/*
 * Copyright (c) 2019, 2023, Oracle and/or its affiliates. All rights reserved.
 * Licensed under the MIT License.
 */
package ai.onnxruntime;

import static org.junit.jupiter.api.Assertions.assertArrayEquals;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertNotNull;
import static org.junit.jupiter.api.Assertions.assertSame;
import static org.junit.jupiter.api.Assertions.assertTrue;
import static org.junit.jupiter.api.Assertions.fail;

import ai.onnxruntime.OrtSession.Result;
import ai.onnxruntime.OrtSession.SessionOptions;
import ai.onnxruntime.OrtSession.SessionOptions.ExecutionMode;
import ai.onnxruntime.OrtSession.SessionOptions.OptLevel;
import java.io.File;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.nio.FloatBuffer;
import java.nio.LongBuffer;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.EnumSet;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Optional;
import java.util.Set;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;
import java.util.function.BiFunction;
import java.util.logging.Logger;
import java.util.regex.Pattern;
import java.util.stream.Collectors;
import java.util.stream.Stream;
import org.junit.jupiter.api.Assertions;
import org.junit.jupiter.api.Disabled;
import org.junit.jupiter.api.Test;
import org.junit.jupiter.api.condition.EnabledIfSystemProperty;

/** Tests for the onnx-runtime Java interface. */
public class InferenceTest {
  private static final Logger logger = Logger.getLogger(InferenceTest.class.getName());

  private static final String propertiesFile = "Properties.txt";

  private static final Pattern inputPBPattern = Pattern.compile("input_*.pb");
  private static final Pattern outputPBPattern = Pattern.compile("output_*.pb");

  private static final OrtEnvironment env = OrtEnvironment.getEnvironment();

  @Test
  public void environmentTest() {
    // Checks that the environment instance is the same.
    OrtEnvironment otherEnv = OrtEnvironment.getEnvironment();
    assertSame(env, otherEnv);
    otherEnv = OrtEnvironment.getEnvironment("test-name");
    assertSame(env, otherEnv);
  }

  @Test
  public void testVersion() {
    String version = env.getVersion();
    Assertions.assertFalse(version.isEmpty());
  }

  @Test
  public void createSessionFromPath() throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/squeezenet.onnx").toString();
    try (OrtSession.SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      assertNotNull(session);
      assertEquals(1, session.getNumInputs()); // 1 input node
      Map<String, NodeInfo> inputInfoList = session.getInputInfo();
      assertNotNull(inputInfoList);
      assertEquals(1, inputInfoList.size());
      NodeInfo input = inputInfoList.get("data_0");
      assertEquals("data_0", input.getName()); // input node name
      assertTrue(input.getInfo() instanceof TensorInfo);
      TensorInfo inputInfo = (TensorInfo) input.getInfo();
      assertEquals(OnnxJavaType.FLOAT, inputInfo.type);
      int[] expectedInputDimensions = new int[] {1, 3, 224, 224};
      assertEquals(expectedInputDimensions.length, inputInfo.shape.length);
      for (int i = 0; i < expectedInputDimensions.length; i++) {
        assertEquals(expectedInputDimensions[i], inputInfo.shape[i]);
      }

      assertEquals(1, session.getNumOutputs()); // 1 output node
      Map<String, NodeInfo> outputInfoList = session.getOutputInfo();
      assertNotNull(outputInfoList);
      assertEquals(1, outputInfoList.size());
      NodeInfo output = outputInfoList.get("softmaxout_1");
      assertEquals("softmaxout_1", output.getName()); // output node name
      assertTrue(output.getInfo() instanceof TensorInfo);
      TensorInfo outputInfo = (TensorInfo) output.getInfo();
      assertEquals(OnnxJavaType.FLOAT, outputInfo.type);
      int[] expectedOutputDimensions = new int[] {1, 1000, 1, 1};
      assertEquals(expectedOutputDimensions.length, outputInfo.shape.length);
      for (int i = 0; i < expectedOutputDimensions.length; i++) {
        assertEquals(expectedOutputDimensions[i], outputInfo.shape[i]);
      }
    }
  }

  @Test
  public void morePartialInputsTest() throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/partial-inputs-test-2.onnx").toString();
    try (OrtSession.SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      assertNotNull(session);
      assertEquals(3, session.getNumInputs());
      assertEquals(1, session.getNumOutputs());

      // Input and output collections.
      Map<String, OnnxTensor> inputMap = new HashMap<>();
      Set<String> requestedOutputs = new HashSet<>();

      BiFunction<Result, String, Float> unwrapFunc =
          (r, s) -> {
            try {
              return ((float[]) r.get(s).get().getValue())[0];
            } catch (OrtException e) {
              return Float.NaN;
            }
          };

      // Graph has three scalar inputs, a, b, c, and a single output, ab.
      OnnxTensor a = OnnxTensor.createTensor(env, new float[] {2.0f});
      OnnxTensor b = OnnxTensor.createTensor(env, new float[] {3.0f});
      OnnxTensor c = OnnxTensor.createTensor(env, new float[] {5.0f});

      // Request all outputs, supply all inputs
      inputMap.put("a:0", a);
      inputMap.put("b:0", b);
      inputMap.put("c:0", c);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        assertEquals(1, r.size());
        float abVal = unwrapFunc.apply(r, "ab:0");
        assertEquals(6.0f, abVal, 1e-10);
      }

      // Don't specify an output, expect all of them returned.
      try (Result r = session.run(inputMap)) {
        assertEquals(1, r.size());
        float abVal = unwrapFunc.apply(r, "ab:0");
        assertEquals(6.0f, abVal, 1e-10);
      }

      inputMap.clear();
      requestedOutputs.clear();

      // Request single output ab, supply required inputs
      inputMap.put("a:0", a);
      inputMap.put("b:0", b);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        assertEquals(1, r.size());
        float abVal = unwrapFunc.apply(r, "ab:0");
        assertEquals(6.0f, abVal, 1e-10);
      }
      inputMap.clear();
      requestedOutputs.clear();

      // Request output but don't supply the inputs
      inputMap.put("c:0", c);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        fail("Expected to throw OrtException due to incorrect inputs");
      } catch (OrtException e) {
        // System.out.println(e.getMessage());
        // pass
      }
      inputMap.clear();
      requestedOutputs.clear();

      // Request output but don't supply all the inputs
      inputMap.put("b:0", b);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        fail("Expected to throw OrtException due to incorrect inputs");
      } catch (OrtException e) {
        // System.out.println(e.getMessage());
        // pass
      }
    }
  }

  @Test
  public void partialInputsTest() throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/partial-inputs-test.onnx").toString();
    try (OrtSession.SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      assertNotNull(session);
      assertEquals(3, session.getNumInputs());
      assertEquals(3, session.getNumOutputs());

      // Input and output collections.
      Map<String, OnnxTensor> inputMap = new HashMap<>();
      Set<String> requestedOutputs = new HashSet<>();

      BiFunction<Result, String, Float> unwrapFunc =
          (r, s) -> {
            try {
              return ((float[]) r.get(s).get().getValue())[0];
            } catch (OrtException e) {
              return Float.NaN;
            }
          };

      // Graph has three scalar inputs, a, b, c, and three outputs, ab, bc, ab + bc.
      OnnxTensor a = OnnxTensor.createTensor(env, new float[] {2.0f});
      OnnxTensor b = OnnxTensor.createTensor(env, new float[] {3.0f});
      OnnxTensor c = OnnxTensor.createTensor(env, new float[] {5.0f});

      // Request all outputs, supply all inputs
      inputMap.put("a:0", a);
      inputMap.put("b:0", b);
      inputMap.put("c:0", c);
      requestedOutputs.add("ab:0");
      requestedOutputs.add("bc:0");
      requestedOutputs.add("abc:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        assertEquals(3, r.size());
        float abVal = unwrapFunc.apply(r, "ab:0");
        assertEquals(6.0f, abVal, 1e-10);
        float bcVal = unwrapFunc.apply(r, "bc:0");
        assertEquals(15.0f, bcVal, 1e-10);
        float abcVal = unwrapFunc.apply(r, "abc:0");
        assertEquals(21.0f, abcVal, 1e-10);
      }

      // Don't specify an output, expect all of them returned.
      try (Result r = session.run(inputMap)) {
        assertEquals(3, r.size());
        float abVal = unwrapFunc.apply(r, "ab:0");
        assertEquals(6.0f, abVal, 1e-10);
        float bcVal = unwrapFunc.apply(r, "bc:0");
        assertEquals(15.0f, bcVal, 1e-10);
        float abcVal = unwrapFunc.apply(r, "abc:0");
        assertEquals(21.0f, abcVal, 1e-10);
      }

      inputMap.clear();
      requestedOutputs.clear();

