/* Copyright (c) 2018 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 "paddle/fluid/operators/math/concat.h"
#include <gtest/gtest.h>
#include <vector>
#include "paddle/fluid/framework/tensor_util.h"

using namespace paddle::framework;
using namespace paddle::platform;

template <typename DeviceContext, typename Place>
void testConcat() {
  Tensor input_a_cpu;
  Tensor input_b_cpu;
  Tensor out_cpu;
  Tensor input_a;
  Tensor input_b;
  Tensor out;

  DeviceContext* context = new DeviceContext(Place());
  //  DeviceContext context(Place());

  /**
   * cast1:
   *    inputs:
   *        t_a.shape: [2, 3, 4]
   *        t_b.shape: [3, 3, 4]
   *    output:
   *        out.shape: [5, 3, 4]
   */
  auto dim_a = make_ddim({2, 3, 4});
  auto dim_b = make_ddim({3, 3, 4});
  auto dim_out = make_ddim({5, 3, 4});

  input_a.mutable_data<int>(dim_a, Place());
  input_b.mutable_data<int>(dim_b, Place());
  out.mutable_data<int>(dim_out, Place());

  if (paddle::platform::is_gpu_place(Place())) {
    input_a_cpu.mutable_data<int>(dim_a, CPUPlace());
    input_b_cpu.mutable_data<int>(dim_b, CPUPlace());
    out_cpu.mutable_data<int>(dim_out, CPUPlace());
  }

  int* a_ptr;
  int* b_ptr;
  if (paddle::platform::is_gpu_place(Place())) {
    a_ptr = input_a_cpu.data<int>();
    b_ptr = input_b_cpu.data<int>();
  } else {
    a_ptr = input_a.data<int>();
    b_ptr = input_b.data<int>();
  }

  for (int i = 0; i < 2 * 3 * 4; ++i) {
    a_ptr[i] = i;
  }
  for (int i = 0; i < 3 * 3 * 4; ++i) {
    b_ptr[i] = i;
  }

  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(input_a_cpu, Place(), *context, &input_a, true);
    TensorCopy(input_b_cpu, Place(), *context, &input_b, true);
  }

  std::vector<Tensor> input;
  input.push_back(input_a);
  input.push_back(input_b);

  paddle::operators::math::ConcatFunctor<DeviceContext, int> concat_functor;
  concat_functor(*context, input, 0, &out);

  // check the dim of input_a, input_b
  PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
  PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);

  int* out_ptr;
  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(out, CPUPlace(), *context, &out_cpu, true);
    out_ptr = out_cpu.data<int>();
  } else {
    out_ptr = out.data<int>();
  }

  int cols = 2 * 3 * 4;
  int idx_a = 0, idx_b = 0;
  for (int j = 0; j < 5 * 3 * 4; ++j) {
    if (j >= cols) {
      PADDLE_ENFORCE_EQ(out_ptr[j], b_ptr[idx_b]);
      ++idx_b;
    } else {
      PADDLE_ENFORCE_EQ(out_ptr[j], a_ptr[idx_a]);
      ++idx_a;
    }
  }
  //
  /**
    * cast2:
    *    inputs:
    *        t_a.shape: [2, 3, 4]
    *        t_b.shape: [2, 4, 4]
    *    output:
    *        out.shape: [2, 7, 4]
    */
  dim_a = make_ddim({2, 3, 4});
  dim_b = make_ddim({2, 4, 4});
  dim_out = make_ddim({2, 7, 4});

  input_a.Resize(dim_a);
  input_b.Resize(dim_b);
  out.Resize(dim_out);
  if (paddle::platform::is_gpu_place(Place())) {
    input_a_cpu.Resize(dim_a);
    input_b_cpu.Resize(dim_b);
    out_cpu.Resize(dim_out);
  }

  if (paddle::platform::is_gpu_place(Place())) {
    a_ptr = input_a_cpu.data<int>();
    b_ptr = input_b_cpu.data<int>();
  } else {
    a_ptr = input_a.data<int>();
    b_ptr = input_b.data<int>();
  }

  for (int i = 0; i < 2 * 3 * 4; ++i) {
    a_ptr[i] = i;
  }
  for (int i = 0; i < 2 * 4 * 4; ++i) {
    b_ptr[i] = i;
  }

  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(input_a_cpu, Place(), *context, &input_a, true);
    TensorCopy(input_b_cpu, Place(), *context, &input_b, true);
  }

  input.clear();
  input.push_back(input_a);
  input.push_back(input_b);

  concat_functor(*context, input, 1, &out);

  // check the dim of input_a, input_b
  PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
  PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);

  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(out, CPUPlace(), *context, &out_cpu, true);
    out_ptr = out_cpu.data<int>();
  } else {
    out_ptr = out.data<int>();
  }

  cols = 3 * 4;
  idx_a = 0, idx_b = 0;
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 28; ++j) {
      if (j >= cols) {
        PADDLE_ENFORCE_EQ(out_ptr[i * 28 + j], b_ptr[idx_b]);
        ++idx_b;
      } else {
        PADDLE_ENFORCE_EQ(out_ptr[i * 28 + j], a_ptr[idx_a]);
        ++idx_a;
      }
    }
  }

