#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

#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) {

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

#include <memory>

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

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

namespace mlir {
namespace TFL {
namespace tac {
namespace {
// CPU

#include "tensorflow/compiler/mlir/tensorflow/translate/mlir_roundtrip_flags.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

// Load a TF model from a GraphDef definition or a TF control flow dialect MLIR

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

namespace mlir {
namespace TFL {
namespace tac {

// Device attribute string on the TFL dialect.
constexpr char kDevice[] = "tac.device";

// Inference type.
constexpr char kInferenceType[] = "tac.inference_type";

// Inference type.
constexpr char kSkipTargetAnnotation[] = "tac.skip_target_annotation";

// TODO(renjieliu): Add more inference types.
enum InferenceType {

      }
      total_count += input_type.getNumElements();
    }
    *count = total_count;
    return true;
  }

  // For conv2d/depthwise_conv/fully_connected ops.
  // This algorithm actually comes from TOCO tooling_util.cc
  static bool GetArithmeticCountForConvAndFullyconnectedOp(mlir::Operation* op,
                                                           int64_t* count) {
    auto weight = op->getOperand(1);

#include "mlir/IR/PatternMatch.h"  // from @llvm-project

namespace mlir {
namespace TFL {
namespace tac {

// nit: Returns all the gpu suitable transformation patterns.
RewritePatternSet GetHardwareRewritePatternsGPU(MLIRContext* context);

}  // namespace tac
}  // namespace TFL
}  // namespace mlir