scripts/map_goodreads.py

#!/usr/bin/env python3
"""Map Vietnamese fiction books to Goodreads entries.

Uses STREAMING mode and EXACT MATCHING ONLY for speed.
Fuzzy matching is too slow for large datasets.

Usage:
    python map_goodreads.py --output mappings.jsonl

Requirements:
    pip install datasets tqdm
"""

import argparse
import json
import re
import unicodedata
from pathlib import Path

from datasets import load_dataset
from rapidfuzz import fuzz
from tqdm import tqdm


def normalize_text(text: str) -> str:
    """Normalize text for matching."""
    if not text:
        return ""
    text = text.lower()
    text = unicodedata.normalize("NFD", text)
    text = "".join(c for c in text if unicodedata.category(c) != "Mn")
    text = re.sub(r"[^\w\s]", " ", text)
    text = re.sub(r"\s+", " ", text).strip()
    return text


def clean_author(author: str) -> str:
    """Clean author string by removing extra metadata."""
    if not author:
        return ""
    # Remove common suffixes
    author = re.split(
        r'\s+(?:LỜI|MỤC|CHƯƠNG|Phần|Dẫn|Nguồn|Bản quyền|Xuất bản|Người dịch|'
        r'Cung cấp|Phát hành|http|www\.|Thể loại|First published|FREE|eBook|\*)',
        author, flags=re.IGNORECASE
    )[0].strip()
    # Remove trailing punctuation and metadata
    author = re.sub(r'[\*\:\.\,]+$', '', author).strip()
    # Remove year patterns at end
    author = re.sub(r'\s+\d{4}\.?$', '', author).strip()
    return author


def clean_title(title: str) -> str:
    """Clean title by removing embedded metadata."""
    if not title:
        return ""

    # Remove ebook project boilerplate
    ebook_patterns = [
        r'\s*Chào mừng các bạn đón đọc.*$',
        r'\s*Với minh họa của chính tác giả.*$',
        r'\s*Nhà xuất bản.*$',
        r'\s*Chuyển sang ấn bản điện tử.*$',
        r'\s*Nguyên tác:.*$',
        r'\s*Thực hiện ebook.*$',
        r'\s*Original title:.*$',
    ]
    for pattern in ebook_patterns:
        title = re.sub(pattern, '', title, flags=re.IGNORECASE)

    # Remove translator info in parentheses: (Bùi Giáng dịch), (Dịch giả: X)
    title = re.sub(r'\s*\([^)]*dịch[^)]*\)', '', title, flags=re.IGNORECASE)
    title = re.sub(r'\s*\(Dịch giả[^)]*\)', '', title, flags=re.IGNORECASE)

    return title.strip()


def is_western_name(text: str) -> bool:
    """Check if text looks like a Western author name."""
    if not text or len(text) < 5:
        return False
    # Western names: mixed case, may contain "de", "von", periods, hyphens
    # Examples: "Antoine de Saint-Exupéry", "J.K. Rowling", "Leo Tolstoy"
    western_pattern = r'^[A-Z][a-z]+(?:\s+(?:de|von|van|du|le|la|the|J\.|K\.|[A-Z]\.)*\s*[A-Z][a-zéèêëàâäùûüôöîïç\-]+)+$'
    return bool(re.match(western_pattern, text))


def extract_foreign_author(text: str) -> tuple[str, str]:
    """Extract foreign author name from text, return (title, author).

