import time
from collections.abc import Iterable, Iterator
from sqlite3 import Connection
from typing import Callable, TypeVar

import Levenshtein
from folkugat_web.config import search as config
from folkugat_web.dal.sql import get_connection
from folkugat_web.dal.sql.temes import properties as properties_dal
from folkugat_web.dal.sql.temes import query as temes_q
from folkugat_web.log import logger
from folkugat_web.model import search as search_model
from folkugat_web.model import temes as model
from folkugat_web.services.temes import properties as properties_service
from folkugat_web.services.temes import query as query_service
from folkugat_web.utils import FnChain

T = TypeVar("T")


def get_query_word_similarity(query_word: str, text_ngrams: search_model.NGrams) -> search_model.SearchMatch:
    n = len(query_word)
    if n < config.MIN_NGRAM_LENGTH:
        return search_model.SearchMatch(distance=0.0, ngram='')
    ns = filter(lambda i: i >= config.MIN_NGRAM_LENGTH, range(
        n - config.QUERY_NGRAM_RANGE, n + config.QUERY_NGRAM_RANGE + 1))
    candidate_ngrams = ((m, ngram)
                        for m, ngrams in map(lambda i: (i, text_ngrams.get(i, [])), ns)
                        for ngram in ngrams)
    return min((search_model.SearchMatch(distance=Levenshtein.distance(query_word, ngram)/m,
                                         ngram=ngram)
                for m, ngram in candidate_ngrams),
               default=search_model.SearchMatch(distance=float("inf"), ngram=""))


def get_query_similarity(query: str, ngrams: search_model.NGrams) -> search_model.SearchMatch:
    query_words = query.lower().split()
    word_matches = map(lambda query_word: get_query_word_similarity(query_word, ngrams), query_words)
    return search_model.SearchMatch.combine_matches(word_matches)


def _build_results_fn(query: str) -> Callable[[Iterable[tuple[T, search_model.NGrams]]],
                                              Iterator[search_model.QueryResult[T]]]:
    def build_result(entry: tuple[T, search_model.NGrams]) -> search_model.QueryResult[T]:
        if len(query) == 0:
            return search_model.QueryResult(
                result=entry[0],
                distance=0,
                ngram="",
            )
        match = get_query_similarity(query, entry[1])
        return search_model.QueryResult(
            result=entry[0],
            distance=match.distance,
            ngram=match.ngram,
        )

    def build_results(entries: Iterable[tuple[T, search_model.NGrams]]) -> Iterator[search_model.QueryResult[T]]:
        return map(build_result, entries)

    return build_results


def _filter_distance(qrs: Iterable[search_model.QueryResult[T]]) -> Iterator[search_model.QueryResult[T]]:
    return filter(lambda qr: qr.distance <= config.SEARCH_DISTANCE_THRESHOLD, qrs)


def _sort_by_distance(qrs: Iterable[search_model.QueryResult[T]]) -> list[search_model.QueryResult[T]]:
    return sorted(qrs, key=lambda qr: qr.distance)


def _query_results_to_temes(
    con: Connection
) -> Callable[[Iterable[search_model.QueryResult[int]]], Iterator[model.Tema]]:
    def fetch_temes(qrs: Iterable[search_model.QueryResult[int]]) -> Iterator[model.Tema]:
        return filter(None, map(lambda qr: temes_q.get_tema_by_id(tema_id=qr.result, con=con), qrs))
    return fetch_temes


def _filter_hidden(hidden: bool) -> Callable[[Iterable[model.Tema]], Iterator[model.Tema]]:
    def filter_hidden(temes: Iterable[model.Tema]) -> Iterator[model.Tema]:
        return filter(lambda t: hidden or not t.hidden, temes)
    return filter_hidden


def _filter_properties(properties: list[str]) -> Callable[[Iterable[model.Tema]], Iterator[model.Tema]]:
    properties_set = set(prop.lower() for prop in properties)

    def has_properties(tema: model.Tema) -> bool:
        tema_properties = {prop.value.lower() for prop in tema.properties}
        return all(prop in tema_properties for prop in properties_set)

    def filter_properties(temes: Iterable[model.Tema]) -> Iterator[model.Tema]:
        return filter(has_properties, temes)

    return filter_properties


def _apply_limit_offset(limit: int, offset: int) -> Callable[[Iterable[T]], list[T]]:
    def apply_limit_offset(temes: Iterable[T]) -> list[T]:
        return list(temes)[offset:offset + limit]
    return apply_limit_offset


def busca_temes(
    query: str,
    properties: list[str],
    hidden: bool = False,
    limit: int = 10,
    offset: int = 0,
) -> list[model.Tema]:
    """
    This function adds properties to Tema
    """
    t0 = time.time()
    with get_connection() as con:
        result = (
            FnChain.transform(temes_q.get_tema_id_to_ngrams(con).items()) |
            _build_results_fn(query) |
            _filter_distance |
            _sort_by_distance |
            _query_results_to_temes(con) |
            _filter_hidden(hidden) |
            properties_service.add_properties_to_temes |
            _filter_properties(properties) |
            query_service.temes_compute_stats |
            _apply_limit_offset(limit=limit, offset=offset)
        ).result()
    logger.info(f"Temes search time: { int((time.time() - t0) * 1000) } ms")
    return result


def _extract_properties(query_results: list[search_model.QueryResult[str]]) -> list[str]:
    return [qr.result for qr in query_results]


def busca_properties(
    query: str,
    limit: int = 10,
    offset: int = 0,
) -> list[str]:
    if not query:
        return []
    t0 = time.time()
    with get_connection() as con:
        result = (
            FnChain.transform(properties_dal.get_property_value_to_ngrams(con).items()) |
            _build_results_fn(query) |
            _filter_distance |
            _sort_by_distance |
            _apply_limit_offset(limit=limit, offset=offset) |
            _extract_properties
        ).result()
    logger.info(f"Properties search time: { int((time.time() - t0) * 1000) } ms")
    return result