/*

 * Copyright (c) 2021, Alliance for Open Media. All rights reserved.

 *

 * This source code is subject to the terms of the BSD 2 Clause License and

 * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License

 * was not distributed with this source code in the LICENSE file, you can

 * obtain it at www.aomedia.org/license/software. If the Alliance for Open

 * Media Patent License 1.0 was not distributed with this source code in the

 * PATENTS file, you can obtain it at www.aomedia.org/license/patent.

 */

#include <assert.h>

#include "config/aom_config.h"

#include "aom_util/aom_pthread.h"

#if CONFIG_TFLITE

#include "tensorflow/lite/c/c_api.h"

#include "av1/encoder/deltaq4_model.c"

#endif

#include "av1/common/common_data.h"

#include "av1/common/enums.h"

#include "av1/common/idct.h"

#include "av1/common/reconinter.h"

#include "av1/encoder/allintra_vis.h"

#include "av1/encoder/aq_variance.h"

#include "av1/encoder/encoder.h"

#include "av1/encoder/ethread.h"

#include "av1/encoder/hybrid_fwd_txfm.h"

#include "av1/encoder/model_rd.h"

#include "av1/encoder/rdopt_utils.h"

#define MB_WIENER_PRED_BLOCK_SIZE BLOCK_128X128

#define MB_WIENER_PRED_BUF_STRIDE 128

// Maximum delta-q range allowed for Variance Boost after scaling

#define VAR_BOOST_MAX_DELTAQ_RANGE 80

// Maximum quantization step boost allowed for Variance Boost

#define VAR_BOOST_MAX_BOOST 8.0

void av1_alloc_mb_wiener_var_pred_buf(AV1_COMMON *cm, ThreadData *td) {

  const int is_high_bitdepth = is_cur_buf_hbd(&td->mb.e_mbd);

  assert(MB_WIENER_PRED_BLOCK_SIZE < BLOCK_SIZES_ALL);

  const int buf_width = block_size_wide[MB_WIENER_PRED_BLOCK_SIZE];

  const int buf_height = block_size_high[MB_WIENER_PRED_BLOCK_SIZE];

  assert(buf_width == MB_WIENER_PRED_BUF_STRIDE);

  const size_t buf_size =

      (buf_width * buf_height * sizeof(*td->wiener_tmp_pred_buf))

      << is_high_bitdepth;

  CHECK_MEM_ERROR(cm, td->wiener_tmp_pred_buf, aom_memalign(32, buf_size));

}

void av1_dealloc_mb_wiener_var_pred_buf(ThreadData *td) {

  aom_free(td->wiener_tmp_pred_buf);

  td->wiener_tmp_pred_buf = NULL;

}

void av1_init_mb_wiener_var_buffer(AV1_COMP *cpi) {

  AV1_COMMON *cm = &cpi->common;

  // This block size is also used to determine number of workers in

  // multi-threading. If it is changed, one needs to change it accordingly in

  // "compute_num_ai_workers()".

  cpi->weber_bsize = BLOCK_8X8;

  if (cpi->oxcf.enable_rate_guide_deltaq) {

    if (cpi->mb_weber_stats && cpi->prep_rate_estimates &&

        cpi->ext_rate_distribution)

      return;

  } else {

    if (cpi->mb_weber_stats) return;

  }

  CHECK_MEM_ERROR(cm, cpi->mb_weber_stats,

                  aom_calloc(cpi->frame_info.mi_rows * cpi->frame_info.mi_cols,

                             sizeof(*cpi->mb_weber_stats)));

  if (cpi->oxcf.enable_rate_guide_deltaq) {

    CHECK_MEM_ERROR(

        cm, cpi->prep_rate_estimates,

        aom_calloc(cpi->frame_info.mi_rows * cpi->frame_info.mi_cols,

                   sizeof(*cpi->prep_rate_estimates)));

    CHECK_MEM_ERROR(

        cm, cpi->ext_rate_distribution,

        aom_calloc(cpi->frame_info.mi_rows * cpi->frame_info.mi_cols,

                   sizeof(*cpi->ext_rate_distribution)));

  }

}

static int64_t get_satd(AV1_COMP *const cpi, BLOCK_SIZE bsize, int mi_row,

                        int mi_col) {

  AV1_COMMON *const cm = &cpi->common;

  const int mi_wide = mi_size_wide[bsize];

  const int mi_high = mi_size_high[bsize];

  const int mi_step = mi_size_wide[cpi->weber_bsize];

  int mb_stride = cpi->frame_info.mi_cols;

  int mb_count = 0;

  int64_t satd = 0;

  for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {

    for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {

      if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)

        continue;

      satd += cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)]

                  .satd;

      ++mb_count;

    }

  }

  if (mb_count) satd = (int)(satd / mb_count);

  satd = AOMMAX(1, satd);

  return (int)satd;

}

static int64_t get_sse(AV1_COMP *const cpi, BLOCK_SIZE bsize, int mi_row,

                       int mi_col) {

  AV1_COMMON *const cm = &cpi->common;

  const int mi_wide = mi_size_wide[bsize];

  const int mi_high = mi_size_high[bsize];

  const int mi_step = mi_size_wide[cpi->weber_bsize];

  int mb_stride = cpi->frame_info.mi_cols;

  int mb_count = 0;

  int64_t distortion = 0;

  for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {

    for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {

      if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)

        continue;

      distortion +=

          cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)]

