// names or patterns, but they should be treated as absolute. At the same time, any hostname
    // presented to this method should also be treated as absolute for the purposes of matching
    // to the server certificate.
    //   www.android.com  matches www.android.com
    //   www.android.com  matches www.android.com.
    //   www.android.com. matches www.android.com.

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.google.common.hash;

import static com.google.common.hash.Hashing.murmur3_32;
import static com.google.common.hash.Hashing.murmur3_32_fixed;
import static java.nio.charset.StandardCharsets.UTF_16;
import static java.nio.charset.StandardCharsets.UTF_16LE;
import static java.nio.charset.StandardCharsets.UTF_8;

import static java.lang.Long.rotateRight;

import com.google.common.annotations.VisibleForTesting;

/**
 * Implementation of Geoff Pike's fingerprint2011 hash function. See {@link Hashing#fingerprint2011}
 * for information on the behaviour of the algorithm.
 *
 * <p>On Intel Core2 2.66, on 1000 bytes, fingerprint2011 takes 0.9 microseconds compared to

        // Both instances should be of the same class
        assertEquals(exception1.getClass(), exception2.getClass());
    }

    public void test_throwAndCatch() {
        // Test throwing and catching the exception
        String expectedMessage = "Expected error";
        try {
            throw new FessSystemException(expectedMessage);
        } catch (FessSystemException e) {

  }

  public void testHashCode_equalsAndSerializable() throws Exception {
    sanityTester().testEqualsAndSerializable();
  }

  public void testRoundTripHashCodeUsingBaseEncoding() {
    HashCode hash1 = Hashing.sha1().hashString("foo", US_ASCII);
    HashCode hash2 = HashCode.fromBytes(BaseEncoding.base16().lowerCase().decode(hash1.toString()));
    assertEquals(hash1, hash2);
  }

  public void testObjectHashCode() {

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

  @Partially.GwtIncompatible("AVAILABLE but requires exceptionType to be Throwable.class")
  public final <X extends Throwable> FluentFuture<V> catching(
      Class<X> exceptionType, Function<? super X, ? extends V> fallback, Executor executor) {
    return (FluentFuture<V>) Futures.catching(this, exceptionType, fallback, executor);
  }

  /**
   * Returns a {@code Future} whose result is taken from this {@code Future} or, if this {@code

        assertEquals("Invalid LDAP parameter", exception.getCause().getCause().getMessage());
    }

    public void test_throwAndCatch() {
        // Test throwing and catching the exception
        String expectedMessage = "LDAP authentication failed";
        try {
            throw new LdapOperationException(expectedMessage);
        } catch (LdapOperationException e) {

        assertEquals(message, exception.getMessage());
        assertNull(exception.getCause());
    }

    public void test_throwAndCatch() {
        // Test throwing and catching the exception
        String message = "Thrown exception test";

        try {
            throw new ScriptEngineException(message);
        } catch (ScriptEngineException e) {

 * little-endian order.
 *
 * <p><b>Warning:</b> The result of calling any methods after calling {@link #hash} is undefined.
 *
 * <p><b>Warning:</b> Using a specific character encoding when hashing a {@link CharSequence} with
 * {@link #putString(CharSequence, Charset)} is generally only useful for cross-language
 * compatibility (otherwise prefer {@link #putUnencodedChars}). However, the character encodings