#include <torch/csrc/Dtype.h>

#include <c10/core/ScalarType.h>
#include <structmember.h>
#include <torch/csrc/DynamicTypes.h>
#include <torch/csrc/Exceptions.h>
#include <torch/csrc/utils/object_ptr.h>
#include <torch/csrc/utils/python_numbers.h>
#include <torch/csrc/utils/python_strings.h>
#include <torch/csrc/utils/tensor_dtypes.h>
#include <torch/csrc/utils/tensor_types.h>
#include <cstring>

#include <torch/csrc/Exceptions.h>

PyObject* THPDtype_New(at::ScalarType scalar_type, const std::string& name) {
  HANDLE_TH_ERRORS
  AT_ASSERT(name.length() < DTYPE_NAME_LEN);
  auto type = (PyTypeObject*)&THPDtypeType;
  auto self = THPObjectPtr{type->tp_alloc(type, 0)};
  if (!self)
    throw python_error();
  auto self_ = reinterpret_cast<THPDtype*>(self.get());
  self_->scalar_type = scalar_type;
  std::strncpy(self_->name, name.c_str(), DTYPE_NAME_LEN);
  return self.release();
  END_HANDLE_TH_ERRORS
}

PyObject* THPDtype_is_floating_point(THPDtype* self, PyObject* noargs) {
  HANDLE_TH_ERRORS
  if (at::isFloatingType(self->scalar_type)) {
    Py_RETURN_TRUE;
  } else {
    Py_RETURN_FALSE;
  }
  END_HANDLE_TH_ERRORS
}

PyObject* THPDtype_itemsize(THPDtype* self, PyObject* noargs) {
  HANDLE_TH_ERRORS
  return THPUtils_packInt64(scalarTypeToTypeMeta(self->scalar_type).itemsize());
  END_HANDLE_TH_ERRORS
}

PyObject* THPDtype_is_complex(THPDtype* self, PyObject* noargs) {
  HANDLE_TH_ERRORS
  if (at::isComplexType(self->scalar_type)) {
    Py_RETURN_TRUE;
  } else {
    Py_RETURN_FALSE;
  }
  END_HANDLE_TH_ERRORS
}

PyObject* THPDtype_is_signed(THPDtype* self, PyObject* noargs) {
  HANDLE_TH_ERRORS
  if (at::isSignedType(self->scalar_type)) {
    Py_RETURN_TRUE;
  } else {
    Py_RETURN_FALSE;
  }
  END_HANDLE_TH_ERRORS
}

PyObject* THPDtype_reduce(PyObject* _self, PyObject* noargs) {
  HANDLE_TH_ERRORS
  /*
   * For singletons, a string is returned. The string should be interpreted
   * as the name of a global variable.
   */
  auto self = (THPDtype*)_self;
  return THPUtils_packString(self->name);
  END_HANDLE_TH_ERRORS
}

PyObject* THPDtype_to_real(PyObject* _self, PyObject* noargs) {
  auto* self = (THPDtype*)_self;
  auto scalar_type = self->scalar_type;
  if (!at::isFloatingType(self->scalar_type)) {
    scalar_type = at::toRealValueType(self->scalar_type);
  }
  return (PyObject*)torch::getTHPDtype(scalar_type);
}

PyObject* THPDtype_to_complex(PyObject* _self, PyObject* noargs) {
  auto* self = (THPDtype*)_self;
  auto scalar_type = self->scalar_type;
  if (!at::isComplexType(self->scalar_type)) {
    scalar_type = at::toComplexType(self->scalar_type);
  }
  return (PyObject*)torch::getTHPDtype(scalar_type);
}

typedef PyObject* (*getter)(PyObject*, void*);

// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,cppcoreguidelines-avoid-non-const-global-variables,modernize-avoid-c-arrays)
static struct PyGetSetDef THPDtype_properties[] = {
    {"is_floating_point",
     (getter)THPDtype_is_floating_point,
     nullptr,
     nullptr,
     nullptr},
    {"is_complex", (getter)THPDtype_is_complex, nullptr, nullptr, nullptr},
    {"is_signed", (getter)THPDtype_is_signed, nullptr, nullptr, nullptr},
    {"itemsize", (getter)THPDtype_itemsize, nullptr, nullptr, nullptr},
    {nullptr}};

// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,cppcoreguidelines-avoid-non-const-global-variables,modernize-avoid-c-arrays)
static PyMethodDef THPDtype_methods[] = {
    {"__reduce__", THPDtype_reduce, METH_NOARGS, nullptr},
    {"to_real", THPDtype_to_real, METH_NOARGS, nullptr},
    {"to_complex", THPDtype_to_complex, METH_NOARGS, nullptr},
    {nullptr} /* Sentinel */
};

PyObject* THPDtype_repr(THPDtype* self) {
  std::string name = self->name;
  return THPUtils_packString("torch." + name);
}

PyTypeObject THPDtypeType = {
    PyVarObject_HEAD_INIT(nullptr, 0) "torch.dtype", /* tp_name */
    sizeof(THPDtype), /* tp_basicsize */
    0, /* tp_itemsize */
    nullptr, /* tp_dealloc */
    0, /* tp_vectorcall_offset */
    nullptr, /* tp_getattr */
    nullptr, /* tp_setattr */
    nullptr, /* tp_reserved */
    (reprfunc)THPDtype_repr, /* tp_repr */
    nullptr, /* tp_as_number */
    nullptr, /* tp_as_sequence */
    nullptr, /* tp_as_mapping */
    nullptr, /* tp_hash  */
    nullptr, /* tp_call */
    nullptr, /* tp_str */
    nullptr, /* tp_getattro */
    nullptr, /* tp_setattro */
    nullptr, /* tp_as_buffer */
    Py_TPFLAGS_DEFAULT, /* tp_flags */
    nullptr, /* tp_doc */
    nullptr, /* tp_traverse */
    nullptr, /* tp_clear */
    nullptr, /* tp_richcompare */
    0, /* tp_weaklistoffset */
    nullptr, /* tp_iter */
    nullptr, /* tp_iternext */
    THPDtype_methods, /* tp_methods */
    nullptr, /* tp_members */
    THPDtype_properties, /* tp_getset */
    nullptr, /* tp_base */
    nullptr, /* tp_dict */
    nullptr, /* tp_descr_get */
    nullptr, /* tp_descr_set */
    0, /* tp_dictoffset */
    nullptr, /* tp_init */
    nullptr, /* tp_alloc */
    nullptr, /* tp_new */
};

void THPDtype_init(PyObject* module) {
  // Set a __dict__ with `__module__` = `torch`. This means
  // `__module__` value will be inherited by instances
  // (i.e. `torch.float32.__module__ == "torch"`). This will prevent
  // Pickle from having to search all of sys.modules in order to find
  // the module when pickling a dtype instance.
  //
  // We have to do this in C++ because extension types are not mutable
  // from Python code.
  //
  // See https://github.com/pytorch/pytorch/issues/65077
  TORCH_INTERNAL_ASSERT(THPDtypeType.tp_dict == nullptr);
  auto dict = THPObjectPtr(PyDict_New());
  if (!dict)
    throw python_error();
  auto torch = THPUtils_packString("torch");
  if (!torch)
    throw python_error();
  if (PyDict_SetItemString(dict, "__module__", torch) < 0) {
    throw python_error();
  }
  THPDtypeType.tp_dict = dict.release();

  if (PyType_Ready(&THPDtypeType) < 0) {
    throw python_error();
  }
  Py_INCREF(&THPDtypeType);
  if (PyModule_AddObject(module, "dtype", (PyObject*)&THPDtypeType) != 0) {
    throw python_error();
  }
}
