#!/usr/bin/env python3
"""Select a compact, semantic terminology context without exposing raw metadata."""

from __future__ import annotations

import argparse
import csv
import hashlib
import io
import json
import re
import unicodedata
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable


SKILL_ROOT = Path(__file__).resolve().parent.parent
TERMINOLOGY_ROOT = SKILL_ROOT / "references" / "terminology"
RAW_ROOT = TERMINOLOGY_ROOT / "raw" / "scientia"
MANIFEST_PATH = RAW_ROOT / "manifest.json"
APPROVED_CORE_PATH = TERMINOLOGY_ROOT / "runtime" / "approved-core.tsv"
CONTEXT_PATH = TERMINOLOGY_ROOT / "runtime" / "context-dependent.tsv"
PENDING_PATH = TERMINOLOGY_ROOT / "maintenance" / "pending-corrections.tsv"

TOKEN_RE = re.compile(r"[0-9a-zа-я]+", re.IGNORECASE)
MAX_INPUT_CHARS = 5_000_000


@dataclass(frozen=True)
class TermRecord:
    english: str
    russian: str
    condition: str
    aliases: tuple[str, ...]
    source: str
    priority: int
    raw_candidate: bool = False


@dataclass(frozen=True)
class Hit:
    start: int
    end: int
    record: TermRecord
    matched: str


def normalized_tokens(value: str) -> tuple[str, ...]:
    value = unicodedata.normalize("NFKC", value).casefold().replace("ё", "е")
    return tuple(TOKEN_RE.findall(value))


def normalized_text(value: str) -> str:
    return " ".join(normalized_tokens(value))


def load_snapshot() -> list[dict[str, object]]:
    manifest = json.loads(MANIFEST_PATH.read_text(encoding="utf-8"))
    snapshot_path = RAW_ROOT / str(manifest["raw_file"])
    payload = snapshot_path.read_bytes()
    if hashlib.sha256(payload).hexdigest() != manifest["snapshot_sha256"]:
        raise ValueError("Scientia snapshot hash does not match manifest")
    entries = [
        json.loads(line)
        for line in payload.decode("utf-8").splitlines()
        if line.strip()
    ]
    if len(entries) != manifest["entry_count"]:
        raise ValueError("Scientia snapshot count does not match manifest")
    return entries


def read_tsv(path: Path, required: set[str]) -> list[dict[str, str]]:
    lines = [
        line
        for line in path.read_text(encoding="utf-8-sig").splitlines()
        if line.strip() and not line.lstrip().startswith("#")
    ]
    if not lines:
        raise ValueError(f"TSV has no header: {path}")
    reader = csv.DictReader(io.StringIO("\n".join(lines)), delimiter="\t")
    fields = set(reader.fieldnames or [])
    missing = required - fields
    if missing:
        raise ValueError(f"TSV {path} is missing columns: {', '.join(sorted(missing))}")
    return [
        {key: (value or "").strip() for key, value in row.items() if key is not None}
        for row in reader
    ]


def split_aliases(value: str) -> tuple[str, ...]:
    return tuple(item.strip() for item in value.split("|") if item.strip())


def lexical_variants(value: str) -> tuple[str, ...]:
    """Return useful written variants without inventing translations."""
    variants = [value]
    without_parentheses = re.sub(r"\([^)]*\)", " ", value).strip()
    if without_parentheses:
        variants.append(without_parentheses)
    for group in re.findall(r"\(([^)]*)\)", value):
        variants.extend(re.split(r"[,;/=]+", group))
    variants.extend(re.split(r"[,;/]+", value))
    result: list[str] = []
    seen: set[tuple[str, ...]] = set()
    for variant in variants:
        variant = variant.strip(" =–—-\t")
        tokens = normalized_tokens(variant)
        if tokens and tokens not in seen:
            result.append(variant)
            seen.add(tokens)
    return tuple(result)