      // Request single output ab, supply all inputs
      inputMap.put("a:0", a);
      inputMap.put("b:0", b);
      inputMap.put("c:0", c);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        assertEquals(1, r.size());
        float abVal = unwrapFunc.apply(r, "ab:0");
        assertEquals(6.0f, abVal, 1e-10);
      }
      inputMap.clear();
      requestedOutputs.clear();

      // Request single output abc, supply all inputs
      inputMap.put("a:0", a);
      inputMap.put("b:0", b);
      inputMap.put("c:0", c);
      requestedOutputs.add("abc:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        assertEquals(1, r.size());
        float abcVal = unwrapFunc.apply(r, "abc:0");
        assertEquals(21.0f, abcVal, 1e-10);
      }
      inputMap.clear();
      requestedOutputs.clear();

      /* The native library does all the computations, rather than the requested subset.
       * Leaving these tests commented out until it's fixed.
      // Request single output ab, supply required inputs
      inputMap.put("a:0",a);
      inputMap.put("b:0",b);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap,requestedOutputs)) {
          assertEquals(1,r.size());
          float abVal = unwrapFunc.apply(r,"ab:0");
          assertEquals(6.0f,abVal,1e-10);
      }
      inputMap.clear();
      requestedOutputs.clear();

      // Request single output bc, supply required inputs
      inputMap.put("b:0",b);
      inputMap.put("c:0",c);
      requestedOutputs.add("bc:0");
      try (Result r = session.run(inputMap,requestedOutputs)) {
          assertEquals(1,r.size());
          float bcVal = unwrapFunc.apply(r,"bc:0");
          assertEquals(15.0f,bcVal,1e-10);
      }
      inputMap.clear();
      requestedOutputs.clear();
      */

      // Request output but don't supply the inputs
      inputMap.put("c:0", c);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        fail("Expected to throw OrtException due to incorrect inputs");
      } catch (OrtException e) {
        // System.out.println(e.getMessage());
        // pass
      }
      inputMap.clear();
      requestedOutputs.clear();

      // Request output but don't supply all the inputs
      inputMap.put("b:0", b);
      requestedOutputs.add("ab:0");
      try (Result r = session.run(inputMap, requestedOutputs)) {
        fail("Expected to throw OrtException due to incorrect inputs");
      } catch (OrtException e) {
        // System.out.println(e.getMessage());
        // pass
      }
    }
  }

  @Test
  public void createSessionFromByteArray() throws IOException, OrtException {
    Path modelPath = TestHelpers.getResourcePath("/squeezenet.onnx");
    byte[] modelBytes = Files.readAllBytes(modelPath);
    try (OrtSession.SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelBytes, options)) {
      assertNotNull(session);
      assertEquals(1, session.getNumInputs()); // 1 input node
      Map<String, NodeInfo> inputInfoList = session.getInputInfo();
      assertNotNull(inputInfoList);
      assertEquals(1, inputInfoList.size());
      NodeInfo input = inputInfoList.get("data_0");
      assertEquals("data_0", input.getName()); // input node name
      assertTrue(input.getInfo() instanceof TensorInfo);
      TensorInfo inputInfo = (TensorInfo) input.getInfo();
      assertEquals(OnnxJavaType.FLOAT, inputInfo.type);
      int[] expectedInputDimensions = new int[] {1, 3, 224, 224};
      assertEquals(expectedInputDimensions.length, inputInfo.shape.length);
      for (int i = 0; i < expectedInputDimensions.length; i++) {
        assertEquals(expectedInputDimensions[i], inputInfo.shape[i]);
      }

      assertEquals(1, session.getNumOutputs()); // 1 output node
      Map<String, NodeInfo> outputInfoList = session.getOutputInfo();
      assertNotNull(outputInfoList);
      assertEquals(1, outputInfoList.size());
      NodeInfo output = outputInfoList.get("softmaxout_1");
      assertEquals("softmaxout_1", output.getName()); // output node name
      assertTrue(output.getInfo() instanceof TensorInfo);
      TensorInfo outputInfo = (TensorInfo) output.getInfo();
      assertEquals(OnnxJavaType.FLOAT, outputInfo.type);
      int[] expectedOutputDimensions = new int[] {1, 1000, 1, 1};
      assertEquals(expectedOutputDimensions.length, outputInfo.shape.length);
      for (int i = 0; i < expectedOutputDimensions.length; i++) {
        assertEquals(expectedOutputDimensions[i], outputInfo.shape[i]);
      }

      // Check the metadata can be extracted
      OnnxModelMetadata metadata = session.getMetadata();
      assertEquals("onnx-caffe2", metadata.getProducerName());
      assertEquals("squeezenet_old", metadata.getGraphName());
      assertEquals("", metadata.getDomain());
      assertEquals("", metadata.getDescription());
      assertEquals(0x7FFFFFFFFFFFFFFFL, metadata.getVersion());
      assertTrue(metadata.getCustomMetadata().isEmpty());
    }
  }

  @Test
  public void inferenceTest() throws OrtException {
    canRunInferenceOnAModel(OptLevel.NO_OPT, ExecutionMode.PARALLEL);
    canRunInferenceOnAModel(OptLevel.NO_OPT, ExecutionMode.SEQUENTIAL);
    canRunInferenceOnAModel(OptLevel.ALL_OPT, ExecutionMode.PARALLEL);
    canRunInferenceOnAModel(OptLevel.ALL_OPT, ExecutionMode.SEQUENTIAL);
  }

  private void canRunInferenceOnAModel(OptLevel graphOptimizationLevel, ExecutionMode exectionMode)
      throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/squeezenet.onnx").toString();

    // Set the graph optimization level for this session.
    try (SessionOptions options = new SessionOptions()) {
      options.setOptimizationLevel(graphOptimizationLevel);
      options.setExecutionMode(exectionMode);

      try (OrtSession session = env.createSession(modelPath, options)) {
        Map<String, NodeInfo> inputMetaMap = session.getInputInfo();
        Map<String, OnnxTensor> container = new HashMap<>();
        NodeInfo inputMeta = inputMetaMap.values().iterator().next();

        float[] inputData =
            TestHelpers.loadTensorFromFile(TestHelpers.getResourcePath("/bench.in"));
        // this is the data for only one input tensor for this model
        Object tensorData =
            OrtUtil.reshape(inputData, ((TensorInfo) inputMeta.getInfo()).getShape());
        OnnxTensor inputTensor = OnnxTensor.createTensor(env, tensorData);
        container.put(inputMeta.getName(), inputTensor);

        // Run the inference
        try (OrtSession.Result results = session.run(container)) {
          assertEquals(1, results.size());

          float[] expectedOutput =
              TestHelpers.loadTensorFromFile(TestHelpers.getResourcePath("/bench.expected_out"));
          // validate the results
          // Only iterates once
          for (Map.Entry<String, OnnxValue> r : results) {
            OnnxValue resultValue = r.getValue();
            assertTrue(resultValue instanceof OnnxTensor);
            OnnxTensor resultTensor = (OnnxTensor) resultValue;
            int[] expectedDimensions =
                new int[] {1, 1000, 1, 1}; // hardcoded for now for the test data
            long[] resultDimensions = resultTensor.getInfo().getShape();
            assertEquals(expectedDimensions.length, resultDimensions.length);

            for (int i = 0; i < expectedDimensions.length; i++) {
              assertEquals(expectedDimensions[i], resultDimensions[i]);
            }

            float[] resultArray = TestHelpers.flattenFloat(resultTensor.getValue());
            assertEquals(expectedOutput.length, resultArray.length);
            assertArrayEquals(expectedOutput, resultArray, 1e-6f);
          }
        } finally {
          inputTensor.close();
        }
      }
    }
  }

  @Test
  public void throwWrongInputName() throws OrtException {
    SqueezeNetTuple tuple = openSessionSqueezeNet();
    try (OrtSession session = tuple.session) {
      float[] inputData = tuple.inputData;
      NodeInfo inputMeta = session.getInputInfo().values().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      long[] inputShape = ((TensorInfo) inputMeta.getInfo()).shape;
      int[] inputDataInt = new int[inputData.length];
      for (int i = 0; i < inputData.length; i++) {
        inputDataInt[i] = (int) inputData[i];
      }
      Object tensor = OrtUtil.reshape(inputDataInt, inputShape);
      container.put("wrong_name", OnnxTensor.createTensor(env, tensor));
      try {
        session.run(container);
        OnnxValue.close(container.values());
        fail("Should throw exception for incorrect name.");
      } catch (OrtException e) {
        OnnxValue.close(container.values());
        String msg = e.getMessage();
        assertTrue(msg.contains("Unknown input name"));
      }
    }
  }

