#pragma once

#include <ATen/Parallel.h>
#include <ATen/TensorUtils.h>
#include <limits>
#include <utility>
#include <cstring>

namespace at {

/*
[collapse dims] Updates sizes, and strides to reflect a "collapse" of
the info, possibly excluding the optional excludeDim. A "collapsed" version
of the info is the fewest dims that order the tensor's elements in the same
way as the original info. If excludeDim is specified, the collapse is the
fewest dims that order the tensor's elements as the original and preserve the
excluded dimension, unless the tensor collapses to a point.

This function returns a pair of values.

1) The (new) index of the preserved dimension if excludeDim is
specified. 0 if the tensor is collapsed to a point. -1
otherwise.

2) The new number of dimensions.
*/
template <typename T>
inline std::pair<int64_t, int64_t> collapse_dims(
    T* sizes,
    T* strides,
    int64_t dims,
    const int excludeDim = -1) {
  TORCH_CHECK(
      excludeDim >= -1 && excludeDim < dims,
      "expected excluded dim between -1 and dims - 1");

  int64_t stopDim = (excludeDim == -1) ? dims : excludeDim;
  int64_t newIndex = -1;
  int64_t oldIndex = 0;
  int64_t remappedExcludedDim = -1;

  while (oldIndex < dims) {
    // Finds a dimension to collapse into
    for (; oldIndex < stopDim; ++oldIndex) {
      if (sizes[oldIndex] == 1) {
        continue;
      }

      ++newIndex;
      sizes[newIndex] = sizes[oldIndex];
      strides[newIndex] = strides[oldIndex];
      ++oldIndex;
      break;
    }

    // Collapses dims
    for (; oldIndex < stopDim; ++oldIndex) {
      if (sizes[oldIndex] == 1) {
        continue;
      }

      if (strides[newIndex] == sizes[oldIndex] * strides[oldIndex]) {
        sizes[newIndex] *= sizes[oldIndex];
        strides[newIndex] = strides[oldIndex];
      } else {
        ++newIndex;
        sizes[newIndex] = sizes[oldIndex];
        strides[newIndex] = strides[oldIndex];
      }
    }

    // Handles excludeDim being set (oldIndex == excludeDim)
    if (oldIndex != dims) {
      // Preserves excluded dimension
      ++newIndex;
      sizes[newIndex] = sizes[oldIndex];
      strides[newIndex] = strides[oldIndex];
      remappedExcludedDim = newIndex;

      // Restarts iteration after excludeDim
      ++oldIndex;
      stopDim = dims;
    }
  }

  // Handles special case of all dims size 1
  if (newIndex == -1 || (newIndex == 0 && sizes[0] == 1)) {
    dims = 1;
    sizes[0] = 1;
    strides[0] = 1;

    return std::pair<int64_t, int64_t>(0, 1);
  }

  dims = newIndex + 1;
  return std::pair<int64_t, int64_t>(remappedExcludedDim, dims);
}

/*
 * The basic strategy for apply is as follows:
 *
 * 1. Starting with the outermost index, loop until we reach a dimension where
 * the data is no longer contiguous, i.e. the stride at that dimension is not
 * equal to the size of the tensor defined by the outer dimensions. Let's call
 * this outer (contiguous) tensor A. Note that if the Tensor is contiguous, then
 * A is equal to the entire Tensor. Let's call the inner tensor B.
 *
 * 2. We loop through the indices in B, starting at its outermost dimension. For
 * example, if B is a 2x2 matrix, then we do:
 *
 * B[0][0]
 * B[0][1]
 * B[1][0]
 * B[1][1]
 *
 * We set the offset into the underlying storage as (storageOffset + stride_B *
 * index_B), i.e. basically we compute the offset into the storage as we would
 * normally for a Tensor. But because we are guaranteed the subsequent data is
 * contiguous in memory, we can simply loop for sizeof(A) iterations and perform
 * the operation, without having to follow the order described by the strides of
 * A.
 *
 * 3. As an optimization, we merge dimensions of A that are contiguous in
 * memory. For example, if A is a 3x3x3x3 tensor narrowed from a 3x3x4x3 tensor,
 * then the first two dimensions can be merged for the purposes of APPLY,
 * reducing the number of nested loops.
 */

