import re

from django.conf import settings
from django.http import Http404
from django.utils.text import slugify
from django.utils.translation import gettext_lazy as _
from django_elasticsearch_dsl import fields
from django_elasticsearch_dsl.registries import registry
from elasticsearch import NotFoundError
from elasticsearch.dsl import Q, Search
from rest_framework.request import Request

from core.models import Brand, Category, Product

SMART_FIELDS = [
    "name^8",
    "name.ngram^8",
    "name.phonetic^6",
    "title^5",
    "title.ngram^4",
    "title.phonetic^2",
    "description^2",
    "description.ngram",
    "description.phonetic",
    "brand_name^5",
    "brand_name.ngram^3",
    "brand_name.auto^4",
    "category_name^3",
    "category_name.ngram^2",
    "category_name.auto^2",
    "sku^9",
    "sku.ngram^6",
    "sku.auto^8",
    "partnumber^10",
    "partnumber.ngram^7",
    "partnumber.auto^9",
]

functions = [
    {
        "filter": Q("term", **{"_index": "products"}),
        "field_value_factor": {
            "field": "brand_priority",
            "modifier": "log1p",
            "factor": 0.2,
            "missing": 0,
        },
        "weight": 0.6,
    },
    {
        "filter": Q("term", **{"_index": "products"}),
        "field_value_factor": {
            "field": "rating",
            "modifier": "log1p",
            "factor": 0.15,
            "missing": 0,
        },
        "weight": 0.5,
    },
    {
        "filter": Q("term", **{"_index": "products"}),
        "field_value_factor": {
            "field": "total_orders",
            "modifier": "log1p",
            "factor": 0.25,
            "missing": 0,
        },
        "weight": 0.7,
    },
    {
        "filter": Q("term", **{"_index": "products"}),
        "field_value_factor": {
            "field": "category_priority",
            "modifier": "log1p",
            "factor": 0.2,
            "missing": 0,
        },
        "weight": 0.6,
    },
    {
        "filter": Q("term", **{"_index": "categories"}),
        "field_value_factor": {
            "field": "priority",
            "modifier": "log1p",
            "factor": 0.25,
            "missing": 0,
        },
        "weight": 0.8,
    },
    {
        "filter": Q("term", **{"_index": "brands"}),
        "field_value_factor": {
            "field": "priority",
            "modifier": "log1p",
            "factor": 0.25,
            "missing": 0,
        },
        "weight": 0.8,
    },
]


def process_query(query: str = "", request: Request | None = None) -> dict[str, list[dict]] | None:
    if not query:
        raise ValueError(_("no search term provided."))

    query = query.strip()
    try:
        exact_shoulds = [
            Q("term", **{"name.raw": {"value": query, "boost": 3.0}}),
            Q("term", **{"slug": {"value": slugify(query), "boost": 2.0}}),
            Q("term", **{"sku.raw": {"value": query.lower(), "boost": 8.0}}),
            Q("term", **{"partnumber.raw": {"value": query.lower(), "boost": 9.0}}),
        ]

        lang = ""
        if request and hasattr(request, "LANGUAGE_CODE") and request.LANGUAGE_CODE:
            lang = request.LANGUAGE_CODE.lower()
        base = lang.split("-")[0] if lang else ""

        is_cjk = base in {"ja", "zh"}
        is_rtl_or_indic = base in {"ar", "hi"}

        fields_all = SMART_FIELDS[:]
        if is_cjk or is_rtl_or_indic:
            fields_all = [f for f in fields_all if ".phonetic" not in f]
            fields_all = [
                f.replace("name.ngram^8", "name.ngram^10").replace("title.ngram^4", "title.ngram^6") for f in fields_all
            ]

        fuzzy = None if (is_cjk or is_rtl_or_indic) else "AUTO:5,8"

        is_code_like = bool(re.search(r"[0-9]", query)) and " " not in query

        text_shoulds = [
            Q(
                "multi_match",
                query=query,
                fields=fields_all,
                operator="and",
                **({"fuzziness": fuzzy} if fuzzy else {}),
            ),
            Q(
                "multi_match",
                query=query,
                fields=[f for f in fields_all if f.endswith(".auto")],
                type="bool_prefix",
            ),
        ]

