import binascii
import errno
import functools
import hashlib
import importlib
import logging
import math
import multiprocessing
import os
import signal
import subprocess
import sys
import tempfile
import threading
import time
from typing import Optional, Sequence, Tuple, Any
import uuid
import grpc
import warnings
try:
    from grpc import aio as aiogrpc
except ImportError:
    from grpc.experimental import aio as aiogrpc

import inspect
from inspect import signature
from pathlib import Path
import numpy as np

import ray
import ray._private.gcs_utils as gcs_utils
import ray.ray_constants as ray_constants
from ray._private.gcs_pubsub import construct_error_message
from ray._private.tls_utils import load_certs_from_env

# Import psutil after ray so the packaged version is used.
import psutil

pwd = None
if sys.platform != "win32":
    import pwd

logger = logging.getLogger(__name__)

# Linux can bind child processes' lifetimes to that of their parents via prctl.
# prctl support is detected dynamically once, and assumed thereafter.
linux_prctl = None

# Windows can bind processes' lifetimes to that of kernel-level "job objects".
# We keep a global job object to tie its lifetime to that of our own process.
win32_job = None
win32_AssignProcessToJobObject = None


def get_user_temp_dir():
    if "RAY_TMPDIR" in os.environ:
        return os.environ["RAY_TMPDIR"]
    elif sys.platform.startswith("linux") and "TMPDIR" in os.environ:
        return os.environ["TMPDIR"]
    elif sys.platform.startswith("darwin") or sys.platform.startswith("linux"):
        # Ideally we wouldn't need this fallback, but keep it for now for
        # for compatibility
        tempdir = os.path.join(os.sep, "tmp")
    else:
        tempdir = tempfile.gettempdir()
    return tempdir


def get_ray_temp_dir():
    return os.path.join(get_user_temp_dir(), "ray")


def _random_string():
    id_hash = hashlib.shake_128()
    id_hash.update(uuid.uuid4().bytes)
    id_bytes = id_hash.digest(ray_constants.ID_SIZE)
    assert len(id_bytes) == ray_constants.ID_SIZE
    return id_bytes


def format_error_message(exception_message, task_exception=False):
    """Improve the formatting of an exception thrown by a remote function.

    This method takes a traceback from an exception and makes it nicer by
    removing a few uninformative lines and adding some space to indent the
    remaining lines nicely.

    Args:
        exception_message (str): A message generated by traceback.format_exc().

    Returns:
        A string of the formatted exception message.
    """
    lines = exception_message.split("\n")
    if task_exception:
        # For errors that occur inside of tasks, remove lines 1 and 2 which are
        # always the same, they just contain information about the worker code.
        lines = lines[0:1] + lines[3:]
        pass
    return "\n".join(lines)


def push_error_to_driver(worker, error_type, message, job_id=None):
    """Push an error message to the driver to be printed in the background.

    Args:
        worker: The worker to use.
        error_type (str): The type of the error.
        message (str): The message that will be printed in the background
            on the driver.
        job_id: The ID of the driver to push the error message to. If this
            is None, then the message will be pushed to all drivers.
    """
    if job_id is None:
        job_id = ray.JobID.nil()
    assert isinstance(job_id, ray.JobID)
    worker.core_worker.push_error(job_id, error_type, message, time.time())


def publish_error_to_driver(error_type,
                            message,
                            job_id=None,
                            redis_client=None,
                            gcs_publisher=None):
    """Push an error message to the driver to be printed in the background.

    Normally the push_error_to_driver function should be used. However, in some
    instances, the raylet client is not available, e.g., because the
    error happens in Python before the driver or worker has connected to the
    backend processes.

    Args:
        error_type (str): The type of the error.
        message (str): The message that will be printed in the background
            on the driver.
        job_id: The ID of the driver to push the error message to. If this
            is None, then the message will be pushed to all drivers.
        redis_client: The redis client to use.
        gcs_publisher: The GCS publisher to use. If specified, ignores
            redis_client.
    """
    if job_id is None:
        job_id = ray.JobID.nil()
    assert isinstance(job_id, ray.JobID)
    error_data = construct_error_message(job_id, error_type, message,
                                         time.time())
    if gcs_publisher:
        gcs_publisher.publish_error(job_id.hex().encode(), error_data)
    elif redis_client:
        pubsub_msg = gcs_utils.PubSubMessage()
        pubsub_msg.id = job_id.binary()
        pubsub_msg.data = error_data.SerializeToString()
        redis_client.publish("ERROR_INFO:" + job_id.hex(),
                             pubsub_msg.SerializeToString())
    else:
        raise ValueError(
            "One of redis_client and gcs_publisher needs to be specified!")


def random_string():
    """Generate a random string to use as an ID.

    Note that users may seed numpy, which could cause this function to generate
    duplicate IDs. Therefore, we need to seed numpy ourselves, but we can't
    interfere with the state of the user's random number generator, so we
    extract the state of the random number generator and reset it after we are
    done.

    TODO(rkn): If we want to later guarantee that these are generated in a
    deterministic manner, then we will need to make some changes here.

