Add Android specific generated test runner.android-mainline-10.0.0_r9android-mainline-10.0.0_r7android-mainline-10.0.0_r5android-mainline-10.0.0_r4android-mainline-10.0.0_r10android-10.0.0_r9android-10.0.0_r8android-10.0.0_r7android-10.0.0_r45android-10.0.0_r44android-10.0.0_r43android-10.0.0_r42android-10.0.0_r41android-10.0.0_r40android-10.0.0_r39android-10.0.0_r38android-10.0.0_r37android-10.0.0_r36android-10.0.0_r35android-10.0.0_r34android-10.0.0_r33android-10.0.0_r32android-10.0.0_r31android-10.0.0_r30android-10.0.0_r14android-10.0.0_r13android-10.0.0_r12android10-qpr3-s1-releaseandroid10-qpr3-releaseandroid10-qpr2-s4-releaseandroid10-qpr2-s3-releaseandroid10-qpr2-s2-releaseandroid10-qpr2-s1-releaseandroid10-qpr2-releaseandroid10-qpr1-mainline-releaseandroid10-mainline-media-releaseandroid10-d4-s1-releaseandroid10-d4-releaseandroid10-c2f2-s2-releaseandroid10-c2f2-s1-releaseandroid10-c2f2-release

am: ea303b6c0d

Change-Id: Id0cddfb502a4c1ad4e40a40f26e755d838dab084

20 files changed, 4506 insertions, 0 deletions

diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/Android.bp b/tensorflow/lite/testing/nnapi_tflite_zip_tests/Android.bp
new file mode 100644
index 00000000000..9af542cd188
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/Android.bp
@@ -0,0 +1,42 @@
+cc_test {
+    name: "TfliteGeneratedNnapiTest",
+    sdk_version: "current",
+    srcs: [
+        "generated_examples_zip_test.cc",
+        "parse_testdata.cc",
+        "tflite_driver.cc",
+        "split.cc",
+        "message.cc",
+        "tokenize.cc",
+    ],
+    data: [
+        "models.tar",
+        "test_manifest.txt",
+    ],
+    include_dirs: [
+        "external/flatbuffers/include",
+        "external/tensorflow",
+    ],
+    cflags: [
+        "-DPLATFORM_POSIX_ANDROID",
+        "-Wall",
+        "-Werror",
+        "-Wextra",
+        "-Wno-extern-c-compat",
+        "-Wno-sign-compare",
+        "-Wno-unused-parameter",
+        "-Wno-unused-private-field",
+    ],
+    stl: "libc++_static",
+    static_libs: [
+        "libtflite_static",
+        "libgmock_ndk",
+    ],
+    shared_libs: [
+        "liblog",
+    ],
+    test_suites: [
+        "general-tests",
+    ],
+}
+
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/AndroidTest.xml b/tensorflow/lite/testing/nnapi_tflite_zip_tests/AndroidTest.xml
new file mode 100644
index 00000000000..2f311463cf0
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/AndroidTest.xml
@@ -0,0 +1,33 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Copyright (C) 2019 The Android Open Source Project
+
+     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.
+-->
+<configuration description="Configuration for TFLite NNAPI zip Tests">
+    <option name="test-suite-tag" value="general-tests" />
+    <option name="config-descriptor:metadata" key="component" value="neuralnetworks" />
+    <target_preparer class="com.android.compatibility.common.tradefed.targetprep.FilePusher">
+        <option name="cleanup" value="true" />
+        <option name="push" value="TfliteGeneratedNnapiTest->/data/local/tmp/TfliteGeneratedNnapiTest" />
+        <option name="push" value="models.tar->/data/local/tmp/models.tar" />
+        <option name="push" value="test_manifest.txt->/data/local/tmp/test_manifest.txt" />
+        <option name="post-push" value="tar xfo /data/local/tmp/models.tar -C /data/local/tmp/" />
+    </target_preparer>
+    <test class="com.android.tradefed.testtype.GTest" >
+        <option name="native-test-device-path" value="/data/local/tmp" />
+        <option name="module-name" value="TfliteGeneratedNnapiTest" />
+        <option name="runtime-hint" value="10m" />
+        <!-- test-timeout unit is ms, value = 10 min -->
+        <option name="native-test-timeout" value="600000" />
+    </test>
+</configuration>
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/generated_examples_zip_test.cc b/tensorflow/lite/testing/nnapi_tflite_zip_tests/generated_examples_zip_test.cc
new file mode 100644
index 00000000000..82279f9ab7a
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/generated_examples_zip_test.cc
@@ -0,0 +1,105 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#include <cstdarg>
+#include <cstdio>
+#include <cstdlib>
+#include <fstream>
+#include <map>
+#include <sstream>
+#include <gtest/gtest.h>
+#include "parse_testdata.h"
+#include "tflite_driver.h"
+#include "util.h"
+
+namespace tflite {
+namespace testing {
+
+namespace {
+bool FLAGS_use_nnapi = true;
+}  // namespace
+
+// Get a list of tests from the manifest file.
+std::vector<string> FindAllTests() {
+  const string test_dir = "/data/local/tmp";
+
+  std::vector<string> test_paths;
+  // Read the newline delimited list of entries in the manifest.
+  std::ifstream manifest_fp(test_dir + "/test_manifest.txt");
+  string manifest((std::istreambuf_iterator<char>(manifest_fp)),
+                   std::istreambuf_iterator<char>());
+  size_t pos = 0;
+  int added = 0;
+  while (true) {
+    size_t end_pos = manifest.find("\n", pos);
+    if (end_pos == string::npos) break;
+    // ignore disabled tests.
+    if (manifest.substr(pos, 8) != "DISABLED") {
+      string filename = manifest.substr(pos, end_pos - pos);
+      test_paths.push_back(test_dir + "/models/" + filename);
+    }
+    pos = end_pos + 1;
+    added += 1;
+  }
+  return test_paths;
+}
+
+class OpsTest : public ::testing::TestWithParam<string> {};
+
+TEST_P(OpsTest, RunZipTests) {
+  string test_path = GetParam();
+  string tflite_test_case = test_path + "_tests.txt";
+  string tflite_dir = test_path.substr(0, test_path.find_last_of("/"));
+  string test_name = test_path.substr(test_path.find_last_of('/'));
+
+  std::ifstream tflite_stream(tflite_test_case);
+  ASSERT_TRUE(tflite_stream.is_open()) << tflite_test_case;
+  tflite::testing::TfLiteDriver test_driver(FLAGS_use_nnapi);
+  test_driver.SetModelBaseDir(tflite_dir);
+
+  bool result = tflite::testing::ParseAndRunTests(&tflite_stream, &test_driver);
+  string message = test_driver.GetErrorMessage();
+  EXPECT_TRUE(result) << message;
+}
+
+struct ZipPathParamName {
+  template <class ParamType>
+  string operator()(const ::testing::TestParamInfo<ParamType>& info) const {
+    string param_name = info.param;
+    size_t last_slash = param_name.find_last_of("\\/");
+    if (last_slash != string::npos) {
+      param_name = param_name.substr(last_slash);
+    }
+    for (size_t index = 0; index < param_name.size(); ++index) {
+      if (!isalnum(param_name[index]) && param_name[index] != '_')
+        param_name[index] = '_';
+    }
+    return param_name;
+  }
+};
+
+INSTANTIATE_TEST_CASE_P(tests, OpsTest, ::testing::ValuesIn(FindAllTests()),
+                        ZipPathParamName());
+
+}  // namespace testing
+}  // namespace tflite
+
+int main(int argc, char** argv) {
+  ::tflite::LogToStderr();
+  ::testing::InitGoogleTest(&argc, argv);
+  return RUN_ALL_TESTS();
+}
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/join.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/join.h
new file mode 100644
index 00000000000..b845f7ee129
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/join.h
@@ -0,0 +1,77 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_JOIN_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_JOIN_H_
+
+#include <cstdlib>
+#include <iomanip>
+#include <sstream>
+
+#include "tensorflow/lite/string_tflite.h"
+
+namespace tflite {
+namespace testing {
+
+// Join a list of data with default precision separated by delimiter.
+template <typename T>
+string JoinDefault(T* data, size_t len, const string& delimiter) {
+  if (len == 0 || data == nullptr) {
+    return "";
+  }
+  std::stringstream result;
+  result << data[0];
+  for (int i = 1; i < len; i++) {
+    result << delimiter << data[i];
+  }
+  return result.str();
+}
+
+// Join a list of data with fixed precision separated by delimiter.
