/**
 * @license
 * Copyright 2020 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 * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
import { identityPoolTest } from './identity_pool_test';
describeWithFlags('maxPool', ALL_ENVS, () => {
    it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {
        const x = tf.tensor3d([123], [1, 1, 1]);
        const result = tf.maxPool(x, 1, 1, 0);
        expectArraysClose(await result.data(), [123]);
    });
    it('x=[3,3,1] f=[2,2] s=1, p=0', async () => {
        // Feed forward.
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
        const result = tf.maxPool(x, 2, 1, 0);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [5, 6, 9, 9]);
    });
    it('x=[3,3,1] f=[2,2] s=1 p=same', async () => {
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
        const result = tf.maxPool(x, 2, 1, 'same');
        const resultData = await result.data();
        tf.test_util.expectArraysClose(resultData, new Float32Array([5, 6, 6, 9, 9, 8, 9, 9, 8]));
    });
    it('x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {
        const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
        const padding = [[0, 0], [1, 2], [0, 1], [0, 0]];
        const result = tf.maxPool(x, 3, 1, padding);
        expect(result.shape).toEqual([4, 2, 1]);
        expectArraysClose(await result.data(), [5, 5, 8, 8, 8, 8, 8, 8]);
    });
    it('x=[2,3,3,1] f=[2,2] s=1', async () => {
        // Feed forward.
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
        const result = tf.maxPool(x, 2, 1, 0);
        expect(result.shape).toEqual([2, 2, 2, 1]);
        expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);
    });
    it('[x=[3,3,1] f=[2,2] s=1 ignores NaNs', async () => {
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 9], [3, 3, 1]);
        const result = tf.maxPool(x, 2, 1, 0);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [5, 6, 7, 9]);
    });
    it('x=[3,3,2] f=[2,2] s=1', async () => {
        // Feed forward.
        const x = tf.tensor3d([1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11], [3, 3, 2]);
        const result = tf.maxPool(x, 2, 1, 0);
        expect(result.shape).toEqual([2, 2, 2]);
        expectArraysClose(await result.data(), [5, 99, 6, 88, 9, 66, 9, 55]);
    });
    it('x=[4,4,1] f=[2,2] s=2', async () => {
        // Feed forward.
        const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
        const result = tf.maxPool(x, 2, 2, 0);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [5, 7, 13, 15]);
    });
    it('x=[2,2,1] f=[2,2] s=1 p=same', async () => {
        // Feed forward.
        const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const fSize = 2;
        const strides = 1;
        const result = tf.maxPool(x, fSize, strides, 'same');
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [4, 4, 4, 4]);
    });
    it('x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode', () => {
        // Feed forward.
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
        const result = tf.maxPool(x, 2, 3, 1);
        expect(result.shape).toEqual([1, 1, 3]);
    });
    it('x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor', () => {
        // Feed forward.
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
        const result = tf.maxPool(x, 1, 2, 1, 'floor');
        expect(result.shape).toEqual([2, 2, 3]);
    });
    it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor', () => {
        // Feed forward.
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
        const result = tf.maxPool(x, 2, 3, 1, 'floor');
        expect(result.shape).toEqual([1, 1, 3]);
    });
    it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round', () => {
        // Feed forward.
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
        const result = tf.maxPool(x, 2, 3, 1, 'round');
        expect(result.shape).toEqual([2, 2, 3]);
    });
    it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil', () => {
        // Feed forward.
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);
        const result = tf.maxPool(x, 2, 3, 1, 'ceil');
        expect(result.shape).toEqual([2, 2, 3]);
    });
    it('throws when x is not rank 3', () => {
        // tslint:disable-next-line:no-any
        const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
        expect(() => tf.maxPool(x, 2, 1, 0)).toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is same', () => {
        const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const pad = 'same';
        const dimRoundingMode = 'round';
        expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is valid', () => {
        const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const pad = 'valid';
        const dimRoundingMode = 'round';
        expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is a non-integer number', () => {
        const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const pad = 1.2;
        const dimRoundingMode = 'round';
        expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
        'number', () => {
        const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]];
        const dimRoundingMode = 'round';
        expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.maxPool({}, 2, 1, 'valid'))
            .toThrowError(/Argument 'x' passed to 'maxPool' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const x = [[[0]]]; // 1x1x1
        const result = tf.maxPool(x, 1, 1, 0);
        expectArraysClose(await result.data(), [0]);
    });
    identityPoolTest(tf.maxPool);
});
describeWithFlags('maxPoolBackprop', ALL_ENVS, () => {
    it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #1', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
        const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient with clones x=[3,3,1] f=[2,2] s=1', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
        const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];
        const dx = tf.grad((x) => tf.maxPool(x.clone(), 2, 1, 0).clone())(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #2', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([9, 5, 6, 6, 8, 4, 9, 5, 10], [3, 3, 1]);
        const expected = [1, 0, 0, 0, 2, 0, 3, 0, 4];
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradients x=[2,3,3,1] f=[2,2] s=1 no duplicate max value', async () => {
        // This test batches the [3,3,1] tests.