    Returns:
        A random byte string of length ray_constants.ID_SIZE.
    """
    # Get the state of the numpy random number generator.
    numpy_state = np.random.get_state()
    # Try to use true randomness.
    np.random.seed(None)
    # Generate the random ID.
    random_id = np.random.bytes(ray_constants.ID_SIZE)
    # Reset the state of the numpy random number generator.
    np.random.set_state(numpy_state)
    return random_id


def decode(byte_str, allow_none=False):
    """Make this unicode in Python 3, otherwise leave it as bytes.

    Args:
        byte_str: The byte string to decode.
        allow_none: If true, then we will allow byte_str to be None in which
            case we will return an empty string. TODO(rkn): Remove this flag.
            This is only here to simplify upgrading to flatbuffers 1.10.0.

    Returns:
        A byte string in Python 2 and a unicode string in Python 3.
    """
    if byte_str is None and allow_none:
        return ""

    if not isinstance(byte_str, bytes):
        raise ValueError(f"The argument {byte_str} must be a bytes object.")
    if sys.version_info >= (3, 0):
        return byte_str.decode("ascii")
    else:
        return byte_str


def ensure_str(s, encoding="utf-8", errors="strict"):
    """Coerce *s* to `str`.

      - `str` -> `str`
      - `bytes` -> decoded to `str`
    """
    if isinstance(s, str):
        return s
    else:
        assert isinstance(s, bytes)
        return s.decode(encoding, errors)


def binary_to_object_ref(binary_object_ref):
    return ray.ObjectRef(binary_object_ref)


def binary_to_task_id(binary_task_id):
    return ray.TaskID(binary_task_id)


def binary_to_hex(identifier):
    hex_identifier = binascii.hexlify(identifier)
    if sys.version_info >= (3, 0):
        hex_identifier = hex_identifier.decode()
    return hex_identifier


def hex_to_binary(hex_identifier):
    return binascii.unhexlify(hex_identifier)


# TODO(qwang): Remove these hepler functions
# once we separate `WorkerID` from `UniqueID`.
def compute_job_id_from_driver(driver_id):
    assert isinstance(driver_id, ray.WorkerID)
    return ray.JobID(driver_id.binary()[0:ray.JobID.size()])


def compute_driver_id_from_job(job_id):
    assert isinstance(job_id, ray.JobID)
    rest_length = ray_constants.ID_SIZE - job_id.size()
    driver_id_str = job_id.binary() + (rest_length * b"\xff")
    return ray.WorkerID(driver_id_str)


def get_cuda_visible_devices():
    """Get the device IDs in the CUDA_VISIBLE_DEVICES environment variable.

    Returns:
        devices (List[str]): If CUDA_VISIBLE_DEVICES is set, returns a
            list of strings representing the IDs of the visible GPUs.
            If it is not set or is set to NoDevFiles, returns empty list.
    """
    gpu_ids_str = os.environ.get("CUDA_VISIBLE_DEVICES", None)

    if gpu_ids_str is None:
        return None

    if gpu_ids_str == "":
        return []

    if gpu_ids_str == "NoDevFiles":
        return []

    # GPU identifiers are given as strings representing integers or UUIDs.
    return list(gpu_ids_str.split(","))


last_set_gpu_ids = None


def set_cuda_visible_devices(gpu_ids):
    """Set the CUDA_VISIBLE_DEVICES environment variable.

    Args:
        gpu_ids (List[str]): List of strings representing GPU IDs.
    """

    if os.environ.get("RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES"):
        return

    global last_set_gpu_ids
    if last_set_gpu_ids == gpu_ids:
        return  # optimization: already set

    os.environ["CUDA_VISIBLE_DEVICES"] = ",".join([str(i) for i in gpu_ids])
    last_set_gpu_ids = gpu_ids


def resources_from_resource_arguments(
        default_num_cpus, default_num_gpus, default_memory,
        default_object_store_memory, default_resources,
        default_accelerator_type, runtime_num_cpus, runtime_num_gpus,
        runtime_memory, runtime_object_store_memory, runtime_resources,
        runtime_accelerator_type):
    """Determine a task's resource requirements.

    Args:
        default_num_cpus: The default number of CPUs required by this function
            or actor method.
        default_num_gpus: The default number of GPUs required by this function
            or actor method.
        default_memory: The default heap memory required by this function
            or actor method.
        default_object_store_memory: The default object store memory required
            by this function or actor method.
        default_resources: The default custom resources required by this
            function or actor method.
        runtime_num_cpus: The number of CPUs requested when the task was
            invoked.
        runtime_num_gpus: The number of GPUs requested when the task was
            invoked.
        runtime_memory: The heap memory requested when the task was invoked.
        runtime_object_store_memory: The object store memory requested when
            the task was invoked.
        runtime_resources: The custom resources requested when the task was
            invoked.

