/*!
 *  Copyright (c) 2015 by Contributors
 * \file ndarray.cc
 * \brief ndarry module of mxnet
 */
#include <dmlc/io.h>
#include <dmlc/logging.h>
#include <dmlc/registry.h>
#include <mxnet/base.h>
#include <mxnet/ndarray.h>
#include <mxnet/resource.h>
#include <mshadow/tensor.h>
#include "./ndarray_function.h"
#include "./autograd.h"

#if MXNET_USE_OPENCV
#include <opencv2/opencv.hpp>
#endif  // MXNET_USE_OPENCV

namespace dmlc {
DMLC_REGISTRY_ENABLE(::mxnet::NDArrayFunctionReg);
}  // namespace dmlc

namespace mxnet {

NDArray NDArray::Reshape(const TShape &shape) const {
  using namespace autograd;
  CHECK_GE(shape_.Size(), shape.Size())
      << "NDArray.Reshape: target shape size is different from current shape";
  NDArray ret = *this;
  ret.shape_ = shape;
  if (AutogradRuntime::Get()->IsTraining()) {
    // fake a Reshape op
    ret.entry_.clear();
    const nnvm::Op* op = nnvm::Op::Get("Reshape");
    nnvm::NodeAttrs attrs;
    attrs.op = op;
    std::ostringstream os;
    os << shape;
    attrs.dict.insert({"shape", os.str()});
    op->attr_parser(&attrs);
    std::vector<NDArray> inputs, outputs;
    inputs.emplace_back(*this);
    outputs.emplace_back(std::move(ret));
    AutogradRuntime::Get()->RecordImperativeFCompute(
      op, attrs, &inputs, &outputs);
    return outputs[0];
  } else {
    return ret;
  }
}


NDArray NDArray::Slice(index_t begin, index_t end) const {
  using namespace autograd;
  NDArray ret = *this;
  CHECK(!is_none()) << "NDArray is not initialized";
  CHECK_GE(shape_[0], end) << "Slice end index out of range";
  size_t length = shape_.ProdShape(1, shape_.ndim());
  ret.offset_ += begin * length;
  ret.shape_[0] = end - begin;
  if (AutogradRuntime::Get()->IsTraining()) {
    // fake a slice_axis op
    ret.entry_.clear();
    const nnvm::Op* op = nnvm::Op::Get("slice_axis");
    nnvm::NodeAttrs attrs;
    attrs.op = op;
    attrs.dict.insert({"axis", "0"});
    attrs.dict.insert({"begin", std::to_string(begin)});
    attrs.dict.insert({"end", std::to_string(end)});
    op->attr_parser(&attrs);
    std::vector<NDArray> inputs, outputs;
    inputs.emplace_back(*this);
    outputs.emplace_back(std::move(ret));
    AutogradRuntime::Get()->RecordImperativeFCompute(
      op, attrs, &inputs, &outputs);
    return outputs[0];
  } else {
    return ret;
  }
}


NDArray NDArray::At(index_t idx) const {
  NDArray ret = this->Slice(idx, idx+1);
  if (shape_.ndim() > 1) {
    return ret.Reshape(TShape(shape_.data()+1, shape_.data()+shape_.ndim()));
  } else {
    return ret;
  }
}

/*!
* \brief run a ternary operation
* \param lhs left operand
* \param mhs middle operand
* \param rhs right operand
* \param out the output ndarray
*/
template<typename OP>
void TernaryOp(const NDArray &lhs,
  const NDArray &mhs,
  const NDArray &rhs,
  NDArray *out) {
  // no check if all of them are on cpu
  if (lhs.ctx().dev_mask() != cpu::kDevMask || mhs.ctx().dev_mask() != cpu::kDevMask
                                            || rhs.ctx().dev_mask() != cpu::kDevMask) {
    CHECK((lhs.ctx() == mhs.ctx()) && (mhs.ctx() == rhs.ctx())) << "operands context mismatch";
  }
  // if out is none, allocate space
  if (out->is_none()) {
    *out = NDArray(OP::GetShape(lhs.shape(), mhs.shape(), rhs.shape()), lhs.ctx(), true);
  } else {
    // no check if both of them are on cpu
    if (lhs.ctx().dev_mask() != cpu::kDevMask ||
      out->ctx().dev_mask() != cpu::kDevMask) {
      CHECK(out->ctx() == lhs.ctx()) << "target context mismatch";
    }
    CHECK(out->shape() == OP::GetShape(lhs.shape(), mhs.shape(), rhs.shape()))
      << "target shape mismatch";
  }
  // important: callback must always capture by value
  NDArray ret = *out;
  // get the const variables
  std::vector<Engine::VarHandle> const_vars;
  if (lhs.var() != ret.var()) const_vars.push_back(lhs.var());
  if (mhs.var() != ret.var()) const_vars.push_back(mhs.var());
  if (rhs.var() != ret.var()) const_vars.push_back(rhs.var());

