import * as tf from '../../dist/tfjs.esm';

import { NetInput, TNetInput, toNetInput } from '../dom/index';
import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
import { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
import { FaceProcessor } from '../faceProcessor/FaceProcessor';
import { FaceExpressions } from './FaceExpressions';

export class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {
  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {
    super('FaceExpressionNet', faceFeatureExtractor);
  }

  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {
    return tf.tidy(() => tf.softmax(this.runNet(input)));
  }

  public async forward(input: TNetInput): Promise<tf.Tensor2D> {
    return this.forwardInput(await toNetInput(input));
  }

  public async predictExpressions(input: TNetInput) {
    const netInput = await toNetInput(input);
    const out = await this.forwardInput(netInput);
    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async (t) => {
      const data = t.dataSync();
      t.dispose();
      return data;
    }));
    out.dispose();

    const predictionsByBatch = probabilitesByBatch
      .map((probabilites) => new FaceExpressions(probabilites as Float32Array));

    return netInput.isBatchInput
      ? predictionsByBatch
      : predictionsByBatch[0];
  }

  protected getDefaultModelName(): string {
    return 'face_expression_model';
  }

  protected getClassifierChannelsIn(): number {
    return 256;
  }

  protected getClassifierChannelsOut(): number {
    return 7;
  }
}