import copy
import importlib
import os
import setuptools
import subprocess
import shutil
import re
import shlex
import sys
import sysconfig

import warnings

import torch
from torch.utils.cpp_extension import _TORCH_PATH
from torch.utils.file_baton import FileBaton
from torch.utils._cpp_extension_versioner import ExtensionVersioner
from torch.utils.hipify.hipify_python import GeneratedFileCleaner

from typing import List, Optional, Union, Tuple
from torch.torch_version import TorchVersion

from setuptools.command.build_ext import build_ext

IS_WINDOWS = sys.platform == 'win32'
IS_MACOS = sys.platform.startswith('darwin')
IS_LINUX = sys.platform.startswith('linux')
LIB_EXT = '.pyd' if IS_WINDOWS else '.so'
EXEC_EXT = '.exe' if IS_WINDOWS else ''
CLIB_PREFIX = '' if IS_WINDOWS else 'lib'
CLIB_EXT = '.dll' if IS_WINDOWS else '.so'
SHARED_FLAG = '/DLL' if IS_WINDOWS else '-shared'

MINIMUM_GCC_VERSION = (5, 0, 0)
MINIMUM_MSVC_VERSION = (19, 0, 24215)

COMMON_MSVC_FLAGS = ['/MD', '/wd4819', '/wd4251', '/wd4244', '/wd4267', '/wd4275', '/wd4018', '/wd4190', '/EHsc']

COMMON_DPCPP_FLAGS = ['-fPIC']

TORCH_LIB_PATH = os.path.join(_TORCH_PATH, 'lib')

JIT_EXTENSION_VERSIONER = ExtensionVersioner()

# Taken directly from python stdlib < 3.9
# See https://github.com/pytorch/pytorch/issues/48617
def _nt_quote_args(args: Optional[List[str]]) -> List[str]:
    """Quote command-line arguments for DOS/Windows conventions.

    Just wraps every argument which contains blanks in double quotes, and
    returns a new argument list.
    """
    # Cover None-type
    if not args:
        return []
    return [f'"{arg}"' if ' ' in arg else arg for arg in args]

def get_default_build_root() -> str:
    r'''
    Returns the path to the root folder under which extensions will built.

    For each extension module built, there will be one folder underneath the
    folder returned by this function. For example, if ``p`` is the path
    returned by this function and ``ext`` the name of an extension, the build
    folder for the extension will be ``p/ext``.

    This directory is **user-specific** so that multiple users on the same
    machine won't meet permission issues.
    '''
    return os.path.realpath(torch._appdirs.user_cache_dir(appname='torch_extensions'))

def _get_exec_path(module_name, path):
    if IS_WINDOWS and TORCH_LIB_PATH not in os.getenv('PATH', '').split(';'):
        torch_lib_in_path = any(
            os.path.exists(p) and os.path.samefile(p, TORCH_LIB_PATH)
            for p in os.getenv('PATH', '').split(';')
        )
        if not torch_lib_in_path:
            os.environ['PATH'] = f"{TORCH_LIB_PATH};{os.getenv('PATH', '')}"
    return os.path.join(path, f'{module_name}{EXEC_EXT}')

def get_dpcpp_complier():
    # build cxx via dpcpp
    dpcpp_cmp = shutil.which('dpcpp')
    if dpcpp_cmp is None:
        raise RuntimeError("Failed to find compiler path from OS PATH")
    _cxxbin = os.getenv("CXX")
    if _cxxbin is not None:
        dpcpp_cmp = _cxxbin
    return dpcpp_cmp

def get_icx_complier():
    # build cc via icx
    icx_cmp = shutil.which('icx')
    if icx_cmp is None:
        raise RuntimeError("Failed to find compiler path from OS PATH")
    _ccbin = os.getenv("CC")
    if _ccbin is not None:
        dpcpp_cmp = _ccbin
    return icx_cmp

def is_ninja_available():
    r'''
    Returns ``True`` if the `ninja <https://ninja-build.org/>`_ build system is
    available on the system, ``False`` otherwise.
    '''
    try:
        subprocess.check_output('ninja --version'.split())
    except Exception:
        return False
    else:
        return True


def verify_ninja_availability():
    r'''
    Raises ``RuntimeError`` if `ninja <https://ninja-build.org/>`_ build system is not
    available on the system, does nothing otherwise.
    '''
    if not is_ninja_available():
        raise RuntimeError("Ninja is required to load C++ extensions")

def _is_cpp_file(path: str) -> bool:
    valid_ext = ['.cpp', '.hpp']
    return os.path.splitext(path)[1] in valid_ext

def _is_c_file(path: str) -> bool:
    valid_ext = ['.c', '.h']
    return os.path.splitext(path)[1] in valid_ext

class DpcppBuildExtension(build_ext, object):
    r'''
    A custom :mod:`setuptools` build extension .
    This :class:`setuptools.build_ext` subclass takes care of passing the
    minimum required compiler flags (e.g. ``-std=c++17``) as well as DPCPP
    compilation.
    When using :class:`DpcppBuildExtension`, it is allowed to supply a dictionary
    for ``extra_compile_args`` (rather than the usual list) that maps from
    languages (``cxx``) to a list of additional compiler flags to supply to the
    compiler.

    ``use_ninja`` (bool): If ``use_ninja`` is ``True`` (default), then we
    attempt to build using the Ninja backend. Ninja greatly speeds up
    compilation compared to the standard ``setuptools.build_ext``.
    Fallbacks to the standard distutils backend if Ninja is not available.

    ``no_python_abi_suffix`` (bool): If ``no_python_abi_suffix`` is ``False`` (default),
    then we attempt to build module with python abi suffix, example:
    output module name: module_name.cpython-37m-x86_64-linux-gnu.so, the
    ``cpython-37m-x86_64-linux-gnu`` is append python abi suffix.

