From 680392cfc69d61ca1f3f3eb81be9da82865fff48 Mon Sep 17 00:00:00 2001 From: YuvrajSingh-mist Date: Wed, 17 Dec 2025 22:05:46 +0000 Subject: [PATCH] added omnidocbench evals with trm and rope++ --- omnidocbench_evals/.github/workflows/cla.yml | 43 + omnidocbench_evals/.gitignore | 9 + omnidocbench_evals/README.md | 84 + omnidocbench_evals/REPORT.md | 199 ++ omnidocbench_evals/pyproject.toml | 162 ++ omnidocbench_evals/run_evaluation.py | 115 ++ omnidocbench_evals/speedrun.sh | 273 +++ .../DeepSeek-OCR-vllm/config.py | 42 + .../DeepSeek-OCR-vllm/deepencoder/__init__.py | 0 .../deepencoder/build_linear.py | 174 ++ .../deepencoder/clip_sdpa.py | 504 +++++ .../deepencoder/sam_vary_sdpa.py | 528 +++++ .../DeepSeek-OCR-vllm/deepseek_ocr.py | 582 ++++++ .../DeepSeek-OCR-vllm/process/__init__.py | 0 .../process/image_process.py | 502 +++++ .../process/ngram_norepeat.py | 40 + .../run_dpsk_ocr_eval_batch.py | 161 ++ .../OmniDocBench/.gitignore | 20 + .../omnidocbench_evals/OmniDocBench/LICENSE 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omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/ocr_utils.py create mode 100644 omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/read_files.py create mode 100644 omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/table_utils.py create mode 100644 omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/README.md create mode 100644 omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/chandra.py create mode 100644 omnidocbench_evals/src/omnidocbench_evals/tools/convert_jsonl_to_markdown.py diff --git a/omnidocbench_evals/.github/workflows/cla.yml b/omnidocbench_evals/.github/workflows/cla.yml new file mode 100644 index 00000000..17b4d757 --- /dev/null +++ b/omnidocbench_evals/.github/workflows/cla.yml @@ -0,0 +1,43 @@ +name: "OmniDocBench CLA Assistant" +on: + issue_comment: + types: [created] + pull_request_target: + types: [opened,closed,synchronize] + +# explicitly configure permissions, in case your GITHUB_TOKEN workflow permissions are set to read-only in repository settings +permissions: + actions: write + contents: write # this can be 'read' if the signatures are in remote repository + pull-requests: write + statuses: write + +jobs: + CLAAssistant: + runs-on: ubuntu-latest + steps: + - name: "CLA Assistant" + if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I hereby sign the CLA') || github.event_name == 'pull_request_target' + uses: contributor-assistant/github-action@v2.5.0 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # the below token should have repo scope and must be manually added by you in the repository's secret + # This token is required only if you have configured to store the signatures in a remote repository/organization + PERSONAL_ACCESS_TOKEN: ${{ secrets.RELEASE_TOKEN }} + with: + path-to-signatures: 'signatures/version1/cla.json' + path-to-document: 'https://github.com/opendatalab/OmniDocBench/blob/main/OmniDocBench_CLA.md' # e.g. a CLA or a DCO document + # branch should not be protected + branch: 'cla' + allowlist: ouyanglinke,wangbinDL,PrinceVictor,ZZZZZQT + + # the followings are the optional inputs - If the optional inputs are not given, then default values will be taken + #remote-organization-name: enter the remote organization name where the signatures should be stored (Default is storing the signatures in the same repository) + #remote-repository-name: enter the remote repository name where the signatures should be stored (Default is storing the signatures in the same repository) + #create-file-commit-message: 'For example: Creating file for storing CLA Signatures' + #signed-commit-message: 'For example: $contributorName has signed the CLA in $owner/$repo#$pullRequestNo' + #custom-notsigned-prcomment: 'pull request comment with Introductory message to ask new contributors to sign' + #custom-pr-sign-comment: 'The signature to be committed in order to sign the CLA' + #custom-allsigned-prcomment: 'pull request comment when all contributors has signed, defaults to **CLA Assistant Lite bot** All Contributors have signed the CLA.' + #lock-pullrequest-aftermerge: false - if you don't want this bot to automatically lock the pull request after merging (default - true) + #use-dco-flag: true - If you are using DCO instead of CLA diff --git a/omnidocbench_evals/.gitignore b/omnidocbench_evals/.gitignore new file mode 100644 index 00000000..1e0cef15 --- /dev/null +++ b/omnidocbench_evals/.gitignore @@ -0,0 +1,9 @@ +# Ignore local cloned/checked-out subprojects and large folders +...existing code... + +outputs/ +*.pyc +__pycache__/ +.env*/ +.venv*/ +*.txt/ \ No newline at end of file diff --git a/omnidocbench_evals/README.md b/omnidocbench_evals/README.md new file mode 100644 index 00000000..81f5bc32 --- /dev/null +++ b/omnidocbench_evals/README.md @@ -0,0 +1,84 @@ +# DeepSeek-OCR vs OLMOCR-2 Evaluation on OmniDocBench + +This repository provides a comparative evaluation of **DeepSeek-OCR** and **OLMOCR-2** on the **OmniDocBench** benchmark. The evaluation assesses document parsing capabilities across text, formulas, tables, and reading order. + +## Overview + +- **DeepSeek-OCR**: A vLLM-based multimodal pipeline for document understanding. +- **OLMOCR-2**: An efficient OCR system using open visual language models. +- **OmniDocBench**: A comprehensive benchmark with 1,355 annotated PDF pages covering diverse document types. + +## Quick Start + +The easiest way to set up and run the complete evaluation is using the automated speedrun script: + +(Use 1xH100 80GB SXM and we used Lambda Labs Lambda Stack 22.04 image for this setup) + +```bash +./speedrun.sh [language_filter] +``` + +**Options:** +- `all` - Evaluate on all languages (default) +- `english` - Evaluate on English documents only +- `simplified_chinese` - Evaluate on simplified Chinese documents only + +**Example:** +```bash +./speedrun.sh all +``` + +This script will automatically: +1. Download the OmniDocBench dataset +2. Set up all three OCR environments (DeepSeek-OCR, OLMOCR-2, Chandra OCR) +3. Run inference on all models +4. Execute comprehensive evaluation +5. Generate comparison results + +### Environment Requirements + +- CUDA 11.8+ with torch 2.6.0+ +- Conda/Miniconda installed +- At least 60GB free disk space for models and data +- For Chandra OCR: `DATALAB_API_KEY` environment variable (optional) +## Results + +After evaluation, results are stored in `OmniDocBench/result/` and a summary CSV is generated at `results_${LANGUAGE_FILTER}.csv`. + +The speedrun script will automatically generate: +- **CSV Summary**: `results_all.csv` (or `results_english.csv`, `results_simplified_chinese.csv` based on language filter) +- **Detailed Metrics**: `src/omnidocbench_evals/OmniDocBench/result/` +- **Model Outputs**: + - DeepSeek-OCR: `outputs/deepseek_ocr/` + - OLMOCR-2: `outputs/olmocr_workspace/markdown/` + - Chandra OCR: `outputs/chandra_ocr/` + +### Key Metrics + +- **Text Accuracy**: Normalized edit distance +- **Formula Accuracy**: Edit distance score +- **Table TEDS Score**: Table structure evaluation +- **Reading Order Accuracy**: Document flow evaluation +- **Overall Score**: `((1 - text_edit) × 100 + table_teds + (1 - edit_distance) × 100) / 3` + +See [`REPORT.md`](REPORT.md) for detailed results and visualizations from our evaluation runs. + +## Troubleshooting + +### CUDA/Memory Issues +- Ensure CUDA 11.8+ is installed: `nvidia-smi` +- Check available GPU memory: `nvidia-smi` +- Reduce batch size if out of memory + +### Dataset Download Issues +- Set HuggingFace token: `export HF_TOKEN="your_token_here"` +- Check internet connection and rate limits + +### Environment Conflicts +- Each OCR system runs in a separate conda environment to avoid conflicts +- Do not mix environments manually + +## Data + +The dataset is automatically downloaded from [OmniDocBench on HuggingFace](https://huggingface.co/datasets/opendatalab/OmniDocBench) to `data/OmniDocBench/` when running the speedrun script. + diff --git a/omnidocbench_evals/REPORT.md b/omnidocbench_evals/REPORT.md new file mode 100644 index 00000000..89349fad --- /dev/null +++ b/omnidocbench_evals/REPORT.md @@ -0,0 +1,199 @@ +# DeepSeek OCR Evaluation Report + +## TL;DR + +This report evaluates the performance of **DeepSeek OCR** (a vLLM-based multimodal pipeline) against **OLmOCR-2** on the OmniDocBench end-to-end benchmark, using 1,355 annotated PDF pages. DeepSeek OCR achieves an overall accuracy of **84.24%**, slightly outperforming OLmOCR-2's **81.56%**, though the difference is not statistically significant (p ≈ 0.305). Key strengths include excellent text and table recovery, with formula parsing as the primary weakness. Confidence intervals are computed using the Wald approximation (z ≈ 1.95). + + +### Direct Comparison: DeepSeek OCR vs OLMOCR-2 + +![DeepSeek OCR vs OLMOCR-2](assets/DeepSeek-OCV%20vs%20Olmocr2.png) + +*Figure: Comparative performance analysis of DeepSeek OCR and OLMOCR-2 on OmniDocBench end-to-end evaluation, highlighting strengths in text recovery and areas for improvement in formula parsing.* + + +## Introduction + +DeepSeek OCR leverages a multimodal language model with image tokenization to extract structured content from documents. This evaluation compares it head-to-head with OLmOCR-2 on OmniDocBench, a comprehensive benchmark for document understanding. Metrics include normalized edit distances for text, formulas, and reading order, plus TEDS scores for tables. + +### Dataset and Setup +- **Benchmark**: OmniDocBench end-to-end evaluation. +- **Sample Size**: 1,355 pages. +- **Systems Compared**: DeepSeek OCR (vLLM pipeline), OLmOCR-2. +- **Metrics Source**: Aggregated results from `results_dpsk-cleaned_quick_match_metric_result.json` and per-page JSONs in `./result/`. + + + +## Definitions + +- **Normalized edit distance**: Measures string dissimilarity (0 = perfect match, 1 = completely different). +- **Accuracy (1 − Edit_dist)**: Proportion correct, e.g., 0.926 = 92.6%. +- **TEDS**: Table Edit Distance-based Similarity (0–100%, higher = better). +- **Table TEDS (structure-only)**: TEDS based only on table layout, ignoring content. +- **Reading-order edit distance**: Dissimilarity in predicted vs. ground-truth reading sequences. +- **Overall score**: Weighted aggregate from OmniDocBench. + +## Visual Comparisons + + +### Overall Performance + +| | | +|-:|:-| +| ![DeepSeek overall](assets/ocr/End2End_OmniDocBench_deepseek_ocr_overall.png) | ![OLmOCR-2 overall](assets/ocr/End2End_OmniDocBench_olmo_ocr_2_overall.png) | +| DeepSeek OCR Overall | OLmOCR-2 Overall | + + +### Language Breakdown — English + +| | | +|-:|:-| +| ![DeepSeek english](assets/ocr/End2End_OmniDocBench_deepseek_ocr_english.png) | ![OLmOCR-2 english](assets/ocr/End2End_OmniDocBench_olmo_ocr_2_english.png) | +| DeepSeek OCR English | OLmOCR-2 English | + +### Language Breakdown — Chinese + +| | | +|-:|:-| +| ![DeepSeek chinese](assets/ocr/End2End_OmniDocBench_deepseek_ocr_chinese.png) | ![OLmOCR-2 chinese](assets/ocr/End2End_OmniDocBench_olmo_ocr_2_chinese.png) | +| DeepSeek OCR Chinese | OLmOCR-2 Chinese | + +### Example Outputs + +| | | +|-:|:-| +| ![Example 1](assets/show1.jpg) | ![Example 2](assets/show2.jpg) | +| Sample 1 | Sample 2 | +| ![Example 3](assets/show3.jpg) | ![Example 4](assets/show4.jpg) | +| Sample 3 | Sample 4 | + +## Methodology Details + +- **CI Calculation**: Wald approximation with z = 1.95 for 95% bands. SE = sqrt(p(1-p)/n), ME = z × SE. +- **Example**: For text-block accuracy (p = 0.926, n = 1355), SE ≈ 0.00711, ME ≈ 0.01386, CI = [0.912, 0.940]. +- **Data Sources**: OmniDocBench pipeline outputs; per-page JSONs for potential deeper analysis. +- **Metric choice (Edit Distance vs CDM)**: For text, formula and reading-order we used normalized Edit Distance (reported as `Edit_dist` and presented as accuracy = 1 − Edit_dist) rather than the CDM metric. Edit Distance is simple, interpretable, and directly available from the OmniDocBench outputs; CDM (Content Distance Metric) is an alternative that emphasizes token-level content differences and could be used in follow-up analyses. + +## Statistical Test: Overall Performance Comparison + +We conducted a z-test for proportions to compare overall accuracies. + +- **z-statistic**: -1.025 +- **p-value**: 0.305 + +**Interpretation**: No significant difference (p > 0.05). DeepSeek's slight edge (84.24% vs. 81.56%) is not conclusive. + +**Test Details**: +- Dataset: Full OmniDocBench set (n = 1,355). +- Observed: DeepSeek = 84.24% (≈1,141 successes), OLmOCR-2 = 81.56% (≈1,105 successes). + +| System | Overall (%) | Successes (approx) | +|---|---:|---:| +| DeepSeek OCR | 84.24 | 1,141 | +| OLmOCR-2 | 81.56 | 1,105 | + + + +## Conclusion + +DeepSeek OCR demonstrates strong document understanding capabilities, particularly for text and tables, on OmniDocBench. While not significantly outperforming OLmOCR-2 in this evaluation, its performance is competitive and reliable. Future work should address formula weaknesses and employ advanced statistical methods for comparisons. + +*Report generated on 2025-10-29. Data from OmniDocBench evaluation.* + +## Definitions + +- Normalized edit distance: A length-normalized measure of how different a predicted string is from the ground truth (0 = identical, 1 = completely different). +- Accuracy (1 − Edit_dist): The complement of normalized edit distance; the proportion correct shown as a percentage (e.g., 0.926 → 92.6%). +- TEDS: Table Edit Distance-based Similarity — a table-quality score derived from tree-edit-distance between predicted and ground-truth table structures and content (reported 0–100%, higher is better). +- Table TEDS (structure-only): The TEDS score computed using only the table’s structural/layout information (cell positions and nesting), ignoring cell text. +- Reading-order edit distance: Normalized edit distance computed on the predicted vs. reference reading-order sequence; lower is better (often reported as accuracy = 1 − value). +- Overall score: The aggregate document-level score from the evaluation run (a summary metric reported by OmniDocBench combining components). + + diff --git a/omnidocbench_evals/pyproject.toml b/omnidocbench_evals/pyproject.toml new file mode 100644 index 00000000..ed1a7980 --- /dev/null +++ b/omnidocbench_evals/pyproject.toml @@ -0,0 +1,162 @@ +[build-system] +requires = ["setuptools>=65.0", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "omnidocbench-evals" +version = "0.1.0" +description = "Evaluation scripts for OmniDocBench OCR models" +readme = "README.md" +license = {text = "MIT"} +authors = [ + {name = "alphaXiv"} +] + +dependencies = [ + "PyMuPDF", + "tqdm", + "jupyter", + "pandas", + "matplotlib", + "datasets", +] + +[project.optional-dependencies] +deepseek-ocr = [ + "torch==2.6.0", + "torchvision==0.21.0", + "torchaudio==2.6.0", + "vllm @ https://github.com/vllm-project/vllm/releases/download/v0.8.5/vllm-0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl", + "transformers", + "tokenizers", + "img2pdf", + "einops", + "easydict", + "addict", + "Pillow", + "numpy", + +] + +chandra-ocr = [ + "chandra-ocr", +] + +omnidocbench = [ + "aiohappyeyeballs==2.4.3", + "aiohttp==3.10.11", + "aiosignal==1.3.1", + "apted==1.0.3", + "argon2-cffi==21.3.0", + "argon2-cffi-bindings==21.2.0", + "async-timeout==5.0.1", + "attrs==24.2.0", + "backcall==0.2.0", + "beautifulsoup4==4.11.1", + "bleach==5.0.1", + "category-encoders==2.5.0", + "certifi==2024.8.30", + "charset-normalizer==3.4.0", + "click==8.1.7", + "contourpy==1.1.1", + "cycler==0.12.1", + "datasets==3.1.0", + "debugpy==1.6.3", + "decorator==5.1.1", + "defusedxml==0.7.1", + "dill==0.3.8", + "entrypoints==0.4", + "evaluate==0.4.3", + "fastjsonschema==2.16.2", + "filelock==3.16.1", + "fonttools==4.55.0", + "frozenlist==1.5.0", + "fsspec==2024.9.0", + "func-timeout==4.3.5", + "huggingface-hub==0.26.2", + "idna==3.10", + "importlib_resources==6.4.5", + "ipykernel==6.16.0", + "ipython==7.34.0", + "ipython-genutils==0.2.0", + "ipywidgets==7.7.2", + "jedi==0.18.1", + "Jinja2==3.1.2", + "joblib==1.2.0", + "jsonpickle==2.2.0", + "jupyterlab-pygments==0.2.2", + "jupyterlab-widgets==1.1.1", + "kiwisolver==1.4.7", + "Levenshtein==0.25.1", + "loguru==0.7.2", + "lxml==4.9.1", + "markdown-it-py==3.0.0", + "matplotlib==3.7.5", + "matplotlib-inline==0.1.6", + "mdurl==0.1.2", + "mistune==2.0.4", + "mmeval==0.2.1", + "multidict==6.1.0", + "multiprocess==0.70.16", + "nbclient==0.6.8", + "nbconvert==7.0.0", + "nbformat==5.6.1", + "nest-asyncio==1.5.5", + "nltk==3.9.1", + "notebook==6.4.12", + "numpy==1.24.4", + "opencv-python==4.10.0.84", + "packaging==24.2", + "pandas==2.0.3", + "pandocfilters==1.5.0", + "parso==0.8.3", + "patsy==0.5.2", + "pexpect==4.8.0", + "pickleshare==0.7.5", + "pillow==10.4.0", + "pkgutil_resolve_name==1.3.10", + "plotly==5.5.0", + "plum-dispatch==1.7.4", + "prometheus-client==0.14.1", + "prompt-toolkit==3.0.31", + "propcache==0.2.0", + "psutil==5.9.2", + "ptyprocess==0.7.0", + "pyarrow==17.0.0", + "Pygments==2.18.0", + "pylatexenc==3.0a30", + "PyNomaly==0.3.4", + "pyparsing==3.1.4", + "pyrsistent==0.18.1", + "python-dateutil==2.9.0.post0", + "python-utils==3.3.3", + "pytz==2024.2", + "PyYAML==6.0.2", + "rapidfuzz==3.9.7", + "regex==2024.11.6", + "requests==2.32.3", + "rich==13.9.4", + "scikit-learn==1.1.2", + "scipy==1.10.1", + "Send2Trash==1.8.0", + "six==1.16.0", + "soupsieve==2.3.2.post1", + "statsmodels==0.13.2", + "tabulate==0.9.0", + "tenacity==8.1.0", + "terminado==0.15.0", + "threadpoolctl==3.1.0", + "tinycss2==1.1.1", + "tornado==6.2", + "tqdm==4.67.1", + "typing_extensions==4.12.2", + "tzdata==2024.2", + "urllib3==2.2.3", + "webencodings==0.5.1", + "widgetsnbextension==3.6.1", + "xxhash==3.5.0", + "yarl==1.15.2", + "zipp==3.20.2", + "pycocotools==2.0.7", + "scikit-image==0.21.0", +] \ No newline at end of file diff --git a/omnidocbench_evals/run_evaluation.py b/omnidocbench_evals/run_evaluation.py new file mode 100644 index 00000000..d8f424b1 --- /dev/null +++ b/omnidocbench_evals/run_evaluation.py @@ -0,0 +1,115 @@ +import os +import pandas as pd +import numpy as np +import json +import sys +import argparse + + +def process_args(args): + parser = argparse.ArgumentParser(description='Process evaluation results and generate summary DataFrame.') + parser.add_argument('--result-dir', type=str, default='./src/omnidocbench_evals/OmniDocBench/result', + help='Path to result directory containing metric_result.json files') + parser.add_argument('--ocr-types', type=str, nargs='+', default=['deepseek', 'olmocr2'], + help='List of OCR types to process (default: deepseek olmocr2)') + parser.add_argument('--language', type=str, default='all', + help='Language filter (all, english, simplified_chinese)') + parser.add_argument('--match-method', type=str, default='quick_match', + help='Match method (default: quick_match)') + parser.add_argument('--output', type=str, default=None, + help='Output CSV file path (optional)') + parameters = parser.parse_args(args) + return parameters + + +def load_and_process_results(result_dir, ocr_types, language, match_method): + """Load metric results and process them into a DataFrame.""" + dict_list = [] + ocr_names = [] + + for ocr_type in ocr_types: + # Construct file name based on naming convention: ocr_type_language_match_method_metric_result.json + result_filename = f'{ocr_type}_{language}_{match_method}_metric_result.json' + result_path = os.path.join(result_dir, result_filename) + + print(f"Loading: {result_path}") + + if not os.path.exists(result_path): + print(f"Warning: Result file not found: {result_path}") + continue + + try: + with open(result_path, 'r', encoding='utf-8') as f: + result = json.load(f) + except Exception as e: + print(f"Error loading {result_path}: {e}") + continue + + save_dict = {} + + # Extract metrics for each category + for category_type, metric in [("text_block", "Edit_dist"), + ("display_formula", "Edit_dist"), + ("table", "TEDS"), + ("table", "TEDS_structure_only"), + ("reading_order", "Edit_dist")]: + try: + if metric == 'CDM' or metric == "TEDS" or metric == "TEDS_structure_only": + if result.get(category_type, {}).get("page", {}).get(metric): + save_dict[category_type+'_'+metric] = result[category_type]["page"][metric]["ALL"] * 100 + else: + save_dict[category_type+'_'+metric] = np.nan + else: + all_metric = result.get(category_type, {}).get("all", {}).get(metric, {}) + save_dict[category_type+'_'+metric] = all_metric.get("ALL_page_avg", np.nan) + except Exception as e: + print(f"Error processing {category_type}_{metric}: {e}") + save_dict[category_type+'_'+metric] = np.nan + + dict_list.append(save_dict) + ocr_names.append(ocr_type) + + if not dict_list: + print("Error: No valid result files found!") + return None + + # Create DataFrame + df = pd.DataFrame(dict_list, index=ocr_names).round(3) + + # Calculate overall score + df['overall'] = ((1 - df['text_block_Edit_dist']/100) * 100 + + (1 - df['display_formula_Edit_dist']/100) * 100 + + df['table_TEDS']) / 3 + + return df + + +if __name__ == '__main__': + parameters = process_args(sys.argv[1:]) + + print("="*80) + print("OmniDocBench Evaluation Results Summary") + print("="*80) + print(f"Result Directory: {parameters.result_dir}") + print(f"OCR Types: {', '.join(parameters.ocr_types)}") + print(f"Language Filter: {parameters.language}") + print(f"Match Method: {parameters.match_method}") + print("="*80) + + df = load_and_process_results(parameters.result_dir, + parameters.ocr_types, + parameters.language, + parameters.match_method) + + if df is not None: + print("\n") + print(df.to_string()) + print("\n") + + # Save to CSV if specified + if parameters.output: + df.to_csv(parameters.output) + print(f"Results saved to: {parameters.output}") + + print("="*80) + diff --git a/omnidocbench_evals/speedrun.sh b/omnidocbench_evals/speedrun.sh new file mode 100755 index 00000000..e9e10188 --- /dev/null +++ b/omnidocbench_evals/speedrun.sh @@ -0,0 +1,273 @@ +#!/bin/bash + +# OmniDocBench-Evals Speedrun Script +# Sets up and runs evaluation for DeepSeek-OCR, OLMOCR-2, and Chandra OCR on OmniDocBench +# Usage: ./speedrun.sh [language_filter] +# Options: all, english, simplified_chinese (default: all) + +set -e + +# Parse language filter argument +LANGUAGE_FILTER="${1:-all}" + +if [[ ! "$LANGUAGE_FILTER" =~ ^(all|english|simplified_chinese)$ ]]; then + echo "Invalid language filter: $LANGUAGE_FILTER" + echo "Usage: ./speedrun.sh [all|english|simplified_chinese]" + echo "Default: all" + exit 1 +fi + +echo "==========================================" +echo "OmniDocBench-Evals Setup and Evaluation" +echo "Language Filter: $LANGUAGE_FILTER" +echo "==========================================" + +# Check if conda is installed +if ! command -v conda &> /dev/null; then + echo "Conda not found. Please install Miniconda or Anaconda from https://docs.conda.io/projects/miniconda/en/latest/" + echo "After installation, run: conda init bash && source ~/.bashrc" + exit 1 +else + echo "Conda is already installed." +fi + +# Ensure conda is available +export PATH="$HOME/miniconda/bin:$PATH" + +# Create data directory +mkdir -p data + +# Check for HF_TOKEN +if [ -z "$HF_TOKEN" ]; then + echo "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads." +fi + +# Download OmniDocBench dataset +echo "Downloading OmniDocBench dataset..." +conda run -n base pip install huggingface_hub +python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='opendatalab/OmniDocBench', repo_type='dataset', local_dir='data/OmniDocBench')" +echo "Dataset downloaded to data/OmniDocBench" + +# Convert images to PDFs for OLMOCR +echo "Converting images to PDFs for OLMOCR..." +# Install basic dependencies +conda run -n base pip install PyMuPDF tqdm + +# Get absolute path +ABS_PATH=$(pwd) + +# Assume the images are in data/OmniDocBench/images/ +# Edit the script to set input_dir and output_dir + +sed -i "s|input_directory = .*|input_directory = \"$ABS_PATH/data/OmniDocBench/images/\"|" src/omnidocbench_evals/OmniDocBench/tools/image_to_pdf.py +sed -i "s|output_directory = .*|output_directory = \"$ABS_PATH/data/OmniDocBench/pdfs/\"|" src/omnidocbench_evals/OmniDocBench/tools/image_to_pdf.py +python src/omnidocbench_evals/OmniDocBench/tools/image_to_pdf.py +echo "PDFs created." + +# Setup DeepSeek-OCR +echo "Setting up DeepSeek-OCR..." + +conda create -n deepseek-ocr python=3.12.9 -y +conda run -n deepseek-ocr pip install -e .[deepseek-ocr] --extra-index-url https://download.pytorch.org/whl/cu118 +conda run -n deepseek-ocr pip install flash_attn==2.7.3 --no-build-isolation + +cd src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm +# Configure config.py with absolute paths +DEEPSEEK_ABS_PATH="$ABS_PATH/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm" +sed -i "s|INPUT_PATH = 'data/OmniDocBench/images/'|INPUT_PATH = '$ABS_PATH/data/OmniDocBench/images/'|" config.py +sed -i "s|OUTPUT_PATH = 'outputs/deepseek_ocr/'|OUTPUT_PATH = '$ABS_PATH/outputs/deepseek_ocr/'|" config.py +echo "DeepSeek-OCR setup complete." + +# Run DeepSeek-OCR inference +echo "Running DeepSeek-OCR inference..." +conda run -n deepseek-ocr python run_dpsk_ocr_eval_batch.py +echo "DeepSeek-OCR inference complete. Outputs in outputs/deepseek_ocr/" + +echo "Cleaning up the outputs/deepseek_ocr/ directory..." +rm -f $ABS_PATH/outputs/deepseek_ocr/*.png + +echo "Cleaning complete. Now setting up olmOCR2.." + +# Setup OLMOCR +echo "Setting up OLMOCR..." +sudo apt-get update +sudo apt-get install -y poppler-utils ttf-mscorefonts-installer msttcorefonts fonts-crosextra-caladea fonts-crosextra-carlito gsfonts lcdf-typetools +conda create -n olmocr python=3.11 -y +# Install olmocr from PyPI +conda run -n olmocr pip install olmocr +conda run -n olmocr pip install vllm==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu128 + +echo "OLMOCR setup complete." + +# Run OLMOCR inference +echo "Running OLMOCR inference..." +# Assume PDFs are in data/OmniDocBench/pdfs/ (created by image_to_pdf.py) +mkdir -p $ABS_PATH/outputs/olmocr_workspace +# Create a text file listing all PDFs +find $ABS_PATH/data/OmniDocBench/pdfs/ -name "*.pdf" -type f > $ABS_PATH/outputs/olmocr_workspace/pdf_list.txt +echo "Found $(wc -l < $ABS_PATH/outputs/olmocr_workspace/pdf_list.txt) PDF files to process" +# Use eval to properly activate the conda environment so vllm is in PATH +eval "$(conda shell.bash hook)" +conda activate olmocr +python -m olmocr.pipeline $ABS_PATH/outputs/olmocr_workspace --markdown --pdfs $ABS_PATH/outputs/olmocr_workspace/pdf_list.txt +conda deactivate +echo "OLMOCR inference complete. Converting JSONL to markdown..." +# Convert JSONL results to markdown files +python $ABS_PATH/src/omnidocbench_evals/tools/convert_jsonl_to_markdown.py +echo "Markdown files created in outputs/olmocr_workspace/markdown/" + +cd $ABS_PATH + +echo "==========================================" + +# Setup Chandra OCR +echo "Setting up Chandra OCR..." +# Reuse olmocr env to avoid conflicts +conda run -n olmocr pip install -e .[chandra-ocr] +cd $ABS_PATH/src/omnidocbench_evals/chandra-ocr +echo "Chandra OCR setup complete." + +# Check for API key +if [ -z "$DATALAB_API_KEY" ]; then + echo "WARNING: DATALAB_API_KEY not set. Please set it with: export DATALAB_API_KEY='your_key'" + echo "Skipping Chandra OCR for now." +else + # Run Chandra OCR + echo "Running Chandra OCR..." + # Assume chandra.py is configured for images in ../../../data/OmniDocBench/images/ + conda run -n olmocr python chandra.py + echo "Chandra OCR complete. Check outputs for results." +fi + +cd $ABS_PATH + +# Evaluation +echo "==========================================" +echo "Starting Evaluation on OmniDocBench..." + +conda create -n omnidocbench-eval python=3.10 -y +conda run -n omnidocbench-eval pip install -e .[omnidocbench] +eval "$(conda shell.bash hook)" +conda activate omnidocbench-eval +echo "Evaluation environment setup complete." + +echo "==========================================" +echo "EVALUATION PHASE" +echo "==========================================" + +echo "" +echo "Running Evaluation on OmniDocBench dataset..." +echo "Language filter: $LANGUAGE_FILTER" +echo "" + +cd $ABS_PATH/src/omnidocbench_evals/OmniDocBench + +# ========================================== +# DEEPSEEK-OCR EVALUATION +# ========================================== +echo "Starting DeepSeek-OCR Evaluation..." +echo "Output directory: $ABS_PATH/outputs/deepseek_ocr/" + +# Update config paths for DeepSeek results +sed -i "s|data_path: .*/OmniDocBench.json|data_path: $ABS_PATH/data/OmniDocBench/OmniDocBench.json|" configs/end2end.yaml +sed -i "s|data_path: output_results_markdown|data_path: $ABS_PATH/outputs/deepseek_ocr/|" configs/end2end.yaml + +# Apply language filter to configuration +if [ "$LANGUAGE_FILTER" != "all" ]; then + echo " → Filtering to: $LANGUAGE_FILTER" + sed -i "s|# filter:|filter:|" configs/end2end.yaml + sed -i "s|# language: english| language: $LANGUAGE_FILTER|" configs/end2end.yaml +else + echo " → Including all languages" + sed -i "s|filter:|# filter:|" configs/end2end.yaml + sed -i "s| language: english|# language: english|" configs/end2end.yaml + sed -i "s| language: simplified_chinese|# language: simplified_chinese|" configs/end2end.yaml +fi + +# Execute DeepSeek evaluation +if [ -f "pdf_validation.py" ]; then + echo " → Running pdf_validation with DeepSeek-OCR results..." + python pdf_validation.py --config configs/end2end.yaml --ocr-type deepseek_ocr --language "$LANGUAGE_FILTER" + echo "✓ DeepSeek-OCR evaluation complete" +else + echo "✗ pdf_validation.py not found. Please run evaluation manually." +fi + +echo "" + +# ========================================== +# OLMOCR2 EVALUATION +# ========================================== +echo "Starting OLMOCR2 Evaluation..." +echo "Output directory: $ABS_PATH/outputs/olmocr_workspace/markdown/" + +# Update config paths for OLMOCR results +sed -i "s|data_path: .*/OmniDocBench.json|data_path: $ABS_PATH/data/OmniDocBench/OmniDocBench.json|" configs/end2end.yaml +sed -i "s|data_path: output_results_markdown|data_path: $ABS_PATH/outputs/olmocr_workspace/markdown/|" configs/end2end.yaml + +# Apply language filter to configuration +if [ "$LANGUAGE_FILTER" != "all" ]; then + echo " → Filtering to: $LANGUAGE_FILTER" + sed -i "s|# filter:|filter:|" configs/end2end.yaml + sed -i "s|# language: english| language: $LANGUAGE_FILTER|" configs/end2end.yaml +else + echo " → Including all languages" + sed -i "s|filter:|# filter:|" configs/end2end.yaml + sed -i "s| language: english|# language: english|" configs/end2end.yaml + sed -i "s| language: simplified_chinese|# language: simplified_chinese|" configs/end2end.yaml +fi + +# Execute OLMOCR2 evaluation +if [ -f "pdf_validation.py" ]; then + echo " → Running pdf_validation with OLMOCR2 results..." + python pdf_validation.py --config configs/end2end.yaml --ocr-type olmocr2 --language "$LANGUAGE_FILTER" + echo "✓ OLMOCR2 evaluation complete" +else + echo "✗ pdf_validation.py not found. Please run evaluation manually." +fi + +echo "" + + +echo "==========================================" +echo "Setup and Evaluation Complete!" +echo "==========================================" +echo "" +echo "Evaluation results saved to:" +echo " • Metric files: src/omnidocbench_evals/OmniDocBench/result/" +echo " • Summary CSV: results_${LANGUAGE_FILTER}.csv" +echo "" + +# ========================================== +# RESULTS SUMMARY +# ========================================== +echo "" +echo "==========================================" +echo "GENERATING RESULTS SUMMARY" +echo "==========================================" +echo "" +echo "Aggregating metrics from both OCR systems..." +echo "Language filter: $LANGUAGE_FILTER" +echo "" + +# Navigate back to omnidocbench_evals root directory +cd $ABS_PATH + +# Generate comprehensive results summary +python run_evaluation.py \ + --result-dir "$ABS_PATH/src/omnidocbench_evals/OmniDocBench/result" \ + --ocr-types deepseek olmocr2 \ + --language "$LANGUAGE_FILTER" \ + --match-method quick_match \ + --output "$ABS_PATH/results_${LANGUAGE_FILTER}.csv" + +echo "" +echo "==========================================" +echo "✓ EVALUATION WORKFLOW COMPLETE!" +echo "==========================================" +echo "" +echo "Results Summary:" +echo " • CSV File: results_${LANGUAGE_FILTER}.csv" +echo " • Detailed Metrics: src/omnidocbench_evals/OmniDocBench/result/" +echo " • Language Filter Applied: $LANGUAGE_FILTER" +echo "" \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py new file mode 100644 index 00000000..26238401 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py @@ -0,0 +1,42 @@ +# TODO: change modes +# Tiny: base_size = 512, image_size = 512, crop_mode = False +# Small: base_size = 640, image_size = 640, crop_mode = False +# Base: base_size = 1024, image_size = 1024, crop_mode = False +# Large: base_size = 1280, image_size = 1280, crop_mode = False +# Gundam: base_size = 1024, image_size = 640, crop_mode = True + +BASE_SIZE = 1024 +IMAGE_SIZE = 640 +CROP_MODE = True +MIN_CROPS= 2 +MAX_CROPS= 6 # max:9; If your GPU memory is small, it is recommended to set it to 6. +MAX_CONCURRENCY = 512 # If you have limited GPU memory, lower the concurrency count. +NUM_WORKERS = 64 # image pre-process (resize/padding) workers +PRINT_NUM_VIS_TOKENS = False +SKIP_REPEAT = True +MODEL_PATH = 'deepseek-ai/DeepSeek-OCR' # change to your model path + +# TODO: change INPUT_PATH +# .pdf: run_dpsk_ocr_pdf.py; +# .jpg, .png, .jpeg: run_dpsk_ocr_image.py; +# Omnidocbench images path: run_dpsk_ocr_eval_batch.py + +INPUT_PATH = '../../../../data/OmniDocBench/images/' +OUTPUT_PATH = '../../../../outputs/deepseek_ocr/' + +PROMPT = '\n<|grounding|>Convert the document to markdown.' +# PROMPT = '\nFree OCR.' +# TODO commonly used prompts +# document: \n<|grounding|>Convert the document to markdown. +# other image: \n<|grounding|>OCR this image. +# without layouts: \nFree OCR. +# figures in document: \nParse the figure. +# general: \nDescribe this image in detail. +# rec: \nLocate <|ref|>xxxx<|/ref|> in the image. +# '先天下之忧而忧' +# ....... + + +from transformers import AutoTokenizer + +TOKENIZER = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True) diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/__init__.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/build_linear.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/build_linear.py new file mode 100644 index 00000000..47dcfc2f --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/build_linear.py @@ -0,0 +1,174 @@ +import torch.nn as nn +import torch +import torch.nn.functional as F +import copy + + +class MlpProjector(nn.Module): + + def __init__(self, cfg): + + super().__init__() + + self.cfg = cfg + + if cfg.projector_type == "identity": + modules = nn.Identity() + + elif cfg.projector_type == "linear": + modules = nn.Linear(cfg.input_dim, cfg.n_embed) + + elif cfg.projector_type == "mlp_gelu": + mlp_depth = cfg.get("depth", 1) + modules = [nn.Linear(cfg.input_dim, cfg.n_embed)] + for _ in range(1, mlp_depth): + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed, cfg.n_embed)) + modules = nn.Sequential(*modules) + + elif cfg.projector_type == "normlayer_downsample_mlp_gelu": + mlp_depth = cfg.get("depth", 1) + mlp_ratio = cfg.get("mlp_ratio", 1) + modules = [ + nn.LayerNorm(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio), + nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio) + ] + for _ in range(1, mlp_depth - 1): + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio)) + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed)) + modules = nn.Sequential(*modules) + + elif cfg.projector_type == "downsample_mlp_gelu": + mlp_depth = cfg.get("depth", 1) + mlp_ratio = cfg.get("mlp_ratio", 1) + modules = [nn.Linear(cfg.input_dim * cfg.downsample_ratio * cfg.downsample_ratio, cfg.n_embed * mlp_ratio)] + for _ in range(1, mlp_depth - 1): + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed * mlp_ratio)) + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed * mlp_ratio, cfg.n_embed)) + modules = nn.Sequential(*modules) + + elif cfg.projector_type == "low_high_hybrid_split_mlp_gelu": + mlp_depth = cfg.get("depth", 1) + self.high_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2) + self.low_up_proj = nn.Linear(cfg.input_dim, cfg.n_embed // 2) + + modules = [] + for _ in range(1, mlp_depth): + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed, cfg.n_embed)) + modules = nn.Sequential(*modules) + + elif cfg.projector_type == "hybrid_split_feature_mlp_gelu": + mlp_depth = cfg.get("depth", 1) + channel_div = cfg.get("channel_div", 0.5) + self.high_up_proj = nn.Linear(cfg.input_dim[0], int(cfg.n_embed * channel_div)) + self.low_up_proj = nn.Linear(cfg.input_dim[1], cfg.n_embed - int(cfg.n_embed * channel_div)) + + modules = [] + for _ in range(1, mlp_depth): + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed, cfg.n_embed)) + modules = nn.Sequential(*modules) + + elif cfg.projector_type == "low_high_split_mlp_gelu": + mlp_depth = cfg.get("depth", 1) + modules = [] + for _ in range(1, mlp_depth): + modules.append(nn.GELU()) + modules.append(nn.Linear(cfg.n_embed // 2, cfg.n_embed // 2)) + modules = nn.Sequential(*modules) + self.high_layers = nn.Sequential(*modules) + self.low_layers = copy.deepcopy(modules) + + else: + raise ValueError(f"Unknown projector type: {cfg.projector_type}") + + if cfg.get("token_pooling", False): + self.token_pooling_layer = nn.Linear(cfg.input_dim * 4, cfg.input_dim) + + if cfg.get("conv_fusion_high_low_features", False): + self.fusion_layer = nn.Linear(cfg.input_dim, cfg.input_dim) + self.layers = modules + + def forward(self, x): + if self.cfg.get("token_pooling", False): + batch_size, wxh, channels = x.shape + w = h = int(wxh**0.5) + x = x.view(batch_size, w, h, channels) + x = x.permute(0, 3, 1, 2) + # import ipdb; ipdb.set_trace() + patches = x.unfold(2, 2, 2).unfold(3, 2, 2) + batch_size, channels, h_patches, w_patches, _, _ = patches.size() + # 在通道维度上拼接 + patches = patches.contiguous().view(batch_size, channels, h_patches * w_patches, -1) + + # 通过线性层 + patches = patches.permute(0, 2, 1, 3).contiguous() + patches = patches.view(batch_size, h_patches * w_patches, channels * 4) + + x = self.token_pooling_layer(patches) + + if self.cfg.get("conv_fusion_high_low_features", False): + x = self.fusion_layer(x[:, 0]) + x[:, 1] + + if self.cfg.projector_type == 'low_high_hybrid_split_mlp_gelu': + high_x, low_x = x[0], x[1] + high_x = self.high_up_proj(high_x) + low_x = self.low_up_proj(low_x) + x = torch.concat([high_x, low_x], dim=-1) + + if self.cfg.projector_type == 'hybrid_split_feature_mlp_gelu': + high_x = x[...,:self.cfg.input_dim[0]] + low_x = x[...,self.cfg.input_dim[0]:] + high_x = self.high_up_proj(high_x) + low_x = self.low_up_proj(low_x) + x = torch.concat([high_x, low_x], dim=-1) + + if self.cfg.projector_type == 'low_high_split_mlp_gelu': + high_x, low_x = x[0], x[1] + high_x = self.high_layers(high_x) + low_x = self.low_layers(low_x) + x = torch.concat([high_x, low_x], dim=-1) + return x + + if self.cfg.projector_type == 'downsample_mlp_gelu' or self.cfg.projector_type == 'normlayer_downsample_mlp_gelu': + bs, hw, input_dim = x.shape + h = w = int((hw) ** 0.5) + + """compute padding""" + if h % self.cfg.downsample_ratio: + pad = self.cfg.downsample_ratio - h % self.cfg.downsample_ratio + else: + pad = 0 + x = x.reshape(bs, h, w, input_dim) + if pad > 0: + x = F.pad(x, (0, 0, 0, pad, 0, pad), "constant", 0) + + """4 to 1 concat""" + x = x.permute(0, 3, 1, 2) # B, C, H, W + x = F.unfold(x, kernel_size=self.cfg.downsample_ratio, stride=self.cfg.downsample_ratio, padding=0) # B, C*4, HW // 4 + x = x.permute(0, 2, 1) + + return self.layers(x) + + @staticmethod + def get_flops_per_sample(cfg): + if cfg.projector_type == "linear": + fwd = 2 * cfg.input_dim * cfg.n_embed + + elif "mlp_gelu" in cfg.projector_type : + mlp_depth = cfg.get("depth", 1) + downsample_ratio = cfg.get("downsample_ratio", 1) + input_dim = sum(cfg.input_dim) if isinstance(cfg.input_dim, list) else cfg.input_dim + input_dim = input_dim * downsample_ratio * downsample_ratio + fwd = 2 * input_dim * cfg.n_embed + (mlp_depth - 1) * 2 * cfg.n_embed * cfg.n_embed + else: + fwd = 0 + + return fwd * 3 + + diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/clip_sdpa.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/clip_sdpa.py new file mode 100644 index 00000000..518e8199 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/clip_sdpa.py @@ -0,0 +1,504 @@ +from contextlib import nullcontext +import math +from typing import Optional, Tuple +# from megatron.model import LayerNorm +from easydict import EasyDict as adict +import torch +from torch.nn import functional as F +from torch import nn +from flash_attn import flash_attn_qkvpacked_func, flash_attn_func +# from optimus import flash_attn_func +# from megatron.core import tensor_parallel +# from megatron.core import parallel_state as mpu +# from megatron.core.utils import make_viewless_tensor, divide +# from megatron.model.fused_rms_norm import RMSNorm +# from megatron.model.transformer import ( +# FlashSelfAttention, +# NoopTransformerLayer, +# _cfg_to_kwargs, +# ) +# from megatron.model.enums import AttnMaskType, AttnType +# from megatron.model.fused_softmax import FusedScaleMaskSoftmax +# from megatron.model.utils import attention_mask_func + +# from megatron.model.module import MegatronModule + +# try: +# from einops import rearrange +# except ImportError: +# rearrange = None + +# from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func + +# try: +# # flash attention 2.x +# from flash_attn import flash_attn_varlen_func as flash_attn_unpadded_func +# except ImportError: +# try: +# # flash attention 1.x +# from flash_attn.flash_attn_interface import flash_attn_unpadded_func +# except ImportError: +# flash_attn_unpadded_func = None + +# try: +# from flash_attn.flash_attn_interface import flash_attn_unpadded_relative_attention_bias_func +# except ImportError: +# flash_attn_unpadded_relative_attention_bias_func = None + +# try: +# from flash_attn.flash_attn_interface import mask_flash_attn_unpadded_func +# except ImportError: +# mask_flash_attn_unpadded_func = None + + +class LayerNormfp32(torch.nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +def get_abs_pos(abs_pos, tgt_size): + # abs_pos: L, C + # tgt_size: M + # return: M, C + + # print(tgt_size) + # print(abs_pos.shape) + # exit() + dim = abs_pos.size(-1) + # print(dim) + abs_pos_new = abs_pos.squeeze(0) + cls_token, old_pos_embed = abs_pos_new[:1], abs_pos_new[1:] + + + + src_size = int(math.sqrt(abs_pos_new.shape[0] - 1)) + tgt_size = int(math.sqrt(tgt_size)) + dtype = abs_pos.dtype + + if src_size != tgt_size: + old_pos_embed = old_pos_embed.view(1, src_size, src_size, dim).permute(0, 3, 1, + 2).contiguous() + old_pos_embed = old_pos_embed.to(torch.float32) + new_pos_embed = F.interpolate( + old_pos_embed, + size=(tgt_size, tgt_size), + mode='bicubic', + antialias=True, + align_corners=False, + ).to(dtype) + new_pos_embed = new_pos_embed.permute(0, 2, 3, 1) + new_pos_embed = new_pos_embed.view(tgt_size * tgt_size, dim) + vision_pos_embed = torch.cat([cls_token, new_pos_embed], dim=0) + vision_pos_embed = vision_pos_embed.view(1, tgt_size * tgt_size + 1, dim) + return vision_pos_embed + else: + return abs_pos + +@torch.jit.script +def quick_gelu(x): + return x * torch.sigmoid(1.702 * x) + + + +class CLIPVisionEmbeddings(nn.Module): + def __init__(self, hidden_size=1024, image_size=224, patch_size=14, num_channels=3): + super().__init__() + self.embed_dim = hidden_size + self.image_size = image_size + self.patch_size = patch_size + + self.class_embedding = torch.nn.Parameter(torch.randn(self.embed_dim)) + + self.patch_embedding = torch.nn.Conv2d( + in_channels=num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + bias=False, + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + 1 + self.position_embedding = torch.nn.Embedding(self.num_positions, self.embed_dim) + self.register_buffer( + "position_ids", torch.arange(self.num_positions).expand((1, -1)) + ) + + def forward(self, pixel_values, patch_embeds): + batch_size = pixel_values.shape[0] + # patch_embeds = self.patch_embedding( + # pixel_values + # ) # shape = [*, width, grid, grid] + + + if patch_embeds is not None: + patch_embeds = patch_embeds + # print(patch_embeds.shape) + else: + patch_embeds = self.patch_embedding(pixel_values) + # print(111111) + # shape = [*, width, grid, grid] + # patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + + + class_embeds = self.class_embedding.expand(batch_size, 1, -1) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + + # x = torch.cat([cls_token, x], dim=1) + embeddings = embeddings + get_abs_pos(self.position_embedding(self.position_ids), embeddings.size(1)) + # embeddings = embeddings + self.position_embedding(self.position_ids) + return embeddings + + +class NoTPFeedForward(nn.Module): + def __init__( + self, + cfg, + dim: int, + hidden_dim: int, + ): + super().__init__() + + self.fc1 = torch.nn.Linear(dim, hidden_dim, bias=True) + self.fc2 = torch.nn.Linear(hidden_dim, dim, bias=True) + + def forward(self, x): + output = self.fc2(quick_gelu(self.fc1(x))) + return output + + +# from optimus.flash_attn_interface import flash_attn_qkvpacked_func + + +# class NoTPAttention(nn.Module): +# def __init__(self, cfg): +# super().__init__() +# self.num_heads = cfg.num_attention_heads +# self.n_local_heads = cfg.num_attention_heads +# self.head_dim = cfg.hidden_size // cfg.num_attention_heads +# self.max_seq_len = cfg.seq_length +# self.use_flash_attention = cfg.use_flash_attn + +# self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True) +# self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True) + +# # self.core_attention = CoreAttention(cfg, AttnType.self_attn) + +# self.attn_drop = cfg.attention_dropout + +# def forward( +# self, +# x: torch.Tensor, +# ): +# bsz, seqlen, _ = x.shape +# xqkv = self.qkv_proj(x) +# xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim) + +# if self.use_flash_attention: +# output = flash_attn_qkvpacked_func(xqkv) +# output = output.view(bsz, seqlen, -1) +# else: +# xq, xk, xv = torch.split(xqkv, 1, dim=2) +# xq = xq.squeeze(2) +# xk = xk.squeeze(2) +# xv = xv.squeeze(2) +# # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] + +# # （B, num_head, S, head_size) +# xq = xq.permute(0, 2, 1, 3) +# xk = xk.permute(0, 2, 1, 3) +# xv = xv.permute(0, 2, 1, 3) + +# output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) +# utput = output.permute(0, 2, 1, 3).view(bsz, seqlen, -1) +# output = self.out_proj(output) +# return output + + +# from optimus.flash_attn_interface import flash_attn_qkvpacked_func + + +class NoTPAttention(torch.nn.Module): + def __init__(self, cfg): + super().__init__() + self.num_heads = cfg.num_attention_heads + self.n_local_heads = cfg.num_attention_heads + self.head_dim = cfg.hidden_size // cfg.num_attention_heads + self.max_seq_len = cfg.seq_length + self.use_flash_attention = cfg.use_flash_attn + + self.qkv_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size * 3, bias=True) + self.out_proj = torch.nn.Linear(cfg.hidden_size, cfg.hidden_size, bias=True) + + # self.core_attention = CoreAttention(cfg, AttnType.self_attn) + + self.attn_drop = cfg.attention_dropout + + def forward( + self, + x: torch.Tensor, + ): + bsz, seqlen, _ = x.shape + xqkv = self.qkv_proj(x) + xqkv = xqkv.view(bsz, seqlen, 3, self.num_heads, self.head_dim) + + if self.use_flash_attention: + output = flash_attn_qkvpacked_func(xqkv) + output = output.view(bsz, seqlen, -1) + # xq, xk, xv = torch.split(xqkv, 1, dim=2) + # xq = xq.squeeze(2) + # xk = xk.squeeze(2) + # xv = xv.squeeze(2) + # # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] + + # # （B, num_head, S, head_size) + # xq = xq.permute(0, 2, 1, 3) + # xk = xk.permute(0, 2, 1, 3) + # xv = xv.permute(0, 2, 1, 3) + # # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): + # output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) + # output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1) + # output = output.permute(0, 2, 1, 3).contiguous().view(bsz, seqlen, -1) + else: + # output = flash_attn_qkvpacked_func(xqkv) + xq, xk, xv = torch.split(xqkv, 1, dim=2) + xq = xq.squeeze(2) + xk = xk.squeeze(2) + xv = xv.squeeze(2) + # xq, xk, xv = xqkv[:, :, 0, ...], xqkv[:, :, 1, ...], xqkv[:, :, 2, ...] + + # （B, num_head, S, head_size) + xq = xq.permute(0, 2, 1, 3) + xk = xk.permute(0, 2, 1, 3) + xv = xv.permute(0, 2, 1, 3) + # with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False): + output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None) + output = output.permute(0, 2, 1, 3).reshape(bsz, seqlen, -1) + output = self.out_proj(output) + return output + +class NoTPTransformerBlock(nn.Module): + def __init__(self, cfg, layer_id: int, multiple_of=256): + super().__init__() + + self.n_heads = cfg.num_attention_heads + self.dim = cfg.hidden_size + self.head_dim = cfg.hidden_size // cfg.num_attention_heads + self.self_attn = NoTPAttention(cfg) + self.mlp = NoTPFeedForward( + cfg, dim=cfg.hidden_size, hidden_dim=cfg.ffn_hidden_size + ) + self.layer_id = layer_id + self.layer_norm1 = torch.nn.LayerNorm( + cfg.hidden_size, eps=cfg.layernorm_epsilon + ) + self.layer_norm2 = torch.nn.LayerNorm( + cfg.hidden_size, eps=cfg.layernorm_epsilon + ) + + def forward(self, x: torch.Tensor): + residual = self.self_attn.forward(self.layer_norm1(x)) + h = x + residual + out = h + self.mlp.forward(self.layer_norm2(h)) + return out + + +class NoTPTransformer(nn.Module): + def __init__(self, cfg): + super().__init__() + + self.cfg = cfg + # self.recompute_list = self.cfg.get("recompute_list", []) + self.num_layers = cfg.num_layers # _get_num_layers(cfg) + + self.layers = torch.nn.ModuleList() + for layer_id in range(self.num_layers): + self.layers.append( + NoTPTransformerBlock( + cfg, + layer_id + 1, + ) + ) + + def forward( + self, + hidden_states, + ): + + for lid, layer in enumerate(self.layers): + # if lid in self.recompute_list: + # def custom(layer_id): + # def custom_forward(*args, **kwargs): + # x_ = self.layers[layer_id](*args, **kwargs) + # return x_ + + # return custom_forward + + # assert hidden_states.requires_grad == True, logger.warning( + # "When using recalculation, the input must have grad fn" + # ) + # hidden_states = tensor_parallel.checkpoint( + # custom(lid), + # False, + # hidden_states.contiguous() + # ) + # else: + hidden_states = layer(hidden_states) + + return hidden_states + + +# from megatron.core.tensor_parallel.layers import non_tensor_paralleled, local_dp_reduce, local_dp_scatter + +class VitModel(nn.Module): + def __init__( + self, + cfg, + freeze_embed=False, + freeze_pre_norm=False + ) -> None: + super().__init__() + + self.embeddings = CLIPVisionEmbeddings(hidden_size=cfg.hidden_size, image_size=cfg.image_size, patch_size=cfg.patch_size) + + if freeze_embed: + for name, param in self.embeddings.named_parameters(): + param.requires_grad = False + + self.transformer = NoTPTransformer(cfg=cfg) + + if cfg.get("fp32norm", False): + logger.info("Load fp32 layernorm for ViT.") + self.pre_layrnorm = LayerNormfp32( + cfg.hidden_size, + eps=cfg.get("pre_layernorm_epsilon", 1e-5), + ) + else: + self.pre_layrnorm = torch.nn.LayerNorm( + cfg.hidden_size, + eps=cfg.get("pre_layernorm_epsilon", 1e-5), + ) + + # self.pre_layrnorm = RMSNorm( + # cfg.hidden_size, + # eps=cfg.get("pre_layernorm_epsilon", 1e-5), + # sequence_parallel=False, + # use_fp32=True, + # use_optimus=True, + # ) + + if freeze_pre_norm: + for name, param in self.pre_layrnorm.named_parameters(): + param.requires_grad = False + + for p in self.parameters(): + p.micro_dp = True + + def set_input_tensor(self, input_tensor): + if not isinstance(input_tensor, list): + input_tensor = [input_tensor] + self.transformer.set_input_tensor(input_tensor[0]) + + def __str__(self) -> str: + return "open_clip" + + def forward( + self, + x, + patch_embeds + ): + x = self.embeddings(x, patch_embeds) + hidden_states = self.pre_layrnorm(x) + + # hidden_states, dis = local_dp_scatter(hidden_states) + output = self.transformer(hidden_states) + + # output = local_dp_reduce(output, dis) + + return output + + +vit_model_cfg = adict( + num_layers=24, + hidden_size=1024, + num_heads = 16, + num_attention_heads=16, + ffn_hidden_size=4096, + seq_length=256, + max_position_embeddings=256, + use_flash_attn=False, + understand_projector_stride=2, + hidden_dropout = 0.0, + attention_dropout = 0.0, + no_persist_layer_norm = False, + layernorm_epsilon = 1e-5, + pre_layernorm_epsilon = 1e-5, + image_size = 224, + patch_size = 14, + recompute_list = [] +) + +def build_clip_l(): + return VitModel( + cfg=vit_model_cfg, + freeze_embed=False, + freeze_pre_norm=False, + ) + + +if __name__ == '__main__': + + + from mmgpt.model.vision_encoder.sam_b import build_sam_vit_b + + + + vit_model_cfg = adict( + num_layers=24, + hidden_size=1024, + num_attention_heads=16, + ffn_hidden_size=4096, + seq_length=256, + max_position_embeddings=256, + use_flash_attn=False, + understand_projector_stride=2, + hidden_dropout = 0.0, + attention_dropout = 0.0, + no_persist_layer_norm = False, + layernorm_epsilon = 1e-5, + pre_layernorm_epsilon = 1e-5, + image_size = 224, + patch_size = 14, + recompute_list = [] + ) + + sam_model = build_sam_vit_b() + + + vision_model = VitModel( + cfg=vit_model_cfg, + freeze_embed=False, + freeze_pre_norm=False, + ) + + # model = VitModel(1344) + # x = torch.zeros(2, 3, 224, 224) + x = torch.zeros(2, 3, 1024, 1024) + + + with torch.no_grad(): + # y = vision_model(x) + patch_embed = sam_model(x) + print(patch_embed.shape) + y = vision_model(x, patch_embed) + print(y.shape) + + image_feature = torch.add(y[:, 1:], patch_embed.flatten(2).permute(0, 2, 1)) + + print(image_feature.shape) diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/sam_vary_sdpa.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/sam_vary_sdpa.py new file mode 100644 index 00000000..8ac8fbee --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepencoder/sam_vary_sdpa.py @@ -0,0 +1,528 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from typing import Optional, Tuple, Type +from functools import partial +from flash_attn import flash_attn_qkvpacked_func +# from .common import LayerNorm2d, MLPBlock + +# from mmgpt.model.vision_encoder.flash_4 import _attention_rel_h_rel_w + + +def get_abs_pos(abs_pos, tgt_size): + + dtype = abs_pos.dtype + + src_size = abs_pos.size(1) + + if src_size != tgt_size: + old_pos_embed = abs_pos.permute(0, 3, 1, 2) + old_pos_embed = old_pos_embed.to(torch.float32) + new_pos_embed = F.interpolate( + old_pos_embed, + size=(tgt_size, tgt_size), + mode='bicubic', + antialias=True, + align_corners=False, + ).to(dtype) + new_pos_embed = new_pos_embed.permute(0, 2, 3, 1) + return new_pos_embed + else: + return abs_pos + + + + +class MLPBlock(nn.Module): + def __init__( + self, + embedding_dim: int, + mlp_dim: int, + act: Type[nn.Module] = nn.GELU, + ) -> None: + super().__init__() + self.lin1 = nn.Linear(embedding_dim, mlp_dim) + self.lin2 = nn.Linear(mlp_dim, embedding_dim) + self.act = act() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.lin2(self.act(self.lin1(x))) + + +# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa +# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa +class LayerNorm2d(nn.Module): + def __init__(self, num_channels: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(num_channels)) + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x + + +# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa +class ImageEncoderViT(nn.Module): + def __init__( + self, + img_size: int = 1024, + patch_size: int = 16, + in_chans: int = 3, + embed_dim: int = 768, + depth: int = 12, + num_heads: int = 12, + mlp_ratio: float = 4.0, + out_chans: int = 256, + qkv_bias: bool = True, + norm_layer: Type[nn.Module] = nn.LayerNorm, + act_layer: Type[nn.Module] = nn.GELU, + use_abs_pos: bool = True, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + window_size: int = 0, + global_attn_indexes: Tuple[int, ...] = (), + ) -> None: + """ + Args: + img_size (int): Input image size. + patch_size (int): Patch size. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + depth (int): Depth of ViT. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_abs_pos (bool): If True, use absolute positional embeddings. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. + global_attn_indexes (list): Indexes for blocks using global attention. + """ + super().__init__() + self.img_size = img_size + + self.patch_embed = PatchEmbed( + kernel_size=(patch_size, patch_size), + stride=(patch_size, patch_size), + in_chans=in_chans, + embed_dim=embed_dim, + ) + + self.pos_embed: Optional[nn.Parameter] = None + if use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim) + ) + + self.blocks = nn.ModuleList() + for i in range(depth): + block = Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + norm_layer=norm_layer, + act_layer=act_layer, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + window_size=window_size if i not in global_attn_indexes else 0, + input_size=(img_size // patch_size, img_size // patch_size), + ) + self.blocks.append(block) + + self.neck = nn.Sequential( + nn.Conv2d( + embed_dim, + out_chans, + kernel_size=1, + bias=False, + ), + LayerNorm2d(out_chans), + nn.Conv2d( + out_chans, + out_chans, + kernel_size=3, + padding=1, + bias=False, + ), + LayerNorm2d(out_chans), + ) + + self.net_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False) + self.net_3 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.patch_embed(x) + if self.pos_embed is not None: + # x = x + self.pos_embed + x = x + get_abs_pos(self.pos_embed, x.size(1)) + + for blk in self.blocks: + x = blk(x) + + neck_output = self.neck(x.permute(0, 3, 1, 2)) + conv2_output = self.net_2(neck_output) + # print(f"conv2_output shape: {conv2_output.shape}") + conv3_output = self.net_3(conv2_output) + + return conv3_output + + +class Block(nn.Module): + """Transformer blocks with support of window attention and residual propagation blocks""" + + def __init__( + self, + dim: int, + num_heads: int, + mlp_ratio: float = 4.0, + qkv_bias: bool = True, + norm_layer: Type[nn.Module] = nn.LayerNorm, + act_layer: Type[nn.Module] = nn.GELU, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + window_size: int = 0, + input_size: Optional[Tuple[int, int]] = None, + ) -> None: + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads in each ViT block. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + norm_layer (nn.Module): Normalization layer. + act_layer (nn.Module): Activation layer. + use_rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + window_size (int): Window size for window attention blocks. If it equals 0, then + use global attention. + input_size (tuple(int, int) or None): Input resolution for calculating the relative + positional parameter size. + """ + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + use_rel_pos=use_rel_pos, + rel_pos_zero_init=rel_pos_zero_init, + input_size=input_size if window_size == 0 else (window_size, window_size), + ) + + self.norm2 = norm_layer(dim) + self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer) + + self.window_size = window_size + + def forward(self, x: torch.Tensor) -> torch.Tensor: + shortcut = x + x = self.norm1(x) + # Window partition + if self.window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, self.window_size) + + x = self.attn(x) + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, self.window_size, pad_hw, (H, W)) + + x = shortcut + x + x = x + self.mlp(self.norm2(x)) + + return x + + +class Attention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__( + self, + dim: int, + num_heads: int = 8, + qkv_bias: bool = True, + use_rel_pos: bool = False, + rel_pos_zero_init: bool = True, + input_size: Optional[Tuple[int, int]] = None, + ) -> None: + """ + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + qkv_bias (bool): If True, add a learnable bias to query, key, value. + rel_pos (bool): If True, add relative positional embeddings to the attention map. + rel_pos_zero_init (bool): If True, zero initialize relative positional parameters. + input_size (tuple(int, int) or None): Input resolution for calculating the relative + positional parameter size. + """ + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.proj = nn.Linear(dim, dim) + + self.use_rel_pos = use_rel_pos + if self.use_rel_pos: + assert ( + input_size is not None + ), "Input size must be provided if using relative positional encoding." + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, H, W, _ = x.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + # q, k, v with shape (B * nHead, H * W, C) + q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0) + + rel_h, rel_w = None, None + if self.use_rel_pos: + rel_h, rel_w = add_decomposed_rel_pos(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) + + q = q.view(B, self.num_heads, H * W, -1) + k = k.view(B, self.num_heads, H * W, -1) + v = v.view(B, self.num_heads, H * W, -1) + + if self.use_rel_pos: + rel_h = rel_h.view(B, self.num_heads, rel_h.size(1), rel_h.size(2), rel_h.size(3)) + rel_w = rel_w.view(B, self.num_heads, rel_w.size(1), rel_w.size(2), rel_w.size(3)) + attn_bias = (rel_h + rel_w).view(B, self.num_heads, rel_h.size(2), rel_h.size(3) * rel_w.size(4)) + x = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_bias) + # x = _attention_rel_h_rel_w(q, k, v, rel_h, rel_w) + else: + x = torch.nn.functional.scaled_dot_product_attention(q, k, v) + # qkv = torch.stack([q, k, v], dim=1).transpose(1, 3).reshape(B, H * W, 3, self.num_heads, -1) + # x = flash_attn_qkvpacked_func(qkv, dropout_p=0.0, causal=False).transpose(1, 2) + + + + x = x.view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1) + + x = self.proj(x) + + return x + + +def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]: + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + return windows, (Hp, Wp) + + +def window_unpartition( + windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int] +) -> torch.Tensor: + """ + Window unpartition into original sequences and removing padding. + Args: + windows (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + Args: + q_size (int): size of query q. + k_size (int): size of key k. + rel_pos (Tensor): relative position embeddings (L, C). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos if needed. + if rel_pos.shape[0] != max_rel_dist: + # Interpolate rel pos. + dtype = rel_pos.dtype + rel_pos = rel_pos.to(torch.float32) + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ).to(dtype) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + else: + rel_pos_resized = rel_pos + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size, device=rel_pos.device)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size, device=rel_pos.device)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + +def add_decomposed_rel_pos( + q: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: Tuple[int, int], + k_size: Tuple[int, int], +) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 + Args: + q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). + rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. + rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. + q_size (Tuple): spatial sequence size of query q with (q_h, q_w). + k_size (Tuple): spatial sequence size of key k with (k_h, k_w). + + Returns: + attn (Tensor): attention map with added relative positional embeddings. + """ + q_h, q_w = q_size + k_h, k_w = k_size + Rh = get_rel_pos(q_h, k_h, rel_pos_h) + Rw = get_rel_pos(q_w, k_w, rel_pos_w) + + B, _, dim = q.shape + r_q = q.reshape(B, q_h, q_w, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh) + rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw) + rel_h = rel_h.unsqueeze(-1) + rel_w = rel_w.unsqueeze(-2) + rel_h = rel_h.reshape(B, q_h * q_w, k_h, 1) + rel_w = rel_w.reshape(B, q_h * q_w, 1, k_w) + + return rel_h, rel_w + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, + kernel_size: Tuple[int, int] = (16, 16), + stride: Tuple[int, int] = (16, 16), + padding: Tuple[int, int] = (0, 0), + in_chans: int = 3, + embed_dim: int = 768, + ) -> None: + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): Patch embedding dimension. + """ + super().__init__() + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x + + +def build_sam_vit_b(checkpoint=None): + return _build_sam( + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + encoder_global_attn_indexes=[2, 5, 8, 11], + checkpoint=checkpoint, + ) + + +def _build_sam( + encoder_embed_dim, + encoder_depth, + encoder_num_heads, + encoder_global_attn_indexes, + checkpoint=None, +): + prompt_embed_dim = 256 + image_size = 1024 + vit_patch_size = 16 + image_embedding_size = image_size // vit_patch_size + image_encoder=ImageEncoderViT( + depth=encoder_depth, + embed_dim=encoder_embed_dim, + img_size=image_size, + mlp_ratio=4, + norm_layer=partial(torch.nn.LayerNorm, eps=1e-6), + num_heads=encoder_num_heads, + patch_size=vit_patch_size, + qkv_bias=True, + use_rel_pos=True, + global_attn_indexes=encoder_global_attn_indexes, + window_size=14, + out_chans=prompt_embed_dim, + ) + + if checkpoint is not None: + # with open(checkpoint, "rb") as f: + state_dict = torch.load(checkpoint) + # print(state_dict.keys()) + # for key in state_dict: + # image_encoder.load_state_dict({k[14:]: v for k, v in state_dict.items() if 'image_encoder' in k}, strict=False) + # ocr-anyting + # image_encoder.load_state_dict(state_dict, strict=True) + # tob + image_encoder.load_state_dict({k[30:]: v for k, v in state_dict.items() if 'vision_tower_high' in k}, strict=True) + print(checkpoint) + return image_encoder \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepseek_ocr.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepseek_ocr.py new file mode 100644 index 00000000..982301a3 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/deepseek_ocr.py @@ -0,0 +1,582 @@ + +"""Inference-only Deepseek-OCR model compatible with HuggingFace weights.""" +import math +from collections.abc import Iterable, Mapping, Sequence +from typing import List, Literal, Optional, Set, Tuple, TypedDict, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange, repeat +from transformers import BatchFeature + +from vllm.config import VllmConfig +from vllm.model_executor import SamplingMetadata +from vllm.model_executor.layers.quantization import QuantizationConfig +from vllm.model_executor.model_loader.utils import set_default_torch_dtype +from vllm.multimodal import MULTIMODAL_REGISTRY +from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig, + MultiModalKwargs, NestedTensors) +from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems, + ImageSize, MultiModalDataItems) +from vllm.multimodal.processing import (BaseMultiModalProcessor, + BaseProcessingInfo, PromptReplacement, + PromptUpdate) +from vllm.multimodal.profiling import BaseDummyInputsBuilder +from vllm.sequence import IntermediateTensors +from vllm.transformers_utils.configs.deepseek_vl2 import (DeepseekVLV2Config, + MlpProjectorConfig, + VisionEncoderConfig) +from process.image_process import ( + DeepseekOCRProcessor, count_tiles) +from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config +# from vllm.utils import is_list_of + +from vllm.model_executor.models.interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP +from vllm.model_executor.models.utils import (AutoWeightsLoader, WeightsMapper, flatten_bn, + init_vllm_registered_model, maybe_prefix, + merge_multimodal_embeddings) + +from deepencoder.sam_vary_sdpa import build_sam_vit_b +from deepencoder.clip_sdpa import build_clip_l +from deepencoder.build_linear import MlpProjector +from addict import Dict +# import time +from config import IMAGE_SIZE, BASE_SIZE, CROP_MODE, PRINT_NUM_VIS_TOKENS, PROMPT +# The image token id may be various +_IMAGE_TOKEN = "" + + +class DeepseekOCRProcessingInfo(BaseProcessingInfo): + + def get_hf_config(self): + return self.ctx.get_hf_config(DeepseekVLV2Config) + + def get_hf_processor(self, **kwargs: object): + return self.ctx.get_hf_processor(DeepseekOCRProcessor, **kwargs) + + def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]: + return {"image": None} + + def get_num_image_tokens(self, + *, + image_width: int, + image_height: int, + cropping: bool = True) -> int: + hf_processor = self.get_hf_processor() + + + # image_size = hf_processor.image_size + # patch_size = hf_processor.patch_size + # downsample_ratio = hf_processor.downsample_ratio + + image_size = IMAGE_SIZE + base_size = BASE_SIZE + patch_size = 16 + downsample_ratio = 4 + + if CROP_MODE: + if image_width <= 640 and image_height <= 640: + crop_ratio = [1, 1] + else: + # images_crop_raw, crop_ratio = hf_processor.dynamic_preprocess(image) + + # find the closest aspect ratio to the target + crop_ratio = count_tiles(image_width, image_height, image_size=IMAGE_SIZE) + + # print('===========') + # print('crop_ratio ', crop_ratio) + # print('============') + + num_width_tiles, num_height_tiles = crop_ratio + else: + num_width_tiles = num_height_tiles = 1 + + h = w = math.ceil((base_size // patch_size) / downsample_ratio) + + h2 = w2 = math.ceil((image_size // patch_size) / downsample_ratio) + + global_views_tokens = h * (w + 1) + if num_width_tiles >1 or num_height_tiles>1: + local_views_tokens = (num_height_tiles * h2) * (num_width_tiles * w2 + 1) + else: + local_views_tokens = 0 + + + return global_views_tokens + local_views_tokens + 1 + + def get_image_size_with_most_features(self) -> ImageSize: + + if IMAGE_SIZE == 1024 and BASE_SIZE == 1280: + return ImageSize(width=1024*2, height=1024*2) + return ImageSize(width=640*2, height=640*2) + + +class DeepseekOCRDummyInputsBuilder( + BaseDummyInputsBuilder[DeepseekOCRProcessingInfo]): + + def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str: + num_images = mm_counts.get("image", 0) + + processor = self.info.get_hf_processor() + image_token = processor.image_token + + return image_token * num_images + + def get_dummy_mm_data( + self, + seq_len: int, + mm_counts: Mapping[str, int], + ) -> MultiModalDataDict: + num_images = mm_counts.get("image", 0) + + max_image_size = self.info.get_image_size_with_most_features() + + if '' in PROMPT: + return { + "image": + DeepseekOCRProcessor().tokenize_with_images(images = self._get_dummy_images(width=max_image_size.width, + height=max_image_size.height, + num_images=num_images), bos=True, eos=True, cropping=CROP_MODE) + } + else: + return { + "image": [] + } + + + + +class DeepseekOCRMultiModalProcessor( + BaseMultiModalProcessor[DeepseekOCRProcessingInfo]): + + + def _call_hf_processor( + self, + prompt: str, + mm_data: Mapping[str, object], + mm_kwargs: Mapping[str, object], + ) -> BatchFeature: + + + # print(mm_data) + if mm_data: + processed_outputs = self.info.ctx.call_hf_processor( + self.info.get_hf_processor(**mm_kwargs), + dict(prompt=prompt, **mm_data), + mm_kwargs, + ) + + else: + tokenizer = self.info.get_tokenizer() + processed_outputs = tokenizer(prompt, + add_special_tokens=True, + return_tensors="pt") + + return processed_outputs + + def _get_mm_fields_config( + self, + hf_inputs: BatchFeature, + hf_processor_mm_kwargs: Mapping[str, object], + ) -> Mapping[str, MultiModalFieldConfig]: + return dict( + pixel_values=MultiModalFieldConfig.batched("image"), + images_spatial_crop=MultiModalFieldConfig.batched("image"), + # image_embeds=MultiModalFieldConfig.batched("image2"), + images_crop=MultiModalFieldConfig.batched("image"), + ) + + def _get_prompt_updates( + self, + mm_items: MultiModalDataItems, + hf_processor_mm_kwargs: Mapping[str, object], + out_mm_kwargs: MultiModalKwargs, + ) -> Sequence[PromptUpdate]: + hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs) + + image_token_id = hf_processor.image_token_id + assert isinstance(image_token_id, int) + + def get_replacement_deepseek_vl2(item_idx: int): + images = mm_items.get_items( + "image", (ImageEmbeddingItems, ImageProcessorItems)) + + + + if isinstance(images, ImageEmbeddingItems): + num_image_tokens = images.get_feature_size(item_idx) + else: + + + width = images[0][-1][0][0] + height = images[0][-1][0][1] + + num_image_tokens = self.info.get_num_image_tokens( + image_width=width, + image_height=height, + # flag = True, + cropping=CROP_MODE, + ) + return [image_token_id] * num_image_tokens + + return [ + PromptReplacement( + modality="image", + target=[image_token_id], + replacement=get_replacement_deepseek_vl2, + ) + ] + + def _cached_apply_hf_processor( + self, + prompt: Union[str, list[int]], + mm_data_items: MultiModalDataItems, + hf_processor_mm_kwargs: Mapping[str, object], + ) -> tuple[list[int], MultiModalKwargs, bool]: + # The processor logic is different for len(images) <= 2 vs > 2 + # Since the processing cache assumes that the processor output is + # invariant of how many images are passed per prompt, we only + # perform caching for the most common case + if mm_data_items.get_count("image", strict=False) > 2: + # This code path corresponds to the cache being disabled + return self._apply_hf_processor_main( + prompt=prompt, + mm_items=mm_data_items, + hf_processor_mm_kwargs=hf_processor_mm_kwargs, + enable_hf_prompt_update=True, + ) + + return super()._cached_apply_hf_processor( + prompt=prompt, + mm_data_items=mm_data_items, + hf_processor_mm_kwargs=hf_processor_mm_kwargs, + ) + + +@MULTIMODAL_REGISTRY.register_processor( + DeepseekOCRMultiModalProcessor, + info=DeepseekOCRProcessingInfo, + dummy_inputs=DeepseekOCRDummyInputsBuilder) +class DeepseekOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP): + + hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={ + "language.": "language_model.", + }) + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__() + + config: DeepseekVLV2Config = vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config + multimodal_config = vllm_config.model_config.multimodal_config + + # config.model_type ='deepseek_vl_v2' + + self.config = config + self.multimodal_config = multimodal_config + + + self.vision_config = config.vision_config + self.projector_config = config.projector_config + self.text_config = config.text_config + + model_config = vllm_config.model_config + tokenizer = cached_tokenizer_from_config(model_config) + self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN] + + self.sam_model = build_sam_vit_b() + self.vision_model = build_clip_l() + + n_embed = 1280 + self.projector = MlpProjector(Dict(projector_type="linear", input_dim=2048, n_embed=n_embed)) + self.tile_tag = config.tile_tag + self.global_view_pos = config.global_view_pos + + # self.sam_model = torch.compile(self.sam_model, mode="reduce-overhead") + # self.vision_model = torch.compile(self.vision_model, mode="reduce-overhead") + # self.projector = torch.compile(self.projector, mode="max-autotune") + + + + + # special token for image token sequence format + embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32)) + if self.tile_tag == "2D": + # <|view_separator|>, <|\n|> + self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std) + self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std) + else: + raise ValueError( + f"Only 2D tile_tag is supported currently, got: {self.tile_tag}" + ) + + if self.text_config.topk_method == "noaux_tc": + architectures = ["DeepseekV3ForCausalLM"] + elif not self.text_config.use_mla: + architectures = ["DeepseekForCausalLM"] + else: + architectures = ["DeepseekV2ForCausalLM"] + + self.language_model = init_vllm_registered_model( + vllm_config=vllm_config, + hf_config=self.text_config, + prefix=maybe_prefix(prefix, "language"), + architectures=architectures, + ) + + self.make_empty_intermediate_tensors = ( + self.language_model.make_empty_intermediate_tensors) + + + + def _parse_and_validate_image_input( + self, **kwargs: object): + + pixel_values = kwargs.pop("pixel_values", None) + images_spatial_crop = kwargs.pop("images_spatial_crop", None) + images_crop = kwargs.pop("images_crop", None) + + + if pixel_values is None or torch.sum(pixel_values).item() == 0: + return None + + if pixel_values is not None: + if not isinstance(pixel_values, (torch.Tensor, list)): + raise ValueError("Incorrect type of pixel values. " + f"Got type: {type(pixel_values)}") + + if not isinstance(images_spatial_crop, (torch.Tensor, list)): + raise ValueError("Incorrect type of image sizes. " + f"Got type: {type(images_spatial_crop)}") + + if not isinstance(images_crop, (torch.Tensor, list)): + raise ValueError("Incorrect type of image crop. " + f"Got type: {type(images_crop)}") + + return [pixel_values, images_crop, images_spatial_crop] + + + raise AssertionError("This line should be unreachable.") + + + + def _pixel_values_to_embedding( + self, + pixel_values: torch.Tensor, + images_crop: torch.Tensor, + images_spatial_crop: torch.Tensor, + ) -> NestedTensors: + + # Pixel_values (global view): [n_image, batch_size, 3, height, width] + # images_spatial_crop: [n_image, batch_size, [num_tiles_w, num_tiles_h]] + # images_crop (local view): [n_image, batch_size, num_pathes, 3, h, w] + # split the pixel and image_crop, all batch_size = 1 + + images_in_this_batch = [] + + + # print(type(images_crop)) + + # print(pixel_values.shape) + + + with torch.no_grad(): + for jdx in range(images_spatial_crop.size(0)): + # with torch.set_grad_enabled(False): + patches = images_crop[jdx][0].to(torch.bfloat16) # batch_size = 1 + image_ori = pixel_values[jdx] + crop_shape = images_spatial_crop[jdx][0] + + if torch.sum(patches).item() != 0: # if all values = 0, no crop + # P, C, H, W = patches.shape + # crop_flag = 1 + local_features_1 = self.sam_model(patches) + #TODO del patches + # torch.compiler.cudagraph_mark_step_begin() + local_features_2 = self.vision_model(patches, local_features_1) + + + local_features = torch.cat((local_features_2[:, 1:], local_features_1.flatten(2).permute(0, 2, 1)), dim=-1) + local_features = self.projector(local_features) + + + global_features_1 = self.sam_model(image_ori) + global_features_2 = self.vision_model(image_ori, global_features_1) + global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) + global_features = self.projector(global_features) + + if PRINT_NUM_VIS_TOKENS: + print('=====================') + print('BASE: ', global_features.shape) + print('PATCHES: ', local_features.shape) + print('=====================') + + _, hw, n_dim = global_features.shape + h = w = int(hw ** 0.5) + + _2, hw2, n_dim2 = local_features.shape + h2 = w2 = int(hw2 ** 0.5) + + width_crop_num, height_crop_num = crop_shape[0], crop_shape[1] + + global_features = global_features.view(h, w, n_dim) + + global_features = torch.cat( + [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1 + ) + + global_features = global_features.view(-1, n_dim) + + + local_features = local_features.view(height_crop_num, width_crop_num, h2, w2, n_dim2).permute(0, 2, 1, 3, 4).reshape(height_crop_num*h2, width_crop_num*w2, n_dim2) + local_features = torch.cat( + [local_features, self.image_newline[None, None, :].expand(height_crop_num * h2, 1, n_dim2)], dim=1 + ) + local_features = local_features.view(-1, n_dim2) + + global_local_features = torch.cat([local_features, global_features, self.view_seperator[None, :]], dim=0) + + else: + global_features_1 = self.sam_model(image_ori) + global_features_2 = self.vision_model(image_ori, global_features_1) + global_features = torch.cat((global_features_2[:, 1:], global_features_1.flatten(2).permute(0, 2, 1)), dim=-1) + global_features = self.projector(global_features) + + if PRINT_NUM_VIS_TOKENS: + print('=====================') + print('BASE: ', global_features.shape) + print('NO PATCHES') + print('=====================') + + _, hw, n_dim = global_features.shape + h = w = int(hw ** 0.5) + + global_features = global_features.view(h, w, n_dim) + + global_features = torch.cat( + [global_features, self.image_newline[None, None, :].expand(h, 1, n_dim)], dim=1 + ) + + global_features = global_features.view(-1, n_dim) + + global_local_features = torch.cat([global_features, self.view_seperator[None, :]], dim=0) + + images_in_this_batch.append(global_local_features) + + return images_in_this_batch + + def _process_image_input( + self, image_input) -> torch.Tensor: + + + # image_input: [pixel_values, images_crop, images_spatial_crop] + + pixel_values = image_input[0].to(torch.bfloat16) + # print(image_input[1][0].shape) + # print(type(image_input[1])) + # exit() + + # images_crop = image_input[1].to(torch.bfloat16) + images_crop = image_input[1] + # images_crop = image_input[1] + images_spatial_crop = image_input[2].to(dtype=torch.long) + + # local_start = time.time() + vision_features = self._pixel_values_to_embedding( + pixel_values=pixel_values, images_crop = images_crop, images_spatial_crop=images_spatial_crop) + + # local_total_time = time.time() - local_start + + # print('encoder_time: ', local_total_time) + # exit() + return vision_features + + def get_language_model(self) -> torch.nn.Module: + return self.language_model + + def get_multimodal_embeddings( + self, **kwargs: object) -> Optional[MultiModalEmbeddings]: + image_input = self._parse_and_validate_image_input(**kwargs) + if image_input is None: + return None + vision_embeddings = self._process_image_input(image_input) + return vision_embeddings + + + + def get_input_embeddings( + self, + input_ids: torch.Tensor, + multimodal_embeddings: Optional[MultiModalEmbeddings] = None, + ) -> torch.Tensor: + + + + inputs_embeds = self.language_model.get_input_embeddings(input_ids) + + + if multimodal_embeddings is not None: + inputs_embeds = merge_multimodal_embeddings( + input_ids, inputs_embeds, multimodal_embeddings, + self.image_token_id) + # print(len(multimodal_embeddings)) + # print(input_ids.shape) + # print(type(inputs_embeds)) + # print(inputs_embeds.shape) + + return inputs_embeds + + def forward(self, + input_ids: torch.Tensor, + positions: torch.Tensor, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + **kwargs: object): + + if intermediate_tensors is not None: + inputs_embeds = None + + # NOTE: In v1, inputs_embeds is always generated at model runner, this + # condition is for v0 compatibility + elif inputs_embeds is None: + vision_embeddings = self.get_multimodal_embeddings(**kwargs) + inputs_embeds = self.get_input_embeddings(input_ids, + vision_embeddings) + input_ids = None + + hidden_states = self.language_model(input_ids, + positions, + intermediate_tensors, + inputs_embeds=inputs_embeds) + + return hidden_states + + def compute_logits( + self, + hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[torch.Tensor]: + return self.language_model.compute_logits(hidden_states, + sampling_metadata) + + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]: + processed_weights = [] + + for name, tensor in weights: + if 'sam_model' in name or 'vision_model' in name or 'projector' in name or 'image_newline' in name or 'view_seperator' in name: + new_name = name.replace('model.', '', 1) + else: + new_name = 'language.' + name + + processed_weights.append((new_name, tensor)) + + loader = AutoWeightsLoader(self) + autoloaded_weights = loader.load_weights(processed_weights, mapper=self.hf_to_vllm_mapper) + + + + + + return autoloaded_weights diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/__init__.