        const dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 5, 6, 6, 8, 4, 9, 5, 10], [2, 3, 3, 1]);
        const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4, 1, 0, 0, 0, 2, 0, 3, 0, 4];
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 1', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([0, 0, 0, 0, 5, 0, 0, 0, 0], [3, 3, 1]);
        const expected = [0, 0, 0, 0, 10, 0, 0, 0, 0];
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 2', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([1, 3, 2, 1, 2, 1, 1, 1, 5], [3, 3, 1]);
        const expected = [0, 3, 0, 0, 3, 0, 0, 0, 4];
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[2,3,3,1] f=[2,2] s=1 dup max value in 2nd input', async () => {
        const dy = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
        const x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 9, 8], [2, 3, 3, 1]);
        const expected = new Float32Array([0, 0, 0, 0, 1, 2, 0, 3, 4, 0, 0, 0, 0, 5, 6, 0, 15, 0]);
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[4,4,1] f=[2,2] s=2 test #1', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
        const expected = [0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 3, 0, 4];
        const dx = tf.grad((x) => x.maxPool(2, 2, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[4,4,1] f=[2,2] s=2 test #2', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1], [4, 4, 1]);
        const expected = [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0];
        const dx = tf.grad((x) => x.maxPool(2, 2, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[5,5,1] f=[3,3] s=2 no duplicate max value', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([
            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
        ], [5, 5, 1]);
        const expected = [
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
            0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4
        ];
        const dx = tf.grad((x) => x.maxPool(3, 2, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[5,5,1] f=[3,3] s=2 duplicate max value', async () => {
        const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
        const x = tf.tensor3d([
            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 24,
            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 12
        ], [5, 5, 1]);
        const expected = [
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
        ];
        const dx = tf.grad((x) => x.maxPool(3, 2, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    // Max pool backprop depth > 1.
    it('gradient x=[3,3,2] f=[2,2] s=1, no duplicate max value', async () => {
        // This test combines the first two 3x3x1 tests with no duplicates to
        // make depth=2,
        // dy is slightly modified to show the difference.
        const dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);
        const x = tf.tensor3d([1, 99, 2, 55, 3, 66, 4, 66, 5, 88, 6, 44, 7, 99, 8, 55, 9, 100], [3, 3, 2]);
        const expected = [0, 44, 0, 0, 0, 0, 0, 0, 1, 33, 2, 0, 0, 22, 3, 0, 4, 11];
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[3,3,2] f=[2,2] s=1 duplicate max value', async () => {
        // This test combines the first two 3x3x1 tests with duplicates to
        // make depth=2,
        // dy is slightly modified to show the difference.
        const dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);
        const x = tf.tensor3d([0, 1, 0, 3, 0, 2, 0, 1, 5, 2, 0, 1, 0, 1, 0, 1, 0, 5], [3, 3, 2]);
        const expected = new Float32Array([0, 0, 0, 77, 0, 0, 0, 0, 10, 22, 0, 0, 0, 0, 0, 0, 0, 11]);
        const dx = tf.grad((x) => x.maxPool(2, 1, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[4,4,2] f=[2,2] s=1', async () => {
        // This test combines the first two 4x4x1 tests with duplicates to make
        // depth=2,
        // dy is slightly modified to show the difference.
