#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"  // IWYU pragma: keep

#define DEBUG_TYPE "quantize-composite-functions"

namespace mlir::quant::stablehlo {

#define GEN_PASS_DEF_QUANTIZECOMPOSITEFUNCTIONSPASS
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h.inc"

namespace {

// CHECK:  return %[[RESULT]]
}

func.func @QDQsFollowedByTranspose(tensor<1x2xf32>) -> (tensor<2x1xf32>) {
^bb0(%arg0: tensor<1x2xf32>):
  %cst_0 = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0){qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>}: (tensor<1x2xf32>) -> (tensor<1x2x!quant.uniform<u8:f32, 1.0>>)
  %1 = "tfl.dequantize"(%0): (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1x2xf32>)

// CHECK:           %[[VAL_4:.*]] = "tfl.quantize"(%[[VAL_3]]) <{qtype = tensor<?x2048x!quant.uniform<i8:f32, 9.000000e-01:-128>>}> : (tensor<?x2048x!quant.uniform<i8:f32, 6.000000e-01:-128>>) -> tensor<?x2048x!quant.uniform<i8:f32, 9.000000e-01:-128>>

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-post-quantize | FileCheck %s

// CHECK-LABEL: @remove_volatile_qdq
func.func @remove_volatile_qdq() -> tensor<3x2xf32> {
  // CHECK: %[[CST:.*]] = stablehlo.constant
  // CHECK-NOT: "quantfork.qcast"
  // CHECK-NOT: "quantfork.dcast"
  // CHECK: return %[[CST]]
  %cst = stablehlo.constant dense<[[-0.960978984, -0.390246302], [-0.790828585, -0.601039409], [-1.0280807, -1.02731466]]> : tensor<3x2xf32>

namespace mlir {
namespace lite {

// Supported resulting types from quantization process.
enum class BufferType { QUANTIZED_INT8, QUANTIZED_FLOAT16 };

// Stores information about how to quantize a user-specified custom operation.
// CustomOpInfo contains info of its corresponding CustomOp registered in the
// CustomOpMap. 'quantizable_input_indices' is used to determine which indices

    *   `tf.einsum()`raises `ValueError` for unsupported equations like
        `"ii->"`.
    *   Add DCT-I and IDCT-I in `tf.signal.dct` and `tf.signal.idct`.
    *   Add LU decomposition op.
    *   Add quantile loss to gradient boosted trees in estimator.
    *   Add `round_mode` to `QuantizeAndDequantizeV2` op to select rounding
        algorithm.
    *   Add `unicode_encode`, `unicode_decode`, `unicode_decode_with_offsets`,

        per_axis_type.getStorageTypeMin(), per_axis_type.getStorageTypeMax());
  }

  auto quantize = builder.create<quantfork::QuantizeCastOp>(
      q_op.getLoc(), new_value_type.clone(new_qtype), new_value);
  auto dequantize = builder.create<quantfork::DequantizeCastOp>(
      dq_op.getLoc(), new_value_type, quantize.getResult());
  return dequantize.getResult();
}

// stats.Pause slice will be reused if large enough, reallocated otherwise.
// ReadGCStats may use the full capacity of the stats.Pause slice.
// If stats.PauseQuantiles is non-empty, ReadGCStats fills it with quantiles
// summarizing the distribution of pause time. For example, if
// len(stats.PauseQuantiles) is 5, it will be filled with the minimum,
// 25%, 50%, 75%, and maximum pause times.
func ReadGCStats(stats *GCStats) {

					}
				}
			}
		}
	}

	var memstats runtime.MemStats
	runtime.ReadMemStats(&memstats)

	gcStats := debug.GCStats{
		// If stats.PauseQuantiles is non-empty, ReadGCStats fills
		// it with quantiles summarizing the distribution of pause time.
		// For example, if len(stats.PauseQuantiles) is 5, it will be
		// filled with the minimum, 25%, 50%, 75%, and maximum pause times.
		PauseQuantiles: make([]time.Duration, 5),
	}

#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/lite/tools/optimize/operator_property.h"

//===----------------------------------------------------------------------===//
// The prepare-quantize Pass for LSTM.
//
namespace mlir {
namespace TFL {

constexpr double power_of_two_scale = 32768.0;

// Same with the ordering of //tensorflow/compiler/mlir/lite/ir/tfl_ops.td