export type SupportedTypedArray = Int8Array | Uint8Array | Int16Array | Uint16Array | Int32Array | Uint32Array | Float32Array | Float64Array | BigInt64Array | BigUint64Array;
export type elementTypeString = "u8" | "u32" | "u16" | "u64" | "i8" | "i64" | "i32" | "i16" | "f64" | "f32" | "string";
export type OVAny = string | number | boolean;
/**
 * Core represents an OpenVINO runtime Core entity.
 *
 * User applications can create several Core class instances.
 * In that case the device plugins will still share underlying resources
 * (such as OCL context) in per-device singleton.
 */
export interface Core {
    /**
     * It constructs a new Core object.
     */
    new (): Core;
    /**
     * Registers extensions to a Core object.
     * @param libraryPath Path to the library with ov::Extension.
     */
    addExtension(libraryPath: string): void;
    /**
     * Asynchronously creates a compiled model from a source {@link Model} object.
     *
     * You can create as many compiled models as needed and use them
     * simultaneously (up to the limitation of the hardware resources).
     * @param model The {@link Model} object acquired from {@link Core.readModel}
     * @param deviceName The name of a device, to which the model is loaded.
     * @param config An object with the key-value pairs
     * (property name, property value): relevant only for this load operation.
     */
    compileModel(model: Model, deviceName: string, config?: Record<string, OVAny>): Promise<CompiledModel>;
    /**
     * Asynchronously reads a model and creates a compiled model
     * from the IR/ONNX/PDPD file.
     *
     * This can be more efficient
     * than using {@link Core.readModel} + core.compileModel(Model) flow
     * especially for cases when caching is enabled and a cached model is
     * available. You can create as many compiled models as needed and use
     * them simultaneously (up to the limitation of the hardware resources).
     * @param modelPath The path to a model.
     * @param deviceName The name of a device, to which a model is loaded.
     * @param config An object with the key-value pairs
     * (property name, property value): relevant only for this load operation.
     */
    compileModel(modelPath: string, deviceName: string, config?: Record<string, OVAny>): Promise<CompiledModel>;
    /**
     * A synchronous version of {@link Core.compileModel}.
     * It creates a compiled model from a source model object.
     */
    compileModelSync(model: Model, deviceName: string, config?: Record<string, OVAny>): CompiledModel;
    /**
     * A synchronous version of {@link Core.compileModel}.
     * It reads a model and creates a compiled model from the IR/ONNX/PDPD file.
     */
    compileModelSync(modelPath: string, deviceName: string, config?: Record<string, OVAny>): CompiledModel;
    /**
     * It returns a list of available inference devices.
     * Core objects go over all registered plugins.
     * @returns The list of devices may include any of the following: CPU, GPU.0,
     * GPU.1, NPU… If there is more than one device of a specific type, they are
     * enumerated with .# suffix. Such enumerated devices can later be used
     * as a device name in all Core methods, like compile_model, query_model,
     * set_property and so on.
     */
    getAvailableDevices(): string[];
    /**
     * It gets the properties dedicated to device behaviour.
     * @param propertyName A property name.
     */
    getProperty(propertyName: string): OVAny;
    /**
     * It gets the properties dedicated to device behaviour.
     * @param deviceName The name of a device, the properties of which you get.
     * @param propertyName Property name.
     */
    getProperty(deviceName: string, propertyName: string): OVAny;
    /**
     * It returns information on the version of device plugins.
     * @param deviceName A device name to identify a plugin.
     */
    getVersions(deviceName: string): {
        [deviceName: string]: {
            buildNumber: string;
            description: string;
        };
    };
    /**
     * Asynchronously imports a previously exported compiled model from a Tensor.
     * @param modelTensor The tensor containing the exported compiled model data.
     * The tensor must be of type u8 with shape [byte_count].
     * @param device The name of a device, for which you import a compiled model.
     * Note, if the device name was not used to compile the original model,
     * an exception is thrown.
     * @param config An object with the key-value pairs
     * (property name, property value): relevant only for this load operation.
     */
    importModel(modelTensor: Tensor, device: string, config?: Record<string, OVAny>): Promise<CompiledModel>;
    /**
     * Asynchronously imports a previously exported compiled model.
     * @param modelStream The input stream that contains a model,
     * previously exported with the {@link CompiledModel.exportModelSync} method.