              .distortion;

      ++mb_count;

    }

  }

  if (mb_count) distortion = (int)(distortion / mb_count);

  distortion = AOMMAX(1, distortion);

  return (int)distortion;

}

static double get_max_scale(const AV1_COMP *const cpi, BLOCK_SIZE bsize,

                            int mi_row, int mi_col) {

  const AV1_COMMON *const cm = &cpi->common;

  const int mi_wide = mi_size_wide[bsize];

  const int mi_high = mi_size_high[bsize];

  const int mi_step = mi_size_wide[cpi->weber_bsize];

  int mb_stride = cpi->frame_info.mi_cols;

  double min_max_scale = 10.0;

  for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {

    for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {

      if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)

        continue;

      const WeberStats *weber_stats =

          &cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)];

      if (weber_stats->max_scale < 1.0) continue;

      if (weber_stats->max_scale < min_max_scale)

        min_max_scale = weber_stats->max_scale;

    }

  }

  return min_max_scale;

}

static int get_window_wiener_var(const AV1_COMP *const cpi, BLOCK_SIZE bsize,

                                 int mi_row, int mi_col) {

  const AV1_COMMON *const cm = &cpi->common;

  const int mi_wide = mi_size_wide[bsize];

  const int mi_high = mi_size_high[bsize];

  const int mi_step = mi_size_wide[cpi->weber_bsize];

  int sb_wiener_var = 0;

  int mb_stride = cpi->frame_info.mi_cols;

  int mb_count = 0;

  double base_num = 1;

  double base_den = 1;

  double base_reg = 1;

  for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {

    for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {

      if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)

        continue;

      const WeberStats *weber_stats =

          &cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)];

      base_num += ((double)weber_stats->distortion) *

                  sqrt((double)weber_stats->src_variance) *

                  weber_stats->rec_pix_max;

      base_den += fabs(

          weber_stats->rec_pix_max * sqrt((double)weber_stats->src_variance) -

          weber_stats->src_pix_max * sqrt((double)weber_stats->rec_variance));

      base_reg += sqrt((double)weber_stats->distortion) *

                  sqrt((double)weber_stats->src_pix_max) * 0.1;

      ++mb_count;

    }

  }

  sb_wiener_var =

      (int)(((base_num + base_reg) / (base_den + base_reg)) / mb_count);

  sb_wiener_var = AOMMAX(1, sb_wiener_var);

  return (int)sb_wiener_var;

}

static int get_var_perceptual_ai(const AV1_COMP *const cpi, BLOCK_SIZE bsize,

                                 int mi_row, int mi_col) {

  const AV1_COMMON *const cm = &cpi->common;

  const int mi_wide = mi_size_wide[bsize];

  const int mi_high = mi_size_high[bsize];

  int sb_wiener_var = get_window_wiener_var(cpi, bsize, mi_row, mi_col);

  if (mi_row >= (mi_high / 2)) {

    sb_wiener_var =

        AOMMIN(sb_wiener_var,

               get_window_wiener_var(cpi, bsize, mi_row - mi_high / 2, mi_col));

  }

  if (mi_row <= (cm->mi_params.mi_rows - mi_high - (mi_high / 2))) {

    sb_wiener_var =

        AOMMIN(sb_wiener_var,

               get_window_wiener_var(cpi, bsize, mi_row + mi_high / 2, mi_col));

  }

  if (mi_col >= (mi_wide / 2)) {

    sb_wiener_var =

        AOMMIN(sb_wiener_var,

               get_window_wiener_var(cpi, bsize, mi_row, mi_col - mi_wide / 2));

  }

  if (mi_col <= (cm->mi_params.mi_cols - mi_wide - (mi_wide / 2))) {

    sb_wiener_var =

        AOMMIN(sb_wiener_var,

               get_window_wiener_var(cpi, bsize, mi_row, mi_col + mi_wide / 2));

  }

  return sb_wiener_var;

}

static int rate_estimator(const tran_low_t *qcoeff, int eob, TX_SIZE tx_size) {

  const SCAN_ORDER *const scan_order = &av1_scan_orders[tx_size][DCT_DCT];

  assert((1 << num_pels_log2_lookup[txsize_to_bsize[tx_size]]) >= eob);

  int rate_cost = 1;

  for (int idx = 0; idx < eob; ++idx) {

    int abs_level = abs(qcoeff[scan_order->scan[idx]]);

    rate_cost += (int)(log1p(abs_level) / log(2.0)) + 1 + (abs_level > 0);

  }

  return (rate_cost << AV1_PROB_COST_SHIFT);

}

void av1_calc_mb_wiener_var_row(AV1_COMP *const cpi, MACROBLOCK *x,

                                MACROBLOCKD *xd, const int mi_row,

                                int16_t *src_diff, tran_low_t *coeff,

                                tran_low_t *qcoeff, tran_low_t *dqcoeff,

                                double *sum_rec_distortion,

                                double *sum_est_rate, uint8_t *pred_buffer) {

  AV1_COMMON *const cm = &cpi->common;

  uint8_t *buffer = cpi->source->y_buffer;

  int buf_stride = cpi->source->y_stride;

