/**
 * @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('oneHot', ALL_ENVS, () => {
    it('Depth 1 throws error', () => {
        const indices = tf.tensor1d([0, 0, 0], 'int32');
        expect(() => tf.oneHot(indices, 1)).toThrowError();
    });
    it('Depth 2, diagonal', async () => {
        const indices = tf.tensor1d([0, 1], 'int32');
        const res = tf.oneHot(indices, 2);
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose(await res.data(), [1, 0, 0, 1]);
    });
    it('Scalar input as Tensor', async () => {
        const indices = tf.scalar(2, 'int32');
        const res = tf.oneHot(indices, 4);
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [0, 0, 1, 0]);
    });
    it('Scalar input as number', async () => {
        const indices = 2;
        const res = tf.oneHot(indices, 4);
        expect(res.shape).toEqual([4]);
        expectArraysClose(await res.data(), [0, 0, 1, 0]);
    });
    it('oneHot with chaining compiles', () => {
        const indices = 2;
        // Asserts that there is no compiler error.
        tf.oneHot(indices, 4).toFloat();
    });
    it('Depth 2, transposed diagonal', async () => {
        const indices = tf.tensor1d([1, 0], 'int32');
        const res = tf.oneHot(indices, 2);
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose(await res.data(), [0, 1, 1, 0]);
    });
    it('Depth 3, 4 events', async () => {
        const indices = tf.tensor1d([2, 1, 2, 0], 'int32');
        const res = tf.oneHot(indices, 3);
        expect(res.shape).toEqual([4, 3]);
        expectArraysClose(await res.data(), [0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]);
    });
    it('Out of range events do not trigger onValue', async () => {
        const indices = tf.tensor1d([-1, 5, 12345], 'int32');
        const res = tf.oneHot(indices, 5);
        expect(res.shape).toEqual([3, 5]);
        expectArraysClose(await res.data(), [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
    });
    it('Depth 2 onValue=3, offValue=-2', async () => {
        const indices = tf.tensor1d([0, 1], 'int32');
        const res = tf.oneHot(indices, 2, 3, -2);
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose(await res.data(), [3, -2, -2, 3]);
    });
    it('indices not int32 throws error', () => {
        const indices = tf.tensor1d([0, 1], 'float32');
        expect(() => tf.oneHot(indices, 2)).toThrowError();
    });
    it('check output dtype', () => {
        const expectedType = 'int32';
        const indices = tf.tensor1d([0, 1], 'int32');
        const res = tf.oneHot(indices, 2);
        expect(res.dtype).toEqual(expectedType);
    });
    it('check specified output dtype', () => {
        const expectedType = 'float32';
        const indices = tf.tensor1d([0, 1], 'int32');
        const res = tf.oneHot(indices, 2, 1, 0, 'float32');
        expect(res.dtype).toEqual(expectedType);
    });
    it('oneHot accepts a tensor-like object', async () => {
        const res = tf.oneHot([0, 1], 2);
        expect(res.shape).toEqual([2, 2]);
        expectArraysClose(await res.data(), [1, 0, 0, 1]);
    });
    it('has gradient', async () => {
        const a = tf.tensor1d([0, 1, 2], 'int32');
        const dy = tf.ones([3, 3], 'float32');
        const da = tf.grad((x) => tf.oneHot(x, 3))(a, dy);
        expect(da.dtype).toBe('float32');
        expect(da.shape).toEqual([3]);
        expectArraysClose(await da.data(), [0, 0, 0]);
    });
    it('gradient with clones', async () => {
        const a = tf.tensor1d([0, 1, 2], 'int32');
        const dy = tf.ones([3, 3], 'float32');
        const da = tf.grad((x) => tf.oneHot(x.clone(), 3).clone())(a, dy);
        expect(da.dtype).toBe('float32');
        expect(da.shape).toEqual([3]);
        expectArraysClose(await da.data(), [0, 0, 0]);
    });
    it('gradient when indices is 3d', async () => {
        const a = tf.tensor3d([1, 2, 3, 4], [1, 2, 2], 'int32');
        const dy = tf.ones([1, 2, 2, 3], 'float32');
        const depth = 3;
        const da = tf.grad(x => tf.oneHot(x, depth))(a, dy);
        expect(da.dtype).toBe('float32');
        expect(da.shape).toEqual(a.shape);
        expectArraysClose(await da.data(), [0, 0, 0, 0]);
    });
    it('oneHot with indices as 2d', async () => {
        const indices = tf.tensor2d([[1, 3], [2, 3]], [2, 2], 'int32');
        const depth = 4;
        const res = tf.oneHot(indices, depth);
        expect(res.shape).toEqual([2, 2, depth]);
        expectArraysClose(await res.data(), [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1]);
    });
    it('Supports chaining', async () => {
        const indices = tf.tensor2d([[1, 2, 3], [2, 3, 1], [4, 5, 6]], [3, 3], 'int32');
        const depth = 6;
        const onValue = 3;
        const offValue = 7;
        const res = indices.oneHot(depth, onValue, offValue);
        expect(res.shape).toEqual([3, 3, 6]);
        expectArraysClose(await res.data(), [
            7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7,
            7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 7, 7,
