/* Copyright 2018 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 "tensorflow/lite/toco/import_tensorflow.h"
#include "tensorflow/lite/toco/toco_port.h"

#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/attr_value_util.h"
#include "tensorflow/core/framework/node_def.pb.h"
#include "tensorflow/core/framework/node_def_builder.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/lite/testing/util.h"

namespace toco {

using tensorflow::AttrValue;
using tensorflow::DT_BOOL;
using tensorflow::DT_COMPLEX64;
using tensorflow::DT_FLOAT;
using tensorflow::DT_INT32;
using tensorflow::DT_INT64;
using tensorflow::DT_QUINT8;
using tensorflow::DT_STRING;
using tensorflow::NodeDef;
using tensorflow::Status;

namespace internal {
using ConverterType = tensorflow::Status (*)(
    const NodeDef& node, const TensorFlowImportFlags& tf_import_flags,
    Model* model);
using ConverterMapType = std::unordered_map<std::string, ConverterType>;

ConverterMapType GetTensorFlowNodeConverterMap();
Status ImportTensorFlowNode(const NodeDef&, const TensorFlowImportFlags&,
                            Model*, const ConverterMapType&);
}  // namespace internal

namespace {

Status ImportNode(const NodeDef& node, Model* model) {
  const auto converter = internal::GetTensorFlowNodeConverterMap();
  return internal::ImportTensorFlowNode(node, TensorFlowImportFlags(), model,
                                        converter);
}

Status ImportFlexNode(const NodeDef& node, Model* model) {
  // Empty converter => all nodes are flex nodes.
  const auto converter = internal::ConverterMapType();
  return internal::ImportTensorFlowNode(node, TensorFlowImportFlags(), model,
                                        converter);
}

Status ImportNode(const NodeDef& node) {
  Model model;
  return ImportNode(node, &model);
}

NodeDef BuildNode(
    const std::string& op,
    const std::vector<std::initializer_list<int>>& output_shapes) {
  NodeDef node;
  node.set_op(op);
  node.set_name("Node1");
  node.add_input();
  node.set_input(0, "Node0");

  AttrValue::ListValue* shapes =
      (*node.mutable_attr())["_output_shapes"].mutable_list();
  for (const auto& output_shape : output_shapes) {
    tensorflow::TensorShapeProto* shape = shapes->add_shape();
    for (int64_t output_shape_dim : output_shape) {
      auto shape_dim = shape->add_dim();
      shape_dim->set_size(output_shape_dim);
    }
  }

  return node;
}

namespace {
void BuildConstNode(std::initializer_list<int64_t> shape,
                    tensorflow::DataType dtype, int64_t num_elements,
                    NodeDef* node) {
  node->set_op("Const");
  node->set_name("Node1");

  // An attribute describing the type of this const node.
  AttrValue dtype_attr;
  SetAttrValue(dtype, &dtype_attr);
  (*node->mutable_attr())["dtype"] = dtype_attr;

  // An attribute describing the content of this const node.
  tensorflow::TensorProto t;
  t.set_dtype(dtype);
  auto* s = t.mutable_tensor_shape();
  for (auto d : shape) {
    s->add_dim()->set_size(d);
  }

  // TODO(ahentz): also need to test via tensor_content()
  switch (dtype) {
    case DT_FLOAT:
      for (int64_t i = 0; i < num_elements; ++i) {
        t.add_float_val(i / 10000.0);
      }
      break;
    case DT_INT32:
      for (int64_t i = 0; i < num_elements; ++i) {
        t.add_int_val(i % std::numeric_limits<int>::max());
      }
      break;
    case DT_QUINT8:
      for (int64_t i = 0; i < num_elements; ++i) {
        t.add_int_val(i % std::numeric_limits<uint8_t>::max());
      }
      break;
    case DT_INT64:
      for (int64_t i = 0; i < num_elements; ++i) {
        t.add_int64_val(i);
      }
      break;
    case DT_STRING:
      break;
    case DT_BOOL:
      for (int64_t i = 0; i < num_elements; ++i) {
        t.add_bool_val(i % 2);
      }
      break;
    case DT_COMPLEX64:
      for (int64_t i = 0; i < num_elements; ++i) {
        t.add_scomplex_val(i / 10000.0);
        t.add_scomplex_val(-i / 10000.0);
      }
      break;
    default:
      break;
  }

