/**
 * @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('frame', ALL_ENVS, () => {
    it('3 length frames', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 3;
        const frameStep = 1;
        const output = tf.signal.frame(input, frameLength, frameStep);
        expect(output.shape).toEqual([3, 3]);
        expectArraysClose(await output.data(), [1, 2, 3, 2, 3, 4, 3, 4, 5]);
    });
    it('3 length frames with step 2', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 3;
        const frameStep = 2;
        const output = tf.signal.frame(input, frameLength, frameStep);
        expect(output.shape).toEqual([2, 3]);
        expectArraysClose(await output.data(), [1, 2, 3, 3, 4, 5]);
    });
    it('3 length frames with step 5', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 3;
        const frameStep = 5;
        const output = tf.signal.frame(input, frameLength, frameStep);
        expect(output.shape).toEqual([1, 3]);
        expectArraysClose(await output.data(), [1, 2, 3]);
    });
    it('Exceeding frame length', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 6;
        const frameStep = 1;
        const output = tf.signal.frame(input, frameLength, frameStep);
        expect(output.shape).toEqual([0, 6]);
        expectArraysClose(await output.data(), []);
    });
    it('Zero frame step', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 6;
        const frameStep = 0;
        const output = tf.signal.frame(input, frameLength, frameStep);
        expect(output.shape).toEqual([0, 6]);
        expectArraysClose(await output.data(), []);
    });
    it('Padding with default value', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 3;
        const frameStep = 3;
        const padEnd = true;
        const output = tf.signal.frame(input, frameLength, frameStep, padEnd);
        expect(output.shape).toEqual([2, 3]);
        expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 0]);
    });
    it('Padding with the given value', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 3;
        const frameStep = 3;
        const padEnd = true;
        const padValue = 100;
        const output = tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);
        expect(output.shape).toEqual([2, 3]);
        expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 100]);
    });
    it('Padding all remaining frames with step=1', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 4;
        const frameStep = 1;
        const padEnd = true;
        const output = tf.signal.frame(input, frameLength, frameStep, padEnd);
        expect(output.shape).toEqual([5, 4]);
        expectArraysClose(await output.data(), [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 0, 4, 5, 0, 0, 5, 0, 0, 0]);
    });
    it('Padding all remaining frames with step=1 and given pad-value', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const frameLength = 4;
        const frameStep = 1;
        const padEnd = true;
        const padValue = 42;
        const output = tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);
        expect(output.shape).toEqual([5, 4]);
        expectArraysClose(await output.data(), [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 42, 4, 5, 42, 42, 5, 42, 42, 42]);
    });
    it('Padding all remaining frames with step=2', async () => {
        const input = tf.tensor1d([1, 2, 3, 4, 5]);
        const output = tf.signal.frame(input, 4, 2, true);
        expect(output.shape).toEqual([3, 4]);
        expectArraysClose(await output.data(), [1, 2, 3, 4, 3, 4, 5, 0, 5, 0, 0, 0]);
    });
});
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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('frame', ALL_ENVS, () => {\n  it('3 length frames', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 3;\n    const frameStep = 1;\n    const output = tf.signal.frame(input, frameLength, frameStep);\n    expect(output.shape).toEqual([3, 3]);\n    expectArraysClose(await output.data(), [1, 2, 3, 2, 3, 4, 3, 4, 5]);\n  });\n\n  it('3 length frames with step 2', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 3;\n    const frameStep = 2;\n    const output = tf.signal.frame(input, frameLength, frameStep);\n    expect(output.shape).toEqual([2, 3]);\n    expectArraysClose(await output.data(), [1, 2, 3, 3, 4, 5]);\n  });\n\n  it('3 length frames with step 5', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 3;\n    const frameStep = 5;\n    const output = tf.signal.frame(input, frameLength, frameStep);\n    expect(output.shape).toEqual([1, 3]);\n    expectArraysClose(await output.data(), [1, 2, 3]);\n  });\n\n  it('Exceeding frame length', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 6;\n    const frameStep = 1;\n    const output = tf.signal.frame(input, frameLength, frameStep);\n    expect(output.shape).toEqual([0, 6]);\n    expectArraysClose(await output.data(), []);\n  });\n\n  it('Zero frame step', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 6;\n    const frameStep = 0;\n    const output = tf.signal.frame(input, frameLength, frameStep);\n    expect(output.shape).toEqual([0, 6]);\n    expectArraysClose(await output.data(), []);\n  });\n\n  it('Padding with default value', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 3;\n    const frameStep = 3;\n    const padEnd = true;\n    const output = tf.signal.frame(input, frameLength, frameStep, padEnd);\n    expect(output.shape).toEqual([2, 3]);\n    expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 0]);\n  });\n\n  it('Padding with the given value', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 3;\n    const frameStep = 3;\n    const padEnd = true;\n    const padValue = 100;\n    const output =\n        tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);\n    expect(output.shape).toEqual([2, 3]);\n    expectArraysClose(await output.data(), [1, 2, 3, 4, 5, 100]);\n  });\n\n  it('Padding all remaining frames with step=1', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const frameLength = 4;\n    const frameStep = 1;\n    const padEnd = true;\n    const output = tf.signal.frame(input, frameLength, frameStep, padEnd);\n    expect(output.shape).toEqual([5, 4]);\n    expectArraysClose(\n        await output.data(),\n        [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 0, 4, 5, 0, 0, 5, 0, 0, 0]);\n  });\n\n  it('Padding all remaining frames with step=1 and given pad-value',\n     async () => {\n       const input = tf.tensor1d([1, 2, 3, 4, 5]);\n       const frameLength = 4;\n       const frameStep = 1;\n       const padEnd = true;\n       const padValue = 42;\n       const output =\n           tf.signal.frame(input, frameLength, frameStep, padEnd, padValue);\n       expect(output.shape).toEqual([5, 4]);\n       expectArraysClose(\n           await output.data(),\n           [1, 2, 3, 4, 2, 3, 4, 5, 3, 4, 5, 42, 4, 5, 42, 42, 5, 42, 42, 42]);\n     });\n\n  it('Padding all remaining frames with step=2', async () => {\n    const input = tf.tensor1d([1, 2, 3, 4, 5]);\n    const output = tf.signal.frame(input, 4, 2, true);\n    expect(output.shape).toEqual([3, 4]);\n    expectArraysClose(\n        await output.data(), [1, 2, 3, 4, 3, 4, 5, 0, 5, 0, 0, 0]);\n  });\n});\n"]}