/* Copyright (c) 2016 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 <string>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/float16.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"

namespace py = pybind11;

namespace paddle {

namespace pybind {

namespace details {

template <bool less, size_t I, typename... ARGS>
struct CastToPyBufferImpl;

template <size_t I, typename... ARGS>
struct CastToPyBufferImpl<false, I, ARGS...> {
  py::buffer_info operator()(framework::Tensor &tensor) {
    PADDLE_THROW("This type of tensor cannot be expose to Python");
    return py::buffer_info();
  }
};

template <size_t I, typename... ARGS>
struct CastToPyBufferImpl<true, I, ARGS...> {
  using CUR_TYPE = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
  py::buffer_info operator()(framework::Tensor &tensor) {
    if (std::type_index(typeid(CUR_TYPE)) == tensor.type()) {
      auto dim_vec = framework::vectorize(tensor.dims());
      std::vector<size_t> dims_outside;
      std::vector<size_t> strides;
      dims_outside.resize(dim_vec.size());
      strides.resize(dim_vec.size());

      size_t prod = 1;
      for (size_t i = dim_vec.size(); i != 0; --i) {
        dims_outside[i - 1] = (size_t)dim_vec[i - 1];
        strides[i - 1] = sizeof(CUR_TYPE) * prod;
        prod *= dims_outside[i - 1];
      }
      framework::Tensor dst_tensor;
      if (paddle::platform::is_gpu_place(tensor.place())) {
#ifdef PADDLE_WITH_CUDA
        auto *src_ptr = static_cast<const void *>(tensor.data<CUR_TYPE>());
        auto *dst_ptr = static_cast<void *>(dst_tensor.mutable_data<CUR_TYPE>(
            tensor.dims(), platform::CPUPlace()));

        platform::DeviceContextPool &pool =
            platform::DeviceContextPool::Instance();
        auto dev_ctx = static_cast<const platform::CUDADeviceContext *>(
            pool.Get(tensor.place()));

        paddle::platform::GpuMemcpyAsync(
            dst_ptr, src_ptr, sizeof(CUR_TYPE) * tensor.numel(),
            cudaMemcpyDeviceToHost, dev_ctx->stream());
#else
        PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
#endif
      } else if (paddle::platform::is_cpu_place(tensor.place())) {
        dst_tensor = tensor;
      }

      if (std::type_index(typeid(CUR_TYPE)) ==
          std::type_index(typeid(platform::float16))) {
        return py::buffer_info(dst_tensor.data<CUR_TYPE>(), sizeof(CUR_TYPE),
                               "e", /* np.dtype('e') == np.float16 */
                               (size_t)framework::arity(dst_tensor.dims()),
                               dims_outside, strides);
      } else {
        return py::buffer_info(dst_tensor.data<CUR_TYPE>(), sizeof(CUR_TYPE),
                               py::format_descriptor<CUR_TYPE>::format(),
                               (size_t)framework::arity(dst_tensor.dims()),
                               dims_outside, strides);
      }
    } else {
      constexpr bool less = I + 1 < std::tuple_size<std::tuple<ARGS...>>::value;
      return CastToPyBufferImpl<less, I + 1, ARGS...>()(tensor);
    }
  }
};

}  // namespace details

inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
  auto buffer_info =
      details::CastToPyBufferImpl<true, 0, float, int, double, int64_t, bool,
                                  platform::float16>()(tensor);
  return buffer_info;
}

template <typename T>
T TensorGetElement(framework::Tensor &self, size_t offset) {
  if (platform::is_cpu_place(self.place())) {
    return self.data<T>()[offset];
  } else {
    std::shared_ptr<framework::Tensor> dst(new framework::Tensor);
    framework::TensorCopy(self, platform::CPUPlace(), dst.get());
    return dst->data<T>()[offset];
  }
}

// TODO(dzhwinter) : fix the redundent Tensor allocate and free
template <typename T>
void TensorSetElement(framework::Tensor &self, size_t offset, T elem) {
  if (platform::is_gpu_place(self.place())) {
    std::shared_ptr<framework::Tensor> dst(new framework::Tensor);
    framework::TensorCopy(self, platform::CPUPlace(), dst.get());
    dst->data<T>()[offset] = elem;
    framework::TensorCopy(*dst.get(), self.place(), &self);

  } else if (platform::is_cpu_place(self.place())) {
    self.data<T>()[offset] = elem;
  }
}

template <typename T>
void PyCPUTensorSetFromArray(
    framework::Tensor &self,
    py::array_t<T, py::array::c_style | py::array::forcecast> array,
    paddle::platform::CPUPlace &place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (size_t i = 0; i < array.ndim(); ++i) {
    dims.push_back((int)array.shape()[i]);
  }

  self.Resize(framework::make_ddim(dims));
  auto *dst = self.mutable_data<T>(place);
  std::memcpy(dst, array.data(), sizeof(T) * array.size());
}

template <>
void PyCPUTensorSetFromArray(
    framework::Tensor &self,
    py::array_t<uint16_t, py::array::c_style | py::array::forcecast> array,
    paddle::platform::CPUPlace &place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (size_t i = 0; i < array.ndim(); ++i) {
    dims.push_back((int)array.shape()[i]);
  }

  self.Resize(framework::make_ddim(dims));
  auto *dst = self.mutable_data<platform::float16>(place);
  std::memcpy(dst, array.data(), sizeof(uint16_t) * array.size());
}

#ifdef PADDLE_WITH_CUDA
template <typename T>
void PyCUDATensorSetFromArray(
    framework::Tensor &self,
    py::array_t<T, py::array::c_style | py::array::forcecast> array,
    paddle::platform::CUDAPlace &place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (size_t i = 0; i < array.ndim(); ++i) {
    dims.push_back((int)array.shape()[i]);
  }

  self.Resize(framework::make_ddim(dims));
  auto *dst = self.mutable_data<T>(place);

  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
  auto dev_ctx =
      static_cast<const platform::CUDADeviceContext *>(pool.Get(place));
  paddle::platform::GpuMemcpyAsync(dst, array.data(), sizeof(T) * array.size(),
                                   cudaMemcpyHostToDevice, dev_ctx->stream());
}

template <>
void PyCUDATensorSetFromArray(
    framework::Tensor &self,
    py::array_t<uint16_t, py::array::c_style | py::array::forcecast> array,
    paddle::platform::CUDAPlace &place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (size_t i = 0; i < array.ndim(); ++i) {
    dims.push_back((int)array.shape()[i]);
  }

  self.Resize(framework::make_ddim(dims));
  auto *dst = self.mutable_data<platform::float16>(place);

  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
  auto dev_ctx =
      static_cast<const platform::CUDADeviceContext *>(pool.Get(place));
  paddle::platform::GpuMemcpyAsync(dst, array.data(),
                                   sizeof(uint16_t) * array.size(),
                                   cudaMemcpyHostToDevice, dev_ctx->stream());
}
#endif

}  // namespace pybind
}  // namespace paddle