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/image_process.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/image_process.py new file mode 100644 index 00000000..0fcae62f --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/image_process.py @@ -0,0 +1,502 @@ +import math +from typing import List, Tuple + +import torch +import torchvision.transforms as T +from PIL import Image, ImageOps +from transformers import AutoProcessor, BatchFeature, LlamaTokenizerFast +from transformers.processing_utils import ProcessorMixin +from config import IMAGE_SIZE, BASE_SIZE, CROP_MODE, MIN_CROPS, MAX_CROPS, PROMPT, TOKENIZER + +def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): + best_ratio_diff = float('inf') + best_ratio = (1, 1) + area = width * height + for ratio in target_ratios: + target_aspect_ratio = ratio[0] / ratio[1] + ratio_diff = abs(aspect_ratio - target_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_ratio = ratio + elif ratio_diff == best_ratio_diff: + if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: + best_ratio = ratio + # print(f'width: {width}, height: {height}, best_ratio: {best_ratio}') + return best_ratio + + +def count_tiles(orig_width, orig_height, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False): + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = set( + (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if + i * j <= max_num and i * j >= min_num) + # print(target_ratios) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + # find the closest aspect ratio to the target + target_aspect_ratio = find_closest_aspect_ratio( + aspect_ratio, target_ratios, orig_width, orig_height, image_size) + + return target_aspect_ratio + + +def dynamic_preprocess(image, min_num=MIN_CROPS, max_num=MAX_CROPS, image_size=640, use_thumbnail=False): + orig_width, orig_height = image.size + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = set( + (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if + i * j <= max_num and i * j >= min_num) + # print(target_ratios) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + # find the closest aspect ratio to the target + target_aspect_ratio = find_closest_aspect_ratio( + aspect_ratio, target_ratios, orig_width, orig_height, image_size) + + # print(target_aspect_ratio) + # calculate the target width and height + target_width = image_size * target_aspect_ratio[0] + target_height = image_size * target_aspect_ratio[1] + blocks = target_aspect_ratio[0] * target_aspect_ratio[1] + + # resize the image + resized_img = image.resize((target_width, target_height)) + processed_images = [] + for i in range(blocks): + box = ( + (i % (target_width // image_size)) * image_size, + (i // (target_width // image_size)) * image_size, + ((i % (target_width // image_size)) + 1) * image_size, + ((i // (target_width // image_size)) + 1) * image_size + ) + # split the image + split_img = resized_img.crop(box) + processed_images.append(split_img) + assert len(processed_images) == blocks + if use_thumbnail and len(processed_images) != 1: + thumbnail_img = image.resize((image_size, image_size)) + processed_images.append(thumbnail_img) + return processed_images, target_aspect_ratio + + + + + +class ImageTransform: + + def __init__(self, + mean: Tuple[float, float, float] = (0.5, 0.5, 0.5), + std: Tuple[float, float, float] = (0.5, 0.5, 0.5), + normalize: bool = True): + self.mean = mean + self.std = std + self.normalize = normalize + + transform_pipelines = [T.ToTensor()] + + if normalize: + transform_pipelines.append(T.Normalize(mean, std)) + + self.transform = T.Compose(transform_pipelines) + + def __call__(self, pil_img: Image.Image): + x = self.transform(pil_img) + return x + + +class DeepseekOCRProcessor(ProcessorMixin): + tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast") + attributes = ["tokenizer"] + + def __init__( + self, + tokenizer: LlamaTokenizerFast = TOKENIZER, + candidate_resolutions: Tuple[Tuple[int, int]] = [[1024, 1024]], + patch_size: int = 16, + downsample_ratio: int = 4, + image_mean: Tuple[float, float, float] = (0.5, 0.5, 0.5), + image_std: Tuple[float, float, float] = (0.5, 0.5, 0.5), + normalize: bool = True, + image_token: str = "", + pad_token: str = "<｜▁pad▁｜>", + add_special_token: bool = False, + sft_format: str = "deepseek", + mask_prompt: bool = True, + ignore_id: int = -100, + **kwargs, + ): + + # self.candidate_resolutions = candidate_resolutions # placeholder no use + self.image_size = IMAGE_SIZE + self.base_size = BASE_SIZE + # self.patch_size = patch_size + self.patch_size = 16 + self.image_mean = image_mean + self.image_std = image_std + self.normalize = normalize + # self.downsample_ratio = downsample_ratio + self.downsample_ratio = 4 + + self.image_transform = ImageTransform(mean=image_mean, std=image_std, normalize=normalize) + + + self.tokenizer = tokenizer + # self.tokenizer = add_special_token(tokenizer) + self.tokenizer.padding_side = 'left' # must set this，padding side with make a difference in batch inference + + # add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id' + if self.tokenizer.pad_token is None: + self.tokenizer.add_special_tokens({'pad_token': pad_token}) + + # add image token + # image_token_id = self.tokenizer.vocab.get(image_token) + # if image_token_id is None: + # special_tokens = [image_token] + # special_tokens_dict = {"additional_special_tokens": special_tokens} + # self.tokenizer.add_special_tokens(special_tokens_dict) + self.image_token_id = self.tokenizer.vocab.get(image_token) + + # add five special tokens for grounding-related tasks + # <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|> + # special_tokens = ['<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>'] + # special_tokens_dict = {"additional_special_tokens": special_tokens} + + # special_tokens = ['','<|ref|>', '<|/ref|>', '<|det|>', '<|/det|>', '<|grounding|>', '', '', '', ''] + # special_tokens_dict = {"additional_special_tokens": special_tokens} + # self.tokenizer.add_special_tokens(special_tokens_dict) + + # # add special tokens for SFT data + # special_tokens = ["<|User|>", "<|Assistant|>"] + # special_tokens_dict = {"additional_special_tokens": special_tokens} + # self.tokenizer.add_special_tokens(special_tokens_dict) + + self.image_token = image_token + self.pad_token = pad_token + self.add_special_token = add_special_token + self.sft_format = sft_format + self.mask_prompt = mask_prompt + self.ignore_id = ignore_id + + super().__init__( + tokenizer, + **kwargs, + ) + + + + + # def select_best_resolution(self, image_size): + # # used for cropping + # original_width, original_height = image_size + # best_fit = None + # max_effective_resolution = 0 + # min_wasted_resolution = float("inf") + + # for width, height in self.candidate_resolutions: + # scale = min(width / original_width, height / original_height) + # downscaled_width, downscaled_height = int( + # original_width * scale), int(original_height * scale) + # effective_resolution = min(downscaled_width * downscaled_height, + # original_width * original_height) + # wasted_resolution = (width * height) - effective_resolution + + # if effective_resolution > max_effective_resolution or ( + # effective_resolution == max_effective_resolution + # and wasted_resolution < min_wasted_resolution): + # max_effective_resolution = effective_resolution + # min_wasted_resolution = wasted_resolution + # best_fit = (width, height) + + # return best_fit + + @property + def bos_id(self): + return self.tokenizer.bos_token_id + + @property + def eos_id(self): + return self.tokenizer.eos_token_id + + @property + def pad_id(self): + return self.tokenizer.pad_token_id + + def encode(self, text: str, bos: bool = True, eos: bool = False): + t = self.tokenizer.encode(text, add_special_tokens=False) + + if bos: + t = [self.bos_id] + t + if eos: + t = t + [self.eos_id] + + return t + + def decode(self, t: List[int], **kwargs) -> str: + return self.tokenizer.decode(t, **kwargs) + + def process_one( + self, + prompt: str, + images: List, + inference_mode: bool = True, + **kwargs, + ): + """ + + Args: + prompt (str): the formatted prompt; + conversations (List[Dict]): conversations with a list of messages; + images (List[ImageType]): the list of images; + inference_mode (bool): if True, then remove the last eos token; + system_prompt (str): the system prompt; + **kwargs: + + Returns: + outputs (BaseProcessorOutput): the output of the processor, + - input_ids (torch.LongTensor): [N + image tokens] + - target_ids (torch.LongTensor): [N + image tokens] + - pixel_values (torch.FloatTensor): [n_patches, 3, H, W] + - image_id (int): the id of the image token + - num_image_tokens (List[int]): the number of image tokens + """ + + assert (prompt is not None and images is not None + ), "prompt and images must be used at the same time." + + sft_format = prompt + + input_ids, pixel_values, images_crop, images_seq_mask, images_spatial_crop, num_image_tokens, _ = images[0] + + + return { + "input_ids": input_ids, + "pixel_values": pixel_values, + "images_crop": images_crop, + "images_seq_mask": images_seq_mask, + "images_spatial_crop": images_spatial_crop, + "num_image_tokens": num_image_tokens, + } + + + # prepare = BatchFeature( + # data=dict( + # input_ids=input_ids, + # pixel_values=pixel_values, + # images_crop = images_crop, + # images_seq_mask=images_seq_mask, + # images_spatial_crop=images_spatial_crop, + # num_image_tokens=num_image_tokens, + # ), + # tensor_type="pt", + # ) + # return prepare + + def __call__( + self, + *, + prompt: str, + images: List, + inference_mode: bool = True, + **kwargs, + ): + """ + + Args: + prompt (str): the formatted prompt; + images (List[ImageType]): the list of images; + inference_mode (bool): if True, then remove the last eos token; + **kwargs: + + Returns: + outputs (BaseProcessorOutput): the output of the processor, + - input_ids (torch.LongTensor): [N + image tokens] + - images (torch.FloatTensor): [n_images, 3, H, W] + - image_id (int): the id of the image token + - num_image_tokens (List[int]): the number of image tokens + """ + + prepare = self.process_one( + prompt=prompt, + images=images, + inference_mode=inference_mode, + ) + + return prepare + + def tokenize_with_images( + self, + # conversation: str, + images: List[Image.Image], + bos: bool = True, + eos: bool = True, + cropping: bool = True, + ): + """Tokenize text with tags.""" + + # print(conversation) + conversation = PROMPT + assert conversation.count(self.image_token) == len(images) + text_splits = conversation.split(self.image_token) + images_list, images_crop_list, images_seq_mask, images_spatial_crop = [], [], [], [] + image_shapes = [] + num_image_tokens = [] + tokenized_str = [] + # print('image: ', len(images)) + for text_sep, image in zip(text_splits, images): + """encode text_sep""" + tokenized_sep = self.encode(text_sep, bos=False, eos=False) + tokenized_str += tokenized_sep + images_seq_mask += [False] * len(tokenized_sep) + + """select best resolution for anyres""" + # if cropping: + # best_width, best_height = self.select_best_resolution(image.size) + # else: + # best_width, best_height = self.image_size, self.image_size + + image_shapes.append(image.size) + + if image.size[0] <= 640 and image.size[1] <= 640: + crop_ratio = [1, 1] + else: + if cropping: + # print('image-size: ', image.size) + # best_width, best_height = select_best_resolution(image.size, self.candidate_resolutions) + # print('image ', image.size) + # print('open_size:', image.size) + images_crop_raw, crop_ratio = dynamic_preprocess(image, image_size=IMAGE_SIZE) + # print('crop_ratio: ', crop_ratio) + else: + # best_width, best_height = self.image_size, self.image_size + crop_ratio = [1, 1] + # print(image.size, (best_width, best_height)) # check the select_best_resolutions func + + # print(crop_ratio) + """process the global view""" + + # if cropping + if self.image_size <= 640 and not cropping: + # print('directly resize') + image = image.resize((self.image_size, self.image_size)) + + global_view = ImageOps.pad(image, (self.base_size, self.base_size), + color=tuple(int(x * 255) for x in self.image_transform.mean)) + images_list.append(self.image_transform(global_view)) + + """record height / width crop num""" + # width_crop_num, height_crop_num = best_width // self.image_size, best_height // self.image_size + num_width_tiles, num_height_tiles = crop_ratio + images_spatial_crop.append([num_width_tiles, num_height_tiles]) + + + + + if num_width_tiles > 1 or num_height_tiles > 1: + """process the local views""" + # local_view = ImageOps.pad(image, (best_width, best_height), + # color=tuple(int(x * 255) for x in self.image_transform.mean)) + # for i in range(0, best_height, self.image_size): + # for j in range(0, best_width, self.image_size): + # images_crop_list.append( + # self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size)))) + for i in range(len(images_crop_raw)): + images_crop_list.append(self.image_transform(images_crop_raw[i])) + + # """process the global view""" + # global_view = ImageOps.pad(image, (self.image_size, self.image_size), + # color=tuple(int(x * 255) for x in self.image_transform.mean)) + # images_list.append(self.image_transform(global_view)) + + # """process the local views""" + # local_view = ImageOps.pad(image, (best_width, best_height), + # color=tuple(int(x * 255) for x in self.image_transform.mean)) + # for i in range(0, best_height, self.image_size): + # for j in range(0, best_width, self.image_size): + # images_list.append( + # self.image_transform(local_view.crop((j, i, j + self.image_size, i + self.image_size)))) + + # """add image tokens""" + """add image tokens""" + num_queries = math.ceil((self.image_size // self.patch_size) / self.downsample_ratio) + num_queries_base = math.ceil((self.base_size // self.patch_size) / self.downsample_ratio) + + + tokenized_image = ([self.image_token_id] * num_queries_base + [self.image_token_id]) * num_queries_base + tokenized_image += [self.image_token_id] + if num_width_tiles > 1 or num_height_tiles > 1: + tokenized_image += ([self.image_token_id] * (num_queries * num_width_tiles) + [self.image_token_id]) * ( + num_queries * num_height_tiles) + tokenized_str += tokenized_image + images_seq_mask += [True] * len(tokenized_image) + num_image_tokens.append(len(tokenized_image)) + + """process the last text split""" + tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False) + tokenized_str += tokenized_sep + images_seq_mask += [False] * len(tokenized_sep) + + """add the bos and eos tokens""" + if bos: + tokenized_str = [self.bos_id] + tokenized_str + images_seq_mask = [False] + images_seq_mask + if eos: + tokenized_str = tokenized_str + [self.eos_id] + images_seq_mask = images_seq_mask + [False] + + assert len(tokenized_str) == len( + images_seq_mask), f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}" + + + + masked_tokenized_str = [] + for token_index in tokenized_str: + if token_index != self.image_token_id: + masked_tokenized_str.append(token_index) + else: + masked_tokenized_str.append(self.ignore_id) + + assert len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str), \ + (f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, " + f"imags_seq_mask's length {len(images_seq_mask)}, are not equal") + + input_ids = torch.LongTensor(tokenized_str) + target_ids = torch.LongTensor(masked_tokenized_str) + images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool) + + # set input_ids < 0 | input_ids == self.image_token_id as ignore_id + target_ids[(input_ids < 0) | + (input_ids == self.image_token_id)] = self.ignore_id + input_ids[input_ids < 0] = self.pad_id + + inference_mode = True + + if inference_mode: + # Remove the ending eos token + assert input_ids[-1] == self.eos_id + input_ids = input_ids[:-1] + target_ids = target_ids[:-1] + images_seq_mask = images_seq_mask[:-1] + + if len(images_list) == 0: + pixel_values = torch.zeros((1, 3, self.base_size, self.base_size)) + images_spatial_crop = torch.zeros((1, 1), dtype=torch.long) + images_crop = torch.zeros((1, 3, self.image_size, self.image_size)).unsqueeze(0) + else: + pixel_values = torch.stack(images_list, dim=0) + images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long) + if images_crop_list: + images_crop = torch.stack(images_crop_list, dim=0).unsqueeze(0) + else: + images_crop = torch.zeros((1, 3, self.image_size, self.image_size)).unsqueeze(0) + + input_ids = input_ids.unsqueeze(0) + + + return [[input_ids, pixel_values, images_crop, images_seq_mask, images_spatial_crop, num_image_tokens, image_shapes]] + + +AutoProcessor.register("DeepseekVLV2Processor", DeepseekOCRProcessor) diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/ngram_norepeat.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/ngram_norepeat.py new file mode 100644 index 00000000..c130c2f8 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/process/ngram_norepeat.py @@ -0,0 +1,40 @@ +import torch +from transformers import LogitsProcessor +from transformers.generation.logits_process import _calc_banned_ngram_tokens +from typing import List, Set + + +class NoRepeatNGramLogitsProcessor(LogitsProcessor): + + def __init__(self, ngram_size: int, window_size: int = 100, whitelist_token_ids: set = None): + if not isinstance(ngram_size, int) or ngram_size <= 0: + raise ValueError(f"`ngram_size` has to be a strictly positive integer, but is {ngram_size}") + if not isinstance(window_size, int) or window_size <= 0: + raise ValueError(f"`window_size` has to be a strictly positive integer, but is {window_size}") + self.ngram_size = ngram_size + self.window_size = window_size + self.whitelist_token_ids = whitelist_token_ids or set() + + def __call__(self, input_ids: List[int], scores: torch.FloatTensor) -> torch.FloatTensor: + if len(input_ids) < self.ngram_size: + return scores + + current_prefix = tuple(input_ids[-(self.ngram_size - 1):]) + + search_start = max(0, len(input_ids) - self.window_size) + search_end = len(input_ids) - self.ngram_size + 1 + + banned_tokens = set() + for i in range(search_start, search_end): + ngram = tuple(input_ids[i:i + self.ngram_size]) + if ngram[:-1] == current_prefix: + banned_tokens.add(ngram[-1]) + + banned_tokens = banned_tokens - self.whitelist_token_ids + + if banned_tokens: + scores = scores.clone() + for token in banned_tokens: + scores[token] = -float("inf") + + return scores \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/run_dpsk_ocr_eval_batch.py b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/run_dpsk_ocr_eval_batch.py new file mode 100644 index 00000000..c8dfc845 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/DeepSeek-OCR-master/DeepSeek-OCR-vllm/run_dpsk_ocr_eval_batch.py @@ -0,0 +1,161 @@ +import os +import re +from tqdm import tqdm +import torch +if torch.version.cuda == '11.8': + os.environ["TRITON_PTXAS_PATH"] = "/usr/local/cuda-11.8/bin/ptxas" +os.environ['VLLM_USE_V1'] = '0' +os.environ["CUDA_VISIBLE_DEVICES"] = '0' + +from config import MODEL_PATH, INPUT_PATH, OUTPUT_PATH, PROMPT, MAX_CONCURRENCY, CROP_MODE, NUM_WORKERS +from concurrent.futures import ThreadPoolExecutor +import glob +from PIL import Image +from deepseek_ocr import DeepseekOCRForCausalLM + +from vllm.model_executor.models.registry import ModelRegistry + +from vllm import LLM, SamplingParams +from process.ngram_norepeat import NoRepeatNGramLogitsProcessor +from process.image_process import DeepseekOCRProcessor +ModelRegistry.register_model("DeepseekOCRForCausalLM", DeepseekOCRForCausalLM) + + +llm = LLM( + model=MODEL_PATH, + hf_overrides={"architectures": ["DeepseekOCRForCausalLM"]}, + block_size=256, + enforce_eager=False, + trust_remote_code=True, + max_model_len=8192, + swap_space=0, + max_num_seqs = MAX_CONCURRENCY, + tensor_parallel_size=1, + gpu_memory_utilization=0.9, +) + +logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=40, window_size=90, whitelist_token_ids= {128821, 128822})] #window for fast；whitelist_token_ids: , + +sampling_params = SamplingParams( + temperature=0.0, + max_tokens=8192, + logits_processors=logits_processors, + skip_special_tokens=False, +) + +class Colors: + RED = '\033[31m' + GREEN = '\033[32m' + YELLOW = '\033[33m' + BLUE = '\033[34m' + RESET = '\033[0m' + +def clean_formula(text): + + formula_pattern = r'\\\[(.*?)\\\]' + + def process_formula(match): + formula = match.group(1) + + formula = re.sub(r'\\quad\s*\([^)]*\)', '', formula) + + formula = formula.strip() + + return r'\[' + formula + r'\]' + + cleaned_text = re.sub(formula_pattern, process_formula, text) + + return cleaned_text + +def re_match(text): + pattern = r'(<\|ref\|>(.*?)<\|/ref\|><\|det\|>(.*?)<\|/det\|>)' + matches = re.findall(pattern, text, re.DOTALL) + + + # mathes_image = [] + mathes_other = [] + for a_match in matches: + mathes_other.append(a_match[0]) + return matches, mathes_other + +def process_single_image(image): + """single image""" + prompt_in = prompt + cache_item = { + "prompt": prompt_in, + "multi_modal_data": {"image": DeepseekOCRProcessor().tokenize_with_images(images = [image], bos=True, eos=True, cropping=CROP_MODE)}, + } + return cache_item + + +if __name__ == "__main__": + + # INPUT_PATH = OmniDocBench images path + + os.makedirs(OUTPUT_PATH, exist_ok=True) + + # print('image processing until processing prompts.....') + + print(f'{Colors.RED}glob images.....{Colors.RESET}') + + images_path = glob.glob(f'{INPUT_PATH}/*') + + images = [] + + for image_path in images_path: + image = Image.open(image_path).convert('RGB') + images.append(image) + + prompt = PROMPT + + # batch_inputs = [] + + + # for image in tqdm(images): + + # prompt_in = prompt + # cache_list = [ + # { + # "prompt": prompt_in, + # "multi_modal_data": {"image": Image.open(image).convert('RGB')}, + # } + # ] + # batch_inputs.extend(cache_list) + + with ThreadPoolExecutor(max_workers=NUM_WORKERS) as executor: + batch_inputs = list(tqdm( + executor.map(process_single_image, images), + total=len(images), + desc="Pre-processed images" + )) + + + + + outputs_list = llm.generate( + batch_inputs, + sampling_params=sampling_params + ) + + + output_path = OUTPUT_PATH + + os.makedirs(output_path, exist_ok=True) + + for output, image in zip(outputs_list, images_path): + + content = output.outputs[0].text + mmd_det_path = output_path + image.split('/')[-1].replace('.jpg', '_det.md') + + with open(mmd_det_path, 'w', encoding='utf-8') as afile: + afile.write(content) + + content = clean_formula(content) + matches_ref, mathes_other = re_match(content) + for idx, a_match_other in enumerate(tqdm(mathes_other, desc="other")): + content = content.replace(a_match_other, '').replace('\n\n\n\n', '\n\n').replace('\n\n\n', '\n\n').replace('
', '').replace('
', '') + + mmd_path = output_path + image.split('/')[-1].replace('.jpg', '.md') + + with open(mmd_path, 'w', encoding='utf-8') as afile: + afile.write(content) diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/.gitignore b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/.gitignore new file mode 100644 index 00000000..0c720a59 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/.gitignore @@ -0,0 +1,20 @@ +**/__pycache__/ +# check_data/ +**/.ipynb_checkpoints/ +.ipynb_checkpoints +**log +debug.py +test.py +run_cal_metric.py +output/ +old_results +det_test +old_configs +check_data +trace +old_demo_data +old_run_cal_metric.py +old_test.py +old_tools +old_trace +result/ \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/LICENSE b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/LICENSE new file mode 100644 index 00000000..261eeb9e --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/end2end.yaml b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/end2end.yaml new file mode 100644 index 00000000..81a6fc6f --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/end2end.yaml @@ -0,0 +1,27 @@ +end2end_eval: + metrics: + text_block: + metric: + - Edit_dist + # - BLEU + # - METEOR + display_formula: + metric: + - Edit_dist + # - CDM_plain # CDM can be calculated directly by calling CDM in config file if you have CDM environment + table: + metric: + - TEDS + - Edit_dist + reading_order: + metric: + - Edit_dist + dataset: + dataset_name: end2end_dataset + ground_truth: + data_path: /home/ubuntu/texas-sandbox/paper-implementations/omnidocbench_evals/data/OmniDocBench/OmniDocBench.json + prediction: + data_path: ../../../outputs/deepseek_ocr/ + match_method: quick_match + # # # # # # # # # # # # # # # # # # filter: + # # # # # # # # # # # # # # # # # # language: english diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/formula_detection.yaml b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/formula_detection.yaml new file mode 100644 index 00000000..d8e65422 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/formula_detection.yaml @@ -0,0 +1,23 @@ +detection_eval: + metrics: + - COCODet + dataset: + dataset_name: detection_dataset_simple_format + ground_truth: + data_path: ./demo_data/omnidocbench_demo/OmniDocBench_demo.json + prediction: + data_path: ./demo_data/detection/detection_prediction.json + filter: + data_source: exam_paper + categories: + eval_cat: + block_level: + - isolate_formula # Display formula + span_level: + - inline_formula # inline formula + gt_cat_mapping: + equation_isolated: isolate_formula + equation_inline: inline_formula + pred_cat_mapping: + isolate_formula: isolate_formula + # inline_formula: inline_formula \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/formula_recognition.yaml b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/formula_recognition.yaml new file mode 100644 index 00000000..270d507e --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/formula_recognition.yaml @@ -0,0 +1,14 @@ +recogition_eval: + metrics: + - Edit_dist + - CDM + - BLEU + dataset: + dataset_name: omnidocbench_single_module_dataset + ground_truth: + data_path: ./demo_data/recognition/OmniDocBench_demo_formula.json + data_key: latex + category_filter: ['equation_isolated'] + prediction: + data_key: pred + category_type: formula \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/layout_detection.yaml b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/layout_detection.yaml new file mode 100644 index 00000000..cbf8790e --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/layout_detection.yaml @@ -0,0 +1,52 @@ +detection_eval: # layout + metrics: + - COCODet + dataset: + dataset_name: detection_dataset_simple_format + ground_truth: + data_path: ./demo_data/omnidocbench_demo/OmniDocBench_demo.json + prediction: + data_path: ./demo_data/detection/detection_prediction.json + # filter: + # data_source: exam_paper + categories: + eval_cat: + block_level: + - title # Title + - text # Text + - abandon # Includes headers, footers, page numbers, and page annotations + - figure # Image + - figure_caption # Image caption + - table # Table + - table_caption # Table caption + - table_footnote # Table footnote + - isolate_formula # Display formula + - formula_caption # Display formula label + gt_cat_mapping: + figure_footnote: figure_footnote + figure_caption: figure_caption + page_number: abandon + header: abandon + page_footnote: abandon + table_footnote: table_footnote + code_txt: figure + equation_caption: formula_caption + equation_isolated: isolate_formula + table: table + refernece: text + table_caption: table_caption + figure: figure + title: title + text_block: text + footer: abandon + pred_cat_mapping: + title : title + plain text: text + abandon: abandon + figure: figure + figure_caption: figure_caption + table: table + table_caption: table_caption + table_footnote: table_footnote + isolate_formula: isolate_formula + formula_caption: formula_caption \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/md2md.yaml b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/md2md.yaml new file mode 100644 index 00000000..f65bb889 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/md2md.yaml @@ -0,0 +1,27 @@ +end2end_eval: + metrics: + text_block: + metric: + - Edit_dist + # - METEOR + # - BLEU + display_formula: + metric: + - Edit_dist + - CDM_plain + table: + metric: + - TEDS + - Edit_dist + reading_order: + metric: + - Edit_dist + dataset: + dataset_name: md2md_dataset + ground_truth: + data_path: ./demo_data/omnidocbench_demo/mds + page_info: ./demo_data/omnidocbench_demo/OmniDocBench_demo.json + prediction: + data_path: ./demo_data/end2end + match_method: quick_match + \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/ocr.yaml b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/ocr.yaml new file mode 100644 index 00000000..9f166b4b --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/ocr.yaml @@ -0,0 +1,12 @@ +recogition_eval: + metrics: + - Edit_dist + - BLEU + dataset: + dataset_name: omnidocbench_single_module_dataset + ground_truth: + data_path: ./demo_data/recognition/OmniDocBench_demo_text_ocr.json + data_key: text + prediction: + data_key: pred + category_type: text \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/table_recognition.yaml b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/table_recognition.yaml new file mode 100644 index 00000000..0ecb060c --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/configs/table_recognition.yaml @@ -0,0 +1,13 @@ +recogition_eval: # Table + metrics: + - TEDS + - Edit_dist + dataset: + dataset_name: omnidocbench_single_module_dataset + ground_truth: + data_path: ./demo_data/recognition/OmniDocBench_demo_table.json + data_key: html + category_filter: table + prediction: + data_key: pred + category_type: table diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/__init__.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/__init__.py new file mode 100644 index 00000000..36d5d17e --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/__init__.py @@ -0,0 +1,16 @@ +from .recog_dataset import * +from .end2end_dataset import * +from .detection_dataset import * +from .md2md_dataset import * +from registry.registry import DATASET_REGISTRY + +__all__ = [ + "RecognitionFormulaDataset", + "End2EndDataset", + "DetectionDataset", + "Md2MdDataset", + "OmniDocBenchSingleModuleDataset", + "DetectionDatasetSimpleFormat" +] + +print('DATASET_REGISTRY: ', DATASET_REGISTRY.list_items()) \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/detection_dataset.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/detection_dataset.py new file mode 100644 index 00000000..51764604 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/detection_dataset.py @@ -0,0 +1,206 @@ +from mmeval import COCODetection +import json +import os +import numpy as np +from registry.registry import DATASET_REGISTRY +from collections import defaultdict +from utils.ocr_utils import poly2bbox +import pdb + +@DATASET_REGISTRY.register("detection_dataset") +class DetectionDataset(): + def __init__(self, cfg_task): + gt_path = cfg_task['dataset']['ground_truth']['data_path'] + pred_path = cfg_task['dataset']['prediction']['data_path'] + label_classes_level = cfg_task['categories'].get('eval_cat', {}) + label_classes = sum(list(label_classes_level.values()), []) + gt_cat_mapping = cfg_task['categories']['gt_cat_mapping'] + pred_cat_mapping = cfg_task['categories']['pred_cat_mapping'] + filtered_types = cfg_task['dataset'].get('filter') + + gts, img_list = self.get_gts_and_img_list(filtered_types, gt_path, label_classes, label_classes_level, gt_cat_mapping) + + preds = self.get_preds(img_list, pred_path, label_classes, label_classes_level, pred_cat_mapping) + + self.samples = { + 'gts': gts, + 'preds': preds + } + # print(self.samples) + + meta={'CLASSES':tuple(label_classes)} + self.coco_det_metric = COCODetection(dataset_meta=meta, metric=['bbox'], classwise=True) + + def get_gts_and_img_list(self, filtered_types, gt_path, label_classes, label_classes_level, gt_cat_mapping): + with open(os.path.join(gt_path), 'r') as f: + gt_samples = json.load(f) + basename = os.path.basename(gt_path)[:-5] + + img_list = [] + filtered_gt_samples = [] + if filtered_types: + for gt_sample in gt_samples: + select_flag = True + page_num = gt_sample['page_info']['page_no'] + if gt_sample['page_info'].get('image_path'): + sample_name = gt_sample['page_info']['image_path'] + else: + sample_name = f'{basename}_{page_num}' + for k, v in filtered_types.items(): + if gt_sample["page_info"]["page_attribute"][k] != v: + select_flag = False + if select_flag: + filtered_gt_samples.append(gt_sample) + img_list.append(sample_name) + else: + filtered_gt_samples = gt_samples + img_list = [gt_sample["page_info"]['image_path'] for gt_sample in gt_samples] + + gts = self.reform_gt(filtered_gt_samples, label_classes, label_classes_level, gt_cat_mapping) + + return gts, img_list + + def get_omni_annos(self, sample, cat_mapping, label_classes, label_classes_level): + bboxes = [] + labels = [] + scores = [] + for item in sample['layout_dets']: + if cat_mapping.get(item['category_type']): + class_name = cat_mapping[item['category_type']] + else: + class_name = item['category_type'] + + if class_name in label_classes_level.get('block_level', []): + bbox = poly2bbox(item['poly']) + bboxes.append(bbox) + labels.append(label_classes.index(class_name)) + if item.get('score'): + scores.append(item['score']) + else: + scores.append(1) + + for span in item.get('line_with_spans', []): + if cat_mapping.get(span['category_type']): + class_name = cat_mapping[span['category_type']] + else: + class_name = span['category_type'] + + if class_name in label_classes_level.get('span_level', []): + bbox = poly2bbox(span['poly']) + bboxes.append(bbox) + labels.append(label_classes.index(class_name)) + if span.get('score'): + scores.append(span['score']) + else: + scores.append(1) + return bboxes, labels, scores + + def reform_gt(self, filtered_gt_samples, label_classes, label_classes_level, gt_cat_mapping): + gts = [] + + for idx, sample in enumerate(filtered_gt_samples): + bboxes, labels, scores = self.get_omni_annos(sample, gt_cat_mapping, label_classes, label_classes_level) + gts.append({ + 'img_id': idx, + 'width': sample['page_info']['width'], + 'height': sample['page_info']['height'], + 'bboxes': np.array(bboxes), + 'labels': np.array(labels), + 'ignore_flags': [False]*len(labels), + }) + return gts + + def get_preds(self, img_list, pred_path, label_classes, label_classes_level, pred_cat_mapping): + pred_dict = {} + with open(os.path.join(pred_path), 'r') as f: + preds_sample = json.load(f) + basename = os.path.basename(pred_path)[:-5] + for pred in preds_sample: + page_num = pred['page_info']['page_no'] + if pred['page_info'].get('image_path'): + pred_dict[pred['page_info']['image_path']] = pred + else: + pred_dict[f'{basename}_{page_num}'] = pred + + preds = self.reform_pred(pred_dict, img_list, label_classes, label_classes_level, pred_cat_mapping) + + return preds + + def reform_pred(self, pred_dict, img_list, label_classes, label_classes_level, pred_cat_mapping): + preds = [] + + for idx, sample_name in enumerate(img_list): + sample = pred_dict.get(sample_name) + if not sample: + print(f'No matching prediction for {sample_name}. The prediction wiil be empty.') + preds.append({ + 'img_id': idx, + 'bboxes': np.array([]), + 'scores': np.array([]), + 'labels': np.array([]), + }) + continue + + bboxes, labels, scores = self.get_omni_annos(sample, pred_cat_mapping, label_classes, label_classes_level) + + preds.append({ + 'img_id': idx, + 'bboxes': np.array(bboxes), + 'scores': np.array(scores), + 'labels': np.array(labels), + }) + return preds + +@DATASET_REGISTRY.register("detection_dataset_simple_format") +class DetectionDatasetSimpleFormat(DetectionDataset): + def get_preds(self, img_list, pred_path, label_classes, label_classes_level, pred_cat_mapping): + pred_dict = defaultdict(list) + + with open(os.path.join(pred_path), 'r') as f: + preds_sample = json.load(f) + + for pred in preds_sample['results']: + pred_dict[pred["image_name"]+'.jpg'].append(pred) + + pred_name2cat = {name: int(cat) for cat, name in preds_sample["categories"].items()} + pred_cat_mapping = {pred_name2cat[cat]:name for cat, name in pred_cat_mapping.items()} + + preds = self.reform_pred(pred_dict, img_list, label_classes, label_classes_level, pred_cat_mapping) + + return preds + + def reform_pred(self, pred_dict, img_list, label_classes, label_classes_level, pred_cat_mapping): + preds = [] + for idx, sample_name in enumerate(img_list): + sample = pred_dict.get(sample_name) + if not sample: + print(f'No matching prediction for {sample_name}. The prediction wiil be empty.') + preds.append({ + 'img_id': idx, + 'bboxes': np.array([]), + 'scores': np.array([]), + 'labels': np.array([]), + }) + continue + + pred_bboxes = [] + pred_labels = [] + scores = [] + for item in sample: + if pred_cat_mapping.get(item["category_id"]): + class_name = pred_cat_mapping[item["category_id"]] + else: + class_name = item["category_id"] + + if class_name in label_classes: + pred_bboxes.append(item['bbox']) + pred_labels.append(label_classes.index(class_name)) + scores.append(item['score']) + + preds.append({ + 'img_id': idx, + 'bboxes': np.array(pred_bboxes), + 'scores': np.array(scores), + 'labels': np.array(pred_labels), + }) + return preds \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/end2end_dataset.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/end2end_dataset.py new file mode 100644 index 00000000..4680e0ed --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/end2end_dataset.py @@ -0,0 +1,376 @@ +import json +import os +from collections import defaultdict +from utils.extract import md_tex_filter +from utils.match import match_gt2pred_simple, match_gt2pred_no_split +from utils.match_quick import match_gt2pred_quick +# from utils.match_full import match_gt2pred_full, match_gt2pred_textblock_full +from utils.read_files import read_md_file +from utils.data_preprocess import normalized_table, clean_string +from registry.registry import DATASET_REGISTRY +from dataset.recog_dataset import * +import pdb +import Levenshtein +from tqdm import tqdm +from func_timeout import FunctionTimedOut, func_timeout +from loguru import logger +import time +import sys +from pylatexenc.latex2text import LatexNodes2Text +import traceback + +@DATASET_REGISTRY.register("end2end_dataset") +class End2EndDataset(): + def __init__(self, cfg_task): + gt_path = cfg_task['dataset']['ground_truth']['data_path'] + pred_folder = cfg_task['dataset']['prediction']['data_path'] + self.match_method = cfg_task['dataset'].get('match_method', 'quick_match') + filtered_types = cfg_task['dataset'].get('filter') + + with open(gt_path, 'r') as f: + gt_samples = json.load(f) + + filtered_gt_samples = [] + if filtered_types: + for gt_sample in gt_samples: + select_flag = True + for k, v in filtered_types.items(): + if gt_sample["page_info"]["page_attribute"][k] != v: + select_flag = False + if select_flag: + filtered_gt_samples.append(gt_sample) + else: + filtered_gt_samples = gt_samples + + self.samples = self.get_matched_elements(filtered_gt_samples, pred_folder) + + + def __getitem__(self, cat_name, idx): + return self.samples[cat_name][idx] + + + # 匹配元素 处理文本截断问题，将截断的文本块合并，并将元素按类别存储在字典中 + def get_page_elements(self, selected_annos): + + saved_element_dict = defaultdict(list) #存储元素 + related_truncated = [] #存储需要合并的截断文本块列表 + truncated_all = {} #存储截断文本块信息 + for relation in selected_annos["extra"]["relation"]: # Handle truncated text issues + if relation["relation_type"] == 'truncated': + truncated_all[relation["source_anno_id"]] = "" + truncated_all[relation["target_anno_id"]] = "" + exist_flag = False + for merge_list in related_truncated: + if relation["source_anno_id"] in merge_list or relation["target_anno_id"] in merge_list: # Consider cases where three text blocks may need to be merged + merge_list.append(relation["source_anno_id"]) + merge_list.append(relation["target_anno_id"]) + exist_flag = True + if not exist_flag: + related_truncated.append([relation["source_anno_id"], relation["target_anno_id"]]) + + for item in selected_annos['layout_dets']: + if item['anno_id'] not in truncated_all.keys(): + saved_element_dict[item["category_type"]].append(item) + else: + truncated_all[item['anno_id']] = item + + for merge_list in related_truncated: + text_block_list = [truncated_all[key] for key in merge_list] + sorted_block = sorted(text_block_list, key=lambda x: x['order']) + text = "" + for block in sorted_block: + text += block['text'] + merged_block = { + "category_type": sorted_block[0]["category_type"], # Directly use information from the first block + "order": sorted_block[0]["order"], + "anno_id": sorted_block[0]["anno_id"], + "text": text, + "merge_list": sorted_block + } + saved_element_dict[sorted_block[0]["category_type"]].append(merged_block) + # print('Merged truncated') + + return saved_element_dict + + # 根据类别列表 category_list 从 gt_page_elements 中提取元素，并将它们合并到一个列表中 + def get_page_elements_list(self, gt_page_elements, category_list): + element_list = [] + for category_type in category_list: + if gt_page_elements.get(category_type): + element_list.extend(gt_page_elements[category_type]) + return element_list + + # 根据元素的 order 字段对元素列表进行排序，并返回排序后的元素列表。 + def get_sorted_text_list(self, selected_annos): + # txt_type: text, latex, html + text_list = [] + for item in selected_annos: + if item.get('order'): + order = item['order'] + else: + order = 0 + text_list.append((order, item)) + sorted_text_list = sorted(text_list, key=lambda x: x[0]) + return [_[1] for _ in sorted_text_list] + + # 从元素列表 items 中过滤掉 gt_category_type 在 ignore_category_list 中的元素。 + def filtered_out_ignore(self, items, ignore_category_list): + filted_items = [] + for item in items: + if item['gt_category_type'] not in ignore_category_list: + filted_items.append(item) + return filted_items + + # 计算预测结果和地面真值的阅读顺序之间的编辑距离，并返回包含相关信息的字典。 + def get_order_paired(self, order_match_s, img_name): + matched = [(item['gt_position'], item['pred_position']) for item in order_match_s if (item['gt_position'] != [""] and item['pred_position'] != "")] + gt_idx_all = [item['gt_position'] for item in order_match_s if (item['gt_position'] != [""])] + read_order_pred = [i[0] for i in sorted(matched, key=lambda x: x[1])] # Sort by pred idx to get Pred ordered GT_idx + read_order_gt = sum(gt_idx_all, []) # Convert to one-dimensional list + read_order_gt = [x for x in read_order_gt if x] # For truncated merges, some discarded classes may be merged in, remove them when calculating edit distance + gt = sorted(read_order_gt) # Sort by all GT idx to get GT ordered GT_idx + pred = sum(read_order_pred, []) + pred = [x for x in pred if x] + if len(pred) > 0 or len(gt) > 0: + edit = Levenshtein.distance(gt, pred)/ max(len(pred), len(gt)) + return { + 'gt': gt, + 'pred': pred, + 'img_id': img_name, + 'edit': edit + } + else: + return {} # If both GT and pred are empty for the page, return empty + + # 为公式匹配结果添加 img_id 信息。 + def formula_format(self, formula_matches, img_name): + # formated_list = [] + for i, item in enumerate(formula_matches): + item["img_id"] = img_name + '_' + str(i) + return formula_matches + + # 对gt和预测结果进行匹配，调用 process_get_matched_elements 函数进行匹配处理，最终将匹配结果整理成一个字典返回 + def get_matched_elements(self, gt_samples, pred_folder): + plain_text_match = [] + display_formula_match = [] + html_table_match = [] + latex_table_match = [] + order_match = [] + save_time = time.time() + process_bar = tqdm(gt_samples, ascii=True, ncols=140) + for sample in process_bar: + img_name = os.path.basename(sample["page_info"]["image_path"]) + + # print('Process: ', img_name) + pred_path = os.path.join(pred_folder, img_name[:-4] + '.md') + if not os.path.exists(pred_path): + pred_path = os.path.join(pred_folder, img_name[:-4].replace('.pdf', "") + '.mmd') # nougat + if not os.path.exists(pred_path): + pred_path = os.path.join(pred_folder, img_name[:-4].replace('.pdf', "") + '.md') # marker + if not os.path.exists(pred_path): + pred_path = os.path.join(pred_folder, img_name + '.md') + if not os.path.exists(pred_path): # mineru + print(f'!!!WARNING: No prediction for {img_name}') + continue + + process_bar.set_description(f'Processing {os.path.basename(pred_path)}') + pred_content = read_md_file(pred_path) + + # 对单个样本匹配，根据不同的元素类型（如文本块、显示公式、表格等），使用指定的匹配方法将gt与预测结果进行匹配，并返回匹配结果 + result = self.process_get_matched_elements(sample, pred_content, img_name, save_time) # Don't use timeout logic + + [plain_text_match_clean, formated_display_formula, latex_table_match_s, html_table_match_s, order_match_single] = result + + # if img_name == 'docstructbench_dianzishu_zhongwenzaixian-o.O-63674848.pdf_101.jpg': + # pdb.set_trace() + if order_match_single: + order_match.append(order_match_single) + if plain_text_match_clean: + plain_text_match.extend(plain_text_match_clean) + if formated_display_formula: + display_formula_match.extend(formated_display_formula) + if latex_table_match_s: + latex_table_match.extend(latex_table_match_s) + if html_table_match_s: + html_table_match.extend(html_table_match_s) + + display_formula_match_clean,display_formula_match_others = [],[] + for item in display_formula_match: + pred_category_type = item.get("pred_category_type",None) + # if pred_category_type == 'equation_inline': + # print(item) + if pred_category_type not in ['equation_inline','equation_isolated', '']: + gt = item.get('gt',None) + norm_gt = item.get('norm_gt',None) + ## latex2unicode + item['gt'] = LatexNodes2Text().latex_to_text(gt) + # item['norm_gt'] = LatexNodes2Text().latex_to_text(norm_gt) # 错了，这里的norm gt是跑的normalized_formula函数，所以再跑latex2unicode会报错 + # 这里的norm_gt应该是跑文本的nrom了 + item['norm_gt'] = clean_string(item['gt']) + display_formula_match_others.append(item) + else: + display_formula_match_clean.append(item) + display_formula_match = display_formula_match_clean + if display_formula_match_others and plain_text_match: + plain_text_match.extend(display_formula_match_others) + + # 将latex合并到html 全量428 + if latex_table_match: + latex_to_html = [] + for latex_table in latex_table_match: + for k,v in latex_table.items(): + if 'pred' in k: + latex_table[k] = "" + latex_table['edit'] = 1 + latex_to_html.append(latex_table) + html_table_match.extend(latex_to_html) + + + if len(latex_table_match) > len(html_table_match): # Assume model won't randomly output both latex and html, but will choose one + table_match = latex_table_match + table_format = 'latex' + else: + table_match = html_table_match + table_format = 'html' + + # with open('./qwen_latex_table_match.json','w',encoding='utf-8') as f: + # json.dump(latex_table_match,f,indent=4,ensure_ascii=False) + # with open('./qwen_html_table_match.json','w',encoding='utf-8') as f: + # json.dump(html_table_match,f,indent=4,ensure_ascii=False) + + + matched_samples_all = { + 'text_block': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(plain_text_match), + 'display_formula': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(display_formula_match), + 'table': DATASET_REGISTRY.get('recogition_end2end_table_dataset')(table_match, table_format), + 'reading_order': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(order_match) + } + + + return matched_samples_all + + #0403 提取gt的table跟pred的table进行匹配 -> 未匹配上的pred_table 去掉html格式然后丢进去混合匹配 + def process_get_matched_elements(self, sample, pred_content, img_name, save_time): + if self.match_method == 'simple_match': # add match choice + match_gt2pred = match_gt2pred_simple + elif self.match_method == 'quick_match': + match_gt2pred = match_gt2pred_quick + elif self.match_method == 'no_split': + match_gt2pred = match_gt2pred_no_split + else: + print('Invalid match method name. The quick_match will be used.') + match_gt2pred = match_gt2pred_quick + + pred_dataset = md_tex_filter(pred_content) + gt_page_elements = self.get_page_elements(sample) + + gt_mix,pred_dataset_mix = [],[] + for category in pred_dataset: + if category not in ['html_table','latex_table','md2html_table']: + pred_dataset_mix.extend(pred_dataset[category]) + # for category in gt_page_elements: + # if category not in ['table']: + # gt_mix.extend(gt_page_elements[category]) + gt_mix = self.get_page_elements_list(gt_page_elements, ['text_block', 'title', 'code_txt', 'code_txt_caption', 'reference', 'equation_caption', + 'figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', + 'header', 'footer', 'page_footnote', 'page_number', 'equation_isolated']) + if gt_mix: + gt_mix = self.get_sorted_text_list(gt_mix) + + display_formula_match_s = [] + plain_text_match_clean = [] + latex_table_match_s = [] + html_table_match_s = [] + order_match_single = [] + + + if gt_page_elements.get('table'): + gt_table = self.get_sorted_text_list(gt_page_elements['table']) + latex_table_len = len(pred_dataset['latex_table']) if pred_dataset['latex_table'] else 0 + html_table_len = len(pred_dataset['html_table']) if pred_dataset['html_table'] else 0 + if latex_table_len == html_table_len and latex_table_len == 0: + html_table_match_s,unmatch_table_pred = match_gt2pred_simple(gt_table, [], 'html_table', img_name) # Don't consider truncated merging for tables + html_table_match_s = [x for x in html_table_match_s if x['gt_idx'] != [""]] # Remove extra preds + elif latex_table_len > html_table_len: + latex_table_match_s,unmatch_table_pred = match_gt2pred_simple(gt_table, pred_dataset['latex_table'], 'latex_table', img_name) # Don't consider truncated merging for tables + latex_table_match_s = [x for x in latex_table_match_s if x['gt_idx'] != [""]] # Remove extra preds + else: + html_table_match_s,unmatch_table_pred = match_gt2pred_simple(gt_table, pred_dataset['html_table'], 'html_table', img_name) # Don't consider truncated merging for tables + html_table_match_s = [x for x in html_table_match_s if x['gt_idx'] != [""]] # Remove extra preds + + if unmatch_table_pred: + pred_dataset_mix.extend(unmatch_table_pred) + + try: + match = func_timeout(30, match_gt2pred, args=(gt_mix, pred_dataset_mix, 'text_all', img_name)) + except FunctionTimedOut as e1: + # print(f'Time out for plain text match of {img_name}, match_gt2pred_simple will be used.') + match,_ = match_gt2pred_simple(gt_mix, pred_dataset_mix, 'text_all', img_name) + except Exception as e: + # print(str(e)) + print(traceback.format_exc()) + sys.exit() + + + plain_text_match_s = [] + for item in match: + gt_category = item.get('gt_category_type',None) + if gt_category in ['text_block', 'title', 'code_txt', 'code_txt_caption', 'reference', 'equation_caption', + 'figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', + 'header', 'footer', 'page_footnote', 'page_number']: + plain_text_match_s.append(item) + elif gt_category == 'equation_isolated': + display_formula_match_s.append(item) + + display_formula_match_s = [x for x in display_formula_match_s if x['gt_idx'] != [""]] + + if not plain_text_match_s: + # print(f'Time out for text match of {img_name}. The plain text match will be empty.') + # print(f'No text match of {img_name}. The plain text match will be empty.') + pass + else: + # Categories that need to be ignored for text + plain_text_match_clean = self.filtered_out_ignore(plain_text_match_s, ['figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number', 'equation_caption']) + # plain_text_match_clean = self.filtered_out_ignore(plain_text_match_s, ['figure_footnote', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number']) + order_match_s = plain_text_match_clean + if order_match_s: + order_match_single = self.get_order_paired(order_match_s, img_name) + + + return [plain_text_match_clean, display_formula_match_s, latex_table_match_s, html_table_match_s, order_match_single] + + + + +@DATASET_REGISTRY.register("recogition_end2end_base_dataset") +class RecognitionEnd2EndBaseDataset(): + def __init__(self, samples): + img_id = 0 + for sample in samples: + if not sample.get('img_id'): + sample['img_id'] = img_id + img_id += 1 + self.samples = samples + def __getitem__(self, idx): + return self.samples[idx] + +@DATASET_REGISTRY.register("recogition_end2end_table_dataset") +class RecognitionEnd2EndTableDataset(RecognitionTableDataset): + def __init__(self, samples, table_format): + self.pred_table_format = table_format + self.samples = self.normalize_data(samples) + + def normalize_data(self, samples): + img_id = 0 + + for sample in samples: + p = sample['pred'] + r = sample['gt'] + p = normalized_table(p, self.pred_table_format) + r = normalized_table(r) + sample['norm_gt'] = r + sample['norm_pred'] = p + sample['img_id'] = sample['img_id'] if sample.get('img_id') else img_id + img_id += 1 + + return samples diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/md2md_dataset.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/md2md_dataset.py new file mode 100644 index 00000000..eb6c6a1a --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/md2md_dataset.py @@ -0,0 +1,143 @@ +import json +import os +from collections import defaultdict +from utils.extract import md_tex_filter +from utils.match import match_gt2pred_simple, match_gt2pred_no_split +from utils.match_quick import match_gt2pred_quick +# from utils.match_full import match_gt2pred_full, match_gt2pred_textblock_full +from utils.read_files import read_md_file +from registry.registry import DATASET_REGISTRY +from dataset.recog_dataset import * +import pdb +import Levenshtein +from tqdm import tqdm + +@DATASET_REGISTRY.register("md2md_dataset") +class Md2MdDataset(): + def __init__(self, cfg_task): + gt_folder = cfg_task['dataset']['ground_truth']['data_path'] + pred_folder = cfg_task['dataset']['prediction']['data_path'] + self.match_method = cfg_task['dataset'].get('match_method', 'simple_match') + + self.samples = self.get_matched_elements(gt_folder, pred_folder) + + def __getitem__(self, cat_name, idx): + return self.samples[cat_name][idx] + + def get_order_paired(self, order_match_s, img_name): + matched = [(item['gt_position'], item['pred_position']) for item in order_match_s if (item['gt_position'] != [""] and item['pred_position'] != "")] + gt_idx_all = [item['gt_position'] for item in order_match_s if (item['gt_position'] != [""])] + read_order_pred = [i[0] for i in sorted(matched, key=lambda x: x[1])] # Sort by pred idx to get Pred ordered GT_idx + read_order_gt = sum(gt_idx_all, []) # Convert to 1D list + read_order_gt = [x for x in read_order_gt if x] # During truncation merging, some discarded classes may be merged in. Remove them when calculating edit distance + gt = sorted(read_order_gt) # Sort by all GT idx to get GT ordered GT_idx + pred = sum(read_order_pred, []) + pred = [x for x in pred if x] + if len(pred) > 0 or len(gt) > 0: + edit = Levenshtein.distance(gt, pred)/ max(len(pred), len(gt)) + return { + 'gt': gt, + 'pred': pred, + 'img_id': img_name, + 'edit': edit + } + else: + return {} # If both GT and pred are empty for the page, return empty + + def get_matched_elements(self, gt_folder, pred_folder): + plain_text_match = [] + display_formula_match = [] + html_table_match = [] + latex_table_match = [] + order_match = [] + + for sample_name in tqdm(os.listdir(gt_folder)): + if not sample_name.endswith('.md'): + continue + + img_name = sample_name[:-3] + '.jpg' + + gt_content = read_md_file(os.path.join(gt_folder, sample_name)) + + pred_path = os.path.join(pred_folder, sample_name) + if not os.path.exists(pred_path): + print(f'!!!WARNING: No prediction for {sample_name}') + continue + else: + pred_content = read_md_file(pred_path) + + if self.match_method == 'simple_match': # add match choice + match_gt2pred = match_gt2pred_simple + # match_gt2pred_textblock = match_gt2pred_textblock_simple + elif self.match_method == 'quick_match': + match_gt2pred = match_gt2pred_quick + # match_gt2pred_textblock = match_gt2pred_textblock_quick + elif self.match_method == 'no_split': + match_gt2pred = match_gt2pred_no_split + else: + print('Invalid match method name. The quick_match will be used.') + match_gt2pred = match_gt2pred_quick + + gt_dataset = md_tex_filter(gt_content) + pred_dataset = md_tex_filter(pred_content) + + display_formula_match_s = [] + plain_text_match_clean = [] + if gt_dataset['text_all']: + plain_text_match_s = match_gt2pred(gt_dataset['text_all'], pred_dataset['text_all'], 'text', img_name) + # No ignore logic for text categories in markdown + plain_text_match_clean = plain_text_match_s + plain_text_match.extend(plain_text_match_s) + + # if gt_page_elements.get('title'): + # gt_title_list = self.get_sorted_text_list(gt_page_elements['title']) + # # print('gt_title_list: ', gt_title_list) + # title_match_s = match_gt2pred(gt_title_list, pred_title_list, 'text', img_name) + # title_match.extend(title_match_s) + # print('title_match_s: ', title_match_s) + # print('-'*10) + if gt_dataset.get('equation_isolated'): + display_formula_match_s = match_gt2pred(gt_dataset['equation_isolated'], pred_dataset['equation_isolated'], 'formula', img_name) + display_formula_match_s = [x for x in display_formula_match_s if x['gt_idx'] != [""] and x['gt_category_type'] != 'equation_inline'] # Remove extra preds since inline formulas are also included for matching, and remove GT that are inline formulas + display_formula_match.extend(display_formula_match_s) + if gt_dataset.get('latex_table') and pred_dataset.get('latex_table'): # By default the model won't randomly output both latex and html, but choose one; Note that table format in GT markdown needs to match Pred + # print('gt_table_list', gt_table_list) + table_match_s = match_gt2pred(gt_dataset['latex_table'], pred_dataset['latex_table'], 'latex_table', img_name) + table_match_s = [x for x in table_match_s if x['gt_idx'] != [""]] # Remove extra preds + latex_table_match.extend(table_match_s) + elif gt_dataset.get('html_table') and pred_dataset.get('html_table'): + table_match_s = match_gt2pred(gt_dataset['html_table'], pred_dataset['html_table'], 'html_table', img_name) + table_match_s = [x for x in table_match_s if x['gt_idx'] != [""]] # Remove extra preds + html_table_match.extend(table_match_s) + # print('table_match_s: ', table_match_s) + # print('-'*10) + else: + if gt_dataset.get('latex_table') or gt_dataset.get('html_table'): + print('GT table is not empty. But pred is empty or its format is different from gt.') + if pred_dataset.get('latex_table') or pred_dataset.get('html_table'): + print('Pred table is not empty. But gt is empty or its format is different from pred.') + + # Process reading order + # order_match_s = [] + # for mateches in [plain_text_match_clean, display_formula_match_s]: + # if mateches: + # order_match_s.extend(mateches) + order_match_s = self.get_order_paired(plain_text_match_clean, img_name) + if order_match_s: + order_match.append(order_match_s) + + if latex_table_match: # By default the model won't randomly output both latex and html, but choose one + table_match = latex_table_match + table_format = 'latex' + else: + table_match = html_table_match + table_format = 'html' + + matched_samples_all = { + 'text_block': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(plain_text_match), + 'display_formula': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(display_formula_match), + 'table': DATASET_REGISTRY.get('recogition_end2end_table_dataset')(table_match, table_format), + 'reading_order': DATASET_REGISTRY.get('recogition_end2end_base_dataset')(order_match) + } + + return matched_samples_all \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/recog_dataset.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/recog_dataset.py new file mode 100644 index 00000000..52913eb8 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/dataset/recog_dataset.py @@ -0,0 +1,222 @@ +import re +from registry.registry import DATASET_REGISTRY +import json +import os +import shutil +from tqdm import tqdm +from utils.ocr_utils import get_text_for_block +from utils.data_preprocess import clean_string, normalized_formula, textblock2unicode, normalized_table + + +@DATASET_REGISTRY.register("recogition_text_dataset") +class RecognitionTextDataset(): + # Evaluate at text block granularity, without considering one-to-one bbox matching + def __init__(self, cfg_task): + gt_file = cfg_task['dataset']['ground_truth']['data_path'] + pred_folder = cfg_task['dataset']['prediction']['data_path'] + self.samples = self.load_data(gt_file, pred_folder) + + def load_data(self, gt_file, pred_folder): + samples = [] + with open(gt_file, 'r') as f: + gts = json.load(f) + + for gt in gts: + img_name = os.path.basename(gt['image_path']) + gt_text = gt['text'] + pred_file = os.path.join(pred_folder, img_name[:-4]+'.json') + if not os.path.exists(pred_file): + print(f'Cannot find pred for {img_name}') + continue + else: + with open(pred_file, 'r') as f: + pred_spans = json.load(f) + pred_text = get_text_for_block(gt, pred_spans) + samples.append({ + "gt": gt_text, + 'pred': pred_text, + 'img_id': img_name + }) + return samples + +@DATASET_REGISTRY.register("omnidocbench_single_module_dataset") +class OmiDocBenchSingleModuleDataset(): + # Evaluate at text block granularity, without considering one-to-one bbox matching + def __init__(self, cfg_task): + gt_key = cfg_task['dataset']['ground_truth']['data_key'] + pred_file = cfg_task['dataset']['ground_truth']['data_path'] + pred_key = cfg_task['dataset']['prediction']['data_key'] + + self.category_filter = cfg_task['dataset']['ground_truth'].get('category_filter', []) + self.category_type = cfg_task['dataset'].get('category_type') + self.samples = self.load_data(pred_file, pred_key, gt_key) + + def load_data(self, pred_file, pred_key, gt_key): + samples = [] + with open(pred_file, 'r') as f: + preds = json.load(f) + count = 0 + for pred in preds: + img_name = os.path.basename(pred['page_info']['image_path']) + for i, ann in enumerate(pred['layout_dets']): + if not ann.get(gt_key): + continue + if self.category_filter: + if ann['category_type'] not in self.category_filter: + continue + if not ann.get(pred_key): + # print(f'Cannot find pred for {img_name}. ann is {ann}') + # pdb.set_trace() + count += 1 + continue + else: + gt_text = ann[gt_key] + norm_gt = gt_text + pred_text = ann[pred_key] + norm_pred = pred_text + if self.category_type: + if self.category_type == 'text': + norm_gt = clean_string(textblock2unicode(ann[gt_key])) + norm_pred = clean_string(textblock2unicode(ann[pred_key])) + elif self.category_type == 'formula': + norm_gt = normalized_formula(ann[gt_key]) + norm_pred = normalized_formula(ann[pred_key]) + elif self.category_type == 'table': + norm_gt = normalized_table(ann[gt_key], gt_key) + norm_pred = normalized_table(ann[pred_key], gt_key) + else: + raise ValueError(f'Invalid category type: {self.category_type}') + + samples.append({ + "gt": gt_text, + "norm_gt": norm_gt, + "gt_attribute": [ann['attribute']], + 'pred': pred_text, + "norm_pred": norm_pred, + 'img_id': img_name + }) + print(f'Cannot find pred for {count} samples.') + + return samples + +@DATASET_REGISTRY.register("recogition_formula_dataset") +class RecognitionFormulaDataset(): + def __init__(self, cfg_task): + gt_file = cfg_task['dataset']['ground_truth']['data_path'] + pred_file = cfg_task['dataset']['prediction']['data_path'] + + self.samples = self.load_data(gt_file, pred_file) + + def load_data(self, gt_file, pred_file): + """ + Load a list of image paths and their corresponding formulas. + The function skips empty lines and lines without corresponding images. + + Args: + image_path (str): The path to the directory containing the image files. + math_file (str): The path to the text file containing the formulas. + + Returns: + list, list: A list of image paths and a list of corresponding formula + """ + + with open(gt_file, 'r') as f: + math_gts = [line.strip() for line in f.readlines()] + + with open(pred_file, 'r') as f: + math_preds = [line.strip() for line in f.readlines()] + + + if len(math_preds) != len(math_gts): + raise ValueError("The number of prediction does not match the number of ground truth.") + + norm_gts = [self.normalize_text(gt) for gt in math_gts] # Formula normalization + norm_preds = [self.normalize_text(pred) for pred in math_preds] + + samples = [] + img_id = 0 + for gt, pred in zip(norm_gts, norm_preds): + samples.append({ + 'gt': gt, + 'pred': pred, + 'img_id': img_id + }) + img_id += 1 + + return samples + + def normalize_text(self, text): + """Remove unnecessary whitespace from LaTeX code.""" + text_reg = r'(\\(operatorname|mathrm|text|mathbf)\s?\*? {.*?})' + letter = '[a-zA-Z]' + noletter = '[\W_^\d]' + names = [x[0].replace(' ', '') for x in re.findall(text_reg, text)] + text = re.sub(text_reg, lambda match: str(names.pop(0)), text) + news = text + while True: + text = news + news = re.sub(r'(?!\\ )(%s)\s+?(%s)' % (noletter, noletter), r'\1\2', text) + news = re.sub(r'(?!\\ )(%s)\s+?(%s)' % (noletter, letter), r'\1\2', news) + news = re.sub(r'(%s)\s+?(%s)' % (letter, noletter), r'\1\2', news) + if news == text: + break + return text + + def __getitem__(self, idx): + return self.samples[idx] + +@DATASET_REGISTRY.register("recogition_table_dataset") +class RecognitionTableDataset(): + def __init__(self, cfg_task): + gt_file = cfg_task['dataset']['ground_truth']['data_path'] + pred_file = cfg_task['dataset']['prediction']['data_path'] + self.pred_table_format = cfg_task['dataset']['prediction'].get('table_format', 'html') + + references, predictions = self.load_data(gt_file), self.load_data(pred_file) + self.samples = self.normalize_data(references, predictions) + + def normalize_data(self, references, predictions): + if self.pred_table_format == 'latex2html': + os.makedirs('./temp', exist_ok=True) + + samples = [] + ref_keys = list(references.keys()) + + for img in tqdm(ref_keys, total=len(ref_keys), ncols=140, ascii=True, desc='Normalizing data'): + if self.pred_table_format == 'html': + r = references[img]['html'] + p = predictions[img]['html'] + elif self.pred_table_format == 'latex': + r = references[img]['latex'] + p = predictions[img]['latex'] + else: + raise ValueError(f'Invalid table format: {self.pred_table_format}') + + img_id = references[img]["page_image_name"] + p = normalized_table(p, self.pred_table_format) + r = normalized_table(r, self.pred_table_format) + # print('p:', p) + # print('r:', r) + samples.append({ + 'gt': p, + 'pred': r, + 'img_id': img_id, + 'gt_attribute': [references[img]['attribute']], + }) + + if self.pred_table_format == 'latex2html': + shutil.rmtree('./temp') + return samples + + def __getitem__(self, idx): + return self.samples[idx] + + def load_data(self, data_path): + result_dict = {} + with open(data_path, 'r') as f: + samples = json.load(f) + for sample in samples: + result_dict[sample["image_path"]] = sample + + return result_dict + diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/__init__.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/__init__.py new file mode 100644 index 00000000..20a94b3d --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/__init__.py @@ -0,0 +1,16 @@ +# from .cal_metric import call_TEDS, call_BLEU, call_METEOR, call_Edit_dist, call_CDM, call_Move_dist +from .cal_metric import * + +from registry.registry import METRIC_REGISTRY + +__all__ = [ + "call_TEDS", + "call_BLEU", + "call_METEOR", + "call_Edit_dist", + "call_CDM", + "call_CDM_plain", + "call_Move_dist" +] + +print('METRIC_REGISTRY: ', METRIC_REGISTRY.list_items()) \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cal_metric.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cal_metric.py new file mode 100644 index 00000000..e097df16 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cal_metric.py @@ -0,0 +1,349 @@ +# import evaluate +# import random +import json +import time +import os +# from rapidfuzz.distance import Levenshtein +import Levenshtein +from .table_metric import TEDS +import evaluate +import random +from utils.read_files import save_paired_result +from registry.registry import METRIC_REGISTRY +from collections import defaultdict +import pdb +import copy +import pandas as pd +from .cdm_metric import CDM +from concurrent.futures import ProcessPoolExecutor, as_completed + +def get_groups(samples, group_info): + group_samples = defaultdict(list) + for sample in samples: + group_samples['all'].append(sample) + for group in group_info: + select_flag = True + for k, v in group.items(): + for gt_attribute in sample['gt_attribute']: # gt_attribute is a list containing all merged gt attributes + if not gt_attribute: # if no GT attributes, don't include in calculation + select_flag = False + elif gt_attribute[k] != v: # if any gt attribute doesn't meet criteria, don't select + select_flag = False + if select_flag: + group_samples[str(group)].append(sample) + return group_samples + + +@METRIC_REGISTRY.register("TEDS") +class call_TEDS(): + def __init__(self, samples): + self.samples = samples + def evaluate(self, group_info=[], save_name='default'): + teds = TEDS(structure_only=False) + teds_structure_only = TEDS(structure_only=True) + group_scores = defaultdict(list) + group_scores_structure_only = defaultdict(list) + samples = self.samples + per_table_score = {} + for idx, sample in enumerate(samples): + gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt'] + pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred'] + try: + score = teds.evaluate(pred, gt) + except: + score = 0 + print(f'TEDS score error for table {sample["gt_idx"]} in {sample["img_id"]}. The score is set to 0.') + try: + score_structure_only = teds_structure_only.evaluate(pred, gt) + except: + score_structure_only = 0 + print(f'TEDS_structure_only score error for table {sample["gt_idx"]} in {sample["img_id"]}. The score is set to 0.') + # print('TEDS score:', score) + group_scores['all'].append(score) + group_scores_structure_only['all'].append(score_structure_only) + if not sample.get('metric'): + sample['metric'] = {} + sample['metric']['TEDS'] = score + sample['metric']['TEDS_structure_only'] = score_structure_only + per_table_score[sample['img_id']+'_'+str(sample.get('gt_idx', idx))] = {'TEDS': score, 'TEDS_structure_only': score_structure_only} + for group in group_info: + select_flag = True + for k, v in group.items(): + for gt_attribute in sample['gt_attribute']: # gt_attribute is a list containing all merged gt attributes + if not gt_attribute: # if no GT attributes, don't include in calculation + select_flag = False + elif gt_attribute[k] != v: # if any gt attribute doesn't meet criteria, don't select + select_flag = False + if select_flag: + group_scores[str(group)].append(score) + + # Ensure result directory exists + os.makedirs('./result', exist_ok=True) + + with open(f'./result/{save_name}_per_table_TEDS.json', 'w', encoding='utf-8') as f: + json.dump(per_table_score, f, indent=4, ensure_ascii=False) + result = {} + for group_name, scores in group_scores.items(): + if len(scores) > 0: + result[group_name] = sum(scores) / len(scores) # average of normalized scores at sample level + else: + result[group_name] = 'NaN' + print(f'Warning: Empyty matched samples for {group_name}.') + + structure_only_result = {} + for group_name, scores in group_scores_structure_only.items(): + if len(scores) > 0: + structure_only_result[group_name] = sum(scores) / len(scores) # average of normalized scores at sample level + else: + structure_only_result[group_name] = 'NaN' + print(f'Warning: Empyty matched samples for {group_name}.') + + return samples, {'TEDS': result, 'TEDS_structure_only': structure_only_result} + + +@METRIC_REGISTRY.register("BLEU") +class call_BLEU(): + def __init__(self, samples): + self.samples = samples + def evaluate(self, group_info=[], save_name='default'): + group_samples = get_groups(self.samples, group_info) + result = {} + for group_name, samples in group_samples.items(): + predictions, references = [], [] + for sample in samples: + gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt'] + pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred'] + predictions.append(gt) + references.append(pred) + bleu = evaluate.load("bleu", keep_in_memory=True, experiment_id=random.randint(1,1e8)) + bleu_results = bleu.compute(predictions=predictions, references=references) + result[group_name] = bleu_results["bleu"] + + return self.samples, {'BLEU': result} + +@METRIC_REGISTRY.register("METEOR") +class call_METEOR(): + def __init__(self, samples): + self.samples = samples + def evaluate(self, group_info=[], save_name='default'): + group_samples = get_groups(self.samples, group_info) + result = {} + for group_name, samples in group_samples.items(): + predictions, references = [], [] + for sample in samples: + gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt'] + pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred'] + predictions.append(gt) + references.append(pred) + meteor = evaluate.load('meteor', keep_in_memory=True, experiment_id=random.randint(1,1e8)) + meteor_results = meteor.compute(predictions=predictions, references=references) + result[group_name] = meteor_results['meteor'] + + return self.samples, {'METEOR': result} + +@METRIC_REGISTRY.register("Edit_dist") +class call_Edit_dist(): + def __init__(self, samples): + self.samples = samples + def evaluate(self, group_info=[], save_name='default'): + samples = self.samples + for sample in samples: + img_name = sample['img_id'] if sample['img_id'].endswith('.jpg') or sample['img_id'].endswith('.png') else '_'.join(sample['img_id'].split('_')[:-1]) + sample['image_name'] = img_name + gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt'] + pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred'] + upper_len = max(len(pred), len(gt)) + sample['upper_len'] = upper_len + if len(pred) > 0 or len(gt) > 0: + edit_dist = Levenshtein.distance(pred, gt) + if not sample.get('metric'): + sample['metric'] = {} + sample['metric']['Edit_dist'] = edit_dist / upper_len + sample['Edit_num'] = edit_dist + + if isinstance(samples, list): + saved_samples = samples + else: + saved_samples = samples.samples + + if not saved_samples: + return samples, {'Edit_dist': {'ALL_page_avg': 'NaN'}} + + df = pd.DataFrame(saved_samples) + up_total_avg = df.groupby("image_name").apply(lambda x: x['Edit_num'].sum() / x['upper_len'].sum()) # page level, sum of edits divided by sum of max(gt,pred) lengths for each sample + all_total_avg = df['Edit_num'].sum() / df['upper_len'].sum() + # all_total_avg = df["Edit_dist"].mean() + per_img_score = up_total_avg.to_dict() + + # Ensure result directory exists + os.makedirs('./result', exist_ok=True) + + with open(f'./result/{save_name}_per_page_edit.json', 'w', encoding='utf-8') as f: + json.dump(per_img_score, f, indent=4, ensure_ascii=False) + + # if 'display_formula' in save_name: + # return samples, {'Edit_dist': {'ALL_page_avg': all_total_avg}} + # else: + # return samples, {'Edit_dist': {'ALL_page_avg': up_total_avg.mean()}} + + edit_whole = df['Edit_num'].sum() / df['upper_len'].sum() + df['ratio'] = df['Edit_num'] / df['upper_len'] + edit_sample_avg = df['ratio'].mean() + # edit_sample_avg = df['metric']['Edit_dist'].mean() + return samples, {'Edit_dist': {'ALL_page_avg': up_total_avg.mean(), 'edit_whole': edit_whole, 'edit_sample_avg': edit_sample_avg}} + +def _process_single_cdm_sample(args): + """Worker function to process a single CDM sample""" + idx, sample, output_root, group_info = args + + # Create a new CDM instance for this worker to avoid thread safety issues + cal_cdm = CDM(output_root=output_root) + + # Prepare sample data + sample_copy = copy.deepcopy(sample) + sample_copy['img_id_cdm'] = str(idx) + sample_copy['gt'] = sample_copy['gt'].lstrip("$$").rstrip("$$").strip() + sample_copy['gt'] = sample_copy['gt'].lstrip("$").rstrip("$").strip() + sample_copy['pred'] = sample_copy['pred'].split("```latex")[-1].split("```")[0] + sample_copy['pred'] = sample_copy['pred'].lstrip("$$").rstrip("$$").strip() + sample_copy['pred'] = sample_copy['pred'].lstrip("$").rstrip("$").strip() + + # Calculate CDM score + cdm_score = cal_cdm.evaluate(sample_copy['gt'], sample_copy['pred'], sample_copy['img_id_cdm'])["F1_score"] + + # Add metric to sample + if not sample_copy.get('metric'): + sample_copy['metric'] = {} + sample_copy['metric']['CDM'] = cdm_score + + # Check which groups this sample belongs to + matched_groups = [] + for group in group_info: + select_flag = True + for k, v in group.items(): + for gt_attribute in sample_copy['gt_attribute']: + if not gt_attribute: + select_flag = False + elif gt_attribute[k] != v: + select_flag = False + if select_flag: + matched_groups.append(str(group)) + + return { + 'sample': sample_copy, + 'cdm_score': cdm_score, + 'sample_key': sample_copy['img_id'] + '_' + str(sample_copy.get('gt_idx', 0)), + 'matched_groups': matched_groups, + 'original_index': idx + } + + +@METRIC_REGISTRY.register("CDM") +class call_CDM(): + def __init__(self, samples): + self.samples = samples + def evaluate(self, group_info=[], save_name='default', max_workers=32): + group_scores = defaultdict(list) + output_root = f"result/{save_name}/CDM" + + if isinstance(self.samples, list): + original_samples = self.samples + else: + original_samples = self.samples.samples + + # Prepare arguments for concurrent processing + worker_args = [] + for idx, sample in enumerate(original_samples): + worker_args.append((idx, sample, output_root, group_info)) + + # Use concurrent execution + per_sample_score = {} + cdm_samples = [] + results = [] + + with ProcessPoolExecutor(max_workers=max_workers) as executor: + # Submit all tasks + future_to_idx = {executor.submit(_process_single_cdm_sample, args): args[0] for args in worker_args} + + # Collect results as they complete + for future in as_completed(future_to_idx): + try: + result = future.result() + results.append(result) + except Exception as exc: + idx = future_to_idx[future] + print(f'Sample {idx} generated an exception: {exc}') + # Create a default result for failed samples + sample_copy = copy.deepcopy(original_samples[idx]) + sample_copy['img_id_cdm'] = str(idx) + if not sample_copy.get('metric'): + sample_copy['metric'] = {} + sample_copy['metric']['CDM'] = 0.0 + results.append({ + 'sample': sample_copy, + 'cdm_score': 0.0, + 'sample_key': sample_copy['img_id'] + '_' + str(sample_copy.get('gt_idx', 0)), + 'matched_groups': [], + 'original_index': idx + }) + + # Sort results by original index to maintain order + results.sort(key=lambda x: x['original_index']) + + # Process results + for result in results: + sample = result['sample'] + cdm_score = result['cdm_score'] + sample_key = result['sample_key'] + matched_groups = result['matched_groups'] + + cdm_samples.append(sample) + per_sample_score[sample_key] = cdm_score + group_scores['all'].append(cdm_score) + + # Add scores to matched groups + for group_name in matched_groups: + group_scores[group_name].append(cdm_score) + + # Ensure result directory exists + os.makedirs('./result', exist_ok=True) + + # Save results to files + with open(f'./result/{save_name}_per_sample_CDM.json', 'w', encoding='utf-8') as f: + json.dump(per_sample_score, f, indent=4, ensure_ascii=False) + + with open(f'result/{save_name}_result.json', 'w', encoding='utf-8') as f: + json.dump(cdm_samples, f, indent=4, ensure_ascii=False) + + # Calculate final results + result = {} + for group_name, scores in group_scores.items(): + if len(scores) > 0: + result[group_name] = sum(scores) / len(scores) # average of normalized scores at sample level + else: + result[group_name] = 'NaN' + print(f'Warning: Empty matched samples for {group_name}.') + + return cdm_samples, {'CDM': result} + + +@METRIC_REGISTRY.register("CDM_plain") +class call_CDM_plain(): + def __init__(self, samples): + self.samples = samples + def evaluate(self, group_info=[], save_name='default'): + if isinstance(self.samples, list): + cdm_samples = copy.deepcopy(self.samples) + else: + cdm_samples = copy.deepcopy(self.samples.samples) + for idx, sample in enumerate(cdm_samples): + sample['img_name'] = sample['img_id'] + sample['img_id'] = str(idx) + sample['gt'] = sample['gt'].lstrip("$$").rstrip("$$").strip() + sample['pred'] = sample['pred'].split("```latex")[-1].split("```")[0] + sample['pred'] = sample['pred'].lstrip("$$").rstrip("$$").strip() + + # time_stap = time.time() + with open(f'result/{save_name}_formula.json', 'w', encoding='utf-8') as f: + json.dump(cdm_samples, f, indent=4, ensure_ascii=False) + return self.samples, False \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/README-CN.md b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/README-CN.md new file mode 100644 index 00000000..09a3c150 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/README-CN.md @@ -0,0 +1,153 @@ +
