#pragma once

#include <c10/core/Device.h>
#include <c10/core/Layout.h>
#include <c10/core/MemoryFormat.h>
#include <c10/core/QScheme.h>
#include <c10/core/Scalar.h>
#include <c10/core/ScalarType.h>
#include <c10/core/Storage.h>
#include <ATen/core/TensorAccessor.h>
#include <c10/core/TensorImpl.h>
#include <c10/core/UndefinedTensorImpl.h>
#include <c10/util/Exception.h>
#include <c10/util/Deprecated.h>
#include <c10/util/Optional.h>
#include <c10/util/intrusive_ptr.h>
#include <ATen/core/DeprecatedTypePropertiesRegistry.h>
#include <ATen/core/DeprecatedTypeProperties.h>
#include <ATen/core/NamedTensor.h>

namespace caffe2 {
class Tensor;
}
namespace c10{
struct TensorOptions;
}
namespace at {
struct Generator;
struct Type;
class DeprecatedTypeProperties;
class Tensor;
} // namespace at
namespace at {
namespace indexing {
struct TensorIndex;
} // namespace indexing
} // namespace at

namespace torch { namespace autograd {

struct Node;

}} // namespace torch::autograd

namespace at {

class Tensor;
using TensorList = ArrayRef<Tensor>;

struct Quantizer;
// This is temporary typedef to enable Quantizer in aten native function API
// we'll remove them when we are actually exposing Quantizer class
// to frontend
using QuantizerPtr = c10::intrusive_ptr<Quantizer>;
using ConstQuantizerPtr = const c10::intrusive_ptr<Quantizer>&;

namespace impl {
inline bool variable_excluded_from_dispatch() {
  return c10::impl::tls_local_dispatch_key_set().excluded_.has(DispatchKey::VariableTensorId);
}
}

// Tensor is a "generic" object holding a pointer to the underlying TensorImpl object, which
// has an embedded reference count. In this way, Tensor is similar to boost::intrusive_ptr.
//
// For example:
//
// void func(Tensor a) {
//   Tensor b = a;
//   ...
// }
//
// In this example, when we say Tensor b = a, we are creating a new object that points to the
// same underlying TensorImpl, and bumps its reference count. When b goes out of scope, the
// destructor decrements the reference count by calling release() on the TensorImpl it points to.
// The existing constructors, operator overloads, etc. take care to implement the correct semantics.
//
// Note that Tensor can also be NULL, i.e. it is not associated with any underlying TensorImpl, and
// special care must be taken to handle this.
class CAFFE2_API Tensor {
 public:
  Tensor(){};
  // This constructor should not be used by end users and is an implementation
  // detail invoked by autogenerated code.
  explicit Tensor(
      c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl> tensor_impl)
      : impl_(std::move(tensor_impl)) {
    if (impl_.get() == nullptr) {
      throw std::runtime_error("TensorImpl with nullptr is not supported");
    }
  }
  Tensor(const Tensor&) = default;
  Tensor(Tensor&&) = default;


 public:
  // Creates a new wrapper from TensorImpl. Intentionally a free method because
  // it should be used with care. Checks necessary invariants
  static Tensor wrap_tensor_impl(
      c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl> tensor_impl) {
    Tensor r(std::move(tensor_impl));
    r.enforce_invariants();
    return r;
  }

  int64_t dim() const {
    return impl_->dim();
  }
  int64_t storage_offset() const {
    return impl_->storage_offset();
  }

  TensorImpl * unsafeGetTensorImpl() const {
    return impl_.get();
  }
  TensorImpl * unsafeReleaseTensorImpl() {
    return impl_.release();
  }
  const c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl>& getIntrusivePtr() const {
    return impl_;
  }

