/* 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 <Python.h>
#include <algorithm>
#include <memory>
#include <string>
#include <tuple>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/strided_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 {

template <typename T>
T TensorGetElement(const framework::Tensor &self, size_t offset) {
  PADDLE_ENFORCE_LT(offset, self.numel());
  T b = static_cast<T>(0);
  if (platform::is_cpu_place(self.place())) {
    b = self.data<T>()[offset];
#ifdef PADDLE_WITH_CUDA
  } else {
    const T *a = self.data<T>();
    auto p = boost::get<platform::CUDAPlace>(self.place());
    paddle::memory::Copy(platform::CPUPlace(), &b, p, a + offset, sizeof(T),
                         nullptr);
#endif
  }
  return b;
}

template <typename T>
void TensorSetElement(framework::Tensor *self, size_t offset, T elem) {
  PADDLE_ENFORCE_LT(offset, self->numel());
  if (platform::is_cpu_place(self->place())) {
    self->mutable_data<T>(self->place())[offset] = elem;
#ifdef PADDLE_WITH_CUDA
  } else {
    auto p = boost::get<platform::CUDAPlace>(self->place());
    T *a = self->mutable_data<T>(p);
    paddle::memory::Copy(p, a + offset, platform::CPUPlace(), &elem, sizeof(T),
                         nullptr);
#endif
  }
}

template <typename T>
void PyCPUTensorSetFromArray(
    framework::Tensor *self,
    pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
        array,
    paddle::platform::CPUPlace place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
    dims.push_back(static_cast<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 <>
// This following specialization maps uint16_t in the parameter type to
// platform::float16.
inline void PyCPUTensorSetFromArray(
    framework::Tensor *self,
    pybind11::array_t<uint16_t,
                      pybind11::array::c_style | pybind11::array::forcecast>
        array,
    paddle::platform::CPUPlace place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
    dims.push_back(static_cast<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());
}

template <typename T, size_t D>
void _sliceCompute(const framework::Tensor *in, framework::Tensor *out,
                   const platform::CPUDeviceContext &ctx,
                   const std::vector<int> &axes,
                   const std::vector<int> &starts) {
  auto &eigen_place = *ctx.eigen_device();
  auto place = in->place();
  auto out_dims = out->dims();
  auto in_dims = in->dims();

  auto offsets = Eigen::array<int, D>();
  auto extents = Eigen::array<int, D>();
  for (size_t i = 0; i < D; ++i) {
    offsets[i] = 0;
    extents[i] = out_dims[i];
  }
  int start;
  for (size_t i = 0; i < axes.size(); ++i) {
    start = starts[i];
    if (start < 0) {
      start = (start + in_dims[axes[i]]);
    }
    start = std::max(start, 0);
    offsets[axes[i]] = start;
  }
  auto in_t =
      framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
          *in);
  auto out_t =
      framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
          *out);
  out_t.device(eigen_place) = in_t.slice(offsets, extents);
}

template <typename T>
void _concatCompute(const std::vector<paddle::framework::Tensor> &ins,
                    paddle::framework::Tensor *out,
                    const platform::CPUDeviceContext &ctx, int64_t axis) {
  if (axis == 0 && ins.size() < 10) {
    size_t output_offset = 0;
    for (auto &in : ins) {
      auto in_stride = framework::stride_numel(in.dims());
      auto out_stride = framework::stride_numel(out->dims());
      paddle::operators::StridedNumelCopyWithAxis<T>(
          ctx, axis, out->data<T>() + output_offset, out_stride, in.data<T>(),
          in_stride, in_stride[axis]);
      output_offset += in_stride[axis];
    }
  } else {
    paddle::operators::math::ConcatFunctor<platform::CPUDeviceContext, T>
        concat_functor;
    concat_functor(ctx, ins, static_cast<int>(axis), out);
  }
}