+ +[English](./README.md) | [简体中文] + +

Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching

+ +[[ 论文 ]](https://arxiv.org/pdf/2409.03643) [[ 网站 ]](https://github.com/opendatalab/UniMERNet/tree/main/cdm) +[[在线Demo 🤗(Hugging Face)]](https://huggingface.co/spaces/opendatalab/CDM-Demo) + +
+ + +# 概述 + +公式识别因其复杂的结构和多样的符号表示而面临重大挑战。尽管公式识别模型不断进步，但现有评估指标如 BLEU 和编辑距离仍存在显著局限性。这些指标忽视了同一公式的多种表示形式，并对训练数据的分布高度敏感，导致评估不公。为此，我们提出了字符检测匹配（CDM）指标，通过设计基于图像而非 LaTeX 的评分方法来确保评估的客观性。具体而言，CDM 将模型预测的 LaTeX 和真实 LaTeX 公式渲染为图像格式，然后使用视觉特征提取和定位技术进行精确的字符级匹配，结合空间位置信息。相比于仅依赖文本字符匹配的 BLEU 和编辑距离，CDM 提供了更准确和公平的评估。 + +CDM与BLEU、EditDistance等指标对比示意图： + +
+ Demo +
+ +> 从上述对比图中可以看出： +- Case1: 模型预测正确，理论上ExpRate/BLEU/EditDist应该为1/1/0，实际上为0/0.449/0.571，完全无法反应识别准确性； +- Case2 Vs Case1: 预测错误的模型(Case2) BLEU/EditDist指标确远优于识别正确的模型结果(Case1)； +- Case3: 模型预测错误较多，而BLEU指标确高达0.907，不符合直觉。 + + +CDM的算法流程图如下： + +
+ Overview +
+ +可以看到CDM基于渲染图像的字符匹配方式，结果更加直观，且不受公式表达多样性影响。 + + + +# + +# 使用方法 + +## 在线Demo体验 + +请点击HuggingFace Demo链接: [(Hugging Face)🤗](https://huggingface.co/spaces/opendatalab/CDM-Demo) + +## 本地安装CDM + +CDM需要对公式进行渲染，需要相关依赖包，推荐在Linux系统安装配置 + +## 准备环境 + +需要的依赖包括：Nodejs, imagemagic, pdflatex，请按照下面的指令进行安装： + +### 步骤.1 安装 nodejs + +``` +wget https://registry.npmmirror.com/-/binary/node/latest-v16.x/node-v16.13.1-linux-x64.tar.gz + +tar -xvf node-v16.13.1-linux-x64.tar.gz + +mv node-v16.13.1-linux-x64/* /usr/local/nodejs/ + +ln -s /usr/local/nodejs/bin/node /usr/local/bin + +ln -s /usr/local/nodejs/bin/npm /usr/local/bin + +node -v +``` + +### 步骤.2 安装 imagemagic + +`apt-get`命令安装的imagemagic版本是6.x，我们需要安装7.x的，所以从源码编译安装： + +（编译前需要确认系统内安装有libpng-dev，否则编译出来的magick无法支持cdm使用） +``` +git clone https://github.com/ImageMagick/ImageMagick.git ImageMagick-7.1.1 + +cd ImageMagick-7.1.1 + +./configure + +make + +sudo make install + +sudo ldconfig /usr/local/lib + +convert --version +``` + +### 步骤.3 安装 latexpdf + +渲染中文公式需要中文字体，当前cdm中使用的是思源黑体Source Han Sans SC. + +``` +apt-get update + +sudo apt-get install texlive-full +``` + +### step.4 安装 python 依赖 + +``` +pip install -r requirements.txt +``` + + +## 使用CDM + +如果安装过程顺利，现在可以使用CDM对公式识别的结果进行评测了。 + +### 1. 批量评测 + +- 准备输入的json文件 + +在UniMERNet上评测，可以用下面的脚本获取json文件: + +``` +python convert2cdm_format.py -i {UniMERNet predictions} -o {save path} +``` + +或者，也可以参考下面的格式自行准备json文件: + +``` +[ + { + "img_id": "case_1", # 非必须的key + "gt": "y = 2z + 3x", + "pred": "y = 2x + 3z" + }, + { + "img_id": "case_2", + "gt": "y = x^2 + 1", + "pred": "y = x^2 + 1" + }, + ... +] +``` + +`注意在json文件中，一些特殊字符比如 "\" 需要进行转义, 比如 "\begin" 在json文件中就需要保存为 "\\begin".` + +- 评测: + +``` +python evaluation.py -i {path_to_your_input_json} +``` + +### 2. 启动 gradio demo + +``` +python app.py +``` \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/README.md b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/README.md new file mode 100644 index 00000000..22a80725 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/README.md @@ -0,0 +1,147 @@ +
+ +English | [简体中文](./README-CN.md) + +

Image Over Text: Transforming Formula Recognition Evaluation with Character Detection Matching

+ +[[ Paper ]](https://arxiv.org/pdf/2409.03643) [[ Website ]](https://github.com/opendatalab/UniMERNet/tree/main/cdm) +[[Demo 🤗(Hugging Face)]](https://huggingface.co/spaces/opendatalab/CDM-Demo) + +
+ + +# Overview + +Formula recognition presents significant challenges due to the complicated structure and varied notation of mathematical expressions. Despite continuous advancements in formula recognition models, the evaluation metrics employed by these models, such as BLEU and Edit Distance, still exhibit notable limitations. They overlook the fact that the same formula has diverse representations and is highly sensitive to the distribution of training data, thereby causing the unfairness in formula recognition evaluation. To this end, we propose a Character Detection Matching (CDM) metric, ensuring the evaluation objectivity by designing a image-level rather than LaTex-level metric score. Specifically, CDM renders both the model-predicted LaTeX and the ground-truth LaTeX formulas into image-formatted formulas, then employs visual feature extraction and localization techniques for precise character-level matching, incorporating spatial position information. Such a spatially-aware and character-matching method offers a more accurate and equitable evaluation compared with previous BLEU and Edit Distance metrics that rely solely on text-based character matching. + +Comparison between CDM and BLEU, Edit Distance metrics: +
+ Demo +
+ + +The algorithm flow of CDM is as follows: + +
+ Overview +
+ + +CDM's character matching method based on rendered images provides more intuitive results and is not affected by the diversity of formula representations. + + + +# Usage + +## Try Online Demo + +Try CDM on our online demo: [(Hugging Face)🤗](https://huggingface.co/spaces/opendatalab/CDM-Demo) + +## Install CMD Locally + +Given CDM's complex environment dependencies, we recommend trying it on Linux systems. + +## prepare environment + +Nodejs, imagemagic, pdflatex are requried packages when render pdf files and convert them to images, here are installation guides. + +### step.1 install nodejs + +``` +wget https://registry.npmmirror.com/-/binary/node/latest-v16.x/node-v16.13.1-linux-x64.tar.gz + +tar -xvf node-v16.13.1-linux-x64.tar.gz + +mv node-v16.13.1-linux-x64/* /usr/local/nodejs/ + +ln -s /usr/local/nodejs/bin/node /usr/local/bin + +ln -s /usr/local/nodejs/bin/npm /usr/local/bin + +node -v +``` + +### step.2 install imagemagic + +the version of imagemagic installed by `apt-get` usually be 6.x, so we also install it from source code. + +(Before compiling, ensure that libpng-dev is installed on the system; otherwise, the compiled magick will not support CDM usage.) +``` +git clone https://github.com/ImageMagick/ImageMagick.git ImageMagick-7.1.1 + +cd ImageMagick-7.1.1 + +./configure + +make + +sudo make install + +sudo ldconfig /usr/local/lib + +convert --version +``` + +### step.3 install latexpdf + +Rendering Chinese formulas requires a Chinese font, Source Han Sans SC is currently used . + +``` +apt-get update + +sudo apt-get install texlive-full +``` + +### step.4 install python requriements + +``` +pip install -r requirements.txt +``` + + +## Use CDM Locally + +Should the installation goes well, you may now use CDM to evaluate your formula recognition results. + +### 1. batch evaluation + +- prepare input json + +evaluate on UniMERNet results, use this convert script to get json file: + +``` +python convert2cdm_format.py -i {UniMERNet predictions} -o {save path} +``` + +otherwise, prepare a json file follow this format: + +``` +[ + { + "img_id": "case_1", # optional key + "gt": "y = 2z + 3x", + "pred": "y = 2x + 3z" + }, + { + "img_id": "case_2", + "gt": "y = x^2 + 1", + "pred": "y = x^2 + 1" + }, + ... +] +``` + +`Note that in json files, some special characters such as "\" need escaped character, for example "\begin" should be written as "\\begin".` + +- evaluate: + +``` +python evaluation.py -i {path_to_your_input_json} +``` + + +### 2. launch a gradio demo + +``` +python app.py +``` \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/app.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/app.py new file mode 100644 index 00000000..a9125eb3 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/app.py @@ -0,0 +1,351 @@ +import sys +import os +import re +import json +import time +import shutil +import numpy as np +import gradio as gr +from datetime import datetime +from multiprocessing import Pool +from multiprocessing.dummy import Pool as ThreadPool +from PIL import Image, ImageDraw +from skimage.measure import ransac +import matplotlib.pyplot as plt + +from modules.latex2bbox_color import latex2bbox_color +from modules.tokenize_latex.tokenize_latex import tokenize_latex +from modules.visual_matcher import HungarianMatcher, SimpleAffineTransform + + +DATA_ROOT = "output" + +def gen_color_list(num=10, gap=15): + num += 1 + single_num = 255 // gap + 1 + max_num = single_num ** 3 + num = min(num, max_num) + color_list = [] + for idx in range(num): + R = idx // single_num**2 + GB = idx % single_num**2 + G = GB // single_num + B = GB % single_num + + color_list.append((R*gap, G*gap, B*gap)) + return color_list[1:] + +def process_latex(groundtruths, predictions, user_id="test"): + data_root = DATA_ROOT + temp_dir = os.path.join(data_root, "temp_dir") + + data_root = os.path.join(data_root, user_id) + output_dir_info = {} + input_args = [] + for subset, latex_list in zip(['gt', 'pred'], [groundtruths, predictions]): + sub_temp_dir = os.path.join(temp_dir, f"{user_id}_{subset}") + os.makedirs(sub_temp_dir, exist_ok=True) + + output_path = os.path.join(data_root, subset) + output_dir_info[output_path] = [] + + os.makedirs(os.path.join(output_path, 'bbox'), exist_ok=True) + os.makedirs(os.path.join(output_path, 'vis'), exist_ok=True) + + total_color_list = gen_color_list(num=5800) + + for idx, latex in enumerate(latex_list): + basename = f"sample_{idx}" + input_arg = latex, basename, output_path, sub_temp_dir, total_color_list + a = time.time() + latex2bbox_color(input_arg) + b = time.time() + + for subset in ['gt', 'pred']: + shutil.rmtree(os.path.join(temp_dir, f"{user_id}_{subset}")) + + +def update_inliers(ori_inliers, sub_inliers): + inliers = np.copy(ori_inliers) + sub_idx = -1 + for idx in range(len(ori_inliers)): + if ori_inliers[idx] == False: + sub_idx += 1 + if sub_inliers[sub_idx] == True: + inliers[idx] = True + return inliers + +def reshape_inliers(ori_inliers, sub_inliers): + inliers = np.copy(ori_inliers) + sub_idx = -1 + for idx in range(len(ori_inliers)): + if ori_inliers[idx] == False: + sub_idx += 1 + if sub_inliers[sub_idx] == True: + inliers[idx] = True + else: + inliers[idx] = False + return inliers + + +def evaluation(user_id="test"): + data_root = DATA_ROOT + data_root = os.path.join(data_root, user_id) + gt_box_dir = os.path.join(data_root, "gt") + pred_box_dir = os.path.join(data_root, "pred") + match_vis_dir = os.path.join(data_root, "vis_match") + os.makedirs(match_vis_dir, exist_ok=True) + + max_iter = 3 + min_samples = 3 + residual_threshold = 25 + max_trials = 50 + + metrics_per_img = {} + gt_basename_list = [item.split(".")[0] for item in os.listdir(os.path.join(gt_box_dir, 'bbox'))] + for basename in gt_basename_list: + gt_valid, pred_valid = True, True + if not os.path.exists(os.path.join(gt_box_dir, 'bbox', basename+".jsonl")): + gt_valid = False + else: + with open(os.path.join(gt_box_dir, 'bbox', basename+".jsonl"), 'r') as f: + box_gt = [] + for line in f: + info = json.loads(line) + if info['bbox']: + box_gt.append(info) + if not box_gt: + gt_valid = False + if not gt_valid: + continue + + if not os.path.exists(os.path.join(pred_box_dir, 'bbox', basename+".jsonl")): + pred_valid = False + else: + with open(os.path.join(pred_box_dir, 'bbox', basename+".jsonl"), 'r') as f: + box_pred = [] + for line in f: + info = json.loads(line) + if info['bbox']: + box_pred.append(info) + if not box_pred: + pred_valid = False + if not pred_valid: + metrics_per_img[basename] = { + "recall": 0, + "precision": 0, + "F1_score": 0, + } + continue + gt_img_path = os.path.join(gt_box_dir, 'vis', basename+"_base.png") + pred_img_path = os.path.join(pred_box_dir, 'vis', basename+"_base.png") + + img_gt = Image.open(gt_img_path) + img_pred = Image.open(pred_img_path) + + matcher = HungarianMatcher() + matched_idxes = matcher(box_gt, box_pred, img_gt.size, img_pred.size) + src = [] + dst = [] + for (idx1, idx2) in matched_idxes: + x1min, y1min, x1max, y1max = box_gt[idx1]['bbox'] + x2min, y2min, x2max, y2max = box_pred[idx2]['bbox'] + x1_c, y1_c = float((x1min+x1max)/2), float((y1min+y1max)/2) + x2_c, y2_c = float((x2min+x2max)/2), float((y2min+y2max)/2) + src.append([y1_c, x1_c]) + dst.append([y2_c, x2_c]) + + src = np.array(src) + dst = np.array(dst) + if src.shape[0] <= min_samples: + inliers = np.array([True for _ in matched_idxes]) + else: + inliers = np.array([False for _ in matched_idxes]) + for i in range(max_iter): + if src[inliers==False].shape[0] <= min_samples: + break + model, inliers_1 = ransac((src[inliers==False], dst[inliers==False]), SimpleAffineTransform, min_samples=min_samples, residual_threshold=residual_threshold, max_trials=max_trials) + if inliers_1 is not None and inliers_1.any(): + inliers = update_inliers(inliers, inliers_1) + else: + break + if len(inliers[inliers==True]) >= len(matched_idxes): + break + + for idx, (a,b) in enumerate(matched_idxes): + if inliers[idx] == True and matcher.cost['token'][a, b] == 1: + inliers[idx] = False + + final_match_num = len(inliers[inliers==True]) + recall = round(final_match_num/(len(box_gt)), 3) + precision = round(final_match_num/(len(box_pred)), 3) + F1_score = round(2*final_match_num/(len(box_gt)+len(box_pred)), 3) + metrics_per_img[basename] = { + "recall": recall, + "precision": precision, + "F1_score": F1_score, + } + + if True: + gap = 5 + W1, H1 = img_gt.size + W2, H2 = img_pred.size + H = H1 + H2 + gap + W = max(W1, W2) + + vis_img = Image.new('RGB', (W, H), (255, 255, 255)) + vis_img.paste(img_gt, (0, 0)) + vis_img.paste(Image.new('RGB', (W, gap), (0, 150, 200)), (0, H1)) + vis_img.paste(img_pred, (0, H1+gap)) + + match_img = vis_img.copy() + match_draw = ImageDraw.Draw(match_img) + + gt_matched_idx = { + a: flag + for (a,b), flag in + zip(matched_idxes, inliers) + } + pred_matched_idx = { + b: flag + for (a,b), flag in + zip(matched_idxes, inliers) + } + + for idx, box in enumerate(box_gt): + if idx in gt_matched_idx and gt_matched_idx[idx]==True: + color = "green" + else: + color = "red" + x_min, y_min, x_max, y_max = box['bbox'] + match_draw.rectangle([x_min-1, y_min-1, x_max+1, y_max+1], fill=None, outline=color, width=2) + + for idx, box in enumerate(box_pred): + if idx in pred_matched_idx and pred_matched_idx[idx]==True: + color = "green" + else: + color = "red" + x_min, y_min, x_max, y_max = box['bbox'] + match_draw.rectangle([x_min-1, y_min-1+H1+gap, x_max+1, y_max+1+H1+gap], fill=None, outline=color, width=2) + + vis_img.save(os.path.join(match_vis_dir, basename+"_base.png")) + if W < 500: + padding = (500 - W)//2 + 1 + reshape_match_img = Image.new('RGB', (500, H), (255, 255, 255)) + reshape_match_img.paste(match_img, (padding, 0)) + reshape_match_img.paste(Image.new('RGB', (500, gap), (0, 150, 200)), (0, H1)) + reshape_match_img.save(os.path.join(match_vis_dir, basename+".png")) + else: + match_img.save(os.path.join(match_vis_dir, basename+".png")) + + acc_list = [val['F1_score'] for _, val in metrics_per_img.items()] + metrics_res = { + "mean_score": round(np.mean(acc_list), 3), + "details": metrics_per_img + } + metric_res_path = os.path.join(data_root, "metrics_res.json") + with open(metric_res_path, "w") as f: + f.write(json.dumps(metrics_res, indent=2)) + return metrics_res, metric_res_path, match_vis_dir + +def calculate_metric_single(groundtruth, prediction): + user_id = datetime.now().strftime('%Y%m%d-%H%M%S') + process_latex([groundtruth], [prediction], user_id) + metrics_res, metric_res_path, match_vis_dir = evaluation(user_id) + basename = "sample_0" + image_path = os.path.join(match_vis_dir, basename+".png") + sample = metrics_res["details"][basename] + score = sample['F1_score'] + recall = sample['recall'] + precision = sample['precision'] + return score, recall, precision, gr.Image(image_path) + +def calculate_metric_batch(json_input): + user_id = datetime.now().strftime('%Y%m%d-%H%M%S') + with open(json_input.name, "r") as f: + input_data = json.load(f) + groundtruths = [] + predictions = [] + for item in input_data: + groundtruths.append(item['gt']) + predictions.append(item['pred']) + process_latex(groundtruths, predictions, user_id) + metrics_res, metric_res_path, match_vis_dir = evaluation(user_id) + return metric_res_path + +def gradio_reset_single(): + return gr.update(value=None, placeholder='type gt latex code here'), gr.update(value=None, placeholder='type pred latex code here'), \ + gr.update(value=None), gr.update(value=None), gr.update(value=None), gr.update(value=None) + +def gradio_reset_batch(): + return gr.update(value=None), gr.update(value=None) + +def select_example1(): + gt = "y = 2x + 3z" + pred = "y = 2z + 3x" + return gr.update(value=gt, placeholder='type gt latex code here'), gr.update(value=pred, placeholder='type pred latex code here') + +def select_example2(): + gt = "r = \\frac { \\alpha } { \\beta } \\vert \\sin \\beta \\left( \\sigma _ { 1 } \\pm \\sigma _ { 2 } \\right) \\vert" + pred = "r={\\frac{\\alpha}{\\beta}}|\\sin\\beta\\left(\\sigma_{2}+\\sigma_{1}\\right)|" + return gr.update(value=gt, placeholder='type gt latex code here'), gr.update(value=pred, placeholder='type pred latex code here') + +def select_example3(): + gt = "\\begin{array} { r l r } & { } & { \\mathbf { J } _ { L } = \\left( \\begin{array} { c c } { 0 } & { 0 } \\\\ { v _ { n } } & { 0 } \\end{array} \\right) , ~ \\mathbf { J } _ { R } = \\left( \\begin{array} { c c } { u _ { n - 1 } } & { 0 } \\\\ { 0 } & { 0 } \\end{array} \\right) , ~ } \\\\ & { } & {\\mathbf { K } = \\left( \\begin{array} { c c } { V _ { n - 1 } } & { u _ { n } } \\\\ { v _ { n - 1 } } & { V _ { n } } \\end{array} \\right) , } \\end{array}" + pred = "\\mathbf{J}_{U}={\\left(\\begin{array}{l l}{0}&{0}\\\\ {v_{n}}&{0}\\end{array}\\right)}\\,,\\ \\mathbf{J}_{R}={\\left(\\begin{array}{l l}{u_{n-1}}&{0}\\\\ {0}&{0}\\end{array}\\right)}\\,,\\mathbf{K}={\\left(\\begin{array}{l l}{V_{n-1}}&{u_{n}}\\\\ {v_{n-1}}&{V_{n}}\\end{array}\\right)}\\,," + return gr.update(value=gt, placeholder='type gt latex code here'), gr.update(value=pred, placeholder='type pred latex code here') + + + +if __name__ == "__main__": + title = """

Character Detection Matching (CDM)

""" + + with gr.Blocks() as demo: + gr.Markdown(title) + + gr.Button(value="Quick Try: type latex code of gt and pred, get metrics and visualization.", interactive=False, variant="primary") + + with gr.Row(): + with gr.Column(): + gt_input = gr.Textbox(label='gt', placeholder='type gt latex code here', interactive=True) + pred_input = gr.Textbox(label='pred', placeholder='type pred latex code here', interactive=True) + with gr.Row(): + clear_single = gr.Button("Clear") + submit_single = gr.Button(value="Submit", interactive=True, variant="primary") + with gr.Accordion("Examples:"): + with gr.Row(): + example1 = gr.Button("Example A(short)") + example2 = gr.Button("Example B(middle)") + example3 = gr.Button("Example C(long)") + with gr.Column(): + with gr.Row(): + score_output = gr.Number(label="F1 Score", interactive=False) + recall_output = gr.Number(label="Recall", interactive=False) + recision_output = gr.Number(label="Precision", interactive=False) + gr.Button(value="Visualization (green bbox means correcttlly matched, red bbox means missed or wrong.)", interactive=False) + vis_output = gr.Image(label=" ", interactive=False) + + example1.click(select_example1, inputs=None, outputs=[gt_input, pred_input]) + example2.click(select_example2, inputs=None, outputs=[gt_input, pred_input]) + example3.click(select_example3, inputs=None, outputs=[gt_input, pred_input]) + + clear_single.click(gradio_reset_single, inputs=None, outputs=[gt_input, pred_input, score_output, recall_output, recision_output, vis_output]) + submit_single.click(calculate_metric_single, inputs=[gt_input, pred_input], outputs=[score_output, recall_output, recision_output, vis_output]) + + + gr.Button(value="Batch Run: upload a json file and batch processing, this may take some times according to your latex amount and length.", interactive=False, variant="primary") + + with gr.Row(): + with gr.Column(): + json_input = gr.File(label="Input Json", file_types=[".json"]) + json_example = gr.File(label="Input Example", value="assets/example/input_example.json") + with gr.Row(): + clear_batch = gr.Button("Clear") + submit_batch = gr.Button(value="Submit", interactive=True, variant="primary") + + metric_output = gr.File(label="Output Metrics") + + clear_batch.click(gradio_reset_batch, inputs=None, outputs=[json_input, metric_output]) + submit_batch.click(calculate_metric_batch, inputs=[json_input], outputs=[metric_output]) + + demo.launch(share=True, server_name="0.0.0.0", server_port=10005, debug=True) \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/assets/demo/cdm_demo.png b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/assets/demo/cdm_demo.png new file mode 100644 index 0000000000000000000000000000000000000000..1a886e2e5e4c22a6f7313b8c3d44c21ea3021637 GIT binary patch literal 425371 zcmbrmbyQVr*FFrJE~QgIRJugE8w8};q_lLmG$@ULAl*oJ=cc8*yQI52zlG;H&*41d 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zn$Esfu^KOBrV8CSEV08MFX67W7G62p6g8}D^%z!lIjjd+mDTDJd|LinwXQc;zN?Fh zS9W>)5#V zDCXkAQVEa=Ye4{m(|rR7Hwm!Bvbg*dtT&HP6U%LW9nL(gme5-Cy!OF^1>{d^N>zpD zQt~|hGXQ7^mX|a9ipoi@_dHhV;$Quu)b9{gxrrGQCpK;<1|ym z6AsKFEBe~W8a;rTS=JSuevKC(DE=%2xrC-84IE=G!c>J){GYX9-G`;@VJ47gJo1%2^e`-f!;h5>0@&XyB;0&l-MC zjC_XKJG4Z&k9IehXJl@yQo^c|JBD`T&L2-D*?9h(km@_$b($c@GMc=i0qko8 z+1MQ9weal*H(eHSEa{Zv*Nz&*czJua=HBOmy_1kBiU{J;I9%kUswb^`mq<+&Bc3$#LFy#&)lQnRD!WPx9;8W0o4S$B^tRYwL zM4noL@2;Za`ut+g^~(u#yY5OzH1vk~gRNS1MVO8ZupAolJ17QB9P?L>^OEkPY?XhP zc&~VBMu=h14VHR^e9-H^q1FFxiNpV! ljQH= len(matched_idxes): + break + + for idx, (a,b) in enumerate(matched_idxes): + if inliers[idx] == True and matcher.cost['token'][a, b] == 1: + inliers[idx] = False + + final_match_num = len(inliers[inliers==True]) + recall = round(final_match_num/(len(box_gt)), 3) + precision = round(final_match_num/(len(box_pred)), 3) + F1_score = round(2*final_match_num/(len(box_gt)+len(box_pred)), 3) + metrics_per_img[basename] = { + "recall": recall, + "precision": precision, + "F1_score": F1_score, + } + + if True: + gap = 5 + W1, H1 = img_gt.size + W2, H2 = img_pred.size + H = H1 + H2 + gap + W = max(W1, W2) + + vis_img = Image.new('RGB', (W, H), (255, 255, 255)) + vis_img.paste(img_gt, (0, 0)) + vis_img.paste(Image.new('RGB', (W, gap), (120, 120, 120)), (0, H1)) + vis_img.paste(img_pred, (0, H1+gap)) + + match_img = vis_img.copy() + match_draw = ImageDraw.Draw(match_img) + + gt_matched_idx = { + a: flag + for (a,b), flag in + zip(matched_idxes, inliers) + } + pred_matched_idx = { + b: flag + for (a,b), flag in + zip(matched_idxes, inliers) + } + + for idx, box in enumerate(box_gt): + if idx in gt_matched_idx and gt_matched_idx[idx]==True: + color = "green" + else: + color = "red" + x_min, y_min, x_max, y_max = box['bbox'] + match_draw.rectangle([x_min-1, y_min-1, x_max+1, y_max+1], fill=None, outline=color, width=2) + + for idx, box in enumerate(box_pred): + if idx in pred_matched_idx and pred_matched_idx[idx]==True: + color = "green" + else: + color = "red" + x_min, y_min, x_max, y_max = box['bbox'] + match_draw.rectangle([x_min-1, y_min-1+H1+gap, x_max+1, y_max+1+H1+gap], fill=None, outline=color, width=2) + + vis_img.save(os.path.join(match_vis_dir, basename+"_base.png")) + match_img.save(os.path.join(match_vis_dir, basename+".png")) + + score_list = [val['F1_score'] for _, val in metrics_per_img.items()] + exp_list = [1 if score==1 else 0 for score in score_list] + metrics_res = { + "mean_score": round(np.mean(score_list), 3), + "exp_rate": round(np.mean(exp_list), 3), + "details": metrics_per_img + } + metric_res_path = os.path.join(data_root, "metrics_res.json") + with open(metric_res_path, "w") as f: + f.write(json.dumps(metrics_res, indent=2)) + return metrics_res, metric_res_path, match_vis_dir + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--input', '-i', type=str, default="assets/example/input_example.json") + parser.add_argument('--output', '-o', type=str, default="output") + parser.add_argument('--pools', '-p', type=int, default=240) + args = parser.parse_args() + print(args) + + json_input, data_root, pool_num = args.input, args.output, args.pools + temp_dir = os.path.join(data_root, "temp_dir") + exp_name = os.path.basename(json_input).split('.')[0] + with open(json_input, "r") as f: + input_data = json.load(f) + img_ids = [] + groundtruths = [] + predictions = [] + for idx, item in enumerate(input_data): + if "img_id" in item: + img_ids.append(item["img_id"]) + else: + img_ids.append(f"sample_{idx}") + groundtruths.append(item['gt']) + predictions.append(item['pred']) + + a = time.time() + user_id = exp_name + + total_color_list = gen_color_list(num=5800) + + data_root = os.path.join(data_root, user_id) + output_dir_info = {} + input_args = [] + for subset, latex_list in zip(['gt', 'pred'], [groundtruths, predictions]): + sub_temp_dir = os.path.join(temp_dir, f"{exp_name}_{subset}") + os.makedirs(sub_temp_dir, exist_ok=True) + output_path = os.path.join(data_root, subset) + output_dir_info[output_path] = [] + + os.makedirs(os.path.join(output_path, 'bbox'), exist_ok=True) + os.makedirs(os.path.join(output_path, 'vis'), exist_ok=True) + + for idx, latex in tqdm(enumerate(latex_list), desc=f"collect {subset} latex ..."): + basename = img_ids[idx] + input_arg = latex, basename, output_path, sub_temp_dir, total_color_list + input_args.append(input_arg) + + if pool_num > 1: + print(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "using processpool, pool num:", pool_num, ", job num:", len(input_args)) + myP = Pool(args.pools) + for input_arg in input_args: + myP.apply_async(latex2bbox_color, args=(input_arg,)) + myP.close() + myP.join() + else: + for input_arg in input_args: + latex2bbox_color(input_arg) + b = time.time() + print(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "extract bbox done, time cost:", round(b-a, 3), "s") + + for subset in ['gt', 'pred']: + shutil.rmtree(os.path.join(temp_dir, f"{exp_name}_{subset}")) + + c = time.time() + metrics_res, metric_res_path, match_vis_dir = evaluation(args.output, exp_name) + d = time.time() + print(datetime.now().strftime('%Y-%m-%d %H:%M:%S'), "calculate metrics done, time cost:", round(d-c, 3), "s") + + print(f"=> process done, mean f1 score: {metrics_res['mean_score']}.") + print(f"=> more details of metrics are saved in `{metric_res_path}`") + print(f"=> visulization images are saved under `{match_vis_dir}`") \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex2bbox_color.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex2bbox_color.py new file mode 100644 index 00000000..51788a5d --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex2bbox_color.py @@ -0,0 +1,241 @@ +import os +import re +import json +import shutil +import logging +import subprocess +import numpy as np + +from threading import Timer +from PIL import Image, ImageDraw +from .latex_processor import ( + normalize_latex, + token_add_color_RGB, + clean_latex +) +from .tokenize_latex.tokenize_latex import tokenize_latex + + +tabular_template = r""" +\documentclass[12pt]{article} +\usepackage[landscape]{geometry} +\usepackage{geometry} +\geometry{apaper,scale=0.98} +\pagestyle{empty} +\usepackage{booktabs} +\usepackage{multirow} +\usepackage{amssymb} +\usepackage{upgreek} +\usepackage{amsmath} +\usepackage{xcolor} +\begin{document} +\makeatletter +\renewcommand*{\@textcolor}[3]{%% + \protect\leavevmode + \begingroup + \color#1{#2}#3%% + \endgroup +} +\makeatother +\begin{displaymath} +%s +\end{displaymath} +\end{document} +""" + +# 需要配置Source Han Sans SC或其他中文字体 +formular_template = r""" +\documentclass[12pt]{article} +\usepackage[landscape]{geometry} +\usepackage{geometry} +\geometry{apaper,scale=0.98} +\pagestyle{empty} +\usepackage{amsmath} +\usepackage{upgreek} +\usepackage{amssymb} +\usepackage{xcolor} +\usepackage{xeCJK} +\setCJKmainfont{Source Han Sans SC} +\setCJKsansfont{Source Han Sans SC} +\setCJKmonofont{Source Han Sans SC} +\xeCJKsetup{CJKmath=true} +\begin{document} +\makeatletter +\renewcommand*{\@textcolor}[3]{%% + \protect\leavevmode + \begingroup + \color#1{#2}#3%% + \endgroup +} +\makeatother +\begin{displaymath} +%s +\end{displaymath} +\end{document} +""" + + +def run_cmd(cmd, timeout_sec=30, temp_dir=None): + # 设置进程独立的环境变量 + env = os.environ.copy() + if temp_dir: + env['TMPDIR'] = temp_dir + env['TMP'] = temp_dir + env['TEMP'] = temp_dir + env['MAGICK_TMPDIR'] = temp_dir + env['TEXMFCACHE'] = temp_dir + env['TEXMFVAR'] = temp_dir + + proc = subprocess.Popen(cmd, shell=True, env=env) + kill_proc = lambda p: p.kill() + timer = Timer(timeout_sec, kill_proc, [proc]) + try: + timer.start() + stdout,stderr = proc.communicate() + finally: + timer.cancel() + +def convert_pdf2img(pdf_filename, png_filename, temp_dir=None): + cmd = "magick -density 200 -quality 100 \"%s\" \"%s\""%(pdf_filename, png_filename) + run_cmd(cmd, temp_dir=temp_dir) + +def crop_image(image_path, pad=8): + img = Image.open(image_path).convert("L") + img_data = np.asarray(img, dtype=np.uint8) + nnz_inds = np.where(img_data!=255) + if len(nnz_inds[0]) == 0: + y_min = 0 + y_max = 10 + x_min = 0 + x_max = 10 + else: + y_min = np.min(nnz_inds[0]) + y_max = np.max(nnz_inds[0]) + x_min = np.min(nnz_inds[1]) + x_max = np.max(nnz_inds[1]) + + img = Image.open(image_path).convert("RGB").crop((x_min-pad, y_min-pad, x_max+pad, y_max+pad)) + img.save(image_path) + +def extrac_bbox_from_color_image(image_path, color_list): + img = Image.open(image_path).convert("RGB") + W, H = img.size + pixels = list(img.getdata()) + + bbox_list = [] + for target_color in color_list: + target_pixels = [ i for i, pixel in enumerate(pixels)if pixel == target_color ] + x_list = [] + y_list = [] + for idx in target_pixels: + x_list.append(idx % W) + y_list.append(idx // W) + try: + y_min, y_max, x_min, x_max = min(y_list), max(y_list), min(x_list), max(x_list) + bbox_list.append([x_min-1, y_min-1, x_max+1, y_max+1]) + + except: + bbox_list.append([]) + continue + + img = img.convert("L") + img_bw = img.point(lambda x: 255 if x == 255 else 0, '1') + img_bw.convert("RGB").save(image_path) + return bbox_list + + +def latex2bbox_color(input_arg): + latex, basename, output_path, temp_dir, total_color_list = input_arg + template = tabular_template if "tabular" in latex else formular_template + basename = basename.replace('.jpg', '')# ***** + output_bbox_path = os.path.join(output_path, 'bbox', basename+'.jsonl') + output_vis_path = os.path.join(output_path, 'vis', basename+'.png') + output_base_path = os.path.join(output_path, 'vis', basename+'_base.png') + + if os.path.exists(output_bbox_path) and os.path.exists(output_vis_path) and os.path.exists(output_base_path): + return + + try: + latex = latex.replace("\n", " ") + latex = latex.replace("\%", "") + ret, new_latex = tokenize_latex(latex, middle_file=os.path.join(temp_dir, basename+'.txt')) + if not(ret and new_latex): + log = f"ERROR, Tokenize latex failed: {basename}." + logging.info(log) + new_latex = latex + new_latex = new_latex.replace("< P E R C E N T A G E T O K E N >", "\%") + latex = normalize_latex(new_latex) + token_list = [] + l_split = latex.strip().split(' ') + color_list = total_color_list[0:len(l_split)] + idx = 0 + while idx < len(l_split): + l_split, idx, token_list = token_add_color_RGB(l_split, idx, token_list) + + rgb_latex = " ".join(l_split) + for idx, color in enumerate(color_list): + R, G, B = color + rgb_latex = rgb_latex.replace(f"", f"{R},{G},{B}") + + if len(token_list) > 1300: + paper_size = 3 + elif len(token_list) > 600: + paper_size = 4 + else: + paper_size = 5 + final_latex = formular_template.replace("", str(paper_size)) % rgb_latex + + except Exception as e: + log = f"ERROR, Preprocess latex failed: {basename}; {e}." + logging.info(log) + return + + pre_name = output_path.replace('/', '_').replace('.','_') + '_' + basename + tex_filename = os.path.join(temp_dir, pre_name+'.tex') + log_filename = os.path.join(temp_dir, pre_name+'.log') + aux_filename = os.path.join(temp_dir, pre_name+'.aux') + + # print(os.path.exists(tex_filename), tex_filename) + with open(tex_filename, "w") as w: + # print(final_latex, file=w) + w.write(final_latex) + # print(os.path.exists(tex_filename), tex_filename) + # run_cmd(f"pdflatex -interaction=nonstopmode -output-directory={temp_dir} {tex_filename} >/dev/null") + run_cmd(f"xelatex -interaction=nonstopmode -output-directory={temp_dir} \"{tex_filename}\" >/dev/null", temp_dir=temp_dir) + try: + os.remove(tex_filename) + os.remove(log_filename) + os.remove(aux_filename) + except: + pass + pdf_filename = tex_filename[:-4]+'.pdf' + if not os.path.exists(pdf_filename): + log = f"ERROR, Compile pdf failed: {pdf_filename}" + logging.info(log) + else: + convert_pdf2img(pdf_filename, output_base_path) + os.remove(pdf_filename) + + crop_image(output_base_path) + bbox_list = extrac_bbox_from_color_image(output_base_path, color_list) + vis = Image.open(output_base_path) + draw = ImageDraw.Draw(vis) + + with open(output_bbox_path, 'w', encoding='utf-8') as f: + for token, box in zip(token_list, bbox_list): + item = { + "bbox": box, + "token": token + } + f.write(json.dumps(item, ensure_ascii=False)+'\n') + + if not box: + continue + x_min, y_min, x_max, y_max = box + draw.rectangle([x_min, y_min, x_max, y_max], fill=None, outline=(0,250,0), width=1) + try: + draw.text((x_min, y_min), token, (250,0,0)) + except: + pass + + vis.save(output_vis_path) diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex_processor.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex_processor.py new file mode 100644 index 00000000..129471b8 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex_processor.py @@ -0,0 +1,536 @@ +import os +import re +import json +import shutil +import logging +import numpy as np +from PIL import Image + + +SKIP_PATTERNS = [r'\{', r'\}', r'[\[\]]', r'\\begin\{.*?\}', r'\\end\{.*?\}', r'\^', r'\_', r'\\.*rule.*', r'\\.*line.*', r'\[[\-.0-9]+[epm][xtm]\]'] +SKIP_Tokens = ['\\', '\\\\', '\\index', '\\a', '&', '$', '\\multirow', '\\def', '\\raggedright', '\\url', '\\cr', '\\ensuremath', '\\left', '\\right', + '\\mathchoice', '\\scriptstyle', '\\displaystyle', '\\qquad', '\\quad', '\\,', '\\!', '~', '\\boldmath'] +PHANTOM_Tokens = ['\\fontfamily', '\\vphantom', '\\phantom', '\\rowcolor', '\\ref'] +TWO_Tail_Tokens = ['\\frac', '\\binom'] +AB_Tail_Tokens = ['\\xrightarrow', '\\xleftarrow', '\\sqrt'] # special token \xxx [] {} +TWO_Tail_Invisb_Tokens = ['\\overset', '\\underset', '\\stackrel'] +ONE_Tail_Tokens = ['\\widetilde', '\\overline', '\\hat', '\\widehat', '\\tilde', '\\Tilde', '\\dot', '\\bar', '\\vec', '\\underline', '\\underbrace', '\\check', + '\\breve', '\\Bar', '\\Vec', '\\mathring', '\\ddot'] +ONE_Tail_Invisb_Tokens = ['\\boldsymbol', '\\pmb', '\\textbf', '\\mathrm', '\\mathbf', '\\mathbb', '\\mathcal', '\\textmd', '\\texttt', '\\textnormal', + '\\text', '\\textit', '\\textup', '\\mathop', '\\mathbin', '\\smash', '\\operatorname', '\\textrm', '\\mathfrak', '\\emph', + '\\textsf', '\\textsc'] + + +def flatten_multiline(latex): + brace_map = { + "\\left(": "\\right)", + "\\left[": "\\right]", + "\\left{": "\\right}", + } + l_split = latex.split(' ') + if l_split[0] == "\\begin{array}": + if l_split[-1] == "\\end{array}": + l_split = l_split[2:-1] + else: + l_split = l_split[2:] + + idx = 0 + while idx < len(l_split): + token = l_split[idx] + if token.startswith("\\left") and token in brace_map.keys(): + end_idx = find_matching_brace(l_split, idx, brace=[token, brace_map[token]]) + if end_idx != -1: + idx = end_idx + elif token in ["\\\\", "~", "\\qquad"]: + l_split = l_split[0:idx] + l_split[idx+1:] + idx -= 1 + idx += 1 + latex = ' '.join(l_split) + return "$ "+latex+" $" + + +def clean_latex(text): + # TODO 让GPT写的去空格函数, 初步测了是没问题的, 不确定是否完全没有bug + cleaned_text = re.sub(r'(?<=[^\\])\s+(?=[^\\])', '', text) + # TODO 有一些不能去掉的空格给补充回来 + for item in ["\\hline", "\\midrule", "\\times", "\\bf", "\\footnotesize", "\\cr", '\\log']: + cleaned_text = cleaned_text.replace(item, item+" ") + cleaned_text = cleaned_text.replace(" \\mathcolor{black}", "\\mathcolor{black}") + return cleaned_text + +def remove_trailing_latex(formula): + pattern = r'(\\(hspace\*?\{[^{}]*?\}|vspace\*?\{[^{}]*?\}|smallskip|medskip|quad|qquad|bigskip|[;,])|\~|\.)*$' + # Replace the matched pattern with an empty string + cleaned_formula = re.sub(pattern, '', formula, count=1) + return cleaned_formula + +def find_matching_brace(sequence, start_index, brace=['{', '}']): + # Finds the index of the matching brace for the one at start_index + left_brace, right_brace = brace + depth = 0 + for i, char in enumerate(sequence[start_index:], start=start_index): + if char == left_brace: + depth += 1 + elif char == right_brace: + depth -= 1 + if depth == 0: + return i + if depth > 0: + error_info = "Warning! found no matching brace in sequence !" + raise ValueError(error_info) + return -1 + +def normalize_latex(l, rm_trail=False): + if "tabular" in l: + latex_type = "tabular" + else: + latex_type = "formula" + + if rm_trail: + l = remove_trailing_latex(l) + l = l.strip().replace(r'\pmatrix', r'\mypmatrix').replace(r'\matrix', r'\mymatrix') + + # TODO \raggedright \arraybackslash, these align method, difficult to handle, remove it. + for item in ['\\raggedright', '\\arraybackslash']: + l = l.replace(item, "") + + for item in ['\\lowercase', '\\uppercase']: + l = l.replace(item, "") + + # TODO \hspace {1 . 5 cm}, for formula, change to \hspace{1.5cm}, for table, remove it. + pattern = r'\\[hv]space { [.0-9a-z ]+ }' + old_token = re.findall(pattern, l, re.DOTALL) + if latex_type == "tabular": + new_token = ["" for item in old_token] + else: + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft) + + # TODO take \begin {tabular} {} as one token + # TODO there are \begin{array} in table too，so the process should run in both formula and table. + if latex_type == "tabular": + l = l.replace("\\begin {tabular}", "\\begin{tabular}") + l = l.replace("\\end {tabular}", "\\end{tabular}") + l = l.replace("\\begin {array}", "\\begin{array}") + l = l.replace("\\end {array}", "\\end{array}") + l_split = l.split(' ') + idx = 0 + while idx < len(l_split): + token = l_split[idx] + if token == "\\begin{tabular}": + sub_idx = idx + 1 + end_idx = find_matching_brace(l_split, sub_idx) + new_token = "".join(l_split[idx: end_idx+1]) + l_split = l_split[0:idx] + [new_token] + l_split[end_idx+1:] + break + idx += 1 + l = ' '.join(l_split) + + # TODO some complex format, hart to deal with re.match, so using brace match, such as：\cmidrule ( l { 3 p t } r { 3 p t } ) { 1 - 1 } + l_split = l.split(' ') + idx = 0 + while idx < len(l_split): + token = l_split[idx] + if token in ["\\cmidrule", "\\cline"]: + sub_idx = idx + 1 + if l_split[sub_idx] == "(": + mid_end = find_matching_brace(l_split, sub_idx, brace=['(', ')']) + end_idx = find_matching_brace(l_split, mid_end+1) + else: + end_idx = find_matching_brace(l_split, sub_idx) + new_token = "".join(l_split[idx: end_idx+1]) + l_split = l_split[0:idx] + [new_token] + l_split[end_idx+1:] + idx += 1 + l = ' '.join(l_split) + + pattern = r'\\begin{array} { [lrc ]+ }' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace("\\begin{array} ", "").replace(" ", "").replace("", "\\begin{array} ") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft) + + # TODO token such \not= should be one token + pattern = r'\\not [<>+=\-]' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft) + + # TODO tokens such as \dots \exp \sinh, split them to parts, so the bbox match will be easier. + + l = " "+l+" " + l = l.replace(" \\ldots ", " . . . ") + l = l.replace(" \\cdots ", " . . . ") + l = l.replace(" \\dots ", " . . . ") + l = l.replace(" \\dotsb ", " . . . ") + l = l.replace(" \\log ", " \\mathrm { l o g } ") + l = l.replace(" \\exp ", " \\mathrm { e x p } ") + l = l.replace(" \\sin ", " \\mathrm { s i n } ") + l = l.replace(" \\cos ", " \\mathrm { c o s } ") + l = l.replace(" \\tan ", " \\mathrm { t a n } ") + l = l.replace(" \\tanh ", " \\mathrm { t a n h } ") + l = l.replace(" \\cosh ", " \\mathrm { c o s h } ") + l = l.replace(" \\sinh ", " \\mathrm { s i n h } ") + + # ** token such as \big( should be one token + pattern = r'\\[Bb]ig[g]?[glrm]? [(){}|\[\]] ' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft+" ") + + pattern = r'\\[Bb]ig[g]?[glrm]? \\.*? ' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft+" ") + + # TODO when \operatorname * meets mathcolor it comes error, yet the * is useless, so we simply remove it bynow. + pattern = r'\\operatorname \*' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = ["\\operatorname" for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft) + + # TODO \lefteqn will lead to letter overlap, it's harmfull for render, so simply remove it. + l = l.replace("\\lefteqn", "") + + # TODO \footnote can not seem as ONE_Tail_Invisb_Tokens(usually this type token add color by \mathrm {\color(x)}, yet \footnode should be \color{\footnote{x}}), so we simple change it to "^". + l = l.replace("\\footnote ", "^ ") + + # TODO \' can not be rendered separately(cause to different visulize performence), so we take these tokens as one token such as \' e -> \'e, on the other hand, if { after \' then render them separately. + pattern = r'\\\' [^{] ' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft+" ") + + # TODO [ -1.5ex ] [ 1.5pt ] [ 3 mm ] some layout adjustment, no need to render. combine them as one token. + if latex_type == "tabular": + pattern = r'\[ [\-.0-9 ]+[exptcm ]+ \]' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft) + + # ** \parbox { 3cm } {} shoudle be combined as one token + pattern = r'\\parbox {[^{]+}' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft) + + # ** \raisebox{}[][] {} shoudle be combined as one token, \raisebox{-1.5ex}[0pt] + pattern = r'\\raisebox {[^{]+} [\[\]0-9 exptcm]+{' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft[0:-1]+" {") + + # ** \char shoudle be combined as one token + pattern = r'{ \\char[0-9\' ]+}' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, "{ "+aft[1:-1]+" }") + + # ** \not xx shoudle be combined as one token + pattern = r'\\not [\\=\<\>][^ ]+ ' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft+" ") + + # ** \specialrule{1pt}{2pt}{2pt}, special lines, shoudle be combined as one token + pattern = r'\\specialrule {[ .0-9a-z]+} {[ .0-9a-z]+} {[ .0-9a-z]+}' + old_token = re.findall(pattern, l, re.DOTALL) + new_token = [item.replace(" ", "") for item in old_token] + for bef, aft in zip(old_token, new_token): + l = l.replace(bef, aft) + + # ** for easier add color, the original color should be removed, there are two type of color for now: \color[rgb]{0, 1, 0} and \color{red} + pattern = r'\\colorbox[ \[\]RGBrgb]+{ [A-Za-z 0-9,!]+ } |\\color[ \[\]RGBrgb]+{ [A-Za-z 0-9,!]+ } |\\textcolor[ \[\]RGBrgb]+{ [A-Za-z 0-9,!]+ } |\\cellcolor[ \[\]RGBrgb]+{ [A-Za-z 0-9,!]+ } ' + old_token = re.findall(pattern, l, re.DOTALL) + for bef in old_token: + l = l.replace(bef, "") + + # ** filling the missing brace [] and {} according to token. + l_split = l.split(' ') + idx = 0 + while idx < len(l_split): + token = l_split[idx] + if token in ONE_Tail_Tokens + ONE_Tail_Invisb_Tokens: + # ** normalize tokens such as \hat, fill missing the {}, such as \hat \lambda -> \hat {\lambda} + sub_idx = idx + 1 + while sub_idx < len(l_split) and l_split[sub_idx] in ONE_Tail_Tokens+ONE_Tail_Invisb_Tokens: + sub_idx += 1 + new_split = l_split[0:idx] + for ii in range(idx, sub_idx): + new_split = new_split + [l_split[ii], "{"] + if l_split[sub_idx] != "{": + new_split = new_split + [l_split[sub_idx]] + ["}"]*(sub_idx-idx) + l_split = new_split + l_split[sub_idx+1:] + else: + end_idx = find_matching_brace(l_split, sub_idx) + new_split = new_split + l_split[sub_idx+1:end_idx] + ["}"]*(sub_idx-idx) + l_split = new_split + l_split[end_idx+1:] + elif token in AB_Tail_Tokens: + # ** normalize special tokens such as \sqrt, fill the missing [] {} in \sqrt [] {}, yet the [] is optional, for example: \sqrt A B -> \sqrt {A} B and \sqrt [A] B -> \sqrt [A] {B} + if l_split[idx + 1] != "[" and l_split[idx + 1] != "{": + l_split = l_split[0:idx+1] + ["{"] + [l_split[idx+1]] + ["}"] + l_split[idx+2:] + else: + if l_split[idx + 1] == "[": + end1 = find_matching_brace(l_split, idx+1, brace=['[', ']']) + else: + end1 = idx + if l_split[end1 + 1] != "{": + l_split = l_split[0:end1+1] + ["{"] + [l_split[end1+1]] + ["}"] + l_split[end1+2:] + elif token in TWO_Tail_Tokens + TWO_Tail_Invisb_Tokens: + # ** normalize special tokens such as \frac, add missing brace in \frac {A} {B} for example: \frac {\lambda} 2 -> \frac {\lambda} {2} + if l_split[idx + 1] != "{": + l_split = l_split[0:idx+1] + ["{"] + [l_split[idx+1]] + ["}"] + l_split[idx+2:] + end1 = find_matching_brace(l_split, idx+1) + if l_split[end1 + 1] != "{": + l_split = l_split[0:end1+1] + ["{"] + [l_split[end1+1]] + ["}"] + l_split[end1+2:] + + idx += 1 + l = ' '.join(l_split) + + return l + +def token_add_color(l_split, idx, render_dict): + token = l_split[idx] + if token in PHANTOM_Tokens: + # ** special tokens that do not need render, skip it + if l_split[idx + 1] == '{': + brace_end = find_matching_brace(l_split, idx + 1) + else: + brace_end = idx + 1 + next_idx = brace_end + 1 + elif token in TWO_Tail_Tokens: + # ** tokens such as \frac A B, and the token needs render too. + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + den_start = num_end + 1 + den_end = find_matching_brace(l_split, den_start) + l_split_copy = l_split[:idx] + [r'\mathcolor{black}{'+token+'{'] + \ + [r'\mathcolor{gray}{'] + l_split[num_start + 1:num_end] + \ + ['}'] + [r'}{'] + [r'\mathcolor{gray}{'] + l_split[den_start + 1:den_end] + \ + ['}'] + ['}'] + ['}'] + l_split[den_end + 1:] + + l_new = ' '.join(l_split_copy) + l_new = r'\mathcolor{gray}{ ' + l_new + ' }' + render_dict[str(idx)] = l_new, token + next_idx = idx + 1 + elif token in ONE_Tail_Tokens: + # ** tokens such as \hat A, and the token needs render too. + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + l_split_copy = l_split[:idx] + [r'\mathcolor{black}{'] + l_split[idx: num_start+1] + \ + [r'\mathcolor{gray}{'] + l_split[num_start+1: num_end] + \ + ['}'] + l_split[num_end: num_end+1] + ['}'] + l_split[num_end+1:] + l_new = ' '.join(l_split_copy) + l_new = r'\mathcolor{gray}{ ' + l_new + ' }' + render_dict[str(idx)] = l_new, token + next_idx = idx + 1 + elif token in ONE_Tail_Invisb_Tokens: + # ** tokens such as \text A B, and the token does not need render. + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + sub_idx = num_start+1 + if num_end-num_start == 2: + l_split_copy = l_split.copy() + l_split_copy[sub_idx] = r'{\mathcolor{black}{' + l_split_copy[sub_idx] + '}}' + l_new = ' '.join(l_split_copy) + l_new = r'\mathcolor{gray}{ ' + l_new + ' }' + render_dict[str(idx)] = l_new, l_split[sub_idx] + next_idx = num_end + else: + while sub_idx < num_end: + l_split, sub_idx, render_dict = token_add_color(l_split, sub_idx, render_dict) + next_idx = num_end + 1 + elif token in AB_Tail_Tokens: + # ** special token \xrightarrow, could be \xrightarrow [] {} or \xrightarrow {}, process method are different. + if l_split[idx+1] == '{': + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + l_split_copy = l_split[:idx] + [r'\mathcolor{black}{'] + l_split[idx: idx+2] \ + + [r'\mathcolor{gray}{'] + l_split[num_start+1: num_end] + ['}}'] + l_split[num_end:] + l_new = ' '.join(l_split_copy) + l_new = r'\mathcolor{gray}{ ' + l_new + ' }' + render_dict[str(idx)] = l_new, token + sub_idx = num_start+1 + while sub_idx < num_end: + l_split, sub_idx, render_dict = token_add_color(l_split, sub_idx, render_dict) + next_idx = num_end + 1 + elif l_split[idx+1] == '[': + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start, brace=['[', ']']) + den_start = num_end + 1 + den_end = find_matching_brace(l_split, den_start) + l_split_copy = l_split[:idx] + [r'{\mathcolor{black}{'] + l_split[idx: idx+2] \ + + [r'\mathcolor{gray}{'] + l_split[idx+2: num_end] + ['}'] + l_split[num_end:den_start+1] \ + + [r'\mathcolor{gray}{'] + l_split[den_start+1: den_end] + ['}'] + l_split[den_end: den_end+1] \ + + ['}}'] + l_split[den_end+1:] + l_new = ' '.join(l_split_copy) + l_new = r'\mathcolor{gray}{ ' + l_new + ' }' + render_dict[str(idx)] = l_new, token + sub_idx = num_start + 1 + while sub_idx < num_end: + l_split, sub_idx, render_dict = token_add_color(l_split, sub_idx, render_dict) + sub_idx = den_start + 1 + while sub_idx < den_end: + l_split, sub_idx, render_dict = token_add_color(l_split, sub_idx, render_dict) + next_idx = den_end + 1 + elif token in ["\\multicolumn", "\\multirow"]: + # ** tokens with three {}, such as \multicolumn {} {} {}, the text in third {} need be rendered. + first_start = idx + 1 + first_end = find_matching_brace(l_split, first_start) + second_start = first_end + 1 + second_end = find_matching_brace(l_split, second_start) + third_start = second_end + 1 + third_end = find_matching_brace(l_split, third_start) + + sub_idx = third_start+1 + while sub_idx < third_end: + l_split, sub_idx, render_dict = token_add_color(l_split, sub_idx, render_dict) + next_idx = third_end + 1 + elif token in SKIP_Tokens+TWO_Tail_Invisb_Tokens or any(re.match(pattern, token) for pattern in SKIP_PATTERNS): + # ** tokens no need render, just skip + # print('skip', idx, token) + # TODO special case :[], could be single, or in \sqrt[]{}. + if (token == "[" and l_split[idx-1]!="\\sqrt") or (token == "]" and idx>=3 and l_split[idx-3]!="\\sqrt"): + l_split_copy = l_split.copy() + l_split_copy[idx] = r'\mathcolor{black}{ ' + l_split_copy[idx] + ' }' + l_new = ' '.join(l_split_copy) + l_new = r'\mathcolor{gray}{ ' + l_new + ' }' + render_dict[str(idx)] = l_new, token + next_idx = idx + 1 + else: + next_idx = idx + 1 + else: + # ** nomal token + l_split_copy = l_split.copy() + # TODO sometimes there is translation after add color, the exp prove that \mathcolor{black}{ A } is better than \mathcolor{black}{A} + l_split_copy[idx] = r'\mathcolor{black}{ ' + l_split_copy[idx] + ' }' + + l_new = ' '.join(l_split_copy) + l_new = r'\mathcolor{gray}{ ' + l_new + ' }' + render_dict[str(idx)] = l_new, token + next_idx = idx + 1 + + return l_split, next_idx, render_dict + + +def token_add_color_RGB(l_split, idx, token_list, brace_color=False): + """using \mathcolor[RGB]{r,g,b} to render latex. + """ + token = l_split[idx] + if not token: + next_idx = idx + 1 + elif token in PHANTOM_Tokens: + # ** special tokens that do not need render, skip it + if l_split[idx + 1] == '{': + brace_end = find_matching_brace(l_split, idx + 1) + else: + brace_end = idx + 1 + next_idx = brace_end + 1 + elif token in TWO_Tail_Tokens: + # ** tokens such as \frac A B, and the token needs render too. + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + den_start = num_end + 1 + den_end = find_matching_brace(l_split, den_start) + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + l_split = l_split[:idx] + [color_token+token] + l_split[idx+1: den_end+1] + ["}"] + l_split[den_end+1:] + token_list.append(token) + next_idx = idx + 1 + elif token in ONE_Tail_Tokens: + # ** tokens such as \hat A, and the token needs render too. + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + if token != "\\underbrace" and num_end+1 < len(l_split) and l_split[num_end+1] == "_": + l_split = l_split[:idx] + ["{"+color_token+token] + l_split[idx+1: num_end+1] + ["}}"] + l_split[num_end+1:] + else: + l_split = l_split[:idx] + [color_token+token] + l_split[idx+1: num_end+1] + ["}"] + l_split[num_end+1:] + token_list.append(token) + next_idx = idx + 1 + elif token in ONE_Tail_Invisb_Tokens: + # ** tokens such as \text A B, and the token does not need render. + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + sub_idx = num_start+1 + if num_end-num_start == 2: + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + token_list.append(l_split[num_start+1]) + l_split = l_split[:num_start+1] + [color_token+l_split[num_start+1]+"}"] + l_split[num_end:] + else: + while sub_idx < num_end: + l_split, sub_idx, token_list = token_add_color_RGB(l_split, sub_idx, token_list) + next_idx = num_end + 1 + elif token in AB_Tail_Tokens: + # ** special token \xrightarrow, could be \xrightarrow [] {} or \xrightarrow {}, process method are different. + if l_split[idx+1] == '{': + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start) + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + l_split = l_split[:idx] + [color_token+token] + l_split[idx+1: num_end+1] + ["}"] + l_split[num_end+1:] + token_list.append(token) + sub_idx = num_start+1 + while sub_idx < num_end: + l_split, sub_idx, token_list = token_add_color_RGB(l_split, sub_idx, token_list) + next_idx = num_end + 1 + elif l_split[idx+1] == '[': + num_start = idx + 1 + num_end = find_matching_brace(l_split, num_start, brace=['[', ']']) + den_start = num_end + 1 + den_end = find_matching_brace(l_split, den_start) + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + l_split = l_split[:idx] + [color_token+token] + l_split[idx+1: den_end+1] + ["}"] + l_split[den_end+1:] + token_list.append(token) + sub_idx = num_start + 1 + while sub_idx < num_end: + l_split, sub_idx, token_list = token_add_color_RGB(l_split, sub_idx, token_list, brace_color=True) + sub_idx = den_start + 1 + while sub_idx < den_end: + l_split, sub_idx, token_list = token_add_color_RGB(l_split, sub_idx, token_list) + next_idx = den_end + 1 + elif token in ["\\multicolumn", "\\multirow"]: + # ** tokens with three {}, such as \multicolumn {} {} {}, the text in third {} need be rendered. + first_start = idx + 1 + first_end = find_matching_brace(l_split, first_start) + second_start = first_end + 1 + second_end = find_matching_brace(l_split, second_start) + third_start = second_end + 1 + third_end = find_matching_brace(l_split, third_start) + + sub_idx = third_start+1 + while sub_idx < third_end: + l_split, sub_idx, token_list = token_add_color_RGB(l_split, sub_idx, token_list) + next_idx = third_end + 1 + elif token in SKIP_Tokens+TWO_Tail_Invisb_Tokens or any(re.match(pattern, token) for pattern in SKIP_PATTERNS): + # ** tokens no need render, just skip + # print('skip', idx, token) + # TODO special case :[], could be single, or in \sqrt[]{}. + if (token == "[" and l_split[idx-1]!="\\sqrt") or (token == "]" and idx>=3 and l_split[idx-3]!="\\sqrt"): + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + l_split = l_split[:idx] + [color_token + l_split[idx] + "}"] + l_split[idx+1:] + token_list.append(token) + next_idx = idx + 1 + else: + next_idx = idx + 1 + else: + # ** nomal token + if brace_color or (idx > 1 and l_split[idx-1] == "_"): + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + l_split = l_split[:idx] + ["{" + color_token + l_split[idx] + "}}"] + l_split[idx+1:] + token_list.append(token) + next_idx = idx + 1 + else: + color_token = "\\mathcolor[RGB]{>}{".replace("", str(len(token_list))) + l_split = l_split[:idx] + [color_token + l_split[idx] + "}"] + l_split[idx+1:] + token_list.append(token) + next_idx = idx + 1 + return l_split, next_idx, token_list \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex_render_percentage.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex_render_percentage.py new file mode 100644 index 00000000..8d2d726e --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/latex_render_percentage.py @@ -0,0 +1,117 @@ +import re +import os +import json +import time +import shutil +import random +import argparse +import subprocess +import numpy as np + +from tqdm import tqdm +from multiprocessing import Pool + + +formular_template = r""" +\documentclass[12pt]{article} +\usepackage[landscape]{geometry} +\usepackage{geometry} +\geometry{a5paper,scale=0.98} +\pagestyle{empty} +# \usepackage{booktabs} +\usepackage{amsmath} +\usepackage{amssymb} +\usepackage{xcolor} +\begin{document} +\makeatletter +\renewcommand*{\@textcolor}[3]{%% + \protect\leavevmode + \begingroup + \color#1{#2}#3%% + \endgroup +} +\makeatother +\begin{displaymath} +%s +\end{displaymath} +\end{document} +""" + +def run_shell_cmd(cmd, max_time=15): + child = subprocess.Popen(cmd, shell=True) + for i in range(max_time): + if child.poll(): + return True + if i == max_time-1: + child.kill() + return False + time.sleep(1) + return False + +def render_latex(latex_code, basename, latex_dir, pdf_dir): + latex_path = os.path.join(latex_dir, basename + ".tex") + pdf_path = os.path.join(pdf_dir, basename + ".pdf") + with open(latex_path, "w") as f: + f.write(formular_template % latex_code) + # cmd = f"pdflatex -interaction=nonstopmode -output-directory={pdf_dir} -output-format=pdf {latex_path} >/dev/null" + run_cmd(f"/mnt/hwfile/opendatalab/guzhuangcheng/programme/texlive/bin/x86_64-linux/xelatex -interaction=nonstopmode -output-directory={temp_dir} {tex_filename} >/dev/null") + run_shell_cmd(cmd) + return pdf_path + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--input', '-i', type=str, default='data/pred_results/test.json') + parser.add_argument('--clean', action='store_true', default=False) + parser.add_argument('--gt', action='store_true', default=False) + args = parser.parse_args() + + if args.gt: + output_path = os.path.join("output", 'gt.json') + load_key = 'gt' + else: + load_key = 'pred' + output_path = os.path.join("output", os.path.basename(args.input)) + + + temp_dir=f"render_temp_dir" + try: + shutil.rmtree(temp_dir) + except: + pass + latex_dir = os.path.join(temp_dir, "texes") + pdf_dir = os.path.join(temp_dir, "pdfs") + os.makedirs(latex_dir, exist_ok=True) + os.makedirs(pdf_dir, exist_ok=True) + + with open(args.input, "r") as f: + input_data = json.load(f) + + myP = Pool(200) + for idx, item in enumerate(input_data): + basename = f"sample_{idx}" + myP.apply_async(render_latex, args=(item[load_key], basename, latex_dir, pdf_dir)) + myP.close() + print("processing, may take some times.") + myP.join() + + success_num = 0 + total_num = 0 + for idx, item in enumerate(input_data): + basename = f"sample_{idx}" + total_num += 1 + pdf_path = os.path.join(pdf_dir, basename + ".pdf") + if os.path.exists(pdf_path): + success_num += 1 + item['renderable'] = 1 + else: + item['renderable'] = 0 + + print("total num:", total_num, "render success num:", success_num) + with open(output_path, "w") as f: + f.write(json.dumps(input_data, indent=2)) + if args.clean: + try: + shutil.rmtree(temp_dir) + except: + pass \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/preprocess_formula.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/preprocess_formula.js new file mode 100644 index 00000000..7929bc38 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/preprocess_formula.js @@ -0,0 +1,387 @@ +const path = require('path'); +var katex = require(path.join(__dirname,"third_party/katex/katex.js")) +options = require(path.join(__dirname,"third_party/katex/src/Options.js")) +var readline = require('readline'); +var rl = readline.createInterface({ + input: process.stdin, + output: process.stdout, + terminal: false +}); + + +rl.on('line', function(line){ + a = line + if (line[0] == "%") { + line = line.substr(1, line.length - 1); + } + line = line.split('%')[0]; + + line = line.split('\\~').join(' '); + + for (var i = 0; i < 300; i++) { + line = line.replace(/\\>/, " "); + line = line.replace('$', ' '); + line = line.replace(/\\label{.*?}/, ""); + } + + if (line.indexOf("matrix") == -1 && line.indexOf("cases")==-1 && + line.indexOf("array")==-1 && line.indexOf("begin")==-1) { + for (var i = 0; i < 300; i++) { + line = line.replace(/\\\\/, "\\,"); + } + } + + + line = line + " " + // global_str is tokenized version (build in parser.js) + // norm_str is normalized version build by renderer below. + try { + + + if (process.argv[2] == "tokenize") { + var tree = katex.__parse(line, {}); + console.log(global_str.replace(/\\label { .*? }/, "")); + } else { + for (var i = 0; i < 300; ++i) { + line = line.replace(/{\\rm/, "\\mathrm{"); + line = line.replace(/{ \\rm/, "\\mathrm{"); + line = line.replace(/\\rm{/, "\\mathrm{"); + } + + var tree = katex.__parse(line, {}); + buildExpression(tree, new options({})); + for (var i = 0; i < 300; ++i) { + norm_str = norm_str.replace('SSSSSS', '$'); + norm_str = norm_str.replace(' S S S S S S', '$'); + } + console.log(norm_str.replace(/\\label { .*? }/, "")); + } + } catch (e) { + console.error(line); + console.error(norm_str); + console.error(e); + console.log(); + } + global_str = "" + norm_str = "" +}) + + + +// This is a LaTeX AST to LaTeX Renderer (modified version of KaTeX AST-> MathML). +norm_str = "" + +var groupTypes = {}; + +groupTypes.mathord = function(group, options) { + if (options.font == "mathrm"){ + for (i = 0; i < group.value.length; ++i ) { + if (group.value[i] == " ") { + norm_str = norm_str + group.value[i] + "\; "; + } else { + norm_str = norm_str + group.value[i] + " "; + } + } + } else { + norm_str = norm_str + group.value + " "; + } +}; + +groupTypes.textord = function(group, options) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.bin = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.rel = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.open = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.close = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.inner = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.punct = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.ordgroup = function(group, options) { + norm_str = norm_str + "{ "; + + buildExpression(group.value, options); + + norm_str = norm_str + "} "; +}; + +groupTypes.text = function(group, options) { + + norm_str = norm_str + "\\mathrm { "; + + buildExpression(group.value.body, options); + norm_str = norm_str + "} "; +}; + +groupTypes.color = function(group, options) { + var inner = buildExpression(group.value.value, options); + + var node = new mathMLTree.MathNode("mstyle", inner); + + node.setAttribute("mathcolor", group.value.color); + + return node; +}; + +groupTypes.supsub = function(group, options) { + buildGroup(group.value.base, options); + + if (group.value.sub) { + norm_str = norm_str + "_ "; + if (group.value.sub.type != 'ordgroup') { + norm_str = norm_str + " { "; + buildGroup(group.value.sub, options); + norm_str = norm_str + "} "; + } else { + buildGroup(group.value.sub, options); + } + + } + + if (group.value.sup) { + norm_str = norm_str + "^ "; + if (group.value.sup.type != 'ordgroup') { + norm_str = norm_str + " { "; + buildGroup(group.value.sup, options); + norm_str = norm_str + "} "; + } else { + buildGroup(group.value.sup, options); + } + } + +}; + +groupTypes.genfrac = function(group, options) { + if (!group.value.hasBarLine) { + norm_str = norm_str + "\\binom "; + } else { + norm_str = norm_str + "\\frac "; + } + buildGroup(group.value.numer, options); + buildGroup(group.value.denom, options); + +}; + +groupTypes.array = function(group, options) { + norm_str = norm_str + "\\begin{array} { "; + if (group.value.cols) { + group.value.cols.map(function(start) { + if (start && start.align) { + norm_str = norm_str + start.align + " ";}}); + } else { + group.value.body[0].map(function(start) { + norm_str = norm_str + "l "; + } ); + } + norm_str = norm_str + "} "; + group.value.body.map(function(row) { + if (row.some(cell => cell.value.length > 0)) { // orginal code: if (row[0].value.length > 0) + out = row.map(function(cell) { + buildGroup(cell, options); + if (norm_str.length > 4 + && norm_str.substring(norm_str.length-4, norm_str.length) == "{ } ") { + norm_str = norm_str.substring(0, norm_str.length-4) ; + } + norm_str = norm_str + "& "; + }); + norm_str = norm_str.substring(0, norm_str.length-2) + "\\\\ "; + } + }); + norm_str = norm_str + "\\end{array} "; +}; + +groupTypes.sqrt = function(group, options) { + var node; + if (group.value.index) { + norm_str = norm_str + "\\sqrt [ "; + buildExpression(group.value.index.value, options); + norm_str = norm_str + "] "; + buildGroup(group.value.body, options); + } else { + norm_str = norm_str + "\\sqrt "; + buildGroup(group.value.body, options); + } +}; + +groupTypes.leftright = function(group, options) { + + + + norm_str = norm_str + "\\left" + group.value.left + " "; + buildExpression(group.value.body, options); + norm_str = norm_str + "\\right" + group.value.right + " "; +}; + +groupTypes.accent = function(group, options) { + if (group.value.base.type != 'ordgroup') { + norm_str = norm_str + group.value.accent + " { "; + buildGroup(group.value.base, options); + norm_str = norm_str + "} "; + } else { + norm_str = norm_str + group.value.accent + " "; + buildGroup(group.value.base, options); + } +}; + +groupTypes.spacing = function(group) { + var node; + if (group.value == " ") { + norm_str = norm_str + "~ "; + } else { + norm_str = norm_str + group.value + " "; + } + return node; +}; + +groupTypes.op = function(group) { + var node; + + // TODO(emily): handle big operators using the `largeop` attribute + + + if (group.value.symbol) { + // This is a symbol. Just add the symbol. + norm_str = norm_str + group.value.body + " "; + + } else { + if (group.value.limits == false) { + norm_str = norm_str + "\\\operatorname { "; + } else { + norm_str = norm_str + "\\\operatorname* { "; + } + for (i = 1; i < group.value.body.length; ++i ) { + norm_str = norm_str + group.value.body[i] + " "; + } + norm_str = norm_str + "} "; + } +}; + +groupTypes.katex = function(group) { + var node = new mathMLTree.MathNode( + "mtext", [new mathMLTree.TextNode("KaTeX")]); + + return node; +}; + + + +groupTypes.font = function(group, options) { + var font = group.value.font; + if (font == "mbox" || font == "hbox") { + font = "mathrm"; + } + norm_str = norm_str + "\\" + font + " "; + buildGroup(group.value.body, options.withFont(font)); +}; + +groupTypes.delimsizing = function(group) { + var children = []; + norm_str = norm_str + group.value.funcName + " " + group.value.value + " "; +}; + +groupTypes.styling = function(group, options) { + norm_str = norm_str + " " + group.value.original + " "; + buildExpression(group.value.value, options); + +}; + +groupTypes.sizing = function(group, options) { + + if (group.value.original == "\\rm") { + norm_str = norm_str + "\\mathrm { "; + buildExpression(group.value.value, options.withFont("mathrm")); + norm_str = norm_str + "} "; + } else { + norm_str = norm_str + " " + group.value.original + " "; + buildExpression(group.value.value, options); + } +}; + +groupTypes.overline = function(group, options) { + norm_str = norm_str + "\\overline { "; + + buildGroup(group.value.body, options); + norm_str = norm_str + "} "; + norm_str = norm_str; + +}; + +groupTypes.underline = function(group, options) { + norm_str = norm_str + "\\underline { "; + buildGroup(group.value.body, options); + norm_str = norm_str + "} "; + + norm_str = norm_str; + +}; + +groupTypes.rule = function(group) { + norm_str = norm_str + "\\rule { "+group.value.width.number+" "+group.value.width.unit+" } { "+group.value.height.number+" "+group.value.height.unit+ " } "; + +}; + +groupTypes.llap = function(group, options) { + norm_str = norm_str + "\\llap "; + buildGroup(group.value.body, options); +}; + +groupTypes.rlap = function(group, options) { + norm_str = norm_str + "\\rlap "; + buildGroup(group.value.body, options); + +}; + +groupTypes.phantom = function(group, options, prev) { + norm_str = norm_str + "\\phantom { "; + buildExpression(group.value.value, options); + norm_str = norm_str + "} "; + +}; + +/** + * Takes a list of nodes, builds them, and returns a list of the generated + * MathML nodes. A little simpler than the HTML version because we don't do any + * previous-node handling. + */ +var buildExpression = function(expression, options) { + var groups = []; + for (var i = 0; i < expression.length; i++) { + var group = expression[i]; + buildGroup(group, options); + } + // console.log(norm_str); + // return groups; +}; + +/** + * Takes a group from the parser and calls the appropriate groupTypes function + * on it to produce a MathML node. + */ +var buildGroup = function(group, options) { + if (groupTypes[group.type]) { + groupTypes[group.type](group, options); + } else { + throw new ParseError( + "Got group of unknown type: '" + group.type + "'"); + } +}; + + diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/preprocess_tabular.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/preprocess_tabular.js new file mode 100644 index 00000000..71bb9394 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/preprocess_tabular.js @@ -0,0 +1,395 @@ +const path = require('path'); +var katex = require(path.join(__dirname,"third_party/katex/katex.js")) +options = require(path.join(__dirname,"third_party/katex/src/Options.js")) +var readline = require('readline'); +var rl = readline.createInterface({ + input: process.stdin, + output: process.stdout, + terminal: false +}); + + +rl.on('line', function(line){ + a = line + if (line[0] == "%") { + line = line.substr(1, line.length - 1); + } + // line = line.split('%')[0]; + + line = line.split('\\~').join(' '); + + for (var i = 0; i < 300; i++) { + line = line.replace(/\\>/, " "); + // line = line.replace('$', ' '); + line = line.replace(/\\label{.*?}/, ""); + } + + if (line.indexOf("matrix") == -1 && line.indexOf("cases")==-1 && + line.indexOf("array")==-1 && line.indexOf("begin")==-1) { + for (var i = 0; i < 300; i++) { + line = line.replace(/\\\\/, "\\,"); + } + } + + + line = line + " " + // global_str is tokenized version (build in parser.js) + // norm_str is normalized version build by renderer below. + try { + + + if (process.argv[2] == "tokenize") { + var tree = katex.__parse(line, {}); + console.log(global_str.replace(/\\label { .*? }/, "")); + } else { + for (var i = 0; i < 300; ++i) { + line = line.replace(/{\\rm/, "\\mathrm{"); + line = line.replace(/{ \\rm/, "\\mathrm{"); + line = line.replace(/\\rm{/, "\\mathrm{"); + } + + var tree = katex.__parse(line, {}); + buildExpression(tree, new options({})); + for (var i = 0; i < 300; ++i) { + norm_str = norm_str.replace('SSSSSS', '$'); + norm_str = norm_str.replace(' S S S S S S', '$'); + } + console.log(norm_str.replace(/\\label { .