/* eslint-disable no-var */
// @ts-nocheck

/*
 * Copyright (c) 2019 Genome Research Ltd.
 * Author(s): James Bonfield
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 *    1. Redistributions of source code must retain the above copyright notice,
 *       this list of conditions and the following disclaimer.
 *
 *    2. Redistributions in binary form must reproduce the above
 *       copyright notice, this list of conditions and the following
 *       disclaimer in the documentation and/or other materials provided
 *       with the distribution.
 *
 *    3. Neither the names Genome Research Ltd and Wellcome Trust Sanger
 *       Institute nor the names of its contributors may be used to endorse
 *       or promote products derived from this software without specific
 *       prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY GENOME RESEARCH LTD AND CONTRIBUTORS "AS
 * IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED
 * TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
 * PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL GENOME RESEARCH
 * LTD OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
 * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */

// An adaptive probability model for encoding and decoding of symbols
// within a given alphabet, using the range coder to get/put the
// compressed data.

const MAX_FREQ = (1 << 16) - 17
const STEP = 16

export default class ByteModel {
  constructor(max_sym = 256) {
    this.total_freq = max_sym
    this.max_sym = max_sym - 1
    this.S = []
    this.F = []

    for (let i = 0; i <= this.max_sym; i++) {
      this.S[i] = i
      this.F[i] = 1
    }
  }

  ModelDecode(src, rc) {
    // Find symbol
    const freq = rc.RangeGetFrequency(this.total_freq)

    // Linear scan to find cumulative frequency 'freq'
    let acc = 0
    let x = 0
    while (acc + this.F[x] <= freq) {
      acc += this.F[x++]
    }

    //	for (var acc = 0; (acc += this.F[x]) <= freq; x++)
    //	    ;
    //	acc -= this.F[x];

    // Update range coder
    rc.RangeDecode(src, acc, this.F[x], this.total_freq)

    // Update model
    this.F[x] += STEP
    this.total_freq += STEP
    if (this.total_freq > MAX_FREQ) {
      this.ModelRenormalise()
    }

    // Keep symbols approximately frequency sorted
    const sym = this.S[x]
    if (x > 0 && this.F[x] > this.F[x - 1]) {
      let tmp = this.F[x]
      this.F[x] = this.F[x - 1]
      this.F[x - 1] = tmp

      tmp = this.S[x]
      this.S[x] = this.S[x - 1]
      this.S[x - 1] = tmp
    }

    return sym
  }

  ModelRenormalise() {
    // Halve all the frequencies, being careful not to hit zero
    this.total_freq = 0
    for (let i = 0; i <= this.max_sym; i++) {
      this.F[i] -= Math.floor(this.F[i] / 2)
      this.total_freq += this.F[i]
    }
  }

  ModelEncode(dst, rc, sym) {
    // Find cumulative frequency
    let acc = 0
    for (var x = 0; this.S[x] != sym; x++) {
      acc += this.F[x]
    }

    // Encode
    rc.RangeEncode(dst, acc, this.F[x], this.total_freq)

    // Update model
    this.F[x] += STEP
    this.total_freq += STEP
    if (this.total_freq > MAX_FREQ) {
      // FIXME x2
      this.ModelRenormalise()
    }

    // Keep symbols approximately frequency sorted
    var sym = this.S[x]
    if (x > 0 && this.F[x] > this.F[x - 1]) {
      let tmp = this.F[x]
      this.F[x] = this.F[x - 1]
      this.F[x - 1] = tmp

      tmp = this.S[x]
      this.S[x] = this.S[x - 1]
      this.S[x - 1] = tmp
    }
  }
}