/**

* @license

* Copyright 2018 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 * as tf from '../index';

import {ALL_ENVS, describeWithFlags} from '../jasmine_util';

import {expectArraysClose} from '../test_util';

describeWithFlags('scatterND', ALL_ENVS, () => {

it('should work for 2d', async () => {

const indices = tf.tensor1d([0, 4, 2], 'int32');

const updates = tf.tensor2d(

[100, 101, 102, 777, 778, 779, 1000, 1001, 1002], [3, 3], 'int32');

const shape = [5, 3];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual(updates.dtype);

expectArraysClose(

await result.data(),

[100, 101, 102, 0, 0, 0, 1000, 1001, 1002, 0, 0, 0, 777, 778, 779]);

});

it('should work for simple 1d', async () => {

const indices = tf.tensor1d([3], 'int32');

const updates = tf.tensor1d([101], 'float32');

const shape = [5];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual(updates.dtype);

expectArraysClose(await result.data(), [0, 0, 0, 101, 0]);

});

it('should work for multiple 1d', async () => {

const indices = tf.tensor1d([0, 4, 2], 'int32');

const updates = tf.tensor1d([100, 101, 102], 'float32');

const shape = [5];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual(updates.dtype);

expectArraysClose(await result.data(), [100, 0, 102, 0, 101]);

});

it('should work for high rank updates', async () => {

const indices = tf.tensor2d([0, 2], [2, 1], 'int32');

const updates = tf.tensor3d(

[

5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,

5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8

],

[2, 4, 4], 'float32');

const shape = [4, 4, 4];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual(updates.dtype);

expectArraysClose(await result.data(), [

5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,

8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0

]);

});

it('should work for high rank indices', async () => {

const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');

const updates = tf.tensor1d([10, 20], 'float32');

const shape = [3, 3];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual(updates.dtype);

expectArraysClose(await result.data(), [0, 20, 10, 0, 0, 0, 0, 0, 0]);

});

it('should work for high rank indices and update', () => {

const indices = tf.tensor2d([1, 0, 0, 1, 0, 1], [3, 2], 'int32');

const updates = tf.ones([3, 256], 'float32');

const shape = [2, 2, 256];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual(updates.dtype);

});

it('should sum the duplicated indices', async () => {

const indices = tf.tensor1d([0, 4, 2, 1, 3, 0], 'int32');

const updates = tf.tensor1d([10, 20, 30, 40, 50, 60], 'float32');

const shape = [8];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual(updates.dtype);

expectArraysClose(await result.data(), [70, 40, 30, 50, 20, 0, 0, 0]);

});

it('should work for tensorLike input', async () => {

const indices = [0, 4, 2];

const updates = [100, 101, 102];

const shape = [5];

const result = tf.scatterND(indices, updates, shape);

expect(result.shape).toEqual(shape);

expect(result.dtype).toEqual('float32');

expectArraysClose(await result.data(), [100, 0, 102, 0, 101]);

});

it('should throw error when indices type is not int32', () => {

const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'float32');

const updates = tf.tensor1d([10, 20], 'float32');

const shape = [3, 3];

expect(() => tf.scatterND(indices, updates, shape)).toThrow();

});

it('should throw error when indices and update mismatch', () => {

const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');

const updates = tf.tensor2d(

[100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004],

[3, 4], 'float32');

const shape = [5, 3];

expect(() => tf.scatterND(indices, updates, shape)).toThrow();

});

it('should throw error when indices and update count mismatch', () => {

const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');

const updates =

tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');

const shape = [5, 3];

expect(() => tf.scatterND(indices, updates, shape)).toThrow();

});

it('should throw error when indices are scalar', () => {

const indices = tf.scalar(1, 'int32');

const updates =

tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');

const shape = [5, 3];

expect(() => tf.scatterND(indices, updates, shape)).toThrow();

});

it('should throw error when update is scalar', () => {

const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');

const updates = tf.scalar(1, 'float32');

const shape = [5, 3];

expect(() => tf.scatterND(indices, updates, shape)).toThrow();

});

});