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import re |
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import json |
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from tqdm import tqdm |
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from loguru import logger |
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from pathlib import Path |
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from typing import Tuple, List |
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from dataclasses import dataclass |
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project_root = Path(__file__).parent.parent.parent |
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@dataclass |
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class Problem: |
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match: re.Match |
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@dataclass |
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class Solution: |
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match: re.Match |
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def clean_text(text: str) -> str: |
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text = text.replace( |
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'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.', |
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'' |
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) |
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text = text.replace( |
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'- 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).', |
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'' |
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) |
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return text |
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def find_problem_with_solution( |
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text: str, |
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problem_parttern: re.Pattern, |
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solution_pattern: re.Pattern |
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) -> int: |
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""" |
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Find the problem with solution start position in the text. |
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Args: |
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text (str): The text to search. |
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Returns: |
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int: The start position of the problem with solution. |
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""" |
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matchs = list(problem_parttern.finditer(text)) |
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for index, match in enumerate(matchs): |
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section_end_position = matchs[index + 1].start() if index + 1 < len(matchs) else len(text) |
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if solution_pattern.search(text[match.start():section_end_position]): |
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return match.start() |
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return 0 |
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def analyze(text: str) -> Tuple[List[Problem | Solution], int]: |
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""" |
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Analyze the text and return the tags and problem number. |
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Args: |
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text (str): The markdown text to analyze. |
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Returns: |
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Tuple[List[Problem | Solution], int]: A tuple containing the tags and problem number. |
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""" |
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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) |
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solution_pattern = re.compile(r'(?:\n|\n\#+\s+)(?:answer\:|solution(?:\s+\d+)?(?:\:|\.)|Proposed by:.*?\n)\s*', re.IGNORECASE) |
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start_position = find_problem_with_solution(text, problem_pattern, solution_pattern) |
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tags: List[Problem | Solution] = [] |
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tags.extend([Problem(x) for x in problem_pattern.finditer(text, start_position)]) |
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problem_num = len(tags) |
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tags.extend([Solution(x) for x in solution_pattern.finditer(text, start_position)]) |
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tags.sort(key=lambda x: x.match.start()) |
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return tags, problem_num |
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def segment(text: str, tags: List[Problem | Solution]) -> List[str]: |
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starts = [] |
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ends = [] |
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for i in range(len(tags)): |
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starts.append(tags[i].match.end()) |
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if i + 1 < len(tags): |
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ends.append(tags[i + 1].match.start()) |
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else: |
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ends.append(len(text)) |
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return [text[start:end].strip() for start, end in zip(starts, ends)] |
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def join(tags: List[Problem | Solution], segments: List[str]) -> List[Tuple[str, str, str, str, str]]: |
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problem, solution = '', '' |
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problem_label, problem_match, solution_match = '', '', '' |
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pairs = [] |
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for tag, segment in zip(tags, segments): |
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if isinstance(tag, Problem): |
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problem = segment |
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problem_match = tag.match.group(0) |
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problem_label = tag.match.group(1) or tag.match.group(2) or tag.match.group(3) |
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elif problem.strip() != "": |
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solution = segment |
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solution_match = tag.match.group(0) |
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if solution.strip() == "": |
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continue |
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pairs.append((problem, solution, problem_label, problem_match, solution_match)) |
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return pairs |
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def write_pairs(output_file: Path, pairs): |
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year = re.search(r'(\d{4})', output_file.stem).group(1) |
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problem_type_mapping = { |
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"-alg-": "Algebra", |
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"-comb-": "Combinatorics", |
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"-geo-": "Geometry", |
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} |
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problem_type = None |
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for _k, _v in problem_type_mapping.items(): |
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if _k in output_file.stem: |
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problem_type = _v |
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break |
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output_jsonl_text = "" |
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for problem, solution, problem_label, problem_match, solution_match in pairs: |
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output_jsonl_text += json.dumps( |
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{ |
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'year': year, |
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'tier': "T4", |
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'problem_label': problem_label, |
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'problem_type': problem_type, |
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"exam": "HMMT", |
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'problem': problem, |
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'solution': solution, |
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'metadata': { |
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'resource_path': output_file.relative_to(project_root).as_posix(), |
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'problem_match': problem_match, |
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'solution_match': solution_match |
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} |
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}, |
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ensure_ascii=False |
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) + '\n' |
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output_file.write_text(output_jsonl_text, encoding="utf-8") |
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def main(): |
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compet_base_path = Path(__file__).resolve().parent.parent |
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compet_md_path = compet_base_path / "md" |
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seg_output_path = compet_base_path / "segmented" |
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total_problem_count = 0 |
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total_solution_count = 0 |
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for hmmt_md in tqdm(list(compet_md_path.glob('**/*.md')), desc='Segmenting'): |
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output_file = seg_output_path / hmmt_md.relative_to(compet_md_path).with_suffix('.jsonl') |
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output_file.parent.mkdir(parents=True, exist_ok=True) |
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text = '\n' + clean_text(hmmt_md.read_text(encoding="utf-8")) |
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tags, problem_num = analyze(text) |
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segments = segment(text, tags) |
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pairs = join(tags, segments) |
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if pairs and problem_num > 0: |
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write_pairs(output_file, pairs) |
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total_problem_count += problem_num |
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total_solution_count += len(pairs) |
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else: |
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logger.warning(f"No problem found in {hmmt_md}") |
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logger.info(f"Total problem count: {total_problem_count}") |
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logger.info(f"Total solution count: {total_solution_count}") |
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if __name__ == '__main__': |
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main() |
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