void _getSliceinfo(const framework::Tensor &self, py::object obj,
                   const int64_t dim, int64_t *pstart, int64_t *pstop,
                   int64_t *pstep, int64_t *pslicelength) {
  auto &start = *pstart;
  auto &stop = *pstop;
  auto &step = *pstep;
  auto &slicelength = *pslicelength;
  const framework::DDim &srcDDim = self.dims();
  if (dim < 0 || dim >= srcDDim.size()) {
    throw py::index_error();
  }
  if (py::isinstance<py::slice>(obj)) {
    size_t lstart, lstop, lstep, lslicelength;
    py::slice s = static_cast<py::slice>(obj);
    if (!s.compute(srcDDim[dim], &lstart, &lstop, &lstep, &lslicelength)) {
      throw py::index_error();
    }
    start = static_cast<int64_t>(lstart);
    stop = static_cast<int64_t>(lstop);
    step = static_cast<int64_t>(lstep);
    slicelength = static_cast<int64_t>(lslicelength);
  } else if (py::isinstance<py::int_>(obj)) {
    start = static_cast<int64_t>(static_cast<py::int_>(obj));
    if (std::abs(start) >= srcDDim[dim]) {
      throw py::index_error();
    }
    start = (start >= 0) ? start : srcDDim[dim] - start;
    stop = start + 1;
    step = 1;
    slicelength = 1;
  } else {
    throw py::index_error();
  }
}

inline framework::Tensor *_getTensor(const framework::Tensor &self,
                                     const framework::DDim &ddim) {
  framework::Tensor *output = new framework::Tensor();
  output->Resize(ddim);
  auto place = self.place();
  if (platform::is_cpu_place(place)) {
    output->mutable_data(boost::get<platform::CPUPlace>(place), self.type());
#ifdef PADDLE_WITH_CUDA
  } else {
    if (platform::is_cuda_pinned_place(place)) {
      output->mutable_data(boost::get<platform::CUDAPinnedPlace>(place),
                           self.type());
    } else if ((platform::is_gpu_place(place))) {
      output->mutable_data(boost::get<platform::CUDAPlace>(place), self.type());
    }
#endif
  }
  return output;
}

template <typename T>
void _sliceDapper(const framework::Tensor *in, framework::Tensor *out,
                  const platform::CPUDeviceContext &ctx,
                  const std::vector<int> &axes, const std::vector<int> &starts,
                  int size) {
  switch (size) {
    case 1:
      _sliceCompute<T, 1>(in, out, ctx, axes, starts);
      break;
    case 2:
      _sliceCompute<T, 2>(in, out, ctx, axes, starts);
      break;
    case 3:
      _sliceCompute<T, 3>(in, out, ctx, axes, starts);
      break;
    case 4:
      _sliceCompute<T, 4>(in, out, ctx, axes, starts);
      break;
    case 5:
      _sliceCompute<T, 5>(in, out, ctx, axes, starts);
      break;
    case 6:
      _sliceCompute<T, 6>(in, out, ctx, axes, starts);
      break;
    case 7:
      _sliceCompute<T, 7>(in, out, ctx, axes, starts);
      break;
    case 8:
      _sliceCompute<T, 8>(in, out, ctx, axes, starts);
      break;
    case 9:
      _sliceCompute<T, 9>(in, out, ctx, axes, starts);
      break;
    default:
      PADDLE_THROW("dim size not exepected, current is %d", size);
      break;
  }
}

template <typename T>
inline framework::Tensor *_sliceWrapper(const framework::Tensor &self,
                                        const platform::CPUDeviceContext &ctx,
                                        py::object obj, int dim, int64_t start,
                                        int64_t slicelength) {
  framework::DDim dstDDim = self.dims();
  dstDDim[dim] = static_cast<int64_t>(slicelength);
  std::vector<int> axes({dim});
  std::vector<int> starts({static_cast<int>(start)});
  framework::Tensor *output = _getTensor(self, dstDDim);
  _sliceDapper<T>(&self, output, ctx, axes, starts, dstDDim.size());
  return output;
}

template <typename T>
inline framework::Tensor *_sliceAndConcat(const framework::Tensor &self,
                                          py::object obj, int dim) {
  platform::CPUDeviceContext ctx;
  int64_t start, stop, step, slicelength;
  _getSliceinfo(self, obj, dim, &start, &stop, &step, &slicelength);
  if (step == 1 || slicelength == 1) {
    return _sliceWrapper<T>(self, ctx, obj, dim, start, slicelength);
  } else {
    std::vector<framework::Tensor> ins;
    for (auto i = 0; i < slicelength; ++i, start += step) {
      ins.emplace_back(*_sliceWrapper<T>(self, ctx, obj, dim, start, 1));
    }