    .. note::
        By default, the Ninja backend uses #CPUS + 2 workers to build the
        extension. This may use up too many resources on some systems. One
        can control the number of workers by setting the `MAX_JOBS` environment
        variable to a non-negative number.
    '''
    @classmethod
    def with_options(cls, **options):
        r'''
        Returns a subclass with alternative constructor that extends any original keyword
        arguments to the original constructor with the given options.
        '''
        class cls_with_options(cls):  # type: ignore[misc, valid-type]
            def __init__(self, *args, **kwargs):
                kwargs.update(options)
                super().__init__(*args, **kwargs)

        return cls_with_options

    def __init__(self, *args, **kwargs) -> None:
        super(DpcppBuildExtension, self).__init__(*args, **kwargs)
        self.no_python_abi_suffix = kwargs.get("no_python_abi_suffix", False)

        self.use_ninja = kwargs.get('use_ninja', True)
        if self.use_ninja:
            # Test if we can use ninja. Fallback otherwise.
            msg = ('Attempted to use ninja as the BuildExtension backend but '
                   '{}. Falling back to using the slow distutils backend.')
            if not is_ninja_available():
                warnings.warn(msg.format('we could not find ninja.'))
                self.use_ninja = False

    def finalize_options(self) -> None:
        super().finalize_options()
        if self.use_ninja:
            self.force = True

    def build_extensions(self) -> None:
        dpcpp_ext = False
        extension_iter = iter(self.extensions)
        extension = next(extension_iter, None)
        while not dpcpp_ext and extension:
            extension = next(extension_iter, None)

        for extension in self.extensions:
            # Ensure at least an empty list of flags for 'cxx' when
            # extra_compile_args is a dict. Otherwise, default torch
            # flags do not get passed. Necessary when only one of 'cxx' is
            # passed to extra_compile_args in DPCPPExtension, i.e.
            #   DPCPPExtension(..., extra_compile_args={'cxx': [...]})
            if isinstance(extension.extra_compile_args, dict):
                for ext in ['cxx']:
                    if ext not in extension.extra_compile_args:
                        extension.extra_compile_args[ext] = []

            self._add_compile_flag(extension, '-DTORCH_API_INCLUDE_EXTENSION_H')
            # See note [Pybind11 ABI constants]
            for name in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
                val = getattr(torch._C, f"_PYBIND11_{name}")
                if val is not None and not IS_WINDOWS:
                    self._add_compile_flag(extension, f'-DPYBIND11_{name}="{val}"')
            self._define_torch_extension_name(extension)
            self._add_gnu_cpp_abi_flag(extension)

        # Save the original _compile method for later.
        if self.compiler.compiler_type == 'msvc':
            original_compile = self.compiler.compile
            original_spawn = self.compiler.spawn
        else:
            original_compile = self.compiler._compile

        def append_std17_if_no_std_present(cflags) -> None:
            cpp_format_prefix = '/{}:' if self.compiler.compiler_type == 'msvc' else '-{}='
            cpp_flag_prefix = cpp_format_prefix.format('std')
            cpp_flag = cpp_flag_prefix + 'c++17'
            if not any(flag.startswith(cpp_flag_prefix) for flag in cflags):
                cflags.append(cpp_flag)

        def unix_dpcpp_flags(cflags):
            cflags = (COMMON_DPCPP_FLAGS + cflags)
            return cflags

        def convert_to_absolute_paths_inplace(paths):
            # Helper function. See Note [Absolute include_dirs]
            if paths is not None:
                for i in range(len(paths)):
                    if not os.path.isabs(paths[i]):
                        paths[i] = os.path.abspath(paths[i])

        def unix_wrap_single_compile(obj, src, ext, cc_args, extra_postargs, pp_opts) -> None:
            # Copy before we make any modifications.
            cflags = copy.deepcopy(extra_postargs)
            try:
                original_compiler = self.compiler.compiler_so
                if _is_cpp_file(src):
                    _cxxbin = get_dpcpp_complier()
                    self.compiler.set_executable('compiler_so', _cxxbin)
                    if isinstance(cflags, dict):
                        cflags = cflags['cxx']
                    else:
                        cflags = unix_dpcpp_flags(cflags)
                elif _is_c_file(src):
                    _ccbin = get_icx_complier()
                    self.compiler.set_executable('compiler_so', _ccbin)
                    if isinstance(cflags, dict):
                        cflags = cflags['cxx']
                    else:
                        cflags = unix_dpcpp_flags(cflags)
                elif isinstance(cflags, dict):
                    cflags = cflags['cxx']
                append_std17_if_no_std_present(cflags)

                original_compile(obj, src, ext, cc_args, cflags, pp_opts)
            finally:
                # Put the original compiler back in place.
                self.compiler.set_executable('compiler_so', original_compiler)

        def unix_wrap_ninja_compile(sources,
                                    output_dir=None,
                                    macros=None,
                                    include_dirs=None,
                                    debug=0,
                                    extra_preargs=None,
                                    extra_postargs=None,
                                    depends=None):
            r"""Compiles sources by outputting a ninja file and running it."""
            # NB: I copied some lines from self.compiler (which is an instance
            # of distutils.UnixCCompiler). See the following link.
            # https://github.com/python/cpython/blob/f03a8f8d5001963ad5b5b28dbd95497e9cc15596/Lib/distutils/ccompiler.py#L564-L567
            # This can be fragile, but a lot of other repos also do this
            # (see https://github.com/search?q=_setup_compile&type=Code)
            # so it is probably OK; we'll also get CI signal if/when
            # we update our python version (which is when distutils can be
            # upgraded)

            # Use absolute path for output_dir so that the object file paths
            # (`objects`) get generated with absolute paths.
            output_dir = os.path.abspath(output_dir)

            # See Note [Absolute include_dirs]
            convert_to_absolute_paths_inplace(self.compiler.include_dirs)

            _, objects, extra_postargs, pp_opts, _ = \
                self.compiler._setup_compile(output_dir, macros,
                                             include_dirs, sources,
                                             depends, extra_postargs)
            common_cflags = self.compiler._get_cc_args(pp_opts, debug, extra_preargs)
            extra_cc_cflags = self.compiler.compiler_so[1:]

            # extra_postargs can be either:
            # - a dict mapping cxx to extra flags
            # - a list of extra flags.
            if isinstance(extra_postargs, dict):
                post_cflags = extra_postargs['cxx']
            else:
                post_cflags = list(extra_postargs)
            append_std17_if_no_std_present(post_cflags)