  // redirect everything to mshadow operations
  switch (lhs.ctx().dev_mask()) {
  case cpu::kDevMask: {
    Engine::Get()->PushSync([lhs, mhs, rhs, ret](RunContext ctx) {
      TBlob tmp = ret.data();
      ndarray::Eval<cpu, OP>(lhs.data(), mhs.data(), rhs.data(), &tmp, ctx);
    }, lhs.ctx(), const_vars, { ret.var() },
    FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
    break;
  }
#if MXNET_USE_CUDA
  case gpu::kDevMask: {
    Engine::Get()->PushSync([lhs, mhs, rhs, ret](RunContext ctx) {
      TBlob tmp = ret.data();
      ndarray::Eval<gpu, OP>(lhs.data(), mhs.data(), rhs.data(), &tmp, ctx);
      // Wait GPU kernel to complete
      ctx.get_stream<gpu>()->Wait();
    }, lhs.ctx(), const_vars, { ret.var() },
    FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
    break;
  }
#endif
  default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
  }
}

/*!
 * \brief run a binary operation
 * \param lhs left operand
 * \param rhs right operand
 * \param out the output ndarray
 * \param binary_op the real
 */
template<typename OP>
void BinaryOp(const NDArray &lhs,
              const NDArray &rhs,
              NDArray *out) {
  // no check if both of them are on cpu
  if (lhs.ctx().dev_mask() != cpu::kDevMask || rhs.ctx().dev_mask() != cpu::kDevMask) {
    CHECK(lhs.ctx() == rhs.ctx()) << "operands context mismatch";
  }
  // if out is none, allocate space
  if (out->is_none()) {
    *out = NDArray(OP::GetShape(lhs.shape(), rhs.shape()), lhs.ctx(), true, lhs.dtype());
  } else {
    // no check if both of them are on cpu
    if (lhs.ctx().dev_mask() != cpu::kDevMask ||
        out->ctx().dev_mask() != cpu::kDevMask) {
      CHECK(out->ctx() == lhs.ctx()) << "target context mismatch";
    }
    CHECK(out->shape() == OP::GetShape(lhs.shape(), rhs.shape()))
        << "target shape mismatch";
  }
  // important: callback must always capture by value
  NDArray ret = *out;
  // get the const variables
  std::vector<Engine::VarHandle> const_vars;
  if (lhs.var() != ret.var()) const_vars.push_back(lhs.var());
  if (rhs.var() != ret.var()) const_vars.push_back(rhs.var());

  // redirect everything to mshadow operations
  switch (lhs.ctx().dev_mask()) {
    case cpu::kDevMask: {
      Engine::Get()->PushSync([lhs, rhs, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Eval<cpu, OP>(lhs.data(), rhs.data(), &tmp, ctx);
        }, lhs.ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#if MXNET_USE_CUDA
    case gpu::kDevMask: {
      Engine::Get()->PushSync([lhs, rhs, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Eval<gpu, OP>(lhs.data(), rhs.data(), &tmp, ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, lhs.ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#endif
    default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
  }
}

void SetValueOp(const real_t &rhs, NDArray *out) {
  CHECK_NE(out->is_none(), true) << "Set value target must not be empty";
  // important: callback must always capture by value
  NDArray ret = *out;
  switch (ret.ctx().dev_mask()) {
    case cpu::kDevMask: {
      Engine::Get()->PushSync([rhs, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Eval<cpu>(rhs, &tmp, ctx);
        }, ret.ctx(), {}, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#if MXNET_USE_CUDA
    case gpu::kDevMask: {
      Engine::Get()->PushSync([rhs, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Eval<gpu>(rhs, &tmp, ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, ret.ctx(), {}, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#endif
    default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
  }
}

