chore: import upstream snapshot with attribution

tensorflow/lite/kernels/matrix_diag_test.cc

@@ -0,0 +1,110 @@
+/* Copyright 2019 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.
+==============================================================================*/
+#include <stdint.h>
+
+#include <memory>
+#include <vector>
+
+#include <gmock/gmock.h>
+#include <gtest/gtest.h>
+#include "tensorflow/lite/core/interpreter.h"
+#include "tensorflow/lite/kernels/test_util.h"
+#include "tensorflow/lite/schema/schema_generated.h"
+
+namespace tflite {
+namespace {
+
+using ::testing::ElementsAre;
+using ::testing::ElementsAreArray;
+
+template <typename T>
+class MatrixDiagOpModel : public SingleOpModel {
+ public:
+  explicit MatrixDiagOpModel(const TensorData& input) {
+    input_ = AddInput(input);
+    output_ = AddOutput({input.type, {}});
+
+    SetBuiltinOp(BuiltinOperator_MATRIX_DIAG, BuiltinOptions_MatrixDiagOptions,
+                 CreateMatrixDiagOptions(builder_).Union());
+    BuildInterpreter({GetShape(input_)});
+  }
+
+  int input() { return input_; }
+
+  std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
+  std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
+  TfLiteType GetOutputType() {
+    TfLiteTensor* t = interpreter_->tensor(output_);
+    return t->type;
+  }
+
+ private:
+  int input_;
+  int output_;
+};
+
+// Use the machinery of TYPED_TEST_SUITE to test all supported types.
+// See
+// https://github.com/google/googletest/blob/master/googletest/docs/advanced.md#typed-tests
+// for details.
+template <typename T>
+class MatrixDiagOpTest : public ::testing::Test {};
+
+using TypesUnderTest =
+    ::testing::Types<TypeUnion<int32_t>, TypeUnion<float>, TypeUnion<int16_t>,
+                     TypeUnion<int8_t>, TypeUnion<uint8_t>>;
+TYPED_TEST_SUITE(MatrixDiagOpTest, TypesUnderTest);
+
+TYPED_TEST(MatrixDiagOpTest, ThreeByThreeDiag) {
+  MatrixDiagOpModel<typename TypeParam::ScalarType> model(
+      {TypeParam::tensor_type, {3}});
+  model.template PopulateTensor<typename TypeParam::ScalarType>(model.input(),
+                                                                {1, 2, 3});
+  ASSERT_EQ(model.Invoke(), kTfLiteOk);
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 3));
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0,  //
+                                                   0, 2, 0,  //
+                                                   0, 0, 3}));
+  EXPECT_THAT(model.GetOutputType(), TypeParam::tflite_type);
+}
+
+// Additional special cases.
+TEST(MatrixDiagTest, Int32TestTwoDimDiag) {
+  MatrixDiagOpModel<int32_t> model({TensorType_INT32, {2, 4}});
+  model.PopulateTensor<int32_t>(model.input(), {1, 2, 3, 4, 5, 6, 7, 8});
+  ASSERT_EQ(model.Invoke(), kTfLiteOk);
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 4, 4));
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0, 0,  //
+                                                   0, 2, 0, 0,  //
+                                                   0, 0, 3, 0,  //
+                                                   0, 0, 0, 4,  //
+                                                   5, 0, 0, 0,  //
+                                                   0, 6, 0, 0,  //
+                                                   0, 0, 7, 0,  //
+                                                   0, 0, 0, 8}));
+  EXPECT_THAT(model.GetOutputType(), TfLiteType::kTfLiteInt32);
+}
+
+TEST(MatrixDiagTest, DegenerateCase) {
+  MatrixDiagOpModel<uint8_t> model({TensorType_UINT8, {1}});
+  model.PopulateTensor<uint8_t>(model.input(), {1});
+  ASSERT_EQ(model.Invoke(), kTfLiteOk);
+  EXPECT_THAT(model.GetOutputShape(), ElementsAre(1, 1));
+  EXPECT_THAT(model.GetOutput(), ElementsAreArray({1}));
+  EXPECT_THAT(model.GetOutputType(), TfLiteType::kTfLiteUInt8);
+}
+
+}  // namespace
+}  // namespace tflite