# Copyright (c) 2024, Tri Dao.

import sys
import warnings
import os
import re
import shutil
import ast
from pathlib import Path
from packaging.version import parse, Version
import platform

from setuptools import setup, find_packages
import subprocess

import urllib.request
import urllib.error
from wheel.bdist_wheel import bdist_wheel as _bdist_wheel

import torch
from torch.utils.cpp_extension import BuildExtension, CppExtension, CUDAExtension, CUDA_HOME, HIP_HOME


with open("README.md", "r", encoding="utf-8") as fh:
    long_description = fh.read()


# ninja build does not work unless include_dirs are abs path
this_dir = os.path.dirname(os.path.abspath(__file__))

PACKAGE_NAME = "causal_conv1d"

BASE_WHEEL_URL = "https://github.com/Dao-AILab/causal-conv1d/releases/download/{tag_name}/{wheel_name}"

# FORCE_BUILD: Force a fresh build locally, instead of attempting to find prebuilt wheels
# SKIP_CUDA_BUILD: Intended to allow CI to use a simple `python setup.py sdist` run to copy over raw files, without any cuda compilation
FORCE_BUILD = os.getenv("CAUSAL_CONV1D_FORCE_BUILD", "FALSE") == "TRUE"
SKIP_CUDA_BUILD = os.getenv("CAUSAL_CONV1D_SKIP_CUDA_BUILD", "FALSE") == "TRUE"
# For CI, we want the option to build with C++11 ABI since the nvcr images use C++11 ABI
FORCE_CXX11_ABI = os.getenv("CAUSAL_CONV1D_FORCE_CXX11_ABI", "FALSE") == "TRUE"


def get_platform():
    """
    Returns the platform name as used in wheel filenames.
    """
    if sys.platform.startswith("linux"):
        return "linux_x86_64"
    elif sys.platform == "darwin":
        mac_version = ".".join(platform.mac_ver()[0].split(".")[:2])
        return f"macosx_{mac_version}_x86_64"
    elif sys.platform == "win32":
        return "win_amd64"
    else:
        raise ValueError("Unsupported platform: {}".format(sys.platform))


def get_cuda_bare_metal_version(cuda_dir):
    raw_output = subprocess.check_output(
        [cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True
    )
    output = raw_output.split()
    release_idx = output.index("release") + 1
    bare_metal_version = parse(output[release_idx].split(",")[0])

    return raw_output, bare_metal_version


def get_hip_version(rocm_dir):

    hipcc_bin = "hipcc" if rocm_dir is None else os.path.join(rocm_dir, "bin", "hipcc")
    try:
        raw_output = subprocess.check_output(
            [hipcc_bin, "--version"], universal_newlines=True
        )
    except Exception as e:
        print(
            f"hip installation not found: {e} ROCM_PATH={os.environ.get('ROCM_PATH')}"
        )
        return None, None
    
    for line in raw_output.split("\n"):
        if "HIP version" in line:
            rocm_version = parse(line.split()[-1].replace("-", "+")) # local version is not parsed correctly
            return line, rocm_version

    return None, None


def get_torch_hip_version():
    if torch.version.hip:
        return parse(torch.version.hip.split()[-1].replace("-", "+"))
    else:
        return None


def check_if_hip_home_none(global_option: str) -> None:
    
    if HIP_HOME is not None:
        return
    # warn instead of error because user could be downloading prebuilt wheels, so hipcc won't be necessary
    # in that case.
    warnings.warn(
        f"{global_option} was requested, but hipcc was not found.  Are you sure your environment has hipcc available?"
    )


def check_if_cuda_home_none(global_option: str) -> None:
    if CUDA_HOME is not None:
        return
    # warn instead of error because user could be downloading prebuilt wheels, so nvcc won't be necessary
    # in that case.
    warnings.warn(
        f"{global_option} was requested, but nvcc was not found.  Are you sure your environment has nvcc available?  "
        "If you're installing within a container from https://hub.docker.com/r/pytorch/pytorch, "
        "only images whose names contain 'devel' will provide nvcc."
    )


def append_nvcc_threads(nvcc_extra_args):
    return nvcc_extra_args + ["--threads", "4"]


cmdclass = {}
ext_modules = []


HIP_BUILD = bool(torch.version.hip)

if not SKIP_CUDA_BUILD:

    print("\n\ntorch.__version__  = {}\n\n".format(torch.__version__))
    TORCH_MAJOR = int(torch.__version__.split(".")[0])
    TORCH_MINOR = int(torch.__version__.split(".")[1])


    cc_flag = []

    if HIP_BUILD:
        check_if_hip_home_none(PACKAGE_NAME)

        rocm_home = os.getenv("ROCM_PATH")
        _, hip_version = get_hip_version(rocm_home)

