/**
 * @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, expectArraysEqual } from '../test_util';
describeWithFlags('sum', ALL_ENVS, () => {
    it('basic', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const result = tf.sum(a);
        expectArraysClose(await result.data(), 7);
    });
    it('propagates NaNs', async () => {
        const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
        expectArraysEqual(await tf.sum(a).data(), NaN);
    });
    it('sum over dtype int32', async () => {
        const a = tf.tensor1d([1, 5, 7, 3], 'int32');
        const sum = tf.sum(a);
        expectArraysEqual(await sum.data(), 16);
    });
    it('sum over dtype bool', async () => {
        const a = tf.tensor1d([true, false, false, true, true], 'bool');
        const sum = tf.sum(a);
        expectArraysEqual(await sum.data(), 3);
    });
    it('sums all values in 2D array with keep dim', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.sum(a, null, true /* keepDims */);
        expect(res.shape).toEqual([1, 1]);
        expectArraysClose(await res.data(), [7]);
    });
    it('sums across axis=0 in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.sum(a, [0]);
        expect(res.shape).toEqual([2]);
        expectArraysClose(await res.data(), [4, 3]);
    });
    it('sums across axis=0 in 2D array, keepDims', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.sum(a, [0], true /* keepDims */);
        expect(res.shape).toEqual([1, 2]);
        expectArraysClose(await res.data(), [4, 3]);
    });
    it('sums across axis=1 in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.sum(a, [1]);
        expect(res.shape).toEqual([3]);
        expectArraysClose(await res.data(), [3, 3, 1]);
    });
    it('2D, axis=1 provided as number', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
        const res = tf.sum(a, 1);
        expect(res.shape).toEqual([2]);
        expectArraysClose(await res.data(), [6, 1]);
    });
    it('2D, axis = -1 provided as number', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
        const res = tf.sum(a, -1);
        expect(res.shape).toEqual([2]);
        expectArraysClose(await res.data(), [6, 1]);
    });
    it('sums across axis=0,1 in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.sum(a, [0, 1]);
        expect(res.shape).toEqual([]);
        expectArraysClose(await res.data(), [7]);
    });
    it('2D, axis=[-1,-2] in 2D array', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const res = tf.sum(a, [-1, -2]);
        expect(res.shape).toEqual([]);
        expectArraysClose(await res.data(), [7]);
    });
    it('4D, axis=[2, 2, 1], need permutation.', async () => {
        const a = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
        const res = tf.sum(a, 0);
        expect(res.shape).toEqual([2, 2, 1]);
        expectArraysClose(await res.data(), [1, 2, 3, 4]);
    });
    it('gradients: sum(2d)', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const dy = tf.scalar(10);
        const gradients = tf.grad(a => a.sum())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [10, 10, 10, 10, 10, 10]);
    });
    it('gradient with clones', async () => {
        const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
        const dy = tf.scalar(10);
        const gradients = tf.grad(a => a.clone().sum().clone())(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [10, 10, 10, 10, 10, 10]);
    });
    it('gradients: sum(2d, axis=0)', async () => {
        const a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);
        const dy = tf.tensor1d([10, 20]);
        const axis = 0;
        const gradients = tf.grad(a => a.sum(axis))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [10, 20, 10, 20, 10, 20]);
    });
    it('gradients: sum(2d, axis=1)', async () => {
        const a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);
        const dy = tf.tensor1d([10, 20, 30]);
        const axis = 1;
        const gradients = tf.grad(a => a.sum(axis))(a, dy);
        expect(gradients.shape).toEqual(a.shape);
        expect(gradients.dtype).toEqual('float32');
        expectArraysClose(await gradients.data(), [10, 10, 20, 20, 30, 30]);
    });
    it('throws when passed a non-tensor', () => {
        expect(() => tf.sum({}))
            .toThrowError(/Argument 'x' passed to 'sum' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const result = tf.sum([[1, 2], [3, 0], [0, 1]]);
        expectArraysClose(await result.data(), 7);
    });
    it('throws error for string tensor', () => {
        expect(() => tf.sum(['a']))
