//go:build !noonnx

package compactonnx

import (
	"bytes"
	"encoding/json"
	"errors"
	"fmt"
	"io"
	"math"
	"os"
	"path/filepath"
	"runtime"
	"strconv"
	"sync"

	"huggingface.co/turenlabs/Vigil/source/pkg/compactmodel"
	ort "github.com/yalue/onnxruntime_go"
)

var compactRuntime struct {
	sync.Mutex
	initialized bool
	libraryPath string
	version     string
}

// Model owns one immutable in-memory ONNX session and its bound tensors.
type Model struct {
	metadata Metadata
	markers  Markers
	session  *ort.AdvancedSession
	input    *ort.Tensor[float32]
	output   *ort.Tensor[float32]
	mu       sync.Mutex
	closed   bool
}

// Load validates the adjacent pair, initializes ONNX Runtime, validates the
// graph interface, and creates a session directly from the verified bytes.
func Load(config Config) (*Model, error) {
	pair, err := readArtifactPair(config.ModelPath, config.MetadataPath)
	if err != nil {
		return nil, err
	}
	metadata, err := decodeMetadata(pair.metadata)
	if err != nil {
		return nil, err
	}
	if err := validateMetadata(metadata, pair); err != nil {
		return nil, err
	}
	runtimeVersion, err := initializeRuntime(config.RuntimeLibrary)
	if err != nil {
		return nil, err
	}
	if err := validateGraph(pair.model, metadata); err != nil {
		return nil, err
	}

	input, err := ort.NewEmptyTensor[float32](ort.NewShape(1, compactmodel.TotalFeatures))
	if err != nil {
		return nil, fmt.Errorf("create compact ONNX input: %w", err)
	}
	output, err := ort.NewEmptyTensor[float32](ort.NewShape(1, 1))
	if err != nil {
		input.Destroy()
		return nil, fmt.Errorf("create compact ONNX output: %w", err)
	}
	session, err := ort.NewAdvancedSessionWithONNXData(
		pair.model,
		[]string{InputName},
		[]string{OutputName},
		[]ort.Value{input},
		[]ort.Value{output},
		nil,
	)
	if err != nil {
		output.Destroy()
		input.Destroy()
		return nil, fmt.Errorf("create compact ONNX session: %w", err)
	}

	return &Model{
		metadata: metadata,
		markers: Markers{
			ModelLoaded:                 true,
			ModelType:                   "compact_hashed_linear_onnx",
			ModelSHA256:                 pair.modelSHA256,
			ModelSizeBytes:              int64(len(pair.model)),
			MetadataSHA256:              pair.metadataSHA256,
			PreprocessingContractSHA256: compactmodel.ContractSHA256(),
			Runtime:                     "onnxruntime",
			RuntimeVersion:              runtimeVersion,
			FallbackUsed:                false,
		},
		session: session,
		input:   input,
		output:  output,
	}, nil
}

func decodeMetadata(data []byte) (Metadata, error) {
	decoder := json.NewDecoder(bytes.NewReader(data))
	decoder.DisallowUnknownFields()
	var metadata Metadata
	if err := decoder.Decode(&metadata); err != nil {
		return Metadata{}, fmt.Errorf("decode compact metadata: %w", err)
	}
	var trailing any
	if err := decoder.Decode(&trailing); !errors.Is(err, io.EOF) {
		if err == nil {
			return Metadata{}, errors.New("decode compact metadata: trailing JSON value")
		}
		return Metadata{}, fmt.Errorf("decode compact metadata trailer: %w", err)
	}
	return metadata, nil
}

