"""Rebuild COMP2211 Spring 2024 midterm into subquestions."""

from __future__ import annotations

import json
import re
from dataclasses import dataclass
from pathlib import Path

import fitz

from app.services.supabase_client import get_supabase


EXAM_KEY = "COMP2211-2024-spring-midterm"
ROOT = Path(__file__).resolve().parent.parent
QUESTION_PDF = ROOT / "pastpaper-scraper" / "papers" / "COMP2211" / "(COMP2211)[2024](s)midterm~=rcidkjgf^_82003.pdf"
ANSWER_PDF = ROOT / "pastpaper-scraper" / "papers" / "COMP2211" / "(COMP2211)[2024](s)midterm~=ubrzkjmz^_90406.pdf"
PROBLEM_SEED_PATH = ROOT / "pastpaper-scraper" / "reviews" / "COMP2211" / "problem_seed.json"
TRUE_FALSE_OPTIONS = [{"label": "True", "text": "True"}, {"label": "False", "text": "False"}]


@dataclass(frozen=True)
class ChildSpec:
    question_number: str
    parent_question: str
    top_level_number: str
    path: tuple[str, ...]
    score: float
    question_type: str
    question_format: str | None = None
    analytics_topic: str | None = None
    topic_primary: str | None = None
    topic_tags: tuple[str, ...] | None = None
    skill_tags: tuple[str, ...] | None = None
    page_number: int = 1


def short_answer(
    question_number: str,
    parent_question: str,
    top_level_number: str,
    path: tuple[str, ...],
    score: float,
    *,
    analytics_topic: str | None = None,
    topic_primary: str | None = None,
    topic_tags: tuple[str, ...] | None = None,
    skill_tags: tuple[str, ...] | None = None,
    page_number: int,
) -> ChildSpec:
    return ChildSpec(
        question_number=question_number,
        parent_question=parent_question,
        top_level_number=top_level_number,
        path=path,
        score=score,
        question_type="long_question",
        question_format="short_answer",
        analytics_topic=analytics_topic,
        topic_primary=topic_primary,
        topic_tags=topic_tags,
        skill_tags=skill_tags,
        page_number=page_number,
    )


