from __future__ import annotations

import pytest

from app.lang import detect_language


@pytest.mark.parametrize(
    "text",
    [
        # The live regression: langdetect scores this pt at 99.9%.
        "hola, ¿hacéis envíos a Portugal?",
        "que productos vendeis y de que material son",
        "quiero devolver mi pedido",
        # langdetect scores this one as Catalan.
        "envianme a Lisboa el paquete por favor",
        "¿cuánto tarda el envío a Canarias?",
        # live bug: this all-caps Spanish was answered in HUNGARIAN (langdetect
        # misfired 'hu'); the expanded markers ("soy","mandais") now win.
        "SOY MELILLA, MANDAIS A MELILLA",
        "soy de melilla, mandais cosas a melilla?",
    ],
)
def test_spanish_detected(text):
    assert detect_language(text) == "español"


@pytest.mark.parametrize(
    "text",
    [
        "bom dia, que produtos tendes?",
        "qual é o prazo de entrega para Lisboa?",
        "obrigado, quero devolver o meu pedido",
        "não recebi a minha encomenda",
    ],
)
def test_portuguese_detected(text):
    assert detect_language(text) == "portugués de Portugal (pt-PT)"


def test_catalan_detected_not_portuguese():
    # regression: "bon dia, què veneu?" was answered in Portuguese
    assert detect_language("bon dia, què veneu?") == "català"
    assert detect_language("bona tarda, voleu ajudar-me amb això?") == "català"


@pytest.mark.parametrize(
    "text,expected",
    [
        ("مرحبا، ماذا تبيعون في متجركم؟", "árabe (Arabic)"),
        ("你们卖什么产品？我想了解一下", "chino simplificado (Chinese)"),
        ("こんにちは、どんな商品がありますか？", "japonés (Japanese)"),
        ("안녕하세요, 어떤 제품을 판매하나요?", "coreano (Korean)"),
        ("Здравствуйте, какие товары у вас есть?", "ruso (Russian)"),
        ("नमस्ते, आप क्या बेचते हैं?", "hindi (Hindi)"),
        ("Merhaba, hangi ürünleri satıyorsunuz?", "turco (Turkish)"),
        ("Hallo, welke producten verkoopt u?", "neerlandés (Dutch)"),
        # genuine Hungarian must STILL detect (the Melilla fix must not over-suppress hu)
        ("Szeretném tudni a szállítási költségeket Budapestre kérem", "húngaro (Hungarian)"),
    ],
)
def test_world_languages_detected(text, expected):
    assert detect_language(text) == expected


def test_english_detected():
    assert detect_language("where is my order, can you check the tracking?") == "English"


def test_english_with_stray_spanish_word_stays_english():
    # regression: "mi" must NOT flip a clearly-English sentence to Spanish
    assert detect_language("where is mi product please") == "English"
    assert detect_language("hello, can you help me find a product") == "English"


@pytest.mark.parametrize(
    "text",
    [
        # the real production bug: plain Spanish WITHOUT ¿¡ñ accents was pinned
        # English because the Spanish preposition "a" tripped _looks_english.
        "Hola, voy a comprar la Toorx msx70 y quiero saber los plazos de entrega "
        "para envios a la peninsula.",
        # same defect without a leading greeting (exercises the score path)
        "quiero comprar la bici y saber los plazos de envios a la peninsula",
        "necesito ayuda para elegir y comprar productos para mi pedido",
    ],
)
def test_plain_spanish_without_accents_is_spanish(text):
    # regression: a strong Spanish word-score must beat the "a"/"to" English bias
    assert detect_language(text) == "español"


@pytest.mark.parametrize(
    "text",
    [
        "i want to talk to a human",          # was detected Swahili
        "hello can you help me",               # was detected Welsh
        "i need help with my order please",
        "can you tell me where my product is",
    ],
)
def test_short_english_not_misdetected(text):
    # regression: short English must NOT be pinned to an exotic language
    assert detect_language(text) == "English"


@pytest.mark.parametrize(
    "text",
    [
        # regression: langdetect guessed Iberian, no markers → was pinned español
        "do you have garden hoses?",
        "do you sell garden products?",
    ],
)
def test_english_misclassified_as_iberian_stays_english(text):
    assert detect_language(text) == "English"


@pytest.mark.parametrize(
    "text,expected",
    [
        # regression: dense non-Latin under 12 chars returned None (no pin → drift)
        ("你们卖什么？", "chino simplificado (Chinese)"),
        ("你们有花园用的水管吗？", "chino simplificado (Chinese)"),
        ("정원용 호스 있나요?", "coreano (Korean)"),
        ("ガーデンホース", "japonés (Japanese)"),
        ("لديكم خراطيم؟", "árabe (Arabic)"),
    ],
)
def test_short_nonlatin_detected_by_script(text, expected):
    assert detect_language(text) == expected


def test_short_cyrillic_pins_a_cyrillic_language():
    # exact Slavic language varies, but it must pin a Cyrillic language (not None)
    cyrillic = {"ruso (Russian)", "ucraniano (Ukrainian)", "búlgaro (Bulgarian)",
                "macedonio (Macedonian)"}
    assert detect_language("у вас есть садовые шланги?") in cyrillic


