"""
Single source of truth for every language SpeechM supports.

Adding a language = adding one row to LANGUAGES below. The per-engine code
maps, the Whisper prompt hints and the PDF font selection are all derived from
it. Keep this in sync with frontend/src/lib/languages.ts.
"""

from typing import Dict, List, Optional

# Script -> font file registered by the PDF exporter.
# The files themselves are fetched in the Dockerfile; NotoSans-Regular covers
# Latin, Cyrillic and Greek, so those three scripts share it.
SCRIPT_FONTS: Dict[str, str] = {
    "latin": "NotoSans-Regular.ttf",
    "cyrillic": "NotoSans-Regular.ttf",
    "greek": "NotoSans-Regular.ttf",
    "devanagari": "NotoSansDevanagari-Regular.ttf",
    "bengali": "NotoSansBengali-Regular.ttf",
    "gurmukhi": "NotoSansGurmukhi-Regular.ttf",
    "gujarati": "NotoSansGujarati-Regular.ttf",
    "telugu": "NotoSansTelugu-Regular.ttf",
    "tamil": "NotoSansTamil-Regular.ttf",
    "kannada": "NotoSansKannada-Regular.ttf",
    "malayalam": "NotoSansMalayalam-Regular.ttf",
    "arabic": "NotoSansArabic-Regular.ttf",
    "hebrew": "NotoSansHebrew-Regular.ttf",
    "thai": "NotoSansThai-Regular.ttf",
    "hanSc": "NotoSansSC-Regular.ttf",
    "japanese": "NotoSansJP-Regular.ttf",
    "korean": "NotoSansKR-Regular.ttf",
}


class Language:
    """One supported language and its per-engine identifiers."""

    __slots__ = ("code", "label", "nllb", "bcp47", "script", "sarvam", "indic", "prompt")

    def __init__(
        self,
        code: str,
        label: str,
        nllb: str,
        bcp47: str,
        script: str,
        sarvam: Optional[str] = None,
        indic: bool = False,
        prompt: str = "",
    ):
        self.code = code          # ISO 639-1, what Whisper expects
        self.label = label
        self.nllb = nllb          # FLORES-200, what NLLB expects
        self.bcp47 = bcp47        # Azure Speech / Web Speech API
        self.script = script
        self.sarvam = sarvam      # Sarvam locale; None where Sarvam has no coverage
        self.indic = indic        # eligible for IndicTrans2 routing against English
        self.prompt = prompt      # Whisper initial_prompt hint


LANGUAGES: List[Language] = [
    # --- Indian languages ---------------------------------------------------
    Language("en", "English", "eng_Latn", "en-US", "latin", sarvam="en-IN"),
    Language("hi", "Hindi", "hin_Deva", "hi-IN", "devanagari", sarvam="hi-IN", indic=True,
             prompt="नमस्ते, क्या, क्यों, कैसे, धन्यवाद, भारत, समाचार, चर्चा।"),
    Language("te", "Telugu", "tel_Telu", "te-IN", "telugu", sarvam="te-IN", indic=True,
             prompt="వస్తున్నాయి, విదేశీ, మెరుపులు, ఆరంభంలో, విధ్వంసం, ధన్యవాదాలు, నమస్కారం, ప్రశ్నలు, చర్చ, భారత్."),
    Language("ta", "Tamil", "tam_Taml", "ta-IN", "tamil", sarvam="ta-IN", indic=True,
             prompt="வணக்கம், நன்றி."),
    Language("kn", "Kannada", "kan_Knda", "kn-IN", "kannada", sarvam="kn-IN", indic=True,
             prompt="ನಮಸ್ಕಾರ, ಧನ್ಯವಾದಗಳು."),
    Language("ml", "Malayalam", "mal_Mlym", "ml-IN", "malayalam", sarvam="ml-IN", indic=True,
             prompt="നമസ്കാരം, നന്ദി."),
    Language("bn", "Bengali", "ben_Beng", "bn-IN", "bengali", sarvam="bn-IN", indic=True),
    Language("mr", "Marathi", "mar_Deva", "mr-IN", "devanagari", sarvam="mr-IN", indic=True),
    Language("gu", "Gujarati", "guj_Gujr", "gu-IN", "gujarati", sarvam="gu-IN", indic=True),
    Language("pa", "Punjabi", "pan_Guru", "pa-IN", "gurmukhi", sarvam="pa-IN", indic=True),
    Language("ur", "Urdu", "urd_Arab", "ur-IN", "arabic"),

    # --- European -----------------------------------------------------------
    Language("es", "Spanish", "spa_Latn", "es-ES", "latin"),
    Language("fr", "French", "fra_Latn", "fr-FR", "latin"),
    Language("de", "German", "deu_Latn", "de-DE", "latin"),
    Language("it", "Italian", "ita_Latn", "it-IT", "latin"),
    Language("pt", "Portuguese", "por_Latn", "pt-BR", "latin"),
    Language("nl", "Dutch", "nld_Latn", "nl-NL", "latin"),
    Language("pl", "Polish", "pol_Latn", "pl-PL", "latin"),
    Language("sv", "Swedish", "swe_Latn", "sv-SE", "latin"),
    Language("da", "Danish", "dan_Latn", "da-DK", "latin"),
    Language("nb", "Norwegian", "nob_Latn", "nb-NO", "latin"),
    Language("fi", "Finnish", "fin_Latn", "fi-FI", "latin"),
    Language("cs", "Czech", "ces_Latn", "cs-CZ", "latin"),
    Language("sk", "Slovak", "slk_Latn", "sk-SK", "latin"),
    Language("hu", "Hungarian", "hun_Latn", "hu-HU", "latin"),
    Language("ro", "Romanian", "ron_Latn", "ro-RO", "latin"),
    Language("hr", "Croatian", "hrv_Latn", "hr-HR", "latin"),
    Language("ca", "Catalan", "cat_Latn", "ca-ES", "latin"),
    Language("tr", "Turkish", "tur_Latn", "tr-TR", "latin"),
    Language("el", "Greek", "ell_Grek", "el-GR", "greek"),
    Language("ru", "Russian", "rus_Cyrl", "ru-RU", "cyrillic"),
    Language("uk", "Ukrainian", "ukr_Cyrl", "uk-UA", "cyrillic"),
    Language("bg", "Bulgarian", "bul_Cyrl", "bg-BG", "cyrillic"),