/*!
 * \brief run a binary operation
 * \param lhs left operand
 * \param rhs right operand
 * \param out the output ndarray
 * \param binary_op the real
 */
template<typename OP, bool reverse>
void ScalarOp(const NDArray &lhs,
              const real_t &rhs,
              NDArray *out) {
  if (out->is_none()) {
    *out = NDArray(lhs.shape(), lhs.ctx(), true, lhs.dtype());
  } else {
    CHECK(out->ctx() == lhs.ctx()) << "target context mismatch";
    CHECK(out->shape() == lhs.shape()) << "target shape mismatch";
  }
  // important: callback must always capture by value
  NDArray ret = *out;
  // get the const variables
  std::vector<Engine::VarHandle> const_vars;
  if (lhs.var() != ret.var()) const_vars.push_back(lhs.var());

  // redirect everything to mshadow operations
  switch (lhs.ctx().dev_mask()) {
    case cpu::kDevMask: {
      Engine::Get()->PushSync([lhs, rhs, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Eval<cpu, OP, reverse>(lhs.data(), rhs, &tmp, ctx);
        }, lhs.ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#if MXNET_USE_CUDA
    case gpu::kDevMask: {
      Engine::Get()->PushSync([lhs, rhs, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Eval<gpu, OP, reverse>(lhs.data(), rhs, &tmp, ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, lhs.ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#endif
    default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
  }
}

void CopyFromTo(const NDArray &from, NDArray *to, int priority) {
  if (from.var() == to->var()) {
    // skip to copy to itself
    return;
  }
  CHECK(from.shape() == to->shape())
      << "operands shape mismatch"
      << "from.shape = " << from.shape() << " to.shape=" << to->shape();
  CHECK(from.shape().ndim() != 0)
      << "source operands have zero dimension shape";
  // important: callback must always capture by value
  NDArray ret = *to;
  int a = from.ctx().dev_mask();
  int b = to->ctx().dev_mask();

  std::vector<Engine::VarHandle> const_vars;
  if (from.var() != ret.var()) const_vars.push_back(from.var());

  if (a == cpu::kDevMask && b == cpu::kDevMask) {
    Engine::Get()->PushSync([from, ret](RunContext ctx) {
        TBlob tmp = ret.data();
        ndarray::Copy<cpu, cpu>(from.data(), &tmp,
                                from.ctx(), ret.ctx(), ctx);
      }, from.ctx(), const_vars, {ret.var()},
      FnProperty::kNormal, priority, PROFILER_MESSAGE("CopyCPU2CPU"));
  } else {
#if MXNET_USE_CUDA
    if (a == cpu::kDevMask && b == gpu::kDevMask) {
      Engine::Get()->PushSync([from, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Copy<cpu, gpu>(from.data(), &tmp,
                                  from.ctx(), ret.ctx(), ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, ret.ctx(), const_vars, {ret.var()},
        FnProperty::kCopyToGPU, priority, PROFILER_MESSAGE("CopyCPU2GPU"));
    } else if (a == gpu::kDevMask && b == cpu::kDevMask) {
      Engine::Get()->PushSync([from, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Copy<gpu, cpu>(from.data(), &tmp,
                                  from.ctx(), ret.ctx(), ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, from.ctx(), const_vars, {ret.var()},
        FnProperty::kCopyFromGPU, priority, PROFILER_MESSAGE("CopyGPU2CPU"));
    } else if (a == gpu::kDevMask && b == gpu::kDevMask) {
      Engine::Get()->PushSync([from, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::Copy<gpu, gpu>(from.data(), &tmp,
                                  from.ctx(), ret.ctx(), ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, from.ctx(), const_vars, {ret.var()},
        from.dtype() != ret.dtype() ? FnProperty::kNormal : FnProperty::kCopyFromGPU,
        priority, PROFILER_MESSAGE("CopyGPU2GPU"));
    } else {
      LOG(FATAL) << "unknown device mask";
    }
#else
    LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
#endif
  }
}