            _write_ninja_file_and_compile_objects(
                sources=sources,
                objects=objects,
                cflags=[shlex.quote(f) for f in extra_cc_cflags + common_cflags],
                post_cflags=[shlex.quote(f) for f in post_cflags],
                build_directory=output_dir,
                verbose=True)

            # Return *all* object filenames, not just the ones we just built.
            return objects

        if self.compiler.compiler_type == 'msvc':
            raise 'Not implemented'
        else:
            if self.use_ninja:
                self.compiler.compile = unix_wrap_ninja_compile
            else:
                self.compiler._compile = unix_wrap_single_compile

        build_ext.build_extensions(self)

    def _add_compile_flag(self, extension, flag):
        extension.extra_compile_args = copy.deepcopy(extension.extra_compile_args)
        if isinstance(extension.extra_compile_args, dict):
            for args in extension.extra_compile_args.values():
                args.append(flag)
        else:
            extension.extra_compile_args.append(flag)

    def _define_torch_extension_name(self, extension):
        # pybind11 doesn't support dots in the names
        # so in order to support extensions in the packages
        # like torch._C, we take the last part of the string
        # as the library name
        names = extension.name.split('.')
        name = names[-1]
        define = f'-DTORCH_EXTENSION_NAME={name}'
        self._add_compile_flag(extension, define)

    def _add_gnu_cpp_abi_flag(self, extension):
        # use the same CXX ABI as what PyTorch was compiled with
        self._add_compile_flag(extension, '-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI)))

SUBPROCESS_DECODE_ARGS = ('oem',) if IS_WINDOWS else ()

ABI_INCOMPATIBILITY_WARNING = '''
                               !! WARNING !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Your compiler ({}) may be ABI-incompatible with PyTorch!
Please use a compiler that is ABI-compatible with GCC 5.0 and above.
See https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html.
See https://gist.github.com/goldsborough/d466f43e8ffc948ff92de7486c5216d6
for instructions on how to install GCC 5 or higher.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
                              !! WARNING !!
'''
WRONG_COMPILER_WARNING = '''
                               !! WARNING !!
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Your compiler ({user_compiler}) is not compatible with the compiler Pytorch was
built with for this platform, which is {pytorch_compiler} on {platform}. Please
use {pytorch_compiler} to to compile your extension. Alternatively, you may
compile PyTorch from source using {user_compiler}, and then you can also use
{user_compiler} to compile your extension.
See https://github.com/pytorch/pytorch/blob/master/CONTRIBUTING.md for help
with compiling PyTorch from source.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
                              !! WARNING !!
'''

BUILT_FROM_SOURCE_VERSION_PATTERN = re.compile(r'\d+\.\d+\.\d+\w+\+\w+')

def _is_binary_build() -> bool:
    return not BUILT_FROM_SOURCE_VERSION_PATTERN.match(torch.version.__version__)


def _accepted_compilers_for_platform() -> List[str]:
    # gnu-c++ and gnu-cc are the conda gcc compilers
    return ['clang++', 'clang'] if IS_MACOS else ['dpcpp', 'icx']

def check_compiler_ok_for_platform(compiler: str) -> bool:
    r'''
    Verifies that the compiler is the expected one for the current platform.
    Args:
        compiler (str): The compiler executable to check.
    Returns:
        True if the compiler is gcc/g++ on Linux or clang/clang++ on macOS,
        and always True for Windows.
    '''

    if IS_WINDOWS:
        return True
    which = subprocess.check_output(['which', compiler], stderr=subprocess.STDOUT)
    # Use os.path.realpath to resolve any symlinks, in particular from 'c++' to e.g. 'g++'.
    compiler_path = os.path.realpath(which.decode(*SUBPROCESS_DECODE_ARGS).strip())

    # Check the compiler name
    if any(name in compiler_path for name in _accepted_compilers_for_platform()):
        return True
    # If compiler wrapper is used try to infer the actual compiler by invoking it with -v flag
    version_string = subprocess.check_output([compiler, '-v'], stderr=subprocess.STDOUT).decode(*SUBPROCESS_DECODE_ARGS)
    if IS_LINUX:
        # Check for 'gcc' or 'g++' for sccache warpper
        pattern = re.compile("^COLLECT_GCC=(.*)$", re.MULTILINE)
        results = re.findall(pattern, version_string)
        if len(results) != 1:
            return False
        compiler_path = os.path.realpath(results[0].strip())
        # On RHEL/CentOS c++ is a gcc compiler wrapper
        if os.path.basename(compiler_path) == 'c++' and 'gcc version' in version_string:
            return True
        return any(name in compiler_path for name in _accepted_compilers_for_platform())
    if IS_MACOS:
        # Check for 'clang' or 'clang++'
        return version_string.startswith("Apple clang")
    return False

def check_compiler_abi_compatibility(compiler) -> bool:
    r'''
    Verifies that the given compiler is ABI-compatible with PyTorch.

    Args:
        compiler (str): The compiler executable name to check (e.g. ``g++``).
            Must be executable in a shell process.

    Returns:
        False if the compiler is (likely) ABI-incompatible with PyTorch,
        else True.
    '''
    if not _is_binary_build():
        return True
    if os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') in ['ON', '1', 'YES', 'TRUE', 'Y']:
        return True

    # First check if the compiler is one of the expected ones for the particular platform.
    if not check_compiler_ok_for_platform(compiler):
        warnings.warn(WRONG_COMPILER_WARNING.format(
            user_compiler=compiler,
            pytorch_compiler=_accepted_compilers_for_platform()[0],
            platform=sys.platform))
        return False

    if IS_MACOS:
        # There is no particular minimum version we need for clang, so we're good here.
        return True
    try:
        if IS_LINUX:
            minimum_required_version = MINIMUM_GCC_VERSION
            versionstr = subprocess.check_output([compiler, '-dumpfullversion', '-dumpversion'])
            version = versionstr.decode(*SUBPROCESS_DECODE_ARGS).strip().split('.')
        else:
            minimum_required_version = MINIMUM_MSVC_VERSION
            compiler_info = subprocess.check_output(compiler, stderr=subprocess.STDOUT)
            match = re.search(r'(\d+)\.(\d+)\.(\d+)', compiler_info.decode(*SUBPROCESS_DECODE_ARGS).strip())
            version = ['0', '0', '0'] if match is None else list(match.groups())
    except Exception:
        _, error, _ = sys.exc_info()
        warnings.warn(f'Error checking compiler version for {compiler}: {error}')
        return False

