#pragma once

#ifdef TORCH_ASSERT_NO_OPERATORS
#error This change adds a dependency on native_functions.yaml,            \
  meaning the file will need to be re-compiled every time an operator     \
  is changed or added. Consider if your change would be better placed in  \
  another file, or if a more specific header might achieve the same goal. \
  See NOTE: [Tensor vs. TensorBase]
#endif

#include <c10/core/Device.h>
#include <c10/core/Layout.h>
#include <c10/core/MemoryFormat.h>
#include <c10/core/QScheme.h>
#include <c10/core/Stream.h>
#include <c10/core/Scalar.h>
#include <c10/core/ScalarType.h>
#include <c10/core/ScalarTypeToTypeMeta.h>
#include <c10/core/Storage.h>
#include <c10/core/TensorImpl.h>
#include <c10/core/UndefinedTensorImpl.h>
#include <c10/core/WrapDimMinimal.h>
#include <c10/util/Exception.h>
#include <c10/util/Deprecated.h>
#include <c10/util/MaybeOwned.h>
#include <c10/util/Optional.h>
#include <c10/util/OptionalArrayRef.h>
#include <c10/util/intrusive_ptr.h>
#include <c10/macros/Export.h>
#include <ATen/core/CheckMemoryFormat.h>
#include <ATen/core/DeprecatedTypePropertiesRegistry.h>
#include <ATen/core/DeprecatedTypeProperties.h>
#include <ATen/core/NamedTensor.h>
#include <ATen/core/QuantizerBase.h>
#include <c10/core/SymInt.h>
#include <ATen/core/TensorAccessor.h>
#include <ATen/core/TensorBase.h>


#include <ATen/MethodOperators.h>

namespace c10{
template<class T> class List;
template<class T> class IListRef;
}
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 OptionalTensorRef;
class Tensor;
using TensorList = ArrayRef<Tensor>;
using ITensorList = c10::IListRef<Tensor>;

using Stream = c10::Stream;

// 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 TORCH_API Tensor: public TensorBase {
 protected:
  // Create a Tensor with a +0 reference count. Special care must be
  // taken to avoid decrementing this reference count at destruction
  // time. Intended to support MaybeOwnedTraits<Tensor>.
  explicit Tensor(unsafe_borrow_t, const TensorBase& rhs): TensorBase(unsafe_borrow_t{}, rhs) {}
  friend MaybeOwnedTraits<Tensor>;
  friend OptionalTensorRef;

 public:
  Tensor() = default;
  // 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)
      : TensorBase(std::move(tensor_impl)) {}
  Tensor(const Tensor &tensor) = default;
  Tensor(Tensor &&tensor) = default;

  // Implicitly move-constructible from TensorBase, but must be explicit to increase refcount
  explicit Tensor(const TensorBase &base): TensorBase(base) {}
  /*implicit*/ Tensor(TensorBase &&base): TensorBase(std::move(base)) {}

  // 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) {
    return TensorBase::wrap_tensor_impl(std::move(tensor_impl));
  }

  Tensor contiguous(MemoryFormat memory_format=MemoryFormat::Contiguous) const {
    return TensorBase::contiguous(memory_format);
  }

  Tensor conj() const {
    if (!this->is_complex()) {
      return *this;
    }

    switch (this->layout()) {
      case at::kSparse:
      case at::kSparseCsr:
      case at::kSparseCsc:
      case at::kSparseBsr:
      case at::kSparseBsc:
        return this->conj_physical();
      default:
        return this->_conj();
    }
  }

  // Aliased by Dimname overloads, so need explicit using
  using TensorBase::size;
  using TensorBase::sym_size;
  using TensorBase::stride;

  /// Should be used if *this can reasonably be expected to be contiguous and
  /// performance is important.
  /// Compared to contiguous, it saves a reference count
  /// increment/decrement if *this is already contiguous, at the cost
  /// in all cases of an extra pointer of stack usage, an extra branch
  /// to access, and an extra branch at destruction time.
  c10::MaybeOwned<Tensor> expect_contiguous(MemoryFormat memory_format=MemoryFormat::Contiguous) const &;

