# -----------------------------------------------------------------------------
# Author: Marina
# Date: 2024-11-15
# -----------------------------------------------------------------------------
''' Script to segment IMO shortlist md files using regex. It takes as input
the file en-compendium.md in en-shortlist and outputs the segmentation 
(problem/solution pairs) in en-shortlist-seg
To run: 
`python segment_compendium.py`
To debug (or see covered use cases by regex):
`pytest test_segment_compendium`
'''

import os 
import re
import pandas as pd


base = 'en-shortlist'
seg_base = 'en-shortlist-seg'
basename = 'en-compendium'


level1_re = re.compile(r"^##\s+(Problems|Solutions|Notation and Abbreviations)$")
year_re = re.compile(r"^[^=]*,\s+(\d{4})\s*$")
problem_section_re = re.compile(r"^###\s+(\d+\.\d+\.\d+)\s+(.+)$")
solution_section_re = re.compile(r"^###\s+(\d+\.\d+)\s+([\w\s]+)\s+(\d{4})$")
problem_or_solution_re = re.compile(r"^(?:\[.*?\])?\s*(\d+)\s*\.\s*(.+)$")


def add_content(current_dict):
    required_keys = ["year", "category", "section_label", "label", "lines"]
    if not all(current_dict[key] for key in required_keys):
        return
    text_str = " ".join(current_dict["lines"]).strip()
    entry = {
        "year": current_dict["year"],
        "category": current_dict["category"],
        "section": current_dict["section_label"],
        "label": current_dict["label"],
    }
    if current_dict["class"] == "problem":
        entry["problem"] = text_str
        current_dict["problems"].append(entry)
    elif current_dict["class"] == "solution":
        entry["solution"] = text_str
        current_dict["solutions"].append(entry)


def get_category(s:str):
    cat = None
    if 'contest' in s.lower():
        cat = 'contest'
    elif 'shortlisted' in s.lower():
        cat = 'shortlisted'
    elif 'longlisted' in s.lower():
        cat = 'longlisted'
    return cat


def get_matching_section_label(s:str):
    """
    extracts the section number to be used a a join key to pair a problem and solution
    for problems: 3.44.1 -> 44
    for solutions: 4.20 -> 20
    """
    return s.split('.')[1]


def parse(file):
    with open(file, 'r') as file:
        content = file.read()
    # problems, solutions = [], []
    current = {
        "year": None,
        "category": None,
        "section_label": None,
        "label": None,  
        "class": None,
        "lines": [],
        "problems": [],
        "solutions": []
    }
    for line in content.splitlines():
        if match := level1_re.match(line):
            add_content(current)
            title, = match.groups()
            current["class"] = {
                "Problems": "problem",
                "Solutions": "solution",
            }.get(title, "other")
            current["lines"] = []
        elif match := year_re.match(line):
            add_content(current)
            current["year"] = match.group(1)
            current["lines"] = []
        elif match := problem_section_re.match(line):
            add_content(current)
            number, title = match.groups()
            current["section_label"] = get_matching_section_label(number)
            current["category"] = get_category(title)
            current["lines"] = []
        elif match := solution_section_re.match(line):
            add_content(current)
            number, title, year = match.groups() 
            current["section_label"] = get_matching_section_label(number)
            current["category"] = get_category(title)
            current["year"] = year
            current["lines"] = []
        elif match := problem_or_solution_re.match(line):
            add_content(current)
            current["label"] = match.group(1)
            current["lines"] = [line]
        else:
            if current["lines"]:
                current["lines"].append(line)
    problems_df = pd.DataFrame(current["problems"])
    solutions_df = pd.DataFrame(current["solutions"])
    return problems_df, solutions_df

def join(problems_df, solutions_df):
    pairs_df = problems_df.merge(solutions_df, on=["year", "category", "section", "label"], how="outer")
    return pairs_df

def add_metadata(pairs_df):
    problem_type_mapping = {
    "A": "Algebra",
    "C": "Combinatorics",
    "G": "Geometry",
    "N": "Number Theory"
    }
    pairs_df['problem_type'] = pairs_df['problem'].str.extract(r'^\d+\.\s*([ACGN])\d*')[0]
    pairs_df['problem_type'] = pairs_df['problem_type'] .map(problem_type_mapping)
    pairs_df['tier'] = 0 # according to omnimath 
    pairs_df.rename(columns={"category": "problem_phase"}, inplace=True)
    pairs_df = pairs_df.drop(columns=['section', 'label'])
    return pairs_df

def write_pairs(filename, pairs_df):
    pairs_df.to_json(filename, orient="records", lines=True)


problems, solutions = parse(f"{base}/{basename}.md")
pairs_df = join(problems, solutions)
pairs_df = pairs_df[pairs_df.notnull().all(axis=1)]
pairs_df = add_metadata(pairs_df)
write_pairs(f"{seg_base}/{basename}.jsonl", pairs_df)

# problems contains duplicate problems (since problem in Shortlist appears in Contest, and problem in Longlist appeasr in Shortlist)
# >>>print(len(problems))
# 2460
# >>>print(len(solutions))
# 961
# print(len(pairs_df))
# 960