     * @param device The name of a device, for which you import a compiled model.
     * Note, if the device name was not used to compile the original model,
     * an exception is thrown.
     * @param config An object with the key-value pairs
     * (property name, property value): relevant only for this load operation.
     */
    importModel(modelStream: Buffer, device: string, config?: Record<string, OVAny>): Promise<CompiledModel>;
    /**
     * A synchronous version of {@link Core.importModel}.
     * It imports a previously exported compiled model from a Tensor.
     */
    importModelSync(modelTensor: Tensor, device: string, config?: Record<string, OVAny>): CompiledModel;
    /**
     * A synchronous version of {@link Core.importModel}.
     * It imports a previously exported compiled model.
     */
    importModelSync(modelStream: Buffer, device: string, config?: Record<string, OVAny>): CompiledModel;
    /**
     * It reads models from the IR / ONNX / PDPD / TF and TFLite formats.
     * @param modelPath The path to a model
     * in the IR / ONNX / PDPD / TF or TFLite format.
     * @param weightsPath The path to a data file for the IR format (.bin):
     * if the path is empty, it tries to read the bin file with the same name
     * as xml and if the bin file with the same name was not found, it loads
     * IR without weights.
     * For the ONNX format (.onnx), the weights parameter is not used.
     * For the PDPD format (.pdmodel), the weights parameter is not used.
     * For the TF format (.pb), the weights parameter is not used.
     * For the TFLite format (*.tflite), the weights parameter is not used.
     */
    readModel(modelPath: string, weightsPath?: string): Promise<Model>;
    /**
     * It reads models from IR / ONNX / PDPD / TF and TFLite formats.
     * @param model A string with model in IR / ONNX / PDPD / TF
     * and TFLite format.
     * @param weights Tensor with weights. Reading ONNX / PDPD / TF
     * and TFLite models doesn’t support loading weights from weights tensors.
     */
    readModel(model: string, weights: Tensor): Promise<Model>;
    /**
     * It reads models from the IR / ONNX / PDPD / TF and TFLite formats.
     * @param modelBuffer Binary data with a model
     * in the IR / ONNX / PDPD / TF or TFLite format.
     * @param weightsBuffer Binary data with tensor data.
     */
    readModel(modelBuffer: Uint8Array, weightsBuffer?: Uint8Array): Promise<Model>;
    /**
     * A synchronous version of {@link Core.readModel}.
     * It reads models from the IR / ONNX / PDPD / TF and TFLite formats.
     */
    readModelSync(modelPath: string, weightsPath?: string): Model;
    /**
     * A synchronous version of {@link Core.readModel}.
     * It reads models from the IR / ONNX / PDPD / TF and TFLite formats.
     */
    readModelSync(model: string, weights: Tensor): Model;
    /**
     * A synchronous version of {@link Core.readModel}.
     * It reads models from the IR / ONNX / PDPD / TF and TFLite formats.
     */
    readModelSync(modelBuffer: Uint8Array, weightsBuffer?: Uint8Array): Model;
    /**
     * It sets the properties.
     * @param properties An object with the property name - property value pairs.
     */
    setProperty(properties: Record<string, OVAny>): void;
    /**
     * It sets the properties for a device.
     * @param deviceName The name of a device.
     * @param properties An object with the property name - property value pairs.
     */
    setProperty(deviceName: string, properties: Record<string, OVAny>): void;
    /**
     * It queries the device if it supports specified model with the specified
     * properties.
     * @param model The passed model to query the property.
     * @param deviceName The name of a device.
     * @param properties An object with the property name - property value pairs.
     * An object with the key-value pairs. (property name, property value).
     */
    queryModel(model: Model, deviceName: string, properties?: Record<string, OVAny>): {
        [key: string]: string;
    };
}
export interface Model {
    /**
     * It constructs a default Model object. Use {@link Core.readModel}
     * to read Model from supported file format.
     */
    new (): Model;
    /**
     * It returns a cloned model.
     */
    clone(): Model;
    /**
     * It gets the friendly name for a model. If a friendly name is not set
     * via {@link Model.setFriendlyName}, a unique model name is returned.
     * @returns A string with a friendly name of the model.
     */
    getFriendlyName(): string;
    /**
     * It gets the unique name of the model.