  bool defined() const {
    return impl_;
  }

  void reset() {
    impl_.reset();
  }

  // The following overloads are very intruiging.  Consider the following
  // program:
  //
  //    x[1] = 3;
  //
  // We would expect that the first entry of x is written to 3.  But how can we
  // actually achieve this?  x[1] evaluates to a tensor...
  //
  // The answer is, using a ref-qualifier.  x[1] is an rvalue, which cannot be
  // (profitably) assigned to in the traditional sense, so we overload
  // assignment to mean, "Actually, copy 3 into the tensor data."  This is done
  // with an rvalue-reference ref-qualified overload (the methods with && at the
  // end of their type.)
  //
  // There's one more fly in the ointment: We also want
  //
  //    Tensor x = y;
  //
  // to work, and we want it NOT to copy.  So we need a traditional operator=
  // overload.  But we MUST specify a mutable lvalue ref-qualifier, to
  // disambiguate the traditional overload from the rvalue-reference
  // ref-qualified overload.  Otherwise, it will be ambiguous, because
  // a non ref-qualified method is eligible for all situations.

  // Unfortunately, we have to write these constructors out manually
  // to work around an MSVC bug:
  //    error C2580: 'at::Tensor &at::Tensor::operator =(const at::Tensor &) &':
  //    multiple versions of a defaulted special member functions are not allowed
  // Tensor& operator=(const Tensor&) & = default;
  // Tensor& operator=(Tensor&&) & = default;
  Tensor& operator=(const Tensor& x) & {
    impl_ = x.impl_;
    return *this;
  }
  Tensor& operator=(Tensor&& x) & {
    impl_ = std::move(x.impl_);
    return *this;
  }

  Tensor& operator=(Scalar v) &&;
  Tensor& operator=(const Tensor&) &&;
  Tensor& operator=(Tensor&&) &&;

  bool is_same(const Tensor& other) const noexcept {
    return impl_ == other.impl_;
  }
  size_t use_count() const noexcept {
    return impl_.use_count();
  }
  size_t weak_use_count() const noexcept {
    return impl_.weak_use_count();
  }

  std::string toString() const;

  IntArrayRef sizes() const {
    return impl_->sizes();
  }
  IntArrayRef strides() const {
    return impl_->strides();
  }
  // See impl::get_opt_names in ATen/NamedTensor.h for docs.
  optional<DimnameList> opt_names() const {
    return impl::get_opt_names(unsafeGetTensorImpl());
  }
  // See impl::get_names in ATen/NamedTensor.h for docs.
  DimnameList names() const {
    return impl::get_names(unsafeGetTensorImpl());
  }
  int64_t ndimension() const {
    return dim();
  }

  bool is_contiguous(at::MemoryFormat memory_format=at::MemoryFormat::Contiguous) const {
    return impl_->is_contiguous(memory_format);
  }

  bool is_non_overlapping_and_dense() const {
    return impl_->is_non_overlapping_and_dense();
  }

  at::MemoryFormat suggest_memory_format(
      bool channels_last_strides_exact_match = false) const {
    // Setting channels_last_strides_exact_match to true forces function to
    // check 0,1 - sized dimention strides.
    if (!is_mkldnn() && !is_sparse() && impl_->is_strides_like_channels_last()) {
      if (!channels_last_strides_exact_match || get_channels_last_strides(sizes()) == strides()) {
        return at::MemoryFormat::ChannelsLast;
      }
    }
    return at::MemoryFormat::Contiguous;
  }

  // Total bytes consumed by the "view" of elements of the array.  Does not
  // include size of metadata.  The number reported here does not necessarily
  // correspond to the true physical memory consumed by a tensor; instead,
  // it reports the memory the tensor would take *if* it were contiguous.
  // Defined to be numel() * itemsize()
  size_t nbytes() const {
    return impl_->numel() * impl_->itemsize();
  }

  int64_t numel() const {
    return impl_->numel();
  }

  // Length of one array element in bytes.  This is the traditional
  // Numpy naming.
  size_t itemsize() const {
    return impl_->itemsize();
  }

  // Same as itemsize().  This is the PyTorch naming.
  int64_t element_size() const {
    return static_cast<int64_t>(impl_->itemsize());
  }