    Handles patterns like:
    - "HOÀNG TỬ BÉ Antoine de Saint-Exupéry" -> ("HOÀNG TỬ BÉ", "Antoine de Saint-Exupéry")
    - "Antoine de Saint-Exupéry HOÀNG TỬ BÉ" -> ("HOÀNG TỬ BÉ", "Antoine de Saint-Exupéry")
    - "Hoàng Tử Bé ANTOINE DE SAINT EXUPÉRY" -> ("Hoàng Tử Bé", "Antoine De Saint Exupéry")
    """
    if not text:
        return "", ""

    # Define character classes
    VN_UPPER = r'A-ZÀÁẢÃẠĂẰẮẲẴẶÂẦẤẨẪẬĐÈÉẺẼẸÊỀẾỂỄỆÌÍỈĨỊÒÓỎÕỌÔỒỐỔỖỘƠỜỚỞỠỢÙÚỦŨỤƯỪỨỬỮỰỲÝỶỸỴ'
    VN_LOWER = r'a-zàáảãạăằắẳẵặâầấẩẫậđèéẻẽẹêềếểễệìíỉĩịòóỏõọôồốổỗộơờớởỡợùúủũụưừứửữựỳýỷỹỵ'
    WESTERN_LOWER = r'a-zàáảãạăằắẳẵặâầấẩẫậđèéẻẽẹêềếểễệìíỉĩịòóỏõọôồốổỗộơờớởỡợùúủũụưừứửữựỳýỷỹỵéèêëàâäùûüôöîïç'

    # Compound name part: handles "Saint-Exupéry" = Name[-Name]*
    COMPOUND_NAME = rf'[A-Z][{WESTERN_LOWER}]+(?:-[A-Z][{WESTERN_LOWER}]+)*'

    # Western author name pattern: "Antoine de Saint-Exupéry", "Leo Tolstoy"
    WESTERN_NAME = (
        rf'[A-Z][{WESTERN_LOWER}]+'  # First name
        r'(?:'
        r'\s+(?:de|von|van|du|le|la|di|da|del|dos|das)'  # Particle
        rf'\s+{COMPOUND_NAME}'  # Compound name after particle
        r')?'
        rf'(?:\s+{COMPOUND_NAME})*'  # More name parts
    )

    # Pattern 1: Vietnamese Title case followed by author in ALL CAPS (Western name)
    # e.g., "Hoàng Tử Bé ANTOINE DE SAINT EXUPÉRY Chào mừng..."
    # Must check this FIRST before Pattern 2, otherwise "Hoàng Tử Bé" matches as Western name
    match = re.match(
        rf'^([{VN_UPPER}][{VN_LOWER}]+(?:\s+[{VN_UPPER}][{VN_LOWER}]+)*)\s+'
        r'([A-ZÉÈÊËÀÂÄÙÛÜÔÖÎÏÇ][A-ZÉÈÊËÀÂÄÙÛÜÔÖÎÏÇ\s\-]+?)'
        r'(?=\s+(?:Chào|Nhà|Với|dịch|Dịch|Nguồn|http|[A-Z][a-z])|$)',
        text
    )
    if match:
        title = match.group(1).strip()
        author_caps = match.group(2).strip()
        if len(title) >= 3 and len(author_caps) >= 5:
            # Check it's mostly uppercase (foreign name)
            upper_ratio = sum(1 for c in author_caps if c.isupper()) / max(len(author_caps.replace(' ', '')), 1)
            if upper_ratio > 0.7:
                return title, author_caps.title()

    # Pattern 2: Vietnamese ALL CAPS title followed by Western name (Title case)
    # e.g., "HOÀNG TỬ BÉ Antoine de Saint-Exupéry"
    match = re.match(
        rf'^([{VN_UPPER}][{VN_UPPER}\s]+?)\s+({WESTERN_NAME})(?=\s*$|\s+[{VN_UPPER}]?[{VN_LOWER}])',
        text
    )
    if match:
        vn_title = match.group(1).strip()
        author = match.group(2).strip()
        if len(vn_title) >= 3 and len(author) >= 5:
            return vn_title, author

    # Pattern 3: Western name (Title case) followed by Vietnamese ALL CAPS title
    # e.g., "Antoine de Saint-Exupéry HOÀNG TỬ BÉ"
    match = re.match(
        rf'^({WESTERN_NAME})\s+([{VN_UPPER}][{VN_UPPER}\s]+?)(?=\s+[{VN_UPPER}]?[{VN_LOWER}]|\s*$)',
        text
    )
    if match:
        author = match.group(1).strip()
        vn_title = match.group(2).strip()
        if len(vn_title) >= 3 and len(author) >= 5:
            return vn_title, author

