/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.
 */

#ifndef SINGA_CORE_TENSOR_H_
#define SINGA_CORE_TENSOR_H_

#include <vector>
#include <tuple>
#include <memory>

#include "singa/core/common.h"
#include "singa/core/device.h"
#include "singa/proto/core.pb.h"
#include "singa/utils/logging.h"

using std::vector;
using std::tuple;
namespace singa {

typedef vector<size_t> Shape;
/// hardcode the width of types defined in DataType
const size_t kDataWidth[] = {sizeof(float),  sizeof(float) / 2,
                             sizeof(int),    sizeof(char),
                             sizeof(double), sizeof(unsigned char)
                            };
inline size_t SizeOf(DataType t) {
  static_assert(kNumDataType == sizeof(kDataWidth) / sizeof(size_t),
                "Num of data types not match num of data width");
  CHECK_GT(kNumDataType, t);
  return kDataWidth[t];
}

/// A Tensor instance is a multi-dimensional array resident on a Device
/// (default device is the host CPU). The internal data is allocated in lazy
/// manner.
/// Linear algebra, neural net and random operations are provided against
/// Tensor.
/// For all operations, if the result tensor is passed as an argument,
/// then it must be set up correctly (shape, device). Otherwise, runtime error
/// like SegmentFault would happen. Simple type/device check would be conducted.
class Tensor {
 public:
  ~Tensor();
  Tensor();

  /// Constructor using default device.
  explicit Tensor(const Shape &shape, DataType dtype = kFloat32);

  /// Constructor with shape, device and data type
  Tensor(const Shape &shape,
         std::shared_ptr<Device> dev,
         DataType dtype = kFloat32);

  /// Copy constructor.  No deep copy.
  Tensor(const Tensor &from);

  /// Move constructor.  No deep copy.
  Tensor(Tensor &&from);

  // --------------------------------------------------------------------------
  // ---Following methods return info of the class without making any changes--
  // --------------------------------------------------------------------------

  /// For functions in xx_math.cc to access the block.
  /// Users should not operate against Block directly.
  /// block_ is allocated in constructors.
  Block *block() const { return block_; }

  std::shared_ptr<Device> device() const { return device_; }

  /// Return immutable Tensor values with given type.
  template <typename SType>
  const SType *data() const {
    return static_cast<const SType *>(block()->data());
  }

  /// data type, including kFloat16, kFloat32, kInt
  const DataType data_type() const { return data_type_; }

  const Shape &shape() const { return shape_; }

  const size_t shape(const size_t idx) const {
    CHECK_LT(idx, shape_.size());
    return shape_.at(idx);
  }

  size_t nDim() const { return shape_.size(); }

  bool empty() const { return nDim() == 0; }

  /// The stride should decrease except dim with stride=0 due to broadcasting
  bool transpose() const {
    if (!stride_.empty()) {
      auto last = stride_.front();
      for (auto s : stride_) {
        if (s > last && last > 0)  
          return true;
        if (s > 0)
          last = s;
      }
    }
    return false;
  }

  const vector<int>& stride() const { return stride_; }

  /// Return true if the content of the tensor is initialized
  bool initailized() const {
    return block_ != nullptr && block_->initialized();
  }

  /// Return number of total elements
  size_t Size() const {
    if (block_ == nullptr) return 0u;
    CHECK_EQ(block_->size() % SizeOf(data_type_), 0u);
    return block_->size() / SizeOf(data_type_);
  }

  /// Return memory size (i.e., Bytes)
  size_t MemSize() const { return block_->size(); }

  /// used for swig code to convert Tensor into numpy array.
  /// It gets data into 'value'
  template <typename SType>
  void GetValue(SType *value, const size_t num) {
    CHECK(device_ == defaultDevice);
    const SType* ptr = data<SType>();
    for (size_t i = 0; i < num; i++) value[i] = ptr[i];
  }

  /// Serialize data, shape and transpose to protobuf object.
  void ToProto(singa::TensorProto *proto) const;

  /// Return average L1 norm
  float L1() const;

  /// Return average L2 norm
  float L2() const;
  // --------------------------------------------------------------------------
  // ---Following methods changes the internal data
  // --------------------------------------------------------------------------

  /// Set each element of the tensor to be x
  template <typename SType>
  void SetValue(const SType x);

  /// For init the tensor values, copy 'num' elements from 'src' to the internal
  /// memory with 'offset' (elements).
  template <typename SType>
  void CopyDataFromHostPtr(const SType *src, const size_t num,
                           const size_t offset = 0);

