquet_path: Optional[Path] = self._get_cached_metadata_parquet() - if parquet_path is None: - existing_metadata = parquet_schema.find_metadata_parquet(self.input_dir) - if existing_metadata is not None: - parquet_path = self._cache_metadata_parquet(existing_metadata) - if parquet_path is None: - ensured = parquet_schema.ensure_metadata_parquet(self.output_dir) - if ensured is not None: - parquet_path = self._cache_metadata_parquet(ensured) - if parquet_path is None: - ensured = parquet_schema.ensure_metadata_parquet(self.input_dir) - if ensured is not None: - parquet_path = self._cache_metadata_parquet(ensured) - if parquet_path is None: - metadata_target = self.output_dir / "download_results" / "download_results.parquet" - self.logger.info( - "Cleaner: no metadata parquet found; will bootstrap %s when metrics become available.", - metadata_target, - ) - else: - metadata_target = parquet_path - parquet_path = self._cache_metadata_parquet(metadata_target) + if write_cleaned_files: + if self.cleaned_markdown_dir.exists(): + shutil.rmtree(self.cleaned_markdown_dir) + self.cleaned_markdown_dir.mkdir(parents=True, exist_ok=True) + + # Prepare parquet helper + parquet_schema = ParquetSchema({"url_column": self.url_column}) + parquet_path = self._resolve_clean_metrics_parquet(parquet_schema) import os records: list = [] # will hold metrics for parquet merge @@ -224,6 +2991,18 @@ def _page_count_for(stem: str) -> Optional[int]: report_parquet_path = self.cleaned_markdown_dir.parent / "cleaning_report.parquet" md_files = sorted(input_dir.glob("*.md")) + if md_files: + # Skip per-page-range chunk markdown when the canonical merged + # doc.md exists alongside doc__pNNNNN-NNNNN.md outputs from the + # OCR runner. Cleaning both would double-count the same content. + canonical_files = {canonical_stem(path.name): path for path in md_files if "__p" not in path.stem} + if canonical_files: + filtered_md_files = [] + for path in md_files: + if "__p" in path.stem and canonical_stem(path.name) in canonical_files: + continue + filtered_md_files.append(path) + md_files = filtered_md_files total_files = len(md_files) self.logger.info( @@ -346,6 +3125,8 @@ def finalize(self) -> None: stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, + encoding="utf-8", + errors="replace", bufsize=1, ) try: @@ -445,7 +3226,9 @@ def finalize(self) -> None: try: self.logger.info("Scoring cleaned markdown files with glossapi_rs_noise …") noise_mod = self._load_rust_extension( - "glossapi_rs_noise", "rust/glossapi_rs_noise/Cargo.toml" + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=("score_markdown_directory_detailed",), ) results = noise_mod.score_markdown_directory_detailed( str(self.cleaned_markdown_dir), os.cpu_count() @@ -700,6 +3483,1143 @@ def _merge_reason(value: str) -> str: if write_cleaned_files: self.markdown_dir = self.cleaned_markdown_dir + def clean_ocr( + self, + input_dir: Union[str, Path] = None, + num_threads: int = None, + drop_bad: bool = False, + *, + min_repeat_run: int = 6, + write_cleaned_files: bool = True, + write_debug_files: bool = False, + debug_output_dir: Union[str, Path, None] = None, + min_progress_steps: int = 10, + min_repeat_steps: int = 8, + min_same_digit_steps: int = 10, + word_rep_threshold: int = 4, + word_min_period: int = 3, + word_window: int = DEFAULT_OCR_WORD_REPEAT_WINDOW, + ) -> None: + """Clean OCR markdown with the shared page loop and update OCR-noise metrics. + + The OCR profile keeps the existing canonical script metrics columns + (`percentage_greek`, `latin_percentage`, `polytonic_ratio`) and adds + OCR-specific noise diagnostics. The same combined page analyzer drives + both clean and debug outputs: + - clean mode writes pipeline-ready markdown to ``self.cleaned_markdown_dir`` + - debug mode writes annotated markdown and a structured match index under + ``debug_output_dir`` (default: ``self.output_dir / "debug"``) + """ + from glossapi.parquet_schema import ParquetSchema + + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + parquet_schema = ParquetSchema({"url_column": self.url_column}) + parquet_path = self._resolve_clean_metrics_parquet(parquet_schema) + parquet_path.parent.mkdir(parents=True, exist_ok=True) + + noise_mod = self._load_rust_extension( + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=( + "score_markdown_directory_ocr_profile", + "find_numeric_debug_page_spans", + "evaluate_page_character_noise", + ), + ) + n_threads = int(num_threads or os.cpu_count() or 4) + render_workers = _default_combined_ocr_render_workers( + noise_mod=noise_mod, + requested_workers=None, + max_workers=n_threads, + ) + md_files = sorted(input_dir.glob("*.md")) + if md_files: + # Skip per-page-range chunk markdown when the canonical merged + # doc.md exists alongside doc__pNNNNN-NNNNN.md outputs from the + # OCR runner. Cleaning both would double-count the same content. + canonical_files = {canonical_stem(path.name): path for path in md_files if "__p" not in path.stem} + if canonical_files: + filtered_md_files = [] + for path in md_files: + if "__p" in path.stem and canonical_stem(path.name) in canonical_files: + continue + filtered_md_files.append(path) + md_files = filtered_md_files + debug_dir: Optional[Path] = None + debug_manifest_path: Optional[Path] = None + debug_page_metrics_path: Optional[Path] = None + debug_match_index_path: Optional[Path] = None + debug_summary_path: Optional[Path] = None + if write_debug_files: + debug_dir = Path(debug_output_dir) if debug_output_dir is not None else (self.output_dir / "debug") + if debug_dir.exists(): + shutil.rmtree(debug_dir) + debug_dir.mkdir(parents=True, exist_ok=True) + debug_manifest_path = debug_dir / "manifest.jsonl" + debug_page_metrics_path = debug_dir / "page_metrics.jsonl" + debug_match_index_path = debug_dir / "match_index.jsonl" + debug_summary_path = debug_dir / "summary.json" + + if write_cleaned_files: + if self.cleaned_markdown_dir.exists(): + shutil.rmtree(self.cleaned_markdown_dir) + self.cleaned_markdown_dir.mkdir(parents=True, exist_ok=True) + + if write_cleaned_files or write_debug_files: + mode_label = "clean+debug" if write_cleaned_files and write_debug_files else ("debug" if write_debug_files else "clean") + self.logger.info( + "Running shared OCR %s loop over %d markdown files (workers=%d)…", + mode_label, + len(md_files), + render_workers, + ) + + if write_debug_files: + rows: List[Dict[str, Any]] = [] + total_page_times: List[float] = [] + table_page_times: List[float] = [] + numeric_page_times: List[float] = [] + latex_page_times: List[float] = [] + shared_page_times: List[float] = [] + hybrid_page_times: List[float] = [] + char_eval_times: List[float] = [] + bad_char_ratios: List[float] = [] + + def _consume_debug_doc_result( + doc_result: Dict[str, Any], + *, + page_metrics_handle: Any, + match_index_handle: Any, + ) -> None: + rows.append(dict(doc_result["row"])) + for page_row in doc_result["page_metric_rows"]: + page_metrics_handle.write(json.dumps(page_row, ensure_ascii=False)) + page_metrics_handle.write("\n") + total_page_times.append(float(page_row["total_page_seconds"])) + table_page_times.append(float(page_row["table_seconds"])) + numeric_page_times.append(float(page_row["numeric_seconds"])) + latex_page_times.append(float(page_row["latex_seconds"])) + hybrid_page_times.append(float(page_row["hybrid_seconds"])) + shared_page_times.append(float(page_row["shared_repeat_seconds"])) + char_eval_times.append(float(page_row["char_eval_seconds"])) + bad_char_ratios.append(float(page_row["bad_char_ratio"])) + for match_row in doc_result["match_index_rows"]: + match_index_handle.write(json.dumps(match_row, ensure_ascii=False)) + match_index_handle.write("\n") + + if _can_use_combined_ocr_process_pool(noise_mod, render_workers): + if write_cleaned_files: + jobs = [ + ( + str(source_path), + str(self.cleaned_markdown_dir / source_path.name), + str(debug_dir / source_path.name), + int(min_progress_steps), + int(min_repeat_steps), + int(min_same_digit_steps), + int(word_rep_threshold), + int(word_min_period), + int(word_window), + ) + for source_path in md_files + ] + else: + jobs = [ + ( + str(source_path), + str(debug_dir / source_path.name), + int(min_progress_steps), + int(min_repeat_steps), + int(min_same_digit_steps), + int(word_rep_threshold), + int(word_min_period), + int(word_window), + ) + for source_path in md_files + ] + with debug_page_metrics_path.open("w", encoding="utf-8") as page_metrics_handle, debug_match_index_path.open("w", encoding="utf-8") as match_index_handle: + with _combined_ocr_process_pool_warning_ctx(): + with ProcessPoolExecutor( + max_workers=render_workers, + mp_context=mp.get_context("fork"), + initializer=_init_combined_ocr_worker, + ) as executor: + if write_cleaned_files: + iterator = executor.map(_process_combined_ocr_dual_document_job, jobs) + else: + iterator = executor.map(_process_combined_ocr_debug_document_job, jobs) + for doc_result in iterator: + _consume_debug_doc_result( + doc_result, + page_metrics_handle=page_metrics_handle, + match_index_handle=match_index_handle, + ) + else: + if write_cleaned_files: + def _run_dual_doc(source_path: Path) -> Dict[str, Any]: + return _process_combined_ocr_document( + source_path, + clean_output_path=self.cleaned_markdown_dir / source_path.name, + debug_output_path=debug_dir / source_path.name, + noise_mod=noise_mod, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + include_page_metrics=True, + include_match_index=True, + ) + run_doc = _run_dual_doc + else: + def _run_debug_doc(source_path: Path) -> Dict[str, Any]: + return _process_combined_ocr_debug_document( + source_path, + debug_dir / source_path.name, + noise_mod=noise_mod, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + ) + run_doc = _run_debug_doc + + with debug_page_metrics_path.open("w", encoding="utf-8") as page_metrics_handle, debug_match_index_path.open("w", encoding="utf-8") as match_index_handle: + with ThreadPoolExecutor(max_workers=render_workers) as executor: + for doc_result in executor.map(run_doc, md_files): + _consume_debug_doc_result( + doc_result, + page_metrics_handle=page_metrics_handle, + match_index_handle=match_index_handle, + ) + + with debug_manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + debug_summary = { + "doc_count": len(rows), + "matched_doc_count": sum(1 for row in rows if int(row["matched_page_count"]) > 0), + "matched_page_count": int(sum(int(row["matched_page_count"]) for row in rows)), + "match_count": int(sum(int(row.get("match_count", 0)) for row in rows)), + "table_match_count": int(sum(int(row["table_match_count"]) for row in rows)), + "numeric_match_count": int(sum(int(row["numeric_match_count"]) for row in rows)), + "latex_match_count": int(sum(int(row["latex_match_count"]) for row in rows)), + "hybrid_match_count": int(sum(int(row["hybrid_match_count"]) for row in rows)), + "word_match_count": int(sum(int(row["word_match_count"]) for row in rows)), + "word_rep_threshold": int(word_rep_threshold), + "word_min_period": int(word_min_period), + "word_window": int(word_window), + "total_page_seconds": _summarize_metric(total_page_times), + "table_seconds": _summarize_metric(table_page_times), + "numeric_seconds": _summarize_metric(numeric_page_times), + "latex_seconds": _summarize_metric(latex_page_times), + "hybrid_seconds": _summarize_metric(hybrid_page_times), + "shared_repeat_seconds": _summarize_metric(shared_page_times), + "char_eval_seconds": _summarize_metric(char_eval_times), + "bad_char_ratio": _summarize_metric(bad_char_ratios), + } + debug_summary_path.write_text(json.dumps(debug_summary, ensure_ascii=False, indent=2), encoding="utf-8") + else: + if _can_use_combined_ocr_process_pool(noise_mod, render_workers): + jobs = [ + ( + str(source_path), + str(self.cleaned_markdown_dir / source_path.name), + int(min_progress_steps), + int(min_repeat_steps), + int(min_same_digit_steps), + int(word_rep_threshold), + int(word_min_period), + int(word_window), + ) + for source_path in md_files + ] + with _combined_ocr_process_pool_warning_ctx(): + with ProcessPoolExecutor( + max_workers=render_workers, + mp_context=mp.get_context("fork"), + initializer=_init_combined_ocr_worker, + ) as executor: + list(executor.map(_process_combined_ocr_clean_document_job, jobs)) + else: + def _run_clean_doc(source_path: Path) -> None: + _process_combined_ocr_clean_document( + source_path, + self.cleaned_markdown_dir / source_path.name, + noise_mod=noise_mod, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + ) + + with ThreadPoolExecutor(max_workers=render_workers) as executor: + list(executor.map(_run_clean_doc, md_files)) + + + self.logger.info( + "Scoring OCR markdown files with glossapi_rs_noise OCR profile on %d markdown files…", + len(md_files), + ) + + results = noise_mod.score_markdown_directory_ocr_profile( + str(input_dir), + n_threads, + int(min_repeat_run), + ) + df_updates = pd.DataFrame(list(results)) + if df_updates.empty: + self.good_files = [] + self.logger.info("OCR cleaning found no markdown files under %s", input_dir) + return + + df_updates["filename"] = df_updates["path"].apply( + lambda value: f"{Path(str(value)).stem}.pdf" + ) + df_updates["polytonic_ratio"] = pd.to_numeric( + df_updates["polytonic_ratio"], errors="coerce" + ).round(2) + df_updates["percentage_greek"] = pd.to_numeric( + df_updates["percentage_greek"], errors="coerce" + ).round(3) + df_updates["latin_percentage"] = pd.to_numeric( + df_updates["latin_percentage"], errors="coerce" + ).round(3) + df_updates["ocr_repeat_suspicious_line_ratio"] = pd.to_numeric( + df_updates["ocr_repeat_suspicious_line_ratio"], errors="coerce" + ).round(4) + df_updates["ocr_noise_flags"] = ( + df_updates["ocr_noise_flags"].fillna("").astype(str) + ) + + update_columns = [ + "filename", + "percentage_greek", + "latin_percentage", + "polytonic_ratio", + "ocr_noise_suspect", + "ocr_noise_flags", + "ocr_repeat_phrase_run_max", + "ocr_repeat_line_run_max", + "ocr_repeat_suspicious_line_count", + "ocr_repeat_suspicious_line_ratio", + ] + + df = self._load_metrics_dataframe(parquet_path, df_updates.get("filename")) + self._ensure_metric_columns( + df, + { + "filter": "ok", + "percentage_greek": pd.NA, + "latin_percentage": pd.NA, + "polytonic_ratio": pd.NA, + "ocr_noise_suspect": False, + "ocr_noise_flags": "", + "ocr_repeat_phrase_run_max": pd.NA, + "ocr_repeat_line_run_max": pd.NA, + "ocr_repeat_suspicious_line_count": pd.NA, + "ocr_repeat_suspicious_line_ratio": pd.NA, + }, + ) + df = self._merge_metric_dataframe(df, df_updates[update_columns]) + + if "filter" not in df.columns: + df["filter"] = "ok" + else: + df["filter"] = df["filter"].fillna("ok").astype(str) + + suspect_mask = df["ocr_noise_suspect"].fillna(False).astype(bool) + if bool(suspect_mask.any()): + current = df.loc[suspect_mask, "filter"].astype(str) + + def _append_ocr_noise(value: str) -> str: + if value == "ok" or not value: + return "ocr_noise" + tokens = [token for token in value.split(";") if token] + if "ocr_noise" not in tokens: + tokens.append("ocr_noise") + return ";".join(tokens) + + df.loc[suspect_mask, "filter"] = current.apply(_append_ocr_noise) + + parquet_schema.write_metadata_parquet(df, parquet_path) + self.logger.info("OCR metrics updated in %s", parquet_path) + + filenames = df.get("filename", pd.Series(dtype=str)) + if drop_bad: + good_df = df[~df["ocr_noise_suspect"].fillna(False).astype(bool)] + filenames = good_df.get("filename", pd.Series(dtype=str)) + self.logger.info( + "After OCR filtering, %d good files remain", + len(filenames.dropna()), + ) + self.good_files = [canonical_stem(f) for f in filenames.dropna().astype(str).tolist()] + if write_cleaned_files: + self.markdown_dir = self.cleaned_markdown_dir + + def clean_ocr_debug( + self, + output_dir: Union[str, Path], + input_dir: Union[str, Path] = None, + num_threads: int = None, + *, + min_repeat_run: int = 6, + max_pages: Optional[int] = 1000, + sample_seed: int = 0, + ) -> List[Dict[str, Any]]: + """Export page-level OCR debug files for repeated-pattern matches. + + Only pages that contain OCR repetition matches are exported. Each output page + contains inline `...` tags around the matched spans. + """ + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for stale in output_dir.glob("*.md"): + stale.unlink() + manifest_path = output_dir / "manifest.jsonl" + if manifest_path.exists(): + manifest_path.unlink() + + noise_mod = self._load_rust_extension( + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=("export_ocr_match_debug_pages",), + ) + n_threads = int(num_threads or os.cpu_count() or 4) + self.logger.info( + "Exporting OCR debug matches from %s into %s with glossapi_rs_noise…", + input_dir, + output_dir, + ) + + rows = list( + noise_mod.export_ocr_match_debug_pages( + str(input_dir), + str(output_dir), + n_threads, + int(min_repeat_run), + None if max_pages is None else int(max_pages), + int(sample_seed), + ) + ) + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(dict(row), ensure_ascii=False)) + handle.write("\n") + + self.logger.info( + "Exported %d OCR debug pages with matches to %s", + len(rows), + output_dir, + ) + return [dict(row) for row in rows] + + def clean_ocr_numeric_debug( + self, + output_dir: Union[str, Path], + input_dir: Union[str, Path] = None, + num_threads: int = None, + *, + min_progress_steps: int = 10, + min_repeat_steps: int = 8, + min_same_digit_steps: int = 10, + max_pages: Optional[int] = 1000, + sample_seed: int = 0, + ) -> List[Dict[str, Any]]: + """Export page-level OCR debug files for numeric-only collapse patterns.""" + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for stale in output_dir.glob("*.md"): + stale.unlink() + manifest_path = output_dir / "manifest.jsonl" + if manifest_path.exists(): + manifest_path.unlink() + + noise_mod = self._load_rust_extension( + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=("export_numeric_match_debug_pages",), + ) + n_threads = int(num_threads or os.cpu_count() or 4) + self.logger.info( + "Exporting OCR numeric debug matches from %s into %s with glossapi_rs_noise…", + input_dir, + output_dir, + ) + + rows = list( + noise_mod.export_numeric_match_debug_pages( + str(input_dir), + str(output_dir), + n_threads, + int(min_progress_steps), + int(min_repeat_steps), + int(min_same_digit_steps), + None if max_pages is None else int(max_pages), + int(sample_seed), + ) + ) + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(dict(row), ensure_ascii=False)) + handle.write("\n") + + self.logger.info( + "Exported %d OCR numeric debug pages with matches to %s", + len(rows), + output_dir, + ) + return [dict(row) for row in rows] + + def clean_token_category_debug( + self, + output_dir: Union[str, Path], + category_specs_path: Union[str, Path], + input_dir: Union[str, Path] = None, + num_threads: int = None, + *, + max_pages: Optional[int] = 1000, + sample_seed: int = 0, + synthetic_page_target_chars: int = 4000, + synthetic_page_min_header_chars: int = 1200, + synthetic_page_hard_max_chars: int = 6000, + ) -> List[Dict[str, Any]]: + """Export synthetic-page debug files for token/category review experiments. + + This is the debug substrate for token-noise and normalization review work. + It mirrors the OCR debug workflow style: Rust-backed matching, annotated + debug pages, manifest output, and a compact summary for later review steps. + """ + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for stale in output_dir.glob("*.md"): + stale.unlink() + manifest_path = output_dir / "manifest.jsonl" + if manifest_path.exists(): + manifest_path.unlink() + page_metrics_path = output_dir / "page_metrics.jsonl" + if page_metrics_path.exists(): + page_metrics_path.unlink() + match_index_path = output_dir / "match_index.jsonl" + if match_index_path.exists(): + match_index_path.unlink() + summary_path = output_dir / "summary.json" + if summary_path.exists(): + summary_path.unlink() + + category_specs_path = Path(category_specs_path) + noise_mod = self._load_rust_extension( + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=("export_token_category_debug_pages",), + ) + n_threads = int(num_threads or os.cpu_count() or 4) + self.logger.info( + "Exporting token category debug pages from %s into %s using specs %s with glossapi_rs_noise…", + input_dir, + output_dir, + category_specs_path, + ) + + rows = list( + noise_mod.export_token_category_debug_pages( + str(input_dir), + str(output_dir), + str(category_specs_path), + n_threads, + None if max_pages is None else int(max_pages), + int(sample_seed), + int(synthetic_page_target_chars), + int(synthetic_page_min_header_chars), + int(synthetic_page_hard_max_chars), + ) + ) + + manifest_rows: List[Dict[str, Any]] = [] + page_metric_rows: List[Dict[str, Any]] = [] + match_index_rows: List[Dict[str, Any]] = [] + category_page_counter: Counter[str] = Counter() + category_match_counter: Counter[str] = Counter() + pattern_family_page_counter: Counter[str] = Counter() + pattern_family_match_counter: Counter[str] = Counter() + page_kind_counter: Counter[str] = Counter() + + for raw_row in rows: + row = dict(raw_row) + page_text = str(row.pop("page_text", "")) + matches = json.loads(str(row.pop("matches_json", "[]"))) + manifest_rows.append(row) + page_metric_rows.append(_build_token_category_page_metric_row(row, matches)) + match_index_rows.extend( + _build_token_category_match_index_rows(page_text, matches, page_row=row) + ) + page_kind_counter[str(row.get("page_kind", ""))] += 1 + for category in str(row.get("match_categories", "")).split(","): + if category: + category_page_counter[category] += 1 + for family in str(row.get("match_pattern_families", "")).split(","): + if family: + pattern_family_page_counter[family] += 1 + for match in matches: + for category in list(match.get("categories") or []): + category_match_counter[str(category)] += 1 + for family in list(match.get("pattern_families") or []): + pattern_family_match_counter[str(family)] += 1 + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in manifest_rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + with page_metrics_path.open("w", encoding="utf-8") as handle: + for row in page_metric_rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + with match_index_path.open("w", encoding="utf-8") as handle: + for row in match_index_rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + summary = { + "input_dir": str(input_dir), + "output_dir": str(output_dir), + "category_specs_path": str(category_specs_path), + "page_count": len(manifest_rows), + "match_count": int(len(match_index_rows)), + "category_page_counts": dict(category_page_counter), + "category_match_counts": dict(category_match_counter), + "pattern_family_page_counts": dict(pattern_family_page_counter), + "pattern_family_match_counts": dict(pattern_family_match_counter), + "page_kind_counts": dict(page_kind_counter), + "synthetic_page_target_chars": int(synthetic_page_target_chars), + "synthetic_page_min_header_chars": int(synthetic_page_min_header_chars), + "synthetic_page_hard_max_chars": int(synthetic_page_hard_max_chars), + } + summary_path.write_text( + json.dumps(summary, ensure_ascii=False, indent=2), + encoding="utf-8", + ) + + self.logger.info( + "Exported %d token category debug pages with matches to %s", + len(manifest_rows), + output_dir, + ) + return manifest_rows + + def clean_ocr_numeric_word_debug_docs( + self, + output_dir: Union[str, Path], + input_dir: Union[str, Path] = None, + *, + max_docs: Optional[int] = 100, + doc_offset: int = 0, + doc_workers: Optional[int] = None, + min_progress_steps: int = 10, + min_repeat_steps: int = 8, + min_same_digit_steps: int = 10, + word_rep_threshold: int = 4, + word_min_period: int = 3, + word_window: int = DEFAULT_OCR_WORD_REPEAT_WINDOW, + ) -> List[Dict[str, Any]]: + """Annotate complete markdown documents with table, numeric, LaTeX, hybrid, then shared-repeat matches. + + Default repetition threshold for both word and LaTeX repeat detection is 4. + """ + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for stale in output_dir.glob("*.md"): + stale.unlink() + manifest_path = output_dir / "manifest.jsonl" + if manifest_path.exists(): + manifest_path.unlink() + page_metrics_path = output_dir / "page_metrics.jsonl" + if page_metrics_path.exists(): + page_metrics_path.unlink() + match_index_path = output_dir / "match_index.jsonl" + if match_index_path.exists(): + match_index_path.unlink() + summary_path = output_dir / "summary.json" + if summary_path.exists(): + summary_path.unlink() + + noise_mod = self._load_rust_extension( + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=("find_numeric_debug_page_spans", "evaluate_page_character_noise"), + ) + + all_source_paths = sorted(input_dir.glob("*.md")) + doc_offset = max(0, int(doc_offset)) + if max_docs is not None: + source_paths = all_source_paths[doc_offset : doc_offset + int(max_docs)] + else: + source_paths = all_source_paths[doc_offset:] + render_workers = _default_combined_ocr_render_workers( + noise_mod=noise_mod, + requested_workers=doc_workers, + max_workers=int(os.cpu_count() or 1), + ) + + self.logger.info( + "Exporting combined OCR table+numeric+latex+hybrid+word debug docs from %s into %s for %d documents (offset=%d, workers=%d)", + input_dir, + output_dir, + len(source_paths), + doc_offset, + render_workers, + ) + + rows: List[Dict[str, Any]] = [] + total_page_times: List[float] = [] + table_page_times: List[float] = [] + numeric_page_times: List[float] = [] + latex_page_times: List[float] = [] + shared_page_times: List[float] = [] + hybrid_page_times: List[float] = [] + char_eval_times: List[float] = [] + bad_char_ratios: List[float] = [] + def _consume_doc_result( + doc_result: Dict[str, Any], + *, + page_metrics_handle: Any, + match_index_handle: Any, + ) -> None: + rows.append(dict(doc_result["row"])) + for page_row in doc_result["page_metric_rows"]: + page_metrics_handle.write(json.dumps(page_row, ensure_ascii=False)) + page_metrics_handle.write("\n") + total_page_times.append(float(page_row["total_page_seconds"])) + table_page_times.append(float(page_row["table_seconds"])) + numeric_page_times.append(float(page_row["numeric_seconds"])) + latex_page_times.append(float(page_row["latex_seconds"])) + hybrid_page_times.append(float(page_row["hybrid_seconds"])) + shared_page_times.append(float(page_row["shared_repeat_seconds"])) + char_eval_times.append(float(page_row["char_eval_seconds"])) + bad_char_ratios.append(float(page_row["bad_char_ratio"])) + for match_row in doc_result["match_index_rows"]: + match_index_handle.write(json.dumps(match_row, ensure_ascii=False)) + match_index_handle.write("\n") + if _can_use_combined_ocr_process_pool(noise_mod, render_workers): + jobs = [ + ( + str(source_path), + str(output_dir / source_path.name), + int(min_progress_steps), + int(min_repeat_steps), + int(min_same_digit_steps), + int(word_rep_threshold), + int(word_min_period), + int(word_window), + ) + for source_path in source_paths + ] + iterator: Iterable[Dict[str, Any]] + with page_metrics_path.open("w", encoding="utf-8") as page_metrics_handle, match_index_path.open("w", encoding="utf-8") as match_index_handle: + with _combined_ocr_process_pool_warning_ctx(): + with ProcessPoolExecutor( + max_workers=render_workers, + # Match the clean-mode executor policy so debug and + # clean keep the same performance shape and worker init. + mp_context=mp.get_context("fork"), + initializer=_init_combined_ocr_worker, + ) as executor: + iterator = executor.map(_process_combined_ocr_debug_document_job, jobs) + for doc_result in iterator: + _consume_doc_result( + doc_result, + page_metrics_handle=page_metrics_handle, + match_index_handle=match_index_handle, + ) + else: + def _run_debug_doc(source_path: Path) -> Dict[str, Any]: + return _process_combined_ocr_debug_document( + source_path, + output_dir / source_path.name, + noise_mod=noise_mod, + min_progress_steps=int(min_progress_steps), + min_repeat_steps=int(min_repeat_steps), + min_same_digit_steps=int(min_same_digit_steps), + word_rep_threshold=int(word_rep_threshold), + word_min_period=int(word_min_period), + word_window=int(word_window), + ) + + with page_metrics_path.open("w", encoding="utf-8") as page_metrics_handle, match_index_path.open("w", encoding="utf-8") as match_index_handle: + with ThreadPoolExecutor(max_workers=render_workers) as executor: + for doc_result in executor.map(_run_debug_doc, source_paths): + _consume_doc_result( + doc_result, + page_metrics_handle=page_metrics_handle, + match_index_handle=match_index_handle, + ) + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + summary = { + "doc_count": len(rows), + "matched_doc_count": sum(1 for row in rows if int(row["matched_page_count"]) > 0), + "matched_page_count": int(sum(int(row["matched_page_count"]) for row in rows)), + "match_count": int(sum(int(row.get("match_count", 0)) for row in rows)), + "table_match_count": int(sum(int(row["table_match_count"]) for row in rows)), + "numeric_match_count": int(sum(int(row["numeric_match_count"]) for row in rows)), + "latex_match_count": int(sum(int(row["latex_match_count"]) for row in rows)), + "hybrid_match_count": int(sum(int(row["hybrid_match_count"]) for row in rows)), + "word_match_count": int(sum(int(row["word_match_count"]) for row in rows)), + "word_rep_threshold": int(word_rep_threshold), + "word_min_period": int(word_min_period), + "word_window": int(word_window), + "total_page_seconds": _summarize_metric(total_page_times), + "table_seconds": _summarize_metric(table_page_times), + "numeric_seconds": _summarize_metric(numeric_page_times), + "latex_seconds": _summarize_metric(latex_page_times), + "hybrid_seconds": _summarize_metric(hybrid_page_times), + "shared_repeat_seconds": _summarize_metric(shared_page_times), + "char_eval_seconds": _summarize_metric(char_eval_times), + "bad_char_ratio": _summarize_metric(bad_char_ratios), + } + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + self.logger.info( + "Exported %d combined OCR debug docs to %s", + len(rows), + output_dir, + ) + return rows + + def clean_ocr_hybrid_debug( + self, + output_dir: Union[str, Path], + input_dir: Union[str, Path] = None, + *, + max_docs: Optional[int] = 100, + doc_offset: int = 0, + ) -> List[Dict[str, Any]]: + """Export only matched pages for local hybrid numbered repetitions.""" + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for stale in output_dir.glob("*.md"): + stale.unlink() + manifest_path = output_dir / "manifest.jsonl" + if manifest_path.exists(): + manifest_path.unlink() + summary_path = output_dir / "summary.json" + if summary_path.exists(): + summary_path.unlink() + + all_source_paths = sorted(list(Path(input_dir).glob("*.md")) + list(Path(input_dir).glob("*.txt"))) + doc_offset = max(0, int(doc_offset)) + if max_docs is not None: + source_paths = all_source_paths[doc_offset : doc_offset + int(max_docs)] + else: + source_paths = all_source_paths[doc_offset:] + + self.logger.info( + "Exporting hybrid OCR debug pages from %s into %s for %d documents (offset=%d)", + input_dir, + output_dir, + len(source_paths), + doc_offset, + ) + + rows: List[Dict[str, Any]] = [] + page_times: List[float] = [] + + for source_path in source_paths: + text = source_path.read_text(encoding="utf-8") + pages = text.split(PAGE_SPLIT_MARKER) + for page_index, page in enumerate(pages, start=1): + page_start = time.perf_counter() + hybrid_spans = _find_hybrid_numbered_repeat_spans(page, blocked_spans=[]) + page_elapsed = time.perf_counter() - page_start + page_times.append(page_elapsed) + if not hybrid_spans: + continue + + annotated_page, page_types, _, _, _, _, _ = _annotate_page_with_labeled_spans( + page, + hybrid_spans, + ) + hybrid_count = _count_hybrid_matches_in_page(page, hybrid_spans) + output_name = f"{source_path.stem}__debug_page_{page_index:05d}.md" + output_path = output_dir / output_name + output_path.write_text(annotated_page, encoding="utf-8") + rows.append( + { + "source_path": str(source_path), + "output_path": str(output_path), + "source_stem": source_path.stem, + "base_stem": canonical_stem(source_path.stem), + "page_number": page_index, + "page_index_in_file": page_index, + "hybrid_match_count": hybrid_count, + "match_types": ",".join(page_types), + "page_seconds": page_elapsed, + } + ) + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + summary = { + "doc_count": len(source_paths), + "matched_page_count": len(rows), + "hybrid_match_count": int(sum(int(row["hybrid_match_count"]) for row in rows)), + "page_seconds": _summarize_metric(page_times), + } + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + self.logger.info( + "Exported %d hybrid OCR debug pages to %s", + len(rows), + output_dir, + ) + return rows + + def clean_ocr_latex_slot_progression_debug( + self, + output_dir: Union[str, Path], + input_dir: Union[str, Path] = None, + *, + max_docs: Optional[int] = 1000, + doc_offset: int = 0, + ) -> List[Dict[str, Any]]: + """Export only matched pages for local LaTeX slot-progression runs.""" + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for stale in output_dir.glob("*.md"): + stale.unlink() + manifest_path = output_dir / "manifest.jsonl" + if manifest_path.exists(): + manifest_path.unlink() + summary_path = output_dir / "summary.json" + if summary_path.exists(): + summary_path.unlink() + + all_source_paths = sorted(list(Path(input_dir).glob("*.md")) + list(Path(input_dir).glob("*.txt"))) + doc_offset = max(0, int(doc_offset)) + if max_docs is not None: + source_paths = all_source_paths[doc_offset : doc_offset + int(max_docs)] + else: + source_paths = all_source_paths[doc_offset:] + + self.logger.info( + "Exporting LaTeX slot-progression debug pages from %s into %s for %d documents (offset=%d)", + input_dir, + output_dir, + len(source_paths), + doc_offset, + ) + + rows: List[Dict[str, Any]] = [] + page_times: List[float] = [] + + for source_path in source_paths: + text = source_path.read_text(encoding="utf-8") + pages = text.split(PAGE_SPLIT_MARKER) + for page_index, page in enumerate(pages, start=1): + page_start = time.perf_counter() + latex_spans = _find_latex_slot_progression_spans(page, blocked_spans=[]) + page_elapsed = time.perf_counter() - page_start + page_times.append(page_elapsed) + if not latex_spans: + continue + + annotated_page, page_types, _, _, latex_count, _, _ = _annotate_page_with_labeled_spans( + page, + latex_spans, + ) + output_name = f"{source_path.stem}__debug_page_{page_index:05d}.md" + output_path = output_dir / output_name + output_path.write_text(annotated_page, encoding="utf-8") + rows.append( + { + "source_path": str(source_path), + "output_path": str(output_path), + "source_stem": source_path.stem, + "base_stem": canonical_stem(source_path.stem), + "page_number": page_index, + "page_index_in_file": page_index, + "latex_match_count": latex_count, + "match_types": ",".join(page_types), + "page_seconds": page_elapsed, + } + ) + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + summary = { + "doc_count": len(source_paths), + "matched_page_count": len(rows), + "latex_match_count": int(sum(int(row["latex_match_count"]) for row in rows)), + "page_seconds": _summarize_metric(page_times), + } + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + self.logger.info( + "Exported %d LaTeX slot-progression debug pages to %s", + len(rows), + output_dir, + ) + return rows + + def clean_ocr_latex_debug( + self, + output_dir: Union[str, Path], + input_dir: Union[str, Path] = None, + *, + max_docs: Optional[int] = 1000, + doc_offset: int = 0, + word_rep_threshold: int = 4, + word_min_period: int = 3, + word_window: int = DEFAULT_OCR_WORD_REPEAT_WINDOW, + ) -> List[Dict[str, Any]]: + """Export only matched pages for all LaTeX repeat classes.""" + if input_dir is None: + input_dir = self.markdown_dir + else: + input_dir = Path(input_dir) + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + for stale in output_dir.glob("*.md"): + stale.unlink() + manifest_path = output_dir / "manifest.jsonl" + if manifest_path.exists(): + manifest_path.unlink() + summary_path = output_dir / "summary.json" + if summary_path.exists(): + summary_path.unlink() + + all_source_paths = sorted(list(Path(input_dir).glob("*.md")) + list(Path(input_dir).glob("*.txt"))) + doc_offset = max(0, int(doc_offset)) + if max_docs is not None: + source_paths = all_source_paths[doc_offset : doc_offset + int(max_docs)] + else: + source_paths = all_source_paths[doc_offset:] + + self.logger.info( + "Exporting LaTeX debug pages from %s into %s for %d documents (offset=%d)", + input_dir, + output_dir, + len(source_paths), + doc_offset, + ) + + rows: List[Dict[str, Any]] = [] + page_times: List[float] = [] + + for source_path in source_paths: + text = source_path.read_text(encoding="utf-8") + pages = text.split(PAGE_SPLIT_MARKER) + for page_index, page in enumerate(pages, start=1): + page_start = time.perf_counter() + latex_spans = _find_latex_repeat_spans( + page, + blocked_spans=[], + rep_threshold=int(word_rep_threshold), + min_period=int(word_min_period), + window=int(word_window), + ) + page_elapsed = time.perf_counter() - page_start + page_times.append(page_elapsed) + if not latex_spans: + continue + + annotated_page, page_types, _, _, latex_count, _, _ = _annotate_page_with_labeled_spans( + page, + latex_spans, + ) + output_name = f"{source_path.stem}__debug_page_{page_index:05d}.md" + output_path = output_dir / output_name + output_path.write_text(annotated_page, encoding="utf-8") + rows.append( + { + "source_path": str(source_path), + "output_path": str(output_path), + "source_stem": source_path.stem, + "base_stem": canonical_stem(source_path.stem), + "page_number": page_index, + "page_index_in_file": page_index, + "latex_match_count": latex_count, + "match_types": ",".join(page_types), + "page_seconds": page_elapsed, + } + ) + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + summary = { + "doc_count": len(source_paths), + "matched_page_count": len(rows), + "latex_match_count": int(sum(int(row["latex_match_count"]) for row in rows)), + "word_rep_threshold": int(word_rep_threshold), + "word_min_period": int(word_min_period), + "word_window": int(word_window), + "page_seconds": _summarize_metric(page_times), + } + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + self.logger.info( + "Exported %d LaTeX debug pages to %s", + len(rows), + output_dir, + ) + return rows + def filter(self, *args, **kwargs): # type: ignore[override] """Deprecated: use :py:meth:`clean` instead. Retained for one release.""" self.logger.warning("Corpus.filter() is deprecated – calling clean() instead") diff --git a/src/glossapi/corpus/phase_download.py b/src/glossapi/corpus/phase_download.py index 38179fd..c543076 100644 --- a/src/glossapi/corpus/phase_download.py +++ b/src/glossapi/corpus/phase_download.py @@ -19,6 +19,7 @@ import pandas as pd from .._naming import canonical_stem +from ..gloss_browser_downloader import BrowserGlossDownloader from ..gloss_downloader import GlossDownloader # Avoid importing section/classifier here; download phase does not use them. from .corpus_skiplist import _SkiplistManager, _resolve_skiplist_path @@ -212,6 +213,22 @@ def _looks_like_list(s: str) -> bool: # Initialize downloader configuration (kwargs take precedence) dl_cfg = dict(self.downloader_config) dl_cfg.update(kwargs) + browser_mode = dl_cfg.pop('browser_mode', None) + if browser_mode is not None and 'download_mode' not in dl_cfg: + dl_cfg['download_mode'] = 'browser' if browser_mode else 'standard' + download_mode = str(dl_cfg.pop('download_mode', 'standard')).strip().lower() + policy_requested = bool(dl_cfg.get('download_policy_file') or dl_cfg.get('download_policy')) + if download_mode in {'standard', 'default', 'http'} and not policy_requested: + downloader_cls = GlossDownloader + default_download_route = 'standard' + elif download_mode in {'browser', 'browser_protected'} or policy_requested: + downloader_cls = BrowserGlossDownloader + default_download_route = 'browser' if download_mode in {'browser', 'browser_protected'} else 'standard' + elif download_mode in {'auto', 'browser_fallback'}: + downloader_cls = BrowserGlossDownloader + default_download_route = 'auto' + else: + raise ValueError(f"Unsupported download_mode: {download_mode}") # Allow caller to override which column holds links if links_column: url_column = links_column @@ -232,14 +249,18 @@ def _looks_like_list(s: str) -> bool: except Exception: pass - downloader = GlossDownloader( - url_column=url_column, - output_dir=str(self.output_dir), - log_level=self.logger.level, - verbose=verbose if verbose is not None else self.verbose, + downloader_kwargs = { + "url_column": url_column, + "output_dir": str(self.output_dir), + "log_level": self.logger.level, + "verbose": verbose if verbose is not None else self.verbose, **{k: v for k, v in dl_cfg.items() if k not in {'input_parquet'}}, - _used_filename_bases=used_bases - ) + "_used_filename_bases": used_bases, + } + if downloader_cls is BrowserGlossDownloader: + downloader_kwargs["default_download_route"] = default_download_route + + downloader = downloader_cls(**downloader_kwargs) # Download files self.logger.info(f"Downloading files from URLs in {input_parquet}...") diff --git a/src/glossapi/corpus/phase_export.py b/src/glossapi/corpus/phase_export.py index 26a6a82..4bcc6a8 100644 --- a/src/glossapi/corpus/phase_export.py +++ b/src/glossapi/corpus/phase_export.py @@ -471,8 +471,6 @@ def _normalize_value(value: Any) -> Any: chunk_paths: List[Path] = entry.get("chunk_paths", []) or [] base_path: Optional[Path] = entry.get("base_path") representative_path: Optional[Path] = base_path - if representative_path is None and chunk_paths: - representative_path = sorted(chunk_paths, key=_chunk_sort_key)[0] base_metadata = metadata_by_stem.get(stem) chunk_metadata = metadata_chunks_by_stem.get(stem, []) if base_metadata is None and not chunk_metadata: @@ -480,17 +478,11 @@ def _normalize_value(value: Any) -> Any: metadata = _aggregate_metadata(stem, base_metadata, chunk_metadata) metadata = {k: _normalize_value(v) for k, v in metadata.items()} original_filename_value = metadata.get("filename") - if chunk_paths: - ordered_chunks = sorted(chunk_paths, key=_chunk_sort_key) - parts: List[str] = [] - for path in ordered_chunks: - parts.append(path.read_text(encoding="utf-8")) - document_text = "\n".join(parts) - elif representative_path is not None: - document_text = representative_path.read_text(encoding="utf-8") - else: + if base_path is None or not base_path.exists(): continue + document_text = base_path.read_text(encoding="utf-8") + filetype = metadata.get("filetype") or metadata.get("file_ext") if not filetype: filename_candidate = original_filename_value or metadata.get("filename") diff --git a/src/glossapi/corpus/phase_extract.py b/src/glossapi/corpus/phase_extract.py index a584eaf..476c3c6 100644 --- a/src/glossapi/corpus/phase_extract.py +++ b/src/glossapi/corpus/phase_extract.py @@ -13,7 +13,7 @@ import sys import time from pathlib import Path -from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union +from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union import numpy as np import pandas as pd @@ -37,6 +37,147 @@ def _maybe_import_torch(force: bool = False): return _maybe_import_torch_fallback(force=force) +def _resolve_docling_max_batch_files(default: int = 1) -> int: + """Resolve the per-worker Docling document batch size for Phase-1 extraction. + + GlossAPI keeps the default conservative because fresh GPU nodes have been + more sensitive to bootstrap/runtime drift than to raw scheduler limits. + Strong GPUs can still be benchmarked explicitly by raising this knob. + """ + + fallback = max(1, int(default)) + raw = os.getenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES") + if not raw: + return fallback + try: + return max(1, int(raw)) + except Exception: + return fallback + + +def _resolve_docling_batch_target_pages(default: int = 256) -> int: + """Resolve the target page budget per queued Docling extraction work item.""" + + fallback = max(1, int(default)) + raw = os.getenv("GLOSSAPI_DOCLING_BATCH_TARGET_PAGES") + if not raw: + return fallback + try: + return max(1, int(raw)) + except Exception: + return fallback + + +def _estimate_extract_work_pages(path: Path) -> int: + """Best-effort PDF page estimate used for Phase-1 queue packing.""" + + suffix = path.suffix.lower() + if suffix != ".pdf": + return 1 + + try: + import pypdfium2 as pdfium # type: ignore + + pdf = pdfium.PdfDocument(str(path)) + try: + return max(1, int(len(pdf))) + finally: + close = getattr(pdf, "close", None) + if callable(close): + close() + except Exception: + pass + + for module_name, attr_name in ( + ("pypdf", "PdfReader"), + ("PyPDF2", "PdfReader"), + ): + try: + module = __import__(module_name, fromlist=[attr_name]) + reader_cls = getattr(module, attr_name) + reader = reader_cls(str(path)) + return max(1, int(len(reader.pages))) + except Exception: + continue + + return 1 + + +def _build_extract_work_items( + paths: Iterable[Path], + *, + max_batch_files: int, + target_batch_pages: int, + long_pdf_page_threshold: int = 600, + page_counter: Optional[Callable[[Path], int]] = None, +) -> List[List[Path]]: + """Pack extraction work into steadier page-budget batches for multi-GPU runs.""" + + files = [Path(path) for path in paths] + if not files: + return [] + + max_files = max(1, int(max_batch_files)) + target_pages = max(1, int(target_batch_pages)) + long_threshold = max(1, int(long_pdf_page_threshold)) + counter = page_counter or _estimate_extract_work_pages + + packed: List[Tuple[List[Path], int]] = [] + standalone: List[Tuple[List[Path], int]] = [] + + for path in files: + try: + est_pages = max(1, int(counter(path))) + except Exception: + est_pages = 1 + + if path.suffix.lower() == ".pdf" and est_pages > long_threshold: + standalone.append(([path], est_pages)) + continue + + best_idx: Optional[int] = None + best_leftover: Optional[int] = None + for idx, (bundle_paths, bundle_pages) in enumerate(packed): + if len(bundle_paths) >= max_files: + continue + new_pages = bundle_pages + est_pages + if bundle_paths and new_pages > target_pages: + continue + leftover = max(0, target_pages - new_pages) + if best_leftover is None or leftover < best_leftover: + best_idx = idx + best_leftover = leftover + if best_idx is None: + packed.append(([path], est_pages)) + else: + packed[best_idx][0].append(path) + packed[best_idx] = (packed[best_idx][0], packed[best_idx][1] + est_pages) + + work_items = standalone + packed + work_items.sort(key=lambda item: item[1], reverse=True) + return [bundle_paths for bundle_paths, _ in work_items] + + +def _resolve_docling_queue_policy(extractor: Any | None = None) -> Tuple[int, int]: + """Return the Docling queue packing knobs the multi-GPU planner should use.""" + + max_batch_files = _resolve_docling_max_batch_files() + long_pdf_page_threshold = 600 + if extractor is None: + return max_batch_files, long_pdf_page_threshold + try: + max_batch_files = max(1, int(getattr(extractor, "max_batch_files", max_batch_files))) + except Exception: + max_batch_files = _resolve_docling_max_batch_files() + try: + long_pdf_page_threshold = max( + 1, int(getattr(extractor, "long_pdf_page_threshold", long_pdf_page_threshold)) + ) + except Exception: + long_pdf_page_threshold = 600 + return max_batch_files, long_pdf_page_threshold + + class ExtractPhaseMixin: def prime_extractor( self, @@ -96,25 +237,36 @@ def prime_extractor( except Exception: images_scale_env = "1.25" + if force_ocr: + self.logger.warning( + "Corpus.extract(force_ocr=True) is deprecated and no longer executes OCR. " + "Use Corpus.ocr(backend='deepseek') for OCR remediation." + ) + # Hard GPU preflight before we attempt to build OCR/enrichment pipelines self._gpu_preflight( accel_type=accel_type, - require_ocr=bool(force_ocr), + require_ocr=False, require_math=bool(formula_enrichment or code_enrichment), require_backend_gpu=(backend_choice == "docling"), ) # Configure batch/backend policy based on resolved choice if backend_choice == "docling": - # Keep docling runs conservative: process one document per batch for stability - self.extractor.configure_batch_policy("docling", max_batch_files=1, prefer_safe_backend=False) + # Keep docling runs conservative by default, but expose an explicit + # Phase-1 tuning hook for benchmark nodes and strong GPUs. + self.extractor.configure_batch_policy( + "docling", + max_batch_files=_resolve_docling_max_batch_files(), + prefer_safe_backend=False, + ) else: self.extractor.configure_batch_policy("safe", max_batch_files=1, prefer_safe_backend=True) # Ensure converter exists (reuse when unchanged) self.extractor.ensure_extractor( - enable_ocr=bool(force_ocr), - force_full_page_ocr=bool(force_ocr), + enable_ocr=False, + force_full_page_ocr=False, formula_enrichment=bool(formula_enrichment), code_enrichment=bool(code_enrichment), images_scale=float(images_scale_env), @@ -136,12 +288,12 @@ def _resolve_phase1_backend( raise ValueError( f"Invalid phase1_backend='{requested}'. Expected one of: 'auto', 'safe', 'docling'." ) - needs_gpu = bool(force_ocr or formula_enrichment or code_enrichment) + needs_gpu = bool(formula_enrichment or code_enrichment) if choice == "auto": choice = "docling" if needs_gpu else "safe" if choice == "safe" and needs_gpu: self.logger.info( - "Phase-1 backend 'safe' overridden to 'docling' because OCR/math enrichment was requested." + "Phase-1 backend 'safe' overridden to 'docling' because math/code enrichment was requested." ) choice = "docling" return choice @@ -154,12 +306,12 @@ def _gpu_preflight( require_math: bool, require_backend_gpu: bool = False, ) -> None: - """Abort early when GPU OCR/math is requested but CUDA is unavailable.""" + """Abort early when GPU-backed Docling work is requested but CUDA is unavailable.""" if not (require_ocr or require_math or require_backend_gpu): return instructions = ( - "GPU OCR and math enrichment require CUDA-enabled torch and onnxruntime-gpu. " + "GPU-backed Docling extraction and math enrichment require CUDA-enabled torch. " "Install the CUDA wheels and ensure NVIDIA drivers expose the desired devices." ) @@ -167,30 +319,15 @@ def _gpu_preflight( accel_lower = str(accel_type or "").strip().lower() if accel_lower.startswith("cpu"): raise RuntimeError( - "GPU OCR was requested (force_ocr/math) but accel_type='CPU'. " + "GPU-backed Docling extraction was requested but accel_type='CPU'. " f"{instructions}" ) - try: - import onnxruntime as _ort # type: ignore - providers = _ort.get_available_providers() - except Exception as exc: - raise RuntimeError( - "onnxruntime not available while attempting GPU OCR. " - "Install onnxruntime-gpu and rerun." - ) from exc - - if "CUDAExecutionProvider" not in providers: - raise RuntimeError( - "CUDAExecutionProvider missing from onnxruntime providers. " - f"Detected providers={providers}. {instructions}" - ) - torch_mod = _maybe_import_torch(force=True) if torch_mod is None or not getattr(torch_mod, "cuda", None) or not torch_mod.cuda.is_available(): raise RuntimeError( - "Torch CUDA is not available but GPU OCR/math was requested. " - "Install the CUDA wheel (e.g. torch==2.5.1+cu121) and ensure CUDA drivers/devices are visible." + "Torch CUDA is not available but GPU-backed Docling extraction/math was requested. " + "Install the CUDA wheel and ensure CUDA drivers/devices are visible." ) device_count = torch_mod.cuda.device_count() @@ -208,13 +345,12 @@ def _gpu_preflight( if not self._gpu_banner_logged: self.logger.info( - "GPU preflight: using torch + onnxruntime GPU backends; ensure CUDA drivers are available." + "GPU preflight: using torch-backed Docling extraction; ensure CUDA drivers are available." ) self._gpu_banner_logged = True self.logger.info( - "GPU preflight OK: providers=%s torch_devices=%s", - ",".join(providers), + "GPU preflight OK: torch_devices=%s", ", ".join(device_names) or "", ) @@ -237,6 +373,7 @@ def extract( export_doc_json: bool = True, emit_formula_index: bool = False, phase1_backend: str = "auto", + workers_per_device: int = 1, _prepared: bool = False, ) -> None: """ @@ -250,8 +387,9 @@ def extract( export_doc_json: When True (default), writes Docling layout JSON to `json/.docling.json(.zst)` emit_formula_index: Also emit `json/.formula_index.jsonl` (default: False) phase1_backend: Selects the Phase-1 backend. ``"auto"`` (default) keeps the safe backend unless - OCR/math is requested, ``"safe"`` forces the PyPDFium backend, and ``"docling"`` forces the - Docling backend. + math/code enrichment is requested, ``"safe"`` forces the PyPDFium backend, and ``"docling"`` + forces the Docling backend. + workers_per_device: Number of extraction workers to bind to each visible GPU when ``use_gpus='multi'``. """ if not file_paths: @@ -425,12 +563,17 @@ def extract( except Exception: threads_effective = int(num_threads) if isinstance(num_threads, int) else max(2, 2 * max(1, len(devs))) - batch_hint = 5 if backend_choice == "docling" and not force_ocr else 1 + workers_per_device = max(1, int(workers_per_device or 1)) + configured_batch_hint = 1 + if backend_choice == "docling": + extractor = getattr(self, "extractor", None) + configured_batch_hint, _ = _resolve_docling_queue_policy(extractor) self.logger.info( - "Phase-1 config: backend=%s batch_size=%s threads=%s skip_existing=%s benchmark=%s", + "Phase-1 config: backend=%s max_batch_files=%s threads=%s workers_per_device=%s skip_existing=%s benchmark=%s", backend_choice, - batch_hint, + configured_batch_hint, threads_effective, + workers_per_device, bool(skip_existing), bool(benchmark_mode), ) @@ -464,15 +607,44 @@ def extract( return # Dynamic work queue across GPUs + from .corpus_orchestrator import gpu_extract_worker_queue from multiprocessing import get_context ctx = get_context("spawn") manager = ctx.Manager() task_q = ctx.Queue() result_q = ctx.Queue() status_map = manager.dict() - path_list = [str(p.resolve()) for p in pending_files] - for full_path in path_list: - task_q.put(full_path) + batch_target_pages = 1 + configured_max_batch_files = 1 + long_pdf_page_threshold = 600 + work_items: List[List[Path]] = [[Path(p)] for p in pending_files] + extractor = getattr(self, "extractor", None) + configured_max_batch_files, long_pdf_page_threshold = _resolve_docling_queue_policy(extractor) + if backend_choice == "docling": + batch_target_pages = _resolve_docling_batch_target_pages() + work_items = _build_extract_work_items( + pending_files, + max_batch_files=configured_max_batch_files, + target_batch_pages=batch_target_pages, + long_pdf_page_threshold=long_pdf_page_threshold, + ) + queue_items = [[str(path.resolve()) for path in item] for item in work_items] + for queue_item in queue_items: + task_q.put(queue_item) + total_estimated_pages = 0 + try: + total_estimated_pages = sum(_estimate_extract_work_pages(path) for path in pending_files) + except Exception: + total_estimated_pages = 0 + self.logger.info( + "Phase-1 dispatch: %d file(s) -> %d work item(s) (backend=%s max_batch_files=%d target_pages=%d est_pages=%d)", + len(pending_files), + len(queue_items), + backend_choice, + configured_max_batch_files, + batch_target_pages, + total_estimated_pages, + ) worker_log_dir_env = os.environ.get("GLOSSAPI_WORKER_LOG_DIR") worker_log_dir_to_use = worker_log_dir_env if not worker_log_dir_to_use: @@ -494,14 +666,29 @@ def extract( marker_base.mkdir(parents=True, exist_ok=True) except Exception as exc: self.logger.debug("Unable to prepare marker directory %s: %s", marker_base, exc) - procs: List[Any] = [] - proc_gpu: Dict[int, int] = {} - marker_files: Dict[int, Path] = {dev_id: marker_base / f"gpu{dev_id}.current" for dev_id in devs} + worker_specs: List[Dict[str, Any]] = [] for dev_id in devs: + for worker_slot in range(workers_per_device): + worker_specs.append( + { + "device_id": int(dev_id), + "worker_slot": int(worker_slot), + "worker_key": f"gpu{dev_id}-w{worker_slot}", + } + ) + procs: List[Any] = [] + proc_specs: Dict[int, Dict[str, Any]] = {} + marker_files: Dict[str, Path] = { + spec["worker_key"]: marker_base / f"{spec['worker_key']}.current" + for spec in worker_specs + } + for spec in worker_specs: p = ctx.Process( target=gpu_extract_worker_queue, args=( - dev_id, + spec["device_id"], + spec["worker_slot"], + spec["worker_key"], str(self.input_dir), str(self.output_dir), task_q, @@ -524,7 +711,7 @@ def extract( p.start() procs.append(p) if p.pid is not None: - proc_gpu[p.pid] = dev_id + proc_specs[p.pid] = dict(spec) active = list(procs) any_fail = False last_summary = time.time() @@ -541,20 +728,21 @@ def extract( procs.remove(p) pid = p.pid or -1 heartbeat[pid] = time.time() - gpu_id = proc_gpu.pop(pid, None) + worker_spec = proc_specs.pop(pid, None) + worker_key = worker_spec["worker_key"] if worker_spec else None if p.exitcode not in (0, None): any_fail = True self.logger.warning("GPU worker pid=%s exited with code %s", p.pid, p.exitcode) current_paths: List[str] = [] stems_for_skip: List[str] = [] - if gpu_id is not None: - current_entry = status_map.pop(gpu_id, None) + if worker_key is not None: + current_entry = status_map.pop(worker_key, None) if current_entry: if not isinstance(current_entry, (list, tuple, set)): current_entry = [current_entry] current_paths = [str(x) for x in current_entry] stems_for_skip = [canonical_stem(path) for path in current_paths] - marker_path = marker_files.get(gpu_id) + marker_path = marker_files.get(worker_key) if marker_path: try: marker_path.unlink(missing_ok=True) @@ -565,12 +753,17 @@ def extract( state_mgr.save(processed_files, problematic_files) if stems_for_skip: skip_mgr.add(stems_for_skip) - if gpu_id is not None: - self.logger.info("Respawning GPU%s worker after crash.", gpu_id) + if worker_spec is not None: + self.logger.info( + "Respawning %s after crash.", + worker_spec["worker_key"], + ) replacement = ctx.Process( target=gpu_extract_worker_queue, args=( - gpu_id, + worker_spec["device_id"], + worker_spec["worker_slot"], + worker_spec["worker_key"], str(self.input_dir), str(self.output_dir), task_q, @@ -594,13 +787,13 @@ def extract( procs.append(replacement) active.append(replacement) if replacement.pid is not None: - proc_gpu[replacement.pid] = gpu_id + proc_specs[replacement.pid] = dict(worker_spec) heartbeat[replacement.pid] = time.time() continue else: - if gpu_id is not None: - status_map.pop(gpu_id, None) - marker_path = marker_files.get(gpu_id) + if worker_key is not None: + status_map.pop(worker_key, None) + marker_path = marker_files.get(worker_key) if marker_path: try: marker_path.unlink(missing_ok=True) @@ -628,7 +821,7 @@ def extract( skip_mgr.add(bad_stems) state_mgr.save(processed_files, problematic_files) self.logger.info( - "GPU%s batch complete: +%d processed, +%d problematic (totals: %d processed, %d problematic)", + "%s batch complete: +%d processed, +%d problematic (totals: %d processed, %d problematic)", result.get("worker"), len(ok_stems), len(bad_stems), @@ -642,30 +835,25 @@ def extract( if result.get("exitcode", 0) not in (0, None): any_fail = True self.logger.warning( - "GPU%s reported non-zero exit: %s", result.get("worker"), result.get("exitcode") + "%s reported non-zero exit: %s", result.get("worker"), result.get("exitcode") ) worker_pid = result.get("pid") if worker_pid is not None: heartbeat[worker_pid] = time.time() - worker_gpu = result.get("worker") - if worker_gpu is not None: - try: - worker_gpu_int = int(worker_gpu) - except Exception: - worker_gpu_int = None - else: - status_map.pop(worker_gpu_int, None) - marker_path = marker_files.get(worker_gpu_int) - if marker_path: - try: - marker_path.unlink(missing_ok=True) - except Exception: - pass + worker_key = result.get("worker") + if worker_key is not None: + status_map.pop(worker_key, None) + marker_path = marker_files.get(str(worker_key)) + if marker_path: + try: + marker_path.unlink(missing_ok=True) + except Exception: + pass now = time.time() if now - last_summary > 30: try: - pending = result_q.qsize() + pending = task_q.qsize() except NotImplementedError: pending = -1 self.logger.info( @@ -706,6 +894,13 @@ def extract( pending_item = task_q.get_nowait() if isinstance(pending_item, str) and pending_item.strip(): remaining_after_failure.append(pending_item) + continue + if isinstance(pending_item, (list, tuple, set)): + remaining_after_failure.extend( + str(item).strip() + for item in pending_item + if str(item).strip() + ) except queue.Empty: pass if remaining_after_failure: diff --git a/src/glossapi/corpus/phase_ocr_math.py b/src/glossapi/corpus/phase_ocr_math.py index 4dec423..0d86861 100644 --- a/src/glossapi/corpus/phase_ocr_math.py +++ b/src/glossapi/corpus/phase_ocr_math.py @@ -1,6 +1,7 @@ """OCR and math enrichment helpers split from Corpus.""" from __future__ import annotations +import hashlib import json import logging import math @@ -21,27 +22,195 @@ from .._naming import canonical_stem from ..gloss_downloader import GlossDownloader from ..gloss_section import GlossSection +from ..ocr.deepseek.defaults import ( + DEFAULT_ATTN_BACKEND, + DEFAULT_GPU_MEMORY_UTILIZATION, + DEFAULT_MAX_NEW_TOKENS, + DEFAULT_OCR_PROFILE, + DEFAULT_RENDER_DPI, + DEFAULT_REPAIR_MODE, + DEFAULT_RUNTIME_BACKEND, + DEFAULT_TARGET_BATCH_PAGES, + DEFAULT_WORKERS_PER_GPU, +) # Avoid importing classifier here; OCR/math phase does not require it at import time. from .corpus_skiplist import _SkiplistManager, _resolve_skiplist_path from .corpus_state import _ProcessingStateManager from .corpus_utils import _maybe_import_torch +from .ocr.config import OcrRequest, normalize_ocr_request +from .ocr.math_targets import discover_docling_json_stems, filter_math_only_stems +from .ocr.pipeline import run_ocr_phase +from .ocr.targets import build_ocr_selection + + +def _build_ocr_stage_artifact_update( + *, + markdown_dir: Path, + metrics_dir: Path, + stem: str, +) -> Optional[Dict[str, object]]: + """Return direct OCR-owned artifact fields for one canonical OCR document. + + The OCR stage should hand off the same row identity that upstream stages + produced, with corrected text embedded back into parquet. Markdown and + metrics remain sidecars, but detached markdown alone is not the full stage + contract. + """ + + markdown_path = Path(markdown_dir) / f"{stem}.md" + if not markdown_path.exists(): + return None + text_payload = markdown_path.read_text(encoding="utf-8") + metrics_path = Path(metrics_dir) / f"{stem}.metrics.json" + return { + "text": text_payload, + "ocr_markdown_relpath": str(Path("markdown") / markdown_path.name), + "ocr_metrics_relpath": ( + str(Path("json") / "metrics" / metrics_path.name) if metrics_path.exists() else None + ), + "ocr_text_sha256": hashlib.sha256(text_payload.encode("utf-8")).hexdigest(), + } + + +def _apply_ocr_success_updates( + df_meta: pd.DataFrame, + *, + filenames: List[str], + markdown_dir: Path, + metrics_dir: Path, + backend_norm: str, +) -> pd.DataFrame: + """Apply only direct, obvious OCR-owned metadata updates to the parquet rows.""" + + if "filename" not in df_meta.columns: + return df_meta + + if "filter" not in df_meta.columns: + df_meta["filter"] = "ok" + if "needs_ocr" not in df_meta.columns: + df_meta["needs_ocr"] = False + if "ocr_success" not in df_meta.columns: + df_meta["ocr_success"] = False + if "extraction_mode" not in df_meta.columns: + df_meta["extraction_mode"] = None + + direct_columns = ("text", "ocr_markdown_relpath", "ocr_metrics_relpath", "ocr_text_sha256") + for column in direct_columns: + if column not in df_meta.columns: + df_meta[column] = None + + filename_series = df_meta["filename"].astype(str) + stem_series = filename_series.map(canonical_stem) + + for fname in filenames: + stem = canonical_stem(fname) + mask = stem_series == stem + if not bool(mask.any()): + continue + artifact_update = _build_ocr_stage_artifact_update( + markdown_dir=markdown_dir, + metrics_dir=metrics_dir, + stem=stem, + ) + df_meta.loc[mask, "filter"] = "ok" + df_meta.loc[mask, "needs_ocr"] = False + df_meta.loc[mask, "ocr_success"] = True + if backend_norm == "deepseek": + df_meta.loc[mask, "extraction_mode"] = "deepseek" + if artifact_update is None: + continue + for column, value in artifact_update.items(): + df_meta.loc[mask, column] = value + + return df_meta + + +def _normalize_ocr_target_filenames(*, filenames: List[str], input_dir: Path) -> List[str]: + """Collapse chunk-like metadata rows back to real OCR source files when possible.""" + + source_by_stem: Dict[str, str] = {} + try: + for path in sorted(Path(input_dir).glob("*.pdf")): + source_by_stem.setdefault(canonical_stem(path.name), path.name) + except Exception: + source_by_stem = {} + + normalized: List[str] = [] + seen: Set[str] = set() + for fname in filenames: + resolved = source_by_stem.get(canonical_stem(fname), str(fname)) + if resolved in seen: + continue + normalized.append(resolved) + seen.add(resolved) + return normalized class OcrMathPhaseMixin: + def _refresh_metrics_after_ocr_rerun(self) -> None: + """Refresh OCR-owned and export-owned metrics after OCR remediation. + + `clean_ocr()` and `clean()` remain separate stages on purpose: + + - `clean_ocr()` owns OCR artifact removal and OCR-specific metrics. + - `clean()` owns the broader export-facing clean metrics. + + After OCR reruns we intentionally execute both stages in sequence on the + OCR-cleaned text surface instead of treating one stage as a synonym for + the other. + """ + + self.logger.info( + "Re-running OCR cleaner after OCR rerun to refresh cleaned text and OCR metrics" + ) + self.clean_ocr( + input_dir=self.markdown_dir, + drop_bad=False, + ) + self.logger.info( + "Re-running Rust cleaner in score-only mode on OCR-cleaned markdown to refresh export metrics" + ) + self.clean( + input_dir=self.cleaned_markdown_dir, + drop_bad=False, + write_cleaned_files=False, + ) + def ocr( self, *, fix_bad: bool = True, mode: Optional[str] = None, - backend: str = "rapidocr", + backend: str = "deepseek", device: Optional[str] = None, model_dir: Optional[Union[str, Path]] = None, max_pages: Optional[int] = None, persist_engine: bool = True, limit: Optional[int] = None, - dpi: Optional[int] = None, # reserved for future use - precision: Optional[str] = None, # reserved for future use ("fp16","bf16") - # Integrated math enrichment controls + dpi: Optional[int] = None, + precision: Optional[str] = None, + workers_per_gpu: int = DEFAULT_WORKERS_PER_GPU, + runtime_backend: str = DEFAULT_RUNTIME_BACKEND, + ocr_profile: str = DEFAULT_OCR_PROFILE, + prompt_override: Optional[str] = None, + attn_backend: str = DEFAULT_ATTN_BACKEND, + base_size: Optional[int] = None, + image_size: Optional[int] = None, + crop_mode: Optional[bool] = None, + render_dpi: Optional[int] = DEFAULT_RENDER_DPI, + max_new_tokens: Optional[int] = DEFAULT_MAX_NEW_TOKENS, + repetition_penalty: Optional[float] = None, + no_repeat_ngram_size: Optional[int] = None, + vllm_batch_size: Optional[int] = None, + gpu_memory_utilization: Optional[float] = DEFAULT_GPU_MEMORY_UTILIZATION, + disable_fp8_kv: bool = False, + repair_mode: str = DEFAULT_REPAIR_MODE, + repair_exec_batch_target_pages: Optional[int] = None, + repair_exec_batch_target_items: Optional[int] = None, + scheduler: str = "auto", + target_batch_pages: int = DEFAULT_TARGET_BATCH_PAGES, + shard_pages: int = 0, + shard_threshold_pages: int = 0, math_enhance: bool = True, math_targets: Optional[Dict[str, List[Tuple[int, int]]]] = None, math_batch_size: int = 8, @@ -51,674 +220,373 @@ def ocr( force: Optional[bool] = None, reprocess_completed: Optional[bool] = None, skip_existing: Optional[bool] = None, - # Content debug: keep page separators and truncation markers when True content_debug: bool = False, CONTENT_DEBUG: Optional[bool] = None, - # Back-compat aliases (deprecated): internal_debug: bool = False, INTERNAL_DEBUG: Optional[bool] = None, ) -> None: - """OCR and/or math enrichment with explicit mode control. - - Parameters - - mode: one of - - 'ocr_bad': re‑OCR only documents flagged as bad by Rust cleaner (parquet 'filter' != 'ok'). - - 'math_only': run math enrichment from Docling JSON (generate JSON without OCR when missing). - - 'ocr_bad_then_math': re‑OCR bad documents, then run math enrichment on those. - If not provided, falls back to legacy flags (fix_bad, math_enhance): - fix_bad and math_enhance -> 'ocr_bad_then_math'; - fix_bad only -> 'ocr_bad'; - math_enhance only -> 'math_only'; - neither -> no‑op. - - backend: 'rapidocr' (default) uses the Docling + RapidOCR path via Phase‑1 extract(). - 'deepseek' uses the DeepSeek‑OCR path (no Docling JSON, math unsupported). - - fix_bad: re-run OCR on documents marked bad by the cleaner (default True). - - math_enhance: run math/code enrichment after OCR (default True). - - force: [DEPRECATED] alias for fix_bad retained for backward compatibility. - - reprocess_completed: when False, skip documents already flagged as successfully - OCRed or math-enriched in metadata. Set True to force reprocessing. Defaults to False - unless ``skip_existing`` overrides it. - - skip_existing: legacy alias for ``reprocess_completed`` (``skip_existing=True`` equals - ``reprocess_completed=False``). Prefer the explicit ``reprocess_completed`` toggle. - """ - # Normalize backend - backend_norm = str(backend or "rapidocr").strip().lower() - if backend_norm not in {"rapidocr", "deepseek"}: - raise ValueError("backend must be 'rapidocr' or 'deepseek'") - - # CONTENT_DEBUG override (preferred uppercase alias) - # Priority: CONTENT_DEBUG > INTERNAL_DEBUG > content_debug/internal_debug flags - if CONTENT_DEBUG is not None: - content_debug = bool(CONTENT_DEBUG) - elif INTERNAL_DEBUG is not None: - content_debug = bool(INTERNAL_DEBUG) - elif internal_debug: - content_debug = True - - # Normalize mode from explicit value or legacy flags - mode_norm = None - fix_bad_effective = bool(fix_bad) - if force is not None: - try: - self.logger.warning("Corpus.ocr(force=...) is deprecated; use fix_bad=... instead") - except Exception: - pass - fix_bad_effective = bool(force) - if mode: - m = str(mode).strip().lower() - if m in {"ocr_bad", "math_only", "ocr_bad_then_math"}: - mode_norm = m - else: - self.logger.warning("Unknown mode '%s'; falling back to legacy flags", mode) - if mode_norm is None: - if fix_bad_effective and math_enhance: - mode_norm = "ocr_bad_then_math" - elif fix_bad_effective: - mode_norm = "ocr_bad" - elif math_enhance: - mode_norm = "math_only" - else: - self.logger.info( - "OCR: no operation requested (enable fix_bad and/or math_enhance or set mode='ocr_bad'|'math_only'|'ocr_bad_then_math')" + """OCR and/or math enrichment with explicit mode control.""" + + del limit, dpi + request = normalize_ocr_request( + logger=self.logger, + fix_bad=fix_bad, + mode=mode, + backend=backend, + device=device, + model_dir=model_dir, + max_pages=max_pages, + persist_engine=persist_engine, + precision=precision, + workers_per_gpu=workers_per_gpu, + runtime_backend=runtime_backend, + ocr_profile=ocr_profile, + prompt_override=prompt_override, + attn_backend=attn_backend, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + render_dpi=render_dpi, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + vllm_batch_size=vllm_batch_size, + gpu_memory_utilization=gpu_memory_utilization, + disable_fp8_kv=disable_fp8_kv, + repair_mode=repair_mode, + repair_exec_batch_target_pages=repair_exec_batch_target_pages, + repair_exec_batch_target_items=repair_exec_batch_target_items, + scheduler=scheduler, + target_batch_pages=target_batch_pages, + shard_pages=shard_pages, + shard_threshold_pages=shard_threshold_pages, + math_enhance=math_enhance, + math_targets=math_targets, + math_batch_size=math_batch_size, + math_dpi_base=math_dpi_base, + use_gpus=use_gpus, + devices=devices, + force=force, + reprocess_completed=reprocess_completed, + skip_existing=skip_existing, + content_debug=content_debug, + CONTENT_DEBUG=CONTENT_DEBUG, + internal_debug=internal_debug, + INTERNAL_DEBUG=INTERNAL_DEBUG, + ) + if request is None: + return + if request.mode == "math_only": + self._run_math_only_request(request) + return + run_ocr_phase(self, request) + + def _run_math_only_request(self, request: OcrRequest) -> None: + selection = build_ocr_selection( + self, + mode=request.mode, + reprocess_completed=request.reprocess_completed, + ) + stems = discover_docling_json_stems(self.output_dir) + stems = filter_math_only_stems( + stems=stems, + bad_files=selection.bad_files, + math_done_stems=selection.math_done_stems, + reprocess_completed=request.reprocess_completed, + logger=self.logger, + ) + self._run_math_targets( + stems=stems, + request=request, + skip_mgr=selection.skip_mgr, + skiplist_path=selection.skiplist_path, + ) + + def _run_math_targets( + self, + *, + stems: List[str], + request: OcrRequest, + skip_mgr: Optional[_SkiplistManager], + skiplist_path: Path, + ) -> None: + if not stems: + self.logger.info("No Docling JSON found for math enrichment.") + return + + initial_math_targets = len(stems) + current_skips = skip_mgr.reload() if skip_mgr else set() + if current_skips: + before = len(stems) + stems = [stem for stem in stems if stem not in current_skips] + removed = before - len(stems) + if removed: + self.logger.warning( + "Skip-list %s filtered %d document(s) from Phase-2 math.", + skiplist_path, + removed, ) + if not stems: + self.logger.info("All math targets filtered by skip-list; nothing to do.") return - reprocess_explicit = reprocess_completed is not None - reprocess_flag = bool(reprocess_completed) if reprocess_explicit else False - if skip_existing is not None: - skip_flag = bool(skip_existing) + + self.logger.info( + "Math targets: total=%d kept=%d filtered_skiplist=%d", + initial_math_targets, + len(stems), + initial_math_targets - len(stems), + ) + + local_targets = None + if request.math_targets: + local_targets = {stem: request.math_targets.get(stem) for stem in stems if stem in request.math_targets} + + if str(request.use_gpus).lower() != "multi": + self.formula_enrich_from_json( + files=stems, + device=(request.device or "cuda"), + batch_size=int(request.math_batch_size), + dpi_base=int(request.math_dpi_base), + targets_by_stem=local_targets, + ) + return + + devs = list(request.devices or []) + if not devs: try: - self.logger.warning( - "Corpus.ocr(skip_existing=...) is deprecated; use reprocess_completed=... instead." + proc = subprocess.run( + ["nvidia-smi", "-L"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + timeout=5, ) + if proc.returncode == 0 and proc.stdout: + for line in proc.stdout.splitlines(): + if line.startswith("GPU "): + try: + devs.append(int(line.split(":", 1)[0].split()[1])) + except Exception: + pass except Exception: pass - desired = not skip_flag - if reprocess_explicit and desired != reprocess_flag: + if not devs: + torch_mod = _maybe_import_torch() try: - self.logger.info( - "Corpus.ocr(): skip_existing=%s overrides reprocess_completed=%s (effective reprocess_completed=%s).", - skip_flag, - reprocess_flag, - desired, - ) + if torch_mod is not None and getattr(torch_mod, "cuda", None) and torch_mod.cuda.is_available(): + devs = list(range(torch_mod.cuda.device_count())) except Exception: pass - reprocess_flag = desired - reprocess_completed = reprocess_flag - # DeepSeek semantics note - if backend_norm == "deepseek": + if not devs: + msg = "Multi-GPU math requested but no GPUs detected; aborting math enhancement" + self.logger.error(msg) + raise RuntimeError(msg) + + from multiprocessing import get_context + + ctx = get_context("spawn") + work_q = ctx.Queue() + result_q = ctx.Queue() + manager = ctx.Manager() + status_map = manager.dict() + for stem in stems: + work_q.put(stem) + + worker_log_dir_env = os.environ.get("GLOSSAPI_WORKER_LOG_DIR") + worker_log_dir_to_use = worker_log_dir_env + if not worker_log_dir_to_use: + default_worker_log_dir = self.logs_dir / "math_workers" try: - self.logger.info( - "DeepSeek backend: Phase-2 math is not required; equations are included inline via OCR." + default_worker_log_dir.mkdir(parents=True, exist_ok=True) + worker_log_dir_to_use = str(default_worker_log_dir) + except Exception as exc: + self.logger.warning( + "Unable to prepare worker log directory %s: %s", + default_worker_log_dir, + exc, ) - except Exception: - pass - # Identify bad documents from parquet (Rust cleaner output) - bad_files: List[str] = [] - skipped_completed = 0 - skipped_skiplist = 0 - parquet_meta: Optional["pd.DataFrame"] = None - ocr_done_files: List[str] = [] - ocr_done_stems: Set[str] = set() - math_done_files: List[str] = [] - math_done_stems: Set[str] = set() + worker_log_dir_to_use = None + if worker_log_dir_to_use: + os.environ["GLOSSAPI_WORKER_LOG_DIR"] = worker_log_dir_to_use + marker_base = Path(worker_log_dir_to_use) if worker_log_dir_to_use else (self.logs_dir / "math_workers") try: - from glossapi.parquet_schema import ParquetSchema - parquet_schema = ParquetSchema({"url_column": self.url_column}) - parquet_path = self._resolve_metadata_parquet(parquet_schema, ensure=True, search_input=True) - if parquet_path and parquet_path.exists(): - import pandas as _pd - df = _pd.read_parquet(parquet_path) - if "filename" in df.columns and "needs_ocr" in df.columns: - bad_files = df.loc[df["needs_ocr"] == True, "filename"].dropna().astype(str).tolist() - else: - # No fallback: selection relies strictly on the 'needs_ocr' flag - # populated by the cleaner. If missing, we skip OCR selection. - bad_files = [] - ocr_done: Set[str] = set() - if "ocr_success" in df.columns: - ocr_done_files = df.loc[df["ocr_success"].fillna(False), "filename"].dropna().astype(str).tolist() - ocr_done = {canonical_stem(str(name)) for name in ocr_done_files} - ocr_done_stems = set(ocr_done) - if "math_enriched" in df.columns: - math_done_files = df.loc[df["math_enriched"].fillna(False), "filename"].dropna().astype(str).tolist() - elif "enriched_math" in df.columns: - math_done_files = df.loc[df["enriched_math"].fillna(False), "filename"].dropna().astype(str).tolist() - if math_done_files: - math_done_stems = {canonical_stem(str(name)) for name in math_done_files} - if not reprocess_completed and ocr_done: - before = len(bad_files) - bad_files = [name for name in bad_files if canonical_stem(name) not in ocr_done] - removed = before - len(bad_files) - if removed: - skipped_completed = removed - self.logger.info( - "OCR: skipping %d already completed document(s) (reprocess_completed=False).", - removed, - ) - if reprocess_completed and mode_norm in {"ocr_bad", "ocr_bad_then_math"} and ocr_done_files: - pending = {str(f) for f in bad_files} - for fname in ocr_done_files: - if fname not in pending: - bad_files.append(fname) - pending.add(fname) - parquet_meta = df - else: - parquet_meta = None + marker_base.mkdir(parents=True, exist_ok=True) except Exception: pass + marker_files: Dict[int, Path] = {dev_id: marker_base / f"gpu{dev_id}.current" for dev_id in devs} - ocr_candidates_initial = len(bad_files) - skiplist_path = _resolve_skiplist_path(self.output_dir, self.logger) - skip_mgr = _SkiplistManager(skiplist_path, self.logger) - skip_stems = skip_mgr.load() - if skip_stems: - before = len(bad_files) - bad_files = [name for name in bad_files if canonical_stem(name) not in skip_stems] - removed = before - len(bad_files) - if removed: - skipped_skiplist = removed - self.logger.warning( - "Skip-list %s filtered %d document(s) from Phase-3 OCR.", - skiplist_path, - removed, - ) + procs: List[Any] = [] + active: List[Any] = [] + proc_gpu: Dict[int, int] = {} try: - self.logger.info( - "OCR targets: total=%d kept=%d skipped_completed=%d skipped_skiplist=%d", - ocr_candidates_initial, - len(bad_files), - skipped_completed, - skipped_skiplist, - ) + respawn_cap = int(os.environ.get("GLOSSAPI_MATH_RESPAWN_CAP", "5")) except Exception: - pass + respawn_cap = 5 + respawn_cap = max(0, respawn_cap) + respawn_counts: Dict[int, int] = {dev_id: 0 for dev_id in devs} - # Helper to run Phase‑2 enrichment over stems - def _run_math(stems: List[str]) -> None: - if not stems: - self.logger.info("No Docling JSON found for math enrichment.") - return - initial_math_targets = len(stems) - current_skips = skip_mgr.reload() if skip_mgr else set() - if current_skips: - before = len(stems) - stems = [s for s in stems if s not in current_skips] - removed = before - len(stems) - if removed: - self.logger.warning( - "Skip-list %s filtered %d document(s) from Phase-2 math.", - skiplist_path, - removed, - ) - if not stems: - self.logger.info("All math targets filtered by skip-list; nothing to do.") - return - try: - self.logger.info( - "Math targets: total=%d kept=%d filtered_skiplist=%d", - initial_math_targets, - len(stems), - initial_math_targets - len(stems), - ) - except Exception: - pass - local_targets = None - if math_targets: - local_targets = {s: math_targets.get(s) for s in stems if s in math_targets} - if str(use_gpus).lower() == "multi": - # Detect GPU devices - devs = devices or [] - if not devs: - try: - import subprocess - p = subprocess.run(["nvidia-smi", "-L"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=5) - if p.returncode == 0 and p.stdout: - for line in p.stdout.splitlines(): - if line.startswith("GPU "): - try: - idx = int(line.split(":", 1)[0].split()[1]) - devs.append(idx) - except Exception: - pass - except Exception: - pass - if not devs: - torch_mod = _maybe_import_torch() - try: - if torch_mod is not None and getattr(torch_mod, "cuda", None) and torch_mod.cuda.is_available(): - devs = list(range(torch_mod.cuda.device_count())) - except Exception: - pass - if not devs: - msg = "Multi-GPU math requested but no GPUs detected; aborting math enhancement" - self.logger.error(msg) - raise RuntimeError(msg) - else: - from multiprocessing import get_context - - ctx = get_context("spawn") - work_q = ctx.Queue() - result_q = ctx.Queue() - manager = ctx.Manager() - status_map = manager.dict() - for s in stems: - work_q.put(s) - - worker_log_dir_env = os.environ.get("GLOSSAPI_WORKER_LOG_DIR") - worker_log_dir_to_use = worker_log_dir_env - if not worker_log_dir_to_use: - default_worker_log_dir = self.logs_dir / "math_workers" - try: - default_worker_log_dir.mkdir(parents=True, exist_ok=True) - worker_log_dir_to_use = str(default_worker_log_dir) - except Exception as exc: - self.logger.warning( - "Unable to prepare worker log directory %s: %s", - default_worker_log_dir, - exc, - ) - worker_log_dir_to_use = None - if worker_log_dir_to_use: - os.environ["GLOSSAPI_WORKER_LOG_DIR"] = worker_log_dir_to_use - marker_base = Path(worker_log_dir_to_use) if worker_log_dir_to_use else (self.logs_dir / "math_workers") - try: - marker_base.mkdir(parents=True, exist_ok=True) - except Exception: - pass - marker_files: Dict[int, Path] = {dev_id: marker_base / f"gpu{dev_id}.current" for dev_id in devs} - - procs: List[Any] = [] - active: List[Any] = [] - proc_gpu: Dict[int, int] = {} - try: - respawn_cap = int(os.environ.get("GLOSSAPI_MATH_RESPAWN_CAP", "5")) - except Exception: - respawn_cap = 5 - respawn_cap = max(0, respawn_cap) - respawn_counts: Dict[int, int] = {dev_id: 0 for dev_id in devs} + for dev_id in devs: + proc = ctx.Process( + target=_gpu_math_worker, + args=( + dev_id, + str(self.input_dir), + str(self.output_dir), + work_q, + int(request.math_batch_size), + int(request.math_dpi_base), + request.device or "cuda", + local_targets or {}, + result_q, + status_map, + str(marker_base), + ), + ) + proc.start() + procs.append(proc) + active.append(proc) + if proc.pid is not None: + proc_gpu[proc.pid] = dev_id - for dev_id in devs: - p = ctx.Process( + try: + last_summary = time.time() + while active: + for proc in list(active): + proc.join(timeout=0.05) + if proc.is_alive(): + continue + active.remove(proc) + if proc in procs: + procs.remove(proc) + pid = proc.pid or -1 + gpu_id = proc_gpu.pop(pid, None) + exitcode = proc.exitcode + stems_for_skip: List[str] = [] + if gpu_id is not None: + current_entry = status_map.pop(gpu_id, None) + if current_entry: + if isinstance(current_entry, (list, tuple, set)): + entries = list(current_entry) + else: + entries = [current_entry] + stems_for_skip = [str(item) for item in entries if item] + marker_path = marker_files.get(gpu_id) + if marker_path: + try: + marker_path.unlink(missing_ok=True) + except Exception: + pass + if exitcode not in (0, None) and gpu_id is not None: + if stems_for_skip and skip_mgr is not None: + skip_mgr.add(canonical_stem(stem) for stem in stems_for_skip) + self.logger.warning("Math worker GPU%s exited with %s", gpu_id, exitcode) + respawn_counts[gpu_id] = respawn_counts.get(gpu_id, 0) + 1 + attempts = respawn_counts[gpu_id] + if respawn_cap and attempts > respawn_cap: + self.logger.error( + "Math worker GPU%s exceeded respawn cap (%s); not respawning", + gpu_id, + respawn_cap, + ) + continue + replacement = ctx.Process( target=_gpu_math_worker, args=( - dev_id, + gpu_id, str(self.input_dir), str(self.output_dir), work_q, - int(math_batch_size), - int(math_dpi_base), - device or "cuda", + int(request.math_batch_size), + int(request.math_dpi_base), + request.device or "cuda", local_targets or {}, result_q, status_map, str(marker_base), ), ) - p.start() - procs.append(p) - active.append(p) - if p.pid is not None: - proc_gpu[p.pid] = dev_id + replacement.start() + procs.append(replacement) + active.append(replacement) + if replacement.pid is not None: + proc_gpu[replacement.pid] = gpu_id + continue + while True: try: - last_summary = time.time() - while active: - for p in list(active): - p.join(timeout=0.05) - if p.is_alive(): - continue - active.remove(p) - if p in procs: - procs.remove(p) - pid = p.pid or -1 - gpu_id = proc_gpu.pop(pid, None) - exitcode = p.exitcode - stems_for_skip: List[str] = [] - if gpu_id is not None: - current_entry = status_map.pop(gpu_id, None) - if current_entry: - if isinstance(current_entry, (list, tuple, set)): - entries = list(current_entry) - else: - entries = [current_entry] - stems_for_skip = [str(item) for item in entries if item] - marker_path = marker_files.get(gpu_id) - if marker_path: - try: - marker_path.unlink(missing_ok=True) - except Exception: - pass - if exitcode not in (0, None) and gpu_id is not None: - if stems_for_skip: - skip_mgr.add(canonical_stem(s) for s in stems_for_skip) - self.logger.warning( - "Math worker GPU%s exited with %s", - gpu_id, - exitcode, - ) - respawn_counts[gpu_id] = respawn_counts.get(gpu_id, 0) + 1 - attempts = respawn_counts[gpu_id] - if respawn_cap and attempts > respawn_cap: - self.logger.error( - "Math worker GPU%s exceeded respawn cap (%s); not respawning", - gpu_id, - respawn_cap, - ) - continue - replacement = ctx.Process( - target=_gpu_math_worker, - args=( - gpu_id, - str(self.input_dir), - str(self.output_dir), - work_q, - int(math_batch_size), - int(math_dpi_base), - device or "cuda", - local_targets or {}, - result_q, - status_map, - str(marker_base), - ), - ) - replacement.start() - procs.append(replacement) - active.append(replacement) - if replacement.pid is not None: - proc_gpu[replacement.pid] = gpu_id - continue - - while True: - try: - event = result_q.get_nowait() - except queue.Empty: - break - if not event: - continue - if event.get("event") == "math_batch": - stems_bad = event.get("problematic", []) - if stems_bad: - skip_mgr.add(canonical_stem(s) for s in stems_bad) - worker = event.get("worker") - try: - worker_gpu = int(worker) - except Exception: - worker_gpu = None - if worker_gpu is not None: - status_map.pop(worker_gpu, None) - marker_path = marker_files.get(worker_gpu) - if marker_path: - try: - marker_path.unlink(missing_ok=True) - except Exception: - pass - elif event.get("event") == "exit" and event.get("exitcode", 0) not in (0, None): - self.logger.warning( - "Math worker GPU%s reported exit code %s", - event.get("worker"), - event.get("exitcode"), - ) - - now = time.time() - if now - last_summary > 30: - try: - qsize = work_q.qsize() - except NotImplementedError: - qsize = -1 - self.logger.info( - "Math progress: queue=%d active_workers=%d", - qsize, - len(active), - ) - last_summary = now - - if not active: - break - remaining_after_cap: List[str] = [] - try: - while True: - item = work_q.get_nowait() - if isinstance(item, str) and item.strip(): - remaining_after_cap.append(item) - except queue.Empty: - pass - if remaining_after_cap: - skip_mgr.add(canonical_stem(s) for s in remaining_after_cap) - self.logger.error( - "No active math workers remain; skipped %d pending item(s)", - len(remaining_after_cap), - ) - finally: - for p in procs: - if p.is_alive(): - p.join() + event = result_q.get_nowait() + except queue.Empty: + break + if not event: + continue + if event.get("event") == "math_batch": + stems_bad = event.get("problematic", []) + if stems_bad and skip_mgr is not None: + skip_mgr.add(canonical_stem(stem) for stem in stems_bad) + worker = event.get("worker") try: - manager.shutdown() + worker_gpu = int(worker) except Exception: - pass - if worker_log_dir_env is not None: - os.environ["GLOSSAPI_WORKER_LOG_DIR"] = worker_log_dir_env - else: - os.environ.pop("GLOSSAPI_WORKER_LOG_DIR", None) - return - # Single-GPU path - self.formula_enrich_from_json( - files=stems, - device=(device or "cuda"), - batch_size=int(math_batch_size), - dpi_base=int(math_dpi_base), - targets_by_stem=local_targets, - ) - - # Branches - if mode_norm == "math_only": - if not math_enhance: - self.logger.info("OCR: fix_bad=False and math_enhance=False → nothing to do") - return - # Math-only: ensure JSON exists; if not, generate without OCR - json_dir = self.output_dir / "json" - stems: List[str] = [] - if json_dir.exists(): - stems = sorted({canonical_stem(p) for p in json_dir.glob("*.docling.json*")}) - # Do not generate layout JSON here; Phase‑1 is responsible for JSON artifacts. - # Never run math on files that need OCR - if bad_files: - before = len(stems) - bad_set = {canonical_stem(s) for s in bad_files} - stems = [s for s in stems if s not in bad_set] - removed = before - len(stems) - if removed: - try: - self.logger.info( - "Math-only: skipping %d document(s) flagged for OCR", - removed, - ) - except Exception: - pass - if not reprocess_completed and stems and parquet_meta is not None: - if math_done_stems: - before = len(stems) - stems = [s for s in stems if s not in math_done_stems] - removed = before - len(stems) - if removed: - self.logger.info( - "Math enrichment: skipping %d already enriched document(s) (reprocess_completed=False).", - removed, + worker_gpu = None + if worker_gpu is not None: + status_map.pop(worker_gpu, None) + marker_path = marker_files.get(worker_gpu) + if marker_path: + try: + marker_path.unlink(missing_ok=True) + except Exception: + pass + elif event.get("event") == "exit" and event.get("exitcode", 0) not in (0, None): + self.logger.warning( + "Math worker GPU%s reported exit code %s", + event.get("worker"), + event.get("exitcode"), ) - _run_math(stems) - return - # 'ocr_bad' and 'ocr_bad_then_math' paths: OCR bad files first - if mode_norm in {"ocr_bad", "ocr_bad_then_math"} and not bad_files: - self.logger.info("OCR: no bad documents flagged by cleaner; skipping OCR fix") - if mode_norm == "ocr_bad_then_math": - json_dir = self.output_dir / "json" - stems = [] - if json_dir.exists(): - stems = sorted({canonical_stem(p) for p in json_dir.glob("*.docling.json*")}) - _run_math(stems) - return - - reran_ocr = False - - if mode_norm in {"ocr_bad", "ocr_bad_then_math"}: - if backend_norm == "deepseek": - # DeepSeek path: run OCR via dedicated runner (no Docling JSON) - from glossapi.ocr.deepseek import runner as _deepseek_runner # type: ignore - - try: - _deepseek_runner.run_for_files( - self, - bad_files, - model_dir=Path(model_dir) if model_dir else None, - content_debug=bool(content_debug), - ) - except Exception as _e: - self.logger.error("DeepSeek OCR runner failed: %s", _e) - raise - else: - # RapidOCR/Docling path via Phase-1 extract - self.extract( - input_format="pdf", - num_threads=os.cpu_count() or 4, - accel_type="CUDA", - force_ocr=True, - formula_enrichment=False, - code_enrichment=False, - filenames=bad_files, - skip_existing=False, - use_gpus=use_gpus, - devices=devices, - # Do not generate Docling JSON for OCR targets; math will skip them - export_doc_json=False, - emit_formula_index=False, - phase1_backend="docling", - ) - reran_ocr = True - # Update metadata to reflect successful OCR reruns - try: - from glossapi.parquet_schema import ParquetSchema as _ParquetSchema - - success_files: List[str] = [] - for _fname in bad_files: - stem = canonical_stem(_fname) - if (self.markdown_dir / f"{stem}.md").exists(): - success_files.append(_fname) - - if success_files: - parquet_schema = _ParquetSchema({"url_column": self.url_column}) - parquet_path = self._resolve_metadata_parquet(parquet_schema, ensure=True, search_input=True) - if parquet_path and parquet_path.exists(): - import pandas as _pd - - df_meta = _pd.read_parquet(parquet_path) - if "filename" in df_meta.columns: - if "filter" not in df_meta.columns: - df_meta["filter"] = "ok" - if "needs_ocr" not in df_meta.columns: - df_meta["needs_ocr"] = False - if "ocr_success" not in df_meta.columns: - df_meta["ocr_success"] = False - if "extraction_mode" not in df_meta.columns: - df_meta["extraction_mode"] = None - for _fname in success_files: - mask = df_meta["filename"].astype(str) == str(_fname) - if mask.any(): - df_meta.loc[mask, "filter"] = "ok" - df_meta.loc[mask, "needs_ocr"] = False - df_meta.loc[mask, "ocr_success"] = True - if backend_norm == "deepseek": - df_meta.loc[mask, "extraction_mode"] = "deepseek" - self._cache_metadata_parquet(parquet_path) - parquet_schema.write_metadata_parquet(df_meta, parquet_path) - # Keep sectioner in sync with newly recovered files + now = time.time() + if now - last_summary > 30: try: - stems = [canonical_stem(_f) for _f in success_files] - if hasattr(self, "good_files"): - for _stem in stems: - if _stem not in getattr(self, "good_files", []): - self.good_files.append(_stem) - except Exception: - pass - except Exception as _e: - self.logger.warning("Failed to update OCR success metadata: %s", _e) + qsize = work_q.qsize() + except NotImplementedError: + qsize = -1 + self.logger.info( + "Math progress: queue=%d active_workers=%d", + qsize, + len(active), + ) + last_summary = now - if reran_ocr: + if not active: + break + + remaining_after_cap: List[str] = [] try: - self.logger.info("Re-running Rust cleaner after OCR rerun to refresh metrics") - self.clean( - input_dir=self.markdown_dir, - drop_bad=False, + while True: + item = work_q.get_nowait() + if isinstance(item, str) and item.strip(): + remaining_after_cap.append(item) + except queue.Empty: + pass + if remaining_after_cap: + if skip_mgr is not None: + skip_mgr.add(canonical_stem(stem) for stem in remaining_after_cap) + self.logger.error( + "No active math workers remain; skipped %d pending item(s)", + len(remaining_after_cap), ) - except Exception as _e: - self.logger.warning("Cleaner refresh after OCR failed: %s", _e) - - if mode_norm == "ocr_bad_then_math": + finally: + for proc in procs: + if proc.is_alive(): + proc.join() try: - # Run math only on documents that do NOT require OCR - json_dir = self.output_dir / "json" - stems: List[str] = [] - if json_dir.exists(): - stems = sorted({canonical_stem(p) for p in json_dir.glob("*.docling.json*")}) - bad_set = {canonical_stem(f) for f in bad_files} - if stems: - # When OCR was rerun we now want math on all stems (bad_set included). - # Only skip bad_set when no rerun happened. - if not reran_ocr: - stems = [s for s in stems if s not in bad_set] - if not reprocess_completed: - if math_done_stems: - before = len(stems) - stems = [s for s in stems if s not in math_done_stems] - removed = before - len(stems) - if removed: - self.logger.info( - "Math enrichment: skipping %d already enriched document(s) (reprocess_completed=False).", - removed, - ) - if not stems: - self.logger.info("Math enrichment: no pending documents after filtering.") - return - # Best-effort: ensure placeholder sidecars for metadata-selected math targets - try: - from glossapi.parquet_schema import ParquetSchema as _ParquetSchema - _ps = _ParquetSchema({"url_column": self.url_column}) - _pq = self._resolve_metadata_parquet(_ps, ensure=True, search_input=True) - except Exception: - _pq = None - if _pq and _pq.exists(): - try: - import pandas as _pd, json as _json - _df = _pd.read_parquet(_pq) - if "filename" in _df.columns: - _df['stem'] = _df['filename'].astype(str).str.replace(r"\.pdf$", "", regex=True) - _phase = _df['phase_recommended'].astype(str) == '2A' if 'phase_recommended' in _df.columns else ((_df['filename'] == _df['filename']) & False) - _ft = (_df['formula_total'].fillna(0).astype('float') > 0) if 'formula_total' in _df.columns else ((_df['filename'] == _df['filename']) & False) - _med = (_df['math_equations_detected'].fillna(0).astype('float') > 0) if 'math_equations_detected' in _df.columns else ((_df['filename'] == _df['filename']) & False) - _mask = _phase | _ft | _med - _parq_stems = set(_df.loc[_mask, 'stem'].dropna().astype(str).tolist()) - if _parq_stems: - sc_dir = self.output_dir / 'sidecars' / 'math' - sc_dir.mkdir(parents=True, exist_ok=True) - for _s in (set(stems) | _parq_stems): - _p = sc_dir / f"{_s}.json" - if not _p.exists(): - _p.write_text(_json.dumps({"items": 0, "accepted": 0, "time_sec": 0.0}, ensure_ascii=False), encoding='utf-8') - except Exception: - pass - try: - self.logger.info("OCR: invoking Phase-2 math for stems: %s", ",".join(stems)) - except Exception: - pass - _run_math(stems) - try: - self.logger.info("OCR: Phase-2 math completed for stems: %s", ",".join(stems)) - except Exception: - pass - except Exception as _e: - self.logger.warning("Phase‑2 enrichment after OCR failed: %s", _e) + manager.shutdown() + except Exception: + pass + if worker_log_dir_env is not None: + os.environ["GLOSSAPI_WORKER_LOG_DIR"] = worker_log_dir_env + else: + os.environ.pop("GLOSSAPI_WORKER_LOG_DIR", None) def formula_enrich_from_json( self, diff --git a/src/glossapi/corpus/text_surface_metrics.py b/src/glossapi/corpus/text_surface_metrics.py new file mode 100644 index 0000000..ac83696 --- /dev/null +++ b/src/glossapi/corpus/text_surface_metrics.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +import re + +HTML_COMMENT_RE = re.compile(r"", re.S) +INLINE_DISPLAY_MATH_RE = re.compile(r"(\\\[.*?\\\])|(\\\(.*?\\\))|(\$\$.*?\$\$)", re.S) +CHAR_COUNT_LATEX_ENV_NAMES = ( + "equation", + "equation*", + "align", + "align*", + "gather", + "gather*", + "multline", + "multline*", + "eqnarray", + "eqnarray*", + "comment", +) + + +def _strip_latex_envs_for_char_count(text: str) -> str: + cleaned = text + for env in CHAR_COUNT_LATEX_ENV_NAMES: + escaped = re.escape(env) + cleaned = re.sub( + rf"\\begin\{{{escaped}\}}.*?