import type { Need } from '../../domain/need'
import type { Property } from '../../domain/property'
import type { ScoreFactor } from '../../domain/match'
import { ResultType, AvailabilityStatus, AssetType } from '../../domain/enums'
import {
  HARD_FILTER,
  DATA_QUALITY_MODIFIER,
  CONFIDENCE_MODIFIER,
  HARD_CRITERION_KEYS,
  SOFT_FACTOR_KEYS,
  DEFAULT_SCORING_PROFILES,
} from '../../domain/scoring'
import type { ScoringWeightProfile, HardFilterResult, MatchEngineOutput, SoftFactorKey } from '../../domain/scoring'
import { analyzeTradeOffs, analyzeRisks, identifyMissingData } from './tradeOffAnalyzer'
import { generateNextBestActions } from './rankingEngine'

// ── Profile resolution ────────────────────────────────────────────────────────

function resolveProfile(need: Need, property: Property): ScoringWeightProfile {
  const base = { ...(DEFAULT_SCORING_PROFILES[property.assetType] ?? DEFAULT_SCORING_PROFILES.DEFAULT) }
  const np = need.weightingProfile
  if (!np) return base

  // Apply need's custom core weights, then renormalize the full profile to 1.00
  const CORE = ['area', 'location', 'budget', 'timing', 'prestige', 'accessibility', 'expansionPotential', 'flexibility']
  for (const key of CORE) {
    if (typeof np[key] === 'number') base[key] = np[key]
  }
  const total = Object.values(base).reduce((s, v) => s + v, 0)
  if (total > 0) for (const key of Object.keys(base)) base[key] /= total
  return base
}

// ── Hard Filters ──────────────────────────────────────────────────────────────

export function applyHardFilters(need: Need, property: Property): HardFilterResult {
  const assetOk = property.assetType === need.assetType
    || property.assetType === AssetType.MIXED
    || need.assetType === AssetType.UNKNOWN

  if (!assetOk) {
    return { excluded: true, reason: `Nutzungstyp ${property.assetType} stimmt nicht mit ${need.assetType} überein`, severePenalty: 0 }
  }

  // Area: hard exclude below tolerance
  const areaMin = need.requiredArea?.min ?? 0
  const propArea = property.areaSqmMin ?? property.areaSqm
  if (areaMin > 0 && propArea < areaMin * HARD_FILTER.AREA_MIN_TOLERANCE) {
    return {
      excluded: true,
      reason: `Fläche ${propArea} m² unterschreitet Minimum ${areaMin} m² um mehr als ${Math.round((1 - HARD_FILTER.AREA_MIN_TOLERANCE) * 100)}%`,
      severePenalty: 0,
    }
  }

  // Region exclusion
  const city = property.location.city.toLowerCase()
  const excluded = (need.excludedLocations ?? []).map(l => l.toLowerCase())
  if (excluded.some(e => city.includes(e) || e.includes(city))) {
    return { excluded: true, reason: `Standort ${property.location.city} ist ausgeschlossen`, severePenalty: 0 }
  }

  // Budget: hard exclude if massively over
  const maxBudget = need.budgetRange?.maxPerSqm ?? 0
  if (maxBudget > 0 && property.rentPricePerSqm > maxBudget * HARD_FILTER.BUDGET_EXCLUSION_RATIO) {
    return {
      excluded: true,
      reason: `Miete CHF ${property.rentPricePerSqm}/m² überschreitet Budget CHF ${maxBudget}/m² um mehr als ${Math.round((HARD_FILTER.BUDGET_EXCLUSION_RATIO - 1) * 100)}%`,
      severePenalty: 0,
    }
  }

  // Usage/zoning: occupied property is severe penalty, not exclude
  if (property.availabilityStatus === AvailabilityStatus.OCCUPIED) {
    return { excluded: false, reason: undefined, severePenalty: 25 }
  }

  return { excluded: false, reason: undefined, severePenalty: 0 }
}

// ── Hard Criterion Scorers ────────────────────────────────────────────────────

function scoreArea(need: Need, property: Property, weight: number): ScoreFactor {
  const { min = 0, max = Infinity } = need.requiredArea ?? {}
  const area = property.areaSqm
  const areaMin = property.areaSqmMin ?? area
  const areaMax = property.areaSqmMax ?? area

