//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o /tmp/temp.stablehlo; [ -f /tmp/temp.stablehlo ]


module {
func.func @main(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  %0 = "tf.Sigmoid"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o /tmp/temp.stablehlo; [ -f /tmp/temp.stablehlo ]


module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> {
  %0 = "tf.Mul"(%arg0, %arg0) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}

        def headers = new ArrayList<>()
        headers.add "header.h"
        headers.add "<stdio.h>"
        headers.add "some/path/to/another.h"
        def tempDir = tmpDirProvider.createDir("temp")
        def prefixHeaderFile = new File(tempDir, "prefix-headers.h")

        when:
        PCHUtils.generatePrefixHeaderFile(headers, prefixHeaderFile)

        then:

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o /tmp/temp.stablehlo; [ -f /tmp/temp.stablehlo ]

module {
func.func @main(%arg0 : tensor<5xf32>, %arg1 : tensor<f32>, %arg2 : tensor<f32>) -> tensor<5xf32> {
  %0 = "tf.ClipByValue"(%arg0, %arg1, %arg2) : (tensor<5xf32>, tensor<f32>, tensor<f32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>
}

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o /tmp/temp.stablehlo; [ -f /tmp/temp.stablehlo ]

module {
func.func @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
  %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<i64>) -> tensor<6x3xf32>
  func.return %1 : tensor<6x3xf32>
}

import java.io.File;
import java.util.function.Consumer;

public interface BuildCacheTempFileStore {

    String PARTIAL_FILE_SUFFIX = ".part";

    /**
     * Run the given action with a temp file allocated based on the given cache key.
     * The temp file will be deleted once the action is completed.
     */
    void withTempFile(HashCode key, Consumer<? super File> action);

  %temp = tfr.call @tf__expand_dims(%v1, %axis) : (!tfr.tensor, i32) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %temp : !tfr.tensor
  } else {
    %step = arith.index_cast %cst : i32 to index
    %end = arith.index_cast %n : i32 to index
    %reduce = scf.for %i = %step to %end step %step iter_args(%reduce_iter=%temp) -> !tfr.tensor {

func benchmarkPutObjectPart(b *testing.B, instanceType string, objSize int) {
	// create a temp Erasure/FS backend.
	ctx, cancel := context.WithCancel(context.Background())
	defer cancel()
	objLayer, disks, err := prepareTestBackend(ctx, instanceType)
	if err != nil {
		b.Fatalf("Failed obtaining Temp Backend: <ERROR> %s", err)
	}

// RUN: odml_to_stablehlo %s --allow-tf=false -o /tmp/temp.mlir; [ -f /tmp/temp.mlir ]; [ -f /tmp/debug_stablehlo.mlir ]
// RUN: odml_to_stablehlo %s --allow-tf=true -o /tmp/temp2.mlir; [ -f /tmp/temp2.mlir ]

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 975 : i32}, tf_saved_model.semantics}  {

        long before = countTmpFiles();
        logger.info("Before request. Number of temp files: " + before);
        req.execute(res -> {
            logger.info("Processing request. Number of temp files: " + countTmpFiles());
        }, e -> {});
        long after = countTmpFiles();
        logger.info("After close response. Number of temp files: " + after);

        // ## Assert ##
        assertEquals(before, after);
    }