+template <typename T>
+string Join(T* data, size_t len, const string& delimiter) {
+  if (len == 0 || data == nullptr) {
+    return "";
+  }
+  std::stringstream result;
+  result << std::setprecision(9) << data[0];
+  for (int i = 1; i < len; i++) {
+    result << std::setprecision(9) << delimiter << data[i];
+  }
+  return result.str();
+}
+
+// Join a list of uint8 data separated by a delimiter. Cast data to int before
+// placing it in the string to prevent values from being treated like chars.
+template <>
+inline string Join<uint8_t>(uint8_t* data, size_t len,
+                            const string& delimiter) {
+  if (len == 0 || data == nullptr) {
+    return "";
+  }
+  std::stringstream result;
+  result << static_cast<int>(data[0]);
+  for (int i = 1; i < len; i++) {
+    result << delimiter << static_cast<int>(data[i]);
+  }
+  return result.str();
+}
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TESTING_JOIN_H_
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/known_failures.txt b/tensorflow/lite/testing/nnapi_tflite_zip_tests/known_failures.txt
new file mode 100644
index 00000000000..e69de29bb2d
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/known_failures.txt
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/message.cc b/tensorflow/lite/testing/nnapi_tflite_zip_tests/message.cc
new file mode 100644
index 00000000000..c43cab7c843
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/message.cc
@@ -0,0 +1,99 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#include "message.h"
+
+#include <stack>
+
+#include "tokenize.h"
+
+namespace tflite {
+namespace testing {
+
+// A token processor that builds messages and forward calls to the current
+// message object. Place a new message at the top of the stack when it start
+// and remove it when it is finished.
+class MessageStack : public TokenProcessor {
+ public:
+  // Start a new MessageStack with the given first_node, which will be used to
+  // process freestanding fields and submessages.
+  explicit MessageStack(Message* first_node) {
+    nodes_.push(first_node);
+    valid_ = true;
+  }
+
+  void ConsumeToken(std::string* token) override {
+    if (!valid_) return;
+    Message* current_node = nodes_.top();
+    if (*token == "{") {
+      // This is the beginning of a new message, names after the previous token.
+      if (previous_token_.empty()) {
+        valid_ = false;
+        return;
+      }
+      nodes_.push(current_node ? current_node->AddChild(previous_token_)
+                               : nullptr);
+      previous_token_.clear();
+    } else if (*token == "}") {
+      // A message is being completed. There should be no previous token.  Note
+      // that the top-level message never closes, so we should always have at
+      // least one entry in the stack.
+      if (nodes_.size() == 1 || !previous_token_.empty()) {
+        valid_ = false;
+        return;
+      }
+      if (current_node) {
+        current_node->Finish();
+      }
+      nodes_.pop();
+    } else if (*token == ":") {
+      // We reached the end of the 'key' portion of a field. Store the token
+      // until we have the 'value' portion.
+      if (previous_token_.empty()) {
+        valid_ = false;
+        return;
+      }
+    } else {
+      if (previous_token_.empty()) {
+        previous_token_.swap(*token);
+      } else {
+        // This is the 'value' portion of a field. The previous token is the
+        // 'key'.
+        if (current_node) {
+          current_node->SetField(previous_token_, *token);
+        }
+        previous_token_.clear();
+      }
+    }
+  }
+
+  bool valid() const { return valid_; }
+
+ private:
+  std::stack<Message*> nodes_;
+  std::string previous_token_;
+  bool valid_;
+};
+
+bool Message::Read(std::istream* input, Message* message) {
+  MessageStack stack(message);
+  Tokenize(input, &stack);
+  return stack.valid();
+}
+
+}  // namespace testing
+}  // namespace tflite
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/message.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/message.h
new file mode 100644
index 00000000000..5d3a4896e2a
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/message.h
@@ -0,0 +1,85 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_MESSAGE_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_MESSAGE_H_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace tflite {
+namespace testing {
+
+// A Message is a textual protobuf-like structure that looks like:
+//    tag {
+//      f : "values"
+//      child {
+//        a : 1
+//       }
+//    }
+// This class provides the framework for processing message but does not
+// associate any particular behavior to fields and submessage. In order
+// to properly parse a stream this class must be derived.
+class Message {
+ public:
+  // Reads a stream, tokenizes it and create a new message under the given
+  // top-level message. Returns true if the parsing succeeded.
+  static bool Read(std::istream* input, Message* message);
+
+  Message() {}
+  virtual ~Message() {}
+
+  // Called when a new field is found. For example, when:
+  //   f : "values"
+  // is found, it triggers:
+  //   SetField("f", "values");
+  virtual void SetField(const std::string& name, const std::string& value) {}
+
+  // Called when a submessage is started. For example, when:
+  //   child {
+  // is found, it triggers
+  //   AddChild("child");
+  // If nullptr is returned, the contents of the submessage will be ignored.
+  // Otherwise, the returned Message will be used to handle new fields and new
+  // submessages. The caller should not take ownership of the returned pointer.
+  virtual Message* AddChild(const std::string& name) { return nullptr; }
+
+  // Called when a submessage is completed, that is, whenever a '}' is found.
+  virtual void Finish() {}
+
+ protected:
+  // Takes ownership of the given pointer. Subclasses can use this method if
+  // they don't want to implement their own ownership semantics.
+  Message* Store(Message* n) {
+    children_.emplace_back(n);
+    return n;
+  }
+
+  // Returns a list of all owned submessages.
+  const std::vector<std::unique_ptr<Message>>& Children() const {
+    return children_;
+  }
+
+ private:
+  std::vector<std::unique_ptr<Message>> children_;
+};
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TESTING_MESSAGE_H_
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/models.tar b/tensorflow/lite/testing/nnapi_tflite_zip_tests/models.tar
new file mode 100644
index 00000000000..a172f1f361f
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/models.tar
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/not_supported.txt b/tensorflow/lite/testing/nnapi_tflite_zip_tests/not_supported.txt
new file mode 100644
index 00000000000..96cca6a7a60
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/not_supported.txt
@@ -0,0 +1,536 @@
+abs/abs_input_shape=[]
+abs/abs_input_shape=[1]
+abs/abs_input_shape=[2,3]
+abs/abs_input_shape=[1,1,1,1]
+abs/abs_input_shape=[1,3,4,3]
+abs/abs_input_shape=[3,15,14,3]
+abs/abs_input_shape=[3,1,2,4,6]
+abs/abs_input_shape=[2,2,3,4,5,6]
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[5,5],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[5,5],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[5,5],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[5,5],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[10],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[10],is_arg_max=True,output_type=tf.int64
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[10],is_arg_max=True,output_type=tf.int32
+arg_min_max/arg_min_max_input_dtype=tf.int32,input_shape=[10],is_arg_max=True,output_type=tf.int64
+batch_to_space_nd/batch_to_space_nd_block_shape=[1,4],constant_block_shape=True,constant_crops=True,crops=[[0,0],[0,0]],dtype=tf.float32,input_shape=[12,3,3,1]
+batch_to_space_nd/batch_to_space_nd_block_shape=[1,4],constant_block_shape=True,constant_crops=False,crops=[[0,0],[0,0]],dtype=tf.float32,input_shape=[12,3,3,1]
+batch_to_space_nd/batch_to_space_nd_block_shape=[1,4],constant_block_shape=False,constant_crops=True,crops=[[0,0],[0,0]],dtype=tf.float32,input_shape=[12,3,3,1]
+batch_to_space_nd/batch_to_space_nd_block_shape=[1,4],constant_block_shape=False,constant_crops=False,crops=[[0,0],[0,0]],dtype=tf.float32,input_shape=[12,3,3,1]
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+mean/mean_axis=[-3,-3,-4],const_axis=True,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=False
+mean/mean_axis=[-3,-3,-4],const_axis=False,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=True
+mean/mean_axis=[-3,-3,-4],const_axis=False,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=False
+mean/mean_axis=[-3,2,1],const_axis=True,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=True
+mean/mean_axis=[-3,2,1],const_axis=True,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=False
+mean/mean_axis=[-3,2,1],const_axis=False,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=True
+mean/mean_axis=[-3,2,1],const_axis=False,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=False
+mean/mean_axis=[],const_axis=False,input_dtype=tf.float32,input_shape=[],keepdims=True
+mean/mean_axis=[],const_axis=False,input_dtype=tf.float32,input_shape=[],keepdims=False
+mean/mean_axis=[],const_axis=False,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=True
+mean/mean_axis=[],const_axis=False,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=False
+mean/mean_axis=[],const_axis=False,input_dtype=tf.float32,input_shape=[3,2,4],keepdims=True
+mean/mean_axis=[],const_axis=False,input_dtype=tf.float32,input_shape=[3,2,4],keepdims=False
+mean/mean_axis=None,const_axis=True,input_dtype=tf.float32,input_shape=[],keepdims=True
+mean/mean_axis=None,const_axis=True,input_dtype=tf.float32,input_shape=[],keepdims=False
+mean/mean_axis=None,const_axis=True,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=True
+mean/mean_axis=None,const_axis=True,input_dtype=tf.float32,input_shape=[1,8,8,3],keepdims=False
+mean/mean_axis=None,const_axis=True,input_dtype=tf.float32,input_shape=[3,2,4],keepdims=True
+mean/mean_axis=None,const_axis=True,input_dtype=tf.float32,input_shape=[3,2,4],keepdims=False
\ No newline at end of file
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/parse_testdata.cc b/tensorflow/lite/testing/nnapi_tflite_zip_tests/parse_testdata.cc
new file mode 100644
index 00000000000..a525e1122e3
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/parse_testdata.cc
@@ -0,0 +1,366 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+// Parses tflite example input data.
