/* 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.
==============================================================================*/

#ifndef TENSORFLOW_CORE_GRAPPLER_UTILS_GRAPPLER_TEST_H_
#define TENSORFLOW_CORE_GRAPPLER_UTILS_GRAPPLER_TEST_H_

#include <vector>

#include "absl/strings/string_view.h"
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/utils.h"
#include "tensorflow/core/lib/random/random.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/public/session_options.h"

namespace tensorflow {
namespace grappler {

class GrapplerTest : public ::testing::Test {
 public:
  GrapplerTest();

 protected:
  void DisableAllOptimizers();
  void EnableAllOptimizers();

  std::vector<Tensor> EvaluateNodes(
      const GraphDef& graph, const std::vector<std::string>& node_names) const;

  std::vector<Tensor> EvaluateNodes(
      const GraphDef& graph, const std::vector<std::string>& node_names,
      const std::vector<std::pair<std::string, Tensor>>& inputs) const;

  std::vector<Tensor> EvaluateFetchNodes(const GrapplerItem& item) const;

  NodeDef* AddNode(
      const std::string& name, const std::string& op,
      const std::vector<std::string>& inputs,
      const std::vector<std::pair<std::string, AttrValue>>& attributes,
      GraphDef* graph) const;

  void DisableAllOptimizers(RewriterConfig* cfg);

  // Checks if two graphs are equal. Both graphs must have the same set of nodes
  // with the same inputs and attributes. Nodes can be in different order.
  //
  // NOTE: This function uses EXPECT/ASSERT macros to check node properties
  // equality, and adds all failures to the current test.
  void CompareGraphs(GraphDef want, GraphDef got) const;

  // Checks if two nodes have the same name, op, inputs and attributes.
  //
  // NOTE: This function uses EXPECT/ASSERT macros to check node properties
  // equality, and adds all failures to the current test.
  void CompareNodes(const NodeDef& want, const NodeDef& got) const;

  // Checks if two functions are equal. Both functions must have the same set of
  // nodes with the same inputs and attributes. Nodes can be in different order.
  //
  // NOTE: This function uses EXPECT/ASSERT macros to check node properties
  // equality, and adds all failures to the current test.
  void CompareFunctions(FunctionDef want, FunctionDef got) const;

  // Checks if node 'src' is directly connected to the input($position) of
  // 'dst'.
  bool IsNodesDirectlyConnected(const NodeMap& node_map, const std::string& src,
                                const std::string& dst, int position = 0);

  // Counts nodes of the given op-type in a graph.
  int CountOpNodes(const GraphDef& graph, const std::string& op);

  // Get a random tensor with given shape.
  template <DataType DTYPE>
  Tensor GenerateRandomTensor(const TensorShape& shape) const {
    typedef typename EnumToDataType<DTYPE>::Type T;
    Tensor tensor(DTYPE, shape);
    for (auto i = 0; i < tensor.NumElements(); i++)
      tensor.flat<T>()(i) = i + random::New64() % 10;
    return tensor;
  }

  // Creates a random tensor with given shape using `setRandom`.
  template <DataType DTYPE>
  Tensor GenerateTensorWithSetRandom(const TensorShape& shape) const {
    typedef typename EnumToDataType<DTYPE>::Type T;
    Tensor tensor(DTYPE, shape);
    tensor.flat<T>().setRandom();
    return tensor;
  }

  // Get a constant tensor with given shape.
  template <DataType DTYPE>
  Tensor GenerateConstantTensor(
      const TensorShape& shape,
      typename EnumToDataType<DTYPE>::Type value) const {
    typedef typename EnumToDataType<DTYPE>::Type T;
    Tensor tensor(DTYPE, shape);
    for (auto i = 0; i < tensor.NumElements(); i++) tensor.flat<T>()(i) = value;
    return tensor;
  }

  inline tensorflow::Scope CreateScopeWithDevice(absl::string_view device) {
    return tensorflow::Scope::NewRootScope().WithDevice(std::string(device));
  }

 private:
  SessionOptions options_;
};

}  // end namespace grappler
}  // end namespace tensorflow

#endif  // TENSORFLOW_CORE_GRAPPLER_UTILS_GRAPPLER_TEST_H_
