package multimodal

import (
	"encoding/binary"
	"fmt"
	"math"
	"math/rand"
	"sync"
)

// 多模态Transformer配置
type MultiModalConfig struct {
	HiddenDim     int     // 隐藏层维度
	NumLayers     int     // Transformer层数
	NumHeads      int     // 注意力头数
	MaxSeqLen     int     // 最大序列长度
	VocabSize     int     // 词表大小
	ImagePatchDim int     // 图像patch维度
	AudioFreqDim  int     // 音频频率维度
	DropoutRate   float64 // Dropout概率
}

// 模态类型枚举
type Modality int

const (
	ModalityText  Modality = 0
	ModalityImage Modality = 1
	ModalityAudio Modality = 2
	ModalityVideo Modality = 3
)

// 多模态Token结构
type MultiModalToken struct {
	Embedding    []float64 // Token嵌入向量
	ModalityType Modality  // 模态类型
	Position     int       // 位置索引
	IsSpecial    bool      // 是否为特殊token
}

// 多模态编码器
type MultiModalEncoder struct {
	Config       *MultiModalConfig
	TextEmbed    *EmbeddingLayer   // 文本嵌入层
	ImageEmbed   *PatchEmbedLayer  // 图像patch嵌入层
	AudioEmbed   *PatchEmbedLayer  // 音频patch嵌入层
	VideoEmbed   *PatchEmbedLayer  // 视频帧嵌入层
	ModalityEmbed *EmbeddingLayer  // 模态类型嵌入
	PositionEmbed *EmbeddingLayer  // 位置编码嵌入
	TransformerLayers []*TransformerLayer // Transformer层
	LayerNorm    *LayerNorm         // 层归一化
	OutputProj   *LinearLayer       // 输出投影
}

// 创建新的多模态编码器
func NewMultiModalEncoder(config *MultiModalConfig) *MultiModalEncoder {
	encoder := &MultiModalEncoder{
		Config:       config,
		TextEmbed:    NewEmbeddingLayer(config.VocabSize, config.HiddenDim),
		ImageEmbed:   NewPatchEmbedLayer(config.ImagePatchDim, config.HiddenDim),
		AudioEmbed:   NewPatchEmbedLayer(config.AudioFreqDim, config.HiddenDim),
		VideoEmbed:   NewPatchEmbedLayer(config.ImagePatchDim*3, config.HiddenDim), // 视频考虑时间维度
		ModalityEmbed: NewEmbeddingLayer(4, config.HiddenDim), // 4种模态
		PositionEmbed: NewEmbeddingLayer(config.MaxSeqLen, config.HiddenDim),
		LayerNorm:    NewLayerNorm(config.HiddenDim),
		OutputProj:   NewLinearLayer(config.HiddenDim, config.HiddenDim),
	}

	// 初始化Transformer层
	encoder.TransformerLayers = make([]*TransformerLayer, config.NumLayers)
	for i := 0; i < config.NumLayers; i++ {
		encoder.TransformerLayers[i] = NewTransformerLayer(config)
	}

	return encoder
}

// 前向传播：将多模态输入编码为统一表示
func (e *MultiModalEncoder) Forward(inputs []MultiModalToken) ([]float64, error) {
	if len(inputs) == 0 {
		return nil, fmt.Errorf("empty input sequence")
	}
	if len(inputs) > e.Config.MaxSeqLen {
		return nil, fmt.Errorf("sequence length %d exceeds max %d", len(inputs), e.Config.MaxSeqLen)
	}

	// 1. 获取每个token的嵌入
	seqLen := len(inputs)
	hiddenStates := make([][]float64, seqLen)
	for i, token := range inputs {
		var tokenEmbed []float64

		switch token.ModalityType {
		case ModalityText:
			tokenEmbed = e.TextEmbed.Forward(int(token.Embedding[0]))
		case ModalityImage:
			tokenEmbed = e.ImageEmbed.Forward(token.Embedding)
		case ModalityAudio:
			tokenEmbed = e.AudioEmbed.Forward(token.Embedding)
		case ModalityVideo:
			tokenEmbed = e.VideoEmbed.Forward(token.Embedding)
		default:
			return nil, fmt.Errorf("unknown modality type: %v", token.ModalityType)
		}

