/**
 * @license
 * Copyright 2017 Google Inc. 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 { Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo } from './engine';
import { Features } from './environment_util';
import { KernelBackend } from './kernels/backend';
import { Tensor } from './tensor';
import { TensorContainer } from './tensor_types';
export declare const EPSILON_FLOAT16 = 0.0001;
export declare const EPSILON_FLOAT32 = 1e-7;
export declare class Environment {
    private features;
    private globalEngine;
    private registry;
    backendName: string;
    constructor(features?: Features);
    /**
     * Sets the backend (cpu, webgl, etc) responsible for creating tensors and
     * executing operations on those tensors.
     *
     * Note this disposes the current backend, if any, as well as any tensors
     * associated with it. A new backend is initialized, even if it is of the
     * same type as the previous one.
     *
     * @param backendName The name of the backend. Currently supports
     *     `'webgl'|'cpu'` in the browser, and `'tensorflow'` under node.js
     *     (requires tfjs-node).
     * @param safeMode Defaults to false. In safe mode, you are forced to
     *     construct tensors and call math operations inside a `tidy()` which
     *     will automatically clean up intermediate tensors.
     */
    /** @doc {heading: 'Environment'} */
    static setBackend(backendName: string, safeMode?: boolean): void;
    /**
     * Returns the current backend name (cpu, webgl, etc). The backend is
     * responsible for creating tensors and executing operations on those tensors.
     */
    /** @doc {heading: 'Environment'} */
    static getBackend(): string;
    /**
     * Dispose all variables kept in backend engine.
     */
    /** @doc {heading: 'Environment'} */
    static disposeVariables(): void;
    /**
     * Returns memory info at the current time in the program. The result is an
     * object with the following properties:
     *
     * - `numBytes`: Number of bytes allocated (undisposed) at this time.
     * - `numTensors`: Number of unique tensors allocated.
     * - `numDataBuffers`: Number of unique data buffers allocated
     *   (undisposed) at this time, which is ≤ the number of tensors
     *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same
     *   data buffer with `a`).
     * - `unreliable`: True if the memory usage is unreliable. See `reasons` when
     *    `unrealible` is true.
     * - `reasons`: `string[]`, reasons why the memory is unreliable, present if
     *    `unreliable` is true.
     */
    /** @doc {heading: 'Performance', subheading: 'Memory'} */
    static memory(): MemoryInfo;
    /**
     * Executes the provided function `f()` and returns a promise that resolves
     * with information about the function's memory use:
     * - `newBytes`: tne number of new bytes allocated
     * - `newTensors`: the number of new tensors created
     * - `peakBytes`: the peak number of bytes allocated
     * - `kernels`: an array of objects for each kernel involved that reports
     * their input and output shapes, number of bytes used, and number of new
     * tensors created.
     *
     * ```js
     * const profile = await tf.profile(() => {
     *   const x = tf.tensor1d([1, 2, 3]);
     *   let x2 = x.square();
     *   x2.dispose();
     *   x2 = x.square();
     *   x2.dispose();
     *   return x;
     * });
     *
     * console.log(`newBytes: ${profile.newBytes}`);
     * console.log(`newTensors: ${profile.newTensors}`);
     * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>
     * k.totalBytesSnapshot)}`);
     * ```
     *
     */
    /** @doc {heading: 'Performance', subheading: 'Profile'} */
    static profile(f: () => TensorContainer): Promise<ProfileInfo>;
    /**
     * Executes the provided function `fn` and after it is executed, cleans up all
     * intermediate tensors allocated by `fn` except those returned by `fn`.
     * `fn` must not return a Promise (async functions not allowed). The returned
     * result can be a complex object.
     *
     * Using this method helps avoid memory leaks. In general, wrap calls to
     * operations in `tf.tidy` for automatic memory cleanup.
     *
     * When in safe mode, you must enclose all `tf.Tensor` creation and ops
     * inside a `tf.tidy` to prevent memory leaks.
     *
     * ```js
     * // y = 2 ^ 2 + 1
     * const y = tf.tidy(() => {
     *   // a, b, and one will be cleaned up when the tidy ends.
     *   const one = tf.scalar(1);
     *   const a = tf.scalar(2);
     *   const b = a.square();
     *
     *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
     *
     *   // The value returned inside the tidy function will return
     *   // through the tidy, in this case to the variable y.
     *   return b.add(one);
     * });
     *
     * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
     * y.print();
     * ```
     *
     * @param nameOrFn The name of the closure, or the function to execute.