  C10_DEPRECATED_MESSAGE("Tensor.type() is deprecated. Instead use Tensor.options(), which in many cases (e.g. in a constructor) is a drop-in replacement. If you were using data from type(), that is now available from Tensor itself, so instead of tensor.type().scalar_type(), use tensor.scalar_type() instead and instead of tensor.type().backend() use tensor.device().")
  DeprecatedTypeProperties & type() const {
    return globalDeprecatedTypePropertiesRegistry().getDeprecatedTypeProperties(
        dispatchKeyToBackend(legacyExtractDispatchKey(key_set())),
        scalar_type());
  }
  DispatchKeySet key_set() const {
    return impl_->key_set();
  }
  ScalarType scalar_type() const {
    return typeMetaToScalarType(impl_->dtype());
  }
  bool has_storage() const {
    return defined() && impl_->has_storage();
  }
  const Storage& storage() const {
    return impl_->storage();
  }
  bool is_alias_of(const at::Tensor& other) const{
    return impl_->storage().is_alias_of(other.storage());
  }
  Tensor toType(ScalarType t) const;
  Tensor toBackend(Backend b) const;

  C10_DEPRECATED_MESSAGE("Tensor.is_variable() is deprecated; everything is a variable now. (If you want to assert that variable has been appropriately handled already, use at::impl::variable_excluded_from_dispatch())")
  bool is_variable() const noexcept {
    return !at::impl::variable_excluded_from_dispatch();
  }

  /// Returns a `Tensor`'s layout. Defined in Type.h
  Layout layout() const noexcept;

  /// Returns a `Tensor`'s dtype (`TypeMeta`). Defined in TensorMethods.h
  caffe2::TypeMeta dtype() const noexcept;

  /// Returns a `Tensor`'s device.
  Device device() const;

  /// Returns a `Tensor`'s device index.
  int64_t get_device() const;

  /// Returns if a `Tensor` has CUDA backend.
  bool is_cuda() const;

  /// Returns if a `Tensor` has HIP backend.
  bool is_hip() const;

  /// Returns if a `Tensor` has sparse backend.
  bool is_sparse() const;

  /// Returns if a `Tensor` is mkldnn tensor.
  bool is_mkldnn() const;

  /// Returns if a `Tensor` has quantized backend.
  bool is_quantized() const;

  /// If a tensor is a quantized tensor, returns its quantizer
  /// TODO: it's not in native_functions.yaml yet as it's not exposed to python
  QuantizerPtr quantizer() const;

  /// Returns if a `Tensor` has any dimension names
  bool has_names() const;

  /// Returns a `Tensor`'s dimension names data structure
  const NamedTensorMeta* get_named_tensor_meta() const;
  NamedTensorMeta* get_named_tensor_meta();

  /// Returns the `TensorOptions` corresponding to this `Tensor`. Defined in
  /// TensorOptions.h.
  TensorOptions options() const;

  void* data_ptr() const {
    return this->unsafeGetTensorImpl()->data();
  }

  template <typename T>
  T * data_ptr() const;

  template<typename T>
  C10_DEPRECATED_MESSAGE("Tensor.data<T>() is deprecated. Please use Tensor.data_ptr<T>() instead.")
  T * data() const {
    return data_ptr<T>();
  }

  template <typename T>
  T item() const;

  // Purposely not defined here to avoid inlining
  void print() const;

  // Return a `TensorAccessor` for CPU `Tensor`s. You have to specify scalar type and
  // dimension.
  template<typename T, size_t N>
  TensorAccessor<T,N> accessor() const& {
    static_assert(N > 0, "accessor is used for indexing tensor, for scalars use *data_ptr<T>()");
    TORCH_CHECK(dim() == N, "expected ", N, " dims but tensor has ", dim());
    return TensorAccessor<T,N>(data_ptr<T>(),sizes().data(),strides().data());
  }
  template<typename T, size_t N>
  TensorAccessor<T,N> accessor() && = delete;