    if tuple(map(int, version)) >= minimum_required_version:
        return True

    compiler = f'{compiler} {".".join(version)}'
    warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler))

    return False

def get_compiler_abi_compatibility_and_version(compiler) -> Tuple[bool, TorchVersion]:
    r'''
    Determine if the given compiler is ABI-compatible with PyTorch alongside
    its version.
    Args:
        compiler (str): The compiler executable name to check (e.g. ``g++``).
            Must be executable in a shell process.
    Returns:
        A tuple that contains a boolean that defines if the compiler is (likely) ABI-incompatible with PyTorch,
        followed by a `TorchVersion` string that contains the compiler version separated by dots.
    '''
    if not _is_binary_build():
        return (True, TorchVersion('0.0.0'))
    if os.environ.get('TORCH_DONT_CHECK_COMPILER_ABI') in ['ON', '1', 'YES', 'TRUE', 'Y']:
        return (True, TorchVersion('0.0.0'))

    # First check if the compiler is one of the expected ones for the particular platform.
    if not check_compiler_ok_for_platform(compiler):
        warnings.warn(WRONG_COMPILER_WARNING.format(
            user_compiler=compiler,
            pytorch_compiler=_accepted_compilers_for_platform()[0],
            platform=sys.platform))
        return (False, TorchVersion('0.0.0'))

    if IS_MACOS:
        # There is no particular minimum version we need for clang, so we're good here.
        return (True, TorchVersion('0.0.0'))
    try:
        if IS_LINUX:
            minimum_required_version = MINIMUM_GCC_VERSION
            versionstr = subprocess.check_output([compiler, '-dumpfullversion', '-dumpversion'])
            version = versionstr.decode(*SUBPROCESS_DECODE_ARGS).strip().split('.')
        else:
            minimum_required_version = MINIMUM_MSVC_VERSION
            compiler_info = subprocess.check_output(compiler, stderr=subprocess.STDOUT)
            match = re.search(r'(\d+)\.(\d+)\.(\d+)', compiler_info.decode(*SUBPROCESS_DECODE_ARGS).strip())
            version = ['0', '0', '0'] if match is None else list(match.groups())
    except Exception:
        _, error, _ = sys.exc_info()
        warnings.warn(f'Error checking compiler version for {compiler}: {error}')
        return (False, TorchVersion('0.0.0'))

    if tuple(map(int, version)) >= minimum_required_version:
        return (True, TorchVersion('.'.join(version)))

    compiler = f'{compiler} {".".join(version)}'
    warnings.warn(ABI_INCOMPATIBILITY_WARNING.format(compiler))

    return (False, TorchVersion('.'.join(version)))

def _write_ninja_file_and_compile_objects(
        sources: List[str],
        objects,
        cflags,
        post_cflags,
        build_directory: str,
        verbose: bool) -> None:
    verify_ninja_availability()
    if IS_WINDOWS:
        compiler = os.environ.get('CXX', 'cl')
    else:
        compiler = get_dpcpp_complier()
    get_compiler_abi_compatibility_and_version(compiler)

    build_file_path = os.path.join(build_directory, 'build.ninja')
    if verbose:
        print(f'Emitting ninja build file {build_file_path}...')
    _write_ninja_file(
        path=build_file_path,
        cflags=cflags,
        post_cflags=post_cflags,
        sources=sources,
        objects=objects,
        ldflags=None,
        library_target=None)
    if verbose:
        print('Compiling objects...')
    _run_ninja_build(
        build_directory,
        verbose,
        # It would be better if we could tell users the name of the extension
        # that failed to build but there isn't a good way to get it here.
        error_prefix='Error compiling objects for extension')

def _write_ninja_file_and_build_library(
        name,
        sources: List[str],
        extra_cflags,
        extra_ldflags,
        extra_include_paths,
        build_directory: str,
        verbose: bool,
        is_standalone: bool = False) -> None:
    verify_ninja_availability()
    if IS_WINDOWS:
        compiler = os.environ.get('CXX', 'cl')
    else:
        compiler = get_dpcpp_complier()
    check_compiler_abi_compatibility(compiler)

    extra_ldflags = _prepare_ldflags(
        extra_ldflags or [],
        verbose,
        is_standalone)
    build_file_path = os.path.join(build_directory, 'build.ninja')
    if verbose:
        print(f'Emitting ninja build file {build_file_path}...')
    # NOTE: Emitting a new ninja build file does not cause re-compilation if
    # the sources did not change, so it's ok to re-emit (and it's fast).
    _write_ninja_file_to_build_library(
        path=build_file_path,
        name=name,
        sources=sources,
        extra_cflags=extra_cflags or [],
        extra_ldflags=extra_ldflags or [],
        extra_include_paths=extra_include_paths or [],
        is_standalone=is_standalone)

    if verbose:
        print(f'Building extension module {name}...')
    _run_ninja_build(
        build_directory,
        verbose,
        error_prefix=f"Error building extension '{name}'")

def get_one_api_help():
    oneAPI = _one_api_help()
    return oneAPI

def include_paths() -> List[str]:
    '''
    Get the include paths required to build a DPC++ extension.

    Returns:
        A list of include path strings.
    '''
    # add pytorch include directories
    paths = []
    paths += get_pytorch_include_dir()