\\end\{{{escaped}\}}", + "", + cleaned, + flags=re.S, + ) + return cleaned + + +def sanitized_char_count(content: str) -> tuple[int, bool]: + """Return export-facing non-whitespace char count and emptiness for text. + + The cleaner, export-facing metadata refresh, and OpenArchives patching must + all agree on this contract so they describe the exact published text + surface. + """ + + sanitized = HTML_COMMENT_RE.sub("", content) + sanitized = _strip_latex_envs_for_char_count(sanitized) + sanitized = INLINE_DISPLAY_MATH_RE.sub("", sanitized) + count = sum(1 for ch in sanitized if not ch.isspace()) + return count, count == 0 + diff --git a/src/glossapi/download_policy.py b/src/glossapi/download_policy.py new file mode 100644 index 0000000..36d3ce6 --- /dev/null +++ b/src/glossapi/download_policy.py @@ -0,0 +1,135 @@ +"""Policy routing for downloader selection.""" + +from __future__ import annotations + +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, Iterable, Optional +from urllib.parse import urlparse + +import yaml + +VALID_DOWNLOADERS = {"standard", "browser", "auto"} +ROUTE_OPTION_KEYS = { + "request_timeout", + "ssl_verify", + "ssl_cafile", + "request_method", + "sleep", + "per_domain_concurrency", + "domain_concurrency_floor", + "domain_concurrency_ceiling", + "skip_failed_after", + "domain_cookies", + "browser_timeout_ms", + "browser_post_load_wait_ms", + "browser_engine", + "browser_headless", + "browser_session_ttl_seconds", +} + + +def _normalize_downloader(value: Any, default: str = "standard") -> str: + normalized = str(value or default).strip().lower() + if normalized in {"default", "http"}: + normalized = "standard" + if normalized in {"browser_fallback"}: + normalized = "auto" + if normalized in {"browser_protected"}: + normalized = "browser" + if normalized not in VALID_DOWNLOADERS: + raise ValueError(f"Unsupported downloader route: {value}") + return normalized + + +@dataclass(frozen=True) +class DownloadPolicyMatch: + domains: tuple[str, ...] = () + url_regex: Optional[re.Pattern[str]] = None + + def matches(self, url: str) -> bool: + parsed = urlparse(url) + hostname = (parsed.hostname or "").lower() + if self.domains: + matched_domain = any( + hostname == domain or hostname.endswith(f".{domain}") + for domain in self.domains + ) + if not matched_domain: + return False + if self.url_regex and not self.url_regex.search(url): + return False + return True + + +@dataclass(frozen=True) +class DownloadPolicyRule: + matcher: DownloadPolicyMatch + downloader: str + options: Dict[str, Any] + + def matches(self, url: str) -> bool: + return self.matcher.matches(url) + + +@dataclass(frozen=True) +class DownloadPolicy: + default_downloader: str = "standard" + default_options: Dict[str, Any] | None = None + rules: tuple[DownloadPolicyRule, ...] = () + + def resolve(self, url: str) -> tuple[str, Dict[str, Any]]: + for rule in self.rules: + if rule.matches(url): + return rule.downloader, dict(rule.options) + return self.default_downloader, dict(self.default_options or {}) + + +def _extract_route_options(data: Dict[str, Any]) -> Dict[str, Any]: + return {key: value for key, value in data.items() if key in ROUTE_OPTION_KEYS} + + +def _build_matcher(raw: Dict[str, Any]) -> DownloadPolicyMatch: + domains = tuple(str(item).strip().lower() for item in (raw.get("domains") or []) if str(item).strip()) + url_regex = raw.get("url_regex") + compiled = re.compile(str(url_regex)) if url_regex else None + return DownloadPolicyMatch(domains=domains, url_regex=compiled) + + +def build_download_policy(data: Dict[str, Any]) -> DownloadPolicy: + default_block = dict(data.get("default") or {}) + default_downloader = _normalize_downloader(default_block.get("downloader"), default="standard") + default_options = _extract_route_options(default_block) + + rules = [] + for raw_rule in data.get("rules") or []: + raw_rule = dict(raw_rule or {}) + matcher = _build_matcher(dict(raw_rule.get("match") or {})) + downloader = _normalize_downloader(raw_rule.get("downloader"), default=default_downloader) + options = _extract_route_options(raw_rule) + rules.append(DownloadPolicyRule(matcher=matcher, downloader=downloader, options=options)) + + return DownloadPolicy( + default_downloader=default_downloader, + default_options=default_options, + rules=tuple(rules), + ) + + +def load_download_policy(path: str | Path) -> DownloadPolicy: + policy_path = Path(path).expanduser().resolve() + payload = yaml.safe_load(policy_path.read_text(encoding="utf-8")) or {} + if not isinstance(payload, dict): + raise ValueError("Download policy file must define a mapping at the top level") + return build_download_policy(payload) + + +__all__ = [ + "DownloadPolicy", + "DownloadPolicyMatch", + "DownloadPolicyRule", + "VALID_DOWNLOADERS", + "build_download_policy", + "load_download_policy", +] diff --git a/src/glossapi/gloss_browser_downloader.py b/src/glossapi/gloss_browser_downloader.py new file mode 100644 index 0000000..66a7c6e --- /dev/null +++ b/src/glossapi/gloss_browser_downloader.py @@ -0,0 +1,527 @@ +"""Browser-capable downloader mode for browser-gated file endpoints.""" + +from __future__ import annotations + +import asyncio +import io +import json +import os +import re +import time +from dataclasses import dataclass +from urllib.parse import urlparse +from typing import Any, Dict, Optional, Tuple + +import aiofiles +import aiohttp +from PIL import Image + +from .download_policy import DownloadPolicy, load_download_policy +from .gloss_downloader import GlossDownloader + + +@dataclass +class BrowserSessionState: + user_agent: str + cookie_header: str + cached_at: float + + +class BrowserGlossDownloader(GlossDownloader): + """ + Downloader variant that retries browser-gated file endpoints via Playwright. + + This mode only targets file endpoints that are protected by browser/session + checks. It intentionally does not attempt viewer-style extraction. + """ + + def __init__( + self, + *args, + browser_timeout_ms: int = 60000, + browser_post_load_wait_ms: int = 3000, + browser_engine: str = "chromium", + browser_headless: bool = True, + browser_session_ttl_seconds: int = 900, + browser_max_parallel_bootstraps: int = 2, + default_download_route: str = "auto", + **kwargs, + ): + super().__init__(*args, **kwargs) + self.browser_timeout_ms = int(browser_timeout_ms) + self.browser_post_load_wait_ms = int(browser_post_load_wait_ms) + self.browser_engine = str(browser_engine or "chromium") + self.browser_headless = bool(browser_headless) + self.browser_session_ttl_seconds = int(browser_session_ttl_seconds) + self.browser_max_parallel_bootstraps = max(1, int(browser_max_parallel_bootstraps)) + self.browser_bootstrap_semaphore = asyncio.Semaphore(self.browser_max_parallel_bootstraps) + self._browser_session_cache: Dict[str, BrowserSessionState] = {} + self._browser_session_locks: Dict[str, asyncio.Lock] = {} + self.default_download_route = str(default_download_route or "auto").strip().lower() + self.policy = self._load_policy() + + def _load_policy(self) -> Optional[DownloadPolicy]: + if self.download_policy is not None: + return self.download_policy + if self.download_policy_file: + return load_download_policy(self.download_policy_file) + return None + + def _resolve_route(self, url: str) -> tuple[str, Dict[str, Any]]: + if self.policy is not None: + return self.policy.resolve(url) + return self.default_download_route, {} + + def _route_setting(self, route_options: Dict[str, Any], name: str, fallback: Any) -> Any: + return route_options.get(name, fallback) + + def _domain_key(self, url: str) -> str: + return self._extract_base_domain(url) or (urlparse(url).hostname or "").lower() + + def _choose_browser_bootstrap_url(self, url: str) -> str: + if self._url_looks_like_file_endpoint(url): + return self.get_base_url(url) + return url + + def _should_ignore_navigation_exception(self, url: str, exc: Exception) -> bool: + message = str(exc) + if self._url_looks_like_file_endpoint(url) and "net::ERR_ABORTED" in message: + return True + return False + + def _session_lock_for_domain(self, domain_key: str) -> asyncio.Lock: + lock = self._browser_session_locks.get(domain_key) + if lock is None: + lock = asyncio.Lock() + self._browser_session_locks[domain_key] = lock + return lock + + def _is_browser_session_fresh(self, state: BrowserSessionState, route_options: Dict[str, Any]) -> bool: + ttl = int(self._route_setting(route_options, "browser_session_ttl_seconds", self.browser_session_ttl_seconds)) + if ttl <= 0: + return False + return (time.time() - state.cached_at) < ttl + + def _should_attempt_browser_recovery(self, url: str, html_issue: str) -> bool: + issue = str(html_issue or "").lower() + if "document viewer returned" in issue: + return False + if "challenge page returned" in issue: + return True + if "cookie bootstrap is required" in issue: + return True + if "expected a file-like response but received html instead" in issue: + return self._url_looks_like_file_endpoint(url) + return False + + def _extract_academy_document_id(self, url: str) -> Optional[str]: + parsed = urlparse(str(url or "")) + host = (parsed.hostname or "").lower() + if host != "repository.academyofathens.gr": + return None + match = re.match(r"^/document/(\d+)(?:\.pdf)?/?$", parsed.path or "") + if not match: + return None + return match.group(1) + + async def _fetch_bytes(self, session: aiohttp.ClientSession, url: str) -> bytes: + async with session.get(url, timeout=aiohttp.ClientTimeout(total=min(max(self.request_timeout, 60), 180))) as response: + response.raise_for_status() + return await response.read() + + def _academy_images_to_pdf_bytes(self, image_blobs: list[bytes]) -> bytes: + if not image_blobs: + raise RuntimeError("No Academy image pages available to synthesize PDF") + images = [] + try: + for blob in image_blobs: + img = Image.open(io.BytesIO(blob)).convert("RGB") + images.append(img) + out = io.BytesIO() + images[0].save(out, format="PDF", save_all=True, append_images=images[1:]) + return out.getvalue() + finally: + for img in images: + try: + img.close() + except Exception: + pass + + async def _download_academy_bookreader_pdf(self, url: str) -> Optional[bytes]: + item_id = self._extract_academy_document_id(url) + if not item_id: + return None + + candidate_bases = [ + "https://repo.academyofathens.gr", + "https://digitallibrary.academyofathens.gr", + ] + connector = self._build_ssl_connector() + headers = {"User-Agent": "Mozilla/5.0", "Accept": "application/json,*/*"} + async with aiohttp.ClientSession(connector=connector, headers=headers) as session: + for base_url in candidate_bases: + try: + payload_bytes = await self._fetch_bytes(session, f"{base_url}/archive/bookreader_options/{item_id}") + payload = json.loads(payload_bytes.decode("utf-8", errors="ignore")) + except Exception: + continue + + page_data = payload.get("data") + if not isinstance(page_data, list) or not page_data: + continue + + image_urls: list[str] = [] + for page in page_data: + if not page or not isinstance(page, list): + continue + first = page[0] if page else None + uri = first.get("uri") if isinstance(first, dict) else None + if not uri: + continue + image_urls.append(uri if uri.startswith("http") else f"{base_url}{uri}") + + if not image_urls: + continue + + image_blobs: list[bytes] = [] + try: + for image_url in image_urls: + image_blobs.append(await self._fetch_bytes(session, image_url)) + except Exception: + continue + + try: + return await asyncio.to_thread(self._academy_images_to_pdf_bytes, image_blobs) + except Exception: + continue + return None + + async def _recover_source_specific_html_interstitial( + self, + *, + row_index: int, + url: str, + retry_count: int, + filename_base: Optional[str], + ) -> Optional[Tuple[bool, str, str, str, int]]: + pdf_body = await self._download_academy_bookreader_pdf(url) + if not pdf_body: + return None + + filename = f"{filename_base}.pdf" if filename_base and str(filename_base).strip() else self.generate_filename(row_index, "pdf") + await self._write_recovered_file(row_index, filename, pdf_body) + self.logger.info("Recovered Academy document via bookreader image->PDF fallback: %s -> %s", url, filename) + return True, filename, "pdf", "", retry_count + + def _build_ssl_connector(self) -> Optional[aiohttp.TCPConnector]: + connector = None + if not self.ssl_verify: + connector = aiohttp.TCPConnector(ssl=False) + elif self.ssl_cafile: + import ssl as _ssl + + ctx = _ssl.create_default_context(cafile=self.ssl_cafile) + connector = aiohttp.TCPConnector(ssl=ctx) + return connector + + def _domain_cookies_for_url(self, url: str) -> Dict[str, str]: + cookies: Dict[str, str] = {} + for domain_pattern, domain_cookies in self.domain_cookies.items(): + if domain_pattern in url: + cookies.update(domain_cookies) + return cookies + + async def _write_recovered_file(self, row_index: int, filename: str, body: bytes) -> None: + tmp_path = self.downloads_dir / f".part_browser_{row_index}" + async with aiofiles.open(tmp_path, "wb") as handle: + await handle.write(body) + final_path = self.downloads_dir / filename + os.replace(tmp_path, final_path) + + async def _fetch_with_browser_session_state( + self, + *, + url: str, + referer: Optional[str], + state: BrowserSessionState, + ) -> Tuple[bytes, Dict[str, str], Dict[str, Any]]: + request_headers = { + "User-Agent": state.user_agent, + "Accept": "application/pdf,application/octet-stream,*/*;q=0.8", + } + if state.cookie_header: + request_headers["Cookie"] = state.cookie_header + if referer: + request_headers["Referer"] = referer + + connector = self._build_ssl_connector() + timeout = aiohttp.ClientTimeout(total=min(max(self.request_timeout, 30), 180)) + async with aiohttp.ClientSession(connector=connector) as session: + async with session.get(url, headers=request_headers, timeout=timeout) as response: + response.raise_for_status() + body = await response.read() + response_headers = {str(k): str(v) for k, v in (response.headers or {}).items()} + return body, response_headers, {"candidate_url": url, "session_reused": True} + + async def _bootstrap_browser_session_state( + self, + *, + url: str, + referer: Optional[str], + route_options: Dict[str, Any], + ) -> tuple[BrowserSessionState, list[tuple[str, Dict[str, str], str]]]: + timeout_ms = int(self._route_setting(route_options, "browser_timeout_ms", self.browser_timeout_ms)) + post_load_wait_ms = int( + self._route_setting(route_options, "browser_post_load_wait_ms", self.browser_post_load_wait_ms) + ) + browser_engine = str(self._route_setting(route_options, "browser_engine", self.browser_engine)) + browser_headless = bool(self._route_setting(route_options, "browser_headless", self.browser_headless)) + + try: + from playwright.async_api import async_playwright + except ImportError as exc: # pragma: no cover - exercised via monkeypatch + raise RuntimeError( + "Browser download mode requires the optional 'browser' dependencies " + "(install Playwright and browser binaries)" + ) from exc + + accepted_responses: list[tuple[str, Dict[str, str], str]] = [] + bootstrap_url = self._choose_browser_bootstrap_url(url) + + async with self.browser_bootstrap_semaphore: + async with async_playwright() as playwright: + browser_type = getattr(playwright, browser_engine, None) + if browser_type is None: + raise RuntimeError(f"Unsupported browser engine: {browser_engine}") + + browser = await browser_type.launch(headless=browser_headless) + context = await browser.new_context(ignore_https_errors=not self.ssl_verify) + parsed = urlparse(url) + browser_cookies = [ + { + "name": key, + "value": str(value), + "domain": parsed.hostname or "", + "path": "/", + } + for key, value in self._domain_cookies_for_url(url).items() + ] + if browser_cookies: + await context.add_cookies(browser_cookies) + page = await context.new_page() + if referer: + await page.set_extra_http_headers({"Referer": referer}) + + async def _route_filter(route: Any) -> None: + req = route.request + if req.resource_type in {"image", "media", "font"}: + await route.abort() + return + req_url = str(req.url or "") + if "googletagmanager" in req_url or "google-analytics.com" in req_url: + await route.abort() + return + await route.continue_() + + await page.route("**/*", _route_filter) + + def _record_response(response: Any) -> None: + try: + response_headers = {str(k): str(v) for k, v in (response.headers or {}).items()} + file_ext = self.infer_file_extension(response.url, response_headers, b"") + if file_ext and file_ext != "html" and self.is_supported_format(file_ext): + accepted_responses.append((response.url, response_headers, file_ext)) + except Exception: + return + + page.on("response", _record_response) + + try: + main_response = None + try: + main_response = await page.goto(bootstrap_url, wait_until="networkidle", timeout=timeout_ms) + except Exception as exc: + if not self._should_ignore_navigation_exception(bootstrap_url, exc): + raise + if main_response is not None: + main_headers = {str(k): str(v) for k, v in (main_response.headers or {}).items()} + main_ext = self.infer_file_extension(main_response.url, main_headers, b"") + if main_ext and main_ext != "html" and self.is_supported_format(main_ext): + accepted_responses.insert(0, (main_response.url, main_headers, main_ext)) + if not accepted_responses and post_load_wait_ms > 0: + await page.wait_for_timeout(post_load_wait_ms) + + browser_user_agent = await page.evaluate("() => navigator.userAgent") + browser_cookies = await context.cookies() + finally: + await browser.close() + + cookie_header = "; ".join( + f"{cookie['name']}={cookie['value']}" for cookie in browser_cookies if cookie.get("name") + ) + return BrowserSessionState( + user_agent=browser_user_agent, + cookie_header=cookie_header, + cached_at=time.time(), + ), accepted_responses + + async def _download_via_browser_session( + self, + *, + url: str, + referer: Optional[str], + route_options: Optional[Dict[str, Any]] = None, + force_refresh: bool = False, + ) -> Tuple[bytes, Dict[str, str], Dict[str, Any]]: + options = dict(route_options or {}) + domain_key = self._domain_key(url) + state = self._browser_session_cache.get(domain_key) + if state and self._is_browser_session_fresh(state, options) and not force_refresh: + try: + return await self._fetch_with_browser_session_state(url=url, referer=referer, state=state) + except Exception: + pass + + lock = self._session_lock_for_domain(domain_key) + async with lock: + state = self._browser_session_cache.get(domain_key) + if state and self._is_browser_session_fresh(state, options) and not force_refresh: + try: + return await self._fetch_with_browser_session_state(url=url, referer=referer, state=state) + except Exception: + pass + + state, accepted_responses = await self._bootstrap_browser_session_state( + url=url, + referer=referer, + route_options=options, + ) + self._browser_session_cache[domain_key] = state + candidate_url = accepted_responses[0][0] if accepted_responses else url + body, response_headers, meta = await self._fetch_with_browser_session_state( + url=candidate_url, + referer=referer, + state=state, + ) + meta.update({ + "candidate_url": candidate_url, + "session_reused": False, + "domain_key": domain_key, + }) + return body, response_headers, meta + + async def _download_browser_route( + self, + *, + row_index: int, + url: str, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + route_options: Dict[str, Any], + ) -> Tuple[bool, str, str, str, int]: + try: + body, response_headers, meta = await self._download_via_browser_session( + url=url, + referer=referer, + route_options=route_options, + ) + except Exception as exc: + error_msg = f"Browser-routed download failed: {exc}" + self.logger.warning(error_msg) + return False, "", self._best_effort_url_extension(url), error_msg, retry_count + 1 + return await self._finalize_download_result( + row_index=row_index, + url=meta.get("candidate_url") or url, + resp_headers=response_headers, + content=body, + retry_count=retry_count, + filename_base=filename_base, + referer=referer, + ) + + async def _preflight_download( + self, + *, + row_index: int, + url: str, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + ) -> Optional[Tuple[bool, str, str, str, int]]: + route, route_options = self._resolve_route(url) + if route != "browser": + return None + return await self._download_browser_route( + row_index=row_index, + url=url, + retry_count=retry_count, + filename_base=filename_base, + referer=referer, + route_options=route_options, + ) + + async def _recover_html_interstitial( + self, + *, + row_index: int, + url: str, + headers: Dict[str, str], + content: bytes, + html_issue: str, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + ) -> Optional[Tuple[bool, str, str, str, int]]: + source_specific = await self._recover_source_specific_html_interstitial( + row_index=row_index, + url=url, + retry_count=retry_count, + filename_base=filename_base, + ) + if source_specific is not None: + return source_specific + + route, route_options = self._resolve_route(url) + if route == "standard": + return None + if route == "auto" and not self._should_attempt_browser_recovery(url, html_issue): + return None + + try: + body, response_headers, meta = await self._download_via_browser_session( + url=url, + referer=referer, + route_options=route_options, + ) + except Exception as exc: + message = f"{html_issue}; browser recovery failed: {exc}" + self.logger.warning(message) + return False, "", "html", message, retry_count + 1 + + file_ext = self.infer_file_extension(meta["candidate_url"], response_headers, body) + if file_ext == "html": + message = ( + f"{html_issue}; browser recovery still returned HTML from {meta['candidate_url']}" + ) + self.logger.warning(message) + return False, "", file_ext, message, retry_count + 1 + if not self.is_supported_format(file_ext): + message = ( + f"{html_issue}; browser recovery returned unsupported format: {file_ext}" + ) + self.logger.warning(message) + return False, "", file_ext or "", message, retry_count + 1 + + if filename_base and str(filename_base).strip(): + filename = f"{filename_base}.{file_ext}" + else: + filename = self.generate_filename(row_index, file_ext) + + await self._write_recovered_file(row_index, filename, body) + self.logger.info( + "Recovered browser-gated download via browser mode: %s -> %s", + url, + filename, + ) + return True, filename, file_ext, "", retry_count diff --git a/src/glossapi/gloss_downloader.py b/src/glossapi/gloss_downloader.py index f9a7bf2..45f0d39 100644 --- a/src/glossapi/gloss_downloader.py +++ b/src/glossapi/gloss_downloader.py @@ -141,6 +141,8 @@ def __init__( error_burst_window: int = 20, error_burst_threshold: float = 0.5, park_403_seconds: float = 600.0, + download_policy_file: Optional[Union[str, Path]] = None, + download_policy: Optional[Any] = None, _used_filename_bases: Optional[Set[str]] = None, ): """ @@ -241,6 +243,8 @@ def verbose_log(self, message, level=logging.DEBUG): self.checkpoint_seconds = float(checkpoint_seconds) if checkpoint_seconds else None # Warnings JSON path self.domain_warnings_path = self.output_dir / 'domain_scheduler_warnings.json' + self.download_policy_file = Path(download_policy_file).expanduser().resolve() if download_policy_file else None + self.download_policy = download_policy # Progress logger (separate file; default to output logs dir) self.progress_logger = self.logger @@ -530,12 +534,47 @@ def _extract_base_domain(self, url: str) -> str: except Exception: return '' + def _resolve_route(self, url: str) -> tuple[str, Dict[str, Any]]: + return "standard", {} + + def _route_setting(self, route_options: Optional[Dict[str, Any]], name: str, fallback: Any) -> Any: + if route_options and name in route_options: + return route_options[name] + return fallback + + def _resolve_domain_scheduler_settings( + self, + route_options: Optional[Dict[str, Any]], + ) -> tuple[int, int, int, int]: + floor = max( + 1, + int(self._route_setting(route_options, "domain_concurrency_floor", self.domain_concurrency_floor)), + ) + raw_ceiling = self._route_setting(route_options, "domain_concurrency_ceiling", self.domain_concurrency_ceiling) + if raw_ceiling is None: + ceiling = max(floor, int(self.domain_concurrency_ceiling)) + else: + ceiling = max(floor, int(raw_ceiling)) + start = max( + floor, + min( + int(self._route_setting(route_options, "per_domain_concurrency", self.per_domain_concurrency)), + max(1, self.concurrency), + ceiling, + ), + ) + skip_after = max(1, int(self._route_setting(route_options, "skip_failed_after", self.skip_failed_after))) + return floor, ceiling, start, skip_after + @dataclass class _DomainState: base: str queue: deque = field(default_factory=deque) active: int = 0 concurrency: int = 1 + concurrency_floor: int = 1 + concurrency_ceiling: int = 1 + skip_failed_after: int = 3 successes: int = 0 failures: int = 0 http_429: int = 0 @@ -713,15 +752,17 @@ def _ext_from_magic_bytes(self, content: bytes) -> Optional[str]: if not content: return None head = content[:4096] - # PDF - if head.startswith(b'%PDF-'): + lower_head = head.lower() + lstripped = lower_head.lstrip() + # PDF: allow a small junk prefix before the real header. + pdf_idx = head.find(b'%PDF-') + if 0 <= pdf_idx <= 1024: return 'pdf' # HTML (very simple heuristic) - lower_head = head.lower() - if b' Optional[str]: pass return None + def _looks_like_pdf_bytes(self, content: bytes) -> bool: + """Lightweight PDF sanity check for content we are about to persist as a PDF.""" + if not content: + return False + head = content[:4096] + pdf_idx = head.find(b'%PDF-') + return 0 <= pdf_idx <= 1024 + def infer_file_extension(self, url: str, headers: Dict[str, str], content: bytes) -> str: """Infer the most likely file extension using URL, headers and content bytes""" + # Strong content sniffing first for the two cases that matter most here: + # real PDFs and HTML bodies masquerading as direct-file endpoints. + sniff_ext = self._ext_from_magic_bytes(content) + if sniff_ext == 'pdf': + return 'pdf' + if sniff_ext == 'html': + return 'html' + # 1) URL path extension url_ext = self.get_file_extension_from_url(url) if self.is_supported_format(url_ext): @@ -758,48 +815,125 @@ def infer_file_extension(self, url: str, headers: Dict[str, str], content: bytes if ct_ext and self.is_supported_format(ct_ext): return ct_ext - # 4) Magic byte sniffing - sniff_ext = self._ext_from_magic_bytes(content) + # 4) Magic byte sniffing for the remaining supported formats if sniff_ext and self.is_supported_format(sniff_ext): return sniff_ext # 5) Fall back to URL ext if any, otherwise 'bin' return url_ext if url_ext else 'bin' - - async def download_file(self, row_index: int, url: str, semaphore: Optional[asyncio.Semaphore], - rate_limiter: RateLimiter, retry_count: int = 0, - filename_base: Optional[str] = None, - referer: Optional[str] = None) -> Tuple[bool, str, str, str, int]: + + def _url_looks_like_file_endpoint(self, url: str) -> bool: + """Return True when the URL shape suggests a direct file download endpoint.""" + try: + lowered = str(url or "").lower() + except Exception: + return False + hints = ( + ".pdf", + ".docx", + ".pptx", + ".xml", + ".csv", + "/pdf", + "format=pdf", + "type=pdf", + "download", + "attachment", + "/file", + "getfile.php", + ) + return any(token in lowered for token in hints) + + def _detect_html_interstitial(self, url: str, headers: Dict[str, str], content: bytes) -> Optional[str]: """ - Download a file from a URL - - Args: - row_index: Index in the dataframe - url: URL to download - semaphore: Semaphore for concurrency control - rate_limiter: Rate limiter for API limits - retry_count: Current retry count - Returns: - Tuple[bool, str, str, str, int]: (success, filename, file_ext, error_message, retry_count) + Detect HTML challenge/viewer pages that should not count as successful downloads. + + We still allow regular HTML documents, but fail fast on common interstitials + such as WAF challenge pages and JavaScript-only document viewers. """ - if not url or pd.isna(url): - return False, "", "", "Empty URL", retry_count - - # Get a new user-agent for each request - user_agent = next(self.user_agents) - domain = urlparse(url).netloc - - # Ensure URL has scheme + try: + lower_headers = {str(k).lower(): str(v).lower() for k, v in (headers or {}).items()} + lower_body = (content or b"")[: 1 << 17].decode("utf-8", errors="ignore").lower() + except Exception: + lower_headers = {} + lower_body = "" + + if not lower_body: + return None + + if ( + "x-amzn-waf-action" in lower_headers + or "awswafintegration" in lower_body + or "challenge.js" in lower_body + or "verify that you're not a robot" in lower_body + or "making sure you're not a bot" in lower_body + or "making sure you're not a bot" in lower_body + or "/.within.website/" in lower_body + or "anubis" in lower_body + ): + return ( + "HTML challenge page returned instead of a document; " + "browser automation or cookie bootstrap is required" + ) + + viewer_markers = ( + "fliphtml5_pages", + "monitor:player:html5", + "javascript/loadingjs.js", + "javascript/main.js", + "bookconfig.totalpagecount", + "getfile.php?lib=", + ) + viewer_hits = sum(1 for marker in viewer_markers if marker in lower_body) + if viewer_hits >= 2: + return ( + "HTML document viewer returned instead of a downloadable file; " + "a source-specific fetcher with persisted cookies/redirect handling is required" + ) + + content_type = lower_headers.get("content-type", "") + if self._url_looks_like_file_endpoint(url) and "text/html" in content_type: + return "Expected a file-like response but received HTML instead" + + return None + + async def _recover_html_interstitial( + self, + *, + row_index: int, + url: str, + headers: Dict[str, str], + content: bytes, + html_issue: str, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + ) -> Optional[Tuple[bool, str, str, str, int]]: + """Allow subclasses to recover from HTML interstitials via alternate fetch modes.""" + return None + + async def _preflight_download( + self, + *, + row_index: int, + url: str, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + ) -> Optional[Tuple[bool, str, str, str, int]]: + """Allow subclasses to short-circuit the direct HTTP path for known routes.""" + return None + + def _normalize_request_url(self, url: str) -> str: if not url.startswith(("http://", "https://")): - url = f"https://{url}" - - # Get base URL for referer header + return f"https://{url}" + return url + + def _build_request_headers(self, url: str, user_agent: str, referer: Optional[str]) -> Dict[str, str]: + domain = urlparse(url).netloc base_url = self.get_base_url(url) - - # Enhanced headers with common browser-like attributes to bypass 403 errors - # Prefer caller-provided referer (e.g., the external_link page) - _referer = (referer or '').strip() - headers = { + referer_value = (referer or '').strip() + return { 'User-Agent': user_agent, 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8', 'Accept-Language': 'en-US,en;q=0.5', @@ -813,75 +947,328 @@ async def download_file(self, row_index: int, url: str, semaphore: Optional[asyn 'Pragma': 'no-cache', 'Cache-Control': 'no-cache', 'TE': 'trailers', - 'Referer': _referer if _referer else f"https://www.google.com/search?q={domain}", + 'Referer': referer_value if referer_value else f"https://www.google.com/search?q={domain}", 'Origin': base_url, 'DNT': '1' } - - # Check for domain-specific cookies - cookies = {} + + def _resolve_request_cookies(self, url: str, route_options: Optional[Dict[str, Any]] = None) -> Dict[str, str]: + cookies: Dict[str, str] = {} for domain_pattern, domain_cookies in self.domain_cookies.items(): if domain_pattern in url: cookies.update(domain_cookies) # If the domain needs dynamic values like random IDs - for key, value in cookies.items(): + for key, value in list(cookies.items()): if 'random.randint' in str(value): # Replace with an actual random value (only supporting this pattern for now) - if 'session-id' in value: + if 'session-id' in str(value): cookies[key] = f"session-id-{random.randint(100000000, 999999999)}" + extra_cookies = self._route_setting(route_options, "domain_cookies", None) + if isinstance(extra_cookies, dict): + cookies.update({str(k): str(v) for k, v in extra_cookies.items()}) + return cookies + + def _build_request_timeout( + self, + retry_count: int, + route_options: Optional[Dict[str, Any]] = None, + ) -> aiohttp.ClientTimeout: + base_request_timeout = float(self._route_setting(route_options, "request_timeout", self.request_timeout)) + return aiohttp.ClientTimeout( + total=min(base_request_timeout * (1.5 ** retry_count), 180), # Cap at 3 minutes + connect=min(30 * (1.2 ** retry_count), 60), # Cap connect timeout at 1 minute + sock_connect=min(30 * (1.2 ** retry_count), 60), # Cap socket connect at 1 minute + sock_read=min(60 * (1.2 ** retry_count), 120) # Cap socket read at 2 minutes + ) + + def _build_session_connector( + self, + url: str, + route_options: Optional[Dict[str, Any]] = None, + ) -> Optional[aiohttp.TCPConnector]: + connector = None + url_base = self._extract_base_domain(url) + force_insecure = url_base in getattr(self, '_domains_ssl_insecure', set()) + ssl_verify = bool(self._route_setting(route_options, "ssl_verify", self.ssl_verify)) + ssl_cafile = self._route_setting(route_options, "ssl_cafile", self.ssl_cafile) + if (not ssl_verify) or force_insecure: + connector = aiohttp.TCPConnector(ssl=False) + elif ssl_cafile: + import ssl as _ssl + ctx = _ssl.create_default_context(cafile=str(ssl_cafile)) + connector = aiohttp.TCPConnector(ssl=ctx) + return connector + + async def _bootstrap_download_session( + self, + session: aiohttp.ClientSession, + url: str, + headers: Dict[str, str], + route_options: Optional[Dict[str, Any]] = None, + ) -> Dict[str, str]: + headers = await self.setup_session(session, url, headers) + + # Set a shorter timeout for the initial connection attempt + base_timeout = aiohttp.ClientTimeout(total=10) + try: + # Visit the base domain to establish cookies if needed + base_domain = urlparse(url).netloc + all_cookie_domains = set(self.domain_cookies.keys()) + extra_cookies = self._route_setting(route_options, "domain_cookies", None) + if isinstance(extra_cookies, dict) and extra_cookies: + all_cookie_domains.add(base_domain) + if any(domain in base_domain for domain in all_cookie_domains): + base_url = f"https://{base_domain}" + async with session.get(base_url, headers=headers, timeout=base_timeout): + pass + except Exception as e: + # Non-fatal error, just log and continue + self.logger.debug(f"Initial base URL visit failed: {str(e)}") + return headers + + def _best_effort_url_extension(self, url: str) -> str: + try: + return self.get_file_extension_from_url(url) + except Exception: + return "" + + def _build_output_filename(self, row_index: int, file_ext: str, filename_base: Optional[str]) -> str: + if filename_base and str(filename_base).strip(): + return f"{filename_base}.{file_ext}" + return self.generate_filename(row_index, file_ext) + + def _cleanup_temp_file(self, tmp_path: Optional[Path]) -> None: + if not tmp_path: + return + try: + os.remove(tmp_path) + except Exception: + pass + + def _move_temp_file_to_final(self, tmp_path: Path, filename: str) -> None: + final_path = Path(self.downloads_dir) / filename + try: + os.replace(tmp_path, final_path) + except Exception: + try: + os.rename(tmp_path, final_path) + except Exception: + pass + + async def _finalize_download_result( + self, + *, + row_index: int, + url: str, + resp_headers: Dict[str, str], + content: bytes, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + tmp_path: Optional[Path] = None, + ) -> Tuple[bool, str, str, str, int]: + file_ext = self.infer_file_extension(url, resp_headers, content) + if file_ext == 'html': + html_issue = self._detect_html_interstitial(url, resp_headers, content) + if html_issue: + self._cleanup_temp_file(tmp_path) + recovered = await self._recover_html_interstitial( + row_index=row_index, + url=url, + headers=resp_headers, + content=content, + html_issue=html_issue, + retry_count=retry_count, + filename_base=filename_base, + referer=referer, + ) + if recovered is not None: + return recovered + self.logger.warning(f"HTML interstitial detected for {url}: {html_issue}") + return False, "", file_ext, html_issue, retry_count + if not self.is_supported_format(file_ext): + self._cleanup_temp_file(tmp_path) + self.logger.warning( + f"Unsupported file format after inference: {file_ext}. Supported formats: {', '.join(self.supported_formats)}" + ) + return False, "", file_ext or "", f"Unsupported file format: {file_ext}", retry_count + if file_ext == 'pdf' and not self._looks_like_pdf_bytes(content): + self._cleanup_temp_file(tmp_path) + message = "Invalid PDF signature in downloaded content" + self.logger.warning("%s for %s", message, url) + return False, "", file_ext, message, retry_count + + filename = self._build_output_filename(row_index, file_ext, filename_base) + if tmp_path is not None: + self._move_temp_file_to_final(tmp_path, filename) + else: + await self.write_file(filename, content, self.downloads_dir) + self.logger.info(f"Successfully downloaded {filename} from {url}") + return True, filename, file_ext, "", retry_count + + async def _download_via_streaming_get( + self, + *, + session: aiohttp.ClientSession, + row_index: int, + url: str, + headers: Dict[str, str], + timeout: aiohttp.ClientTimeout, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + ) -> Tuple[bool, str, str, str, int]: + from tenacity import AsyncRetrying + + head = bytearray() + async for attempt in AsyncRetrying( + stop=stop_after_attempt(max(1, int(self.max_retries))), + wait=wait_exponential(multiplier=1, min=1, max=10), + retry=(retry_if_exception_type(aiohttp.ClientError) | + retry_if_exception_type(asyncio.TimeoutError)), + before_sleep=before_sleep_log(logging.getLogger(__name__), logging.INFO), + reraise=True, + ): + with attempt: + async with session.get(url, headers=headers, timeout=timeout) as response: + response.raise_for_status() + resp_headers = dict(response.headers or {}) + tmp_path = Path(self.downloads_dir) / f".part_{row_index}" + async with aiofiles.open(tmp_path, 'wb') as f: + async for chunk in response.content.iter_chunked(1 << 16): + if chunk: + if len(head) < (1 << 16): + need = (1 << 16) - len(head) + head.extend(chunk[:need]) + await f.write(chunk) + return await self._finalize_download_result( + row_index=row_index, + url=url, + resp_headers=resp_headers, + content=bytes(head), + retry_count=retry_count, + filename_base=filename_base, + referer=referer, + tmp_path=tmp_path, + ) + return False, "", "", "Retry exhaustion", retry_count + 1 + + async def _download_via_buffered_request( + self, + *, + session: aiohttp.ClientSession, + requester: str, + row_index: int, + url: str, + headers: Dict[str, str], + timeout: aiohttp.ClientTimeout, + retry_count: int, + filename_base: Optional[str], + referer: Optional[str], + ) -> Tuple[bool, str, str, str, int]: + content, status, resp_headers = await self.make_request( + session, requester, url, headers, timeout + ) + return await self._finalize_download_result( + row_index=row_index, + url=url, + resp_headers=resp_headers, + content=content, + retry_count=retry_count, + filename_base=filename_base, + referer=referer, + ) + + def _build_http_error_result( + self, + url: str, + error: aiohttp.ClientResponseError, + retry_count: int, + ) -> Tuple[bool, str, str, str, int]: + status = error.status + self.logger.warning(f"Received {status} for {url}") + + if self.verbose: + self.logger.debug(f"HTTP Error Details - Status: {error.status}, Message: {error.message}") + self.logger.debug(f"Headers: {error.headers if hasattr(error, 'headers') else 'No headers available'}") + self.logger.debug(f"Request info: {error.request_info if hasattr(error, 'request_info') else 'No request info available'}") + + retry_after = None + try: + hdrs = dict(getattr(error, 'headers', {}) or {}) + for k, v in hdrs.items(): + if k.lower() == 'retry-after': + val = str(v).strip() + if val.isdigit(): + retry_after = int(val) + else: + try: + dt = parsedate_to_datetime(val) + retry_after = max(0, int((dt.timestamp() - time.time()))) + except Exception: + retry_after = None + break + except Exception: + retry_after = None + error_msg = f"HTTP {status}: {str(error)}" + if status in (429, 503) and retry_after is not None: + error_msg += f" retry_after={retry_after}" + return False, "", self._best_effort_url_extension(url), error_msg, retry_count + 1 + + async def download_file(self, row_index: int, url: str, semaphore: Optional[asyncio.Semaphore], + rate_limiter: RateLimiter, retry_count: int = 0, + filename_base: Optional[str] = None, + referer: Optional[str] = None) -> Tuple[bool, str, str, str, int]: + """ + Download a file from a URL + + Args: + row_index: Index in the dataframe + url: URL to download + semaphore: Semaphore for concurrency control + rate_limiter: Rate limiter for API limits + retry_count: Current retry count + Returns: + Tuple[bool, str, str, str, int]: (success, filename, file_ext, error_message, retry_count) + """ + if not url or pd.isna(url): + return False, "", "", "Empty URL", retry_count + + url = self._normalize_request_url(url) + _, route_options = self._resolve_route(url) + user_agent = next(self.user_agents) + headers = self._build_request_headers(url, user_agent, referer) + cookies = self._resolve_request_cookies(url, route_options=route_options) if semaphore: await semaphore.acquire() try: - # Apply rate limiting await rate_limiter.acquire() - - # Implement exponential backoff - sleep_time = self.sleep * (2 ** retry_count) + base_sleep = float(self._route_setting(route_options, "sleep", self.sleep)) + sleep_time = base_sleep * (2 ** retry_count) await asyncio.sleep(random.uniform(sleep_time, sleep_time * 1.5)) - - # Set up timeout with exponential backoff - timeout = aiohttp.ClientTimeout( - total=min(self.request_timeout * (1.5 ** retry_count), 180), # Cap at 3 minutes - connect=min(30 * (1.2 ** retry_count), 60), # Cap connect timeout at 1 minute - sock_connect=min(30 * (1.2 ** retry_count), 60), # Cap socket connect at 1 minute - sock_read=min(60 * (1.2 ** retry_count), 120) # Cap socket read at 2 minutes + preflight = await self._preflight_download( + row_index=row_index, + url=url, + retry_count=retry_count, + filename_base=filename_base, + referer=referer, ) - + if preflight is not None: + return preflight + timeout = self._build_request_timeout(retry_count, route_options=route_options) + try: - # Prepare optional SSL connector - connector = None - # Domain-specific insecure override (discovered via ping) - url_base = self._extract_base_domain(url) - _force_insecure = url_base in getattr(self, '_domains_ssl_insecure', set()) - if (not self.ssl_verify) or _force_insecure: - connector = aiohttp.TCPConnector(ssl=False) - elif self.ssl_cafile: - import ssl as _ssl - ctx = _ssl.create_default_context(cafile=self.ssl_cafile) - connector = aiohttp.TCPConnector(ssl=ctx) - # Create a new session for each download to avoid cookie contamination + connector = self._build_session_connector(url, route_options=route_options) async with aiohttp.ClientSession(cookies=cookies, connector=connector) as session: try: - # Try to access the base domain first to establish cookies - headers = await self.setup_session(session, url, headers) - - # Set a shorter timeout for the initial connection attempt - base_timeout = aiohttp.ClientTimeout(total=10) - try: - # Visit the base domain to establish cookies if needed - base_domain = urlparse(url).netloc - if any(domain in base_domain for domain in self.domain_cookies.keys()): - base_url = f"https://{base_domain}" - async with session.get(base_url, headers=headers, timeout=base_timeout): - pass - except Exception as e: - # Non-fatal error, just log and continue - self.logger.debug(f"Initial base URL visit failed: {str(e)}") - pass - - # Choose request method and perform streaming for GET - requester = self.request_method.lower() + headers = await self._bootstrap_download_session( + session, + url, + headers, + route_options=route_options, + ) + requester = str(self._route_setting(route_options, "request_method", self.request_method)).lower() try: self.verbose_log(f"Attempting download request to URL: {url}") @@ -889,112 +1276,30 @@ async def download_file(self, row_index: int, url: str, semaphore: Optional[asyn self.verbose_log(f"Headers: {headers}") if requester == 'get': - # Streaming GET with retries - from tenacity import AsyncRetrying - head = bytearray() - resp_headers = {} - async for attempt in AsyncRetrying( - stop=stop_after_attempt(max(1, int(self.max_retries))), - wait=wait_exponential(multiplier=1, min=1, max=10), - retry=(retry_if_exception_type(aiohttp.ClientError) | - retry_if_exception_type(asyncio.TimeoutError)), - before_sleep=before_sleep_log(logging.getLogger(__name__), logging.INFO), - reraise=True, - ): - with attempt: - async with session.get(url, headers=headers, timeout=timeout) as response: - response.raise_for_status() - resp_headers = dict(response.headers or {}) - # Write to a temp file first - tmp_path = Path(self.downloads_dir) / f".part_{row_index}" - async with aiofiles.open(tmp_path, 'wb') as f: - async for chunk in response.content.iter_chunked(1 << 16): - if chunk: - if len(head) < (1 << 16): - need = (1 << 16) - len(head) - head.extend(chunk[:need]) - await f.write(chunk) - # Infer extension using URL, headers and first bytes - file_ext = self.infer_file_extension(url, resp_headers, bytes(head)) - if not self.is_supported_format(file_ext): - # Clean up temp and report - try: - os.remove(tmp_path) - except Exception: - pass - self.logger.warning(f"Unsupported file format after inference: {file_ext}. Supported formats: {', '.join(self.supported_formats)}") - return False, "", file_ext or "", f"Unsupported file format: {file_ext}", retry_count - # Decide final filename - if filename_base and str(filename_base).strip(): - filename = f"{filename_base}.{file_ext}" - else: - filename = self.generate_filename(row_index, file_ext) - final_path = Path(self.downloads_dir) / filename - try: - os.replace(tmp_path, final_path) - except Exception: - # Fallback to copy/rename - try: - os.rename(tmp_path, final_path) - except Exception: - pass - self.logger.info(f"Successfully downloaded {filename} from {url}") - return True, filename, file_ext, "", retry_count - else: - # Fallback to non-streaming POST - content, status, resp_headers = await self.make_request( - session, requester, url, headers, timeout + return await self._download_via_streaming_get( + session=session, + row_index=row_index, + url=url, + headers=headers, + timeout=timeout, + retry_count=retry_count, + filename_base=filename_base, + referer=referer, ) - file_ext = self.infer_file_extension(url, resp_headers, content) - if not self.is_supported_format(file_ext): - self.logger.warning(f"Unsupported file format after inference: {file_ext}. Supported formats: {', '.join(self.supported_formats)}") - return False, "", file_ext or "", f"Unsupported file format: {file_ext}", retry_count - if filename_base and str(filename_base).strip(): - filename = f"{filename_base}.{file_ext}" - else: - filename = self.generate_filename(row_index, file_ext) - await self.write_file(filename, content, self.downloads_dir) - self.logger.info(f"Successfully downloaded {filename} from {url}") - return True, filename, file_ext, "", retry_count + return await self._download_via_buffered_request( + session=session, + requester=requester, + row_index=row_index, + url=url, + headers=headers, + timeout=timeout, + retry_count=retry_count, + filename_base=filename_base, + referer=referer, + ) except aiohttp.ClientResponseError as e: - # Handle HTTP errors - status = e.status - self.logger.warning(f"Received {status} for {url}") - - # Detailed verbose logging for HTTP errors - if self.verbose: - self.logger.debug(f"HTTP Error Details - Status: {e.status}, Message: {e.message}") - self.logger.debug(f"Headers: {e.headers if hasattr(e, 'headers') else 'No headers available'}") - self.logger.debug(f"Request info: {e.request_info if hasattr(e, 'request_info') else 'No request info available'}") - - # Build error with optional Retry-After info - retry_after = None - try: - hdrs = dict(getattr(e, 'headers', {}) or {}) - for k, v in hdrs.items(): - if k.lower() == 'retry-after': - val = str(v).strip() - if val.isdigit(): - retry_after = int(val) - else: - try: - dt = parsedate_to_datetime(val) - retry_after = max(0, int((dt.timestamp() - time.time()))) - except Exception: - retry_after = None - break - except Exception: - retry_after = None - error_msg = f"HTTP {status}: {str(e)}" - if status in (429, 503) and retry_after is not None: - error_msg += f" retry_after={retry_after}" - # Best-effort ext from URL if possible - try: - url_ext = self.get_file_extension_from_url(url) - except Exception: - url_ext = "" - return False, "", url_ext, error_msg, retry_count + 1 + return self._build_http_error_result(url, e, retry_count) except Exception as e: error_msg = str(e) @@ -1007,11 +1312,7 @@ async def download_file(self, row_index: int, url: str, semaphore: Optional[asyn import traceback self.logger.debug(f"Traceback: {traceback.format_exc()}") - try: - url_ext = self.get_file_extension_from_url(url) - except Exception: - url_ext = "" - return False, "", url_ext, error_msg, retry_count + 1 + return False, "", self._best_effort_url_extension(url), error_msg, retry_count + 1 except asyncio.TimeoutError: self.logger.error(f"Overall timeout exceeded for {url}") @@ -1023,22 +1324,14 @@ async def download_file(self, row_index: int, url: str, semaphore: Optional[asyn except aiohttp.ClientError as e: error_msg = str(e) self.logger.error(f"ClientError while downloading {url}: {error_msg}") - try: - url_ext = self.get_file_extension_from_url(url) - except Exception: - url_ext = "" - return False, "", url_ext, error_msg, retry_count + 1 + return False, "", self._best_effort_url_extension(url), error_msg, retry_count + 1 except asyncio.TimeoutError: self.logger.error(f"Timeout while downloading {url}") return False, "", "", "Timeout", retry_count + 1 except Exception as e: error_msg = str(e) self.logger.error(f"Error while downloading {url}: {error_msg}") - try: - url_ext = self.get_file_extension_from_url(url) - except Exception: - url_ext = "" - return False, "", url_ext, error_msg, retry_count + 1 + return False, "", self._best_effort_url_extension(url), error_msg, retry_count + 1 finally: if semaphore: try: @@ -1137,6 +1430,8 @@ def _write_checkpoint() -> None: for i, row_idx in enumerate(batch_indices): url = df.loc[row_idx, self.url_column] retry_count = df.loc[row_idx, 'download_retry_count'] + _, route_options = self._resolve_route(url) + _, _, _, skip_after = self._resolve_domain_scheduler_settings(route_options) # Optional per-row referer (e.g., external_link page) ref_val = None if self.referer_column and self.referer_column in df.columns: @@ -1156,7 +1451,7 @@ def _write_checkpoint() -> None: pass # Skip URLs that have failed too many times - if retry_count >= self.skip_failed_after: + if retry_count >= skip_after: self.logger.info(f"Skipping URL at row {row_idx} - too many failures: {retry_count}") continue @@ -1367,6 +1662,7 @@ def _write_checkpoint() -> None: domains: Dict[str, GlossDownloader._DomainState] = {} for idx in row_indices: url = df.at[idx, self.url_column] + _, route_options = self._resolve_route(url) # Determine grouping key if self.scheduler_group_by and self.scheduler_group_by != 'base_domain': key = str(df.at[idx, self.scheduler_group_by]) if self.scheduler_group_by in df.columns else '' @@ -1377,9 +1673,14 @@ def _write_checkpoint() -> None: if not key: key = '' if key not in domains: - # Each group starts with up to per_domain_concurrency, but not exceeding global - start_c = min(self.per_domain_concurrency, max(1, self.concurrency)) - domains[key] = GlossDownloader._DomainState(base=key, concurrency=start_c) + floor_c, ceiling_c, start_c, skip_after = self._resolve_domain_scheduler_settings(route_options) + domains[key] = GlossDownloader._DomainState( + base=key, + concurrency=start_c, + concurrency_floor=floor_c, + concurrency_ceiling=ceiling_c, + skip_failed_after=skip_after, + ) domains[key].queue.append(idx) if not domains: @@ -1638,7 +1939,7 @@ def estimate_eta_s(state: GlossDownloader._DomainState) -> float: if remaining <= 0: return 0.0 avg = state.avg_duration() or 5.0 # default initial guess - eff_c = max(self.domain_concurrency_floor, min(state.concurrency, self.domain_concurrency_ceiling)) + eff_c = max(state.concurrency_floor, min(state.concurrency, state.concurrency_ceiling)) # ETA ≈ remaining * avg / eff_c (assuming steady parallelism) return float(remaining) * avg / max(1, eff_c) @@ -1722,7 +2023,7 @@ async def dispatch_ready(): if pending_domains: active_order.append(pending_domains.popleft()) continue - state.concurrency = max(self.domain_concurrency_floor, 1) + state.concurrency = max(state.concurrency_floor, 1) self.progress_logger.info(f"[park] Unparked domain: {dom}; resuming at concurrency={state.concurrency}") # Attempt to launch up to (state.concurrency - state.active) while ( @@ -1734,7 +2035,7 @@ async def dispatch_ready(): url = df.at[row_idx, self.url_column] retry_count = int(df.at[row_idx, 'download_retry_count']) if 'download_retry_count' in df.columns else 0 # Skip rows with too many failures - if retry_count >= self.skip_failed_after: + if retry_count >= state.skip_failed_after: continue # Launch task t0 = time.time() @@ -1916,7 +2217,7 @@ async def dispatch_ready(): # Dynamic tuning: ease if overloaded if self.dynamic_tuning and should_ease(state): - if state.concurrency > self.domain_concurrency_floor: + if state.concurrency > state.concurrency_floor: state.concurrency -= 1 self.logger.info(f"Easing concurrency for {dom} -> {state.concurrency}") @@ -1936,14 +2237,14 @@ async def dispatch_ready(): if retry_after is None: retry_after = max(1, int(self.ping_recheck_seconds)) state.parked_until = now2 + retry_after - state.concurrency = max(self.domain_concurrency_floor, 1) + state.concurrency = max(state.concurrency_floor, 1) self.progress_logger.info(f"[park] Rate limited: {dom}; parked for {retry_after}s") # Timeout streak -> exponential backoff elif state.timeout_streak >= int(getattr(self, 'timeout_streak_threshold', 5)): backoff = min(float(getattr(self, 'backoff_min_s', 60.0)) * (2 ** max(0, state.ping_failures)), float(getattr(self, 'backoff_max_s', 900.0))) state.ping_failures += 1 state.parked_until = now2 + backoff - state.concurrency = max(self.domain_concurrency_floor, 1) + state.concurrency = max(state.concurrency_floor, 1) state.timeout_streak = 0 self.progress_logger.info(f"[park] Timeout streak: {dom}; parked for {int(backoff)}s (level={state.ping_failures})") else: @@ -1965,7 +2266,7 @@ async def dispatch_ready(): state.eta_exceeded_count += 1 if state.eta_exceeded_count == 1: # Try to increase concurrency gently to improve ETA, up to ceiling - if state.concurrency < self.domain_concurrency_ceiling: + if state.concurrency < state.concurrency_ceiling: state.concurrency += 1 self.logger.info( f"ETA high for {dom} ({int(eta_s)}s). Bumping concurrency -> {state.concurrency}" diff --git a/src/glossapi/gloss_extract.py b/src/glossapi/gloss_extract.py index 4a2477c..1c21cf1 100644 --- a/src/glossapi/gloss_extract.py +++ b/src/glossapi/gloss_extract.py @@ -10,7 +10,6 @@ AcceleratorDevice, AcceleratorOptions, PdfPipelineOptions, - RapidOcrOptions, LayoutOptions, TableStructureOptions, TableFormerMode, @@ -47,9 +46,9 @@ def _maybe_import_torch(*, force: bool = False): MarkdownFormatOption = None CsvFormatOption = None StandardPdfPipeline = None -DoclingParseV2DocumentBackend = None DoclingParseDocumentBackend = None PyPdfiumDocumentBackend = None +_DOCLING_PARSE_BACKEND_NAME = "docling_parse" class _NoOpOption: # minimal stand-ins for optional helpers @@ -84,19 +83,23 @@ def _ensure_docling_converter_loaded() -> None: def _ensure_docling_pipeline_loaded() -> None: global _DOC_PIPELINE_LOADED, StandardPdfPipeline - global DoclingParseV2DocumentBackend, DoclingParseDocumentBackend, PyPdfiumDocumentBackend + global DoclingParseDocumentBackend, PyPdfiumDocumentBackend, _DOCLING_PARSE_BACKEND_NAME if _DOC_PIPELINE_LOADED: return try: StandardPdfPipeline = importlib.import_module( "docling.pipeline.standard_pdf_pipeline" ).StandardPdfPipeline - DoclingParseV2DocumentBackend = importlib.import_module( - "docling.backend.docling_parse_v2_backend" - ).DoclingParseV2DocumentBackend - DoclingParseDocumentBackend = importlib.import_module( - "docling.backend.docling_parse_backend" - ).DoclingParseDocumentBackend + try: + DoclingParseDocumentBackend = importlib.import_module( + "docling.backend.docling_parse_backend" + ).DoclingParseDocumentBackend + _DOCLING_PARSE_BACKEND_NAME = "docling_parse" + except Exception: + DoclingParseDocumentBackend = importlib.import_module( + "docling.backend.docling_parse_v2_backend" + ).DoclingParseV2DocumentBackend + _DOCLING_PARSE_BACKEND_NAME = "docling_parse_v2" PyPdfiumDocumentBackend = importlib.import_module( "docling.backend.pypdfium2_backend" ).PyPdfiumDocumentBackend @@ -106,11 +109,8 @@ def _ensure_docling_pipeline_loaded() -> None: from docling.pipeline.simple_pipeline import SimplePipeline -# Ensure RapidOCR plugin is registered for factory-based OCR construction -import docling.models.rapid_ocr_model # noqa: F401 -from .ocr.rapidocr._paths import resolve_packaged_onnx_and_keys -from .ocr.rapidocr.pool import GLOBAL_RAPID_OCR_POOL import inspect +from .ocr.docling_pipeline import build_layout_pipeline import ftfy import logging @@ -328,7 +328,7 @@ def _apply_thread_caps(self) -> None: self._thread_caps_applied = True def release_resources(self) -> None: - """Release Docling converters, pooled RapidOCR engines, and GPU caches.""" + """Release Docling converters and GPU caches.""" try: self.converter = None except Exception: @@ -343,10 +343,6 @@ def release_resources(self) -> None: setattr(self, attr, None) except Exception: pass - try: - GLOBAL_RAPID_OCR_POOL.clear() - except Exception: - pass torch_mod = _maybe_import_torch() if torch_mod is not None and getattr(torch_mod, "cuda", None): try: @@ -390,7 +386,7 @@ def _convert_all_with_timeout(self, files: Iterable[Path], timeout_s: int, **kwa timeout_kw = None backend_cls = getattr(self, "_active_pdf_backend", None) - is_native_backend = backend_cls is DoclingParseV2DocumentBackend if backend_cls else False + is_native_backend = backend_cls is DoclingParseDocumentBackend if backend_cls else False if timeout_kw and not is_native_backend and len(set(budgets)) == 1: kw = dict(raises_on_error=False) @@ -553,12 +549,7 @@ def create_extractor( ocr_langs: list[str] | None = None, profile_timings: bool = True, ): - """Create a document converter with configured options using the canonical builder. - - Delegates PDF pipeline construction to `glossapi.ocr.rapidocr.pipeline.build_rapidocr_pipeline` - to avoid duplicated provider checks and option wiring. Falls back to the legacy - inline path if the canonical builder is unavailable. - """ + """Create a Docling document converter for Phase-1 extraction.""" _ensure_docling_converter_loaded() _ensure_docling_pipeline_loaded() # Enable/disable Docling pipeline timings collection (for benchmarks) @@ -569,176 +560,88 @@ def create_extractor( pass # Record the PDF backend name for provenance (default to native backend) - self.pdf_backend_name = "docling_parse_v2" - self._active_pdf_backend = DoclingParseV2DocumentBackend + self.pdf_backend_name = _DOCLING_PARSE_BACKEND_NAME + self._active_pdf_backend = DoclingParseDocumentBackend # Best-effort Torch preflight only if Phase‑1 is asked to do enrichment try: - if formula_enrichment: + if formula_enrichment or code_enrichment: torch_mod = _maybe_import_torch(force=True) if torch_mod is None: - raise RuntimeError("Torch not available but formula enrichment requested.") + raise RuntimeError("Torch not available but Docling GPU enrichment was requested.") if hasattr(torch_mod, "cuda") and isinstance(getattr(self, "pipeline_options", None), PdfPipelineOptions): dev = getattr(self.pipeline_options, "accelerator_options", None) dv = getattr(dev, "device", None) if (isinstance(dv, str) and dv.lower().startswith("cuda")) and not torch_mod.cuda.is_available(): - raise RuntimeError("Torch CUDA not available but formula enrichment requested.") + raise RuntimeError("Torch CUDA not available but Docling GPU enrichment was requested.") except Exception as e: raise RuntimeError(f"Torch CUDA preflight failed: {e}") - # Build PDF pipeline via the canonical builder (preferred) - opts = None - active_backend = DoclingParseV2DocumentBackend - try: - from .ocr.rapidocr.pipeline import build_layout_pipeline, build_rapidocr_pipeline # type: ignore - except Exception: # pragma: no cover - adapter fallback - from ._pipeline import build_layout_pipeline, build_rapidocr_pipeline # type: ignore - - device_str = self._current_device_str() or "cuda:0" - builder = build_rapidocr_pipeline if enable_ocr else build_layout_pipeline - - try: - _, opts = builder( - device=device_str, - images_scale=float(images_scale), - formula_enrichment=bool(formula_enrichment), - code_enrichment=bool(code_enrichment), - **({"text_score": float(text_score)} if enable_ocr else {}), - ) - - if enable_ocr and hasattr(opts, "ocr_options") and getattr(opts, "ocr_options", None) is not None: - if use_cls is not None: - setattr(opts.ocr_options, "use_cls", bool(use_cls)) # type: ignore[attr-defined] - if ocr_langs: - setattr(opts.ocr_options, "lang", list(ocr_langs)) # type: ignore[attr-defined] - if force_full_page_ocr is not None: - setattr(opts.ocr_options, "force_full_page_ocr", bool(force_full_page_ocr)) # type: ignore[attr-defined] - + if enable_ocr: try: - setattr(opts, "images_scale", float(images_scale)) + self._log.warning( + "Docling Phase-1 OCR is no longer supported. " + "Ignoring enable_ocr/force_full_page_ocr; use Corpus.ocr(backend='deepseek') instead." + ) except Exception: pass - self._active_pdf_options = opts - self._current_ocr_enabled = bool(enable_ocr) - - # Create a multi-format DocumentConverter using the built PDF options - pdf_backend = DoclingParseV2DocumentBackend - if not enable_ocr: - try: - if getattr(self, "use_pypdfium_backend", False): - pdf_backend = PyPdfiumDocumentBackend - self.pdf_backend_name = "pypdfium" - except Exception: - pdf_backend = DoclingParseV2DocumentBackend - if opts is None: - opts = self.pipeline_options - active_backend = pdf_backend - - self.converter = DocumentConverter( - allowed_formats=[ - InputFormat.PDF, - InputFormat.DOCX, - InputFormat.XML_JATS, - InputFormat.HTML, - InputFormat.PPTX, - InputFormat.CSV, - InputFormat.MD, - ], - format_options={ - InputFormat.PDF: PdfFormatOption( - pipeline_options=opts, - pipeline_cls=StandardPdfPipeline, - backend=active_backend, - ), - InputFormat.DOCX: WordFormatOption(pipeline_cls=SimplePipeline), - InputFormat.XML_JATS: XMLJatsFormatOption(), - InputFormat.HTML: HTMLFormatOption(), - InputFormat.PPTX: PowerpointFormatOption(), - InputFormat.CSV: CsvFormatOption(), - InputFormat.MD: MarkdownFormatOption(), - }, - ) - self._active_pdf_backend = active_backend + active_backend = DoclingParseDocumentBackend + device_str = self._current_device_str() or "cuda:0" + _, opts = build_layout_pipeline( + device=device_str, + images_scale=float(images_scale), + formula_enrichment=bool(formula_enrichment), + code_enrichment=bool(code_enrichment), + ) + try: + opts.do_ocr = False + setattr(opts, "images_scale", float(images_scale)) except Exception: - # Fallback to legacy inline configuration path - if enable_ocr: - r = resolve_packaged_onnx_and_keys() - if not (r.det and r.rec and r.cls and r.keys): - raise FileNotFoundError( - "RapidOCR ONNX models/keys not found. Ensure models exist under glossapi.models/rapidocr or set GLOSSAPI_RAPIDOCR_ONNX_DIR." - ) - langs = ocr_langs or ["el", "en"] - ocr_opts = RapidOcrOptions( - backend="onnxruntime", - lang=langs, - force_full_page_ocr=bool(force_full_page_ocr), - use_det=True, - use_cls=bool(use_cls), - use_rec=True, - text_score=float(text_score), - det_model_path=r.det, - rec_model_path=r.rec, - cls_model_path=r.cls, - print_verbose=False, - ) - ocr_opts.rec_keys_path = r.keys - self.pipeline_options.ocr_options = ocr_opts - # Attach core toggles to existing pipeline_options - try: - self.pipeline_options.do_ocr = bool(enable_ocr) - self.pipeline_options.do_formula_enrichment = bool(formula_enrichment) - self.pipeline_options.do_code_enrichment = bool(code_enrichment) - try: - setattr(self.pipeline_options, "images_scale", float(images_scale)) - except Exception: - pass - except Exception: - pass - if not enable_ocr: - try: - setattr(self.pipeline_options, "ocr_options", None) - except Exception: - pass + pass - pdf_backend = DoclingParseV2DocumentBackend - if not enable_ocr: - try: - if getattr(self, "use_pypdfium_backend", False): - pdf_backend = PyPdfiumDocumentBackend - self.pdf_backend_name = "pypdfium" - except Exception: - pdf_backend = DoclingParseV2DocumentBackend - - active_backend = pdf_backend - - self.converter = DocumentConverter( - allowed_formats=[ - InputFormat.PDF, - InputFormat.DOCX, - InputFormat.XML_JATS, - InputFormat.HTML, - InputFormat.PPTX, - InputFormat.CSV, - InputFormat.MD, - ], - format_options={ - InputFormat.PDF: PdfFormatOption( - pipeline_options=self.pipeline_options, - pipeline_cls=StandardPdfPipeline, - backend=active_backend, - ), - }, - ) + self._active_pdf_options = opts + self._current_ocr_enabled = False - self._active_pdf_options = self.pipeline_options - self._current_ocr_enabled = bool(enable_ocr) - self._active_pdf_backend = active_backend + pdf_backend = DoclingParseDocumentBackend + try: + if getattr(self, "use_pypdfium_backend", False): + pdf_backend = PyPdfiumDocumentBackend + self.pdf_backend_name = "pypdfium" + except Exception: + pdf_backend = DoclingParseDocumentBackend + active_backend = pdf_backend + + self.converter = DocumentConverter( + allowed_formats=[ + InputFormat.PDF, + InputFormat.DOCX, + InputFormat.XML_JATS, + InputFormat.HTML, + InputFormat.PPTX, + InputFormat.CSV, + InputFormat.MD, + ], + format_options={ + InputFormat.PDF: PdfFormatOption( + pipeline_options=opts, + pipeline_cls=StandardPdfPipeline, + backend=active_backend, + ), + InputFormat.DOCX: WordFormatOption(pipeline_cls=SimplePipeline), + InputFormat.XML_JATS: XMLJatsFormatOption(), + InputFormat.HTML: HTMLFormatOption(), + InputFormat.PPTX: PowerpointFormatOption(), + InputFormat.CSV: CsvFormatOption(), + InputFormat.MD: MarkdownFormatOption(), + }, + ) + self._active_pdf_backend = active_backend # Record last configuration for reuse try: self._last_extractor_cfg = self._cfg_signature( - enable_ocr=enable_ocr, + enable_ocr=False, force_full_page_ocr=force_full_page_ocr, text_score=text_score, images_scale=images_scale, @@ -914,6 +817,17 @@ def _process_file_chunked(self, file_path: Path, output_dir: Path, timeout_dir: except Exception as e: self._log.error(f"Failed to write chunk manifest for {file_path.name}: {e}") + # Always attempt to assemble whatever chunks succeeded (best-effort) + out_md_path = output_dir / f"{stem}.md" + final_md_written = False + if all_segments: + try: + final_md = "\n\n".join(all_segments) + out_md_path.write_text(final_md, encoding="utf-8") + final_md_written = True + except Exception as e: + self._log.error(f"Failed to assemble final markdown for {file_path.name}: {e}") + if not completed: # Record failure/timeout provenance in parquet try: @@ -928,6 +842,7 @@ def _process_file_chunked(self, file_path: Path, output_dir: Path, timeout_dir: chunk_size=self.chunk_size, chunk_count=len(manifest.get("entries", [])), chunk_manifest_path=manifest_path, + no_partial_output=not final_md_written, ) except Exception as e: self._log.warning(f"Failed to record chunked extraction metadata for {file_path.name}: {e}") @@ -939,14 +854,7 @@ def _process_file_chunked(self, file_path: Path, output_dir: Path, timeout_dir: self._log.error(f"Failed to copy timeout/failed file {file_path.name}: {e}") return False - # Assemble final markdown - try: - final_md = "\n\n".join(all_segments) - out_md_path = output_dir / f"{stem}.md" - with out_md_path.open("w", encoding="utf-8") as fp: - fp.write(final_md) - except Exception as e: - self._log.error(f"Failed to assemble final markdown for {file_path.name}: {e}") + if not final_md_written: return False # Record success provenance in parquet try: @@ -1294,7 +1202,7 @@ def _update_extraction_metadata( if chunk_manifest_path is not None: data["chunk_manifest_path"] = str(chunk_manifest_path) # Backend and failure - backend_name = getattr(self, "pdf_backend_name", None) or ("docling_parse_v2" if getattr(self, "USE_V2", True) else "docling_parse") + backend_name = getattr(self, "pdf_backend_name", None) or _DOCLING_PARSE_BACKEND_NAME data["extraction_backend"] = backend_name if status in ("timeout", "error", "failure"): data["failure_mode"] = status diff --git a/src/glossapi/ocr/__init__.py b/src/glossapi/ocr/__init__.py index bb167c4..df79456 100644 --- a/src/glossapi/ocr/__init__.py +++ b/src/glossapi/ocr/__init__.py @@ -1,7 +1,7 @@ """Lightweight OCR backend package. Exports minimal, import-safe helpers for OCR backends. Heavy -dependencies (vLLM, transformers, PyMuPDF) are imported lazily +dependencies (transformers, PyMuPDF) are imported lazily inside the specific backend functions so importing this package does not require GPU stacks or model weights. """ @@ -12,17 +12,14 @@ __all__ = [ "deepseek", - "rapidocr", "math", "utils", "deepseek_runner", - "rapidocr_dispatch", ] -_SUBPACKAGES = {"deepseek", "rapidocr", "math", "utils"} +_SUBPACKAGES = {"deepseek", "math", "utils"} _ALIASES = { "deepseek_runner": "glossapi.ocr.deepseek.runner", - "rapidocr_dispatch": "glossapi.ocr.rapidocr.dispatch", } diff --git a/src/glossapi/ocr/deepseek/__init__.py b/src/glossapi/ocr/deepseek/__init__.py index 5326c42..a5fb1ca 100644 --- a/src/glossapi/ocr/deepseek/__init__.py +++ b/src/glossapi/ocr/deepseek/__init__.py @@ -1,4 +1,4 @@ -"""DeepSeek OCR backend with a lightweight stub fallback.""" +"""DeepSeek OCR backend.""" from .runner import run_for_files from . import preflight diff --git a/src/glossapi/ocr/deepseek/defaults.py b/src/glossapi/ocr/deepseek/defaults.py new file mode 100644 index 0000000..96fffb4 --- /dev/null +++ b/src/glossapi/ocr/deepseek/defaults.py @@ -0,0 +1,27 @@ +"""Canonical DeepSeek OCR defaults shared across orchestration and CLIs.""" + +from __future__ import annotations + +from typing import Optional + +DEFAULT_RUNTIME_BACKEND = "vllm" +DEFAULT_OCR_PROFILE = "markdown_grounded" +DEFAULT_ATTN_BACKEND = "auto" +DEFAULT_RENDER_DPI = 144 +DEFAULT_MAX_NEW_TOKENS = 2048 +DEFAULT_GPU_MEMORY_UTILIZATION = 0.9 +DEFAULT_REPAIR_MODE = "auto" +DEFAULT_WORKERS_PER_GPU = 1 +DEFAULT_TARGET_BATCH_PAGES = 160 + + +def resolve_render_dpi(value: Optional[int]) -> int: + """Return the canonical render DPI, even when callers pass ``None``.""" + + return DEFAULT_RENDER_DPI if value is None else int(value) + + +def resolve_gpu_memory_utilization(value: Optional[float]) -> float: + """Return the canonical vLLM memory target, even when callers pass ``None``.""" + + return DEFAULT_GPU_MEMORY_UTILIZATION if value is None else float(value) diff --git a/src/glossapi/ocr/deepseek/preflight.py b/src/glossapi/ocr/deepseek/preflight.py index 76810e6..b8638b1 100644 --- a/src/glossapi/ocr/deepseek/preflight.py +++ b/src/glossapi/ocr/deepseek/preflight.py @@ -1,17 +1,17 @@ -"""Preflight checks for the DeepSeek OCR CLI environment.""" +"""Preflight checks for the DeepSeek OCR environment.""" from __future__ import annotations import dataclasses import os -import shutil -import sys from pathlib import Path from typing import Dict, Iterable, List, Optional -DEFAULT_SCRIPT = Path.cwd() / "deepseek-ocr" / "run_pdf_ocr_vllm.py" -DEFAULT_MODEL_DIR = Path.cwd() / "deepseek-ocr" / "DeepSeek-OCR" -DEFAULT_LIB_DIR = Path.cwd() / "deepseek-ocr" / "libjpeg-turbo" / "lib" +from .runtime_paths import resolve_deepseek_python + +REPO_ROOT = Path(__file__).resolve().parents[4] +DEFAULT_SCRIPT = REPO_ROOT / "src" / "glossapi" / "ocr" / "deepseek" / "run_pdf_ocr_transformers.py" +DEFAULT_MODEL_DIR = REPO_ROOT / "deepseek-ocr-2-model" / "DeepSeek-OCR-2" @dataclasses.dataclass(frozen=True) @@ -46,9 +46,6 @@ def summarize(self) -> str: def _ensure_path(path: Path, label: str, errors: List[CheckResult]) -> Optional[Path]: - if not path: - errors.append(CheckResult(label, False, "Not provided")) - return None if not path.exists(): errors.append(CheckResult(label, False, f"Missing at {path}")) return None @@ -58,38 +55,41 @@ def _ensure_path(path: Path, label: str, errors: List[CheckResult]) -> Optional[ def check_deepseek_env( env: Optional[Dict[str, str]] = None, *, - check_flashinfer: bool = True, + check_torch: bool = True, ) -> PreflightReport: - """Validate DeepSeek CLI prerequisites without running the model.""" + """Validate DeepSeek OCR prerequisites without running the model.""" env = dict(env or os.environ) errors: List[CheckResult] = [] warnings: List[CheckResult] = [] infos: List[CheckResult] = [] - allow_cli = env.get("GLOSSAPI_DEEPSEEK_ALLOW_CLI", "0") == "1" - allow_stub = env.get("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "1") == "1" + allow_cli = env.get("GLOSSAPI_DEEPSEEK_ALLOW_CLI", "1") == "1" + allow_stub = env.get("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "0") == "1" if not allow_cli: - warnings.append( + errors.append( CheckResult( "allow_cli", False, - "Set GLOSSAPI_DEEPSEEK_ALLOW_CLI=1 to force the real CLI.", + "DeepSeek OCR requires the real CLI/runtime. Set GLOSSAPI_DEEPSEEK_ALLOW_CLI=1.", ) ) if allow_stub: - warnings.append( + errors.append( CheckResult( "allow_stub", False, - "Set GLOSSAPI_DEEPSEEK_ALLOW_STUB=0 to fail instead of falling back to stub output.", + "Stub execution is no longer supported. Set GLOSSAPI_DEEPSEEK_ALLOW_STUB=0.", ) ) - script = Path(env.get("GLOSSAPI_DEEPSEEK_VLLM_SCRIPT") or DEFAULT_SCRIPT) - _ensure_path(script, "vllm_script", errors) + script = Path( + env.get("GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT") + or DEFAULT_SCRIPT + ) + _ensure_path(script, "runner_script", errors) - python_bin = Path(env.get("GLOSSAPI_DEEPSEEK_TEST_PYTHON") or sys.executable) + python_bin = resolve_deepseek_python(env=env) _ensure_path(python_bin, "deepseek_python", errors) model_dir = Path( @@ -99,7 +99,7 @@ def check_deepseek_env( ) model_dir = _ensure_path(model_dir, "model_dir", errors) if model_dir: - has_weights = any(model_dir.glob("*.safetensors")) or (model_dir / "model-00001-of-000001.safetensors").exists() + has_weights = any(model_dir.glob("*.safetensors")) has_config = (model_dir / "config.json").exists() if not has_weights or not has_config: errors.append( @@ -110,34 +110,21 @@ def check_deepseek_env( ) ) - ld_path_env = env.get("GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH") - lib_dir = Path(ld_path_env) if ld_path_env else DEFAULT_LIB_DIR - _ensure_path(lib_dir, "ld_library_path", errors) - - cc1plus_path = shutil.which("cc1plus", path=env.get("PATH", "")) - if not cc1plus_path: - errors.append( - CheckResult( - "cc1plus", - False, - "C++ toolchain missing (cc1plus not on PATH); install g++ and ensure PATH includes gcc's cc1plus.", - ) - ) - else: - infos.append(CheckResult("cc1plus", True, f"Found at {cc1plus_path}")) - - if check_flashinfer: + if check_torch: try: - import flashinfer # type: ignore + import torch # type: ignore - infos.append(CheckResult("flashinfer", True, f"flashinfer {flashinfer.__version__} import ok")) + infos.append(CheckResult("torch", True, f"torch {torch.__version__} import ok")) + if not torch.cuda.is_available(): + warnings.append(CheckResult("cuda", False, "Torch CUDA is not available.")) except Exception as exc: # pragma: no cover - depends on env - errors.append(CheckResult("flashinfer", False, f"flashinfer import failed: {exc}")) + errors.append(CheckResult("torch", False, f"torch import failed: {exc}")) return PreflightReport(errors=errors, warnings=warnings, infos=infos) def main(argv: Optional[Iterable[str]] = None) -> int: + del argv report = check_deepseek_env() summary = report.summarize() if summary: diff --git a/src/glossapi/ocr/deepseek/run_pdf_ocr_transformers.py b/src/glossapi/ocr/deepseek/run_pdf_ocr_transformers.py new file mode 100644 index 0000000..9b318e1 --- /dev/null +++ b/src/glossapi/ocr/deepseek/run_pdf_ocr_transformers.py @@ -0,0 +1,591 @@ +"""CLI wrapper for DeepSeek-OCR-2 inference over PDF files.""" + +from __future__ import annotations + +import argparse +import json +import logging +import re +import sys +import tempfile +import time +from pathlib import Path +from typing import Iterable, Iterator, List + +from PIL import Image + +SRC_ROOT = Path(__file__).resolve().parents[3] +if str(SRC_ROOT) not in sys.path: + sys.path.insert(0, str(SRC_ROOT)) + +from glossapi.ocr.utils.cleaning import ( # noqa: E402 + apply_early_stop, + canonicalize_markdown, + clean_output, + strip_prompt_echo, +) + +LOGGER = logging.getLogger(__name__) +PROMPT_GROUNDED_MARKDOWN = "\n<|grounding|>Convert the document to markdown. " +PROMPT_PLAIN_OCR = "\nExtract the text from the document page in reading order." +PAGE_SPLIT = "\n<--- Page Split --->\n" +PAGE_SPLIT_RE = re.compile(r"(?:^|\n)(?:\n)?<--- Page Split --->\n?") +DEFAULT_MAX_NEW_TOKENS = 2048 + + +def _profile_defaults(profile: str) -> dict: + profile_norm = str(profile or "markdown_grounded").strip().lower() + if profile_norm == "plain_ocr": + return { + "prompt": PROMPT_PLAIN_OCR, + "base_size": 768, + "image_size": 512, + "crop_mode": True, + } + return { + "prompt": PROMPT_GROUNDED_MARKDOWN, + "base_size": 1024, + "image_size": 768, + "crop_mode": True, + } + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input-dir", required=True) + parser.add_argument("--output-dir", required=True) + parser.add_argument("--model-dir", required=True) + parser.add_argument("--files", nargs="*", default=[]) + parser.add_argument("--page-ranges", nargs="*", default=[]) + parser.add_argument("--max-pages", type=int, default=None) + parser.add_argument("--device", default="cuda") + parser.add_argument("--ocr-profile", default="markdown_grounded", choices=["markdown_grounded", "plain_ocr"]) + parser.add_argument("--prompt-override", default=None) + parser.add_argument("--attn-backend", default="auto", choices=["auto", "flash_attention_2", "sdpa", "eager"]) + parser.add_argument("--base-size", type=int, default=None) + parser.add_argument("--image-size", type=int, default=None) + parser.add_argument("--render-dpi", type=int, default=144) + parser.add_argument("--max-new-tokens", type=int, default=DEFAULT_MAX_NEW_TOKENS) + parser.add_argument("--repetition-penalty", type=float, default=None) + parser.add_argument("--no-repeat-ngram-size", type=int, default=None) + parser.add_argument("--crop-mode", dest="crop_mode", action="store_true") + parser.add_argument("--no-crop-mode", dest="crop_mode", action="store_false") + parser.set_defaults(crop_mode=None) + parser.add_argument("--content-debug", action="store_true") + return parser.parse_args() + + +def _parse_page_range_spec(input_dir: Path, spec: str) -> dict: + try: + name, start_raw, end_raw = str(spec).rsplit(":", 2) + except ValueError as exc: + raise ValueError(f"Invalid page range spec: {spec}") from exc + start_page = int(start_raw) + end_page = int(end_raw) + if start_page <= 0 or end_page < start_page: + raise ValueError(f"Invalid page range bounds: {spec}") + pdf_path = (input_dir / name).resolve() + return { + "pdf_path": pdf_path, + "source_name": str(name), + "source_stem": pdf_path.stem, + "start_page": start_page, + "end_page": end_page, + "stem": f"{pdf_path.stem}__p{start_page:05d}-{end_page:05d}", + } + + +def _iter_pdf_jobs(input_dir: Path, files: List[str], page_ranges: List[str]) -> List[dict]: + jobs: List[dict] = [] + if files: + for name in files: + pdf_path = (input_dir / name).resolve() + jobs.append( + { + "pdf_path": pdf_path, + "source_name": str(name), + "source_stem": pdf_path.stem, + "start_page": 1, + "end_page": None, + "stem": pdf_path.stem, + } + ) + if page_ranges: + jobs.extend(_parse_page_range_spec(input_dir, spec) for spec in page_ranges) + if jobs: + return jobs + return [ + { + "pdf_path": path.resolve(), + "source_name": path.name, + "source_stem": path.stem, + "start_page": 1, + "end_page": None, + "stem": path.stem, + } + for path in sorted(input_dir.glob("*.pdf")) + ] + + +def _resolve_render_window( + *, + doc_page_count: int, + max_pages: int | None, + start_page: int = 1, + end_page: int | None = None, +) -> tuple[int, int] | None: + first_idx = max(0, int(start_page) - 1) + last_idx = int(doc_page_count) - 1 if end_page is None else min(int(doc_page_count) - 1, int(end_page) - 1) + if max_pages is not None: + last_idx = min(last_idx, first_idx + int(max_pages) - 1) + if last_idx < first_idx: + return None + return first_idx, last_idx + + +def _count_rendered_pages( + pdf_path: Path, + max_pages: int | None, + *, + start_page: int = 1, + end_page: int | None = None, +) -> int: + import fitz + + doc = fitz.open(pdf_path) + try: + window = _resolve_render_window( + doc_page_count=int(doc.page_count), + max_pages=max_pages, + start_page=start_page, + end_page=end_page, + ) + if window is None: + return 0 + first_idx, last_idx = window + return max(0, int(last_idx) - int(first_idx) + 1) + finally: + doc.close() + + +def _iter_rendered_pages( + pdf_path: Path, + max_pages: int | None, + render_dpi: int, + *, + start_page: int = 1, + end_page: int | None = None, +) -> Iterator[Image.Image]: + import fitz + + doc = fitz.open(pdf_path) + try: + window = _resolve_render_window( + doc_page_count=int(doc.page_count), + max_pages=max_pages, + start_page=start_page, + end_page=end_page, + ) + if window is None: + return + first_idx, last_idx = window + zoom = float(render_dpi) / 72.0 + matrix = fitz.Matrix(zoom, zoom) + for idx in range(first_idx, last_idx + 1): + page = doc[idx] + pixmap = page.get_pixmap(matrix=matrix, alpha=False) + img = Image.frombytes("RGB", [pixmap.width, pixmap.height], pixmap.samples) + yield img + finally: + doc.close() + + +def _render_pages( + pdf_path: Path, + max_pages: int | None, + render_dpi: int, + *, + start_page: int = 1, + end_page: int | None = None, +) -> List[Image.Image]: + return list( + _iter_rendered_pages( + pdf_path, + max_pages, + render_dpi, + start_page=start_page, + end_page=end_page, + ) + ) + + +def _clean_markdown(text: str) -> str: + text = (text or "").replace("<｜end▁of▁sentence｜>", "").strip() + pattern = re.compile(r"(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)", re.DOTALL) + matches = pattern.findall(text) + for full_match, label, _coords in matches: + if label == "image": + text = text.replace(full_match, "") + else: + text = text.replace(full_match, "") + return text.replace("\\coloneqq", ":=").replace("\\eqqcolon", "=:").strip() + + +def _page_split_comment(page_number: int) -> str: + return f"\n\n<--- Page Split --->\n" + + +def _join_page_outputs(page_outputs: List[str]) -> str: + if not page_outputs: + return "" + first_page = str(page_outputs[0]) + parts = [first_page] + emitted = bool(first_page) + for page_number, page_text in enumerate(page_outputs[1:], start=2): + separator = _page_split_comment(page_number) + if not emitted: + separator = separator.lstrip("\n") + parts.append(separator) + emitted = True + parts.append(str(page_text)) + return "".join(parts) + + +def _split_page_outputs(markdown_text: str) -> List[str]: + content = str(markdown_text or "").rstrip("\n") + if not content: + return [] + return PAGE_SPLIT_RE.split(content) + + +def _serialize_markdown(markdown: str) -> str: + return str(markdown or "").rstrip("\n") + "\n" + + +def _postprocess_page_text( + text: str, + *, + prompt: str, + content_debug: bool, +) -> tuple[str, dict]: + metrics: dict = {} + cleaned = _clean_markdown(text) + cleaned = strip_prompt_echo(cleaned, prompt) + cleaned = clean_output(cleaned, keep_refdet=False, metrics=metrics) + cleaned = canonicalize_markdown(cleaned) + cleaned = apply_early_stop(cleaned, content_debug=content_debug, metrics=metrics) + return cleaned.strip(), metrics + + +def _resolve_attn_backend(attn_backend: str) -> str: + requested = str(attn_backend or "auto").strip().lower() + if requested != "auto": + return requested + try: + import flash_attn # noqa: F401 + return "flash_attention_2" + except Exception: + # DeepSeek-OCR-2's custom decoder path has not behaved reliably with SDPA + # on the stacks we have exercised; if FA2 is unavailable, prefer the known + # fallback instead of silently selecting a backend that then downgrades. + return "eager" + + +def _supports_retry_with_eager(exc: Exception, attn_impl: str) -> bool: + if str(attn_impl) == "eager": + return False + message = str(exc) + markers = ( + "does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention", + 'load your model with the argument `attn_implementation="eager"` meanwhile', + ) + return any(marker in message for marker in markers) + + +def _configure_generate( + model, + *, + max_new_tokens: int | None, + repetition_penalty: float | None, + no_repeat_ngram_size: int | None, +): + if ( + max_new_tokens is None + and repetition_penalty is None + and no_repeat_ngram_size is None + ): + return + capped = None + if max_new_tokens is not None: + capped = int(max_new_tokens) + if capped <= 0: + raise ValueError("max_new_tokens must be > 0") + repetition_penalty_value = None + if repetition_penalty is not None: + repetition_penalty_value = float(repetition_penalty) + if repetition_penalty_value <= 0: + raise ValueError("repetition_penalty must be > 0") + no_repeat_ngram_value = None + if no_repeat_ngram_size is not None: + no_repeat_ngram_value = int(no_repeat_ngram_size) + if no_repeat_ngram_value <= 0: + raise ValueError("no_repeat_ngram_size must be > 0") + original_generate = model.generate + + def _wrapped_generate(*args, **kwargs): + if capped is not None: + current = kwargs.get("max_new_tokens") + if current is None: + kwargs["max_new_tokens"] = capped + else: + kwargs["max_new_tokens"] = min(int(current), capped) + if repetition_penalty_value is not None and kwargs.get("repetition_penalty") is None: + kwargs["repetition_penalty"] = repetition_penalty_value + if no_repeat_ngram_value is not None and kwargs.get("no_repeat_ngram_size") is None: + kwargs["no_repeat_ngram_size"] = no_repeat_ngram_value + return original_generate(*args, **kwargs) + + model.generate = _wrapped_generate + + +def _load_model( + model_dir: Path, + device: str, + attn_backend: str, + max_new_tokens: int | None, + repetition_penalty: float | None, + no_repeat_ngram_size: int | None, +): + import torch + from transformers import AutoModel, AutoTokenizer + + attn_impl = _resolve_attn_backend(attn_backend) + tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True) + try: + model = AutoModel.from_pretrained( + model_dir, + _attn_implementation=attn_impl, + trust_remote_code=True, + use_safetensors=True, + ) + except ValueError as exc: + if not _supports_retry_with_eager(exc, attn_impl): + raise + LOGGER.warning( + "DeepSeek model rejected attention backend `%s`; retrying with eager attention: %s", + attn_impl, + exc, + ) + attn_impl = "eager" + model = AutoModel.from_pretrained( + model_dir, + _attn_implementation=attn_impl, + trust_remote_code=True, + use_safetensors=True, + ) + if device.startswith("cuda"): + model = model.eval().to(device).to(torch.bfloat16) + else: + model = model.eval().to(device) + _configure_generate( + model, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + ) + return tokenizer, model, attn_impl + + +def _infer_page( + model, + tokenizer, + image_path: Path, + output_dir: Path, + *, + prompt: str, + base_size: int, + image_size: int, + crop_mode: bool, +) -> str: + result = model.infer( + tokenizer, + prompt=prompt, + image_file=str(image_path), + output_path=str(output_dir), + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + save_results=False, + eval_mode=True, + ) + return _clean_markdown(str(result)) + + +def _write_outputs( + output_dir: Path, + stem: str, + markdown: str, + page_count: int, + extra_metrics: dict | None = None, +) -> None: + md_dir = output_dir / "markdown" + metrics_dir = output_dir / "json" / "metrics" + progress_dir = output_dir / "sidecars" / "ocr_progress" + md_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + progress_dir.mkdir(parents=True, exist_ok=True) + (md_dir / f"{stem}.md").write_text(_serialize_markdown(markdown), encoding="utf-8") + metrics = { + "page_count": page_count, + "model": "deepseek-ai/DeepSeek-OCR-2", + } + if extra_metrics: + metrics.update(extra_metrics) + (metrics_dir / f"{stem}.metrics.json").write_text(json.dumps(metrics, indent=2), encoding="utf-8") + partial_path = progress_dir / f"{stem}.partial.md" + if partial_path.exists(): + partial_path.unlink() + + +def _write_progress( + output_dir: Path, + stem: str, + page_outputs: List[str], + total_pages: int, + completed_pages: int, +) -> None: + """Emit lightweight progress artifacts during long OCR runs.""" + md_dir = output_dir / "markdown" + metrics_dir = output_dir / "json" / "metrics" + progress_dir = output_dir / "sidecars" / "ocr_progress" + metrics_dir.mkdir(parents=True, exist_ok=True) + progress_dir.mkdir(parents=True, exist_ok=True) + partial_markdown = _join_page_outputs(page_outputs) + if partial_markdown: + (progress_dir / f"{stem}.partial.md").write_text(_serialize_markdown(partial_markdown), encoding="utf-8") + progress = { + "completed_pages": completed_pages, + "total_pages": total_pages, + "status": "running" if completed_pages < total_pages else "complete", + "model": "deepseek-ai/DeepSeek-OCR-2", + } + (metrics_dir / f"{stem}.progress.json").write_text( + json.dumps(progress, indent=2), + encoding="utf-8", + ) + + +def main() -> int: + args = _parse_args() + input_dir = Path(args.input_dir).resolve() + output_dir = Path(args.output_dir).resolve() + model_dir = Path(args.model_dir).resolve() + jobs = _iter_pdf_jobs(input_dir, args.files, args.page_ranges) + if not jobs: + return 0 + + profile_defaults = _profile_defaults(args.ocr_profile) + prompt = str(args.prompt_override) if args.prompt_override else profile_defaults["prompt"] + base_size = int(args.base_size) if args.base_size is not None else int(profile_defaults["base_size"]) + image_size = int(args.image_size) if args.image_size is not None else int(profile_defaults["image_size"]) + crop_mode = bool(args.crop_mode) if args.crop_mode is not None else bool(profile_defaults["crop_mode"]) + + tokenizer, model, attn_impl = _load_model( + model_dir, + args.device, + args.attn_backend, + args.max_new_tokens, + args.repetition_penalty, + args.no_repeat_ngram_size, + ) + + for job in jobs: + pdf_path = Path(job["pdf_path"]) + stem = str(job["stem"]) + doc_start = time.perf_counter() + render_start = time.perf_counter() + images = _render_pages( + pdf_path, + args.max_pages, + args.render_dpi, + start_page=int(job["start_page"]), + end_page=job["end_page"], + ) + render_sec = time.perf_counter() - render_start + page_outputs: List[str] = [] + page_metrics: List[dict] = [] + total_pages = len(images) + _write_progress(output_dir, stem, page_outputs, total_pages, 0) + with tempfile.TemporaryDirectory(prefix=f"{stem}_deepseek_") as tmp_dir_str: + tmp_dir = Path(tmp_dir_str) + for idx, image in enumerate(images): + page_png = tmp_dir / f"page_{idx + 1:04d}.png" + image.save(page_png, format="PNG") + infer_start = time.perf_counter() + raw_page_text = _infer_page( + model, + tokenizer, + page_png, + tmp_dir / f"page_{idx + 1:04d}", + prompt=prompt, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + ) + infer_sec = time.perf_counter() - infer_start + page_text, postprocess_metrics = _postprocess_page_text( + raw_page_text, + prompt=prompt, + content_debug=bool(args.content_debug), + ) + if args.content_debug: + page_text = f"\n{page_text}".strip() + page_outputs.append(page_text) + page_metrics.append( + { + "page_number": int(idx + 1), + "infer_sec": float(infer_sec), + "raw_chars": int(len(str(raw_page_text or "").strip())), + "final_chars": int(len(page_text.strip())), + **postprocess_metrics, + } + ) + _write_progress( + output_dir, + stem, + page_outputs, + total_pages, + idx + 1, + ) + markdown = _join_page_outputs(page_outputs) if page_outputs else "[[Blank page]]" + _write_outputs( + output_dir, + stem, + markdown, + len(images), + extra_metrics={ + "source_file": str(job["source_name"]), + "source_stem": str(job["source_stem"]), + "source_start_page": int(job["start_page"]), + "source_end_page": int(job["start_page"]) + max(0, len(images) - 1), + "ocr_profile": args.ocr_profile, + "attn_backend": attn_impl, + "base_size": base_size, + "image_size": image_size, + "crop_mode": crop_mode, + "render_dpi": int(args.render_dpi), + "max_new_tokens": args.max_new_tokens, + "repetition_penalty": args.repetition_penalty, + "no_repeat_ngram_size": args.no_repeat_ngram_size, + "render_sec": float(render_sec), + "infer_sec_total": float(sum(item["infer_sec"] for item in page_metrics)), + "wall_time_sec": float(time.perf_counter() - doc_start), + "page_metrics": page_metrics, + }, + ) + + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/ocr/deepseek/run_pdf_ocr_vllm.py b/src/glossapi/ocr/deepseek/run_pdf_ocr_vllm.py new file mode 100644 index 0000000..edc838b --- /dev/null +++ b/src/glossapi/ocr/deepseek/run_pdf_ocr_vllm.py @@ -0,0 +1,1382 @@ +"""CLI wrapper for DeepSeek-OCR-2 inference over PDF files using vLLM.""" + +from __future__ import annotations + +import argparse +import json +import logging +import queue +import sys +import threading +import time +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +from PIL import Image + +SRC_ROOT = Path(__file__).resolve().parents[3] +if str(SRC_ROOT) not in sys.path: + sys.path.insert(0, str(SRC_ROOT)) + +from glossapi.ocr.deepseek.run_pdf_ocr_transformers import ( + DEFAULT_MAX_NEW_TOKENS, + _join_page_outputs, + _count_rendered_pages, + _iter_pdf_jobs, + _iter_rendered_pages, + _postprocess_page_text, + _profile_defaults, + _split_page_outputs, + _write_outputs, + _write_progress, +) +from glossapi.ocr.deepseek.work_queue import ( + QUEUE_MAIN, + QUEUE_REPAIR, + STATUS_PENDING, + STATUS_RUNNING, + claim_next_batch, + enqueue_batches, + heartbeat_batch, + mark_batch_done, + mark_batch_failed, + work_queue_counts, +) +from glossapi.ocr.utils.cleaning import StreamingGarbageDetector + +LOGGER = logging.getLogger(__name__) +REPAIR_DARK_THRESHOLD = 235 +EMPTY_PAGE_OVERALL_DARK_MAX = 0.0015 +EMPTY_PAGE_BAND_DARK_MAX = 0.0025 +GARBAGE_EARLY_STOP_MIN_OUTPUT_TOKENS = 48 +GARBAGE_EARLY_STOP_WINDOW_TOKENS = 160 +DEFAULT_REPAIR_EXEC_BATCH_TARGET_PAGES = 48 +DEFAULT_REPAIR_EXEC_BATCH_TARGET_ITEMS = 32 + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input-dir", required=True) + parser.add_argument("--output-dir", required=True) + parser.add_argument("--model-dir", required=True) + parser.add_argument("--files", nargs="*", default=[]) + parser.add_argument("--page-ranges", nargs="*", default=[]) + parser.add_argument("--max-pages", type=int, default=None) + parser.add_argument("--device", default="cuda") + parser.add_argument("--ocr-profile", default="markdown_grounded", choices=["markdown_grounded", "plain_ocr"]) + parser.add_argument("--prompt-override", default=None) + parser.add_argument("--attn-backend", default="vllm") + parser.add_argument("--base-size", type=int, default=None) + parser.add_argument("--image-size", type=int, default=None) + parser.add_argument("--render-dpi", type=int, default=144) + parser.add_argument("--max-new-tokens", type=int, default=DEFAULT_MAX_NEW_TOKENS) + parser.add_argument("--repetition-penalty", type=float, default=None) + parser.add_argument("--no-repeat-ngram-size", type=int, default=None) + parser.add_argument("--crop-mode", dest="crop_mode", action="store_true") + parser.add_argument("--no-crop-mode", dest="crop_mode", action="store_false") + parser.set_defaults(crop_mode=None) + parser.add_argument("--batch-size", type=int, default=8) + parser.add_argument("--gpu-memory-utilization", type=float, default=0.9) + parser.add_argument("--disable-fp8-kv", action="store_true") + parser.add_argument("--repair-mode", default="auto", choices=["auto", "off"]) + parser.add_argument("--content-debug", action="store_true") + parser.add_argument("--work-db", default=None) + parser.add_argument("--worker-id", default=None) + parser.add_argument("--worker-runtime-file", default=None) + parser.add_argument("--work-stale-after-sec", type=float, default=900.0) + parser.add_argument("--work-heartbeat-sec", type=float, default=10.0) + parser.add_argument("--work-max-attempts", type=int, default=2) + parser.add_argument("--repair-exec-batch-target-pages", type=int, default=DEFAULT_REPAIR_EXEC_BATCH_TARGET_PAGES) + parser.add_argument("--repair-exec-batch-target-items", type=int, default=DEFAULT_REPAIR_EXEC_BATCH_TARGET_ITEMS) + return parser.parse_args() + + +def _load_vllm(model_dir: Path, gpu_memory_utilization: float, disable_fp8_kv: bool): + from vllm import LLM + + logits_processors = [] + try: + from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor + + logits_processors.append(NGramPerReqLogitsProcessor) + except Exception as exc: # pragma: no cover - environment dependent + LOGGER.warning("DeepSeek OCR logits processor unavailable in vLLM; continuing without it: %s", exc) + + try: + from transformers import AutoTokenizer + from vllm.sampling_params import SamplingParams + from vllm.v1.sample.logits_processor import AdapterLogitsProcessor + + class _GarbageStopPerReqLogitsProcessor: + def __init__( + self, + tokenizer, + eos_token_id: int | None, + *, + min_output_tokens: int, + window_tokens: int, + ) -> None: + self.tokenizer = tokenizer + self.eos_token_id = eos_token_id + self.min_output_tokens = int(min_output_tokens) + self.window_tokens = int(window_tokens) + self.detector = StreamingGarbageDetector() + self.seen_output_tokens = 0 + + def __call__(self, prompt_ids: list[int], output_ids: list[int], logits): + del prompt_ids + if self.eos_token_id is None: + return logits + current_len = len(output_ids) + if current_len <= self.seen_output_tokens: + return logits + new_ids = output_ids[self.seen_output_tokens :] + self.seen_output_tokens = current_len + if not new_ids: + return logits + new_text = self.tokenizer.decode(new_ids, skip_special_tokens=False) + if new_text: + self.detector.feed(new_text) + if current_len < self.min_output_tokens or self.detector.triggered_reason is None: + return logits + eos_token_id = int(self.eos_token_id) + eos_value = logits[eos_token_id].clone() + logits[:] = float("-inf") + logits[eos_token_id] = eos_value + return logits + + class GarbageEarlyStopLogitsProcessor(AdapterLogitsProcessor): + @classmethod + def validate_params(cls, params: SamplingParams): + extra = params.extra_args or {} + enabled = extra.get("garbage_early_stop") + if enabled is None: + return + if not isinstance(enabled, bool): + raise ValueError("garbage_early_stop must be a bool when provided") + min_output_tokens = extra.get("garbage_min_output_tokens") + if min_output_tokens is not None and int(min_output_tokens) <= 0: + raise ValueError("garbage_min_output_tokens must be > 0") + window_tokens = extra.get("garbage_window_tokens") + if window_tokens is not None and int(window_tokens) <= 0: + raise ValueError("garbage_window_tokens must be > 0") + + def __init__(self, vllm_config, device, is_pin_memory): + super().