    # Pattern 4: English ALL CAPS title followed by Western name (Title case)
    # e.g., "THE LITTLE PRINCE Antoine de Saint-Exupéry"
    match = re.match(
        rf'^([A-Z][A-Z\s]+?)\s+({WESTERN_NAME})(?=\s*$|\s+[{VN_UPPER}]?[{VN_LOWER}])',
        text
    )
    if match:
        title = match.group(1).strip()
        author = match.group(2).strip()
        if len(title) >= 3 and len(author) >= 5:
            return title, author

    return "", ""


def extract_title_author(text: str) -> tuple[str, str]:
    """Extract title and author from Vietnamese book text.

    Handles common Vietnamese book patterns:
    - "TITLE Tác giả: AUTHOR"
    - "TITLE Author: AUTHOR" (English)
    - "TITLE - AUTHOR"
    - "TITLE by AUTHOR"
    - "TITLE\nAUTHOR\nLỜI NÓI ĐẦU..."
    - "HOÀNG TỬ BÉ Antoine de Saint-Exupéry (Bùi Giáng dịch)"
    """
    if not text:
        return "", ""

    # Get first ~1000 chars for analysis
    header = text[:1000].strip()
    lines = header.split("\n")
    if not lines:
        return "", ""

    first_line = lines[0].strip()
    title = ""
    author = ""

    # Skip non-book content (technical docs, etc.)
    skip_patterns = [
        r'^(REC-|W3C|DOCTYPE|<!|<\?|http://|https://|Copyright|\*\s*\*\s*\*)',
        r'^(next\s+contents|properties\s+index)',
    ]
    for pattern in skip_patterns:
        if re.match(pattern, first_line, re.IGNORECASE):
            return "", ""

    # Pre-clean the first line: remove translator parentheses
    first_line_cleaned = clean_title(first_line)

    # NEW Pattern 0: Foreign author embedded in title
    # e.g., "HOÀNG TỬ BÉ Antoine de Saint-Exupéry (Bùi Giáng dịch)"
    foreign_title, foreign_author = extract_foreign_author(first_line_cleaned)
    if foreign_title and foreign_author:
        return clean_title(foreign_title), foreign_author

    # Pattern 1: "Tác giả:" or "Tác Giả:" marker
    tac_gia_match = re.search(
        r'^(.+?)\s*[Tt]ác\s*[Gg]iả\s*[:\-]\s*(.+)',
        first_line_cleaned
    )
    if tac_gia_match:
        title = tac_gia_match.group(1).strip()
        author = clean_author(tac_gia_match.group(2).strip())
        return clean_title(title), author

    # Pattern 2: "Author:" marker (English)
    author_match = re.search(
        r'^(.+?)\s*Author\s*[:\-]\s*(.+)',
        first_line_cleaned, re.IGNORECASE
    )
    if author_match:
        title = author_match.group(1).strip()
        author = clean_author(author_match.group(2).strip())
        return clean_title(title), author

    # Pattern 3: "Dịch giả:" marker (translator) - extract title before it
    dich_gia_match = re.search(
        r'^(.+?)\s*[Dd]ịch\s*[Gg]iả\s*[:\-]\s*(.+)',
        first_line
    )
    if dich_gia_match:
        title = dich_gia_match.group(1).strip()
        # Try to extract author from title (may contain foreign author)
        t, a = extract_foreign_author(title)
        if t and a:
            return clean_title(t), a
        return clean_title(title), ""

