# 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

  const llvm::MemoryBuffer* input =
      source_mgr->getMemoryBuffer(source_mgr->getMainFileID());
  std::string error;
  auto loc =
      mlir::FileLineColLoc::get(context, input->getBufferIdentifier(), 0, 0);

  // Parses input/output names from command line options.
  std::vector<std::string> inputs;
  std::vector<std::string> outputs;

    private final Packer delegate;

    public SnappyCommonsPacker(Packer delegate) {
        this.delegate = delegate;
    }

    @Override
    public void pack(List<DataSource> inputs, DataTarget output) throws IOException {
        delegate.pack(inputs, new DelegatingDataTarget(output) {
            @Override
            public OutputStream openOutput() throws IOException {

    @TaskAction fun action() { println(inputValue.get()) }
}

// tag::gradle-properties-task-inputs[]
tasks.register<PrintValue>("printProperty") {
    // Using the API, provides a lazy Provider<String> wired to a task input
    inputValue = providers.gradleProperty("gradlePropertiesProp")
}

# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,2,8,2 -tf-input-data-types=DT_FLOAT -tf-output-arrays=output_0 -print-function-result-mapping %s -o - 2>&1 | FileCheck %s

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 1
        }
        dim {
          size: 2

            assert state.inputFileProperties as Map == ["known-file": knownFileFingerprint, "input-file": inputFileFingerprint]
        }
        0 * _

        assertOperation()
    }

    def "fails when input properties cannot be snapshot"() {
        def failure = new InputFingerprinter.InputFileFingerprintingException("input", new IOException("Error"))
        when:

// CHECK: name: "bar"
// CHECK-NEXT: op: "_Arg"
// CHECK: name: "[[BAR_ID_0:.*]]"
// CHECK-NEXT: op: "Identity"
// CHECK-NEXT: input: "bar"
// CHECK: name: "[[BAR_ID_1:.*]]"
// CHECK-NEXT: op: "Identity"
// CHECK-NEXT: input: "[[BAR_ID_0]]"
// CHECK: name: "Add"
// CHECK-NEXT: op: "_Retval"

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-tpu-annotate-dynamic-shape-inputs %s | FileCheck %s

// Test that annotate the inputs of the cluster func to be dynamic shaped.

module attributes {tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
   func.func @main(
      %arg0: tensor<2048xi64> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"},

                }
            }
        """
    }

    def "detects input file change just before the task is executed"() {
        def inputFile = file("input.txt")
        buildFile << """
            def inputFile = file("input.txt")
            def outputFile = file("build/output.txt")

            task consumer {
                inputs.file(inputFile)
                outputs.file(outputFile)
                doLast {

            3
          ],
          "name": "output1",
          "quantization": {
          }
        }
      ],
      "inputs": [
        0,
        1
      ],
      "outputs": [
        2,
        3
      ],
      "operators": [
        {
          "inputs": [
            0,
            1
          ],
          "outputs": [
            2,
            3
          ],