import * as tf from '@tensorflow/tfjs'
import { load } from '../src/index'
const fs = require('fs');
const jpeg = require('jpeg-js');


// Fix for JEST
const globalAny: any = global
globalAny.fetch = require('node-fetch')
const timeoutMS = 10000
const NUMBER_OF_CHANNELS = 3


const readImage = (path: string) => {
  const buf = fs.readFileSync(path)
  const pixels = jpeg.decode(buf, true)
  return pixels
}

// @ts-ignore
const imageByteArray = (image, numChannels: number) => {
  const pixels = image.data
  const numPixels = image.width * image.height;
  const values = new Int32Array(numPixels * numChannels);

  for (let i = 0; i < numPixels; i++) {
    for (let channel = 0; channel < numChannels; ++channel) {
      values[i * numChannels + channel] = pixels[i * 4 + channel];
    }
  }

  return values
}

// @ts-ignore
const imageToInput = (image, numChannels: number) => {
  const values = imageByteArray(image, numChannels)
  const outShape = [image.height, image.width, numChannels] as [number, number, number];
  const input = tf.tensor3d(values, outShape, 'int32');

  return input
}

it("Snapshots", async () => {
  const model = await load()
  const logo = readImage(`${__dirname}/../_art/nsfwjs_logo.jpg`)
  const input = imageToInput(logo, NUMBER_OF_CHANNELS)
  const predictions = await model.classify(input)
  expect(predictions).toMatchSnapshot()
}, timeoutMS)