apply spotless after rebase

Signed-off-by: HenryL27 <[email protected]>

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_similarity/TextSimilarityCrossEncoderModel.java

@@ -17,8 +17,6 @@
  */
 package org.opensearch.ml.engine.algorithms.text_similarity;
 
-import static org.opensearch.ml.engine.ModelHelper.PYTORCH_ENGINE;
-
 import java.util.ArrayList;
 import java.util.List;
 
@@ -40,7 +38,7 @@
 
 @Function(FunctionName.TEXT_SIMILARITY)
 public class TextSimilarityCrossEncoderModel extends DLModel {
- 
+
  @Override
  public ModelTensorOutput predict(String modelId, MLInput mlInput) throws TranslateException {
  MLInputDataset inputDataSet = mlInput.getInputDataset();

ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/text_similarity/TextSimilarityTranslator.java

@@ -38,6 +38,7 @@
 
 public class TextSimilarityTranslator extends SentenceTransformerTranslator {
  public final String SIMILARITY_NAME = "similarity";
+
  @Override
  public NDList processInput(TranslatorContext ctx, Input input) {
  String sentence = input.getAsString(0);
@@ -78,13 +79,14 @@ public Output processOutput(TranslatorContext ctx, NDList list) {
  DataType dataType = ndArray.getDataType();
  MLResultDataType mlResultDataType = MLResultDataType.valueOf(dataType.name());
  ByteBuffer buffer = ndArray.toByteBuffer();
- ModelTensor tensor = ModelTensor.builder()
- .name(name)
- .data(data)
- .shape(shape)
- .dataType(mlResultDataType)
- .byteBuffer(buffer)
- .build();
+ ModelTensor tensor = ModelTensor
+ .builder()
+ .name(name)
+ .data(data)
+ .shape(shape)
+ .dataType(mlResultDataType)
+ .byteBuffer(buffer)
+ .build();
  outputs.add(tensor);
  }
 

ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/text_similarity/TextSimilarityCrossEncoderModelTest.java

@@ -17,6 +17,15 @@
  */
 package org.opensearch.ml.engine.algorithms.text_similarity;
 
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertThrows;
+import static org.mockito.ArgumentMatchers.any;
+import static org.mockito.Mockito.doNothing;
+import static org.mockito.Mockito.mock;
+import static org.mockito.Mockito.when;
+import static org.opensearch.ml.engine.algorithms.DLModel.*;
+
 import java.io.File;
 import java.io.IOException;
 import java.net.URISyntaxException;
@@ -30,7 +39,6 @@
 import java.util.Map;
 import java.util.UUID;
 
-import org.apache.commons.lang3.tuple.Pair;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -61,15 +69,6 @@
 import ai.djl.translate.TranslatorContext;
 import lombok.extern.log4j.Log4j2;
 
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-import static org.junit.Assert.assertThrows;
-import static org.mockito.ArgumentMatchers.any;
-import static org.mockito.Mockito.doNothing;
-import static org.mockito.Mockito.mock;
-import static org.mockito.Mockito.when;
-import static org.opensearch.ml.engine.algorithms.DLModel.*;
-
 @Log4j2
 public class TextSimilarityCrossEncoderModelTest {
 
@@ -88,14 +87,15 @@ public void setUp() throws URISyntaxException {
  mlCachePath = Path.of("/tmp/ml_cache" + UUID.randomUUID());
  encryptor = new EncryptorImpl("m+dWmfmnNRiNlOdej/QelEkvMTyH//frS2TBeS2BP4w=");
  mlEngine = new MLEngine(mlCachePath, encryptor);
- model = MLModel.builder()
- .modelFormat(MLModelFormat.TORCH_SCRIPT)
- .name("test_model_name")
- .modelId("test_model_id")
- .algorithm(FunctionName.TEXT_SIMILARITY)
- .version("1.0.0")
- .modelState(MLModelState.TRAINED)
- .build();
+ model = MLModel
+ .builder()
+ .modelFormat(MLModelFormat.TORCH_SCRIPT)
+ .name("test_model_name")
+ .modelId("test_model_id")
+ .algorithm(FunctionName.TEXT_SIMILARITY)
+ .version("1.0.0")
+ .modelState(MLModelState.TRAINED)
+ .build();
  modelHelper = new ModelHelper(mlEngine);
  params = new HashMap<>();
  modelZipFile = new File(getClass().getResource("TinyBERT-CE.zip").toURI());
@@ -104,10 +104,9 @@ public void setUp() throws URISyntaxException {
  params.put(ML_ENGINE, mlEngine);
  textSimilarityCrossEncoderModel = new TextSimilarityCrossEncoderModel();
 