+// Format is ASCII
+// TODO(aselle): Switch to protobuf, but the android team requested a simple
+// ASCII file.
+#include "parse_testdata.h"
+
+#include <cinttypes>
+#include <cmath>
+#include <cstdint>
+#include <cstdio>
+#include <fstream>
+#include <iostream>
+#include <streambuf>
+
+#include "tensorflow/lite/error_reporter.h"
+#include "message.h"
+#include "split.h"
+
+namespace tflite {
+namespace testing {
+namespace {
+
+// Fatal error if parse error occurs
+#define PARSE_CHECK_EQ(filename, current_line, x, y)                         \
+  if ((x) != (y)) {                                                          \
+    fprintf(stderr, "Parse Error @ %s:%d\n  File %s\n  Line %d, %s != %s\n", \
+            __FILE__, __LINE__, filename, current_line + 1, #x, #y);         \
+    return kTfLiteError;                                                     \
+  }
+
+// Breakup a "," delimited line into a std::vector<std::string>.
+// This is extremely inefficient, and just used for testing code.
+// TODO(aselle): replace with absl when we use it.
+std::vector<std::string> ParseLine(const std::string& line) {
+  size_t pos = 0;
+  std::vector<std::string> elements;
+  while (true) {
+    size_t end = line.find(',', pos);
+    if (end == std::string::npos) {
+      elements.push_back(line.substr(pos));
+      break;
+    } else {
+      elements.push_back(line.substr(pos, end - pos));
+    }
+    pos = end + 1;
+  }
+  return elements;
+}
+
+}  // namespace
+
+// Given a `filename`, produce a vector of Examples corresopnding
+// to test cases that can be applied to a tflite model.
+TfLiteStatus ParseExamples(const char* filename,
+                           std::vector<Example>* examples) {
+  std::ifstream fp(filename);
+  if (!fp.good()) {
+    fprintf(stderr, "Could not read '%s'\n", filename);
+    return kTfLiteError;
+  }
+  std::string str((std::istreambuf_iterator<char>(fp)),
+                  std::istreambuf_iterator<char>());
+  size_t pos = 0;
+
+  // \n and , delimit parse a file.
+  std::vector<std::vector<std::string>> csv;
+  while (true) {
+    size_t end = str.find('\n', pos);
+
+    if (end == std::string::npos) {
+      csv.emplace_back(ParseLine(str.substr(pos)));
+      break;
+    }
+    csv.emplace_back(ParseLine(str.substr(pos, end - pos)));
+    pos = end + 1;
+  }
+
+  int current_line = 0;
+  PARSE_CHECK_EQ(filename, current_line, csv[0][0], "test_cases");
+  int example_count = std::stoi(csv[0][1]);
+  current_line++;
+
+  auto parse_tensor = [&filename, &current_line,
+                       &csv](FloatTensor* tensor_ptr) {
+    PARSE_CHECK_EQ(filename, current_line, csv[current_line][0], "dtype");
+    current_line++;
+    // parse shape
+    PARSE_CHECK_EQ(filename, current_line, csv[current_line][0], "shape");
+    size_t elements = 1;
+    FloatTensor& tensor = *tensor_ptr;
+
+    for (size_t i = 1; i < csv[current_line].size(); i++) {
+      const auto& shape_part_to_parse = csv[current_line][i];
+      if (shape_part_to_parse.empty()) {
+        // Case of a 0-dimensional shape
+        break;
+      }
+      int shape_part = std::stoi(shape_part_to_parse);
+      elements *= shape_part;
+      tensor.shape.push_back(shape_part);
+    }
+    current_line++;
+    // parse data
+    PARSE_CHECK_EQ(filename, current_line, csv[current_line].size() - 1,
+                   elements);
+    for (size_t i = 1; i < csv[current_line].size(); i++) {
+      tensor.flat_data.push_back(std::stof(csv[current_line][i]));
+    }
+    current_line++;
+
+    return kTfLiteOk;
+  };
+
+  for (int example_idx = 0; example_idx < example_count; example_idx++) {
+    Example example;
+    PARSE_CHECK_EQ(filename, current_line, csv[current_line][0], "inputs");
+    int inputs = std::stoi(csv[current_line][1]);
+    current_line++;
+    // parse dtype
+    for (int input_index = 0; input_index < inputs; input_index++) {
+      example.inputs.push_back(FloatTensor());
+      TF_LITE_ENSURE_STATUS(parse_tensor(&example.inputs.back()));
+    }
+
+    PARSE_CHECK_EQ(filename, current_line, csv[current_line][0], "outputs");
+    int outputs = std::stoi(csv[current_line][1]);
+    current_line++;
+    for (int input_index = 0; input_index < outputs; input_index++) {
+      example.outputs.push_back(FloatTensor());
+      TF_LITE_ENSURE_STATUS(parse_tensor(&example.outputs.back()));
+    }
+    examples->emplace_back(example);
+  }
+  return kTfLiteOk;
+}
+
+TfLiteStatus FeedExample(tflite::Interpreter* interpreter,
+                         const Example& example) {
+  // Resize inputs to match example & allocate.
+  for (size_t i = 0; i < interpreter->inputs().size(); i++) {
+    int input_index = interpreter->inputs()[i];
+
+    TF_LITE_ENSURE_STATUS(
+        interpreter->ResizeInputTensor(input_index, example.inputs[i].shape));
+  }
+  TF_LITE_ENSURE_STATUS(interpreter->AllocateTensors());
+  // Copy data into tensors.
+  for (size_t i = 0; i < interpreter->inputs().size(); i++) {
+    int input_index = interpreter->inputs()[i];
+    if (float* data = interpreter->typed_tensor<float>(input_index)) {
+      for (size_t idx = 0; idx < example.inputs[i].flat_data.size(); idx++) {
+        data[idx] = example.inputs[i].flat_data[idx];
+      }
+    } else if (int32_t* data =
+                   interpreter->typed_tensor<int32_t>(input_index)) {
+      for (size_t idx = 0; idx < example.inputs[i].flat_data.size(); idx++) {
+        data[idx] = example.inputs[i].flat_data[idx];
+      }
+    } else if (int64_t* data =
+                   interpreter->typed_tensor<int64_t>(input_index)) {
+      for (size_t idx = 0; idx < example.inputs[i].flat_data.size(); idx++) {
+        data[idx] = example.inputs[i].flat_data[idx];
+      }
+    } else {
+      fprintf(stderr, "input[%zu] was not float or int data\n", i);
+      return kTfLiteError;
+    }
+  }
+  return kTfLiteOk;
+}
+
+TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter,
+                          const Example& example) {
+  constexpr double kRelativeThreshold = 1e-2f;
+  constexpr double kAbsoluteThreshold = 1e-4f;
+
+  ErrorReporter* context = DefaultErrorReporter();
+  int model_outputs = interpreter->outputs().size();
+  TF_LITE_ENSURE_EQ(context, model_outputs, example.outputs.size());
+  for (size_t i = 0; i < interpreter->outputs().size(); i++) {
+    bool tensors_differ = false;
+    int output_index = interpreter->outputs()[i];
+    if (const float* data = interpreter->typed_tensor<float>(output_index)) {
+      for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) {
+        float computed = data[idx];
+        float reference = example.outputs[0].flat_data[idx];
+        float diff = std::abs(computed - reference);
+        // For very small numbers, try absolute error, otherwise go with
+        // relative.