		// 2. 添加模态类型嵌入
		modalityEmbed := e.ModalityEmbed.Forward(int(token.ModalityType))
		for j := 0; j < len(tokenEmbed); j++ {
			tokenEmbed[j] += modalityEmbed[j]
		}

		// 3. 添加位置编码
		posEmbed := e.PositionEmbed.Forward(token.Position)
		for j := 0; j < len(tokenEmbed); j++ {
			tokenEmbed[j] += posEmbed[j]
		}

		hiddenStates[i] = tokenEmbed
	}

	// 4. 通过所有Transformer层
	for _, layer := range e.TransformerLayers {
		var err error
		hiddenStates, err = layer.Forward(hiddenStates)
		if err != nil {
			return nil, fmt.Errorf("transformer layer error: %v", err)
		}
	}

	// 5. 最终层归一化和投影
	output := make([]float64, e.Config.HiddenDim)
	for _, state := range hiddenStates {
		normalized := e.LayerNorm.Forward(state)
		projected := e.OutputProj.Forward(normalized)
		for j := 0; j < len(output); j++ {
			output[j] += projected[j]
		}
	}

	// 取平均作为序列表示
	for j := 0; j < len(output); j++ {
		output[j] /= float64(seqLen)
	}

	return output, nil
}

// Transformer层
type TransformerLayer struct {
	Config      *MultiModalConfig
	SelfAttn    *MultiHeadAttention
	CrossAttn   *MultiHeadAttention // 跨模态注意力
	FFN         *FeedForwardNetwork
	Norm1       *LayerNorm
	Norm2       *LayerNorm
	Norm3       *LayerNorm
	Dropout     float64
}

func NewTransformerLayer(config *MultiModalConfig) *TransformerLayer {
	return &TransformerLayer{
		Config:    config,
		SelfAttn:  NewMultiHeadAttention(config.HiddenDim, config.NumHeads),
		CrossAttn: NewMultiHeadAttention(config.HiddenDim, config.NumHeads),
		FFN:       NewFeedForwardNetwork(config.HiddenDim, config.HiddenDim*4),
		Norm1:     NewLayerNorm(config.HiddenDim),
		Norm2:     NewLayerNorm(config.HiddenDim),
		Norm3:     NewLayerNorm(config.HiddenDim),
		Dropout:   config.DropoutRate,
	}
}

func (l *TransformerLayer) Forward(inputs [][]float64) ([][]float64, error) {
	// 自注意力子层
	residual := inputs
	normalized := make([][]float64, len(inputs))
	for i, inp := range inputs {
		normalized[i] = l.Norm1.Forward(inp)
	}
	attnOutput, err := l.SelfAttn.Forward(normalized)
	if err != nil {
		return nil, err
	}
	// 残差连接
	for i := range inputs {
		for j := range inputs[i] {
			attnOutput[i][j] += residual[i][j]
		}
	}

	// 跨模态注意力子层（与文本模态进行交互）
	residual = attnOutput
	normalized = make([][]float64, len(attnOutput))
	for i, inp := range attnOutput {
		normalized[i] = l.Norm2.Forward(inp)
	}
	// 使用文本模态作为query，其他模态作为key/value
	crossOutput, err := l.CrossAttn.Forward(normalized)
	if err != nil {
		return nil, err
	}
	for i := range crossOutput {
		for j := range crossOutput[i] {
			crossOutput[i][j] += residual[i][j]
		}
	}