def load_pending_keys(
    entries: list[dict[str, object]],
) -> tuple[set[str], set[str], set[tuple[str, ...]]]:
    rows = read_tsv(
        PENDING_PATH,
        {"english_raw", "russian_raw", "status"},
    )
    by_english = {normalized_text(str(entry["en_raw"])): entry for entry in entries}
    blocked_english: set[str] = set()
    blocked_russian: set[str] = set()
    pending_forms: set[tuple[str, ...]] = set()
    for row in rows:
        if row["status"] != "pending":
            raise ValueError("Only pending records belong in pending-corrections.tsv")
        key = normalized_text(row["english_raw"])
        source = by_english.get(key)
        if source is None or str(source["en_raw"]) != row["english_raw"]:
            raise ValueError(f"Pending record is absent from raw snapshot: {row['english_raw']}")
        if str(source["ru_raw"]) != row["russian_raw"]:
            raise ValueError(f"Pending Russian raw value differs from snapshot: {row['english_raw']}")
        blocked_english.add(key)
        blocked_russian.add(normalized_text(row["russian_raw"]))
        for value in (row["english_raw"], row["russian_raw"]):
            for variant in lexical_variants(value):
                tokens = normalized_tokens(variant)
                if tokens:
                    pending_forms.add(tokens)
    return blocked_english, blocked_russian, pending_forms


def load_approved_core(entries: list[dict[str, object]]) -> list[TermRecord]:
    rows = read_tsv(APPROVED_CORE_PATH, {"english", "russian", "sense", "aliases"})
    raw_pairs = {(str(item["en_raw"]), str(item["ru_raw"])) for item in entries}
    records: list[TermRecord] = []
    seen: set[str] = set()
    for row in rows:
        pair = (row["english"], row["russian"])
        if pair not in raw_pairs:
            raise ValueError(
                "approved-core.tsv must contain an exact positive pair from the snapshot: "
                f"{row['english']} — {row['russian']}"
            )
        key = normalized_text(row["english"])
        if not key or key in seen:
            raise ValueError(f"Duplicate or blank approved term: {row['english']}")
        seen.add(key)
        records.append(
            TermRecord(
                english=row["english"],
                russian=row["russian"],
                condition=row["sense"],
                aliases=split_aliases(row["aliases"]),
                source="core glossary",
                priority=200,
            )
        )
    return records


def load_context_records() -> list[TermRecord]:
    rows = read_tsv(
        CONTEXT_PATH,
        {
            "term",
            "language",
            "possible_renderings",
            "decision_rule",
            "context_cues",
            "aliases",
        },
    )
    records: list[TermRecord] = []
    for row in rows:
        if row["language"] not in {"en", "ru"}:
            raise ValueError(f"Unsupported language in context-dependent.tsv: {row['language']}")
        renderings = split_aliases(row["possible_renderings"])
        aliases = split_aliases(row["aliases"]) + renderings
        condition = row["decision_rule"]
        if row["context_cues"]:
            condition += f" Контекстные признаки: {row['context_cues']}."
        records.append(
            TermRecord(
                english=row["term"] if row["language"] == "en" else "",
                russian=row["possible_renderings"],
                condition=condition,
                aliases=aliases,
                source="context guide",
                priority=190,
            )
        )
    return records


def load_custom_glossary(path: Path, source: str, priority: int) -> list[TermRecord]:
    rows = read_tsv(path, {"english", "russian", "sense", "aliases"})
    records: list[TermRecord] = []
    seen: set[str] = set()
    for row in rows:
        if not row["english"] or not row["russian"] or not row["sense"]:
            raise ValueError(f"Custom glossary has a blank required value: {path}")
        key = normalized_text(row["english"])
        if key in seen:
            raise ValueError(f"Duplicate custom term in {path}: {row['english']}")
        seen.add(key)
        records.append(
            TermRecord(
                english=row["english"],
                russian=row["russian"],
                condition=row["sense"],
                aliases=split_aliases(row["aliases"]),
                source=source,
                priority=priority,
            )
        )
    return records


def load_raw_records(
    entries: list[dict[str, object]],
    blocked_english: set[str],
    blocked_russian: set[str],
) -> list[TermRecord]:
    records: list[TermRecord] = []
    for entry in entries:
        english = str(entry["en_raw"])
        russian = str(entry["ru_raw"])
        if normalized_text(english) in blocked_english or normalized_text(russian) in blocked_russian:
            continue
        records.append(
            TermRecord(
                english=english,
                russian=russian,
                condition=(
                    "Сверить значение, поддомен и носитель свойства с исходным текстом; "
                    "пара Scientia сама по себе не утверждает выбор."
                ),
                aliases=(),
                source="Scientia",
                priority=100,
                raw_candidate=True,
            )
        )
    return records


def record_forms(record: TermRecord, include_raw_single: bool) -> set[tuple[str, ...]]:
    values: list[str] = []
    for value in (record.english, record.russian, *record.aliases):
        values.extend(lexical_variants(value))
    forms = {normalized_tokens(value) for value in values if normalized_tokens(value)}
    if record.raw_candidate and not include_raw_single:
        forms = {form for form in forms if len(form) >= 2}
    return forms


def find_spans(tokens: tuple[str, ...], needle: tuple[str, ...]) -> Iterable[tuple[int, int]]:
    size = len(needle)
    if not size or size > len(tokens):
        return
    for start in range(len(tokens) - size + 1):
        if tokens[start : start + size] == needle:
            yield start, start + size


def choose_non_overlapping(hits: list[Hit]) -> list[Hit]:
    # Longest phrase wins; project/company authority breaks equal-span ties.
    ranked = sorted(
        hits,
        key=lambda hit: (
            -(hit.end - hit.start),
            -hit.record.priority,
            hit.start,
            normalized_text(hit.record.english or hit.record.russian),
        ),
    )
    selected: list[Hit] = []
    occupied: set[int] = set()
    for hit in ranked:
        positions = set(range(hit.start, hit.end))
        if positions & occupied:
            continue
        selected.append(hit)
        occupied.update(positions)
    return sorted(selected, key=lambda hit: (hit.start, hit.end))


def scan_text(
    text: str,
    records: list[TermRecord],
    include_raw_single: bool,
    explicit_label: str | None = None,
    pending_forms: set[tuple[str, ...]] | None = None,
) -> list[Hit]:
    tokens = normalized_tokens(text)
    pending_spans = [
        span
        for form in (pending_forms or set())
        for span in find_spans(tokens, form)
    ]
    hits: list[Hit] = []
    for record in records:
        for form in record_forms(record, include_raw_single):
            for start, end in find_spans(tokens, form):
                hit_length = end - start
                if any(
                    pending_end - pending_start > hit_length
                    and start < pending_end
                    and pending_start < end
                    for pending_start, pending_end in pending_spans
                ):
                    continue
                matched = explicit_label or " ".join(tokens[start:end])
                hits.append(Hit(start, end, record, matched))
    return choose_non_overlapping(hits)


def fuzzy_raw_matches(query: str, raw_records: list[TermRecord]) -> list[TermRecord]:
    query_tokens = normalized_tokens(query)
    if not query_tokens:
        return []
    ranked: list[tuple[int, int, TermRecord]] = []
    query_set = set(query_tokens)
    for record in raw_records:
        best = 0
        best_length = 0
        for form in record_forms(record, include_raw_single=True):
            if form == query_tokens:
                score = 100
            elif len(query_tokens) <= len(form) and any(
                form[index : index + len(query_tokens)] == query_tokens
                for index in range(len(form) - len(query_tokens) + 1)
            ):
                score = 90
            elif len(query_tokens) >= 2 and query_set.issubset(set(form)):
                score = 70
            else:
                score = 0
            if score > best:
                best = score
                best_length = len(form)
        if best:
            ranked.append((best, best_length, record))
    ranked.sort(
        key=lambda item: (
            -item[0],
            item[1],
            normalized_text(item[2].english),
        )
    )
    return [item[2] for item in ranked[:3]]


def escape_markdown(value: str) -> str:
    return value.replace("|", "\\|").replace("\r", " ").replace("\n", " ").strip()


def serialize_hits(hits: list[Hit], diagnostic: bool = False) -> list[dict[str, str]]:
    rows: list[dict[str, str]] = []
    for hit in hits:
        row = {
            "matched": hit.matched,
            "english": hit.record.english,
            "russian": hit.record.russian,
            "condition": hit.record.condition,
        }
        if diagnostic:
            row["source"] = hit.record.source
        rows.append(row)
    return rows


def print_markdown(rows: list[dict[str, str]]) -> None:
    if not rows:
        print("Подходящих терминологических записей не найдено.")
        return
    print("| Найдено | English | Русский / варианты | Условие выбора |")
    print("|---|---|---|---|")
    for row in rows:
        print(
            "| "
            + " | ".join(
                escape_markdown(row[key])
                for key in ("matched", "english", "russian", "condition")
            )
            + " |"
        )