+        bool local_tensors_differ =
+            std::abs(reference) < kRelativeThreshold
+                ? diff > kAbsoluteThreshold
+                : diff > kRelativeThreshold * std::abs(reference);
+        if (local_tensors_differ) {
+          fprintf(stdout, "output[%zu][%zu] did not match %f vs reference %f\n",
+                  i, idx, data[idx], reference);
+          tensors_differ = local_tensors_differ;
+        }
+      }
+    } else if (const int32_t* data =
+                   interpreter->typed_tensor<int32_t>(output_index)) {
+      for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) {
+        int32_t computed = data[idx];
+        int32_t reference = example.outputs[0].flat_data[idx];
+        if (std::abs(computed - reference) > 0) {
+          fprintf(stderr, "output[%zu][%zu] did not match %d vs reference %d\n",
+                  i, idx, computed, reference);
+          tensors_differ = true;
+        }
+      }
+    } else if (const int64_t* data =
+                   interpreter->typed_tensor<int64_t>(output_index)) {
+      for (size_t idx = 0; idx < example.outputs[i].flat_data.size(); idx++) {
+        int64_t computed = data[idx];
+        int64_t reference = example.outputs[0].flat_data[idx];
+        if (std::abs(computed - reference) > 0) {
+          fprintf(stderr,
+                  "output[%zu][%zu] did not match %" PRId64
+                  " vs reference %" PRId64 "\n",
+                  i, idx, computed, reference);
+          tensors_differ = true;
+        }
+      }
+    } else {
+      fprintf(stderr, "output[%zu] was not float or int data\n", i);
+      return kTfLiteError;
+    }
+    fprintf(stderr, "\n");
+    if (tensors_differ) return kTfLiteError;
+  }
+  return kTfLiteOk;
+}
+
+// Process an 'invoke' message, triggering execution of the test runner, as
+// well as verification of outputs. An 'invoke' message looks like:
+//   invoke {
+//     id: xyz
+//     input: 1,2,1,1,1,2,3,4
+//     output: 4,5,6
+//   }
+class Invoke : public Message {
+ public:
+  explicit Invoke(TestRunner* test_runner) : test_runner_(test_runner) {
+    expected_inputs_ = test_runner->GetInputs();
+    expected_outputs_ = test_runner->GetOutputs();
+  }
+
+  void SetField(const std::string& name, const std::string& value) override {
+    if (name == "id") {
+      test_runner_->SetInvocationId(value);
+    } else if (name == "input") {
+      if (expected_inputs_.empty()) {
+        return test_runner_->Invalidate("Too many inputs");
+      }
+      test_runner_->SetInput(*expected_inputs_.begin(), value);
+      expected_inputs_.erase(expected_inputs_.begin());
+    } else if (name == "output") {
+      if (expected_outputs_.empty()) {
+        return test_runner_->Invalidate("Too many outputs");
+      }
+      test_runner_->SetExpectation(*expected_outputs_.begin(), value);
+      expected_outputs_.erase(expected_outputs_.begin());
+    }
+  }
+  void Finish() override {
+    test_runner_->Invoke();
+    test_runner_->CheckResults();
+  }
+
+ private:
+  std::vector<int> expected_inputs_;
+  std::vector<int> expected_outputs_;
+
+  TestRunner* test_runner_;
+};
+
+// Process an 'reshape' message, triggering resizing of the input tensors via
+// the test runner. A 'reshape' message looks like:
+//   reshape {
+//     input: 1,2,1,1,1,2,3,4
+//   }
+class Reshape : public Message {
+ public:
+  explicit Reshape(TestRunner* test_runner) : test_runner_(test_runner) {
+    expected_inputs_ = test_runner->GetInputs();
+  }
+
+  void SetField(const std::string& name, const std::string& value) override {
+    if (name == "input") {
+      if (expected_inputs_.empty()) {
+        return test_runner_->Invalidate("Too many inputs to reshape");
+      }
+      test_runner_->ReshapeTensor(*expected_inputs_.begin(), value);
+      expected_inputs_.erase(expected_inputs_.begin());
+    }
+  }
+
+ private:
+  std::vector<int> expected_inputs_;
+  TestRunner* test_runner_;
+};
+
+// This is the top-level message in a test file.
+class TestData : public Message {
+ public:
+  explicit TestData(TestRunner* test_runner)
+      : test_runner_(test_runner), num_invocations_(0), max_invocations_(-1) {}
+  void SetMaxInvocations(int max) { max_invocations_ = max; }
+  void SetField(const std::string& name, const std::string& value) override {
+    if (name == "load_model") {
+      test_runner_->LoadModel(value);
+    } else if (name == "init_state") {
+      test_runner_->AllocateTensors();
+      for (int id : Split<int>(value, ",")) {
+        test_runner_->ResetTensor(id);
+      }
+    }
+  }
+  Message* AddChild(const std::string& s) override {
+    if (s == "invoke") {
+      test_runner_->AllocateTensors();
+      if (max_invocations_ == -1 || num_invocations_ < max_invocations_) {
+        ++num_invocations_;
+        return Store(new Invoke(test_runner_));
+      } else {
+        return nullptr;
+      }
+    } else if (s == "reshape") {
+      return Store(new Reshape(test_runner_));
+    }
+    return nullptr;
+  }
+
+ private:
+  TestRunner* test_runner_;
+  int num_invocations_;
+  int max_invocations_;
+};
+
+bool ParseAndRunTests(std::istream* input, TestRunner* test_runner,
+                      int max_invocations) {
+  TestData test_data(test_runner);
+  test_data.SetMaxInvocations(max_invocations);
+  Message::Read(input, &test_data);
+  return test_runner->IsValid() && test_runner->GetOverallSuccess();
+}
+
+}  // namespace testing
+}  // namespace tflite
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/parse_testdata.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/parse_testdata.h
new file mode 100644
index 00000000000..0220a098df5
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/parse_testdata.h
@@ -0,0 +1,78 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_PARSE_TESTDATA_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_PARSE_TESTDATA_H_
+
+#include <vector>
+#include "tensorflow/lite/interpreter.h"
+#include "test_runner.h"
+
+namespace tflite {
+namespace testing {
+
+// Shape and data for a float tensor
+struct FloatTensor {
+  std::vector<int> shape;
+  std::vector<float> flat_data;
+};
+
+// A prescribed input, output example
+struct Example {
+  std::vector<FloatTensor> inputs;
+  std::vector<FloatTensor> outputs;
+};
+
+// Parses an example input and output file (used for unit tests)
+TfLiteStatus ParseExamples(const char* filename,
+                           std::vector<Example>* examples);
+
+// Inputs Tensors into a TensorFlow lite interpreter. Note, this will run
+// interpreter.AllocateTensors();
+TfLiteStatus FeedExample(tflite::Interpreter* interpreter, const Example&);
+
+// Check outputs against (already) evaluated result.
+TfLiteStatus CheckOutputs(tflite::Interpreter* interpreter, const Example&);
+
+// Parses a test description and feeds the given test runner with data.
+// The input format is similar to an ASCII proto:
+//   // Loads model 'add.bin' from the TestRunner's model directory.
+//   load_model: "add.bin"
+//   // Changes the shape of inputs, provided in the same order they appear
+//   // in the model.
+//   reshape {
+//     input: "1,224,224,3"
+//     input: "1,3,4,1"
+//   }
+//   // Fills the given persistent tensors with zeros.
+//   init_state: 0,1,2,3
+//   // Invokes the interpreter with the given input and checks that it
+//   // produces the expected output. Inputs and outputs should be specified in
+//   // the order they appear in the model.
+//   invoke {
+//     input: "1,2,3,4,56"
+//     input: "0.1,0.2,0.3,4.3,56.4"
+//     output: "12,3,4,545,3"
+//     output: "0.01,0.02"
+//   }
+bool ParseAndRunTests(std::istream* input, TestRunner* test_runner,
+                      int max_invocations = -1);
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TESTING_ANDROID_PARSE_TESTDATA_H_
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/split.cc b/tensorflow/lite/testing/nnapi_tflite_zip_tests/split.cc
new file mode 100644
index 00000000000..eb1b15da7cc
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/split.cc
@@ -0,0 +1,45 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#include "split.h"
+
+namespace tflite {
+namespace testing {
+
+std::vector<std::pair<size_t, size_t>> SplitToPos(const string& s,
+                                                  const string& delimiter) {
+  std::vector<std::pair<size_t, size_t>> fields;
+  if (delimiter.length() == 0) {
+    fields.emplace_back(0, s.length());
+    return fields;
+  }
+  size_t pos = 0;
+  size_t start = 0;
+  while ((pos = s.find(delimiter, start)) != string::npos) {
+    if (pos != start) {
+      fields.emplace_back(start, pos);
+    }
+    start = pos + delimiter.length();
+  }
+  if (start != s.length()) {
+    fields.emplace_back(start, s.length());
+  }
+  return fields;
+}
+
+}  // namespace testing
+}  // namespace tflite
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/split.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/split.h
new file mode 100644
index 00000000000..cdb53817d43
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/split.h
@@ -0,0 +1,99 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_SPLIT_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_SPLIT_H_
+
+#include <cstdlib>
+#include <string>
+#include <utility>
+#include <vector>
+#include "tensorflow/lite/string_tflite.h"
+
+namespace tflite {
+namespace testing {
+
+// Splits a string based on the given delimiter string. Each pair in the
+// returned vector has the start and past-the-end positions for each of the
+// parts of the original string. Empty fields are not represented in the
+// output.
