super.setUp();

        StandardCrawlerContainer container = new StandardCrawlerContainer().singleton("sitemapsHelper", SitemapsHelper.class)//
                .singleton("sitemapsRule", SitemapsRule.class)//
                .singleton("fileRule", RegexRule.class)//
                .singleton("ruleManager", RuleManagerImpl.class);

        ruleManager = container.getComponent("ruleManager");

 * encompasses both of these, 'container' is used.
 *
 * @author George van den Driessche
 */
@GwtCompatible
@NullMarked
public interface TestContainerGenerator<T, E extends @Nullable Object> {
  /** Returns the sample elements that this generate populates its container with. */
  SampleElements<E> samples();

  /**

 *
 * TODO you don't need the container in here with the active maps (jvz).
 * @since 3.0
 */
@Named
@Singleton
@Deprecated
public class DefaultConflictResolverFactory implements ConflictResolverFactory, Contextualizable {
    // fields -----------------------------------------------------------------

    /**
     * The plexus container used to obtain instances from.
     */

# ==============================================================================
cat <<EOF
IMPORTANT: These tests ran under docker. This script does not clean up the
container for you! You can delete the container with:

$ docker rm -f tf

You can also execute more commands within the container with e.g.:

$ docker exec tf bazel clean
$ docker exec -it tf bash
EOF

 */
package org.codelibs.fess.crawler.container;

import org.codelibs.core.lang.StringUtil;

/**
 * The CrawlerContainer interface provides methods for managing components
 * within a crawler container. It includes methods to retrieve components,
 * check availability, and destroy the container. Additionally, it provides
 * a default method to initialize the container with specific protocol handlers.
 *
 */

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

    public ZipExtractor zipExtractor;

    @Override
    protected void setUp() throws Exception {
        super.setUp();
        StandardCrawlerContainer container = new StandardCrawlerContainer();
        container.singleton("archiveStreamFactory", ArchiveStreamFactory.class)
                .singleton("compressorStreamFactory", CompressorStreamFactory.class)

    @Override
    protected void setUp() throws Exception {
        super.setUp();
        StandardCrawlerContainer container = new StandardCrawlerContainer().singleton("contentLengthHelper", ContentLengthHelper.class);
        contentLengthHelper = container.getComponent("contentLengthHelper");
    }

    public void test_getMaxLength() {
        final String mimeType = "text/plain";

-endif
-
 LDLIBS-test-container = $(libsupport)
 
 others += test-container
@@ -182,9 +175,6 @@ others-noinstall += test-container
 others += shell-container echo-container true-container
 others-noinstall += shell-container echo-container true-container
 
-others += $(LINKS_DSO_PROGRAM)
-others-noinstall += $(LINKS_DSO_PROGRAM)
-
 $(objpfx)test-container : $(libsupport)

    public TextExtractor textExtractor;

    @Override
    protected void setUp() throws Exception {
        super.setUp();
        StandardCrawlerContainer container = new StandardCrawlerContainer().singleton("textExtractor", TextExtractor.class);
        textExtractor = container.getComponent("textExtractor");
    }

    public void test_getText() {
        final InputStream in = ResourceUtil.getResourceAsStream("extractor/test.txt");