package multimodal

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

// Multi-modal Transformer configuration
type MultiModalConfig struct {
	HiddenDim     int     // Hidden layer dimension
	NumLayers     int     // Number of Transformer layers
	NumHeads      int     // Number of attention heads
	MaxSeqLen     int     // Maximum sequence length
	VocabSize     int     // Vocabulary size
	ImagePatchDim int     // Image patch dimension
	AudioFreqDim  int     // Audio frequency dimension
	DropoutRate   float64 // Dropout probability
}

// Modality type enumeration
type Modality int

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

// Multi-modal token structure
type MultiModalToken struct {
	Embedding    []float64 // Token embedding vector
	ModalityType Modality  // Modality type
	Position     int       // Position index
	IsSpecial    bool      // Whether it is a special token
}

// Multi-modal encoder
type MultiModalEncoder struct {
	Config        *MultiModalConfig
	TextEmbed     *EmbeddingLayer   // Text embedding layer
	ImageEmbed    *PatchEmbedLayer  // Image patch embedding layer
	AudioEmbed    *PatchEmbedLayer  // Audio patch embedding layer
	VideoEmbed    *PatchEmbedLayer  // Video frame embedding layer
	ModalityEmbed *EmbeddingLayer   // Modality type embedding
	PositionEmbed *EmbeddingLayer   // Position encoding embedding
	TransformerLayers []*TransformerLayer // Transformer layers
	LayerNorm     *LayerNorm         // Layer normalization
	OutputProj    *LinearLayer       // Output projection
}

// Create a new multi-modal encoder
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), // Video considers temporal dimension
		ModalityEmbed: NewEmbeddingLayer(4, config.HiddenDim), // 4 modalities
		PositionEmbed: NewEmbeddingLayer(config.MaxSeqLen, config.HiddenDim),
		LayerNorm:     NewLayerNorm(config.HiddenDim),
		OutputProj:    NewLinearLayer(config.HiddenDim, config.HiddenDim),
	}

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

	return encoder
}

// Forward: Encode multi-modal input into a unified representation
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. Get the embedding for each token
	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. Add modality type embedding
		modalityEmbed := e.ModalityEmbed.Forward(int(token.ModalityType))
		for j := 0; j < len(tokenEmbed); j++ {
			tokenEmbed[j] += modalityEmbed[j]
		}

		// 3. Add position encoding
		posEmbed := e.PositionEmbed.Forward(token.Position)
		for j := 0; j < len(tokenEmbed); j++ {
			tokenEmbed[j] += posEmbed[j]
		}

		hiddenStates[i] = tokenEmbed
	}

	// 4. Pass through all Transformer layers
	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. Final layer normalization and projection
	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]
		}
	}

	// Average as sequence representation
	for j := 0; j < len(output); j++ {
		output[j] /= float64(seqLen)
	}

	return output, nil
}

// Transformer layer
type TransformerLayer struct {
	Config    *MultiModalConfig
	SelfAttn  *MultiHeadAttention
	CrossAttn *MultiHeadAttention // Cross-modal attention
	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) {
	// Self-attention sublayer
	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
	}
	// Residual connection
	for i := range inputs {
		for j := range inputs[i] {
			attnOutput[i][j] += residual[i][j]
		}
	}

	// Cross-modal attention sublayer (interact with text modality)
	residual = attnOutput
	normalized = make([][]float64, len(attnOutput))
	for i, inp := range attnOutput {
		normalized[i] = l.Norm2.Forward(inp)
	}
	// Use text modality as query, other modalities as 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]
		}
	}

	// Feed-forward network sublayer
	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
}

// Multi-modal contrastive loss function
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])
		}
	}

	// Compute cross-entropy loss
	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)
}

// Cosine similarity computation
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)
}

// Helper structures: Embedding layer, linear layer, layer normalization, etc.
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")
	}

	// Compute 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)
	}

	// Compute attention per head
	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

			// Compute attention scores for the current head
			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
				}
			}

			// Weighted sum
			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()

	// Output projection
	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 activation
		for j := range intermediate {
			if intermediate[j] < 0 {
				intermediate[j] = 0
			}
		}
		outputs[i] = f.W2.Forward(intermediate)
	}
	return outputs
}

// Multi-modal data loader
type MultiModalDataLoader struct {
	TextData     []string
	ImagePaths   []string
	AudioPaths   []string
	BatchSize    int
	PrefetchSize int
	textQueue    chan []string
	imageQueue   chan [][]float64
	audioQueue   chan [][]float64
}

// Asynchronous data preprocessing
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 {
				// Asynchronously load and preprocess images
				images[j] = loadAndProcessImage(path)
			}
			l.imageQueue <- images
		}
		close(l.imageQueue)
	}()
}

// Multi-modal inference service
type MultiModalService struct {
	encoder *MultiModalEncoder
	cache   *sync.Map
	mu      sync.RWMutex
}

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

// Process multi-modal request
func (s *MultiModalService) ProcessRequest(ctx context.Context, req *MultiModalRequest) (*MultiModalResponse, error) {
	// 1. Check cache
	cacheKey := generateCacheKey(req)
	if cached, ok := s.cache.Load(cacheKey); ok {
		return cached.(*MultiModalResponse), nil
	}

	// 2. Parse multi-modal data from request
	tokens, err := parseMultiModalInput(req)
	if err != nil {
		return nil, fmt.Errorf("parse input error: %v", err)
	}

	// 3. Model inference
	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. Post-processing
	response := &MultiModalResponse{
		Embeddings:    embeddings,
		InferenceTime: inferenceTime,
		ModalityStats: getModalityStats(tokens),
	}

	// 5. Cache result (based on TTL)
	s.cache.Store(cacheKey, response)

	return response, nil
}

// Degradation strategy: When input for a modality is abnormal, use default values or ignore that modality
func (s *MultiModalService) degradedInference(req *MultiModalRequest) (*MultiModalResponse, error) {
	// Attempt to parse all modalities, using placeholders for failed modalities
	tokens, failedModalities := parseWithFallback(req)
	
	// Log degradation
	if len(failedModalities) > 0 {
		log.Warnf("degraded inference for modalities: %v", failedModalities)
	}

	return s.encoder.Forward(tokens)
}