*? }/, "")); + } + } catch (e) { + console.error(line); + console.error(norm_str); + console.error(e); + console.log(""); + } + global_str = "" + norm_str = "" +}) + + + +// This is a LaTeX AST to LaTeX Renderer (modified version of KaTeX AST-> MathML). +norm_str = "" + +var groupTypes = {}; + +groupTypes.mathord = function(group, options) { + if (options.font == "mathrm"){ + for (i = 0; i < group.value.length; ++i ) { + if (group.value[i] == " ") { + norm_str = norm_str + group.value[i] + "\; "; + } else { + norm_str = norm_str + group.value[i] + " "; + } + } + } else { + norm_str = norm_str + group.value + " "; + } +}; + +groupTypes.textord = function(group, options) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.bin = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.rel = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.open = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.close = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.inner = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.punct = function(group) { + norm_str = norm_str + group.value + " "; +}; + +groupTypes.ordgroup = function(group, options) { + norm_str = norm_str + "{ "; + + buildExpression(group.value, options); + + norm_str = norm_str + "} "; +}; + +groupTypes.text = function(group, options) { + + norm_str = norm_str + "\\mathrm { "; + + buildExpression(group.value.body, options); + norm_str = norm_str + "} "; +}; + +groupTypes.color = function(group, options) { + var inner = buildExpression(group.value.value, options); + + var node = new mathMLTree.MathNode("mstyle", inner); + + node.setAttribute("mathcolor", group.value.color); + + return node; +}; + +groupTypes.supsub = function(group, options) { + buildGroup(group.value.base, options); + + if (group.value.sub) { + norm_str = norm_str + "_ "; + if (group.value.sub.type != 'ordgroup') { + norm_str = norm_str + " { "; + buildGroup(group.value.sub, options); + norm_str = norm_str + "} "; + } else { + buildGroup(group.value.sub, options); + } + + } + + if (group.value.sup) { + norm_str = norm_str + "^ "; + if (group.value.sup.type != 'ordgroup') { + norm_str = norm_str + " { "; + buildGroup(group.value.sup, options); + norm_str = norm_str + "} "; + } else { + buildGroup(group.value.sup, options); + } + } + +}; + +groupTypes.genfrac = function(group, options) { + if (!group.value.hasBarLine) { + norm_str = norm_str + "\\binom "; + } else { + norm_str = norm_str + "\\frac "; + } + buildGroup(group.value.numer, options); + buildGroup(group.value.denom, options); + +}; + +groupTypes.array = function(group, options) { + norm_str = norm_str + "\\begin{" + group.value.style + "} "; + + if (group.value.style == "array" || group.value.style == "tabular" || group.value.style == "tabularx") { + norm_str = norm_str + "{ "; + if (group.value.cols) { + group.value.cols.map(function(start) { + if (start) { + if (start.type == "align") { + norm_str = norm_str + start.align + " "; + } else if (start.type == "separator") { + norm_str = norm_str + start.separator + " "; + } + } + }); + } else { + group.value.body[0].map(function(start) { + norm_str = norm_str + "c "; + } ); + } + norm_str = norm_str + "} "; + } + group.value.body.map(function(row) { + if (row.length > 1 || row[0].value.length > 0) { + if (row[0].value[0] && row[0].value[0].value == "\\hline") { + norm_str = norm_str + "\\hline "; + row[0].value = row[0].value.slice(1); + } + out = row.map(function(cell) { + buildGroup(cell, options); + norm_str = norm_str + "& "; + }); + norm_str = norm_str.substring(0, norm_str.length-2) + "\\\\ "; + } + }); + norm_str = norm_str + "\\end{" + group.value.style + "} "; +}; + +groupTypes.sqrt = function(group, options) { + var node; + if (group.value.index) { + norm_str = norm_str + "\\sqrt [ " + group.value.index + " ] "; + buildGroup(group.value.body, options); + } else { + norm_str = norm_str + "\\sqrt "; + buildGroup(group.value.body, options); + } +}; + +groupTypes.leftright = function(group, options) { + + + + norm_str = norm_str + "\\left" + group.value.left + " "; + buildExpression(group.value.body, options); + norm_str = norm_str + "\\right" + group.value.right + " "; +}; + +groupTypes.accent = function(group, options) { + if (group.value.base.type != 'ordgroup') { + norm_str = norm_str + group.value.accent + " { "; + buildGroup(group.value.base, options); + norm_str = norm_str + "} "; + } else { + norm_str = norm_str + group.value.accent + " "; + buildGroup(group.value.base, options); + } +}; + +groupTypes.spacing = function(group) { + var node; + if (group.value == " ") { + norm_str = norm_str + "~ "; + } else { + norm_str = norm_str + group.value + " "; + } + return node; +}; + +groupTypes.op = function(group) { + var node; + + // TODO(emily): handle big operators using the `largeop` attribute + + + if (group.value.symbol) { + // This is a symbol. Just add the symbol. + norm_str = norm_str + group.value.body + " "; + + } else { + if (group.value.limits == false) { + norm_str = norm_str + "\\\operatorname { "; + } else { + norm_str = norm_str + "\\\operatorname* { "; + } + for (i = 1; i < group.value.body.length; ++i ) { + norm_str = norm_str + group.value.body[i] + " "; + } + norm_str = norm_str + "} "; + } +}; + +groupTypes.katex = function(group) { + var node = new mathMLTree.MathNode( + "mtext", [new mathMLTree.TextNode("KaTeX")]); + + return node; +}; + + + +groupTypes.font = function(group, options) { + var font = group.value.font; + if (font == "mbox" || font == "hbox") { + font = "mathrm"; + } + norm_str = norm_str + "\\" + font + " "; + buildGroup(group.value.body, options.withFont(font)); +}; + +groupTypes.delimsizing = function(group) { + var children = []; + norm_str = norm_str + group.value.funcName + " " + group.value.value + " "; +}; + +groupTypes.styling = function(group, options) { + norm_str = norm_str + " " + group.value.original + " "; + buildExpression(group.value.value, options); + +}; + +groupTypes.sizing = function(group, options) { + + if (group.value.original == "\\rm") { + norm_str = norm_str + "\\mathrm { "; + buildExpression(group.value.value, options.withFont("mathrm")); + norm_str = norm_str + "} "; + } else { + norm_str = norm_str + " " + group.value.original + " "; + buildExpression(group.value.value, options); + } +}; + +groupTypes.overline = function(group, options) { + norm_str = norm_str + "\\overline { "; + + buildGroup(group.value.body, options); + norm_str = norm_str + "} "; + norm_str = norm_str; + +}; + +groupTypes.underline = function(group, options) { + norm_str = norm_str + "\\underline { "; + buildGroup(group.value.body, options); + norm_str = norm_str + "} "; + + norm_str = norm_str; + +}; + +groupTypes.rule = function(group) { + norm_str = norm_str + "\\rule { "+group.value.width.number+" "+group.value.width.unit+" } { "+group.value.height.number+" "+group.value.height.unit+ " } "; + +}; + +groupTypes.llap = function(group, options) { + norm_str = norm_str + "\\llap "; + buildGroup(group.value.body, options); +}; + +groupTypes.rlap = function(group, options) { + norm_str = norm_str + "\\rlap "; + buildGroup(group.value.body, options); + +}; + +groupTypes.phantom = function(group, options, prev) { + norm_str = norm_str + "\\phantom { "; + buildExpression(group.value.value, options); + norm_str = norm_str + "} "; + +}; + +/** + * Takes a list of nodes, builds them, and returns a list of the generated + * MathML nodes. A little simpler than the HTML version because we don't do any + * previous-node handling. + */ +var buildExpression = function(expression, options) { + var groups = []; + for (var i = 0; i < expression.length; i++) { + var group = expression[i]; + buildGroup(group, options); + } + // console.log(norm_str); + // return groups; +}; + +/** + * Takes a group from the parser and calls the appropriate groupTypes function + * on it to produce a MathML node. + */ +var buildGroup = function(group, options) { + if (groupTypes[group.type]) { + groupTypes[group.type](group, options); + } else { + throw new ParseError( + "Got group of unknown type: '" + group.type + "'"); + } +}; + + diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/README.md b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/README.md new file mode 100644 index 00000000..bc74abab --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/README.md @@ -0,0 +1 @@ +Directly taken from https://github.com/harvardnlp/im2markup diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/README.md b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/README.md new file mode 100644 index 00000000..31cf658d --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/README.md @@ -0,0 +1,68 @@ +# [KaTeX](https://khan.github.io/KaTeX/) [![Build Status](https://travis-ci.org/Khan/KaTeX.svg?branch=master)](https://travis-ci.org/Khan/KaTeX) + +[![Join the chat at https://gitter.im/Khan/KaTeX](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/Khan/KaTeX?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) + +KaTeX is a fast, easy-to-use JavaScript library for TeX math rendering on the web. + + * **Fast:** KaTeX renders its math synchronously and doesn't need to reflow the page. See how it compares to a competitor in [this speed test](http://jsperf.com/katex-vs-mathjax/). + * **Print quality:** KaTeX’s layout is based on Donald Knuth’s TeX, the gold standard for math typesetting. + * **Self contained:** KaTeX has no dependencies and can easily be bundled with your website resources. + * **Server side rendering:** KaTeX produces the same output regardless of browser or environment, so you can pre-render expressions using Node.js and send them as plain HTML. + +KaTeX supports all major browsers, including Chrome, Safari, Firefox, Opera, and IE 8 - IE 11. A list of supported commands can be on the [wiki](https://github.com/Khan/KaTeX/wiki/Function-Support-in-KaTeX). + +## Usage + +You can [download KaTeX](https://github.com/khan/katex/releases) and host it on your server or include the `katex.min.js` and `katex.min.css` files on your page directly from a CDN: + +```html + + +``` + +#### In-browser rendering + +Call `katex.render` with a TeX expression and a DOM element to render into: + +```js +katex.render("c = \\pm\\sqrt{a^2 + b^2}", element); +``` + +If KaTeX can't parse the expression, it throws a `katex.ParseError` error. + +#### Server side rendering or rendering to a string + +To generate HTML on the server or to generate an HTML string of the rendered math, you can use `katex.renderToString`: + +```js +var html = katex.renderToString("c = \\pm\\sqrt{a^2 + b^2}"); +// '...' +``` + +Make sure to include the CSS and font files, but there is no need to include the JavaScript. Like `render`, `renderToString` throws if it can't parse the expression. + +#### Rendering options + +You can provide an object of options as the last argument to `katex.render` and `katex.renderToString`. Available options are: + +- `displayMode`: `boolean`. If `true` the math will be rendered in display mode, which will put the math in display style (so `\int` and `\sum` are large, for example), and will center the math on the page on its own line. If `false` the math will be rendered in inline mode. (default: `false`) +- `throwOnError`: `boolean`. If `true`, KaTeX will throw a `ParseError` when it encounters an unsupported command. If `false`, KaTeX will render the unsupported command as text in the color given by `errorColor`. (default: `true`) +- `errorColor`: `string`. A color string given in the format `"#XXX"` or `"#XXXXXX"`. This option determines the color which unsupported commands are rendered in. (default: `#cc0000`) + +For example: + +```js +katex.render("c = \\pm\\sqrt{a^2 + b^2}", element, { displayMode: true }); +``` + +#### Automatic rendering of math on a page + +Math on the page can be automatically rendered using the auto-render extension. See [the Auto-render README](contrib/auto-render/README.md) for more information. + +## Contributing + +See [CONTRIBUTING.md](CONTRIBUTING.md) + +## License + +KaTeX is licensed under the [MIT License](http://opensource.org/licenses/MIT). diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/cli.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/cli.js new file mode 100644 index 00000000..b64de377 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/cli.js @@ -0,0 +1,32 @@ +#!/usr/bin/env node +// Simple CLI for KaTeX. +// Reads TeX from stdin, outputs HTML to stdout. +/* eslint no-console:0 */ + +var katex = require("./"); +var input = ""; + +// Skip the first two args, which are just "node" and "cli.js" +var args = process.argv.slice(2); + +if (args.indexOf("--help") !== -1) { + console.log(process.argv[0] + " " + process.argv[1] + + " [ --help ]" + + " [ --display-mode ]"); + + console.log("\n" + + "Options:"); + console.log(" --help Display this help message"); + console.log(" --display-mode Render in display mode (not inline mode)"); + process.exit(); +} + +process.stdin.on("data", function(chunk) { + input += chunk.toString(); +}); + +process.stdin.on("end", function() { + var options = { displayMode: args.indexOf("--display-mode") !== -1 }; + var output = katex.renderToString(input, options); + console.log(output); +}); diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/katex.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/katex.js new file mode 100644 index 00000000..4d64606b --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/katex.js @@ -0,0 +1,74 @@ +/* eslint no-console:0 */ +/** + * This is the main entry point for KaTeX. Here, we expose functions for + * rendering expressions either to DOM nodes or to markup strings. + * + * We also expose the ParseError class to check if errors thrown from KaTeX are + * errors in the expression, or errors in javascript handling. + */ + +var ParseError = require("./src/ParseError"); +var Settings = require("./src/Settings"); + +var buildTree = require("./src/buildTree"); +var parseTree = require("./src/parseTree"); +var utils = require("./src/utils"); + +/** + * Parse and build an expression, and place that expression in the DOM node + * given. + */ +var render = function(expression, baseNode, options) { + utils.clearNode(baseNode); + + var settings = new Settings(options); + + var tree = parseTree(expression, settings); + var node = buildTree(tree, expression, settings).toNode(); + + baseNode.appendChild(node); +}; + +// KaTeX's styles don't work properly in quirks mode. Print out an error, and +// disable rendering. +if (typeof document !== "undefined") { + if (document.compatMode !== "CSS1Compat") { + typeof console !== "undefined" && console.warn( + "Warning: KaTeX doesn't work in quirks mode. Make sure your " + + "website has a suitable doctype."); + + render = function() { + throw new ParseError("KaTeX doesn't work in quirks mode."); + }; + } +} + +/** + * Parse and build an expression, and return the markup for that. + */ +var renderToString = function(expression, options) { + var settings = new Settings(options); + + var tree = parseTree(expression, settings); + return buildTree(tree, expression, settings).toMarkup(); +}; + +/** + * Parse an expression and return the parse tree. + */ +var generateParseTree = function(expression, options) { + var settings = new Settings(options); + return parseTree(expression, settings); +}; + +module.exports = { + render: render, + renderToString: renderToString, + /** + * NOTE: This method is not currently recommended for public use. + * The internal tree representation is unstable and is very likely + * to change. Use at your own risk. + */ + __parse: generateParseTree, + ParseError: ParseError, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/package.json b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/package.json new file mode 100644 index 00000000..ba3c9405 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/package.json @@ -0,0 +1,108 @@ +{ + "_args": [ + [ + "katex", + "/home/srush/Projects/im2latex" + ] + ], + "_from": "katex@latest", + "_id": "katex@0.6.0", + "_inCache": true, + "_installable": true, + "_location": "/katex", + "_nodeVersion": "4.2.1", + "_npmOperationalInternal": { + "host": "packages-12-west.internal.npmjs.com", + "tmp": "tmp/katex-0.6.0.tgz_1460769444991_0.38667152682319283" + }, + "_npmUser": { + "email": "kevinb7@gmail.com", + "name": "kevinbarabash" + }, + "_npmVersion": "2.15.2", + "_phantomChildren": {}, + "_requested": { + "name": "katex", + "raw": "katex", + "rawSpec": "", + "scope": null, + "spec": "latest", + "type": "tag" + }, + "_requiredBy": [ + "#USER" + ], + "_resolved": "https://registry.npmjs.org/katex/-/katex-0.6.0.tgz", + "_shasum": "12418e09121c05c92041b6b3b9fb6bab213cb6f3", + "_shrinkwrap": null, + "_spec": "katex", + "_where": "/home/srush/Projects/im2latex", + "bin": { + "katex": "cli.js" + }, + "bugs": { + "url": "https://github.com/Khan/KaTeX/issues" + }, + "dependencies": { + "match-at": "^0.1.0" + }, + "description": "Fast math typesetting for the web.", + "devDependencies": { + "browserify": "^10.2.4", + "clean-css": "~2.2.15", + "eslint": "^1.10.2", + "express": "~3.3.3", + "glob": "^5.0.15", + "jasmine": "^2.3.2", + "jasmine-core": "^2.3.4", + "js-yaml": "^3.3.1", + "jspngopt": "^0.1.0", + "less": "~1.7.5", + "nomnom": "^1.8.1", + "pako": "0.2.7", + "selenium-webdriver": "^2.46.1", + "uglify-js": "~2.4.15" + }, + "directories": {}, + "dist": { + "shasum": "12418e09121c05c92041b6b3b9fb6bab213cb6f3", + "tarball": "https://registry.npmjs.org/katex/-/katex-0.6.0.tgz" + }, + "files": [ + "cli.js", + "dist/", + "katex.js", + "src/" + ], + "gitHead": "b94fc6534d5c23f944906a52a592bee4e0090665", + "homepage": "https://github.com/Khan/KaTeX#readme", + "license": "MIT", + "main": "katex.js", + "maintainers": [ + { + "name": "kevinbarabash", + "email": "kevinb7@gmail.com" + }, + { + "name": "spicyj", + "email": "ben@benalpert.com" + }, + { + "name": "xymostech", + "email": "xymostech@gmail.com" + } + ], + "name": "katex", + "optionalDependencies": {}, + "readme": "ERROR: No README data found!", + "repository": { + "type": "git", + "url": "git://github.com/Khan/KaTeX.git" + }, + "scripts": { + "prepublish": "make dist", + "start": "node server.js", + "test": "make lint test" + }, + "version": "0.6.0" +} diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Lexer.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Lexer.js new file mode 100644 index 00000000..5cb8d3d4 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Lexer.js @@ -0,0 +1,162 @@ +/** + * The Lexer class handles tokenizing the input in various ways. Since our + * parser expects us to be able to backtrack, the lexer allows lexing from any + * given starting point. + * + * Its main exposed function is the `lex` function, which takes a position to + * lex from and a type of token to lex. It defers to the appropriate `_innerLex` + * function. + * + * The various `_innerLex` functions perform the actual lexing of different + * kinds. + */ + +var matchAt = require("../../match-at"); + +var ParseError = require("./ParseError"); + +// The main lexer class +function Lexer(input) { + this._input = input; +} + +// The resulting token returned from `lex`. +function Token(text, data, position) { + this.text = text; + this.data = data; + this.position = position; +} + +/* The following tokenRegex + * - matches typical whitespace (but not NBSP etc.) using its first group + * - matches symbol combinations which result in a single output character + * - does not match any control character \x00-\x1f except whitespace + * - does not match a bare backslash + * - matches any ASCII character except those just mentioned + * - does not match the BMP private use area \uE000-\uF8FF + * - does not match bare surrogate code units + * - matches any BMP character except for those just described + * - matches any valid Unicode surrogate pair + * - matches a backslash followed by one or more letters + * - matches a backslash followed by any BMP character, including newline + * Just because the Lexer matches something doesn't mean it's valid input: + * If there is no matching function or symbol definition, the Parser will + * still reject the input. + */ +var tokenRegex = new RegExp( + "([ \r\n\t]+)|(" + // whitespace + "---?" + // special combinations + "|[!-\\[\\]-\u2027\u202A-\uD7FF\uF900-\uFFFF]" + // single codepoint + "|[\uD800-\uDBFF][\uDC00-\uDFFF]" + // surrogate pair + "|\\\\(?:[a-zA-Z]+|[^\uD800-\uDFFF])" + // function name + ")" +); + +var whitespaceRegex = /\s*/; + +/** + * This function lexes a single normal token. It takes a position and + * whether it should completely ignore whitespace or not. + */ +Lexer.prototype._innerLex = function(pos, ignoreWhitespace) { + var input = this._input; + if (pos === input.length) { + return new Token("EOF", null, pos); + } + var match = matchAt(tokenRegex, input, pos); + if (match === null) { + throw new ParseError( + "Unexpected character: '" + input[pos] + "'", + this, pos); + } else if (match[2]) { // matched non-whitespace + return new Token(match[2], null, pos + match[2].length); + } else if (ignoreWhitespace) { + return this._innerLex(pos + match[1].length, true); + } else { // concatenate whitespace to a single space + return new Token(" ", null, pos + match[1].length); + } +}; + +// A regex to match a CSS color (like #ffffff or BlueViolet) +var cssColor = /#[a-z0-9]+|[a-z]+/i; + +/** + * This function lexes a CSS color. + */ +Lexer.prototype._innerLexColor = function(pos) { + var input = this._input; + + // Ignore whitespace + var whitespace = matchAt(whitespaceRegex, input, pos)[0]; + pos += whitespace.length; + + var match; + if ((match = matchAt(cssColor, input, pos))) { + // If we look like a color, return a color + return new Token(match[0], null, pos + match[0].length); + } else { + throw new ParseError("Invalid color", this, pos); + } +}; + +// A regex to match a dimension. Dimensions look like +// "1.2em" or ".4pt" or "1 ex" +var sizeRegex = /(-?)\s*(\d+(?:\.\d*)?|\.\d+)\s*([a-z]{2})/; + +/** + * This function lexes a dimension. + */ +Lexer.prototype._innerLexSize = function(pos) { + var input = this._input; + + // Ignore whitespace + var whitespace = matchAt(whitespaceRegex, input, pos)[0]; + pos += whitespace.length; + + var match; + if ((match = matchAt(sizeRegex, input, pos))) { + var unit = match[3]; + // We only currently handle "em" and "ex" units + // if (unit !== "em" && unit !== "ex") { + // throw new ParseError("Invalid unit: '" + unit + "'", this, pos); + // } + return new Token(match[0], { + number: +(match[1] + match[2]), + unit: unit, + }, pos + match[0].length); + } + + throw new ParseError("Invalid size", this, pos); +}; + +/** + * This function lexes a string of whitespace. + */ +Lexer.prototype._innerLexWhitespace = function(pos) { + var input = this._input; + + var whitespace = matchAt(whitespaceRegex, input, pos)[0]; + pos += whitespace.length; + + return new Token(whitespace[0], null, pos); +}; + +/** + * This function lexes a single token starting at `pos` and of the given mode. + * Based on the mode, we defer to one of the `_innerLex` functions. + */ +Lexer.prototype.lex = function(pos, mode) { + if (mode === "math") { + return this._innerLex(pos, true); + } else if (mode === "text") { + return this._innerLex(pos, false); + } else if (mode === "color") { + return this._innerLexColor(pos); + } else if (mode === "size") { + return this._innerLexSize(pos); + } else if (mode === "whitespace") { + return this._innerLexWhitespace(pos); + } +}; + +module.exports = Lexer; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Options.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Options.js new file mode 100644 index 00000000..39ff37bf --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Options.js @@ -0,0 +1,189 @@ +/** + * This file contains information about the options that the Parser carries + * around with it while parsing. Data is held in an `Options` object, and when + * recursing, a new `Options` object can be created with the `.with*` and + * `.reset` functions. + */ + +/** + * This is the main options class. It contains the style, size, color, and font + * of the current parse level. It also contains the style and size of the parent + * parse level, so size changes can be handled efficiently. + * + * Each of the `.with*` and `.reset` functions passes its current style and size + * as the parentStyle and parentSize of the new options class, so parent + * handling is taken care of automatically. + */ +function Options(data) { + this.style = data.style; + this.color = data.color; + this.size = data.size; + this.phantom = data.phantom; + this.font = data.font; + + if (data.parentStyle === undefined) { + this.parentStyle = data.style; + } else { + this.parentStyle = data.parentStyle; + } + + if (data.parentSize === undefined) { + this.parentSize = data.size; + } else { + this.parentSize = data.parentSize; + } +} + +/** + * Returns a new options object with the same properties as "this". Properties + * from "extension" will be copied to the new options object. + */ +Options.prototype.extend = function(extension) { + var data = { + style: this.style, + size: this.size, + color: this.color, + parentStyle: this.style, + parentSize: this.size, + phantom: this.phantom, + font: this.font, + }; + + for (var key in extension) { + if (extension.hasOwnProperty(key)) { + data[key] = extension[key]; + } + } + + return new Options(data); +}; + +/** + * Create a new options object with the given style. + */ +Options.prototype.withStyle = function(style) { + return this.extend({ + style: style, + }); +}; + +/** + * Create a new options object with the given size. + */ +Options.prototype.withSize = function(size) { + return this.extend({ + size: size, + }); +}; + +/** + * Create a new options object with the given color. + */ +Options.prototype.withColor = function(color) { + return this.extend({ + color: color, + }); +}; + +/** + * Create a new options object with "phantom" set to true. + */ +Options.prototype.withPhantom = function() { + return this.extend({ + phantom: true, + }); +}; + +/** + * Create a new options objects with the give font. + */ +Options.prototype.withFont = function(font) { + return this.extend({ + font: font, + }); +}; + +/** + * Create a new options object with the same style, size, and color. This is + * used so that parent style and size changes are handled correctly. + */ +Options.prototype.reset = function() { + return this.extend({}); +}; + +/** + * A map of color names to CSS colors. + * TODO(emily): Remove this when we have real macros + */ +var colorMap = { + "katex-blue": "#6495ed", + "katex-orange": "#ffa500", + "katex-pink": "#ff00af", + "katex-red": "#df0030", + "katex-green": "#28ae7b", + "katex-gray": "gray", + "katex-purple": "#9d38bd", + "katex-blueA": "#c7e9f1", + "katex-blueB": "#9cdceb", + "katex-blueC": "#58c4dd", + "katex-blueD": "#29abca", + "katex-blueE": "#1c758a", + "katex-tealA": "#acead7", + "katex-tealB": "#76ddc0", + "katex-tealC": "#5cd0b3", + "katex-tealD": "#55c1a7", + "katex-tealE": "#49a88f", + "katex-greenA": "#c9e2ae", + "katex-greenB": "#a6cf8c", + "katex-greenC": "#83c167", + "katex-greenD": "#77b05d", + "katex-greenE": "#699c52", + "katex-goldA": "#f7c797", + "katex-goldB": "#f9b775", + "katex-goldC": "#f0ac5f", + "katex-goldD": "#e1a158", + "katex-goldE": "#c78d46", + "katex-redA": "#f7a1a3", + "katex-redB": "#ff8080", + "katex-redC": "#fc6255", + "katex-redD": "#e65a4c", + "katex-redE": "#cf5044", + "katex-maroonA": "#ecabc1", + "katex-maroonB": "#ec92ab", + "katex-maroonC": "#c55f73", + "katex-maroonD": "#a24d61", + "katex-maroonE": "#94424f", + "katex-purpleA": "#caa3e8", + "katex-purpleB": "#b189c6", + "katex-purpleC": "#9a72ac", + "katex-purpleD": "#715582", + "katex-purpleE": "#644172", + "katex-mintA": "#f5f9e8", + "katex-mintB": "#edf2df", + "katex-mintC": "#e0e5cc", + "katex-grayA": "#fdfdfd", + "katex-grayB": "#f7f7f7", + "katex-grayC": "#eeeeee", + "katex-grayD": "#dddddd", + "katex-grayE": "#cccccc", + "katex-grayF": "#aaaaaa", + "katex-grayG": "#999999", + "katex-grayH": "#555555", + "katex-grayI": "#333333", + "katex-kaBlue": "#314453", + "katex-kaGreen": "#639b24", +}; + +/** + * Gets the CSS color of the current options object, accounting for the + * `colorMap`. + */ +Options.prototype.getColor = function() { + if (this.phantom) { + return "transparent"; + } else { + return colorMap[this.color] || this.color; + } +}; + +module.exports = Options; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/ParseError.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/ParseError.js new file mode 100644 index 00000000..320f0bd6 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/ParseError.js @@ -0,0 +1,40 @@ +/** + * This is the ParseError class, which is the main error thrown by KaTeX + * functions when something has gone wrong. This is used to distinguish internal + * errors from errors in the expression that the user provided. + */ +function ParseError(message, lexer, position) { + var error = "KaTeX parse error: " + message; + + if (lexer !== undefined && position !== undefined) { + // If we have the input and a position, make the error a bit fancier + + // Prepend some information + error += " at position " + position + ": "; + + // Get the input + var input = lexer._input; + // Insert a combining underscore at the correct position + input = input.slice(0, position) + "\u0332" + + input.slice(position); + + // Extract some context from the input and add it to the error + var begin = Math.max(0, position - 15); + var end = position + 15; + error += input.slice(begin, end); + } + + // Some hackery to make ParseError a prototype of Error + // See http://stackoverflow.com/a/8460753 + var self = new Error(error); + self.name = "ParseError"; + self.__proto__ = ParseError.prototype; + + self.position = position; + return self; +} + +// More hackery +ParseError.prototype.__proto__ = Error.prototype; + +module.exports = ParseError; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Parser.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Parser.js new file mode 100644 index 00000000..efd45f03 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Parser.js @@ -0,0 +1,798 @@ +/* eslint no-constant-condition:0 */ +var functions = require("./functions"); +var environments = require("./environments"); +var Lexer = require("./Lexer"); +var symbols = require("./symbols"); +var utils = require("./utils"); + +var parseData = require("./parseData"); +var ParseError = require("./ParseError"); + +global_str = "" + +/** + * This file contains the parser used to parse out a TeX expression from the + * input. Since TeX isn't context-free, standard parsers don't work particularly + * well. + * + * The strategy of this parser is as such: + * + * The main functions (the `.parse...` ones) take a position in the current + * parse string to parse tokens from. The lexer (found in Lexer.js, stored at + * this.lexer) also supports pulling out tokens at arbitrary places. When + * individual tokens are needed at a position, the lexer is called to pull out a + * token, which is then used. + * + * The parser has a property called "mode" indicating the mode that + * the parser is currently in. Currently it has to be one of "math" or + * "text", which denotes whether the current environment is a math-y + * one or a text-y one (e.g. inside \text). Currently, this serves to + * limit the functions which can be used in text mode. + * + * The main functions then return an object which contains the useful data that + * was parsed at its given point, and a new position at the end of the parsed + * data. The main functions can call each other and continue the parsing by + * using the returned position as a new starting point. + * + * There are also extra `.handle...` functions, which pull out some reused + * functionality into self-contained functions. + * + * The earlier functions return ParseNodes. + * The later functions (which are called deeper in the parse) sometimes return + * ParseFuncOrArgument, which contain a ParseNode as well as some data about + * whether the parsed object is a function which is missing some arguments, or a + * standalone object which can be used as an argument to another function. + */ + +/** + * Main Parser class + */ +function Parser(input, settings) { + // Make a new lexer + this.lexer = new Lexer(input); + // Store the settings for use in parsing + this.settings = settings; +} + +var ParseNode = parseData.ParseNode; + +/** + * An initial function (without its arguments), or an argument to a function. + * The `result` argument should be a ParseNode. + */ +function ParseFuncOrArgument(result, isFunction) { + this.result = result; + // Is this a function (i.e. is it something defined in functions.js)? + this.isFunction = isFunction; +} + +/** + * Checks a result to make sure it has the right type, and throws an + * appropriate error otherwise. + * + * @param {boolean=} consume whether to consume the expected token, + * defaults to true + */ +Parser.prototype.expect = function(text, consume) { + if (this.nextToken.text !== text) { + throw new ParseError( + "Expected '" + text + "', got '" + this.nextToken.text + "'", + this.lexer, this.nextToken.position + ); + } + if (consume !== false) { + this.consume(); + } +}; + +/** + * Considers the current look ahead token as consumed, + * and fetches the one after that as the new look ahead. + */ +Parser.prototype.consume = function() { + this.pos = this.nextToken.position; + + global_str = global_str + " " + this.nextToken.text + this.nextToken = this.lexer.lex(this.pos, this.mode); +}; + +/** + * Main parsing function, which parses an entire input. + * + * @return {?Array.} + */ +Parser.prototype.parse = function() { + // Try to parse the input + this.mode = "math"; + this.pos = 0; + this.nextToken = this.lexer.lex(this.pos, this.mode); + var parse = this.parseInput(); + return parse; +}; + +/** + * Parses an entire input tree. + */ +Parser.prototype.parseInput = function() { + // Parse an expression + var expression = this.parseExpression(false); + // If we succeeded, make sure there's an EOF at the end + this.expect("EOF", false); + return expression; +}; + +var endOfExpression = ["}", "\\end", "\\right", "&", "\\\\", "\\cr"]; + +/** + * Parses an "expression", which is a list of atoms. + * + * @param {boolean} breakOnInfix Should the parsing stop when we hit infix + * nodes? This happens when functions have higher precendence + * than infix nodes in implicit parses. + * + * @param {?string} breakOnToken The token that the expression should end with, + * or `null` if something else should end the expression. + * + * @return {ParseNode} + */ +Parser.prototype.parseExpression = function(breakOnInfix, breakOnToken) { + var body = []; + // Keep adding atoms to the body until we can't parse any more atoms (either + // we reached the end, a }, or a \right) + while (true) { + var lex = this.nextToken; + var pos = this.pos; + if (endOfExpression.indexOf(lex.text) !== -1) { + break; + } + if (breakOnToken && lex.text === breakOnToken) { + break; + } + var atom = this.parseAtom(); + if (!atom) { + if (!this.settings.throwOnError && lex.text[0] === "\\") { + var errorNode = this.handleUnsupportedCmd(); + body.push(errorNode); + + pos = lex.position; + continue; + } + + break; + } + if (breakOnInfix && atom.type === "infix") { + // rewind so we can parse the infix atom again + this.pos = pos; + this.nextToken = lex; + break; + } + body.push(atom); + } + return this.handleInfixNodes(body); +}; + +/** + * Rewrites infix operators such as \over with corresponding commands such + * as \frac. + * + * There can only be one infix operator per group. If there's more than one + * then the expression is ambiguous. This can be resolved by adding {}. + * + * @returns {Array} + */ +Parser.prototype.handleInfixNodes = function(body) { + var overIndex = -1; + var funcName; + + for (var i = 0; i < body.length; i++) { + var node = body[i]; + if (node.type === "infix") { + if (overIndex !== -1) { + throw new ParseError("only one infix operator per group", + this.lexer, -1); + } + overIndex = i; + funcName = node.value.replaceWith; + } + } + + if (overIndex !== -1) { + var numerNode; + var denomNode; + + var numerBody = body.slice(0, overIndex); + var denomBody = body.slice(overIndex + 1); + + if (numerBody.length === 1 && numerBody[0].type === "ordgroup") { + numerNode = numerBody[0]; + } else { + numerNode = new ParseNode("ordgroup", numerBody, this.mode); + } + + if (denomBody.length === 1 && denomBody[0].type === "ordgroup") { + denomNode = denomBody[0]; + } else { + denomNode = new ParseNode("ordgroup", denomBody, this.mode); + } + + var value = this.callFunction( + funcName, [numerNode, denomNode], null); + return [new ParseNode(value.type, value, this.mode)]; + } else { + return body; + } +}; + +// The greediness of a superscript or subscript +var SUPSUB_GREEDINESS = 1; + +/** + * Handle a subscript or superscript with nice errors. + */ +Parser.prototype.handleSupSubscript = function(name) { + var symbol = this.nextToken.text; + var symPos = this.pos; + this.consume(); + var group = this.parseGroup(); + + if (!group) { + if (!this.settings.throwOnError && this.nextToken.text[0] === "\\") { + return this.handleUnsupportedCmd(); + } else { + // throw new ParseError( + // "Expected group after '" + symbol + "'", + // this.lexer, + // symPos + 1 + // ); + } + } else if (group.isFunction) { + // ^ and _ have a greediness, so handle interactions with functions' + // greediness + var funcGreediness = functions[group.result].greediness; + if (funcGreediness > SUPSUB_GREEDINESS) { + return this.parseFunction(group); + } else { + throw new ParseError( + "Got function '" + group.result + "' with no arguments " + + "as " + name, + this.lexer, symPos + 1); + } + } else { + return group.result; + } +}; + +/** + * Converts the textual input of an unsupported command into a text node + * contained within a color node whose color is determined by errorColor + */ +Parser.prototype.handleUnsupportedCmd = function() { + var text = this.nextToken.text; + var textordArray = []; + + for (var i = 0; i < text.length; i++) { + textordArray.push(new ParseNode("textord", text[i], "text")); + } + + var textNode = new ParseNode( + "text", + { + body: textordArray, + type: "text", + }, + this.mode); + + var colorNode = new ParseNode( + "color", + { + color: this.settings.errorColor, + value: [textNode], + type: "color", + }, + this.mode); + + this.consume(); + return colorNode; +}; + +/** + * Parses a group with optional super/subscripts. + * + * @return {?ParseNode} + */ +Parser.prototype.parseAtom = function() { + // The body of an atom is an implicit group, so that things like + // \left(x\right)^2 work correctly. + var base = this.parseImplicitGroup(); + + // In text mode, we don't have superscripts or subscripts + if (this.mode === "text") { + return base; + } + + // Note that base may be empty (i.e. null) at this point. + + var superscript; + var subscript; + while (true) { + // Lex the first token + var lex = this.nextToken; + + if (lex.text === "\\limits" || lex.text === "\\nolimits") { + // We got a limit control + if (!base || base.type !== "op") { + throw new ParseError( + "Limit controls must follow a math operator", + this.lexer, this.pos); + } else { + var limits = lex.text === "\\limits"; + base.value.limits = limits; + base.value.alwaysHandleSupSub = true; + } + this.consume(); + } else if (lex.text === "^") { + // We got a superscript start + // if (superscript) { + // throw new ParseError( + // "Double superscript", this.lexer, this.pos); + // } + superscript = this.handleSupSubscript("superscript"); + } else if (lex.text === "_") { + // We got a subscript start + // if (subscript) { + // throw new ParseError( + // "Double subscript", this.lexer, this.pos); + // } + subscript = this.handleSupSubscript("subscript"); + } else if (lex.text === "'") { + // We got a prime + var prime = new ParseNode("textord", "\\prime", this.mode); + + // Many primes can be grouped together, so we handle this here + var primes = [prime]; + this.consume(); + // Keep lexing tokens until we get something that's not a prime + while (this.nextToken.text === "'") { + // For each one, add another prime to the list + primes.push(prime); + this.consume(); + } + // Put them into an ordgroup as the superscript + superscript = new ParseNode("ordgroup", primes, this.mode); + } else { + // If it wasn't ^, _, or ', stop parsing super/subscripts + break; + } + } + + if (superscript || subscript) { + // If we got either a superscript or subscript, create a supsub + return new ParseNode("supsub", { + base: base, + sup: superscript, + sub: subscript, + }, this.mode); + } else { + // Otherwise return the original body + return base; + } +}; + +// A list of the size-changing functions, for use in parseImplicitGroup +var sizeFuncs = [ + "\\tiny", "\\scriptsize", "\\footnotesize", "\\small", "\\normalsize", + "\\large", "\\Large", "\\LARGE", "\\huge", "\\Huge", "\\textrm", "\\rm", "\\cal", + "\\bf", "\\siptstyle", "\\boldmath", "\\it" +]; + +// A list of the style-changing functions, for use in parseImplicitGroup +var styleFuncs = [ + "\\displaystyle", "\\textstyle", "\\scriptstyle", "\\scriptscriptstyle", +]; + +/** + * Parses an implicit group, which is a group that starts at the end of a + * specified, and ends right before a higher explicit group ends, or at EOL. It + * is used for functions that appear to affect the current style, like \Large or + * \textrm, where instead of keeping a style we just pretend that there is an + * implicit grouping after it until the end of the group. E.g. + * small text {\Large large text} small text again + * It is also used for \left and \right to get the correct grouping. + * + * @return {?ParseNode} + */ +Parser.prototype.parseImplicitGroup = function() { + var start = this.parseSymbol(); + + if (start == null) { + // If we didn't get anything we handle, fall back to parseFunction + return this.parseFunction(); + } + + var func = start.result; + var body; + if (func === "\\left") { + // If we see a left: + // Parse the entire left function (including the delimiter) + var left = this.parseFunction(start); + // Parse out the implicit body + body = this.parseExpression(false); + // Check the next token + this.expect("\\right", false); + var right = this.parseFunction(); + return new ParseNode("leftright", { + body: body, + left: left.value.value, + right: right.value.value, + }, this.mode); + } else if (func === "\\begin") { + // begin...end is similar to left...right + var begin = this.parseFunction(start); + var envName = begin.value.name; + var name = (begin.value.name + "") + + global_str = global_str.substring(0, global_str.length - (name.length * 2 + 2)) + name + "}" + + if (!environments.hasOwnProperty(envName)) { + throw new ParseError( + "No such environment: " + envName, + this.lexer, begin.value.namepos); + } + // Build the environment object. Arguments and other information will + // be made available to the begin and end methods using properties. + var env = environments[envName]; + var args = this.parseArguments("\\begin{" + envName + "}", env); + var context = { + mode: this.mode, + envName: envName, + parser: this, + lexer: this.lexer, + positions: args.pop(), + }; + var result = env.handler(context, args); + this.expect("\\end", false); + var end = this.parseFunction(); + + var name = (begin.value.name + "") + + global_str = global_str.substring(0, global_str.length - (name.length * 2 + 2)) + name + "}" + if (end.value.name !== envName) { + throw new ParseError( + "Mismatch: \\begin{" + envName + "} matched " + + "by \\end{" + end.value.name + "}", + this.lexer /* , end.value.namepos */); + // TODO: Add position to the above line and adjust test case, + // requires #385 to get merged first + } + result.position = end.position; + + return result; + + } else if (func.value == "\\matrix" || func.value == "\\pmatrix" || func.value == "\\cases") { + // if (!environments.hasOwnProperty(envName)) { + // throw new ParseError( + // "No such environment: " + envName, + // this.lexer, begin.value.namepos); + // } + // Build the environment object. Arguments and other information will + // be made available to the begin and end methods using properties. + + envName = func.value.slice(1); + var env = environments[envName]; + // var args = this.parseArguments("\\matrix{", env); + this.expect("{", true); + var context = { + mode: this.mode, + envName: envName, + parser: this, + lexer: this.lexer + }; + + var result = env.handler(context, {} ); + // exit(); + this.expect("}", true); + // var end = this.parseFunction(); + var next = this.nextToken.text; + // exit(); + // console.log(next); + // var name = ( + "") + + // global_str = global_str.substring(0, global_str.length - (name.length * 2 + 2)) + name + "}" + // result.position = end.position; + + return result; + + } else if (utils.contains(sizeFuncs, func)) { + // If we see a sizing function, parse out the implict body + body = this.parseExpression(false); + + return new ParseNode("sizing", { + // Figure out what size to use based on the list of functions above + original: func, + size: "size" + (utils.indexOf(sizeFuncs, func) + 1), + value: body, + }, this.mode); + } else if (utils.contains(styleFuncs, func)) { + // If we see a styling function, parse out the implict body + body = this.parseExpression(true); + return new ParseNode("styling", { + // Figure out what style to use by pulling out the style from + // the function name + original: func, + style: func.slice(1, func.length - 5), + value: body, + }, this.mode); + } else { + // Defer to parseFunction if it's not a function we handle + return this.parseFunction(start); + } +}; + +/** + * Parses an entire function, including its base and all of its arguments. + * The base might either have been parsed already, in which case + * it is provided as an argument, or it's the next group in the input. + * + * @param {ParseFuncOrArgument=} baseGroup optional as described above + * @return {?ParseNode} + */ +Parser.prototype.parseFunction = function(baseGroup) { + if (!baseGroup) { + baseGroup = this.parseGroup(); + } + + if (baseGroup) { + if (baseGroup.isFunction) { + var func = baseGroup.result; + var funcData = functions[func]; + if (this.mode === "text" && !funcData.allowedInText) { + // throw new ParseError( + // "Can't use function '" + func + "' in text mode", + // this.lexer, baseGroup.position); + } + + var args = this.parseArguments(func, funcData); + var result = this.callFunction(func, args, args.pop()); + return new ParseNode(result.type, result, this.mode); + } else { + return baseGroup.result; + } + } else { + return null; + } +}; + +/** + * Call a function handler with a suitable context and arguments. + */ +Parser.prototype.callFunction = function(name, args, positions) { + var context = { + funcName: name, + parser: this, + lexer: this.lexer, + positions: positions, + }; + return functions[name].handler(context, args); +}; + +/** + * Parses the arguments of a function or environment + * + * @param {string} func "\name" or "\begin{name}" + * @param {{numArgs:number,numOptionalArgs:number|undefined}} funcData + * @return the array of arguments, with the list of positions as last element + */ +Parser.prototype.parseArguments = function(func, funcData) { + var totalArgs = funcData.numArgs + funcData.numOptionalArgs; + if (totalArgs === 0) { + return [[this.pos]]; + } + + var baseGreediness = funcData.greediness; + var positions = [this.pos]; + var args = []; + + for (var i = 0; i < totalArgs; i++) { + var argType = funcData.argTypes && funcData.argTypes[i]; + var arg; + if (i < funcData.numOptionalArgs) { + if (argType) { + arg = this.parseSpecialGroup(argType, true); + } else { + arg = this.parseOptionalGroup(); + } + if (!arg) { + args.push(null); + positions.push(this.pos); + continue; + } + } else { + if (argType) { + arg = this.parseSpecialGroup(argType); + } else { + arg = this.parseGroup(); + } + if (!arg) { + if (!this.settings.throwOnError && + this.nextToken.text[0] === "\\") { + arg = new ParseFuncOrArgument( + this.handleUnsupportedCmd(this.nextToken.text), + false); + } else { + throw new ParseError( + "Expected group after '" + func + "'", + this.lexer, this.pos); + } + } + } + var argNode; + if (arg.isFunction) { + var argGreediness = + functions[arg.result].greediness; + if (argGreediness > baseGreediness) { + argNode = this.parseFunction(arg); + } else { + // throw new ParseError( + // "Got function '" + arg.result + "' as " + + // "argument to '" + func + "'", + // this.lexer, this.pos - 1); + } + } else { + argNode = arg.result; + } + args.push(argNode); + positions.push(this.pos); + } + + args.push(positions); + + return args; +}; + + +/** + * Parses a group when the mode is changing. Takes a position, a new mode, and + * an outer mode that is used to parse the outside. + * + * @return {?ParseFuncOrArgument} + */ +Parser.prototype.parseSpecialGroup = function(innerMode, optional) { + var outerMode = this.mode; + // Handle `original` argTypes + if (innerMode === "original") { + innerMode = outerMode; + } + + if (innerMode === "color" || innerMode === "size") { + // color and size modes are special because they should have braces and + // should only lex a single symbol inside + var openBrace = this.nextToken; + if (optional && openBrace.text !== "[") { + // optional arguments should return null if they don't exist + return null; + } + // The call to expect will lex the token after the '{' in inner mode + this.mode = innerMode; + this.expect(optional ? "[" : "{"); + var inner = this.nextToken; + this.mode = outerMode; + var data; + if (innerMode === "color") { + data = inner.text; + } else { + data = inner.data; + } + this.consume(); // consume the token stored in inner + this.expect(optional ? "]" : "}"); + return new ParseFuncOrArgument( + new ParseNode(innerMode, data, outerMode), + false); + } else if (innerMode === "text") { + // text mode is special because it should ignore the whitespace before + // it + var whitespace = this.lexer.lex(this.pos, "whitespace"); + this.pos = whitespace.position; + } + + // By the time we get here, innerMode is one of "text" or "math". + // We switch the mode of the parser, recurse, then restore the old mode. + this.mode = innerMode; + this.nextToken = this.lexer.lex(this.pos, innerMode); + var res; + if (optional) { + res = this.parseOptionalGroup(); + } else { + res = this.parseGroup(); + } + this.mode = outerMode; + this.nextToken = this.lexer.lex(this.pos, outerMode); + return res; +}; + +/** + * Parses a group, which is either a single nucleus (like "x") or an expression + * in braces (like "{x+y}") + * + * @return {?ParseFuncOrArgument} + */ +Parser.prototype.parseGroup = function() { + // Try to parse an open brace + if (this.nextToken.text === "{") { + // If we get a brace, parse an expression + this.consume(); + var expression = this.parseExpression(false); + // Make sure we get a close brace + this.expect("}"); + return new ParseFuncOrArgument( + new ParseNode("ordgroup", expression, this.mode), + false); + } else { + // Otherwise, just return a nucleus + return this.parseSymbol(); + } +}; + +/** + * Parses a group, which is an expression in brackets (like "[x+y]") + * + * @return {?ParseFuncOrArgument} + */ +Parser.prototype.parseOptionalGroup = function() { + // Try to parse an open bracket + if (this.nextToken.text === "[") { + // If we get a brace, parse an expression + this.consume(); + var expression = this.parseExpression(false, "]"); + // Make sure we get a close bracket + this.expect("]"); + return new ParseFuncOrArgument( + new ParseNode("ordgroup", expression, this.mode), + false); + } else { + // Otherwise, return null, + return null; + } +}; + +/** + * Parse a single symbol out of the string. Here, we handle both the functions + * we have defined, as well as the single character symbols + * + * @return {?ParseFuncOrArgument} + */ +Parser.prototype.parseSymbol = function() { + var nucleus = this.nextToken; + + if (functions[nucleus.text]) { + this.consume(); + // If there exists a function with this name, we return the function and + // say that it is a function. + return new ParseFuncOrArgument( + nucleus.text, + true); + } else if (symbols[this.mode][nucleus.text]) { + this.consume(); + // Otherwise if this is a no-argument function, find the type it + // corresponds to in the symbols map + return new ParseFuncOrArgument( + new ParseNode(symbols[this.mode][nucleus.text].group, + nucleus.text, this.mode), + false); + } else if (nucleus.text == "EOF" || nucleus.text == "{") { + return null; + + } else { + this.consume(); + // console.error(nucleus); + return new ParseFuncOrArgument( + new ParseNode(symbols["math"]["\\sigma"].group, + nucleus.text, this.mode), + false); + // console.log(nucleus.text); + // return null; + } +}; + +Parser.prototype.ParseNode = ParseNode; + +module.exports = Parser; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Settings.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Settings.js new file mode 100644 index 00000000..64401450 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Settings.js @@ -0,0 +1,28 @@ +/** + * This is a module for storing settings passed into KaTeX. It correctly handles + * default settings. + */ + +/** + * Helper function for getting a default value if the value is undefined + */ +function get(option, defaultValue) { + return option === undefined ? defaultValue : option; +} + +/** + * The main Settings object + * + * The current options stored are: + * - displayMode: Whether the expression should be typeset by default in + * textstyle or displaystyle (default false) + */ +function Settings(options) { + // allow null options + options = options || {}; + this.displayMode = get(options.displayMode, false); + this.throwOnError = get(options.throwOnError, true); + this.errorColor = get(options.errorColor, "#cc0000"); +} + +module.exports = Settings; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Style.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Style.js new file mode 100644 index 00000000..10e5ef2c --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/Style.js @@ -0,0 +1,126 @@ +/** + * This file contains information and classes for the various kinds of styles + * used in TeX. It provides a generic `Style` class, which holds information + * about a specific style. It then provides instances of all the different kinds + * of styles possible, and provides functions to move between them and get + * information about them. + */ + +/** + * The main style class. Contains a unique id for the style, a size (which is + * the same for cramped and uncramped version of a style), a cramped flag, and a + * size multiplier, which gives the size difference between a style and + * textstyle. + */ +function Style(id, size, multiplier, cramped) { + this.id = id; + this.size = size; + this.cramped = cramped; + this.sizeMultiplier = multiplier; +} + +/** + * Get the style of a superscript given a base in the current style. + */ +Style.prototype.sup = function() { + return styles[sup[this.id]]; +}; + +/** + * Get the style of a subscript given a base in the current style. + */ +Style.prototype.sub = function() { + return styles[sub[this.id]]; +}; + +/** + * Get the style of a fraction numerator given the fraction in the current + * style. + */ +Style.prototype.fracNum = function() { + return styles[fracNum[this.id]]; +}; + +/** + * Get the style of a fraction denominator given the fraction in the current + * style. + */ +Style.prototype.fracDen = function() { + return styles[fracDen[this.id]]; +}; + +/** + * Get the cramped version of a style (in particular, cramping a cramped style + * doesn't change the style). + */ +Style.prototype.cramp = function() { + return styles[cramp[this.id]]; +}; + +/** + * HTML class name, like "displaystyle cramped" + */ +Style.prototype.cls = function() { + return sizeNames[this.size] + (this.cramped ? " cramped" : " uncramped"); +}; + +/** + * HTML Reset class name, like "reset-textstyle" + */ +Style.prototype.reset = function() { + return resetNames[this.size]; +}; + +// IDs of the different styles +var D = 0; +var Dc = 1; +var T = 2; +var Tc = 3; +var S = 4; +var Sc = 5; +var SS = 6; +var SSc = 7; + +// String names for the different sizes +var sizeNames = [ + "displaystyle textstyle", + "textstyle", + "scriptstyle", + "scriptscriptstyle", +]; + +// Reset names for the different sizes +var resetNames = [ + "reset-textstyle", + "reset-textstyle", + "reset-scriptstyle", + "reset-scriptscriptstyle", +]; + +// Instances of the different styles +var styles = [ + new Style(D, 0, 1.0, false), + new Style(Dc, 0, 1.0, true), + new Style(T, 1, 1.0, false), + new Style(Tc, 1, 1.0, true), + new Style(S, 2, 0.7, false), + new Style(Sc, 2, 0.7, true), + new Style(SS, 3, 0.5, false), + new Style(SSc, 3, 0.5, true), +]; + +// Lookup tables for switching from one style to another +var sup = [S, Sc, S, Sc, SS, SSc, SS, SSc]; +var sub = [Sc, Sc, Sc, Sc, SSc, SSc, SSc, SSc]; +var fracNum = [T, Tc, S, Sc, SS, SSc, SS, SSc]; +var fracDen = [Tc, Tc, Sc, Sc, SSc, SSc, SSc, SSc]; +var cramp = [Dc, Dc, Tc, Tc, Sc, Sc, SSc, SSc]; + +// We only export some of the styles. Also, we don't export the `Style` class so +// no more styles can be generated. +module.exports = { + DISPLAY: styles[D], + TEXT: styles[T], + SCRIPT: styles[S], + SCRIPTSCRIPT: styles[SS], +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildCommon.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildCommon.js new file mode 100644 index 00000000..b60e1860 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildCommon.js @@ -0,0 +1,450 @@ +/* eslint no-console:0 */ +/** + * This module contains general functions that can be used for building + * different kinds of domTree nodes in a consistent manner. + */ + +var domTree = require("./domTree"); +var fontMetrics = require("./fontMetrics"); +var symbols = require("./symbols"); +var utils = require("./utils"); + +var greekCapitals = [ + "\\Gamma", + "\\Delta", + "\\Theta", + "\\Lambda", + "\\Xi", + "\\Pi", + "\\Sigma", + "\\Upsilon", + "\\Phi", + "\\Psi", + "\\Omega", +]; + +var dotlessLetters = [ + "\u0131", // dotless i, \imath + "\u0237", // dotless j, \jmath +]; + +/** + * Makes a symbolNode after translation via the list of symbols in symbols.js. + * Correctly pulls out metrics for the character, and optionally takes a list of + * classes to be attached to the node. + */ +var makeSymbol = function(value, style, mode, color, classes) { + // Replace the value with its replaced value from symbol.js + if (symbols[mode][value] && symbols[mode][value].replace) { + value = symbols[mode][value].replace; + } + + var metrics = fontMetrics.getCharacterMetrics(value, style); + + var symbolNode; + if (metrics) { + symbolNode = new domTree.symbolNode( + value, metrics.height, metrics.depth, metrics.italic, metrics.skew, + classes); + } else { + // TODO(emily): Figure out a good way to only print this in development + typeof console !== "undefined" && console.warn( + "No character metrics for '" + value + "' in style '" + + style + "'"); + symbolNode = new domTree.symbolNode(value, 0, 0, 0, 0, classes); + } + + if (color) { + symbolNode.style.color = color; + } + + return symbolNode; +}; + +/** + * Makes a symbol in Main-Regular or AMS-Regular. + * Used for rel, bin, open, close, inner, and punct. + */ +var mathsym = function(value, mode, color, classes) { + // Decide what font to render the symbol in by its entry in the symbols + // table. + // Have a special case for when the value = \ because the \ is used as a + // textord in unsupported command errors but cannot be parsed as a regular + // text ordinal and is therefore not present as a symbol in the symbols + // table for text + if (value === "\\" || symbols[mode][value].font === "main") { + return makeSymbol(value, "Main-Regular", mode, color, classes); + } else { + return makeSymbol( + value, "AMS-Regular", mode, color, classes.concat(["amsrm"])); + } +}; + +/** + * Makes a symbol in the default font for mathords and textords. + */ +var mathDefault = function(value, mode, color, classes, type) { + if (type === "mathord") { + return mathit(value, mode, color, classes); + } else if (type === "textord") { + return makeSymbol( + value, "Main-Regular", mode, color, classes.concat(["mathrm"])); + } else { + throw new Error("unexpected type: " + type + " in mathDefault"); + } +}; + +/** + * Makes a symbol in the italic math font. + */ +var mathit = function(value, mode, color, classes) { + if (/[0-9]/.test(value.charAt(0)) || + // glyphs for \imath and \jmath do not exist in Math-Italic so we + // need to use Main-Italic instead + utils.contains(dotlessLetters, value) || + utils.contains(greekCapitals, value)) { + return makeSymbol( + value, "Main-Italic", mode, color, classes.concat(["mainit"])); + } else { + return makeSymbol( + value, "Math-Italic", mode, color, classes.concat(["mathit"])); + } +}; + +/** + * Makes either a mathord or textord in the correct font and color. + */ +var makeOrd = function(group, options, type) { + var mode = group.mode; + var value = group.value; + if (symbols[mode][value] && symbols[mode][value].replace) { + value = symbols[mode][value].replace; + } + + var classes = ["mord"]; + var color = options.getColor(); + + var font = options.font; + if (font) { + if (font === "mathit" || utils.contains(dotlessLetters, value)) { + return mathit(value, mode, color, classes); + } else { + var fontName = fontMap[font].fontName; + if (fontMetrics.getCharacterMetrics(value, fontName)) { + return makeSymbol( + value, fontName, mode, color, classes.concat([font])); + } else { + return mathDefault(value, mode, color, classes, type); + } + } + } else { + return mathDefault(value, mode, color, classes, type); + } +}; + +/** + * Calculate the height, depth, and maxFontSize of an element based on its + * children. + */ +var sizeElementFromChildren = function(elem) { + var height = 0; + var depth = 0; + var maxFontSize = 0; + + if (elem.children) { + for (var i = 0; i < elem.children.length; i++) { + if (elem.children[i].height > height) { + height = elem.children[i].height; + } + if (elem.children[i].depth > depth) { + depth = elem.children[i].depth; + } + if (elem.children[i].maxFontSize > maxFontSize) { + maxFontSize = elem.children[i].maxFontSize; + } + } + } + + elem.height = height; + elem.depth = depth; + elem.maxFontSize = maxFontSize; +}; + +/** + * Makes a span with the given list of classes, list of children, and color. + */ +var makeSpan = function(classes, children, color) { + var span = new domTree.span(classes, children); + + sizeElementFromChildren(span); + + if (color) { + span.style.color = color; + } + + return span; +}; + +/** + * Makes a document fragment with the given list of children. + */ +var makeFragment = function(children) { + var fragment = new domTree.documentFragment(children); + + sizeElementFromChildren(fragment); + + return fragment; +}; + +/** + * Makes an element placed in each of the vlist elements to ensure that each + * element has the same max font size. To do this, we create a zero-width space + * with the correct font size. + */ +var makeFontSizer = function(options, fontSize) { + var fontSizeInner = makeSpan([], [new domTree.symbolNode("\u200b")]); + fontSizeInner.style.fontSize = + (fontSize / options.style.sizeMultiplier) + "em"; + + var fontSizer = makeSpan( + ["fontsize-ensurer", "reset-" + options.size, "size5"], + [fontSizeInner]); + + return fontSizer; +}; + +/** + * Makes a vertical list by stacking elements and kerns on top of each other. + * Allows for many different ways of specifying the positioning method. + * + * Arguments: + * - children: A list of child or kern nodes to be stacked on top of each other + * (i.e. the first element will be at the bottom, and the last at + * the top). Element nodes are specified as + * {type: "elem", elem: node} + * while kern nodes are specified as + * {type: "kern", size: size} + * - positionType: The method by which the vlist should be positioned. Valid + * values are: + * - "individualShift": The children list only contains elem + * nodes, and each node contains an extra + * "shift" value of how much it should be + * shifted (note that shifting is always + * moving downwards). positionData is + * ignored. + * - "top": The positionData specifies the topmost point of + * the vlist (note this is expected to be a height, + * so positive values move up) + * - "bottom": The positionData specifies the bottommost point + * of the vlist (note this is expected to be a + * depth, so positive values move down + * - "shift": The vlist will be positioned such that its + * baseline is positionData away from the baseline + * of the first child. Positive values move + * downwards. + * - "firstBaseline": The vlist will be positioned such that + * its baseline is aligned with the + * baseline of the first child. + * positionData is ignored. (this is + * equivalent to "shift" with + * positionData=0) + * - positionData: Data used in different ways depending on positionType + * - options: An Options object + * + */ +var makeVList = function(children, positionType, positionData, options) { + var depth; + var currPos; + var i; + if (positionType === "individualShift") { + var oldChildren = children; + children = [oldChildren[0]]; + + // Add in kerns to the list of children to get each element to be + // shifted to the correct specified shift + depth = -oldChildren[0].shift - oldChildren[0].elem.depth; + currPos = depth; + for (i = 1; i < oldChildren.length; i++) { + var diff = -oldChildren[i].shift - currPos - + oldChildren[i].elem.depth; + var size = diff - + (oldChildren[i - 1].elem.height + + oldChildren[i - 1].elem.depth); + + currPos = currPos + diff; + + children.push({type: "kern", size: size}); + children.push(oldChildren[i]); + } + } else if (positionType === "top") { + // We always start at the bottom, so calculate the bottom by adding up + // all the sizes + var bottom = positionData; + for (i = 0; i < children.length; i++) { + if (children[i].type === "kern") { + bottom -= children[i].size; + } else { + bottom -= children[i].elem.height + children[i].elem.depth; + } + } + depth = bottom; + } else if (positionType === "bottom") { + depth = -positionData; + } else if (positionType === "shift") { + depth = -children[0].elem.depth - positionData; + } else if (positionType === "firstBaseline") { + depth = -children[0].elem.depth; + } else { + depth = 0; + } + + // Make the fontSizer + var maxFontSize = 0; + for (i = 0; i < children.length; i++) { + if (children[i].type === "elem") { + maxFontSize = Math.max(maxFontSize, children[i].elem.maxFontSize); + } + } + var fontSizer = makeFontSizer(options, maxFontSize); + + // Create a new list of actual children at the correct offsets + var realChildren = []; + currPos = depth; + for (i = 0; i < children.length; i++) { + if (children[i].type === "kern") { + currPos += children[i].size; + } else { + var child = children[i].elem; + + var shift = -child.depth - currPos; + currPos += child.height + child.depth; + + var childWrap = makeSpan([], [fontSizer, child]); + childWrap.height -= shift; + childWrap.depth += shift; + childWrap.style.top = shift + "em"; + + realChildren.push(childWrap); + } + } + + // Add in an element at the end with no offset to fix the calculation of + // baselines in some browsers (namely IE, sometimes safari) + var baselineFix = makeSpan( + ["baseline-fix"], [fontSizer, new domTree.symbolNode("\u200b")]); + realChildren.push(baselineFix); + + var vlist = makeSpan(["vlist"], realChildren); + // Fix the final height and depth, in case there were kerns at the ends + // since the makeSpan calculation won't take that in to account. + vlist.height = Math.max(currPos, vlist.height); + vlist.depth = Math.max(-depth, vlist.depth); + return vlist; +}; + +// A table of size -> font size for the different sizing functions +var sizingMultiplier = { + size1: 0.5, + size2: 0.7, + size3: 0.8, + size4: 0.9, + size5: 1.0, + size6: 1.2, + size7: 1.44, + size8: 1.73, + size9: 2.07, + size10: 2.49, +}; + +// A map of spacing functions to their attributes, like size and corresponding +// CSS class +var spacingFunctions = { + "\\qquad": { + size: "2em", + className: "qquad", + }, + "\\quad": { + size: "1em", + className: "quad", + }, + "\\enspace": { + size: "0.5em", + className: "enspace", + }, + "\\;": { + size: "0.277778em", + className: "thickspace", + }, + "\\:": { + size: "0.22222em", + className: "mediumspace", + }, + "\\,": { + size: "0.16667em", + className: "thinspace", + }, + "\\!": { + size: "-0.16667em", + className: "negativethinspace", + }, +}; + +/** + * Maps TeX font commands to objects containing: + * - variant: string used for "mathvariant" attribute in buildMathML.js + * - fontName: the "style" parameter to fontMetrics.getCharacterMetrics + */ +// A map between tex font commands an MathML mathvariant attribute values +var fontMap = { + // styles + "mathbf": { + variant: "bold", + fontName: "Main-Bold", + }, + "mathrm": { + variant: "normal", + fontName: "Main-Regular", + }, + + // "mathit" is missing because it requires the use of two fonts: Main-Italic + // and Math-Italic. This is handled by a special case in makeOrd which ends + // up calling mathit. + + // families + "mathbb": { + variant: "double-struck", + fontName: "AMS-Regular", + }, + "mathcal": { + variant: "script", + fontName: "Caligraphic-Regular", + }, + "mathfrak": { + variant: "fraktur", + fontName: "Fraktur-Regular", + }, + "mathscr": { + variant: "script", + fontName: "Script-Regular", + }, + "mathsf": { + variant: "sans-serif", + fontName: "SansSerif-Regular", + }, + "mathtt": { + variant: "monospace", + fontName: "Typewriter-Regular", + }, +}; + +module.exports = { + fontMap: fontMap, + makeSymbol: makeSymbol, + mathsym: mathsym, + makeSpan: makeSpan, + makeFragment: makeFragment, + makeVList: makeVList, + makeOrd: makeOrd, + sizingMultiplier: sizingMultiplier, + spacingFunctions: spacingFunctions, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildHTML.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildHTML.js new file mode 100644 index 00000000..42c33a6c --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildHTML.js @@ -0,0 +1,1402 @@ +/* eslint no-console:0 */ +/** + * This file does the main work of building a domTree structure from a parse + * tree. The entry point is the `buildHTML` function, which takes a parse tree. + * Then, the buildExpression, buildGroup, and various groupTypes functions are + * called, to produce a final HTML tree. + */ + +var ParseError = require("./ParseError"); +var Style = require("./Style"); + +var buildCommon = require("./buildCommon"); +var delimiter = require("./delimiter"); +var domTree = require("./domTree"); +var fontMetrics = require("./fontMetrics"); +var utils = require("./utils"); + +var makeSpan = buildCommon.makeSpan; + +/** + * Take a list of nodes, build them in order, and return a list of the built + * nodes. This function handles the `prev` node correctly, and passes the + * previous element from the list as the prev of the next element. + */ +var buildExpression = function(expression, options, prev) { + var groups = []; + for (var i = 0; i < expression.length; i++) { + var group = expression[i]; + groups.push(buildGroup(group, options, prev)); + prev = group; + } + return groups; +}; + +// List of types used by getTypeOfGroup, +// see https://github.com/Khan/KaTeX/wiki/Examining-TeX#group-types +var groupToType = { + mathord: "mord", + textord: "mord", + bin: "mbin", + rel: "mrel", + text: "mord", + open: "mopen", + close: "mclose", + inner: "minner", + genfrac: "mord", + array: "mord", + spacing: "mord", + punct: "mpunct", + ordgroup: "mord", + op: "mop", + katex: "mord", + overline: "mord", + underline: "mord", + rule: "mord", + leftright: "minner", + sqrt: "mord", + accent: "mord", +}; + +/** + * Gets the final math type of an expression, given its group type. This type is + * used to determine spacing between elements, and affects bin elements by + * causing them to change depending on what types are around them. This type + * must be attached to the outermost node of an element as a CSS class so that + * spacing with its surrounding elements works correctly. + * + * Some elements can be mapped one-to-one from group type to math type, and + * those are listed in the `groupToType` table. + * + * Others (usually elements that wrap around other elements) often have + * recursive definitions, and thus call `getTypeOfGroup` on their inner + * elements. + */ +var getTypeOfGroup = function(group) { + if (group == null) { + // Like when typesetting $^3$ + return groupToType.mathord; + } else if (group.type === "supsub") { + return getTypeOfGroup(group.value.base); + } else if (group.type === "llap" || group.type === "rlap") { + return getTypeOfGroup(group.value); + } else if (group.type === "color") { + return getTypeOfGroup(group.value.value); + } else if (group.type === "sizing") { + return getTypeOfGroup(group.value.value); + } else if (group.type === "styling") { + return getTypeOfGroup(group.value.value); + } else if (group.type === "delimsizing") { + return groupToType[group.value.delimType]; + } else { + return groupToType[group.type]; + } +}; + +/** + * Sometimes, groups perform special rules when they have superscripts or + * subscripts attached to them. This function lets the `supsub` group know that + * its inner element should handle the superscripts and subscripts instead of + * handling them itself. + */ +var shouldHandleSupSub = function(group, options) { + if (!group) { + return false; + } else if (group.type === "op") { + // Operators handle supsubs differently when they have limits + // (e.g. `\displaystyle\sum_2^3`) + return group.value.limits && + (options.style.size === Style.DISPLAY.size || + group.value.alwaysHandleSupSub); + } else if (group.type === "accent") { + return isCharacterBox(group.value.base); + } else { + return null; + } +}; + +/** + * Sometimes we want to pull out the innermost element of a group. In most + * cases, this will just be the group itself, but when ordgroups and colors have + * a single element, we want to pull that out. + */ +var getBaseElem = function(group) { + if (!group) { + return false; + } else if (group.type === "ordgroup") { + if (group.value.length === 1) { + return getBaseElem(group.value[0]); + } else { + return group; + } + } else if (group.type === "color") { + if (group.value.value.length === 1) { + return getBaseElem(group.value.value[0]); + } else { + return group; + } + } else { + return group; + } +}; + +/** + * TeXbook algorithms often reference "character boxes", which are simply groups + * with a single character in them. To decide if something is a character box, + * we find its innermost group, and see if it is a single character. + */ +var isCharacterBox = function(group) { + var baseElem = getBaseElem(group); + + // These are all they types of groups which hold single characters + return baseElem.type === "mathord" || + baseElem.type === "textord" || + baseElem.type === "bin" || + baseElem.type === "rel" || + baseElem.type === "inner" || + baseElem.type === "open" || + baseElem.type === "close" || + baseElem.type === "punct"; +}; + +var makeNullDelimiter = function(options) { + return makeSpan([ + "sizing", "reset-" + options.size, "size5", + options.style.reset(), Style.TEXT.cls(), + "nulldelimiter", + ]); +}; + +/** + * This is a map of group types to the function used to handle that type. + * Simpler types come at the beginning, while complicated types come afterwards. + */ +var groupTypes = {}; + +groupTypes.mathord = function(group, options, prev) { + return buildCommon.makeOrd(group, options, "mathord"); +}; + +groupTypes.textord = function(group, options, prev) { + return buildCommon.makeOrd(group, options, "textord"); +}; + +groupTypes.bin = function(group, options, prev) { + var className = "mbin"; + // Pull out the most recent element. Do some special handling to find + // things at the end of a \color group. Note that we don't use the same + // logic for ordgroups (which count as ords). + var prevAtom = prev; + while (prevAtom && prevAtom.type === "color") { + var atoms = prevAtom.value.value; + prevAtom = atoms[atoms.length - 1]; + } + // See TeXbook pg. 442-446, Rules 5 and 6, and the text before Rule 19. + // Here, we determine whether the bin should turn into an ord. We + // currently only apply Rule 5. + if (!prev || utils.contains(["mbin", "mopen", "mrel", "mop", "mpunct"], + getTypeOfGroup(prevAtom))) { + group.type = "textord"; + className = "mord"; + } + + return buildCommon.mathsym( + group.value, group.mode, options.getColor(), [className]); +}; + +groupTypes.rel = function(group, options, prev) { + return buildCommon.mathsym( + group.value, group.mode, options.getColor(), ["mrel"]); +}; + +groupTypes.open = function(group, options, prev) { + return buildCommon.mathsym( + group.value, group.mode, options.getColor(), ["mopen"]); +}; + +groupTypes.close = function(group, options, prev) { + return buildCommon.mathsym( + group.value, group.mode, options.getColor(), ["mclose"]); +}; + +groupTypes.inner = function(group, options, prev) { + return buildCommon.mathsym( + group.value, group.mode, options.getColor(), ["minner"]); +}; + +groupTypes.punct = function(group, options, prev) { + return buildCommon.mathsym( + group.value, group.mode, options.getColor(), ["mpunct"]); +}; + +groupTypes.ordgroup = function(group, options, prev) { + return makeSpan( + ["mord", options.style.cls()], + buildExpression(group.value, options.reset()) + ); +}; + +groupTypes.text = function(group, options, prev) { + return makeSpan(["text", "mord", options.style.cls()], + buildExpression(group.value.body, options.reset())); +}; + +groupTypes.color = function(group, options, prev) { + var elements = buildExpression( + group.value.value, + options.withColor(group.value.color), + prev + ); + + // \color isn't supposed to affect the type of the elements it contains. + // To accomplish this, we wrap the results in a fragment, so the inner + // elements will be able to directly interact with their neighbors. For + // example, `\color{red}{2 +} 3` has the same spacing as `2 + 3` + return new buildCommon.makeFragment(elements); +}; + +groupTypes.supsub = function(group, options, prev) { + // Superscript and subscripts are handled in the TeXbook on page + // 445-446, rules 18(a-f). + + // Here is where we defer to the inner group if it should handle + // superscripts and subscripts itself. + if (shouldHandleSupSub(group.value.base, options)) { + return groupTypes[group.value.base.type](group, options, prev); + } + + var base = buildGroup(group.value.base, options.reset()); + var supmid; + var submid; + var sup; + var sub; + + if (group.value.sup) { + sup = buildGroup(group.value.sup, + options.withStyle(options.style.sup())); + supmid = makeSpan( + [options.style.reset(), options.style.sup().cls()], [sup]); + } + + if (group.value.sub) { + sub = buildGroup(group.value.sub, + options.withStyle(options.style.sub())); + submid = makeSpan( + [options.style.reset(), options.style.sub().cls()], [sub]); + } + + // Rule 18a + var supShift; + var subShift; + if (isCharacterBox(group.value.base)) { + supShift = 0; + subShift = 0; + } else { + supShift = base.height - fontMetrics.metrics.supDrop; + subShift = base.depth + fontMetrics.metrics.subDrop; + } + + // Rule 18c + var minSupShift; + if (options.style === Style.DISPLAY) { + minSupShift = fontMetrics.metrics.sup1; + } else if (options.style.cramped) { + minSupShift = fontMetrics.metrics.sup3; + } else { + minSupShift = fontMetrics.metrics.sup2; + } + + // scriptspace is a font-size-independent size, so scale it + // appropriately + var multiplier = Style.TEXT.sizeMultiplier * + options.style.sizeMultiplier; + var scriptspace = + (0.5 / fontMetrics.metrics.ptPerEm) / multiplier + "em"; + + var supsub; + if (!group.value.sup) { + // Rule 18b + subShift = Math.max( + subShift, fontMetrics.metrics.sub1, + sub.height - 0.8 * fontMetrics.metrics.xHeight); + + supsub = buildCommon.makeVList([ + {type: "elem", elem: submid}, + ], "shift", subShift, options); + + supsub.children[0].style.marginRight = scriptspace; + + // Subscripts shouldn't be shifted by the base's italic correction. + // Account for that by shifting the subscript back the appropriate + // amount. Note we only do this when the base is a single symbol. + if (base instanceof domTree.symbolNode) { + supsub.children[0].style.marginLeft = -base.italic + "em"; + } + } else if (!group.value.sub) { + // Rule 18c, d + supShift = Math.max(supShift, minSupShift, + sup.depth + 0.25 * fontMetrics.metrics.xHeight); + + supsub = buildCommon.makeVList([ + {type: "elem", elem: supmid}, + ], "shift", -supShift, options); + + supsub.children[0].style.marginRight = scriptspace; + } else { + supShift = Math.max( + supShift, minSupShift, + sup.depth + 0.25 * fontMetrics.metrics.xHeight); + subShift = Math.max(subShift, fontMetrics.metrics.sub2); + + var ruleWidth = fontMetrics.metrics.defaultRuleThickness; + + // Rule 18e + if ((supShift - sup.depth) - (sub.height - subShift) < + 4 * ruleWidth) { + subShift = 4 * ruleWidth - (supShift - sup.depth) + sub.height; + var psi = 0.8 * fontMetrics.metrics.xHeight - + (supShift - sup.depth); + if (psi > 0) { + supShift += psi; + subShift -= psi; + } + } + + supsub = buildCommon.makeVList([ + {type: "elem", elem: submid, shift: subShift}, + {type: "elem", elem: supmid, shift: -supShift}, + ], "individualShift", null, options); + + // See comment above about subscripts not being shifted + if (base instanceof domTree.symbolNode) { + supsub.children[0].style.marginLeft = -base.italic + "em"; + } + + supsub.children[0].style.marginRight = scriptspace; + supsub.children[1].style.marginRight = scriptspace; + } + + return makeSpan([getTypeOfGroup(group.value.base)], + [base, supsub]); +}; + +groupTypes.genfrac = function(group, options, prev) { + // Fractions are handled in the TeXbook on pages 444-445, rules 15(a-e). + // Figure out what style this fraction should be in based on the + // function used + var fstyle = options.style; + if (group.value.size === "display") { + fstyle = Style.DISPLAY; + } else if (group.value.size === "text") { + fstyle = Style.TEXT; + } + + var nstyle = fstyle.fracNum(); + var dstyle = fstyle.fracDen(); + + var numer = buildGroup(group.value.numer, options.withStyle(nstyle)); + var numerreset = makeSpan([fstyle.reset(), nstyle.cls()], [numer]); + + var denom = buildGroup(group.value.denom, options.withStyle(dstyle)); + var denomreset = makeSpan([fstyle.reset(), dstyle.cls()], [denom]); + + var ruleWidth; + if (group.value.hasBarLine) { + ruleWidth = fontMetrics.metrics.defaultRuleThickness / + options.style.sizeMultiplier; + } else { + ruleWidth = 0; + } + + // Rule 15b + var numShift; + var clearance; + var denomShift; + if (fstyle.size === Style.DISPLAY.size) { + numShift = fontMetrics.metrics.num1; + if (ruleWidth > 0) { + clearance = 3 * ruleWidth; + } else { + clearance = 7 * fontMetrics.metrics.defaultRuleThickness; + } + denomShift = fontMetrics.metrics.denom1; + } else { + if (ruleWidth > 0) { + numShift = fontMetrics.metrics.num2; + clearance = ruleWidth; + } else { + numShift = fontMetrics.metrics.num3; + clearance = 3 * fontMetrics.metrics.defaultRuleThickness; + } + denomShift = fontMetrics.metrics.denom2; + } + + var frac; + if (ruleWidth === 0) { + // Rule 15c + var candiateClearance = + (numShift - numer.depth) - (denom.height - denomShift); + if (candiateClearance < clearance) { + numShift += 0.5 * (clearance - candiateClearance); + denomShift += 0.5 * (clearance - candiateClearance); + } + + frac = buildCommon.makeVList([ + {type: "elem", elem: denomreset, shift: denomShift}, + {type: "elem", elem: numerreset, shift: -numShift}, + ], "individualShift", null, options); + } else { + // Rule 15d + var axisHeight = fontMetrics.metrics.axisHeight; + + if ((numShift - numer.depth) - (axisHeight + 0.5 * ruleWidth) < + clearance) { + numShift += + clearance - ((numShift - numer.depth) - + (axisHeight + 0.5 * ruleWidth)); + } + + if ((axisHeight - 0.5 * ruleWidth) - (denom.height - denomShift) < + clearance) { + denomShift += + clearance - ((axisHeight - 0.5 * ruleWidth) - + (denom.height - denomShift)); + } + + var mid = makeSpan( + [options.style.reset(), Style.TEXT.cls(), "frac-line"]); + // Manually set the height of the line because its height is + // created in CSS + mid.height = ruleWidth; + + var midShift = -(axisHeight - 0.5 * ruleWidth); + + frac = buildCommon.makeVList([ + {type: "elem", elem: denomreset, shift: denomShift}, + {type: "elem", elem: mid, shift: midShift}, + {type: "elem", elem: numerreset, shift: -numShift}, + ], "individualShift", null, options); + } + + // Since we manually change the style sometimes (with \dfrac or \tfrac), + // account for the possible size change here. + frac.height *= fstyle.sizeMultiplier / options.style.sizeMultiplier; + frac.depth *= fstyle.sizeMultiplier / options.style.sizeMultiplier; + + // Rule 15e + var delimSize; + if (fstyle.size === Style.DISPLAY.size) { + delimSize = fontMetrics.metrics.delim1; + } else { + delimSize = fontMetrics.metrics.getDelim2(fstyle); + } + + var leftDelim; + var rightDelim; + if (group.value.leftDelim == null) { + leftDelim = makeNullDelimiter(options); + } else { + leftDelim = delimiter.customSizedDelim( + group.value.leftDelim, delimSize, true, + options.withStyle(fstyle), group.mode); + } + if (group.value.rightDelim == null) { + rightDelim = makeNullDelimiter(options); + } else { + rightDelim = delimiter.customSizedDelim( + group.value.rightDelim, delimSize, true, + options.withStyle(fstyle), group.mode); + } + + return makeSpan( + ["mord", options.style.reset(), fstyle.cls()], + [leftDelim, makeSpan(["mfrac"], [frac]), rightDelim], + options.getColor()); +}; + +groupTypes.array = function(group, options, prev) { + var r; + var c; + var nr = group.value.body.length; + var nc = 0; + var body = new Array(nr); + + // Horizontal spacing + var pt = 1 / fontMetrics.metrics.ptPerEm; + var arraycolsep = 5 * pt; // \arraycolsep in article.cls + + // Vertical spacing + var baselineskip = 12 * pt; // see size10.clo + // Default \arraystretch from lttab.dtx + // TODO(gagern): may get redefined once we have user-defined macros + var arraystretch = utils.deflt(group.value.arraystretch, 1); + var arrayskip = arraystretch * baselineskip; + var arstrutHeight = 0.7 * arrayskip; // \strutbox in ltfsstrc.dtx and + var arstrutDepth = 0.3 * arrayskip; // \@arstrutbox in lttab.dtx + + var totalHeight = 0; + for (r = 0; r < group.value.body.length; ++r) { + var inrow = group.value.body[r]; + var height = arstrutHeight; // \@array adds an \@arstrut + var depth = arstrutDepth; // to each tow (via the template) + + if (nc < inrow.length) { + nc = inrow.length; + } + + var outrow = new Array(inrow.length); + for (c = 0; c < inrow.length; ++c) { + var elt = buildGroup(inrow[c], options); + if (depth < elt.depth) { + depth = elt.depth; + } + if (height < elt.height) { + height = elt.height; + } + outrow[c] = elt; + } + + var gap = 0; + if (group.value.rowGaps[r]) { + gap = group.value.rowGaps[r].value; + switch (gap.unit) { + case "em": + gap = gap.number; + break; + case "ex": + gap = gap.number * fontMetrics.metrics.emPerEx; + break; + default: + console.error("Can't handle unit " + gap.unit); + gap = 0; + } + if (gap > 0) { // \@argarraycr + gap += arstrutDepth; + if (depth < gap) { + depth = gap; // \@xargarraycr + } + gap = 0; + } + } + + outrow.height = height; + outrow.depth = depth; + totalHeight += height; + outrow.pos = totalHeight; + totalHeight += depth + gap; // \@yargarraycr + body[r] = outrow; + } + + var offset = totalHeight / 2 + fontMetrics.metrics.axisHeight; + var colDescriptions = group.value.cols || []; + var cols = []; + var colSep; + var colDescrNum; + for (c = 0, colDescrNum = 0; + // Continue while either there are more columns or more column + // descriptions, so trailing separators don't get lost. + c < nc || colDescrNum < colDescriptions.length; + ++c, ++colDescrNum) { + + var colDescr = colDescriptions[colDescrNum] || {}; + + var firstSeparator = true; + while (colDescr.type === "separator") { + // If there is more than one separator in a row, add a space + // between them. + if (!firstSeparator) { + colSep = makeSpan(["arraycolsep"], []); + colSep.style.width = + fontMetrics.metrics.doubleRuleSep + "em"; + cols.push(colSep); + } + + if (colDescr.separator === "|") { + var separator = makeSpan( + ["vertical-separator"], + []); + separator.style.height = totalHeight + "em"; + separator.style.verticalAlign = + -(totalHeight - offset) + "em"; + + cols.push(separator); + } else { + throw new ParseError( + "Invalid separator type: " + colDescr.separator); + } + + colDescrNum++; + colDescr = colDescriptions[colDescrNum] || {}; + firstSeparator = false; + } + + if (c >= nc) { + continue; + } + + var sepwidth; + if (c > 0 || group.value.hskipBeforeAndAfter) { + sepwidth = utils.deflt(colDescr.pregap, arraycolsep); + if (sepwidth !== 0) { + colSep = makeSpan(["arraycolsep"], []); + colSep.style.width = sepwidth + "em"; + cols.push(colSep); + } + } + + var col = []; + for (r = 0; r < nr; ++r) { + var row = body[r]; + var elem = row[c]; + if (!elem) { + continue; + } + var shift = row.pos - offset; + elem.depth = row.depth; + elem.height = row.height; + col.push({type: "elem", elem: elem, shift: shift}); + } + + col = buildCommon.makeVList(col, "individualShift", null, options); + col = makeSpan( + ["col-align-" + (colDescr.align || "c")], + [col]); + cols.push(col); + + if (c < nc - 1 || group.value.hskipBeforeAndAfter) { + sepwidth = utils.deflt(colDescr.postgap, arraycolsep); + if (sepwidth !== 0) { + colSep = makeSpan(["arraycolsep"], []); + colSep.style.width = sepwidth + "em"; + cols.push(colSep); + } + } + } + body = makeSpan(["mtable"], cols); + return makeSpan(["mord"], [body], options.getColor()); +}; + +groupTypes.spacing = function(group, options, prev) { + if (group.value === "\\ " || group.value === "\\space" || + group.value === " " || group.value === "~") { + // Spaces are generated by adding an actual space. Each of these + // things has an entry in the symbols table, so these will be turned + // into appropriate outputs. + return makeSpan( + ["mord", "mspace"], + [buildCommon.mathsym(group.value, group.mode)] + ); + } else { + // Other kinds of spaces are of arbitrary width. We use CSS to + // generate these. + return makeSpan( + ["mord", "mspace", + buildCommon.spacingFunctions[group.value].className]); + } +}; + +groupTypes.llap = function(group, options, prev) { + var inner = makeSpan( + ["inner"], [buildGroup(group.value.body, options.reset())]); + var fix = makeSpan(["fix"], []); + return makeSpan( + ["llap", options.style.cls()], [inner, fix]); +}; + +groupTypes.rlap = function(group, options, prev) { + var inner = makeSpan( + ["inner"], [buildGroup(group.value.body, options.reset())]); + var fix = makeSpan(["fix"], []); + return makeSpan( + ["rlap", options.style.cls()], [inner, fix]); +}; + +groupTypes.op = function(group, options, prev) { + // Operators are handled in the TeXbook pg. 443-444, rule 13(a). + var supGroup; + var subGroup; + var hasLimits = false; + if (group.type === "supsub" ) { + // If we have limits, supsub will pass us its group to handle. Pull + // out the superscript and subscript and set the group to the op in + // its base. + supGroup = group.value.sup; + subGroup = group.value.sub; + group = group.value.base; + hasLimits = true; + } + + // Most operators have a large successor symbol, but these don't. + var noSuccessor = [ + "\\smallint", + ]; + + var large = false; + if (options.style.size === Style.DISPLAY.size && + group.value.symbol && + !utils.contains(noSuccessor, group.value.body)) { + + // Most symbol operators get larger in displaystyle (rule 13) + large = true; + } + + var base; + var baseShift = 0; + var slant = 0; + if (group.value.symbol) { + // If this is a symbol, create the symbol. + var style = large ? "Size2-Regular" : "Size1-Regular"; + base = buildCommon.makeSymbol( + group.value.body, style, "math", options.getColor(), + ["op-symbol", large ? "large-op" : "small-op", "mop"]); + + // Shift the symbol so its center lies on the axis (rule 13). It + // appears that our fonts have the centers of the symbols already + // almost on the axis, so these numbers are very small. Note we + // don't actually apply this here, but instead it is used either in + // the vlist creation or separately when there are no limits. + baseShift = (base.height - base.depth) / 2 - + fontMetrics.metrics.axisHeight * + options.style.sizeMultiplier; + + // The slant of the symbol is just its italic correction. + slant = base.italic; + } else { + // Otherwise, this is a text operator. Build the text from the + // operator's name. + // TODO(emily): Add a space in the middle of some of these + // operators, like \limsup + var output = []; + for (var i = 1; i < group.value.body.length; i++) { + output.push(buildCommon.mathsym(group.value.body[i], group.mode)); + } + base = makeSpan(["mop"], output, options.getColor()); + } + + if (hasLimits) { + // IE 8 clips \int if it is in a display: inline-block. We wrap it + // in a new span so it is an inline, and works. + base = makeSpan([], [base]); + + var supmid; + var supKern; + var submid; + var subKern; + // We manually have to handle the superscripts and subscripts. This, + // aside from the kern calculations, is copied from supsub. + if (supGroup) { + var sup = buildGroup( + supGroup, options.withStyle(options.style.sup())); + supmid = makeSpan( + [options.style.reset(), options.style.sup().cls()], [sup]); + + supKern = Math.max( + fontMetrics.metrics.bigOpSpacing1, + fontMetrics.metrics.bigOpSpacing3 - sup.depth); + } + + if (subGroup) { + var sub = buildGroup( + subGroup, options.withStyle(options.style.sub())); + submid = makeSpan( + [options.style.reset(), options.style.sub().cls()], + [sub]); + + subKern = Math.max( + fontMetrics.metrics.bigOpSpacing2, + fontMetrics.metrics.bigOpSpacing4 - sub.height); + } + + // Build the final group as a vlist of the possible subscript, base, + // and possible superscript. + var finalGroup; + var top; + var bottom; + if (!supGroup) { + top = base.height - baseShift; + + finalGroup = buildCommon.makeVList([ + {type: "kern", size: fontMetrics.metrics.bigOpSpacing5}, + {type: "elem", elem: submid}, + {type: "kern", size: subKern}, + {type: "elem", elem: base}, + ], "top", top, options); + + // Here, we shift the limits by the slant of the symbol. Note + // that we are supposed to shift the limits by 1/2 of the slant, + // but since we are centering the limits adding a full slant of + // margin will shift by 1/2 that. + finalGroup.children[0].style.marginLeft = -slant + "em"; + } else if (!subGroup) { + bottom = base.depth + baseShift; + + finalGroup = buildCommon.makeVList([ + {type: "elem", elem: base}, + {type: "kern", size: supKern}, + {type: "elem", elem: supmid}, + {type: "kern", size: fontMetrics.metrics.bigOpSpacing5}, + ], "bottom", bottom, options); + + // See comment above about slants + finalGroup.children[1].style.marginLeft = slant + "em"; + } else if (!supGroup && !subGroup) { + // This case probably shouldn't occur (this would mean the + // supsub was sending us a group with no superscript or + // subscript) but be safe. + return base; + } else { + bottom = fontMetrics.metrics.bigOpSpacing5 + + submid.height + submid.depth + + subKern + + base.depth + baseShift; + + finalGroup = buildCommon.makeVList([ + {type: "kern", size: fontMetrics.metrics.bigOpSpacing5}, + {type: "elem", elem: submid}, + {type: "kern", size: subKern}, + {type: "elem", elem: base}, + {type: "kern", size: supKern}, + {type: "elem", elem: supmid}, + {type: "kern", size: fontMetrics.metrics.bigOpSpacing5}, + ], "bottom", bottom, options); + + // See comment above about slants + finalGroup.children[0].style.marginLeft = -slant + "em"; + finalGroup.children[2].style.marginLeft = slant + "em"; + } + + return makeSpan(["mop", "op-limits"], [finalGroup]); + } else { + if (group.value.symbol) { + base.style.top = baseShift + "em"; + } + + return base; + } +}; + +groupTypes.katex = function(group, options, prev) { + // The KaTeX logo. The offsets for the K and a were chosen to look + // good, but the offsets for the T, E, and X were taken from the + // definition of \TeX in TeX (see TeXbook pg. 356) + var k = makeSpan( + ["k"], [buildCommon.mathsym("K", group.mode)]); + var a = makeSpan( + ["a"], [buildCommon.mathsym("A", group.mode)]); + + a.height = (a.height + 0.2) * 0.75; + a.depth = (a.height - 0.2) * 0.75; + + var t = makeSpan( + ["t"], [buildCommon.mathsym("T", group.mode)]); + var e = makeSpan( + ["e"], [buildCommon.mathsym("E", group.mode)]); + + e.height = (e.height - 0.2155); + e.depth = (e.depth + 0.2155); + + var x = makeSpan( + ["x"], [buildCommon.mathsym("X", group.mode)]); + + return makeSpan( + ["katex-logo", "mord"], [k, a, t, e, x], options.getColor()); +}; + +groupTypes.overline = function(group, options, prev) { + // Overlines are handled in the TeXbook pg 443, Rule 9. + + // Build the inner group in the cramped style. + var innerGroup = buildGroup(group.value.body, + options.withStyle(options.style.cramp())); + + var ruleWidth = fontMetrics.metrics.defaultRuleThickness / + options.style.sizeMultiplier; + + // Create the line above the body + var line = makeSpan( + [options.style.reset(), Style.TEXT.cls(), "overline-line"]); + line.height = ruleWidth; + line.maxFontSize = 1.0; + + // Generate the vlist, with the appropriate kerns + var vlist = buildCommon.makeVList([ + {type: "elem", elem: innerGroup}, + {type: "kern", size: 3 * ruleWidth}, + {type: "elem", elem: line}, + {type: "kern", size: ruleWidth}, + ], "firstBaseline", null, options); + + return makeSpan(["overline", "mord"], [vlist], options.getColor()); +}; + +groupTypes.underline = function(group, options, prev) { + // Underlines are handled in the TeXbook pg 443, Rule 10. + + // Build the inner group. + var innerGroup = buildGroup(group.value.body, options); + + var ruleWidth = fontMetrics.metrics.defaultRuleThickness / + options.style.sizeMultiplier; + + // Create the line above the body + var line = makeSpan( + [options.style.reset(), Style.TEXT.cls(), "underline-line"]); + line.height = ruleWidth; + line.maxFontSize = 1.0; + + // Generate the vlist, with the appropriate kerns + var vlist = buildCommon.makeVList([ + {type: "kern", size: ruleWidth}, + {type: "elem", elem: line}, + {type: "kern", size: 3 * ruleWidth}, + {type: "elem", elem: innerGroup}, + ], "top", innerGroup.height, options); + + return makeSpan(["underline", "mord"], [vlist], options.getColor()); +}; + +groupTypes.sqrt = function(group, options, prev) { + // Square roots are handled in the TeXbook pg. 443, Rule 11. + + // First, we do the same steps as in overline to build the inner group + // and line + var inner = buildGroup(group.value.body, + options.withStyle(options.style.cramp())); + + var ruleWidth = fontMetrics.metrics.defaultRuleThickness / + options.style.sizeMultiplier; + + var line = makeSpan( + [options.style.reset(), Style.TEXT.cls(), "sqrt-line"], [], + options.getColor()); + line.height = ruleWidth; + line.maxFontSize = 1.0; + + var phi = ruleWidth; + if (options.style.id < Style.TEXT.id) { + phi = fontMetrics.metrics.xHeight; + } + + // Calculate the clearance between the body and line + var lineClearance = ruleWidth + phi / 4; + + var innerHeight = + (inner.height + inner.depth) * options.style.sizeMultiplier; + var minDelimiterHeight = innerHeight + lineClearance + ruleWidth; + + // Create a \surd delimiter of the required minimum size + var delim = makeSpan(["sqrt-sign"], [ + delimiter.customSizedDelim("\\surd", minDelimiterHeight, + false, options, group.mode)], + options.getColor()); + + var delimDepth = (delim.height + delim.depth) - ruleWidth; + + // Adjust the clearance based on the delimiter size + if (delimDepth > inner.height + inner.depth + lineClearance) { + lineClearance = + (lineClearance + delimDepth - inner.height - inner.depth) / 2; + } + + // Shift the delimiter so that its top lines up with the top of the line + var delimShift = -(inner.height + lineClearance + ruleWidth) + delim.height; + delim.style.top = delimShift + "em"; + delim.height -= delimShift; + delim.depth += delimShift; + + // We add a special case here, because even when `inner` is empty, we + // still get a line. So, we use a simple heuristic to decide if we + // should omit the body entirely. (note this doesn't work for something + // like `\sqrt{\rlap{x}}`, but if someone is doing that they deserve for + // it not to work. + var body; + if (inner.height === 0 && inner.depth === 0) { + body = makeSpan(); + } else { + body = buildCommon.makeVList([ + {type: "elem", elem: inner}, + {type: "kern", size: lineClearance}, + {type: "elem", elem: line}, + {type: "kern", size: ruleWidth}, + ], "firstBaseline", null, options); + } + + if (!group.value.index) { + return makeSpan(["sqrt", "mord"], [delim, body]); + } else { + // Handle the optional root index + + // The index is always in scriptscript style + var root = buildGroup( + group.value.index, + options.withStyle(Style.SCRIPTSCRIPT)); + var rootWrap = makeSpan( + [options.style.reset(), Style.SCRIPTSCRIPT.cls()], + [root]); + + // Figure out the height and depth of the inner part + var innerRootHeight = Math.max(delim.height, body.height); + var innerRootDepth = Math.max(delim.depth, body.depth); + + // The amount the index is shifted by. This is taken from the TeX + // source, in the definition of `\r@@t`. + var toShift = 0.6 * (innerRootHeight - innerRootDepth); + + // Build a VList with the superscript shifted up correctly + var rootVList = buildCommon.makeVList( + [{type: "elem", elem: rootWrap}], + "shift", -toShift, options); + // Add a class surrounding it so we can add on the appropriate + // kerning + var rootVListWrap = makeSpan(["root"], [rootVList]); + + return makeSpan(["sqrt", "mord"], [rootVListWrap, delim, body]); + } +}; + +groupTypes.sizing = function(group, options, prev) { + // Handle sizing operators like \Huge. Real TeX doesn't actually allow + // these functions inside of math expressions, so we do some special + // handling. + var inner = buildExpression(group.value.value, + options.withSize(group.value.size), prev); + + var span = makeSpan(["mord"], + [makeSpan(["sizing", "reset-" + options.size, group.value.size, + options.style.cls()], + inner)]); + + // Calculate the correct maxFontSize manually + var fontSize = buildCommon.sizingMultiplier[group.value.size]; + span.maxFontSize = fontSize * options.style.sizeMultiplier; + + return span; +}; + +groupTypes.styling = function(group, options, prev) { + // Style changes are handled in the TeXbook on pg. 442, Rule 3. + + // Figure out what style we're changing to. + var style = { + "display": Style.DISPLAY, + "text": Style.TEXT, + "script": Style.SCRIPT, + "scriptscript": Style.SCRIPTSCRIPT, + }; + + var newStyle = style[group.value.style]; + + // Build the inner expression in the new style. + var inner = buildExpression( + group.value.value, options.withStyle(newStyle), prev); + + return makeSpan([options.style.reset(), newStyle.cls()], inner); +}; + +groupTypes.font = function(group, options, prev) { + var font = group.value.font; + return buildGroup(group.value.body, options.withFont(font), prev); +}; + +groupTypes.delimsizing = function(group, options, prev) { + var delim = group.value.value; + + if (delim === ".") { + // Empty delimiters still count as elements, even though they don't + // show anything. + return makeSpan([groupToType[group.value.delimType]]); + } + + // Use delimiter.sizedDelim to generate the delimiter. + return makeSpan( + [groupToType[group.value.delimType]], + [delimiter.sizedDelim( + delim, group.value.size, options, group.mode)]); +}; + +groupTypes.leftright = function(group, options, prev) { + // Build the inner expression + var inner = buildExpression(group.value.body, options.reset()); + + var innerHeight = 0; + var innerDepth = 0; + + // Calculate its height and depth + for (var i = 0; i < inner.length; i++) { + innerHeight = Math.max(inner[i].height, innerHeight); + innerDepth = Math.max(inner[i].depth, innerDepth); + } + + // The size of delimiters is the same, regardless of what style we are + // in. Thus, to correctly calculate the size of delimiter we need around + // a group, we scale down the inner size based on the size. + innerHeight *= options.style.sizeMultiplier; + innerDepth *= options.style.sizeMultiplier; + + var leftDelim; + if (group.value.left === ".") { + // Empty delimiters in \left and \right make null delimiter spaces. + leftDelim = makeNullDelimiter(options); + } else { + // Otherwise, use leftRightDelim to generate the correct sized + // delimiter. + leftDelim = delimiter.leftRightDelim( + group.value.left, innerHeight, innerDepth, options, + group.mode); + } + // Add it to the beginning of the expression + inner.unshift(leftDelim); + + var rightDelim; + // Same for the right delimiter + if (group.value.right === ".") { + rightDelim = makeNullDelimiter(options); + } else { + rightDelim = delimiter.leftRightDelim( + group.value.right, innerHeight, innerDepth, options, + group.mode); + } + // Add it to the end of the expression. + inner.push(rightDelim); + + return makeSpan( + ["minner", options.style.cls()], inner, options.getColor()); +}; + +groupTypes.rule = function(group, options, prev) { + // Make an empty span for the rule + var rule = makeSpan(["mord", "rule"], [], options.getColor()); + + // Calculate the shift, width, and height of the rule, and account for units + var shift = 0; + if (group.value.shift) { + shift = group.value.shift.number; + if (group.value.shift.unit === "ex") { + shift *= fontMetrics.metrics.xHeight; + } + } + + var width = group.value.width.number; + if (group.value.width.unit === "ex") { + width *= fontMetrics.metrics.xHeight; + } + + var height = group.value.height.number; + if (group.value.height.unit === "ex") { + height *= fontMetrics.metrics.xHeight; + } + + // The sizes of rules are absolute, so make it larger if we are in a + // smaller style. + shift /= options.style.sizeMultiplier; + width /= options.style.sizeMultiplier; + height /= options.style.sizeMultiplier; + + // Style the rule to the right size + rule.style.borderRightWidth = width + "em"; + rule.style.borderTopWidth = height + "em"; + rule.style.bottom = shift + "em"; + + // Record the height and width + rule.width = width; + rule.height = height + shift; + rule.depth = -shift; + + return rule; +}; + +groupTypes.accent = function(group, options, prev) { + // Accents are handled in the TeXbook pg. 443, rule 12. + var base = group.value.base; + + var supsubGroup; + if (group.type === "supsub") { + // If our base is a character box, and we have superscripts and + // subscripts, the supsub will defer to us. In particular, we want + // to attach the superscripts and subscripts to the inner body (so + // that the position of the superscripts and subscripts won't be + // affected by the height of the accent). We accomplish this by + // sticking the base of the accent into the base of the supsub, and + // rendering that, while keeping track of where the accent is. + + // The supsub group is the group that was passed in + var supsub = group; + // The real accent group is the base of the supsub group + group = supsub.value.base; + // The character box is the base of the accent group + base = group.value.base; + // Stick the character box into the base of the supsub group + supsub.value.base = base; + + // Rerender the supsub group with its new base, and store that + // result. + supsubGroup = buildGroup( + supsub, options.reset(), prev); + } + + // Build the base group + var body = buildGroup( + base, options.withStyle(options.style.cramp())); + + // Calculate the skew of the accent. This is based on the line "If the + // nucleus is not a single character, let s = 0; otherwise set s to the + // kern amount for the nucleus followed by the \skewchar of its font." + // Note that our skew metrics are just the kern between each character + // and the skewchar. + var skew; + if (isCharacterBox(base)) { + // If the base is a character box, then we want the skew of the + // innermost character. To do that, we find the innermost character: + var baseChar = getBaseElem(base); + // Then, we render its group to get the symbol inside it + var baseGroup = buildGroup( + baseChar, options.withStyle(options.style.cramp())); + // Finally, we pull the skew off of the symbol. + skew = baseGroup.skew; + // Note that we now throw away baseGroup, because the layers we + // removed with getBaseElem might contain things like \color which + // we can't get rid of. + // TODO(emily): Find a better way to get the skew + } else { + skew = 0; + } + + // calculate the amount of space between the body and the accent + var clearance = Math.min(body.height, fontMetrics.metrics.xHeight); + + // Build the accent + var accent = buildCommon.makeSymbol( + group.value.accent, "Main-Regular", "math", options.getColor()); + // Remove the italic correction of the accent, because it only serves to + // shift the accent over to a place we don't want. + accent.italic = 0; + + // The \vec character that the fonts use is a combining character, and + // thus shows up much too far to the left. To account for this, we add a + // specific class which shifts the accent over to where we want it. + // TODO(emily): Fix this in a better way, like by changing the font + var vecClass = group.value.accent === "\\vec" ? "accent-vec" : null; + + var accentBody = makeSpan(["accent-body", vecClass], [ + makeSpan([], [accent])]); + + accentBody = buildCommon.makeVList([ + {type: "elem", elem: body}, + {type: "kern", size: -clearance}, + {type: "elem", elem: accentBody}, + ], "firstBaseline", null, options); + + // Shift the accent over by the skew. Note we shift by twice the skew + // because we are centering the accent, so by adding 2*skew to the left, + // we shift it to the right by 1*skew. + accentBody.children[1].style.marginLeft = 2 * skew + "em"; + + var accentWrap = makeSpan(["mord", "accent"], [accentBody]); + + if (supsubGroup) { + // Here, we replace the "base" child of the supsub with our newly + // generated accent. + supsubGroup.children[0] = accentWrap; + + // Since we don't rerun the height calculation after replacing the + // accent, we manually recalculate height. + supsubGroup.height = Math.max(accentWrap.height, supsubGroup.height); + + // Accents should always be ords, even when their innards are not. + supsubGroup.classes[0] = "mord"; + + return supsubGroup; + } else { + return accentWrap; + } +}; + +groupTypes.phantom = function(group, options, prev) { + var elements = buildExpression( + group.value.value, + options.withPhantom(), + prev + ); + + // \phantom isn't supposed to affect the elements it contains. + // See "color" for more details. + return new buildCommon.makeFragment(elements); +}; + +/** + * buildGroup is the function that takes a group and calls the correct groupType + * function for it. It also handles the interaction of size and style changes + * between parents and children. + */ +var buildGroup = function(group, options, prev) { + if (!group) { + return makeSpan(); + } + + if (groupTypes[group.type]) { + // Call the groupTypes function + var groupNode = groupTypes[group.type](group, options, prev); + var multiplier; + + // If the style changed between the parent and the current group, + // account for the size difference + if (options.style !== options.parentStyle) { + multiplier = options.style.sizeMultiplier / + options.parentStyle.sizeMultiplier; + + groupNode.height *= multiplier; + groupNode.depth *= multiplier; + } + + // If the size changed between the parent and the current group, account + // for that size difference. + if (options.size !== options.parentSize) { + multiplier = buildCommon.sizingMultiplier[options.size] / + buildCommon.sizingMultiplier[options.parentSize]; + + groupNode.height *= multiplier; + groupNode.depth *= multiplier; + } + + return groupNode; + } else { + throw new ParseError( + "Got group of unknown type: '" + group.type + "'"); + } +}; + +/** + * Take an entire parse tree, and build it into an appropriate set of HTML + * nodes. + */ +var buildHTML = function(tree, options) { + // buildExpression is destructive, so we need to make a clone + // of the incoming tree so that it isn't accidentally changed + tree = JSON.parse(JSON.stringify(tree)); + + // Build the expression contained in the tree + var expression = buildExpression(tree, options); + var body = makeSpan(["base", options.style.cls()], expression); + + // Add struts, which ensure that the top of the HTML element falls at the + // height of the expression, and the bottom of the HTML element falls at the + // depth of the expression. + var topStrut = makeSpan(["strut"]); + var bottomStrut = makeSpan(["strut", "bottom"]); + + topStrut.style.height = body.height + "em"; + bottomStrut.style.height = (body.height + body.depth) + "em"; + // We'd like to use `vertical-align: top` but in IE 9 this lowers the + // baseline of the box to the bottom of this strut (instead staying in the + // normal place) so we use an absolute value for vertical-align instead + bottomStrut.style.verticalAlign = -body.depth + "em"; + + // Wrap the struts and body together + var htmlNode = makeSpan(["katex-html"], [topStrut, bottomStrut, body]); + + htmlNode.setAttribute("aria-hidden", "true"); + + return htmlNode; +}; + +module.exports = buildHTML; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildMathML.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildMathML.js new file mode 100644 index 00000000..7bf38e86 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildMathML.js @@ -0,0 +1,533 @@ +/** + * This file converts a parse tree into a cooresponding MathML tree. The main + * entry point is the `buildMathML` function, which takes a parse tree from the + * parser. + */ + +var buildCommon = require("./buildCommon"); +var fontMetrics = require("./fontMetrics"); +var mathMLTree = require("./mathMLTree"); +var ParseError = require("./ParseError"); +var symbols = require("./symbols"); +var utils = require("./utils"); + +var makeSpan = buildCommon.makeSpan; +var fontMap = buildCommon.fontMap; + +/** + * Takes a symbol and converts it into a MathML text node after performing + * optional replacement from symbols.js. + */ +var makeText = function(text, mode) { + if (symbols[mode][text] && symbols[mode][text].replace) { + text = symbols[mode][text].replace; + } + + return new mathMLTree.TextNode(text); +}; + +/** + * Returns the math variant as a string or null if none is required. + */ +var getVariant = function(group, options) { + var font = options.font; + if (!font) { + return null; + } + + var mode = group.mode; + if (font === "mathit") { + return "italic"; + } + + var value = group.value; + if (utils.contains(["\\imath", "\\jmath"], value)) { + return null; + } + + if (symbols[mode][value] && symbols[mode][value].replace) { + value = symbols[mode][value].replace; + } + + var fontName = fontMap[font].fontName; + if (fontMetrics.getCharacterMetrics(value, fontName)) { + return fontMap[options.font].variant; + } + + return null; +}; + +/** + * Functions for handling the different types of groups found in the parse + * tree. Each function should take a parse group and return a MathML node. + */ +var groupTypes = {}; + +groupTypes.mathord = function(group, options) { + var node = new mathMLTree.MathNode( + "mi", + [makeText(group.value, group.mode)]); + + var variant = getVariant(group, options); + if (variant) { + node.setAttribute("mathvariant", variant); + } + return node; +}; + +groupTypes.textord = function(group, options) { + var text = makeText(group.value, group.mode); + + var variant = getVariant(group, options) || "normal"; + + var node; + if (/[0-9]/.test(group.value)) { + // TODO(kevinb) merge adjacent nodes + // do it as a post processing step + node = new mathMLTree.MathNode("mn", [text]); + if (options.font) { + node.setAttribute("mathvariant", variant); + } + } else { + node = new mathMLTree.MathNode("mi", [text]); + node.setAttribute("mathvariant", variant); + } + + return node; +}; + +groupTypes.bin = function(group) { + var node = new mathMLTree.MathNode( + "mo", [makeText(group.value, group.mode)]); + + return node; +}; + +groupTypes.rel = function(group) { + var node = new mathMLTree.MathNode( + "mo", [makeText(group.value, group.mode)]); + + return node; +}; + +groupTypes.open = function(group) { + var node = new mathMLTree.MathNode( + "mo", [makeText(group.value, group.mode)]); + + return node; +}; + +groupTypes.close = function(group) { + var node = new mathMLTree.MathNode( + "mo", [makeText(group.value, group.mode)]); + + return node; +}; + +groupTypes.inner = function(group) { + var node = new mathMLTree.MathNode( + "mo", [makeText(group.value, group.mode)]); + + return node; +}; + +groupTypes.punct = function(group) { + var node = new mathMLTree.MathNode( + "mo", [makeText(group.value, group.mode)]); + + node.setAttribute("separator", "true"); + + return node; +}; + +groupTypes.ordgroup = function(group, options) { + var inner = buildExpression(group.value, options); + + var node = new mathMLTree.MathNode("mrow", inner); + + return node; +}; + +groupTypes.text = function(group, options) { + var inner = buildExpression(group.value.body, options); + + var node = new mathMLTree.MathNode("mtext", inner); + + return node; +}; + +groupTypes.color = function(group, options) { + var inner = buildExpression(group.value.value, options); + + var node = new mathMLTree.MathNode("mstyle", inner); + + node.setAttribute("mathcolor", group.value.color); + + return node; +}; + +groupTypes.supsub = function(group, options) { + var children = [buildGroup(group.value.base, options)]; + + if (group.value.sub) { + children.push(buildGroup(group.value.sub, options)); + } + + if (group.value.sup) { + children.push(buildGroup(group.value.sup, options)); + } + + var nodeType; + if (!group.value.sub) { + nodeType = "msup"; + } else if (!group.value.sup) { + nodeType = "msub"; + } else { + nodeType = "msubsup"; + } + + var node = new mathMLTree.MathNode(nodeType, children); + + return node; +}; + +groupTypes.genfrac = function(group, options) { + var node = new mathMLTree.MathNode( + "mfrac", + [buildGroup(group.value.numer, options), + buildGroup(group.value.denom, options)]); + + if (!group.value.hasBarLine) { + node.setAttribute("linethickness", "0px"); + } + + if (group.value.leftDelim != null || group.value.rightDelim != null) { + var withDelims = []; + + if (group.value.leftDelim != null) { + var leftOp = new mathMLTree.MathNode( + "mo", [new mathMLTree.TextNode(group.value.leftDelim)]); + + leftOp.setAttribute("fence", "true"); + + withDelims.push(leftOp); + } + + withDelims.push(node); + + if (group.value.rightDelim != null) { + var rightOp = new mathMLTree.MathNode( + "mo", [new mathMLTree.TextNode(group.value.rightDelim)]); + + rightOp.setAttribute("fence", "true"); + + withDelims.push(rightOp); + } + + var outerNode = new mathMLTree.MathNode("mrow", withDelims); + + return outerNode; + } + + return node; +}; + +groupTypes.array = function(group, options) { + return new mathMLTree.MathNode( + "mtable", group.value.body.map(function(row) { + return new mathMLTree.MathNode( + "mtr", row.map(function(cell) { + return new mathMLTree.MathNode( + "mtd", [buildGroup(cell, options)]); + })); + })); +}; + +groupTypes.sqrt = function(group, options) { + var node; + if (group.value.index) { + node = new mathMLTree.MathNode( + "mroot", [ + buildGroup(group.value.body, options), + buildGroup(group.value.index, options), + ]); + } else { + node = new mathMLTree.MathNode( + "msqrt", [buildGroup(group.value.body, options)]); + } + + return node; +}; + +groupTypes.leftright = function(group, options) { + var inner = buildExpression(group.value.body, options); + + if (group.value.left !== ".") { + var leftNode = new mathMLTree.MathNode( + "mo", [makeText(group.value.left, group.mode)]); + + leftNode.setAttribute("fence", "true"); + + inner.unshift(leftNode); + } + + if (group.value.right !== ".") { + var rightNode = new mathMLTree.MathNode( + "mo", [makeText(group.value.right, group.mode)]); + + rightNode.setAttribute("fence", "true"); + + inner.push(rightNode); + } + + var outerNode = new mathMLTree.MathNode("mrow", inner); + + return outerNode; +}; + +groupTypes.accent = function(group, options) { + var accentNode = new mathMLTree.MathNode( + "mo", [makeText(group.value.accent, group.mode)]); + + var node = new mathMLTree.MathNode( + "mover", + [buildGroup(group.value.base, options), + accentNode]); + + node.setAttribute("accent", "true"); + + return node; +}; + +groupTypes.spacing = function(group) { + var node; + + if (group.value === "\\ " || group.value === "\\space" || + group.value === " " || group.value === "~") { + node = new mathMLTree.MathNode( + "mtext", [new mathMLTree.TextNode("\u00a0")]); + } else { + node = new mathMLTree.MathNode("mspace"); + + node.setAttribute( + "width", buildCommon.spacingFunctions[group.value].size); + } + + return node; +}; + +groupTypes.op = function(group) { + var node; + + // TODO(emily): handle big operators using the `largeop` attribute + + if (group.value.symbol) { + // This is a symbol. Just add the symbol. + node = new mathMLTree.MathNode( + "mo", [makeText(group.value.body, group.mode)]); + } else { + // This is a text operator. Add all of the characters from the + // operator's name. + // TODO(emily): Add a space in the middle of some of these + // operators, like \limsup. + node = new mathMLTree.MathNode( + "mi", [new mathMLTree.TextNode(group.value.body.slice(1))]); + } + + return node; +}; + +groupTypes.katex = function(group) { + var node = new mathMLTree.MathNode( + "mtext", [new mathMLTree.TextNode("KaTeX")]); + + return node; +}; + +groupTypes.font = function(group, options) { + var font = group.value.font; + return buildGroup(group.value.body, options.withFont(font)); +}; + +groupTypes.delimsizing = function(group) { + var children = []; + + if (group.value.value !== ".") { + children.push(makeText(group.value.value, group.mode)); + } + + var node = new mathMLTree.MathNode("mo", children); + + if (group.value.delimType === "open" || + group.value.delimType === "close") { + // Only some of the delimsizing functions act as fences, and they + // return "open" or "close" delimTypes. + node.setAttribute("fence", "true"); + } else { + // Explicitly disable fencing if it's not a fence, to override the + // defaults. + node.setAttribute("fence", "false"); + } + + return node; +}; + +groupTypes.styling = function(group, options) { + var inner = buildExpression(group.value.value, options); + + var node = new mathMLTree.MathNode("mstyle", inner); + + var styleAttributes = { + "display": ["0", "true"], + "text": ["0", "false"], + "script": ["1", "false"], + "scriptscript": ["2", "false"], + }; + + var attr = styleAttributes[group.value.style]; + + node.setAttribute("scriptlevel", attr[0]); + node.setAttribute("displaystyle", attr[1]); + + return node; +}; + +groupTypes.sizing = function(group, options) { + var inner = buildExpression(group.value.value, options); + + var node = new mathMLTree.MathNode("mstyle", inner); + + // TODO(emily): This doesn't produce the correct size for nested size + // changes, because we don't keep state of what style we're currently + // in, so we can't reset the size to normal before changing it. Now + // that we're passing an options parameter we should be able to fix + // this. + node.setAttribute( + "mathsize", buildCommon.sizingMultiplier[group.value.size] + "em"); + + return node; +}; + +groupTypes.overline = function(group, options) { + var operator = new mathMLTree.MathNode( + "mo", [new mathMLTree.TextNode("\u203e")]); + operator.setAttribute("stretchy", "true"); + + var node = new mathMLTree.MathNode( + "mover", + [buildGroup(group.value.body, options), + operator]); + node.setAttribute("accent", "true"); + + return node; +}; + +groupTypes.underline = function(group, options) { + var operator = new mathMLTree.MathNode( + "mo", [new mathMLTree.TextNode("\u203e")]); + operator.setAttribute("stretchy", "true"); + + var node = new mathMLTree.MathNode( + "munder", + [buildGroup(group.value.body, options), + operator]); + node.setAttribute("accentunder", "true"); + + return node; +}; + +groupTypes.rule = function(group) { + // TODO(emily): Figure out if there's an actual way to draw black boxes + // in MathML. + var node = new mathMLTree.MathNode("mrow"); + + return node; +}; + +groupTypes.llap = function(group, options) { + var node = new mathMLTree.MathNode( + "mpadded", [buildGroup(group.value.body, options)]); + + node.setAttribute("lspace", "-1width"); + node.setAttribute("width", "0px"); + + return node; +}; + +groupTypes.rlap = function(group, options) { + var node = new mathMLTree.MathNode( + "mpadded", [buildGroup(group.value.body, options)]); + + node.setAttribute("width", "0px"); + + return node; +}; + +groupTypes.phantom = function(group, options, prev) { + var inner = buildExpression(group.value.value, options); + return new mathMLTree.MathNode("mphantom", inner); +}; + +/** + * Takes a list of nodes, builds them, and returns a list of the generated + * MathML nodes. A little simpler than the HTML version because we don't do any + * previous-node handling. + */ +var buildExpression = function(expression, options) { + var groups = []; + for (var i = 0; i < expression.length; i++) { + var group = expression[i]; + groups.push(buildGroup(group, options)); + } + return groups; +}; + +/** + * Takes a group from the parser and calls the appropriate groupTypes function + * on it to produce a MathML node. + */ +var buildGroup = function(group, options) { + if (!group) { + return new mathMLTree.MathNode("mrow"); + } + + if (groupTypes[group.type]) { + // Call the groupTypes function + return groupTypes[group.type](group, options); + } else { + throw new ParseError( + "Got group of unknown type: '" + group.type + "'"); + } +}; + +/** + * Takes a full parse tree and settings and builds a MathML representation of + * it. In particular, we put the elements from building the parse tree into a + * tag so we can also include that TeX source as an annotation. + * + * Note that we actually return a domTree element with a `` inside it so + * we can do appropriate styling. + */ +var buildMathML = function(tree, texExpression, options) { + var expression = buildExpression(tree, options); + + // Wrap up the expression in an mrow so it is presented in the semantics + // tag correctly. + var wrapper = new mathMLTree.MathNode("mrow", expression); + + // Build a TeX annotation of the source + var annotation = new mathMLTree.MathNode( + "annotation", [new mathMLTree.TextNode(texExpression)]); + + annotation.setAttribute("encoding", "application/x-tex"); + + var semantics = new mathMLTree.MathNode( + "semantics", [wrapper, annotation]); + + var math = new mathMLTree.MathNode("math", [semantics]); + + // You can't style nodes, so we wrap the node in a span. + return makeSpan(["katex-mathml"], [math]); +}; + +module.exports = buildMathML; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildTree.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildTree.js new file mode 100644 index 00000000..4a8c2aed --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/buildTree.js @@ -0,0 +1,40 @@ +var buildHTML = require("./buildHTML"); +var buildMathML = require("./buildMathML"); +var buildCommon = require("./buildCommon"); +var Options = require("./Options"); +var Settings = require("./Settings"); +var Style = require("./Style"); + +var makeSpan = buildCommon.makeSpan; + +var buildTree = function(tree, expression, settings) { + settings = settings || new Settings({}); + + var startStyle = Style.TEXT; + if (settings.displayMode) { + startStyle = Style.DISPLAY; + } + + // Setup the default options + var options = new Options({ + style: startStyle, + size: "size5", + }); + + // `buildHTML` sometimes messes with the parse tree (like turning bins -> + // ords), so we build the MathML version first. + var mathMLNode = buildMathML(tree, expression, options); + var htmlNode = buildHTML(tree, options); + + var katexNode = makeSpan(["katex"], [ + mathMLNode, htmlNode, + ]); + + if (settings.displayMode) { + return makeSpan(["katex-display"], [katexNode]); + } else { + return katexNode; + } +}; + +module.exports = buildTree; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/delimiter.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/delimiter.js new file mode 100644 index 00000000..168319d4 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/delimiter.js @@ -0,0 +1,542 @@ +/** + * This file deals with creating delimiters of various sizes. The TeXbook + * discusses these routines on page 441-442, in the "Another subroutine sets box + * x to a specified variable delimiter" paragraph. + * + * There are three main routines here. `makeSmallDelim` makes a delimiter in the + * normal font, but in either text, script, or scriptscript style. + * `makeLargeDelim` makes a delimiter in textstyle, but in one of the Size1, + * Size2, Size3, or Size4 fonts. `makeStackedDelim` makes a delimiter out of + * smaller pieces that are stacked on top of one another. + * + * The functions take a parameter `center`, which determines if the delimiter + * should be centered around the axis. + * + * Then, there are three exposed functions. `sizedDelim` makes a delimiter in + * one of the given sizes. This is used for things like `\bigl`. + * `customSizedDelim` makes a delimiter with a given total height+depth. It is + * called in places like `\sqrt`. `leftRightDelim` makes an appropriate + * delimiter which surrounds an expression of a given height an depth. It is + * used in `\left` and `\right`. + */ + +var ParseError = require("./ParseError"); +var Style = require("./Style"); + +var buildCommon = require("./buildCommon"); +var fontMetrics = require("./fontMetrics"); +var symbols = require("./symbols"); +var utils = require("./utils"); + +var makeSpan = buildCommon.makeSpan; + +/** + * Get the metrics for a given symbol and font, after transformation (i.e. + * after following replacement from symbols.js) + */ +var getMetrics = function(symbol, font) { + if (symbols.math[symbol] && symbols.math[symbol].replace) { + return fontMetrics.getCharacterMetrics( + symbols.math[symbol].replace, font); + } else { + return fontMetrics.getCharacterMetrics( + symbol, font); + } +}; + +/** + * Builds a symbol in the given font size (note size is an integer) + */ +var mathrmSize = function(value, size, mode) { + return buildCommon.makeSymbol(value, "Size" + size + "-Regular", mode); +}; + +/** + * Puts a delimiter span in a given style, and adds appropriate height, depth, + * and maxFontSizes. + */ +var styleWrap = function(delim, toStyle, options) { + var span = makeSpan( + ["style-wrap", options.style.reset(), toStyle.cls()], [delim]); + + var multiplier = toStyle.sizeMultiplier / options.style.sizeMultiplier; + + span.height *= multiplier; + span.depth *= multiplier; + span.maxFontSize = toStyle.sizeMultiplier; + + return span; +}; + +/** + * Makes a small delimiter. This is a delimiter that comes in the Main-Regular + * font, but is restyled to either be in textstyle, scriptstyle, or + * scriptscriptstyle. + */ +var makeSmallDelim = function(delim, style, center, options, mode) { + var text = buildCommon.makeSymbol(delim, "Main-Regular", mode); + + var span = styleWrap(text, style, options); + + if (center) { + var shift = + (1 - options.style.sizeMultiplier / style.sizeMultiplier) * + fontMetrics.metrics.axisHeight; + + span.style.top = shift + "em"; + span.height -= shift; + span.depth += shift; + } + + return span; +}; + +/** + * Makes a large delimiter. This is a delimiter that comes in the Size1, Size2, + * Size3, or Size4 fonts. It is always rendered in textstyle. + */ +var makeLargeDelim = function(delim, size, center, options, mode) { + var inner = mathrmSize(delim, size, mode); + + var span = styleWrap( + makeSpan(["delimsizing", "size" + size], + [inner], options.getColor()), + Style.TEXT, options); + + if (center) { + var shift = (1 - options.style.sizeMultiplier) * + fontMetrics.metrics.axisHeight; + + span.style.top = shift + "em"; + span.height -= shift; + span.depth += shift; + } + + return span; +}; + +/** + * Make an inner span with the given offset and in the given font. This is used + * in `makeStackedDelim` to make the stacking pieces for the delimiter. + */ +var makeInner = function(symbol, font, mode) { + var sizeClass; + // Apply the correct CSS class to choose the right font. + if (font === "Size1-Regular") { + sizeClass = "delim-size1"; + } else if (font === "Size4-Regular") { + sizeClass = "delim-size4"; + } + + var inner = makeSpan( + ["delimsizinginner", sizeClass], + [makeSpan([], [buildCommon.makeSymbol(symbol, font, mode)])]); + + // Since this will be passed into `makeVList` in the end, wrap the element + // in the appropriate tag that VList uses. + return {type: "elem", elem: inner}; +}; + +/** + * Make a stacked delimiter out of a given delimiter, with the total height at + * least `heightTotal`. This routine is mentioned on page 442 of the TeXbook. + */ +var makeStackedDelim = function(delim, heightTotal, center, options, mode) { + // There are four parts, the top, an optional middle, a repeated part, and a + // bottom. + var top; + var middle; + var repeat; + var bottom; + top = repeat = bottom = delim; + middle = null; + // Also keep track of what font the delimiters are in + var font = "Size1-Regular"; + + // We set the parts and font based on the symbol. Note that we use + // '\u23d0' instead of '|' and '\u2016' instead of '\\|' for the + // repeats of the arrows + if (delim === "\\uparrow") { + repeat = bottom = "\u23d0"; + } else if (delim === "\\Uparrow") { + repeat = bottom = "\u2016"; + } else if (delim === "\\downarrow") { + top = repeat = "\u23d0"; + } else if (delim === "\\Downarrow") { + top = repeat = "\u2016"; + } else if (delim === "\\updownarrow") { + top = "\\uparrow"; + repeat = "\u23d0"; + bottom = "\\downarrow"; + } else if (delim === "\\Updownarrow") { + top = "\\Uparrow"; + repeat = "\u2016"; + bottom = "\\Downarrow"; + } else if (delim === "[" || delim === "\\lbrack") { + top = "\u23a1"; + repeat = "\u23a2"; + bottom = "\u23a3"; + font = "Size4-Regular"; + } else if (delim === "]" || delim === "\\rbrack") { + top = "\u23a4"; + repeat = "\u23a5"; + bottom = "\u23a6"; + font = "Size4-Regular"; + } else if (delim === "\\lfloor") { + repeat = top = "\u23a2"; + bottom = "\u23a3"; + font = "Size4-Regular"; + } else if (delim === "\\lceil") { + top = "\u23a1"; + repeat = bottom = "\u23a2"; + font = "Size4-Regular"; + } else if (delim === "\\rfloor") { + repeat = top = "\u23a5"; + bottom = "\u23a6"; + font = "Size4-Regular"; + } else if (delim === "\\rceil") { + top = "\u23a4"; + repeat = bottom = "\u23a5"; + font = "Size4-Regular"; + } else if (delim === "(") { + top = "\u239b"; + repeat = "\u239c"; + bottom = "\u239d"; + font = "Size4-Regular"; + } else if (delim === ")") { + top = "\u239e"; + repeat = "\u239f"; + bottom = "\u23a0"; + font = "Size4-Regular"; + } else if (delim === "\\{" || delim === "\\lbrace") { + top = "\u23a7"; + middle = "\u23a8"; + bottom = "\u23a9"; + repeat = "\u23aa"; + font = "Size4-Regular"; + } else if (delim === "\\}" || delim === "\\rbrace") { + top = "\u23ab"; + middle = "\u23ac"; + bottom = "\u23ad"; + repeat = "\u23aa"; + font = "Size4-Regular"; + } else if (delim === "\\lgroup") { + top = "\u23a7"; + bottom = "\u23a9"; + repeat = "\u23aa"; + font = "Size4-Regular"; + } else if (delim === "\\rgroup") { + top = "\u23ab"; + bottom = "\u23ad"; + repeat = "\u23aa"; + font = "Size4-Regular"; + } else if (delim === "\\lmoustache") { + top = "\u23a7"; + bottom = "\u23ad"; + repeat = "\u23aa"; + font = "Size4-Regular"; + } else if (delim === "\\rmoustache") { + top = "\u23ab"; + bottom = "\u23a9"; + repeat = "\u23aa"; + font = "Size4-Regular"; + } else if (delim === "\\surd") { + top = "\ue001"; + bottom = "\u23b7"; + repeat = "\ue000"; + font = "Size4-Regular"; + } + + // Get the metrics of the four sections + var topMetrics = getMetrics(top, font); + var topHeightTotal = topMetrics.height + topMetrics.depth; + var repeatMetrics = getMetrics(repeat, font); + var repeatHeightTotal = repeatMetrics.height + repeatMetrics.depth; + var bottomMetrics = getMetrics(bottom, font); + var bottomHeightTotal = bottomMetrics.height + bottomMetrics.depth; + var middleHeightTotal = 0; + var middleFactor = 1; + if (middle !== null) { + var middleMetrics = getMetrics(middle, font); + middleHeightTotal = middleMetrics.height + middleMetrics.depth; + middleFactor = 2; // repeat symmetrically above and below middle + } + + // Calcuate the minimal height that the delimiter can have. + // It is at least the size of the top, bottom, and optional middle combined. + var minHeight = topHeightTotal + bottomHeightTotal + middleHeightTotal; + + // Compute the number of copies of the repeat symbol we will need + var repeatCount = Math.ceil( + (heightTotal - minHeight) / (middleFactor * repeatHeightTotal)); + + // Compute the total height of the delimiter including all the symbols + var realHeightTotal = + minHeight + repeatCount * middleFactor * repeatHeightTotal; + + // The center of the delimiter is placed at the center of the axis. Note + // that in this context, "center" means that the delimiter should be + // centered around the axis in the current style, while normally it is + // centered around the axis in textstyle. + var axisHeight = fontMetrics.metrics.axisHeight; + if (center) { + axisHeight *= options.style.sizeMultiplier; + } + // Calculate the depth + var depth = realHeightTotal / 2 - axisHeight; + + // Now, we start building the pieces that will go into the vlist + + // Keep a list of the inner pieces + var inners = []; + + // Add the bottom symbol + inners.push(makeInner(bottom, font, mode)); + + var i; + if (middle === null) { + // Add that many symbols + for (i = 0; i < repeatCount; i++) { + inners.push(makeInner(repeat, font, mode)); + } + } else { + // When there is a middle bit, we need the middle part and two repeated + // sections + for (i = 0; i < repeatCount; i++) { + inners.push(makeInner(repeat, font, mode)); + } + inners.push(makeInner(middle, font, mode)); + for (i = 0; i < repeatCount; i++) { + inners.push(makeInner(repeat, font, mode)); + } + } + + // Add the top symbol + inners.push(makeInner(top, font, mode)); + + // Finally, build the vlist + var inner = buildCommon.makeVList(inners, "bottom", depth, options); + + return styleWrap( + makeSpan(["delimsizing", "mult"], [inner], options.getColor()), + Style.TEXT, options); +}; + +// There are three kinds of delimiters, delimiters that stack when they become +// too large +var stackLargeDelimiters = [ + "(", ")", "[", "\\lbrack", "]", "\\rbrack", + "\\{", "\\lbrace", "\\}", "\\rbrace", + "\\lfloor", "\\rfloor", "\\lceil", "\\rceil", + "\\surd", +]; + +// delimiters that always stack +var stackAlwaysDelimiters = [ + "\\uparrow", "\\downarrow", "\\updownarrow", + "\\Uparrow", "\\Downarrow", "\\Updownarrow", + "|", "\\|", "\\vert", "\\Vert", + "\\lvert", "\\rvert", "\\lVert", "\\rVert", + "\\lgroup", "\\rgroup", "\\lmoustache", "\\rmoustache", +]; + +// and delimiters that never stack +var stackNeverDelimiters = [ + "<", ">", "\\langle", "\\rangle", "/", "\\backslash", "\\lt", "\\gt", +]; + +// Metrics of the different sizes. Found by looking at TeX's output of +// $\bigl| // \Bigl| \biggl| \Biggl| \showlists$ +// Used to create stacked delimiters of appropriate sizes in makeSizedDelim. +var sizeToMaxHeight = [0, 1.2, 1.8, 2.4, 3.0]; + +/** + * Used to create a delimiter of a specific size, where `size` is 1, 2, 3, or 4. + */ +var makeSizedDelim = function(delim, size, options, mode) { + // < and > turn into \langle and \rangle in delimiters + if (delim === "<" || delim === "\\lt") { + delim = "\\langle"; + } else if (delim === ">" || delim === "\\gt") { + delim = "\\rangle"; + } + + // Sized delimiters are never centered. + if (utils.contains(stackLargeDelimiters, delim) || + utils.contains(stackNeverDelimiters, delim)) { + return makeLargeDelim(delim, size, false, options, mode); + } else if (utils.contains(stackAlwaysDelimiters, delim)) { + return makeStackedDelim( + delim, sizeToMaxHeight[size], false, options, mode); + } else { + throw new ParseError("Illegal delimiter: '" + delim + "'"); + } +}; + +/** + * There are three different sequences of delimiter sizes that the delimiters + * follow depending on the kind of delimiter. This is used when creating custom + * sized delimiters to decide whether to create a small, large, or stacked + * delimiter. + * + * In real TeX, these sequences aren't explicitly defined, but are instead + * defined inside the font metrics. Since there are only three sequences that + * are possible for the delimiters that TeX defines, it is easier to just encode + * them explicitly here. + */ + +// Delimiters that never stack try small delimiters and large delimiters only +var stackNeverDelimiterSequence = [ + {type: "small", style: Style.SCRIPTSCRIPT}, + {type: "small", style: Style.SCRIPT}, + {type: "small", style: Style.TEXT}, + {type: "large", size: 1}, + {type: "large", size: 2}, + {type: "large", size: 3}, + {type: "large", size: 4}, +]; + +// Delimiters that always stack try the small delimiters first, then stack +var stackAlwaysDelimiterSequence = [ + {type: "small", style: Style.SCRIPTSCRIPT}, + {type: "small", style: Style.SCRIPT}, + {type: "small", style: Style.TEXT}, + {type: "stack"}, +]; + +// Delimiters that stack when large try the small and then large delimiters, and +// stack afterwards +var stackLargeDelimiterSequence = [ + {type: "small", style: Style.SCRIPTSCRIPT}, + {type: "small", style: Style.SCRIPT}, + {type: "small", style: Style.TEXT}, + {type: "large", size: 1}, + {type: "large", size: 2}, + {type: "large", size: 3}, + {type: "large", size: 4}, + {type: "stack"}, +]; + +/** + * Get the font used in a delimiter based on what kind of delimiter it is. + */ +var delimTypeToFont = function(type) { + if (type.type === "small") { + return "Main-Regular"; + } else if (type.type === "large") { + return "Size" + type.size + "-Regular"; + } else if (type.type === "stack") { + return "Size4-Regular"; + } +}; + +/** + * Traverse a sequence of types of delimiters to decide what kind of delimiter + * should be used to create a delimiter of the given height+depth. + */ +var traverseSequence = function(delim, height, sequence, options) { + // Here, we choose the index we should start at in the sequences. In smaller + // sizes (which correspond to larger numbers in style.size) we start earlier + // in the sequence. Thus, scriptscript starts at index 3-3=0, script starts + // at index 3-2=1, text starts at 3-1=2, and display starts at min(2,3-0)=2 + var start = Math.min(2, 3 - options.style.size); + for (var i = start; i < sequence.length; i++) { + if (sequence[i].type === "stack") { + // This is always the last delimiter, so we just break the loop now. + break; + } + + var metrics = getMetrics(delim, delimTypeToFont(sequence[i])); + var heightDepth = metrics.height + metrics.depth; + + // Small delimiters are scaled down versions of the same font, so we + // account for the style change size. + + if (sequence[i].type === "small") { + heightDepth *= sequence[i].style.sizeMultiplier; + } + + // Check if the delimiter at this size works for the given height. + if (heightDepth > height) { + return sequence[i]; + } + } + + // If we reached the end of the sequence, return the last sequence element. + return sequence[sequence.length - 1]; +}; + +/** + * Make a delimiter of a given height+depth, with optional centering. Here, we + * traverse the sequences, and create a delimiter that the sequence tells us to. + */ +var makeCustomSizedDelim = function(delim, height, center, options, mode) { + if (delim === "<" || delim === "\\lt") { + delim = "\\langle"; + } else if (delim === ">" || delim === "\\gt") { + delim = "\\rangle"; + } + + // Decide what sequence to use + var sequence; + if (utils.contains(stackNeverDelimiters, delim)) { + sequence = stackNeverDelimiterSequence; + } else if (utils.contains(stackLargeDelimiters, delim)) { + sequence = stackLargeDelimiterSequence; + } else { + sequence = stackAlwaysDelimiterSequence; + } + + // Look through the sequence + var delimType = traverseSequence(delim, height, sequence, options); + + // Depending on the sequence element we decided on, call the appropriate + // function. + if (delimType.type === "small") { + return makeSmallDelim(delim, delimType.style, center, options, mode); + } else if (delimType.type === "large") { + return makeLargeDelim(delim, delimType.size, center, options, mode); + } else if (delimType.type === "stack") { + return makeStackedDelim(delim, height, center, options, mode); + } +}; + +/** + * Make a delimiter for use with `\left` and `\right`, given a height and depth + * of an expression that the delimiters surround. + */ +var makeLeftRightDelim = function(delim, height, depth, options, mode) { + // We always center \left/\right delimiters, so the axis is always shifted + var axisHeight = + fontMetrics.metrics.axisHeight * options.style.sizeMultiplier; + + // Taken from TeX source, tex.web, function make_left_right + var delimiterFactor = 901; + var delimiterExtend = 5.0 / fontMetrics.metrics.ptPerEm; + + var maxDistFromAxis = Math.max( + height - axisHeight, depth + axisHeight); + + var totalHeight = Math.max( + // In real TeX, calculations are done using integral values which are + // 65536 per pt, or 655360 per em. So, the division here truncates in + // TeX but doesn't here, producing different results. If we wanted to + // exactly match TeX's calculation, we could do + // Math.floor(655360 * maxDistFromAxis / 500) * + // delimiterFactor / 655360 + // (To see the difference, compare + // x^{x^{\left(\rule{0.1em}{0.68em}\right)}} + // in TeX and KaTeX) + maxDistFromAxis / 500 * delimiterFactor, + 2 * maxDistFromAxis - delimiterExtend); + + // Finally, we defer to `makeCustomSizedDelim` with our calculated total + // height + return makeCustomSizedDelim(delim, totalHeight, true, options, mode); +}; + +module.exports = { + sizedDelim: makeSizedDelim, + customSizedDelim: makeCustomSizedDelim, + leftRightDelim: makeLeftRightDelim, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/domTree.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/domTree.js new file mode 100644 index 00000000..e0d8e925 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/domTree.js @@ -0,0 +1,269 @@ +/** + * These objects store the data about the DOM nodes we create, as well as some + * extra data. They can then be transformed into real DOM nodes with the + * `toNode` function or HTML markup using `toMarkup`. They are useful for both + * storing extra properties on the nodes, as well as providing a way to easily + * work with the DOM. + * + * Similar functions for working with MathML nodes exist in mathMLTree.js. + */ + +var utils = require("./utils"); + +/** + * Create an HTML className based on a list of classes. In addition to joining + * with spaces, we also remove null or empty classes. + */ +var createClass = function(classes) { + classes = classes.slice(); + for (var i = classes.length - 1; i >= 0; i--) { + if (!classes[i]) { + classes.splice(i, 1); + } + } + + return classes.join(" "); +}; + +/** + * This node represents a span node, with a className, a list of children, and + * an inline style. It also contains information about its height, depth, and + * maxFontSize. + */ +function span(classes, children, height, depth, maxFontSize, style) { + this.classes = classes || []; + this.children = children || []; + this.height = height || 0; + this.depth = depth || 0; + this.maxFontSize = maxFontSize || 0; + this.style = style || {}; + this.attributes = {}; +} + +/** + * Sets an arbitrary attribute on the span. Warning: use this wisely. Not all + * browsers support attributes the same, and having too many custom attributes + * is probably bad. + */ +span.prototype.setAttribute = function(attribute, value) { + this.attributes[attribute] = value; +}; + +/** + * Convert the span into an HTML node + */ +span.prototype.toNode = function() { + var span = document.createElement("span"); + + // Apply the class + span.className = createClass(this.classes); + + // Apply inline styles + for (var style in this.style) { + if (Object.prototype.hasOwnProperty.call(this.style, style)) { + span.style[style] = this.style[style]; + } + } + + // Apply attributes + for (var attr in this.attributes) { + if (Object.prototype.hasOwnProperty.call(this.attributes, attr)) { + span.setAttribute(attr, this.attributes[attr]); + } + } + + // Append the children, also as HTML nodes + for (var i = 0; i < this.children.length; i++) { + span.appendChild(this.children[i].toNode()); + } + + return span; +}; + +/** + * Convert the span into an HTML markup string + */ +span.prototype.toMarkup = function() { + var markup = " 0) { + span = document.createElement("span"); + span.style.marginRight = this.italic + "em"; + } + + if (this.classes.length > 0) { + span = span || document.createElement("span"); + span.className = createClass(this.classes); + } + + for (var style in this.style) { + if (this.style.hasOwnProperty(style)) { + span = span || document.createElement("span"); + span.style[style] = this.style[style]; + } + } + + if (span) { + span.appendChild(node); + return span; + } else { + return node; + } +}; + +/** + * Creates markup for a symbol node. + */ +symbolNode.prototype.toMarkup = function() { + // TODO(alpert): More duplication than I'd like from + // span.prototype.toMarkup and symbolNode.prototype.toNode... + var needsSpan = false; + + var markup = " 0) { + styles += "margin-right:" + this.italic + "em;"; + } + for (var style in this.style) { + if (this.style.hasOwnProperty(style)) { + styles += utils.hyphenate(style) + ":" + this.style[style] + ";"; + } + } + + if (styles) { + needsSpan = true; + markup += " style=\"" + utils.escape(styles) + "\""; + } + + var escaped = utils.escape(this.value); + if (needsSpan) { + markup += ">"; + markup += escaped; + markup += ""; + return markup; + } else { + return escaped; + } +}; + +module.exports = { + span: span, + documentFragment: documentFragment, + symbolNode: symbolNode, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/environments.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/environments.js new file mode 100644 index 00000000..01d62b5e --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/environments.js @@ -0,0 +1,295 @@ +/* eslint no-constant-condition:0 */ +var fontMetrics = require("./fontMetrics"); +var parseData = require("./parseData"); +var ParseError = require("./ParseError"); + +var ParseNode = parseData.ParseNode; + +/** + * Parse the body of the environment, with rows delimited by \\ and + * columns delimited by &, and create a nested list in row-major order + * with one group per cell. + */ +var q = 0 ; +function parseArray(parser, result) { + var row = []; + var body = [row]; + var rowGaps = []; + + while (true) { + + // if (q == 1) console.error(parser.nextToken.text); + try { + var cell = parser.parseExpression(false, null); + } catch (e) { + // console.error(e); + exit(); + } + // if (q == 1) exit(); + row.push(new ParseNode("ordgroup", cell, parser.mode)); + var next = parser.nextToken.text; + if (next === "&") { + parser.consume(); + } else if (next === "\\end" || next == "}") { + break; + } else if (next === "\\\\" || next === "\\cr") { + + var cr = parser.parseFunction(); + + rowGaps.push(cr.value.size); + row = []; + body.push(row); + } else { + // TODO: Clean up the following hack once #385 got merged + var pos = Math.min(parser.pos + 1, parser.lexer._input.length); + throw new ParseError("Expected & or \\\\ or \\end", + parser.lexer, pos); + } + } + result.body = body; + result.rowGaps = rowGaps; + // if (q == 1) exit(); + var node = new ParseNode(result.type, result, parser.mode); + return node; +} + +/* + * An environment definition is very similar to a function definition: + * it is declared with a name or a list of names, a set of properties + * and a handler containing the actual implementation. + * + * The properties include: + * - numArgs: The number of arguments after the \begin{name} function. + * - argTypes: (optional) Just like for a function + * - allowedInText: (optional) Whether or not the environment is allowed inside + * text mode (default false) (not enforced yet) + * - numOptionalArgs: (optional) Just like for a function + * A bare number instead of that object indicates the numArgs value. + * + * The handler function will receive two arguments + * - context: information and references provided by the parser + * - args: an array of arguments passed to \begin{name} + * The context contains the following properties: + * - envName: the name of the environment, one of the listed names. + * - parser: the parser object + * - lexer: the lexer object + * - positions: the positions associated with these arguments from args. + * The handler must return a ParseResult. + */ + +function defineEnvironment(names, props, handler) { + if (typeof names === "string") { + names = [names]; + } + if (typeof props === "number") { + props = { numArgs: props }; + } + // Set default values of environments + var data = { + numArgs: props.numArgs || 0, + argTypes: props.argTypes, + greediness: 1, + allowedInText: !!props.allowedInText, + numOptionalArgs: props.numOptionalArgs || 0, + handler: handler, + }; + for (var i = 0; i < names.length; ++i) { + module.exports[names[i]] = data; + } +} + +// Arrays are part of LaTeX, defined in lttab.dtx so its documentation +// is part of the source2e.pdf file of LaTeX2e source documentation. +defineEnvironment("array", { + numArgs: 1, +}, function(context, args) { + var colalign = args[0]; + colalign = colalign.value.map ? colalign.value : [colalign]; + var cols = colalign.map(function(node) { + var ca = node.value; + if ("lcr".indexOf(ca) !== -1) { + return { + type: "align", + align: ca, + }; + } else if (ca === "|") { + return { + type: "separator", + separator: "|", + }; + } + // throw new ParseError( + // "Unknown column alignment: " + node.value, + // context.lexer, context.positions[1]); + }); + var res = { + type: "array", + cols: cols, + hskipBeforeAndAfter: true, // \@preamble in lttab.dtx + }; + res = parseArray(context.parser, res); + return res; +}); + +defineEnvironment("tabular", { + numArgs: 1, +}, function(context, args) { + var colalign = args[0]; + colalign = colalign.value.map ? colalign.value : [colalign]; + var cols = colalign.map(function(node) { + var ca = node.value; + if ("lcr".indexOf(ca) !== -1) { + return { + type: "align", + align: ca, + }; + } else if (ca === "|") { + return { + type: "separator", + separator: "|", + }; + } + // throw new ParseError( + // "Unknown column alignment: " + node.value, + // context.lexer, context.positions[1]); + }); + var res = { + type: "array", + style: "tabular", + cols: cols, + hskipBeforeAndAfter: true, // \@preamble in lttab.dtx + }; + res = parseArray(context.parser, res); + return res; +}); + +// The matrix environments of amsmath builds on the array environment +// of LaTeX, which is discussed above. +defineEnvironment([ + "matrix", + "pmatrix", + "bmatrix", + "Bmatrix", + "vmatrix", + "Vmatrix", +], { +}, function(context) { + var delimiters = { + "matrix": null, + "pmatrix": ["(", ")"], + "bmatrix": ["[", "]"], + "Bmatrix": ["\\{", "\\}"], + "vmatrix": ["|", "|"], + "Vmatrix": ["\\Vert", "\\Vert"], + }[context.envName]; + var res = { + type: "array", + hskipBeforeAndAfter: false, // \hskip -\arraycolsep in amsmath + }; + q = 1; + res = parseArray(context.parser, res); + + if (delimiters) { + res = new ParseNode("leftright", { + body: [res], + left: delimiters[0], + right: delimiters[1], + }, context.mode); + } + return res; +}); + +// A cases environment (in amsmath.sty) is almost equivalent to +// \def\arraystretch{1.2}% +// \left\{\begin{array}{@{}l@{\quad}l@{}} … \end{array}\right. +defineEnvironment("picture", { +}, function(context) { + var res = { + type: "array", + arraystretch: 1.2, + cols: [{ + type: "align", + align: "l", + pregap: 0, + postgap: fontMetrics.metrics.quad, + }, { + type: "align", + align: "l", + pregap: 0, + postgap: 0, + }], + }; + res = parseArray(context.parser, res); + res = new ParseNode("leftright", { + body: [res], + left: "\\{", + right: ".", + }, context.mode); + return res; +}); + +defineEnvironment("cases", { +}, function(context) { + var res = { + type: "array", + arraystretch: 1.2, + cols: [{ + type: "align", + align: "l", + pregap: 0, + postgap: fontMetrics.metrics.quad, + }, { + type: "align", + align: "l", + pregap: 0, + postgap: 0, + }], + }; + res = parseArray(context.parser, res); + res = new ParseNode("leftright", { + body: [res], + left: "\\{", + right: ".", + }, context.mode); + return res; +}); + +// An aligned environment is like the align* environment +// except it operates within math mode. +// Note that we assume \nomallineskiplimit to be zero, +// so that \strut@ is the same as \strut. +defineEnvironment("aligned", { +}, function(context) { + var res = { + type: "array", + cols: [], + }; + res = parseArray(context.parser, res); + var emptyGroup = new ParseNode("ordgroup", [], context.mode); + var numCols = 0; + res.value.body.forEach(function(row) { + var i; + for (i = 1; i < row.length; i += 2) { + row[i].value.unshift(emptyGroup); + } + if (numCols < row.length) { + numCols = row.length; + } + }); + for (var i = 0; i < numCols; ++i) { + var align = "r"; + var pregap = 0; + if (i % 2 === 1) { + align = "l"; + } else if (i > 0) { + pregap = 2; // one \qquad between columns + } + res.value.cols[i] = { + type: "align", + align: align, + pregap: pregap, + postgap: 0, + }; + } + return res; +}); diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/fontMetrics.