    Returns:
        A dictionary of the resource requirements for the task.
    """
    if runtime_resources is not None:
        resources = runtime_resources.copy()
    elif default_resources is not None:
        resources = default_resources.copy()
    else:
        resources = {}

    if "CPU" in resources or "GPU" in resources:
        raise ValueError("The resources dictionary must not "
                         "contain the key 'CPU' or 'GPU'")
    elif "memory" in resources or "object_store_memory" in resources:
        raise ValueError("The resources dictionary must not "
                         "contain the key 'memory' or 'object_store_memory'")

    assert default_num_cpus is not None
    resources["CPU"] = (default_num_cpus
                        if runtime_num_cpus is None else runtime_num_cpus)

    if runtime_num_gpus is not None:
        resources["GPU"] = runtime_num_gpus
    elif default_num_gpus is not None:
        resources["GPU"] = default_num_gpus

    # Order of arguments matter for short circuiting.
    memory = runtime_memory or default_memory
    object_store_memory = (runtime_object_store_memory
                           or default_object_store_memory)
    if memory is not None:
        resources["memory"] = ray_constants.to_memory_units(
            memory, round_up=True)
    if object_store_memory is not None:
        resources["object_store_memory"] = ray_constants.to_memory_units(
            object_store_memory, round_up=True)

    if runtime_accelerator_type is not None:
        resources[f"{ray_constants.RESOURCE_CONSTRAINT_PREFIX}"
                  f"{runtime_accelerator_type}"] = 0.001
    elif default_accelerator_type is not None:
        resources[f"{ray_constants.RESOURCE_CONSTRAINT_PREFIX}"
                  f"{default_accelerator_type}"] = 0.001

    return resources


class Unbuffered(object):
    """There's no "built-in" solution to programatically disabling buffering of
    text files. Ray expects stdout/err to be text files, so creating an
    unbuffered binary file is unacceptable.

    See
    https://mail.python.org/pipermail/tutor/2003-November/026645.html.
    https://docs.python.org/3/library/functions.html#open

    """

    def __init__(self, stream):
        self.stream = stream

    def write(self, data):
        self.stream.write(data)
        self.stream.flush()

    def writelines(self, datas):
        self.stream.writelines(datas)
        self.stream.flush()

    def __getattr__(self, attr):
        return getattr(self.stream, attr)


def open_log(path, unbuffered=False, **kwargs):
    """
    Opens the log file at `path`, with the provided kwargs being given to
    `open`.
    """
    # Disable buffering, see test_advanced_3.py::test_logging_to_driver
    kwargs.setdefault("buffering", 1)
    kwargs.setdefault("mode", "a")
    kwargs.setdefault("encoding", "utf-8")
    stream = open(path, **kwargs)
    if unbuffered:
        return Unbuffered(stream)
    else:
        return stream


def get_system_memory():
    """Return the total amount of system memory in bytes.

    Returns:
        The total amount of system memory in bytes.
    """
    # Try to accurately figure out the memory limit if we are in a docker
    # container. Note that this file is not specific to Docker and its value is
    # often much larger than the actual amount of memory.
    docker_limit = None
    memory_limit_filename = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
    if os.path.exists(memory_limit_filename):
        with open(memory_limit_filename, "r") as f:
            docker_limit = int(f.read())

    # Use psutil if it is available.
    psutil_memory_in_bytes = psutil.virtual_memory().total

    if docker_limit is not None:
        # We take the min because the cgroup limit is very large if we aren't
        # in Docker.
        return min(docker_limit, psutil_memory_in_bytes)

    return psutil_memory_in_bytes


def _get_docker_cpus(
        cpu_quota_file_name="/sys/fs/cgroup/cpu/cpu.cfs_quota_us",
        cpu_period_file_name="/sys/fs/cgroup/cpu/cpu.cfs_period_us",
        cpuset_file_name="/sys/fs/cgroup/cpuset/cpuset.cpus"
) -> Optional[float]:
    # TODO (Alex): Don't implement this logic oursleves.
    # Docker has 2 underyling ways of implementing CPU limits:
    # https://docs.docker.com/config/containers/resource_constraints/#configure-the-default-cfs-scheduler
    # 1. --cpuset-cpus 2. --cpus or --cpu-quota/--cpu-period (--cpu-shares is a
    # soft limit so we don't worry about it). For Ray's purposes, if we use
    # docker, the number of vCPUs on a machine is whichever is set (ties broken
    # by smaller value).

    cpu_quota = None
    # See: https://bugs.openjdk.java.net/browse/JDK-8146115
    if os.path.exists(cpu_quota_file_name) and os.path.exists(
            cpu_quota_file_name):
        try:
            with open(cpu_quota_file_name, "r") as quota_file, open(
                    cpu_period_file_name, "r") as period_file:
                cpu_quota = float(quota_file.read()) / float(
                    period_file.read())
        except Exception as e:
            logger.exception("Unexpected error calculating docker cpu quota.",
                             e)
    if (cpu_quota is not None) and (cpu_quota < 0):
        cpu_quota = None

    cpuset_num = None
    if os.path.exists(cpuset_file_name):
        try:
            with open(cpuset_file_name) as cpuset_file:
                ranges_as_string = cpuset_file.read()
                ranges = ranges_as_string.split(",")
                cpu_ids = []
                for num_or_range in ranges:
                    if "-" in num_or_range:
                        start, end = num_or_range.split("-")
                        cpu_ids.extend(list(range(int(start), int(end) + 1)))
                    else:
                        cpu_ids.append(int(num_or_range))
                cpuset_num = len(cpu_ids)
        except Exception as e:
            logger.exception("Unexpected error calculating docker cpuset ids.",
                             e)

    if cpu_quota and cpuset_num:
        return min(cpu_quota, cpuset_num)
    else:
        return cpu_quota or cpuset_num


def get_k8s_cpus(cpu_share_file_name="/sys/fs/cgroup/cpu/cpu.shares") -> float:
    """Get number of CPUs available for use by this container, in terms of
    cgroup cpu shares.