void ElementwiseSum(const std::vector<NDArray> &source, NDArray *out, int priority) {
  std::vector<Engine::VarHandle> const_vars;
  const_vars.reserve(source.size());
  for (size_t i = 0; i < source.size(); ++i) {
    if (source[i].var() != out->var()) {
      const_vars.push_back(source[i].var());
    }
    CHECK_EQ(source[i].shape() , out->shape())
        << "operands shape mismatch";
    if (out->ctx().dev_mask() == cpu::kDevMask) {
      CHECK_EQ(source[i].ctx().dev_mask(),  cpu::kDevMask)
          << "operands context mismatch";
    } else {
      CHECK(source[i].ctx() == out->ctx())
          << "operands context mismatch";
    }
  }
  // important: callback must always capture by value
  NDArray ret = *out;

  switch (out->ctx().dev_mask()) {
    case cpu::kDevMask: {
      Engine::Get()->PushSync([source, ret](RunContext ctx) {
          std::vector<TBlob> source_tblob(source.size());
          for (size_t i = 0; i < source.size(); ++i) {
            source_tblob[i] = source[i].data();
          }
          TBlob tmp = ret.data();
          ndarray::ElementwiseSum<cpu>(source_tblob, &tmp, ctx);
        }, out->ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, priority, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#if MXNET_USE_CUDA
    case gpu::kDevMask: {
      Engine::Get()->PushSync([source, ret](RunContext ctx) {
          std::vector<TBlob> source_tblob(source.size());
          for (size_t i = 0; i < source.size(); ++i) {
            source_tblob[i] = source[i].data();
          }
          TBlob tmp = ret.data();
          ndarray::ElementwiseSum<gpu>(source_tblob, &tmp, ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, out->ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, priority, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#endif
    default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
  }
}

void ClipOp(const NDArray &src,
            const real_t &a_min, const real_t &a_max,
            NDArray *out) {
  if (out->is_none()) {
    *out = NDArray(src.shape(), src.ctx(), true, src.dtype());
  } else {
    CHECK(out->ctx() == src.ctx()) << "target context mismatch";
    CHECK(out->shape() == src.shape()) << "target shape mismatch";
  }
  NDArray ret = *out;
  std::vector<Engine::VarHandle> const_vars;
  if (src.var() != ret.var()) const_vars.push_back(src.var());
  switch (src.ctx().dev_mask()) {
    case cpu::kDevMask: {
      Engine::Get()->PushSync([src, a_min, a_max, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::EvalClip<cpu>(src.data(), a_min, a_max, &tmp, ctx);
        }, src.ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
    #if MXNET_USE_CUDA
    case gpu::kDevMask: {
      Engine::Get()->PushSync([src, a_min, a_max, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::EvalClip<gpu>(src.data(), a_min, a_max, &tmp, ctx);
        }, src.ctx(), const_vars, {ret.var()},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
    #endif
    default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
  }
}

inline void CopyFromToSimple(const NDArray &from, NDArray *to) {
  CopyFromTo(from, to, 0);
}

template<typename Distribution>
void SampleOP(const real_t &a,
              const real_t &b,
              NDArray *out) {
  CHECK(!out->is_none());
  Resource resource = ResourceManager::Get()->Request(
      out->ctx(), ResourceRequest::kRandom);
  // important: callback must always capture by value
  NDArray ret = *out;
  // redirect everything to mshadow operations
  switch (out->ctx().dev_mask()) {
    case cpu::kDevMask: {
      Engine::Get()->PushSync([a, b, resource, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::EvalRandom<cpu, Distribution>(a, b, resource, &tmp, ctx);
        }, out->ctx(), {}, {ret.var(), resource.var},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#if MXNET_USE_CUDA
    case gpu::kDevMask: {
      Engine::Get()->PushSync([a, b, resource, ret](RunContext ctx) {
          TBlob tmp = ret.data();
          ndarray::EvalRandom<gpu, Distribution>(a, b, resource, &tmp, ctx);
          // Wait GPU kernel to complete
          ctx.get_stream<gpu>()->Wait();
        }, out->ctx(), {}, {ret.var(), resource.var},
        FnProperty::kNormal, 0, PROFILER_MESSAGE_FUNCNAME);
      break;
    }
#endif
    default: LOG(FATAL) << MXNET_GPU_NOT_ENABLED_ERROR;
  }
}