	// 前馈网络子层
	residual = crossOutput
	normalized = make([][]float64, len(crossOutput))
	for i, inp := range crossOutput {
		normalized[i] = l.Norm3.Forward(inp)
	}
	ffnOutput := l.FFN.Forward(normalized)
	for i := range ffnOutput {
		for j := range ffnOutput[i] {
			ffnOutput[i][j] += residual[i][j]
		}
	}

	return ffnOutput, nil
}

// 多模态对比损失函数
func ContrastiveLoss(textEmbeddings, imageEmbeddings [][]float64, temperature float64) float64 {
	batchSize := len(textEmbeddings)
	similarityMatrix := make([][]float64, batchSize)
	for i := 0; i < batchSize; i++ {
		similarityMatrix[i] = make([]float64, batchSize)
		for j := 0; j < batchSize; j++ {
			similarityMatrix[i][j] = CosineSimilarity(textEmbeddings[i], imageEmbeddings[j])
		}
	}

	// 计算交叉熵损失
	var loss float64
	for i := 0; i < batchSize; i++ {
		var sumExp float64
		for j := 0; j < batchSize; j++ {
			sumExp += math.Exp(similarityMatrix[i][j] / temperature)
		}
		loss -= math.Log(math.Exp(similarityMatrix[i][i]/temperature) / sumExp)
	}

	return loss / float64(batchSize)
}

// 余弦相似度计算
func CosineSimilarity(a, b []float64) float64 {
	var dot, normA, normB float64
	for i := 0; i < len(a); i++ {
		dot += a[i] * b[i]
		normA += a[i] * a[i]
		normB += b[i] * b[i]
	}
	return dot / (math.Sqrt(normA) * math.Sqrt(normB) + 1e-8)
}

// 辅助结构：嵌入层、线性层、层归一化等
type EmbeddingLayer struct {
	Weights [][]float64
	Dim     int
}

func NewEmbeddingLayer(vocabSize, dim int) *EmbeddingLayer {
	weights := make([][]float64, vocabSize)
	for i := 0; i < vocabSize; i++ {
		weights[i] = make([]float64, dim)
		for j := 0; j < dim; j++ {
			weights[i][j] = rand.NormFloat64() * 0.02
		}
	}
	return &EmbeddingLayer{Weights: weights, Dim: dim}
}

func (e *EmbeddingLayer) Forward(idx int) []float64 {
	return e.Weights[idx]
}

type PatchEmbedLayer struct {
	Projection *LinearLayer
}

func NewPatchEmbedLayer(inputDim, outputDim int) *PatchEmbedLayer {
	return &PatchEmbedLayer{
		Projection: NewLinearLayer(inputDim, outputDim),
	}
}

func (p *PatchEmbedLayer) Forward(input []float64) []float64 {
	return p.Projection.Forward(input)
}

type LinearLayer struct {
	Weights [][]float64
	Bias    []float64
	InDim   int
	OutDim  int
}

func NewLinearLayer(inDim, outDim int) *LinearLayer {
	weights := make([][]float64, outDim)
	for i := 0; i < outDim; i++ {
		weights[i] = make([]float64, inDim)
		for j := 0; j < inDim; j++ {
			weights[i][j] = rand.NormFloat64() * math.Sqrt(2.0/float64(inDim))
		}
	}
	return &LinearLayer{
		Weights: weights,
		Bias:    make([]float64, outDim),
		InDim:   inDim,
		OutDim:  outDim,
	}
}

func (l *LinearLayer) Forward(input []float64) []float64 {
	output := make([]float64, l.OutDim)
	for i := 0; i < l.OutDim; i++ {
		var sum float64
		for j := 0; j < l.InDim; j++ {
			sum += l.Weights[i][j] * input[j]
		}
		output[i] = sum + l.Bias[i]
	}
	return output
}