  // Use .contiguous() instead. Trying to borrow from a prvalue Tensor
  // will only lead to trouble and dangling references.
  c10::MaybeOwned<Tensor> expect_contiguous(MemoryFormat memory_format=MemoryFormat::Contiguous) && = delete;

  // 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;

  // Also MSVC will wrongly issue the following warning with the aforementioned fix
  //    warning C4522: 'at::Tensor': multiple assignment operators specified
  // Let's just skip the warning.
  //
  // TODO: temporarily disabled

  Tensor& operator=(const TensorBase& x) & {
    impl_ = x.getIntrusivePtr();
    return *this;
  }
  Tensor& operator=(TensorBase&& x) & noexcept {
    impl_ = x.unsafeReleaseIntrusivePtr();
    return *this;
  }

  Tensor& operator=(const Tensor &x) & {
    return operator=(static_cast<const TensorBase&>(x));
  }
  Tensor& operator=(Tensor &&x) & noexcept {
    return operator=(static_cast<TensorBase&&>(x));
  }

  Tensor& operator=(const Scalar &v) && {
    return fill_(v);
  }
  Tensor& operator=(const Tensor &rhs) && {
    return copy_(rhs);
  }
  Tensor& operator=(Tensor&& rhs) && {
    return copy_(rhs);
  }

  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());
  }

  Tensor toType(ScalarType t) const {
    return to(options().dtype(t), /*non_blocking*/ false, /*copy*/ false);
  }

  // TODO: Deprecate me
  Tensor toBackend(Backend b) const {
    return to(options().device(backendToDeviceType(b)).layout(layout_from_backend(b)), /*non_blocking*/ false, /*copy*/ false);
  }

  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();
  }

  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;

  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 {
    return bitwise_not();
  }
  Tensor operator-() const {
    return neg();
  }
  Tensor& operator+=(const Tensor & other) {
    return add_(other);
  }
  Tensor& operator+=(const Scalar & other) {
    return add_(other);
  }
  Tensor& operator-=(const Tensor & other) {
    return sub_(other);
  }
  Tensor& operator-=(const Scalar & other) {
    return sub_(other);
  }
  Tensor& operator*=(const Tensor & other) {
    return mul_(other);
  }
  Tensor& operator*=(const Scalar & other) {
    return mul_(other);
  }
  Tensor& operator/=(const Tensor & other) {
    return div_(other);
  }
  Tensor& operator/=(const Scalar & other) {
    return div_(other);
  }
  Tensor& operator&=(const Tensor & other) {
    return bitwise_and_(other);
  }
  Tensor& operator|=(const Tensor & other) {
    return bitwise_or_(other);
  }
  Tensor& operator^=(const Tensor & other) {
    return bitwise_xor_(other);
  }
  Tensor operator[](const Scalar & index) const {
    if (!index.isIntegral(false)) {
      TORCH_CHECK_INDEX(false, "Can only index tensors with integral scalars");
    }
    return this->operator[](index.toLong());
  }
  Tensor operator[](const Tensor & index) const {
    // These properties are checked in the Scalar constructor, but we already
    // check them here to provide more useful diagnostics for the user.
    if (!index.defined()) {
      TORCH_CHECK_INDEX(false, "Can only index with tensors that are defined");
    }
    if (index.dim() != 0) {
      TORCH_CHECK_INDEX(false,
                        "Can only index with tensors that are scalars (zero-dim)");
    }
    // The Scalar(Tensor) constructor is explicit, so we need to call it.
    return this->operator[](index.item());
  }
  Tensor operator[](int64_t index) const {
    return select(0, index);
  }

  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, const 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, const Scalar& v);

  Tensor cpu() const {
    return to(options().device(DeviceType::CPU), /*non_blocking*/ false, /*copy*/ false);
  }

  // TODO: The Python version also accepts arguments
  Tensor cuda() const {
    return to(options().device(DeviceType::CUDA), /*non_blocking*/ false, /*copy*/ false);
  }

  Tensor hip() const {
    return to(options().device(DeviceType::HIP), /*non_blocking*/ false, /*copy*/ false);
  }

  Tensor ve() const {
    return to(options().device(DeviceType::VE), /*non_blocking*/ false, /*copy*/ false);
  }