scripts/defextra_markers.py

from __future__ import annotations

import importlib
import re
import unicodedata
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, Iterable, Optional

try:
    etree = importlib.import_module("lxml.etree")
except Exception as exc:
    raise RuntimeError(
        "Missing lxml dependency. Install with `pip install lxml`.",
    ) from exc

NS = {"tei": "http://www.tei-c.org/ns/1.0"}

BLOCK_TAGS = {
    "head",
    "p",
    "item",
    "title",
    "label",
    "figDesc",
    "caption",
    "cell",
    "ab",
    "note",
    "quote",
}

HASH_BASE = 1_000_003
HASH_MASK = (1 << 64) - 1
HASH_VERSION = "tokhash_v2"
ANCHOR_WINDOW = 12
ANCHOR_WINDOW_ALT = 6


def normalize_paper_id(paper_id: str) -> str:
    raw = paper_id.strip()
    if raw.lower().startswith("doi:"):
        raw = raw[4:]
    if raw.startswith("dx.doi.org/"):
        raw = raw[len("dx.doi.org/") :]
    if "doi.org/" in raw:
        raw = raw.split("doi.org/", 1)[1]
    if raw.startswith("http://") or raw.startswith("https://"):
        match = re.search(r"arxiv\.org/(abs|pdf)/([^?#]+)", raw)
        if match:
            return match.group(2).replace(".pdf", "")
        match = re.search(r"/pii/([^/?#]+)", raw)
        if match:
            return match.group(1)
        match = re.search(r"10\.[0-9]+/([^?#]+)", raw)
        if match:
            return match.group(1)
        parts = [p for p in re.split(r"[/?#]", raw) if p]
        if parts:
            return parts[-1]
    match = re.search(r"10\.[0-9]+/([^\s]+)", raw)
    if match:
        return match.group(1)
    return raw


def normalize_arxiv(value: str) -> str:
    cleaned = value.strip()
    match = re.search(
        r"(\d{4}\.\d{4,5}v\d+|[a-z-]+/\d{7}v\d+)",
        cleaned,
        re.I,
    )
    if match:
        return match.group(1)
    return cleaned.replace("arXiv:", "").strip()


def normalize_doi(value: str) -> str:
    cleaned = value.strip()
    if cleaned.startswith("https://doi.org/"):
        cleaned = cleaned[len("https://doi.org/") :]
    if cleaned.startswith("http://doi.org/"):
        cleaned = cleaned[len("http://doi.org/") :]
    return cleaned.lower()


def doi_suffix(value: str) -> str:
    cleaned = normalize_doi(value)
    match = re.search(r"10\.[0-9]+/(.+)", cleaned)
    if match:
        return match.group(1)
    return cleaned


def extract_ids_from_tei(path: Path) -> tuple[Optional[str], Optional[str]]:
    try:
        root = etree.parse(str(path)).getroot()
    except (OSError, etree.XMLSyntaxError):
        return None, None
    bibl = root.find(
        ".//tei:teiHeader/tei:fileDesc/tei:sourceDesc/tei:biblStruct",
        namespaces=NS,
    )
    if bibl is None:
        return None, None
    idnos = bibl.findall(".//tei:idno", namespaces=NS)
    doi = arxiv = None
    for idno in idnos:
        if idno.text is None:
            continue
        value = idno.text.strip()
        if not value:
            continue
        id_type = (idno.get("type") or "").lower()
        if doi is None and (
            id_type == "doi" or value.lower().startswith("10.")
        ):
            doi = value
        if arxiv is None and (
            "arxiv" in id_type or value.lower().startswith("arxiv")
        ):
            arxiv = normalize_arxiv(value)
    return doi, arxiv


def extract_title_from_tei(path: Path) -> Optional[str]:
    try:
        root = etree.parse(str(path)).getroot()
    except (OSError, etree.XMLSyntaxError):
        return None
    for path_expr in (
        ".//tei:teiHeader/tei:fileDesc/tei:titleStmt/tei:title",
        ".//tei:teiHeader/tei:fileDesc/tei:sourceDesc/tei:biblStruct/tei:analytic/tei:title",
        ".//tei:teiHeader/tei:fileDesc/tei:sourceDesc/tei:biblStruct/tei:monogr/tei:title",
    ):
        node = root.find(path_expr, namespaces=NS)
        if node is None:
            continue
        text = "".join(node.itertext()).strip()
        if text:
            return text
    return None


