MLECO-3611: Formatting fixes for generated files.

Template files updated for generated files to adhere to
coding guidelines and clang format configuration. There
will still be unavoidable violations, but most of the
others have been fixed.

Change-Id: Ia03db40f8c62a369f2b07fe02eea65e41993a523
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>

1 files changed, 19 insertions, 20 deletions

diff --git a/tests/use_case/asr/InferenceTestWav2Letter.cc b/tests/use_case/asr/InferenceTestWav2Letter.cc
index e6012c3..991617c 100644
--- a/tests/use_case/asr/InferenceTestWav2Letter.cc
+++ b/tests/use_case/asr/InferenceTestWav2Letter.cc
@@ -1,6 +1,6 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates
+ * <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,10 +14,10 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+#include "BufAttributes.hpp"
 #include "TensorFlowLiteMicro.hpp"
-#include "Wav2LetterModel.hpp"
 #include "TestData_asr.hpp"
-#include "BufAttributes.hpp"
+#include "Wav2LetterModel.hpp"
 
 #include <catch.hpp>
 #include <random>
@@ -51,11 +51,9 @@ bool RunInferenceRandom(arm::app::Model& model)
 
     std::random_device rndDevice;
     std::mt19937 mersenneGen{rndDevice()};
-    std::uniform_int_distribution<short> dist {-128, 127};
+    std::uniform_int_distribution<short> dist{-128, 127};
 
-    auto gen = [&dist, &mersenneGen](){
-                   return dist(mersenneGen);
-               };
+    auto gen = [&dist, &mersenneGen]() { return dist(mersenneGen); };
 
     std::vector<int8_t> randomAudio(inputTensor->bytes);
     std::generate(std::begin(randomAudio), std::end(randomAudio), gen);
@@ -64,21 +62,22 @@ bool RunInferenceRandom(arm::app::Model& model)
     return true;
 }
 
-TEST_CASE("Running random inference with TensorFlow Lite Micro and Wav2LetterModel Int8", "[Wav2Letter]")
+TEST_CASE("Running random inference with TensorFlow Lite Micro and Wav2LetterModel Int8",
+          "[Wav2Letter]")
 {
     arm::app::Wav2LetterModel model{};
 
     REQUIRE_FALSE(model.IsInited());
     REQUIRE(model.Init(arm::app::tensorArena,
-                    sizeof(arm::app::tensorArena),
-                    arm::app::asr::GetModelPointer(),
-                    arm::app::asr::GetModelLen()));
+                       sizeof(arm::app::tensorArena),
+                       arm::app::asr::GetModelPointer(),
+                       arm::app::asr::GetModelLen()));
     REQUIRE(model.IsInited());
 
     REQUIRE(RunInferenceRandom(model));
 }
 
-template<typename T>
+template <typename T>
 void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::Model& model)
 {
     TfLiteTensor* inputTensor = model.GetInputTensor(0);
@@ -101,9 +100,10 @@ void TestInference(const T* input_goldenFV, const T* output_goldenFV, arm::app::
 TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter]")
 {
     REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_IFM_FILES);
-    for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
-        auto input_goldenFV = get_ifm_data_array(i);;
-        auto output_goldenFV = get_ofm_data_array(i);
+    for (uint32_t i = 0; i < NUMBER_OF_IFM_FILES; ++i) {
+        auto input_goldenFV = GetIfmDataArray(i);
+        ;
+        auto output_goldenFV = GetOfmDataArray(i);
 
         DYNAMIC_SECTION("Executing inference with re-init")
         {
@@ -111,13 +111,12 @@ TEST_CASE("Running inference with Tflu and Wav2LetterModel Int8", "[Wav2Letter]"
 
             REQUIRE_FALSE(model.IsInited());
             REQUIRE(model.Init(arm::app::tensorArena,
-                    sizeof(arm::app::tensorArena),
-                    arm::app::asr::GetModelPointer(),
-                    arm::app::asr::GetModelLen()));
+                               sizeof(arm::app::tensorArena),
+                               arm::app::asr::GetModelPointer(),
+                               arm::app::asr::GetModelLen()));
             REQUIRE(model.IsInited());
 
             TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
-
         }
     }
 }