import { CareInfo } from '../types';

const { SEARCH_INTENT_CONFIG } = require('../constants/searchIntentConfig');

type SearchIntentConfig = {
  aliases?: string[];
  entryHints?: string[];
  lightHints?: string[];
};

export interface HybridSearchEntryLike {
  name: string;
  botanicalName?: string;
  description?: string;
  categories?: string[];
  careInfo?: Partial<CareInfo> | null;
}

interface RankedEntry<T> {
  entry: T;
  score: number;
}

const normalizeArray = (values: string[]): string[] => {
  return [...new Set(values.map((value) => normalizeSearchText(value)).filter(Boolean))];
};

export const normalizeSearchText = (value: string): string => {
  return value
    .toLowerCase()
    .normalize('NFD')
    .replace(/[\u0300-\u036f]/g, '')
    .trim()
    .replace(/[^a-z0-9\s_-]+/g, ' ')
    .replace(/[_-]+/g, ' ')
    .replace(/\s+/g, ' ');
};

const tokenize = (normalizedValue: string): string[] => {
  return normalizedValue.split(' ').filter(Boolean);
};

const tokenSetFromQuery = (normalizedQuery: string): Set<string> => {
  const rawTokens = tokenize(normalizedQuery);
  const noise = new Set<string>(SEARCH_INTENT_CONFIG.noiseTokens.map((token: string) => normalizeSearchText(token)));
  return new Set(rawTokens.filter((token) => !noise.has(token)));
};

const includesPhrase = (normalizedQuery: string, normalizedAlias: string, queryTokens: Set<string>): boolean => {
  if (!normalizedAlias) return false;
  if (normalizedQuery.includes(normalizedAlias)) return true;

  const aliasTokens = tokenize(normalizedAlias);
  if (aliasTokens.length <= 1) return queryTokens.has(normalizedAlias);
  return aliasTokens.every((token) => queryTokens.has(token));
};

const detectQueryIntents = (normalizedQuery: string): string[] => {
  const queryTokens = tokenSetFromQuery(normalizedQuery);
  const intents = (Object.entries(SEARCH_INTENT_CONFIG.intents) as Array<[string, SearchIntentConfig]>)
    .filter(([, value]) =>
      (value.aliases || []).some((alias) => includesPhrase(normalizedQuery, normalizeSearchText(alias), queryTokens)))
    .map(([intentId]) => intentId);
  return intents;
};

const getLevenshteinDistance = (left: string, right: string): number => {
  const rows = left.length + 1;
  const cols = right.length + 1;
  const matrix: number[][] = Array.from({ length: rows }, (_, rowIndex) => [rowIndex]);

  for (let col = 0; col < cols; col += 1) {
    matrix[0][col] = col;
  }

  for (let row = 1; row < rows; row += 1) {
    for (let col = 1; col < cols; col += 1) {
      const cost = left[row - 1] === right[col - 1] ? 0 : 1;
      matrix[row][col] = Math.min(
        matrix[row - 1][col] + 1,
        matrix[row][col - 1] + 1,
        matrix[row - 1][col - 1] + cost,
      );
    }
  }

  return matrix[left.length][right.length];
};

const fuzzyBonus = (normalizedQuery: string, candidates: string[]): number => {
  if (normalizedQuery.length < 3 || normalizedQuery.length > 32) return 0;

  let best = Number.POSITIVE_INFINITY;
  candidates.forEach((candidate) => {
    if (!candidate) return;
    tokenize(candidate).forEach((token) => {
      best = Math.min(best, getLevenshteinDistance(normalizedQuery, token));
    });
    best = Math.min(best, getLevenshteinDistance(normalizedQuery, candidate));
  });

  if (best === 1) return 14;
  if (best === 2) return 8;
  return 0;
};

const scoreTextMatch = (normalizedQuery: string, normalizedTarget: string, exact: number, prefix: number, contains: number): number => {
  if (!normalizedQuery || !normalizedTarget) return 0;
  if (normalizedTarget === normalizedQuery) return exact;
  if (normalizedTarget.startsWith(normalizedQuery)) return prefix;
  if (normalizedTarget.includes(normalizedQuery)) return contains;
  return 0;
};

const buildDerivedIntentSignals = (entry: HybridSearchEntryLike): string[] => {
  const normalizedDescription = normalizeSearchText(entry.description || '');
  const normalizedLight = normalizeSearchText(entry.careInfo?.light || '');

  const derivedSignals = new Set<string>();
  const normalizedCategories = (entry.categories || []).map((category) => normalizeSearchText(category));
  normalizedCategories.forEach((category) => derivedSignals.add(category));