    # add oneAPI include directories
    paths += get_one_api_help().get_include_dirs()

    return paths

def library_paths() -> List[str]:
    paths = []
    paths += get_pytorch_lib_dir()
    paths += get_one_api_help().get_library_dirs()

    return paths

def _prepare_ldflags(extra_ldflags, verbose, is_standalone):
    if IS_WINDOWS:
        python_path = os.path.dirname(sys.executable)
        python_lib_path = os.path.join(python_path, 'libs')

        extra_ldflags.append('c10.lib')
        extra_ldflags.append('torch.lib')
        extra_ldflags.append(f'/LIBPATH:{TORCH_LIB_PATH}')
        if not is_standalone:
            extra_ldflags.append('torch_python.lib')
            extra_ldflags.append(f'/LIBPATH:{python_lib_path}')

    else:
        extra_ldflags.append(f'-L{TORCH_LIB_PATH}')
        extra_ldflags.append('-lc10')
        extra_ldflags.append('-ltorch')
        if not is_standalone:
            extra_ldflags.append('-ltorch_python')

        if is_standalone and "TBB" in torch.__config__.parallel_info():
            extra_ldflags.append('-ltbb')

        if is_standalone:
            extra_ldflags.append(f"-Wl,-rpath,{TORCH_LIB_PATH}")

    library_dirs = library_paths()
    # Append oneMKL link parameters, detailed please reference:
    # https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-link-line-advisor.html
    oneapi_link_args = []
    oneapi_link_args += [f'-L{x}' for x in library_dirs]
    # oneapi_link_args += ['-fsycl-device-code-split=per_kernel']
    oneapi_link_args += ['-Wl,--start-group']
    oneapi_link_args += [f'{x}' for x in get_one_api_help().get_onemkl_libraries()]
    oneapi_link_args += ['-Wl,--end-group']
    oneapi_link_args += ['-lsycl', '-lOpenCL', '-lpthread', '-lm', '-ldl']
    oneapi_link_args += ['-ldnnl']

    # Append IPEX link parameters.
    oneapi_link_args += [f'-L{x}' for x in get_one_api_help().get_default_lib_dir()]
    oneapi_link_args += ['-lintel-ext-pt-gpu']

    extra_ldflags += oneapi_link_args

    return extra_ldflags

PLAT_TO_VCVARS = {
    'win32': 'x86',
    'win-amd64': 'x86_amd64',
}

def _get_num_workers(verbose: bool) -> Optional[int]:
    max_jobs = os.environ.get('MAX_JOBS')
    if max_jobs is not None and max_jobs.isdigit():
        if verbose:
            print(f'Using envvar MAX_JOBS ({max_jobs}) as the number of workers...')
        return int(max_jobs)
    if verbose:
        print('Allowing ninja to set a default number of workers... '
              '(overridable by setting the environment variable MAX_JOBS=N)')
    return None

def _run_ninja_build(build_directory: str, verbose: bool, error_prefix: str) -> None:
    command = ['ninja', '-v']
    num_workers = _get_num_workers(verbose)
    if num_workers is not None:
        command.extend(['-j', str(num_workers)])
    env = os.environ.copy()
    # Try to activate the vc env for the users
    if IS_WINDOWS and 'VSCMD_ARG_TGT_ARCH' not in env:
        from setuptools import distutils

        plat_name = distutils.util.get_platform()
        plat_spec = PLAT_TO_VCVARS[plat_name]

        vc_env = distutils._msvccompiler._get_vc_env(plat_spec)
        vc_env = {k.upper(): v for k, v in vc_env.items()}
        for k, v in env.items():
            uk = k.upper()
            if uk not in vc_env:
                vc_env[uk] = v
        env = vc_env
    try:
        sys.stdout.flush()
        sys.stderr.flush()
        # Warning: don't pass stdout=None to subprocess.run to get output.
        # subprocess.run assumes that sys.__stdout__ has not been modified and
        # attempts to write to it by default.  However, when we call _run_ninja_build
        # from ahead-of-time cpp extensions, the following happens:
        # 1) If the stdout encoding is not utf-8, setuptools detachs __stdout__.
        #    https://github.com/pypa/setuptools/blob/7e97def47723303fafabe48b22168bbc11bb4821/setuptools/dist.py#L1110
        #    (it probably shouldn't do this)
        # 2) subprocess.run (on POSIX, with no stdout override) relies on
        #    __stdout__ not being detached:
        #    https://github.com/python/cpython/blob/c352e6c7446c894b13643f538db312092b351789/Lib/subprocess.py#L1214
        # To work around this, we pass in the fileno directly and hope that
        # it is valid.
        stdout_fileno = 1
        subprocess.run(
            command,
            stdout=stdout_fileno if verbose else subprocess.PIPE,
            stderr=subprocess.STDOUT,
            cwd=build_directory,
            check=True,
            env=env)
    except subprocess.CalledProcessError as e:
        # Python 2 and 3 compatible way of getting the error object.
        _, error, _ = sys.exc_info()
        # error.output contains the stdout and stderr of the build attempt.
        message = error_prefix
        # `error` is a CalledProcessError (which has an `ouput`) attribute, but
        # mypy thinks it's Optional[BaseException] and doesn't narrow
        if hasattr(error, 'output') and error.output:  # type: ignore[union-attr]
            message += f": {error.output.decode(*SUBPROCESS_DECODE_ARGS)}"  # type: ignore[union-attr]
        raise RuntimeError(message) from e

def _get_build_directory(name: str, verbose: bool) -> str:
    root_extensions_directory = os.environ.get('TORCH_EXTENSIONS_DIR')
    if root_extensions_directory is None:
        root_extensions_directory = get_default_build_root()

        # TODO: hard code as xpu. will check xpu_available when it is ready.
        xpu_str = ('xpu')  # type: ignore[attr-defined]
        python_version = f'py{sys.version_info.major}{sys.version_info.minor}'
        build_folder = f'{python_version}_{xpu_str}'

        root_extensions_directory = os.path.join(
            root_extensions_directory, build_folder)

    if verbose:
        print(f'Using {root_extensions_directory} as PyTorch extensions root...')

    build_directory = os.path.join(root_extensions_directory, name)
    if not os.path.exists(build_directory):
        if verbose:
            print(f'Creating extension directory {build_directory}...')
        # This is like mkdir -p, i.e. will also create parent directories.
        os.makedirs(build_directory, exist_ok=True)

    return build_directory

def _import_module_from_library(module_name, path, is_python_module):
    filepath = os.path.join(path, f"{module_name}{LIB_EXT}")
    if is_python_module:
        # https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
        spec = importlib.util.spec_from_file_location(module_name, filepath)
        module = importlib.util.module_from_spec(spec)
        assert isinstance(spec.loader, importlib.abc.Loader)
        spec.loader.exec_module(module)
        return module
    else:
        torch.ops.load_library(filepath)

def _write_ninja_file_to_build_library(path,
                                       name,
                                       sources,
                                       extra_cflags,
                                       extra_ldflags,
                                       extra_include_paths,
                                       is_standalone) -> None:
    extra_cflags = [flag.strip() for flag in extra_cflags]
    extra_ldflags = [flag.strip() for flag in extra_ldflags]
    extra_include_paths = [flag.strip() for flag in extra_include_paths]