+std::vector<std::pair<size_t, size_t>> SplitToPos(const string& s,
+                                                  const string& delimiter);
+
+// Splits the given string and converts each part to the given T.
+template <typename T>
+std::vector<T> Split(const string& s, const string& delimiter);
+
+template <>
+inline std::vector<string> Split(const string& s, const string& delimiter) {
+  std::vector<string> fields;
+  for (const auto& p : SplitToPos(s, delimiter)) {
+    fields.push_back(s.substr(p.first, p.second - p.first));
+  }
+  return fields;
+}
+
+template <>
+inline std::vector<int> Split(const string& s, const string& delimiter) {
+  std::vector<int> fields;
+  for (const auto& p : SplitToPos(s, delimiter)) {
+    fields.push_back(strtol(s.data() + p.first, nullptr, 10));
+  }
+  return fields;
+}
+
+template <>
+inline std::vector<int64_t> Split(const string& s, const string& delimiter) {
+  std::vector<int64_t> fields;
+  for (const auto& p : SplitToPos(s, delimiter)) {
+    fields.push_back(strtoll(s.data() + p.first, nullptr, 10));
+  }
+  return fields;
+}
+
+template <>
+inline std::vector<float> Split(const string& s, const string& delimiter) {
+  std::vector<float> fields;
+  for (const auto& p : SplitToPos(s, delimiter)) {
+    fields.push_back(strtod(s.data() + p.first, nullptr));
+  }
+  return fields;
+}
+
+template <>
+inline std::vector<uint8_t> Split(const string& s, const string& delimiter) {
+  std::vector<uint8_t> fields;
+  for (const auto& p : SplitToPos(s, delimiter)) {
+    fields.push_back(strtol(s.data() + p.first, nullptr, 10));
+  }
+  return fields;
+}
+
+template <>
+inline std::vector<bool> Split(const string& s, const string& delimiter) {
+  std::vector<bool> fields;
+  for (const auto& p : SplitToPos(s, delimiter)) {
+    fields.push_back(
+        static_cast<bool>(strtol(s.data() + p.first, nullptr, 10)));
+  }
+  return fields;
+}
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TESTING_ANDROID_SPLIT_H_
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/test_manifest.txt b/tensorflow/lite/testing/nnapi_tflite_zip_tests/test_manifest.txt
new file mode 100644
index 00000000000..ff0d85d71ad
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/test_manifest.txt
@@ -0,0 +1,2105 @@
+add/add_activation=True,dtype=tf.float32,input_shape_1=[1,3,4,3],input_shape_2=[1,3,4,3]
+DISABLED_add/add_activation=True,dtype=tf.int32,input_shape_1=[1,3,4,3],input_shape_2=[1,3,4,3]
+add/add_activation=False,dtype=tf.float32,input_shape_1=[5],input_shape_2=[5]
+add/add_activation=True,dtype=tf.float32,input_shape_1=[5],input_shape_2=[5]
+add/add_activation=True,dtype=tf.float32,input_shape_1=[1,3,4,3],input_shape_2=[3]
+add/add_activation=False,dtype=tf.float32,input_shape_1=[1,3,4,3],input_shape_2=[3]
+DISABLED_add/add_activation=True,dtype=tf.int32,input_shape_1=[1,3,4,3],input_shape_2=[3]
+DISABLED_add/add_activation=False,dtype=tf.int32,input_shape_1=[1,3,4,3],input_shape_2=[3]
+DISABLED_add/add_activation=True,dtype=tf.int64,input_shape_1=[1,3,4,3],input_shape_2=[3]
+DISABLED_add/add_activation=False,dtype=tf.int64,input_shape_1=[1,3,4,3],input_shape_2=[3]
+add/add_activation=True,dtype=tf.float32,input_shape_1=[3],input_shape_2=[1,3,4,3]
+add/add_activation=False,dtype=tf.float32,input_shape_1=[3],input_shape_2=[1,3,4,3]
+DISABLED_add/add_activation=True,dtype=tf.int32,input_shape_1=[3],input_shape_2=[1,3,4,3]
+DISABLED_add/add_activation=False,dtype=tf.int32,input_shape_1=[3],input_shape_2=[1,3,4,3]
+DISABLED_add/add_activation=False,dtype=tf.float32,input_shape_1=[],input_shape_2=[]
+DISABLED_add/add_activation=False,dtype=tf.float32,input_shape_1=[0],input_shape_2=[1]
+avg_pool/avg_pool_data_format='NHWC',input_shape=[1,1,1,1],ksize=[1,1,1,1],padding='SAME',strides=[1,1,1,1]
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+depthwiseconv/depthwiseconv_channel_multiplier=2,constant_filter=True,data_format='NHWC',dilations=[1,2,2,1],filter_size=[3,3],input_shape=[1,10,10,3],padding='SAME',rate=[1,1],strides=[1,1,1,1]
+depthwiseconv/depthwiseconv_channel_multiplier=2,constant_filter=True,data_format='NHWC',dilations=[1,2,2,1],filter_size=[3,3],input_shape=[1,10,10,3],padding='SAME',rate=[1,1],strides=[1,3,3,1]
+depthwiseconv/depthwiseconv_channel_multiplier=2,constant_filter=True,data_format='NHWC',dilations=[1,2,2,1],filter_size=[3,3],input_shape=[1,10,10,3],padding='VALID',rate=[1,1],strides=[1,1,1,1]
+depthwiseconv/depthwiseconv_channel_multiplier=2,constant_filter=True,data_format='NHWC',dilations=[1,2,2,1],filter_size=[3,3],input_shape=[1,10,10,3],padding='VALID',rate=[1,1],strides=[1,3,3,1]
+depthwiseconv/depthwiseconv_channel_multiplier=2,constant_filter=False,data_format='NHWC',dilations=[1,2,2,1],filter_size=[3,3],input_shape=[1,3,4,3],padding='SAME',rate=[1,1],strides=[1,1,1,1]
+depthwiseconv/depthwiseconv_channel_multiplier=2,constant_filter=False,data_format='NHWC',dilations=[1,2,2,1],filter_size=[3,3],input_shape=[1,3,4,3],padding='SAME',rate=[1,1],strides=[1,3,3,1]
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+depthwiseconv/depthwiseconv_channel_multiplier=2,constant_filter=False,data_format='NHWC',dilations=[1,2,2,1],filter_size=[3,3],input_shape=[1,10,10,3],padding='VALID',rate=[1,1],strides=[1,1,1,1]
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+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=None,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=None,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=None,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=None,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=None,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=None,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=None,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=None,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=2,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=2,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=2,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=1,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=None,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=None,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=None,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=2,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=2,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=2,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=3,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=None,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=1,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_strided_slice/strided_slice_begin=[0,-1],begin_mask=2,constant_indices=False,dtype=tf.float32,end=[2,-3],end_mask=2,index_type=tf.int32,input_shape=[2,3],shrink_axis_mask=-1,strides=[1,-1]
+DISABLED_transpose/transpose_constant_perm=True,dtype=tf.int32,input_shape=[2,2,3],perm=[0,1,2]
+DISABLED_transpose/transpose_constant_perm=True,dtype=tf.int32,input_shape=[2,2,3],perm=[0,2,1]
+DISABLED_transpose/transpose_constant_perm=True,dtype=tf.int64,input_shape=[2,2,3],perm=[0,1,2]
+DISABLED_transpose/transpose_constant_perm=True,dtype=tf.int64,input_shape=[2,2,3],perm=[0,2,1]
+transpose/transpose_constant_perm=True,dtype=tf.float32,input_shape=[2,2,3],perm=[0,1,2]
+transpose/transpose_constant_perm=True,dtype=tf.float32,input_shape=[2,2,3],perm=[0,2,1]
+transpose/transpose_constant_perm=True,dtype=tf.float32,input_shape=[1,2,3,4],perm=[0,1,2,3]
+transpose/transpose_constant_perm=True,dtype=tf.float32,input_shape=[1,2,3,4],perm=[3,0,1,2]
+DISABLED_transpose/transpose_constant_perm=True,dtype=tf.float32,input_shape=[1,2,3,4,5],perm=[4,3,2,1,0]
+lstm/lstm_dtype=tf.float32,input_vec_size=3,num_batchs=1,num_cells=4,split_tflite_lstm_inputs=False,time_step_size=1
\ No newline at end of file
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/test_runner.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/test_runner.h
new file mode 100644
index 00000000000..1a8f7659c7d
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/test_runner.h
@@ -0,0 +1,133 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_TEST_RUNNER_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_TEST_RUNNER_H_
+
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+#include "tensorflow/lite/string_tflite.h"
+
+namespace tflite {
+namespace testing {
+
+// This is the base class for processing test data. Each one of the virtual
+// methods must be implemented to forward the data to the appropriate executor
+// (e.g. TF Lite's interpreter, or the NNAPI).