  AttrValue value_attr;
  SetAttrValue(t, &value_attr);
  (*node->mutable_attr())["value"] = value_attr;
}
}  //  namespace

class ShapeImportTest : public ::testing::TestWithParam<tensorflow::DataType> {
};

TEST_P(ShapeImportTest, ShapeElementIsNegative) {
  NodeDef node;
  BuildConstNode({1, -2, 10}, GetParam(), 0, &node);
  auto status = ImportNode(node);
  EXPECT_EQ(
      status.error_message(),
      "Tensor shape should not include negative values\n\t (while processing "
      "node 'Node1')");
}

TEST_P(ShapeImportTest, ShapeElementIsZero) {
  NodeDef node;
  // Const nodes with zero-sized, non-scalar shapes are still not importable.
  BuildConstNode({1, 0, 10}, GetParam(), 0, &node);

  Model model;
  EXPECT_TRUE(ImportNode(node, &model).ok());

  const auto& array = model.GetArray("Node1");
  EXPECT_THAT(array.shape().dims(), ::testing::ElementsAre());
}

// Note how this is subtly different thant ShapeElementIsZero above, where toco
// removes all shape information after import.
TEST_P(ShapeImportTest, ShapeIsOneDimZero) {
  NodeDef node;
  BuildConstNode({0}, GetParam(), 0, &node);

  Model model;
  EXPECT_TRUE(ImportNode(node, &model).ok());

  const auto& array = model.GetArray("Node1");
  // We would like to have [0] shapes actually import correctly, but
  // for some reason that slows everything down.
  EXPECT_THAT(array.shape().dims(), ::testing::ElementsAre());
}

TEST_P(ShapeImportTest, ShapeElementTooLarge) {
  NodeDef node;
  BuildConstNode({3000000000}, GetParam(), 0, &node);
  auto status = ImportNode(node);
  EXPECT_EQ(status.error_message(),
            "Shape element overflows\n\t (while processing node 'Node1')");
}

TEST_P(ShapeImportTest, ShapeTooLarge) {
  NodeDef node;
  BuildConstNode({1000000, 2000000, 2000000, 2000000}, GetParam(), 0, &node);
  auto status = ImportNode(node);
  EXPECT_EQ(status.error_message(),
            "Tensor shape is too large\n\t (while processing node 'Node1')");
}

TEST_P(ShapeImportTest, ValidShapeButZeroElements) {
  NodeDef node;
  BuildConstNode({1, 2, 2, 2}, GetParam(), 0, &node);
  auto status = ImportNode(node);
  EXPECT_THAT(status.error_message(),
              ::testing::MatchesRegex(
                  "Neither input_content .0. nor .*_val .0. have the right "
                  "dimensions .8. for this .* tensor\n\t .while processing "
                  "node 'Node1'."));
}

std::vector<tensorflow::DataType> TestTypes() {
  return {DT_FLOAT, DT_INT32, DT_INT64, DT_BOOL, DT_QUINT8, DT_COMPLEX64};
}

INSTANTIATE_TEST_CASE_P(ShapeImportTest, ShapeImportTest,
                        ::testing::ValuesIn(TestTypes()));

TEST(ImportTest, Complex64ConstNode) {
  NodeDef node;
  BuildConstNode({1, 2, 3}, DT_COMPLEX64, 6, &node);
  Model model;
  EXPECT_TRUE(ImportNode(node, &model).ok());
  const auto& array = model.GetArray("Node1");
  EXPECT_EQ(ArrayDataType::kComplex64, array.data_type);
  EXPECT_EQ(6, array.GetBuffer<ArrayDataType::kComplex64>().Length());
  int64_t i = 0;
  for (const auto& datum : array.GetBuffer<ArrayDataType::kComplex64>().data) {
    EXPECT_EQ(i / 10000.0f, std::real(datum));
    EXPECT_EQ(-i / 10000.0f, std::imag(datum));
    i++;
  }
}

std::vector<std::pair<tensorflow::DataType, ArrayDataType>> UnaryTestTypes() {
  return {{DT_FLOAT, ArrayDataType::kFloat},
          {DT_INT32, ArrayDataType::kInt32},
          {DT_INT64, ArrayDataType::kInt64}};
}

class TypeImportTest : public ::testing::TestWithParam<
                           std::pair<tensorflow::DataType, ArrayDataType>> {
 protected:
  TypeImportTest() {}

  void BuildUnaryNode(const std::string& op_name, tensorflow::DataType dtype,
                      NodeDef* node) {
    node->set_op(op_name);
    node->set_name("Node1");

    node->add_input();
    node->set_input(0, "Node0");

