Feature/add t bool test

tensorflow-core/tensorflow-core-api/src/main/java/org/tensorflow/NativeFunction.java

@@ -20,9 +20,7 @@
 
 import com.google.protobuf.InvalidProtocolBufferException;
 import java.util.ArrayDeque;
-import java.util.ArrayList;
 import java.util.Collection;
-import java.util.Collections;
 import java.util.LinkedHashSet;
 import java.util.List;
 import java.util.Map;
@@ -92,7 +90,7 @@ public synchronized List<String> getDependencies() {
                   }
                 });
       }
-      dependencies = Collections.unmodifiableList(new ArrayList<>(deps));
+      dependencies = List.copyOf(deps);
     }
 
     return dependencies;

tensorflow-core/tensorflow-core-api/src/test/java/org/tensorflow/types/TBoolTest.java

@@ -0,0 +1,156 @@
+/*
+ *  Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+ *
+ *  Licensed 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.
+ *  =======================================================================
+ */
+
+package org.tensorflow.types;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertNotNull;
+
+import org.junit.jupiter.api.Test;
+import org.tensorflow.EagerSession;
+import org.tensorflow.ndarray.NdArray;
+import org.tensorflow.ndarray.NdArrays;
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.ndarray.index.Indices;
+import org.tensorflow.op.Ops;
+import org.tensorflow.op.core.Constant;
+import org.tensorflow.op.math.LogicalAnd;
+import org.tensorflow.op.math.LogicalNot;
+import org.tensorflow.op.math.LogicalOr;
+
+public class TBoolTest {
+
+  @Test
+  public void createScalar() {
+    TBool tensorT = TBool.scalarOf(true);
+    assertNotNull(tensorT);
+    assertEquals(Shape.scalar(), tensorT.shape());
+    assertEquals(true, tensorT.getObject());
+
+    TBool tensorF = TBool.scalarOf(false);
+    assertNotNull(tensorF);
+    assertEquals(Shape.scalar(), tensorF.shape());
+    assertEquals(false, tensorF.getObject());
+  }
+
+  @Test
+  public void createVector() {
+    TBool tensor = TBool.vectorOf(true, false);
+    assertNotNull(tensor);
+    assertEquals(Shape.of(2), tensor.shape());
+    assertEquals(true, tensor.getObject(0));
+    assertEquals(false, tensor.getObject(1));
+  }
+
+  @Test
+  public void createCopy() {
+    NdArray<Boolean> bools =
+        NdArrays.ofObjects(Boolean.class, Shape.of(2, 2))
+            .setObject(true, 0, 0)
+            .setObject(false, 0, 1)
+            .setObject(false, 1, 0)
+            .setObject(true, 1, 1);
+
+    TBool tensor = TBool.tensorOf(bools);
+    assertNotNull(tensor);
+    bools.scalars().forEachIndexed((idx, s) -> assertEquals(s.getObject(), tensor.getObject(idx)));
+  }
+
+  @Test
+  public void initializeTensorsWithBools() {
+    // Allocate a tensor of booleans of the shape (2, 3, 2)
+    TBool tensor = TBool.tensorOf(Shape.of(2, 3, 2));
+
+    assertEquals(3, tensor.rank());
+    assertEquals(12, tensor.size());
+    NdArray<Boolean> data = (NdArray<Boolean>) tensor;
+
+    try (EagerSession session = EagerSession.create()) {
+      Ops tf = Ops.create(session);
+
+      // Initialize tensor memory with falses and take a snapshot
+      data.scalars().forEach(scalar -> ((NdArray<Boolean>) scalar).setObject(false));
+      Constant<TBool> x = tf.constantOf(tensor);
+
+      // Initialize the same tensor memory with trues and take a snapshot
+      data.scalars().forEach(scalar -> ((NdArray<Boolean>) scalar).setObject(true));
+      Constant<TBool> y = tf.constantOf(tensor);
+
+      // Calculate x AND y and validate the result
+      LogicalAnd xAndY = tf.math.logicalAnd(x, y);
+      ((NdArray<Boolean>) xAndY.asTensor())
+          .scalars()
+          .forEach(scalar -> assertEquals(false, scalar.getObject()));
+
+      // Calculate x OR y and validate the result
+      LogicalOr xOrY = tf.math.logicalOr(x, y);
+      ((NdArray<Boolean>) xOrY.asTensor())
+          .scalars()
+          .forEach(scalar -> assertEquals(true, scalar.getObject()));
+
+      // Calculate !x and validate the result against y
+      LogicalNot notX = tf.math.logicalNot(x);
+      assertEquals(y.asTensor(), notX.asTensor());
+    }
+  }
+
+  @Test
+  public void setAndCompute() {
+    NdArray<Boolean> heapData =
+        NdArrays.ofBooleans(Shape.of(4))
+            .setObject(true, 0)
+            .setObject(false, 1)
+            .setObject(true, 2)
+            .setObject(false, 3);
+
+    // Creates a 2x2 matrix
+    try (TBool tensor = TBool.tensorOf(Shape.of(2, 2))) {
+      NdArray<Boolean> data = (NdArray<Boolean>) tensor;
+
+      // Copy first 2 values of the vector to the first row of the matrix
+      data.set(heapData.slice(Indices.range(0, 2)), 0);
+
+      // Copy values at an odd position in the vector as the second row of the matrix
+      data.set(heapData.slice(Indices.odd()), 1);
+
+      assertEquals(true, data.getObject(0, 0));
+      assertEquals(false, data.getObject(0, 1));
+      assertEquals(false, data.getObject(1, 0));
+      assertEquals(false, data.getObject(1, 1));
+
+      // Read rows of the tensor in reverse order
+      NdArray<Boolean> flippedData = data.slice(Indices.flip(), Indices.flip());
+
+      assertEquals(false, flippedData.getObject(0, 0));
+      assertEquals(false, flippedData.getObject(0, 1));
+      assertEquals(false, flippedData.getObject(1, 0));
+      assertEquals(true, flippedData.getObject(1, 1));
+
+      try (EagerSession session = EagerSession.create()) {
+        Ops tf = Ops.create(session);
+
+        LogicalNot sub = tf.math.logicalNot(tf.constantOf(tensor));
+        NdArray<Boolean> result = (NdArray<Boolean>) sub.asTensor();
+
+        assertEquals(false, result.getObject(0, 0));
+        assertEquals(true, result.getObject(0, 1));
+        assertEquals(true, result.getObject(1, 0));
+        assertEquals(true, result.getObject(1, 1));
+      }
+    }
+  }
+}