    This is the number of CPUs K8s has assigned to the container based
    on pod spec requests and limits.

    Note: using cpu_quota as in _get_docker_cpus() works
    only if the user set CPU limit in their pod spec (in addition to CPU
    request). Otherwise, the quota is unset.
    """
    try:
        cpu_shares = int(open(cpu_share_file_name).read())
        container_num_cpus = cpu_shares / 1024
        return container_num_cpus
    except Exception as e:
        logger.exception("Error computing CPU limit of Ray Kubernetes pod.", e)
        return 1.0


def get_num_cpus() -> int:
    if "KUBERNETES_SERVICE_HOST" in os.environ:
        # If in a K8S pod, use cgroup cpu shares and round up.
        return int(math.ceil(get_k8s_cpus()))
    cpu_count = multiprocessing.cpu_count()
    if os.environ.get("RAY_USE_MULTIPROCESSING_CPU_COUNT"):
        logger.info(
            "Detected RAY_USE_MULTIPROCESSING_CPU_COUNT=1: Using "
            "multiprocessing.cpu_count() to detect the number of CPUs. "
            "This may be inconsistent when used inside docker. "
            "To correctly detect CPUs, unset the env var: "
            "`RAY_USE_MULTIPROCESSING_CPU_COUNT`.")
        return cpu_count
    try:
        # Not easy to get cpu count in docker, see:
        # https://bugs.python.org/issue36054
        docker_count = _get_docker_cpus()
        if docker_count is not None and docker_count != cpu_count:
            if "RAY_DISABLE_DOCKER_CPU_WARNING" not in os.environ:
                logger.warning(
                    "Detecting docker specified CPUs. In "
                    "previous versions of Ray, CPU detection in containers "
                    "was incorrect. Please ensure that Ray has enough CPUs "
                    "allocated. As a temporary workaround to revert to the "
                    "prior behavior, set "
                    "`RAY_USE_MULTIPROCESSING_CPU_COUNT=1` as an env var "
                    "before starting Ray. Set the env var: "
                    "`RAY_DISABLE_DOCKER_CPU_WARNING=1` to mute this warning.")
            # TODO (Alex): We should probably add support for fractional cpus.
            if int(docker_count) != float(docker_count):
                logger.warning(
                    f"Ray currently does not support initializing Ray"
                    f"with fractional cpus. Your num_cpus will be "
                    f"truncated from {docker_count} to "
                    f"{int(docker_count)}.")
            docker_count = int(docker_count)
            cpu_count = docker_count

    except Exception:
        # `nproc` and cgroup are linux-only. If docker only works on linux
        # (will run in a linux VM on other platforms), so this is fine.
        pass

    return cpu_count


def get_used_memory():
    """Return the currently used system memory in bytes

    Returns:
        The total amount of used memory
    """
    # Try to accurately figure out the memory usage if we are in a docker
    # container.
    docker_usage = None
    memory_usage_filename = "/sys/fs/cgroup/memory/memory.usage_in_bytes"
    if os.path.exists(memory_usage_filename):
        with open(memory_usage_filename, "r") as f:
            docker_usage = int(f.read())

    # Use psutil if it is available.
    psutil_memory_in_bytes = psutil.virtual_memory().used

    if docker_usage is not None:
        # We take the min because the cgroup limit is very large if we aren't
        # in Docker.
        return min(docker_usage, psutil_memory_in_bytes)

    return psutil_memory_in_bytes


def estimate_available_memory():
    """Return the currently available amount of system memory in bytes.

    Returns:
        The total amount of available memory in bytes. Based on the used
        and total memory.

    """
    return get_system_memory() - get_used_memory()


def get_shared_memory_bytes():
    """Get the size of the shared memory file system.

    Returns:
        The size of the shared memory file system in bytes.
    """
    # Make sure this is only called on Linux.
    assert sys.platform == "linux" or sys.platform == "linux2"

    shm_fd = os.open("/dev/shm", os.O_RDONLY)
    try:
        shm_fs_stats = os.fstatvfs(shm_fd)
        # The value shm_fs_stats.f_bsize is the block size and the
        # value shm_fs_stats.f_bavail is the number of available
        # blocks.
        shm_avail = shm_fs_stats.f_bsize * shm_fs_stats.f_bavail
    finally:
        os.close(shm_fd)

    return shm_avail


def check_oversized_function(pickled: bytes, name: str, obj_type: str,
                             worker: "ray.Worker") -> None:
    """Send a warning message if the pickled function is too large.