  /// Copy data from another Tensor which may be on a diff device.
  /// Meta data would not be copied!
  void CopyData(const Tensor &other);

  /// Deserialize data, shape and transpose from protobuf object.
  void FromProto(const singa::TensorProto &proto);


  /// TODO(wangwei) merge RepeatData into  Repeat?
  void RepeatData(const vector<size_t>& repeats, int axis, int total_repeats,
                  const Tensor &other);

  // --------------------------------------------------------------------------
  // ---Following methods returns a new Tensor without change original tensor
  // --------------------------------------------------------------------------

  Tensor Repeat(const vector<size_t>& repeats, int axis,
                std::shared_ptr<Device> device = nullptr);

  /// return an exactly the same Tensor with data been deep copied to the given
  /// device. If 'device' is nullptr, then clone it one the current device.
  Tensor Clone(std::shared_ptr<Device> device = nullptr) const;

  // --------------------------------------------------------------------------
  // ---Following methods change the tensor and return itself
  // --------------------------------------------------------------------------
  /// Copy assignment
  Tensor &operator=(const Tensor &in);

  /// Move assignment
  Tensor &operator=(Tensor &&in);

  Tensor &operator+=(const Tensor &in);

  Tensor &operator-=(const Tensor &in);

  Tensor &operator*=(const Tensor &in);

  Tensor &operator/=(const Tensor &in);

  // Scalar operations.

  /// SType is a scalar type
  template <typename SType>
  Tensor &operator+=(const SType x);

  /// SType is a scalar type
  template <typename SType>
  Tensor &operator-=(const SType x);

  /// SType is a scalar type
  template <typename SType>
  Tensor &operator*=(const SType x);

  /// SType is a scalar type
  template <typename SType>
  Tensor &operator/=(const SType x);

  /// change the shape (and stride); the block may be reallocated.
  Tensor &Reshape(const Shape &shape);


  /// Resize the memory and return itself
  Tensor& Resize(const Shape& shape);

  /// Matrix transpose.  Valid only if shape.size() == 2.
  Tensor& T();

  /// Reverse the shape vector
  Tensor& Transpose();

  /// Change the axes
  Tensor& Transpose(const vector<size_t> &axes);

  /// Return a view of the input tensor whose shape is broadcasted to be
  /// compitable with the given shape
  Tensor& Broadcast(const Shape& shape);

  /// Reset the shape, device, and data type as given tensor.
  /// If block size changes, then reallocate a new block.
  /// The previous block would be deleted.
  Tensor& ResetLike(const Tensor &t);

  /// Reset the data type, it would reallocate block if type changes.
  Tensor& AsType(const DataType type);

  /// Reset the device.
  /// If the target device is a diff device, then do deep data copy.
  Tensor& ToDevice(std::shared_ptr<Device> dev);

  /// Equivalent to ToDevice(host_dev).
  Tensor& ToHost();

 protected:

  //generate strides automatically if stride field is not passed
  void generate_stride() {
    stride_.clear();
    if (shape_.size() == 0) {
      stride_.push_back(1);
      return;
    }

    size_t dim = Size();
    int cumulative_product = 1;
    for (size_t n = 0; n < shape_.size(); ++n) {
      cumulative_product = cumulative_product * shape_[n];
      stride_.push_back(dim / cumulative_product);
    }
  }

  void set_strides(const vector<int> new_strides) {
    stride_ = new_strides;
  }

 protected:
  DataType data_type_ = kFloat32;
  std::shared_ptr<Device> device_ = nullptr;
  /// Note: block_ is allocated in lazy manner to avoid frequent malloc/free.
  /// If you want to get an allocated Block, use block() instead of block_.
  Block *block_ = nullptr;
  Shape shape_ = {};
  vector<int> stride_ = {};
}; //end of tensor class


inline size_t Product(const Shape &shape, int start = 0, size_t len = 0) {
  if (len == 0) len = shape.size();
  if (len == 0) return 0;
  CHECK_LE(len, shape.size());
  size_t v = 1;
  for (unsigned int i = start; i < len; i++) v *= shape[i];
  return v;
}


inline void CheckDataTypeAndLang(const Tensor &in1, const Tensor &in2) {
  CHECK_EQ(in1.data_type(), in2.data_type());
  CHECK_EQ(in1.device()->lang(), in2.device()->lang());
}


template <typename FromType, typename ToType>
ToType TypeCast(const FromType &x) {
  // TODO(wangwei) cast fp16; prevent some casts, e.g., float to char
  return static_cast<ToType>(x);
}