  let score: number
  let explanation: string

  // Flexible property — check range overlap
  const overlap = areaMin <= max && areaMax >= min
  if (overlap) {
    score = 100
    explanation = `Fläche ${areaMin === areaMax ? `${area}` : `${areaMin}–${areaMax}`} m² deckt Bedarf ${min}–${max} m² ab`
  } else if (area > max) {
    const ratio = area / max
    score = ratio <= HARD_FILTER.AREA_MAX_RATIO
      ? Math.max(40, Math.round(100 - (ratio - 1) * 50))
      : 20
    explanation = `Fläche ${area} m² überschreitet Maximum ${max} m² (${Math.round((ratio - 1) * 100)}% zu viel)`
  } else {
    // Area between tolerance and min — mild penalty
    const ratio = area / min
    score = Math.round(40 + ratio * 30)
    explanation = `Fläche ${area} m² leicht unter Minimum ${min} m²`
  }

  return { criterion: 'area', weight, score, contribution: score * weight, explanation }
}

function scoreLocation(need: Need, property: Property, weight: number): ScoreFactor {
  const city = property.location.city.toLowerCase()
  const canton = (property.location.canton ?? '').toLowerCase()
  const preferred = (need.preferredLocations ?? []).map(l => l.toLowerCase())

  let score: number
  let explanation: string

  if (preferred.length === 0) {
    score = 70
    explanation = 'Kein Standortwunsch — neutral bewertet'
  } else if (preferred.some(p => city.includes(p) || p.includes(city))) {
    score = 100
    explanation = `Standort ${property.location.city} entspricht Präferenz`
  } else if (canton && preferred.some(p => p.includes(canton) || canton.includes(p))) {
    score = 60
    explanation = `Gleicher Kanton wie Präferenz (${property.location.canton})`
  } else {
    score = 35
    explanation = `Standort ${property.location.city} nicht in Präferenzliste`
  }

  return { criterion: 'location', weight, score, contribution: score * weight, explanation }
}

function scoreBudget(need: Need, property: Property, weight: number): ScoreFactor {
  const maxBudget = need.budgetRange?.maxPerSqm ?? 0
  const rent = property.rentPricePerSqm

  let score: number
  let explanation: string

  if (maxBudget <= 0) {
    score = 60
    explanation = 'Kein Budget angegeben — neutral bewertet'
  } else if (rent <= maxBudget) {
    const ratio = rent / maxBudget
    // Very cheap can indicate quality issues — slight penalty below 50% of budget
    score = ratio >= 0.50 ? 100 : 88
    explanation = `Miete CHF ${rent}/m² liegt ${Math.round((1 - ratio) * 100)}% unter Budget CHF ${maxBudget}/m²`
  } else {
    const overRatio = rent / maxBudget
    if (overRatio <= HARD_FILTER.BUDGET_MODERATE_RATIO) {
      score = 75
      explanation = `Miete CHF ${rent}/m² leicht über Budget (+${Math.round((overRatio - 1) * 100)}%)`
    } else if (overRatio <= HARD_FILTER.BUDGET_SEVERE_RATIO) {
      score = 45
      explanation = `Miete CHF ${rent}/m² merklich über Budget (+${Math.round((overRatio - 1) * 100)}%)`
    } else {
      score = 20
      explanation = `Miete CHF ${rent}/m² stark über Budget (+${Math.round((overRatio - 1) * 100)}%)`
    }
  }

  return { criterion: 'budget', weight, score, contribution: score * weight, explanation }
}

function scoreTiming(need: Need, property: Property, weight: number): ScoreFactor {
  const isFutureSignal = property.resultType === ResultType.FUTURE_AVAILABILITY

  const rawDate = property.availabilityDate
  const propDate = rawDate && rawDate !== '' ? new Date(rawDate) : null
  const earliest = need.timing?.earliestMoveIn ? new Date(need.timing.earliestMoveIn) : null
  const latest = need.timing?.latestMoveIn ? new Date(need.timing.latestMoveIn) : null
  const graceMs = HARD_FILTER.TIMING_GRACE_DAYS * 86_400_000

  let score: number
  let explanation: string

  // CRITICAL RULE: Future availability is never treated as confirmed
  if (isFutureSignal) {
    if (!propDate) {
      score = 30
      explanation = 'Zukünftiges Signal — kein Datum, Verfügbarkeit unbestätigt'
    } else if (latest && propDate.getTime() > latest.getTime() + graceMs) {
      score = 20
      explanation = `Zukünftiges Signal — erwartet ${rawDate}, nach gewünschtem Zeitfenster (unbestätigt)`
    } else if (earliest && propDate.getTime() < earliest.getTime()) {
      score = 50
      explanation = `Zukünftiges Signal — erwartet ${rawDate}, vor gewünschtem Einzug (unbestätigt)`
    } else {
      score = 42
      explanation = `Zukünftiges Signal — Zeitfenster passt, Verfügbarkeit jedoch unbestätigt`
    }
    return { criterion: 'timing', weight, score, contribution: score * weight, explanation }
  }

