import os
import re
import subprocess
import warnings
from typing import List


def print_rank_0(message: str) -> None:
    """
    Print on only one process to avoid spamming.
    """
    try:
        import torch.distributed as dist
        if not dist.is_initialized():
            is_main_rank = True
        else:
            is_main_rank = dist.get_rank() == 0
    except ImportError:
        is_main_rank = True

    if is_main_rank:
        print(message)


def get_cuda_version_in_pytorch() -> List[int]:
    """
    This function returns the CUDA version in the PyTorch build.

    Returns:
        The CUDA version required by PyTorch, in the form of tuple (major, minor).
    """
    import torch

    try:
        torch_cuda_major = torch.version.cuda.split(".")[0]
        torch_cuda_minor = torch.version.cuda.split(".")[1]
    except:
        raise ValueError(
            "[extension] Cannot retrive the CUDA version in the PyTorch binary given by torch.version.cuda")
    return torch_cuda_major, torch_cuda_minor


def get_cuda_bare_metal_version(cuda_dir) -> List[int]:
    """
    Get the System CUDA version from nvcc.

    Args:
        cuda_dir (str): the directory for CUDA Toolkit.

    Returns:
        The CUDA version required by PyTorch, in the form of tuple (major, minor).
    """
    nvcc_path = os.path.join(cuda_dir, 'bin/nvcc')

    if cuda_dir is None:
        raise ValueError(
            f"[extension] The argument cuda_dir is None, but expected to be a string. Please make sure your have exported the environment variable CUDA_HOME correctly."
        )

    # check for nvcc path
    if not os.path.exists(nvcc_path):
        raise FileNotFoundError(
            f"[extension] The nvcc compiler is not found in {nvcc_path}, please make sure you have set the correct value for CUDA_HOME."
        )

    # parse the nvcc -v output to obtain the system cuda version
    try:
        raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True)
        output = raw_output.split()
        release_idx = output.index("release") + 1
        release = output[release_idx].split(".")
        bare_metal_major = release[0]
        bare_metal_minor = release[1][0]
    except:
        raise ValueError(
            f"[extension] Failed to parse the nvcc output to obtain the system CUDA bare metal version. The output for 'nvcc -v' is \n{raw_output}"
        )

    return bare_metal_major, bare_metal_minor


def check_system_pytorch_cuda_match(cuda_dir):
    bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)
    torch_cuda_major, torch_cuda_minor = get_cuda_version_in_pytorch()

    if bare_metal_major != torch_cuda_major:
        raise Exception(
            f'[extension] Failed to build PyTorch extension because the detected CUDA version ({bare_metal_major}.{bare_metal_minor}) '
            f'mismatches the version that was used to compile PyTorch ({torch_cuda_major}.{torch_cuda_minor}).'
            'Please make sure you have set the CUDA_HOME correctly and installed the correct PyTorch in https://pytorch.org/get-started/locally/ .'
        )

    print(bare_metal_minor != torch_cuda_minor)
    if bare_metal_minor != torch_cuda_minor:
        warnings.warn(
            f"[extension] The CUDA version on the system ({bare_metal_major}.{bare_metal_minor}) does not match with the version ({torch_cuda_major}.{torch_cuda_minor}) torch was compiled with. "
            "The mismatch is found in the minor version. As the APIs are compatible, we will allow compilation to proceed. "
            "If you encounter any issue when using the built kernel, please try to build it again with fully matched CUDA versions"
        )
    return True


def get_pytorch_version() -> List[int]:
    """
    This functions finds the PyTorch version.

    Returns:
        A tuple of integers in the form of (major, minor, patch).
    """
    import torch
    torch_version = torch.__version__.split('+')[0]
    TORCH_MAJOR = int(torch_version.split('.')[0])
    TORCH_MINOR = int(torch_version.split('.')[1])
    TORCH_PATCH = int(torch_version.split('.')[2])
    return TORCH_MAJOR, TORCH_MINOR, TORCH_PATCH


def check_pytorch_version(min_major_version, min_minor_version) -> bool:
    """
    Compare the current PyTorch version with the minium required version.

