/**
 * @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('cos', ALL_ENVS, () => {
  it('basic', async () => {
    // Covers every 1/4pi range from -4pi to 4pi.
    const values = [1, 3, 4, 6, 7, 9, 10, 12, -1, -3, -4, -6, -7, -9, -10, -12];
    const a = tf.tensor1d(values);
    const result = tf.cos(a);

    const expected = [];
    for (let i = 0; i < a.size; i++) {
      expected[i] = Math.cos(values[i]);
    }
    expectArraysClose(await result.data(), expected);
  });

  it('propagates NaNs', async () => {
    const a = tf.tensor1d([4, NaN, 0]);
    const res = tf.cos(a);
    expectArraysClose(await res.data(), [Math.cos(4), NaN, Math.cos(0)]);
  });

  it('gradients: Scalar', async () => {
    const a = tf.scalar(5);
    const dy = tf.scalar(8);

    const gradients = tf.grad(a => tf.cos(a))(a, dy);

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(await gradients.data(), [8 * Math.sin(5) * -1]);
  });

  it('gradient with clones', async () => {
    const a = tf.scalar(5);
    const dy = tf.scalar(8);

    const gradients = tf.grad(a => tf.cos(a.clone()).clone())(a, dy);

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(await gradients.data(), [8 * Math.sin(5) * -1]);
  });

  it('gradients: Tensor1D', async () => {
    const a = tf.tensor1d([-1, 2, 3, -5]);
    const dy = tf.tensor1d([1, 2, 3, 4]);

    const gradients = tf.grad(a => tf.cos(a))(a, dy);

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(
        await gradients.data(),
        [
          1 * Math.sin(-1) * -1, 2 * Math.sin(2) * -1, 3 * Math.sin(3) * -1,
          4 * Math.sin(-5) * -1
        ],
        1e-1);
  });

  it('gradients: Tensor2D', async () => {
    const a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
    const dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);

    const gradients = tf.grad(a => tf.cos(a))(a, dy);

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(
        await gradients.data(),
        [
          1 * Math.sin(-3) * -1, 2 * Math.sin(1) * -1, 3 * Math.sin(2) * -1,
          4 * Math.sin(3) * -1
        ],
        1e-1);
  });

  it('throws when passed a non-tensor', () => {
    expect(() => tf.cos({} as tf.Tensor))
        .toThrowError(/Argument 'x' passed to 'cos' must be a Tensor/);
  });

  it('accepts a tensor-like object', async () => {
    const values = [1, -3, 2, 7, -4];
    const result = tf.cos(values);

    const expected = [];
    for (let i = 0; i < values.length; i++) {
      expected[i] = Math.cos(values[i]);
    }
    expectArraysClose(await result.data(), expected);
  });

  it('throws for string tensor', () => {
    expect(() => tf.cos('q'))
        .toThrowError(/Argument 'x' passed to 'cos' must be float32/);
  });

  it('throws for int32 tensor', async () => {
    expect(() => tf.cos(tf.tensor([10], [1], 'int32')))
        .toThrowError(/Argument 'x' passed to 'cos' must be float32/);
  });
});
