mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.
I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.
I used the following script to do the canonicalization:
```
import subprocess
import re
import os.path
files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
for fn in files:
if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
continue
if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
continue
with open(fn, 'r') as f:
c = f.read()
def fmt(p):
return "#include <{}>".format(p)
def repl(m):
p = m.group(1)
if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
return fmt(p)
if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
return fmt(p)
for root in ["aten/src", "torch/lib", ""]:
for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
new_p = os.path.relpath(os.path.join(bad_root, p), root)
if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
return fmt(new_p)
print("ERROR: ", fn, p)
return m.group(0)
new_c = re.sub(r'#include "([^"]+)"', repl, c)
if new_c != c:
print(fn)
with open(fn, 'w') as f:
f.write(new_c)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849
Reviewed By: dzhulgakov
Differential Revision: D13363445
Pulled By: ezyang
fbshipit-source-id: 52361f878a672785f9306c9e9ab2513128092b68
186 lines
6.9 KiB
C++
186 lines
6.9 KiB
C++
#include <torch/csrc/Generator.h>
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#include <structmember.h>
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#include <ATen/ATen.h>
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#include <TH/TH.h>
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#include <torch/csrc/THP.h>
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#include <torch/csrc/Exceptions.h>
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#include <torch/csrc/autograd/python_variable.h>
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#include <torch/csrc/autograd/generated/VariableType.h>
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#include <torch/csrc/utils/tensor_types.h>
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#include <torch/csrc/autograd/generated/variable_factories.h>
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using namespace at;
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using namespace torch;
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PyObject *THPGeneratorClass = nullptr;
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PyObject * THPGenerator_New()
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{
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PyObject *args = PyTuple_New(0);
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if (!args) {
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PyErr_SetString(PyExc_RuntimeError, "Could not create a new generator object - "
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"failed to allocate argument tuple");
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return nullptr;
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}
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PyObject *result = PyObject_Call((PyObject*)THPGeneratorClass, args, nullptr);
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Py_DECREF(args);
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return result;
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}
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PyObject * THPGenerator_NewWithGenerator(at::Generator& cdata)
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{
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auto type = (PyTypeObject*)THPGeneratorClass;
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auto self = THPObjectPtr{type->tp_alloc(type, 0)};
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if (!self) throw python_error();
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auto self_ = reinterpret_cast<THPGenerator*>(self.get());
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self_->cdata = &cdata;
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return self.release();
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}
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static void THPGenerator_dealloc(THPGenerator* self)
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{
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if (self->owner) {
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delete self->cdata;
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}
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Py_TYPE(self)->tp_free((PyObject*)self);
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}
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static PyObject * THPGenerator_pynew(PyTypeObject *type, PyObject *args, PyObject *kwargs)
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{
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HANDLE_TH_ERRORS
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if ((args && PyTuple_Size(args) != 0) || kwargs) {
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THPUtils_setError("torch.Generator constructor doesn't accept any arguments");
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return nullptr;
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}
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THPGeneratorPtr self((THPGenerator *)type->tp_alloc(type, 0));
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// having to pick a specific type rather than just a backend here is strange,
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// but we don't really have fully fledged backend objects.
