package rag

import (
	"bytes"
	"encoding/json"
	"errors"
	"fmt"
	"gf-lt/config"
	"gf-lt/models"
	"log/slog"
	"net/http"

	"github.com/sugarme/tokenizer"
	"github.com/sugarme/tokenizer/pretrained"
	"github.com/yalue/onnxruntime_go"
)

// Embedder defines the interface for embedding text
type Embedder interface {
	Embed(text string) ([]float32, error)
	EmbedSlice(lines []string) ([][]float32, error)
}

// APIEmbedder implements embedder using an API (like Hugging Face, OpenAI, etc.)
type APIEmbedder struct {
	logger *slog.Logger
	client *http.Client
	cfg    *config.Config
}

func NewAPIEmbedder(l *slog.Logger, cfg *config.Config) *APIEmbedder {
	return &APIEmbedder{
		logger: l,
		client: &http.Client{},
		cfg:    cfg,
	}
}

func (a *APIEmbedder) Embed(text string) ([]float32, error) {
	payload, err := json.Marshal(
		map[string]any{"input": text, "encoding_format": "float"},
	)
	if err != nil {
		a.logger.Error("failed to marshal payload", "err", err.Error())
		return nil, err
	}
	req, err := http.NewRequest("POST", a.cfg.EmbedURL, bytes.NewReader(payload))
	if err != nil {
		a.logger.Error("failed to create new req", "err", err.Error())
		return nil, err
	}
	if a.cfg.HFToken != "" {
		req.Header.Add("Authorization", "Bearer "+a.cfg.HFToken)
	}
	resp, err := a.client.Do(req)
	if err != nil {
		a.logger.Error("failed to embed text", "err", err.Error())
		return nil, err
	}
	defer resp.Body.Close()
	if resp.StatusCode != 200 {
		err = fmt.Errorf("non 200 response; code: %v", resp.StatusCode)
		a.logger.Error(err.Error())
		return nil, err
	}
	embResp := &models.LCPEmbedResp{}
	if err := json.NewDecoder(resp.Body).Decode(&embResp); err != nil {
		a.logger.Error("failed to decode embedding response", "err", err.Error())
		return nil, err
	}
	if len(embResp.Data) == 0 || len(embResp.Data[0].Embedding) == 0 {
		err = errors.New("empty embedding response")
		a.logger.Error("empty embedding response")
		return nil, err
	}
	return embResp.Data[0].Embedding, nil
}

func (a *APIEmbedder) EmbedSlice(lines []string) ([][]float32, error) {
	payload, err := json.Marshal(
		map[string]any{"input": lines, "encoding_format": "float"},
	)
	if err != nil {
		a.logger.Error("failed to marshal payload", "err", err.Error())
		return nil, err
	}
	req, err := http.NewRequest("POST", a.cfg.EmbedURL, bytes.NewReader(payload))
	if err != nil {
		a.logger.Error("failed to create new req", "err", err.Error())
		return nil, err
	}
	if a.cfg.HFToken != "" {
		req.Header.Add("Authorization", "Bearer "+a.cfg.HFToken)
	}
	resp, err := a.client.Do(req)
	if err != nil {
		a.logger.Error("failed to embed text", "err", err.Error())
		return nil, err
	}
	defer resp.Body.Close()
	if resp.StatusCode != 200 {
		err = fmt.Errorf("non 200 response; code: %v", resp.StatusCode)
		a.logger.Error(err.Error())
		return nil, err
	}
	embResp := &models.LCPEmbedResp{}
	if err := json.NewDecoder(resp.Body).Decode(&embResp); err != nil {
		a.logger.Error("failed to decode embedding response", "err", err.Error())
		return nil, err
	}
	if len(embResp.Data) == 0 {
		err = errors.New("empty embedding response")
		a.logger.Error("empty embedding response")
		return nil, err
	}

	// Collect all embeddings from the response
	embeddings := make([][]float32, len(embResp.Data))
	for i := range embResp.Data {
		if len(embResp.Data[i].Embedding) == 0 {
			err = fmt.Errorf("empty embedding at index %d", i)
			a.logger.Error("empty embedding", "index", i)
			return nil, err
		}
		embeddings[i] = embResp.Data[i].Embedding
	}

	// Sort embeddings by index to match the order of input lines
	// API responses may not be in order
	for _, data := range embResp.Data {
		if data.Index >= len(embeddings) || data.Index < 0 {
			err = fmt.Errorf("invalid embedding index %d", data.Index)
			a.logger.Error("invalid embedding index", "index", data.Index)
			return nil, err
		}
		embeddings[data.Index] = data.Embedding
	}
	return embeddings, nil
}

// 1. Loading ONNX models locally
// 2. Using a Go ONNX runtime (like gorgonia/onnx or similar)
// 3. Converting text to embeddings without external API calls
type ONNXEmbedder struct {
	session   *onnxruntime_go.DynamicAdvancedSession
	tokenizer *tokenizer.Tokenizer
	dims      int // embedding dimension (e.g., 768)
	logger    *slog.Logger
}

func NewONNXEmbedder(modelPath, tokenizerPath string, dims int, logger *slog.Logger) (*ONNXEmbedder, error) {
	// Load tokenizer using sugarme/tokenizer
	tok, err := pretrained.FromFile(tokenizerPath)
	if err != nil {
		return nil, fmt.Errorf("failed to load tokenizer: %w", err)
	}
	// Create ONNX session
	session, err := onnxruntime_go.NewDynamicAdvancedSession(
		modelPath, // onnx/embedgemma/model_q4.onnx
		[]string{"input_ids", "attention_mask"},
		[]string{"sentence_embedding"},
		nil, // optional options
	)
	if err != nil {
		return nil, fmt.Errorf("failed to create ONNX session: %w", err)
	}
	return &ONNXEmbedder{
		session:   session,
		tokenizer: tok,
		dims:      dims,
		logger:    logger,
	}, nil
}