    # Pattern 4: "Nguyên tác:" marker - original work
    nguyen_tac_match = re.search(
        r'^(.+?)\s*Nguyên\s*tác\s*[:\-]\s*(.+)',
        first_line_cleaned, re.IGNORECASE
    )
    if nguyen_tac_match:
        title = nguyen_tac_match.group(1).strip()
        # Look for author after original title
        remainder = nguyen_tac_match.group(2).strip()
        # Author might be in next segment
        for line in lines[1:5]:
            line = line.strip()
            if line and len(line) < 60:
                author = clean_author(line)
                break
        return clean_title(title), author

    # Pattern 5: "Nguồn:" marker - title before, ignore source
    nguon_match = re.search(r'^(.+?)\s*Nguồn\s*:', first_line_cleaned)
    if nguon_match:
        title = nguon_match.group(1).strip()
        for line in lines[1:5]:
            line = line.strip()
            if line and not re.match(r'^(LỜI|MỤC|CHƯƠNG|Phần|\d+|http)', line, re.IGNORECASE):
                author = clean_author(line)
                break
        return clean_title(title), author

    # Pattern 6: Vietnamese author name pattern in title
    # e.g., "CHU DỊCH QUỐC VĂN DIỄN GIẢI Sào Nam Phan Bội Châu LỜI GIỚI THIỆU"
    vn_name_match = re.search(
        r'^(.+?)\s+([A-ZÀÁẢÃẠĂẰẮẲẴẶÂẦẤẨẪẬĐÈÉẺẼẸÊỀẾỂỄỆÌÍỈĨỊÒÓỎÕỌÔỒỐỔỖỘƠỜỚỞỠỢÙÚỦŨỤƯỪỨỬỮỰỲÝỶỸỴ][a-zàáảãạăằắẳẵặâầấẩẫậđèéẻẽẹêềếểễệìíỉĩịòóỏõọôồốổỗộơờớởỡợùúủũụưừứửữựỳýỷỹỵ]+(?:\s+[A-ZÀÁẢÃẠĂẰẮẲẴẶÂẦẤẨẪẬĐÈÉẺẼẸÊỀẾỂỄỆÌÍỈĨỊÒÓỎÕỌÔỒỐỔỖỘƠỜỚỞỠỢÙÚỦŨỤƯỪỨỬỮỰỲÝỶỸỴ][a-zàáảãạăằắẳẵặâầấẩẫậđèéẻẽẹêềếểễệìíỉĩịòóỏõọôồốổỗộơờớởỡợùúủũụưừứửữựỳýỷỹỵ]+){1,4})\s+(?:LỜI|MỤC|CHƯƠNG|Phần)',
        first_line_cleaned
    )
    if vn_name_match:
        title = vn_name_match.group(1).strip()
        author = vn_name_match.group(2).strip()
        return clean_title(title), author

    # Pattern 7: " - " separator
    if " - " in first_line_cleaned:
        parts = first_line_cleaned.split(" - ", 1)
        title = parts[0].strip()
        author = clean_author(parts[1].strip()) if len(parts) > 1 else ""
        return clean_title(title), author

    # Pattern 8: " by " (English)
    if " by " in first_line_cleaned.lower():
        parts = re.split(r'\s+by\s+', first_line_cleaned, flags=re.IGNORECASE)
        title = parts[0].strip()
        author = clean_author(parts[1].strip()) if len(parts) > 1 else ""
        return clean_title(title), author

    # Pattern 9: Content markers in first line - title is before
    content_markers = [
        'LỜI NÓI ĐẦU', 'LỜI GIỚI THIỆU', 'LỜI TỰA', 'MỤC LỤC',
        'CHƯƠNG', 'Phần', 'Dẫn nhập', 'Lời mở đầu', 'PHẦN',
        'Tác phẩm', 'Bản quyền', 'Xuất bản', 'Chào mừng các bạn'
    ]
    for marker in content_markers:
        if marker.upper() in first_line_cleaned.upper():
            idx = first_line_cleaned.upper().find(marker.upper())
            if idx > 5:
                title = first_line_cleaned[:idx].strip()
                # Try to extract author from this title segment
                t, a = extract_foreign_author(title)
                if t and a:
                    return clean_title(t), a
                return clean_title(title), author

