# Test importing a saved model with 2 signatures that are using a same
# BatchFunction Op, which references to a same inference_func from graph_def
# library. The result should be that both signatures uses the same
# BatchFunction Op (the shared_name is the same) and the same copy of
# inference_func.

# CHECK: f = @inference_func[[post_fix:[^,]*]]

  %2 = "tf.Identity"(%arg0) : (tensor<1x3xf32>) -> tensor<1x3xf32>
  func.return %2 : tensor<1x3xf32>
}

// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<1x3xf32>) -> tensor<*xf32> {
  // CHECK:  "tf.BatchFunction"
  // CHECK-SAME: allowed_batch_sizes = [6]
  // CHECK-SAME: batch_timeout_micros = 100000 : i64
  // CHECK-SAME: batching_queue = ""
  // CHECK-SAME: container = ""
  // CHECK-SAME: enable_large_batch_splitting = false

// assigned inside the function called by "tf.BatchFunction".

module {
// CHECK-LABEL: @serving_default
  func.func @serving_default() -> (tensor<*xi32>) {
    // expected-remark@below {{device: "", container: "", shared_name: "var_0", is_potentially_written: false}}
    %0 = "tf.VarHandleOp"() {shared_name = "var_0"} : () -> tensor<!tf_type.resource<tensor<2xi32>>>
    %1 = "tf.BatchFunction"(%0) {
        f = @called_by_batch_func,

// the functions invoked by BatchFunction with the same shared_name and the
// function `compute_2` will not be removed as the shared_name is different.

// CHECK-LABEL: func private @batch_0
// CHECK: f = @compute_0
func.func private @batch_0(%arg0: tensor<?x?xi32>) -> tensor<*xi32> {

# CHECK: "tf.BatchFunction"
# CHECK-SAME: shared_name = "batch_node"

# CHECK: func private @create_resource
# CHECK: tf.HashTableV2
# CHECK-SAME: shared_name = "hash_table_node@create_resource"
# CHECK: "tf.Variable"
# CHECK-SAME: shared_name = "variable_node"
# CHECK: "tf.VariableV2"
# CHECK-SAME: shared_name = "variable_v2_node"
# CHECK: "tf.BatchFunction"

// CHECK: return %[[identity]] : tensor<1x3x2x2xf32>

// -----

// Tests that `tf.BatchFunction` is inlined.

func.func @serving_default(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {

  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>

  // CHECK: "tf.BatchFunction"(%arg0, %0)
  // CHECK: operandSegmentSizes = array<i32: 1, 1>

  func.return %2 : tensor<1x3xf32>
}

// CHECK-LABEL: func @main
func.func @main(%arg0: tensor<1x3xf32>) -> tensor<*xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "input:0", outputs = "batch/BatchFunction:0"}} {
  %0 = "tf.VarHandleOp"() {device = "/device:CPU:0", container = "", shared_name = "variable"} : () -> tensor<!tf_type.resource<tensor<1x3xf32>>>

// This test verifies the error when two functions are different but invoked by
// the batch functions with same shared_name.

func.func private @batch_0(%arg0: tensor<?x?xi32>) -> tensor<*xi32> {

  // CHECK:  "tf._TfrtGetResource"()
  %cst = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> {device = "/CPU:0"} : () -> tensor<1xi32>
  // CHECK:  "tf.BatchFunction"