            .toThrowError(/Argument 'x' passed to 'sum' 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, expectArraysEqual} from '../test_util';\n\ndescribeWithFlags('sum', ALL_ENVS, () => {\n  it('basic', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const result = tf.sum(a);\n    expectArraysClose(await result.data(), 7);\n  });\n\n  it('propagates NaNs', async () => {\n    const a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);\n    expectArraysEqual(await tf.sum(a).data(), NaN);\n  });\n\n  it('sum over dtype int32', async () => {\n    const a = tf.tensor1d([1, 5, 7, 3], 'int32');\n    const sum = tf.sum(a);\n    expectArraysEqual(await sum.data(), 16);\n  });\n\n  it('sum over dtype bool', async () => {\n    const a = tf.tensor1d([true, false, false, true, true], 'bool');\n    const sum = tf.sum(a);\n    expectArraysEqual(await sum.data(), 3);\n  });\n\n  it('sums all values in 2D array with keep dim', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.sum(a, null, true /* keepDims */);\n\n    expect(res.shape).toEqual([1, 1]);\n    expectArraysClose(await res.data(), [7]);\n  });\n\n  it('sums across axis=0 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.sum(a, [0]);\n\n    expect(res.shape).toEqual([2]);\n    expectArraysClose(await res.data(), [4, 3]);\n  });\n\n  it('sums across axis=0 in 2D array, keepDims', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.sum(a, [0], true /* keepDims */);\n\n    expect(res.shape).toEqual([1, 2]);\n    expectArraysClose(await res.data(), [4, 3]);\n  });\n\n  it('sums across axis=1 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.sum(a, [1]);\n\n    expect(res.shape).toEqual([3]);\n    expectArraysClose(await res.data(), [3, 3, 1]);\n  });\n\n  it('2D, axis=1 provided as number', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);\n    const res = tf.sum(a, 1);\n\n    expect(res.shape).toEqual([2]);\n    expectArraysClose(await res.data(), [6, 1]);\n  });\n\n  it('2D, axis = -1 provided as number', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);\n    const res = tf.sum(a, -1);\n\n    expect(res.shape).toEqual([2]);\n    expectArraysClose(await res.data(), [6, 1]);\n  });\n\n  it('sums across axis=0,1 in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.sum(a, [0, 1]);\n\n    expect(res.shape).toEqual([]);\n    expectArraysClose(await res.data(), [7]);\n  });\n\n  it('2D, axis=[-1,-2] in 2D array', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const res = tf.sum(a, [-1, -2]);\n\n    expect(res.shape).toEqual([]);\n    expectArraysClose(await res.data(), [7]);\n  });\n\n  it('4D, axis=[2, 2, 1], need permutation.', async () => {\n    const a = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n    const res = tf.sum(a, 0);\n\n    expect(res.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await res.data(), [1, 2, 3, 4]);\n  });\n\n  it('gradients: sum(2d)', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const dy = tf.scalar(10);\n\n    const gradients = tf.grad(a => a.sum())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [10, 10, 10, 10, 10, 10]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);\n    const dy = tf.scalar(10);\n\n    const gradients = tf.grad(a => a.clone().sum().clone())(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [10, 10, 10, 10, 10, 10]);\n  });\n\n  it('gradients: sum(2d, axis=0)', async () => {\n    const a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);\n    const dy = tf.tensor1d([10, 20]);\n    const axis = 0;\n\n    const gradients = tf.grad(a => a.sum(axis))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [10, 20, 10, 20, 10, 20]);\n  });\n\n  it('gradients: sum(2d, axis=1)', async () => {\n    const a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);\n    const dy = tf.tensor1d([10, 20, 30]);\n    const axis = 1;\n\n    const gradients = tf.grad(a => a.sum(axis))(a, dy);\n\n    expect(gradients.shape).toEqual(a.shape);\n    expect(gradients.dtype).toEqual('float32');\n    expectArraysClose(await gradients.data(), [10, 10, 20, 20, 30, 30]);\n  });\n\n  it('throws when passed a non-tensor', () => {\n    expect(() => tf.sum({} as tf.Tensor))\n        .toThrowError(/Argument 'x' passed to 'sum' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const result = tf.sum([[1, 2], [3, 0], [0, 1]]);\n    expectArraysClose(await result.data(), 7);\n  });\n\n  it('throws error for string tensor', () => {\n    expect(() => tf.sum(['a']))\n        .toThrowError(/Argument 'x' passed to 'sum' must be numeric tensor/);\n  });\n});\n"]}