    # Default: first line is title, look for author in next lines
    title = first_line_cleaned

    for line in lines[1:5]:
        line = line.strip()
        if not line:
            continue
        if re.match(r'^(LỜI|MỤC|CHƯƠNG|Phần|\d+|http|www\.|Copyright|©|\*|Chào mừng)', line, re.IGNORECASE):
            continue
        if len(line) > 80:
            continue
        author = clean_author(line)
        break

    return clean_title(title), author


def load_vietnamese_books_streaming(source: str, limit: int | None = None) -> list[dict]:
    """Load Vietnamese books using streaming mode."""
    print(f"Loading Vietnamese books from {source} (streaming)...")
    ds = load_dataset(source, split="train", streaming=True)

    books = []
    for i, row in enumerate(tqdm(ds, desc="Loading VN books", total=limit)):
        if limit and i >= limit:
            break
        text = row.get("text", "")
        title, author = extract_title_author(text)
        books.append({
            "id": f"vn_{i:06d}",
            "title": title,
            "author": author,
        })

    print(f"Loaded {len(books)} Vietnamese books")
    return books


def find_matches_streaming(
    vn_books: list[dict],
    goodreads_source: str = "BrightData/Goodreads-Books",
    goodreads_limit: int | None = None,
    fuzzy_threshold: int = 85,
) -> list[dict]:
    """Stream through Goodreads and find matches (exact + fuzzy)."""

    # Build index of Vietnamese books by normalized title
    vn_index: dict[str, list[dict]] = {}
    vn_titles_set: set[str] = set()
    vn_title_lengths: set[int] = set()

    for book in vn_books:
        norm_title = normalize_text(book["title"])
        if norm_title:
            if norm_title not in vn_index:
                vn_index[norm_title] = []
            vn_index[norm_title].append(book)
            vn_titles_set.add(norm_title)
            vn_title_lengths.add(len(norm_title))

    # Pre-compute length ranges for fuzzy matching
    min_len = min(vn_title_lengths) if vn_title_lengths else 0
    max_len = max(vn_title_lengths) if vn_title_lengths else 0

    print(f"Built index with {len(vn_index)} unique normalized titles")
    print(f"Title length range: {min_len} - {max_len} chars")

    # Track matches and candidates for fuzzy matching
    matches: dict[str, dict | None] = {book["id"]: None for book in vn_books}
    fuzzy_candidates: list[tuple[str, dict]] = []  # (norm_title, gr_data)

    print(f"\nPhase 1: Streaming Goodreads for exact matches...")
    ds = load_dataset(goodreads_source, split="train", streaming=True)

    exact_count = 0
    for i, row in enumerate(tqdm(ds, desc="Exact matching", total=goodreads_limit)):
        if goodreads_limit and i >= goodreads_limit:
            break

        gr_title = row.get("name", "")
        if not gr_title:
            continue

        norm_gr_title = normalize_text(gr_title)
        if not norm_gr_title:
            continue

        gr_data = {
            "goodreads_id": row.get("id", ""),
            "goodreads_url": row.get("url", ""),
            "goodreads_title": gr_title,
            "goodreads_author": row.get("author", ""),
            "goodreads_rating": row.get("star_rating"),
            "goodreads_num_ratings": row.get("num_ratings"),
        }

        # Exact title matches (O(1) lookup)
        if norm_gr_title in vn_index:
            for vn_book in vn_index[norm_gr_title]:
                vn_id = vn_book["id"]
                if matches[vn_id] is None:
                    exact_count += 1
                    matches[vn_id] = {
                        **gr_data,
                        "match_type": "exact_title",
                        "match_score": 100,
                    }