  (Object.entries(SEARCH_INTENT_CONFIG.intents) as Array<[string, SearchIntentConfig]>).forEach(([intentId, intentConfig]) => {
    const entryHints = normalizeArray(intentConfig.entryHints || []);
    if (entryHints.some((hint) => normalizedDescription.includes(hint))) {
      derivedSignals.add(intentId);
    }

    const lightHints = normalizeArray(intentConfig.lightHints || []);
    if (lightHints.some((hint) => normalizedLight.includes(hint))) {
      derivedSignals.add(intentId);
    }
  });

  return [...derivedSignals];
};

export const scoreHybridEntry = (entry: HybridSearchEntryLike, query: string): number => {
  const normalizedQuery = normalizeSearchText(query);
  if (!normalizedQuery) return 0;

  const normalizedName = normalizeSearchText(entry.name || '');
  const normalizedBotanical = normalizeSearchText(entry.botanicalName || '');
  const normalizedDescription = normalizeSearchText(entry.description || '');
  const normalizedCategories = (entry.categories || []).map((category) => normalizeSearchText(category));
  const derivedSignals = buildDerivedIntentSignals(entry);
  const requestedIntents = detectQueryIntents(normalizedQuery);

  let score = 0;
  score += Math.max(
    scoreTextMatch(normalizedQuery, normalizedName, 140, 100, 64),
    scoreTextMatch(normalizedQuery, normalizedBotanical, 130, 96, 58),
  );

  if (normalizedDescription.includes(normalizedQuery)) {
    score += 24;
  }

  score += fuzzyBonus(normalizedQuery, [normalizedName, normalizedBotanical, ...normalizedCategories]);

  let matchedIntentCount = 0;
  requestedIntents.forEach((intentId) => {
    const categoryHit = normalizedCategories.includes(intentId);
    const derivedHit = derivedSignals.includes(intentId);
    if (categoryHit) {
      score += 92;
      matchedIntentCount += 1;
      return;
    }
    if (derivedHit) {
      score += 56;
      matchedIntentCount += 1;
    }
  });

  if (matchedIntentCount >= 2) {
    score += 38 * matchedIntentCount;
  } else if (matchedIntentCount === 1) {
    score += 10;
  }

  const queryTokens = [...tokenSetFromQuery(normalizedQuery)];
  if (queryTokens.length > 1) {
    const searchableText = [normalizedName, normalizedBotanical, normalizedDescription, ...normalizedCategories, ...derivedSignals].join(' ');
    const tokenHits = queryTokens.filter((token) => searchableText.includes(token)).length;
    score += tokenHits * 8;
    if (tokenHits === queryTokens.length && queryTokens.length > 0) {
      score += 16;
    }
  }

  return score;
};

export const rankHybridEntries = <T extends HybridSearchEntryLike>(
  entries: T[],
  query: string,
  limit = 30,
): RankedEntry<T>[] => {
  const normalizedQuery = normalizeSearchText(query);
  if (!normalizedQuery) {
    return entries.slice(0, limit).map((entry) => ({ entry, score: 0 }));
  }

  return entries
    .map((entry) => ({ entry, score: scoreHybridEntry(entry, normalizedQuery) }))
    .filter((candidate) => candidate.score > 0)
    .sort((left, right) =>
      right.score - left.score ||
      left.entry.name.length - right.entry.name.length ||
      left.entry.name.localeCompare(right.entry.name))
    .slice(0, limit);
};