from __future__ import print_function

# Unlike the rest of the PyTorch this file must be python2 compliant.
# This script outputs relevant system environment info
# Run it with `python collect_env.py`.
import datetime
import locale
import re
import subprocess
import sys
import os
from collections import namedtuple


try:
    import torch

    TORCH_AVAILABLE = True
except (ImportError, NameError, AttributeError, OSError):
    TORCH_AVAILABLE = False

# System Environment Information
SystemEnv = namedtuple(
    "SystemEnv",
    [
        "torch_version",
        "is_debug_build",
        "cuda_compiled_version",
        "gcc_version",
        "clang_version",
        "cmake_version",
        "os",
        "libc_version",
        "python_version",
        "python_platform",
        "is_cuda_available",
        "cuda_runtime_version",
        "nvidia_driver_version",
        "nvidia_gpu_models",
        "cudnn_version",
        "pip_version",  # 'pip' or 'pip3'
        "pip_packages",
        "conda_packages",
        "hip_compiled_version",
        "hip_runtime_version",
        "miopen_runtime_version",
        "caching_allocator_config",
        "is_xnnpack_available",
    ],
)


def run(command):
    """Returns (return-code, stdout, stderr)"""
    p = subprocess.Popen(
        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True
    )
    raw_output, raw_err = p.communicate()
    rc = p.returncode
    if get_platform() == "win32":
        enc = "oem"
    else:
        enc = locale.getpreferredencoding()
    output = raw_output.decode(enc)
    err = raw_err.decode(enc)
    return rc, output.strip(), err.strip()


def run_and_read_all(run_lambda, command):
    """Runs command using run_lambda; reads and returns entire output if rc is 0"""
    rc, out, _ = run_lambda(command)
    if rc != 0:
        return None
    return out


def run_and_parse_first_match(run_lambda, command, regex):
    """Runs command using run_lambda, returns the first regex match if it exists"""
    rc, out, _ = run_lambda(command)
    if rc != 0:
        return None
    match = re.search(regex, out)
    if match is None:
        return None
    return match.group(1)


def run_and_return_first_line(run_lambda, command):
    """Runs command using run_lambda and returns first line if output is not empty"""
    rc, out, _ = run_lambda(command)
    if rc != 0:
        return None
    return out.split("\n")[0]


def get_conda_packages(run_lambda):
    conda = os.environ.get("CONDA_EXE", "conda")
    out = run_and_read_all(run_lambda, "{} list".format(conda))
    if out is None:
        return out

    return "\n".join(
        line
        for line in out.splitlines()
        if not line.startswith("#")
        and any(
            name in line
            for name in {
                "torch",
                "numpy",
                "cudatoolkit",
                "soumith",
                "mkl",
                "magma",
                "mkl",
            }
        )
    )


def get_gcc_version(run_lambda):
    return run_and_parse_first_match(run_lambda, "gcc --version", r"gcc (.*)")


def get_clang_version(run_lambda):
    return run_and_parse_first_match(
        run_lambda, "clang --version", r"clang version (.*)"
    )


def get_cmake_version(run_lambda):
    return run_and_parse_first_match(run_lambda, "cmake --version", r"cmake (.*)")


def get_nvidia_driver_version(run_lambda):
    if get_platform() == "darwin":
        cmd = "kextstat | grep -i cuda"
        return run_and_parse_first_match(
            run_lambda, cmd, r"com[.]nvidia[.]CUDA [(](.*?)[)]"
        )
    smi = get_nvidia_smi()
    return run_and_parse_first_match(run_lambda, smi, r"Driver Version: (.*?) ")


def get_gpu_info(run_lambda):
    if get_platform() == "darwin" or (
        TORCH_AVAILABLE
        and hasattr(torch.version, "hip")
        and torch.version.hip is not None
    ):
        if TORCH_AVAILABLE and torch.cuda.is_available():
            return torch.cuda.get_device_name(None)
        return None
    smi = get_nvidia_smi()
    uuid_regex = re.compile(r" \(UUID: .+?\)")
    rc, out, _ = run_lambda(smi + " -L")
    if rc != 0:
        return None
    # Anonymize GPUs by removing their UUID
    return re.sub(uuid_regex, "", out)


def get_running_cuda_version(run_lambda):
    return run_and_parse_first_match(run_lambda, "nvcc --version", r"release .+ V(.*)")


