| import re |
| import json |
|
|
| from tqdm import tqdm |
| from loguru import logger |
|
|
| from pathlib import Path |
| from typing import Tuple, List |
|
|
|
|
| project_root = Path(__file__).parent.parent.parent |
| problem_tag = 'Problem' |
| solution_tag = 'Solution' |
| problem_pattern = re.compile(r'(?:\n|# )Opgave\s+(\d+)\.', re.IGNORECASE) |
| solution_pattern = re.compile(r'(?:\n|# )Oplossing(?:\s+(\d+)\.|\s+(I{1,3}|IV|V|VI{1,3}|IX)\.|\.|\n|:)', re.IGNORECASE) |
|
|
|
|
| def analyze(text: str) -> Tuple[List, int]: |
| """ |
| Analyze the text and return the tags and problem number. |
| |
| Args: |
| text (str): The markdown text to analyze. |
| |
| Returns: |
| Tuple[List, int]: A tuple containing the tags and problem number. |
| """ |
| tags = [] |
| tags.extend([(x, problem_tag) for x in problem_pattern.finditer(text)]) |
| problem_num = len(tags) |
|
|
| tags.extend([(x, solution_tag) for x in solution_pattern.finditer(text)]) |
| tags.sort(key=lambda x: x[0].start()) |
| return tags, problem_num |
|
|
|
|
| def segment(text: str, tags): |
| starts = [] |
| ends = [] |
|
|
| for i in range(len(tags)): |
| starts.append(tags[i][0].end()) |
| if i + 1 < len(tags): |
| ends.append(tags[i + 1][0].start()) |
| else: |
| ends.append(len(text)) |
|
|
| return [text[start:end].strip().rstrip("#").strip() for start, end in zip(starts, ends)] |
|
|
|
|
| def join(tags, segments): |
| problem, solution = '', '' |
| problem_label, problem_match, solution_match = '', '', '' |
| pairs = [] |
|
|
| has_solution = any([tag[1] == solution_tag for tag in tags]) |
|
|
| for tag, segment in zip(tags, segments): |
| if tag[1] == problem_tag: |
| problem = segment |
| problem_match = tag[0].group(0) |
| problem_label = tag[0].group(1) if tag[0].group(1) else tag[0].group(2) |
|
|
| |
| if not has_solution: |
| pairs.append((problem, '', problem_label, problem_match, '')) |
| else: |
| solution = segment |
| solution_match = tag[0].group(0) |
| pairs.append((problem, solution, problem_label, problem_match, solution_match)) |
|
|
| return pairs |
|
|
|
|
| def write_pairs(output_file: Path, pairs): |
| year = re.search(r'(\d{4})', output_file.stem).group(1) |
|
|
| output_jsonl_text = "" |
| for problem, solution, problem_label, problem_match, solution_match in pairs: |
| output_jsonl_text += json.dumps( |
| { |
| 'year': year, |
| 'tier': 'T1', |
| 'problem_label': problem_label, |
| 'problem_type': None, |
| "exam": "Dutch_TST", |
| 'problem': problem, |
| 'solution': solution, |
| 'metadata': { |
| 'resource_path': output_file.relative_to(project_root).as_posix(), |
| 'problem_match': problem_match, |
| 'solution_match': solution_match |
| } |
| }, |
| ensure_ascii=False |
| ) + '\n' |
|
|
| output_file.write_text(output_jsonl_text, encoding="utf-8") |
|
|
|
|
| def main(): |
| compet_base_path = Path(__file__).resolve().parent.parent |
| compet_md_path = compet_base_path / "md" |
| seg_output_path = compet_base_path / "segmented" |
|
|
| total_problem_count = 0 |
| total_solution_count = 0 |
|
|
| for dutch_tst_md in tqdm(list(compet_md_path.glob('**/*.md')), desc='Segmenting'): |
| output_file = seg_output_path / dutch_tst_md.relative_to(compet_md_path).with_suffix('.jsonl') |
| output_file.parent.mkdir(parents=True, exist_ok=True) |
|
|
| text = '\n' + dutch_tst_md.read_text(encoding="utf-8") |
|
|
| if all([y not in dutch_tst_md.name for y in ["2007", "2008"]]): |
| tags, problem_num = analyze(text) |
|
|
| if problem_num != 4 and problem_num != 5: |
| logger.warning(f"{dutch_tst_md} problem number is {problem_num}") |
|
|
| segments = segment(text, tags) |
| pairs = join(tags, segments) |
|
|
| if pairs and problem_num > 0: |
| write_pairs(output_file, pairs) |
| total_problem_count += problem_num |
| total_solution_count += len(pairs) |
| else: |
| logger.warning(f"No problem found in {dutch_tst_md}") |
| |
| logger.info(f"Total problem count: {total_problem_count}") |
| logger.info(f"Total solution count: {total_solution_count}") |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