CHILDREN: list[ChildSpec] = [
    ChildSpec("1a", "1", "1", ("a",), 0.5, "true_false", "true_false", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("concept_check", "code_tracing"), page_number=3),
    ChildSpec("1b", "1", "1", ("b",), 0.5, "true_false", "true_false", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("concept_check", "broadcasting"), page_number=3),
    ChildSpec("1c", "1", "1", ("c",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "algorithm_property"), page_number=3),
    ChildSpec("1d", "1", "1", ("d",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "tie_reasoning"), page_number=3),
    ChildSpec("1e", "1", "1", ("e",), 0.5, "true_false", "true_false", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("concept_check", "cross_validation"), page_number=3),
    ChildSpec("1f", "1", "1", ("f",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "clustering_property"), page_number=3),
    ChildSpec("1g", "1", "1", ("g",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "robustness_reasoning"), page_number=3),
    ChildSpec("1h", "1", "1", ("h",), 0.5, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "decision_boundary"), page_number=3),
    ChildSpec("1i", "1", "1", ("i",), 0.5, "true_false", "true_false", "Perceptron and MLP", "Perceptron and MLP", ("Perceptron and MLP",), ("concept_check", "optimization_reasoning"), page_number=3),
    ChildSpec("1j", "1", "1", ("j",), 0.5, "true_false", "true_false", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("concept_check", "clustering_property"), page_number=3),
    short_answer("2a_i", "2a", "2", ("a", "i"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4),
    short_answer("2a_ii", "2a", "2", ("a", "ii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("code_tracing",), page_number=4),
    short_answer("2a_iii", "2a", "2", ("a", "iii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("array_manipulation",), page_number=5),
    short_answer("2a_iv", "2a", "2", ("a", "iv"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("array_construction",), page_number=5),
    short_answer("2a_v", "2a", "2", ("a", "v"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("aggregation",), page_number=5),
    short_answer("2a_vi", "2a", "2", ("a", "vi"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("transpose",), page_number=6),
    short_answer("2a_vii", "2a", "2", ("a", "vii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("matrix_multiplication",), page_number=6),
    short_answer("2a_viii", "2a", "2", ("a", "viii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("dot_product",), page_number=6),
    short_answer("2a_ix", "2a", "2", ("a", "ix"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting",), page_number=6),
    short_answer("2a_x", "2a", "2", ("a", "x"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("error_reasoning",), page_number=7),
    short_answer("2a_xi", "2a", "2", ("a", "xi"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("broadcasting",), page_number=7),
    short_answer("2a_xii", "2a", "2", ("a", "xii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("slicing",), page_number=7),
    short_answer("2a_xiii", "2a", "2", ("a", "xiii"), 1, analytics_topic="Python Fundamentals", topic_primary="Python Fundamentals", topic_tags=("Python Fundamentals",), skill_tags=("views_vs_copies",), page_number=7),
    ChildSpec("2b", "2", "2", ("b",), 6, "long_question", "coding", "Python Fundamentals", "Python Fundamentals", ("Python Fundamentals",), ("implementation", "vectorization", "similarity_computation"), page_number=8),
    ChildSpec("3a", "3", "3", ("a",), 5.5, "long_question", "long_answer", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("manual_computation", "metric_reasoning"), page_number=10),
    short_answer("3b", "3", "3", ("b",), 1, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("metric_reasoning",), page_number=11),
    ChildSpec("3c", "3", "3", ("c",), 2.5, "long_question", "long_answer", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("manual_computation", "metric_reasoning"), page_number=11),
    short_answer("3d", "3", "3", ("d",), 1, analytics_topic="Evaluation and Validation", topic_primary="Evaluation and Validation", topic_tags=("Evaluation and Validation",), skill_tags=("metric_reasoning",), page_number=12),
    ChildSpec("3e", "3", "3", ("e",), 6, "long_question", "coding", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("implementation", "metrics", "vectorization"), page_number=12),
    ChildSpec("4a", "4", "4", ("a",), 4, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("manual_computation", "gaussian_nb"), page_number=15),
    ChildSpec("4b", "4", "4", ("b",), 3, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("manual_computation", "likelihood_reasoning"), page_number=15),
    ChildSpec("4c", "4", "4", ("c",), 4, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("laplace_smoothing", "likelihood_reasoning"), page_number=16),
    short_answer("4d", "4", "4", ("d",), 2, analytics_topic="Probabilistic Models", topic_primary="Probabilistic Models", topic_tags=("Probabilistic Models",), skill_tags=("prior_reasoning",), page_number=17),
    ChildSpec("4e", "4", "4", ("e",), 3, "long_question", "long_answer", "Probabilistic Models", "Probabilistic Models", ("Probabilistic Models",), ("posterior_reasoning", "classification_decision"), page_number=17),
    ChildSpec("5a", "5", "5", ("a",), 3, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("distance_calculation", "weighted_knn"), page_number=18),
    ChildSpec("5b", "5", "5", ("b",), 13, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("cross_validation", "manual_tracing", "model_selection"), page_number=18),
    short_answer("5c", "5", "5", ("c",), 2, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("test_error", "model_selection"), page_number=20),
    ChildSpec("6a", "6", "6", ("a",), 6, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("manual_computation", "clustering"), page_number=21),
    ChildSpec("6b", "6", "6", ("b",), 6, "long_question", "long_answer", "KNN and Clustering", "KNN and Clustering", ("KNN and Clustering",), ("manual_computation", "clustering"), page_number=22),
    short_answer("6c", "6", "6", ("c",), 2, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("outlier_reasoning",), page_number=22),
    short_answer("6d", "6", "6", ("d",), 2, analytics_topic="KNN and Clustering", topic_primary="KNN and Clustering", topic_tags=("KNN and Clustering",), skill_tags=("model_selection", "threshold_reasoning"), page_number=22),
    ChildSpec("7", "7", "7", (), 10, "long_question", "long_answer", "Evaluation and Validation", "Evaluation and Validation", ("Evaluation and Validation",), ("cross_validation", "data_leakage_reasoning"), page_number=23),
]


MARKER_RE = re.compile(r"(?m)^\(([a-z]+|[ivx]+)\)\s*")


def split_sections(text: str) -> tuple[str, dict[str, str]]:
    matches = list(MARKER_RE.finditer(text))
    if not matches:
        return text.strip(), {}
    intro = text[: matches[0].start()].strip()
    sections: dict[str, str] = {}
    for idx, match in enumerate(matches):
        marker = match.group(1)
        end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text)
        sections[marker] = text[match.start() : end].strip()
    return intro, sections


def extract_segment(text: str, path: tuple[str, ...]) -> str:
    if not path:
        return text.strip()
    current = text.strip()
    carried_intro: list[str] = []
    for depth, marker in enumerate(path):
        intro, sections = split_sections(current)
        if depth == 0 and intro:
            carried_intro.append(intro)
        current = sections.get(marker, current)
    return "\n".join(part for part in [*carried_intro, current] if part).strip()


def extract_pages(pdf_path: Path, start: int, end: int) -> str:
    doc = fitz.open(pdf_path)
    try:
        return "\n".join(doc[i].get_text("text") for i in range(start - 1, end))
    finally:
        doc.close()


def load_seed_rows() -> dict[str, dict]:
    data = json.loads(PROBLEM_SEED_PATH.read_text())
    return {row["question_number"]: row for row in data if row["source_exam_key"] == EXAM_KEY}


