// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail --dump-input-filter=all
// RUN: tf-tfrt-opt -pass-pipeline='builtin.module(tf-to-tfrt{target-tpurt=true tpu-use-core-selector=false})' %s | FileCheck %s --dump-input=fail --dump-input-filter=all

// CHECK-LABEL: func @_tfrt_fallback_init
// CHECK-SAME: {{.*}} !tfrt.chain
// CHECK: tfrt_fallback_async.createop(%arg0) key(0) device("/device:CPU:0") "tf.ParseExampleV2"()

                    def input = inputArtifact.get().asFile
                    def output = outputs.file(input.name + ".blue")
                    assert output.parentFile.directory && output.parentFile.list().length == 0
                    println "Transforming \${input.name} to \${output.name}"
                    println "Input exists: \${input.exists()}"

  func.func @gather_with_float_output(%arg0: tensor<1x3x2x1024xf32> {tf_saved_model.index_path = ["input1"]}, %arg1: tensor<1xi32> {tf_saved_model.index_path = ["input2"]}) -> (tensor<1x3x1x1xf32> {tf_saved_model.index_path = ["output"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

  (TF_MatMulOp:$matmul
    (TF_SubOp (TF_CastOp $input, $truncate1), $input_zp),
    (TF_SubOp (TF_CastOp $weight, $truncate2), $weight_zp),
    $transpose_a, $transpose_b, $grad_a, $grad_b),
  (CreateXlaDotV2OpFromTfMatMulOp
    $input, $weight, $input_zp, $weight_zp, $matmul, $transpose_a, $transpose_b),
  [(IsInt8ElementType $input),
   (IsInt8ElementType $weight),
   (HasRank $input),
   (HasRank $weight),

                @Input
                URL getDirect() { null }

                @Input
                Provider<URL> getProviderInput() { propertyInput }

                @Input
                abstract Property<URL> getPropertyInput();

                @Input
                abstract SetProperty<URL> getSetPropertyInput();

                @Input

        input = new Input(this.inputStream, bufferSize);
    }

    @Override
    protected int maybeReadBytes(byte[] buffer, int offset, int count) {
        return input.read(buffer, offset, count);
    }

    @Override
    protected long maybeSkip(long count) throws IOException {
        // Work around some bugs in Input.skip()
        int remaining = input.limit() - input.position();

            "unmodified/input": fingerprint("input", 3),
            "moved/new/input": fingerprint("input", 3)
        ]) == [
            modified("modified-with-collision/input": "input"),
            removed("removed/input": "input"),
            modified("modified/input": "input"),
            added("added/input": "input")
        ]
    }

    "input": "aa--",
    "output": "aa--"
  },
  {
    "input": "a†--",
    "output": "xn--a---kp0a"
  },
  {
    "input": "ab--c",
    "output": "ab--c"
  },
  {
    "comment": "Label with leading hyphen",
    "input": "-x",
    "output": "-x"
  },
  {
    "input": "-†",
    "output": "xn----xhn"
  },
  {
    "input": "-x.xn--zca",
    "output": "-x.xn--zca"

// L2Loss is lowered using the formula,
// L2Loss(input) = Sum(input * input) / 2

// TODO(hinsu): Support unranked tensors once b/144593778 is resolved to not
// cause result type mismatch.
def LowerL2LossOp :
  Pat<(TF_L2LossOp AnyRankedTensor:$input),
      (TF_DivOp
        (TF_SumOp (TF_MulOp $input, $input),
                  (TF_ConstOp (GetAllAxes $input)),
                  /*keep_dims=*/ConstBoolAttrFalse),

                outFile = file("out.txt")
            }
        """
        def input = file("in.txt")
        def output = file("out.txt")
        def configurationCache = newConfigurationCacheFixture()

        when:
        input.text = "12"
        configurationCacheRun(":transformer")

        then: