#!/usr/bin/env python3
"""Build Linear A model-ready training data from libation tables and lineara.xyz.

Reads:
  - libation_tables.json (inscription metadata + transcriptions)
  - sign_to_ipa.json (syllabogram → IPA mapping)
  - LinearAInscriptions.js (fetched from lineara.xyz, cached locally)

Outputs:
  - linear_a_words.tsv (all unique word forms with IPA and SCA)
  - linear_a_corpus.txt (one inscription per line, IPA-converted words)
"""

from __future__ import annotations

import json
import re
import sys
import urllib.request
from pathlib import Path
from typing import Dict, List, Optional, Tuple

_HERE = Path(__file__).resolve().parent
_CACHE_JS = _HERE / "LinearAInscriptions.js"
_LINEARA_URL = "https://www.lineara.xyz/LinearAInscriptions.js"


def load_sign_map(path: Path) -> Dict[str, str]:
    """Load sign_to_ipa.json."""
    with path.open("r", encoding="utf-8") as f:
        return json.load(f)


# Alternate notations used by lineara.xyz for undeciphered signs.
_SIGN_ALIASES = {
    "pa₃": "*56",
    "pa3": "*56",
}


def syllables_to_ipa(word: str, sign_map: Dict[str, str]) -> Optional[str]:
    """Convert a hyphen-delimited transliteration to IPA.

    E.g. "A-TA-I-*301-WA-JA" → "ataiΘwaja"

    Returns None if any syllable is unrecognizable (damaged, numeric, etc.).
    """
    syllables = word.split("-")
    ipa_parts: List[str] = []
    for syl in syllables:
        key = syl.lower().strip()
        if not key:
            continue
        # Normalize alternate notations
        key = _SIGN_ALIASES.get(key, key)
        if key in sign_map:
            ipa_parts.append(sign_map[key])
        else:
            # Skip numeric tokens (commodity quantities), damaged signs, etc.
            return None
    if not ipa_parts:
        return None
    return "".join(ipa_parts)


def ipa_to_sca(ipa: str) -> str:
    """Generate an uppercase SCA (Sound Class Approximation) from IPA.

    For CV syllabograms that map directly, this is just uppercase ASCII.
    For placeholders Θ and Φ, keep them as-is in SCA.
    """
    result: List[str] = []
    for ch in ipa:
        if ch in ("Θ", "Φ"):
            result.append(ch)
        elif ch.isascii() and ch.isalpha():
            result.append(ch.upper())
        else:
            # Non-ASCII IPA character — keep uppercase if possible
            result.append(ch.upper())
    return "".join(result)


def fetch_lineara_js(cache_path: Path) -> str:
    """Fetch LinearAInscriptions.js, caching locally."""
    if cache_path.exists():
        return cache_path.read_text(encoding="utf-8")
    print(f"Fetching {_LINEARA_URL} ...")
    req = urllib.request.Request(_LINEARA_URL, headers={"User-Agent": "LinearA-build/1.0"})
    with urllib.request.urlopen(req, timeout=30) as resp:
        data = resp.read().decode("utf-8")
    cache_path.write_text(data, encoding="utf-8")
    print(f"  Cached to {cache_path}")
    return data


def parse_lineara_js(js_text: str) -> Dict[str, List[str]]:
    """Parse the JS Map to extract transliteratedWords per inscription ID.

    Returns dict of inscription_id → list of transliterated word strings.
    """
    inscriptions: Dict[str, List[str]] = {}

    # The file has format: ["IOZa2",{...}]
    # We look for Za inscriptions and extract their transliteratedWords.
    # Use regex to find each entry key and its transliteratedWords array.
    # Since the JS is not valid JSON, we parse carefully.

    # Strategy: find all ["<key>Za<num>", ...] patterns and extract transliteratedWords
    # More robust: iterate line by line looking for Za entries

    # Find all entry starts: ["<ID>",{
    entry_pattern = re.compile(r'\["([A-Za-z]+Za\d+[a-z]?)",\s*\{')
    tw_pattern = re.compile(r'"transliteratedWords"\s*:\s*\[([^\]]*)\]')

    # Split into entries by looking for ["...Za...",{
    # We'll use a simpler approach: find each Za entry and extract its content
    pos = 0
    while pos < len(js_text):
        m = entry_pattern.search(js_text, pos)
        if not m:
            break
        entry_id = m.group(1)
        entry_start = m.start()

        # Find the transliteratedWords array in the next ~5000 chars
        chunk_end = min(len(js_text), entry_start + 10000)
        chunk = js_text[entry_start:chunk_end]

        tw_match = tw_pattern.search(chunk)
        if tw_match:
            raw = tw_match.group(1)
            # Parse the array elements (strings)
            words: List[str] = []
            for item in re.findall(r'"([^"]*)"', raw):
                # Skip word separators: ⦁ (U+2981) and 𐄁 (U+10101) and newlines
                if item in ("\u2981", "\U00010101", "\n", ""):
                    continue
                words.append(item)
            inscriptions[entry_id] = words

        pos = m.end()

    return inscriptions


def normalize_inscription_id(json_id: str) -> str:
    """Convert JSON id like 'IO Za 2' to lineara.xyz key like 'IOZa2'."""
    return json_id.replace(" ", "")


def is_transliterable_word(word: str) -> bool:
    """Check if a word from lineara.xyz is a transliterable syllabic word.

