namespace {

// Calculates the number of bins from the range and bin width.
inline int32_t CalculateActualNumBins(const float min_value,
                                      const float max_value,
                                      const float bin_width) {
  const float lower_bound = CalculateLowerBound(min_value, bin_width);
  return std::ceil((max_value - lower_bound) / bin_width);
}

      mse_tuple = self._get_weighted_mean_squared_error(quant_min, quant_max)
      mse_min = min(mse_tuple, mse_min)
    # Extract (quant_min, quant_max) from (mse_error, quant_min, quant_max).
    min_value, max_value = mse_min[1], mse_min[2]
    return min_value, max_value


@_implements(_CalibrationMethod.CALIBRATION_METHOD_MIN_MAX)
class _MinMax(_CalibrationAlgorithmBase):

namespace mlir {
namespace TFL {

template <class TFFakeQuantOp>
struct FetchMinMaxAttrs {
  using AttrType = FloatAttr;
  bool operator()(TFFakeQuantOp tf_op, AttrType &min_value,
                  AttrType &max_value) const {
    min_value = tf_op.getMinAttr();
    max_value = tf_op.getMaxAttr();
    return true;  // Successfully matched and fetched.
  }
};

template <class TFFakeQuantOp>

        return Long.MAX_VALUE;
      }
      if (end < start && result > 0) { // underflow
        return Long.MIN_VALUE;
      }
      return result;
    }

    @Override
    public Long minValue() {
      return Long.MIN_VALUE;
    }

    @Override
    public Long maxValue() {
      return Long.MAX_VALUE;
    }

    private Object readResolve() {

    statistics.min_max_statistics.global_min = 1.0
    statistics.min_max_statistics.global_max = 5.0

    min_value, max_value = calibration_algorithm.get_min_max_value(
        statistics, calib_opts
    )
    self.assertAllEqual((min_value, max_value), (1.0, 5.0))

  def test_average_min_max(self):
    calib_opts = stablehlo_quant_config_pb2.CalibrationOptions(

            ContiguousSet.closedOpen(1, 1),
            ContiguousSet.closedOpen(Integer.MIN_VALUE, Integer.MIN_VALUE),
            ImmutableSortedSet.of(),
            ImmutableSet.of())
        .testEquals();
    // not testing hashCode for these because it takes forever to compute
    assertEquals(
        ContiguousSet.closed(Integer.MIN_VALUE, Integer.MAX_VALUE),
        ContiguousSet.create(Range.<Integer>all(), integers()));

namespace mlir {
namespace quant {

template <class TFFakeQuantOp>
struct FetchMinMaxAttrs {
  using AttrType = FloatAttr;
  bool operator()(TFFakeQuantOp tf_op, AttrType &min_value,
                  AttrType &max_value) const {
    min_value = tf_op.getMinAttr();
    max_value = tf_op.getMaxAttr();
    return true;  // Successfully matched and fetched.
  }
};

template <class TFFakeQuantOp>

                                                     &histogram_output));
    if (num_bins > 0) {
      const float min_value = min_output->scalar<float>()();
      const float max_value = max_output->scalar<float>()();
      CalculateHistogramStatistics(context, input_tensor, min_value, max_value,
                                   num_bins, histogram_output);
    }
  }

 private:
  std::string id_;

// number of bins might be smaller than the given `num_bins`.
inline float CalculateBinWidth(const float min_value, const float max_value,
                               const int32_t num_bins) {
  const float raw_bin_width = (max_value - min_value) / num_bins;
  return std::pow(2, std::ceil(std::log2(raw_bin_width)));
}

// Calculates the lower bound of the histogram. The lower bound is in form of

    List<long[]> ordered =
        Arrays.asList(
            new long[] {},
            new long[] {MIN_VALUE},
            new long[] {MIN_VALUE, MIN_VALUE},
            new long[] {MIN_VALUE, (long) 1},
            new long[] {(long) 1},
            new long[] {(long) 1, MIN_VALUE},
            new long[] {MAX_VALUE, MAX_VALUE - (long) 1},
            new long[] {MAX_VALUE, MAX_VALUE},