#pragma once

#include <ATen/Tensor.h>
#include <c10/core/TensorImpl.h>
#include <c10/util/Exception.h>

namespace at {
struct TORCH_API SparseTensorImpl : public TensorImpl {
  // Stored in COO format, indices + values.

  // INVARIANTS:
  // sparse_dim: range [0, len(shape)]; sparse_dim + dense_dim = len(shape)
  // dense_dim : range [0, len(shape)]; sparse_dim + dense_dim = len(shape)
  // _indices.shape: dimensionality: 2,  shape: (sparse_dim, nnz)
  // _values.shape:  dimensionality: 1 + dense_dim.  shape: (nnz, shape[sparse_dim:])

  int64_t sparse_dim_ = 0; // number of sparse dimensions
  int64_t dense_dim_ = 0; // number of dense dimensions

  Tensor indices_; // always a LongTensor
  Tensor values_;

  // A sparse tensor is 'coalesced' if every index occurs at most once in
  // the indices tensor, and the indices are in sorted order.  (This means
  // that it is very easy to convert a coalesced tensor to CSR format: you
  // need only compute CSR format indices.)
  //
  // Most math operations can only be performed on coalesced sparse tensors,
  // because many algorithms proceed by merging two sorted lists (of indices).
  bool coalesced_ = false;

public:
  // Public for now...
  explicit SparseTensorImpl(at::DispatchKeySet, const caffe2::TypeMeta);

  int64_t nnz() const { return values_.size(0); }
  int64_t sparse_dim() const { return sparse_dim_; }
  int64_t dense_dim() const { return dense_dim_; }
  bool coalesced() const { return coalesced_; }
  Tensor indices() const { return indices_; }
  Tensor values() const { return values_; }

  IntArrayRef strides() const override;
  bool is_contiguous(at::MemoryFormat memory_format=at::MemoryFormat::Contiguous) const override;
  int64_t stride(int64_t d) const override;
  void set_size(int64_t dim, int64_t new_size) override;
  void set_stride(int64_t dim, int64_t new_stride) override;
  void set_storage_offset(int64_t storage_offset) override;

#ifdef DEBUG
  bool has_storage() const override;
#endif
  const Storage& storage() const override;

  // WARNING: This function does NOT preserve invariants of sparse_dim/dense_dim with
  // respect to indices and values
  void raw_resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
    TORCH_CHECK(allow_tensor_metadata_change(), "raw_resize_ ", err_msg_tensor_metadata_change_not_allowed);
    sizes_and_strides_.set_sizes(size);
    sparse_dim_ = sparse_dim;
    dense_dim_ = dense_dim;
    refresh_numel();
  }

  // NOTE: This function preserves invariants of sparse_dim/dense_dim with respect to
  // indices and values.
  //
  // NOTE: This function supports the following cases:
  // 1. When we keep the number of dense dimensions unchanged, and NOT shrinking the size of
  // any of the dense dimensions.
  // 2. When we keep the number of sparse dimensions unchanged, and NOT shrinking the size of
  // any of the sparse dimensions.
  // 3. When the sparse tensor has zero nnz, in which case we are free to change the shapes of
  // both its sparse and dense dimensions.
  //
  // This function DOESN'T support (and will throw an error) the following cases:
  // 1. When we attempt to change the number of sparse dimensions on a non-empty sparse tensor
  // (such an operation will invalidate the indices stored).
  // 2. When we attempt to change the number of dense dimensions on a non-empty sparse tensor
  // (such an operation will behave differently from an equivalent dense tensor's resize method,
  // and for API consistency we don't support it).
  // 3. When we attempt to shrink the size of any of the dense dimensions on a non-empty sparse tensor
  // (such an operation will behave differently from an equivalent dense tensor's resize method,
  // and for API consistency we don't support it).
  // 4. When we attempt to shrink the size of any of the sparse dimensions on a non-empty sparse tensor
  // (this could make some of the stored indices out-of-bound and thus unsafe).
  void resize_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
    TORCH_CHECK(allow_tensor_metadata_change(), "resize_ ", err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(sparse_dim + dense_dim == static_cast<int64_t>(size.size()), "number of dimensions must be sparse_dim (", sparse_dim, ") + dense_dim (", dense_dim, "), but got ", size.size());
    if (nnz() > 0) {
      auto alt_options_msg = "You could try the following options:\n\
1. If you need an empty sparse tensor of this size, call `x = torch.sparse_coo_tensor(size)`.\n\
2. If you need to resize this tensor, you have the following options:\n\
    1. For both sparse and dense dimensions, keep the number of them constant and the size of them non-shrinking, and then try the same call again.\n\
    2. Or, create a new sparse tensor with the correct indices and values from this sparse tensor.";