        const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
        const x = tf.tensor3d([
            0, 1, 1, 2, 2, 2, 3, 1, 4, 1, 5, 1, 6, 1, 7, 1,
            8, 1, 9, 1, 10, 1, 11, 1, 12, 1, 13, 2, 14, 2, 15, 1
        ], [4, 4, 2]);
        const expected = [
            0, 0, 0, 11, 0, 22, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 33, 0, 44, 4, 0
        ];
        const dx = tf.grad((x) => x.maxPool(2, 2, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=[3,3,1] f=3 s=1 p=explicit', async () => {
        const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [4, 2, 1]);
        const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);
        const padding = [[0, 0], [1, 2], [0, 1], [0, 0]];
        const expected = [0, 0, 0, 0, 0, 12, 0, 0, 108];
        const dx = tf.grad((x) => x.maxPool(3, 1, padding))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
    it('gradient x=5x5x2, f=3, s=2 no duplicate max value', async () => {
        // This test combines the first two 5x5x1 tests with duplicates to make
        // depth=2,
        // dy is slightly modified to show the difference.
        const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
        const x = tf.tensor3d([
            0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8,
            8, 9, 9, 10, 10, 11, 11, 12, 24, 13, 13, 14, 14, 15, 15, 16, 16,
            17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 12
        ], [5, 5, 2]);
        const expected = [
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 1, 110, 0, 0, 2, 0, 0, 0, 0, 0,
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 4, 0
        ];
        const dx = tf.grad((x) => x.maxPool(3, 2, 0))(x, dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), expected);
    });
});
//# 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* @license\n * Copyright 2020 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\nimport { identityPoolTest } from './identity_pool_test';\n\ndescribeWithFlags('maxPool', ALL_ENVS, () => {\n  it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', async () => {\n    const x = tf.tensor3d([123], [1, 1, 1]);\n\n    const result = tf.maxPool(x, 1, 1, 0);\n\n    expectArraysClose(await result.data(), [123]);\n  });\n\n  it('x=[3,3,1] f=[2,2] s=1, p=0', async () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);\n\n    const result = tf.maxPool(x, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 6, 9, 9]);\n  });\n\n  it('x=[3,3,1] f=[2,2] s=1 p=same', async () => {\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);\n\n    const result = tf.maxPool(x, 2, 1, 'same');\n    const resultData = await result.data();\n\n    tf.test_util.expectArraysClose(\n        resultData, new Float32Array([5, 6, 6, 9, 9, 8, 9, 9, 8]));\n  });\n\n  it('x=[3,3,1] f=[3,3] s=1 p=explicit', async () => {\n    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);\n    const padding =\n        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const result = tf.maxPool(x, 3, 1, padding);\n\n    expect(result.shape).toEqual([4, 2, 1]);\n    expectArraysClose(await result.data(), [5, 5, 8, 8, 8, 8, 8, 8]);\n  });\n\n  it('x=[2,3,3,1] f=[2,2] s=1', async () => {\n    // Feed forward.\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);\n\n    const result = tf.maxPool(x, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 6, 9, 9, 5, 6, 8, 9]);\n  });\n\n  it('[x=[3,3,1] f=[2,2] s=1 ignores NaNs', async () => {\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 9], [3, 3, 1]);\n\n    const result = tf.maxPool(x, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 6, 7, 9]);\n  });\n\n  it('x=[3,3,2] f=[2,2] s=1', async () => {\n    // Feed forward.\n    const x = tf.tensor3d(\n        [1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11],\n        [3, 3, 2]);\n\n    const result = tf.maxPool(x, 2, 1, 0);\n\n    expect(result.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await result.data(), [5, 99, 6, 88, 9, 66, 9, 55]);\n  });\n\n  it('x=[4,4,1] f=[2,2] s=2', async () => {\n    // Feed forward.\n    const x = tf.tensor3d(\n        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);\n\n    const result = tf.maxPool(x, 2, 2, 0);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [5, 7, 13, 15]);\n  });\n\n  it('x=[2,2,1] f=[2,2] s=1 p=same', async () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const fSize = 2;\n    const strides = 1;\n    const result = tf.maxPool(x, fSize, strides, 'same');\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [4, 4, 4, 4]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 default dimRoundingMode', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.maxPool(x, 2, 3, 1);\n\n    expect(result.shape).toEqual([1, 1, 3]);\n  });\n\n  it('x=[2,2,3] f=[1,1] s=2 p=1 dimRoundingMode=floor', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.maxPool(x, 1, 2, 1, 'floor');\n\n    expect(result.shape).toEqual([2, 2, 3]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=floor', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.maxPool(x, 2, 3, 1, 'floor');\n\n    expect(result.shape).toEqual([1, 1, 3]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=round', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.maxPool(x, 2, 3, 1, 'round');\n\n    expect(result.shape).toEqual([2, 2, 3]);\n  });\n\n  it('x=[2,2,3] f=[2,2] s=3 p=1 dimRoundingMode=ceil', () => {\n    // Feed forward.