package utils

import (
	"fmt"
	"math"
	"math/rand"
	"strconv"
	"strings"
	"sync"
	"time"

	"github.com/xrash/smetrics"
)

func GetInvitation() string {
	var bs = make([]byte, 6)
	for i := 0; i < 6; i++ {
		rand.Seed(time.Now().UnixNano())
		flag := rand.Intn(2)
		if flag == 1 {
			bs[i] = byte(rand.Float64()*10 + 48)
		} else {
			bs[i] = byte(rand.Float64()*26 + 65)
		}
	}
	return string(bs)
}

func GetInvitationLen(length int) string {
	var bs = make([]byte, length)
	for i := 0; i < length; i++ {
		rand.Seed(time.Now().UnixNano())
		flag := rand.Intn(3)
		if flag == 1 {
			bs[i] = byte(rand.Float64()*10 + 48)
		} else if flag == 0 {
			bs[i] = byte(rand.Float64()*26 + 65)
		} else {
			bs[i] = byte(rand.Float64()*26 + 97)
		}
	}
	return string(bs)
}

func StringFloat64ToInt(s string) int {
	f1, err := strconv.ParseFloat(s, 64)
	if err != nil {
		return 0
	}
	s1 := strconv.FormatFloat(math.Ceil(f1), 'f', -1, 64)
	i, _ := strconv.Atoi(s1)
	return i
}

// FindMostSimilarCode 使用协程并发地对比目标特征码与一组候选特征码，
// 并找出相似度最高的特征码（若相似度高于阈值）及其相似度。
func FindMostSimilarCode(targetCode string, candidateCodes []string, similarityThreshold float64, boostThreshold float64, prefixSize int) (string, float64, bool) {
	var wg sync.WaitGroup
	maxSimilarity := 0.0
	var closestCode string
	foundAboveThreshold := false

	resultChan := make(chan struct {
		similarity float64
		code       string
	})

	for _, candidate := range candidateCodes {
		wg.Add(1)
		go func(candidate string) {
			defer wg.Done()
			similarity := smetrics.JaroWinkler(targetCode, candidate, boostThreshold, prefixSize)
			resultChan <- struct {
				similarity float64
				code       string
			}{similarity, candidate}
		}(candidate)
	}

	go func() {
		wg.Wait()
		close(resultChan)
	}()

	for result := range resultChan {
		if result.similarity > maxSimilarity {
			maxSimilarity = result.similarity
			closestCode = result.code
			if maxSimilarity >= similarityThreshold {
				foundAboveThreshold = true
			}
		}
	}

	return closestCode, maxSimilarity, foundAboveThreshold
}

// 计算余弦相似度
func CosineSimilarity(vec1Str, vec2Str string) float64 {
	vec1, _ := parseVectorFromString(vec1Str)
	vec2, _ := parseVectorFromString(vec2Str)

	dotProduct := 0.0
	vec1Norm := 0.0
	vec2Norm := 0.0

	for i := range vec1 {
		dotProduct += vec1[i] * vec2[i]
		vec1Norm += vec1[i] * vec1[i]
		vec2Norm += vec2[i] * vec2[i]
	}

	if vec1Norm == 0 || vec2Norm == 0 {
		return 0
	}

	return dotProduct / (math.Sqrt(vec1Norm) * math.Sqrt(vec2Norm))
}

func parseVectorFromString(vecStr string) ([]float64, error) {
	vec := strings.Split(vecStr, ",")
	var parsedVec []float64
	for _, strVal := range vec {
		floatVal, err := strconv.ParseFloat(strVal, 64)
		if err != nil {
			return nil, fmt.Errorf("failed to parse float value '%s': %w", strVal, err)
		}
		parsedVec = append(parsedVec, floatVal)
	}
	return parsedVec, nil
}

// float64转string的函数
func Float64ToString(input float64) string {
	return strconv.FormatFloat(input, 'f', -1, 64)
}