def extract_text_from_pdf(path: Path) -> str:
    pdfplumber_module: Any | None
    try:
        pdfplumber_module = importlib.import_module("pdfplumber")
    except Exception:
        pdfplumber_module = None
    pdfplumber_any: Any = pdfplumber_module
    if pdfplumber_any is not None:
        try:
            texts = []
            with pdfplumber_any.open(str(path)) as pdf:
                for page in pdf.pages:
                    page_text = page.extract_text() or ""
                    words_text = ""
                    try:
                        words = page.extract_words(
                            use_text_flow=True,
                            keep_blank_chars=False,
                        )
                        if words:
                            words_text = " ".join(
                                w.get("text", "") for w in words
                            )
                    except Exception:
                        words_text = ""
                    if words_text and words_text not in page_text:
                        if page_text:
                            page_text = f"{page_text}\n{words_text}"
                        else:
                            page_text = words_text
                    texts.append(page_text)
            return "\n\n".join(texts)
        except Exception:
            # pdfplumber can choke on some PDFs; fall back below.
            pass
    try:
        from pdfminer.high_level import extract_text
    except Exception:  # pragma: no cover - optional dependency
        try:
            from PyPDF2 import PdfReader
        except Exception as py_exc:  # pragma: no cover
            raise RuntimeError(
                "PDF fallback extraction requires pdfminer.six "
                "or PyPDF2. Install with `pip install pdfminer.six`.",
            ) from py_exc
        reader = PdfReader(str(path))
        texts = []
        for page in reader.pages:
            texts.append(page.extract_text() or "")
        return "\n\n".join(texts)
    return extract_text(str(path))


def build_tei_index(tei_dirs: Iterable[Path]) -> Dict[str, Path]:
    index: Dict[str, Path] = {}
    for tei_dir in tei_dirs:
        if not tei_dir.exists():
            continue
        for path in tei_dir.glob("*.grobid.tei.xml"):
            stem = path.name[: -len(".grobid.tei.xml")]
            if stem.startswith("paper_"):
                stem = stem[len("paper_") :]
            index.setdefault(stem, path)
    return index


def _local_name(tag: str) -> str:
    return tag.split("}", 1)[-1] if "}" in tag else tag


def extract_blocks_from_tei(path: Path) -> list[str]:
    root = etree.parse(str(path)).getroot()
    blocks: list[str] = []

    def add_blocks(elem) -> None:
        tag = _local_name(elem.tag)
        if tag in BLOCK_TAGS:
            text = "".join(elem.itertext()).strip()
            if text:
                number = elem.get("n")
                if number and tag in {"head", "label"}:
                    text = f"{number} {text}".strip()
                blocks.append(text)
            return
        for child in elem:
            add_blocks(child)

    for abstract in root.xpath(
        ".//tei:teiHeader//tei:abstract",
        namespaces=NS,
    ):
        add_blocks(abstract)

    text_root = root.find(".//tei:text", namespaces=NS)
    if text_root is not None:
        add_blocks(text_root)
    return blocks


def _normalize_token(token: str) -> str:
    return unicodedata.normalize("NFKC", token).lower()


HYPHEN_CHARS = {
    "-",
    "\u2010",
    "\u2011",
    "\u2012",
    "\u2013",
    "\u2014",
    "\u2212",
}
SOFT_HYPHEN = "\u00ad"

CITATION_BRACKET_RE = re.compile(r"\[[^\]]{0,120}\]")
CITATION_PAREN_RE = re.compile(r"\([^\)]{0,120}\)")


def _looks_like_bracket_citation(text: str) -> bool:
    return any(ch.isdigit() for ch in text)


def _looks_like_paren_citation(text: str) -> bool:
    if not any(ch.isdigit() for ch in text):
        return False
    lowered = text.lower()
    if "et al" in lowered:
        return True
    if re.search(r"\b(19|20)\d{2}\b", text):
        return True
    return False


def strip_citations(
    text: str,
    *,
    strip_brackets: bool = True,
    strip_parens: bool = False,
) -> str:
    if not text:
        return text
    spans: list[tuple[int, int]] = []
    if strip_brackets:
        for match in CITATION_BRACKET_RE.finditer(text):
            if _looks_like_bracket_citation(match.group(0)):
                spans.append((match.start(), match.end()))
    if strip_parens:
        for match in CITATION_PAREN_RE.finditer(text):
            if _looks_like_paren_citation(match.group(0)):
                spans.append((match.start(), match.end()))
    if not spans:
        return text
    spans.sort()
    merged: list[tuple[int, int]] = []
    for start, end in spans:
        if not merged or start > merged[-1][1]:
            merged.append((start, end))
        else:
            merged[-1] = (merged[-1][0], max(merged[-1][1], end))
    parts = []
    cursor = 0
    for start, end in merged:
        if cursor < start:
            parts.append(text[cursor:start])
        parts.append(" ")
        cursor = end
    if cursor < len(text):
        parts.append(text[cursor:])
    return "".join(parts)