  // Return a `GenericPackedTensorAccessor` for CUDA `Tensor`s. You have to specify scalar type and
  // dimension. You can optionally specify RestrictPtrTraits as a template parameter to
  // cast the data pointer to a __restrict__ pointer.
  // In order to use this, your CUDA kernel has to take a corresponding GenericPackedTensorAccessor
  // as an argument.
  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
  GenericPackedTensorAccessor<T,N,PtrTraits,index_t> generic_packed_accessor() const& {
    static_assert(N > 0, "accessor is used for indexing tensor, for scalars use *data_ptr<T>()");
    TORCH_CHECK(dim() == N, "expected ", N, " dims but tensor has ", dim());
    return GenericPackedTensorAccessor<T,N,PtrTraits,index_t>(static_cast<typename PtrTraits<T>::PtrType>(data_ptr<T>()),sizes().data(),strides().data());
  }
  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
  GenericPackedTensorAccessor<T,N> generic_packed_accessor() && = delete;

  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits>
  PackedTensorAccessor32<T,N,PtrTraits> packed_accessor32() const& {
    return generic_packed_accessor<T,N,PtrTraits,int32_t>();
  }
  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits>
  PackedTensorAccessor32<T,N,PtrTraits> packed_accessor32() && = delete;

  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits>
  PackedTensorAccessor64<T,N,PtrTraits> packed_accessor64() const& {
    return generic_packed_accessor<T,N,PtrTraits,int64_t>();
  }
  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits>
  PackedTensorAccessor64<T,N,PtrTraits> packed_accessor64() && = delete;

  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
  C10_DEPRECATED_MESSAGE("packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead")
  GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() const & {
    return generic_packed_accessor<T,N,PtrTraits,index_t>();
  }
  template<typename T, size_t N, template <typename U> class PtrTraits = DefaultPtrTraits, typename index_t = int64_t>
  C10_DEPRECATED_MESSAGE("packed_accessor is deprecated, use packed_accessor32 or packed_accessor64 instead")
  GenericPackedTensorAccessor<T,N,PtrTraits,index_t> packed_accessor() && = delete;

  Tensor operator-() const;
  Tensor& operator+=(const Tensor & other);
  Tensor& operator+=(Scalar other);
  Tensor& operator-=(const Tensor & other);
  Tensor& operator-=(Scalar other);
  Tensor& operator*=(const Tensor & other);
  Tensor& operator*=(Scalar other);
  Tensor& operator/=(const Tensor & other);
  Tensor& operator/=(Scalar other);
  Tensor operator[](Scalar index) const;
  Tensor operator[](Tensor index) const;
  Tensor operator[](int64_t index) const;

  Tensor index(ArrayRef<at::indexing::TensorIndex> indices) const;
  Tensor index(std::initializer_list<at::indexing::TensorIndex> indices) const;

  Tensor & index_put_(ArrayRef<at::indexing::TensorIndex> indices, Tensor const & rhs);
  Tensor & index_put_(ArrayRef<at::indexing::TensorIndex> indices, Scalar v);
  Tensor & index_put_(std::initializer_list<at::indexing::TensorIndex> indices, Tensor const & rhs);
  Tensor & index_put_(std::initializer_list<at::indexing::TensorIndex> indices, Scalar v);

  Tensor cpu() const;
  Tensor cuda() const;
  Tensor hip() const;

  // ~~~~~ Autograd API ~~~~~

  Tensor& set_requires_grad(bool requires_grad) {
    impl_->set_requires_grad(requires_grad);
    return *this;
  }
  bool requires_grad() const {
    return impl_->requires_grad();
  }

  Tensor& grad() {
    return impl_->grad();
  }
  const Tensor& grad() const {
    return impl_->grad();
  }

  // STOP.  Thinking of adding a method here, which only makes use
  // of other ATen methods?  Define it in native_functions.yaml.

