/**
 * @license
 * Copyright 2023 Google LLC.
 * 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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-layers/dist/layers/nlp/einsum_dense" />
/**
 *  TFJS-based einsum dense layer.
 */
import { Tensor, Tensor2D, serialization } from '@tensorflow/tfjs-core';
import { ConstraintIdentifier } from '../../constraints';
import { Layer, LayerArgs } from '../../engine/topology';
import { InitializerIdentifier } from '../../initializers';
import { ActivationIdentifier } from '../../keras_format/activation_config';
import { Shape } from '../../keras_format/common';
import { RegularizerIdentifier } from '../../regularizers';
import { Kwargs } from '../../types';
import { LayerVariable } from '../../variables';
/**
 * Analyzes an einsum string to determine the required weight shape.
 */
export declare function analyzeEinsumString(equation: string, biasAxes: string, inputShape: Shape, outputShape: Shape): [Shape, Shape, Shape];
/**
 * Analyze an pre-split einsum string to find the weight shape.
 */
export declare function analyzeSplitString(splitString: RegExpMatchArray, biasAxes: string, inputShape: Shape, outputShape: Shape | number, leftElided?: boolean): [Shape, Shape, Shape];
export declare interface EinsumDenseArgs extends LayerArgs {
    /**
     * An equation describing the einsum to perform. This equation must be a
     * valid einsum string of the form `ab,bc->ac`, `...ab,bc->...ac`, or
     * `ab...,bc->ac...` where 'ab', 'bc', and 'ac' can be any valid einsum
     * axis expression sequence.
     */
    equation: string;
    /**
     * The expected shape of the output tensor (excluding the batch dimension and
     * any dimensions represented by ellipses). You can specify None for any
     * dimension that is unknown or can be inferred from the input shape.
     */
    outputShape: Shape | number;
    /**
     * Activation function to use. If you don't specify anything, no activation
     * is applied (that is, a "linear" activation: `a(x) = x`).
     */
    activation?: ActivationIdentifier;
    /**
     * A string containing the output dimension(s) to apply a bias to. Each
     * character in the `biasAxes` string should correspond to a character
     * in the output portion of the `equation` string.
     */
    biasAxes?: string;
    /**
     * Initializer for the `kernel` weights matrix.
     * Defaults to `"glorotUniform"`.
     */
    kernelInitializer?: InitializerIdentifier;
    /**
     * Initializer for the bias vector.
     * Defaults to `"zeros"`.
     */
    biasInitializer?: InitializerIdentifier;
    /**
     * Regularizer function applied to the `kernel` weights matrix.
     */
    kernelRegularizer?: RegularizerIdentifier;
    /**
     * Regularizer function applied to the bias vector.
     */
    biasRegularizer?: RegularizerIdentifier;
    /**
     * Regularizer function applied to the output of the layer (its "activation").
     */
    activityRegularizer?: RegularizerIdentifier;
    /**
     * Constraint function applied to the `kernel` weights matrix.
     */
    kernelConstraint?: ConstraintIdentifier;
    /**
     * Constraint function applied to the bias vector.
     */
    biasConstraint?: ConstraintIdentifier;
}
/**
 * A layer that uses `tf.einsum` as the backing computation.
 *
 * This layer can perform einsum calculations of arbitrary dimensionality.
 *
 * Examples:
 *
 * **Biased dense layer with einsums**
 *
 * This example shows how to instantiate a standard Keras dense layer using
 * einsum operations. This example is equivalent to
 * tf.layers.Dense({units: 64, useBias: true})`.
 *
 * const layer = new EinsumDense({
 *    equation: "ab,bc->ac", outputShape: 4, biasAxes: "c"});
 * const inputTensor = tf.input({shape: [32]});
 * const outputTensor = layer.call(inputTensor);
 * console.log(outputTensor);  // [null, 64]
 *
 * **Applying a dense layer to a sequence**
 *
 * This example shows how to instantiate a layer that applies the same dense
 * operation to every element in a sequence. Here, the `outputShape` has two
 * values (since there are two non-batch dimensions in the output); the first
 * dimension in the `outputShape` is `null`, because the sequence dimension
 * `b` has an unknown shape.
 *
 * ```js
 * const layer = new EinsumDense({
 *    equation: "abc,cd->abd", outputShape: [null, 64], biasAxes: "d"});
 * const inputTensor = tf.input({shape: [32, 128]});
 * const outputTensor = layer.call(inputTensor);
 * console.log(outputTensor);  // [null, 32, 64]
 * ```
 *
 * **Applying a dense layer to a sequence using ellipses**
 *
 * This example shows how to instantiate a layer that applies the same dense
 * operation to every element in a sequence, but uses the ellipsis notation
 * instead of specifying the batch and sequence dimensions.
 *
 * Because we are using ellipsis notation and have specified only one axis, the
 * `outputShape` arg is a single value. When instantiated in this way, the
 * layer can handle any number of sequence dimensions - including the case
 * where no sequence dimension exists.
 *
 * ```js
 * const layer = new EinsumDense({
 *    equation: "...x,xy->...y", outputShape: 64, biasAxes: "y"});
 * const inputTensor = tf.input({shape: [32, 128]});
 * const outputTensor = layer.call(inputTensor);
 * console.log(outputTensor);  // [null, 32, 64]
 * ``
 */
export declare class EinsumDense extends Layer {
    /** @nocollapse */
    static readonly className = "EinsumDense";
    private readonly equation;
    private readonly biasAxes;
    private readonly partialOutputShape;
    private readonly activation;
    private readonly kernelInitializer;
    private readonly biasInitializer;
    private readonly kernelRegularizer;
    private readonly biasRegularizer;
    private readonly kernelConstraint;
    private readonly biasConstraint;
    private fullOutputShape;
    private _kernel;
    private _bias;
    constructor(args: EinsumDenseArgs);
    get kernel(): LayerVariable;
    get bias(): LayerVariable;
    build(inputShape: Shape): void;
    computeOutputShape(_: Shape): Shape;
    getConfig(): serialization.ConfigDict;
    call(inputs: Tensor | Tensor[], kwargs: Kwargs): Tensor | Tensor2D;
}