     * @returns A string with the name of the model.
     */
    getName(): string;
    /**
     * It returns the operators(nodes) in the model.
     * @returns An array of Node objects.
     */
    getOps(): Node[];
    /**
     * It returns the shape of the element at the specified index.
     * @param index The index of the element.
     */
    getOutputShape(index: number): number[];
    /**
     * It returns the number of the model outputs.
     */
    getOutputSize(): number;
    /**
     * It gets the element type of a specific output of the model.
     * @param index The index of the output.
     */
    getOutputElementType(index: number): string;
    /**
     * It gets the input of the model.
     * If a model has more than one input, this method throws an exception.
     */
    input(): Output;
    /**
     * It gets the input of the model identified by the tensor name.
     * @param name The tensor name.
     */
    input(name: string): Output;
    /**
     * It gets the input of the model identified by the index.
     * @param index The index of the input.
     */
    input(index: number): Output;
    /**
     * It returns true if any of the op’s defined in the model contains a partial
     * shape.
     */
    isDynamic(): boolean;
    /**
     * It gets the output of the model.
     * If a model has more than one output, this method throws an exception.
     */
    output(): Output;
    /**
     * It gets the output of the model identified by the tensor name.
     * @param name The tensor name.
     */
    output(name: string): Output;
    /**
     * It gets the output of the model identified by the index.
     * @param index The index of the input.
     */
    output(index: number): Output;
    /**
     * Sets a friendly name for the model. This does not overwrite the unique
     * model name and is retrieved via {@link Model.getFriendlyName}.
     * Mainly used for debugging.
     * @param name The string to set as the friendly name.
     */
    setFriendlyName(name: string): void;
    /** Reshapes model input.
     * @param partialShape The {@link PartialShape} object
     * or its string representation to reshape the model input.
     * @param variablesShapes New shapes for variables.
     */
    reshape(partialShape: PartialShape | string, variablesShapes?: Record<string, PartialShape | string>): Model;
    /** Reshapes model inputs.
     * @param partialShapes A Map with partial shapes.
     * @param key The key is a model input index, tensor name
     * or {@link Output} object.
     * @param value The value is a {@link PartialShape} or
     * a string representation of {@link PartialShape}.
     * @param variablesShapes New shapes for variables.
     */
    reshape(partialShapes: Map<number | string | Output, PartialShape | string>, variablesShapes?: Record<string, PartialShape | string>): Model;
    /**
     * It gets all the model inputs as an array.
     */
    inputs: Output[];
    /**
     * It gets all the model outputs as an array
     */
    outputs: Output[];
}
export interface Node {
    /**
     * It constructs a default Node object.
     */
    new (): Node;
    /**
     * It gets the unique name of the node.
     * @returns A string with the name of the node.
     */
    getName(): string;
}
/**
 * CompiledModel represents a model that is compiled for a specific device
 * by applying multiple optimization transformations,
 * then mapping to compute kernels.
 */
export interface CompiledModel {
    /**
     * It constructs a default CompiledModel object. Use {@link Core.compileModel}
     * or {@link Core.importModel} to get model compiled for a specific device.
     */
    new (): CompiledModel;
    /** It gets all inputs of a compiled model. */
    inputs: Output[];
    /** It gets all outputs of a compiled model. */
    outputs: Output[];
    /**
     * It gets the property for the current compiled model.
     * @param propertyName A string to get the property value.
     * @returns The property value.
     */
    getProperty(propertyName: string): OVAny;
    /**
     * It creates an inference request object used to infer the compiled model.
     * @return {InferRequest}
     */
    createInferRequest(): InferRequest;
    /**
     * It exports the compiled model to binary data.
     * @remarks
     * The exported model can be imported via the {@link Core.importModelSync}.
     * @return {Buffer} The binary data that contains the compiled model.
     */
    exportModelSync(): Buffer;
    /**
     * It gets a single output of a compiled model.
     * If a model has more than one output, this method throws an exception.
     * @returns {Output} A compiled model output.
     */
    output(): Output;
    /**
     * It gets output of a compiled model identified by an index.
     * @param index An output tensor index.
     * @returns {Output} A compiled model output.
     */
    output(index: number): Output;
    /**
     * It gets output of a compiled model identified by a tensorName.
     * @param name An output tensor name.