  //example
  //Tensor * add(Tensor & b);
  ${tensor_method_declarations}

  // We changed .dtype() to return a TypeMeta in #12766. Ideally, we want the
  // at::kDouble and its friends to be TypeMeta's, but that hasn't happened yet.
  // Before that change, we make this method to maintain BC for C++ usage like
  // `x.to(y.dtype)`.
  // TODO: remove following two after at::kDouble and its friends are TypeMeta's.
  inline Tensor to(caffe2::TypeMeta type_meta, bool non_blocking=false, bool copy=false) const {
    return this->to(/*scalar_type=*/typeMetaToScalarType(type_meta), non_blocking, copy);
  }
  inline Tensor to(Device device, caffe2::TypeMeta type_meta, bool non_blocking=false, bool copy=false) const {
    return this->to(device, /*scalar_type=*/typeMetaToScalarType(type_meta), non_blocking, copy);
  }

  template <typename F, typename... Args>
  auto m(F func, Args&&... params) const -> decltype(func(*this, std::forward<Args>(params)...)) {
    return func(*this, std::forward<Args>(params)...);
  }

  /// NOTE: This is similar to the legacy `.data()` function on `Variable`, and is intended
  /// to be used from functions that need to access the `Variable`'s equivalent `Tensor`
  /// (i.e. `Tensor` that shares the same storage and tensor metadata with the `Variable`).
  ///
  /// One notable difference with the legacy `.data()` function is that changes to the
  /// returned `Tensor`'s tensor metadata (e.g. sizes / strides / storage / storage_offset)
  /// will not update the original `Variable`, due to the fact that this function
  /// shallow-copies the `Variable`'s underlying TensorImpl.
  at::Tensor tensor_data() const;

  /// NOTE: `var.variable_data()` in C++ has the same semantics as `tensor.data`
  /// in Python, which create a new `Variable` that shares the same storage and
  /// tensor metadata with the original `Variable`, but with a completely new
  /// autograd history.
  ///
  /// NOTE: If we change the tensor metadata (e.g. sizes / strides /
  /// storage / storage_offset) of a variable created from `var.variable_data()`, those
  /// changes will not update the original variable `var`. In `.variable_data()`, we set
  /// `allow_tensor_metadata_change_` to false to make such changes explicitly illegal,
  /// in order to prevent users from changing metadata of `var.variable_data()`
  /// and expecting the original variable `var` to also be updated.
  at::Tensor variable_data() const;

  // Gradient Node and Edges
  //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  /// Gets the gradient function of the `Variable`. If this is a leaf variable,
  /// the pointer returned will be null.
  ///
  /// For View Variables:
  /// Gets the up-to-date grad_fn. If the shared data or base was modified, we
  /// re-create the grad_fn to express the up-to-date view relationship between
  /// this and the base Variable.
  const std::shared_ptr<torch::autograd::Node>& grad_fn() const;

  // Hooks
  //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  template <typename T>
  using hook_return_void_t = std::enable_if_t<std::is_void<typename std::result_of<T&(Tensor)>::type>::value, unsigned>;
  template <typename T>
  using hook_return_var_t = std::enable_if_t<std::is_same<typename std::result_of<T&(Tensor)>::type, Tensor>::value, unsigned>;

  // Returns the index of the hook in the list which can be used to remove hook
  // Register a hook with no return value
  template <typename T>
  hook_return_void_t<T> register_hook(T&& hook) const;
  // Register a hook with variable return value
  template <typename T>
  hook_return_var_t<T> register_hook(T&& hook) const;

private:
  unsigned _register_hook(std::function<Tensor(const Tensor&)> hook) const;

public:

  // Remove hook at given position
  void remove_hook(unsigned pos) const;

  // View Variables
  //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  /// Returns true if this `Variable` is a view of another `Variable`.
  bool is_view() const;

  /// Returns the `Variable` that this `Variable` is a view of. If this
  /// `Variable` is not a view, throw a `std::runtime_error`.
  const Tensor& _base() const;

  // Miscellaneous
  //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

  const std::string& name() const;

protected:
  friend class ::caffe2::Tensor;

  void enforce_invariants();
  c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl> impl_;
};

namespace detail {
// Helper creator for Tensor class which doesn't requires the users to pass
// in an intrusive_ptr instead it just converts the argument passed to
// requested intrusive_ptr type.
template <typename T, typename... Args>
Tensor make_tensor(Args&&... args) {
  return Tensor(c10::make_intrusive<T>(std::forward<Args>(args)...));
}

} // namespace detail

static inline DispatchKey legacyExtractDispatchKey(const Tensor& t) {
  return legacyExtractDispatchKey(t.key_set());
}

} // namespace at