func (e *ONNXEmbedder) Embed(text string) ([]float32, error) {
	// 1. Tokenize
	encoding, err := e.tokenizer.Encode(text, true) // true = add special tokens
	if err != nil {
		return nil, fmt.Errorf("tokenization failed: %w", err)
	}
	// Convert []int32 to []int64 for ONNX
	inputIDs := make([]int64, len(encoding.GetIDs()))
	for i, id := range encoding.GetIDs() {
		inputIDs[i] = int64(id)
	}
	attentionMask := make([]int64, len(encoding.GetAttentionMask()))
	for i, m := range encoding.GetAttentionMask() {
		attentionMask[i] = int64(m)
	}
	// 2. Create input tensors (shape: [1, seq_len])
	seqLen := int64(len(inputIDs))
	inputIDsTensor, err := onnxruntime_go.NewTensor(onnxruntime_go.NewShape(1, seqLen), inputIDs)
	if err != nil {
		return nil, fmt.Errorf("failed to create input_ids tensor: %w", err)
	}
	defer inputIDsTensor.Destroy()
	maskTensor, err := onnxruntime_go.NewTensor(onnxruntime_go.NewShape(1, seqLen), attentionMask)
	if err != nil {
		return nil, fmt.Errorf("failed to create attention_mask tensor: %w", err)
	}
	defer maskTensor.Destroy()
	// 3. Create output tensor (shape: [1, dims])
	outputTensor, err := onnxruntime_go.NewEmptyTensor[float32](onnxruntime_go.NewShape(1, int64(e.dims)))
	if err != nil {
		return nil, fmt.Errorf("failed to create output tensor: %w", err)
	}
	defer outputTensor.Destroy()
	// 4. Run inference
	err = e.session.Run(
		map[string]*onnxruntime_go.Tensor{
			"input_ids":      inputIDsTensor,
			"attention_mask": maskTensor,
		},
		[]string{"sentence_embedding"},
		[]*onnxruntime_go.Tensor{outputTensor},
	)
	if err != nil {
		return nil, fmt.Errorf("inference failed: %w", err)
	}
	// 5. Extract data
	outputData := outputTensor.GetData()
	// outputTensor is owned by us, but GetData returns a slice that remains valid until Destroy.
	// We need to copy if we want to keep it after Destroy (we defer Destroy, so copy now).
	embedding := make([]float32, len(outputData))
	copy(embedding, outputData)
	return embedding, nil
}

// EmbedSlice (batch) – to be implemented properly
func (e *ONNXEmbedder) EmbedSlice(texts []string) ([][]float32, error) {
	if len(texts) == 0 {
		return nil, nil
	}
	// 1. Tokenize all texts and find max length for padding
	encodings := make([]*tokenizer.Encoding, len(texts))
	maxLen := 0
	for i, txt := range texts {
		enc, err := e.tokenizer.Encode(txt, true)
		if err != nil {
			return nil, fmt.Errorf("tokenization failed at index %d: %w", i, err)
		}
		encodings[i] = enc
		if l := len(enc.GetIDs()); l > maxLen {
			maxLen = l
		}
	}
	// 2. Build padded input_ids and attention_mask (shape: [batch, maxLen])
	batchSize := len(texts)
	inputIDs := make([]int64, batchSize*maxLen)
	attentionMask := make([]int64, batchSize*maxLen)
	for i, enc := range encodings {
		ids := enc.GetIDs()
		mask := enc.GetAttentionMask()
		offset := i * maxLen
		// copy actual tokens
		for j := 0; j < len(ids); j++ {
			inputIDs[offset+j] = int64(ids[j])
			attentionMask[offset+j] = int64(mask[j])
		}
		// remaining positions (padding) are already zero-initialized
	}
	// 3. Create tensors
	inputIDsTensor, err := onnxruntime_go.NewTensor(
		onnxruntime_go.NewShape(int64(batchSize), int64(maxLen)),
		inputIDs,
	)
	if err != nil {
		return nil, err
	}
	defer inputIDsTensor.Destroy()
	maskTensor, err := onnxruntime_go.NewTensor(
		onnxruntime_go.NewShape(int64(batchSize), int64(maxLen)),
		attentionMask,
	)
	if err != nil {
		return nil, err
	}
	defer maskTensor.Destroy()
	outputTensor, err := onnxruntime_go.NewEmptyTensor[float32](
		onnxruntime_go.NewShape(int64(batchSize), int64(e.dims)),
	)
	if err != nil {
		return nil, err
	}
	defer outputTensor.Destroy()
	// 4. Run
	err = e.session.Run(
		map[string]*onnxruntime_go.Tensor{
			"input_ids":      inputIDsTensor,
			"attention_mask": maskTensor,
		},
		[]string{"sentence_embedding"},
		[]*onnxruntime_go.Tensor{outputTensor},
	)
	if err != nil {
		return nil, err
	}
	// 5. Extract batch results
	outputData := outputTensor.GetData()
	embeddings := make([][]float32, batchSize)
	for i := 0; i < batchSize; i++ {
		start := i * e.dims
		emb := make([]float32, e.dims)
		copy(emb, outputData[start:start+e.dims])
		embeddings[i] = emb
	}
	return embeddings, nil
}