    AttrValue dtype_attr;
    SetAttrValue(dtype, &dtype_attr);
    (*node->mutable_attr())["T"] = dtype_attr;
  }
};

TEST_P(TypeImportTest, BasicTypeInference) {
  NodeDef node;
  BuildUnaryNode("Atan", GetParam().first, &node);

  Model model;
  EXPECT_TRUE(ImportNode(node, &model).ok());

  ASSERT_THAT(model.operators.size(), ::testing::Ge(1));
  ASSERT_EQ(model.operators[0]->type, OperatorType::kUnsupported);
  const TensorFlowUnsupportedOperator* op =
      static_cast<const TensorFlowUnsupportedOperator*>(
          model.operators[0].get());
  ASSERT_THAT(op->output_data_types, ::testing::ElementsAre(GetParam().second));
}
INSTANTIATE_TEST_CASE_P(BasicTypeInference, TypeImportTest,
                        ::testing::ValuesIn(UnaryTestTypes()));

TEST(ImportTest, TypeInferenceWithFixedOutputType) {
  // Create an op that has a fixed output type (bool).
  Model model;
  EXPECT_TRUE(ImportNode(BuildNode("IsFinite", {{1, 2}, {2, 3}}), &model).ok());
  ASSERT_THAT(model.operators.size(), ::testing::Ge(1));
  ASSERT_EQ(model.operators[0]->type, OperatorType::kUnsupported);
  const TensorFlowUnsupportedOperator* op =
      static_cast<const TensorFlowUnsupportedOperator*>(
          model.operators[0].get());

  // The static output type should be indicated in the imported op.
  ASSERT_THAT(op->output_data_types,
              ::testing::ElementsAre(ArrayDataType::kBool));
}

TEST(ImportTest, FailedTypeInference) {
  // Create a unary op with no Type ("T") annotation.
  NodeDef node;
  node.set_op("Atan");
  node.set_name("Node1");
  node.add_input();
  node.set_input(0, "Node0");

  Model model;
  EXPECT_TRUE(ImportNode(node, &model).ok());

  ASSERT_THAT(model.operators.size(), ::testing::Ge(1));
  ASSERT_EQ(model.operators[0]->type, OperatorType::kUnsupported);
  const TensorFlowUnsupportedOperator* op =
      static_cast<const TensorFlowUnsupportedOperator*>(
          model.operators[0].get());
  ASSERT_TRUE(op->output_data_types.empty());
}

TEST(ImportTest, UnsupportedOpWithOutputShapes) {
  // Create an unsupported op with output shapes.
  Model model;
  EXPECT_TRUE(ImportNode(BuildNode("Atan", {{1, 2}, {2, 3}}), &model).ok());
  ASSERT_THAT(model.operators.size(), ::testing::Ge(1));
  ASSERT_EQ(model.operators[0]->type, OperatorType::kUnsupported);
  const TensorFlowUnsupportedOperator* op =
      static_cast<const TensorFlowUnsupportedOperator*>(
          model.operators[0].get());

  // The output shapes should be imported.
  ASSERT_EQ(op->output_shapes.size(), 2);
  ASSERT_THAT(op->output_shapes[0].dims(), ::testing::ElementsAre(1, 2));
  ASSERT_THAT(op->output_shapes[1].dims(), ::testing::ElementsAre(2, 3));
}

TEST(ImportTest, UnsupportedOpWithWildcardOutputShapes) {
  // Create an unsupported op with wildcard output shapes.
  Model model;
  EXPECT_TRUE(ImportNode(BuildNode("Atan", {{-1, 2}}), &model).ok());
  ASSERT_THAT(model.operators.size(), ::testing::Ge(1));
  ASSERT_EQ(model.operators[0]->type, OperatorType::kUnsupported);
  const TensorFlowUnsupportedOperator* op =
      static_cast<const TensorFlowUnsupportedOperator*>(
          model.operators[0].get());

