/**
 * @license
 * Copyright 2017 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { ArrayMap, BackendValues, DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap } from './types';
export interface TensorData<D extends DataType> {
    dataId?: DataId;
    values?: DataTypeMap[D];
}
export interface Backend {
}
/**
 * A mutable object, similar to `tf.Tensor`, that allows users to set values
 * at locations before converting to an immutable `tf.Tensor`.
 *
 * See `tf.buffer` for creating a tensor buffer.
 *
 * @doc {heading: 'Tensors', subheading: 'Classes'}
 */
export declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
    dtype: D;
    size: number;
    shape: ShapeMap[R];
    strides: number[];
    values: DataTypeMap[D];
    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
    /**
     * Sets a value in the buffer at a given location.
     *
     * @param value The value to set.
     * @param locs  The location indices.
     *
     * @doc {heading: 'Tensors', subheading: 'Creation'}
     */
    set(value: SingleValueMap[D], ...locs: number[]): void;
    /**
     * Returns the value in the buffer at the provided location.
     *
     * @param locs The location indices.
     *
     * @doc {heading: 'Tensors', subheading: 'Creation'}
     */
    get(...locs: number[]): SingleValueMap[D];
    locToIndex(locs: number[]): number;
    indexToLoc(index: number): number[];
    readonly rank: number;
    /**
     * Creates an immutable `tf.Tensor` object from the buffer.
     *
     * @doc {heading: 'Tensors', subheading: 'Creation'}
     */
    toTensor(): Tensor<R>;
}
export interface TensorTracker {
    makeTensor(values: DataValues, shape: number[], dtype: DataType, backend?: Backend): Tensor;
    makeVariable(initialValue: Tensor, trainable?: boolean, name?: string, dtype?: DataType): Variable;
    incRef(a: Tensor, backend: Backend): void;
    disposeTensor(t: Tensor): void;
    disposeVariable(v: Variable): void;
    read(dataId: DataId): Promise<BackendValues>;
    readSync(dataId: DataId): BackendValues;
}
/**
 * The Tensor class calls into this handler to delegate chaining operations.
 */
export interface OpHandler {
    cast<T extends Tensor>(x: T, dtype: DataType): T;
    buffer<R extends Rank, D extends DataType>(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]): TensorBuffer<R, D>;
    print<T extends Tensor>(x: T, verbose: boolean): void;
    clone<T extends Tensor>(x: T): T;
}
/**
 * An external consumer can register itself as the tensor tracker. This way
 * the Tensor class can notify the tracker for every tensor created and
 * disposed.
 */
export declare function setTensorTracker(fn: () => TensorTracker): void;
/**
 * An external consumer can register itself as the op handler. This way the
 * Tensor class can have chaining methods that call into ops via the op
 * handler.
 */
export declare function setOpHandler(handler: OpHandler): void;
/**
 * Sets the deprecation warning function to be used by this file. This way the
 * Tensor class can be a leaf but still use the environment.
 */
export declare function setDeprecationWarningFn(fn: (msg: string) => void): void;
/**
 * We wrap data id since we use weak map to avoid memory leaks.
 * Since we have our own memory management, we have a reference counter
 * mapping a tensor to its data, so there is always a pointer (even if that
 * data is otherwise garbage collectable).
 * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/
 * Global_Objects/WeakMap
 */
export declare type DataId = object;
export declare namespace Tensor { }
/**
 * A `tf.Tensor` object represents an immutable, multidimensional array of
 * numbers that has a shape and a data type.
 *
 * See `tf.tensor` for details on how to create a `tf.Tensor`.
 *
 * @doc {heading: 'Tensors', subheading: 'Classes'}
 */
export declare class Tensor<R extends Rank = Rank> {
    /** Unique id of this tensor. */
    readonly id: number;
    /**
     * Id of the bucket holding the data for this tensor. Multiple arrays can
     * point to the same bucket (e.g. when calling array.reshape()).