void SampleUniform(real_t begin, real_t end, NDArray *out) {
  SampleOP<ndarray::UniformDistribution>(begin, end, out);
}

void SampleGaussian(real_t mu, real_t sigma, NDArray *out) {
  SampleOP<ndarray::GaussianDistribution>(mu, sigma, out);
}

void SampleExponential(real_t lambda, NDArray *out) {
  if ( out->ctx().dev_mask() != cpu::kDevMask ) {
    LOG(FATAL) <<"exponential sampling only valid on cpu";
  }
  real_t dummy;
  SampleOP<ndarray::ExponentialDistribution>(lambda, dummy, out);
}

void SamplePoisson(real_t lambda, NDArray *out) {
  if ( out->ctx().dev_mask() != cpu::kDevMask ) {
    LOG(FATAL) <<"poisson sampling only valid on cpu";
  }
  real_t dummy;
  SampleOP<ndarray::PoissonDistribution>(lambda, dummy, out);
}

void SampleNegBinomial(int32_t k, real_t p, NDArray *out) {
  if ( out->ctx().dev_mask() != cpu::kDevMask ) {
    LOG(FATAL) <<"negative binomial sampling only valid on cpu";
  }
  SampleOP<ndarray::NegBinomialDistribution>(k, p, out);
}

void SampleGenNegBinomial(real_t mu, real_t alpha, NDArray *out) {
  if ( out->ctx().dev_mask() != cpu::kDevMask ) {
    LOG(FATAL) <<"negative binomial sampling only valid on cpu";
  }
  SampleOP<ndarray::GenNegBinomialDistribution>(mu, alpha, out);
}

void RandomSeed(uint32_t seed) {
  ResourceManager::Get()->SeedRandom(seed);
}

template<typename OP>
inline NDArray BinaryOpRet(const NDArray &lhs,
                           const NDArray &rhs) {
  NDArray ret;
  BinaryOp<OP>(lhs, rhs, &ret);
  return ret;
}

template<typename OP, bool reverse>
inline NDArray ScalarOpRet(const NDArray &lhs,
                           const real_t &rhs) {
  NDArray ret;
  ScalarOp<OP, reverse>(lhs, rhs, &ret);
  return ret;
}

template<typename OP>
inline NDArray &BinaryOpApply(NDArray *dst,
                              const NDArray &src) {
  BinaryOp<OP>(*dst, src, dst);
  return *dst;
}

template<typename OP>
inline NDArray &ScalarOpApply(NDArray *dst,
                             const real_t &src) {
  ScalarOp<OP, false>(*dst, src, dst);
  return *dst;
}

// Binary
NDArray operator+(const NDArray &lhs, const NDArray &rhs) {
  return BinaryOpRet<ndarray::Plus>(lhs, rhs);
}
NDArray operator-(const NDArray &lhs, const NDArray &rhs) {
  return BinaryOpRet<ndarray::Minus>(lhs, rhs);
}
NDArray operator*(const NDArray &lhs, const NDArray &rhs) {
  return BinaryOpRet<ndarray::Mul>(lhs, rhs);
}
NDArray operator/(const NDArray &lhs, const NDArray &rhs) {
  return BinaryOpRet<ndarray::Div>(lhs, rhs);
}
// Scalar
NDArray operator+(const NDArray &lhs, const real_t &rhs) {
  return ScalarOpRet<ndarray::Plus, false>(lhs, rhs);
}
NDArray operator-(const NDArray &lhs, const real_t &rhs) {
  return ScalarOpRet<ndarray::Minus, false>(lhs, rhs);
}
NDArray operator*(const NDArray &lhs, const real_t &rhs) {
  return ScalarOpRet<ndarray::Mul, false>(lhs, rhs);
}
NDArray operator/(const NDArray &lhs, const real_t &rhs) {
  return ScalarOpRet<ndarray::Div, false>(lhs, rhs);
}

// Binary
NDArray &NDArray::operator=(real_t scalar) {
  SetValueOp(scalar, this);
  return *this;
}