type LayerNorm struct {
	Gamma []float64
	Beta  []float64
	Dim   int
}

func NewLayerNorm(dim int) *LayerNorm {
	gamma := make([]float64, dim)
	beta := make([]float64, dim)
	for i := 0; i < dim; i++ {
		gamma[i] = 1.0
		beta[i] = 0.0
	}
	return &LayerNorm{Gamma: gamma, Beta: beta, Dim: dim}
}

func (l *LayerNorm) Forward(input []float64) []float64 {
	var mean, variance float64
	for _, v := range input {
		mean += v
	}
	mean /= float64(l.Dim)

	for _, v := range input {
		variance += (v - mean) * (v - mean)
	}
	variance /= float64(l.Dim)

	output := make([]float64, l.Dim)
	for i, v := range input {
		output[i] = l.Gamma[i]*(v-mean)/math.Sqrt(variance+1e-5) + l.Beta[i]
	}
	return output
}

type MultiHeadAttention struct {
	NumHeads int
	HeadDim  int
	HiddenDim int
	Wq, Wk, Wv *LinearLayer
	Wo        *LinearLayer
}

func NewMultiHeadAttention(hiddenDim, numHeads int) *MultiHeadAttention {
	headDim := hiddenDim / numHeads
	return &MultiHeadAttention{
		NumHeads:  numHeads,
		HeadDim:   headDim,
		HiddenDim: hiddenDim,
		Wq:        NewLinearLayer(hiddenDim, hiddenDim),
		Wk:        NewLinearLayer(hiddenDim, hiddenDim),
		Wv:        NewLinearLayer(hiddenDim, hiddenDim),
		Wo:        NewLinearLayer(hiddenDim, hiddenDim),
	}
}

func (m *MultiHeadAttention) Forward(inputs [][]float64) ([][]float64, error) {
	seqLen := len(inputs)
	if seqLen == 0 {
		return nil, fmt.Errorf("empty input for attention")
	}

	// 计算Q、K、V
	q := make([][]float64, seqLen)
	k := make([][]float64, seqLen)
	v := make([][]float64, seqLen)
	for i, inp := range inputs {
		q[i] = m.Wq.Forward(inp)
		k[i] = m.Wk.Forward(inp)
		v[i] = m.Wv.Forward(inp)
	}

	// 分头计算注意力
	output := make([][]float64, seqLen)
	for i := range output {
		output[i] = make([]float64, m.HiddenDim)
	}

	var wg sync.WaitGroup
	for h := 0; h < m.NumHeads; h++ {
		wg.Add(1)
		go func(headIdx int) {
			defer wg.Done()
			start := headIdx * m.HeadDim
			end := start + m.HeadDim

			// 计算当前头的注意力分数
			attnScores := make([][]float64, seqLen)
			for i := 0; i < seqLen; i++ {
				attnScores[i] = make([]float64, seqLen)
				for j := 0; j < seqLen; j++ {
					var score float64
					for d := start; d < end; d++ {
						score += q[i][d] * k[j][d]
					}
					attnScores[i][j] = score / math.Sqrt(float64(m.HeadDim))
				}
			}

			// Softmax
			for i := 0; i < seqLen; i++ {
				var maxScore float64
				for j := 0; j < seqLen; j++ {
					if attnScores[i][j] > maxScore {
						maxScore = attnScores[i][j]
					}
				}
				var sumExp float64
				for j := 0; j < seqLen; j++ {
					attnScores[i][j] = math.Exp(attnScores[i][j] - maxScore)
					sumExp += attnScores[i][j]
				}
				for j := 0; j < seqLen; j++ {
					attnScores[i][j] /= sumExp
				}
			}

			// 加权求和
			for i := 0; i < seqLen; i++ {
				for d := start; d < end; d++ {
					var sum float64
					for j := 0; j < seqLen; j++ {
						sum += attnScores[i][j] * v[j][d]
					}
					output[i][d] = sum
				}
			}
		}(h)
	}
	wg.Wait()