  MB_MODE_INFO mbmi;

  memset(&mbmi, 0, sizeof(mbmi));

  MB_MODE_INFO *mbmi_ptr = &mbmi;

  xd->mi = &mbmi_ptr;

  const BLOCK_SIZE bsize = cpi->weber_bsize;

  const TX_SIZE tx_size = max_txsize_lookup[bsize];

  const int block_size = tx_size_wide[tx_size];

  const int coeff_count = block_size * block_size;

  const int mb_step = mi_size_wide[bsize];

  const BitDepthInfo bd_info = get_bit_depth_info(xd);

  const MultiThreadInfo *const mt_info = &cpi->mt_info;

  const AV1EncAllIntraMultiThreadInfo *const intra_mt = &mt_info->intra_mt;

  AV1EncRowMultiThreadSync *const intra_row_mt_sync =

      &cpi->ppi->intra_row_mt_sync;

  const int mi_cols = cm->mi_params.mi_cols;

  const int mt_thread_id = mi_row / mb_step;

  // TODO(chengchen): test different unit step size

  const int mt_unit_step = mi_size_wide[MB_WIENER_MT_UNIT_SIZE];

  const int mt_unit_cols = (mi_cols + (mt_unit_step >> 1)) / mt_unit_step;

  int mt_unit_col = 0;

  const int is_high_bitdepth = is_cur_buf_hbd(xd);

  uint8_t *dst_buffer = pred_buffer;

  const int dst_buffer_stride = MB_WIENER_PRED_BUF_STRIDE;

  if (is_high_bitdepth) {

    uint16_t *pred_buffer_16 = (uint16_t *)pred_buffer;

    dst_buffer = CONVERT_TO_BYTEPTR(pred_buffer_16);

  }

  for (int mi_col = 0; mi_col < mi_cols; mi_col += mb_step) {

    if (mi_col % mt_unit_step == 0) {

      intra_mt->intra_sync_read_ptr(intra_row_mt_sync, mt_thread_id,

                                    mt_unit_col);

#if CONFIG_MULTITHREAD

      const int num_workers =

          AOMMIN(mt_info->num_mod_workers[MOD_AI], mt_info->num_workers);

      if (num_workers > 1) {

        const AV1EncRowMultiThreadInfo *const enc_row_mt = &mt_info->enc_row_mt;

        pthread_mutex_lock(enc_row_mt->mutex_);

        const bool exit = enc_row_mt->mb_wiener_mt_exit;

        pthread_mutex_unlock(enc_row_mt->mutex_);

        // Stop further processing in case any worker has encountered an error.

        if (exit) break;

      }

#endif

    }

    PREDICTION_MODE best_mode = DC_PRED;

    int best_intra_cost = INT_MAX;

    const int mi_width = mi_size_wide[bsize];

    const int mi_height = mi_size_high[bsize];

    set_mode_info_offsets(&cpi->common.mi_params, &cpi->mbmi_ext_info, x, xd,

                          mi_row, mi_col);

    set_mi_row_col(xd, &xd->tile, mi_row, mi_height, mi_col, mi_width,

                   AOMMIN(mi_row + mi_height, cm->mi_params.mi_rows),

                   AOMMIN(mi_col + mi_width, cm->mi_params.mi_cols));

    set_plane_n4(xd, mi_size_wide[bsize], mi_size_high[bsize],

                 av1_num_planes(cm));

    xd->mi[0]->bsize = bsize;

    xd->mi[0]->motion_mode = SIMPLE_TRANSLATION;

    // Set above and left mbmi to NULL as they are not available in the

    // preprocessing stage.

    // They are used to detemine intra edge filter types in intra prediction.

    if (xd->up_available) {

      xd->above_mbmi = NULL;

    }

    if (xd->left_available) {

      xd->left_mbmi = NULL;

    }

    uint8_t *mb_buffer =

        buffer + mi_row * MI_SIZE * buf_stride + mi_col * MI_SIZE;

    for (PREDICTION_MODE mode = INTRA_MODE_START; mode < INTRA_MODE_END;

         ++mode) {

      // TODO(chengchen): Here we use src instead of reconstructed frame as

      // the intra predictor to make single and multithread version match.