- inputDataSet = TextSimilarityInputDataSet.builder()
- .textDocs(Arrays.asList(
- "That is a happy dog", 
- "it's summer"))
+ inputDataSet = TextSimilarityInputDataSet
+ .builder()
+ .textDocs(Arrays.asList("That is a happy dog", "it's summer"))
  .queryText("it's summer")
  .build();
  }
@@ -128,15 +127,15 @@ public void test_TextSimilarity_Translator_ProcessInput() throws URISyntaxExcept
  when(input.getAsString(0)).thenReturn(testSentence);
  when(input.getAsString(1)).thenReturn(testSentence);
  NDArray indiceNdArray = mock(NDArray.class);
- when(indiceNdArray.toLongArray()).thenReturn(new long[]{102l, 101l});
+ when(indiceNdArray.toLongArray()).thenReturn(new long[] { 102l, 101l });
  when(manager.create((long[]) any())).thenReturn(indiceNdArray);
  doNothing().when(indiceNdArray).setName(any());
  NDList outputList = textSimilarityTranslator.processInput(translatorContext, input);
  assertEquals(3, outputList.size());
  Iterator<NDArray> iterator = outputList.iterator();
  while (iterator.hasNext()) {
  NDArray ndArray = iterator.next();
- long [] output = ndArray.toLongArray();
+ long[] output = ndArray.toLongArray();
  assertEquals(2, output.length);
  }
  }
@@ -155,10 +154,10 @@ public void test_TextSimilarity_Translator_ProcessOutput() throws URISyntaxExcep
  when(ndArray.nonzero()).thenReturn(ndArray);
  when(ndArray.squeeze()).thenReturn(ndArray);
  when(ndArray.getFloat(any())).thenReturn(1.0f);
- when(ndArray.toArray()).thenReturn(new Number[]{1.245f});
+ when(ndArray.toArray()).thenReturn(new Number[] { 1.245f });
  when(ndArray.getName()).thenReturn("output");
  when(ndArray.getShape()).thenReturn(shape);
- when(shape.getShape()).thenReturn(new long[]{1});
+ when(shape.getShape()).thenReturn(new long[] { 1 });
  when(ndArray.getDataType()).thenReturn(DataType.FLOAT32);
  List<NDArray> ndArrayList = Collections.singletonList(ndArray);
  NDList ndList = new NDList(ndArrayList);
@@ -181,7 +180,7 @@ public void initModel_predict_TorchScript_CrossEncoder() throws URISyntaxExcepti
  ModelTensorOutput output = (ModelTensorOutput) textSimilarityCrossEncoderModel.predict(mlInput);
  List<ModelTensors> mlModelOutputs = output.getMlModelOutputs();
  assertEquals(2, mlModelOutputs.size());
- for (int i=0;i<mlModelOutputs.size();i++) {
+ for (int i = 0; i < mlModelOutputs.size(); i++) {
  ModelTensors tensors = mlModelOutputs.get(i);
  List<ModelTensor> mlModelTensors = tensors.getMlModelTensors();
  assertEquals(1, mlModelTensors.size());
@@ -190,13 +189,14 @@ public void initModel_predict_TorchScript_CrossEncoder() throws URISyntaxExcepti
  textSimilarityCrossEncoderModel.close();
  }
 
-
  @Test
  public void initModel_NullModelHelper() throws URISyntaxException {
  Map<String, Object> params = new HashMap<>();
  params.put(MODEL_ZIP_FILE, new File(getClass().getResource("TinyBERT-CE.zip").toURI()));
- IllegalArgumentException e = assertThrows(IllegalArgumentException.class, 
- () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor));
+ IllegalArgumentException e = assertThrows(
+ IllegalArgumentException.class,
+ () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor)
+ );
  assert (e.getMessage().equals("model helper is null"));
  }
 
