text: // Copyright (c) 2019 PaddlePaddle 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 <gtest/gtest.h>
  #include "lite/api/paddle_use_kernels.h"
  #include "lite/api/paddle_use_ops.h"
  #include "lite/core/arena/framework.h"
  #include "lite/tests/utils/fill_data.h"
  
  namespace paddle {
  namespace lite {
  
  std::vector<int> CalStrides(const DDim& dims) {
    int dsize = dims.size();
    std::vector<int> strides(dsize, 1);
    for (int i = dsize - 2; i >= 0; i--) {
      strides[i] = strides[i + 1] * dims[i + 1];
    }
    return strides;
  }
  
  std::vector<int> CalIndex(const std::vector<int>& strides, int offset) {
    int dsize = strides.size();
    std::vector<int> index(dsize, 0);
    for (int i = 0; i < dsize; i++) {
      index[i] = offset / strides[i];
      offset %= strides[i];
    }
    return index;
  }
  
  std::vector<int> TransIndex(const std::vector<int>& in_index,
                              const std::vector<int>& axis) {
    std::vector<int> out_index(in_index.size(), 0);
    for (int i = 0; i < axis.size(); i++) {
      out_index[i] = in_index[axis[i]];
    }
    return out_index;
  }
  
  int CalOffset(const std::vector<int>& strides, const std::vector<int>& index) {
    int offset = 0;
    for (int i = 0; i < index.size(); i++) {
      offset += strides[i] * index[i];
    }
    return offset;
  }
  
  class TransposeComputeTester : public arena::TestCase {
   protected:
    // common attributes for this op.
    std::string op_type_ = "transpose2";
    std::string input_ = "x";
    std::string output_ = "out";
    std::string xshape_ = "xshape";
    DDim dims_;
    std::vector<int> axis_;
  
   public:
    TransposeComputeTester(const Place& place,
                           const std::string& alias,
                           DDim dims,
                           std::vector<int> axis)
        : TestCase(place, alias), dims_(dims), axis_(axis) {}
  
    void RunBaseline(Scope* scope) override {
      auto* out = scope->NewTensor(output_);
      CHECK(out);
  
      auto* x = scope->FindTensor(input_);
  
      std::vector<int64_t> out_shape(dims_.size(), 0);
      for (size_t i = 0; i < dims_.size(); i++) {
        out_shape[i] = dims_[axis_[i]];
      }
      out->Resize(out_shape);
      auto out_dims = out->dims();
  
      std::vector<int> x_strides = CalStrides(dims_);
      std::vector<int> out_strides = CalStrides(out_dims);
  
      auto x_data = x->data<float>();
      auto out_data = out->mutable_data<float>();
  
      for (int i = 0; i < dims_.production(); i++) {
        std::vector<int> x_index = CalIndex(x_strides, i);
        std::vector<int> out_index = TransIndex(x_index, axis_);
        int out_offset = CalOffset(out_strides, out_index);
        out_data[out_offset] = x_data[i];
      }
  
      if (op_type_ == "transpose2") {
        auto* xshape = scope->NewTensor(xshape_);
        auto xshape_dims = dims_.Vectorize();
        xshape_dims.insert(xshape_dims.begin(), 0);
        xshape->Resize(xshape_dims);
      }
    }
  
    void PrepareOpDesc(cpp::OpDesc* op_desc) {
      op_desc->SetType(op_type_);
      op_desc->SetInput("X", {input_});
      op_desc->SetOutput("Out", {output_});
      if (op_type_ == "transpose2") {
        op_desc->SetOutput("XShape", {xshape_});
      }
      op_desc->SetAttr("axis", axis_);
    }
  
    void PrepareData() override {
      std::vector<float> din(dims_.production());
      fill_data_rand(din.data(), -1.f, 1.f, dims_.production());
      SetCommonTensor(input_, dims_, din.data());
    }
  };
  
  void TestTranspose2D(Place place, float abs_error) {
    DDim x_dims{{4, 5}};
    std::vector<std::vector<int>> axes{{0, 1}, {1, 0}};
    for (auto axis : axes) {
      std::unique_ptr<arena::TestCase> tester(
          new TransposeComputeTester(place, "def", x_dims, axis));
      arena::Arena arena(std::move(tester), place, abs_error);
      arena.TestPrecision({"xshape"});
    }
  }
  
  void TestTranspose3D(Place place, float abs_error) {
    DDim x_dims{{3, 4, 5}};
    std::vector<std::vector<int>> axes{
        {0, 1, 2}, {0, 2, 1}, {1, 0, 2}, {2, 1, 0}};
    for (auto axis : axes) {
      std::unique_ptr<arena::TestCase> tester(
          new TransposeComputeTester(place, "def", x_dims, axis));
      arena::Arena arena(std::move(tester), place, abs_error);
      arena.TestPrecision({"xshape"});
    }
  }
  
  void TestTranspose4D(Place place, float abs_error) {
    DDim x_dims{{2, 3, 4, 5}};
    std::vector<std::vector<int>> axes{
        {0, 1, 2, 3}, {0, 1, 3, 2}, {0, 2, 1, 3}, {3, 1, 2, 0}, {3, 1, 0, 2}};
    for (auto axis : axes) {
      std::unique_ptr<arena::TestCase> tester(
          new TransposeComputeTester(place, "def", x_dims, axis));
      arena::Arena arena(std::move(tester), place, abs_error);
      arena.TestPrecision({"xshape"});
    }
  }
  
  TEST(Transpose, precision) {
    LOG(INFO) << "test Transpose op";
    float abs_error = 2e-5;
    Place place;
  #if defined(LITE_WITH_XPU) && defined(LITE_WITH_XTCL)
    place = TARGET(kXPU);
  #elif defined(LITE_WITH_NPU)
    place = TARGET(kNPU);
    abs_error = 1e-2;  // Using fp16 in NPU
  #else
    return;
  #endif
  
    TestTranspose2D(place, abs_error);
    TestTranspose3D(place, abs_error);
    TestTranspose4D(place, abs_error);
  }
  
  }  // namespace lite
  }  // namespace paddle
  