import re import json from tqdm import tqdm from loguru import logger from pathlib import Path from typing import Tuple, List from dataclasses import dataclass project_root = Path(__file__).parent.parent.parent @dataclass class Problem: match: re.Match @dataclass class Solution: match: re.Match def clean_text(text: str) -> str: text = text.replace( 'For a discussion, see\nW. Morris and V. Soltan. The Erdős-Szekeres Problem on Points in Convex Postion-A Survey, Bulletin of the American Math Monthly. 37 (2000), 437-458.\n\nThis article is available at\nhttp://www.ams.org/bull/2000-37-04/S0273-0979-00-00877-6/home.html.\nIf $N(7)=33$, the highest sure score on this problem would be $32-6=26$. It is not known whether there exist arbitrarily large sets of points that will fool the graders.\n\n## The unexamined life is not worth living.', '' ) text = text.replace( '- Bishop: This piece can move any number of squares diagonally if there are no other pieces along its path.\n- Rook: This piece can move any number of squares either vertically or horizontally if there are no other pieces along its path\n- Knight: This piece can move either two squares along a row and one square along a column or two squares along a column and one square along a row.\n- King: This piece can move to any open adjacent square (including diagonally).', '' ) return text def find_problem_with_solution( text: str, problem_parttern: re.Pattern, solution_pattern: re.Pattern ) -> int: """ Find the problem with solution start position in the text. Args: text (str): The text to search. Returns: int: The start position of the problem with solution. """ matchs = list(problem_parttern.finditer(text)) for index, match in enumerate(matchs): section_end_position = matchs[index + 1].start() if index + 1 < len(matchs) else len(text) if solution_pattern.search(text[match.start():section_end_position]): return match.start() return 0 def analyze(text: str) -> Tuple[List[Problem | Solution], int]: """ Analyze the text and return the tags and problem number. Args: text (str): The markdown text to analyze. Returns: Tuple[List[Problem | Solution], int]: A tuple containing the tags and problem number. """ problem_pattern = re.compile(r'(?:\n|\n\#+\s+)(?:(\d{1,2})\.\s+(?:problem\:\s*|\$?\[.+?\]\$?)?|problem\s+?(\w+)\s+\[\d+(?:\spoints)?\]|\$([H|M|T]_\{\d+\})\$\.)', re.IGNORECASE) solution_pattern = re.compile(r'(?:\n|\n\#+\s+)(?:answer\:|solution(?:\s+\d+)?(?:\:|\.)|Proposed by:.*?\n)\s*', re.IGNORECASE) start_position = find_problem_with_solution(text, problem_pattern, solution_pattern) tags: List[Problem | Solution] = [] tags.extend([Problem(x) for x in problem_pattern.finditer(text, start_position)]) problem_num = len(tags) tags.extend([Solution(x) for x in solution_pattern.finditer(text, start_position)]) tags.sort(key=lambda x: x.match.start()) return tags, problem_num def segment(text: str, tags: List[Problem | Solution]) -> List[str]: starts = [] ends = [] for i in range(len(tags)): starts.append(tags[i].match.end()) if i + 1 < len(tags): ends.append(tags[i + 1].match.start()) else: ends.append(len(text)) return [text[start:end].strip() for start, end in zip(starts, ends)] def join(tags: List[Problem | Solution], segments: List[str]) -> List[Tuple[str, str, str, str, str]]: problem, solution = '', '' problem_label, problem_match, solution_match = '', '', '' pairs = [] for tag, segment in zip(tags, segments): if isinstance(tag, Problem): problem = segment problem_match = tag.match.group(0) problem_label = tag.match.group(1) or tag.match.group(2) or tag.match.group(3) elif problem.strip() != "": solution = segment solution_match = tag.match.group(0) if solution.strip() == "": continue 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) problem_type_mapping = { "-alg-": "Algebra", "-comb-": "Combinatorics", "-geo-": "Geometry", } problem_type = None for _k, _v in problem_type_mapping.items(): if _k in output_file.stem: problem_type = _v break output_jsonl_text = "" for problem, solution, problem_label, problem_match, solution_match in pairs: output_jsonl_text += json.dumps( { 'year': year, 'tier': "T4", 'problem_label': problem_label, 'problem_type': problem_type, "exam": "HMMT", '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 hmmt_md in tqdm(list(compet_md_path.glob('**/*.md')), desc='Segmenting'): output_file = seg_output_path / hmmt_md.relative_to(compet_md_path).with_suffix('.jsonl') output_file.parent.mkdir(parents=True, exist_ok=True) text = '\n' + clean_text(hmmt_md.read_text(encoding="utf-8")) tags, problem_num = analyze(text) 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 {hmmt_md}") logger.info(f"Total problem count: {total_problem_count}") logger.info(f"Total solution count: {total_solution_count}") if __name__ == '__main__': main()