    Args:
        pickled: the pickled function.
        name: name of the pickled object.
        obj_type: type of the pickled object, can be 'function',
            'remote function', or 'actor'.
        worker: the worker used to send warning message. message will be logged
            locally if None.
    """
    length = len(pickled)
    if length <= ray_constants.FUNCTION_SIZE_WARN_THRESHOLD:
        return
    elif length < ray_constants.FUNCTION_SIZE_ERROR_THRESHOLD:
        warning_message = (
            "The {} {} is very large ({} MiB). "
            "Check that its definition is not implicitly capturing a large "
            "array or other object in scope. Tip: use ray.put() to put large "
            "objects in the Ray object store.").format(obj_type, name,
                                                       length // (1024 * 1024))
        if worker:
            push_error_to_driver(
                worker,
                ray_constants.PICKLING_LARGE_OBJECT_PUSH_ERROR,
                "Warning: " + warning_message,
                job_id=worker.current_job_id)
    else:
        error = (
            "The {} {} is too large ({} MiB > FUNCTION_SIZE_ERROR_THRESHOLD={}"
            " MiB). Check that its definition is not implicitly capturing a "
            "large array or other object in scope. Tip: use ray.put() to "
            "put large objects in the Ray object store.").format(
                obj_type, name, length // (1024 * 1024),
                ray_constants.FUNCTION_SIZE_ERROR_THRESHOLD // (1024 * 1024))
        raise ValueError(error)


def is_main_thread():
    return threading.current_thread().getName() == "MainThread"


def detect_fate_sharing_support_win32():
    global win32_job, win32_AssignProcessToJobObject
    if win32_job is None and sys.platform == "win32":
        import ctypes
        try:
            from ctypes.wintypes import BOOL, DWORD, HANDLE, LPVOID, LPCWSTR
            kernel32 = ctypes.WinDLL("kernel32")
            kernel32.CreateJobObjectW.argtypes = (LPVOID, LPCWSTR)
            kernel32.CreateJobObjectW.restype = HANDLE
            sijo_argtypes = (HANDLE, ctypes.c_int, LPVOID, DWORD)
            kernel32.SetInformationJobObject.argtypes = sijo_argtypes
            kernel32.SetInformationJobObject.restype = BOOL
            kernel32.AssignProcessToJobObject.argtypes = (HANDLE, HANDLE)
            kernel32.AssignProcessToJobObject.restype = BOOL
            kernel32.IsDebuggerPresent.argtypes = ()
            kernel32.IsDebuggerPresent.restype = BOOL
        except (AttributeError, TypeError, ImportError):
            kernel32 = None
        job = kernel32.CreateJobObjectW(None, None) if kernel32 else None
        job = subprocess.Handle(job) if job else job
        if job:
            from ctypes.wintypes import DWORD, LARGE_INTEGER, ULARGE_INTEGER

            class JOBOBJECT_BASIC_LIMIT_INFORMATION(ctypes.Structure):
                _fields_ = [
                    ("PerProcessUserTimeLimit", LARGE_INTEGER),
                    ("PerJobUserTimeLimit", LARGE_INTEGER),
                    ("LimitFlags", DWORD),
                    ("MinimumWorkingSetSize", ctypes.c_size_t),
                    ("MaximumWorkingSetSize", ctypes.c_size_t),
                    ("ActiveProcessLimit", DWORD),
                    ("Affinity", ctypes.c_size_t),
                    ("PriorityClass", DWORD),
                    ("SchedulingClass", DWORD),
                ]

            class IO_COUNTERS(ctypes.Structure):
                _fields_ = [
                    ("ReadOperationCount", ULARGE_INTEGER),
                    ("WriteOperationCount", ULARGE_INTEGER),
                    ("OtherOperationCount", ULARGE_INTEGER),
                    ("ReadTransferCount", ULARGE_INTEGER),
                    ("WriteTransferCount", ULARGE_INTEGER),
                    ("OtherTransferCount", ULARGE_INTEGER),
                ]

            class JOBOBJECT_EXTENDED_LIMIT_INFORMATION(ctypes.Structure):
                _fields_ = [
                    ("BasicLimitInformation",
                     JOBOBJECT_BASIC_LIMIT_INFORMATION),
                    ("IoInfo", IO_COUNTERS),
                    ("ProcessMemoryLimit", ctypes.c_size_t),
                    ("JobMemoryLimit", ctypes.c_size_t),
                    ("PeakProcessMemoryUsed", ctypes.c_size_t),
                    ("PeakJobMemoryUsed", ctypes.c_size_t),
                ]

            debug = kernel32.IsDebuggerPresent()