    # Turn into absolute paths so we can emit them into the ninja build
    # file wherever it is.
    user_includes = [os.path.abspath(file) for file in extra_include_paths]

    # include_paths() gives us the location of torch/extension.h
    system_includes = include_paths()
    # sysconfig.get_path('include') gives us the location of Python.h
    # Explicitly specify 'posix_prefix' scheme on non-Windows platforms to workaround error on some MacOS
    # installations where default `get_path` points to non-existing `/Library/Python/M.m/include` folder
    python_include_path = sysconfig.get_path('include', scheme='nt' if IS_WINDOWS else 'posix_prefix')
    if python_include_path is not None:
        system_includes.append(python_include_path)

    # Windows does not understand `-isystem`.
    if IS_WINDOWS:
        user_includes += system_includes
        system_includes.clear()

    common_cflags = []
    if not is_standalone:
        common_cflags.append(f'-DTORCH_EXTENSION_NAME={name}')
        common_cflags.append('-DTORCH_API_INCLUDE_EXTENSION_H')

    # Note [Pybind11 ABI constants]
    #
    # Pybind11 before 2.4 used to build an ABI strings using the following pattern:
    # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_BUILD_TYPE}__"
    # Since 2.4 compier type, stdlib and build abi parameters are also encoded like this:
    # f"__pybind11_internals_v{PYBIND11_INTERNALS_VERSION}{PYBIND11_INTERNALS_KIND}{PYBIND11_COMPILER_TYPE}
    # {PYBIND11_STDLIB}{PYBIND11_BUILD_ABI}{PYBIND11_BUILD_TYPE}__"
    #
    # This was done in order to further narrow down the chances of compiler ABI incompatibility
    # that can cause a hard to debug segfaults.
    # For PyTorch extensions we want to relax those restrictions and pass compiler, stdlib and abi properties
    # captured during PyTorch native library compilation in torch/csrc/Module.cpp

    for pname in ["COMPILER_TYPE", "STDLIB", "BUILD_ABI"]:
        pval = getattr(torch._C, f"_PYBIND11_{pname}")
        if pval is not None and not IS_WINDOWS:
            common_cflags.append(f'-DPYBIND11_{pname}=\\"{pval}\\"')

    common_cflags += [f'-I{include}' for include in user_includes]
    common_cflags += [f'-isystem {include}' for include in system_includes]

    common_cflags += ['-D_GLIBCXX_USE_CXX11_ABI=' + str(int(torch._C._GLIBCXX_USE_CXX11_ABI))]

    if IS_WINDOWS:
        cflags = common_cflags + COMMON_MSVC_FLAGS + extra_cflags
        cflags = _nt_quote_args(cflags)
    else:
        cflags = common_cflags + ['-fPIC', '-std=c++17'] + extra_cflags

    def object_file_path(source_file: str) -> str:
        # '/path/to/file.cpp' -> 'file'
        file_name = os.path.splitext(os.path.basename(source_file))[0]
        target = f'{file_name}.o'
        return target

    objects = [object_file_path(src) for src in sources]
    ldflags = ([] if is_standalone else [SHARED_FLAG]) + extra_ldflags

    # The darwin linker needs explicit consent to ignore unresolved symbols.
    if IS_MACOS:
        ldflags.append('-undefined dynamic_lookup')
    elif IS_WINDOWS:
        ldflags = _nt_quote_args(ldflags)

    ext = EXEC_EXT if is_standalone else LIB_EXT
    library_target = f'{name}{ext}'

    _write_ninja_file(
        path=path,
        cflags=cflags,
        post_cflags=None,
        sources=sources,
        objects=objects,
        ldflags=ldflags,
        library_target=library_target)

def _jit_compile(name,
                 sources,
                 extra_cflags,
                 extra_ldflags,
                 extra_include_paths,
                 build_directory: str,
                 verbose: bool,
                 is_python_module,
                 is_standalone,
                 keep_intermediates=True) -> None:
    if is_python_module and is_standalone:
        raise ValueError("`is_python_module` and `is_standalone` are mutually exclusive.")

    old_version = JIT_EXTENSION_VERSIONER.get_version(name)
    version = JIT_EXTENSION_VERSIONER.bump_version_if_changed(
        name,
        sources,
        build_arguments=[extra_cflags, extra_ldflags, extra_include_paths],
        build_directory=build_directory,
        with_cuda=False,
        is_python_module=is_python_module,
        is_standalone=is_standalone,
    )
    if version > 0:
        if version != old_version and verbose:
            print(f'The input conditions for extension module {name} have changed. ' +
                  f'Bumping to version {version} and re-building as {name}_v{version}...')
        name = f'{name}_v{version}'

    if version != old_version:
        baton = FileBaton(os.path.join(build_directory, 'lock'))
        if baton.try_acquire():
            try:
                with GeneratedFileCleaner(keep_intermediates=keep_intermediates) as clean_ctx:
                    _write_ninja_file_and_build_library(
                        name=name,
                        sources=sources,
                        extra_cflags=extra_cflags or [],
                        extra_ldflags=extra_ldflags or [],
                        extra_include_paths=extra_include_paths or [],
                        build_directory=build_directory,
                        verbose=verbose,
                        is_standalone=is_standalone)
            finally:
                baton.release()
        else:
            baton.wait()
    elif verbose:
        print('No modifications detected for re-loaded extension '
              f'module {name}, skipping build step...')

    if verbose:
        print(f'Loading extension module {name}...')

    if is_standalone:
        return _get_exec_path(name, build_directory)

    return _import_module_from_library(name, build_directory, is_python_module)

def load(name,
         sources: Union[str, List[str]],
         extra_cflags=None,
         extra_ldflags=None,
         extra_include_paths=None,
         build_directory=None,
         verbose=False,
         is_python_module=True,
         is_standalone=False,
         keep_intermediates=True):
    r'''
    Loads a PyTorch C++ extension just-in-time (JIT).

    To load an extension, a Ninja build file is emitted, which is used to
    compile the given sources into a dynamic library. This library is
    subsequently loaded into the current Python process as a module and
    returned from this function, ready for use.