        if is_code_like:
            text_shoulds.extend(
                [
                    Q("term", **{"sku.raw": {"value": query.lower(), "boost": 12.0}}),
                    Q("term", **{"partnumber.raw": {"value": query.lower(), "boost": 14.0}}),
                    Q("prefix", **{"sku.raw": {"value": query.lower(), "boost": 6.0}}),
                    Q("prefix", **{"partnumber.raw": {"value": query.lower(), "boost": 7.0}}),
                ]
            )

        query_base = Q(
            "bool",
            should=exact_shoulds + text_shoulds,
            minimum_should_match=1,
        )

        def build_search(indexes, size):
            return (
                Search(index=indexes)
                .query(query_base)
                .extra(
                    rescore={
                        "window_size": 200,
                        "query": {
                            "rescore_query": Q(
                                "function_score",
                                query=Q("match_all"),
                                functions=functions,
                                boost_mode="sum",
                                score_mode="sum",
                                max_boost=2.0,
                            ).to_dict(),
                            "query_weight": 1.0,
                            "rescore_query_weight": 1.0,
                        },
                    }
                )
                .extra(size=size, track_total_hits=True)
            )

        search_cats = build_search(["categories"], size=22)
        search_brands = build_search(["brands"], size=22)
        search_products = build_search(["products"], size=44)

        resp_cats = search_cats.execute()
        resp_brands = search_brands.execute()
        resp_products = search_products.execute()

        results: dict = {"products": [], "categories": [], "brands": [], "posts": []}
        uuids_by_index: dict[str, list] = {"products": [], "categories": [], "brands": []}
        hit_cache: list = []

        for h in list(resp_cats.hits[:12]) + list(resp_brands.hits[:12]) + list(resp_products.hits[:26]):
            hit_cache.append(h)
            if getattr(h, "uuid", None):
                uuids_by_index.setdefault(h.meta.index, []).append(str(h.uuid))

        products_by_uuid = {}
        brands_by_uuid = {}
        cats_by_uuid = {}

        if request:
            if uuids_by_index.get("products"):
                products_by_uuid = {
                    str(p.uuid): p
                    for p in Product.objects.filter(uuid__in=uuids_by_index["products"])
                    .select_related("brand", "category")
                    .prefetch_related("images")
                }
            if uuids_by_index.get("brands"):
                brands_by_uuid = {str(b.uuid): b for b in Brand.objects.filter(uuid__in=uuids_by_index["brands"])}
            if uuids_by_index.get("categories"):
                cats_by_uuid = {str(c.uuid): c for c in Category.objects.filter(uuid__in=uuids_by_index["categories"])}

        for hit in hit_cache:
            obj_uuid = getattr(hit, "uuid", None) or hit.meta.id
            obj_name = getattr(hit, "name", None) or getattr(hit, "title", None) or "N/A"
            obj_slug = getattr(hit, "slug", "") or (
                slugify(obj_name) if hit.meta.index in {"brands", "categories"} else ""
            )

            image_url = None
            idx = hit.meta.index
            if idx == "products" and request:
                prod = products_by_uuid.get(str(obj_uuid))
                if prod:
                    first = prod.images.order_by("priority").first()
                    if first and first.image:
                        image_url = request.build_absolute_uri(first.image.url)
            elif idx == "brands" and request:
                brand = brands_by_uuid.get(str(obj_uuid))
                if brand and brand.small_logo:
                    image_url = request.build_absolute_uri(brand.small_logo.url)
            elif idx == "categories" and request:
                cat = cats_by_uuid.get(str(obj_uuid))
                if cat and cat.image:
                    image_url = request.build_absolute_uri(cat.image.url)

            hit_result = {
                "uuid": str(obj_uuid),
                "name": obj_name,
                "slug": obj_slug,
                "image": image_url,
            }

            if settings.DEBUG:
                if idx == "products":
                    hit_result["rating_debug"] = getattr(hit, "rating", 0)
                    hit_result["total_orders_debug"] = getattr(hit, "total_orders", 0)
                    hit_result["brand_priority_debug"] = getattr(hit, "brand_priority", 0)
                    hit_result["category_priority_debug"] = getattr(hit, "category_priority", 0)
                if idx in ("brands", "categories"):
                    hit_result["priority_debug"] = getattr(hit, "priority", 0)

            results[idx].append(hit_result)