def get_cudnn_version(run_lambda):
    """This will return a list of libcudnn.so; it's hard to tell which one is being used"""
    if get_platform() == "win32":
        system_root = os.environ.get("SYSTEMROOT", "C:\\Windows")
        cuda_path = os.environ.get("CUDA_PATH", "%CUDA_PATH%")
        where_cmd = os.path.join(system_root, "System32", "where")
        cudnn_cmd = '{} /R "{}\\bin" cudnn*.dll'.format(where_cmd, cuda_path)
    elif get_platform() == "darwin":
        # CUDA libraries and drivers can be found in /usr/local/cuda/. See
        # https://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#install
        # https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac
        # Use CUDNN_LIBRARY when cudnn library is installed elsewhere.
        cudnn_cmd = "ls /usr/local/cuda/lib/libcudnn*"
    else:
        cudnn_cmd = 'ldconfig -p | grep libcudnn | rev | cut -d" " -f1 | rev'
    rc, out, _ = run_lambda(cudnn_cmd)
    # find will return 1 if there are permission errors or if not found
    if len(out) == 0 or (rc != 1 and rc != 0):
        l = os.environ.get("CUDNN_LIBRARY")
        if l is not None and os.path.isfile(l):
            return os.path.realpath(l)
        return None
    files_set = set()
    for fn in out.split("\n"):
        fn = os.path.realpath(fn)  # eliminate symbolic links
        if os.path.isfile(fn):
            files_set.add(fn)
    if not files_set:
        return None
    # Alphabetize the result because the order is non-deterministic otherwise
    files = list(sorted(files_set))
    if len(files) == 1:
        return files[0]
    result = "\n".join(files)
    return "Probably one of the following:\n{}".format(result)


def get_nvidia_smi():
    # Note: nvidia-smi is currently available only on Windows and Linux
    smi = "nvidia-smi"
    if get_platform() == "win32":
        system_root = os.environ.get("SYSTEMROOT", "C:\\Windows")
        program_files_root = os.environ.get("PROGRAMFILES", "C:\\Program Files")
        legacy_path = os.path.join(
            program_files_root, "NVIDIA Corporation", "NVSMI", smi
        )
        new_path = os.path.join(system_root, "System32", smi)
        smis = [new_path, legacy_path]
        for candidate_smi in smis:
            if os.path.exists(candidate_smi):
                smi = '"{}"'.format(candidate_smi)
                break
    return smi


def get_platform():
    if sys.platform.startswith("linux"):
        return "linux"
    elif sys.platform.startswith("win32"):
        return "win32"
    elif sys.platform.startswith("cygwin"):
        return "cygwin"
    elif sys.platform.startswith("darwin"):
        return "darwin"
    else:
        return sys.platform


def get_mac_version(run_lambda):
    return run_and_parse_first_match(run_lambda, "sw_vers -productVersion", r"(.*)")


def get_windows_version(run_lambda):
    system_root = os.environ.get("SYSTEMROOT", "C:\\Windows")
    wmic_cmd = os.path.join(system_root, "System32", "Wbem", "wmic")
    findstr_cmd = os.path.join(system_root, "System32", "findstr")
    return run_and_read_all(
        run_lambda, "{} os get Caption | {} /v Caption".format(wmic_cmd, findstr_cmd)
    )


def get_lsb_version(run_lambda):
    return run_and_parse_first_match(
        run_lambda, "lsb_release -a", r"Description:\t(.*)"
    )


def check_release_file(run_lambda):
    return run_and_parse_first_match(
        run_lambda, "cat /etc/*-release", r'PRETTY_NAME="(.*)"'
    )


def get_os(run_lambda):
    from platform import machine

    platform = get_platform()

    if platform == "win32" or platform == "cygwin":
        return get_windows_version(run_lambda)

    if platform == "darwin":
        version = get_mac_version(run_lambda)
        if version is None:
            return None
        return "macOS {} ({})".format(version, machine())

    if platform == "linux":
        # Ubuntu/Debian based
        desc = get_lsb_version(run_lambda)
        if desc is not None:
            return "{} ({})".format(desc, machine())

        # Try reading /etc/*-release
        desc = check_release_file(run_lambda)
        if desc is not None:
            return "{} ({})".format(desc, machine())

        return "{} ({})".format(platform, machine())