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self->cdata = at::CPU(at::kFloat).generator().release();
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self->owner = true;
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return (PyObject*)self.release();
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_getState(THPGenerator *self)
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{
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using namespace torch::autograd;
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HANDLE_TH_ERRORS
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THGenerator *generator = THPGenerator_TH_CData(self);
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Variable var = torch::empty({0}, at::device(at::kCPU).dtype(at::kByte));
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THByteTensor_getRNGState(generator, (THByteTensor*)(var.data().unsafeGetTensorImpl()));
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return THPVariable_Wrap(std::move(var));
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_setState(THPGenerator *self, PyObject *_new_state)
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{
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using namespace torch::autograd;
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HANDLE_TH_ERRORS
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if (!THPVariable_Check(_new_state)) {
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throw TypeError("expected a torch.ByteTensor, but got %s", Py_TYPE(_new_state)->tp_name);
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}
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auto& tensor = ((THPVariable*)_new_state)->cdata.data();
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if (tensor.type() != CPU(kByte)) {
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auto type_name = torch::utils::type_to_string(tensor.type());
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throw TypeError("expected a torch.ByteTensor, but got %s", type_name.c_str());
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}
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THGenerator *generator = THPGenerator_TH_CData(self);
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THByteTensor_setRNGState(generator, (THByteTensor*)tensor.unsafeGetTensorImpl());
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Py_INCREF(self);
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return (PyObject*)self;
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_manualSeed(THPGenerator *self, PyObject *seed)
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{
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HANDLE_TH_ERRORS
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auto generator = self->cdata;
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THPUtils_assert(THPUtils_checkLong(seed), "manual_seed expected a long, "
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"but got %s", THPUtils_typename(seed));
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generator->manualSeed(THPUtils_unpackLong(seed));
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Py_INCREF(self);
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return (PyObject*)self;
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_seed(THPGenerator *self)
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{
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HANDLE_TH_ERRORS
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return THPUtils_packUInt64(self->cdata->seed());
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END_HANDLE_TH_ERRORS
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}
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static PyObject * THPGenerator_initialSeed(THPGenerator *self)
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{
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HANDLE_TH_ERRORS
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return THPUtils_packUInt64(self->cdata->initialSeed());
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END_HANDLE_TH_ERRORS
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}
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static PyMethodDef THPGenerator_methods[] = {
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{"get_state", (PyCFunction)THPGenerator_getState, METH_NOARGS, nullptr},
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{"set_state", (PyCFunction)THPGenerator_setState, METH_O, nullptr},
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{"manual_seed", (PyCFunction)THPGenerator_manualSeed, METH_O, nullptr},
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{"seed", (PyCFunction)THPGenerator_seed, METH_NOARGS, nullptr},
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{"initial_seed", (PyCFunction)THPGenerator_initialSeed, METH_NOARGS, nullptr},
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{nullptr}
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};
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static struct PyMemberDef THPGenerator_members[] = {
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{(char*)"_cdata", T_ULONGLONG, offsetof(THPGenerator, cdata), READONLY, nullptr},
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{nullptr}
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};
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PyTypeObject THPGeneratorType = {
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PyVarObject_HEAD_INIT(nullptr, 0)
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"torch._C.Generator", /* tp_name */
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sizeof(THPGenerator), /* tp_basicsize */
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0, /* tp_itemsize */
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(destructor)THPGenerator_dealloc, /* tp_dealloc */
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nullptr, /* tp_print */
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nullptr, /* tp_getattr */
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nullptr, /* tp_setattr */
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nullptr, /* tp_reserved */
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nullptr, /* tp_repr */
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nullptr, /* tp_as_number */
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nullptr, /* tp_as_sequence */
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nullptr, /* tp_as_mapping */
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nullptr, /* tp_hash */
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nullptr, /* tp_call */
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nullptr, /* tp_str */
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nullptr, /* tp_getattro */
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nullptr, /* tp_setattro */
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nullptr, /* tp_as_buffer */
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Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */
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nullptr, /* tp_doc */
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nullptr, /* tp_traverse */
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nullptr, /* tp_clear */
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nullptr, /* tp_richcompare */
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0, /* tp_weaklistoffset */
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nullptr, /* tp_iter */
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nullptr, /* tp_iternext */
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THPGenerator_methods, /* tp_methods */
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THPGenerator_members, /* tp_members */
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nullptr, /* tp_getset */
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nullptr, /* tp_base */
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nullptr, /* tp_dict */
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nullptr, /* tp_descr_get */
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nullptr, /* tp_descr_set */
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0, /* tp_dictoffset */
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nullptr, /* tp_init */
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nullptr, /* tp_alloc */
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THPGenerator_pynew, /* tp_new */
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};
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bool THPGenerator_init(PyObject *module)
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{
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THPGeneratorClass = (PyObject*)&THPGeneratorType;
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if (PyType_Ready(&THPGeneratorType) < 0)
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return false;
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Py_INCREF(&THPGeneratorType);
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PyModule_AddObject(module, "Generator", (PyObject *)&THPGeneratorType);
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return true;
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}
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