      // Ideally we want to use the reconstructed.

      av1_predict_intra_block(

          xd, cm->seq_params->sb_size, cm->seq_params->enable_intra_edge_filter,

          block_size, block_size, tx_size, mode, 0, 0, FILTER_INTRA_MODES,

          mb_buffer, buf_stride, dst_buffer, dst_buffer_stride, 0, 0, 0);

      av1_subtract_block(bd_info, block_size, block_size, src_diff, block_size,

                         mb_buffer, buf_stride, dst_buffer, dst_buffer_stride);

      av1_quick_txfm(0, tx_size, bd_info, src_diff, block_size, coeff);

      int intra_cost = aom_satd(coeff, coeff_count);

      if (intra_cost < best_intra_cost) {

        best_intra_cost = intra_cost;

        best_mode = mode;

      }

    }

    av1_predict_intra_block(

        xd, cm->seq_params->sb_size, cm->seq_params->enable_intra_edge_filter,

        block_size, block_size, tx_size, best_mode, 0, 0, FILTER_INTRA_MODES,

        mb_buffer, buf_stride, dst_buffer, dst_buffer_stride, 0, 0, 0);

    av1_subtract_block(bd_info, block_size, block_size, src_diff, block_size,

                       mb_buffer, buf_stride, dst_buffer, dst_buffer_stride);

    av1_quick_txfm(0, tx_size, bd_info, src_diff, block_size, coeff);

    const struct macroblock_plane *const p = &x->plane[0];

    uint16_t eob;

    const SCAN_ORDER *const scan_order = &av1_scan_orders[tx_size][DCT_DCT];

    QUANT_PARAM quant_param;

    int pix_num = 1 << num_pels_log2_lookup[txsize_to_bsize[tx_size]];

    av1_setup_quant(tx_size, 0, AV1_XFORM_QUANT_FP, 0, &quant_param);

#if CONFIG_AV1_HIGHBITDEPTH

    if (is_cur_buf_hbd(xd)) {

      av1_highbd_quantize_fp_facade(coeff, pix_num, p, qcoeff, dqcoeff, &eob,

                                    scan_order, &quant_param);

    } else {

      av1_quantize_fp_facade(coeff, pix_num, p, qcoeff, dqcoeff, &eob,

                             scan_order, &quant_param);

    }

#else

    av1_quantize_fp_facade(coeff, pix_num, p, qcoeff, dqcoeff, &eob, scan_order,

                           &quant_param);

#endif  // CONFIG_AV1_HIGHBITDEPTH

    if (cpi->oxcf.enable_rate_guide_deltaq) {

      const int rate_cost = rate_estimator(qcoeff, eob, tx_size);

      cpi->prep_rate_estimates[(mi_row / mb_step) * cpi->frame_info.mi_cols +

                               (mi_col / mb_step)] = rate_cost;

    }

    av1_inverse_transform_block(xd, dqcoeff, 0, DCT_DCT, tx_size, dst_buffer,

                                dst_buffer_stride, eob, 0);

    WeberStats *weber_stats =

        &cpi->mb_weber_stats[(mi_row / mb_step) * cpi->frame_info.mi_cols +

                             (mi_col / mb_step)];

    weber_stats->rec_pix_max = 1;

    weber_stats->rec_variance = 0;

    weber_stats->src_pix_max = 1;

    weber_stats->src_variance = 0;

    weber_stats->distortion = 0;

    int64_t src_mean = 0;

    int64_t rec_mean = 0;

    int64_t dist_mean = 0;

    for (int pix_row = 0; pix_row < block_size; ++pix_row) {

      for (int pix_col = 0; pix_col < block_size; ++pix_col) {

        int src_pix, rec_pix;

#if CONFIG_AV1_HIGHBITDEPTH

        if (is_cur_buf_hbd(xd)) {

          uint16_t *src = CONVERT_TO_SHORTPTR(mb_buffer);

          uint16_t *rec = CONVERT_TO_SHORTPTR(dst_buffer);

          src_pix = src[pix_row * buf_stride + pix_col];

          rec_pix = rec[pix_row * dst_buffer_stride + pix_col];

        } else {

          src_pix = mb_buffer[pix_row * buf_stride + pix_col];

          rec_pix = dst_buffer[pix_row * dst_buffer_stride + pix_col];

        }

#else

        src_pix = mb_buffer[pix_row * buf_stride + pix_col];

        rec_pix = dst_buffer[pix_row * dst_buffer_stride + pix_col];

#endif

        src_mean += src_pix;

        rec_mean += rec_pix;

        dist_mean += src_pix - rec_pix;

        weber_stats->src_variance += src_pix * src_pix;

        weber_stats->rec_variance += rec_pix * rec_pix;

        weber_stats->src_pix_max = AOMMAX(weber_stats->src_pix_max, src_pix);

        weber_stats->rec_pix_max = AOMMAX(weber_stats->rec_pix_max, rec_pix);

        weber_stats->distortion += (src_pix - rec_pix) * (src_pix - rec_pix);

      }

    }

    if (cpi->oxcf.intra_mode_cfg.auto_intra_tools_off) {

      *sum_rec_distortion += weber_stats->distortion;

      int est_block_rate = 0;

      int64_t est_block_dist = 0;

      model_rd_sse_fn[MODELRD_LEGACY](cpi, x, bsize, 0, weber_stats->distortion,

                                      pix_num, &est_block_rate,

                                      &est_block_dist);

      *sum_est_rate += est_block_rate;

    }

    weber_stats->src_variance -= (src_mean * src_mean) / pix_num;

    weber_stats->rec_variance -= (rec_mean * rec_mean) / pix_num;

    weber_stats->distortion -= (dist_mean * dist_mean) / pix_num;

    weber_stats->satd = best_intra_cost;

    qcoeff[0] = 0;

    int max_scale = 0;

    for (int idx = 1; idx < coeff_count; ++idx) {

      const int abs_qcoeff = abs(qcoeff[idx]);

      max_scale = AOMMAX(max_scale, abs_qcoeff);