__init__(vllm_config, device, is_pin_memory) + self._tokenizer = AutoTokenizer.from_pretrained(str(model_dir), trust_remote_code=True) + self._eos_token_id = self._tokenizer.eos_token_id + + def is_argmax_invariant(self) -> bool: + return False + + def new_req_logits_processor(self, params: SamplingParams): + extra = params.extra_args or {} + if not bool(extra.get("garbage_early_stop", False)): + return None + return _GarbageStopPerReqLogitsProcessor( + self._tokenizer, + self._eos_token_id, + min_output_tokens=int( + extra.get("garbage_min_output_tokens", GARBAGE_EARLY_STOP_MIN_OUTPUT_TOKENS) + ), + window_tokens=int( + extra.get("garbage_window_tokens", GARBAGE_EARLY_STOP_WINDOW_TOKENS) + ), + ) + + logits_processors.append(GarbageEarlyStopLogitsProcessor) + except Exception as exc: # pragma: no cover - environment dependent + LOGGER.warning("Garbage-stop logits processor unavailable in vLLM; continuing without it: %s", exc) + + engine_kwargs = { + "model": str(model_dir), + "tokenizer": str(model_dir), + "trust_remote_code": True, + "dtype": "bfloat16", + "enable_prefix_caching": False, + "mm_processor_cache_gb": 0, + "gpu_memory_utilization": float(gpu_memory_utilization), + "tensor_parallel_size": 1, + } + if disable_fp8_kv: + engine_kwargs["kv_cache_dtype"] = "auto" + if logits_processors: + engine_kwargs["logits_processors"] = logits_processors + return LLM(**engine_kwargs) + + +def _sampling_params(max_new_tokens: int | None, *, enable_garbage_early_stop: bool): + from vllm import SamplingParams + + return SamplingParams( + temperature=0.0, + max_tokens=int(max_new_tokens or DEFAULT_MAX_NEW_TOKENS), + skip_special_tokens=False, + extra_args={ + "ngram_size": 30, + "window_size": 90, + "whitelist_token_ids": {128821, 128822}, + "garbage_early_stop": bool(enable_garbage_early_stop), + "garbage_min_output_tokens": int(GARBAGE_EARLY_STOP_MIN_OUTPUT_TOKENS), + "garbage_window_tokens": int(GARBAGE_EARLY_STOP_WINDOW_TOKENS), + }, + ) + + +def _batched(items: List[dict], batch_size: int) -> List[List[dict]]: + size = max(1, int(batch_size)) + return [items[idx : idx + size] for idx in range(0, len(items), size)] + + +def _image_content_stats(image: Image.Image) -> dict: + sample = image.convert("L") + sample.thumbnail((256, 256)) + width, height = sample.size + pixels = list(sample.getdata()) + + def _dark_ratio(y0: int, y1: int) -> float: + values = [] + for row in range(y0, y1): + start = row * width + values.extend(pixels[start : start + width]) + total = len(values) + if total <= 0: + return 0.0 + dark = sum(1 for value in values if value < REPAIR_DARK_THRESHOLD) + return float(dark) / float(total) + + half = max(1, height // 2) + third = max(1, height // 3) + top_third_end = min(height, third) + middle_third_end = min(height, third * 2) + dark_total = sum(1 for value in pixels if value < REPAIR_DARK_THRESHOLD) + return { + "top_dark_ratio": _dark_ratio(0, half), + "bottom_dark_ratio": _dark_ratio(half, height), + "top_third_dark_ratio": _dark_ratio(0, top_third_end), + "middle_third_dark_ratio": _dark_ratio(top_third_end, middle_third_end), + "bottom_third_dark_ratio": _dark_ratio(middle_third_end, height), + "overall_dark_ratio": float(dark_total) / float(max(1, len(pixels))), + } + + +def _text_quality_metrics(text: str) -> dict: + stripped = str(text or "").strip() + letters = sum(1 for ch in stripped if ch.isalpha()) + digits = sum(1 for ch in stripped if ch.isdigit()) + pua_chars = sum( + 1 + for ch in stripped + if 0xE000 <= ord(ch) <= 0xF8FF + or 0xF0000 <= ord(ch) <= 0xFFFFD + or 0x100000 <= ord(ch) <= 0x10FFFD + ) + lines = [line.strip() for line in stripped.splitlines() if line.strip()] + avg_line_length = (sum(len(line) for line in lines) / float(len(lines))) if lines else 0.0 + score = float(letters) + (0.10 * float(len(stripped))) + (0.05 * float(digits)) - (20.0 * float(pua_chars)) + return { + "chars": int(len(stripped)), + "letters": int(letters), + "digits": int(digits), + "pua_chars": int(pua_chars), + "line_count": int(len(lines)), + "avg_line_length": float(avg_line_length), + "quality_score": float(score), + } + + +def _is_effectively_empty_page(image_stats: dict, repair_mode: str) -> bool: + if str(repair_mode or "off").strip().lower() != "auto": + return False + overall_dark = float(image_stats.get("overall_dark_ratio", 0.0)) + if overall_dark > EMPTY_PAGE_OVERALL_DARK_MAX: + return False + return all( + float(image_stats.get(key, 0.0)) <= EMPTY_PAGE_BAND_DARK_MAX + for key in ( + "top_dark_ratio", + "bottom_dark_ratio", + "top_third_dark_ratio", + "middle_third_dark_ratio", + "bottom_third_dark_ratio", + ) + ) + + +def _resolve_job_image(item: dict) -> Tuple[Image.Image, bool]: + image = item.get("image") + if isinstance(image, Image.Image): + return image, False + return Image.open(item["image_path"]).convert("RGB"), True + + +def _close_job_image(item: dict) -> None: + image = item.pop("image", None) + if isinstance(image, Image.Image): + image.close() + + +def _empty_page_metric(*, page_number: int, image_stats: dict) -> dict: + return { + "page_number": int(page_number), + "infer_sec": 0.0, + "raw_chars": 0, + "final_chars": 0, + "first_pass_quality_score": 0.0, + "first_pass_letters": 0, + "first_pass_digits": 0, + "first_pass_pua_chars": 0, + "repair_strategy": "skip_empty", + "repair_reason": "empty_page", + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": True, + "garbage_early_stop_applied": False, + **image_stats, + } + + +def _utc_now_iso(now_ts: Optional[float] = None) -> str: + return time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime(float(now_ts) if now_ts is not None else time.time())) + + +def _write_worker_runtime(runtime_file: Optional[Path], state: dict) -> None: + if runtime_file is None: + return + runtime_path = Path(runtime_file).expanduser().resolve() + runtime_path.parent.mkdir(parents=True, exist_ok=True) + payload = dict(state) + payload["updated_at"] = _utc_now_iso() + runtime_path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") + + +def _build_jobs_from_batch(input_dir: Path, batch: dict) -> List[dict]: + files = list(batch.get("files") or []) + page_ranges = list(batch.get("page_ranges") or []) + return _iter_pdf_jobs(input_dir, files, page_ranges) + + +def _iter_selected_rendered_pages( + pdf_path: Path, + *, + render_dpi: int, + source_page_numbers: List[int], +): + import fitz + + doc = fitz.open(pdf_path) + try: + zoom = float(render_dpi) / 72.0 + matrix = fitz.Matrix(zoom, zoom) + for source_page_number in source_page_numbers: + idx = int(source_page_number) - 1 + if idx < 0 or idx >= int(doc.page_count): + raise ValueError(f"Requested page {source_page_number} outside document bounds for {pdf_path}") + page = doc[idx] + pixmap = page.get_pixmap(matrix=matrix, alpha=False) + yield int(source_page_number), Image.frombytes("RGB", [pixmap.width, pixmap.height], pixmap.samples) + finally: + doc.close() + + +def _emit_progress(output_dir: Path, stem: str, state: dict) -> None: + _write_progress( + output_dir, + stem, + state["page_outputs"], + int(state["total_pages"]), + int(state["completed_pages"]), + ) + + +def _resolve_repair_disposition(*, repair_text: str, repair_postprocess: dict) -> dict: + if bool(repair_postprocess.get("early_stops", 0)): + return { + "final_text": "", + "repair_applied": False, + "page_dropped_after_repair": True, + "drop_reason": "repeat_garbage_cutoff", + } + if repair_text.strip(): + return { + "final_text": repair_text, + "repair_applied": True, + "page_dropped_after_repair": False, + "drop_reason": None, + } + return { + "final_text": None, + "repair_applied": False, + "page_dropped_after_repair": False, + "drop_reason": None, + } + + +def _repair_summary_from_page_metrics(page_metrics: List[dict], repair_mode: str) -> dict: + return { + "repair_mode": str(repair_mode), + "pages_flagged": int(sum(1 for item in page_metrics if str(item.get("repair_strategy")) != "none")), + "pages_repaired": int(sum(1 for item in page_metrics if bool(item.get("repair_applied")))), + "plain_repairs": int( + sum(1 for item in page_metrics if str(item.get("repair_profile")) == "plain_ocr" and bool(item.get("repair_applied"))) + ), + "tiled_repairs": 0, + "pages_dropped_after_repeat_cutoff": int(sum(1 for item in page_metrics if bool(item.get("page_dropped_after_repair")))), + "empty_pages_skipped": int(sum(1 for item in page_metrics if bool(item.get("empty_page_skipped")))), + "pages_with_early_stop": int(sum(1 for item in page_metrics if bool(item.get("garbage_early_stop_applied")))), + } + + +def _load_persisted_doc_state(output_dir: Path, stem: str) -> dict: + markdown_path = output_dir / "markdown" / f"{stem}.md" + metrics_path = output_dir / "json" / "metrics" / f"{stem}.metrics.json" + metrics = json.loads(metrics_path.read_text(encoding="utf-8")) + page_count = int(metrics.get("page_count", 0)) + page_outputs = _split_page_outputs(markdown_path.read_text(encoding="utf-8")) if markdown_path.exists() else [] + if len(page_outputs) < page_count: + page_outputs.extend([""] * (page_count - len(page_outputs))) + elif len(page_outputs) > page_count: + page_outputs = page_outputs[:page_count] + metrics_by_page = { + int(item["page_number"]): dict(item) + for item in list(metrics.get("page_metrics") or []) + if item is not None and "page_number" in item + } + page_metrics = [metrics_by_page.get(page_number) for page_number in range(1, page_count + 1)] + extra_metrics = dict(metrics) + extra_metrics.pop("page_count", None) + extra_metrics.pop("model", None) + return { + "stem": stem, + "page_outputs": page_outputs, + "page_metrics": page_metrics, + "total_pages": page_count, + "extra_metrics": extra_metrics, + } + + +def _build_repair_batches(*, doc_states: Dict[str, dict], retry_pages_by_stem: Dict[str, List[int]], origin_batch_id: int) -> List[dict]: + batches: List[dict] = [] + for stem, retry_pages in sorted(retry_pages_by_stem.items()): + unique_retry_pages = sorted({int(page_number) for page_number in retry_pages}) + if not unique_retry_pages: + continue + state = doc_states[stem] + batches.append( + { + "queue_key": f"repair:{int(origin_batch_id)}:{stem}", + "origin_batch_id": int(origin_batch_id), + "stem": stem, + "pdf_path": str(state["pdf_path"]), + "source_name": str(state["source_name"]), + "source_stem": str(state["source_stem"]), + "source_start_page": int(state["source_start_page"]), + "source_end_page": int(state["source_start_page"]) + max(0, int(state["total_pages"]) - 1), + "repair_page_numbers": unique_retry_pages, + "pages": int(len(unique_retry_pages)), + } + ) + return batches + + +def _claim_additional_repair_batches( + work_db: Path, + *, + worker_id: str, + stale_after_sec: float, + first_batch: dict, + target_pages: int, + target_items: int, +) -> List[dict]: + claimed_batches = [dict(first_batch)] + first_batch_pages = max(0, int(first_batch.get("pages", len(list(first_batch.get("repair_page_numbers") or []))))) + claimed_pages = first_batch_pages + target_pages = max(1, int(target_pages)) + target_items = max(1, int(target_items)) + if "batch_id" in first_batch: + heartbeat_batch(work_db, batch_id=int(first_batch["batch_id"]), worker_id=worker_id) + while len(claimed_batches) < target_items and claimed_pages < target_pages: + next_batch = claim_next_batch( + work_db, + worker_id=worker_id, + stale_after_sec=stale_after_sec, + queue_name=QUEUE_REPAIR, + ) + if next_batch is None: + break + claimed_batches.append(dict(next_batch)) + claimed_pages += max(0, int(next_batch.get("pages", 0))) + return claimed_batches + + +def _repair_batch_result( + *, + batch: dict, + render_sec_total: float, + infer_sec_total: float, + first_infer_started_at: Optional[float], + last_infer_completed_at: Optional[float], + batch_wall_time_sec: float, + execution_pack_batch_ids: List[int], + execution_pack_pages: int, +) -> dict: + batch_pages = int(batch.get("pages", len(list(batch.get("repair_page_numbers") or [])))) + return { + "docs": 1, + "pages": int(batch_pages), + "render_sec_total": float(render_sec_total), + "infer_sec_total": float(infer_sec_total), + "first_infer_started_at": _utc_now_iso(first_infer_started_at) if first_infer_started_at is not None else None, + "last_infer_completed_at": _utc_now_iso(last_infer_completed_at) if last_infer_completed_at is not None else None, + "batch_wall_time_sec": float(batch_wall_time_sec), + "execution_pack_batch_ids": [int(batch_id) for batch_id in execution_pack_batch_ids], + "execution_pack_pages": int(execution_pack_pages), + "execution_pack_items": int(len(execution_pack_batch_ids)), + "queue_name": QUEUE_REPAIR, + "batch_id": int(batch["batch_id"]) if "batch_id" in batch else None, + } + + +def _run_vllm_batch( + llm, + *, + batch: List[dict], + prompt: str, + sampling_params, +) -> List[dict]: + if not batch: + return [] + + prompt_batch = [] + opened_images: List[Image.Image] = [] + keys: List[tuple[str, int]] = [] + for item in batch: + image, should_close = _resolve_job_image(item) + if should_close: + opened_images.append(image) + keys.append((str(item["stem"]), int(item["page_number"]))) + prompt_batch.append( + { + "prompt": prompt, + "multi_modal_data": {"image": image}, + } + ) + + try: + infer_start = time.perf_counter() + batch_outputs = llm.generate(prompt_batch, sampling_params=sampling_params) + infer_sec = time.perf_counter() - infer_start + finally: + for image in opened_images: + image.close() + + per_item_sec = infer_sec / max(1, len(batch)) + results: List[dict] = [] + for item, key, output in zip(batch, keys, batch_outputs): + raw_text = "" + if getattr(output, "outputs", None): + raw_text = str(output.outputs[0].text) + results.append( + { + "key": key, + "item": item, + "raw_text": raw_text, + "infer_sec": float(per_item_sec), + } + ) + return results + + +def _generate_batch_outputs( + llm, + *, + jobs: List[dict], + prompt: str, + batch_size: int, + sampling_params, +) -> List[dict]: + outputs_by_key: Dict[tuple[str, int], dict] = {} + for batch in _batched(jobs, batch_size): + for result in _run_vllm_batch( + llm, + batch=batch, + prompt=prompt, + sampling_params=sampling_params, + ): + outputs_by_key[result["key"]] = { + "item": result["item"], + "raw_text": result["raw_text"], + "infer_sec": result["infer_sec"], + } + return [outputs_by_key[(str(item["stem"]), int(item["page_number"]))] for item in jobs] + + +def _run_jobs_to_outputs( + args: argparse.Namespace, + *, + jobs_to_run: List[dict], + output_dir: Path, + work_db: Optional[Path], + origin_batch_id: Optional[int], + llm, + prompt: str, + plain_prompt: str, + base_size: int, + image_size: int, + crop_mode: bool, + sampling_params, +) -> dict: + batch_wall_start = time.perf_counter() + batch_size = max(1, int(args.batch_size)) + doc_states: Dict[str, dict] = {} + plain_retry_jobs: List[dict] = [] + retry_pages_by_stem: Dict[str, List[int]] = {} + state_lock = threading.Lock() + render_queue: "queue.Queue[dict | None]" = queue.Queue(maxsize=max(2, batch_size * 2)) + producer_errors: List[BaseException] = [] + first_infer_started_at: Optional[float] = None + last_infer_completed_at: Optional[float] = None + shared_repair_queue = ( + work_db is not None + and origin_batch_id is not None + and str(args.repair_mode or "off").strip().lower() == "auto" + ) + + def _render_producer() -> None: + try: + for job in jobs_to_run: + pdf_path = Path(job["pdf_path"]) + stem = str(job["stem"]) + doc_start = time.perf_counter() + total_pages = _count_rendered_pages( + pdf_path, + args.max_pages, + start_page=int(job["start_page"]), + end_page=job["end_page"], + ) + state = { + "stem": stem, + "pdf_path": str(pdf_path), + "source_name": str(job["source_name"]), + "source_stem": str(job["source_stem"]), + "source_start_page": int(job["start_page"]), + "page_outputs": [""] * total_pages, + "page_metrics": [None] * total_pages, + "render_sec": 0.0, + "doc_start": float(doc_start), + "completed_pages": 0, + "total_pages": total_pages, + } + with state_lock: + doc_states[stem] = state + _emit_progress(output_dir, stem, state) + + render_start = time.perf_counter() + for page_number, image in enumerate( + _iter_rendered_pages( + pdf_path, + args.max_pages, + args.render_dpi, + start_page=int(job["start_page"]), + end_page=job["end_page"], + ), + start=1, + ): + image_stats = _image_content_stats(image) + if _is_effectively_empty_page(image_stats, args.repair_mode): + with state_lock: + state["page_metrics"][page_number - 1] = _empty_page_metric( + page_number=page_number, + image_stats=image_stats, + ) + state["completed_pages"] = int(state["completed_pages"]) + 1 + _emit_progress(output_dir, stem, state) + image.close() + continue + render_queue.put( + { + "stem": stem, + "page_number": int(page_number), + "image": image, + "image_stats": image_stats, + } + ) + + with state_lock: + state["render_sec"] = float(time.perf_counter() - render_start) + except BaseException as exc: # pragma: no cover - exercised in integration flows + producer_errors.append(exc) + finally: + render_queue.put(None) + + producer = threading.Thread(target=_render_producer, name="deepseek-vllm-render", daemon=True) + producer.start() + + in_flight_batch: List[dict] = [] + producer_done = False + queue_wait_timeout = 0.05 + queue_flush_marker = "__flush__" + try: + while not producer_done or in_flight_batch: + if not producer_done and len(in_flight_batch) < batch_size: + try: + item = render_queue.get(timeout=queue_wait_timeout) + except queue.Empty: + item = queue_flush_marker if in_flight_batch else None + if item is None: + if producer.is_alive(): + continue + producer_done = True + elif item == queue_flush_marker: + pass + else: + in_flight_batch.append(item) + if len(in_flight_batch) < batch_size: + continue + + if not in_flight_batch: + continue + + batch_infer_started_at = time.time() + if first_infer_started_at is None: + first_infer_started_at = batch_infer_started_at + batch_results = _run_vllm_batch( + llm, + batch=in_flight_batch, + prompt=prompt, + sampling_params=sampling_params, + ) + last_infer_completed_at = time.time() + for result in batch_results: + item = result["item"] + state = doc_states[item["stem"]] + raw_text = str(result["raw_text"]) + image_stats = dict(item.get("image_stats", {})) + page_text, postprocess_metrics = _postprocess_page_text( + raw_text, + prompt=prompt, + content_debug=bool(args.content_debug), + ) + if args.content_debug: + page_text = f"\n{page_text}".strip() + quality = _text_quality_metrics(page_text) + metric = { + "page_number": int(item["page_number"]), + "infer_sec": float(result["infer_sec"]), + "raw_chars": int(len(raw_text.strip())), + "final_chars": int(len(page_text.strip())), + "first_pass_quality_score": float(quality["quality_score"]), + "first_pass_letters": int(quality["letters"]), + "first_pass_digits": int(quality["digits"]), + "first_pass_pua_chars": int(quality["pua_chars"]), + "repair_strategy": "plain" if bool(postprocess_metrics.get("early_stops", 0)) else "none", + "repair_reason": "early_stop_markdown_garbage" if bool(postprocess_metrics.get("early_stops", 0)) else None, + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": False, + "garbage_early_stop_applied": bool(postprocess_metrics.get("early_stops", 0)), + **image_stats, + **postprocess_metrics, + } + with state_lock: + state["page_outputs"][item["page_number"] - 1] = page_text + state["page_metrics"][item["page_number"] - 1] = metric + state["completed_pages"] = int(state["completed_pages"]) + 1 + _emit_progress(output_dir, item["stem"], state) + + if bool(postprocess_metrics.get("early_stops", 0)) and str(args.repair_mode or "off").strip().lower() == "auto": + if shared_repair_queue: + retry_pages_by_stem.setdefault(str(item["stem"]), []).append(int(item["page_number"])) + _close_job_image(item) + else: + plain_retry_jobs.append(item) + else: + _close_job_image(item) + + in_flight_batch = [] + + producer.join() + if producer_errors: + raise producer_errors[0] + + if plain_retry_jobs: + repair_started_at = time.time() + if first_infer_started_at is None: + first_infer_started_at = repair_started_at + plain_repair_outputs = _generate_batch_outputs( + llm, + jobs=plain_retry_jobs, + prompt=plain_prompt, + batch_size=batch_size, + sampling_params=sampling_params, + ) + last_infer_completed_at = time.time() + for result in plain_repair_outputs: + item = result["item"] + state = doc_states[item["stem"]] + metric = state["page_metrics"][item["page_number"] - 1] + repair_text, repair_postprocess = _postprocess_page_text( + str(result["raw_text"]), + prompt=plain_prompt, + content_debug=bool(args.content_debug), + ) + if args.content_debug: + repair_text = f"\n{repair_text}".strip() + metric["repair_attempted"] = True + metric["repair_infer_sec"] = float(result["infer_sec"]) + metric["repair_raw_chars"] = int(len(str(result["raw_text"]).strip())) + metric["repair_profile"] = "plain_ocr" + disposition = _resolve_repair_disposition( + repair_text=repair_text, + repair_postprocess=repair_postprocess, + ) + repair_effective_text = disposition["final_text"] or "" + metric["repair_final_chars"] = int(len(repair_effective_text.strip())) + metric["repair_quality_score"] = float(_text_quality_metrics(repair_effective_text)["quality_score"]) + metric["repair_garbage_early_stop_applied"] = bool(repair_postprocess.get("early_stops", 0)) + metric["repair_applied"] = bool(disposition["repair_applied"]) + metric["page_dropped_after_repair"] = bool(disposition["page_dropped_after_repair"]) + if disposition["drop_reason"] is not None: + metric["drop_reason"] = str(disposition["drop_reason"]) + metric.update({f"repair_{key}": value for key, value in repair_postprocess.items()}) + metric["infer_sec"] = float(metric["infer_sec"]) + float(result["infer_sec"]) + with state_lock: + if disposition["final_text"] is not None: + state["page_outputs"][item["page_number"] - 1] = repair_effective_text + metric["final_chars"] = int(len(repair_effective_text.strip())) + _emit_progress(output_dir, item["stem"], state) + _close_job_image(item) + finally: + for item in in_flight_batch: + _close_job_image(item) + for item in plain_retry_jobs: + _close_job_image(item) + + for stem, state in doc_states.items(): + markdown = _join_page_outputs(state["page_outputs"]) if state["page_outputs"] else "[[Blank page]]" + page_metrics = sorted( + [item for item in state["page_metrics"] if item], + key=lambda item: int(item["page_number"]), + ) + repair_summary = _repair_summary_from_page_metrics(page_metrics, str(args.repair_mode)) + _write_outputs( + output_dir, + stem, + markdown, + int(state["total_pages"]), + extra_metrics={ + "source_file": str(state["source_name"]), + "source_stem": str(state["source_stem"]), + "source_start_page": int(state["source_start_page"]), + "source_end_page": int(state["source_start_page"]) + max(0, len(page_metrics) - 1), + "ocr_profile": args.ocr_profile, + "attn_backend": "vllm", + "runtime_backend": "vllm", + "base_size": base_size, + "image_size": image_size, + "crop_mode": crop_mode, + "render_dpi": int(args.render_dpi), + "max_new_tokens": args.max_new_tokens, + "batch_size": int(args.batch_size), + "gpu_memory_utilization": float(args.gpu_memory_utilization), + "disable_fp8_kv": bool(args.disable_fp8_kv), + "repair_mode": str(args.repair_mode), + "render_sec": float(state["render_sec"]), + "infer_sec_total": float(sum(item["infer_sec"] for item in page_metrics)), + "wall_time_sec": float(time.perf_counter() - float(state["doc_start"])), + "repair_summary": repair_summary, + "page_metrics": page_metrics, + }, + ) + if shared_repair_queue and retry_pages_by_stem: + enqueue_batches( + work_db, + queue_name=QUEUE_REPAIR, + batches=_build_repair_batches( + doc_states=doc_states, + retry_pages_by_stem=retry_pages_by_stem, + origin_batch_id=int(origin_batch_id), + ), + ) + + return { + "docs": int(len(doc_states)), + "pages": int(sum(int(state["total_pages"]) for state in doc_states.values())), + "render_sec_total": float(sum(float(state["render_sec"]) for state in doc_states.values())), + "infer_sec_total": float( + sum( + sum(float(item["infer_sec"]) for item in state["page_metrics"] if item is not None) + for state in doc_states.values() + ) + ), + "first_infer_started_at": _utc_now_iso(first_infer_started_at) if first_infer_started_at is not None else None, + "last_infer_completed_at": _utc_now_iso(last_infer_completed_at) if last_infer_completed_at is not None else None, + "repair_batches_enqueued": int(sum(1 for pages in retry_pages_by_stem.values() if pages)), + "batch_wall_time_sec": float(time.perf_counter() - batch_wall_start), + } + + +def _run_repair_batches_to_outputs( + args: argparse.Namespace, + *, + batches: List[dict], + output_dir: Path, + llm, + plain_prompt: str, + sampling_params, +) -> dict: + batch_wall_start = time.perf_counter() + claimed_batches = [dict(batch) for batch in batches] + if not claimed_batches: + return { + "docs": 0, + "pages": 0, + "render_sec_total": 0.0, + "infer_sec_total": 0.0, + "first_infer_started_at": None, + "last_infer_completed_at": None, + "batch_wall_time_sec": float(time.perf_counter() - batch_wall_start), + "per_batch_results": {}, + } + + state_by_stem: Dict[str, dict] = {} + repair_jobs: List[dict] = [] + per_batch_results: Dict[int, dict] = {} + execution_pack_batch_ids = [int(batch["batch_id"]) for batch in claimed_batches if "batch_id" in batch] + execution_pack_pages = int(sum(max(0, int(batch.get("pages", 0))) for batch in claimed_batches)) + render_sec_total = 0.0 + + for batch in claimed_batches: + batch_id = int(batch["batch_id"]) if "batch_id" in batch else None + stem = str(batch["stem"]) + state = state_by_stem.get(stem) + if state is None: + state = _load_persisted_doc_state(output_dir, stem) + state_by_stem[stem] = state + source_start_page = int(batch["source_start_page"]) + repair_page_numbers = sorted({int(page_number) for page_number in list(batch.get("repair_page_numbers") or [])}) + render_start = time.perf_counter() + if repair_page_numbers: + source_page_numbers = [source_start_page + page_number - 1 for page_number in repair_page_numbers] + for source_page_number, image in _iter_selected_rendered_pages( + Path(str(batch["pdf_path"])), + render_dpi=int(args.render_dpi), + source_page_numbers=source_page_numbers, + ): + repair_jobs.append( + { + "batch_id": batch_id, + "stem": stem, + "page_number": int(source_page_number) - source_start_page + 1, + "image": image, + } + ) + render_sec = float(time.perf_counter() - render_start) + render_sec_total += render_sec + if batch_id is not None: + per_batch_results[batch_id] = _repair_batch_result( + batch=batch, + render_sec_total=render_sec, + infer_sec_total=0.0, + first_infer_started_at=None, + last_infer_completed_at=None, + batch_wall_time_sec=float(time.perf_counter() - batch_wall_start), + execution_pack_batch_ids=execution_pack_batch_ids, + execution_pack_pages=execution_pack_pages, + ) + + first_infer_started_at: Optional[float] = None + last_infer_completed_at: Optional[float] = None + if repair_jobs: + first_infer_started_at = time.time() + repair_outputs = _generate_batch_outputs( + llm, + jobs=repair_jobs, + prompt=plain_prompt, + batch_size=max(1, int(args.batch_size)), + sampling_params=sampling_params, + ) + last_infer_completed_at = time.time() + else: + repair_outputs = [] + + try: + for result in repair_outputs: + item = result["item"] + stem = str(item["stem"]) + page_number = int(item["page_number"]) + batch_id = int(item["batch_id"]) if item.get("batch_id") is not None else None + state = state_by_stem[stem] + metric = state["page_metrics"][page_number - 1] + if metric is None: + metric = { + "page_number": page_number, + "infer_sec": 0.0, + "raw_chars": 0, + "final_chars": 0, + "first_pass_quality_score": 0.0, + "first_pass_letters": 0, + "first_pass_digits": 0, + "first_pass_pua_chars": 0, + "repair_strategy": "plain", + "repair_reason": "early_stop_markdown_garbage", + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": False, + "garbage_early_stop_applied": False, + } + state["page_metrics"][page_number - 1] = metric + repair_text, repair_postprocess = _postprocess_page_text( + str(result["raw_text"]), + prompt=plain_prompt, + content_debug=bool(args.content_debug), + ) + if args.content_debug: + repair_text = f"\n{repair_text}".strip() + metric["repair_attempted"] = True + metric["repair_infer_sec"] = float(result["infer_sec"]) + metric["repair_raw_chars"] = int(len(str(result["raw_text"]).strip())) + metric["repair_profile"] = "plain_ocr" + disposition = _resolve_repair_disposition( + repair_text=repair_text, + repair_postprocess=repair_postprocess, + ) + repair_effective_text = disposition["final_text"] or "" + metric["repair_final_chars"] = int(len(repair_effective_text.strip())) + metric["repair_quality_score"] = float(_text_quality_metrics(repair_effective_text)["quality_score"]) + metric["repair_garbage_early_stop_applied"] = bool(repair_postprocess.get("early_stops", 0)) + metric["repair_applied"] = bool(disposition["repair_applied"]) + metric["page_dropped_after_repair"] = bool(disposition["page_dropped_after_repair"]) + if disposition["drop_reason"] is not None: + metric["drop_reason"] = str(disposition["drop_reason"]) + metric.update({f"repair_{key}": value for key, value in repair_postprocess.items()}) + metric["infer_sec"] = float(metric.get("infer_sec", 0.0)) + float(result["infer_sec"]) + if disposition["final_text"] is not None: + state["page_outputs"][page_number - 1] = repair_effective_text + metric["final_chars"] = int(len(repair_effective_text.strip())) + if batch_id is not None and batch_id in per_batch_results: + per_batch_results[batch_id]["infer_sec_total"] = float( + per_batch_results[batch_id]["infer_sec_total"] + float(result["infer_sec"]) + ) + _close_job_image(item) + finally: + for item in repair_jobs: + _close_job_image(item) + + for stem, state in state_by_stem.items(): + page_metrics = sorted([item for item in state["page_metrics"] if item], key=lambda item: int(item["page_number"])) + extra_metrics = dict(state["extra_metrics"]) + extra_metrics["repair_summary"] = _repair_summary_from_page_metrics( + page_metrics, + extra_metrics.get("repair_mode", args.repair_mode), + ) + extra_metrics["page_metrics"] = page_metrics + extra_metrics["infer_sec_total"] = float(sum(float(item["infer_sec"]) for item in page_metrics)) + _write_outputs( + output_dir, + stem, + _join_page_outputs(state["page_outputs"]) if state["page_outputs"] else "[[Blank page]]", + int(state["total_pages"]), + extra_metrics=extra_metrics, + ) + + batch_wall_time_sec = float(time.perf_counter() - batch_wall_start) + for batch_id, result in per_batch_results.items(): + result["first_infer_started_at"] = ( + _utc_now_iso(first_infer_started_at) if first_infer_started_at is not None else None + ) + result["last_infer_completed_at"] = ( + _utc_now_iso(last_infer_completed_at) if last_infer_completed_at is not None else None + ) + result["batch_wall_time_sec"] = batch_wall_time_sec + + return { + "docs": int(len(state_by_stem)), + "pages": int( + sum(max(0, int(batch.get("pages", len(list(batch.get("repair_page_numbers") or []))))) for batch in claimed_batches) + ), + "render_sec_total": float(render_sec_total), + "infer_sec_total": float(sum(float(result["infer_sec"]) for result in repair_outputs)), + "first_infer_started_at": _utc_now_iso(first_infer_started_at) if first_infer_started_at is not None else None, + "last_infer_completed_at": _utc_now_iso(last_infer_completed_at) if last_infer_completed_at is not None else None, + "batch_wall_time_sec": batch_wall_time_sec, + "per_batch_results": per_batch_results, + } + + +def _run_repair_batch_to_outputs( + args: argparse.Namespace, + *, + batch: dict, + output_dir: Path, + llm, + plain_prompt: str, + sampling_params, +) -> dict: + result = _run_repair_batches_to_outputs( + args, + batches=[batch], + output_dir=output_dir, + llm=llm, + plain_prompt=plain_prompt, + sampling_params=sampling_params, + ) + batch_id = int(batch["batch_id"]) if "batch_id" in batch else None + if batch_id is not None and batch_id in result["per_batch_results"]: + return dict(result["per_batch_results"][batch_id]) + result.pop("per_batch_results", None) + return result + + +def _queue_has_pending_or_running(counts: Dict[str, object], queue_name: str) -> bool: + queue_counts = counts.get("by_queue", {}).get(queue_name, {}) + return int(queue_counts.get(STATUS_PENDING, 0)) > 0 or int(queue_counts.get(STATUS_RUNNING, 0)) > 0 + + +def _claim_next_phase_batch( + work_db: Path, + *, + worker_id: str, + stale_after_sec: float, +) -> Tuple[Optional[str], Optional[Dict[str, object]], bool]: + batch = claim_next_batch( + work_db, + worker_id=worker_id, + stale_after_sec=stale_after_sec, + queue_name=QUEUE_MAIN, + ) + if batch is not None: + return QUEUE_MAIN, batch, False + + counts = work_queue_counts(work_db) + # Repairs are a distinct global phase: no worker should start repair work + # while any first-pass batch is still pending or running elsewhere. + if _queue_has_pending_or_running(counts, QUEUE_MAIN): + return None, None, True + + batch = claim_next_batch( + work_db, + worker_id=worker_id, + stale_after_sec=stale_after_sec, + queue_name=QUEUE_REPAIR, + ) + if batch is not None: + return QUEUE_REPAIR, batch, False + + counts = work_queue_counts(work_db) + if _queue_has_pending_or_running(counts, QUEUE_REPAIR): + return None, None, True + return None, None, False + + +def _run_work_queue( + args: argparse.Namespace, + *, + input_dir: Path, + output_dir: Path, + llm, + prompt: str, + plain_prompt: str, + base_size: int, + image_size: int, + crop_mode: bool, + sampling_params, +) -> int: + work_db = Path(str(args.work_db)).expanduser().resolve() + worker_id = str(args.worker_id or f"worker-{int(time.time())}") + runtime_file = Path(str(args.worker_runtime_file)).expanduser().resolve() if args.worker_runtime_file else None + heartbeat_interval = float(max(1.0, args.work_heartbeat_sec)) + stale_after_sec = float(max(30.0, args.work_stale_after_sec)) + max_attempts = int(max(1, args.work_max_attempts)) + runtime_state = { + "worker_id": worker_id, + "status": "starting", + "started_at": _utc_now_iso(), + "engine_ready_at": _utc_now_iso(), + "current_batch_id": None, + "current_queue_name": None, + "current_batch_ids": [], + "completed_batches": [], + "first_batch_started_at": None, + "last_batch_finished_at": None, + } + _write_worker_runtime(runtime_file, runtime_state) + + while True: + queue_name, batch, should_wait = _claim_next_phase_batch( + work_db, + worker_id=worker_id, + stale_after_sec=stale_after_sec, + ) + if batch is None: + if should_wait: + time.sleep(min(heartbeat_interval, 1.0)) + continue + runtime_state["status"] = "complete" + runtime_state["current_batch_id"] = None + runtime_state["current_queue_name"] = None + _write_worker_runtime(runtime_file, runtime_state) + return 0 + + claimed_batches = [dict(batch)] + if queue_name == QUEUE_REPAIR: + claimed_batches = _claim_additional_repair_batches( + work_db, + worker_id=worker_id, + stale_after_sec=stale_after_sec, + first_batch=batch, + target_pages=int(args.repair_exec_batch_target_pages), + target_items=int(args.repair_exec_batch_target_items), + ) + batch_ids = [int(claimed_batch["batch_id"]) for claimed_batch in claimed_batches if "batch_id" in claimed_batch] + batch_id = batch_ids[0] + heartbeat_stop = threading.Event() + + def _heartbeat_loop() -> None: + while not heartbeat_stop.wait(heartbeat_interval): + for heartbeat_batch_id in batch_ids: + heartbeat_batch(work_db, batch_id=heartbeat_batch_id, worker_id=worker_id) + runtime_state["heartbeat_at"] = _utc_now_iso() + _write_worker_runtime(runtime_file, runtime_state) + + heartbeat_thread = threading.Thread(target=_heartbeat_loop, name=f"{worker_id}-heartbeat", daemon=True) + heartbeat_thread.start() + try: + runtime_state["status"] = f"running_{queue_name}" + runtime_state["current_batch_id"] = batch_id + runtime_state["current_queue_name"] = queue_name + runtime_state["current_batch_ids"] = batch_ids + runtime_state["current_batch_pages"] = int(sum(int(claimed_batch.get("pages", 0)) for claimed_batch in claimed_batches)) + runtime_state["heartbeat_at"] = _utc_now_iso() + _write_worker_runtime(runtime_file, runtime_state) + if queue_name == QUEUE_MAIN: + result = _run_jobs_to_outputs( + args, + jobs_to_run=_build_jobs_from_batch(input_dir, batch), + output_dir=output_dir, + work_db=work_db, + origin_batch_id=batch_id, + llm=llm, + prompt=prompt, + plain_prompt=plain_prompt, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + sampling_params=sampling_params, + ) + per_batch_results = {batch_id: dict(result)} + else: + result = _run_repair_batches_to_outputs( + args, + batches=claimed_batches, + output_dir=output_dir, + llm=llm, + plain_prompt=plain_prompt, + sampling_params=sampling_params, + ) + per_batch_results = dict(result.get("per_batch_results") or {}) + if runtime_state["first_batch_started_at"] is None: + runtime_state["first_batch_started_at"] = result.get("first_infer_started_at") + runtime_state["last_batch_finished_at"] = result.get("last_infer_completed_at") + runtime_state["completed_batches"].extend( + { + "batch_id": int(claimed_batch["batch_id"]), + "queue_name": queue_name, + } + for claimed_batch in claimed_batches + if "batch_id" in claimed_batch + ) + for claimed_batch in claimed_batches: + claimed_batch_id = int(claimed_batch["batch_id"]) + mark_batch_done( + work_db, + batch_id=claimed_batch_id, + worker_id=worker_id, + result=per_batch_results.get(claimed_batch_id, dict(result)), + ) + except Exception as exc: + runtime_state["status"] = "failed" + runtime_state["current_batch_id"] = batch_id + runtime_state["current_queue_name"] = queue_name + runtime_state["last_error"] = str(exc) + _write_worker_runtime(runtime_file, runtime_state) + for claimed_batch in claimed_batches: + mark_batch_failed( + work_db, + batch_id=int(claimed_batch["batch_id"]), + worker_id=worker_id, + error=str(exc), + max_attempts=max_attempts, + ) + raise + finally: + heartbeat_stop.set() + heartbeat_thread.join(timeout=max(1.0, heartbeat_interval)) + runtime_state["current_batch_id"] = None + runtime_state["current_queue_name"] = None + runtime_state["current_batch_ids"] = [] + _write_worker_runtime(runtime_file, runtime_state) + + +def main() -> int: + args = _parse_args() + input_dir = Path(args.input_dir).resolve() + output_dir = Path(args.output_dir).resolve() + model_dir = Path(args.model_dir).resolve() + + profile_defaults = _profile_defaults(args.ocr_profile) + prompt = str(args.prompt_override) if args.prompt_override else profile_defaults["prompt"] + plain_prompt = _profile_defaults("plain_ocr")["prompt"] + base_size = int(args.base_size) if args.base_size is not None else int(profile_defaults["base_size"]) + image_size = int(args.image_size) if args.image_size is not None else int(profile_defaults["image_size"]) + crop_mode = bool(args.crop_mode) if args.crop_mode is not None else bool(profile_defaults["crop_mode"]) + + llm = _load_vllm( + model_dir, + gpu_memory_utilization=float(args.gpu_memory_utilization), + disable_fp8_kv=bool(args.disable_fp8_kv), + ) + sampling_params = _sampling_params( + args.max_new_tokens, + enable_garbage_early_stop=str(args.repair_mode or "off").strip().lower() == "auto", + ) + + if args.work_db: + return _run_work_queue( + args, + input_dir=input_dir, + output_dir=output_dir, + llm=llm, + prompt=prompt, + plain_prompt=plain_prompt, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + sampling_params=sampling_params, + ) + + jobs_to_run = _iter_pdf_jobs(input_dir, args.files, args.page_ranges) + if not jobs_to_run: + return 0 + _run_jobs_to_outputs( + args, + jobs_to_run=jobs_to_run, + output_dir=output_dir, + work_db=None, + origin_batch_id=None, + llm=llm, + prompt=prompt, + plain_prompt=plain_prompt, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + sampling_params=sampling_params, + ) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/ocr/deepseek/runner.py b/src/glossapi/ocr/deepseek/runner.py index d68f05c..c140965 100644 --- a/src/glossapi/ocr/deepseek/runner.py +++ b/src/glossapi/ocr/deepseek/runner.py @@ -1,22 +1,72 @@ -"""DeepSeek OCR runner with stub and optional CLI dispatch.""" +"""DeepSeek OCR runner.""" from __future__ import annotations +from contextlib import ExitStack +import calendar import json import logging import os +import re +import signal import shutil import subprocess import sys +import threading +import time from pathlib import Path from typing import Any, Dict, Iterable, List, Optional +from glossapi.ocr.deepseek.scheduling import ( + SourceDocument, + assign_batches_to_lanes, + build_exact_fill_batches, + build_fixed_shard_slices, + build_whole_document_slices, + pack_slices_into_batches, +) +from glossapi.ocr.deepseek.runtime_paths import resolve_deepseek_python +from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _join_page_outputs, _split_page_outputs, _write_outputs +from glossapi.ocr.deepseek.work_queue import ( + STATUS_DONE, + STATUS_FAILED, + init_work_db, + iter_work_items, + requeue_worker_batches, + work_queue_counts, +) + try: import pypdfium2 as _pypdfium2 except Exception: # pragma: no cover - optional dependency _pypdfium2 = None LOGGER = logging.getLogger(__name__) +REPO_ROOT = Path(__file__).resolve().parents[4] +DEFAULT_SCRIPT = REPO_ROOT / "src" / "glossapi" / "ocr" / "deepseek" / "run_pdf_ocr_transformers.py" +DEFAULT_VLLM_SCRIPT = REPO_ROOT / "src" / "glossapi" / "ocr" / "deepseek" / "run_pdf_ocr_vllm.py" +AUTO_VLLM_BATCH_PAGE_CAP = 160 +DEFAULT_MAX_NEW_TOKENS = 2048 +DEFAULT_WORKER_RESPAWN_CAP = 3 +DEFAULT_WORK_ITEM_MAX_ATTEMPTS = 2 +DEFAULT_WORK_STALE_AFTER_SEC = 900.0 +DEFAULT_WORK_HEARTBEAT_SEC = 10.0 +DEFAULT_TELEMETRY_INTERVAL_SEC = 15.0 +SHARD_STEM_RE = re.compile(r"^(?P.+)__p(?P\d{5})-(?P\d{5})$") +REASSEMBLED_CONFIG_KEYS = ( + "ocr_profile", + "attn_backend", + "runtime_backend", + "base_size", + "image_size", + "crop_mode", + "render_dpi", + "max_new_tokens", + "batch_size", + "gpu_memory_utilization", + "disable_fp8_kv", + "repair_mode", +) def _page_count(pdf_path: Path) -> int: @@ -28,17 +78,42 @@ def _page_count(pdf_path: Path) -> int: return 0 -def _run_cli( +def _build_cli_command( input_dir: Path, output_dir: Path, *, + files: List[str], + page_ranges: Optional[List[str]], + model_dir: Path, python_bin: Optional[Path], script: Path, max_pages: Optional[int], content_debug: bool, - gpu_memory_utilization: Optional[float] = None, - disable_fp8_kv: bool = False, -) -> None: + device: Optional[str], + ocr_profile: str, + prompt_override: Optional[str], + attn_backend: str, + base_size: Optional[int], + image_size: Optional[int], + crop_mode: Optional[bool], + render_dpi: Optional[int], + max_new_tokens: Optional[int], + repetition_penalty: Optional[float], + no_repeat_ngram_size: Optional[int], + runtime_backend: str, + vllm_batch_size: Optional[int], + gpu_memory_utilization: Optional[float], + disable_fp8_kv: bool, + repair_mode: Optional[str], + repair_exec_batch_target_pages: Optional[int] = None, + repair_exec_batch_target_items: Optional[int] = None, + work_db: Optional[Path] = None, + worker_id: Optional[str] = None, + worker_runtime_file: Optional[Path] = None, + work_stale_after_sec: Optional[float] = None, + work_heartbeat_sec: Optional[float] = None, + work_max_attempts: Optional[int] = None, +) -> List[str]: python_exe = Path(python_bin) if python_bin else Path(sys.executable) cmd: List[str] = [ str(python_exe), @@ -47,151 +122,1435 @@ def _run_cli( str(input_dir), "--output-dir", str(output_dir), + "--model-dir", + str(model_dir), ] + if files: + cmd += ["--files", *files] + if page_ranges: + cmd += ["--page-ranges", *page_ranges] if max_pages is not None: cmd += ["--max-pages", str(max_pages)] if content_debug: cmd.append("--content-debug") - if gpu_memory_utilization is not None: - cmd += ["--gpu-memory-utilization", str(gpu_memory_utilization)] - if disable_fp8_kv: - cmd.append("--no-fp8-kv") + if device: + cmd += ["--device", str(device)] + if ocr_profile: + cmd += ["--ocr-profile", str(ocr_profile)] + if prompt_override: + cmd += ["--prompt-override", str(prompt_override)] + if attn_backend: + cmd += ["--attn-backend", str(attn_backend)] + if base_size is not None: + cmd += ["--base-size", str(int(base_size))] + if image_size is not None: + cmd += ["--image-size", str(int(image_size))] + if crop_mode is True: + cmd.append("--crop-mode") + elif crop_mode is False: + cmd.append("--no-crop-mode") + if render_dpi is not None: + cmd += ["--render-dpi", str(int(render_dpi))] + if max_new_tokens is not None: + cmd += ["--max-new-tokens", str(int(max_new_tokens))] + if work_db is not None: + cmd += ["--work-db", str(work_db)] + if worker_id: + cmd += ["--worker-id", str(worker_id)] + if worker_runtime_file is not None: + cmd += ["--worker-runtime-file", str(worker_runtime_file)] + if work_stale_after_sec is not None: + cmd += ["--work-stale-after-sec", str(float(work_stale_after_sec))] + if work_heartbeat_sec is not None: + cmd += ["--work-heartbeat-sec", str(float(work_heartbeat_sec))] + if work_max_attempts is not None: + cmd += ["--work-max-attempts", str(int(work_max_attempts))] + if repetition_penalty is not None: + cmd += ["--repetition-penalty", str(float(repetition_penalty))] + if no_repeat_ngram_size is not None: + cmd += ["--no-repeat-ngram-size", str(int(no_repeat_ngram_size))] + runtime_backend_norm = str(runtime_backend or "transformers").strip().lower() + if runtime_backend_norm == "vllm": + if vllm_batch_size is not None: + cmd += ["--batch-size", str(int(vllm_batch_size))] + if gpu_memory_utilization is not None: + cmd += ["--gpu-memory-utilization", str(float(gpu_memory_utilization))] + if disable_fp8_kv: + cmd.append("--disable-fp8-kv") + if repair_mode: + cmd += ["--repair-mode", str(repair_mode)] + if repair_exec_batch_target_pages is not None: + cmd += ["--repair-exec-batch-target-pages", str(int(repair_exec_batch_target_pages))] + if repair_exec_batch_target_items is not None: + cmd += ["--repair-exec-batch-target-items", str(int(repair_exec_batch_target_items))] + return cmd + +def _build_env( + *, + python_bin: Optional[Path], + visible_device: Optional[int] = None, + script: Optional[Path] = None, +) -> Dict[str, str]: env = os.environ.copy() + if python_bin: + python_path = Path(python_bin).expanduser() + venv_bin = str(python_path.parent) + env["PATH"] = f"{venv_bin}:{env.get('PATH', '')}" + env["VIRTUAL_ENV"] = str(python_path.parent.parent) + if script is not None: + script_path = Path(script).expanduser().resolve() + src_root = next((parent for parent in script_path.parents if (parent / "glossapi").is_dir()), None) + if src_root is not None: + src_root_str = str(src_root) + existing_pythonpath = str(env.get("PYTHONPATH", "")).strip() + pythonpath_entries = [src_root_str] + if existing_pythonpath: + pythonpath_entries.extend( + entry + for entry in existing_pythonpath.split(os.pathsep) + if entry and entry != src_root_str + ) + env["PYTHONPATH"] = os.pathsep.join(pythonpath_entries) + env.pop("PYTHONHOME", None) + if visible_device is not None: + env["CUDA_VISIBLE_DEVICES"] = str(visible_device) if shutil.which("cc1plus", path=env.get("PATH", "")) is None: - # FlashInfer JIT (via vLLM) needs a C++ toolchain; add a known cc1plus location if missing. for candidate in sorted(Path("/usr/lib/gcc/x86_64-linux-gnu").glob("*/cc1plus")): - env["PATH"] = f"{candidate.parent}:{env.get('PATH','')}" + env["PATH"] = f"{candidate.parent}:{env.get('PATH', '')}" break + ld_entries: List[str] = [] + if python_bin: + # Keep the venv path semantics instead of resolving the interpreter symlink + # back to `/usr/bin/python...`; the wheel-managed CUDA libs live under the + # virtualenv tree, not under the system interpreter location. + venv_root = Path(python_bin).expanduser().parent.parent + for site_packages in sorted((venv_root / "lib").glob("python*/site-packages")): + nvidia_root = site_packages / "nvidia" + if not nvidia_root.is_dir(): + continue + for lib_dir in sorted(nvidia_root.glob("*/lib")): + if lib_dir.is_dir(): + ld_entries.append(str(lib_dir)) ld_path = env.get("GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH") if ld_path: - env["LD_LIBRARY_PATH"] = f"{ld_path}:{env.get('LD_LIBRARY_PATH','')}" + ld_entries.extend(entry for entry in str(ld_path).split(os.pathsep) if entry) + existing_ld = str(env.get("LD_LIBRARY_PATH", "")).strip() + if existing_ld: + ld_entries.extend(entry for entry in existing_ld.split(os.pathsep) if entry) + if ld_entries: + deduped: List[str] = [] + seen: Set[str] = set() + for entry in ld_entries: + if entry and entry not in seen: + seen.add(entry) + deduped.append(entry) + env["LD_LIBRARY_PATH"] = os.pathsep.join(deduped) + return env + + +def _run_cli( + input_dir: Path, + output_dir: Path, + *, + files: List[str], + model_dir: Path, + python_bin: Optional[Path], + script: Path, + max_pages: Optional[int], + content_debug: bool, + device: Optional[str], + ocr_profile: str, + prompt_override: Optional[str], + attn_backend: str, + base_size: Optional[int], + image_size: Optional[int], + crop_mode: Optional[bool], + render_dpi: Optional[int], + max_new_tokens: Optional[int], + repetition_penalty: Optional[float], + no_repeat_ngram_size: Optional[int], + runtime_backend: str, + vllm_batch_size: Optional[int], + gpu_memory_utilization: Optional[float], + disable_fp8_kv: bool, + repair_mode: Optional[str], + repair_exec_batch_target_pages: Optional[int], + repair_exec_batch_target_items: Optional[int], + visible_device: Optional[int] = None, +) -> None: + cmd = _build_cli_command( + input_dir=input_dir, + output_dir=output_dir, + files=files, + page_ranges=None, + model_dir=model_dir, + python_bin=python_bin, + script=script, + max_pages=max_pages, + content_debug=content_debug, + device=device, + ocr_profile=ocr_profile, + prompt_override=prompt_override, + attn_backend=attn_backend, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + render_dpi=render_dpi, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + runtime_backend=runtime_backend, + vllm_batch_size=vllm_batch_size, + gpu_memory_utilization=gpu_memory_utilization, + disable_fp8_kv=disable_fp8_kv, + repair_mode=repair_mode, + repair_exec_batch_target_pages=repair_exec_batch_target_pages, + repair_exec_batch_target_items=repair_exec_batch_target_items, + ) + env = _build_env(python_bin=python_bin, visible_device=visible_device, script=script) - LOGGER.info("Running DeepSeek CLI: %s", " ".join(cmd)) + LOGGER.info("Running DeepSeek OCR CLI: %s", " ".join(cmd)) subprocess.run(cmd, check=True, env=env) # nosec: controlled arguments -def _run_one_pdf(pdf_path: Path, md_out: Path, metrics_out: Path, cfg: Dict[str, Any]) -> Dict[str, Any]: - """Stub processor for a single PDF.""" - page_count = _page_count(pdf_path) - max_pages = cfg.get("max_pages") - if max_pages is not None and page_count: - page_count = min(page_count, max_pages) +def _parse_device_index(device: Optional[str]) -> Optional[int]: + if not device: + return None + value = str(device).strip().lower() + if value.startswith("cuda:"): + suffix = value.split(":", 1)[1] + if suffix.isdigit(): + return int(suffix) + return None + + +def _detect_visible_gpus() -> List[int]: + visible = os.environ.get("CUDA_VISIBLE_DEVICES", "").strip() + if visible: + parsed = [piece.strip() for piece in visible.split(",") if piece.strip()] + if parsed and all(piece.isdigit() for piece in parsed): + return [int(piece) for piece in parsed] + torch_mod = None + try: # pragma: no cover - best effort + import torch as torch_mod # type: ignore + except Exception: # pragma: no cover - optional import + torch_mod = None + if torch_mod is not None: + try: + if torch_mod.cuda.is_available(): + return list(range(torch_mod.cuda.device_count())) + except Exception: + pass + try: # pragma: no cover - shell fallback + proc = subprocess.run( + ["nvidia-smi", "-L"], + check=False, + capture_output=True, + text=True, + timeout=5, + ) + devices: List[int] = [] + if proc.returncode == 0: + for line in proc.stdout.splitlines(): + if line.startswith("GPU "): + prefix = line.split(":", 1)[0] + idx = prefix.split()[1] + if idx.isdigit(): + devices.append(int(idx)) + return devices + except Exception: + return [] + + +def _resolve_lane_devices( + *, + use_gpus: Optional[str], + devices: Optional[List[int]], + workers_per_gpu: int, + device: Optional[str], +) -> List[int]: + if devices: + resolved = [int(dev) for dev in devices] + if resolved: + return resolved + if str(use_gpus or "single").strip().lower() == "multi": + resolved = _detect_visible_gpus() + if resolved: + return resolved + if workers_per_gpu > 1: + from_device = _parse_device_index(device) + if from_device is not None: + return [from_device] + visible = os.environ.get("CUDA_VISIBLE_DEVICES", "").strip() + if visible: + first = visible.split(",", 1)[0].strip() + if first.isdigit(): + return [int(first)] + return [0] + return [] + + +def _effective_page_count(pdf_path: Path, max_pages: Optional[int]) -> int: + count = _page_count(pdf_path) + if max_pages is not None and count > 0: + return min(count, int(max_pages)) + return max(1, count) + + +def _source_documents( + *, + file_list: List[str], + input_root: Path, + max_pages: Optional[int], +) -> List[SourceDocument]: + documents: List[SourceDocument] = [] + for name in file_list: + pdf_path = (input_root / name).resolve() + documents.append( + SourceDocument( + name=str(name), + pages=int(_effective_page_count(pdf_path, max_pages)), + ) + ) + return documents + + +def _plan_lanes( + *, + file_list: List[str], + input_root: Path, + lane_devices: List[int], + workers_per_gpu: int, + max_pages: Optional[int], +) -> List[Dict[str, Any]]: + lanes: List[Dict[str, Any]] = [] + lane_id = 0 + for visible_device in lane_devices: + for _ in range(max(1, int(workers_per_gpu))): + lanes.append( + { + "lane_id": lane_id, + "visible_device": int(visible_device), + "files": [], + "weight": 0, + } + ) + lane_id += 1 + if not lanes: + return [] + + weighted_files = [] + for name in file_list: + pdf_path = (input_root / name).resolve() + weighted_files.append((name, _effective_page_count(pdf_path, max_pages))) + weighted_files.sort(key=lambda item: (-item[1], item[0])) + + for name, weight in weighted_files: + lane = min(lanes, key=lambda item: int(item["weight"])) + lane["files"].append(name) + lane["weight"] = int(lane["weight"]) + int(weight) + return lanes + + +def _resolve_scheduler( + *, + scheduler: Optional[str], + runtime_backend: str, + lane_devices: List[int], + workers_per_gpu: int, +) -> str: + scheduler_norm = str(scheduler or "auto").strip().lower() + if scheduler_norm not in {"auto", "whole_doc", "fixed_shard", "exact_fill"}: + raise ValueError("scheduler must be one of 'auto', 'whole_doc', 'fixed_shard', or 'exact_fill'") + if scheduler_norm != "auto": + return scheduler_norm + runtime_backend_norm = str(runtime_backend or "transformers").strip().lower() + lane_count = max(1, len(lane_devices)) * max(1, int(workers_per_gpu)) + if runtime_backend_norm == "vllm" and lane_count > 1: + return "exact_fill" + return "whole_doc" + + +def _plan_lane_batches( + *, + file_list: List[str], + input_root: Path, + lane_devices: List[int], + workers_per_gpu: int, + max_pages: Optional[int], + runtime_backend: str, + scheduler: Optional[str], + target_batch_pages: int, + shard_pages: int, + shard_threshold_pages: int, +) -> List[Dict[str, Any]]: + documents = _source_documents( + file_list=file_list, + input_root=input_root, + max_pages=max_pages, + ) + scheduler_norm = _resolve_scheduler( + scheduler=scheduler, + runtime_backend=runtime_backend, + lane_devices=lane_devices, + workers_per_gpu=workers_per_gpu, + ) + if scheduler_norm == "exact_fill": + batches = build_exact_fill_batches( + documents, + target_batch_pages=max(1, int(target_batch_pages)), + ) + else: + if scheduler_norm == "fixed_shard": + slices = build_fixed_shard_slices( + documents, + shard_pages=max(1, int(shard_pages)), + shard_threshold_pages=max(0, int(shard_threshold_pages)), + ) + else: + slices = build_whole_document_slices(documents) + batches = pack_slices_into_batches( + slices, + target_batch_pages=max(1, int(target_batch_pages)), + ) + lanes = assign_batches_to_lanes( + batches, + devices=lane_devices, + workers_per_gpu=workers_per_gpu, + ) + return [lane.to_dict() for lane in lanes if lane.batches] + + +def _plan_work_batches( + *, + file_list: List[str], + input_root: Path, + max_pages: Optional[int], + runtime_backend: str, + scheduler: Optional[str], + lane_devices: List[int], + workers_per_gpu: int, + target_batch_pages: int, + shard_pages: int, + shard_threshold_pages: int, +) -> List[Dict[str, Any]]: + documents = _source_documents( + file_list=file_list, + input_root=input_root, + max_pages=max_pages, + ) + scheduler_norm = _resolve_scheduler( + scheduler=scheduler, + runtime_backend=runtime_backend, + lane_devices=lane_devices, + workers_per_gpu=workers_per_gpu, + ) + if scheduler_norm == "exact_fill": + batches = build_exact_fill_batches( + documents, + target_batch_pages=max(1, int(target_batch_pages)), + ) + else: + if scheduler_norm == "fixed_shard": + slices = build_fixed_shard_slices( + documents, + shard_pages=max(1, int(shard_pages)), + shard_threshold_pages=max(0, int(shard_threshold_pages)), + ) + else: + slices = build_whole_document_slices(documents) + batches = pack_slices_into_batches( + slices, + target_batch_pages=max(1, int(target_batch_pages)), + ) + return [batch.to_dict() for batch in batches if int(batch.pages) > 0] + + +def _auto_vllm_batch_size( + *, + runtime_backend: str, + file_list: List[str], + input_root: Path, + max_pages: Optional[int], +) -> Optional[int]: + if str(runtime_backend or "").strip().lower() != "vllm": + return None + total_pages = 0 + for name in file_list: + pdf_path = (input_root / name).resolve() + total_pages += int(_effective_page_count(pdf_path, max_pages)) + if total_pages <= 0: + return 1 + return min(int(total_pages), int(AUTO_VLLM_BATCH_PAGE_CAP)) + + +def _auto_vllm_batch_size_for_pages(*, runtime_backend: str, pages: int) -> Optional[int]: + if str(runtime_backend or "").strip().lower() != "vllm": + return None + if int(pages) <= 0: + return 1 + return min(int(pages), int(AUTO_VLLM_BATCH_PAGE_CAP)) + + +def _flatten_lane_batches(lane: Dict[str, Any]) -> Dict[str, Any]: + files: List[str] = [] + page_ranges: List[str] = [] + pages = 0 + planned_batch_pages: List[int] = [] + for batch in list(lane.get("batches") or []): + batch_pages = int(batch.get("pages", 0)) + pages += batch_pages + planned_batch_pages.append(batch_pages) + files.extend(list(batch.get("files") or [])) + page_ranges.extend(list(batch.get("page_ranges") or [])) + return { + "files": files, + "page_ranges": page_ranges, + "pages": int(pages), + "planned_batch_count": len(planned_batch_pages), + "planned_batch_pages": planned_batch_pages, + } + + +def _utc_now_iso(now_ts: Optional[float] = None) -> str: + return time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime(float(now_ts) if now_ts is not None else time.time())) + + +def _parse_utc_iso(value: Optional[str]) -> Optional[float]: + if not value: + return None + try: + return float(calendar.timegm(time.strptime(str(value), "%Y-%m-%dT%H:%M:%SZ"))) + except Exception: + return None + + +def _run_text_command(cmd: List[str]) -> str: + proc = subprocess.run(cmd, check=True, capture_output=True, text=True) # nosec: controlled args + return str(proc.stdout or "").strip() + + +def _process_group_members(pgid: int) -> List[int]: + proc = subprocess.run(["pgrep", "-g", str(int(pgid))], check=False, capture_output=True, text=True) # nosec: controlled args + if int(proc.returncode) not in {0, 1}: + return [] + members: List[int] = [] + for line in str(proc.stdout or "").splitlines(): + line = line.strip() + if line: + try: + members.append(int(line)) + except ValueError: + continue + return members + + +def _wait_for_process_group_exit(pgid: int, *, timeout_sec: float) -> bool: + deadline = time.time() + float(max(0.0, timeout_sec)) + while time.time() <= deadline: + if not _process_group_members(pgid): + return True + time.sleep(0.2) + return not _process_group_members(pgid) + + +def _terminate_worker_process_group(worker: Dict[str, Any]) -> bool: + pgid = int(worker["proc"].pid) + worker_id = str(worker["worker_id"]) + for sig, grace_sec in ((signal.SIGTERM, 5.0), (signal.SIGKILL, 5.0)): + try: + os.killpg(pgid, sig) + except ProcessLookupError: + return True + except Exception as exc: + LOGGER.warning("Failed to signal worker process group %s pgid=%s: %s", worker_id, pgid, exc) + return False + if _wait_for_process_group_exit(pgid, timeout_sec=grace_sec): + return True + LOGGER.warning("Worker process group %s pgid=%s did not exit cleanly", worker_id, pgid) + return False + + +def _launch_worker_process(cmd: List[str], *, fh, env: Dict[str, str]) -> subprocess.Popen: + return subprocess.Popen( + cmd, + stdout=fh, + stderr=subprocess.STDOUT, + env=env, + start_new_session=True, + ) # nosec: controlled args + + +def _parse_csv_table(text: str, columns: List[str]) -> List[Dict[str, str]]: + rows: List[Dict[str, str]] = [] + for raw_line in str(text or "").splitlines(): + line = raw_line.strip() + if not line: + continue + parts = [piece.strip() for piece in line.split(",")] + if len(parts) < len(columns): + parts.extend([""] * (len(columns) - len(parts))) + rows.append({name: str(parts[idx]) for idx, name in enumerate(columns)}) + return rows - md_lines = [ - f"# DeepSeek OCR (stub) — {pdf_path.name}", - "", - f"Pages: {page_count if page_count else 'unknown'}", + +def _collect_gpu_snapshot(*, visible_devices: List[int]) -> Dict[str, Any]: + gpu_text = _run_text_command( + [ + "nvidia-smi", + f"--id={','.join(str(device) for device in visible_devices)}", + "--query-gpu=index,name,utilization.gpu,memory.used,memory.total,temperature.gpu,power.draw,persistence_mode", + "--format=csv,noheader,nounits", + ] + ) + process_text = _run_text_command( + [ + "nvidia-smi", + "--query-compute-apps=gpu_uuid,pid,process_name,used_memory", + "--format=csv,noheader,nounits", + ] + ) + return { + "captured_at": _utc_now_iso(), + "gpus": _parse_csv_table( + gpu_text, + [ + "index", + "name", + "utilization_gpu", + "memory_used_mib", + "memory_total_mib", + "temperature_c", + "power_draw_w", + "persistence_mode", + ], + ), + "processes": _parse_csv_table( + process_text, + [ + "gpu_uuid", + "pid", + "process_name", + "used_memory_mib", + ], + ), + } + + +def _read_worker_runtime(runtime_path: Path) -> Dict[str, Any]: + try: + return json.loads(Path(runtime_path).read_text(encoding="utf-8")) + except Exception: + return {} + + +def _write_runtime_summary(*, runtime_dir: Path, db_path: Path) -> Path: + runtime_dir.mkdir(parents=True, exist_ok=True) + workers = [] + first_batch_started = [] + last_batch_finished = [] + engine_ready = [] + for path in sorted(runtime_dir.glob("worker_*.runtime.json")): + data = _read_worker_runtime(path) + workers.append(data) + first_batch_started_ts = _parse_utc_iso(data.get("first_batch_started_at")) + last_batch_finished_ts = _parse_utc_iso(data.get("last_batch_finished_at")) + engine_ready_ts = _parse_utc_iso(data.get("engine_ready_at")) + if first_batch_started_ts is not None: + first_batch_started.append(first_batch_started_ts) + if last_batch_finished_ts is not None: + last_batch_finished.append(last_batch_finished_ts) + if engine_ready_ts is not None: + engine_ready.append(engine_ready_ts) + steady_summary = { + "first_batch_started_at": _utc_now_iso(min(first_batch_started)) if first_batch_started else None, + "last_batch_finished_at": _utc_now_iso(max(last_batch_finished)) if last_batch_finished else None, + "all_workers_ready_at": _utc_now_iso(max(engine_ready)) if engine_ready else None, + "first_batch_to_last_batch_window_sec": ( + float(max(last_batch_finished) - min(first_batch_started)) + if first_batch_started and last_batch_finished + else None + ), + "all_workers_ready_to_last_batch_window_sec": ( + float(max(last_batch_finished) - max(engine_ready)) + if engine_ready and last_batch_finished + else None + ), + } + summary_path = runtime_dir / "runtime_summary.json" + summary_path.write_text( + json.dumps( + { + "generated_at": _utc_now_iso(), + "queue_counts": work_queue_counts(db_path), + "work_items": list(iter_work_items(db_path)), + "workers": workers, + "steady_state": steady_summary, + }, + indent=2, + ), + encoding="utf-8", + ) + return summary_path + + +def _query_persistence_mode(*, visible_devices: List[int]) -> List[Dict[str, str]]: + raw = _run_text_command( + [ + "nvidia-smi", + f"--id={','.join(str(device) for device in visible_devices)}", + "--query-gpu=index,persistence_mode", + "--format=csv,noheader,nounits", + ] + ) + return _parse_csv_table(raw, ["index", "persistence_mode"]) + + +def _ensure_gpu_preflight(*, visible_devices: List[int], mode: str) -> Dict[str, Any]: + mode_norm = str(mode or "warn").strip().lower() + status = { + "mode": mode_norm, + "checked_at": _utc_now_iso(), + "before": _query_persistence_mode(visible_devices=visible_devices), + "changed": False, + } + disabled = [item for item in status["before"] if str(item.get("persistence_mode", "")).lower() != "enabled"] + if not disabled or mode_norm == "off": + status["after"] = list(status["before"]) + return status + if mode_norm == "ensure": + try: + subprocess.run(["sudo", "-n", "nvidia-smi", "-pm", "1"], check=True, capture_output=True, text=True) # nosec: controlled args + status["changed"] = True + except Exception as exc: + status["ensure_error"] = str(exc) + status["after"] = _query_persistence_mode(visible_devices=visible_devices) + return status + + +def _collect_xid_faults(*, start_utc_iso: str) -> Dict[str, Any]: + cmd = [ + "journalctl", + "-k", + "--since", + str(start_utc_iso), + "--no-pager", ] - if cfg.get("content_debug"): - md_lines.append("") - md_lines.append("") - md_out.parent.mkdir(parents=True, exist_ok=True) - md_out.write_text("\n".join(md_lines) + "\n", encoding="utf-8") + try: + output = _run_text_command(cmd) + except Exception as exc: + return { + "supported": False, + "error": str(exc), + "faults": [], + } + faults = [line for line in output.splitlines() if "NVRM: Xid" in line] + return { + "supported": True, + "faults": faults, + } + + +def _start_gpu_telemetry( + *, + telemetry_path: Path, + visible_devices: List[int], + interval_sec: float, + stop_event: threading.Event, +) -> threading.Thread: + telemetry_path.parent.mkdir(parents=True, exist_ok=True) + + def _loop() -> None: + while not stop_event.wait(float(max(1.0, interval_sec))): + try: + with telemetry_path.open("a", encoding="utf-8") as fh: + fh.write(json.dumps({"kind": "sample", **_collect_gpu_snapshot(visible_devices=visible_devices)}) + "\n") + except Exception as exc: # pragma: no cover - best effort logging + LOGGER.warning("GPU telemetry sample failed: %s", exc) + + thread = threading.Thread(target=_loop, name="deepseek-gpu-telemetry", daemon=True) + thread.start() + return thread + + +def _parse_shard_stem(stem: str) -> Optional[Dict[str, Any]]: + match = SHARD_STEM_RE.match(str(stem)) + if match is None: + return None + return { + "source_stem": str(match.group("source_stem")), + "start_page": int(match.group("start")), + "end_page": int(match.group("end")), + } + + +def _split_markdown_pages(markdown_text: str, *, expected_pages: int) -> List[str]: + pages = _split_page_outputs(markdown_text) + if len(pages) < int(expected_pages): + pages.extend([""] * (int(expected_pages) - len(pages))) + elif len(pages) > int(expected_pages): + pages = pages[: int(expected_pages)] + return pages + + +def _archive_shard_artifact(*, out_root: Path, source_path: Path, relative_path: Path) -> None: + archive_path = out_root / "sidecars" / "ocr_shards" / relative_path + archive_path.parent.mkdir(parents=True, exist_ok=True) + if archive_path.exists(): + archive_path.unlink() + source_path.replace(archive_path) + + +def _reassemble_canonical_output_for_source( + *, + out_root: Path, + pdf_path: Path, + source_name: str, +) -> bool: + md_dir = out_root / "markdown" + metrics_dir = out_root / "json" / "metrics" + source_stem = Path(source_name).stem + canonical_md = md_dir / f"{source_stem}.md" + canonical_metrics = metrics_dir / f"{source_stem}.metrics.json" + if canonical_md.exists() and canonical_metrics.exists(): + return True + + shard_records: List[Dict[str, Any]] = [] + for metrics_path in sorted(metrics_dir.glob(f"{source_stem}__p*.metrics.json")): + shard_stem = metrics_path.name.removesuffix(".metrics.json") + shard_md = md_dir / f"{shard_stem}.md" + if not shard_md.exists(): + continue + shard_meta = _parse_shard_stem(shard_stem) + if shard_meta is None: + continue + metrics = json.loads(metrics_path.read_text(encoding="utf-8")) + start_page = int(metrics.get("source_start_page", shard_meta["start_page"])) + end_page = int(metrics.get("source_end_page", shard_meta["end_page"])) + shard_records.append( + { + "stem": shard_stem, + "md_path": shard_md, + "metrics_path": metrics_path, + "metrics": metrics, + "start_page": start_page, + "end_page": end_page, + } + ) + + if not shard_records: + return False + + shard_records.sort(key=lambda item: (int(item["start_page"]), int(item["end_page"]), str(item["stem"]))) + page_count = max(int(_page_count(pdf_path)), max(int(item["end_page"]) for item in shard_records)) + merged_pages = [""] * int(page_count) + merged_page_metrics: List[Optional[Dict[str, Any]]] = [None] * int(page_count) + merged_extra_metrics: Dict[str, Any] = {} + repair_totals: Dict[str, int] = {} + render_sec_total = 0.0 + infer_sec_total = 0.0 + wall_time_sec_total = 0.0 + reassembled_ranges: List[Dict[str, int]] = [] + + for shard in shard_records: + metrics = dict(shard["metrics"]) + start_page = int(shard["start_page"]) + end_page = int(shard["end_page"]) + expected_pages = max(0, end_page - start_page + 1) + reassembled_ranges.append({"start_page": start_page, "end_page": end_page}) + + shard_pages = _split_markdown_pages( + shard["md_path"].read_text(encoding="utf-8"), + expected_pages=expected_pages, + ) + for offset, page_text in enumerate(shard_pages): + merged_pages[start_page - 1 + offset] = page_text + + for idx, page_metric in enumerate(list(metrics.get("page_metrics") or []), start=1): + absolute_page = start_page + int(page_metric.get("page_number", idx)) - 1 + if absolute_page <= 0 or absolute_page > int(page_count): + continue + merged_metric = dict(page_metric) + merged_metric["page_number"] = int(absolute_page) + merged_page_metrics[absolute_page - 1] = merged_metric + + render_sec_total += float(metrics.get("render_sec", 0.0)) + infer_sec_total += float(metrics.get("infer_sec_total", 0.0)) + wall_time_sec_total += float(metrics.get("wall_time_sec", 0.0)) + for key, value in dict(metrics.get("repair_summary") or {}).items(): + if key == "repair_mode": + continue + repair_totals[key] = int(repair_totals.get(key, 0)) + int(value) + for key in REASSEMBLED_CONFIG_KEYS: + if key in metrics and key not in merged_extra_metrics: + merged_extra_metrics[key] = metrics[key] + + merged_extra_metrics.update( + { + "source_file": str(source_name), + "source_stem": str(source_stem), + "source_start_page": 1, + "source_end_page": int(page_count), + "reassembled_from_shards": True, + "reassembled_shard_count": len(shard_records), + "reassembled_source_ranges": reassembled_ranges, + "render_sec": float(render_sec_total), + "infer_sec_total": float(infer_sec_total), + "wall_time_sec": float(wall_time_sec_total), + "wall_time_sec_semantics": "sum_of_shard_wall_times", + "page_metrics": [item for item in merged_page_metrics if item is not None], + } + ) + if repair_totals: + merged_extra_metrics["repair_summary"] = { + "repair_mode": str(merged_extra_metrics.get("repair_mode", "unknown")), + **{key: int(value) for key, value in repair_totals.items()}, + } + + merged_markdown = _join_page_outputs(merged_pages) if merged_pages else "[[Blank page]]" + _write_outputs( + output_dir=out_root, + stem=source_stem, + markdown=merged_markdown, + page_count=int(page_count), + extra_metrics=merged_extra_metrics, + ) + for shard in shard_records: + _archive_shard_artifact( + out_root=out_root, + source_path=Path(shard["md_path"]), + relative_path=Path("markdown") / Path(shard["md_path"]).name, + ) + _archive_shard_artifact( + out_root=out_root, + source_path=Path(shard["metrics_path"]), + relative_path=Path("json") / "metrics" / Path(shard["metrics_path"]).name, + ) + return True + + +def _ensure_canonical_outputs(*, out_root: Path, pdf_root: Path, file_list: List[str]) -> None: + for name in file_list: + pdf_path = (pdf_root / name).resolve() + if _reassemble_canonical_output_for_source( + out_root=out_root, + pdf_path=pdf_path, + source_name=name, + ): + continue + + + +def _run_multi_cli( + *, + input_root: Path, + out_root: Path, + file_list: List[str], + lane_devices: List[int], + workers_per_gpu: int, + model_root: Path, + python_exe: Path, + script_path: Path, + max_pages: Optional[int], + content_debug: bool, + log_dir: Path, + ocr_profile: str, + prompt_override: Optional[str], + attn_backend: str, + base_size: Optional[int], + image_size: Optional[int], + crop_mode: Optional[bool], + render_dpi: Optional[int], + max_new_tokens: Optional[int], + repetition_penalty: Optional[float], + no_repeat_ngram_size: Optional[int], + runtime_backend: str, + vllm_batch_size: Optional[int], + gpu_memory_utilization: Optional[float], + disable_fp8_kv: bool, + repair_mode: Optional[str], + repair_exec_batch_target_pages: Optional[int], + repair_exec_batch_target_items: Optional[int], + scheduler: Optional[str], + target_batch_pages: int, + shard_pages: int, + shard_threshold_pages: int, +) -> None: + if str(runtime_backend or "").strip().lower() == "vllm": + batches = _plan_work_batches( + file_list=file_list, + input_root=input_root, + max_pages=max_pages, + runtime_backend=runtime_backend, + scheduler=scheduler, + lane_devices=lane_devices, + workers_per_gpu=workers_per_gpu, + target_batch_pages=target_batch_pages, + shard_pages=shard_pages, + shard_threshold_pages=shard_threshold_pages, + ) + if not batches: + return + + log_dir.mkdir(parents=True, exist_ok=True) + runtime_dir = out_root / "sidecars" / "ocr_runtime" + runtime_dir.mkdir(parents=True, exist_ok=True) + work_db = runtime_dir / "work_queue.sqlite" + init_work_db(work_db, batches=batches, replace=True) + + visible_devices = sorted({int(device) for device in lane_devices}) + preflight_mode = str(os.environ.get("GLOSSAPI_DEEPSEEK_GPU_PREFLIGHT", "ensure")).strip().lower() + preflight = _ensure_gpu_preflight(visible_devices=visible_devices, mode=preflight_mode) + (runtime_dir / "gpu_preflight.json").write_text(json.dumps(preflight, indent=2), encoding="utf-8") + + telemetry_path = runtime_dir / "gpu_telemetry.jsonl" + with telemetry_path.open("a", encoding="utf-8") as fh: + fh.write(json.dumps({"kind": "preflight", **preflight}) + "\n") + fh.write(json.dumps({"kind": "initial_sample", **_collect_gpu_snapshot(visible_devices=visible_devices)}) + "\n") + + telemetry_stop = threading.Event() + telemetry_thread = _start_gpu_telemetry( + telemetry_path=telemetry_path, + visible_devices=visible_devices, + interval_sec=float(os.environ.get("GLOSSAPI_DEEPSEEK_TELEMETRY_INTERVAL_SEC", DEFAULT_TELEMETRY_INTERVAL_SEC)), + stop_event=telemetry_stop, + ) + stale_after_sec = float(os.environ.get("GLOSSAPI_DEEPSEEK_WORK_STALE_AFTER_SEC", DEFAULT_WORK_STALE_AFTER_SEC)) + heartbeat_sec = float(os.environ.get("GLOSSAPI_DEEPSEEK_WORK_HEARTBEAT_SEC", DEFAULT_WORK_HEARTBEAT_SEC)) + respawn_cap = int(os.environ.get("GLOSSAPI_DEEPSEEK_WORKER_RESPAWN_CAP", DEFAULT_WORKER_RESPAWN_CAP)) + work_max_attempts = int( + max(1, int(os.environ.get("GLOSSAPI_DEEPSEEK_WORK_ITEM_MAX_ATTEMPTS", DEFAULT_WORK_ITEM_MAX_ATTEMPTS))) + ) + xid_start = _utc_now_iso() + + def _start_worker(*, worker_id: str, visible_device: int, respawns: int) -> Dict[str, Any]: + log_path = log_dir / f"{worker_id}.r{int(respawns)}.log" + fh = log_path.open("w", encoding="utf-8") + resolved_vllm_batch_size = ( + int(vllm_batch_size) + if vllm_batch_size is not None + else _auto_vllm_batch_size_for_pages( + runtime_backend=runtime_backend, + pages=int(target_batch_pages), + ) + ) + cmd = _build_cli_command( + input_dir=input_root, + output_dir=out_root, + files=[], + page_ranges=None, + model_dir=model_root, + python_bin=python_exe, + script=script_path, + max_pages=max_pages, + content_debug=content_debug, + device="cuda", + ocr_profile=ocr_profile, + prompt_override=prompt_override, + attn_backend=attn_backend, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + render_dpi=render_dpi, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + runtime_backend=runtime_backend, + vllm_batch_size=resolved_vllm_batch_size, + gpu_memory_utilization=gpu_memory_utilization, + disable_fp8_kv=disable_fp8_kv, + repair_mode=repair_mode, + repair_exec_batch_target_pages=repair_exec_batch_target_pages, + repair_exec_batch_target_items=repair_exec_batch_target_items, + work_db=work_db, + worker_id=worker_id, + worker_runtime_file=runtime_dir / f"{worker_id}.runtime.json", + work_stale_after_sec=stale_after_sec, + work_heartbeat_sec=heartbeat_sec, + work_max_attempts=work_max_attempts, + ) + env = _build_env(python_bin=python_exe, visible_device=visible_device, script=script_path) + LOGGER.info( + "Running DeepSeek OCR worker=%s visible_gpu=%s batches=%d: %s", + worker_id, + visible_device, + len(batches), + " ".join(cmd), + ) + proc = _launch_worker_process(cmd, fh=fh, env=env) + return { + "worker_id": worker_id, + "visible_device": int(visible_device), + "proc": proc, + "fh": fh, + "log_path": log_path, + "respawns": int(respawns), + } + + active_workers: List[Dict[str, Any]] = [] + worker_index = 0 + for visible_device in lane_devices: + for _ in range(max(1, int(workers_per_gpu))): + worker_id = f"worker_{worker_index:02d}_gpu{int(visible_device)}" + active_workers.append(_start_worker(worker_id=worker_id, visible_device=int(visible_device), respawns=0)) + worker_index += 1 + + failures: List[str] = [] + try: + while active_workers: + time.sleep(0.5) + for worker in list(active_workers): + rc = worker["proc"].poll() + if rc is None: + continue + worker["fh"].close() + active_workers.remove(worker) + if int(rc) == 0: + continue + error_message = f"{worker['worker_id']} rc={int(rc)} log={worker['log_path']}" + LOGGER.warning("DeepSeek OCR worker failed: %s", error_message) + _terminate_worker_process_group(worker) + requeue_worker_batches( + work_db, + worker_id=str(worker["worker_id"]), + error=error_message, + max_attempts=work_max_attempts, + ) + counts = work_queue_counts(work_db) + # Only respawn while there is retryable work left in the + # durable queue; terminally failed items should stop the run. + remaining_work = int(counts.get("pending", 0)) + int(counts.get("running", 0)) + if remaining_work > 0 and int(worker["respawns"]) < respawn_cap: + active_workers.append( + _start_worker( + worker_id=str(worker["worker_id"]), + visible_device=int(worker["visible_device"]), + respawns=int(worker["respawns"]) + 1, + ) + ) + continue + failures.append(error_message) + counts = work_queue_counts(work_db) + if int(counts.get(STATUS_FAILED, 0)) > 0 or int(counts.get(STATUS_DONE, 0)) < int(counts.get("total", 0)): + failures.append(f"incomplete_work queue_counts={counts}") + finally: + for worker in list(active_workers): + _terminate_worker_process_group(worker) + try: + worker["proc"].wait(timeout=5) + except Exception: + pass + worker["fh"].close() + telemetry_stop.set() + telemetry_thread.join(timeout=max(1.0, DEFAULT_TELEMETRY_INTERVAL_SEC)) + with telemetry_path.open("a", encoding="utf-8") as fh: + fh.write(json.dumps({"kind": "final_sample", **_collect_gpu_snapshot(visible_devices=visible_devices)}) + "\n") + fh.write(json.dumps({"kind": "xid_faults", **_collect_xid_faults(start_utc_iso=xid_start)}) + "\n") + _write_runtime_summary(runtime_dir=runtime_dir, db_path=work_db) + + if failures: + raise RuntimeError("DeepSeek OCR multi-worker failure(s): " + "; ".join(failures)) + return + + lanes = _plan_lane_batches( + file_list=file_list, + input_root=input_root, + lane_devices=lane_devices, + workers_per_gpu=workers_per_gpu, + max_pages=max_pages, + runtime_backend=runtime_backend, + scheduler=scheduler, + target_batch_pages=target_batch_pages, + shard_pages=shard_pages, + shard_threshold_pages=shard_threshold_pages, + ) + if not lanes: + return + + log_dir.mkdir(parents=True, exist_ok=True) + failures: List[str] = [] + with ExitStack() as stack: + procs = [] + + for lane in lanes: + lane_id = int(lane["lane_id"]) + visible_device = int(lane["visible_device"]) + lane_plan = _flatten_lane_batches(lane) + files = list(lane_plan["files"]) + page_ranges = list(lane_plan["page_ranges"]) + pages = int(lane_plan["pages"]) + if pages <= 0: + continue + resolved_vllm_batch_size = ( + int(vllm_batch_size) + if vllm_batch_size is not None + else _auto_vllm_batch_size_for_pages( + runtime_backend=runtime_backend, + pages=min(int(target_batch_pages), int(pages)), + ) + ) + log_path = log_dir / f"lane_{lane_id:02d}_gpu{visible_device}.log" + fh = stack.enter_context(log_path.open("w", encoding="utf-8")) + cmd = _build_cli_command( + input_dir=input_root, + output_dir=out_root, + files=files, + page_ranges=page_ranges, + model_dir=model_root, + python_bin=python_exe, + script=script_path, + max_pages=max_pages, + content_debug=content_debug, + device="cuda", + ocr_profile=ocr_profile, + prompt_override=prompt_override, + attn_backend=attn_backend, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + render_dpi=render_dpi, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + runtime_backend=runtime_backend, + vllm_batch_size=resolved_vllm_batch_size, + gpu_memory_utilization=gpu_memory_utilization, + disable_fp8_kv=disable_fp8_kv, + repair_mode=repair_mode, + repair_exec_batch_target_pages=repair_exec_batch_target_pages, + repair_exec_batch_target_items=repair_exec_batch_target_items, + ) + env = _build_env(python_bin=python_exe, visible_device=visible_device, script=script_path) + LOGGER.info( + "Running DeepSeek OCR lane=%s visible_gpu=%s pages=%s planned_batches=%s files=%d ranges=%d: %s", + lane_id, + visible_device, + pages, + lane_plan["planned_batch_count"], + len(files), + len(page_ranges), + " ".join(cmd), + ) + proc = subprocess.Popen(cmd, stdout=fh, stderr=subprocess.STDOUT, env=env) # nosec: controlled args + procs.append((lane, log_path, proc)) - metrics = {"page_count": page_count} - metrics_out.parent.mkdir(parents=True, exist_ok=True) - metrics_out.write_text(json.dumps(metrics, indent=2), encoding="utf-8") - return metrics + for lane, log_path, proc in procs: + rc = proc.wait() + if rc != 0: + failures.append( + f"lane={lane['lane_id']} gpu={lane['visible_device']} rc={rc} log={log_path}" + ) + if failures: + raise RuntimeError("DeepSeek OCR multi-worker failure(s): " + "; ".join(failures)) def run_for_files( self_ref: Any, files: Iterable[str], *, - model_dir: Optional[Path] = None, # kept for API compatibility + model_dir: Optional[Path] = None, output_dir: Optional[Path] = None, - log_dir: Optional[Path] = None, # unused placeholder to mirror rapidocr + log_dir: Optional[Path] = None, # kept for API compatibility max_pages: Optional[int] = None, - allow_stub: bool = True, - allow_cli: bool = False, + allow_stub: bool = False, # ignored after stub removal; kept for compatibility + allow_cli: bool = True, # ignored after stub removal; kept for compatibility python_bin: Optional[Path] = None, vllm_script: Optional[Path] = None, content_debug: bool = False, persist_engine: bool = True, # placeholder for future session reuse precision: Optional[str] = None, # reserved - device: Optional[str] = None, # reserved + device: Optional[str] = None, + runtime_backend: str = "transformers", + ocr_profile: str = "markdown_grounded", + prompt_override: Optional[str] = None, + attn_backend: str = "auto", + base_size: Optional[int] = None, + image_size: Optional[int] = None, + crop_mode: Optional[bool] = None, + render_dpi: Optional[int] = None, + max_new_tokens: Optional[int] = DEFAULT_MAX_NEW_TOKENS, + repetition_penalty: Optional[float] = None, + no_repeat_ngram_size: Optional[int] = None, + use_gpus: Optional[str] = None, + devices: Optional[List[int]] = None, + workers_per_gpu: int = 1, gpu_memory_utilization: Optional[float] = None, disable_fp8_kv: bool = False, + vllm_batch_size: Optional[int] = None, + repair_mode: str = "auto", + repair_exec_batch_target_pages: Optional[int] = None, + repair_exec_batch_target_items: Optional[int] = None, + scheduler: str = "auto", + target_batch_pages: int = AUTO_VLLM_BATCH_PAGE_CAP, + shard_pages: int = 0, + shard_threshold_pages: int = 0, **_: Any, ) -> Dict[str, Any]: - """Run DeepSeek OCR for the provided files. + """Run DeepSeek OCR for the provided files.""" + + requested_stub = bool(allow_stub) + del allow_stub, allow_cli, persist_engine, precision + if requested_stub or os.environ.get("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "0") == "1": + raise RuntimeError( + "DeepSeek stub execution has been removed. " + "Unset GLOSSAPI_DEEPSEEK_ALLOW_STUB and configure the real DeepSeek runtime." + ) - Returns a mapping of stem -> minimal metadata (page_count). - """ + runtime_backend_norm = str( + runtime_backend or os.environ.get("GLOSSAPI_DEEPSEEK_RUNTIME_BACKEND", "transformers") + ).strip().lower() + if runtime_backend_norm not in {"transformers", "vllm"}: + raise ValueError("runtime_backend must be 'transformers' or 'vllm'") file_list = [str(f) for f in files or []] if not file_list: return {} input_root = Path(getattr(self_ref, "input_dir", ".")).resolve() + pdf_root = (input_root / "downloads") if (input_root / "downloads").exists() else input_root out_root = Path(output_dir) if output_dir else Path(getattr(self_ref, "output_dir", input_root)) md_dir = out_root / "markdown" metrics_dir = out_root / "json" / "metrics" md_dir.mkdir(parents=True, exist_ok=True) metrics_dir.mkdir(parents=True, exist_ok=True) - env_allow_stub = os.environ.get("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "1") == "1" - env_allow_cli = os.environ.get("GLOSSAPI_DEEPSEEK_ALLOW_CLI", "0") == "1" + model_root = Path( + model_dir + or os.environ.get("GLOSSAPI_DEEPSEEK_MODEL_DIR", "") + or (REPO_ROOT / "deepseek-ocr-2-model" / "DeepSeek-OCR-2") + ) + if not model_root.exists(): + raise FileNotFoundError( + "DeepSeek model directory not found. Set model_dir or GLOSSAPI_DEEPSEEK_MODEL_DIR." + ) - use_cli = allow_cli or env_allow_cli - use_stub = allow_stub and env_allow_stub + default_script = DEFAULT_VLLM_SCRIPT if runtime_backend_norm == "vllm" else DEFAULT_SCRIPT + script_path = Path( + vllm_script + or os.environ.get("GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT", "") + or default_script + ) + if not script_path.exists(): + raise FileNotFoundError(f"DeepSeek OCR runner script not found: {script_path}") - script_path = Path(vllm_script) if vllm_script else Path.cwd() / "deepseek-ocr" / "run_pdf_ocr_vllm.py" - # Optional GPU memory utilization override (env wins over kwarg) - env_gpu_mem = os.environ.get("GLOSSAPI_DEEPSEEK_GPU_MEMORY_UTILIZATION") - gpu_mem_fraction = gpu_memory_utilization - if env_gpu_mem: - try: - gpu_mem_fraction = float(env_gpu_mem) - except Exception: - gpu_mem_fraction = gpu_memory_utilization - disable_fp8_kv = disable_fp8_kv or os.environ.get("GLOSSAPI_DEEPSEEK_NO_FP8_KV") == "1" + python_exe = resolve_deepseek_python(explicit_python=python_bin) + if not python_exe.exists(): + raise FileNotFoundError(f"DeepSeek Python interpreter not found: {python_exe}") - if use_cli and script_path.exists(): - try: - _run_cli( - input_root, - out_root, - python_bin=python_bin, - script=script_path, + lane_devices = _resolve_lane_devices( + use_gpus=use_gpus, + devices=devices, + workers_per_gpu=int(max(1, workers_per_gpu)), + device=device, + ) + multi_requested = str(use_gpus or "single").strip().lower() == "multi" or int(max(1, workers_per_gpu)) > 1 + if multi_requested and lane_devices: + _run_multi_cli( + input_root=pdf_root, + out_root=out_root, + file_list=file_list, + lane_devices=lane_devices, + workers_per_gpu=int(max(1, workers_per_gpu)), + model_root=model_root, + python_exe=python_exe, + script_path=script_path, + max_pages=max_pages, + content_debug=content_debug, + log_dir=Path(log_dir) if log_dir else (out_root / "logs" / "deepseek_workers"), + ocr_profile=ocr_profile, + prompt_override=prompt_override, + attn_backend=attn_backend, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + render_dpi=render_dpi, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + runtime_backend=runtime_backend_norm, + vllm_batch_size=vllm_batch_size, + gpu_memory_utilization=gpu_memory_utilization, + disable_fp8_kv=disable_fp8_kv, + repair_mode=repair_mode, + repair_exec_batch_target_pages=repair_exec_batch_target_pages, + repair_exec_batch_target_items=repair_exec_batch_target_items, + scheduler=scheduler, + target_batch_pages=int(max(1, target_batch_pages)), + shard_pages=int(max(0, shard_pages)), + shard_threshold_pages=int(max(0, shard_threshold_pages)), + ) + else: + resolved_vllm_batch_size = ( + int(vllm_batch_size) + if vllm_batch_size is not None + else _auto_vllm_batch_size( + runtime_backend=runtime_backend_norm, + file_list=file_list, + input_root=pdf_root, max_pages=max_pages, - content_debug=content_debug, - gpu_memory_utilization=gpu_mem_fraction, - disable_fp8_kv=disable_fp8_kv, ) - results: Dict[str, Any] = {} - for name in file_list: - pdf_path = (input_root / name).resolve() - stem = Path(name).stem - md_path = md_dir / f"{stem}.md" - metrics_path = metrics_dir / f"{stem}.metrics.json" - if not md_path.exists() or not md_path.read_text(encoding="utf-8").strip(): - placeholder = [ - f"# DeepSeek OCR — {pdf_path.name}", - "", - "[[Blank page]]", - ] - md_path.parent.mkdir(parents=True, exist_ok=True) - md_path.write_text("\n".join(placeholder) + "\n", encoding="utf-8") - page_count = _page_count(pdf_path) - if not metrics_path.exists(): - metrics_path.parent.mkdir(parents=True, exist_ok=True) - metrics_path.write_text(json.dumps({"page_count": page_count}, indent=2), encoding="utf-8") - results[stem] = {"page_count": page_count} - return results - except Exception as exc: - if not use_stub: - raise - LOGGER.warning("DeepSeek CLI failed (%s); falling back to stub output", exc) + ) + _run_cli( + input_dir=pdf_root, + output_dir=out_root, + files=file_list, + page_ranges=None, + model_dir=model_root, + python_bin=python_exe, + script=script_path, + max_pages=max_pages, + content_debug=content_debug, + device=device, + ocr_profile=ocr_profile, + prompt_override=prompt_override, + attn_backend=attn_backend, + base_size=base_size, + image_size=image_size, + crop_mode=crop_mode, + render_dpi=render_dpi, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + no_repeat_ngram_size=no_repeat_ngram_size, + runtime_backend=runtime_backend_norm, + vllm_batch_size=resolved_vllm_batch_size, + gpu_memory_utilization=gpu_memory_utilization, + disable_fp8_kv=disable_fp8_kv, + repair_mode=repair_mode, + repair_exec_batch_target_pages=repair_exec_batch_target_pages, + repair_exec_batch_target_items=repair_exec_batch_target_items, + ) + + _ensure_canonical_outputs(out_root=out_root, pdf_root=pdf_root, file_list=file_list) - cfg = {"max_pages": max_pages, "content_debug": content_debug} results: Dict[str, Any] = {} for name in file_list: - pdf_path = (input_root / name).resolve() + pdf_path = (pdf_root / name).resolve() stem = Path(name).stem md_path = md_dir / f"{stem}.md" metrics_path = metrics_dir / f"{stem}.metrics.json" - results[stem] = _run_one_pdf(pdf_path, md_path, metrics_path, cfg) + if not md_path.exists(): + raise FileNotFoundError(f"DeepSeek OCR did not produce markdown for {name}: {md_path}") + text_payload = md_path.read_text(encoding="utf-8") + page_count = _page_count(pdf_path) + result_payload: Optional[Dict[str, Any]] = None + if metrics_path.exists(): + try: + result_payload = json.loads(metrics_path.read_text(encoding="utf-8")) + except Exception: + result_payload = None + if result_payload is None: + result_payload = {"page_count": page_count} + else: + result_payload.setdefault("page_count", page_count) + if not text_payload.strip(): + result_payload["empty_markdown"] = True + metrics_path.write_text(json.dumps(result_payload, indent=2), encoding="utf-8") + LOGGER.warning("DeepSeek OCR produced empty markdown for %s: %s", name, md_path) + results[stem] = result_payload + continue + results[stem] = result_payload + if not metrics_path.exists(): + metrics_path.write_text(json.dumps(result_payload, indent=2), encoding="utf-8") return results diff --git a/src/glossapi/ocr/deepseek/runtime_paths.py b/src/glossapi/ocr/deepseek/runtime_paths.py new file mode 100644 index 0000000..a442010 --- /dev/null +++ b/src/glossapi/ocr/deepseek/runtime_paths.py @@ -0,0 +1,91 @@ +"""Resolve DeepSeek runtime paths for split-runtime GlossAPI installs.""" + +from __future__ import annotations + +import os +import sys +from pathlib import Path +from typing import Dict, Iterable, List, Optional + +REPO_ROOT = Path(__file__).resolve().parents[4] + + +def _runtime_sort_key(candidate: Path) -> tuple[int, int, str]: + name = candidate.parent.parent.name + if name == "deepseek": + return (1, 0, name) + if name.startswith("deepseek"): + suffix = name[len("deepseek") :] + if suffix.isdigit(): + return (0, -int(suffix), name) + return (2, 0, name) + + +def _candidate_deepseek_pythons( + *, + explicit_python: Optional[Path | str] = None, + env: Optional[Dict[str, str]] = None, + repo_root: Optional[Path] = None, +) -> List[Path]: + resolved_env = dict(env or os.environ) + root = Path(repo_root) if repo_root is not None else REPO_ROOT + + candidates: List[Path] = [] + + def _append(candidate: Optional[Path | str]) -> None: + if not candidate: + return + path = Path(candidate).expanduser() + if path not in candidates: + candidates.append(path) + + _append(explicit_python) + _append(resolved_env.get("GLOSSAPI_DEEPSEEK_PYTHON")) + _append(resolved_env.get("GLOSSAPI_DEEPSEEK_TEST_PYTHON")) + + venv_root = root / "dependency_setup" / ".venvs" + if venv_root.exists(): + for candidate in sorted(venv_root.glob("deepseek*/bin/python"), key=_runtime_sort_key): + _append(candidate) + + _append(sys.executable) + return candidates + + +def resolve_deepseek_python( + *, + explicit_python: Optional[Path | str] = None, + env: Optional[Dict[str, str]] = None, + repo_root: Optional[Path] = None, +) -> Path: + """Return the best available DeepSeek Python interpreter path. + + Preference order: + 1. explicit function argument + 2. explicit environment override + 3. validated repo-local DeepSeek venv(s) + 4. current process interpreter + """ + + resolved_env = dict(env or os.environ) + explicit_candidate = Path(explicit_python).expanduser() if explicit_python else None + if explicit_candidate is not None: + return explicit_candidate + + for key in ("GLOSSAPI_DEEPSEEK_PYTHON", "GLOSSAPI_DEEPSEEK_TEST_PYTHON"): + raw = resolved_env.get(key) + if raw: + return Path(raw).expanduser() + + candidates = _candidate_deepseek_pythons( + explicit_python=None, + env={}, + repo_root=repo_root, + ) + for candidate in candidates: + if candidate.exists(): + return candidate + return candidates[0] + + +__all__ = ["resolve_deepseek_python"] diff --git a/src/glossapi/ocr/deepseek/scheduling.py b/src/glossapi/ocr/deepseek/scheduling.py new file mode 100644 index 0000000..339b3e6 --- /dev/null +++ b/src/glossapi/ocr/deepseek/scheduling.py @@ -0,0 +1,242 @@ +"""Scheduling helpers for DeepSeek OCR page-range planning. + +The core abstraction is a divisible PDF page stream. We can cut a document into +page ranges exactly where a batch boundary needs it, then reconstruct outputs +later by `(doc_id, page_number)`. +""" + +from __future__ import annotations + +from dataclasses import dataclass, field +import heapq +from typing import Iterable, List, Optional + + +@dataclass(frozen=True) +class SourceDocument: + name: str + pages: int + + +@dataclass(frozen=True) +class WorkSlice: + source_name: str + source_pages: int + start_page: int + end_page: int + + @property + def pages(self) -> int: + return int(self.end_page) - int(self.start_page) + 1 + + @property + def is_full_document(self) -> bool: + return int(self.start_page) == 1 and int(self.end_page) == int(self.source_pages) + + @property + def item_id(self) -> str: + if self.is_full_document: + return str(self.source_name) + return f"{self.source_name}:{int(self.start_page)}:{int(self.end_page)}" + + @property + def cli_file(self) -> Optional[str]: + return str(self.source_name) if self.is_full_document else None + + @property + def cli_page_range(self) -> Optional[str]: + if self.is_full_document: + return None + return self.item_id + + def to_dict(self) -> dict: + return { + "item_id": self.item_id, + "pages": int(self.pages), + "file": self.cli_file, + "page_range": self.cli_page_range, + "source_name": str(self.source_name), + "start_page": int(self.start_page), + "end_page": int(self.end_page), + "is_full_document": bool(self.is_full_document), + } + + +@dataclass +class DocumentCursor: + name: str + total_pages: int + next_page: int = 1 + + @property + def remaining_pages(self) -> int: + return max(0, int(self.total_pages) - int(self.next_page) + 1) + + def take(self, requested_pages: int) -> WorkSlice: + take_pages = min(max(1, int(requested_pages)), int(self.remaining_pages)) + start_page = int(self.next_page) + end_page = start_page + take_pages - 1 + self.next_page = end_page + 1 + return WorkSlice( + source_name=str(self.name), + source_pages=int(self.total_pages), + start_page=int(start_page), + end_page=int(end_page), + ) + + +@dataclass +class BatchPlan: + batch_id: int + items: List[WorkSlice] = field(default_factory=list) + + @property + def pages(self) -> int: + return sum(int(item.pages) for item in self.items) + + def to_dict(self) -> dict: + return { + "batch_id": int(self.batch_id), + "item_ids": [item.item_id for item in self.items], + "files": [item.cli_file for item in self.items if item.cli_file], + "page_ranges": [item.cli_page_range for item in self.items if item.cli_page_range], + "pages": int(self.pages), + "items": [item.to_dict() for item in self.items], + } + + +@dataclass +class LanePlan: + lane_id: int + visible_device: int + batches: List[BatchPlan] = field(default_factory=list) + + @property + def assigned_pages(self) -> int: + return sum(int(batch.pages) for batch in self.batches) + + def to_dict(self) -> dict: + return { + "lane_id": int(self.lane_id), + "visible_device": int(self.visible_device), + "assigned_pages": int(self.assigned_pages), + "batches": [batch.to_dict() for batch in self.batches], + } + + +def build_whole_document_slices(documents: Iterable[SourceDocument]) -> List[WorkSlice]: + return [ + WorkSlice( + source_name=str(doc.name), + source_pages=int(doc.pages), + start_page=1, + end_page=int(doc.pages), + ) + for doc in documents + ] + + +def build_fixed_shard_slices( + documents: Iterable[SourceDocument], + *, + shard_pages: int, + shard_threshold_pages: int, +) -> List[WorkSlice]: + shard_size = max(0, int(shard_pages)) + threshold = max(0, int(shard_threshold_pages)) + slices: List[WorkSlice] = [] + for doc in documents: + total_pages = int(doc.pages) + if shard_size <= 0 or total_pages <= max(threshold, shard_size): + slices.extend(build_whole_document_slices([doc])) + continue + start_page = 1 + while start_page <= total_pages: + end_page = min(total_pages, start_page + shard_size - 1) + slices.append( + WorkSlice( + source_name=str(doc.name), + source_pages=total_pages, + start_page=int(start_page), + end_page=int(end_page), + ) + ) + start_page = end_page + 1 + return slices + + +def build_exact_fill_batches( + documents: Iterable[SourceDocument], + *, + target_batch_pages: int, +) -> List[BatchPlan]: + target = max(1, int(target_batch_pages)) + heap: List[tuple[int, int, DocumentCursor]] = [] + for idx, doc in enumerate(documents): + cursor = DocumentCursor(name=str(doc.name), total_pages=int(doc.pages)) + if cursor.remaining_pages > 0: + heapq.heappush(heap, (-int(cursor.remaining_pages), idx, cursor)) + + batches: List[BatchPlan] = [] + while heap: + remaining_capacity = int(target) + items: List[WorkSlice] = [] + while remaining_capacity > 0 and heap: + _neg_remaining, idx, cursor = heapq.heappop(heap) + take_pages = min(int(cursor.remaining_pages), int(remaining_capacity)) + items.append(cursor.take(take_pages)) + remaining_capacity -= int(take_pages) + if cursor.remaining_pages > 0: + heapq.heappush(heap, (-int(cursor.remaining_pages), idx, cursor)) + batches.append(BatchPlan(batch_id=len(batches), items=items)) + return batches + + +def pack_slices_into_batches( + slices: Iterable[WorkSlice], + *, + target_batch_pages: int, +) -> List[BatchPlan]: + target = max(1, int(target_batch_pages)) + ordered = sorted(list(slices), key=lambda item: (-int(item.pages), item.item_id)) + batches: List[BatchPlan] = [] + current: List[WorkSlice] = [] + current_pages = 0 + + def flush() -> None: + nonlocal current, current_pages + if not current: + return + batches.append(BatchPlan(batch_id=len(batches), items=list(current))) + current = [] + current_pages = 0 + + for item in ordered: + item_pages = int(item.pages) + if current and current_pages + item_pages > target: + flush() + current.append(item) + current_pages += item_pages + if current_pages >= target: + flush() + flush() + return batches + + +def assign_batches_to_lanes( + batches: Iterable[BatchPlan], + *, + devices: List[int], + workers_per_gpu: int, +) -> List[LanePlan]: + lanes: List[LanePlan] = [] + lane_id = 0 + for visible_device in devices: + for _ in range(max(1, int(workers_per_gpu))): + lanes.append(LanePlan(lane_id=lane_id, visible_device=int(visible_device))) + lane_id += 1 + for batch in batches: + lane = min(lanes, key=lambda item: (int(item.assigned_pages), int(item.lane_id))) + lane.batches.append(batch) + return lanes + diff --git a/src/glossapi/ocr/deepseek/work_queue.py b/src/glossapi/ocr/deepseek/work_queue.py new file mode 100644 index 0000000..9cf8d0b --- /dev/null +++ b/src/glossapi/ocr/deepseek/work_queue.py @@ -0,0 +1,380 @@ +"""Durable batch queue helpers for multi-GPU DeepSeek OCR runs.""" + +from __future__ import annotations + +import json +import sqlite3 +import time +from pathlib import Path +from typing import Any, Dict, Iterable, Optional + +STATUS_DONE = "done" +STATUS_FAILED = "failed" +STATUS_PENDING = "pending" +STATUS_RUNNING = "running" +QUEUE_MAIN = "main" +QUEUE_REPAIR = "repair" + + +def _empty_counts() -> Dict[str, int]: + return { + STATUS_PENDING: 0, + STATUS_RUNNING: 0, + STATUS_DONE: 0, + STATUS_FAILED: 0, + "total": 0, + } + + +def _normalize_queue_name(queue_name: str) -> str: + queue_norm = str(queue_name or QUEUE_MAIN).strip().lower() + if queue_norm not in {QUEUE_MAIN, QUEUE_REPAIR}: + raise ValueError(f"Unsupported queue name: {queue_name}") + return queue_norm + + +def _connect(db_path: Path) -> sqlite3.Connection: + db_path = Path(db_path).expanduser().resolve() + db_path.parent.mkdir(parents=True, exist_ok=True) + conn = sqlite3.connect(str(db_path), timeout=30.0, isolation_level=None) + conn.row_factory = sqlite3.Row + conn.execute("PRAGMA journal_mode=WAL") + conn.execute("PRAGMA synchronous=NORMAL") + return conn + + +def init_work_db(db_path: Path, *, batches: Iterable[Dict[str, Any]], replace: bool = True) -> None: + db_path = Path(db_path).expanduser().resolve() + if replace and db_path.exists(): + db_path.unlink() + with _connect(db_path) as conn: + conn.executescript( + """ + CREATE TABLE IF NOT EXISTS work_items ( + batch_id INTEGER PRIMARY KEY, + queue_name TEXT NOT NULL, + queue_key TEXT NOT NULL UNIQUE, + batch_json TEXT NOT NULL, + pages INTEGER NOT NULL, + status TEXT NOT NULL, + worker_id TEXT, + attempt_count INTEGER NOT NULL DEFAULT 0, + started_at REAL, + finished_at REAL, + last_heartbeat REAL, + last_error TEXT, + result_json TEXT + ); + CREATE INDEX IF NOT EXISTS idx_work_items_status ON work_items(status); + CREATE INDEX IF NOT EXISTS idx_work_items_queue_status ON work_items(queue_name, status); + CREATE INDEX IF NOT EXISTS idx_work_items_worker ON work_items(worker_id); + """ + ) + rows = [ + ( + int(batch["batch_id"]), + QUEUE_MAIN, + str(batch.get("queue_key") or f"{QUEUE_MAIN}:{int(batch['batch_id'])}"), + json.dumps(batch, sort_keys=True), + int(batch.get("pages", 0)), + STATUS_PENDING, + ) + for batch in batches + ] + conn.executemany( + """ + INSERT OR REPLACE INTO work_items(batch_id, queue_name, queue_key, batch_json, pages, status) + VALUES (?, ?, ?, ?, ?, ?) + """, + rows, + ) + + +def enqueue_batches( + db_path: Path, + *, + queue_name: str, + batches: Iterable[Dict[str, Any]], +) -> list[int]: + queue_norm = _normalize_queue_name(queue_name) + inserted_ids: list[int] = [] + with _connect(db_path) as conn: + _with_transaction(conn) + next_batch_id = int( + conn.execute("SELECT COALESCE(MAX(batch_id), -1) + 1 AS next_id FROM work_items").fetchone()["next_id"] + ) + for batch in batches: + payload = dict(batch) + queue_key = str(payload.get("queue_key") or f"{queue_norm}:{next_batch_id}") + row = conn.execute( + "SELECT batch_id FROM work_items WHERE queue_key = ?", + (queue_key,), + ).fetchone() + if row is None: + batch_id = int(payload.get("batch_id", next_batch_id)) + next_batch_id = max(next_batch_id, batch_id + 1) + else: + batch_id = int(row["batch_id"]) + payload["batch_id"] = batch_id + payload["queue_name"] = queue_norm + payload_json = json.dumps(payload, sort_keys=True) + pages = int(payload.get("pages", 0)) + if row is None: + conn.execute( + """ + INSERT INTO work_items(batch_id, queue_name, queue_key, batch_json, pages, status) + VALUES (?, ?, ?, ?, ?, ?) + """, + (batch_id, queue_norm, queue_key, payload_json, pages, STATUS_PENDING), + ) + else: + conn.execute( + """ + UPDATE work_items + SET queue_name = ?, batch_json = ?, pages = ?, status = ?, worker_id = NULL, attempt_count = 0, + started_at = NULL, finished_at = NULL, last_heartbeat = NULL, last_error = NULL, result_json = NULL + WHERE batch_id = ? + """, + (queue_norm, payload_json, pages, STATUS_PENDING, batch_id), + ) + inserted_ids.append(batch_id) + conn.commit() + return inserted_ids + + +def _with_transaction(conn: sqlite3.Connection) -> None: + conn.execute("BEGIN IMMEDIATE") + + +def requeue_stale_running_batches( + db_path: Path, + *, + stale_after_sec: float, + now_ts: Optional[float] = None, +) -> int: + now_value = float(now_ts) if now_ts is not None else float(time.time()) + cutoff = now_value - float(max(1.0, stale_after_sec)) + with _connect(db_path) as conn: + _with_transaction(conn) + cursor = conn.execute( + """ + UPDATE work_items + SET status = ?, worker_id = NULL, started_at = NULL, finished_at = NULL + WHERE status = ? AND COALESCE(last_heartbeat, started_at, 0) < ? + """, + (STATUS_PENDING, STATUS_RUNNING, cutoff), + ) + conn.commit() + return int(cursor.rowcount or 0) + + +def requeue_worker_batches( + db_path: Path, + *, + worker_id: str, + error: Optional[str] = None, + max_attempts: int = 2, +) -> int: + max_attempts_value = max(1, int(max_attempts)) + with _connect(db_path) as conn: + _with_transaction(conn) + # `attempt_count` is incremented on claim. With the default max_attempts=2 + # each work item gets one retry after its first failed claim, then becomes + # terminally failed instead of bouncing forever between workers. + cursor = conn.execute( + """ + UPDATE work_items + SET status = CASE WHEN attempt_count < ? THEN ? ELSE ? END, + worker_id = CASE WHEN attempt_count < ? THEN NULL ELSE ? END, + started_at = NULL, + finished_at = NULL, + last_heartbeat = NULL, + last_error = ?, + result_json = NULL + WHERE status = ? AND worker_id = ? + """, + ( + max_attempts_value, + STATUS_PENDING, + STATUS_FAILED, + max_attempts_value, + str(worker_id), + str(error) if error else None, + STATUS_RUNNING, + str(worker_id), + ), + ) + conn.commit() + return int(cursor.rowcount or 0) + + +def claim_next_batch( + db_path: Path, + *, + worker_id: str, + stale_after_sec: float, + queue_name: str = QUEUE_MAIN, + now_ts: Optional[float] = None, +) -> Optional[Dict[str, Any]]: + queue_norm = _normalize_queue_name(queue_name) + now_value = float(now_ts) if now_ts is not None else float(time.time()) + cutoff = now_value - float(max(1.0, stale_after_sec)) + with _connect(db_path) as conn: + _with_transaction(conn) + conn.execute( + """ + UPDATE work_items + SET status = ?, worker_id = NULL, started_at = NULL, finished_at = NULL + WHERE status = ? AND COALESCE(last_heartbeat, started_at, 0) < ? + """, + (STATUS_PENDING, STATUS_RUNNING, cutoff), + ) + row = conn.execute( + """ + SELECT batch_id, batch_json + FROM work_items + WHERE status = ? AND queue_name = ? + ORDER BY batch_id ASC + LIMIT 1 + """, + (STATUS_PENDING, queue_norm), + ).fetchone() + if row is None: + conn.commit() + return None + conn.execute( + """ + UPDATE work_items + SET status = ?, worker_id = ?, attempt_count = attempt_count + 1, started_at = ?, last_heartbeat = ?, last_error = NULL + WHERE batch_id = ? + """, + (STATUS_RUNNING, str(worker_id), now_value, now_value, int(row["batch_id"])), + ) + conn.commit() + return json.loads(str(row["batch_json"])) + + +def heartbeat_batch(db_path: Path, *, batch_id: int, worker_id: str, now_ts: Optional[float] = None) -> None: + now_value = float(now_ts) if now_ts is not None else float(time.time()) + with _connect(db_path) as conn: + conn.execute( + """ + UPDATE work_items + SET last_heartbeat = ? + WHERE batch_id = ? AND status = ? AND worker_id = ? + """, + (now_value, int(batch_id), STATUS_RUNNING, str(worker_id)), + ) + + +def mark_batch_done( + db_path: Path, + *, + batch_id: int, + worker_id: str, + result: Optional[Dict[str, Any]] = None, + now_ts: Optional[float] = None, +) -> None: + now_value = float(now_ts) if now_ts is not None else float(time.time()) + with _connect(db_path) as conn: + conn.execute( + """ + UPDATE work_items + SET status = ?, finished_at = ?, last_heartbeat = ?, result_json = ? + WHERE batch_id = ? AND worker_id = ? + """, + ( + STATUS_DONE, + now_value, + now_value, + json.dumps(result, sort_keys=True) if result is not None else None, + int(batch_id), + str(worker_id), + ), + ) + + +def mark_batch_failed( + db_path: Path, + *, + batch_id: int, + worker_id: str, + error: str, + max_attempts: int = 2, + now_ts: Optional[float] = None, +) -> None: + now_value = float(now_ts) if now_ts is not None else float(time.time()) + max_attempts_value = max(1, int(max_attempts)) + with _connect(db_path) as conn: + conn.execute( + """ + UPDATE work_items + SET status = CASE WHEN attempt_count < ? THEN ? ELSE ? END, + worker_id = CASE WHEN attempt_count < ? THEN NULL ELSE ? END, + started_at = NULL, + finished_at = ?, + last_heartbeat = ?, + last_error = ?, + result_json = NULL + WHERE batch_id = ? AND worker_id = ? + """, + ( + max_attempts_value, + STATUS_PENDING, + STATUS_FAILED, + max_attempts_value, + str(worker_id), + now_value, + now_value, + str(error), + int(batch_id), + str(worker_id), + ), + ) + + +def work_queue_counts(db_path: Path) -> Dict[str, int]: + counts = _empty_counts() + counts["by_queue"] = { + QUEUE_MAIN: _empty_counts(), + QUEUE_REPAIR: _empty_counts(), + } + with _connect(db_path) as conn: + for row in conn.execute("SELECT queue_name, status, COUNT(*) AS count FROM work_items GROUP BY queue_name, status"): + queue_name = _normalize_queue_name(str(row["queue_name"])) + status = str(row["status"]) + count = int(row["count"]) + counts[status] = int(counts.get(status, 0)) + count + counts["total"] += count + counts["by_queue"][queue_name][status] = count + counts["by_queue"][queue_name]["total"] += count + return counts + + +def iter_work_items(db_path: Path) -> Iterable[Dict[str, Any]]: + with _connect(db_path) as conn: + for row in conn.execute( + """ + SELECT batch_id, queue_name, queue_key, batch_json, pages, status, worker_id, attempt_count, started_at, + finished_at, last_heartbeat, last_error, result_json + FROM work_items + ORDER BY batch_id ASC + """ + ): + item = json.loads(str(row["batch_json"])) + item.update( + { + "queue_name": str(row["queue_name"]), + "queue_key": str(row["queue_key"]), + "status": str(row["status"]), + "worker_id": row["worker_id"], + "attempt_count": int(row["attempt_count"]), + "started_at": row["started_at"], + "finished_at": row["finished_at"], + "last_heartbeat": row["last_heartbeat"], + "last_error": row["last_error"], + "result": json.loads(str(row["result_json"])) if row["result_json"] else None, + "pages": int(row["pages"]), + } + ) + yield item diff --git a/src/glossapi/ocr/docling/__init__.py b/src/glossapi/ocr/docling/__init__.py new file mode 100644 index 0000000..28d4b0a --- /dev/null +++ b/src/glossapi/ocr/docling/__init__.py @@ -0,0 +1,5 @@ +"""Docling PDF pipeline helpers used by GlossAPI.""" + +from .pipeline import build_layout_pipeline + +__all__ = ["build_layout_pipeline"] diff --git a/src/glossapi/ocr/docling/pipeline.py b/src/glossapi/ocr/docling/pipeline.py new file mode 100644 index 0000000..df23030 --- /dev/null +++ b/src/glossapi/ocr/docling/pipeline.py @@ -0,0 +1,187 @@ +from __future__ import annotations + +import os +from typing import Tuple + +from docling.datamodel.pipeline_options import ( + AcceleratorDevice, + AcceleratorOptions, + PdfPipelineOptions, + TableFormerMode, + TableStructureOptions, +) + +try: # pragma: no cover - depends on installed Docling version + from docling.datamodel.pipeline_options import LayoutOptions +except ImportError: # pragma: no cover - older Docling versions + LayoutOptions = None + +try: # pragma: no cover - depends on installed Docling version + from docling.datamodel.pipeline_options import PictureDescriptionApiOptions +except ImportError: # pragma: no cover - older Docling versions + PictureDescriptionApiOptions = None + +try: # pragma: no cover - depends on installed Docling version + from docling.datamodel.pipeline_options import ThreadedPdfPipelineOptions +except ImportError: # pragma: no cover - older Docling versions + ThreadedPdfPipelineOptions = None + +try: # pragma: no cover - depends on installed Docling version + from docling.datamodel.settings import settings as docling_settings +except ImportError: # pragma: no cover - older Docling versions + docling_settings = None + + +def _resolve_accelerator(device: str | None) -> Tuple[AcceleratorOptions, bool]: + """Return accelerator options and whether CUDA was requested.""" + dev = device or "cuda:0" + if isinstance(dev, str) and dev.lower().startswith(("cuda", "mps", "cpu")): + try: + acc = AcceleratorOptions(device=dev) + except Exception: + acc = AcceleratorOptions(device=dev.split(":", 1)[0]) + want_cuda = dev.lower().startswith("cuda") + else: + want_cuda = str(dev).lower().startswith("cuda") + acc = AcceleratorOptions( + device=AcceleratorDevice.CUDA if want_cuda else AcceleratorDevice.CPU + ) + return acc, want_cuda + + +def _apply_common_pdf_options( + *, + acc: AcceleratorOptions, + images_scale: float, + formula_enrichment: bool, + code_enrichment: bool, +) -> PdfPipelineOptions: + def _supports_kwarg(model_cls, field_name: str) -> bool: + fields = getattr(model_cls, "model_fields", None) or getattr(model_cls, "__fields__", None) + if fields is None: + return True + return field_name in fields + + options_cls = ThreadedPdfPipelineOptions or PdfPipelineOptions + table_opts = TableStructureOptions(mode=TableFormerMode.ACCURATE) + try: + if hasattr(table_opts, "do_cell_matching"): + table_opts.do_cell_matching = True + except Exception: + pass + + option_kwargs = { + "accelerator_options": acc, + "do_ocr": False, + "do_table_structure": True, + "do_formula_enrichment": bool(formula_enrichment), + "do_code_enrichment": bool(code_enrichment), + "force_backend_text": False, + "generate_parsed_pages": False, + "allow_external_plugins": True, + } + if LayoutOptions is not None and _supports_kwarg(options_cls, "layout_options"): + option_kwargs["layout_options"] = LayoutOptions() + if _supports_kwarg(options_cls, "table_structure_options"): + option_kwargs["table_structure_options"] = table_opts + opts = options_cls(**{key: value for key, value in option_kwargs.items() if _supports_kwarg(options_cls, key)}) + try: + if hasattr(opts, "do_picture_description"): + opts.do_picture_description = False + if PictureDescriptionApiOptions is not None and getattr(opts, "picture_description_options", None) is None: + opts.picture_description_options = PictureDescriptionApiOptions() + if hasattr(opts, "enable_remote_services"): + opts.enable_remote_services = False + except Exception: + pass + try: + setattr(opts, "images_scale", images_scale) + except Exception: + pass + _apply_runtime_overrides(opts) + return opts + + +def _apply_runtime_overrides(opts: PdfPipelineOptions) -> None: + """Apply optional runtime tuning knobs exposed by newer Docling releases.""" + + int_env_map = { + "GLOSSAPI_DOCLING_LAYOUT_BATCH_SIZE": "layout_batch_size", + "GLOSSAPI_DOCLING_TABLE_BATCH_SIZE": "table_batch_size", + "GLOSSAPI_DOCLING_OCR_BATCH_SIZE": "ocr_batch_size", + "GLOSSAPI_DOCLING_QUEUE_MAX_SIZE": "queue_max_size", + "GLOSSAPI_DOCLING_DOCUMENT_TIMEOUT": "document_timeout", + } + float_env_map = { + "GLOSSAPI_DOCLING_BATCH_POLL_INTERVAL": "batch_polling_interval_seconds", + } + + for env_name, attr_name in int_env_map.items(): + raw = os.getenv(env_name) + if not raw: + continue + try: + value = int(raw) + except ValueError: + continue + if value <= 0 or not hasattr(opts, attr_name): + continue + try: + setattr(opts, attr_name, value) + except Exception: + pass + + for env_name, attr_name in float_env_map.items(): + raw = os.getenv(env_name) + if not raw: + continue + try: + value = float(raw) + except ValueError: + continue + if value <= 0 or not hasattr(opts, attr_name): + continue + try: + setattr(opts, attr_name, value) + except Exception: + pass + + raw_page_batch_size = os.getenv("GLOSSAPI_DOCLING_PAGE_BATCH_SIZE") + if raw_page_batch_size and docling_settings is not None: + try: + page_batch_size = int(raw_page_batch_size) + except ValueError: + page_batch_size = 0 + if page_batch_size > 0: + try: + perf_settings = getattr(docling_settings, "perf", None) + if perf_settings is not None and hasattr(perf_settings, "page_batch_size"): + setattr(perf_settings, "page_batch_size", page_batch_size) + except Exception: + pass + + +def build_layout_pipeline( + *, + device: str = "cuda:0", + images_scale: float = 1.25, + formula_enrichment: bool = False, + code_enrichment: bool = False, +) -> Tuple[object, PdfPipelineOptions]: + """Create a Docling layout-only PDF pipeline.""" + + acc, _ = _resolve_accelerator(device) + opts = _apply_common_pdf_options( + acc=acc, + images_scale=float(images_scale), + formula_enrichment=formula_enrichment, + code_enrichment=code_enrichment, + ) + + try: + from docling.pipelines.standard_pdf_pipeline import StandardPdfPipeline # type: ignore + except Exception: # pragma: no cover + from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline # type: ignore + + pipeline = StandardPdfPipeline(opts) # type: ignore[arg-type] + return pipeline, opts diff --git a/src/glossapi/ocr/docling_pipeline.py b/src/glossapi/ocr/docling_pipeline.py new file mode 100644 index 0000000..4a96e09 --- /dev/null +++ b/src/glossapi/ocr/docling_pipeline.py @@ -0,0 +1,5 @@ +"""Compatibility wrapper for the canonical Docling pipeline builder.""" + +from .docling.pipeline import build_layout_pipeline + +__all__ = ["build_layout_pipeline"] diff --git a/src/glossapi/ocr/rapidocr/__init__.py b/src/glossapi/ocr/rapidocr/__init__.py deleted file mode 100644 index c0d1232..0000000 --- a/src/glossapi/ocr/rapidocr/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -"""RapidOCR subpackage with lazy re-exports.""" - -from __future__ import annotations - -from importlib import import_module -from typing import Any - -__all__ = [ - "dispatch", - "docling_pipeline", - "pool", - "safe", - "onnx", - "_paths", - "pipeline", -] - - -def __getattr__(name: str) -> Any: - if name in __all__: - return import_module(f"glossapi.ocr.rapidocr.{name}") - raise AttributeError(name) - - -def __dir__() -> list[str]: - return sorted(set(globals().keys()) | set(__all__)) diff --git a/src/glossapi/ocr/rapidocr/__init__.py.backup b/src/glossapi/ocr/rapidocr/__init__.py.backup deleted file mode 100644 index 865f119..0000000 --- a/src/glossapi/ocr/rapidocr/__init__.py.backup +++ /dev/null @@ -1,6 +0,0 @@ -"""RapidOCR subpackage (shim).""" - -from __future__ import annotations - -__all__ = ["dispatch"] - diff --git a/src/glossapi/ocr/rapidocr/_paths.py b/src/glossapi/ocr/rapidocr/_paths.py deleted file mode 100644 index 4c1cc2a..0000000 --- a/src/glossapi/ocr/rapidocr/_paths.py +++ /dev/null @@ -1,114 +0,0 @@ -from __future__ import annotations - -from dataclasses import dataclass -from pathlib import Path -from typing import Optional, Tuple -import importlib -import os - - -@dataclass -class ResolvedOnnx: - det: Optional[str] - rec: Optional[str] - cls: Optional[str] - keys: Optional[str] - - -def _find_first(base: Path, patterns: list[str]) -> Optional[str]: - for pat in patterns: - for p in base.rglob(pat): - if p.is_file(): - return str(p) - return None - - -def _resolve_packaged_cls_fallback() -> Optional[str]: - try: - rapidocr = importlib.import_module("rapidocr") - base = Path(rapidocr.__file__).resolve().parent / "models" - pref = base / "ch_ppocr_mobile_v2.0_cls_infer.onnx" - if pref.exists(): - return str(pref) - return _find_first(base, ["*cls*infer*.onnx", "*cls*.onnx"]) - except Exception: - return None - - -def resolve_packaged_onnx_and_keys() -> ResolvedOnnx: - """Locate ONNX det/rec/cls and Greek keys packaged with the glossapi package. - - Search order: - 1) GLOSSAPI_RAPIDOCR_ONNX_DIR (env var) with heuristic file names - 2) Under the installed glossapi package folder `models/` and common subfolders - 3) CLS only: fallback to RapidOCR’s bundled cls model if missing - """ - # 1) Explicit override directory - override = os.getenv("GLOSSAPI_RAPIDOCR_ONNX_DIR") - if override: - base = Path(override) - det = _find_first(base, [ - "**/det/**/inference.onnx", - "*det*server*onnx", - "*PP*det*.onnx", - "det*.onnx", - ]) - rec = _find_first(base, [ - "**/rec/**/inference.onnx", - "*el*rec*onnx", - "*greek*rec*onnx", - "*PP*rec*.onnx", - "rec*.onnx", - ]) - cls = _find_first(base, ["*cls*infer*.onnx", "*cls*.onnx"]) - keys = _find_first(base, ["*greek*keys*.txt", "*ppocr*keys*.txt", "*keys*.txt"]) - if det or rec or cls or keys: - return ResolvedOnnx(det, rec, cls, keys) - - # 2) Search inside installed glossapi package - try: - glossapi = importlib.import_module("glossapi") - pkg_root = Path(glossapi.__file__).resolve().parent - # Candidate asset directories inside the package - candidates = [ - pkg_root / "models", - pkg_root / "models" / "rapidocr", - pkg_root / "models" / "rapidocr" / "onnx", - pkg_root / "models" / "rapidocr" / "keys", - pkg_root / "resources", - pkg_root / "assets", - pkg_root / "data", - ] - det = rec = cls = keys = None - for base in candidates: - if not base.exists(): - continue - det = det or _find_first(base, [ - "**/det/**/inference.onnx", - "*det*server*onnx", - "*PP*det*.onnx", - "det*.onnx", - ]) - rec = rec or _find_first(base, [ - "**/rec/**/inference.onnx", - "*el*rec*onnx", - "*greek*rec*onnx", - "*PP*rec*.onnx", - "rec*.onnx", - ]) - cls = cls or _find_first(base, ["*cls*infer*.onnx", "*cls*.onnx"]) - keys = keys or _find_first(base, ["*greek*keys*.txt", "*ppocr*keys*.txt", "*keys*.txt"]) - - if cls is None: - cls = _resolve_packaged_cls_fallback() - return ResolvedOnnx(det, rec, cls, keys) - except Exception: - return ResolvedOnnx(None, None, _resolve_packaged_cls_fallback(), None) - - -def summarize_resolution() -> Tuple[bool, str]: - r = resolve_packaged_onnx_and_keys() - ok = bool(r.det and r.rec and r.cls and r.keys) - msg = f"det={bool(r.det)} rec={bool(r.rec)} cls={bool(r.cls)} keys={bool(r.keys)}" - return ok, msg - diff --git a/src/glossapi/ocr/rapidocr/dispatch.py b/src/glossapi/ocr/rapidocr/dispatch.py deleted file mode 100644 index 7deeba2..0000000 --- a/src/glossapi/ocr/rapidocr/dispatch.py +++ /dev/null @@ -1,33 +0,0 @@ -from __future__ import annotations - -from typing import Iterable, Optional - - -def run_via_extract( - corpus, - files: Iterable[str], - *, - export_doc_json: bool = False, - internal_debug: bool = False, - content_debug: Optional[bool] = None, -) -> None: - """Thin adapter that forwards to Corpus.extract for RapidOCR/Docling. - - This exists for symmetry with deepseek_runner and to keep the OCR package - as the single entry point for OCR backends. - """ - # Note: internal_debug/content_debug are no-ops for the Docling/RapidOCR path. - # Docling's output already produces a single concatenated Markdown document. - corpus.extract( - input_format="pdf", - num_threads=1, # let extract decide; override in tests if needed - accel_type="CUDA", - force_ocr=True, - formula_enrichment=False, - code_enrichment=False, - filenames=list(files), - skip_existing=False, - export_doc_json=bool(export_doc_json), - emit_formula_index=bool(export_doc_json), - phase1_backend="docling", - ) diff --git a/src/glossapi/ocr/rapidocr/docling_pipeline.py b/src/glossapi/ocr/rapidocr/docling_pipeline.py deleted file mode 100644 index bb8988f..0000000 --- a/src/glossapi/ocr/rapidocr/docling_pipeline.py +++ /dev/null @@ -1,501 +0,0 @@ -"""Docling + RapidOCR (ONNX) pipeline for batch PDF OCR. - -Provides build_pipeline() and convert_dir() mirroring the behavior of the -repro script greek_pdf_ocr.py, but self-contained inside glossapi and with -packaged ONNX models/keys. Includes robust logging and native Docling timeout. -""" -from __future__ import annotations - -import argparse -import logging -import os -import sys -import time -import inspect -import importlib -from pathlib import Path -from typing import Iterable, Optional, Tuple - -from docling.datamodel.base_models import InputFormat -from docling.datamodel.pipeline_options import ( - AcceleratorDevice, - AcceleratorOptions, - LayoutOptions, - PdfPipelineOptions, - RapidOcrOptions, - TableFormerMode, - TableStructureOptions, -) -from docling.document_converter import ( - ConversionResult, - DocumentConverter, - PdfFormatOption, -) -from docling.datamodel.settings import settings - -from glossapi.ocr.rapidocr._paths import resolve_packaged_onnx_and_keys -from glossapi.metrics import compute_per_page_metrics -# Ensure RapidOCR factory is registered (avoids masked errors in older paths) -import docling.models.rapid_ocr_model # noqa: F401 - - -log = logging.getLogger(__name__) - - -def _maybe_import_torch(*, force: bool = False): - torch_mod = sys.modules.get("torch") - if torch_mod is not None: - return torch_mod - try: - return importlib.import_module("torch") # type: ignore - except Exception: - return None - return None - - -def _available_ort_providers() -> str: - try: - import onnxruntime as ort # type: ignore - return ",".join(ort.get_available_providers()) - except Exception as e: - return f"unavailable: {e}" - - -def _supports_native_timeout(converter: DocumentConverter) -> Optional[str]: - try: - sig = inspect.signature(converter.convert) - for name in ("timeout", "timeout_s"): - if name in sig.parameters: - return name - except Exception: - pass - return None - - -def _convert_with_timeout(converter: DocumentConverter, *, source: str, raises_on_error: bool, timeout_s: Optional[int] = None, **kwargs): - kw = dict(raises_on_error=raises_on_error) - kw.update(kwargs) - if timeout_s is not None: - tkw = _supports_native_timeout(converter) - if tkw: - kw[tkw] = int(timeout_s) - return converter.convert(source=source, **kw) - - -def _convert_all_with_timeout(converter: DocumentConverter, *, sources: Iterable[str], raises_on_error: bool, timeout_s: Optional[int] = None, **kwargs): - kw = dict(raises_on_error=raises_on_error) - kw.update(kwargs) - if timeout_s is not None: - tkw = _supports_native_timeout(converter) - if tkw: - kw[tkw] = int(timeout_s) - return list(converter.convert_all(sources, **kw)) - - -def build_pipeline( - *, - device: str = "cuda:0", - text_score: float = 0.45, - images_scale: float = 1.25, - formula_enrichment: bool = False, - code_enrichment: bool = False, -) -> Tuple[object, PdfPipelineOptions]: - # Delegate to canonical pipeline builder to avoid duplication - try: - from glossapi.ocr.rapidocr.pipeline import build_rapidocr_pipeline # type: ignore - except Exception as _e: # pragma: no cover - # Backward-compat fallback: inline builder (kept minimal to satisfy tests) - from docling.datamodel.pipeline_options import AcceleratorOptions, TableStructureOptions, TableFormerMode, LayoutOptions, PdfPipelineOptions, RapidOcrOptions # type: ignore - dev = device or "cuda:0" - acc = AcceleratorOptions(device=dev) - r = resolve_packaged_onnx_and_keys() - if not (r.det and r.rec and r.cls and r.keys): - raise FileNotFoundError("Packaged RapidOCR ONNX models/keys not found under glossapi.models.") - ocr_opts = RapidOcrOptions( - backend="onnxruntime", lang=["el", "en"], force_full_page_ocr=False, - use_det=True, use_cls=False, use_rec=True, text_score=text_score, - det_model_path=r.det, rec_model_path=r.rec, cls_model_path=r.cls, print_verbose=False, - ) - ocr_opts.rec_keys_path = r.keys - table_opts = TableStructureOptions(mode=TableFormerMode.ACCURATE) - opts = PdfPipelineOptions( - accelerator_options=acc, - ocr_options=ocr_opts, - layout_options=LayoutOptions(), - do_ocr=True, - do_table_structure=True, - do_formula_enrichment=bool(formula_enrichment), - do_code_enrichment=bool(code_enrichment), - force_backend_text=False, - generate_parsed_pages=False, - table_structure_options=table_opts, - allow_external_plugins=True, - ) - try: - setattr(opts, "images_scale", images_scale) - except Exception: - pass - from docling.document_converter import DocumentConverter, PdfFormatOption # type: ignore - from docling.datamodel.base_models import InputFormat # type: ignore - return DocumentConverter(format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=opts)}), opts - return build_rapidocr_pipeline( - device=device, - text_score=text_score, - images_scale=images_scale, - formula_enrichment=formula_enrichment, - code_enrichment=code_enrichment, - ) - - -def convert_dir( - input_dir: Path, - output_dir: Path, - *, - device: str = "cuda:0", - text_score: float = 0.45, - images_scale: float = 1.25, - formula_enrichment: bool = False, - code_enrichment: bool = False, - normalize_output: bool = True, - timeout_s: Optional[int] = 600, -) -> None: - input_dir = Path(input_dir) - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - # Device-aware preflight: only enforce CUDA provider when device requests CUDA - want_cuda = isinstance(device, str) and device.lower().startswith("cuda") - if want_cuda: - try: - import onnxruntime as _ort # type: ignore - _providers = _ort.get_available_providers() - if "CUDAExecutionProvider" not in _providers: - raise RuntimeError(f"CUDAExecutionProvider not available in onnxruntime providers={_providers}") - except Exception as e: - raise RuntimeError(f"onnxruntime-gpu not available or misconfigured: {e}") - if formula_enrichment and want_cuda: - try: - torch_mod = _maybe_import_torch(force=True) - if torch_mod is None or not torch_mod.cuda.is_available(): - raise RuntimeError("Torch CUDA not available but formula enrichment requested.") - except Exception as e: - raise RuntimeError(f"Torch CUDA preflight failed: {e}") - - # Optional: tune CodeFormula batch size and math precision when enrichment is requested - if formula_enrichment: - try: - torch_mod = _maybe_import_torch() - if torch_mod is not None and getattr(torch_mod, "cuda", None) and torch_mod.cuda.is_available(): - try: - torch_mod.set_float32_matmul_precision("high") - except Exception: - pass - except Exception: - pass - - engine, opts = build_pipeline( - device=device, - text_score=text_score, - images_scale=images_scale, - formula_enrichment=formula_enrichment, - code_enrichment=code_enrichment, - ) - - # Logging block - log.info("Docling+RapidOCR pipeline ready") - log.info("device=%s text_score=%.2f images_scale=%.2f formula=%s code=%s", device, text_score, images_scale, formula_enrichment, code_enrichment) - log.info("ORT providers: %s", _available_ort_providers()) - log.info("Caches: HF_HOME=%s XDG_CACHE_HOME=%s DOCLING_CACHE_DIR=%s", os.getenv("HF_HOME"), os.getenv("XDG_CACHE_HOME"), os.getenv("DOCLING_CACHE_DIR")) - try: - r = resolve_packaged_onnx_and_keys() - import os as _os - log.info( - "Models: det=%s rec=%s cls=%s keys=%s", - _os.path.basename(r.det) if r.det else None, - _os.path.basename(r.rec) if r.rec else None, - _os.path.basename(r.cls) if r.cls else None, - _os.path.basename(r.keys) if r.keys else None, - ) - except Exception: - pass - - # Collect PDFs - pdfs = sorted(str(p) for p in input_dir.rglob("*.pdf") if p.is_file()) - if not pdfs: - log.warning("No PDFs under %s", input_dir) - return - - # Enable timing profile - try: - settings.debug.profile_pipeline_timings = True - except Exception: - pass - - total_start = time.time() - # If we got a StandardPdfPipeline, it has a .convert method similar in spirit - # to DocumentConverter.convert; detect native timeout support by signature. - def _native_timeout_kw(obj) -> Optional[str]: - try: - sig = inspect.signature(obj.convert) - for name in ("timeout", "timeout_s"): - if name in sig.parameters: - return name - except Exception: - return None - return None - - tkw = _native_timeout_kw(engine) - for src in pdfs: - try: - kwargs = {} - if tkw and timeout_s is not None: - kwargs[tkw] = int(timeout_s) - conv = engine.convert(source=src, **kwargs) # type: ignore - _export(conv, output_dir, normalize_output=normalize_output) - # Per-page metrics and per-page console logs - try: - per_page = compute_per_page_metrics(conv) - # Harmonize with GlossExtract: write to sibling json/metrics/ - metrics_dir = output_dir.parent / "json" / "metrics" - metrics_dir.mkdir(parents=True, exist_ok=True) - pp = metrics_dir / f"{Path(src).stem}.per_page.metrics.json" - import json as _json - pp.write_text(_json.dumps(per_page, ensure_ascii=False, indent=2), encoding="utf-8") - for row in per_page.get("pages", []): - log.info("[PAGE] %s p%d: parse=%.3fs ocr=%.3fs formulas=%d code=%d", - Path(src).name, - int(row.get("page_no", 0)), - float(row.get("parse_sec", 0.0)), - float(row.get("ocr_sec", 0.0)), - int(row.get("formula_count", 0)), - int(row.get("code_count", 0))) - except Exception as _e: - log.warning("Failed to compute per-page metrics for %s: %s", src, _e) - log.info("[OK] %s", src) - except Exception as e: - log.error("[FAIL] %s: %s", src, e) - log.info("Done in %.2fs", time.time() - total_start) - - -def _normalize_text(s: str) -> str: - import unicodedata, re - zw = re.compile(r"[\u200B\u200C\u200D\uFEFF]") - s = unicodedata.normalize("NFC", s) - return zw.sub("", s) - - -def _normalize_obj(o): - if isinstance(o, str): - return _normalize_text(o) - if isinstance(o, list): - return [_normalize_obj(x) for x in o] - if isinstance(o, dict): - return {k: _normalize_obj(v) for k, v in o.items()} - return o - - -def _export(conv: ConversionResult, out_dir: Path, *, normalize_output: bool) -> None: - doc = conv.document - p = Path(conv.input.file) - md_path = out_dir / f"{p.stem}.md" - # Write Docling JSON under sibling json/ directory (no JSON in markdown dir) - json_dir = out_dir.parent / "json" - json_dir.mkdir(parents=True, exist_ok=True) - json_path = json_dir / f"{p.stem}.docling.json" - # Harmonize metrics location with GlossExtract: sibling json/metrics/ - metrics_dir = out_dir.parent / "json" / "metrics" - metrics_dir.mkdir(parents=True, exist_ok=True) - metrics_path = metrics_dir / f"{p.stem}.metrics.json" - - md = doc.export_to_markdown() - if normalize_output: - md = _normalize_text(md) - md_path.write_text(md, encoding="utf-8") - # Export DoclingDocument JSON via helper (compressed by default) - try: - from glossapi.ocr.utils.json_io import export_docling_json # type: ignore - # Attach minimal meta for provenance - meta = {"source_pdf_relpath": str(p)} - export_docling_json(doc, json_path, compress="zstd", meta=meta) # type: ignore[arg-type] - except Exception: - # Fallback: write plain JSON under json/ without compression - try: - import json as _json - dd = doc.export_to_dict() - if normalize_output: - dd = _normalize_obj(dd) - json_path.write_text(_json.dumps(dd, ensure_ascii=False, indent=2), encoding="utf-8") - except Exception: - pass - - # Timings if present - try: - from typing import Any, Dict, List - def _q(vals: list[float], q: float) -> float: - if not vals: - return 0.0 - s = sorted(vals) - i = int(round((len(s) - 1) * q)) - return float(s[i]) - metrics: Dict[str, Any] = {"file": str(p), "timings": {}} - for key, item in conv.timings.items(): - times = list(item.times) - cnt = int(item.count) - tot = float(sum(times)) if times else 0.0 - avg = float(tot / cnt) if cnt else 0.0 - metrics["timings"][key] = { - "scope": str(item.scope.value) if hasattr(item, "scope") else "unknown", - "count": cnt, - "total_sec": tot, - "avg_sec": avg, - "p50_sec": _q(times, 0.50), - "p90_sec": _q(times, 0.90), - } - import json as _json - metrics_path.write_text(_json.dumps(metrics, ensure_ascii=False, indent=2), encoding="utf-8") - except Exception: - pass - - -def _compute_per_page_metrics(conv: ConversionResult): - try: - doc = conv.document - except Exception: - return {"pages": []} - try: - page_count = len(doc.pages) # type: ignore[attr-defined] - except Exception: - page_count = 0 - timings = {} - try: - for key, item in conv.timings.items(): - times = list(item.times) - timings[key] = { - "scope": str(getattr(getattr(item, 'scope', None), 'value', 'unknown')), - "times": times, - "total": float(sum(times)) if times else float(getattr(item, 'total', 0.0)), - } - except Exception: - pass - def _pt(k): - arr = timings.get(k, {}).get("times", []) or [] - if page_count and len(arr) == page_count: - return [float(x) for x in arr] - return [float(x) for x in (arr + [0.0] * page_count)[:page_count]] - ocr = _pt("ocr") - parse = _pt("page_parse") - layout = _pt("layout") - table = _pt("table_structure") - # counts with sanitization and capping - fcnt = [0] * max(1, page_count) - fch = [0] * max(1, page_count) - ftr = [0] * max(1, page_count) - ftrc = [0] * max(1, page_count) - ccnt = [0] * max(1, page_count) - try: - as_dict = doc.export_to_dict() - import re as _re - _run_pat = _re.compile(r"\\\\\s*&(?P(?:\\quad|\\;|\\:|\\,|\\\\s|\s){200,})") - _ws_collapse = _re.compile(r"(?:(?:\\quad|\\;|\\:|\\,|\\\\s)|\s){2,}") - _CAP = 3000 - def _sanitize(s: str): - dropped=0 - m=_run_pat.search(s) - if m: - s_new=s[:m.start('ws')]; dropped+=len(s)-len(s_new); s=s_new - if len(s)>_CAP: - cut=s.rfind('\\\\',0,_CAP); cut = cut if cut>=0 else _CAP; dropped+=len(s)-cut; s=s[:cut] - s2=_ws_collapse.sub(' ', s) - return s2, dropped - def _walk(label, cnt, chars=False): - for node in as_dict.get("texts", []): - if str(node.get("label")) != label: - continue - raw = str(node.get("text") or node.get("orig") or "") - txt, dropped = _sanitize(raw) if label=='formula' else (raw,0) - ch = len(txt) - for prov in node.get("prov", []) or []: - pno = int(prov.get("page_no") or 0) - if 1 <= pno <= len(cnt): - cnt[pno - 1] += 1 - if chars: - fch[pno - 1] += ch - if label=='formula' and dropped: - ftr[pno - 1] += 1 - ftrc[pno - 1] += int(dropped) - _walk("formula", fcnt, True) - _walk("code", ccnt, False) - except Exception: - pass - try: - den_total = float(timings.get("doc_enrich", {}).get("total", 0.0)) - except Exception: - den_total = 0.0 - shares = [0.0] * max(1, page_count) - if den_total and page_count: - s = float(sum(fch)) or float(sum(fcnt)) or 0.0 - if s > 0: - base = fch if sum(fch) > 0 else fcnt - shares = [den_total * (float(x) / s) for x in base] - rows = [] - n = max(page_count, len(ocr), len(parse)) - for i in range(n): - rows.append({ - "page_no": i + 1, - "ocr_sec": float(ocr[i]) if i < len(ocr) else 0.0, - "parse_sec": float(parse[i]) if i < len(parse) else 0.0, - "layout_sec": float(layout[i]) if i < len(layout) else 0.0, - "table_sec": float(table[i]) if i < len(table) else 0.0, - "formula_count": int(fcnt[i]) if i < len(fcnt) else 0, - "formula_chars": int(fch[i]) if i < len(fch) else 0, - "formula_truncated": int(ftr[i]) if i < len(ftr) else 0, - "formula_truncated_chars": int(ftrc[i]) if i < len(ftrc) else 0, - "code_count": int(ccnt[i]) if i < len(ccnt) else 0, - "doc_enrich_share_sec": float(shares[i]) if i < len(shares) else 0.0, - }) - return {"file": str(getattr(conv.input.file, 'name', 'unknown')), "page_count": int(page_count), "totals": {"doc_enrich_total_sec": den_total}, "pages": rows} - - -def _setup_logging(level: int = logging.INFO) -> None: - logging.basicConfig(level=level, format="%(asctime)s %(levelname)s %(name)s: %(message)s") - - -if __name__ == "__main__": - _setup_logging() - ap = argparse.ArgumentParser(description="Batch OCR with Docling + RapidOCR (ONNX)") - ap.add_argument("input_dir", type=Path) - ap.add_argument("output_dir", type=Path) - ap.add_argument("--device", default=os.getenv("GLOSSAPI_DOCLING_DEVICE", "cuda:0")) - ap.add_argument("--text-score", type=float, default=float(os.getenv("GLOSSAPI_TEXT_SCORE", "0.45"))) - ap.add_argument("--images-scale", type=float, default=float(os.getenv("GLOSSAPI_IMAGES_SCALE", "1.25"))) - ap.add_argument("--docling-formula", dest="docling_formula", action="store_true", help="Enable formula enrichment (CodeFormula)") - ap.add_argument("--no-docling-formula", dest="docling_formula", action="store_false") - ap.set_defaults(docling_formula=False) - ap.add_argument("--formula-batch", type=int, default=int(os.getenv("GLOSSAPI_FORMULA_BATCH", "8")), help="CodeFormula batch size (default 8)") - ap.add_argument("--docling-code", dest="docling_code", action="store_true", help="Enable code enrichment") - ap.add_argument("--no-docling-code", dest="docling_code", action="store_false") - ap.set_defaults(docling_code=False) - ap.add_argument("--normalize-output", action="store_true") - ap.add_argument("--no-normalize-output", dest="normalize_output", action="store_false") - ap.set_defaults(normalize_output=True) - ap.add_argument("--timeout-s", type=int, default=int(os.getenv("GLOSSAPI_DOCLING_TIMEOUT", "600"))) - args = ap.parse_args() - # Apply formula batch size if requested - try: - if getattr(args, "docling_formula", False): - from docling.models.code_formula_model import CodeFormulaModel # type: ignore - if isinstance(args.formula_batch, int) and args.formula_batch > 0: - CodeFormulaModel.elements_batch_size = int(args.formula_batch) # type: ignore[attr-defined] - except Exception: - pass - convert_dir( - args.input_dir, - args.output_dir, - device=args.device, - text_score=args["text_score"] if isinstance(args, dict) else args.text_score, - images_scale=args.images_scale, - formula_enrichment=args.docling_formula, - code_enrichment=args.docling_code, - normalize_output=args.normalize_output, - timeout_s=args.timeout_s, - ) diff --git a/src/glossapi/ocr/rapidocr/docling_pipeline.py.backup b/src/glossapi/ocr/rapidocr/docling_pipeline.py.backup deleted file mode 100644 index f80344d..0000000 --- a/src/glossapi/ocr/rapidocr/docling_pipeline.py.backup +++ /dev/null @@ -1,501 +0,0 @@ -"""Docling + RapidOCR (ONNX) pipeline for batch PDF OCR. - -Provides build_pipeline() and convert_dir() mirroring the behavior of the -repro script greek_pdf_ocr.py, but self-contained inside glossapi and with -packaged ONNX models/keys. Includes robust logging and native Docling timeout. -""" -from __future__ import annotations - -import argparse -import logging -import os -import sys -import time -import inspect -import importlib -from pathlib import Path -from typing import Iterable, Optional, Tuple - -from docling.datamodel.base_models import InputFormat -from docling.datamodel.pipeline_options import ( - AcceleratorDevice, - AcceleratorOptions, - LayoutOptions, - PdfPipelineOptions, - RapidOcrOptions, - TableFormerMode, - TableStructureOptions, -) -from docling.document_converter import ( - ConversionResult, - DocumentConverter, - PdfFormatOption, -) -from docling.datamodel.settings import settings - -from glossapi._rapidocr_paths import resolve_packaged_onnx_and_keys -from glossapi.metrics import compute_per_page_metrics -# Ensure RapidOCR factory is registered (avoids masked errors in older paths) -import docling.models.rapid_ocr_model # noqa: F401 - - -log = logging.getLogger(__name__) - - -def _maybe_import_torch(*, force: bool = False): - torch_mod = sys.modules.get("torch") - if torch_mod is not None: - return torch_mod - try: - return importlib.import_module("torch") # type: ignore - except Exception: - return None - return None - - -def _available_ort_providers() -> str: - try: - import onnxruntime as ort # type: ignore - return ",".join(ort.get_available_providers()) - except Exception as e: - return f"unavailable: {e}" - - -def _supports_native_timeout(converter: DocumentConverter) -> Optional[str]: - try: - sig = inspect.signature(converter.convert) - for name in ("timeout", "timeout_s"): - if name in sig.parameters: - return name - except Exception: - pass - return None - - -def _convert_with_timeout(converter: DocumentConverter, *, source: str, raises_on_error: bool, timeout_s: Optional[int] = None, **kwargs): - kw = dict(raises_on_error=raises_on_error) - kw.update(kwargs) - if timeout_s is not None: - tkw = _supports_native_timeout(converter) - if tkw: - kw[tkw] = int(timeout_s) - return converter.convert(source=source, **kw) - - -def _convert_all_with_timeout(converter: DocumentConverter, *, sources: Iterable[str], raises_on_error: bool, timeout_s: Optional[int] = None, **kwargs): - kw = dict(raises_on_error=raises_on_error) - kw.update(kwargs) - if timeout_s is not None: - tkw = _supports_native_timeout(converter) - if tkw: - kw[tkw] = int(timeout_s) - return list(converter.convert_all(sources, **kw)) - - -def build_pipeline( - *, - device: str = "cuda:0", - text_score: float = 0.45, - images_scale: float = 1.25, - formula_enrichment: bool = False, - code_enrichment: bool = False, -) -> Tuple[object, PdfPipelineOptions]: - # Delegate to canonical pipeline builder to avoid duplication - try: - from glossapi._pipeline import build_rapidocr_pipeline # type: ignore - except Exception as _e: # pragma: no cover - # Backward-compat fallback: inline builder (kept minimal to satisfy tests) - from docling.datamodel.pipeline_options import AcceleratorOptions, TableStructureOptions, TableFormerMode, LayoutOptions, PdfPipelineOptions, RapidOcrOptions # type: ignore - dev = device or "cuda:0" - acc = AcceleratorOptions(device=dev) - r = resolve_packaged_onnx_and_keys() - if not (r.det and r.rec and r.cls and r.keys): - raise FileNotFoundError("Packaged RapidOCR ONNX models/keys not found under glossapi.models.") - ocr_opts = RapidOcrOptions( - backend="onnxruntime", lang=["el", "en"], force_full_page_ocr=False, - use_det=True, use_cls=False, use_rec=True, text_score=text_score, - det_model_path=r.det, rec_model_path=r.rec, cls_model_path=r.cls, print_verbose=False, - ) - ocr_opts.rec_keys_path = r.keys - table_opts = TableStructureOptions(mode=TableFormerMode.ACCURATE) - opts = PdfPipelineOptions( - accelerator_options=acc, - ocr_options=ocr_opts, - layout_options=LayoutOptions(), - do_ocr=True, - do_table_structure=True, - do_formula_enrichment=bool(formula_enrichment), - do_code_enrichment=bool(code_enrichment), - force_backend_text=False, - generate_parsed_pages=False, - table_structure_options=table_opts, - allow_external_plugins=True, - ) - try: - setattr(opts, "images_scale", images_scale) - except Exception: - pass - from docling.document_converter import DocumentConverter, PdfFormatOption # type: ignore - from docling.datamodel.base_models import InputFormat # type: ignore - return DocumentConverter(format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=opts)}), opts - return build_rapidocr_pipeline( - device=device, - text_score=text_score, - images_scale=images_scale, - formula_enrichment=formula_enrichment, - code_enrichment=code_enrichment, - ) - - -def convert_dir( - input_dir: Path, - output_dir: Path, - *, - device: str = "cuda:0", - text_score: float = 0.45, - images_scale: float = 1.25, - formula_enrichment: bool = False, - code_enrichment: bool = False, - normalize_output: bool = True, - timeout_s: Optional[int] = 600, -) -> None: - input_dir = Path(input_dir) - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - # Device-aware preflight: only enforce CUDA provider when device requests CUDA - want_cuda = isinstance(device, str) and device.lower().startswith("cuda") - if want_cuda: - try: - import onnxruntime as _ort # type: ignore - _providers = _ort.get_available_providers() - if "CUDAExecutionProvider" not in _providers: - raise RuntimeError(f"CUDAExecutionProvider not available in onnxruntime providers={_providers}") - except Exception as e: - raise RuntimeError(f"onnxruntime-gpu not available or misconfigured: {e}") - if formula_enrichment and want_cuda: - try: - torch_mod = _maybe_import_torch(force=True) - if torch_mod is None or not torch_mod.cuda.is_available(): - raise RuntimeError("Torch CUDA not available but formula enrichment requested.") - except Exception as e: - raise RuntimeError(f"Torch CUDA preflight failed: {e}") - - # Optional: tune CodeFormula batch size and math precision when enrichment is requested - if formula_enrichment: - try: - torch_mod = _maybe_import_torch() - if torch_mod is not None and getattr(torch_mod, "cuda", None) and torch_mod.cuda.is_available(): - try: - torch_mod.set_float32_matmul_precision("high") - except Exception: - pass - except Exception: - pass - - engine, opts = build_pipeline( - device=device, - text_score=text_score, - images_scale=images_scale, - formula_enrichment=formula_enrichment, - code_enrichment=code_enrichment, - ) - - # Logging block - log.info("Docling+RapidOCR pipeline ready") - log.info("device=%s text_score=%.2f images_scale=%.2f formula=%s code=%s", device, text_score, images_scale, formula_enrichment, code_enrichment) - log.info("ORT providers: %s", _available_ort_providers()) - log.info("Caches: HF_HOME=%s XDG_CACHE_HOME=%s DOCLING_CACHE_DIR=%s", os.getenv("HF_HOME"), os.getenv("XDG_CACHE_HOME"), os.getenv("DOCLING_CACHE_DIR")) - try: - r = resolve_packaged_onnx_and_keys() - import os as _os - log.info( - "Models: det=%s rec=%s cls=%s keys=%s", - _os.path.basename(r.det) if r.det else None, - _os.path.basename(r.rec) if r.rec else None, - _os.path.basename(r.cls) if r.cls else None, - _os.path.basename(r.keys) if r.keys else None, - ) - except Exception: - pass - - # Collect PDFs - pdfs = sorted(str(p) for p in input_dir.rglob("*.pdf") if p.is_file()) - if not pdfs: - log.warning("No PDFs under %s", input_dir) - return - - # Enable timing profile - try: - settings.debug.profile_pipeline_timings = True - except Exception: - pass - - total_start = time.time() - # If we got a StandardPdfPipeline, it has a .convert method similar in spirit - # to DocumentConverter.convert; detect native timeout support by signature. - def _native_timeout_kw(obj) -> Optional[str]: - try: - sig = inspect.signature(obj.convert) - for name in ("timeout", "timeout_s"): - if name in sig.parameters: - return name - except Exception: - return None - return None - - tkw = _native_timeout_kw(engine) - for src in pdfs: - try: - kwargs = {} - if tkw and timeout_s is not None: - kwargs[tkw] = int(timeout_s) - conv = engine.convert(source=src, **kwargs) # type: ignore - _export(conv, output_dir, normalize_output=normalize_output) - # Per-page metrics and per-page console logs - try: - per_page = compute_per_page_metrics(conv) - # Harmonize with GlossExtract: write to sibling json/metrics/ - metrics_dir = output_dir.parent / "json" / "metrics" - metrics_dir.mkdir(parents=True, exist_ok=True) - pp = metrics_dir / f"{Path(src).stem}.per_page.metrics.json" - import json as _json - pp.write_text(_json.dumps(per_page, ensure_ascii=False, indent=2), encoding="utf-8") - for row in per_page.get("pages", []): - log.info("[PAGE] %s p%d: parse=%.3fs ocr=%.3fs formulas=%d code=%d", - Path(src).name, - int(row.get("page_no", 0)), - float(row.get("parse_sec", 0.0)), - float(row.get("ocr_sec", 0.0)), - int(row.get("formula_count", 0)), - int(row.get("code_count", 0))) - except Exception as _e: - log.warning("Failed to compute per-page metrics for %s: %s", src, _e) - log.info("[OK] %s", src) - except Exception as e: - log.error("[FAIL] %s: %s", src, e) - log.info("Done in %.2fs", time.time() - total_start) - - -def _normalize_text(s: str) -> str: - import unicodedata, re - zw = re.compile(r"[\u200B\u200C\u200D\uFEFF]") - s = unicodedata.normalize("NFC", s) - return zw.sub("", s) - - -def _normalize_obj(o): - if isinstance(o, str): - return _normalize_text(o) - if isinstance(o, list): - return [_normalize_obj(x) for x in o] - if isinstance(o, dict): - return {k: _normalize_obj(v) for k, v in o.items()} - return o - - -def _export(conv: ConversionResult, out_dir: Path, *, normalize_output: bool) -> None: - doc = conv.document - p = Path(conv.input.file) - md_path = out_dir / f"{p.stem}.md" - # Write Docling JSON under sibling json/ directory (no JSON in markdown dir) - json_dir = out_dir.parent / "json" - json_dir.mkdir(parents=True, exist_ok=True) - json_path = json_dir / f"{p.stem}.docling.json" - # Harmonize metrics location with GlossExtract: sibling json/metrics/ - metrics_dir = out_dir.parent / "json" / "metrics" - metrics_dir.mkdir(parents=True, exist_ok=True) - metrics_path = metrics_dir / f"{p.stem}.metrics.json" - - md = doc.export_to_markdown() - if normalize_output: - md = _normalize_text(md) - md_path.write_text(md, encoding="utf-8") - # Export DoclingDocument JSON via helper (compressed by default) - try: - from glossapi.ocr.utils.json_io import export_docling_json # type: ignore - # Attach minimal meta for provenance - meta = {"source_pdf_relpath": str(p)} - export_docling_json(doc, json_path, compress="zstd", meta=meta) # type: ignore[arg-type] - except Exception: - # Fallback: write plain JSON under json/ without compression - try: - import json as _json - dd = doc.export_to_dict() - if normalize_output: - dd = _normalize_obj(dd) - json_path.write_text(_json.dumps(dd, ensure_ascii=False, indent=2), encoding="utf-8") - except Exception: - pass - - # Timings if present - try: - from typing import Any, Dict, List - def _q(vals: list[float], q: float) -> float: - if not vals: - return 0.0 - s = sorted(vals) - i = int(round((len(s) - 1) * q)) - return float(s[i]) - metrics: Dict[str, Any] = {"file": str(p), "timings": {}} - for key, item in conv.timings.items(): - times = list(item.times) - cnt = int(item.count) - tot = float(sum(times)) if times else 0.0 - avg = float(tot / cnt) if cnt else 0.0 - metrics["timings"][key] = { - "scope": str(item.scope.value) if hasattr(item, "scope") else "unknown", - "count": cnt, - "total_sec": tot, - "avg_sec": avg, - "p50_sec": _q(times, 0.50), - "p90_sec": _q(times, 0.90), - } - import json as _json - metrics_path.write_text(_json.dumps(metrics, ensure_ascii=False, indent=2), encoding="utf-8") - except Exception: - pass - - -def _compute_per_page_metrics(conv: ConversionResult): - try: - doc = conv.document - except Exception: - return {"pages": []} - try: - page_count = len(doc.pages) # type: ignore[attr-defined] - except Exception: - page_count = 0 - timings = {} - try: - for key, item in conv.timings.items(): - times = list(item.times) - timings[key] = { - "scope": str(getattr(getattr(item, 'scope', None), 'value', 'unknown')), - "times": times, - "total": float(sum(times)) if times else float(getattr(item, 'total', 0.0)), - } - except Exception: - pass - def _pt(k): - arr = timings.get(k, {}).get("times", []) or [] - if page_count and len(arr) == page_count: - return [float(x) for x in arr] - return [float(x) for x in (arr + [0.0] * page_count)[:page_count]] - ocr = _pt("ocr") - parse = _pt("page_parse") - layout = _pt("layout") - table = _pt("table_structure") - # counts with sanitization and capping - fcnt = [0] * max(1, page_count) - fch = [0] * max(1, page_count) - ftr = [0] * max(1, page_count) - ftrc = [0] * max(1, page_count) - ccnt = [0] * max(1, page_count) - try: - as_dict = doc.export_to_dict() - import re as _re - _run_pat = _re.compile(r"\\\\\s*&(?P(?:\\quad|\\;|\\:|\\,|\\\\s|\s){200,})") - _ws_collapse = _re.compile(r"(?:(?:\\quad|\\;|\\:|\\,|\\\\s)|\s){2,}") - _CAP = 3000 - def _sanitize(s: str): - dropped=0 - m=_run_pat.search(s) - if m: - s_new=s[:m.start('ws')]; dropped+=len(s)-len(s_new); s=s_new - if len(s)>_CAP: - cut=s.rfind('\\\\',0,_CAP); cut = cut if cut>=0 else _CAP; dropped+=len(s)-cut; s=s[:cut] - s2=_ws_collapse.sub(' ', s) - return s2, dropped - def _walk(label, cnt, chars=False): - for node in as_dict.get("texts", []): - if str(node.get("label")) != label: - continue - raw = str(node.get("text") or node.get("orig") or "") - txt, dropped = _sanitize(raw) if label=='formula' else (raw,0) - ch = len(txt) - for prov in node.get("prov", []) or []: - pno = int(prov.get("page_no") or 0) - if 1 <= pno <= len(cnt): - cnt[pno - 1] += 1 - if chars: - fch[pno - 1] += ch - if label=='formula' and dropped: - ftr[pno - 1] += 1 - ftrc[pno - 1] += int(dropped) - _walk("formula", fcnt, True) - _walk("code", ccnt, False) - except Exception: - pass - try: - den_total = float(timings.get("doc_enrich", {}).get("total", 0.0)) - except Exception: - den_total = 0.0 - shares = [0.0] * max(1, page_count) - if den_total and page_count: - s = float(sum(fch)) or float(sum(fcnt)) or 0.0 - if s > 0: - base = fch if sum(fch) > 0 else fcnt - shares = [den_total * (float(x) / s) for x in base] - rows = [] - n = max(page_count, len(ocr), len(parse)) - for i in range(n): - rows.append({ - "page_no": i + 1, - "ocr_sec": float(ocr[i]) if i < len(ocr) else 0.0, - "parse_sec": float(parse[i]) if i < len(parse) else 0.0, - "layout_sec": float(layout[i]) if i < len(layout) else 0.0, - "table_sec": float(table[i]) if i < len(table) else 0.0, - "formula_count": int(fcnt[i]) if i < len(fcnt) else 0, - "formula_chars": int(fch[i]) if i < len(fch) else 0, - "formula_truncated": int(ftr[i]) if i < len(ftr) else 0, - "formula_truncated_chars": int(ftrc[i]) if i < len(ftrc) else 0, - "code_count": int(ccnt[i]) if i < len(ccnt) else 0, - "doc_enrich_share_sec": float(shares[i]) if i < len(shares) else 0.0, - }) - return {"file": str(getattr(conv.input.file, 'name', 'unknown')), "page_count": int(page_count), "totals": {"doc_enrich_total_sec": den_total}, "pages": rows} - - -def _setup_logging(level: int = logging.INFO) -> None: - logging.basicConfig(level=level, format="%(asctime)s %(levelname)s %(name)s: %(message)s") - - -if __name__ == "__main__": - _setup_logging() - ap = argparse.ArgumentParser(description="Batch OCR with Docling + RapidOCR (ONNX)") - ap.add_argument("input_dir", type=Path) - ap.add_argument("output_dir", type=Path) - ap.add_argument("--device", default=os.getenv("GLOSSAPI_DOCLING_DEVICE", "cuda:0")) - ap.add_argument("--text-score", type=float, default=float(os.getenv("GLOSSAPI_TEXT_SCORE", "0.45"))) - ap.add_argument("--images-scale", type=float, default=float(os.getenv("GLOSSAPI_IMAGES_SCALE", "1.25"))) - ap.add_argument("--docling-formula", dest="docling_formula", action="store_true", help="Enable formula enrichment (CodeFormula)") - ap.add_argument("--no-docling-formula", dest="docling_formula", action="store_false") - ap.set_defaults(docling_formula=False) - ap.add_argument("--formula-batch", type=int, default=int(os.getenv("GLOSSAPI_FORMULA_BATCH", "8")), help="CodeFormula batch size (default 8)") - ap.add_argument("--docling-code", dest="docling_code", action="store_true", help="Enable code enrichment") - ap.add_argument("--no-docling-code", dest="docling_code", action="store_false") - ap.set_defaults(docling_code=False) - ap.add_argument("--normalize-output", action="store_true") - ap.add_argument("--no-normalize-output", dest="normalize_output", action="store_false") - ap.set_defaults(normalize_output=True) - ap.add_argument("--timeout-s", type=int, default=int(os.getenv("GLOSSAPI_DOCLING_TIMEOUT", "600"))) - args = ap.parse_args() - # Apply formula batch size if requested - try: - if getattr(args, "docling_formula", False): - from docling.models.code_formula_model import CodeFormulaModel # type: ignore - if isinstance(args.formula_batch, int) and args.formula_batch > 0: - CodeFormulaModel.elements_batch_size = int(args.formula_batch) # type: ignore[attr-defined] - except Exception: - pass - convert_dir( - args.input_dir, - args.output_dir, - device=args.device, - text_score=args["text_score"] if isinstance(args, dict) else args.text_score, - images_scale=args.images_scale, - formula_enrichment=args.docling_formula, - code_enrichment=args.docling_code, - normalize_output=args.normalize_output, - timeout_s=args.timeout_s, - ) diff --git a/src/glossapi/ocr/rapidocr/onnx.py b/src/glossapi/ocr/rapidocr/onnx.py deleted file mode 100644 index 57430d1..0000000 --- a/src/glossapi/ocr/rapidocr/onnx.py +++ /dev/null @@ -1,105 +0,0 @@ -"""OCR helpers for GlossAPI using Docling + RapidOCR (ONNXRuntime). - -GPU-first OCR that auto-discovers packaged ONNX models and Greek keys within -the installed `glossapi` package. Designed as a drop-in for Corpus.ocr(). -""" -from __future__ import annotations - -from pathlib import Path -from typing import Optional, Dict, Any, Tuple - -_PIPELINE_CACHE: dict[str, Tuple[object, object]] = {} - - -def _build_pipeline( - device: Optional[str] = None, - *, - use_cls: Optional[bool] = None, - text_score: Optional[float] = None, - images_scale: Optional[float] = None, -): - # Delegate to canonical builder to avoid duplication - from glossapi.ocr.rapidocr.pipeline import build_rapidocr_pipeline - - engine, opts = build_rapidocr_pipeline( - device=(device or "cuda:0"), - text_score=(0.45 if text_score is None else float(text_score)), - images_scale=(1.25 if images_scale is None else float(images_scale)), - formula_enrichment=False, - code_enrichment=False, - ) - # Apply use_cls override if requested - try: - if use_cls is not None and hasattr(opts, "ocr_options"): - setattr(opts.ocr_options, "use_cls", bool(use_cls)) # type: ignore[attr-defined] - except Exception: - pass - return engine, opts - - -def run_rapidocr_onnx( - pdf_path: Path | str, - *, - device: Optional[str] = None, - use_cls: Optional[bool] = None, - text_score: Optional[float] = None, - images_scale: Optional[float] = None, - max_pages: Optional[int] = None, -) -> Dict[str, Any]: - """Run Docling + RapidOCR (ONNX) OCR on a PDF and return markdown text. - - Returns - ------- - dict with keys: - - markdown_text: str - - duration_s: float - - pages: int - - models: dict with file names of det/rec/cls/keys - """ - from time import perf_counter - pdf_p = Path(pdf_path) - if not pdf_p.exists(): - raise FileNotFoundError(pdf_p) - - key = str(device or "cuda:0").lower() - cached = _PIPELINE_CACHE.get(key) - if cached is None: - pipe, r = _build_pipeline(device=device, use_cls=use_cls, text_score=text_score, images_scale=images_scale) - _PIPELINE_CACHE[key] = (pipe, r) - else: - pipe, r = cached # type: ignore[misc] - - t0 = perf_counter() - conv = pipe.convert(source=str(pdf_p)) # type: ignore[attr-defined] - doc = conv.document - md_text = doc.export_to_markdown() - duration = perf_counter() - t0 - - # Attempt to get page count from conv/document - pages = 0 - try: - if hasattr(doc, "pages"): - pages = len(doc.pages) # type: ignore[attr-defined] - except Exception: - pages = 0 - - # Return model identifiers as file names only (no full paths) - import os as _os - models = { - "det": _os.path.basename(r.det) if r.det else None, - "rec": _os.path.basename(r.rec) if r.rec else None, - "cls": _os.path.basename(r.cls) if r.cls else None, - "keys": _os.path.basename(r.keys) if r.keys else None, - } - - return { - "markdown_text": md_text or "", - "duration_s": duration, - "pages": int(pages), - "models": models, - } - - -__all__ = [ - "run_rapidocr_onnx", -] diff --git a/src/glossapi/ocr/rapidocr/pipeline.py b/src/glossapi/ocr/rapidocr/pipeline.py deleted file mode 100644 index a623c3d..0000000 --- a/src/glossapi/ocr/rapidocr/pipeline.py +++ /dev/null @@ -1,229 +0,0 @@ -from __future__ import annotations - -import logging -from typing import Tuple - -from docling.datamodel.base_models import InputFormat -from docling.datamodel.pipeline_options import ( - AcceleratorDevice, - AcceleratorOptions, - LayoutOptions, - PictureDescriptionApiOptions, - PdfPipelineOptions, - RapidOcrOptions, - TableFormerMode, - TableStructureOptions, -) -from docling.document_converter import DocumentConverter, PdfFormatOption - -from ._paths import resolve_packaged_onnx_and_keys -from .pool import GLOBAL_RAPID_OCR_POOL -from .safe import SafeRapidOcrModel, patch_docling_rapidocr - -_logger = logging.getLogger(__name__) - -patch_docling_rapidocr() - - -def _resolve_accelerator(device: str | None) -> Tuple[AcceleratorOptions, bool]: - """Return accelerator options and whether CUDA was requested.""" - dev = device or "cuda:0" - if isinstance(dev, str) and dev.lower().startswith(("cuda", "mps", "cpu")): - acc = AcceleratorOptions(device=dev) - want_cuda = dev.lower().startswith("cuda") - else: - want_cuda = str(dev).lower().startswith("cuda") - acc = AcceleratorOptions( - device=AcceleratorDevice.CUDA if want_cuda else AcceleratorDevice.CPU - ) - return acc, want_cuda - - -def _apply_common_pdf_options( - *, - acc: AcceleratorOptions, - images_scale: float, - formula_enrichment: bool, - code_enrichment: bool, -) -> PdfPipelineOptions: - table_opts = TableStructureOptions(mode=TableFormerMode.ACCURATE) - try: - if hasattr(table_opts, "do_cell_matching"): - table_opts.do_cell_matching = True - except Exception: - pass - - opts = PdfPipelineOptions( - accelerator_options=acc, - layout_options=LayoutOptions(), - do_ocr=False, - do_table_structure=True, - do_formula_enrichment=bool(formula_enrichment), - do_code_enrichment=bool(code_enrichment), - force_backend_text=False, - generate_parsed_pages=False, - table_structure_options=table_opts, - allow_external_plugins=True, - ) - # Prefer lightweight placeholder picture descriptions to avoid heavy VLM backends. - try: - if hasattr(opts, "do_picture_description"): - opts.do_picture_description = False - if getattr(opts, "picture_description_options", None) is None: - opts.picture_description_options = PictureDescriptionApiOptions() - if hasattr(opts, "enable_remote_services"): - opts.enable_remote_services = False - except Exception: - pass - try: - setattr(opts, "images_scale", images_scale) - except Exception: - pass - return opts - - -def build_layout_pipeline( - *, - device: str = "cuda:0", - images_scale: float = 1.25, - formula_enrichment: bool = False, - code_enrichment: bool = False, -) -> Tuple[object, PdfPipelineOptions]: - """Builder for a Docling PDF pipeline without RapidOCR. - - Returns ``(converter, PdfPipelineOptions)`` where ``converter`` is a - ``StandardPdfPipeline`` configured for layout extraction only. - """ - - acc, _ = _resolve_accelerator(device) - opts = _apply_common_pdf_options( - acc=acc, - images_scale=float(images_scale), - formula_enrichment=formula_enrichment, - code_enrichment=code_enrichment, - ) - - try: - from docling.pipelines.standard_pdf_pipeline import StandardPdfPipeline # type: ignore - except Exception: # pragma: no cover - from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline # type: ignore - - pipeline = StandardPdfPipeline(opts) # type: ignore[arg-type] - return pipeline, opts - - -def build_rapidocr_pipeline( - *, - device: str = "cuda:0", - text_score: float = 0.45, - images_scale: float = 1.25, - formula_enrichment: bool = False, - code_enrichment: bool = False, -) -> Tuple[object, PdfPipelineOptions]: - """Canonical builder for Docling + RapidOCR pipeline. - - Returns a tuple (engine, PdfPipelineOptions). Prefers explicit RapidOCR injection - when supported; otherwise returns a DocumentConverter using the factory path. - """ - - def _fallback_layout(reason: str) -> Tuple[object, PdfPipelineOptions]: - _logger.warning( - "RapidOCR pipeline fallback: %s. Using Docling layout-only configuration.", - reason, - ) - pipeline, opts = build_layout_pipeline( - device=device, - images_scale=images_scale, - formula_enrichment=formula_enrichment, - code_enrichment=code_enrichment, - ) - return pipeline, opts - - acc, want_cuda = _resolve_accelerator(device) - - # Optional provider preflight only when CUDA requested - if want_cuda: - try: - import onnxruntime as ort # type: ignore - - prov = ort.get_available_providers() - if "CUDAExecutionProvider" not in prov: - raise RuntimeError(f"CUDAExecutionProvider not available: {prov}") - except Exception as e: # pragma: no cover - raise RuntimeError(f"onnxruntime-gpu not available or misconfigured: {e}") - - r = resolve_packaged_onnx_and_keys() - if not (r.det and r.rec and r.cls and r.keys): - return _fallback_layout("packaged RapidOCR ONNX assets missing") - - ocr_opts = RapidOcrOptions( - backend="onnxruntime", - lang=["el", "en"], - force_full_page_ocr=False, - use_det=True, - use_cls=False, - use_rec=True, - text_score=text_score, - det_model_path=r.det, - rec_model_path=r.rec, - cls_model_path=r.cls, - print_verbose=False, - ) - ocr_opts.rec_keys_path = r.keys - - opts = _apply_common_pdf_options( - acc=acc, - images_scale=float(images_scale), - formula_enrichment=formula_enrichment, - code_enrichment=code_enrichment, - ) - opts.do_ocr = True - opts.ocr_options = ocr_opts - - # Prefer explicit injection of RapidOCR model when available - try: - from docling.models.rapid_ocr_model import RapidOcrModel # type: ignore - - try: - from docling.pipelines.standard_pdf_pipeline import StandardPdfPipeline # type: ignore - except Exception: # pragma: no cover - from docling.pipeline.standard_pdf_pipeline import StandardPdfPipeline # type: ignore - - import inspect - - sig = inspect.signature(StandardPdfPipeline.__init__) - if "ocr_model" not in sig.parameters: - raise RuntimeError("Docling build does not support RapidOCR injection") - - def _factory(): - try: - return SafeRapidOcrModel(True, None, ocr_opts, acc) # type: ignore[arg-type] - except Exception: # pragma: no cover - # Fall back to the stock implementation if our wrapper misbehaves. - return RapidOcrModel(True, None, ocr_opts, acc) # type: ignore[arg-type] - - pooled_model = GLOBAL_RAPID_OCR_POOL.get( - str(acc.device), - ocr_opts, - _factory, - expected_type=SafeRapidOcrModel, - ) - pipeline = StandardPdfPipeline(opts, ocr_model=pooled_model) # type: ignore - return pipeline, opts - except Exception as exc: - _logger.warning( - "RapidOCR injection unavailable (%s); using DocumentConverter factory path.", - exc, - ) - - # Fallback: use DocumentConverter factory - try: - converter = DocumentConverter( - format_options={InputFormat.PDF: PdfFormatOption(pipeline_options=opts)} - ) - return converter, opts - except Exception as exc: - return _fallback_layout(f"DocumentConverter failed: {exc}") - - -__all__ = ["build_layout_pipeline", "build_rapidocr_pipeline"] diff --git a/src/glossapi/ocr/rapidocr/pool.py b/src/glossapi/ocr/rapidocr/pool.py deleted file mode 100644 index db1e8f2..0000000 --- a/src/glossapi/ocr/rapidocr/pool.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Shared RapidOCR engine pooling utilities.""" -from __future__ import annotations - -from dataclasses import dataclass -from threading import Lock -from typing import Callable, Dict, Optional, Union, Type - -from docling.datamodel.pipeline_options import RapidOcrOptions - - -@dataclass(frozen=True) -class _PoolKey: - device: str - det_model_path: str - rec_model_path: str - cls_model_path: str - lang: Tuple[str, ...] - text_score: float - use_det: bool - use_cls: bool - use_rec: bool - - -class RapidOcrEnginePool: - """Process-local cache of RapidOCR models keyed by configuration.""" - - def __init__(self) -> None: - self._lock = Lock() - self._cache: Dict[_PoolKey, object] = {} - - def _make_key(self, device: str, opts: RapidOcrOptions) -> _PoolKey: - lang = tuple(opts.lang or []) - return _PoolKey( - device=str(device), - det_model_path=str(getattr(opts, "det_model_path", "")), - rec_model_path=str(getattr(opts, "rec_model_path", "")), - cls_model_path=str(getattr(opts, "cls_model_path", "")), - lang=lang, - text_score=float(getattr(opts, "text_score", 0.0)), - use_det=bool(getattr(opts, "use_det", True)), - use_cls=bool(getattr(opts, "use_cls", False)), - use_rec=bool(getattr(opts, "use_rec", True)), - ) - - def get( - self, - device: str, - opts: RapidOcrOptions, - factory: Callable[[], object], - *, - expected_type: Optional[Union[Type[object], tuple[Type[object], ...]]] = None, - ) -> object: - key = self._make_key(device, opts) - with self._lock: - model = self._cache.get(key) - if expected_type is not None and model is not None and not isinstance(model, expected_type): - self._cache.pop(key, None) - model = None - if model is None: - model = factory() - if expected_type is None or isinstance(model, expected_type): - self._cache[key] = model - return model - - def clear(self) -> None: - with self._lock: - self._cache.clear() - - -GLOBAL_RAPID_OCR_POOL = RapidOcrEnginePool() - -__all__ = ["RapidOcrEnginePool", "GLOBAL_RAPID_OCR_POOL"] diff --git a/src/glossapi/ocr/rapidocr/safe.py b/src/glossapi/ocr/rapidocr/safe.py deleted file mode 100644 index 5534563..0000000 --- a/src/glossapi/ocr/rapidocr/safe.py +++ /dev/null @@ -1,301 +0,0 @@ -"""Temporary wrappers around Docling's RapidOCR integration. - -The upstream Docling release (2.48.x) does not tolerate RapidOCR returning -``None`` for a given crop. That bubbles up as an AttributeError inside the -conversion loop and the entire document fails. Until Docling includes a fix, we -wrap the loader so that ``None`` simply means "no detections" and processing -continues. Once Docling ships a release with the guard we can drop this shim and -revert to the vanilla ``RapidOcrModel``. -""" - -from __future__ import annotations - -import importlib.util -import sys -from collections.abc import Iterable -from pathlib import Path -from typing import Optional, Type - -import numpy - -from docling.datamodel.base_models import Page -from docling.datamodel.document import ConversionResult -from docling.datamodel.pipeline_options import OcrOptions, RapidOcrOptions -from docling.models.rapid_ocr_model import RapidOcrModel as _RapidOcrModel -from docling.models.rapid_ocr_model import TextCell, _log -from docling.utils.profiling import TimeRecorder -from docling_core.types.doc import BoundingBox, CoordOrigin -from docling_core.types.doc.page import BoundingRectangle - -from ._paths import resolve_packaged_onnx_and_keys - - -class SafeRapidOcrModel(_RapidOcrModel): - """Drop-in RapidOCR wrapper that copes with ``None`` OCR results. - - Docling 2.48.0 assumes ``self.reader`` always returns an object with - ``boxes/txts/scores``. RapidOCR occasionally yields ``None`` for problematic - crops, which crashes the extractor. We normalise the return value before the - original list(zip(...)) call and treat anything unexpected as "no boxes". - Remove this once Docling hardens the upstream implementation. - """ - - # NOTE: keep signature identical so StandardPdfPipeline can instantiate it. - _rapidocr_available: Optional[bool] = None - - def __init__( - self, - enabled: bool, - artifacts_path: Optional[Path], - options: RapidOcrOptions, - accelerator_options, - ): - rapidocr_available = self._rapidocr_available - if rapidocr_available is None: - rapidocr_available = bool( - importlib.util.find_spec("rapidocr") is not None or "rapidocr" in sys.modules - ) - SafeRapidOcrModel._rapidocr_available = rapidocr_available - - effective_enabled = bool(enabled and rapidocr_available) - if enabled and not rapidocr_available: - _log.warning( - "RapidOCR python package not found; continuing with Docling pipeline OCR disabled." - ) - - if effective_enabled: - try: - resolved = resolve_packaged_onnx_and_keys() - - _log.warning( - 'SafeRapidOcrModel initial options: det=%s rec=%s cls=%s keys=%s', - getattr(options, 'det_model_path', None), - getattr(options, 'rec_model_path', None), - getattr(options, 'cls_model_path', None), - getattr(options, 'rec_keys_path', None), - ) - - if resolved.det: - options.det_model_path = resolved.det - if resolved.rec: - options.rec_model_path = resolved.rec - if resolved.cls: - options.cls_model_path = resolved.cls - if resolved.keys: - options.rec_keys_path = resolved.keys - - try: - from rapidocr.ch_ppocr_rec import main as _rapidocr_rec_main - - if not getattr(_rapidocr_rec_main.TextRecognizer, '_glossapi_patch', False): - original_get_character_dict = _rapidocr_rec_main.TextRecognizer.get_character_dict - - def _patched_get_character_dict(self, cfg): - try: - current_keys = cfg.get('keys_path', None) - current_rec_keys = cfg.get('rec_keys_path', None) - if current_rec_keys is None and current_keys is not None: - cfg['rec_keys_path'] = current_keys - _log.warning('Patched RapidOCR cfg: set rec_keys_path from keys_path=%s', current_keys) - else: - _log.warning('Patched RapidOCR cfg: existing rec_keys_path=%s keys_path=%s', current_rec_keys, current_keys) - except Exception: - _log.warning('RapidOCR cfg inspection failed', exc_info=True) - return original_get_character_dict(self, cfg) - - _rapidocr_rec_main.TextRecognizer.get_character_dict = _patched_get_character_dict - _rapidocr_rec_main.TextRecognizer._glossapi_patch = True - except Exception: - _log.warning('Failed to patch RapidOCR TextRecognizer for keys fallback', exc_info=True) - - _log.warning( - 'SafeRapidOcrModel using packaged assets: det=%s rec=%s cls=%s keys=%s', - options.det_model_path, - options.rec_model_path, - options.cls_model_path, - options.rec_keys_path, - ) - except Exception: - _log.warning( - 'SafeRapidOcrModel bootstrap failed to resolve packaged assets', - exc_info=True, - ) - - super().__init__( - enabled=effective_enabled, - artifacts_path=artifacts_path, - options=options, - accelerator_options=accelerator_options, - ) - - @classmethod - def get_options_type(cls) -> Type[OcrOptions]: - return RapidOcrOptions - - def _normalise_result(self, result): - """Return an iterable of (bbox, text, score) triples. - - RapidOCR returns ``None`` or semi-populated structures in some corner - cases. We swallow those and log a one-line warning so the page still - progresses through the pipeline. - """ - - if result is None: - _log.warning("RapidOCR returned None; skipping crop") - return [] - boxes = getattr(result, "boxes", None) - txts = getattr(result, "txts", None) - scores = getattr(result, "scores", None) - if boxes is None or txts is None or scores is None: - _log.warning("RapidOCR returned incomplete data; treating crop as empty") - return [] - try: - return list(zip(boxes.tolist(), txts, scores)) - except Exception as exc: # pragma: no cover - defensive only - _log.warning("RapidOCR result normalisation failed: %s", exc) - return [] - - def __call__( - self, conv_res: ConversionResult, page_batch: Iterable[Page] - ) -> Iterable[Page]: - if not self.enabled: - yield from page_batch - return - - for page in page_batch: - assert page._backend is not None - if not page._backend.is_valid(): - yield page - continue - - with TimeRecorder(conv_res, "ocr"): - ocr_rects = self.get_ocr_rects(page) - - all_ocr_cells = [] - for ocr_rect in ocr_rects: - if ocr_rect.area() == 0: - continue - high_res_image = page._backend.get_page_image( - scale=self.scale, cropbox=ocr_rect - ) - im = numpy.array(high_res_image) - raw_result = self.reader( - im, - use_det=self.options.use_det, - use_cls=self.options.use_cls, - use_rec=self.options.use_rec, - ) - result = self._normalise_result(raw_result) - del high_res_image - del im - - if not result: - continue - - cells = [ - TextCell( - index=ix, - text=line[1], - orig=line[1], - confidence=line[2], - from_ocr=True, - rect=BoundingRectangle.from_bounding_box( - BoundingBox.from_tuple( - coord=( - (line[0][0][0] / self.scale) + ocr_rect.l, - (line[0][0][1] / self.scale) + ocr_rect.t, - (line[0][2][0] / self.scale) + ocr_rect.l, - (line[0][2][1] / self.scale) + ocr_rect.t, - ), - origin=CoordOrigin.TOPLEFT, - ) - ), - ) - for ix, line in enumerate(result) - ] - all_ocr_cells.extend(cells) - - self.post_process_cells(all_ocr_cells, page) - - from docling.datamodel.settings import settings - - if settings.debug.visualize_ocr: - self.draw_ocr_rects_and_cells(conv_res, page, ocr_rects) - - yield page - - -def patch_docling_rapidocr() -> bool: - """Replace Docling's RapidOcrModel with the safe shim if available.""" - - try: - import docling.models.rapid_ocr_model as rapid_module - except Exception: # pragma: no cover - Docling missing - return False - - current = getattr(rapid_module, "RapidOcrModel", None) - if current is SafeRapidOcrModel: - return False - - rapid_module.RapidOcrModel = SafeRapidOcrModel - try: - from docling.models.factories import get_ocr_factory # type: ignore - import logging - except Exception: - return True - - try: - factory = get_ocr_factory() - options_type = SafeRapidOcrModel.get_options_type() - - if hasattr(factory, "classes"): - factory.classes[options_type] = SafeRapidOcrModel - elif hasattr(factory, "_classes"): - factory._classes[options_type] = SafeRapidOcrModel - logging.getLogger(__name__).info( - "Registered SafeRapidOcrModel for %s", options_type - ) - try: - from docling.pipeline import standard_pdf_pipeline as _std_pdf # type: ignore - from docling.datamodel.pipeline_options import RapidOcrOptions # type: ignore - from functools import lru_cache - except Exception as _exc: # pragma: no cover - best effort - logging.getLogger(__name__).warning( - "Docling factory patch limited to local mutation: %s", _exc - ) - else: - original_get_factory = getattr( - _std_pdf.get_ocr_factory, "__wrapped__", _std_pdf.get_ocr_factory - ) - - def _ensure_safe(factory_obj): - try: - current = factory_obj.classes.get(RapidOcrOptions) - if current is not SafeRapidOcrModel: - factory_obj.classes[RapidOcrOptions] = SafeRapidOcrModel - except AttributeError: - current = getattr(factory_obj, "_classes", {}).get(RapidOcrOptions) - if current is not SafeRapidOcrModel: - getattr(factory_obj, "_classes", {})[RapidOcrOptions] = SafeRapidOcrModel - return factory_obj - - @lru_cache(maxsize=None) - def _patched_get_ocr_factory(allow_external_plugins: bool = False): - return _ensure_safe(original_get_factory(allow_external_plugins)) - - _patched_get_ocr_factory.__wrapped__ = original_get_factory # type: ignore[attr-defined] - _std_pdf.get_ocr_factory = _patched_get_ocr_factory # type: ignore[attr-defined] - try: - _ensure_safe(_std_pdf.get_ocr_factory(False)) - except Exception: - pass - except Exception as exc: # pragma: no cover - best effort - import logging - - logging.getLogger(__name__).warning( - "Failed to re-register SafeRapidOcrModel: %s", exc - ) - return True - - -__all__ = ["SafeRapidOcrModel", "patch_docling_rapidocr"] diff --git a/src/glossapi/ocr/utils/cleaning.py b/src/glossapi/ocr/utils/cleaning.py index 9b4e287..c194c72 100644 --- a/src/glossapi/ocr/utils/cleaning.py +++ b/src/glossapi/ocr/utils/cleaning.py @@ -260,11 +260,207 @@ def _detect_repeated_lines_cut(text: str, *, threshold: int = 10) -> Optional[in return None +def _is_private_use_char(ch: str) -> bool: + codepoint = ord(ch) + return ( + 0xE000 <= codepoint <= 0xF8FF + or 0xF0000 <= codepoint <= 0xFFFFD + or 0x100000 <= codepoint <= 0x10FFFD + ) + + +def _is_symbol_garbage_char(ch: str) -> bool: + if _is_private_use_char(ch): + return True + return ch in { + "•", + "", + "·", + "◦", + "▪", + "▫", + "‣", + "∙", + "⋅", + "●", + "○", + "◉", + "◌", + "◆", + "◇", + "■", + "□", + "▲", + "△", + "▼", + "▽", + "►", + "◄", + "◊", + "", + "", + "", + "", + "", + "", + } + + +def _detect_symbol_garbage_cut(text: str, *, threshold: int = 16) -> Optional[int]: + """Cut on long runs of isolated bullet/dingbat/private-use symbols. + + This targets the common DeepSeek garbage mode where the model emits long + whitespace-separated runs of bullets or private-use glyphs instead of text. + """ + if threshold <= 1: + return 0 + run_count = 0 + run_start: Optional[int] = None + last_non_ws = -10_000 + for index, ch in enumerate(text): + if ch.isspace(): + continue + if _is_symbol_garbage_char(ch): + if run_count == 0 or (index - last_non_ws) > 3: + run_start = index + run_count = 1 + else: + run_count += 1 + last_non_ws = index + if run_count >= threshold: + return run_start + continue + run_count = 0 + run_start = None + last_non_ws = index + return None + + +NUMERIC_LIST_TOKEN_PATTERN = re.compile(r"(? Optional[int]: + """Cut on degenerate `1. 2. 3. ...` style list output.""" + if threshold <= 1: + return 0 + matches = list(NUMERIC_LIST_TOKEN_PATTERN.finditer(text)) + if len(matches) < threshold: + return None + run_start = matches[0].start() + run_count = 1 + prev_value = int(matches[0].group(1)) + prev_end = matches[0].end() + for match in matches[1:]: + current_value = int(match.group(1)) + gap = text[prev_end : match.start()] + if current_value == prev_value + 1 and len(gap) <= 4 and gap.strip() == "": + run_count += 1 + else: + run_start = match.start() + run_count = 1 + if run_count >= threshold: + return run_start + prev_value = current_value + prev_end = match.end() + return None + + +class StreamingGarbageDetector: + """Incremental detector for common OCR garbage generation modes. + + This is designed for hot decode loops: feed only newly decoded text chunks + and keep O(1) mutable state instead of rescanning the whole suffix. + """ + + def __init__( + self, + *, + symbol_threshold: int = 16, + numeric_list_threshold: int = 12, + ) -> None: + self.symbol_threshold = int(symbol_threshold) + self.numeric_list_threshold = int(numeric_list_threshold) + self._symbol_run = 0 + self._numeric_run = 0 + self._expected_next_number: Optional[int] = None + self._digits_buffer: str = "" + self.triggered_reason: Optional[str] = None + + def reset(self) -> None: + self._symbol_run = 0 + self._numeric_run = 0 + self._expected_next_number = None + self._digits_buffer = "" + self.triggered_reason = None + + def _reset_numeric(self) -> None: + self._numeric_run = 0 + self._expected_next_number = None + self._digits_buffer = "" + + def _feed_symbol_char(self, ch: str) -> bool: + if ch.isspace(): + return False + if _is_symbol_garbage_char(ch): + self._symbol_run += 1 + if self._symbol_run >= self.symbol_threshold: + self.triggered_reason = "symbol_garbage" + return True + return False + self._symbol_run = 0 + return False + + def _feed_numeric_char(self, ch: str) -> bool: + if ch.isspace(): + if self._digits_buffer: + self._reset_numeric() + return False + if "0" <= ch <= "9": + self._digits_buffer += ch + return False + if ch in {".", ")"} and self._digits_buffer: + value = int(self._digits_buffer) + self._digits_buffer = "" + if self._expected_next_number is None: + if value == 1: + self._numeric_run = 1 + self._expected_next_number = 2 + else: + self._reset_numeric() + else: + if value == self._expected_next_number: + self._numeric_run += 1 + self._expected_next_number += 1 + elif value == 1: + self._numeric_run = 1 + self._expected_next_number = 2 + else: + self._reset_numeric() + if self._numeric_run >= self.numeric_list_threshold: + self.triggered_reason = "numeric_list_garbage" + return True + return False + self._reset_numeric() + return False + + def feed(self, text: str) -> bool: + if self.triggered_reason is not None: + return True + for ch in str(text or ""): + if self._feed_symbol_char(ch): + return True + if self._feed_numeric_char(ch): + return True + return False + + def detect_early_stop_index( text: str, *, line_repeat_threshold: int = 10, char_repeat_threshold: int = 200, + symbol_garbage_threshold: int = 16, + numeric_list_threshold: int = 12, ) -> Optional[int]: """Find earliest cut index based on repetition heuristics. @@ -273,11 +469,12 @@ def detect_early_stop_index( """ idx_char = _detect_repeated_char_cut(text, threshold=char_repeat_threshold) idx_line = _detect_repeated_lines_cut(text, threshold=line_repeat_threshold) - if idx_char is None: - return idx_line - if idx_line is None: - return idx_char - return min(idx_char, idx_line) + idx_symbol = _detect_symbol_garbage_cut(text, threshold=symbol_garbage_threshold) + idx_numeric = _detect_numeric_list_garbage_cut(text, threshold=numeric_list_threshold) + candidates = [idx for idx in (idx_char, idx_line, idx_symbol, idx_numeric) if idx is not None] + if not candidates: + return None + return min(candidates) def apply_early_stop( @@ -286,6 +483,8 @@ def apply_early_stop( content_debug: bool = False, line_repeat_threshold: int = 10, char_repeat_threshold: int = 200, + symbol_garbage_threshold: int = 16, + numeric_list_threshold: int = 12, metrics: Optional[dict] = None, ) -> str: """Apply early termination heuristics to ``text`` and optionally append notice. @@ -299,6 +498,8 @@ def apply_early_stop( text, line_repeat_threshold=line_repeat_threshold, char_repeat_threshold=char_repeat_threshold, + symbol_garbage_threshold=symbol_garbage_threshold, + numeric_list_threshold=numeric_list_threshold, ) if cut is None: return text diff --git a/src/glossapi/scripts/build_ocr_golden_pages.py b/src/glossapi/scripts/build_ocr_golden_pages.py new file mode 100644 index 0000000..f6bb5b9 --- /dev/null +++ b/src/glossapi/scripts/build_ocr_golden_pages.py @@ -0,0 +1,223 @@ +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Dict, Iterable, List, Sequence, Tuple + +PAGE_SPLIT_MARKER = "<--- Page Split --->" + + +def _read_jsonl(path: Path) -> List[Dict[str, object]]: + return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + + +def _stable_sort_rows(rows: Sequence[Dict[str, object]], seed: str) -> List[Dict[str, object]]: + def _key(row: Dict[str, object]) -> str: + basis = f"{seed}|{row['source_stem']}|{row['page_number']}" + return hashlib.sha1(basis.encode("utf-8")).hexdigest() + + return sorted(rows, key=_key) + + +def _take_rows( + rows: Sequence[Dict[str, object]], + selected_keys: set[Tuple[str, int]], + *, + limit: int, + seed: str, +) -> List[Dict[str, object]]: + out: List[Dict[str, object]] = [] + for row in _stable_sort_rows(rows, seed): + key = (str(row["source_stem"]), int(row["page_number"])) + if key in selected_keys: + continue + out.append(row) + selected_keys.add(key) + if len(out) >= limit: + break + return out + + +def _split_pages(path: Path) -> List[str]: + return path.read_text(encoding="utf-8", errors="ignore").split(PAGE_SPLIT_MARKER) + + +def build_ocr_goldens( + *, + run_dir: Path, + source_dir: Path, + output_dir: Path, + seed: str = "ocr-golden-v1", +) -> Dict[str, object]: + page_metrics = _read_jsonl(run_dir / "page_metrics.jsonl") + manifest_rows = _read_jsonl(run_dir / "manifest.jsonl") + source_by_stem = {Path(str(row["source_path"])).stem: Path(str(row["source_path"])) for row in manifest_rows} + output_by_stem = {Path(str(row["output_path"])).stem: Path(str(row["output_path"])) for row in manifest_rows} + + for target in (output_dir / "inputs", output_dir / "expected"): + target.mkdir(parents=True, exist_ok=True) + for stale in target.iterdir(): + if stale.is_file(): + stale.unlink() + for stale_name in ("manifest.jsonl", "summary.json"): + stale = output_dir / stale_name + if stale.exists(): + stale.unlink() + + source_pages_cache: Dict[str, List[str]] = {} + output_pages_cache: Dict[str, List[str]] = {} + + rows_with_features: List[Dict[str, object]] = [] + for row in page_metrics: + stem = str(row["source_stem"]) + source_path = source_by_stem.get(stem) + output_path = output_by_stem.get(stem) + if source_path is None or output_path is None: + continue + if stem not in source_pages_cache: + source_pages_cache[stem] = _split_pages(source_path) + output_pages_cache[stem] = _split_pages(output_path) + page_idx = int(row["page_number"]) - 1 + source_page = source_pages_cache[stem][page_idx] + output_page = output_pages_cache[stem][page_idx] + feature_row = dict(row) + feature_row["has_table_html"] = " 0 + ] + feature_row["positive_categories"] = positive_categories + rows_with_features.append(feature_row) + + selected_keys: set[Tuple[str, int]] = set() + selected_rows: List[Tuple[str, Dict[str, object]]] = [] + + def add_bucket(label: str, candidates: Iterable[Dict[str, object]], limit: int) -> None: + bucket = _take_rows(list(candidates), selected_keys, limit=limit, seed=f"{seed}:{label}") + for item in bucket: + selected_rows.append((label, item)) + + add_bucket( + "hybrid_positive", + [row for row in rows_with_features if int(row.get("hybrid_match_count", 0)) > 0], + 9999, + ) + add_bucket( + "latex_positive", + [row for row in rows_with_features if int(row.get("latex_match_count", 0)) > 0], + 9999, + ) + add_bucket( + "mixed_positive", + [row for row in rows_with_features if len(list(row.get("positive_categories", []))) >= 2], + 120, + ) + add_bucket( + "numeric_positive", + [row for row in rows_with_features if int(row.get("numeric_match_count", 0)) > 0], + 140, + ) + add_bucket( + "word_positive", + [row for row in rows_with_features if int(row.get("word_match_count", 0)) > 0], + 140, + ) + add_bucket( + "table_positive", + [row for row in rows_with_features if int(row.get("table_match_count", 0)) > 0], + 180, + ) + add_bucket( + "table_kept_conversion", + [ + row + for row in rows_with_features + if row.get("has_table_html") + and all(int(row.get(f"{category}_match_count", 0)) == 0 for category in ("table", "numeric", "latex", "hybrid", "word")) + ], + 60, + ) + add_bucket( + "negative_plain", + [ + row + for row in rows_with_features + if not row.get("has_table_html") + and all(int(row.get(f"{category}_match_count", 0)) == 0 for category in ("table", "numeric", "latex", "hybrid", "word")) + ], + 60, + ) + + manifest_out = output_dir / "manifest.jsonl" + summary_out = output_dir / "summary.json" + written_rows: List[Dict[str, object]] = [] + category_counts: Dict[str, int] = {} + + for idx, (label, row) in enumerate(selected_rows, start=1): + stem = str(row["source_stem"]) + page_number = int(row["page_number"]) + base_name = f"{idx:04d}__{stem}__page_{page_number:05d}" + input_path = output_dir / "inputs" / f"{base_name}.md" + expected_path = output_dir / "expected" / f"{base_name}.md" + input_path.write_text(str(row["source_page"]), encoding="utf-8") + expected_path.write_text(str(row["expected_page"]), encoding="utf-8") + + category_counts[label] = category_counts.get(label, 0) + 1 + written_rows.append( + { + "case_id": base_name, + "label": label, + "source_stem": stem, + "page_number": page_number, + "input_path": str(input_path), + "expected_path": str(expected_path), + "source_path": str(source_by_stem[stem]), + "output_path": str(output_by_stem[stem]), + "match_counts": { + category: int(row.get(f"{category}_match_count", 0)) + for category in ("table", "numeric", "latex", "hybrid", "word") + }, + "has_table_html": bool(row.get("has_table_html")), + } + ) + + with manifest_out.open("w", encoding="utf-8") as handle: + for row in written_rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + summary = { + "run_dir": str(run_dir), + "source_dir": str(source_dir), + "output_dir": str(output_dir), + "case_count": len(written_rows), + "category_counts": category_counts, + } + summary_out.