      TORCH_CHECK(sparse_dim == sparse_dim_,
        "changing the number of sparse dimensions (from ", sparse_dim_, " to ", sparse_dim, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);

      TORCH_CHECK(dense_dim == dense_dim_,
        "changing the number of dense dimensions (from ", dense_dim_, " to ", dense_dim, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);

      bool shrinking_sparse_dims = false;
      bool shrinking_dense_dim = false;
      auto sparse_size_original = sizes().slice(0, sparse_dim);
      auto sparse_size_new = size.slice(0, sparse_dim);
      for (int64_t i = 0; i < sparse_dim; i++) {
        if (sparse_size_new[i] < sparse_size_original[i]) {
          shrinking_sparse_dims = true;
          break;
        }
      }
      auto dense_size_original = sizes().slice(sparse_dim);
      auto dense_size_new = size.slice(sparse_dim);
      for (int64_t i = 0; i < dense_dim; i++) {
        if (dense_size_new[i] < dense_size_original[i]) {
          shrinking_dense_dim = true;
          break;
        }
      }

      TORCH_CHECK(!shrinking_sparse_dims,
        "shrinking the size of sparse dimensions (from ", sparse_size_original, " to ", sparse_size_new, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);

      TORCH_CHECK(!shrinking_dense_dim,
        "shrinking the size of dense dimensions (from ", dense_size_original, " to ", dense_size_new, ") on a non-empty sparse tensor is not supported.\n", alt_options_msg);
    }

    const bool size_equals_sizes = std::equal(size.begin(), size.end(), sizes_and_strides_.sizes_begin(), sizes_and_strides_.sizes_end());
    if ((!size_equals_sizes) || (sparse_dim != sparse_dim_) || (dense_dim != dense_dim_)) {
      auto nnz = values().size(0);
      std::vector<int64_t> values_size = {nnz};
      auto dense_size = size.slice(sparse_dim);
      values_size.insert(values_size.end(), dense_size.begin(), dense_size.end());
      values_.resize_(values_size);
      indices_.resize_({sparse_dim, nnz});
    }

    if (!size_equals_sizes) {
      sizes_and_strides_.set_sizes(size);
    }
    sparse_dim_ = sparse_dim;
    dense_dim_ = dense_dim;
    refresh_numel();
  }

  // NOTE: this function will resize the sparse tensor and also set `indices` and `values` to empty.
  void resize_and_clear_(int64_t sparse_dim, int64_t dense_dim, IntArrayRef size) {
    TORCH_CHECK(allow_tensor_metadata_change(), "resize_and_clear_ ", err_msg_tensor_metadata_change_not_allowed);
    TORCH_CHECK(sparse_dim + dense_dim == static_cast<int64_t>(size.size()), "number of dimensions must be sparse_dim (", sparse_dim, ") + dense_dim (", dense_dim, "), but got ", size.size());

    sizes_and_strides_.set_sizes(size);
    sparse_dim_ = sparse_dim;
    dense_dim_ = dense_dim;

    auto empty_indices = at::empty({sparse_dim, 0}, indices().options());
    std::vector<int64_t> values_size = {0};
    auto dense_size = sizes().slice(sparse_dim);
    values_size.insert(values_size.end(), dense_size.begin(), dense_size.end());
    auto empty_values = at::empty(values_size, values().options());
    set_indices_and_values_unsafe(empty_indices, empty_values);
    refresh_numel();
  }

  void set_coalesced(bool coalesced) {
    TORCH_CHECK(allow_tensor_metadata_change(), "set_coalesced ", err_msg_tensor_metadata_change_not_allowed);
    coalesced_ = coalesced;
  }