            7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7
        ]);
    });
});
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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('oneHot', ALL_ENVS, () => {\n  it('Depth 1 throws error', () => {\n    const indices = tf.tensor1d([0, 0, 0], 'int32');\n    expect(() => tf.oneHot(indices, 1)).toThrowError();\n  });\n\n  it('Depth 2, diagonal', async () => {\n    const indices = tf.tensor1d([0, 1], 'int32');\n    const res = tf.oneHot(indices, 2);\n\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [1, 0, 0, 1]);\n  });\n\n  it('Scalar input as Tensor', async () => {\n    const indices = tf.scalar(2, 'int32');\n    const res = tf.oneHot(indices, 4);\n\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [0, 0, 1, 0]);\n  });\n\n  it('Scalar input as number', async () => {\n    const indices = 2;\n    const res = tf.oneHot(indices, 4);\n\n    expect(res.shape).toEqual([4]);\n    expectArraysClose(await res.data(), [0, 0, 1, 0]);\n  });\n\n  it('oneHot with chaining compiles', () => {\n    const indices = 2;\n    // Asserts that there is no compiler error.\n    tf.oneHot(indices, 4).toFloat();\n  });\n\n  it('Depth 2, transposed diagonal', async () => {\n    const indices = tf.tensor1d([1, 0], 'int32');\n    const res = tf.oneHot(indices, 2);\n\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [0, 1, 1, 0]);\n  });\n\n  it('Depth 3, 4 events', async () => {\n    const indices = tf.tensor1d([2, 1, 2, 0], 'int32');\n    const res = tf.oneHot(indices, 3);\n\n    expect(res.shape).toEqual([4, 3]);\n    expectArraysClose(await res.data(), [0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]);\n  });\n\n  it('Out of range events do not trigger onValue', async () => {\n    const indices = tf.tensor1d([-1, 5, 12345], 'int32');\n    const res = tf.oneHot(indices, 5);\n    expect(res.shape).toEqual([3, 5]);\n    expectArraysClose(\n        await res.data(), [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);\n  });\n\n  it('Depth 2 onValue=3, offValue=-2', async () => {\n    const indices = tf.tensor1d([0, 1], 'int32');\n    const res = tf.oneHot(indices, 2, 3, -2);\n\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [3, -2, -2, 3]);\n  });\n\n  it('indices not int32 throws error', () => {\n    const indices = tf.tensor1d([0, 1], 'float32');\n    expect(() => tf.oneHot(indices, 2)).toThrowError();\n  });\n\n  it('check output dtype', () => {\n    const expectedType = 'int32';\n    const indices = tf.tensor1d([0, 1], 'int32');\n    const res = tf.oneHot(indices, 2);\n\n    expect(res.dtype).toEqual(expectedType);\n  });\n\n  it('check specified output dtype', () => {\n    const expectedType = 'float32';\n    const indices = tf.tensor1d([0, 1], 'int32');\n    const res = tf.oneHot(indices, 2, 1, 0, 'float32');\n\n    expect(res.dtype).toEqual(expectedType);\n  });\n\n  it('oneHot accepts a tensor-like object', async () => {\n    const res = tf.oneHot([0, 1], 2);\n    expect(res.shape).toEqual([2, 2]);\n    expectArraysClose(await res.data(), [1, 0, 0, 1]);\n  });\n\n  it('has gradient', async () => {\n    const a = tf.tensor1d([0, 1, 2], 'int32');\n    const dy = tf.ones([3, 3], 'float32');\n    const da = tf.grad((x: tf.Tensor1D) => tf.oneHot(x, 3))(a, dy);\n\n    expect(da.dtype).toBe('float32');\n    expect(da.shape).toEqual([3]);\n    expectArraysClose(await da.data(), [0, 0, 0]);\n  });\n\n  it('gradient with clones', async () => {\n    const a = tf.tensor1d([0, 1, 2], 'int32');\n    const dy = tf.ones([3, 3], 'float32');\n    const da =\n        tf.grad((x: tf.Tensor1D) => tf.oneHot(x.clone(), 3).clone())(a, dy);\n\n    expect(da.dtype).toBe('float32');\n    expect(da.shape).toEqual([3]);\n    expectArraysClose(await da.data(), [0, 0, 0]);\n  });\n\n  it('gradient when indices is 3d', async () => {\n    const a = tf.tensor3d([1, 2, 3, 4], [1, 2, 2], 'int32');\n    const dy = tf.ones([1, 2, 2, 3], 'float32');\n    const depth = 3;\n    const da = tf.grad(x => tf.oneHot(x, depth))(a, dy);\n    expect(da.dtype).toBe('float32');\n    expect(da.shape).toEqual(a.shape);\n    expectArraysClose(await da.data(), [0, 0, 0, 0]);\n  });\n\n  it('oneHot with indices as 2d', async () => {\n    const indices = tf.tensor2d([[1, 3], [2, 3]], [2, 2], 'int32');\n    const depth = 4;\n    const res = tf.oneHot(indices, depth);\n    expect(res.shape).toEqual([2, 2, depth]);\n    expectArraysClose(\n        await res.data(), [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1]);\n  });\n\n  it('Supports chaining', async () => {\n    const indices =\n        tf.tensor2d([[1, 2, 3], [2, 3, 1], [4, 5, 6]], [3, 3], 'int32');\n    const depth = 6;\n    const onValue = 3;\n    const offValue = 7;\n    const res = indices.oneHot(depth, onValue, offValue);\n\n    expect(res.shape).toEqual([3, 3, 6]);\n    expectArraysClose(await res.data(), [\n      7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7,\n      7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 3, 7, 7, 7, 7,\n      7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7, 3, 7, 7, 7, 7, 7, 7\n    ]);\n  });\n});\n"]}