     * @returns {Output} A compiled model output.
     */
    output(name: string): Output;
    /**
     * It gets a single input of a compiled model.
     * If a model has more than one input, this method throws an exception.
     * @returns {Output} A compiled model input.
     */
    input(): Output;
    /**
     * It gets input of a compiled model identified by an index.
     * @param index An input tensor index.
     * @returns {Output} A compiled model input.
     */
    input(index: number): Output;
    /**
     * It gets input of a compiled model identified by a tensorName.
     * @param name An input tensor name.
     * @returns {Output} A compiled model input.
     */
    input(name: string): Output;
    /**
     * It sets properties for the current compiled model. Properties
     * can be retrieved via {@link CompiledModel.getProperty}.
     * @param property An object with the key-value pairs.
     * (property name, property value)
     */
    setProperty(properties: Record<string, OVAny>): void;
}
/**
 * The {@link Tensor} is a lightweight class that represents data used for
 * inference.
 *
 * @remarks
 * The tensor memory is shared with the TypedArray. That is,
 * the responsibility for maintaining the reference to the TypedArray lies with
 * the user. Any action performed on the TypedArray will be reflected in this
 * tensor memory.
 */
export interface Tensor {
    /**
     * It constructs a tensor using the element type and shape. The new tensor
     * data will be allocated by default.
     * @param type The element type of the new tensor.
     * @param shape The shape of the new tensor.
     */
    new (type: element | elementTypeString, shape: number[]): Tensor;
    /**
     * It constructs a tensor using the element type and shape. The new tensor
     * wraps allocated host memory.
     * @param type The element type of the new tensor.
     * @param shape The shape of the new tensor.
     * @param tensorData A subclass of TypedArray that will be wrapped
     * by a {@link Tensor}.
     */
    new (type: element | elementTypeString, shape: number[], tensorData: SupportedTypedArray): Tensor;
    /**
     * It constructs a tensor using the element type and shape. The strings from
     * the array are used to fill the new tensor. Each element of a string tensor
     * is a string of arbitrary length, including an empty string.
     */
    new (tensorData: string[]): Tensor;
    /**
     * This property provides access to the tensor's data.
     *
     * Its getter returns a subclass of TypedArray that corresponds to the
     * tensor element type, e.g. Float32Array corresponds to float32. The
     * content of the TypedArray subclass is a copy of the tensor underlaying
     * memory.
     *
     * Its setter fills the underlaying tensor memory by copying the binary data
     * buffer from the TypedArray subclass. An exception will be thrown if the
     * size or type of array does not match the tensor.
     */
    data: SupportedTypedArray;
    /**
     * It gets the tensor element type.
     */
    getElementType(): element;
    /**
     * It gets tensor data.
     * @returns A subclass of TypedArray corresponding to the tensor
     * element type, e.g. Float32Array corresponds to float32.
     */
    getData(): SupportedTypedArray;
    /**
     * It gets the tensor shape.
     */
    getShape(): number[];
    /**
     * It gets the tensor size as a total number of elements.
     */
    getSize(): number;
    /**
     * Reports whether the tensor is continuous or not.
     */
    isContinuous(): boolean;
    /**
     * Sets the shape of the tensor.
     * @param shape - Array of dimensions for the new shape
     */
    setShape(shape: number[]): void;
    /**
     * Copies data from this tensor to a destination tensor.
     * The destination tensor must have the same shape and element type.
     * @param tensor The destination tensor to which the data will be copied.
     */
    copyTo(tensor: Tensor): void;
}
/**
 * The {@link InferRequest} object is used to make predictions and can be run in
 * asynchronous or synchronous manners.
 */
export interface InferRequest {
    /**
     * It constructs a default InferRequest object.
     * Use {@link CompiledModel.createInferRequest}
     * to get InferRequest object specific for a given deployed model.
     */
    new (): InferRequest;
    /**
     * It infers specified input(s) in the synchronous mode.
     * @remarks
     * Inputs have to be specified earlier using {@link InferRequest.setTensor}
     * or {@link InferRequest.setInputTensor}
     */
    infer(): {
        [outputName: string]: Tensor;
    };
    /**
     * It infers specified input(s) in the synchronous mode.
     * @param inputData An object with the key-value pairs where the key is the
     * input name and value can be either a tensor or a TypedArray.