NDArray &NDArray::operator+=(const NDArray &src) {
  return BinaryOpApply<ndarray::Plus>(this, src);
}
NDArray &NDArray::operator-=(const NDArray &src) {
  return BinaryOpApply<ndarray::Minus>(this, src);
}
NDArray &NDArray::operator*=(const NDArray &src) {
  return BinaryOpApply<ndarray::Mul>(this, src);
}
NDArray &NDArray::operator/=(const NDArray &src) {
  return BinaryOpApply<ndarray::Div>(this, src);
}
// Scalar
NDArray &NDArray::operator+=(const real_t &src) {
  return ScalarOpApply<ndarray::Plus>(this, src);
}
NDArray &NDArray::operator-=(const real_t &src) {
  return ScalarOpApply<ndarray::Minus>(this, src);
}
NDArray &NDArray::operator*=(const real_t &src) {
  return ScalarOpApply<ndarray::Mul>(this, src);
}
NDArray &NDArray::operator/=(const real_t &src) {
  return ScalarOpApply<ndarray::Div>(this, src);
}

void NDArray::Save(dmlc::Stream *strm) const {
  // save shape
  shape_.Save(strm);
  if (is_none()) return;
  // save context
  Context ctx = this->ctx();
  ctx.Save(strm);
  TBlob save_data;
  NDArray temp;
  if (ctx.dev_mask() != cpu::kDevMask) {
    temp = this->Copy(Context::CPU());
    temp.WaitToRead();
    save_data = temp.data();
  } else {
    this->WaitToRead();
    save_data = this->data();
  }
  // save type flag
  int32_t type_flag = save_data.type_flag_;
  strm->Write(&type_flag, sizeof(type_flag));
  CHECK(save_data.CheckContiguous());
  size_t type_size = mshadow::mshadow_sizeof(type_flag);
  strm->Write(save_data.dptr_, type_size * shape_.Size());
}

bool NDArray::Load(dmlc::Stream *strm) {
  // load shape
  TShape shape;
  if (!shape.Load(strm)) return false;
  if (shape.ndim() == 0) {
    *this = NDArray(); return true;
  }
  // load context
  Context ctx;
  if (!ctx.Load(strm)) return false;
  // load type flag
  int32_t type_flag;
  if (strm->Read(&type_flag, sizeof(type_flag)) != sizeof(type_flag)) return false;
  // load data into CPU
  NDArray temp(shape, Context::CPU(), false, type_flag);
  TBlob load_data = temp.data();
  size_t type_size = mshadow::mshadow_sizeof(type_flag);
  size_t nread = type_size * shape.Size();

  if (strm->Read(load_data.dptr_, nread) != nread) return false;
  if (ctx.dev_mask() == cpu::kDevMask) {
    *this = std::move(temp); return true;
  } else {
#if MXNET_USE_CUDA
    *this = temp.Copy(ctx); return true;
#else
    *this = std::move(temp); return true;
#endif
  }
}


const uint64_t kMXAPINDArrayListMagic = 0x112;

void NDArray::Save(dmlc::Stream* fo,
                   const std::vector<NDArray>& data,
                   const std::vector<std::string>& names) {
  uint64_t header = kMXAPINDArrayListMagic, reserved = 0;
  fo->Write(&header, sizeof(header));
  fo->Write(&reserved, sizeof(reserved));
  fo->Write(data);
  fo->Write(names);
}

void NDArray::Load(dmlc::Stream* fi,
                   std::vector<NDArray>* data,
                   std::vector<std::string>* keys) {
  uint64_t header, reserved;
  CHECK(fi->Read(&header))
      << "Invalid NDArray file format";
  CHECK(fi->Read(&reserved))
      << "Invalid NDArray file format";
  CHECK(header == kMXAPINDArrayListMagic)
      << "Invalid NDArray file format";
  CHECK(fi->Read(data))
      << "Invalid NDArray file format";
  CHECK(fi->Read(keys))
      << "Invalid NDArray file format";
  CHECK(keys->size() == 0 || keys->size() == data->size())
      << "Invalid NDArray file format";
}