    By default, the directory to which the build file is emitted and the
    resulting library compiled to is ``<tmp>/torch_extensions/<name>``, where
    ``<tmp>`` is the temporary folder on the current platform and ``<name>``
    the name of the extension. This location can be overridden in two ways.
    First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it
    replaces ``<tmp>/torch_extensions`` and all extensions will be compiled
    into subfolders of this directory. Second, if the ``build_directory``
    argument to this function is supplied, it overrides the entire path, i.e.
    the library will be compiled into that folder directly.

    To compile the sources, the default system compiler (``c++``) is used,
    which can be overridden by setting the ``CXX`` environment variable. To pass
    additional arguments to the compilation process, ``extra_cflags`` or
    ``extra_ldflags`` can be provided. For example, to compile your extension
    with optimizations, pass ``extra_cflags=['-O3']``. You can also use
    ``extra_cflags`` to pass further include directories.

    Args:
        name: The name of the extension to build. This MUST be the same as the
            name of the pybind11 module!
        sources: A list of relative or absolute paths to C++ source files.
        extra_cflags: optional list of compiler flags to forward to the build.
        extra_ldflags: optional list of linker flags to forward to the build.
        extra_include_paths: optional list of include directories to forward
            to the build.
        build_directory: optional path to use as build workspace.
        verbose: If ``True``, turns on verbose logging of load steps.
        is_python_module: If ``True`` (default), imports the produced shared
            library as a Python module. If ``False``, behavior depends on
            ``is_standalone``.
        is_standalone: If ``False`` (default) loads the constructed extension
            into the process as a plain dynamic library. If ``True``, build a
            standalone executable.

    Returns:
        If ``is_python_module`` is ``True``:
            Returns the loaded PyTorch extension as a Python module.

        If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``:
            Returns nothing. (The shared library is loaded into the process as
            a side effect.)

        If ``is_standalone`` is ``True``.
            Return the path to the executable. (On Windows, TORCH_LIB_PATH is
            added to the PATH environment variable as a side effect.)

    Example:
        >>> from intel_extension_for_pytorch.xpu.cpp_extension import load
        >>> module = load(
                name='extension',
                sources=['extension.cpp', 'extension_kernel.cpp'],
                extra_cflags=['-O2'],
                verbose=True)
    '''
    return _jit_compile(
        name,
        [sources] if isinstance(sources, str) else sources,
        extra_cflags,
        extra_ldflags,
        extra_include_paths,
        build_directory or _get_build_directory(name, verbose),
        verbose,
        is_python_module,
        is_standalone,
        keep_intermediates=keep_intermediates)

def _write_ninja_file(path,
                      cflags,
                      post_cflags,
                      sources,
                      objects,
                      ldflags,
                      library_target) -> None:
    r"""Write a ninja file that does the desired compiling and linking.
    `path`: Where to write this file
    `cflags`: list of flags to pass to $cxx. Can be None.
    `post_cflags`: list of flags to append to the $cxx invocation. Can be None.
    `sources`: list of paths to source files
    `objects`: list of desired paths to objects, one per source.
    `ldflags`: list of flags to pass to linker. Can be None.
    `library_target`: Name of the output library. Can be None; in that case,
                      we do no linking.
    """
    def sanitize_flags(flags):
        if flags is None:
            return []
        else:
            return [flag.strip() for flag in flags]

    cflags = sanitize_flags(cflags)
    post_cflags = sanitize_flags(post_cflags)
    ldflags = sanitize_flags(ldflags)

    # Sanity checks...
    assert len(sources) == len(objects)
    assert len(sources) > 0

    if IS_WINDOWS:
        compiler = os.environ.get('CXX', 'cl')
    else:
        compiler = get_dpcpp_complier()

    # Version 1.3 is required for the `deps` directive.
    config = ['ninja_required_version = 1.3']
    config.append(f'cxx = {compiler}')

    flags = [f'cflags = {" ".join(cflags)}']
    flags.append(f'post_cflags = {" ".join(post_cflags)}')
    flags.append(f'ldflags = {" ".join(ldflags)}')

    # Turn into absolute paths so we can emit them into the ninja build
    # file wherever it is.
    sources = [os.path.abspath(file) for file in sources]

    # See https://ninja-build.org/build.ninja.html for reference.
    compile_rule = ['rule compile']
    if IS_WINDOWS:
        compile_rule.append(
            '  command = cl /showIncludes $cflags -c $in /Fo$out $post_cflags')
        compile_rule.append('  deps = msvc')
    else:
        compile_rule.append(
            '  command = $cxx -MMD -MF $out.d $cflags -c $in -o $out $post_cflags')
        compile_rule.append('  depfile = $out.d')
        compile_rule.append('  deps = gcc')

    # Emit one build rule per source to enable incremental build.
    build = []
    for source_file, object_file in zip(sources, objects):
        rule = 'compile'
        if IS_WINDOWS:
            source_file = source_file.replace(':', '$:')
            object_file = object_file.replace(':', '$:')
        source_file = source_file.replace(" ", "$ ")
        object_file = object_file.replace(" ", "$ ")
        build.append(f'build {object_file}: {rule} {source_file}')

    if library_target is not None:
        link_rule = ['rule link']
        if IS_WINDOWS:
            cl_paths = subprocess.check_output(['where',
                                                'cl']).decode(*SUBPROCESS_DECODE_ARGS).split('\r\n')
            if len(cl_paths) >= 1:
                cl_path = os.path.dirname(cl_paths[0]).replace(':', '$:')
            else:
                raise RuntimeError("MSVC is required to load C++ extensions")
            link_rule.append(f'  command = "{cl_path}/link.exe" $in /nologo $ldflags /out:$out')
        else:
            link_rule.append('  command = $cxx $in $ldflags -o $out')

        link = [f'build {library_target}: link {" ".join(objects)}']

        default = [f'default {library_target}']
    else:
        link_rule, link, default = [], [], []