            # Defined in <WinNT.h>; also available here:
            # https://docs.microsoft.com/en-us/windows/win32/api/jobapi2/nf-jobapi2-setinformationjobobject
            JobObjectExtendedLimitInformation = 9
            JOB_OBJECT_LIMIT_BREAKAWAY_OK = 0x00000800
            JOB_OBJECT_LIMIT_DIE_ON_UNHANDLED_EXCEPTION = 0x00000400
            JOB_OBJECT_LIMIT_KILL_ON_JOB_CLOSE = 0x00002000
            buf = JOBOBJECT_EXTENDED_LIMIT_INFORMATION()
            buf.BasicLimitInformation.LimitFlags = (
                (0 if debug else JOB_OBJECT_LIMIT_KILL_ON_JOB_CLOSE)
                | JOB_OBJECT_LIMIT_DIE_ON_UNHANDLED_EXCEPTION
                | JOB_OBJECT_LIMIT_BREAKAWAY_OK)
            infoclass = JobObjectExtendedLimitInformation
            if not kernel32.SetInformationJobObject(
                    job, infoclass, ctypes.byref(buf), ctypes.sizeof(buf)):
                job = None
        win32_AssignProcessToJobObject = (kernel32.AssignProcessToJobObject
                                          if kernel32 is not None else False)
        win32_job = job if job else False
    return bool(win32_job)


def detect_fate_sharing_support_linux():
    global linux_prctl
    if linux_prctl is None and sys.platform.startswith("linux"):
        try:
            from ctypes import c_int, c_ulong, CDLL
            prctl = CDLL(None).prctl
            prctl.restype = c_int
            prctl.argtypes = [c_int, c_ulong, c_ulong, c_ulong, c_ulong]
        except (AttributeError, TypeError):
            prctl = None
        linux_prctl = prctl if prctl else False
    return bool(linux_prctl)


def detect_fate_sharing_support():
    result = None
    if sys.platform == "win32":
        result = detect_fate_sharing_support_win32()
    elif sys.platform.startswith("linux"):
        result = detect_fate_sharing_support_linux()
    return result


def set_kill_on_parent_death_linux():
    """Ensures this process dies if its parent dies (fate-sharing).

    Linux-only. Must be called in preexec_fn (i.e. by the child).
    """
    if detect_fate_sharing_support_linux():
        import signal
        PR_SET_PDEATHSIG = 1
        if linux_prctl(PR_SET_PDEATHSIG, signal.SIGKILL, 0, 0, 0) != 0:
            import ctypes
            raise OSError(ctypes.get_errno(), "prctl(PR_SET_PDEATHSIG) failed")
    else:
        assert False, "PR_SET_PDEATHSIG used despite being unavailable"


def set_kill_child_on_death_win32(child_proc):
    """Ensures the child process dies if this process dies (fate-sharing).

    Windows-only. Must be called by the parent, after spawning the child.

    Args:
        child_proc: The subprocess.Popen or subprocess.Handle object.
    """

    if isinstance(child_proc, subprocess.Popen):
        child_proc = child_proc._handle
    assert isinstance(child_proc, subprocess.Handle)

    if detect_fate_sharing_support_win32():
        if not win32_AssignProcessToJobObject(win32_job, int(child_proc)):
            import ctypes
            raise OSError(ctypes.get_last_error(),
                          "AssignProcessToJobObject() failed")
    else:
        assert False, "AssignProcessToJobObject used despite being unavailable"


def set_sigterm_handler(sigterm_handler):
    """Registers a handler for SIGTERM in a platform-compatible manner."""
    if sys.platform == "win32":
        # Note that these signal handlers only work for console applications.
        # TODO(mehrdadn): implement graceful process termination mechanism
        # SIGINT is Ctrl+C, SIGBREAK is Ctrl+Break.
        signal.signal(signal.SIGBREAK, sigterm_handler)
    else:
        signal.signal(signal.SIGTERM, sigterm_handler)


def try_make_directory_shared(directory_path):
    try:
        os.chmod(directory_path, 0o0777)
    except OSError as e:
        # Silently suppress the PermissionError that is thrown by the chmod.
        # This is done because the user attempting to change the permissions
        # on a directory may not own it. The chmod is attempted whether the
        # directory is new or not to avoid race conditions.
        # ray-project/ray/#3591
        if e.errno in [errno.EACCES, errno.EPERM]:
            pass
        else:
            raise


def try_to_create_directory(directory_path):
    """Attempt to create a directory that is globally readable/writable.

    Args:
        directory_path: The path of the directory to create.
    """
    directory_path = os.path.expanduser(directory_path)
    os.makedirs(directory_path, exist_ok=True)
    # Change the log directory permissions so others can use it. This is
    # important when multiple people are using the same machine.
    try_make_directory_shared(directory_path)


def try_to_symlink(symlink_path, target_path):
    """Attempt to create a symlink.

    If the symlink path exists and isn't a symlink, the symlink will not be
    created. If a symlink exists in the path, it will be attempted to be
    removed and replaced.