NDArray NDArray::Copy(Context ctx) const {
  NDArray ret(shape(), ctx, true, dtype_);
  CopyFromTo(*this, &ret);
  return ret;
}

void NDArray::SyncCopyFromCPU(const void *data, size_t size) const {
  TShape dshape = this->shape();
  CHECK_EQ(dshape.Size(), size)
      << "Memory size do not match";
  TBlob src((void*)data, dshape, cpu::kDevMask, this->dtype_); // NOLINT(*)

  if (this->ctx().dev_mask() == cpu::kDevMask) {
    this->WaitToWrite();
    RunContext rctx;
    rctx.stream = nullptr;
    TBlob dst = this->data();
    ndarray::Copy<cpu, cpu>(src, &dst, Context::CPU(), Context::CPU(), rctx);
  } else {
#if MXNET_USE_CUDA
    Engine::Get()->PushSync([&](RunContext rctx) {
        TBlob dst = this->data();
        ndarray::Copy<cpu, gpu>(src, &dst,
                                Context::CPU(), this->ctx(), rctx);
        // Wait GPU kernel to complete
        rctx.get_stream<gpu>()->Wait();
      }, this->ctx(), {}, {this->var()},
      FnProperty::kCopyToGPU, 0, PROFILER_MESSAGE("SyncCopyCPU2GPU"));
    this->WaitToRead();
#else
    LOG(FATAL) << "GPU is not enabled";
#endif
  }
}

void NDArray::SyncCopyToCPU(void *data, size_t size) const {
  TShape dshape = this->shape();
  CHECK_EQ(dshape.Size(), size)
      << "Memory size do not match";
  TBlob dst(data, dshape, cpu::kDevMask, this->dtype_); // NOLINT(*)

  if (this->ctx().dev_mask() == cpu::kDevMask) {
    this->WaitToRead();
    RunContext rctx;
    rctx.stream = nullptr;
    ndarray::Copy<cpu, cpu>(this->data(), &dst,
                            Context::CPU(), Context::CPU(), rctx);
  } else {
#if MXNET_USE_CUDA
    Engine::Get()->PushSync([&](RunContext rctx) {
        ndarray::Copy<gpu, cpu>(this->data(), &dst,
                                this->ctx(), Context::CPU(), rctx);
        // Wait GPU kernel to complete
        rctx.get_stream<gpu>()->Wait();
      }, this->ctx(), {this->var()}, {},
      FnProperty::kCopyFromGPU, 0, PROFILER_MESSAGE("SyncCopyGPU2CPU"));
    this->WaitToWrite();
#else
    LOG(FATAL) << "GPU is not enabled";
#endif
  }
}

#if MXNET_PREDICT_ONLY == 0
// register API function
// those with underscore will be registered at NDArray
MXNET_REGISTER_NDARRAY_FUN(_set_value)
.set_function(SetValueOp);


MXNET_REGISTER_NDARRAY_FUN(_onehot_encode)
.set_function(BinaryOp<ndarray::OneHotEncode>);

MXNET_REGISTER_NDARRAY_FUN(choose_element_0index)
.set_function(BinaryOp<ndarray::MatChooseRowElem>)
.describe("Choose one element from each line(row for python, column for R/Julia)"
          " in lhs according to index indicated by rhs."
          " This function assume rhs uses 0-based index.");

MXNET_REGISTER_NDARRAY_FUN(fill_element_0index)
.set_function(TernaryOp<ndarray::MatFillRowElem>)
.describe("Fill one element of each line(row for python, column for R/Julia)"
" in lhs according to index indicated by rhs and values indicated by mhs."
" This function assume rhs uses 0-based index.");

// register API function
// those with underscore will be registered at NDArray


// copy function is special
// that we need to remove kAcceptEmptyMutateTarget from it
MXNET_REGISTER_NDARRAY_FUN(_copyto)
.set_function(CopyFromToSimple)
.set_type_mask(kNDArrayArgBeforeScalar);

void Imdecode(NDArray *ret, NDArray mean, size_t index,
              size_t x0, size_t y0, size_t x1, size_t y1, size_t n_channels,
              size_t size, char *str_img) {
#if MXNET_USE_OPENCV
  cv::Mat buf(1, size, CV_8U, str_img);
  cv::Mat res = cv::imdecode(buf, n_channels == 1 ? 0 : -1);
  CHECK(res.data != NULL) << "OpenCV Failed to decode image";
  CHECK_LE(n_channels, static_cast<size_t>(res.channels()));
  if (y1 - y0 == 0) {
    x0 = 0;
    x1 = res.cols;
    y0 = 0;
    y1 = res.rows;
  }
  CHECK(x1 <= static_cast<size_t>(res.cols) &&
        y1 <= static_cast<size_t>(res.rows));

