// limitations under the License.

package cluster

// DebugInfo contains minimal information about remote clusters.
// This struct is defined here, in a package that avoids many imports, since xds/debug usually
// affects agent binary size. We avoid embedding other parts of a "remote cluster" struct like kube clients.
type DebugInfo struct {
	ID         ID     `json:"id"`
	SecretName string `json:"secretName"`

		}
	}
	_ = w.Flush()
	return nil
}

func parseClusterStatuses(input map[string][]byte) (map[string][]cluster.DebugInfo, error) {
	statuses := make(map[string][]cluster.DebugInfo, len(input))
	for istiodKey, bytes := range input {
		var parsed []cluster.DebugInfo
		if err := json.Unmarshal(bytes, &parsed); err != nil {
			return nil, err
		}
		statuses[istiodKey] = parsed
	}
	return statuses, nil

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir --mlir-print-debuginfo -o - | FileCheck %s

// CHECK: #[[LOC0:.*]] = loc("<stdin>":0:0)
// CHECK: "tfl.split"
// CHECK-SAME: loc(#[[SPLIT_LOC:.*]])
// CHECK: #[[LOC1:.*]] = loc("output0"(#[[LOC0]]))
// CHECK: #[[LOC2:.*]] = loc("output1"(#[[LOC0]]))
// CHECK: #[[SPLIT_LOC]] = loc(fused[#[[LOC1]], #[[LOC2]]])

{
  "version": 3,
  "operator_codes": [
    {

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s --mlir-print-debuginfo --print-after-all -tf-input-arrays=arg -tf-input-data-types=DT_INT32 -tf-input-shapes=8 -tf-output-arrays=arg -o - | FileCheck %s

node {
  name: "arg"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
versions {
  producer: 27

// RUN: tf-opt %s -mlir-print-debuginfo -mlir-print-local-scope | tf-opt -mlir-print-debuginfo -mlir-print-op-generic -mlir-print-local-scope | FileCheck %s

// This file should be written in the generic form with debug locations.
// (that is, as if printed with `-mlir-print-debuginfo -mlir-print-op-generic`).
// The test parses the file, prints it in the pretty form with debug locations,
// then parses it back, then prints it in the generic form again.

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

node {
  name: "Quantized_Constant"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_QUINT8
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_QUINT8
        tensor_shape {
        }
        int_val: 7
      }
    }

// RUN: tf-mlir-translate -mlir-tf-str-attr-to-mlir %s -mlir-print-debuginfo -mlir-print-local-scope | FileCheck %s

"\0A\0Amodule attributes {tf.versions = {producer = 888 : i32}} {\0A func.func @main(%arg0: tensor<?xi32>) -> tensor<?xi32> {\0A %0 = \22tf.Identity\22(%arg0) : (tensor<?xi32>) -> tensor<?xi32> loc(unknown)\0A return %0 : tensor<?xi32> loc(unknown)\0A } loc(unknown)\0A} loc(unknown)"

// Test simple serialized computation consisting of a function named `main`

// Restore variable and resources in the SavedModel export dir for the
// indicated metagraph.
// The recommended way to load a saved model is to call LoadSavedModel,
// which provides an already initialized Metagraph, Session, and DebugInfo.
Status RestoreSession(const RunOptions& run_options,
                      const MetaGraphDef& meta_graph, const string& export_dir,
                      std::unique_ptr<Session>* session);

		{ID: "config", SyncStatus: "synced"},
		{ID: "c0", SecretName: "istio-system/s0", SyncStatus: "synced"},
		{ID: "c1", SecretName: "istio-system/s1", SyncStatus: "syncing"},
	})

	// Sync the last one
	c.component.ForCluster("c1").Synced.Store(true)
	assert.EventuallyEqual(t, c.controller.ListRemoteClusters, []cluster.DebugInfo{
		{ID: "config", SyncStatus: "synced"},

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false -mlir-print-debuginfo %s -o - | FileCheck %s

node {
  name: "PartitionedCall"
  op: "PartitionedCall"
  attr {
    key: "Tin"
    value {
      list {
      }
    }
  }
  attr {
    key: "Tout"
    value {
      list {
        type: DT_UINT8
      }
    }
  }
  attr {
    key: "_gradient_op_type"
    value {