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + return summary + + +def main() -> None: + parser = argparse.ArgumentParser(description="Build OCR golden page fixtures from a combined debug run.") + parser.add_argument("--run-dir", required=True, type=Path) + parser.add_argument("--source-dir", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--seed", default="ocr-golden-v1") + args = parser.parse_args() + + summary = build_ocr_goldens( + run_dir=args.run_dir, + source_dir=args.source_dir, + output_dir=args.output_dir, + seed=args.seed, + ) + print(json.dumps(summary, ensure_ascii=False, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/src/glossapi/scripts/deepseek_pipeline_benchmark.py b/src/glossapi/scripts/deepseek_pipeline_benchmark.py new file mode 100644 index 0000000..4ffb064 --- /dev/null +++ b/src/glossapi/scripts/deepseek_pipeline_benchmark.py @@ -0,0 +1,401 @@ +from __future__ import annotations + +import argparse +import json +import random +import shutil +import subprocess +import time +from pathlib import Path +from typing import Any, Dict, List, Optional + +from glossapi.ocr.deepseek.scheduling import ( + SourceDocument, + assign_batches_to_lanes, + build_exact_fill_batches, + build_fixed_shard_slices, + build_whole_document_slices, + pack_slices_into_batches, +) + + +def _parse_devices(spec: str) -> List[int]: + tokens = [piece.strip() for piece in str(spec or "").split(",") if piece.strip()] + if not tokens: + raise argparse.ArgumentTypeError("--devices must contain at least one GPU id") + try: + return [int(token) for token in tokens] + except ValueError as exc: + raise argparse.ArgumentTypeError(f"Invalid GPU list: {spec}") from exc + + +def _parse_args() -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.deepseek_pipeline_benchmark", + description="Benchmark DeepSeek OCR pipeline throughput for different scheduling strategies.", + ) + p.add_argument("--repo", required=True) + p.add_argument("--input-dir", required=True) + p.add_argument("--output-dir", required=True) + p.add_argument("--python-bin", required=True) + p.add_argument("--model-dir", required=True) + p.add_argument("--label", required=True) + p.add_argument("--mode", default="static", choices=["static", "streaming"]) + p.add_argument( + "--scheduler", + default="whole_doc", + choices=["whole_doc", "fixed_shard", "exact_fill"], + ) + p.add_argument("--devices", default="0,1,2,3,4,5,6,7") + p.add_argument("--workers-per-gpu", type=int, default=1) + p.add_argument("--max-docs", type=int, default=None) + p.add_argument("--doc-order", default="name", choices=["name", "random", "largest_first"]) + p.add_argument("--seed", type=int, default=20260330) + p.add_argument("--target-batch-pages", type=int, default=160) + p.add_argument("--stream-batch-pages", type=int, default=160) + p.add_argument("--shard-pages", type=int, default=0) + p.add_argument("--shard-threshold-pages", type=int, default=0) + p.add_argument("--runtime-backend", default="vllm", choices=["transformers", "vllm"]) + p.add_argument("--ocr-profile", default="markdown_grounded", choices=["markdown_grounded", "plain_ocr"]) + p.add_argument("--prompt-override", default=None) + p.add_argument("--repair-mode", default="auto", choices=["auto", "off"]) + p.add_argument("--attn-backend", default="auto") + p.add_argument("--base-size", type=int, default=None) + p.add_argument("--image-size", type=int, default=None) + p.add_argument("--render-dpi", type=int, default=144) + p.add_argument("--max-new-tokens", type=int, default=2048) + p.add_argument("--vllm-batch-size", type=int, default=None) + p.add_argument("--gpu-memory-utilization", type=float, default=0.9) + p.add_argument("--disable-fp8-kv", action="store_true") + p.add_argument("--clean", action="store_true") + return p.parse_args() + + +def _weighted_documents( + *, + input_dir: Path, + max_docs: Optional[int], + doc_order: str, + seed: int, +) -> List[SourceDocument]: + from glossapi.ocr.deepseek import runner as deepseek_runner + + documents = [ + SourceDocument(name=path.name, pages=int(deepseek_runner._effective_page_count(path, None))) + for path in sorted(input_dir.glob("*.pdf")) + ] + if doc_order == "largest_first": + documents.sort(key=lambda item: (-int(item.pages), str(item.name))) + elif doc_order == "random": + rng = random.Random(int(seed)) + rng.shuffle(documents) + if max_docs is not None: + documents = documents[: max(0, int(max_docs))] + return documents + + +def _plan_lanes( + *, + documents: List[SourceDocument], + devices: List[int], + workers_per_gpu: int, + scheduler: str, + target_batch_pages: int, + shard_pages: int, + shard_threshold_pages: int, +) -> List[Dict[str, Any]]: + scheduler_norm = str(scheduler or "whole_doc").strip().lower() + if scheduler_norm == "exact_fill": + batches = build_exact_fill_batches(documents, target_batch_pages=max(1, int(target_batch_pages))) + else: + if scheduler_norm == "fixed_shard": + slices = build_fixed_shard_slices( + documents, + shard_pages=max(1, int(shard_pages)), + shard_threshold_pages=max(0, int(shard_threshold_pages)), + ) + else: + slices = build_whole_document_slices(documents) + batches = pack_slices_into_batches(slices, target_batch_pages=max(1, int(target_batch_pages))) + lanes = assign_batches_to_lanes( + batches, + devices=devices, + workers_per_gpu=max(1, int(workers_per_gpu)), + ) + return [lane.to_dict() for lane in lanes if lane.batches] + + +def _collect_repair_metrics(run_dir: Path) -> Dict[str, int]: + metrics_dir = run_dir / "json" / "metrics" + totals = { + "docs_with_metrics": 0, + "pages_flagged": 0, + "pages_repaired": 0, + "plain_repairs": 0, + "tiled_repairs": 0, + } + if not metrics_dir.exists(): + return totals + for path in metrics_dir.glob("*.metrics.json"): + try: + data = json.loads(path.read_text(encoding="utf-8")) + except Exception: + continue + totals["docs_with_metrics"] += 1 + summary = data.get("repair_summary") or {} + totals["pages_flagged"] += int(summary.get("pages_flagged", 0)) + totals["pages_repaired"] += int(summary.get("pages_repaired", 0)) + totals["plain_repairs"] += int(summary.get("plain_repairs", 0)) + totals["tiled_repairs"] += int(summary.get("tiled_repairs", 0)) + return totals + + +def _collect_runtime_summary(run_dir: Path) -> Dict[str, Any]: + summary_path = run_dir / "sidecars" / "ocr_runtime" / "runtime_summary.json" + if not summary_path.exists(): + return {} + try: + return json.loads(summary_path.read_text(encoding="utf-8")) + except Exception: + return {} + + +def _flatten_lane_batches(lane: Dict[str, Any]) -> Dict[str, Any]: + files: List[str] = [] + page_ranges: List[str] = [] + pages = 0 + planned_batch_pages: List[int] = [] + for batch in list(lane.get("batches") or []): + batch_pages = int(batch.get("pages", 0)) + pages += batch_pages + planned_batch_pages.append(batch_pages) + files.extend(list(batch.get("files") or [])) + page_ranges.extend(list(batch.get("page_ranges") or [])) + return { + "files": files, + "page_ranges": page_ranges, + "pages": int(pages), + "planned_batch_count": len(planned_batch_pages), + "planned_batch_pages": planned_batch_pages, + } + + +def main() -> int: + args = _parse_args() + repo = Path(args.repo).resolve() + input_dir = Path(args.input_dir).resolve() + output_root = Path(args.output_dir).resolve() + python_bin = Path(args.python_bin).expanduser() + model_dir = Path(args.model_dir).resolve() + devices = _parse_devices(args.devices) + + from glossapi.ocr.deepseek import runner as deepseek_runner + + documents = _weighted_documents( + input_dir=input_dir, + max_docs=args.max_docs, + doc_order=args.doc_order, + seed=int(args.seed), + ) + if not documents: + raise SystemExit("No PDFs found for benchmark input set.") + lanes = _plan_lanes( + documents=documents, + devices=devices, + workers_per_gpu=max(1, int(args.workers_per_gpu)), + scheduler=str(args.scheduler), + target_batch_pages=int(args.target_batch_pages), + shard_pages=int(args.shard_pages), + shard_threshold_pages=int(args.shard_threshold_pages), + ) + + run_dir = output_root / args.label + if args.clean and run_dir.exists(): + shutil.rmtree(run_dir) + run_dir.mkdir(parents=True, exist_ok=True) + logs_dir = run_dir / "logs" + logs_dir.mkdir(parents=True, exist_ok=True) + (run_dir / "lane_plan.json").write_text(json.dumps(lanes, indent=2), encoding="utf-8") + + script_path = ( + deepseek_runner.DEFAULT_VLLM_SCRIPT + if str(args.runtime_backend) == "vllm" + else deepseek_runner.DEFAULT_SCRIPT + ) + py_env = {"PYTHONPATH": str(repo / "src")} + + def start_lane(lane: Dict[str, Any]) -> Dict[str, Any]: + lane_id = int(lane["lane_id"]) + visible_device = int(lane["visible_device"]) + lane_plan = _flatten_lane_batches(lane) + files = list(lane_plan["files"]) + page_ranges = list(lane_plan["page_ranges"]) + pages = int(lane_plan["pages"]) + resolved_vllm_batch_size = ( + int(args.vllm_batch_size) + if args.vllm_batch_size is not None + else min(max(1, int(args.target_batch_pages)), max(1, pages)) + ) + log_path = logs_dir / f"lane_{lane_id:02d}_gpu{visible_device}.log" + fh = log_path.open("w", encoding="utf-8") + cmd = deepseek_runner._build_cli_command( + input_dir=input_dir, + output_dir=run_dir, + files=files, + page_ranges=page_ranges, + model_dir=model_dir, + python_bin=python_bin, + script=script_path, + max_pages=None, + content_debug=False, + device="cuda", + ocr_profile=str(args.ocr_profile), + prompt_override=args.prompt_override, + attn_backend=str(args.attn_backend), + base_size=args.base_size, + image_size=args.image_size, + crop_mode=None, + render_dpi=int(args.render_dpi), + max_new_tokens=args.max_new_tokens, + repetition_penalty=None, + no_repeat_ngram_size=None, + runtime_backend=str(args.runtime_backend), + vllm_batch_size=resolved_vllm_batch_size, + gpu_memory_utilization=float(args.gpu_memory_utilization), + disable_fp8_kv=bool(args.disable_fp8_kv), + repair_mode=str(args.repair_mode), + ) + env = deepseek_runner._build_env(python_bin=python_bin, visible_device=visible_device) + env["PYTHONPATH"] = f"{py_env['PYTHONPATH']}:{env['PYTHONPATH']}" if env.get("PYTHONPATH") else py_env["PYTHONPATH"] + proc = subprocess.Popen(cmd, stdout=fh, stderr=subprocess.STDOUT, env=env) # nosec: controlled args + return { + "lane_id": lane_id, + "visible_device": visible_device, + "batch_id": 0, + "pages": pages, + "files": files, + "page_ranges": page_ranges, + "planned_batch_count": int(lane_plan["planned_batch_count"]), + "planned_batch_pages": list(lane_plan["planned_batch_pages"]), + "resolved_vllm_batch_size": resolved_vllm_batch_size, + "log_path": str(log_path), + "fh": fh, + "proc": proc, + "start_ts": time.perf_counter(), + "cmd": cmd, + } + + global_start = time.perf_counter() + active: List[Dict[str, Any]] = [start_lane(lane) for lane in lanes] + + batch_results: List[Dict[str, Any]] = [] + while active: + time.sleep(0.2) + for item in list(active): + rc = item["proc"].poll() + if rc is None: + continue + end_ts = time.perf_counter() + item["fh"].close() + elapsed = max(0.000001, float(end_ts - item["start_ts"])) + batch_results.append( + { + "lane_id": int(item["lane_id"]), + "visible_device": int(item["visible_device"]), + "batch_id": int(item["batch_id"]), + "pages": int(item["pages"]), + "files": list(item["files"]), + "page_ranges": list(item.get("page_ranges") or []), + "planned_batch_count": int(item.get("planned_batch_count", 1)), + "planned_batch_pages": list(item.get("planned_batch_pages") or []), + "return_code": int(rc), + "resolved_vllm_batch_size": int(item["resolved_vllm_batch_size"]), + "start_offset_sec": float(item["start_ts"] - global_start), + "end_offset_sec": float(end_ts - global_start), + "elapsed_sec": float(elapsed), + "sec_per_page": float(elapsed / max(1, int(item["pages"]))), + "log_path": str(item["log_path"]), + "cmd": item["cmd"], + } + ) + active.remove(item) + + total_elapsed = max(0.000001, time.perf_counter() - global_start) + total_pages = sum(int(doc.pages) for doc in documents) + failures = [item for item in batch_results if int(item["return_code"]) != 0] + + lane_results: List[Dict[str, Any]] = [] + for lane in lanes: + lane_batches = [item for item in batch_results if int(item["lane_id"]) == int(lane["lane_id"])] + if not lane_batches: + continue + lane_start = min(float(item["start_offset_sec"]) for item in lane_batches) + lane_end = max(float(item["end_offset_sec"]) for item in lane_batches) + lane_elapsed = max(0.000001, lane_end - lane_start) + lane_pages = sum(int(item["pages"]) for item in lane_batches) + lane_results.append( + { + "lane_id": int(lane["lane_id"]), + "visible_device": int(lane["visible_device"]), + "batch_count": len(lane_batches), + "pages": int(lane_pages), + "active_elapsed_sec": float(lane_elapsed), + "sec_per_page": float(lane_elapsed / max(1, lane_pages)), + "all_return_codes_zero": all(int(item["return_code"]) == 0 for item in lane_batches), + } + ) + + gpu_results: List[Dict[str, Any]] = [] + for visible_device in sorted({int(item["visible_device"]) for item in batch_results}): + gpu_batches = [item for item in batch_results if int(item["visible_device"]) == visible_device] + gpu_start = min(float(item["start_offset_sec"]) for item in gpu_batches) + gpu_end = max(float(item["end_offset_sec"]) for item in gpu_batches) + gpu_elapsed = max(0.000001, gpu_end - gpu_start) + gpu_pages = sum(int(item["pages"]) for item in gpu_batches) + gpu_results.append( + { + "visible_device": visible_device, + "batch_count": len(gpu_batches), + "pages": int(gpu_pages), + "active_elapsed_sec": float(gpu_elapsed), + "sec_per_page": float(gpu_elapsed / max(1, gpu_pages)), + "all_return_codes_zero": all(int(item["return_code"]) == 0 for item in gpu_batches), + } + ) + + repair_metrics = _collect_repair_metrics(run_dir) + runtime_summary = _collect_runtime_summary(run_dir) + summary = { + "label": str(args.label), + "status": "pass" if not failures else "fail", + "mode": str(args.mode), + "scheduler": str(args.scheduler), + "runtime_backend": str(args.runtime_backend), + "ocr_profile": str(args.ocr_profile), + "repair_mode": str(args.repair_mode), + "devices": devices, + "workers_per_gpu": int(args.workers_per_gpu), + "doc_order": str(args.doc_order), + "target_batch_pages": int(args.target_batch_pages), + "stream_batch_pages": int(args.stream_batch_pages), + "docs": len(documents), + "pages": int(total_pages), + "shard_pages": int(args.shard_pages), + "shard_threshold_pages": int(args.shard_threshold_pages), + "wall_time_sec": float(total_elapsed), + "sec_per_page": float(total_elapsed / max(1, total_pages)), + "batch_results": batch_results, + "lane_results": lane_results, + "gpu_results": gpu_results, + "repair_metrics": repair_metrics, + "runtime_summary": runtime_summary, + "steady_state": dict(runtime_summary.get("steady_state") or {}), + "failures": failures, + } + (run_dir / "pipeline_benchmark_summary.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") + print(json.dumps(summary, indent=2)) + return 1 if failures else 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/deepseek_runtime_report.py b/src/glossapi/scripts/deepseek_runtime_report.py new file mode 100644 index 0000000..cb93729 --- /dev/null +++ b/src/glossapi/scripts/deepseek_runtime_report.py @@ -0,0 +1,286 @@ +from __future__ import annotations + +import argparse +import json +import os +import platform +import subprocess +import sys +from pathlib import Path +from typing import Any, Dict, Iterable, List, Optional + + +PACKAGE_NAMES = ( + "torch", + "vllm", + "transformers", + "nvidia.cuda_runtime", + "nvidia.cuda_nvrtc", +) + + +def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.deepseek_runtime_report", + description="Print a reproducible DeepSeek OCR runtime report for a GlossAPI checkout.", + ) + p.add_argument("--repo-root", default=".") + p.add_argument("--python-bin", default="") + p.add_argument("--json", action="store_true") + return p.parse_args(argv) + + +def _detect_python_bin(repo_root: Path, explicit: str) -> Path: + if str(explicit).strip(): + path = Path(explicit).expanduser() + if not path.is_absolute(): + path = repo_root / path + return path.absolute() + candidates = ( + repo_root / "dependency_setup" / ".venvs" / "deepseek" / "bin" / "python", + repo_root / "dependency_setup" / "deepseek_uv" / ".venv" / "bin" / "python", + ) + for candidate in candidates: + if candidate.exists(): + return candidate.absolute() + return Path(sys.executable).absolute() + + +def _read_os_release() -> Dict[str, str]: + path = Path("/etc/os-release") + if not path.exists(): + return {} + out: Dict[str, str] = {} + for line in path.read_text(encoding="utf-8").splitlines(): + if "=" not in line: + continue + key, value = line.split("=", 1) + out[key] = value.strip().strip('"') + return out + + +def _run_text(*cmd: str) -> str: + try: + completed = subprocess.run( + list(cmd), + check=False, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + ) + except FileNotFoundError: + return "" + return completed.stdout.strip() + + +def _gpu_rows() -> List[Dict[str, str]]: + text = _run_text( + "nvidia-smi", + "--query-gpu=index,name,driver_version,memory.total", + "--format=csv,noheader,nounits", + ) + rows: List[Dict[str, str]] = [] + for line in text.splitlines(): + parts = [part.strip() for part in line.split(",")] + if len(parts) != 4: + continue + rows.append( + { + "index": parts[0], + "name": parts[1], + "driver_version": parts[2], + "memory_total_mib": parts[3], + } + ) + return rows + + +def _python_json(python_bin: Path, code: str) -> Dict[str, Any]: + completed = subprocess.run( + [str(python_bin), "-c", code], + check=False, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + if completed.returncode != 0: + return { + "ok": False, + "stdout": completed.stdout.strip(), + "stderr": completed.stderr.strip(), + } + try: + return {"ok": True, "data": json.loads(completed.stdout)} + except json.JSONDecodeError: + return { + "ok": False, + "stdout": completed.stdout.strip(), + "stderr": completed.stderr.strip(), + } + + +def _package_report(python_bin: Path) -> Dict[str, Any]: + code = """ +import importlib +import json +import os +import sys + +mods = {} +for name in %s: + try: + mod = importlib.import_module(name) + mods[name] = { + "version": getattr(mod, "__version__", None), + "file": getattr(mod, "__file__", None), + } + except Exception as exc: + mods[name] = {"error": repr(exc)} + +payload = { + "python_version": sys.version, + "executable": sys.executable, + "virtual_env": os.environ.get("VIRTUAL_ENV"), + "ld_library_path": os.environ.get("LD_LIBRARY_PATH"), + "packages": mods, +} +print(json.dumps(payload)) +""" % (repr(PACKAGE_NAMES),) + return _python_json(python_bin, code) + + +def _site_package_nvidia_libs(venv_root: Path) -> List[Path]: + libs: List[Path] = [] + for site_packages in sorted((venv_root / "lib").glob("python*/site-packages")): + for lib_dir in sorted((site_packages / "nvidia").glob("*/lib")): + if lib_dir.is_dir(): + libs.append(lib_dir) + return libs + + +def _interesting_libs(lib_dir: Path) -> List[str]: + names = [] + for child in sorted(lib_dir.iterdir()): + if not child.is_file(): + continue + name = child.name + if any(token in name for token in ("libcudart", "libnvrtc", "libcudnn", "libcuda")): + names.append(name) + return names + + +def _venv_root(python_bin: Path) -> Path: + return python_bin.parent.parent + + +def _pip_freeze_subset(python_bin: Path) -> List[str]: + text = _run_text(str(python_bin), "-m", "pip", "freeze") + prefixes = ( + "torch", + "vllm", + "transformers", + "nvidia-cuda", + "nvidia-cudnn", + "xformers", + "flash-attn", + ) + lines = [] + for line in text.splitlines(): + normalized = line.strip().lower() + if any(normalized.startswith(prefix) for prefix in prefixes): + lines.append(line.strip()) + return lines + + +def _report(repo_root: Path, python_bin: Path) -> Dict[str, Any]: + os_release = _read_os_release() + venv_root = _venv_root(python_bin) + lib_dirs = _site_package_nvidia_libs(venv_root) + return { + "repo_root": str(repo_root), + "repo_head": _run_text("git", "-C", str(repo_root), "rev-parse", "HEAD"), + "hostname": platform.node(), + "os_release": { + "PRETTY_NAME": os_release.get("PRETTY_NAME"), + "VERSION_ID": os_release.get("VERSION_ID"), + }, + "python_bin": str(python_bin), + "venv_root": str(venv_root), + "gpus": _gpu_rows(), + "python_env": _package_report(python_bin), + "nvidia_lib_dirs": [ + { + "path": str(lib_dir), + "interesting_libs": _interesting_libs(lib_dir), + } + for lib_dir in lib_dirs + ], + "pip_freeze_subset": _pip_freeze_subset(python_bin), + "selected_env": { + "CUDA_VISIBLE_DEVICES": os.environ.get("CUDA_VISIBLE_DEVICES"), + "LD_LIBRARY_PATH": os.environ.get("LD_LIBRARY_PATH"), + "VIRTUAL_ENV": os.environ.get("VIRTUAL_ENV"), + }, + } + + +def _print_text(report: Dict[str, Any]) -> None: + print(f"repo_root: {report['repo_root']}") + print(f"repo_head: {report['repo_head']}") + print(f"hostname: {report['hostname']}") + os_release = report["os_release"] + print(f"os: {os_release.get('PRETTY_NAME')} (VERSION_ID={os_release.get('VERSION_ID')})") + print(f"python_bin: {report['python_bin']}") + print(f"venv_root: {report['venv_root']}") + print() + print("gpus:") + for row in report["gpus"]: + print( + f" - index={row['index']} name={row['name']} " + f"driver={row['driver_version']} memory_mib={row['memory_total_mib']}" + ) + print() + print("python_env:") + py_env = report["python_env"] + print(f" ok: {py_env.get('ok')}") + if py_env.get("ok"): + data = py_env["data"] + print(f" executable: {data.get('executable')}") + print(f" python_version: {data.get('python_version')}") + print(f" virtual_env: {data.get('virtual_env')}") + print(f" ld_library_path: {data.get('ld_library_path')}") + for name, package in data.get("packages", {}).items(): + print(f" {name}: {package}") + else: + print(f" stdout: {py_env.get('stdout')}") + print(f" stderr: {py_env.get('stderr')}") + print() + print("nvidia_lib_dirs:") + for item in report["nvidia_lib_dirs"]: + print(f" - path: {item['path']}") + for lib in item["interesting_libs"]: + print(f" {lib}") + print() + print("pip_freeze_subset:") + for line in report["pip_freeze_subset"]: + print(f" - {line}") + print() + print("selected_env:") + for key, value in report["selected_env"].items(): + print(f" {key}={value}") + + +def main(argv: Optional[List[str]] = None) -> int: + args = _parse_args(argv) + repo_root = Path(args.repo_root).expanduser().resolve() + python_bin = _detect_python_bin(repo_root, str(args.python_bin or "")) + report = _report(repo_root, python_bin) + if args.json: + print(json.dumps(report, indent=2, ensure_ascii=False)) + else: + _print_text(report) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/extract_checkpoint_benchmark.py b/src/glossapi/scripts/extract_checkpoint_benchmark.py new file mode 100644 index 0000000..ec5800d --- /dev/null +++ b/src/glossapi/scripts/extract_checkpoint_benchmark.py @@ -0,0 +1,276 @@ +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import re +import shutil +import time +from pathlib import Path +from typing import Any, Dict, List, Optional + +from glossapi import Corpus + + +HEADER_RE = re.compile(r"(?m)^[ \t]{0,3}#{1,6}\s+\S") + +TUNING_ENV_VARS = ( + "GLOSSAPI_DOCLING_MAX_BATCH_FILES", + "GLOSSAPI_DOCLING_BATCH_TARGET_PAGES", + "GLOSSAPI_DOCLING_LAYOUT_BATCH_SIZE", + "GLOSSAPI_DOCLING_TABLE_BATCH_SIZE", + "GLOSSAPI_DOCLING_OCR_BATCH_SIZE", + "GLOSSAPI_DOCLING_PAGE_BATCH_SIZE", +) + +TUNING_ARG_TO_ENV = { + "docling_max_batch_files": "GLOSSAPI_DOCLING_MAX_BATCH_FILES", + "docling_batch_target_pages": "GLOSSAPI_DOCLING_BATCH_TARGET_PAGES", + "docling_layout_batch_size": "GLOSSAPI_DOCLING_LAYOUT_BATCH_SIZE", + "docling_table_batch_size": "GLOSSAPI_DOCLING_TABLE_BATCH_SIZE", + "docling_ocr_batch_size": "GLOSSAPI_DOCLING_OCR_BATCH_SIZE", + "docling_page_batch_size": "GLOSSAPI_DOCLING_PAGE_BATCH_SIZE", +} + + +def _runtime_env_snapshot() -> Dict[str, str]: + return {name: os.getenv(name, "") for name in TUNING_ENV_VARS} + + +def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.extract_checkpoint_benchmark", + description=( + "Run a strict Phase-1 extraction benchmark on a fixed PDF set and audit " + "canonical markdown outputs for presence, byte size, header counts, and drift." + ), + ) + p.add_argument("--input-dir", required=True) + p.add_argument("--output-dir", required=True) + p.add_argument("--report-path", required=True) + p.add_argument("--baseline-report", default="") + p.add_argument("--phase1-backend", default="docling", choices=["auto", "safe", "docling"]) + p.add_argument("--accel-type", default="CUDA") + p.add_argument("--num-threads", type=int, default=1) + p.add_argument("--use-gpus", default="single", choices=["single", "multi"]) + p.add_argument("--devices", nargs="*", type=int, default=None) + p.add_argument("--workers-per-device", type=int, default=1) + p.add_argument("--benchmark-mode", action="store_true") + p.add_argument("--docling-max-batch-files", type=int, default=None) + p.add_argument("--docling-batch-target-pages", type=int, default=None) + p.add_argument("--docling-layout-batch-size", type=int, default=None) + p.add_argument("--docling-table-batch-size", type=int, default=None) + p.add_argument("--docling-ocr-batch-size", type=int, default=None) + p.add_argument("--docling-page-batch-size", type=int, default=None) + p.add_argument("--filenames", nargs="*", default=[]) + p.add_argument("--clean-output-dir", action="store_true") + p.add_argument("--log-level", default="INFO") + return p.parse_args(argv) + + +def _apply_cli_tuning_overrides(args: argparse.Namespace) -> None: + for arg_name, env_name in TUNING_ARG_TO_ENV.items(): + value = getattr(args, arg_name, None) + if value is None: + continue + os.environ[env_name] = str(int(value)) + + +def _count_pdf_pages(pdf_path: Path) -> int: + try: + import fitz + + doc = fitz.open(pdf_path) + try: + return int(doc.page_count) + finally: + doc.close() + except Exception: + pass + + try: + import pypdfium2 as pdfium + + pdf = pdfium.PdfDocument(str(pdf_path)) + try: + return int(len(pdf)) + finally: + try: + pdf.close() + except Exception: + pass + except Exception: + pass + + try: + from pypdf import PdfReader + + return int(len(PdfReader(str(pdf_path)).pages)) + except Exception as exc: + try: + from PyPDF2 import PdfReader # type: ignore + + return int(len(PdfReader(str(pdf_path)).pages)) + except Exception as exc2: + raise RuntimeError(f"Unable to count PDF pages for {pdf_path}: {exc2}") from exc2 + + +def _sha256_bytes(data: bytes) -> str: + return hashlib.sha256(data).hexdigest() + + +def _markdown_headers(text: str) -> int: + return int(len(HEADER_RE.findall(text or ""))) + + +def _inventory_markdown(markdown_dir: Path, *, pdf_paths: List[Path]) -> Dict[str, Dict[str, Any]]: + inventory: Dict[str, Dict[str, Any]] = {} + for pdf_path in pdf_paths: + stem = pdf_path.stem + md_path = markdown_dir / f"{stem}.md" + present = md_path.exists() + payload = md_path.read_bytes() if present else b"" + text = payload.decode("utf-8") if present else "" + inventory[stem] = { + "filename": pdf_path.name, + "markdown_path": str(md_path), + "present": bool(present), + "byte_size": int(len(payload)), + "header_count": _markdown_headers(text), + "sha256": _sha256_bytes(payload) if present else None, + } + return inventory + + +def _compare_inventory( + current_inventory: Dict[str, Dict[str, Any]], + baseline_inventory: Dict[str, Dict[str, Any]], +) -> Dict[str, Any]: + added = [] + missing = [] + byte_size_changed = [] + header_count_changed = [] + sha_changed = [] + for stem, current in sorted(current_inventory.items()): + baseline = baseline_inventory.get(stem) + if baseline is None: + added.append(stem) + continue + if bool(baseline.get("present")) and not bool(current.get("present")): + missing.append(stem) + if int(baseline.get("byte_size", 0)) != int(current.get("byte_size", 0)): + byte_size_changed.append(stem) + if int(baseline.get("header_count", 0)) != int(current.get("header_count", 0)): + header_count_changed.append(stem) + if baseline.get("sha256") != current.get("sha256"): + sha_changed.append(stem) + for stem, baseline in sorted(baseline_inventory.items()): + if stem in current_inventory: + continue + if bool(baseline.get("present")): + missing.append(stem) + return { + "added_markdown": added, + "missing_markdown": sorted(set(missing)), + "byte_size_changed": byte_size_changed, + "header_count_changed": header_count_changed, + "sha_changed": sha_changed, + } + + +def _load_baseline_inventory(path: Path) -> Dict[str, Dict[str, Any]]: + payload = json.loads(path.read_text(encoding="utf-8")) + return dict(payload.get("markdown_inventory") or {}) + + +def main(argv: Optional[List[str]] = None) -> int: + args = _parse_args(argv) + _apply_cli_tuning_overrides(args) + input_dir = Path(args.input_dir).expanduser().resolve() + output_dir = Path(args.output_dir).expanduser().resolve() + report_path = Path(args.report_path).expanduser().resolve() + report_path.parent.mkdir(parents=True, exist_ok=True) + + pdf_paths = sorted(input_dir.glob("*.pdf")) + if args.filenames: + selected = {str(name) for name in args.filenames} + pdf_paths = [path for path in pdf_paths if path.name in selected] + if not pdf_paths: + raise SystemExit(f"No PDF files selected under {input_dir}") + + if bool(args.clean_output_dir) and output_dir.exists(): + shutil.rmtree(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + total_pages = int(sum(_count_pdf_pages(path) for path in pdf_paths)) + start_ts = time.time() + start_perf = time.perf_counter() + + corpus = Corpus(input_dir=input_dir, output_dir=output_dir) + corpus.extract( + input_format="pdf", + accel_type=str(args.accel_type), + num_threads=int(args.num_threads), + phase1_backend=str(args.phase1_backend), + use_gpus=str(args.use_gpus), + devices=list(args.devices) if args.devices else None, + workers_per_device=int(args.workers_per_device), + benchmark_mode=bool(args.benchmark_mode), + filenames=[path.name for path in pdf_paths], + ) + + elapsed_sec = float(time.perf_counter() - start_perf) + end_ts = time.time() + markdown_dir = output_dir / "markdown" + inventory = _inventory_markdown(markdown_dir, pdf_paths=pdf_paths) + markdown_present = int(sum(1 for item in inventory.values() if bool(item["present"]))) + + report: Dict[str, Any] = { + "input_dir": str(input_dir), + "output_dir": str(output_dir), + "started_at": int(start_ts), + "finished_at": int(end_ts), + "elapsed_sec": elapsed_sec, + "files_total": int(len(pdf_paths)), + "pages_total": int(total_pages), + "pages_per_sec": (float(total_pages) / elapsed_sec) if elapsed_sec > 0 else None, + "phase1_backend": str(args.phase1_backend), + "accel_type": str(args.accel_type), + "num_threads": int(args.num_threads), + "use_gpus": str(args.use_gpus), + "devices": list(args.devices) if args.devices else [], + "workers_per_device": int(args.workers_per_device), + "benchmark_mode": bool(args.benchmark_mode), + "runtime_env": _runtime_env_snapshot(), + "markdown_present": markdown_present, + "markdown_missing": int(len(pdf_paths) - markdown_present), + "markdown_inventory": inventory, + } + + baseline_raw = str(args.baseline_report or "").strip() + if baseline_raw: + baseline_path = Path(baseline_raw).expanduser().resolve() + if baseline_path.exists(): + report["comparison"] = _compare_inventory( + inventory, + _load_baseline_inventory(baseline_path), + ) + else: + report["comparison_error"] = f"Baseline report not found: {baseline_path}" + + report_path.write_text(json.dumps(report, indent=2, sort_keys=True), encoding="utf-8") + print(json.dumps({ + "files_total": report["files_total"], + "pages_total": report["pages_total"], + "elapsed_sec": round(report["elapsed_sec"], 3), + "pages_per_sec": round(report["pages_per_sec"], 4) if report["pages_per_sec"] is not None else None, + "markdown_present": report["markdown_present"], + "markdown_missing": report["markdown_missing"], + "report_path": str(report_path), + }, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/full_pipeline_checkpoint.py b/src/glossapi/scripts/full_pipeline_checkpoint.py new file mode 100644 index 0000000..406b8ed --- /dev/null +++ b/src/glossapi/scripts/full_pipeline_checkpoint.py @@ -0,0 +1,263 @@ +from __future__ import annotations + +import argparse +import json +import shutil +import time +from pathlib import Path +from typing import Any, Dict, List, Optional + +import pandas as pd + +from glossapi import Corpus +from glossapi.scripts.extract_checkpoint_benchmark import _apply_cli_tuning_overrides + + +def _parse_int_list(values: Optional[List[int]]) -> List[int]: + return list(values or []) + + +def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.full_pipeline_checkpoint", + description=( + "Run a sample GlossAPI pipeline checkpoint from extract through JSONL export " + "and write a compact timing/continuity report." + ), + ) + p.add_argument("--input-dir", required=True) + p.add_argument("--output-dir", required=True) + p.add_argument("--export-path", required=True) + p.add_argument("--report-path", required=True) + p.add_argument("--clean-output-dir", action="store_true") + p.add_argument("--skip-extract", action="store_true") + p.add_argument("--skip-clean", action="store_true") + p.add_argument("--skip-ocr", action="store_true") + + p.add_argument("--phase1-backend", default="docling", choices=["auto", "safe", "docling"]) + p.add_argument("--accel-type", default="CUDA") + p.add_argument("--num-threads", type=int, default=1) + p.add_argument("--use-gpus", default="single", choices=["single", "multi"]) + p.add_argument("--devices", nargs="*", type=int, default=None) + p.add_argument("--workers-per-device", type=int, default=1) + p.add_argument("--benchmark-mode", action="store_true") + p.add_argument("--filenames", nargs="*", default=[]) + p.add_argument("--drop-bad", action="store_true") + + p.add_argument("--docling-max-batch-files", type=int, default=None) + p.add_argument("--docling-batch-target-pages", type=int, default=None) + p.add_argument("--docling-layout-batch-size", type=int, default=None) + p.add_argument("--docling-table-batch-size", type=int, default=None) + p.add_argument("--docling-ocr-batch-size", type=int, default=None) + p.add_argument("--docling-page-batch-size", type=int, default=None) + + p.add_argument("--ocr-backend", default="deepseek") + p.add_argument("--ocr-runtime-backend", default="vllm") + p.add_argument("--ocr-use-gpus", default="single", choices=["single", "multi"]) + p.add_argument("--ocr-devices", nargs="*", type=int, default=None) + p.add_argument("--ocr-workers-per-gpu", type=int, default=1) + p.add_argument("--ocr-vllm-batch-size", type=int, default=None) + p.add_argument("--ocr-repair-exec-batch-target-pages", type=int, default=None) + p.add_argument("--ocr-repair-exec-batch-target-items", type=int, default=None) + p.add_argument("--ocr-target-batch-pages", type=int, default=160) + p.add_argument("--ocr-render-dpi", type=int, default=None) + p.add_argument("--ocr-scheduler", default="auto") + p.add_argument("--ocr-math-enhance", action="store_true") + + p.add_argument("--text-key", default="text") + p.add_argument("--metadata-key", default="pipeline_metadata") + return p.parse_args(argv) + + +def _read_metadata_counts(parquet_path: Path) -> Dict[str, int]: + if not parquet_path.exists(): + return { + "rows_total": 0, + "needs_ocr_true": 0, + "ocr_success_true": 0, + "text_nonempty": 0, + } + df = pd.read_parquet(parquet_path) + if df.empty: + return { + "rows_total": 0, + "needs_ocr_true": 0, + "ocr_success_true": 0, + "text_nonempty": 0, + } + text_series = df["text"] if "text" in df.columns else pd.Series([], dtype=object) + text_nonempty = int( + sum(bool(str(value).strip()) for value in text_series.fillna("").tolist()) + ) if len(text_series) else 0 + needs_ocr_true = int(df["needs_ocr"].fillna(False).astype(bool).sum()) if "needs_ocr" in df.columns else 0 + ocr_success_true = int(df["ocr_success"].fillna(False).astype(bool).sum()) if "ocr_success" in df.columns else 0 + return { + "rows_total": int(len(df)), + "needs_ocr_true": needs_ocr_true, + "ocr_success_true": ocr_success_true, + "text_nonempty": text_nonempty, + } + + +def _count_jsonl_records(path: Path) -> int: + if not path.exists(): + return 0 + with path.open("r", encoding="utf-8") as fp: + return sum(1 for line in fp if line.strip()) + + +def _export_jsonl_with_retry( + corpus: Corpus, + *, + export_path: Path, + metadata_path: Path, + text_key: str, + metadata_key: str, + post_ocr_counts: Dict[str, int], + max_attempts: int = 4, + retry_delay_sec: float = 1.0, +) -> int: + needs_retry = int(post_ocr_counts.get("text_nonempty", 0) or 0) > 0 + attempts = max_attempts if needs_retry else 1 + + for attempt in range(attempts): + if export_path.exists(): + export_path.unlink() + corpus.jsonl( + export_path, + text_key=text_key, + metadata_key=metadata_key, + include_remaining_metadata=False, + metadata_path=metadata_path, + ) + export_records = _count_jsonl_records(export_path) + if export_records > 0 or attempt == attempts - 1: + return export_records + time.sleep(retry_delay_sec) + return 0 + + +def main(argv: Optional[List[str]] = None) -> int: + args = _parse_args(argv) + _apply_cli_tuning_overrides(args) + + input_dir = Path(args.input_dir).expanduser().resolve() + output_dir = Path(args.output_dir).expanduser().resolve() + export_path = Path(args.export_path).expanduser().resolve() + report_path = Path(args.report_path).expanduser().resolve() + + if bool(args.clean_output_dir) and output_dir.exists(): + shutil.rmtree(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + report_path.parent.mkdir(parents=True, exist_ok=True) + export_path.parent.mkdir(parents=True, exist_ok=True) + + corpus = Corpus(input_dir=input_dir, output_dir=output_dir) + metadata_path = output_dir / "download_results" / "download_results.parquet" + + started_at = time.time() + skipped_phases: List[str] = [] + + if bool(args.skip_extract): + skipped_phases.append("extract") + extract_elapsed = 0.0 + else: + extract_start = time.perf_counter() + corpus.extract( + input_format="pdf", + accel_type=str(args.accel_type), + num_threads=int(args.num_threads), + phase1_backend=str(args.phase1_backend), + use_gpus=str(args.use_gpus), + devices=_parse_int_list(args.devices), + workers_per_device=int(args.workers_per_device), + benchmark_mode=bool(args.benchmark_mode), + filenames=list(args.filenames or []), + ) + extract_elapsed = float(time.perf_counter() - extract_start) + post_extract_counts = _read_metadata_counts(metadata_path) + + if bool(args.skip_clean): + skipped_phases.append("clean") + clean_elapsed = 0.0 + else: + clean_start = time.perf_counter() + corpus.clean(drop_bad=bool(args.drop_bad)) + clean_elapsed = float(time.perf_counter() - clean_start) + post_clean_counts = _read_metadata_counts(metadata_path) + + if bool(args.skip_ocr): + skipped_phases.append("ocr") + ocr_elapsed = 0.0 + else: + ocr_start = time.perf_counter() + corpus.ocr( + backend=str(args.ocr_backend), + runtime_backend=str(args.ocr_runtime_backend), + use_gpus=str(args.ocr_use_gpus), + devices=_parse_int_list(args.ocr_devices), + workers_per_gpu=int(args.ocr_workers_per_gpu), + vllm_batch_size=args.ocr_vllm_batch_size, + repair_exec_batch_target_pages=args.ocr_repair_exec_batch_target_pages, + repair_exec_batch_target_items=args.ocr_repair_exec_batch_target_items, + target_batch_pages=int(args.ocr_target_batch_pages), + render_dpi=args.ocr_render_dpi, + scheduler=str(args.ocr_scheduler), + math_enhance=bool(args.ocr_math_enhance), + ) + ocr_elapsed = float(time.perf_counter() - ocr_start) + post_ocr_counts = _read_metadata_counts(metadata_path) + + export_start = time.perf_counter() + export_records = _export_jsonl_with_retry( + corpus, + export_path=export_path, + metadata_path=metadata_path, + text_key=str(args.text_key), + metadata_key=str(args.metadata_key), + post_ocr_counts=post_ocr_counts, + ) + export_elapsed = float(time.perf_counter() - export_start) + + finished_at = time.time() + report: Dict[str, Any] = { + "input_dir": str(input_dir), + "output_dir": str(output_dir), + "export_path": str(export_path), + "metadata_path": str(metadata_path), + "started_at": int(started_at), + "finished_at": int(finished_at), + "elapsed_total_sec": float(finished_at - started_at), + "skipped_phases": list(skipped_phases), + "extract_elapsed_sec": extract_elapsed, + "clean_elapsed_sec": clean_elapsed, + "ocr_elapsed_sec": ocr_elapsed, + "export_elapsed_sec": export_elapsed, + "post_extract_counts": post_extract_counts, + "post_clean_counts": post_clean_counts, + "post_ocr_counts": post_ocr_counts, + "export_records": int(export_records), + } + report_path.write_text(json.dumps(report, indent=2, sort_keys=True), encoding="utf-8") + print( + json.dumps( + { + "extract_elapsed_sec": round(extract_elapsed, 3), + "clean_elapsed_sec": round(clean_elapsed, 3), + "ocr_elapsed_sec": round(ocr_elapsed, 3), + "export_elapsed_sec": round(export_elapsed, 3), + "rows_total": post_ocr_counts["rows_total"], + "needs_ocr_after_clean": post_clean_counts["needs_ocr_true"], + "ocr_success_after_ocr": post_ocr_counts["ocr_success_true"], + "export_records": int(export_records), + "report_path": str(report_path), + }, + indent=2, + sort_keys=True, + ) + ) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/install_glossapi.py b/src/glossapi/scripts/install_glossapi.py new file mode 100644 index 0000000..195d662 --- /dev/null +++ b/src/glossapi/scripts/install_glossapi.py @@ -0,0 +1,230 @@ +"""Guided installer for GlossAPI extras.""" + +from __future__ import annotations + +import argparse +import os +import shlex +import subprocess +import shutil +import sys +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Iterable, List, Optional, Sequence, Set + + +PHASE_TO_EXTRAS: Dict[str, Set[str]] = { + "download": set(), + "browser_download": {"browser"}, + "extract": {"docling"}, + "ocr": set(), + "docs": {"docs"}, +} + + +@dataclass(frozen=True) +class InstallPlan: + phases: tuple[str, ...] + extras: tuple[str, ...] + editable: bool + include_cuda: bool + needs_deepseek_runtime: bool + + +def _supports_color() -> bool: + return sys.stdout.isatty() and os.environ.get("TERM") not in {"", "dumb", None} + + +def _style(text: str, code: str) -> str: + if not _supports_color(): + return text + return f"\033[{code}m{text}\033[0m" + + +def _prompt_yes_no(question: str, default: bool = False) -> bool: + suffix = "[Y/n]" if default else "[y/N]" + while True: + raw = input(f"{question} {suffix} ").strip().lower() + if not raw: + return default + if raw in {"y", "yes"}: + return True + if raw in {"n", "no"}: + return False + print("Please answer 'y' or 'n'.") + + +def _resolve_phase_selection(tokens: Iterable[str]) -> List[str]: + resolved: List[str] = [] + seen: Set[str] = set() + for token in tokens: + phase = str(token).strip().lower() + if not phase: + continue + if phase not in PHASE_TO_EXTRAS: + raise ValueError(f"Unsupported phase '{token}'. Valid phases: {', '.join(sorted(PHASE_TO_EXTRAS))}") + if phase not in seen: + seen.add(phase) + resolved.append(phase) + return resolved + + +def build_install_plan( + *, + phases: Sequence[str], + editable: bool, + include_cuda: bool, +) -> InstallPlan: + selected = _resolve_phase_selection(phases) + extras: Set[str] = set() + for phase in selected: + extras.update(PHASE_TO_EXTRAS[phase]) + if include_cuda: + extras.add("cuda") + return InstallPlan( + phases=tuple(selected), + extras=tuple(sorted(extras)), + editable=bool(editable), + include_cuda=bool(include_cuda), + needs_deepseek_runtime=("ocr" in selected), + ) + + +def build_pip_command(plan: InstallPlan, repo_root: Path) -> List[str]: + target = "." + if plan.extras: + target = f".[{','.join(plan.extras)}]" + cmd = [sys.executable, "-m", "pip", "install"] + if plan.editable: + cmd.append("-e") + cmd.append(target) + return cmd + + +def build_deepseek_command(repo_root: Path) -> Optional[List[str]]: + script = repo_root / "dependency_setup" / "setup_deepseek_uv.sh" + if not script.exists(): + return None + shell = shutil.which("bash") or shutil.which("sh") + if not shell: + return None + return [shell, str(script)] + + +def _interactive_plan(default_editable: bool) -> InstallPlan: + print(_style("GlossAPI Installer", "1;36")) + print("Select only the phases you plan to use so optional dependencies stay minimal.\n") + + selected: List[str] = ["download"] + print(_style("Core", "1;37")) + print(" download: base downloader/data pipeline dependencies") + if _prompt_yes_no("Add browser-gated download support?", default=False): + selected.append("browser_download") + if _prompt_yes_no("Add extraction support (Docling)?", default=False): + selected.append("extract") + if _prompt_yes_no("Add OCR support (DeepSeek backend)?", default=False): + selected.append("ocr") + if _prompt_yes_no("Add docs tooling?", default=False): + selected.append("docs") + include_cuda = _prompt_yes_no("Include CUDA extras where relevant?", default=False) + editable = _prompt_yes_no("Install in editable mode?", default=default_editable) + return build_install_plan(phases=selected, editable=editable, include_cuda=include_cuda) + + +def _plan_summary(plan: InstallPlan, command: Sequence[str]) -> str: + extras = ", ".join(plan.extras) if plan.extras else "(none)" + phases = ", ".join(plan.phases) if plan.phases else "(none)" + return "\n".join( + [ + _style("Install plan", "1;32"), + f" phases: {phases}", + f" extras: {extras}", + f" editable: {'yes' if plan.editable else 'no'}", + f" command: {shlex.join(command)}", + f" deepseek runtime: {'separate setup required' if plan.needs_deepseek_runtime else 'not requested'}", + ] + ) + + +def build_arg_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + prog="python install_glossapi.py", + description="Guided installer for GlossAPI optional dependency groups.", + ) + parser.add_argument( + "--phases", + default="", + help=( + "Comma-separated phases to install. Valid values: " + + ", ".join(sorted(PHASE_TO_EXTRAS)) + + ". If omitted, an interactive wizard is shown." + ), + ) + parser.add_argument( + "--cuda", + action="store_true", + help="Include the CUDA extra.", + ) + parser.add_argument( + "--editable", + dest="editable", + action="store_true", + help="Install in editable mode.", + ) + parser.add_argument( + "--no-editable", + dest="editable", + action="store_false", + help="Install as a regular package.", + ) + parser.set_defaults(editable=True) + parser.add_argument( + "--dry-run", + action="store_true", + help="Print the computed pip command without running it.", + ) + parser.add_argument( + "--yes", + action="store_true", + help="Skip confirmation prompts in non-interactive mode.", + ) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_arg_parser().parse_args(argv) + repo_root = Path(__file__).resolve().parents[3] + + if args.phases.strip(): + plan = build_install_plan( + phases=[token for token in args.phases.split(",") if token.strip()], + editable=args.editable, + include_cuda=bool(args.cuda), + ) + else: + plan = _interactive_plan(default_editable=bool(args.editable)) + + command = build_pip_command(plan, repo_root) + print(_plan_summary(plan, command)) + deepseek_command = build_deepseek_command(repo_root) if plan.needs_deepseek_runtime else None + if deepseek_command: + print(f" deepseek command: {shlex.join(deepseek_command)}") + + if args.dry_run: + return 0 + if not args.yes and not args.phases.strip(): + if not _prompt_yes_no("Run this install command now?", default=True): + print("Aborted.") + return 1 + + completed = subprocess.run(command, cwd=repo_root) + if completed.returncode != 0: + return int(completed.returncode) + if plan.needs_deepseek_runtime and deepseek_command: + print(_style("Provisioning dedicated DeepSeek runtime…", "1;33")) + completed = subprocess.run(deepseek_command, cwd=repo_root) + return int(completed.returncode) + + +if __name__ == "__main__": # pragma: no cover - CLI entrypoint + raise SystemExit(main()) diff --git a/src/glossapi/scripts/ocr_gpu_batch.py b/src/glossapi/scripts/ocr_gpu_batch.py index 2183664..2646baa 100644 --- a/src/glossapi/scripts/ocr_gpu_batch.py +++ b/src/glossapi/scripts/ocr_gpu_batch.py @@ -115,15 +115,21 @@ def main(argv: Optional[List[str]] = None) -> int: "--force-ocr", dest="force_ocr", action="store_true", - help="Force GPU OCR during extraction (default).", + help="Deprecated no-op retained for compatibility; OCR now runs through Corpus.ocr(...).", ) parser.add_argument( "--no-force-ocr", dest="force_ocr", action="store_false", - help="Skip forced OCR (only run math/layout).", + help="Explicitly disable the deprecated Phase-1 OCR flag.", + ) + parser.set_defaults(force_ocr=False) + parser.add_argument( + "--workers-per-device", + type=int, + default=1, + help="Number of extraction workers to bind to each visible GPU (default: 1).", ) - parser.set_defaults(force_ocr=True) parser.add_argument( "--dry-run", action="store_true", @@ -182,6 +188,7 @@ def main(argv: Optional[List[str]] = None) -> int: export_doc_json=True, emit_formula_index=emit_formula_index, phase1_backend=args.phase1_backend, + workers_per_device=max(1, int(args.workers_per_device)), ) print("[ocr_gpu_batch] Extraction complete.") @@ -190,4 +197,3 @@ def main(argv: Optional[List[str]] = None) -> int: if __name__ == "__main__": # pragma: no cover - CLI entrypoint raise SystemExit(main()) - diff --git a/src/glossapi/scripts/openarchives_download_freeze.py b/src/glossapi/scripts/openarchives_download_freeze.py new file mode 100644 index 0000000..e358781 --- /dev/null +++ b/src/glossapi/scripts/openarchives_download_freeze.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +import argparse +import logging +from pathlib import Path +from typing import List, Optional + +from glossapi import Corpus +from glossapi.scripts.openarchives_ocr_run_node import ( + DEFAULT_DOWNLOAD_CONCURRENCY, + DEFAULT_DOWNLOAD_TIMEOUT, + _load_frame, + _normalize_download_results, + _prepare_download_input, + _write_canonical_metadata, +) + + +def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_download_freeze", + description=( + "Materialize one OpenArchives manifest into a canonical GlossAPI downloads root " + "without starting OCR. This is the reproducible PDF-freeze entrypoint." + ), + ) + p.add_argument("--input-parquet", required=True) + p.add_argument("--work-root", required=True) + p.add_argument("--python-log-level", default="INFO") + p.add_argument("--download-concurrency", type=int, default=DEFAULT_DOWNLOAD_CONCURRENCY) + p.add_argument("--download-timeout", type=int, default=DEFAULT_DOWNLOAD_TIMEOUT) + p.add_argument("--download-mode", default="auto") + p.add_argument("--download-scheduler-mode", default="per_domain") + p.add_argument("--download-group-by", default="base_domain") + p.add_argument("--download-policy-file", default="") + p.add_argument("--supported-formats", default="pdf") + p.add_argument("--dry-run", action="store_true") + return p.parse_args(argv) + + +def main(argv: Optional[List[str]] = None) -> int: + args = _parse_args(argv) + input_path = Path(args.input_parquet).expanduser().resolve() + work_root = Path(args.work_root).expanduser().resolve() + work_root.mkdir(parents=True, exist_ok=True) + manifests_dir = work_root / "manifests" + manifests_dir.mkdir(parents=True, exist_ok=True) + + manifest_df = _prepare_download_input(_load_frame(input_path)) + download_input = manifests_dir / "download_input.parquet" + manifest_df.to_parquet(download_input, index=False) + + metadata_path = work_root / "download_results" / "download_results.parquet" + if not metadata_path.exists(): + metadata_path.parent.mkdir(parents=True, exist_ok=True) + _write_canonical_metadata(work_root, manifest_df) + + if args.dry_run: + return 0 + + corpus = Corpus( + input_dir=work_root / "downloads", + output_dir=work_root, + metadata_path=metadata_path, + log_level=getattr(logging, str(args.python_log_level).upper(), logging.INFO), + verbose=False, + ) + dl_df = corpus.download( + input_parquet=download_input, + links_column="url", + download_mode=str(args.download_mode), + parallelize_by=str(args.download_group_by), + concurrency=int(args.download_concurrency), + request_timeout=int(args.download_timeout), + scheduler_mode=str(args.download_scheduler_mode), + supported_formats=[part.strip() for part in str(args.supported_formats).split(",") if part.strip()], + download_policy_file=(str(args.download_policy_file) if str(args.download_policy_file or "").strip() else None), + ) + canonical_df = _normalize_download_results(shard_df=manifest_df, download_results_df=dl_df, url_column="url") + _write_canonical_metadata(work_root, canonical_df) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_download_probe.py b/src/glossapi/scripts/openarchives_download_probe.py new file mode 100644 index 0000000..d253b9b --- /dev/null +++ b/src/glossapi/scripts/openarchives_download_probe.py @@ -0,0 +1,158 @@ +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Iterable, Optional +from urllib.parse import urlparse + +import pandas as pd + +from glossapi import Corpus + + +def _parse_args(argv: Optional[list[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_download_probe", + description=( + "Sample OpenArchives OCR-target PDFs by host, run a controlled download probe, " + "and write per-host success summaries." + ), + ) + p.add_argument("--parquet", required=True, help="needs_ocr_enriched parquet with pdf_url and filename columns") + p.add_argument("--output-dir", required=True) + p.add_argument("--policy-file", default="") + p.add_argument("--samples-per-host", type=int, default=12) + p.add_argument("--max-hosts", type=int, default=12) + p.add_argument("--seed", type=int, default=42) + p.add_argument("--concurrency", type=int, default=12) + p.add_argument("--request-timeout", type=int, default=60) + p.add_argument("--scheduler-mode", default="per_domain") + p.add_argument("--download-group-by", default="base_domain") + p.add_argument("--hosts", nargs="*", default=None, help="Optional explicit host allowlist") + p.add_argument("--dry-run", action="store_true") + return p.parse_args(argv) + + +def _host_from_url(url: str) -> str: + try: + return (urlparse(str(url)).hostname or "").lower() + except Exception: + return "" + + +def _prepare_probe_frame( + df: pd.DataFrame, + *, + samples_per_host: int, + max_hosts: int, + seed: int, + hosts: Optional[Iterable[str]] = None, +) -> pd.DataFrame: + frame = df.copy() + if "pdf_url" not in frame.columns or "filename" not in frame.columns: + raise SystemExit("Probe parquet must include at least 'pdf_url' and 'filename' columns") + frame["host"] = frame["pdf_url"].astype(str).map(_host_from_url) + frame = frame[frame["host"].astype(bool)].copy() + if hosts: + allowed = {str(h).strip().lower() for h in hosts if str(h).strip()} + frame = frame[frame["host"].isin(allowed)].copy() + ranked_hosts = ( + frame.groupby("host", dropna=False) + .size() + .sort_values(ascending=False) + .head(max(1, int(max_hosts))) + .index.tolist() + ) + probe = frame[frame["host"].isin(ranked_hosts)].copy() + sampled = ( + probe.groupby("host", group_keys=True) + .apply( + lambda grp: grp.sample(n=min(len(grp), int(samples_per_host)), random_state=int(seed)), + include_groups=False, + ) + .reset_index(level=0) + .reset_index(drop=True) + ) + sampled["url"] = sampled["pdf_url"].astype(str) + sampled["base_domain"] = sampled["pdf_url"].astype(str).map( + lambda s: f"{urlparse(str(s)).scheme or 'https'}://{(urlparse(str(s)).netloc or '').lower()}".rstrip("/") + if _host_from_url(str(s)) + else "" + ) + return sampled + + +def _summary_payload(df: pd.DataFrame, *, source_rows: int) -> dict: + out = df.copy() + if "download_success" not in out.columns: + out["download_success"] = False + grouped = ( + out.groupby("host", dropna=False) + .agg( + docs=("host", "size"), + successes=("download_success", lambda s: int(pd.Series(s).fillna(False).sum())), + failures=("download_success", lambda s: int((~pd.Series(s).fillna(False)).sum())), + ) + .reset_index() + .sort_values(["docs", "successes"], ascending=[False, False]) + ) + return { + "source_rows": int(source_rows), + "probe_rows": int(len(out)), + "hosts": grouped.to_dict(orient="records"), + } + + +def main(argv: Optional[list[str]] = None) -> int: + args = _parse_args(argv) + parquet_path = Path(args.parquet).expanduser().resolve() + output_dir = Path(args.output_dir).expanduser().resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + source_df = pd.read_parquet(parquet_path) + probe_df = _prepare_probe_frame( + source_df, + samples_per_host=int(args.samples_per_host), + max_hosts=int(args.max_hosts), + seed=int(args.seed), + hosts=args.hosts, + ) + probe_input = output_dir / "probe_input.parquet" + probe_df.to_parquet(probe_input, index=False) + + if args.dry_run: + summary = _summary_payload(probe_df, source_rows=len(source_df)) + (output_dir / "probe_summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + return 0 + + corpus = Corpus( + input_dir=output_dir / "downloads", + output_dir=output_dir, + log_level="INFO", + verbose=False, + ) + results = corpus.download( + input_parquet=probe_input, + links_column="url", + parallelize_by=str(args.download_group_by), + concurrency=int(args.concurrency), + request_timeout=int(args.request_timeout), + scheduler_mode=str(args.scheduler_mode), + download_policy_file=(str(args.policy_file) if str(args.policy_file or "").strip() else None), + ) + merged = results.merge( + probe_df[["url", "host", "filename"]], + on="url", + how="left", + suffixes=("", "_probe"), + ) + merged_path = output_dir / "probe_results.parquet" + merged.to_parquet(merged_path, index=False) + summary = _summary_payload(merged, source_rows=len(source_df)) + (output_dir / "probe_summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_hf_refresh.py b/src/glossapi/scripts/openarchives_hf_refresh.py new file mode 100644 index 0000000..133852f --- /dev/null +++ b/src/glossapi/scripts/openarchives_hf_refresh.py @@ -0,0 +1,232 @@ +from __future__ import annotations + +import argparse +import io +import json +import re +from pathlib import Path +from typing import Dict, Iterable, Optional, Sequence + +import pandas as pd +import zstandard as zstd + +from glossapi.scripts.openarchives_ocr_enrich import _resolve_jsonl_path + + +PIPELINE_FIELDS = ( + "greek_badness_score", + "mojibake_badness_score", + "latin_percentage", + "polytonic_ratio", + "char_count_no_comments", + "is_empty", + "filter", + "needs_ocr", + "ocr_success", + "quality_method", + "reevaluated_at", +) + + +def _parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_hf_refresh", + description=( + "Refresh the canonical OpenArchives HF jsonl.zst shards in place from a refreshed " + "document-level parquet and update the dataset card counts." + ), + ) + p.add_argument("--dataset-root", required=True, help="Local clone/snapshot root of the HF dataset repo.") + p.add_argument("--metadata-parquet", required=True, help="Refreshed document-level parquet with source_jsonl/doc ids.") + p.add_argument("--output-root", default="", help="Optional separate output root. Defaults to in-place dataset-root.") + p.add_argument("--readme-path", default="README.md", help="Dataset card path relative to dataset-root/output-root.") + p.add_argument("--dry-run", action="store_true") + return p.parse_args(argv) + + +def _normalize_source_key(dataset_root: Path, recorded_path: str) -> str: + resolved = _resolve_jsonl_path(dataset_root, recorded_path) + return str(resolved.relative_to(dataset_root)) + + +def _clean_value(value: object) -> object: + if pd.isna(value): # type: ignore[arg-type] + return None + if isinstance(value, pd.Timestamp): + return value.isoformat() + if hasattr(value, "item"): + try: + return value.item() + except Exception: + return value + return value + + +def _build_update_index(metadata_df: pd.DataFrame, *, dataset_root: Path) -> Dict[str, Dict[str, dict]]: + required = {"source_doc_id", "source_jsonl"} + missing = sorted(required - set(metadata_df.columns)) + if missing: + raise SystemExit(f"Metadata parquet missing required column(s): {', '.join(missing)}") + updates: Dict[str, Dict[str, dict]] = {} + work = metadata_df.copy() + work["_source_key"] = work["source_jsonl"].astype(str).map(lambda p: _normalize_source_key(dataset_root, p)) + for _, row in work.iterrows(): + source_key = str(row["_source_key"]) + doc_id = str(row["source_doc_id"] or "") + payload = {field: _clean_value(row[field]) for field in PIPELINE_FIELDS if field in row.index} + updates.setdefault(source_key, {})[doc_id] = payload + return updates + + +def _iter_jsonl_rows(path: Path) -> Iterable[dict]: + dctx = zstd.ZstdDecompressor() + with path.open("rb") as fh, dctx.stream_reader(fh) as reader: + text_reader = io.TextIOWrapper(reader, encoding="utf-8") + for line in text_reader: + yield json.loads(line) + + +def _write_jsonl_rows(path: Path, rows: Iterable[dict]) -> int: + path.parent.mkdir(parents=True, exist_ok=True) + cctx = zstd.ZstdCompressor(level=3) + count = 0 + with path.open("wb") as fh: + with cctx.stream_writer(fh) as writer: + for row in rows: + payload = (json.dumps(row, ensure_ascii=False) + "\n").encode("utf-8") + writer.write(payload) + count += 1 + return count + + +def _refresh_readme(readme_text: str, *, total_docs: int, needs_ocr_docs: int) -> str: + title_text = f"OpenArchives.gr {total_docs:,} docs".replace(",", ",") + percent = (100.0 * needs_ocr_docs / total_docs) if total_docs else 0.0 + pct_text = f"{percent:.2f}%" + + replacements = [ + (r"pretty_name:\s*OpenArchives\.gr [^\n]+", f"pretty_name: {title_text}"), + (r"# OpenArchives\.gr [^\n]+", f"# {title_text}"), + ( + r"- Σύνολο markdown αρχείων: \*\*[0-9,]+\*\* (?:from|από) openarchives\.gr", + f"- Σύνολο markdown αρχείων: **{total_docs:,}** από openarchives.gr", + ), + ( + r"- Total markdown files: \*\*[0-9,]+\*\* from openarchives\.gr", + f"- Total markdown files: **{total_docs:,}** from openarchives.gr", + ), + ( + r"- Τα χαμηλής ποιότητας αρχεία που ενδέχεται να χρειάζονται OCR επεξεργασία επισημαίνονται με τη στήλη `needs_ocr`: \*\*[0-9,]+ / [0-9,]+ \([0-9.]+%\)\*\*", + f"- Τα χαμηλής ποιότητας αρχεία που ενδέχεται να χρειάζονται OCR επεξεργασία επισημαίνονται με τη στήλη `needs_ocr`: **{needs_ocr_docs:,} / {total_docs:,} ({pct_text})**", + ), + ( + r"- Lower-quality files that may require OCR reprocessing are marked by the `needs_ocr` indicator: \*\*[0-9,]+ / [0-9,]+ \([0-9.]+%\)\*\*", + f"- Lower-quality files that may require OCR reprocessing are marked by the `needs_ocr` indicator: **{needs_ocr_docs:,} / {total_docs:,} ({pct_text})**", + ), + ] + updated = readme_text + for pattern, replacement in replacements: + updated = re.sub(pattern, replacement, updated) + return updated + + +def _refresh_shard( + *, + input_path: Path, + output_path: Path, + updates: Dict[str, dict], + dry_run: bool, +) -> dict: + total = 0 + matched = 0 + needs_ocr = 0 + unmatched_doc_ids: list[str] = [] + rows_out: list[dict] = [] + + for row in _iter_jsonl_rows(input_path): + total += 1 + doc_id = str(row.get("doc_id") or "") + payload = updates.get(doc_id) + if payload is not None: + pipeline = dict(row.get("pipeline_metadata") or {}) + pipeline.update({k: v for k, v in payload.items() if v is not None}) + row["pipeline_metadata"] = pipeline + matched += 1 + else: + unmatched_doc_ids.append(doc_id) + pipeline = row.get("pipeline_metadata") or {} + if bool(pipeline.get("needs_ocr")): + needs_ocr += 1 + rows_out.append(row) + + if not dry_run: + _write_jsonl_rows(output_path, rows_out) + + return { + "path": str(input_path), + "total_rows": total, + "matched_rows": matched, + "unmatched_rows": total - matched, + "needs_ocr_rows": needs_ocr, + "sample_unmatched_doc_ids": unmatched_doc_ids[:5], + } + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = _parse_args(argv) + dataset_root = Path(args.dataset_root).expanduser().resolve() + output_root = Path(args.output_root).expanduser().resolve() if str(args.output_root).strip() else dataset_root + output_root.mkdir(parents=True, exist_ok=True) + metadata_path = Path(args.metadata_parquet).expanduser().resolve() + + metadata_df = pd.read_parquet(metadata_path).copy() + updates_by_shard = _build_update_index(metadata_df, dataset_root=dataset_root) + + summaries: list[dict] = [] + total_rows = 0 + matched_rows = 0 + needs_ocr_rows = 0 + shard_root = dataset_root / "data" / "openarchives" + for rel_key, updates in sorted(updates_by_shard.items()): + input_path = dataset_root / rel_key + output_path = output_root / rel_key + summary = _refresh_shard( + input_path=input_path, + output_path=output_path, + updates=updates, + dry_run=bool(args.dry_run), + ) + summaries.append(summary) + total_rows += int(summary["total_rows"]) + matched_rows += int(summary["matched_rows"]) + needs_ocr_rows += int(summary["needs_ocr_rows"]) + + readme_rel = Path(args.readme_path) + readme_in = dataset_root / readme_rel + readme_out = output_root / readme_rel + if readme_in.exists() and not args.dry_run: + readme_text = readme_in.read_text(encoding="utf-8") + readme_out.write_text( + _refresh_readme(readme_text, total_docs=matched_rows, needs_ocr_docs=int(metadata_df["needs_ocr"].fillna(False).sum())), + encoding="utf-8", + ) + + summary = { + "dataset_root": str(dataset_root), + "output_root": str(output_root), + "metadata_parquet": str(metadata_path), + "shards_touched": len(summaries), + "total_rows_seen": total_rows, + "matched_rows": matched_rows, + "unmatched_rows": total_rows - matched_rows, + "needs_ocr_rows_after_refresh": needs_ocr_rows, + "metadata_rows": int(len(metadata_df)), + "metadata_needs_ocr_rows": int(metadata_df["needs_ocr"].fillna(False).sum()) if "needs_ocr" in metadata_df.columns else None, + "sample_shards": summaries[:5], + } + print(json.dumps(summary, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_ocr_cutoff_shards.py b/src/glossapi/scripts/openarchives_ocr_cutoff_shards.py new file mode 100644 index 0000000..8548faa --- /dev/null +++ b/src/glossapi/scripts/openarchives_ocr_cutoff_shards.py @@ -0,0 +1,194 @@ +from __future__ import annotations + +import argparse +import hashlib +import json +from pathlib import Path +from typing import Dict, List, Optional, Sequence, Tuple + +import pandas as pd + +from glossapi.scripts.openarchives_ocr_shards import ( + PAGE_COLUMN_CANDIDATES, + _assign_rows, + _coerce_bool_series, + _resolve_page_column, + _resolve_targets, +) + + +def _parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_ocr_cutoff_shards", + description=( + "Build OCR shard manifests from the materialized local PDFs available at a cutoff, " + "plus residual manifests for missing OCR targets." + ), + ) + p.add_argument("--parquet", required=True) + p.add_argument("--output-dir", required=True) + p.add_argument("--local-download-root", action="append", default=[]) + p.add_argument("--nodes", type=int, default=4) + p.add_argument("--pages-per-hour-per-node", type=float, default=50700.0) + p.add_argument("--filename-column", default="filename") + p.add_argument("--needs-ocr-column", default="needs_ocr") + p.add_argument("--page-column", default=None) + p.add_argument("--allow-threshold-derive", action="store_true") + p.add_argument("--greek-threshold", type=float, default=60.0) + p.add_argument("--mojibake-threshold", type=float, default=0.1) + p.add_argument("--key-column", default="source_doc_id") + p.add_argument("--cutoff-id", default="") + return p.parse_args(argv) + + +def _canonical_stem_from_row(row: pd.Series, filename_column: str) -> str: + if "filename_base" in row.index and str(row.get("filename_base") or "").strip(): + return str(row.get("filename_base")).strip() + return Path(str(row.get(filename_column) or "")).stem + + +def _scan_local_pdfs(roots: Sequence[Path]) -> Dict[str, Tuple[Path, Path]]: + available: Dict[str, Tuple[Path, Path]] = {} + for root in roots: + root = root.expanduser().resolve() + if not root.exists(): + continue + for pdf in sorted(p for p in root.rglob("*.pdf") if p.is_file()): + stem = pdf.stem + if stem not in available: + available[stem] = (root, pdf) + return available + + +def _stable_item_id(cutoff_id: str, key_value: str, stem: str) -> str: + payload = f"{cutoff_id}|{key_value}|{stem}" + return hashlib.sha256(payload.encode("utf-8")).hexdigest() + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = _parse_args(argv) + parquet_path = Path(args.parquet).expanduser().resolve() + output_dir = Path(args.output_dir).expanduser().resolve() + output_dir.mkdir(parents=True, exist_ok=True) + local_roots = [Path(p).expanduser().resolve() for p in (args.local_download_root or [])] + if not local_roots: + raise SystemExit("Pass at least one --local-download-root.") + + df = pd.read_parquet(parquet_path).copy() + if args.filename_column not in df.columns: + raise SystemExit(f"Filename column '{args.filename_column}' not found in parquet.") + + page_column = _resolve_page_column(df, args.page_column) + target_mask = _resolve_targets( + df, + needs_ocr_column=str(args.needs_ocr_column), + allow_threshold_derive=bool(args.allow_threshold_derive), + greek_threshold=float(args.greek_threshold), + mojibake_threshold=float(args.mojibake_threshold), + ) + target_df = df.loc[target_mask].copy() + if target_df.empty: + raise SystemExit("No OCR target rows selected at cutoff.") + + cutoff_id = str(args.cutoff_id or pd.Timestamp.utcnow().strftime("%Y%m%dT%H%M%SZ")) + target_df["filename_base"] = target_df.apply( + lambda row: _canonical_stem_from_row(row, str(args.filename_column)), + axis=1, + ) + available = _scan_local_pdfs(local_roots) + + rows_available: List[Dict[str, object]] = [] + rows_missing: List[Dict[str, object]] = [] + key_column = str(args.key_column) + preserve_columns = [c for c in target_df.columns if c not in {"filename_base"}] + + for row in target_df.to_dict(orient="records"): + stem = str(row.get("filename_base") or "") + key_value = str(row.get(key_column) or stem or row.get(args.filename_column) or "") + base = {col: row.get(col) for col in preserve_columns} + item_id = _stable_item_id(cutoff_id, key_value, stem) + if stem in available: + root, pdf_path = available[stem] + rel_path = pdf_path.relative_to(root) + out = dict(base) + out["source_filename"] = str(row.get(args.filename_column) or "") + out["filename"] = pdf_path.name + out["md_filename"] = f"{stem}.md" + out["filename_base"] = stem + out["ocr_item_id"] = item_id + out["ocr_cutoff_id"] = cutoff_id + out["local_pdf_path"] = str(pdf_path) + out["local_pdf_root"] = str(root) + out["local_pdf_relpath"] = str(rel_path) + out["available_at_cutoff"] = True + rows_available.append(out) + else: + out = dict(base) + out["filename_base"] = stem + out["ocr_item_id"] = item_id + out["ocr_cutoff_id"] = cutoff_id + out["available_at_cutoff"] = False + rows_missing.append(out) + + available_df = pd.DataFrame(rows_available) + missing_df = pd.DataFrame(rows_missing) + available_path = output_dir / "openarchives_ocr_available_at_cutoff.parquet" + missing_path = output_dir / "openarchives_ocr_missing_at_cutoff.parquet" + if not available_df.empty: + bins = _assign_rows(available_df, page_column=page_column, node_count=int(args.nodes)) + else: + bins = [] + + summaries: List[Dict[str, object]] = [] + total_pages = 0 + total_docs = 0 + for node in bins: + node_id = int(node["node_id"]) + node_df = pd.DataFrame(list(node["rows"])) + if "_pages_int" in node_df.columns: + node_df = node_df.drop(columns=["_pages_int"]) + node_df["shard_id"] = f"node-{node_id:02d}" + node_df["node_id"] = node_id + out_path = output_dir / f"openarchives_ocr_shard_node_{node_id:02d}.parquet" + node_df.to_parquet(out_path, index=False) + node_pages = int(node["pages_total"]) + node_docs = int(node["docs_total"]) + total_pages += node_pages + total_docs += node_docs + summaries.append( + { + "node_id": node_id, + "manifest_path": str(out_path), + "docs_total": node_docs, + "pages_total": node_pages, + "eta_hours_at_validated_speed": float(node_pages / float(args.pages_per_hour_per_node)), + } + ) + + available_df.to_parquet(available_path, index=False) + missing_df.to_parquet(missing_path, index=False) + overall = { + "source_parquet": str(parquet_path), + "cutoff_id": cutoff_id, + "nodes": int(args.nodes), + "key_column": key_column, + "filename_column": str(args.filename_column), + "page_column": str(page_column), + "available_docs_total": int(len(available_df)), + "available_pages_total": int(total_pages), + "missing_docs_total": int(len(missing_df)), + "missing_pages_total": int(pd.to_numeric(missing_df.get(page_column, pd.Series(dtype=float)), errors="coerce").fillna(0).sum()) if not missing_df.empty else 0, + "pages_per_hour_per_node": float(args.pages_per_hour_per_node), + "eta_hours_one_node": float(total_pages / float(args.pages_per_hour_per_node)) if total_pages else 0.0, + "eta_hours_all_nodes": float(total_pages / (float(args.pages_per_hour_per_node) * max(1, int(args.nodes)))) if total_pages else 0.0, + "available_manifest_path": str(available_path), + "missing_manifest_path": str(missing_path), + "node_summaries": summaries, + } + (output_dir / "openarchives_ocr_cutoff_summary.json").write_text(json.dumps(overall, indent=2), encoding="utf-8") + print(json.dumps(overall, indent=2)) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_ocr_enrich.py b/src/glossapi/scripts/openarchives_ocr_enrich.py new file mode 100644 index 0000000..7bfd767 --- /dev/null +++ b/src/glossapi/scripts/openarchives_ocr_enrich.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +import argparse +import io +import json +from pathlib import Path +from typing import Dict, Iterable, Optional, Sequence + +import pandas as pd +import zstandard as zstd + + +def _parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_ocr_enrich", + description="Enrich OpenArchives OCR routing rows with page counts and PDF URLs from raw JSONL shards.", + ) + p.add_argument("--parquet", required=True, help="Canonical parquet after OpenArchives cleaning/fill.") + p.add_argument("--raw-repo-root", required=True, help="Local root of the raw HF OpenArchives dataset.") + p.add_argument("--output-parquet", required=True, help="Where the enriched parquet will be written.") + p.add_argument("--filename-column", default="filename") + p.add_argument("--doc-id-column", default="source_doc_id") + p.add_argument("--source-jsonl-column", default="source_jsonl") + p.add_argument("--needs-ocr-column", default="needs_ocr") + p.add_argument( + "--allow-threshold-derive", + action="store_true", + help="If needs_ocr is missing, derive targets from greek/mojibake thresholds.", + ) + p.add_argument("--greek-threshold", type=float, default=60.0) + p.add_argument("--mojibake-threshold", type=float, default=0.1) + return p.parse_args(argv) + + +def _coerce_bool_series(series: pd.Series) -> pd.Series: + if series.dtype == bool: + return series.fillna(False) + lowered = series.astype(str).str.strip().str.lower() + return lowered.isin({"1", "true", "t", "yes", "y"}) + + +def _resolve_targets( + df: pd.DataFrame, + *, + needs_ocr_column: str, + allow_threshold_derive: bool, + greek_threshold: float, + mojibake_threshold: float, +) -> pd.Series: + if needs_ocr_column in df.columns: + return _coerce_bool_series(df[needs_ocr_column]) + if not allow_threshold_derive: + raise SystemExit( + f"Column '{needs_ocr_column}' not found and threshold derivation is disabled." + ) + greek = pd.to_numeric(df.get("greek_badness_score"), errors="coerce") + moj = pd.to_numeric(df.get("mojibake_badness_score"), errors="coerce") + if greek is None and moj is None: + raise SystemExit( + "Cannot derive OCR targets: neither needs_ocr nor greek/mojibake badness columns are present." + ) + greek_mask = (greek > float(greek_threshold)).fillna(False) if greek is not None else False + moj_mask = (moj > float(mojibake_threshold)).fillna(False) if moj is not None else False + return greek_mask | moj_mask + + +def _resolve_jsonl_path(raw_repo_root: Path, recorded_path: str) -> Path: + candidate = Path(recorded_path) + if candidate.exists(): + return candidate + + marker = "data/openarchives/" + text = str(recorded_path) + idx = text.find(marker) + if idx != -1: + rel = Path(text[idx:]) + rewritten = raw_repo_root / rel + if rewritten.exists(): + return rewritten + + name = Path(recorded_path).name + matches = list((raw_repo_root / "data" / "openarchives").glob(f"**/{name}")) + if len(matches) == 1: + return matches[0] + raise FileNotFoundError(f"could not resolve JSONL path for {recorded_path}") + + +def _pick_pdf_url(source_meta: dict) -> str: + for key in ("refined_pdf_links_json", "pdf_links_json"): + value = source_meta.get(key) + url = _normalize_pdf_link(value) + if url: + return url + for key in ("external_link", "handle_url", "url"): + value = source_meta.get(key) + if isinstance(value, str) and value.strip(): + return value.strip() + return "" + + +def _normalize_pdf_link(value: object) -> str: + if value is None: + return "" + if isinstance(value, str): + text = value.strip() + if not text: + return "" + if text.startswith("http://") or text.startswith("https://"): + return text + try: + parsed = json.loads(text) + except Exception: + return text + return _normalize_pdf_link(parsed) + if isinstance(value, list): + for item in value: + normalized = _normalize_pdf_link(item) + if normalized: + return normalized + return "" + if isinstance(value, dict): + for key in ("url", "href", "pdf_url", "link"): + if key in value: + normalized = _normalize_pdf_link(value[key]) + if normalized: + return normalized + return "" + return "" + + +def _coerce_page_count(value: object) -> Optional[int]: + if value is None: + return None + try: + return max(1, int(float(value))) + except Exception: + return None + + +def _enrich_targets( + targets: pd.DataFrame, + *, + raw_repo_root: Path, + doc_id_column: str, + source_jsonl_column: str, +) -> pd.DataFrame: + work = targets.copy() + work["_resolved_jsonl"] = work[source_jsonl_column].map( + lambda p: str(_resolve_jsonl_path(raw_repo_root, str(p))) + ) + grouped: Dict[str, Dict[str, int]] = {} + for row_index, row in work[[doc_id_column, "_resolved_jsonl"]].iterrows(): + grouped.setdefault(str(row["_resolved_jsonl"]), {})[str(row[doc_id_column])] = int(row_index) + + dctx = zstd.ZstdDecompressor() + for jsonl_path, doc_map in grouped.items(): + with Path(jsonl_path).open("rb") as fh, dctx.stream_reader(fh) as reader: + text_reader = io.TextIOWrapper(reader, encoding="utf-8") + for line in text_reader: + record = json.loads(line) + doc_id = str(record.get("doc_id") or "") + row_index = doc_map.get(doc_id) + if row_index is None: + continue + pipeline = record.get("pipeline_metadata") or {} + source_meta = record.get("source_metadata") or {} + page_count = _coerce_page_count(pipeline.get("page_count")) + pages_total = _coerce_page_count(pipeline.get("pages_total")) + if page_count is None: + page_count = pages_total + if pages_total is None: + pages_total = page_count + work.at[row_index, "page_count_source"] = page_count + work.at[row_index, "pages_total_source"] = pages_total + work.at[row_index, "pdf_url"] = _pick_pdf_url(source_meta) + work.at[row_index, "source_collection_slug"] = source_meta.get("collection_slug") or "" + work.at[row_index, "source_language_code"] = source_meta.get("language_code") or "" + + return work.drop(columns=["_resolved_jsonl"]) + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = _parse_args(argv) + parquet_path = Path(args.parquet).expanduser().resolve() + raw_repo_root = Path(args.raw_repo_root).expanduser().resolve() + output_path = Path(args.output_parquet).expanduser().resolve() + output_path.parent.mkdir(parents=True, exist_ok=True) + + df = pd.read_parquet(parquet_path) + for required in (args.filename_column, args.doc_id_column, args.source_jsonl_column): + if required not in df.columns: + raise SystemExit(f"Required column '{required}' not found in parquet.") + + target_mask = _resolve_targets( + df, + needs_ocr_column=str(args.needs_ocr_column), + allow_threshold_derive=bool(args.allow_threshold_derive), + greek_threshold=float(args.greek_threshold), + mojibake_threshold=float(args.mojibake_threshold), + ) + targets = df.loc[target_mask].copy() + if targets.empty: + raise SystemExit("No OCR target rows selected; enriched parquet was not created.") + + enriched_targets = _enrich_targets( + targets, + raw_repo_root=raw_repo_root, + doc_id_column=str(args.doc_id_column), + source_jsonl_column=str(args.source_jsonl_column), + ) + + enriched_targets.to_parquet(output_path, index=False) + summary = { + "source_parquet": str(parquet_path), + "output_parquet": str(output_path), + "target_docs": int(len(enriched_targets)), + "page_count_source_non_null": int(enriched_targets["page_count_source"].notna().sum()), + "pdf_url_non_empty": int(enriched_targets["pdf_url"].fillna("").astype(str).str.len().gt(0).sum()), + "pages_total_sum": int(pd.to_numeric(enriched_targets["page_count_source"], errors="coerce").fillna(0).sum()), + } + print(json.dumps(summary, indent=2)) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_ocr_merge.py b/src/glossapi/scripts/openarchives_ocr_merge.py new file mode 100644 index 0000000..b88b8c3 --- /dev/null +++ b/src/glossapi/scripts/openarchives_ocr_merge.py @@ -0,0 +1,209 @@ +from __future__ import annotations + +import argparse +import hashlib +import re +import shutil +from pathlib import Path +from typing import Dict, List, Optional + +import pandas as pd + + +_MARKDOWN_SHARD_RE = re.compile(r"^(?P.+)__p(?P\d+)-(?P\d+)\.md$") + + +def _parse_args(argv: List[str] | None = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_ocr_merge", + description="Merge shard-level OCR metadata back into a canonical GlossAPI download_results parquet.", + ) + p.add_argument("--master-parquet", required=True) + p.add_argument("--shard-parquets", nargs="+", required=True) + p.add_argument("--output-parquet", required=True) + p.add_argument("--key-column", default="filename") + p.add_argument("--preserve-master-columns", default="") + p.add_argument("--artifact-work-roots", nargs="*", default=[]) + p.add_argument("--artifact-output-root", default="") + return p.parse_args(argv) + + +def _normalize_key(df: pd.DataFrame, key: str) -> pd.Series: + if key not in df.columns: + raise SystemExit(f"Key column '{key}' not present in dataframe.") + return df[key].astype(str).str.strip() + + +def _merge_markdown_parts(parts: List[str]) -> str: + merged: List[str] = [] + for part in parts: + if not part: + continue + if merged and not merged[-1].endswith("\n"): + merged[-1] = merged[-1] + "\n" + merged.append(part) + return "".join(merged) + + +def _copy_once(src: Path, dst: Path) -> None: + dst.parent.mkdir(parents=True, exist_ok=True) + if dst.exists(): + return + shutil.copy2(src, dst) + + +def _resolve_markdown_payload( + *, + stem: str, + md_name: str, + work_roots: List[Path], + output_root: Optional[Path], +) -> tuple[Optional[str], Optional[str]]: + markdown_out = output_root / "markdown" if output_root is not None else None + shard_out = output_root / "sidecars" / "ocr_shards" / "markdown" if output_root is not None else None + + for root in work_roots: + canonical_src = root / "markdown" / f"{stem}.md" + if canonical_src.exists(): + payload = canonical_src.read_text(encoding="utf-8") + if markdown_out is not None: + _copy_once(canonical_src, markdown_out / md_name) + return payload, str(Path("markdown") / md_name) + return payload, None + + shard_sources = [] + for candidate in sorted((root / "markdown").glob(f"{stem}__p*.md")): + match = _MARKDOWN_SHARD_RE.match(candidate.name) + if not match or match.group("stem") != stem: + continue + shard_sources.append((int(match.group("start")), candidate)) + if not shard_sources: + continue + + shard_sources.sort(key=lambda item: item[0]) + payload = _merge_markdown_parts([path.read_text(encoding="utf-8") for _, path in shard_sources]) + if markdown_out is not None: + destination = markdown_out / md_name + destination.parent.mkdir(parents=True, exist_ok=True) + destination.write_text(payload, encoding="utf-8") + if shard_out is not None: + for _, shard_path in shard_sources: + _copy_once(shard_path, shard_out / shard_path.name) + return payload, str(Path("markdown") / md_name) + return payload, None + return None, None + + +def _collect_artifact_updates( + *, + shard_rows: pd.DataFrame, + work_roots: List[Path], + output_root: Optional[Path], +) -> tuple[int, pd.DataFrame]: + copied = 0 + markdown_out = output_root / "markdown" if output_root is not None else None + metrics_out = output_root / "json" / "metrics" if output_root is not None else None + if metrics_out is not None: + metrics_out.mkdir(parents=True, exist_ok=True) + updates: List[Dict[str, object]] = [] + for row in shard_rows.to_dict(orient="records"): + merge_key = str(row.get("_merge_key") or "").strip() + stem = str(row.get("filename_base") or Path(str(row.get("filename") or "")).stem).strip() + if not stem: + continue + md_name = str(row.get("md_filename") or f"{stem}.md") + md_payload, md_relpath = _resolve_markdown_payload( + stem=stem, + md_name=md_name, + work_roots=work_roots, + output_root=output_root, + ) + if md_payload is not None and markdown_out is not None: + copied += 1 + metrics_relpath = None + for suffix in (".metrics.json", ".per_page.metrics.json"): + for root in work_roots: + src = root / "json" / "metrics" / f"{stem}{suffix}" + if src.exists(): + if metrics_out is not None: + _copy_once(src, metrics_out / src.name) + copied += 1 + metrics_relpath = str(Path("json") / "metrics" / src.name) + break + if metrics_relpath is not None: + break + updates.append( + { + "_merge_key": merge_key, + "text": md_payload, + "ocr_markdown_relpath": md_relpath, + "ocr_metrics_relpath": metrics_relpath, + "ocr_text_sha256": ( + hashlib.sha256(md_payload.encode("utf-8")).hexdigest() + if isinstance(md_payload, str) + else None + ), + } + ) + return copied, pd.DataFrame(updates) + + +def main(argv: List[str] | None = None) -> int: + args = _parse_args(argv) + master_path = Path(args.master_parquet).expanduser().resolve() + out_path = Path(args.output_parquet).expanduser().resolve() + out_path.parent.mkdir(parents=True, exist_ok=True) + + preserve_master_columns = [c.strip() for c in str(args.preserve_master_columns or "").split(",") if c.strip()] + master = pd.read_parquet(master_path).copy() + master["_merge_key"] = _normalize_key(master, str(args.key_column)) + + shard_frames: List[pd.DataFrame] = [] + for shard in args.shard_parquets: + shard_df = pd.read_parquet(Path(shard).expanduser().resolve()).copy() + shard_df["_merge_key"] = _normalize_key(shard_df, str(args.key_column)) + shard_frames.append(shard_df) + shards = pd.concat(shard_frames, ignore_index=True) + shards = shards.drop_duplicates(subset=["_merge_key"], keep="last") + + master = master.set_index("_merge_key", drop=False) + shards = shards.set_index("_merge_key", drop=False) + + for column in shards.columns: + if column == "_merge_key": + continue + if column in preserve_master_columns: + continue + master.loc[shards.index, column] = shards[column] + + copied = 0 + if args.artifact_work_roots: + roots = [Path(p).expanduser().resolve() for p in args.artifact_work_roots] + artifact_output_root = ( + Path(args.artifact_output_root).expanduser().resolve() + if str(args.artifact_output_root or "").strip() + else None + ) + copied, artifact_updates = _collect_artifact_updates( + shard_rows=shards.reset_index(drop=True), + work_roots=roots, + output_root=artifact_output_root, + ) + if not artifact_updates.empty: + artifact_updates = artifact_updates.drop_duplicates(subset=["_merge_key"], keep="last").set_index("_merge_key") + for column in artifact_updates.columns: + if column in preserve_master_columns: + continue + if column not in master.columns: + master[column] = None + mask = artifact_updates[column].notna() + if bool(mask.any()): + master.loc[artifact_updates.index[mask], column] = artifact_updates.loc[mask, column] + master = master.reset_index(drop=True).drop(columns=["_merge_key"], errors="ignore") + master.to_parquet(out_path, index=False) + print(f"Merged {len(shards)} shard row(s) into {master_path} -> {out_path}; copied {copied} artifact file(s)") + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_ocr_run_node.py b/src/glossapi/scripts/openarchives_ocr_run_node.py new file mode 100644 index 0000000..aeb2751 --- /dev/null +++ b/src/glossapi/scripts/openarchives_ocr_run_node.py @@ -0,0 +1,377 @@ +from __future__ import annotations + +import argparse +import json +import logging +import os +import socket +import threading +import time +from pathlib import Path +from typing import Any, Dict, List, Optional + +import pandas as pd + +from glossapi import Corpus +from glossapi.parquet_schema import ParquetSchema + + +DEFAULT_DOWNLOAD_CONCURRENCY = 24 +DEFAULT_DOWNLOAD_TIMEOUT = 60 +DEFAULT_HEARTBEAT_INTERVAL = 60 + + +def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_ocr_run_node", + description=( + "Materialize one OpenArchives OCR shard into a normal GlossAPI corpus root, " + "download its PDFs, and run DeepSeek OCR with the standardized settings." + ), + ) + p.add_argument("--shard-parquet", required=True) + p.add_argument("--work-root", required=True) + p.add_argument("--python-log-level", default="INFO") + p.add_argument("--download-concurrency", type=int, default=DEFAULT_DOWNLOAD_CONCURRENCY) + p.add_argument("--download-timeout", type=int, default=DEFAULT_DOWNLOAD_TIMEOUT) + p.add_argument("--download-scheduler-mode", default="per_domain") + p.add_argument("--download-group-by", default="base_domain") + p.add_argument("--download-policy-file", default="") + p.add_argument("--heartbeat-path") + p.add_argument("--heartbeat-interval", type=int, default=DEFAULT_HEARTBEAT_INTERVAL) + p.add_argument("--instance-id", default="") + p.add_argument("--node-id", default="") + p.add_argument("--dry-run", action="store_true") + p.add_argument("--skip-download", action="store_true") + p.add_argument("--scheduler", default="whole_doc") + p.add_argument("--target-batch-pages", type=int, default=160) + p.add_argument("--shard-pages", type=int, default=0) + p.add_argument("--shard-threshold-pages", type=int, default=0) + p.add_argument("--workers-per-gpu", type=int, default=1) + p.add_argument("--runtime-backend", default="vllm") + p.add_argument("--ocr-profile", default="markdown_grounded") + p.add_argument("--max-new-tokens", type=int, default=2048) + p.add_argument("--render-dpi", type=int, default=144) + p.add_argument("--repair-mode", default="auto") + p.add_argument("--repair-exec-batch-target-pages", type=int, default=None) + p.add_argument("--repair-exec-batch-target-items", type=int, default=None) + p.add_argument("--gpu-memory-utilization", type=float, default=0.9) + return p.parse_args(argv) + + +def _hostname() -> str: + try: + return socket.gethostname() + except Exception: + return "" + + +def _atomic_write_json(path: Path, payload: Dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") + os.replace(tmp, path) + + +def _prepare_download_input(df: pd.DataFrame) -> pd.DataFrame: + required = {"filename", "pdf_url"} + missing = sorted(required - set(df.columns)) + if missing: + raise SystemExit(f"Shard parquet missing required column(s): {', '.join(missing)}") + out = df.copy() + out["url"] = out["pdf_url"].astype(str) + out["filename_base"] = out["filename"].astype(str).map(lambda s: Path(s).stem) + return out + + +def _prepare_materialized_input(df: pd.DataFrame) -> pd.DataFrame: + if "filename" not in df.columns: + raise SystemExit("Shard parquet missing required column: filename") + out = df.copy() + if "filename_base" not in out.columns: + out["filename_base"] = out["filename"].astype(str).map(lambda s: Path(s).stem) + return out + + +def _load_frame(path: Path) -> pd.DataFrame: + return pd.read_parquet(path).copy() + + +def _normalize_download_results( + *, + shard_df: pd.DataFrame, + download_results_df: pd.DataFrame, + url_column: str = "url", +) -> pd.DataFrame: + shard = shard_df.copy() + if "filename_base" not in shard.columns: + shard["filename_base"] = shard["filename"].astype(str).map(lambda s: Path(s).stem) + + dl = download_results_df.copy() + if "filename_base" not in dl.columns: + dl["filename_base"] = dl["filename"].astype(str).map(lambda s: Path(s).stem) + + merged = dl.merge( + shard, + on="filename_base", + how="left", + suffixes=("", "_shard"), + ) + if "filename_shard" in merged.columns: + merged["filename"] = merged["filename_shard"].fillna(merged["filename"]) + merged = merged.drop(columns=["filename_shard"]) + if "pdf_url" in merged.columns and url_column in merged.columns: + merged[url_column] = merged["pdf_url"].fillna(merged[url_column]) + elif "pdf_url" in merged.columns and url_column not in merged.columns: + merged[url_column] = merged["pdf_url"] + if "download_success" not in merged.columns: + merged["download_success"] = False + if "download_error" not in merged.columns: + merged["download_error"] = "" + if "ocr_success" not in merged.columns: + merged["ocr_success"] = False + if "needs_ocr" not in merged.columns: + merged["needs_ocr"] = True + return merged + + +def _write_canonical_metadata(work_root: Path, df: pd.DataFrame) -> Path: + schema = ParquetSchema({"url_column": "url"}) + canonical = work_root / "download_results" / "download_results.parquet" + canonical.parent.mkdir(parents=True, exist_ok=True) + normalized = schema.normalize_metadata_frame(df) + schema.write_metadata_parquet(normalized, canonical) + return canonical + + +def _normalize_materialized_results( + *, + shard_df: pd.DataFrame, + downloads_dir: Path, +) -> pd.DataFrame: + out = shard_df.copy() + if "filename_base" not in out.columns: + out["filename_base"] = out["filename"].astype(str).map(lambda s: Path(s).stem) + if "local_pdf_path" in out.columns: + local_exists = out["local_pdf_path"].astype(str).map(lambda s: Path(s).exists()) + else: + local_exists = out["filename"].astype(str).map(lambda s: (downloads_dir / s).exists()) + out["download_success"] = local_exists.astype(bool) + out["download_error"] = out["download_success"].map(lambda ok: "" if ok else "materialized_pdf_missing") + if "needs_ocr" not in out.columns: + out["needs_ocr"] = True + if "ocr_success" not in out.columns: + out["ocr_success"] = False + if "url" not in out.columns: + if "pdf_url" in out.columns: + out["url"] = out["pdf_url"].fillna("").astype(str) + else: + out["url"] = "" + return out + + +def _read_progress(parquet_path: Path, page_col: str = "page_count_source") -> Dict[str, Any]: + try: + df = pd.read_parquet(parquet_path) + except Exception as exc: + return {"parquet_error": str(exc)} + total_docs = int(len(df)) + docs_done = int(df.get("ocr_success", pd.Series(dtype=bool)).fillna(False).sum()) if "ocr_success" in df.columns else 0 + total_pages = 0 + pages_done = 0 + if page_col in df.columns: + page_values = pd.to_numeric(df[page_col], errors="coerce").fillna(0) + total_pages = int(page_values.sum()) + if "ocr_success" in df.columns: + pages_done = int(page_values[df["ocr_success"].fillna(False)].sum()) + return { + "docs_total": total_docs, + "docs_done": docs_done, + "pages_total": total_pages, + "pages_done": pages_done, + } + + +class _HeartbeatThread(threading.Thread): + def __init__( + self, + *, + heartbeat_path: Path, + interval: int, + parquet_path: Path, + context: Dict[str, Any], + ) -> None: + super().__init__(daemon=True) + self.heartbeat_path = heartbeat_path + self.interval = max(10, int(interval)) + self.parquet_path = parquet_path + self.context = dict(context) + self.stage = "init" + self.error = "" + self.stop_event = threading.Event() + self.started_at = time.time() + + def set_stage(self, stage: str) -> None: + self.stage = str(stage) + + def set_error(self, error: str) -> None: + self.error = str(error) + + def stop(self) -> None: + self.stop_event.set() + + def _payload(self) -> Dict[str, Any]: + payload = dict(self.context) + payload.update( + { + "timestamp": int(time.time()), + "hostname": _hostname(), + "stage": self.stage, + "error": self.error, + "uptime_sec": round(time.time() - self.started_at, 1), + "parquet_path": str(self.parquet_path), + } + ) + payload.update(_read_progress(self.parquet_path)) + return payload + + def run(self) -> None: + while not self.stop_event.is_set(): + try: + _atomic_write_json(self.heartbeat_path, self._payload()) + except Exception: + pass + self.stop_event.wait(self.interval) + try: + _atomic_write_json(self.heartbeat_path, self._payload()) + except Exception: + pass + + +def main(argv: Optional[List[str]] = None) -> int: + args = _parse_args(argv) + shard_path = Path(args.shard_parquet).expanduser().resolve() + work_root = Path(args.work_root).expanduser().resolve() + work_root.mkdir(parents=True, exist_ok=True) + manifests_dir = work_root / "manifests" + manifests_dir.mkdir(parents=True, exist_ok=True) + + raw_shard_df = _load_frame(shard_path) + shard_df = ( + _prepare_materialized_input(raw_shard_df) + if args.skip_download + else _prepare_download_input(raw_shard_df) + ) + download_input = manifests_dir / "download_input.parquet" + if not args.skip_download: + shard_df.to_parquet(download_input, index=False) + + metadata_path = work_root / "download_results" / "download_results.parquet" + if not metadata_path.exists(): + metadata_path.parent.mkdir(parents=True, exist_ok=True) + if args.skip_download: + _write_canonical_metadata( + work_root, + _normalize_materialized_results(shard_df=shard_df, downloads_dir=work_root / "downloads"), + ) + else: + _write_canonical_metadata(work_root, shard_df) + + heartbeat: Optional[_HeartbeatThread] = None + if args.heartbeat_path: + heartbeat = _HeartbeatThread( + heartbeat_path=Path(args.heartbeat_path).expanduser().resolve(), + interval=int(args.heartbeat_interval), + parquet_path=metadata_path, + context={ + "instance_id": str(args.instance_id or ""), + "node_id": str(args.node_id or ""), + "shard_parquet": str(shard_path), + "work_root": str(work_root), + }, + ) + heartbeat.start() + + try: + if args.dry_run: + if heartbeat: + heartbeat.set_stage("dry_run") + return 0 + + if args.skip_download: + if heartbeat: + heartbeat.set_stage("materialized") + canonical_df = _normalize_materialized_results( + shard_df=shard_df, + downloads_dir=work_root / "downloads", + ) + metadata_path = _write_canonical_metadata(work_root, canonical_df) + else: + corpus = Corpus( + input_dir=work_root / "downloads", + output_dir=work_root, + metadata_path=metadata_path, + log_level=getattr(logging, str(args.python_log_level).upper(), logging.INFO), + verbose=False, + ) + + if heartbeat: + heartbeat.set_stage("download") + dl_df = corpus.download( + input_parquet=download_input, + links_column="url", + parallelize_by=str(args.download_group_by), + concurrency=int(args.download_concurrency), + request_timeout=int(args.download_timeout), + scheduler_mode=str(args.download_scheduler_mode), + download_policy_file=(str(args.download_policy_file) if str(args.download_policy_file or "").strip() else None), + ) + canonical_df = _normalize_download_results(shard_df=shard_df, download_results_df=dl_df, url_column="url") + metadata_path = _write_canonical_metadata(work_root, canonical_df) + if heartbeat: + heartbeat.parquet_path = metadata_path + heartbeat.set_stage("ocr") + + corpus = Corpus( + input_dir=work_root / "downloads", + output_dir=work_root, + metadata_path=metadata_path, + log_level=getattr(logging, str(args.python_log_level).upper(), logging.INFO), + verbose=False, + ) + corpus.ocr( + fix_bad=True, + mode="ocr_bad", + backend="deepseek", + runtime_backend=str(args.runtime_backend), + ocr_profile=str(args.ocr_profile), + use_gpus="multi", + workers_per_gpu=int(args.workers_per_gpu), + render_dpi=int(args.render_dpi), + max_new_tokens=int(args.max_new_tokens), + repair_mode=str(args.repair_mode), + repair_exec_batch_target_pages=args.repair_exec_batch_target_pages, + repair_exec_batch_target_items=args.repair_exec_batch_target_items, + scheduler=str(args.scheduler), + target_batch_pages=int(args.target_batch_pages), + shard_pages=int(args.shard_pages), + shard_threshold_pages=int(args.shard_threshold_pages), + gpu_memory_utilization=float(args.gpu_memory_utilization), + math_enhance=False, + ) + if heartbeat: + heartbeat.set_stage("done") + return 0 + except Exception as exc: + if heartbeat: + heartbeat.set_stage("failed") + heartbeat.set_error(str(exc)) + raise + finally: + if heartbeat: + heartbeat.stop() + heartbeat.join(timeout=5) + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_ocr_shards.py b/src/glossapi/scripts/openarchives_ocr_shards.py new file mode 100644 index 0000000..e68833c --- /dev/null +++ b/src/glossapi/scripts/openarchives_ocr_shards.py @@ -0,0 +1,224 @@ +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Dict, List, Optional, Sequence + +import pandas as pd + + +PAGE_COLUMN_CANDIDATES: Sequence[str] = ( + "page_count_source", + "pages_total_source", + "pages_total", + "page_count", + "total_pages", + "num_pages", + "pages", +) + + +def _parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_ocr_shards", + description="Create page-balanced OCR shard manifests from a canonical GlossAPI parquet.", + ) + p.add_argument("--parquet", required=True, help="Canonical download_results parquet with needs_ocr flags.") + p.add_argument("--output-dir", required=True, help="Directory where shard manifests and summaries will be written.") + p.add_argument("--nodes", type=int, default=4, help="Number of OCR nodes to shard across.") + p.add_argument( + "--pages-per-hour-per-node", + type=float, + default=50700.0, + help="Validated throughput per OCR node, used for ETA calculations.", + ) + p.add_argument("--filename-column", default="filename") + p.add_argument("--needs-ocr-column", default="needs_ocr") + p.add_argument( + "--page-column", + default=None, + help="Explicit page-count column. If omitted, the script searches common page columns.", + ) + p.add_argument( + "--copy-columns", + default="", + help="Comma-separated extra metadata columns to preserve in every shard manifest.", + ) + p.add_argument( + "--allow-threshold-derive", + action="store_true", + help="If needs_ocr is missing, derive the target set from greek/mojibake thresholds.", + ) + p.add_argument("--greek-threshold", type=float, default=60.0) + p.add_argument("--mojibake-threshold", type=float, default=0.1) + return p.parse_args(argv) + + +def _resolve_page_column(df: pd.DataFrame, explicit: Optional[str]) -> str: + if explicit: + if explicit not in df.columns: + raise SystemExit(f"--page-column '{explicit}' not found in parquet.") + return explicit + for candidate in PAGE_COLUMN_CANDIDATES: + if candidate in df.columns: + return candidate + raise SystemExit( + "No page-count column found. Expected one of: " + + ", ".join(PAGE_COLUMN_CANDIDATES) + + " or pass --page-column." + ) + + +def _coerce_bool_series(series: pd.Series) -> pd.Series: + if series.dtype == bool: + return series.fillna(False) + lowered = series.astype(str).str.strip().str.lower() + return lowered.isin({"1", "true", "t", "yes", "y"}) + + +def _resolve_targets( + df: pd.DataFrame, + *, + needs_ocr_column: str, + allow_threshold_derive: bool, + greek_threshold: float, + mojibake_threshold: float, +) -> pd.Series: + if needs_ocr_column in df.columns: + return _coerce_bool_series(df[needs_ocr_column]) + if not allow_threshold_derive: + raise SystemExit( + f"Column '{needs_ocr_column}' not found and threshold derivation is disabled." + ) + greek = pd.to_numeric(df.get("greek_badness_score"), errors="coerce") + moj = pd.to_numeric(df.get("mojibake_badness_score"), errors="coerce") + if greek is None and moj is None: + raise SystemExit( + "Cannot derive OCR targets: neither needs_ocr nor greek/mojibake badness columns are present." + ) + greek_mask = (greek > float(greek_threshold)).fillna(False) if greek is not None else False + moj_mask = (moj > float(mojibake_threshold)).fillna(False) if moj is not None else False + return greek_mask | moj_mask + + +def _page_int(value: object) -> int: + try: + return max(1, int(value)) + except Exception: + return 1 + + +def _make_node_bins(node_count: int) -> List[Dict[str, object]]: + return [ + { + "node_id": idx, + "pages_total": 0, + "docs_total": 0, + "rows": [], + } + for idx in range(max(1, int(node_count))) + ] + + +def _assign_rows(df: pd.DataFrame, *, page_column: str, node_count: int) -> List[Dict[str, object]]: + ordered = df.copy() + ordered["_pages_int"] = ordered[page_column].map(_page_int) + ordered = ordered.sort_values(["_pages_int"], ascending=[False]).reset_index(drop=True) + bins = _make_node_bins(node_count) + for row in ordered.to_dict(orient="records"): + node = min(bins, key=lambda item: (int(item["pages_total"]), int(item["node_id"]))) + row["node_id"] = int(node["node_id"]) + node["rows"].append(row) + node["docs_total"] = int(node["docs_total"]) + 1 + node["pages_total"] = int(node["pages_total"]) + int(row["_pages_int"]) + return bins + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = _parse_args(argv) + parquet_path = Path(args.parquet).expanduser().resolve() + output_dir = Path(args.output_dir).expanduser().resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + df = pd.read_parquet(parquet_path) + if args.filename_column not in df.columns: + raise SystemExit(f"Filename column '{args.filename_column}' not found in parquet.") + + page_column = _resolve_page_column(df, args.page_column) + target_mask = _resolve_targets( + df, + needs_ocr_column=str(args.needs_ocr_column), + allow_threshold_derive=bool(args.allow_threshold_derive), + greek_threshold=float(args.greek_threshold), + mojibake_threshold=float(args.mojibake_threshold), + ) + shard_df = df.loc[target_mask].copy() + if shard_df.empty: + raise SystemExit("No OCR target rows selected; shard manifests were not created.") + + copy_columns = [c.strip() for c in str(args.copy_columns or "").split(",") if c.strip()] + selected_columns = [args.filename_column, page_column] + for optional in [ + "needs_ocr", + "greek_badness_score", + "mojibake_badness_score", + "ocr_success", + "source_row", + "document_type", + ] + copy_columns: + if optional in shard_df.columns and optional not in selected_columns: + selected_columns.append(optional) + shard_df = shard_df[selected_columns].copy() + + bins = _assign_rows(shard_df, page_column=page_column, node_count=int(args.nodes)) + summaries: List[Dict[str, object]] = [] + total_pages = 0 + total_docs = 0 + for node in bins: + node_id = int(node["node_id"]) + rows = list(node["rows"]) + node_df = pd.DataFrame(rows) + if "_pages_int" in node_df.columns: + node_df = node_df.drop(columns=["_pages_int"]) + node_df["shard_id"] = f"node-{node_id:02d}" + node_df["node_id"] = node_id + out_path = output_dir / f"openarchives_ocr_shard_node_{node_id:02d}.parquet" + node_df.to_parquet(out_path, index=False) + + node_pages = int(node["pages_total"]) + node_docs = int(node["docs_total"]) + total_pages += node_pages + total_docs += node_docs + summaries.append( + { + "node_id": node_id, + "manifest_path": str(out_path), + "docs_total": node_docs, + "pages_total": node_pages, + "eta_hours_at_validated_speed": float(node_pages / float(args.pages_per_hour_per_node)), + } + ) + + overall = { + "source_parquet": str(parquet_path), + "nodes": int(args.nodes), + "filename_column": str(args.filename_column), + "page_column": str(page_column), + "docs_total": int(total_docs), + "pages_total": int(total_pages), + "pages_per_hour_per_node": float(args.pages_per_hour_per_node), + "eta_hours_one_node": float(total_pages / float(args.pages_per_hour_per_node)), + "eta_hours_all_nodes": float(total_pages / (float(args.pages_per_hour_per_node) * max(1, int(args.nodes)))), + "node_summaries": summaries, + } + (output_dir / "openarchives_ocr_shard_summary.json").write_text( + json.dumps(overall, indent=2), + encoding="utf-8", + ) + print(json.dumps(overall, indent=2)) + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(main()) diff --git a/src/glossapi/scripts/openarchives_pdf_stage_pull.py b/src/glossapi/scripts/openarchives_pdf_stage_pull.py new file mode 100644 index 0000000..330f0c5 --- /dev/null +++ b/src/glossapi/scripts/openarchives_pdf_stage_pull.py @@ -0,0 +1,737 @@ +from __future__ import annotations + +import argparse +import csv +import json +import os +import re +import signal +import sqlite3 +import subprocess +import sys +import time +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Iterable, Optional, Sequence + + +def utc_now() -> str: + return datetime.now(timezone.utc).replace(microsecond=0).isoformat() + + +@dataclass(frozen=True) +class TransferItem: + canonical_filename: str + remote_path: str + remote_size_bytes: int + remote_name: str + + +SCHEMA = """ +CREATE TABLE IF NOT EXISTS transfer_items ( + canonical_filename TEXT PRIMARY KEY, + remote_path TEXT NOT NULL, + remote_size_bytes INTEGER NOT NULL, + remote_name TEXT NOT NULL, + status TEXT NOT NULL DEFAULT 'pending', + attempts INTEGER NOT NULL DEFAULT 0, + priority_rank INTEGER NOT NULL DEFAULT 0, + last_error TEXT NOT NULL DEFAULT '', + transfer_started_at TEXT, + transfer_finished_at TEXT, + last_seen_size_bytes INTEGER NOT NULL DEFAULT 0 +); +""" + +PDF_NAME_PATTERN = re.compile(r"([A-Za-z0-9._-]+\.pdf(?:\.