    // do the concat operation
    framework::DDim dstDDim = self.dims();
    dstDDim[dim] = static_cast<int64_t>(slicelength);
    framework::Tensor *output1 = _getTensor(self, dstDDim);
    _concatCompute<T>(ins, output1, ctx, dim);
    return output1;
  }
}

inline framework::Tensor *_sliceTensor(const framework::Tensor &self,
                                       py::object obj, int dim) {
  auto src_type = self.type();
  switch (src_type) {
    case framework::proto::VarType::FP16:
      return _sliceAndConcat<paddle::platform::float16>(self, obj, dim);
    case framework::proto::VarType::FP32:
      return _sliceAndConcat<float>(self, obj, dim);
    case framework::proto::VarType::FP64:
      return _sliceAndConcat<double>(self, obj, dim);
    case framework::proto::VarType::INT32:
      return _sliceAndConcat<int>(self, obj, dim);
    case framework::proto::VarType::INT64:
      return _sliceAndConcat<int64_t>(self, obj, dim);
    case framework::proto::VarType::BOOL:
      return _sliceAndConcat<bool>(self, obj, dim);
    case framework::proto::VarType::INT16:
      return _sliceAndConcat<bool>(self, obj, dim);
    case framework::proto::VarType::UINT8:
      return _sliceAndConcat<bool>(self, obj, dim);
    default:
      PADDLE_THROW("Not support type %d", src_type);
  }
}

inline framework::Tensor *_pySliceTensor(const framework::Tensor &self,
                                         py::object obj) {
  if (py::isinstance<py::tuple>(obj)) {
    py::list l = static_cast<py::list>(obj);
    std::unique_ptr<framework::Tensor> target;
    framework::Tensor *src = const_cast<framework::Tensor *>(&self);
    for (auto i = 0; i < static_cast<int>(l.size()); ++i) {
      src = _sliceTensor(*src, l[i], i);
      if (i + 1 == static_cast<int>(l.size())) {
        return src;
      } else {
        target.reset(src);
      }
    }
    return nullptr;
  } else {
    return _sliceTensor(self, obj, 0);
  }
}

inline framework::Tensor *PySliceTensor(const framework::Tensor &self,
                                        py::object obj) {
  if (platform::is_gpu_place(self.place())) {
    std::unique_ptr<framework::Tensor> holder;
    framework::Tensor src;
    framework::TensorCopySync(self, platform::CPUPlace(), &src);
    framework::Tensor *output = _pySliceTensor(src, obj);
    holder.reset(output);
    framework::Tensor *dst = _getTensor(*output, output->dims());
    framework::TensorCopySync(*output, self.place(), dst);
    return dst;
  } else {
    return _pySliceTensor(self, obj);
  }
}

#ifdef PADDLE_WITH_CUDA
template <typename T>
void PyCUDATensorSetFromArray(
    framework::Tensor *self,
    pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
        array,
    paddle::platform::CUDAPlace place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
    dims.push_back(static_cast<int>(array.shape()[i]));
  }

  self->Resize(framework::make_ddim(dims));
  auto *dst = self->mutable_data<T>(place);
  paddle::platform::GpuMemcpySync(dst, array.data(), sizeof(T) * array.size(),
                                  cudaMemcpyHostToDevice);
}

template <>
// This following specialization maps uint16_t in the parameter type to
// platform::float16.
inline void PyCUDATensorSetFromArray(
    framework::Tensor *self,
    pybind11::array_t<uint16_t,
                      pybind11::array::c_style | pybind11::array::forcecast>
        array,
    paddle::platform::CUDAPlace place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
    dims.push_back(static_cast<int>(array.shape()[i]));
  }