+class TestRunner {
+ public:
+  TestRunner() {}
+  virtual ~TestRunner() {}
+
+  // Load the given model, as a path relative to SetModelBaseDir().
+  virtual void LoadModel(const string& bin_file_path) = 0;
+
+  // Return the list of input tensors in the loaded model.
+  virtual const std::vector<int>& GetInputs() = 0;
+
+  // Return the list of output tensors in the loaded model.
+  virtual const std::vector<int>& GetOutputs() = 0;
+
+  // Prepare for a run by resize the given tensor. The given 'id' is
+  // guaranteed to be one of the ids returned by GetInputs().
+  virtual void ReshapeTensor(int id, const string& csv_values) = 0;
+
+  // Reserve memory for all tensors.
+  virtual void AllocateTensors() = 0;
+
+  // Set the given tensor to some initial state, usually zero. This is
+  // used to reset persistent buffers in a model.
+  virtual void ResetTensor(int id) = 0;
+
+  // Define the contents of the given input tensor. The given 'id' is
+  // guaranteed to be one of the ids returned by GetInputs().
+  virtual void SetInput(int id, const string& values_as_string) = 0;
+
+  // Define what should be expected for an output tensor after Invoke() runs.
+  // The given 'id' is guaranteed to be one of the ids returned by
+  // GetOutputs().
+  virtual void SetExpectation(int id, const string& values_as_string) = 0;
+
+  // Run the model.
+  virtual void Invoke() = 0;
+
+  // Verify that the contents of all outputs conform to the existing
+  // expectations. Return true if there are no expectations or they are all
+  // satisfied.
+  virtual bool CheckResults() = 0;
+
+  // Read contents of tensor into csv format.
+  // The given 'id' is guaranteed to be one of the ids returned by GetOutputs().
+  virtual string ReadOutput(int id) = 0;
+
+  // Set the base path for loading models.
+  void SetModelBaseDir(const string& path) {
+    model_base_dir_ = path;
+    if (path[path.length() - 1] != '/') {
+      model_base_dir_ += "/";
+    }
+  }
+
+  // Return the full path of a model.
+  string GetFullPath(const string& path) { return model_base_dir_ + path; }
+
+  // Give an id to the next invocation to make error reporting more meaningful.
+  void SetInvocationId(const string& id) { invocation_id_ = id; }
+  const string& GetInvocationId() const { return invocation_id_; }
+
+  // Invalidate the test runner, preventing it from executing any further.
+  void Invalidate(const string& error_message) {
+    std::cerr << error_message << std::endl;
+    error_message_ = error_message;
+  }
+  bool IsValid() const { return error_message_.empty(); }
+  const string& GetErrorMessage() const { return error_message_; }
+
+  // Handle the overall success of this test runner. This will be true if all
+  // invocations were successful.
+  void SetOverallSuccess(bool value) { overall_success_ = value; }
+  bool GetOverallSuccess() const { return overall_success_; }
+
+ protected:
+  // A helper to check of the given number of values is consistent with the
+  // number of bytes in a tensor of type T. When incompatibles sizes are found,
+  // the test runner is invalidated and false is returned.
+  template <typename T>
+  bool CheckSizes(size_t tensor_bytes, size_t num_values) {
+    size_t num_tensor_elements = tensor_bytes / sizeof(T);
+    if (num_tensor_elements != num_values) {
+      Invalidate("Expected '" + std::to_string(num_tensor_elements) +
+                 "' elements for a tensor, but only got '" +
+                 std::to_string(num_values) + "'");
+      return false;
+    }
+    return true;
+  }
+
+ private:
+  string model_base_dir_;
+  string invocation_id_;
+  bool overall_success_ = true;
+
+  string error_message_;
+};
+
+}  // namespace testing
+}  // namespace tflite
+#endif  // TENSORFLOW_LITE_TESTING_ANDROID_TEST_RUNNER_H_
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/tflite_driver.cc b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tflite_driver.cc
new file mode 100644
index 00000000000..86f55cfcccc
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tflite_driver.cc
@@ -0,0 +1,412 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#include "tflite_driver.h"
+
+#include <iostream>
+
+#include "tensorflow/lite/builtin_op_data.h"
+#include "tensorflow/lite/delegates/nnapi/nnapi_delegate.h"
+#include "tensorflow/lite/kernels/register.h"
+#include "tensorflow/lite/kernels/register_ref.h"
+#include "tensorflow/lite/string_util.h"
+#include "join.h"
+#include "split.h"
+
+namespace tflite {
+namespace testing {
+
+namespace {
+
+// Returns the value in the given position in a tensor.
+template <typename T>
+T Value(const TfLitePtrUnion& data, int index);
+template <>
+float Value(const TfLitePtrUnion& data, int index) {
+  return data.f[index];
+}
+template <>
+int32_t Value(const TfLitePtrUnion& data, int index) {
+  return data.i32[index];
+}
+template <>
+int64_t Value(const TfLitePtrUnion& data, int index) {
+  return data.i64[index];
+}
+template <>
+uint8_t Value(const TfLitePtrUnion& data, int index) {
+  return data.uint8[index];
+}
+template <>
+bool Value(const TfLitePtrUnion& data, int index) {
+  return data.b[index];
+}
+
+template <typename T>
+void SetTensorData(const std::vector<T>& values, TfLitePtrUnion* data) {
+  T* input_ptr = reinterpret_cast<T*>(data->raw);
+  for (const T& v : values) {
+    *input_ptr = v;
+    ++input_ptr;
+  }
+}
+
+}  // namespace
+
+class TfLiteDriver::Expectation {
+ public:
+  Expectation() {
+    data_.raw = nullptr;
+    num_elements_ = 0;
+  }
+  ~Expectation() { delete[] data_.raw; }
+  template <typename T>
+  void SetData(const string& csv_values) {
+    const auto& values = testing::Split<T>(csv_values, ",");
+    num_elements_ = values.size();
+    data_.raw = new char[num_elements_ * sizeof(T)];
+    SetTensorData(values, &data_);
+  }
+
+  bool Check(bool verbose, const TfLiteTensor& tensor);
+
+ private:
+  template <typename T>
+  bool TypedCheck(bool verbose, const TfLiteTensor& tensor) {
+    // TODO(ahentz): must find a way to configure the tolerance.
+    constexpr double kRelativeThreshold = 1e-2f;
+    constexpr double kAbsoluteThreshold = 1e-4f;
+
+    size_t tensor_size = tensor.bytes / sizeof(T);
+
+    if (tensor_size != num_elements_) {
+      std::cerr << "Expected a tensor with " << num_elements_
+                << " elements, got " << tensor_size << std::endl;
+      std::cerr << "while checking tensor " << tensor.name << std::endl;
+      return false;
+    }
+
+    bool good_output = true;
+    for (int i = 0; i < tensor_size; ++i) {
+      float computed = Value<T>(tensor.data, i);
+      float reference = Value<T>(data_, i);
+      float diff = std::abs(computed - reference);
+      bool error_is_large = false;
+      // For very small numbers, try absolute error, otherwise go with
+      // relative.
