content: package org.apache.beam.examples;
  /*
   * Licensed to the Apache Software Foundation (ASF) under one
   * or more contributor license agreements.  See the NOTICE file
   * distributed with this work for additional information
   * regarding copyright ownership.  The ASF licenses this file
   * to you under the Apache License, Version 2.0 (the
   * "License"); you may not use this file except in compliance
   * with the License.  You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * 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.
   */
  
  import java.util.ArrayList;
  import java.util.List;
  
  import org.apache.beam.sdk.Pipeline;
  import org.apache.beam.sdk.extensions.python.PythonExternalTransform;
  import org.apache.beam.sdk.io.TextIO;
  import org.apache.beam.sdk.options.Description;
  import org.apache.beam.sdk.options.PipelineOptions;
  import org.apache.beam.sdk.options.PipelineOptionsFactory;
  import org.apache.beam.sdk.options.Validation.Required;
  import org.apache.beam.sdk.values.PCollection;
  
  public class MultiLangRunInference {
      public interface MultiLanguageOptions extends PipelineOptions {
  
          @Description("Path to an input file that contains the IMDB review dataset.")
          @Required
          String getInputFile();
  
          void setInputFile(String value);
  
          @Description("Path to a stored model which will be used for inference.")
          @Required
          String getModelPath();
  
          void setModelPath(String value);
  
          @Description("Path to for the output file, which will contain the predictions.")
          @Required
          String getOutputFile();
  
          void setOutputFile(String value);
  
          @Description("Name of the model on HuggingFace.")
          @Required
          String getModelName();
  
          void setModelName(String value);
  
          @Description("Path to a locally available package compiled as a tarball. "
              + "This package should contain the python transforms used in the pipeline.")
          @Required
          String getLocalPackage();
  
          void setLocalPackage(String value);
      }
  
      public static void main(String[] args) {
  
          MultiLanguageOptions options = PipelineOptionsFactory.fromArgs(args).withValidation()
                  .as(MultiLanguageOptions.class);
  
          Pipeline p = Pipeline.create(options);
          PCollection<String> input = p.apply("Read Input", TextIO.read().from(options.getInputFile()));
          
  
          List<String> local_packages=new ArrayList<String>();
          local_packages.add("transformers==4.26.0");
          local_packages.add("torch==1.13.1");
          local_packages.add(options.getLocalPackage()); 
  
          List<String> packages=new ArrayList<String>();  
          input.apply("Predict", PythonExternalTransform.<PCollection<String>, PCollection<String>>from(
                  "multi_language_custom_transform.composite_transform.InferenceTransform")
                  .withKwarg("model", options.getModelName())
                  .withKwarg("model_path", options.getModelPath())
                  .withExtraPackages(local_packages)
                  )
                  .apply("Write Output", TextIO.write().to(options.getOutputFile()));
  
          p.run().waitUntilFinish();
      }
  }
  