  @Test
  public void throwWrongInputType() throws OrtException {
    SqueezeNetTuple tuple = openSessionSqueezeNet();
    try (OrtSession session = tuple.session) {

      float[] inputData = tuple.inputData;
      NodeInfo inputMeta = session.getInputInfo().values().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      long[] inputShape = ((TensorInfo) inputMeta.getInfo()).shape;
      int[] inputDataInt = new int[inputData.length];
      for (int i = 0; i < inputData.length; i++) {
        inputDataInt[i] = (int) inputData[i];
      }
      Object tensor = OrtUtil.reshape(inputDataInt, inputShape);
      container.put(inputMeta.getName(), OnnxTensor.createTensor(env, tensor));
      try {
        session.run(container);
        OnnxValue.close(container.values());
        fail("Should throw exception for incorrect type.");
      } catch (OrtException e) {
        OnnxValue.close(container.values());
        String msg = e.getMessage();
        assertTrue(msg.contains("Unexpected input data type"));
      }
    }
  }

  @Test
  public void throwExtraInputs() throws OrtException {
    SqueezeNetTuple tuple = openSessionSqueezeNet();
    try (OrtSession session = tuple.session) {

      float[] inputData = tuple.inputData;
      NodeInfo inputMeta = session.getInputInfo().values().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      long[] inputShape = ((TensorInfo) inputMeta.getInfo()).shape;
      int[] inputDataInt = new int[inputData.length];
      for (int i = 0; i < inputData.length; i++) {
        inputDataInt[i] = (int) inputData[i];
      }
      Object tensor = OrtUtil.reshape(inputDataInt, inputShape);
      container.put(inputMeta.getName(), OnnxTensor.createTensor(env, tensor));
      container.put("extra", OnnxTensor.createTensor(env, tensor));
      try {
        session.run(container);
        OnnxValue.close(container.values());
        fail("Should throw exception for too many inputs.");
      } catch (OrtException e) {
        OnnxValue.close(container.values());
        String msg = e.getMessage();
        assertTrue(msg.contains("Unexpected number of inputs"));
      }
    }
  }

  @Test
  public void testMultiThreads() throws OrtException, InterruptedException {
    int numThreads = 10;
    int loop = 10;
    SqueezeNetTuple tuple = openSessionSqueezeNet();
    try (OrtSession session = tuple.session) {

      float[] inputData = tuple.inputData;
      float[] expectedOutput = tuple.outputData;
      NodeInfo inputMeta = session.getInputInfo().values().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      long[] inputShape = ((TensorInfo) inputMeta.getInfo()).shape;
      Object tensor = OrtUtil.reshape(inputData, inputShape);
      container.put(inputMeta.getName(), OnnxTensor.createTensor(env, tensor));
      ExecutorService executor = Executors.newFixedThreadPool(numThreads);
      for (int i = 0; i < numThreads; i++) {
        executor.submit(
            () -> {
              for (int j = 0; j < loop; j++) {
                try (OrtSession.Result result = session.run(container)) {
                  OnnxValue resultTensor = result.get(0);
                  float[] resultArray = TestHelpers.flattenFloat(resultTensor.getValue());
                  assertEquals(expectedOutput.length, resultArray.length);
                  assertArrayEquals(expectedOutput, resultArray, 1e-6f);
                } catch (OrtException e) {
                  throw new IllegalStateException("Failed to execute a scoring operation", e);
                }
              }
            });
      }
      executor.shutdown();
      executor.awaitTermination(1, TimeUnit.MINUTES);
      OnnxValue.close(container.values());
      assertTrue(executor.isTerminated());
    }
  }

  @Test
  public void testProviders() {
    EnumSet<OrtProvider> providers = OrtEnvironment.getAvailableProviders();
    int providersSize = providers.size();
    assertTrue(providersSize > 0);
    assertTrue(providers.contains(OrtProvider.CPU));

    // Check that the providers are a copy of the original, note this does not enable the DNNL
    // provider
    providers.add(OrtProvider.DNNL);
    assertEquals(providersSize, OrtEnvironment.getAvailableProviders().size());
  }

  @Test
  public void testSymbolicDimensionAssignment() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/capi_symbolic_dims.onnx").toString();

    // Check the dimension is symbolic
    try (SessionOptions options = new SessionOptions()) {
      try (OrtSession session = env.createSession(modelPath, options)) {
        Map<String, NodeInfo> infoMap = session.getInputInfo();
        TensorInfo aInfo = (TensorInfo) infoMap.get("A").getInfo();
        assertArrayEquals(new long[] {-1, 2}, aInfo.shape);
      }
    }
    // Check that when the options are assigned it overrides the symbolic dimension
    try (SessionOptions options = new SessionOptions()) {
      options.setSymbolicDimensionValue("n", 5);
      try (OrtSession session = env.createSession(modelPath, options)) {
        Map<String, NodeInfo> infoMap = session.getInputInfo();
        TensorInfo aInfo = (TensorInfo) infoMap.get("A").getInfo();
        assertArrayEquals(new long[] {5, 2}, aInfo.shape);
      }
    }
  }

  @Test
  @EnabledIfSystemProperty(named = "USE_CUDA", matches = "1")
  public void testCUDA() throws OrtException {
    runProvider(OrtProvider.CUDA);
  }

  @Test
  @EnabledIfSystemProperty(named = "USE_ROCM", matches = "1")
  public void testROCM() throws OrtException {
    runProvider(OrtProvider.ROCM);
  }

  @Test
  @EnabledIfSystemProperty(named = "USE_TENSORRT", matches = "1")
  public void testTensorRT() throws OrtException {
    runProvider(OrtProvider.TENSOR_RT);
  }

  @Test
  @EnabledIfSystemProperty(named = "USE_OPENVINO", matches = "1")
  public void testOpenVINO() throws OrtException {
    runProvider(OrtProvider.OPEN_VINO);
  }

  @Test
  @EnabledIfSystemProperty(named = "USE_DNNL", matches = "1")
  public void testDNNL() throws OrtException {
    runProvider(OrtProvider.DNNL);
  }

  @Test
  @EnabledIfSystemProperty(named = "USE_XNNPACK", matches = "1")
  public void testXNNPACK() throws OrtException {
    runProvider(OrtProvider.XNNPACK);
  }

  @Test
  @EnabledIfSystemProperty(named = "USE_COREML", matches = "1")
  public void testCoreML() throws OrtException {
    runProvider(OrtProvider.CORE_ML);
  }

  private void runProvider(OrtProvider provider) throws OrtException {
    EnumSet<OrtProvider> providers = OrtEnvironment.getAvailableProviders();
    assertTrue(providers.size() > 1);
    assertTrue(providers.contains(OrtProvider.CPU));
    assertTrue(providers.contains(provider));
    SqueezeNetTuple tuple = openSessionSqueezeNet(EnumSet.of(provider));
    try (OrtSession session = tuple.session) {
      float[] inputData = tuple.inputData;
      float[] expectedOutput = tuple.outputData;
      NodeInfo inputMeta = session.getInputInfo().values().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      long[] inputShape = ((TensorInfo) inputMeta.getInfo()).shape;
      Object tensor = OrtUtil.reshape(inputData, inputShape);
      container.put(inputMeta.getName(), OnnxTensor.createTensor(env, tensor));
      try (OrtSession.Result result = session.run(container)) {
        OnnxValue resultTensor = result.get(0);
        float[] resultArray = TestHelpers.flattenFloat(resultTensor.getValue());
        assertEquals(expectedOutput.length, resultArray.length);
        if (provider == OrtProvider.CORE_ML) {
          // CoreML gives slightly different answers on a 2020 13" M1 MBP
          assertArrayEquals(expectedOutput, resultArray, 1e-2f);
        } else {
          assertArrayEquals(expectedOutput, resultArray, 1e-6f);
        }
      } catch (OrtException e) {
        throw new IllegalStateException("Failed to execute a scoring operation", e);
      }
      OnnxValue.close(container.values());
    }
  }

  @Test
  public void testExternalInitializers() throws IOException, OrtException {
    String modelPath = TestHelpers.getResourcePath("/java-external-matmul.onnx").toString();