  // NOTE: this function is only used internally and not exposed to Python frontend
  void set_nnz_and_narrow(int64_t new_nnz) {
    TORCH_CHECK(allow_tensor_metadata_change(), "set_nnz_and_narrow ", err_msg_tensor_metadata_change_not_allowed);
    AT_ASSERT(new_nnz <= nnz());
    indices_ = indices_.narrow(1, 0, new_nnz);
    values_ = values_.narrow(0, 0, new_nnz);
  }

  // Takes indices and values and directly puts them into the sparse tensor, no copy.
  // NOTE: this function is unsafe because it doesn't check whether any indices are
  // out of boundaries of `sizes`, so it should ONLY be used where we know that the
  // indices are guaranteed to be within bounds.
  // This used to be called THSTensor_(_move)
  // NB: This used to be able to avoid a refcount bump, but I was too lazy to
  // make it happen
  void set_indices_and_values_unsafe(const Tensor& indices, const Tensor& values);

  /**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change) const override {
    auto impl = c10::make_intrusive<SparseTensorImpl>(key_set(), dtype());
    copy_tensor_metadata(
      /*src_impl=*/this,
      /*dest_impl=*/impl.get(),
      /*version_counter=*/version_counter,
      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
    impl->refresh_numel();
    return impl;
  }

  /**
   * Return a TensorImpl that is a shallow-copy of this TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  c10::intrusive_ptr<TensorImpl> shallow_copy_and_detach(
      c10::VariableVersion&& version_counter,
      bool allow_tensor_metadata_change) const override {
    auto impl = c10::make_intrusive<SparseTensorImpl>(key_set(), dtype());
    copy_tensor_metadata(
      /*src_impl=*/this,
      /*dest_impl=*/impl.get(),
      /*version_counter=*/std::move(version_counter),
      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change);
    impl->refresh_numel();
    return impl;
  }

  /**
   * Shallow-copies data from another TensorImpl into this TensorImpl.
   *
   * For why this function doesn't check this TensorImpl's `allow_tensor_metadata_change_`,
   * see NOTE [ TensorImpl Shallow-Copying ].
   */
  void shallow_copy_from(const c10::intrusive_ptr<TensorImpl>& impl) override {
    AT_ASSERT(has_compatible_shallow_copy_type(impl->key_set()));
    auto sparse_impl = static_cast<const SparseTensorImpl*>(impl.get());
    copy_tensor_metadata(
      /*src_impl=*/sparse_impl,
      /*dest_impl=*/this,
      /*version_counter=*/version_counter(),
      /*allow_tensor_metadata_change=*/allow_tensor_metadata_change());
    refresh_numel();
  }
private:
    explicit SparseTensorImpl(at::DispatchKeySet, const caffe2::TypeMeta, at::Tensor indices, at::Tensor values);

  /**
   * Copy the tensor metadata fields (e.g. sizes / strides / storage pointer / storage_offset)
   * from one TensorImpl to another TensorImpl.
   *
   * For usage of `version_counter` and `allow_tensor_metadata_change`, see NOTE [ TensorImpl Shallow-Copying ].
   */
  static void copy_tensor_metadata(
      const SparseTensorImpl* src_sparse_impl,
      SparseTensorImpl* dest_sparse_impl,
      const c10::VariableVersion& version_counter,
      bool allow_tensor_metadata_change) {
    TensorImpl::copy_tensor_metadata(src_sparse_impl, dest_sparse_impl, version_counter, allow_tensor_metadata_change);

    // Sparse-specific fields
    dest_sparse_impl->sparse_dim_ = src_sparse_impl->sparse_dim();
    dest_sparse_impl->dense_dim_ = src_sparse_impl->dense_dim();
    dest_sparse_impl->indices_ = src_sparse_impl->indices();
    dest_sparse_impl->values_ = src_sparse_impl->values();
    dest_sparse_impl->coalesced_ = src_sparse_impl->coalesced();
  }
};

} // namespace at