@@ -205,16 +205,20 @@ public void initModel_NullMLEngine() throws URISyntaxException {
  Map<String, Object> params = new HashMap<>();
  params.put(MODEL_ZIP_FILE, new File(getClass().getResource("TinyBERT-CE.zip").toURI()));
  params.put(MODEL_HELPER, modelHelper);
- IllegalArgumentException e = assertThrows(IllegalArgumentException.class, 
- () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor));
+ IllegalArgumentException e = assertThrows(
+ IllegalArgumentException.class,
+ () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor)
+ );
  assert (e.getMessage().equals("ML engine is null"));
  }
 
  @Test
  public void initModel_NullModelId() {
  model.setModelId(null);
- IllegalArgumentException e = assertThrows(IllegalArgumentException.class, 
- () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor));
+ IllegalArgumentException e = assertThrows(
+ IllegalArgumentException.class,
+ () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor)
+ );
  assert (e.getMessage().equals("model id is null"));
  }
 
@@ -224,8 +228,7 @@ public void initModel_WrongModelFile() throws URISyntaxException {
  params.put(MODEL_HELPER, modelHelper);
  params.put(MODEL_ZIP_FILE, new File(getClass().getResource("../text_embedding/wrong_zip_with_2_pt_file.zip").toURI()));
  params.put(ML_ENGINE, mlEngine);
- MLException e = assertThrows(MLException.class, 
- () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor));
+ MLException e = assertThrows(MLException.class, () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor));
  Throwable rootCause = e.getCause();
  assert (rootCause instanceof IllegalArgumentException);
  assert (rootCause.getMessage().equals("found multiple models"));
@@ -234,39 +237,52 @@ public void initModel_WrongModelFile() throws URISyntaxException {
  @Test
  public void initModel_WrongFunctionName() {
  MLModel mlModel = model.toBuilder().algorithm(FunctionName.KMEANS).build();
- IllegalArgumentException e = assertThrows(IllegalArgumentException.class, 
- () -> textSimilarityCrossEncoderModel.initModel(mlModel, params, encryptor));
+ IllegalArgumentException e = assertThrows(
+ IllegalArgumentException.class,
+ () -> textSimilarityCrossEncoderModel.initModel(mlModel, params, encryptor)
+ );
  assert (e.getMessage().equals("wrong function name"));
  }
 
  @Test
  public void predict_NullModelHelper() {
- IllegalArgumentException e = assertThrows(IllegalArgumentException.class, 
- () -> textSimilarityCrossEncoderModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(inputDataSet).build()));
+ IllegalArgumentException e = assertThrows(
+ IllegalArgumentException.class,
+ () -> textSimilarityCrossEncoderModel
+ .predict(MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(inputDataSet).build())
+ );
  assert (e.getMessage().equals("model not deployed"));
  }
 
  @Test
  public void predict_NullModelId() {
  model.setModelId(null);
- IllegalArgumentException e = assertThrows(IllegalArgumentException.class, 
- () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor));
+ IllegalArgumentException e = assertThrows(
+ IllegalArgumentException.class,
+ () -> textSimilarityCrossEncoderModel.initModel(model, params, encryptor)
+ );
  assert (e.getMessage().equals("model id is null"));
- IllegalArgumentException e2 = assertThrows(IllegalArgumentException.class, 
- () -> textSimilarityCrossEncoderModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(inputDataSet).build()));
+ IllegalArgumentException e2 = assertThrows(
+ IllegalArgumentException.class,
+ () -> textSimilarityCrossEncoderModel
+ .predict(MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(inputDataSet).build())
+ );
  assert (e2.getMessage().equals("model not deployed"));
  }
 
  @Test
  public void predict_AfterModelClosed() {
  textSimilarityCrossEncoderModel.initModel(model, params, encryptor);
  textSimilarityCrossEncoderModel.close();
- MLException e = assertThrows(MLException.class, 
- () -> textSimilarityCrossEncoderModel.predict(MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(inputDataSet).build()));
+ MLException e = assertThrows(
+ MLException.class,
+ () -> textSimilarityCrossEncoderModel
+ .predict(MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(inputDataSet).build())
+ );
  log.info(e.getMessage());
  assert (e.getMessage().startsWith("Failed to inference TEXT_SIMILARITY"));
  }
- 
+
  @After
  public void tearDown() {
  FileUtils.deleteFileQuietly(mlCachePath);