     * TypedArray will be wrapped into Tensor underneath using the input shape
     * and element type of the deployed model.
     */
    infer(inputData: {
        [inputName: string]: Tensor | SupportedTypedArray;
    }): {
        [outputName: string]: Tensor;
    };
    /**
     * It infers specified input(s) in the synchronous mode.
     * @param inputData An array with tensors or TypedArrays. TypedArrays will be
     * wrapped into Tensors underneath using the input shape and element type
     * of the deployed model. If the model has multiple inputs, the Tensors
     * and TypedArrays must be passed in the correct order.
     */
    infer(inputData: Tensor[] | SupportedTypedArray[]): {
        [outputName: string]: Tensor;
    };
    /**
     * It infers specified input(s) in the asynchronous mode.
     * @param inputData An object with the key-value pairs where the key is the
     * input name and value is a tensor or an array with tensors. If the model has
     * multiple inputs, the Tensors must be passed in the correct order.
     */
    inferAsync(inputData: {
        [inputName: string]: Tensor;
    } | Tensor[]): Promise<{
        [outputName: string]: Tensor;
    }>;
    /**
     * It gets the compiled model used by the InferRequest object.
     */
    getCompiledModel(): CompiledModel;
    /**
     * It gets the input tensor for inference.
     * @returns The input tensor for the model. If the model has several inputs,
     * an exception is thrown.
     */
    getInputTensor(): Tensor;
    /**
     * It gets the input tensor for inference.
     * @param idx An index of the tensor to get.
     * @returns A tensor at the specified index. If the tensor with the specified
     * idx is not found, an exception is thrown.
     */
    getInputTensor(idx: number): Tensor;
    /**
     * It gets the output tensor for inference.
     * @returns The output tensor for the model. If the model has several outputs,
     * an exception is thrown.
     */
    getOutputTensor(): Tensor;
    /**
     * It gets the output tensor for inference.
     * @param idx An index of the tensor to get.
     * @returns A tensor at the specified index. If the tensor with the specified
     * idx is not found, an exception is thrown.
     */
    getOutputTensor(idx?: number): Tensor;
    /**
     * It gets an input/output tensor for inference.
     *
     * @remarks
     * If a tensor with the specified name or port is not found, an exception
     * is thrown.
     * @param nameOrOutput The name of the tensor or output object.
     */
    getTensor(nameOrOutput: string | Output): Tensor;
    /**
     * It sets the input tensor to infer models with a single input.
     * @param tensor The input tensor. The element type and shape of the tensor
     * must match the type and size of the model's input element. If the model
     * has several inputs, an exception is thrown.
     */
    setInputTensor(tensor: Tensor): void;
    /**
     * It sets the input tensor to infer.
     * @param idx The input tensor index. If idx is greater than the number of
     * model inputs, an exception is thrown.
     * @param tensor The input tensor. The element type and shape of the tensor
     * must match the input element type and size of the model.
     */
    setInputTensor(idx: number, tensor: Tensor): void;
    /**
     * It sets the output tensor to infer models with a single output.
     * @param tensor The output tensor. The element type and shape of the tensor
     * must match the output element type and size of the model. If the model
     * has several outputs, an exception is thrown.
     */
    setOutputTensor(tensor: Tensor): void;
    /**
     * It sets the output tensor to infer.
     * @param idx The output tensor index.
     * @param tensor The output tensor. The element type and shape of the tensor
     * must match the output element type and size of the model.
     */
    setOutputTensor(idx: number, tensor: Tensor): void;
    /**
     * It sets the input/output tensor to infer.
     * @param name The input or output tensor name.
     * @param tensor The tensor. The element type and shape of the tensor
     * must match the input/output element type and size of the model.