    # 'Blocks' should be separated by newlines, for visual benefit.
    blocks = [config, flags, compile_rule]

    blocks += [link_rule, build, link, default]
    with open(path, 'w') as build_file:
        for block in blocks:
            lines = '\n'.join(block)
            build_file.write(f'{lines}\n\n')

def _get_dpcpp_root():
    # TODO: Need to decouple with toolchain env scripts
    dpcpp_root = os.getenv('CMPLR_ROOT')
    return dpcpp_root

def _get_onemkl_root():
    # TODO: Need to decouple with toolchain env scripts
    path = os.getenv('MKLROOT')
    return path

def _get_onednn_root():
    # TODO: Need to decouple with toolchain env scripts
    path = os.getenv('DNNLROOT')
    return path

class _one_api_help:
    __dpcpp_root = None
    __onemkl_root = None
    __onednn_root = None
    __default_root = None

    def __init__(self):
        self.__dpcpp_root = _get_dpcpp_root()
        self.__onemkl_root = _get_onemkl_root()
        self.__onednn_root = _get_onednn_root()

        CUR_DIR = os.path.dirname(__file__)
        self.__default_root = os.path.dirname(CUR_DIR)

        self.check_onednn_cfg()
        self.check_dpcpp_cfg()
        self.check_onemkl_cfg()

    def check_onemkl_cfg(self):
        if self.__onemkl_root is None:
            raise 'Didn\'t detect mkl root. Please source <oneapi_dir>/mkl/<version>/env/vars.sh '

    def check_onednn_cfg(self):
        if self.__onednn_root is None:
            raise 'Didn\'t detect dnnl root. Please source <oneapi_dir>/dnnl/<version>/env/vars.sh '
        else:
            warnings.warn("This extension has static linked onednn library. Please attaction to \
                that, this path of onednn version maybe not match with the built-in version.")

    def check_dpcpp_cfg(self):
        if self.__dpcpp_root is None:
            raise 'Didn\'t detect dpcpp root. Please source <oneapi_dir>/compiler/<version>/env/vars.sh '

    def get_default_include_dir(self):
        return [os.path.join(self.__default_root, 'include')]

    def get_default_lib_dir(self):
        return [os.path.join(self.__default_root, 'lib')]

    def get_dpcpp_include_dir(self):
        return [
            os.path.join(self.__dpcpp_root, 'linux', 'include'),
            os.path.join(self.__dpcpp_root, 'linux', 'include', 'sycl')
        ]

    def get_onemkl_include_dir(self):
        return [os.path.join(self.__onemkl_root, 'include')]

    def get_onednn_include_dir(self):
        return [os.path.join(self.__onednn_root, 'include')]

    def get_onednn_lib_dir(self):
        return [os.path.join(self.__onednn_root, 'lib')]

    def is_onemkl_ready(self):
        if self.__onemkl_root is None:
            return False
        return True

    def is_onednn_ready(self):
        if self.__onednn_root is None:
            return False
        return True

    def get_library_dirs(self):
        library_dirs = []
        library_dirs += [f'{x}' for x in self.get_default_lib_dir()]
        library_dirs += [f'{x}' for x in self.get_onednn_lib_dir()]
        return library_dirs

    def get_include_dirs(self):
        include_dirs = []
        include_dirs += [f'{x}' for x in self.get_dpcpp_include_dir()]
        include_dirs += [f'{x}' for x in self.get_onemkl_include_dir()]
        include_dirs += [f'{x}' for x in self.get_onednn_include_dir()]
        include_dirs += [f'{x}' for x in self.get_default_include_dir()]
        return include_dirs

    def get_onemkl_libraries(self):
        MKLROOT = self.__onemkl_root
        return [
            f'{MKLROOT}/lib/intel64/libmkl_sycl.a',
            f'{MKLROOT}/lib/intel64/libmkl_intel_ilp64.a',
            f'{MKLROOT}/lib/intel64/libmkl_sequential.a',
            f'{MKLROOT}/lib/intel64/libmkl_core.a',
        ]

def get_pytorch_include_dir():
    lib_include = os.path.join(_TORCH_PATH, 'include')
    paths = [
        lib_include,
        # Remove this once torch/torch.h is officially no longer supported for C++ extensions.
        os.path.join(lib_include, 'torch', 'csrc', 'api', 'include'),
        # Some internal (old) Torch headers don't properly prefix their includes,
        # so we need to pass -Itorch/lib/include/TH as well.
        os.path.join(lib_include, 'TH')
    ]
    return paths

def get_pytorch_lib_dir():
    return [os.path.join(_TORCH_PATH, 'lib')]


def DPCPPExtension(name, sources, *args, **kwargs):
    r'''
    Creates a :class:`setuptools.Extension` for DPCPP/C++.
    Convenience method that creates a :class:`setuptools.Extension` with the
    bare minimum (but often sufficient) arguments to build a DPCPP/C++
    extension.
    All arguments are forwarded to the :class:`setuptools.Extension`
    constructor.
    Example:
        >>> from intel_extension_for_pytorch.xpu.utils import DpcppBuildExtension, DPCPPExtension
        >>> setup(
                name='dpcpp_extension',
                ext_modules=[
                    DPCPPExtension(
                            name='dpcpp_extension',
                            sources=['extension.cpp', 'extension_kernel.cpp'],
                            extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']})
                ],
                cmdclass={
                    'build_ext': DpcppBuildExtension
                })
    '''

    library_dirs = kwargs.get('library_dirs', [])
    library_dirs += library_paths()
    kwargs['library_dirs'] = library_dirs

    libraries = kwargs.get('libraries', [])
    libraries.append('c10')
    libraries.append('torch')
    libraries.append('torch_cpu')
    libraries.append('torch_python')

    # Append oneDNN link parameters.
    libraries.append('dnnl')
    kwargs['libraries'] = libraries

    include_dirs = kwargs.get('include_dirs', [])
    include_dirs += include_paths()
    kwargs['include_dirs'] = include_dirs

    kwargs['language'] = 'c++'

    extra_compile_args = kwargs.get('extra_compile_args', {})
    extra_link_args = kwargs.get('extra_link_args', [])
    # add oneapi link parameters
    extra_link_args = _prepare_ldflags(extra_link_args, False, False)

    # todo: add dpcpp parameter support.
    kwargs['extra_link_args'] = extra_link_args
    kwargs['extra_compile_args'] = extra_compile_args

    return setuptools.Extension(name, sources, *args, **kwargs)