+      if (std::abs(reference) < kRelativeThreshold) {
+        error_is_large = (diff > kAbsoluteThreshold);
+      } else {
+        error_is_large = (diff > kRelativeThreshold * std::abs(reference));
+      }
+      if (error_is_large) {
+        good_output = false;
+        if (verbose) {
+          std::cerr << "  index " << i << ": got " << computed
+                    << ", but expected " << reference << std::endl;
+        }
+      }
+    }
+    return good_output;
+  }
+
+  TfLitePtrUnion data_;
+  size_t num_elements_;
+};
+
+template <>
+void TfLiteDriver::Expectation::SetData<string>(const string& csv_values) {
+  std::cerr << "String tensor not supported!" << std::endl;
+}
+
+template <>
+bool TfLiteDriver::Expectation::TypedCheck<string>(bool verbose,
+                                                   const TfLiteTensor& tensor) {
+  if (tensor.data.raw == nullptr) {
+    if (verbose) {
+      std::cerr << "  got empty string" << std::endl;
+    }
+    return false;
+  }
+  int expected_num_strings = GetStringCount(data_.raw);
+  int returned_num_strings = GetStringCount(tensor.data.raw);
+  if (expected_num_strings != returned_num_strings) {
+    if (verbose) {
+      std::cerr << "  string count differ: got " << returned_num_strings
+                << ", but expected " << expected_num_strings << std::endl;
+    }
+    return false;
+  }
+  for (int i = 0; i < returned_num_strings; ++i) {
+    auto expected_ref = GetString(data_.raw, i);
+    auto returned_ref = GetString(tensor.data.raw, i);
+    if (expected_ref.len != returned_ref.len) {
+      if (verbose) {
+        std::cerr << "  index " << i << ": got string of size "
+                  << returned_ref.len << ", but expected size "
+                  << expected_ref.len << std::endl;
+      }
+      return false;
+    }
+    if (strncmp(expected_ref.str, returned_ref.str, returned_ref.len) != 0) {
+      if (verbose) {
+        std::cerr << "  index " << i << ": strings are different" << std::endl;
+      }
+      return false;
+    }
+  }
+
+  return true;
+}
+
+bool TfLiteDriver::Expectation::Check(bool verbose,
+                                      const TfLiteTensor& tensor) {
+  switch (tensor.type) {
+    case kTfLiteFloat32:
+      return TypedCheck<float>(verbose, tensor);
+    case kTfLiteInt32:
+      return TypedCheck<int32_t>(verbose, tensor);
+    case kTfLiteInt64:
+      return TypedCheck<int64_t>(verbose, tensor);
+    case kTfLiteUInt8:
+      return TypedCheck<uint8_t>(verbose, tensor);
+    case kTfLiteBool:
+      return TypedCheck<bool>(verbose, tensor);
+    case kTfLiteString:
+      return TypedCheck<string>(verbose, tensor);
+    default:
+      fprintf(stderr, "Unsupported type %d in Check\n", tensor.type);
+      return false;
+  }
+}
+
+TfLiteDriver::TfLiteDriver(bool use_nnapi, const string& delegate_name,
+                           bool reference_kernel)
+    : use_nnapi_(use_nnapi) {
+  if (reference_kernel) {
+    resolver_.reset(new ops::builtin::BuiltinRefOpResolver);
+  } else {
+    resolver_.reset(new ops::builtin::BuiltinOpResolver);
+  }
+#if 0
+  if (delegate_name == "FLEX") {
+    delegate_ = FlexDelegate::Create();
+  }
+#endif
+}
+
+TfLiteDriver::~TfLiteDriver() {
+  for (auto t : tensors_to_deallocate_) {
+    DeallocateStringTensor(t.second);
+  }
+  interpreter_.reset();
+}
+
+void TfLiteDriver::AllocateTensors() {
+  if (must_allocate_tensors_) {
+    if (interpreter_->AllocateTensors() != kTfLiteOk) {
+      Invalidate("Failed to allocate tensors");
+      return;
+    }
+    ResetLSTMStateTensors();
+    must_allocate_tensors_ = false;
+  }
+}
+
+void TfLiteDriver::LoadModel(const string& bin_file_path) {
+  if (!IsValid()) return;
+
+  model_ = FlatBufferModel::BuildFromFile(GetFullPath(bin_file_path).c_str());
+  if (!model_) {
+    Invalidate("Failed to mmap model " + bin_file_path);
+    return;
+  }
+  InterpreterBuilder(*model_, *resolver_)(&interpreter_);
+  if (!interpreter_) {
+    Invalidate("Failed build interpreter");
+    return;
+  }
+#if 0
+  if (delegate_) {
+    if (interpreter_->ModifyGraphWithDelegate(delegate_.get()) != kTfLiteOk) {
+      Invalidate("Unable to the build graph using the delegate");
+      return;
+    }
+  }
+#endif
+
+  must_allocate_tensors_ = true;
+}
+
+void TfLiteDriver::ResetTensor(int id) {
+  if (!IsValid()) return;
+  auto* tensor = interpreter_->tensor(id);
+  memset(tensor->data.raw, 0, tensor->bytes);
+}
+
+void TfLiteDriver::ReshapeTensor(int id, const string& csv_values) {
+  if (!IsValid()) return;
+  if (interpreter_->ResizeInputTensor(
+          id, testing::Split<int>(csv_values, ",")) != kTfLiteOk) {
+    Invalidate("Failed to resize input tensor " + std::to_string(id));
+    return;
+  }
+  must_allocate_tensors_ = true;
+}
+
+void TfLiteDriver::SetInput(int id, const string& csv_values) {
+  if (!IsValid()) return;
+  auto* tensor = interpreter_->tensor(id);
+  switch (tensor->type) {
+    case kTfLiteFloat32: {
+      const auto& values = testing::Split<float>(csv_values, ",");
+      if (!CheckSizes<float>(tensor->bytes, values.size())) return;
+      SetTensorData(values, &tensor->data);
+      break;
+    }
+    case kTfLiteInt32: {
+      const auto& values = testing::Split<int32_t>(csv_values, ",");
+      if (!CheckSizes<int32_t>(tensor->bytes, values.size())) return;
+      SetTensorData(values, &tensor->data);
+      break;
+    }
+    case kTfLiteInt64: {
+      const auto& values = testing::Split<int64_t>(csv_values, ",");
+      if (!CheckSizes<int64_t>(tensor->bytes, values.size())) return;
+      SetTensorData(values, &tensor->data);
+      break;
+    }
+    case kTfLiteUInt8: {
+      const auto& values = testing::Split<uint8_t>(csv_values, ",");
+      if (!CheckSizes<uint8_t>(tensor->bytes, values.size())) return;
+      SetTensorData(values, &tensor->data);
+      break;
+    }
+    case kTfLiteBool: {
+      const auto& values = testing::Split<bool>(csv_values, ",");
+      if (!CheckSizes<bool>(tensor->bytes, values.size())) return;
+      SetTensorData(values, &tensor->data);
+      break;
+    }
+    case kTfLiteString: {
+      Invalidate("String tensor not supported!");
+      break;
+    }
+    default:
+      Invalidate("Unsupported tensor type!");
+      return;
+  }
+}
+
+void TfLiteDriver::SetExpectation(int id, const string& csv_values) {
+  if (!IsValid()) return;
+  auto* tensor = interpreter_->tensor(id);
+  if (expected_output_.count(id) != 0) {
+    Invalidate("Overridden expectation for tensor.");
+  }
+  expected_output_[id].reset(new Expectation);
+  switch (tensor->type) {
+    case kTfLiteFloat32:
+      expected_output_[id]->SetData<float>(csv_values);
+      break;
+    case kTfLiteInt32:
+      expected_output_[id]->SetData<int32_t>(csv_values);
+      break;
+    case kTfLiteInt64:
+      expected_output_[id]->SetData<int64_t>(csv_values);
+      break;
+    case kTfLiteUInt8:
+      expected_output_[id]->SetData<uint8_t>(csv_values);
+      break;
+    case kTfLiteBool:
+      expected_output_[id]->SetData<bool>(csv_values);
+      break;
+    case kTfLiteString:
+      expected_output_[id]->SetData<string>(csv_values);
+      break;
+    default:
+      Invalidate("Unsupported tensor type!");
+      return;
+  }
+}
+
+void TfLiteDriver::Invoke() {
+  if (!IsValid()) return;
+  if (use_nnapi_) {
+    if (interpreter_->ModifyGraphWithDelegate(NnApiDelegate()) != kTfLiteOk) {
+      Invalidate("Unable to the build graph using NNAPI delegate");
+    }
+  }
+  if (interpreter_->Invoke() != kTfLiteOk) {
+    Invalidate("Failed to invoke interpreter");
+  }
+}
+
+bool TfLiteDriver::CheckResults() {
+  if (!IsValid()) return false;
+  bool success = true;
+  for (const auto& p : expected_output_) {
+    int id = p.first;
+    auto* tensor = interpreter_->tensor(id);
+    if (!p.second->Check(/*verbose=*/false, *tensor)) {
+      // Do not invalidate anything here. Instead, simply output the
+      // differences and return false. Invalidating would prevent all
+      // subsequent invocations from running..
