#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/nnapi_hardware.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

struct DeviceTransformNNAPIPass

#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
// Gpu hardware class which handles GPU capabilities in TFLite.
// This is used by TAC to get op supported/ op cost estimates on GPU.
class GpuHardware : public TargetHardware {
 public:

  %1 = "tfl.mul"(%0, %arg2) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

#include <string>

#include "llvm/ADT/StringRef.h"
#include "mlir/IR/Operation.h"  // from @llvm-project

namespace mlir {
namespace TFL {
namespace tac {

// Interface name here is the "hook" between the CallOp and FuncOps.
// Take the following example:
//
// call @func_1_CPU {tac.interface_name = "func_1"}
//
// "func_1" is the interface name where "func_1_cpu" is the real implementation

#include "mlir/IR/MLIRContext.h"  // from @llvm-project
#include "mlir/IR/PatternMatch.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {

// Returns true if 'op' is supported to run on 'hardware'.
bool IsSupported(Operation* op, const std::string& hardware);

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/gpu_hardware.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

struct DeviceTransformGPUPass

        "//tensorflow/compiler/mlir/lite/experimental/tac:tflite_importer_exporter",
        "//tensorflow/compiler/mlir/lite/experimental/tac/hardwares:all-target-hardwares",
        "//tensorflow/compiler/mlir/lite/experimental/tac/hardwares:target_hardware",
        "//tensorflow/compiler/mlir/lite/experimental/tac/utils",
        "//tensorflow/compiler/mlir/tensorflow",
        "//third_party/python_runtime:headers",  # buildcleaner: keep

#define TENSORFLOW_COMPILER_MLIR_LITE_EXPERIMENTAL_TAC_EXAMPLES_EXAMPLE_HARDWARE_H_

#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/simple_hardware.h"

namespace mlir {
namespace TFL {
namespace tac {

class ExampleHardware : public SimpleHardware {
 public:
  static constexpr char kId[] = "ExampleHardware";

  mlir::RewritePatternSet GetTransformations(

#define TENSORFLOW_COMPILER_MLIR_LITE_EXPERIMENTAL_TAC_HARDWARES_NNAPI_HARDWARE_H_

#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/simple_hardware.h"

namespace mlir {
namespace TFL {
namespace tac {

class NNAPIHardware : public SimpleHardware {
 public:
  static constexpr char kId[] = "NNAPI";

  mlir::RewritePatternSet GetTransformations(

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"

#include "llvm/Support/Casting.h"
#include "mlir/IR/OpDefinition.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/utils/utils.h"

namespace mlir {
namespace TFL {
namespace tac {

bool NotTFLQuantDequantizeOp(Operation* op) {