/* eslint-env browser, mocha */

describe('convolutional layer: Convolution1D', 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

  const testParams = [
    {
      inputShape: [5, 2],
      kernelShape: [4, 3],
      attrs: { activation: 'linear', borderMode: 'valid', subsampleLength: 1, bias: true }
    },
    {
      inputShape: [6, 3],
      kernelShape: [4, 3],
      attrs: { activation: 'linear', borderMode: 'valid', subsampleLength: 1, bias: false }
    },
    {
      inputShape: [4, 6],
      kernelShape: [2, 3],
      attrs: { activation: 'sigmoid', borderMode: 'same', subsampleLength: 2, bias: true }
    },
    {
      inputShape: [8, 3],
      kernelShape: [2, 7],
      attrs: { activation: 'tanh', borderMode: 'same', subsampleLength: 1, bias: true }
    }
  ]

  before(function () {
    console.log('\n%cconvolutional layer: Convolution1D', styles.h1)
  })

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

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

    testParams.forEach(({ inputShape, kernelShape, attrs }, i) => {
      const key = `convolutional.Convolution1D.${i}`
      const [inputLength, inputFeatures] = inputShape
      const [nbFilter, filterLength] = kernelShape
      const title = `[${key}] [CPU] test: ${nbFilter} length ${filterLength} filters on ${inputLength}x${inputFeatures} input, activation='${attrs.activation}', border_mode='${attrs.borderMode}', subsampleLength=${attrs.subsampleLength}, bias=${attrs.bias}`

      it(title, function () {
        console.log(`\n%c${title}`, styles.h3)
        let testLayer = new layers.Convolution1D(Object.assign({ nbFilter, filterLength }, attrs))
        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)
    })

    testParams.forEach(({ inputShape, kernelShape, attrs }, i) => {
      const key = `convolutional.Convolution1D.${i}`
      const [inputLength, inputFeatures] = inputShape
      const [nbFilter, filterLength] = kernelShape
      const title = `[${key}] [GPU] test: ${nbFilter} length ${filterLength} filters on ${inputLength}x${inputFeatures} input, activation='${attrs.activation}', border_mode='${attrs.borderMode}', subsampleLength=${attrs.subsampleLength}, bias=${attrs.bias}`

      it(title, function () {
        console.log(`\n%c${title}`, styles.h3)
        let testLayer = new layers.Convolution1D(Object.assign({ nbFilter, filterLength }, attrs, { 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))
      })
    })
  })
})