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

template <typename T>
void PyCUDAPinnedTensorSetFromArray(
    framework::Tensor *self,
    pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
        array,
    const paddle::platform::CUDAPinnedPlace &place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
    dims.push_back(static_cast<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 <>
// This following specialization maps uint16_t in the parameter type to
// platform::float16.
inline void PyCUDAPinnedTensorSetFromArray(
    framework::Tensor *self,
    pybind11::array_t<uint16_t,
                      pybind11::array::c_style | pybind11::array::forcecast>
        array,
    const paddle::platform::CUDAPinnedPlace &place) {
  std::vector<int64_t> dims;
  dims.reserve(array.ndim());
  for (decltype(array.ndim()) i = 0; i < array.ndim(); ++i) {
    dims.push_back(static_cast<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());
}
#endif

namespace details {

template <typename T>
struct ValidDTypeToPyArrayChecker {
  static constexpr bool kValue = false;
};

#define DECLARE_VALID_DTYPE_TO_PY_ARRAY(type) \
  template <>                                 \
  struct ValidDTypeToPyArrayChecker<type> {   \
    static constexpr bool kValue = true;      \
  }

DECLARE_VALID_DTYPE_TO_PY_ARRAY(platform::float16);
DECLARE_VALID_DTYPE_TO_PY_ARRAY(float);
DECLARE_VALID_DTYPE_TO_PY_ARRAY(double);
DECLARE_VALID_DTYPE_TO_PY_ARRAY(bool);
DECLARE_VALID_DTYPE_TO_PY_ARRAY(int8_t);
DECLARE_VALID_DTYPE_TO_PY_ARRAY(uint8_t);
DECLARE_VALID_DTYPE_TO_PY_ARRAY(int);
DECLARE_VALID_DTYPE_TO_PY_ARRAY(int64_t);

inline std::string TensorDTypeToPyDTypeStr(
    framework::proto::VarType::Type type) {
#define TENSOR_DTYPE_TO_PY_DTYPE(T, proto_type)                             \
  if (type == proto_type) {                                                 \
    if (std::is_same<T, platform::float16>::value) {                        \
      return "e";                                                           \
    } else {                                                                \
      constexpr auto kIsValidDType = ValidDTypeToPyArrayChecker<T>::kValue; \
      PADDLE_ENFORCE(kIsValidDType,                                         \
                     "This type of tensor cannot be expose to Python");     \
      return py::format_descriptor<T>::format();                            \
    }                                                                       \
  }

  _ForEachDataType_(TENSOR_DTYPE_TO_PY_DTYPE);
#undef TENSOR_DTYPE_TO_PY_DTYPE
  PADDLE_THROW("Unsupported data type %d", static_cast<int>(type));
}

}  // namespace details

inline py::array TensorToPyArray(const framework::Tensor &tensor) {
  if (!tensor.IsInitialized()) {
    return py::array();
  }
  bool is_gpu_tensor = platform::is_gpu_place(tensor.place());
  const auto &tensor_dims = tensor.dims();
  auto tensor_dtype = tensor.type();
  size_t sizeof_dtype = framework::SizeOfType(tensor_dtype);

  std::vector<size_t> py_dims(tensor_dims.size());
  std::vector<size_t> py_strides(tensor_dims.size());

  size_t numel = 1;
  for (int i = tensor_dims.size() - 1; i >= 0; --i) {
    py_dims[i] = (size_t)tensor_dims[i];
    py_strides[i] = sizeof_dtype * numel;
    numel *= py_dims[i];
  }

  const void *tensor_buf_ptr = tensor.data<void>();

  std::string py_dtype_str = details::TensorDTypeToPyDTypeStr(tensor.type());

  if (!is_gpu_tensor) {
    return py::array(py::buffer_info(
        const_cast<void *>(tensor_buf_ptr), sizeof_dtype, py_dtype_str,
        static_cast<size_t>(tensor.dims().size()), py_dims, py_strides));
  }

#ifdef PADDLE_WITH_CUDA
  py::array py_arr(py::dtype(py_dtype_str.c_str()), py_dims, py_strides);
  PADDLE_ENFORCE(py_arr.writeable() && py_arr.owndata(),
                 "PyArray must be writable and own data, otherwise memory leak "
                 "or double free would occur");

  size_t copy_bytes = sizeof_dtype * numel;
  paddle::platform::GpuMemcpySync(py_arr.mutable_data(), tensor_buf_ptr,
                                  copy_bytes, cudaMemcpyDeviceToHost);
  return py_arr;
#else
  PADDLE_THROW("CUDAPlace is not supported when not compiled with CUDA");
#endif
}

}  // namespace pybind
}  // namespace paddle