def tokenize_text(
    text: str,
    *,
    return_spans: bool = False,
) -> tuple[list[str], Optional[list[tuple[int, int]]]]:
    tokens: list[str] = []
    spans: list[tuple[int, int]] = []
    i = 0
    while i < len(text):
        ch = text[i]
        if ch == SOFT_HYPHEN:
            i += 1
            continue
        if ch.isalnum():
            start = i
            last_idx = i
            last_alpha = ch.isalpha()
            token_chars = [ch]
            i += 1
            while i < len(text):
                ch = text[i]
                if ch == SOFT_HYPHEN:
                    i += 1
                    continue
                if ch.isalnum():
                    is_alpha = ch.isalpha()
                    if is_alpha != last_alpha:
                        break
                    token_chars.append(ch)
                    last_idx = i
                    last_alpha = is_alpha
                    i += 1
                    continue
                if ch in HYPHEN_CHARS and last_alpha:
                    j = i + 1
                    while j < len(text) and text[j].isspace():
                        j += 1
                    if j < len(text) and text[j].isalpha():
                        i = j
                        continue
                break
            tokens.append(_normalize_token("".join(token_chars)))
            if return_spans:
                spans.append((start, last_idx + 1))
        else:
            i += 1
    return tokens, spans if return_spans else None


def hash_token(token: str) -> int:
    import hashlib

    digest = hashlib.blake2b(token.encode("utf-8"), digest_size=8).digest()
    return int.from_bytes(digest, "big")


def hash_token_sequence(tokens: list[str]) -> tuple[int, str, int]:
    import hashlib

    rolling = 0
    normalized = [_normalize_token(token) for token in tokens]
    for token in normalized:
        rolling = ((rolling * HASH_BASE) + hash_token(token)) & HASH_MASK
    joined = " ".join(normalized).encode("utf-8")
    sha = hashlib.sha256(joined).hexdigest()
    return rolling, sha, len(normalized)


@dataclass
class TokenIndex:
    doc_text: str
    tokens: list[str]
    spans: list[tuple[int, int]]
    token_hashes: list[int]
    rolling_cache: dict[int, dict[int, list[int]]] = field(
        default_factory=dict,
    )

    @classmethod
    def from_text(cls, doc_text: str) -> "TokenIndex":
        tokens, spans = tokenize_text(doc_text, return_spans=True)
        token_hashes = [hash_token(t) for t in tokens]
        return cls(
            doc_text=doc_text,
            tokens=tokens,
            spans=spans or [],
            token_hashes=token_hashes,
        )

    def _build_rolling_index(self, window: int) -> dict[int, list[int]]:
        if window in self.rolling_cache:
            return self.rolling_cache[window]
        index: dict[int, list[int]] = {}
        if window <= 0 or window > len(self.tokens):
            self.rolling_cache[window] = index
            return index

        pow_base = 1
        for _ in range(window - 1):
            pow_base = (pow_base * HASH_BASE) & HASH_MASK

        rolling = 0
        for i in range(window):
            rolling = (
                (rolling * HASH_BASE) + self.token_hashes[i]
            ) & HASH_MASK
        index.setdefault(rolling, []).append(0)

        for i in range(1, len(self.tokens) - window + 1):
            remove = (self.token_hashes[i - 1] * pow_base) & HASH_MASK
            rolling = (rolling - remove) & HASH_MASK
            rolling = (
                (rolling * HASH_BASE) + self.token_hashes[i + window - 1]
            ) & HASH_MASK
            index.setdefault(rolling, []).append(i)

        self.rolling_cache[window] = index
        return index

    def _positions_for_hash(
        self,
        window: int,
        target_hash: int,
        target_sha: str,
    ) -> list[int]:
        index = self._build_rolling_index(window)
        candidates = index.get(target_hash, [])
        if not candidates:
            return []
        import hashlib

        positions: list[int] = []
        for start_idx in candidates:
            end_idx = start_idx + window - 1
            if end_idx >= len(self.tokens):
                continue
            token_slice = self.tokens[start_idx : start_idx + window]
            sha = hashlib.sha256(
                " ".join(token_slice).encode("utf-8"),
            ).hexdigest()
            if sha == target_sha:
                positions.append(start_idx)
        return positions

    def find_token_span_by_hash(
        self,
        window: int,
        target_hash: int,
        target_sha: str,
    ) -> Optional[tuple[int, int]]:
        positions = self._positions_for_hash(window, target_hash, target_sha)
        if not positions:
            return None
        start_idx = positions[0]
        end_idx = start_idx + window - 1
        return start_idx, end_idx

    def find_token_positions_by_hash(
        self,
        window: int,
        target_hash: int,
        target_sha: str,
    ) -> list[int]:
        return self._positions_for_hash(window, target_hash, target_sha)

    def find_span_by_hash(
        self,
        window: int,
        target_hash: int,
        target_sha: str,
    ) -> Optional[tuple[int, int]]:
        span = self.find_token_span_by_hash(window, target_hash, target_sha)
        if span is None:
            return None
        start_idx, end_idx = span
        start_char = self.spans[start_idx][0]
        end_char = self.spans[end_idx][1]
        return start_char, end_char
        return None