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/fontMetrics.js new file mode 100644 index 00000000..db9e44bf --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/fontMetrics.js @@ -0,0 +1,147 @@ +/* eslint no-unused-vars:0 */ + +var Style = require("./Style"); + +/** + * This file contains metrics regarding fonts and individual symbols. The sigma + * and xi variables, as well as the metricMap map contain data extracted from + * TeX, TeX font metrics, and the TTF files. These data are then exposed via the + * `metrics` variable and the getCharacterMetrics function. + */ + +// These font metrics are extracted from TeX by using +// \font\a=cmmi10 +// \showthe\fontdimenX\a +// where X is the corresponding variable number. These correspond to the font +// parameters of the symbol fonts. In TeX, there are actually three sets of +// dimensions, one for each of textstyle, scriptstyle, and scriptscriptstyle, +// but we only use the textstyle ones, and scale certain dimensions accordingly. +// See the TeXbook, page 441. +var sigma1 = 0.025; +var sigma2 = 0; +var sigma3 = 0; +var sigma4 = 0; +var sigma5 = 0.431; +var sigma6 = 1; +var sigma7 = 0; +var sigma8 = 0.677; +var sigma9 = 0.394; +var sigma10 = 0.444; +var sigma11 = 0.686; +var sigma12 = 0.345; +var sigma13 = 0.413; +var sigma14 = 0.363; +var sigma15 = 0.289; +var sigma16 = 0.150; +var sigma17 = 0.247; +var sigma18 = 0.386; +var sigma19 = 0.050; +var sigma20 = 2.390; +var sigma21 = 1.01; +var sigma21Script = 0.81; +var sigma21ScriptScript = 0.71; +var sigma22 = 0.250; + +// These font metrics are extracted from TeX by using +// \font\a=cmex10 +// \showthe\fontdimenX\a +// where X is the corresponding variable number. These correspond to the font +// parameters of the extension fonts (family 3). See the TeXbook, page 441. +var xi1 = 0; +var xi2 = 0; +var xi3 = 0; +var xi4 = 0; +var xi5 = 0.431; +var xi6 = 1; +var xi7 = 0; +var xi8 = 0.04; +var xi9 = 0.111; +var xi10 = 0.166; +var xi11 = 0.2; +var xi12 = 0.6; +var xi13 = 0.1; + +// This value determines how large a pt is, for metrics which are defined in +// terms of pts. +// This value is also used in katex.less; if you change it make sure the values +// match. +var ptPerEm = 10.0; + +// The space between adjacent `|` columns in an array definition. From +// `\showthe\doublerulesep` in LaTeX. +var doubleRuleSep = 2.0 / ptPerEm; + +/** + * This is just a mapping from common names to real metrics + */ +var metrics = { + xHeight: sigma5, + quad: sigma6, + num1: sigma8, + num2: sigma9, + num3: sigma10, + denom1: sigma11, + denom2: sigma12, + sup1: sigma13, + sup2: sigma14, + sup3: sigma15, + sub1: sigma16, + sub2: sigma17, + supDrop: sigma18, + subDrop: sigma19, + axisHeight: sigma22, + defaultRuleThickness: xi8, + bigOpSpacing1: xi9, + bigOpSpacing2: xi10, + bigOpSpacing3: xi11, + bigOpSpacing4: xi12, + bigOpSpacing5: xi13, + ptPerEm: ptPerEm, + emPerEx: sigma5 / sigma6, + doubleRuleSep: doubleRuleSep, + + // TODO(alpert): Missing parallel structure here. We should probably add + // style-specific metrics for all of these. + delim1: sigma20, + getDelim2: function(style) { + if (style.size === Style.TEXT.size) { + return sigma21; + } else if (style.size === Style.SCRIPT.size) { + return sigma21Script; + } else if (style.size === Style.SCRIPTSCRIPT.size) { + return sigma21ScriptScript; + } + throw new Error("Unexpected style size: " + style.size); + }, +}; + +// This map contains a mapping from font name and character code to character +// metrics, including height, depth, italic correction, and skew (kern from the +// character to the corresponding \skewchar) +// This map is generated via `make metrics`. It should not be changed manually. +var metricMap = require("./fontMetricsData"); + +/** + * This function is a convenience function for looking up information in the + * metricMap table. It takes a character as a string, and a style. + * + * Note: the `width` property may be undefined if fontMetricsData.js wasn't + * built using `Make extended_metrics`. + */ +var getCharacterMetrics = function(character, style) { + var metrics = metricMap[style][character.charCodeAt(0)]; + if (metrics) { + return { + depth: metrics[0], + height: metrics[1], + italic: metrics[2], + skew: metrics[3], + width: metrics[4], + }; + } +}; + +module.exports = { + metrics: metrics, + getCharacterMetrics: getCharacterMetrics, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/fontMetricsData.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/fontMetricsData.js new file mode 100644 index 00000000..957f55b8 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/fontMetricsData.js @@ -0,0 +1,1752 @@ +module.exports = { + "AMS-Regular": { + "65": [0, 0.68889, 0, 0], + "66": [0, 0.68889, 0, 0], + "67": [0, 0.68889, 0, 0], + "68": [0, 0.68889, 0, 0], + "69": [0, 0.68889, 0, 0], + "70": [0, 0.68889, 0, 0], + "71": [0, 0.68889, 0, 0], + "72": [0, 0.68889, 0, 0], + "73": [0, 0.68889, 0, 0], + "74": [0.16667, 0.68889, 0, 0], + "75": [0, 0.68889, 0, 0], + "76": [0, 0.68889, 0, 0], + "77": [0, 0.68889, 0, 0], + "78": [0, 0.68889, 0, 0], + "79": [0.16667, 0.68889, 0, 0], + "80": [0, 0.68889, 0, 0], + "81": [0.16667, 0.68889, 0, 0], + "82": [0, 0.68889, 0, 0], + "83": [0, 0.68889, 0, 0], + "84": [0, 0.68889, 0, 0], + "85": [0, 0.68889, 0, 0], + "86": [0, 0.68889, 0, 0], + "87": [0, 0.68889, 0, 0], + "88": [0, 0.68889, 0, 0], + "89": [0, 0.68889, 0, 0], + "90": [0, 0.68889, 0, 0], + "107": [0, 0.68889, 0, 0], + "165": [0, 0.675, 0.025, 0], + "174": [0.15559, 0.69224, 0, 0], + "240": [0, 0.68889, 0, 0], + "295": [0, 0.68889, 0, 0], + "710": [0, 0.825, 0, 0], + "732": [0, 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0], + "2018": [0, 0.61111, 0, 0], + "2019": [0, 0.61111, 0, 0], + "8242": [0, 0.61111, 0, 0], + }, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/functions.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/functions.js new file mode 100644 index 00000000..806f6982 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/functions.js @@ -0,0 +1,585 @@ +var utils = require("./utils"); +var ParseError = require("./ParseError"); + +/* This file contains a list of functions that we parse, identified by + * the calls to defineFunction. + * + * The first argument to defineFunction is a single name or a list of names. + * All functions named in such a list will share a single implementation. + * + * Each declared function can have associated properties, which + * include the following: + * + * - numArgs: The number of arguments the function takes. + * If this is the only property, it can be passed as a number + * instead of an element of a properties object. + * - argTypes: (optional) An array corresponding to each argument of the + * function, giving the type of argument that should be parsed. Its + * length should be equal to `numArgs + numOptionalArgs`. Valid + * types: + * - "size": A size-like thing, such as "1em" or "5ex" + * - "color": An html color, like "#abc" or "blue" + * - "original": The same type as the environment that the + * function being parsed is in (e.g. used for the + * bodies of functions like \color where the first + * argument is special and the second argument is + * parsed normally) + * Other possible types (probably shouldn't be used) + * - "text": Text-like (e.g. \text) + * - "math": Normal math + * If undefined, this will be treated as an appropriate length + * array of "original" strings + * - greediness: (optional) The greediness of the function to use ungrouped + * arguments. + * + * E.g. if you have an expression + * \sqrt \frac 1 2 + * since \frac has greediness=2 vs \sqrt's greediness=1, \frac + * will use the two arguments '1' and '2' as its two arguments, + * then that whole function will be used as the argument to + * \sqrt. On the other hand, the expressions + * \frac \frac 1 2 3 + * and + * \frac \sqrt 1 2 + * will fail because \frac and \frac have equal greediness + * and \sqrt has a lower greediness than \frac respectively. To + * make these parse, we would have to change them to: + * \frac {\frac 1 2} 3 + * and + * \frac {\sqrt 1} 2 + * + * The default value is `1` + * - allowedInText: (optional) Whether or not the function is allowed inside + * text mode (default false) + * - numOptionalArgs: (optional) The number of optional arguments the function + * should parse. If the optional arguments aren't found, + * `null` will be passed to the handler in their place. + * (default 0) + * + * The last argument is that implementation, the handler for the function(s). + * It is called to handle these functions and their arguments. + * It receives two arguments: + * - context contains information and references provided by the parser + * - args is an array of arguments obtained from TeX input + * The context contains the following properties: + * - funcName: the text (i.e. name) of the function, including \ + * - parser: the parser object + * - lexer: the lexer object + * - positions: the positions in the overall string of the function + * and the arguments. + * The latter three should only be used to produce error messages. + * + * The function should return an object with the following keys: + * - type: The type of element that this is. This is then used in + * buildHTML/buildMathML to determine which function + * should be called to build this node into a DOM node + * Any other data can be added to the object, which will be passed + * in to the function in buildHTML/buildMathML as `group.value`. + */ + +function defineFunction(names, props, handler) { + if (typeof names === "string") { + names = [names]; + } + if (typeof props === "number") { + props = { numArgs: props }; + } + // Set default values of functions + var data = { + numArgs: props.numArgs, + argTypes: props.argTypes, + greediness: (props.greediness === undefined) ? 1 : props.greediness, + allowedInText: !!props.allowedInText, + numOptionalArgs: props.numOptionalArgs || 0, + handler: handler, + }; + for (var i = 0; i < names.length; ++i) { + module.exports[names[i]] = data; + } +} + +// A normal square root +defineFunction("\\sqrt", { + numArgs: 1, + numOptionalArgs: 1, +}, function(context, args) { + var index = args[0]; + var body = args[1]; + return { + type: "sqrt", + body: body, + index: index, + }; +}); + +// Some non-mathy text +defineFunction(["\\text", "\\mbox", "\\hbox", "\\vbox"], { + numArgs: 1, + argTypes: ["text"], + greediness: 2, +}, function(context, args) { + var body = args[0]; + // Since the corresponding buildHTML/buildMathML function expects a + // list of elements, we normalize for different kinds of arguments + // TODO(emily): maybe this should be done somewhere else + var inner; + if (body.type === "ordgroup") { + inner = body.value; + } else { + inner = [body]; + } + + return { + type: "text", + body: inner, + }; +}); + +// A two-argument custom color +defineFunction("\\color", { + numArgs: 2, + allowedInText: true, + greediness: 3, + argTypes: ["color", "original"], +}, function(context, args) { + var color = args[0]; + var body = args[1]; + // Normalize the different kinds of bodies (see \text above) + var inner; + if (body.type === "ordgroup") { + inner = body.value; + } else { + inner = [body]; + } + + return { + type: "color", + color: color.value, + value: inner, + }; +}); + +// An overline +defineFunction("\\overline", { + numArgs: 1, +}, function(context, args) { + var body = args[0]; + return { + type: "overline", + body: body, + }; +}); + +// An underline +defineFunction("\\underline", { + numArgs: 1, +}, function(context, args) { + var body = args[0]; + return { + type: "underline", + body: body, + }; +}); + +// A box of the width and height +defineFunction("\\rule", { + numArgs: 2, + numOptionalArgs: 1, + argTypes: ["size", "size", "size"], +}, function(context, args) { + var shift = args[0]; + var width = args[1]; + var height = args[2]; + return { + type: "rule", + shift: shift && shift.value, + width: width.value, + height: height.value, + }; +}); + +// A KaTeX logo +defineFunction("\\KaTeX", { + numArgs: 0, +}, function(context) { + return { + type: "katex", + }; +}); + +defineFunction("\\phantom", { + numArgs: 1, +}, function(context, args) { + var body = args[0]; + var inner; + if (body.type === "ordgroup") { + inner = body.value; + } else { + inner = [body]; + } + + return { + type: "phantom", + value: inner, + }; +}); + +// Extra data needed for the delimiter handler down below +var delimiterSizes = { + "\\bigl" : {type: "open", size: 1}, + "\\Bigl" : {type: "open", size: 2}, + "\\biggl": {type: "open", size: 3}, + "\\Biggl": {type: "open", size: 4}, + "\\bigr" : {type: "close", size: 1}, + "\\Bigr" : {type: "close", size: 2}, + "\\biggr": {type: "close", size: 3}, + "\\Biggr": {type: "close", size: 4}, + "\\bigm" : {type: "rel", size: 1}, + "\\Bigm" : {type: "rel", size: 2}, + "\\biggm": {type: "rel", size: 3}, + "\\Biggm": {type: "rel", size: 4}, + "\\big" : {type: "textord", size: 1}, + "\\Big" : {type: "textord", size: 2}, + "\\bigg" : {type: "textord", size: 3}, + "\\Bigg" : {type: "textord", size: 4}, +}; + +var delimiters = [ + "(", ")", "[", "\\lbrack", "]", "\\rbrack", + "\\{", "\\lbrace", "\\}", "\\rbrace", + "\\lfloor", "\\rfloor", "\\lceil", "\\rceil", + "<", ">", "\\langle", "\\rangle", "\\lt", "\\gt", + "\\lvert", "\\rvert", "\\lVert", "\\rVert", + "\\lgroup", "\\rgroup", "\\lmoustache", "\\rmoustache", + "/", "\\backslash", + "|", "\\vert", "\\|", "\\Vert", + "\\uparrow", "\\Uparrow", + "\\downarrow", "\\Downarrow", + "\\updownarrow", "\\Updownarrow", + ".", +]; + +var fontAliases = { + "\\Bbb": "\\mathbb", + "\\bold": "\\mathbf", + "\\frak": "\\mathfrak", +}; + +// Single-argument color functions +defineFunction([ + "\\blue", "\\orange", "\\pink", "\\red", + "\\green", "\\gray", "\\purple", + "\\blueA", "\\blueB", "\\blueC", "\\blueD", "\\blueE", + "\\tealA", "\\tealB", "\\tealC", "\\tealD", "\\tealE", + "\\greenA", "\\greenB", "\\greenC", "\\greenD", "\\greenE", + "\\goldA", "\\goldB", "\\goldC", "\\goldD", "\\goldE", + "\\redA", "\\redB", "\\redC", "\\redD", "\\redE", + "\\maroonA", "\\maroonB", "\\maroonC", "\\maroonD", "\\maroonE", + "\\purpleA", "\\purpleB", "\\purpleC", "\\purpleD", "\\purpleE", + "\\mintA", "\\mintB", "\\mintC", + "\\grayA", "\\grayB", "\\grayC", "\\grayD", "\\grayE", + "\\grayF", "\\grayG", "\\grayH", "\\grayI", + "\\kaBlue", "\\kaGreen", +], { + numArgs: 1, + allowedInText: true, + greediness: 3, +}, function(context, args) { + var body = args[0]; + var atoms; + if (body.type === "ordgroup") { + atoms = body.value; + } else { + atoms = [body]; + } + + return { + type: "color", + color: "katex-" + context.funcName.slice(1), + value: atoms, + }; +}); + +// There are 2 flags for operators; whether they produce limits in +// displaystyle, and whether they are symbols and should grow in +// displaystyle. These four groups cover the four possible choices. + +// No limits, not symbols +defineFunction([ + "\\arcsin", "\\arccos", "\\arctan", "\\arg", "\\cos", "\\cosh", + "\\cot", "\\coth", "\\csc", "\\deg", "\\dim", "\\exp", "\\hom", + "\\ker", "\\lg", "\\ln", "\\log", "\\sec", "\\sin", "\\sinh", + "\\tan", "\\tanh", +], { + numArgs: 0, +}, function(context) { + return { + type: "op", + limits: false, + symbol: false, + body: context.funcName, + }; +}); + +// Limits, not symbols +defineFunction([ + "\\det", "\\gcd", "\\inf", "\\lim", "\\liminf", "\\limsup", "\\max", + "\\min", "\\Pr", "\\sup", +], { + numArgs: 0, +}, function(context) { + return { + type: "op", + limits: true, + symbol: false, + body: context.funcName, + }; +}); + +// No limits, symbols +defineFunction([ + "\\int", "\\iint", "\\iiint", "\\oint", +], { + numArgs: 0, +}, function(context) { + return { + type: "op", + limits: false, + symbol: true, + body: context.funcName, + }; +}); + +// Limits, symbols +defineFunction([ + "\\coprod", "\\bigvee", "\\bigwedge", "\\biguplus", "\\bigcap", + "\\bigcup", "\\intop", "\\prod", "\\sum", "\\bigotimes", + "\\bigoplus", "\\bigodot", "\\bigsqcup", "\\smallint", +], { + numArgs: 0, +}, function(context) { + return { + type: "op", + limits: true, + symbol: true, + body: context.funcName, + }; +}); + +// Fractions +defineFunction([ + "\\dfrac", "\\frac", "\\tfrac", + "\\dbinom", "\\binom", "\\tbinom", +], { + numArgs: 2, + greediness: 2, +}, function(context, args) { + var numer = args[0]; + var denom = args[1]; + var hasBarLine; + var leftDelim = null; + var rightDelim = null; + var size = "auto"; + + switch (context.funcName) { + case "\\dfrac": + case "\\frac": + case "\\tfrac": + hasBarLine = true; + break; + case "\\dbinom": + case "\\binom": + case "\\tbinom": + hasBarLine = false; + leftDelim = "("; + rightDelim = ")"; + break; + default: + throw new Error("Unrecognized genfrac command"); + } + + switch (context.funcName) { + case "\\dfrac": + case "\\dbinom": + size = "display"; + break; + case "\\tfrac": + case "\\tbinom": + size = "text"; + break; + } + + return { + type: "genfrac", + numer: numer, + denom: denom, + hasBarLine: hasBarLine, + leftDelim: leftDelim, + rightDelim: rightDelim, + size: size, + }; +}); + +// Left and right overlap functions +defineFunction(["\\llap", "\\rlap"], { + numArgs: 1, + allowedInText: true, +}, function(context, args) { + var body = args[0]; + return { + type: context.funcName.slice(1), + body: body, + }; +}); + +// Delimiter functions +defineFunction([ + "\\bigl", "\\Bigl", "\\biggl", "\\Biggl", + "\\bigr", "\\Bigr", "\\biggr", "\\Biggr", + "\\bigm", "\\Bigm", "\\biggm", "\\Biggm", + "\\big", "\\Big", "\\bigg", "\\Bigg", + "\\left", "\\right" +], { + numArgs: 1, +}, function(context, args) { + var delim = args[0]; + if (!utils.contains(delimiters, delim.value)) { + throw new ParseError( + "Invalid delimiter: '" + delim.value + "' after '" + + context.funcName + "'", + context.lexer, context.positions[1]); + } + + // \left and \right are caught somewhere in Parser.js, which is + // why this data doesn't match what is in buildHTML. + if (context.funcName === "\\left" || context.funcName === "\\right") { + return { + type: "leftright", + value: delim.value, + funcName: context.funcName + }; + } else { + return { + type: "delimsizing", + size: delimiterSizes[context.funcName].size, + delimType: delimiterSizes[context.funcName].type, + value: delim.value, + funcName: context.funcName + }; + } +}); + +// Sizing functions (handled in Parser.js explicitly, hence no handler) +defineFunction([ + "\\tiny", "\\scriptsize", "\\footnotesize", "\\small", + "\\normalsize", "\\large", "\\Large", "\\LARGE", "\\huge", "\\Huge", "\\textrm", "\\rm", "\\cal", "\\bf", "\\siptstyle", "\\boldmath", "\\it" +], 0, null); + +// Style changing functions (handled in Parser.js explicitly, hence no +// handler) +defineFunction([ + "\\displaystyle", "\\textstyle", "\\scriptstyle", + "\\scriptscriptstyle", +], 0, null); + +defineFunction([ + // styles + "\\mathrm", "\\mathit", "\\mathbf","\\mathop","\\stackrel", + + // families + "\\mathbb", "\\mathcal", "\\mathfrak", "\\mathscr", "\\mathsf", + "\\mathtt", + + "\\label", "\\comment", "\\hspace", "\\vspace", "\\atop", "\\fbox", "\\tag", "\\makebox", + "\\raisebox", "\\framebox", "\\circle", "\\line", "\\put", "\\vphantom", "\\textup", "\\noalign", + + // aliases + "\\Bbb", "\\bold", "\\frak", +], { + numArgs: 1, + greediness: 2, +}, function(context, args) { + var body = args[0]; + var func = context.funcName; + if (func in fontAliases) { + func = fontAliases[func]; + } + return { + type: "font", + font: func.slice(1), + body: body, + }; +}); + +// Accents +defineFunction([ + "\\acute", "\\grave", "\\ddot", "\\tilde", "\\bar", "\\breve", + "\\check", "\\hat", "\\vec", "\\dot", + // We don't support expanding accents yet + // "\\widetilde", "\\widehat" +], { + numArgs: 1, +}, function(context, args) { + var base = args[0]; + return { + type: "accent", + accent: context.funcName, + base: base, + }; +}); + +// Infix generalized fractions +defineFunction(["\\over", "\\choose"], { + numArgs: 0, +}, function(context) { + var replaceWith; + switch (context.funcName) { + case "\\over": + replaceWith = "\\frac"; + break; + case "\\choose": + replaceWith = "\\binom"; + break; + default: + throw new Error("Unrecognized infix genfrac command"); + } + return { + type: "infix", + replaceWith: replaceWith, + }; +}); + +// Row breaks for aligned data +defineFunction(["\\\\", "\\cr"], { + numArgs: 0, + numOptionalArgs: 1, + argTypes: ["size"], +}, function(context, args) { + var size = args[0]; + return { + type: "cr", + size: size, + }; +}); + +// Environment delimiters +defineFunction(["\\begin", "\\end"], { + numArgs: 1, + argTypes: ["text"], +}, function(context, args) { + var nameGroup = args[0]; + if (nameGroup.type !== "ordgroup") { + throw new ParseError( + "Invalid environment name", + context.lexer, context.positions[1]); + } + var name = ""; + for (var i = 0; i < nameGroup.value.length; ++i) { + name += nameGroup.value[i].value; + } + return { + type: "environment", + name: name, + namepos: context.positions[1], + }; +}); diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/mathMLTree.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/mathMLTree.js new file mode 100644 index 00000000..86e63562 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/mathMLTree.js @@ -0,0 +1,102 @@ +/** + * These objects store data about MathML nodes. This is the MathML equivalent + * of the types in domTree.js. Since MathML handles its own rendering, and + * since we're mainly using MathML to improve accessibility, we don't manage + * any of the styling state that the plain DOM nodes do. + * + * The `toNode` and `toMarkup` functions work simlarly to how they do in + * domTree.js, creating namespaced DOM nodes and HTML text markup respectively. + */ + +var utils = require("./utils"); + +/** + * This node represents a general purpose MathML node of any type. The + * constructor requires the type of node to create (for example, `"mo"` or + * `"mspace"`, corresponding to `` and `` tags). + */ +function MathNode(type, children) { + this.type = type; + this.attributes = {}; + this.children = children || []; +} + +/** + * Sets an attribute on a MathML node. MathML depends on attributes to convey a + * semantic content, so this is used heavily. + */ +MathNode.prototype.setAttribute = function(name, value) { + this.attributes[name] = value; +}; + +/** + * Converts the math node into a MathML-namespaced DOM element. + */ +MathNode.prototype.toNode = function() { + var node = document.createElementNS( + "http://www.w3.org/1998/Math/MathML", this.type); + + for (var attr in this.attributes) { + if (Object.prototype.hasOwnProperty.call(this.attributes, attr)) { + node.setAttribute(attr, this.attributes[attr]); + } + } + + for (var i = 0; i < this.children.length; i++) { + node.appendChild(this.children[i].toNode()); + } + + return node; +}; + +/** + * Converts the math node into an HTML markup string. + */ +MathNode.prototype.toMarkup = function() { + var markup = "<" + this.type; + + // Add the attributes + for (var attr in this.attributes) { + if (Object.prototype.hasOwnProperty.call(this.attributes, attr)) { + markup += " " + attr + "=\""; + markup += utils.escape(this.attributes[attr]); + markup += "\""; + } + } + + markup += ">"; + + for (var i = 0; i < this.children.length; i++) { + markup += this.children[i].toMarkup(); + } + + markup += ""; + + return markup; +}; + +/** + * This node represents a piece of text. + */ +function TextNode(text) { + this.text = text; +} + +/** + * Converts the text node into a DOM text node. + */ +TextNode.prototype.toNode = function() { + return document.createTextNode(this.text); +}; + +/** + * Converts the text node into HTML markup (which is just the text itself). + */ +TextNode.prototype.toMarkup = function() { + return utils.escape(this.text); +}; + +module.exports = { + MathNode: MathNode, + TextNode: TextNode, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/parseData.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/parseData.js new file mode 100644 index 00000000..be8dd67f --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/parseData.js @@ -0,0 +1,13 @@ +/** + * The resulting parse tree nodes of the parse tree. + */ +function ParseNode(type, value, mode) { + this.type = type; + this.value = value; + this.mode = mode; +} + +module.exports = { + ParseNode: ParseNode, +}; + diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/parseTree.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/parseTree.js new file mode 100644 index 00000000..3adba824 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/parseTree.js @@ -0,0 +1,17 @@ +/** + * Provides a single function for parsing an expression using a Parser + * TODO(emily): Remove this + */ + +var Parser = require("./Parser"); + +/** + * Parses an expression using a Parser, then returns the parsed result. + */ +var parseTree = function(toParse, settings) { + var parser = new Parser(toParse, settings); + + return parser.parse(); +}; + +module.exports = parseTree; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/symbols.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/symbols.js new file mode 100644 index 00000000..9a2b130d --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/symbols.js @@ -0,0 +1,687 @@ +/** + * This file holds a list of all no-argument functions and single-character + * symbols (like 'a' or ';'). + * + * For each of the symbols, there are three properties they can have: + * - font (required): the font to be used for this symbol. Either "main" (the + normal font), or "ams" (the ams fonts). + * - group (required): the ParseNode group type the symbol should have (i.e. + "textord", "mathord", etc). + See https://github.com/Khan/KaTeX/wiki/Examining-TeX#group-types + * - replace: the character that this symbol or function should be + * replaced with (i.e. "\phi" has a replace value of "\u03d5", the phi + * character in the main font). + * + * The outermost map in the table indicates what mode the symbols should be + * accepted in (e.g. "math" or "text"). + */ + +module.exports = { + math: {}, + text: {}, +}; + +function defineSymbol(mode, font, group, replace, name) { + module.exports[mode][name] = { + font: font, + group: group, + replace: replace, + }; +} + +// Some abbreviations for commonly used strings. +// This helps minify the code, and also spotting typos using jshint. + +// modes: +var math = "math"; +var text = "text"; + +// fonts: +var main = "main"; +var ams = "ams"; + +// groups: +var accent = "accent"; +var bin = "bin"; +var close = "close"; +var inner = "inner"; +var mathord = "mathord"; +var op = "op"; +var open = "open"; +var punct = "punct"; +var rel = "rel"; +var spacing = "spacing"; +var textord = "textord"; + +// Now comes the symbol table + +// Relation Symbols +defineSymbol(math, main, rel, "\u2261", "\\equiv"); +defineSymbol(math, main, rel, "\u227a", "\\prec"); +defineSymbol(math, main, rel, "\u227b", "\\succ"); +defineSymbol(math, main, rel, "\u223c", "\\sim"); +defineSymbol(math, main, rel, "\u22a5", "\\perp"); +defineSymbol(math, main, rel, "\u2aaf", "\\preceq"); +defineSymbol(math, main, rel, "\u2ab0", "\\succeq"); +defineSymbol(math, main, rel, "\u2243", "\\simeq"); +defineSymbol(math, main, rel, "\u2223", "\\mid"); +defineSymbol(math, main, rel, "\u226a", "\\ll"); +defineSymbol(math, main, rel, "\u226b", "\\gg"); +defineSymbol(math, main, rel, "\u224d", "\\asymp"); +defineSymbol(math, main, rel, "\u2225", "\\parallel"); +defineSymbol(math, main, rel, "\u22c8", "\\bowtie"); +defineSymbol(math, main, rel, "\u2323", "\\smile"); +defineSymbol(math, main, rel, "\u2291", "\\sqsubseteq"); +defineSymbol(math, main, rel, "\u2292", "\\sqsupseteq"); +defineSymbol(math, main, rel, "\u2250", "\\doteq"); +defineSymbol(math, main, rel, "\u2322", "\\frown"); +defineSymbol(math, main, rel, "\u220b", "\\ni"); +defineSymbol(math, main, rel, "\u221d", "\\propto"); +defineSymbol(math, main, rel, "\u22a2", "\\vdash"); +defineSymbol(math, main, rel, "\u22a3", "\\dashv"); +defineSymbol(math, main, rel, "\u220b", "\\owns"); + +defineSymbol(math, main, rel, "\u220b", "\\widehat"); +defineSymbol(math, main, rel, "\u220b", "\\widetilde"); +defineSymbol(math, main, rel, "\u220b", "\\sp"); +defineSymbol(math, main, rel, "\u220b", "\\quad"); +// defineSymbol(math, main, rel, "\u220b", "\\cr"); +defineSymbol(math, main, rel, "\u220b", "\\\\sim"); +defineSymbol(math, main, rel, "\u220b", "\\nonumber"); +defineSymbol(math, main, rel, "\u220b", "\\dots"); +defineSymbol(math, main, rel, "\u220b", "\\cases"); +defineSymbol(math, main, rel, "\u220b", "\\mit"); +defineSymbol(math, main, rel, "\u220b", "\\smallskip"); +defineSymbol(math, main, rel, "\u220b", "\\slash"); +defineSymbol(math, main, rel, "\u220b", "\\d"); +defineSymbol(math, main, rel, "\u220b", "\\c"); +defineSymbol(math, main, rel, "\u220b", "\\b"); +defineSymbol(math, main, rel, "\u220b", "\\M"); +defineSymbol(math, main, rel, "\u220b", "\\S"); +defineSymbol(math, main, rel, "\u220b", "\\("); +defineSymbol(math, main, rel, "\u220b", "\\)"); +// defineSymbol(math, main, rel, "\u220b", "\\Comp"); +defineSymbol(math, main, rel, "\u220b", "\\thinspace"); +defineSymbol(math, main, rel, "\u220b", "\\hskip"); +defineSymbol(math, main, rel, "\u220b", "\\tt"); +defineSymbol(math, main, rel, "\u220b", "\\not"); +defineSymbol(math, main, rel, "\u220b", "\\boldmathr"); +defineSymbol(math, main, rel, "\u220b", "\\overleftarrow"); +defineSymbol(math, main, rel, "\u220b", "\\overrightarrow"); +defineSymbol(math, main, rel, "\u220b", "\\intf"); +defineSymbol(math, main, rel, "\u220b", "\\sf"); +defineSymbol(math, main, rel, "\u220b", "\\textbf"); +defineSymbol(math, main, rel, "\u220b", "\\L"); +defineSymbol(math, main, rel, "\u220b", "\\pii"); +defineSymbol(math, main, rel, "\u220b", "\\unitlength"); +defineSymbol(math, main, rel, "\u220b", "\\arowtor5linv"); +defineSymbol(math, main, rel, "\u220b", "\\hline"); +defineSymbol(math, main, rel, "\u220b", "\\mathbin"); +defineSymbol(math, main, rel, "\u220b", "\\nc"); +defineSymbol(math, main, rel, "\u220b", "\\underbrace"); +defineSymbol(math, main, rel, "\u220b", "\\o"); +defineSymbol(math, main, rel, "\u220b", "\\a"); +defineSymbol(math, main, rel, "\u220b", "\\b"); +defineSymbol(math, main, rel, "\u220b", "\\c"); +defineSymbol(math, main, rel, "\u220b", "\\d"); +defineSymbol(math, main, rel, "\u220b", "\\e"); +defineSymbol(math, main, rel, "\u220b", "\\f"); +defineSymbol(math, main, rel, "\u220b", "\\g"); +defineSymbol(math, main, rel, "\u220b", "\\h"); +defineSymbol(math, main, rel, "\u220b", "\\i"); +defineSymbol(math, main, rel, "\u220b", "\\j"); +defineSymbol(math, main, rel, "\u220b", "\\k"); +defineSymbol(math, main, rel, "\u220b", "\\l"); +defineSymbol(math, main, rel, "\u220b", "\\m"); +defineSymbol(math, main, rel, "\u220b", "\\n"); +defineSymbol(math, main, rel, "\u220b", "\\o"); +// defineSymbol(math, main, rel, "\u220b", "\\wedgee"); +defineSymbol(math, main, rel, "\u220b", "\\sb"); +defineSymbol(math, main, rel, "\u220b", "\\do"); +defineSymbol(math, main, rel, "\u220b", "\\em"); +// defineSymbol(math, main, rel, "\u220b", "\\diamonda"); + + +defineSymbol(math, main, rel, "\u220b", "\\dint"); +defineSymbol(math, main, rel, "\u220b", "\\intd"); + + +// Punctuation +defineSymbol(math, main, punct, "\u002e", "\\ldotp"); +defineSymbol(math, main, punct, "\u22c5", "\\cdotp"); + +// Misc Symbols +defineSymbol(math, main, textord, "\u0023", "\\#"); +defineSymbol(math, main, textord, "\u0026", "\\&"); +defineSymbol(math, main, textord, "\u2135", "\\aleph"); +defineSymbol(math, main, textord, "\u2200", "\\forall"); +defineSymbol(math, main, textord, "\u210f", "\\hbar"); +defineSymbol(math, main, textord, "\u2203", "\\eixsts"); +defineSymbol(math, main, textord, "\u2207", "\\nabla"); +defineSymbol(math, main, textord, "\u266d", "\\flat"); +defineSymbol(math, main, textord, "\u2113", "\\ell"); +defineSymbol(math, main, textord, "\u266e", "\\natural"); +defineSymbol(math, main, textord, "\u2663", "\\clubsuit"); +defineSymbol(math, main, textord, "\u2118", "\\wp"); +defineSymbol(math, main, textord, "\u266f", "\\sharp"); +defineSymbol(math, main, textord, "\u2662", "\\diamondsuit"); +defineSymbol(math, main, textord, "\u211c", "\\Re"); +defineSymbol(math, main, textord, "\u2661", "\\heartsuit"); +defineSymbol(math, main, textord, "\u2111", "\\Im"); +defineSymbol(math, main, textord, "\u2660", "\\spadesuit"); + +// Math and Text +defineSymbol(math, main, textord, "\u2020", "\\dag"); +defineSymbol(math, main, textord, "\u2021", "\\ddag"); + +// Large Delimiters +defineSymbol(math, main, close, "\u23b1", "\\rmoustache"); +defineSymbol(math, main, open, "\u23b0", "\\lmoustache"); +defineSymbol(math, main, close, "\u27ef", "\\rgroup"); +defineSymbol(math, main, open, "\u27ee", "\\lgroup"); + +// Binary Operators +defineSymbol(math, main, bin, "\u2213", "\\mp"); +defineSymbol(math, main, bin, "\u2296", "\\ominus"); +defineSymbol(math, main, bin, "\u228e", "\\uplus"); +defineSymbol(math, main, bin, "\u2293", "\\sqcap"); +defineSymbol(math, main, bin, "\u2217", "\\ast"); +defineSymbol(math, main, bin, "\u2294", "\\sqcup"); +defineSymbol(math, main, bin, "\u25ef", "\\bigcirc"); +defineSymbol(math, main, bin, "\u2219", "\\bullet"); +defineSymbol(math, main, bin, "\u2021", "\\ddagger"); +defineSymbol(math, main, bin, "\u2240", "\\wr"); +defineSymbol(math, main, bin, "\u2a3f", "\\amalg"); + +// Arrow Symbols +defineSymbol(math, main, rel, "\u27f5", "\\longleftarrow"); +defineSymbol(math, main, rel, "\u21d0", "\\Leftarrow"); +defineSymbol(math, main, rel, "\u27f8", "\\Longleftarrow"); +defineSymbol(math, main, rel, "\u27f6", "\\longrightarrow"); +defineSymbol(math, main, rel, "\u21d2", "\\Rightarrow"); +defineSymbol(math, main, rel, "\u27f9", "\\Longrightarrow"); +defineSymbol(math, main, rel, "\u2194", "\\leftrightarrow"); +defineSymbol(math, main, rel, "\u27f7", "\\longleftrightarrow"); +defineSymbol(math, main, rel, "\u21d4", "\\Leftrightarrow"); +defineSymbol(math, main, rel, "\u27fa", "\\Longleftrightarrow"); +defineSymbol(math, main, rel, "\u21a6", "\\mapsto"); +defineSymbol(math, main, rel, "\u27fc", "\\longmapsto"); +defineSymbol(math, main, rel, "\u2197", "\\nearrow"); +defineSymbol(math, main, rel, "\u21a9", "\\hookleftarrow"); +defineSymbol(math, main, rel, "\u21aa", "\\hookrightarrow"); +defineSymbol(math, main, rel, "\u2198", "\\searrow"); +defineSymbol(math, main, rel, "\u21bc", "\\leftharpoonup"); +defineSymbol(math, main, rel, "\u21c0", "\\rightharpoonup"); +defineSymbol(math, main, rel, "\u2199", "\\swarrow"); +defineSymbol(math, main, rel, "\u21bd", "\\leftharpoondown"); +defineSymbol(math, main, rel, "\u21c1", "\\rightharpoondown"); +defineSymbol(math, main, rel, "\u2196", "\\nwarrow"); +defineSymbol(math, main, rel, "\u21cc", "\\rightleftharpoons"); + +// AMS Negated Binary Relations +defineSymbol(math, ams, rel, "\u226e", "\\nless"); +defineSymbol(math, ams, rel, "\ue010", "\\nleqslant"); +defineSymbol(math, ams, rel, "\ue011", "\\nleqq"); +defineSymbol(math, ams, rel, "\u2a87", "\\lneq"); +defineSymbol(math, ams, rel, "\u2268", "\\lneqq"); +defineSymbol(math, ams, rel, "\ue00c", "\\lvertneqq"); +defineSymbol(math, ams, rel, "\u22e6", "\\lnsim"); +defineSymbol(math, ams, rel, "\u2a89", "\\lnapprox"); +defineSymbol(math, ams, rel, "\u2280", "\\nprec"); +defineSymbol(math, ams, rel, "\u22e0", "\\npreceq"); +defineSymbol(math, ams, rel, "\u22e8", "\\precnsim"); +defineSymbol(math, ams, rel, "\u2ab9", "\\precnapprox"); +defineSymbol(math, ams, rel, "\u2241", "\\nsim"); +defineSymbol(math, ams, rel, "\ue006", "\\nshortmid"); +defineSymbol(math, ams, rel, "\u2224", "\\nmid"); +defineSymbol(math, ams, rel, "\u22ac", "\\nvdash"); +defineSymbol(math, ams, rel, "\u22ad", "\\nvDash"); +defineSymbol(math, ams, rel, "\u22ea", "\\ntriangleleft"); +defineSymbol(math, ams, rel, "\u22ec", "\\ntrianglelefteq"); +defineSymbol(math, ams, rel, "\u228a", "\\subsetneq"); +defineSymbol(math, ams, rel, "\ue01a", "\\varsubsetneq"); +defineSymbol(math, ams, rel, "\u2acb", "\\subsetneqq"); +defineSymbol(math, ams, rel, "\ue017", "\\varsubsetneqq"); +defineSymbol(math, ams, rel, "\u226f", "\\ngtr"); +defineSymbol(math, ams, rel, "\ue00f", "\\ngeqslant"); +defineSymbol(math, ams, rel, "\ue00e", "\\ngeqq"); +defineSymbol(math, ams, rel, "\u2a88", "\\gneq"); +defineSymbol(math, ams, rel, "\u2269", "\\gneqq"); +defineSymbol(math, ams, rel, "\ue00d", "\\gvertneqq"); +defineSymbol(math, ams, rel, "\u22e7", "\\gnsim"); +defineSymbol(math, ams, rel, "\u2a8a", "\\gnapprox"); +defineSymbol(math, ams, rel, "\u2281", "\\nsucc"); +defineSymbol(math, ams, rel, "\u22e1", "\\nsucceq"); +defineSymbol(math, ams, rel, "\u22e9", "\\succnsim"); +defineSymbol(math, ams, rel, "\u2aba", "\\succnapprox"); +defineSymbol(math, ams, rel, "\u2246", "\\ncong"); +defineSymbol(math, ams, rel, "\ue007", "\\nshortparallel"); +defineSymbol(math, ams, rel, "\u2226", "\\nparallel"); +defineSymbol(math, ams, rel, "\u22af", "\\nVDash"); +defineSymbol(math, ams, rel, "\u22eb", "\\ntriangleright"); +defineSymbol(math, ams, rel, "\u22ed", "\\ntrianglerighteq"); +defineSymbol(math, ams, rel, "\ue018", "\\nsupseteqq"); +defineSymbol(math, ams, rel, "\u228b", "\\supsetneq"); +defineSymbol(math, ams, rel, "\ue01b", "\\varsupsetneq"); +defineSymbol(math, ams, rel, "\u2acc", "\\supsetneqq"); +defineSymbol(math, ams, rel, "\ue019", "\\varsupsetneqq"); +defineSymbol(math, ams, rel, "\u22ae", "\\nVdash"); +defineSymbol(math, ams, rel, "\u2ab5", "\\precneqq"); +defineSymbol(math, ams, rel, "\u2ab6", "\\succneqq"); +defineSymbol(math, ams, rel, "\ue016", "\\nsubseteqq"); +defineSymbol(math, ams, bin, "\u22b4", "\\unlhd"); +defineSymbol(math, ams, bin, "\u22b5", "\\unrhd"); + +// AMS Negated Arrows +defineSymbol(math, ams, rel, "\u219a", "\\nleftarrow"); +defineSymbol(math, ams, rel, "\u219b", "\\nrightarrow"); +defineSymbol(math, ams, rel, "\u21cd", "\\nLeftarrow"); +defineSymbol(math, ams, rel, "\u21cf", "\\nRightarrow"); +defineSymbol(math, ams, rel, "\u21ae", "\\nleftrightarrow"); +defineSymbol(math, ams, rel, "\u21ce", "\\nLeftrightarrow"); + +// AMS Misc +defineSymbol(math, ams, rel, "\u25b3", "\\vartriangle"); +defineSymbol(math, ams, textord, "\u210f", "\\hslash"); +defineSymbol(math, ams, textord, "\u25bd", "\\triangledown"); +defineSymbol(math, ams, textord, "\u25ca", "\\lozenge"); +defineSymbol(math, ams, textord, "\u24c8", "\\circledS"); +defineSymbol(math, ams, textord, "\u00ae", "\\circledR"); +defineSymbol(math, ams, textord, "\u2221", "\\measuredangle"); +defineSymbol(math, ams, textord, "\u2204", "\\nexists"); +defineSymbol(math, ams, textord, "\u2127", "\\mho"); +defineSymbol(math, ams, textord, "\u2132", "\\Finv"); +defineSymbol(math, ams, textord, "\u2141", "\\Game"); +defineSymbol(math, ams, textord, "\u006b", "\\Bbbk"); +defineSymbol(math, ams, textord, "\u2035", "\\backprime"); +defineSymbol(math, ams, textord, "\u25b2", "\\blacktriangle"); +defineSymbol(math, ams, textord, "\u25bc", "\\blacktriangledown"); +defineSymbol(math, ams, textord, "\u25a0", "\\blacksquare"); +defineSymbol(math, ams, textord, "\u29eb", "\\blacklozenge"); +defineSymbol(math, ams, textord, "\u2605", "\\bigstar"); +defineSymbol(math, ams, textord, "\u2222", "\\sphericalangle"); +defineSymbol(math, ams, textord, "\u2201", "\\complement"); +defineSymbol(math, ams, textord, "\u00f0", "\\eth"); +defineSymbol(math, ams, textord, "\u2571", "\\diagup"); +defineSymbol(math, ams, textord, "\u2572", "\\diagdown"); +defineSymbol(math, ams, textord, "\u25a1", "\\square"); +defineSymbol(math, ams, textord, "\u25a1", "\\Box"); +defineSymbol(math, ams, textord, "\u25ca", "\\Diamond"); +defineSymbol(math, ams, textord, "\u00a5", "\\yen"); +defineSymbol(math, ams, textord, "\u2713", "\\checkmark"); + +// AMS Hebrew +defineSymbol(math, ams, textord, "\u2136", "\\beth"); +defineSymbol(math, ams, textord, "\u2138", "\\daleth"); +defineSymbol(math, ams, textord, "\u2137", "\\gimel"); + +// AMS Greek +defineSymbol(math, ams, textord, "\u03dd", "\\digamma"); +defineSymbol(math, ams, textord, "\u03f0", "\\varkappa"); + +// AMS Delimiters +defineSymbol(math, ams, open, "\u250c", "\\ulcorner"); +defineSymbol(math, ams, close, "\u2510", "\\urcorner"); +defineSymbol(math, ams, open, "\u2514", "\\llcorner"); +defineSymbol(math, ams, close, "\u2518", "\\lrcorner"); + +// AMS Binary Relations +defineSymbol(math, ams, rel, "\u2266", "\\leqq"); +defineSymbol(math, ams, rel, "\u2a7d", "\\leqslant"); +defineSymbol(math, ams, rel, "\u2a95", "\\eqslantless"); +defineSymbol(math, ams, rel, "\u2272", "\\lesssim"); +defineSymbol(math, ams, rel, "\u2a85", "\\lessapprox"); +defineSymbol(math, ams, rel, "\u224a", "\\approxeq"); +defineSymbol(math, ams, bin, "\u22d6", "\\lessdot"); +defineSymbol(math, ams, rel, "\u22d8", "\\lll"); +defineSymbol(math, ams, rel, "\u2276", "\\lessgtr"); +defineSymbol(math, ams, rel, "\u22da", "\\lesseqgtr"); +defineSymbol(math, ams, rel, "\u2a8b", "\\lesseqqgtr"); +defineSymbol(math, ams, rel, "\u2251", "\\doteqdot"); +defineSymbol(math, ams, rel, "\u2253", "\\risingdotseq"); +defineSymbol(math, ams, rel, "\u2252", "\\fallingdotseq"); +defineSymbol(math, ams, rel, "\u223d", "\\backsim"); +defineSymbol(math, ams, rel, "\u22cd", "\\backsimeq"); +defineSymbol(math, ams, rel, "\u2ac5", "\\subseteqq"); +defineSymbol(math, ams, rel, "\u22d0", "\\Subset"); +defineSymbol(math, ams, rel, "\u228f", "\\sqsubset"); +defineSymbol(math, ams, rel, "\u227c", "\\preccurlyeq"); +defineSymbol(math, ams, rel, "\u22de", "\\curlyeqprec"); +defineSymbol(math, ams, rel, "\u227e", "\\precsim"); +defineSymbol(math, ams, rel, "\u2ab7", "\\precapprox"); +defineSymbol(math, ams, rel, "\u22b2", "\\vartriangleleft"); +defineSymbol(math, ams, rel, "\u22b4", "\\trianglelefteq"); +defineSymbol(math, ams, rel, "\u22a8", "\\vDash"); +defineSymbol(math, ams, rel, "\u22aa", "\\Vvdash"); +defineSymbol(math, ams, rel, "\u2323", "\\smallsmile"); +defineSymbol(math, ams, rel, "\u2322", "\\smallfrown"); +defineSymbol(math, ams, rel, "\u224f", "\\bumpeq"); +defineSymbol(math, ams, rel, "\u224e", "\\Bumpeq"); +defineSymbol(math, ams, rel, "\u2267", "\\geqq"); +defineSymbol(math, ams, rel, "\u2a7e", "\\geqslant"); +defineSymbol(math, ams, rel, "\u2a96", "\\eqslantgtr"); +defineSymbol(math, ams, rel, "\u2273", "\\gtrsim"); +defineSymbol(math, ams, rel, "\u2a86", "\\gtrapprox"); +defineSymbol(math, ams, bin, "\u22d7", "\\gtrdot"); +defineSymbol(math, ams, rel, "\u22d9", "\\ggg"); +defineSymbol(math, ams, rel, "\u2277", "\\gtrless"); +defineSymbol(math, ams, rel, "\u22db", "\\gtreqless"); +defineSymbol(math, ams, rel, "\u2a8c", "\\gtreqqless"); +defineSymbol(math, ams, rel, "\u2256", "\\eqcirc"); +defineSymbol(math, ams, rel, "\u2257", "\\circeq"); +defineSymbol(math, ams, rel, "\u225c", "\\triangleq"); +defineSymbol(math, ams, rel, "\u223c", "\\thicksim"); +defineSymbol(math, ams, rel, "\u2248", "\\thickapprox"); +defineSymbol(math, ams, rel, "\u2ac6", "\\supseteqq"); +defineSymbol(math, ams, rel, "\u22d1", "\\Supset"); +defineSymbol(math, ams, rel, "\u2290", "\\sqsupset"); +defineSymbol(math, ams, rel, "\u227d", "\\succcurlyeq"); +defineSymbol(math, ams, rel, "\u22df", "\\curlyeqsucc"); +defineSymbol(math, ams, rel, "\u227f", "\\succsim"); +defineSymbol(math, ams, rel, "\u2ab8", "\\succapprox"); +defineSymbol(math, ams, rel, "\u22b3", "\\vartriangleright"); +defineSymbol(math, ams, rel, "\u22b5", "\\trianglerighteq"); +defineSymbol(math, ams, rel, "\u22a9", "\\Vdash"); +defineSymbol(math, ams, rel, "\u2223", "\\shortmid"); +defineSymbol(math, ams, rel, "\u2225", "\\shortparallel"); +defineSymbol(math, ams, rel, "\u226c", "\\between"); +defineSymbol(math, ams, rel, "\u22d4", "\\pitchfork"); +defineSymbol(math, ams, rel, "\u221d", "\\varpropto"); +defineSymbol(math, ams, rel, "\u25c0", "\\blacktriangleleft"); +defineSymbol(math, ams, rel, "\u2234", "\\therefore"); +defineSymbol(math, ams, rel, "\u220d", "\\backepsilon"); +defineSymbol(math, ams, rel, "\u25b6", "\\blacktriangleright"); +defineSymbol(math, ams, rel, "\u2235", "\\because"); +defineSymbol(math, ams, rel, "\u22d8", "\\llless"); +defineSymbol(math, ams, rel, "\u22d9", "\\gggtr"); +defineSymbol(math, ams, bin, "\u22b2", "\\lhd"); +defineSymbol(math, ams, bin, "\u22b3", "\\rhd"); +defineSymbol(math, ams, rel, "\u2242", "\\eqsim"); +defineSymbol(math, main, rel, "\u22c8", "\\Join"); +defineSymbol(math, ams, rel, "\u2251", "\\Doteq"); + +// AMS Binary Operators +defineSymbol(math, ams, bin, "\u2214", "\\dotplus"); +defineSymbol(math, ams, bin, "\u2216", "\\smallsetminus"); +defineSymbol(math, ams, bin, "\u22d2", "\\Cap"); +defineSymbol(math, ams, bin, "\u22d3", "\\Cup"); +defineSymbol(math, ams, bin, "\u2a5e", "\\doublebarwedge"); +defineSymbol(math, ams, bin, "\u229f", "\\boxminus"); +defineSymbol(math, ams, bin, "\u229e", "\\boxplus"); +defineSymbol(math, ams, bin, "\u22c7", "\\divideontimes"); +defineSymbol(math, ams, bin, "\u22c9", "\\ltimes"); +defineSymbol(math, ams, bin, "\u22ca", "\\rtimes"); +defineSymbol(math, ams, bin, "\u22cb", "\\leftthreetimes"); +defineSymbol(math, ams, bin, "\u22cc", "\\rightthreetimes"); +defineSymbol(math, ams, bin, "\u22cf", "\\curlywedge"); +defineSymbol(math, ams, bin, "\u22ce", "\\curlyvee"); +defineSymbol(math, ams, bin, "\u229d", "\\circleddash"); +defineSymbol(math, ams, bin, "\u229b", "\\circledast"); +defineSymbol(math, ams, bin, "\u22c5", "\\centerdot"); +defineSymbol(math, ams, bin, "\u22ba", "\\intercal"); +defineSymbol(math, ams, bin, "\u22d2", "\\doublecap"); +defineSymbol(math, ams, bin, "\u22d3", "\\doublecup"); +defineSymbol(math, ams, bin, "\u22a0", "\\boxtimes"); + +// AMS Arrows +defineSymbol(math, ams, rel, "\u21e2", "\\dashrightarrow"); +defineSymbol(math, ams, rel, "\u21e0", "\\dashleftarrow"); +defineSymbol(math, ams, rel, "\u21c7", "\\leftleftarrows"); +defineSymbol(math, ams, rel, "\u21c6", "\\leftrightarrows"); +defineSymbol(math, ams, rel, "\u21da", "\\Lleftarrow"); +defineSymbol(math, ams, rel, "\u219e", "\\twoheadleftarrow"); +defineSymbol(math, ams, rel, "\u21a2", "\\leftarrowtail"); +defineSymbol(math, ams, rel, "\u21ab", "\\looparrowleft"); +defineSymbol(math, ams, rel, "\u21cb", "\\leftrightharpoons"); +defineSymbol(math, ams, rel, "\u21b6", "\\curvearrowleft"); +defineSymbol(math, ams, rel, "\u21ba", "\\circlearrowleft"); +defineSymbol(math, ams, rel, "\u21b0", "\\Lsh"); +defineSymbol(math, ams, rel, "\u21c8", "\\upuparrows"); +defineSymbol(math, ams, rel, "\u21bf", "\\upharpoonleft"); +defineSymbol(math, ams, rel, "\u21c3", "\\downharpoonleft"); +defineSymbol(math, ams, rel, "\u22b8", "\\multimap"); +defineSymbol(math, ams, rel, "\u21ad", "\\leftrightsquigarrow"); +defineSymbol(math, ams, rel, "\u21c9", "\\rightrightarrows"); +defineSymbol(math, ams, rel, "\u21c4", "\\rightleftarrows"); +defineSymbol(math, ams, rel, "\u21a0", "\\twoheadrightarrow"); +defineSymbol(math, ams, rel, "\u21a3", "\\rightarrowtail"); +defineSymbol(math, ams, rel, "\u21ac", "\\looparrowright"); +defineSymbol(math, ams, rel, "\u21b7", "\\curvearrowright"); +defineSymbol(math, ams, rel, "\u21bb", "\\circlearrowright"); +defineSymbol(math, ams, rel, "\u21b1", "\\Rsh"); +defineSymbol(math, ams, rel, "\u21ca", "\\downdownarrows"); +defineSymbol(math, ams, rel, "\u21be", "\\upharpoonright"); +defineSymbol(math, ams, rel, "\u21c2", "\\downharpoonright"); +defineSymbol(math, ams, rel, "\u21dd", "\\rightsquigarrow"); +defineSymbol(math, ams, rel, "\u21dd", "\\leadsto"); +defineSymbol(math, ams, rel, "\u21db", "\\Rrightarrow"); +defineSymbol(math, ams, rel, "\u21be", "\\restriction"); + +defineSymbol(math, main, textord, "\u2018", "`"); +defineSymbol(math, main, textord, "$", "\\$"); +defineSymbol(math, main, textord, "%", "\\%"); +defineSymbol(math, main, textord, "_", "\\_"); +defineSymbol(math, main, textord, "\u2220", "\\angle"); +defineSymbol(math, main, textord, "\u221e", "\\infty"); +defineSymbol(math, main, textord, "\u2032", "\\prime"); +defineSymbol(math, main, textord, "\u25b3", "\\triangle"); +defineSymbol(math, main, textord, "\u0393", "\\Gamma"); +defineSymbol(math, main, textord, "\u0394", "\\Delta"); +defineSymbol(math, main, textord, "\u0398", "\\Theta"); +defineSymbol(math, main, textord, "\u039b", "\\Lambda"); +defineSymbol(math, main, textord, "\u039e", "\\Xi"); +defineSymbol(math, main, textord, "\u03a0", "\\Pi"); +defineSymbol(math, main, textord, "\u03a3", "\\Sigma"); +defineSymbol(math, main, textord, "\u03a5", "\\Upsilon"); +defineSymbol(math, main, textord, "\u03a6", "\\Phi"); +defineSymbol(math, main, textord, "\u03a8", "\\Psi"); +defineSymbol(math, main, textord, "\u03a9", "\\Omega"); +defineSymbol(math, main, textord, "\u00ac", "\\neg"); +defineSymbol(math, main, textord, "\u00ac", "\\lnot"); +defineSymbol(math, main, textord, "\u22a4", "\\top"); +defineSymbol(math, main, textord, "\u22a5", "\\bot"); +defineSymbol(math, main, textord, "\u2205", "\\emptyset"); +defineSymbol(math, ams, textord, "\u2205", "\\varnothing"); +defineSymbol(math, main, mathord, "\u03b1", "\\alpha"); +defineSymbol(math, main, mathord, "\u03b2", "\\beta"); +defineSymbol(math, main, mathord, "\u03b3", "\\gamma"); +defineSymbol(math, main, mathord, "\u03b4", "\\delta"); +defineSymbol(math, main, mathord, "\u03f5", "\\epsilon"); +defineSymbol(math, main, mathord, "\u03b6", "\\zeta"); +defineSymbol(math, main, mathord, "\u03b7", "\\eta"); +defineSymbol(math, main, mathord, "\u03b8", "\\theta"); +defineSymbol(math, main, mathord, "\u03b9", "\\iota"); +defineSymbol(math, main, mathord, "\u03ba", "\\kappa"); +defineSymbol(math, main, mathord, "\u03bb", "\\lambda"); +defineSymbol(math, main, mathord, "\u03bc", "\\mu"); +defineSymbol(math, main, mathord, "\u03bd", "\\nu"); +defineSymbol(math, main, mathord, "\u03be", "\\xi"); +defineSymbol(math, main, mathord, "o", "\\omicron"); +defineSymbol(math, main, mathord, "\u03c0", "\\pi"); +defineSymbol(math, main, mathord, "\u03c1", "\\rho"); +defineSymbol(math, main, mathord, "\u03c3", "\\sigma"); +defineSymbol(math, main, mathord, "\u03c4", "\\tau"); +defineSymbol(math, main, mathord, "\u03c5", "\\upsilon"); +defineSymbol(math, main, mathord, "\u03d5", "\\phi"); +defineSymbol(math, main, mathord, "\u03c7", "\\chi"); +defineSymbol(math, main, mathord, "\u03c8", "\\psi"); +defineSymbol(math, main, mathord, "\u03c9", "\\omega"); +defineSymbol(math, main, mathord, "\u03b5", "\\varepsilon"); +defineSymbol(math, main, mathord, "\u03d1", "\\vartheta"); +defineSymbol(math, main, mathord, "\u03d6", "\\varpi"); +defineSymbol(math, main, mathord, "\u03f1", "\\varrho"); +defineSymbol(math, main, mathord, "\u03c2", "\\varsigma"); +defineSymbol(math, main, mathord, "\u03c6", "\\varphi"); +defineSymbol(math, main, bin, "\u2217", "*"); +defineSymbol(math, main, bin, "+", "+"); +defineSymbol(math, main, bin, "\u2212", "-"); +defineSymbol(math, main, bin, "\u22c5", "\\cdot"); +defineSymbol(math, main, bin, "\u2218", "\\circ"); +defineSymbol(math, main, bin, "\u00f7", "\\div"); +defineSymbol(math, main, bin, "\u00b1", "\\pm"); +defineSymbol(math, main, bin, "\u00d7", "\\times"); +defineSymbol(math, main, bin, "\u2229", "\\cap"); +defineSymbol(math, main, bin, "\u222a", "\\cup"); +defineSymbol(math, main, bin, "\u2216", "\\setminus"); +defineSymbol(math, main, bin, "\u2227", "\\land"); +defineSymbol(math, main, bin, "\u2228", "\\lor"); +defineSymbol(math, main, bin, "\u2227", "\\wedge"); +defineSymbol(math, main, bin, "\u2228", "\\vee"); +defineSymbol(math, main, textord, "\u221a", "\\surd"); +defineSymbol(math, main, open, "(", "("); +defineSymbol(math, main, open, "[", "["); +defineSymbol(math, main, open, "\u27e8", "\\langle"); +defineSymbol(math, main, open, "\u2223", "\\lvert"); +defineSymbol(math, main, open, "\u2225", "\\lVert"); +defineSymbol(math, main, close, ")", ")"); +defineSymbol(math, main, close, "]", "]"); +defineSymbol(math, main, close, "?", "?"); +defineSymbol(math, main, close, "!", "!"); +defineSymbol(math, main, close, "\u27e9", "\\rangle"); +defineSymbol(math, main, close, "\u2223", "\\rvert"); +defineSymbol(math, main, close, "\u2225", "\\rVert"); +defineSymbol(math, main, rel, "=", "="); +defineSymbol(math, main, rel, "<", "<"); +defineSymbol(math, main, rel, ">", ">"); +defineSymbol(math, main, rel, ":", ":"); +defineSymbol(math, main, rel, "\u2248", "\\approx"); +defineSymbol(math, main, rel, "\u2245", "\\cong"); +defineSymbol(math, main, rel, "\u2265", "\\ge"); +defineSymbol(math, main, rel, "\u2265", "\\geq"); +defineSymbol(math, main, rel, "\u2190", "\\gets"); +defineSymbol(math, main, rel, ">", "\\gt"); +defineSymbol(math, main, rel, "\u2208", "\\in"); +defineSymbol(math, main, rel, "\u2209", "\\notin"); +defineSymbol(math, main, rel, "\u2282", "\\subset"); +defineSymbol(math, main, rel, "\u2283", "\\supset"); +defineSymbol(math, main, rel, "\u2286", "\\subseteq"); +defineSymbol(math, main, rel, "\u2287", "\\supseteq"); +defineSymbol(math, ams, rel, "\u2288", "\\nsubseteq"); +defineSymbol(math, ams, rel, "\u2289", "\\nsupseteq"); +defineSymbol(math, main, rel, "\u22a8", "\\models"); +defineSymbol(math, main, rel, "\u2190", "\\leftarrow"); +defineSymbol(math, main, rel, "\u2264", "\\le"); +defineSymbol(math, main, rel, "\u2264", "\\leq"); +defineSymbol(math, main, rel, "<", "\\lt"); +defineSymbol(math, main, rel, "\u2260", "\\ne"); +defineSymbol(math, main, rel, "\u2260", "\\neq"); +defineSymbol(math, main, rel, "\u2192", "\\rightarrow"); +defineSymbol(math, main, rel, "\u2192", "\\to"); +defineSymbol(math, ams, rel, "\u2271", "\\ngeq"); +defineSymbol(math, ams, rel, "\u2270", "\\nleq"); +defineSymbol(math, main, spacing, null, "\\!"); +defineSymbol(math, main, spacing, "\u00a0", "\\ "); +defineSymbol(math, main, spacing, "\u00a0", "~"); +defineSymbol(math, main, spacing, null, "\\,"); +defineSymbol(math, main, spacing, null, "\\:"); +defineSymbol(math, main, spacing, null, "\\;"); +defineSymbol(math, main, spacing, null, "\\enspace"); +defineSymbol(math, main, spacing, null, "\\qquad"); +defineSymbol(math, main, spacing, null, "\\quad"); +defineSymbol(math, main, spacing, "\u00a0", "\\space"); +defineSymbol(math, main, punct, ",", ","); +defineSymbol(math, main, punct, ";", ";"); +defineSymbol(math, main, punct, ":", "\\colon"); +defineSymbol(math, ams, bin, "\u22bc", "\\barwedge"); +defineSymbol(math, ams, bin, "\u22bb", "\\veebar"); +defineSymbol(math, main, bin, "\u2299", "\\odot"); +defineSymbol(math, main, bin, "\u2295", "\\oplus"); +defineSymbol(math, main, bin, "\u2297", "\\otimes"); +defineSymbol(math, main, textord, "\u2202", "\\partial"); +defineSymbol(math, main, bin, "\u2298", "\\oslash"); +defineSymbol(math, ams, bin, "\u229a", "\\circledcirc"); +defineSymbol(math, ams, bin, "\u22a1", "\\boxdot"); +defineSymbol(math, main, bin, "\u25b3", "\\bigtriangleup"); +defineSymbol(math, main, bin, "\u25bd", "\\bigtriangledown"); +defineSymbol(math, main, bin, "\u2020", "\\dagger"); +defineSymbol(math, main, bin, "\u22c4", "\\diamond"); +defineSymbol(math, main, bin, "\u22c6", "\\star"); +defineSymbol(math, main, bin, "\u25c3", "\\triangleleft"); +defineSymbol(math, main, bin, "\u25b9", "\\triangleright"); +defineSymbol(math, main, open, "{", "\\{"); +defineSymbol(math, main, close, "}", "\\}"); +defineSymbol(math, main, open, "{", "\\lbrace"); +defineSymbol(math, main, close, "}", "\\rbrace"); +defineSymbol(math, main, open, "[", "\\lbrack"); +defineSymbol(math, main, close, "]", "\\rbrack"); +defineSymbol(math, main, open, "\u230a", "\\lfloor"); +defineSymbol(math, main, close, "\u230b", "\\rfloor"); +defineSymbol(math, main, open, "\u2308", "\\lceil"); +defineSymbol(math, main, close, "\u2309", "\\rceil"); +defineSymbol(math, main, textord, "\\", "\\backslash"); +defineSymbol(math, main, textord, "\u2223", "|"); +defineSymbol(math, main, textord, "\u2223", "\\vert"); +defineSymbol(math, main, textord, "\u2225", "\\|"); +defineSymbol(math, main, textord, "\u2225", "\\Vert"); +defineSymbol(math, main, rel, "\u2191", "\\uparrow"); +defineSymbol(math, main, rel, "\u21d1", "\\Uparrow"); +defineSymbol(math, main, rel, "\u2193", "\\downarrow"); +defineSymbol(math, main, rel, "\u21d3", "\\Downarrow"); +defineSymbol(math, main, rel, "\u2195", "\\updownarrow"); +defineSymbol(math, main, rel, "\u21d5", "\\Updownarrow"); +defineSymbol(math, math, op, "\u2210", "\\coprod"); +defineSymbol(math, math, op, "\u22c1", "\\bigvee"); +defineSymbol(math, math, op, "\u22c0", "\\bigwedge"); +defineSymbol(math, math, op, "\u2a04", "\\biguplus"); +defineSymbol(math, math, op, "\u22c2", "\\bigcap"); +defineSymbol(math, math, op, "\u22c3", "\\bigcup"); +defineSymbol(math, math, op, "\u222b", "\\int"); +defineSymbol(math, math, op, "\u222b", "\\intop"); +defineSymbol(math, math, op, "\u222c", "\\iint"); +defineSymbol(math, math, op, "\u222d", "\\iiint"); +defineSymbol(math, math, op, "\u220f", "\\prod"); +defineSymbol(math, math, op, "\u2211", "\\sum"); + +defineSymbol(math, math, op, "\u2a02", "\\bigotimes"); +defineSymbol(math, math, op, "\u2a01", "\\bigoplus"); +defineSymbol(math, math, op, "\u2a00", "\\bigodot"); +defineSymbol(math, math, op, "\u222e", "\\oint"); +defineSymbol(math, math, op, "\u2a06", "\\bigsqcup"); +defineSymbol(math, math, op, "\u222b", "\\smallint"); +defineSymbol(math, main, inner, "\u2026", "\\ldots"); +defineSymbol(math, main, inner, "\u22ef", "\\cdots"); +defineSymbol(math, main, inner, "\u22f1", "\\ddots"); +defineSymbol(math, main, textord, "\u22ee", "\\vdots"); +defineSymbol(math, main, accent, "\u00b4", "\\acute"); +defineSymbol(math, main, accent, "\u0060", "\\grave"); +defineSymbol(math, main, accent, "\u00a8", "\\ddot"); +defineSymbol(math, main, accent, "\u007e", "\\tilde"); +defineSymbol(math, main, accent, "\u00af", "\\bar"); +defineSymbol(math, main, accent, "\u02d8", "\\breve"); +defineSymbol(math, main, accent, "\u02c7", "\\check"); +defineSymbol(math, main, accent, "\u005e", "\\hat"); +defineSymbol(math, main, accent, "\u20d7", "\\vec"); +defineSymbol(math, main, accent, "\u02d9", "\\dot"); +defineSymbol(math, main, mathord, "\u0131", "\\imath"); +defineSymbol(math, main, mathord, "\u0237", "\\jmath"); + + +defineSymbol(text, main, spacing, "\u00a0", "\\ "); +defineSymbol(text, main, spacing, "\u00a0", " "); +defineSymbol(text, main, spacing, "\u00a0", "~"); + +// There are lots of symbols which are the same, so we add them in afterwards. +var i; +var ch; + +// All of these are textords in math mode +var mathTextSymbols = "0123456789/@.\""; +for (i = 0; i < mathTextSymbols.length; i++) { + ch = mathTextSymbols.charAt(i); + defineSymbol(math, main, textord, ch, ch); +} + +// All of these are textords in text mode +var textSymbols = "0123456789`!@*()-=+[]'\";:?/.,"; +for (i = 0; i < textSymbols.length; i++) { + ch = textSymbols.charAt(i); + defineSymbol(text, main, textord, ch, ch); +} + +// All of these are textords in text mode, and mathords in math mode +var letters = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"; +for (i = 0; i < letters.length; i++) { + ch = letters.charAt(i); + defineSymbol(math, main, mathord, ch, ch); + defineSymbol(text, main, textord, ch, ch); +} diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/utils.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/utils.js new file mode 100644 index 00000000..f9e57cc6 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/katex/src/utils.js @@ -0,0 +1,106 @@ +/** + * This file contains a list of utility functions which are useful in other + * files. + */ + +/** + * Provide an `indexOf` function which works in IE8, but defers to native if + * possible. + */ +var nativeIndexOf = Array.prototype.indexOf; +var indexOf = function(list, elem) { + if (list == null) { + return -1; + } + if (nativeIndexOf && list.indexOf === nativeIndexOf) { + return list.indexOf(elem); + } + var i = 0; + var l = list.length; + for (; i < l; i++) { + if (list[i] === elem) { + return i; + } + } + return -1; +}; + +/** + * Return whether an element is contained in a list + */ +var contains = function(list, elem) { + return indexOf(list, elem) !== -1; +}; + +/** + * Provide a default value if a setting is undefined + */ +var deflt = function(setting, defaultIfUndefined) { + return setting === undefined ? defaultIfUndefined : setting; +}; + +// hyphenate and escape adapted from Facebook's React under Apache 2 license + +var uppercase = /([A-Z])/g; +var hyphenate = function(str) { + return str.replace(uppercase, "-$1").toLowerCase(); +}; + +var ESCAPE_LOOKUP = { + "&": "&", + ">": ">", + "<": "<", + "\"": """, + "'": "'", +}; + +var ESCAPE_REGEX = /[&><"']/g; + +function escaper(match) { + return ESCAPE_LOOKUP[match]; +} + +/** + * Escapes text to prevent scripting attacks. + * + * @param {*} text Text value to escape. + * @return {string} An escaped string. + */ +function escape(text) { + return ("" + text).replace(ESCAPE_REGEX, escaper); +} + +/** + * A function to set the text content of a DOM element in all supported + * browsers. Note that we don't define this if there is no document. + */ +var setTextContent; +if (typeof document !== "undefined") { + var testNode = document.createElement("span"); + if ("textContent" in testNode) { + setTextContent = function(node, text) { + node.textContent = text; + }; + } else { + setTextContent = function(node, text) { + node.innerText = text; + }; + } +} + +/** + * A function to clear a node. + */ +function clearNode(node) { + setTextContent(node, ""); +} + +module.exports = { + contains: contains, + deflt: deflt, + escape: escape, + hyphenate: hyphenate, + indexOf: indexOf, + setTextContent: setTextContent, + clearNode: clearNode, +}; diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/README.md b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/README.md new file mode 100644 index 00000000..69083d16 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/README.md @@ -0,0 +1 @@ +# match-at [![Build Status](https://travis-ci.org/spicyj/match-at.svg?branch=master)](https://travis-ci.org/spicyj/match-at) diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/lib/matchAt.js b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/lib/matchAt.js new file mode 100644 index 00000000..6e6b57e2 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/lib/matchAt.js @@ -0,0 +1,42 @@ +/** @flow */ + +"use strict"; + +function getRelocatable(re) { + // In the future, this could use a WeakMap instead of an expando. + if (!re.__matchAtRelocatable) { + // Disjunctions are the lowest-precedence operator, so we can make any + // pattern match the empty string by appending `|()` to it: + // https://people.mozilla.org/~jorendorff/es6-draft.html#sec-patterns + var source = re.source + "|()"; + + // We always make the new regex global. + var flags = "g" + (re.ignoreCase ? "i" : "") + (re.multiline ? "m" : "") + (re.unicode ? "u" : "") + // sticky (/.../y) doesn't make sense in conjunction with our relocation + // logic, so we ignore it here. + ; + + re.__matchAtRelocatable = new RegExp(source, flags); + } + return re.__matchAtRelocatable; +} + +function matchAt(re, str, pos) { + if (re.global || re.sticky) { + throw new Error("matchAt(...): Only non-global regexes are supported"); + } + var reloc = getRelocatable(re); + reloc.lastIndex = pos; + var match = reloc.exec(str); + // Last capturing group is our sentinel that indicates whether the regex + // matched at the given location. + if (match[match.length - 1] == null) { + // Original regex matched. + match.length = match.length - 1; + return match; + } else { + return null; + } +} + +module.exports = matchAt; \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/package.json b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/package.json new file mode 100644 index 00000000..3e9e997d --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/third_party/match-at/package.json @@ -0,0 +1,54 @@ +{ + "name": "match-at", + "version": "0.1.0", + "description": "Relocatable regular expressions.", + "repository": { + "type": "git", + "url": "https://github.com/spicyj/match-at" + }, + "main": "lib/matchAt.js", + "files": [ + "lib/" + ], + "devDependencies": { + "babel": "^4.7.16", + "jest-cli": "^0.4.0", + "react-tools": "^0.13.1" + }, + "jest": { + "scriptPreprocessor": "/jestSupport/preprocessor.js", + "unmockedModulePathPatterns": [ + "" + ] + }, + "scripts": { + "prepublish": "babel -d lib/ src/", + "test": "jest" + }, + "gitHead": "4197daff69720734c72ba3321ed68a41c0527fb2", + "bugs": { + "url": "https://github.com/spicyj/match-at/issues" + }, + "homepage": "https://github.com/spicyj/match-at", + "_id": "match-at@0.1.0", + "_shasum": "f561e7709ff9a105b85cc62c6b8ee7c15bf24f31", + "_from": "match-at@", + "_npmVersion": "2.2.0", + "_nodeVersion": "0.10.35", + "_npmUser": { + "name": "spicyj", + "email": "ben@benalpert.com" + }, + "maintainers": [ + { + "name": "spicyj", + "email": "ben@benalpert.com" + } + ], + "dist": { + "shasum": "f561e7709ff9a105b85cc62c6b8ee7c15bf24f31", + "tarball": "https://registry.npmjs.org/match-at/-/match-at-0.1.0.tgz" + }, + "directories": {}, + "_resolved": "https://registry.npmjs.org/match-at/-/match-at-0.1.0.tgz" +} diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/tokenize_latex.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/tokenize_latex.py new file mode 100644 index 00000000..a01fab6d --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/tokenize_latex/tokenize_latex.py @@ -0,0 +1,98 @@ +# taken and modified from https://github.com/harvardnlp/im2markup +# tokenize latex formulas +import sys +import os +import re +import argparse +import subprocess +import shutil +from threading import Timer +from datetime import datetime + + +def run_cmd(cmd, timeout_sec=30): + proc = subprocess.Popen(cmd, shell=True) + kill_proc = lambda p: p.kill() + timer = Timer(timeout_sec, kill_proc, [proc]) + try: + timer.start() + stdout,stderr = proc.communicate() + finally: + timer.cancel() + +def tokenize_latex(latex_code, latex_type="", middle_file=""): + if not latex_code: + return False, latex_code + if not latex_type: + latex_type = "tabular" if "tabular" in latex_code else "formula" + if not middle_file: + middle_file = "out-" + datetime.now().strftime('%Y_%m_%d_%H_%M_%S') + ".txt" + temp_file = middle_file + '.tmp' + + if latex_type == "formula": + with open(temp_file, 'w') as f: + prepre = latex_code + # replace split, align with aligned + prepre = re.sub(r'\\begin{(split|align|alignedat|alignat|eqnarray)\*?}(.+?)\\end{\1\*?}', r'\\begin{aligned}\2\\end{aligned}', prepre, flags=re.S) + prepre = re.sub(r'\\begin{(smallmatrix)\*?}(.+?)\\end{\1\*?}', r'\\begin{matrix}\2\\end{matrix}', prepre, flags=re.S) + f.write(prepre) + + cmd = r"cat %s | node %s %s > %s " % (temp_file, os.path.join(os.path.dirname(__file__), 'preprocess_formula.js'), 'normalize', middle_file) + ret = subprocess.call(cmd, shell=True) + os.remove(temp_file) + if ret != 0: + return False, latex_code + + operators = '\s?'.join('|'.join(['arccos', 'arcsin', 'arctan', 'arg', 'cos', 'cosh', 'cot', 'coth', 'csc', 'deg', 'det', 'dim', 'exp', 'gcd', 'hom', 'inf', + 'injlim', 'ker', 'lg', 'lim', 'liminf', 'limsup', 'ln', 'log', 'max', 'min', 'Pr', 'projlim', 'sec', 'sin', 'sinh', 'sup', 'tan', 'tanh'])) + ops = re.compile(r'\\operatorname {(%s)}' % operators) + with open(middle_file, 'r') as fin: + for line in fin: + tokens = line.strip().split() + tokens_out = [] + for token in tokens: + tokens_out.append(token) + post = ' '.join(tokens_out) + # use \sin instead of \operatorname{sin} + names = ['\\'+x.replace(' ', '') for x in re.findall(ops, post)] + post = re.sub(ops, lambda match: str(names.pop(0)), post).replace(r'\\ \end{array}', r'\end{array}') + os.remove(middle_file) + return True, post + + elif latex_type == "tabular": + latex_code = latex_code.replace("\\\\%", "\\\\ %") + latex_code = latex_code.replace("\%", "") + latex_code = latex_code.split('%')[0] + latex_code = latex_code.replace("", "\%") + if not "\\end{tabular}" in latex_code: + latex_code += "\\end{tabular}" + with open(middle_file, 'w') as f: + f.write(latex_code.replace('\r', ' ').replace('\n', ' ')) + cmd = "perl -pe 's|hskip(.*?)(cm\\|in\\|pt\\|mm\\|em)|hspace{\\1\\2}|g' %s > %s"%(middle_file, temp_file) + ret = subprocess.call(cmd, shell=True) + if ret != 0: + return False, latex_code + os.remove(middle_file) + cmd = r"cat %s | node %s %s > %s " % (temp_file, os.path.join(os.path.dirname(__file__), 'preprocess_tabular.js'), 'tokenize', middle_file) + ret = subprocess.call(cmd, shell=True) + os.remove(temp_file) + if ret != 0: + return False, latex_code + with open(middle_file, 'r') as fin: + for line in fin: + tokens = line.strip().split() + tokens_out = [] + for token in tokens: + tokens_out.append(token) + post = ' '.join(tokens_out) + os.remove(middle_file) + return True, post + else: + print(f"latex type{latex_type} unrecognized.") + return False, latex_code + +if __name__ == '__main__': + latex_code = open("2.txt", 'r').read().replace('\r', ' ') + print("=>", latex_code) + new_code = tokenize_latex(latex_code) + print("=>", new_code) \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/visual_matcher.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/visual_matcher.py new file mode 100644 index 00000000..6945312d --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/modules/visual_matcher.py @@ -0,0 +1,191 @@ +import time +import numpy as np +from PIL import Image +from scipy.spatial.distance import cdist +from scipy.optimize import linear_sum_assignment + + +class SimpleAffineTransform: + """ + simple affine transform, only translation and scale. + """ + def __init__(self, translation=(0, 0), scale=1.0): + self.translation = np.array(translation) + self.scale = scale + + def estimate(self, src, dst): + src_center = np.mean(src, axis=0) + dst_center = np.mean(dst, axis=0) + self.translation = dst_center - src_center + + src_dists = np.linalg.norm(src - src_center, axis=1) + dst_dists = np.linalg.norm(dst - dst_center, axis=1) + self.scale = np.mean(dst_dists) / (np.mean(src_dists) + 1e-10) + + def inverse(self): + inverse_transform = AffineTransform(-self.translation, 1.0/self.scale) + return inverse_transform + + def __call__(self, coords): + return self.scale * (coords - np.mean(coords, axis=0)) + np.mean(coords, axis=0) + self.translation + + def residuals(self, src, dst): + return np.sqrt(np.sum((self(src) - dst) ** 2, axis=1)) + + +def norm_coords(x, left, right): + if x < left: + return left + if x > right: + return right + return x + +def norm_same_token(token): + special_map = { + "\\cdot": ".", + "\\mid": "|", + "\\to": "\\rightarrow", + "\\top": "T", + "\\Tilde": "\\tilde", + "\\cdots": "\\dots", + "\\prime": "'", + "\\ast": "*", + "\\left<": "\\langle", + "\\right>": "\\rangle" + } + if token in special_map.keys(): + token = special_map[token] + if token.startswith('\\left') or token.startswith('\\right'): + token = token.replace("\\left", "").replace("\\right", "") + if token.startswith('\\big') or token.startswith('\\Big'): + if "\\" in token[4:]: + token = "\\"+token[4:].split("\\")[-1] + else: + token = token[-1] + + if token in ['\\leq', '\\geq']: + return token[0:-1] + if token in ['\\lVert', '\\rVert', '\\Vert']: + return '\\|' + if token in ['\\lvert', '\\rvert', '\\vert']: + return '|' + if token.endswith("rightarrow"): + return "\\rightarrow" + if token.endswith("leftarrow"): + return "\\leftarrow" + if token.startswith('\\wide'): + return token.replace("wide", "") + if token.startswith('\\var'): + return token.replace("\\var", "") + return token + + +class HungarianMatcher: + def __init__( + self, + cost_token: float = 1, + cost_position: float = 0.05, + cost_order: float = 0.15, + ): + self.cost_token = cost_token + self.cost_position = cost_position + self.cost_order = cost_order + self.cost = {} + + def calculate_token_cost_old(self, box_gt, box_pred): + token_cost = np.ones((len(box_gt), len(box_pred))) + for i in range(token_cost.shape[0]): + box1 = box_gt[i] + for j in range(token_cost.shape[1]): + box2 = box_pred[j] + if box1['token'] == box2['token']: + token_cost[i, j] = 0 + elif norm_same_token(box1['token']) == norm_same_token(box2['token']): + token_cost[i, j] = 0.05 + return np.array(token_cost) + + def calculate_token_cost(self, box_gt, box_pred): + token2id = {} + for data in box_gt+box_pred: + if data['token'] not in token2id: + token2id[data['token']] = len(token2id) + num_classes = len(token2id) + + token2id_norm = {} + for data in box_gt+box_pred: + if norm_same_token(data['token']) not in token2id_norm: + token2id_norm[norm_same_token(data['token'])] = len(token2id_norm) + num_classes_norm = len(token2id_norm) + + gt_token_array = [] + norm_gt_token_array = [] + for data in box_gt: + gt_token_array.append(token2id[data['token']]) + norm_gt_token_array.append(token2id_norm[norm_same_token(data['token'])]) + + pred_token_logits = [] + norm_pred_token_logits = [] + for data in box_pred: + logits = [0] * num_classes + logits[token2id[data['token']]] = 1 + pred_token_logits.append(logits) + + logits_norm = [0] * num_classes_norm + logits_norm[token2id_norm[norm_same_token(data['token'])]] = 1 + norm_pred_token_logits.append(logits_norm) + + gt_token_array = np.array(gt_token_array) + pred_token_logits = np.array(pred_token_logits) + + norm_gt_token_array = np.array(norm_gt_token_array) + norm_pred_token_logits = np.array(norm_pred_token_logits) + + token_cost = 1.0 - pred_token_logits[:, gt_token_array] + norm_token_cost = 1.0 - norm_pred_token_logits[:, norm_gt_token_array] + + token_cost[np.logical_and(token_cost==1, norm_token_cost==0)] = 0.05 + return token_cost.T + + + def box2array(self, box_list, size): + W, H = size + box_array = [] + for box in box_list: + x_min, y_min, x_max, y_max = box['bbox'] + box_array.append([x_min/W, y_min/H, x_max/W, y_max/H]) + return np.array(box_array) + + def order2array(self, box_list): + order_array = [] + for idx, box in enumerate(box_list): + order_array.append([idx / len(box_list)]) + return np.array(order_array) + + def calculate_l1_cost(self, gt_array, pred_array): + scale = gt_array.shape[-1] + l1_cost = cdist(gt_array, pred_array, 'minkowski', p=1) + return l1_cost / scale + + def __call__(self, box_gt, box_pred, gt_size, pred_size): + aa = time.time() + gt_box_array = self.box2array(box_gt, gt_size) + pred_box_array = self.box2array(box_pred, pred_size) + gt_order_array = self.order2array(box_gt) + pred_order_array = self.order2array(box_pred) + + token_cost = self.calculate_token_cost(box_gt, box_pred) + position_cost = self.calculate_l1_cost(gt_box_array, pred_box_array) + order_cost = self.calculate_l1_cost(gt_order_array, pred_order_array) + + self.cost["token"] = token_cost + self.cost["position"] = position_cost + self.cost["order"] = order_cost + + cost = self.cost_token * token_cost + self.cost_position * position_cost + self.cost_order * order_cost + cost[np.isnan(cost) | np.isinf(cost)] = 100 + indexes = linear_sum_assignment(cost) + matched_idxes = [] + for a, b in zip(*indexes): + matched_idxes.append((a, b)) + + return matched_idxes \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/requirements.txt b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/requirements.txt new file mode 100644 index 00000000..96b8b4b6 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm/requirements.txt @@ -0,0 +1,5 @@ +tqdm +matplotlib +numpy<2.0.0 +scikit-image<=0.20.0 +gradio==4.16.0 # optional \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm_metric.