inline Tensor sort_strides(Tensor& tensor_) {
  IntArrayRef strides = tensor_.strides();
  std::vector<int64_t> indices;
  indices.reserve(tensor_.ndimension());
  for (int64_t i = 0; i < tensor_.ndimension(); i++) {
    indices.push_back(i);
  }
  std::sort(indices.begin(), indices.end(), [&strides](int64_t i1, int64_t i2) {
    return strides[i1] > strides[i2];
  });
  Tensor tensor = tensor_.permute(indices);
  return tensor;
}

template <typename T, int N>
struct strided_tensor_iter_fixed {
 public:
  T* data_ = NULL;
  int64_t dim_ = 0;

  int64_t counter_[N] = {0};
  int64_t sizes_[N] = {0};
  int64_t strides_[N] = {0};

  strided_tensor_iter_fixed(strided_tensor_iter_fixed const&) = delete;
  void operator=(strided_tensor_iter_fixed const& x) = delete;
  strided_tensor_iter_fixed(strided_tensor_iter_fixed&&) = default;
  strided_tensor_iter_fixed(Tensor& tensor, bool sort_strides = false)
      : data_(tensor.data_ptr<T>()) {
    std::memset(counter_, 0, sizeof(int64_t) * N);
    if (tensor.dim() > 0) {
      std::memcpy(
          sizes_, tensor.sizes().data(), tensor.dim() * sizeof(int64_t));
      std::memcpy(
          strides_,
          tensor.strides().data(),
          tensor.dim() * sizeof(int64_t));
    }
    dim_ = std::get<1>(collapse_dims(sizes_, strides_, tensor.ndimension()));
  }
};

template <typename T>
struct strided_tensor_iter {
 private:
 public:
  T* data_ = NULL;
  int64_t dim_;

  std::vector<int64_t> counter_;
  std::vector<int64_t> sizes_;
  std::vector<int64_t> strides_;

  strided_tensor_iter(strided_tensor_iter const&) = delete;
  void operator=(strided_tensor_iter const& x) = delete;
  strided_tensor_iter(strided_tensor_iter&&) = default;
  strided_tensor_iter(Tensor& tensor)
      : data_(tensor.data_ptr<T>()),
        dim_(tensor.ndimension()),
        counter_(dim_, 0),
        sizes_(tensor.sizes().vec()),
        strides_(tensor.strides().vec()) {
    dim_ = std::get<1>(collapse_dims(sizes_.data(), strides_.data(), dim_));
  }
};

inline bool _all_equal_numel(at::ArrayRef<Tensor> tensors) {
  if (tensors.size() == 0)
    return true;
  int64_t all_numel = tensors[0].numel();
  for (size_t i = 1; i < tensors.size(); i++) {
    if (tensors[i].numel() != all_numel)
      return false;
  }
  return true;
}

inline std::string _all_equal_numel_error(at::ArrayRef<Tensor> tensors) {
  std::ostringstream oss;
  oss << "inconsistent tensor size, expected ";
  for (size_t i = 0; i < tensors.size() - 1; i++) {
    oss << tensors[i].sizes() << ", ";
  }
  oss << "and " << tensors[tensors.size() - 1].sizes()
      << " to have the same number of elements, but got ";
  for (size_t i = 0; i < tensors.size() - 1; i++) {
    oss << tensors[i].numel() << ", ";
  }
  oss << "and " << tensors[tensors.size() - 1].numel()
      << " elements respectively";
  return oss.str();
}

inline bool _apply_preamble(ArrayRef<Tensor> tensors) {
  checkDeviceType("CPU_tensor_apply", tensors, kCPU);
  checkLayout("CPU_tensor_apply", tensors, kStrided);
  if (!_all_equal_numel(tensors))
    AT_ERROR(_all_equal_numel_error(tensors));
  // An empty tensor has no elements
  for (auto& t : tensors)
    if (t.numel() == 0)
      return false;
  return true;
}