  // Wildcard shapes aren't yet supported.
  ASSERT_TRUE(op->output_shapes.empty());
}

TEST(ImportTest, UnsupportedOpWithMultipleOutputs) {
  // This test needs an existing TensorFlow op to run correctly, because it
  // read the OpDef from the global registry. The complex output setup of
  // ParseExample allows us to test all nuances here, but we will need to add
  // attributes to match the specification in the OpDef.
  NodeDef node = BuildNode("ParseExample", {});

  // Nsparse defines how many sparse indices and shapes there are. Here we set
  // Nsparse to 2, meaning there will be 2 INT64 tensors for 'sparse_indices'
  // and 2 INT64 tensors for 'sparse_shapes. The type of those tensors is
  // defined in the OpDef.
  {
    AttrValue value_attr;
    SetAttrValue(2, &value_attr);
    (*node.mutable_attr())["Nsparse"] = value_attr;
  }

  // The there will be a number of 'sparse_values' tensors, defined by the
  // attribute 'sparse_types', which is a list of types.
  {
    AttrValue value_attr;
    std::vector<tensorflow::DataType> types;
    types.push_back(tensorflow::DT_FLOAT);
    types.push_back(tensorflow::DT_STRING);
    SetAttrValue(types, &value_attr);
    (*node.mutable_attr())["sparse_types"] = value_attr;
  }

  // And finally there will be 'dense_values' tensors, which are controlled by
  // the 'Tdense' attribute.
  {
    AttrValue value_attr;
    std::vector<tensorflow::DataType> types;
    types.push_back(tensorflow::DT_STRING);
    types.push_back(tensorflow::DT_FLOAT);
    types.push_back(tensorflow::DT_INT64);
    SetAttrValue(types, &value_attr);
    (*node.mutable_attr())["Tdense"] = value_attr;
  }

  Model model;
  EXPECT_TRUE(ImportFlexNode(node, &model).ok());

  ASSERT_THAT(model.operators.size(), ::testing::Ge(1));
  ASSERT_EQ(model.operators[0]->type, OperatorType::kUnsupported);
  const TensorFlowUnsupportedOperator* op =
      static_cast<const TensorFlowUnsupportedOperator*>(
          model.operators[0].get());

  ASSERT_EQ(op->outputs.size(), 9);
  ASSERT_EQ(op->output_data_types.size(), 9);

  // The 'sparse_indices' output tensors.
  ASSERT_EQ(op->outputs[0], "Node1");
  ASSERT_EQ(op->outputs[1], "Node1:1");
  ASSERT_EQ(op->output_data_types[0], ArrayDataType::kInt64);
  ASSERT_EQ(op->output_data_types[1], ArrayDataType::kInt64);

  // The 'sparse_values' output tensors.
  ASSERT_EQ(op->outputs[2], "Node1:2");
  ASSERT_EQ(op->outputs[3], "Node1:3");
  ASSERT_EQ(op->output_data_types[2], ArrayDataType::kFloat);
  ASSERT_EQ(op->output_data_types[3], ArrayDataType::kString);

  // The 'sparse_shapes' output tensors.
  ASSERT_EQ(op->outputs[4], "Node1:4");
  ASSERT_EQ(op->outputs[5], "Node1:5");
  ASSERT_EQ(op->output_data_types[4], ArrayDataType::kInt64);
  ASSERT_EQ(op->output_data_types[5], ArrayDataType::kInt64);

  // The 'dense_shapes' output tensors.
  ASSERT_EQ(op->outputs[6], "Node1:6");
  ASSERT_EQ(op->outputs[7], "Node1:7");
  ASSERT_EQ(op->outputs[8], "Node1:8");
  ASSERT_EQ(op->output_data_types[6], ArrayDataType::kString);
  ASSERT_EQ(op->output_data_types[7], ArrayDataType::kFloat);
  ASSERT_EQ(op->output_data_types[8], ArrayDataType::kInt64);
}

}  // namespace
}  // namespace toco

int main(int argc, char** argv) {
  ::tflite::LogToStderr();
  ::testing::InitGoogleTest(&argc, argv);
  ::toco::port::InitGoogleWasDoneElsewhere();
  return RUN_ALL_TESTS();
}