    }

    weber_stats->max_scale = max_scale;

    if ((mi_col + mb_step) % mt_unit_step == 0 ||

        (mi_col + mb_step) >= mi_cols) {

      intra_mt->intra_sync_write_ptr(intra_row_mt_sync, mt_thread_id,

                                     mt_unit_col, mt_unit_cols);

      ++mt_unit_col;

    }

  }

  // Set the pointer to null since mbmi is only allocated inside this function.

  xd->mi = NULL;

}

static void calc_mb_wiener_var(AV1_COMP *const cpi, double *sum_rec_distortion,

                               double *sum_est_rate) {

  MACROBLOCK *x = &cpi->td.mb;

  MACROBLOCKD *xd = &x->e_mbd;

  const BLOCK_SIZE bsize = cpi->weber_bsize;

  const int mb_step = mi_size_wide[bsize];

  DECLARE_ALIGNED(32, int16_t, src_diff[32 * 32]);

  DECLARE_ALIGNED(32, tran_low_t, coeff[32 * 32]);

  DECLARE_ALIGNED(32, tran_low_t, qcoeff[32 * 32]);

  DECLARE_ALIGNED(32, tran_low_t, dqcoeff[32 * 32]);

  for (int mi_row = 0; mi_row < cpi->frame_info.mi_rows; mi_row += mb_step) {

    av1_calc_mb_wiener_var_row(cpi, x, xd, mi_row, src_diff, coeff, qcoeff,

                               dqcoeff, sum_rec_distortion, sum_est_rate,

                               cpi->td.wiener_tmp_pred_buf);

  }

}

static int64_t estimate_wiener_var_norm(AV1_COMP *const cpi,

                                        const BLOCK_SIZE norm_block_size) {

  const AV1_COMMON *const cm = &cpi->common;

  int64_t norm_factor = 1;

  assert(norm_block_size >= BLOCK_16X16 && norm_block_size <= BLOCK_128X128);

  const int norm_step = mi_size_wide[norm_block_size];

  double sb_wiener_log = 0;

  double sb_count = 0;

  for (int mi_row = 0; mi_row < cm->mi_params.mi_rows; mi_row += norm_step) {

    for (int mi_col = 0; mi_col < cm->mi_params.mi_cols; mi_col += norm_step) {

      const int sb_wiener_var =

          get_var_perceptual_ai(cpi, norm_block_size, mi_row, mi_col);

      const int64_t satd = get_satd(cpi, norm_block_size, mi_row, mi_col);

      const int64_t sse = get_sse(cpi, norm_block_size, mi_row, mi_col);

      const double scaled_satd = (double)satd / sqrt((double)sse);

      sb_wiener_log += scaled_satd * log(sb_wiener_var);

      sb_count += scaled_satd;

    }

  }

  if (sb_count > 0) norm_factor = (int64_t)(exp(sb_wiener_log / sb_count));

  norm_factor = AOMMAX(1, norm_factor);

  return norm_factor;

}

static void automatic_intra_tools_off(AV1_COMP *cpi,

                                      const double sum_rec_distortion,

                                      const double sum_est_rate) {

  if (!cpi->oxcf.intra_mode_cfg.auto_intra_tools_off) return;

  // Thresholds

  const int high_quality_qindex = 128;

  const double high_quality_bpp = 2.0;

  const double high_quality_dist_per_pix = 4.0;

  AV1_COMMON *const cm = &cpi->common;

  const int qindex = cm->quant_params.base_qindex;

  const double dist_per_pix =

      (double)sum_rec_distortion / (cm->width * cm->height);

  // The estimate bpp is not accurate, an empirical constant 100 is divided.

  const double estimate_bpp = sum_est_rate / (cm->width * cm->height * 100);

  if (qindex < high_quality_qindex && estimate_bpp > high_quality_bpp &&

      dist_per_pix < high_quality_dist_per_pix) {

    cpi->oxcf.intra_mode_cfg.enable_smooth_intra = 0;

    cpi->oxcf.intra_mode_cfg.enable_paeth_intra = 0;

    cpi->oxcf.intra_mode_cfg.enable_cfl_intra = 0;

    cpi->oxcf.intra_mode_cfg.enable_diagonal_intra = 0;

  }

}

static void ext_rate_guided_quantization(AV1_COMP *cpi) {

  // Calculation uses 8x8.

  const int mb_step = mi_size_wide[cpi->weber_bsize];

  // Accumulate to 16x16, step size is in the unit of mi.

  const int block_step = 4;

  const char *filename = cpi->oxcf.rate_distribution_info;

  FILE *pfile = fopen(filename, "r");

  if (pfile == NULL) {

    assert(pfile != NULL);

    return;

  }

  double ext_rate_sum = 0.0;

  for (int row = 0; row < cpi->frame_info.mi_rows; row += block_step) {

    for (int col = 0; col < cpi->frame_info.mi_cols; col += block_step) {

      float val;

      const int fields_converted = fscanf(pfile, "%f", &val);

      if (fields_converted != 1) {

        assert(fields_converted == 1);

        fclose(pfile);

        return;

