#!/usr/bin/env bash
set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
MODEL_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
REPO_DIR="$(cd "${MODEL_DIR}/.." && pwd)"
IMAGE="openexplorer/ai_toolchain_ubuntu_20_x5_cpu:v1.2.8"

docker run --rm --platform linux/amd64 \
  -v "${REPO_DIR}:/workspace/smart_car_2026" \
  "${IMAGE}" bash -lc '
    set -euo pipefail
    cd /workspace/smart_car_2026/model/x5_quantization
    python3 extract_yolo26_bpu.py \
      --input ../best.onnx \
      --output best_bpu.onnx
    python3 prepare_calibration.py \
      --dataset /workspace/smart_car_2026/dataset \
      --output calibration_data \
      --samples 50
    cat > best_bpu_int8.yaml <<"YAML"
model_parameters:
  onnx_model: "./best_bpu.onnx"
  march: "bayes-e"
  layer_out_dump: false
  working_dir: "mapper_output_bpu"
  output_model_file_prefix: "best_bpu_bayese_640x640_nv12"

input_parameters:
  input_name: "images"
  input_type_rt: "nv12"
  input_type_train: "rgb"
  input_layout_train: "NCHW"
  norm_type: "data_scale"
  scale_value: 0.003921568627451

calibration_parameters:
  cal_data_dir: "./calibration_data"
  cal_data_type: "float32"
  calibration_type: "default"
  optimization: "set_Softmax_input_int8,set_Softmax_output_int8"

compiler_parameters:
  jobs: 8
  compile_mode: "latency"
  debug: true
  optimize_level: "O3"
YAML
    hb_mapper checker \
      --model best_bpu.onnx \
      --model-type onnx \
      --march bayes-e \
      --input-shape images 1x3x640x640
    hb_mapper makertbin \
      --config best_bpu_int8.yaml \
      --model-type onnx
    ls -lh mapper_output_bpu/best_bpu_bayese_640x640_nv12.bin
  '