def test_unsure_latin_text_not_forced():
    # gibberish/ambiguous Latin → don't force a wrong language (let LLM decide)
    assert detect_language("zxcv qwer asdf zxcv") is None


def test_short_greetings_pinned_by_lexicon():
    # regression: "buenas" was answered in Portuguese
    assert detect_language("buenas") == "español"
    assert detect_language("hola") == "español"
    assert detect_language("bonjour") == "français"
    assert detect_language("hello") == "English"
    assert detect_language("olá") == "portugués de Portugal (pt-PT)"


def test_unknown_short_text_not_pinned():
    assert detect_language("xy") is None
    assert detect_language("") is None


def test_language_guard_flags_drift():
    from app.lang import needs_language_repair

    ko = "coreano (Korean)"
    zh = "chino simplificado (Chinese)"
    # drift: Korean pinned but answer is Spanish → must repair
    assert needs_language_repair("Sí, disponemos de mangueras para jardín.", ko) is True
    assert needs_language_repair("Sí, tenemos varias opciones.", zh) is True
    # correct: answer in the pinned script → no repair (Latin product names allowed)
    assert needs_language_repair("네, 정원용 호스가 있습니다. Gobeflat Medium 모델입니다.", ko) is False
    assert needs_language_repair("我们有花园用的水管。PVC 平面水管。", zh) is False
    # Latin-script languages are NOT force-repaired by design: the model mirrors the
    # customer's language (system prompt), and a heuristic Latin pin can be wrong —
    # repairing toward a wrong pin is exactly what caused the Hungarian/Catalan
    # misfire. So Latin always returns False (trust the model), only non-Latin repairs.
    assert needs_language_repair("Sí, tenemos varias mangueras de jardín disponibles para ti.", "français") is False
    assert needs_language_repair("Bonjour, nous avons plusieurs tuyaux d'arrosage disponibles.", "français") is False
    assert needs_language_repair("anything", None) is False


def test_strip_emojis_removes_pictographs_keeps_text():
    from app.lang import strip_emojis

    assert strip_emojis("¡Hola! 😊 ¿En qué te ayudo?") == "¡Hola! ¿En qué te ayudo?"
    assert strip_emojis("Tenemos mangueras 🌿✅ disponibles") == "Tenemos mangueras disponibles"
    # non-Latin text must survive untouched (emoji ranges don't overlap scripts)
    assert strip_emojis("정원용 호스 😀 있습니다") == "정원용 호스 있습니다"
    assert strip_emojis("我们有水管 🚀") == "我们有水管"
    assert strip_emojis("normal text") == "normal text"


def test_strip_links_removes_fabricated_links():
    from app.lang import strip_links

    # markdown link → plain text (the inline form is the real CTA, no 404 link)
    out = strip_links("Rellena el [Formulario de contacto](https://flexigobe.com/contacto) abajo.")
    assert "https://" not in out and "](" not in out
    assert "Formulario de contacto" in out  # keep the words, drop the link
    # bare URL removed
    assert "http" not in strip_links("Visita https://flexigobe.com/contacto para más.")
    # clean text untouched
    assert strip_links("Rellena el formulario que aparece abajo.") == \
        "Rellena el formulario que aparece abajo."


def test_prompt_forbids_emojis():
    from app.prompts import build_system_prompt

    assert "emoji" in build_system_prompt("X").lower()


def test_prompt_pins_detected_language_else_mirrors():
    # The store context is heavily Spanish and drags the model into Spanish even for
    # an English customer (observed live), so a detected language is pinned EXPLICITLY;
    # when detection is unsure (None) we mirror. The Hungarian misfire is fixed at the
    # DETECTOR (see test_spanish_detected), not by refusing to pin.
    from app.prompts import build_system_prompt

    pinned = build_system_prompt("Tienda", "English").lower()
    assert "english" in pinned and "entire reply" in pinned
    unsure = build_system_prompt("Tienda", None).lower()
    assert "same language" in unsure and "mirror" in unsure


def test_iso_code_reverses_the_pin_friendly_display_name():
    """detect_language() returns a display name meant for the LLM prompt
    ("English", "español"), not a wire code -- iso_code() converts it back for
    callers (the widget matching its own UI language to the conversation)."""
    from app.lang import iso_code

    assert iso_code("English") == "en"
    assert iso_code("español") == "es"
    assert iso_code("français") == "fr"
    assert iso_code("chino simplificado (Chinese)") == "zh"
    assert iso_code("chino tradicional (Chinese)") == "zh"  # both zh variants collapse
    assert iso_code("not a real language") is None
    assert iso_code(None) is None
    assert iso_code("") is None


def test_prompt_forbids_redirecting_customer_to_third_parties():
    """Regression: the bot once told a Spanish customer to 'visit Toorx's website
    / contact their customer service' instead of helping. The prompt must forbid
    sending the customer to a manufacturer/third party and keep them with the
    store's own team."""
    from app.prompts import build_system_prompt

    p = build_system_prompt("Tienda X").lower()
    assert "third party" in p and "manufacturer" in p
    assert "escalate_to_human" in p