    // Run by loading the external initializer from disk
    // initializer is 1...16 in a 4x4 matrix.
    try (SessionOptions options = new SessionOptions()) {
      try (OrtSession session = env.createSession(modelPath, options)) {
        try (OnnxTensor t = OnnxTensor.createTensor(env, new float[][] {{1, 2, 3, 4}});
            OrtSession.Result res = session.run(Collections.singletonMap("input", t))) {
          OnnxTensor output = (OnnxTensor) res.get(0);
          float[][] outputArr = (float[][]) output.getValue();
          assertArrayEquals(new float[] {90, 100, 110, 120}, outputArr[0]);
        }
      }
    }
    // Run by overriding the initializer with the identity matrix
    try (SessionOptions options = new SessionOptions()) {
      OnnxTensor tensor = TestHelpers.makeIdentityMatrixBuf(env, 4);
      options.addExternalInitializers(Collections.singletonMap("tensor", tensor));
      try (OrtSession session = env.createSession(modelPath, options)) {
        try (OnnxTensor t = OnnxTensor.createTensor(env, new float[][] {{1, 2, 3, 4}});
            OrtSession.Result res = session.run(Collections.singletonMap("input", t))) {
          OnnxTensor output = (OnnxTensor) res.get(0);
          float[][] outputArr = (float[][]) output.getValue();
          assertArrayEquals(new float[] {1, 2, 3, 4}, outputArr[0]);
        }
      }
      tensor.close();
    }
    // Run by overriding the initializer with the identity matrix loaded from a byte array
    byte[] modelBytes =
        Files.readAllBytes(TestHelpers.getResourcePath("/java-external-matmul.onnx"));
    try (SessionOptions options = new SessionOptions()) {
      OnnxTensor tensor = TestHelpers.makeIdentityMatrixBuf(env, 4);
      options.addExternalInitializers(Collections.singletonMap("tensor", tensor));
      try (OrtSession session = env.createSession(modelBytes, options)) {
        try (OnnxTensor t = OnnxTensor.createTensor(env, new float[][] {{1, 2, 3, 4}});
            OrtSession.Result res = session.run(Collections.singletonMap("input", t))) {
          OnnxTensor output = (OnnxTensor) res.get(0);
          float[][] outputArr = (float[][]) output.getValue();
          assertArrayEquals(new float[] {1, 2, 3, 4}, outputArr[0]);
        }
      }
      tensor.close();
    }
  }

  @Test
  public void testOverridingInitializer() throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/java-matmul.onnx").toString();

    // Run with the normal initializer
    // initializer is 1...16 in a 4x4 matrix.
    try (SessionOptions options = new SessionOptions()) {
      try (OrtSession session = env.createSession(modelPath, options)) {
        try (OnnxTensor t = OnnxTensor.createTensor(env, new float[][] {{1, 2, 3, 4}});
            OrtSession.Result res = session.run(Collections.singletonMap("input", t))) {
          OnnxTensor output = (OnnxTensor) res.get(0);
          float[][] outputArr = (float[][]) output.getValue();
          assertArrayEquals(new float[] {90, 100, 110, 120}, outputArr[0]);
        }
      }
    }
    // Run by overriding the initializer with the identity matrix
    try (SessionOptions options = new SessionOptions()) {
      OnnxTensor tensor = TestHelpers.makeIdentityMatrixBuf(env, 4);
      options.addInitializer("tensor", tensor);
      try (OrtSession session = env.createSession(modelPath, options)) {
        try (OnnxTensor t = OnnxTensor.createTensor(env, new float[][] {{1, 2, 3, 4}});
            OrtSession.Result res = session.run(Collections.singletonMap("input", t))) {
          OnnxTensor output = (OnnxTensor) res.get(0);
          float[][] outputArr = (float[][]) output.getValue();
          assertArrayEquals(new float[] {1, 2, 3, 4}, outputArr[0]);
        }
      }
      tensor.close();
    }
  }

  private static File getTestModelsDir() throws IOException {
    // get build directory, append downloaded models location
    String cwd = System.getProperty("user.dir");
    List<String> props = Files.readAllLines(Paths.get(cwd, propertiesFile));
    String modelsRelDir = props.get(0).split("=")[1].trim();
    File modelsDir = Paths.get(cwd, "../../..", modelsRelDir, "models").toFile();
    return modelsDir;
  }

  private static Map<String, String> getSkippedModels() {
    Map<String, String> skipModels = new HashMap<>();
    skipModels.put("mxnet_arcface", "Model is an invalid ONNX model");
    skipModels.put("tf_inception_v2", "TODO: Debug failing model, skipping for now");
    skipModels.put("fp16_inception_v1", "16-bit float not supported type in C#.");
    skipModels.put("fp16_shufflenet", "16-bit float not supported type in C#.");
    skipModels.put("fp16_tiny_yolov2", "16-bit float not supported type in C#.");
    skipModels.put(
        "BERT_Squad",
        "Could not find an implementation for the node bert / embeddings / one_hot:OneHot(9)");
    skipModels.put("mlperf_ssd_mobilenet_300", "Could not find file output_0.pb");
    skipModels.put("tf_resnet_v1_50", "result mismatch when Conv BN Fusion is applied");
    skipModels.put("tf_resnet_v1_101", "result mismatch when Conv BN Fusion is applied");
    skipModels.put("tf_resnet_v1_152", "result mismatch when Conv BN Fusion is applied");

    // The following models fails on nocontribops win CI
    skipModels.put("test_tiny_yolov2", "Fails when ContribOps is disabled");
    skipModels.put("mask_rcnn_keras", "Pad is not a registered function/op");

    return skipModels;
  }

  private static String getCustomOpLibraryName() {
    String customLibraryName = "";
    String osName = System.getProperty("os.name").toLowerCase();
    if (osName.contains("windows")) {
      // In windows we start in the wrong working directory relative to the custom_op_library.dll
      // So we look it up as a classpath resource and resolve it to a real path
      customLibraryName = TestHelpers.getResourcePath("/custom_op_library.dll").toString();
    } else if (osName.contains("mac")) {
      customLibraryName = TestHelpers.getResourcePath("/libcustom_op_library.dylib").toString();
    } else if (osName.contains("linux")) {
      customLibraryName = TestHelpers.getResourcePath("/libcustom_op_library.so").toString();
    } else {
      fail("Unknown os/platform '" + osName + "'");
    }

    return customLibraryName;
  }

  public static List<String[]> getModelsForTest() throws IOException {
    File modelsDir = getTestModelsDir();
    Map<String, String> skipModels = getSkippedModels();
    ArrayList<String[]> output = new ArrayList<>();

    for (File opsetDirectory : modelsDir.listFiles(File::isDirectory)) {
      for (File modelDir : opsetDirectory.listFiles(File::isDirectory)) {
        if (!skipModels.containsKey(modelDir.getName())) {
          output.add(new String[] {modelDir.getParentFile().getName(), modelDir.getName()});
        }
      } // model
    } // opset

    return output;
  }

  public static Set<String[]> getSkippedModelForTest() throws IOException {
    File modelsDir = getTestModelsDir();
    Map<String, String> skipModels = getSkippedModels();
    Set<String[]> output = new HashSet<>();

    for (File opsetDirectory : modelsDir.listFiles(File::isDirectory)) {
      for (File modelDir : opsetDirectory.listFiles(File::isDirectory)) {
        if (skipModels.containsKey(modelDir.getName())) {
          output.add(new String[] {modelDir.getParentFile().getName(), modelDir.getName()});
        }
      } // model
    } // opset

    return output;
  }

  @Disabled
  @Test
  public void testAllPretrainedModels() throws IOException {
    List<String[]> testModels = getModelsForTest();
    Set<String[]> skipModels = getSkippedModelForTest();
    for (String[] model : testModels) {
      if (!skipModels.contains(model)) {
        testPreTrainedModel(model[0], model[1]);
      }
    }
  }

  public void testPreTrainedModel(String opset, String modelName) throws IOException {
    File modelsDir = getTestModelsDir();
    String onnxModelFileName = null;