      }

      ext_rate_sum += val;

      cpi->ext_rate_distribution[(row / mb_step) * cpi->frame_info.mi_cols +

                                 (col / mb_step)] = val;

    }

  }

  fclose(pfile);

  int uniform_rate_sum = 0;

  for (int row = 0; row < cpi->frame_info.mi_rows; row += block_step) {

    for (int col = 0; col < cpi->frame_info.mi_cols; col += block_step) {

      int rate_sum = 0;

      for (int r = 0; r < block_step; r += mb_step) {

        for (int c = 0; c < block_step; c += mb_step) {

          const int mi_row = row + r;

          const int mi_col = col + c;

          rate_sum += cpi->prep_rate_estimates[(mi_row / mb_step) *

                                                   cpi->frame_info.mi_cols +

                                               (mi_col / mb_step)];

        }

      }

      uniform_rate_sum += rate_sum;

    }

  }

  const double scale = uniform_rate_sum / ext_rate_sum;

  cpi->ext_rate_scale = scale;

}

void av1_set_mb_wiener_variance(AV1_COMP *cpi) {

  AV1_COMMON *const cm = &cpi->common;

  const SequenceHeader *const seq_params = cm->seq_params;

  if (aom_realloc_frame_buffer(

          &cm->cur_frame->buf, cm->width, cm->height, seq_params->subsampling_x,

          seq_params->subsampling_y, seq_params->use_highbitdepth,

          cpi->oxcf.border_in_pixels, cm->features.byte_alignment, NULL, NULL,

          NULL, cpi->alloc_pyramid, 0))

    aom_internal_error(cm->error, AOM_CODEC_MEM_ERROR,

                       "Failed to allocate frame buffer");

  av1_alloc_mb_wiener_var_pred_buf(&cpi->common, &cpi->td);

  cpi->norm_wiener_variance = 0;

  MACROBLOCK *x = &cpi->td.mb;

  MACROBLOCKD *xd = &x->e_mbd;

  // xd->mi needs to be setup since it is used in av1_frame_init_quantizer.

  MB_MODE_INFO mbmi;

  memset(&mbmi, 0, sizeof(mbmi));

  MB_MODE_INFO *mbmi_ptr = &mbmi;

  xd->mi = &mbmi_ptr;

  cm->quant_params.base_qindex = cpi->oxcf.rc_cfg.cq_level;

  av1_frame_init_quantizer(cpi);

  double sum_rec_distortion = 0.0;

  double sum_est_rate = 0.0;

  MultiThreadInfo *const mt_info = &cpi->mt_info;

  const int num_workers =

      AOMMIN(mt_info->num_mod_workers[MOD_AI], mt_info->num_workers);

  AV1EncAllIntraMultiThreadInfo *const intra_mt = &mt_info->intra_mt;

  intra_mt->intra_sync_read_ptr = av1_row_mt_sync_read_dummy;

  intra_mt->intra_sync_write_ptr = av1_row_mt_sync_write_dummy;

  // Calculate differential contrast for each block for the entire image.

  // TODO(chengchen): properly accumulate the distortion and rate in

  // av1_calc_mb_wiener_var_mt(). Until then, call calc_mb_wiener_var() if

  // auto_intra_tools_off is true.

  if (num_workers > 1 && !cpi->oxcf.intra_mode_cfg.auto_intra_tools_off) {

    intra_mt->intra_sync_read_ptr = av1_row_mt_sync_read;

    intra_mt->intra_sync_write_ptr = av1_row_mt_sync_write;

    av1_calc_mb_wiener_var_mt(cpi, num_workers, &sum_rec_distortion,

                              &sum_est_rate);

  } else {

    calc_mb_wiener_var(cpi, &sum_rec_distortion, &sum_est_rate);

  }

  // Determine whether to turn off several intra coding tools.

  automatic_intra_tools_off(cpi, sum_rec_distortion, sum_est_rate);

  // Read external rate distribution and use it to guide delta quantization

  if (cpi->oxcf.enable_rate_guide_deltaq) ext_rate_guided_quantization(cpi);

  const BLOCK_SIZE norm_block_size = cm->seq_params->sb_size;

  cpi->norm_wiener_variance = estimate_wiener_var_norm(cpi, norm_block_size);

  const int norm_step = mi_size_wide[norm_block_size];

  double sb_wiener_log = 0;

  double sb_count = 0;

  for (int its_cnt = 0; its_cnt < 2; ++its_cnt) {

    sb_wiener_log = 0;

    sb_count = 0;

    for (int mi_row = 0; mi_row < cm->mi_params.mi_rows; mi_row += norm_step) {

      for (int mi_col = 0; mi_col < cm->mi_params.mi_cols;

           mi_col += norm_step) {

        int sb_wiener_var =

            get_var_perceptual_ai(cpi, norm_block_size, mi_row, mi_col);

        double beta = (double)cpi->norm_wiener_variance / sb_wiener_var;

        double min_max_scale = AOMMAX(

            1.0, get_max_scale(cpi, cm->seq_params->sb_size, mi_row, mi_col));

        beta = AOMMIN(beta, 4);

        beta = AOMMAX(beta, 0.25);

        if (beta < 1 / min_max_scale) continue;

        sb_wiener_var = (int)(cpi->norm_wiener_variance / beta);

        int64_t satd = get_satd(cpi, norm_block_size, mi_row, mi_col);

        int64_t sse = get_sse(cpi, norm_block_size, mi_row, mi_col);

        double scaled_satd = (double)satd / sqrt((double)sse);

        sb_wiener_log += scaled_satd * log(sb_wiener_var);

        sb_count += scaled_satd;