Tensor Boradcast(const Shape& shape);

/// Reshape the given tensor and generate a new tensor; the total vol should match
/// which shares the memory with in if possible
Tensor Reshape(const Tensor &in, const Shape &s);

Tensor Resize(const Tensor &in, const Shape &s);

/// Reverse the shape vector
Tensor Transpose(const Tensor& in);

/// Return a view of the input tensor whose shape is broadcasted to be
/// compitable with the given shape
Tensor Broadcast(const Tensor& in, const Shape& shape);

/// Change the axes
Tensor Transpose(const Tensor& in, const vector<size_t> &axes);

/// Copy 'num' elements of src to dst.
/// The first 'src_offset' ('dst_offset') elements will be skipped.
void CopyDataToFrom(Tensor *dst, const Tensor &src, const size_t num,
                    const size_t dst_offset = 0, const size_t src_offset = 0);


void RepeatDataToFrom(bool broadcast_flag, const vector<size_t>& repeats, int axis,
                      Tensor *dst, const Tensor &in, const size_t num);

// =============Element-wise operations====================================
Tensor Abs(const Tensor &in);
Tensor Exp(const Tensor &in);
Tensor Log(const Tensor &in);
Tensor ReLU(const Tensor &in);
Tensor Sigmoid(const Tensor &in);
Tensor Sign(const Tensor &in);
Tensor Sqrt(const Tensor &in);
Tensor Square(const Tensor &in);
Tensor Tanh(const Tensor &in);
Tensor Transform(const Tensor &in);

void Abs(const Tensor &in, Tensor *out);
void Exp(const Tensor &in, Tensor *out);
void Log(const Tensor &in, Tensor *out);
void ReLU(const Tensor &in, Tensor *out);
void Sigmoid(const Tensor &in, Tensor *out);
void Sign(const Tensor &in, Tensor *out);
void Sqrt(const Tensor &in, Tensor *out);
void Square(const Tensor &in, Tensor *out);
void Tanh(const Tensor &in, Tensor *out);
void Transform(const Tensor &in, Tensor *out);

/// Element-wise opeartion, out[i]=in[i]^x
template <typename SType>
Tensor Pow(const Tensor &in, const SType x);
/// Element-wise opeartion, out[i]=in[i]^x
template <typename SType>
void Pow(const Tensor &in, const SType x, Tensor *out);
/// Element-wise opeartion, out[i]=baes[i]^exp[i]
Tensor Pow(const Tensor &base, const Tensor &exp);
/// Element-wise opeartion, out[i]=baes[i]^exp[i]
void Pow(const Tensor &base, const Tensor &exp, Tensor *out);

/// Element-wise operation, out[i]= (in[i] < x) ? 1.f : 0.f
template <typename SType>
Tensor operator<(const Tensor &in, const SType x);
template <typename SType>
void LT(const Tensor &in, const SType x, Tensor *out);

/// Element-wise operation, out[i]= (in1[i] < in2[i]) ? 1.f : 0.f
Tensor operator<(const Tensor &in1, const Tensor& in2);
void LT(const Tensor &in1, const Tensor& in2, Tensor *out);

/// Element-wise operation, out[i]= (in[i] <= x) ? 1.f : 0.f
template <typename SType>
Tensor operator<=(const Tensor &in, const SType x);
template <typename SType>
void LE(const Tensor &in, const SType x, Tensor *out);

/// Element-wise operation, out[i]= (in1[i] <= in2[i]) ? 1.f : 0.f
Tensor operator<=(const Tensor &in1, const Tensor& in2);
void LE(const Tensor &in1, const Tensor& in2, Tensor *out);

/// Element-wise operation, out[i]= (in[i] > x) ? 1.f : 0.f
template <typename SType>
Tensor operator>(const Tensor &in, const SType x);
template <typename SType>
void GT(const Tensor &in, const SType x, Tensor *out);

/// Element-wise operation, out[i]= (in1[i] > in2[i]) ? 1.f : 0.f
Tensor operator>(const Tensor &in1, const Tensor& in2);
void GT(const Tensor &in1, const Tensor& in2, Tensor *out);


/// Element-wise operation, out[i]= (in[i] >= x) ? 1.f : 0.f
template <typename SType>
Tensor operator>=(const Tensor &in, const SType x);
template <typename SType>
void GE(const Tensor &in, const SType x, Tensor *out);