  if (ret->is_none()) {
    *ret = NDArray(mshadow::Shape3(n_channels, y1-y0, x1-x0),
                   Context::CPU(), false,
                   mean.is_none() ? mshadow::default_type_flag : mean.dtype());
  }
  NDArray buff;
  if (ret->shape().ndim() == 3) {
    buff = ret->Reshape(mshadow::Shape4(1, ret->shape()[0], ret->shape()[1], ret->shape()[2]));
  } else {
    CHECK_EQ(ret->shape().ndim(), 4U);
    buff = ret->Slice(index, index+1);
  }
  CHECK_EQ(buff.ctx().dev_mask(), cpu::kDevMask);
  CHECK_EQ(n_channels, buff.shape()[1]);
  CHECK_EQ(y1-y0, buff.shape()[2]);
  CHECK_EQ(x1-x0, buff.shape()[3]);
  buff.WaitToWrite();
  if (mean.is_none()) {
    MSHADOW_TYPE_SWITCH(buff.dtype(), DType, {
      mshadow::Tensor<cpu, 4, DType> tensor = buff.data().get<cpu, 4, DType>();
      for (index_t i = 0; i < y1-y0; i++) {
        uchar* im_data = res.ptr<uchar>(y0+i) + res.channels()*x0;
        for (index_t j = 0; j < x1-x0; j++) {
          for (index_t k = 0; k < n_channels; k++) {
            tensor[0][k][i][j] = DType(im_data[k]);  // NOLINT(*)
          }
          im_data += res.channels();
        }
      }
    })
  } else {
    CHECK_EQ(mean.dtype(), buff.dtype());
    CHECK_EQ(mean.ctx().dev_mask(), cpu::kDevMask);
    CHECK_EQ(mean.shape()[0], buff.shape()[1]);
    CHECK_EQ(mean.shape()[1], buff.shape()[2]);
    CHECK_EQ(mean.shape()[2], buff.shape()[3]);
    mean.WaitToRead();
    MSHADOW_TYPE_SWITCH(buff.dtype(), DType, {
      mshadow::Tensor<cpu, 4, DType> tensor = buff.data().get<cpu, 4, DType>();
      mshadow::Tensor<cpu, 3, DType> tmean = mean.data().get<cpu, 3, DType>();
      for (index_t i = 0; i < y1-y0; i++) {
        uchar* im_data = res.ptr<uchar>(y0+i) + res.channels()*x0;
        for (index_t j = 0; j < x1-x0; j++) {
          for (index_t k = 0; k < n_channels; k++) {
            tensor[0][k][i][j] = DType(im_data[k]) - tmean[k][i][j];  // NOLINT(*)
          }
          im_data += res.channels();
        }
      }
    })
  }
#else
  LOG(FATAL) << "Compile with OpenCV for image decoding.";
#endif  // MXNET_USE_OPENCV
}

MXNET_REGISTER_NDARRAY_FUN(_imdecode)
.set_type_mask(kAcceptEmptyMutateTarget | kNDArrayArgBeforeScalar)
.set_body([](NDArray **u, real_t *s, NDArray **out,
             int num_params, char **param_keys, char **param_vals) {
    CHECK_EQ(num_params, 1);
    Imdecode(out[0], *u[0],
             static_cast<size_t>(s[0]),
             static_cast<size_t>(s[1]),
             static_cast<size_t>(s[2]),
             static_cast<size_t>(s[3]),
             static_cast<size_t>(s[4]),
             static_cast<size_t>(s[5]),
             static_cast<size_t>(s[6]),
             param_vals[0]);
  })
.set_num_use_vars(1)
.set_num_scalars(7)
.set_num_mutate_vars(1)
.describe("Decode an image, clip to (x0, y0, x1, y1), subtract mean, and write to buffer")
.add_argument("mean", "NDArray-or-Symbol", "image mean")
.add_argument("index", "int", "buffer position for output")
.add_argument("x0", "int", "x0")
.add_argument("y0", "int", "y0")
.add_argument("x1", "int", "x1")
.add_argument("y1", "int", "y1")
.add_argument("c", "int", "channel")
.add_argument("size", "int", "length of str_img");
#endif
}  // namespace mxnet