        return results
    except NotFoundError as nfe:
        raise Http404 from nfe


LANGUAGE_ANALYZER_MAP = {
    "cs": "czech",
    "da": "danish",
    "de": "german",
    "en": "english",
    "es": "spanish",
    "fr": "french",
    "it": "italian",
    "nl": "dutch",
    "pt": "portuguese",
    "ro": "romanian",
    "ja": "cjk_search",
    "zh": "cjk_search",
    "ar": "arabic_search",
    "hi": "indic_search",
    "ru": "russian",
    "pl": "standard",
    "kk": "standard",
}


def _lang_analyzer(lang_code: str) -> str:
    base = lang_code.split("-")[0].lower()
    return LANGUAGE_ANALYZER_MAP.get(base, "icu_query")


class ActiveOnlyMixin:
    """QuerySet & indexing helpers, so only *active* objects are indexed."""

    def get_queryset(self):
        return super().get_queryset().filter(is_active=True)

    def should_index_object(self, obj):
        return getattr(obj, "is_active", False)


COMMON_ANALYSIS = {
    "char_filter": {
        "icu_nfkc_cf": {"type": "icu_normalizer", "name": "nfkc_cf"},
    },
    "filter": {
        "edge_ngram_filter": {"type": "edge_ngram", "min_gram": 1, "max_gram": 20},
        "ngram_filter": {"type": "ngram", "min_gram": 2, "max_gram": 20},
        "cjk_bigram": {"type": "cjk_bigram"},
        "icu_folding": {"type": "icu_folding"},
        "double_metaphone": {"type": "phonetic", "encoder": "double_metaphone", "replace": False},
        "arabic_norm": {"type": "arabic_normalization"},
        "indic_norm": {"type": "indic_normalization"},
    },
    "analyzer": {
        "icu_query": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding"],
        },
        "autocomplete": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding", "edge_ngram_filter"],
        },
        "autocomplete_search": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding"],
        },
        "name_ngram": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding", "ngram_filter"],
        },
        "name_phonetic": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding", "double_metaphone"],
        },
        "cjk_search": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding", "cjk_bigram"],
        },
        "arabic_search": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding", "arabic_norm"],
        },
        "indic_search": {
            "type": "custom",
            "char_filter": ["icu_nfkc_cf"],
            "tokenizer": "icu_tokenizer",
            "filter": ["lowercase", "icu_folding", "indic_norm"],
        },
    },
    "normalizer": {
        "lc_norm": {
            "type": "custom",
            "filter": ["lowercase", "icu_folding"],
        }
    },
}


def add_multilang_fields(cls):
    """
    Dynamically add multilingual name/description fields and prepare methods to guard against None.
    """
    for code, _lang in settings.LANGUAGES:
        lc = code.replace("-", "_").lower()
        # name_{lc}
        name_field = f"name_{lc}"
        setattr(
            cls,
            name_field,
            fields.TextField(
                attr=name_field,
                analyzer=_lang_analyzer(code),
                copy_to="name",
                fields={
                    "raw": fields.KeywordField(ignore_above=256),
                    "ngram": fields.TextField(analyzer="name_ngram", search_analyzer="icu_query"),
                    "phonetic": fields.TextField(analyzer="name_phonetic"),
                },
            ),
        )

        # prepare_name_{lc} to ensure no None values
        def make_prepare(attr):
            return lambda self, instance: getattr(instance, attr, "") or ""

        setattr(cls, f"prepare_{name_field}", make_prepare(name_field))

        # description_{lc}
        desc_field = f"description_{lc}"
        setattr(
            cls,
            desc_field,
            fields.TextField(
                attr=desc_field,
                analyzer=_lang_analyzer(code),
                copy_to="description",
                fields={
                    "raw": fields.KeywordField(ignore_above=256),
                    "ngram": fields.TextField(analyzer="name_ngram", search_analyzer="icu_query"),
                    "phonetic": fields.TextField(analyzer="name_phonetic"),
                },
            ),
        )
        setattr(cls, f"prepare_{desc_field}", make_prepare(desc_field))


def populate_index():
    for doc in registry.get_documents(set(registry.get_models())):
        qs = doc().get_indexing_queryset()
        doc().update(qs, parallel=True, refresh=True)