  /**
    * cast3:
    *    inputs:
    *        t_a.shape: [2, 3, 5]
    *        t_b.shape: [2, 3, 4]
    *    output:
    *        out.shape: [2, 3, 9]
    */
  dim_a = make_ddim({2, 3, 4});
  dim_b = make_ddim({2, 3, 5});
  dim_out = make_ddim({2, 3, 9});

  input_a.Resize(dim_a);
  input_b.Resize(dim_b);
  out.Resize(dim_out);
  if (paddle::platform::is_gpu_place(Place())) {
    input_a_cpu.Resize(dim_a);
    input_b_cpu.Resize(dim_b);
    out_cpu.Resize(dim_out);
  }

  if (paddle::platform::is_gpu_place(Place())) {
    a_ptr = input_a_cpu.data<int>();
    b_ptr = input_b_cpu.data<int>();
  } else {
    a_ptr = input_a.data<int>();
    b_ptr = input_b.data<int>();
  }

  for (int i = 0; i < 2 * 3 * 4; ++i) {
    a_ptr[i] = i;
  }
  for (int i = 0; i < 2 * 3 * 5; ++i) {
    b_ptr[i] = i;
  }

  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(input_a_cpu, Place(), *context, &input_a, true);
    TensorCopy(input_b_cpu, Place(), *context, &input_b, true);
  }

  input.clear();
  input.push_back(input_a);
  input.push_back(input_b);

  concat_functor(*context, input, 2, &out);

  // check the dim of input_a, input_b
  PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
  PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);

  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(out, CPUPlace(), *context, &out_cpu, true);
    out_ptr = out_cpu.data<int>();
  } else {
    out_ptr = out.data<int>();
  }

  // check the data
  cols = 4;
  idx_a = 0, idx_b = 0;
  for (int i = 0; i < 6; ++i) {
    for (int j = 0; j < 9; ++j) {
      if (j >= cols) {
        PADDLE_ENFORCE_EQ(out_ptr[i * 9 + j], b_ptr[idx_b]);
        ++idx_b;
      } else {
        PADDLE_ENFORCE_EQ(out_ptr[i * 9 + j], a_ptr[idx_a]);
        ++idx_a;
      }
    }
  }

  /**
    * cast4:
    *    inputs:
    *        axis = 1
    *        t_a.shape: [2, 3, 4]
    *        t_b.shape: [2, 3, 4]
    *    output:
    *        out.shape: [2, 6, 4]
    */
  dim_a = make_ddim({2, 3, 4});
  dim_b = make_ddim({2, 3, 4});
  dim_out = make_ddim({2, 6, 4});

  input_a.Resize(dim_a);
  input_b.Resize(dim_b);
  out.Resize(dim_out);
  if (paddle::platform::is_gpu_place(Place())) {
    input_a_cpu.Resize(dim_a);
    input_b_cpu.Resize(dim_b);
    out_cpu.Resize(dim_out);
  }

  if (paddle::platform::is_gpu_place(Place())) {
    a_ptr = input_a_cpu.data<int>();
    b_ptr = input_b_cpu.data<int>();
  } else {
    a_ptr = input_a.data<int>();
    b_ptr = input_b.data<int>();
  }

  for (int i = 0; i < 2 * 3 * 4; ++i) {
    a_ptr[i] = i;
  }
  for (int i = 0; i < 2 * 3 * 4; ++i) {
    b_ptr[i] = i;
  }

  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(input_a_cpu, Place(), *context, &input_a, true);
    TensorCopy(input_b_cpu, Place(), *context, &input_b, true);
  }

  input.clear();
  input.push_back(input_a);
  input.push_back(input_b);

  concat_functor(*context, input, 1, &out);

  // check the dim of input_a, input_b
  PADDLE_ENFORCE_EQ(input_a.dims(), dim_a);
  PADDLE_ENFORCE_EQ(input_b.dims(), dim_b);

  if (paddle::platform::is_gpu_place(Place())) {
    TensorCopy(out, CPUPlace(), *context, &out_cpu, true);
    out_ptr = out_cpu.data<int>();
  } else {
    out_ptr = out.data<int>();
  }

  // check the data
  cols = 12;
  idx_a = 0, idx_b = 0;
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 24; ++j) {
      if (j >= cols) {
        PADDLE_ENFORCE_EQ(out_ptr[i * 24 + j], b_ptr[idx_b]);
        ++idx_b;
      } else {
        PADDLE_ENFORCE_EQ(out_ptr[i * 24 + j], a_ptr[idx_a]);
        ++idx_a;
      }
    }
  }
}

TEST(math, concat) {
  testConcat<paddle::platform::CPUDeviceContext, paddle::platform::CPUPlace>();
#ifdef PADDLE_WITH_CUDA
  testConcat<paddle::platform::CUDADeviceContext,
             paddle::platform::CUDAPlace>();
#endif
}