    Args:
        min_major_version (int): the minimum major version of PyTorch required
        min_minor_version (int): the minimum minor version of PyTorch required

    Returns:
        A boolean value. The value is True if the current pytorch version is acceptable and False otherwise.
    """
    # get pytorch version
    torch_major, torch_minor, _ = get_pytorch_version()

    # if the
    if torch_major < min_major_version or (torch_major == min_major_version and torch_minor < min_minor_version):
        raise RuntimeError(
            f"[extension] Colossal-AI requires Pytorch {min_major_version}.{min_minor_version} or newer.\n"
            "The latest stable release can be obtained from https://pytorch.org/get-started/locally/")


def check_cuda_availability():
    """
    Check if CUDA is available on the system.

    Returns:
        A boolean value. True if CUDA is available and False otherwise.
    """
    import torch
    return torch.cuda.is_available()


def set_cuda_arch_list(cuda_dir):
    """
    This function sets the PyTorch TORCH_CUDA_ARCH_LIST variable for ahead-of-time extension compilation.
    Ahead-of-time compilation occurs when CUDA_EXT=1 is set when running 'pip install'.
    """
    cuda_available = check_cuda_availability()

    # we only need to set this when CUDA is not available for cross-compilation
    if not cuda_available:
        warnings.warn(
            '\n[extension]  PyTorch did not find available GPUs on this system.\n'
            'If your intention is to cross-compile, this is not an error.\n'
            'By default, Colossal-AI will cross-compile for \n'
            '1. Pascal (compute capabilities 6.0, 6.1, 6.2),\n'
            '2. Volta (compute capability 7.0)\n'
            '3. Turing (compute capability 7.5),\n'
            '4. Ampere (compute capability 8.0, 8.6)if the CUDA version is >= 11.0\n'
            '\nIf you wish to cross-compile for a single specific architecture,\n'
            'export TORCH_CUDA_ARCH_LIST="compute capability" before running setup.py.\n')

        if os.environ.get("TORCH_CUDA_ARCH_LIST", None) is None:
            bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(cuda_dir)

            arch_list = ['6.0', '6.1', '6.2', '7.0', '7.5']

            if int(bare_metal_major) == 11:
                if int(bare_metal_minor) == 0:
                    arch_list.append('8.0')
                else:
                    arch_list.append('8.0')
                    arch_list.append('8.6')

            arch_list_str = ';'.join(arch_list)
            os.environ["TORCH_CUDA_ARCH_LIST"] = arch_list_str
        return False
    return True


def get_cuda_cc_flag() -> List[str]:
    """
    This function produces the cc flags for your GPU arch

    Returns:
        The CUDA cc flags for compilation.
    """

    # only import torch when needed
    # this is to avoid importing torch when building on a machine without torch pre-installed
    # one case is to build wheel for pypi release
    import torch

    cc_flag = []
    for arch in torch.cuda.get_arch_list():
        res = re.search(r'sm_(\d+)', arch)
        if res:
            arch_cap = res[1]
            if int(arch_cap) >= 60:
                cc_flag.extend(['-gencode', f'arch=compute_{arch_cap},code={arch}'])
    return cc_flag


def append_nvcc_threads(nvcc_extra_args: List[str]) -> List[str]:
    """
    This function appends the threads flag to your nvcc args.

    Returns:
        The nvcc compilation flags including the threads flag.
    """
    from torch.utils.cpp_extension import CUDA_HOME

    bare_metal_major, bare_metal_minor = get_cuda_bare_metal_version(CUDA_HOME)
    if int(bare_metal_major) >= 11 and int(bare_metal_minor) >= 2:
        return nvcc_extra_args + ["--threads", "4"]
    return nvcc_extra_args