  const isNow = property.availabilityStatus === AvailabilityStatus.AVAILABLE_NOW
    || property.availabilityStatus === AvailabilityStatus.AVAILABLE_SOON

  if (isNow) {
    const tooEarly = earliest && new Date() < earliest
    score = tooEarly ? 80 : 100
    explanation = tooEarly
      ? `Sofort verfügbar — Einzug jedoch erst ab ${need.timing?.earliestMoveIn} geplant`
      : 'Sofort verfügbar — entspricht Verfügbarkeitswunsch'
    return { criterion: 'timing', weight, score, contribution: score * weight, explanation }
  }

  if (!propDate) {
    score = 38
    explanation = 'Kein Verfügbarkeitsdatum angegeben'
    return { criterion: 'timing', weight, score, contribution: score * weight, explanation }
  }

  if (earliest && latest) {
    const t = propDate.getTime()
    if (t >= earliest.getTime() && t <= latest.getTime()) {
      score = 95
      explanation = `Verfügbar ${rawDate} liegt im Einzugsfenster`
    } else if (t < earliest.getTime()) {
      const diff = earliest.getTime() - t
      score = diff < graceMs ? 80 : 65
      explanation = `Verfügbar ${rawDate} vor gewünschtem Einzug — kurze Leerstandszeit`
    } else {
      const diff = t - latest.getTime()
      score = diff < graceMs ? 55 : 28
      explanation = `Verfügbar ${rawDate} nach gewünschtem Zeitfenster`
    }
  } else {
    score = 65
    explanation = `Verfügbar ${rawDate}`
  }

  return { criterion: 'timing', weight, score, contribution: score * weight, explanation }
}

// ── Soft Factor Scorer ────────────────────────────────────────────────────────

function scoreSoftFactor(key: SoftFactorKey, weight: number, property: Property): ScoreFactor {
  const sf = property.softFactors
  const hf = property.hardFacts

  const rawValue = (() => {
    switch (key) {
      case 'prestige':           return sf?.prestigeScore ?? sf?.prestige
      case 'accessibility':      return sf?.commuterAccessScore ?? sf?.accessibility
        ?? (hf?.publicTransportScore !== undefined ? hf.publicTransportScore / 10 : undefined)
      case 'expansionPotential': return sf?.expansionPotentialScore
      case 'flexibility':        return sf?.flexibilityScore
      case 'visibility':         return sf?.visibilityScore
      case 'footfall':           return sf?.footfallScore
      case 'talentAccess':       return sf?.talentAccessScore ?? sf?.talentAccess
      case 'esg':                return sf?.esgScore
      case 'taxEnvironment':     return sf?.taxEnvironmentScore
      default:                   return undefined
    }
  })()

  if (rawValue === undefined || rawValue === null) {
    // Missing data → neutral 50 (does not help, does not hurt)
    return {
      criterion: key,
      weight,
      score: 50,
      contribution: 50 * weight,
      explanation: `${key}: keine Daten verfügbar — neutral bewertet`,
    }
  }

  // Soft factor values are 0–1 scale → convert to 0–100
  const score = Math.round(Math.min(100, Math.max(0, rawValue * 100)))
  const LABELS: Record<SoftFactorKey, string> = {
    prestige: 'Prestige', accessibility: 'Erreichbarkeit', expansionPotential: 'Expansion',
    flexibility: 'Flexibilität', visibility: 'Sichtbarkeit', footfall: 'Passantenfrequenz',
    talentAccess: 'Talent-Zugang', esg: 'ESG', taxEnvironment: 'Steuerumfeld',
  }
  return {
    criterion: key,
    weight,
    score,
    contribution: score * weight,
    explanation: `${LABELS[key] ?? key}: ${score}/100`,
  }
}

// ── Modifier Calculators ──────────────────────────────────────────────────────

export function calcDataQualityModifier(property: Property): number {
  const s = property.dataQuality?.score ?? 0.5
  if (s >= 0.85) return DATA_QUALITY_MODIFIER.EXCELLENT
  if (s >= 0.70) return DATA_QUALITY_MODIFIER.GOOD
  if (s >= 0.55) return DATA_QUALITY_MODIFIER.FAIR
  if (s >= 0.40) return DATA_QUALITY_MODIFIER.POOR
  return DATA_QUALITY_MODIFIER.CRITICAL
}

export function calcConfidenceModifier(property: Property): number {
  let mod = 0
  if (property.resultType === ResultType.FUTURE_AVAILABILITY) {
    mod += CONFIDENCE_MODIFIER.FUTURE_AVAILABILITY
  } else if (property.resultType === ResultType.EXTERNAL_MARKET) {
    mod += CONFIDENCE_MODIFIER.EXTERNAL_MARKET
  } else {
    // VERIFIED_PORTFOLIO
    mod += property.confidenceScore >= 0.80
      ? CONFIDENCE_MODIFIER.VERIFIED_HIGH
      : CONFIDENCE_MODIFIER.VERIFIED_MEDIUM
  }
  if (property.confidenceScore < 0.50) {
    mod += CONFIDENCE_MODIFIER.LOW_CONFIDENCE
  }
  return mod
}