\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 2, 3]);\n    const result = tf.maxPool(x, 2, 3, 1, 'ceil');\n\n    expect(result.shape).toEqual([2, 2, 3]);\n  });\n\n  it('throws when x is not rank 3', () => {\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);\n\n    expect(() => tf.maxPool(x, 2, 1, 0)).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is same', () => {\n    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n    const pad = 'same';\n    const dimRoundingMode = 'round';\n\n    expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', () => {\n    const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n    const pad = 'valid';\n    const dimRoundingMode = 'round';\n\n    expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     () => {\n       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n       const pad = 1.2;\n       const dimRoundingMode = 'round';\n\n       expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n     });\n\n  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +\n         'number',\n     () => {\n       const x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n\n       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as\n           tf.backend_util.ExplicitPadding;\n       const dimRoundingMode = 'round';\n\n       expect(() => tf.maxPool(x, 2, 1, pad, dimRoundingMode)).toThrowError();\n     });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.maxPool({} as tf.Tensor3D, 2, 1, 'valid'))\n        .toThrowError(/Argument 'x' passed to 'maxPool' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const x = [[[0]]];  // 1x1x1\n    const result = tf.maxPool(x, 1, 1, 0);\n    expectArraysClose(await result.data(), [0]);\n  });\n\n  identityPoolTest(tf.maxPool);\n});\n\ndescribeWithFlags('maxPoolBackprop', ALL_ENVS, () => {\n  it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #1', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);\n    const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient with clones x=[3,3,1] f=[2,2] s=1', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);\n    const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];\n\n    const dx = tf.grad(\n        (x: tf.Tensor3D) => tf.maxPool(x.clone(), 2, 1, 0).clone())(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #2', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d([9, 5, 6, 6, 8, 4, 9, 5, 10], [3, 3, 1]);\n    const expected = [1, 0, 0, 0, 2, 0, 3, 0, 4];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradients x=[2,3,3,1] f=[2,2] s=1 no duplicate max value', async () => {\n    // This test batches the [3,3,1] tests.\n    const dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);\n    const x = tf.tensor4d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 5, 6, 6, 8, 4, 9, 5, 10], [2, 3, 3, 1]);\n    const expected = [0, 0, 0, 0, 1, 2, 0, 3, 4, 1, 0, 0, 0, 2, 0, 3, 0, 4];\n\n    const dx = tf.grad((x: tf.Tensor4D) => x.maxPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 1', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d([0, 0, 0, 0, 5, 0, 0, 0, 0], [3, 3, 1]);\n    const expected = [0, 0, 0, 0, 10, 0, 0, 0, 0];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 2', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d([1, 3, 2, 1, 2, 1, 1, 1, 5], [3, 3, 1]);\n    const expected = [0, 3, 0, 0, 3, 0, 0, 0, 4];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[2,3,3,1] f=[2,2] s=1 dup max value in 2nd input',\n     async () => {\n       const dy = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);\n       const x = tf.tensor4d(\n           [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 9, 8],\n           [2, 3, 3, 1]);\n       const expected = new Float32Array(\n           [0, 0, 0, 0, 1, 2, 0, 3, 4, 0, 0, 0, 0, 5, 6, 0, 15, 0]);\n\n       const dx = tf.grad((x: tf.Tensor4D) => x.maxPool(2, 1, 0))(x, dy);\n\n       expect(dx.shape).toEqual(x.shape);\n       expectArraysClose(await dx.data(), expected);\n     });\n\n  it('gradient x=[4,4,1] f=[2,2] s=2 test #1', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d(\n        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);\n    const expected = [0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 3, 0, 