     */
    dataId: DataId;
    /** The shape of the tensor. */
    readonly shape: ShapeMap[R];
    /** Number of elements in the tensor. */
    readonly size: number;
    /** The data type for the array. */
    readonly dtype: DataType;
    /** The rank type for the array (see `Rank` enum). */
    readonly rankType: R;
    /** Whether this tensor has been globally kept. */
    kept: boolean;
    /** The id of the scope this tensor is being tracked in. */
    scopeId: number;
    /**
     * Number of elements to skip in each dimension when indexing. See
     * https://docs.scipy.org/doc/numpy/reference/generated/\
     * numpy.ndarray.strides.html
     */
    readonly strides: number[];
    constructor(shape: ShapeMap[R], dtype: DataType, dataId: DataId, id: number);
    readonly rank: number;
    /**
     * Returns a promise of `tf.TensorBuffer` that holds the underlying data.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    buffer<D extends DataType = 'float32'>(): Promise<TensorBuffer<R, D>>;
    /**
     * Returns a `tf.TensorBuffer` that holds the underlying data.
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    bufferSync<D extends DataType = 'float32'>(): TensorBuffer<R, D>;
    /**
     * Returns the tensor data as a nested array. The transfer of data is done
     * asynchronously.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    array(): Promise<ArrayMap[R]>;
    /**
     * Returns the tensor data as a nested array. The transfer of data is done
     * synchronously.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    arraySync(): ArrayMap[R];
    /**
     * Asynchronously downloads the values from the `tf.Tensor`. Returns a
     * promise of `TypedArray` that resolves when the computation has finished.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
    /**
     * Synchronously downloads the values from the `tf.Tensor`. This blocks the
     * UI thread until the values are ready, which can cause performance issues.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
    /** Returns the underlying bytes of the tensor's data. */
    bytes(): Promise<Uint8Array[] | Uint8Array>;
    /**
     * Disposes `tf.Tensor` from memory.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    dispose(): void;
    protected isDisposedInternal: boolean;
    readonly isDisposed: boolean;
    throwIfDisposed(): void;
    /**
     * Prints the `tf.Tensor`. See `tf.print` for details.
     *
     * @param verbose Whether to print verbose information about the tensor,
     *    including dtype and size.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    print(verbose?: boolean): void;
    /**
     * Returns a copy of the tensor. See `tf.clone` for details.
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    clone<T extends Tensor>(this: T): T;
    /**
     * Returns a human-readable description of the tensor. Useful for logging.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    toString(verbose?: boolean): string;
    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
}
export declare function getGlobalTensorClass(): typeof Tensor;
export interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {
    dtype: NumericDataType;
    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
}
export interface StringTensor<R extends Rank = Rank> extends Tensor<R> {
    dtype: 'string';
    dataSync<D extends DataType = 'string'>(): DataTypeMap[D];
    data<D extends DataType = 'string'>(): Promise<DataTypeMap[D]>;
}
/** @doclink Tensor */
export declare type Scalar = Tensor<Rank.R0>;
/** @doclink Tensor */
export declare type Tensor1D = Tensor<Rank.R1>;
/** @doclink Tensor */
export declare type Tensor2D = Tensor<Rank.R2>;
/** @doclink Tensor */
export declare type Tensor3D = Tensor<Rank.R3>;
/** @doclink Tensor */
export declare type Tensor4D = Tensor<Rank.R4>;
/** @doclink Tensor */
export declare type Tensor5D = Tensor<Rank.R5>;
/** @doclink Tensor */
export declare type Tensor6D = Tensor<Rank.R6>;
/**
 * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.
 *
 * @doc {heading: 'Tensors', subheading: 'Classes'}
 */
export declare class Variable<R extends Rank = Rank> extends Tensor<R> {
    trainable: boolean;
    name: string;
    constructor(initialValue: Tensor<R>, trainable: boolean, name: string, tensorId: number);
    /**
     * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have
     * the same shape and dtype as the old `tf.Tensor`.
     *
     * @param newValue New tensor to be assigned to this variable.
     *
     * @doc {heading: 'Tensors', subheading: 'Classes'}
     */
    assign(newValue: Tensor<R>): void;
    dispose(): void;
}