func validateMetadata(metadata Metadata, pair artifactPair) error {
	if metadata.SchemaVersion != MetadataSchema {
		return fmt.Errorf("compact metadata schema mismatch: got %q", metadata.SchemaVersion)
	}
	if metadata.Model.SHA256 != pair.modelSHA256 {
		return errors.New("compact metadata model SHA-256 mismatch")
	}
	if metadata.Model.SizeBytes != int64(len(pair.model)) {
		return errors.New("compact metadata model size mismatch")
	}
	if metadata.Model.SizeBytes <= 0 || metadata.Model.SizeBytes > MaxModelBytes {
		return errors.New("compact metadata model size is outside the release limit")
	}
	if metadata.Model.Family != "hashed-word-char-linear" {
		return errors.New("compact metadata model family mismatch")
	}
	if metadata.Model.InputName != InputName || metadata.Model.InputFeatures != compactmodel.TotalFeatures {
		return errors.New("compact metadata input contract mismatch")
	}
	if metadata.Model.OutputName != OutputName || metadata.Model.OutputElements != 1 {
		return errors.New("compact metadata output contract mismatch")
	}
	if err := compactmodel.ValidateMetadata(metadata.preprocessingMetadata()); err != nil {
		return err
	}
	if math.IsNaN(metadata.Threshold) || math.IsInf(metadata.Threshold, 0) || metadata.Threshold <= 0 || metadata.Threshold >= 1 {
		return errors.New("compact metadata threshold must be finite and between zero and one")
	}
	return nil
}

func validateGraph(data []byte, metadata Metadata) error {
	inputs, outputs, err := ort.GetInputOutputInfoWithONNXData(data)
	if err != nil {
		return fmt.Errorf("inspect compact ONNX graph: %w", err)
	}
	if len(inputs) != 1 || !validTensor(inputs[0], InputName, compactmodel.TotalFeatures) {
		return fmt.Errorf("compact ONNX input mismatch: want one float tensor %s [batch,%d]", InputName, compactmodel.TotalFeatures)
	}
	if len(outputs) != 1 || !validTensor(outputs[0], OutputName, 1) {
		return fmt.Errorf("compact ONNX output mismatch: want one float tensor %s [batch,1]", OutputName)
	}
	modelMetadata, err := ort.GetModelMetadataWithONNXData(data)
	if err != nil {
		return fmt.Errorf("inspect compact ONNX metadata: %w", err)
	}
	defer modelMetadata.Destroy()
	required := map[string]string{
		"model_family":        "hashed-word-char-linear",
		"word_features":       strconv.Itoa(compactmodel.WordFeatures),
		"char_features":       strconv.Itoa(compactmodel.CharFeatures),
		"structured_features": strconv.Itoa(compactmodel.StructuredFeatures),
		"preprocessing":       "sklearn-murmurhash3 word(1,2)+char(4)+package-structure-v1",
	}
	for key, want := range required {
		got, found, err := modelMetadata.LookupCustomMetadataMap(key)
		if err != nil || !found || got != want {
			return fmt.Errorf("compact ONNX internal metadata mismatch for %s", key)
		}
	}
	thresholdText, found, err := modelMetadata.LookupCustomMetadataMap("threshold")
	if err != nil || !found {
		return errors.New("compact ONNX internal threshold metadata is missing")
	}
	threshold, err := strconv.ParseFloat(thresholdText, 64)
	if err != nil || threshold != metadata.Threshold {
		return errors.New("compact ONNX internal threshold metadata mismatch")
	}
	return nil
}

func validTensor(info ort.InputOutputInfo, name string, width int) bool {
	if info.Name != name || info.OrtValueType != ort.ONNXTypeTensor || info.DataType != ort.TensorElementDataTypeFloat {
		return false
	}
	return len(info.Dimensions) == 2 &&
		(info.Dimensions[0] == -1 || info.Dimensions[0] == 1) &&
		info.Dimensions[1] == int64(width)
}

func initializeRuntime(explicit string) (string, error) {
	compactRuntime.Lock()
	defer compactRuntime.Unlock()
	if compactRuntime.initialized {
		if explicit != "" {
			resolved, err := canonicalRuntimeLibrary(explicit)
			if err != nil {
				return "", err
			}
			if resolved != compactRuntime.libraryPath {
				return "", errors.New("ONNX Runtime is already initialized from a different library")
			}
		}
		return compactRuntime.version, nil
	}
	if ort.IsInitialized() {
		return "", errors.New("ONNX Runtime was initialized outside the compact loader; exact runtime provenance is unavailable")
	}
	library := explicit
	if library == "" {
		library = os.Getenv("ONNXRUNTIME_LIB")
	}
	if library == "" {
		library = discoverRuntimeLibrary()
	}
	if library == "" {
		return "", errors.New("ONNX Runtime library is required; compact scoring has no fallback")
	}
	resolved, err := canonicalRuntimeLibrary(library)
	if err != nil {
		return "", err
	}
	ort.SetSharedLibraryPath(resolved)
	if err := ort.InitializeEnvironment(); err != nil {
		return "", fmt.Errorf("initialize ONNX Runtime: %w", err)
	}
	compactRuntime.initialized = true
	compactRuntime.libraryPath = resolved
	compactRuntime.version = ort.GetVersion()
	return compactRuntime.version, nil
}