inline int64_t _max_dim_tensors(ArrayRef<Tensor> tensors) {
  int64_t dim = 0;
  for (auto& t : tensors)
    dim = std::max(dim, t.ndimension());
  return dim;
}

inline void iterate(int64_t size){};

template <typename Arg, typename... Args>
inline void iterate(int64_t size, Arg& iter, Args&... iter_tail) {
  iter.counter_[iter.dim_ - 1] += size;
  iter.data_ = iter.data_ + size * iter.strides_[iter.dim_ - 1];
  iterate(size, iter_tail...);
}

inline bool iterate_continue() {
  return true;
};

template <typename Arg, typename... Args>
inline bool iterate_continue(Arg& iter, Args&... iter_tail) {
  return iter.counter_[iter.dim_ - 1] < iter.sizes_[iter.dim_ - 1] &&
      iterate_continue(iter_tail...);
}

inline int64_t max_iterate_size() {
  return std::numeric_limits<int64_t>::max();
};

template <typename Arg, typename... Args>
inline int64_t max_iterate_size(Arg& iter, Args&... iter_tail) {
  return std::min(
      (iter.sizes_[iter.dim_ - 1] - iter.counter_[iter.dim_ - 1]),
      max_iterate_size(iter_tail...));
}

inline void iterate_overflow(){};

template <typename Arg, typename... Args>
inline void iterate_overflow(Arg& iter, Args&... iter_tail) {
  if (iter.counter_[iter.dim_ - 1] == iter.sizes_[iter.dim_ - 1]) {
    for (int64_t i = iter.dim_ - 1; i > 0; i--) {
      if (iter.counter_[i] == iter.sizes_[i]) {
        iter.counter_[i] = 0;
        iter.counter_[i - 1]++;
        iter.data_ = iter.data_ - (iter.sizes_[i] * iter.strides_[i]) +
            iter.strides_[i - 1];
      }
    }
  }
  iterate_overflow(iter_tail...);
}

inline void forward(int64_t offset){};

template <typename Arg, typename... Args>
inline void forward(int64_t offset, Arg& iter, Args&... iter_tail) {
  int64_t multi = offset;
  for (int64_t i = iter.dim_ - 1; i >= 0; i--) {
    int64_t inc = multi % iter.sizes_[i];
    multi = multi / iter.sizes_[i];
    iter.data_ = iter.data_ + inc * iter.strides_[i];
    iter.counter_[i] += inc;
  }
  forward(offset, iter_tail...);
}

inline int64_t max_dim() {
  return 0;
}

template <typename Arg, typename... Args>
inline int64_t max_dim(Arg& iter, Args&... iter_tail) {
  return std::max(iter.dim_, max_dim(iter_tail...));
}

inline void apply_op(){};

template <typename Op, typename... Args>
inline void
apply_op(int64_t numel, int64_t offset, const Op& op, Args... iters) {
  // For 0-dim tensors
  if (numel == 1 && max_dim(iters...) == 0) {
    op(*iters.data_...);
    return;
  }
  if (offset > 0)
    forward(offset, iters...);
  // Splitting this into chunks helps the compiler create faster assembly
  for (int64_t i = 0; i < numel;) {
    for (; iterate_continue(iters...) && i < numel;) {
      op(*iters.data_...);
      iterate(1, iters...);
      i++;
    }
    iterate_overflow(iters...);
  }
}

/*
  Apply a pointwise operator to sequence of tensors

  The calling convention for op is a function/functor that takes the same
  number of pointers of type scalar as the number of given tensors. For example,
  to compute a = b * c, op would be of the form:
  [](scalar* a_val, const scalar* b_val, const scalar* c_val) { a_val[0] =
  b_val[0] * c_val[0]; };
*/

template <typename scalar1, typename scalar2, typename Op>
inline void CPU_tensor_apply2(Tensor tensor1, Tensor tensor2, const Op op) {