    Args:
        symlink_path: The path at which to create the symlink.
        target_path: The path the symlink should point to.
    """
    symlink_path = os.path.expanduser(symlink_path)
    target_path = os.path.expanduser(target_path)

    if os.path.exists(symlink_path):
        if os.path.islink(symlink_path):
            # Try to remove existing symlink.
            try:
                os.remove(symlink_path)
            except OSError:
                return
        else:
            # There's an existing non-symlink file, don't overwrite it.
            return

    try:
        os.symlink(target_path, symlink_path)
    except OSError:
        return


def get_user():
    if pwd is None:
        return ""
    try:
        return pwd.getpwuid(os.getuid()).pw_name
    except Exception:
        return ""


def get_function_args(callable):
    all_parameters = frozenset(signature(callable).parameters)
    return list(all_parameters)


def get_conda_bin_executable(executable_name):
    """
    Return path to the specified executable, assumed to be discoverable within
    the 'bin' subdirectory of a conda installation.  Adapted from
    https://github.com/mlflow/mlflow.
    """

    # Use CONDA_EXE as per https://github.com/conda/conda/issues/7126
    if "CONDA_EXE" in os.environ:
        conda_bin_dir = os.path.dirname(os.environ["CONDA_EXE"])
        return os.path.join(conda_bin_dir, executable_name)
    return executable_name


def get_conda_env_dir(env_name):
    """Find and validate the conda directory for a given conda environment.

    For example, given the environment name `tf1`, this function checks
    the existence of the corresponding conda directory, e.g.
    `/Users/scaly/anaconda3/envs/tf1`, and returns it.
    """
    conda_prefix = os.environ.get("CONDA_PREFIX")
    if conda_prefix is None:
        # The caller is neither in a conda env or in (base) env.  This is rare
        # because by default, new terminals start in (base), but we can still
        # support this case.
        conda_exe = os.environ.get("CONDA_EXE")
        if conda_exe is None:
            raise ValueError("Cannot find environment variables set by conda. "
                             "Please verify conda is installed.")
        # Example: CONDA_EXE=$HOME/anaconda3/bin/python
        # Strip out /bin/python by going up two parent directories.
        conda_prefix = str(Path(conda_exe).parent.parent)

    # There are two cases:
    # 1. We are in a conda (base) env: CONDA_DEFAULT_ENV=base and
    #    CONDA_PREFIX=$HOME/anaconda3
    # 2. We are in a user-created conda env: CONDA_DEFAULT_ENV=$env_name and
    #    CONDA_PREFIX=$HOME/anaconda3/envs/$current_env_name
    if os.environ.get("CONDA_DEFAULT_ENV") == "base":
        # Caller's curent environment is (base).
        # Not recommended by conda, but we can still support it.
        if (env_name == "base"):
            # Desired environment is (base), located at e.g. $HOME/anaconda3
            env_dir = conda_prefix
        else:
            # Desired environment is user-created, e.g.
            # $HOME/anaconda3/envs/$env_name
            env_dir = os.path.join(conda_prefix, "envs", env_name)
    else:
        # Now `conda_prefix` should be something like
        # $HOME/anaconda3/envs/$current_env_name
        # We want to replace the last component with the desired env name.
        conda_envs_dir = os.path.split(conda_prefix)[0]
        env_dir = os.path.join(conda_envs_dir, env_name)
    if not os.path.isdir(env_dir):
        raise ValueError(
            "conda env " + env_name +
            " not found in conda envs directory. Run `conda env list` to " +
            "verify the name is correct.")
    return env_dir


def get_call_location(back=1):
    """
    Get the location (filename and line number) of a function caller, `back`
    frames up the stack.

    Args:
        back (int): The number of frames to go up the stack, not including this
            function.
    """
    stack = inspect.stack()
    try:
        frame = stack[back + 1]
        return f"{frame.filename}:{frame.lineno}"
    except IndexError:
        return "UNKNOWN"


# Used to only print a deprecation warning once for a given function if we
# don't wish to spam the caller.
_PRINTED_WARNING = set()


# The following is inspired by
# https://github.com/tensorflow/tensorflow/blob/dec8e0b11f4f87693b67e125e67dfbc68d26c205/tensorflow/python/util/deprecation.py#L274-L329
def deprecated(instructions=None,
               removal_release=None,
               removal_date=None,
               warn_once=True):
    """
    Creates a decorator for marking functions as deprecated. The decorator
    will log a deprecation warning on the first (or all, see `warn_once` arg)
    invocations, and will otherwise leave the wrapped function unchanged.

    Args:
        instructions (str): Instructions for the caller to update their code.
        removal_release (str): The release in which this deprecated function
            will be removed. Only one of removal_release and removal_date
            should be specified. If neither is specfieid, we'll warning that
            the function will be removed "in a future release".
        removal_date (str): The date on which this deprecated function will be
            removed. Only one of removal_release and removal_date should be
            specified. If neither is specfieid, we'll warning that
            the function will be removed "in a future release".
        warn_once (bool): If true, the deprecation warning will only be logged
            on the first invocation. Otherwise, the deprecation warning will
            be logged on every invocation. Defaults to True.

    Returns:
        A decorator to be used for wrapping deprecated functions.
    """
    if removal_release is not None and removal_date is not None:
        raise ValueError(
            "Only one of removal_release and removal_date should be specified."
        )

    def deprecated_wrapper(func):
        @functools.wraps(func)
        def new_func(*args, **kwargs):
            global _PRINTED_WARNING
            if func not in _PRINTED_WARNING:
                if warn_once:
                    _PRINTED_WARNING.add(func)
                msg = ("From {}: {} (from {}) is deprecated and will ".format(
                    get_call_location(), func.__name__,
                    func.__module__) + "be removed " +
                       (f"in version {removal_release}."
                        if removal_release is not None else
                        f"after {removal_date}"
                        if removal_date is not None else "in a future version")
                       + (f" {instructions}"
                          if instructions is not None else ""))
                warnings.warn(msg)
            return func(*args, **kwargs)

        return new_func

    return deprecated_wrapper


def import_attr(full_path: str):
    """Given a full import path to a module attr, return the imported attr.