+      std::cerr << "There were errors in invocation '" << GetInvocationId()
+                << "', output tensor '" << id << "':" << std::endl;
+      p.second->Check(/*verbose=*/true, *tensor);
+      success = false;
+      SetOverallSuccess(false);
+    }
+  }
+  expected_output_.clear();
+  return success;
+}
+
+void TfLiteDriver::ResetLSTMStateTensors() {
+  interpreter_->ResetVariableTensors();
+}
+
+string TfLiteDriver::ReadOutput(int id) {
+  auto* tensor = interpreter_->tensor(id);
+  int num_elements = 1;
+
+  for (int i = 0; i < tensor->dims->size; ++i) {
+    num_elements *= tensor->dims->data[i];
+  }
+
+  switch (tensor->type) {
+    case kTfLiteFloat32:
+      return JoinDefault(tensor->data.f, num_elements, ",");
+    case kTfLiteInt32:
+      return JoinDefault(tensor->data.i32, num_elements, ",");
+    case kTfLiteInt64:
+      return JoinDefault(tensor->data.i64, num_elements, ",");
+    case kTfLiteUInt8:
+      return Join(tensor->data.uint8, num_elements, ",");
+    case kTfLiteInt8:
+      return JoinDefault(tensor->data.int8, num_elements, ",");
+    case kTfLiteBool:
+      return JoinDefault(tensor->data.b, num_elements, ",");
+    default:
+      Invalidate("Unsupported tensor type!");
+      return "";
+  }
+}
+
+}  // namespace testing
+}  // namespace tflite
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/tflite_driver.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tflite_driver.h
new file mode 100644
index 00000000000..b3834215d7a
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tflite_driver.h
@@ -0,0 +1,86 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_TFLITE_DRIVER_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_TFLITE_DRIVER_H_
+
+#include <map>
+#include <memory>
+
+#include "tensorflow/lite/interpreter.h"
+#include "tensorflow/lite/kernels/register.h"
+#include "tensorflow/lite/kernels/register_ref.h"
+#include "tensorflow/lite/model.h"
+#include "test_runner.h"
+
+namespace tflite {
+namespace testing {
+
+// A test runner that feeds inputs into TF Lite and verifies its outputs.
+class TfLiteDriver : public TestRunner {
+ public:
+  explicit TfLiteDriver(bool use_nnapi, const string& delegate = "",
+                        bool reference_kernel = false);
+  ~TfLiteDriver() override;
+
+  void LoadModel(const string& bin_file_path) override;
+  const std::vector<int>& GetInputs() override {
+    return interpreter_->inputs();
+  }
+  const std::vector<int>& GetOutputs() override {
+    return interpreter_->outputs();
+  }
+  void ReshapeTensor(int id, const string& csv_values) override;
+  void AllocateTensors() override;
+  void ResetTensor(int id) override;
+  void SetInput(int id, const string& csv_values) override;
+  void SetExpectation(int id, const string& csv_values) override;
+  void Invoke() override;
+  bool CheckResults() override;
+  string ReadOutput(int id) override;
+
+ private:
+  void DeallocateStringTensor(TfLiteTensor* t) {
+    if (t) {
+      free(t->data.raw);
+      t->data.raw = nullptr;
+    }
+  }
+  void AllocateStringTensor(int id, size_t num_bytes, TfLiteTensor* t) {
+    t->data.raw = reinterpret_cast<char*>(malloc(num_bytes));
+    t->bytes = num_bytes;
+    tensors_to_deallocate_[id] = t;
+  }
+
+  void ResetLSTMStateTensors();
+
+  class Expectation;
+
+  std::unique_ptr<OpResolver> resolver_;
+  std::unique_ptr<TfLiteDelegate> delegate_;
+  bool use_nnapi_ = false;
+  std::unique_ptr<FlatBufferModel> model_;
+  std::unique_ptr<Interpreter> interpreter_;
+  std::map<int, std::unique_ptr<Expectation>> expected_output_;
+  bool must_allocate_tensors_ = true;
+  std::map<int, TfLiteTensor*> tensors_to_deallocate_;
+};
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TESTING_ANDROID_TFLITE_DRIVER_H_
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/tokenize.cc b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tokenize.cc
new file mode 100644
index 00000000000..95251dcd679
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tokenize.cc
@@ -0,0 +1,98 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#include "tokenize.h"
+#include <istream>
+#include <string>
+#include "tensorflow/lite/string_tflite.h"
+
+namespace tflite {
+namespace testing {
+
+void Tokenize(std::istream* input, TokenProcessor* processor) {
+  enum State { kBuildQuotedToken, kBuildToken, kIdle };
+
+  std::string current_token;
+  State state = kIdle;
+  auto start_token = [&](char c) {
+    state = kBuildToken;
+    current_token.clear();
+    current_token = c;
+  };
+  auto issue_token = [&]() {
+    state = kIdle;
+    processor->ConsumeToken(&current_token);
+    current_token.clear();
+  };
+  auto start_quoted_token = [&]() {
+    state = kBuildQuotedToken;
+    current_token.clear();
+  };
+  auto issue_quoted_token = [&]() {
+    state = kIdle;
+    processor->ConsumeToken(&current_token);
+    current_token.clear();
+  };
+  auto issue_delim = [&](char d) {
+    current_token = string(1, d);
+    processor->ConsumeToken(&current_token);
+    current_token.clear();
+  };
+  auto is_delim = [](char c) { return c == '{' || c == '}' || c == ':'; };
+  auto is_quote = [](char c) { return c == '"'; };
+
+  for (auto it = std::istreambuf_iterator<char>(*input);
+       it != std::istreambuf_iterator<char>(); ++it) {
+    switch (state) {
+      case kIdle:
+        if (is_delim(*it)) {
+          issue_delim(*it);
+        } else if (is_quote(*it)) {
+          start_quoted_token();
+        } else if (!isspace(*it)) {
+          start_token(*it);
+        }
+        break;
+      case kBuildToken:
+        if (is_delim(*it)) {
+          issue_token();
+          issue_delim(*it);
+        } else if (is_quote(*it)) {
+          issue_token();
+          start_quoted_token();
+        } else if (isspace(*it)) {
+          issue_token();
+        } else {
+          current_token += *it;
+        }
+        break;
+      case kBuildQuotedToken:
+        if (is_quote(*it)) {
+          issue_quoted_token();
+        } else {
+          current_token += *it;
+        }
+        break;
+    }
+  }
+  if (state != kIdle) {
+    issue_token();
+  }
+}
+
+}  // namespace testing
+}  // namespace tflite
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/tokenize.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tokenize.h
new file mode 100644
index 00000000000..31b84c2b82c
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/tokenize.h
@@ -0,0 +1,45 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_TOKENIZE_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_TOKENIZE_H_
+
+#include <istream>
+#include <string>
+
+namespace tflite {
+namespace testing {
+
+// Process tokens coming from Tokenize().
+class TokenProcessor {
+ public:
+  virtual ~TokenProcessor() {}
+  // Process a single token. The token won't be reused, so it is OK to call
+  // token.swap().
+  virtual void ConsumeToken(std::string* token) = 0;
+};
+
+// Tokenize a stream on whitespaces, colons and curly braces. Whitespaces are
+// removed from the tokens and double-quotes can be used to avoid that. Note
+// that there is no way to escape double-quotes, so there's no way to have a
+// double-quote inside a token.
+void Tokenize(std::istream* input, TokenProcessor* processor);
+
+}  // namespace testing
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TESTING_ANDROID_TOKENIZE_H_
diff --git a/tensorflow/lite/testing/nnapi_tflite_zip_tests/util.h b/tensorflow/lite/testing/nnapi_tflite_zip_tests/util.h
new file mode 100644
index 00000000000..883c5e07e38
--- /dev/null
+++ b/tensorflow/lite/testing/nnapi_tflite_zip_tests/util.h
@@ -0,0 +1,62 @@
+/* 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.
+==============================================================================*/
+// NOTE: this is a Android version of the file with the same name in parent folder.
+// The main difference is the removal of absl, re2 and tensorflow core dependencies.
+
+#ifndef TENSORFLOW_LITE_TESTING_ANDROID_UTIL_H_
+#define TENSORFLOW_LITE_TESTING_ANDROID_UTIL_H_
+
+#include <cstdio>
+
+#include "tensorflow/lite/core/api/error_reporter.h"
+#include "tensorflow/lite/string_tflite.h"
+
+namespace tflite {
+
+// An ErrorReporter that collects error message in a string, in addition
+// to printing to stderr.
+class TestErrorReporter : public ErrorReporter {
+ public:
+  int Report(const char* format, va_list args) override {
+    char buffer[1024];
+    int size = vsnprintf(buffer, sizeof(buffer), format, args);
+    fprintf(stderr, "%s", buffer);
+    error_messages_ += buffer;
+    num_calls_++;
+    return size;
+  }
+
+  void Reset() {
+    num_calls_ = 0;
+    error_messages_.clear();
+  }
+
+  int num_calls() const { return num_calls_; }
+  const string& error_messages() const { return error_messages_; }
+
+ private:
+  int num_calls_ = 0;
+  string error_messages_;
+};
+
+inline void LogToStderr() {
+#ifdef PLATFORM_GOOGLE
+  FLAGS_logtostderr = true;
+#endif
+}
+
+}  // namespace tflite
+
+#endif  // TENSORFLOW_LITE_TESTING_ANDROID_UTIL_H_