4];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 2, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[4,4,1] f=[2,2] s=2 test #2', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d(\n        [1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1], [4, 4, 1]);\n    const expected = [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 2, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[5,5,1] f=[3,3] s=2 no duplicate max value', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d(\n        [\n          0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12,\n          13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24\n        ],\n        [5, 5, 1]);\n    const expected = [\n      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n      0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4\n    ];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 2, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[5,5,1] f=[3,3] s=2 duplicate max value', async () => {\n    const dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);\n    const x = tf.tensor3d(\n        [\n          0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 24,\n          13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 12\n        ],\n        [5, 5, 1]);\n    const expected = [\n      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,\n      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n    ];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 2, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  // Max pool backprop depth > 1.\n  it('gradient x=[3,3,2] f=[2,2] s=1, no duplicate max value', async () => {\n    // This test combines the first two 3x3x1 tests with no duplicates to\n    // make depth=2,\n    // dy is slightly modified to show the difference.\n    const dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);\n    const x = tf.tensor3d(\n        [1, 99, 2, 55, 3, 66, 4, 66, 5, 88, 6, 44, 7, 99, 8, 55, 9, 100],\n        [3, 3, 2]);\n    const expected = [0, 44, 0, 0, 0, 0, 0, 0, 1, 33, 2, 0, 0, 22, 3, 0, 4, 11];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[3,3,2] f=[2,2] s=1 duplicate max value', async () => {\n    // This test combines the first two 3x3x1 tests with duplicates to\n    // make depth=2,\n    // dy is slightly modified to show the difference.\n    const dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);\n    const x = tf.tensor3d(\n        [0, 1, 0, 3, 0, 2, 0, 1, 5, 2, 0, 1, 0, 1, 0, 1, 0, 5], [3, 3, 2]);\n    const expected = new Float32Array(\n        [0, 0, 0, 77, 0, 0, 0, 0, 10, 22, 0, 0, 0, 0, 0, 0, 0, 11]);\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 1, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[4,4,2] f=[2,2] s=1', async () => {\n    // This test combines the first two 4x4x1 tests with duplicates to make\n    // depth=2,\n    // dy is slightly modified to show the difference.\n    const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);\n    const x = tf.tensor3d(\n        [\n          0, 1, 1, 2, 2,  2, 3,  1, 4,  1, 5,  1, 6,  1, 7,  1,\n          8, 1, 9, 1, 10, 1, 11, 1, 12, 1, 13, 2, 14, 2, 15, 1\n        ],\n        [4, 4, 2]);\n    const expected = [\n      0, 0, 0, 11, 0, 22, 0, 0, 0, 0, 1, 0,  0, 0,  2, 0,\n      0, 0, 0, 0,  0, 0,  0, 0, 0, 0, 3, 33, 0, 44, 4, 0\n    ];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(2, 2, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=[3,3,1] f=3 s=1 p=explicit', async () => {\n    const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [4, 2, 1]);\n    const x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8], [3, 3, 1]);\n    const padding =\n        [[0, 0], [1, 2], [0, 1], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const expected = [0, 0, 0, 0, 0, 12, 0, 0, 108];\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 1, padding))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n\n  it('gradient x=5x5x2, f=3, s=2 no duplicate max value', async () => {\n    // This test combines the first two 5x5x1 tests with duplicates to make\n    // depth=2,\n    // dy is slightly modified to show the difference.\n    const dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);\n    const x = tf.tensor3d(\n        [\n          0,  0,  1,  1,  2,  2,  3,  3,  4,  4,  5,  5,  6,  6,  7,  7,  8,\n          8,  9,  9,  10, 10, 11, 11, 12, 24, 13, 13, 14, 14, 15, 15, 16, 16,\n          17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 12\n        ],\n        [5, 5, 2]);\n    const expected = [\n      0, 0, 0, 0, 0, 0, 0, 0, 0,   0, 0, 0, 0, 0, 0, 0, 0,\n      0, 0, 0, 0, 0, 0, 0, 1, 110, 0, 0, 2, 0, 0, 0, 0, 0,\n      0, 0, 0, 0, 0, 0, 0, 0, 0,   0, 3, 0, 0, 0, 4, 0\n    ];\n\n    const dx = tf.grad((x: tf.Tensor3D) => x.maxPool(3, 2, 0))(x, dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), expected);\n  });\n});\n"]}