#include "tensorflow/core/ir/utils/shape_inference_utils.h"

namespace mlir {

class Operation;

namespace TF {

// Runs TensorFlow shape inference associated to the op type registered in the
// TensorFlow op registry based on the Graph version, operands, and attributes.
// Invoking this shape function will create conversions of parameters to the
// TensorFlow Graph equivalent data structures and back to MLIR equivalent data

          password: ${{ secrets.DOCKERHUB_TOKEN }}
      -
        name: Login to GCR
        uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a # v2.1.0
        with:
          registry: gcr.io
          username: _json_key
          password: ${{ secrets.GCP_CREDS }}
      -
        name: Generate variables for cache busting and tag naming
        run: |

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

namespace mlir {

// Inserts common Tensorflow dialects used for offline tools.
void RegisterCommonToolingDialects(mlir::DialectRegistry& registry);

};  // namespace mlir

_CalibrationMethod = (
    stablehlo_quant_config_pb2.CalibrationOptions.CalibrationMethod
)
_REGISTRY = {}


def _implements(calib_method: _CalibrationMethod):
  def decorator(cls):
    assert calib_method not in _REGISTRY
    _REGISTRY[calib_method] = cls
    return cls

  return decorator


class _CalibrationAlgorithmBase(abc.ABC):

  absl::flat_hash_set<std::string> denylisted_mlir_op_names;
  for (auto& entry : denylisted_ops) {
    denylisted_mlir_op_names.insert(TfLiteToMlir(entry));
  }

  DialectRegistry registry;
  MLIRContext context(registry);
  StatusScopedDiagnosticHandler statusHandler(&context,
                                              /*propagate=*/true);

  // Import input_model to a MLIR module

class LowerGlobalsToMlProgram
    : public impl::LowerGlobalsToMlProgramPassBase<LowerGlobalsToMlProgram> {
 public:
  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<mlir::tf_saved_model::TensorFlowSavedModelDialect,
                    ml_program::MLProgramDialect>();
  }

  void runOnOperation() override {

// quantization case.
class PrepareQuantizeDRQPass
    : public PassWrapper<PrepareQuantizeDRQPass, OperationPass<ModuleOp>> {
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, ::mlir::quant::QuantizationDialect,
                    ::mlir::quantfork::QuantizationForkDialect>();
  }

 public:

    // This is a brief description of the pass.
    return "Insert custom aggregation ops for the calibration procedure";
  }

  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<TF::TensorFlowDialect>();
  }

  void runOnOperation() override;

 private:
  enum TestCase {
    TEST_CASE_MIN_MAX,
    TEST_CASE_AVERAGE_MIN_MAX,

    // This is a brief description of the pass.
    return "Apply graph optimizations such as fusing and constant folding to "
           "prepare lifting.";
  }

  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, arith::ArithDialect>();
  }

  void runOnOperation() override;

 private:
  Option<OpSet> op_set_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),

struct DecomposeReduceDatasetPass
    : public impl::DecomposeReduceDatasetPassBase<DecomposeReduceDatasetPass> {
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<tf_device::TensorFlowDeviceDialect>();
  }

  void runOnOperation() override;
};

// Create the AnonymousIterator for `reduce_dataset` with `dataset_types` using
// `builder`.