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        description=(
            "Select relevant approved, context-dependent and Scientia terminology without "
            "loading the full snapshot into model context."
        )
    )
    parser.add_argument("--query", action="append", default=[], help="A Russian or English term")
    parser.add_argument("--text", action="append", default=[], help="Source text to scan")
    parser.add_argument(
        "--text-file",
        action="append",
        default=[],
        type=Path,
        help="UTF-8 source file to scan",
    )
    parser.add_argument(
        "--company-glossary",
        action="append",
        default=[],
        type=Path,
        help="Approved company TSV using the project template schema",
    )
    parser.add_argument(
        "--project-glossary",
        action="append",
        default=[],
        type=Path,
        help="Approved project TSV using runtime/project/template.tsv",
    )
    parser.add_argument("--limit", type=int, default=12, help="Global result limit (1-50)")
    parser.add_argument("--format", choices=["markdown", "json"], default="markdown")
    parser.add_argument(
        "--diagnostic",
        action="store_true",
        help="Include glossary origin; allowed only with --format json",
    )
    parser.add_argument(
        "--include-raw-candidates",
        action="store_true",
        help=(
            "Research mode: include unapproved raw Scientia candidate pairs. "
            "By default only approved and context-dependent records are returned."
        ),
    )
    parser.add_argument(
        "--no-raw-candidates",
        action="store_true",
        help=argparse.SUPPRESS,
    )
    return parser


def main() -> int:
    args = build_parser().parse_args()
    if not (args.query or args.text or args.text_file):
        raise ValueError("Provide at least one --query, --text or --text-file")
    if len(args.query) > 30:
        raise ValueError("At most 30 explicit queries are allowed per call")
    if not 1 <= args.limit <= 50:
        raise ValueError("--limit must be between 1 and 50")
    if args.diagnostic and args.format != "json":
        raise ValueError("--diagnostic is allowed only with --format json")
    if any(not query.strip() for query in args.query):
        raise ValueError("Queries must not be blank")
    if args.include_raw_candidates and args.no_raw_candidates:
        raise ValueError(
            "--include-raw-candidates conflicts with deprecated --no-raw-candidates"
        )

    source_texts = [text for text in args.text]
    source_texts.extend(path.read_text(encoding="utf-8-sig") for path in args.text_file)
    if sum(len(text) for text in (*args.query, *source_texts)) > MAX_INPUT_CHARS:
        raise ValueError(f"Combined source text exceeds {MAX_INPUT_CHARS} characters")

    entries = load_snapshot()
    blocked_english, blocked_russian, pending_forms = load_pending_keys(entries)
    raw_records = load_raw_records(entries, blocked_english, blocked_russian)
    records: list[TermRecord] = []
    for path in args.project_glossary:
        records.extend(load_custom_glossary(path, "project glossary", 400))
    for path in args.company_glossary:
        records.extend(load_custom_glossary(path, "company glossary", 300))
    records.extend(load_approved_core(entries))
    records.extend(load_context_records())
    if args.include_raw_candidates:
        records.extend(raw_records)

    all_hits: list[Hit] = []
    for query in args.query:
        query_hits = scan_text(
            query,
            records,
            include_raw_single=len(normalized_tokens(query)) == 1,
            explicit_label=query,
            pending_forms=pending_forms,
        )
        if not query_hits and args.include_raw_candidates:
            query_has_pending_span = any(
                next(find_spans(normalized_tokens(query), form), None) is not None
                for form in pending_forms
            )
            if not query_has_pending_span:
                query_hits = [
                    Hit(0, max(1, len(normalized_tokens(query))), record, query)
                    for record in fuzzy_raw_matches(query, raw_records)
                ]
        all_hits.extend(query_hits)
    for text in source_texts:
        all_hits.extend(
            scan_text(
                text,
                records,
                include_raw_single=False,
                pending_forms=pending_forms,
            )
        )

    # Keep the first occurrence and the highest-priority decision for each concept.
    deduplicated: list[Hit] = []
    positions: dict[str, int] = {}
    for hit in all_hits:
        key = normalized_text(hit.record.english or hit.record.russian)
        if key not in positions:
            positions[key] = len(deduplicated)
            deduplicated.append(hit)
            continue
        index = positions[key]
        if hit.record.priority > deduplicated[index].record.priority:
            deduplicated[index] = hit
    deduplicated = deduplicated[: args.limit]
    rows = serialize_hits(deduplicated, diagnostic=args.diagnostic)

    if args.format == "json":
        print(json.dumps(rows, ensure_ascii=False, indent=2))
    else:
        print_markdown(rows)
    return 0


if __name__ == "__main__":
    raise SystemExit(main())