// Test the output from TPU op is properly awaited before its use by map_fn.
// CHECK-LABEL: @main
// CHECK-SAME: ([[input0:%.*]]: !tf_mlrt.tensor, [[input1:%.*]]: !tf_mlrt.tensor)
func.func @main(%input0: tensor<i32>, %input1: tensor<i32>, %input2: tensor<!tf_type.variant<tensor<*xf32>>> ) -> tensor<i32> {
  %0 = "tf.Cast"(%input0) {__op_key = 0: i32, device = "/device:CPU:0"} : (tensor<i32>) -> tensor<f32>
  // CHECK: tf_mlrt_tpu.compile_and_execute

}

// -----

// Check number of replicated inputs is evenly divisible by 'n'.
func.func @verifier_replicate_bad_operandSegmentSizes(%arg0: tensor<*xi32>) {
  "tf_device.replicate" (%arg0, %arg0, %arg0, %arg0) ({
// expected-error@-1 {{'tf_device.replicate' op expects number of replicated inputs (4) to be evenly divisible by 'n' (3)}}
  ^entry(%input0: tensor<*xi32>, %input1: tensor<*xi32>):
    tf_device.return

  // CHECK-DAG:  [[READ1:%.+]] = "tf.ReadVariableOp"([[ARG1]])
  // CHECK:      [[INPUT0:%.+]] = "tf.TPUPartitionedInputV2"([[READ0]], [[READ1]])
  // CHECK-DAG:  [[READ2:%.+]] = "tf.ReadVariableOp"([[ARG0]])
  // CHECK-DAG:  [[READ3:%.+]] = "tf.ReadVariableOp"([[ARG1]])
  // CHECK:      [[INPUT1:%.+]] = "tf.TPUPartitionedInputV2"([[READ2]], [[READ3]])

# RUN: not tf_tfl_translate -tf-input-arrays=input0 -tf-input-shapes=-1 -tf-input-data-types=DT_FLOAT -tf-output-arrays=add %s 2>&1 | FileCheck %s
# CHECK: error: The Graph contains unsupported `StatefulPartionedCallOp`(s)

node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {

  }
}
node {
  name: "ParseExample/ParseExampleV2"
  op: "ParseExampleV2"
  input: "input0"
  input: "ParseExample/ParseExampleV2/names"
  input: "ParseExample/ParseExampleV2/sparse_keys"
  input: "ParseExample/ParseExampleV2/dense_keys"
  input: "ParseExample/ParseExampleV2/ragged_keys"
  input: "ParseExample/Const"
  input: "ParseExample/Const_1"
  attr {
    key: "Tdense"
    value {
      list {

  EXPECT_THAT(input2->type, Eq(TensorType_INT8));

  // Check if the quantization params of the minimum/maximum inputs match
  // after requantization
  EXPECT_THAT(input1->quantization->scale, Eq(input2->quantization->scale));
  EXPECT_THAT(input1->quantization->zero_point,
              Eq(input2->quantization->zero_point));

  // Check the input quantization params match the output ones.

        
        input2 = getResourceFile("2.txt")
        output2 = signing.signatureType.fileFor(input2)
        input2Artifact = new DefaultPublishArtifact(input2.name, "Text File", "txt", null, null, input2)
        
        [output1, output2].each { output ->
            assert !output.exists() || output.delete()
        }
        
        assert input1.text && input2.text  // don't care what it is, just need some
    }
    

      // CHECK-NEXT: op: "ParseExampleV2"
      // CHECK-NEXT: input: "input0"
      // CHECK-NEXT: input: "tf.Const{{_.*_3}}"
      // CHECK-NEXT: input: "tf.Const{{_.*_5}}"
      // CHECK-NEXT: input: "tf.Const{{_.*_2}}"
      // CHECK-NEXT: input: "tf.Const{{_.*_4}}"
      // CHECK-NEXT: input: "tf.Const"
      // CHECK-NEXT: input: "tf.Const{{_.*_1}}"
      // CHECK-NEXT: attr {
      // CHECK-NEXT:   key: "Tdense"

            }
        """
        when:
        run "myTask"
        then:
        output.contains "Input: inputDirectory [inputs]"
        output.contains "Input: inputFile [input.txt]"
        output.contains "Input: inputFiles [input1.txt, input2.txt]"
        output.contains "Input: nested.inputFile [input-nested.txt]"
        output.contains "Output: namedOutputDirectories.one [outputs-one]"

                @TaskAction
                void doStuff() {
                    outputFile.text = nested.input
                }
            }

            class NestedBean {
                @Input
                input
            }

            class OtherNestedBean {
                @Input
                input
            }

            boolean useOther = providers.gradleProperty('useOther').present