      }

    }

    if (sb_count > 0)

      cpi->norm_wiener_variance = (int64_t)(exp(sb_wiener_log / sb_count));

    cpi->norm_wiener_variance = AOMMAX(1, cpi->norm_wiener_variance);

  }

  // Set the pointer to null since mbmi is only allocated inside this function.

  xd->mi = NULL;

  aom_free_frame_buffer(&cm->cur_frame->buf);

  av1_dealloc_mb_wiener_var_pred_buf(&cpi->td);

}

static int get_rate_guided_quantizer(const AV1_COMP *const cpi,

                                     BLOCK_SIZE bsize, int mi_row, int mi_col) {

  // Calculation uses 8x8.

  const int mb_step = mi_size_wide[cpi->weber_bsize];

  // Accumulate to 16x16

  const int block_step = mi_size_wide[BLOCK_16X16];

  double sb_rate_hific = 0.0;

  double sb_rate_uniform = 0.0;

  for (int row = mi_row; row < mi_row + mi_size_wide[bsize];

       row += block_step) {

    for (int col = mi_col; col < mi_col + mi_size_high[bsize];

         col += block_step) {

      sb_rate_hific +=

          cpi->ext_rate_distribution[(row / mb_step) * cpi->frame_info.mi_cols +

                                     (col / mb_step)];

      for (int r = 0; r < block_step; r += mb_step) {

        for (int c = 0; c < block_step; c += mb_step) {

          const int this_row = row + r;

          const int this_col = col + c;

          sb_rate_uniform +=

              cpi->prep_rate_estimates[(this_row / mb_step) *

                                           cpi->frame_info.mi_cols +

                                       (this_col / mb_step)];

        }

      }

    }

  }

  sb_rate_hific *= cpi->ext_rate_scale;

  const double weight = 1.0;

  const double rate_diff =

      weight * (sb_rate_hific - sb_rate_uniform) / sb_rate_uniform;

  double scale = pow(2, rate_diff);

  scale = scale * scale;

  double min_max_scale = AOMMAX(1.0, get_max_scale(cpi, bsize, mi_row, mi_col));

  scale = 1.0 / AOMMIN(1.0 / scale, min_max_scale);

  const AV1_COMMON *const cm = &cpi->common;

  const int base_qindex = cm->quant_params.base_qindex;

  int offset =

      av1_get_deltaq_offset(cm->seq_params->bit_depth, base_qindex, scale);

  const DeltaQInfo *const delta_q_info = &cm->delta_q_info;

  const int max_offset = delta_q_info->delta_q_res * 10;

  offset = AOMMIN(offset, max_offset - 1);

  offset = AOMMAX(offset, -max_offset + 1);

  int qindex = cm->quant_params.base_qindex + offset;

  qindex = AOMMIN(qindex, MAXQ);

  qindex = AOMMAX(qindex, MINQ);

  if (base_qindex > MINQ) qindex = AOMMAX(qindex, MINQ + 1);

  return qindex;

}

int av1_get_sbq_perceptual_ai(const AV1_COMP *const cpi, BLOCK_SIZE bsize,

                              int mi_row, int mi_col) {

  if (cpi->oxcf.enable_rate_guide_deltaq) {

    return get_rate_guided_quantizer(cpi, bsize, mi_row, mi_col);

  }

  const AV1_COMMON *const cm = &cpi->common;

  const int base_qindex = cm->quant_params.base_qindex;

  int sb_wiener_var = get_var_perceptual_ai(cpi, bsize, mi_row, mi_col);

  int offset = 0;

  double beta = (double)cpi->norm_wiener_variance / sb_wiener_var;

  double min_max_scale = AOMMAX(1.0, get_max_scale(cpi, bsize, mi_row, mi_col));

  beta = 1.0 / AOMMIN(1.0 / beta, min_max_scale);

  // Cap beta such that the delta q value is not much far away from the base q.

  beta = AOMMIN(beta, 4);

  beta = AOMMAX(beta, 0.25);

  offset = av1_get_deltaq_offset(cm->seq_params->bit_depth, base_qindex, beta);

  const DeltaQInfo *const delta_q_info = &cm->delta_q_info;

  offset = AOMMIN(offset, delta_q_info->delta_q_res * 20 - 1);

  offset = AOMMAX(offset, -delta_q_info->delta_q_res * 20 + 1);

  int qindex = cm->quant_params.base_qindex + offset;

  qindex = AOMMIN(qindex, MAXQ);

  qindex = AOMMAX(qindex, MINQ);

  if (base_qindex > MINQ) qindex = AOMMAX(qindex, MINQ + 1);

  return qindex;

}

void av1_init_mb_ur_var_buffer(AV1_COMP *cpi) {

  AV1_COMMON *cm = &cpi->common;

  if (cpi->mb_delta_q) return;

  CHECK_MEM_ERROR(cm, cpi->mb_delta_q,

                  aom_calloc(cpi->frame_info.mb_rows * cpi->frame_info.mb_cols,

                             sizeof(*cpi->mb_delta_q)));

}

#if CONFIG_TFLITE

static int model_predict(BLOCK_SIZE block_size, int num_cols, int num_rows,

                         int bit_depth, uint8_t *y_buffer, int y_stride,

                         float *predicts0, float *predicts1) {

  // Create the model and interpreter options.

