// attributes into legacy attributes. For example,  _replication_info=X
// and _xla_compile_device_type=TPU should be replaced with _tpu_replicate=X.
// This ensures the unified attributes not get exposed outside of the MLIR
// bridge with V1 pipeline in some cases.

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

                    }
                }
                MethodVisitorScope bridge = new MethodVisitorScopeImpl(
                    clazz.visitor.visitMethod(methodData.access, methodData.name, methodData.descriptor, methodData.signature, methodData.exceptions)
                );
                bridge.visitCode();
                bridge.visitEnd();
            }
            return original;
        }),

import org.apache.maven.artifact.repository.layout.ArtifactRepositoryLayout;
import org.apache.maven.artifact.repository.layout.DefaultRepositoryLayout;
import org.apache.maven.bridge.MavenRepositorySystem;
import org.eclipse.sisu.Priority;

@Named
@Priority(10)
@Singleton
public class TestMavenRepositorySystem extends MavenRepositorySystem {

    @Inject

// Whether this type is allowed to have a TF2XLA fallback.
bool IsOpAllowedTf2xlaFallback(const TypeID& type_id);

// Whether this type is Preferred to use a TF2XLA fallback kernel when using
// the MLIR bridge. If this is true, then the TF2XLA fallback kernel will be
// used over the MLIR lowering.
bool IsOpAllowedTf2xlaPreferred(const TypeID& type_id);

}  // namespace mhlo
}  // namespace mlir

namespace tensorflow {

// An OpKernel that compiles an op to an XLA computation and runs it. Unlike
// XlaLaunch this doesn't rely on any rewrites of the graphdef - it will run a
// vanilla TensorFlow op as long as the bridge supports it.
class XlaCompileOnDemandOp : public OpKernel {
 public:
  explicit XlaCompileOnDemandOp(OpKernelConstruction* ctx)
      : OpKernel(ctx),
        platform_info_(XlaPlatformInfoFromDevice(ctx->device())) {}

}

// Adds the list of ops that are only supported in the old bridge.
// TODO(b/168036682): Remove bounded dynamism ops now that MLIR bridge supports
// bounded dynamism.
// TODO(b/257574556): Remove the need for this manual list by making use of old
// bridge phase 2 op list.
void AddOldBridgeOnlyOps(MLIRContext* context,

#define GEN_PASS_DECL_CONVERTTFQUANTOPSTOMHLO
#define GEN_PASS_DECL_CONVERTTFQUANTTYPES
#define GEN_PASS_DECL_VERIFYQUANTLEGALIZATION
#define GEN_PASS_DECL_OPTIMIZEINTGRAPH
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h.inc"
}  // namespace mlir::quant::stablehlo

#include "tsl/platform/statusor.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {

// Compiles a serialized MLIR module and returns a serialized MLIR module of the
// result of running all the MLIR Bridge passes. If compile_to_xla_hlo is true
// then those passes include all the Legalization to XLA HLO which is returned
// in the compilation_result.
absl::StatusOr<std::string> CompileFromMlirToXlaHlo(

#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/tf_stablehlo_pass.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/cc/pass_pipeline.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/cc/run_passes.h"

// `max_iterations` are reached. Returns failure if resource ops decomposition
// does not converge after `max_iterations`.
// TODO(prakalps): This can be useful to a lot of other passes in bridge.
// Extract out as a separate utility.
LogicalResult ApplyPatternsLocallyUntilConverged(
    Operation* op_with_regions, FrozenRewritePatternSet& patterns,
    int max_iterations) {
  bool changed = true;