/// Element-wise operation, out[i]= (in1[i] >= in2[i]) ? 1.f : 0.f
Tensor operator>=(const Tensor &in1, const Tensor& in2);
void GE(const Tensor &in1, const Tensor& in2, Tensor *out);


Tensor operator+(const Tensor &lhs, const Tensor &rhs);
void Add(const Tensor &lhs, const Tensor &rhs, Tensor *out);
Tensor operator-(const Tensor &lhs, const Tensor &rhs);
void Sub(const Tensor &lhs, const Tensor &rhs, Tensor *out);
Tensor operator*(const Tensor &lhs, const Tensor &rhs);
void EltwiseMult(const Tensor &lhs, const Tensor &rhs, Tensor *out);
Tensor operator/(const Tensor &lhs, const Tensor &rhs);
void Div(const Tensor &lhs, const Tensor &rhs, Tensor *out);

template <typename SType>
Tensor operator+(const Tensor &in, const SType x);
template <typename SType>
void Add(const Tensor &in, const SType x, Tensor *out);

template <typename SType>
Tensor operator-(const Tensor &in, const SType x);
template <typename SType>
void Sub(const Tensor &in, const SType x, Tensor *out);

template <typename SType>
Tensor operator*(const Tensor &in, const SType x);
template <typename SType>
void EltwiseMult(const Tensor &in, const SType x, Tensor *out);

/// For each element e of Tensor 'in', compute e / x
template <typename SType>
Tensor operator/(const Tensor &in, const SType x);
/// For each element e of Tensor 'in', compute e / x into out
template <typename SType>
void Div(const Tensor &in, const SType x, Tensor *out);

/// For each element e of Tensor 'in', compute x/e
template <typename SType>
Tensor Div(const SType x, const Tensor &in);
/// For each element e of Tensor 'in', compute x/e into 'out'
template <typename SType>
void Div(const SType x, const Tensor &in, Tensor *out);

template <typename SType = float>
SType Sum(const Tensor &in);


// ============Matrix (row/column) operations==================================
/// Average elements in the Tensor, currently only support vector and matrix.
/// if 'axis' is 0, average all rows into a single row
/// if 'axis' is 1, average all columns into a single column
/// TODO(wangwei) support arbitrary Tensor like numpy.average
Tensor Average(const Tensor &in, const int axis);

/// Add column 'v' with each column of matrix M
void AddColumn(const Tensor &v, Tensor *M);
/// For each column 'c' of matrix out, do c=alpha*v + beta*c
template <typename SType>
void AddColumn(const SType alpha, const SType beta, const Tensor &v,
               Tensor *out);
/// Add row 'v' with each row of matrix M; write results into 'out'
void AddRow(const Tensor &v, Tensor *out);
/// For each row 'r' of matrix out, do r=alpha*v + beta*r
template <typename SType>
void AddRow(const SType alpha, const SType beta, const Tensor &v, Tensor *M);
/// Divide column 'v' by each column of matrix M; write results into 'out'
void DivColumn(const Tensor &v, Tensor *M);
/// Divide row 'v' by each row of matrix M; write results into 'out'
void DivRow(const Tensor &v, Tensor *M);
/// Multiply column 'v' and each column of matrix M; write results into 'out'
void MultColumn(const Tensor &v, Tensor *M);
/// Multiply row 'v' with each row of matrix M; write results into 'out'
void MultRow(const Tensor &v, Tensor *M);
/// Do softmax for each row. 'in' could be a 1-d or 2-d Tensor.
Tensor SoftMax(const Tensor &in);

Tensor RowMax(const Tensor &in);
/// Do softmax for each row. 'in' could be a 1-d or 2-d Tensor.
void SoftMax(const Tensor &in, Tensor *out);
/// Sub column 'v' by each column of matrix M
void SubColumn(const Tensor &v, Tensor *M);
/// Sub row 'v' by each row of matrix M; write results into 'out'
void SubRow(const Tensor &v, Tensor *M);
/// Sum all columns of matrix M into a single column as 'out'
void SumColumns(const Tensor &M, Tensor *out);
/// Sum all rows of matrix M into a single row as 'out'
void SumRows(const Tensor &M, Tensor *out);

/// Sum elements in the Tensor, currently only support vector and matrix.
/// if 'axis' is 0, sum all rows into a single row
/// if 'axis' is 1, sum all columns into a single column
/// TODO(wangwei) support arbitrary Tensor like numpy.sum
Tensor Sum(const Tensor &in, const int axis);