def build_source_rows(existing_rows: dict[str, dict]) -> dict[str, dict]:
    seed_rows = load_seed_rows()
    rows = dict(seed_rows)
    if "5" in rows:
        rows["5"] = {
            **rows["5"],
            "question_text": extract_pages(QUESTION_PDF, 18, 20),
            "raw_answer_text": extract_pages(ANSWER_PDF, 21, 25),
            "page_number": 18,
            "analytics_topic": "KNN and Clustering",
            "topic_primary": "KNN and Clustering",
            "topic_tags": ["KNN and Clustering"],
            "skill_tags": ["manual_computation", "distance_calculation", "algorithm_tracing"],
            "difficulty": "medium",
        }
    else:
        rows["5"] = {
            **seed_rows["5"],
            "question_text": extract_pages(QUESTION_PDF, 18, 20),
            "raw_answer_text": extract_pages(ANSWER_PDF, 21, 25),
            "page_number": 18,
        }
    if "7" in rows:
        rows["7"] = {
            **rows["7"],
            "question_text": extract_pages(QUESTION_PDF, 23, 24),
            "raw_answer_text": extract_pages(ANSWER_PDF, 31, 34),
            "page_number": 23,
            "analytics_topic": "Evaluation and Validation",
            "topic_primary": "Evaluation and Validation",
            "topic_tags": ["Evaluation and Validation"],
            "skill_tags": ["cross_validation", "data_leakage_reasoning"],
            "difficulty": "medium",
        }
    else:
        rows["7"] = {
            **seed_rows["7"],
            "question_text": extract_pages(QUESTION_PDF, 23, 24),
            "raw_answer_text": extract_pages(ANSWER_PDF, 31, 34),
            "page_number": 23,
        }
    return rows


def extract_true_false_answers(answer_text: str) -> dict[str, str]:
    answers: dict[str, str] = {}
    table_match = re.search(r"Answer\s+([TF\s]+)", answer_text, re.S)
    if table_match:
        seq = re.findall(r"[TF]", table_match.group(1))
        if len(seq) >= 10:
            for idx, val in enumerate(seq[:10]):
                answers[chr(ord("a") + idx)] = val
            return answers
    lines = [line.strip() for line in answer_text.splitlines() if line.strip()]
    current_letter: str | None = None
    for line in lines:
        m = re.fullmatch(r"\(([a-j])\)", line)
        if m:
            current_letter = m.group(1)
            continue
        if current_letter and line in {"T", "F"}:
            answers[current_letter] = line
            current_letter = None
    if answers:
        return answers
    seq = re.findall(r"\b([TF])\b", answer_text)
    if len(seq) >= 10:
        for idx, val in enumerate(seq[:10]):
            answers[chr(ord("a") + idx)] = val
    return answers


def main() -> None:
    sb = get_supabase()
    paper = sb.table("papers").select("id").eq("source_exam_key", EXAM_KEY).execute().data[0]
    paper_id = paper["id"]
    current_rows = (
        sb.table("paper_questions")
        .select("*")
        .eq("paper_id", paper_id)
        .order("display_order")
        .execute()
        .data
    )
    existing_by_number = {row["question_number"]: row for row in current_rows}
    parent_rows = build_source_rows(existing_by_number)
    tf_answers = extract_true_false_answers(parent_rows["1"]["raw_answer_text"] or "")

    inserts = []
    for display_order, child in enumerate(CHILDREN, start=1):
        parent = parent_rows[child.top_level_number]
        existing = existing_by_number.get(child.question_number, {})
        question_text = extract_segment(parent["question_text"] or "", child.path)
        raw_answer_text = extract_segment(parent["raw_answer_text"] or "", child.path) if child.path else (parent["raw_answer_text"] or "")
        options = None
        correct_option = None
        if child.question_type == "true_false":
            options = TRUE_FALSE_OPTIONS
            correct_option = tf_answers.get(child.path[0])

        inserts.append(
            {
                "paper_id": paper_id,
                "question_number": child.question_number,
                "parent_question": child.parent_question,
                "display_order": display_order,
                "question_type": child.question_type,
                "question_format": child.question_format,
                "question_text": question_text,
                "score": child.score,
                "page_number": child.page_number,
                "page_y_ratio": existing.get("page_y_ratio"),
                "options": options,
                "correct_option": correct_option,
                "correct_answer": None,
                "raw_answer_text": raw_answer_text,
                "topics": existing.get("topics") or (list(child.topic_tags) if child.topic_tags else parent.get("topics")),
                "topic_primary": existing.get("topic_primary") or child.topic_primary or parent.get("topic_primary"),
                "analytics_topic": existing.get("analytics_topic") or child.analytics_topic or parent.get("analytics_topic"),
                "topic_tags": existing.get("topic_tags") or (list(child.topic_tags) if child.topic_tags else parent.get("topic_tags")),
                "skill_tags": existing.get("skill_tags") or (list(child.skill_tags) if child.skill_tags else parent.get("skill_tags")),
                "difficulty": existing.get("difficulty") or parent.get("difficulty"),
                "knowledge_reminder": existing.get("knowledge_reminder", ""),
                "ai_hint": existing.get("ai_hint", ""),
                "solution": existing.get("solution", ""),
            }
        )

    sb.table("paper_questions").delete().eq("paper_id", paper_id).execute()
    sb.table("paper_questions").insert(inserts).execute()
    sb.table("papers").update({"question_count": len(inserts), "status": "processing"}).eq("id", paper_id).execute()
    print(f"Inserted {len(inserts)} rows for {EXAM_KEY}.")


if __name__ == "__main__":
    main()