    Path modelDir = Paths.get(modelsDir.getAbsolutePath(), opset, modelName);

    try {
      File[] onnxModelFiles =
          modelDir.toFile().listFiles((dir, filename) -> filename.contains(".onnx"));
      boolean validModelFound = false;
      if (onnxModelFiles.length > 0) {
        // TODO remove file "._resnet34v2.onnx" from test set
        for (int i = 0; i < onnxModelFiles.length; i++) {
          if (!onnxModelFiles[i].getName().equals("._resnet34v2.onnx")) {
            onnxModelFiles[0] = onnxModelFiles[i];
            validModelFound = true;
          }
        }
      }

      if (validModelFound) {
        onnxModelFileName = onnxModelFiles[0].getAbsolutePath();
      } else {
        String modelNamesList =
            Stream.of(onnxModelFiles).map(File::getName).collect(Collectors.joining(","));
        throw new RuntimeException(
            "Opset "
                + opset
                + " Model "
                + modelName
                + ". Can't determine model file name. Found these: "
                + modelNamesList);
      }

      try (OrtSession session = env.createSession(onnxModelFileName)) {
        String testDataDirNamePattern;
        if (opset.equals("opset9") && modelName.equals("LSTM_Seq_lens_unpacked")) {
          testDataDirNamePattern = "seq_lens"; // discrepency in data directory
        } else {
          testDataDirNamePattern = "test_data";
        }
        Map<String, NodeInfo> inMeta = session.getInputInfo();
        Map<String, NodeInfo> outMeta = session.getOutputInfo();
        File testDataDir =
            modelDir.toFile().listFiles(f -> f.getName().startsWith(testDataDirNamePattern))[0];
        Map<String, OnnxTensor> inputContainer = new HashMap<>();
        Map<String, OnnxTensor> outputContainer = new HashMap<>();
        for (File f :
            testDataDir.listFiles((dir, name) -> inputPBPattern.matcher(name).matches())) {
          TestHelpers.StringTensorPair o = TestHelpers.loadTensorFromFilePb(env, f, inMeta);
          inputContainer.put(o.string, o.tensor);
        }
        for (File f :
            testDataDir.listFiles((dir, name) -> outputPBPattern.matcher(name).matches())) {
          TestHelpers.StringTensorPair o = TestHelpers.loadTensorFromFilePb(env, f, outMeta);
          outputContainer.put(o.string, o.tensor);
        }

        try (Result resultCollection = session.run(inputContainer)) {
          for (Map.Entry<String, OnnxValue> result : resultCollection) {
            Assertions.assertTrue(outMeta.containsKey(result.getKey()));
            OnnxTensor outputValue = outputContainer.get(result.getKey());
            if (outputValue == null) {
              outputValue =
                  outputContainer
                      .values()
                      .iterator()
                      .next(); // in case the output data file does not contain the name
            }
            if (result.getValue() instanceof OnnxTensor) {
              OnnxTensor resultTensor = (OnnxTensor) result.getValue();
              Assertions.assertEquals(outputValue.getByteBuffer(), resultTensor.getByteBuffer());
            } else {
              fail("testPretrainedModel cannot handle non-tensor outputs yet");
            }
          }
        }
      }
    } catch (OrtException ex) {
      String msg =
          "Opset "
              + opset
              + ", Model "
              + modelName
              + ": ModelFile = "
              + onnxModelFileName
              + " error = "
              + ex.getMessage();
      throw new RuntimeException(msg, ex);
    }
  }

  @Test
  public void testModelInputFLOAT() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_FLOAT.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      long[] shape = new long[] {1, 5};
      Map<String, OnnxTensor> container = new HashMap<>();
      float[] flatInput = new float[] {1.0f, 2.0f, -3.0f, Float.MIN_VALUE, Float.MAX_VALUE};
      Object tensorIn = OrtUtil.reshape(flatInput, shape);
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      float[] resultArray;
      try (OrtSession.Result res = session.run(container)) {
        resultArray = TestHelpers.flattenFloat(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray, 1e-6f);
        float[] resultBufferArray = new float[flatInput.length];
        ((OnnxTensor) res.get(0)).getFloatBuffer().get(resultBufferArray);
        assertArrayEquals(flatInput, resultBufferArray, 1e-6f);
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testModelInputBuffer() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_FLOAT.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      long[] shape = new long[] {1, 5};
      Map<String, OnnxTensor> container = new HashMap<>();
      float[] inputArr =
          new float[] {
            1.0f, -2.0f, 3.0f, -4.0f, 5.0f, -6.0f, 7.0f, -8.0f, 9.0f, -10.0f, 11.0f, -12.0f, 13.0f,
            -14.0f, 15
          };
      FloatBuffer buffer = FloatBuffer.wrap(inputArr);
      FloatBuffer directBuffer =
          ByteBuffer.allocateDirect(inputArr.length * 4)
              .order(ByteOrder.nativeOrder())
              .asFloatBuffer()
              .put(buffer);
      buffer.rewind();
      directBuffer.rewind();
      float[] resultArray;

      // Test loading from buffer
      for (int i = 0; i < 3; i++) {
        // Set limits
        buffer.position(i * 5);
        buffer.limit((i + 1) * 5);
        directBuffer.position(i * 5);
        directBuffer.limit((i + 1) * 5);

        // Check regular buffer (copies to direct)
        OnnxTensor newTensor = OnnxTensor.createTensor(env, buffer, shape);
        container.put(inputName, newTensor);
        try (OrtSession.Result res = session.run(container)) {
          resultArray = TestHelpers.flattenFloat(res.get(0).getValue());
          assertArrayEquals(Arrays.copyOfRange(inputArr, i * 5, (i + 1) * 5), resultArray, 1e-6f);
          OnnxValue.close(container);
        }
        container.clear();
        // buffer should be unchanged
        assertEquals(i * 5, buffer.position());

        // Check direct buffer (no-copy)
        newTensor = OnnxTensor.createTensor(env, directBuffer, shape);
        container.put(inputName, newTensor);
        try (OrtSession.Result res = session.run(container)) {
          resultArray = TestHelpers.flattenFloat(res.get(0).getValue());
          assertArrayEquals(Arrays.copyOfRange(inputArr, i * 5, (i + 1) * 5), resultArray, 1e-6f);
          OnnxValue.close(container);
        }
        container.clear();
        // direct buffer should be unchanged
        assertEquals(i * 5, directBuffer.position());
      }
    }
  }

  @Test
  public void testRunOptions() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_BOOL.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options);
        OrtSession.RunOptions runOptions = new OrtSession.RunOptions()) {
      runOptions.setRunTag("monkeys");
      assertEquals("monkeys", runOptions.getRunTag());
      runOptions.setLogLevel(OrtLoggingLevel.ORT_LOGGING_LEVEL_FATAL);
      assertEquals(OrtLoggingLevel.ORT_LOGGING_LEVEL_FATAL, runOptions.getLogLevel());
      runOptions.setLogVerbosityLevel(9000);
      assertEquals(9000, runOptions.getLogVerbosityLevel());
      runOptions.setTerminate(true);
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      boolean[] flatInput = new boolean[] {true, false, true, false, true};
      Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container, runOptions)) {
        fail("Should have terminated.");
      } catch (OrtException e) {
        assertTrue(e.getMessage().contains("Exiting due to terminate flag being set to true."));
        assertEquals(OrtException.OrtErrorCode.ORT_FAIL, e.getCode());
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testExtraSessionOptions() throws OrtException, IOException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_BOOL.pb").toString();
    File tmpPath = File.createTempFile("onnx-runtime-profiling", "file");
    tmpPath.deleteOnExit();

    try (SessionOptions options = new SessionOptions()) {
      options.setCPUArenaAllocator(true);
      options.setMemoryPatternOptimization(true);
      options.enableProfiling(tmpPath.getAbsolutePath());
      options.setLoggerId("monkeys");
      options.setSessionLogLevel(OrtLoggingLevel.ORT_LOGGING_LEVEL_FATAL);
      options.setSessionLogVerbosityLevel(5);
      Map<String, String> configEntries = options.getConfigEntries();
      assertTrue(configEntries.isEmpty());
      options.addConfigEntry("key", "value");
      assertEquals("value", configEntries.get("key"));
      try {
        options.addConfigEntry("", "invalid key");
        fail("Add config entry with empty key should have failed");
      } catch (OrtException e) {
        assertTrue(e.getMessage().contains("Config key is empty"));
        assertEquals(OrtException.OrtErrorCode.ORT_INVALID_ARGUMENT, e.getCode());
      }
      try (OrtSession session = env.createSession(modelPath, options)) {
        String inputName = session.getInputNames().iterator().next();
        Map<String, OnnxTensor> container = new HashMap<>();
        boolean[] flatInput = new boolean[] {true, false, true, false, true};
        Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
        OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
        container.put(inputName, ov);
        try (OrtSession.Result res = session.run(container)) {
          boolean[] resultArray = TestHelpers.flattenBoolean(res.get(0).getValue());
          assertArrayEquals(flatInput, resultArray);
        }
        // Check that the profiling start time doesn't throw
        long profilingStartTime = session.getProfilingStartTimeInNs();