    For example, the following are equivalent:
        MyClass = import_attr("module.submodule.MyClass")
        from module.submodule import MyClass

    Returns:
        Imported attr
    """
    if full_path is None:
        raise TypeError("import path cannot be None")
    last_period_idx = full_path.rfind(".")
    attr_name = full_path[last_period_idx + 1:]
    module_name = full_path[:last_period_idx]
    module = importlib.import_module(module_name)
    return getattr(module, attr_name)


def get_wheel_filename(
        sys_platform: str = sys.platform,
        ray_version: str = ray.__version__,
        py_version: str = f"{sys.version_info.major}{sys.version_info.minor}"
) -> str:
    """Returns the filename used for the nightly Ray wheel.

    Args:
        sys_platform (str): The platform as returned by sys.platform. Examples:
            "darwin", "linux", "win32"
        ray_version (str): The Ray version as returned by ray.__version__ or
            `ray --version`.  Examples: "2.0.0.dev0"
        py_version (str):
            The major and minor Python versions concatenated.  Examples: "36",
            "37", "38", "39"
    Returns:
        The wheel file name.  Examples:
            ray-2.0.0.dev0-cp38-cp38-manylinux2014_x86_64.whl
    """
    assert py_version in ["36", "37", "38", "39"], py_version

    os_strings = {
        "darwin": "macosx_10_15_x86_64"
        if py_version in ["38", "39"] else "macosx_10_15_intel",
        "linux": "manylinux2014_x86_64",
        "win32": "win_amd64"
    }

    assert sys_platform in os_strings, sys_platform

    wheel_filename = (
        f"ray-{ray_version}-cp{py_version}-"
        f"cp{py_version}{'m' if py_version in ['36', '37'] else ''}"
        f"-{os_strings[sys_platform]}.whl")

    return wheel_filename


def get_master_wheel_url(
        ray_commit: str = ray.__commit__,
        sys_platform: str = sys.platform,
        ray_version: str = ray.__version__,
        py_version: str = f"{sys.version_info.major}{sys.version_info.minor}"
) -> str:
    """Return the URL for the wheel from a specific commit."""
    filename = get_wheel_filename(
        sys_platform=sys_platform,
        ray_version=ray_version,
        py_version=py_version)
    return (f"http://d168575n8y1h5x.cloudfront.net/main/"
            f"{ray_commit}/{filename}")


def get_release_wheel_url(
        ray_commit: str = ray.__commit__,
        sys_platform: str = sys.platform,
        ray_version: str = ray.__version__,
        py_version: str = f"{sys.version_info.major}{sys.version_info.minor}"
) -> str:
    """Return the URL for the wheel for a specific release."""
    filename = get_wheel_filename(
        sys_platform=sys_platform,
        ray_version=ray_version,
        py_version=py_version)
    return (f"http://d168575n8y1h5x.cloudfront.net/staging/"
            f"{ray_version}/{ray_commit}/{filename}")
    # e.g. http://d168575n8y1h5x.cloudfront.net/staging/1.4.0rc1/e7c7
    # f6371a69eb727fa469e4cd6f4fbefd143b4c/ray-1.4.0rc1-cp36-cp36m-manylinux201
    # 4_x86_64.whl


def validate_namespace(namespace: str):
    if not isinstance(namespace, str):
        raise TypeError("namespace must be None or a string.")
    elif namespace == "":
        raise ValueError("\"\" is not a valid namespace. "
                         "Pass None to not specify a namespace.")


def init_grpc_channel(address: str,
                      options: Optional[Sequence[Tuple[str, Any]]] = None,
                      asynchronous: bool = False):
    grpc_module = aiogrpc if asynchronous else grpc
    if os.environ.get("RAY_USE_TLS", "0").lower() in ("1", "true"):
        server_cert_chain, private_key, ca_cert = load_certs_from_env()
        credentials = grpc.ssl_channel_credentials(
            certificate_chain=server_cert_chain,
            private_key=private_key,
            root_certificates=ca_cert)
        channel = grpc_module.secure_channel(
            address, credentials, options=options)
    else:
        channel = grpc_module.insecure_channel(address, options=options)

    return channel


def check_dashboard_dependencies_installed() -> bool:
    """Returns True if Ray Dashboard dependencies are installed.

    Checks to see if we should start the dashboard agent or not based on the
    Ray installation version the user has installed (ray vs. ray[default]).
    Unfortunately there doesn't seem to be a cleaner way to detect this other
    than just blindly importing the relevant packages.

    """
    try:
        import ray.dashboard.optional_deps  # noqa: F401

        return True
    except ImportError:
        return False