        
        # if HIP_HOME is not None:
        #     if hip_version < Version("6.0"):
        #         raise RuntimeError(
        #             f"{PACKAGE_NAME} is only supported on ROCm 6.0 and above.  "
        #             "Note: make sure HIP has a supported version by running hipcc --version."
        #         )
        #     if hip_version == Version("6.0"):
        #         warnings.warn(
        #             f"{PACKAGE_NAME} requires a patch to be applied when running on ROCm 6.0. "
        #             "Refer to the README.md for detailed instructions.",
        #             UserWarning
        #         )

        cc_flag.append("-DBUILD_PYTHON_PACKAGE")

    else:
        check_if_cuda_home_none(PACKAGE_NAME)
        # Check, if CUDA11 is installed for compute capability 8.0
        
        if CUDA_HOME is not None:
            _, bare_metal_version = get_cuda_bare_metal_version(CUDA_HOME)
            if bare_metal_version < Version("11.6"):
                raise RuntimeError(
                    f"{PACKAGE_NAME} is only supported on CUDA 11.6 and above.  "
                    "Note: make sure nvcc has a supported version by running nvcc -V."
                )
                    
        cc_flag.append("-gencode")
        cc_flag.append("arch=compute_53,code=sm_53")
        cc_flag.append("-gencode")
        cc_flag.append("arch=compute_62,code=sm_62")
        cc_flag.append("-gencode")
        cc_flag.append("arch=compute_70,code=sm_70")
        cc_flag.append("-gencode")
        cc_flag.append("arch=compute_72,code=sm_72")
        cc_flag.append("-gencode")
        cc_flag.append("arch=compute_80,code=sm_80")
        cc_flag.append("-gencode")
        cc_flag.append("arch=compute_87,code=sm_87")
        if bare_metal_version >= Version("11.8"):
            cc_flag.append("-gencode")
            cc_flag.append("arch=compute_90,code=sm_90")

    # HACK: The compiler flag -D_GLIBCXX_USE_CXX11_ABI is set to be the same as
    # torch._C._GLIBCXX_USE_CXX11_ABI
    # https://github.com/pytorch/pytorch/blob/8472c24e3b5b60150096486616d98b7bea01500b/torch/utils/cpp_extension.py#L920
    if FORCE_CXX11_ABI:
        torch._C._GLIBCXX_USE_CXX11_ABI = True


    if HIP_BUILD:
        extra_compile_args = {
            "cxx": ["-O3", "-std=c++17"],
            "nvcc": [
                "-O3",
                "-std=c++17",
                f"--offload-arch={os.getenv('HIP_ARCHITECTURES', 'native')}",
                "-U__CUDA_NO_HALF_OPERATORS__",
                "-U__CUDA_NO_HALF_CONVERSIONS__",
                "-fgpu-flush-denormals-to-zero",
            ]
            + cc_flag,
        }
    else:
        extra_compile_args = {
            "cxx": ["-O3"],
            "nvcc": append_nvcc_threads(
                [
                    "-O3",
                    "-U__CUDA_NO_HALF_OPERATORS__",
                    "-U__CUDA_NO_HALF_CONVERSIONS__",
                    "-U__CUDA_NO_BFLOAT16_OPERATORS__",
                    "-U__CUDA_NO_BFLOAT16_CONVERSIONS__",
                    "-U__CUDA_NO_BFLOAT162_OPERATORS__",
                    "-U__CUDA_NO_BFLOAT162_CONVERSIONS__",
                    "--expt-relaxed-constexpr",
                    "--expt-extended-lambda",
                    "--use_fast_math",
                    "--ptxas-options=-v",
                    "-lineinfo",
                ]
                + cc_flag
            ),
        }

    ext_modules.append(
        CUDAExtension(
            name="causal_conv1d_cuda",
            sources=[
                "csrc/causal_conv1d.cpp",
                "csrc/causal_conv1d_fwd.cu",
                "csrc/causal_conv1d_bwd.cu",
                "csrc/causal_conv1d_update.cu",
            ],
            extra_compile_args=extra_compile_args,
            include_dirs=[Path(this_dir) / "csrc" / "causal_conv1d"],
        )
    )


def get_package_version():
    with open(Path(this_dir) / "causal_conv1d" / "__init__.py", "r") as f:
        version_match = re.search(r"^__version__\s*=\s*(.*)$", f.read(), re.MULTILINE)
    public_version = ast.literal_eval(version_match.group(1))
    local_version = os.environ.get("CAUSAL_CONV1D_LOCAL_VERSION")
    if local_version:
        return f"{public_version}+{local_version}"
    else:
        return str(public_version)


def get_wheel_url():