        // Check the profiling output doesn't throw
        String profilingOutput = session.endProfiling();
        File profilingOutputFile = new File(profilingOutput);
        profilingOutputFile.deleteOnExit();
        try (OrtSession.Result res = session.run(container)) {
          boolean[] resultArray = TestHelpers.flattenBoolean(res.get(0).getValue());
          assertArrayEquals(flatInput, resultArray);
        }
        OnnxValue.close(container);
      }
    }
    try (SessionOptions options = new SessionOptions()) {
      options.setCPUArenaAllocator(false);
      options.setMemoryPatternOptimization(false);
      options.enableProfiling(tmpPath.getAbsolutePath());
      options.disableProfiling();
      options.setSessionLogVerbosityLevel(0);
      try (OrtSession session = env.createSession(modelPath, options)) {
        String inputName = session.getInputNames().iterator().next();
        Map<String, OnnxTensor> container = new HashMap<>();
        boolean[] flatInput = new boolean[] {true, false, true, false, true};
        Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
        OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
        container.put(inputName, ov);
        try (OrtSession.Result res = session.run(container)) {
          boolean[] resultArray = TestHelpers.flattenBoolean(res.get(0).getValue());
          assertArrayEquals(flatInput, resultArray);
        }
        OnnxValue.close(container);
      }
    }
  }

  @Test
  public void testLoadCustomLibrary() throws OrtException {
    // This test is disabled on Android.
    if (!OnnxRuntime.isAndroid()) {
      String customLibraryName = getCustomOpLibraryName();
      String customOpLibraryTestModel =
          TestHelpers.getResourcePath("/custom_op_library/custom_op_test.onnx").toString();

      try (SessionOptions options = new SessionOptions()) {
        options.registerCustomOpLibrary(customLibraryName);
        if (OnnxRuntime.extractCUDA()) {
          options.addCUDA();
        }
        try (OrtSession session = env.createSession(customOpLibraryTestModel, options)) {
          Map<String, OnnxTensor> container = new HashMap<>();

          // prepare expected inputs and outputs
          float[] flatInputOne =
              new float[] {
                1.1f, 2.2f, 3.3f, 4.4f, 5.5f, 6.6f, 7.7f, 8.8f, 9.9f, 10.0f, 11.1f, 12.2f, 13.3f,
                14.4f, 15.5f
              };
          Object tensorIn = OrtUtil.reshape(flatInputOne, new long[] {3, 5});
          OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
          container.put("input_1", ov);

          float[] flatInputTwo =
              new float[] {
                15.5f, 14.4f, 13.3f, 12.2f, 11.1f, 10.0f, 9.9f, 8.8f, 7.7f, 6.6f, 5.5f, 4.4f, 3.3f,
                2.2f, 1.1f
              };
          tensorIn = OrtUtil.reshape(flatInputTwo, new long[] {3, 5});
          ov = OnnxTensor.createTensor(env, tensorIn);
          container.put("input_2", ov);

          int[] flatOutput = new int[] {17, 17, 17, 17, 17, 17, 18, 18, 18, 17, 17, 17, 17, 17, 17};

          try (OrtSession.Result res = session.run(container)) {
            OnnxTensor outputTensor = (OnnxTensor) res.get(0);
            assertArrayEquals(new long[] {3, 5}, outputTensor.getInfo().shape);
            int[] resultArray = TestHelpers.flattenInteger(res.get(0).getValue());
            assertArrayEquals(flatOutput, resultArray);
          }
          OnnxValue.close(container);
        }
      }
    }
  }

  @Test
  public void testLoadCustomOpsUsingFunction() throws OrtException {
    // This test is disabled on Android.
    if (!OnnxRuntime.isAndroid()) {
      String customLibraryName = getCustomOpLibraryName();
      String customOpLibraryTestModel =
          TestHelpers.getResourcePath("/custom_op_library/custom_op_test.onnx").toString();

      try (SessionOptions options = new SessionOptions()) {
        String osName = System.getProperty("os.name").toLowerCase();
        boolean isWindows = osName.contains("windows");
        boolean isMac = osName.contains("mac");

        // on Windows and mac, Java.System.load will make the symbols from the loaded library
        // available.
        // on other platforms the dlsym uses RTLD_LOCAL so they're not. Would need to use something
        // like
        // https://github.com/java-native-access/jna to achieve that.
        // As we have unit tests that validate the custom op registration across all platforms, we
        // settle for just
        // making sure the ORT API function can be called and behaves as expected.
        try {
          // manually load the library. typically we'd expect the user to link against the library,
          // but doing that here would conflict with testLoadCustomLibrary needing to test ORT
          // loading
          // the library.
          System.load(customLibraryName);
          options.registerCustomOpsUsingFunction("RegisterCustomOps");

          if (isWindows || isMac) {
            if (OnnxRuntime.extractCUDA()) {
              options.addCUDA();
            }
            try (OrtSession session = env.createSession(customOpLibraryTestModel, options)) {
              // if model was loaded the op registration was successful
            }
          } else {
            fail("Expected to throw OrtException due System.load not using RTLD_GLOBAL");
          }
        } catch (OrtException e) {
          System.out.println(e.getMessage());
          assertTrue(
              !(isWindows || isMac), "Expected to not throw OrtException on Windows or macOS");
          assertTrue(e.getMessage().contains("Failed to get symbol RegisterCustomOps"));
        }
      }
    }
  }

  @Test
  public void testModelMetadata() throws OrtException {
    String modelPath =
        TestHelpers.getResourcePath("/model_with_valid_ort_config_json.onnx").toString();

    try (OrtSession session = env.createSession(modelPath)) {
      OnnxModelMetadata modelMetadata = session.getMetadata();

      Assertions.assertEquals(1, modelMetadata.getVersion());

      Assertions.assertEquals("Hari", modelMetadata.getProducerName());

      Assertions.assertEquals("matmul test", modelMetadata.getGraphName());

      Assertions.assertEquals("", modelMetadata.getDomain());

      Assertions.assertEquals(
          "This is a test model with a valid ORT config Json", modelMetadata.getDescription());

      Assertions.assertEquals("graph description", modelMetadata.getGraphDescription());

      Assertions.assertEquals(2, modelMetadata.getCustomMetadata().size());
      Assertions.assertEquals("dummy_value", modelMetadata.getCustomMetadata().get("dummy_key"));
      Assertions.assertEquals(
          "{\"session_options\": {\"inter_op_num_threads\": 5, \"intra_op_num_threads\": 2, \"graph_optimization_level\": 99, \"enable_profiling\": 1}}",
          modelMetadata.getCustomMetadata().get("ort_config"));
    }
  }

  @Test
  public void testModelInputBOOL() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_BOOL.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      long[] shape = new long[] {1, 5};

      // Test array input
      boolean[] flatInput = new boolean[] {true, false, true, false, true};
      Object tensorIn = OrtUtil.reshape(flatInput, shape);
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        boolean[] resultArray = TestHelpers.flattenBoolean(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
      container.clear();

      // Test direct buffer input
      ByteBuffer dirBuf = ByteBuffer.allocateDirect(5).order(ByteOrder.nativeOrder());
      dirBuf.put((byte) 1);
      dirBuf.put((byte) 0);
      dirBuf.put((byte) 1);
      dirBuf.put((byte) 0);
      dirBuf.put((byte) 1);
      dirBuf.rewind();
      ov = OnnxTensor.createTensor(env, dirBuf, shape, OnnxJavaType.BOOL);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        boolean[] resultArray = TestHelpers.flattenBoolean(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
      container.clear();

      // Test non-direct buffer input
      ByteBuffer buf = ByteBuffer.allocate(5);
      buf.put((byte) 1);
      buf.put((byte) 0);
      buf.put((byte) 1);
      buf.put((byte) 0);
      buf.put((byte) 1);
      buf.rewind();
      ov = OnnxTensor.createTensor(env, buf, shape, OnnxJavaType.BOOL);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        boolean[] resultArray = TestHelpers.flattenBoolean(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
      container.clear();
    }
  }

