/**
 * @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';
describeWithFlags('cumsum', ALL_ENVS, () => {
    it('1D standard', async () => {
        const res = tf.tensor1d([1, 2, 3, 4]).cumsum();
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [1, 3, 6, 10]);
    });
    it('1D reverse', async () => {
        const reverse = true;
        const exclusive = false;
        const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [10, 9, 7, 4]);
    });
    it('1D exclusive', async () => {
        const exclusive = true;
        const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive);
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [0, 1, 3, 6]);
    });
    it('1D exclusive reverse', async () => {
        const reverse = true;
        const exclusive = true;
        const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [9, 7, 4, 0]);
    });
    it('gradient: 1D', async () => {
        const a = tf.tensor1d([1, 2, 3]);
        const dy = tf.tensor1d([4, 5, 6]);
        const da = tf.grad(x => tf.cumsum(x))(a, dy);
        expect(da.shape).toEqual([3]);
        expectArraysClose(await da.data(), [15, 11, 6]);
    });
    it('gradient with clones', async () => {
        const a = tf.tensor1d([1, 2, 3]);
        const dy = tf.tensor1d([4, 5, 6]);
        const da = tf.grad(x => tf.cumsum(x.clone()).clone())(a, dy);
        expect(da.shape).toEqual([3]);
        expectArraysClose(await da.data(), [15, 11, 6]);
    });
    it('2D standard', async () => {
        const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1);
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose(await res.data(), [1, 3, 3, 7]);
    });
    it('2D reverse exclusive', async () => {
        const reverse = true;
        const exclusive = true;
        const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1, exclusive, reverse);
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose(await res.data(), [2, 0, 4, 0]);
    });
    it('2D axis=0', async () => {
        const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum();
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose(await res.data(), [1, 2, 4, 6]);
    });
    it('3D standard', async () => {
        const res = tf.tensor3d([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).cumsum(2);
        expect(res.shape).toEqual([2, 2, 2]);
        expectArraysClose(await res.data(), [0, 1, 2, 5, 4, 9, 6, 13]);
    });
    it('4d axis=2', async () => {
        const input = tf.ones([1, 32, 46, 4]);
        const res = tf.cumsum(input, 2, false, false);
        expect(res.shape).toEqual([1, 32, 46, 4]);
        const earlySlice = tf.slice(res, [0, 0, 0, 0], [1, 1, 46, 1]);
        const lateSlice = tf.slice(res, [0, 31, 0, 0], [1, 1, 46, 1]);
        const expectedDataInEachSlice = [
            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
            33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46
        ];
        expectArraysClose(await earlySlice.data(), expectedDataInEachSlice);
        expectArraysClose(await lateSlice.data(), expectedDataInEachSlice);
    });
    it('handle permutation properly', async () => {
        const res = tf.ones([1, 240, 1, 10]).cumsum(1);
        expect(res.shape).toEqual([1, 240, 1, 10]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.cumsum({}))
            .toThrowError(/Argument 'x' passed to 'cumsum' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const res = tf.cumsum([1, 2, 3, 4]);
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [1, 3, 6, 10]);
    });
    it('throws error for string tensor', () => {
        expect(() => tf.cumsum([
            'a', 'b', 'c'
        ])).toThrowError(/Argument 'x' passed to 'cumsum' must be numeric tensor/);
    });
});
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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';\n\ndescribeWithFlags('cumsum', ALL_ENVS, () => {\n  it('1D standard', async () => {\n    const res = tf.tensor1d([1, 2, 3, 4]).cumsum();\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [1, 3, 6, 10]);\n  });\n\n  it('1D reverse', async () => {\n    const reverse = true;\n    const exclusive = false;\n    const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [10, 9, 7, 4]);\n  });\n\n  it('1D exclusive', async () => {\n    const exclusive = true;\n    const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive);\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [0, 1, 3, 6]);\n  });\n\n  it('1D exclusive reverse', async () => {\n    const reverse = true;\n    const exclusive = true;\n    const res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [9, 7, 4, 0]);\n  });\n\n  it('gradient: 1D', async () => {\n    const a = tf.tensor1d([1, 2, 3]);\n    const dy = tf.tensor1d([4, 5, 6]);\n    const da = tf.grad(x => tf.cumsum(x))(a, dy);\n\n    expect(da.shape).toEqual([3]);\n    expectArraysClose(await da.data(), [15, 11, 6]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.tensor1d([1, 2, 3]);\n    const dy = tf.tensor1d([4, 5, 6]);\n    const da = tf.grad(x => tf.cumsum(x.clone()).clone())(a, dy);\n\n    expect(da.shape).toEqual([3]);\n    expectArraysClose(await da.data(), [15, 11, 6]);\n  });\n\n  it('2D standard', async () => {\n    const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1);\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [1, 3, 3, 7]);\n  });\n\n  it('2D reverse exclusive', async () => {\n    const reverse = true;\n    const exclusive = true;\n    const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1, exclusive, reverse);\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [2, 0, 4, 0]);\n  });\n\n  it('2D axis=0', async () => {\n    const res = tf.tensor2d([[1, 2], [3, 4]]).cumsum();\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [1, 2, 4, 6]);\n  });\n\n  it('3D standard', async () => {\n    const res = tf.tensor3d([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).cumsum(2);\n    expect(res.shape).toEqual([2, 2, 2]);\n    expectArraysClose(await res.data(), [0, 1, 2, 5, 4, 9, 6, 13]);\n  });\n\n  it('4d axis=2', async () => {\n    const input = tf.ones([1, 32, 46, 4]);\n    const res = tf.cumsum(input, 2, false, false);\n\n    expect(res.shape).toEqual([1, 32, 46, 4]);\n\n    const earlySlice = tf.slice(res, [0, 0, 0, 0], [1, 1, 46, 1]);\n    const lateSlice = tf.slice(res, [0, 31, 0, 0], [1, 1, 46, 1]);\n    const expectedDataInEachSlice = [\n      1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15, 16,\n      17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,\n      33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46\n    ];\n    expectArraysClose(await earlySlice.data(), expectedDataInEachSlice);\n    expectArraysClose(await lateSlice.data(), expectedDataInEachSlice);\n  });\n\n  it('handle permutation properly', async () => {\n    const res = tf.ones([1, 240, 1, 10]).cumsum(1);\n    expect(res.shape).toEqual([1, 240, 1, 10]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.cumsum({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'cumsum' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const res = tf.cumsum([1, 2, 3, 4]);\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [1, 3, 6, 10]);\n  });\n\n  it('throws error for string tensor', () => {\n    expect(() => tf.cumsum([\n      'a', 'b', 'c'\n    ])).toThrowError(/Argument 'x' passed to 'cumsum' must be numeric tensor/);\n  });\n});\n"]}