    # Determine the version numbers that will be used to determine the correct wheel
    torch_version_raw = parse(torch.__version__)

    if HIP_BUILD:
        # We're using the HIP version used to build torch, not the one currently installed
        torch_hip_version = get_torch_hip_version()
        hip_version = f"{torch_hip_version.major}{torch_hip_version.minor}"
    else:
        # We're using the CUDA version used to build torch, not the one currently installed
        # _, cuda_version_raw = get_cuda_bare_metal_version(CUDA_HOME)
        torch_cuda_version = parse(torch.version.cuda)
        # For CUDA 11, we only compile for CUDA 11.8, and for CUDA 12 we only compile for CUDA 12.2
        # to save CI time. Minor versions should be compatible.
        torch_cuda_version = parse("11.8") if torch_cuda_version.major == 11 else parse("12.2")
        cuda_version = f"{torch_cuda_version.major}{torch_cuda_version.minor}"
    
    gpu_compute_version = hip_version if HIP_BUILD else cuda_version
    cuda_or_hip = "hip" if HIP_BUILD else "cu"

    python_version = f"cp{sys.version_info.major}{sys.version_info.minor}"
    platform_name = get_platform()
    causal_conv1d_version = get_package_version()

    torch_version = f"{torch_version_raw.major}.{torch_version_raw.minor}"
    cxx11_abi = str(torch._C._GLIBCXX_USE_CXX11_ABI).upper()

    # Determine wheel URL based on CUDA version, torch version, python version and OS
    wheel_filename = f"{PACKAGE_NAME}-{causal_conv1d_version}+{cuda_or_hip}{gpu_compute_version}torch{torch_version}cxx11abi{cxx11_abi}-{python_version}-{python_version}-{platform_name}.whl"

    wheel_url = BASE_WHEEL_URL.format(
        tag_name=f"v{causal_conv1d_version}", wheel_name=wheel_filename
    )
    return wheel_url, wheel_filename


class CachedWheelsCommand(_bdist_wheel):
    """
    The CachedWheelsCommand plugs into the default bdist wheel, which is ran by pip when it cannot
    find an existing wheel (which is currently the case for all installs). We use
    the environment parameters to detect whether there is already a pre-built version of a compatible
    wheel available and short-circuits the standard full build pipeline.
    """

    def run(self):
        if FORCE_BUILD:
            return super().run()

        wheel_url, wheel_filename = get_wheel_url()
        print("Guessing wheel URL: ", wheel_url)
        try:
            urllib.request.urlretrieve(wheel_url, wheel_filename)

            # Make the archive
            # Lifted from the root wheel processing command
            # https://github.com/pypa/wheel/blob/cf71108ff9f6ffc36978069acb28824b44ae028e/src/wheel/bdist_wheel.py#LL381C9-L381C85
            if not os.path.exists(self.dist_dir):
                os.makedirs(self.dist_dir)

            impl_tag, abi_tag, plat_tag = self.get_tag()
            archive_basename = f"{self.wheel_dist_name}-{impl_tag}-{abi_tag}-{plat_tag}"

            wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
            print("Raw wheel path", wheel_path)
            shutil.move(wheel_filename, wheel_path)
        except urllib.error.HTTPError:
            print("Precompiled wheel not found. Building from source...")
            # If the wheel could not be downloaded, build from source
            super().run()


setup(
    name=PACKAGE_NAME,
    version=get_package_version(),
    packages=find_packages(
        exclude=(
            "build",
            "csrc",
            "include",
            "tests",
            "dist",
            "docs",
            "benchmarks",
            "causal_conv1d.egg-info",
        )
    ),
    author="Tri Dao",
    author_email="tri@tridao.me",
    description="Causal depthwise conv1d in CUDA, with a PyTorch interface",
    long_description=long_description,
    long_description_content_type="text/markdown",
    url="https://github.com/Dao-AILab/causal-conv1d",
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: BSD License",
        "Operating System :: Unix",
    ],
    ext_modules=ext_modules,
    cmdclass={"bdist_wheel": CachedWheelsCommand, "build_ext": BuildExtension}
    if ext_modules
    else {
        "bdist_wheel": CachedWheelsCommand,
    },
    python_requires=">=3.8",
    install_requires=[
        "torch",
        "packaging",
        "ninja",
    ],
)