// ================Random operations==========================================
/// For each element x set x = 1 if random() < p; otherwise x = 1.
template <typename SType>
void Bernoulli(const SType p, Tensor *out);
/// Fill in Tensor 't' following Gaussian distribution.
template <typename SType>
void Gaussian(const SType mean, const SType std, Tensor *out);
/// Fill in Tensor 't' following uniform distribution.
template <typename SType>
void Uniform(const SType low, const SType high, Tensor *out);

// ================Blas operations============================================
// TODO(wangwei) make amax/amin/asum a member function of tensor

/// out = alpha*in + out
template <typename SType>
void Axpy(SType alpha, const Tensor &in, Tensor *out);

/// Do matrix vector multipication or matrix matrix multiplication depdending
/// on the Tensor shape.  result = A * B
Tensor Mult(const Tensor &A, const Tensor &B);
/// Do matrix vector multipication or matrix matrix multiplication depdending
/// on the Tensor shape.  C = A * B
void Mult(const Tensor &A, const Tensor &B, Tensor *C);
/// Do matrix vector multipication or matrix matrix multiplication depdending
/// on the Tensor shape. out = alpha lhs * rhs + beta * out
template <typename SType>
void Mult(const SType alpha, const Tensor &A, const Tensor &B, const SType beta,
          Tensor *C);

// *****************
// Misc.
// ****************
/// Compute the cross entropy loss given the prediction probability 'p' and
/// the target (ground truth) labels 't'. 'p' could be either a 1-d vector for
/// a single instance or a 2-d matrix for a batch of instances. t[i]
/// could be the ground truth label index or a label weighted
/// array of the i-th instance. For example, if there are 3 candidate labels for
/// each instance, t[i] could be 2 or [0, 0, 1]. If one instance could have
/// multiple labels, then t[i] could be [1, 0, 1].
/// The loss is computed into p.

void ComputeCrossEntropy(const Tensor &p, const Tensor &t, Tensor *loss);

/// Compute the dx, given prediction probability 'p' (p=softmax(x)) and
/// the target (ground truth) labels 't'. 'p' and 't' are either 1-d vector
/// or 2-d matrix. 'grad' has the same shape as 'p'. dx is computed into p.

void SoftmaxCrossEntropyBwd(const Tensor &t, Tensor *p);

/// To be called by pysinga autograd operations;
/// swig ignores the const qualifier http://www.swig.org/Doc3.0/SWIGPlus.html#SWIGPlus_const
Tensor CrossEntropyFwd(const Tensor& p, const Tensor& t);
Tensor SoftmaxCrossEntropyBwd(const Tensor& p, const Tensor& t);

/// Return a tensor consisting of rows ([start, end)) from 'in'. It copies the
/// values from 'in'. 'in' ia a 2D Tensor.
Tensor CopyRows(const Tensor &in, const size_t start, const size_t end);
/// Alias of CopyRows
Tensor SliceRows(const Tensor &in, const size_t start, const size_t end);
/// Slice the input tensor along the give axis to generate a new tensor
Tensor SliceOn(const Tensor &in, const size_t start, const size_t end,
               int axis);
/// Return a tensor consisting of columns ([start, end)) from 'in'. It copies
/// the values from 'in'. 'in' is a  2D Tensor.
Tensor CopyColumns(const Tensor &in, const size_t start, const size_t end);
/// Alias of CopyColumns
Tensor SliceColumns(const Tensor &in, const size_t start, const size_t end);
/// Return a tensor which is vertically stacked from tensors in 'in'. Each
/// tensor in 'in' is a 2D tensor. Values are copied, no memory sharing.
Tensor ConcatenateRows(const vector<Tensor> &in);
/// Return a tensor concatenated of the input tensors along the give axis.
Tensor ConcatOn(const std::vector<Tensor> &in, int axis);
/// Alias name for function ConcatenateRows
Tensor ConcatRows(const vector<Tensor> &in);
/// Return a tensor which is horizontally stacked from tensors in 'in'. Each
/// tensor in 'in' is a 2D tensor. Values are copied, no memory sharing.
Tensor ConcatenateColumns(const vector<Tensor> &in);
/// Alias name for function ConcatenateColumns
Tensor ConcatColumns(const vector<Tensor> &in);
}  // namespace singa

#endif  // SINGA_CORE_TENSOR_H_