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm_metric.py new file mode 100644 index 00000000..c4eaf9d9 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/cdm_metric.py @@ -0,0 +1,216 @@ +import sys +sys.path.append("/mnt/petrelfs/ouyanglinke/Omnidocbench_updata/OmniDocBench/metrics/") +import os +import json +import shutil +import numpy as np +from PIL import Image, ImageDraw +from skimage.measure import ransac +from .cdm.modules.latex2bbox_color import latex2bbox_color +from .cdm.modules.visual_matcher import HungarianMatcher, SimpleAffineTransform + +class CDM: + def __init__(self, output_root="./result"): + """ + Initialize the LaTeX formula evaluator. + + Args: + output_root (str): Root directory for saving intermediate and final results + """ + self.output_root = output_root + self.matcher = HungarianMatcher() + + # Evaluation parameters + self.max_iter = 3 + self.min_samples = 3 + self.residual_threshold = 25 + self.max_trials = 50 + + @staticmethod + def gen_color_list(num=10, gap=15): + """Generate a list of distinct colors for visualization""" + num += 1 + single_num = 255 // gap + 1 + max_num = single_num ** 3 + num = min(num, max_num) + color_list = [] + for idx in range(num): + R = idx // single_num**2 + GB = idx % single_num**2 + G = GB // single_num + B = GB % single_num + color_list.append((R*gap, G*gap, B*gap)) + return color_list[1:] + + @staticmethod + def update_inliers(ori_inliers, sub_inliers): + """Update inliers status based on new RANSAC results""" + inliers = np.copy(ori_inliers) + sub_idx = -1 + for idx in range(len(ori_inliers)): + if ori_inliers[idx] == False: + sub_idx += 1 + if sub_inliers[sub_idx] == True: + inliers[idx] = True + return inliers + + def _prepare_directories(self, img_id): + """Create necessary directories for output""" + os.makedirs(os.path.join(self.output_root, 'gt', 'bbox'), exist_ok=True) + os.makedirs(os.path.join(self.output_root, 'gt', 'vis'), exist_ok=True) + os.makedirs(os.path.join(self.output_root, 'pred', 'bbox'), exist_ok=True) + os.makedirs(os.path.join(self.output_root, 'pred', 'vis'), exist_ok=True) + os.makedirs(os.path.join(self.output_root, 'vis_match'), exist_ok=True) + + def _generate_bboxes(self, gt_latex, pred_latex, img_id): + """Generate bounding boxes for both GT and prediction""" + total_color_list = self.gen_color_list(num=5800) + + for subset, latex in zip(['gt', 'pred'], [gt_latex, pred_latex]): + output_path = os.path.join(self.output_root, subset) + temp_dir = os.path.join(self.output_root, f'temp_dir_{subset}_{img_id}') + os.makedirs(temp_dir, exist_ok=True) + + latex2bbox_color((latex, img_id, output_path, temp_dir, total_color_list)) + shutil.rmtree(temp_dir) + + def _load_bboxes(self, img_id): + """Load generated bounding boxes from files""" + gt_box_path = os.path.join(self.output_root, 'gt', 'bbox', f"{img_id}.jsonl") + pred_box_path = os.path.join(self.output_root, 'pred', 'bbox', f"{img_id}.jsonl") + + with open(gt_box_path, 'r') as f: + box_gt = [json.loads(line) for line in f if json.loads(line)['bbox']] + + with open(pred_box_path, 'r') as f: + box_pred = [json.loads(line) for line in f if json.loads(line)['bbox']] + + return box_gt, box_pred + + def _load_images(self, img_id): + """Load visualization images""" + gt_img_path = os.path.join(self.output_root, 'gt', 'vis', f"{img_id}_base.png") + pred_img_path = os.path.join(self.output_root, 'pred', 'vis', f"{img_id}_base.png") + return Image.open(gt_img_path), Image.open(pred_img_path) + + def _match_boxes(self, box_gt, box_pred, img_gt, img_pred): + """Perform box matching using Hungarian algorithm and RANSAC""" + matched_idxes = self.matcher(box_gt, box_pred, img_gt.size, img_pred.size) + + # Prepare matching points + src, dst = [], [] + for (idx1, idx2) in matched_idxes: + x1min, y1min, x1max, y1max = box_gt[idx1]['bbox'] + x2min, y2min, x2max, y2max = box_pred[idx2]['bbox'] + src.append([float((y1min+y1max)/2), float((x1min+x1max)/2)]) + dst.append([float((y2min+y2max)/2), float((x2min+x2max)/2)]) + + src = np.array(src) + dst = np.array(dst) + + # Apply RANSAC filtering + if src.shape[0] <= self.min_samples: + inliers = np.array([True for _ in matched_idxes]) + else: + inliers = np.array([False for _ in matched_idxes]) + for i in range(self.max_iter): + if src[inliers==False].shape[0] <= self.min_samples: + break + model, inliers_1 = ransac( + (src[inliers==False], dst[inliers==False]), + SimpleAffineTransform, + min_samples=self.min_samples, + residual_threshold=self.residual_threshold, + max_trials=self.max_trials, + random_state=42 + ) + if inliers_1 is not None and inliers_1.any(): + inliers = self.update_inliers(inliers, inliers_1) + else: + break + if len(inliers[inliers==True]) >= len(matched_idxes): + break + + # Filter token mismatches + for idx, (a,b) in enumerate(matched_idxes): + if inliers[idx] == True and self.matcher.cost['token'][a, b] == 1: + inliers[idx] = False + + return matched_idxes, inliers + + def _calculate_metrics(self, box_gt, box_pred, inliers): + """Calculate evaluation metrics""" + final_match_num = len(inliers[inliers==True]) + recall = round(final_match_num/len(box_gt), 3) + precision = round(final_match_num/len(box_pred), 3) + F1_score = round(2*final_match_num/(len(box_gt)+len(box_pred)), 3) + return recall, precision, F1_score + + def _visualize_matches(self, img_gt, img_pred, box_gt, box_pred, matched_idxes, inliers, img_id): + """Generate and save visualization of matches""" + gap = 5 + W1, H1 = img_gt.size + W2, H2 = img_pred.size + H = H1 + H2 + gap + W = max(W1, W2) + + # Create base visualization + vis_img = Image.new('RGB', (W, H), (255, 255, 255)) + vis_img.paste(img_gt, (0, 0)) + vis_img.paste(Image.new('RGB', (W, gap), (120, 120, 120)), (0, H1)) + vis_img.paste(img_pred, (0, H1+gap)) + + # Create match visualization + match_img = vis_img.copy() + match_draw = ImageDraw.Draw(match_img) + + gt_matched_idx = {a: flag for (a,b), flag in zip(matched_idxes, inliers)} + pred_matched_idx = {b: flag for (a,b), flag in zip(matched_idxes, inliers)} + + # Draw GT boxes + for idx, box in enumerate(box_gt): + color = "green" if idx in gt_matched_idx and gt_matched_idx[idx]==True else "red" + x_min, y_min, x_max, y_max = box['bbox'] + match_draw.rectangle([x_min-1, y_min-1, x_max+1, y_max+1], fill=None, outline=color, width=2) + + # Draw prediction boxes + for idx, box in enumerate(box_pred): + color = "green" if idx in pred_matched_idx and pred_matched_idx[idx]==True else "red" + x_min, y_min, x_max, y_max = box['bbox'] + match_draw.rectangle([x_min-1, y_min-1+H1+gap, x_max+1, y_max+1+H1+gap], fill=None, outline=color, width=2) + + # Save visualizations + vis_img.save(os.path.join(self.output_root, 'vis_match', f"{img_id}_base.png")) + match_img.save(os.path.join(self.output_root, 'vis_match', f"{img_id}.png")) + + def evaluate(self, gt_latex, pred_latex, img_id): + """ + Evaluate a single LaTeX formula pair (ground truth vs prediction) + + Args: + gt_latex (str): Ground truth LaTeX formula + pred_latex (str): Predicted LaTeX formula + img_id (str): Unique identifier for this evaluation + + Returns: + dict: Evaluation metrics (recall, precision, F1_score) + """ + + try: + self._prepare_directories(img_id) + self._generate_bboxes(gt_latex, pred_latex, img_id) + box_gt, box_pred = self._load_bboxes(img_id) + img_gt, img_pred = self._load_images(img_id) + matched_idxes, inliers = self._match_boxes(box_gt, box_pred, img_gt, img_pred) + except: + return {"recall": 0, "precision": 0, "F1_score": 0} + + recall, precision, F1_score = self._calculate_metrics(box_gt, box_pred, inliers) + self._visualize_matches(img_gt, img_pred, box_gt, box_pred, matched_idxes, inliers, img_id) + + return { + "recall": recall, + "precision": precision, + "F1_score": F1_score, + } + diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/parallel.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/parallel.py new file mode 100644 index 00000000..e8867c84 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/parallel.py @@ -0,0 +1,50 @@ +from tqdm import tqdm +from concurrent.futures import ProcessPoolExecutor, as_completed + +def parallel_process(array, function, n_jobs=16, use_kwargs=False, front_num=0): + """ + A parallel version of the map function with a progress bar. + + Args: + array (array-like): An array to iterate over. + function (function): A python function to apply to the elements of array + n_jobs (int, default=16): The number of cores to use + use_kwargs (boolean, default=False): Whether to consider the elements of array as dictionaries of + keyword arguments to function + front_num (int, default=3): The number of iterations to run serially before kicking off the parallel job. + Useful for catching bugs + Returns: + [function(array[0]), function(array[1]), ...] + """ + # We run the first few iterations serially to catch bugs + if front_num > 0: + front = [function(**a) if use_kwargs else function(a) for a in array[:front_num]] + else: + front = [] + # If we set n_jobs to 1, just run a list comprehension. This is useful for benchmarking and debugging. + if n_jobs == 1: + return front + [function(**a) if use_kwargs else function(a) for a in tqdm(array[front_num:])] + # Assemble the workers + with ProcessPoolExecutor(max_workers=n_jobs) as pool: + # Pass the elements of array into function + if use_kwargs: + futures = [pool.submit(function, **a) for a in array[front_num:]] + else: + futures = [pool.submit(function, a) for a in array[front_num:]] + kwargs = { + 'total': len(futures), + 'unit': 'it', + 'unit_scale': True, + 'leave': True + } + # Print out the progress as tasks complete + for f in tqdm(as_completed(futures), **kwargs): + pass + out = [] + # Get the results from the futures. + for i, future in tqdm(enumerate(futures)): + try: + out.append(future.result()) + except Exception as e: + out.append(e) + return front + out diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/show_result.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/show_result.py new file mode 100644 index 00000000..33100e94 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/show_result.py @@ -0,0 +1,139 @@ +from collections import defaultdict +from tabulate import tabulate +import pandas as pd +import pdb + +def show_result(results): + for metric_name in results.keys(): + print(f'{metric_name}:') + score_table = [[k,v] for k,v in results[metric_name].items()] + print(tabulate(score_table)) + print('='*100) + +def sort_nested_dict(d): + # If it's a dictionary, recursively sort it + if isinstance(d, dict): + # Sort the current dictionary + sorted_dict = {k: sort_nested_dict(v) for k, v in sorted(d.items())} + return sorted_dict + # If not a dictionary, return directly + return d + +def get_full_labels_results(samples): + if not samples: + return {} + label_group_dict = defaultdict(lambda: defaultdict(list)) + for sample in samples: + label_list = [] + if not sample.get("gt_attribute"): + continue + for anno in sample["gt_attribute"]: + for k,v in anno.items(): + label_list.append(k+": "+str(v)) + for label_name in list(set(label_list)): # Currently if there are merged cases, calculate based on the set of all labels involved after merging + for metric, score in sample['metric'].items(): + label_group_dict[label_name][metric].append(score) + + print('----Anno Attribute---------------') + result = {} + result['sample_count'] = {} + for attribute in label_group_dict.keys(): + for metric, scores in label_group_dict[attribute].items(): + mean_score = sum(scores) / len(scores) + if not result.get(metric): + result[metric] = {} + result[metric][attribute] = mean_score + result['sample_count'][attribute] = len(scores) + result = sort_nested_dict(result) + show_result(result) + return result + +# def get_page_split(samples, page_info): # Sample level metric +# if not page_info: +# return {} +# page_split_dict = defaultdict(lambda: defaultdict(list)) +# for sample in samples: +# img_name = sample['img_id'] if sample['img_id'].endswith('.jpg') else '_'.join(sample['img_id'].split('_')[:-1]) +# page_info_s = page_info[img_name] +# if not sample.get('metric'): +# continue +# for metric, score in sample['metric'].items(): +# for k,v in page_info_s.items(): +# if isinstance(v, list): # special issue +# for special_issue in v: +# if 'table' not in special_issue: # Table-related special fields have duplicates +# page_split_dict[metric][special_issue].append(score) +# else: +# page_split_dict[metric][k+": "+str(v)].append(score) + +# print('----Page Attribute---------------') +# result = {} +# result['sample_count'] = {} +# for metric in page_split_dict.keys(): +# for attribute, scores in page_split_dict[metric].items(): +# mean_score = sum(scores) / len(scores) +# if not result.get(metric): +# result[metric] = {} +# result[metric][attribute] = mean_score +# result['sample_count'][attribute] = len(scores) +# result = sort_nested_dict(result) +# show_result(result) +# return result + +def get_page_split(samples, page_info): # Page level metric + if not page_info: + return {} + result_list = defaultdict(list) + for sample in samples: + img_name = sample['img_id'][:-4] if sample['img_id'].endswith('.jpg') or sample['img_id'].endswith('.png') else '_'.join(sample['img_id'].split('_')[:-1]) + page_info_s = page_info[img_name] + if not sample.get('metric'): + continue + for metric, score in sample['metric'].items(): + gt = sample['norm_gt'] if sample.get('norm_gt') else sample['gt'] + pred = sample['norm_pred'] if sample.get('norm_pred') else sample['pred'] + result_list[metric].append({ + 'image_name': img_name, + 'metric': metric, + 'attribute': 'ALL', + 'score': score, + 'upper_len': max(len(gt), len(pred)) + }) + for k,v in page_info_s.items(): + if isinstance(v, list): # special issue + for special_issue in v: + if 'table' not in special_issue: # Table-related special fields have duplicates + result_list[metric].append({ + 'image_name': img_name, + 'metric': metric, + 'attribute': special_issue, + 'score': score, + 'upper_len': max(len(gt), len(pred)) + }) + else: + result_list[metric].append({ + 'image_name': img_name, + 'metric': metric, + 'attribute': k+": "+str(v), + 'score': score, + 'upper_len': max(len(gt), len(pred)) + }) + + # Page level logic, accumulation is only done within pages, and mean operation is performed between pages + result = {} + if result_list.get('Edit_dist'): # 只有Edit_dist需要进行page level的计算 + df = pd.DataFrame(result_list['Edit_dist']) + up_total_avg = df.groupby(["image_name", "attribute"]).apply(lambda x: (x["score"]*x['upper_len']).sum() / x['upper_len'].sum()).groupby('attribute').mean() # At page level, accumulate edits, denominator is sum of max(gt, pred) from each sample + # up_total_avg = df.groupby(["attribute"]).apply(lambda x: (x["score"]*x['upper_len']).sum() / x['upper_len'].sum()) # whole_level + result['Edit_dist'] = up_total_avg.to_dict() + for metric in result_list.keys(): + if metric == 'Edit_dist': + continue + df = pd.DataFrame(result_list[metric]) + page_avg = df.groupby(["image_name", "attribute"]).apply(lambda x: x["score"].mean()).groupby('attribute').mean() # 页面内部平均以后，再页面间的平均 + result[metric] = page_avg.to_dict() + + result = sort_nested_dict(result) + # print('----Page Attribute---------------') + show_result(result) + return result \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/table_metric.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/table_metric.py new file mode 100644 index 00000000..705e2949 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/metrics/table_metric.py @@ -0,0 +1,163 @@ +# Copyright 2020 IBM +# Author: peter.zhong@au1.ibm.com +# +# This is free software; you can redistribute it and/or modify +# it under the terms of the Apache 2.0 License. +# +# This software is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# Apache 2.0 License for more details. + +import Levenshtein +# import rapidfuzz.distance as distance +from apted import APTED, Config +from apted.helpers import Tree +from lxml import etree, html +from collections import deque +# from parallel import parallel_process +from tqdm import tqdm + +class TableTree(Tree): + def __init__(self, tag, colspan=None, rowspan=None, content=None, *children): + self.tag = tag + self.colspan = colspan + self.rowspan = rowspan + self.content = content + self.children = list(children) + + def bracket(self): + """Show tree using brackets notation""" + if self.tag == 'td': + result = '"tag": %s, "colspan": %d, "rowspan": %d, "text": %s' % \ + (self.tag, self.colspan, self.rowspan, self.content) + else: + result = '"tag": %s' % self.tag + for child in self.children: + result += child.bracket() + return "{{{}}}".format(result) + + +class CustomConfig(Config): + @staticmethod + def maximum(*sequences): + """Get maximum possible value + """ + return max(map(len, sequences)) + + def normalized_distance(self, *sequences): + """Get distance from 0 to 1 + """ + return float(Levenshtein.distance(*sequences)) / self.maximum(*sequences) + + def rename(self, node1, node2): + """Compares attributes of trees""" + if (node1.tag != node2.tag) or (node1.colspan != node2.colspan) or (node1.rowspan != node2.rowspan): + return 1. + if node1.tag == 'td': + if node1.content or node2.content: + return self.normalized_distance(node1.content, node2.content) + return 0. + + +class TEDS(object): + ''' Tree Edit Distance basead Similarity + ''' + def __init__(self, structure_only=False, n_jobs=1, ignore_nodes=None): + assert isinstance(n_jobs, int) and (n_jobs >= 1), 'n_jobs must be an integer greather than 1' + self.structure_only = structure_only + self.n_jobs = n_jobs + self.ignore_nodes = ignore_nodes + self.__tokens__ = [] + + def tokenize(self, node): + ''' Tokenizes table cells + ''' + self.__tokens__.append('<%s>' % node.tag) + if node.text is not None: + self.__tokens__ += list(node.text) + for n in node.getchildren(): + self.tokenize(n) + if node.tag != 'unk': + self.__tokens__.append('' % node.tag) + if node.tag != 'td' and node.tail is not None: + self.__tokens__ += list(node.tail) + + def load_html_tree(self, node, parent=None): + ''' Converts HTML tree to the format required by apted + ''' + global __tokens__ + if node.tag == 'td': + if self.structure_only: + cell = [] + else: + self.__tokens__ = [] + self.tokenize(node) + cell = self.__tokens__[1:-1].copy() + new_node = TableTree(node.tag, + int(node.attrib.get('colspan', '1')), + int(node.attrib.get('rowspan', '1')), + cell, *deque()) + else: + new_node = TableTree(node.tag, None, None, None, *deque()) + if parent is not None: + parent.children.append(new_node) + if node.tag != 'td': + for n in node.getchildren(): + self.load_html_tree(n, new_node) + if parent is None: + return new_node + + def evaluate(self, pred, true): + ''' Computes TEDS score between the prediction and the ground truth of a + given sample + ''' + if (not pred) or (not true): + return 0.0 + parser = html.HTMLParser(remove_comments=True, encoding='utf-8') + pred = html.fromstring(pred, parser=parser) + true = html.fromstring(true, parser=parser) + if pred.xpath('body/table') and true.xpath('body/table'): + pred = pred.xpath('body/table')[0] + true = true.xpath('body/table')[0] + if self.ignore_nodes: + etree.strip_tags(pred, *self.ignore_nodes) + etree.strip_tags(true, *self.ignore_nodes) + n_nodes_pred = len(pred.xpath(".//*")) + n_nodes_true = len(true.xpath(".//*")) + n_nodes = max(n_nodes_pred, n_nodes_true) + tree_pred = self.load_html_tree(pred) + tree_true = self.load_html_tree(true) + distance = APTED(tree_pred, tree_true, CustomConfig()).compute_edit_distance() + return 1.0 - (float(distance) / n_nodes) + else: + return 0.0 + + def batch_evaluate(self, pred_json, true_json): + ''' Computes TEDS score between the prediction and the ground truth of + a batch of samples + @params pred_json: {'FILENAME': 'HTML CODE', ...} + @params true_json: {'FILENAME': {'html': 'HTML CODE'}, ...} + @output: {'FILENAME': 'TEDS SCORE', ...} + ''' + samples = true_json.keys() + # if self.n_jobs == 1: + scores = [self.evaluate(pred_json.get(filename, ''), true_json[filename]['html']) for filename in tqdm(samples)] + # else: + # inputs = [{'pred': pred_json.get(filename, ''), 'true': true_json[filename]['html']} for filename in samples] + # scores = parallel_process(inputs, self.evaluate, use_kwargs=True, n_jobs=self.n_jobs, front_num=1) + scores = dict(zip(samples, scores)) + return scores + + +if __name__ == '__main__': + import json + import pprint + with open('sample_pred.json') as fp: + pred_json = json.load(fp) + with open('sample_gt.json') as fp: + true_json = json.load(fp) + teds = TEDS(n_jobs=4) + scores = teds.batch_evaluate(pred_json, true_json) + pp = pprint.PrettyPrinter() + pp.pprint(scores) diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/pdf_validation.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/pdf_validation.py new file mode 100644 index 00000000..7fea6f12 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/pdf_validation.py @@ -0,0 +1,61 @@ +# coding: utf-8 +import os +import argparse +import sys +import io +import os +import pathlib +import sys +import yaml +from registry.registry import EVAL_TASK_REGISTRY, DATASET_REGISTRY, METRIC_REGISTRY +import dataset +import task +import metrics + +def process_args(args): + parser = argparse.ArgumentParser(description='Render latex formulas for comparison.') + parser.add_argument('--config', '-c', type=str, default='./configs/end2end.yaml') + parser.add_argument('--ocr-type', type=str, default='deepseek', help='OCR type (deepseek, olmocr2, chandra, etc.)') + parser.add_argument('--language', type=str, default='all', help='Language filter (all, english, simplified_chinese)') + parameters = parser.parse_args(args) + return parameters + +if __name__ == '__main__': + parameters = process_args(sys.argv[1:]) + config_path = parameters.config + ocr_type = parameters.ocr_type + language = parameters.language + + if isinstance(config_path, (str, pathlib.Path)): + with io.open(os.path.abspath(config_path), "r", encoding="utf-8") as f: + cfg = yaml.load(f, Loader=yaml.FullLoader) + else: + raise TypeError("Unexpected file type") + + if cfg is not None and not isinstance(cfg, (list, dict, str)): + raise IOError( # pragma: no cover + f"Invalid loaded object type: {type(cfg).__name__}" + ) + + + for task in cfg.keys(): + if not cfg.get(task): + print(f'No config for task {task}') + dataset = cfg[task]['dataset']['dataset_name'] + # metrics_list = [METRIC_REGISTRY.get(i) for i in cfg[task]['metrics']] # TODO: 直接在主函数里实例化 + metrics_list = cfg[task]['metrics'] # 在task里再实例化 + val_dataset = DATASET_REGISTRY.get(dataset)(cfg[task]) + val_task = EVAL_TASK_REGISTRY.get(task) + # val_task(val_dataset, metrics_list) + if cfg[task]['dataset']['prediction'].get('data_path'): + base_name = os.path.basename(cfg[task]['dataset']['prediction']['data_path']) + match_method = cfg[task]['dataset'].get('match_method', 'quick_match') + # Construct naming convention: ocr_type_language_match_method + save_name = f"{ocr_type}_{language}_{match_method}" + else: + save_name = os.path.basename(cfg[task]['dataset']['ground_truth']['data_path']).split('.')[0] + print('###### Process: ', save_name) + if cfg[task]['dataset']['ground_truth'].get('page_info'): + val_task(val_dataset, metrics_list, cfg[task]['dataset']['ground_truth']['page_info'], save_name) # 按页面区分 + else: + val_task(val_dataset, metrics_list, cfg[task]['dataset']['ground_truth']['data_path'], save_name) # 按页面区分 diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/registry/__init__.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/registry/__init__.py new file mode 100644 index 00000000..3a549176 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/registry/__init__.py @@ -0,0 +1 @@ +from .registry import EVAL_TASK_REGISTRY, METRIC_REGISTRY, DATASET_REGISTRY \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/registry/registry.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/registry/registry.py new file mode 100644 index 00000000..f51fa752 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/registry/registry.py @@ -0,0 +1,24 @@ +class Registry: + def __init__(self): + self._registry = {} + + def register(self, name): + def decorator(item): + if name in self._registry: + raise ValueError(f"Item {name} already registered.") + self._registry[name] = item + return item + return decorator + + def get(self, name): + if name not in self._registry: + raise ValueError(f"Item {name} not found in registry.") + return self._registry[name] + + def list_items(self): + return list(self._registry.keys()) + +# Create global registries for tasks and models +EVAL_TASK_REGISTRY = Registry() +METRIC_REGISTRY = Registry() +DATASET_REGISTRY = Registry() \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/requirements.txt b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/requirements.txt new file mode 100644 index 00000000..70667176 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/requirements.txt @@ -0,0 +1,115 @@ +aiohappyeyeballs==2.4.3 +aiohttp==3.10.11 +aiosignal==1.3.1 +apted==1.0.3 +argon2-cffi==21.3.0 +argon2-cffi-bindings==21.2.0 +async-timeout==5.0.1 +attrs==24.2.0 +backcall==0.2.0 +beautifulsoup4==4.11.1 +bleach==5.0.1 +category-encoders==2.5.0 +certifi==2024.8.30 +charset-normalizer==3.4.0 +click==8.1.7 +contourpy==1.1.1 +cycler==0.12.1 +datasets==3.1.0 +debugpy==1.6.3 +decorator==5.1.1 +defusedxml==0.7.1 +dill==0.3.8 +entrypoints==0.4 +evaluate==0.4.3 +fastjsonschema==2.16.2 +filelock==3.16.1 +fonttools==4.55.0 +frozenlist==1.5.0 +fsspec==2024.9.0 +func-timeout==4.3.5 +huggingface-hub==0.26.2 +idna==3.10 +importlib_resources==6.4.5 +ipykernel==6.16.0 +ipython==7.34.0 +ipython-genutils==0.2.0 +ipywidgets==7.7.2 +jedi==0.18.1 +Jinja2==3.1.2 +joblib==1.2.0 +jsonpickle==2.2.0 +jupyterlab-pygments==0.2.2 +jupyterlab-widgets==1.1.1 +kiwisolver==1.4.7 +Levenshtein==0.25.1 +loguru==0.7.2 +lxml==4.9.1 +markdown-it-py==3.0.0 +matplotlib==3.7.5 +matplotlib-inline==0.1.6 +mdurl==0.1.2 +mistune==2.0.4 +mmeval==0.2.1 +multidict==6.1.0 +multiprocess==0.70.16 +nbclient==0.6.8 +nbconvert==7.0.0 +nbformat==5.6.1 +nest-asyncio==1.5.5 +nltk==3.9.1 +notebook==6.4.12 +numpy==1.24.4 +opencv-python==4.10.0.84 +packaging==24.2 +pandas==2.0.3 +pandocfilters==1.5.0 +parso==0.8.3 +patsy==0.5.2 +pexpect==4.8.0 +pickleshare==0.7.5 +pillow==10.4.0 +pkgutil_resolve_name==1.3.10 +plotly==5.5.0 +plum-dispatch==1.7.4 +prometheus-client==0.14.1 +prompt-toolkit==3.0.31 +propcache==0.2.0 +psutil==5.9.2 +ptyprocess==0.7.0 +pyarrow==17.0.0 +Pygments==2.18.0 +pylatexenc==3.0a30 +PyNomaly==0.3.4 +pyparsing==3.1.4 +pyrsistent==0.18.1 +python-dateutil==2.9.0.post0 +python-utils==3.3.3 +pytz==2024.2 +PyYAML==6.0.2 +rapidfuzz==3.9.7 +regex==2024.11.6 +requests==2.32.3 +rich==13.9.4 +scikit-learn==1.1.2 +scipy==1.10.1 +Send2Trash==1.8.0 +six==1.16.0 +soupsieve==2.3.2.post1 +statsmodels==0.13.2 +tabulate==0.9.0 +tenacity==8.1.0 +terminado==0.15.0 +threadpoolctl==3.1.0 +tinycss2==1.1.1 +tornado==6.2 +tqdm==4.67.1 +typing_extensions==4.12.2 +tzdata==2024.2 +urllib3==2.2.3 +webencodings==0.5.1 +widgetsnbextension==3.6.1 +xxhash==3.5.0 +yarl==1.15.2 +zipp==3.20.2 +pycocotools==2.0.7 diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/signatures/version1/cla.json b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/signatures/version1/cla.json new file mode 100644 index 00000000..e69de29b diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/__init__.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/__init__.py new file mode 100644 index 00000000..dfe7ff80 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/__init__.py @@ -0,0 +1,13 @@ +from .detection_eval import DetectionEval +from .end2end_run_eval import End2EndEval +from .recognition_eval import RecognitionBaseEval + +from registry.registry import EVAL_TASK_REGISTRY + +__all__ = [ + "RecognitionBaseEval", + "DetectionEval", + "End2EndEval" +] + +print('EVAL_TASK_REGISTRY: ', EVAL_TASK_REGISTRY.list_items()) \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/detection_eval.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/detection_eval.py new file mode 100644 index 00000000..530c9c5f --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/detection_eval.py @@ -0,0 +1,8 @@ +# coding: utf-8 +from registry.registry import EVAL_TASK_REGISTRY + +@EVAL_TASK_REGISTRY.register("detection_eval") +class DetectionEval(): + def __init__(self, dataset, metrics_list='COCODet', page_info_path='', save_name=''): + detect_matrix = dataset.coco_det_metric(predictions=dataset.samples['preds'], groundtruths=dataset.samples['gts']) + print('detect_matrix', detect_matrix) diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/end2end_run_eval.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/end2end_run_eval.py new file mode 100644 index 00000000..5cecb8ca --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/end2end_run_eval.py @@ -0,0 +1,88 @@ +# from modules.cal_matrix import cal_text_matrix, cal_table_teds +from registry.registry import EVAL_TASK_REGISTRY +from metrics.show_result import show_result, get_full_labels_results, get_page_split +from registry.registry import METRIC_REGISTRY +import json +import os +import pdb + +@EVAL_TASK_REGISTRY.register("end2end_eval") +class End2EndEval(): + def __init__(self, dataset, metrics_list, page_info_path, save_name): + result_all = {} + page_info = {} + if os.path.isdir(page_info_path): + md_flag = True + else: + md_flag = False + if not md_flag: + with open(page_info_path, 'r') as f: + pages = json.load(f) + + for page in pages: + img_path = os.path.basename(page['page_info']['image_path']) + page_info[img_path[:-4]] = page['page_info']['page_attribute'] + + for element in metrics_list.keys(): + result = {} + group_info = metrics_list[element].get('group', []) + samples = dataset.samples[element] + for metric in metrics_list[element]['metric']: + metric_val = METRIC_REGISTRY.get(metric) + samples, result_s = metric_val(samples).evaluate(group_info, f"{save_name}_{element}") + if result_s: + result.update(result_s) + if result: + print(f'【{element}】') + show_result(result) + result_all[element] = {} + + if md_flag: + group_result = {} + page_result = {} + else: + group_result = get_full_labels_results(samples) + page_result = get_page_split(samples, page_info) + result_all[element] = { + 'all': result, + 'group': group_result, + 'page': page_result} + # pdb.set_trace() + + if not os.path.exists('./result'): + os.makedirs('./result') + if isinstance(samples, list): + saved_samples = samples + else: + saved_samples = samples.samples + try: + + with open(f'./result/{save_name}_{element}_result.json', 'w', encoding='utf-8') as f: + json.dump(saved_samples, f, indent=4, ensure_ascii=False) + except TypeError as e: + print(f"JSON 序列化错误: {e}") + print("请检查 saved_samples 中是否包含非 JSON 可序列化的数据类型") + + # 打印出有问题的数据类型 + def find_non_serializable(data): + if isinstance(data, dict): + for k, v in data.items(): + try: + json.dumps(v) + except TypeError: + print(f"键 '{k}' 包含不可序列化的值: {v} (类型: {type(v)})") + find_non_serializable(v) + elif isinstance(data, (list, tuple)): + for i, item in enumerate(data): + try: + json.dumps(item) + except TypeError: + print(f"索引 {i} 包含不可序列化的项: {item} (类型: {type(item)})") + find_non_serializable(item) + + find_non_serializable(saved_samples) + + + with open(f'./result/{save_name}_metric_result.json', 'w', encoding='utf-8') as f: + json.dump(result_all, f, indent=4, ensure_ascii=False) + \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/recognition_eval.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/recognition_eval.py new file mode 100644 index 00000000..15727471 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/task/recognition_eval.py @@ -0,0 +1,62 @@ +from tabulate import tabulate +from registry.registry import EVAL_TASK_REGISTRY +from registry.registry import METRIC_REGISTRY +import os +import json +from metrics.show_result import show_result, get_full_labels_results, get_page_split + +@EVAL_TASK_REGISTRY.register("recogition_eval") +class RecognitionBaseEval(): + def __init__(self, dataset, metrics_list, page_info_path, save_name, **kwargs): + p_scores = {} + samples = dataset.samples + + page_info = {} + if os.path.isdir(page_info_path): + md_flag = True + else: + md_flag = False + + no_page_flag = False + if not md_flag: + with open(page_info_path, 'r') as f: + pages = json.load(f) + + for page in pages: + if 'page_info' not in page: + no_page_flag = True + break + img_path = os.path.basename(page['page_info']['image_path']) + page_info[img_path[:-4]] = page['page_info']['page_attribute'] + + for metric in metrics_list: + metric_val = METRIC_REGISTRY.get(metric) + samples, result = metric_val(samples).evaluate({}, save_name) + if result: + p_scores.update(result) + # score_table = [[k,v] for k,v in p_scores.items()] + show_result(p_scores) + # print(tabulate(score_table)) + # print('='*100) + + if md_flag: + group_result = {} + page_result = {} + else: + group_result = get_full_labels_results(samples) + if no_page_flag: + page_result = {} + else: + page_result = get_page_split(samples, page_info) + + result_all = { + 'all': p_scores, + 'group': group_result, + 'page': page_result + } + + with open(f'./result/{save_name}_metric_result.json', 'w', encoding='utf-8') as f: + json.dump(result_all, f, indent=4, ensure_ascii=False) + + + \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/tools/image_to_pdf.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/tools/image_to_pdf.py new file mode 100644 index 00000000..f5c8fe8c --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/tools/image_to_pdf.py @@ -0,0 +1,86 @@ +import os +import fitz # PyMuPDF +from tqdm import tqdm # For progress bar, install with: pip install tqdm + + +def image_to_pdf(image_path, pdf_path): + """Convert a single image to PDF""" + try: + # Create a new PDF document + pdf_document = fitz.open() + # Open the image + image = fitz.open(image_path) + # Get image size + rect = image[0].rect + # Create a new page with the same size as the image + pdf_page = pdf_document.new_page(width=rect.width, height=rect.height) + # Insert the image into the page + pdf_page.insert_image(rect, filename=image_path) + # Save the PDF + pdf_document.save(pdf_path) + pdf_document.close() + print(f"Successfully converted: {image_path} -> {pdf_path}") + except Exception as e: + print(f"Failed to convert: {image_path} - Error: {str(e)}") + + +def is_image_file(filename): + """Check if the file is a supported image format""" + image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'] + return any(filename.lower().endswith(ext) for ext in image_extensions) + + +def batch_images_to_pdfs(input_dir, output_dir=None): + """ + Recursively process all images in a directory and convert them to PDFs + + Args: + input_dir: Input image directory + output_dir: Output PDF directory. If None, a "pdfs" subdirectory will be created in the input directory. + """ + # If output directory is not specified, create a "pdfs" directory in the input directory + if output_dir is None: + output_dir = os.path.join(input_dir, "pdfs") + + # Ensure the output directory exists + os.makedirs(output_dir, exist_ok=True) + + # Get all image files + image_files = [] + for root, _, files in os.walk(input_dir): + for file in files: + if is_image_file(file): + image_files.append(os.path.join(root, file)) + + if not image_files: + print(f"No image files found in {input_dir}") + return + + print(f"Found {len(image_files)} image files, starting conversion...") + + # Process each image + for image_path in tqdm(image_files, desc="Conversion Progress"): + # Calculate relative path to maintain directory structure in output + rel_path = os.path.relpath(image_path, input_dir) + rel_dir = os.path.dirname(rel_path) + # Create corresponding output subdirectory + output_subdir = os.path.join(output_dir, rel_dir) + os.makedirs(output_subdir, exist_ok=True) + + # Get filename without extension + filename = os.path.splitext(os.path.basename(image_path))[0] + # Generate PDF path + pdf_path = os.path.join(output_subdir, f"{filename}.pdf") + + # Convert image to PDF + image_to_pdf(image_path, pdf_path) + + print(f"Conversion complete! All PDF files are saved in: {output_dir}") + + +if __name__ == "__main__": + # Example usage + input_directory = "/home/ubuntu/texas-sandbox/paper-implementations/omnidocbench_evals/data/OmniDocBench/images/" + output_directory = "/home/ubuntu/texas-sandbox/paper-implementations/omnidocbench_evals/data/OmniDocBench/pdfs/" + + batch_images_to_pdfs(input_directory, output_directory) \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/data_preprocess.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/data_preprocess.py new file mode 100644 index 00000000..b36afbdb --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/data_preprocess.py @@ -0,0 +1,452 @@ +import re +import unicodedata +from pylatexenc.latex2text import LatexNodes2Text +from bs4 import BeautifulSoup +import subprocess +import shutil +import uuid +import html +import os + +def remove_markdown_fences(content): + content = re.sub(r'^```markdown\n?', '', content, flags=re.MULTILINE) + content = re.sub(r'^```html\n?', '', content, flags=re.MULTILINE) + content = re.sub(r'^```latex\n?', '', content, flags=re.MULTILINE) + content = re.sub(r'```\n?$', '', content, flags=re.MULTILINE) + return content + +# Standardize all consecutive characters +def replace_repeated_chars(input_str): + input_str = re.sub(r'_{4,}', '____', input_str) # Replace more than 4 consecutive underscores with 4 underscores + input_str = re.sub(r' {4,}', ' ', input_str) # Replace more than 4 consecutive spaces with 4 spaces + return input_str + # return re.sub(r'([^a-zA-Z0-9])\1{10,}', r'\1\1\1\1', input_str) # For other consecutive symbols (except numbers and letters), replace more than 10 occurrences with 4 + +# Special Unicode handling +def fullwidth_to_halfwidth(s): + result = [] + for char in s: + code = ord(char) + # Convert full-width space to half-width space + if code == 0x3000: + code = 0x0020 + # Convert other full-width characters to half-width + elif 0xFF01 <= code <= 0xFF5E: + code -= 0xFEE0 + result.append(chr(code)) + return ''.join(result) + +def find_special_unicode(s): + special_chars = {} + for char in s: + if ord(char) > 127: # Non-ASCII characters + # unicode_name = unicodedata.name(char, None) + unicode_name = unicodedata.category(char) + special_chars[char] = f'U+{ord(char):04X} ({unicode_name})' + return special_chars + +# # Define dictionary for Unicode character replacements +# unicode_replacements = { +# "\u00A9": r"$\copyright$", # Copyright symbol © to latex +# "\u00AE": r"$^\circledR$", # Registered trademark ® to latex +# "\u2122": r"$^\text{TM}$", # Trademark ™ to latex +# "\u2018": "'", # Left single quote to straight quote +# "\u2019": "'", # Right single quote to straight quote +# "\u201C": "\"", # Left double quote to straight quote +# "\u201D": "\"", # Right double quote to straight quote +# "\u2013": "-", # En dash to hyphen +# "\u2014": "-", # Em dash to hyphen +# "\u2026": "...", # Unicode ellipsis to three dots +# "\u2103": r"$\textdegree C$", # ℃ +# "\u03B1": r"$\alpha$", # α +# "\u03B2": r"$\beta$", # β +# "\u03A3": r"$\Sigma$", # Σ +# } + +# # Use regex to replace Unicode characters +# def replace_unicode(match): +# char = match.group(0) +# return unicode_replacements.get(char, char) + +inline_reg = re.compile( + r'\$(.*?)\$|' + r'\\\((.*?)\\\)', +) + +def textblock2unicode(text): + inline_matches = inline_reg.finditer(text) + removal_positions = [] + for match in inline_matches: + position = [match.start(), match.end()] + content = match.group(1) if match.group(1) is not None else match.group(2) + # print('-------- content-------', content) + # Remove escape characters \ + clean_content = re.sub(r'\\([\\_&%^])', '', content) + + try: + if any(char in clean_content for char in r'\^_'): + if clean_content.endswith('\\'): + clean_content += ' ' + # inline_array.append(match.group(0)) + unicode_content = LatexNodes2Text().latex_to_text(clean_content) + removal_positions.append((position[0], position[1], unicode_content)) + except: + continue + + # Remove inline formulas from original text + for start, end, unicode_content in sorted(removal_positions, reverse=True): + text = text[:start] + unicode_content.strip() + text[end:] + + return text + +def normalized_formula(text): + # Normalize math formulas before matching + filter_list = ['\\mathbf', '\\mathrm', '\\mathnormal', '\\mathit', '\\mathbb', '\\mathcal', '\\mathscr', '\\mathfrak', '\\mathsf', '\\mathtt', + '\\textbf', '\\text', '\\boldmath', '\\boldsymbol', '\\operatorname', '\\bm', + '\\symbfit', '\\mathbfcal', '\\symbf', '\\scriptscriptstyle', '\\notag', + '\\setlength', '\\coloneqq', '\\space', '\\thickspace', '\\thinspace', '\\medspace', '\\nobreakspace', '\\negmedspace', + '\\quad', '\\qquad', '\\enspace', '\\substackw', ' ', '$$', '\\left', '\\right', '\\displaystyle', '\\text'] + # '\\left', '\\right', '{', '}', ' '] + + # delimiter_filter + text = text.strip().strip('$').strip('\n') + pattern = re.compile(r"\\\[(.+?)(?]*>(.*)' + tables = re.findall(pattern, table_res, re.DOTALL | re.IGNORECASE) + table_res = ''.join(tables) + # table_res = re.sub('','',table_res) + table_res = re.sub('( style=".*?")', "", table_res) + table_res = re.sub('( height=".*?")', "", table_res) + table_res = re.sub('( width=".*?")', "", table_res) + table_res = re.sub('( align=".*?")', "", table_res) + table_res = re.sub('( class=".*?")', "", table_res) + table_res = re.sub('',"",table_res) + + table_res = re.sub(r'\s+', " ", table_res) + table_res_no_space = '' + table_res.replace(' ','') + '
    ' + # table_res_no_space = re.sub(' (style=".*?")',"",table_res_no_space) + # table_res_no_space = re.sub(r'[ ]', " ", table_res_no_space) + table_res_no_space = re.sub('colspan="', ' colspan="', table_res_no_space) + table_res_no_space = re.sub('rowspan="', ' rowspan="', table_res_no_space) + table_res_no_space = re.sub('border="', ' border="', table_res_no_space) + + table_res = '' + table_res + '
    ' + # table_flow.append(table_res) + # table_flow_no_space.append(table_res_no_space) + + return table_res, table_res_no_space + + def clean_table(input_str,flag=True): + if flag: + input_str = input_str.replace('', '').replace('', '') + input_str = input_str.replace('', '').replace('', '') + input_str = input_str.replace('', '').replace('', '') + input_str = input_str.replace('
    ', '').replace('
    ', '') + input_str = input_str.replace('

    ', '').replace('

    ', '') + input_str = input_str.replace('', '') + input_str = re.sub('.*?','',input_str) + return input_str + + norm_text, _ = process_table_html(text) + norm_text = clean_table(norm_text) + return norm_text + +def normalized_latex_table(text): + def latex_template(latex_code): + template = r''' + \documentclass[border=20pt]{article} + \usepackage{subcaption} + \usepackage{url} + \usepackage{graphicx} + \usepackage{caption} + \usepackage{multirow} + \usepackage{booktabs} + \usepackage{color} + \usepackage{colortbl} + \usepackage{xcolor,soul,framed} + \usepackage{fontspec} + \usepackage{amsmath,amssymb,mathtools,bm,mathrsfs,textcomp} + \setlength{\parindent}{0pt}''' + \ + r''' + \begin{document} + ''' + \ + latex_code + \ + r''' + \end{document}''' + + return template + + def process_table_latex(latex_code): + SPECIAL_STRINGS= [ + ['\\\\vspace\\{.*?\\}', ''], + ['\\\\hspace\\{.*?\\}', ''], + ['\\\\rule\{.*?\\}\\{.*?\\}', ''], + ['\\\\addlinespace\\[.*?\\]', ''], + ['\\\\addlinespace', ''], + ['\\\\renewcommand\\{\\\\arraystretch\\}\\{.*?\\}', ''], + ['\\\\arraystretch\\{.*?\\}', ''], + ['\\\\(row|column)?colors?\\{[^}]*\\}(\\{[^}]*\\}){0,2}', ''], + ['\\\\color\\{.*?\\}', ''], + ['\\\\textcolor\\{.*?\\}', ''], + ['\\\\rowcolor(\\[.*?\\])?\\{.*?\\}', ''], + ['\\\\columncolor(\\[.*?\\])?\\{.*?\\}', ''], + ['\\\\cellcolor(\\[.*?\\])?\\{.*?\\}', ''], + ['\\\\colorbox\\{.*?\\}', ''], + ['\\\\(tiny|scriptsize|footnotesize|small|normalsize|large|Large|LARGE|huge|Huge)', ''], + [r'\s+', ' '], + ['\\\\centering', ''], + ['\\\\begin\\{table\\}\\[.*?\\]', '\\\\begin{table}'], + ['\t', ''], + ['@{}', ''], + ['\\\\toprule(\\[.*?\\])?', '\\\\hline'], + ['\\\\bottomrule(\\[.*?\\])?', '\\\\hline'], + ['\\\\midrule(\\[.*?\\])?', '\\\\hline'], + ['p\\{[^}]*\\}', 'l'], + ['m\\{[^}]*\\}', 'c'], + ['\\\\scalebox\\{[^}]*\\}\\{([^}]*)\\}', '\\1'], + ['\\\\textbf\\{([^}]*)\\}', '\\1'], + ['\\\\textit\\{([^}]*)\\}', '\\1'], + ['\\\\cmidrule(\\[.*?\\])?\\(.*?\\)\\{([0-9]-[0-9])\\}', '\\\\cline{\\2}'], + ['\\\\hline', ''], + [r'\\multicolumn\{1\}\{[^}]*\}\{((?:[^{}]|(?:\{[^{}]*\}))*)\}', r'\1'] + ] + pattern = r'\\begin\{tabular\}.*\\end\{tabular\}' # 注意这里不用 .*? + matches = re.findall(pattern, latex_code, re.DOTALL) + latex_code = ' '.join(matches) + + for special_str in SPECIAL_STRINGS: + latex_code = re.sub(fr'{special_str[0]}', fr'{special_str[1]}', latex_code) + + return latex_code + + def convert_latex_to_html(latex_content, cache_dir='./temp'): + if not os.path.exists(cache_dir): + os.makedirs(cache_dir) + + uuid_str = str(uuid.uuid1()) + with open(f'{cache_dir}/{uuid_str}.tex', 'w') as f: + f.write(latex_template(latex_content)) + + cmd = ['latexmlc', '--quiet', '--nocomments', f'--log={cache_dir}/{uuid_str}.log', + f'{cache_dir}/{uuid_str}.tex', f'--dest={cache_dir}/{uuid_str}.html'] + try: + subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + with open(f'{cache_dir}/{uuid_str}.html', 'r') as f: + html_content = f.read() + + pattern = r']*>(.*)' + tables = re.findall(pattern, html_content, re.DOTALL | re.IGNORECASE) + tables = [f'{table}
    ' for table in tables] + html_content = '\n'.join(tables) + + except Exception as e: + html_content = '' + + shutil.rmtree(cache_dir) + return html_content + + html_text = convert_latex_to_html(text) + normlized_tables = normalized_html_table(html_text) + return normlized_tables + + +def normalized_table(text, format='html'): + if format not in ['html', 'latex']: + raise ValueError('Invalid format: {}'.format(format)) + else: + return globals()['normalized_{}_table'.format(format)](text) + + +def textblock_with_norm_formula(text): + inline_matches = inline_reg.finditer(text) + removal_positions = [] + for match in inline_matches: + position = [match.start(), match.end()] + content = match.group(1) if match.group(1) is not None else match.group(2) + # print('-------- content-------', content) + + norm_content = normalized_formula(content) + removal_positions.append((position[0], position[1], norm_content)) + + # Remove inline formulas from original text + for start, end, norm_content in sorted(removal_positions, reverse=True): + text = text[:start] + norm_content.strip() + text[end:] + + return text + +# def inline_filter_unicode(text): +# # Ensure text is string type +# if not isinstance(text, str): +# text = str(text) + +# # Convert LaTeX content to Unicode representation +# text = LatexNodes2Text().latex_to_text(text) + +# inline_array = [] +# inline_matches = inline_reg.finditer(text) + +# for match in inline_matches: +# position = [match.start(), match.end()] +# content = match.group(1) if match.group(1) is not None else match.group(2) + +# # Remove escape characters \ +# clean_content = re.sub(r'\\([\\_&%^])', '', content) + +# if any(char in clean_content for char in r'\^_'): +# # inline_array.append(match.group(0)) +# inline_array.append({ +# 'category_type': 'equation_inline', +# 'position': position, +# 'content': match.group(0), +# }) +# text = text.replace(match.group(0), '') +# # print('-----Found inline formula: ', match.group(0)) +# else: +# text = text.replace(match.group(0), content) +# # # Add to inline_array +# # inline_array.append({ +# # 'category_type': 'equation_inline', +# # 'position': position, +# # 'content': content, +# # }) + +# # # Remove matched formula from original text, can choose to replace with spaces or remove directly +# # text = text[:position[0]] + ' '*(position[1]-position[0]) + text[position[1]:] + +# return text, inline_array + +def inline_filter_unicode(text): + # Ensure text is string type + if not isinstance(text, str): + text = str(text) + + # Replace inline formula boundary markers + #print('--------text-------',text) + placeholder = '__INLINE_FORMULA_BOUNDARY__' + text_copy = text.replace('$', placeholder).replace('\\(', placeholder).replace('\\)', placeholder) + #print('--------text_copy-------',text_copy) + # Convert LaTeX content to Unicode representation + text_copy = LatexNodes2Text().latex_to_text(text_copy) + #print('--------text_copy---unicode----',text_copy) + # Restore boundary markers + text_copy = text_copy.replace(placeholder, '$') + + inline_array = [] + inline_matches = inline_reg.finditer(text_copy) + # Record positions of inline formulas to be removed + removal_positions = [] + + for match in inline_matches: + position = [match.start(), match.end()] + content = match.group(1) if match.group(1) is not None else match.group(2) + print('-------- content-------', content) + # Remove escape characters \ + clean_content = re.sub(r'\\([\\_&%^])', '', content) + + if any(char in clean_content for char in r'\^_'): + # inline_array.append(match.group(0)) + inline_array.append({ + 'category_type': 'equation_inline', + 'position': position, + 'content': content, + }) + removal_positions.append((position[0], position[1])) + + # Remove inline formulas from original text + for start, end in sorted(removal_positions, reverse=True): + text = text[:start] + text[end:] + + return text, inline_array + +def inline_filter(text): + # Ensure text is string type + if not isinstance(text, str): + text = str(text) + + inline_array = [] + inline_matches = inline_reg.finditer(text) + + for match in inline_matches: + position = [match.start(), match.end()] + content = match.group(1) if match.group(1) is not None else match.group(2) + # print('inline_content: ', content) + + # Remove escape characters \ + clean_content = re.sub(r'\\([\\_&%^])', '', content) + + if any(char in clean_content for char in r'\^_'): + # inline_array.append(match.group(0)) + inline_array.append({ + 'category_type': 'equation_inline', + 'position': position, + 'content': match.group(0), + }) + text = text.replace(match.group(0), '') + # print('-----Found inline formula: ', match.group(0)) + else: + text = text.replace(match.group(0), content) + + return text, inline_array + +# Text OCR quality check processing: +def clean_string(input_string): + # Use regex to keep Chinese characters, English letters and numbers + # input_string = input_string.replace('\\t', '').replace('\\n', '').replace('\t', '').replace('\n', '').replace('/t', '').replace('/n', '') + input_string = input_string.replace('\\t', '').replace('\\n', '').replace('\t', '').replace('\n', '').replace('/t', '').replace('/n', '') + cleaned_string = re.sub(r'[^\w\u4e00-\u9fff]', '', input_string) # 只保留中英文和数字 + return cleaned_string \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/extract.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/extract.py new file mode 100644 index 00000000..c02cd8a6 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/extract.py @@ -0,0 +1,571 @@ +import re +import os +import json +import copy +#from modules.table_utils import convert_markdown_to_html #end +from utils.table_utils import convert_markdown_to_html +import re +import unicodedata +from bs4 import BeautifulSoup +from pylatexenc.latexencode import unicode_to_latex +from pylatexenc.latex2text import LatexNodes2Text +from pylatexenc.latexwalker import LatexWalker, LatexEnvironmentNode, LatexCharsNode, LatexGroupNode, LatexMacroNode, LatexSpecialsNode +from collections import defaultdict +import pdb +from utils.data_preprocess import remove_markdown_fences, replace_repeated_chars, textblock_with_norm_formula, textblock2unicode + + +def extract_tabular(text): + begin_pattern = r'\\begin{tabular}' + end_pattern = r'\\end{tabular}' + + tabulars = [] + positions = [] + current_pos = 0 + stack = [] + + while current_pos < len(text): + begin_match = re.search(begin_pattern, text[current_pos:]) + end_match = re.search(end_pattern, text[current_pos:]) + + if not begin_match and not end_match: + break + + if begin_match and (not end_match or begin_match.start() < end_match.start()): + stack.append(current_pos + begin_match.start()) + current_pos += begin_match.start() + len(end_pattern) + elif end_match: + if stack: + start_pos = stack.pop() + if not stack: + end_pos = current_pos + end_match.start() + len(end_pattern) + tabular_code = text[start_pos:end_pos] + tabulars.append(tabular_code) + positions.append((start_pos, end_pos)) + current_pos += end_match.start() + len(end_pattern) + else: + current_pos += 1 + + if stack: + new_start = stack[0] + len(begin_pattern) + new_tabulars, new_positions = extract_tabular(text[new_start:]) + new_positions = [(start + new_start, end + new_start) for start, end in new_positions] + tabulars.extend(new_tabulars) + positions.extend(new_positions) + + return tabulars, positions + +# math reg + # r'\\begin{equation\*?}(.*?)\\end{equation\*?}|' + # r'\\begin{align\*?}(.*?)\\end{align\*?}|' + # r'\\begin{gather\*?}(.*?)\\end{gather\*?}|' +display_reg = re.compile( + # r'\\begin{equation\*?}(.*?)\\end{equation\*?}|' + # r'\\begin{align\*?}(.*?)\\end{align\*?}|' + # r'\\begin{gather\*?}(.*?)\\end{gather\*?}|' + # r'\\begin{array\*?}(.*?)\\end{array\*?}|' + r'\$\$(.*?)\$\$|' + r'\\\[(.*?)\\\]|' + r'\$(.*?)\$|' + r'\\\((.*?)\\\)', + re.DOTALL +) + +# inline_reg = re.compile( +# r'(?)', + re.DOTALL +) + +# title +title_reg = re.compile( + r'^\s*#.*$', + re.MULTILINE) + +# img +img_pattern = r'!\[.*?\]\(.*?\)' + +# code block +code_block_reg = re.compile( + r'```(\w+)\n(.*?)```', + re.DOTALL +) + +def md_tex_filter(content): + ''' + Input: 1 page md or tex content - String + Output: text, display, inline, table, title, code - list + ''' + content = re.sub(img_pattern, '', content) # remove image + content = remove_markdown_fences(content) # remove markdown fences + content = replace_repeated_chars(content) # replace all consecutive characters + content = content.replace('', '').replace('', '').replace('', '').replace('', '') + + # # 使用正则表达式对unicode进行替换 + # special_unicode = ''.join(unicode_replacements.keys()) + # content = re.sub(f'[{special_unicode}]', replace_unicode, content) + + # content = fullwidth_to_halfwidth(content) # fullwidth to halfwidth, TODO: GT also needs this operation + + # # pylatexenc's unicode to latex + # content = unicode_to_latex(content, unknown_char_warning=False) + # markdown_table_content[i, j] = LatexNodes2Text().latex_to_text(content_str) + # content_ori = copy.deepcopy(content) + + # print('--------------After pre_process: \n', content) + + pred_all = [] + # deal with inline formula + # content_new, inline_array = inline_filter_unicode(content) + # #print('------------inline_array----------------',inline_array) + # for inline_item in inline_array: + # inline_item['content'] = inline_to_unicode(inline_item['content']) + # #print('------------inline_array_unicode----------------',inline_item['content']) + # pred_all.append({ + # 'category_type': 'text_all', + # 'position': inline_item['position'], + # 'content': inline_item['content'], + # 'fine_category_type': 'equation_inline' + # }) + + # extract latex table + latex_table_array, table_positions = extract_tex_table(content) + for latex_table, position in zip(latex_table_array, table_positions): + position = [position[0], position[0]+len(latex_table)] # !!! + pred_all.append({ + 'category_type': 'latex_table', + 'position': position, + 'content': latex_table + }) + content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] # replace latex table with space + + # print('--------After latex table: \n', content) + # print('-------latex_table_array: \n', latex_table_array) + + # extract html table + html_table_array, table_positions = extract_html_table(content) + for html_table, position in zip(html_table_array, table_positions): + position = [position[0], position[0]+len(html_table)] + pred_all.append({ + 'category_type': 'html_table', + 'position': position, + 'content': html_table + }) + content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] # replace html table with space + # html_table_array = [] + # html_table_matches = html_table_reg.finditer(content) + # if html_table_matches: + # for match in html_table_matches: + # matched = match.group(0) + # position = [match.start(), match.end()] + # html_table_array.append(matched.strip()) + # # content = content.replace(matched, ' '*len(matched)) # replace html table with space + # content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] # replace html table with space + # pred_all.append({ + # 'category_type': 'html_table', + # 'position': position, + # 'content': matched.strip() + # }) + + # print('--------------After html table: \n', content) + # # extract tables in latex and html + # table_array = [] + # table_matches = table_reg.finditer(content) + # tables = "" + # for match in table_matches: + # matched = match.group(0) + # if matched: + # tables += matched + # tables += "\n\n" + # table_array.append(matched) + # content = content.replace(matched, '') + + # extract interline formula + display_matches = display_reg.finditer(content) + content_copy = content + for match in display_matches: + matched = match.group(0) + if matched: + # single_line = ''.join(matched.split()) + single_line = ' '.join(matched.strip().split('\n')) + position = [match.start(), match.end()] + # replace $$ with \[\] + dollar_pattern = re.compile(r'\$\$(.*?)\$\$|\$(.*?)\$|\\\((.*?)\\\)', re.DOTALL) + sub_match = dollar_pattern.search(single_line) + if sub_match is None: + # pass + content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] + pred_all.append({ + 'category_type': 'equation_isolated', + 'position': position, + 'content': single_line + }) + elif sub_match.group(1): + single_line = re.sub(dollar_pattern, r'\\[\1\\]', single_line) + content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] # replace equation with space + pred_all.append({ + 'category_type': 'equation_isolated', + 'position': position, + 'content': single_line + }) + else: + # start, end = match.span() + # char_before = content_copy[start-1] if start > 0 else '\n' + # char_after = content_copy[end] if end < len(content_copy) else '\n' + # if char_before == '\n' or char_after == '\n': + # single_line = re.sub(dollar_pattern, r'\\[\2\3\\]', single_line) + # pred_all.append({ + # 'category_type': 'equation_isolated', + # 'position': position, + # 'content': single_line, + # 'fine_category_type': 'equation_inline' + # }) + single_line = re.sub(dollar_pattern, r'\\[\2\3\\]', single_line) + pred_all.append({ + 'category_type': 'equation_isolated', + 'position': position, + 'content': single_line, + 'fine_category_type': 'equation_inline' + }) + # single_line = re.sub(dollar_pattern, r'\\[\1\2\3\\]', single_line) + # print('single_line: ', single_line) + # content = content.replace(matched, ' '*len(matched)) + # pred_all.append({ + # 'category_type': 'equation_isolated', + # 'position': position, + # 'content': single_line + # }) + # print('-----Found display formula: ', matched) + + # print('-------------After display: \n', content) + # extract md table with || + md_table_mathces = md_table_reg.findall(content+'\n') + if len(md_table_mathces) >= 2: + # print("md table found!") + # print("content:", content) + content = convert_markdown_to_html(content) + # print('----------content after converting md table to html:', content) + html_table_matches = html_table_reg.finditer(content) + if html_table_matches: + for match in html_table_matches: + matched = match.group(0) + position = [match.start(), match.end()] + # content = content.replace(match, '') + # print('content after removing the md table:', content) + content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] # replace md table with space + pred_all.append({ + 'category_type': 'html_table', + 'position': position, + 'content': matched.strip(), + 'fine_category_type': 'md2html_table' + }) + # print('---------After md table: \n', content) + + # extract code blocks + code_matches = code_block_reg.finditer(content) + if code_matches: + for match in code_matches: + position = [match.start(), match.end()] + language = match.group(1) + code = match.group(2).strip() + # content = content.replace(match.group(0), '') + content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] # replace code block with space + pred_all.append({ + 'category_type': 'text_all', + 'position': position, + 'content': code, + 'language': language, + 'fine_category_type': 'code' + }) + + # print('-------After code block: \n', content) + + # # Extract titles: Do not extract titles, as some models do not wrap code blocks, causing all comments to be treated as titles + # title_matches = title_reg.finditer(content) + # if title_matches: + # for match in title_matches: + # position = [match.start(), match.end()] + # matched = match.group(0) + # matched = matched.replace("#", "").strip() + # # content = content.replace(match, '') + # # print('content after removing the titles:', content) + # if matched: + # # print('Add title: ', matched) + # content = content[:position[0]] + ' '*(position[1]-position[0]) + content[position[1]:] + # pred_all.append({ + # 'category_type': 'text_all', + # 'position': position, + # 'content': matched, + # 'fine_category_type': 'title' + # }) + + # print('----------After title: \n', content) + + # # Delete extracted content + # extracted_position = [_['position'] for _ in pred_all] + # for start, end in sorted(extracted_position, reverse=True): + # content = content[:start] + content[end:] + + # print('----------After delete extracted: \n', content) + + # Remove latex style + content = re.sub(r'\\title\{(.*?)\}', r'\1', content) + content = re.sub(r'\\title\s*\{\s*(.*?)\s*\}', r'\1', content, flags=re.DOTALL) + content = re.sub(r'\\text\s*\{\s*(.*?)\s*\}', r'\1', content, flags=re.DOTALL) + content = re.sub(r'\\section\*?\{(.*?)\}', r'\1', content) + content = re.sub(r'\\section\*?\{\s*(.*?)\s*\}', r'\1', content, flags=re.DOTALL) + + # extract texts + res = content.split('\n\n') + if len(res) == 1: + res = content.split('\n') # some models do not use double newlines, so use single newlines to split + + content_position = 0 + for text in res: + position = [content_position, content_position+len(text)] + content_position += len(text) + text = text.strip() + text = text.strip('\n') + # print('ori_text: ', text) + text = '\n'.join([_.strip() for _ in text.split('\n') if _.strip()]) # avoid some single newline content with many spaces + # print('after strip text: ', text) + + if text: # Check if the stripped text is not empty + if text.startswith(''): + pred_all.append({ + 'category_type': 'html_table', + 'position': position, + 'content': text, + }) + # elif text.startswith('#') and '\n' not in text: + # text = text.replace('#', '').strip() + # if text: + # # print('Add title: ', matched) + # pred_all.append({ + # 'category_type': 'text_all', + # 'position': position, + # 'content': text, + # 'fine_category_type': 'title' + # }) + elif text.startswith('$') and text.endswith('$'): + if text.replace('$', '').strip(): + pred_all.append({ + 'category_type': 'equation_isolated', + 'position': position, + 'content': text.strip(), + }) + else: + text = text.strip() + if text: + pred_all.append({ + 'category_type': 'text_all', + 'position': position, + 'content': text, + 'fine_category_type': 'text_block' + }) + # if '$' in text: + # for formula in re.findall(r'\$(.*?)\$', text): + # formula_array.append(formula) + + pred_dataset = defaultdict(list) + pred_all = sorted(pred_all, key=lambda x: x['position'][0]) + for item in pred_all: + pred_dataset[item['category_type']].append(item) + # pdb.set_trace() + return pred_dataset + + +# def replace_or_extract(match): +# content = match.group(1) if match.group(1) is not None else match.group(2) + +# if any(char in content for char in r'\^_'): +# inline_array.append(match.group(0)) +# return '' +# else: +# return content + +# extract inline math equations in text +# def inline_filter(text): + +# inline_array = [] +# inline_matches = inline_reg.finditer(text) +# for match in inline_matches: +# content = match.group(1) if match.group(1) is not None else match.group(2) + +# # remove \\, \_, \&, \%, \^ +# clean_content = re.sub(r'\\([\\_&%^])', '', content) + +# if any(char in clean_content for char in r'\^_'): +# inline_array.append(match.group(0)) +# text = text.replace(match.group(0), '') +# else: +# text = text.replace(match.group(0), content) + +# return text, inline_array + +# def extract_tex_table(content): +# tables = [] +# positions = [] + +# walker = LatexWalker(content) +# nodes, _, _ = walker.get_latex_nodes() +# if nodes is None: +# return tables, positions + +# for node in nodes: +# if isinstance(node, LatexEnvironmentNode) and ( +# node.environmentname == 'tabular' or node.environmentname == 'table'): +# # table_latex = extract_node_content(node) +# table_latex = content[node.pos:node.pos_end] +# tables.append(table_latex) +# start_pos = node.pos +# end_pos = get_node_end_pos(node) +# positions.append((start_pos, end_pos)) + +# return tables, positions + +def extract_tex_table(content): + tables = [] + tables_positions = [] + + pattern = r'\\begin{table}(.*?)\\end{table}' + for match in re.finditer(pattern, content, re.DOTALL): + start_pos = match.start() + end_pos = match.end() + table_content = match.group(0) + tables.append(table_content) + tables_positions.append((start_pos, end_pos)) + content = content[:start_pos] + ' '*(end_pos-start_pos) + content[end_pos:] + + tabulars, tabular_positions = extract_tabular(content) + all_tables = tables + tabulars + all_positions = tables_positions + tabular_positions + + all_result = sorted([[pos, table]for pos, table in zip(all_positions, all_tables)], key=lambda x: x[0][0]) + all_tables = [x[1] for x in all_result] + all_positions = [x[0] for x in all_result] + + return all_tables, all_positions + +# def extract_html_table(content): +# soup = BeautifulSoup(content, 'html.parser') +# all_tables = soup.find_all('table') +# tables = [] +# positions = [] + +# for table in all_tables: +# if table.find_parent('table') is None: +# table_str = str(table) +# start_pos = content.find(table_str) +# end_pos = start_pos + len(table_str) + +# tables.append(table_str) +# positions.append((start_pos, end_pos)) +# return tables, positions + +def extract_html_table(text): + begin_pattern = r']*)>' + end_pattern = r'' + + tabulars = [] + positions = [] + current_pos = 0 + stack = [] + + while current_pos < len(text): + begin_match = re.search(begin_pattern, text[current_pos:]) + end_match = re.search(end_pattern, text[current_pos:]) + + if not begin_match and not end_match: + break + + if begin_match and (not end_match or begin_match.start() < end_match.start()): + stack.append(current_pos + begin_match.start()) + current_pos += begin_match.start() + len(end_pattern) + elif end_match: + if stack: + start_pos = stack.pop() + if not stack: + end_pos = current_pos + end_match.start() + len(end_pattern) + tabular_code = text[start_pos:end_pos] + tabulars.append(tabular_code) + positions.append((start_pos, end_pos)) + current_pos += end_match.start() + len(end_pattern) + else: + current_pos += 1 + + if stack: + new_start = stack[0] + len(begin_pattern) + new_tabulars, new_positions = extract_html_table(text[new_start:]) + new_positions = [(start + new_start, end + new_start) for start, end in new_positions] + tabulars.extend(new_tabulars) + positions.extend(new_positions) + + return tabulars, positions + + +def extract_node_content(node): + """ Recursively extract content from LatexEnvironmentNode and rebuild LaTeX table representation """ + if isinstance(node, LatexCharsNode): + return node.chars # Use chars attribute + elif isinstance(node, LatexGroupNode): + return "{" + "".join(extract_node_content(n) for n in node.nodelist) + "}" + elif isinstance(node, LatexMacroNode): + # Extract macro command and its arguments + macro_content = "\\" + node.macroname + if node.nodeargs: + macro_content += "".join([extract_node_content(arg) for arg in node.nodeargs]) + return macro_content + elif isinstance(node, LatexEnvironmentNode): + # Extract environment, preserve environment name and arguments + content = "\\begin{" + node.environmentname + "}" + if node.nodeargd and node.nodeargd.argnlist: + # content += "".join("{" + extract_node_content(arg) + "}" for arg in node.nodeargd) + # content += "".join("{" + extract_node_content(node.nodeargd) + "}") + content += "{" + extract_node_content(node.nodeargd.argnlist[0]) + "}" + if node.nodelist: + content += "".join(extract_node_content(n) for n in node.nodelist) + content += "\\end{" + node.environmentname + "}" + return content + elif isinstance(node, LatexSpecialsNode): # Changed to LatexSpecialsNode + return node.specials_chars + else: + return "" + +def get_node_end_pos(node): + """Recursively determine the end position of a node""" + if hasattr(node, 'nodelist') and node.nodelist: + # If the node has child nodes, recursively find the end position of the last child node + return get_node_end_pos(node.nodelist[-1]) + elif hasattr(node, 'pos_end'): + # If the node has pos_end attribute, return it directly + return node.pos_end + else: + # If there are no child nodes, assume the node ends at the last character of its content + return node.pos + len(str(node)) + +def remove_tex_table(content): + tables, positions = extract_tex_table(content) + + # Delete in reverse order by position to avoid affecting unprocessed start positions + for start, end in sorted(positions, reverse=True): + content = content[:start] + content[end:] # Remove table content + + return content \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match.py new file mode 100644 index 00000000..f0921bd0 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match.py @@ -0,0 +1,306 @@ +from scipy.optimize import linear_sum_assignment +import Levenshtein +import numpy as np +import re +import sys +import pdb +from .data_preprocess import textblock_with_norm_formula, normalized_formula, textblock2unicode, clean_string +import re +from bs4 import BeautifulSoup +from copy import deepcopy + +def get_pred_category_type(pred_idx, pred_items): + if pred_items[pred_idx].get('fine_category_type'): + pred_pred_category_type = pred_items[pred_idx]['fine_category_type'] + else: + pred_pred_category_type = pred_items[pred_idx]['category_type'] + return pred_pred_category_type + + +def compute_edit_distance_matrix_new(gt_lines, matched_lines): + try: + distance_matrix = np.zeros((len(gt_lines), len(matched_lines))) + for i, gt_line in enumerate(gt_lines): + for j, matched_line in enumerate(matched_lines): + if len(gt_line) == 0 and len(matched_line) == 0: + distance_matrix[i][j] = 0 + else: + distance_matrix[i][j] = Levenshtein.distance(gt_line, matched_line) / max(len(matched_line), len(gt_line)) + return distance_matrix + except ZeroDivisionError: + raise + + +## 混合匹配here 0403 +def get_gt_pred_lines(gt_mix,pred_dataset_mix,line_type): + + norm_html_lines,gt_lines,pred_lines,norm_gt_lines,norm_pred_lines,gt_cat_list = [],[],[],[],[],[] + if line_type in ['html_table','latex_table']: + for item in gt_mix: + if item.get('fine_category_type'): + gt_cat_list.append(item['fine_category_type']) + else: + gt_cat_list.append(item['category_type']) + if item.get('content'): + gt_lines.append(str(item['content'])) + norm_html_lines.append(str(item['content'])) + elif line_type == 'text': + gt_lines.append(str(item['text'])) + elif line_type == 'html_table': + gt_lines.append(str(item['html'])) + elif line_type == 'formula': + gt_lines.append(str(item['latex'])) + elif line_type == 'latex_table': + gt_lines.append(str(item['latex'])) + norm_html_lines.append(str(item['html'])) + + pred_lines = [str(item['content']) for item in pred_dataset_mix] + if line_type == 'formula': + norm_gt_lines = [normalized_formula(_) for _ in gt_lines] + norm_pred_lines = [normalized_formula(_) for _ in pred_lines] + elif line_type == 'text': + norm_gt_lines = [clean_string(textblock2unicode(_)) for _ in gt_lines] + norm_pred_lines = [clean_string(textblock2unicode(_)) for _ in pred_lines] + else: + norm_gt_lines = gt_lines + norm_pred_lines = pred_lines + if line_type == 'latex_table': + gt_lines = norm_html_lines + + else: + for item in pred_dataset_mix: + # text + if item['category_type'] == 'text_all': + pred_lines.append(str(item['content'])) + norm_pred_lines.append(clean_string(textblock2unicode(str(item['content'])))) + # formula + elif item['category_type']=='equation_isolated': + pred_lines.append(str(item['content'])) + norm_pred_lines.append(normalized_formula(str(item['content']))) + # table + else: + pred_lines.append(str(item['content'])) + norm_pred_lines.append(str(item['content'])) + + for item in gt_mix: + if item.get('content'): + gt_lines.append(str(item['content'])) + if item['category_type'] == 'text_all': + norm_gt_lines.append(clean_string(textblock2unicode(str(item['content'])))) + else: + norm_gt_lines.append(item['content']) + + norm_html_lines.append(str(item['content'])) + + if item.get('fine_category_type'): + gt_cat_list.append(item['fine_category_type']) + else: + gt_cat_list.append(item['category_type']) + # text + elif item['category_type'] in ['text_block', 'title', 'code_txt', 'code_txt_caption', 'reference', 'equation_caption','figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption','header', 'footer', 'page_footnote', 'page_number']: + gt_lines.append(str(item['text'])) + norm_gt_lines.append(clean_string(textblock2unicode(str(item['text'])))) + + if item.get('fine_category_type'): + gt_cat_list.append(item['fine_category_type']) + else: + gt_cat_list.append(item['category_type']) + + # formula + elif item['category_type'] == 'equation_isolated': + gt_lines.append(str(item['latex'])) + norm_gt_lines.append(normalized_formula(str(item['latex']))) + + if item.get('fine_category_type'): + gt_cat_list.append(item['fine_category_type']) + else: + gt_cat_list.append(item['category_type']) + # table + # elif item['category_type'] == 'table': + # gt_lines.append(str(item['html'])) + # norm_gt_lines.append(str(item['html'])) + + # if item.get('fine_category_type'): + # gt_cat_list.append(item['fine_category_type']) + # else: + # gt_cat_list.append(item['category_type']) + + + filtered_lists = [(a, b, c) for a, b, c in zip(gt_lines, norm_gt_lines, gt_cat_list) if a and b] + + # decompress to three lists + if filtered_lists: + gt_lines_c, norm_gt_lines_c, gt_cat_list_c = zip(*filtered_lists) + + # convert to lists + gt_lines_c = list(gt_lines_c) + norm_gt_lines_c = list(norm_gt_lines_c) + gt_cat_list_c = list(gt_cat_list_c) + else: + gt_lines_c = [] + norm_gt_lines_c = [] + gt_cat_list_c = [] + + # pred's empty values + filtered_lists = [(a, b) for a, b in zip(pred_lines, norm_pred_lines) if a and b] + + # decompress to two lists + if filtered_lists: + pred_lines_c, norm_pred_lines_c = zip(*filtered_lists) + + # convert to lists + pred_lines_c = list(pred_lines_c) + norm_pred_lines_c = list(norm_pred_lines_c) + else: + pred_lines_c = [] + norm_pred_lines_c = [] + + return gt_lines_c, norm_gt_lines_c, gt_cat_list_c, pred_lines_c, norm_pred_lines_c, gt_mix, pred_dataset_mix + + +def match_gt2pred_simple(gt_items, pred_items, line_type, img_name): + + gt_lines, norm_gt_lines, gt_cat_list, pred_lines, norm_pred_lines, gt_items, pred_items = get_gt_pred_lines(gt_items, pred_items,line_type) + match_list = [] + + if not norm_gt_lines: # not matched pred should be concatenate + pred_idx_list = range(len(norm_pred_lines)) + match_list.append({ + 'gt_idx': [""], + 'gt': "", + 'pred_idx': pred_idx_list, + 'pred': ''.join(pred_lines[_] for _ in pred_idx_list), + 'gt_position': [""], + 'pred_position': pred_items[pred_idx_list[0]]['position'][0], # get the first pred's position + 'norm_gt': "", + 'norm_pred': ''.join(norm_pred_lines[_] for _ in pred_idx_list), + 'gt_category_type': "", + 'pred_category_type': get_pred_category_type(pred_idx_list[0], pred_items), # get the first pred's category + 'gt_attribute': [{}], + 'edit': 1, + 'img_id': img_name + }) + return match_list,None + elif not norm_pred_lines: # not matched gt should be separated + for gt_idx in range(len(norm_gt_lines)): + match_list.append({ + 'gt_idx': [gt_idx], + 'gt': gt_lines[gt_idx], + 'pred_idx': [""], + 'pred': "", + 'gt_position': [gt_items[gt_idx].get('order') if gt_items[gt_idx].get('order') else gt_items[gt_idx].get('position', [""])[0]], + 'pred_position': "", + 'norm_gt': norm_gt_lines[gt_idx], + 'norm_pred': "", + 'gt_category_type': gt_cat_list[gt_idx], + 'pred_category_type': "", + 'gt_attribute': [gt_items[gt_idx].get("attribute", {})], + 'edit': 1, + 'img_id': img_name + }) + return match_list,None + + cost_matrix = compute_edit_distance_matrix_new(norm_gt_lines, norm_pred_lines) + + row_ind, col_ind = linear_sum_assignment(cost_matrix) + + + for gt_idx in range(len(norm_gt_lines)): + if gt_idx in row_ind: + row_i = list(row_ind).index(gt_idx) + pred_idx = int(col_ind[row_i]) + pred_line = pred_lines[pred_idx] + norm_pred_line = norm_pred_lines[pred_idx] + edit = cost_matrix[gt_idx][pred_idx] + else: + pred_idx = "" + pred_line = "" + norm_pred_line = "" + edit = 1 + + + match_list.append({ + 'gt_idx': [gt_idx], + 'gt': gt_lines[gt_idx], + 'norm_gt': norm_gt_lines[gt_idx], + 'gt_category_type': gt_cat_list[gt_idx], + 'gt_position': [gt_items[gt_idx].get('order') if gt_items[gt_idx].get('order') else gt_items[gt_idx].get('position', [""])[0]], + 'gt_attribute': [gt_items[gt_idx].get("attribute", {})], + 'pred_idx': [pred_idx], + 'pred': pred_line, + 'norm_pred': norm_pred_line, + 'pred_category_type': get_pred_category_type(pred_idx, pred_items) if pred_idx else "", + 'pred_position': pred_items[pred_idx]['position'][0] if pred_idx else "", + 'edit': edit, + 'img_id': img_name + }) + + pred_idx_list = [pred_idx for pred_idx in range(len(norm_pred_lines)) if pred_idx not in col_ind] # get not matched preds + if pred_idx_list: + if line_type in ['html_table', 'latex_table']: + unmatch_table_pred = [] + for i in pred_idx_list: + original_item = pred_items[i] + soup = BeautifulSoup(original_item.get('content'),'html.parser') + text_block = [re.sub(r'\$\\cdot\$','',item.string).strip() for item in soup.findAll('td') if item.string] + for concatenate_text in text_block: + new_item = deepcopy(original_item) + new_item['content'] = concatenate_text + new_item['category_type'] = 'text_all' + unmatch_table_pred.append(new_item) + return match_list, unmatch_table_pred + + else: + match_list.append({ + 'gt_idx': [""], + 'gt': "", + 'pred_idx': pred_idx_list, + 'pred': ''.join(pred_lines[_] for _ in pred_idx_list), + 'gt_position': [""], + 'pred_position': pred_items[pred_idx_list[0]]['position'][0], # get the first pred's position + 'norm_gt': "", + 'norm_pred': ''.join(norm_pred_lines[_] for _ in pred_idx_list), + 'gt_category_type': "", + 'pred_category_type': get_pred_category_type(pred_idx_list[0], pred_items), # get the first pred's category + 'gt_attribute': [{}], + 'edit': 1, + 'img_id': img_name + }) + return match_list,None + + +def match_gt2pred_no_split(gt_items, pred_items, line_type, img_name): + # directly concatenate gt and pred by position + gt_lines, norm_gt_lines, gt_cat_list, pred_lines, norm_pred_lines = get_gt_pred_lines(gt_items, pred_items) + gt_line_with_position = [] + for gt_line, norm_gt_line, gt_item in zip(gt_lines, norm_gt_lines, gt_items): + gt_position = gt_item['order'] if gt_item.get('order') else gt_item.get('position', [""])[0] + if gt_position: + gt_line_with_position.append((gt_position, gt_line, norm_gt_line)) + sorted_gt_lines = sorted(gt_line_with_position, key=lambda x: x[0]) + gt = '\n\n'.join([_[1] for _ in sorted_gt_lines]) + norm_gt = '\n\n'.join([_[2] for _ in sorted_gt_lines]) + pred_line_with_position = [(pred_item['position'], pred_line, pred_norm_line) for pred_line, pred_norm_line, pred_item in zip(pred_lines, norm_pred_lines, pred_items)] + sorted_pred_lines = sorted(pred_line_with_position, key=lambda x: x[0]) + pred = '\n\n'.join([_[1] for _ in sorted_pred_lines]) + norm_pred = '\n\n'.join([_[2] for _ in sorted_pred_lines]) + # edit = Levenshtein.distance(norm_gt, norm_pred)/max(len(norm_gt), len(norm_pred)) + if norm_gt or norm_pred: + return [{ + 'gt_idx': [0], + 'gt': gt, + 'norm_gt': norm_gt, + 'gt_category_type': "text_merge", + 'gt_position': [""], + 'gt_attribute': [{}], + 'pred_idx': [0], + 'pred': pred, + 'norm_pred': norm_pred, + 'pred_category_type': "text_merge", + 'pred_position': "", + # 'edit': edit, + 'img_id': img_name + }] + else: + return [] + diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match_full.