    # Unknown platform
    return platform


def get_python_platform():
    import platform

    return platform.platform()


def get_libc_version():
    import platform

    if get_platform() != "linux":
        return "N/A"
    return "-".join(platform.libc_ver())


def get_pip_packages(run_lambda):
    """Returns `pip list` output. Note: will also find conda-installed pytorch
    and numpy packages."""
    # People generally have `pip` as `pip` or `pip3`
    # But here it is incoved as `python -mpip`
    def run_with_pip(pip):
        out = run_and_read_all(run_lambda, "{} list --format=freeze".format(pip))
        return "\n".join(
            line
            for line in out.splitlines()
            if any(
                name in line
                for name in {
                    "torch",
                    "numpy",
                    "mypy",
                }
            )
        )

    pip_version = "pip3" if sys.version[0] == "3" else "pip"
    out = run_with_pip(sys.executable + " -mpip")

    return pip_version, out


def get_cachingallocator_config():
    ca_config = os.environ.get("PYTORCH_CUDA_ALLOC_CONF", "")
    return ca_config


def is_xnnpack_available():
    if TORCH_AVAILABLE:
        import torch.backends.xnnpack

        return str(torch.backends.xnnpack.enabled)  # type: ignore[attr-defined]
    else:
        return "N/A"


def get_env_info():
    run_lambda = run
    pip_version, pip_list_output = get_pip_packages(run_lambda)

    if TORCH_AVAILABLE:
        version_str = torch.__version__
        debug_mode_str = str(torch.version.debug)
        cuda_available_str = str(torch.cuda.is_available())
        cuda_version_str = torch.version.cuda
        if (
            not hasattr(torch.version, "hip") or torch.version.hip is None
        ):  # cuda version
            hip_compiled_version = hip_runtime_version = miopen_runtime_version = "N/A"
        else:  # HIP version
            cfg = torch._C._show_config().split("\n")
            hip_runtime_version = [
                s.rsplit(None, 1)[-1] for s in cfg if "HIP Runtime" in s
            ][0]
            miopen_runtime_version = [
                s.rsplit(None, 1)[-1] for s in cfg if "MIOpen" in s
            ][0]
            cuda_version_str = "N/A"
            hip_compiled_version = torch.version.hip
    else:
        version_str = debug_mode_str = cuda_available_str = cuda_version_str = "N/A"
        hip_compiled_version = hip_runtime_version = miopen_runtime_version = "N/A"

    sys_version = sys.version.replace("\n", " ")

    return SystemEnv(
        torch_version=version_str,
        is_debug_build=debug_mode_str,
        python_version="{} ({}-bit runtime)".format(
            sys_version, sys.maxsize.bit_length() + 1
        ),
        python_platform=get_python_platform(),
        is_cuda_available=cuda_available_str,
        cuda_compiled_version=cuda_version_str,
        cuda_runtime_version=get_running_cuda_version(run_lambda),
        nvidia_gpu_models=get_gpu_info(run_lambda),
        nvidia_driver_version=get_nvidia_driver_version(run_lambda),
        cudnn_version=get_cudnn_version(run_lambda),
        hip_compiled_version=hip_compiled_version,
        hip_runtime_version=hip_runtime_version,
        miopen_runtime_version=miopen_runtime_version,
        pip_version=pip_version,
        pip_packages=pip_list_output,
        conda_packages=get_conda_packages(run_lambda),
        os=get_os(run_lambda),
        libc_version=get_libc_version(),
        gcc_version=get_gcc_version(run_lambda),
        clang_version=get_clang_version(run_lambda),
        cmake_version=get_cmake_version(run_lambda),
        caching_allocator_config=get_cachingallocator_config(),
        is_xnnpack_available=is_xnnpack_available(),
    )


env_info_fmt = """
PyTorch version: {torch_version}
Is debug build: {is_debug_build}
CUDA used to build PyTorch: {cuda_compiled_version}
ROCM used to build PyTorch: {hip_compiled_version}

OS: {os}
GCC version: {gcc_version}
Clang version: {clang_version}
CMake version: {cmake_version}
Libc version: {libc_version}