[A-Za-z0-9_-]+)?)", re.IGNORECASE) +FILENAME_KEYS = ("filename", "canonical_filename", "md_filename", "source_filename") + + +def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m glossapi.scripts.openarchives_pdf_stage_pull", + description="Resumable staged pull of OpenArchives PDFs from the Greece storage box.", + ) + p.add_argument("--manifest", required=True, help="TSV manifest with canonical_filename, remote_path, remote_size_bytes, remote_name.") + p.add_argument("--work-root", required=True, help="Root directory for downloads, partials, logs, and state.") + p.add_argument("--remote-host", default="debian@83.212.80.170") + p.add_argument("--password-env", default="GREECE_BOX_PASSWORD", help="Environment variable containing the remote SSH password.") + p.add_argument("--transport", choices=("sftp", "rsync"), default="sftp") + p.add_argument("--max-attempts", type=int, default=20) + p.add_argument("--connect-timeout", type=int, default=30) + p.add_argument("--io-timeout", type=int, default=180) + p.add_argument("--sleep-after-failure", type=float, default=10.0) + p.add_argument("--summary-interval-seconds", type=float, default=5.0) + p.add_argument("--limit", type=int, default=0, help="Optional limit for testing.") + p.add_argument( + "--priority-dir", + default=None, + help="Directory of dynamic priority files or filename lists. Items here are transferred first.", + ) + p.add_argument( + "--priority-only", + action="store_true", + help="Transfer only files currently present in the priority set; do not fall through to the rest of the manifest.", + ) + return p.parse_args(argv) + + +class TransferState: + def __init__(self, db_path: Path): + self.db_path = db_path + self.db_path.parent.mkdir(parents=True, exist_ok=True) + self.conn = sqlite3.connect(str(self.db_path)) + self.conn.execute("PRAGMA journal_mode=WAL") + self.conn.execute(SCHEMA) + self._ensure_columns() + self.conn.commit() + + def close(self) -> None: + self.conn.close() + + def _ensure_columns(self) -> None: + cols = {row[1] for row in self.conn.execute("PRAGMA table_info(transfer_items)").fetchall()} + if "priority_rank" not in cols: + self.conn.execute("ALTER TABLE transfer_items ADD COLUMN priority_rank INTEGER NOT NULL DEFAULT 0") + + def sync_manifest(self, items: Iterable[TransferItem]) -> None: + rows = [ + (item.canonical_filename, item.remote_path, int(item.remote_size_bytes), item.remote_name) + for item in items + ] + self.conn.executemany( + """ + INSERT INTO transfer_items ( + canonical_filename, remote_path, remote_size_bytes, remote_name, status + ) VALUES (?, ?, ?, ?, 'pending') + ON CONFLICT(canonical_filename) DO UPDATE SET + remote_path=excluded.remote_path, + remote_size_bytes=excluded.remote_size_bytes, + remote_name=excluded.remote_name + """, + rows, + ) + self.conn.commit() + + def reset_stale_in_progress(self) -> None: + self.conn.execute( + """ + UPDATE transfer_items + SET status='pending', + last_error=CASE + WHEN last_error = '' THEN 'Recovered from interrupted transfer' + ELSE last_error || ' | Recovered from interrupted transfer' + END + WHERE status='in_progress' + """ + ) + self.conn.commit() + + def mark_completed_if_present(self, downloads_dir: Path, partial_dir: Path) -> None: + cur = self.conn.execute( + "SELECT canonical_filename, remote_size_bytes, status FROM transfer_items" + ) + updates = [] + for canonical_filename, remote_size_bytes, status in cur.fetchall(): + final_path = downloads_dir / canonical_filename + if final_path.exists() and final_path.stat().st_size == int(remote_size_bytes): + updates.append((int(remote_size_bytes), utc_now(), canonical_filename)) + continue + part_path = partial_dir / f"{canonical_filename}.part" + if part_path.exists() and status == "completed": + self.conn.execute( + """ + UPDATE transfer_items + SET status='pending', + last_error='Final file missing; resuming from partial', + transfer_finished_at=NULL + WHERE canonical_filename=? + """, + (canonical_filename,), + ) + if updates: + self.conn.executemany( + """ + UPDATE transfer_items + SET status='completed', + last_seen_size_bytes=?, + transfer_finished_at=?, + last_error='' + WHERE canonical_filename=? + """, + updates, + ) + self.conn.commit() + + def next_item(self, *, max_attempts: int, priority_only: bool = False) -> Optional[sqlite3.Row]: + self.conn.row_factory = sqlite3.Row + if priority_only: + cur = self.conn.execute( + """ + SELECT * + FROM transfer_items + WHERE status IN ('pending', 'failed') + AND attempts < ? + AND priority_rank > 0 + ORDER BY priority_rank DESC, attempts ASC, canonical_filename ASC + LIMIT 1 + """, + (max_attempts,), + ) + else: + cur = self.conn.execute( + """ + SELECT * + FROM transfer_items + WHERE status IN ('pending', 'failed') + AND attempts < ? + ORDER BY priority_rank DESC, attempts ASC, canonical_filename ASC + LIMIT 1 + """, + (max_attempts,), + ) + return cur.fetchone() + + def mark_in_progress(self, canonical_filename: str, current_size: int) -> None: + self.conn.execute( + """ + UPDATE transfer_items + SET status='in_progress', + attempts=attempts+1, + transfer_started_at=?, + last_seen_size_bytes=?, + last_error='' + WHERE canonical_filename=? + """, + (utc_now(), int(current_size), canonical_filename), + ) + self.conn.commit() + + def mark_completed(self, canonical_filename: str, size_bytes: int) -> None: + self.conn.execute( + """ + UPDATE transfer_items + SET status='completed', + transfer_finished_at=?, + last_seen_size_bytes=?, + last_error='' + WHERE canonical_filename=? + """, + (utc_now(), int(size_bytes), canonical_filename), + ) + self.conn.commit() + + def mark_failed(self, canonical_filename: str, error: str, size_bytes: int) -> None: + self.conn.execute( + """ + UPDATE transfer_items + SET status='failed', + last_error=?, + last_seen_size_bytes=? + WHERE canonical_filename=? + """, + (str(error), int(size_bytes), canonical_filename), + ) + self.conn.commit() + + def counts(self) -> dict[str, int]: + cur = self.conn.execute( + """ + SELECT status, COUNT(*) AS c + FROM transfer_items + GROUP BY status + """ + ) + counts = {"pending": 0, "in_progress": 0, "completed": 0, "failed": 0} + for status, count in cur.fetchall(): + counts[str(status)] = int(count) + counts["total"] = sum(counts.values()) + return counts + + def byte_counts(self) -> dict[str, int]: + cur = self.conn.execute( + """ + SELECT + COALESCE(SUM(remote_size_bytes), 0) AS bytes_total, + COALESCE(SUM(CASE WHEN status = 'completed' THEN remote_size_bytes ELSE 0 END), 0) AS bytes_completed, + COALESCE(SUM(CASE WHEN status = 'in_progress' THEN last_seen_size_bytes ELSE 0 END), 0) AS bytes_in_progress + FROM transfer_items + """ + ) + row = cur.fetchone() + bytes_total = int(row[0] or 0) + bytes_completed = int(row[1] or 0) + bytes_in_progress = int(row[2] or 0) + bytes_remaining = max(0, bytes_total - bytes_completed) + return { + "bytes_total": bytes_total, + "bytes_completed": bytes_completed, + "bytes_in_progress": bytes_in_progress, + "bytes_remaining": bytes_remaining, + } + + def set_priorities(self, canonical_filenames: set[str]) -> None: + self.conn.execute("UPDATE transfer_items SET priority_rank=0 WHERE priority_rank != 0") + if canonical_filenames: + batch = [] + for name in sorted(canonical_filenames): + batch.append(name) + if len(batch) >= 500: + placeholders = ",".join("?" for _ in batch) + self.conn.execute( + f"UPDATE transfer_items SET priority_rank=100 WHERE canonical_filename IN ({placeholders})", + batch, + ) + batch.clear() + if batch: + placeholders = ",".join("?" for _ in batch) + self.conn.execute( + f"UPDATE transfer_items SET priority_rank=100 WHERE canonical_filename IN ({placeholders})", + batch, + ) + self.conn.commit() + + def priority_counts(self) -> dict[str, int]: + cur = self.conn.execute( + """ + SELECT + COALESCE(SUM(CASE WHEN priority_rank > 0 THEN 1 ELSE 0 END), 0) AS priority_total, + COALESCE(SUM(CASE WHEN priority_rank > 0 AND status='pending' THEN 1 ELSE 0 END), 0) AS priority_pending, + COALESCE(SUM(CASE WHEN priority_rank > 0 AND status='completed' THEN 1 ELSE 0 END), 0) AS priority_completed, + COALESCE(SUM(CASE WHEN priority_rank > 0 AND status='failed' THEN 1 ELSE 0 END), 0) AS priority_failed + FROM transfer_items + """ + ) + row = cur.fetchone() + return { + "priority_total": int(row[0] or 0), + "priority_pending": int(row[1] or 0), + "priority_completed": int(row[2] or 0), + "priority_failed": int(row[3] or 0), + } + + +def read_manifest(path: Path) -> list[TransferItem]: + items: list[TransferItem] = [] + with path.open("r", encoding="utf-8", newline="") as handle: + reader = csv.DictReader(handle, delimiter="\t") + required = {"canonical_filename", "remote_path", "remote_size_bytes", "remote_name"} + if not required.issubset(reader.fieldnames or set()): + raise SystemExit(f"Manifest missing required columns: {sorted(required)}") + for row in reader: + items.append( + TransferItem( + canonical_filename=str(row["canonical_filename"]).strip(), + remote_path=str(row["remote_path"]).strip(), + remote_size_bytes=int(row["remote_size_bytes"]), + remote_name=str(row["remote_name"]).strip(), + ) + ) + return items + + +def write_json(path: Path, payload: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + tmp = path.with_suffix(path.suffix + ".tmp") + tmp.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") + os.replace(tmp, path) + + +def append_event(path: Path, payload: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("a", encoding="utf-8") as handle: + handle.write(json.dumps(payload, ensure_ascii=False) + "\n") + + +def sshpass_env(password_env: str) -> dict[str, str]: + env = os.environ.copy() + secret = env.get(password_env) + if not secret: + raise SystemExit(f"Password env var '{password_env}' is not set.") + env["SSHPASS"] = secret + return env + + +def ssh_transport_options(connect_timeout: int) -> list[str]: + return [ + "-o", + "BatchMode=no", + "-o", + "PreferredAuthentications=password", + "-o", + "PubkeyAuthentication=no", + "-o", + "KbdInteractiveAuthentication=yes", + "-o", + f"ConnectTimeout={int(connect_timeout)}", + "-o", + "ServerAliveInterval=15", + "-o", + "ServerAliveCountMax=3", + "-o", + "ConnectionAttempts=3", + "-o", + "StrictHostKeyChecking=no", + "-o", + "UserKnownHostsFile=/tmp/greece_box_known_hosts", + ] + + +def canonicalize_pdf_name(raw: str) -> Optional[str]: + text = os.path.basename(str(raw).strip()) + if not text: + return None + lower = text.lower() + marker = ".pdf." + if marker in lower: + idx = lower.index(marker) + return text[: idx + 4] + if lower.endswith(".pdf"): + return text + return None + + +def _walk_json_strings(obj) -> Iterable[str]: + if isinstance(obj, dict): + for key, value in obj.items(): + if isinstance(key, str): + yield key + yield from _walk_json_strings(value) + elif isinstance(obj, list): + for item in obj: + yield from _walk_json_strings(item) + elif isinstance(obj, str): + yield obj + + +def _extract_priority_filenames_from_csv(path: Path) -> set[str]: + results: set[str] = set() + with path.open("r", encoding="utf-8", errors="ignore", newline="") as handle: + reader = csv.DictReader(handle) + fields = {field.strip() for field in (reader.fieldnames or []) if field} + keyed = any(key in fields for key in FILENAME_KEYS) + for row in reader: + if keyed: + for key in FILENAME_KEYS: + value = row.get(key) + if value: + canonical = canonicalize_pdf_name(value) + if canonical is not None: + results.add(canonical) + break + else: + for value in row.values(): + if not value: + continue + for match in PDF_NAME_PATTERN.findall(str(value)): + canonical = canonicalize_pdf_name(match) + if canonical is not None: + results.add(canonical) + return results + + +def _extract_priority_filenames_from_json(path: Path) -> set[str]: + results: set[str] = set() + data = json.loads(path.read_text(encoding="utf-8", errors="ignore")) + for text in _walk_json_strings(data): + canonical = canonicalize_pdf_name(text) + if canonical is not None: + results.add(canonical) + continue + for match in PDF_NAME_PATTERN.findall(text): + canonical = canonicalize_pdf_name(match) + if canonical is not None: + results.add(canonical) + return results + + +def _extract_priority_filenames_from_text(path: Path) -> set[str]: + results: set[str] = set() + text = path.read_text(encoding="utf-8", errors="ignore") + for line in text.splitlines(): + canonical = canonicalize_pdf_name(line) + if canonical is not None: + results.add(canonical) + for match in PDF_NAME_PATTERN.findall(text): + canonical = canonicalize_pdf_name(match) + if canonical is not None: + results.add(canonical) + return results + + +def load_priority_filenames(priority_dir: Path) -> set[str]: + results: set[str] = set() + if not priority_dir.exists(): + return results + for path in sorted(priority_dir.rglob("*")): + if not path.is_file(): + continue + direct = canonicalize_pdf_name(path.name) + if direct is not None: + results.add(direct) + continue + suffix = path.suffix.lower() + try: + if suffix == ".csv": + results.update(_extract_priority_filenames_from_csv(path)) + elif suffix == ".json": + results.update(_extract_priority_filenames_from_json(path)) + elif suffix in {".txt", ".list", ".lst", ".log"}: + results.update(_extract_priority_filenames_from_text(path)) + else: + continue + except Exception: + continue + return results + + +def rsync_one( + *, + remote_host: str, + remote_path: str, + temp_path: Path, + password_env: str, + connect_timeout: int, + io_timeout: int, +) -> subprocess.CompletedProcess[str]: + ssh_cmd = ( + "ssh " + "-o BatchMode=no " + "-o PreferredAuthentications=password " + "-o PubkeyAuthentication=no " + "-o KbdInteractiveAuthentication=yes " + f"-o ConnectTimeout={int(connect_timeout)} " + "-o ServerAliveInterval=15 " + "-o ServerAliveCountMax=3 " + "-o ConnectionAttempts=3 " + "-o StrictHostKeyChecking=no " + "-o UserKnownHostsFile=/tmp/greece_box_known_hosts" + ) + cmd = [ + "sshpass", + "-e", + "rsync", + "-av", + "--partial", + "--append-verify", + "--inplace", + f"--timeout={int(io_timeout)}", + "-e", + ssh_cmd, + f"{remote_host}:{remote_path}", + str(temp_path), + ] + return subprocess.run(cmd, capture_output=True, text=True, env=sshpass_env(password_env)) + + +def sftp_one( + *, + remote_host: str, + remote_path: str, + temp_path: Path, + password_env: str, + connect_timeout: int, + io_timeout: int, +) -> subprocess.CompletedProcess[str]: + cmd = [ + "sshpass", + "-e", + "sftp", + *ssh_transport_options(connect_timeout), + "-b", + "-", + remote_host, + ] + batch = f'reget "{remote_path}" "{temp_path}"\n' + return subprocess.run(cmd, capture_output=True, text=True, env=sshpass_env(password_env), input=batch) + + +def run(argv: Optional[Sequence[str]] = None) -> int: + args = parse_args(argv) + manifest_path = Path(args.manifest).expanduser().resolve() + work_root = Path(args.work_root).expanduser().resolve() + priority_dir = Path(args.priority_dir).expanduser().resolve() if args.priority_dir else (work_root / "unreachable_from_source_20260331") + downloads_dir = work_root / "downloads" + partial_dir = work_root / "partials" + logs_dir = work_root / "logs" + state_dir = work_root / "state" + downloads_dir.mkdir(parents=True, exist_ok=True) + partial_dir.mkdir(parents=True, exist_ok=True) + logs_dir.mkdir(parents=True, exist_ok=True) + state_dir.mkdir(parents=True, exist_ok=True) + + state = TransferState(state_dir / "transfer_state.sqlite3") + items = read_manifest(manifest_path) + if args.limit and int(args.limit) > 0: + items = items[: int(args.limit)] + state.sync_manifest(items) + state.reset_stale_in_progress() + state.mark_completed_if_present(downloads_dir, partial_dir) + manifest_names = {item.canonical_filename for item in items} + + stop_requested = False + + def _handle_signal(signum, _frame) -> None: + nonlocal stop_requested + stop_requested = True + print(f"[transfer] signal {signum} received; stopping after current file", file=sys.stderr) + + signal.signal(signal.SIGINT, _handle_signal) + signal.signal(signal.SIGTERM, _handle_signal) + + last_summary_ts = 0.0 + current_path = state_dir / "current_transfer.json" + summary_path = state_dir / "summary.json" + events_path = logs_dir / "events.jsonl" + priority_summary_path = state_dir / "priority_summary.json" + priority_available_path = state_dir / "priority_available_in_manifest.txt" + priority_missing_path = state_dir / "priority_missing_in_manifest.txt" + last_priority_set: Optional[set[str]] = None + + def refresh_priorities() -> dict[str, int]: + nonlocal last_priority_set + requested = load_priority_filenames(priority_dir) + if last_priority_set is None or requested != last_priority_set: + available = requested & manifest_names + missing = requested - manifest_names + state.set_priorities(available) + priority_available_path.write_text( + "".join(f"{name}\n" for name in sorted(available)), + encoding="utf-8", + ) + priority_missing_path.write_text( + "".join(f"{name}\n" for name in sorted(missing)), + encoding="utf-8", + ) + write_json( + priority_summary_path, + { + "updated_at": utc_now(), + "priority_dir": str(priority_dir), + "priority_only": bool(args.priority_only), + "requested_total": len(requested), + "available_in_manifest_total": len(available), + "missing_in_manifest_total": len(missing), + }, + ) + last_priority_set = requested + return state.priority_counts() + + priority_counts = refresh_priorities() + + while not stop_requested: + priority_counts = refresh_priorities() + row = state.next_item(max_attempts=int(args.max_attempts), priority_only=bool(args.priority_only)) + if row is None: + write_json(summary_path, {"updated_at": utc_now(), **state.counts(), **state.byte_counts(), **priority_counts, "priority_only": bool(args.priority_only), "done": True}) + break + + canonical = str(row["canonical_filename"]) + remote_path = str(row["remote_path"]) + remote_size = int(row["remote_size_bytes"]) + final_path = downloads_dir / canonical + temp_path = partial_dir / f"{canonical}.part" + current_size = temp_path.stat().st_size if temp_path.exists() else 0 + + state.mark_in_progress(canonical, current_size) + write_json( + current_path, + { + "updated_at": utc_now(), + "transport": str(args.transport), + "canonical_filename": canonical, + "remote_path": remote_path, + "remote_size_bytes": remote_size, + "partial_path": str(temp_path), + "partial_size_bytes": current_size, + "attempt_number": int(row["attempts"]) + 1, + }, + ) + append_event( + events_path, + { + "ts": utc_now(), + "event": "start", + "transport": str(args.transport), + "canonical_filename": canonical, + "remote_path": remote_path, + "remote_size_bytes": remote_size, + "partial_size_bytes": current_size, + "attempt_number": int(row["attempts"]) + 1, + }, + ) + + transfer_kwargs = { + "remote_host": str(args.remote_host), + "remote_path": remote_path, + "temp_path": temp_path, + "password_env": str(args.password_env), + "connect_timeout": int(args.connect_timeout), + "io_timeout": int(args.io_timeout), + } + if str(args.transport) == "rsync": + result = rsync_one(**transfer_kwargs) + else: + result = sftp_one(**transfer_kwargs) + + if result.returncode == 0 and temp_path.exists(): + actual_size = temp_path.stat().st_size + if remote_size > 0 and actual_size != remote_size: + state.mark_failed( + canonical, + f"Size mismatch after transfer: expected {remote_size}, got {actual_size}", + actual_size, + ) + else: + final_path.parent.mkdir(parents=True, exist_ok=True) + os.replace(temp_path, final_path) + state.mark_completed(canonical, actual_size) + append_event( + events_path, + { + "ts": utc_now(), + "event": "completed", + "transport": str(args.transport), + "canonical_filename": canonical, + "size_bytes": actual_size, + }, + ) + else: + actual_size = temp_path.stat().st_size if temp_path.exists() else 0 + error = (result.stderr or result.stdout or "").strip()[-4000:] + state.mark_failed(canonical, error or f"transfer failed with code {result.returncode}", actual_size) + append_event( + events_path, + { + "ts": utc_now(), + "event": "failed", + "transport": str(args.transport), + "canonical_filename": canonical, + "return_code": int(result.returncode), + "partial_size_bytes": actual_size, + "error": error or f"transfer failed with code {result.returncode}", + }, + ) + time.sleep(float(args.sleep_after_failure)) + + now = time.time() + if now - last_summary_ts >= float(args.summary_interval_seconds): + priority_counts = refresh_priorities() + write_json(summary_path, {"updated_at": utc_now(), **state.counts(), **state.byte_counts(), **priority_counts, "priority_only": bool(args.priority_only), "done": False}) + last_summary_ts = now + + if current_path.exists(): + try: + current_path.unlink() + except Exception: + pass + + priority_counts = refresh_priorities() + write_json(summary_path, {"updated_at": utc_now(), **state.counts(), **state.byte_counts(), **priority_counts, "priority_only": bool(args.priority_only), "done": True}) + state.close() + return 0 + + +if __name__ == "__main__": # pragma: no cover + raise SystemExit(run()) diff --git a/src/glossapi/scripts/review_manifest_materialize.py b/src/glossapi/scripts/review_manifest_materialize.py new file mode 100644 index 0000000..56fc7b1 --- /dev/null +++ b/src/glossapi/scripts/review_manifest_materialize.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +import argparse +import json +import re +import shutil +from collections import Counter +from pathlib import Path +from typing import Dict, Iterable, List + + +_SAFE_LABEL_RE = re.compile(r"[^a-z0-9._-]+") + + +def _slugify_label(value: object) -> str: + text = str(value).strip().lower() + text = text.replace(" ", "_") + text = _SAFE_LABEL_RE.sub("_", text) + text = text.strip("._-") + return text or "unlabeled" + + +def _format_metadata_lines(row: Dict[str, object], source_field: str, label_field: str, category_name: str) -> List[str]: + lines = [ + f"REVIEW_CATEGORY: {category_name}", + f"REVIEW_LABEL: {row.get(label_field, '')}", + ] + for key, value in row.items(): + if key in {source_field, label_field}: + continue + if isinstance(value, (dict, list)): + rendered = json.dumps(value, ensure_ascii=False) + else: + rendered = str(value) + lines.append(f"{key.upper()}: {rendered}") + return lines + + +def _read_manifest_rows(path: Path) -> List[Dict[str, object]]: + return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + + +def _write_review_copy( + src: Path, + dest: Path, + row: Dict[str, object], + source_field: str, + label_field: str, + category_name: str, +) -> None: + body = src.read_text(encoding="utf-8", errors="ignore") + header = "\n".join(_format_metadata_lines(row, source_field, label_field, category_name)) + dest.write_text(f"{header}\n\n=== REVIEW_SOURCE_CONTENT ===\n{body}", encoding="utf-8") + + +def materialize_manifest_categories( + manifest_path: Path, + output_dir: Path, + *, + source_field: str = "path", + label_field: str = "label", + category_name: str | None = None, +) -> Dict[str, object]: + rows = _read_manifest_rows(manifest_path) + category_name = category_name or label_field + + if output_dir.exists(): + for stale in output_dir.rglob("*.txt"): + stale.unlink() + for stale in output_dir.rglob("*.json"): + stale.unlink() + for stale in output_dir.rglob("*.jsonl"): + stale.unlink() + output_dir.mkdir(parents=True, exist_ok=True) + + labels_dir = output_dir / "by_label" + labels_dir.mkdir(parents=True, exist_ok=True) + + label_counts: Counter[str] = Counter() + written_rows: List[Dict[str, object]] = [] + + for row in rows: + if source_field not in row or label_field not in row: + raise KeyError(f"Manifest row missing required fields: {source_field!r}, {label_field!r}") + + src = Path(str(row[source_field])) + label = str(row[label_field]) + label_slug = _slugify_label(label) + dest_dir = labels_dir / label_slug + dest_dir.mkdir(parents=True, exist_ok=True) + dest = dest_dir / src.name + if dest.exists(): + stem = dest.stem + suffix = dest.suffix + counter = 2 + while True: + candidate = dest_dir / f"{stem}__dup{counter}{suffix}" + if not candidate.exists(): + dest = candidate + break + counter += 1 + + _write_review_copy(src, dest, row, source_field, label_field, category_name) + label_counts[label] += 1 + written_rows.append( + { + "label": label, + "label_slug": label_slug, + "source_path": str(src), + "copied_path": str(dest), + } + ) + + manifest_out = output_dir / "materialized_manifest.jsonl" + with manifest_out.open("w", encoding="utf-8") as handle: + for row in written_rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + summary = { + "manifest_path": str(manifest_path), + "output_dir": str(output_dir), + "category_name": category_name, + "source_field": source_field, + "label_field": label_field, + "row_count": len(rows), + "label_counts": dict(label_counts), + "label_dirs": { + _slugify_label(label): str(labels_dir / _slugify_label(label)) + for label in sorted(label_counts) + }, + } + (output_dir / "summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + return summary + + +def main() -> None: + parser = argparse.ArgumentParser(description="Materialize categorized review copies from a JSONL manifest.") + parser.add_argument("--manifest", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--source-field", default="path") + parser.add_argument("--label-field", default="label") + parser.add_argument("--category-name", default=None) + args = parser.parse_args() + + materialize_manifest_categories( + args.manifest, + args.output_dir, + source_field=args.source_field, + label_field=args.label_field, + category_name=args.category_name, + ) + + +if __name__ == "__main__": + main() diff --git a/src/glossapi/scripts/table_markdown_audit.py b/src/glossapi/scripts/table_markdown_audit.py new file mode 100644 index 0000000..1bba05d --- /dev/null +++ b/src/glossapi/scripts/table_markdown_audit.py @@ -0,0 +1,522 @@ +from __future__ import annotations + +import argparse +import html +import json +import re +from collections import Counter +from dataclasses import dataclass +from html.parser import HTMLParser +from pathlib import Path +from typing import Dict, List, Optional, Sequence, Tuple + + +TABLE_BLOCK_RE = re.compile(r"(?is)") +ROW_RE = re.compile(r"(?is).*?") +CELL_RE = re.compile(r"(?is)<(td|th)\b(.*?)>(.*?)") +ATTR_RE = re.compile(r'([A-Za-z_:][-A-Za-z0-9_:.]*)\s*=\s*(".*?"|\'.*?\'|[^\s>]+)', re.S) +TAG_RE = re.compile(r"(?is)<[^>]+>") +DISALLOWED_TAG_RE = re.compile(r"(?is)]*>") +BREAK_TAG_RE = re.compile(r"(?is)") + + +@dataclass +class ParsedCell: + tag: str + text: str + rowspan: int + colspan: int + + +@dataclass +class TableAudit: + source_path: str + source_stem: str + table_index_in_doc: int + global_index: int + html: str + status: str + convertible: bool + broken: bool + reasons: List[str] + row_count: int + col_count: int + nonempty_ratio: float + duplicate_rows: int + header_mode: str + spans_present: bool + markdown: Optional[str] + + +class _CellHTMLNormalizer(HTMLParser): + def __init__(self) -> None: + super().__init__(convert_charrefs=True) + self.parts: List[str] = [] + self.link_stack: List[Optional[str]] = [] + + def _append_break(self) -> None: + if self.parts and not self.parts[-1].endswith("\n"): + self.parts.append("\n") + + def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None: + tag = tag.lower() + attr_map = {key.lower(): (value or "") for key, value in attrs} + if tag == "br": + self._append_break() + return + if tag in {"p", "div", "li"}: + self._append_break() + if tag == "li": + self.parts.append("- ") + return + if tag in {"sub", "sup"}: + self.parts.append(f"<{tag}>") + return + if tag == "img": + alt = " ".join(attr_map.get("alt", "").split()) + if alt: + self.parts.append(alt) + return + if tag == "a": + href = attr_map.get("href", "").strip() + self.link_stack.append(href or None) + self.parts.append("[") + return + + def handle_endtag(self, tag: str) -> None: + tag = tag.lower() + if tag in {"p", "div", "li"}: + self._append_break() + return + if tag in {"sub", "sup"}: + self.parts.append(f"") + return + if tag == "a": + href = self.link_stack.pop() if self.link_stack else None + if href: + self.parts.append(f"]({href})") + else: + self.parts.append("]") + + def handle_data(self, data: str) -> None: + self.parts.append(data) + + def get_text(self) -> str: + return "".join(self.parts) + + +def _parse_attrs(attr_text: str) -> Dict[str, str]: + attrs: Dict[str, str] = {} + for key, raw_value in ATTR_RE.findall(attr_text): + value = raw_value.strip() + if len(value) >= 2 and value[0] == value[-1] and value[0] in {"'", '"'}: + value = value[1:-1] + attrs[key.lower()] = html.unescape(value) + return attrs + + +def _normalize_cell_html(cell_html: str) -> str: + parser = _CellHTMLNormalizer() + parser.feed(cell_html) + parser.close() + text = parser.get_text() + text = BREAK_TAG_RE.sub("\n", text) + text = DISALLOWED_TAG_RE.sub(" ", text) + text = html.unescape(text) + lines = [" ".join(line.split()) for line in text.splitlines()] + return "\n".join(line for line in lines if line).strip() + + +def _parse_table_rows(table_html: str) -> Tuple[List[List[ParsedCell]], List[str]]: + reasons: List[str] = [] + if re.search(r"(?is) Tuple[Optional[List[List[str]]], List[str]]: + reasons: List[str] = [] + active_rowspans: Dict[int, int] = {} + expanded_rows: List[List[str]] = [] + max_cols = 0 + + for parsed_row in parsed_rows: + row: List[str] = [] + col_idx = 0 + + def fill_active_until_free() -> None: + nonlocal col_idx + while active_rowspans.get(col_idx, 0) > 0: + row.append("") + active_rowspans[col_idx] -= 1 + if active_rowspans[col_idx] <= 0: + del active_rowspans[col_idx] + col_idx += 1 + + fill_active_until_free() + for cell in parsed_row: + fill_active_until_free() + row.append(cell.text) + if cell.rowspan > 1: + active_rowspans[col_idx] = max(active_rowspans.get(col_idx, 0), cell.rowspan - 1) + start_col = col_idx + col_idx += 1 + for extra in range(1, cell.colspan): + row.append("") + if cell.rowspan > 1: + active_rowspans[start_col + extra] = max( + active_rowspans.get(start_col + extra, 0), cell.rowspan - 1 + ) + col_idx += 1 + fill_active_until_free() + + max_cols = max(max_cols, len(row)) + expanded_rows.append(row) + + while active_rowspans: + row: List[str] = [] + col_idx = 0 + max_active_col = max(active_rowspans) + while col_idx <= max_active_col: + if active_rowspans.get(col_idx, 0) > 0: + row.append("") + active_rowspans[col_idx] -= 1 + if active_rowspans[col_idx] <= 0: + del active_rowspans[col_idx] + else: + row.append("") + col_idx += 1 + max_cols = max(max_cols, len(row)) + expanded_rows.append(row) + + if max_cols == 0 or not expanded_rows: + reasons.append("empty_grid") + return None, reasons + + for row in expanded_rows: + if len(row) < max_cols: + row.extend([""] * (max_cols - len(row))) + return expanded_rows, reasons + + +def _markdown_escape(text: str) -> str: + text = text.replace("\\", "\\\\") + text = text.replace("|", "\\|") + text = text.replace("\n", "
") + return text + + +def _format_markdown_row(values: Sequence[str], widths: Sequence[int]) -> str: + padded = [value.ljust(width) for value, width in zip(values, widths)] + return "| " + " | ".join(padded) + " |" + + +def _should_infer_header_row(grid: Sequence[Sequence[str]]) -> bool: + if len(grid) < 2: + return False + first_row = grid[0] + if not first_row: + return False + return all(any(ch.isalnum() for ch in cell) for cell in first_row) + + +def _grid_to_markdown(grid: Sequence[Sequence[str]], header_mode: str) -> str: + if not grid: + return "" + cols = len(grid[0]) + if header_mode in {"explicit_first_row", "inferred_first_row"}: + header = [_markdown_escape(cell) for cell in grid[0]] + data_rows = list(grid[1:]) + else: + header = [""] * cols + data_rows = list(grid) + escaped_rows = [[_markdown_escape(cell) for cell in row] for row in data_rows] + sep = ["---"] * cols + widths = [ + max( + len(header[idx]), + len(sep[idx]), + *(len(row[idx]) for row in escaped_rows), + ) + for idx in range(cols) + ] + + lines = [ + _format_markdown_row(header, widths), + _format_markdown_row(sep, widths), + ] + for row in escaped_rows: + lines.append(_format_markdown_row(row, widths)) + return "\n".join(lines) + + +def _assess_content( + grid: Sequence[Sequence[str]], + *, + spans_present: bool, +) -> Tuple[bool, List[str], float, int]: + total_cells = sum(len(row) for row in grid) + nonempty_cells = sum(1 for row in grid for cell in row if any(ch.isalnum() for ch in cell)) + nonempty_ratio = (nonempty_cells / total_cells) if total_cells else 0.0 + + row_keys = [] + for row in grid: + normalized = tuple(" ".join(cell.split()).casefold() for cell in row) + nonempty_in_row = sum(1 for cell in normalized if any(ch.isalnum() for ch in cell)) + if nonempty_in_row >= 2: + row_keys.append(normalized) + duplicate_rows = sum(freq - 1 for freq in Counter(row_keys).values() if freq >= 2) + + reasons: List[str] = [] + broken = False + if total_cells >= 18 and nonempty_ratio <= 0.15: + broken = True + reasons.append("near_empty_table") + if spans_present and total_cells >= 4 and nonempty_ratio <= 0.34: + broken = True + reasons.append("sparse_span_shell") + if len(grid) >= 4 and duplicate_rows >= 2: + broken = True + reasons.append("repeated_rows") + return broken, reasons, round(nonempty_ratio, 4), duplicate_rows + + +def audit_table(source_path: Path, table_index_in_doc: int, global_index: int, table_html: str) -> TableAudit: + parsed_rows, parse_reasons = _parse_table_rows(table_html) + spans_present = any(cell.rowspan > 1 or cell.colspan > 1 for row in parsed_rows for cell in row) + explicit_header = bool(parsed_rows and any(cell.tag == "th" for cell in parsed_rows[0])) + grid, expand_reasons = _expand_rows(parsed_rows) + reasons = list(dict.fromkeys(parse_reasons + expand_reasons)) + + if grid is None: + return TableAudit( + source_path=str(source_path), + source_stem=source_path.stem, + table_index_in_doc=table_index_in_doc, + global_index=global_index, + html=table_html, + status="broken_or_ambiguous", + convertible=False, + broken=True, + reasons=reasons or ["parse_failure"], + row_count=0, + col_count=0, + nonempty_ratio=0.0, + duplicate_rows=0, + header_mode="none", + spans_present=spans_present, + markdown=None, + ) + + broken, content_reasons, nonempty_ratio, duplicate_rows = _assess_content( + grid, + spans_present=spans_present, + ) + reasons = list(dict.fromkeys(reasons + content_reasons)) + if explicit_header: + header_mode = "explicit_first_row" + elif _should_infer_header_row(grid): + header_mode = "inferred_first_row" + else: + header_mode = "blank_first_row" + markdown = _grid_to_markdown(grid, header_mode=header_mode) + + if any(reason in {"nested_table", "invalid_rowspan", "invalid_colspan"} for reason in reasons): + status = "broken_or_ambiguous" + convertible = False + markdown = None + broken = True + else: + status = "convertible_but_broken" if broken else "convertible_clean" + convertible = True + + return TableAudit( + source_path=str(source_path), + source_stem=source_path.stem, + table_index_in_doc=table_index_in_doc, + global_index=global_index, + html=table_html, + status=status, + convertible=convertible, + broken=broken, + reasons=reasons, + row_count=len(grid), + col_count=len(grid[0]) if grid else 0, + nonempty_ratio=nonempty_ratio, + duplicate_rows=duplicate_rows, + header_mode=header_mode, + spans_present=spans_present, + markdown=markdown, + ) + + +def iter_tables(markdown_dir: Path): + global_index = 0 + for source_path in sorted(markdown_dir.glob("*.md")): + text = source_path.read_text(encoding="utf-8", errors="ignore") + table_index = 0 + for match in TABLE_BLOCK_RE.finditer(text): + table_index += 1 + global_index += 1 + yield source_path, table_index, global_index, match.group(0) + + +def write_review_file(output_dir: Path, audit: TableAudit) -> str: + filename = f"{audit.global_index:05d}__{audit.source_stem}__table_{audit.table_index_in_doc:03d}.txt" + output_path = output_dir / filename + lines = [ + f"SOURCE_PATH: {audit.source_path}", + f"SOURCE_STEM: {audit.source_stem}", + f"TABLE_INDEX_IN_DOC: {audit.table_index_in_doc}", + f"GLOBAL_INDEX: {audit.global_index}", + f"STATUS: {audit.status}", + f"CONVERTIBLE: {audit.convertible}", + f"BROKEN: {audit.broken}", + f"REASONS: {', '.join(audit.reasons) if audit.reasons else 'none'}", + f"ROWS: {audit.row_count}", + f"COLS: {audit.col_count}", + f"NONEMPTY_RATIO: {audit.nonempty_ratio}", + f"DUPLICATE_ROWS: {audit.duplicate_rows}", + f"HEADER_MODE: {audit.header_mode}", + f"SPANS_PRESENT: {audit.spans_present}", + "", + "=== HTML ===", + audit.html, + "", + "=== GITHUB_MD ===", + audit.markdown if audit.markdown is not None else "UNAVAILABLE", + "", + ] + output_path.write_text("\n".join(lines), encoding="utf-8") + return str(output_path) + + +def write_clean_markdown_file(output_dir: Path, audit: TableAudit) -> Optional[str]: + if audit.markdown is None: + return None + filename = f"{audit.global_index:05d}__{audit.source_stem}__table_{audit.table_index_in_doc:03d}.md" + output_path = output_dir / filename + output_path.write_text( + "\n".join( + [ + "## ORIGINAL_HTML", + "", + audit.html, + "", + "## GITHUB_MD", + "", + audit.markdown, + "", + ] + ), + encoding="utf-8", + ) + return str(output_path) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Audit HTML tables and export GitHub Markdown conversions.") + parser.add_argument("--input-dir", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + parser.add_argument("--max-tables", type=int, default=1000) + args = parser.parse_args() + + output_dir = args.output_dir + output_dir.mkdir(parents=True, exist_ok=True) + tables_dir = output_dir / "tables" + tables_dir.mkdir(parents=True, exist_ok=True) + clean_md_dir = output_dir / "github_md_tables" + clean_md_dir.mkdir(parents=True, exist_ok=True) + + manifest_path = output_dir / "manifest.jsonl" + summary_path = output_dir / "summary.json" + if manifest_path.exists(): + manifest_path.unlink() + if summary_path.exists(): + summary_path.unlink() + for stale in tables_dir.glob("*.txt"): + stale.unlink() + for stale in clean_md_dir.glob("*.md"): + stale.unlink() + + rows = [] + audited = 0 + for source_path, table_index, global_index, table_html in iter_tables(args.input_dir): + audited += 1 + audit = audit_table(source_path, table_index, global_index, table_html) + output_path = write_review_file(tables_dir, audit) + markdown_path = write_clean_markdown_file(clean_md_dir, audit) + row = { + "source_path": audit.source_path, + "source_stem": audit.source_stem, + "table_index_in_doc": audit.table_index_in_doc, + "global_index": audit.global_index, + "status": audit.status, + "convertible": audit.convertible, + "broken": audit.broken, + "reasons": audit.reasons, + "row_count": audit.row_count, + "col_count": audit.col_count, + "nonempty_ratio": audit.nonempty_ratio, + "duplicate_rows": audit.duplicate_rows, + "header_mode": audit.header_mode, + "spans_present": audit.spans_present, + "output_path": output_path, + "markdown_output_path": markdown_path, + } + rows.append(row) + if audited >= args.max_tables: + break + + with manifest_path.open("w", encoding="utf-8") as handle: + for row in rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + reason_counts = Counter(reason for row in rows for reason in row["reasons"]) + status_counts = Counter(row["status"] for row in rows) + summary = { + "input_dir": str(args.input_dir), + "output_dir": str(output_dir), + "github_md_dir": str(clean_md_dir), + "audited_table_count": len(rows), + "convertible_count": sum(1 for row in rows if row["convertible"]), + "broken_count": sum(1 for row in rows if row["broken"]), + "status_counts": dict(status_counts), + "reason_counts": dict(reason_counts), + } + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/src/glossapi/scripts/table_sentence_context_review.py b/src/glossapi/scripts/table_sentence_context_review.py new file mode 100644 index 0000000..6e2a074 --- /dev/null +++ b/src/glossapi/scripts/table_sentence_context_review.py @@ -0,0 +1,256 @@ +from __future__ import annotations + +import argparse +import importlib.util +import json +import re +import sys +from collections import Counter +from pathlib import Path +from typing import Dict, List, Optional, Sequence, Tuple + +PAGE_SPLIT_MARKER = "<--- Page Split --->" +TABLE_BLOCK_RE = re.compile(r"(?is)") +WORD_RE = re.compile(r"[^\W\d_]+", re.UNICODE) + + +_TABLE_AUDIT_PATH = Path(__file__).with_name("table_markdown_audit.py") +_TABLE_AUDIT_SPEC = importlib.util.spec_from_file_location("table_markdown_audit_local", _TABLE_AUDIT_PATH) +assert _TABLE_AUDIT_SPEC and _TABLE_AUDIT_SPEC.loader +_TABLE_AUDIT_MODULE = importlib.util.module_from_spec(_TABLE_AUDIT_SPEC) +sys.modules[_TABLE_AUDIT_SPEC.name] = _TABLE_AUDIT_MODULE +_TABLE_AUDIT_SPEC.loader.exec_module(_TABLE_AUDIT_MODULE) +_expand_rows = _TABLE_AUDIT_MODULE._expand_rows +_parse_table_rows = _TABLE_AUDIT_MODULE._parse_table_rows + + +def _extract_review_html(review_text: str) -> str: + return review_text.split("=== HTML ===\n", 1)[1].split("\n\n=== GITHUB_MD ===", 1)[0] + + +def _flatten_nonempty_cells(table_html: str) -> List[str]: + parsed_rows, _ = _parse_table_rows(table_html) + grid, _ = _expand_rows(parsed_rows) + if not grid: + return [] + nonempty: List[str] = [] + for row in grid: + for cell in row: + normalized = " ".join(cell.split()) + if any(ch.isalnum() for ch in normalized): + nonempty.append(normalized) + return nonempty + + +def _is_sentence_shell_candidate(review_row: Dict[str, object], table_html: str) -> Tuple[bool, Dict[str, int]]: + nonempty_cells = _flatten_nonempty_cells(table_html) + word_count = sum(len(WORD_RE.findall(cell)) for cell in nonempty_cells) + max_cell_len = max((len(cell) for cell in nonempty_cells), default=0) + metrics = { + "nonempty_cell_count": len(nonempty_cells), + "word_count": word_count, + "max_cell_len": max_cell_len, + } + is_candidate = ( + bool(review_row.get("broken")) + and "sparse_span_shell" in list(review_row.get("reasons", [])) + and len(nonempty_cells) == 1 + and word_count >= 6 + and max_cell_len >= 40 + ) + return is_candidate, metrics + + +def _find_table_page_context( + source_path: Path, + table_index_in_doc: int, +) -> Tuple[int, int, int, int, str, str, str]: + text = source_path.read_text(encoding="utf-8", errors="ignore") + pages = text.split(PAGE_SPLIT_MARKER) + seen = 0 + for page_idx, page in enumerate(pages): + matches = list(TABLE_BLOCK_RE.finditer(page)) + if seen + len(matches) < table_index_in_doc: + seen += len(matches) + continue + local_idx = table_index_in_doc - seen - 1 + match = matches[local_idx] + prev_page = pages[page_idx - 1] if page_idx > 0 else "" + curr_page = page + next_page = pages[page_idx + 1] if page_idx + 1 < len(pages) else "" + return page_idx, match.start(), match.end(), len(pages), prev_page, curr_page, next_page + raise ValueError(f"Could not find table {table_index_in_doc} in {source_path}") + + +def _smart_join(before_text: str, inline_text: str, after_text: str) -> str: + left = before_text.rstrip() + right = after_text.lstrip() + insertion = inline_text.strip() + + if left and not left.endswith(("\n", " ", "(", "[", "{", "“", "\"", "'")): + if left[-1].isalnum() and insertion and insertion[0].isalnum(): + left += " " + if right and not right.startswith(("\n", " ", ".", ",", ";", ":", "!", "?", ")", "]", "}", "”", "\"", "'")): + if insertion and insertion[-1].isalnum() and right[0].isalnum(): + insertion += " " + return left + insertion + right + + +def _context_fit_guess(before_text: str, inline_text: str, after_text: str) -> Tuple[bool, List[str]]: + reasons: List[str] = [] + word_count = len(WORD_RE.findall(inline_text)) + if word_count < 6: + reasons.append("short_inline_text") + left_window = before_text[-4:] + right_window = after_text[:4] + left_blockish = (not before_text) or ("\n" in left_window) or before_text.endswith((" ", "\t")) + right_blockish = (not after_text) or ("\n" in right_window) or after_text.startswith((" ", "\t")) + if not left_blockish: + reasons.append("not_block_isolated_left") + if not right_blockish: + reasons.append("not_block_isolated_right") + fit = word_count >= 6 and left_blockish and right_blockish + return fit, reasons + + +def _format_three_page_context( + prev_page: str, + curr_page: str, + next_page: str, + start: int, + end: int, + inline_text: str, +) -> Tuple[str, str]: + tagged_current = curr_page[:start] + "[[[TABLE_START]]]" + curr_page[start:end] + "[[[TABLE_END]]]" + curr_page[end:] + replaced_current = ( + curr_page[:start] + + "[[[INLINE_TEXT_START]]]" + + inline_text + + "[[[INLINE_TEXT_END]]]" + + curr_page[end:] + ) + original_context = ( + f"=== PAGE -1 ===\n{prev_page}\n\n" + f"=== PAGE 0 ===\n{tagged_current}\n\n" + f"=== PAGE +1 ===\n{next_page}\n" + ) + replaced_context = ( + f"=== PAGE -1 ===\n{prev_page}\n\n" + f"=== PAGE 0 ===\n{replaced_current}\n\n" + f"=== PAGE +1 ===\n{next_page}\n" + ) + return original_context, replaced_context + + +def main() -> None: + parser = argparse.ArgumentParser(description="Export 3-page context review files for sentence-in-table shells.") + parser.add_argument("--audit-dir", required=True, type=Path) + parser.add_argument("--output-dir", required=True, type=Path) + args = parser.parse_args() + + audit_dir = args.audit_dir + manifest_path = audit_dir / "manifest.jsonl" + output_dir = args.output_dir + output_dir.mkdir(parents=True, exist_ok=True) + contexts_dir = output_dir / "contexts" + contexts_dir.mkdir(parents=True, exist_ok=True) + + summary_path = output_dir / "summary.json" + review_manifest_path = output_dir / "manifest.jsonl" + if summary_path.exists(): + summary_path.unlink() + if review_manifest_path.exists(): + review_manifest_path.unlink() + for stale in contexts_dir.glob("*.txt"): + stale.unlink() + + rows = [json.loads(line) for line in manifest_path.read_text(encoding="utf-8").splitlines() if line.strip()] + review_rows: List[Dict[str, object]] = [] + + for row in rows: + review_text = Path(str(row["output_path"])).read_text(encoding="utf-8") + table_html = _extract_review_html(review_text) + is_candidate, metrics = _is_sentence_shell_candidate(row, table_html) + if not is_candidate: + continue + + inline_text = _flatten_nonempty_cells(table_html)[0] + page_idx, start, end, page_count, prev_page, curr_page, next_page = _find_table_page_context( + Path(str(row["source_path"])), + int(row["table_index_in_doc"]), + ) + fit_guess, fit_reasons = _context_fit_guess(curr_page[:start], inline_text, curr_page[end:]) + original_context, replaced_context = _format_three_page_context( + prev_page, + curr_page, + next_page, + start, + end, + inline_text, + ) + filename = f"{int(row['global_index']):05d}__{row['source_stem']}__table_{int(row['table_index_in_doc']):03d}.txt" + output_path = contexts_dir / filename + output_path.write_text( + "\n".join( + [ + f"SOURCE_PATH: {row['source_path']}", + f"SOURCE_STEM: {row['source_stem']}", + f"TABLE_INDEX_IN_DOC: {row['table_index_in_doc']}", + f"GLOBAL_INDEX: {row['global_index']}", + f"PAGE_INDEX_ZERO_BASED: {page_idx}", + f"PAGE_NUMBER_ONE_BASED: {page_idx + 1}", + f"PAGE_COUNT: {page_count}", + f"FIT_GUESS: {fit_guess}", + f"FIT_REASONS: {', '.join(fit_reasons) if fit_reasons else 'none'}", + f"INLINE_TEXT_WORDS: {metrics['word_count']}", + f"INLINE_TEXT_CHARS: {metrics['max_cell_len']}", + "", + "=== INLINE_TEXT ===", + inline_text, + "", + "=== ORIGINAL_CONTEXT_3P ===", + original_context, + "", + "=== REPLACED_CONTEXT_3P ===", + replaced_context, + "", + ] + ), + encoding="utf-8", + ) + review_rows.append( + { + "source_path": row["source_path"], + "source_stem": row["source_stem"], + "table_index_in_doc": row["table_index_in_doc"], + "global_index": row["global_index"], + "page_number": page_idx + 1, + "page_count": page_count, + "fit_guess": fit_guess, + "fit_reasons": fit_reasons, + "inline_text_words": metrics["word_count"], + "inline_text_chars": metrics["max_cell_len"], + "output_path": str(output_path), + } + ) + + with review_manifest_path.open("w", encoding="utf-8") as handle: + for row in review_rows: + handle.write(json.dumps(row, ensure_ascii=False)) + handle.write("\n") + + fit_counter = Counter(bool(row["fit_guess"]) for row in review_rows) + reason_counter = Counter(reason for row in review_rows for reason in row["fit_reasons"]) + summary = { + "audit_dir": str(audit_dir), + "output_dir": str(output_dir), + "candidate_count": len(review_rows), + "fit_guess_count": fit_counter.get(True, 0), + "fit_guess_rate": round((fit_counter.get(True, 0) / len(review_rows)), 4) if review_rows else 0.0, + "fit_reason_counts": dict(reason_counter), + } + summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8") + + +if __name__ == "__main__": + main() diff --git a/tests/test_browser_gloss_downloader.py b/tests/test_browser_gloss_downloader.py new file mode 100644 index 0000000..ab94f15 --- /dev/null +++ b/tests/test_browser_gloss_downloader.py @@ -0,0 +1,477 @@ +import asyncio +import io + +import pandas as pd +from PIL import Image + +from glossapi import Corpus +from glossapi.download_policy import build_download_policy +from glossapi.gloss_browser_downloader import BrowserGlossDownloader, BrowserSessionState +import glossapi.corpus.phase_download as phase_download_mod + + +def test_browser_downloader_skips_viewer_interstitial(tmp_path, monkeypatch): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + called = False + + async def _fake_browser_download(**kwargs): + nonlocal called + called = True + return b"%PDF-1.7\n", {"Content-Type": "application/pdf"}, {"candidate_url": kwargs["url"]} + + monkeypatch.setattr(downloader, "_download_via_browser_session", _fake_browser_download) + + result = asyncio.run( + downloader._recover_html_interstitial( + row_index=0, + url="https://freader.ekt.gr/eadd/index.php?doc=60819&lang=el", + headers={"Content-Type": "text/html"}, + content=b"", + html_issue=( + "HTML document viewer returned instead of a downloadable file; " + "a source-specific fetcher with persisted cookies/redirect handling is required" + ), + retry_count=0, + filename_base="AAA_000", + referer=None, + ) + ) + + assert result is None + assert called is False + + +def test_browser_downloader_recovers_challenge_page(tmp_path, monkeypatch): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + async def _fake_browser_download(**kwargs): + return ( + b"%PDF-1.7\n%dummy\n", + {"Content-Type": "application/pdf"}, + {"candidate_url": "https://example.org/file.pdf"}, + ) + + monkeypatch.setattr(downloader, "_download_via_browser_session", _fake_browser_download) + + result = asyncio.run( + downloader._recover_html_interstitial( + row_index=0, + url="https://example.org/file.pdf", + headers={"Content-Type": "text/html"}, + content=b"challenge", + html_issue=( + "HTML challenge page returned instead of a document; " + "browser automation or cookie bootstrap is required" + ), + retry_count=1, + filename_base="AAA_000", + referer=None, + ) + ) + + assert result == (True, "AAA_000.pdf", "pdf", "", 1) + assert (tmp_path / "downloads" / "AAA_000.pdf").read_bytes().startswith(b"%PDF-1.7") + assert not (tmp_path / "downloads" / ".part_browser_0").exists() + + +def test_browser_downloader_detects_anubis_challenge(tmp_path): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + issue = downloader._detect_html_interstitial( + "https://dias.library.tuc.gr/view/view/manf/77495", + {"Content-Type": "text/html"}, + b"Making sure you're not a bot!" + b"anubis /.within.website/", + ) + + assert issue is not None + assert "challenge page returned" in issue.lower() + + +def test_infer_file_extension_prefers_html_magic_over_pdf_url(tmp_path): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + file_ext = downloader.infer_file_extension( + "https://repository.academyofathens.gr/document/43963.pdf", + {"Content-Type": "text/html"}, + b"spa shell", + ) + + assert file_ext == "html" + + +def test_infer_file_extension_accepts_pdf_header_after_small_prefix(tmp_path): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + file_ext = downloader.infer_file_extension( + "https://pergamos.lib.uoa.gr/uoa/dl/object/1316268/file.pdf", + {"Content-Type": "application/pdf"}, + b"test123%PDF-1.5\nrest", + ) + + assert file_ext == "pdf" + + +def test_finalize_download_result_rejects_invalid_pdf_payload(tmp_path): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + result = asyncio.run( + downloader._finalize_download_result( + row_index=0, + url="https://example.org/file.pdf", + resp_headers={"Content-Type": "application/pdf"}, + content=b"this is not a pdf payload", + retry_count=0, + filename_base="AAA_000", + referer=None, + ) + ) + + assert result[0] is False + assert result[2] == "pdf" + assert "invalid pdf signature" in result[3].lower() + assert not (tmp_path / "downloads" / "AAA_000.pdf").exists() + + +def test_browser_downloader_recovers_academy_bookreader_pdf(tmp_path, monkeypatch): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path), default_download_route="standard") + + async def _fake_download_academy(url: str): + return b"%PDF-1.4\n%academy\n" + + monkeypatch.setattr(downloader, "_download_academy_bookreader_pdf", _fake_download_academy) + + result = asyncio.run( + downloader._recover_html_interstitial( + row_index=0, + url="https://repository.academyofathens.gr/document/43963.pdf", + headers={"Content-Type": "text/html"}, + content=b"", + html_issue="Expected a file-like response but received HTML instead", + retry_count=0, + filename_base="AAA_000", + referer=None, + ) + ) + + assert result == (True, "AAA_000.pdf", "pdf", "", 0) + assert (tmp_path / "downloads" / "AAA_000.pdf").read_bytes().startswith(b"%PDF-1.4") + + +def test_academy_images_to_pdf_bytes_builds_pdf(tmp_path): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + blobs = [] + for color in ("red", "blue"): + image = Image.new("RGB", (16, 16), color=color) + buf = io.BytesIO() + image.save(buf, format="JPEG") + blobs.append(buf.getvalue()) + + pdf_bytes = downloader._academy_images_to_pdf_bytes(blobs) + + assert pdf_bytes.startswith(b"%PDF-") + + +def test_browser_downloader_domain_cookie_lookup(tmp_path): + downloader = BrowserGlossDownloader( + output_dir=str(tmp_path), + domain_cookies={"eur-lex.europa.eu": {"token": "abc123"}}, + ) + + cookies = downloader._domain_cookies_for_url( + "https://eur-lex.europa.eu/legal-content/EL/TXT/PDF/?uri=OJ:L_202502360" + ) + + assert cookies == {"token": "abc123"} + + +def test_browser_downloader_bootstrap_url_uses_base_for_file_endpoints(tmp_path): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + assert downloader._choose_browser_bootstrap_url( + "https://eur-lex.europa.eu/legal-content/EL/TXT/PDF/?uri=OJ:L_202502360" + ) == "https://eur-lex.europa.eu" + + +def test_browser_downloader_ignores_err_aborted_for_file_navigation(tmp_path): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path)) + + assert downloader._should_ignore_navigation_exception( + "https://eur-lex.europa.eu/legal-content/EL/TXT/PDF/?uri=OJ:L_202502360", + RuntimeError("Page.goto: net::ERR_ABORTED"), + ) + assert not downloader._should_ignore_navigation_exception( + "https://example.org/article", + RuntimeError("Page.goto: net::ERR_ABORTED"), + ) + + +def test_browser_downloader_uses_default_browser_route_for_preflight(tmp_path, monkeypatch): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path), default_download_route="browser") + + async def _fake_download_browser_route(**kwargs): + return True, "AAA_000.pdf", "pdf", "", 0 + + monkeypatch.setattr(downloader, "_download_browser_route", _fake_download_browser_route) + + result = asyncio.run( + downloader._preflight_download( + row_index=0, + url="https://example.org/file.pdf", + retry_count=0, + filename_base="AAA_000", + referer=None, + ) + ) + + assert result == (True, "AAA_000.pdf", "pdf", "", 0) + + +def test_browser_downloader_reuses_cached_domain_session(tmp_path, monkeypatch): + downloader = BrowserGlossDownloader(output_dir=str(tmp_path), default_download_route="auto") + bootstraps = 0 + fetches = 0 + + async def _fake_fetch_with_browser_session_state(**kwargs): + nonlocal fetches + fetches += 1 + return b"%PDF-1.7\n", {"Content-Type": "application/pdf"}, {"candidate_url": kwargs["url"]} + + async def _bootstrap(**kwargs): + nonlocal bootstraps + bootstraps += 1 + return BrowserSessionState(user_agent="UA", cookie_header="a=b", cached_at=10_000.0), [] + + monkeypatch.setattr(downloader, "_bootstrap_browser_session_state", _bootstrap) + monkeypatch.setattr(downloader, "_fetch_with_browser_session_state", _fake_fetch_with_browser_session_state) + monkeypatch.setattr("glossapi.gloss_browser_downloader.time.time", lambda: 10_100.0) + + first = asyncio.run( + downloader._download_via_browser_session(url="https://eur-lex.europa.eu/file.pdf", referer=None) + ) + second = asyncio.run( + downloader._download_via_browser_session(url="https://eur-lex.europa.eu/file2.pdf", referer=None) + ) + + assert first[0].startswith(b"%PDF") + assert second[0].startswith(b"%PDF") + assert bootstraps == 1 + assert fetches == 2 + + +def test_browser_downloader_policy_routes_domain_to_browser(tmp_path, monkeypatch): + policy = build_download_policy( + { + "default": {"downloader": "standard"}, + "rules": [ + { + "match": {"domains": ["eur-lex.europa.eu"]}, + "downloader": "browser", + "browser_timeout_ms": 1234, + } + ], + } + ) + downloader = BrowserGlossDownloader( + output_dir=str(tmp_path), + download_policy=policy, + default_download_route="standard", + ) + + observed = {} + + async def _fake_download_browser_route(**kwargs): + observed.update(kwargs) + return True, "AAA_000.pdf", "pdf", "", 0 + + monkeypatch.setattr(downloader, "_download_browser_route", _fake_download_browser_route) + + result = asyncio.run( + downloader._preflight_download( + row_index=0, + url="https://eur-lex.europa.eu/legal-content/EL/TXT/PDF/?uri=OJ:L_202502360", + retry_count=0, + filename_base="AAA_000", + referer=None, + ) + ) + + assert result == (True, "AAA_000.pdf", "pdf", "", 0) + assert observed["route_options"]["browser_timeout_ms"] == 1234 + + +def test_download_policy_preserves_transport_and_scheduler_options(): + policy = build_download_policy( + { + "default": {"downloader": "standard"}, + "rules": [ + { + "match": {"domains": ["ikee.lib.auth.gr"]}, + "downloader": "standard", + "request_timeout": 120, + "ssl_verify": False, + "per_domain_concurrency": 2, + "domain_concurrency_floor": 1, + "domain_concurrency_ceiling": 3, + "skip_failed_after": 5, + "domain_cookies": {"sessionid": "abc"}, + } + ], + } + ) + + route, options = policy.resolve("https://ikee.lib.auth.gr/record/123/files/file.pdf") + + assert route == "standard" + assert options["request_timeout"] == 120 + assert options["ssl_verify"] is False + assert options["per_domain_concurrency"] == 2 + assert options["domain_concurrency_floor"] == 1 + assert options["domain_concurrency_ceiling"] == 3 + assert options["skip_failed_after"] == 5 + assert options["domain_cookies"] == {"sessionid": "abc"} + + +def test_browser_downloader_route_options_apply_standard_transport_settings(tmp_path): + policy = build_download_policy( + { + "default": {"downloader": "standard"}, + "rules": [ + { + "match": {"domains": ["ktisis.cut.ac.cy"]}, + "downloader": "standard", + "request_timeout": 90, + "ssl_verify": False, + "per_domain_concurrency": 2, + "domain_concurrency_floor": 1, + "domain_concurrency_ceiling": 2, + "skip_failed_after": 4, + "domain_cookies": {"sessionid": "abc"}, + } + ], + } + ) + downloader = BrowserGlossDownloader( + output_dir=str(tmp_path), + download_policy=policy, + default_download_route="standard", + ) + + async def _build_connector(): + return downloader._build_session_connector( + "https://ktisis.cut.ac.cy/items/123/file.pdf", + route_options=route_options, + ) + + route, route_options = downloader._resolve_route("https://ktisis.cut.ac.cy/items/123/file.pdf") + timeout = downloader._build_request_timeout(0, route_options=route_options) + connector = asyncio.run(_build_connector()) + cookies = downloader._resolve_request_cookies( + "https://ktisis.cut.ac.cy/items/123/file.pdf", + route_options=route_options, + ) + floor, ceiling, start, skip_after = downloader._resolve_domain_scheduler_settings(route_options) + + assert route == "standard" + assert timeout.total == 90 + assert connector is not None + assert cookies["sessionid"] == "abc" + assert (floor, ceiling, start, skip_after) == (1, 2, 2, 4) + + +def test_corpus_download_mode_selects_browser_downloader(tmp_path, monkeypatch): + input_df = pd.DataFrame({"url": ["https://example.org/file.pdf"]}) + input_parquet = tmp_path / "urls.parquet" + input_df.to_parquet(input_parquet, index=False) + + observed = {} + + class DummyBrowserDownloader: + def __init__(self, *args, **kwargs): + observed["cls"] = "browser" + observed["kwargs"] = kwargs + + def download_files(self, input_parquet: str, **kwargs): + return pd.DataFrame( + { + "url": ["https://example.org/file.pdf"], + "filename": ["AAA_000.pdf"], + "download_success": [True], + "download_error": [""], + } + ) + + monkeypatch.setattr(phase_download_mod, "BrowserGlossDownloader", DummyBrowserDownloader) + + corpus = Corpus(input_dir=tmp_path, output_dir=tmp_path) + result = corpus.download(input_parquet=input_parquet, download_mode="browser") + + assert observed["cls"] == "browser" + assert observed["kwargs"]["default_download_route"] == "browser" + assert bool(result["download_success"].iloc[0]) is True + assert (tmp_path / "download_results" / f"download_results_{input_parquet.name}").exists() + + +def test_corpus_browser_mode_alias_selects_browser_downloader(tmp_path, monkeypatch): + input_df = pd.DataFrame({"url": ["https://example.org/file.pdf"]}) + input_parquet = tmp_path / "urls.parquet" + input_df.to_parquet(input_parquet, index=False) + + observed = {} + + class DummyBrowserDownloader: + def __init__(self, *args, **kwargs): + observed["cls"] = "browser" + + def download_files(self, input_parquet: str, **kwargs): + return pd.DataFrame( + { + "url": ["https://example.org/file.pdf"], + "filename": ["AAA_000.pdf"], + "download_success": [True], + "download_error": [""], + } + ) + + monkeypatch.setattr(phase_download_mod, "BrowserGlossDownloader", DummyBrowserDownloader) + + corpus = Corpus(input_dir=tmp_path, output_dir=tmp_path) + corpus.download(input_parquet=input_parquet, browser_mode=True) + + assert observed["cls"] == "browser" + + +def test_corpus_policy_file_selects_browser_router(tmp_path, monkeypatch): + input_df = pd.DataFrame({"url": ["https://eur-lex.europa.eu/file.pdf"]}) + input_parquet = tmp_path / "urls.parquet" + input_df.to_parquet(input_parquet, index=False) + policy_path = tmp_path / "download_policy.yml" + policy_path.write_text( + "default:\n downloader: standard\nrules:\n - match:\n domains: [eur-lex.europa.eu]\n downloader: browser\n", + encoding="utf-8", + ) + + observed = {} + + class DummyBrowserDownloader: + def __init__(self, *args, **kwargs): + observed["kwargs"] = kwargs + + def download_files(self, input_parquet: str, **kwargs): + return pd.DataFrame( + { + "url": ["https://eur-lex.europa.eu/file.pdf"], + "filename": ["AAA_000.pdf"], + "download_success": [True], + "download_error": [""], + } + ) + + monkeypatch.setattr(phase_download_mod, "BrowserGlossDownloader", DummyBrowserDownloader) + + corpus = Corpus(input_dir=tmp_path, output_dir=tmp_path) + corpus.download(input_parquet=input_parquet, download_policy_file=policy_path) + + assert observed["kwargs"]["download_policy_file"] == policy_path.resolve() + assert observed["kwargs"]["default_download_route"] == "standard" diff --git a/tests/test_corpus_clean_enhancements.py b/tests/test_corpus_clean_enhancements.py index b876a20..5a31ffa 100644 --- a/tests/test_corpus_clean_enhancements.py +++ b/tests/test_corpus_clean_enhancements.py @@ -1,5 +1,6 @@ from __future__ import annotations +import json import os from pathlib import Path @@ -7,6 +8,15 @@ import pytest from glossapi import Corpus +from glossapi.corpus.phase_clean import ( + DEFAULT_OCR_WORD_REPEAT_WINDOW, + _find_word_repeat_spans, + _find_word_repeat_spans_python, + _merge_labeled_raw_spans, + _normalize_alnum_with_map_skip_tags, +) +from glossapi.scripts.table_markdown_audit import audit_table, write_clean_markdown_file +from glossapi.scripts.review_manifest_materialize import materialize_manifest_categories LATEX_MOJIBAKE_MD = """# Sample Document @@ -55,6 +65,171 @@ def _run_clean_and_read_row( return row.iloc[0] +def _run_clean_ocr_and_read_row( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + drop_bad: bool = False, +) -> pd.Series: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + corpus.clean_ocr(drop_bad=drop_bad) + parquet = corpus.output_dir / "download_results" / "download_results.parquet" + df = pd.read_parquet(parquet) + row = df[df["filename"] == f"{stem}.pdf"] + assert not row.empty, "Expected OCR metrics entry for generated markdown" + return row.iloc[0] + + +def _run_clean_ocr_and_read_cleaned_text( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + write_cleaned_files: bool = True, +) -> str: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + corpus.clean_ocr(write_cleaned_files=write_cleaned_files) + cleaned_path = corpus.cleaned_markdown_dir / f"{stem}.md" + assert cleaned_path.exists(), f"Expected cleaned markdown output at {cleaned_path}" + return cleaned_path.read_text(encoding="utf-8") + + +def _run_clean_ocr_debug_export( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + max_pages: int | None = 1000, +) -> tuple[list[dict], Path]: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + debug_dir = corpus.output_dir / "ocr_debug" + rows = corpus.clean_ocr_debug(debug_dir, max_pages=max_pages) + return rows, debug_dir + + +def _run_clean_ocr_numeric_debug_export( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + max_pages: int | None = 1000, +) -> tuple[list[dict], Path]: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + debug_dir = corpus.output_dir / "ocr_numeric_debug" + rows = corpus.clean_ocr_numeric_debug(debug_dir, max_pages=max_pages) + return rows, debug_dir + + +def _run_clean_token_category_debug_export( + corpus: Corpus, + markdown_text: str, + specs: list[dict], + *, + stem: str = "sample", + max_pages: int | None = 1000, + synthetic_page_target_chars: int = 4000, + synthetic_page_min_header_chars: int = 1200, + synthetic_page_hard_max_chars: int = 6000, +) -> tuple[list[dict], Path]: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + specs_path = corpus.output_dir / "token_category_specs.json" + specs_path.write_text(json.dumps(specs, ensure_ascii=False, indent=2), encoding="utf-8") + debug_dir = corpus.output_dir / "token_category_debug" + rows = corpus.clean_token_category_debug( + debug_dir, + specs_path, + max_pages=max_pages, + synthetic_page_target_chars=synthetic_page_target_chars, + synthetic_page_min_header_chars=synthetic_page_min_header_chars, + synthetic_page_hard_max_chars=synthetic_page_hard_max_chars, + ) + return rows, debug_dir + + +def _run_clean_ocr_numeric_word_debug_docs( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + max_docs: int | None = 100, +) -> tuple[list[dict], Path]: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + debug_dir = corpus.output_dir / "ocr_numeric_word_debug" + rows = corpus.clean_ocr_numeric_word_debug_docs(debug_dir, max_docs=max_docs) + return rows, debug_dir + + +def _run_clean_ocr_hybrid_debug_export( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + max_docs: int | None = 100, +) -> tuple[list[dict], Path]: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + debug_dir = corpus.output_dir / "ocr_hybrid_debug" + rows = corpus.clean_ocr_hybrid_debug(debug_dir, max_docs=max_docs) + return rows, debug_dir + + +def _run_clean_ocr_latex_slot_progression_debug_export( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + max_docs: int | None = 1000, +) -> tuple[list[dict], Path]: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + debug_dir = corpus.output_dir / "ocr_latex_slot_progression_debug" + rows = corpus.clean_ocr_latex_slot_progression_debug(debug_dir, max_docs=max_docs) + return rows, debug_dir + + +def _run_clean_ocr_latex_debug_export( + corpus: Corpus, + markdown_text: str, + *, + stem: str = "sample", + max_docs: int | None = 1000, +) -> tuple[list[dict], Path]: + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(markdown_text, encoding="utf-8") + debug_dir = corpus.output_dir / "ocr_latex_debug" + rows = corpus.clean_ocr_latex_debug(debug_dir, max_docs=max_docs) + return rows, debug_dir + + +def test_merge_labeled_raw_spans_merges_same_type_with_gap_of_40() -> None: + text = "A" * 10 + ("x" * 40) + "B" * 10 + spans = [ + {"start": 0, "end": 10, "match_types": ["word_repeat"], "category": "word"}, + {"start": 50, "end": 60, "match_types": ["word_repeat"], "category": "word"}, + ] + merged = _merge_labeled_raw_spans(text, spans) + assert len(merged) == 1 + assert merged[0]["start"] == 0 + assert merged[0]["end"] == 60 + + +def test_merge_labeled_raw_spans_does_not_merge_same_type_with_gap_of_41() -> None: + text = "A" * 10 + ("x" * 41) + "B" * 10 + spans = [ + {"start": 0, "end": 10, "match_types": ["word_repeat"], "category": "word"}, + {"start": 51, "end": 61, "match_types": ["word_repeat"], "category": "word"}, + ] + merged = _merge_labeled_raw_spans(text, spans) + assert len(merged) == 2 + + def test_clean_skips_latex_blocks_for_mojibake(tmp_path: Path) -> None: corpus = _build_corpus(tmp_path) row = _run_clean_and_read_row(corpus, LATEX_MOJIBAKE_MD, stem="latex-case") @@ -88,3 +263,1602 @@ def test_clean_flags_uppercase_glyph_noise(tmp_path: Path) -> None: filter_value = row.get("filter") or "" assert "mojibake>0.1" in filter_value or "non_greek_text" in filter_value assert bool(row.get("needs_ocr", False)) + + +def test_clean_ocr_populates_script_metrics(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + row = _run_clean_ocr_and_read_row( + corpus, + "Αυτή είναι η πρώτη σελίδα.\n<--- Page Split --->\nΚαὶ αὕτη εἶναι ἡ δευτέρα.", + stem="ocr-script-metrics", + ) + assert float(row.get("percentage_greek") or 0.0) > 70.0 + assert float(row.get("latin_percentage") or 0.0) < 5.0 + assert float(row.get("polytonic_ratio") or 0.0) > 0.0 + assert not bool(row.get("ocr_noise_suspect", False)) + assert (row.get("filter") or "") == "ok" + + +def test_clean_ocr_writes_cleaned_markdown_with_combined_loop(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + content = _run_clean_ocr_and_read_cleaned_text( + corpus, + ( + "1111 1 1 1 1 1 1 1 1 1 1\n" + "<--- Page Split --->\n" + "1. Από το 2020, η αγορά των εργασιών των εργασιών των εργασιών των εργασιών των εργασιώ\n" + "
NameScore
Alice10
\n" + ), + stem="ocr-clean-shared-loop", + ) + assert "<--- Page Split --->" in content + assert "" not in content + assert "| Name" in content + assert "| Alice" in content + assert corpus.markdown_dir == corpus.cleaned_markdown_dir + + +def test_clean_ocr_drops_sentence_shell_and_repeated_row_tables(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + content = _run_clean_ocr_and_read_cleaned_text( + corpus, + ( + "Πρόλογος\n" + "
Η οινοφόρος άμπελος αναπτύχθηκε στην Αρμενία, νότια της Κασπίας
\n" + "
StateValue
Alpha10
Beta20
Alpha10
Beta20
\n" + "Επίλογος\n" + ), + stem="ocr-clean-drop-tables", + ) + assert "" not in content + assert "Η οινοφόρος άμπελος" not in content + assert "| Alpha" not in content + assert "Πρόλογος" in content + assert "Επίλογος" in content + + +def test_clean_ocr_supports_score_only_mode(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + md_path = corpus.markdown_dir / "ocr-clean-score-only.md" + md_path.write_text("Κανονικό περιεχόμενο.\n", encoding="utf-8") + corpus.clean_ocr(write_cleaned_files=False) + assert not any(corpus.cleaned_markdown_dir.glob("*.md")) + assert corpus.markdown_dir == corpus.output_dir / "markdown" + + +def test_clean_ocr_ignores_chunk_markdown_when_canonical_doc_exists(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + (corpus.markdown_dir / "doc.md").write_text("Κανονικό κείμενο.\n", encoding="utf-8") + (corpus.markdown_dir / "doc__p00001-00010.md").write_text("Θορυβώδες chunk.\n", encoding="utf-8") + + corpus.clean_ocr() + + cleaned_files = sorted(path.name for path in corpus.cleaned_markdown_dir.glob("*.md")) + assert cleaned_files == ["doc.md"] + + parquet = corpus.output_dir / "download_results" / "download_results.parquet" + df = pd.read_parquet(parquet) + assert "doc.pdf" in df["filename"].tolist() + + +def test_clean_ocr_supports_combined_clean_and_debug_outputs(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + stem = "ocr-clean-debug" + source_text = ( + "Πρόλογος\n" + "
Η οινοφόρος άμπελος αναπτύχθηκε στην Αρμενία, νότια της Κασπίας
\n" + "<--- Page Split --->\n" + "των εργασιών των εργασιών των εργασιών των εργασιών των εργασιώ\n" + ) + md_path = corpus.markdown_dir / f"{stem}.md" + md_path.write_text(source_text, encoding="utf-8") + + corpus.clean_ocr(write_cleaned_files=True, write_debug_files=True) + + cleaned_path = corpus.cleaned_markdown_dir / f"{stem}.md" + debug_dir = corpus.output_dir / "debug" + debug_path = debug_dir / f"{stem}.md" + assert cleaned_path.exists() + assert debug_path.exists() + + cleaned_text = cleaned_path.read_text(encoding="utf-8") + debug_text = debug_path.read_text(encoding="utf-8") + assert "= 2 + + match_rows = [ + json.loads(line) + for line in (debug_dir / "match_index.jsonl").read_text(encoding="utf-8").strip().splitlines() + ] + assert len(match_rows) >= 2 + source_pages = source_text.split("<--- Page Split --->") + for row in match_rows: + page_text = source_pages[int(row["page_number"]) - 1] + assert page_text[int(row["start_char"]):int(row["end_char"])] == row["matched_text"] + word_row = next(row for row in match_rows if row["match_type"] == "word_repeat") + assert int(word_row["repeat_count"]) >= 3 + assert int(word_row["period"]) > 0 + + page_metrics_rows = (debug_dir / "page_metrics.jsonl").read_text(encoding="utf-8").strip().splitlines() + assert len(page_metrics_rows) == 2 + summary = json.loads((debug_dir / "summary.json").read_text(encoding="utf-8")) + assert summary["doc_count"] == 1 + assert summary["match_count"] >= 2 + + +def test_clean_ocr_ignores_numeric_lists_and_dotted_values(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + row = _run_clean_ocr_and_read_row( + corpus, + "1. 2. 3. 4. 5. 6. 7.\n9.9.9.9.9\n", + stem="ocr-non-repeat-noise", + drop_bad=True, + ) + assert not bool(row.get("ocr_noise_suspect", False)) + assert int(row.get("ocr_repeat_phrase_run_max") or 0) == 0 + assert int(row.get("ocr_repeat_line_run_max") or 0) == 0 + flags = row.get("ocr_noise_flags") or "" + assert flags == "" + assert "ocr_noise" not in (row.get("filter") or "") + assert "ocr-non-repeat-noise" in corpus.good_files + + +def test_clean_ocr_flags_repeated_phrase_noise(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + row = _run_clean_ocr_and_read_row( + corpus, + "0 0 0 0 0 0\n1.1\n1.1\n1.1\n1.1\n1.1\n1.1\n", + stem="ocr-repeat-noise", + drop_bad=True, + ) + assert bool(row.get("ocr_noise_suspect", False)) + assert int(row.get("ocr_repeat_phrase_run_max") or 0) >= 6 + assert int(row.get("ocr_repeat_line_run_max") or 0) >= 6 + flags = row.get("ocr_noise_flags") or "" + assert "repeat_phrase_run" in flags + assert "repeat_line_run" in flags + assert "ocr_noise" in (row.get("filter") or "") + assert "ocr-repeat-noise" not in corpus.good_files + + +def test_clean_ocr_debug_exports_annotated_pages(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_debug_export( + corpus, + ( + "Κανονική πρώτη σελίδα.\n" + "<--- Page Split --->\n" + "1. 2. 3. 4. 5. 6. 7.\n" + "0 0 0 0 0 0\n" + "1.1\n1.1\n1.1\n1.1\n1.1\n1.1\n" + ), + stem="ocr-debug-source", + ) + assert len(rows) == 1 + row = rows[0] + assert row["page_number"] == 2 + assert row["page_index_in_file"] == 2 + assert row["match_count"] >= 2 + assert "repeat_phrase_run" in row["match_types"] + assert "repeat_line_run" in row["match_types"] + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "1. 2. 3. 4. 5. 6. 7." in content + assert "0 0 0 0 0 0" in content + assert "1.1" in content + + manifest = debug_dir / "manifest.jsonl" + lines = manifest.read_text(encoding="utf-8").strip().splitlines() + assert len(lines) == 1 + + +def test_clean_ocr_debug_respects_sample_limit(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + md_path = corpus.markdown_dir / "ocr-debug-many.md" + md_path.write_text( + ( + "0 0 0 0 0 0\n" + "<--- Page Split --->\n" + "0 0 0 0 0 0\n" + "<--- Page Split --->\n" + "0 0 0 0 0 0\n" + ), + encoding="utf-8", + ) + debug_dir = corpus.output_dir / "ocr_debug" + rows = corpus.clean_ocr_debug(debug_dir, max_pages=2, sample_seed=0) + assert len(rows) == 2 + manifest = debug_dir / "manifest.jsonl" + lines = manifest.read_text(encoding="utf-8").strip().splitlines() + assert len(lines) == 2 + + +def test_clean_token_category_debug_exports_synthetic_pages(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_token_category_debug_export( + corpus, + ( + "# Section One\n\n" + "Κανονικό κείμενο χωρίς ύποπτα μοτίβα.\n\n" + "# Section Two\n\n" + "Intro .................. 15\n" + "GLYPH<1> GLYPH<2> GLYPH<3>\n" + ), + specs=[ + { + "category": "glyph_font_like", + "pattern_family": "glyph_marker", + "pattern": r"GLYPH<\\d+>", + }, + { + "category": "dot_leader_like", + "pattern_family": "dot_run", + "pattern": r"\\.{4,}", + }, + ], + synthetic_page_target_chars=120, + synthetic_page_min_header_chars=20, + synthetic_page_hard_max_chars=240, + ) + + assert len(rows) == 1 + row = rows[0] + assert row["page_kind"].startswith("synthetic") + assert row["match_count"] >= 2 + assert "glyph_font_like" in row["match_categories"] + assert "dot_leader_like" in row["match_categories"] + assert "glyph_marker" in row["match_pattern_families"] + assert "dot_run" in row["match_pattern_families"] + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "" in content + assert ".................." in content + + manifest = debug_dir / "manifest.jsonl" + page_metrics = debug_dir / "page_metrics.jsonl" + match_index = debug_dir / "match_index.jsonl" + summary = debug_dir / "summary.json" + assert manifest.exists() + assert page_metrics.exists() + assert match_index.exists() + assert summary.exists() + page_metric_rows = [ + json.loads(line) + for line in page_metrics.read_text(encoding="utf-8").strip().splitlines() + ] + assert len(page_metric_rows) == 1 + assert page_metric_rows[0]["page_kind"].startswith("synthetic") + assert page_metric_rows[0]["category_match_counts"]["glyph_font_like"] >= 1 + match_rows = [ + json.loads(line) + for line in match_index.read_text(encoding="utf-8").strip().splitlines() + ] + assert any(row["category"] == "glyph_font_like" for row in match_rows) + assert any(row["category"] == "dot_leader_like" for row in match_rows) + assert all("context_excerpt" in row for row in match_rows) + summary_data = json.loads(summary.read_text(encoding="utf-8")) + assert summary_data["page_count"] == 1 + assert summary_data["category_page_counts"]["glyph_font_like"] == 1 + assert summary_data["category_match_counts"]["glyph_font_like"] >= 1 + + +def test_clean_ocr_numeric_debug_flags_ascending_sequences(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + ( + "Κανονικό κείμενο.\n" + "<--- Page Split --->\n" + "1. 2. 3. 4. 5. 6. 7. 8. 9. 10.\n" + ), + stem="ocr-numeric-progress", + ) + assert len(rows) == 1 + row = rows[0] + assert row["page_number"] == 2 + assert "ascending_numeric_sequence" in row["match_types"] + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert ( + "1. 2. 3. 4. 5. 6. 7. 8. 9. 10" + in content + ) + + +def test_clean_ocr_numeric_debug_flags_compact_repeated_numbers(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + "2.2.2.2.2.2.2.2.\n", + stem="ocr-numeric-compact-repeat", + ) + assert len(rows) == 1 + row = rows[0] + assert "repeat_numeric_run" in row["match_types"] + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "2.2.2.2.2.2.2.2" in content + + +def test_clean_ocr_numeric_debug_flags_same_digit_runs(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + "1111 1 1 1 111 11 1 111 1 11\n", + stem="ocr-numeric-same-digit", + ) + assert len(rows) == 1 + row = rows[0] + assert "same_digit_numeric_run" in row["match_types"] + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert ( + "1111 1 1 1 111 11 1 111 1 11" + in content + ) + + +def test_clean_ocr_numeric_debug_merges_close_same_category_spans(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + "1111 1 1 1 1 1 1 1 1 1 1 xy 1111 1 1 1 1 1 1 1 1 1 1\n", + stem="ocr-numeric-gap-merge", + ) + assert len(rows) == 1 + exported = Path(rows[0]["output_path"]) + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert content.count("") == 1 + assert ( + "" + "1111 1 1 1 1 1 1 1 1 1 1 xy 1111 1 1 1 1 1 1 1 1 1 1" + "" + in content + ) + + +def test_clean_ocr_numeric_debug_flags_numeric_page_collapse(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + tokens = ("22 2 22 6 22 8 22 1 22 7 22 5 " * 12).strip() + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + tokens + "\n", + stem="ocr-numeric-page-collapse", + ) + assert len(rows) == 1 + row = rows[0] + assert "numeric_page_collapse" in row["match_types"] + assert row["match_count"] == 1 + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "" in content + assert tokens in content + + +def test_clean_ocr_numeric_debug_page_collapse_ignores_punctuation_only_tokens( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + tokens = ("1 1 . 1 1 . 2 2 . 2 2 . " * 16).strip() + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + tokens + "\n", + stem="ocr-numeric-page-collapse-punct", + ) + assert len(rows) == 1 + row = rows[0] + assert "numeric_page_collapse" in row["match_types"] + assert row["match_count"] == 1 + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "" in content + assert tokens in content + + +def test_clean_ocr_numeric_debug_page_collapse_ignores_container_tokens( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + numeric_body = ("11 11 11 22 22 22 33 33 33 44 44 44 " * 8).strip() + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + f"```\n( {numeric_body} )\n```\n", + stem="ocr-numeric-page-collapse-fenced", + ) + assert len(rows) == 1 + row = rows[0] + assert "numeric_page_collapse" in row["match_types"] + assert row["match_count"] == 1 + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "" in content + assert numeric_body in content + + +def test_clean_ocr_numeric_debug_page_collapse_accepts_dotted_numeric_tokens( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + dotted_tokens = " ".join(f"{major}.{minor}." for major in range(1, 6) for minor in range(1, 21)) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + dotted_tokens + "\n", + stem="ocr-numeric-page-collapse-dotted", + ) + assert len(rows) == 1 + row = rows[0] + assert "numeric_page_collapse" in row["match_types"] + assert row["match_count"] == 1 + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "" in content + assert dotted_tokens in content + + +def test_clean_ocr_numeric_debug_page_collapse_accepts_compact_numeric_atom_pages( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + compact_tokens = " ".join(["1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1."] * 20) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + compact_tokens + "\n", + stem="ocr-numeric-page-collapse-compact-atoms", + ) + assert len(rows) == 1 + row = rows[0] + assert "numeric_page_collapse" in row["match_types"] + assert row["match_count"] == 1 + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "" in content + assert compact_tokens in content + + +def test_clean_ocr_numeric_debug_flags_numeric_block_after_heading(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + numeric_block = "\n\n".join( + f"{i}.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.{i}.1.1.1.1.1.1.1.1.1.1.1.1.1.1" + for i in range(1, 27) + ) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + f"1\n\n## ΑΠΡΙΛΙΟΣ\n\n1\n\n{numeric_block}\n", + stem="ocr-numeric-block-heading", + ) + assert len(rows) == 1 + row = rows[0] + assert "numeric_block_collapse" in row["match_types"] + assert row["match_count"] == 1 + + exported = Path(row["output_path"]) + assert exported.exists() + assert exported.parent == debug_dir + content = exported.read_text(encoding="utf-8") + assert "## ΑΠΡΙΛΙΟΣ" in content + assert "" in content + assert numeric_block in content + + +def test_clean_ocr_numeric_word_debug_docs_runs_numeric_then_word(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + ( + "1111 1 1 1 1 1 1 1 1 1 1\n" + "<--- Page Split --->\n" + "1. Από το 2020, η αγορά των εργασιών των εργασιών των εργασιών των εργασιών των εργασιώ\n" + "
Standard nameStandard nameStandard name
\n" + ), + stem="ocr-number-word-doc", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["page_count"] == 2 + assert row["matched_page_count"] == 2 + assert row["numeric_match_count"] >= 1 + assert row["word_match_count"] >= 1 + assert "word_repeat" in row["match_types"] + + exported = debug_dir / "ocr-number-word-doc.md" + content = exported.read_text(encoding="utf-8") + assert "<--- Page Split --->" in content + assert content.count("") == 1 + assert "" in content + assert "Standard name" not in content + + summary = json.loads((debug_dir / "summary.json").read_text(encoding="utf-8")) + assert summary["doc_count"] == 1 + assert summary["match_count"] >= 2 + assert summary["numeric_match_count"] >= 1 + assert summary["word_match_count"] >= 1 + + page_metrics = (debug_dir / "page_metrics.jsonl").read_text(encoding="utf-8").strip().splitlines() + assert len(page_metrics) == 2 + match_index = [ + json.loads(line) + for line in (debug_dir / "match_index.jsonl").read_text(encoding="utf-8").strip().splitlines() + ] + assert any(row["match_type"] == "same_digit_numeric_run" for row in match_index) + assert any(row["match_type"] == "word_repeat" for row in match_index) + + +def test_rust_word_repeat_spans_match_python_reference(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + corpus._load_rust_extension( + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=("find_word_repeat_spans",), + ) + cases = [ + "των εργασιών των εργασιών των εργασιών των εργασιών των εργασιώ", + "1.1 Hypergeometric function 1.1.1 Hypergeometric function 1.1.2 Hypergeometric function 1.1.3 Hypergeometric function", + r"\Delta \Delta \Delta \Delta \Delta", + "το σημείο 1, το σημείο 2, το σημείο 3, το σημείο 4, το σημείο 5, το σημείο 6", + ] + for text in cases: + normalized, _ = _normalize_alnum_with_map_skip_tags(text) + assert _find_word_repeat_spans( + normalized, + rep_threshold=4, + min_period=3, + window=96, + ) == _find_word_repeat_spans_python( + normalized, + rep_threshold=4, + min_period=3, + window=96, + ) + + +def test_long_accent_shift_repeat_needs_wider_default_window() -> None: + line_a = "\"Ελληνική\" ειδήματα, θανεί σει σάφησαν τ' άγχιλίαν" + line_b = "\"Ελληνική\" ειδήματα, θανεί σει σάφησαν τ' άγχίλιαν" + normalized, _ = _normalize_alnum_with_map_skip_tags("\n".join([line_a, line_b] * 6)) + + legacy_spans = _find_word_repeat_spans_python( + normalized, + rep_threshold=4, + min_period=3, + window=96, + ) + default_spans = _find_word_repeat_spans_python( + normalized, + rep_threshold=4, + min_period=3, + window=DEFAULT_OCR_WORD_REPEAT_WINDOW, + ) + + assert legacy_spans == [] + assert default_spans + assert default_spans[0]["period"] == 40 + assert default_spans[0]["repetitions"] >= 6 + + +def test_clean_ocr_numeric_word_debug_docs_flags_empty_html_table_collapse(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + empty_table = ( + "" + "" + "" + "" + "" + "" + "" + "
\n" + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + empty_table, + stem="ocr-empty-table", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["table_match_count"] == 1 + assert "table_repeat" in row["match_types"] + + content = (debug_dir / "ocr-empty-table.md").read_text(encoding="utf-8") + assert "" not in content + assert "|" in content + + summary = json.loads((debug_dir / "summary.json").read_text(encoding="utf-8")) + assert summary["table_match_count"] == 1 + + +def test_clean_ocr_numeric_word_debug_docs_flags_repeated_html_table_rows(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + repeated_table = ( + "" + "" + "" + "" + "" + "" + "
StateValue
Alpha10
Beta20
Alpha10
Beta20
\n" + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated_table, + stem="ocr-repeated-table-rows", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["table_match_count"] == 1 + assert "table_repeat" in row["match_types"] + + content = (debug_dir / "ocr-repeated-table-rows.md").read_text(encoding="utf-8") + assert "" not in content + assert "| Alpha" in content or "| Beta" in content + + +def test_clean_ocr_numeric_word_debug_docs_ignores_small_distinct_html_table(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + ( + "" + "" + "" + "" + "
NameScore
Alice10
Bob11
\n" + ), + stem="ocr-distinct-table", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["table_match_count"] == 0 + assert "table_repeat" not in row["match_types"] + + content = (debug_dir / "ocr-distinct-table.md").read_text(encoding="utf-8") + assert "" not in content + assert "| Name" in content + assert "| Alice" in content + + +def test_clean_ocr_numeric_word_debug_docs_flags_sentence_shell_table(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + ( + "
" + "Η οινοφόρος άμπελος αναπτύχθηκε στην Αρμενία, νότια της Κασπίας" + "