    # --- Middle East --------------------------------------------------------
    Language("ar", "Arabic", "arb_Arab", "ar-SA", "arabic"),
    Language("he", "Hebrew", "heb_Hebr", "he-IL", "hebrew"),
    Language("fa", "Persian", "pes_Arab", "fa-IR", "arabic"),

    # --- East & Southeast Asia ---------------------------------------------
    Language("zh", "Chinese (Mandarin)", "zho_Hans", "zh-CN", "hanSc"),
    Language("ja", "Japanese", "jpn_Jpan", "ja-JP", "japanese"),
    Language("ko", "Korean", "kor_Hang", "ko-KR", "korean"),
    Language("vi", "Vietnamese", "vie_Latn", "vi-VN", "latin"),
    Language("th", "Thai", "tha_Thai", "th-TH", "thai"),
    Language("id", "Indonesian", "ind_Latn", "id-ID", "latin"),
    Language("ms", "Malay", "zsm_Latn", "ms-MY", "latin"),
    Language("tl", "Filipino", "tgl_Latn", "fil-PH", "latin"),

    # --- Africa -------------------------------------------------------------
    Language("sw", "Swahili", "swh_Latn", "sw-KE", "latin"),
    Language("af", "Afrikaans", "afr_Latn", "af-ZA", "latin"),
]

BY_CODE: Dict[str, Language] = {lang.code: lang for lang in LANGUAGES}
BY_NLLB: Dict[str, Language] = {lang.nllb: lang for lang in LANGUAGES}


def resolve(code: Optional[str]) -> Optional[Language]:
    """Look a language up by short code ('te') or FLORES-200 code ('tel_Telu')."""
    if not code:
        return None
    return BY_CODE.get(code) or BY_NLLB.get(code)


def to_short(code: Optional[str], default: Optional[str] = None) -> Optional[str]:
    """Normalise any supported spelling to our canonical ISO 639-1 code."""
    lang = resolve(code)
    return lang.code if lang else (default if default is not None else code)


def to_nllb(code: Optional[str], default: str = "eng_Latn") -> str:
    """Normalise any supported spelling to a FLORES-200 code."""
    lang = resolve(code)
    return lang.nllb if lang else (code or default)


def to_bcp47(code: Optional[str], default: str = "en-US") -> str:
    """Azure Speech / Web Speech locale for a language."""
    lang = resolve(code)
    return lang.bcp47 if lang else (code or default)


def to_sarvam(code: Optional[str], default: str = "en-IN") -> str:
    """Sarvam locale. Falls back to the default where Sarvam has no coverage."""
    lang = resolve(code)
    return lang.sarvam if lang and lang.sarvam else default


def supports_sarvam(code: Optional[str]) -> bool:
    lang = resolve(code)
    return bool(lang and lang.sarvam)


# Scripts whose glyphs need HarfBuzz shaping (reordering, conjuncts, cursive
# joining) to render correctly. Latin, Cyrillic, Greek and CJK do not.
SHAPING_SCRIPTS = {
    "devanagari", "bengali", "gurmukhi", "gujarati",
    "telugu", "tamil", "kannada", "malayalam",
    "arabic", "hebrew", "thai",
}


def script_for(code: Optional[str]) -> str:
    """Script a language is written in.

    Accepts short codes ("te"), FLORES-200 codes ("tel_Telu") and the legacy
    "<iso639-1>_<Script>" spellings ("te_Telu") that older clients still send.
    """
    lang = resolve(code)
    if lang:
        return lang.script

    # Unknown code: match on the script suffix so old saved transcripts keep
    # rendering in the right script rather than silently falling back to Latin.
    if code and "_" in code:
        suffix = "_" + code.split("_", 1)[1]
        for candidate in LANGUAGES:
            if candidate.nllb.endswith(suffix):
                return candidate.script
    return "latin"


def needs_shaping(code: Optional[str]) -> bool:
    """Whether the PDF exporter should turn text shaping on for this language."""
    return script_for(code) in SHAPING_SCRIPTS


def font_for(code: Optional[str]) -> str:
    """Font file able to render this language's script in an exported PDF."""
    return SCRIPT_FONTS.get(script_for(code), SCRIPT_FONTS["latin"])


# Whisper initial_prompt hints, only where a script needs pinning.
LANGUAGE_PROMPTS: Dict[str, str] = {lang.code: lang.prompt for lang in LANGUAGES if lang.prompt}

# Languages IndicTrans2 handles better than NLLB when paired with English.
INDIC_LANGS = {lang.code for lang in LANGUAGES if lang.indic}

# Every font file the PDF exporter may ask for, for Dockerfile/preflight checks.
REQUIRED_FONTS = sorted(set(SCRIPT_FONTS.values()))