Python version: {python_version}
Python platform: {python_platform}
Is CUDA available: {is_cuda_available}
CUDA runtime version: {cuda_runtime_version}
GPU models and configuration: {nvidia_gpu_models}
Nvidia driver version: {nvidia_driver_version}
cuDNN version: {cudnn_version}
HIP runtime version: {hip_runtime_version}
MIOpen runtime version: {miopen_runtime_version}
Is XNNPACK available: {is_xnnpack_available}

Versions of relevant libraries:
{pip_packages}
{conda_packages}
""".strip()


def pretty_str(envinfo):
    def replace_nones(dct, replacement="Could not collect"):
        for key in dct.keys():
            if dct[key] is not None:
                continue
            dct[key] = replacement
        return dct

    def replace_bools(dct, true="Yes", false="No"):
        for key in dct.keys():
            if dct[key] is True:
                dct[key] = true
            elif dct[key] is False:
                dct[key] = false
        return dct

    def prepend(text, tag="[prepend]"):
        lines = text.split("\n")
        updated_lines = [tag + line for line in lines]
        return "\n".join(updated_lines)

    def replace_if_empty(text, replacement="No relevant packages"):
        if text is not None and len(text) == 0:
            return replacement
        return text

    def maybe_start_on_next_line(string):
        # If `string` is multiline, prepend a \n to it.
        if string is not None and len(string.split("\n")) > 1:
            return "\n{}\n".format(string)
        return string

    mutable_dict = envinfo._asdict()

    # If nvidia_gpu_models is multiline, start on the next line
    mutable_dict["nvidia_gpu_models"] = maybe_start_on_next_line(
        envinfo.nvidia_gpu_models
    )

    # If the machine doesn't have CUDA, report some fields as 'No CUDA'
    dynamic_cuda_fields = [
        "cuda_runtime_version",
        "nvidia_gpu_models",
        "nvidia_driver_version",
    ]
    all_cuda_fields = dynamic_cuda_fields + ["cudnn_version"]
    all_dynamic_cuda_fields_missing = all(
        mutable_dict[field] is None for field in dynamic_cuda_fields
    )
    if (
        TORCH_AVAILABLE
        and not torch.cuda.is_available()
        and all_dynamic_cuda_fields_missing
    ):
        for field in all_cuda_fields:
            mutable_dict[field] = "No CUDA"
        if envinfo.cuda_compiled_version is None:
            mutable_dict["cuda_compiled_version"] = "None"

    # Replace True with Yes, False with No
    mutable_dict = replace_bools(mutable_dict)

    # Replace all None objects with 'Could not collect'
    mutable_dict = replace_nones(mutable_dict)

    # If either of these are '', replace with 'No relevant packages'
    mutable_dict["pip_packages"] = replace_if_empty(mutable_dict["pip_packages"])
    mutable_dict["conda_packages"] = replace_if_empty(mutable_dict["conda_packages"])

    # Tag conda and pip packages with a prefix
    # If they were previously None, they'll show up as ie '[conda] Could not collect'
    if mutable_dict["pip_packages"]:
        mutable_dict["pip_packages"] = prepend(
            mutable_dict["pip_packages"], "[{}] ".format(envinfo.pip_version)
        )
    if mutable_dict["conda_packages"]:
        mutable_dict["conda_packages"] = prepend(
            mutable_dict["conda_packages"], "[conda] "
        )
    return env_info_fmt.format(**mutable_dict)


def get_pretty_env_info():
    return pretty_str(get_env_info())


def main():
    print("Collecting environment information...")
    output = get_pretty_env_info()
    print(output)

    if (
        TORCH_AVAILABLE
        and hasattr(torch, "utils")
        and hasattr(torch.utils, "_crash_handler")
    ):
        minidump_dir = torch.utils._crash_handler.DEFAULT_MINIDUMP_DIR
        if sys.platform == "linux" and os.path.exists(minidump_dir):
            dumps = [
                os.path.join(minidump_dir, dump) for dump in os.listdir(minidump_dir)
            ]
            latest = max(dumps, key=os.path.getctime)
            ctime = os.path.getctime(latest)
            creation_time = datetime.datetime.fromtimestamp(ctime).strftime(
                "%Y-%m-%d %H:%M:%S"
            )
            msg = (
                "\n*** Detected a minidump at {} created on {}, ".format(
                    latest, creation_time
                )
                + "if this is related to your bug please include it when you file a report ***"
            )
            print(msg, file=sys.stderr)


if __name__ == "__main__":
    main()