    Filters out:
    - Pure numbers (commodity counts)
    - Words with embedded Unicode Linear A characters (untransliterable signs)
    - Empty strings
    """
    if not word or not word.strip():
        return False
    # Skip pure numeric tokens
    if re.match(r"^\d+$", word.strip()):
        return False
    # Skip if contains Linear A Unicode block chars (U+10600-U+1077F)
    for ch in word:
        cp = ord(ch)
        if 0x10600 <= cp <= 0x1077F:
            return False
    # Skip single punctuation or special chars
    clean = word.strip()
    if len(clean) <= 0:
        return False
    return True


def build_corpus(
    libation_path: Path,
    sign_map: Dict[str, str],
    lineara_inscriptions: Dict[str, List[str]],
) -> Tuple[List[Dict[str, str]], List[str]]:
    """Build word list and corpus lines from all sources.

    Returns:
        (word_records, corpus_lines) where:
        - word_records: list of dicts with Word, IPA, SCA, Source, Concept_ID, Cognate_Set_ID
        - corpus_lines: list of strings, one per inscription (IPA words space-separated)
    """
    with libation_path.open("r", encoding="utf-8") as f:
        data = json.load(f)

    all_words: Dict[str, Dict[str, str]] = {}  # IPA → record
    corpus_lines: List[str] = []
    inscriptions_used = 0

    for insc in data["inscriptions"]:
        insc_id = insc["id"]
        norm_id = normalize_inscription_id(insc_id)

        # Gather transliterated words from lineara.xyz first (most complete)
        words_raw: List[str] = []
        source_tag = "linear_a_za"

        if norm_id in lineara_inscriptions:
            words_raw = lineara_inscriptions[norm_id]
        # Also check sub-parts (e.g. IOZa2 may also have IOZa2a, IOZa2b)
        for key in lineara_inscriptions:
            if key.startswith(norm_id) and key != norm_id and key not in [
                k for k in lineara_inscriptions if k == norm_id
            ]:
                words_raw.extend(lineara_inscriptions[key])

        # If lineara.xyz has no data, fall back to libation_tables.json
        if not words_raw and insc.get("word_segmentation"):
            for seg in insc["word_segmentation"]:
                # Skip placeholders like [dedicant_name], [damaged/unreadable]
                if seg.startswith("["):
                    continue
                # Handle multi-word segments like "i-pi-na-ma si-ru-te"
                for part in seg.split():
                    words_raw.append(part.upper())

        if not words_raw:
            continue

        # Convert each word to IPA
        line_ipa_words: List[str] = []
        for word_raw in words_raw:
            if not is_transliterable_word(word_raw):
                continue

            word_clean = word_raw.strip()
            ipa = syllables_to_ipa(word_clean, sign_map)
            if ipa is None:
                continue

            sca = ipa_to_sca(ipa)

            if ipa not in all_words:
                all_words[ipa] = {
                    "Word": word_clean,
                    "IPA": ipa,
                    "SCA": sca,
                    "Source": source_tag,
                    "Concept_ID": "-",
                    "Cognate_Set_ID": "-",
                }

            line_ipa_words.append(ipa)

        if line_ipa_words:
            corpus_lines.append(" ".join(line_ipa_words))
            inscriptions_used += 1

    print(f"Inscriptions with data: {inscriptions_used}")
    print(f"Unique word forms: {len(all_words)}")
    print(f"Corpus lines: {len(corpus_lines)}")

    # Sort word records alphabetically by IPA
    word_records = sorted(all_words.values(), key=lambda r: r["IPA"])
    return word_records, corpus_lines


def write_tsv(records: List[Dict[str, str]], path: Path) -> None:
    """Write word records to TSV."""
    cols = ["Word", "IPA", "SCA", "Source", "Concept_ID", "Cognate_Set_ID"]
    with path.open("w", encoding="utf-8", newline="") as f:
        f.write("\t".join(cols) + "\n")
        for rec in records:
            f.write("\t".join(rec[c] for c in cols) + "\n")
    print(f"Wrote {len(records)} rows to {path}")


def write_corpus(lines: List[str], path: Path) -> None:
    """Write corpus lines."""
    with path.open("w", encoding="utf-8", newline="") as f:
        for line in lines:
            f.write(line + "\n")
    print(f"Wrote {len(lines)} lines to {path}")


def main() -> None:
    sign_map_path = _HERE / "sign_to_ipa.json"
    libation_path = _HERE / "libation_tables.json"
    words_out = _HERE / "linear_a_words.tsv"
    corpus_out = _HERE / "linear_a_corpus.txt"

    if not sign_map_path.exists():
        print(f"ERROR: {sign_map_path} not found", file=sys.stderr)
        sys.exit(1)
    if not libation_path.exists():
        print(f"ERROR: {libation_path} not found", file=sys.stderr)
        sys.exit(1)

    sign_map = load_sign_map(sign_map_path)
    print(f"Loaded {len(sign_map)} sign mappings")

    # Fetch and parse lineara.xyz data
    js_text = fetch_lineara_js(_CACHE_JS)
    lineara_inscriptions = parse_lineara_js(js_text)
    za_count = len(lineara_inscriptions)
    print(f"Parsed {za_count} Za inscriptions from lineara.xyz")

    word_records, corpus_lines = build_corpus(libation_path, sign_map, lineara_inscriptions)

    write_tsv(word_records, words_out)
    write_corpus(corpus_lines, corpus_out)

    print("\nDone. Files created:")
    print(f"  {words_out}")
    print(f"  {corpus_out}")


if __name__ == "__main__":
    main()