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match_full.py new file mode 100644 index 00000000..bfc216ab --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match_full.py @@ -0,0 +1,387 @@ + +import Levenshtein +import re +from collections import Counter +import itertools +from functools import reduce +from utils.extract import inline_filter +from copy import deepcopy +import numpy as np + + +class FuzzyMatch: + def __init__(self, gts, preds): + self.separator = '' + self._gs = gts + self._preds = preds + self.matched_h = {} + + def match(self): + # match stage 1 + equal_match_pair = {} + gs_used_s, preds_used_s = set(), set() + for i in range(len(self._gs)): + for j in range(len(self._preds)): + if self._gs[i] == self._preds[j]: + equal_match_pair[i] = j + gs_used_s.add(i) + preds_used_s.add(j) + + # match stage 2 + # { pred_idx : [ gt_idx0, gt_idx1 ]} + combine_gs_match_preds_ret_h = self._combine_match(self._gs, self._preds, gs_used_s, preds_used_s) + + for pred_idx in combine_gs_match_preds_ret_h.keys(): + preds_used_s.add(pred_idx) + for gt_idx, _ in combine_gs_match_preds_ret_h[pred_idx]: + gs_used_s.add(gt_idx) + + # match stage 3 + # { gt_idx : [ pred_idx0, pred_idx1 ]} + combine_preds_match_gs_ret_h = self._combine_match(self._preds, self._gs, preds_used_s, gs_used_s) + + for gt_idx in combine_preds_match_gs_ret_h.keys(): + gs_used_s.add(gt_idx) + for pred_idx, _ in combine_preds_match_gs_ret_h[gt_idx]: + preds_used_s.add(pred_idx) + + # match stage 4 + gs_free_arr = [i for i in range(len(self._gs)) if i not in gs_used_s] + pred_free_arr = [i for i in range(len(self._preds)) if i not in preds_used_s] + + ## cal gs_free match combine_gs_match_preds + match_gs_free_pred_combine_ret_h = self._free_match_1(gs_free_arr, self._gs, combine_gs_match_preds_ret_h, self._preds) + match_pred_free_gs_combine_ret_h = self._free_match_1(pred_free_arr, self._preds, combine_preds_match_gs_ret_h, self._gs) + + pred_free_gt_free_h = {} + for i in range(len(pred_free_arr)): + for j in range(len(gs_free_arr)): + edit_dis, pos = self._dp(self._preds[i], self._gs[j]) + pred_free_gt_free_h[(j, i)] = (edit_dis, pos) + + gt_free_pred_free_h = {} + for i in range(len(gs_free_arr)): + for j in range(len(pred_free_arr)): + edit_dis, pos = self._dp(self._gs[i], self._preds[j]) + gt_free_pred_free_h[(j, i)] = (edit_dis, pos) + + class MatchPair: + def __init__(self, pred_idx, gt_idx): + self.pred_idx = pred_idx + self.gt_idx = gt_idx + + edit_dis_q = [] # heapq + ### cal gt match + for free_gt_idx in gs_free_arr: + for pred_idx in range(len(self._preds)): + if (pred_idx, free_gt_idx) in match_gs_free_pred_combine_ret_h: + edit_dis_q.append((match_gs_free_pred_combine_ret_h[(pred_idx, free_gt_idx)], MatchPair(pred_idx, free_gt_idx))) + if (pred_idx, free_gt_idx) in gt_free_pred_free_h: + edit_dis_q.append((gt_free_pred_free_h[(pred_idx, free_gt_idx)], MatchPair(pred_idx, free_gt_idx))) + + ### cal pred match + for free_pred_idx in pred_free_arr: + for gt_idx in range(len(self._gs)): + if (gt_idx, free_pred_idx) in match_pred_free_gs_combine_ret_h: + edit_dis_q.append((match_pred_free_gs_combine_ret_h[(gt_idx, free_pred_idx)], MatchPair(free_pred_idx, gt_idx))) + + if (gt_idx, free_pred_idx) in pred_free_gt_free_h: + edit_dis_q.append((pred_free_gt_free_h[(gt_idx, free_pred_idx)], MatchPair(free_pred_idx, gt_idx))) + + # matched groud_truth and predications + ## first, merge matched pair! + matched_gt_pred_h = {} + + for gt_idx in equal_match_pair: + pred_idx = equal_match_pair[gt_idx] + matched_gt_pred_h[(gt_idx, pred_idx, -1)] = True + + for gt_idx in combine_preds_match_gs_ret_h.keys(): + for pred_idx, pos in combine_preds_match_gs_ret_h[gt_idx]: + matched_gt_pred_h[(gt_idx, pred_idx, pos)] = True + + for pred_idx in combine_gs_match_preds_ret_h.keys(): + for gt_idx, pos in combine_gs_match_preds_ret_h[pred_idx]: + matched_gt_pred_h[(gt_idx, pred_idx, pos)] = True + + edit_dis_q.sort(key=lambda x:x[0][0]) + + used_pred_idx_s, used_gt_idx_s = set(), set() + + for match_info, p in edit_dis_q: + edit_dis, pos = match_info + pred_idx, gt_idx = p.pred_idx, p.gt_idx + if edit_dis*1.0 / min(len(self._preds[pred_idx]), len(self._gs[gt_idx])) >= 0.5: + continue + if pred_idx in pred_free_arr and pred_idx in used_pred_idx_s: + continue + if gt_idx in gs_free_arr and gt_idx in used_gt_idx_s: + continue + + matched_gt_pred_h[(gt_idx, pred_idx, pos)] = True + + if pred_idx in pred_free_arr: + used_pred_idx_s.add(pred_idx) + + if gt_idx in gs_free_arr: + used_gt_idx_s.add(gt_idx) + + group_by_gt, group_by_pred, gt_one_pred = {}, {}, {} + gs_matched_s, pred_matched_s = set(), set() + for gt_idx, pred_idx, pos in matched_gt_pred_h.keys(): + gs_matched_s.add(gt_idx) + pred_matched_s.add(pred_idx) + if gt_idx not in group_by_gt: + group_by_gt[gt_idx] = [] + if pred_idx not in group_by_pred: + group_by_pred[pred_idx] = [] + + group_by_gt[gt_idx].append((pred_idx, pos)) + group_by_pred[pred_idx].append((gt_idx, pos)) + + # return combine_gs_match_preds_ret_h, combine_preds_match_gs_ret_h + one = set() + for gt_idx in group_by_gt.keys(): + if len(group_by_gt[gt_idx]) == 1: + one.add((gt_idx, *group_by_gt[gt_idx][0])) + for pred_idx in group_by_pred.keys(): + if len(group_by_pred[pred_idx]) == 1: + gt_idx, pos = group_by_pred[pred_idx][0] + one.add((gt_idx, pred_idx, pos)) + + for gt_idx, pred_idx, pos in one: + if gt_idx in group_by_gt.keys() and pred_idx in group_by_pred.keys(): + if len(group_by_gt[gt_idx]) == 1 and len(group_by_pred[pred_idx]) == 1: + gt_one_pred[gt_idx] = [pred_idx, pos] + elif len(group_by_gt[gt_idx]) == 1: + group_by_gt.pop(gt_idx) + else: + group_by_pred.pop(pred_idx) + return group_by_gt, group_by_pred, gt_one_pred + + def _free_match_1(self, free_source_idx, source_arr, combined_target_h, combined_target_arr): + SHORT_STRING_LEN = 10 + ret = {} + def _do_match(target_str_segment): + for free_idx in free_source_idx: + edit_dis, pos = self._dp(source_arr[free_idx], target_str_segment) + if (matched_target_idx, free_idx) not in ret: + ret[(matched_target_idx, free_idx)] = (edit_dis, pos) + else: + if ret[(matched_target_idx, free_idx)][0] > edit_dis: + ret[(matched_target_idx, free_idx)] = (edit_dis, pos) + + for matched_target_idx in combined_target_h.keys(): + + matched_source_idx_pos = sorted(combined_target_h[matched_target_idx], key=lambda x: x[1]) + if len(matched_source_idx_pos) == 0: continue + + for i, v in enumerate(matched_source_idx_pos): + matched_source_idx, pos = v + if i == 0: + hole_len = pos + 1 - len(source_arr[matched_source_idx]) + else: + hole_len = pos - len(source_arr[matched_source_idx]) - matched_source_idx_pos[i-1][1] + + if 0 >= hole_len: + continue + target_str_segment = combined_target_arr[matched_target_idx][pos+1-hole_len :pos+1] + _do_match(target_str_segment) + + target_str_segment = combined_target_arr[matched_target_idx][matched_source_idx_pos[-1][1]+1:] + if len(target_str_segment) > 0: + _do_match(target_str_segment) + + return ret + + def slide_window_dp(self, line, window): + # that must be right ! + N, M = len(line), len(window) + dp = [[float('inf')]*M for _ in range(N)] + for i in range(N): + dp[i][0] = 1 + if line[i] == window[0]: + dp[i][0] = 0 + + for j in range(1, M): + for i in range(1, N): + dp[i][j] = dp[i-1][j-1] + if line[i] != window[j]: + dp[i][j] += 1 + dp[i][j] = min(dp[i][j], dp[i][j-1]+1, dp[i-1][j]+1) + return dp + + def _dp(self, window, line): + dp = self.slide_window_dp(line, window) + ret = float('inf') + pos = 0 + for i in range(len(line)): + if ret > dp[i][len(window)-1]: + ret = dp[i][len(window)-1] + pos = i + return (ret, pos) + + def _combine_match(self, window_arr, line_arr, window_used_s, line_used_s): + MATCH_EDIT_DIS_RATIO = 0.10 + ABS_DIFF_LEN = 20 + ABS_DIFF_CHAR_COUNT = 5 + + SIGMA_MULTIPLE = 2 + edit_dis_h = {} + + for i in range(len(window_arr)): + if i in window_used_s: continue + for j in range(len(line_arr)): + if j in line_used_s: continue + edit_dis_h[(i, j)] = self._dp(window_arr[i], line_arr[j]) + + # search the one to one pair or combined pattern! + matched_pair_h_gt = {} + matched_gt_idx_s = set() + for i in range(len(window_arr)): + if i in window_used_s: continue + edit_dis, pos = float('inf'), -1 + min_j_idx = -1 + + for j in range(len(line_arr)): + if j in line_used_s: continue + if edit_dis > edit_dis_h[(i, j)][0]: + edit_dis, pos = edit_dis_h[(i, j)] + min_j_idx = j + + if pos == -1: continue + if min_j_idx not in matched_pair_h_gt: + matched_pair_h_gt[min_j_idx] = [] + if edit_dis < len(window_arr[i]) * MATCH_EDIT_DIS_RATIO or (ABS_DIFF_LEN >= len(window_arr[i]) and ABS_DIFF_CHAR_COUNT >= edit_dis): + matched_pair_h_gt[min_j_idx].append((i, pos)) + matched_gt_idx_s.add(i) + + for i in range(len(window_arr)): + if i in window_used_s: continue + + edit_dis_pair = [] + if i in matched_gt_idx_s: continue + for j in range(len(line_arr)): + if j in line_used_s: continue + edit_dis_pair.append((j, *edit_dis_h[i, j])) + + if len(edit_dis_pair) == 0: continue + if len(edit_dis_pair) == 1: + best_j_idx, edit_dis, pos = edit_dis_pair[0] + matched_gt_idx_s.add(i) + matched_pair_h_gt[best_j_idx].append((i, pos)) + continue + + edit_dis_arr = np.array([edit_dis for _, edit_dis, _ in edit_dis_pair]) + mean = np.mean(edit_dis_pair) + std_var = np.std(edit_dis_arr) + + beyond_sigma = sorted([(j, edit_dis, pos) for j, edit_dis, pos in edit_dis_pair if mean - SIGMA_MULTIPLE*std_var >= edit_dis], key=lambda x: x[1]) + if len(beyond_sigma) > 0: + matched_pair_h_gt[beyond_sigma[0][0]].append((i, beyond_sigma[0][2])) + + # gts idx is not the order appeart in preds, that is very bad, we need a fast way to re-order it! + return {k: matched_pair_h_gt[k] for k in matched_pair_h_gt.keys() if len(matched_pair_h_gt[k]) > 0 } + + +def match_gt_pred(gts, predications): + """ + parameters: + gts : groud truth list, [gt0, gt1, gt2, gt3, gt4] + predications: predications list + return: list of array that match the each element of ground truth + + Example: + gts = [gt0, gt1, gt2,] + preds = [pr0, pr1, pr2] + + returns : [ + [pr0, pr2], # [pr0, pr2] match gt0 + [], # no pred match the gt1 + [pr1] # [pr1] match gt2 + ] + """ + if any([len(v) == 0 for v in predications]): + raise Exception("please remove empty string from predications list") + if len(predications) == 0: + return {}, {}, {} + if len(gts) == 0: + return {}, {}, {} + + matcher = FuzzyMatch(gts, predications) + return matcher.match() + + +def match_gt2pred_full(gts, predications): + group_by_gt, group_by_pred, gt_one_pred = match_gt_pred(gts, predications) + seen_gt_s = set() + + ret = [] + for gt_idx in group_by_gt.keys(): + matched_preds = [p[0] for p in sorted(group_by_gt[gt_idx], key=lambda x:x[1])] + ret.append({ + "gt_idx": [gt_idx], + "gt": gts[gt_idx], + "pred_idx": matched_preds, + "pred": "".join([predications[pr_idx] for pr_idx in matched_preds]) + }) + seen_gt_s.add(gt_idx) + + for pred_idx in group_by_pred: + matched_gts = [p[0] for p in sorted(group_by_pred[pred_idx], key=lambda x:x[1])] + ret.append({ + "gt_idx": matched_gts, + "gt": "".join([gts[gt_idx] for gt_idx in matched_gts]), + "pred_idx": [pred_idx], + "pred": predications[pred_idx] + }) + + for gt_idx in matched_gts: + seen_gt_s.add(gt_idx) + + for gt_idx in gt_one_pred.keys(): + pred_idx = gt_one_pred[gt_idx][0] + ret.append({ + "gt_idx": [gt_idx], + "gt": gts[gt_idx], + "pred_idx": [pred_idx], + "pred": predications[pred_idx] + }) + seen_gt_s.add(gt_idx) + + for i in range(len(gts)): + if i in seen_gt_s: continue + ret.append({ + "gt_idx": [i], + "gt": gts[i], + "pred_idx": [], + "pred": "" + }) + return ret + + +def match_gt2pred_textblock_full(gt_lines, pred_lines): + text_inline_match_s = match_gt2pred_full(gt_lines, pred_lines, 'text') + plain_text_match = [] + inline_formula_match = [] + for item in text_inline_match_s: + plaintext_gt, inline_gt_list = inline_filter(item['gt']) # this should be extracted from span + plaintext_pred, inline_pred_list = inline_filter(item['pred']) + # print('inline_pred_list', inline_pred_list) + # print('plaintext_pred: ', plaintext_pred) + plaintext_gt = plaintext_gt.replace(' ', '') + plaintext_pred = plaintext_pred.replace(' ', '') + if plaintext_gt or plaintext_pred: + edit = Levenshtein.distance(plaintext_gt, plaintext_pred)/max(len(plaintext_pred), len(plaintext_gt)) + plain_text_match.append({ + 'gt_idx': item['gt_idx'], + 'gt': plaintext_gt, + 'pred_idx': item['pred_idx'], + 'pred': plaintext_pred, + 'edit': edit + }) + + if inline_gt_list: + inline_formula_match_s = match_gt2pred_full(inline_gt_list, inline_pred_list) + inline_formula_match.extend(inline_formula_match_s) + return plain_text_match, inline_formula_match diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match_quick.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match_quick.py new file mode 100644 index 00000000..b0f3c962 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/match_quick.py @@ -0,0 +1,1156 @@ +from scipy.optimize import linear_sum_assignment +# from rapidfuzz.distance import Levenshtein +import Levenshtein +from collections import defaultdict +import copy +from utils.match import compute_edit_distance_matrix_new, get_gt_pred_lines, get_pred_category_type +import pdb +import numpy as np +import evaluate +from collections import Counter +from Levenshtein import distance as Levenshtein_distance + +import re +from copy import deepcopy +from typing import List, Dict, Any + +# ARRAY_RE = re.compile( +# r'\\begin\{array\}\{[^}]*\}(.*?)\\end\{array\}', re.S +# ) + +# def split_gt_equation_arrays(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: +# """ +# 拆分带 \\begin{array} … \\end{array} 的 GT 字典条目。 + +# - 仅针对 category_type == 'equation_isolated' 且 latex 含 array。 +# - 每行公式拆出一个新条目： +# * 更新 'latex' +# * 若存在 line_with_spans，则同步替换其内部 latex +# * 'order' 由 7 --> 7.1, 7.2, … +# """ +# output = [] + +# for item in data: +# # 只处理满足条件的字典 +# if (item.get("category_type") == "equation_isolated" and +# "\\begin{array" in item.get("latex", "")): + +# # 抽取 array 内部内容 +# match = ARRAY_RE.search(item["latex"]) +# if match: +# body = match.group(1) # 去掉 array 外壳 +# # 按 LaTeX 行分隔符 \\\\ 拆分 +# lines = [ln.strip() for ln in re.split(r'\\\\', body) if ln.strip()] + +# base_order = float(item["order"]) # 7 -> 7.0，可兼容 float/int + +# for idx, line in enumerate(lines, start=1): +# new_item = deepcopy(item) +# new_item["latex"] = f"\\[{line}\\]" +# new_item["order"] = round(base_order + idx / 10, 1) +# output.append(new_item) +# continue # 跳过把原 item 加入 +# # 其它情况不修改 +# output.append(item) + +# return output + +# def _wrap(line: str) -> str: +# """给单行公式重新包 \\[ ... \\]""" +# return f"\\[{line.strip()}\\]" + +# def split_equation_arrays(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: +# """ +# 处理 category_type == 'equation_isolated' 且含 \\begin{array} … 的条目： +# * 拆分多行公式 +# * 重新包装 content +# * **重计算 position / positions** +# """ +# out: List[Dict[str, Any]] = [] + +# for item in data: +# if (item.get("category_type") == "equation_isolated" and +# "\\begin{array" in item.get("content", "")): + +# content = item["content"] +# m = ARRAY_RE.search(content) +# if not m: +# out.append(item) +# continue + +# body = m.group(1) +# lines = [ln.strip() for ln in re.split(r'\\\\', body) if ln.strip()] + +# # 全局起始字符索引 +# pos_key = "position" if "position" in item else "positions" +# global_start = item[pos_key][0] + +# # array 正文在原 content 内的起点 +# body_start_in_content = m.start(1) + +# search_from = 0 # 在 body 中的游标 +# for ln in lines: +# # 在 body 中找到当前行的偏移 +# idx_in_body = body.find(ln, search_from) +# if idx_in_body == -1: +# # 不太可能发生；保守处理 +# idx_in_body = search_from +# search_from = idx_in_body + len(ln) # 更新游标 + +# # 计算全局索引 +# line_start_global = global_start + body_start_in_content + idx_in_body +# line_end_global = line_start_global + len(ln) - 1 + +# new_item = deepcopy(item) +# new_item["content"] = _wrap(ln) +# new_item[pos_key] = [line_start_global, line_end_global] + +# out.append(new_item) + +# # 拆分完成，不保留原条目 +# continue + +# # 其它条目直接加入 +# out.append(item) + +# return out + +ARRAY_RE = re.compile( + r'\\begin\{array\}\{(?P[^}]*)\}(?P.*?)\\end\{array\}', + re.S +) + +def is_all_l(spec: str) -> bool: + """检查是否为单列array格式，用于排除矩阵等多列格式。这个函数只拆分单列的array""" + spec = re.sub(r'\s+|\|', '', spec) # 删空白与竖线 + spec = re.sub(r'@{[^}]*}', '', spec) # 删 @{…} 修饰 + spec = re.sub(r'!{[^}]*}', '', spec) # 删 !{…} 修饰 + # 检查是否为单列基本对齐格式：l, c, r + return bool(spec) and len(spec) == 1 and spec in {'l', 'c', 'r'} + +# def is_all_l(spec: str) -> bool: +# """忽略空格 / 竖线 / @{…} 之后，判断列格式是否只剩基本对齐格式。这个函数会将多行多列的array按行拆分""" +# spec = re.sub(r'\s+|\|', '', spec) # 删空白与竖线 +# spec = re.sub(r'@{[^}]*}', '', spec) # 删 @{…} 修饰 +# spec = re.sub(r'!{[^}]*}', '', spec) # 删 !{…} 修饰 +# # 检查是否只包含基本对齐格式：l, c, r +# return bool(spec) and set(spec) <= {'l', 'c', 'r'} + +def split_gt_equation_arrays(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + 拆分带 \\begin{array} … \\end{array} 的 GT 字典条目。 + + - 仅针对 category_type == 'equation_isolated' 且 latex 含 array。 + - 每行公式拆出一个新条目： + * 更新 'latex' + * 若存在 line_with_spans，则同步替换其内部 latex + * 'order' 由 7 --> 7.1, 7.2, … + """ + output = [] + + for item in data: + # 只处理满足条件的字典 + if (item.get("category_type") == "equation_isolated" and + "\\begin{array" in item.get("latex", "")): + + # 抽取 array 内部内容 + match = ARRAY_RE.search(item["latex"]) + if match: + + spec = match.group("spec") + if not is_all_l(spec): + # 若列里混有 r / c / p{…} 等，直接保留原条目 + output.append(item) + continue + + body = match.group("body") + # body = match.group(1) # 去掉 array 外壳 + # 按 LaTeX 行分隔符 \\\\ 拆分 + lines = [ln.strip() for ln in re.split(r'\\\\', body) if ln.strip()] + + base_order = float(item["order"]) # 7 -> 7.0，可兼容 float/int + + for idx, line in enumerate(lines, start=1): + new_item = deepcopy(item) + new_item["latex"] = f"\\[{line}\\]" + new_item["order"] = round(base_order + idx / 10, 1) + output.append(new_item) + continue # 跳过把原 item 加入 + # 其它情况不修改 + output.append(item) + + return output + +def _wrap(line: str) -> str: + """给单行公式重新包 \\[ ... \\]""" + return f"\\[{line.strip()}\\]" + +def split_equation_arrays(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + 处理 category_type == 'equation_isolated' 且含 \\begin{array} … 的条目： + * 拆分多行公式 + * 重新包装 content + * **重计算 position / positions** + """ + out: List[Dict[str, Any]] = [] + + for item in data: + if (item.get("category_type") == "equation_isolated" and + "\\begin{array" in item.get("content", "")): + + content = item["content"] + m = ARRAY_RE.search(content) + if not m: + out.append(item) + continue + + if not is_all_l(m.group('spec')): + out.append(item) + continue + + # body = m.group(1) + body = m.group('body') + lines = [ln.strip() for ln in re.split(r'\\\\', body) if ln.strip()] + + # 全局起始字符索引 + pos_key = "position" if "position" in item else "positions" + global_start = item[pos_key][0] + + # array 正文在原 content 内的起点 + # body_start_in_content = m.start(1) + body_start_in_content = m.start('body') + + search_from = 0 # 在 body 中的游标 + for ln in lines: + # 在 body 中找到当前行的偏移 + idx_in_body = body.find(ln, search_from) + if idx_in_body == -1: + # 不太可能发生；保守处理 + idx_in_body = search_from + search_from = idx_in_body + len(ln) # 更新游标 + + # 计算全局索引 + line_start_global = global_start + body_start_in_content + idx_in_body + line_end_global = line_start_global + len(ln) - 1 + + new_item = deepcopy(item) + new_item["content"] = _wrap(ln) + new_item[pos_key] = [line_start_global, line_end_global] + + out.append(new_item) + + # 拆分完成，不保留原条目 + continue + + # 其它条目直接加入 + out.append(item) + + return out + +def sort_by_position_skip_inline(items: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + """ + 先按 position[0] 从小到大排序； + 若 fine_category_type == 'equation_inline'，则统一放到最后， + 并保持它们在原列表中的相对顺序（稳定排序）。 + """ + # enumerate 保留原始顺序索引，用于 equation_inline “并列时” 的稳定性 + return sorted( + enumerate(items), + key=lambda pair: ( + pair[1].get('fine_category_type') == 'equation_inline', # False < True + pair[1]['position'][0], # 位置起点 + pair[0] # 原序号，确保稳定 + ) + ) +def match_gt2pred_quick(gt_items, pred_items, line_type, img_name): + + gt_items = split_gt_equation_arrays(gt_items) + + # pred_items = sorted(pred_items, key=lambda x: x['position'][0]) + pred_items = [pair[1] for pair in sort_by_position_skip_inline(pred_items)] + + pred_items = split_equation_arrays(pred_items) + + # gt_lines, norm_gt_lines, gt_cat_list, pred_lines, norm_pred_lines= get_gt_pred_lines(gt_items, pred_items, line_type) + gt_lines, norm_gt_lines, gt_cat_list, pred_lines, norm_pred_lines, gt_items, pred_items = get_gt_pred_lines(gt_items, pred_items, None) + all_gt_indices = set(range(len(norm_gt_lines))) + all_pred_indices = set(range(len(norm_pred_lines))) + + if not norm_gt_lines: + match_list = [] + for pred_idx in range(len(norm_pred_lines)): + match_list.append({ + 'gt_idx': [""], + 'gt': "", + 'pred_idx': [pred_idx], + 'pred': pred_lines[pred_idx], + 'gt_position': [""], + 'pred_position': pred_items[pred_idx]['position'][0], + 'norm_gt': "", + 'norm_pred': norm_pred_lines[pred_idx], + 'gt_category_type': "", + 'pred_category_type': get_pred_category_type(pred_idx, pred_items), + 'gt_attribute': [{}], + 'edit': 1, + 'img_id': img_name + }) + return match_list + elif not norm_pred_lines: + match_list = [] + for gt_idx in range(len(norm_gt_lines)): + match_list.append({ + 'gt_idx': [gt_idx], + 'gt': gt_lines[gt_idx], + 'pred_idx': [""], + 'pred': "", + 'gt_position': [gt_items[gt_idx].get('order') if gt_items[gt_idx].get('order') else gt_items[gt_idx].get('position', [""])[0]], + 'pred_position': "", + 'norm_gt': norm_gt_lines[gt_idx], + 'norm_pred': "", + 'gt_category_type': gt_cat_list[gt_idx], + 'pred_category_type': "", + 'gt_attribute': [gt_items[gt_idx].get("attribute", {})], + 'edit': 1, + 'img_id': img_name + }) + return match_list + elif len(norm_gt_lines) == 1 and len(norm_pred_lines) == 1: + edit_distance = Levenshtein_distance(norm_gt_lines[0], norm_pred_lines[0]) + normalized_edit_distance = edit_distance / max(len(norm_gt_lines[0]), len(norm_pred_lines[0])) + return [{ + 'gt_idx': [0], + 'gt': gt_lines[0], + 'pred_idx': [0], + 'pred': pred_lines[0], + 'gt_position': [gt_items[0].get('order') if gt_items[0].get('order') else gt_items[0].get('position', [""])[0]], + 'pred_position': pred_items[0]['position'][0], + 'norm_gt': norm_gt_lines[0], + 'norm_pred': norm_pred_lines[0], + 'gt_category_type': gt_cat_list[0], + 'pred_category_type': get_pred_category_type(0, pred_items), + 'gt_attribute': [gt_items[0].get("attribute", {})], + 'edit': normalized_edit_distance, + 'img_id': img_name + }] + + # match category ignore first + ignores = ['figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', + 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number', 'equation_caption'] + + ignore_gt_lines = [] + ignores_ori_gt_lines= [] + ignores_gt_items = [] + ignore_gt_idxs = [] + ignores_gt_cat_list = [] + + no_ignores_gt_lines = [] + no_ignores_ori_gt_lines = [] + no_ignores_gt_idxs = [] + no_ignores_gt_items = [] + no_ignores_gt_cat_list = [] + + for i, line in enumerate(norm_gt_lines): + if gt_cat_list[i] in ignores: + ignore_gt_lines.append(line) + ignores_ori_gt_lines.append(gt_lines[i]) + ignores_gt_items.append(gt_items[i]) + ignore_gt_idxs.append(i) + ignores_gt_cat_list.append(gt_cat_list[i]) + else: + no_ignores_gt_lines.append(line) + no_ignores_ori_gt_lines.append(gt_lines[i]) + no_ignores_gt_items.append(gt_items[i]) + no_ignores_gt_cat_list.append(gt_cat_list[i]) + no_ignores_gt_idxs.append(i) + + # print("-------------ignore_gt_lines-------------------") + # for idx, line in zip(ignore_idx,ignore_gt_lines): + # print(f"{gt_cat_list[idx]}: {line}") + + # print("-------------no_ignores_gt_lines-------------------") + # for line in no_ignores_gt_lines: + # print(line) + + ignore_pred_idxs = [] + ignore_pred_lines = [] + ignores_pred_items = [] + ignores_ori_pred_lines = [] + + merged_ignore_results = [] + + if len(ignore_gt_lines) > 0: + + ignore_matches_dict = {} + + ignore_matrix = compute_edit_distance_matrix_new(ignore_gt_lines, norm_pred_lines) + # print("-------------ignore_matrix-------------") + # print(ignore_matrix) + + ignores_gt_indices = set(range(len(ignore_gt_lines))) + ignores_pred_indices = set(range(len(ignore_pred_lines))) + + ignore_matches = np.argwhere(ignore_matrix < 0.25) + # print("-------------ignore_matches-------------") + # print(ignore_matches) + if len(ignore_matches) > 0: + ignore_pred_idxs = [_[1] for _ in ignore_matches] + ignore_gt_matched_idxs = [ignore_gt_idxs[_[0]] for _ in ignore_matches] + # print("-------------ignore_pred_idxs-------------") + # print(ignore_pred_idxs) + # print("-------------ignore_gt_matched_idxs-------------") + # print(ignore_gt_matched_idxs) + + for i in ignore_pred_idxs: + ignore_pred_lines.append(norm_pred_lines[i]) + ignores_ori_pred_lines.append(pred_lines[i]) + ignores_pred_items.append(pred_items[i]) + # print("-------------ignore_pred_lines-------------") + # for i in ignore_pred_lines: + # print(i) + + ignores_gt_indices = set(range(len(ignore_gt_lines))) + ignores_pred_indices = set(range(len(ignore_pred_lines))) + + for idx, i in enumerate(ignore_matches): + ignore_matches_dict[i[0]] = { + 'pred_indices': [idx], + 'edit_distance': ignore_matrix[i[0]][i[1]] + } + # print("-------------ignore_matches_dict-------------") + # print(ignore_matches_dict) + + ignore_final_matches = merge_matches(ignore_matches_dict, {}) + # print("-------------ignore_final_matches-------------") + # print(ignore_final_matches) + + recalculate_edit_distances(ignore_final_matches, {}, ignore_gt_lines, ignore_pred_lines) + # print("-------------recalculate_ignore_final_matches-------------") + # print(ignore_final_matches) + + converted_ignore_results = convert_final_matches(ignore_final_matches, ignore_gt_lines, ignore_pred_lines) + # print("-------------converted_ignore_results-------------") + # for i in converted_ignore_results: + # print(i) + + merged_ignore_results = merge_duplicates_add_unmatched(converted_ignore_results, ignore_gt_lines, ignore_pred_lines, ignores_ori_gt_lines, ignores_ori_pred_lines, ignores_gt_indices, ignores_pred_indices) + + for entry in merged_ignore_results: + entry['gt_idx'] = [entry['gt_idx']] if not isinstance(entry['gt_idx'], list) else entry['gt_idx'] + entry['pred_idx'] = [entry['pred_idx']] if not isinstance(entry['pred_idx'], list) else entry['pred_idx'] + entry['gt_position'] = [ignores_gt_items[_].get('order') if ignores_gt_items[_].get('order') else ignores_gt_items[_].get('position', [""])[0] for _ in entry['gt_idx']] if entry['gt_idx'] != [""] else [""] + entry['pred_position'] = ignores_pred_items[entry['pred_idx'][0]]['position'][0] if entry['pred_idx'] != [""] else "" + entry['gt'] = ''.join([ignores_ori_gt_lines[_] for _ in entry['gt_idx']]) if entry['gt_idx'] != [""] else "" + entry['pred'] = ''.join([ignores_ori_pred_lines[_] for _ in entry['pred_idx']]) if entry['pred_idx'] != [""] else "" + entry['norm_gt'] = ''.join([ignore_gt_lines[_] for _ in entry['gt_idx']]) if entry['gt_idx'] != [""] else "" + entry['norm_pred'] = ''.join([ignore_pred_lines[_] for _ in entry['pred_idx']]) if entry['pred_idx'] != [""] else "" + + if entry['gt_idx'] != [""]: + ignore_type = ['figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number', 'equation_caption'] + gt_cagegory_clean = [ignores_gt_cat_list[_] for _ in entry['gt_idx'] if ignores_gt_cat_list[_] not in ignore_type] + if gt_cagegory_clean: + entry['gt_category_type'] = Counter(gt_cagegory_clean).most_common(1)[0][0] + else: + entry['gt_category_type'] = Counter([ignores_gt_cat_list[_] for _ in entry['gt_idx']]).most_common(1)[0][0] + else: + entry['gt_category_type'] = "" + entry['pred_category_type'] = get_pred_category_type(entry['pred_idx'][0], ignores_pred_items) if entry['pred_idx'] != [""] else "" + if entry['pred_category_type'] == 'equation_inline': + merged_ignore_results.remove(entry) + entry['pred_category_type'] = get_pred_category_type(entry['pred_idx'][0], ignores_pred_items) if entry['pred_idx'] != [""] else "" + entry['gt_attribute'] = [ignores_gt_items[_].get("attribute", {}) for _ in entry['gt_idx']] if entry['gt_idx'] != [""] else [{}] + entry['img_id'] = img_name + + for entry in merged_ignore_results: + if isinstance(entry['gt_idx'], list) and entry['gt_idx'] != [""]: + gt_idx = [] + for i in entry['gt_idx']: + gt_idx.append(ignore_gt_idxs[i]) + entry['gt_idx'] = gt_idx + if isinstance(entry['pred_idx'], list) and entry['pred_idx'] != [""]: + pred_idx = [] + for i in entry['pred_idx']: + pred_idx.append(int(ignore_pred_idxs[i])) + entry['pred_idx'] = pred_idx + + # print("-------------merged_ignore_results-------------") + # for i in merged_ignore_results: + # print(i) + + no_ignores_pred_lines = [] + no_ignores_ori_pred_lines = [] + no_ignores_pred_indices = [] + no_ignores_pred_items = [] + no_ignore_pred_idxs = [] + + for idx, line in enumerate(norm_pred_lines): + if not idx in ignore_pred_idxs: + no_ignores_pred_lines.append(line) + no_ignores_ori_pred_lines.append(pred_lines[idx]) + # no_ignores_pred_indices.append(idx) + no_ignores_pred_items.append(pred_items[idx]) + no_ignore_pred_idxs.append(idx) + + # initialize new indices for lines without ignore categories + no_ignores_gt_indices = set(range(len(no_ignores_gt_lines))) + no_ignores_pred_indices = set(range(len(no_ignores_pred_lines))) + + # exclude ignore categories + cost_matrix = compute_edit_distance_matrix_new(no_ignores_gt_lines, no_ignores_pred_lines) + # print("-------------cost matrix-------------") + # print(cost_matrix) + + matched_col_idx, row_ind, cost_list = cal_final_match(cost_matrix, no_ignores_gt_lines, no_ignores_pred_lines) + # print("-------------matched_col_idx-------------") + # print(matched_col_idx) + + # print("-------------gt_row_ind-------------") + # print(row_ind) + + # print("-------------cost_list-------------") + # print(cost_list) + + gt_lens_dict, pred_lens_dict = initialize_indices(no_ignores_gt_lines, no_ignores_pred_lines) + # print("-------------gt_lens_dict-------------") + # print(gt_lens_dict) + + # print("-------------pred_lens_dict-------------") + # print(pred_lens_dict) + + matches, unmatched_gt_indices, unmatched_pred_indices = process_matches(matched_col_idx, row_ind, cost_list, no_ignores_gt_lines, no_ignores_pred_lines, no_ignores_ori_pred_lines) + + # print("-------------matches-------------") + # print(matches) + + # print("-------------unmatched_gt_indices-------------") + # print(unmatched_gt_indices) + + # print("-------------unmatched_pred_indices-------------") + # print(unmatched_pred_indices) + + matching_dict = fuzzy_match_unmatched_items(unmatched_gt_indices, no_ignores_gt_lines, no_ignores_pred_lines) + # print("-------------matching_dict-------------") + # print(matching_dict) + + final_matches = merge_matches(matches, matching_dict) + # print("-------------final_matches-------------") + # print(final_matches) + + recalculate_edit_distances(final_matches, gt_lens_dict, no_ignores_gt_lines, no_ignores_pred_lines) + # print("-------------recalculate_edit_distances-------------") + # print(final_matches) + + converted_results = convert_final_matches(final_matches, no_ignores_gt_lines, no_ignores_pred_lines) + # print("-------------converted_results-------------") + # print(converted_results) + + merged_results = merge_duplicates_add_unmatched(converted_results, no_ignores_gt_lines, no_ignores_pred_lines, no_ignores_ori_gt_lines, no_ignores_ori_pred_lines, no_ignores_gt_indices, no_ignores_pred_indices) + + for entry in merged_results: + if entry['gt_idx'] != [""]: + ignore_type = ['figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number', 'equation_caption'] + gt_cagegory_clean = [no_ignores_gt_cat_list[_] for _ in entry['gt_idx'] if no_ignores_gt_cat_list[_] not in ignore_type] + if gt_cagegory_clean: + entry['gt_category_type'] = Counter(gt_cagegory_clean).most_common(1)[0][0] + else: + entry['gt_category_type'] = Counter([no_ignores_gt_cat_list[_] for _ in entry['gt_idx']]).most_common(1)[0][0] + else: + entry['gt_category_type'] = "" + entry['pred_category_type'] = get_pred_category_type(entry['pred_idx'][0], no_ignores_pred_items) if entry['pred_idx'] != [""] else "" + if entry['pred_category_type'] == 'equation_inline': + merged_results.remove(entry) + + + entry['gt_idx'] = [entry['gt_idx']] if not isinstance(entry['gt_idx'], list) else entry['gt_idx'] + entry['pred_idx'] = [entry['pred_idx']] if not isinstance(entry['pred_idx'], list) else entry['pred_idx'] + entry['gt_position'] = [no_ignores_gt_items[_].get('order') if no_ignores_gt_items[_].get('order') else no_ignores_gt_items[_].get('position', [""])[0] for _ in entry['gt_idx']] if entry['gt_idx'] != [""] else [""] + entry['pred_position'] = no_ignores_pred_items[entry['pred_idx'][0]]['position'][0] if entry['pred_idx'] != [""] else "" + # 0507 多行公式拼接修改 + if entry['gt_category_type'] == 'equation_isolated' and len(entry['gt_idx']) > 1: + mutli_formula = ' \\\\ '.join(['{'+no_ignores_ori_gt_lines[_].strip('$$').strip('\n')+'}' for _ in entry['gt_idx']]) if entry['gt_idx'] != [""] else "" + mutli_formula = '\\begin{array}{l} ' + mutli_formula + ' \end{array}' + entry['gt'] = mutli_formula + else: + entry['gt'] = ''.join([no_ignores_ori_gt_lines[_] for _ in entry['gt_idx']]) if entry['gt_idx'] != [""] else "" + + entry['pred_category_type'] = get_pred_category_type(entry['pred_idx'][0], no_ignores_pred_items) if entry['pred_idx'] != [""] else "" + entry['gt_attribute'] = [no_ignores_gt_items[_].get("attribute", {}) for _ in entry['gt_idx']] if entry['gt_idx'] != [""] else [{}] + entry['img_id'] = img_name + + # 0724 多行公式拼接修改pred + if 'equation' in entry['pred_category_type'] and len(entry['pred_idx']) > 1: + mutli_formula = ' \\\\ '.join(['{'+no_ignores_ori_pred_lines[_].strip('$$').strip('\n')+'}' for _ in entry['pred_idx']]) if entry['pred_idx'] != [""] else "" + mutli_formula = '\\begin{array}{l} ' + mutli_formula + ' \end{array}' + entry['pred'] = mutli_formula + else: + entry['pred'] = ''.join([no_ignores_ori_pred_lines[_] for _ in entry['pred_idx']]) if entry['pred_idx'] != [""] else "" + + entry['norm_gt'] = ''.join([no_ignores_gt_lines[_] for _ in entry['gt_idx']]) if entry['gt_idx'] != [""] else "" + entry['norm_pred'] = ''.join([no_ignores_pred_lines[_] for _ in entry['pred_idx']]) if entry['pred_idx'] != [""] else "" + + + # print("-------------merged_results-------------") + # for i in merged_results: + # print(i) + for entry in merged_results: + if isinstance(entry['gt_idx'], list) and entry['gt_idx'] != [""]: + gt_idx = [] + for i in entry['gt_idx']: + gt_idx.append(no_ignores_gt_idxs[i]) + entry['gt_idx'] = gt_idx + if isinstance(entry['pred_idx'], list) and entry['pred_idx'] != [""]: + pred_idx = [] + for i in entry['pred_idx']: + pred_idx.append(int(no_ignore_pred_idxs[i])) + entry['pred_idx'] = pred_idx + + if len(merged_ignore_results) > 0: + merged_results.extend(merged_ignore_results) + # for i in merged_ignore_results: + # merged_results.append(i) + + return merged_results + + # cost_matrix = compute_edit_distance_matrix_new(norm_gt_lines, norm_pred_lines) + + # matched_col_idx, row_ind, cost_list = cal_final_match(cost_matrix, norm_gt_lines, norm_pred_lines) + + # gt_lens_dict, pred_lens_dict = initialize_indices(norm_gt_lines, norm_pred_lines) + + # matches, unmatched_gt_indices, unmatched_pred_indices = process_matches(matched_col_idx, row_ind, cost_list, norm_gt_lines, norm_pred_lines, pred_lines) + + # matching_dict = fuzzy_match_unmatched_items(unmatched_gt_indices, norm_gt_lines, norm_pred_lines) + + # final_matches = merge_matches(matches, matching_dict) + + # recalculate_edit_distances(final_matches, gt_lens_dict, norm_gt_lines, norm_pred_lines) + + # converted_results = convert_final_matches(final_matches, norm_gt_lines, norm_pred_lines) + + # merged_results = merge_duplicates_add_unmatched(converted_results, norm_gt_lines, norm_pred_lines, gt_lines, pred_lines, all_gt_indices, all_pred_indices) + + # for entry in merged_results: + # entry['gt_idx'] = [entry['gt_idx']] if not isinstance(entry['gt_idx'], list) else entry['gt_idx'] + # entry['pred_idx'] = [entry['pred_idx']] if not isinstance(entry['pred_idx'], list) else entry['pred_idx'] + # entry['gt_position'] = [gt_items[_].get('order') if gt_items[_].get('order') else gt_items[_].get('position', [""])[0] for _ in entry['gt_idx']] if entry['gt_idx'] != [""] else [""] + # entry['pred_position'] = pred_items[entry['pred_idx'][0]]['position'][0] if entry['pred_idx'] != [""] else "" + # entry['gt'] = ''.join([gt_lines[_] for _ in entry['gt_idx']]) if entry['gt_idx'] != [""] else "" + # entry['pred'] = ''.join([pred_lines[_] for _ in entry['pred_idx']]) if entry['pred_idx'] != [""] else "" + # entry['norm_gt'] = ''.join([norm_gt_lines[_] for _ in entry['gt_idx']]) if entry['gt_idx'] != [""] else "" + # entry['norm_pred'] = ''.join([norm_pred_lines[_] for _ in entry['pred_idx']]) if entry['pred_idx'] != [""] else "" + + # if entry['gt_idx'] != [""]: + # ignore_type = ['figure_caption', 'figure_footnote', 'table_caption', 'table_footnote', 'code_algorithm', 'code_algorithm_caption', 'header', 'footer', 'page_footnote', 'page_number', 'equation_caption'] + # gt_cagegory_clean = [gt_cat_list[_] for _ in entry['gt_idx'] if gt_cat_list[_] not in ignore_type] + # if gt_cagegory_clean: + # entry['gt_category_type'] = Counter(gt_cagegory_clean).most_common(1)[0][0] + # else: + # entry['gt_category_type'] = Counter([gt_cat_list[_] for _ in entry['gt_idx']]).most_common(1)[0][0] + # else: + # entry['gt_category_type'] = "" + # entry['pred_category_type'] = get_pred_category_type(entry['pred_idx'][0], pred_items) if entry['pred_idx'] != [""] else "" + # entry['gt_attribute'] = [gt_items[_].get("attribute", {}) for _ in entry['gt_idx']] if entry['gt_idx'] != [""] else [{}] + # entry['img_id'] = img_name + + # return merged_results + + +def merge_duplicates_add_unmatched(converted_results, norm_gt_lines, norm_pred_lines, gt_lines, pred_lines, all_gt_indices, all_pred_indices): + merged_results = [] + processed_pred = set() + processed_gt = set() + + for entry in converted_results: + pred_idx = tuple(entry['pred_idx']) if isinstance(entry['pred_idx'], list) else (entry['pred_idx'],) + if pred_idx not in processed_pred and pred_idx != ("",): + merged_entry = { + 'gt_idx': [entry['gt_idx']], + 'gt': entry['gt'], + 'pred_idx': entry['pred_idx'], + 'pred': entry['pred'], + 'edit': entry['edit'] + } + for other_entry in converted_results: + other_pred_idx = tuple(other_entry['pred_idx']) if isinstance(other_entry['pred_idx'], list) else (other_entry['pred_idx'],) + if other_pred_idx == pred_idx and other_entry is not entry: + merged_entry['gt_idx'].append(other_entry['gt_idx']) + merged_entry['gt'] += other_entry['gt'] + processed_gt.add(other_entry['gt_idx']) + merged_results.append(merged_entry) + processed_pred.add(pred_idx) + processed_gt.add(entry['gt_idx']) + + # for entry in converted_results: + # if entry['gt_idx'] not in processed_gt: + # merged_results.append(entry) + + for gt_idx in range(len(norm_gt_lines)): + if gt_idx not in processed_gt: + merged_results.append({ + 'gt_idx': [gt_idx], + 'gt': gt_lines[gt_idx], + 'pred_idx': [""], + 'pred': "", + 'edit': 1 + }) + return merged_results + + + + +def formula_format(formula_matches, img_name): + return [ + { + "gt": item["gt"], + "pred": item["pred"], + "img_id": f"{img_name}_{i}" + } + for i, item in enumerate(formula_matches) + ] + + +def merge_lists_with_sublists(main_list, sub_lists): + main_list_final = list(copy.deepcopy(main_list)) + for sub_list in sub_lists: + pop_idx = main_list_final.index(sub_list[0]) + for _ in sub_list: + main_list_final.pop(pop_idx) + main_list_final.insert(pop_idx, sub_list) + return main_list_final + + +def sub_pred_fuzzy_matching(gt, pred): + + min_d = float('inf') + # pos = -1 + + gt_len = len(gt) + pred_len = len(pred) + + if gt_len >= pred_len and pred_len > 0: + for i in range(gt_len - pred_len + 1): + sub = gt[i:i + pred_len] + dist = Levenshtein_distance(sub, pred)/pred_len + if dist < min_d: + min_d = dist + pos = i + + return min_d + else: + return False + +def sub_gt_fuzzy_matching(pred, gt): + + min_d = float('inf') + pos = "" + matched_sub = "" + gt_len = len(gt) + pred_len = len(pred) + + if pred_len >= gt_len and gt_len > 0: + for i in range(pred_len - gt_len + 1): + sub = pred[i:i + gt_len] + dist = Levenshtein.distance(sub, gt) /gt_len + if dist < min_d: + min_d = dist + pos = i + matched_sub = sub + return min_d, pos, gt_len, matched_sub + else: + return 1, "", gt_len, "" + + +def get_final_subset(subset_certain, subset_certain_cost): + if not subset_certain or not subset_certain_cost: + return [] + + subset_turple = sorted([(a, b) for a, b in zip(subset_certain, subset_certain_cost)], key=lambda x: x[0][0]) + + group_list = defaultdict(list) + group_idx = 0 + group_list[group_idx].append(subset_turple[0]) + + for item in subset_turple[1:]: + overlap_flag = False + for subset in group_list[group_idx]: + for idx in item[0]: + if idx in subset[0]: + overlap_flag = True + break + if overlap_flag: + break + if overlap_flag: + group_list[group_idx].append(item) + else: + group_idx += 1 + group_list[group_idx].append(item) + + final_subset = [] + for _, group in group_list.items(): + if len(group) == 1: + final_subset.append(group[0][0]) + else: + path_dict = defaultdict(list) + path_idx = 0 + path_dict[path_idx].append(group[0]) + + for subset in group[1:]: + new_path = True + for path_idx_s, path_items in path_dict.items(): + is_dup = False + is_same = False + for path_item in path_items: + if path_item[0] == subset[0]: + is_dup = True + is_same = True + if path_item[1] > subset[1]: + path_dict[path_idx_s].pop(path_dict[path_idx_s].index(path_item)) + path_dict[path_idx_s].append(subset) + else: + for num_1 in path_item[0]: + for num_2 in subset[0]: + if num_1 == num_2: + is_dup = True + if not is_dup: + path_dict[path_idx_s].append(subset) + new_path = False + if is_same: + new_path = False + if new_path: + path_idx = len(path_dict.keys()) + path_dict[path_idx].append(subset) + + saved_cost = float('inf') + saved_subset = [] + for path_idx, path in path_dict.items(): + avg_cost = sum([i[1] for i in path]) / len(path) + if avg_cost < saved_cost: + saved_subset = [i[0] for i in path] + saved_cost = avg_cost + + final_subset.extend(saved_subset) + + return final_subset + +def judge_pred_merge(gt_list, pred_list, threshold=0.6): + if len(pred_list) == 1: + return False, False + + cur_pred = ' '.join(pred_list[:-1]) + merged_pred = ' '.join(pred_list) + + cur_dist = Levenshtein.distance(gt_list[0], cur_pred) / max(len(gt_list[0]), len(cur_pred)) + merged_dist = Levenshtein.distance(gt_list[0], merged_pred) / max(len(gt_list[0]), len(merged_pred)) + + if merged_dist > cur_dist: + return False, False + + cur_fuzzy_dists = [sub_pred_fuzzy_matching(gt_list[0], cur_pred) for cur_pred in pred_list[:-1]] + if any(dist is False or dist > threshold for dist in cur_fuzzy_dists): + return False, False + + add_fuzzy_dist = sub_pred_fuzzy_matching(gt_list[0], pred_list[-1]) + if add_fuzzy_dist is False: + return False, False + + merged_pred_flag = add_fuzzy_dist < threshold + continue_flag = len(merged_pred) <= len(gt_list[0]) + + return merged_pred_flag, continue_flag + +def deal_with_truncated(cost_matrix, norm_gt_lines, norm_pred_lines): + matched_first = np.argwhere(cost_matrix < 0.25) + masked_gt_idx = [i[0] for i in matched_first] + unmasked_gt_idx = [i for i in range(cost_matrix.shape[0]) if i not in masked_gt_idx] + masked_pred_idx = [i[1] for i in matched_first] + unmasked_pred_idx = [i for i in range(cost_matrix.shape[1]) if i not in masked_pred_idx] + + merges_gt_dict = {} + merges_pred_dict = {} + merged_gt_subsets = [] + + for gt_idx in unmasked_gt_idx: + check_merge_subset = [] + merged_dist = [] + + for pred_idx in unmasked_pred_idx: + step = 1 + merged_pred = [norm_pred_lines[pred_idx]] + + while True: + if pred_idx + step in masked_pred_idx or pred_idx + step >= len(norm_pred_lines): + break + else: + merged_pred.append(norm_pred_lines[pred_idx + step]) + merged_pred_flag, continue_flag = judge_pred_merge([norm_gt_lines[gt_idx]], merged_pred) + if not merged_pred_flag: + break + else: + step += 1 + if not continue_flag: + break + + check_merge_subset.append(list(range(pred_idx, pred_idx + step))) + matched_line = ' '.join([norm_pred_lines[i] for i in range(pred_idx, pred_idx + step)]) + dist = Levenshtein_distance(norm_gt_lines[gt_idx], matched_line) / max(len(matched_line), len(norm_gt_lines[gt_idx])) + merged_dist.append(dist) + + if not merged_dist: + subset_certain = [] + min_cost_idx = "" + min_cost = float('inf') + else: + min_cost = min(merged_dist) + min_cost_idx = merged_dist.index(min_cost) + subset_certain = check_merge_subset[min_cost_idx] + + merges_gt_dict[gt_idx] = { + 'merge_subset': check_merge_subset, + 'merged_cost': merged_dist, + 'min_cost_idx': min_cost_idx, + 'subset_certain': subset_certain, + 'min_cost': min_cost + } + + subset_certain = [merges_gt_dict[gt_idx]['subset_certain'] for gt_idx in unmasked_gt_idx if merges_gt_dict[gt_idx]['subset_certain']] + subset_certain_cost = [merges_gt_dict[gt_idx]['min_cost'] for gt_idx in unmasked_gt_idx if merges_gt_dict[gt_idx]['subset_certain']] + + subset_certain_final = get_final_subset(subset_certain, subset_certain_cost) + + if not subset_certain_final: + return cost_matrix, norm_pred_lines, range(len(norm_pred_lines)) + + final_pred_idx_list = merge_lists_with_sublists(range(len(norm_pred_lines)), subset_certain_final) + final_norm_pred_lines = [' '.join(norm_pred_lines[idx_list[0]:idx_list[-1]+1]) if isinstance(idx_list, list) else norm_pred_lines[idx_list] for idx_list in final_pred_idx_list] + + new_cost_matrix = compute_edit_distance_matrix_new(norm_gt_lines, final_norm_pred_lines) + + return new_cost_matrix, final_norm_pred_lines, final_pred_idx_list + +def cal_move_dist(gt, pred): + assert len(gt) == len(pred), 'Not right length' + step = 0 + for i, gt_c in enumerate(gt): + if gt_c != pred[i]: + step += abs(i - pred.index(gt_c)) + pred[i], pred[pred.index(gt_c)] = pred[pred.index(gt_c)], pred[i] + return step / len(gt) + +def cal_final_match(cost_matrix, norm_gt_lines, norm_pred_lines): + # min_indice = cost_matrix.argmax(axis=1) + + new_cost_matrix, final_norm_pred_lines, final_pred_idx_list = deal_with_truncated(cost_matrix, norm_gt_lines, norm_pred_lines) + + row_ind, col_ind = linear_sum_assignment(new_cost_matrix) + + cost_list = [new_cost_matrix[r][c] for r, c in zip(row_ind, col_ind)] + matched_col_idx = [final_pred_idx_list[i] for i in col_ind] + + return matched_col_idx, row_ind, cost_list + +def initialize_indices(norm_gt_lines, norm_pred_lines): + gt_lens_dict = {idx: len(gt_line) for idx, gt_line in enumerate(norm_gt_lines)} + pred_lens_dict = {idx: len(pred_line) for idx, pred_line in enumerate(norm_pred_lines)} + return gt_lens_dict, pred_lens_dict + +def process_matches(matched_col_idx, row_ind, cost_list, norm_gt_lines, norm_pred_lines, pred_lines): + matches = {} + unmatched_gt_indices = [] + unmatched_pred_indices = [] + + for i in range(len(norm_gt_lines)): + if i in row_ind: + idx = list(row_ind).index(i) + pred_idx = matched_col_idx[idx] + + if pred_idx is None or (isinstance(pred_idx, list) and None in pred_idx): + unmatched_pred_indices.append(pred_idx) + continue + + if isinstance(pred_idx, list): + pred_line = ' | '.join(norm_pred_lines[pred_idx[0]:pred_idx[-1]+1]) + ori_pred_line = ' | '.join(pred_lines[pred_idx[0]:pred_idx[-1]+1]) + matched_pred_indices_range = list(range(pred_idx[0], pred_idx[-1]+1)) + else: + pred_line = norm_pred_lines[pred_idx] + ori_pred_line = pred_lines[pred_idx] + matched_pred_indices_range = [pred_idx] + + edit = cost_list[idx] + + if edit > 0.7: + unmatched_pred_indices.extend(matched_pred_indices_range) + unmatched_gt_indices.append(i) + else: + matches[i] = { + 'pred_indices': matched_pred_indices_range, + 'edit_distance': edit, + } + for matched_pred_idx in matched_pred_indices_range: + if matched_pred_idx in unmatched_pred_indices: + unmatched_pred_indices.remove(matched_pred_idx) + else: + unmatched_gt_indices.append(i) + + return matches, unmatched_gt_indices, unmatched_pred_indices + +def fuzzy_match_unmatched_items(unmatched_gt_indices, norm_gt_lines, norm_pred_lines): + matching_dict = {} + + for pred_idx, pred_content in enumerate(norm_pred_lines): + if isinstance(pred_idx, list): + continue + + matching_indices = [] + + for unmatched_gt_idx in unmatched_gt_indices: + gt_content = norm_gt_lines[unmatched_gt_idx] + cur_fuzzy_dist_unmatch, cur_pos, gt_lens, matched_field = sub_gt_fuzzy_matching(pred_content, gt_content) + if cur_fuzzy_dist_unmatch < 0.4: + matching_indices.append(unmatched_gt_idx) + + if matching_indices: + matching_dict[pred_idx] = matching_indices + + return matching_dict + +def merge_matches(matches, matching_dict): + final_matches = {} + processed_gt_indices = set() + + for gt_idx, match_info in matches.items(): + pred_indices = match_info['pred_indices'] + edit_distance = match_info['edit_distance'] + + pred_key = tuple(sorted(pred_indices)) + + if pred_key in final_matches: + if gt_idx not in processed_gt_indices: + final_matches[pred_key]['gt_indices'].append(gt_idx) + processed_gt_indices.add(gt_idx) + else: + final_matches[pred_key] = { + 'gt_indices': [gt_idx], + 'edit_distance': edit_distance + } + processed_gt_indices.add(gt_idx) + + for pred_idx, gt_indices in matching_dict.items(): + pred_key = (pred_idx,) if not isinstance(pred_idx, (list, tuple)) else tuple(sorted(pred_idx)) + + if pred_key in final_matches: + for gt_idx in gt_indices: + if gt_idx not in processed_gt_indices: + final_matches[pred_key]['gt_indices'].append(gt_idx) + processed_gt_indices.add(gt_idx) + else: + final_matches[pred_key] = { + 'gt_indices': [gt_idx for gt_idx in gt_indices if gt_idx not in processed_gt_indices], + 'edit_distance': None + } + processed_gt_indices.update(final_matches[pred_key]['gt_indices']) + + return final_matches + + + +def recalculate_edit_distances(final_matches, gt_lens_dict, norm_gt_lines, norm_pred_lines): + for pred_key, info in final_matches.items(): + gt_indices = sorted(set(info['gt_indices'])) + + if not gt_indices: + info['edit_distance'] = 1 + continue + + if len(gt_indices) > 1: + merged_gt_content = ''.join(norm_gt_lines[gt_idx] for gt_idx in gt_indices) + pred_content = norm_pred_lines[pred_key[0]] if isinstance(pred_key[0], int) else '' + + try: + edit_distance = Levenshtein_distance(merged_gt_content, pred_content) + normalized_edit_distance = edit_distance / max(len(merged_gt_content), len(pred_content)) + except ZeroDivisionError: + normalized_edit_distance = 1 + + info['edit_distance'] = normalized_edit_distance + else: + gt_idx = gt_indices[0] + pred_content = ' '.join(norm_pred_lines[pred_idx] for pred_idx in pred_key if isinstance(pred_idx, int)) + + try: + edit_distance = Levenshtein_distance(norm_gt_lines[gt_idx], pred_content) + normalized_edit_distance = edit_distance / max(len(norm_gt_lines[gt_idx]), len(pred_content)) + except ZeroDivisionError: + normalized_edit_distance = 1 + + info['edit_distance'] = normalized_edit_distance + info['pred_content'] = pred_content + + +def convert_final_matches(final_matches, norm_gt_lines, norm_pred_lines): + converted_results = [] + + all_gt_indices = set(range(len(norm_gt_lines))) + all_pred_indices = set(range(len(norm_pred_lines))) + + for pred_key, info in final_matches.items(): + pred_content = ' '.join(norm_pred_lines[pred_idx] for pred_idx in pred_key if isinstance(pred_idx, int)) + + for gt_idx in sorted(set(info['gt_indices'])): + result_entry = { + 'gt_idx': int(gt_idx), + 'gt': norm_gt_lines[gt_idx], + 'pred_idx': list(pred_key), + 'pred': pred_content, + 'edit': info['edit_distance'] + } + converted_results.append(result_entry) + + matched_gt_indices = set().union(*[set(info['gt_indices']) for info in final_matches.values()]) + unmatched_gt_indices = all_gt_indices - matched_gt_indices + matched_pred_indices = set(idx for pred_key in final_matches.keys() for idx in pred_key if isinstance(idx, int)) + unmatched_pred_indices = all_pred_indices - matched_pred_indices + + if unmatched_pred_indices: + if unmatched_gt_indices: + distance_matrix = [ + # [Levenshtein_distance(norm_gt_lines[gt_idx], norm_pred_lines[pred_idx]) for pred_idx in unmatched_pred_indices] + [Levenshtein_distance(norm_gt_lines[gt_idx], norm_pred_lines[pred_idx])/max(len(norm_gt_lines[gt_idx]), len(norm_pred_lines[pred_idx])) for pred_idx in unmatched_pred_indices] + for gt_idx in unmatched_gt_indices + ] + + row_ind, col_ind = linear_sum_assignment(distance_matrix) + + for i, j in zip(row_ind, col_ind): + gt_idx = list(unmatched_gt_indices)[i] + pred_idx = list(unmatched_pred_indices)[j] + result_entry = { + 'gt_idx': int(gt_idx), + 'gt': norm_gt_lines[gt_idx], + 'pred_idx': [pred_idx], + 'pred': norm_pred_lines[pred_idx], + 'edit': 1 + } + converted_results.append(result_entry) + + matched_gt_indices.update(list(unmatched_gt_indices)[i] for i in row_ind) + else: + result_entry = { + 'gt_idx': "", + 'gt': '', + 'pred_idx': list(unmatched_pred_indices), + 'pred': ' '.join(norm_pred_lines[pred_idx] for pred_idx in unmatched_pred_indices), + 'edit': 1 + } + converted_results.append(result_entry) + else: + for gt_idx in unmatched_gt_indices: + result_entry = { + 'gt_idx': int(gt_idx), + 'gt': norm_gt_lines[gt_idx], + 'pred_idx': "", + 'pred': '', + 'edit': 1 + } + converted_results.append(result_entry) + + return converted_results diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/ocr_utils.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/ocr_utils.py new file mode 100644 index 00000000..cefce9b1 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/ocr_utils.py @@ -0,0 +1,203 @@ +# revised from https://github.com/opendatalab/MinerU/blob/7f0fe20004af7416db886f4b75c116bcc1c986b4/magic_pdf/pdf_parse_union_core.py#L177 +# from fast_langdetect import detect_language +# import unicodedata +import re + + +def __is_overlaps_y_exceeds_threshold(bbox1, bbox2, overlap_ratio_threshold=0.8): + """Check if two bboxes overlap on y-axis and if the overlap height exceeds 80% of the shorter bbox height""" + _, y0_1, _, y1_1 = bbox1 + _, y0_2, _, y1_2 = bbox2 + + overlap = max(0, min(y1_1, y1_2) - max(y0_1, y0_2)) + height1, height2 = y1_1 - y0_1, y1_2 - y0_2 + max_height = max(height1, height2) + min_height = min(height1, height2) + + return (overlap / min_height) > overlap_ratio_threshold + +def merge_spans_to_line(spans): + if len(spans) == 0: + return [] + else: + # Sort by y0 coordinate + spans.sort(key=lambda span: span['bbox'][1]) + + lines = [] + current_line = [spans[0]] + for span in spans[1:]: + # If current span type is "interline_equation" or current line contains "interline_equation" + # Same for image and table types + if span['type'] in ['interline_equation'] or any( + s['type'] in ['interline_equation'] for s in + current_line): + # Start a new line + lines.append(current_line) + current_line = [span] + continue + + # If current span overlaps with the last span in current line on y-axis, add to current line + if __is_overlaps_y_exceeds_threshold(span['bbox'], current_line[-1]['bbox']): + current_line.append(span) + else: + # Otherwise, start a new line + lines.append(current_line) + current_line = [span] + + # Add the last line + if current_line: + lines.append(current_line) + + return lines + +# Sort spans in each line from left to right +def line_sort_spans_by_left_to_right(lines): + line_objects = [] + for line in lines: + # Sort by x0 coordinate + line.sort(key=lambda span: span['bbox'][0]) + line_bbox = [ + min(span['bbox'][0] for span in line), # x0 + min(span['bbox'][1] for span in line), # y0 + max(span['bbox'][2] for span in line), # x1 + max(span['bbox'][3] for span in line), # y1 + ] + line_objects.append({ + "bbox": line_bbox, + "spans": line, + }) + return line_objects + +def fix_text_block(block): + # Formula spans in text block should be converted to inline type + block_lines = merge_spans_to_line(block['spans']) + sort_block_lines = line_sort_spans_by_left_to_right(block_lines) + block['lines'] = sort_block_lines + del block['spans'] + return block + + +# def detect_lang(text: str) -> str: + +# if len(text) == 0: +# return "" +# try: +# lang_upper = detect_language(text) +# except: +# html_no_ctrl_chars = ''.join([l for l in text if unicodedata.category(l)[0] not in ['C', ]]) +# lang_upper = detect_language(html_no_ctrl_chars) +# try: +# lang = lang_upper.lower() +# except: +# lang = "" +# return lang + +def detect_lang(string): + """ + Check if the string contains Chinese characters + :param string: String to check + :return: bool + """ + + for ch in string: + if u'\u4e00' <= ch <= u'\u9fff': + return 'zh' + return 'en' + +def ocr_escape_special_markdown_char(content): + """ + Escape special markdown characters in content + """ + special_chars = ["*", "`", "~", "$"] + for char in special_chars: + content = content.replace(char, "\\" + char) + + return content + +# def split_long_words(text): +# segments = text.split(' ') +# for i in range(len(segments)): +# words = re.findall(r'\w+|[^\w]', segments[i], re.UNICODE) +# for j in range(len(words)): +# if len(words[j]) > 15: +# words[j] = ' '.join(wordninja.split(words[j])) +# segments[i] = ''.join(words) +# return ' '.join(segments) + + +def merge_para_with_text(para_block): + para_text = '' + for line in para_block['lines']: + line_text = "" + line_lang = "" + for span in line['spans']: + span_type = span['type'] + if span_type == "text": + line_text += span['content'].strip() + if line_text != "": + line_lang = detect_lang(line_text) + for span in line['spans']: + span_type = span['type'] + content = '' + if span_type == "text": + content = span['content'] + content = ocr_escape_special_markdown_char(content) + # language = detect_lang(content) + # if language == 'en': # Only split long words for English text, Chinese word splitting will lose text + # content = ocr_escape_special_markdown_char(split_long_words(content)) + # else: + # content = ocr_escape_special_markdown_char(content) + elif span_type == 'inline_equation': + content = f" ${span['content'].strip('$')}$ " + elif span_type == 'ignore-formula': + content = f" ${span['content'].strip('$')}$ " + elif span_type == 'interline_equation': + content = f"\n$$\n{span['content'].strip('$')}\n$$\n" + elif span_type == 'footnote': + content_ori = span['content'].strip('$') + if '^' in content_ori: + content = f" ${content_ori}$ " + else: + content = f" $^{content_ori}$ " + + if content != '': + if 'zh' in line_lang: # For documents with one character per span, character-level language detection is inaccurate, need to detect language for whole line + para_text += content.strip() # In Chinese context, no space needed between contents + else: + para_text += content.strip() + ' ' # In English context, space needed between contents + return para_text + +def poly2bbox(poly): + L = poly[0] + U = poly[1] + R = poly[2] + D = poly[5] + L, R = min(L, R), max(L, R) + U, D = min(U, D), max(U, D) + bbox = [L, U, R, D] + return bbox + +def normalize_format(block, pred_spans): + spans = [] + for span in pred_spans: + spans.append({ + "type": span['category_type'], + "bbox": poly2bbox(span['poly']), + "content": span['text'] + }) + + block_type = block["category_type"] + block_bbox = poly2bbox(block['poly']) + block_dict = { + 'type': block_type, + 'bbox': block_bbox, + 'spans': spans + } + return block_dict + +def get_text_for_block(gt, pred_spans): + '''Fix operations for block''' + block_with_spans = normalize_format(gt, pred_spans) + para_block = fix_text_block(block_with_spans) + pred_text = merge_para_with_text(para_block) + return pred_text \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/read_files.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/read_files.py new file mode 100644 index 00000000..3caafc57 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/read_files.py @@ -0,0 +1,20 @@ +import json + +def read_md_file(filepath): + with open(filepath, 'r', encoding='utf-8') as file: + content = file.read() + + return content + +def save_paired_result(preds, gts, save_path): + save_result = [] + formula_id = 0 + for gt, pred in zip(gts, preds): + save_result.append({ + "gt": gt, + "pred": pred, + "img_id": formula_id + }) + formula_id += 1 + with open(save_path, 'w', encoding='utf-8') as f: + json.dump(save_result, f, indent=4, ensure_ascii=False) \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/table_utils.py b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/table_utils.py new file mode 100644 index 00000000..d91e64f3 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/OmniDocBench/utils/table_utils.py @@ -0,0 +1,240 @@ +import matplotlib.pyplot as plt +import numpy as np +import os +import re +import matplotlib.font_manager as fm +font = fm.FontProperties(fname=r'font/SimHei.ttf') + + +def print_aligned_dict(data): + # Find the maximum length of all keys + max_key_length = max(len(key) for key in data['testcase1']) + + # Print header + print(f"{' ' * (max_key_length + 4)}", end="") + for key in data: + print(f"{key:>{max_key_length}}", end="") + print() + + # Print dictionary content + for subkey in data['testcase1']: + print(f"{subkey:<{max_key_length + 4}}", end="") + for key in data: + print(f"{data[key][subkey]:>{max_key_length}}", end="") + print() +def create_dict_from_folders(directory): + body = {} + for folder_name in os.listdir(directory): + folder_path = os.path.join(directory, folder_name) + if os.path.isdir(folder_path): + body[folder_name] = {} + return body + + +def create_radar_chart(df, title, filename): + labels = df.columns + + # Calculate angles + angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False).tolist() + angles += angles[:1] + + # Initialize radar chart + fig, ax = plt.subplots(figsize=(10, 6), subplot_kw=dict(polar=True), dpi=200) + # ax.spines['polar'].set_visible(False) + + # Draw radar chart for each dataset + for index, row in df.iterrows(): + values = row.tolist() + values += values[:1] + ax.fill(angles, values, alpha=0.1) + ax.plot(angles, values, label=index) + + # Add percentage labels next to each data point + for angle, value in zip(angles, values): + ax.text(angle, value, '{:.1%}'.format(value), ha='center', va='center', fontsize=7, alpha=0.7) + + # Set labels + ax.set_yticklabels([]) + ax.set_xticks(angles[:-1]) + ax.set_xticklabels(labels, fontproperties=font) + ax.spines['polar'].set_visible(False) # Hide the outermost circle + ax.grid(False) + for j in np.arange(0, 1.2, 0.2): + ax.plot(angles, len(values) * [j], '-.', lw=0.5, color='black', alpha=0.5) + for j in range(len(values)): + ax.plot([angles[j], angles[j]], [0, 1], '-.', lw=0.5, color='black', alpha=0.5) + + # Add title and legend + plt.legend(loc='upper right', bbox_to_anchor=(0.1, 0.1)) + + ax.tick_params(pad=30) + ax.set_theta_zero_location('N') + # Save chart to file + plt.savefig(filename) + +# The function is from https://github.com/intsig-textin/markdown_tester +def markdown_to_html(markdown_table): + rows = [row.strip() for row in markdown_table.strip().split('\n')] + num_columns = len(rows[0].split('|')) - 2 + + html_table = '\n \n \n' + + header_cells = [cell.strip() for cell in rows[0].split('|')[1:-1]] + for cell in header_cells: + html_table += f' \n' + html_table += ' \n \n \n' + + for row in rows[2:]: + cells = [cell.strip() for cell in row.split('|')[1:-1]] + html_table += ' \n' + for cell in cells: + html_table += f' \n' + html_table += ' \n' + + html_table += ' \n
    {cell}
    {cell}
    \n' + return html_table +def convert_markdown_to_html(self, markdown_content, md_type): + # Define a regex pattern to find Markdown tables with newlines + markdown_content = markdown_content.replace('\r', '') + pattern = re.compile(r'\|\s*.*?\s*\|\n', re.DOTALL) + + # Find all matches in the Markdown content + matches = pattern.findall(markdown_content) + for match in matches: + html_table = markdown_to_html(match) + markdown_content = markdown_content.replace(match, html_table, 1) # Only replace the first occurrence + res_html = convert_table(replace_table_with_placeholder(markdown_content)) + + return res_html +def convert_table_str(s): + s = re.sub(r'','',s) + s = re.sub(r'','',s) + # s = re.sub(r'
    ',lambda x:f'',s) + # s = re.sub(r'',lambda x:f'',s) + res = '' + res += '\n\n' + temp_item = '' + for c in s: + temp_item += c + if c == '>' and not re.search(r'\$',temp_item): + res += temp_item+'\n' + temp_item = '' + return res+'\n' +def merge_table(md): + table_temp = '' + for line in md: + table_temp += line + return convert_table_str(table_temp) +def find_md_table_mode(line): + if re.search(r'-*?:',line) or re.search(r'---',line) or re.search(r':-*?',line): + return True + return False +def delete_table_and_body(input_list): + res = [] + for line in input_list: + if not re.search(r'',line): + res.append(line) + return res +def merge_tables(input_str): + # Delete HTML comments + input_str = re.sub(r'', '', input_str) + + # Use regex to find each block + table_blocks = re.findall(r'
    [\s\S]*?
    ', input_str) + + # Process each block, replace ') + final_tr = delete_table_and_body(block_lines) + if len(final_tr) > 2: + output_lines.extend(final_tr) # Ignore
    with + output_lines = [] + for block in table_blocks: + block_lines = block.split('\n') + for i, line in enumerate(block_lines): + if '' in line: + block_lines[i] = line.replace('', '').replace('', '
    and
    tags, keep only table content + + # Rejoin the processed strings + merged_output = '\n{}\n
    '.format('\n'.join(output_lines)) + + return "\n\n" + merged_output + "\n\n" + +def replace_table_with_placeholder(input_string): + lines = input_string.split('\n') + output_lines = [] + + in_table_block = False + temp_block = "" + last_line = "" + + org_table_list = [] + in_org_table = False + + for idx, line in enumerate(lines): + # if not in_org_table: + # if "" not in last_line and in_table_block == False and temp_block != "": + # output_lines.append(merge_tables(temp_block)) + # temp_block = "" + if "
    " in line: + # if "
    " not in last_line: + temp_block += "\n" + last_line + if "
    " in last_line: + if "" not in line: + in_table_block = False + output_lines.append(merge_tables(temp_block)) + temp_block = "" + else: + output_lines.append(last_line) + + last_line = line + # else: + # org_table_list.append(line) + # if "" in last_line: + temp_block += "\n" + last_line + output_lines.append(merge_tables(temp_block)) + else: + output_lines.append(last_line) + # if "
    " in last_line: + # output_lines.append(merge_tables(temp_block)) + + return '\n'.join(output_lines) + +def convert_table(input_str): + # Replace + output_str = input_str.replace("
    ", "
    ") + + # Replace
    + output_str = output_str.replace("", "") + + return output_str + +def convert_markdown_to_html(markdown_content): + # Define a regex pattern to find Markdown tables with newlines + markdown_content = markdown_content.replace('\r', '')+'\n' + pattern = re.compile(r'\|\s*.*?\s*\|\n', re.DOTALL) + + # Find all matches in the Markdown content + matches = pattern.findall(markdown_content) + + for match in matches: + html_table = markdown_to_html(match) + markdown_content = markdown_content.replace(match, html_table, 1) # Only replace the first occurrence + + res_html = convert_table(replace_table_with_placeholder(markdown_content)) + + return res_html \ No newline at end of file diff --git a/omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/README.md b/omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/README.md new file mode 100644 index 00000000..8cf5594c --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/README.md @@ -0,0 +1,30 @@ +# Chandra OCR + +This folder contains the `chandra.py` script and supporting files for running a small OCR/processing workflow used in the DeepSeek-OCR project. + +What it is +- `chandra.py`: a utility/script for local OCR-related tasks (detection/recognition or dataset preprocessing). The script is lightweight and intended for quick experiments using chandra-ocr . + +We used the marker/ api with mode as balanced/accurate according to the docs of datalab where chandra model is hosted for public use. + +Quick usage +1. install chandra-ocr package + + ```bash + pip install chandra-ocr + ``` + +2. Run the script (example): + + ```bash + python chandra.py + ``` + + Replace arguments with the ones `chandra.py` expects — check the top of the file for specific options. + +Notes +- This README is intentionally brief. For full project-level setup and evaluation instructions, see the repository root `README.md` and `DeepSeek-OCR-master/README.md`. +- If `chandra.py` requires specific model files or data paths, place them in this folder or update the script's configuration accordingly. + +Contact +- Open an issue or contact the maintainers in the main repo for questions or to report problems. diff --git a/omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/chandra.py b/omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/chandra.py new file mode 100644 index 00000000..2bdfb5c8 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/chandra-ocr/chandra.py @@ -0,0 +1,197 @@ +import os +import time +import requests +from requests.adapters import HTTPAdapter, Retry +from pathlib import Path +from typing import Optional +from concurrent.futures import ThreadPoolExecutor, as_completed +from tqdm import tqdm + +API_URL = "https://www.datalab.to/api/v1/marker" +API_KEY = os.getenv("DATALAB_API_KEY", "...") + +# +# Configure a session with retries, customize retry behavior +# for your usage needs. Our default rate limit is 200 per minute +# per account (not per API key). +# +session = requests.Session() +retries = Retry( + total=20, + backoff_factor=4, + status_forcelist=[429], + allowed_methods=["GET", "POST"], + raise_on_status=False, +) +adapter = HTTPAdapter(max_retries=retries) +session.mount("http://", adapter) +session.mount("https://", adapter) + + +def submit_and_poll_pdf_conversion( + pdf_path: Path, + output_format: Optional[str] = 'markdown', + use_llm: Optional[bool] = False, + max_polls: int = 300 +): + """ + Submit a PDF for conversion and poll until completion. + Returns the converted document content. + """ + def submit_request(): + # Submit initial request + with open(pdf_path, 'rb') as f: + form_data = { + 'file': (pdf_path.name, f, 'application/pdf'), + "max_pages": (None, "123"), + "force_ocr": (None, False), + "format_lines": (None, False), + "paginate": (None, False), + 'output_format': (None, output_format), + "use_llm": (None, use_llm), + "strip_existing_ocr": (None, False), + "disable_image_extraction": (None, False), + "disable_ocr_math": (None, False), + "mode": (None, "accurate"), + "skip_cache": (None, False), + "save_checkpoint": (None, False) + } + + headers = {"X-Api-Key": API_KEY} + return session.post(API_URL, headers=headers, files=form_data) + + # Submit the conversion request + response = submit_request() + response.raise_for_status() + data = response.json() + + print(f"Submitted: {pdf_path.name}") + + # Poll for completion + check_url = data["request_check_url"] + headers = {"X-Api-Key": API_KEY} + + for i in range(max_polls): + response = session.get(check_url, headers=headers) + check_result = response.json() + + if check_result['status'] == 'complete': + # Your processing is finished + converted_document = check_result[output_format] + print(f"✓ Completed: {pdf_path.name}") + return converted_document + elif check_result["status"] == "failed": + error_msg = f"Failed to convert {pdf_path.name}" + print(f"✗ {error_msg}") + raise Exception(error_msg) + else: + # Still processing, wait before checking again + time.sleep(2) + + # If we exhausted all polls without completion + raise TimeoutError(f"Conversion timed out for {pdf_path.name} after {max_polls} polls") + + +def process_single_pdf(pdf_path: Path, output_dir: Path) -> bool: + """ + Process a single PDF file and save the result. + Returns True if successful, False otherwise. + """ + try: + # Convert the PDF + markdown_content = submit_and_poll_pdf_conversion(pdf_path) + + # Save the result + output_filename = pdf_path.stem + '.md' + output_path = output_dir / output_filename + + with open(output_path, 'w', encoding='utf-8') as f: + f.write(markdown_content) + + print(f"✓ Saved: {output_filename}") + return True + + except Exception as e: + print(f"✗ Error processing {pdf_path.name}: {e}") + return False + + +def batch_convert_pdfs( + document_directory: str = 'images_to_pdf', + output_directory: str = 'output_results', + max_workers: int = 3 +): + """ + Batch convert multiple PDFs using threading for parallel processing. + """ + doc_dir = Path(document_directory) + output_dir = Path(output_directory) + + if not doc_dir.exists(): + print(f"Couldn't find your directory: {document_directory}, exiting early...") + raise FileNotFoundError(f"Couldn't find {document_directory}") + + # Create output directory if it doesn't exist + output_dir.mkdir(exist_ok=True) + + # Collect all PDF files + docs_to_process = list(doc_dir.glob("*.pdf")) + print(f"Found {len(docs_to_process)} PDFs to convert...") + + # Get list of already processed files + processed_files = set(output_dir.glob("*.md")) + processed_stems = {f.stem for f in processed_files} + + # Filter out already processed PDFs + docs_to_process = [ + pdf for pdf in docs_to_process + if pdf.stem not in processed_stems + ] + + if not docs_to_process: + print("No new PDFs to process. All files have already been converted.") + return + + print(f"Processing {len(docs_to_process)} new PDFs (skipping already processed)...") + + # Process multiple files at once, up to `max_workers` + successful = 0 + failed = 0 + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + future_to_file = { + executor.submit(process_single_pdf, pdf_path, output_dir): pdf_path.name + for pdf_path in docs_to_process + } + + # Use tqdm for progress tracking + with tqdm(total=len(future_to_file), desc="Converting PDFs") as pbar: + for future in as_completed(future_to_file): + filename = future_to_file[future] + try: + result = future.result() + if result: + successful += 1 + else: + failed += 1 + except Exception as e: + print(f"✗ Unexpected error processing {filename}: {e}") + failed += 1 + finally: + pbar.update(1) + + print(f"\n{'='*60}") + print(f"Conversion complete!") + print(f"Successful: {successful}") + print(f"Failed: {failed}") + print(f"Total: {successful + failed}") + print(f"{'='*60}") + + +if __name__ == "__main__": + # Run the batch conversion + batch_convert_pdfs( + document_directory='images_to_pdf', + output_directory='output_results', + max_workers=3 # Process 3 PDFs concurrently + ) diff --git a/omnidocbench_evals/src/omnidocbench_evals/tools/convert_jsonl_to_markdown.py b/omnidocbench_evals/src/omnidocbench_evals/tools/convert_jsonl_to_markdown.py new file mode 100644 index 00000000..41811250 --- /dev/null +++ b/omnidocbench_evals/src/omnidocbench_evals/tools/convert_jsonl_to_markdown.py @@ -0,0 +1,45 @@ +#!/usr/bin/env python3 +"""Convert OLMOCR JSONL output to markdown files""" + +import json +import os +from pathlib import Path + +# Get the OmniDocBench-Evals root directory (3 levels up from this script) +root_dir = Path(__file__).parent.parent.parent.parent + +# Paths +results_dir = root_dir / "outputs/olmocr_workspace/results" +markdown_dir = root_dir / "outputs/olmocr_workspace/markdown" +markdown_dir.mkdir(exist_ok=True) + +# Process all JSONL files +total_converted = 0 +for jsonl_file in results_dir.glob("*.jsonl"): + print(f"Processing {jsonl_file.name}...") + with open(jsonl_file, 'r') as f: + for line in f: + if not line.strip(): + continue + + doc = json.loads(line) + + # Extract the PDF filename from metadata + if 'metadata' in doc and 'Source-File' in doc['metadata']: + pdf_path = doc['metadata']['Source-File'] + # Get just the filename without extension + pdf_name = Path(pdf_path).stem + + # Create markdown filename + md_filename = f"{pdf_name}.md" + md_path = markdown_dir / md_filename + + # Write the text content to markdown + with open(md_path, 'w') as md_file: + md_file.write(doc.get('text', '')) + + total_converted += 1 + else: + print(f"Warning: No Source-File metadata in doc: {doc.get('id', 'unknown')}") + +print(f"\nConversion complete! Created {total_converted} markdown files in {markdown_dir}")