/* eslint-env browser, mocha */

describe('core layer: Dense', 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%ccore layer: Dense', styles.h1)
  })

  /*********************************************************
  * CPU
  *********************************************************/

  describe('CPU', function () {
    before(function () {
      console.log('\n%cCPU', styles.h2)
    })

    it('[core.Dense.0] [CPU] should produce expected values', function () {
      const key = 'core.Dense.0'
      console.log(`\n%c[${key}] [CPU] test 1`, styles.h3)
      let testLayer = new layers.Dense({ outputDim: 2 })
      testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
      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('[core.Dense.1] [CPU] should produce expected values, with sigmoid activation function', function () {
      const key = 'core.Dense.1'
      console.log(`\n%c[${key}] [CPU] test 2 (with sigmoid activation)`, styles.h3)
      let testLayer = new layers.Dense({ outputDim: 2, activation: 'sigmoid' })
      testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
      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('[core.Dense.2] [CPU] should produce expected values, with softplus activation function and no bias', function () {
      const key = 'core.Dense.2'
      console.log(`\n%c[${key}] [CPU] test 3 (with softplus activation and no bias)`, styles.h3)
      let testLayer = new layers.Dense({ outputDim: 2, activation: 'softplus', bias: false })
      testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
      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))
    })
  })

  /*********************************************************
  * GPU
  *********************************************************/

  describe('GPU', function () {
    before(function () {
      console.log('\n%cGPU', styles.h2)
    })

    it('[core.Dense.3] [GPU] should produce expected values', function () {
      const key = 'core.Dense.3'
      console.log(`\n%c[${key}] [GPU] test 1`, styles.h3)
      let testLayer = new layers.Dense({ outputDim: 2, gpu: true })
      testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
      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('[core.Dense.4] [GPU] should produce expected values, with sigmoid activation function', function () {
      const key = 'core.Dense.4'
      console.log(`\n%c[${key}] [GPU] test 2 (with sigmoid activation)`, styles.h3)
      let testLayer = new layers.Dense({ outputDim: 2, activation: 'sigmoid', gpu: true })
      testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
      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('[core.Dense.5] [GPU] should produce expected values, with softplus activation function and no bias', function () {
      const key = 'core.Dense.5'
      console.log(`\n%c[${key}] [GPU] test 3 (with softplus activation and no bias)`, styles.h3)
      let testLayer = new layers.Dense({ outputDim: 2, activation: 'softplus', bias: false, gpu: true })
      testLayer.setWeights(TEST_DATA[key].weights.map(w => new KerasJS.Tensor(w.data, w.shape)))
      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))
    })
  })
})