  @Test
  public void testModelInputINT32() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_INT32.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      int[] flatInput = new int[] {1, -2, -3, Integer.MIN_VALUE, Integer.MAX_VALUE};
      Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        int[] resultArray = TestHelpers.flattenInteger(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testModelInputDOUBLE() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_DOUBLE.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      double[] flatInput = new double[] {1.0, 2.0, -3.0, 5, 5};
      Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        double[] resultArray = TestHelpers.flattenDouble(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray, 1e-6f);
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testModelInputINT8() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_INT8.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      byte[] flatInput = new byte[] {1, 2, -3, Byte.MIN_VALUE, Byte.MAX_VALUE};
      Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        byte[] resultArray = TestHelpers.flattenByte(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testModelInputUINT8() throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/test_types_UINT8.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      byte[] flatInput = new byte[] {1, 2, -3, Byte.MIN_VALUE, Byte.MAX_VALUE};
      ByteBuffer data = ByteBuffer.wrap(flatInput);
      long[] shape = new long[] {1, 5};
      OnnxTensor ov = OnnxTensor.createTensor(env, data, shape, OnnxJavaType.UINT8);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        byte[] resultArray = TestHelpers.flattenByte(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testModelInputINT16() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_INT16.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      short[] flatInput = new short[] {1, 2, 3, Short.MIN_VALUE, Short.MAX_VALUE};
      Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        short[] resultArray = TestHelpers.flattenShort(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testModelInputINT64() throws OrtException {
    // model takes 1x5 input of fixed type, echoes back
    String modelPath = TestHelpers.getResourcePath("/test_types_INT64.pb").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      String inputName = session.getInputNames().iterator().next();
      Map<String, OnnxTensor> container = new HashMap<>();
      long[] flatInput = new long[] {1, 2, -3, Long.MIN_VALUE, Long.MAX_VALUE};
      Object tensorIn = OrtUtil.reshape(flatInput, new long[] {1, 5});
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);
      try (OrtSession.Result res = session.run(container)) {
        long[] resultArray = TestHelpers.flattenLong(res.get(0).getValue());
        assertArrayEquals(flatInput, resultArray);
      }
      OnnxValue.close(container);
    }
  }

  @Test
  public void testModelSequenceOfMapIntFloat() throws OrtException {
    // test model trained using lightgbm classifier
    // produces 2 named outputs
    //   "label" is a tensor,
    //   "probabilities" is a sequence<map<int64, float>>
    // https://github.com/onnx/sklearn-onnx/blob/master/docs/examples/plot_pipeline_lightgbm.py

    String modelPath = TestHelpers.getResourcePath("/test_sequence_map_int_float.pb").toString();
    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {

      Map<String, NodeInfo> outputInfos = session.getOutputInfo();
      Iterator<NodeInfo> valuesItr = outputInfos.values().iterator();
      NodeInfo firstOutputInfo = valuesItr.next();
      NodeInfo secondOutputInfo = valuesItr.next();
      assertTrue(firstOutputInfo.getInfo() instanceof TensorInfo);
      assertTrue(secondOutputInfo.getInfo() instanceof SequenceInfo);
      assertEquals(OnnxJavaType.INT64, ((TensorInfo) firstOutputInfo.getInfo()).type);
      assertTrue(((SequenceInfo) secondOutputInfo.getInfo()).sequenceOfMaps);
      assertEquals(OnnxJavaType.UNKNOWN, ((SequenceInfo) secondOutputInfo.getInfo()).sequenceType);
      MapInfo mapInfo = ((SequenceInfo) secondOutputInfo.getInfo()).mapInfo;
      assertNotNull(mapInfo);
      assertEquals(OnnxJavaType.INT64, mapInfo.keyType);
      assertEquals(OnnxJavaType.FLOAT, mapInfo.valueType);

      Map<String, OnnxTensor> container = new HashMap<>();
      long[] shape = new long[] {1, 2};
      float[] flatInput = new float[] {5.8f, 2.8f};
      Object tensorIn = OrtUtil.reshape(flatInput, shape);
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(session.getInputNames().iterator().next(), ov);

      try (OrtSession.Result outputs = session.run(container)) {
        assertEquals(2, outputs.size());

        // first output is a tensor containing label
        OnnxValue firstOutput = outputs.get(firstOutputInfo.getName()).get();
        assertTrue(firstOutput instanceof OnnxTensor);

        // try-cast as a tensor
        long[] labelOutput = (long[]) firstOutput.getValue();

        // Label 1 should have highest probability
        assertEquals(1, labelOutput[0]);
        assertEquals(1, labelOutput.length);

        // second output is a sequence<map<int64, float>>
        // try-cast to an sequence of NOV
        OnnxValue secondOutput = outputs.get(secondOutputInfo.getName()).get();
        assertTrue(secondOutput instanceof OnnxSequence);
        SequenceInfo sequenceInfo = ((OnnxSequence) secondOutput).getInfo();
        assertTrue(sequenceInfo.sequenceOfMaps);
        assertEquals(OnnxJavaType.INT64, sequenceInfo.mapInfo.keyType);
        assertEquals(OnnxJavaType.FLOAT, sequenceInfo.mapInfo.valueType);

        // try-cast first element in sequence to map/dictionary type
        @SuppressWarnings("unchecked")
        Map<Long, Float> map =
            (Map<Long, Float>) ((List<OnnxMap>) secondOutput.getValue()).get(0).getValue();
        assertEquals(0.25938290, map.get(0L), 1e-6);
        assertEquals(0.40904793, map.get(1L), 1e-6);
        assertEquals(0.33156919, map.get(2L), 1e-6);
      }
      ov.close();
    }
  }

  @Test
  public void testModelSequenceOfMapStringFloat() throws OrtException {
    // test model trained using lightgbm classifier
    // produces 2 named outputs
    //   "label" is a tensor,
    //   "probabilities" is a sequence<map<int64, float>>
    // https://github.com/onnx/sklearn-onnx/blob/master/docs/examples/plot_pipeline_lightgbm.py
    String modelPath = TestHelpers.getResourcePath("/test_sequence_map_string_float.pb").toString();
    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {

      Map<String, NodeInfo> outputInfos = session.getOutputInfo();
      Iterator<NodeInfo> valuesItr = outputInfos.values().iterator();
      NodeInfo firstOutputInfo = valuesItr.next();
      NodeInfo secondOutputInfo = valuesItr.next();
      assertTrue(firstOutputInfo.getInfo() instanceof TensorInfo);
      assertTrue(secondOutputInfo.getInfo() instanceof SequenceInfo);
      assertEquals(OnnxJavaType.STRING, ((TensorInfo) firstOutputInfo.getInfo()).type);
      assertTrue(((SequenceInfo) secondOutputInfo.getInfo()).sequenceOfMaps);
      assertEquals(OnnxJavaType.UNKNOWN, ((SequenceInfo) secondOutputInfo.getInfo()).sequenceType);
      MapInfo mapInfo = ((SequenceInfo) secondOutputInfo.getInfo()).mapInfo;
      assertNotNull(mapInfo);
      assertEquals(OnnxJavaType.STRING, mapInfo.keyType);
      assertEquals(OnnxJavaType.FLOAT, mapInfo.valueType);

      Map<String, OnnxTensor> container = new HashMap<>();
      long[] shape = new long[] {1, 2};
      float[] flatInput = new float[] {5.8f, 2.8f};
      Object tensorIn = OrtUtil.reshape(flatInput, shape);
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(session.getInputNames().iterator().next(), ov);

      try (OrtSession.Result outputs = session.run(container)) {
        assertEquals(2, outputs.size());

        // first output is a tensor containing label
        OnnxValue firstOutput = outputs.get(firstOutputInfo.getName()).get();
        assertTrue(firstOutput instanceof OnnxTensor);

        // try-cast as a tensor
        String[] labelOutput = (String[]) firstOutput.getValue();

        // Label 1 should have highest probability
        assertEquals("1", labelOutput[0]);
        assertEquals(1, labelOutput.length);

        // second output is a sequence<map<int64, float>>
        // try-cast to an sequence of NOV
        OnnxValue secondOutput = outputs.get(secondOutputInfo.getName()).get();
        assertTrue(secondOutput instanceof OnnxSequence);
        SequenceInfo sequenceInfo = ((OnnxSequence) secondOutput).getInfo();
        assertTrue(sequenceInfo.sequenceOfMaps);
        assertEquals(OnnxJavaType.STRING, sequenceInfo.mapInfo.keyType);
        assertEquals(OnnxJavaType.FLOAT, sequenceInfo.mapInfo.valueType);

        // try-cast first element in sequence to map/dictionary type
        @SuppressWarnings("unchecked")
        Map<String, Float> map =
            (Map<String, Float>) ((List<OnnxMap>) secondOutput.getValue()).get(0).getValue();
        assertEquals(0.25938290, map.get("0"), 1e-6);
        assertEquals(0.40904793, map.get("1"), 1e-6);
        assertEquals(0.33156919, map.get("2"), 1e-6);
      }
      ov.close();
    }
  }