        # Store potential fuzzy candidates (filter by length + popularity)
        gr_len = len(norm_gr_title)
        num_ratings = gr_data.get("goodreads_num_ratings") or 0
        # Keep only titles within length range AND with some ratings (popular books)
        if min_len * 0.6 <= gr_len <= max_len * 1.4:
            if num_ratings >= 10 or len(fuzzy_candidates) < 500000:
                fuzzy_candidates.append((norm_gr_title, gr_data))

    print(f"\nExact matches: {exact_count}")
    print(f"Fuzzy candidates: {len(fuzzy_candidates)}")

    # Phase 2: Fuzzy matching for unmatched books
    unmatched_books = [b for b in vn_books if matches[b["id"]] is None and b["title"]]

    if unmatched_books and fuzzy_candidates:
        print(f"\nPhase 2: Fuzzy matching {len(unmatched_books)} unmatched books...")
        print(f"Using {len(fuzzy_candidates)} Goodreads candidates")

        # Build lookup dict for fuzzy candidates
        gr_title_to_data = {t: d for t, d in fuzzy_candidates}
        gr_titles = list(gr_title_to_data.keys())

        fuzzy_count = 0
        from rapidfuzz import process

        for vn_book in tqdm(unmatched_books, desc="Fuzzy matching"):
            vn_id = vn_book["id"]
            norm_vn_title = normalize_text(vn_book["title"])
            if not norm_vn_title or len(norm_vn_title) < 5:
                continue

            # Use rapidfuzz's optimized extractOne
            result = process.extractOne(
                norm_vn_title,
                gr_titles,
                scorer=fuzz.ratio,
                score_cutoff=fuzzy_threshold,
            )

            if result:
                matched_title, score, _ = result
                gr_data = gr_title_to_data[matched_title]
                fuzzy_count += 1
                matches[vn_id] = {
                    **gr_data,
                    "match_type": "fuzzy_title",
                    "match_score": int(score),
                }

        print(f"Fuzzy matches: {fuzzy_count}")

    # Build results
    results = []
    for vn_book in vn_books:
        vn_id = vn_book["id"]
        result = {
            "vn_id": vn_id,
            "vn_title": vn_book["title"],
            "vn_author": vn_book["author"],
        }
        if matches[vn_id]:
            result.update(matches[vn_id])
        else:
            result["goodreads_id"] = None
        results.append(result)

    return results


def main():
    parser = argparse.ArgumentParser(description="Map Vietnamese books to Goodreads")
    parser.add_argument(
        "--output", "-o",
        default="mappings.jsonl",
        help="Output file for mappings (JSONL format)",
    )
    parser.add_argument(
        "--vn-source",
        default="tmnam20/Vietnamese-Book-Corpus",
        help="Vietnamese books dataset on HuggingFace",
    )
    parser.add_argument(
        "--vn-limit",
        type=int,
        default=None,
        help="Limit Vietnamese books to process",
    )
    parser.add_argument(
        "--goodreads-limit",
        type=int,
        default=None,
        help="Limit Goodreads books to scan",
    )
    args = parser.parse_args()

    # Load Vietnamese books
    vn_books = load_vietnamese_books_streaming(args.vn_source, args.vn_limit)

    # Find matches by streaming Goodreads
    results = find_matches_streaming(
        vn_books,
        goodreads_limit=args.goodreads_limit,
    )

    # Save results
    output_path = Path(args.output)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    with open(output_path, "w", encoding="utf-8") as f:
        for r in results:
            f.write(json.dumps(r, ensure_ascii=False) + "\n")

    matched = sum(1 for r in results if r.get("goodreads_id"))
    print(f"\n{'='*50}")
    print(f"Results saved to: {output_path}")
    print(f"Total Vietnamese books: {len(vn_books)}")
    print(f"Matched to Goodreads: {matched} ({100*matched/len(vn_books):.1f}%)")
    print(f"Unmatched: {len(vn_books) - matched}")


if __name__ == "__main__":
    main()