==============================================================================*/
#include "tensorflow/compiler/mlir/quantization/stablehlo/cc/debugger.h"

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/tf_quant_ops.h"

namespace stablehlo::quantization {

void DisableDebugging(mlir::ModuleOp module_op) {
  module_op.walk(

#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"

namespace mlir::quant::stablehlo {

// Returns StableHLO quantization specs for an op.
std::unique_ptr<OpQuantSpec> GetStableHloOpQuantSpec(Operation* op);

// Returns quantization constraints (ex: fixed output, same scale) given

namespace stablehlo::quantization {

using ::tensorflow::quantization::RepresentativeDatasetFile;

absl::StatusOr<absl::flat_hash_map<std::string, RepresentativeDatasetFile>>
CreateRepresentativeDatasetFileMap(absl::Span<const RepresentativeDatasetConfig>
                                       representative_dataset_configs) {
  absl::flat_hash_map<std::string, RepresentativeDatasetFile>

#define TENSORFLOW_COMPILER_MLIR_QUANTIZATION_STABLEHLO_QUANTIZE_PASSES_H_

#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_options.pb.h"

namespace stablehlo {
namespace quantization {
// Adds passes for quantization of individual quantizable components.
// (i.e. activation, weight, bias)
void AddQuantizationPasses(mlir::PassManager& pass_manager,

#include "absl/container/flat_hash_map.h"
#include "absl/status/statusor.h"
#include "absl/types/span.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"

namespace stablehlo::quantization {

// Translates a set of `RepresentativeDatsetConfig` to signature key ->
// `RepresentativeDatasetFile` mapping. This is useful when using

            32,
            32,
            3
          ],
          "name": "arg0",
          "quantization": {
          }
        },
        {
          "shape": [
            16,
            3,
            3,
            3
          ],
          "name": "arg1",
          "quantization": {
          }
        },
        {
          "shape": [
            0
          ],

        {
          "shape": [
            1
          ],
          "name": "split_dim",
          "quantization": {
          }
        },
        {
          "shape": [
            256,
            32,
            32,
            3
          ],
          "name": "input",
          "quantization": {
          }
        },
        {
          "shape": [
            128,
            32,

#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_config.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_options.pb.h"

namespace mlir::quant::stablehlo {

#include "tensorflow/compiler/mlir/quantization/common/ir/FakeQuantSupport.h"
#include "tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.h"

//===----------------------------------------------------------------------===//
// The Pass to add default quantization parameters for the activations which
// don't have quantization information. These default parameters are usually

  // CHECK-NEXT:       name: "arg0",
  // CHECK-NEXT:       quantization: {
  // CHECK-EMPTY:
  // CHECK-NEXT:       },
  // CHECK-NEXT:       has_rank: true
  // CHECK-NEXT:     }, {
  // CHECK-NEXT:       shape: [ 4 ],
  // CHECK-NEXT:       buffer: 2,
  // CHECK-NEXT:       name: "Const",
  // CHECK-NEXT:       quantization: {
  // CHECK-EMPTY:
  // CHECK-NEXT:       },
  // CHECK-NEXT:       has_rank: true