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authorHenning Baldersheim <balder@yahoo-inc.com>2022-12-04 23:02:42 +0100
committerGitHub <noreply@github.com>2022-12-04 23:02:42 +0100
commit5381962e747ea9ac515abcf51dc70ad40bbbf70d (patch)
treed25af24e6a007091bb4a321a63747f1c7b439045 /vespajlib/src/main/java/com/yahoo/tensor/serialization
parent2555b49b4390ed9dac9eab3959d6cc6e3511515c (diff)
Revert "Revert collect(Collectors.toList())"
Diffstat (limited to 'vespajlib/src/main/java/com/yahoo/tensor/serialization')
-rw-r--r--vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java2
-rw-r--r--vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java8
2 files changed, 5 insertions, 5 deletions
diff --git a/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java b/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java
index b683519988c..a7afc1efc6d 100644
--- a/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java
+++ b/vespajlib/src/main/java/com/yahoo/tensor/serialization/JsonFormat.java
@@ -124,7 +124,7 @@ public class JsonFormat {
private static void encodeBlocks(MixedTensor tensor, Cursor cursor) {
var mappedDimensions = tensor.type().dimensions().stream().filter(d -> d.isMapped())
- .map(d -> TensorType.Dimension.mapped(d.name())).collect(Collectors.toList());
+ .map(d -> TensorType.Dimension.mapped(d.name())).toList();
if (mappedDimensions.size() < 1) {
throw new IllegalArgumentException("Should be ensured by caller");
}
diff --git a/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java b/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java
index 2a55ac658b1..c18e9f179d6 100644
--- a/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java
+++ b/vespajlib/src/main/java/com/yahoo/tensor/serialization/MixedBinaryFormat.java
@@ -43,7 +43,7 @@ class MixedBinaryFormat implements BinaryFormat {
}
private void encodeSparseDimensions(GrowableByteBuffer buffer, MixedTensor tensor) {
- List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).collect(Collectors.toList());
+ List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).toList();
buffer.putInt1_4Bytes(sparseDimensions.size());
for (TensorType.Dimension dimension : sparseDimensions) {
buffer.putUtf8String(dimension.name());
@@ -51,7 +51,7 @@ class MixedBinaryFormat implements BinaryFormat {
}
private void encodeDenseDimensions(GrowableByteBuffer buffer, MixedTensor tensor) {
- List<TensorType.Dimension> denseDimensions = tensor.type().dimensions().stream().filter(d -> d.isIndexed()).collect(Collectors.toList());
+ List<TensorType.Dimension> denseDimensions = tensor.type().dimensions().stream().filter(d -> d.isIndexed()).toList();
buffer.putInt1_4Bytes(denseDimensions.size());
for (TensorType.Dimension dimension : denseDimensions) {
buffer.putUtf8String(dimension.name());
@@ -71,7 +71,7 @@ class MixedBinaryFormat implements BinaryFormat {
}
private void encodeCells(GrowableByteBuffer buffer, MixedTensor tensor, Consumer<Double> consumer) {
- List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).collect(Collectors.toList());
+ List<TensorType.Dimension> sparseDimensions = tensor.type().dimensions().stream().filter(d -> !d.isIndexed()).toList();
long denseSubspaceSize = tensor.denseSubspaceSize();
if (sparseDimensions.size() > 0) {
buffer.putInt1_4Bytes((int)(tensor.size() / denseSubspaceSize)); // XXX: Size truncation
@@ -137,7 +137,7 @@ class MixedBinaryFormat implements BinaryFormat {
}
private void decodeCells(GrowableByteBuffer buffer, MixedTensor.BoundBuilder builder, TensorType type, Supplier<Double> supplier) {
- List<TensorType.Dimension> sparseDimensions = type.dimensions().stream().filter(d -> !d.isIndexed()).collect(Collectors.toList());
+ List<TensorType.Dimension> sparseDimensions = type.dimensions().stream().filter(d -> !d.isIndexed()).toList();
TensorType sparseType = MixedTensor.createPartialType(type.valueType(), sparseDimensions);
long denseSubspaceSize = builder.denseSubspaceSize();