     *     If a name is provided, the 2nd argument should be the function.
     *     If debug mode is on, the timing and the memory usage of the function
     *     will be tracked and displayed on the console using the provided name.
     * @param fn The function to execute.
     */
    /** @doc {heading: 'Performance', subheading: 'Memory'} */
    static tidy<T extends TensorContainer>(nameOrFn: string | ScopeFn<T>, fn?: ScopeFn<T>): T;
    /**
     * Disposes any `tf.Tensor`s found within the provided object.
     *
     * @param container an object that may be a `tf.Tensor` or may directly
     *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If
     *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing
     *     happens. In general it is safe to pass any object here, except that
     *     `Promise`s are not supported.
     */
    /** @doc {heading: 'Performance', subheading: 'Memory'} */
    static dispose(container: TensorContainer): void;
    /**
     * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed
     * automatically.
     *
     * ```js
     * let b;
     * const y = tf.tidy(() => {
     *   const one = tf.scalar(1);
     *   const a = tf.scalar(2);
     *
     *   // b will not be cleaned up by the tidy. a and one will be cleaned up
     *   // when the tidy ends.
     *   b = tf.keep(a.square());
     *
     *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);
     *
     *   // The value returned inside the tidy function will return
     *   // through the tidy, in this case to the variable y.
     *   return b.add(one);
     * });
     *
     * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
     * console.log('y:');
     * y.print();
     * console.log('b:');
     * b.print();
     * ```
     *
     * @param result The tensor to keep from being disposed.
     */
    /** @doc {heading: 'Performance', subheading: 'Memory'} */
    static keep<T extends Tensor>(result: T): T;
    /**
     * Executes `f()` and returns a promise that resolves with timing
     * information.
     *
     * The result is an object with the following properties:
     *
     * - `wallMs`: Wall execution time.
     * - `kernelMs`: Kernel execution time, ignoring data transfer.
     * - On `WebGL` The following additional properties exist:
     *   - `uploadWaitMs`: CPU blocking time on texture uploads.
     *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
     *
     * ```js
     * const x = tf.randomNormal([20, 20]);
     * const time = await tf.time(() => x.matMul(x));
     *
     * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);
     * ```
     *
     * @param f The function to execute and time.
     */
    /** @doc {heading: 'Performance', subheading: 'Timing'} */
    static time(f: () => void): Promise<TimingInfo>;
    get<K extends keyof Features>(feature: K): Features[K];
    getFeatures(): Features;
    set<K extends keyof Features>(feature: K, value: Features[K]): void;
    private getBestBackendName;
    private evaluateFeature;
    setFeatures(features: Features): void;
    reset(): void;
    readonly backend: KernelBackend;
    findBackend(name: string): KernelBackend;
    /**
     * Registers a global backend. The registration should happen when importing
     * a module file (e.g. when importing `backend_webgl.ts`), and is used for
     * modular builds (e.g. custom tfjs bundle with only webgl support).
     *
     * @param factory The backend factory function. When called, it should
     * return an instance of the backend.
     * @param priority The priority of the backend (higher = more important).
     *     In case multiple backends are registered, the priority is used to find
     *     the best backend. Defaults to 1.
     * @return False if the creation/registration failed. True otherwise.
     */
    registerBackend(name: string, factory: () => KernelBackend, priority?: number): boolean;
    removeBackend(name: string): void;
    readonly engine: Engine;
    private initEngine;
    readonly global: {
        ENV: Environment;
    };
}
/**
 * Enables production mode which disables correctness checks in favor of
 * performance.
 */
/** @doc {heading: 'Environment'} */
export declare function enableProdMode(): void;
/**
 * Enables debug mode which will log information about all executed kernels:
 * the ellapsed time of the kernel execution, as well as the rank, shape, and
 * size of the output tensor.
 *
 * Debug mode will significantly slow down your application as it will
 * download the result of every operation to the CPU. This should not be used in
 * production. Debug mode does not affect the timing information of the kernel
 * execution as we do not measure download time in the kernel execution time.
 *
 * See also: `tf.profile`, `tf.memory`.
 */
/** @doc {heading: 'Environment'} */
export declare function enableDebugMode(): void;
/** Globally disables deprecation warnings */
export declare function disableDeprecationWarnings(): void;
/** Warn users about deprecated functionality. */
export declare function deprecationWarn(msg: string): void;
export declare let ENV: Environment;