  @Test
  public void testModelSequenceOfTensors() throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/test_sequence_tensors.onnx").toString();

    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {
      Map<String, NodeInfo> outputInfos = session.getOutputInfo();
      NodeInfo outputInfo = outputInfos.get("output_sequence");
      assertTrue(outputInfo.getInfo() instanceof SequenceInfo);

      Map<String, OnnxTensor> container = new HashMap<>();
      OnnxTensor firstInputTensor =
          OnnxTensor.createTensor(
              env, OrtUtil.reshape(new long[] {1, 2, 3, 4, 5, 6}, new long[] {2, 3}));
      OnnxTensor secondInputTensor =
          OnnxTensor.createTensor(
              env, OrtUtil.reshape(new long[] {7, 8, 9, 10, 11, 12}, new long[] {2, 3}));

      container.put("tensor1", firstInputTensor);
      container.put("tensor2", secondInputTensor);

      try (OrtSession.Result outputs = session.run(container)) {
        // output is a sequence<tensors>
        Optional<OnnxValue> output = outputs.get("output_sequence");
        assertTrue(output.isPresent());
        assertTrue(output.get() instanceof OnnxSequence);

        // cast to a sequence
        OnnxSequence seq = (OnnxSequence) output.get();

        // make sure that the sequence holds only 2 elements (tensors)
        assertEquals(2, seq.getInfo().length);

        // try-cast the elements in sequence to tensor type
        List<? extends OnnxValue> elements = seq.getValue();
        assertEquals(2, elements.size());
        assertTrue(elements.get(0) instanceof OnnxTensor);
        assertTrue(elements.get(1) instanceof OnnxTensor);

        OnnxTensor firstTensor = (OnnxTensor) elements.get(0);
        OnnxTensor secondTensor = (OnnxTensor) elements.get(1);

        LongBuffer outputBuf = firstTensor.getLongBuffer();

        // make sure the tensors in the output sequence hold the correct values
        assertEquals(1, outputBuf.get(0));
        assertEquals(2, outputBuf.get(1));
        assertEquals(3, outputBuf.get(2));
        assertEquals(4, outputBuf.get(3));
        assertEquals(5, outputBuf.get(4));
        assertEquals(6, outputBuf.get(5));

        outputBuf = secondTensor.getLongBuffer();

        assertEquals(7, outputBuf.get(0));
        assertEquals(8, outputBuf.get(1));
        assertEquals(9, outputBuf.get(2));
        assertEquals(10, outputBuf.get(3));
        assertEquals(11, outputBuf.get(4));
        assertEquals(12, outputBuf.get(5));

        firstTensor.close();
        secondTensor.close();
      }
    }
  }

  @Test
  public void testModelSerialization() throws OrtException, IOException {
    String cwd = System.getProperty("user.dir");
    Path squeezeNet = TestHelpers.getResourcePath("/squeezenet.onnx");
    String modelPath = squeezeNet.toString();
    File tmpFile = File.createTempFile("optimized-squeezenet", ".onnx");
    String modelOutputPath = tmpFile.getAbsolutePath();
    Assertions.assertEquals(0, tmpFile.length());
    // Set the optimized model file path to assert that no exception are thrown.
    try (SessionOptions options = new SessionOptions()) {
      options.setOptimizedModelFilePath(modelOutputPath);
      options.setOptimizationLevel(OptLevel.BASIC_OPT);
      try (OrtSession session = env.createSession(modelPath, options)) {
        Assertions.assertNotNull(session);
      } finally {
        Assertions.assertTrue(tmpFile.length() > 0);
      }
    } finally {
      tmpFile.delete();
    }
  }

  @Test
  public void testStringIdentity() throws OrtException {
    String modelPath = TestHelpers.getResourcePath("/identity_string.onnx").toString();
    try (SessionOptions options = new SessionOptions();
        OrtSession session = env.createSession(modelPath, options)) {

      Map<String, NodeInfo> outputInfos = session.getOutputInfo();
      ValueInfo firstOutputInfo = outputInfos.values().iterator().next().getInfo();
      assertTrue(firstOutputInfo instanceof TensorInfo);
      assertEquals(OnnxJavaType.STRING, ((TensorInfo) firstOutputInfo).type);

      String inputName = session.getInputNames().iterator().next();

      Map<String, OnnxTensor> container = new HashMap<>();
      String[][] tensorIn =
          new String[][] {new String[] {"this", "is"}, new String[] {"identity", "test \u263A"}};
      OnnxTensor ov = OnnxTensor.createTensor(env, tensorIn);
      container.put(inputName, ov);

      try (OrtSession.Result outputs = session.run(container)) {
        assertEquals(1, outputs.size());

        OnnxValue firstOutput = outputs.get(0);
        assertTrue(firstOutput instanceof OnnxTensor);

        String[][] labelOutput = (String[][]) firstOutput.getValue();

        assertEquals("this", labelOutput[0][0]);
        assertEquals("is", labelOutput[0][1]);
        assertEquals("identity", labelOutput[1][0]);
        assertEquals("test \u263A", labelOutput[1][1]);
        assertEquals(2, labelOutput.length);
        assertEquals(2, labelOutput[0].length);
        assertEquals(2, labelOutput[1].length);

        OnnxValue.close(container);
        container.clear();
      }

      String[] tensorInFlatArr = new String[] {"this", "is", "identity", "test \u263A"};
      ov = OnnxTensor.createTensor(env, tensorInFlatArr, new long[] {2, 2});
      container.put(inputName, ov);

      try (OrtSession.Result outputs = session.run(container)) {
        assertEquals(1, outputs.size());

        OnnxValue firstOutput = outputs.get(0);
        assertTrue(firstOutput instanceof OnnxTensor);

        String[][] labelOutput = (String[][]) firstOutput.getValue();

        assertEquals("this", labelOutput[0][0]);
        assertEquals("is", labelOutput[0][1]);
        assertEquals("identity", labelOutput[1][0]);
        assertEquals("test \u263A", labelOutput[1][1]);
        assertEquals(2, labelOutput.length);
        assertEquals(2, labelOutput[0].length);
        assertEquals(2, labelOutput[1].length);
      }
    }
  }

  /** Carrier tuple for the squeeze net model. */
  public static class SqueezeNetTuple {
    public final OrtSession session;
    public final float[] inputData;
    public final float[] outputData;

    public SqueezeNetTuple(OrtSession session, float[] inputData, float[] outputData) {
      this.session = session;
      this.inputData = inputData;
      this.outputData = outputData;
    }
  }

  private static SqueezeNetTuple openSessionSqueezeNet() throws OrtException {
    return openSessionSqueezeNet(EnumSet.noneOf(OrtProvider.class));
  }

  /**
   * Loads the squeezenet model into a session using the supplied providers.
   *
   * @param providers The providers to activate.
   * @return The squeezenet session, input and output.
   * @throws OrtException If the native code failed.
   */
  private static SqueezeNetTuple openSessionSqueezeNet(EnumSet<OrtProvider> providers)
      throws OrtException {
    Path squeezeNet = TestHelpers.getResourcePath("/squeezenet.onnx");
    String modelPath = squeezeNet.toString();
    SessionOptions options = new SessionOptions();
    for (OrtProvider p : providers) {
      switch (p) {
        case CUDA:
          options.addCUDA();
          break;
        case DNNL:
          options.addDnnl(false);
          break;
        case OPEN_VINO:
          options.addOpenVINO("");
          break;
        case TENSOR_RT:
          options.addTensorrt(0);
          break;
        case NNAPI:
          options.addNnapi();
          break;
        case DIRECT_ML:
          options.addDirectML(0);
          break;
        case ACL:
          options.addACL(false);
          break;
        case ARM_NN:
          options.addArmNN(false);
          break;
        case ROCM:
          options.addROCM();
          break;
        case CORE_ML:
          options.addCoreML();
          break;
        case XNNPACK:
          options.addXnnpack(Collections.emptyMap());
          break;
        case VITIS_AI:
        case RK_NPU:
        case MI_GRAPH_X:
        default:
          logger.warning("Unsupported provider in Java test " + p);
      }
    }
    OrtSession session = env.createSession(modelPath, options);
    float[] inputData = TestHelpers.loadTensorFromFile(TestHelpers.getResourcePath("/bench.in"));
    float[] expectedOutput =
        TestHelpers.loadTensorFromFile(TestHelpers.getResourcePath("/bench.expected_out"));
    return new SqueezeNetTuple(session, inputData, expectedOutput);
  }
}