\n" + ), + stem="ocr-sentence-shell-table", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["table_match_count"] == 1 + assert "table_repeat" in row["match_types"] + + content = (debug_dir / "ocr-sentence-shell-table.md").read_text(encoding="utf-8") + assert "" not in content + + +def test_clean_ocr_numeric_word_debug_docs_transfers_pure_numeric_repeats_to_numeric( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + "12 12 12 12 12 12 12 12 12 12 12 12\n", + stem="ocr-number-transfer", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["numeric_match_count"] >= 1 + assert row["word_match_count"] == 0 + assert "numeric_repeat" in row["match_types"] + assert "word_repeat" not in row["match_types"] + + content = (debug_dir / "ocr-number-transfer.md").read_text(encoding="utf-8") + assert "12 12 12 12 12 12 12 12 12 12 12 12" in content + + +def test_clean_ocr_numeric_word_debug_docs_flags_hybrid_progression(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + "1.1 Hypergeometric function 1.1.1 Hypergeometric function 1.1.2 Hypergeometric function 1.1.3 Hypergeometric function 1.1.4 Hypergeometric function\n", + stem="ocr-combined-hybrid", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["hybrid_match_count"] >= 1 + assert "hybrid_repeat" in row["match_types"] + + content = (debug_dir / "ocr-combined-hybrid.md").read_text(encoding="utf-8") + assert "= 1 + + +def test_clean_ocr_numeric_word_debug_docs_ignores_latex_in_shared_repeat(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + r"\[ S=\frac{1}{16\pi}\int\sqrt{-g}d^{4}x\left[\phi R-\frac{\omega(\phi)}{\phi}\phi_{,a}\phi^{,a}+2\phi\lambda(\phi)\right]+S_{M} \quad (149) \]" + "\n", + stem="ocr-latex-ignore", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["word_match_count"] == 0 + assert row["latex_match_count"] == 0 + assert "word_repeat" not in row["match_types"] + content = (debug_dir / "ocr-latex-ignore.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + r"\( \varepsilon_{H} = \frac{1}{2} \left( \frac{1}{2} \left( \frac{1}{2} \left( \frac{1}{2} \left( x \right) \right) \right) \right) \)" + + "\n", + stem="ocr-latex-structural-repeat", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + assert "latex_repeat" in row["match_types"] + content = (debug_dir / "ocr-latex-structural-repeat.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + r"\[ uαuαuαuαuαuαuαuαuα \]" + + "\n", + stem="ocr-latex-markup-repeat", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + assert "latex_repeat" in row["match_types"] + content = (debug_dir / "ocr-latex-markup-repeat.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + r"\[ K:\mathrm{\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa} \]" + + "\n", + stem="ocr-latex-text-wrapper-noise", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + assert "latex_repeat" in row["match_types"] + content = (debug_dir / "ocr-latex-text-wrapper-noise.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + r"\[ K:\mathrm{\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa\iota\kappa \]" + + "\n", + stem="ocr-latex-unclosed-text-wrapper-noise", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + assert "latex_repeat" in row["match_types"] + content = (debug_dir / "ocr-latex-unclosed-text-wrapper-noise.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + r"\[ \delta R^{\mu\nu}=g^{\mu\alpha}g^{\nu\beta}\left(\nabla_{\kappa}\left(\delta g_{\nu\alpha}\right)\right). \]" + + "\n", + stem="ocr-latex-bookkeeping-ignore", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] == 0 + assert "latex_repeat" not in row["match_types"] + content = (debug_dir / "ocr-latex-bookkeeping-ignore.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + repeated = "\n\n".join( + [ + r"\[ N_{bd} = \frac{f_{bk} \cdot l_e \cdot \pi \cdot d_b}{\gamma_b} \]", + r"\[ N_{bd} = \frac{f_{bk} \cdot l_e \cdot \pi \cdot d_b}{\gamma_b} \]", + r"\[ N_{bd} = \frac{f_{bk} \cdot l_e \cdot \pi \cdot d_b}{\gamma_b} \]", + r"\[ N_{bd} = \frac{f_{bk} \cdot l_e \cdot \pi \cdot d_b}{\gamma_b} \]", + ] + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-consecutive-exact", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-consecutive-exact.md").read_text(encoding="utf-8") + assert content.count(" None: + corpus = _build_corpus(tmp_path) + repeated = "\n\n".join( + [ + r"\( N_{bd} = \frac{0,6Mpa \cdot 0,4m \cdot \pi \cdot 0,02m}{1,5} = 10,05KN \)", + r"\( N_{bd} = \frac{0,6Mpa \cdot 0,4m \cdot \pi \cdot 0,03m}{1,5} = 15,07KN \)", + r"\( N_{bd} = \frac{0,6Mpa \cdot 0,4m \cdot \pi \cdot 0,04m}{1,5} = 20,10KN \)", + r"\( N_{bd} = \frac{0,6Mpa \cdot 0,4m \cdot \pi \cdot 0,05m}{1,5} = 25,12KN \)", + ] + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-consecutive-template", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-consecutive-template.md").read_text(encoding="utf-8") + assert content.count(" None: + corpus = _build_corpus(tmp_path) + repeated = "\n\n".join([r"\( \Delta \)", r"\( \Delta \)", r"\( \Delta \)", r"\( \Delta \)"]) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-delta-run", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-delta-run.md").read_text(encoding="utf-8") + assert content.count(" None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + ( + r"\( Q^{I} \) : \( \uparrow\uparrow\uparrow \) + \( \uparrow\downarrow\downarrow \) + ..." + "\n\n" + r"\( Q^{IV} \) : \( \uparrow\uparrow\uparrow \) + \( \downarrow\downarrow\downarrow \) + ..." + "\n" + ), + stem="ocr-latex-diagram-ignore", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] == 0 + assert "latex_repeat" not in row["match_types"] + content = (debug_dir / "ocr-latex-diagram-ignore.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + warmup = [r"\( \alpha \)", r"\( \beta \)", r"\( \gamma \)", r"\( \gamma \)"] + block = [ + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \beta \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \gamma \)", + ] + repeated = " ".join(warmup + block + block) + " \\( \\alpha" + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-short-atom-block", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-short-atom-block.md").read_text(encoding="utf-8") + assert " \( \alpha" not in content + assert r"\( \alpha" in content + + +def test_clean_ocr_numeric_word_debug_docs_ignores_nonrepeating_short_atom_inventory( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + repeated = " ".join( + [ + r"\( \alpha \)", + r"\( \beta \)", + r"\( \gamma \)", + r"\( \delta \)", + r"\( \omega \)", + r"\( \mu \)", + r"\( \nu \)", + r"\( \lambda \)", + ] + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-short-atom-inventory-ignore", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] == 0 + content = (debug_dir / "ocr-latex-short-atom-inventory-ignore.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + repeated = " ".join( + [ + r"\( \alpha\beta\gamma\delta \)", + r"\( \alpha\beta\gamma\delta \)", + r"\( \alpha\beta\gamma\delta \)", + r"\( \alpha\beta\gamma\delta \)", + r"\( \alpha\beta\gamma \)", + r"\( \alpha\beta\gamma \)", + r"\( \alpha\beta\gamma \)", + r"\( \alpha\beta\gamma \)", + r"\( \alpha\beta\gamma \)", + ] + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-short-atom-segment-repeat", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-short-atom-segment-repeat.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + repeated = r"\( \Delta_{i}\Delta_{i}\Delta_{i}\Delta_{i}\Delta_{i}\Delta_{i}\Delta_{i}\Delta \)" + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-short-atom-chain-segment", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-short-atom-chain-segment.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + command_run = (r"\cdots" * 18) + (r"\vdots") + (r"\cdots" * 18) + (r"\ddots") + (r"\cdots" * 18) + repeated = rf"\[ \begin{{aligned}}{command_run}\end{{aligned}} \]" + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-internal-small-vocab-command-run", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-internal-small-vocab-command-run.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + repeated = "\n\n".join( + [ + r"\[ \frac{d^2\Psi}{dr_*^2} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^2\Psi}{dr_*^2} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^3\Psi}{dr_*^3} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^3\Psi}{dr^3} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^4\Psi}{dr_*^4} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^4\Psi}{dr^4} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^5\Psi}{dr_*^5} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^5\Psi}{dr^5} + (\omega^2 - V(r))\Psi = 0 \]", + ] + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-derivative-ladder", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-derivative-ladder.md").read_text(encoding="utf-8") + assert content.count("") + + +def test_clean_ocr_numeric_word_debug_docs_ignores_small_parameterized_formula_family( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + repeated = "\n\n".join( + [ + r"\( f_{11}(k) = (1 - 0.0561)^{k-1}0.0561 \)", + r"\( f_{12}(k) = (1 - 0.0617)^{k-1}0.0617 \)", + r"\( f_{21}(k) = (1 - 0.1057)^{k-1}0.1057 \)", + r"\( f_{22}(k) = (1 - 0.1724)^{k-1}0.1724 \)", + ] + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-parameter-family-ignore", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] == 0 + content = (debug_dir / "ocr-latex-parameter-family-ignore.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + repeated = " ".join( + [ + r"\( \tilde{p}_{(1,1)(1,2)}^{\prime} \)", + r"\( \tilde{p}_{(1,1)(2,0)}^{\prime} \)", + r"\( \tilde{p}_{(1,1)(1,0)}^{\prime} \)", + r"\( \tilde{p}_{(2,0)(1,0)}^{\prime} \)", + r"\( \tilde{p}_{(2,0)(2,1)}^{\prime} \)", + r"\( \tilde{p}_{(2,0)(2,0)}^{\prime} \)", + ] + ) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + repeated + "\n", + stem="ocr-latex-symbol-inventory-ignore", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] == 0 + content = (debug_dir / "ocr-latex-symbol-inventory-ignore.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + r"where \( \Delta \) CFF = \( \Delta \) CFF(t) - \( \Delta \) CFF(t-1)." + "\n", + stem="ocr-latex-delta-definition-ignore", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["latex_match_count"] == 0 + content = (debug_dir / "ocr-latex-delta-definition-ignore.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + vertical_numbers = "\n\n".join(str(i) for i in range(0, 121)) + rows, debug_dir = _run_clean_ocr_numeric_debug_export( + corpus, + vertical_numbers + "\n", + stem="ocr-vertical-numeric-page", + ) + assert len(rows) == 1 + row = rows[0] + assert "numeric_page_collapse" in row["match_types"] + + content = Path(row["output_path"]).read_text(encoding="utf-8") + assert "" in content + assert "100" in content + assert "120" in content + + +def test_clean_ocr_numeric_word_debug_docs_records_bad_char_metrics(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_numeric_word_debug_docs( + corpus, + "Κανονική γραμμή\n<--- Page Split --->\n## \x01\x02\x00 漢 \uf0b7\n", + stem="ocr-bad-char-metrics", + max_docs=1, + ) + assert len(rows) == 1 + + page_metric_rows = [ + json.loads(line) + for line in (debug_dir / "page_metrics.jsonl").read_text(encoding="utf-8").strip().splitlines() + ] + assert len(page_metric_rows) == 2 + second_page = page_metric_rows[1] + assert second_page["bad_char_count"] >= 4 + assert second_page["bad_char_ratio"] > 0.0 + assert second_page["control_count"] >= 3 + assert second_page["cjk_count"] >= 1 + assert second_page["private_use_count"] >= 1 + + summary = json.loads((debug_dir / "summary.json").read_text(encoding="utf-8")) + assert summary["bad_char_ratio"]["max"] > 0.0 + + +def test_clean_ocr_numeric_word_debug_docs_respects_doc_offset(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + (corpus.markdown_dir / "a-first.md").write_text("χωρίς επανάληψη\n", encoding="utf-8") + (corpus.markdown_dir / "b-second.md").write_text( + r"\( \Delta \)" + "\n\n" + r"\( \Delta \)" + "\n\n" + r"\( \Delta \)" + "\n\n" + r"\( \Delta \)" + "\n", + encoding="utf-8", + ) + + debug_dir = corpus.output_dir / "ocr_numeric_word_debug" + rows = corpus.clean_ocr_numeric_word_debug_docs(debug_dir, max_docs=1, doc_offset=1) + + assert len(rows) == 1 + row = rows[0] + assert row["source_stem"] == "b-second" + assert row["latex_match_count"] >= 1 + assert not (debug_dir / "a-first.md").exists() + assert (debug_dir / "b-second.md").exists() + + +def test_clean_ocr_hybrid_debug_flags_same_body_numbered_progression(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + "1. Απόκτηση της αξίας του αξιώματος. " + "2. Απόκτηση της αξίας του αξιώματος. " + "3. Απόκτηση της αξίας του αξιώματος. " + "4. Απόκτηση της αξίας του αξιώματος. " + "5. Απόκτηση της αξίας του αξιώματος.\n" + ), + stem="ocr-hybrid-same-body", + max_docs=1, + ) + assert len(rows) == 1 + content = (debug_dir / "ocr-hybrid-same-body__debug_page_00001.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + "1.1 Hypergeometric function " + "1.1.1 Hypergeometric function " + "1.1.2 Hypergeometric function " + "1.1.3 Hypergeometric function " + "1.1.4 Hypergeometric function " + "1.1.5 Hypergeometric function\n" + ), + stem="ocr-hybrid-hierarchical", + max_docs=1, + ) + assert len(rows) == 1 + content = (debug_dir / "ocr-hybrid-hierarchical__debug_page_00001.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + "1. Σχεδία 1.1. Σχεδία 1.2. Σχεδία 1.3. Σχεδία 1.4. Σχεδία 1.5. Σχεδ\n" + ), + stem="ocr-hybrid-partial-tail", + max_docs=1, + ) + assert len(rows) == 1 + content = (debug_dir / "ocr-hybrid-partial-tail__debug_page_00001.md").read_text(encoding="utf-8") + assert "1.5. Σχεδ" in content + assert content.index("1.5. Σχεδ") < content.index("") + + +def test_clean_ocr_hybrid_debug_flags_body_cycle_progression(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + "1. Εισαγωγή 2. Φυσικοχημικές ιδιότητες 3. Φάσεις 4. Επιπλοκές " + "5. Εισαγωγή 6. Φυσικοχημικές ιδιότητες 7. Φάσεις 8. Επιπλοκές " + "9. Εισαγωγή 10. Φυσικοχημικές ιδιότητες 11. Φάσεις 12. Επιπλοκές\n" + ), + stem="ocr-hybrid-cycle", + max_docs=1, + ) + assert len(rows) == 1 + content = (debug_dir / "ocr-hybrid-cycle__debug_page_00001.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + "1. Από το σημείο 1, το σημείο 2, το σημείο 3, " + "το σημείο 4, το σημείο 5, το σημείο 6, το σημείο 7.\n" + ), + stem="ocr-hybrid-inline-progress", + max_docs=1, + ) + assert len(rows) == 1 + content = (debug_dir / "ocr-hybrid-inline-progress__debug_page_00001.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + "1. Από το σημείο 1, το σημείο 2, το σημείο 3, " + "το σημείο 4, το σημείο 5.\n" + ), + stem="ocr-hybrid-inline-short-ignore", + max_docs=1, + ) + assert rows == [] + assert not any(debug_dir.glob("*.md")) + + +def test_clean_ocr_hybrid_debug_ignores_diverse_numbered_list(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + "1. Εισαγωγή 2. Μέθοδοι 3. Αποτελέσματα 4. Συζήτηση 5. Συμπεράσματα\n" + ), + stem="ocr-hybrid-diverse-ignore", + max_docs=1, + ) + assert rows == [] + assert not any(debug_dir.glob("*.md")) + + +def test_clean_ocr_hybrid_debug_ignores_markup_number_progression(tmp_path: Path) -> None: + corpus = _build_corpus(tmp_path) + rows, debug_dir = _run_clean_ocr_hybrid_debug_export( + corpus, + ( + 'Π ' + 'Π ' + 'Π ' + 'Π\n' + ), + stem="ocr-hybrid-markup-ignore", + max_docs=1, + ) + assert rows == [] + assert not any(debug_dir.glob("*.md")) + + +def test_clean_ocr_latex_debug_exports_short_atom_block_pages( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + warmup = [r"\( \alpha \)", r"\( \beta \)", r"\( \gamma \)", r"\( \gamma \)"] + block = [ + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \beta \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \alpha \)", + r"\( \gamma \)", + ] + markdown_text = ( + "Κανονική πρώτη σελίδα.\n" + "<--- Page Split --->\n" + + " ".join(warmup + block + block) + + " \\( \\alpha" + + "\n" + "<--- Page Split --->\n" + "Κανονική τρίτη σελίδα.\n" + ) + rows, debug_dir = _run_clean_ocr_latex_debug_export( + corpus, + markdown_text, + stem="ocr-latex-debug-short-atom", + max_docs=1, + ) + assert len(rows) == 1 + row = rows[0] + assert row["page_number"] == 2 + assert row["latex_match_count"] >= 1 + content = (debug_dir / "ocr-latex-debug-short-atom__debug_page_00002.md").read_text(encoding="utf-8") + assert " \( \alpha" not in content + assert r"\( \alpha" in content + + +def test_clean_ocr_latex_slot_progression_debug_flags_derivative_ladder( + tmp_path: Path, +) -> None: + corpus = _build_corpus(tmp_path) + repeated = "\n\n".join( + [ + r"\[ \frac{d^2\Psi}{dr_*^2} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^2\Psi}{dr^2} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^3\Psi}{dr_*^3} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^3\Psi}{dr^3} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^4\Psi}{dr_*^4} + (\omega^2 - V(r))\Psi = 0 \]", + r"\[ \frac{d^4\Psi}{dr^4} + (\omega^2 - V(r))\Psi = 0 \]", + ] + ) + rows, debug_dir = _run_clean_ocr_latex_slot_progression_debug_export( + corpus, + repeated + "\n", + stem="ocr-latex-slot-derivative", + max_docs=1, + ) + assert len(rows) == 1 + content = (debug_dir / "ocr-latex-slot-derivative__debug_page_00001.md").read_text(encoding="utf-8") + assert " None: + corpus = _build_corpus(tmp_path) + repeated = "\n\n".join( + [ + r"\( f_{11}(k) = (1 - 0.0561)^{k-1}0.0561 \)", + r"\( f_{12}(k) = (1 - 0.0617)^{k-1}0.0617 \)", + r"\( f_{21}(k) = (1 - 0.1057)^{k-1}0.1057 \)", + r"\( f_{22}(k) = (1 - 0.1724)^{k-1}0.1724 \)", + ] + ) + rows, debug_dir = _run_clean_ocr_latex_slot_progression_debug_export( + corpus, + repeated + "\n", + stem="ocr-latex-slot-parameter-family-ignore", + max_docs=1, + ) + assert rows == [] + assert not any(debug_dir.glob("*.md")) + + +def test_review_manifest_materialize_creates_labeled_copies(tmp_path: Path) -> None: + source_dir = tmp_path / "contexts" + source_dir.mkdir() + first = source_dir / "case_001.txt" + second = source_dir / "case_002.txt" + first.write_text("alpha body\n", encoding="utf-8") + second.write_text("beta body\n", encoding="utf-8") + + manifest = tmp_path / "semantic_review_manifest.jsonl" + manifest.write_text( + "\n".join( + [ + json.dumps( + { + "path": str(first), + "label": "fits_semantically", + "confidence": "high", + "notes": "complete", + }, + ensure_ascii=False, + ), + json.dumps( + { + "path": str(second), + "label": "fits_but_truncated_or_incomplete", + "confidence": "medium", + "notes": "cut off", + }, + ensure_ascii=False, + ), + ] + ) + + "\n", + encoding="utf-8", + ) + + output_dir = tmp_path / "categorized" + summary = materialize_manifest_categories( + manifest, + output_dir, + category_name="semantic_fit", + ) + + assert summary["row_count"] == 2 + fit_copy = output_dir / "by_label" / "fits_semantically" / "case_001.txt" + trunc_copy = output_dir / "by_label" / "fits_but_truncated_or_incomplete" / "case_002.txt" + assert fit_copy.exists() + assert trunc_copy.exists() + + fit_text = fit_copy.read_text(encoding="utf-8") + assert "REVIEW_CATEGORY: semantic_fit" in fit_text + assert "REVIEW_LABEL: fits_semantically" in fit_text + assert "=== REVIEW_SOURCE_CONTENT ===" in fit_text + assert "alpha body" in fit_text + + +def test_table_markdown_audit_preserves_semantic_inline_html() -> None: + audit = audit_table( + Path("/tmp/demo.md"), + 1, + 1, + ( + "" + "" + "" + "
Line A
Line B		xi2source\"diagram\"
" + ), + ) + assert audit.convertible is True + assert audit.markdown is not None + assert "Line A
Line B" in audit.markdown + assert "xi2" in audit.markdown + assert "[source](https://example.com)" in audit.markdown + assert "diagram" in audit.markdown + + +def test_table_markdown_audit_writes_clean_markdown_file(tmp_path: Path) -> None: + audit = audit_table( + Path("/tmp/demo.md"), + 1, + 7, + "
ΑΒ
12
", + ) + output = write_clean_markdown_file(tmp_path, audit) + assert output is not None + path = Path(output) + assert path.exists() + text = path.read_text(encoding="utf-8") + assert text.startswith("## ORIGINAL_HTML") + assert "## GITHUB_MD" in text + assert "" in text + assert "Α" in text + assert "1" in text diff --git a/tests/test_corpus_guards.py b/tests/test_corpus_guards.py index 29db5be..d6911ee 100644 --- a/tests/test_corpus_guards.py +++ b/tests/test_corpus_guards.py @@ -50,12 +50,6 @@ def make_corpus(tmp_path): return Corpus(input_dir=input_dir, output_dir=output_dir) -def set_onnx_providers(monkeypatch, providers): - stub = SimpleNamespace(get_available_providers=lambda: providers) - monkeypatch.setitem(sys.modules, "onnxruntime", stub) - return stub - - def set_torch_stub(monkeypatch, *, available: bool, device_count: int): cuda_ns = SimpleNamespace( is_available=lambda: available, @@ -66,22 +60,23 @@ def set_torch_stub(monkeypatch, *, available: bool, device_count: int): return torch_ns -def test_prime_extractor_requires_cuda_for_ocr(tmp_path, monkeypatch): +def test_prime_extractor_force_ocr_is_ignored_for_backend_selection(tmp_path, monkeypatch): corpus = make_corpus(tmp_path) corpus.extractor = DummyExtractor() - set_torch_stub(monkeypatch, available=True, device_count=1) - set_onnx_providers(monkeypatch, ["CPUExecutionProvider"]) + set_torch_stub(monkeypatch, available=False, device_count=0) - with pytest.raises(RuntimeError) as exc: - corpus.prime_extractor( - input_format="pdf", - accel_type="CUDA", - force_ocr=True, - phase1_backend="docling", - ) + corpus.prime_extractor( + input_format="pdf", + accel_type="CPU", + force_ocr=True, + phase1_backend="auto", + ) - assert "CUDAExecutionProvider" in str(exc.value) + assert corpus.extractor.last_policy == "safe" + ensure_kwargs = corpus.extractor.ensure_calls[0] + assert ensure_kwargs["enable_ocr"] is False + assert ensure_kwargs["force_full_page_ocr"] is False def test_prime_extractor_requires_cuda_for_docling_backend(tmp_path, monkeypatch): @@ -89,8 +84,6 @@ def test_prime_extractor_requires_cuda_for_docling_backend(tmp_path, monkeypatch corpus.extractor = DummyExtractor() set_torch_stub(monkeypatch, available=False, device_count=0) - set_onnx_providers(monkeypatch, ["CUDAExecutionProvider"]) - with pytest.raises(RuntimeError) as exc: corpus.prime_extractor( input_format="pdf", @@ -106,8 +99,6 @@ def test_prime_extractor_configures_safe_backend_for_text_layer(tmp_path, monkey corpus.extractor = DummyExtractor() set_torch_stub(monkeypatch, available=True, device_count=1) - set_onnx_providers(monkeypatch, ["CUDAExecutionProvider"]) - corpus.prime_extractor( input_format="pdf", accel_type="CPU", @@ -120,26 +111,24 @@ def test_prime_extractor_configures_safe_backend_for_text_layer(tmp_path, monkey assert corpus.extractor.ensure_calls[0]["enable_ocr"] is False -def test_prime_extractor_configures_docling_backend_for_ocr(tmp_path, monkeypatch): +def test_prime_extractor_configures_docling_backend_explicitly(tmp_path, monkeypatch): corpus = make_corpus(tmp_path) corpus.extractor = DummyExtractor() + monkeypatch.delenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES", raising=False) set_torch_stub(monkeypatch, available=True, device_count=2) - set_onnx_providers(monkeypatch, ["CUDAExecutionProvider"]) - corpus.prime_extractor( input_format="pdf", accel_type="CUDA", - force_ocr=True, - phase1_backend="auto", + phase1_backend="docling", ) assert corpus.extractor.last_policy == "docling" assert corpus.extractor.last_max_batch_files == 1 assert corpus.extractor.last_prefer_safe_backend is False ensure_kwargs = corpus.extractor.ensure_calls[0] - assert ensure_kwargs["enable_ocr"] is True - assert ensure_kwargs["force_full_page_ocr"] is True + assert ensure_kwargs["enable_ocr"] is False + assert ensure_kwargs["force_full_page_ocr"] is False def test_prime_extractor_requires_cuda_for_formula_enrichment(tmp_path, monkeypatch): @@ -147,8 +136,6 @@ def test_prime_extractor_requires_cuda_for_formula_enrichment(tmp_path, monkeypa corpus.extractor = DummyExtractor() set_torch_stub(monkeypatch, available=False, device_count=0) - set_onnx_providers(monkeypatch, ["CUDAExecutionProvider"]) - with pytest.raises(RuntimeError) as exc: corpus.prime_extractor( input_format="pdf", @@ -203,6 +190,8 @@ def extract(self, *, file_paths=None, **kwargs): with pytest.raises(SystemExit) as exit_info: corpus_mod.gpu_extract_worker_queue( device_id=0, + worker_slot=0, + worker_key="gpu0-w0", in_dir=str(tmp_path), out_dir=str(tmp_path), work_q=work_q, @@ -240,4 +229,116 @@ def extract(self, *, file_paths=None, **kwargs): assert processed_batches == [["doc.pdf"]] assert work_q.empty() + + +def test_gpu_worker_accepts_bundled_work_items(tmp_path, monkeypatch): + import glossapi.corpus as corpus_mod + + processed_batches = [] + + class FakeCorpus: + def __init__(self, input_dir, output_dir): + self.input_dir = input_dir + self.output_dir = output_dir + self.extractor = SimpleNamespace(max_batch_files=1) + + def prime_extractor(self, *args, **kwargs): + return None + + def extract(self, *, file_paths=None, **kwargs): + processed_batches.append(list(file_paths or [])) + return None + + monkeypatch.setattr(corpus_mod, "Corpus", FakeCorpus) + monkeypatch.setattr("glossapi.Corpus", FakeCorpus) + monkeypatch.delenv("GLOSSAPI_WORKER_LOG_DIR", raising=False) + + work_q = queue.Queue() + work_q.put(["doc-a.pdf", "doc-b.pdf"]) + result_q = queue.Queue() + status_map: dict = {} + + with pytest.raises(SystemExit) as exit_info: + corpus_mod.gpu_extract_worker_queue( + device_id=0, + worker_slot=0, + worker_key="gpu0-w0", + in_dir=str(tmp_path), + out_dir=str(tmp_path), + work_q=work_q, + force=False, + fe=False, + ce=False, + use_cls_w=False, + skip=False, + input_fmt="pdf", + threads=1, + benchmark=False, + export_json=False, + emit_index=False, + backend="safe", + result_q=result_q, + status_map=status_map, + marker_dir=None, + ) + + assert exit_info.value.code == 0 + assert processed_batches == [["doc-a.pdf", "doc-b.pdf"]] + assert status_map == {} + + +def test_gpu_worker_keeps_singleton_queue_items_separate(tmp_path, monkeypatch): + import glossapi.corpus as corpus_mod + + processed_batches = [] + + class FakeCorpus: + def __init__(self, input_dir, output_dir): + self.input_dir = input_dir + self.output_dir = output_dir + self.extractor = SimpleNamespace(max_batch_files=2) + + def prime_extractor(self, *args, **kwargs): + return None + + def extract(self, *, file_paths=None, **kwargs): + processed_batches.append(list(file_paths or [])) + return None + + monkeypatch.setattr(corpus_mod, "Corpus", FakeCorpus) + monkeypatch.setattr("glossapi.Corpus", FakeCorpus) + monkeypatch.delenv("GLOSSAPI_WORKER_LOG_DIR", raising=False) + + work_q = queue.Queue() + work_q.put("doc-a.pdf") + work_q.put("doc-b.pdf") + result_q = queue.Queue() + status_map: dict = {} + + with pytest.raises(SystemExit) as exit_info: + corpus_mod.gpu_extract_worker_queue( + device_id=0, + worker_slot=0, + worker_key="gpu0-w0", + in_dir=str(tmp_path), + out_dir=str(tmp_path), + work_q=work_q, + force=False, + fe=False, + ce=False, + use_cls_w=False, + skip=False, + input_fmt="pdf", + threads=1, + benchmark=False, + export_json=False, + emit_index=False, + backend="docling", + result_q=result_q, + status_map=status_map, + marker_dir=None, + ) + + assert exit_info.value.code == 0 + assert processed_batches == [["doc-a.pdf"], ["doc-b.pdf"]] assert status_map == {} diff --git a/tests/test_corpus_ocr_modules.py b/tests/test_corpus_ocr_modules.py new file mode 100644 index 0000000..4d5fedd --- /dev/null +++ b/tests/test_corpus_ocr_modules.py @@ -0,0 +1,211 @@ +import json +from pathlib import Path + +import pandas as pd + +from glossapi import Corpus +from glossapi.corpus.ocr.artifacts import apply_ocr_success_updates +from glossapi.corpus.ocr.config import normalize_ocr_request +from glossapi.corpus.ocr.targets import build_ocr_selection +from glossapi.ocr.deepseek.defaults import DEFAULT_GPU_MEMORY_UTILIZATION, DEFAULT_RENDER_DPI + + +def _mk_corpus(tmp_path: Path) -> Corpus: + root = tmp_path / "corpus" + root.mkdir() + return Corpus(input_dir=root, output_dir=root) + + +def test_normalize_ocr_request_uses_shared_vllm_defaults(tmp_path): + corpus = _mk_corpus(tmp_path) + + request = normalize_ocr_request( + logger=corpus.logger, + fix_bad=True, + mode="ocr_bad", + backend="deepseek", + device=None, + model_dir=None, + max_pages=None, + persist_engine=True, + precision=None, + runtime_backend="vllm", + render_dpi=None, + gpu_memory_utilization=None, + math_enhance=False, + force=None, + reprocess_completed=None, + skip_existing=None, + ) + + assert request is not None + assert request.render_dpi == DEFAULT_RENDER_DPI + assert request.gpu_memory_utilization == DEFAULT_GPU_MEMORY_UTILIZATION + + +def test_build_ocr_selection_collapses_chunk_rows_and_skips_completed(tmp_path): + corpus = _mk_corpus(tmp_path) + (corpus.input_dir / "needs.pdf").write_bytes(b"%PDF-1.4\n%stub\n") + + dl_dir = corpus.output_dir / "download_results" + dl_dir.mkdir(parents=True, exist_ok=True) + pd.DataFrame( + [ + {"filename": "needs.pdf", corpus.url_column: "", "needs_ocr": True, "ocr_success": False}, + {"filename": "needs__p0001-0002.pdf", corpus.url_column: "", "needs_ocr": True, "ocr_success": False}, + {"filename": "done.pdf", corpus.url_column: "", "needs_ocr": True, "ocr_success": True}, + ] + ).to_parquet(dl_dir / "download_results.parquet", index=False) + + selection = build_ocr_selection( + corpus, + mode="ocr_bad", + reprocess_completed=False, + ) + + assert selection.bad_files == ["needs.pdf"] + assert selection.ocr_candidates_initial == 2 + assert selection.skipped_completed == 1 + assert selection.skipped_skiplist == 0 + assert selection.ocr_done_stems == {"done"} + + +def test_apply_ocr_success_updates_maps_canonical_artifacts_by_stem(tmp_path): + markdown_dir = tmp_path / "markdown" + metrics_dir = tmp_path / "json" / "metrics" + markdown_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + + (markdown_dir / "needs.md").write_text("fixed markdown\n", encoding="utf-8") + (metrics_dir / "needs.metrics.json").write_text('{"page_count": 1}\n', encoding="utf-8") + + df = pd.DataFrame( + [ + {"filename": "needs.pdf", "needs_ocr": True, "ocr_success": False}, + {"filename": "needs__p0001-0002.pdf", "needs_ocr": True, "ocr_success": False}, + ] + ) + + updated = apply_ocr_success_updates( + df, + filenames=["needs.pdf"], + markdown_dir=markdown_dir, + metrics_dir=metrics_dir, + backend_norm="deepseek", + ).set_index("filename") + + assert bool(updated.loc["needs.pdf", "ocr_success"]) is True + assert bool(updated.loc["needs__p0001-0002.pdf", "ocr_success"]) is True + assert updated.loc["needs.pdf", "text"] == "fixed markdown\n" + assert updated.loc["needs__p0001-0002.pdf", "text"] == "fixed markdown\n" + assert updated.loc["needs.pdf", "ocr_markdown_relpath"] == "markdown/needs.md" + assert updated.loc["needs__p0001-0002.pdf", "ocr_metrics_relpath"] == "json/metrics/needs.metrics.json" + assert updated.loc["needs.pdf", "extraction_mode"] == "deepseek" + + +def test_ocr_pipeline_exports_cleaned_and_raw_text_side_by_side(tmp_path, monkeypatch): + corpus = _mk_corpus(tmp_path) + + dl_dir = corpus.output_dir / "download_results" + dl_dir.mkdir(parents=True, exist_ok=True) + pd.DataFrame( + [ + { + "filename": "doc.pdf", + corpus.url_column: "https://example.com/doc.pdf", + "needs_ocr": True, + "ocr_success": False, + } + ] + ).to_parquet(dl_dir / "download_results.parquet", index=False) + (corpus.input_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n%stub\n") + + raw_markdown = ( + "Κανονική πρώτη σελίδα.\n" + "<--- Page Split --->\n" + "1. 2. 3. 4. 5. 6. 7.\n" + "0 0 0 0 0 0\n" + "1.1\n1.1\n1.1\n1.1\n1.1\n1.1\n" + ) + + from glossapi.ocr.deepseek import runner + + def fake_run_for_files(self_ref, files, **kwargs): + markdown_dir = self_ref.output_dir / "markdown" + metrics_dir = self_ref.output_dir / "json" / "metrics" + markdown_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + for current in files: + stem = Path(current).stem + (markdown_dir / f"{stem}.md").write_text(raw_markdown, encoding="utf-8") + (metrics_dir / f"{stem}.metrics.json").write_text( + json.dumps( + { + "page_count": 2, + "pages": [ + {"page_no": 1, "formula_count": 0, "code_count": 0}, + {"page_no": 2, "formula_count": 0, "code_count": 0}, + ], + } + ) + + "\n", + encoding="utf-8", + ) + return {"doc": {"page_count": 2}} + + monkeypatch.setattr(runner, "run_for_files", fake_run_for_files) + + calls = [] + original_clean_ocr = corpus.clean_ocr + original_clean = corpus.clean + original_markdown_dir = corpus.markdown_dir + original_cleaned_markdown_dir = corpus.cleaned_markdown_dir + + def record_clean_ocr(*args, **kwargs): + calls.append( + ( + "clean_ocr", + Path(str(kwargs.get("input_dir"))), + kwargs.get("write_cleaned_files", True), + ) + ) + return original_clean_ocr(*args, **kwargs) + + def record_clean(*args, **kwargs): + calls.append( + ( + "clean", + Path(str(kwargs.get("input_dir"))), + kwargs.get("write_cleaned_files", True), + ) + ) + return original_clean(*args, **kwargs) + + monkeypatch.setattr(corpus, "clean_ocr", record_clean_ocr) + monkeypatch.setattr(corpus, "clean", record_clean) + + corpus.ocr(backend="deepseek", fix_bad=True, math_enhance=False) + + assert calls[0] == ("clean_ocr", original_markdown_dir, True) + assert calls[1] == ("clean", original_cleaned_markdown_dir, False) + + raw_text = (original_markdown_dir / "doc.md").read_text(encoding="utf-8") + cleaned_text = (original_cleaned_markdown_dir / "doc.md").read_text(encoding="utf-8") + assert raw_text == raw_markdown + assert cleaned_text != raw_text + assert "1.1\n1.1" in raw_text + assert "1.1\n1.1" not in cleaned_text + + out_path = corpus.output_dir / "export.jsonl" + corpus.jsonl(out_path) + records = [json.loads(line) for line in out_path.read_text(encoding="utf-8").splitlines() if line] + assert len(records) == 1 + record = records[0] + + assert record["document"] == cleaned_text + assert record["text"] == raw_text + assert record["filename"] == "doc" + assert record["url"] == "https://example.com/doc.pdf" + assert record["ocr_success"] is True + assert record["extraction_mode"] == "deepseek" + assert record["page_count"] == 2 diff --git a/tests/test_deepseek_multi_gpu_runtime.py b/tests/test_deepseek_multi_gpu_runtime.py new file mode 100644 index 0000000..e465949 --- /dev/null +++ b/tests/test_deepseek_multi_gpu_runtime.py @@ -0,0 +1,515 @@ +import json +from pathlib import Path +from types import SimpleNamespace + + +def test_build_env_adds_wheel_managed_cuda_lib_dirs(tmp_path): + from glossapi.ocr.deepseek import runner + + venv_root = tmp_path / "venv" + python_bin = venv_root / "bin" / "python" + python_bin.parent.mkdir(parents=True, exist_ok=True) + python_bin.write_text("") + cuda_runtime_lib = venv_root / "lib" / "python3.11" / "site-packages" / "nvidia" / "cuda_runtime" / "lib" + cublas_lib = venv_root / "lib" / "python3.11" / "site-packages" / "nvidia" / "cublas" / "lib" + cuda_runtime_lib.mkdir(parents=True, exist_ok=True) + cublas_lib.mkdir(parents=True, exist_ok=True) + + env = runner._build_env(python_bin=python_bin, visible_device=1, script=None) + + assert env["CUDA_VISIBLE_DEVICES"] == "1" + ld_entries = env["LD_LIBRARY_PATH"].split(":") + assert str(cuda_runtime_lib) in ld_entries + assert str(cublas_lib) in ld_entries + + +def test_build_env_uses_virtualenv_path_when_python_bin_is_symlink(tmp_path): + from glossapi.ocr.deepseek import runner + + venv_root = tmp_path / "venv" + python_bin = venv_root / "bin" / "python" + python_bin.parent.mkdir(parents=True, exist_ok=True) + python_bin.symlink_to("/usr/bin/python3") + cuda_runtime_lib = venv_root / "lib" / "python3.11" / "site-packages" / "nvidia" / "cuda_runtime" / "lib" + cuda_runtime_lib.mkdir(parents=True, exist_ok=True) + + env = runner._build_env(python_bin=python_bin, visible_device=0, script=None) + + ld_entries = env["LD_LIBRARY_PATH"].split(":") + assert str(cuda_runtime_lib) in ld_entries + + +def test_resolve_deepseek_python_prefers_repo_local_runtime(tmp_path): + from glossapi.ocr.deepseek import runtime_paths + + repo_root = tmp_path / "repo" + python_bin = repo_root / "dependency_setup" / ".venvs" / "deepseek31111" / "bin" / "python" + python_bin.parent.mkdir(parents=True, exist_ok=True) + python_bin.write_text("", encoding="utf-8") + + resolved = runtime_paths.resolve_deepseek_python(env={}, repo_root=repo_root) + + assert resolved == python_bin + + +def test_resolve_deepseek_python_prefers_versioned_runtime_over_generic_alias(tmp_path): + from glossapi.ocr.deepseek import runtime_paths + + repo_root = tmp_path / "repo" + generic = repo_root / "dependency_setup" / ".venvs" / "deepseek" / "bin" / "python" + versioned = repo_root / "dependency_setup" / ".venvs" / "deepseek31111" / "bin" / "python" + generic.parent.mkdir(parents=True, exist_ok=True) + versioned.parent.mkdir(parents=True, exist_ok=True) + generic.write_text("", encoding="utf-8") + versioned.write_text("", encoding="utf-8") + + resolved = runtime_paths.resolve_deepseek_python(env={}, repo_root=repo_root) + + assert resolved == versioned + + +def test_work_queue_requeues_stale_running_batch(tmp_path): + from glossapi.ocr.deepseek import work_queue + + db_path = tmp_path / "work.sqlite" + work_queue.init_work_db( + db_path, + batches=[ + { + "batch_id": 0, + "pages": 12, + "files": ["a.pdf"], + "page_ranges": [], + "items": [], + } + ], + ) + + claimed = work_queue.claim_next_batch( + db_path, + worker_id="worker-a", + stale_after_sec=30.0, + now_ts=100.0, + ) + + assert claimed["batch_id"] == 0 + assert work_queue.work_queue_counts(db_path)["running"] == 1 + + requeued = work_queue.requeue_stale_running_batches( + db_path, + stale_after_sec=30.0, + now_ts=200.0, + ) + + assert requeued == 1 + assert work_queue.work_queue_counts(db_path)["pending"] == 1 + + +def test_work_queue_mark_done_persists_result(tmp_path): + from glossapi.ocr.deepseek import work_queue + + db_path = tmp_path / "work.sqlite" + work_queue.init_work_db( + db_path, + batches=[ + { + "batch_id": 1, + "pages": 8, + "files": [], + "page_ranges": ["b.pdf:1:8"], + "items": [], + } + ], + ) + + work_queue.claim_next_batch( + db_path, + worker_id="worker-b", + stale_after_sec=60.0, + now_ts=50.0, + ) + work_queue.mark_batch_done( + db_path, + batch_id=1, + worker_id="worker-b", + result={"pages": 8, "first_infer_started_at": "2026-04-02T10:00:00Z"}, + now_ts=75.0, + ) + + items = list(work_queue.iter_work_items(db_path)) + + assert items[0]["status"] == work_queue.STATUS_DONE + assert items[0]["result"]["pages"] == 8 + assert work_queue.work_queue_counts(db_path)["done"] == 1 + + +def test_work_queue_repair_enqueue_reuses_queue_key(tmp_path): + from glossapi.ocr.deepseek import work_queue + + db_path = tmp_path / "work.sqlite" + work_queue.init_work_db(db_path, batches=[]) + + inserted = work_queue.enqueue_batches( + db_path, + queue_name=work_queue.QUEUE_REPAIR, + batches=[ + { + "queue_key": "repair:5:doc", + "stem": "doc", + "repair_page_numbers": [2, 5], + "pages": 2, + } + ], + ) + claimed = work_queue.claim_next_batch( + db_path, + worker_id="worker-r", + stale_after_sec=60.0, + queue_name=work_queue.QUEUE_REPAIR, + now_ts=10.0, + ) + work_queue.mark_batch_done( + db_path, + batch_id=claimed["batch_id"], + worker_id="worker-r", + result={"pages": 2}, + now_ts=12.0, + ) + + inserted_again = work_queue.enqueue_batches( + db_path, + queue_name=work_queue.QUEUE_REPAIR, + batches=[ + { + "queue_key": "repair:5:doc", + "stem": "doc", + "repair_page_numbers": [2], + "pages": 1, + } + ], + ) + repair_item = [ + item + for item in work_queue.iter_work_items(db_path) + if item["queue_name"] == work_queue.QUEUE_REPAIR + ][0] + + assert inserted_again == inserted + assert repair_item["batch_id"] == inserted[0] + assert repair_item["status"] == work_queue.STATUS_PENDING + assert repair_item["repair_page_numbers"] == [2] + + +def test_work_queue_marks_batch_failed_after_one_retry(tmp_path): + from glossapi.ocr.deepseek import work_queue + + db_path = tmp_path / "work.sqlite" + work_queue.init_work_db( + db_path, + batches=[ + { + "batch_id": 2, + "pages": 4, + "files": ["c.pdf"], + "page_ranges": [], + "items": [], + } + ], + ) + + first = work_queue.claim_next_batch( + db_path, + worker_id="worker-a", + stale_after_sec=60.0, + now_ts=10.0, + ) + work_queue.mark_batch_failed( + db_path, + batch_id=first["batch_id"], + worker_id="worker-a", + error="first failure", + max_attempts=2, + now_ts=20.0, + ) + + second = work_queue.claim_next_batch( + db_path, + worker_id="worker-b", + stale_after_sec=60.0, + now_ts=30.0, + ) + work_queue.mark_batch_failed( + db_path, + batch_id=second["batch_id"], + worker_id="worker-b", + error="second failure", + max_attempts=2, + now_ts=40.0, + ) + + item = list(work_queue.iter_work_items(db_path))[0] + + assert item["attempt_count"] == 2 + assert item["status"] == work_queue.STATUS_FAILED + assert item["worker_id"] == "worker-b" + assert item["last_error"] == "second failure" + + +def test_claim_additional_repair_batches_packs_multiple_items(tmp_path): + from glossapi.ocr.deepseek import run_pdf_ocr_vllm + from glossapi.ocr.deepseek import work_queue + + db_path = tmp_path / "work.sqlite" + work_queue.init_work_db(db_path, batches=[]) + inserted = work_queue.enqueue_batches( + db_path, + queue_name=work_queue.QUEUE_REPAIR, + batches=[ + {"queue_key": "repair:1:a", "batch_id": 10, "stem": "a", "repair_page_numbers": [1, 2], "pages": 2}, + {"queue_key": "repair:1:b", "batch_id": 11, "stem": "b", "repair_page_numbers": [3, 4], "pages": 2}, + {"queue_key": "repair:1:c", "batch_id": 12, "stem": "c", "repair_page_numbers": [5], "pages": 1}, + ], + ) + assert inserted == [10, 11, 12] + + first = work_queue.claim_next_batch( + db_path, + worker_id="worker-pack", + stale_after_sec=60.0, + queue_name=work_queue.QUEUE_REPAIR, + now_ts=10.0, + ) + packed = run_pdf_ocr_vllm._claim_additional_repair_batches( + db_path, + worker_id="worker-pack", + stale_after_sec=60.0, + first_batch=first, + target_pages=4, + target_items=8, + ) + + assert [int(batch["batch_id"]) for batch in packed] == [10, 11] + counts = work_queue.work_queue_counts(db_path) + assert counts["by_queue"][work_queue.QUEUE_REPAIR][work_queue.STATUS_RUNNING] == 2 + assert counts["by_queue"][work_queue.QUEUE_REPAIR][work_queue.STATUS_PENDING] == 1 + + +def test_claim_next_phase_batch_switches_to_repair_after_main_drains(tmp_path): + from glossapi.ocr.deepseek import run_pdf_ocr_vllm + from glossapi.ocr.deepseek import work_queue + + db_path = tmp_path / "work.sqlite" + work_queue.init_work_db( + db_path, + batches=[ + { + "batch_id": 0, + "pages": 8, + "files": ["a.pdf"], + "page_ranges": [], + "items": [], + } + ], + ) + claimed = work_queue.claim_next_batch( + db_path, + worker_id="worker-main", + stale_after_sec=60.0, + now_ts=10.0, + ) + work_queue.mark_batch_done( + db_path, + batch_id=claimed["batch_id"], + worker_id="worker-main", + result={"pages": 8}, + now_ts=20.0, + ) + work_queue.enqueue_batches( + db_path, + queue_name=work_queue.QUEUE_REPAIR, + batches=[ + { + "queue_key": "repair:0:doc", + "stem": "doc", + "repair_page_numbers": [2, 5], + "pages": 2, + } + ], + ) + + queue_name, batch, should_wait = run_pdf_ocr_vllm._claim_next_phase_batch( + db_path, + worker_id="worker-repair", + stale_after_sec=60.0, + ) + + assert queue_name == work_queue.QUEUE_REPAIR + assert batch is not None + assert batch["queue_key"] == "repair:0:doc" + assert should_wait is False + + +def test_runner_runtime_summary_reports_steady_state_windows(tmp_path): + from glossapi.ocr.deepseek import runner + from glossapi.ocr.deepseek import work_queue + + runtime_dir = tmp_path / "runtime" + runtime_dir.mkdir(parents=True, exist_ok=True) + (runtime_dir / "worker_00.runtime.json").write_text( + json.dumps( + { + "worker_id": "worker_00", + "engine_ready_at": "2026-04-02T10:00:10Z", + "first_batch_started_at": "2026-04-02T10:00:20Z", + "last_batch_finished_at": "2026-04-02T10:05:20Z", + } + ), + encoding="utf-8", + ) + (runtime_dir / "worker_01.runtime.json").write_text( + json.dumps( + { + "worker_id": "worker_01", + "engine_ready_at": "2026-04-02T10:00:12Z", + "first_batch_started_at": "2026-04-02T10:00:24Z", + "last_batch_finished_at": "2026-04-02T10:04:20Z", + } + ), + encoding="utf-8", + ) + db_path = tmp_path / "work.sqlite" + work_queue.init_work_db( + db_path, + batches=[ + {"batch_id": 0, "pages": 50, "files": ["a.pdf"], "page_ranges": [], "items": []}, + {"batch_id": 1, "pages": 50, "files": ["b.pdf"], "page_ranges": [], "items": []}, + ], + ) + work_queue.claim_next_batch(db_path, worker_id="worker_00", stale_after_sec=60.0, now_ts=1.0) + work_queue.mark_batch_done(db_path, batch_id=0, worker_id="worker_00", now_ts=2.0) + work_queue.claim_next_batch(db_path, worker_id="worker_01", stale_after_sec=60.0, now_ts=3.0) + work_queue.mark_batch_done(db_path, batch_id=1, worker_id="worker_01", now_ts=4.0) + + summary_path = runner._write_runtime_summary(runtime_dir=runtime_dir, db_path=db_path) + summary = json.loads(summary_path.read_text(encoding="utf-8")) + + assert summary["queue_counts"]["done"] == 2 + assert summary["steady_state"]["first_batch_started_at"] == "2026-04-02T10:00:20Z" + assert summary["steady_state"]["all_workers_ready_at"] == "2026-04-02T10:00:12Z" + assert summary["steady_state"]["last_batch_finished_at"] == "2026-04-02T10:05:20Z" + assert summary["steady_state"]["first_batch_to_last_batch_window_sec"] == 300.0 + assert summary["steady_state"]["all_workers_ready_to_last_batch_window_sec"] == 308.0 + assert summary["queue_counts"]["by_queue"]["main"]["done"] == 2 + assert summary["queue_counts"]["by_queue"]["repair"]["done"] == 0 + + +def test_runner_preflight_can_ensure_persistence_mode(monkeypatch): + from glossapi.ocr.deepseek import runner + + responses = [ + [{"index": "0", "persistence_mode": "Disabled"}], + [{"index": "0", "persistence_mode": "Enabled"}], + ] + + monkeypatch.setattr(runner, "_query_persistence_mode", lambda *, visible_devices: responses.pop(0)) + + calls = {} + + def fake_run(cmd, check, capture_output, text): + calls["cmd"] = cmd + return SimpleNamespace(returncode=0) + + monkeypatch.setattr(runner.subprocess, "run", fake_run) + + status = runner._ensure_gpu_preflight(visible_devices=[0], mode="ensure") + + assert calls["cmd"] == ["sudo", "-n", "nvidia-smi", "-pm", "1"] + assert status["changed"] is True + assert status["after"] == [{"index": "0", "persistence_mode": "Enabled"}] + + +def test_build_cli_command_includes_work_queue_flags(tmp_path): + from glossapi.ocr.deepseek.runner import _build_cli_command + + cmd = _build_cli_command( + input_dir=tmp_path / "in", + output_dir=tmp_path / "out", + files=[], + page_ranges=None, + model_dir=tmp_path / "model", + python_bin=Path("/usr/bin/python3"), + script=tmp_path / "run_vllm.py", + max_pages=None, + content_debug=False, + device="cuda", + ocr_profile="markdown_grounded", + prompt_override=None, + attn_backend="vllm", + base_size=None, + image_size=None, + crop_mode=None, + render_dpi=144, + max_new_tokens=2048, + repetition_penalty=None, + no_repeat_ngram_size=None, + runtime_backend="vllm", + vllm_batch_size=96, + gpu_memory_utilization=0.9, + disable_fp8_kv=False, + repair_mode="auto", + repair_exec_batch_target_pages=48, + repair_exec_batch_target_items=32, + work_db=tmp_path / "work.sqlite", + worker_id="worker_00_gpu0", + worker_runtime_file=tmp_path / "worker_00.runtime.json", + work_stale_after_sec=900.0, + work_heartbeat_sec=10.0, + work_max_attempts=2, + ) + + assert "--work-db" in cmd + assert str(tmp_path / "work.sqlite") in cmd + assert "--worker-id" in cmd and "worker_00_gpu0" in cmd + assert "--worker-runtime-file" in cmd and str(tmp_path / "worker_00.runtime.json") in cmd + assert "--work-stale-after-sec" in cmd and "900.0" in cmd + assert "--work-heartbeat-sec" in cmd and "10.0" in cmd + assert "--work-max-attempts" in cmd and "2" in cmd + assert "--repair-exec-batch-target-pages" in cmd and "48" in cmd + assert "--repair-exec-batch-target-items" in cmd and "32" in cmd + + +def test_launch_worker_process_uses_start_new_session(monkeypatch): + from glossapi.ocr.deepseek import runner + + calls = {} + + def fake_popen(cmd, stdout, stderr, env, start_new_session): + calls["cmd"] = cmd + calls["start_new_session"] = start_new_session + return SimpleNamespace(pid=1234) + + monkeypatch.setattr(runner.subprocess, "Popen", fake_popen) + + proc = runner._launch_worker_process(["python", "worker.py"], fh=object(), env={"A": "1"}) + + assert calls["cmd"] == ["python", "worker.py"] + assert calls["start_new_session"] is True + assert proc.pid == 1234 + + +def test_terminate_worker_process_group_signals_group(monkeypatch): + from glossapi.ocr.deepseek import runner + + signals = [] + monkeypatch.setattr(runner.os, "killpg", lambda pgid, sig: signals.append((pgid, sig))) + monkeypatch.setattr(runner, "_wait_for_process_group_exit", lambda pgid, *, timeout_sec: True) + + ok = runner._terminate_worker_process_group( + { + "worker_id": "worker_00_gpu0", + "proc": SimpleNamespace(pid=4321), + } + ) + + assert ok is True + assert signals == [(4321, runner.signal.SIGTERM)] diff --git a/tests/test_deepseek_preflight.py b/tests/test_deepseek_preflight.py index 1900a2b..73e761d 100644 --- a/tests/test_deepseek_preflight.py +++ b/tests/test_deepseek_preflight.py @@ -1,5 +1,4 @@ import sys -from pathlib import Path from glossapi.ocr.deepseek.preflight import check_deepseek_env @@ -9,45 +8,34 @@ def test_preflight_reports_missing_components(tmp_path): "GLOSSAPI_DEEPSEEK_ALLOW_CLI": "0", "GLOSSAPI_DEEPSEEK_ALLOW_STUB": "1", "GLOSSAPI_DEEPSEEK_TEST_PYTHON": str(tmp_path / "missing_python"), - "GLOSSAPI_DEEPSEEK_VLLM_SCRIPT": str(tmp_path / "missing_script.py"), + "GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT": str(tmp_path / "missing_script.py"), "GLOSSAPI_DEEPSEEK_MODEL_DIR": str(tmp_path / "missing_model"), - "GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH": str(tmp_path / "missing_lib"), - "PATH": str(tmp_path), # no cc1plus here } - report = check_deepseek_env(env, check_flashinfer=False) + report = check_deepseek_env(env, check_torch=False) names = {c.name for c in report.errors} + assert "allow_cli" in names + assert "allow_stub" in names assert "deepseek_python" in names - assert "vllm_script" in names + assert "runner_script" in names assert "model_dir" in names - assert "ld_library_path" in names - assert "cc1plus" in names assert not report.ok def test_preflight_passes_with_complete_env(tmp_path): - script = tmp_path / "run_pdf_ocr_vllm.py" + script = tmp_path / "run_pdf_ocr_transformers.py" script.write_text("#!/usr/bin/env python3\n", encoding="utf-8") - model_dir = tmp_path / "DeepSeek-OCR" + model_dir = tmp_path / "DeepSeek-OCR-2" model_dir.mkdir() (model_dir / "config.json").write_text("{}", encoding="utf-8") (model_dir / "model-00001-of-000001.safetensors").write_bytes(b"stub") - lib_dir = tmp_path / "libjpeg" - lib_dir.mkdir() - fake_bin = tmp_path / "bin" - fake_bin.mkdir() - cc1plus = fake_bin / "cc1plus" - cc1plus.write_text("#!/bin/sh\nexit 0\n", encoding="utf-8") - cc1plus.chmod(0o755) env = { "GLOSSAPI_DEEPSEEK_ALLOW_CLI": "1", "GLOSSAPI_DEEPSEEK_ALLOW_STUB": "0", "GLOSSAPI_DEEPSEEK_TEST_PYTHON": sys.executable, - "GLOSSAPI_DEEPSEEK_VLLM_SCRIPT": str(script), + "GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT": str(script), "GLOSSAPI_DEEPSEEK_MODEL_DIR": str(model_dir), - "GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH": str(lib_dir), - "PATH": str(fake_bin), } - report = check_deepseek_env(env, check_flashinfer=False) + report = check_deepseek_env(env, check_torch=False) assert report.ok assert not report.errors diff --git a/tests/test_deepseek_runner_contract.py b/tests/test_deepseek_runner_contract.py new file mode 100644 index 0000000..6c1b07b --- /dev/null +++ b/tests/test_deepseek_runner_contract.py @@ -0,0 +1,866 @@ +import json +import sys +from pathlib import Path +from types import SimpleNamespace + +import pandas as pd +import pytest + + +def _mk_corpus(tmp_path: Path): + from glossapi import Corpus + + root = tmp_path / "corpus" + root.mkdir() + return Corpus(input_dir=root, output_dir=root) + + +def test_deepseek_backend_rejects_stub_mode(tmp_path, monkeypatch): + corpus = _mk_corpus(tmp_path) + + dl_dir = corpus.output_dir / "download_results" + dl_dir.mkdir(parents=True, exist_ok=True) + fname = "doc.pdf" + df = pd.DataFrame( + [{"filename": fname, corpus.url_column: "", "needs_ocr": True, "ocr_success": False}] + ) + parquet_path = dl_dir / "download_results.parquet" + df.to_parquet(parquet_path, index=False) + (corpus.input_dir / fname).write_bytes(b"%PDF-1.4\n%real\n") + + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "1") + + with pytest.raises(RuntimeError, match="stub execution has been removed"): + corpus.ocr(backend="deepseek", fix_bad=True, math_enhance=False) + + updated = pd.read_parquet(parquet_path).set_index("filename") + assert bool(updated.loc[fname, "ocr_success"]) is False + assert bool(updated.loc[fname, "needs_ocr"]) is True + + +def test_progress_artifacts_stay_out_of_canonical_markdown(tmp_path): + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _write_outputs, _write_progress + + output_dir = tmp_path / "output" + _write_progress( + output_dir=output_dir, + stem="doc", + page_outputs=["page one"], + total_pages=5, + completed_pages=1, + ) + + canonical_markdown = output_dir / "markdown" / "doc.md" + progress_markdown = output_dir / "sidecars" / "ocr_progress" / "doc.partial.md" + progress_json = output_dir / "json" / "metrics" / "doc.progress.json" + + assert not canonical_markdown.exists() + assert progress_markdown.exists() + assert progress_json.exists() + + _write_outputs(output_dir=output_dir, stem="doc", markdown="final", page_count=5) + + assert canonical_markdown.exists() + assert canonical_markdown.read_text(encoding="utf-8") == "final\n" + assert not progress_markdown.exists() + + +def test_page_output_helpers_roundtrip_numbered_blank_pages(): + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _join_page_outputs, _split_page_outputs + + page_outputs = ["page one", "", "page three"] + + markdown = _join_page_outputs(page_outputs) + + assert markdown == ( + "page one\n" + "\n" + "<--- Page Split --->\n" + "\n" + "\n" + "<--- Page Split --->\n" + "page three" + ) + assert _split_page_outputs(markdown) == page_outputs + + +def test_write_outputs_preserves_blank_first_page_structure(tmp_path): + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _join_page_outputs, _split_page_outputs, _write_outputs + + output_dir = tmp_path / "output" + markdown = _join_page_outputs(["", "page two"]) + + _write_outputs(output_dir=output_dir, stem="doc", markdown=markdown, page_count=2) + + written = (output_dir / "markdown" / "doc.md").read_text(encoding="utf-8") + assert written == ( + "\n" + "<--- Page Split --->\n" + "page two\n" + ) + assert _split_page_outputs(written) == ["", "page two"] + + +def test_auto_attn_backend_prefers_eager_when_flash_attn_is_unavailable(monkeypatch): + import builtins + + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _resolve_attn_backend + + original_import = builtins.__import__ + + def fake_import(name, globals=None, locals=None, fromlist=(), level=0): + if name == "flash_attn": + raise ImportError("flash_attn unavailable") + return original_import(name, globals, locals, fromlist, level) + + monkeypatch.setattr(builtins, "__import__", fake_import) + assert _resolve_attn_backend("auto") == "eager" + + +def test_runner_uses_downloads_subdir_when_present(tmp_path, monkeypatch): + from glossapi.ocr.deepseek import runner + + corpus = _mk_corpus(tmp_path) + downloads_dir = corpus.input_dir / "downloads" + downloads_dir.mkdir(parents=True, exist_ok=True) + (downloads_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n%real\n") + + calls = {} + + def fake_run_cli(input_dir, output_dir, **kwargs): + calls["input_dir"] = input_dir + md_dir = output_dir / "markdown" + metrics_dir = output_dir / "json" / "metrics" + md_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + (md_dir / "doc.md").write_text("ok\n", encoding="utf-8") + (metrics_dir / "doc.metrics.json").write_text('{"page_count": 1}', encoding="utf-8") + + monkeypatch.setattr(runner, "_run_cli", fake_run_cli) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(tmp_path)) + monkeypatch.setenv( + "GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT", + str(Path(runner.__file__).resolve().parent / "run_pdf_ocr_transformers.py"), + ) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_PYTHON", sys.executable) + + result = runner.run_for_files(corpus, ["doc.pdf"]) + + assert calls["input_dir"] == downloads_dir.resolve() + assert result["doc"]["page_count"] == 1 + + +def test_build_cli_command_includes_speed_flags(tmp_path): + from glossapi.ocr.deepseek.runner import _build_cli_command + + cmd = _build_cli_command( + input_dir=tmp_path / "in", + output_dir=tmp_path / "out", + files=["a.pdf"], + page_ranges=None, + model_dir=tmp_path / "model", + python_bin=Path("/usr/bin/python3"), + script=tmp_path / "run.py", + max_pages=1, + content_debug=False, + device="cuda", + ocr_profile="plain_ocr", + prompt_override="custom prompt", + attn_backend="flash_attention_2", + base_size=768, + image_size=512, + crop_mode=True, + render_dpi=144, + max_new_tokens=1024, + repetition_penalty=1.05, + no_repeat_ngram_size=12, + runtime_backend="transformers", + vllm_batch_size=None, + gpu_memory_utilization=None, + disable_fp8_kv=False, + repair_mode=None, + ) + + assert "--ocr-profile" in cmd and "plain_ocr" in cmd + assert "--prompt-override" in cmd and "custom prompt" in cmd + assert "--attn-backend" in cmd and "flash_attention_2" in cmd + assert "--base-size" in cmd and "768" in cmd + assert "--image-size" in cmd and "512" in cmd + assert "--crop-mode" in cmd + assert "--render-dpi" in cmd and "144" in cmd + assert "--max-new-tokens" in cmd and "1024" in cmd + + +def test_deepseek_default_max_new_tokens_is_standardized(): + from glossapi.ocr.deepseek import runner + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import DEFAULT_MAX_NEW_TOKENS + + assert DEFAULT_MAX_NEW_TOKENS == 2048 + assert runner.DEFAULT_MAX_NEW_TOKENS == 2048 + + +def test_build_cli_command_includes_vllm_flags(tmp_path): + from glossapi.ocr.deepseek.runner import _build_cli_command + + cmd = _build_cli_command( + input_dir=tmp_path / "in", + output_dir=tmp_path / "out", + files=["a.pdf"], + page_ranges=None, + model_dir=tmp_path / "model", + python_bin=Path("/usr/bin/python3"), + script=tmp_path / "run_vllm.py", + max_pages=1, + content_debug=False, + device="cuda", + ocr_profile="markdown_grounded", + prompt_override=None, + attn_backend="auto", + base_size=None, + image_size=None, + crop_mode=None, + render_dpi=110, + max_new_tokens=768, + repetition_penalty=None, + no_repeat_ngram_size=None, + runtime_backend="vllm", + vllm_batch_size=16, + gpu_memory_utilization=0.92, + disable_fp8_kv=True, + repair_mode="auto", + ) + + assert "--batch-size" in cmd and "16" in cmd + assert "--gpu-memory-utilization" in cmd and "0.92" in cmd + assert "--disable-fp8-kv" in cmd + assert "--repair-mode" in cmd and "auto" in cmd + + +def test_build_env_prepends_script_src_to_pythonpath(tmp_path, monkeypatch): + import os + + from glossapi.ocr.deepseek.runner import _build_env + + repo_root = tmp_path / "repo" + script = repo_root / "src" / "glossapi" / "ocr" / "deepseek" / "run_pdf_ocr_vllm.py" + script.parent.mkdir(parents=True, exist_ok=True) + script.write_text("# stub\n", encoding="utf-8") + (repo_root / "src" / "glossapi").mkdir(parents=True, exist_ok=True) + + monkeypatch.setenv("PYTHONPATH", os.pathsep.join(["/tmp/old-a", "/tmp/old-b"])) + env = _build_env( + python_bin=Path("/usr/bin/python3"), + visible_device=1, + script=script, + ) + + assert env["PYTHONPATH"].split(os.pathsep) == [ + str((repo_root / "src").resolve()), + "/tmp/old-a", + "/tmp/old-b", + ] + assert env["CUDA_VISIBLE_DEVICES"] == "1" + + +def test_build_cli_command_includes_page_ranges(tmp_path): + from glossapi.ocr.deepseek.runner import _build_cli_command + + cmd = _build_cli_command( + input_dir=tmp_path / "in", + output_dir=tmp_path / "out", + files=[], + page_ranges=["a.pdf:1:64", "b.pdf:65:128"], + model_dir=tmp_path / "model", + python_bin=Path("/usr/bin/python3"), + script=tmp_path / "run_vllm.py", + max_pages=None, + content_debug=False, + device="cuda", + ocr_profile="markdown_grounded", + prompt_override=None, + attn_backend="auto", + base_size=None, + image_size=None, + crop_mode=None, + render_dpi=144, + max_new_tokens=1024, + repetition_penalty=None, + no_repeat_ngram_size=None, + runtime_backend="vllm", + vllm_batch_size=32, + gpu_memory_utilization=0.9, + disable_fp8_kv=False, + repair_mode="auto", + ) + + assert "--page-ranges" in cmd + assert "a.pdf:1:64" in cmd + assert "b.pdf:65:128" in cmd + + +def test_vllm_empty_page_detector_is_conservative(): + from glossapi.ocr.deepseek.run_pdf_ocr_vllm import _is_effectively_empty_page + + empty_page = { + "top_dark_ratio": 0.0004, + "bottom_dark_ratio": 0.0006, + "top_third_dark_ratio": 0.0002, + "middle_third_dark_ratio": 0.0005, + "bottom_third_dark_ratio": 0.0007, + "overall_dark_ratio": 0.0008, + } + non_empty_sparse_page = { + "top_dark_ratio": 0.003, + "bottom_dark_ratio": 0.004, + "top_third_dark_ratio": 0.0028, + "middle_third_dark_ratio": 0.0031, + "bottom_third_dark_ratio": 0.0042, + "overall_dark_ratio": 0.0022, + } + assert _is_effectively_empty_page(empty_page, "auto") is True + assert _is_effectively_empty_page(non_empty_sparse_page, "auto") is False + assert _is_effectively_empty_page(empty_page, "off") is False + + +def test_repair_disposition_drops_repeat_garbage_cutoff(): + from glossapi.ocr.deepseek.run_pdf_ocr_vllm import _resolve_repair_disposition + + disposition = _resolve_repair_disposition( + repair_text="garbage", + repair_postprocess={"early_stops": 1}, + ) + + assert disposition == { + "final_text": "", + "repair_applied": False, + "page_dropped_after_repair": True, + "drop_reason": "repeat_garbage_cutoff", + } + + +def test_repair_batch_updates_persisted_outputs_with_repeat_cutoff_drop(tmp_path, monkeypatch): + from PIL import Image + + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _join_page_outputs, _split_page_outputs, _write_outputs + from glossapi.ocr.deepseek.run_pdf_ocr_vllm import _run_repair_batch_to_outputs + + output_dir = tmp_path / "output" + _write_outputs( + output_dir=output_dir, + stem="doc", + markdown=_join_page_outputs(["bad first page", "page two"]), + page_count=2, + extra_metrics={ + "repair_mode": "auto", + "page_metrics": [ + { + "page_number": 1, + "infer_sec": 1.0, + "raw_chars": 20, + "final_chars": 14, + "repair_strategy": "plain", + "repair_reason": "early_stop_markdown_garbage", + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": False, + "garbage_early_stop_applied": True, + }, + { + "page_number": 2, + "infer_sec": 0.5, + "raw_chars": 8, + "final_chars": 8, + "repair_strategy": "none", + "repair_reason": None, + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": False, + "garbage_early_stop_applied": False, + }, + ], + "repair_summary": {"repair_mode": "auto", "pages_flagged": 1, "pages_repaired": 0}, + }, + ) + + monkeypatch.setattr( + "glossapi.ocr.deepseek.run_pdf_ocr_vllm._iter_selected_rendered_pages", + lambda pdf_path, *, render_dpi, source_page_numbers: [(1, Image.new("RGB", (4, 4), "white"))], + ) + monkeypatch.setattr( + "glossapi.ocr.deepseek.run_pdf_ocr_vllm._generate_batch_outputs", + lambda llm, *, jobs, prompt, batch_size, sampling_params: [ + {"item": jobs[0], "raw_text": "still broken", "infer_sec": 0.25} + ], + ) + monkeypatch.setattr( + "glossapi.ocr.deepseek.run_pdf_ocr_vllm._postprocess_page_text", + lambda text, *, prompt, content_debug: ("garbage", {"early_stops": 1}), + ) + + result = _run_repair_batch_to_outputs( + SimpleNamespace(render_dpi=144, batch_size=8, content_debug=False, repair_mode="auto"), + batch={ + "stem": "doc", + "pdf_path": str(tmp_path / "doc.pdf"), + "source_start_page": 1, + "repair_page_numbers": [1], + }, + output_dir=output_dir, + llm=object(), + plain_prompt="plain prompt", + sampling_params=object(), + ) + + markdown = (output_dir / "markdown" / "doc.md").read_text(encoding="utf-8") + metrics = json.loads((output_dir / "json" / "metrics" / "doc.metrics.json").read_text(encoding="utf-8")) + + assert result["pages"] == 1 + assert _split_page_outputs(markdown) == ["", "page two"] + assert metrics["repair_summary"]["pages_dropped_after_repeat_cutoff"] == 1 + + +def test_repair_batch_pack_updates_multiple_stems(tmp_path, monkeypatch): + from PIL import Image + + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _join_page_outputs, _split_page_outputs, _write_outputs + from glossapi.ocr.deepseek.run_pdf_ocr_vllm import _run_repair_batches_to_outputs + + output_dir = tmp_path / "output" + _write_outputs( + output_dir=output_dir, + stem="doc_a", + markdown=_join_page_outputs(["bad a", "page two a"]), + page_count=2, + extra_metrics={ + "repair_mode": "auto", + "page_metrics": [ + { + "page_number": 1, + "infer_sec": 1.0, + "raw_chars": 10, + "final_chars": 5, + "repair_strategy": "plain", + "repair_reason": "early_stop_markdown_garbage", + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": False, + "garbage_early_stop_applied": True, + }, + { + "page_number": 2, + "infer_sec": 0.5, + "raw_chars": 9, + "final_chars": 9, + "repair_strategy": "none", + "repair_reason": None, + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": False, + "garbage_early_stop_applied": False, + }, + ], + "repair_summary": {"repair_mode": "auto", "pages_flagged": 1, "pages_repaired": 0}, + }, + ) + _write_outputs( + output_dir=output_dir, + stem="doc_b", + markdown=_join_page_outputs(["bad b"]), + page_count=1, + extra_metrics={ + "repair_mode": "auto", + "page_metrics": [ + { + "page_number": 1, + "infer_sec": 0.7, + "raw_chars": 8, + "final_chars": 5, + "repair_strategy": "plain", + "repair_reason": "early_stop_markdown_garbage", + "repair_attempted": False, + "repair_applied": False, + "page_dropped_after_repair": False, + "empty_page_skipped": False, + "garbage_early_stop_applied": True, + } + ], + "repair_summary": {"repair_mode": "auto", "pages_flagged": 1, "pages_repaired": 0}, + }, + ) + + monkeypatch.setattr( + "glossapi.ocr.deepseek.run_pdf_ocr_vllm._iter_selected_rendered_pages", + lambda pdf_path, *, render_dpi, source_page_numbers: [ + (page_number, Image.new("RGB", (4, 4), "white")) for page_number in source_page_numbers + ], + ) + monkeypatch.setattr( + "glossapi.ocr.deepseek.run_pdf_ocr_vllm._generate_batch_outputs", + lambda llm, *, jobs, prompt, batch_size, sampling_params: [ + {"item": job, "raw_text": f"fixed-{job['stem']}-{job['page_number']}", "infer_sec": 0.25} + for job in jobs + ], + ) + monkeypatch.setattr( + "glossapi.ocr.deepseek.run_pdf_ocr_vllm._postprocess_page_text", + lambda text, *, prompt, content_debug: (text, {"early_stops": 0}), + ) + + result = _run_repair_batches_to_outputs( + SimpleNamespace(render_dpi=144, batch_size=8, content_debug=False, repair_mode="auto"), + batches=[ + { + "batch_id": 10, + "stem": "doc_a", + "pdf_path": str(tmp_path / "doc_a.pdf"), + "source_start_page": 1, + "repair_page_numbers": [1], + "pages": 1, + }, + { + "batch_id": 11, + "stem": "doc_b", + "pdf_path": str(tmp_path / "doc_b.pdf"), + "source_start_page": 1, + "repair_page_numbers": [1], + "pages": 1, + }, + ], + output_dir=output_dir, + llm=object(), + plain_prompt="plain prompt", + sampling_params=object(), + ) + + markdown_a = (output_dir / "markdown" / "doc_a.md").read_text(encoding="utf-8") + markdown_b = (output_dir / "markdown" / "doc_b.md").read_text(encoding="utf-8") + metrics_a = json.loads((output_dir / "json" / "metrics" / "doc_a.metrics.json").read_text(encoding="utf-8")) + metrics_b = json.loads((output_dir / "json" / "metrics" / "doc_b.metrics.json").read_text(encoding="utf-8")) + + assert result["pages"] == 2 + assert result["docs"] == 2 + assert set(result["per_batch_results"]) == {10, 11} + assert _split_page_outputs(markdown_a)[0] == "fixed-doc_a-1" + assert _split_page_outputs(markdown_b)[0] == "fixed-doc_b-1" + assert metrics_a["repair_summary"]["pages_repaired"] == 1 + assert metrics_b["repair_summary"]["pages_repaired"] == 1 + + +def test_vllm_progress_sidecar_keeps_absolute_page_numbers(tmp_path): + from glossapi.ocr.deepseek.run_pdf_ocr_vllm import _emit_progress + + state = { + "page_outputs": ["", "page two"], + "total_pages": 2, + "completed_pages": 2, + } + + _emit_progress(tmp_path / "output", "doc", state) + + partial_markdown = (tmp_path / "output" / "sidecars" / "ocr_progress" / "doc.partial.md").read_text( + encoding="utf-8" + ) + assert partial_markdown == ( + "\n" + "<--- Page Split --->\n" + "page two\n" + ) + + +def test_early_stop_detects_symbol_and_numeric_list_garbage(): + from glossapi.ocr.utils.cleaning import detect_early_stop_index + + symbol_garbage = "Κανονικό κείμενο\n" + (" " * 20) + numeric_list_garbage = "Πρόλογος\n" + " ".join(f"{idx}." for idx in range(1, 20)) + + symbol_cut = detect_early_stop_index(symbol_garbage) + numeric_cut = detect_early_stop_index(numeric_list_garbage) + + assert symbol_cut is not None + assert "Κανονικό κείμενο" in symbol_garbage[:symbol_cut] + assert numeric_cut is not None + assert "Πρόλογος" in numeric_list_garbage[:numeric_cut] + + +def test_runner_selects_vllm_script_when_requested(tmp_path, monkeypatch): + from glossapi.ocr.deepseek import runner + + corpus = _mk_corpus(tmp_path) + (corpus.input_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n%real\n") + + calls = {} + + def fake_run_cli(input_dir, output_dir, **kwargs): + calls["script"] = kwargs["script"] + calls["runtime_backend"] = kwargs["runtime_backend"] + md_dir = output_dir / "markdown" + metrics_dir = output_dir / "json" / "metrics" + md_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + (md_dir / "doc.md").write_text("ok\n", encoding="utf-8") + (metrics_dir / "doc.metrics.json").write_text('{"page_count": 1}', encoding="utf-8") + + monkeypatch.setattr(runner, "_run_cli", fake_run_cli) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(tmp_path)) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_PYTHON", sys.executable) + + result = runner.run_for_files(corpus, ["doc.pdf"], runtime_backend="vllm") + + assert calls["runtime_backend"] == "vllm" + assert Path(calls["script"]).name == "run_pdf_ocr_vllm.py" + assert result["doc"]["page_count"] == 1 + + +def test_runner_resolves_standard_vllm_defaults_when_omitted(tmp_path, monkeypatch): + from glossapi.ocr.deepseek import runner + from glossapi.ocr.deepseek.defaults import DEFAULT_GPU_MEMORY_UTILIZATION, DEFAULT_RENDER_DPI + + corpus = _mk_corpus(tmp_path) + (corpus.input_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n%real\n") + + calls = {} + + def fake_run_cli(input_dir, output_dir, **kwargs): + calls["input_dir"] = input_dir + calls["kwargs"] = dict(kwargs) + md_dir = output_dir / "markdown" + metrics_dir = output_dir / "json" / "metrics" + md_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + (md_dir / "doc.md").write_text("ok\n", encoding="utf-8") + (metrics_dir / "doc.metrics.json").write_text('{"page_count": 1}', encoding="utf-8") + + monkeypatch.setattr(runner, "_run_cli", fake_run_cli) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(tmp_path)) + monkeypatch.setenv( + "GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT", + str(Path(runner.__file__).resolve().parent / "run_pdf_ocr_vllm.py"), + ) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_PYTHON", sys.executable) + + runner.run_for_files( + corpus, + ["doc.pdf"], + runtime_backend="vllm", + render_dpi=None, + gpu_memory_utilization=None, + ) + + assert calls["input_dir"] == corpus.input_dir.resolve() + assert calls["kwargs"]["render_dpi"] == DEFAULT_RENDER_DPI + assert calls["kwargs"]["gpu_memory_utilization"] == DEFAULT_GPU_MEMORY_UTILIZATION + + +def test_runner_prefers_repo_local_deepseek_runtime_when_env_missing(tmp_path, monkeypatch): + from glossapi.ocr.deepseek import runner, runtime_paths + + corpus = _mk_corpus(tmp_path) + (corpus.input_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n%real\n") + + repo_root = tmp_path / "repo" + python_bin = repo_root / "dependency_setup" / ".venvs" / "deepseek31111" / "bin" / "python" + python_bin.parent.mkdir(parents=True, exist_ok=True) + python_bin.write_text("", encoding="utf-8") + monkeypatch.setattr(runtime_paths, "REPO_ROOT", repo_root) + + calls = {} + + def fake_run_cli(input_dir, output_dir, **kwargs): + calls["python_bin"] = kwargs["python_bin"] + md_dir = output_dir / "markdown" + metrics_dir = output_dir / "json" / "metrics" + md_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + (md_dir / "doc.md").write_text("ok\n", encoding="utf-8") + (metrics_dir / "doc.metrics.json").write_text('{"page_count": 1}', encoding="utf-8") + + monkeypatch.setattr(runner, "_run_cli", fake_run_cli) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(tmp_path)) + monkeypatch.delenv("GLOSSAPI_DEEPSEEK_PYTHON", raising=False) + monkeypatch.delenv("GLOSSAPI_DEEPSEEK_TEST_PYTHON", raising=False) + + result = runner.run_for_files(corpus, ["doc.pdf"], runtime_backend="vllm") + + assert calls["python_bin"] == python_bin + assert result["doc"]["page_count"] == 1 + + +def test_runner_forwards_scheduler_controls_to_multi_cli(tmp_path, monkeypatch): + from glossapi.ocr.deepseek import runner + + corpus = _mk_corpus(tmp_path) + (corpus.input_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n%real\n") + + calls = {} + + def fake_run_multi_cli(**kwargs): + calls.update(kwargs) + md_dir = kwargs["out_root"] / "markdown" + metrics_dir = kwargs["out_root"] / "json" / "metrics" + md_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + (md_dir / "doc.md").write_text("ok\n", encoding="utf-8") + (metrics_dir / "doc.metrics.json").write_text('{"page_count": 1}', encoding="utf-8") + + monkeypatch.setattr(runner, "_run_multi_cli", fake_run_multi_cli) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(tmp_path)) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_PYTHON", sys.executable) + + result = runner.run_for_files( + corpus, + ["doc.pdf"], + runtime_backend="vllm", + use_gpus="multi", + devices=[0, 1], + scheduler="exact_fill", + target_batch_pages=196, + shard_pages=64, + shard_threshold_pages=256, + ) + + assert calls["scheduler"] == "exact_fill" + assert calls["target_batch_pages"] == 196 + assert calls["shard_pages"] == 64 + assert calls["shard_threshold_pages"] == 256 + assert result["doc"]["page_count"] == 1 + + + +def test_runner_reassembles_exact_fill_shards_into_canonical_outputs(tmp_path, monkeypatch): + import json + + from glossapi.ocr.deepseek import runner + from glossapi.ocr.deepseek.run_pdf_ocr_transformers import _join_page_outputs, _write_outputs + + corpus = _mk_corpus(tmp_path) + downloads_dir = corpus.input_dir / "downloads" + downloads_dir.mkdir(parents=True, exist_ok=True) + (downloads_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n%real\n") + + def fake_run_multi_cli(*, out_root, **kwargs): + del kwargs + common_metrics = { + "source_file": "doc.pdf", + "source_stem": "doc", + "ocr_profile": "markdown_grounded", + "attn_backend": "vllm", + "runtime_backend": "vllm", + "batch_size": 96, + "repair_mode": "auto", + } + _write_outputs( + output_dir=out_root, + stem="doc__p00001-00002", + markdown=_join_page_outputs(["page one", "page two"]), + page_count=2, + extra_metrics={ + **common_metrics, + "source_start_page": 1, + "source_end_page": 2, + "render_sec": 1.5, + "infer_sec_total": 2.5, + "wall_time_sec": 3.5, + "repair_summary": {"repair_mode": "auto", "pages_flagged": 1, "pages_repaired": 1}, + "page_metrics": [ + {"page_number": 1, "infer_sec": 1.0, "repair_strategy": "none", "repair_applied": False}, + {"page_number": 2, "infer_sec": 1.5, "repair_strategy": "plain", "repair_applied": True}, + ], + }, + ) + _write_outputs( + output_dir=out_root, + stem="doc__p00003-00004", + markdown=_join_page_outputs(["page three", "page four"]), + page_count=2, + extra_metrics={ + **common_metrics, + "source_start_page": 3, + "source_end_page": 4, + "render_sec": 0.5, + "infer_sec_total": 1.5, + "wall_time_sec": 2.0, + "repair_summary": {"repair_mode": "auto", "pages_flagged": 0, "pages_repaired": 0}, + "page_metrics": [ + {"page_number": 1, "infer_sec": 0.7, "repair_strategy": "none", "repair_applied": False}, + {"page_number": 2, "infer_sec": 0.8, "repair_strategy": "none", "repair_applied": False}, + ], + }, + ) + + monkeypatch.setattr(runner, "_run_multi_cli", fake_run_multi_cli) + monkeypatch.setattr(runner, "_page_count", lambda path: 4) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(tmp_path)) + monkeypatch.setenv( + "GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT", + str(Path(runner.__file__).resolve().parent / "run_pdf_ocr_vllm.py"), + ) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_PYTHON", sys.executable) + + result = runner.run_for_files( + corpus, + ["doc.pdf"], + use_gpus="multi", + devices=[0, 1], + runtime_backend="vllm", + scheduler="exact_fill", + target_batch_pages=2, + ) + + canonical_md = corpus.output_dir / "markdown" / "doc.md" + canonical_metrics = corpus.output_dir / "json" / "metrics" / "doc.metrics.json" + assert canonical_md.exists() + assert canonical_metrics.exists() + assert canonical_md.read_text(encoding="utf-8") == _join_page_outputs( + ["page one", "page two", "page three", "page four"] + ) + "\n" + + metrics = json.loads(canonical_metrics.read_text(encoding="utf-8")) + assert metrics["reassembled_from_shards"] is True + assert metrics["reassembled_shard_count"] == 2 + assert [item["page_number"] for item in metrics["page_metrics"]] == [1, 2, 3, 4] + assert metrics["repair_summary"]["pages_flagged"] == 1 + assert metrics["repair_summary"]["pages_repaired"] == 1 + assert result["doc"]["page_count"] == 4 + + assert not (corpus.output_dir / "markdown" / "doc__p00001-00002.md").exists() + assert (corpus.output_dir / "sidecars" / "ocr_shards" / "markdown" / "doc__p00001-00002.md").exists() + assert (corpus.output_dir / "sidecars" / "ocr_shards" / "json" / "metrics" / "doc__p00003-00004.metrics.json").exists() + + +def test_vllm_batch_outputs_accept_in_memory_images_without_disk_roundtrip(): + from PIL import Image + + from glossapi.ocr.deepseek.run_pdf_ocr_vllm import _generate_batch_outputs + + class FakeOutput: + def __init__(self, text): + self.outputs = [type("TokenOutput", (), {"text": text})()] + + class FakeLLM: + def generate(self, prompt_batch, sampling_params=None): + del sampling_params + assert len(prompt_batch) == 2 + assert all(item["multi_modal_data"]["image"].mode == "RGB" for item in prompt_batch) + return [FakeOutput("alpha"), FakeOutput("beta")] + + jobs = [ + {"stem": "doc", "page_number": 1, "image": Image.new("RGB", (4, 4), color="white")}, + {"stem": "doc", "page_number": 2, "image": Image.new("RGB", (4, 4), color="black")}, + ] + outputs = _generate_batch_outputs( + FakeLLM(), + jobs=jobs, + prompt="prompt", + batch_size=2, + sampling_params=object(), + ) + + assert [item["raw_text"] for item in outputs] == ["alpha", "beta"] + assert jobs[0]["image"].size == (4, 4) + assert jobs[1]["image"].size == (4, 4) + for item in jobs: + item["image"].close() diff --git a/tests/test_deepseek_runner_stub.py b/tests/test_deepseek_runner_stub.py deleted file mode 100644 index aee5177..0000000 --- a/tests/test_deepseek_runner_stub.py +++ /dev/null @@ -1,59 +0,0 @@ -from pathlib import Path - -import pandas as pd - - -def _mk_corpus(tmp_path: Path): - from glossapi import Corpus - - root = tmp_path / "corpus" - root.mkdir() - return Corpus(input_dir=root, output_dir=root) - - -def test_deepseek_backend_stub_runs_and_updates_parquet(tmp_path, monkeypatch): - corpus = _mk_corpus(tmp_path) - - # Seed a minimal metadata parquet with one bad file - dl_dir = corpus.output_dir / "download_results" - dl_dir.mkdir(parents=True, exist_ok=True) - fname = "doc.pdf" - df = pd.DataFrame( - [{"filename": fname, corpus.url_column: "", "needs_ocr": True, "ocr_success": False}] - ) - parquet_path = dl_dir / "download_results.parquet" - df.to_parquet(parquet_path, index=False) - - # Create an empty placeholder file for the PDF - (corpus.input_dir / fname).write_bytes(b"%PDF-1.4\n%stub\n") - - # Monkeypatch the runner internal to avoid heavy imports - from glossapi.ocr.deepseek import runner - - def fake_run_one(pdf_path, md_out, metrics_out, cfg): - md_out.parent.mkdir(parents=True, exist_ok=True) - metrics_out.parent.mkdir(parents=True, exist_ok=True) - md_out.write_text("deepseek stub output\n", encoding="utf-8") - metrics_out.write_text("{\n \"page_count\": 1\n}\n", encoding="utf-8") - return {"page_count": 1} - - monkeypatch.setattr(runner, "_run_one_pdf", fake_run_one) - - # Run OCR via dispatcher - corpus.ocr(backend="deepseek", fix_bad=True, math_enhance=False) - - # Artifacts exist - stem = "doc" - md = corpus.output_dir / "markdown" / f"{stem}.md" - metrics = corpus.output_dir / "json" / "metrics" / f"{stem}.metrics.json" - assert md.exists(), "Markdown output should be created by deepseek stub" - assert metrics.exists(), "Metrics JSON should be created by deepseek stub" - - # Parquet updated - updated = pd.read_parquet(parquet_path).set_index("filename") - row = updated.loc[fname] - assert bool(row["ocr_success"]) is True - assert bool(row["needs_ocr"]) is False - # extraction_mode is optional; if present assert value - if "extraction_mode" in updated.columns: - assert updated.loc[fname, "extraction_mode"] == "deepseek" diff --git a/tests/test_deepseek_scheduling.py b/tests/test_deepseek_scheduling.py new file mode 100644 index 0000000..25983a8 --- /dev/null +++ b/tests/test_deepseek_scheduling.py @@ -0,0 +1,238 @@ +from pathlib import Path + + +def _touch_files(root: Path, names: list[str]) -> None: + root.mkdir(parents=True, exist_ok=True) + for name in names: + (root / name).write_bytes(b"%PDF-1.4\n%stub\n") + + +def test_plan_lanes_balances_weighted_docs_greedily(monkeypatch, tmp_path): + from glossapi.ocr.deepseek import runner + + weights = { + "huge.pdf": 500, + "mid_a.pdf": 300, + "mid_b.pdf": 300, + "small_a.pdf": 200, + "tiny_a.pdf": 100, + "tiny_b.pdf": 100, + } + _touch_files(tmp_path, list(weights)) + + monkeypatch.setattr(runner, "_page_count", lambda path: weights[path.name]) + lanes = runner._plan_lanes( + file_list=["tiny_b.pdf", "mid_a.pdf", "huge.pdf", "small_a.pdf", "tiny_a.pdf", "mid_b.pdf"], + input_root=tmp_path, + lane_devices=[0, 1, 2], + workers_per_gpu=1, + max_pages=None, + ) + + assert [int(lane["weight"]) for lane in lanes] == [500, 500, 500] + assigned = [name for lane in lanes for name in lane["files"]] + assert sorted(assigned) == sorted(weights) + assert len(assigned) == len(set(assigned)) + + +def test_auto_vllm_batch_size_caps_total_pages(monkeypatch, tmp_path): + from glossapi.ocr.deepseek import runner + + weights = { + "a.pdf": 90, + "b.pdf": 120, + "c.pdf": 400, + } + _touch_files(tmp_path, list(weights)) + monkeypatch.setattr(runner, "_page_count", lambda path: weights[path.name]) + + capped = runner._auto_vllm_batch_size( + runtime_backend="vllm", + file_list=list(weights), + input_root=tmp_path, + max_pages=None, + ) + reduced = runner._auto_vllm_batch_size( + runtime_backend="vllm", + file_list=list(weights), + input_root=tmp_path, + max_pages=20, + ) + + assert capped == 160 + assert reduced == 60 + + +def test_auto_scheduler_prefers_exact_fill_for_multi_gpu_vllm(): + from glossapi.ocr.deepseek import runner + + assert runner._resolve_scheduler( + scheduler="auto", + runtime_backend="vllm", + lane_devices=[0, 1], + workers_per_gpu=1, + ) == "exact_fill" + assert runner._resolve_scheduler( + scheduler="auto", + runtime_backend="transformers", + lane_devices=[0, 1], + workers_per_gpu=1, + ) == "whole_doc" + + +def test_fixed_shard_builder_only_splits_large_docs(): + from glossapi.ocr.deepseek.scheduling import SourceDocument, build_fixed_shard_slices + + documents = [ + SourceDocument(name="huge.pdf", pages=310), + SourceDocument(name="mid.pdf", pages=120), + SourceDocument(name="small.pdf", pages=40), + ] + + slices = build_fixed_shard_slices(documents, shard_pages=128, shard_threshold_pages=200) + + assert [item.item_id for item in slices] == [ + "huge.pdf:1:128", + "huge.pdf:129:256", + "huge.pdf:257:310", + "mid.pdf", + "small.pdf", + ] + + +def test_exact_fill_batches_split_documents_to_fill_target(): + from glossapi.ocr.deepseek.scheduling import SourceDocument, build_exact_fill_batches + + documents = [ + SourceDocument(name="a.pdf", pages=200), + SourceDocument(name="b.pdf", pages=60), + SourceDocument(name="c.pdf", pages=60), + SourceDocument(name="d.pdf", pages=20), + ] + + batches = build_exact_fill_batches(documents, target_batch_pages=160) + + assert [batch.pages for batch in batches] == [160, 160, 20] + assert [item.item_id for item in batches[0].items] == ["a.pdf:1:160"] + assert set(item.item_id for item in batches[1].items) == {"a.pdf:161:200", "b.pdf", "c.pdf"} + assert [item.item_id for item in batches[2].items] == ["d.pdf"] + + +def test_assign_batches_to_lanes_balances_full_batches(): + from glossapi.ocr.deepseek.scheduling import ( + BatchPlan, + WorkSlice, + assign_batches_to_lanes, + ) + + batches = [ + BatchPlan(batch_id=0, items=[WorkSlice("a.pdf", 160, 1, 160)]), + BatchPlan(batch_id=1, items=[WorkSlice("b.pdf", 160, 1, 160)]), + BatchPlan(batch_id=2, items=[WorkSlice("c.pdf", 160, 1, 160)]), + BatchPlan(batch_id=3, items=[WorkSlice("d.pdf", 20, 1, 20)]), + ] + + lanes = assign_batches_to_lanes(batches, devices=[0, 1], workers_per_gpu=1) + + assert sorted(lane.assigned_pages for lane in lanes) == [180, 320] + assert [len(lane.batches) for lane in lanes] == [2, 2] + + +def test_benchmark_planner_exact_fill_mixes_ranges_and_whole_docs(): + from glossapi.ocr.deepseek.scheduling import SourceDocument + from glossapi.scripts.deepseek_pipeline_benchmark import _plan_lanes + + lanes = _plan_lanes( + documents=[ + SourceDocument(name="monster.pdf", pages=200), + SourceDocument(name="tiny.pdf", pages=20), + SourceDocument(name="mid.pdf", pages=60), + SourceDocument(name="mid2.pdf", pages=60), + ], + devices=[0, 1], + workers_per_gpu=1, + scheduler="exact_fill", + target_batch_pages=160, + shard_pages=0, + shard_threshold_pages=0, + ) + + all_ranges = [ + spec + for lane in lanes + for batch in lane["batches"] + for spec in batch.get("page_ranges", []) + ] + all_files = [ + name + for lane in lanes + for batch in lane["batches"] + for name in batch.get("files", []) + ] + assert "monster.pdf:1:160" in all_ranges + assert "monster.pdf:161:200" in all_ranges + assert sorted(all_files) == ["mid.pdf", "mid2.pdf", "tiny.pdf"] + + +def test_benchmark_planner_whole_doc_preserves_whole_files(): + from glossapi.ocr.deepseek.scheduling import SourceDocument + from glossapi.scripts.deepseek_pipeline_benchmark import _plan_lanes + + lanes = _plan_lanes( + documents=[ + SourceDocument(name="monster.pdf", pages=1085), + SourceDocument(name="a.pdf", pages=200), + SourceDocument(name="b.pdf", pages=200), + ], + devices=[0, 1], + workers_per_gpu=1, + scheduler="whole_doc", + target_batch_pages=160, + shard_pages=0, + shard_threshold_pages=0, + ) + + assigned = [name for lane in lanes for batch in lane["batches"] for name in batch["files"]] + assert sorted(assigned) == ["a.pdf", "b.pdf", "monster.pdf"] + + +def test_runner_lane_batches_exact_fill_split_large_docs(monkeypatch, tmp_path): + from glossapi.ocr.deepseek import runner + + weights = { + "monster.pdf": 200, + "mid.pdf": 60, + "mid2.pdf": 60, + "tiny.pdf": 20, + } + _touch_files(tmp_path, list(weights)) + monkeypatch.setattr(runner, "_page_count", lambda path: weights[path.name]) + + lanes = runner._plan_lane_batches( + file_list=list(weights), + input_root=tmp_path, + lane_devices=[0, 1], + workers_per_gpu=1, + max_pages=None, + runtime_backend="vllm", + scheduler="exact_fill", + target_batch_pages=160, + shard_pages=0, + shard_threshold_pages=0, + ) + + all_ranges = [ + spec + for lane in lanes + for batch in lane["batches"] + for spec in batch.get("page_ranges", []) + ] + all_files = [ + name + for lane in lanes + for batch in lane["batches"] + for name in batch.get("files", []) + ] + assert "monster.pdf:1:160" in all_ranges + assert "monster.pdf:161:200" in all_ranges + assert sorted(all_files) == ["mid.pdf", "mid2.pdf", "tiny.pdf"] diff --git a/tests/test_docling_pipeline_tuning.py b/tests/test_docling_pipeline_tuning.py new file mode 100644 index 0000000..d57aadb --- /dev/null +++ b/tests/test_docling_pipeline_tuning.py @@ -0,0 +1,35 @@ +from glossapi.ocr.docling import pipeline as docling_pipeline + + +def test_apply_common_pdf_options_prefers_threaded_pipeline_options_when_available(): + acc, _ = docling_pipeline._resolve_accelerator("cuda:0") + opts = docling_pipeline._apply_common_pdf_options( + acc=acc, + images_scale=1.25, + formula_enrichment=False, + code_enrichment=False, + ) + + expected_cls = docling_pipeline.ThreadedPdfPipelineOptions or docling_pipeline.PdfPipelineOptions + assert isinstance(opts, expected_cls) + + +def test_apply_runtime_overrides_updates_docling_page_batch_size(monkeypatch): + class Perf: + page_batch_size = 4 + + class Settings: + perf = Perf() + + monkeypatch.setenv("GLOSSAPI_DOCLING_PAGE_BATCH_SIZE", "8") + monkeypatch.setattr(docling_pipeline, "docling_settings", Settings(), raising=False) + + acc, _ = docling_pipeline._resolve_accelerator("cuda:0") + docling_pipeline._apply_common_pdf_options( + acc=acc, + images_scale=1.25, + formula_enrichment=False, + code_enrichment=False, + ) + + assert Settings.perf.page_batch_size == 8 diff --git a/tests/test_extract_checkpoint_benchmark.py b/tests/test_extract_checkpoint_benchmark.py new file mode 100644 index 0000000..aefa3e5 --- /dev/null +++ b/tests/test_extract_checkpoint_benchmark.py @@ -0,0 +1,85 @@ +import json +from pathlib import Path + +from glossapi.scripts import extract_checkpoint_benchmark as benchmark + + +def test_markdown_headers_counts_markdown_headings(): + text = "# Title\n\ntext\n## Subtitle\n\nnot a header\n### Third\n" + assert benchmark._markdown_headers(text) == 3 + + +def test_compare_inventory_detects_presence_size_header_and_sha_changes(): + baseline = { + "a": {"present": True, "byte_size": 10, "header_count": 1, "sha256": "old"}, + "b": {"present": True, "byte_size": 20, "header_count": 0, "sha256": "same"}, + } + current = { + "a": {"present": True, "byte_size": 12, "header_count": 2, "sha256": "new"}, + "c": {"present": True, "byte_size": 5, "header_count": 0, "sha256": "other"}, + } + diff = benchmark._compare_inventory(current, baseline) + assert diff["added_markdown"] == ["c"] + assert diff["missing_markdown"] == ["b"] + assert diff["byte_size_changed"] == ["a"] + assert diff["header_count_changed"] == ["a"] + assert diff["sha_changed"] == ["a"] + + +def test_load_baseline_inventory_reads_report_payload(tmp_path): + report_path = tmp_path / "baseline.json" + report_path.write_text( + json.dumps({"markdown_inventory": {"doc": {"present": True, "byte_size": 1, "header_count": 0}}}), + encoding="utf-8", + ) + assert benchmark._load_baseline_inventory(report_path)["doc"]["present"] is True + + +def test_inventory_markdown_marks_missing_files(tmp_path): + input_pdf = tmp_path / "sample.pdf" + input_pdf.write_bytes(b"%PDF-1.4\n") + markdown_dir = tmp_path / "markdown" + markdown_dir.mkdir() + inventory = benchmark._inventory_markdown(markdown_dir, pdf_paths=[input_pdf]) + assert inventory["sample"]["present"] is False + assert inventory["sample"]["byte_size"] == 0 + assert inventory["sample"]["header_count"] == 0 + + +def test_runtime_env_snapshot_captures_docling_batch_knobs(monkeypatch): + monkeypatch.setenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES", "2") + monkeypatch.setenv("GLOSSAPI_DOCLING_BATCH_TARGET_PAGES", "384") + monkeypatch.setenv("GLOSSAPI_DOCLING_PAGE_BATCH_SIZE", "8") + + snapshot = benchmark._runtime_env_snapshot() + + assert snapshot["GLOSSAPI_DOCLING_MAX_BATCH_FILES"] == "2" + assert snapshot["GLOSSAPI_DOCLING_BATCH_TARGET_PAGES"] == "384" + assert snapshot["GLOSSAPI_DOCLING_PAGE_BATCH_SIZE"] == "8" + + +def test_apply_cli_tuning_overrides_sets_docling_env(monkeypatch): + for env_name in benchmark.TUNING_ENV_VARS: + monkeypatch.delenv(env_name, raising=False) + + args = benchmark._parse_args( + [ + "--input-dir", + "/tmp/in", + "--output-dir", + "/tmp/out", + "--report-path", + "/tmp/report.json", + "--docling-max-batch-files", + "2", + "--docling-batch-target-pages", + "512", + "--docling-page-batch-size", + "8", + ] + ) + benchmark._apply_cli_tuning_overrides(args) + + assert benchmark._runtime_env_snapshot()["GLOSSAPI_DOCLING_MAX_BATCH_FILES"] == "2" + assert benchmark._runtime_env_snapshot()["GLOSSAPI_DOCLING_BATCH_TARGET_PAGES"] == "512" + assert benchmark._runtime_env_snapshot()["GLOSSAPI_DOCLING_PAGE_BATCH_SIZE"] == "8" diff --git a/tests/test_full_pipeline_checkpoint.py b/tests/test_full_pipeline_checkpoint.py new file mode 100644 index 0000000..5c36250 --- /dev/null +++ b/tests/test_full_pipeline_checkpoint.py @@ -0,0 +1,271 @@ +import json + +import pandas as pd + +from glossapi.scripts import full_pipeline_checkpoint as checkpoint + + +def test_read_metadata_counts_handles_missing_and_populated_parquet(tmp_path): + missing = checkpoint._read_metadata_counts(tmp_path / "missing.parquet") + assert missing["rows_total"] == 0 + + parquet_path = tmp_path / "download_results.parquet" + pd.DataFrame( + [ + {"filename": "a.pdf", "needs_ocr": True, "ocr_success": False, "text": ""}, + {"filename": "b.pdf", "needs_ocr": False, "ocr_success": True, "text": "hello"}, + ] + ).to_parquet(parquet_path, index=False) + + counts = checkpoint._read_metadata_counts(parquet_path) + assert counts == { + "rows_total": 2, + "needs_ocr_true": 1, + "ocr_success_true": 1, + "text_nonempty": 1, + } + + +def test_full_pipeline_checkpoint_main_writes_summary(tmp_path, monkeypatch): + class DummyCorpus: + def __init__(self, input_dir, output_dir): + self.input_dir = input_dir + self.output_dir = output_dir + + def _metadata_path(self): + path = self.output_dir / "download_results" / "download_results.parquet" + path.parent.mkdir(parents=True, exist_ok=True) + return path + + def extract(self, **kwargs): + md = self.output_dir / "markdown" + md.mkdir(parents=True, exist_ok=True) + (md / "doc.md").write_text("raw text", encoding="utf-8") + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": False, "ocr_success": False, "text": ""}] + ).to_parquet(self._metadata_path(), index=False) + + def clean(self, **kwargs): + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": True, "ocr_success": False, "text": ""}] + ).to_parquet(self._metadata_path(), index=False) + + def ocr(self, **kwargs): + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": False, "ocr_success": True, "text": "fixed text"}] + ).to_parquet(self._metadata_path(), index=False) + + def jsonl(self, output_path, **kwargs): + output_path.write_text(json.dumps({"text": "fixed text"}) + "\n", encoding="utf-8") + + monkeypatch.setattr(checkpoint, "Corpus", DummyCorpus) + + input_dir = tmp_path / "in" + input_dir.mkdir() + (input_dir / "doc.pdf").write_bytes(b"%PDF-1.4\n") + + output_dir = tmp_path / "out" + export_path = tmp_path / "export.jsonl" + report_path = tmp_path / "report.json" + + rc = checkpoint.main( + [ + "--input-dir", + str(input_dir), + "--output-dir", + str(output_dir), + "--export-path", + str(export_path), + "--report-path", + str(report_path), + ] + ) + + assert rc == 0 + report = json.loads(report_path.read_text(encoding="utf-8")) + assert report["post_clean_counts"]["needs_ocr_true"] == 1 + assert report["post_ocr_counts"]["ocr_success_true"] == 1 + assert report["export_records"] == 1 + + +def test_full_pipeline_checkpoint_can_resume_from_ocr_phase(tmp_path, monkeypatch): + class DummyCorpus: + def __init__(self, input_dir, output_dir): + self.input_dir = input_dir + self.output_dir = output_dir + + def _metadata_path(self): + path = self.output_dir / "download_results" / "download_results.parquet" + path.parent.mkdir(parents=True, exist_ok=True) + return path + + def extract(self, **kwargs): + raise AssertionError("extract should have been skipped") + + def clean(self, **kwargs): + raise AssertionError("clean should have been skipped") + + def ocr(self, **kwargs): + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": False, "ocr_success": True, "text": "fixed text"}] + ).to_parquet(self._metadata_path(), index=False) + + def jsonl(self, output_path, **kwargs): + output_path.write_text(json.dumps({"text": "fixed text"}) + "\n", encoding="utf-8") + + monkeypatch.setattr(checkpoint, "Corpus", DummyCorpus) + + input_dir = tmp_path / "in" + input_dir.mkdir() + output_dir = tmp_path / "out" + metadata_path = output_dir / "download_results" / "download_results.parquet" + metadata_path.parent.mkdir(parents=True, exist_ok=True) + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": True, "ocr_success": False, "text": ""}] + ).to_parquet(metadata_path, index=False) + + export_path = tmp_path / "export.jsonl" + report_path = tmp_path / "report.json" + + rc = checkpoint.main( + [ + "--input-dir", + str(input_dir), + "--output-dir", + str(output_dir), + "--export-path", + str(export_path), + "--report-path", + str(report_path), + "--skip-extract", + "--skip-clean", + ] + ) + + assert rc == 0 + report = json.loads(report_path.read_text(encoding="utf-8")) + assert report["skipped_phases"] == ["extract", "clean"] + assert report["post_extract_counts"]["needs_ocr_true"] == 1 + assert report["post_ocr_counts"]["ocr_success_true"] == 1 + assert report["export_records"] == 1 + + +def test_full_pipeline_checkpoint_forwards_repair_exec_batch_controls(tmp_path, monkeypatch): + captured = {} + + class DummyCorpus: + def __init__(self, input_dir, output_dir): + self.input_dir = input_dir + self.output_dir = output_dir + + def _metadata_path(self): + path = self.output_dir / "download_results" / "download_results.parquet" + path.parent.mkdir(parents=True, exist_ok=True) + return path + + def extract(self, **kwargs): + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": True, "ocr_success": False, "text": ""}] + ).to_parquet(self._metadata_path(), index=False) + + def clean(self, **kwargs): + return None + + def ocr(self, **kwargs): + captured.update(kwargs) + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": False, "ocr_success": True, "text": "fixed text"}] + ).to_parquet(self._metadata_path(), index=False) + + def jsonl(self, output_path, **kwargs): + output_path.write_text(json.dumps({"text": "fixed text"}) + "\n", encoding="utf-8") + + monkeypatch.setattr(checkpoint, "Corpus", DummyCorpus) + + input_dir = tmp_path / "in" + input_dir.mkdir() + output_dir = tmp_path / "out" + export_path = tmp_path / "export.jsonl" + report_path = tmp_path / "report.json" + + rc = checkpoint.main( + [ + "--input-dir", + str(input_dir), + "--output-dir", + str(output_dir), + "--export-path", + str(export_path), + "--report-path", + str(report_path), + "--ocr-repair-exec-batch-target-pages", + "64", + "--ocr-repair-exec-batch-target-items", + "24", + ] + ) + + assert rc == 0 + assert captured["repair_exec_batch_target_pages"] == 64 + assert captured["repair_exec_batch_target_items"] == 24 + + +def test_full_pipeline_checkpoint_retries_empty_export_when_ocr_text_exists(tmp_path, monkeypatch): + calls = {"jsonl": 0} + + class DummyCorpus: + def __init__(self, input_dir, output_dir): + self.input_dir = input_dir + self.output_dir = output_dir + + def _metadata_path(self): + path = self.output_dir / "download_results" / "download_results.parquet" + path.parent.mkdir(parents=True, exist_ok=True) + return path + + def extract(self, **kwargs): + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": True, "ocr_success": False, "text": ""}] + ).to_parquet(self._metadata_path(), index=False) + + def clean(self, **kwargs): + return None + + def ocr(self, **kwargs): + pd.DataFrame( + [{"filename": "doc.pdf", "needs_ocr": False, "ocr_success": True, "text": "fixed text"}] + ).to_parquet(self._metadata_path(), index=False) + + def jsonl(self, output_path, **kwargs): + calls["jsonl"] += 1 + if calls["jsonl"] == 1: + output_path.write_text("", encoding="utf-8") + return + output_path.write_text(json.dumps({"text": "fixed text"}) + "\n", encoding="utf-8") + + monkeypatch.setattr(checkpoint, "Corpus", DummyCorpus) + + input_dir = tmp_path / "in" + input_dir.mkdir() + output_dir = tmp_path / "out" + export_path = tmp_path / "export.jsonl" + report_path = tmp_path / "report.json" + + rc = checkpoint.main( + [ + "--input-dir", + str(input_dir), + "--output-dir", + str(output_dir), + "--export-path", + str(export_path), + "--report-path", + str(report_path), + ] + ) + + assert rc == 0 + assert calls["jsonl"] == 2 + report = json.loads(report_path.read_text(encoding="utf-8")) + assert report["post_ocr_counts"]["text_nonempty"] == 1 + assert report["export_records"] == 1 diff --git a/tests/test_gloss_downloader_dynamic_html.py b/tests/test_gloss_downloader_dynamic_html.py new file mode 100644 index 0000000..a1bd678 --- /dev/null +++ b/tests/test_gloss_downloader_dynamic_html.py @@ -0,0 +1,53 @@ +from glossapi.gloss_downloader import GlossDownloader + + +def test_detects_waf_challenge_html(tmp_path): + downloader = GlossDownloader(output_dir=str(tmp_path)) + url = "https://eur-lex.europa.eu/legal-content/EL/TXT/PDF/?uri=OJ:L_202502360" + headers = { + "Content-Type": "text/html; charset=UTF-8", + "x-amzn-waf-action": "challenge", + } + body = b""" + + + """ + + assert downloader.infer_file_extension(url, headers, body) == "html" + error = downloader._detect_html_interstitial(url, headers, body) + + assert error is not None + assert "challenge page" in error.lower() + + +def test_detects_js_document_viewer_html(tmp_path): + downloader = GlossDownloader(output_dir=str(tmp_path)) + url = "https://freader.ekt.gr/eadd/index.php?doc=60819&lang=el" + headers = { + "Content-Type": "text/html; charset=UTF-8", + } + body = b""" + + + + + """ + + assert downloader.infer_file_extension(url, headers, body) == "html" + error = downloader._detect_html_interstitial(url, headers, body) + + assert error is not None + assert "document viewer" in error.lower() + + +def test_regular_html_document_is_still_allowed(tmp_path): + downloader = GlossDownloader(output_dir=str(tmp_path)) + url = "https://example.org/article" + headers = { + "Content-Type": "text/html; charset=UTF-8", + } + body = b"""Article +

Normal HTML document

Body text.

