"@local_tsl//tsl/platform:errors",
    ],
)

tf_cc_test(
    name = "cluster_tf_test",
    srcs = ["cluster_tf_test.cc"],
    data = [
        "testdata/empty_func.mlir",
        "testdata/invalid_executor.mlir",
        "testdata/multiple_submodules.mlir",
    ],
    deps = [
        ":cluster_tf",
        "//tensorflow/compiler/mlir:register_common_dialects",

  OwningOpRef<mlir::ModuleOp> mlir_module_;
};

TEST_F(SessionClusterTensorflowDialectTest, ClustersTf) {
  CellReader<int64_t> compilation_status(kCompilationStreamz);

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(
      RunSessionTf2xlaClusteringBridge(*mlir_module_,
                                       /*is_in_fallback_enabled_mode=*/false));

  OwningOpRef<mlir::ModuleOp> mlir_module_;
};

TEST_F(TensorflowDialectToExecutorTest, ConvertsToExecutor) {
  CellReader<int64_t> compilation_status(kCompilationStreamz);

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(ExportFromTensorflowDialectToExecutor(*mlir_module_));

  EXPECT_EQ(compilation_status.Delta("success"), 1);
}

TEST_F(TensorflowDialectToExecutorTest, ErrorsWhenCannotConvert) {

				if fname != p.funcs[u.fnfid].fname {
					// New function; emit entry for previous one.
					if err := emitFunc(u); err != nil {
						return err
					}
				}
			}
			tstmts += uint64(u.NxStmts)
			count := p.unitTable[u]
			if count != 0 {
				cstmts += uint64(u.NxStmts)
			}
		}
		if err := emitFunc(extcu{}); err != nil {
			return err
		}
	}
	if _, err := fmt.Fprintf(tabber, "%s\t%s\t%.1f%%\n",

  OwningOpRef<mlir::ModuleOp> mlir_module_;
};

TEST_F(TensorflowDialectToExecutorTest, ConvertsToExecutor) {
  CellReader<int64_t> compilation_status(kExportStreamzName);

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(ExportFromTensorflowDialectToExecutor(*mlir_module_));

  EXPECT_EQ(compilation_status.Delta(kExportSuccess), 1);
  EXPECT_EQ(compilation_status.Delta(kExportFailed), 0);
}

	for idx := 0; idx < nFuncs; idx++ {
		b.wrUint32(w, uint32(foff))
		foff += int64(len(b.funcs[idx].encoded))
	}
	return off + (int64(len(b.funcs)) * 4)
}

func (b *CoverageMetaDataBuilder) emitFunc(w io.WriteSeeker, off int64, f funcDesc) (int64, error) {
	ew := len(f.encoded)
	if nw, err := w.Write(f.encoded); err != nil {
		return 0, err
	} else if ew != nw {

    ],
)

tf_cc_test(
    name = "lower_cluster_to_runtime_ops_test",
    srcs = ["lower_cluster_to_runtime_ops_test.cc"],
    data = [
        "testdata/basic_cluster.mlir",
        "testdata/empty_func.mlir",
        "testdata/malformed_cluster.mlir",
    ],
    deps = [
        ":lower_cluster_to_runtime_ops",
        "//tensorflow/compiler/mlir:register_common_dialects",

  OwningOpRef<mlir::ModuleOp> mlir_module_;

  Env* env_;
  std::string test_dir_;
  std::string test_group_name_;
};

TEST_F(LowerClusterToRuntimeOpsTest, SanityCheck) {
  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(RunLowerClusterToRuntimeOpsPassPipeline(
      *mlir_module_, DeviceType(DEVICE_TPU_XLA_JIT)));
}

TEST_F(LowerClusterToRuntimeOpsTest, LowersClusterOpsTPU) {

		for k := uint(0); k < atl; k++ {
			if emit {
				emitFunc(&dentries[idx], dcsites[idx], k, atl)
			}
			idx++
		}
		// now process any closures it contains
		ncl := 0
		for idx < nd {
			nfn := dentries[idx].fname
			if !clore.MatchString(nfn) {
				break
			}
			ncl++
			if emit {
				emitFunc(&dentries[idx], dcsites[idx], 0, 1)
			}
			idx++
		}
		return idx

func.func @check_sharding_attrs_exists_for_empty_cluster_func() {
  "tf_device.cluster_func"() {func = @empty_func, step_marker_location = "", num_cores_per_replica = 1 : i64} : () -> ()
  // CHECK: input_sharding_configuration = []
  // CHECK: output_sharding_configuration = []
  func.return
}

// CHECK-LABEL: func @empty_func() {
func.func @empty_func() {
  func.return
}

// -----