  TfLiteModel *model =

      TfLiteModelCreate(av1_deltaq4_model_file, av1_deltaq4_model_fsize);

  if (model == NULL) return 1;

  TfLiteInterpreterOptions *options = TfLiteInterpreterOptionsCreate();

  TfLiteInterpreterOptionsSetNumThreads(options, 2);

  if (options == NULL) {

    TfLiteModelDelete(model);

    return 1;

  }

  // Create the interpreter.

  TfLiteInterpreter *interpreter = TfLiteInterpreterCreate(model, options);

  if (interpreter == NULL) {

    TfLiteInterpreterOptionsDelete(options);

    TfLiteModelDelete(model);

    return 1;

  }

  // Allocate tensors and populate the input tensor data.

  TfLiteInterpreterAllocateTensors(interpreter);

  TfLiteTensor *input_tensor = TfLiteInterpreterGetInputTensor(interpreter, 0);

  if (input_tensor == NULL) {

    TfLiteInterpreterDelete(interpreter);

    TfLiteInterpreterOptionsDelete(options);

    TfLiteModelDelete(model);

    return 1;

  }

  size_t input_size = TfLiteTensorByteSize(input_tensor);

  float *input_data = aom_calloc(input_size, 1);

  if (input_data == NULL) {

    TfLiteInterpreterDelete(interpreter);

    TfLiteInterpreterOptionsDelete(options);

    TfLiteModelDelete(model);

    return 1;

  }

  const int num_mi_w = mi_size_wide[block_size];

  const int num_mi_h = mi_size_high[block_size];

  for (int row = 0; row < num_rows; ++row) {

    for (int col = 0; col < num_cols; ++col) {

      const int row_offset = (row * num_mi_h) << 2;

      const int col_offset = (col * num_mi_w) << 2;

      uint8_t *buf = y_buffer + row_offset * y_stride + col_offset;

      int r = row_offset, pos = 0;

      const float base = (float)((1 << bit_depth) - 1);

      while (r < row_offset + (num_mi_h << 2)) {

        for (int c = 0; c < (num_mi_w << 2); ++c) {

          input_data[pos++] = bit_depth > 8

                                  ? (float)*CONVERT_TO_SHORTPTR(buf + c) / base

                                  : (float)*(buf + c) / base;

        }

        buf += y_stride;

        ++r;

      }

      TfLiteTensorCopyFromBuffer(input_tensor, input_data, input_size);

      // Execute inference.

      if (TfLiteInterpreterInvoke(interpreter) != kTfLiteOk) {

        TfLiteInterpreterDelete(interpreter);

        TfLiteInterpreterOptionsDelete(options);

        TfLiteModelDelete(model);

        return 1;

      }

      // Extract the output tensor data.

      const TfLiteTensor *output_tensor =

          TfLiteInterpreterGetOutputTensor(interpreter, 0);

      if (output_tensor == NULL) {

        TfLiteInterpreterDelete(interpreter);

        TfLiteInterpreterOptionsDelete(options);

        TfLiteModelDelete(model);

        return 1;

      }

      size_t output_size = TfLiteTensorByteSize(output_tensor);

      float output_data[2];

      TfLiteTensorCopyToBuffer(output_tensor, output_data, output_size);

      predicts0[row * num_cols + col] = output_data[0];

      predicts1[row * num_cols + col] = output_data[1];

    }

  }

  // Dispose of the model and interpreter objects.

  TfLiteInterpreterDelete(interpreter);

  TfLiteInterpreterOptionsDelete(options);

  TfLiteModelDelete(model);

  aom_free(input_data);

  return 0;

}

void av1_set_mb_ur_variance(AV1_COMP *cpi) {

  const AV1_COMMON *cm = &cpi->common;

  const CommonModeInfoParams *const mi_params = &cm->mi_params;

  uint8_t *y_buffer = cpi->source->y_buffer;

  const int y_stride = cpi->source->y_stride;

  const int block_size = cpi->common.seq_params->sb_size;

  const uint32_t bit_depth = cpi->td.mb.e_mbd.bd;

  const int num_mi_w = mi_size_wide[block_size];

  const int num_mi_h = mi_size_high[block_size];

  const int num_cols = (mi_params->mi_cols + num_mi_w - 1) / num_mi_w;

  const int num_rows = (mi_params->mi_rows + num_mi_h - 1) / num_mi_h;

  // TODO(sdeng): fit a better model_1; disable it at this time.