"""
extract factors the build is dependent on:
[X] compute capability  
    [ ] TODO: Q - What if we have multiple GPUs of different makes?
- CUDA version
- Software:
    - CPU-only: only CPU quantization functions (no optimizer, no matrix multipl)
    - CuBLAS-LT: full-build 8-bit optimizer
    - no CuBLAS-LT: no 8-bit matrix multiplication (`nomatmul`)

alle Binaries packagen

evaluation:
    - if paths faulty, return meaningful error
    - else:
        - determine CUDA version
        - determine capabilities
        - based on that set the default path
"""

import ctypes
import shlex
import subprocess
from os import environ as env
from pathlib import Path
from typing import Set, Union

from .utils import print_err, warn_of_missing_prerequisite


def execute_and_return(command_string: str) -> Tuple[str, str]:
    def _decode(subprocess_err_out_tuple):
        return tuple(
            to_decode.decode("UTF-8").strip()
            for to_decode in subprocess_err_out_tuple
        )

    def execute_and_return_decoded_std_streams(command_string):
        return _decode(
            subprocess.Popen(
                shlex.split(command_string),
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
            ).communicate()
        )

    std_out, std_err = execute_and_return_decoded_std_streams()
    return std_out, std_err


def check_cuda_result(cuda, result_val):
    if result_val != 0:
        # TODO: undefined name 'error_str'
        cuda.cuGetErrorString(result_val, ctypes.byref(error_str))
        print("Count not initialize CUDA - failure!")
        raise Exception("CUDA exception!")
    return result_val


# taken from https://gist.github.com/f0k/63a664160d016a491b2cbea15913d549
def get_compute_capability():
    libnames = ("libcuda.so", "libcuda.dylib", "cuda.dll")
    for libname in libnames:
        try:
            cuda = ctypes.CDLL(libname)
        except OSError:
            continue
        else:
            break
    else:
        raise OSError("could not load any of: " + " ".join(libnames))

    nGpus = ctypes.c_int()
    cc_major = ctypes.c_int()
    cc_minor = ctypes.c_int()

    result = ctypes.c_int()
    device = ctypes.c_int()
    # TODO: local variable 'context' is assigned to but never used
    context = ctypes.c_void_p()
    # TODO: local variable 'error_str' is assigned to but never used
    error_str = ctypes.c_char_p()

    result = check_cuda_result(cuda, cuda.cuInit(0))

    result = check_cuda_result(cuda, cuda.cuDeviceGetCount(ctypes.byref(nGpus)))
    ccs = []
    for i in range(nGpus.value):
        result = check_cuda_result(
            cuda, cuda.cuDeviceGet(ctypes.byref(device), i)
        )
        result = check_cuda_result(
            cuda,
            cuda.cuDeviceComputeCapability(
                ctypes.byref(cc_major), ctypes.byref(cc_minor), device
            ),
        )
        ccs.append(f"{cc_major.value}.{cc_minor.value}")

    # TODO: handle different compute capabilities; for now, take the max
    ccs.sort()
    # return ccs[-1]
    return ccs


CUDA_RUNTIME_LIB: str = "libcudart.so"


def tokenize_paths(paths: str) -> Set[Path]:
    return {Path(ld_path) for ld_path in paths.split(":") if ld_path}


def get_cuda_runtime_lib_path(
    # TODO: replace this with logic for all paths in env vars
    LD_LIBRARY_PATH: Union[str, None] = env.get("LD_LIBRARY_PATH")
) -> Union[Path, None]:
    """# TODO: add doc-string"""

    if not LD_LIBRARY_PATH:
        warn_of_missing_prerequisite(
            "LD_LIBRARY_PATH is completely missing from environment!"
        )
        return None

    ld_library_paths: Set[Path] = tokenize_paths(LD_LIBRARY_PATH)

    non_existent_directories: Set[Path] = {
        path for path in ld_library_paths if not path.exists()
    }

    if non_existent_directories:
        print_err(
            "WARNING: The following directories listed your path were found to "
            f"be non-existent: {non_existent_directories}"
        )

    cuda_runtime_libs: Set[Path] = {
        path / CUDA_RUNTIME_LIB
        for path in ld_library_paths
        if (path / CUDA_RUNTIME_LIB).is_file()
    } - non_existent_directories

    if len(cuda_runtime_libs) > 1:
        err_msg = (
            f"Found duplicate {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
        )
        raise FileNotFoundError(err_msg)

    elif len(cuda_runtime_libs) < 1:
        err_msg = (
            f"Did not find {CUDA_RUNTIME_LIB} files: {cuda_runtime_libs}.."
        )
        raise FileNotFoundError(err_msg)

    single_cuda_runtime_lib_dir = next(iter(cuda_runtime_libs))
    return single_cuda_runtime_lib_dir


def evaluate_cuda_setup():
    cuda_path = get_cuda_runtime_lib_path()
    cc = get_compute_capability()
    binary_name = "libbitsandbytes_cpu.so"

    if not (has_gpu := bool(cc)):
        print(
            "WARNING: No GPU detected! Check our CUDA paths. Processing to load CPU-only library..."
        )
        return binary_name

    has_cublaslt = cc in ["7.5", "8.0", "8.6"]

    # TODO:
    # (1) Model missing cases (no CUDA installed by CUDA driver (nvidia-smi accessible)
    # (2) Multiple CUDA versions installed

    cuda_home = str(Path(cuda_path).parent.parent)
    ls_output, err = execute_and_return(f"{cuda_home}/bin/nvcc --version")
    cuda_version = (
        ls_output.split("\n")[3].split(",")[-1].strip().lower().replace("v", "")
    )
    major, minor, revision = cuda_version.split(".")
    cuda_version_string = f"{major}{minor}"

    binary_name = f'libbitsandbytes_cuda{cuda_version_string}_{("cublaslt" if has_cublaslt else "")}.so'

    return binary_name