/* eslint-env browser, mocha */

describe('convolutional layer: ZeroPadding2D', function () {
  const assert = chai.assert
  const styles = testGlobals.styles
  const logTime = testGlobals.logTime
  const stringifyCondensed = testGlobals.stringifyCondensed
  const approxEquals = KerasJS.testUtils.approxEquals
  const layers = KerasJS.layers

  before(function () {
    console.log('\n%cconvolutional layer: ZeroPadding2D', styles.h1)
  })

  it(`[convolutional.ZeroPadding2D.0] padding 1,1 on 3x5x2 input, dimOrdering=tf`, function () {
    const key = `convolutional.ZeroPadding2D.0`
    console.log(`\n%c[${key}] padding 1,1 on 3x5x2 input, dimOrdering=tf`, styles.h3)
    let testLayer = new layers.ZeroPadding2D({ padding: [1, 1], dimOrdering: 'tf' })
    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
    const startTime = performance.now()
    t = testLayer.call(t)
    const endTime = performance.now()
    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
    logTime(startTime, endTime)
    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
    const shapeExpected = TEST_DATA[key].expected.shape
    assert.deepEqual(t.tensor.shape, shapeExpected)
    assert.isTrue(approxEquals(t.tensor, dataExpected))
  })

  it(`[convolutional.ZeroPadding2D.1] padding 1,1 on 3x5x2 input, dimOrdering=th`, function () {
    const key = `convolutional.ZeroPadding2D.1`
    console.log(`\n%c[${key}] padding 1,1 on 3x5x2 input, dimOrdering=th`, styles.h3)
    let testLayer = new layers.ZeroPadding2D({ padding: [1, 1], dimOrdering: 'th' })
    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
    const startTime = performance.now()
    t = testLayer.call(t)
    const endTime = performance.now()
    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
    logTime(startTime, endTime)
    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
    const shapeExpected = TEST_DATA[key].expected.shape
    assert.deepEqual(t.tensor.shape, shapeExpected)
    assert.isTrue(approxEquals(t.tensor, dataExpected))
  })

  it(`[convolutional.ZeroPadding2D.2] padding 3,2 on 2x6x4 input, dimOrdering=tf`, function () {
    const key = `convolutional.ZeroPadding2D.2`
    console.log(`\n%c[${key}] padding 3,2 on 2x6x4 input, dimOrdering=tf`, styles.h3)
    let testLayer = new layers.ZeroPadding2D({ padding: [3, 2], dimOrdering: 'tf' })
    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
    const startTime = performance.now()
    t = testLayer.call(t)
    const endTime = performance.now()
    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
    logTime(startTime, endTime)
    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
    const shapeExpected = TEST_DATA[key].expected.shape
    assert.deepEqual(t.tensor.shape, shapeExpected)
    assert.isTrue(approxEquals(t.tensor, dataExpected))
  })

  it(`[convolutional.ZeroPadding2D.3] padding 3,2 on 2x6x4 input, dimOrdering=th`, function () {
    const key = `convolutional.ZeroPadding2D.3`
    console.log(`\n%c[${key}] padding 3,2 on 2x6x4 input, dimOrdering=th`, styles.h3)
    let testLayer = new layers.ZeroPadding2D({ padding: [3, 2], dimOrdering: 'th' })
    let t = new KerasJS.Tensor(TEST_DATA[key].input.data, TEST_DATA[key].input.shape)
    console.log('%cin', styles.h4, stringifyCondensed(t.tensor))
    const startTime = performance.now()
    t = testLayer.call(t)
    const endTime = performance.now()
    console.log('%cout', styles.h4, stringifyCondensed(t.tensor))
    logTime(startTime, endTime)
    const dataExpected = new Float32Array(TEST_DATA[key].expected.data)
    const shapeExpected = TEST_DATA[key].expected.shape
    assert.deepEqual(t.tensor.shape, shapeExpected)
    assert.isTrue(approxEquals(t.tensor, dataExpected))
  })
})