""" + + assert downloader.infer_file_extension(url, headers, body) == "html" + assert downloader._detect_html_interstitial(url, headers, body) is None diff --git a/tests/test_install_glossapi.py b/tests/test_install_glossapi.py new file mode 100644 index 0000000..5226429 --- /dev/null +++ b/tests/test_install_glossapi.py @@ -0,0 +1,51 @@ +from pathlib import Path + +from glossapi.scripts.install_glossapi import ( + build_deepseek_command, + build_install_plan, + build_pip_command, +) + + +def test_build_install_plan_collects_phase_extras(): + plan = build_install_plan( + phases=["download", "browser_download", "extract", "ocr"], + editable=True, + include_cuda=False, + ) + + assert plan.phases == ("download", "browser_download", "extract", "ocr") + assert set(plan.extras) == {"browser", "docling"} + assert plan.editable is True + assert plan.needs_deepseek_runtime is True + + +def test_build_install_plan_adds_cuda_extra(): + plan = build_install_plan( + phases=["download"], + editable=False, + include_cuda=True, + ) + + assert set(plan.extras) == {"cuda"} + assert plan.editable is False + assert plan.needs_deepseek_runtime is False + + +def test_build_pip_command_uses_editable_install(): + plan = build_install_plan( + phases=["download", "browser_download"], + editable=True, + include_cuda=False, + ) + command = build_pip_command(plan, Path("/tmp/repo")) + + assert command[:4] == [command[0], "-m", "pip", "install"] + assert "-e" in command + assert command[-1] == ".[browser]" + + +def test_build_deepseek_command_points_to_setup_script(): + command = build_deepseek_command(Path("/tmp/repo")) + + assert command is None or command[0] diff --git a/tests/test_jsonl_export.py b/tests/test_jsonl_export.py index e05caa0..aecd7a3 100644 --- a/tests/test_jsonl_export.py +++ b/tests/test_jsonl_export.py @@ -458,6 +458,39 @@ def test_jsonl_export_sharded(tmp_path): assert len(seen_doc_ids) == len(texts) +def test_jsonl_prefers_base_markdown_when_chunks_exist(tmp_path): + corpus = Corpus(input_dir=tmp_path / "in_chunks", output_dir=tmp_path / "out_chunks") + + base_text = "## Base Title\n\nMerged body from extraction." + base_path = corpus.cleaned_markdown_dir / "chunked.md" + base_path.parent.mkdir(parents=True, exist_ok=True) + base_path.write_text(base_text, encoding="utf-8") + + chunk_dir = corpus.cleaned_markdown_dir / "chunks" / "chunked" + chunk_dir.mkdir(parents=True, exist_ok=True) + (chunk_dir / "chunked__p0001-0002.md").write_text("chunk-one", encoding="utf-8") + (chunk_dir / "chunked__p0003-0004.md").write_text("chunk-two", encoding="utf-8") + + _write_download_results( + corpus.output_dir / "download_results" / "download_results.parquet", + [ + { + "filename": "chunked.pdf", + "filter": "ok", + "needs_ocr": False, + "is_empty": False, + "char_count_no_comments": 10, + } + ], + ) + + out_path = corpus.output_dir / "chunked.jsonl" + corpus.jsonl(out_path) + + record = json.loads(out_path.read_text(encoding="utf-8").strip()) + assert record["document"] == base_text + + @pytest.mark.skipif(not _HAS_DATASETS, reason="datasets package is not installed") def test_hf_streaming_loader_example(tmp_path): corpus = Corpus(input_dir=tmp_path / "in7", output_dir=tmp_path / "out7") @@ -531,5 +564,6 @@ def test_pyarrow_filter_example(tmp_path): table = dataset.to_table(filter=(ds.field("lang") == "el") & (ds.field("year") >= 2019)) assert set(table.column("doc_id").to_pylist()) == {"a"} + def _expected_doc_id(filename: str) -> str: return hashlib.sha256(filename.encode("utf-8")).hexdigest() diff --git a/tests/test_metadata_fallback.py b/tests/test_metadata_fallback.py index f899f17..53524eb 100644 --- a/tests/test_metadata_fallback.py +++ b/tests/test_metadata_fallback.py @@ -210,6 +210,8 @@ def test_canonical_stem_variants(): "beta.metrics.json": "beta", "gamma.per_page.metrics.json": "gamma", "delta.with.dots.pdf": "delta.with.dots", + "needs__p0001-0002.pdf": "needs", + "needs__p00001-00096.md": "needs", } for source, expected in cases.items(): assert canonical_stem(source) == expected diff --git a/tests/test_ocr_backends_smoke.py b/tests/test_ocr_backends_smoke.py index 0419ba4..096bf73 100644 --- a/tests/test_ocr_backends_smoke.py +++ b/tests/test_ocr_backends_smoke.py @@ -1,3 +1,4 @@ +import hashlib from pathlib import Path import pandas as pd @@ -11,7 +12,7 @@ def _mk_corpus(tmp_path: Path): return Corpus(input_dir=root, output_dir=root) -def test_cross_backend_smoke_with_stubs(tmp_path, monkeypatch): +def test_deepseek_ocr_then_math_only_smoke(tmp_path, monkeypatch): corpus = _mk_corpus(tmp_path) # Two PDFs: one needs OCR, one does not (for math-only later) @@ -28,7 +29,7 @@ def test_cross_backend_smoke_with_stubs(tmp_path, monkeypatch): parquet_path = dl_dir / "download_results.parquet" df.to_parquet(parquet_path, index=False) - # DeepSeek stub for OCR + # DeepSeek runner is stubbed here only to avoid the heavy model during unit tests. from glossapi.ocr.deepseek import runner def fake_run_for_files(self_ref, files, **kwargs): @@ -45,7 +46,7 @@ def fake_run_for_files(self_ref, files, **kwargs): # Run DeepSeek OCR for bad files corpus.ocr(backend="deepseek", fix_bad=True, math_enhance=True, mode="ocr_bad_then_math") - # RapidOCR math-only pass: ensure JSON for clean.pdf and run math + # Math-only pass: ensure JSON for clean.pdf and run math json_dir = corpus.output_dir / "json" json_dir.mkdir(parents=True, exist_ok=True) (json_dir / "clean.docling.json").write_text("{}", encoding="utf-8") @@ -58,9 +59,58 @@ def fake_enrich(files=None, **kwargs): monkeypatch.setattr(corpus, "formula_enrich_from_json", fake_enrich) - corpus.ocr(backend="rapidocr", fix_bad=False, math_enhance=True, mode="math_only") + corpus.ocr(backend="deepseek", fix_bad=False, math_enhance=True, mode="math_only") # Verify updated = pd.read_parquet(parquet_path).set_index("filename") assert bool(updated.loc["needs.pdf", "ocr_success"]) is True + assert bool(updated.loc["needs.pdf", "needs_ocr"]) is False + assert updated.loc["needs.pdf", "text"] == "ds md\n" + assert updated.loc["needs.pdf", "ocr_markdown_relpath"] == "markdown/needs.md" + assert updated.loc["needs.pdf", "ocr_metrics_relpath"] == "json/metrics/needs.metrics.json" + assert ( + updated.loc["needs.pdf", "ocr_text_sha256"] + == hashlib.sha256(b"ds md\n").hexdigest() + ) assert captured.get("files") == ["clean"], "Math-only should run for non-OCR stem only" + + +def test_deepseek_ocr_normalizes_chunk_rows_to_real_source_pdf(tmp_path, monkeypatch): + corpus = _mk_corpus(tmp_path) + + (corpus.input_dir / "needs.pdf").write_bytes(b"%PDF-1.4\n%stub\n") + + dl_dir = corpus.output_dir / "download_results" + dl_dir.mkdir(parents=True, exist_ok=True) + parquet_path = dl_dir / "download_results.parquet" + pd.DataFrame( + [ + {"filename": "needs.pdf", corpus.url_column: "", "needs_ocr": True, "ocr_success": False}, + {"filename": "needs__p0001-0002.pdf", corpus.url_column: "", "needs_ocr": True, "ocr_success": False}, + ] + ).to_parquet(parquet_path, index=False) + + from glossapi.ocr.deepseek import runner + + captured = {} + + def fake_run_for_files(self_ref, files, **kwargs): + captured["files"] = list(files) + markdown_dir = corpus.output_dir / "markdown" + metrics_dir = corpus.output_dir / "json" / "metrics" + markdown_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + (markdown_dir / "needs.md").write_text("normalized md\n", encoding="utf-8") + (metrics_dir / "needs.metrics.json").write_text("{\n \"page_count\": 1\n}\n", encoding="utf-8") + return {"needs": {"page_count": 1}} + + monkeypatch.setattr(runner, "run_for_files", fake_run_for_files) + + corpus.ocr(backend="deepseek", fix_bad=True, math_enhance=False, mode="ocr_bad") + + assert captured["files"] == ["needs.pdf"] + updated = pd.read_parquet(parquet_path).set_index("filename") + assert bool(updated.loc["needs.pdf", "ocr_success"]) is True + assert bool(updated.loc["needs__p0001-0002.pdf", "ocr_success"]) is True + assert updated.loc["needs.pdf", "text"] == "normalized md\n" + assert updated.loc["needs__p0001-0002.pdf", "text"] == "normalized md\n" diff --git a/tests/test_ocr_dispatch_backends.py b/tests/test_ocr_dispatch_backends.py index 965692b..08eb326 100644 --- a/tests/test_ocr_dispatch_backends.py +++ b/tests/test_ocr_dispatch_backends.py @@ -51,29 +51,156 @@ def fail_math(*args, **kwargs): assert calls.get("files") == [fname] -def test_rapidocr_backend_routes_to_extract_with_docling(tmp_path, monkeypatch): +def test_deepseek_backend_forwards_repair_exec_batch_controls(tmp_path, monkeypatch): corpus = _mk_corpus(tmp_path) - # Seed minimal metadata parquet that flags a single file for OCR dl_dir = corpus.output_dir / "download_results" dl_dir.mkdir(parents=True, exist_ok=True) + fname = "doc.pdf" df = pd.DataFrame([ - {"filename": "doc.pdf", corpus.url_column: "", "needs_ocr": True, "ocr_success": False} + {"filename": fname, corpus.url_column: "", "needs_ocr": True, "ocr_success": False} ]) df.to_parquet(dl_dir / "download_results.parquet", index=False) + (corpus.input_dir / fname).write_bytes(b"%PDF-1.4\n%stub\n") + + from glossapi.ocr.deepseek import runner + + calls = {} + + def fake_run_for_files(self_ref, files, **kwargs): + calls["files"] = list(files) + calls["kwargs"] = dict(kwargs) + return {"doc": {"page_count": 1}} + + monkeypatch.setattr(runner, "run_for_files", fake_run_for_files) + + corpus.ocr( + backend="deepseek", + fix_bad=True, + math_enhance=False, + mode="ocr_bad", + repair_exec_batch_target_pages=64, + repair_exec_batch_target_items=24, + ) + + assert calls.get("files") == [fname] + assert calls["kwargs"]["repair_exec_batch_target_pages"] == 64 + assert calls["kwargs"]["repair_exec_batch_target_items"] == 24 + + +def test_invalid_backend_is_rejected(tmp_path): + corpus = _mk_corpus(tmp_path) + with pytest.raises(ValueError, match="backend must be 'deepseek'"): + corpus.ocr(backend="bogus", fix_bad=True, math_enhance=False) + + +def test_deepseek_backend_forwards_parallelism_controls(tmp_path, monkeypatch): + corpus = _mk_corpus(tmp_path) + + dl_dir = corpus.output_dir / "download_results" + dl_dir.mkdir(parents=True, exist_ok=True) + fname = "doc.pdf" + pd.DataFrame( + [{"filename": fname, corpus.url_column: "", "needs_ocr": True, "ocr_success": False}] + ).to_parquet(dl_dir / "download_results.parquet", index=False) + (corpus.input_dir / fname).write_bytes(b"%PDF-1.4\n%stub\n") + + from glossapi.ocr.deepseek import runner + + calls = {} + + def fake_run_for_files(self_ref, files, **kwargs): + calls["files"] = list(files) + calls["kwargs"] = dict(kwargs) + return {"doc": {"page_count": 1}} + + monkeypatch.setattr(runner, "run_for_files", fake_run_for_files) + + corpus.ocr( + backend="deepseek", + fix_bad=True, + math_enhance=False, + use_gpus="multi", + devices=[1, 3], + workers_per_gpu=2, + ocr_profile="plain_ocr", + attn_backend="sdpa", + base_size=640, + image_size=448, + crop_mode=True, + render_dpi=120, + max_pages=7, + max_new_tokens=2048, + repetition_penalty=1.08, + no_repeat_ngram_size=12, + ) + + assert calls["files"] == [fname] + assert calls["kwargs"]["use_gpus"] == "multi" + assert calls["kwargs"]["devices"] == [1, 3] + assert calls["kwargs"]["workers_per_gpu"] == 2 + assert calls["kwargs"]["ocr_profile"] == "plain_ocr" + assert calls["kwargs"]["attn_backend"] == "sdpa" + assert calls["kwargs"]["base_size"] == 640 + assert calls["kwargs"]["image_size"] == 448 + assert calls["kwargs"]["crop_mode"] is True + assert calls["kwargs"]["render_dpi"] == 120 + assert calls["kwargs"]["max_pages"] == 7 + assert calls["kwargs"]["max_new_tokens"] == 2048 + assert calls["kwargs"]["repetition_penalty"] == 1.08 + assert calls["kwargs"]["no_repeat_ngram_size"] == 12 + + +def test_deepseek_rerun_refreshes_with_clean_ocr_then_score_only_clean(tmp_path, monkeypatch): + corpus = _mk_corpus(tmp_path) + + dl_dir = corpus.output_dir / "download_results" + dl_dir.mkdir(parents=True, exist_ok=True) + fname = "doc.pdf" + pd.DataFrame( + [{"filename": fname, corpus.url_column: "", "needs_ocr": True, "ocr_success": False}] + ).to_parquet(dl_dir / "download_results.parquet", index=False) + (corpus.input_dir / fname).write_bytes(b"%PDF-1.4\n%stub\n") + + from glossapi.ocr.deepseek import runner + + def fake_run_for_files(self_ref, files, **kwargs): + markdown_dir = self_ref.output_dir / "markdown" + metrics_dir = self_ref.output_dir / "json" / "metrics" + markdown_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + for current in files: + stem = Path(current).stem + (markdown_dir / f"{stem}.md").write_text("ocr text\n", encoding="utf-8") + (metrics_dir / f"{stem}.metrics.json").write_text( + "{\"page_count\": 1}\n", + encoding="utf-8", + ) + return {"doc": {"page_count": 1}} + + monkeypatch.setattr(runner, "run_for_files", fake_run_for_files) + + calls = [] + original_markdown_dir = corpus.markdown_dir - captured = {} + def fake_clean_ocr(*args, **kwargs): + calls.append( + ("clean_ocr", kwargs.get("input_dir"), kwargs.get("write_cleaned_files", True)) + ) + corpus.cleaned_markdown_dir.mkdir(parents=True, exist_ok=True) + corpus.markdown_dir = corpus.cleaned_markdown_dir - def fake_extract(**kwargs): - captured.update(kwargs) - return None + def fake_clean(*args, **kwargs): + calls.append(("clean", kwargs.get("input_dir"), kwargs.get("write_cleaned_files", True))) - monkeypatch.setattr(corpus, "extract", fake_extract) + monkeypatch.setattr(corpus, "clean_ocr", fake_clean_ocr) + monkeypatch.setattr(corpus, "clean", fake_clean) - corpus.ocr(backend="rapidocr", fix_bad=True, math_enhance=False, use_gpus="single", devices=[0]) + corpus.ocr(backend="deepseek", fix_bad=True, math_enhance=False) - assert captured, "Expected extract() to be called for rapidocr backend" - assert captured.get("force_ocr") is True - assert captured.get("phase1_backend") == "docling" - files = captured.get("filenames") or [] - assert files and files[0] == "doc.pdf" + assert calls[0][0] == "clean_ocr" + assert Path(str(calls[0][1])) == original_markdown_dir + assert calls[0][2] is True + assert calls[1][0] == "clean" + assert Path(str(calls[1][1])) == corpus.cleaned_markdown_dir + assert calls[1][2] is False diff --git a/tests/test_ocr_golden_pages.py b/tests/test_ocr_golden_pages.py new file mode 100644 index 0000000..6274d96 --- /dev/null +++ b/tests/test_ocr_golden_pages.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import difflib +import json +from pathlib import Path + +from glossapi import Corpus +from glossapi.corpus.phase_clean import _render_combined_ocr_debug_page + + +GOLDEN_DIR = Path( + "/home/foivos/data/openarchives_ocr_ingest_20260403/debug/ocr_golden_pages_first300_20260410" +) + + +def _load_manifest_rows() -> list[dict]: + manifest_path = GOLDEN_DIR / "manifest.jsonl" + assert manifest_path.exists(), f"Missing OCR golden manifest: {manifest_path}" + return [ + json.loads(line) + for line in manifest_path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + + +def _format_diff(case_id: str, expected: str, actual: str) -> str: + diff = list( + difflib.unified_diff( + expected.splitlines(), + actual.splitlines(), + fromfile=f"{case_id}:expected", + tofile=f"{case_id}:actual", + lineterm="", + n=3, + ) + ) + return "\n".join(diff[:120]) + + +def test_combined_ocr_real_goldens_match_exact_output(tmp_path: Path) -> None: + rows = _load_manifest_rows() + assert len(rows) >= 300, f"Expected hundreds of real OCR golden cases, got {len(rows)}" + + corpus = Corpus(input_dir=tmp_path / "input", output_dir=tmp_path / "output") + corpus.input_dir.mkdir(parents=True, exist_ok=True) + corpus.output_dir.mkdir(parents=True, exist_ok=True) + noise_mod = corpus._load_rust_extension( + "glossapi_rs_noise", + "rust/glossapi_rs_noise/Cargo.toml", + required_attrs=("find_numeric_debug_page_spans", "evaluate_page_character_noise"), + ) + + mismatches: list[str] = [] + for row in rows: + case_id = str(row["case_id"]) + input_path = Path(str(row["input_path"])) + expected_path = Path(str(row["expected_path"])) + page_text = input_path.read_text(encoding="utf-8") + expected = expected_path.read_text(encoding="utf-8") + actual = _render_combined_ocr_debug_page( + page_text, + noise_mod=noise_mod, + min_progress_steps=10, + min_repeat_steps=8, + min_same_digit_steps=10, + word_rep_threshold=4, + word_min_period=3, + word_window=96, + )["annotated_page"] + if actual != expected: + mismatches.append(_format_diff(case_id, expected, actual)) + if len(mismatches) >= 5: + break + + assert not mismatches, "\n\n".join(mismatches) diff --git a/tests/test_ocr_imports.py b/tests/test_ocr_imports.py index 3487619..094e72b 100644 --- a/tests/test_ocr_imports.py +++ b/tests/test_ocr_imports.py @@ -8,32 +8,19 @@ def test_import_ocr_package_is_lightweight(): import glossapi.ocr as ocr assert hasattr(ocr, "deepseek") - assert hasattr(ocr, "rapidocr") # New subpackages remain importable lazily import glossapi.ocr.deepseek.runner as deepseek_runner - import glossapi.ocr.rapidocr.dispatch as rapid_dispatch assert ocr.deepseek.runner is deepseek_runner - assert ocr.rapidocr.dispatch is rapid_dispatch assert ocr.deepseek_runner is deepseek_runner - assert ocr.rapidocr_dispatch is rapid_dispatch assert hasattr(deepseek_runner, "run_for_files") - assert hasattr(rapid_dispatch, "run_via_extract") # Utilities module always available (pure Python) from glossapi.ocr.utils import json_io as utils_json assert hasattr(utils_json, "export_docling_json") - if importlib.util.find_spec("docling") is not None: - try: - from glossapi.ocr.rapidocr import pool as rapid_pool - except ModuleNotFoundError: - pytest.skip("Docling optional dependencies not available") - else: - assert hasattr(rapid_pool, "GLOBAL_RAPID_OCR_POOL") - if importlib.util.find_spec("docling_core") is not None: try: from glossapi.ocr.math import enrich_from_docling_json, RoiEntry diff --git a/tests/test_openarchives_download_freeze.py b/tests/test_openarchives_download_freeze.py new file mode 100644 index 0000000..e76b24e --- /dev/null +++ b/tests/test_openarchives_download_freeze.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd + +import glossapi.scripts.openarchives_download_freeze as freeze_mod +from glossapi.scripts.openarchives_download_freeze import main + + +def test_download_freeze_dry_run_materializes_manifest(tmp_path: Path) -> None: + src = tmp_path / "input.parquet" + pd.DataFrame( + [ + { + "filename": "ABC_001.pdf", + "pdf_url": "https://example.com/a.pdf", + "needs_ocr": True, + } + ] + ).to_parquet(src, index=False) + + work_root = tmp_path / "work" + rc = main(["--input-parquet", str(src), "--work-root", str(work_root), "--dry-run"]) + assert rc == 0 + assert (work_root / "manifests" / "download_input.parquet").exists() + assert (work_root / "download_results" / "download_results.parquet").exists() + + +def test_download_freeze_uses_pdf_only_auto_mode(tmp_path: Path, monkeypatch) -> None: + src = tmp_path / "input.parquet" + pd.DataFrame( + [ + { + "filename": "ABC_001.pdf", + "pdf_url": "https://example.com/a.pdf", + "needs_ocr": True, + } + ] + ).to_parquet(src, index=False) + + observed = {} + + class DummyCorpus: + def __init__(self, *args, **kwargs): + observed["init"] = kwargs + + def download(self, **kwargs): + observed["download"] = kwargs + return pd.DataFrame( + [ + { + "url": "https://example.com/a.pdf", + "filename": "ABC_001.pdf", + "download_success": True, + "download_error": "", + "file_ext": "pdf", + } + ] + ) + + monkeypatch.setattr(freeze_mod, "Corpus", DummyCorpus) + + work_root = tmp_path / "work" + rc = main(["--input-parquet", str(src), "--work-root", str(work_root)]) + + assert rc == 0 + assert observed["download"]["download_mode"] == "auto" + assert observed["download"]["supported_formats"] == ["pdf"] diff --git a/tests/test_openarchives_download_probe.py b/tests/test_openarchives_download_probe.py new file mode 100644 index 0000000..0213438 --- /dev/null +++ b/tests/test_openarchives_download_probe.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd + +from glossapi.scripts.openarchives_download_probe import _prepare_probe_frame + + +def test_prepare_probe_frame_limits_per_host_and_adds_runtime_columns() -> None: + df = pd.DataFrame( + [ + {"filename": "a.pdf", "pdf_url": "https://ikee.lib.auth.gr/file/a.pdf"}, + {"filename": "b.pdf", "pdf_url": "https://ikee.lib.auth.gr/file/b.pdf"}, + {"filename": "c.pdf", "pdf_url": "https://ikee.lib.auth.gr/file/c.pdf"}, + {"filename": "d.pdf", "pdf_url": "https://dspace.lib.ntua.gr/file/d.pdf"}, + {"filename": "e.pdf", "pdf_url": "https://dspace.lib.ntua.gr/file/e.pdf"}, + ] + ) + + out = _prepare_probe_frame( + df, + samples_per_host=2, + max_hosts=2, + seed=7, + ) + + counts = out.groupby("host").size().to_dict() + assert counts["ikee.lib.auth.gr"] == 2 + assert counts["dspace.lib.ntua.gr"] == 2 + assert set(out["url"]) <= set(df["pdf_url"]) + assert set(out["base_domain"]) == {"https://ikee.lib.auth.gr", "https://dspace.lib.ntua.gr"} diff --git a/tests/test_openarchives_hf_refresh.py b/tests/test_openarchives_hf_refresh.py new file mode 100644 index 0000000..81f015e --- /dev/null +++ b/tests/test_openarchives_hf_refresh.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +import io +import json +from pathlib import Path + +import pandas as pd +import zstandard as zstd + +from glossapi.scripts.openarchives_hf_refresh import main + + +def _write_jsonl_zst(path: Path, rows: list[dict]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + cctx = zstd.ZstdCompressor(level=3) + with path.open("wb") as fh: + with cctx.stream_writer(fh) as writer: + for row in rows: + writer.write((json.dumps(row, ensure_ascii=False) + "\n").encode("utf-8")) + + +def _read_jsonl_zst(path: Path) -> list[dict]: + dctx = zstd.ZstdDecompressor() + with path.open("rb") as fh, dctx.stream_reader(fh) as reader: + text = io.TextIOWrapper(reader, encoding="utf-8").read() + return [json.loads(line) for line in text.splitlines() if line.strip()] + + +def test_openarchives_hf_refresh_updates_pipeline_metadata_and_readme(tmp_path: Path) -> None: + dataset_root = tmp_path / "openarchives.gr" + shard_path = dataset_root / "data" / "openarchives" / "shard_001" / "chunk-000.jsonl.zst" + _write_jsonl_zst( + shard_path, + [ + { + "doc_id": "doc-a", + "filename": "AAA_000", + "text": "alpha", + "source_metadata": {"filename": "AAA_000.pdf"}, + "pipeline_metadata": {"needs_ocr": False, "greek_badness_score": 1.0}, + }, + { + "doc_id": "doc-b", + "filename": "BBB_000", + "text": "beta", + "source_metadata": {"filename": "BBB_000.pdf"}, + "pipeline_metadata": {"needs_ocr": False, "greek_badness_score": 2.0}, + }, + ], + ) + (dataset_root / "README.md").write_text( + "---\npretty_name: OpenArchives.gr 191,000 docs\n---\n\n# OpenArchives.gr 191,000 docs\n\n" + "- Σύνολο markdown αρχείων: **191,301** from openarchives.gr\n" + "- Τα χαμηλής ποιότητας αρχεία που ενδέχεται να χρειάζονται OCR επεξεργασία επισημαίνονται με τη στήλη `needs_ocr`: **23,083 / 191,301 (12.07%)**\n" + "- Total markdown files: **191,301** from openarchives.gr\n" + "- Lower-quality files that may require OCR reprocessing are marked by the `needs_ocr` indicator: **23,083 / 191,301 (12.07%)**\n", + encoding="utf-8", + ) + + metadata = tmp_path / "filled_document_level.parquet" + pd.DataFrame( + [ + { + "source_doc_id": "doc-a", + "source_jsonl": str(shard_path), + "needs_ocr": True, + "ocr_success": False, + "greek_badness_score": 72.0, + "mojibake_badness_score": 0.2, + "latin_percentage": 33.3, + "polytonic_ratio": 0.0, + "char_count_no_comments": 1234.0, + "is_empty": False, + "filter": "ok", + "quality_method": "refresh", + "reevaluated_at": "2026-03-31T12:00:00+00:00", + }, + { + "source_doc_id": "doc-b", + "source_jsonl": str(shard_path), + "needs_ocr": False, + "ocr_success": False, + "greek_badness_score": 2.0, + "mojibake_badness_score": 0.0, + "latin_percentage": 22.0, + "polytonic_ratio": 0.0, + "char_count_no_comments": 456.0, + "is_empty": True, + "filter": "empty_text==0", + "quality_method": "refresh", + "reevaluated_at": "2026-03-31T12:00:00+00:00", + }, + ] + ).to_parquet(metadata, index=False) + + out_root = tmp_path / "out" + rc = main( + [ + "--dataset-root", + str(dataset_root), + "--metadata-parquet", + str(metadata), + "--output-root", + str(out_root), + ] + ) + assert rc == 0 + + rows = _read_jsonl_zst(out_root / "data" / "openarchives" / "shard_001" / "chunk-000.jsonl.zst") + assert rows[0]["pipeline_metadata"]["needs_ocr"] is True + assert rows[0]["pipeline_metadata"]["greek_badness_score"] == 72.0 + assert rows[1]["pipeline_metadata"]["is_empty"] is True + assert rows[1]["pipeline_metadata"]["filter"] == "empty_text==0" + + readme = (out_root / "README.md").read_text(encoding="utf-8") + assert "OpenArchives.gr 2 docs" in readme + assert "**1 / 2 (50.00%)**" in readme + + +def test_openarchives_hf_refresh_dry_run_does_not_write_outputs(tmp_path: Path) -> None: + dataset_root = tmp_path / "openarchives.gr" + shard_path = dataset_root / "data" / "openarchives" / "shard_001" / "chunk-000.jsonl.zst" + _write_jsonl_zst( + shard_path, + [ + { + "doc_id": "doc-a", + "filename": "AAA_000", + "text": "alpha", + "source_metadata": {}, + "pipeline_metadata": {"needs_ocr": False}, + } + ], + ) + (dataset_root / "README.md").write_text("# OpenArchives.gr 191,000 docs\n", encoding="utf-8") + metadata = tmp_path / "filled_document_level.parquet" + pd.DataFrame( + [ + { + "source_doc_id": "doc-a", + "source_jsonl": str(shard_path), + "needs_ocr": True, + } + ] + ).to_parquet(metadata, index=False) + + out_root = tmp_path / "out" + rc = main( + [ + "--dataset-root", + str(dataset_root), + "--metadata-parquet", + str(metadata), + "--output-root", + str(out_root), + "--dry-run", + ] + ) + assert rc == 0 + assert not (out_root / "data" / "openarchives" / "shard_001" / "chunk-000.jsonl.zst").exists() diff --git a/tests/test_openarchives_ocr_enrich.py b/tests/test_openarchives_ocr_enrich.py new file mode 100644 index 0000000..16d683a --- /dev/null +++ b/tests/test_openarchives_ocr_enrich.py @@ -0,0 +1,144 @@ +from __future__ import annotations + +import io +import json +from pathlib import Path + +import pandas as pd +import zstandard as zstd + +from glossapi.scripts.openarchives_ocr_enrich import main + + +def _write_jsonl_zst(path: Path, rows: list[dict]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + payload = "\n".join(json.dumps(row, ensure_ascii=False) for row in rows).encode("utf-8") + cctx = zstd.ZstdCompressor() + path.write_bytes(cctx.compress(payload)) + + +def test_openarchives_ocr_enrich_extracts_page_counts_and_pdf_url(tmp_path): + raw_root = tmp_path / "raw" / "openarchives.gr" + jsonl_path = raw_root / "data" / "openarchives" / "shard_01" / "chunk-000.jsonl.zst" + _write_jsonl_zst( + jsonl_path, + [ + { + "doc_id": "doc-a", + "filename": "AAA_000", + "text": "alpha", + "pipeline_metadata": {"page_count": 98, "pages_total": 98}, + "source_metadata": { + "pdf_links_json": "https://example.com/a.pdf", + "collection_slug": "Dione", + "language_code": "el", + }, + }, + { + "doc_id": "doc-b", + "filename": "BBB_000", + "text": "beta", + "pipeline_metadata": {"pages_total": 12}, + "source_metadata": { + "pdf_links_json": json.dumps( + [ + {"url": "https://example.com/b.pdf"}, + {"url": "https://example.com/b2.pdf"}, + ] + ), + "collection_slug": "Pandemos", + "language_code": "el", + }, + }, + ], + ) + + parquet = tmp_path / "document_level.parquet" + pd.DataFrame( + [ + { + "source_doc_id": "doc-a", + "filename": "AAA_000.pdf", + "source_jsonl": str(jsonl_path), + "needs_ocr": True, + }, + { + "source_doc_id": "doc-b", + "filename": "BBB_000.pdf", + "source_jsonl": str(jsonl_path), + "needs_ocr": True, + }, + { + "source_doc_id": "doc-c", + "filename": "CCC_000.pdf", + "source_jsonl": str(jsonl_path), + "needs_ocr": False, + }, + ] + ).to_parquet(parquet, index=False) + + output = tmp_path / "enriched.parquet" + rc = main( + [ + "--parquet", + str(parquet), + "--raw-repo-root", + str(raw_root), + "--output-parquet", + str(output), + ] + ) + assert rc == 0 + + enriched = pd.read_parquet(output).sort_values("filename").reset_index(drop=True) + assert enriched["filename"].tolist() == ["AAA_000.pdf", "BBB_000.pdf"] + assert enriched["page_count_source"].tolist() == [98, 12] + assert enriched["pages_total_source"].tolist() == [98, 12] + assert enriched["pdf_url"].tolist() == ["https://example.com/a.pdf", "https://example.com/b.pdf"] + assert enriched["source_collection_slug"].tolist() == ["Dione", "Pandemos"] + + +def test_openarchives_ocr_enrich_resolves_rewritten_source_jsonl_path(tmp_path): + raw_root = tmp_path / "raw" / "openarchives.gr" + jsonl_path = raw_root / "data" / "openarchives" / "shard_02" / "chunk-001.jsonl.zst" + _write_jsonl_zst( + jsonl_path, + [ + { + "doc_id": "doc-x", + "filename": "XXX_000", + "text": "x", + "pipeline_metadata": {"page_count": 7}, + "source_metadata": {"external_link": "https://example.com/x"}, + } + ], + ) + + parquet = tmp_path / "document_level.parquet" + pd.DataFrame( + [ + { + "source_doc_id": "doc-x", + "filename": "XXX_000.pdf", + "source_jsonl": "/home/foivos/data/glossapi_raw/hf/openarchives.gr/data/openarchives/shard_02/chunk-001.jsonl.zst", + "needs_ocr": True, + } + ] + ).to_parquet(parquet, index=False) + + output = tmp_path / "enriched.parquet" + rc = main( + [ + "--parquet", + str(parquet), + "--raw-repo-root", + str(raw_root), + "--output-parquet", + str(output), + ] + ) + assert rc == 0 + + enriched = pd.read_parquet(output) + assert int(enriched.loc[0, "page_count_source"]) == 7 + assert enriched.loc[0, "pdf_url"] == "https://example.com/x" diff --git a/tests/test_openarchives_ocr_run_node.py b/tests/test_openarchives_ocr_run_node.py new file mode 100644 index 0000000..0b66d52 --- /dev/null +++ b/tests/test_openarchives_ocr_run_node.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +from pathlib import Path + +import pandas as pd + +from glossapi.scripts.openarchives_ocr_run_node import ( + _normalize_download_results, + _prepare_download_input, +) + + +def test_prepare_download_input_adds_url_and_filename_base() -> None: + df = pd.DataFrame( + [ + { + "filename": "ABC_001.pdf", + "pdf_url": "https://example.com/a.pdf", + "needs_ocr": True, + } + ] + ) + out = _prepare_download_input(df) + assert out.loc[0, "url"] == "https://example.com/a.pdf" + assert out.loc[0, "filename_base"] == "ABC_001" + + +def test_normalize_download_results_preserves_shard_filename_and_metadata() -> None: + shard = pd.DataFrame( + [ + { + "filename": "ABC_001.pdf", + "pdf_url": "https://example.com/a.pdf", + "filename_base": "ABC_001", + "needs_ocr": True, + "source_doc_id": "doc-1", + } + ] + ) + dl = pd.DataFrame( + [ + { + "filename": "ABC_001.pdf", + "filename_base": "ABC_001", + "download_success": True, + "download_error": "", + "url": "https://example.com/a.pdf", + } + ] + ) + out = _normalize_download_results(shard_df=shard, download_results_df=dl) + assert out.loc[0, "filename"] == "ABC_001.pdf" + assert out.loc[0, "source_doc_id"] == "doc-1" + assert bool(out.loc[0, "download_success"]) is True + assert bool(out.loc[0, "needs_ocr"]) is True diff --git a/tests/test_openarchives_ocr_shards.py b/tests/test_openarchives_ocr_shards.py new file mode 100644 index 0000000..d616225 --- /dev/null +++ b/tests/test_openarchives_ocr_shards.py @@ -0,0 +1,287 @@ +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import pandas as pd + +from glossapi.scripts import openarchives_ocr_cutoff_shards, openarchives_ocr_merge, openarchives_ocr_shards + + +def test_openarchives_ocr_shards_balances_pages(tmp_path: Path) -> None: + df = pd.DataFrame( + [ + {"filename": "a.pdf", "needs_ocr": True, "pages_total": 100}, + {"filename": "b.pdf", "needs_ocr": True, "pages_total": 90}, + {"filename": "c.pdf", "needs_ocr": True, "pages_total": 40}, + {"filename": "d.pdf", "needs_ocr": True, "pages_total": 30}, + {"filename": "skip.pdf", "needs_ocr": False, "pages_total": 999}, + ] + ) + source = tmp_path / "download_results.parquet" + out_dir = tmp_path / "shards" + df.to_parquet(source, index=False) + + rc = openarchives_ocr_shards.main( + [ + "--parquet", + str(source), + "--output-dir", + str(out_dir), + "--nodes", + "2", + ] + ) + assert rc == 0 + + summary = json.loads((out_dir / "openarchives_ocr_shard_summary.json").read_text()) + assert summary["docs_total"] == 4 + assert summary["pages_total"] == 260 + manifests = sorted(out_dir.glob("openarchives_ocr_shard_node_*.parquet")) + assert len(manifests) == 2 + page_totals = [int(pd.read_parquet(path)["pages_total"].sum()) for path in manifests] + assert max(page_totals) - min(page_totals) <= 20 + + +def test_openarchives_ocr_merge_updates_master(tmp_path: Path) -> None: + master = pd.DataFrame( + [ + {"filename": "a.pdf", "needs_ocr": True, "ocr_success": False}, + {"filename": "b.pdf", "needs_ocr": True, "ocr_success": False}, + ] + ) + shard = pd.DataFrame( + [ + {"filename": "a.pdf", "needs_ocr": False, "ocr_success": True, "ocr_node_id": 2}, + ] + ) + master_path = tmp_path / "master.parquet" + shard_path = tmp_path / "shard.parquet" + out_path = tmp_path / "merged.parquet" + master.to_parquet(master_path, index=False) + shard.to_parquet(shard_path, index=False) + + rc = openarchives_ocr_merge.main( + [ + "--master-parquet", + str(master_path), + "--shard-parquets", + str(shard_path), + "--output-parquet", + str(out_path), + ] + ) + assert rc == 0 + + merged = pd.read_parquet(out_path).set_index("filename") + assert bool(merged.loc["a.pdf", "ocr_success"]) is True + assert bool(merged.loc["a.pdf", "needs_ocr"]) is False + assert int(merged.loc["a.pdf", "ocr_node_id"]) == 2 + assert bool(merged.loc["b.pdf", "ocr_success"]) is False + + +def test_openarchives_ocr_cutoff_shards_uses_only_available_local_pdfs(tmp_path: Path) -> None: + df = pd.DataFrame( + [ + {"source_doc_id": "doc-1", "filename": "a.html", "filename_base": "A", "needs_ocr": True, "pages_total_source": 100}, + {"source_doc_id": "doc-2", "filename": "b.html", "filename_base": "B", "needs_ocr": True, "pages_total_source": 50}, + {"source_doc_id": "doc-3", "filename": "c.html", "filename_base": "C", "needs_ocr": False, "pages_total_source": 999}, + ] + ) + source = tmp_path / "master.parquet" + downloads = tmp_path / "downloads" + downloads.mkdir() + (downloads / "A.pdf").write_bytes(b"%PDF-1.4\n") + df.to_parquet(source, index=False) + + out_dir = tmp_path / "cutoff" + rc = openarchives_ocr_cutoff_shards.main( + [ + "--parquet", + str(source), + "--output-dir", + str(out_dir), + "--local-download-root", + str(downloads), + "--nodes", + "2", + "--cutoff-id", + "cutoff-x", + ] + ) + assert rc == 0 + summary = json.loads((out_dir / "openarchives_ocr_cutoff_summary.json").read_text()) + assert summary["available_docs_total"] == 1 + assert summary["missing_docs_total"] == 1 + shard = pd.read_parquet(out_dir / "openarchives_ocr_shard_node_00.parquet") + assert shard.loc[0, "source_filename"] == "a.html" + assert shard.loc[0, "filename"] == "A.pdf" + assert shard.loc[0, "md_filename"] == "A.md" + assert bool(shard.loc[0, "available_at_cutoff"]) is True + missing = pd.read_parquet(out_dir / "openarchives_ocr_missing_at_cutoff.parquet") + assert set(missing["source_doc_id"]) == {"doc-2"} + + +def test_openarchives_ocr_merge_copies_markdown_artifacts(tmp_path: Path) -> None: + master = pd.DataFrame( + [ + {"source_doc_id": "doc-1", "filename": "a.html", "md_filename": "a.md", "needs_ocr": True, "ocr_success": False}, + ] + ) + shard = pd.DataFrame( + [ + { + "source_doc_id": "doc-1", + "filename": "A.pdf", + "filename_base": "A", + "md_filename": "A.md", + "needs_ocr": False, + "ocr_success": True, + }, + ] + ) + master_path = tmp_path / "master.parquet" + shard_path = tmp_path / "shard.parquet" + out_path = tmp_path / "merged.parquet" + work_root = tmp_path / "node00" + (work_root / "markdown").mkdir(parents=True) + (work_root / "json" / "metrics").mkdir(parents=True) + (work_root / "markdown" / "A.md").write_text("ocr text", encoding="utf-8") + (work_root / "json" / "metrics" / "A.metrics.json").write_text("{}", encoding="utf-8") + master.to_parquet(master_path, index=False) + shard.to_parquet(shard_path, index=False) + + rc = openarchives_ocr_merge.main( + [ + "--master-parquet", + str(master_path), + "--shard-parquets", + str(shard_path), + "--output-parquet", + str(out_path), + "--key-column", + "source_doc_id", + "--preserve-master-columns", + "filename,md_filename", + "--artifact-work-roots", + str(work_root), + "--artifact-output-root", + str(tmp_path / "final"), + ] + ) + assert rc == 0 + merged = pd.read_parquet(out_path).set_index("source_doc_id") + assert merged.loc["doc-1", "filename"] == "a.html" + assert bool(merged.loc["doc-1", "ocr_success"]) is True + assert merged.loc["doc-1", "text"] == "ocr text" + assert merged.loc["doc-1", "ocr_markdown_relpath"] == "markdown/A.md" + assert merged.loc["doc-1", "ocr_metrics_relpath"] == "json/metrics/A.metrics.json" + assert merged.loc["doc-1", "ocr_text_sha256"] == hashlib.sha256(b"ocr text").hexdigest() + assert (tmp_path / "final" / "markdown" / "A.md").exists() + assert (tmp_path / "final" / "json" / "metrics" / "A.metrics.json").exists() + + +def test_openarchives_ocr_merge_embeds_text_without_copy_root(tmp_path: Path) -> None: + master = pd.DataFrame( + [ + {"source_doc_id": "doc-1", "filename": "a.html", "needs_ocr": True, "ocr_success": False}, + ] + ) + shard = pd.DataFrame( + [ + { + "source_doc_id": "doc-1", + "filename": "A.pdf", + "filename_base": "A", + "md_filename": "A.md", + "needs_ocr": False, + "ocr_success": True, + }, + ] + ) + master_path = tmp_path / "master.parquet" + shard_path = tmp_path / "shard.parquet" + out_path = tmp_path / "merged.parquet" + work_root = tmp_path / "node00" + (work_root / "markdown").mkdir(parents=True) + (work_root / "json" / "metrics").mkdir(parents=True) + (work_root / "markdown" / "A.md").write_text("embedded text", encoding="utf-8") + (work_root / "json" / "metrics" / "A.metrics.json").write_text("{}", encoding="utf-8") + master.to_parquet(master_path, index=False) + shard.to_parquet(shard_path, index=False) + + rc = openarchives_ocr_merge.main( + [ + "--master-parquet", + str(master_path), + "--shard-parquets", + str(shard_path), + "--output-parquet", + str(out_path), + "--key-column", + "source_doc_id", + "--artifact-work-roots", + str(work_root), + ] + ) + assert rc == 0 + + merged = pd.read_parquet(out_path).set_index("source_doc_id") + assert merged.loc["doc-1", "text"] == "embedded text" + assert pd.isna(merged.loc["doc-1", "ocr_markdown_relpath"]) + + +def test_openarchives_ocr_merge_unifies_markdown_shards(tmp_path: Path) -> None: + master = pd.DataFrame( + [ + {"source_doc_id": "doc-1", "filename": "a.html", "md_filename": "a.md", "needs_ocr": True, "ocr_success": False}, + ] + ) + shard = pd.DataFrame( + [ + { + "source_doc_id": "doc-1", + "filename": "A.pdf", + "filename_base": "A", + "md_filename": "A.md", + "needs_ocr": False, + "ocr_success": True, + }, + ] + ) + master_path = tmp_path / "master.parquet" + shard_path = tmp_path / "shard.parquet" + out_path = tmp_path / "merged.parquet" + work_root = tmp_path / "node00" + markdown_dir = work_root / "markdown" + markdown_dir.mkdir(parents=True) + (markdown_dir / "A__p00001-00096.md").write_text("part one", encoding="utf-8") + (markdown_dir / "A__p00097-00179.md").write_text("part two\n", encoding="utf-8") + master.to_parquet(master_path, index=False) + shard.to_parquet(shard_path, index=False) + + rc = openarchives_ocr_merge.main( + [ + "--master-parquet", + str(master_path), + "--shard-parquets", + str(shard_path), + "--output-parquet", + str(out_path), + "--key-column", + "source_doc_id", + "--artifact-work-roots", + str(work_root), + "--artifact-output-root", + str(tmp_path / "final"), + ] + ) + assert rc == 0 + + merged = pd.read_parquet(out_path).set_index("source_doc_id") + assert merged.loc["doc-1", "text"] == "part one\npart two\n" + assert merged.loc["doc-1", "ocr_markdown_relpath"] == "markdown/A.md" + assert (tmp_path / "final" / "markdown" / "A.md").read_text(encoding="utf-8") == "part one\npart two\n" + assert (tmp_path / "final" / "sidecars" / "ocr_shards" / "markdown" / "A__p00001-00096.md").exists() + assert (tmp_path / "final" / "sidecars" / "ocr_shards" / "markdown" / "A__p00097-00179.md").exists() diff --git a/tests/test_openarchives_pdf_stage_pull.py b/tests/test_openarchives_pdf_stage_pull.py new file mode 100644 index 0000000..fbdfbed --- /dev/null +++ b/tests/test_openarchives_pdf_stage_pull.py @@ -0,0 +1,252 @@ +from __future__ import annotations + +import subprocess +from pathlib import Path + +from glossapi.scripts.openarchives_pdf_stage_pull import ( + TransferItem, + TransferState, + canonicalize_pdf_name, + load_priority_filenames, + read_manifest, + run, +) + + +def _write_manifest(path: Path) -> None: + path.write_text( + "\t".join(["canonical_filename", "remote_path", "remote_size_bytes", "remote_name"]) + + "\n" + + "\t".join(["AAA_456.pdf", "/remote/AAA_456.pdf", "10", "AAA_456.pdf"]) + + "\n" + + "\t".join(["VFK_368.pdf", "/remote/VFK_368.pdf.Ac6Dc3BA", "20", "VFK_368.pdf.Ac6Dc3BA"]) + + "\n", + encoding="utf-8", + ) + + +def test_read_manifest_parses_rows(tmp_path: Path) -> None: + manifest = tmp_path / "manifest.tsv" + _write_manifest(manifest) + + items = read_manifest(manifest) + + assert items == [ + TransferItem("AAA_456.pdf", "/remote/AAA_456.pdf", 10, "AAA_456.pdf"), + TransferItem("VFK_368.pdf", "/remote/VFK_368.pdf.Ac6Dc3BA", 20, "VFK_368.pdf.Ac6Dc3BA"), + ] + + +def test_transfer_state_resets_stale_and_marks_completed(tmp_path: Path) -> None: + db_path = tmp_path / "state.sqlite3" + downloads = tmp_path / "downloads" + partials = tmp_path / "partials" + downloads.mkdir() + partials.mkdir() + state = TransferState(db_path) + state.sync_manifest( + [ + TransferItem("AAA_456.pdf", "/remote/AAA_456.pdf", 10, "AAA_456.pdf"), + TransferItem("BBB_001.pdf", "/remote/BBB_001.pdf", 12, "BBB_001.pdf"), + ] + ) + + state.mark_in_progress("AAA_456.pdf", 5) + (downloads / "BBB_001.pdf").write_bytes(b"x" * 12) + + state.reset_stale_in_progress() + state.mark_completed_if_present(downloads, partials) + + cur = state.conn.execute( + "SELECT canonical_filename, status, last_seen_size_bytes, last_error FROM transfer_items ORDER BY canonical_filename" + ) + rows = cur.fetchall() + assert rows[0][0] == "AAA_456.pdf" + assert rows[0][1] == "pending" + assert "Recovered from interrupted transfer" in rows[0][3] + assert rows[1][0] == "BBB_001.pdf" + assert rows[1][1] == "completed" + assert rows[1][2] == 12 + + counts = state.counts() + assert counts["pending"] == 1 + assert counts["completed"] == 1 + state.close() + + +def test_transfer_state_next_item_respects_attempt_limit(tmp_path: Path) -> None: + state = TransferState(tmp_path / "state.sqlite3") + state.sync_manifest( + [ + TransferItem("AAA_456.pdf", "/remote/AAA_456.pdf", 10, "AAA_456.pdf"), + TransferItem("BBB_001.pdf", "/remote/BBB_001.pdf", 12, "BBB_001.pdf"), + ] + ) + state.conn.execute( + "UPDATE transfer_items SET status='failed', attempts=25 WHERE canonical_filename='AAA_456.pdf'" + ) + state.conn.execute( + "UPDATE transfer_items SET status='failed', attempts=2 WHERE canonical_filename='BBB_001.pdf'" + ) + state.conn.commit() + + row = state.next_item(max_attempts=20) + + assert row is not None + assert row["canonical_filename"] == "BBB_001.pdf" + state.close() + + +def test_load_priority_filenames_supports_lists_and_suffix_forms(tmp_path: Path) -> None: + priority_dir = tmp_path / "priority" + priority_dir.mkdir() + (priority_dir / "manual.txt").write_text( + "AAA_456.pdf\n" + "/tmp/VFK_368.pdf.Ac6Dc3BA\n" + "ignore me\n", + encoding="utf-8", + ) + (priority_dir / "BBB_001.pdf").write_text("", encoding="utf-8") + + names = load_priority_filenames(priority_dir) + + assert names == {"AAA_456.pdf", "VFK_368.pdf", "BBB_001.pdf"} + assert canonicalize_pdf_name("VFK_368.pdf.Ac6Dc3BA") == "VFK_368.pdf" + + +def test_transfer_state_priorities_are_selected_first(tmp_path: Path) -> None: + state = TransferState(tmp_path / "state.sqlite3") + state.sync_manifest( + [ + TransferItem("AAA_456.pdf", "/remote/AAA_456.pdf", 10, "AAA_456.pdf"), + TransferItem("BBB_001.pdf", "/remote/BBB_001.pdf", 12, "BBB_001.pdf"), + TransferItem("CCC_002.pdf", "/remote/CCC_002.pdf", 14, "CCC_002.pdf"), + ] + ) + state.set_priorities({"CCC_002.pdf"}) + + row = state.next_item(max_attempts=20) + + assert row is not None + assert row["canonical_filename"] == "CCC_002.pdf" + counts = state.priority_counts() + assert counts["priority_total"] == 1 + assert counts["priority_pending"] == 1 + state.close() + + +def test_transfer_state_priority_only_skips_non_priority(tmp_path: Path) -> None: + state = TransferState(tmp_path / "state.sqlite3") + state.sync_manifest( + [ + TransferItem("AAA_456.pdf", "/remote/AAA_456.pdf", 10, "AAA_456.pdf"), + TransferItem("BBB_001.pdf", "/remote/BBB_001.pdf", 12, "BBB_001.pdf"), + ] + ) + state.set_priorities({"BBB_001.pdf"}) + + row = state.next_item(max_attempts=20, priority_only=True) + assert row is not None + assert row["canonical_filename"] == "BBB_001.pdf" + + state.mark_in_progress("BBB_001.pdf", 0) + state.mark_completed("BBB_001.pdf", 12) + + row2 = state.next_item(max_attempts=20, priority_only=True) + assert row2 is None + state.close() + + +def test_load_priority_filenames_ignores_parquet_and_reads_csv_columns(tmp_path: Path) -> None: + priority_dir = tmp_path / "priority" + priority_dir.mkdir() + (priority_dir / "unreachable_from_source_20260331.csv").write_text( + "filename,source_unreachable_reason\n" + "ZFV_051.pdf,connect_timeout\n" + "ZGA_056.pdf,connect_timeout\n", + encoding="utf-8", + ) + (priority_dir / "unreachable_from_source_20260331.parquet").write_bytes(b"PAR1junkZXY_999.pdfjunk") + + names = load_priority_filenames(priority_dir) + + assert names == {"ZFV_051.pdf", "ZGA_056.pdf"} + + +def test_run_uses_rsync_transport_when_requested(tmp_path: Path, monkeypatch) -> None: + manifest = tmp_path / "manifest.tsv" + _write_manifest(manifest) + work_root = tmp_path / "work" + seen: list[str] = [] + + def _fake_rsync_one(**kwargs): + seen.append("rsync") + Path(kwargs["temp_path"]).parent.mkdir(parents=True, exist_ok=True) + Path(kwargs["temp_path"]).write_bytes(b"x" * 10) + return subprocess.CompletedProcess(args=["rsync"], returncode=0, stdout="", stderr="") + + def _fake_sftp_one(**kwargs): + seen.append("sftp") + return subprocess.CompletedProcess(args=["sftp"], returncode=1, stdout="", stderr="unexpected") + + monkeypatch.setenv("GREECE_BOX_PASSWORD", "secret") + monkeypatch.setattr("glossapi.scripts.openarchives_pdf_stage_pull.rsync_one", _fake_rsync_one) + monkeypatch.setattr("glossapi.scripts.openarchives_pdf_stage_pull.sftp_one", _fake_sftp_one) + + rc = run( + [ + "--manifest", + str(manifest), + "--work-root", + str(work_root), + "--transport", + "rsync", + "--limit", + "1", + "--summary-interval-seconds", + "0", + ] + ) + + assert rc == 0 + assert seen == ["rsync"] + assert (work_root / "downloads" / "AAA_456.pdf").exists() + + +def test_run_priority_only_ignores_non_priority_items(tmp_path: Path, monkeypatch) -> None: + manifest = tmp_path / "manifest.tsv" + _write_manifest(manifest) + work_root = tmp_path / "work" + priority_dir = tmp_path / "priority" + priority_dir.mkdir() + (priority_dir / "priority.csv").write_text("filename\nVFK_368.pdf\n", encoding="utf-8") + seen: list[str] = [] + + def _fake_sftp_one(**kwargs): + seen.append(Path(kwargs["remote_path"]).name) + size = 20 if "VFK_368" in kwargs["remote_path"] else 10 + Path(kwargs["temp_path"]).parent.mkdir(parents=True, exist_ok=True) + Path(kwargs["temp_path"]).write_bytes(b"x" * size) + return subprocess.CompletedProcess(args=["sftp"], returncode=0, stdout="", stderr="") + + monkeypatch.setenv("GREECE_BOX_PASSWORD", "secret") + monkeypatch.setattr("glossapi.scripts.openarchives_pdf_stage_pull.sftp_one", _fake_sftp_one) + + rc = run( + [ + "--manifest", + str(manifest), + "--work-root", + str(work_root), + "--priority-dir", + str(priority_dir), + "--priority-only", + "--summary-interval-seconds", + "0", + ] + ) + + assert rc == 0 + assert seen == ["VFK_368.pdf.Ac6Dc3BA"] + assert (work_root / "downloads" / "VFK_368.pdf").exists() + assert not (work_root / "downloads" / "AAA_456.pdf").exists() diff --git a/tests/test_phase_extract_tuning.py b/tests/test_phase_extract_tuning.py new file mode 100644 index 0000000..3b32792 --- /dev/null +++ b/tests/test_phase_extract_tuning.py @@ -0,0 +1,87 @@ +from pathlib import Path + +from glossapi.corpus.phase_extract import ( + _build_extract_work_items, + _resolve_docling_batch_target_pages, + _resolve_docling_max_batch_files, + _resolve_docling_queue_policy, +) + + +def test_resolve_docling_max_batch_files_defaults_to_conservative_batch(monkeypatch): + monkeypatch.delenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES", raising=False) + assert _resolve_docling_max_batch_files() == 1 + + +def test_resolve_docling_max_batch_files_accepts_explicit_override(monkeypatch): + monkeypatch.setenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES", "4") + assert _resolve_docling_max_batch_files() == 4 + + +def test_resolve_docling_max_batch_files_ignores_invalid_values(monkeypatch): + monkeypatch.setenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES", "not-an-int") + assert _resolve_docling_max_batch_files() == 1 + + +def test_resolve_docling_batch_target_pages_defaults(monkeypatch): + monkeypatch.delenv("GLOSSAPI_DOCLING_BATCH_TARGET_PAGES", raising=False) + assert _resolve_docling_batch_target_pages() == 256 + + +def test_resolve_docling_batch_target_pages_accepts_override(monkeypatch): + monkeypatch.setenv("GLOSSAPI_DOCLING_BATCH_TARGET_PAGES", "384") + assert _resolve_docling_batch_target_pages() == 384 + + +def test_resolve_docling_queue_policy_uses_env_when_extractor_is_unprimed(monkeypatch): + monkeypatch.setenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES", "2") + assert _resolve_docling_queue_policy(None) == (2, 600) + + +def test_resolve_docling_queue_policy_prefers_extractor_values(monkeypatch): + class Extractor: + max_batch_files = 3 + long_pdf_page_threshold = 900 + + monkeypatch.setenv("GLOSSAPI_DOCLING_MAX_BATCH_FILES", "2") + assert _resolve_docling_queue_policy(Extractor()) == (3, 900) + + +def test_build_extract_work_items_packs_smaller_files_by_page_budget(): + paths = [Path("a.pdf"), Path("b.pdf"), Path("c.pdf"), Path("d.pdf")] + pages = { + "a.pdf": 140, + "b.pdf": 120, + "c.pdf": 110, + "d.pdf": 90, + } + + items = _build_extract_work_items( + paths, + max_batch_files=2, + target_batch_pages=250, + long_pdf_page_threshold=600, + page_counter=lambda path: pages[path.name], + ) + + assert [[p.name for p in item] for item in items] == [["a.pdf", "c.pdf"], ["b.pdf", "d.pdf"]] + + +def test_build_extract_work_items_keeps_long_pdf_as_standalone_work_item(): + paths = [Path("huge.pdf"), Path("small-a.pdf"), Path("small-b.pdf")] + pages = { + "huge.pdf": 1200, + "small-a.pdf": 100, + "small-b.pdf": 80, + } + + items = _build_extract_work_items( + paths, + max_batch_files=3, + target_batch_pages=250, + long_pdf_page_threshold=600, + page_counter=lambda path: pages[path.name], + ) + + assert [p.name for p in items[0]] == ["huge.pdf"] + assert sorted(p.name for p in items[1]) == ["small-a.pdf", "small-b.pdf"] diff --git a/tests/test_pipeline_smoke.py b/tests/test_pipeline_smoke.py index 4fe7464..f673a83 100644 --- a/tests/test_pipeline_smoke.py +++ b/tests/test_pipeline_smoke.py @@ -1,4 +1,5 @@ import os +import sys from pathlib import Path import pandas as pd @@ -7,10 +8,6 @@ pytest.importorskip("docling") pytest.importorskip("glossapi_rs_cleaner") -pytest.importorskip( - "onnxruntime", reason="RapidOCR/DeepSeek end-to-end tests require onnxruntime" -) -import onnxruntime as ort # noqa: E402 from glossapi import Corpus from glossapi.corpus import _resolve_skiplist_path @@ -106,11 +103,8 @@ def _assert_dir_contents( pytest.fail(f"Unexpected file {entry} in {root}") -@pytest.mark.rapidocr -def test_pipeline_smoke_and_artifacts(tmp_path): +def test_pipeline_smoke_and_artifacts(tmp_path, monkeypatch): assert torch.cuda.is_available(), "CUDA GPU expected for pipeline smoke test" - providers = ort.get_available_providers() - assert "CUDAExecutionProvider" in providers, f"CUDAExecutionProvider missing: {providers}" device_idx = 0 if torch.cuda.device_count() > 1: @@ -132,7 +126,6 @@ def test_pipeline_smoke_and_artifacts(tmp_path): num_threads=1, emit_formula_index=True, phase1_backend="docling", - force_ocr=True, use_gpus="single", devices=[device_idx], ) @@ -146,6 +139,21 @@ def test_pipeline_smoke_and_artifacts(tmp_path): assert bool(needs.get("blank.pdf")), "Blank PDF should be flagged for OCR" assert not bool(needs.get("text.pdf")) + from glossapi.ocr.deepseek import runner as deepseek_runner + + def fake_run_for_files(self_ref, files, **kwargs): + markdown_dir = self_ref.output_dir / "markdown" + metrics_dir = self_ref.output_dir / "json" / "metrics" + markdown_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + for name in files: + stem = Path(name).stem + (markdown_dir / f"{stem}.md").write_text("[[Blank page]]\n", encoding="utf-8") + (metrics_dir / f"{stem}.metrics.json").write_text("{\n \"page_count\": 1\n}\n", encoding="utf-8") + return {Path(name).stem: {"page_count": 1} for name in files} + + monkeypatch.setattr(deepseek_runner, "run_for_files", fake_run_for_files) + corpus.ocr( mode="ocr_bad", use_gpus="single", @@ -193,15 +201,8 @@ def test_pipeline_smoke_and_artifacts(tmp_path): assert sections_file.exists() -@pytest.mark.rapidocr def test_docling_math_pipeline_with_mixed_pdfs(tmp_path, monkeypatch): assert torch.cuda.is_available(), "CUDA GPU expected for docling pipeline test" - providers = ort.get_available_providers() - assert "CUDAExecutionProvider" in providers, f"CUDAExecutionProvider missing: {providers}" - - assert torch.cuda.is_available(), "CUDA GPU expected for docling pipeline test" - providers = ort.get_available_providers() - assert "CUDAExecutionProvider" in providers, f"CUDAExecutionProvider missing: {providers}" device_idx = 0 if torch.cuda.device_count() > 1: @@ -242,7 +243,6 @@ def test_docling_math_pipeline_with_mixed_pdfs(tmp_path, monkeypatch): num_threads=1, emit_formula_index=True, phase1_backend="docling", - force_ocr=True, use_gpus="single", devices=[device_idx], ) @@ -256,6 +256,25 @@ def test_docling_math_pipeline_with_mixed_pdfs(tmp_path, monkeypatch): assert bool(greek_row["needs_ocr"]), "Greek consonant doc should require OCR rerun" assert "non_greek_text" in str(greek_row.get("filter", "")), "Filter should record non-Greek text" + from glossapi.ocr.deepseek import runner as deepseek_runner + + def fake_run_for_files(self_ref, files, **kwargs): + markdown_dir = self_ref.output_dir / "markdown" + metrics_dir = self_ref.output_dir / "json" / "metrics" + markdown_dir.mkdir(parents=True, exist_ok=True) + metrics_dir.mkdir(parents=True, exist_ok=True) + for name in files: + stem = Path(name).stem + if stem == "greek_consonants": + text = documents["greek_consonants"] + else: + text = documents.get(stem) or "[[Blank page]]" + (markdown_dir / f"{stem}.md").write_text(f"{text}\n", encoding="utf-8") + (metrics_dir / f"{stem}.metrics.json").write_text("{\n \"page_count\": 1\n}\n", encoding="utf-8") + return {Path(name).stem: {"page_count": 1} for name in files} + + monkeypatch.setattr(deepseek_runner, "run_for_files", fake_run_for_files) + corpus.ocr( fix_bad=True, math_enhance=True, @@ -268,6 +287,15 @@ def test_docling_math_pipeline_with_mixed_pdfs(tmp_path, monkeypatch): assert not bool(greek_after["needs_ocr"]), "Greek consonant doc should be resolved after OCR rerun" assert bool(greek_after.get("ocr_success", False)), "OCR rerun should mark greek consonant doc as success" + corpus.ocr( + backend="deepseek", + fix_bad=False, + math_enhance=True, + mode="math_only", + use_gpus="single", + devices=[device_idx], + ) + json_dir = corpus_dir / "json" assert json_dir.exists(), "Docling JSON directory should exist after extraction" for stem in documents: @@ -304,11 +332,8 @@ def test_docling_math_pipeline_with_mixed_pdfs(tmp_path, monkeypatch): assert not skiplist_path.read_text(encoding="utf-8").strip(), "Fatal skip-list should remain empty" -@pytest.mark.rapidocr def test_clean_skips_files_with_successful_ocr(tmp_path, monkeypatch): assert torch.cuda.is_available(), "CUDA GPU expected for OCR recovery test" - providers = ort.get_available_providers() - assert "CUDAExecutionProvider" in providers, f"CUDAExecutionProvider missing: {providers}" device_idx = 0 if torch.cuda.device_count() > 1: @@ -351,7 +376,6 @@ def test_clean_skips_files_with_successful_ocr(tmp_path, monkeypatch): accel_type="CUDA", num_threads=1, phase1_backend="docling", - force_ocr=True, use_gpus="single", devices=[device_idx], ) @@ -384,8 +408,8 @@ def test_deepseek_cli_pipeline_with_synthetic_pdfs(tmp_path, monkeypatch): script = Path( os.environ.get( - "GLOSSAPI_DEEPSEEK_VLLM_SCRIPT", - Path.cwd() / "deepseek-ocr" / "run_pdf_ocr_vllm.py", + "GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT", + Path.cwd() / "src" / "glossapi" / "ocr" / "deepseek" / "run_pdf_ocr_transformers.py", ) ) if not script.exists(): @@ -393,8 +417,8 @@ def test_deepseek_cli_pipeline_with_synthetic_pdfs(tmp_path, monkeypatch): python_bin = Path( os.environ.get( - "GLOSSAPI_DEEPSEEK_TEST_PYTHON", - Path("/mnt/data/glossAPI/deepseek_venv/bin/python"), + "GLOSSAPI_DEEPSEEK_PYTHON", + os.environ.get("GLOSSAPI_DEEPSEEK_TEST_PYTHON", sys.executable), ) ) if not python_bin.exists(): @@ -409,29 +433,17 @@ def test_deepseek_cli_pipeline_with_synthetic_pdfs(tmp_path, monkeypatch): if not model_dir.exists(): pytest.skip(f"DeepSeek model directory missing: {model_dir}") - lib_path = os.environ.get("GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH") - if not lib_path: - candidate = Path.cwd() / "deepseek-ocr" / "libjpeg-turbo" / "lib" - if candidate.exists(): - lib_path = str(candidate) - if not lib_path or not Path(lib_path).exists(): - pytest.skip("Set GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH to the libjpeg-turbo library directory") - - providers = ort.get_available_providers() - assert "CUDAExecutionProvider" in providers, f"CUDAExecutionProvider missing: {providers}" - device_idx = 0 if torch.cuda.device_count() > 1: device_idx = torch.cuda.current_device() - # Force the CLI path (no stub fallback) and point to the desired interpreter/script. + # Force the real runner path and point to the desired interpreter/script. monkeypatch.delenv("PYTEST_CURRENT_TEST", raising=False) - monkeypatch.setenv("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "0") monkeypatch.setenv("GLOSSAPI_DEEPSEEK_ALLOW_CLI", "1") + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_ALLOW_STUB", "0") monkeypatch.setenv("GLOSSAPI_DEEPSEEK_PYTHON", str(python_bin)) - monkeypatch.setenv("GLOSSAPI_DEEPSEEK_VLLM_SCRIPT", str(script)) - monkeypatch.setenv("GLOSSAPI_DEEPSEEK_LD_LIBRARY_PATH", lib_path) - monkeypatch.setenv("VLLM_ALLOW_REMOTE_CODE", "1") + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_RUNNER_SCRIPT", str(script)) + monkeypatch.setenv("GLOSSAPI_DEEPSEEK_MODEL_DIR", str(model_dir)) existing_py_path = os.environ.get("PYTHONPATH", "") src_path = str(Path.cwd() / "src") if existing_py_path: @@ -439,13 +451,6 @@ def test_deepseek_cli_pipeline_with_synthetic_pdfs(tmp_path, monkeypatch): else: monkeypatch.setenv("PYTHONPATH", src_path) - import glossapi.ocr.deepseek.runner as deepseek_runner - - def _raise_if_stub(*_args, **_kwargs): - raise AssertionError("DeepSeek fallback stub should not run in CLI smoke test") - - monkeypatch.setattr(deepseek_runner, "_run_one_pdf", _raise_if_stub) - corpus_dir = tmp_path / "corpus" corpus_dir.mkdir() @@ -461,7 +466,6 @@ def _raise_if_stub(*_args, **_kwargs): num_threads=1, emit_formula_index=True, phase1_backend="docling", - force_ocr=True, use_gpus="single", devices=[device_idx], ) diff --git a/tests/test_rapidocr_patch.py b/tests/test_rapidocr_patch.py deleted file mode 100644 index 93a8ca5..0000000 --- a/tests/test_rapidocr_patch.py +++ /dev/null @@ -1,368 +0,0 @@ -import importlib -import sys -import types -from pathlib import Path -from types import SimpleNamespace - -import numpy as np -import pytest - - -def _clear_modules(prefix: str) -> None: - for name in list(sys.modules): - if name == prefix or name.startswith(f"{prefix}."): - sys.modules.pop(name, None) - - -def _install_docling_stub(*, supports_injection: bool) -> None: - _clear_modules("docling") - _clear_modules("docling_core") - _clear_modules("glossapi") - - def register(name: str) -> types.ModuleType: - module = types.ModuleType(name) - sys.modules[name] = module - return module - - docling = register("docling") - register("docling.backend") - register("docling.backend.docling_parse_backend").DoclingParseDocumentBackend = object - register("docling.backend.docling_parse_v2_backend").DoclingParseV2DocumentBackend = object - register("docling.backend.pypdfium2_backend").PyPdfiumDocumentBackend = object - - base_models = register("docling.datamodel.base_models") - - class InputFormat: - PDF = "pdf" - DOCX = "docx" - XML_JATS = "xml" - HTML = "html" - PPTX = "pptx" - CSV = "csv" - MD = "md" - - class ConversionStatus: - SUCCESS = "success" - PARTIAL_SUCCESS = "partial" - - class Page: - def __init__(self): - self._backend = types.SimpleNamespace( - is_valid=lambda: True, - get_page_image=lambda *args, **kwargs: types.SimpleNamespace() - ) - - base_models.InputFormat = InputFormat - base_models.ConversionStatus = ConversionStatus - base_models.Page = Page - - pipeline_opts = register("docling.datamodel.pipeline_options") - - class AcceleratorDevice: - AUTO = "auto" - CUDA = "cuda" - MPS = "mps" - CPU = "cpu" - - class AcceleratorOptions: - def __init__(self, num_threads=None, device=None): - self.num_threads = num_threads - self.device = device - - class PdfPipelineOptions: - def __init__(self, **_kwargs): - self.ocr_options = None - self.do_ocr = False - - class RapidOcrOptions: - def __init__(self, **kwargs): - for key, value in kwargs.items(): - setattr(self, key, value) - self.rec_keys_path = None - - class OcrOptions: - pass - - class LayoutOptions: - pass - - class TableStructureOptions: - def __init__(self, mode=None): - self.mode = mode - self.do_cell_matching = False - - class TableFormerMode: - ACCURATE = "accurate" - - class PictureDescriptionApiOptions: - pass - - pipeline_opts.AcceleratorDevice = AcceleratorDevice - pipeline_opts.AcceleratorOptions = AcceleratorOptions - pipeline_opts.PdfPipelineOptions = PdfPipelineOptions - pipeline_opts.RapidOcrOptions = RapidOcrOptions - pipeline_opts.OcrOptions = OcrOptions - pipeline_opts.LayoutOptions = LayoutOptions - pipeline_opts.TableStructureOptions = TableStructureOptions - pipeline_opts.TableFormerMode = TableFormerMode - pipeline_opts.PictureDescriptionApiOptions = PictureDescriptionApiOptions - - register("docling.datamodel.document").ConversionResult = object - - settings_mod = register("docling.datamodel.settings") - - class _Debug: - def __init__(self): - self.profile_pipeline_timings = False - self.visualize_ocr = False - - class _Settings: - def __init__(self): - self.debug = _Debug() - - settings_mod.settings = _Settings() - - converter_mod = register("docling.document_converter") - - class DocumentConverter: - def __init__(self, *args, **kwargs): - self.args = args - self.kwargs = kwargs - - class PdfFormatOption: - def __init__(self, *args, **kwargs): - self.args = args - self.kwargs = kwargs - - converter_mod.DocumentConverter = DocumentConverter - converter_mod.PdfFormatOption = PdfFormatOption - converter_mod.WordFormatOption = object - converter_mod.HTMLFormatOption = object - converter_mod.XMLJatsFormatOption = object - converter_mod.PowerpointFormatOption = object - converter_mod.MarkdownFormatOption = object - converter_mod.CsvFormatOption = object - - register("docling.pipeline.simple_pipeline").SimplePipeline = object - - pipelines_mod = register("docling.pipelines.standard_pdf_pipeline") - pipeline_mod = register("docling.pipeline.standard_pdf_pipeline") - - if supports_injection: - class StandardPdfPipeline: - def __init__(self, opts, ocr_model=None, **_): - self.opts = opts - self.ocr_model = ocr_model - else: - class StandardPdfPipeline: - def __init__(self, opts, **_): - self.opts = opts - - pipelines_mod.StandardPdfPipeline = StandardPdfPipeline - pipeline_mod.StandardPdfPipeline = StandardPdfPipeline - - rapid_module = register("docling.models.rapid_ocr_model") - - class DummyReader: - def __call__(self, *_args, **_kwargs): - return [] - - class RapidOcrModel: - def __init__(self, enabled, artifacts_path, options, accelerator_options): - self.enabled = enabled - self.reader = DummyReader() - self.options = options - - def get_ocr_rects(self, _page): - return [] - - def post_process_cells(self, _cells, _page): - pass - - class TextCell: - def __init__(self, **kwargs): - self.__dict__.update(kwargs) - - class _Log: - @staticmethod - def warning(_msg, *args, **kwargs): - return None - - rapid_module.RapidOcrModel = RapidOcrModel - rapid_module.TextCell = TextCell - rapid_module._log = _Log() - - utils_mod = register("docling.utils") - profiling_mod = register("docling.utils.profiling") - - class TimeRecorder: - def __init__(self, *_args, **_kwargs): - pass - - def __enter__(self): - return self - - def __exit__(self, *exc): - return False - - profiling_mod.TimeRecorder = TimeRecorder - utils_mod.profiling = profiling_mod - - register("docling.models") - - core_doc = register("docling_core.types.doc") - - class BoundingBox: - @staticmethod - def from_tuple(coord, origin=None): - return SimpleNamespace(coord=coord, origin=origin) - - class CoordOrigin: - TOPLEFT = "topleft" - - core_doc.BoundingBox = BoundingBox - core_doc.CoordOrigin = CoordOrigin - - core_page = register("docling_core.types.doc.page") - - class BoundingRectangle: - @staticmethod - def from_bounding_box(box): - return box - - core_page.BoundingRectangle = BoundingRectangle - - -def _install_onnxruntime_stub(): - sys.modules['onnxruntime'] = types.SimpleNamespace( - get_available_providers=lambda: ['CUDAExecutionProvider'] - ) - - -def _make_safe_ocr() -> SimpleNamespace: - """Return an instantiated SafeRapidOcrModel with stubbed dependencies.""" - rapid_opts = sys.modules['docling.datamodel.pipeline_options'].RapidOcrOptions() - accel_opts = sys.modules['docling.datamodel.pipeline_options'].AcceleratorOptions(device='cuda:0') - from glossapi.ocr.rapidocr.safe import SafeRapidOcrModel - - return SafeRapidOcrModel(enabled=True, artifacts_path=None, options=rapid_opts, accelerator_options=accel_opts) - - -@pytest.fixture(autouse=True) -def _cleanup_modules(): - yield - for name in [n for n in list(sys.modules) if n.startswith('glossapi') and '_rapidocr_paths' not in n]: - if name.startswith('glossapi_rs_'): - continue - sys.modules.pop(name, None) - _clear_modules('docling') - _clear_modules('docling_core') - sys.modules.pop('onnxruntime', None) - - -def test_patch_runs_on_import(): - _install_docling_stub(supports_injection=True) - _install_onnxruntime_stub() - - importlib.import_module('glossapi') - rapid_module = sys.modules['docling.models.rapid_ocr_model'] - from glossapi.ocr.rapidocr.safe import SafeRapidOcrModel, patch_docling_rapidocr - - assert rapid_module.RapidOcrModel is SafeRapidOcrModel - - patch_docling_rapidocr() - assert rapid_module.RapidOcrModel is SafeRapidOcrModel - - -def test_build_rapidocr_pipeline_injects_when_supported(monkeypatch): - _install_docling_stub(supports_injection=True) - _install_onnxruntime_stub() - - glossapi_mod = importlib.import_module('glossapi') - pipeline = importlib.reload(importlib.import_module('glossapi.ocr.rapidocr.pipeline')) - - monkeypatch.setattr( - pipeline, - 'resolve_packaged_onnx_and_keys', - lambda: SimpleNamespace(det='det', rec='rec', cls='cls', keys='keys'), - ) - - captured = {} - - def fake_pool_get(device, opts, factory, expected_type): - model = factory() - assert isinstance(model, pipeline.SafeRapidOcrModel) - assert expected_type is pipeline.SafeRapidOcrModel - captured['device'] = device - captured['opts'] = opts - return SimpleNamespace() - - monkeypatch.setattr(pipeline, 'GLOBAL_RAPID_OCR_POOL', SimpleNamespace(get=fake_pool_get)) - - engine, opts = pipeline.build_rapidocr_pipeline(device='cuda:0') - assert hasattr(engine, 'ocr_model') - assert captured['device'] == 'cuda:0' - assert opts.do_ocr is True - - -def test_build_rapidocr_pipeline_falls_back_without_injection(monkeypatch): - _install_docling_stub(supports_injection=False) - _install_onnxruntime_stub() - - importlib.import_module('glossapi') - pipeline = importlib.reload(importlib.import_module('glossapi.ocr.rapidocr.pipeline')) - - monkeypatch.setattr( - pipeline, - 'resolve_packaged_onnx_and_keys', - lambda: SimpleNamespace(det='det', rec='rec', cls='cls', keys='keys'), - ) - - def fail_pool(*_args, **_kwargs): - raise AssertionError('Pool should not be used when injection unsupported') - - monkeypatch.setattr(pipeline, 'GLOBAL_RAPID_OCR_POOL', SimpleNamespace(get=fail_pool)) - - engine, opts = pipeline.build_rapidocr_pipeline(device='cuda:0') - converter_mod = importlib.import_module('docling.document_converter') - assert isinstance(engine, converter_mod.DocumentConverter) - assert opts.do_ocr is True - - -def test_safe_rapidocr_normalises_none(monkeypatch): - _install_docling_stub(supports_injection=True) - _install_onnxruntime_stub() - - importlib.import_module('glossapi') - model = _make_safe_ocr() - - assert model._normalise_result(None) == [] - - -def test_safe_rapidocr_normalises_incomplete_and_valid_data(monkeypatch): - _install_docling_stub(supports_injection=True) - _install_onnxruntime_stub() - - importlib.import_module('glossapi') - model = _make_safe_ocr() - - class IncompleteResult: - boxes = None - txts = ['foo'] - scores = [0.9] - - assert model._normalise_result(IncompleteResult()) == [] - - box = np.array([ - [[0.0, 0.0], [1.0, 0.0], [1.0, 1.0], [0.0, 1.0]], - ]) - - class FullResult: - boxes = box - txts = ['foo'] - scores = [0.9] - - output = model._normalise_result(FullResult()) - assert output == [ - (box[0].tolist(), 'foo', 0.9) - ] diff --git a/tests/test_streaming_garbage_detector.py b/tests/test_streaming_garbage_detector.py new file mode 100644 index 0000000..0d12fdd --- /dev/null +++ b/tests/test_streaming_garbage_detector.py @@ -0,0 +1,83 @@ +from pathlib import Path + +import pytest + +from glossapi.ocr.utils.cleaning import StreamingGarbageDetector + + +DOWNLOAD_EXPORT = ( + Path.home() + / "Downloads" + / "deepseek_ocr_43pdfs_allpages_20260331" +) + + +def _stream_detect(text: str, *, chunk_size: int) -> tuple[bool, str | None]: + detector = StreamingGarbageDetector() + for idx in range(0, len(text), max(1, int(chunk_size))): + if detector.feed(text[idx : idx + chunk_size]): + return True, detector.triggered_reason + return False, detector.triggered_reason + + +def _load_real_markdown_garbage() -> str: + root = DOWNLOAD_EXPORT / "corrections_markdown_garbage" + if not root.exists(): + pytest.skip(f"missing local export: {root}") + for path in sorted(root.glob("*__markdown_original.md")): + text = path.read_text(encoding="utf-8", errors="ignore") + if "\uf0b7" in text or "" in text or "" in text: + return text + pytest.skip("no local symbol-garbage sample found") + + +def _load_real_empty_page_numeric_garbage() -> str: + if not DOWNLOAD_EXPORT.exists(): + pytest.skip(f"missing local export: {DOWNLOAD_EXPORT}") + preferred = DOWNLOAD_EXPORT / ( + "000008__04afb897cb954a76fe378b2ca22f2f059097876fa60a57666de75e37319e5968__p0008__markdown_original.md" + ) + candidates = [preferred] if preferred.exists() else sorted(DOWNLOAD_EXPORT.glob("*__markdown_original.md")) + for path in candidates: + text = path.read_text(encoding="utf-8", errors="ignore") + if "1. 2. 3." in text: + return text + pytest.skip("no local numeric-list garbage sample found") + + +@pytest.mark.parametrize("chunk_size", [1, 2, 5, 17]) +def test_streaming_detector_catches_symbol_garbage_across_chunks(chunk_size): + text = "Κανονικό κείμενο\n" + (" " * 20) + triggered, reason = _stream_detect(text, chunk_size=chunk_size) + assert triggered is True + assert reason == "symbol_garbage" + + +@pytest.mark.parametrize("chunk_size", [1, 2, 4, 11]) +def test_streaming_detector_catches_numeric_list_garbage_across_chunks(chunk_size): + text = " ".join(f"{idx}." for idx in range(1, 25)) + triggered, reason = _stream_detect(text, chunk_size=chunk_size) + assert triggered is True + assert reason == "numeric_list_garbage" + + +def test_streaming_detector_ignores_non_ascii_digit_glyphs(): + triggered, reason = _stream_detect("x³ y² z¹", chunk_size=1) + assert triggered is False + assert reason is None + + +@pytest.mark.parametrize("chunk_size", [1, 3, 9, 23]) +def test_streaming_detector_real_faulty_page_from_downloads(chunk_size): + text = _load_real_markdown_garbage() + triggered, reason = _stream_detect(text, chunk_size=chunk_size) + assert triggered is True + assert reason == "symbol_garbage" + + +@pytest.mark.parametrize("chunk_size", [1, 3, 8, 21]) +def test_streaming_detector_real_empty_page_generation_from_downloads(chunk_size): + text = _load_real_empty_page_numeric_garbage() + triggered, reason = _stream_detect(text, chunk_size=chunk_size) + assert triggered is True + assert reason == "numeric_list_garbage"