@dataclass
class DocIndex:
    doc_text: str
    norm_space: str
    norm_space_map: list[int]
    norm_nospace: str
    norm_nospace_map: list[int]

    @classmethod
    def from_tei(cls, tei_path: Path) -> "DocIndex":
        blocks = extract_blocks_from_tei(tei_path)
        doc_text = " ".join(blocks)
        return cls.from_text(doc_text)

    @classmethod
    def from_text(cls, doc_text: str) -> "DocIndex":
        norm_space: list[str] = []
        norm_space_map: list[int] = []
        norm_nospace: list[str] = []
        norm_nospace_map: list[int] = []
        prev_space = False
        i = 0
        while i < len(doc_text):
            ch = doc_text[i]
            if ch == "-" and i > 0 and doc_text[i - 1].isalpha():
                j = i + 1
                while j < len(doc_text) and doc_text[j].isspace():
                    j += 1
                if j < len(doc_text) and doc_text[j].isalpha():
                    i = j
                    continue
            lower = ch.lower()
            if lower.isalnum():
                norm_space.append(lower)
                norm_space_map.append(i)
                norm_nospace.append(lower)
                norm_nospace_map.append(i)
                prev_space = False
            else:
                if not prev_space:
                    norm_space.append(" ")
                    norm_space_map.append(i)
                    prev_space = True
            i += 1

        while norm_space and norm_space[0] == " ":
            norm_space.pop(0)
            norm_space_map.pop(0)
        while norm_space and norm_space[-1] == " ":
            norm_space.pop()
            norm_space_map.pop()

        return cls(
            doc_text=doc_text,
            norm_space="".join(norm_space),
            norm_space_map=norm_space_map,
            norm_nospace="".join(norm_nospace),
            norm_nospace_map=norm_nospace_map,
        )

    def find_span(self, query: str) -> Optional[tuple[int, int, str]]:
        if not query:
            return None
        n_q, n_q_ns = _normalize_query(query)
        idx = self.norm_space.find(n_q)
        if idx != -1:
            start = self.norm_space_map[idx]
            end = self.norm_space_map[idx + len(n_q) - 1] + 1
            return start, end, "space"

        trimmed = re.sub(r"^\s*\d+(?:\.\d+)*\s+", "", query)
        if trimmed != query:
            n_q_trim, n_q_trim_ns = _normalize_query(trimmed)
            idx = self.norm_space.find(n_q_trim)
            if idx != -1:
                start = self.norm_space_map[idx]
                end = self.norm_space_map[idx + len(n_q_trim) - 1] + 1
                return start, end, "space_trim"
            n_q_ns = n_q_trim_ns

        idx = self.norm_nospace.find(n_q_ns)
        if idx != -1:
            start = self.norm_nospace_map[idx]
            end = self.norm_nospace_map[idx + len(n_q_ns) - 1] + 1
            return start, end, "nospace"
        return None

    def extract_span(self, start: Optional[int], end: Optional[int]) -> str:
        if start is None or end is None:
            return ""
        if start < 0 or end > len(self.doc_text) or start >= end:
            return ""
        return self.doc_text[start:end]


def _normalize_query(text: str) -> tuple[str, str]:
    norm_space: list[str] = []
    norm_nospace: list[str] = []
    prev_space = False
    i = 0
    while i < len(text):
        ch = text[i]
        if ch == "-" and i > 0 and text[i - 1].isalpha():
            j = i + 1
            while j < len(text) and text[j].isspace():
                j += 1
            if j < len(text) and text[j].isalpha():
                i = j
                continue
        lower = ch.lower()
        if lower.isalnum():
            norm_space.append(lower)
            norm_nospace.append(lower)
            prev_space = False
        else:
            if not prev_space:
                norm_space.append(" ")
                prev_space = True
        i += 1

    while norm_space and norm_space[0] == " ":
        norm_space.pop(0)
    while norm_space and norm_space[-1] == " ":
        norm_space.pop()
    return "".join(norm_space), "".join(norm_nospace)