/* Copyright (c) 2021 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. */

#pragma once

#include "paddle/pten/api/ext/exception.h"
#include "paddle/pten/api/include/tensor.h"

namespace paddle {
namespace experimental {

template <typename T>
class ScalarArrayBase {
 public:
  // Constructor support implicit
  ScalarArrayBase() = default;

  ScalarArrayBase(const std::vector<int64_t>& vec) : array_(vec) {}  // NOLINT

  ScalarArrayBase(const std::vector<int32_t>& vec) {  // NOLINT
    array_.insert(array_.begin(), vec.begin(), vec.end());
  }

  ScalarArrayBase(std::initializer_list<int64_t> array_list)
      : array_(array_list) {}

  ScalarArrayBase(const int64_t* date_value, int64_t n) {
    AssignData(date_value, n);
  }

  ScalarArrayBase(const int32_t* date_value, int64_t n) {
    AssignData(date_value, n);
  }

  bool IsInitByTensor() const { return is_init_by_tensor_; }

  void setInitByTensor(bool val) { is_init_by_tensor_ = val; }

  // The Tensor must have one dim
  ScalarArrayBase(const T& tensor) {  // NOLINT
    is_init_by_tensor_ = true;
    size_t n = tensor.numel();
    array_.reserve(n);
    switch (tensor.dtype()) {
      case DataType::INT32:
        AssignData(tensor.template data<int32_t>(), n);
        break;
      case DataType::INT64:
        AssignData(tensor.template data<int64_t>(), n);
        break;
      default:
        PD_THROW(
            "Data type error. Currently, The data type of ScalarArrayBase "
            "only supports Tensor with int32 and int64, "
            "but now received `",
            tensor.dtype(),
            "`.");
    }
  }

  // The Tensor in vec must have only one element
  ScalarArrayBase(const std::vector<T>& tensor_list) {  // NOLINT
    is_init_by_tensor_ = true;

    for (size_t i = 0; i < tensor_list.size(); ++i) {
      DataType data_type = tensor_list[i].dtype();
      switch (data_type) {
        case DataType::INT32:
          array_.push_back(*tensor_list[i].template data<int32_t>());
          break;
        case DataType::INT64:
          array_.push_back(*tensor_list[i].template data<int64_t>());
          break;
        default:
          PD_THROW(
              "Data type error. Currently, The data type of ScalarArrayBase "
              "only supports Tensor with int32 and int64, "
              "but now received `",
              data_type,
              "`.");
      }
    }
  }

  template <typename OtherT>
  ScalarArrayBase(const ScalarArrayBase<OtherT>& other)
      : array_(other.GetData()) {}

  const std::vector<int64_t>& GetData() const { return array_; }

 private:
  /// \brief Assign the data_ from const data pointer value of type T.
  template <typename TYPE>
  void AssignData(const TYPE* value_data, int64_t n) {
    if (value_data || n == 0) {
      array_.reserve(n);
      for (auto i = 0; i < n; ++i) {
        array_.push_back(static_cast<int64_t>(value_data[i]));
      }
    } else {
      PD_THROW("The input data pointer is null.");
    }
  }

 private:
  // TODO(zhangyunfei) Replace std::vector with a more efficient container
  // structure.
  std::vector<int64_t> array_;
  bool is_init_by_tensor_{false};
};

using ScalarArray =
    paddle::experimental::ScalarArrayBase<paddle::experimental::Tensor>;

}  // namespace experimental
}  // namespace paddle

namespace pten {

class DenseTensor;
using ScalarArray = paddle::experimental::ScalarArrayBase<DenseTensor>;

}  // namespace pten