func canonicalRuntimeLibrary(path string) (string, error) {
	if hasTraversal(path) {
		return "", errors.New("ONNX Runtime path contains traversal")
	}
	abs, err := filepath.Abs(path)
	if err != nil {
		return "", fmt.Errorf("resolve ONNX Runtime path: %w", err)
	}
	resolved, err := filepath.EvalSymlinks(abs)
	if err != nil {
		return "", fmt.Errorf("resolve ONNX Runtime library: %w", err)
	}
	info, err := os.Lstat(resolved)
	if err != nil {
		return "", fmt.Errorf("inspect ONNX Runtime library: %w", err)
	}
	if info.Mode()&os.ModeSymlink != 0 || !info.Mode().IsRegular() {
		return "", errors.New("ONNX Runtime library is not a regular file")
	}
	return resolved, nil
}

func discoverRuntimeLibrary() string {
	name := "libonnxruntime.so"
	switch runtime.GOOS {
	case "darwin":
		name = "libonnxruntime.dylib"
	case "windows":
		name = "onnxruntime.dll"
	}
	var candidates []string
	if executable, err := os.Executable(); err == nil {
		directory := filepath.Dir(executable)
		candidates = append(candidates, filepath.Join(directory, name), filepath.Join(directory, "lib", name))
	}
	if working, err := os.Getwd(); err == nil {
		candidates = append(candidates, filepath.Join(working, name), filepath.Join(working, "lib", name))
	}
	switch runtime.GOOS {
	case "darwin":
		candidates = append(candidates, filepath.Join("/opt/homebrew/lib", name), filepath.Join("/usr/local/lib", name))
	case "linux":
		candidates = append(candidates, filepath.Join("/usr/lib", name), filepath.Join("/usr/local/lib", name), filepath.Join("/usr/lib/x86_64-linux-gnu", name), filepath.Join("/usr/lib/aarch64-linux-gnu", name))
	}
	for _, candidate := range candidates {
		if _, err := os.Stat(candidate); err == nil {
			return candidate
		}
	}
	return ""
}

// Score vectorizes and scores one whole package. There is no fallback branch.
func (model *Model) Score(pkg compactmodel.Package) (float64, error) {
	vector, err := compactmodel.Vectorize(pkg)
	if err != nil {
		return 0, err
	}
	model.mu.Lock()
	defer model.mu.Unlock()
	if model.closed || model.session == nil {
		return 0, errors.New("compact ONNX model is closed")
	}
	copy(model.input.GetData(), vector)
	if err := model.session.Run(); err != nil {
		return 0, fmt.Errorf("run compact ONNX: %w", err)
	}
	values := model.output.GetData()
	if len(values) != 1 {
		return 0, fmt.Errorf("compact ONNX output length mismatch: got %d", len(values))
	}
	probability := float64(values[0])
	if math.IsNaN(probability) || math.IsInf(probability, 0) || probability < 0 || probability > 1 {
		return 0, errors.New("compact ONNX output is not a finite probability")
	}
	return probability, nil
}

func (model *Model) Threshold() float64 { return model.metadata.Threshold }
func (model *Model) Markers() Markers   { return model.markers }

// Close releases the session and bound tensors. The process-global runtime is
// intentionally retained because other loaded sessions may still use it.
func (model *Model) Close() error {
	model.mu.Lock()
	defer model.mu.Unlock()
	if model.closed {
		return nil
	}
	model.closed = true
	var errs []error
	if model.session != nil {
		errs = append(errs, model.session.Destroy())
	}
	if model.output != nil {
		errs = append(errs, model.output.Destroy())
	}
	if model.input != nil {
		errs = append(errs, model.input.Destroy())
	}
	return errors.Join(errs...)
}