# Copyright (c) Facebook, Inc. and its affiliates.

from __future__ import (
    absolute_import,
    division,
    print_function,
    unicode_literals,
)
import locale
import re
import subprocess
import sys
import os
from collections import namedtuple

try:
    import nle
    import minihack
    import gym

    NLE_AVAILABLE = True
    MINIHACK_AVAILABLE = True
    GYM_AVAILABLE = True
except (ImportError, NameError, AttributeError):
    NLE_AVAILABLE = False
    MINIHACK_AVAILABLE = False
    GYM_AVAILABLE = False
try:
    import torch

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

PY3 = sys.version_info >= (3, 0)

# System Environment Information
SystemEnv = namedtuple(
    "SystemEnv",
    [
        "minihack_version",
        "nle_version",
        "gym_version",
        "torch_version",
        "is_debug_build",
        "cuda_compiled_version",
        "gcc_version",
        "cmake_version",
        "os",
        "python_version",
        "is_cuda_available",
        "cuda_runtime_version",
        "nvidia_driver_version",
        "nvidia_gpu_models",
        "cudnn_version",
        "pip_version",  # 'pip' or 'pip3'
        "pip_packages",
        "conda_packages",
    ],
)


def run(command):
    """Returns (return-code, stdout, stderr)"""
    p = subprocess.Popen(
        command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True
    )
    output, err = p.communicate()
    rc = p.returncode
    if PY3:
        enc = locale.getpreferredencoding()
        output = output.decode(enc)
        err = 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 get_conda_packages(run_lambda):
    if get_platform() == "win32":
        grep_cmd = r'findstr /R "torch soumith mkl magma"'
    else:
        grep_cmd = r'grep "torch\|soumith\|mkl\|magma"'
    conda = os.environ.get("CONDA_EXE", "conda")
    out = run_and_read_all(run_lambda, conda + " list | " + grep_cmd)
    if out is None:
        return out
    # Comment starting at beginning of line
    comment_regex = re.compile(r"^#.*\n")
    return re.sub(comment_regex, "", out)


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


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":
        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"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":
        cudnn_cmd = 'where /R "%CUDA_PATH%\\bin" cudnn*.dll'
    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 # noqa
        # https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installmac # noqa
        # 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):
        cl = os.environ.get("CUDNN_LIBRARY")
        if cl is not None and os.path.isfile(cl):
            return os.path.realpath(cl)
        return None
    files = set()
    for fn in out.split("\n"):
        fn = os.path.realpath(fn)  # eliminate symbolic links
        if os.path.isfile(fn):
            files.add(fn)
    if not files:
        return None
    # Alphabetize the result because the order is non-deterministic otherwise
    files = list(sorted(files))
    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":
        smi = '"C:\\Program Files\\NVIDIA Corporation\\NVSMI\\%s"' % smi
    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):
    return run_and_read_all(
        run_lambda, "wmic os get Caption | findstr /v Caption"
    )


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):
    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 "Mac OSX {}".format(version)

    if platform == "linux":
        # Ubuntu/Debian based
        desc = get_lsb_version(run_lambda)
        if desc is not None:
            return desc

        # Try reading /etc/*-release
        desc = check_release_file(run_lambda)
        if desc is not None:
            return desc

        return platform

    # Unknown platform
    return platform


def get_pip_packages(run_lambda):
    # People generally have `pip` as `pip` or `pip3`
    def run_with_pip(pip):
        if get_platform() == "win32":
            grep_cmd = r'findstr /R "numpy torch"'
        else:
            grep_cmd = r'grep "torch\|numpy"'
        return run_and_read_all(
            run_lambda, pip + " list --format=freeze | " + grep_cmd
        )

    if not PY3:
        return "pip", run_with_pip("pip")

    # Try to figure out if the user is running pip or pip3.
    out2 = run_with_pip("pip")
    out3 = run_with_pip("pip3")

    num_pips = len([x for x in [out2, out3] if x is not None])
    if num_pips == 0:
        return "pip", out2

    if num_pips == 1:
        if out2 is not None:
            return "pip", out2
        return "pip3", out3

    # num_pips is 2. Return pip3 by default b/c that most likely
    # is the one associated with Python 3
    return "pip3", out3


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

    if NLE_AVAILABLE:
        nle_version = nle.__version__
    else:
        nle_version = "N/A"

    if MINIHACK_AVAILABLE:
        minihack_version = minihack.__version__
    else:
        minihack_version = "N/A"

    if GYM_AVAILABLE:
        gym_version = gym.__version__
    else:
        gym_version = "N/A"

    if TORCH_AVAILABLE:
        torch_version_str = torch.__version__
        torch_debug_mode_str = torch.version.debug
        cuda_available_str = torch.cuda.is_available()
        cuda_version_str = torch.version.cuda
    else:
        torch_version_str = (
            torch_debug_mode_str
        ) = cuda_available_str = cuda_version_str = "N/A"

    return SystemEnv(
        minihack_version=minihack_version,
        nle_version=nle_version,
        gym_version=gym_version,
        torch_version=torch_version_str,
        is_debug_build=torch_debug_mode_str,
        python_version="{}.{}".format(
            sys.version_info[0], sys.version_info[1]
        ),
        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),
        pip_version=pip_version,
        pip_packages=pip_list_output,
        conda_packages=get_conda_packages(run_lambda),
        os=get_os(run_lambda),
        gcc_version=get_gcc_version(run_lambda),
        cmake_version=get_cmake_version(run_lambda),
    )


env_info_fmt = """
MiniHack version: {minihack_version}
NLE version: {nle_version}
Gym version: {gym_version}
PyTorch version: {torch_version}
Is debug build: {is_debug_build}
CUDA used to build PyTorch: {cuda_compiled_version}

OS: {os}
GCC version: {gcc_version}
CMake version: {cmake_version}

Python version: {python_version}
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}

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 __name__ == "__main__":
    main()
