/*!
 *  Copyright (c) 2015 by Contributors
 * \file ndarray_op.h
 * \brief the real execution functions of ndarray operations
 */
#ifndef MXNET_NDARRAY_NDARRAY_FUNCTION_H_
#define MXNET_NDARRAY_NDARRAY_FUNCTION_H_

#include <dmlc/logging.h>
#include <mshadow/tensor.h>
#include <mxnet/base.h>
#include <mxnet/resource.h>
#include <vector>
#include "../operator/mshadow_op.h"

namespace mxnet {
/*! \brief namespace to support all possible Ndarray operator */
namespace ndarray {
struct BinaryBase {
  inline static TShape GetShape(const TShape &lshape, const TShape &rshape) {
    CHECK(lshape == rshape) << "operands shape mismatch";
    CHECK(lshape.ndim() != 0) << "source operand have zero dimension shape";
    return lshape;
  }
};

// operators
struct Plus : public BinaryBase {
  typedef mshadow::op::plus mshadow_op;
};

struct Minus : public BinaryBase {
  typedef mshadow::op::minus mshadow_op;
};

struct Mul : public BinaryBase {
  typedef mshadow::op::mul mshadow_op;
};

struct Div : public BinaryBase {
  typedef mshadow::op::div mshadow_op;
};

struct Mod : public BinaryBase {
  typedef op::mshadow_op::mod mshadow_op;
};

struct ClipMin : public BinaryBase {
  struct mshadow_op {
    template<typename DType>
    MSHADOW_XINLINE static DType Map(DType a, DType b) {
      if (a < b) {
        return b;
      } else {
        return a;
      }
    }
  };
};

struct ClipMax : public BinaryBase {
  struct mshadow_op {
    template<typename DType>
    MSHADOW_XINLINE static DType Map(DType a, DType b) {
      if (a > b) {
        return b;
      } else {
        return a;
      }
    }
  };
};


struct OneHotEncode {
  inline static TShape GetShape(const TShape &index, const TShape &proptype) {
    CHECK(index.ndim() == 1 && proptype.ndim() == 2) << "OneHotEncode only support 1d index.";
    CHECK_EQ(index[0], proptype[0]) << "OneHotEncode shape inconsistent";
    return proptype;
  }
};

struct MatChooseRowElem {
  inline static TShape GetShape(const TShape &lshape, const TShape &rshape) {
    CHECK(lshape.ndim() == 2 && rshape.ndim() == 1)
        << "choose_row_element only support 2D Matrix and 1D index";
    CHECK_EQ(lshape[0], rshape[0]) << "choose_row_element index and matrix shape mismatch";
    return rshape;
  }
};

struct MatFillRowElem {
  inline static TShape GetShape(const TShape &lshape, const TShape &mshape, const TShape &rshape) {
    CHECK(lshape.ndim() == 2 && mshape.ndim() == 1 && rshape.ndim() == 1)
        << "fill_row_element only support 2D Matrix, 1D value and 1D index";
    CHECK((lshape[0] == mshape[0]) && (mshape[0] == rshape[0]))
        << "choose_row_element index vector, value vector and matrix shape mismatch";
    return lshape;
  }
};

// type holder for random number generators
struct UniformDistribution {};

struct GaussianDistribution {};

struct GammaDistribution {};

struct ExponentialDistribution {};

struct PoissonDistribution {};

struct NegBinomialDistribution {};

struct GenNegBinomialDistribution {};

template<typename Device>
void EvalClip(const TBlob &src, const real_t &a_min, const real_t &a_max,
              TBlob *ret, RunContext ctx);

template<typename Device, typename OP>
void Eval(const TBlob &lhs, const TBlob &mhs, const TBlob &rhs, TBlob *ret, RunContext ctx);

template<typename Device, typename OP>
void Eval(const TBlob &lhs, const TBlob &rhs, TBlob *ret, RunContext ctx);

template<typename Device, typename OP>
void Eval(const TBlob &src, TBlob *ret, RunContext ctx);

template<typename Device, typename OP, bool reverse>
void Eval(const TBlob &lhs, const real_t &rhs, TBlob *ret, RunContext ctx);

template<typename Device>
void Eval(const real_t &rhs, TBlob *ret, RunContext ctx);

template<typename Device, typename Distribution>
void EvalRandom(const real_t &a,
                const real_t &b,
                const Resource &resource,
                TBlob *ret,  RunContext ctx);

// copy function when only cpu is involved
template<typename DeviceFrom, typename DeviceTo>
void Copy(const TBlob &from, TBlob *to,
          Context from_ctx, Context to_ctx,
          RunContext ctx);

template<typename Device>
void ElementwiseSum(const std::vector<TBlob> source,
                    TBlob *out,
                    RunContext ctx);

// broadcasting
template <typename Device>
void EvalBroadcast(TBlob const& src, TBlob* ret, int size, RunContext ctx);

}  // namespace ndarray
}  // namespace mxnet
#endif  // MXNET_NDARRAY_NDARRAY_FUNCTION_H_