*/
package org.codelibs.fess.util;

import static org.hamcrest.MatcherAssert.assertThat;
import static org.hamcrest.Matchers.is;

import java.lang.reflect.Constructor;
import java.lang.reflect.InvocationTargetException;
import java.lang.reflect.Method;
import java.util.Arrays;
import java.util.List;

import org.codelibs.fess.unit.UnitFessTestCase;

public class KuromojiCSVUtilTest extends UnitFessTestCase {

/**
 * An entry in a reference map.
 *
 * <p>Entries in the map can be in the following states:
 *
 * <p>Valid:
 *
 * <ul>
 *   <li>Live: valid key/value are set
 *   <li>Loading: loading is pending
 * </ul>
 *
 * <p>Invalid:
 *
 * <ul>
 *   <li>Expired: time expired (key/value may still be set)
 *   <li>Collected: key/value was partially collected, but not yet cleaned up

        // Test path with leading backslash (should be stripped)
        request.setPath("\\test\\file.txt");
        assertEquals("\\test\\file.txt", request.getPath());

        // Test path with trailing backslash (should be stripped)
        request.setPath("test\\directory\\");
        assertEquals("\\test\\directory", request.getPath());

        // Test path with both leading and trailing backslash

{* ../../docs_src/events/tutorial003.py hl[16,19] *}

Here we are simulating the expensive *startup* operation of loading the model by putting the (fake) model function in the dictionary with machine learning models before the `yield`. This code will be executed **before** the application **starts taking requests**, during the *startup*.

    sink().write(value.toByteArray())
  }

  fun writeLong(v: Long) {
    val sink = sink()

    val lengthBitCount: Int =
      if (v < 0L) {
        65 - java.lang.Long.numberOfLeadingZeros(v xor -1L)
      } else {
        65 - java.lang.Long.numberOfLeadingZeros(v)
      }

    val lengthByteCount = (lengthBitCount + 7) / 8
    for (shift in (lengthByteCount - 1) * 8 downTo 0 step 8) {

   * Returns a cache loader that uses {@code function} to load keys, without supporting either
   * reloading or bulk loading. This allows creating a cache loader using a lambda expression.
   *
   * <p>The returned object is serializable if {@code function} is serializable.
   *
   * @param function the function to be used for loading values; must never return {@code null}
   * @return a cache loader that loads values by passing each key to {@code function}

		Help:           help,
		VariableLabels: labels,
	}
}

type metricValue struct {
	Labels map[string]string
	Value  float64
}

// MetricValues - type to set metric values retrieved while loading metrics. A
// value of this type is passed to the `MetricsLoaderFn`.
type MetricValues struct {
	values      map[MetricName][]metricValue
	descriptors map[MetricName]MetricDescriptor
}

### Saved graphs and checkpoints

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the
TensorFlow process effectively executes arbitrary code.

The risk of loading untrusted checkpoints depends on the code or graph that you

// Configure SSL settings
container.singleton("httpClient", HcHttpClient.class, client -> {
    client.setTrustAllCertificates(true);  // For testing only
});
```

### Debug Logging

Enable debug logging by adding to your logging configuration:

```xml
<logger name="org.codelibs.fess.crawler" level="DEBUG"/>
<logger name="org.codelibs.fess.crawler.client" level="DEBUG"/>

See src/README.vendor for information on loading vendored packages