/**
 * @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('atanh', ALL_ENVS, () => {
  it('basic', async () => {
    const values = [-0.25, 0.25, 0.5, .75, -0.4];
    const a = tf.tensor1d(values);
    const result = tf.atanh(a);

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

  it('scalar', async () => {
    const value = 0.2;
    const a = tf.scalar(value);
    const result = tf.atanh(a);

    const expected = [Math.atanh(value)];
    expectArraysClose(await result.data(), expected);
  });

  it('tensor2d', async () => {
    const values = [0.2, 0.3, 0.4, 0.5];
    const a = tf.tensor2d(values, [2, 2]);
    const result = tf.atanh(a);

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

  it('propagates NaNs', async () => {
    const a = tf.tensor1d([0.5, NaN, 0]);
    const res = tf.atanh(a);
    expectArraysClose(await res.data(), [Math.atanh(0.5), NaN, Math.atanh(0)]);
  });

  it('NaN outside function domain', async () => {
    const a = tf.tensor1d([-2, 0, 2]);
    const res = tf.atanh(a);
    expectArraysClose(await res.data(), [NaN, Math.atanh(0), NaN]);
  });

  it('gradients: Scalar', async () => {
    const a = tf.scalar(0.5);
    const dy = tf.scalar(8);

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

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(await gradients.data(), [8 / (1 - 0.5 * 0.5)]);
  });

  it('gradient with clones', async () => {
    const a = tf.scalar(0.5);
    const dy = tf.scalar(8);

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

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(await gradients.data(), [8 / (1 - 0.5 * 0.5)]);
  });

  it('gradients: Tensor1D', async () => {
    const aValues = [-0.1, 0.2, 0.3, -0.5];
    const dyValues = [1, 2, 3, 4];
    const a = tf.tensor1d(aValues);
    const dy = tf.tensor1d(dyValues);

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

    const expected = [];
    for (let i = 0; i < a.size; i++) {
      expected[i] = dyValues[i] / (1 - Math.pow(aValues[i], 2));
    }

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(await gradients.data(), expected);
  });

  it('gradients: Tensor2D', async () => {
    const aValues = [-0.3, 0.1, 0.2, 0.3];
    const dyValues = [1, 2, 3, 4];
    const a = tf.tensor2d(aValues, [2, 2]);
    const dy = tf.tensor2d(dyValues, [2, 2]);

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

    const expected = [];
    for (let i = 0; i < a.size; i++) {
      expected[i] = dyValues[i] / (1 - Math.pow(aValues[i], 2));
    }

    expect(gradients.shape).toEqual(a.shape);
    expect(gradients.dtype).toEqual('float32');
    expectArraysClose(await gradients.data(), expected);
  });

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

  it('accepts a tensor-like object', async () => {
    const result = tf.atanh(0.2);
    expectArraysClose(await result.data(), [Math.atanh(0.2)]);
  });

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