// ── Main Engine Function ──────────────────────────────────────────────────────

export function calculateScore(need: Need, property: Property): MatchEngineOutput {
  const hardFilter = applyHardFilters(need, property)

  if (hardFilter.excluded) {
    return {
      propertyId: property.id,
      needId: need.id,
      excluded: true,
      excludedReason: hardFilter.reason,
      finalScore: 0,
      hardMatchScore: 0,
      softFactorScore: 0,
      dataQualityModifier: 0,
      confidenceModifier: 0,
      positiveFactors: [],
      negativeFactors: [],
      allHardFactors: [],
      allSoftFactors: [],
      tradeOffs: [],
      risks: [],
      missingData: identifyMissingData(property, need),
      nextBestActions: [],
    }
  }

  const profile = resolveProfile(need, property)

  // ── Hard criteria scoring ──────────────────────────────────────────────────
  const hardFactors: ScoreFactor[] = [
    scoreArea(need, property, profile.area),
    scoreLocation(need, property, profile.location),
    scoreBudget(need, property, profile.budget),
    scoreTiming(need, property, profile.timing),
  ]
  const hardWeightSum = HARD_CRITERION_KEYS.reduce((s, k) => s + profile[k], 0)
  const hardRaw = hardFactors.reduce((s, f) => s + f.contribution, 0)
  const hardMatchScore = hardWeightSum > 0 ? Math.min(100, Math.round(hardRaw / hardWeightSum)) : 0

  // ── Soft factor scoring ────────────────────────────────────────────────────
  const softFactors: ScoreFactor[] = SOFT_FACTOR_KEYS
    .filter(k => (profile[k] ?? 0) > 0)
    .map(k => scoreSoftFactor(k, profile[k], property))
  const softWeightSum = SOFT_FACTOR_KEYS.reduce((s, k) => s + (profile[k] ?? 0), 0)
  const softRaw = softFactors.reduce((s, f) => s + f.contribution, 0)
  const softFactorScore = softWeightSum > 0 ? Math.min(100, Math.round(softRaw / softWeightSum)) : 50

  // ── Modifiers ──────────────────────────────────────────────────────────────
  const dqMod = calcDataQualityModifier(property)
  const confMod = calcConfidenceModifier(property)

  // ── Final score: weighted sum of both groups + modifiers ──────────────────
  // Each group already normalized 0–100; combine per SCORE_SPLIT, then apply modifiers
  const baseScore = hardMatchScore * 0.60 + softFactorScore * 0.40
  const rawFinal = baseScore + dqMod + confMod - hardFilter.severePenalty
  const finalScore = Math.round(Math.min(100, Math.max(0, rawFinal)))

  // ── Factor classification ──────────────────────────────────────────────────
  const allFactors = [...hardFactors, ...softFactors]
  const THRESHOLD_POSITIVE = 70
  const THRESHOLD_NEGATIVE = 45
  const positiveFactors = allFactors
    .filter(f => f.score >= THRESHOLD_POSITIVE)
    .sort((a, b) => b.contribution - a.contribution)
    .slice(0, 4)
  const negativeFactors = allFactors
    .filter(f => f.score < THRESHOLD_NEGATIVE)
    .sort((a, b) => a.contribution - b.contribution)
    .slice(0, 4)

  const tradeOffs = analyzeTradeOffs(hardFactors, softFactors, need, property)
  const risks = analyzeRisks(property, hardFactors)
  const missingData = identifyMissingData(property, need)

  const output: MatchEngineOutput = {
    propertyId: property.id,
    needId: need.id,
    excluded: false,
    finalScore,
    hardMatchScore,
    softFactorScore,
    dataQualityModifier: dqMod,
    confidenceModifier: confMod,
    positiveFactors,
    negativeFactors,
    allHardFactors: hardFactors,
    allSoftFactors: softFactors,
    tradeOffs,
    risks,
    missingData,
    nextBestActions: [],  // filled by rankingEngine
  }

  output.nextBestActions = generateNextBestActions(output, property, need)
  return output
}