     */
    setTensor(name: string, tensor: Tensor): void;
}
export type Dimension = number | [number, number];
export interface Output {
    new (): Output;
    anyName: string;
    shape: number[];
    toString(): string;
    getAnyName(): string;
    getShape(): number[];
    getPartialShape(): PartialShape;
}
export interface InputTensorInfo {
    setElementType(elementType: element | elementTypeString): InputTensorInfo;
    setLayout(layout: string): InputTensorInfo;
    setShape(shape: number[]): InputTensorInfo;
}
export interface OutputTensorInfo {
    setElementType(elementType: element | elementTypeString): InputTensorInfo;
    setLayout(layout: string): InputTensorInfo;
}
export interface PreProcessSteps {
    resize(algorithm: resizeAlgorithm | string): PreProcessSteps;
}
export interface InputModelInfo {
    setLayout(layout: string): InputModelInfo;
}
export interface InputInfo {
    tensor(): InputTensorInfo;
    preprocess(): PreProcessSteps;
    model(): InputModelInfo;
}
export interface OutputInfo {
    tensor(): OutputTensorInfo;
}
export interface PrePostProcessor {
    new (model: Model): PrePostProcessor;
    build(): PrePostProcessor;
    input(idxOrTensorName?: number | string): InputInfo;
    output(idxOrTensorName?: number | string): OutputInfo;
}
export interface PartialShape {
    /**
     * It constructs a PartialShape by passed string.
     * Omit parameter to create empty shape.
     * @param [shape] String representation of the shape.
     */
    new (shape?: string): PartialShape;
    isStatic(): boolean;
    isDynamic(): boolean;
    toString(): string;
    getDimensions(): Dimension[];
}
/**
 * Callback function type for AsyncInferQueue operations.
 * @param inferRequest The {@link InferRequest} object from the queue's pool.
 * It allows to access input and output tensors.
 * @param userData User data that was passed to the startAsync method. If data was not
 * passed, it will be undefined.
 * @param error Optional error that occurred during inference, if any.
 */
export type AsyncInferQueueCallback = (error: null | Error, inferRequest: InferRequest, userData: object) => void;
export interface AsyncInferQueue {
    /**
     * Creates AsyncInferQueue.
     * @param compiledModel The compiledModel that will be used
     * to create InferRequests in the pool.
     * @param jobs Number of InferRequest objects in the pool. If not provided,
     * jobs number will be set automatically to the optimal number.
     */
    new (compiledModel: CompiledModel, jobs?: number): AsyncInferQueue;
    /**
     * Sets unified callback on all InferRequests from queue's pool.
     * The callback that was previously set will be replaced.
     * @param callback - Any function that matches callback's requirements.
     */
    setCallback(callback: AsyncInferQueueCallback): void;
    /**
     * It starts asynchronous inference for the specified input data.
     * @param inputData An object with the key-value pairs where the key is the
     * input name and value is a tensor or an array with tensors.
     * @param userData User data that will be passed to the callback.
     * @returns A Promise that can be used to track the callback completion.
     */
    startAsync(inputData: {
        [inputName: string]: Tensor;
    } | Tensor[], userData?: object): Promise<object>;
    /**
     * Releases resources associated with this AsyncInferQueue instance.
     * Call this method after all `startAsync` requests have completed
     * and the AsyncInferQueue is no longer needed.
     */
    release(): void;
}
export declare enum element {
    u8,
    u32,
    u16,
    u64,
    i8,
    i16,
    i32,
    i64,
    f32,
    f64,
    string
}
export declare enum resizeAlgorithm {
    RESIZE_NEAREST,
    RESIZE_CUBIC,
    RESIZE_LINEAR
}
export interface NodeAddon {
    Core: Core;
    Model: Model;
    CompiledModel: CompiledModel;
    Tensor: Tensor;
    InferRequest: InferRequest;
    Output: Output;
    PartialShape: PartialShape;
    AsyncInferQueue: AsyncInferQueue;
    preprocess: {
        resizeAlgorithm: typeof resizeAlgorithm;
        PrePostProcessor: PrePostProcessor;
    };
    /**
     * It saves a model into IR files (xml and bin).
     * Floating point weights are compressed to FP16 by default.
     * This method saves a model to IR applying all necessary transformations
     * that usually applied in model conversion flow provided by mo tool.
     * Particularly, floating point weights are compressed to FP16,
     * debug information in model nodes are cleaned up, etc.
     * @param model The model which will be
     * converted to IR representation and saved.
     * @param path The path for saving the model.
     * @param compressToFp16 Whether to compress
     * floating point weights to FP16. Default is set to `true`.
     */
    saveModelSync(model: Model, path: string, compressToFp16?: boolean): void;
    element: typeof element;
    resizeAlgorithm: typeof resizeAlgorithm;
}
declare const _default: NodeAddon;
export default _default;