	// 输出投影
	for i := 0; i < seqLen; i++ {
		output[i] = m.Wo.Forward(output[i])
	}

	return output, nil
}

type FeedForwardNetwork struct {
	W1 *LinearLayer
	W2 *LinearLayer
}

func NewFeedForwardNetwork(hiddenDim, intermediateDim int) *FeedForwardNetwork {
	return &FeedForwardNetwork{
		W1: NewLinearLayer(hiddenDim, intermediateDim),
		W2: NewLinearLayer(intermediateDim, hiddenDim),
	}
}

func (f *FeedForwardNetwork) Forward(inputs [][]float64) [][]float64 {
	outputs := make([][]float64, len(inputs))
	for i, inp := range inputs {
		intermediate := f.W1.Forward(inp)
		// ReLU激活
		for j := range intermediate {
			if intermediate[j] < 0 {
				intermediate[j] = 0
			}
		}
		outputs[i] = f.W2.Forward(intermediate)
	}
	return outputs
}

// 多模态数据加载器
type MultiModalDataLoader struct {
	TextData    []string
	ImagePaths  []string
	AudioPaths  []string
	BatchSize   int
	PrefetchSize int
	textQueue   chan []string
	imageQueue  chan [][]float64
	audioQueue  chan [][]float64
}

// 异步数据预处理
func (l *MultiModalDataLoader) StartPreprocessing() {
	go func() {
		for i := 0; i < len(l.TextData); i += l.BatchSize {
			batch := l.TextData[i:min(i+l.BatchSize, len(l.TextData))]
			l.textQueue <- batch
		}
		close(l.textQueue)
	}()

	go func() {
		for i := 0; i < len(l.ImagePaths); i += l.BatchSize {
			batch := l.ImagePaths[i:min(i+l.BatchSize, len(l.ImagePaths))]
			images := make([][]float64, len(batch))
			for j, path := range batch {
				// 异步加载和预处理图像
				images[j] = loadAndProcessImage(path)
			}
			l.imageQueue <- images
		}
		close(l.imageQueue)
	}()
}

// 多模态推理服务
type MultiModalService struct {
	encoder *MultiModalEncoder
	cache   *sync.Map
	mu      sync.RWMutex
}

func NewMultiModalService(config *MultiModalConfig) *MultiModalService {
	return &MultiModalService{
		encoder: NewMultiModalEncoder(config),
		cache:   &sync.Map{},
	}
}

// 处理多模态请求
func (s *MultiModalService) ProcessRequest(ctx context.Context, req *MultiModalRequest) (*MultiModalResponse, error) {
	// 1. 检查缓存
	cacheKey := generateCacheKey(req)
	if cached, ok := s.cache.Load(cacheKey); ok {
		return cached.(*MultiModalResponse), nil
	}

	// 2. 解析请求中的多模态数据
	tokens, err := parseMultiModalInput(req)
	if err != nil {
		return nil, fmt.Errorf("parse input error: %v", err)
	}

	// 3. 模型推理
	start := time.Now()
	embeddings, err := s.encoder.Forward(tokens)
	if err != nil {
		return nil, fmt.Errorf("inference error: %v", err)
	}
	inferenceTime := time.Since(start)

	// 4. 后处理
	response := &MultiModalResponse{
		Embeddings:     embeddings,
		InferenceTime:  inferenceTime,
		ModalityStats:  getModalityStats(tokens),
	}

	// 5. 缓存结果（根据TTL）
	s.cache.Store(cacheKey, response)

	return response, nil
}

// 降级策略：当某种模态输入异常时，使用默认值或忽略该模态
func (s *MultiModalService) degradedInference(req *MultiModalRequest) (*MultiModalResponse, error) {
	// 尝试解析所有模态，对失败的模态使用占位符
	tokens, failedModalities := parseWithFallback(req)
	
	// 记录降级日志
	if len(failedModalities) > 0 {
		log.Warnf("degraded inference for modalities: %v", failedModalities)
	}

	return s.encoder.Forward(tokens)
}