*/
package org.codelibs.core.exception;

import static org.codelibs.core.collection.ArrayUtil.asArray;

import org.codelibs.core.beans.Converter;

/**
 * Exception thrown when an error occurs in a {@link Converter}.
 *
 * @author higa
 */
public class ConverterRuntimeException extends ClRuntimeException {

    private static final long serialVersionUID = 1L;

    /**

  }

  /**
   * Returns a serializable {@code Converter} that converts strings from this format to {@code
   * targetFormat}.
   *
   * @since 16.0
   */
  public Converter<String, String> converterTo(CaseFormat targetFormat) {
    return new StringConverter(this, targetFormat);
  }

  private static final class StringConverter extends Converter<String, String>
      implements Serializable {

 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
 * either express or implied. See the License for the specific language
 * governing permissions and limitations under the License.
 */
package org.codelibs.fess.suggest.converter;

import java.io.IOException;
import java.io.Reader;
import java.io.StringReader;
import java.util.ArrayList;
import java.util.List;

import org.apache.lucene.analysis.TokenStream;

        // Test creating default reading converter
        ReadingConverter converter = SuggestUtil.createDefaultReadingConverter(client, settings);
        assertNotNull(converter);
    }

    @Test
    public void testCreateDefaultContentsReadingConverter() {
        // Test creating default contents reading converter
        ReadingConverter converter = SuggestUtil.createDefaultContentsReadingConverter(client, settings);

Por exemplo, você não pode colocar um modelo do Pydantic em uma `JSONResponse` sem antes convertê-lo em um `dict` com todos os tipos de dados (como `datetime`, `UUID`, etc) convertidos para tipos compatíveis com JSON.

Para esses casos, você pode usar o `jsonable_encoder` para converter seus dados antes de repassá-los para a resposta:

{* ../../docs_src/response_directly/tutorial001_py310.py hl[5:6,20:21] *}

 * <li>{@link #badWords} - List of bad words.
 * <li>{@link #parallel} - Flag for parallel processing.
 * <li>{@link #readingConverter} - Reading converter.
 * <li>{@link #contentsReadingConverter} - Contents reading converter.
 * <li>{@link #normalizer} - Normalizer.
 * <li>{@link #analyzer} - Suggest analyzer.
 * <li>{@link #contentsParser} - Contents parser.
 * <li>{@link #suggestWriter} - Suggest writer.

Então, mesmo com o código acima que não usa Pydantic explicitamente, o FastAPI está usando Pydantic para converter essas dataclasses padrão para a versão do Pydantic.

E claro, ele suporta o mesmo:

* validação de dados
* serialização de dados
* documentação de dados, etc.

import org.codehaus.plexus.component.configurator.converters.AbstractConfigurationConverter;
import org.codehaus.plexus.component.configurator.converters.basic.AbstractBasicConverter;
import org.codehaus.plexus.component.configurator.converters.composite.ObjectWithFieldsConverter;
import org.codehaus.plexus.component.configurator.converters.lookup.ConverterLookup;

    private final JavadocConverter javadocConverter;
    private final GenerationListener listener;

    public ClassDocMethodsBuilder(JavadocConverter converter, GenerationListener listener) {
        this.javadocConverter = converter;
        this.listener = listener;
    }

    /**
     * Builds the methods and script blocks of the given class. Assumes properties have already been built.
     */

  }

  /**
   * Returns a serializable {@code Converter} that converts strings from this format to {@code
   * targetFormat}.
   *
   * @since 16.0
   */
  public Converter<String, String> converterTo(CaseFormat targetFormat) {
    return new StringConverter(this, targetFormat);
  }

  private static final class StringConverter extends Converter<String, String>
      implements Serializable {